Ecography 36: 1076–1085, 2013
doi: 10.1111/j.1600-0587.2013.00158.x
© 2013 The Authors. Ecography © 2013 Nordic Society Oikos
Subject Editor: Jens-Christian Svenning. Accepted 30 January 2013
Adult age of vascular plant species along an elevational land-use
and climate gradient
Michael P. Nobis and Fritz H. Schweingruber­
M. P. Nobis ([email protected]) and F. H. Schweingruber, Swiss Federal Research Inst. WSL, Zürcherstrasse 111, CH-8903 Birmensdorf,
Switzerland.­
Trait variation across species plays a fundamental role in ecology and evolution, but quantitative analyses of key lifehistory traits under natural conditions generally do not include a large number of species. In a comparative study, we
analyzed interspecific variation in adult age as a minimum estimate of the lifespan of 708 vascular plant species along
elevational gradients from 263–3175 m a.s.l. and compared this variation with predictions from r-K selection theory
and the metabolic theory of ecology (MTE). Age data based on annual ring counts of root collars and rhizomes were
combined with a systematic sample of current species distributions in Switzerland (453 plots, each 1 km2). Elevation
and temperature trends were investigated by regression analyses of the variation in adult age across species and species
assemblages (median adult age) at the landscape level. We included climate, land use and geology as environmental
predictors in multiple regressions and considered phylogeny by eigenvector filtering. We found a general increase in
adult age towards higher elevations at the level of overall interspecific variation, and this trend was also detectable
within individual plant families. Species generally had a shorter lifespan under warmer climates and, in agreement
with r-K prediction, in lowland agricultural landscapes. We found an exponential adult age–temperature relationship
that is consistent with MTE. The estimate of the MTE parameter ‘activation energy’ for median adult age in multiple
regression was 0.65 eV (95% CI 0.62–0.69 eV) which coincided with the predicted range of 0.60–0.70 eV. Our
results imply that climate warming could accelerate species turnover rates by favoring short-lived species over the whole
range of life histories and species assemblages. Besides the strong temperature relationship, residual variability and
confounding factors demonstrate the need for additional research about interactions between broad-scale constraints
and more local drivers of life-history variation.
Trait variation among species is an evident characteristic of
biodiversity, even though evolutionary, environmental and
ecophysiological constraints mean that not all trait combi­
nations actually occur in nature (Charnov 1993, Ricklefs
and Wikelski 2002, Reich et al. 2003). Trade-offs between
traits allow for niche separation of species within communities as well as along broad-scale environmental gradients
(Silvertown 2004, Moles et al. 2007), facilitating species
co-existence at various spatial scales and contributing to the
maintenance of biodiversity.
One of the fundamental life-history traits of a species is
its lifespan (Roff 1992, Stearns 1992), i.e. the age at death
for individuals that have already reached the last stage of
the life cycle (Franco and Silvertown 1996). In vascular
plants, the subject of this study, lifespan varies from eight
weeks in Arabidopsis thaliana (Hensel et al. 1993) to about
4900 yr in Pinus longaeva (Currey 1965), and the variation
is even greater if genets of clonal plants are included (e.g.
43 600 yr for Lomatia tasmanica, Lynch et al. 1998). A
number of theories have been developed to explain this
huge variability in life histories. In addition to general theories of ageing, such as the rate of living theory (Pearl 1928)
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or the free-radical theory (Harman 1956), in ecology the
classical r-K selection theory (MacArthur and Wilson 1967,
Pianka 1970) was one of the first predictive models of
life-history evolution. As a consequence of variation in population densities, this theory assumes general trade-offs
between functional traits for species with short vs long life
cycles. In vascular plants, r-K selection for lifespan is related
to trait syndromes involving relative growth rate (negative
correlation; Stearns 1992), seed mass (positive; Silvertown
1981, Rees 1996), seed dormancy (negative; Rees 1996),
seed persistence (negative; Thompson et al. 1998, Endels
et al. 2007), and fecundity and dispersability (negative;
Ehrlén and van Groenendael 1998). These trait syndromes
are often influenced by disturbances (e.g. soil disturbance,
grazing, fire) which in general favor r-strategists and shortlived species (McIntyre et al. 1995).
In recent years, the metabolic theory of ecology (MTE)
has attracted much attention because it provides ‘a basis
for using first principles of physics, chemistry, and biology
to link the biology of individual organisms to the ecology of
populations, communities, and ecosystems’ (Brown et al.
2004). MTE is a quantitative theory that relates the body
mass and temperature dependency of metabolic rates to
biological rates in general. It is based on relationships that
have been studied for many years, i.e. mass-dependent
metabolic rates (Kleiber 1932) and the temperature dependency of chemical reactions (Arrhenius 1889). According
to MTE, biological times like lifespan (l ) can be expected to
scale reciprocally with rates as
l  M1/4 eE/kT
where T is the temperature of the organism, M is the
mass of the organism, E is the activation energy, and k is
the Boltzmann’s constant (Brown et al. 2004). MTE is a
general theory that links the metabolism of individuals to a
wide range of physiological to macroecological processes
and patterns, making clear, testable predictions. According
to MTE, the temperature dependency of lifespan should
be exponential with a predicted activation energy within
the range of 0.60–0.70 eV (Brown et al. 2004).
Testing such relationships at the landscape scale is
difficult because accurate quantitative data are scarce. The
evident lack of livespan data for a large number of species
sampled under natural conditions has been noted previously (Rees 1996, Weiher et al. 1999), and the central
question of ‘how perennial are perennials?’ (Ehrlén and
Lehtilä 2002) still remains unanswered for most plant species. Therefore, and in contrast to previous studies about
intraspecific variation in the lifespan of selected species
(Körner 2003, von Arx et al. 2006, Giménez-Benavides et al.
2011), our main objective here was to analyze the general
lifespan variation in vascular plants along elevational
gradients for a large fraction of the species pool. For this
reason, we combined a new comprehensive data source
for adult plant age based on counts of annual rings as a
minimum estimate of lifespan (Landolt et al. 2010), with
independently sampled landscape-level species distributions
from the Alps (Swiss Biodiversity Monitoring program;
Weber et al. 2004).
Assuming r-K selection, we expected adult age to decline
in landscapes characterized by frequent disturbances, such as
in agricultural or urban areas, and low precipitation, favoring
sparse vegetation with short-lived species. In contrast, MTE
predicts a general and uniform adult age–temperature relationship along the entire elevational temperature gradient.
Trait–environment relationships might change over
different levels of organization, from individuals to ecosystems (Violle et al. 2007). We addressed this possibility
by analyzing the environmental covariation of adult age as
a species life-history trait as well as a property within
species assemblages at the landscape level. Overall, we
addressed the following main questions: 1) is there a general
elevational gradient in the variation of adult age across
vascular plant species? 2) If so, does this relationship
change between adult age as a minimum estimate of the
species life-history trait lifespan and adult age as a property
of species assemblages at the landscape level? Finally, 3) how
well does the variation in adult age along elevational landuse and temperature gradients coincide with predictions
from r-K selection and the metabolic theory of ecology?
Methods
Study area
Our study area was Switzerland, which covers 41 284 km2 in
central Europe (45°49′–47°48′N latitude, 5°57′–10°30′E
longitude; Fig. 1). About 70% of the country is mountainous and the average elevation is ca 1300 m a.s.l., ranging
from 193 to 4634 m a.s.l. Mean annual temperature varies
from 210.5 to 12.5°C, and mean annual precipitation varies from 438 to 2950 mm (reference period 1961–1990;
Zimmermann and Kienast 1999).
Adult-age data
Our adult age data were based on counts of annual rings.
This technique is well established for trees and shrubs but
is also feasible for many herbs, especially dicotyledons
outside the tropics with differential growth in different
Figure 1. Location of sample plots (n  453) and the six biogeographic regions of Switzerland: 1  Jura, 2  Central Plateau, 3  Northern
Prealps, 4  Western Central Alps, 5  Eastern Central Alps, 6  Southern Alps; following Gonseth et al. (2001).
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seasons of the year (Schweingruber and Poschlod 2005).
The data were collected by F. H. Schweingruber and
published in Flora indicativa (Landolt et al. 2010), a
recent source of species characteristics including annual
ring based age data from root collars and rhizomes of
1030 vascular plant taxa of Switzerland and the Alps.
Specimens were collected under natural conditions with the
aims of 1) representing typical age of mature, full-grown
plants rather than notably long-lived individuals, 2) sampling a large number of different plant families and
3) including species with different elevational distributions (Schweingruber et al. 2011). The age data of the 708
analyzed species (see below) were derived from 2449 individual specimens, with a maximum of 40 and an average
of three specimens per species. In cases where several
specimens were collected for an individual species (527 out
of 708 species), the age of the oldest plant was considered
for analyses. In summary, adult age can be taken as a minimum estimate of the species’ common lifespan.
In order to evaluate a potential elevation bias in sample
size, we calculated the correlation between the sample size
of individuals collected for age detection and the average
elevation of species occurrences (r  0.09, p  0.05; species
data see below). This correlation was not significantly
different from zero if three outlier species with the largest
sample sizes were removed (r  0.06, p  0.11).
Plant distribution data
Information about the distributions of vascular plants
and about species assemblages at the landscape level were
derived from the Swiss Biodiversity Monitoring program
(Weber et al. 2004). On a systematic national grid, plots of
1 km2 in area were surveyed using standardized samples
of transect-like areas 5 m wide and with a total length of
2500 m, thereby providing a species list for each 1 km2
plot (Plattner et al. 2004). These investigated sample areas
were established along paths or streets and were considered
representative of the main different types of land use and
habitats. We used data from 468 plots recorded during the
first survey (2001–2005) combined with data from eight
plots located in urban environments recorded in 2006
(476 plots total). Plots with a lake fraction  50% and
some plots near the border of Switzerland were excluded
from analyses due to biased or missing data. The remaining
data set included 453 plots (Fig. 1) with 104 225 occurrences of 1737 vascular plant species. Within this set of
species, adult-age data were available for 708 species,
i.e. 41% of all species from the first survey period or about
one quarter of the total Swiss vascular plant flora. The average observed species richness per plot was 230  64 species
(mean  SD), and the average number of species with
available adult age data per plot was 110  31 (mean  SD).
For analyses at the species level, only species with at
least five records were considered in order to get more
robust estimates of the average elevation of occurrence
(567 species with 56 601 occurrences; 386 species with
age data from root collar counts; 181 species using
rhizome counts).
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Phylogenetic and taxonomic data
To consider phylogenetic relationships in our trait analysis,
we used the topology of a phylogenetic supertree fully based
on published DNA sequences (Durka 2002). Supplemental
data on new species of the Swiss sample were provided by
W. Durka (pers. comm.), yielding a final supertree including
all 708 species with available distribution and adult-age
data. In order to enable analyses of single plant families,
species were allocated to a given plant family according to
Flora indicativa (Landolt et al. 2010).
Environmental data
For the analyses of environmental covariates of adult age,
we used the average elevation (m a.s.l.) of the BDM plots
as a general proxy as well as the following more direct
variables: 1) three climate variables, i.e. ‘mean annual temperature’ TY (°C) as a relevant variable for MTE as well as
‘mean annual precipitation’ PY (mm) and ‘water balance
in July’ WB7 (mm) as indicators of drought that might
favor r-strategists and short-lived species; 2) three land cover
variables based on remote sensing (aerial photography) that
were related to disturbances at the landscape level that
might favor short-lived species, i.e. ‘urban areas’ Urban (%),
‘agricultural areas of the lowlands’ AgriLow (%) and ‘agri­
cultural areas of the Alps’ AgriAlp (%); and 3) one variable
related to geology, i.e. ‘calcareous substrate’ Calc (%) because
geology is known as an important factor influencing species
richness in the study area (Wohlgemuth et al. 2008).
We used two different types of agricultural areas because
of clear land-use and vegetation differences between arable
land in the lowlands and mountain meadows or alpine
pastures. All predictor variables were originally available as
1 ha grids and were subsequently aggregated to the 1 km2
resolution of the distribution data by calculating the arithmetic mean per 1 km2 for climate variables and using the
percentage within the 1 km2 plot for the categorical land
cover and geology variables (for further details see
Wohlgemuth et al. 2008, Nobis et al. 2009). Collinearity
among the environmental predictors at the plot level was low
(r  0.70; Dormann et al. in press), with exception of PY
and WB7 where the Pearson correlation coefficient was
r  0.72.
For each species, we calculated the arithmetic mean of
the environmental covariates considering all plots where it
occurred. In contrast to plot-level data, these data were
highly collinear, with the highest correlations occurring
between PY and WB7 (r  0.86) and between TY and
Agrilow (r  0.83; plot level r  0.59).
Data analyses
Potential bias of reduced species lists
Because only species with available adult age data could be
considered, i.e. 708 out of 1737 species of the first BDM
survey, we tested how well these species represent species
richness and species composition of the full sample. We
did this by correlation analyses of species richness per plot
and species co-occurrence analyses through comparisons
of plot scores on multivariate ordination axes for the reduced
and full species lists. For ordination, we used detrended
correspondence analysis (DCA, R-package VEGAN ver.
1.17-10) because there was an arch effect in correspondence
analysis and the length of the first DCA-axis (10.45 standard
deviations) indicated pronounced unimodal species responses
(Hill and Gauch 1980). All analyses were performed using
R ver. 2.15.0 (R Development Core Team).
Elevational gradient of adult age
We initially analyzed the variation in adult age between
species directly along the elevation gradient using linear
least-squares regression of adult age on the average elevation
of species occurrence. We considered all species with at
least five occurrences in order to get more robust elevation
estimates. This analysis was repeated for the most speciesrich families separately, i.e. families having at least 20 species
in our sample (each species with five or more occurrences).
Age data were log-transformed to match regression assumptions tested by diagnostic plots (residuals vs fitted values,
QQ-plots, Cook’s distance). To account for multiple testing,
we used Holm’s correction (Quinn and Keough 2002)
for the significance levels of the regression slopes.
Elevational gradient of adult age within species
assemblages
To detect changing impacts of environmental factors
along the elevation gradient, we iteratively reran hierarchical
partitioning and multiple regression for plots within elevation intervals. Starting with plots having an average plot
elevation between 250 and 1000 m a.s.l., we moved this
750 m elevation interval by 100 m steps to high mountains
until the highest plot was included.
To reduce spatial autocorrelation in the model residuals
and to test the robustness of regression results, we simultaneously applied spatial and spatio-phylogenetic eigenvector
filtering (Kühn et al. 2009). Spatial eigenvectors were
derived from a geographical distance matrix of the plots
that was subjected to a principal coordinates analysis
(PCoA). The spatio-phylogenetic eigenvectors were based
on the co-occurrence matrix of phylogenetic branches
within sites subjected to correspondence analysis. These
eigenvectors represent the information of co-occurring
phylogenetic branches in space, and they are therefore called
spatio-phylogenetic eigenvectors. For eigenvector filtering,
spatio- and spatio-pyhlogenetic eigenvectors were selected
to best reduce spatial autocorrelation of model residuals
(Kühn et al. 2009). The multiple least-square regression and
hierarchical partitioning were then re-run with the selected
filters included along with the significant environmental
predictors selected by the initial stepwise regression.
As a general characteristic of the adult-age distribution
within species assemblages at the landscape level, we used
the median adult age of all species in a given BDM plot
with available adult age data. We calculated bootstrapped
medians (mean of the medians of 1000 bootstrap samples
per species list) to obtain more robust estimates. These
median adult-age values were thereafter analyzed along
the elevation gradient by linear least-square regression
as described above. We calculated median adult age of
species assemblages based on all available adult age data
as well as separately for species with root collar and rhizome data.
Results
Environmental covariates
Adult-age variation between species
Based on the predicted adult age–temperature relationship
of MTE, we made regressions of the ln-transformed adult
age on 1/kT for species and species assemblages, where k is
the Boltzmann’s constant and T is the mean annual temperature in Kelvin. The slope of the regression line represents
an estimate of the activation energy according to MTE
(Brown et al. 2004). This estimate and its 95%-confidence
interval was compared to the predicted range of 0.6–0.7 eV
(Brown et al. 2004).
The covariation between the median adult age and
multiple environmental factors was analyzed by multiple
least-square regression and hierarchical partitioning
(Chevan and Sutherland 1991). Starting with the full environmental model, a stepwise model was built by backward
elimination of non-significant covariates. Again, we used
the natural logarithm of adult age and replaced TY by 1/kT
to get estimates for the activation energy of MTE in the
multiple regression framework. For hierarchical partitioning, we used the R-package ‘hier.part’ (Mac Nally and
Walsh 2004) with R-squared as the goodness-of-fit measure.
Potential bias of reduced species lists
Species with adult age data were representative of the full
monitoring sample in terms of diversity patterns: the
number of species per 1 km2 plot with available adult age
data was highly correlated with the total number of observed
species per plot (r  0.95, p  0.001). Plot scores of the first
four DCA axes were also highly correlated (DCA1: r  0.99;
DCA2: r  0.96; DCA3: r  0.95; DCA4: r  0.86;
p  0.001 in all cases).
We found a weak, but significant linear increase of log10
adult age towards higher elevations (slope  3.58e-04,
SE  3.18e-05, t  11.24, n  567, p  0.001, R2  0.181;
Fig. 2). The slope of the regression line was positive for
all species-rich families, and it was significant (pHolm  0.05)
for four out of the eight families (Fig. 3). In some families,
there was a strong increase with elevation, e.g. Caryophyllaceae.
Corresponding to the elevational gradient, model fit of
the linear regression of ln-transformed adult age of species
on 1/kT was low (R2  0.174). The estimated activation
energy from the slope of the regression line was 1.10 eV
(SE  0.10, t  10.93, n  567, p  0.001) with a 95%confidence interval of 0.90–1.30 eV.
Multiple linear regression of ln-transformed adult age of
species on environmental covariates did not considerably
improve the model fit (adjusted R2  0.227). Because of
highly collinear environmental data, estimates of parameter
coefficients were not considered further. In hierarchical partitioning, AgriLow showed the highest independent effect
(40.4%), followed by TY (20.3%) and AgriAlp (12.7%).
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Figure 2. Adult age of single species (n  567; log10 scale) and
average elevation of species occurrences. A linear regression line
is shown with  95% CI for the regression line and the response.
Adult-age variation between species assemblages
The general trend of increasing adult age of species with
average elevation of occurrence scaled up to a strong linear
relationship between the log10-transformed median adult
age of species assemblages and the average plot elevation
(R2  0.894; slope  2.54e-04, SE  4.13e-06, t  61.59,
n  453, p  0.001; Fig. 4A). Bootstrapping improved the
model fit without affecting the estimated slope (without
bootstrapping: R2  0.865, slope  2.54e-04). Median
adult age and elevation were also highly correlated if
analyzed independently for rhizome and root collar data
(model fit R2  0.736 and R2  0.887, respectively). The
correlation between median adult age based on both sources
of age data was high (r  0.87, p  0.001), with outliers
being related to plots with only a small number of species
used for median adult-age (Fig. 5). In general, species
assemblages with low median adult age had higher values
of median adult age for rhizome data than for root collar
data (Fig. 5).
Model fit was high for the linear regression of
ln-transformed median adult age on 1/kT (R2  0.846;
Fig. 4B), with an estimated activation energy of 0.79 eV
(SE  0.02 eV, t  49.79, n  453, p  0.001) and a
95%-confidence interval of 0.76–0.82 eV. The estimated
activation energy was 0.85 eV (CI-95%  0.82–0.89 eV) for
root collar data, whereas it was only 0.69 eV (CI-95%  0.63–0.71 eV) for rhizome data.
Multiple linear regression of ln-transformed median
adult age on environmental covariates showed a good fit for
the full model (adjusted R2  0.892; Table 1). Stepwise
regression revealed that mean annual temperature (TY),
agricultural areas in the lowlands (AgriLow) and calcareous
substrate (Calc) were significant environmental covariates.
In combination, these three covariates explained 89.1% of
the variance in median adult age (Table 1). According to
hierarchical partitioning, TY showed the highest inde­
pendent effect, followed by AgriLow and Calc. The three
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signi­ficant covariates were all negatively correlated with
adult age, i.e. species generally had a shorter lifespan
under warmer climates, in lowland agricultural areas, and
on calcareous substrates. After replacing TY by 1/kT in
the stepwise model, the estimated activation energy was
0.65 eV (CI-95%  0.62–0.69 eV; Table 1). For median
adult age, the estimated activation energy was 0.72 eV
(CI-95%  0.68–0.76 eV) for root collar data and 0.57 eV
(CI-95%  0.52–0.61 eV) for rhizome data.
For the stepwise model, eigenvector filtering selected
none of the pure spatial eigenvectors, but two of the
spatio-phylogenetic eigenvectors, namely the 1st and 3rd.
The 1st eigenvector showed a strong independent effect
in hierarchical partitioning when added to the stepwise
model, mainly at the expense of mean annual temperature
(Table 1). The ranking of the environmental covariates
based on their independent effect as well as the sign of
their coefficients did not change when the two eigenvectors
were added, but the coefficient of calcareous substrates
was no longer significant. Due to the fact that the 1st
eigenvector was highly correlated with mean annual temperature (r  0.96) no activation energy of MTE was estimated from model coefficients.
For the three significant environmental covariates
determined from stepwise regression, hierarchical partitioning and modeling results within elevation bands of 750 m
from lowlands to high mountains (Fig. 6) revealed reduced
model fits, but clear changes in both the independent
effects of the covariates and their ranking.
Discussion
Our results revealed a general trend of increasing adult age
of vascular plant species along an elevation gradient. This
trend was detected at the level of overall interspecific
variation, within different plant families, and for species
assemblages at the landscape level. These results are in
accordance with previous studies that report elevational
gradients of plant age for intraspecific variation (Körner
2003, von Arx et al. 2006, Munch and Salinas 2009,
Giménez-Benavides et al. 2011) and with the rarity of annuals at alpine elevations or arctic latitudes (Warming 1909,
Molisch 1929, Klimeš 2003). Since our results are based on
a standardized sample that covers a large fraction of the
vascular plant flora of the study area, as well as the main
patterns in species richness and composition (DCA), the
results strongly support a general trend of increasing adult
age with elevation for vascular plants in the Alps.
At the species level, however, adult age showed huge
variability and the elevational trend was weak (Fig. 2). In
addition to adult age as a rough estimate of lifespan, there
are some clear explanations for this finding. Life histories
often vary greatly among plant species over short distances
and independently from elevation, for instance between
crop-field annuals and nearby forest species. As we used
1 km2 species occurrence data, we were not able to account
for local habitat effects. Nevertheless, agricultural land use
in the lowlands, representing habitat information at the
landscape level, showed the strongest independent effect
on adult age. In addition, different elevational trends
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Figure 3. Adult age of single species (log10 scale) and average elevation of occurrence along the elevational gradient for plant families with 20 or more species in our sample. pHolm-values refer
to the slope of the regression line after applying the Holm correction for multiple testing.
Figure 4. Relationship between median adult age and (A) the average plot elevation, and (B) mean annual temperature. Temperature is
expressed by 1/kT, where k is the Boltzmann’s constant and T is absolute temperature in Kelvin. Linear regression lines are shown with  95% CI for the predicted regression line and the response. Median adult age values are log10 scale (A) and ln scale (B), respectively.
for plant families (Fig. 3) indicate that differences between
phylogenetic lineages contribute to the variability of adult
age found for the overall elevational trend at the species
level.
Despite the observed large variation in life histories,
median adult age showed a very strong elevational trend
as a property of species assemblages at the landscape level
(Fig. 4A), with median adult age about four-times higher
in high mountains than in lowlands. As pointed out by
Körner (2007), in comparative ecology the interpretation
of elevation gradients is often complicated by confounding
effects between environmental factors. This applies to
Figure 5. Relationship between median adult age when calculated
separately for root collar and rhizome data. Solid and dotted lines
represent a major axis regression with  95% CI for the regression
line. The dashed line is the 1:1 line. Open circles refer to species
assemblages where median adult age was based on  20 species.
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our study, where we found significant effects of climate,
land use and geology on median adult age. Moreover,
these factors revealed changing impacts on adult age with
elevation (Fig. 6). In the lowlands, the dominant effect of
land use by agriculture is consistent with r-K selection that
disturbances favor r-strategists and therefore reduced adult
age. In contrast, our second r-K selection prediction of
reduced adult age with low precipitation or water balance
in July, indicators of drought, was not confirmed. This
result is probably due to the fact that the lowest precipitation (minimum PY in our sample  616 mm yr21) observed
in the study region is not low enough to cause drought stress
for most species. The dominant overall environmental
factor influencing median adult age, however, was mean
annual temperature.
The exponential temperature dependence of adult age is
in accordance with expectations of the metabolic theory
of ecology. However, from direct regressions of adult age
on temperature, we found that the estimated activation
energy of 1.10 eV at the species level and 0.79 eV at the
species assemblage level, as well as their 95%-confidence
intervals, are clearly above the MTE-predicted range of
0.6–0.7 eV. On the other hand, these estimates were
biased by confounding factors, as the proportion of agri­
cultural areas in the lowlands was the main factor driving
adult age at the species level (hierarchical partitioning)
and an important one at the species assemblage level
(multiple regression). From this finding, we conclude that
corresponding anthropogenic habitats increase the number
of short-lived species and therefore the estimated acti­
vation energy. This effect is reduced when scaling up
from the individual species level to species assemblages
(1.10 eV → 0.79 eV). After including land use as a
confounding factor contributing to the temperaturedependency of median adult age, the estimated activation
energy for MTE of 0.65 eV (CI-95%  0.62–0.69 eV) coincides nicely with the predicted range of 0.6–0.7 eV.
Table 1. Summary of multiple linear regressions (coefficient estimates) and of hierarchical partitioning (independent effects) for the median
adult age of species assemblages at the landscape level (natural logarithm) and environmental covariates.
Full model
Parameter
(Intercept)
TY
AgriLow
Calc
Urban
AgriAlp
PY
WB7
Filter:
P1
P3
Adjusted R2
Slope of 1/kT
95% CI
Stepwise model
Stepwise model and filtering
Coefficient
estimate
Independent
effect (%)
Coefficient
estimate
Independent
effect (%)
Coefficient
estimate
Independent
effect (%)
2.290e  00***
29.985e-02***
22.401e-03***
21.986e-03***
27.087e-04 ns
26.978e-04 ns
2.218e-05 ns
24.851e-04 ns
–
54.1
17.1
12.3
5.2
4.6
4.1
2.5
2.276e  00***
29.828e-02***
22.221e-03***
22.199e-03***
–
–
–
–
–
63.7
22.3
14.1
–
–
–
–
7.494e-01***
21.263e-04***
24.593e-04***
21.240e-04 ns
–
–
–
–
–
35.2
14.5
9.0
–
–
–
–
–
–
–
–
–
–
–
–
1.268e-01***
22.319e-02***
39.7
1.6
0.892
0.661
0.623–0.698
0.891
0.652
0.619–0.685
0.937
na
na
­ otes: ‘***’  p  0.001; ‘ns’  non-significant, p  0.05; ‘–’  not used in the given model; P1 and P2 are the 1st and 3rd spatio-phylogenetic
N
eigenvectors selected by eigenvector filtering; ‘na’  not calculated because of high collinearity between temperature and P1.
This finding can be interpreted as strong support for
MTE. However, the activation energy independently
estimated from rhizome and root collar data (0.57 and
0.72 eV, respectively) did not fit as well even though
confidence intervals overlapped. Both types of age data
clearly represent species sets with different life form
spectra. Root collar data include many short-lived species
(e.g. annuals and biennials) which are absent or rarely
found in the rhizome data. These constraints imposed by
specific life forms might explain why species assemblages
with low median adult age generally had higher values
for rhizome data than for root collar data (Fig. 5) and
consequently why deviations from the MTE-predicted
activation energy were greater. From the MTE perspective,
it can be argued that, to a certain degree, pooling both
sources of age data levels out a bias in estimated activation
energy caused by different life forms.
Despite the fact that MTE makes strong predictions, the
way the theory has been tested in literature so far as well as
in our study can be criticized. Usually, overlaps of observed
confidence intervals with the predicted range of 0.6–0.7 eV
for the activation energy (some studies use 0.2–1.2 eV,
Munch and Salinas 2009) are interpreted as evidence of
support for the metabolic theory. With this approach,
the MTE prediction itself is the null-hypothesis. As a
consequence, the weaker the observed relationship and the
larger the confidence interval, the more likely it is to accept
the null-hypothesis and ‘support’ MTE. Therefore, more
appropriate null models which explicitly exclude MTE
assumptions are strongly needed to test the theory by
rejection of the null hypothesis. However, in our study the
estimated activation energy for median adult age of the
most comprehensive analysis, including all adult age data
as well as confounding factors, not only falls within the
Figure 6. Hierarchical partitioning and the independent effects of covariates of median adult age calculated for 750 m elevation
bands moved by 100 m steps from lowlands to high mountains. Rectangles: agricultural land use in the lowlands; filled circles: mean
annual temperature; triangles: calcareous substrates. The grey background gives the model performance (R2) of the multiple linear regression. Lines are fitted with cubic-smoothing splines (DF  6).
1083
predicted range of 0.6–0.7 eV, but also covers the entire confidence interval. Therefore, MTE accurately predicted this
adult age–temperature relationship even if the common
approach can be criticized in general.
MTE is still controversially discussed in macroecology
and tends, for example, to be a poor predictor for the temperature–species richness relationship (Algar et al. 2007,
Hawkins et al. 2007, McCain and Sanders 2010). The
theory, however, seems to be much better supported by
the life history of species. Again, studies of intraspecific
variation in lifespan support our findings. For example,
Munch and Salinas (2009) found that MTE accurately predicts the intraspecific temperature–lifespan relationship for
a wide range of ectotherms.
Despite the strength of our findings, there are limitations
of our study that have to be addressed. First, the age
data were based on varying and generally small numbers of
specimens per species, and the representativeness of the
age information of a single species might therefore be
questionable. The weak correlation between the sample size
and the average elevation of species occurrences, however,
indicates that this bias in adult-age data is not a major confounding factor for the patterns observed. In addition, only
adult individuals growing under natural conditions were
sampled, rather than notably long-lived individuals. This
precaution seems to be a more important standardization
for representing the common natural situation than just
a higher number of specimens per species (Schweingruber
et al. 2012). Even though the small number of specimens
introduced noise, it is unlikely that this had much influence
on the general adult age gradient. The same argument
holds for the fact that age data and environmental information were derived from independent data sets. This might
again have attenuated the strength of the relationships but
could hardly cause the detected elevation and temperature
gradients of adult age.
Another potential limitation to our study is the phylo­
genetic bias in our age data. Monocot species could not
be considered due to methodological difficulties in age
detection (Schweingruber and Poschlod 2005), and our
results are therefore based on dicots. However, we are not
aware of any evidence that the elevational gradient of adult
age is generally different in monocots. In fact, a high number of gramineous annuals occur in the lowlands of the
study area (e.g. species of the genera Aira, Apera, Bromus,
Digitaria, Echinochloa, Eragrostis, Hordeum, Panicum,
Setaria and Vulpia), but no single annual Gramineae species
has a high mountain core area.
As a second phylogenetic aspect, the 1st spatio-phylogenetic
eigenvector reduced spatial autocorrelation in the residuals
of multiple regression and was highly collinear with temperature. The observed temperature dependency including
the MTE interpretation for median adult age is, therefore,
affected by the phylogenetic structure of the species assemblages in space. However, the interpretation of these
structures is difficult and involves ecological processes
like species assemblage rules or biological invasions, as well
as evolutionary processes like regional diversification or
trait evolution, and these steps were clearly beyond the scope
of our study.
1084
Conclusions and implications
In spite of these limitations, our results clearly support
a gradual adult age–temperature gradient with increasing
adult age towards higher elevations and a notable variability
within perennials. Thus, the common classification of
annuals, biennials and perennials falls short of sufficiently
reflecting the environmental covariation of adult age
and quantitative data are indispensable. The adult age–
temperature relationship should also offer general insights
into species range and vegetation dynamics. This implies,
for instance, decreasing population and vegetation dyna­
mics along the elevational gradient, a pattern that is
already known from empirical studies (Stephenson and
Mantgem 2005). Furthermore, our results imply that climate warming could accelerate species turnover rates and
vegetation dynamics by favoring short-lived species at the
expense of more long-lived species, with land use as a major
confounding factor. In this context, MTE is a promising
theory that not only provides a theoretical basis on first
principles in trait analyses but also offers the chance to
be implemented in analyses of species range shifts and vegetation dynamics under climate change.
Although MTE is ‘emphatically not a theory of everything’ (Brown et al. 2004), there is increasing support of
MTE for the lifespan of individuals, bridging the intraand interspecific variation of this key life-history trait
along elevational and latitudinal gradients for a wide range
of taxa. However, confounding factors and the remaining
variability in adult age around a general temperature trend,
which is explained well by MTE, calls for more detailed
research on interactions between broad-scale constraints and
local factors.­­­­­­
Acknowledgements – We thank the Swiss Federal Office for the
Environment (FOEN) and the BDM Coordination Office
(Hintermann and Weber AG, Reinach) for providing the data set of
the Swiss Biodiversity Monitoring program and all botanists
who contributed to this unique data set. We are also grateful
to Walter Durka for the phylogenetic supertree, Niklaus E.
Zimmermann and Peter B. Pearman for helpful discussions,
and Melissa Dawes for revising the English.
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