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Ecological theory posits that temporal stability patterns in plant populations are associated with differences in species’ ecological strategies. However, empirical evidence is lacking about which traits, or trade-offs, underlie species stability, specially across different ecosystems. To address this, we compiled a global collection of long-term permanent vegetation records (>7000 plots from 78 datasets) from a wide range of habitats and combined this with existing trait databases. We tested whether the observed inter-annual variability in species abundance (coefficient of variation) was related to multiple individual traits and multivariate axes of trait variations (PCoA axes). We found that species with greater leaf dry matter content and seed mass were consistently more stable over time (lower variability in species abundance) although other leaf traits played a significant role as well, albeit weaker. Using multivariate axes did not improve predictions by specific traits. Our results confirm existing theory, providing compelling empirical evidence on the importance of specific traits, which point at ecological trade-offs in different resource use and dispersal strategies, on the stability of plant populations worldwide.
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Functional traits trade-offs define plant population stability worldwide
Luisa Conti1,2*, Enrique Valencia3, Thomas Galland2,4, Lars Götzenberger2,4, Jan Lepš4,5, Anna E-
Vojtkó2,4, Carlos P. Carmona6, Maria Májeková7, Jiří Danihelka8,9, Jürgen Dengler10,11,12, David J.
Eldridge13, Marc Estiarte14,15, Ricardo García-González16, Eric Garnier17, Daniel Gómez18, Věra
Hadincová9, Susan P. Harrison19, Tomáš Herben20,9, Ricardo Ibáñez21, Anke Jentsch22, Norbert
Juergens23, Miklós Kertész24, Katja Klumpp25, František Krahulec9, Frédérique Louault25, Rob H.
Marrs26, Gábor Ónodi24, Robin J. Pakeman27, Meelis Pärtel6, Begoña Peco28, Josep Peñuelas14,15,
Marta Rueda29, Wolfgang Schmidt30, Ute Schmiedel23, Martin Schuetz31, Hana Skalova9, Petr
Šmilauer32, Marie Šmilauerová4, Christian Smit33, MingHua Song34, Martin Stock35, James Val13,
Vigdis Vandvik36, David Ward37, Karsten Wesche38, 39, Susan K. Wiser40, Ben A. Woodcock41, Truman
P. Young42, 43, Fei-Hai Yu44, Martin Zobel6, Francesco de Bello45
1Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha – Suchdol,
Czech Republic
2Institute of Botany, Czech Academy of Sciences, Třeboň, Czech Republic.
3Departamento de Biología y Geología, Física y Química Inorgánica, Escuela Superior de Ciencias
Experimentales y Tecnología, Universidad Rey Juan Carlos, Móstoles, Spain.
4Department of Botany, Faculty of Sciences, University of South Bohemia, České Budějovice, Czech
Republic.
5Institute of Entomology, Czech Academy of Sciences, Ceske Budejovice, Czech Republic.
6Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.
7Plant Ecology Group, Institute of Evolution and Ecology, University of Tübingen, Tübingen,
Germany.
8Department of Botany and Zoology, Masaryk University, Czech Republic
9Institute of Botany, Czech Academy of Sciences, Průhonice, Czech Republic
10Vegetation Ecology, Institute of Natural Resource Sciences (IUNR), Zurich University of Applied
Sciences (ZHAW), Wädenswil, Switzerland
11Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of
Bayreuth, Bayreuth, Germany
12German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
13Centre for Ecosystem Studies, School of Biological, Earth and Environmental Sciences, University
of New South Wales, Sydney, Australia
14CREAF, Cerdanyola del Vallès, Catalonia, Spain
15CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain
16Pyrenean Institute of Ecology (IPE-CSIC), Jaca-Zaragoza, Spain
17Centre d'Ecologie Fonctionnelle et Evolutive, Université de Montpellier, CNRS, EPHE, IRD,
Université Paul-Valéry Montpellier 3, Montpellier, France
18Pyrenean Institute of Ecology (IPE-CSIC), Jaca-Zaragoza, Spain
19Department of Environmental Science and Policy, University of California Davis, CA, USA
20Department of Botany, Faculty of Science, Charles University, Praha, Czech Republic
21Department of Environmental Biology, University of Navarra, Pamplona, Spain
22Disturbance Ecology and Vegetation Dynamics, Bayreuth Center of Ecology and Environmental
Research, University of Bayreuth, Bayreuth, Germany
23Research Unit Biodiversity, Evolution & Ecology (BEE) of Plants, Institute of Plant Science and
Microbiology, University of Hamburg, Germany
24Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, Hungary
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25Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Ecosystème Prairial, 63000 Clermont
Ferrand, France
26School of Environmental Sciences, University of Liverpool, Liverpool L69 3GP, UK
27The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK
28Terrestrial Ecology Group (TEG), Department of Ecology, Institute for Biodiversity and Global
Change, Autonomous University of Madrid, Madrid, Spain
29Department of Plant Biology and Ecology, University of Seville, Sevilla, Spain
30Department of Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen,
Germany
31Community Ecology, Swiss Federal Institute for Forest, Snow and Landscape Research,
Birmensdorf, Switzerland
32Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, České
Budějovice, Czech Republic
33Conservation Ecology Group, Groningen Institute for Evolutionary Life Sciences, Groningen, The
Netherlands
34Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences
and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
35Wadden Sea National Park of Schleswig-Holstein, Tönning, Germany
36Department of Biological Sciences, University of Bergen, Norway
37Department of Biological Sciences, Kent State University, Kent, USA
38Botany Department, Senckenberg, Natural History Museum Goerlitz, Görlitz, Germany
39International Institute Zittau, Technische Universität Dresden, Dresden, Germany
40Manaaki Whenua – Landcare Research, Lincoln, New Zealand
41UK Centre for Ecology & Hydrology, Crowmarsh Gifford, UK
42Department of Plant Sciences, University of California, Davis, CA, USA
43Mpala Research Centre, Nanyuki, Kenya
44Institute of Wetland Ecology & Clone Ecology / Zhejiang Provincial Key Laboratory of Plant
Evolutionary Ecology and Conservation, Taizhou University, Taizhou, China
45CIDE-CSIC, Valencia, Spain
*Correspondence:
Luisa Conti
Faculty of Environmental Sciences
Czech University of Life Sciences Prague
Kamýcká 129, 165 00 Praha-Suchdol
conti@fzp.czu.cz
The following Supporting Information is available for this article:
Fig. S1 Mean species variability (CV) and categorical traits
Fig. S2 Mean species variability detrended (CVt3) and traits
Fig. S3 Species’ mean abundance and standard deviation, and traits
Fig. S4 Random slope effects in single trait models.
Table. S1 Mean species variability (CV) and PCoA axes
Table S2 Dataset information (Separate file: “Table S2 Datasets information.xlsx”)
Table S3 Functional traits information (Separate file: “Table S3 Traits information.xlsx”)
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Summary
1. Ecological theory posits that temporal stability patterns in plant populations are associated
with differences in species’ ecological strategies. However, empirical evidence is lacking
about which traits, or trade-offs, underlie species stability, specially across different
ecosystems.
2. To address this, we compiled a global collection of long-term permanent vegetation records
(>7000 plots from 78 datasets) from a wide range of habitats and combined this with existing
trait databases. We tested whether the observed inter-annual variability in species
abundance (coefficient of variation) was related to multiple individual traits and multivariate
axes of trait variations (PCoA axes).
3. We found that species with greater leaf dry matter content and seed mass were consistently
more stable over time (lower variability in species abundance) although other leaf traits
played a significant role as well, albeit weaker. Using multivariate axes did not improve
predictions by specific traits.
4. Our results confirm existing theory, providing compelling empirical evidence on the
importance of specific traits, which point at ecological trade-offs in different resource use
and dispersal strategies, on the stability of plant populations worldwide.
Keywords: acquisitive; conservative; dispersal; global database; long-term experiment; resource
use; temporal patterns; variability
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Introduction
Identifying the drivers of temporal stability in plant populations and communities has consequences
for maintenance of multiple ecosystem functions over time, including carbon sequestration, fodder
resources for livestock, and nutrient cycling (Tilman & Downing, 1994; Hautier et al., 2015; Isbell et
al., 2018). One of the main determinants of community stability is the cumulative temporal
variability in the abundances of individual species’ populations (Thibaut & Connolly, 2013; Hallett et
al., 2014; Májeková et al., 2014). Lower temporal variability in individual population abundances at
a given site generally increases overall community stability (Lepš et al., 1982, 2018; Pimm, 1984;
McCann, 2000). Accordingly, assessing the drivers of temporal variability in populations is necessary
to understand and forecast the potential consequences of increasingly common environmental
perturbations (Easterling et al., 2000; Lloret et al., 2012).
While empirical evidence is still scarce and ambiguous, theoretical predictions suggest that
the drivers of temporal variability in plant populations are related to different ecological
characteristics of species (e.g., r/K life history strategies, MacArthur & Wilson, 1967). These
differences can be described through functional traits that determine how plants respond to
environmental factors, affect other trophic levels, and influence ecosystem properties (Lavorel &
Garnier, 2002; Kattge et al., 2011; Garnier et al., 2016). Specifically, differences in functional traits
among species result in varied responses to the environment that might lead to different patterns
of demography, adaptation, and distribution, thus giving rise to different population fluctuations
over time (e.g. Angert et al., 2009; Metz et al., 2010; Adler et al., 2013; Májeková et al., 2014).
Assessing differences in functional traits between species, as well as the relationship of these
differences to specific ecological patterns, has been a long-standing focus in plant ecology leading
to a search for general trait trade-offs across taxa and ecosystems (e.g. Díaz et al., 2016). Trait trade-
offs are generally understood as a shift in the balance of resource allocation to maximise fitness
within the constraints of finite resources (e.g. Grime’s C-S-R strategy scheme; Grime, 1977). Traits
linked to specific axes of ecological differentiation are key in understanding major trade-offs in plant
strategies, such as the trade-off between leaf maximum photosynthetic rate and leaf longevity, also
known as the leaf economic spectrum (Wright et al., 2004).
Indeed, different specific trade-offs can underlie differences in species’ temporal patterns,
both within and between community types. For example, species that are able to respond quickly
to environmental variability, e.g. acquisitive resource-use strategy, fast-growing species that invest
in organs for rapid resource acquisition and/or high dispersal ability, should sustain higher temporal
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variation in population size, and will be favoured in sites where disturbance and/or environmental
instability determine a fluctuation in resources (MacArthur & Wilson, 1967; Westoby, 1998). In
contrast, species adapted to endure environmental variability, e.g. conservative resource-use
strategy, slow-growing and long-lived species that invest in structural tissues and permanence, are
thought to persist during unfavourable periods due to resources stored from previous, more
favourable years (Reich, 2014), and will exhibit less temporal variability (MacArthur & Wilson, 1967;
Grime, 2001; Wright et al., 2004). These species are expected to be favoured in more stable and
predictable environments (Kraft et al., 2014).
It remains unclear though whether the potential relationship between species’ traits and
species’ stability would be detected through differences in single traits or combined axes of
differentiation that incorporate multiple traits (Westoby, 1998; Laughlin, 2014; Díaz, et al. 2016).
Several ecological strategy schemes, such as the classic r/K selection (MacArthur & Wilson, 1967)
and C-S-R (Grime, 1977) theories, as well as the Leaf-Height-Seed scheme (´LHS´; Westoby, 1998),
can theoretically help predicting how functional trade-offs determine species’ temporal strategies
and their fitness across different types of environments. The LHS scheme for instance, is based on
three independent plant traits which should provide key proxies for independent trade-offs in plants
(stress adaptation, competition, and response to disturbance respectively; Westoby, 1998).
Interestingly, only a few empirical studies have linked differences in temporal strategies to
functional traits within plant communities (Adler et al., 2006; Angert et al., 2009; Metz et al., 2010;
Májeková et al. 2014; Craven et al., 2018). For example, Májeková et al. (2014) empirically
confirmed that herbaceous species with a more conservative resource-use strategy (i.e., those with
higher leaf dry matter content - LDMC) have more stable populations over time. A similar
relationship was found at the community level, where communities including a greater abundance
of species with high LDMC were more stable (Polley et al., 2013; Chollet et al., 2014). A recent global
meta-analysis of sown grasslands suggested that an increase in the abundance of rapidly-growing
species can destabilize community biomass over time (Craven et al., 2018). This is supported by
empirical demonstrations that community stability is predicted by the functional traits of the
dominant species rather than by species diversity per se (Lepš et al., 1982). Further, only Májeková
et al. (2014) tested whether trait-based predictions of population temporal variability were
consistent across different managements, i.e. fertilization and competitor-removal treatments,
generally finding minor differences and consistent predictions for LDMC. Ultimately, global
empirical evidence of a general link between quantitative functional traits and the temporal
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variability of populations, and whether this link is maintained despite differences in community
types and environmental conditions, is still missing (de Bello et al., 2021).
Here, using a global compilation of long-term, recurrently monitored vegetation plots,
encompassing different habitat types (https://lotvs.csic.es/), we determine which plant traits better
predict the temporal stability of plant populations. We expect that populations of species with more
acquisitive and higher dispersal-ability traits will tend to be more variable over time, while those of
species with more conservative trait values and lower dispersal ability will tend to be more stable
over time. We also expect to find global empirical evidence of the generality of these relationships.
Materials and Methods
Plots and population’s stability
We used 78 datasets consisting of a total of 7396 permanent plots of natural and semi-natural
vegetation that have been consistently sampled for periods of between six and 99 years, depending
on the dataset (Supporting Information Table S2; Valencia et al. 2020a, Sperandii et al. 2021). These
datasets were collected from study sites around the globe, and differ in sampling method (e.g.,
above-ground biomass, visual species cover estimates, species individual frequencies), plot size, and
study duration. The studies that generated the datasets sampled different types of vegetation and
covered a wide array of biomes, with mean annual precipitation spanning from 140 mm to 2211
mm, highest temperature of the warmest month spanning from 11.3°C to 35.7°C, and lowest
temperature of the coldest month spanning from -35.3°C to 7.7°C (Supporting Information Table
S2).
First, for each plot we quantified the inter-annual variability in the size of each species’
population using the coefficient of variation (CV) of abundance over time, i.e. the standard deviation
of species abundance over mean species abundance (Májeková et al., 2014; de Bello et al., 2021).
Since a fundamental differentiation between growing strategies corresponds to woody versus non-
woody species (Reich, 2014; Díaz et al., 2016), here we focused on non-woody species, thus
excluding forest overstories and woody species’ seedlings when present. Moreover, based on the
collected data available, we could not distinguish adult woody individuals from seedlings, with
seedlings most likely being the cause of high variability in woody species’ CV values (see Supporting
Information Fig. S1a). To avoid using biased CV values (increased CV for very sporadic species), we
excluded those species that occurred in fewer than 30% of the sampling events across the time
series for a given plot (Májeková et al., 2014). Further, to account for variability in CV values
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between and inside the datasets, mostly due to differences in abiotic, biotic, and management
conditions, we calculated the average CV value for each species in each dataset, standardizing and
scaling these averages within each dataset (z-scores). This resulted in a total of 3,397 species per
dataset CV values. To account for potential effects of temporal directional trends in vegetation
affecting CV (Valencia et al., 2020b) we also computed a detrended version of CV (CVt3) which gave
very similar results to the basic CV calculations (see Supporting Information Fig. S2).
Functional traits
For all the species in our dataset, we collected trait information from the TRY global database
(Kattge et al., 2020). We specifically focussed on continuous traits of herbaceous species, which
were more appropriate for depicting trait trade-offs along axes of species differentiation (see
Supporting Information Fig. S1). Specifically, we analysed plant height, seed mass, specific stem
density, LDMC, specific leaf area (SLA), leaf nitrogen content per unit mass, and leaf phosphorus
content per unit mass (see Garnier et al., 2017 for trait name nomenclature and definitions). For
each species, we averaged trait values across all standard measurements obtained from TRY,
excluding those performed under explicit treatments, on juveniles, and outliers. The traits were log-
transformed when their distribution was skewed (using natural logarithm). For details on the traits
used, their summary statistics, their correlations, and their coverage in each dataset, see Supporting
Information Table S3. To take into account multivariate trade-offs between species, we also
considered axes of functional variation derived from multivariate analyses (Principal Coordinates
Analysis, PCoA). The traits considered were weakly inter-correlated, with the two major axes of trait
differentiation from PCoA, linked mainly to LDMC and seed mass (see Supporting Information Table
S1 for details). The taxonomic names follow the nomenclature of ‘The Plant List’
(www.theplantlist.org). Nomenclature was standardized using the R package ‘Taxonstand’ (Cayuela
et al., 2017).
Data analyses
To quantify how the considered continuous traits were linked to species CV, we used linear mixed
effect models (‘lmer’ function in R package “lme4”, Bates et al., 2014). As a response variable, we
used the mean CV for each species in each dataset, standardized as mentioned above. As predictors,
we included all the continuous traits listed above, scaled and centered. To account for the
taxonomic and spatial structure of the data, we included both species identity and dataset identifier
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as random intercept factors. We visually checked the compliance of model residuals with normality
and homoscedasticity. To assess the goodness-of-fit of the full model, marginal and conditional R2
were calculated (Nakagawa & Schielzeth, 2013; Nakagawa et al., 2017). Then, we compared the
marginal R2 of nested models, each differing in the subset of predictors that were included;
therefore, we fitted a final model that included a subset of the original predictors used and had the
highest marginal R2. Similar models were run using, instead of single traits, both the multivariate
PCoA axes that resulted from the combination of traits. We also fitted separate models using each
a single trait of those emerging as stronger in the final multivariate model (See Supporting
Information Table S1). In addition, to explore the consistency of the stability-trait relationships
across datasets, we fitted models using each single trait and adding random slope effect for the
datasets (Supporting Information Fig. S4). Finally, similar models were run also on the two
components determining species’ CV separately, i.e. mean abundance and standard deviation of
abundance in time, also standardizing these variables within each dataset (Supporting Information
Fig. S3).
Results
We were able to detect two sets of key continuous functional traits playing a consistent major role
in species’ population temporal stability: one linked exclusively to seed mass, and the other linked
to the leaf economic spectrum, i.e. LDMC, SLA, and Leaf N content. Based on the final linear mixed
effect model, these two sets of traits had the most influence on species CV among the continuous
traits considered (Table 1; Figure 1, Figure 2).
We found significant negative coefficients with population variability for LDMC and for seed
mass (Table 1; Fig. 1). These coefficients indicate that species with greater LDMC and greater seed
mass were more stable (i.e. lower CV values; Fig. 2a). In contrast, we found positive coefficients for
SLA and Leaf N content, although the effect was statistically significant only for SLA. For these traits,
the larger the trait value, the higher the species CV and therefore the less stable the species
populations (Fig. 2b,d). It should be noted that the effect of these traits was rather consistent across
datasets (low deviation of the datasets’ random slope effect compared to the main effect slope for
both the models using LDMC and seed mass; Supporting Information Fig. S4).
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Table 1. Model’s summary for both the full model, containing all the predictors, and the final model,
containing only a subset of the initial predictors. Estimates and relative standard errors are shown.
R2 (fixed): variation explained by fixed factors; R2 (total): variation explained by both fixed and
random factors. P-values calculated using Satterthwaite approximation for degrees of freedom.
***p-value<=0.001; **p-value<=0.01; *p-value<=0.05.
Full model
Final model
(Intercept)
-0.10
-0.03
(0.06)
(0.04)
Plant height
-0.01
(0.09)
Leaf N content
0.03
0.06
(0.08)
(0.04)
Leaf P content
0.04
(0.07)
Seed mass
-0.12
-0.08 *
(0.08)
(0.04)
SLA
0.02
0.09 *
(0.09)
(0.04)
LDMC
-0.23 **
-0.21 ***
(0.07)
(0.04)
SSD
0.06
(0.06)
N
676
1630
Species
93
395
Datasets
67
77
R2 (fixed)
0.05
0.07
R2 (total)
0.13
0.18
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Figure 1. Coefficient plot showing estimate values and their 68% (thin line) and 95% (thick line)
confidence intervals of the final linear mixed effect model fitted. To explain species CV, the final
model included leaf dry matter content (LDMC); seed mass transformed through natural logarithm
(Seed Mass); specific leaf area transformed through natural logarithm (SLA); and Leaf N content.
Figure 2. Regression plots of the final model showing the effects of leaf dry matter content (LDMC,
a), specific leaf area (SLA, b), seed mass (c), and leaf N (d) content on the CV of species.
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Similar results were found using either of the two first PCoA axes based on multiple traits
(Supporting Information Table S1), although with a slightly lower predictive power (R2 fixed was
0.05 compared to 0.07 in the final model with individual traits). Therefore, results from PCoA did
not improve results from single traits. We also fitted models using the single PCoA axis and the single
traits. In this case as well single trait models explained a higher variability compared to the models
with the single PCoA axis (PCoA Axis 1 model’s R2 fixed was 0.040 vs 0.050 when using LDMC; PCoA
Axis 2 model’s R2 fixed was 0.003 vs 0.005 when using seed mass; Supporting Information Table. S1).
Finally, when the two components determining species’ CV were analysed separately, i.e. species’
mean abundance and standard deviation of abundance over time, the model predicting mean
abundance was stronger compared to the one for standard deviation of abundance over time (with
significant results and a higher R2 fixed; see Supporting Information Fig. S3) although LDMC
predicted significantly both mean abundance and its standard deviation.
Discussion
By analysing a large worldwide compilation of permanent vegetation plot records, we confirmed
the generality of theoretical predictions relating key functional traits to plant population stability
over time. We specifically found that the abundance of species with greater LDMC and a bigger seed
mass were the most stable over time. Ultimately, these results suggest that common functional
trade-offs related to resource use and dispersal consistently define herbaceous plant population
stability worldwide.
We identified two likely functional trade-offs that influence stability. Specifically, differences
associated with the leaf economic spectrum (in our case linked to LDMC, SLA and N content values)
define trade-offs in terms of slow-fast resource acquisition (Wright et al., 2004; Díaz et al., 2016).
Differences in seed mass values represent the competition-colonization (seedling establishment)
trade-off (Turnbull et al., 1999) related to the species’ dispersal and establishment strategy.
Moreover, when analysing multivariate functional differentiation in herbaceous species, these set
of traits were the ones most strongly associated with the two first principal axes (Supporting
Information Table S1), further confirming the importance of these two functional differentiation
axes. These findings are broadly consistent with Diaz et al. (2016), who found that the main
differentiation between species was related to leaf and size-related (whole plant and seed) traits.
At the same time, it is interesting to notice that, in our case, combined trait information in the form
of plant spectra (i.e. via the PCoA axes) lost some ecological explanatory power compared to specific
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trait effects. This suggests that, in the case of predicting species stability, using specific functional
traits could be more effective than using axes of functional variation based on multiple traits, in
which case their individual effects could be possibly blurred.
Both the two main functional traits ultimately related to the populations’ temporal patterns
are intrinsically linked to how the species adapt to patterns of resource availability and disturbance.
Higher LDMC values, as well as smaller SLA and N content values, correspond to a slow return of
investments in nutrients, lower potential relative growth rate, and longer leaf and whole-plant
lifespan (Wright et al., 2004; Pérez-Harguindeguy et al., 2013). This implies higher potential of
buffered population growth. In fact, slow-growing and long-lived species, for example with higher
values of LDMC, could have an advantage in unfavourable years due to resources stored from
previous, more favourable years, thus maintaining buffered population growth and consequently
more stable populations (Májeková et al., 2014; Reich, 2014). Similarly, larger seed mass means
greater resources stored that tend to help the young seedling establish and survive in the face of
stress with the cost of short-distance dispersal, while smaller seeds (also in combination with seed
shape) are typically related to greater longevity in seed banks and dispersal over longer distances
(Thompson et al., 1993; Turnbull et al., 1999; Moles & Westoby, 2006). Therefore, species
germinating from seeds with a larger mass are more likely to survive during adverse years and so
their populations are more stable in a given site compared to species with smaller seeds, which will
tend to maintain their populations through permanence in seed banks, which enables proper
germination timing (Venable & Brown, 1988; Metz et al., 2010). In addition, species with greater
seed mass might be favoured in communities where gaps are scarce, which are usually dominated
by perennial species (with higher LDMC values) and are more stable. Large seeds will tend to remain
closer to the mother plant than small seeds, thus increasing the stabilizing effects on populations.
Small seeded species still maintain a buffered population growth (Pake & Venable, 1995), yet their
above-ground abundance will be more variable over time, because they usually germinate only in
favourable years. This explanation is particularly supported, for example, for short-lived plants
(annuals and biennial species together, Table S3), which tend to be less stable over time (Fig. S1b)
and are generally associated with the small-seed strategy at a global scale (Westoby, 1998).
It is important to consider that the same traits that predicted species variability, using CV,
also predicted the components of CV, i.e. species means and standard deviation (SD). Clearly the SD
in species fluctuation is inherently increasing with species means, following the so-called Taylor’s
power law (Lepš, 2004). This leads to the use of CV in the study of stability, as a more “scaled”
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measure of species variability. At the same time, when the CV is negatively correlated to species
means, as in our case (R=-0.46, which corresponds to the case of a slope in the Taylor’s power law
being lower than 2), it implies that more dominant species tend to fluctuate comparatively less than
subordinate species. This is an important observation because this scenario implies that the same
type of species that are dominant, e.g. with high LDMC, are also the more stable ones. Since
dominant species were key drivers of the stability of the communities considered in our study
(Valencia et al., 2020a) the results of the present study indicate that the same traits that determine
species dominance also determine species stability, which is a key message for any attempt to
predict both community structure and its potential to buffer environmental fluctuations (de Bello
et al., 2021).
Our results show worldwide evidence that species with more conservative leaf economics
and greater seed mass are generally more stable, i.e. less variable over time, and therefore confirm
theoretical assumptions as well as previous localized empirical evidence on the interdependence
between these traits, their relative trade-offs, and population temporal stability (e.g. MacArthur &
Wilson, 1967; Májeková et al., 2014). In addition, our results show the global validity of these trade-
offs, found across a variety of abiotic and biotic conditions. Overall, our findings contribute to a
better understanding of the drivers of plant population temporal stability, which has important
implications for the conservation of ecosystem functions over time across the world.
Acknowledgements
This research was funded by Czech Science Foundation Grant GACR16-15012S and Czech Academy
of Sciences Grant RVO 67985939. RJP was supported by the Scottish Government’s Rural and
Environmental Sciences and Analytical Services division. MP and CPC were supported by the
Estonian Research Council grant (PRG609, PSG293). MP and MZ were supported by the European
Regional Development Fund (Centre of Excellence EcolChange). SKW was supported by the
Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and
Employment. EV was funded by the 2017 program for attracting and retaining talent of
Comunidad de Madrid (n° 2017-T2/AMB-5406). RM was supported by Defra and the Leverhulme
Trust.
Author Contributions
FdB and EV conceived the idea together with LC, EV and TG gathered the data, LC prepared the
data, performed the analyses, and wrote the first draft of the manuscript. LG, JL, AE-V, CC, and
MM, helped with data preparation and/or statistical analyses. The rest of the authors contributed
with data. All the authors actively participated in the writing.
Data Availability
All the metrics used in the analyses are available at https://doi.org/10.5281/zenodo.6720583
under CC-BY licence. For access to the LOTVS datasets in full please refer to https://lotvs.csic.es/
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The following Supporting Information is available for this article:
Fig. S1 Mean species variability (CV) and categorical traits
Fig. S2 Mean species variability detrended (CVt3) and traits
Fig. S3 Species’ mean abundance and standard deviation, and traits
Fig. S4 Random slope effects in single trait models.
Table. S1 Mean species variability (CV) and PCoA axes
Table S2 Dataset information (Separate file: “Table S2 Datasets information.xlsx”)
Table S3 Functional traits information (Separate file: “Table S3 Traits information.xlsx”)
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Supporting Information
Fig. S1 Species variability (CV) and categorical traits. Here we show results of the models fitted
using single categorical traits as predictors for the mean species CV at dataset level (i.e. analogous
models as the final model in the main text): woodiness (a), life span (b), life form (c), growth form
(d). Coefficient plots of these linear mixed models are shown in red (estimates and respective 95%
confidence intervals). Intercept was excluded from the model to better understand the differences
across trait categories.
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Fig. S2 Mean species variability detrended (CVt3) and traits. We computed the detrended version
of CV using the moving window method in Valencia et al. (2020b). We then fitted a model analogous
to the final model in the main text. Results were very similar to those in the main text and are not
further discussed. In this model, R2 (fixed) was 0.05 while R2 (total) was 0.16.
Fig. S3 Species’ mean abundance and standard deviation, and traits. We fitted analogous models
as the final model in the main text but using either the mean abundance of each species in each
dataset (a), or their standard deviation (b), both these variables where scaled and centered within
each dataset. The model using the mean abundance was stronger, with more significant results
and higher R2 (fixed 0.05, total 0.22), compared to the model using the standard deviation (R2
fixed 0.04, total 0.19). Moreover, the positive effect of LDMC on the species’ standard deviation is
due to the known relationship between variation and mean abundance (Pearson’s correlation
coefficient between CV and mean abundance is -0.46).
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Fig. S4 Random slope effects in single trait models. To see the variability across datasets of the
relationships found in the main results, we fitted two separate models explaining mean (dataset
level) species variability (CV) with each the two main traits emerging from the final model in the
main text, i.e leaf dry matter content (LDMC) and seed mass, adding a random slope effect. Here,
caterpillar plots show the resulting random effect slope for each of the datasets analyzed in the
model using LDMC (a) and the model using seed mass (b).
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Table. S1 Mean species variability (CV) and PCoA axes and single traits. We performed a PCoA
considering all continuous traits. We used a PCoA instead of a PCA as we couldn’t use a correlation-
matrix PCA because of the missing trait data. Trait data was centered and scaled, as well as log-
transformed when its distribution was skewed. We used Gower's distance to generate the pairwise
distance matrix, which was corrected through squared-root transformation. We found that the first
two axes resulting from the PCoA explained 15% and 14% of the variability (when considering only
positive eigenvalues, i.e. the metric part of the distance matrix, which explained 82% of the total
variability). Moreover, these two axes were highly correlated (r>0.8) to leaf dry matter content
(LDMC) and seed mass, respectively. We fitted a linear mixed effect model analogous to the final
model in the main manuscript but using the values from these two axes. We found that despite the
high correlation with the traits explaining CV in the main results, the results using the PCoA axis
were not as clear as when using the trait values. Their R2 (fixed) value was of 0.05, compared to 0.07
in the main results (Tab.1). For a fair comparison, we also fitted models using the single PCoA axis
and the single traits, also in this case the single trait models explained a higher variability compared
to the models with the single PCoA axis. All predictors were mean-centred and scaled by 1 standard
deviation, to be able to compare results across all models. R2 (fixed): variation explained by fixed
factors; R2 (total): variation explained by both fixed and random factors. P-values calculated using
Satterthwaite approximation for degrees of freedom. ***p-value<=0.001; **p-value<=0.01; *p-
value<=0.05.
PCoA Axes
PCoA Axis 1
PCoA Axis 2
Seed Mass
(Intercept)
0.06 *
0.06 *
0.04
0.05
(0.03)
(0.03)
(0.03)
(0.02)
PCoA
Axis1
1.95 ***
1.94 ***
(0.23)
(0.24)
PCoA
Axis2
0.60 *
0.57 *
(0.27)
(0.28)
LDMC
Seed Mass
-0.04 **
(0.01)
N
2238
2238
2238
2979
Species
716
716
716
1211
Datasets
77
77
77
78
R2 (fixed)
0.05
0.04
0.003
0.005
R2 (total)
0.17
0.16
0.16
0.19
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... Species have unique tolerance and avoidance strategies for environmental disturbance. The tolerance to grazing indicates the sensitivity of species to grazing, and the drought tolerance reflects the sensitivity of species to precipitation (Serra-Maluquer et al., 2022;Conti et al., 2022). Previous studies on the impacts of two-factors on vegetation characteristics seldom included the sensitivity of species to factors to two factors, because there are few comprehensive divisions of functional groups according to the degree of sensitivity. ...
Article
Several studies have explored the influence of grazing or precipitation addition (PA), two important components of human activities and global climate change on the structure and function of communities. However, the response of communities to a combination of grazing and PA remains largely unexplored. We investigated the impact of grazing and PA on the relationship between aboveground biomass (AGB) and species richness (SR) of communities in three-year field experiments conducted in a typical steppe in the Loess Plateau, using a split-plot design with grazing as the main-plot factor and PA as the split-plot factor. AGB and SR have response threshold value to PA, which was decreased by grazing for AGB, but increased for SR. This indicates that implementing grazing management strategies is conducive to strengthening the protection of biodiversity in arid and semi-arid grasslands. Grazing promoted the AGB-SR coupling of the community by increasing the SR of medium drought tolerance (MD), low drought tolerance, and grazing tolerant functional groups. Grazing also accelerated the AGB-SR decoupling of the community by changing the AGB of high drought tolerance, MD, high grazing tolerance, and medium grazing tolerance functional groups. PA mediated changes in MD and SR of both drought and grazing tolerant functional groups and AGB of low grazing tolerance promoted the coupling of AGB-SR of the community. The Two-dimension functional groups classification method reflects the changes of AGB and SR in communities more reasonable than the division of single-factor functional groups.
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