Biogeosciences, 9, 775–801, 2012
www.biogeosciences.net/9/775/2012/
doi:10.5194/bg-9-775-2012
© Author(s) 2012. CC Attribution 3.0 License.
Biogeosciences
Coordination of physiological and structural traits in Amazon
forest trees
S. Patiño1,2,† , N. M. Fyllas2 , T. R. Baker2 , R. Paiva3 , C. A. Quesada2-4 , A. J. B. Santos3,4,† , M. Schwarz1 ,
H. ter Steege5 , O. L. Phillips2 , and J. Lloyd2,6
1 Max-Planck-Institut
für Biogeochemie, Postfach 100164, 07701, Jena, Germany
of Geography, University of Leeds, LS2 9JT UK
3 Institito Nacional de Pesquisas da Amazônia, Manaus, AM, Brazil
4 Departamento de Ecologia, Universidade de Brası́lia, DF, Brazil
5 Dept. of Plant Ecology and Biodiversity, Utrecht University, The Netherlands
6 School of Earth and Environmental Sciences, James Cook University, Cairns, Qld 4871, Australia
† deceased
2 School
Correspondence to: J. Lloyd ([email protected])
Received: 5 May 2011 – Published in Biogeosciences Discuss.: 25 May 2011
Revised: 16 November 2011 – Accepted: 18 January 2012 – Published: 16 February 2012
Abstract. Many plant traits covary in a non-random manner reflecting interdependencies associated with “ecological
strategy” dimensions. To understand how plants integrate
their structural and physiological investments, data on leaf
and leaflet size and the ratio of leaf area to sapwood area
(8LS ) obtained for 1020 individual trees (encompassing 661
species) located in 52 tropical forest plots across the Amazon Basin were incorporated into an analysis utilising existing data on species maximum height (Hmax ), seed size,
leaf mass per unit area (MA ), foliar nutrients and δ 13 C, and
branch xylem density (ρx ).
Utilising a common principal components approach allowing eigenvalues to vary between two soil fertility dependent
species groups, five taxonomically controlled trait dimensions were identified. The first involves primarily cations,
foliar carbon and MA and is associated with differences in
foliar construction costs. The second relates to some components of the classic “leaf economic spectrum”, but with
increased individual leaf areas and a higher 8LS newly identified components for tropical tree species. The third relates
primarily to increasing Hmax and hence variations in light
acquisition strategy involving greater MA , reductions in 8LS
and less negative δ 13 C. Although these first three dimensions
were more important for species from high fertility sites the
final two dimensions were more important for low fertility
species and were associated with variations linked to reproductive and shade tolerance strategies.
Environmental conditions influenced structural traits with
ρx of individual species decreasing with increased soil fertility and higher temperatures. This soil fertility response
appears to be synchronised with increases in foliar nutrient
concentrations and reductions in foliar [C]. Leaf and leaflet
area and 8LS were less responsive to the environment than
ρx .
Thus, although genetically determined foliar traits such as
those associated with leaf construction costs coordinate independently of structural characteristics such as maximum
height, others such as the classical “leaf economic spectrum”
covary with structural traits such as leaf size and 8LS . Coordinated structural and physiological adaptions are also associated with light acquisition/shade tolerance strategies with
several traits such as MA and [C] being significant components of more than one ecological strategy dimension. This
is argued to be a consequence of a range of different potential underlying causes for any observed variation in such
“ambiguous” traits. Environmental effects on structural and
physiological characteristics are also coordinated but in a different way to the gamut of linkages associated with genotypic differences.
1
Introduction
Plant traits are widely used in ecology and biogeochemistry.
In particular, sets of functional characters can serve as the
basis for identifying important adaptations that improve the
success of different taxa at different environments. Over the
last decade significant advances have been made in terms of
our understanding of plant trait inter-relationships and associated trade-offs (Reich et al., 1997; Westoby et al., 2002),
Published by Copernicus Publications on behalf of the European Geosciences Union.
776
especially in terms of the so called “leaf economic spectrum”
(Wright et al., 2004) with well documented systematic and
co-ordinated changes in leaf nitrogen and phosphorus concentrations, leaf mass per unit area, MA and leaf lifetimes.
Attention has also been paid to the relationships between
physiological and structural characteristics of leaves and
other plant traits. For example, it has been reported that
leaf size declines with wood density, ρw (Pickup et al., 2005;
Wright et al., 2006, 2007; Malhado et al., 2009) and it has
been suggested that this is because the ratio of leaf area
to sapwood area (8LS ) should also decline with increasing wood density due to hydraulic constraints (Wright et
al., 2007). Nevertheless, although 8LS may decline with
ρw for trees in some ecosystems that are clearly waterlimited (Ackerly, 2004; Cavender-Bares et al., 2004), 8LS
sometimes actually increases with ρw (Wright et al., 2006;
Meinzer et al., 2008). The latter study also found that associated with these higher 8LS and high wood density stems
were lower stem hydraulic conductances, more negative midday leaf water potentials, and more negative bulk leaf osmotic potentials at zero turgor. Thus, leaves of some high
wood density species may be characterised by physiological
and structural adaptations allowing them to function at more
severe water deficits than is the case for low wood density
species.
The Panama study of Meinzer et al. (2008) also found that
higher ρw species tended to have higher MA . Although similar positive correlations between MA and ρw have also been
reported for other ecosystems (e.g. for sclerophyllous forest: Ishida et al., 2008) when examining the bivariate relationship between ρw and MA across a range of tropical forest
sites, Wright et al. (2007) observed no significant relationship. Likewise, when examining variation in leaf and stem
traits for 17 dipterocarp species growing in a common garden in southern China, Zhang and Cao (2009) also found no
significant correlation between ρw and MA .
Variations in MA may also be related to a suite of additional plant physiological characteristics (Poorter et al.,
2009), varying negatively with dry-weight foliar nitrogen
and phosphorus concentrations (Wright et al., 2004; Fyllas
et al., 2009) as well as tending to increase with increasing
tree height (Thomas and Bazzaz, 1999; Kenzo et al., 2006;
Lloyd et al., 2010). Potential tree height, Hmax , has also been
related to a number of wood traits (Chave et al., 2009) with
taller plants tending to have bigger conduits in their trunks,
but fewer conduits overall (Coomes et al., 2007).
Within a given stand, taller and generally more lightdemanding rain forest species also tend to have larger leaves,
this being associated with shallower crown and a more efficient light capture (Poorter et al., 2006; Poorter and Rozendaal, 2008). Leaf–size may also be influenced by other
factors. For example, Australian rain forests growing on
oligotrophic soils typically have a greater abundance of
smaller leaved species than for nearby forests found on more
mesotrophic soil types (Webb, 1968).
Biogeosciences, 9, 775–801, 2012
S. Patiño et al.: Tropical tree trait dimensions
Seed size may also relate to the above plant functional
traits. For example, one of “Corner’s rules” describes a tendency for species with thick twigs to have large appendages
(leaves and fruit). The range of viable seed size also tends to
increase with plant height (Moles et al., 2005; Grubb et al.,
2005). Forests on the more fertile soils of western Amazonia tend to have smaller average seed masses than their less
fertile counterparts on the Guyana Shield and elsewhere (ter
Steege et al., 2006), this perhaps being related to several advantages attributable to large seeded species under nutrientpoor conditions, viz. greater initial nutrient stores, greater
initial root zone expansion, and increased mychorrizal infection, all of which would be expected to increase the probability of seedling survival (Foster, 1986).
This paper presents new data on leaf and leaflet size and
8LS for 661 species located in 52 plots across the Amazon
Basin. The trees sampled form a subset of those also examined for variations in branch xylem density (Patiño et al.,
2009), and for foliar nutrients, MA and δ 13 C (Fyllas et al.,
2009), which had previously been analysed separately. We
here investigate the inter-relationships between these structural and physiological parameters also considering taxonomic variations in Hmax (Baker et al., 2009) and seed mass
(ter Steege and Hammond, 2001; ter Steege et al., 2006).
Specifically, we were interested to assess the degree to which
the observed variations in the studied structural and physiological traits were coordinated with each other into identifiable integrated trait dimensions: for example, those associated with leaf construction costs, light acquisition, and/or
shade tolerance.
2
2.1
Materials and methods
Study sites
In the analysis here, RAINFOR sample plots have been aggregated as discussed in Fyllas et al. (2009), with further
plot details available in Patiño et al. (2009) and Quesada
et al. (2010). Ten plots in Fyllas et al. (2009) have not
been included due to insufficient structural trait data having been collected, but the range of soils encountered here
is still substantial with the sum of exchangeable bases (0–
0.3 m), for example ranging from less than 1 mmolc kg−1 to
nearly 100 mmolc kg−1 . Total soil phosphorus ranged from
26 mg kg−1 for an ortseinc podzol to 727 mg kg−1 for a eutric cambisol (Quesada et al., 2010). Mean annual precipitation varies from less than 1.5 m a−1 on sites at the north and
southern periphery of the basin to more than 3.0 m a−1 for
sub-montane sites close to the Andes.
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
2.2
777
Structural traits
For most trees sampled in Patiño et al. (2009) and Fyllas et
al. (2009), and from the same terminal branches for which
data has already been presented in those studies, all leaves
from the branch had also been counted. From that branch,
a sub-sample of 10–20 leaves was randomly chosen to estimate individual leaf area, LA , and leaflet area, `A (when
a species had compound leaves), and to estimate the total
leaf area of the branch. All age and size leaves or leaflets
were selected for this analysis except for very young leaves
or those which were obviously senescent. The chosen leaves
were usually scanned fresh on the same day of collection.
When this was not possible the same day, they were stored
for a maximum of two days in sealed plastic bags to avoid
desiccation and any consequent reduction of the leaf area.
Scans were analysed using “Win Folia Basic 2001a” (Regent Instruments Inc., 4040 rue Blain Quebec, QC., G2B 5C3
Canada) to obtain LA and `A .
The distal (sapwood + pith) and pith diameters for each
branch were also measured with a digital caliper (Mitutoyo
Corporation, Japan) with sapwood area, AS , then estimated
by subtracting pith area from the total branch area with
8LS =nL̄A /AS where n is the number of leaves distal to the
piece of branch sampled and L̄A is the average area of the individual leaves sub-sampled for the estimation of LA and/or
`A .
Branch xylem density data for the same samples were obtained as described in Patiño et al. (2009). In brief, this
consisted of the estimation of the volume of a branch segment, approximately 1 cm in diameter and 5–10 cm long using calipers, with the pith removed as necessary and dry
weight subsequently determined. Species maximum height
taken from the database developed by Baker et al. (2009)
with estimates made to the species level for 80% of the trees
identified, and the bulk of the remainder being genus level
averages. Seed mass (S) was taken as a genus level dependent variable and was already on a log10 ordinal scale (ter
Steege et al., 2006).
2.3
Physiological foliar traits
Foliar traits used here are as described/measured in Fyllas et
al. (2009) and Lloyd et al. (2010) and include leaf mass per
unit area (MA ) and foliar [N], [C], [P], [Ca], [K] and [Mg] expressed on dry-weight basis. Foliar 13 C/12 C discrimination,
1, was estimated from measurements of foliar δ 13 C (Fyllas
et al., 2009) using an assumed value for the isotopic composition of source air equal to −8.0 ‰ (Farquhar et al., 1989)
and subsequently transformed to a diffusional limitation index, , according to (Fyllas et al., 2012)
r
= 1−
(1 − 4.4)/25.6 − 0.2
0.8
www.biogeosciences.net/9/775/2012/
which utilises the well known relationship between 1 and
the ratio of internal to ambient CO2 concentrations, ci /ca
(Farquhar et al., 1989). Equation (1) assumes that at current day ca , photosynthesis can be considered a roughly linear function of ci and with a maximum practical ci /ca (indicating minimal diffusional limitation) of 0.8. Here we have
taken a value of 4.4 ‰ for the fractionation against 13 CO2
during diffusion into the leaf and 30.0 ‰ for the fractionation against 13 CO2 during photosynthetic fixation (Farquhar
et al., 1989). Increasing values are associated with lower
ci /ca , and thus, other things being equal, a higher water use
efficiency, W , this being the ratio of carbon gained to water
lost during photosynthetic CO2 assimilation. Equation (1)
relies on a simplified expression for 1 which ignores difference between gas- and liquid-phase fractionations within
the leaf (Farquhar et al., 1989), but this should not seriously
compromise its utility in the current context.
2.4
Climate and soils
The soil and climate predictors table used was the same as
in Fyllas et al. (2009), using a set of measured soil properties (Quesada et al., 2010) with precipitation variables
and temperature from the “WorldClim” dataset (http://www.
worldclim.org). Estimates of mean annual solar radiation are
from New et al. (2002). As in Fyllas et al. (2009) we separate
soils into two fertility classes based on their total phosphorus
concentration and the total sum of reserve bases, (Quesada et
al., 2010). In brief this categorisation gives rise to arenosols,
podzols, ferralsols, and most acrisols being classified as low
fertility soils. High fertility soils include plinthosols, cambisols, fluvisols, gleysols and most alisols.
2.5
Statistical analysis
This paper implements a similar set of statistical analyses to
that described in detail in Fyllas et al. (2009). Preliminary
tests included analysis of normality (Shapiro-Wilk) and homogeneity of variance (Fligner-Killeen) for each of the structural traits of interest. The foliar related structural traits (LA ,
`A and 8LS ) presented a right skewed distribution and thus
were all log10 transformed. As ρx , Hmax and S (the latter
already provided as size classes on a log10 scale) were more
or less symmetrically distributed around their mean we did
not apply this transformation for these variables, even though
the Shapiro test failed to identify strict normality. The nonparametric Kruskal-Wallis test (Hollander and Wolfe, 1999)
was used to explore for differences between fertility groups
as well as for differences between families, genera within a
family and species within a genus. All analyses were performed with the R statistical platform (R Development Core
Team, 2010).
(1)
Biogeosciences, 9, 775–801, 2012
778
2.5.1
S. Patiño et al.: Tropical tree trait dimensions
Partitioning of variance and estimation of
taxonomic and environmental effects
A multilevel model was initially fitted for all traits (including
those previously analysed separately in Fyllas et al. (2009)
and Patiño et al. (2009) because this was a slightly different
dataset), except Hmax and S according to
2 = µ + p + f/g/s + ,
(2)
where µ is the overall mean value of each trait, 2; p is the
plot effect, i.e. the effect of the location that each individual
is found, and f/g/s represents the genetic structure of the data,
i.e. that each individual belongs to a species (s), nested in a
genus (g), nested in a family (f ), and is the error term.
All parameters were estimated by the Residual Maximum
Likelihood (REML) method with the lme4 library available within R (Bates and Sarkor, 2007). Fyllas et al. (2009)
have already discussed further details of the above formulations and the advantage in being able to partition the variance
from the family to the species level, also taking into account
the location (thus the environmental contribution to trait variation) where the trait was measured. The Supplementary Information (II) of that paper also provides an empirical validation of the approach used. Note, that whilst theoretically
possible, we do not include interaction terms in Eq. (2), this
is because there is insufficient species replication across different sites. Nevertheless, investigations into the likely magnitude of such effects have been undertaken as part of the
analyses in both Patiño et al. (2009) and Fyllas et al. (2009)
and have not been found to be significant. Again we were interested in exploring the taxonomic (estimated as the sum of
family ± genus ± species random effects) and environmental
terms, using bivariate relationships as well as multiple nonparametric regressions of plot effect contributions on a set of
environmental predictors. For the latter we used Kendall’s τ
as our measure of association calculating the significance of
partial correlations using our own specifically written code,
using the R statistical platform.
For Hmax and S no multilevel model was fitted or environmental effect assumed, the available data being considered
to express directly the genetic potential of each species. We
also note that our estimates of S are resolved at the genus
level only (ter Steege and Hammond, 2001) and are only on
a log10 categorical scale. This introduces potential errors into
the analyses where S is involved because all other traits have
been resolved at the species level. Thus, even though a small
portion of the observed variation in S generally occurs at the
species level (Casper et al., 1992), bivariate and multivariate analyses involving this trait as presented here may carry
somewhat more “noise” than would otherwise be the case.
2.5.2
Bivariate relationships
Relationships were initially assessed with the Pearson’s correlation coefficient (r) with subsequent Standardized MaBiogeosciences, 9, 775–801, 2012
jor Axis (SMA) line fits where significant correlations were
identified. In this study, SMA line fits are applied to the
raw dataset (including all measured traits and thus intraspecific variation), to the taxonomic component of trait variation
(i.e. each species is represented by a single data point) as well
as to the plot level effects (i.e. each plot is contributing a single data point). In each case we initially fitted separate lines
for each fertility group, and when a common SMA slope was
identified we tested for differences in elevation and/or slope
between fertility groups, using the smartr library available within R (Warton et al., 2006).
We explored the plot level effect of each structural trait,
through non-parametric correlation analysis on selected soil
and environmental predictors, with the soil variables reduced
to three principal axes to avoid multicollinearity (Fyllas et
al., 2009). The climatic variables of mean annual temperature, total annual precipitation, dry season precipitation and
mean annual radiation were also examined. As extensively
discussed in Fyllas et al. (2009) we dealt with spatial autocorrelation issues by fitting appropriate simultaneous autoregressive models (SAR) which include a spatial error term
(Lichstein et al., 2002) to help interpret the significance of
full and partial Kendall’s τ coefficients as a measure of association between plot-level trait effects and environmental
predictors.
2.5.3
Multivariate analyses
Inferred taxonomic effects were analysed jointly for species
found on fertile versus infertile soils (excluding those
found on both soil types) by calculating separate variance–
covariance matrices for the two species groups and then
using the common principal components (CPC) model of
Flury (1988) as implemented by Phillips and Arnold (1999).
Within this model, it is assumed that the two populations of
species have the same eigenvectors (principal components;
denoted here as U ) but that the relative loading of the various U as expressed through their eigenvalues (λ) may potentially vary between the two populations. Flury’s model
provides a hierarchy of tests corresponding to a range of possible relationships between matrices including equality, proportionality, common principal components, partial common
principal components or unrelated (Flury, 1988; Phillips and
Arnold, 1999). CPC can thus be seen as a method for summarizing the variation in two or more matrices. Nevertheless,
caution needs to be applied when using CPC to address the
more complex goal of diagnosing and understanding the nature of the changes that underlie the difference between the
matrices. This is because CPC tends to spread any differences over many of the vectors it extracts and often over all
of them (Houle et al., 2002).
As the CPC model does not strictly apply to correlation
matrices (Flury, 1988), we standardised each variable before
calculating the input variance–covariance matrix by dividing
each variable by its observed range (across both high and
www.biogeosciences.net/9/775/2012/
779
Population density
Population density
S. Patiño et al.: Tropical tree trait dimensions
log10[leaf area] (m2)
Population density
Population density
log10[leaf mass per unit area] ( g m-2) log10[leaf area:sapwood area] ( m-2 cm-2)
Population density
Population density
***
log10[leaflet area] (m2)
Diffusional limitation index
Population density
Population density
Branch xylem density (kg m-3)
Maximum species height (m)
-6
-4
-2
0
2
log10[seed mass] (g)
Fig. 1. Probability density histograms of raw data per fertility group for leaf area (LA ; m2 ), leaflet area (`A ; m2 ), leaf mass per unit area
(MA ; g m−2 ), (m2 ), leaf area:sapwood area ratio (8LS ; cm2 m−2 ), branch xylem density (ρx ; kg m−3 ), = stomatal limitation index
(dimensionless; see Eq. 1), species maximum height (Hmax ; m) and seed mass (S; g). Open red bars represent low and blue dashed bars high
soil fertility plots, as defined by the quantitative determinations of the level of total reserve bases from 0.0–0.3 m depth (Fyllas et al., 2009;
Quesada et al., 2010). Also given for each histogram are the mean and the variance for each trait. Significant differences in mean values
and/or variances between the two fertility groups were identified with the Fligner-Killeen test respectively. Significance codes: *** < 0.001,
** < 0.01,* < 0.05.
Fig. 1. Probability density histograms of raw data per fertility group
for leaf area (LA ; m2 ), leaflet area (`A ; m2 ), leaf mass per unit area
−2
2 −2
(MAsoils)
; gm
), (mby2 ),
leaf
area:sapwood
areamultivariate
ratio (Φ
cmPCA
mof the),derived
low fertility
as first proposed
Gower
(1966)
but, due
All other
analyses
LS ; (e.g.
to the presence of the occasional outlier, taking the effective −3 environmental effects) were implemented with the ade4
branch
xylem
(ρxof; thekgU and
m ),
=(Thioulouse
stomatal
index
range as
the 0.1 to 0.9
quantiles.density
Standard errors
package
et al.,limitation
1997) available within
the R staλ for the
CPC
models
were
estimated
assuming
asymptotic
tistical
platform
with
the
environmental
effect
PCA
(dimensionless; see Eq. 1), species maximum
height (Hmax ; m) and undernormality as described in Flury (1988).
taken on the correlation matrix.
seed mass (S; g). Open red bars represent low and blue dashed bars
high soil fertility plots, as defined by the quantitativeBiogeosciences,
determinations
www.biogeosciences.net/9/775/2012/
9, 775–801, 2012
of the level of total reserve bases from 0.0–0.3 m depth (Fyllas et
780
3
3.1
S. Patiño et al.: Tropical tree trait dimensions
Results
Trait distribution in relation to soil type
The structural traits distributions along with those for MA
and for the complete dataset divided to low and high fertility groups are shown in Fig. 1 with overall mean values,
range and variances for each plot for all traits also provided
in the Supplementary Information (Table S1). The three leaf
related traits introduced here (LA , `A and 8LS ) did not differ
significantly between low and high fertility sites (Fig. 1). On
the other hand, ρx and S showed significant differences between the two fertility groups, with their distributions shifted
to the left for fertile sites, i.e. higher ρx and S were found for
species found on infertile soils. This is similar to the shifted
distributions identified for most leaf mineral concentrations
across fertility gradients (Fyllas et al., 2009) but in the opposite direction, i.e. with higher structural carbon and lower
mineral investment in less fertile environments. As expected
from our prior analysis of the statistical distribution of foliar
δ 13 C (Fyllas et al., 2009), the diffusional limitation index of
Eq. 1 tended to be lower for trees growing on low fertility soils. Despite a difference in variance between low and
high fertility sites, there was, however, no overall effect of
soil fertility classification on the average Hmax .
3.2
Partitioning of the variance
The variation apportioned to different taxonomic levels
varies for each of the traits examined (Fig. 2). When leaf size
was expressed per leaflet, most of the variation was attributed
at the species level (0.31) with the overall taxonomic component (i.e. family ± genus ± species) adding up to a very high
(0.62) proportion. When leaf size was expressed at the leaf
level, most of the variation was attributed at the family level
(0.29) with a very high overall taxonomic component (0.71).
In contrast to LA and `A , plot level contributions to the total
variance were substantial for the other structural traits: being
around 0.30 for ρx and 0.27 for 8LS . These are not necessarily higher than their respective taxonomic components, but
underline the importance of the site growing conditions in influencing structural traits such as ρx and 8LS . As for the foliar traits reported in Fyllas et al. (2009) this must have direct
implications for different physiological processes. In that
study, leaf mass per unit area and [C], [N] and [Mg] emerged
as highly constrained by the taxonomic affiliation, but with
others, such as [P], [K] and [Ca] also strongly influenced by
site growing conditions. That study also found foliar δ 13 C to
be strongly influenced by site growing conditions, consistent
with its analogue here () having its environmental component as the dominant source for its variation. Overall, there
was a tendency for the residual component (related to intraspecies variations not accountable for by different plot locations and experimental error) to increase as the proportion of
variation accountable for by taxonomic affiliation declined
Biogeosciences, 9, 775–801, 2012
Diffusional limitation
index
Leaf area: sapwood
area ratio
Branch xylem
density
Leaf area
Leaflet area
Proportion of total variance
Fig. 2. Partitioning of the total variance for each studied property
into taxonomic (family/genus/species), environmental (plot) and erFig. 2. Partitioning of the total variance for each structural property
ror (residual) components. Traits are sorted from less to more taxinto taxonomic (family/genus/species), environmental (plot) and an
onomically constrained. Significance of each variance component
error (residual) components. Foliar properties are sorted from less
was
tested with a likelihood ratio test (Galwey, 2006). Significance
to more taxonomically constrained. Significance of each variance
codes:
*** <
0.001,
< 0.01,*
< 0.05.ratio test (Galwey, 2006).
component
was
tested**with
a likelihood
Significance codes: *** < 0.001, ** < 0.01,* < 0.05.
and with the proportion attributable to plot location tending
to increase as the residual component became larger.
3.3
Bivariate relationships: raw data
These are not considered in any detail here, but for the interested reader data are summarised in the Supplementary Information, Table S2A.
3.4
Bivariate relationships: taxonomic components
Considering data from both low and high fertility sites together, Table 1 lists correlations and SMA slopes for the
derived taxonomic components with this same information
shown in more detail (including confidence intervals) in the
Supplementary Information (Table S2A) and with low and
high fertility species separated for OLS and SMA regression
analyses in Table S2B. Within Table 1, the SMA slopes reflect the relationship y ↔ x, with the x as the column headers and the y being the row labels. Figures 3 through 6 illustrate the more important relationships involving the sampled structural traits. Due to considerations associated with
multiple testing, we focus only on relationships significant
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
781
Table 1. Relationships between the derived genetic components of the observed plant traits: MA = leaf mass per unit area (gm−2 ); elemental
concentrations are on a dry weight basis (mg g−1 ), LA = leaf area (m2 ), `A = leaflet area (m2 ), 8LS = leaf area:sapwood area ratio
(cm2 g−1 ), ρx = branch xylem density (kg m−3 ), = stomatal limitation index (see Eq. 1), S = seed mass (g), Hmax = species maximum
height (m). Values above the diagonal represent the slope of the relationship (y axis as columns labels, x axis as row labels). Values below
S. Patiño et al.: Tropical tree trait dimensions
the diagonal represent the correlation coefficient. Values significant at P < 0.05 are given in bold. NS = no slope estimated as the relationship
was not significant.
[C]
log[N]
log[P]
log[Ca]
log[K]
log[Mg]
log(LA )
log(`A )
log(8LS )
ρx
log(S)
Hmax
−
0.15
−0.43
−0.41
−0.07
−0.28
−0.14
−0.09
0.17
−0.24
0.13
0.09
0.12
0.17
0.37
−
0.07
-0.02
−0.51
−0.45
−0.45
0.14
−0.08
0.06
0.07
0.03
0.18
0.04
−1.01
NS
−
0.66
0.02
0.18
0.05
0.27
−0.11
0.20
−0.08
0.23
−0.16
−0.03
−1.21
NS
1.20
−
0.14
0.46
0.18
0.37
0.04
0.14
−0.20
0.27
−0.08
0.00
NS
−6.28
NS
1.93
−
0.46
0.65
−0.03
0.07
0.00
−0.21
0.12
−0.34
−0.06
−1.65
−4.43
1.63
1.36
0.7
−
0.59
−0.01
0.18
0.04
−0.24
0.06
−0.23
−0.02
−2.18
−5.85
NS
1.79
0.93
1.32
−
−0.14
0.15
−0.09
−0.12
0.09
−0.25
−0.08
NS
17.30
6.36
5.37
NS
NS
−2.98
−
0.41
0.26
−0.10
0.12
0.02
−0.02
4.32
NS
−4.18
NS
NS
2.60
1.97
0.65
−
0.01
−0.22
−0.08
0.00
0.00
−1.27
NS
1.22
1.03
NS
NS
NS
0.19
NS
−
0.07
−0.08
−0.10
−0.07
0.88
NS
NS
−0.72
−0.37
−0.52
−0.40
−0.13
−0.2
NS
−
−0.09
0.25
−0.03
NS
NS
0.22
0.19
0.10
NS
0.10
0.04
NS
NS
NS
−
−0.20
0.11
19.2
51.4
−18.6
NS
−8.3
−11.4
−8.8
NS
NS
NS
21.6
−81.8
−
0.14
167
NS
NS
NS
NS
NS
NS
NS
NS
NS
NS
731
8.8
−
Leaf mass per unit area (g m-2)
log(MA )
[C]
log[N]
log[P]
log[Ca]
log[K]
log[Mg]
log(LA )
log(`A )
log(8LS )
ρx
log(S)
Hmax
MA
(a)
(b)
Seed mass (mg)
Variable
Species maximum height (m)
Fig. 3. Standard Major Axis (SMA) regression lines between species maximum height (Hmax ) and the derived taxonomic components of
leaf mass per unit area (MA ) for the same species and the associated average seed mass (S) for the associated genus. Red open circles
indicate species found on low fertility sites and the blue open circles indicate species found on high fertility sites. Species found on both soil
fertility groups are indicated with closed circles (see text for details). Red solid lines show the SMA model fit for low fertility species which
is significantly different to the blue solid lines for high fertility soil species.
Fig. 3. Standard Major Axis (SMA) regressions lines between
species maximum height (Hmax ) and the derived taxonomic components
of leaf
mass per
unit area
(MA ) for theSMA
sameslope
species
and intercept between the species associated
at p ≤ 0.001 though, where
interesting
and/or
informative,
and/or
the
associated
average
seed
mass
(S)
for
the
associated
genus.
Red
statistically less significant relationships are also considered.
with the two soil fertility classes (see Supplementary Inforopen circles indicate species found on low fertility
sites and
theS2B)
blue we have fitted separate lines for species
mation,
Table
open
circles
indicate
species
found
on
high
fertility
sites.
Species
found on low and high fertility soils. This shows that for
3.4.1 Maximum tree height
found on both soil fertility groups are indicated species
with closed
circles with low fertility soils, both MA and S
associated
(see
text
for
details).
Red
solid
lines
show
the
SMA
model
fit
which higher at a given Hmax than their higher
tend to be slightly
Generally only poor correlations were observed for Hmax ,
is
significantly
different
to
the
blue
solid
lines
for
high
fertility
soil
fertility counterparts.
Especially for S ↔ Hmax the variathese being significant only for log10 (MA ) (p ≤ 0.001) and
species.
tion is considerable, particularly at low Hmax , with S varying
log10 (S) (p ≤ 0.01).
The MA ↔ Hmax and S ↔ Hmax relathree orders of magnitude for Hmax between 10 and 30 m.
tionships are shown in Fig. 3. Here, due to differences in the
www.biogeosciences.net/9/775/2012/
Biogeosciences, 9, 775–801, 2012
(a)
(b)
(d)
(c)
Seed mass(g)
Leaf potassium (mg g-1)
Leaf phosphorus (mg g-1)
S. Patiño et al.: Tropical tree trait dimensions
Leaf mass per unit area (g m-2)
782
Branch xylem density (kg m-3)
Fig. 4. Standard Major Axis (SMA) regression lines between the derived species components of branch xylem density (ρx ) and those for
mass per unit area (MA ), foliar [P] and foliar [K] for the same species and the average seed mass (S) for the associated genus. Red open
circles indicate species found on low fertility sites and the blue open circles indicate species found on high fertility sites. Species found on
both soil fertility groups are indicated with closed circles (see text for details). The black solid lines show the SMA model fit which did not
depend on soil fertility.
Fig. 4. Standard Major Axis (SMA) regressions lines between the
derived species components of branch xylem density (ρx ) and those
3.4.2 Branch xylem density
3.4.3 Leaf area: sapwood area ratio
for mass per unit area (MA ), foliar [P] and foliar [K] for the same
As detailed in Table 1, the derived taxonomic component
Reasonably strong correlations were found for log10 (8LS )
species
the average
mass
(S)
for
the
associated
genus.
Red
of ρx and
was negatively
correlatedseed
with log
[P],
log
[Ca],
with
log10 (MA ), log
10
10
10 [N] and log10 (LA ) (p ≤ 0.001) with
(`A ) and positively
associated
withon
log10
(S) fertility
the relationship
log10blue
(8LS ) and log10 [P] also sig10 [K], log10
openlogcircles
indicate
species
found
low
sitesbetween
and the
(p ≤ 0.001). A weaker but significant positive correlation
nificant (p ≤ 0.01). The relevant biplots are shown in Fig. 5.
openwascircles
indicate
species
found
on highThe
fertility
sites.
Species
also observed
with log10 (M
correlation
slope for the
taxonomic
component MA ↔ 8LS relationA ) and a negative
with log10 [Mg] (p ≤ 0.01). Of minor significance was a negship is 1/−1.27 = −0.79. Thus, as 8LS increases across
found
both with
soillogfertility
groups are indicated
circles
ativeon
association
species,with
then Mclosed
A declines proportionally less. That is to
10 (LA ) (p ≤ 0.05). Some of these
illustrated in
Fig. black
4 which shows
rela- show
say, species
a higher
8LS also
(seerelationships
text for are
details).
The
solidthelines
the with
SMA
model
fittend to carry a greater
tionships between ρx and both [P] and [K] to be particularly
weight of (generally larger) leaves per unit stem area with
which
did not
depend
on for
soil
fertility.
compelling
and, as
is also the case
MA and
S, with no difthose leaves also tending to have higher foliar [N] and [P].
ference for species associated with low versus high fertility
soils.
Biogeosciences, 9, 775–801, 2012
www.biogeosciences.net/9/775/2012/
Leaf nitrogen (mg g-1)
(a)
783
(b)
(d)
(c)
Leaf area (m2)
Leaf phosphorus (mg g-1)
Leaf mass per unit area (g m-2)
S. Patiño et al.: Tropical tree trait dimensions
Leaf area:sapwood area ratio (m2 cm-2)
Fig. 5. Standard Major Axis (SMA) regression lines between the derived species components of leaf area/sapwood area ratio (8LS ) and
those for mass per unit area MA , foliar [N], foliar [P] and average leaf size for the same species.Red open circles indicate species found
on low fertility sites and the blue open circles indicate species found on high fertility sites. Species found on both soil fertility groups are
indicated with closed circles (see text for details). Solid lines show the SMA model fit which did not depend on soil fertility.
Fig. 5. Standard Major Axis (SMA) regressions lines between the
Leaf nutrients
and other structural
traits
3.5 Common
Component
modelling
derived3.4.4species
components
of leaf
area/sapwood
area Principal
ratio (Φ
LS )
(taxonomic components)
Strongfor
positive
correlations
(p ≤ 0.001)
and those
mass
per unit
areawere
Malso
foliar [N], foliar [P] and averA , observed
for log10 (LA ) with log10 [N] and log10 [P] as well as between
Results from the CPC modelling are shown in Table 2, with
age leaf
forS. the
same both
species.Red
open circles
indicate
species
log10size
[Ca] and
Interestingly,
the slope and intercept
the full model
output, details
of the rationale for eigenvecof these relationships are dependent on the soil fertility with
tor inclusion and assessments of the overall model fit given
found which
on low
fertility sites and the blue open circles
indicate species
a species is associated (Supplementary Information
in the Supplementary Information Tables S3, S4 and S5 and
found onsites.
low fertility
soils tend tofound
have
their
The five eigenvectors selected
found Table
on S2B).
highSpecies
fertility
Species
on accompanying
both soilcaptions.
fertility
a higher LA at any given foliar [N] and/or [P].
are listed in Table 2 in order of their importance, as derived
groups are
indicated
with
circles
(see text
forcharacteristic
details).rootsSolid
For the
[Ca] ↔ S pairing
the closed
negative slope
is also large
from the
(eigenvectors, λ). These results
(−8.3), though in this case with no soil fertility effect decan be interpreted as in the case of an ordinary principal comlines show
the SMA
model
which
not depend
on soil fertility.
tected. Though
not shown
in Fig. 6,fit
also
of note isdid
the posiponents analysis, the difference here being that the relative
tive [C]↔ S relationship (p ≤ 0.001) with species with a low
seed mass also tending to have a low foliar carbon content.
www.biogeosciences.net/9/775/2012/
weightings (λ) have been allowed to differ for species on
high vs. low fertility soils.
The first eigenvector, U1 , had somewhat higher λ for high
vs. low fertility associated species (accounting for 0.24 and
0.27 of the dataset variance respectively) and with high positive coefficients for all three foliar cations and to a lesser
Biogeosciences, 9, 775–801, 2012
imensions
784
S. Patiño et al.: Tropical tree trait dimensions
Table 2. Common principal component analysis of derived genetic effects for species associated with low and high fertility soils. Values in
brackets represent standard errors for each component. Coefficients given in bold are either those whose absolute values are 0.50 or more,
or 0.30 or more with a standard error of less than 0.1. MA = leaf mass per unit area; elemental concentrations are on a dry weight basis,
LA = leaf area; 8LS = leaf area:sapwood area ratio, ρx = branch xylem density, = diffusion limitation index (see Eq. 1), S = seed mass,
Hmax = species maximum height.
Variable
log(MA )
[C]
log[N]
log[P]
log[Ca]
log[K]
log[Mg]
log(LA )
log(8LS )
ρx
log(S)
Hmax
Characteristic roots
λlow,j
λhigh,j
U1
U2
Component
U3
U4
U5
−0.22(0.05)
−0.35 (0.05)
0.15 (0.10)
0.25 (0.08)
0.42 (0.03)
0.48 (0.02)
0.49 (0.04)
−0.01 (0.09)
−0.01 (0.07)
−0.14 (0.03)
0.10 (0.04)
−0.23 (0.03)
−0.10 (0.04)
−0.23 (0.06)
0.24 (0.07)
0.53 (0.04)
0.45 (0.05)
−0.13 (0.08)
−0.01 (0.09)
−0.21 (0.09)
0.48 (0.05)
0.29 (0.06)
−0.03 (0.05)
0.14 (0.05)
0.01 (0.06)
0.07 (0.06)
0.44 (0.07)
0.01 (0.07)
−0.02 (0.09)
0.12 (0.09)
0.15 (0.09)
0.00 (0.08)
0.07 (0.06)
0.25 (0.13)
−0.44 (0.11)
−0.22 (0.10)
0.39 (0.09)
0.19 (0.10)
0.53 (0.10)
−0.22 (0.11)
0.06 (0.12)
0.22 (0.09)
0.31 (0.05)
−0.31 (0.06)
0.16 (0.09)
0.06 (0.07)
−0.35 (0.16)
−0.53 (0.16)
0.12 (0.21)
−0.10 (0.13)
0.48 (0.10)
−0.13 (0.22)
0.35 (0.09)
0.34 (0.09)
−0.03 (0.08)
0.08 (0.06)
0.00 (0.08)
0.05 (0.11)
0.19 (0.07)
−0.16 (0.10)
0.18 (0.11)
0.26 (0.11)
0.60 (0.08)
0.59 (0.08)
−0.47 (0.09)
1876 (259)
2341 (237)
1472 (203)
1641 (166)
641(89)
898 (91)
717 (99)
564 (57)
698 ( 96)
318 ( 32)
Table 3. Bivariate relationships for the derived environmental component of the observed plant traits.Values above the diagonal represent the
slope of the relationship (y axis as columns labels, x axis as row labels). Values below the diagonal represent the correlation coefficient. Values
significant at P < 0.05 are given in bold. NS = no slope estimated as the relationship was not significant. For units and symbols, see Table 1.
7
log[N] log[P] log[Ca] log[K] log[Mg] log(LA ) log(`A ) log(8LS )
ρx
S. Patiño et al.: Tropical tree trait dimensions
log(MA )
−1.06
NS
NS
NS
−3.49
NS
0.57
any −given 0.31
species)
with the NS
results −4.97
shown in Table
4. −1.32
This
sed before. It is thus
here
[C]
0.63
− −3.38
NS −15.86
NS
−4.22
NS
NS
NS
4.10
1.82
shows
that 0.33
theseem
total to
variation
in4.68
the
11 traits
examined
any given 3.06
species) with
the NS
results shown in T
doesofnot
have been
recognised
before.
It is thusNS
here
log[N]
−0.52 S.−0.30
−
2.69
NS
1.25
NS
NS
PatiñoS.
et Patiño
al.:
Tropical
etfirst
al.:PCA
tree
Tropical
trait (ů
tree
dimensions
trait
dimensions
could
be
explained
by
the
axis
)
with
ρ
an
imshows
that
0.33
of
the
total
variation
in the 11 tra
1
x
denoted0.48
as PFL .−
−0.04 −0.09
1.74
1.53
NS
NS
NS
NS −0.45
0.20
cted, all of whichlog[P]
we beportant
contributor
and
this
also
relating
positively
to
foliar
could
be
explained
by
the
first
PCA
axis (ů 1 ) w
log[Ca]
−0.28
−0.54
0.28
0.50
−
0.88
0.27
NS
NS
NS
−0.26
NS
t (see Supplementary InOverall the five eigenvectors selected, all of which we beany given
any
species)
given
with
species)
the with
results
theshown
results
inshown
Table
not
does
not
to have
seem
been
toall
have
recognised
been
recognised
before.
Itbefore.
isNS
thus It
here
is thus
here
[C]
anddoes
M
butseem
negatively
with
foliar
nutrients
examined
portant
contributor
and
this
also
relating
positi
−0.01
−0.13
0.23
0.74
0.49
−
NS
NS
NS
−0.30
0.13
A ,lieve
he total variance log[K]
for both
to be physiologically relevant (see Supplementary Inshows
that
shows
0.33
that
of
the
0.33
total
of
variation
the
total
variation
in
the
11
in
traits
theex
1
and
alsodenoted
with
.
The
second
axis
of
the
PCA
on
the
plot
eflog[Mg]
−0.54
−0.72
0.28
0.04
0.50
0.05
−
NS
NS
NS
NS
NS
[C]
and
M
,
but
negatively
with
all
foliar
nutrie
as
denoted
.as PFL . for 0.68 of the total variance for both
A
PFLaccounted
formation),
could
be
could
explained
be
explained
by
the
first
by
PCA
the
first
axis
PCA
(ů
)
axis
with
(ů
ρ
log(LA )
0.08correlation
−0.06
−0.09
−0.03
0.11
0.21
0.07
−
0.85
NS
NS
NS
1
fects
matrix
(ů
)
is
also
significant,
accounting
and
also
with
.
The
second
axis
of
the
PCA
o
Overall
the
Overall
five eigenvectors
the
eigenvectors
selected, selected,
all of which
all of
wewhich
be- we be2 fivesoil
low and
high
fertility
species.
es (normalised to ± 100)
portant
contributor
and
this also
and
this
relating
also
positively
relating
p
log(`A )
0.07
−0.20
−0.16
−0.11
0.09negative
0.20
0.15
0.90 portant
− contributor
NS
−0.79
−0.35
for
0.25
of
the
variance,
with
substantial
weightings
fects
correlation
matrix
(ů
)
is
also
significan
lieve
to
be
lieve
physiologically
to
be
physiologically
relevant
(see
relevant
Supplementary
(see
Supplementary
InIn2
pplementary Information
The 0.36
first three
axes species
scores
to ±0.07
100)[C] 0.08
log(8LS )
−0.29
−0.21 (normalised
0.10
−A
NS
and
[C]
but
negatively
M
, butNS
negatively
with
foliar
with nutrients
all foliar
nu
ex
A , and
for
MAformation),
, −0.25
foliar
[C]
and against
−0.07
(and
to0.68
a−0.02
lesser
extent
[P])variance
forM
0.25
of the
variance,
withall
substantial
negativ
formation),
accounted
accounted
for
of
forthe
0.68
total
of foliar
variance
the total
for
both for both
lack of any systematic
are
plotted
each
other
in
Supplementary
Information
ρx
0.08
0.27 −0.22 −0.64
−0.46 −0.82
−0.06
−0.25 and
−0.31
0.17
−Theaxis
NS
also
with
and
also
.
The
with
second
.
second
of
the
axis
PCA
of
the
on
the
PC
being balanced
positive
weightings
for
foliar [Mg]
in parfor MA , foliar [C] and (and to a lesser exte
low0.27
and
high
low
and
fertility
high
soil
fertility
species.
soil
species.
cores as expectedfor the
Fig.by
S1.
shows
the0.25
required
any systematic
0.32
0.24This 0.49
0.31lack of
−0.22
−0.14 fects
−0.28
−0.09
−0.18
− )significant,
correlation
fects
correlation
matrix
(ůmatrix
also
(ů
acc
2 ) is weightings
2 is also
ticular,
but
also
with
contributions
from
Φ
and
ρ
.
being
balanced
by positive
forsignifi
foliar
LS
x
The
first
The
three
first
axes
three
species
axes
scores
species
(normalised
scores
(normalised
to
±
100)
to
±
100)
ciple components model.
correlations between the species scores as expected for thefor 0.25
of
for
the
0.25
variance,
of
the
variance,
with
substantial
with
substantial
negative
we
but also with contributions from ΦLS ne
an
are plotted
areagainst
plotted
each
against
other
each
other
in Supplementary
Information
Information
ns of these three trait dioutput
from
any
good
fitinofSupplementary
a principle
components
model.for Mticular,
,
for
foliar
MA[C]
, foliar
and [C](and
andtoa(and
lesser
to extent
a lesser
fo
A
Fig.
S1.
Fig.
This
S1.
shows
This
the
shows
required
the
required
lack
of
any
lack
systematic
of
any
systematic
8a also showing that it is
Clearly a wide range of combinations of these three trait di-being balanced
being
balanced
by
positive
by
weightings
positive
weightings
for
foliar
for
[Mg
f
correlations
correlations
between
between
the plot
species
the
scores
species
asscores
expected
as expected
for that
the itfor
the
3.8 negative
Relationship
between
effect
PCAs
and
typically associated
with [P], and
mensions
can for
occur.
But[C]
with
Fig. 8a
also
showing
isticular,
extent foliar
coefficients
foliar
with
coefficients
for foliar
[N] and
[P]
aswith
well
as with
LA contributions
and, from
to a ΦLSfrom
but
ticular,
also
but
also
contributions
and
Φ
ρ
x
L
output
from
output
anyspeaking)
from
goodany
fit of
good
a principle
fit of a principle
components
components
model. with
model. 3.8 Relationship between plot effect .
soil/climate
(generally
only
species
typically
associated
and but
or both PDJ
smaller
lesser extent,
8LS . Also notable
are modestly negative coRW . still significant coefficients for MA and S. In
Clearly aClearly
wide range
a wide
of range
combinations
of combinations
of these three
of these
traitthree
di- trait disoil/climate
that haveseems
scores forefficients
both PDJ
onents of theterms
three of
major
cations, carbon and high
MA , fertility
this firstsoils
component
forand
MA RW
and. foliar [Mg]. In terms of [N], [P] and
mensionsmensions
can occur.can
But
occur.
with But
Fig.with
8a also
Fig.showing
8a also showing
that it is that it is
diagrammaticsimilar
form. to
This
7 shows
major
components
threetomajor
that first described
byFigure
Poorter
andspeaking)
dethe
Jong
(1999)
Mtypically
U2the
, seems
components
of what isbetween
considA ,of
3.8some
Relationship
3.8 Relationship
between
plot effect
plot PCA
effe
(generally
(generally
speaking)
only
species
only
typically
species
associated
associated
with reflect
with
The
most
significant
relationships
between
the
PCA
site
axis
o be “shared”,
CPCs and
their(PDJ)
overlap
of traits in diagrammatic
form.leaf
This
andespecially
thus we dub it the Poorter-De
Jong
dimension,
ered the classic
economic
spectrum
(Reich
et
al.,
1997;
soil/climate
soil/climate
scores of
Table
4,
and
previously
soil
climate
most significant relationships between the
and
and
.
. Thelabel
high
fertility
high
soils
fertility
soils
havecalculated
that
scores
have
forscores
both
for
both
PDJ
PDJ
RW
RWthus
illustrates
thatthat
many
traits
seem
to and
be
“shared”,
especially
. , RW
Wright
et al.,
2004).
We
this the Reich-Wright dir all three of PDJ
PDJ
characteristics
of7the
samethe
are the
shown
in Fig.
9.
First,
the
scores of Table 4, and previously calculated soi
Figure
Figure
shows
7sites
shows
major
components
major
components
of
the
three
of
the
major
three
major
mension,
,
of
tropical
tree
functional
trait coordination.
M
which
isůanas
important
factor
forfirst
all soil
threePCA
of RW , RW
A9
the component,
top panel
ofU
Fig.
shows
function
ofdiagrammatic
the
PDJ ∩
RW ) and
1for
CPCs
and
their
and
overlap
theiraan
ofoverlap
traits
in
of
traits
in diagrammatic
form.PDJ
This
form. This characteristics of the same sites are shown in Fig
Theofsecond
accounts
additional
2 , CPCs
most
The
significant
most
significant
relationships
relationships
between
the
between
[Mg] varying0.18
in opposite
and
.many
Also
occurring
in be
( PDJ
∩Although
of
theTheseem
axisdataset
of illustrates
Fyllas
etillustrates
al.
(2009),
the
latter
considered
atostrong
in-) and
top panel
of Fig.
ů1 as
function
ofPCA
the
FW
RW
that
that
traits
many
seem
traits
to
seem
“shared”,
be “shared”,
especially
especially
and 0.19 of the
variances
for
low
and
high
fertilHmax
would
to have
little9 shows
influence
ona eiscores
of
scores
Table
4,
of
and
Table
previously
4,
and
previously
calculated
calculated
soil
and
ese two traitity
dimensions.
same
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
.
The
strong
tegratedM
measure
of
soil
fertility
and
denoted
axis
of
Fyllas
et
al.
(2009),
the
latter
considere
F
species respectively,
and
is
characterised
by
high
positive
ther
or
it
emerges
as
the
dominant
term
for
U
PDJ
RW
3
Mis
which
important
is an important
factor forfactor
all three
for of
all three
A which
A an
PDJ ,of RW
PDJ , characteristics
RW
characteristics
of
the
same
of
the
sites
same
are
shown
sites
are
in
shown
Fig.
FF
directions
with
respect
to
M
for
these
two
trait
dimensions.
relationship
observed
suggests
a
strong
integrated
response
tegrated measure of soil fertility and denoted9. in
A
n the same direction relaand tropical
and
. Also
. Also
occurring
in fertility,
( PDJinwith
∩( PDJ
)∩and
of )the
and oftop
thepanel
top
of
Fig.
panel
9
shows
of
Fig.
ů
9
shows
as
a
function
ů
as
a
of
function
the
first
of
s
FW
FWoccurring
RW
RW
1
1
of
Amazon
forest
trees
to
soil
most
nurelationship
observed
suggests
a
strong
integra
a high estimated standard
Intersecting RW and FW and in the same direction relasame 2012
signsame
is [C],
sign
butisfoliar
with
[C], but
[P]
with
[Mg]
[P]
and
varying
[Mg]invarying
opposite
in opposite
of
axis of
et Fyllas
al. (2009),
etforest
al.the
(2009),
latterto
the
considered
latter
consid
a stw
Biogeosciences, 9,
775–801,
www.biogeosciences.net/9/775/2012/
trients
increasing,
with
[C]and
andwith
ρx adecreasing
as
of Fyllas
Amazon
tropical
trees
soil
fertility,
tive to and
MA is
ΦLS . Although
high estimated
standardaxis
included LA in ( RW ∩
directions
directions
with
respect
with
to
respect
M
for
to
these
M
for
two
these
trait
dimensions.
two
trait
dimensions.
.
Th
tegrated
measure
tegrated
of
measure
soil
fertility
of
soil
and
fertility
denoted
and
denote
A
A this plot of ů
F
increases.
Interestingly,
the
Kendall’s
τ
for
trients
increasing,
and
with
foliar
[C]
and
ρ
F
x d
error as part of FW , we have also included LA1 in ( RW ∩relationship
ies in the opposite direcrelationship
observed
observed
suggests suggests
athe
strong
aintegrated
strong
Intersecting
and
in
same
in the
direction
same direction
relarela- F increases.
versus Intersecting
is greater
than
forand
any of
theand
original
variInterestingly,
Kendall’s
τ forinttr
RW and
RW
FW
FW
F of 0.63
), this
also
showing
that
varies
the
opposite
of Amazon
of Amazon
tropical
forest
tropical
trees
forest
to soil
trees
fertility,
to soil with
fertilm
FW
tive to M
tive
to
ΦLS
M
. Aet
Although
isal.
ΦLS
. Although
with
with
estimated
ain
estimated
standarddirecstandard
ables examined
by
(2009),
theaithigh
highest
ofhigh
which
versus
A isFyllas
RW cf.
FW .
F of 0.63 is greater than for any of the
Variable
log(MA )
[C]
any given species) with the results shown in Table 4. T
shows that 0.33 of the total variation in the 11 traits examin
denoted as PFL .
could be explained by the first PCA axis (ů 1 ) with ρx an
Overall the five eigenvectors selected, all of which we beportant contributor and this also relating785
positively to fo
S. Patiño et al.: Tropical
tree
trait
dimensions
lieve to be physiologically relevant (see Supplementary In[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examin
A
formation), accounted for 0.68 of the total variance for both
and
also
with
.
The
second
axis
of
the
PCA
on
the
plot
low and high fertility soil species.
along with MA and, of opposite sign, 8LS . Also of note here
fects
correlation
matrix
(ů
)
is
also
significant,
account
2
The first three axes species scores (normalised
to ±
100) high value for the coefficient of the diffusion
is the
relatively
for 0.25 of the variance, with substantial negative weighti
are plotted against each other in Supplementarylimitation
Information
index, which is positively associated with both
for MA , foliar [C] and (and to a lesser extent foliar [
Fig. S1. This shows the required lack of anyHmax
systematic
and MA . Interestingly,
for this component LA varies
being balanced by positive weightings
for foliar [Mg] in p
correlations between the species scores as expected
for
the
in the opposite direction to 8LS (albeit with a large stanticular,
but
also
with
contributions
from
ΦLS and ρx .
S. Patiño
et al.:components
Tropical
tree
trait suggesting
dimensions that there is a tendency towards conoutput from any good fit of
a principle
model.
dard
error)
Clearly a wide range of combinations of these three
trait disiderably
fewer but also significantly larger leaves in taller
mensions can occur. But with
8a also
it
is
any but
given
species) with the res
does Fig.
not seem
to showing
havestatured
beenthat
recognised
before.
is thus
here
species.
There It
also
being
a modest
significant
3.8
Relationship
between
plot
effect
a
(generally speaking) onlydenoted
species as
typically
associated
with
shows
that
0.33
of the PCAs
total varia
negative contribution of ρx to this dimension. We consider
PFL .
soil/climate
could
be
explained
by
the
first
P
. its own
high fertility soils that have scores
forthe
both
U3and
, which
on
accounts
for be0.08 and 0.10 of the variOverall
five eigenvectors
selected,
all of
which we
PDJ
RW
portant
contributor
and
this
als
Figure 7 shows the major
components
of theation
threein
major
lieve
to be physiologically
relevant
(see Supplementary
the dataset
respectively, toIn-contain several features
[C] and M
negatively with
Patiño
et al.:
Tropical
tree
trait
dimensions
A , butfor
CPCs and their overlap ofS.
traits
in diagrammatic
form.
This
formation),
accounted
for
0.68
of
the total
variance
for bothand Westoby
similar
to
those
described
by Falster
(2005)
The
most
significant
relationships
between
the PCA site a
and
also
with
.
illustrates that many traitslow
seem
be fertility
“shared”,
andtohigh
soilespecially
species.
climax
tropical forest in Australia, and it is thus denoted asThe second ax
scores
of
Table
4,
and
previously
calculated
soil
fects
correlation
matrix
(ů clim
is
15
20
25 does
30
2 )res
any
given
species)
withand
the
not
seem
to
have
been
recognised
before.
It
is
thus
here
The for
firstallthree
(normalised to ± 100)
. , scores
FW
MA which is an important
factor
threeaxes
of species
PDJ
RW
-1
characteristics
of
the
same
sites
are
shown
in
Fig.
9.
First,
for
0.25
of
the
variance,
with
su
shows
that
0.33
of
the
total
varia
Foliar [N] (mg g )denoted
are plotted against
each The
otherfourth
in Supplementary Information
axis is dominated by S and 8LS
PFL
∩ . RW ) and
of the component
and FW . Also occurring in ( as
topofpanel
ofsystematic
Fig. 9 shows ůcould
a be
function
theand
firstsoil
P
for
foliar of
[C]
(and
1 asM
A , explained
Fig.
S1. PDJ
Thisfive
shows
the these
required
lack
any
with
coefficients
of
different
sign. Associated
with the by the first P
Overall
the
eigenvectors
selected,
all
of
which
we besame sign is [C], but with [P] and [Mg] varying in opposite
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
being
balanced
by
positive
weig
portant[P]
contributor
correlations
between higher
the species
as
expected
for Inthe
to these
be physiologically
relevant
(see lower
Supplementary
S arescores
also
[Ca] but
higher
foliar
and LA . and this also
directions with respect to lieve
MA for
two trait dimensions.
. Thewith
stro
tegrated
measure
of soil fertility
and
ticular,
butAdenoted
also
contributio
F
[C]
and
M
, but with
negatively
output
from
any
good
fit
of
a
principle
components
model.
formation), accountedWith
for 0.68
of the
totalfor
variance
for both and higher standard
lower
values
their coefficients
relationship
observed
suggests
a
strong
integrated
respo
Intersecting RW and FW
and
in
the
same
direction
relaand also
withterms.
. The second ax
Clearly
a wide
range soil
of
combinations
threesign,
trait are
di- the M
low
and high
fertility
species.
errors,
also beingof
ofthese
different
[N]
A and
of
Amazon
tropical
forestfects
trees
to soil
fertility,
with(ůmost
tive to MA is ΦLS . Although
with
a can
highoccur.
estimated
standard
correlation
matrix
S.
Patiño
et
al.:
Tropical
tree
trait
dimensions
2 ) is
mensions
But
with
Fig.
8a
also
showing
that
it
is
The first three axes As
species
scores (normalised
to ± 100)
mentioned
intrients
the Discussion,
U
forand
0.09
4 (accounting
increasing,
and
with
foliar
[C]
ρ
decreasing
x
for
0.25
of
the
variance,
with su
3.8
Relationship
between
(generally
speaking)
species
typically
associated
with
error as part of FW , we are
have
also included
LAonly
in
(
∩
RW
plotted
against
each
other
in
Supplementary
Information
and 0.07 of the population
variance for low and
high fertility
τ forand
this plot
of
F increases. Interestingly,
forthe
MKendall’s
, foliar
(and
soil/climate
A
any
given[C]
species)with
t
does
notin
seem
tospecies
haverequired
been
recognised
before.
ItbeisRW
thus
here
This
shows
the
lack
of
any
systematic
and
.
highitS1.
fertility
soils
have
scores
for
both
that
varies
thethat
opposite
direcrespectively)
seems
to
dominated
by
the
PDJ
FW ), this also showing Fig.
versus F of 0.63 is greater
thanbalanced
forpresence
any of
the
original
v
being
by
positive
weig
shows
that
0.33
of
the
tota
correlations
between
the
species
scores
as
expected
for
the
7 shows
the
of the
major
seeded members
ofthree
the Leguminaceae
imporas FW
. largecomponents
PFL
. ofmajor
tion relative to MA and ΦLS Figure
fordenoted
ables
examined
by Fyllasticular,
et al.whose
(2009),
the
highest
of wh
RW cf.
but also
with
contributio
be explained
by the
output
from
anyoverlap
good
fitof
oftraits
a principle
components
model.
CPCs and
their
in phytogeography
diagrammatic
form.
This
tance
in
the
of
Amazon
has with
already
Overall
the
five
eigenvectors
selected,
allfor
of foliar
which
we Comparison
be-forestcould
was
0.56
[P].
Fyllas
et
al.
(20
The most
significant
relationshi
portant
contributor
and th
Clearly
a wide
range
of
combinations
these
three
trait
illustrates
that
many
traits
seem relevant
to beofalso
“shared”,
especially
lieve
to be
physiologically
(see
Supplementary
Inbeen
recognised
by
ter
Steege
et
al.
We
therefore
3.6 Bivariate relationships:
environmental
components
shows
that
thediů 2 (2006).
contains
significant
weightings
of
le
scores[C]
of and
Table
previous
M4,
but negative
mensions
can
occur.
But
with
Fig.
8aall
also
it for
isindividually,
A ,and
formation),
accounted
for
0.68
of
theshowing
total
both
denote
this
dimension
as variance
.that
TS
MA which
is an
important
factor
for
three
of
,that,
level
variables
were
all
strongly
correla
PDJ
RW
characteristics
of the.
same
3.8
Relationship
between
and
with
Thesites
sec
(generally
speaking)
only
species
typically
associated
with
low
and
highfertility
fertility
soil
species.
data from1.5
both
low
and
high
sites
The
lasttoeigenvector
included
inthe
our
analysis,
U5Aalso
, )differs
0.5 Considering 1.0
2.0
with
mean
annual
precipitation
(P
viz.9positive
.
Also
occurring
in
(
∩
)
and
of
and
top
panel
of
Fig.
shows
ů1corre
as a(
FW
PDJ
RW
soil/climate
fects
correlation
matrix
-1 high fertility
gether, Foliar
Table 3[P]lists
correlations
and
SMA
slopes
for
the
enThe
first
three
axes
species
scores
(normalised
to
±
100)
and
have [P]
scores
for
both
others
intions
having
substantially
greater
importance
(mg g ) same sign is soils
PDJ ain
RW
with
foliar
[C].and M
a negative
correlation
A and
[C], that
but from
with
and
[Mg]
varying
opposite
axis
offorFyllas
et al.
(2009),
thew
0.25
of
the
variance,
w
vironmental effects with this
information
provided
in more
are 7plotted
against
other
in Supplementary
Information
Figure
shows
the for
major
of trait
the
three
low
fertility
versus
high
fertility
speciesnot
(accounting
for
foliar
[Mg].
It major
is therefore
surprising,
asofissoil
shown
in
directions
with
respect
toeach
MAcomponents
for these
two
dimensions.
tegrated
measure
fertility
for
M
,
foliar
[C]
and
detail (including confidence
intervals)
in the
Supplementary
Fig.
S1.
This
shows
the0.04
lack
ofform.
any
systematic
CPCs
and their
overlap
of traits
inrequired
diagrammatic
This
0.09
and
of the
population
This
second
panel
ofvariances
Fig.
9, thatrespectively).
ů 2 and PA A
also
show strong
as
relationship
observed
suggests
being
balanced
by
positiv
Intersecting
andtraits
and
in be
the“shared”,
same direction
rela-for the
The
most
significant
relationshi
FW
Information (Table S2A). illustrates
As
forcorrelations
Table
the between
SMA
slopes
rethe
species
scores
as
expected
that1, RW
many
seem
to
especially
ciation, but by
withHexamination
ofhaving
Tabletropical
4 also suggesting
t
component is characterised
MAAmazon
oppomax and of
forest
trees
but4,also
with
cont
tive
tooutput
MAxisas
Φthe
Although
with
a high
estimated
standardmodel.
scoresticular,
of Table
and
previousl
LS . any
flect the relationship y ↔ x,
with
the
column
headers
from
good
fit
of
principle
for any
given
species,
Φ
and
ρ
also
decline
w
site
signs
(in
tocomponents
) ,and
with both
higher
S
and
also
LS
w
FW
MA
which
is an
important
factor
foracontrast
all
three
of
PDJ
RW
trients increasing,
with
fol
characteristics
of theand
same
sites
S.
Patiño
et
S.,range
al.:
Patiño
Tropical
et
al.:tree
Tropical
trait
dimensions
tree
trait
dimensions
error as
part
we
have
also
included
L
in
( three
∩; this
and the y being the row labels.
For
theof
traits,
the
Clearly
astructural
wide
ofassociated
combinations
trait
diFW
RW
increasing
precipitation
and,
somewhat
counter intuitiv
being
withof
a Athese
lower
H
also
being along
max
increases.
Interestingly,
thea
.
Also
occurring
in
(
∩
)
and
of
the
and
F panel
top
of
Fig.
9
shows
ů
as
FW
PDJ
RW
1
most significant relationshipsFW
are
all
and
appear
mensions
can
occur.
with
Fig.with
8a
showing
that
it is
increasing.
),
thisisnegative
also
showing
that
itbevaries
thealso
opposite
direcwith
aBut
less
substantial
but
significant
coefficient
forF ρof
Alsois greater tha
x . 0.63
versus
same[Ca],
sign
[C], speaking)
but with
[P]
and
[Mg]invarying
in
opposite
axis
of3.8
Fyllas
etgiven
al. (2009),
thew
Relationship
betw
tween ρx and log10 [P], log
log
to
a
lesser
only
species
typically
associated
with
any
any
species)
given
does
not and,
seem
does
to
not
have
seem
been
to
have
recognised
been
recognised
before.
It
before.
is
thus
here
Itfoliar
is
thus
here
10 (generally
10 [K]
oftoΦinfluence
in
characterising
U
are
greater
[C]
asso5
cf.
.
tion relative
to respect
MA and
for
ables
examined
by
Fyllas
et al.
directions
with
M
for
these
two
trait
dimensions.
RW
FW
LSA
tegrated
measure
of
soil
fertility
Finally,
as
in
Fyllas
et
al.
(2009)
we
show
values
soil/climate
extent log10 (`A ). The slopes observed
(−0.26soils
tociated
−0.41)
shows thatshows
0.33 of
that
the
and Although,
.
high denoted
fertility
that.have
for both
with
the . higher
MA and
U5 for
presents
asdenoted
asare,scores
PDJ .
RWwas
PFL
PFL
foliar
[P].
Compar
relationship
observed
partial
τ (denoted
τ0.56
all
traits
ofsuggests
interest
Intersecting
and for
and
in the Kendall’s
same direction
relap ) forcould
however, much less than for
the associated
the
taxRWslopes
FW
be
explained
could
be
ex
b
Figure
7
shows
the
major
components
of
the
three
major
some
combinations
as reported
previously
inwe
the
literOverall
the
Overall
fivetrait
eigenvectors
the
five eigenvectors
selected,
all
selected,
of which
all we
of
which
be-shows
be3.6 Bivariate
relationships:
environmental
also
that
the T
ůforest
2,contains
of
Amazon
tropical
as components
ů 1 and
ů 2 as functions
of portant
Patrees
and
tive in
to Table
MA is1Φ(−0.37
with a highwell
estimated
standard
F , T , contributor
a
onomic components as listed
to
−0.72).
LS . Although
portant
con
a
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
lieve to believe
physiologically
to this
be physiologically
relevantmostly
(see
relevant
Supplementary
(see with
Supplementary
In-variables
In-that,
ature,
component,
related
species
found
at τindividually
level
trients
increasing,
with
fol
in Table
5. ( Here∩the calculated
value
of and
and
asso
p
The
most
significant
relat
[C]
and
M
[C]
,
but
and
nega
M
error
as
partformation),
ofdatathat
,many
we
have
also
included
L
in
A
A
FW
A
RW
illustrates
traits
seem
to
be
“shared”,
especially
Considering
from
both
low
and
high
fertility
sites
toformation),
accounted
accounted
for
0.68
of
for
the
0.68
total
of
variance
the
total
for
variance
both
for
both
low fertility soils,ated
doesprobability
not seem togiving
have been
recognised
bemean annual
precipitatio
increases.
Interestingly,
the
anwith
of
the with
effect
of
eK
Findication
3.7 Principal component analysis of environmental efscores
of
Table
4, and
pre
and
also
and
.
also
The
w
gether,
Table
3
lists
correlations
and
SMA
slopes
for
the
enlow
and
high
low
fertility
and
high
soil
fertility
species.
soil
species.
),
this
also
showing
that
it
varies
in
the
opposite
direcfore. It is thus here
denoted
as PFL
tions after
with
foliar
[C]
and Mthe
an
all three of
, RWversus
A
PDJ. parameter
0.63correlation
is greater
than
soil/environmental
accounting
F of
2
6
10 FW
14MA which is an important factor for
fects
characteristics
of for
thecorre
same
fects
fects
ma
vironmental
effects
with
this
information
provided
inselected,
more
The
first
three
The
first
axes
three
species
axes
scores
species
(normalised
scores
(normalised
toTaking
±
100)
to
±account
100)
Overall
the
five
eigenvectors
all
of
which
we
befoliar
[Mg].
It
is
therefore
not
-1 tion
relative
to
M
and
Φ
for
cf.
.
ables
examined
by
Fyllas
et
al.
RW
FW
A
LS
fect
of
the
other
four.
into
to
the
poten
and FW . Also occurring in ( PDJ ∩ RW ) and of the
Foliar [Ca] (mg g )
top panel
of Fig.
9 0.25
shows
for 0.25
offor
the
varian
ofů
detail (including
confidence
intervals)
ineach
theSupplementary
Supplementary
are plotted
are
against
each
against
other
in
other
in
Supplementary
Information
Information
to
be
physiologically
relevant
(see
Supplementary
second
panel
of InFig.
9,
that ů 2eta
was
0.56
for
foliar
[P].
Compar
Especially given the strong relationships
ρplotted
the
effects
of spatial
autocorrelation
(Fyllas
same signbetween
is [C],lieve
but
with
[P]
andconfounding
[Mg] varying
in opposite
x and
axis
of
Fyllas
et
al.
(2009
for
M
,
foliar
for
M
[C]
,
fo
a
A
A
Information
(Table
S2A).
As
for
Table
1, thethe
SMA
slopes
re-systematic
Fig.
S1.
Fig.
This
shows
This
shows
required
lack
required
ofof
any
lack
ofrelationships
any
systematic
ciation,
butfor
with
examination
os
formation),
accounted
for
0.68
the
total
variance
both
3.6
Bivariate
components
also
shows
that
the
ů 2being
contains
foliar cation environmental
components
(Fig.8)
, itS1.
was
ofthe
we
only
consider
with
pbalanced
≤
0.01
or
bet
directionsrelationships:
with respect
toenvironmental
M
these two
trait dimensions.
tegrated
measure
of
soil
f
A for 2009)
being
by
balan
po
flect the
relationship
ylow
↔
x,
with
thefertility
x as the
column
Fig. 6. Standard Major Axis (SMA) regression lines
between
decorrelations
correlations
between
the
between
species
the
scores
species
asheaders
scores
expected
as for
expected
the
for
thethat,
any
given
species,
both
Φ
and
high
soil
species.
level
variables
individually
additional interest to see if coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τforshown
inticular,
Fig.
9, but
Table
5 sugge
relationship
observed
sug
ticular,
also
with
but
Intersecting
and
and
in
the
same
direction
relaRW
FW
rived taxonomic components of foliar [N] and foliar
[P] yand
leafthe output
and the
being
rowany
labels.
For
the
structural
the (normalised
output
from
good
fit
of
good
aaxes
principle
fitto
ofbea traits,
components
principle
components
model.
model.
increasing
precipitation
and, s
data
both
low
and
high
fertility
sites
toThe
first
three
species
scores
tothe
± 100)
with
mean
annual
precipitation
chemical responses to the Considering
environment
existisfrom
for
Amazon
for-any
the
superior
predictors
than
individual
variab
of Amazon
tropical
forest
tive
to
M
Φ
with
a of
high
estimated
standard
Arelationships
LS . Although
mass per unit area (LA ) for the top two panels and
between
species
most
significant
are
allcombinations
negative
and
appear
beClearly
a
wide
Clearly
range
a
wide
of
range
combinations
of
these
three
of
these
trait
three
ditrait
diwith
increasing.
gether,
Table
3
lists
correlations
and
SMA
slopes
for
the
enare
plotted
against
each
other
in
Supplementary
Information
with
[C]
and[K]
M
an
est. We therefore undertook (S)
a PCA
analysis
of the full plot
the only exception being tions
Ta . In
thatfoliar
case,
[N],
and
A
trients
increasing,
and
w
estimated foliar [Ca] associated average seed mass
for
the
astween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
error
aseffects
part10ofand
,S1.
we
have
also
included
L
in8a( also
∩
mensions
can
occur.
can
But
occur.
with
Fig.
But
8a
with
also
Fig.
that
showing
itany
is [Mg].
that
itItisisregressing
x mensions
FW
Ashowing
RW
vironmental
with
this
information
provided
in
more
10
10
Fig.
This
shows
the
required
lack
ofpresent
systematic
foliar
therefore
not
s
effects
correlation
matrix
(excluding
H
S
both
of
all
show
relationships
not
when
the
max
increases.
Interestingl
F 3.8
sociated genus
(bottom
panel).
Open regressions
circles indicate
species
found
Finally,
as inRelationship
Fyllas
al.p
Fig. 6. Standard
Major
Axis
(SMA)
lines
between
derived
taxonomic
3.8 etRelat
extent
log10
(`A ). The
slopes
observed
(−0.26
tospecies
−0.41)
are,direc(generally
(generally
speaking)
speaking)
only
only
typically
associated
typically
associated
with
with
detail
(including
intervals)
in
the
Supplementary
also
showing
that
itspecies
varies
inPCs
the
opposite
correlations
between
the
species
scores
as
expected
for
the
second
panel
of
Fig.
9,
that
ů
a
FW ), thisconfidence
which
were
considered
to
be
environmentally
invariant
for
effect
as
dependent
variables.
2
versus
of
0.63
is
great
F soil/climate
fertility
andfoliar
the closed
indicate
species
found
Kendall’s partial
τ (denoted
τ
soil/c
however,
less
than
for
associated
for
taxomponentsonoflow
foliar
[N]sites
and
[P] circles
and leaf
mass
permuch
unit
area
(L
)from
for
the1,
top
Information
(Table
S2A).
As
for
Table
theslopes
SMA
slopes
reAthe
andcomponents
. examination
high
fertility
high
soils
fertility
that
have
soils
scores
that
have
for
both
for
both
ciation,
but RW
with
of
output
any
good
fit scores
of
a.the
principle
model.
PDJ
PDJ
RW .and
tion
relative
to
M
and
ΦLS
for
ables
examined
by
Fyllas
on high fertility sites. Species found on both soil
fertility
groups
RW cf. to
FW
A
well
as
ů
and
ů
as
function
1
2
onomic
components
as
listed
in
Table
1
(−0.37
−0.72).
the associated
relationship
↔
x, with
the
x range
asthe
themajor
column
headers
wo panels are
anddesignated
between
species estimated foliarflect
[Ca]
mass
for
given
species,
both
Figure y7average
shows
Figure
the
shows
major
components
components
of the three
major
the any
three
major
Clearly
a7 seed
wide
of
combinations
ofofthese
three
traitHere
di- foliar
was
0.56
for
[P].ΦCL
by a “+” (see text for details). For the top two panin Table
5.
the calcula
andspecies
the3.6y being
the
row
labels.
For
the
structural
traits,
the
S) for the associated
genus.
Open
circles
indicate
found
on
low
fertility
sites
increasing
precipitation
and,
so
CPCs
and
CPCs
their
overlap
and
their
of
overlap
traits
in
of
diagrammatic
traits
in
diagrammatic
form.
This
form.
This
mensions
can
occur.
Figure
8a
also
shows
that
it
is
(generally
Bivariate
relationships:
environmental
components
els, solid lines show the SMA fit for low fertility soil
which
also shows
that
the an
ůmost
2 con
ated probability
giving
inds
3.7 species
Principal
component
analysis
of environmental
efThe most
The
significant
most
significant
relationships
are
all
negative
and
appear
bewith
increasing.
illustrates
illustrates
that
many
that
traits
many
seem
traits
to
be
seem
“shared”,
to
be
“shared”,
especially
especially
speaking)
only
species
typically
associated
with
high
fertility
nd the closed
circles
indicate
species
found
on
high
fertility
sites.
Species
found
on
are significantly different to the dashed lines for high fertility
soil
levelscores
variables
that, indivi
soil/environmental
fects
of parameter
Table
scores
4,ofanT
tween
ρx and
log10data
[P],soils
log
[Ca],
log
[K]
and, for
to
aboth
lesser to10
10two
Considering
from
both
low
and
high
fertility
species.groups
For the bottom
panel the solid
shows
the text
SMA
model
.the
that
have
high
scores
with
mean
annual
precip
PDJ
RW
M
whichMis
which
important
is
antop
important
factor
for
all
factor
three
forofallsites
three
, of
,
oth soil fertility
are designated
bylines
a ”+”
(see
for
For
the
A details).
Aan
PDJand
RW
PDJ
RW
fect
of
other
four.
Taking
Finally,
as
in
Fyllas
et
al.
characteristics
characteris
of
the
extent
log
(`
).
The
slopes
observed
(−0.26
to
−0.41)
are,
A
10 Table
whichshow
did notthe
depend
on soil
fertility.
3 strong
listsFigure
correlations
andbetween
SMA slopes
forthethe en-of
7Also
shows
the
three
major
tions
with
foliar
and
anels, solidfitlines
SMA
fit for
low fertility
soilgether,
species
which
are
significantly
Especially
given
the
relationships
and
confounding
effects
of[C]
spatial
and
and
. Also
occurring
. associated
occurring
inthe
( major
inρ∩xcomponents
(the
)∩
and of
the
) and
of
the
Kendall’s
partial
τ
(denoted
τopM
top
panel
of
top
Fig.
panel
9
sh
FW
FW
PDJfor
RW
PDJ
RW
however,
much
less
than
for
the
slopes
taxvironmental
effects
with
this
information
provided
in
more2009)foliar
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
[Mg].
It
is
therefor
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
we
only
consider
relation
same
sign
same
is
[C],
sign
but
is
with
[C],
[P]
but
and
with
[Mg]
[P]
and
varying
[Mg]
in
varying
opposite
in
opposite
ifferent to the dashed lines for high fertility soil species.
For
the
bottom
panel
the
solid
well as ů 1 axis
and of
ů 2Fyllas
asaxis
functions
et
of al.
Fyl(
onomicdetail
components
as confidence
listed in Table
1 (−0.37
to −0.72).
(including
intervals)
in the
Supplementary
second
panel
of
Fig.
tha
additional
interest
todirections
see respect
if coordinated
structural/leaf
bioAsTable
for
the5.(full)
Kendall’s
τ9,sho
directions
with
with
to respect
MA for
tothese
MAtwo
for these
trait
dimensions.
two trait
dimensions.
in
Here
the
calculat
tegrated
measure
tegrated
of
ms
nes shows the SMA model fit which did not depend
on
soil
fertility.
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
reciation,
but
with
examina
chemical
responses
to
the environment
exist
for
Amazon
forthe
to berelationship
superior
predictors
ated
probability
giving
an
indi
relationshi
observed
3.7
Principal
component
analysis
of
environmental
efwww.biogeosciences.net/9/775/2012/
Biogeosciences,
9,
775–801,
2012
Intersecting
Intersecting
and
and
and
in
the
and
same
in
direction
the
same
reladirection
relaRW
RW
FW
FW
flect
the relationship
y↔
x,
with the x asofthe
column
headersthe only
for exception
any
givenparameter
species,
est. We
therefore
undertook
PCA
the
fulla plot
being
Ta . bo
soil/environmental
of Amazon
of
tropical
Amazon
faI
fects
tiveytobeing
M
tive
isthe
ΦtoLS
M
. aA
Although
is ΦLSanalysis
.For
Although
with
a structural
high
with
estimated
high estimated
standard
standard
A
and
the
row
labels.
the
traits,
the
increasing
precipitation
a
effects correlation matrix (excluding Hmax and S both of
all show
relationships
not
pres
fect
of
the
other
four.
Taking
trients
increasing,
trients
incr
an
most
significant
relationships
are
all
negative
and
appear
beerror
as
part
error
of
as
part
,
we
of
have
,
also
we
have
included
also
L
included
in
(
L
∩
in
(
∩
FW
FW
A
RW
A
RW
with
increasing.
which were
considered
to relationships
be environmentally invariant
for
effect PCs asFeffects
dependent
variabla
Especially
the log
strong
the
confounding
ofF spatial
increases.
increase
Interes
x andto
tweengiven
ρx and
[P], log [Ca], between
log [K]ρand,
a lesser
0.010
0.010
.1.0
0.1
Seed mass (g)
10
0.001
Leaf
area (m2)
Leaf area (m-2)
0.100
0.001
Leaf area (m2)
0.100
does not seem to have been recognised before. It is thus here
786
S. Patiño et al.: Tropical tree trait dimensions
Д PDJ
Д RW
[K]
[P]
[C] [Mg] [Ca]
[N]
LA
Φ
ρ
Table 4. Summary of the Principal Components Analysis of the
correlation matrix for the derived environmental/soil effects on observed structural and physiological traits. Coefficients given in bold
are those whose values are 0.3 or more. MA = leaf mass per unit
area; elemental concentrations are on a dry weight basis, LA = leaf
area; 8LS = leaf area: sapwood area ratio, ρx = branch xylem density, = diffusion limitation index (see Eq. 1).
Variable
M
Component
S. Patiño
S. Patiño
et al.: Tropical
et al.: Tropical
tree
treedimensions
trait dimensions
7
7
A trait
ů1
ů2
atiño
ait dimensions
S.etPatiño
al.: Tropical
et al.: Tropical
tree traittree
dimensions
trait
dimensions
7
7
7
LS
log(MA )
−0.196 −0.443
any given
any species)
given species)
with the
with
results
the results
shownshown
in Table
in 4.
Table
This
4. This
does not
does
seem
notto
seem
havetobeen
haverecognised
been recognised
before.before.
It is thus
It ishere
thus here
[C]
−0.300 −0.412
shows
shows
that
0.33
that
of
0.33
the
total
of
the
variation
total
variation
in
the
11
in
the
traits
11
examined
traits
examined
any
given
species)
with
the
any
results
given
any
shown
species)
given
in
species)
with
Table
the
4.
with
results
This
the
shown
results
in
shown
Table
in
4.
Table
This
4.
This
ognised
notdoes
seem
before.
not
to seem
have
It
been
is
to
thus
have
recognised
here
been
recognised
before.
It
before.
is
thus
It
here
is
thus
here
denoted
denoted
as XPFL
as . PFL .
0.320
0.111
log[N]
tree
Tropical
trait dimensions
tree. trait Overall
dimensions
7 the
could
be
could
explained
bevariation
explained
by11
the
by
first
PCA
first
axis
PCA
(ůaxis
(ů
ρx an imshows
that 0.33
of the
total
variation
shows
in0.33
thethat
11
of the
traits
0.33
total
of
examined
the
variation
total
in7the
intraits
the
11
examined
traits
examined
1 ) with
1 )ρwith
x an imoted
denoted
as
Overall
the fivethe
eigenvectors
fivemax
eigenvectors
selected,
selected,
all ofshows
which
all ofthat
which
we
bewe
belog[P]
0.406
−0.276
PFLas
PFL .
portant
portant
contributor
contributor
and
this
and
also
this
relating
also
relating
positively
positively
to
foliar
to foliar
could
be
explained
by
the
first
could
PCA
be
could
explained
axis
be
(ů
explained
)
with
by
the
ρ
first
an
by
imPCA
the
first
axis
PCA
(ů
)
axis
with
(ů
ρ
)
an
with
imρ
an
im1
x
1
1
x
x
lieve
bebephysiologically
to be physiologically
relevant
relevant
(see
(see
In- In- log[CA ]
0.453
0.099
verall
selected,
the
Overall
five
all ofeigenvectors
the
which
fivetolieve
eigenvectors
we
selected,
selected,
all of which
all ofwe
which
be- Supplementary
we
be-Supplementary
[C]
and
[C]
M
and
,
but
M
negatively
,
but
negatively
with
all
with
foliar
all
nutrients
foliar
nutrients
examined
examined
e
trait
dimensions
S.
Patiño
S.
et
Patiño
al.:
Tropical
et
al.:
Tropical
tree
trait
tree
dimensions
trait
dimensions
7
7
7
portant
contributor
and
this
portant
also
relating
contributor
portant
positively
contributor
and
this
to
and
foliar
also
this
relating
also
relating
positively
positively
to
foliar
to
foliar
A
A
any
given
any
species)
given
with
species)
the
results
with
the
shown
results
in
shown
Table
4.
in
Table
This
4.
This
en
o
have
recognised
been
recognised
before.
It
before.
is
thus
here
It
is
thus
here
log[K]
0.392
−0.300
formation),
formation),
accounted
accounted
for(see
0.68
for
of0.68
theInof
total
thevariance
total
for both
for both
eevant
to lieve
be(see
physiologically
toSupplementary
be physiologically
relevant
Inrelevant
(see
Supplementary
Supplementary
In- variance
and
also
and
with
also
.
with
The
.
second
The
second
axis
of
axis
the
of
PCA
the
on
PCA
the
on
plot
the
efplot ef[C]
and
M
,
but
negatively
[C]
with
and
all
M
[C]
foliar
,
and
but
nutrients
M
negatively
,
but
examined
negatively
with
all
foliar
all
nutrients
foliar
nutrients
examined
examined
shows
that
shows
0.33
of
that
the
0.33
total
of
variation
the
total
in
variation
the
11
traits
in
the
examined
11
traits
examined
A
A
A
log[M
]
0.245
0.416
low
and
low
high
and
fertility
high
fertility
soil
species.
soil
species.
g
mation),
of. the
formation),
total
accounted
variance
accounted
forfor
0.68
both
for
of the
0.68total
of the
variance
total variance
for both for both
FL
fects
correlation
fects
correlation
matrix
matrix
(ů
is(ůalso
)7is
significant,
also
significant,
accounting
accounting
and
also
with
.
The
second
and
axis
also
of
and
with
the
also
.
PCA
with
The
on
second
.
the
The
plot
axis
second
efof
the
axis
PCA
of
the
on
the
plot
the
efplot
efno
Tropical
et
al.:
Tropical
tree
trait
tree
dimensions
trait
dimensions
27)PCA
2on
could
be
explained
could
be
explained
by
the
first
by
PCA
the
first
axis
PCA
(ů
)
with
axis
(ů
ρ
an
)
with
imρ
an
im0.087
−0.009
log(L
)
any
given
species)
with
the
results
shown
any
given
in
any
Table
species)
given
4.
species)
This
with
the
with
results
the
shown
results
in
shown
Table
in
4.
Table
This
4.
This
recognised
does
before.
not
does
seem
It
is
not
thus
to
seem
have
here
to
been
have
recognised
been
recognised
before.
before.
It
is
thus
It
here
is
thus
here
1
x
1
x
A
and
es.
low
high
and
fertility
high
soil
fertility
species.
soil
species.
The
first
The
three
first
axes
three
species
axes
species
scores
scores
(normalised
(normalised
to
±
100)
to
±
100)
vectors
five eigenvectors
selected, all
selected,
of which
all we
of which
be- we befor
0.25
for
0.25
the
variance,
of
the
variance,
with
substantial
with
substantial
negative
negative
weightings
weightings
fects
correlation
matrix
(ů
fects
is
correlation
also
fects
significant,
correlation
accounting
(ů
matrix
)of
also
significant,
isthe
also
significant,
accounting
accounting
log(8
)(ůfoliar
0.025
0.271
portant
contributor
and
this
also
and
relating
this
also
positively
relating
positively
to
tototal
foliar
2 ) variation
2
2 )of
shows
0.33
the
total
in
shows
thematrix
11
that
shows
traits
0.33
examined
that
ofis
the
0.33
total
variation
variation
in
the 11
in
traits
the
11
examined
traits
examined
FW
LS
are
plotted
are
against
against
each
other
each
in
other
Supplementary
inofto
Supplementary
Information
Information
he
scores
first
The
(normalised
three
first
axes
three
toaxes
± 100)
scores
species
(normalised
(normalised
to
±portant
100)
±contributor
100)
lly
siologically
relevant
(see
relevant
Supplementary
(see
InIn-that
denoted
denoted
asspecies
asplotted
.Supplementary
. scores
PFL
PFL
for
M
for
,
foliar
M
,
[C]
foliar
and
[C]
and
(and
to
(and
a
lesser
to
a
lesser
extent
extent
foliar
foliar
for
0.25
of
the
variance,
with
for
substantial
0.25
for
of
the
0.25
negative
variance,
of
the
variance,
weightings
with
substantial
with
substantial
negative
negative
weightings
weightings
ρ
−0.383
0.287
A
[C]
and
M
[C]
,lack
but
and
negatively
M
,given
but
negatively
with
all
foliar
nutrients
all
foliar
examined
nutrients
examined
xAbe
could
be
explained
by
the
first
PCA
axis
could
(ů
be
with
explained
ρx explained
an
by
imthe first
by
the
PCA
first
axis
(ů 1axis
) with
(ů ρ
an imρx an[P])
im- [P])
AInformation
A
ot
o seem
have
to
been
have
recognised
been
recognised
before.
before.
It
iseigenvectors
thus
Itselected,
here
isrequired
thus
here
any
given
any
species)
species)
with
the
with
results
the
shown
results
in
shown
Table
in4.Table
This
4. PCA
This
1 )could
1 )x with
Fig.
S1.
Fig.
This
S1.
shows
This
shows
the
the
required
of
lack
any
ofsystematic
any
plotted
nors
Supplementary
areof
against
plotted
each
against
other
each
in
other
Supplementary
in
Supplementary
Information
rounted
0.68
forthe
0.68
total
variance
the
total
for
variance
both
for
both
selected,
Overall
all
ofInformation
which
Overall
five
we
the
eigenvectors
befive
selected,
all
of
which
all
of
we
which
bewesystematic
be
0.174
−0.340
being
balanced
being
balanced
by
positive
by
positive
weightings
weightings
for
foliar
for
[Mg]
foliar
in
[Mg]
parfor
M
,
foliar
[C]
and
for
(and
M
to
,
for
a
foliar
lesser
M
[C]
,
extent
foliar
and
[C]
foliar
(and
and
[P])
to
a
(and
lesser
to
a
extent
lesser
foliar
extent
[P])
foliar
[P])
Fig.
7.
Euler
diagram
showing
overlaps
between
the
first
three
also
with
and
also
.scores
The
with
second
The
axis
second
of
the
axis
PCA
of
on
the
the
PCA
plot
on
efthe
plot
efA
A
A
portant
contributor
and
this
also
relating
portant
positively
portant
contributor
to
contributor
foliar
and
this
and
also
this
relating
also relating
positively
positively
to foliarto foliarin parshows
that
shows
0.33
that
of
the
0.33
total
of
the
variation
total
variation
in
the
11
intraits
the
11
examined
traits
examined
correlations
between
between
theand
species
the
scores
as
expected
as.
expected
for
the
for
the
uired
S1.
lack
This
S1.
of
shows
This
any
systematic
shows
required
lack
of
lack
any
systematic
ofspecies
any
systematic
lrelevant
species.
soil
(see
lieve
Supplementary
tocorrelations
lieve
bethe
physiologically
to
bethe
physiologically
In-required
relevant
relevant
(see
Supplementary
(see
Supplementary
InIndertility
as
. Fig.
. species.
FL
PFL
ticular,
ticular,
but
also
but
with
also
contributions
with
contributions
from
Φ
from
and
ΦLS
ρxexamined
and
. ρ .
dimensions
for
the
individual
measured
traits
(where
significant):
being
balanced
by
positive
being
weightings
balanced
being
for
balanced
by
foliar
positive
[Mg]
by
positive
weightings
in
parweightings
for
foliar
for
[Mg]
foliar
in
[Mg]
parin
parLS
fects
correlation
fects
correlation
matrix
(ů
matrix
)
is
also
(ů
significant,
)
is
also
significant,
accounting
accounting
[C]
and
,bebut
negatively
with
allboth
foliar
[C]
and
nutrients
[C]
Mfirst
and
,axis
but
examined
M
negatively
negatively
foliar
nutrients
foliar6.23
nutrients
examined
2 for
2by
could
be
could
explained
be
explained
by
the
first
the
PCA
PCA
(ůA1,axis
)but
with
(ůρ1with
)x with
an all
imρwith
imA
AEigenvalue
output
output
from
any
from
good
fit
good
of
aM
fitwe
principle
offor
avariance
principle
components
components
model.
model.
x an all
elations
es
scores
correlations
between
as
expected
between
the accounted
species
for
the
the
scores
species
scores
expected
as
expected
the
for
the
2.54 x
pecies
ee
scores
species
(normalised
scores
(normalised
to
±
100)
toas
±
100)
.68
of
the
formation),
total
variance
formation),
for
both
accounted
for
0.68
for
of
0.68
the
total
of
the
total
variance
for
both
five
allaxes
the
eigenvectors
five
eigenvectors
selected,
selected,
all
of
which
allany
of
we
which
beblue; positive relationship
with
dimension,
red;
negative
relationticular,
but
also
with
contributions
ticular,
but
ticular,
from
also
Φ
but
with
and
also
contributions
ρ
with
.
contributions
from
Φ
from
and
Φ
ρ
.
and
ρ
.
for
0.25
of
for
the
0.25
variance,
of
the
with
variance,
substantial
with
substantial
negative
weightings
negative
weightings
LS
x
LS
LS
x
x
and
also
with
.
The
second
axis
of
and
the
PCA
also
and
with
on
also
the
.
plot
with
The
ef.
second
The
axis
second
of
the
axis
PCA
of
the
on
PCA
the
plot
on
the
efplot efportant
portant
contributor
contributor
anddithis
and
also
relating
also relating
positively
positively
to foliarto foliar
Clearly
aahigh
wide
asoil
range
wide
of
range
combinations
of
combinations
of these
of three
these
trait
three
trait
di-this
principle
ut
output
any
components
from
good
any
fitand
good
of
model.
principle
fit (see
of
aInformation
principle
components
components
model.
model.
other
inst
each
in Supplementary
other
inClearly
Supplementary
Information
ecies.
low
and
low
high
fertility
fertility
species.
soil
species.
siologically
befrom
physiologically
relevant
(see
Supplementary
Supplementary
InInship
withrelevant
dimension,
black;
offor
different
sign
depending
on
the
diProportion
of
variance
explained
0.33
0.25
M
,
foliar
for
M
[C]
,
foliar
and
[C]
(and
and
to
a
(and
lesser
to
extent
a
lesser
foliar
extent
[P])
foliar
[P])
fects
correlation
matrix
(ů
)
is
also
fects
significant,
correlation
fects
accounting
correlation
matrix
(ů
matrix
)
is
(ů
also
)
significant,
is
also
significant,
accounting
accounting
Awith
A
[C]
and
[C]
M
and
,±but
M
negatively
, but
negatively
with
foliarall
nutrients
foliar nutrients
examined
examined
2100)
2
2
mensions
mensions
can
occur.
can
occur.
But
with
But
Fig.
8aFig.
also
8a
showing
also
showing
that
it that
is
it is all with
A±
nations
rly
aClearly
wide
of
these
range
alack
wide
three
range
combinations
trait
of
dicombinations
of
these
of
three
these
trait
three
ditrait
dihe
shows
required
the(normalised
required
any
lack
of
any
systematic
es
scores
The
first
three
to
first
±
axes
100)
three
species
axes
scores
(normalised
scores
(normalised
toA
to
100)
on),
ounted
accounted
for
0.68
for
ofofThe
0.68
the
total
ofsystematic
the
variance
total
variance
forspecies
both
both
mension.
Abbreviations
are
as
in for
Table
5,variance,
with
the
three
trait
dibeing
balanced
being
by
balanced
positive
by
weightings
positive
weightings
for
foliar
[Mg]
for
foliar
in
par[Mg]
in
parfor
0.25
of
the
with
substantial
for
0.25
negative
for
of
the
0.25
weightings
variance,
of
the
variance,
with
substantial
substantial
negative
negative
weightings
weightings
3.8
Relationship
3.8
Relationship
between
plot
effect
effect
PCAs
PCAs
and and
and
also
and
with
also
.
with
The
.
second
The
axis
second
of
the
axis
PCA
of
the
on
PCA
the between
plot
onwith
the
efplot
ef- plot
(generally
(generally
speaking)
speaking)
only
species
only
species
typically
typically
associated
associated
with
with
Fig.
sions
8a
can
also
occur.
showing
can
But
occur.
with
that
But
it
Fig.
is
with
8a
also
Fig.
showing
8a
also
showing
that
it
is
that
it
is
tween
species
the
scores
species
as
scores
expected
as
expected
for
the
for
the
er
inmensions
Supplementary
are
plotted
are
against
plotted
Information
against
each
other
each
in
other
Supplementary
in
Supplementary
Information
Information
dertility
high
fertility
soil
species.
soil
species.
mensions as defined in Sect. 3.5
ticular,
but
ticular,
also
with
but
also
contributions
with
contributions
from
Φ
from
and
ρ
Φ
.
and
ρ
.
for
M
,
foliar
[C]
and
(and
to
a
for
lesser
M
extent
for
,
foliar
M
foliar
,
[C]
foliar
and
[P])
[C]
(and
and
to
(and
a
lesser
to
a
extent
lesser
foliar
extent
[P])
foliar
[P])
soil/climate
soil/climate
LS
x
LS
x
fects
correlation
fects
correlation
matrix
(ů
matrix
)
is
also
(ů
)
significant,
is
also
significant,
accounting
accounting
3.8
Relationship
between
3.8
Relationship
plot
3.8
effect
Relationship
PCAs
between
between
and
plot
effect
plot
effect
PCAs
PCAs
and
and
A
A
cies
erally
(generally
speaking)
speaking)
only
species
with
only
typically
species
typically
associated
associated
with
with
tfirst
yequired
ofgood
atypically
principle
fit
of S1.
aassociated
principle
components
components
model.
model.
Fig.
lack
of
Fig.
any
This
S1.
systematic
shows
This
the
shows
required
the
required
lack
of
lack
any
of
systematic
any and
systematic
ee
axes
three
species
axes
species
scores
(normalised
scores
(normalised
to
±
100)
to
± 100)
and. 2 RW
. 2 A
high
fertility
high
fertility
soils
that
soils
have
that
scores
have
scores
for
both
for
both
PDJ
PDJ RW
being
balanced
by
positive
weightings
being
for
foliar
balanced
being
[Mg]
balanced
by
in
positive
parby
positive
weightings
weightings
for
foliar
for
[Mg]
foliar
in
[Mg]
parin
par3.7
Principal
component
analysis
of
for
0.25
for
of
the
0.25
variance,
of
the
variance,
with
substantial
with
substantial
negative
negative
weightings
weightings
soil/climate
soil/climate
soil/climate
combinations
range
of
combinations
these
three
trait
didiecies
scores
correlations
asof
correlations
between
for
the
the
species
the
species
scores
as
scores
expected
expected
for
the
themajor
inst
ted
each
against
other
each
inexpected
other
Supplementary
inof
Supplementary
Information
Information
and
.between
andPDJ
and
. as
. of
ores
fertility
high
for
both
soils
fertility
that
soils
have
that
scores
have
for
scores
both
forthe
both
Figure
Figure
7these
shows
7three
shows
thetrait
major
major
components
components
of RW
the
three
thefor
major
three
PDJ
RW
PDJ
RW
ticular,
but
also
from
ticular,
Φ
ticular,
and
ρlesser
with
contributions
with
contributions
from Φ
from
andΦρLS
for with
MAcomponents
for
,contributions
foliar
M
, foliar
and
[C]
(and
and
to
(and
aalso
to aalso
extent
lesser
foliar
extent
[P])
foliar
[P])
environmental
effects
LSbut
x .but
LS
x . and ρx .
A [C]
occur.
with
But
with
8a
Fig.
showing
8alack
also
that
showing
it
isof
that
itsystematic
isthree
a principle
output
components
from
output
any
from
model.
good
any
fittheir
good
aany
fit
principle
of
aof
components
model.
1.
shows
This
the
shows
the
required
of
lack
any
systematic
CPCs
CPCs
and
their
and
overlap
overlap
of
traits
ofprinciple
in
traits
diagrammatic
in diagrammatic
form.model.
form.
This This
omponents
gure
7Fig.
Figure
shows
ofrequired
7also
the
the
shows
three
major
the
major
components
major
components
of
the
the
major
three
major
being
balanced
being
balanced
by
positive
by
positive
weightings
weightings
for
foliar
for
[Mg]
foliar
in
[Mg]
parin
parThe
most
The
significant
most
significant
relationships
relationships
between
between
the
PCA
thesite
PCA
axis
site axis
3.8
Relationship
3.8
Relationship
between
between
plot
effect
plot
PCAs
effect
and
PCAs
and
aking)
species
only
typically
species
associated
typically
associated
with
with
mbinations
ofoverlap
these
Clearly
a species
wide
three
range
atrait
wide
diof
range
combinations
of
combinations
of to
these
of“shared”,
three
trait
three
di- traitespecially
ditween
between
theClearly
species
the
scores
as
scores
expected
astraits
expected
for
the
for
the
illustrates
illustrates
that
many
that
many
traits
seem
traits
seem
be
tothese
be
“shared”,
especially
Cs
syions
in
and
CPCs
diagrammatic
their
and
their
form.
of
overlap
traits
This
of
in
traits
diagrammatic
in
diagrammatic
form.
form.
This
illustrates
that
many
seem
to
beThis
“shared”,
especially
Given
the
correlations
between
the
environmental
effects
for
ticular,
but
ticular,
also
but
with
also
contributions
with
contributions
from
Φ
from
and
Φ
ρ
.
and
ρ
.
scores
scores
of
Table
of
4,
Table
and
4,
previously
and
previously
calculated
calculated
soil
and
soil
climate
and
climate
The
most
significant
relationships
The
most
between
The
significant
most
the
significant
PCA
relationships
site
relationships
axis
between
between
the
PCA
the
site
PCA
axis
site
axis
soil/climate
soil/climate
LS
LS
x
x
Fig.
8a
mensions
also
showing
mensions
can
occur.
that
can
But
occur.
isseem
But
Fig.
with
8a
Fig.
also
8a
showing
also
showing
that
it of
is,that
it is
from
yth
any
fit
good
of
athat
principle
of
aM
principle
components
components
model.
model.
trates
m
to
illustrates
be
that
“shared”,
many
traits
many
especially
seem
traits
to
be
be
“shared”,
and
and
.“shared”,
. especially
ve
ilsgood
scores
that
have
for
scores
both
for
both
M
which
is
important
factor
for
all
three
of
PDJ
PDJ
RW
RW
Afit
PDJ
M
which
isitan
important
iswith
anto
important
factor
for
factor
allespecially
for
three
all
three
, RW
A which
A an
PDJ
PDJ
RW,
RW
ρ
and
several
foliar
nutrients
(Table
3;
Fig.
8),
it
was
of
x
characteristics
characteristics
of
the
same
of
the
sites
same
are
sites
shown
are
shown
in
Fig.
in
9.
Fig.
First,
9.
the
First,
the
scores
of
Table
4,
and
previously
scores
of
calculated
scores
Table
of
4,
Table
soil
and
and
previously
4,
climate
and
previously
calculated
calculated
soil
and
soil
climate
and
climate
3.8
Relationship
between
plot
3.8
effect
Relationship
3.8
PCAs
Relationship
and
between
between
plot
effect
plot
effect
PCAs
PCAs
and
and
pecies
typically
(generally
(generally
associated
speaking)
speaking)
with
only
species
only
species
typically
typically
associated
associated
with
range
a for
wide
of
range
combinations
of
combinations
of
these
of
three
these
trait
three
ditraitof
ajor
ws
the
components
components
ofFW
the
three
of
major
the
three
major
and
. factor
occurring
in
(three
∩
) ,andRW
of) with
the
same
PDJ
RW
or
which
M
all
ismajor
which
three
an
important
of
isand
an
important
for
factor
all
three
for
all
of
,Also
, diA
PDJ
RWAlso
PDJ
RW
PDJ
.
.
occurring
Also
occurring
in
(
in
(
∩
∩
and
)
of
and
the
of
the
and
additional
interest
to
see
if
coordinated
structural/leaf
biotop
panel
top
of
panel
Fig.
of
9
shows
Fig.
9
shows
ů
as
a
ů
function
as
a
function
of
the
first
of
the
soil
first
PCA
soil
PCA
FW
FW
PDJ
PDJ
RW
RW
characteristics
of
the
same
characteristics
sites
are
characteristics
shown
of
in
the
Fig.
same
9.
of
First,
the
sites
same
the
are
sites
shown
are
in
shown
Fig.
9.
in
First,
Fig.
9.
the
First,
the
soil/climate
soil/climate
soil/climate
1
1
occur.
ns
canBut
occur.
with
But
Fig.
with
8afertility
also
Fig.
8a
showing
also
showing
that
it[Mg]
isscores
that
it for
is PDJ
of
overlap
traits
inhigh
ofboth
diagrammatic
traits
in
diagrammatic
form.
This
form.
This
and
. is[P]
andPDJ
and
.direc.
scores
for
fertility
high
soils
that
soils
have
that
scores
have
for
both
both
sign
[C],
with
and
varying
in
PDJ
RW
RW
RW
same
sign
same
is
sign
[C],
[C],
with
but
[P]
and
[P]
[Mg]
and
varying
[Mg]
varying
opposite
in
opposite
( FW
. only
Also
∩FW
occurring
.only
Also
)is
and
occurring
ofbut
inthe
(typically
inbut
∩
( associated
)∩with
and
of
the
and
ofopposite
the
and
chemical
responses
the
environment
exist
for Amazon
axis
of
axis
Fyllas
etaxis
al.ato(2009),
et
al.
(2009),
the
latter
the
considered
latter
considered
a strong
a forstrong
in- intop
panel
ofmost
Fig.
9) shows
ůsignificant
as
panel
ainfunction
top
of
panel
Fig.
of 9the
of
shows
Fig.
first
9soil
ů1PCA
shows
as
PCA
aof
function
ůFyllas
as
function
of
the
first
of soil
the
first
PCA
soil PCA
PDJ
RW
PDJ
PDJ
RW
RW
The
The
significant
most
relationships
relationships
between
the
between
site
the
PCA
site
axis
er
diagram
showing
overlaps
between
the
first
three
dimensions
in
terms
1top
1
3.8
Relationship
3.8
Relationship
between
between
plot
effect
plot
effect
PCAs
PCAs
and
and
aking)
lly
speaking)
species
species
typically
associated
with
with
many
seem
traits
to Figure
be
seem
“shared”,
to
be
“shared”,
especially
especially
reits
components
of
the
7
Figure
shows
three
7
major
the
shows
major
the
components
major
components
of
the
three
of
the
major
three
major
tions
with
respect
to
M
for
these
two
trait
dimensions.
InA
directions
with
respect
with
respect
to
M
tofor
M
these
for
two
these
trait
two
dimensions.
trait
dimensions.
and
sign
same
[Mg]
is [C],
sign
varying
but
isdirections
[C],
with
in opposite
but
[P]with
and [Mg]
[P]
and
varying
[Mg]
in
opposite
in(2009),
opposite
est.
We
therefore
undertook
PCA
analysis
ofF .the
full
plotstrong
The
strong
.
The
tegrated
tegrated
measure
measure
of
soil
of
fertility
soil
fertility
and
denoted
and
denoted
Avarying
Aal.
axis
of
Fyllas
et
axis
the
of
latter
Fyllas
axis
considered
of
et
Fyllas
al.
(2009),
a
et
strong
al.
the
(2009),
inlatter
the
considered
latter
considered
a
strong
a
instrong
inF
scores
of
Table
scores
4,
of
and
Table
previously
4,
and
previously
calculated
calculated
soil
and
climate
soil
and
climate
soil/climate
soil/climate
dual
measured
traits
(where
significant):
blue;
with
raits
insoils
diagrammatic
CPCs
and
CPCs
their
form.
and
overlap
their
This
overlap
ofFW
traits
of
in
traits
diagrammatic
in
form.positive
This
form. This
and
and
.thetrait
. direction
rtility
ils
that
have
that
scores
have
for
scores
both
for
both
tersecting
and
and
in
same
relative
torelationship
PDJ
PDJ
RW
RWdiagrammatic
RW
nt
nctions
important
factor
for
factor
all
three
for
of
all
three
of
,these
, trait
RW
PDJ
RW
or
these
directions
with
two
respect
trait
with
dimensions.
to
respect
M
for
to
M
for
two
these
two
dimensions.
dimensions.
effects
correlation
(excluding
and
Sresponse
bothaxis
of
relationship
observed
observed
suggests
a strong
aHPCA
strong
integrated
integrated
response
. significant
The
strong
. suggests
The
. The
strong
tegrated
measure
of
soil
fertility
tegrated
and
tegrated
measure
denoted
measure
of
soil
fertility
ofrelationship
soil
fertility
denoted
and
denoted
APDJ
A
max
characteristics
characteristics
of
the
same
ofespecially
sites
the
same
areThe
shown
sites
in
Fig.
shown
9.
First,
inand
Fig.
the
9.matrix
First,
the
Intersecting
Intersecting
and
and
and
in
and
the
in
same
the
direction
same
direction
relarelaFare
F
F
The
most
significant
relationships
between
most
the
The
PCA
most
site
significant
axis
relationships
relationships
between
between
the
the
site
PCA
axis
site
RW
RW
FW
FW
seem
to
be
illustrates
“shared”,
illustrates
that
especially
many
that
traits
many
seem
traits
to
seem
be
“shared”,
to
be
“shared”,
especially
re
ws
7
the
shows
major
the
components
major
components
of
the
three
of
the
major
three
major
M
is
8
.
Although
with
a
high
estimated
standard
error
as
d;
negative
relationship
with
dimension,
black;
of
different
sign
dependA
LS ) ∩
of
Amazon
of
Amazon
tropical
trees
forest
totrees
soil
to
fertility,
soil
fertility,
with
most
with
numost nuing
so in
occurring
inthe
( same
in
∩
( RW
and
ofLS
the
)is
and
of
thesame
were
considered
toPCA
beintegrated
environmentally
invariant
for
observed
relationship
strong
relationship
observed
observed
suggests
suggests
atropical
strong
aforest
integrated
strong
response
response
top
panel
of
top
Fig.
panel
9estimated
shows
of
Fig.
ůa91standard
as
shows
acalculated
function
ůintegrated
aof
of
function
theresponse
first
of
soil
the
PCA
first
soil
PDJ
PDJ
RW
RW
tive
to
tive
Mrelais FW
M
Φ
. relationship
Although
Φ
.the
Although
with
arelawith
high
asuggests
high
estimated
standard
rsecting
nd
Intersecting
and
direction
and
and
in
the
and
same
in
direction
direction
relascores
of
Table
4,
and
previously
scores
scores
soil
Table
and
of
4,
climate
Table
and
previously
4,
and
previously
calculated
calculated
soil
and
soil
climate
and
climate
1 aswhich
Ato
A
LS
RW
FW
nd
their
overlap
of[Mg]
traits
of
inAtraits
diagrammatic
in
diagrammatic
form.
This
form.
This
part
of
,in
we
have
also
included
L
inof
(three
∩, Amazon
),of, this
FW
A all
RW
actor
for
all
M
three
M
isstandard
which
an
important
is
an
important
factor
for
factor
all
three
for
of
,varying
A
PDJ
RW
PDJ
RW
PDJ
RW
mension.
trients
trients
increasing,
increasing,
and
with
and
foliar
with
[C]
foliar
and
[C]
ρFirst,
and
decreasing
ρthe
decreasing
as
as
C],
hoverlap
[P]
but
and
with
[P]
and
varying
[Mg]
opposite
in
opposite
of
tropical
forest
trees
of
to
soil
Amazon
fertility,
tropical
forest
tropical
with
most
forest
nutothe
soil
trees
fertility,
to
soil
fertility,
with
most
with
numost
any
given
with
the
results
shown
Table
4.
This
xnuxFirst,
axis
of
Fyllas
axis
et
of
al.
Fyllas
(2009),
etFW
al.
the
(2009),
latter
considered
the
latter
considered
a species)
strong
inathe
strong
inwith
to
M
ative
high
ismany
to
Φestimated
M
.which
is
Although
Φto
.be
Although
with
apart
high
with
estimated
a FW
high
estimated
standard
standard
characteristics
ofalso
the
same
sites
are
shown
in
characteristics
Fig.
9.trees
of
First,
the
the
same
of
sites
same
are
sites
shown
are
inshown
Fig.
9.inin
Fig.
9.
the
The
most
The
significant
relationships
relationships
between
between
PCA
the
site
PCA
axis
site
axis
A
LSseem
A
LS
error
as
error
part
asof
of
,Amazon
we
have
,the
we
also
have
included
included
Lmost
L
(significant
in
(characteristics
∩ RW
∩
FW
A inrelative
ARW
many
es
that
traits
traits
seem
“shared”,
to
be
“shared”,
especially
especially
also
showing
that
it
varies
in
opposite
direction
increases.
increases.
Interestingly,
Interestingly,
the
Kendall’s
the
Kendall’s
τ
for
this
τ
for
plot
this
of
plot
ů
of
ů
orespect
M
for
to
these
M
two
for
these
trait
dimensions.
two
trait
dimensions.
trients
increasing,
and
with
trients
foliar
increasing,
trients
[C]
and
increasing,
ρ
and
decreasing
with
and
foliar
with
as
[C]
foliar
and
[C]
ρ
decreasing
and
ρ
decreasing
as
as
in
(error
∩
)(Also
and
of
.also
occurring
Also
the
occurring
in included
(tegrated
inof
(∩L
∩)ofscores
and
the
and
ofpreviously
the
and
and
that
ofF 9.the
total
11the
traits
examined
F
1
1
.FThe
strong
strong
measure
tegrated
of
measure
soil
fertility
of
soil
and
fertility
denoted
and
denoted
A
A FW
x ofin
top
panel
Fig.
9∩(shows
ů∩of
as
aand
function
top
panel
ofxshows
the
top
of
first
panel
Fig.
90.33
of
shows
PCA
Fig.
ůThe
shows
axfunction
ůvariation
a function
thethe
first
of
soil
first
PCA
soil PCA
PDJ
RW
FW
PDJ
PDJ
RW
RW
F soil
scores
Table
4,
Table
4,
and
previously
calculated
calculated
soil
and
soil
climate
and
1)of
1 as
1 asclimate
also
as
part
included
of
asfactor
part
LM
,Afor
we
in.factor
have
,for
we
∩
have
included
also
L
(that
in
FW
Aof
FW
RW
A in
RW
A it
RWin
),
this
also
this
showing
also
that
varies
varies
in varying
the
opposite
the
opposite
direcdirecto
and
8
for
cf.
. it
FW
FW
RW
FW
LS
nrP]
ich
important
is
an
important
three
all
of
three
of
,showing
, and
PDJ
RW
PDJ
RW
versus
of
0.63
of
is
0.63
greater
isfirst
greater
than
for
than
any
ofa any
the
original
increases.
Interestingly,
the
increases.
Kendall’s
Interestingly,
τsites
for
this
plot
the
ofal.
Kendall’s
ůshown
Kendall’s
τFig.
for
this
τconsidered
plot
for
this
ofaxis
ůconsidered
plot
ů 1of
and
[Mg]
same
varying
sign
same
in
[C],
sign
opposite
but
is),
[C],
with
but
[P]
with
and
[P]
[Mg]
varying
[Mg]
in opposite
inthe
opposite
could
be
explained
by
the
PCA
(ů
) with
ρstrong
anvariimF
F
relationship
relationship
suggests
aincreases.
suggests
strong
integrated
aInterestingly,
strong
integrated
response
response
F axis
Fobserved
Fsame
1(2009),
1for
of
Fyllas
etobserved
al. (2009),
the
latter
considered
of
Fyllas
axis
aofversus
strong
et
Fyllas
inetthe
al.
(2009),
the
latter
the
latter
strong
athe
inin- varix original
1of
characteristics
characteristics
of
ofaxis
the
same
are
sites
shown
are
in
Fig.
9.
in
First,
9.
the
First,
the
and
inFW
the
and
same
inisall
the
direction
same
reladirection
relaRW
FWand
t ),varies
this
also
in
),the
this
showing
opposite
also
showing
that
direcvaries
that
in
varies
the
opposite
in
the
opposite
direcdirecW
FW
tion
relative
tion
relative
toRW
M
toand
M
Φ)of
and
for
Φ
for
cf.
cf.
.than
.versus
examined
ables
examined
by
Fyllas
by
Fyllas
etoriginal
al.
(2009),
et
al.
(2009),
theThe
highest
the
highest
oftowhich
of which
versus
of
0.63
is
greater
versus
any
of
0.63
the
isof
greater
original
0.63
is
greater
varifor
than
any
of
for
the
any
ofdenoted
the
original
varivarifor
these
directions
two
trait
directions
with
dimensions.
respect
with
to
respect
for
to
M
these
for
two
these
trait
two
dimensions.
trait
dimensions.
RW
RW
FW
FW
A
LS
LS
Amazon
of
tropical
Amazon
forest
tropical
trees
forest
to tegrated
soil
trees
fertility,
to
with
fertility,
most
numost
nuso
Also
in
(estimated
in
(∩itestimated
∩)AitM
and
of
the
and
of
the
portant
contributor
and
this
also
relating
foliar
F
Fand
The
strong
strong
strong
tegrated
measure
of
soil
fertility
denoted
tegrated
measure
measure
of
soilwith
fertility
of
soil
fertility
and
and
denoted
A
A
A
top
panel
top
of
panel
Fig.
9for
ofshows
Fig.
9ůofshows
aables
function
ůsoil
athan
function
of
the
first
of
the
soil
first
PCA
soil
PCA
FW
PDJ
RW
ough
Although
with
aoccurring
high
with
aPDJ
high
standard
standard
F
F .positively
F . The
1 Fas
1 .as
LS .occurring
rrelative
tion
cf.
to
relative
M
and
.
to
M
Φ
for
Φ
for
cf.
cf.
.
.
was
0.56
was
for
0.56
foliar
for
[P].
foliar
Comparison
[P].
Comparison
with
Fyllas
with
Fyllas
et
al.
(2009)
et
al.
ables
examined
by
Fyllas
et
ables
al.
(2009),
examined
ables
the
examined
by
highest
Fyllas
by
of
et
Fyllas
which
al.
(2009),
et
al.
the
(2009),
highest
the
of
highest
which
of
which
RW
FW
RW
RW
FW
FW
A
A
LS
LS
trients
increasing,
trients
increasing,
and
with
and
foliar
with
[C]
foliar
and
ρ
[C]
decreasing
and
ρ
decreasing
as
as
C],
gn
but
is
[C],
with
but
[P]
with
[P]
[Mg]
and
varying
[Mg]
varying
in
opposite
in
opposite
[C]
and
M
negatively
with
all
foliar nutrients
examined
observed
suggests
strong
relationship
integrated
relationship
observed
response
observed
suggests
suggests
aainstrong
a integrated
strong
integrated
response
response(2009)
xthe
xconsidered
axis
Fyllas
axis
ofet
Fyllas
al.a(2009),
et
al. (2009),
the
latter
considered
latter
a strong
strong
inA , but
and
inwe
the
Intersecting
same
Intersecting
direction
relaand
and
in∩the
andsame
in theof
direction
same
direction
relarelaW
RW
FW
FW
e have
have
included
also
L
included
in (and
LRW
∩
in
(relationship
FW ,also
A
RW
A
RW
3.6
Bivariate
3.6
Bivariate
relationships:
relationships:
environmental
environmental
components
components
3.6
Bivariate
relationships:
environmental
components
also
shows
also
shows
that
the
that
ů
the
contains
ů
contains
significant
significant
weightings
weightings
leafwas
0.56
for
foliar
[P].
Comparison
was
0.56
was
for
with
0.56
foliar
Fyllas
for
[P].
foliar
et
Comparison
al.
(2009)
[P].
Comparison
with
Fyllas
with
et
Fyllas
al.
(2009)
et
al.
(2009)
2
2
increases.
increases.
Interestingly,
Interestingly,
the
Kendall’s
the
Kendall’s
τ
for
this
plot
τ
for
of
this
ů
plot
of
ů
ns
respect
with
to
respect
M
for
to
M
these
for
two
these
trait
two
dimensions.
trait
dimensions.
of
Amazon
tropical
forest
trees
to
soil
of
fertility,
Amazon
of
with
Amazon
tropical
most
tropical
forest
nutrees
forest
to
trees
soil
fertility,
to
soil
fertility,
with
most
with
numost
nuandfertility
alsodenoted
with
of the PCA on the of
plot
ef-of leaf1 second
1axis
. The
strong
strong
measure
measure
of
soil fertility
of soil
and
and.
denoted
h with a high
tive Ato
estimated
M
tive
toΦM
standard
. isAlthough
ΦLS . Although
withF a high
withtegrated
estimated
aFhigh tegrated
estimated
standard
standard
FThe
F . The
A isA
LSA
goenvironmental
that
showing
it varies
that
initcomponents
the
varies
opposite
in
the
direcopposite
direcBivariate
3.6
Bivariate
relationships:
relationships:
environmental
environmental
components
components
level
variables
level
variables
that,
individually,
that,
individually,
were
all
were
strongly
all
strongly
correlated
correlated
also
shows
that
the
ů
contains
also
significant
shows
also
that
shows
weightings
the
ů
that
contains
the
of
ů
leafcontains
significant
significant
weightings
weightings
of
leafof
leafversus
versus
of
0.63
is
of
greater
0.63
is
than
greater
for
any
than
of
for
the
any
original
of
the
varioriginal
vari2
2
2
trients
increasing,
and
with
foliar
[C]
trients
and
ρ
increasing,
trients
decreasing
increasing,
and
as
with
and
foliar
with
[C]
foliar
and
[C]
ρ
and
decreasing
ρ
decreasing
as
as
fects
correlation
matrix
(ů
)
is
also
significant,
accounting
Frelationship
F
relationship
suggests
suggests
ax stronga integrated
strong integrated
response
x
2 response x
cting
andRW
andas
and
in
the
and
same
in
the
direction
relarelaRW also
FW
ave
included
error
error
part
LFW
of
as
( part
of
,∩
wesame
have
, direction
we
also
have
included
also
included
L
in
(high
Lobserved
in
∩sites
( observed
∩ toA in
FW
RW
FW
A
A
RW
RW
Considering
Considering
data
from
data
both
from
low
both
and
low
high
and
fertility
fertility
tosites
with
mean
with
mean
annual
annual
precipitation
precipitation
(P
)
viz.
(P
)
positive
viz.
positive
correlacorrelalevel
variables
that,
individually,
level
variables
were
level
all
variables
that,
strongly
individually,
that,
correlated
individually,
were
all
were
strongly
all
strongly
correlated
correlated
Φ
M
for
and
Φ
cf.
for
.
cf.
.
A
A
ables
examined
ables
examined
by
Fyllas
by
et
al.
Fyllas
(2009),
et
al.
the
(2009),
highest
the
of
highest
which
of
which
increases.
Interestingly,
the
Kendall’s
increases.
τ
for
this
increases.
Interestingly,
plot
of
ů
Interestingly,
the
Kendall’s
the
Kendall’s
τ
for
this
τ
plot
for
this
of
ů
plot
of
ů
RW
FW
RW
FW
LS
A
LS
of
Amazon
of
Amazon
tropical
tropical
forest
trees
forest
to
trees
soil
fertility,
to
soil
fertility,
with
most
with
numost
nufor
0.25
of
the
variance,
with
substantial
negative
weightings
F
F
F
1
1
1
M
. isAlthough
ΦLS . Although
with
a high
with
estimated
a high
estimated
standard
standard
LSA
gether,
Table
3and
Table
lists
3correlations
lists
correlations
and
SMA
and
slopes
SMA
slopes
for
the
for
enthe
enow
sidering
Considering
high
data
fertility
from
data
both
sites
from
low
toboth
low
high
and
high
fertility
sites
tosites
toat
itand
varies
inConsidering
the
),gether,
this
opposite
also
),
this
showing
direcalso
showing
that
itfertility
varies
that
itand
in
varies
the
opposite
in
the
opposite
direcdirecdata
from
both
low0.56
high
fertility
sites
totions
tions
foliar
with
[C]
foliar
and
[C]
M
and
and
M
a
negative
and
a
negative
correlation
correlation
with
with
FW
FW
with
mean
annual
precipitation
with
mean
(P
with
)
annual
viz.
mean
positive
precipitation
annual
correlaprecipitation
(P
)
viz.
(P
positive
)
viz.
positive
correlacorrelaA
A
was
for
was
foliar
0.56
[P].
for
foliar
Comparison
[P].
Comparison
with
Fyllas
with
et
al.
Fyllas
(2009)
et
al.
(2009)
A
A
A
versus
of
0.63
is
greater
than
for
any
versus
of
the
versus
of
original
0.63
of
is
varigreater
0.63
is
than
greater
for
than
any
for
of
the
any
original
of
the
original
varivaritrients
increasing,
trients
increasing,
and
with
and
foliar
with
[C]
foliar
and
[C]
ρ
and
decreasing
ρ
decreasing
as
as
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
F
F A
F
x
x
of
,SMA
we3FW
have
, we
also
included
also
included
LAcomponents
incorrelations
( Lslopes
inwith
∩(foliar
∩information
FW
RW
RW
vironmental
effects
effects
with
this
information
this
provided
provided
intions
more
in the
more
er,
spart
and
Table
gether,
Table
slopes
correlations
3.have
for
lists
the
correlations
enSMA
and
SMA
for
slopes
the
enfor
the
engether,
Table
3and
and
SMA
slopes
for
the
foliar
[Mg].
foliar
[Mg].
It
is
therefore
It
is
therefore
not
surprising,
not
surprising,
as
is
shown
as
is
shown
in
the
in
the
eships:
relationships:
environmental
environmental
components
with
[C]
and
M
tions
and
with
a
negative
foliar
with
[C]
correlation
foliar
and
[C]
M
and
with
and
M
a
negative
and
a
negative
correlation
correlation
with
with
also
shows
also
that
shows
the
ů
that
contains
the
ů
significant
contains
significant
weightings
weightings
of
leafof
leaffor
tion
cf.lists
relative
tion
relative
tovironmental
M
tolists
M
ΦAtions
forA
Φ
for
cf.
cf.
.
.
A
A
A
ables
examined
by
Fyllas
et
al.
(2009),
ables
examined
highest
ables
examined
of
by
which
Fyllas
by
et
Fyllas
al.
(2009),
et
al.
(2009),
the
highest
the
highest
of
which
of
which
2
2
increases.
increases.
Interestingly,
Interestingly,
the
Kendall’s
the
Kendall’s
τ
for
this
τ
plot
for
this
of
ů
plot
of
ů
RW
FW
RW
RW
FW
FW
A and
LSand
LS
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parF
F
1
1
(including
detail
(including
confidence
intervals)
intervals)
inthat,
the
in
Supplementary
thethat,
Supplementary
nmental
vironmental
provided
with
effects
in
with
more
information
this
information
provided
provided
more
in
more
onformation
this
showing
alsoeffects
showing
that
itdetail
varies
thatthis
itin
varies
the
opposite
inconfidence
the
opposite
direcdirecsecond
second
panel
of
panel
Fig.
of
9,
Fig.
that
9,
ů
that
and
ů
P
and
also
P
show
also
strong
show
strong
assoassoenvironmental
effects
with
this
information
provided
in
more
foliar
[Mg].
Itinisversus
therefore
foliar
not
surprising,
[Mg].
foliar
It
[Mg].
is
as
therefore
is
shown
It
is
therefore
not
in
the
surprising,
not
surprising,
as
is
shown
as
is
in
shown
the
in
the
2
2
A
A
level
variables
level
variables
individually,
individually,
were
all
strongly
were
all
correlated
strongly
correlated
was
0.56
for
foliar
[P].
Comparison
with
was
0.56
Fyllas
was
for
et
0.56
foliar
al.
(2009)
for
[P].
foliar
Comparison
[P].
Comparison
with
Fyllas
with
et
Fyllas
al.
(2009)
et
al.
(2009)
versus
of
0.63
of
is
greater
0.63
is
than
greater
for
than
any
for
of
the
any
original
of
the
original
varivariticular,
but
also
with
contributions
from
8
and
ρ
.
F
F
x
LS
Information
Information
(Table
(Table
S2A).
S2A).
As
for
As
Table
for
1,
Table
SMA
1,ůannual
the
slopes
SMA
slopes
rereilM
ervals)
(including
detail
(including
thedetail
confidence
Supplementary
confidence
intervals)
in
the
Supplementary
in
Supplementary
both
ata
from
low
both
and
high
low
and
fertility
high
sites
fertility
tosites
to-the
ciation,
with
but
with
examination
Table
of
4Table
also
4suggesting
suggesting
that that
second
panel
of
Fig.
9,mean
that
second
and
panel
P
second
ofA
panel
Fig.
show
9,
of
strong
that
Fig.
ůbut
9,
assoand
that
ůAthe
also
and
P
show
strong
show
assostrong
asso(including
confidence
intervals)
in
the
Supplementary
ps:
3.6
Bivariate
3.6
components
Bivariate
relationships:
relationships:
environmental
components
components
with
mean
with
annual
precipitation
precipitation
(P
)shows
viz.
(P
positive
)ciation,
viz.
correlapositive
2examined
A also
2
2examination
A also
also
shows
that
the
ůthe
contains
also
weightings
also
that
shows
the
of
that
ůleafůhighest
contains
significant
significant
weightings
weightings
ofalso
leafof leafative
and
to in
M
Φ
for
ΦLS
for
cf.
. intervals)
. environmental
A
ables
examined
bysignificant
Fyllas
by
et
Fyllas
al.
(2009),
et
al.
(2009),
the
highest
the
of
which
ofof
which
2ables
2Pcontains
2correlaRW
RW
FWcf.
FW
Aenvironmental
A
LSand
flect
the
flect
relationship
the
relationship
ythe
↔
x,
y
with
↔
x,
the
with
x
as
the
the
x
column
as
the
column
headers
headers
or
rmation
Table
Information
1,
(Table
theSMA
SMA
S2A).
(Table
slopes
As
S2A).
for
reTable
As
for
1,
SMA
1,
the
slopes
SMA
reslopes
reelations
lists
correlations
and
and
slopes
SMA
for
slopes
the
enforTable
the
enfor
any
for
given
any
given
species,
species,
both
Φ
both
and
Φ
ρ
and
also
ρ
decline
also
decline
with
ciation,
but
with
examination
ciation,
of
Table
but
ciation,
with
4
also
but
examination
suggesting
with
examination
of
that
Table
of
4
also
Table
suggesting
4
also
suggesting
that
that
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
reLS
LSall
w strongly
w
tions
tions
foliar
with
[C]
foliar
and
M[C]
and
and
aM
negative
and
acorrelation
negative
correlation
with
with
level
variables
that,
individually,
were
level
all
variables
level
variables
correlated
that,
individually,
that,
individually,
were
alleffect
were
strongly
correlated
3.8
Relationship
between
plot
PCAs
andcorrelated with
A[P].
Astrongly
was
0.56
was
for
0.56
foliar
for
foliar
Comparison
[P].
Comparison
with
Fyllas
with
etFyllas
al.
(2009)
et
al.
(2009)
and
the
and
y
being
the
y
the
being
row
the
labels.
row
labels.
For
the
For
structural
the
structural
traits,
traits,
the
the
the
the
flect
relationship
x
as
the
the
relationship
column
y
↔
x,
headers
with
y
↔
the
x,
with
x
as
the
column
x
as
the
headers
column
headers
fects
this
with
information
this
information
provided
provided
in
more
in
more
increasing
increasing
precipitation
precipitation
and,
somewhat
and,
somewhat
counter
counter
intuitively,
intuitively,
hth
low
and
Considering
high
fertility
Considering
data
sites
from
data
toboth
from
low
both
and
low
high
and
fertility
high
fertility
sites
tosites
tofor
any
given
species,
both
for
Φ
any
and
for
given
any
ρ
species,
also
given
decline
species,
both
Φ
with
both
and
Φ
ρ
and
also
ρ
decline
also
with
decline
with
flect
the
relationship
y
↔
x,
with
the
x
as
the
column
headers
foliar
[Mg].
foliar
It
is
[Mg].
therefore
It
is
therefore
not
surprising,
not
surprising,
as
is
shown
as
in
is
shown
the
in
the
ivariate
relationships:
relationships:
environmental
environmental
components
components
LS
LS
LS
w
with
mean annual
precipitation
with
)w viz.
positive
withsoil/climate
annual
mean
correlaannual
precipitation
precipitation
(PwAof
) viz.
(PApositive
) viz. positive
correlacorrelaalso shows
alsothat
shows
the that
ů (P
the
ů 2 mean
contains
significant
significant
weightings
weightings
of leafleafA
2 contains
most
most
significant
relationships
relationships
are
all
are
negative
all
negative
and
appear
and
appear
bebe.the
yand
being
thethe
structural
ythe
being
row
the
labels.
row
the
For
labels.
the
structural
For
the
structural
traits,
the
traits,
the
nce
g For
confidence
intervals)
intervals)
in
the
Supplementary
in
the
with
with
increasing.
increasing.
ions
and
gether,
SMA
slopes
Table
gether,
for
3being
Table
lists
the
correlations
en3Supplementary
lists
correlations
and
SMA
and
slopes
SMA
for
slopes
the
enfor
the
enincreasing
precipitation
and,
increasing
somewhat
increasing
precipitation
counter
precipitation
intuitively,
and,
somewhat
and,
somewhat
counter
counter
intuitively,
intuitively,
second
panel
second
of
Fig.
panel
9,
that
of
Fig.
ů
9,
and
that
P
ů
also
and
show
P
also
strong
show
assostrong
assoand
thetraits,
ysignificant
the
row
labels.
For
the
structural
traits,
the
tions with foliar
and M
and
a that,
negative
tions
tions
correlation
foliar
foliar
with
and
M
andMacorrelated
negative
and a negative
correlation
correlation
with with
2individually,
Aindividually,
2with
Awith
level[C]
variables
level
variables
that,
were
all[C]
were
strongly
all[C]
strongly
correlated
A
Aand
A
tween
tween
ρhigh
and
ρwith
log
and
[P],
log
log
[P],
[Ca],
log
[Ca],
[K]
log
and,
[K]
to
and,
ain
lesser
toTable
lesser
t.reAs
significant
all
most
negative
significant
relationships
and
appear
relationships
are
beall
negative
are
allinformation
negative
and
appear
and
beappear
bexin
xhigh
able
S2A).
for
Table
As
1,
for
the
Table
SMA
1,
slopes
the
SMA
reslopes
reis
information
vironmental
provided
vironmental
effects
more
effects
this
with
this
information
provided
provided
inwith
more
more
10
10
10
10
10
10
with
increasing.
with
examination
increasing.
with
a increasing.
ata
ering
from
data
both
from
low
both
and
low
and
fertility
fertility
sites
tosites
tociation,
ciation,
with
examination
but
of
4The
of
also
Table
suggesting
4is
also
suggesting
that
that
most
significant
relationships
are
allbut
negative
and
appear
bemost
significant
relationships
between
theisPCA
foliar
[Mg].
Itlog
is
therefore
not
surprising,
foliar
as
[Mg].
foliar
is
shown
It)[Mg].
therefore
inApositive
It
the
is
therefore
not
surprising,
not
surprising,
as is shown
as
in
shown
thesiteinaxis
the
with
mean
with
annual
mean
precipitation
annual
precipitation
(P
viz.
(P
)
viz.
positive
correlacorrelaA
Finally,Finally,
as in as
Fyllas
in Fyllas
et al. et
(2009)
al. (2009)
we show
we show
valuesvalues
for for
extent
log
(`
log
).10
The
(`
).slopes
The
slopes
observed
observed
(−0.26
(−0.26
toto
−0.41)
to
−0.41)
are,
are,
en
Ca],
tween
log
and
[K]
log
ρx
and
and,
[P],
log
to
aextent
[P],
lesser
[Ca],
log
[Ca],
[K]
log
and,
[K]
to
alog
and,
lesser
to
a
lesser
Alog
A
nship
x,ρwith
y3↔
the
x,
with
as
the
the
column
x
as
the
headers
column
headers
x
xtween
intervals)
detail
in
the
(including
detail
Supplementary
(including
confidence
confidence
intervals)
intervals)
in
the
Supplementary
in
the
Supplementary
10
10
10
10
10
10
10
10
Table
lists
correlations
lists
correlations
and
SMA
and
slopes
SMA
for
slopes
the
enfor
the
enfor
any
given
for
any
species,
given
both
species,
Φ
both
and
Φ
ρ
also
and
decline
ρ
also
with
decline
with
second
panel
of
Fig.
9,
that
ů
and
P
second
also
show
panel
second
strong
of
panel
Fig.
asso9,
of
that
Fig.
ů
9,
and
that
P
ů
and
also
P
show
also
strong
show
assostrong
assoscores
of
Table
4,
and
previously
calculated
soil
and
climate
ρlog
and
log
[P],
log
[Ca],
[K]
and,
a
lesser
LSA[C]
w
LSM
wa negative
tions
with
tions
foliar
with
foliar
and
MAand
and
a Anegative
and
correlation
correlation
2[C]
2
A
2 withA
x
10
10
10
Kendall’s
Kendall’s
partial
τ we
(denoted
τ (denoted
τpshow
) forτpfor
all
) for
traits
allfor
of
traits
interest
of interest
as
as
Finally,
asfor
in
Fyllas
et
al.
Finally,
(2009)
Finally,
as
we
in show
Fyllas
as
in
values
etFyllas
al. partial
for
(2009)
et al.
(2009)
show
we
values
values
however,
however,
much
much
than
less
for
than
the
associated
the
associated
slopes
slopes
for
the
for
taxthe
taxbserved
log
extent
(`
(−0.26
log
).the
The
to
(`
slopes
−0.41)
). (Table
The
observed
are,
slopes
observed
(−0.26
to
(−0.26
−0.41)
to
are,
−0.41)
are,
gfects
labels.
the
row
For
labels.
structural
For
the
structural
traits,
the
traits,
the
A
A
snt
for
Table
Information
1,
the
Information
SMA
slopes
S2A).
(Table
re-less
As
S2A).
formore
Table
As
for
1,
Table
the
SMA
1,[Mg].
the
slopes
SMA
reslopes
10
10information
ental
with
effects
this
with
this
information
provided
provided
in
in
more
increasing
increasing
precipitation
precipitation
and,
somewhat
and,
somewhat
counter
intuitively,
counter
intuitively,
ciation,
but
with
examination
of
Table
ciation,
4not
also
ciation,
but
suggesting
with
but
examination
with
that
examination
ofin
Table
ofin
4are
Table
also
suggesting
4 alsoinsuggesting
that9. First,
that
extent
log
slopes
observed
(−0.26
to
−0.41)
are,
characteristics
of
the
same
sites
Fig.
foliar
foliar
It[Mg].
is
therefore
It isretherefore
surprising,
not
surprising,
as
is
shown
as
is
shown
the
theshown
10 (`A ). TheKendall’s
well
as
well
ů
and
ů
ů
and
as
ů
functions
as
functions
of
of
,
,
T
,
,
P
,
T
and
,
P
Q
and
Q
partial
τ
(denoted
Kendall’s
τ
)
for
Kendall’s
partial
all
traits
τ
partial
(denoted
of
interest
τ
(denoted
τ
as
)
for
all
τ
)
traits
for
all
of
traits
interest
of
interest
as
as
1
1
2
2
F
T
F
a
T
a
a
a
a
a
onomic
onomic
components
components
as
listed
as
in
listed
Table
in
1
Table
(−0.37
1
(−0.37
to
−0.72).
to
−0.72).
p
p
p
eships
ever,
associated
however,
much
less
slopes
much
than
for
less
for
the
than
the
taxassociated
for
the
associated
slopes
for
slopes
the
taxfor
the
taxtincluding
relationships
are
all
negative
are
all
and
negative
appear
and
beappear
bethe xflect
ashowever,
the
theflect
column
relationship
the
relationship
ySupplementary
↔than
x, with
yfor
↔with
the
x, with
x
asincreasing.
the
the
xcolumn
as
the
column
headers
headers
gwith
confidence
confidence
intervals)
intervals)
inheaders
theless
in
the
Supplementary
with
slopes
increasing.
any
given
species,
both
Φ
and
forρůwany
also
for
given
species,
given
with
species,
both
ΦLS
both
and
ρassoand
also
ρdecline
decline
withfirst with
second
panel
second
ofpanel
Fig.
9,of
that
Fig.
9,
and
that
Pdecline
ůany
and
also
Pshow
also
strong
show
assostrong
much
for
the
associated
for
the
top
of
Fig.
9Here
shows
ůΦ1calculated
as
function
of of
the
soilassociLStaxLS
wa
w also
2the
A
2panel
AHere
in
Table
in
Table
5.
5.
the
calculated
the
value
value
of
τ
and
τ
associand
well
as
ů
and
ů
as
functions
well
as
of
well
ů
and
as
ů
as
and
functions
ů
as
functions
of
of
,
,
T
,
P
and
Q
,
,
T
,
,
P
,
and
T
,
Q
P
and
Q
p
p
1
2
1
F
T
2
1
a
a
2
a
F
T
F
a
T
a
a
a
a
a
nmic
Table
components
onomic
1
(−0.37
components
as
to
listed
−0.72).
as
in
listed
Table
in
1
(−0.37
Table
1
to
(−0.37
−0.72).
to
−0.72).
og
[P],
[Ca],
log
log
[Ca],
[K]
and,
log
to
[K]
a
and,
lesser
to
a
lesser
els.
For
and
the
structural
the
and
y
being
the
traits,
y
the
being
row
the
the
labels.
row
For
labels.
the
For
structural
the
structural
traits,
the
traits,
the
able
ation
(TableAs
S2A).
for Table
Ascomponents
for10
1,Table
the SMA
1, as
theslopes
SMAreslopes
re10S2A).
1010
increasing
precipitation
and,
somewhat
counter
increasing
precipitation
intuitively,
precipitation
and,
somewhat
and,
somewhat
counter
counter
intuitively,
intuitively,
ciation,
ciation,
but to
with
but
examination
withincreasing
examination
ofated
Table
of4of
Table
also
suggesting
4 giving
also
suggesting
that
that
onomic
in
Table
1 (−0.37
−0.72).
axis
Fyllas
et al.
(2009),
the
latter
a strong
probability
ated
probability
giving
an
indication
indication
ofconsidered
the of
effect
the effect
of
each
of each
in listed
Table
5.analysis
the
calculated
in
Table
invalue
5.(2009)
Table
Here
5.τPCA
the
and
Here
calculated
associthe
calculated
value
ofvalue
τpfor
and
ofan
τassociassoci3.7
Principal
3.7
component
component
analysis
of
of
environmental
efefFinally,
as
Finally,
inenvironmental
Fyllas
as
inet
Fyllas
al.
etofal.
we
(2009)
show
we
values
show
forvalues
p
p and
opes
).relationship
The
observed
observed
to
(−0.26
−0.41)
to
are,
−0.41)
ps
are
most
significant
most
and
appear
significant
relationships
berelationships
areare,
all
negative
areHere
all
and
appear
and
beappear
beenship
yallslopes
↔negative
x, with
y(−0.26
↔
the
x, with
x
as
the
thePrincipal
x
column
as
the
column
headers
headers
with
increasing.
with
increasing.
with
increasing.
for negative
any
for
given
any
species,
given species,
both
ΦLS
both
and
Φ
ρ
and
also
ρ
decline
also
decline
with
with
LS
w
w
soil/environmental
soil/environmental
parameter
parameter
after
accounting
after
accounting
for
the
for
efthe efated
probability
giving
an
ated
indication
probability
ated
of
probability
the
giving
effect
an
giving
of
indication
each
an
indication
of
the
effect
of
the
of
effect
each
of
each
fects
fects
nalysis
3.7
oftween
Principal
environmental
component
component
efanalysis
of
environmental
oftraits,
environmental
efefKendall’s
Kendall’s
partial
τto(denoted
partial
τlesser
) for of
all interest
traits ofasinterest as
p ) for allτptraits
for
less
the
than
associated
for
theρtween
associated
slopes
for
slopes
the
taxfor
tax[Ca],
log
and
tolog
ρxanalysis
aFor
and
lesser
[P],
log
log
[P],
[Ca],
log
log
[Ca],
log
and,
and,
a lesser
toτ precipitation
a(denoted
g10
yPrincipal
the
being
row
the
labels.
row
For
labels.
the
structural
the
structural
traits,
the
the
xand,
10 [K]
10
10
10the
10
10 [K]
10 [K]
increasing
increasing
precipitation
and, somewhat
and,offect
somewhat
counter
counter
intuitively,
intuitively,
fect
the
of
other
the
four.
other
Taking
four.
Taking
into
account
into
account
to
the
to
potential
the
potential
soil/environmental
parameter
soil/environmental
after
soil/environmental
accounting
parameter
for
the
parameter
efafter
accounting
after
accounting
for
the
for
efthe
effects
fects
well
as(−0.26
ůbewell
and(−0.26
asů−0.41)
functions
ů 2 (2009)
as functions
of Finally,
,Finally,
T
, PaFyllas
and
, for
Tin
P
and(2009)
Finally,
as
in
Fyllas
et
al.
we
asavalues
Fyllas
etQaal. (2009)
we show
wevalues
show for
values for
Biogeosciences,
9,
2012
www.biogeosciences.net/9/775/2012/
1
2ů 1asand
F , show
Tof
F, in
Tas
a ,Qet
a a al.
nents
inasTable
listed
1in
(−0.37
Table
to
1log
−0.72).
toThe
−0.72).
sgnificant
observed
extent
(−0.26
log
extent
to
−0.41)
).(−0.37
The
(`
are,
slopes
).775–801,
observed
slopes
observed
to
to
−0.41)
are,
are,
tsted
relationships
relationships
are
all
negative
are
all
negative
and
appear
and
beappear
A
A
10 (`
10
with
between
increasing.
with
increasing.
Especially
Especially
given
the
given
strong
the
strong
relationships
relationships
between
ρ
and
ρother
the
and
the
confounding
confounding
effects
effects
ofassocispatial
of
autocorrelation
autocorrelation
(Fyllas
et al.,
fect
of
the
other
four.
Taking
fect
of
into
fect
account
the
four.
to
other
the
Taking
potential
four.
into
Taking
account
into
account
to
thespatial
potential
totraits
the
potential
xpthe
xof
in
Table
in
5.
Table
Here
5.
the
Here
calculated
the
calculated
value
of
value
τ
and
of
associτ
and
Kendall’s
partial
τ
(denoted
τ
)
for
Kendall’s
all
traits
Kendall’s
of
partial
interest
τ
partial
(denoted
as
τ
(denoted
τ
)
for
τ
all
)
for
all
of
traits
interest
of
interest
as(Fyllas
as et al.,
p
p
p
p
the
associated
however,
slopes
however,
much
for
less
the
much
taxthan
less
for
than
the
associated
for
the
associated
slopes
for
slopes
the
taxfor
the
taxρog
and
[P],
log
log
[P],
[Ca],
log
log
[Ca],
[K]
log
and,
[K]
to
and,
a
lesser
to
a
lesser
x 10
10 10
10 10
10
foliar
foliar
cation
cation
environmental
environmental
components
components
(Fig.8)
(Fig.8)
,
it
was
,
it
of
was
of
2009)
2009)
we
only
we
consider
only
consider
relationships
relationships
with
p
with
≤
0.01
p
≤
or
0.01
better.
or
ecially
ationships
Especially
given
between
the
given
strong
ρ
the
and
relationships
strong
the
relationships
between
between
ρ
and
the
ρ
and
the
confounding
effects
of
spatial
confounding
autocorrelation
confounding
effects
(Fyllas
of
effects
spatial
et
of
al.,
autocorrelation
spatial
autocorrelation
(Fyllas
et
(Fyllas
al.,
et
al.,
ated
probability
ated
probability
giving
an
giving
indication
an
indication
of
the
effect
of
the
of
effect
each
of
each
x
x
x
well
as ůin1 Table
and
ů12 (−0.37
as−0.72).
functions
as, well
ůeta1 , and
ůand
ů12 and
asQafunctions
ů 2values
as
functions
of
, al.
T
Pasa (2009)
,, P
and
Qaa and Qabetter.
ldog
ent
analysis
of
analysis
environmental
of
environmental
efefFinally,
as to
in −0.72).
Fyllas
asof inwell
Fyllas
(2009)
al.
we
show
we
show
for
values
for
Fet
T
F , of
T, T
Fa
Ta, T
a, P
in10
Table
1The
(−0.37
onomic
components
toobserved
−0.72).
components
as(−0.26
as
in
Table
(−0.37
to
).component
The
(`Aslopes
).onomic
observed
slopes
(−0.26
tolisted
−0.41)
tolisted
−0.41)
are,
are,Finally,
additional
additional
interest
interest
to
see
to
if
see
coordinated
if
coordinated
structural/leaf
structural/leaf
biobioAs
for
As
the
for
(full)
the
Kendall’s
(full)
Kendall’s
τ
shown
τ
shown
in
Fig.
in
9,
Fig.
Table
9,
5
Table
suggests
5 suggests
mponents
rr,less
cation
foliar
environmental
cation
(Fig.8)
environmental
,
it
was
components
of
components
(Fig.8)
,
(Fig.8)
it
was
,
of
it
was
of
2009)
we
only
consider
relationships
2009)
we
2009)
only
with
we
consider
p
≤
only
0.01
consider
relationships
or
better.
relationships
with
p
≤
with
0.01
p
or
≤
better.
0.01
or
better.
soil/environmental
soil/environmental
parameter
parameter
after
accounting
after
accounting
for
the
effor
the
efin
Table
5.
Here
the
calculated
value
in
Table
of
in
τ
5.
Table
and
Here
associ5.
Here
calculated
the
calculated
value
of
value
τ
and
of
τ
associand
associKendall’s
Kendall’s
partial τpartial
(denoted
τ (denoted
τp ) pfor τall
for alloftraits
interest
of interest
as
as p
p
p ) traits
much
thanless
forthan
the associated
for the associated
slopes for
slopes
the taxfor the tax-
S
H
Ø
Д
thecontributions
required
lack
anymatrix
also
The
axissignificant,
of the PCAaccounting
on the plot
with
from
Φof
and
ρx.
.systematic
LS with
fects and
correlation
(ůsecond
) is also
ef2
being
balanced
by
positive
weightings
for foliar [Mg] in par00)
he species
scores
as
expected
for
the
fects
correlation
matrix
(ů
)
is
also
significant,
accounting
2
negative
± 100) for 0.25 of the variance, with substantial
ticular,
but alsoweightings
with contributions from ΦLS and ρx .
on
fit of
a for
principle
components
model.
for
0.25
of
the
variance,
with
substantial
negative
weightings
ip
between
plot
effect
PCAs
and
S.
Pati
ño
et
al.:
Tropical
tree
trait
dimensions
787
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
rmation
A
between
plot
effect
PCAs
and
tic
etip
frait
combinations
of
three
di- (and tofor
Mthese
[C]trait
andweightings
a lesser
extentinfoliar
A , foliar
dimensions
dimensions
7 7
beingfor
balanced
by
positive
foliar [Mg]
par- [P])
etematic
he
ut with ticular,
Fig.
8aKendall’s
also
showing
that
it
is
Table 5.
partial correlation
coefficient,
τweightings
contribution
(plotin
effect
estimate) of each foliar property (conbeing
balanced
by
positive
for
foliar
[Mg]
parP , for the environmental
also with contributions from Φ
and
ρx . estimated as detailed in Maghsoodloo and Laszlo
for the trolling forbut
LStheir
the effects of the other environmental predictors)
with
significance
el.
3.8 (p
Relationship
plot ateffect
PCAs
and
nly
species
typically
associated
with
ticular,
but
also
with
contributions
from
Φ
andthe
ρxbetween
.results
LS
Pallos
(1981).
Bold
values
indicate
a
very
strong
correlation
<
0.001)
and
italics
indicate
significant
correlations
p <4.
0.01;
seeThis
text
cant
relationships
between
the
PCAhere
site axis
any
any
given
given
species)
species)
with
with
the
results
shown
shown
in
Table
in
Table
4.
This
gnised
cognised
before.
before.
It
is
It
thus
is
thus
here
model.
disoil/climate
ant
relationships
between
the
PCA
for details.
MA = leaf
mass
persite
unitaxis
area; elemental concentrations
are on a dry weight basis, LA = leaf area; `A = leaflet area, 8LS = leaf
, and
previously
calculated
soil
and
climate
and
.
have
scores
for
both
PDJ
RW
shows
shows
that
that
0.33
0.33
of
the
of the
total
total
variation
variation
the
11ů 211
traits
trait
diarea/sapwood
area
ratio,
ρ
=
branch
xylem
density,
=
diffusion
limitation
index
(see
Eq. 1) in
and the
ůin
are traits
the examined
first examined
two principal
x
1 and
, the
and
previously
calculated
soil
and
climate
is same
sites
are
shown
in
Fig.
9.
First,
the
components of theof
PCA
analysis
on the
environmental effects correlation matrix (See Table 4).
major
components
the
three
major
could
could
beplot
be
explained
explained
the
the
firstfirst
PCA
PCA
axisaxis
(ů 1(ů
) with
ρx ρanx an
im-imhat
it isůsites
the
same
are
shown
inthe
Fig.first
9. First,
the
1 ) with
Relationship
between
effectby by
PCAs
and
9elected,
as aall
function
of
soil
PCA
sshows
selected,
all
of
which
of which
we
we
bebeith
13.8
9
shows
ů
as
a
function
of
the
first
soil
PCA
3.8
Relationship
between
plot
effect
PCAs
ped
of(2009),
traits
inlatter
diagrammatic
form.
This
1 the
portant
portant
contributor
andand
thisthis
also
relating
relating
toů 1 foliar
to foliar
with
MA in[C]
[N] contributor
[P]
[Ca]
[K]
[Mg]
LA also
log(8LS ) positively
ρpositively
ů2
al.
considered
a strong
x
soil/climate
7`a and
ant
levant
(see(see
Supplementary
Supplementary
InInThe
most
significant
relationships
between
the
PCA
site axis
.
al.
(2009),
the
latter
considered
a
strong
inW
Soil
fertility
PCA
axis, FF . The
−0.20
−0.23
0.20and
0.48M
0.48 negatively
0.33
0.22 −0.09
−0.07
−0.04
−0.32
0.20 examined
0.56
0.00
strong
of soilseem
fertilityto
and
soil/climate
raits
bedenoted
“shared”,
especially
[C]
[C]
and
M0.04
but
negatively
with
with
all
all
foliar
foliar
nutrients
nutrients
examined
A , −0.27
A , but
derved
.
8jor
fofthe
of
the
total
total
variance
variance
both
both
Soil
texture
PCA for
axis, for
0.05
0.10
0.12
−0.17 −0.18 −0.03
0.02
0.05
0.19
0.02 −0.22 −0.07
.
The
strong
soil
fertility
and
denoted
TF
RW
scores
of−0.41Table
4,−0.08
and−0.04
previously
calculated
soil0.21and climate
suggests Mean
a strong
integrated
response
annual temperature,
Ta
0.11
0.051
−0.38
−0.26
−0.08
0.03second
0.07
0.35
−0.13
and
and
alsoalso
with
with
. .
The
The
second
axis
axis
of the
of0.17
the
PCA
PCA
on
on
the−0.23
the
plotplot
ef- efrved
suggests
a
strong
integrated
response
ant
factor
for
all
three
of
,
PDJ
RW
Mean
annual
precipitation,
P
0.33
0.30
−0.18
0.17
−0.01
0.11
−0.31
−0.01
−0.01
−0.12
0.24
−0.07
−0.44
s.
cies.
e
major
a
cal
forest trees
togiven
soil fertility,
with most
nuanyMean
species)
with
the
results
shown
in
Table
4.
This
ere
characteristics
of
the
same
sites
are
shown
in
Fig.
9. First, the
his
annual
radiation,
Q
−0.06
0.15
0.02
0.12
−0.14
0.08
0.00
−0.11
−0.10
0.08
0.02
0.12
−0.04
−0.11
a
aland
forest
trees
to soil
fertility,
with
most nufects
fects
correlation
correlation
matrix
matrix
(ů
(ů
)
is
)
also
is
also
significant,
significant,
accounting
accounting
2
2
g,
with
foliar
[C]
and
ρ
decreasing
as
The
most
significant
relationships
between
the
PCA
axisů1 as a function of the first soil PCA
x 100)
m.
This
ores
scores
(normalised
(normalised
to
±
toRW
±)100)
inshows
( PDJ
and
of
the
that∩
0.33
of
the
total
variation
in panel
the 11
traits
top
of
Fig.examined
9site
shows
lly
,rring
and with
foliar
[C]
and
ρfor
decreasing
as
xsignificant
The
most
relationships
between
the
PCA
site
axis negative
erestingly,
the
Kendall’s
τ
this
plot
of
ů
for
for
0.25
0.25
of
the
of
the
variance,
variance,
with
with
substantial
substantial
negative
weightings
weightings
1
ofInformation
Table
4, and
previously
calculated
soil and
climate
pecially
Supplementary
inis Supplementary
Information
with
[P] scores
and
[Mg]
varying
in
could
be
explained
byopposite
the
axisof(ůFyllas
im- the latter considered a strong inerestingly,
the
Kendall’s
τoffor
this
plot
of
ů 1 first PCA
axis
etρal.
(2009),
1 ) with
x an
3
greater
than
for
any
the
original
varibescores
Table
4,
and
calculated
soil
climate
forpreviously
for
MAare
M
,A
foliar
, foliar
[C]
andand
(and
and
(and
to
atolesser
a lesser
extent
extent
foliar
foliar
[P])[P])
RW
3quired
greater
than
for
any
ofof
the
varicharacteristics
oforiginal
the
same
shown
in[C]
Fig.
9.
First,
the
red
lack
of
of
any
any
systematic
systematic
to
M
for
two
trait
dimensions.
integrated
measure
of soil fertility
denoted FF. . The
portant
contributor
and
thissites
also
relating
positively
to
foliar
by
et
al.these
(2009),
the
highest
of
which
The strong
tegrated
measure
of
soil
fertility
andand
denoted
A lack
,isFyllas
J
RW
Incharacteristics
of
the
same
sites
are
shown
in
Fig.
9.
First,
the
being
being
balanced
balanced
by
by
positive
positive
weightings
weightings
for
for
foliar
foliar
[Mg]
[Mg]
in
parin
pary
Fyllas
et
al.
(2009),
the
highest
of
which
strong
relationship
observed
suggests
an
integrated
response
he
top
panel
9negatively
shows
ůrelaas a function
the first
soil PCA
arscores
[P].scores
Comparison
with
Fyllas
et
al.the
(2009)
cies
as in
expected
as
expected
fordirection
for
the
1 with
[C]
and
Mof
,Fig.
but
allrelationship
foliarof
nutrients
examined
observed
suggests
ato strong
integrated
response
AFyllas
and
the
same
FW
ar of
[P].
Comparison
with
et
al. of
(2009)
of
Amazon
tropical
forest
trees
fertility,
doth
the
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
ticular,
ticular,
but
but
also
also
with
with
contributions
contributions
from
from
ΦLS
Φsoil
and
and
ρx .ρwith
1
he
ů
contains
significant
weightings
leafLS
x . most nuite
2
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
ininciple
principle
components
components
model.
model.
andsignificant
also
with
. The
second
axis of
PCAtrients
on
the
plot
efincreasing,
and trees
with foliar
[C] and
ρx decreasing
ofthe
Amazon
tropical
forest
to soil
fertility,
withasmost nuepposite
ůindividually,
weightings
ofstandard
leafhough
with
aaxis
high
estimated
2 contains
at,
were
all
strongly
correlated
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
inns.
increases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of ů 1
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
F
inations
tions
offects
these
of these
three
three
traittrait
didiat,
individually,
were
all
strongly
correlated
F accounting
correlation
matrix
(ů 2 ) is also
significant,
trients
increasing,
and
with
foliar
[C]
and
ρx decreasing
as
al
precipitation
(P
correlaA ) viz. positive
00)
ensions.
versus
of
0.63
is
greater
than
for
any
of
the
original
variwe
have
also
included
L
in
(
∩
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
F
A
RW
F
al
precipitation
(P
)
viz.
positive
correlaA
observed
a strong
integrated
response et al.Kendall’s
g.Fig.
8a
also
also
showing
that
that
it isit issuggests
la[C]
and8a
Mrelationship
and
ashowing
negative
correlation
with
for
0.25
of the
variance,
with substantial
negative
weightings
A
τ forofthis
plot of ů 1
ablesInterestingly,
examined by Fyllasthe
(2009), the highest
which
F increases.
on
C]
and
M
and
a
negative
correlation
with
relationship
observed
suggests
a
strong
integrated
response
A
on
relatherefore
not
surprising,
as
is
shown
in
the
3.8
3.8
Relationship
Relationship
between
between
plot
plot
effect
effect
PCAs
PCAs
and
and
ng
that
it
varies
in
the
opposite
direcwas
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nues
ecies
typically
typically
associated
associated
with
with
ard
for
MA , foliar
and
(and to aversus
lesser extent
foliar
[P]) than for any of the original variis greater
F of 0.63
therefore
not
surprising,
is[C]
shown
inassothe
tic
also shows
that the
ů 2 contains
ofPincreasing,
Amazon
tropical
forest
trees
to soil
fertility,
with
most
nu- significant weightings of leafFig.
9, thattrients
ů 2 and
alsoas
show
strong
A
tandard
soil/climate
soil/climate
and
with
foliar
[C]
and
ρ
decreasing
as
xlevel[Mg]
being
balanced
by
positive
weightings
for
foliar
in
parcf.
.
dexamination
Φ
for
ig.
9,
that
ůboth
and
P
also
show
strong
assoables
examined
by
Fyllas
et
al. (2009),
the highest
2 of
A
and
and
.
.
es
ores
for
for
both
RW
FW
LS
variables
that,
individually,
all strongly
correlatedof which
∩
PDJ
PDJ
RW
RW
Table
4 also
suggesting that
he
trients
increasing,
and the
with
foliar [C]
and
ρmean
decreasing
as (Pwere
x plot
examination
of
Table
4
also
suggesting
that
increases.
Interestingly,
Kendall’s
τ
for
this
of
ů
with
annual
precipitation
)
viz.
positive
correlations
∩
F
1
A
ticular,
but
with
contributions
from
ΦLS for
andfoliar
ρx . [P]. Comparison with Fyllas et al. (2009)
was 0.56
ecies,
both
ΦLSthe
ρwalso
also
decline
with
mponents
components
of
ofand
the
three
three
major
major
el.RW both
Interestingly,
Kendall’s
τ original
for
this
ů 1a negative correlation with foliar
ecwith
foliar
[C] plot
and Mof
ecies,
Φsomewhat
ρw
also
decline
with thanthe
F increases.
A and
LS and
versus
of
0.63
is
greater
for
any
of
the
varipitation
and,
counter
intuitively,
Fform.
nships:
environmental
components
also
shows
that
the
ů
contains
significant
weightings
edidirecin
ts
diagrammatic
in
diagrammatic
form.
This
This
2
[Mg].
It
is
therefore
not
surprising,
as is shown
in the second of leafitation and, somewhat
counter
intuitively,
versus
0.63
is The
greater
than
for
any
of
the
original
variF of
g.
ables
examined
by
Fyllas
et
al.
(2009),
the
highest
of
which
The
most
most
significant
significant
relationships
relationships
between
theshow
the
PCA
PCA
site
site
axisaxis
panel
of
Fig. 9,
that between
ů 2 and
PA also
strong
association,
level
variables
that,
individually,
were
all
strongly
correlated
g.
em
to be
to be
“shared”,
“shared”,
especially
especially
is
ables
examined
by
Fyllas
et
al.
(2009),
the
highest
of
which
but
with
examination
of
Table
4
also
suggesting
that
for any
was
0.56high
for
foliar
[P].sites
Comparison
with
Fyllas
et
al.
(2009)
scores
scores
of
Table
of
Table
4,
and
4,
and
previously
previously
calculated
calculated
soil
soil
and
and
climate
climate
both et
low
and
fertility
toFyllas
al.
(2009)
we
show
values
for
witheffect
meangiven
annual
precipitation
viz. with
positive
A )decline
3.8
Relationship
between
plot
PCAs
and
species,
both
8LS and ρw(P
also
increas- correlaith
Fyllas
al.three
wetraits
show
values
for
tor
for
for
alletall
three
of
ofall
, for
,RW
was
0.56
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
PDJ
PDJ
RWcontains
nts
τ (denoted
τp(2009)
)shows
for
of
interest
as
also
that
the
ů
significant
weightings
of
leafcharacteristics
characteristics
of
the
of
the
same
same
sites
sites
are
are
shown
shown
in
Fig.
in
Fig.
9.
First,
9.
First,
thethe
rrelations
and
SMA
slopes
for
the
en2
precipitation
and,M
somewhat
intuitively,
with
with ing
foliar
[C] and
a negative
correlation
with
A and counter
τ as
(denoted
τp ) also
for
all
traits
of
interest
as containstions
soil/climate
onents
shows
that
the
ů
significant
weightings
of
leafůn
functions
of
,
,
T
,
P
and
Q
2(
F
T
a
a
a
2
.
increasing.
∩
∩
)
and
)
and
of
of
the
the
W
levelRW
were
correlated
top
top
panel
panel
of
Fig.
ofallFig.
9strongly
shows
9 shows
as
ů1 aasfunction
a function
the
of the
firstfirst
soil
PCA
PCA in the
PDJ
PDJ
RW
th
this
information
provided
inQmore
1 therefore
ofvariables
, Tτthat,
Pa individually,
and
foliar
[Mg].
It ůis
not of
surprising,
assoil
is
shown
2 as
F , Tof
a , and
a
re
thefunctions
calculated
value
associp
Finally, as correlated
in Fyllas et al. (2009) we show values for
level
variables
that,
individually,
were
all
strongly
tojor
dand
[Mg]
[Mg]
varying
varying
opposite
in
opposite
re
the
calculated
value
of
andprecipitation
associmean
annual
(Psecond
positive
correlaaxisaxis
of Fyllas
of
Fyllas
et al.
et
al.
(2009),
(2009),
the9,
the
latter
latter
considered
a strong
ashow
inindence
intervals)
ininofthe
p
A ) viz.
giving
anwith
indication
theτSupplementary
effect
of
each
panel
of Fig.
ůconsidered
PAall also
strong
assoKendall’s
partial
τthat
(denoted
τp ) for
traits
ofstrong
interest
as
2 and
ites
towith
mean
annual
precipitation
(P
)
viz.
positive
correlagiving
an
indication
of
the
effect
of
each
enA
his
rA).
for
these
these
two
two
trait
trait
dimensions.
dimensions.
well
as
ů
and
ů
as
functions
of
,
,
T
,
P
and
Q
tions
with
foliar
[C]
and
M
and
a
negative
correlation
with
al
parameter
after
accounting
for
the
ef.
The
.
The
strong
strong
tegrated
tegrated
measure
measure
of
soil
of
soil
fertility
fertility
and
and
denoted
denoted
F
T
a
a
a
1
2
As for Table 1, the SMA slopes reA
F 4Falso suggesting that
ciation, but
examination
of Table
enTheafter
most
significant
relationships
theinwith
PCA
siteHere
axisthewith
althe
parameter
accounting
forpotential
the
ef-and MA between
Table
5.
calculated value of τp and associtions
with
foliar
[C]
and
a
negative
correlation
four.
Taking
into
account
to
the
ore
lly
foliar
[Mg].
is
therefore
not
surprising,
asgiven
is
shown
in agiving
the
relationship
relationship
observed
suggests
suggests
strong
a strong
integrated
integrated
response
response
↔
x,the
the
xaccount
as theIttocolumn
headers
and
inmore
inwith
the
same
same
relarelaforobserved
any
species,
both
also
with
intodirection
the
potential
ated and
probability
an Φ
indication
ofdecline
each
LS andof ρthe
w effect
nfour.
scores
ofdirection
Table
4,
and
previously
calculated
soil
climate
ects
of Taking
spatial
autocorrelation
(Fyllas
et
al.,
foliar
[Mg].
It
is
therefore
not
surprising,
as
is
shown
in
the
ary
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assosoil/environmental
parameter
after
accounting
for
the
effect
of
Amazon
of
Amazon
tropical
tropical
forest
forest
trees
trees
to
soil
to
soil
fertility,
fertility,
with
with
most
most
nunuw
labels.
For
the
structural
traits,
the
cts
of
spatial
autocorrelation
(Fyllas
et
al.,
2
A
with
h a high
arelationships
high
estimated
estimated
increasing
precipitation
and, somewhat counter intuitively,
RW
onsider
withstandard
p ≤standard
0.01
orFig.
better.
mentary
characteristics
of
the
same
sites
are
shown
inalso
Fig.
9.other
First,
second
panel
of
9,
that
ů
and
P
show
strong
assoof the
four. the
Taking
into account the potential confoundre2
A
nsider
relationships
with
p
≤
0.01
or
better.
ciation,
but
with
examination
of
Table
4
also
suggesting
that
trients
trients
increasing,
increasing,
and
and
with
with
foliar
foliar
[C]
[C]
andand
ρx (Fyllas
ρdecreasing
as as
nships
are
allLnegative
and∩
beKendall’s
τ shown
inL
Fig.
9, Table
5 appear
suggests
x decreasing
with
increasing.
ing
effects
of
spatial
autocorrelation
et al., 2009) we
he
e
so
also
included
included
in
(
in
(
∩
opes
retop
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
A
A
RW
RW
1
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
ciation,
but
with
examination
of
Table
4
also
suggesting
that
ers
iorlog
predictors
the
individual
variables,
for than
any
given
species,
Φ
and Interestingly,
ρw Interestingly,
also
with
increases.
thethe
Kendall’s
Kendall’s
τ for
this
plot
plot
of As
ůof1forů 1
,ite
logthe
[K]
and,
to a both
lesser
onlydecline
consider
relationships
with
pτ≤for
0.01this
or
better.
F increases.
F LS
10 [Ca],
10
ior
predictors
than
individual
variables,
headers
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
infor
any
given
species,
both
Φ
and
ρ
also
decline
with
on
being
Tincreasing
. Inthe
thatopposite
case,
[N],
[K]
and
ρ
varies
it
varies
in
in
opposite
direcdirecthe
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
the
athe
w
he
LS
w
as greater
in
Fyllas
al.any
(2009)
we
show
values for
precipitation
and,
somewhat
counter
intuitively,
versus
versus
0.63
of 0.63
is greater
is
than
than
foretfor
any
of
the
of the
original
original
varivarislopes
observed
(−0.26
−0.41)
are,
F of
FFinally,
n being
Ttegrated
. In that
case,
[N],to[K]
and plot
ρfertility
a
w
ns.
to
be
superior
predictors
than
the
individual
variables,
the
ships
not
present
when
regressing
the
aits,
the
.
The
strong
measure
of
soil
and
denoted
F
increasing
precipitation
and,
somewhat
counter
intuitively,
beKendall’s
partial
)the
for
all
as
ships
present
the the
plot
cf.
cf.
.when
. regressing
or
with
increasing.
ables
ables
examined
examined
by by
Fyllas
Fyllas
etτal.
et(denoted
al.
(2009),
(2009),
the
highest
highest
of
of
which
p
n RW
fornot
the
associated
slopes for
taxRW
FW
FW
only
exception
being
Ta . Inτ
that
case,
[N],traits
[K] which
andof
ρw interest
all
pendent
variables.
pear
berelationship
observed
suggests
a
strong
integrated
response
lawith
increasing.
endent
ser
relationships
not with
present
when
regressing
the
plot
, al.
Ta(2009)
, Pefwell
asfoliar
ů[P].
and
ůComparison
ofFyllas
waswas
0.56
0.56
for
for
foliar
[P].
Comparison
with
Fyllas
etT ,al.
(2009)
1show
2 as functions
Fet
a and Qa
listedvariables.
in Table 1 (−0.37 to −0.72).
a
lesser
fect
PCs
as
dependent
variables.
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuFinally,
as
in Fyllas also
et also
al.shows
(2009)
we
show
values
for
ard
inthat
Table
5.2 ůcontains
Here
the
calculated
value ofofτleafnvironmental
:re,environmental
components
components
Fig. 8. Standard
Major Axis (SMA) regression
lines
between
the
shows
that
thethe
ůwe
contains
significant
significant
weightings
of
leaf- associp and
2
Finally,
as
in
Fyllas
et
al.
(2009)
show
values
for weightings
41)
are,
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
derived
environmental
components
of
branch
xylem
density
(ρ
)
x
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
interest
x
p variables
axated that,
probability
givingwere
anwere
indication
ofcorrelated
the
effect of each
∩ analysis
onent
ofandenvironmental
eflevel
level
variables
that,
individually,
individually,
all all
strongly
strongly
correlated
and foliar
[P]
foliar partial
[K]. Open circles
indicate
species
on all
Kendall’s
τfunctions
(denoted
τpfound
) for
traits
ofand
interest
as
the
taxincreases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
F
1
well
as
ů
and
ů
as
of
,
,
T
,
P
Q
4
Discussion
1 sites
2 toF annual
a precipitation
a
a (P
.ow
andand
high
high
fertility
fertility
sites
tolow
fertility
sites
and the
closed
circleswith
indicate
species
found
onT precipitation
soil/environmental
parameter
accounting
for the efwith
mean
mean
annual
(P
) Aviz.
)after
viz.
positive
positive
correlacorrelaAQ
ecwell
as
ů
and
ů
as
functions
of
,
,
T
,
P
and
1
2
F
T
a
a
a
high
fertility
sites.
Species
found
on
both
soil
fertility
groups
are
0.72).
versus
of
0.63
is
greater
than
for
any
of
the
original
variFfor
inMajor
Table
5. for
Here
the calculated
value
of
τSome
and
associStandard
Axis
(SMA)
regressions
lines
between
the
derived
environp
and
ns and
SMA
SMA
slopes
slopes
the
the
en-enfect
of[C]
the[C]
other
four.
Taking
into
account
towith
the
potential
tions
tions
with
with
foliar
and
and
M
M
and
a negative
a negative
correlation
correlation
with
of the
data
used
here
have
been
presented previously
designated
by
a “+” (see
text for
details).
Solid
lines
show
the foliar
SMA
A
A and
in
Table
5.
Here
the
calculated
value
of
τ
and
associp
ables
examined
by
Fyllas
et
al.
(2009),
the
highest
of
which
mponents
of
branch
xylem
density
(ρ
)
and
foliar
[P]
and
foliar
[K].
Open
ated
probability
giving
an
indication
of
the
effect
of
each
(Fyllas
et
al.,
2009;
Patiño
et
al.,
2009),
with
the
current
efmodel
fits.
x
ormation
information
provided
provided
in more
in more
rong
relationships
between
ρx and
the
confounding
effects
of
spatial
autocorrelation
foliar
foliar
[Mg].
[Mg].
It isIt therefore
isoftherefore
notnot
surprising,
surprising,
as is
as shown
is shown
in (Fyllas
the
in the et al.,
ated for
probability
giving
an
indication
the
effect
of
eachdatasets
analysis
integrating
those
with structural traits inntal
efwas
0.56
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
icate
species
found
on
low
fertility
sites
and
the
close
circles
indicate
species
soil/environmental
parameter
after
accounting
for
the
efntervals)
vals) in
the
in the
Supplementary
Supplementary
ental
components
(Fig.8) , it was
of
2009)
weFig.
only
relationships
p, S≤and
0.01
or better.
second
second
panel
panel
of Fig.
of
9,
that
9,consider
that
ů 2part
ůand
and
PAstudy
P
also
also
show
strong
assoassotroduced
as
this
LAshow
, `with
, 8LS
H
2ofthe
A (viz.
Astrong
max )
soil/environmental
parameter
after
accounting
for
efnts
high fertility
sites.
Species
found
on
bothsignificant
soil fertility
groups
are
designated
also
shows
that
the
ů
contains
weightings
of
leaf2
fect
of
the
other
four.
Taking
into
account
to
the
potential
Table
or Table
the
1, the
SMA
SMA
slopes
slopes
re- re- ciation,
see
if 1,
coordinated
structural/leaf
biofor
the
(full) Kendall’s
τ shown
in
Fig.
9, Table
5 suggests
ciation,
butAs
but
with
with
examination
examination
ofpotential
Table
of Table
4 also
4 also
suggesting
suggesting
thatthat
see
text for
details).
lines
show
the
SMA
model
fits.
fect
ofSolid
the
other
four.
Taking
into
account
tocorrelated
theet
level
variables
that,
individually,
were
all
strongly
he
confounding
effects
of
spatial
autocorrelation
(Fyllas
al.,
Biogeosciences,
9,
775–801,
2012
ith
the
the
x asx www.biogeosciences.net/9/775/2012/
the
as the
column
column
headers
he environment
existheaders
for
Amazonfor
for-anyany
the
tospecies,
be superior
the
individual
variables,
given
given
species,
both
both
Φpredictors
ΦLS
and
and
ρwthan
ρalso
decline
decline
with
with
LS
w also
toand
the with
confounding
effects
of for
spatial
autocorrelation
(Fyllas
et al.,
mean
annual
precipitation
(P
)
viz.
positive
correlaA
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
better.
For
s. For
theathe
structural
structural
traits,
traits,
ertook
PCA
analysis
ofthe
thethefullincreasing
plot
the
only
exception
being
Ta . In
that
case,
[N], [K] and ρw
increasing
precipitation
precipitation
and,
somewhat
counter
counter
intuitively,
intuitively,
enwas
of
2009)
we
only
consider
relationships
with
p ≤and,
0.01
orsomewhat
better.
tions
with
foliar
[C]
and
M
and
a
negative
correlation
with
A
ioAs
for
the
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
are
all all
negative
negative
andH
and
appear
appear
be-S
be-both
trix
(excluding
and
of
show relationships not present when regressing the plot
with
with
increasing.
all
increasing.
max
788
S. Patiño et al.: Tropical tree trait dimensions
mate. To this extent they reflect a systematic component of
intra-species variability, i.e. that predictable from where a
particular species is growing, as opposed to a more random
within population component, such as might be expected
when comparing the same species growing nearby under the
same edaphic and climatic conditions. This portion, along
with experimental error is theoretically included in the residual component of the analysis (i.e., that not accounted for
by the fitted model itself ) which, as shown in Figure 2, can
sometimes be substantial. The extent to which this component of trait variation relates to within plot variability in microclimate or soil characteristics (rather than intrinsic withinspecies differences or sampling/measurement error) remains
to be established.
4.1
4.1.1
Fig. 9. Relationship between derived environmental effect principal
components (Table 5) and soil/environmental parameters for various plots across the Amazon. Top panel, first principal component
of the environmental effects versus the first principal component of
the PCA of soil chemical and physical characteristics as derived by
Fyllas et al. (2009) on the basis of data provided by Quesada et
al. (2010). Second panel, second principal component of the environmental effects PCA versus mean annual precipitation. Open circles indicate low fertility sites and the closed circles indicate high
fertility sites as defined by Fyllas et al. (2009).
as well as with foliar 13 C/12 C ratios as reinterpreted through
Bivariate relationships for the taxonomic
component of trait variation
Maximum tree height, branch xylem density and
leaf mass per unit area
These three structural traits have often been associated with
each other with significant positive ρx ↔MA correlations
such as for our taxonomic component in Fig. 4 also reported
by Bucci et al. (2004), Ishida et al. (2008) and Meinzer
et al. (2008). Those studies interpreted this relationship in
terms of higher density wood species having lower hydraulic
conductances leading to a requirement for more robust leaves
capable of sustaining more severe soil water deficits. This
notion is supported by more negative osmotic potentials being reported for the leaves of higher MA and ρx species
(Bucci et al., 2004; Ishida et al., 2008; Meinzer et al., 2008).
On the other hand, it is also the case that MA tends to increase
with actual or potential (maximum) tree height (Falster and
Westoby, 2005; Kenzo et al., 2008; Lloyd et al., 2010) and
that ρw and Hmax are sometimes negatively correlated (Falster and Westoby, 2005; van Gelder et al., 2006). This then
implying that ρw and MA should be negatively (as opposed
to positively) correlated as well.
One reason for this apparent contradiction may be that
wood density and xylem vessel traits do not necessarily represent the same axis of ecophysiological variation (Preston et
al., 2006; Martinéz–Cabrera et al., 2009; Poorter et al., 2010;
Baraloto et al., 2010). For example, decreasing wood density
associated with increasing foliar P concentration and lower
LMA is also likely associated with decreasing investment
in wood physical and chemical defences (Augspurger, 1984;
Putz et al., 1983; King, 1986; Chao et al., 2008), including
resistance against breakage (Romero and Bolker, 2008). One
interpretation of Fig. 4a–c is then simply that tropical tree
species with traits associated with a higher photosynthetic
potential such as a high foliar [P] (Domingues et al., 2010),
also tend to invest less towards wood defensive strategies (but
see Larjavaara and Muller-Landau, 2010).
ship between
environmental
effect principal components
the diffusionalderived
limitation index,
, as defined by Eq. (1). We
first consider the bivariate relationships between the strucnvironmental
parameters
for
various
plots
across the Amazon. Top
tural components
introduced as part
of this
study as well
as
relationships between these structural traits and the others
l component
of the environmental effects versus the first principal
already presented (Fyllas et al., 2009; Patiño et al., 2009),
this then
being extendedand
to a consideration
of how
variaCA of soil
chemical
physical
characteristics
as derived by Fyltions in these traits coordinate in response to differences in
species
Here we
emphasise
that, as es-et al. (2010). Second panel,
he basis
ofand/or
dataenvironment.
provided
by
Quesada
timated within the study, our “environmental effects” reflect
mponent
of theof taxon
environmental
versus mean annual premodulation
specific trait values byeffects
soils and/or PCA
clicles indicate low fertility sites and the closed circles indicate high
Biogeosciences, 9, 775–801, 2012
www.biogeosciences.net/9/775/2012/
ned by Fyllas et al. (2009).
S. Patiño et al.: Tropical tree trait dimensions
789
Our observation of significant within-species variation in
4.1.2 Leaf size, nutrients and 8LS
ρx as illustrated in more detail by Patiño et al. (2009) and
Species with intrinsically higher foliar nutrient concentraalso observed for ρw by Omolodun et al. (1991), Hernández
tions also tend to be found on more fertile soils (Fyllas et
and Restrepo (1995), Gonzalez and Fisher (1998), Weber
al., 2009), and so the positive correlation between the taxoand Montes (2008) and Sungpalee et al. (2009), shows imnomic components of leaf size variation, foliar [N] and foportant intraspecific variation in xylem and/or wood density
liar [P] observed here (Fig. 6) is consistent with the obsereven within one plot (as also evidenced by the “residual”
vation that Australian tropical forest tree species associated
term for ρx in Fig. 2) as well as being systematically affected
with poorer soils tend to have smaller leaves than those assoby soil fertility (Table 5). Thus, although we do not dispute
ciated with more eutric conditions (Webb, 1968). This was
that xylem traits and ρw /ρx may not necessarily be closely or
also found to be the case for south-eastern Australian woodmechanistically linked (as discussed above), studies which
land species once precipitation effects were also taken into
simply compare wood density “species values” as measured
account (McDonald et al., 2003). Such a relationship has also
in one study or studies with values of ρw /ρx for the same
been observed for pre-montane subtropical forest species in
species but gathered from a completely independent source
Argentina (Easdale and Healey, 2009) and has been sug(Russo et al., 2010; Zanne et al., 2010) are effectively comgested to be a widespread phenomenon (Givnish, 1987) perparing bananas with wombats. Thus, also not employing rohaps being explainable by low N and/or P leaves typically
bust regression techniques more applicable to such analyses
having lower gas exchange rates than those of a higher fertil(McKean et al., 2009) they must under-estimate the actual
al tree trait dimensions
ity status (Domingues7 et al., 2010); with associated lower lasignificance of any relationship, be it functional or not.
tent heat loss rates due to lower stomatal conductances. This
So, does the observation of large diameter xylem vessels
any
given
species)
with
the
results
shown
Table
been recognised
before.
It
is
thus
here
would in
give
rise 4.
to aThis
greater rate of sensible heat loss being rewith a high KS also being associated with a greater Hmax
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
quired
to
avoid
over-heating
during times of high insolation
(e.g., Poorter et al., 2010; Zach et al., 2010) mean that the
could
be
explained
by
the
first
PCA
axis
(ů
)
with
ρ
an
imbeing
achieved
through
the
higher
boundary layer conduc1
x
tendency
of which
matureweforests
envectors selected,
all of
be- species of a greater Hmax to also
portant
contributor
and
this
also
relating
positively
to
foliar
tance
of
smaller
leaf
sizes
(Yates
et
al.,
2010). Alternatively,
cally relevanthave
(seea Supplementary
In- and Westoby, 2005; van Gelder et
lower ρw (Falster
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
A
and
consistent
with
the
general
notion
of
plants growing on
for 0.68 of the
foral.,
both
al., total
2006;variance
Baker et
2009; Poorter et al., 2009) is indicaalso
with
.
The
second
axis
of
the
PCA
on
the
plot
efless
fertile
soils
having
more
conservative
growth strategies
soil species. tive of some sort of functionaland
linkage? Or does it more simfects correlation matrix (ů 2 ) is also (Westoby
significant,et accounting
al.,
2002),
smaller
leaves
may
be favoured on
species scores
(normalised
to
±
100)
ply reflect that the fast-growing and light-demanding species
for 0.25 of the variance, with substantial
negative
weightings
low
nutrient
soils
despite
their
relatively
higher
construction
h other in Supplementary
Information
characteristic of “dynamic” tropical forests also tend to have
for MA , foliar [C] and (and to a costs.
lesser This
extentis foliar
[P])they also have shorter expansion times
because
the requireda lower
lack ofρwany
systematic
– this presumably allowing a faster height and
being balanced by positive weightings
for an
foliar
[Mg] in parwith
associated
reduction in herbivory losses during this
he species scores
as expected
for the
diameter
growth rate?
On the basis of the discussion above,
ticular,
but
also
with
contributions
from
Φ
and
ρx . of foliar development (Moles and Westoby,
LS
susceptible
phase
fit of a principle
components
model.
we suggest the latter, also noting that ρw is actually gener2000). If the “heat budget” explanation were to be correct,
of combinations
thesecorrelated
three traitwith
di- juvenile light–exposure than Hmax
ally of
better
But with Fig.(van
8a also
showing
that
it
is
then an even better correlation with `A would be expected for
Gelder et al., 2006; Poorter et al., 2009).
3.8
Relationship
between
plot
effect
only species typically
associated
with
both
foliarPCAs
[N] andand
[P]. But this was not the case (Table 1)
The positive relationship between MA and Hmax of Fig. 3a
soil/climate
with
both
foliar
[N]
and [P] much more closely correlated
and
.
have scores and
for both
as alsoPDJ
evident inRW
the data of Falster and Westoby (2005)
with
L
.
On
the
other
hand, the relationship between leaf
A
major components
of
the
three
major
can also be inferred from the positive MA vs. tree height
size
and
expansion
time
does not appear to differ strongly
p of traits in relationships
diagrammatic as
form.
This
reported by Thomas and Bazzaz (1999),
The most significant relationships between
thesimple
PCA site
axis
between
vs.
compound
leaves (Moles and Westoby,
raits seem toKenzo
be “shared”,
especially
et al. (2006) and Lloyd et al. (2010). This is also
scores of Table 4, and previously calculated
soil
and
climatethat the herbivory hypothesis may be
2000).
This
suggests
analysis of Table 3, with the
within
tant factor forseen
all three
of FW
, the
PDJin
RW CPC characteristics
of the same sites are shown
in Fig.
9. First, the
the more
correct.
leaves
of
(potentially)
taller
trees
being thicker (Kenzo et
urring in ( PDJ ∩ RW ) and of the
top panel of Fig. 9 shows ů1 as a function of the first soil PCA
2006;
Rozendaal
et al., 2006) with a greater mesophyll
Although not significant across the dataset as a whole,
with [P] and al.,
[Mg]
varying
in opposite
axis of Fyllas et al. (2009), the latter considered a strong inthickness
associated
with
a
higher
photosynthetic
capacity
there
was a significant negative correlation between LA
to MA for these two trait dimensions.
tegrated measure of soil fertility and denoted F . The strong
per unit area (Kenzo et al., relationship
2006). Thisobserved
increasesuggests
in MA a strong
and integrated
ρx for species
characteristic of low fertility sites (r 2 =
response
FW and in the same direction relawith tree height being mostly of
associated
with
a
greater
mes−0.17,p
≤
0.05:
Supplementary
Information, Table S2B) as
Amazon tropical forest trees to soil fertility, with most nuhough with a high estimated standard
ophyll thickness should allowtrients
for a more
efficient
use
of
the
has
also
been
reported
for
Australian
tree/shrub species by
increasing, and with foliar [C] and ρx decreasing as
we have alsohigher
included
LAofin insolation
( RW ∩ towards the canopy top through
rates
Pickup
et
al.
(2005)
and
Wright
et
al.
(2007)
and for neotropF increases. Interestingly, the Kendall’s τ for this plot of ů 1
ing that it varies
in the
opposite direchigher
photosynthetic
capacities
per
unit
leaf
area
(Rijkers
ical
forest
tree
species
by
Swenson
and
Enquist
(2008), Malversus F of 0.63 is greater than for any of the original variet
al.,
2000).
Along
with
more
negative
osmotic
potentials,
hado
et
al.
(2009),
Baraloto
et
al.
(2010)
and,
with
a much
d ΦLS for RW cf. FW .
ables examined by Fyllas et al. (2009), the highest of which
2 = −0.02) by Wright et al. (2006). Exthe greater tissue densities associated
with
a
higher
M
and
lower
correlation
(r
A
was 0.56 for foliar [P]. Comparison
with Fyllas et al. (2009)
Hmax shouldcomponents
also help sustainalso
leaves
ofthat
such
trees insignificant
actlyweightings
as to why of
this
should be the case is currently unclear.
onships: environmental
shows
thetaller
ů 2 contains
leafthe face of the more severe water
deficits
expected
for
sun
Earlier
arguments
have
level variables that, individually, were all strongly correlated revolved around not only ρx and KS
m both low and
high leaves
fertilityhigher
sites up
to- in thewith
exposed
canopy
(Cavaleri
et al., 2010; (PAbeing
but also with the assumption that variamean
annual precipitation
) viz. closely
positivelinked,
correlarrelations and
SMAetslopes
for the enLloyd
al., 2010).
tions
in
L
should
to
tions with foliar [C] and MA and a negative correlation
witha large extent reflect variations in 8LS
A
ith this information provided in more
(Wright
et shown
al., 2006).
foliar [Mg]. It is therefore not surprising,
as is
in theBut, as discussed in Sect. 4.1.1, wood
dence intervals) in the Supplementary
andstrong
plant hydraulics
may not be as closely linked as
second panel of Fig. 9, that ů 2 and PAdensity
also show
assoA). As for Table 1, the SMA slopes reciation, but with examination of Table 4 also suggesting that
↔ x, with the x as the column headers
for any given species, both ΦLS and ρw also decline with
www.biogeosciences.net/9/775/2012/
Biogeosciences, 9, 775–801, 2012
w labels. For
the structural traits, the
increasing precipitation and, somewhat counter intuitively,
onships are all negative and appear bewith increasing.
], log10 [Ca], log10 [K] and, to a lesser
Finally, as in Fyllas et al. (2009) we show values for
slopes observed (−0.26 to −0.41) are,
790
once thought and, although 8LS is indeed correlated with
LA (Fig. 6), our data do not actually show any appreciable
correlations between 8LS and ρx (Table 1; Supplementary
Information, Table S2B). This suggests that for tropical trees
at least, this correlation may be more “casual” than mechanistic. Indeed, both for the dataset as a whole and for the individual fertility groupings, ρx was better (negatively) correlated with `A than LA (Table 1, Supplementary Information,
Table S2B). Given that compound leaves are generally associated with faster diameter increment species (Givnish, 1978;
Malhado et al., 2010) as is a generally lower ρx (Keeling et
al., 2008) this then suggests that the negative correlation between laminar size and wood density may just reflect both
traits being associated with faster growth rates. As well as
tending to have lower ρw (Sect. 4.1.2) such species also tend
to exhibit less branching than more shade tolerant species
(Poorter et al., 2006; Poorter and Rozendaal, 2006; Takahashi and Mikami, 2008). Presumably (along with wider
spacings) this allows for larger leafed upper-canopy species
to have greater rates of direct light interception (Falster and
Westoby, 2003).
Increasing MA with decreasing 8LS as shown in Fig. 5
does not seem to have been detected in other studies with
tropical tree species (Meinzer et al., 2008; Zhang and Cao,
2009). Although it is notable that working with a range of
emergent or upper-canopy dipterocarp species, Zhang and
Chao (2009) did find a significant negative relationship between 8LS and leaf thickness, the latter being associated
with variations in MA with tree height for dipterocarp species
(Kenzo et al., 2006). Sampling across a range of sites in
south-eastern Australia, Pickup et al. (2005) also found a
negative relationship between MA with 8LS but this relationship was, overall, not significant for species sampled within
individual sites. Our own data suggest a stronger linkage of
MA with 8LS than either LA or (indeed even of different
sign) `A . This suggests (as is discussed further in Sect. 4.2)
that this linkage may be mostly related to plant hydraulics
considerations. The positive relationship between `A and
MA may reflect constraints on the range of possible combinations of leaf(let) size and MA , with larger laminar areas
necessarily requiring a greater (minimum) MA due to structural constraints (Grubb, 1998).
Not surprisingly, LA and 8LS were related, but with a scaling coefficient of only 0.17, meaning that a greater leaf size
was to a substantial degree compensated for by reduced numbers of leaves per unit sapwood area AS . This points to 8LS
being a relatively invariate trait as has also been reported by
others (e.g., Westoby and Wright, 2003). Of note, 8LS was
also correlated with foliar [P] and [N] (Fig. 5), although this
correlation was weaker for LA , especially in the case of foliar phosphorus. But for both nitrogen and phosphorus, the
slope was still positive and close to 1.0. Thus tropical tree
species with larger leaves tend to have not only higher [P] and
[N] (and by implication higher gas exchange rates) but also a
higher 8LS . As there is little evidence of greater diffusional
Biogeosciences, 9, 775–801, 2012
S. Patiño et al.: Tropical tree trait dimensions
limitations on gas exchange for such leaves (as shown by the
lack of any significant relationship between 8LS , [N], [P] or
LA with ), this implies that accompanying a higher 8LS
are also increased KS as also observed by Vander Willigen et
al. (2000) for subtropical trees and also by Cavender–Bares
and Holbrook (2001) for a range of Quercus species.
4.1.3
Seed mass
We first note that unlike the other parameters investigated in
this study, seed mass has been resolved only at the genus
level. This is potentially an issue as there are large genera
present in this dataset (e.g. Pouteria, Ocotea and Eschweilera) within which there may be a rather broad variation in
seed mass that has the potential to mask causative patterns
reported here (C. Baraloto, personal communication, 2011).
Nevertheless, as is evident from Fig. 1, seed mass varies by
nearly five orders of magnitude which is much greater than
the relative variability even in leaf area. Thus, although it
must be accepted that any causative relationships may well
have been stronger if seed mass had been more accurately
determined, where relationships have been found in this data
there is little reason to suspect that they are an artifact of our
less than ideal species level measurements of S.
Bearing this in mind, we note that, as has been reported
by others, seed mass showed significant positive correlations
with both Hmax (Fig. 3; Foster and Janson, 1985; Hammond
and Brown, 1995; Kelly, 1995; Metcalfe and Grubb, 1995;
Grubb and Coomes, 1997), and ρw (Fig. 4; ter Steege and
Hammond, 2001), although the latter relationship was not
detected by Wright et al. (2006), perhaps because of methodological issues (Williamson and Weimann, 2010). Generally
speaking, a greater seed size should confer a greater ability
for survival and thus tend to be favoured under less favorable environmental conditions such as deep shade or nutrient
poor soils (Westoby et al., 2002; ter Steege et al., 2006). This
readily provides a basis for indirect correlations between S
and wood/stem density to exist as high values of ρx or ρw are
similarly associated with shade and/or dystrophic soil conditions (Sect. 4.1; Kitajima, 1994). More controversial is the
basis of the relationship between S and Hmax . For example,
the suggestion of Moles et al. (2005) that, by analogy with
Charnov’s life history theory for mammals, larger statured
species may have larger seeds because they require a longer
juvenile period has been contested by Grubb et al. (2005)
who maintain that it is simply the range of feasible seed sizes
that a species can have that increases with Hmax . Moreover,
for tropical trees at least, there is probably little correlation
between juvenile period and Hmax , with faster-growing lowwood density pioneer type trees attaining greater heights than
their smaller statured shade counterparts and in a shorter time
(Baker et al., 2009). Indeed, by applying a general scaling
model Falster et al. (2008) showed that longer juvenile periods alone are not sufficient to generate a correlation between
height and seed size. They suggested that size-asymmetric
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
competition among recruits (i.e. competition for light) may
be the main factor having caused evolution towards larger
offspring size. In this scheme of things, correlations with
adult height come about because larger adults have a greater
total reproductive output, thus generating more intense competition among recruits. That model tested dynamics only
with a single species at a time, but it is likely to still apply
in more complex species systems such as tropical forests,
even though relative size at the onset of maturity is much
more variable for tropical trees species than for animal systems (Thomas, 1996; Wright et al., 2005). We also consider
it unlikely that simple physical constraints can account for
much of the relationships (also seen in Fig. 3) as even small
statured species can have reasonably large seeds and/or fruits
(for example Theobromba, or many members of the genus
Licania: Prance, 1972). Likewise, wind dispersed species
have both small seeds and a tendency to occur in the upper
canopy strata where higher wind velocities aiding dispersal
are greater (Hughes et al., 1994), one obvious example from
the Amazon Basin being the widespread neotropical species
Jacaranda copaia (Jones et al., 2005).
As was also found by Wright et al. (2006), the study gives
little support for one of “Corner’s rules”, viz. that due to their
mutual dependence on the available supporting twig mass
that leaf size and seed size should be positively correlated
(Corner, 1949). There may be two reasons for this. First, as
pointed out by Grubb et al. (2005) such biomechanical explanations would only be expected to apply where there is little
flexibility in the number of fruits per inflorescence. Second,
as for 8LS (Fig. 5) the ratio of total leaf area to the supporting stem mass is to a large degree independent of LA (Wright
et al., 2007). Indeed, if anything, what our data suggest is
that reproductive structures compete with leaves for available
space as there is a nearly significant correlation between 8LS
and S (r 2 = −0.09,p = 0.07) with this negative relationship
significant for the low fertility species (Supplementary Information, Table 2). Thus, in contrast to vegetation types
from more xeric habitats where leaf areas may be substantially constrained by hydraulic considerations, leaf area per
unit available stem area or mass may actually be constrained
by the requirements for simultaneous allocation of available
carbohydrate to reproductive structures for most tropical forest trees. That being consistent with their tropical forest productivity being carbon limited as argued by Lloyd and Farquhar (2008).
Competition between foliage and developing fruit may
also be the reason for the negative relationship between seed
size and foliar [Ca] shown in Fig. 6, an observation also
made for sub–tropical montane tree species by Easdale and
Healey (2009). It has long been known that calcium is relatively immobile in plants (e.g., Kirby and Pilbeam, 1984)
with high rates of calcium supply to developing fruit essential
for cell wall development and for longer term maintenance
of membrane integrity. Sufficient levels of calcium are also
required to maintain the integrity of the fruit flesh includwww.biogeosciences.net/9/775/2012/
791
ing resistance to fungal attack even after abscissed from the
plant (Bangerth, 1979). Due to its immobility, this calcium
accumulation in fruit tissues must occur at the expense of the
leaves, and thus Fig. 6 does not necessarily imply that Ca
itself may be limiting for either reproductive tissue development or leaf physiological function. Indeed, the SMA slope
fit of −8.3 suggests that for each doubling of S foliar [Ca]
declines by only about 10 %, a value roughly consistent with
the similar [Ca] in both seed and leaf tissue (as evidenced
from the seed data of Grubb and Coomes (1997)) and with
about 0.1 of total South American tropical forest “soft” litterfall occurring as reproductive organs (Chave et al., 2010).
Even though such a result does not, therefore, necessarily
imply direct effect of Ca availability on tree function, it is interesting to note that species growing on extremely cation
poor spodosols are characterised by relatively small seed
masses as compared to more fertile nearby forests (Grubb
and Coomes, 1997) as well as with leaf photosynthetic rates
showing an apparent dependence of leaf calcium concentrations (Reich et al., 1995). Moreover, for forests on such nutrient poor soils, carbon allocation to photosynthetic organs is
apparently prioritised over that to reproduction (Chave et al.,
2010). This is consistent with neotropical forest reproductive
structure frequency being highly sensitive to soil fertility as
inferred (apparently) from soil nitrogen status (Gentry and
Emmons, 1987), and markedly lower for forests growing on
less fertile soils. Overall, these observations suggest, as also
discussed in Sect. 4.1.2, that foliar and reproductive tissue
development may be in direct competition for either carbon
or available nutrients where soil fertility is low.
4.2
Integration of structural and physiological traits
Although an examination of the various bivariate relationships, as discussed in Sect. 4.1 has hopefully proved informative, it is also of additional interest to quantify the extent
to which all the various traits examined coordinate in their
variability as a whole. In this respect, PCA was considered
the most appropriate approach, as the first dimension of a
PCA analysis can also be considered (with data normalisations prior to analysis as undertaken here) as the multivariate equivalent of an SMA model fit (Warton et al., 2006).
We therefore interpret Table 2 as indicating five discrete integrated trait dimensions of tropical tree function and with
the relative importance of these effects varying between high
and low fertility species. This interpretation is made even
though some of the measured properties such as MA and ρx
are modelled as having significant contributions to several
dimensions. This is argued as reasonable on two counts.
First, variations in some of the traits measured may have
different underlying causes. For example, changes in MA
may be a consequence of variations in leaf thickness, tissue
density or both (Witkowski and Lamont, 1991; Niinemets,
1999; Poorter et al., 2009) and likewise, variations in ρx
could reflect differences in the proportions of gas, air and
Biogeosciences, 9, 775–801, 2012
for MA , foliarlow
[C]and
andhigh
(and
to a soil
lesser
extent foliar [P])
and also with . The second axis of
required lack of any S.
systematic
fertility
species.
Patiño et al.: Tropical
tree trait
dimensions
7
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parfects correlation matrix (ů 2 ) is also
ecies scores as expected for the
The first three axes species scores (normalised to ± 100)
ticular, but also with contributions from ΦLS and ρx .
for 0.25 of the variance, with substant
f a principle components model.
are plotted
against
Information
any given species)
with the results
in [C]
Table
4. This
does not seem to have been recognised
before.
It iseach
thusother
here in Supplementary
fortree
Mshown
, foliar
and
(and to a
Atrait
mbinations of792
these three trait diS.
Pati
ño
et
al.:
Tropical
dimensions
Fig. S1. This shows the requiredshows
lack that
of any
systematic
0.33
of
the
total
variation
in
the
11
traits
examined
being
balanced
by
positive
weighting
denoted
as
.
S. Patiño correlations
et al.: Tropical
tree
trait
dimensions
ith Fig. 8a also showing that it is PFL
between the species scores
as explained
expected for
the first PCA axis (ů ) with ρ an imcould
be
by
the
1
x
ticular,
but
also
with
contributions
fro
Overall
five
selected,
all of which
we of
be-a principle
3.8eigenvectors
Relationship
plot
and
species typically
associated
withthe
output
from
good
components
model.
(Fig. PCAs
6)portant
within
this dimension.
Such
an
involvement
of L
dry matter
content
(for hydrated
tissue)
in a between
wideany
range
of fiteffect
Afoliar
contributor
and
this
also
relating
positively
to
lieve to be physiologically
relevant
(see
Supplementary
Insoil/climate
the results
not seem
to have
been
recognised
Itand
thus
here
Clearly
a wide
range
of combinations
ofis these
three acquisition/utilisation
traitany
di-given
scores for both
inbefore.
the [C]
classic
resource
spectrum
has shown in T
combinations
2008).does
Second,
as selective
pressures
PDJ and (Poorter,
RW .
M
withspecies)
all foliarwith
nutrients
examined
A , but negatively
formation),
accounted for 0.68mensions
of the total
variance
for both
shows
that
0.33
of
the
total
variation
can
occur.
But
with
Fig.
8a
also
showing
that
it
is
r components
the three itmajor
also been
from
data analysis
29the
sub–
areofmultiple,
is quite likelydenoted
that contrasting
as PFL . combinations
andsuggested
also with .
Thea second
axis of involving
the PCA on
plot in
ef-the 11 tr
low and high fertility soil species.
3.8
Relationship
between
S.
Patiño
et
al.:
Tropical
tree
trait
dimensions
could
be
explained
by
the
first
PCA
axis
(ůplot
(generally
speaking)
only
species
typically
associated
with
1) w
raits in diagrammatic
form.traits
Thishave evolvedOverall
tropical
montane
tree
12also
ha ofsignificant,
permanentaccounting
samof individual
for different
reasons.
the five
eigenvectors selected,
all of
which
we
be-species
fects
correlation
matrixacross
(ů 2 ) is
The first threeThe
axes
species
scoresrelationships
(normalised between
to ± 100)the PCA
most
significant
site
axis
soil/climate
portant
contributor
and
this
also
relating
posit
seem to be “shared”, especially
and
. (Easdale
fertility soils relevant
that have(see
scores
both
lieve to behigh
physiologically
Supplementary
ple
plots
Tucumán,
Argentina
and Healey,
forin0.25
of PDJ
theInvariance,
substantial
negative2009).
weightings
RWwith
are plotted against
eachofother
in4,
Supplementary
Information
scores
Table
and previously
calculated
soil
and
climate before. It
[C]
M
, but
negatively
with
all [P])
foliar
Ato
any extent
given
species)
withnutrie
the
does
not0.68
seem
tothe
have
been
recognised
isand
thus
here
formation),
accounted
for
of
total
variance
for
both
Figure
7
shows
the
major
components
of
the
three
major
:
Leaf
structural
costs
and
lifespan
4.2.1
Although
not
considered
significant
on
the
basis
of
penalty
for
M
,
foliar
[C]
and
(and
a
lesser
foliar
PDJ
actor
alltrait
three
of Fig.
,
A
opicalfor
tree
dimensions
7
PDJ S1.RWThis shows
the required
anyaresystematic
characteristics
of thelack
sameofsites
shown in Fig. 9. First, the
and
also
with
.
The
second
axis
of
the
PCA
shows
that
0.33
of
the
total
vao
low and high
fertility
soil overlap
species.
CPCs
and
their
of traits
in being
diagrammatic
form.
This weightings
balancedcorrelations
by positive
for size
foliarand
[Mg]
in parcorrected
p–values,
between leaf
[N]
denoted
as PFL
between
thethat
species
scores
for .of
the
in ( PDJ ∩
and
the considered
top panel
of Fig.
9primary
shows as
ůdimension
as a function
theal.:
first
soil PCA
RW ) correlations
Although
it isofoften
the
The
most
significant
relationships
bet
fects
correlation
matrix
(ů
)
is
also
significan
1 expected
S.
Patiño
et
Tropical
tree
trait
dimensions
2
could
be
explained
by
the
fir
illustrates
that
many
traits
seem
toticular,
especially
The
three
axes
species
scores
(normalised
to
± also
100)
but
with
contributions
ΦLS(and
and hence
ρx .
and
[P]
ofbein
a “shared”,
similar
strength
to that
Overall
the
five
eigenvectors
selected,
all
of which
wereported
be- fromhere
any
given
species)
with
the
results
shown
Table
aveand
been
recognised
before.
It
is thus
here
output
from
anyaxis
good
fit
of first
aproposed
principle
components
P]
[Mg]
ineconomic
opposite
ofis
Fyllas
et
al. (2009),
the
latter
considered
a strong
in- 4. This for 0.25 ofscores
ofvarying
the leaf
spectrum
that
by
Wright
et model.
ofportant
Tablewith
4,contributor
and
previously
calc
the variance,
substantial
negati
are
plotted
against
each
other
in
Supplementary
Information
lieve
to
be
physiologically
relevant
(see
Supplementary
In)
were
also
reported
for
tropical
for- and this
included
as
part
of
RW
M
which
is
an
important
factor
for
all
three
,
shows
that
0.33
of the three
total
variation
11 strong
traits examined
Aofof
PDJ for
RWM , foliar
Clearly
a wide range
of combinations
of fertility
these
trait
di- inFthe
MA for these two
trait dimensions.
.
The
tegrated
measure
soil
and
denoted
al. (2004)
viz. systematic
variations
in
rates
photosynthetic
characteristics
of
the
same
sites
are
shw
[C]
and
(and
to
a
lesser
ext
A
andinM
, but
negatively
A
any
given
species
does
notlack
seem
have
been
recognised
before.
It issubstrates
thus[C]
here
Fig.
S1. Fig.
This
shows
the
required
ofto0.68
any
systematic
formation),
accounted
for
of
theρtotal
variance
for
est
leaves
sampled
across
a range
ofboth
soil
French
could
be explained
by athe
first
PCA
axis
(ůresponse
im-of
mensions
can
occur.
But
with
8a
also
showing
that
it
is
1 )∩withRW
x) an
.
Also
occurring
in
(
and
the
and
relationship
observed
suggests
strong
integrated
top
panel
of
Fig.
9
shows
ů
as
a
funct
FW
PDJ
being
balanced
by
positive
weightings
for
folia
carbon
acquisitions
(dry
weight
basis)
being
linked
with
foeigenvectors
selected,
all
of
which
we
beand
in
the
same
direction
rela1
and also
with
. that
The
second
W
S. Patiño
al.:
Tropical
tree
dimensions
shows
0.33
of
correlations
between
species
scores
asPFL
expected
foretto
the
lowthe
and
high
fertility
soil
species.
Guiana
(Baraloto
al.,
2010).
Theytrait
concluded,
however,
denoted
as
.positively
3.8
Relationship
between
plot
effect
PCAs
and
contributor
and
this
also
relating
foliar
speaking)
only
species
typically
associated
with
same
sign istrees
[C],
with
[P]
and
[Mg]
varying
in
opposite
ofInAmazon
tropical
forest
tobut
soil
fertility,
with
most
nu- et
ofwith
Fyllas
et al.
(2009),
the
ticular,
butaxis
also
contributions
from
Φlatter
an
relevant
(see(generally
Supplementary
liar
dry–weight
concentrations
of portant
nitrogen,
phosphorus,
M
hlogically
with a high
estimated
standard
LS
fects
correlation
matrix
(ů
A
2)
could
be
explained
output
from
any
good
fit
of
a
principle
components
model.
The
first [C]
three
axes
species
scores
(normalised
±of100)
that
Lfive
was
nottrait
closely
linkedtoall
with
either we
[N]beor [P]. This
Overall
the
eigenvectors
selected,
which
soil/climate
A nutrients
[C]
and
M
but
negatively
with
all
foliar
examined
A ,both
directions
with
respect
to
M
for
these
two
dimensions.
trients
increasing,
and
with
foliar
and
ρ
decreasing
as
tegrated
measure
of
soil
fertility
and
A
x
and
.
high
fertility
soils
that
have
scores
for
nted
for
0.68
of
the
total
variance
for
both
and
leaf
longetivity,
our
analysis
found
that
U
(accountfor
0.25
of
the
variance,
with
PDJ
RW of thesedoes
1
portant
contributo
ave also included LA in ( RW ∩
not
seem
to
have
been
recognised
before.
It
is
thus
here
Clearly
a
wide
range
of
combinations
three
trait
diare
against
each
other
in of
Supplementary
Information
lieve
to τbe
relevant
Supplementary
Incould
bePCA
for
several
reasons.
First,
their sampling
strategy
also
with
.ofplotted
The
second
axis
ofphysiologically
the
on
plot
ef- (see
increases.
Interestingly,
the
Kendall’s
for
this
plot
ůthe
relationship
observed
astro
Fthe
1 direction relaity soil species.
7 shows
major
components
the
three
major
ing for the Figure
greatest
component
ofand
the
total
variation
in
the
for M
[C]M
and
(a
Intersecting
and
andthe
in required
the same
A , foliar
[C]suggests
and
n
RW
FW
mensions
can
occur.
But
with
Fig.
8aalso
also
showing
that
it(undefined)
isPFL
A , but
Fig.
S1.
This
shows
lack
of
any
systematic
formation),
accounted
for
0.68
the
total
variance
for
both
at it varies in the opposite direccovered
a
range
of
soil
types
and
as
discussed
in
denoted
as
.
fects
correlation
matrix
(ů
)
is
significant,
accounting
2
versus of
of
0.63
is
greater
than
for
any
of
the
original
variof
Amazon
tropical
forest
trees
to
soi
F all,
CPCs
their
overlap
traits
in
diagrammatic
form.
This
axes species dataset)
scores (normalised
to ±nitrogen
100)
being
balanced
by
positive
wT
did
notand
involve
at
and
was
actually
domtive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
3.8
Relationship
between
plot
effect
and
also
with
.
A only
LSspecies
(generally
speaking)
typically
associated
with
correlations
between
the
species
scores
as
expected
for
the
low
and
high
fertility
soilweightings
species.
4.3,
are
likely
have modulated
foliar
nutrient
Overall
the
five to
eigenvectors
selected,
all of
which
we
be- [C
forseem
0.25byto
of the
variance,
with
substantial
negative
The
most
significant
relationships
between
the
PCA
site
axisfoliar
for other
ables traits
examined
et al.sign)
(2009),
theSect.
highest
ofthese
which
RW cf.
FW .byillustrates
S each
trients
increasing,
and
with
that
many
be
“shared”,
especially
in
Supplementary
Information
ticular,
but
also
with
contribu
inated
leaf cation
concentrations
and
(ofFyllas
opposite
soil/climate
fects
correlation
error
as
part
of
,aany
wefor
have
also
included
L
in
( and
∩sampling
output
from
good
fit
of
anot
principle
components
model.
FW
A physiologically
RW
The
first
three
axes
species
scores
(normalised
to
±
100)
and
.4,[P])
highfor
fertility
soils
that
have
scores
both
lieve
to
be
relevant
(see
Supplementary
In-Kendam
levels
but
L
.
Second,
our
has
covered
a
much
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
scores
of
Table
previously
calculated
soil
and
climate
PDJ
RW
A
A
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
increases.
Interestingly,
the
ows the required
lackMof
any
systematic
F
,
low carbon
content.
We
suggest
that
this
dimension,
for
0.25
of
the var
PDJ
factor
for
allthis
three
of ashowing
, range
A which is an important
PDJ
RW
Clearly
wide
of
combinations
ofin
these
three
trait
diare
plotted
each
other
in
Supplementary
Information
formation),
accounted
for
0.68
ofshown
the total
variance
forthe
both
),
that
itagainst
varies
in
the
opposite
Figure
7the
shows
the
major
components
of
the
three
major
being
balanced
byalso
positive
weightings
for
foliar
[Mg]
parwider
range
of
environments
soils,
presumably
bringcharacteristics
of
thedirecsame
sites
are
in Fig.
9.
First,
ps:
environmental
components
FW
:enTropical
tree
trait
dimensions
7 and
also
that
ůof
significant
weightings
of
leafversus
greater
than
for
2 contains
the
species
scores
as expected
for
the shows
F of 0.63 isfor
reflects
different
plant
strategies
in
terms
leaf
construcM
,
foliar
[Ca
A
mensions
can
occur.
But
with
Fig.
8a
alsofertility
isthe
occurring
intion
(their
∩ with
)traits
and
of
the
and FW . Alsolevel
Fig.
S1.
This
shows
the
required
lack
any
systematic
low
and
soil
species.
and
overlap
of
in
diagrammatic
form.
This
ticular,
but
also
contributions
from
Φcorrelated
and
ρhigh
.Fig.
top
panel
of
9showing
shows
ůthat
ait
function
of theasby
first
soil PCA
PDJ
RW
ing
wider
species–level
variation
into
dataset
whole.
LS
1ofas
variables
that,
individually,
all
strongly
cf.
relative
toleaves
M
and
Φ
for
ables
examined
Fyllas
et
al.
(2009
RW
FWx.
Awere
LS
ood fit of a tion
principle
components
model. CPCs
being
balanced
by
costs,
with
the
tendency
for
low
M
in
these
of
The
most
significant
relationships
between
the
3.8
Relationship
betwee
A [Mg]
(generally
speaking)
only
species
typically
associated
same
sign
[C],dimensions
but illustrates
with [P]
and
varying
in
opposite
correlations
between
the
species
scores
as
expected
for
that
many
traits seem
to
“shared”,
especially
hge
and
high
fertility
sites
to-isIt
The
three
axes
scores
(normalised
toComparison
±in100)
axis
of
Fyllas
et
al.
latter
considered
a[P].
strong
Thirdly,
our
analysis
shows
the species
LAthe
iswith
also
an
important
comwith
mean
annual
precipitation
(P
viz.be
positive
correlaS. combinations
Patiño
et
al.:
Tropical
tree
trait
7thepreviously
was
0.56
for
foliar
A ) the
tolow
have
been
recognised
before.
is thus
any
given
species)
with
results
shown
infirst
Table
4. (2009),
This
of
of these
three
trait
di-here
ticular,
but
also
ww
high
mineral
content
presumably
attributable
to
a
low
tissue
scores
of
Table
4,
and
calculated
so
soil/climate
directions
respect
to M
these
two
trait
dimensions.
output
from
anytegrated
good
fit
ofboth
aagainst
principle
components
model.
ions
and
SMA
slopes
forshowing
the en-with
are
plotted
each
other
in
Supplementary
Information
.
The
strong
measure
of
soil
fertility
and
denoted
A for
and
.
high
fertility
soils
that
have
scores
for
.
This
means
that
considered
in
simple
bivariponent
of
F
3.6
Bivariate
relationships:
environmental
components
tions
with
foliar
[C]
and
M
and
a
negative
correlation
with
PDJ
RW
FW
M
which
is
an
important
factor
for
all
three
,
also
shows
that
the
ů
contains
signifi
A
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
ur.
But
with
Fig.
8a
also
that
it
is
PDJ
RW
density
associated
thinner,Aless lignified cell walls and
.
characteristics
oftrait
the
same
sites2response
are shown in Fi
PFL
Clearly
athe
range
ofS1.
combinations
ofleaf
these
three
di-ofthat,
is information
provided
inassociated
morewithRW
Fig.
This
shows
the
required
lack
any
systematic
relationship
observed
suggests
a strong
integrated
Figure
7by
shows
components
of
theimthree
major
ate
relationships
such
with
size,
relationships
may
be
Intersecting
and
in be
the
same
relafoliar
[Mg].
Itand
therefore
notdirection
surprising,
aswide
ismajor
shown
in
the
3.8FW
Relationship
between
plot
effect
level
individually,
were
FW
could
explained
the
first
PCA
axis
(ů
) of
with
ρas
an
ng)
only
species
with
any
given
species)
with
the
results
shown
in Table
4.variables
This
does
not
seemtypically
to have
been
recognised
before.
It
is
here
1PCAs
xand
.is thus
Also
occurring
in
(
∩
)
and
the
and
top
panel
of
Fig.
9
shows
ů
as
a
function
the
PDJ
RW
with
the
higher
cations
content
presumably
also
balanced
by
five
eigenvectors
selected,
all
of
which
we
be1
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
itannual
is
intervals)
in the Supplementary
correlations
between
the
species
scores
asadditional
expected
for
of
Amazon
tropical
forest
trees
toexamined
soil
fertility,
with
most
nu-theof(P
Considering
data
from
both
lowclear
and
high
fertility
sites
toCPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
less
than
when
examined
in
conjunction
with
tive to MA is ΦLS
. Although
with
a
high
estimated
standard
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assosoil/climate
with
mean
precipitation
2
A
eysiologically
trait
dimensions
7
portant
contributor
and
this
also
relating
positively
to
foliar
A
shows
that
0.33
of
the
total
variation
in
the
11
traits
same
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
axis
of
Fyllas
et
al.
(2009),
the
latter
considere
and
.
that
have
scores
for
both
relevant
(see
Supplementary
Inhigher
levels
of
organic
acids
(Poorter
and
de
Jong,
1999).
denoted
as
.
PDJ
RW
The
most
significant
relation
3.8 Relationshi
(generally
speaking)
only
species
typically
associated
with
s for Table 1, the PFL
SMA slopes reoutput
from
any
good
fit) with
of
a ρprinciple
components
model.
trients
increasing,
and
with
foliar
[C]
and
ρx decreasing
asand a ne
gether,
Table
3
lists
correlations
and
SMA
slopes
for
the
enillustrates
that
many
traits
seem
to
be
“shared”,
especially
ciation,
but
with
examination
of
Table
4
also
suggesting
that
covariates
as
done
here.
tions
with
foliar
[C]
and
M
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
A
could
be
explained
by
the
first
PCA
axis
(ů
an
imerror
as
part
of
,
we
have
also
included
L
in
(
∩
A
directions
with
respect
to M
these
two trait dimensions.
1
x
FWboth
Acosts
RW
tegrated
measure
of
fertility
denoted
A fortrait
counted
for
0.68
of
theeigenvectors
total
for
the major
components
of
thevariance
threeselected,
major
Such
leaves
also
being
with
lower
overall
Overall
thecolumn
five
all
of
which
we
bescores
of Table
4,
and
previo
soil/climate
S.
Patiño
etconstruction
al.:
Tropical
tree
dimensions
with
the
x as
the
headers
Clearly
aon
wide
range
of combinations
of
these
three
trait
increases.
Interestingly,
the
Kendall’s
τsoil
for
plotand
of
ůnot
vironmental
effects
with
this
information
provided
inof
more
F
1difor
any
given
species,
both
ΦThe
and
ρcontributor
decline
with
and
. isthis
high
fertility
soils
that
have
scores
for
both
Also
identified
as
part
was
8LS
, [Mg].
thisRW
being
consisfoliar
It
therefore
surpri
LS
w also
and
also
with
.
second
axis
of
the
PCA
the
plot
efRW
M
which
is
andirecimportant
factor
for
all
three
, PDJ
portant
and
this
also
relating
positively
to
foliar
Arich
PDJ
RW
any
given
species)
with
the
results
shown
in
Table
4.
This
recognised
before.
It
is
thus
here
relationship
observed
suggests
a
strong
ertility
soil
species.
erlap
of
traits
in
diagrammatic
form.
This
),
this
also
showing
that
it
varies
in
the
opposite
lieve
to
be
physiologically
relevant
(see
Supplementary
Inand
less
investment
of
phenols
and
other
carbon
comcharacteristics
of
the
same
si
Intersecting
and
and
in
the
same
direction
relaFW
RW
FW
tiño etFor
al.:the
Tropical
treetraits,
trait dimensions
7increases
bels.
structural
the
mensions
can
occur.
But
Fig.
8a
also
that
isintegr
versus
of
0.63
is greater
than
for
any
ofwith
the
original
varidetailcorrelation
(including
confidence
intervals)
in the
Supplementary
Fwith
increasing
precipitation
and,
somewhat
counter
intuitively,
Figure
7between
shows
the
major
components
ofwith
the
three
major
with
the
general
trend
of
8LS
to
increassecond
panel
ofshowing
Fig.
9,tothat
ůitfertility,
The
most
significant
relationships
PCA
site
axis
fects
matrix
(ů
)tent
also
significant,
accounting
2 and PA
[C]
and
M
,is
but
negatively
all
foliar
nutrients
examined
2occurring
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
A
of
Amazon
tropical
forest
trees
soil
.
Also
in
(
∩
)
and
of
the
and
ny
traits
seem
to
be
“shared”,
especially
ree
axes
species
scores
(normalised
to
±
100)
formation),
accounted
for
0.68
of
the
total
variance
for
both
top
panel
of
Fig.
9
shows
ů
a
FW
PDJ
RW
pounds
in
defense
(Poorter
and
Villar,
1997).
Presumably
tive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
1
A
LScf.
.been
relative
Φ
ps are all negative andtion
appear
be- to Mwith
(generally
speaking)
only
species
typically
with
ables
examined
by
Fyllas
et
al.
(2009),
the
highest
which
any
given
species)
withassociated
theofresults
shown
does
not
seem
tothe
have
recognised
It
isof
thus
here
Information
(Table
S2A).
Asing
for
Table
1,soil
the
SMA
slopes
reRW
FW
A and
LS for
beincreasing.
CPCs
and
their
overlap
traits
in the
diagrammatic
form.
This
ciation,
but
with
examination
of Tabl
Lbefore.
(Fig.
5d).
Especially
as
there
was
little
contribution
scores
of
Table
4,first
and
previously
calculated
and
climate
for
0.25
of
variance,
with
negative
weightings
and
also
with
.
axis
of
PCA
on
the
plot
efAThe
could
explained
byleaf
the
PCA
axis
(ůsubstantial
with
ρxsecond
an
imtrients
increasing,
and
with
foliar
[C]
and
ρ
1 )with
same
sign
is
[C],
but
[P]
and
[Mg]
varying
in
opposite
ainst
each
other
in
Supplementary
Information
low
and
high
fertility
soil
species.
x
axis
of
Fyllas
et
al.
(2009),
t
associated
with
are
also
variations
in
water
relators
selected,
all
of
which
we
beThe
most
significa
PDJ
mportant
factor
forbeen
all three
, RWIterror
S.
Patiño
etthat
al.:
Tropical
tree
trait
dimensions
[K]
and,
to a of
lesser
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
etthings
al.
(2009)
as here
part
ofthe,PFL
we[C]
have
also
included
L
indimension
(extent
∩
shows
that
0.33
of
the
total
variation
1
PDJ
flect
relationship
ysites
↔
x,
with
the
x
the
column
headers
any
species)
with
the
results
shown
in(Table
Table
4. this
This
not
seemlog
to10have
recognised
before.
is contributor
thus
FW
Aas
10 [Ca],
illustrates
many
traits
seem
to
be
“shared”,
especially
and
.τthe
high
fertility
soils
that
have
scores
forgiven
both
for
any
species,
bothRW
ΦinLS
and
of
this
2),
suggests,
other
denoted
as
characteristics
of.,given
the
same
are
shown
in
Fig.
First,
the
for
M
foliar
and
respect
(and
to
amatrix
lesser
foliar
[P])
PDJ
fects
correlation
(ů
)RW
is
also
significant,
accounting
A
portant
and
this
also
relating
positively
foliar
29.
Interestingly,
the
Kendall’s
for
directions
with
toto
M
for
these
two
trait
dimensions.
ssrelevant
shows
the
required
lack
ofare,
anyit
systematic
F increases.
tegrated
measure
soil
ferti
Ato
The(see
first
three
axes
species
scores
(normalised
toFyllas
±
100)
Supplementary
Intions.
For
example,
seems
reasonable
tothe
expect
that,
asFinally,
as and
in
et al.
(2009)
we
show
values
for
scores
of Table
4,1
3.6
Bivariate
relationships:
environmental
components
observed
(−0.26
to
−0.41)
also
shows
that
the
ů
contains
significant
weightings
of
leafcould
be
explained
by
the
first
PCA
axis
(ů
y
being
the
row
labels.
For
the
structural
traits,
the
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
2
occurring
in
(
∩
)
and
of
the
increasing
precipitation
and,
somew
), ,this
also
showing
it1Avaries
inisvariance,
the
opposite
Figure
7we
shows
the
major
components
ofalso
thethan
three
panel
of
Fig.
shows
ů0.25
aswhich
aweightings
function
of
the
firstdirecsoil
PCA
PDJ
RW
being
balanced
bythat
positive
for
foliar
[Mg]
in parbeing
equal,
that
trees
with
athree
higher
have
ed as
. against
Overall
the
five 9eigenvectors
selected,
all
of
which
befor
of
the
with
substantial
negative
weightings
FW
RW
PFLspecies
M
an
important
factor
for
all
of
, relationship
[C]
Mtop
but
negatively
with
nutrients
examined
PDJ
versus
ofwere
0.63should
is RW
greater
formajor
any sugge
of of
thet
A
etween
the
scores
as
expected
for
the
F
observed
are
plotted
each
other
in Supplementary
Information
.68
of
the
total
variance
for
both
Kendall’s
partial
τand
(denoted
τprelationships
)all
forfoliar
all
traits
ofall
interest
as
sociated
lower
ofand
lignification
reduced
tissue
characteristics
Intersecting
and
and
inand
the
same
direction
relathewith
associated
slopes
for
the
taxlevel
variables
that,
individually,
all
strongly
correlated
RW
FW
contributor
and this
also
relating
po
most
significant
are
negative
and
appear
becould
be
explained
by
the
first
PCA
axis
(ů
with
ρportant
an
imany
given
species
does
not
seem
to
have
been
recognised
before.
Itunit
isin
thus
here
1a)inxextent
but
[P]
and
[Mg]with
varying
in levels
opposite
with
increasing.
CPCs
their
overlap
of
traits
diagrammatic
form.
This
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
ticular,
but
also
with
contributions
from
Φ
and
ρ
.
lieve
to
be
physiologically
relevant
(see
Supplementary
Inincreased
rates
of
water
transport
per
A
.
erall
the
five
eigenvectors
selected,
all
of
which
we
befor
M
,
foliar
[C]
and
(and
to
lesser
foliar
[P])
LS
x
and
also
with
.
The
second
axis
of
the
PCA
on
the
plot
efS
A
cf.
.
tion
relative
to
M
and
Φ
for
ables
examined
by
Fyllas
et
al.
(2009),
the
hig
A
LS
ny
fit1of
a principle
components
model.
ofnegatively
Amazon
tropical
forest
inpositively
( aPDJ
∩to
)(P
and
of
the
and
Fig.
S1.
This
shows
the be
required
lack
of
systematic
pecies.
Considering
datawell
from
both
low
high
fertility
as
ů 1 any
and
ů 2and
ascell
functions
of
,RW
,.[Ca],
TAlso
Plog
and
Q
top
panel
of
Fig.tre
9
FW
RWM
densities,
would
relatively
more
flexible
walls
and
asites
tive
to
M
is
ΦFLS
.toAlthough
with
a aand,
high
estimated
standard
T
a ,FW
a occurring
dpect
ingood
Table
(−0.37
totwo
−0.72).
with
mean
annual
precipitation
)
viz.
positive
correlaA
[C]
and
,
but
with
all
foliar
nu
tween
ρ
and
log
[P],
log
[K]
to
lesser
portant
contributor
and
this
also
relating
foliar
A
shows
that
0.33
of
A
x
to
M
for
these
trait
dimensions.
10
10
10
illustrates
that
many
traits
seem
to
be
“shared”,
especially
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
A
formation),
accounted
for
0.68
of
the
total
variance
for
both
torange
be
physiologically
relevant
(see
Supplementary
In- matrix
balanced
by
positive
weightings
for
[Mg]
in
parAlso
of
note
of[Mg]
lesser
significance
than
the
above) with
denoted
as is
.but
F (though
fects
correlation
(ůslopes
also
significant,
accounting
PFL
wasfoliar
0.56
forFinally,
foliar
[P].
Comparison
Fylla
2 ) isbeing
eies
of combinations
ditrients
increasing,
and
with
same
sign
[C],
with
[P]
and
varying
in
opposite
correlations
species
scores
as
expected
for
the
Table
3 lists
correlations
and
SMA
for
the
enintrait
Table
5. Here
the
calculated
value
of
τ
and
associscores
(normalised
toofthe
±these
100)
axis
of
Fyllas
et
a
lowbetween
bulkgether,
modulus
ofthree
elasticity
(Niinemets,
2001),
also
with
as
in
Fyllas
et
al.
(2009
p
tions
with
foliar
[C]
and
M
and
a
negative
correlation
with
and
also
with
.
The
second
axis
of
the
PC
extent
log
(`A
).M
The
slopes
observed
(−0.26
−0.41)
[C]
, but
negatively
with
all foliar
nutrients
ARWρ∩.
could. be explained
error
as
part
of
,the
we
have
also
included
LA
inexamined
( and
Aspecies.
10and
FW
Aare,
relationship
observed
suggests
awas
strong
integrated
response
low
and
high
fertility
soil
ation),
accounted
for
0.68
of
the
total
variance
for
both
ticular,
but
also
with
contributions
from
Φ
the
increase
into
both
M
and
[Mg]
with
decreasing
Overall
five
eigenvectors
selected,
all
of
which
we
beand
and
inany
theInformation
same
relafor
of
the
variance,
with
substantial
negative
weightings
Bivariate
relationships:
environmental
components
LS
x
RW
M
which
isfor
an
important
factor
for
allůFpartial
three
ofτ tegrated
, measure
FW
also
shows
that
the
contains
significant
weig
occur.
But of
with
Fig.
8a
also
showing
that0.25
it3.6
is
PDJ
RW
2
increases.
Interestingly,
directions
with
respect
to
M
these
two
trait
dimensions.
from
good
fitdirection
of
principle
components
model.
vironmental
with
this
information
provided
in
more
ated
probability
giving
analso
indication
of
the
effect
ofA[Mg].
each
inoutput
Supplementary
A
ter
analysis
environmental
ef-a effects
Kendall’s
(denoted
τ
)
for
the
high
cation
concentrations,
especially
potassium
making
foliar
It
is
therefore
not
surprising,
as
is
shown
in
the
fects
correlation
matrix
(ů
)
is
also
signifi
p
however,
much
less
than
for
the
associated
slopes
for
the
taxand
with
.
The
second
axis
of
the
PCA
on
the
plot
efportant
contributoto
2
ofThe
Amazon
tropical
forest
trees
tobe
soil
fertility,
with
most
nund
high
fertility
soil
species.
first
three
axes
species
scores
(normalised
to
±
100)
),
this
also
showing
that
it
varies
in
the
opposite
direclieve
to
physiologically
relevant
(see
Supplementary
InThe
former
is,
of
course,
well
documented
and,
for
woody
Although
with
a
high
estimated
standard
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
FW
3.8
Relationship
between
plot
effect
PCAs
and
level
variables
that,
individually,
were
stron
A
aking)
only
species
typically
associated
with
versus
of
0.63
is
greater
Clearly
a
wide
range
of
combinations
of
these
three
trait
di(includingsoil/environmental
confidence
intervals)
in
thecorrelation
Supplementary
parameter
after
for
ef. to
Also
occurring
inof(Pthe
)asand
ofMall
the
and
required lacka substantial
of any detail
systematic
relationship
obser
well
as
ů relaand
ůF2with
functions
ofneg
FW
contribution
(in association
with
organic
acids)
second
panel
of
Fig.
9,
that
ů0.25
also
showRW
strong
assoIntersecting
and
and
in
the
same
direction
for
variance,
substantial
1∩
onomic
components
asaccounting
listed
in
Table
1ρthe
(−0.37
−0.72).
fects
matrix
(ů[Mg]
also
significant,
accounting
2 and
APDJ
[C]
and
nt
RW
FW
2 ) is
A , but
trients
increasing,
and
with
foliar
[C]
and
as
are
plotted
against
each
other
in
Supplementary
Information
Considering
data
from
both
low
and
high
fertility
sites
toeecies
first
three
axes
species
scores
(normalised
to
±
100)
x decreasing
formation),
accounted
for
0.68
of
the
total
variance
for
both
being
balanced
by
positive
weightings
for
foliar
in
parplants
at
least,
seems
to
be
associated
with
an
increased
fosoil/climate
with
mean
annual
precipitation
(P
)
viz.
pos
cf.
.
tion
relative
to
M
and
Φ
for
A
,
we
have
also
included
L
in
(
∩
ables
examined
by
Fyllas
et
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
it
is
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
refect RW
of (Olivares
the
four.Medina,
Taking
into
account
towith
potential
RW with
FW
A
LS
same
sign
isnegative
[C],
with
and
[Mg]
varying
inAmazon
opposite
scores
expected
forAthe
FW
RW
of
tropica
in,Table
Table
5.
Here
the
calculated
and
. other
oils
havetoas
scores
for both
ciation,
but
of
4[C]
also
suggesting
that
leaf
tissue
osmotic
and
tive
to Kendall’s
M
Φthe
Although
with
abut
high
standard
forestimated
foliar
and
and
(and
to
a lesser
for
0.25 1992).
of
the
variance,
weightings
PDJpotentials
also
with
.vaT
A is
LS
A
et
al.:that
Tropical
tree
trait
dimensions
7M[P]
increases.
Interestingly,
the
τx.substantial
this
plot
ofexamination
ůthan
Fig.
S1.with
This
shows
the
required
lack
any
systematic
Table
3x
lists
correlations
and
SMA
slopes
for
the
enotted
against
each
other
inand
Supplementary
Information
Falso
1 tions
low
andΦ
high
fertility
soil
species.
ticular,
contributions
from
and
ρof
.for
liar
tissue
density
rather
changes
in probability
leaf
thickness
(Niwith
foliar
[C]
and
M
and
aan
negative
co
LS
3.8
Relationship
between
plot
effect
PCAs
and
A
was
0.56
for
foliar
[P].
Com
(generally
speaking)
species
typically
with
the
relationship
ygether,
↔but
x,associated
with
the
asspatial
the
column
headers
relationships
between
ρthe
confounding
effects
of
autocorrelation
(Fyllas
et
al.,
directions
with
respect
to
M
for
these
two
trait
dimensions.
fows
a principle
components
model.
trients
increasing,
ated
giving
indication
x only
A
for
any
given
species,
both
Φ
and
ρ
also
decline
with
3.7
Principal
component
analysis
of
environmental
efhowing
itThese
varies
inflect
opposite
directhethat
major
components
ofthe
the three
major
being
balanced
by
positive
weightings
for
fom
attributes,
combined
with
the
likely
relatively
low
allofor
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
LS
w
fects
correlation
asfirst
part
ofprovided
, we
have
also
included
L
∩ not surprising,
versus F
of
0.63
isABivariate
greater
than
forthree
any
the
original
varicorrelations
between
the
species
scores
asofaxes
expected
forscores
the
FW
A in
RW
vironmental
effects
with
thiserror
information
in
more
S1.
This shows
the
required
lack
of2009)
any
systematic
The
species
(normalised
to(is±
100)
inemets,
1999;
Poorter
et al.,
2009)
and
with
a
concurrent
refoliar
[Mg].
It
therefore
as
is
3.6
relationships:
environmental
components
soil/climate
also
shows
that
the
ů
contain
and
the
y
being
the
row
labels.
For
the
structural
traits,
the
components
(Fig.8)
,
it
was
of
we
only
consider
relationships
with
p
≤
0.01
or
better.
mbinations
of
these
three
trait
di2
increases.
Inte
soil/environmental
parameter
after
F
increasing
precipitation
and,
somewhat
counter
intuitively,
fects
r
overlap
of
traits
in
diagrammatic
form.
This
and
.
high
fertility
soils
that
have
scores
for
both
ticular,
but
also
with
contributions
from
Φ
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parcation
of
carbon
resources
to
defense
associated
compounds
Intersecting
and
and
in
the
same
direction
relafor
0.25
of
the
var
PDJ
RW
LS
RW
FW Information
cf.
andtoΦbetween
ables
examined
by species)
Fyllas
etaplotted
al.
(2009),
the
highest
of
which
any
given
with
the
results
shown
in
Table
4. inThis
eem
have
beenRW
recognised
before.asItexpected
is thus
here
FW .scores
A
LS for
output
from
any
good
fit intervals)
of
principle
components
model.
detail
(including
confidence
inTable
the
Supplementary
lations
the
species
for
the
are
against
each
other
in
Supplementary
), bethis
also
that
it
varies
the
opposite
direcduction
insuggests
photosynthetic
nutrient
efficiency
when
expressed
second
panel
of
9,
that
ů 2versus
andthat,
PAFindividua
also
show
The
most
significant
relationships
between
the
PCA
site
axis
level
variables
most
significant
relationships
are
all
negative
and
appear
coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τ shown
inFW
Fig.
9,
5showing
ith
Fig.Figure
8a also
showing
it is
ofinto
0.63
fect
ofaFig.
the
other
four.
Taking
a
with
three
increasing.
many
traits
tolignin
bethat
“shared”,
especially
ticular,
but
also
with
contributions
from
Φ
and
ρx .
7seem
shows
the
major
components
of
the
three
major
such
as
and
phenols
suggests
that
in
many
ways
leaves
tivein
to
M11
is
Φdi. lack
Although
with
high
estimated
standard
for M
LS
A
LS
A , foliar [C
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
shows
that
0.33
of
the
total
variation
the
traits
examined
Clearly
a
wide
range
of
combinations
of
these
trait
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
ret
from
any
good
fit
of
a
principle
components
model.
Fig.
S1.
This
shows
the
required
of
any
systematic
Considering
data
from
both
low
and
high
fertility
sites
tociation,
but
with
examination
of
Table
4
also
su
on
a
dry
weight
basis
(Niinemets,
1999;
Domingues
et
al.,
s PFL .typically
scores
of
Table
4,
and
previously
calculated
soil
and
climate
3.8
Relationship
between
plot
effect
PCAs
and
with
mean
annual
precipita
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
nvironment
exist
for
Amazon
forthe
to
be
superior
predictors
than
the
individual
variables,
cf.
.
tion
relative
to
M
and
Φ
for
species
associated
with
ables
examined
by
Especially
given
the
strong
relationships
between
ρ
and
the
confounding
effects
of
spatial
autoco
x
RW
FW
A
LS
10
10 beform.
10expand quite
x
CPCs
and
their
overlap
of
traits
in
diagrammatic
This
being
balanced
by
of
species
with
high
scores
may
able
to
lationships:
environmental
components
PDJ
n
important
factor
for
all
three
of
,
also
shows
the
ůBut
contains
significant
weightings
be
the
first
PCA
axis
(ů
with
ρFW
an
imerror
as
part
of
,for
we
have
also
included
LAfoliar
in
( and
canthat
occur.
with
Fig.
8a
also
showing
that
itleafis
PDJ
RW
flect
the
relationship
yTexplained
↔
x,
with
the
x2010).
as
the
column
wideeigenvectors
range ofofcombinations
of
these
three
traitobserved
di- could
1 )of
xfor
correlations
between
the
species
scores
as
expected
for
the
RW
gether,
Table
3by
lists
correlations
and
SMA
slopes
the
enany
given
species,
both
Φ
ρ∩
also
One
possibility
to
account
for
this
is
low
internal
conFinally,
Fyllas
et
al.
(2009)
we
show
values
forand
characteristics
of
the
same
sites
are
shown
in
Fig.
the
the
five
selected,
all
which
wemensions
besoil/climate
The
most
significant
relationships
between
the
PCA
site
axis
LS0.56
w folia
tions
with
[C]
MwA
extent
log
(`ofA
).
The
slopes
(−0.26
−0.41)
are,
kly
aaillustrates
PCA
analysis
the
full
plot
the
only
exception
being
. 2to
In
that
case,
[N],
[K]
and
ρheaders
was
foliar
cation
components
(Fig.8)
, First,
it
was
of
2009)
we
only
consider
relationships
aenvironmental
w as9.in
10
that
many
traits
seem
to
be
“shared”,
especially
and
. shorter
scores
foroccur.
both
ticular,
butfor
also
rapidly
but
also
be
lived
and
with
more
“deciduous
PDJ
RW
3.8
Relationship
between
plot
effe
level
variables
that,
individually,
were
all
strongly
correlated
portant
contributor
and
this
also
relating
positively
to
foliar
(generally
speaking)
only
species
typically
associated
with
and
the
y
being
the
row
labels.
For
the
structural
traits,
the
can
But
with
Fig.
8a
also
showing
that
it
is
output
from
any
good
fit
of
a
principle
components
model.
vironmental
effects
with
this
information
provided
in
more
lso
occurring
in
(
∩
)
and
of
the
increasing
precipitation
and,
somewhat
count
),
this
also
showing
that
it
varies
in
the
opposite
direcKendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
interest
as
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
PDJ
RW
eions
physiologically
relevant
(see
Supplementary
Inductances
to
CO
transfer
for
higher
Mfor
species
(Lloyd
etis
scores
of
Table
4,
and
previously
calculated
soil
and
climate
foliar
[Mg].
It
therefore
3.6
Bivariate
relationships:
environmental
components
FW
p
1taxhowever,
much
less
than
for
the
associated
slopes
for
the
A
2
excluding
H
and
S
both
of
all
show
relationships
not
present
when
regressing
the
plot
also
shows
that
the
additional
interest
to
see
if
coordinated
structural/leaf
bioAs
the
(full)
Kendall’s
τ
shown
inn
max
from
both
low
high
fertility
sites
torrally
components
ofand
the
three
major
like”
characteristics
than
their
lower
counterparts
(see
soil/climate
3.8
Relationship
between
plot
effect
PCAs
and
with
mean
annual
(P
) viz.
positive
correlaPDJ
M
which
an
important
factor
allfor
three
ofwith
[C]
and
M
,al.
but
negatively
with
all
foliar
nutrients
examined
most
significant
relationships
are
allconfidence
and
appear
bespecies
typically
associated
A
A with
PDJ
RW
Aprecipitation
Clearly
anegative
wide
range
ofůsites
combinations
ofinthese
three
trait
didetail
(including
intervals)
in
the
C],
butspeaking)
[P]
and
[Mg]
infor
opposite
with
increasing.
well
as
ůal.,
as
functions
of
, TFW
, 2006),
Plevel
andvariables
Qa9, than
axis
of, Fyllas
et
(2009),
the
latter
considered
aare
strong
inand
high
fertility
soils
that
have
scores
for
both
),
accounted
forisonly
0.68
of thevarying
total
variance
both
al.,
1992;
Syvertsen
et
1995;
Warren
and
Adams,
characteristics
of
the
same
shown
Fig.
9.
First,
the
second
of
Fig.
that
ůt
1 and
2.Supplementary
F ,be
T
apanel
apredictors
PDJ
RW
onomic
components
as
listed
in
Table
1
(−0.37
to
−0.72).
be
environmentally
invariant
for
effect
PCs
as
dependent
variables.
tha
tion
relative
to
M
and
Φ
for
cf.
.
chemical
responses
to
the
environment
exist
for
Amazon
forthe
to
superior
RW
A
LS
s correlations
andSobrado,
SMA
slopes
for the en- tween
raits
in diagrammatic
form.
This
also
1986).
soil/climate
with
foliar
[C]
and
M
and
aFig.
negative
correlation
with
also
with
.
The
second
axis
of
the
on
the
plot
efρxRW
and
log
[P],
log
[Ca],
log
[K]
and,
toTable
aPCA
lesser
A
can
occur.
But
with
Fig.
8a
also
showing
that
itof
is τpbut
Information
(Table
S2A).
As
for
1,major
the
SMA
slopes
re-soil
hfertility
respect
toFW
M
these
two
traitin
dimensions.
and
.A for
Also
occurring
( The
∩andtions
)est.
and
ofrelationships
the
10
10mensions
10
in
Table
5.
Here
the
calculated
value
and
associThe
strong
tegrated
measure
of
soil
fertility
and
denoted
igh
fertility
soil
species.
Considering
data
from
both
low
and
high
fertility
sites
to. 7and
soils
that
have
scores
for
both
Figure
shows
the
major
components
of
the
three
top
panel
of
9
shows
ů
as
function
of
the
first
PCA
ciation,
with
examination
PDJ
RW
as
perhaps
evidenced
by
a
small
but
significant
positive
conF .a
most
significant
between
the
PCA
site
axis
PDJ
1
with
mean
annua
We
therefore
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
a
s withtothis
provided in more extent
seem
be information
“shared”, especially
Finally,
asfor
in the
Fyllas
et al.
(2009)
we sho
foliar
[Mg].
ItThe
isobserved
therefore
notFyllas
surprising,
ascorrelations
issignificant,
shown
in
the
fects
correlation
matrix
(ů
) strong
also
accounting
3.8AlRelationship
log
slopes
observed
(−0.26
to
−0.41)
are,
2a
speaking)
only
species
typically
associated
with
A ). of
flect
the axis
relationship
ysoil
↔
x,
with
the
x
as
the
column
headers
same
sign
isand
[C],
but
with
[P]
andofscores
[Mg]
in
10
ated
probability
giving
indication
of
the
of
each
relationship
suggests
response
gether,
Table
3is
lists
and
SMA
slopes
enand
their
overlap
of(generally
traits
in
diagrammatic
form.
This
st
three
axes
species
(normalised
torela±varying
100)
of
et
al.
(2009),
the
latter
considered
a strong
infor
anyeffect
given
species,
both
3.7
Principal
component
analysis
environmental
efgure
7and
shows
the
major
components
the
three
major
tribution
inclimate
max
to
this
dimension
±
0.05:
Table
2).
in
the
same
direction
ofCPCs
Table
4,(`opposite
and
previously
calculated
and
3.6
Bivariate
relationships:
environmental
components
tions
with
foliar
effects
correlation
matrix
(excluding
Hintegrated
and
S
both
ofan(0.014
all
show
relationships
not
present
w[C
RW
FW
onfidence
intervals)
inscores
the
Supplementary
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
4.2.2
:
an
extension
of
the
classic
“leaf
economic
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assofor
0.25
of
the
variance,
with
substantial
negative
weightings
The
most
significant
relationships
between
t
soil/climate
p
however,
much
less
than
for
the
associated
slopes
for
the
taxRW
actor
for
all
three
of
,
2
A
and
the
y
being
the
row
labels.
For
the
structural
traits,
the
PDJ
RW
directions
with
respect
to
M
for
these
two
trait
dimensions.
soil/environmental
parameter
after
accounting
for
the
efof
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuvironmental
effects
with
this
information
provided
in
more
illustrates
that
many
traits
seem
to
be
“shared”,
especially
d
against
each
other
in
Supplementary
Information
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
increasing
precipitation
and
A
fects
and
their
overlap
of
traits
in
diagrammatic
form.
This
and
.
high
fertility
soils
that
have
scores
for
both
ternatively,
relatively
more
nitrogen
being
allocated
to
cell
Φ
.
Although
with
a
high
estimated
standard
of the same
sites
are shown
in Fig.
9. First, the invariant for FPDJ
foliar
[Mg].
It
is
t
RW
which
were
considered
to
be
environmentally
effect
PCs
as
dependent
variables.
e LS
S2A). As for Table spectrum”
1, the SMA slopescharacteristics
re- onomic
well
as
ů
and
ů
as
functions
of
,
,
T
ciation,
but
with
examination
of
Table
4
also
suggesting
that
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
scores
of
Table
4,
and
previously
calculated
1
2
F
T
The
most
significant
relationships
between
the
PCA
site
axis
components
as
listed
in
Table
1
(−0.37
to
−0.72).
A
most
significant
relationships
are
all
and
appear
befect
ofPCA
the
other
four.
into
account
tofertility
thesecond
potential
trients
and
with
[C]
and
ρRW
decreasing
as
detail
(including
confidence
inTaking
the
Supplementary
in Intersecting
( that
∩thetraits
) and
of
the
This
required
lack
ofand
any
observed
suggests
a data
strong
integrated
response
with
increasing.
rates
many
seem
to
be
“shared”,
especially
Considering
from
both
low
andmajor
high
to- of Fia
x negative
Figure
7the
shows
the
major
components
ofetthe
three
panel
of
Fig.
9 shows
ů1 as
arelationship
function
offoliar
first
soil
walls
of
low
species
(Onoda
al.,
2004;
Takashima
etsites
panel
and
in
the
same
relawhich
isincreasing,
an
important
factor
for
all
three
of
,intervals)
RW
FW
fip
,PDJ
have
also
L
in
(topsystematic
A direction
PDJ
RW
yFW
↔shows
x,we
with
theRW
x asincluded
the column
A headers
RW ∩M
in
Table
5.
Here
the
calculated
value
of τp
for
any
given
species,
both
Φ
and
ρ
also
decline
with
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parcharacteristics
of
the
same
sites
are
shown
scores
of
Table
4,
and
previously
calculated
soil
and
climate
LS
w
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
strong
between
ρAmazon
effects
ofwould
al.,
increases.
Interestingly,
the
Kendall’s
τ for
this
plot
of
ůcorrelations
Information
(Table
S2A).
As
for
Table
1,
thewith
SMA
slopes
reP] between
and
[Mg]
varying
inAlthough
opposite
ns
the
scores
asgiven
expected
the
tropical
forest
trees
to
fertility,
most
nu10
10
gether,
Table
3soil
lists
and
SMA
foretthe
en-with in
x and
1spatial
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
axis
offor
Fyllas
etF al.
(2009),
the xof
latter
considered
aconfounding
strong
inciation,
but
e
Our
second
identified
CPC,
is
that
usually
considered
al.,
2004),
much
of10
which
beautocorrelation
expected
to slopes
be(Fyllas
in
the
tive
to
isspecies
Φ
.Especially
with
athe
high
estimated
standard
RW
AFor
LS factor
which
islabels.
an M
important
for all
three
of
, relationships
e row
the
structural
traits,
the
PDJ
RW
and
.
Also
occurring
in
(
∩
)
and
of
the
ated
probability
giving
an
indication
of
the
increasing
precipitation
and,
somewhat
counter
intuitively,
3.7
Principal
component
analysis
of
environmental
efso
showing
that
it
varies
in
the
opposite
directicular,
but
also
with
contributions
from
Φ
and
ρ
.
top
panel
of
Fig.
9
shows
ů
as
a
function
FW
PDJ
RW
Finally,
as
in
Fyllas
etofea
characteristics
of
the
same
sites
are
shown
in
Fig.
9.
First,
the
The
most
significa
LS
x
1
extent
log
(`
).
The
slopes
observed
(−0.26
to
−0.41)
are,
foliar
cation
environmental
components
(Fig.8)
,illustrates
it denoted
was
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
better.
versus
0.63 trients
isand
greater
for
any
of
the
original
variA
flect
thethan
relationship
y
↔
x,
with
the
x
as
the
column
headers
Mm
these
two
trait
dimensions.
any
good
fit
of
a
principle
components
model.
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
10
vironmental
effects
with
this
information
provided
in
more
F of
that
many
traits
seem
to
be
“shared”,
especially
.
The
strong
tegrated
measure
of
soil
fertility
A for
x
for
any
given
spe
to
be
the
principal
dimension
of
the
leaf
economic
specform
of
defense
related
proteins
(Feng
et
al.,
2009).
The
deF
lationships
all
and
appear
error
as are
part
ofnegative
, we
have
included
L
in increasing.
( is RW
∩but
same
[C],
with
[P] and
[Mg]
in (full)
opposite
soil/environmental
parameter
afterlatter
FW
Asign
. Also
occurring
in
∩also be) see
and
ofcoordinated
the
with
axis
oftaxFyllas
etKendall’s
al.
(2009),
the
consid
partial
τaccountin
(denoted
top
panel
Fig.
9et
shows
ůfor
as
athe
function
ofof
the
first
soil
PCA
FW
PDJ
RW
scores
of
Table
4,
1varying
however,
much
less
than
the
associated
slopes
M
ΦLS
for (Wright
cf. (etrela.interest
additional
to
iffects
structural/leaf
bioAs
for
the
Kendall’s
τthe
shown
in
Fig.
Table
5 suggests
ables
examined
by
Fyllas
al.
(2009),
highest
which
and
the
ycrease
being
the
row
labels.
For
the
structural
traits,
the
wide
range
of
combinations
of
these
three
traitaspects
diincreases.
Interestingly,
the
Kendall’s
τfor
for
this
plot
of
ůin
RW
FW
A and
detail
(including
confidence
intervals)
the
Supplementary
relationship
observed
suggests
aof
strong
integrated
response
F
19,
increasing
precipi
trum
al.,
2004),
some
of
which
have
also
in
[Mg]
with
higher
values
of
does
not
seem
to
and
in[C],
the
same
direction
RW
M
which
is
an
important
factor
for
all
three
,
W0
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
A
PDJ
RW
directions
with
respect
to
M
for
these
two
trait
dimensions.
fect
of
the
other
four.
Taking
into
account
tot
sign
is
but
with
[P]
and
[Mg]
varying
in
opposite
10 this also showing
10
tegrated
measure
of
soil
fertility
and
denote
A
well
as
ů
and
ů
as
functi
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
in),
that
it
varies
in
the
opposite
direccharacteristics
of
1
2
FW
onomic
components
as
listed
in
Table
1
(−0.37
to
−0.72).
chemical
responses
tothat
theitenvironment
exist
for versus
Amazon
the
to nubethan
superior
predictors
than
the
individual
variables,
wasspecies
0.56
for
foliar
Comparison
with
Fyllas
etbefore
al.
(2009)
most
significant
relationships
are
all
negative
and
appear
occur.
But
with
Fig.
8a to
also
showing
istropical
of
0.63
is
greater
forfor
any
of the
original
variInformation
(Table
S2A).
As
forrelated
Table
1,bethe
SMA
slopes
reof
Amazon
forest
trees
to[P].
soil
fertility,
with
most
Fforwith
increasing
been
presented
for
tropical
forest
tree
(Sandquist
have
been
reported
and
may
be
to
its
role
as
hcan
with
a high
estimated
standard
Finally,
asthe
instrong
Fyllas
et
al.
(2009)
we
show
values
The
slopes
observed
(−0.26
−0.41)
are,
Especially
given
relationships
between
ρ
and
the
confounding
effects
of
spatial
autocorrelation
ions
with
respect
to
M
for
these
two
trait
dimensions.
and
.significant
Also
occurring
in[Ca],
(PCAs
∩and
and
of
observed
suggests
a ρstrong
int9
xeffect
inasthe
Table
5.top and
Here
the
calc
.the
The
tegrated
measure
of
fertility
and
denoted
AandWe
panel
of Fig.
FW
PDJ
Intersecting
and
and
the
same
direction
relaF
espeaking)
relationships:
components
3.8
Relationship
plot
RW
therefore
undertook
PCA
analysis
of
the
full
plot
the
only
exception
being
Tstrong
.RW
In)chlorophyll
that
[N],
[K]
shows
that
the
ůFW
contains
weightings
ofas
leaftween
ρxinsoil
log
[P],
log
log
[K]
and,
tothe
acase,
tion relative
toenvironmental
MAest.
ΦLS
for
cf. and
. aalso
species
typically
associated
with
ables
examined
by
Fyllas
et
al.
highest
of
which
flect
the
relationship
y10relationship
↔
with
xlesser
the
column
headers
ax,
w
2[C]
RW
FW
trients
increasing,
and
with
foliar
and
ρand
as
10
10(2009),
x decreasing
Kendall’s
partial
τFyllas
(denoted
τbetween
all
traits
interest
and
Cordell,
2007;
Wright,
2007;
et
coordination
within
the
molecule.
p )afor
s than
theonly
associated
forSantiago
the
tax- foliar
ave
alsoforincluded
LA effects
in (slopes
∩
cation
environmental
components
(Fig.8)
,strongly
itofcompound
was
of
2009)
we
only
consider
relationships
with
pan
≤ai
same
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
RW
of
Amazon
tropical
forest
trees
to
soil
fertili
ated
probability
giving
relationship
observed
suggests
a
strong
integrated
response
axis
of
Fyllas
et
3.7
Principal
component
analysis
of
environmental
eftive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
Finally,
as
in
ecting
the sameF direction
relasoil/climate
A
LS
correlation
matrix
(excluding
H
and
S
both
of
all
show
relationships
not
present
when
regressing
the
plot
level
variables
that,
individually,
were
all
correlated
extent
log
(`
).
The
slopes
observed
(−0.26
to
−0.41)
are,
RW and
FW and in
was
0.56 for
foliar
with
Fyllas
et
al.For
(2009)
andComparison
the
the
row
labels.
the
traits, the
max
A, [P].
increases.
the
Kendall’s
τThis
this
plot
10
1yainbeing
well
as
ů 1 andto
ů 2see
as2010;
functions
offor
, ůPM
and
Q
and
. Interestingly,
ity
soils
thatinal.,
have
scores
for both
2009;
Baltzer
and
Thomas,
2010;
Baraloto
et
al.,
because,
the
absence
of
variation
in structural
the τwith
withinFis
T , Tof
ato
a Astwo
PDJ
RW
ts
as
listed
Table
1
(−0.37
to
−0.72).
additional
interest
if
coordinated
structural/leaf
biofor
the
(full)
Kendall’s
shown
in
Fig.
9,
Tao
directions
with
respect
for
these
trait
dimensions.
trients
increasing,
and
foliar
[C]
and
soil/environmental
paramete
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nutegrated
fects
from
both
lowopposite
and
high
sites to-error
atM
it3.6
the
direcKendall’s
partial
oata
is ΦBivariate
. Although
with
afertility
high
estimated
standard
relationships:
environmental
components
which
were
considered
to
beTable
environmentally
invariant
for
effect
dependent
variables.
with
annual
precipitation
(P
positive
correlahowever,
much
less
than
for
associated
slopes
for
taxAvaries
LSin
also
shows
that
the
ů)of
contains
significant
weightings
of
leafmost
significant
are
allthe
negative
and
appearmeasure
beA
as
part
of Here
,the
we
have
included
L
in
(asAthe
∩relationships
0.63
ismean
greater
for
anyalso
of
the
original
vari2viz.
FWthan
RW
Finof
5.
calculated
value
τAPCs
and
associ7 shows
the
major components
of
theversus
three
major
canopy
light
regime,
leaf
chlorophyll
contents
should
be
relDomingues
et
al.,
2010).
Although
we
did
not
measure
p
chemical
responses
to
the
environment
exist
for
Amazon
forthe
to
be
superior
predictors
than
the
individ
increases.
Interestingly,
the
Kendall’s
τůf
fect
of
the
other
four.
Takin
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
relationship
obser
3asfor
lists
and
SMA
forables
the
Flog direction
well
ů 1 and
xinwith
Intersecting
and
and
the
same
relations
foliar
andvariables
Mthe
and
aRW
negative
correlation
onomic
aseffect
listed
inwere
Table
1strongly
(−0.37
to
−0.72).
level
that,
individually,
all
correlated
tween
ρFW
and
log
[P],
log
and,
to as
a lesser
. of
Acomponents
examined
by with
Fyllas
et
al.[C]
(2009),
highest
of
which
partcorrelations
of cf.
, photosynthetic
we
have
alsoslopes
included
LAenin This
(ated
∩
x on
RW
S their
FW
RW
10
10 [Ca],
10 [K]
probability
giving
an
indication
of
the
of
each
overlap
ofFW
traits
in
diagrammatic
form.
omponent
analysis
environmental
ef),
this
alsoEspecially
showing
that
it
varies
in
the
opposite
directhe
or respiratory
components,
our
analysis
atively
conserved
an
area
(as
oppossed
to
mass)
basis
(RiFW
est.
We
therefore
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
case,
[N
versus
of
0.63
is
greater
than
for
any
of
a
given
the
strong
relationships
between
ρ
and
the
confounding
effects
of
spati
increases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
of
Amazon
tropica
ffects
with
this
information
provided
in
more
xpositive
in−0.41)
Table are,
5. Her
F Itsignificant
1F correlaConsidering data from both low and
high
fertility
sites
totivewith
torelationships
M
isaccounting
Φet
.extent
Although
with
a)The
high
estimated
standard
foliarThe
[Mg].
isparameter
therefore
not
surprising,
as log
is shown
in
theslopes
most
between
the
PCA
site
axis
with
mean
annual
precipitation
viz.
A
(`(P
).
observed
(−0.26
toto
was
0.56
for
foliar
[P].
Comparison
Fyllas
al.
(2009)
AA
10the
soil/environmental
after
for
eftraits
seem
be
“shared”,
especially
),that
thismany
also showing
thatto
varies
intropical
the
opposite
direcdoes
for
forest
species,
Lversus
should
also
jkers
etLSthan
al.,
Lloyd
etoriginal
al.,
giving
rise
atA
effects
correlation
matrix
(excluding
H
and
Sfor
both
all
show
relationships
not
when
regre
tion
relative
to
M
and
Φof
forPrincipal
cf.[C]
.2000,
examined
byefFyllas
etfor
al.consider
(2009),
theg
max
foliar
cation
environmental
components
, ables
it2010),
was
ofthis
2009)
wepresent
only
relat
0.63
is
greater
any
of(Fig.8)
the
variRW
FW
Aof
LS
trients
increasing,
ng
intervals)
initthat
the
Supplementary
ated
probability
F
gether,
Table
3suggest
lists
correlations
and
SMA
slopes
forscores
the
en3.7
component
analysis
of
environmental
second
panel
Fig.
9,
that
ů
and
P
also
show
strong
assoof
Table
4,
and
previously
calculated
soil
and
climate
ps:confidence
environmental
components
tions
with
foliar
and
M
and
a
negative
correlation
with
however,
much
less
than
for
the
associated
slopes
the
tax2
A
also
shows
that
the
ů
contains
significant
weightings
of
leafA
error
asinto
part
of reductions
, we
also included
LAasindependent
( RW
∩ variables.
2other four. Taking
fectwere
of the
account
to
thehave
potential
FW
incorporating
the
be for
included
part
of
tendant
in
mass
based
magnesium
concentrations
considered
to
be
environmentally
invariant
for
effect
PCs
RW
helative
is vironmental
anS2A).
important
factor
all
three
ofslopes
,effectively
to MAs
ΦLS for
for
cf.information
. ,which
was
0.56
for
foliar
[P].
Comparison
with
additional
interest
to
see
if
coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τFys
ables
examined
by
Fyllas
et
al.
(2009),
the
highest
of
which
PDJ
RW ciation,
increases.
Inte
Table
Table
1,as
theRW
SMA
reFW
A and
soil/environmenta
effects
with
this
provided
in
more
fects
but
with of
examination
of
Table
4shown
also
suggesting
that
characteristics
of
the[Mg].
same
are
in surprising,
Fig.
9. First,
the
foliar
Itsites
is therefore
not
aslisted
is shown
in the
onomic
components
as
in Table
1 (−0.37 toF −0.72).
variables
that,
individually,
were
all
strongly
correlated
he strong relationships
between
ρx and
the
confounding
effects
spatial
autocorrelation
(Fyllas
al.,
this
also
showing
that
itetvaries
in
the
opposite
direcrelationships
between
Llevel
and
foliar
[N]
and/or
[P] status
as
M
decreases.
3.6
Bivariate
relationships:
environmental
components
FW ),
A
A
also
shows
that
the
ů
contains
significant
wf
chemical
responses
to
the
environment
exist
for
Amazon
forthe
to
be
superior
predicto
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
versus
of
0.63
onship
y
↔
x,
with
the
x
as
the
column
headers
2
fect
of
the
other
detail
(including
confidence
intervals)
in
the
Supplementary
.
Also
occurring
in
(
∩
)
and
of
the
F
honmental
low and components
high fertility(Fig.8)
sites
forwe
any
given
both
and
also
decline
withPCA
top
panel
ofspecies,
Fig.
9Ashows
ůpositive
a function
the
first
PDJ tosecond
ofaswith
Fig.
9,
that
ůof2 or
and
PA soil
also
show strong assow
1LS
with
mean
annual
precipitation
(P
) panel
viz.Φ
, it RW
was
ofcomponents
2009)
only
consider
relationships
pρcorrela≤
0.01
better.
Bivariate
relationships:
environmental
level
variables
that,
individually,
were
all
str
est.
We
therefore
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
also
shows
that
the
ů
contains
significant
weightings
of
leaftion
relative
to
M
and
Φ
for
cf.
.
ables
examined
ng
the
row
labels.
For
the
structural
traits,
the
a
Especially
given
the
strong
relationships
between
ρ
and
the
confounding
effec
2
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
reRW
FW
A
LS
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
ions
andifSMA
slopes for
the enx that
increasing
and,
somewhat
counter
intuitively,
axis
of precipitation
Fyllas
etaal.
(2009),
the
latter
considered
acomponent
strong
3.7Table
Principal
analysis
of environmental ef- by
ciation,
but with
examination
of
Table
4 alsoinsuggesting
tions
foliar
[C]
and
M
correlation
with
A and
to see
coordinated
structural/leaf
bio- with
As
for
the
(full)
Kendall’s
τnegative
shown
in
Fig.
9,
5sites
suggests
Considering
data
from
both
lowcation
and
high
fertility
to- and correlated
with
mean
annual
precipitation
(P
)
viz.
effects
correlation
matrix
(excluding
H
S
both
of
all
show
relationships
not
p
level
variables
that,
individually,
were
all
strongly
was
0.56
for
foliar
nt
relationships
are
all
negative
and
appear
beA
max
foliar
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
con
flect
the relationship
y
↔
x,
with
the
x
as
the
column
headers
with
respect
to
M
for
these
two
trait
dimensions.
is
information
provided
in
more
with
increasing.
. Theρwstrong
tegrated
measure
of soil
fertility
and denoted
A exist for Amazon
fects
for any
given
species,
both
ΦLS Fwww.biogeosciences.net/9/775/2012/
and
also decline with
Biogeosciences,
9, 775–801,
2012
foliar
[Mg].
is
therefore
surprising,
as SMA
isthe
shown
in
the
s to the environment
forthe It toto
be superior
predictors
than
individual
variables,
gether,
Table
3with
listsnot
correlations
and
slopes
for
the
enidering
data
both
and
high
fertility
sites
3.6
Bivariate
environmental
components
tions
with
foliareffect
[C]
and
a negative
which
were
considered
toarelationships:
be
invariant
for
PCsM
asAdependent
mean
annual
precipitation
(P
)ifviz.
positive
correlaalso
shows
thatvari
the
log
[P],
log
[Ca],
loglow
and,
to
a the
lesser
additional
interest
toenvironmentally
see
coordinated
structural/leaf
bio-and
As
for
the
(full)
K
Aintegrated
and
the
yfrom
being
the
row
labels.
For
structural
traits,
the
intervals)
in
the
Supplementary
10
10
10 [K]
relationship
observed
suggests
strong
response
increasing
precipitation
and,
somewhat
counter
intuitively,
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assong
and
and
in
the
same
direction
rela2
A
RW
FW
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
case,
[N],
[K]
and
ρ
a information
w forfoliar [Mg]. It is therefore not surprising, a
vironmental
effects
with
this
provided
in more
r,
Table
3significant
lists
correlations
andre-SMA
slopes
for
the
enFinally,
as with
in
Fyllas
et
al.and
(2009)
we
show
values
tions
foliar
[C]
M
and
a
negative
correlation
with
level
variables
tha
).
The
slopes
observed
(−0.26
to
−0.41)
are,
chemical
responses
to
the
environment
exist
for
Amazon
forthe
to
be
superi
A
most
relationships
are
all
negative
and
appear
beA
Especially
given
the
strong
relationships
between
ρ
and
the
s
for
Table
1,
the
SMA
slopes
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuwith
increasing.
x
ciation,
but
examination
of not
Table
4 alsowhen
suggesting
that the plot
is ΦLSeffects
. (excluding
Although
with
a high
A
matrix
Hmax
and estimated
S both
ofstandard
allwith
show
relationships
present
regressing
detail
(including
confidence
Supplementary
with
information
in
more
Considering
data
from
both
and
high
fertility
to-(Fig.8)
Kendall’s
partial
τ (denoted
τp )in
forthe
all
traits
of
as
second
panel
of Fig.
9, thatwith
ů, 2itonly
and
also s
foliar
[Mg].
Itintervals)
isWe
therefore
not
surprising,
as
is
shown
the
mean
hmental
less
thanxρfor
the
associated
slopes
forprovided
the
taxA
est.
therefore
undertook
ainterest
PCA
analysis
of
the sites
full
plot
the
tween
and
logthis
log
logany
[K]
and,
totrients
a lesser
foliar
cation
environmental
components
wasPexception
ofannua
with
the
as
column
headers
x the
increasing,
and
with
foliar
[C]
and
ρxlow
decreasing
asin
10 [P],
10 [Ca],
10
for
given
species,
both
Φ
and
ρ
also
decline
with
LS
w
dered
to
be
environmentally
invariant
for
effect
PCs
as
dependent
variables.
art
of
,
we
have
also
included
L
in
(
∩
Information (Table S2A). As for Table 1, the SMA slopes re(including confidence intervals) in the Supplementary
low
and the
highfive
fertility
soilfive
species.
Overall
eigenvectors
eigenvectors
selected,
all of
selected,
whichRelationship
we
all of
be-which webetween
beRelationship
between
plotOverall
effecttheassociated
PCAs
and
3.8
plot matrix
effect
P
a wide
of(generally
combinations
of these
three
trait
dispecies typically Clearly
associated
withrange3.8
speaking)
only
species
typically
with
fects
portant
contributor
portant
and
contributo
this(ůals
2)
The
first
three
axes
species
scores
(normalised
to
±
100)
lieve
to
be
physiologically
lieve
to
be
physiologically
relevant
(see
relevant
Supplementary
(see
Supplementary
InIn-correlation
soil/climate
soil/climate
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
it
is
for
0.25
of
the
variance,
with
[C]both
and MA , [C]
but and
negatively
MA , but
witn
. variance
scores for both PDJ and RW .
high fertility soils that
have
scores
for both
PDJ and
are
plotted
against
other
in the
Supplementary
formation),
accounted
formation),
for
accounted
0.68
of
forRW
total
0.68
ofbetween
the Information
total
for both
variance
for
3.8
Relationship
plot effect
PCAs
and[C] and (a
(generally speaking) only species typically
associated
with each
foralso
M
, foliar
and
and
. also
The with
second
. aT
Awith
r components of the three major
Figure 7 shows low
theFig.
major
components
ofthe
the
threesoil
major
This
shows
required
lack
of
any
systematic
andS1.
high
low
fertility
and
high
soil
species.
fertility
species.
soil/climate
being
balanced
by
positive
andthree
. firstspecies
high
soils that have
scores
for both
S. Pati
ñofertility
et al.:This
Tropical
tree
793matrix
correlation
fects
correlation
(ů 2 ) wim
PDJ
raits in diagrammatic
form.
CPCstrait
and dimensions
their
overlap
offirst
traits
inRW
diagrammatic
form.species
This
correlations
between
the
species
scores
as
expected
the tofects
The
The
axes
three
axes
scores
(normalised
scores
to
(normalised
± for
100)
±relationships
100)
The
most
significant
relationships
between
the
PCA
site
axis
The
most
significant
between
the
PC
ticular,
but
also
with
contribu
Figure
7
shows
the
major
components
of
the
three
major
for
0.25
of
the
for
variance,
0.25
of
the
with
var
su
seem to be “shared”, especially S. Patiño
illustrates
many
traits
toplotted
be
“shared”,
especially
output
from
any
good
fit ofinaeach
principle
model.
et al.: that
Tropical
tree
traitseem
dimensions
are
plotted
against
are
each
against
other
Supplementary
other components
in Supplementary
Information
scores
of Table
4, and
previously
calculated
soil
and climate
scores
of TableInformation
4, and
previously
calculated
soil
a
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
for
M
,
foliar
for
[C]
M
and
,
foliar
(and
[C
A
A
4.2.3of FW
: ,treeRW
height and
acquisition
4.2.4
:significant
large
seeds
atthree
the expense
of leaf
area
Clearly
aThis
wide
range
of of
combinations
of
traitany
di- systematic
Fig.
shows
S1.three
This
shows
lack
required
ofthese
any
lack
systematic
of
TS
actor for all three
MAlight
which
is an important
factor
for
all
, the
PDJ
PDJ
RW
The
most
relationships
between
the
PCA
site
axis
sameS1.
sites
areFig.
shown
inthe
Fig.required
9.
First,
the
characteristics
of the
same
sites
are
shown
in Fig.
9
illustrates that many characteristics
traits seem toofbethe
“shared”,
especially
being
balanced
being
by
positive
balanced
wei
by
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
it iswith for
correlations
between
correlations
the
between
species
scores
the
species
as
expected
scores
for
as
expected
the
the
any
given
species)
the
results
shown
in
Table
4.
not
seem
to
been
recognised
before.
It
is
thus
here
in ( PDJ ∩ RW ) and of the does
. have
Also
occurring
in
(
∩
)
and
of
the
and
scores
of
Table
4,
and
previously
calculated
soil
and
climate
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
fir
FW
PDJ
RW
1 also
ticular,
but
ticular,
withbut
contributi
also wi
does
Unlike
previous
two dimensions
considered,
3.8
Relationship
betwee
FW
(generally
speaking)
only
typically
associated
with
MAthe
which
is an important
factor
for
all three
of 1 from
,and
output
output
any
good
from
fit
any
of aaspecies
good
principle
fitinofSect.
components
a principle
model.
model.
PDJ
RW
shows
that
of
the
total
variation
in
the
traits
As
mentioned
in
4.1.3,
acomponents
major
in accounting
P] and [Mg] varying
inS.opposite
same
sign
istrait
but
with
[P]latter
[Mg]
varying
in
opposite
characteristics
of
the
same
sites
are
shown
in
Fig.
9.the
First,
the11
axis
of
et
al.dimensions
(2009),
the
considered
strong
axis
of0.33
Fyllas
etfactor
al.
(2009),
latter
considered
a
denoted
asFyllas
. [C],
Patiño
et al.:
Tropical
tree
7 exam
PFL
soil/climate
not involve
foliar
nutrient
concentrations,
but
incorporates
Clearly
aand
wide
Clearly
range
atwo
ofwide
combinations
range
ofFig.
combinations
ofboth
these
three
of
trait
these
dithree
trait
dicould
be
explained
by
the
first
PCA
axis
(ů
)
with
ρ
a
and
.
high
fertility
soils
that
have
scores
for
inmeasure
(five
of
the
and dimensions.
for
this
trait
dimension
is
the
presence
of
many
large
seeded
1
x
MA for these two trait
directions
with
respect
to)M
these
trait
dimensions.
top
panel
of
9
shows
ů
as
a
function
of
the
first
soil
PCA
PDJ
RW
FW . Also occurring
PDJ
RW
.
The
strong
.
tegrated
of∩soil
fertility
and
denoted
tegrated
measure
of
soil
fertility
and
denoted
A for
1
Overall
the
eigenvectors
selected,
all
of
which
we
beF
F
into same
one dimension
in H
, 8LSvarying
, , Figure
MAin and
to
max[Mg]
mensions
can
occur.
But
can
with
occur.
Fig.
But
8a et
with
also
showing
Fig.
8acontributor
also
thatpoor
showing
it
isobserved
itwhom
isa strong
portant
andthat
this
also
relating
positively
to
sign isrela[C],variations
but
with
[P]
and
opposite
shows
the
major
components
ofnutrient
the
three
major
Fabaceae,
especially
on
soils,
for suggests
it aturns
axis
ofany
Fyllas
al.
(2009),
the
latter
considered
inrelationship
observed
suggests
a7mensions
strong
integrated
response
relationship
strong
integrate
lieve
to be
physiologically
relevant
(see
Supplementary
Indirection
Intersecting
and
in
thehere
same
direction
relagiven
species)
with
the
results
shown
in
Table
4.
This
does not
seem
to
have
been
recognised
before.
It
is
thus
W and in the asame
RW
FW and
lesser
extent
ρ
.
This
linkage
is
most
likely
through
the
3.8
Relationship
3.8
Relationship
between
x respect to MA for these two
(generally
speaking)
(generally
only
speaking)
species
only
typically
species
associated
typically
with
associated
with
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
exam
directions
with
trait
dimensions.
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
A LS and
out
do
not
have
a total
8
astropical
they in
would
otherwise
. trees
The
strong
tegrated
measure
oflarge
soil
fertility
denoted
oftive
Amazon
tropical
forest
trees
to
soil
with
most
nu- as
Amazon
forest
toexamined
soil fertility, wit
formation),
0.68
of the
total
variance
for
both
h with a highhydraulics/plant
estimateddenoted
standard
to accounted
MA
is already
ΦLS .for
Although
with
a fertility,
high
estimated
standard
shows
that
0.33
ofofthe
variation
theFsoil/climate
11
traits
The
most
significant
relation
soil/climate
height
considerations
discussed
as
as PFL
.
and
also
with
.
The
second
axis
of
the[C]
PCA
on
p
illustrates
traits
seem
to
be
“shared”,
especially
and
.
and
.
high
fertility
high
soils
fertility
that
soils
scores
that
for
have
both
scores
for
both
observed
suggests
a
strong
integrated
response
to
have
on
the
basis
of
their
other
trait
values.
PDJ
RW
PDJ
RW
trients
increasing,
and
with
foliarthat
[C]many
andbehave
ρrelationship
decreasing
as
trients
increasing,
and
with
foliar
and
ρthe
Intersecting
and
and
in
the
same
direction
relalow
and
high
fertility
soil
species.
xexpected
x de
RW
FW
could
be
explained
by the first PCA axis
(ů 1 )of
with
ρx 4,
an and
im- previo
ave also included
LASect.
in (Overall
error
as
partisofto
we
have
also
included
L
in
(
∩
scores
Table
part of
4.1.1
4.1.2.
That
say,
H
inthe five
eigenvectors
selected,
all
of
which
we
beRW ∩ and
FW , as
A
RW
max
fects
correlation
matrix
(ů
)
is
also
significant,
accou
2
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuFigure
7standard
shows
Figure
the
7major
shows
components
the
major
ofhigh
theofFthree
major
of, the
three
should
best major
be positively
regarded
as
with
acomponents
the
Kendall’s
τThus
for
this
plot
of
ůcontributor
increases.
Interestingly,
the Kendall’s
τ for thi
tive to MA is ΦLS . Although
with
high
estimated
TS
M
which
is(normalised
an important
factor
for
all
The
first
three
axes
species
scores
tospecies
±
100)
Patiño
et aInterestingly,
al.:
Tropical
tree
trait
dimensions
FS.increases.
1 three
PDJ
RW
portant
and
this
also
relating
to
characteristics
offoliar
the same
sit
toofbetrait
physiologically
relevant
(seeASupplementary
Inalieve
suite
adjustments
occur;
these
including
at it varies increases,
the opposite
direcalsoisshowing
that
itfor
varies
inthose
the
opposite
direcfor
0.25
of
theFThis
variance,
with
substantial
negative
weig
FW ), this
trients
increasing,
andthan
with
foliar
[C]
and
ρgreater
decreasing
as
CPCs
and
their
CPCs
overlap
and
their
of
traits
overlap
in
diagrammatic
of
traits
in
diagrammatic
form.
form.
having
a
larger
average
seed
size
with
that
beversus
of
0.63
greater
than
any
of
the
original
variversus
of
0.63
is
than
for
any
of
the
or
xThis
are
plotted
against
each
other
in
Supplementary
Information
F
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
error as
part
of
,
we
have
also
included
L
in
(
∩
A∩
.
Also
occurring
in
(
)
and
of
the
and
FW
A
RW
top
panel
of
Fig.
9
shows
ů
FW
PDJ
RW
formation),
accounted
for
0.68
of
the
total
variance
for
both
The
most
significant
The
most
relationsh
significa
a
reduction
in
8
with
estimates
of
also
suggesting
that
1
LS
for
Mto
foliar
[C] and
(and
toplot
a lesser
foliaa
A ,be
increases.
Interestingly,
the
Kendall’s
τ8
for
this
of ůplot
illustrates
that
illustrates
many
traits
that
seem
many
totraits
be
“shared”,
seem
especially
“shared”,
especially
cf.
.
tion
relative
toby
Mhave
and
Φ
for
examined
Fyllas
et
al.
(2009),
the
highest
of
which
ables
examined
by
Fyllas
et
al.
(2009),
the
highe
ing
with
a
lower
than
average
as
compared
Fassociated
1 extent
RW cf.
FW .
RW
FW
S for
A
LS
Fig.ables
S1.
Thisseem
shows
the
required
lack
of
any
systematic
LS
and
also
with
.
The
second
axis
of
the
PCA
on
the
efany
given
species)
with
the
results
shown
in
Tab
does
not
to
been
recognised
before.
It
is
thus
here
sign
is
[C], butal.:
with
[P] andtree
[Mg]
varying
in opposite
Patiño
Tropical
trait
dimensions
axis
of Fyllas
et and
al.
low
and
high
fertility
soilittospecies.
scores
Table
scores
4,
of (2009),
Table
previous
),
this
also
showing
that
varies
in at
thesame
opposite
direcleaves FW
with
a high
Hmax
also
tend
operate
aComparison
lower
cS.i /c
a .with et
being
balanced
by foliar
positive
weightings
for
foliar
[Mg]4,eti
of
0.63
is greater
than
for
the
original
variwas 0.56
for
foliar the
[P].
Fyllas
et
al.Ffor
(2009)
was
0.56
[P].
Comparison
with
Fyllas
and/or
.the
This
8
to versus
trees
of
an
equivalent
correlations
between
species
as
expected
for
the
FW
RW
LSin
M(normalised
iswith
M
anto
which
is
factor
for
all
factor
three
of
for
all
three
ofisany
,(ůfor
,significant,
fects
correlation
matrix
)0.33
also
accounting
shows
that
ofof
total
variation
the
11 sites
trait
A whichscores
Aimportant
PDJ
PDJ
RWlower
2RW
directions
respect
toan
Mimportant
these
two
trait
dimensions.
tegrated
measure
of
soil
A
characteristics
characteristics
of
the
same
of t
denoted
as
.
The
first
three
axes
species
scores
±
100)
As
it
seems
likely
that
the
higher
M
with
increasing
H
is
PFL
ticular,
but
also
with
contributions
from
Φ
ρferti
A
max
cf.relationships:
.a principle
tioncomponents
relative to MAoutput
and
Φfrom
forany
LS and
x.
ps: environmental
3.6
environmental
components
ables
examined
by
Fyllas
etshows
al.
(2009),
theůby
highest
of
which
RW
FW
LS Bivariate
also
shows
that
the
ůfit
significant
weightings
of
leafalso
that
the
contains
significant
weighti
is
also
accompanied
by
reduction
in
L
suggesting
that
it
is
good
of
components
model.
2 contains
2
A
for
0.25
of
the
variance,
with
substantial
negative
weightings
could
be
explained
the
first
PCA
axis
(ů
)
with
1Fig.
Also
occurring
.
Also
in
occurring
(
∩
in
(
)
and
∩
of
the
)
and
of
the
and
and
relationship
observed
sugges
top
panel
of
Fig.
top
panel
9
shows
of
ů
as9w
a
FW . does
FW
PDJ
RW
PDJ
RW
are plottedtoagainst
each
other
in
Supplementary
Information
Overall
the
five
eigenvectors
selected,
all
of
which
we
beany
given
species)
not
seem
to
have
been
recognised
before.
It
is
thus
here
Intersecting
and
and
in
the
same
direction
relamostly attributable
increased
leaf/mesophyll
thickness
and
1
FW
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
etextent
al.
(2009)
level avariables
that,ofindividually,
were
all strongly
correlated
level
variables
that,
individually,
were
all
strongly
not
so
much
competition
for
lateral
meristems
(Kleiman
and
Clearly
wide range
combinations
ofRW
these
three
trait
difor
M
foliar
[C]
and
(and
to
aopposite
lesser
foliar
[P])
portant
contributor
and
this
also
relating
positive
Aa, high
same
sign
is
same
[C],
but
sign
with
is
[C],
[P]
but
and
with
[Mg]
[P]
varying
and
[Mg]
in
opposite
varying
in
of
Amazon
tropical
forest
tre
axis
of
Fyllas
axis
et
al.
of
(2009),
Fyllas
et
the
a
Fig.
S1.
This
shows
the
required
lack
of
any
systematic
lieve
to
be
physiologically
relevant
(see
Supplementary
Inshows
that
0.33
of
th
tive
to
M
is
Φ
.
Although
with
estimated
standard
hence
increases
in
photosynthetic
capacity
per
unit
leaf
area,
A
LS
3.6 Bivariate
components
h low and high fertility
sites to-relationships:
Considering
dataBut
from
low
and as
high
fertility
toalso
that
thegives
ů 2by
contains
weightings
of
leafdenoted
.shows
with
mean
annual
precipitation
(Palso
positive
correlawith
mean
annual
precipitation
(P
) parviz.
positi
PFL
mensions
canenvironmental
occur.
withboth
Fig.with
8a
showing
that
itsites
isbalanced
A ) viz.
Ain
Aarssen,
2007)
that
rise
tosignificant
the
negative
association
bebeing
positive
weightings
for
foliar
[Mg]
[C]
and
M
with
all
foliar
nutrient
Patiño et al.: A
Tropical
tree
trait dimensions
7 negatively
A , but
directions
directions
respect
to
with
MA
respect
for
these
toboth
M
two
traitthese
dimensions.
two
trait
dimensions.
trients
increasing,
and
with bo
tegrated
measure
tegrated
of explained
soil
measure
fertilit
A for
correlations
between
thein
species
scores
as as
expected
the
formation),
accounted
for
0.68
of
thefor
total
variance
for
could
be
(Sect.
4.1.1),
reduction
ci /c
may
be
attributable
ions and SMAmax
slopes
for
the en-this(generally
gether,
Table
3a [C]
lists
correlations
and
SMA
slopes
for
the
enlevel
variables
that,
individually,
were
all
strongly
correlated
error
part
of
,
we
have
also
included
L
in
(
∩
the
five
eigenvectors
selected,
all
of
which
we
be3.8
Relationship
between
plot
effect
PCAs
tions
with
foliar
and
M
aOverall
negative
correlation
with
tions
with
foliar
[C]
and
M
and
a
negative
corre
FW
A
RW
speaking)
only
species
typically
associated
with
A and
A
tween 8ticular,
S within
this
dimension.
rather
also with
contributions
from
Φincreases.
and
ρsort
.Interestingly,
LS and but
andthe
also
with
.But
The
second
axis
the
PCA
onth
LS some
xof
relationship
observed
relationship
suggests
obser
output
from
anyfoliar
good
fit
of
a
principle
components
model.
F
low
and
high
fertility
soil
species.
portant
contributor
Considering
data increasing
from
both
low
and
high
fertility
sites
toIntersecting
Intersecting
and
and
and
in
the
same
and
direction
in
same
reladirection
relato provided
stomatal
capacity
less
with
H
than
should
is information
in
more
vironmental
effects
with
this
information
provided
more
RW
FW
RW
FW
with
mean
annual
precipitation
(P
)
viz.
positive
correlamax
lieve
to
be
physiologically
relevant
(see
Supplementary
Insoil/climate
[Mg].
It
is
therefore
not
surprising,
as
is
shown
in
the
foliar
[Mg].
It
is
therefore
not
surprising,
as
is sha
A
),
this
also
showing
that
it
varies
in
the
opposite
direcof
mechanical
constraint
such
as
the
total
mass
capable
of
beFW
fects
correlation
matrix
(ů
)
is
also
significant,
and
.
high
fertility
soils
that
have
scores
for
both
2
versus
of
0.63
is
greater
t
PDJ
RW
ofboth
AmazonF[C]
tropical
Amazon
forest
tropica
trees
given
species)
with
the
results
shown
in
Table
4.a negative
Thisfor
sintervals)
not seem in
toAthe
have
recognised
before.
Itofis(including
here
Clearly
wide
range
combinations
ofany
these
three
trait
and
neg
The
first
three
axes
scores
(normalised
100)
Table
3a lists
correlations
and
SMA
slopes
for
the
entive
towould
is
tive
Φ
to
. Although
M
is Φ
with
. to
Although
a±high
estimated
with
aM
high
standard
estimated
standard
A , but
. been
Such
a tendency
to
operate
atthus
a of
lower
c9,
also
Supplementary
detail
intervals)
indithe
Supplementary
A
LS
tions
with
foliar
[C]
with
maxgether,
aspecies
i /c
formation),
accounted
for
0.68
ofand
the
total
variance
second
panel
Fig.confidence
that
ůM
and
PLS
show
strong
assosecond
panel
ofvariance,
Fig.
9,correlation
that
ů 2substantial
and
PAM
also
show
s
A and
2A
A alsoing
borne
per
unit
stem
weight
(Westoby
and
Wright,
2003)
for
0.25
of
the
with
negative
Figure
7
shows
the
major
components
of
the
three
major
cf.
.
tion
relative
to
M
and
Φ
for
ables
examined
by
Fyllas
et
tsoted
al.:
Tropical
tree
trait
dimensions
7
RW
FW
A
LS
trients
increasing,
trients
and
increasing,
with
fo
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
mensions
can for
occur.
But
with
Fig.
8a
also
showing
that
itpart
isfoliar
and
also
with
.sugg
Th
are
plotted
against
each
other
in
Supplementary
Information
with
this
information
provided
in
more
for as
TablePFL
1,help
the
SMA
slopes effects
reInformation
(Table
S2A).
As
Table
1,
the
SMA
slopes
re-therefore
to conserve
water
species
more
likely
to be
higher–
. vironmental
[Mg].
It
is
not
surprising,
isin
shown
in
the
low
and
soil
species.
error
as
part
of
error
,orwe
of
have
also
, we
included
have
also
Lciation,
in
( ,RW
∩discussed
( as
∩Sect.
ciation,
but
with
examination
offor
Table
4ashigh
also
suggesting
that
butasLwith
examination
of4.1.2.
Table
4 also
FW
FW
A included
A in
RW
afertility
simple
competition
carbon
for
M
[C]
and
(and
to
acorrelation
lesser
CPCsflect
andonly
their
overlap
ofytraits
in
diagrammatic
form.
This
Aρxfoliar
3.8
Relationship
between
plot
effect
PCAs
and
was
0.56
for
foliar
[P].extent
Comp
ical
trait
dimensions
7for
increases.
Interestingly,
increases.
Inte
the
could
beinsoexplained
by
the
first
PCA
axis
(ů
)
with
an
im(generally
species
typically
associated
with
fects
ma
Fig.
S1.
This
shows
the
required
lack
of
any
systematic
F
F
(including
confidence
intervals)
in
the
Supplementary
1
withtree
the
x five
asup
the
column
headers
the
relationship
↔
x,
with
the
x
as
the
column
headers
indetail
the canopy
andspeaking)
hence
exposed
to
higher
levels
of
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assoOverall
the
eigenvectors
selected,
all
of
which
we
beThe
first
three
axes
species
scores
(normalised
±positive
100)
for any given species, 3.6
both
ΦLSalso
and
ρ),wthis
also
decline
withthat
for
any
given
species,
bothweightings
Φ
ρwPCA
also sit
de
2balanced
A to
The
most
significant
relationships
between
LS and the
),species)
this
showing
also
that
showing
it also
varies
ininthe
it positively
varies
opposite
in
the
direcopposite
direcbeing
by
foliar
[M
Bivariate
relationships:
environmental
components
FW
FW
illustrates
that
many
traits
seem
tocontributor
beFor
“shared”,
especially
soil/climate
also
that
ůFthe
contain
small
but
significant
contributions
of
M
[N]
and
[P]the
versus
versus
0.63
isoffor
greater
0.63
tha
portant
and
this
relating
to
foliar
2of
for
0.25
varian
A , shows
correlations
between
the
species
scores
as
expected
for
the
any
given
with
the
results
shown
Table
4.
This
eem
have
recognised
before.
Itincreasing
isand
thus
here
F of
Information
(Table
S2A).
Asthe
for
Table
1,the
the
SMA
slopes
rebels.
For
thebeen
structural
traits,
the (see
yprecipitation
being
row
labels.
theRW
structural
traits,
.The
fertility
soils
that
have
scores
for
both
ciation,
but
with
examination
of
Table
also
suggesting
that
e totobe
physiologically
relevant
Supplementary
Inlation
andhigh
an
associated
greater
evaporative
demand
(Lloyd
PDJ
are and
plotted
against
each
other
inscores
Supplementary
Information
and,
somewhat
counter
intuitively,
increasing
and,
somewhat
counter
of
Table
4,precipitation
and4with
previously
calculated
soil
and
cl
ticular,
but
also
contributions
from
Φ
and
LS
level
variables
that,
individua
cf.
.
cf.
.
tion
relative
to
tion
M
relative
and
Φ
to
M
for
and
Φ
for
may
be
mostly
genetic
associations
as
members
of
the
to
ables
examined
ables
by
examined
Fyllas
et
al.
by
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
for
M
,
foliar
[C]
a
RW
FW
RW
FW
A
LS
A
LS
output
from
any
good
fit
of
a
principle
components
model.
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
TS
M
which
is an
important
for
all
three
of
, appear
relationship
ywith
↔
x,
with
the
x as
the factor
column
headers
Aresults
given
species)
with
the
shown
in
Table
4. characteristics
This with
e been
before.
It of
isbethus
here
A
ps
are
negative
and
appear
most
significant
relationships
are
all
and
beA
RW
forPDJ
any
given
species,
both
ρw also
with
mation),
accounted
forthe
0.68
the
total
variance
for
both
Figure
7 shows
the
major
components
of the
three
major
et al.,flect
2010).
.recognised
Fig.
S1.negative
This
shows
theand
required
lack Φ
ofsites
any
systematic
any
increasing.
increasing.
LS
PFLall
ofand
the
same
sitesdecline
are shown
in Fig.
9. Fir
Considering
data
from
both
low
high
fertility
towith
mean
annual
precipitat
was
0.56
for
was
foliar
0.56
[P].
for
Compa
foliar
and
also
with
.
The
second
axis
of
the
PCA
on
the
plot
eftypically
large
seeded
Fabaceae
typically
have
a
lower
M
being
balanced
by
p
Clearly
a
wide
range
of
combinations
of
these
three
trait
dicould
be
explained
by
the
first
PCA
axis
(ů
)
with
ρ
an
imand
the
y
being
the
row
labels.
For
the
structural
traits,
the
shows
that
0.33
of
the
total
variation
in
the
11
traits
examined
A
1 topscores
x
[Ca],
log
and,
to aHlesser
tween
ρ
andoccurring
[P],study,
log(10many
[Ca],
logThis
[K]
and,oftothe
aprecipitation
lesser
increasing
and,
somewhat
counter
intuitively,
and
high
fertility
soilselected,
species.
CPCs
and
their
overlap
intheir
diagrammatic
form.
x traits
Also
in
) and
the
Although
not
determined
inlog
correlations
between
species
as
expected
for
the
five
eigenvectors
allwas
ofand
which
we. of
be10
10 [K]
10
10
panel
of
Fig.
9enshows
ůtions
as awith
function
of[C]
theand
firstM
soi
FW
PDJ
RW
max
1the
Table
3∩
lists
correlations
and
SMA
slopes
for
theas
3.6gether,
3.6berelationships:
Bivariate
relationships:
environmental
components
components
foliar
The
most
relationships
between
PCA
site
also
shows
that
also
the
shows
ů 2axis
contains
that
the
fects
correlation
matrix
(ů
)with
is
also
accounting
A
and
higher
[N]
[P]
than
members
of
plant
families
ticular,
but
also
with
Finally,
as
in
Fyllas
etBivariate
al.
(2009)
we
show
values
for
Finally,
in other
Fyllas
ettheal.
(2009)
we
show
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
it
issignificant,
portant
contributor
and
this
also
relating
positively
to
foliar
most
significant
relationships
are
all
negative
and
appear
could
be
explained
byand
the
first
PCA
axis
(ů
)2environmental
ρand
an
ims physiologically
observed
(−0.26
to
−0.41)
are,
extent
(`
).
The
slopes
observed
(−0.26
to
−0.41)
are,
1
xsignificant
with
increasing.
illustrates
that
many
seem
to
bevironmental
“shared”,
especially
A
The
first three
species
scores
(normalised
to
±
100)
same
signtraits
islog
[C],
but
with
[P]
[Mg]
varying
in
opposite
output
from
any
good
fit
of
a
principle
components
model.
relevant
Supplementary
Inofaxes
the
above
measured
and/or
inferred
traits,
viz.
8
and
10
genvectors
selected,
all
of (see
which
we
beaxis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
stro
LS
effects
with
this
information
provided
in
more
foliar
[Mg].
It
is
therefore
no
3.8
Relationship
between
plot
effect
P
scores
of
Table
4,
and
previously
calculated
soil
and
climate
level
variables
level
that,
variables
individuall
tha
for
0.25
of
the
variance,
with
substantial
negative
weightings
(Fyllas
et
al.,
2009).
On
the
other
hand,
as
is
discussed
in
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
interest
as
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
(generally
speaking)
only
species
typically
associated
with
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
portant
contributor
and
this
also
relating
positively
to
foliar
p
p
Afor
the
associated
slopes
the
taxhowever,
much
less
than
for
the
associated
slopes
for
the
tax-tegratedofmeasure
x
10
10
against
each
other
intotal
Supplementary
directions
with
respect
toall
M
for
these
twoaconfidence
trait
dimensions.
Clearly
wide
range
ofand
combinations
these
three
trait
di-to- and denoted F . The s
,plotted
accounted
for
0.68
offor
for
both
gically
relevant
(see
Supplementary
In-important
Amax
, were
found
tovariance
in10Information
a similar
manner
of soil
fertility
Aas
FW
Mthe
isco-vary
an
factor
for
three
of
detail
(including
intervals)
inFyllas
the
Supplementary
A which
PDJ ,from
RW
Considering
Considering
data
both
data
low
from
both
low
fertility
and
high
sites
fertility
tosites
second
panel
9,
soil/climate
Finally,
as
in
etplot
al.
we
values
forFig.
characteristics
ofthe
the
same
sites
are
shown
inwith
Fig.
9.ofof
First,
with
mean
annual
with
mean
for
M
, of
foliar
[C]
towith
ahigh
extent
foliar
[P])
well
as[C]
ůsystematic
and
ůPanama
functions
well
as
ů(2009)
ůthat
asshow
functions
,and
Toccur.
,PDJ
Pto
and
Q
, the
,that
Ta ,ůP
and
also
with
The
of
the
PCA
on
efSect.
4.1.3,
lower
foliar
[Ca]
levels
associated
larger
extent
log
(`tropical
).lack
The
slopes
observed
(−0.26
to
−0.41)
are,
A.
and
M
, as
but
negatively
with
all
foliar
nutrients
examined
1components
2
F ,second
T
aaxis
a(and
a lesser
1 and
2
Fprecipitatio
T annua
dd S1.
in
Table
(−0.37
torange
−0.72).
onomic
as
listed
in
Table
1both
(−0.37
−0.72).
Afor
A
10
and
.
high
fertility
soils
that
have
scores
for
.gh
This1of
shows
required
of
any
mensions
can
But
Fig.
8a
also
showing
it
isenfertility
soil
species.
for
0.68
the
total
variance
both
RW
across
athe
of
forest
trees
in
by
Meinzer
relationship
observed
suggests
a strong
integrated
res
Intersecting
and
and
in
the
same
direction
relaInformation
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
reRW
FW
gether,
Table
gether,
3
lists
correlations
Table
3
lists
and
correlations
SMA
slopes
and
SMA
for
the
slopes
enfor
the
.
Also
occurring
in
(
∩
)
and
of
the
and
ciation,
but
with
examination
Kendall’s
partial
τFig.
(denoted
τůp1)linked
forparall
traits
of
interest
as
top
panel
of
9inshows
aHere
function
the
first
soil
PCA
FWless than
PDJ
RW
tions
with
foliar
tions
[C]
with
andfoliar
aa
being
balanced
positive
weightings
for
foliar
[Mg]
in
in to
Table
5.associated
Here
the
calculated
value
of
and
associTable
5.as
the of
calculated
value
ofMτAp [C
fects
correlation
matrix
(ů
)τpis
also
significant,
accounting
much
for
the
slopes
for
the
taxand
also
with
.
The
second
axisbyof
the
PCA
the
plot
efseed
size
ison
probably
functionally
though
high
calcium
2of
3.8
Relationship
relations
between
thescores
species
scores
as
expected
for
the
Figure
7
shows
the
major
components
the
three
major
(generally
speaking)
only
species
typically
associated
with
t soil
threespecies.
axes
species
(normalised
±
100)
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
mo
et al.however,
(2008).
Though
in
that
case,
variations
in
ρ
were
conxvarying
tobut
MA
is Φ[P]
Although
with
abut
high
estimated
standard
flect
the
relationship
ywell
↔
x,
with
the
xůas
the
column
headers
LS . and
vironmental
vironmental
effects
with
this
effects
information
with
this
provided
information
in
more
provided
in
more
same
sign
istive
[C],
with
[Mg]
in)also
opposite
for
any
given
species,
both
as
ů
and
as
functions
of
,
,
T
,
P
and
Q
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
infoliar
[Mg].
foliar
It
is
therefore
[Mg].
It
not
is
t
ticular,
with
contributions
from
Φ
and
ρ
.
ated
probability
giving
an
indication
of
the
effect
of
each
ated
probability
giving
an
indication
of
the
effe
for
0.25
of
the
variance,
with
substantial
negative
weightings
1
2
F
T
a
a
a
onomic
components
as
listed
in
Table
1
(−0.37
to
−0.72).
LS
x
fects
correlation
matrix
(ů
is
also
significant,
accounting
tput
analysis
of
environmental
ef3.7
Principal
component
analysis
of
environmental
efrequirements
of
developing
fruits
and
seeds.
2
soil/climate
fromeach
any
good
fitofSupplementary
ofkey
a principle
components
model.
CPCs
andof
their
overlap
of
traits
infertility
diagrammatic
form.
This
other
in
Information
sagainst
species
scores
(normalised
to ±
100)
trients
increasing,
and
with
foliar
[C]precipitation
and ρx decreasi
sidered
importance
in part
terms
trait
coordination,
esand
. and
high
soils
that
have
scores
for
both
and
the
yafter
being
the
labels.
For
the
structural
traits,
the
PDJ
RW
detail
(including
detail
confidence
(including
confidence
in
the
intervals)
Supplementary
in
the
Supplementary
directions
with
respect
toof
MA FW
for
two
trait
dimensions.
increasing
and
error
as
,these
we
have
also
included
Lrow
innegative
(intervals)
∩weightings
in
Table
5.
Here
the
calculated
value
of
τ
associ.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
second
panel
second
of
Fig.
panel
9,
that
of
ů
Fi
a
A
RW
The
most
significant
relationships
between
the
PC
soil/environmental
parameter
accounting
for
the
efsoil/environmental
parameter
after
accounting
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
p
F
2
for
0.25
of
the
variance,
with
substantial
fects
A
arlyother
ashows
wide
range
of combinations
of these
threehydraulic
trait
illustrates
thatdimany
traitssignificant
seem
to be
“shared”,
especially
This
the required
lack
of any
systematic
ach
in Supplementary
Information
increases.
Interestingly,
the
Kendall’s
τ for this plot
pecially
through
linkages
to
plant
parameters
such
F SMA
most
relationships
are
all
negative
and
appear
beFigure
7
shows
the
major
components
of
the
three
major
Information
(Table
Information
S2A).
(Table
As
for
S2A).
Table
As
1,
the
for
Table
slopes
1,
the
reSMA
slopes
rewith
increasing.
ated
probability
giving
an
indication
of
the
effect
of
each
relationship
observed
suggests
a
strong
integrated
response
ciation,
but
with
ciation,
examination
but
with
e
o
3.7
Principal
analysis
of
environmental
ef-the
scores
of
and previously
calculated
fect
of
the
other
four.
Taking
into
account
to the
potential
fectinofpartheTable
other4,four.
Taking into
account soil
to tha
being
balanced
by
positive
weightings
for
foliar
[Mg]
for
M
, foliar
[C]
and
(and
torelaa4.2.5
lesser
extent
foliar
[P])
Intersecting
and
in
the
same
direction
),and
this
also
that
it etvaries
in
opposite
direcRW
FW
FW
nsions
can occur.
Butof
with
Fig.
also
showing
that
is
S.likewise
Patiño
al.:
Tropical
tree
trait
dimensions
s between
the
scores
as8acomponent
expected
the
ws
the required
lack
any
systematic
versus
of
0.63
isform.
greater
than
for any of the origina
:of
shade
tolerance
and
long-term
viability
as species
K
observed
contribution
ofAisshowing
ρit
PFL
MAfor
which
an
important
factor
for
all10ythree
of
,↔
tween
ρsignifiand
log
[P],
[Ca],
log
[K]
and,
to
atrees
lesser
CPCs
and
their
overlap
traits
in
diagrammatic
This
x is
S . Our
PDJ
RW
flect
the
relationship
flect
the
relationship
↔
x,log
with
the
yet
x
x,
as
with
the
column
the
xFas
headers
the
column
headers
xautocorrelation
10
10
soil/environmental
parameter
after
accounting
for
the
efof
Amazon
tropical
forest
toofsoil
fertility,
with
most
nufor
any
given
for
species,
any significant
given
both
spe
Φ9
fects
characteristics
the
same
sites
are
shown
in
Fig.
relationships
between
ρ
and
the
Especially
given
the
strong
relationships
between
ρ
and
the
confounding
effects
of
spatial
(Fyllas
al.,
confounding
effects
of
spatial
autocorrelation
(F
ticular,
but
also
with
contributions
from
Φ
and
ρ
.
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
partive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
x
x
LS
x
3.8
Relationship
between
plot
effect
PCAs
and
A
LS
nerally
speaking)
only
species
typically
associated
with
The
most
mthe
anyspecies
good cant
fit
of(−0.22
a as
principle
components
cf.Athe
.being
tion
to MA and
ΦLS
for ylog
scores
expected
for
therelative
ables
examined
by
Fyllas
et Finally,
al. (2009),
highestetof a
as the
in Fyllas
RW
± 0.10),
though
asmodel.
discussed
in Sect.
4.1.1
taken
extent
(`
). FW
The
slopes
observed
(−0.26
tothe
−0.41)
are,
illustrates
that
many
traits
seem
to
be
“shared”,
especially
and
the
being
and
the
row
y
labels.
the
For
row
the
labels.
structural
For
traits,
structural
the
traits,
the
10
.
Also
occurring
in
(
∩
)
and
of
the
and
fect
of
the
other
four.
Taking
into
account
to
the
potential
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
increasing
precipitation
increasing
precipi
and,
s
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
fir
FW
PDJ
RW
components
(Fig.8)
,
it
was
of
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
better.
2009)
we
only
consider
relationships
with
p
≤
0.0
x
but
also
with contributions
from
ΦLS and ρx . was 0.56 for foliar [P]. Comparison
1 scores of Table 4, an
soil/climate
error
as
ofthree
, ticular,
we
have
also
included
LAmuch
in
(have
∩ recognised
wide
ofacross
combinations
of part
these
trait
difit range
of a soils
principle
model.
with Fyllas
et al.
(
FW
RW
Kendall’s
partial
τ (denoted
any
given
with
the
resu
not
seem
to which
been
before.
It
is negative
thus
here
and
.does
hdfertility
that
have
scores
for
both
acomponents
wide
range
of
species
and
sites
the
strong
relationhowever,
less
than
for
the
associated
slopes
forthe
the
taxPDJ
RW
most
significant
most
relationships
significant
relationships
are
all
negative
are
and
all
appear
beand
appear
beThe
fifth
dimension
identified,
viz.
,al.
includes
aspecies)
signifisame
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
Especially
given
the
strong
relationships
between
ρxcoordinated
and
the
confounding
effects
of
spatial
autocorrelation
(Fyllas
et
al.,
PFL
increases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
M
is
an
important
factor
for
all
three
of
,
with
increasing.
with
increasing
axis
of
Fyllas
et
(2009),
the
latter
considered
a
coordinated
structural/leaf
bioadditional
interest
to
see
if
structural/leaf
bioAs
for
the
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
As
for
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
F
1
A
PDJ
RW
of the
3.6diBivariate
relationships:
environmental
components
can
occur.
But
with
Fig.
8a
showing
thatthree
it Sis
of combinations
ofmajor
theseFW
three
trait
also
shows
the
ů 2M
significant
weightings
o
well
as0.33
ůcharacteristics
and
ů 2total
as functi
shows
that
ofasthe
variat
),xforthis
also
showing
that
it varies
inthe
the
direcFigure
7 shows
the
components
of
the
major
1better.
onomic
components
as
listed
in[P],
Table
1[K]
(−0.37
to10
−0.72).
ship
between
ρalso
and/or
ρthe
and
K
as
observed
by
Meinzer
tween
ρas
and
tween
log
ρ[P],
and
log
log
[Ca],
log
log
[Ca],
and,
log
athat
[K]
lesser
and,
tocontains
aany
lesser
w
cant
positive
contribution
of
increased
,
presumably
denoted
.
x opposite
x
directions
with
respect
to
M
for
these
two
trait
dimensions.
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
versus
of
0.63
is
greater
than
for
of
the
original
vari10
10
10
10
10
A
PFL
.
tegrated
measure
of
soil
fertility
and
denoted
A
nvironment
exist
for
Amazon
chemical
responses
to
environment
exist
for
Amazon
forto
be
superior
predictors
than
the
individual
variables,
the
to
be
superior
predictors
than
the
individua
F
F
. Also occurring
inlevel
( PCAs
∩ and
) and
of in
thebe
and FW
3.8(e.g.,
Relationship
between
plot effect
top
panel
of
Fig.
9al.sh
PDJ
RW
speaking)
only
typically
associated
with
Butand
with
Fig.
8a
also
showing
that
it is
variables
that,
individually,
were
all
strongly
corr
Table
5.
Here
the
could
explained
by
the
first
PF
Finally,
as
Finally,
as
etcalcu
in
Cs
their
overlap
of
traits
inalso
diagrammatic
form.
This
et
al.species
(2008)
and
in
studies
Santiago
et
extent
log
(`
extent
). eigenvectors
The
logsociated
slopes
(`Afor
).observed
The
slopes
(−0.26
observed
to
−0.41)
(−0.26
are,
to
−0.41)
are,
with
a ρrelahigh
tissue
density
(as
oppossed
to Thighest
leaf
thickA
Overall
the
five
selected,
of which
we et
becf.
.asame
tion
relative
totosome
M
and
Φ
for
additional
interest
see
ifother
coordinated
structural/leaf
bioAs
the
(full)
Kendall’s
τPCA
shown
in
Fig.
9,being
Table
5a .suggests
ables
examined
by
al.
(2009),
the
of Fyllas
which
10
10
relationship
observed
suggests
a in
strong
integrate
A only
LS
konly
a PCA
analysis
of
the
full
plot
est.
We
therefore
undertook
PCA
analysis
of
the
full
plot
the
exception
being
Tlow
.most
In
that
case,
[N],
[K]
and
theFyllas
only
exception
In
that
case,
[N],
[
aFW
wall
Intersecting
and
and
in
the
same
direction
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
Considering
data
from
both
and
high
fertility
sites
toThe
significant
relationships
between
the
site
axis
RWRW
FW
soil/climate
3.8
Relationship
between
plot
effect
PCAs
and
axis
of
Fyllas
et
al.co
species
typically
associated
with
with
mean
annual
precipitation
(P
)
viz.
positive
ated
probability
giving
an
portant
contributor
and
this
also
AKendall’s
3.7
Principal
component
analysis
of
environmental
efKendall’s
partial
τ
(denoted
partial
τiτ
strates
that
many
traits
seem
to
be
“shared”,
especially
and
.
ty
soils
that
have
scores
for
both
al.,
2004a)
may
not
necessarily
always
apply.
however,
much
however,
less
than
much
for
the
less
associated
than
for
the
slopes
associated
for
the
slopes
taxfor
the
taxPDJ
RW
lieve
to
be
physiologically
relevant
(see
Supplementary
Inness)
and
associated
increased
leaf
toughness
(Kitajima
and
responses
toallthe
environment
exist
for
Amazon
forthe
to
be
superior
predictors
than
the
individual
variables,
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
of
Amazon
tropical
forest
trees
to
soil
fertility,
wit
excluding Hmaxchemical
and S both
of gether,
effects
correlation
matrix
(excluding
H
and
S
both
of
show
relationships
not
present
when
regressing
the
plot
all
show
relationships
not
present
when
regressi
max
tive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
directions
with
respect
to
M
for
these
two
trait
dimensions.
Table 3Alists
correlations
and
SMA 4,
slopes
for the en- calculated
scores
of Table
and previously
soil foliar
and climate
LS
soil/climate
measure
of
A tions with
[C]toand
M
and
a,tegrated
negative
correlation
soil/environmental
[C]
and
M
but
negatively
with
A as
fects
well
ůA1foliar
and
well
ůparamete
and
ů2
and
. which
have
scores
for
both
7atwhich
shows
components
of
three
2asas
1function
onomic
components
onomic
as
components
listed
in
Table
as
listed
1for
(−0.37
inbeing
Table
to
−0.72).
1acontains
(−0.37
−0.72).
Interestingly,
in
contrast
to
, ,variations
invariables.
were
3.6
Bivariate
relationships:
environmental
components
PDJ
RW
RW
LS
formation),
for
0.68
of
the
total
variance
both
isthe
anmajor
important
factor
for
allthe
three
of major
est.
We
therefore
undertook
aPDJ
PCA
analysis
of8
the
full
plotof
the
only
exception
.9.
In
that
case,
[N],
[K]
and
ρů[C]
Poorter,
2010)
and
with
high
[C]
linked
through
higher
than
also
shows
that
the
ůT
weightings
of
leaftrients
increasing,
and
with
and
ρx de
Abe
RW
environmentally
invariant
for
were
considered
to
beaccounted
environmentally
invariant
effect
PCs
as
dependent
effect
PCs
as significant
dependent
variables.
w
2for
vironmental
effects
with
this
information
provided
in
more
characteristics
the
same
sites
are
shown
in
Fig.
First,
the
relationship
observe
error
as
part
of
,
we
have
also
included
L
in
(
∩
foliar
[Mg].
It
is
therefore
not
surprising,
as
is
shown
Intersecting
and
and
in
the
same
direction
relafect
of
the
other
four.
Takin
FW
A
RW
and
also
with
.
The
second
axi
RW
FW
in
Table
5.
in
Here
Table
the
5.
calcula
Here
overlap
of traits
in the
diagrammatic
form.(excluding
This changes
etheir
major
components
of
three
major
not
accompanied
by
commensurate
in
L
.
Indeed,
low
and
high
fertility
soil
species.
A
effects
correlation
matrix
H
and
S
both
of
all
show
relationships
not
present
when
regressing
the
plot
level
variables
that,
individually,
were
all
strongly
correlated
average
levels
of more
reduced
structural
compounds
such
as
increases.
Interestingly,
theof
Kendall’s
τ for thi
max top
Fsite
∩ (including
the
confidence
intervals)
inFig.
the
Supplementary
panel
of
9A
shows
ů.analysis
as a function
of
first
soil
PCA
FW . Also occurring in ( PDJdetail
RW ) and of
Amazon
tropical
The
most
significant
relationships
between
the
PCA
axis
1Although
second
panel
of
Fig.
that
ůcorrelation
and P
also
show
strong
tive
M
isstrong
ΦLS
with
a the
high
standard
Especially
given
the
relationships
between
ρestimated
andefthe9,
confounding
effects
of
matrix
(ůan
) ind
isg
2efA
ated
probability
ated
giving
probability
xenvironmental
3.7
Principal
component
Principal
component
of
environmental
analysis
2spatia
this
alsodecreasing
showing
that
it3.7
varies
inspecies
the
opposite
directhat
traits
seem Considering
to
beAform.
“shared”,
especially
data
from
both
low
and
high
fertility
sites
toapsign
ofmany
traits
in
This
if diagrammatic
anything,
L
tend
tovarying
increase
with
8three
asto
FW
which
were
considered
to
be),in
environmentally
invariant
for
effect
PCs
as
dependent
variables.
The
first
axes
scores
(normalised
toof
±
100)
with
mean
annual
precipitation
(Pfects
viz.
positive
correlaLS
lignin
as
well
as
the
typically
high
C-content
defense
related
versus
of
0.63
is
than
for
any
of
the
ora
A )greater
F
me
is [C],
but
with [P]
and
[Mg]
opposite
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
reaxis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
intrients
increasing,
scores
of
Table
4,
and
previously
calculated
soil
and
climate
The
most
significant
relationships
between
the
PCA
site
axis
ciation,
but
with
examination
of
Table
4
also
suggestin
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
consider
relat
for
0.25
of
the
variance,
with
sub
soil/environmental
soil/environmenta
parameter
fects
fects
gether,
Tableof3 lists
and
SMA
slopes
forRW
enerror
asthe
part
ofFW .FW
, we
have
also
included
LAAand
in (by
∩et etal.,
seemrespect
tomax
be
“shared”,
especially
. correlations
Thus,
find
integrated
toH
increases
within
RW
are
plotted
against
each
other
in
Supplementary
Information
tions
with
foliar
[C]
and
Mphenols
a (Fine
negative
correlation
with
cf.
tion
relative
to characteristics
M
Φ
for
istraits
an important
factor
for
all three
,relationship
ables
examined
Fyllas
al.
(2009),
the
highe
compounds
such
as
tannins
and
2006;
A and
LSthe
PDJ
RW we
ections
with
to M
twoFW
trait
dimensions.
the
y
↔
x,
with
x
as
the
column
headers
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
A for theseflect
increases.
Intere
ofinterest
theinsame
shown
Fig.
9.
F thespecies,
scores
Table 4,
and previously
calculated
andinclimate
forstructural/leaf
anyFirst,
given
both
Φ, LS
ρwof
alsoTaking
decline
F
additional
tosites
see are
if soil
coordinated
bio- for
As
theand
(full)
Kendall’s
τ sf
Mof
foliar
[C]
and
(and
fect
other
fect
four.
other
Afor
vironmental
effects
with
thisofinformation
more
gether
within
tendency
of
taller
trees
to
Fig.labels.
S1.provided
ThisFW
shows
the
required
of
any
systematic
foliar
[Mg].
is was
therefore
not2009).
surprising,
asthe
isversus
shown
inthe
the
for
foliar
[P].
Comparison
with
Fyllas
),structural
this
also
showing
that
itItvaries
in0.56
the
opposite
direcortant
for
all in
three
ofandFW
,the
Read
and
Stokes,
2006;
Read
et response
al.,
Also
associated
PDJ
RW
Alsofactor
occurring
(FWPDJ
∩inthe
) the
anddirection
thepotentially
and
yofcharacteristics
being
the
row
For
the
traits,
the
relationship
observed
suggests
alack
strong
integrated
of
0.63
ise
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
RW
of
the
same
sites
are
shown
in
Fig.
9.
First,
the
ersecting
same
relaincreasing
precipitation
and,
somewhat
counter
intui
F
1
chemical
responses
to
the
environment
exist
for
Amazon
forthe
to
be
superior
predicto
RW and
being
balanced
by
positive
weigh
Especially
given
Especially
the
strong
given
relationships
the
strong
relationships
between
ρ
and
between
the
ρ
and
the
confounding
confounding
effects
of
spatial
effec
x
x
detail
(including
confidence
intervals)
in the Supplementary
3.6
Bivariate
relationships:
environmental
components
have
fewer
but
larger
leaves
than
their
more
vertically
chalcorrelations
between
the
species
scores
as
expected
for
the
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assoalso
shows
that
the
ů
contains
significant
weighti
2
A
with
this
is
abehigher
, aRW
which
may
suggestive
of a ables
greater
2
isto[C],
[P]∩andRW
[Mg]
varying
insignificant
opposite
relationships
are
all
negative
and
appear
Amazon
tropical
forest
to
soil
fertility,
most
nution
relative
tothe
M
and
Φ
forcomponents
.be
curring
) and
ofmost
the
examined
by
axis
ofest.
Fyllas
et
al.
(2009),
latter
considered
strong
inFW
Atrees
LS
top panel
Fig.
9of
shows
ů1foliar
as
a function
of
the
PCA
MAbut
isinΦ(with
. Although
with
a high
estimated
standard
with
increasing.
WeSMA
therefore
undertook
a first
PCA
analysis
ofcf.
the
full
plot
the of
only
exception
being
TaF
LSPDJ
but
also
with
contribution
foliar
cation
environmental
cation
(Fig.8)
,with
itmodel.
was
(Fig.8)
of
, ticular,
it2009)
was
wesuggesting
only
2009)
consider
we
only
relation
con
Information
(Table
S2A).
As
forof
Table
1,
the
slopes
reoutput
from
any
good
fit
ofenvironmental
acomponents
principle
components
ciation,
but
with
examination
of that,
Table
4leaves
also
that
lenged
counterparts.
But
with
a lower
8
overall.
This
level
variables
individually,
were
all
strongly
LSlog
internal
resistance
to
CO
diffusion
within
the
of
high
2
with
respect
to[Mg]
MA for
these in
two
trait
dimensions.
tween
ρx axis
and
log
[P],
[Ca],
log
[K]
and,
to
a
lesser
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
with
[P]
and
varying
opposite
was
0.56
for
foliar
[
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
x
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
in10 ∩
10headers
F
correlation
matrix
(excluding
H
and S structural/leaf
both
of Asall
show
relationships
not
pr
additional
interest
additional
to
see
interest
if coordinated
toany
seeof
ifthese
structural/leaf
coordinated
biobiothe
(full)
As(PKendall’s
for)the
(full)
τ sho
Ke
max
or as part of lower
have
alsorelationship
included
LyA ↔
in x,
(help
the
with
the
xeffects
as10the
column
Considering
data
from
both
low
and
high
fertility
sites
toFW , we
RW
Clearly
aincreases.
wide
range
oftissue
combinations
three
trait
difor
given
species,
both
Φ
andprecipitation
ρfor
also
decline
with
8LSflect
presumably
serves
to
maintain
favourable
with
mean
annual
viz.
positi
LS
w
Finally,
as
in
Fyllas
et
al.
(2009)
we
value
A show
density
woody
species
(Lloyd
et
al.,
1992;
Syvertsen
3.6
Bivariate
relationships:
environmental
components
extent
log
(`
).
The
slopes
observed
(−0.26
to
−0.41)
are,
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
ct
to
M
for
these
two
trait
dimensions.
also
shows
that
the
ů
relationship
observed
suggests
a
strong
integrated
response
A
F
1
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
A
10
which
were
considered
to befor
environmentally
invariant
for
effect
PCs
as dependent
varia
g RW and FW andand
in the same
direction
relaF the
chemical
responses
tothe
the
responses
environment
to
environment
exist
fortions
Amazon
exist
for
Amazon
fortheand
to
beand
superior
the
topredictors
be superio
the
row
labels.
the
structural
traits,
gether,
Table
3 For
lists
correlations
and
SMA
slopes
enmensions
can
occur.
But
with
Fig.
8athe
also
showing
that
it foris
increasing
precipitation
and,
counter
intuitively,
showing
that the
it varies
in the
opposite
direcrelations
byy being
counteracting
greater
resistances
inFchemical
the
with
foliar
[C]
negative
corre
W ), this alsowater
Kendall’s
partial
τsomewhat
(denoted
τM
allavariables
traits of that,
inter
et
al.,
Warren
and
Adams,
2006).
Interestingly,
as
p )Afor
however,
much
less
than
forversus
the
associated
slopes
for
the
taxof forest
0.63
istrees
greater
than
for
any
of
the
original
varilevel
ofall
Amazon
tropical
to1995;
soil
fertility,
with
most
nu-plot
relationship
observed
suggests
aundertook
strong
integrated
response
Although
a direction
high
estimated
standard
ndis ΦFW
in the with
same
rela-relationships
est.
We
therefore
est.
We
therefore
a
PCA
undertook
analysis
a
PCA
of
the
analysis
full
of
the
full
plot
the
only
exception
the
only
being
exception
T
LS . and
a.
most
significant
are
negative
and
appear
be3.8
Relationship
between
vironmental
effects
with
this
information
provided
in
more
(generally
speaking)
only
species
typically
associated
with
with
increasing.
foliar
[Mg].
issites
therefore
surprising,
hydraulic
pathwayRW
forcf.
potentially
taller trees.
Nevertheless,
well
as
ů 1traits
and
ů 2Itthis
as
functions
ofthere
,asPis
anp
Considering
data
from
both
and
high
fertility
to- not
well
as
these
correlated
leaf
dimension
isT , Trelations
F , mean
a
a sh
n relative with
to M
and Φ
. of Amazon
onomic
as
listed
in
Table
1and
(−0.37
to
−0.72).
ables
examined
by
Fyllas
et[C]
al.
(2009),
the
highest
of in
which
with
annual
FWcomponents
LS for
trients
increasing,
with
foliar
and
ρlow
as
tropical
forest
trees
tolesser
soil
fertility,
with
most
nux decreasing
Although
aAhave
high
estimated
standard
effects
correlation
effects
matrix
correlation
(excluding
matrix
H
(excluding
and
S
H
both
of
and
S
both
of
all
show
relationships
all
show
not
pres
max
max
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
rt of FW , we
also
included
L
in
(
∩
soil/climate
detail
(including
confidence
intervals)
in
the
Supplementary
x
RW 8
10 lower
10LS must
10
second
panel
of
Fig.
9,
that
ů
and
P
also
show
along with a higher ,A this
also
serve
to
in
Table
5.
Here
the
calculated
value
of
τ
and
2
A
gether,
Table
3
lists
correlations
and
SMA
slopes
for
the
enand
.
high
fertility
soils
that
have
scores
for
both
p
the
coordinated
involvement
of
a
lower
H
.
Species
with
was
0.56
forfoliar
foliar
[P].
Comparison
with
Fyllas
et al.
PDJ
RW
tions
with
foliar
[C]as
max
Interestingly,
the
Kendall’s
τ for
plot
ůet
trients
increasing,
and
with
[C]
ρenvironmentally
decreasing
as
F increases.
1 (2009)
xFinally,
as this
in
Fyllas
al.
(2009)
we
show
values
for
which
were
considered
which
were
toand
considered
be
to
be
environmentally
invariant
for
invariant
effect
for
PCs
as
effect
dependent
PCs
as
variab
depe
extent
(`
).
Thedirecslopes
observed
(−0.26
to
−0.41)
are,
(Table
S2A).
Asas
for
Table
1,
the
SMA
slopes
rewe
have
also
included
LA environmental
in
(the
∩ Information
A
10of
RW
ciation,
but
with
examination
of
Table
4
also
sugg
s, also
showing
that itoverall
varies
inlog
opposite
reduce
rates
whole
tree
carbon
gain
such
othated
probability
giving
an
indication
of
the
effect
of
vironmental
effects
with
this
information
provided
in
more
Bivariate
relationships:
components
3.7
Principal
component
analysis
of
environmental
efFigure
7
shows
the
major
components
of
the
three
major
strong
weightings
along
this
trait
dimension
are
also
charalso
shows
that
the
ů
contains
significant
weightings
of
leaf[Mg].asIt is the
versus
of 0.63for
is greater
any plot
of partial
the
vari- τp ) for all traits foliar
2than
increases.
Interestingly,
the
Kendall’s
τ for
this
of original
ů 1 τ (denoted
F slopes
Ffor
Kendall’s
of interest
however,
much
less
than
the of
associated
taxflect
the
relationship
y↔
x, with
thethe
x as
the
column
headers
for
any
given
species,
both
Φ
and
ρ
also
de
wing
that
it
varies
in
the
opposite
direcsoil/environmental
parameter
after
accounting
for
th
LS
w
erwise
might
be
expected
on
the
basis
higher
A
and
detail
(including
confidence
intervals)
in
the
Supplementary
fects
CPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
max
level
variables
that,
individually,
were
all
strongly
correlated
acterised
by
larger
seeds
as
would
be
expected
for
shade
second
panel
of
Fig.
e to MA and ΦLS for onomic
ables
examined
bythan
Fyllas
et
al.of(2009),
highest
versus
ofthe
0.63
is labels.
greater
forstructural
any
thetraits,
original
vari- 2ofaswhich
RW cf. components
FW .
well
asthe
ůthe
functions
ofTheF ,most
, significant
Ta ,somewhat
Pa andrelationship
Qcounter
1 and ůincreasing
Tand,
a
asthe
listed
inFtoTable
1 (−0.37
to
−0.72).
and
ysites
being
row
For
the
precipitation
nsidering
data
from
both
low
and
high
fertility
fect
of
the
other
four.
Taking
into
account
to
the
pot
Information
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
rea
greater
probability
of
high
levels
of
incoming
radiation.
illustrates
that
many
traits
seem
to
be
“shared”,
especially
with
mean
annual
precipitation
(P
)
viz.
positive
correlaciation,
but
with
exa
adapted
trees
(Sect.
4.1.3).
Along
with
a
small
but
signifind ΦLS for RW cf. FW .
was 0.56
for foliar
[P].(2009),
Comparison
with of
Fyllas
et al.the
(2009)
A
ables
examined
by Fyllas
et al.
the
highest
in
Table
5.which
Here
calculated
value of
ofTable
τp and
associmost
significant
relationships
are
all
negative
and
appear
bescores
4, and
previously
with
increasing.
her,
3 lists
and SMA
slopes
for0.56
thethe
Especially
given
strong
relationships
between
ρcontribution
and
the
confounding
effects
ofheaders
spatial
autocorrelation
(Fyllas
flect
relationship
y and
↔
x,
with
the
xPFL
as
the
column
Thiscorrelations
trade-off
associated
with
a was
greater
Henmay
be
one
reax
riateTable
relationships:
environmental
tions
with
foliar
[C]
and
M
athree
negative
correlation
with
for of
any
speci
max
cant
of
ρof
, giving
is
thus
strongly
suggestive
a given
also
shows
that
the
ůthe
significant
weightings
of leafAfor
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
x
2 contains
M
which
is
an
important
factor
all
,
ated
probability
an
indication
of
the
effect
of
each sites
A
PDJ
RW
3.7 Principalcomponents
component
analysis
of
environmental
eftween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
characteristics
of
the
same
x
10
10
10
onmental
effects
with
this
information
provided
in
more
foliar
environmental
components
(Fig.8)
, the
itweightings
was
of
2009)
we
only consider
relationships
with p ≤precipita
0.01 or ab
and
the
y
being
row
labels.
For
the
structural
traits,
the
son for the observation
thatcation
light
demanding
species
with
a
foliar
[Mg].
It
is
therefore
not
surprising,
as
is
shown
in
the
ionships: environmental
components
increasing
level
variables
that,
individually,
were
all
strongly
correlated
coordinated
trait
dimension
associated
with
shade
tolerance
also
shows
that
the
ů
contains
significant
of
leaf2
parameter
accounting
for9 shows
the we
ef-ů show
fects
Finally,
as after
in top
Fyllas
etofal.Fig.
(2009)
and
Also significant
occurring
in
(−0.41)
∩ are
) negative
and
of the
extent
log
(`A
).see
Theifslopes
(−0.26 relationships
tosoil/environmental
panel
a
FW . observed
PDJ are,
RW
1 as
ail
(including
confidence
intervals)
in theshow
Supplementary
additional
interest
towith
coordinated
bioAs
for
the
(full)
Kendall’s
τbeshown
inmuch
Fig.
Table
5 sug
most
all
and
appear
g data
fromlow
both
high fertility
sites
to-10
ρwlow
do and
not
necessarily
higher
above-ground
growth
second
panelprecipitation
ofstructural/leaf
Fig.
9,all
that
ůA
PApositive
also
show
strong
assowith
9,
increasing.
mean
annual
(P
viz.
correla2 )and
level
variables
that,
individually,
were
strongly
correlated
and
longevity.
Not
surprisingly
then,
it
seems
to
play
a
fect
of
the
other
four.
Taking
into
account
to
the
potential
Kendall’s
partial
τ
(denoted
τ
)
for
all
traitstheofl
same
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
p
however,
much
less
than
for
the
associated
slopes
for
the
taxaxis
of
Fyllas
et
al.
(2009),
ormation
(Table
S2A).
As
for
Table
1,
the
SMA
slopes
rechemical
responses
to
the
environment
exist
for
Amazon
forthe
to
be
superior
predictors
than
the
individual
vari
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
ble 3both
listslow
correlations
SMA
slopes
the
en-counterparts
om
and high
fertility
sites
to-for
x(P
ciation,
but
examination
ofinTable
4effects
also
suggesting
that
rates
thanand
their
more
shade
tolerant
(Keeling
10
10
tions
with
foliar
[C]
and
M
and10
a negative
correlation
with
with
mean
annual
precipitation
viz.
positive
correlagreater
role
accounting
for
the
variations
of species
A )A
Especially
given
the
strong
relationships
between
ρetwith
confounding
ofas
spatial
autocorrelation
(Fyllas
well
ů 1trait
and
ůtegrated
ofof et
, al.,
T , P
x and
directions
with
respect
to
M
for
these
two
traitonly
dimensions.
2 as functions
T , in
onomic
components
as listed
in analysis
Table
1 the
(−0.37
to
−0.72).
measure
soil
asfertility
t the
relationship
y↔
x,
with for
theprovided
x est.
asenthe
column
headers
We
therefore
undertook
a and
PCA
of(`not
the
full
plot
the
exception
being
Ta .fertility
In
thatFinally,
case,F
[N],
[K]aFy
an
extent
log
).Aboth
The
slopes
observed
(−0.26
to with
−0.41)
are,
al
effects
with
information
in
more
correlations
and
SMA
slopes
the
A
for
any
given
species,
Φ
and
ρ
also
decline
al.,this
2008).
10
foliar
[Mg].
It
is
therefore
surprising,
as
is
shown
in
the
LS
w
tions
with
foliar
[C]
M
and
a
negative
correlation
with
associated
with
low
fertility
as
opposed
to
high
soils
A
foliar cation environmental components (Fig.8) , it was of
2009) we only consider
relationships
with
p
≤
0.01
or
better.
in
Table
5.
Here
the
calculated
value
of
τpτ aa
relationship
observed
suggests
Kendall’s
partial
Intersecting
and
and
in
the
same
direction
relathe ythis
being
the row
labels.
For
the
structural
traits,
the
effects
correlation
matrix
(excluding
H
and
S
both
of
all
show
relationships
not
present
when
regressing
th
however,
much
less
than
for
the
associated
slopes
for
the
taxRW
FW
uding
confidence
intervals)
in
the
Supplementary
with
information
provided
in
more
max
precipitation
and,
somewhat
counter
intuitively,
second
panel
of Fig.
9,
that
ůindicated
PisAfor
also
show
strong
assofoliar
It is increasing
therefore
not
surprising,
as
shown
in the
2 and
asenvironmental
by
theefdifferent
theof
characteristic
rootsforest
additional interest3.7
to see
if [Mg].
coordinated
structural/leaf
bioAs
the
(full)
Kendall’s
τ for
shown
in
Fig. 9,
atedvalues
probability
giving
anTable
indication
the
Principal
component
of
Amazon
tropical
trees
well5 suggests
as ůof
ůeffe
tive
to
M9,
isanalysis
Φexamination
Although
with
high
estimated
st
significant
relationships
are
negative
andsecond
appear
bewhichslopes
were
considered
toFig.
be
environmentally
invariant
effect
as
dependent
variables.
1 and
2 ta
onomic
components
asafor
listed
inassoTablePCs
1 standard
(−0.37
to −0.72).
A
LS .and
n (Table
S2A).
Asinfor
1, all
the
SMA
refidence
intervals)
theTable
Supplementary
with
increasing.
ciation,
but
with
of
Table
4
also
suggesting
that
panel
of
that
ů
P
also
show
strong
2
A
(λ
=
698,λ
=
318,
Table
2).
chemical
responses
to the
environment
exist
for
Amazon
forthe
to
be
superior
predictors
than
the
individual
variables,
soil/environmental
parameter
after
accounting
low
high
fects
trients
increasing,
and
with
foli
in Table 5. Here
en ρxAs
and
log
[Ca],
logcolumn
[K] and,
to a lesser
lationship
↔
x,[P],
with
the
x
asslopes
the
headers
2A).
forylog
Table
1, the
SMA
10
10
10reerror
as
partofspecies,
ofthe of
,both
we have
also
included
L
( ofwith
forwith
any
given
and
ρw exception
alsothat
decline
ciation,
examination
4Φalso
suggesting
FWTable
A inbeing
RW
LSthe
est.
We
therefore
undertook
abut
PCA
analysis
Ta∩.other
Inforthat
case,
[N],ated
[K]
and
ρw the
the
four.
Taking
intoprobability
account
togiv
th
Interestingly,
K
Finally,
asfull
in plot
Fyllascomponent
et al.only
(2009)
we fect
show
values
F increases.
3.7
Principal
analysis
of
environmental
efent
(`row
observed
(−0.26
tofor
−0.41)
are,
being
labels.
For
the
structural
traits,
the
A ).
y ↔log
x,the
with
theThe
x asslopes
the
column
headers
10
increasing
precipitation
and,
somewhat
counter
intuitively,
given
species,
both
Φ
and
ρ
also
decline
with
LS
w
),
this
also
showing
that
it
varies
in
the
opposite
direceffects correlationEspecially
matrix any
(excluding
H
and
S
both
of
all
show
relationships
not
present
when
regressing
the
plot
given
theFW
strong
relationships
between
ρ
and
the
confounding
effects
of
spatial
autocorrelation
(F
max
x
versus
of
0.63
is
greater
than
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
interest
as
soil/environmental
F
fects
p
wever,
much For
less
than
forallthe
associated
for
the taxwww.biogeosciences.net/9/775/2012/
Biogeosciences, 9, 775–801, 2012
ficantlabels.
relationships
are
negative
appear
berow
the
structural
traits,and
theslopes
with
increasing.
precipitation
and,
somewhat
counter
intuitively,
which
were
considered
to be environmentally
PCs
asFW
dependent
variables.
foliarincreasing
cation
environmental
, itRW
was
2009) we
only consider relationships
withother
pet≤al.
0.0
tion relative
to
Φ(Fig.8)
foreffect
cf. of
(
well ascomponents
ůinvariant
and
ů 2for
asLSfunctions
of
A and
fect by
of Fyllas
the
fou
1M
F , .T , Ta , Pa and Qa ables examined
miclog
components
as listedand
in10
Table
1 be(−0.37
to
−0.72).
and
lognegative
[K] and,
to a lesser
ionships
are all
appear
10 [P],
10 [Ca], log
with
increasing.
additional interestFinally,
to in
seeTable
if
coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
was
0.56
for
foliar
[P].
Compari
5.
Here
the
calculated
value
of
τ
and
associEspecially
given
the
strong
relationships
between
ρ
and
the
confounding
effects
as
in
Fyllas
et
al.
(2009)
we
show
values
for
p
x
). 10
The
slopes
observed
to −0.41) are,
P],
log
[Ca],
log10
[K] and,(−0.26
to a lesser
0 (`A
chemical
responses
to ated
the
environment
exist
for
fortheeffect
toas
be
than
individua
Bivariate
relationships:
environmental
components
shows
that
the we
ůthe
si
probability
giving
indication
ofof
of superior
each
foliar
cation
environmental
components
(Fig.8)
, it also
waspredictors
of
2009)
only
consi
Principal
analysis
ofare,
environmental
ef2 contains
Kendall’s
partial
(denoted
τpan
)Amazon
for
allvalues
traits
interest
Finally,
as3.6in
Fyllas
et τal.
(2009)
we
show
forthe
much
lessobserved
thancomponent
for the
associated
slopes
for the taxe slopes
(−0.26
to −0.41)
for
[C]
and M[C], but
andnegatively
MInnegatively
withS1.
allcontributor
foliar
withnutrients
all
foliar
examined
nutrients
examined
portant
andthe
this
also relating
positively
to
foliar
A , but Fig.
shows
0.33
of the tota
This
shows
required
lack of
any that
systematic
ation),
formation),
accountedaccounted
for 0.68
0.68
total
ofvariance
the totalfor
variance
both for
both
lieveoffor
tothe
be
physiologically
relevant
(see
Supplementary
denotedA as PFL
.
be
and
also
with
and
.
also
The
with
second
.
The
axis
second
of
the
axis
PCA
of
on
the
the
PCA
plot
on
efthe
plot
ef[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
could
be
explained
by
the
A
any
given
species)
with
the
results
shown
in
Table
4.
This
recognised
before.
It
is
thus
here
correlations
between
thewhich
species
and high
lowfertility
and highsoil
fertility
species.
soil species.
formation),
accounted for 0.68 of the total variance
for five
botheigenvectors
Overall the
selected,
all of
we scores
be- as expected for the
fects
correlation
fects correlation
matrix
(ůoutput
matrix
) isalso
also
(ůwith
) any
is.
also
accounting
accounting
and
Thesignificant,
second
of the PCA
on the
plot ef- andtic
2
2significant,
portant
contributor
th
shows(normalised
that
0.33
of to
the±total
variation
the
11 traits
examined
from
fit of a axis
principle
model.
low
and
high
fertility
soil
e first three
The first
axesthree
species
axesscores
species
(normalised
scores
to species.
± 100)
100)
lieve
to beinphysiologically
relevant
(seegood
Supplementary
In- components
for
0.25
of
for
the
0.25
variance,
of
the
with
variance,
substantial
with
substantial
negative
weightings
negative
weightings
fects
correlation
matrix
(ů
)
is
also
significant,
accounting
[C]
and
M
,
but
negativel
2
could
be
explained
by
the
first
PCA
axis
(ů
)
with
ρ
an
imA
1
x
Clearly
a
wide
range
of
combinations
of
these
three
trait
di794
S.
Pati
ño
et
al.:
Tropical
tree
trait
dimensions
lotted
are
against
plotted
each
against
othereach
inweSupplementary
other
in Supplementary
Information
The
first
axesInformation
species scores
(normalised
to ±
100)
formation),
accounted
for 0.68 of the total variance for both
ors selected,
all
of which
be- three
Malso
for
M[C], positively
foliar
and [C]
(and
and
toaofcan
(and
lesser
toextent
a lesser
extent
[P])
[P])
forto
0.25
theoccur.
variance,
with
substantial
negative
A , foliar
and
alsoweightings
with
portant
contributor
andfor
this
relating
foliar
mensions
Butfoliar
with
Fig.foliar
8a also
showing
that .
it isThe seco
S1. Fig.
This(see
S1.
shows
This
theshows
required
the
required
of
any
lacksystematic
of any
are
plotted
against
each
other
in systematic
Supplementary
low
andInformation
highAfertility
soil
species.
relevant
Supplementary
In-lack
being
balanced
being
by
balanced
positive
by
weightings
positive
weightings
for
foliar
[Mg]
for
foliar
in
par[Mg]
in
parfor
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliarwith
[P])
fects
correlation
matrix3.8
(
A
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
A
(generally
speaking)
only
species
typically
associated
lations
correlations
between
the
between
species
the
scores
species
as
scores
expected
as
for
expected
the
for
the
Fig.
S1.
This
shows
the
required
lack
of
any
systematic
.68 of the total
variance
for both of integrated trait dimensionsThe
first threenegative
axes species
scores between
(normalised
±L
100)
relationship
M. Ato
and
be expected
4.2.6
Significance
and
A. would
ticular,
but
ticular,
also
with
but
contributions
also
with
contributions
from
Φ
from
and
ρ
Φ
and
ρ
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parLS
x LS
x
for
0.25
of
the
variance,
w
and also
with
. The
second
axis
of
the
PCA
on
the
plot
efutecies.
from
output
any from
good any
fittheir
ofcorrelations
good
a principle
fit
of
a
components
principle
components
model.
model.
between
the
species
scores
as
expected
for
the
are plotted against
each
other
inas,
Supplementary
Information
and
.
high
fertility
soils
that have
scores
fortoboth
to be
observed
for
example,
was
found
be thePDJ
case
for
components
ticular, but also with contributions from Φfor
. RW
M ρ, xfoliar
[C] and LS and
fects
correlation
matrix
also This
significant,
accounting
2 ) isS1.
rly
a wide
Clearly
range
a wide
of combinations
range
combinations
these
three
offitthese
trait
ditrait (ů
dioutput
fromofany
good
of athree
principle
components
model.
Fig.
shows
required
lackangiosperms
of any
systematic
es scores
(normalised
to ±of100)
7 shows
the
major
components
the Athree
a rangethe
ofFigure
herbaceous
by Shipleyof(1995).
Or,major
being balanced by positiv
for
0.25
theitvariance,
substantial
negative
weightings
ions
mensions
can occur.
can
Butoccur.
with
Fig.
But
with
also
Fig.
showing
8a
also
that
showing
is thatof
itwith
is
Clearly
a8a
wide
range
oftoof
combinations
these
three
dicorrelations
between
species
scoresoverlap
as expected
forinet
the
er
in Supplementary
Information
CPCs
and
their
of Pickup
traits
diagrammatic
form.
Although
it is
axiomatic
that,
be
included
in
any of
the trait
as wasthe
found
in some
cases
by
al. (2005) we
can This
ticular, but also with cont
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
Relationship
3.8
Relationship
between
effect
plot
PCAs
effectseem
and
PCAs
Th
A with
erally
(generally
speaking)
only
species
onlytypically
species
associated
typically
with
with
mensions
can occur.
Fig. have
8a 3.8
also
showing
that
itpredict
is
output
from
any
good
fitthat
ofbetween
aplot
principle
components
model.
equired
lackabove
ofspeaking)
any
systematic
illustrates
that
many
traits
to should
be and
“shared”,
integrated
dimensions,
aBut
trait associated
would
had
to have
in
some
circumstances
there
be no especially
relabeing balanced
by positive
weightings
forwith
foliar of
[Mg]
inRelationship
parsoil/climate
soil/climate
sco
3.8
between
plot
effect
PCAs
and
(generally
speaking)
only
species
typically
associated
Clearly
a
wide
range
combinations
of
these
three
trait
diecies
scores
as
expected
for
the
and
.and isRW
. the mix
fertility
highsoils
fertility
that have
soils scores
that have
for scores
both
for
both
been
measured,
what
is perhaps
morePDJ
subtle,
that
tionship
MA , factor
for example
where
PDJ
RW
A and
Mxbetween
which
isLan
important
for all three
of PDJ
, RW
A
PDJ is
ticular,
but also
with contributions
from
Φ
ρ
.with
ch
soil/climate
LS and
mensions
can
occur.
But
Fig.
8a
also
showing
that
it
is
a
principle
components
model.
and RW
high
fertility
soils
thatthree
have
scores
formajor
both (orPDJ
gure 7 shows
Figure
7suites
shows
major
the
components
major
components
oftogether
the
of
the
ofthe
of
traits
coming
onmajor
anythree
one
PCA
CPC)
the. primary
source
of variation
in in
the( latter (as
LA is) effec.
Also
occurring
∩
and
of
the
and
top
FW
PDJ
RW
3.8 7Relationship
betw
Patiño
et
al.:Figure
Tropical
tree
(generally
species
typically
associatedIndeed,
with although
mbinations
these
three
trait
s andCPCs
theirS.of
overlap
and
their
overlap
traits
inofdidiagrammatic
traits
intrait
diagrammatic
form.
form. This
7 shows
thedimensions
major
components
of the
threespeaking)
major
axis
isof
also
dependent
on
what
is notThis
measured.
For
examtivelyonly
absent
from
this
dimension).
much
same
sign
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
ax
The
most
The
significant
most
significant
relationships
relationships
between
the
between
PCA
site
the
axis
PCA
site
axis
soil/climate
th Fig.
8a many
also
that
it
rates
illustrates
that
that
traits
seem
traits
to isbe
seem
“shared”,
to the
be “shared”,
especially
especially
CPCs
and
their
overlap
of traits
diagrammatic
form.soils
This
ple,showing
ourmany
differentiation
of
first
two in
components
the
and
. twoettrait
high of
fertility
that have
scoreswith
for both
touted
as a fundamental
plantPDJ
trait
(e.g.,
Poorter
al.,dimensions.
2009;
RW
directions
respect
to
M
for
these
teg
A
scores
of
Table
scores
4,
of
and
Table
previously
4,
and
previously
calculated
calculated
soil
and
climate
soil
and
climate
The
most
significant
relationships
between
the
PCA
site
axis
3.8
Relationship
between
plot
effect
PCAs and
Patiño
etassociated
al.:
Tropical
tree
trait
dimensions
peciesS.typically
with
given
with the
results
shown
in Table
4.7 This
does
not
seem
to of
have
been
recognised
is
thus
here
illustrates
that
seem
toIt ,be
“shared”,
especially
mainly
CPC
2,many
viz.
7 any
shows
the species)
major
of et
theal.,
three
major
Asner
et al., components
2011;
Kattge
2011)
MA seems
to us to
PDJ
RW
whichMisAan
which
important
is analysis
an
important
factor
forTable
all
factor
three
for
ofall traits
three
of
,before.
PDJand
RW
PDJoccurred
RW Figure
rel
characteristics
characteristics
of
the
same
of
sites
the
same
are
shown
sites
are
in
Fig.
shown
9.
First,
in
Fig.
the
9.
First,
the
scores
of
Table
4,
previously
calculated
soil
and
climate
soil/climate
Intersecting
and
and
in
the
same
direction
relaFW
shows
of theintotal
variation in
theto11
traits
examined
and
scores for denoted
both
CPCs
and
their
overlap
of traits
diagrammatic
form.
asoccurring
of .[C]
varying
positively
with
M
for
bethat
too0.33
confounded
a RW
measurement
beThis
practically
useful in
as M
PDJ
A
PDJ
is∩an
factor
for
all
three
of of
, 9 shows
PFL
A which
PDJ
RW
. AlsoFW
.consequence
Also
occurring
in
(. RW
( important
and
of
) and
oftop
the
of
panel
top
Fig.
panel
of
Fig.
ů
9
as
shows
a
function
ů
as
a
of
function
the
first
of
soil
the
PCA
first
soil
PCA
FWand
PDJ in
RW
PDJ) ∩
RWthe
characteristics
of
the
same
sites
are
shown
in
Fig.
9.
First,
the
tive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
1
1
The
most significant relat
Ato
LS“shared”,
could
be
explained
by
theresults
first
PCA
axis
) with
an evidenced
imany
given
species)
the
shown
in(ůTable
4. ρThis
does not
seem
to
have
been
recognised
before.allItof
is which
thus
here
1strategies
x as
illustrates
many
traits with
seem
be
especially
r sign
components
of
the
three
major
(this
being
interpreted
as less
allocation
of
photosynthate
tothat
differentiating
different
plant
growth
Overall
the
five
eigenvectors
selected,
besame
is [C],
sign
but
iswith
[C],
[P]
but
and
with
[Mg]
[P]
and
varying
[Mg]
invarying
opposite
opposite
. Also
occurring
in (in
∩we
)axis
and
of
the
and
tri
ofRW
Fyllas
et
of
al.
Fyllas
(2009),
et
al.
the
(2009),
latter
considered
the
latter
considered
a
strong
ina
strong
intop
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
FW
PDJaxis
scores
of
Table
4,
and
pre
1
portant
contributor
and
this
also
positively
to work
foliar
error
astotal
part
of
,relating
we
included
LA would
in ( RW ∩
shows
that
0.33
of the
variation
in
thehave
11
traits
examined
FW
raits
diagrammatic
form.
This
more
reduced
structural
compounds
such
as lignin
in
low
lieve
to to
be
physiologically
relevant
(see
above.
Future
by
itsfertility
contribution
to
the
five
denoted
aswith
.
PFL
MAInwhich
is an
important
factor
for
alletand
three
of
,also
tionsin
directions
with
respect
respect
M
these
Mis
for
these
trait
dimensions.
two
trait
dimensions.
PDJ
RW
same
sign
[C],
butmost
with
[P]Supplementary
and
[Mg]
varying
in
opposite
.
The
strong
.
The
strong
tegrated
measure
tegrated
of
measure
soil
of
soil
and
fertility
denoted
denoted
A forto
Atwo
axis
of
Fyllas
al.
(2009),
the
latter
considered
a
strong
inF
F
F
characteristics
of
the
same
The
significant
relationships
between
the
PCA
site
axis
[C]beand
M
with
all
foliar
examined
could
explained
bynegatively
the
first
PCA
axis
(ů
with
ρx an
imA , but
1 ) nutrients
seem to Overall
be
“shared”,
especially
MA /high
cation
leaves),
butofincreasing
with
decreasing
M
formation),
accounted
for 0.68
the
total
variance
for
both
),in
this
also
showing
that
itmeasurements
varies
in
the opposite
direcbe
better
directed
towards
separate
of . foliar
the five
eigenvectors
selected,
all
of
which
we
beA. Also
FW
directions
with
respect
to
M
for
these
two
trait
dimensions.
ve
relationship
relationship
observed
suggests
observed
a
suggests
strong
integrated
a
strong
integrated
response
response
The
strong
tegrated
measure
of
soil
fertility
and
denoted
occurring
(
∩
)
and
of
the
and
A
secting
Intersecting
and
and
and
in
the
and
same
in
direction
the
same
reladirection
relatop
panel
of
Fig.
9
shows
ů
F
scores
of
Table
4,
and
previously
calculated
soil
and
climate
FW
PDJ
RW
RW
RW
FW
FW
and contributor
also
with
.and
The
second
axis of
the
PCA
ontothe
plot
ef- conportant
this
also relating
positively
foliar
low
and
high
fertility
soil
species.
(this
likely
being(see
an Supplementary
effect of higher
lipid sign
conlieve
towithin
be of
physiologically
relevant
Intissue
density
and
thickness
as
well
asa leaf
dry
matter
actor
for
all
, RW
cf.
.
tion
relative
to
M
and
Φ
for
PDJ
ab
of
Amazon
of
tropical
Amazon
forest
tropical
trees
forest
to
soil
trees
fertility,
to
soil
with
fertility,
most
with
numost
nuRW
FW
A
LS
relationship
observed
suggests
strong
integrated
response
same
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
o MAtive
is Φ
toLSthree
M
. A
Although
is ΦLSRW
. Although
with
a high
with
estimated
a
high
estimated
standard
standard
axis
of
Fyllas
et
al.
(2009
characteristics
of
the
same
sites
are
shown
in
Fig.
9.
First,
the
Intersecting
and
and
in
the
same
direction
relafectsMcorrelation
matrixwith
(ů 2 )allisfoliar
also significant,
accounting
RW
[C]
and
negatively
nutrients
examined
A , but
three
axes
species
scores
(normalised
toboth
100)
tentsfirst
in)higher
photosynthetic
capacity
leaves).
If±
leaf
[C]
iswith
formation),
accounted
for
0.68
of the
totalFW
variance
fortrients
tent
(Witkowski
and
Lamont,
1991;
Wilson
et as
al., 1999).
It nutrients
increasing,
and
with
foliar
and
with
[C]
foliar
and
ρsubstantial
[C]
and
ρxnegative
decreasing
as
ofThe
Amazon
tropical
forest
trees
tothe
soilplot
fertility,
withmeasure
most
directions
respect
to
M
for
these
two
trait
dimensions.
in part
( error
∩The
and
of
the
xofdecreasing
tegrated
of soilwa
f
top.Lincluded
panel
of Fig.
9 shows
ů∩
asincreasing,
a function
of
theof
first
soil
APCA
PDJ
RW
tive
toweM
Although
with
a high
estimated
standard
1
for
0.25
the
variance,
with
weightings
A is Φ
LS
and
also
with
.
second
axis
the
PCA
on
efas
of
as
part
,
we
of
have
,
also
have
included
also
in
(
L
∩
in
(
FW
FW
A
RW
A
RW
are
plotted
against
each
other
in
Supplementary
Information
low
and
high
fertility
soil
species.
omitted
from
the
analysis,
then
these
two
dimensions
actuis
probably
because
of
its
ambiguous
nature
that
M
does
3.6
Bivariate
relationships:
environmental
components
A
als
increases.
increases.
Interestingly,
Interestingly,
the
Kendall’s
the
Kendall’s
τ
for
this
plot
τ
for
of
this
ů
plot
of
ů
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
P] and [Mg] varying inerror
opposite
F Intersecting
F
1
1
relationship
observed
sug
x
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
a
strong
infor
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
and
and
in
the
same
direction
relafects
correlation
matrix
(ů 2 ) is also significant, accounting
A
RW∩
FW
asspecies
part
,(normalised
wethe
have
in
( RW
FW
Fig.
S1.
This
required
lack
ofalso
any
systematic
thisthese
also
),two
showing
this
also
that
showing
itshows
varies
that
inof
it
the
varies
opposite
in
direcopposite
direcThe
first
three
axes
scores
toincluded
±
100) LFAversus
ally
collapse
into
thethe
one
due
to
the
strong
correlations
be-0.63
not
seem
toincreases.
be
asany
good
a any
predictor
of
demographic
ratesplot
as
W ), for
FW
lev
of
of
greater
0.63
is
than
for
than
of
for
the
original
of
the
varioriginal
variInterestingly,
the
Kendall’s
τin
this
of ů 1 forest
trait
dimensions.
Fof
offor
Amazon
tropical
F greater
The
strong
tegrated measure of soilversus
fertility
A
F .variance,
being
by
positive
weightings
for
foliar
[Mg]
partivethe
toand
M
is
Φis
. the
Although
with
a high
estimated
standard
for
0.25
with
substantial
negative
weightings
Adenoted
LSbalanced
correlations
between
the
species
scores
as
expected
for
),
this
also
showing
that
it
varies
in
the
opposite
direcare
plotted
against
each
other
in
Supplementary
Information
Considering
data
from
both
low
and
high
fertility
sites
totween
all
of
the
cations,
nitrogen
and
phosphorus
and
(negfirst
thought,
especially
when
comparisons
are
done
across
FW
cf.
.
cf.
.
elative
tion
to
relative
M
and
to
Φ
M
for
and
Φ
for
ables
examined
ables
examined
by
Fyllas
by
et
al.
Fyllas
(2009),
et
al.
the
(2009),
highest
the
of
highest
which
of
which
versus
of
0.63
is
greater
than
for
any
of
the
original
variRW
FW
RW
FW
A
LSA
LS
trients increasing, and wi
wi
F
relationship
observed suggests
aasfor
strong
integrated
response
ticular,
but also
withalso
contributions
from
Φ RW
and
ρx .[P])
in the
same
direction
rela[C]
and
(and
to acorrelations
lesser
extent
foliar
W and Fig.
A , foliar
error
partM
of
have
included
L2008).
∩SMA
output
from
any
good
ofM
a principle
components
model.
FW , we
A in ( LS
S1.
This
shows
the fit
required
lack
of
any
systematic
gether,
Table
3
lists
and
slopes
forofthe
enatively)
M
not
shown).
This
seems
likely
to
have
different
sites
(Poorter
et
al.,
A (results
tio
cf.
.
tion
relative
to
and
Φ
for
was
0.56
for
was
foliar
0.56
[P].
for
foliar
Comparison
[P].
Comparison
with
Fyllas
with
et
al.
Fyllas
(2009)
et
al.
(2009)
ables
examined
by
Fyllas
et
al.
(2009),
the
which
FW to being
increases.
Interestingl
of AAmazonLStropicalRW
forest trees
soil fertility,
with
most
nu- weightings for foliar [Mg] in par-Fhighest
h withcorrelations
a high
estimated
standard
balanced
by
positive
wide
range
of
combinations
these
three
trait
dibetween
the the
species
scores
asofexpected
for
theFW
vironmental
with
this
information
provided
in
more
been athe
case
for
results
ofincreasing,
Easdale
and
(2009)
),also
this
also
it give
varies
in support
the
opposite
direcOur
results
no
forleafthe supposed
“second
Bivariate
3.6 Clearly
Bivariate
relationships:
relationships:
environmental
environmental
components
components
fol
also
that
the
ůshowing
contains
the
ůthat
contains
significant
weightings
weightings
of
leafwas
0.56
foreffects
foliar
[P].
Comparison
with
Fyllas
etFal.
2ρthat
2significant
versus
of(2009)
0.63 is great
trients
andHealey
withshows
foliar
[C]shows
and
decreasing
as
x
ticular,
but
also
with
contributions
from
Φ
and ρx .of
LSintervals)
ave also
included
L
in
(
∩
mensions
can
occur.
But
with
Fig.
8a
also
showing
that
it
is
A
RW
output
from
any
good
fit
of
a
principle
components
model.
detail
(including
confidence
in
the
Supplementary
and Baraloto
al. (2010)
where, along
with level
MAtion
,variables
cations,
dimension”
of
the
leaf
economics
spectrum
proposed
by
3.6 etBivariate
relationships:
environmental
components
sec
level
that,
variables
individually,
that,
individually,
were
all
strongly
were
all
correlated
strongly
correlated
also
shows
that
the
ů
contains
significant
weightings
of
leafcf.
.
relative
to
M
and
Φ
for
ables
examined
by
Fyllas
2
increases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
RW
FW
A
LS
F
1
3.8 Baltzer
Relationship
between
plot
effect
PCAs
and
(generally
speaking)
only
typically
associated
with
Clearly
a wide
range
ofboth
combinations
of
these
three
trait
diInformation
(Table
S2A).
As
for Table
1,
the
SMA
slopes
re- [P]. cia
idering
Considering
data
from
data
both
from
low
and
high
lowspecies
and
fertility
high
sites
fertility
tosites
toat
it varies
innitrogen
the
opposite
direcand
phosphorus
were
all
considered
part
of
the
one
and
Thomas
(2010).
That
study,
primarily
based
on
with
mean
with
annual
mean
precipitation
annual
precipitation
(P
)
viz.
(P
positive
)
viz.
correlapositive
correlalevel
variables
that,
individually,
were
all
strongly
correlated
A
A
was
0.56
for
foliar
Co
versus
of
0.63
is
greater
than
for
any
of
the
original
variF
soil/climate
mensions
can3 occur.
But
Fig.
8a
also
showing
that
it3.6
isfertility
the
relationship
y ↔did,
x, correlation
with
the
column
er,for
Table
gether,
3high
lists
Table
correlations
lists
correlations
and
SMA
and
slopes
SMA
for
slopes
the
enfor
the
enConsidering
data
from
both
low
and
high
sites
dimension.
and
.tions
fertility
soils
thatwith
have
scores
for
both
for
data
from
Bornean
forest
trees
however,
fail
topositive
differentitions
foliar
with
[C]
foliar
and
Mflect
[C]
and
and
aM
negative
and acorrelation
negative
withthe
with
with
mean
precipitation
(PAx)as
viz.
correlaBivariate
relationships:
environmental
components
PDJ
RW(2009),
Aof
A annual
also
showsheaders
that the ů 2 con
ables
examined
by
Fyllas
et with
al.
thetohighest
which
RW cf.
FW .
3.8
Relationship
between
plot
effect
PCAs
and properties
(generally
speaking)
only
species
typically
associated
with
and
the
y being
the
labels.
For
the
structural
traits,with
the indivi
mental
vironmental
effects
information
with
this
information
provided
in
provided
more
inthree
more
gether,
Table
3 the
lists
correlations
and
SMA
slopes
for
the
eninc
Figure
7this
shows
the
major
components
the[P].
major
foliar
[Mg].
foliar
Itwith
is
[Mg].
therefore
is
not
therefore
surprising,
not
surprising,
as
is row
shown
asAin
isand
the
shown
infoliar
the
Itwith
iseffects
thus
clear,
that
in
presence
ofofadditional
parameter
ate Itbetween
taxonomic
versus
effects
with
foliar
[C]
andsoil
M
a on
negative
correlation
level
variables
that,
was
0.56
for
foliar
Comparison
Fyllas
ettions
al. (2009)
soil/climate
S.
Patiño
et
al.:
Tropical
tree
trait
dimensions
7
most
significant
relationships
are
all
negative
and
appear
belps:
(including
detail
(including
confidence
confidence
intervals)
intervals)
in
the
Supplementary
in
the
Supplementary
vironmental
effects
with
this
information
provided
in
more
wi
Considering
data
from
both
low
and
high
fertility
sites
toCPCs
and
their
overlap
of
traits
in
diagrammatic
form.
This
high fertility
soils
that(for
haveexample
scores
both
second
panel
Fig.
panel
that
of Fig.
ů 2 and
9, done
that
PAůalso
and
P
strong
show
assostrong
assofoliar
[Mg].
isshow
therefore
not
surprising,
as
is mean
shownannual
in the precip
measurements
direct
determination
of. K
ourofdimensions
as9,has
been
here.
And
with
their
“second
dimension”
environmental
components
RW
2 It
A also
with
alsofor
shows
thatPDJ
ů Tropical
significant
weightings
of
leafS )second
2 contains
S.
Patiño
etthe
al.:and
tree
trait
The
most
significant
relationships
between
the
PCA
site
axis
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
mation
Information
(Table
S2A).
(Table
As
for
S2A).
Table
As
1,
for
the
Table
SMA
1,
slopes
the
SMA
reslopes
redetail
(including
confidence
intervals)
in
the
Supplementary
x
10
10
10
gether,
Table
3
lists
correlations
and
SMA
slopes
for
the
enillustrates
that
many
traits
seem
to
be
“shared”,
especially
ciation,
but
ciation,
with
examination
but
with
examination
of
Table
4
of
also
Table
suggesting
4
also
suggesting
that
that
panel
of Fig. 9, that
PAdimension
alsotions
showwith
strong
assoFigure
7 shows
the majormay
components
the
three
major
derived
dimensions
well variables
haveof
been
different.
Neverthe(hardly second
likely to
be orthogonal
toůthe
first
in
any
foliar
[C] and M
2 and
level
that,
individually,
were all strongly
correlated
scores
of
Table
4, species)
and
previously
calculated
soil
and
climate
extent
log
(`
).also
The
slopes
observed
(−0.26
to4.
−0.41)
relationship
flect
relationship
yfertility
↔
with
yseem
↔
the
x,above,
xtotraits
with
ashave
the
the
column
x
asrecognised
the
headers
column
headers
Information
(Table
S2A).
As
for of
Table
1,This
the
SMA
slopes
reA
any
given
with
the
shown
in
Table
does
not
been
before.
It
is
thus
here
10
vironmental
effects
with
this
information
provided
in
more
hthe
low
andthe
high
sites
tofor
any
given
for
any
species,
given
both
species,
Φcorrelaboth
and
ρwith
Φw
and
decline
ρwresults
alsosoil
with
decline
with
ciation,
but
examination
of
Table
4 also
suggesting
that
CPCs
and
their
of
inall
diagrammatic
form.
less,
as x,
discussed
five
identified
relate
in
some
case)
most
likely
simply
reflecting
fertility
effects
onThis
fo-Itare,
LS
LS
foliar
[Mg].
is
therefor
with
mean
annual
precipitation
(P
)
viz.
positive
M
which
isoverlap
an
important
factor
for
all
three
,
A
A
PDJ
RW
any
given
species)
with
the
results
shown
inKe
T
does
not
seem
to xhave
been
recognised
before.
Itthat
isrelationships
thus
here
characteristics
of
the
same
sites
are
shown
9.
the
The
most confidence
significant
between
the
site
axis
however,
much
less
than
for
the
associated
slopes
for
theof
taxhe y and
being
thethe
y slopes
being
row
labels.
the
For
labels.
the
structural
For
structural
traits,
the
traits,
flectrow
the
relationship
ywith
↔
x,
with
the
asthe
the
column
headers
shows
0.33
ofwell
the
total
variation
inFig.
the
11ρFirst,
traits
examined
detail
(including
intervals)
in
the
Supplementary
ions
SMA
for
the
enincreasing
increasing
precipitation
precipitation
and,
somewhat
and,
somewhat
counter
intuitively,
counter
intuitively,
for
any
given
species,
both
ΦinPCA
and
also
decline
with
illustrates
that
many
traits
seem
to the
be
“shared”,
especially
way
to
previously
identified
trait
groupings;
though
in
some
liar
[P]
as
already
documented
by
Fyllas
et
al.
(2009)
denoted
as
.
second
panel
Fig.
9, tha
LS
w
tions
foliar
[C]
and
M
and
a
negative
correlation
with
PFL
A
.
Also
occurring
in
(
∩
)
and
of
the
and
shows
that
0.33
of
the
total
variation
in
the
11
tra
we
top
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
FW
PDJ
RW
scores
of
Table
4,
and
previously
calculated
soil
and
climate
onomic
components
as
listed
in
Table
1
(−0.37
to
−0.72).
1
most significant
relationships
relationships
are
allyfive
negative
are
all
and
negative
appear
and
bedenoted
as
. not
andin
the
being
the
row
labels.
For
structural
the
be
by
firstand,
PCA
axis
ρx an
imPFL
Information
Asexplained
Table
1,below.
the SMA
slopes
re-(ů 1 ) with
issignificant
information
provided
more
increasing.
with
(Table
increasing.
increasing
precipitation
somewhat
counter
intuitively,
ciation,
but
with
examina
foliar
It
is appear
therefore
not
surprising,
as could
isS2A).
shown
inforthe
cases
example
with
Sand
and
M
inbe)the
previously
and
considered
further
Overall
the
eigenvectors
selected,
all
of
which
wetraits,
beA
PFL
M same
which
is(as
an
important
factor
for
all[Mg].
three
of
, with
PDJ
RW
sign
isfor
[C],
but
with
[P]
[Mg]
varying
in
opposite
could
beFig.
explained
bythe
theinfirst
PCA
axis
(ů 1 )inw
axisbeof portant
Fyllas
etcontributor
al.
(2009),
thethe
latter
considered
apositively
strong
characteristics
of
the
same
sites
are
shown
in
9. First,
nintervals)
ρx tween
andAlog
[P],
and
log
[Ca],
[P],
log
log
[Ca],
[K]
and,
log
to
[K]
a
lesser
and,
to
a
lesser
most
significant
relationships
are
all
negative
and
appear
and
this
also
relating
to
foliar
Overall
the
five
eigenvectors
selected,
all
of
which
we
bexthe
flect
the
relationship
y
↔
x,
with
the
x
as
column
headers
inρspecifically
Supplementary
10
10
10
10
10
with
increasing.
for
any
given
species,
bo
second
panel
of
Fig.
9,
that
ů
and
P
also
show
strong
assolieve
to
be
physiologically
relevant
(see
Supplementary
Inlinked
through
the
species
dependent
variance2
A
directions
with
respect
to
M
these
two
trait
dimensions.
portant
contributor
and
this
also
relating
positi
.
Also
occurring
in
(
∩
)
and
of
the
and
ate
.
The
strong
tegrated
measure
of
soil
fertility
and
denoted
A for
Finally,
as
Finally,
in
Fyllas
as
in
et
Fyllas
al.
(2009)
et
al.
we
(2009)
show
we
values
show
for
values
for
3.7
Principal
component
analysis
of
environmental
eftop
panel
of
Fig.
9
shows
ů
as
a
function
of
the
first
soil
PCA
FW
PDJ
RW
F
t
log
extent
(`
).
log
The
(`
slopes
).
The
observed
slopes
(−0.26
observed
to
(−0.26
−0.41)
to
are,
−0.41)
are,
1
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
[C]
and
M
,
but
negatively
with
all
foliar
nutrients
examined
lieve
to
be
physiologically
relevant
(see
Supplementary
InA
and
the
y
being
the
row
labels.
For
the
structural
traits,
the
x
s for Table
the
slopes
reA
10 A 1,covariance
10 SMA
10
10
10
4.3
Coordinated
trait
responses
to
environmental
increasing
precipitation
a
ciation,
but
with
examination
of
Table
4
also
suggesting
that
formation),
accounted
for
0.68
of
the
total
variance
for
both
matrix.
It would
be of
great interest
to see how relationship observed suggests a[C]
and
M
,the
but
negatively
with
all
foliar
same
is
[C],
but
with
[P]
and
[Mg]
varying
indirection
opposite
so
strong
integrated
response
Kendall’s
Kendall’s
partial
τFyllas
(denoted
partial
τ Finally,
τ(denoted
)fects
for
all
τThe
)latter
for
of
all
interest
traits
of
as
Aas
as
in
Fyllas
etappear
al.
(2009)
we
values
for nutrie
axis
of
et
al.
(2009),
the
considered
ainterest
strong
in-show
and
and
in
the
relapwith
ptraits
ver, much
however,
less
much
than
for
less
the
than
associated
for
the
slopes
for
slopes
thesame
taxfor
the
taxextent
log
(`FW
).
The
slopes
observed
(−0.26
tosignificant
−0.41)
are,
and
also
.
second
axis
of
PCA
on
the
plot
efformation),
accounted
for
0.68
of
the
total
variance
for
both
RW
most
relationships
are
all
negative
and
beAassociated
with
the
xIntersecting
assign
the
column
headers
10
variability
with
increasing.
for
any
given
species,
both
Φ
and
ρ
also
decline
with
low
and
high
fertility
soil
species.
the with
identified
trait
combinations
varytrait
with
phylogeny
if of
LS
also
.
The
second
of the
o
directions
respect
to
for
these
two
dimensions.
Amazon
trees
fertility,
with
most
nu-of axis
well
as species.
ůand
and
asůw
ůtaxfunctions
ůtropical
functions
offorest
, matrix
,and
,denoted
,soil
P
,toalso
Twith
,Fplesser
P
and
Qtraits
Kendall’s
partial
τTto
(denoted
all
interest
as PCAfec
.)asignificant,
The
strong
tegrated
measure
ofassoil
fertility
tivestructural
toasMlisted
Φin
. listed
Although
with
high
estimated
standard
1well
2log
1asand
2
F
Tof
aand
F
afor
a accounting
mic
onomic
components
asTable
1Ain
(−0.37
Table
to
1 a(−0.37
−0.72).
toassociated
−0.72).
much
less
than
for
the
slopes
the
fects
correlation
(ů
isand
low
and
high
fertility
soil
A ishowever,
LS
tween
and
log
aaτQ
els.components
For
the
theM
2 )aT
x 100)
increasing
precipitation
and,
somewhat
counter
10 [P],intuitively,
10 [Ca], log10 [K] and,
Thetraits,
first
three
axes
species
scores
to ρ±for
they trace
back
through
evolutionary
time(normalised
as discrete
combifects
correlation
matrix
(ů
)
is
also
significan
Especially
given
the
strong
relationships
between
ρ
and
the
co
trients
increasing,
and
with
[C]
and
ρand
decreasing
Table
5.
in
Table
Here
5.
the
Here
calculated
the
calculated
of
value
of
associτp of
associwell
as
ůvalue
and
asand
functions
, Finally,
Tas
and
relationship
observed
suggests
aůfoliar
strong
integrated
response
xof
p
as inQaFyllas e
1variance,
2τ
F , Tvariance
a2, Px
a7asonomic
components
astrait
listed
in
Table
1in(rela(−0.37
−0.72).
Intersecting
and
inalso
the
same
direction
for
0.25
ofto
the
with
negative
weightings
The
first
three
axes
species
scores
(normalised
±
100)
extent
log
The
slopes
observed
(−0.26
tosubstantial
−0.41)
are,
ps are all
negative
and
appear
beRW
S.asPatiño
al.:
Tropical
tree
As
evidenced
by
the
0.3–0.4
portion
the
total
error
part
ofet
,FW
we and
have
included
LA in
∩to
with
increasing.
10 (`A ).
FW
RW
are
plotted
against
each
other
indimensions
Supplementary
Information
nations.
Nevertheless,
these
ambitions
may
be
confounded
for
0.25
of
the
variance,
with
substantial
foliar
cation
environmental
components
(Fig.8)
,
it
was
ofτnegativ
20
increases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
ated
probability
ated
probability
an
giving
indication
an
indication
of
the
effect
of
the
of
each
effect
of
each
in
Table
5.
Here
the
calculated
value
of
τ
and
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuPrincipal
3.7
Principal
component
component
analysis
of
analysis
environmental
of plotted
environmental
efefFgiving
1
Kendall’s
partial
(deno
p
tive
to
M
is
Φ
.
Although
with
a
high
estimated
standard
for
M
,
foliar
[C]
and
(and
to
a
lesser
extent
foliar
[P])
are
against
each
other
in
Supplementary
Information
however,
much
less
than
for
the
associated
slopes
for
the
tax[Ca],
log
[K]
and,
to
a
lesser
A
LS
sociated A
with the 8LS , ρx and “plot effect” terms (Fig. 2),associ10
10FW
Fig.
S1. showing
This
shows
required
lack
of any
systematic
),traits
this such
also
that itthe
varies
in thein
opposite
direcby
as
M
being
significant
almost
all
dimenA
for
M
,
foliar
[C]
and
(and
to
a
lesser
exte
additional
interest
to
see
if
coordinated
structural/leaf
bioAs
versus
of
0.63
is
greater
than
for
any
of
the
original
varisoil/environmental
soil/environmental
parameter
parameter
after
accounting
after
accounting
for
the
effor
the
efated
probability
giving
anA−0.72).
of well
the
effect
of
trients
increasing,
and
foliar
[C]
and
ρindication
decreasing
as
F
3.7
component
analysis
ofItetenvironmental
efasThis
and
ů 2 as fun
Finally,
as L
in
Fyllas
al.
(2009)
we
show
values
for
xindependent
being
bywith
positive
weightings
forinfoliar
[Mg]
inů 1parFig.
S1.
This
required
lack
of
systematic
onomic
components
as any
listed
inwith
Table
1 (−0.37
to
sfects
observed
(−0.26
to
−0.41)
are,
values
ofbalanced
all
these
traits
are
not
of
a each
errorfects
assions.
part
ofnot
, Principal
we
have
also
included
in
(shows
∩the
any
given
species)
thebeing
results
shown
Table
4.where
does
seem
to
been
recognised
is
thus
here
correlations
between
the
species
scores
as RW
expected
for
the
FW
Abefore.
As
discussed
at
the
start
of
Sect.
4.2.6
this
may
be
balanced
by
positive
weightings
for
foliar
cf.
.
tion
relative
to
M
and
Φ
for
chemical
responses
to
the
environment
exist
for
Amazon
forables
examined
by
Fyllas
et
al.
(2009),
the
highest
of
which
fect
of
the
fect
other
of
the
four.
other
Taking
four.
into
Taking
account
into
to
account
the
potential
to
the
potential
soil/environmental
parameter
after
accounting
for
the
efincreases.
Interestingly,
the
Kendall’s
τ
for
this
plot
of
ů
RW
FW
A
LS
in1examined
5. Here thethe
c
Kendall’s
partial
τ (denoted
for
traits
interest
as the
F all
ticular,
also
with
contributions
ΦLS
and
ρTable
between
the τspecies
scores
asofexpected
for
the
p ) model.
the associated slopes
forfrom
thefects
taxx.
species
is but
growing
and
with
there being
environmenshows
that
0.33
of
total
variation
infrom
the strong
11
traits
output
any
good
fitcorrelations
of
asurrogates
principle
components
this
alsovariations
showing
itAvaries
in
the
opposite
direcbecause
Mbetween
can
be
for
variations
in
denoted
as strong
.in
FW ),the
PFLthat
ticular,
but
also
with
contributions
from
ΦLS the
ana
est.
We
therefore
undertook
a
PCA
analysis
of
full
plotgiving
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
cially
Especially
given
strong
given
the
relationships
relationships
ρ
between
and
the
ρ
and
the
confounding
confounding
effects
of
effects
spatial
of
autocorrelation
spatial
autocorrelation
(Fyllas
et
(Fyllas
al.,
et
al.,
fect
of
the
other
four.
Taking
into
account
to
the
potential
versus
of
0.63
is
greater
than
for
any
of
the
original
varix1 from
xasgood
ated
probability
well
as
ů
and
ů
functions
of
,
,
T
,
P
and
Q
F
3.7
Principal
component
analysis
of
environmental
efoutput
any
fit
of
a
principle
components
model.
2
F
T
a
a
a
d in Table 1 (−0.37
to
−0.72).
tal
correlations
between
all of
log[N],
log[P],
log[Ca]
could
be explained
by theρfirst
PCA
axis
(ů 1 ) with
ρx an
imClearly
arelationships:
wide
range
of environmental
combinations
of
these
three
dix and
tissue
density,
leaf
thickness
or
and
similarly
from
the
Overall
the
five
eigenvectors
selected,
of2009)
which
wetrait
be3.6
Bivariate
effects
correlation
matrix
H
and
S both
of param
all
also
shows
that
the
ůrelationships
weightings
of
leafcf.
. of, all
tion
relative
to
MEspecially
and
ΦLS
for(Fig.8)
r cation
foliar
environmental
cation
environmental
components
components
, both,
it
(Fig.8)
was
itcomponents
was
of
we
2009)
only
consider
we
only
consider
with
p(2009),
≤significant
0.01
with
or
p(excluding
≤
better.
0.01
orofbetter.
given
the
strong
relationships
between
ρvalue
the
confounding
effects
of the
spatial
autocorrelation
(Fyllas
et al.,
ables
examined
by
Fyllas
et
al.
highest
which
max
2 contains
A
RW
FW
soil/environmental
x and
inBut
Table
5.Fig.
the
calculated
of
and
associfects
Clearly
a Here
wide
range
of combinations
of τthese
three
trait
diprelationships
and
log[K].
This
results
in
this
structural
parameter
alignportant
contributor
and
this
also
relating
positively
to
foliar
mensions
can
occur.
with
8a
also
showing
that
it
is
lieve
to
be
physiologically
relevant
(see
Supplementary
Indiscussion
in
Sect.
4.1.1
to
4.1.5
above,
variations
in
[C],
which
were
considered
to
be
environmentally
invariant
for
eff
level
variables
that,
individually,
were
all
strongly
correlated
ional
additional
interest
to
interest
see
if
coordinated
to
see
if
coordinated
structural/leaf
structural/leaf
biobioAs
for
the
As
(full)
for
Kendall’s
the
(full)
Kendall’s
τ
shown
in
τ
Fig.
shown
9,
Table
in
Fig.
5
9,
suggests
Table
5
suggests
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
better.
was
0.56
for
foliar
[P].
Comparison
with
Fyllas
et
al.
(2009)
fect
of
the
other
four.
Ta
ated
probability
giving
an
indication
of
the
effect
of
each
analysis of environmental
efmensions
can
occur.associated
But with Fig.
8a ing
also
showing
thatnegatively
it iselemental
3.8
Relationship
between
effect(including
PCAs
and
[C]
and
M
withconcentrations
all plot
foliar nutrients
examined
itself
along
[C]
(generally
speaking)
only
species
typically
with
A , butwith
data
from
both
low
and
fertility
sites
toformation),
for
0.68
of high
the
total
variance
for
both
8responses
LAenvironment
and
are
all
potentially
attributable
to
a
range
of
3.6 Considering
Bivariate
relationships:
environmental
components
with
mean
annual
precipitation
(P
)
viz.
positive
correlaLS
mical responses
chemical
to ,the
toaccounted
the
environment
exist
for
Amazon
exist
for
forAmazon
forthe
to
be
the
superior
to
be
predictors
superior
predictors
than
the
individual
than
the
individual
variables,
variables,
additional
interest
to
see
if
coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
also
shows
that
the
ů
contains
significant
weightings
of
leafA
3.8
Relationship
between
plot
effect
Especially
given
the
strong
relationships
between
ρ
and
the
confounding
effects
of
sp
soil/environmental
parameter
after
accounting
for
the
ef2
(generally speaking) only species typically
associated
withsecond in
soil/climate
and also
with . weighting)
The
axis
the PCA
on the plot PCA
efwith
afoliar
negative
theofx first
environmental
gether,
Table
3alists
correlations
and
SMA
slopes
the
enlowhigh
andunderlying
high
fertility
soil
species.
and
.environmental
fertility
soils
have
scores
for
both
different
causes.
isthe
also
probably
for
this
reason
PDJ
RW
tions
with
[C]
and
M
and
a[K]
negative
withwevariables,
We therefore
est. We
undertook
therefore
undertook
PCA
analysis
a that
PCA
ofIt
analysis
the
full
of
plot
the
fullfor
plot
the
only
exception
the
only
exception
being
Tthe
being
In
that
Tacase,
.superior
InAthat
[N],
case,
[N],
ρthan
[K]
ρw
chemical
responses
to
environment
exist
for
Amazon
forto
be
predictors
the individual
level
variables
that,
individually,
were
all
strongly
correlated
a .potential
wcorrelation
soil/climate
foliar
cation
components
(Fig.8)
,and
it was
ofand
2009)
only consider re
fect
of
the
other
four.
Taking
into
account
to the
fects
correlation
matrix
(ů
)
is
also
significant,
accounting
and
.
high
fertility
soils
that
have
scores
for
both
axis,
ů
(Table
4),
which
was
itself
closely
correlated
with
a
2
RW
1PDJ
vironmental
effects
with
this
information
provided
inof
more
Considering
data
from
both
low
and
high
fertility
sites
toThe
first
three
axes
species
scores
(normalised
tothree
±
100)
Figure
7
shows
the
major
components
of
the
major
that
considerable
ambiguity
exists
between
different
studies
foliar
[Mg].
It
is
therefore
not
surprising,
as
is
shown
in
the
ts correlation
effects
correlation
matrix
(excluding
matrix
(excluding
H
and
H
S
both
and
of
S
both
all
show
relationships
all
show
relationships
not
present
not
when
present
regressing
when
regressing
the
plot
the
plot
est.
We
therefore
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
case,
[N],
[K]
and ρKendall’s
with
mean
annual
precipitation
(P
)
viz.
positive
correlamax
max
additional
interest
to
see
if
coordinated
structural/leaf
bioAs
for
the
(full)
a
w
relationships
between
ρ
and
the
confounding
effects
of
spatial
autocorrelation
(Fyllas
et
al.,
A
x
for
0.25
of
the
variance,
with
substantial
negative
weightings
Figure
7
shows
the
major
components
of
the
three
major
PCA
of
soil
chemical
and
physical
properties
(Fyllas
et
al.,
detail
(including
confidence
intervals)
in
Supplementary
gether,
3toof
lists
correlations
and
SMA
slopes
for of
the
enare
plotted
against
each
other
in Supplementary
Information
CPCs
their
overlap
of
traits
inthe
diagrammatic
form.
inTable
terms
the
significance
(or
even
the
sign)
some
bivarisecond
panel
of
Fig.
9,
that
ůexist
Pnot
also
show
strong
asso-to bethe
hcomponents
were
which
considered
were
considered
be
environmentally
to
environmentally
invariant
for
invariant
effect
for
PCs
effect
asSThis
dependent
PCs
as
dependent
variables.
correlation
matrix
(excluding
H
and
both
all
show
relationships
present
when
regressing
plot predi
tions
withpof
foliar
[C]
andvariables.
M
negative
correlation
with
2 aand
A
chemical
responses
to
the
environment
for
Amazon
forthe
superior
max
(Fig.8)
,effects
itand
was
ofbe
2009)
we
only
consider
relationships
with
≤
0.01
or
better.
A and
forThe
MA
, foliar
[C]
and
(and
to dimension,
a lesser
extent
foliarto
[P])
most
significant
relationships
between
the
PCA
site
axis
CPCs
and
their
overlap
ofof
traits
inciation,
diagrammatic
form.
2009;
et This
al.,
2010).
This
relating
what
Information
(Table
S2A).
As
forfor
Table
1,
the
SMA
slopes
revironmental
effects
with
this
information
provided
insource
more
Fig.
S1.which
This
shows
the
required
lack
of
any
systematic
illustrates
that
many
traits
seem
to
be
“shared”,
especially
but
examination
ofThe
Table
4 also
suggesting
that
ate
relationships.
For
example,
if
the
variwere
considered
to
beprimary
environmentally
invariant
for
effect
as
dependent
variables.
foliar
[Mg].
It Quesada
iswith
notanalysis
surprising,
as
issignificant
shown
in
the
We
therefore
undertook
aPCs
PCA
of most
the
full
plot
theand
only
exception
coordinated
structural/leaf
bioAs
the
(full)
Kendall’s
τest.
shown
in
Fig.
9,
Table
5therefore
suggests
relationships
betweenbeing
the P
being
balanced
by
positive
weightings
for
foliar
[Mg]
in
parscores
of
Table
4,
and
previously
calculated
soil
climate
illustrates
that
many
traits
seem
to
be
“shared”,
especially
seems
to
be
a
soil
fertility
mediated
effect,
bears
some
resemflect
the
relationship
↔were
x,the
with
the
xthe
as
the
column
headers
detail
(including
confidence
intervals)
ininbe
Supplementary
correlations
scores
asthree
expected
for
thethe
for panel
any matrix
given
species,
Φ
and S
ρwboth
also
decline
with
ation
in
Lwhich
andbetween
Myan
tospecies
be
association
with
(this
of Fig.
9, that ůboth
PLSA
also
show
strong
assoA
A important
FW
effects
correlation
(excluding
H
and
of
all
show
relationships
no
M
is
factor
for
all
of
,second
nvironment
exist
for
Amazon
forthe
to
superior
predictors
than
variables,
2 and
scores
of
Table
4,
and
previously
calculated
soi
max
A
PDJ
RWindividual
ticular,
but RW
alsobut
contributions
from
ΦLS in
and
ρeffect
. First,
characteristics
ofwith
the
same
sites
are
shown
Fig.
x9.
aTable
more
easily
discernible
on ρthe
blance
to
andbeing
the
y(Table
being
the
row
labels.
For
the
structural
traits,
the
x . dependent v
M
which
is components
anslopes
important
factor
for
all
three
of
,with
Information
S2A).
As
for
1,principle
the
SMA
reoutput
from
any
good
fitTable
of
athe
model.
A
PDJ
RW
increasing
precipitation
and,
somewhat
counter
intuitively,
ciation,
but
with
examination
of
4
also
suggesting
that
similar
in
many
ways
to
light
acquisition
axis
idenwhich
were
considered
to
be
environmentally
invariant
for
effect
PCs
as
k a PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
case,
[N],
[K]
and
ρ
characteristics
of
the
same
sites
are
shown
in
Fig
a
w
and FWrelationships
. Also occurring
in
( PDJand
∩ appear
of the Astop
panel of Fig.
9 shows
ů1 such
as a function
of effect
the first
soil
RW ) and
mentioned
inofSect.
a fertility
ρx PCA
has
significant
are
negative
beflectmost
the
relationship
y↔
x,Cao
with
the
xall
as
the
column
headers
Clearly
wide
range
of(2009)
combinations
ofAlso
these
three
traitin
diwith
∩increasing.
for
given
species,
both
ΦLS4.1.1,
and
with on
by Zhang
for
dipterocarps
growing
in
a( any
and
occurring
andplot
the
excluding
Htified
and
Sasign
both
of
show
relationships
not
present
when
regressing
w also
topρthe
panel
ofdecline
Fig.
9 shows
ůstrong
of the fi
FW .[Mg]
PDJ
RW )the
max same
1 as a function
isand
[C],
butall
with
[P]
and
varying
in opposite
axis
of
Fyllas
et
al.
(2009),
latter
considered
a
inbeen
seen
before
as
mediated
by
soil
phosphorus
availability
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
and theChinese
ymensions
being
thecan
row
labels.
For
the
structural
traits,
the
But
with
also
showing
that
itand
is [Mg] precipitation
x invariant
10occur.
10
108a
and, somewhat
counter
intuitively,
common
garden)
then
a Fig.
positive
association
between
same
sign
is
[C],
but
with
[P]increasing
varying in opposite
be environmentally
for
effect
PCs
as
dependent
variables.
axis
of
Fyllas
et
al.
(2009),
the
latter
considered
directions
with
respect
to
M
for
these
two
trait
dimensions.
strong
tegrated
of
soil
fertility
andshow
denoted
A (−0.26 to −0.41) are,
as inmeasure
Fyllas
etbetween
al. (2009)
we
for and
F . The
3.8
Relationship
plot
effect
PCAs
log (`
The slopes
observed
for
eucalypt
and mangrove
(Thomas
et
al., values
2005;
Lovelock
mostextent
significant
relationships
are
allspecies
negative
and
be-to canopy
speaking)
only
A ).
with
Finally,
increasing.
directions
withappear
respect
MAwith
for these
two
trait dimensions.
M(generally
withtypically
leaves
ofassociated
upper
tegrated
of
soil fertility
and denoted F
A and10LA would be expected,
relationship
observed
suggests
a measure
strong
integrated
response
Kendall’s
partial
τ
(denoted
τ
)
for
all
traits
of
interest
as
Intersecting
and
and
in
the
same
direction
relasoil/climate
p
however,
much
less
than
for
the
associated
slopes
for
the
taxRW
FW
et
al.,
2006).
Although
working
with
Brazilian
savanna
trees,
tween
ρ
and
log
[P],
log
[Ca],
log
[K]
and,
to
a
lesser
xhigh
10
10 and
10 than
andFW
. in the same direction relafertility
that
havethicker
scores
for both
trees
being
bothsoils
larger
those
for
trees
lower
relationship
observed
suggests
a
strong integra
PDJ
RW
Intersecting
and
and
RW
of
Amazon
tropical
forest
trees
to
soil
fertility,
with
most
nuwell Bucci
asasů 1in
and
ů(2006)
of we
tive
toThe
MAslopes
is Φ
.observed
Although
with
atohigh
standard
Finally,
Fyllas
al. (2009)
for Qto
2 asetfunctions
F , show
T , Tvalues
a, P
a and
a phosonomic
components
asLS
listed
in Table
1 (−0.37
toestimated
−0.72).
extent
log10Figure
(`
(−0.26
are,
et
al.
found
it of
was
nitrogen
(as
opposed
7 canopy
shows
the
major
components
of
the
three
down
inA ).the
(as
was
found
be−0.41)
case
for major
temAmazon
tropical
forest
trees
to
soil fertility, w
tive
to MtoA is
Φthe
withina Table
highpartial
estimated
standard
LS . Although Kendall’s
trients
increasing,
and
with
foliar
[C]
and
ρ
decreasing
as
5. τHere
the calculated
ofof τinterest
andρxxassoci(denoted
τp ) for
allvalue
traits
as
p in
however,
much
less
thanof
for
the
slopes
for
the taxinduced
changes
and
Kfoliar
error
as their
part
,ofwetraits
haveinalso
included
LA
in1998).
( This
S and[C] and ρ d
FWassociated
RW ∩ phorus) fertilisation that
CPCs
and
overlap
diagrammatic
form.
trients
increasing,
and
with
perate
deciduous
trees,
for
example,
by
Niinemets,
x
increases.
Interestingly,
τand
for
this
plotaxis
of ů 1
ated
probability
an
indication
of
of
error
asofpart
of−0.72).
have
also
L
ingiving
(functions
∩
F
well
asincluded
ůin
and
ůcase
as
of F ,theTKendall’s
, Tthe
Q
3.7 components
Principal component
analysis
environmental
FW , we efA
RW
1 their
2significant
a , Peffect
aattendant
aeach
The
most
relationships
between
the
PCA
site
onomic
as also
listed
intraits
Table
1 (−0.37
with
N-fertilisation
causing
increases
that
many
seem
beto“shared”,
especially
Interestingly,
the variKendall’s τ for t
), this
showing
that
varies
the weight
opposite
direcOnillustrates
theFW
other
hand,
where
foliar
Nitto
and/or
Pindry
conF increases.
versus
of
0.63
is
greater
than
for
any
of
the
original
soil/environmental
parameter
after
accounting
for
the
efF
init Table
5.8the
Here
the
calculated
value 4).
of
τp and soil
associfects
scores
Table
4,direcandhere
previously
calculated
and climate
this also via
showing, that
in
opposite
in
not
detected
(Table
FW ),variation
LSof
versus
0.63
ishighest
greater
for any of the
centrations
are
the
main
source
avaries
F of to
MAtion
which
is an
factor
for all
three
relative
toimportant
MA
and
ΦLSofof
for
cf. of
.RW
ables
examined
by
Fyllas
etofal.
(2009),
the
ofthan
which
PDJ ,then
RW
RW
FWeffect
of
the other
four.
Taking
into
account
the
potential
ated
probability
giving
anthe
indication
the
effectin
of
each
3.7 Principal
component
analysis
environmental
characteristics
of
same
sites
are
shown
Fig.
9. First,
the(2009), the high
tion relative to MA and ΦLS for RW cf. was
.
ables
examined
by
Fyllas
et
al.
FW
0.56
for foliar
[P].
with
Fyllas
al.PCA
(2009)
Especially
strong
relationships
between
ρx )and
effects
of
spatial
autocorrelation
etetsoil
al.,
after
accounting
for
thefirst
efand given
Also
occurring
in ( PDJ
∩ RW
andthe
of soil/environmental
the confounding
fects
top panel
ofparameter
Fig.
9 shows
ůComparison
as
a 0.56
function
of(Fyllas
the
FW . the
1was
for
foliar
[P].
Comparison
3.6 sign
Bivariate
relationships:
environmental
components
also
shows
that
the
ů
contains
significant
weightings
of
leaf- with Fyllas
foliar
cation
environmental
components
(Fig.8)
,
it
was
of
2009)
we
only
consider
relationships
with
p
≤
0.01
or
better.
2
fect
of
the
other
four.
Taking
into
account
to
the
potential
same
is
[C],
but
with
[P]
and
[Mg]
varying
in
opposite
axis of Fyllas
et al. (2009),
the latter
considered
acontains
strong inBiogeosciences, 9, 775–801,3.6
2012Bivariate relationships: environmental
www.biogeosciences.net/9/775/2012/
components
also
shows
that
the
ů
significant
weigh
2
level
variables
that,
individually,
were
all strongly
additional
interest
torespect
see
iftocoordinated
structural/leaf
As for
the
(full)
Kendall’s
τautocorrelation
shown
in Fig.
9,(Fyllas
Table
5etFsuggests
Especially
given
thewith
strong
relationships
ρx trait
and dimensions.
thebio- confounding
effects
of spatial
directions
MA forbetween
these
two
.al.,
Thecorrelated
strongwere all strong
tegrated
measure
of
soil
fertility
and
denoted
level
variables
that,
individually,
Considering
data
from
both
low
and
high
fertility
sites
towith
meanobserved
annual
precipitation
(P0.01
viz.
positive
correlachemical
responses to the
environment
exist
for- relathe
to
befertility
superior
predictors
than
the
individual
variables,
A ) integrated
foliar
cation
environmental
components
, itAmazon
was
ofboth
2009)
werelationship
only
consider
relationships
with
pstrong
≤
or better.
amean
response
Intersecting
and
and(Fig.8)
in
thefor
same
direction
Considering
data
from
lowenand
high
sites
to- suggests
RW
FW
with
annual
precipitation
(PA ) viz. pos
gether,
Table
3 iflists
correlations
and
SMA
slopes
for the
tions
withtropical
foliar
[C]
and
M
and
a fertility,
negative
correlation
with
est. We
therefore
undertook
a
PCA
analysis
of
the
full
plot
the
only
exception
being
T
.
In
that
case,
[N],
[K]
and
ρ
Ato
additional
interest
to
see
coordinated
structural/leaf
bioAs
for
the
(full)
Kendall’s
τ
shown
in
Fig.
9,
Table
5
suggests
a
w
of
Amazon
forest
trees
soil
with
most
nutive to MA is ΦLS . Although
with aTable
high3estimated
standardand SMA slopes for the engether,
lists correlations
tions with foliar [C] and M and
a negative cor
S. Patiño et al.: Tropical tree trait dimensions
It seems likely that higher foliar [P], especially in combination with the lower MA also associated with ů 1 would
give rise to higher photosynthetic rates on an area basis
(Domingues et al., 2010; Mercado et al., 2011). Thus, with
tropical forest tree hydraulics and photosynthetic capacity
being closely linked (Brodribb and Field, 2000; Brodribb et
al., 2002; Santiago et al., 2004a) the likely increase in KS accompanying a decrease in ρx with improved nutrient status
may serve to help maintain some homeostasis in leaf water
relations, this offsetting the higher rates of water-use per leaf
area that would be expected to accompany any increase in
ů 1 . This suggestion supported by the only modest contribution of to this dimension (Table 4). As to how such a
coordination could occur is currently not clear, although the
greater rates of cambial activity in the wood of higher P status
trees giving rise to a lower ρx might be attributable through
sugar signalling mechanisms (Rolland et al., 2006; Hölttä et
al., 2010), this resulting in less secondary thickening of vessels walls and a higher conduit area (Thomas et al., 2005).
Other elements may also be involved though, for example
effects of calcium and/or potassium on sapwood cambial activity (Fromm, 2010).
The second integrated environmental response dimension
identified, ů 2 , essentially represents an integration of previous observed foliar trait responses to precipitation, viz. increased MA , [C] and and decreased [Mg] as mean annual
precipitation increases as detailed in Fyllas et al. (2009). Although this response to PA seems at odds with the general
observation from inter-species analyses that leaves of more
arid environments should have a higher MA and often with
a higher (Miller et al., 2001; Santiago et al., 2004b) as
discussed by Fyllas et al. (2009) this tendency towards more
structurally rigid leaves at higher PA may reflect different
populations of the same species having different characteristics according to their prevailing environment. An aligned
interpretation is that as severe dry season water deficits become increasingly less of a driving force in determining leaf
lifetimes, leaves of any given species become more “evergreen” in their structural characteristics. And indeed it is
worth noting that the distinction between evergreen and deciduous phenologies for tropical forest trees is a somewhat
arbitrary one (Brodribb and Holbrook, 2005; Williams et al.,
2008). In such an interpretation, an increase in with PA
could be interpreted as either a tendency towards more conservative stomatal behavior in evergreen species where the
precipitation regime is not strongly seasonal (Lloyd and Farquhar, 1994) or, alternatively to an increased resistance to
CO2 diffusion within higher MA leaves due to a higher cell
wall resistance (Syvertsen et al., 1995).
Although not emerging as any sort of integrated response
through the PCA analysis of the derived environmental effects, the temperature responses of [N], [K] and ρx are all
also of note; these have already been considered separately
by Fyllas et al. (2009) and Patiño et al. (2009).
www.biogeosciences.net/9/775/2012/
795
5
Conclusions
Extending beyond a simple bivariate analysis approach, this
study has separated environmental from taxonomic effects
for a range of structural and physiological traits for Amazon forest trees then using Common Principal Component
Analysis to reveal as many as five discrete integrated axes
of taxonomic variation. The relative weightings of the axes
varies between low and high fertility soil associated species.
The first component (accounting for the highest proportion
of the total variance in the dataset) was not the classic “leaf
economic spectrum”, but rather relates mostly to variations
in leaf construction costs per unit dry weight. The leaf economic spectrum was the second most important dimension
identified in terms of variance accounted for, with our results suggesting that it also involves differences in leaf size
as well as in leaf area: sapwood area ratios. Our third dimension brings together several structural traits, including
species specific maximum height, individual leaf areas, leaf
mass per unit area and xylem density and leaf magnesium
concentrations. The fourth and fifth dimensions were interpreted as relating to a seed size/leaf area trade-off and shade
tolerance characteristics respectively.
Several traits, in particular leaf mass per unit area, foliar
carbon content and xylem density had significant weighting
on many axes of variation, this being attributed to their somewhat ambiguous “proxy” nature for a range of underlying
and more fundamental plant physiological properties. In particular, variations in twig xylem density may arise as a consequence of differences in a range of different underlying phenomena and with its generally poor correlation with other
plant traits suggesting that it may not be as good a proxy for
plant hydraulic conductivity as once thought.
Significant effects of environment on many plant traits
were also identified. Some of these integrated into discrete dimensions of variation and with discrete but different
changes being associated with variations in soil fertility versus differences in mean annual precipitation. Whether these
differences relate to strict “environmental effects” or reflect
systematic patterns in intra-specific trait variation with soils
and/or climate remains to be established.
Supplementary material related to this
article is available online at:
http://www.biogeosciences.net/9/775/2012/
bg-9-775-2012-supplement.pdf.
Biogeosciences, 9, 775–801, 2012
796
Acknowledgements. We thank our many South American collaborators, also involved in the work described in Fyllas et al.
(2009) and Patiño et al. (2009), for help with logistics and
practical help in the field. Much of the data collection phase of
this work (2001-2004) was supported through the EU FP5 “LBACarbonsink” project with subsequent data analyses supported by
funding through the UK Natural Environment Research Council
“QUERCC” and “TROBIT” consortia. We also thank David
Warton (University of New South Wales) for pointing out to us
the possibilities of CPC analysis and Patrick Phillips (University
of Oregon) for making his CPC model estimation and evaluation
programs freely available. Shiela Lloyd assisted with manuscript
preparation.
Edited by: A. Arneth
The service charges for this open access publication
have been covered by the Max Planck Society.
References
Ackerly, D. D.: Functional strategies of chaparral shrubs in relation
to seasonal water deficit and disturbance, Ecol. Monogr., 74, 25–
44, 2004.
Asner, G. P., Martin, R. E., Tupayachi, R., Emerson, R. Martinez,
P., Sinca, F., Powell, G. V. N. Wright, S. J. and Lugo, A. E.:
Taxonomy and remote sensing of leaf mass per area (LMA) in
humid tropical forests, Ecol. Appl., 21, 85–98, 2011.
Augspurger, C. K.: Seedling survival of tropical tree species: interaction of dispersal distance, light gaps and pathogens, Ecology,
65, 1705–1712, 1984.
Baker, T. R., Phillips, O. L., Laurance, W. F., Pitman, N. C. A.,
Almeida, S., Arroyo, L., DiFiore, A., Erwin, T., Higuchi, N.,
Killeen, T. J., Laurance, S. G., Nascimento, H., Monteagudo,
A., Neill, D. A., Silva, J. N. M., Malhi, Y., López Gonzalez,
G., Peacock, J., Quesada, C. A., Lewis, S. L., and Lloyd, J.:
Do species traits determine patterns of wood production in Amazonian forests?, Biogeosciences, 6, 297–307, doi:10.5194/bg-6297-2009, 2009.
Baltzer, J. L. and Thomas, S. C.: A second dimension
to the leaf economics spectrum predicts edaphic habitat
association in a tropical forest, PLoS ONE, 5, e13163,
doi:10.1371/journal.pone.0013163, 2010.
Bangerth, F.: Calcium related disorders of plants, Ann. Rev. Phytopathol., 17, 97–122, 1979.
Baraloto, C., Paine, T. C. E., Poorter, L., Beauchene, J., Bonal, D.,
Domenach, A.-M., Hérault, B., Patiño, S., Roggy, J.-C., and
Chave, J.: Decoupled leaf and stem economics in rain forest
trees, Ecol. Lett., 13, 1338–1347, 2010.
Bates, D. and Sarkar, D.: lme4: Linear Mixed–Effects Models Using S4 Classes. R package version 0.999375–27, 2007.
Brodribb, T. J. and Field, T. S.: Stem hydraulic supply is linked
to leaf photosynthetic capacity: evidence from New Caledonian
and Tasmanian rainforests, Plant Cell Environ., 23, 1381–1388,
2000.
Brodribb, T. J. and Holbrook, N. M.: Leaf physiology does not
predict leaf habit; examples from tropical fry forest, Trees, 19,
290–295, 2005.
Biogeosciences, 9, 775–801, 2012
S. Patiño et al.: Tropical tree trait dimensions
Brodribb, T. J., Holbrook, N. M., and Gutiérrez, M. V.: Hydraulic
and photosynthetic co-ordination in seasonally dry tropical forest
trees, Plant Cell Environ., 25, 1435–1444, 2002.
Bucci, S. J., Goldstein, F. C., Meinzer, F. C. Scholz, A., Franco, A.
C. and Bustamante, M. C.: Functional convergence in hydraulic
architecture and water relations in tropical savanna trees: From
leaf to whole plant, Tree Physiol., 24, 891–899, 2004.
Bucci, S. J., Scholz, F. G., Goldestein, G., Meinzer, F. V., Franco,
A. C., Campanello, P. I., Villa Lobos-Vega, R., Bustamante, M.,
and Miralles-Wilhelm, F.: Nutrient availability constrains the hydraulic architecture and water relations of savanna trees, Plant
Cell Environ., 29, 253–267, 2006.
Casper, B. B., Heard, S. B., and Apanius, V.: Ecological correlates
of single–seededness in a woody tropical flora, Oecologia, 90,
212–217, 1992.
Cavaleri, M. A., Oberbauer, S. F., Clark, D. B., Clark, D. A., and
Ryan, M. G.: Height is more important than light in determining
leaf morphology in a tropical forest, Ecology, 91, 1730–1739,
2010.
Cavender-Bares, J. and Holbrook, N. M.: Hydraulic properties and
freezing-induced cavitation in sympatric evergreen and deciduous oaks with contrasting habitats, Plant Cell Environ., 24, 1243–
1256, 2001.
Cavender-Bares, J., Kitajima, K., and Bazzaz, F. A.: Multiple
trait associations in relation to habitat differentiation among 17
Floridian oak species, Ecol. Monog., 74, 635–662, 2004.
Chao, K.-J., Phillips, O. L., Gloor, E., Monteagudo, A., TorresLezama, A., and Vásquez-Martı́nez, R.: Growth and wood density predict tree mortality in Amazon forests, J. Ecol., 96, 281–
292, 2008.
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., and
Zanne, E. R.: Towards a worldwide wood economic spectrum,
Ecol. Lett., 12, 351–366, 2009.
Chave, J., Navarrete, D., Almeida, S., Álvarez, E., Aragão, L. E. O.
C., Bonal, D., Châtelet, P., Silva-Espejo, J. E., Goret, J.-Y., von
Hildebrand, P., Jiménez, E., Patiño, S., Peñuela, M. C., Phillips,
O. L., Stevenson, P., and Malhi, Y.: Regional and seasonal patterns of litterfall in tropical South America, Biogeosciences, 7,
43–55, doi:10.5194/bg-7-43-2010, 2010.
Coomes, D. A., Jenkins, K. L., and Cole, L. E. S.: Scaling of three
vascular transport systems along gradients of nutrient supply and
altitude, Biol. Lett., 3, 86–89, 2007.
Corner, E.: The Durian theory, or the origin of the modern tree,
Ann. Bot., 13, 368–414, 1949.
Domingues, T. F., Meir, P., Feldpausch, T. R., Saiz, G., Veenendaal,
E. M., Schrodt, F., Bird, M., Djagbletey, G., Hien, F., Compaore,
H., Diallo, A., Grace, J., and Lloyd, J.: Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa
woodlands, Plant Cell Environ., 33, 959–980, 2010.
Easdale, T. A. and Healey, J. R.: Resource-use-related traits correlate within population turnover rates, but not stem diameter
growth rates in 29 subtropical montane tree species, Perspect.
Plant Ecol. Evol. Syst., 11, 203–218, 2009.
Falster, D. S. and Westoby, M.: Leaf size and angle vary widely
across species: what consequences for light interception?, New
Phytol., 158, 509–525, 2003.
Falster, D. S. and Westoby, M.: Alternative height strategies among
45 dicot rain forest species from tropical Queensland, Australia,
J. Ecol., 93, 521–535, 2005.
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
Falster, D. S., Moles, A. T., and Westoby, M.: A general model
for the scaling of offspring size and adult size, Amer. Nat., 172,
299–317, 2008.
Farquhar, G. D., Ehleringer, J. R., and Hubick, K. T.: Carbon isotope discrimination and photosynthesis, Ann. Rev. Plant Biol.,
40, 503–537. 1989.
Feng, Y.-L., Lei, Y.-B., Wang, R.-F., Callaway, R. M., ValienteBanuet, A., Inderjit, Li, Y.-P., and Zheng, Y.-L.: Evolutionary
tradeoffs for nitrogen allocation to photosynthesis versus cell
walls in an invasive plant, P. Natl. Acad. Sci. USA, 106, 1853–
1856, 2009.
Fine, P. V. A., Miller, Z. J., Mesones, I., Irazuzta, S., Appel, H. M.,
Stevens, M. H. H., Sääksjärvi, I., Schultz, J. C., and Coley, P. D.:
The growth-defense trade-off and habitat specialization by plants
in Amazonian forests, Ecology, 87, S150–S162, 2006.
Flury, B.: Common Principal Components and Related Multivariate
Models, Wiley, New York, 1988.
Foster, S. A.: On the adaptive value of large seeds for tropical moist
forest trees: a review and synthesis, Bot. Rev., 52, 260–299,
1986.
Foster, S. A. and Janson, C. T.: The relationship between seed size
and establishment conditions in tropical woody plants, Ecology,
66, 773–780, 1985.
Fromm, J.: Wood formation in trees in relation to potassium and
calcium nutrition, Tree Physiol., 30, 1140–1147, 2010.
Fyllas, N. M., Patiño, S., Baker, T. R., Bielefeld Nardoto, G., Martinelli, L. A., Quesada, C. A., Paiva, R., Schwarz, M., Horna, V.,
Mercado, L. M., Santos, A., Arroyo, L., Jiménez, E. M., Luizzão,
F. J., Neill, D. A., Silva, N., Prieto, A., Rudas, A., Silviera, M.,
Vieira, I. C. G., Lopez-Gonzalez, G., Malhi, Y., Phillips, O. L.,
and Lloyd, J.: Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate, Biogeosciences, 6,
2677–2708, doi:10.5194/bg-6-2677-2009, 2009.
Fyllas, N. M., Lloyd, J., and Quesada, C. A.: Deriving
plant functional types for Amazonian forests for use in vegetation dynamics models, Perspect. Plant Ecol. Evol. Syst.,
http://dx.doi.org/10.1016/j.ppees.2011.11.001, 2012.
Galwey, N. W.: Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance, Wiley, Chichester, 2006.
Gentry, A. H. and Emmons, L. H.: Geographical variation in fertility, phenology, and composition of the understory of neotropical
forests, Biotropica, 19, 216–227, 1987.
Givnish, T. J.: On the adaptive significance of compound leaves,
with particular reference to tropical trees, in: Tropical Trees as
Living Systems, edited by: Tomlinson, P. B. and Zimmermann,
M. H., Cambridge University Press, Cambridge, UK, 351–380,
1978.
Givnish, T. J.: Comparative studies of leaf form: Assessing the relative roles of selective pressures and phylogenetic constraints,
New Phytol., 106(Suppl.), 131–160, 1987.
Gonzalez, E. and Fisher, R. F.: Variation in selected wood properties
of Vochysia guatemalensis from four sites in Costa Rica, Forest
Sci., 44, 185–191, 1998.
Gower, J. C.: Some distance properties of latent root and vector
methods used in multivariate analyses, Biometrika, 53, 325–338,
1966.
Grubb, P. J.: A reassessment of the strategies of plant which cope
with shortages of resources, Perspect. Plant Ecol. Evol. Syst., 1,
3–31, doi:10.1078/1433-8319-00049, 1998.
www.biogeosciences.net/9/775/2012/
797
Grubb, P. J. and Coomes, D. A.: Seed mass and nutrient content in
nutrient starved tropical rainforest in Venezuela, Seed Sci. Res.,
7, 269–280, 1997.
Grubb, P. J., Coomes, D. A. and Metcalfe, D. J.: Comment on A
Brief History of Seed Size, Science, 310, 783–783, 2005.
Hammond, D. S. and Brown, V. K.: Seed size of woody plants
in relation to disturbance, dispersal, soil type in wet neotropcial
forests, Ecology, 76, 2544–2561, 1995.
Hernández, R. A. and Restrepo, G.: Natural variation in wood properties of Alnus acuminata grown in Colombia, Wood Fiber Sci.,
27, 41–48, 1995.
Hölttä, T., Mäkinen, H., Nöjd, P., Mäkelä, A., and Nikinmaa, E.: A
physiological model of softwood cambial growth, Tree Physiol.,
30, 1235–1252, 2010.
Hollander, M. and Wolfe, D. A.: Nonparametric Statistical Methods, J. Wiley and Sons, New York, 1999.
Houle, D., Mezey, J., and Galpern, P.: Interpretation of the results
of common principal components analyses, Evolution, 56, 433–
440, 2002.
Hughes, L., Dunlop, M., French, K., Leishman, M., Rice, B.,
Rogerson, L., and Westoby, M.: Predicting dispersal spectra: a
minimal set of hypotheses based on plant attributes, J. Ecol., 82,
933–950, 1994.
Ishida, A., Nakano, T., Yazaki, K., Matsuki, S., Koike, N., Lauenstein, D. L., Shimizu, M., and Yamashita, N.: Coordination between leaf and stem traits related to leaf carbon gain and hydraulics across 32 drought-tolerant angiosperms, Oecologia, 156,
193–202, 2008.
Jones, F. A., Chen, J., Weng, G.-J., and Hubbell, S. P.: A genetic
evaluation of seed dispersal in the neotropical tree Jacaranda copaia (Bignoniaceae), Amer. Nat., 166, 543–555, 2005.
Kattge, J., Dı́az, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch,
G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., van Bodegom, P. M.,
Reichstein, M., Soudzilovskaia, N. A., Ackerly, D. D., Anand,
M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker,
R., Blanco, C., Blonder, B., Bond, W., Bradstock, R., Bunker,
D. E., Casanoves, F., Cavender–Bares, J., Chambers, J., Chapin,
F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M.,
Dobrin, B. H., Durka, W., Elser, J., Enquist, B. J., Esser, G.,
Estiarte, M., Fagan, W. F., Fang, J., Fernández, F., Fidelis, A.,
Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R., Green, W., Gutierrez, A. G., Hickler,
T., Higgins, S., Hodgson, J. G., Jalili, A., Jansen, S., Kerkhoff,
A. J., Kirkup, K., Kitajima, K., Kleyer, M., Klotz, S., Knops, J.
M. H., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee,
T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J.,
Llusià, J., Louault, F., Siyan Ma, S., Mahecha, M. D., Manning,
P., Massad, T., Medlyn, B., Messier, J., Moles, A., Müller, S.,
Nadrowski, K., Naeem, S., Niinemets, U., Nöllert, S., Nüske, A.
, Ogaya, R., Oleksyn, J., Onipchenko, V. G., Onoda, Y., Ordoñez,
J., Overbeck, G., Ozinga, W., Patiño, S., Paula, S., Pausas, J. G.,
Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L.,
Poschlod, P., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack,
L., Salgado, B., Sardans, J., Shiodera, S., Shipley, B., Sosinski, E., Soussana, J.-F., Swaine, E., Swenson, N., Thompson, K.,
Thornton, P., Waldram, M., Weiher, W., White, M., Wright, S.
J., Sönke, B., Zaehle, S., Zanne, A. E., and Wirth, Ch.: TRY – a
global database of plant traits, Global. Change Biol., 17, 2905–
Biogeosciences, 9, 775–801, 2012
798
2935, 2011.
Keeling, H. C., Baker, T. R., Monteagudo, A., Vasquez Martinez,
R., and Phillips, O. L.: Contrasting patterns of diameter and
biomass increment across tree functional groups in Amazonian
forests, Oecologia, 158, 521–534, 2008.
Kelly, C. K.: Seed size in tropical trees: a comparative study of
factors affecting seed size in Peruvian angiosperms, Oecologia,
102, 377–388, 1995.
Kenzo, T., Ichie, T., Watanabe, Y., Yoneda, R., Nonomiya, I., and
Koike, T.: Changes in photosynthesis and leaf characteristics
with tree height in five diperocarp species in a tropical rain forest,
Tree Physiol., 26, 865–873, 2006.
King, D. A.: Load bearing capacity of understory treelets of tropical
wet forest, Bull. Torrey Bot. Club, 114, 419–428, 1986.
Kirby, E. A. and Pilbeam, D. J.: Calcium as a plant nutrient, Plant
Cell Environ., 7, 397–405, 1984.
Kitajima, K.: Relative importance of photosynthetic traits and allocation patterns as correlates of seedling shade tolerance of 13
tropical trees, Oecologia, 98, 419–428, 1994.
Kitajima, K. and Poorter, L.: Tissue–level leaf toughness, but not
laminar thickness predicts sapling leaf lifespan and shade tolerance of tropical tree species, New Phytol., 186, 708–721, 2010.
Kleiman, D. and Aarssen, L. W.: The leaf size/number trade off in
trees, J. Ecol., 95, 376–382, 2007.
Larjavaara, M. and Muller-Landau, H. C.:Rethinking the value of
high wood density, Funct. Ecol., 24, 701–705, 2010.
Lichstein, J. W., Simons, T. R., Shriner, S. A., and Franzreb, K.
E.: Spatial autocorrelation and autoregressive models in ecology,
Ecol. Monogr., 72, 445–463, 2002.
Lloyd, J. and Farquhar, G. D.: 13 C discrimination during CO2 assimilation by the terrestrial biosphere, Oecologia, 99, 201–215,
1994.
Lloyd, J. and Farquhar, G. D.: Effects of rising temperatures and
[CO2 ] on the physiology of tropical forest trees, Phil. Trans. Roy.
Soc. Lond., 363B, 1811–1817, 2008.
Lloyd, J., Syvertsen, J. P., Kriedemann,P., and Farquhar, G. D.: Low
conductances for CO2 diffusion from stomata to the sites of carboxylation in leaves of woody species, Plant Cell Environ., 15,
873–889, 1992.
Lloyd, J., Patiño, S., Paiva, R. Q., Nardoto, G. B., Quesada, C.
A., Santos, A. J. B., Baker, T. R., Brand, W. A., Hilke, I., Gielmann, H., Raessler, M., Luizão, F. J., Martinelli, L. A., and Mercado, L. M.: Optimisation of photosynthetic carbon gain and
within-canopy gradients of associated foliar traits for Amazon
forest trees, Biogeosciences, 7, 1833–1859, doi:10.5194/bg-71833-2010, 2010.
Lovelock, C. E., Ball, M. C., Choat, B., Engelbrecht, B. J. Holbrook, N. M., and Feller, M. C.: Linking physiological processes
with mangrove forest structure: phosphorus deficiency limits
canopy development, hydraulic conductivity and photosynthetic
carbon gain in dwarf Rhizophora mangle, Plant Cell Environ.,
29, 793–802, 2006.
McKean, J. W., Terpstra, J. T., and Kloke, J. D.: Computational
rank-based statistics, WIREs Comput. Stat., 1, 132–140, 2009.
Maghsoodloo, S. and Laszlo Pallos, L.: Asymptotic behavior of
Kendalls partial rank correlation coefficient and additional quantile estimates, J. Statist. Comput. Simul., 13, 41–48, 1981.
Malhado, A. C. M., Malhi, Y., Whittaker, R. J., Ladle, R. J., ter
Steege, H., Phillips, O. L., Butt, N., Arag ao, L. E. O. C.,
Biogeosciences, 9, 775–801, 2012
S. Patiño et al.: Tropical tree trait dimensions
Quesada, C. A., Araujo-Murakami, A., Arroyo, L., Peacock, J.,
Lopez-Gonzalez, G., Baker, T. R., Anderson, L. O., Almeida,
S., Higuchi, N., Killeen, T. J., Monteagudo, A., Neill, D., Pitman, N., Prieto, A., Salomão, R. P., Vàsquez-Martnez R., and
Laurance, W. F.: Spatial trends in leaf size of Amazonian rainforest trees, Biogeosciences, 6, 1563–1576, doi:10.5194/bg-61563-2009, 2009.
Malhado, A. C. M., Whittaker, R. J., Malhi, Y., Ladle, R. J., ter
Steege, H., Phillips, O. L., Arag ao, L. E. O. C., Baker, T. R., Arroyo, L., Almeida, S., Higuchi, N., Killeen, T. J., Monteagudo,
A., Pitman, N. C. A., Prieto, A., Salomão, R. P., VàsquezMartnez R., Laurance, W. F., and Ramı̀rez-Angulo, H.: Are
compound leaves an adaptation to seasonal drought or to rapid
growth? Evidence from the Amazon rain forest, Global Ecol.
Biogeog., 19, 852–862, 2010.
Martinéz-Cabrera, M., Jones, H. I., Espino, C. S., Shenk, S., and
Jochen, H.: Wood anatomy and wood density in shrubs: Responses to varying aridity along transcontinental transects, Am.
J. Bot., 96, 1388–1398, 2009.
Meinzer, F. C., Campanello, P. I., Domec, J.-C., Gatti, M. G., Goldstein, G., Villalobos-Vega, R., and Woodruff, D. R.: Constraints
on physiological function associated with branch architecture on
wood density of tropical forest trees, Tree Physiol., 28, 1609–
1617, 2008.
Mercado, L. M., Patiño, S., Domingues, T. F., Fyllas, N. M., Weedon, G. P., Sitch, S., Quesada, C. A., Phillips, O. L., Aragão, L.
E. O. C, Malhi, Y., Dolman, A. J., Restrepo-Coupe, N., Saleska,
S. R., Baker, T.R., Almeida, S., Higuchi, N., and Lloyd, J.: Variations in Amazon forest productivity correlated with foliar nutrients and modelled rates of photosynthetic carbon supply, Phil.
Trans. R. Soc. B, 366, 3316–3329, 2011.
Metcalfe, D. J. and Grubb, P. J.: Seed mass and light requirements
for regeneration in Southeast Asian rain forest, Can. J. Bot., 73,
817–826, 1995.
Miller, J. M., Williams, R. J., and Farquhar, G. D.: Carbon isotope discrimination by a sequence of Eucalyptus species along
a subcontinental rainfall gradient in Australia, Funct. Ecol., 15,
222–232, 2001.
Moles, A. and Westoby, M.: Do small leaves expand faster than
large leaves, and do shorter expansion times reduce herbivore
damage?, Oikos, 90, 517–526, 2000.
Moles, A. T, Ackerly, D. D., Webb, C. O., Tweddle, J. C., Dickie, J.
B., and Westoby, M.: A brief history of seed size, Science, 307,
576–80, 2005.
New, M., Lister, D., Hulme, M., and Makin, I.: A high-resolution
data set of surface climate over global land areas, Climate Res.
21, 1–25, 2002.
Niinemets, U.: Are compound–leaved species woody species inherently shade tolerant? An analysis of species ecological requirements and foliar support costs, Plant Ecol., 134, 1–11, 1998.
Niinemets, U.: Components of leaf mass per unit area – thickness
and density, alter leaf photosynthetic capacity in reverse directions in woody plants, New Phytol., 144, 35–47, 1999.
Niinemets, U.: Global scale climatic controls of leaf dry mass per
unit area, density and thickness in trees and shrubs, Ecology, 82,
453–489, 2001.
Olivares, E. and Medina, E.: Water and nutrient relations of woody
perennials from tropical dry forests, J. Veg. Sci., 3, 383–392,
1992.
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
Omolodun, O. O., Cutter, B. E., Krause, G. F., and McGinnes, E.
A.: Wood quality in Hidegardia berteri (Mast.) Kossern – An
African tropical pioneer species, Wood Fiber Sci., 23, 419–435,
1991.
Onoda, Y., Hikosaka, K., and Hirose, T.: Allocation of nitrogen
to cell walls decreases photosynthetic nitrogen-use efficiency,
Funct. Ecol., 18, 419–425, 2004.
Patiño, S., Lloyd, J., Paiva, R., Baker, T. R., Quesada, C. A.,
Mercado, L. M., Schmerler, J., Schwarz, M., Santos, A. J.
B., Aguilar, A., Czimczik, C. I., Gallo, J., Horna, V., Hoyos,
E. J., Jimenez, E. M., Palomino, W., Peacock, J., Peña-Cruz,
A., Sarmiento, C., Sota, A., Turriago, J. D., Villanueva, B.,
Vitzthum, P., Alvarez, E., Arroyo, L., Baraloto, C., Bonal, D.,
Chave, J., Costa, A. C. L., Herrera, R., Higuchi, N., Killeen, T.,
Leal, E., Luizão, F., Meir, P., Monteagudo, A., Neil, D., NúñezVargas, P., Peñuela, M. C., Pitman, N., Priante Filho, N., Prieto,
A., Panfil, S. N., Rudas, A., Salomão, R., Silva, N., Silveira, M.,
Soares deAlmeida, S., Torres-Lezama, A., Vásquez–Martı́nez,
R., Vieira, I., Malhi, Y., and Phillips, O. L.: Branch xylem
density variations across the Amazon Basin, Biogeosciences, 6,
545–568, doi:10.5194/bg-6-545-2009, 2009.
Phillips, P. C. and Arnold, S. J.: Hierarchical comparison of genetic variance–covariance matrices I. Using the Flury hierarchy,
Evolution, 53, 1506–1515, 1999.
Pickup, M., Westoby M., and Basden A.: Dry mass costs of deploying leaf area in relation to leaf size, Funct. Ecol., 19, 88–97,
2005.
Poorter, H. and Villar, R.: The fate of acquired carbon in plants:
chemical composition and construction costs. edited by: Bazzaz,
F. A., Grace, J., in: Plant Resource Allocation, San Diego, Academic Press, 39–72, 1997.
Poorter, H. and de Jong R.: A comparison of specific leaf area,
chemical composition and leaf construction costs of field plants
from 15 habitats differing in productivity, New Phytol., 143,
163–176, 1999.
Poorter, H., Niinemets, U., Poorter, L., Wright, I. J., and Villar,
R.: Causes and consequences of variation in leaf mass per area
(LMA): a meta-analysis, New Phytol., 182, 565–588, 2009.
Poorter, L.: The relationships of wood-, gas- and water fractions
of tree stems to performance an life history variation of tropical
trees, Ann. Bot., Ann. Bot., 102, 367–375, 2008.
Poorter, L. and Bongers, F.: Leaf traits are good predictors of plant
performance across 53 rain forest species, Ecology, 87, 1733–
1743, 2006.
Poorter, L. and Rozendaal, D. M. A.: Leaf size and display of thirtyeight tropical tree species, Oecologia, 158, 35–46, 2008.
Poorter, L., Bongers L., and Bongers F.: Architecture of 54 moist
forest tree species: traits, trade-offs, and functional groups, Ecology, 87, 1289–1301, 2006.
Poorter, L., Wright, S. J., Paz, H., Ackerly, D. D., Condit, R., IbarraManrı́quez, G., Harms, K. E., Licona, J. C., Martı́nez-Ramos,
M., Mazer, S. J., Muller–Landau, H. C., Penã–Claros, M., Webb,
C. O., and Wright, I. J.: Are functional traits good predictors
of demographic rates? Evidence from five neotropical forests,
Ecology, 89, 1908–1920, 2008.
Poorter, L., McDonald, I., Alarcón, A., Fichtler, E., Licona, J.-C.,
Penã-Claros, M. M., Sterck, F., Villegas, Z., and Sass–Klaassen,
U.: The importance of wood traits and hydraulic conductance
for the performance and life history strategies of 42 rainforest
www.biogeosciences.net/9/775/2012/
799
tree species, New Phytol., 185, 481–492, 2010.
Prance, G. T.: Chrysobalanaceae, Flora Neotropica, 9, 1–409. 1972.
Preston, K. A., Cornwell, W. K., and Denoyer, J.: Wood density
and vessel traits as distinct correlates of ecological strategy in 51
California coast range angiosperms, New Phytol., 170, 807–818,
2006.
Putz, F. E., Coley, P. D., Lu, K., Montalvo, A., and Aiello, A.: Uprooting and snapping of trees: structural determinants and ecological consequences, Can. J. For. Res., 13, 1011–1020, 1983.
Quesada, C. A., Lloyd, J., Schwarz, M., Patiño, S., Baker, T. R., Czimczik, C., Fyllas, N. M., Martinelli, L., Nardoto, G. B., Schmerler, J., Santos, A. J. B., Hodnett, M. G., Herrera, R., Luizão,
F. J., Arneth, A., Lloyd, G., Dezzeo, N., Hilke, I., Kuhlmann,
I., Raessler, M., Brand, W. A., Geilmann, H., Moraes Filho, J.
O., Carvalho, F. P., Araujo Filho, R. N., Chaves, J. E., Cruz Junior, O. F., Pimentel, T. P., and Paiva, R.: Variations in chemical
and physical properties of Amazon forest soils in relation to their
genesis, Biogeosciences, 7, 1515–1541, doi:10.5194/bg-7-15152010, 2010.
R Development Core Team: R: A Language and Environment for
Statistical Computing, Foundation for Statistical Computing, Vienna, Austria, ISBN3-900051-07-0, 2010.
Read, J. and Stokes, A.: Plant biomechanics in an ecological context, Amer. J. Bot., 93, 1546–1565, 2006.
Read, J., Sanson, G. D., Caldwell, E., Clissold, F. J., Chatain, A.,
Peeters, P., Lamont, B. B., de Garine-Wichatitsky, M., Jaffré, T.,
and Kerr, S.: Correlations between leaf toughness and phenolics
among species in contrasting environments of Australia and New
Caledonia, Ann. Bot., 103, 757–767, 2009.
Reich, P. B., Ellsworth, D. S., and Uhl, C.: Leaf carbon and nutrient
assimilation and conservation in species of different successional
status in an oligotrophic Amazonian forest, Funct. Ecol., 9, 65–
76, 1995.
Reich, P. B., Walters, M. B., and Ellsworth, D. S.: From tropics to
tundra: Global convergence in plant functioning, P. Natl. Acad.
Sci. USA, 94, 13730–13734, 1997.
Rolland, F., Baena-Gonzalez, E., and Sheen, J.: Sugar
sensing and signalling in plants: conserved and novel
mechanisms, Ann. Rev. Plant Biol., 57, 675–709,
doi:10.1146/annurev.arplant.57.032905.105441, 2006.
Romero, C. and Bolker, B. M.: Effects of stem anatomical and
structural traits on responses to stem damage: an experimental
study in the Bolivian Amazon, Can. J. For. Res., 38, 611–618,
2008.
Roque, R. M.: Effect of management treatment and growing regions on wood properties of Gmelina arborea in Costa Rica, New
Forests, 28, 325–330, 2004.
Rozendaal, D. M. A., Hurtado, V. H., and Poorter, L.: Plasticity in
leaf traits of 38 tropical tree species in response to light: relationships with light demand and adult stature, Funct. Ecol., 20,
207–216, 2006.
Russo, S. E., Jenkins, K. L., Wiser, S. K., Uriate, M., Duncan, R.
P., and Coomes, D. A.: Interspecific relationships among growth,
mortality and xylem traits of woody species from New Zealand,
Funct. Ecol., 24, 253–262, 2010.
Sandquist, D. R. and Cordell, S.: Functional diversity of carbongain, water-use, and leaf-allocation traits in trees of a threatened
lowland dry forest in Hawaii, Am. J. Bot., 94, 1459–1469, 2007.
Santiago, L. S. and Wright, I. J.: Leaf functional traits of tropical
Biogeosciences, 9, 775–801, 2012
800
forest plants in relation to growth form, Funct. Ecol., 21, 19–27,
2007.
Santiago, L. S., Goldstein, G., Meinzer, F. C., Fisher, J. B.,
Machado, K., Woodruff, D., and Jones, T.: Leaf photosynthetic traits scale with hydraulic conductivity and wood density
in Panamanian forest canopy trees, Oecologia, 140, 543–550,
2004a.
Santiago, L. S., Kitajima, K., Wright, S. J., and Mulkey, S. S.: Co–
ordinated changes in photosynthesis, water relations and leaf nutritional traits of canopy trees along a precipitation gradient in
lowland tropical forest, Oecologia, 139, 495–502, 2004b.
Sarmiento, C., Patiño, S., Paine, C. E. P., Beauchêne, J., Thibaut,
A., and Baraloto, C.: Within-individual variation of trunk and
branch xylem density in tropical trees, Amer. J. Bot., 98, 140–
149, doi:10.3732/ajb.1000034, 2011.
Shipley, B.: Structured interspecific determinants of specific leaf
area in 34 species of herbaceous angiosperms, Func. Ecol., 9,
312–319, 1995.
Sobrado, M. A.: Aspects of tissue water relations and seasonal
changes of leaf water potential components of evergreen and deciduous species coexisting in tropical dry forests, Oecologia, 68,
413–416, 1986.
Sterck, F., van Gelder, H. A., and Poorter, L.: Mechanical branch
constraints contribute to life-history variation across tree species
in a Bolivian forest, J. Ecol. 94, 1192–1200, 2006.
Sungpalee, W., Itoh, A., Kanzaki, M., Sringeryuang, K., Noguchi,
H., Mizuno, T., Teejunuk, S., Hara, M., Chaiudon, K., Yamakura,
T., and Sornngai, A.: Intra-and interspecific variation in wood
density and fine-scale spatial distribution of stand-level wood
density in a northern Thai tropical montane forest, J. Trop. Ecol.,
25, 359–370, 2009.
Swenson, N. G. and Enquist, B. J.: The relationship between stem
and branch wood specific gravity and the ability of each measure
to predict leaf area, Amer. J. Bot., 95, 516–519, 2008.
Syvertsen, J. P., Lloyd, J., McConchie, C., Kriedemann, P. E., and
Farquhar, G. D.: On the site of biophysical constraints to CO2
diffusion through the mesophyll of thick hyperstomatous leaves,
Plant Cell Environ., 18, 149–157, 1995.
Takahashi, K. and Mikami, Y.: A weak relationship between crown
architectural and leaf traits in saplings of eight tropical rain–
forest species in Indonesia, J. Trop. Ecol., 24, 425–432, 2008.
Takashima, T., Hikosaka, K., Hirose, T.: Photosynthesis or persistence: Nitrogen allocation in leaves of evergreen and deciduous
Quercus species, Plant Cell Environ. 27, 1047–1054, 2004.
ter Steege, H. and Hammond, D. S.: Character convergence, diversity, and disturbance in tropical rainforest in Guyana, Ecology,
82, 3197–3212, 2001.
ter Steege, H., Pitman, N. C. A., Phillips, O. L., Chave, J., Sabatier,
D., Duque, A., Molino, J.-F., Prévost, M. F., Spichiger, R.,
Castellanos, H., Hildebrand, P., and Vásquez, R.: Continental–
scale patterns of canopy tree composition and function across
Amazonia, Nature, 443, 444–447, 2006.
Thioulouse, J., Chessel, D., Dolédec, S., and Olivier, J.: ADE–
4: a multivariate analysis and graphical display software, Stat.
Comput., 7, 75–83, 1997.
Thomas, S. C.: Relative size at onset of maturity in rain forest trees:
a comparative analysis of 37 Malaysian species, Oikos, 76, 145–
154, 1996.
Thomas, S. C. and Bazzaz, F. A.: Asymptotic height as a predictor
Biogeosciences, 9, 775–801, 2012
S. Patiño et al.: Tropical tree trait dimensions
of photosynthetic characteristics in Malaysian rain forest trees,
Ecology, 80, 607–1622, 1999.
Thomas, D. S., Montangu, K. D., and Conroy, J. P.: Why does phosphorus limitation increase wood density in Eucalyptus grandis
seedlings?, Tree Physiol., 26, 35–42, 2005.
van Gelder, H. A., Poorter, L., and Sterk, F. J.: Wood mechanics,
allometry and life-history variation in a tropical rain forest tree
community, New Phytol., 171, 367–378, 2006.
Vander Willigen, C., Sherwin, H. W., and Pammenter, N. W.:
Xylem hydraulic characteristics of subtropical trees from contrasting habitats grown under identical environmental conditions,
New Phytol., 145, 51–59, 2000.
Warren, C. A. and Adams, M. A.: Internal conductance does not
scale with photosynthetic capacity: implications for carbon isotope discrimination and the economics of water and nitrogen use
in photosynthesis, Plant Cell Environ., 29, 192–201, 2006.
Warton, D. I., Wright, I. J., Falster, D. S., and Westoby, M.: Bivariate line–fitting methods for allometry, Biol. Rev., 81, 259–291,
2006.
Weber, J. C. and Montes, C. S.: Geographic variation in tree growth
and wood density of Guazuma crinita Mart, in the Peruvian
Amazon, New Forests, 36, 29–52, 2008.
Webb, L. J.: Environmental relationships of the structural types of
Australia rain forest vegetation, Ecology, 49, 296–311, 1968.
Westoby, M. and Wright, I. R.: The leaf size–twig–size spectrum and its relationship to other important spectra of variation
amongst species, Oecologia, 135, 621–628, 2003.
Westoby, M., Falster, D., Moles, A., Vesk, P., and Wright, I.: Plant
ecological strategies: some leading dimensions of variation between species, Ann. Rev. Ecol. Syst., 33, 125–159, 2002.
Williams, L. J., Bunyavejchewin, S., and Baker, P. J.: Deciduousness in a seasonal tropical forest in western Thailand, Oecologia,
155, 571–582, 2008.
Williamson, G. B. and Wiemann, M. C.: Measuring wood specific
gravity... Correctly, Amer. J. Bot., 97, 207–215, 2010.
Wilson, P. J., Thompson, K., and Hodgson, J. G.: Specific leaf
area and leaf dry matter content as alternative predictors of plant
strategies, New Phytol., 143, 155–162, 1999.
Witkowski, E. T. F. and Lamont, B. B.: Leaf specific mass confounds leaf density and thickness, Oecologia, 88, 486–493, 1991.
Wright, I. R., Falster, D. S., Pickup, M., and Westoby, M.: Cross–
species patterns in the coordination between leaf and stem traits,
and their implications for plant hydraulics, Physiol. Plant., 127,
445–456, 2006.
Wright, I. R., Ackerly. D. D., Bongers, F., Harms, K. E., Ibarra–
Manrı́quez, G., Martı́nez–Ramos, M., Mazer, S. J., Muller–
Landau, H. C., Paz, H., Pitman, N. C. A., Poorter, L., Silman,
M., Vriesendorp, C. F., Webb, C. O., Westoby, M., and Wright,
S. J.: Relationships among ecologically important dimensions of
plant trait variation in seven Neotropical forests, Ann Bot., 99,
1003–1015, 2007.
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch,
Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J.
H., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias,
J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C.,
Midgley, J. J., Navas, M. L., Niinemets, U., Oleksyn, J., Osada,
N., Poorter, H., Poot, P., Prior, L., Pyankov, V. I., Roumet, C.,
Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and Villar, R.:
The worldwide leaf economics spectrum, Nature, 428, 821–827,
www.biogeosciences.net/9/775/2012/
S. Patiño et al.: Tropical tree trait dimensions
2004.
Wright, S. J., Jaramillo, M. A., Pavon, J., Condit, R., Hubbell, S.
P., and Foster, R. B.: Reproductive size thresholds in tropical
trees; variations amongst individuals, species and forests, J. Trop.
Ecol., 21, 307–315, 2005.
Yates, M. J., Verboom, G. A., Rebelo, A. G., and Cramer, M. D.:
Ecophysiological significance of leaf size variation in Proteaceae
from the Cape Floristic Region, Funct. Ecol., 24, 485–492, 2010.
Zach, A., Schuldt, B., Brix, S., Horna, V., Culmsee, H., Leuschner,
C.: Vessel diameter and xylem hydraulic conductivity increase
with tree height in tropical rainforest trees in Sulawesi Indonesia,
Flora, 205, 506–512, 2010.
www.biogeosciences.net/9/775/2012/
801
Zanne, A. R., Westoby, M., Falster, S., Ackerly, D. D., Loarise,
S. R., Arnold, S. E. J., and Coomes, D. A.: Angiosperm wood
structure: Global patterns in vessel anatomy and their relation to
wood density and potential conductivity, Amer. J. Bot., 92, 207–
215, 2010.
Zhang, J.-L. and Cao, J.-F.: Stem hydraulics mediates leaf water
status, carbon gain, nutrient efficiencies and plant growth rates
across dipterocarp species, Funct. Ecol., 23, 658–667, 2009.
Biogeosciences, 9, 775–801, 2012