Functional Ecology 2013, 27, 264–272
doi: 10.1111/1365-2435.12018
The environment and space, not phylogeny, determine
trait dispersion in a subtropical forest
Xiaojuan Liu1, Nathan G. Swenson2, Jinlong Zhang1 and Keping Ma*,1
1
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of
Sciences, 20 Nanxincun, Xiangshan, Beijing, 100093 China; and 2Department of Plant Biology, Michigan State
University, East Lansing, Michigan, 48824 USA
Summary
1. A central aim of forest ecologists is to quantify the relative importance of different community assembly mechanisms in tropical and subtropical tree communities. Recent work in this
field has focused on the importance of functional trait similarity and abiotic filtering. While
important, none of this work has simultaneously: linked these trait dispersion patterns to the
underlying abiotic environment, considered dispersal limitation and quantified the degree to
which patterns of trait dispersion may be explained simply by shared ancestry.
2. Here we use data from a subtropical Chinese forest to accomplish this goal. We first examine the trait dispersion (leaf area, specific leaf area, seed mass, wood density, maximum height
and five traits together) on local scales by comparing the observed trait dispersion pattern to
that expected from a null model. Then we use a variance partitioning approach to examine the
degree to which spatial proximity, environmental similarity or the phylogenetic dispersion of
the species determine the observed trait dispersion.
3. The results show that, on local scales, trait dispersion is often non-randomly filtered.
Further the widespread trait clustering observed is largely explained by the environment and
space, while the phylogenetic dispersion of species in a sample explains relatively little. This
result further underscores that inferring an assembly mechanism from a pattern of phylogenetic dispersion is tenuous.
4. The work is important in that it is the first to partition the variation in tree trait diversity
into its spatial, environmental and phylogenetic components and that it demonstrates that
functional trait data often lack enough phylogenetic signal on local scales to confidently link
patterns of trait and phylogenetic dispersion. Ultimately, the findings suggest a strong role for
abiotic filtering and dispersal limitation during community assembly on local spatial scales and
that shared evolutionary history plays a relatively small role.
Key-words: abiotic environment, community assembly, dispersal limitation, evolutionary
history, forest dynamics plot, functional trait, null model
Introduction
The assembly of communities is governed by a combination of niche-based partitioning in space and time as well
as stochastic processes and dispersal limitation (e.g. Gravel
et al. 2006; Adler, HilleRisLambers & Levine 2007). Dissecting the relative contribution of these processes in tropical and subtropical tree communities is particularly
challenging (Wright 2002). This is because the majority of
species are rare, likely dispersal limited on ecological time
scales (Hubbell et al. 1999) and their long life spans make
*Correspondence author. E-mail: [email protected]
them not amenable to experimental investigations. Further, quantifying aspects of form and function related to
fitness across hundreds of species presents a substantial
methodological challenge. Plant ecologists are increasingly
using quantitative functional traits of trees as a way to
meet this challenge (e.g. Kraft, Valencia & Ackerly 2008;
Swenson & Enquist 2009; Paine et al. 2011). These easily
measured traits are generally only proxies of the actual
physiological traits of interest. That said, even measuring
these simple traits in diverse assemblages can take months
or years in the field to collect and they have been shown to
be reliable indicators of plant resource allocation decisions
and ecological strategies (e.g. Westoby 1998; Westoby
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society
Trait dispersion, filtering and phylogeny 265
et al. 2002; Wright et al. 2004; Chave et al. 2009). They
therefore provide the most pragmatic approach for estimating the functional similarity of tens or hundreds of
co-occurring species in tropical and subtropical communities.
Plant functional traits have been used to infer community assembly mechanisms primarily by measuring the trait
diversity in an assemblage and comparing it to that
expected from a null model (Weiher, Clarke & Keddy
1998; Kraft, Valencia & Ackerly 2008). When trait diversity is less than that expected (i.e. trait clustering) an abiotic filtering mechanism is generally inferred and when
diversity is higher than expected the importance of biotic
interactions is inferred. Studies from tropical tree communities to date have generally reported trait clustering in
local assemblages leading to an inference of abiotic filtering (Kraft, Valencia & Ackerly 2008; Swenson & Enquist
2009; Swenson 2011). A missing component of this work is
that these patterns of trait clustering are never explicitly
linked to the abiotic environment that is inferred to filter
the traits. Stronger inferences regarding the role of abiotic
filtering would come from directly assessing the degree to
which the underlying abiotic environment predicts patterns
of trait dispersion. Further, previous tests have generally
failed to account for dispersal limitation and other spatial
processes that are likely important during community
assembly. In sum, we know little about how much of the
observed spatial variation in trait dispersion can be
explained simply by abiotic gradients and/or spatial
proximity.
While a number of research groups have now measured
functional traits across hundreds of co-occurring tropical
tree species, a number of other groups have taken an alternative route to quantifying the ecological similarity of their
study species. In particular, researchers have utilized
phylogenetic distance as a proxy for ecological similarity
(Webb 2000). This approach was traditionally based on
the idea that recently diverged species may be more functionally similar to one another compared to species that
diverged long ago. Practically, this approach is also
appealing because time and financial constraints often
make quantifying relatively easily measured traits impossible and informatics tools now exist to estimate phylogenetic trees for communities within minutes (Webb &
Donoghue 2005). This approach is, of course, more
indirect and will fail if phylogenetic relatedness does not
indicate ecological similarity (Webb et al. 2002; CavenderBares et al. 2004), but it will likely continue to be used.
Further the question of how much does shared evolutionary history matter to community assembly remains
interesting.
As stated above, we have argued for the need to explicitly link the abiotic environment and spatial relationships
to increasingly measured patterns of functional trait dispersion in diverse tree communities. We have also posed
the question of to what degree can patterns of trait dispersion be simply explained by the shared evolutionary history of species. Here we propose that a variance
partitioning approach can be applied to address these challenges simultaneously (Pavoine et al. 2011). Specifically,
partitioning the variation in trait dispersion into its purely
spatial, environmental, and phylogenetic effects and their
joint effects should allow one to answer the degree to
which the abiotic environment, dispersal limitation and/or
shared evolutionary history govern patterns of trait dispersion. It is predicted that dispersal limitation will increase
the amount of variation explained by the spatial component, abiotic interactions will increase the amount of variation explained by the environmental component and
shared evolutionary history will increase the amount of
variation explained by the phylogenetic component
(Fig. 1). If little variation is explained by the environmental component we may infer that patterns of trait clustering are not associated with filtering by the measured
abiotic variables. If little variation is explained by the phylogenetic component we may infer that shared evolutionary history does not explain the patterns of trait dispersion
and importantly that measurements of phylogenetic dispersion may be misleading. If little variation is explained by
the spatial component we may infer a diminished role for
dispersal limitation.
The present work analyzes the functional trait dispersion
of 159 tree species in the 24-ha Gutianshan Forest Dynamics Plot in subtropical China. Specifically, we measured
leaf area, specific leaf area (SLA), seed mass (SM), wood
density (WD) and maximum height for the species in the
plot. Next we quantified the trait dispersion in 600 400 m2
subplots using null models with pairwise and nearest
neighbor trait dispersion metrics. Using soil nutrient and
topography data and a molecular phylogeny we then partitioned the amount of variation in trait dispersion that
Fig. 1. Partitioning the variation of the dependent variable [trait
dispersion (T)] by three sets of independent variables [environment
(E), phylogeny (P) and space (S)]. The rectangular area represents
100% of the variation in trait dispersion. Joint effects are denoted
with ‘+’ signs. ‘U’ is the unexplained fraction. Abiotic filtering is
expected to contribute to the purely environmental (E) and the
joint environmental effects (E + P, E + S and E + P + S). Dispersal limitation is expected to contribute to the purely spatial (S)
and the joint spatial effects (P + S, E + S and E + P + S). The
imprint of shared evolutionary history is expected to contribute to
the purely phylogenetic (P) and the joint environmental effects
(P + S, E + P and E + P + S).
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
266 X. Liu et al.
could be explained by abiotic factors, spatial autocorrelation, and the phylogenetic dispersion of the taxa in the
subplot or their joint effects. The results are discussed with
respect to the general contribution of evolutionary history,
the environment and dispersal limitation to observed
patterns of trait dispersion in this diverse community.
Materials and methods
STUDY LOCATION
The present study was conducted in the Gutianshan (GTS)
24-ha forest dynamics plot (29°15101′–29°15344′ N, 118°
07010′–118°07400′ E), which was established in the summer of 2005 in the Gutianshan National Nature Reserve,
Kaihua County, southeast China. The plot has not been
disturbed in the past 80 years and may be considered to be
a middle-to-late successional forest (Legendre et al. 2009).
The plot contains approximately 140 000 individual trees
(dbh 1 cm) from 49 families, 103 genera and 159 species (Lai et al. 2009; Legendre et al. 2009). The mean
annual temperature in GTS is 153 °C, with the hottest
month being July and the coldest month being January.
The mean annual precipitation is 1963 mm with a prolonged dry period between October and February. The
plot topography is very rugged with altitude ranging from
4463 to 7149 m and with slopes varying from 13 to 62°C.
SOIL AND TOPOGRAPHY DATA COLLECTION
Four topographic variables were quantified for each
20 9 20 m subplot for our analyses: mean elevation, slope,
convexity and aspect. Slope and aspect were measured in
the field; mean elevation and convexity were calculated
from the measured elevation data. Detailed information
regarding the collection of this data is provided in Legendre et al. (2009). A total of 13 soil nutrients (Fe, Mn, Zn,
Cu, K, P, Ca, Mg, B, Al, N, pH, Nmin) were obtained
from soil samples taken from the GTS plot. Soil cores
were taken to a depth of 10cm from 893 points using both
regular and random sampling methods. In particular the
GTS plot was divided into 30 9 30 m grids, with grid
intersections as basal points and two points along 2, 5 and
15 m in a random compass direction from the grid basal
point as two additional points (Zhang et al. 2011). In
total, the four topographic and 13 soil nutrient variables
were utilized to characterize the abiotic environment in the
GTS plot.
FUNCTIONAL TRAIT DATA COLLECTION
Four functional traits for each species were measured from
the field: leaf area (LA), SLA, SM and WD. An additional
trait, maximum height (Hmax), was compiled from the
Flora of China (Wu & Raven 1994–2009) and the Flora
Reipublicae Popularis Sinicae (Editorial Committee
for Flora Reipublicae Popularis Sinicae 1961–2004).
Measurement protocols for LA, SLA and SM followed
Cornelissen et al. (Cornelissen et al. 2003) and WD measurements followed Wright et al. (Wright et al. 2010). Specifically, at least 10 fresh leaf samples for each of the 159
species were taken from the tallest parts of trees, which
were fully expanded and exposed to direct sunlight. The
LA was measured as mean value of one-sided projected
surface area for each species and the SLA was calculated
as the mean value of one-sided surface area divided by the
dry leaf mass. The SM was an average value of oven-dry
mass (under 80 °C for 48 h) from 30 to 200 collected
mature seeds for each species without any appendages. We
collected wood samples from 5 to 15 individuals for each
species. The WD was estimated as a mean ratio of
oven-dry mass to fresh volume.
DNA SEQUENCING AND PHYLOGENETIC INFERENCE
Plant leaf material was collected during the summer of
2009 and 2010 from three individuals for each of the 159
species occurring in the GTS plot. Three common barcoding genes rbcLa, matK and trnH-psbA were amplified
using polymerase chain reaction (PCR) and sequenced following the methods described in (Kress et al. 2009, 2010).
DNA contigs were further assembled using ContigExpress
in VectorNTI11 (Invitrogen Corp., Carlsbad, CA, USA).
Sequences were aligned using MUSCLE 3.8 (Edgar 2004).
We used RAxML via CIPRES to reconstruct a maximum
likelihood (ML) tree (Stamatakis 2006). The ML tree was
further calibrated using nonparametric rate smoothing in
the software r8s (Sanderson 2003). A figure of the phylogenetic tree is available in the supplemental information (see
Fig. S1, Supporting information). After inferring the
molecular phylogeny, the inferred basal topology was compared to the basal topology of the APG III phylogenetic
backbone to determine whether a constraint tree was
needed. The basal topologies were congruent except for
two species that are located in ‘unplaced’ families in the
APG III phylogeny and therefore this lack of congruence is not surprising. Rather than constrain our inferred tree by unplaced
or dubious lineages we kept our original basal topology.
VARIATION PARTITIONING ANALYSES
Trait dispersion analyses
The community trait dispersion was calculated using two
indices. First we calculated a mean pairwise trait distance
(PW) between all individuals in a forest subplot. Second
we calculated a mean nearest neighbor trait distance (NN)
between all individuals in a forest subplot. The distances
between individuals were calculated from a trait dendrogram. The trait dendrogram was generated by first generating a trait Euclidean distance matrix. Hierarchical
clustering was then applied to this matrix to generate the
dendrogram. A dendrogram was used as it is analogous to
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
Trait dispersion, filtering and phylogeny 267
a phylogenetic tree, which we used for the phylogenetic
analyses explained below. We generated six trait dendrograms, including five for each trait and one for all the
traits (AT) combined. The community trait dispersion was
quantified using the observed PW and NN values and
comparing them to a null distribution of PW and NN values. This allowed us to calculate a standardized effect size
for the PW and NN metric (S.E.S. PW and S.E.S. NN
respectively). To generate the null distribution we randomized species names on the trait dendrograms 9999 times
and recalculated the PW and NN metrics for subplots.
This randomization procedure retains observed trait combinations and correlations and only changes the identity of
the species with that trait combination. The 9999 random
values were used to calculate S.E.S. PW and S.E.S. NN as
follows:
S.E.S.PWsample ¼ 1 ðPWsample
meanðPWrandom ÞÞ=sdðPWrandom Þ
eqn 1
S.E.S.NNsample ¼ 1 ðNNsample
meanðNNrandom ÞÞ=sdðNNrandom Þ
eqn 2
Where PWsample and NNsample are the observed PW and
NN for the community and PWrandom and NNrandom are
the 9999 random PW and NN values. Thus S.E.S. PW
indicates the basal or overall trait dispersion of a community, while S.E.S. NN is calculated from the nearest neighbors, therefore indicates the terminal trait dispersion of a
community. The high S.E.S values indicate lower than
expected trait diversity and low S.E.S. values indicate
higher than expected trait diversity.
As the present study was interested in quantifying the
amount of variation in S.E.S. PW and S.E.S. NN that
could be explained by the phylogenetic dispersion of the
assemblage, we calculated two widely used phylogenetic
metrics that are mathematically analogous to S.E.S. PW
and S.E.S. NN. The first was the net relatedness index
(NRI; Webb et al. 2002), which is a phylogenetic analog
of the S.E.S. PW. The second was the nearest taxon index
(NTI; Webb et al. 2002), which is a phylogenetic analog of
S.E.S. NN. The NRI and NTI are calculated using the
same set of equations as S.E.S. PW and S.E.S. NN respectively except they utilize phylogenetic branch lengths
instead of trait dendrogram branch lengths. As with the
functional trait analyses, the phylogenetic analyses
randomized species names on the phylogeny. It therefore
retained the observed phylogenetic topology. In total,
we calculated S.E.S. PW, S.E.S. NN, NRI and NTI for
600 subplots in the GTS each of which was 20 9 20 m in
area.
PCNMs for the spatial variation information
The spatial relationships among the 20 9 20 m subplots
was obtained via principal coordinate analysis (PCoA) of
a truncated distance matrix, which was composed of
Euclidean distance between subplots. We used the function
‘PCNM’ from the R package ‘vegan’ to obtain the spatial
information across the 600 subplots in the GTS plot. The
spatial relationships among the subplots were then decomposed to a set of orthogonal PCNM eigenfunctions (Borcard & Legendre 2002; Legendre, Borcard & Peres-Neto
2008). The PCNM variables were then used to analyze the
spatial variation.
Partitioning the variation into three sets of explanatory
variables
We partitioned the variation in the trait dispersion pattern into an environmental component, a phylogenetic
dispersion component, a spatial component and their
joint effects by using sequential linear regressions followed by subtractions (Desdevises et al. 2003; Cubo et al.
2008) (Fig. 1). Here we briefly explain this procedure.
First, for environmental variables (E), a forward variable
selection procedure was used to extract those variables,
which were significantly related to the dependent variables
(T) (i.e. trait dispersion values). Second, for the spatial
PCNM variables (S), the same method was applied to
reduce the number of explanatory eigenvectors, retaining
only those that were significantly related to the dependent
variables (T). Last, a sequential regression of the dependent variables (T) was computed on E, S and phylogenetic dispersion (P) and their joint variables E + S,
E + P, P + S and E + P + S (Fig. 1). The coefficient of
determination adjusted R2 (R2a) from each part indicated
the proportion of the variance in T that could be
explained exclusively from the individual independent variable or the interaction of multiple independent variables.
All the procedures were carried out in the R statistical
language (R Development Core Team 2012). The forward
selection analyses used the function ‘forward.sel’ in the
‘packfor’ package using 9999 permutations and the variation partitioning and significance tests used the functions
‘varpart’ and ‘anova.cca’ in the ‘vegan’ package using
9999 permutations.
Results
TRAIT DISPERSION PATTERNS
In the GTS plot, the S.E.S. PW of the 600 20 9 20 m subplots primarily showed less trait diversity than expected
given the null distribution. Specifically the majority of subplots had lower than expected trait diversity: LA (998%),
SLA (100%), WD (973%) and AT (983%) and 175%,
507%, 255% and 95% of the subplots had significantly
low trait diversity (Table 1; Fig. 2). On the contrary, SM
(627%) and Hmax (757%) were mostly over-dispersed
with 02% and 58% of each being significantly over-dispersed. Similar to the S.E.S. PW results, the S.E.S. NN
results generally show low trait dispersion in subplots: LA
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
268 X. Liu et al.
Table 1. The proportion of subplots with trait clustering (overdispersion) and the proportion of subplots that are significantly clustered
(overdispersed). S.E.S. PW is the standardized effect size of the mean pairwise trait distance. S.E.S. NN is the standardized effect size of
the mean nearest neighbour trait distance
S.E.S. PW
Leaf area
Specific leaf area
Seed mass
Wood density
Maximum height
All traits
S.E.S. NN
Clustered
(Overdispersed)
(%)
Sig-clustered
(Sig-overdispersed)
(%)
Clustered
(Overdispersed)
(%)
Sig-clustered
(Sig-overdispersed)
(%)
998
1000
373
973
243
983
175 (00)
507 (00)
00 (02)
255 (00)
00 (58)
95 (00)
960
993
600
982
690
908
00
00
00
00
00
117
(02)
(00)
(627)
(27)
(757)
(17)
(40)
(07)
(400)
(18)
(310)
(92)
(033)
(00)
(02)
(02)
(25)
(00)
Fig. 2. The spatial distribution of S.E.S.
PW (the standardized effect size of the
mean pairwise trait distance) based on five
funtional traits and all trait combined of
Gutianshan plot. The color scale on the
right of each map indicates the S.E.S. PW
values. ‘S.E.S. PW > 0’ indicates trait dispersion is clustered; ‘S.E.S. PW < 0’ indicates trait is overdispersed. The lines are
elevation contour lines at 10-m intervals.
(960%), SLA (993%), SM (60%), WD (982%), Hmax
(690%) and AT (908%) (Table 1; Fig. 3), while only AT
had any subplots (117%) with significantly low levels of
trait diversity (Table 1; Fig. 3).
VARIANCE PARTITIONING RESULTS
The amount of variation in S.E.S. PW and S.E.S. NN for
the traits in the GTS plot explained by E, P and S and by
their interaction (E + P, E + S, P + S and E + P + S) is
provided in Fig. 4 and Table S1 (Supporting information).
The variation explained purely by the environmental
component and the whole environmental component (with
joint effects of phylogenetic and/or spatial components)
for all traits was significant (Fig. 4). The variation
explained purely by the spatial component was significant
for all traits except for the S.E.S. NN for Hmax, and the
variation explained by the whole spatial component (with
joint effects of environmental and/or phylogenetic components) for all traits was significant (Fig. 4). The variation
explained by the phylogenetic component was significant
except for the pure components of SM dispersion for both
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
Trait dispersion, filtering and phylogeny 269
Fig. 3. The spatial distribution of S.E.S.
NN (the standardized effect size of the
mean nearest neighbor trait distance) based
on five funtional traits and all trait combined of Gutianshan plot. The color scale
on the right of each map indicates the S.E.
S. NN values. ‘S.E.S. NN > 0’ indicates
trait dispersion is clustered; ‘S.E.S.
NN < 0’ indicates trait is overdispersed.
The lines are elevation contour lines at
10-m intervals.
S.E.S. PW and S.E.S. NN and the WD dispersion for S.E.
S. PW, and the whole phylogenetic component (with joint
effects of environmental and/or spatial components) of
WD dispersion for S.E.S. PW (Fig. 4). See Table S1
(Supporting information) for the detailed P-values of each
portion in trait variation partitioning.
Discussion
Community ecologists have sought to link patterns of
functional trait dispersion to local-scale environments in
order to infer assembly mechanisms. Much of this work
has inferred that the abiotic environment plays an important role in determining community assembly (Cornwell,
Schwilk & Ackerly 2006; Swenson et al. 2007; Kraft,
Valencia & Ackerly 2008; Swenson & Enquist 2009; Uriarte et al. 2010), but this linkage is not always explicitly
quantified. Making this linkage is further complicated by
the fact that abiotic environments generally display strong
spatial autocorrelation. Combine this with the fact that
many species may be dispersal limited and inferences
regarding abiotic filtering during assembly become difficult
without explicitly partitioning the variance in trait dispersion due to space, the abiotic environment or their interactions. Concurrent with the rapid increase in interest in
functional trait analyses of plant communities has been an
increasing interest in the phylogenetic dispersion of
co-occurring species (Webb 2000; Webb et al. 2002;
Swenson et al. 2007; Cavender-Bares et al. 2009). Much of
the early phylogenetic work in community ecology
assumed that ecologically important traits have significant
phylogenetic similarity (sensu Swenson, Anglada-Cordero
& Barone 2011) where closely related species are likely to
have more similar trait values than distantly related species
(Webb 2000; Kembel & Hubbell 2006; Swenson et al.
2006). If this assumption is met, then it is predicted that a
large fraction of the spatial variability in trait dispersion
may be explained by the spatial variability in phylogenetic
dispersion. The present study aimed to disentangle these
three possible drivers of spatial patterns in trait dispersion
by partitioning the variance in trait dispersion explained
by space, the abiotic environment or phylogenetic dispersion or their interactions.
We first quantified the dispersion of trait values inside a
24-ha forest dynamics plot in China called GTS. When
using a pairwise trait dispersion metric (S.E.S. PW) we
found that the majority of traits had less than expected
diversity inside 20 9 20 m subplots. This result is consistent with a filtering of phenotypes into local assemblages,
which is often considered evidence of abiotic filtering. Two
traits, SM and Hmax, had the opposite pattern of higher
than expected diversity. This result is consistent with work
from temperate and tropical plant community studies
(Grime 2006; Kraft, Valencia & Ackerly 2008; Swenson &
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
270 X. Liu et al.
Fig. 4. The variance partitioning results for trait dispersion among
environment, phylogeny and space for Gutianshan plot. The left
side is about S.E.S. PW dispersion (the standardized effect size of
the mean pairwise trait distance) and the right side is about S.E.S.
NN dispersion (the standardized effect size of the mean nearest
neighbor trait distance). The significances of the whole and pure
variation explained by the three independent variables are indicated at the upper left or right corner of the portion. The ‘Env’,
‘Phylo’ and ‘Spatial’ represent the whole variation respectively
explained by the environmental, phylogenetic and spatial components; and the portion inside the circle represents the pure variation explained by the three components. ‘***P < 0001’;
‘**P < 001’; ‘*P < 005’. See Table S1 for the detailed P-values
of the significance for each portion.
Enquist 2009) that have shown overdispersion in these two
traits believed to be linked to regeneration and light niches,
respectively. Thus one may infer that most traits are abiotically filtered into communities while local scale competition
along light and regeneration niche axes also significantly
contributes to the assembly of these tree communities.
We next quantified the trait dispersion using a nearest
neighbor metric (S.E.S. NN). While most values tended to
show low trait diversity within subplots, few subplots had
observed values significantly different than that randomly
expected. Thus we cannot infer any deterministic mechanisms, such as limiting trait similarity, underlying the patterns of trait diversity and the assembly of the GTS tree
community. In sum, the trait results, particularly the S.E.
S. PW results, are consistent with an abiotic filtering mechanism governing community assembly in the forest plot
studied. That said, these analyses were not linked to any
abiotic variables that presumably underlie the filtering of
species nor were we able to determine how much of this filtering could be understood by shared evolutionary history
or dispersal limitation. Therefore we wanted to determine
how much of the trait dispersion in the GTS plot could be
explained by space, the abiotic environment, and/or phylogenetic dispersion using variance partitioning.
The variance partitioning results showed that a significant amount of the variation in trait dispersion in subplots
was explained by the abiotic environment (Fig. 4). Across
all traits, at least 20% of the spatial variation in S.E.S.
PW could be accounted for by the purely environmental
or environment, space, and/or phylogeny interaction. In
one case, SLA, over 50% of the variation in S.E.S. PW
values could be linked to a pure or joint environmental
component (Fig. 4). This is taken as stronger evidence that
the patterns of trait clustering are indeed linked to the abiotic environment thereby strengthening our support for an
abiotic filtering model of community assembly in this forest. Much less of the spatial variation in the S.E.S. NN
values could be explained by environmental variation
(Fig. 4) suggesting that trait spacing within local assemblages had little to do with the underlying abiotic environment and may have a large stochastic component. In most
cases there was also a strong spatial component to the
results suggesting that dispersal limitation and/or spatial
autocorrelation in the environment also explain some of
the trait dispersion in the forest. Thus an abiotic filtering
mechanism certainly does not explain all assembly in this
forest and purely spatial processes also contribute significantly to the observed patterns of co-occurrence.
Along with space and the abiotic environment we quantified the amount of variance in trait dispersion explained
by phylogenetic dispersion. It was expected that if local
patterns of trait dispersion can be predicted based upon
shared evolutionary history, then a large amount of spatial
variation in trait dispersion should be linked to pure or
joint phylogenetic effects. In most cases a significant
amount of variation in trait dispersion could be explained
by the phylogenetic dispersion of the assemblage, but the
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272
Trait dispersion, filtering and phylogeny 271
amount of variation was typically low particularly for S.E.
S. NN (Fig. 4). From this we can infer that examinations
of phylogenetic dispersion in this forest would likely not
reliably indicate the dispersion of the traits studied and
that shared evolutionary history may not be invoked to
explain the assembly of trait values. In other words, phylogenetic dispersion may provide faulty inferences regarding
trait-based assembly in the present study system. Recent
work has highlighted similar difficulties in recovering trait
dispersion patterns from patterns of phylogenetic dispersion (Swenson & Enquist 2009) suggesting that great care
should be used in future studies that only utilize phylogenetic information. We do, though, point out a notable
exception to this general pattern. The spatial distribution
of Hmax dispersion in the GTS plot was predominantly
explained by the spatial variation in phylogenetic dispersion (Fig. 4). This result is important for a couple of reasons. First it shows that the dispersion of this trait has
little to do with the environmental variables measured
which may be expected given this trait is expected to be
related more to the light niche than the soil niche. Second,
it shows that the dispersion of this trait can largely be
explained by shared evolutionary history between species.
This shows that analyses of phylogenetic dispersion may
predict the dispersion of Hmax values in this forest suggesting that phylogenetic dispersion analyses may be useful in
some cases. Lastly, we point out that a large amount of
the variation in trait dispersion in the GTS forest plot
could not be explained by space, the abiotic variables measured, phylogenetic dispersion or their interactions. This
suggests that stochastic processes not linked to spatial distance may play an important role in the assembly of the
forest. An alternative, and perhaps more likely, reason for
this unexplained variation is that the present study failed
to measure key abiotic variables and/or species traits that
contribute greatly to the assembly of this forest.
In summary, the present study has shown that local scale
tree assemblages in a species rich subtropical Chinese forest
plot are often non-randomly filtered sets of the species pool.
This was demonstrated by examining the dispersion of several traits on local scales and showing that trait diversity
within assemblages was typically lower than that expected
given the species pool and a null model. The results are consistent with other studies that have argued for the importance of abiotic filtering during tropical and subtropical
tree community assembly (Lai et al. 2009; Legendre et al.
2009). Unlike previous functional trait analyses of tropical
tree communities we next examined whether the underlying
abiotic environment does indeed predict the trait dispersion
in communities. Our variance partitioning analyses show
that indeed the abiotic environment in this forest does predict the levels of trait clustering demonstrated providing
direct support for an abiotic filtering mechanism. The
results also show a strong spatial component underscoring
the importance of dispersal limitation as well. Lastly, we
have shown that spatial variation in phylogenetic dispersion
in this forest often fails to predict the spatial variation in
trait dispersion thereby casting doubt on the ability of phylogenetic relatedness to predict the mechanisms underlying
trait assembly in this subtropical tree community. Ultimately, by employing a variance partitioning approach we
were able to more directly link patterns of trait dispersion
to the underlying environment and dispersal limitation
while also examining the ability of shared evolutionary
history to predict spatial variation in trait dispersion.
Acknowledgements
This work was supported by a key innovation project of the Chinese Academy of Sciences (KZCX2-YW-430). NGS was supported by a Dimensions
of Biodiversity IRCN grant from the United States National Science Foundation (DEB – 1046113). The authors thank all the field workers for contributions to the establishment and census of Gutianshan 24-ha permanent
plot, and Fang Teng, Chen Shengwen and Lai Zhenxi for species identification. The Administration Bureau of the Gutianshan National Nature
Reserve provided local support. The Biodiversity Committee of the Chinese
Academy of Sciences financially supported the establishment of the GTS
plot.
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Received 21 June 2012; accepted 26 September 2012
Handling Editor: Ken Thompson
Supporting Information
Additional Supporting Information May be found in the online
version of this article:
Fig. S1. The phylogenetic tree for the GTS plot constructed using
DNA barcodes with each clade colored by order.
Table S1. Results of variation partitioning of trait dispersion
among environmental, phylogenetic and spatial components. All
the significance tests were based on 9999 permutations.
© 2012 The Authors. Functional Ecology © 2012 British Ecological Society, Functional Ecology, 27, 264–272