# Trait-environment relationships depend on species life history

Léo Delalandre, Cyrille Violle, Sylvain Coq, Éric Garnier

<figure><img src="image_1.png" /></figure>

## To cite this version:

Léo Delalandre, Cyrille Violle, Sylvain Coq, Éric Garnier. Trait-environment relationships depend on species life history. Journal of Vegetation Science, 2023, 34 (6), pp.e13211. 10.1111/jvs.13211. hal-04785799

HAL Id: hal-04785799

https://hal.inrae.fr/hal-04785799v1

Submitted on 15 Nov 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Trait-environment relationships depend on species life history

Léo Delalandre ID

Cyrille Violle ID

Sylvain Coq ID

Eric Garnier ID

CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

**Correspondence**

Léo Delalandre, CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France.

Email: leo.delalandre@protonmail.com

**Funding information**

European Research Council

## Abstract

**Question:** Predicting the functional response of vegetation to environmental variations remains highly challenging. A first reason is that trait-environment relationships result from joint intraspecific and interspecific responses. A second reason is that most plant communities host sets of species that are expected to display different environmental responses according to their ecophysiological strategies.

**Location:** Mediterranean rangeland, Larzac Plateau, France ("La Fage" INRAE experimental station: 43°55' N, 3°05' E).

**Methods:** In a Mediterranean rangeland, we tested whether changes in trait values in response to management intensification (increased resource availability and grazing intensity) differ between annuals and perennials that coexist locally. To capture the multiple facets of plant phenotypes, we studied nine traits related to carbon acquisition and conservation, plant size, water-use efficiency, phenology and reproduction.

**Results:** For most traits, we evidenced a significant interaction between environment and life history. In particular, changes in trait values were higher in perennials for leaf traits related to tissue density and growth. These differences were explained by: (a) a lower species turnover in annuals, and (b) the presence of species with more distinct trait values across environments in perennials. For most traits, these changes were not accompanied by differential intraspecific variations between annuals and perennials, which invalidates previous theoretical predictions. In addition, the contribution of intraspecific trait variation to among-communities trait variance was higher for annuals than perennials for most traits.

**Conclusion:** Altogether, our findings highlight that changes in trait values with the environment can depend on functional groups (here, life history), which can further critically impact the assessment of the functional response of communities. They challenge the mean field approaches to communities in which trait aggregation is most often blindly applied, without considering potential different trajectories in response to environmental changes at various nested organizational levels.

## KEYWORDS

annual, environmental gradient, functional space, functional trait, grassland, intraspecific trait variation, perennial, plasticity, species turnover

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /></figure>

# 1 | INTRODUCTION

The pressing need to predict the response of vegetation to environmental changes has fostered the exploration of trait-environment relationships worldwide, at scales ranging from infra-species (Sartori et al., 2019) to global patterns (Bruelheide et al., 2018; Joswig et al., 2022). Directional responses of functional traits to environmental gradients have been extensively documented (Cornwell & Ackerly, 2010; Muscarella & Uriarte, 2016). However, discrepancies exist when comparing trait-environment relationships at different organizational levels, including within species, among species and among functional groups (Vellend et al., 2014; Šímová et al., 2018; Garnier et al., 2019; Anderegg, 2023). Yet, comparisons of trait-environment relationships at these different levels remain scarce.

Many plant communities host a mix of annual and perennial species, which can display contrasted environmental responses because of their ecophysiological differences (Brouillette et al., 2014; Kurze et al., 2021). This is, for instance, the case of grasslands, where herbaceous annuals and perennials can coexist but vary in their syndromes of traits (Barbaro et al., 2004). Because annual species have to germinate, grow and produce seeds during one season, they tend to display higher relative growth rates than perennials (Garnier, 1992), and to allocate more resources to seed production (Bazzaz et al., 2000). In turn, perennials tend to invest more resources in roots (Lindberg et al., 2020), can grow bigger and produce denser tissues than annuals, often develop organs dedicated to resource storage above- or below-ground and/or perform vegetative reproduction (Klimešová et al., 2021). In many conditions, annuals and perennials co-occur

in environments varying in several aspects, such as the intensity of disturbance (partial or total destruction of biomass) or stress (scarcity of resources limiting biomass production). These fundamental differences between these two life histories suggest potential group-specific trait-environment relationships.

Because of their short life cycle, annuals can respond to both disturbance and stress by growing fast and spending unfavourable episodes as seeds (Garnier, 1992; Volaire, 2018). In addition, recent work suggests tight trait coordination in annuals (Herron et al., 2021), which could reduce the range of expression of their possible phenotypes. By contrast, perennial species tend to display a wide range of strategies, from ruderal (fast-growing, producing many seeds) to stress-tolerant (slow-growing, with dense and long-lived tissues). Stress-tolerance syndromes have also been identified in annual species (Fernández Ales et al., 1993; Hodgson et al., 2020), but trait variation appears more restricted than for perennials (Hodgson et al., 2020). A null hypothesis would be that changes in trait values across environments are identical among species of the two life histories (Figure 1, case a). Against this, if changes in trait values across environments are due to the replacement of species with contrasted strategies, we expect them to be higher for perennials than for annuals (Figure 1, case b1).

In addition to species turnover, intraspecific trait variation (ITV) plays a major role in community trait changes (Lepš et al., 2011; Siefert et al., 2015), as a result of both genetic differentiation within species and phenotypic plasticity. Whether ITV due to genetic differences should be higher for annuals or for perennials is difficult to predict, because this likely depends on other dimensions of species

<figure><img src="image_6.png" /><figcaption>**FIGURE 1** Trait changes with the environment resulting from both species replacement and intraspecific trait variation (ITV). Triangles represent perennials, and circles represent annuals. Two environments, E1 and E2, are highlighted in grey. Black slopes indicate intraspecific trait change with the environment, along the ranges of environments where the species can grow, and thus connect points belonging to the same species. A species may be present in one environment (E1 or E2), or both. Red slopes indicate overall change in trait value with the environment for each life history. (a) The magnitude of species replacement and intraspecific change in trait value are identical for species of both life histories, resulting in an identical response in trait values across environments E1 and E2. (b1) Replacement of species with contrasted strategies across environmental conditions is higher for perennials, resulting in greater changes in trait value. (b2) ITV (genetic and plastic) is higher for perennials. The opposite hypothesis of higher ITV for annuals is also possible. (c1) The effects of both species turnover and ITV are higher for perennials and have a cumulative effect on changes in trait values. (c2) The effect of species turnover is higher for perennials, and intraspecific variability is higher for annuals, resulting in identical overall change in trait values for the two life histories across environments E1 and E2. All intermediate combinations are possible. Figure adapted from Anderegg (2023).</figcaption></figure>
<figure><img src="image_7.png" /></figure>

life histories (e.g. mode of pollination; Wessinger, 2021), leading to
more or less differentiation among populations. Regarding pheno-
typic plasticity, two types of mechanisms lead to two opposite pre-
dictions in grasslands. On the one hand, perennials face stress and
disturbance in their vegetative phase, and are thus likely to have more
flexible development and thus greater phenotypic plasticity than an-
nuals, which generally grow during a shorter time window and often
escape these environmental pressures (Moriuchi & Winn, 2005).
This would lead to higher ITV in perennials than in annuals (Figure 1,
case b2), and its effect could cumulate with that of species turnover,
leading to even higher changes in trait values for perennials than for
annuals (Figure 1, case c1). On the other hand, because perennials
generally have lower growth rates than annuals, and higher tissue
construction costs (Garnier & Laurent, 1994), their plastic responses
should also be slower and more costly (Stotz et al., 2022), which
could result in annual species being more plastic. In this case, ef-
fects of species turnover and plasticity could compensate each other
(Figure 1, case c2). Moreover, plasticity is not a property of a species
per se, but depends on the trait considered (Pigliucci, 2003; Garnier
et al., 2016; Stotz et al., 2022). The results of the intertwined effects
of species replacement and ITV are thus likely to depend on the trait
under scrutiny. Comparative studies have evidenced syndromes of
traits associated to different organismal functions (Laughlin, 2014;
Díaz et al., 2016), and environmental gradients (Joswig et al., 2022),
which can in turn modulate specific trait-environment relationships
(Wüest et al., 2018). As such, a multi-trait approach can complement
single-trait-environment relationships to assess their robustness to
the type of species involved.

To decipher the relative importance of aforementioned pro-
cesses, we studied annuals and perennials in a Mediterranean
rangeland in which differences in management regime resulted in
two contrasted environmental conditions: (a) intensively managed
plots, characterized by a high level of nutrient resources and high
disturbance by grazing; and (b) extensively managed plots, with low
levels of nutrients and less-intense grazing. To capture the dimen-
sions of plant functioning in these two conditions, we measured
traits related to leaf carbon acquisition and conservation, plant size,
water-use efficiency, phenology and reproduction. For species of
each life history, we measured the overall changes in trait values
with the environment, both for individual traits and for independent
synthetic axes of species' phenotypic space. We compared species
turnover for each life history, and compared the contribution of spe-
cies turnover and intraspecific trait variability with overall changes
in trait values between the two management regimes.

2 | METHODS

2.1 | Study site

The study was carried out on the "Causse du Larzac", a Mediterranean
limestone plateau in southern France, at the INRAE La Fage ex-
perimental station (43°55' N, 3°05' E, 790 m a.s.l.). Mean monthly

temperatures vary between 1°C in January and 19°C in August
(Bernard-Verdier et al., 2012). Annual mean precipitation (1,070mm)
is concentrated in spring and autumn. Two management regimes
were imposed in these calcareous rangelands: (a) the "intensive
management" regime, intensively grazed by sheep and fertilized
(nitrogen and phosphorus additions) since 1978; and (b) the "ex-
tensive management" regime, less intensely grazed and unfertilized
(Bumb et al., 2016, 2018; Garnier et al., 2018), with sandy, superficial
soils (corresponding to the "shallow" conditions of Bernard-Verdier
et al., 2012). Hereafter, these conditions are referred to as the "in-
tensive" and "extensive" regimes. The intensive regime consists in
three contiguous plots representing ca. 7ha each, whereas the ex-
tensive regime is represented by four plots of ca. 15ha each, located
a few hundred metres from the plots of the intensive regime.

Nitrogen (NNI) and phosphorous (PNI) nutrition indices were used to determine nutrient limitation for plant growth in the two regimes. NNI was calculated as the ratio between nitrogen concentration of above-ground biomass and the critical nitrogen concentration (i.e. concentration allowing potential growth), as proposed by Lemaire and Gastal (1997). PNI, which depends on the nitrogen concentration of above-ground biomass, was calculated as proposed by Ducrocq and Duru (1997) and Jouany et al. (2004). Total standing biomass at vegetation peak was measured in plots of the two management regimes. The intensity of disturbance was assessed by the proportion of the maximum standing biomass of the communities removed by sheep (Gardarin et al., 2014). Details on the protocols used to measure these variables are given in Appendix S1.

## 2.2 | Species occurrences and abundances

Species occurrences and abundances were measured using the
point-intercept method (Barkaoui et al., 2013) along sixteen 5 m-long
transects in the intensive regime (Garnier et al., 2018), and along
ten 3 m-long transects in the extensive regime (Bernard-Verdier
et al., 2012). A transect was considered as a community. We defined
a population as individuals of a species present in a given manage-
ment regime. To compare the proportion of annuals versus perenni-
als in each management regime, we computed the relative richness
of both life histories; i.e. the proportion of species belonging to one
or the other life history in each transect. We also computed the rela-
tive abundance of each life history; i.e. the proportion of contacts
represented by annual and perennial species. The species list is given
in Appendix S2.

2.3 | Species turnover within each life history

To estimate species turnover across management regimes, we used
presence/absence data of species of each life history, in each tran-
sect. For a given life history Ih, Ih being either annual or perennial,
we computed Jaccard distance (1 - Jaccard index) between each
possible pair of transects {Tr<sup>Ih</sup><sub>i</sub>, Tr<sup>Ih</sup><sub>j</sub>}, Tr<sub>i</sub> being the presence/absence
<figure><img src="image_8.png" /></figure>

matrix measured in transect *i* of the intensively managed regime, and *Tr*<sub>*j*</sub> the equivalent in the extensively managed regime. This metric equals one when all species differ between two transects, and zero when species composition is the same. Distribution of Jaccard distances was compared for the two life histories using Wilcoxon rang sum tests.

## 2.4 | Trait measurements

Traits were chosen to represent various dimensions of plant phenotype (Table 1) and were measured in the two management conditions. We used data from previous studies conducted on the same site (Bernard-Verdier et al., 2012; Garnier et al., 2018), completed with measurements of traits from 29 annual species. Measurements followed the procedures described in Pérez-Harguindeguy et al. (2013). First, leaf morphology and economics were studied using measurements of leaf area (one-sided area of a leaf), specific leaf area (the ratio of leaf area to leaf dry mass) and leaf dry matter content (the ratio of leaf dry mass to leaf fresh mass), replicated on 10 individuals of a species in each management regime. Samples were pooled into four samples per population, to ensure sufficient material, and were used to measure leaf nitrogen content per unit mass (LNC<sub>m</sub>), leaf carbon content per unit mass (LCC<sub>m</sub>), and the carbon isotope ratio of leaves (Lδ<sup>13</sup>C). Second, measurements of reproductive height were replicated on 30 individuals per species × management. Third, the date of first dispersal was measured as the first event of seed dispersal observed in the field on a weekly basis. Fourth, 10 measurements of seed mass per species × management were made on oven-dried seeds. Each measurement was performed on 5–30 seeds, depending on the species, so that total mass was not lower than 0.5 mg. In the extensive regime, for some species, seed mass in perennials was determined on plants growing slightly away from the place where individuals selected for the measurement of the other traits have been sampled, thus preventing the analysis of intraspecific variability for this trait. Trait values were averaged at the population level (intensive vs extensive management regime).

For each trait, and in each regime, we estimated trait coverage by measuring the proportion of all species present in the transects for which we had a trait value (Appendix S2). We always measured traits for more than 50% of the species present in the vegetation transects, except for the dispersal date of perennials in the intensive regime, where the proportion of species covered was only 16%, and reproductive height and leaf δ<sup>13</sup>C of perennials in the extensive regime, with respectively 21% and 46% of the species covered (Appendix S3). For the multivariate analysis, to have a complete trait matrix (see section below), we removed the date of first dispersal, because it generated an important reduction in multivariate trait coverage (Appendix S3), and completed data with values of reproductive height and leaf δ<sup>13</sup>C measured in the same management regime (i.e. the extensive regime), but away from plots where the other traits were measured.

## 2.5 | Management regime, life history and trait values

For each of the nine traits, we built a model to test the effects of life history, management regime and their interaction on mean trait values of populations. Because some, but not all, species were present in the two conditions, we included species identity as a random effect. For each trait *T*, we thus built a mixed linear model in the form:

$$T_{ijkl} = R_i \times LH_j + (1|S_k) + E_{ijkl}$$

$R_i$ is the fixed effect of management regime *i* (intensive or extensive), $LH_j$ the fixed effect of life history *j* (annual or perennial), $S_k$ is the random effect associated to species *k* and $E_{ijkl}$ is the random error term. Two traits were log-transformed to meet normality assumptions (leaf area and seed mass). Models were tested using type II Wald chi-square tests, with the “Anova” function of the car package. Post-hoc tests were performed using the “cld” function of the mult-comp package, using Tukey correction, to identify pairs of modalities that differed.

To compare shifts in the functional space of annuals and perennials across the two management regimes, we performed a principal component analysis (PCA) on scaled trait values. Within each life history, we compared species position across management regimes, on the first two axes of the PCA, using Student's test with Bonferroni correction. To test whether perennial species could display more distinct trait combinations than annuals, we used species coordinates on the PCA to compute their functional distinctiveness, which is a measure of species' originality in a multidimensional trait space (Grenié et al., 2017).

## 2.6 | Intraspecific trait variation

For each trait, we tested whether: (a) there was significant ITV between the two management regimes, and (b) the magnitude of ITV differed between the two life histories.

First, for each trait, we computed the average intraspecific difference in trait values across species as:

$$\bar{D} = \sum_{i} D_{i} = \sum_{i} (T_{i}^{\text{ext}} - T_{i}^{\text{int}})$$

$T_{i}^{\text{ext}}$ (resp. $T_{i}^{\text{int}}$) being the trait value of species *i* measured in the intensive (resp. extensive) management regime. We then generated 1,000 random values of $\bar{D}$ with no association between direction of ITV and treatment by randomizing the associations between trait value and management regime, and compared the real value of $\bar{D}$ to this random distribution. The *p*-value was computed as the proportion of times the observed value of $\bar{D}$ was lower (or higher, depending on the direction of trait variation) than in the random distribution. Second, we performed Wilcoxon rank sum test to compare the distribution of $\bar{D}$ between annuals and perennials, for each trait.
<table><caption>TABLE 1 Trait names, descriptions and identifications (Trait ID) follow the Thesaurus of Plant Characteristics, TOP (Garnier et al., 2017; accessible online at: https://www.top-thesaurus.org/), when available.</caption><thead><tr><th>Trait</th><th>Abbreviation</th><th>Unit</th><th>Trait ID in TOP</th><th>Related function</th><th>Life-cycle phase</th></tr></thead><tbody><tr><td>Leaf dry matter content</td><td>LDMC</td><td>mg/g</td><td>TOP45</td><td>Leaf tissue density, leaf physical resistance, stress tolerance</td><td>Vegetative growth</td></tr><tr><td>Specific leaf area</td><td>SLA</td><td>m²/kg</td><td>TOP50</td><td>Photosynthetic rate, leaf longevity, relative growth rate</td><td>Vegetative growth</td></tr><tr><td>Leaf area</td><td>LA</td><td>cm²</td><td>TOP25</td><td>Light interception, energy balance</td><td>Vegetative growth</td></tr><tr><td>Leaf carbon content per unit mass</td><td>LCC<sub>m</sub></td><td>mg/g</td><td>TOP452</td><td>Conversion factor from gas exchange to mass-based growth rate, lignin content</td><td>Vegetative growth</td></tr><tr><td>Leaf nitrogen content per unit mass</td><td>LNC<sub>m</sub></td><td>mg/g</td><td>TOP462</td><td>Light capture, photosynthetic rate</td><td>Vegetative growth</td></tr><tr><td>Leaf delta 13C</td><td>Lδ13C</td><td>‰</td><td>Deviation of the leaf ¹³C/¹²C isotopic ratio</td><td>Gas exchange, water-use efficiency</td><td>Vegetative growth</td></tr><tr><td>Reproductive height</td><td>RPH</td><td>cm</td><td>TOP70</td><td>Dispersal distance, light capture, above-ground competition</td><td>Regeneration and vegetative growth</td></tr><tr><td>Date of first dispersal</td><td>Disp</td><td>Day of year (Julian day)</td><td>Day of first seed dispersal observed</td><td>Dispersal and regeneration strategy</td><td>Regeneration</td></tr><tr><td>Seed mass</td><td>SeedMass</td><td>mg</td><td>TOP103</td><td>Dispersal and regeneration strategy, seedling competition</td><td>Regeneration</td></tr></tbody></table>

Note: Additional information taken from Bernard-Verdier et al. (2012) and Pérez-Harguindeguy et al. (2013).

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /></figure>

## 2.7 | Relative contribution of intraspecific trait variation and species turnover

Considering a transect as a community, we computed the relative contribution of ITV to among-communities trait variance (aITV). Following the procedure described by Lepš et al. (2011) and Siefert et al. (2015), we averaged trait values at: (a) the level of the whole study site, and (b) the level of management regime, as we did in the rest of our analyses. For each trait, and for each life history (annual vs perennial), we then computed the mean of trait values weighted by the relative abundance of species (which is a community-weighted mean, CWM, computed for each life history). This was done using either site-level trait values ($CWM_{fixed}$) or traits averaged by management regime ($CWM_{tot}$). Any difference in these two values of CWM would result from ITV across management regimes. We thus defined $CWM_{intra} = CWM_{tot} - CWM_{fixed}$. We decomposed variance at these three levels by computing the sums of squares (SS), using fixed-intercept linear models, and measured the contribution of ITV to among-communities trait variance as $aITV = \ln(SS_{intra}/SS_{fixed})$. Positive values indicated a greater contribution of intraspecific trait variability, and negative values a greater contribution of species turnover, to the total among-community variance. We compared aITV between annuals and perennials.

All analyses were performed using R (version 4.2.1; R Core Team, R Foundation for Statistical Computing, Vienna, AT).

# 3 | RESULTS

## 3.1 | Environmental conditions in the two regimes

NNI was three times higher in the intensive (NNI = 60%) than in the extensive (NNI = 20%) management regimes (Figure 2a), whereas PNI was 2.3 times higher in the intensive regime (80% vs 30%). Maximum standing biomass ranged from 3 to 5 t/ha in the intensive regime, and was only 1 t/ha in the extensive regime (Figure 2b). Some 60% of the biomass produced was removed by grazing in the intensive regime, whereas it was ca. 25% in the extensive regime, although with higher variability (Figure 2c). Annuals represented a median 60% of the species in communities of the intensive regime, and 20% of the species in the extensive regime (Figure 2d). The relative abundance of annuals was between 30% and 70% in the intensive regime, and between 0% and 30% in the extensive regime (Appendix S4).

## 3.2 | Species turnover

Species replacement across management regimes was high for both life histories: Jaccard distance between pairs of species was around 0.8–0.9 for annuals and 0.9–1 for perennials. Species replacement was significantly higher for perennials than for annuals (Wilcoxon rank sum test: W = 29,472, *p* < 0.0001; Appendix S5).

<figure><img src="image_11.png" /></figure>

**FIGURE 2** Characterization of the two management regimes (intensive vs extensive management). (a) Nutrition indices: NNI, nitrogen nutrition index; PNI, phosphorus nutrition index. (b) Maximum standing biomass (t/ha) in the two management regimes. (c) Disturbance, measured as the proportion of biomass grazed. (d) Proportion of species being annuals in transects of the two management regimes.
<figure><img src="image_12.png" /></figure>

## 3.3 | Trait variation across management regimes

Management regime had a significant effect on the values of each trait except seed mass (Appendix S6). Specific leaf area, leaf area, leaf nitrogen content per mass, leaf δ¹³C and height were higher in the intensive regime (Figure 3; Appendix S6). By contrast, leaf dry matter content, leaf carbon content per mass and dispersal date were higher in the extensive regime.

The effect of life history on trait values depended on the management regime: in the intensive regime, annuals and perennials differed significantly only on leaf area and dispersal date, with annuals having smaller leaves and dispersing earlier (Figure 3; Appendix S6). By contrast, in the extensive regime, annuals had higher specific leaf area, and lower leaf dry matter content, leaf carbon content, leaf area and leaf δ¹³C than perennials. They were also smaller and dispersed earlier. In particular, leaf dry matter content of annuals was between 100 and 350 mg/g, whatever the regime (Figure 3), whereas for perennials, it varied in the same range in the intensive regime, but ranged from 200 to 500 mg/g in the extensive regime.

## 3.4 | Changes of species positions in the functional space

The two first axes of the PCA represented 50% of the variance (first axis, 26%; second axis, 24%). The functional space was driven by two orthogonal sets of traits: (a) traits related to size (leaf area, reproductive height, seed mass), as well as leaf carbon isotope ratio and nitrogen content, all positively related to the first axis; and (b) leaf dry matter content and leaf carbon content, positively related to the second axis, against specific leaf area, negatively related to the second axis (Figure 4a; Appendix S7). Changes in trait values were significant for both life histories along the first axis, but only for perennials along the second axis (Figure 4b-e). Species present in the two regimes tended to have intermediate positions along the two axes (Figure 4; Appendix S8, open circles and triangles). For both life histories, species present only in the intensive regime tended to have higher values than other species on the first dimension (Appendix S8A). However, species present only in the extensive regime had higher values in the second dimension within the perennial, but not the annual, life history (Appendix S8B). In addition, the three most functionally distinct species in each management regime were perennials (Appendix S9).

## 3.5 | Intraspecific trait variation

ITV between the two management regimes was significant for all traits except reproductive height and dispersal date (Table 2). In addition, ITV differed between life histories for two traits: leaf area and leaf δ¹³C (Table 2), in which case plasticity was higher for annual species (Figure 3).

## 3.6 | Relative contribution of intraspecific trait variation and species turnover

The contribution of ITV to among-communities trait variance, alTV, was negative for all traits for both life histories – at the exception of leaf nitrogen content leaf δ¹³C in annuals –, which indicates a greater contribution of species turnover to the total among-community variance in trait values. Except from dispersal date, alTV was always higher for annuals than for perennials, indicating a higher effect of intraspecific changes in trait values for annuals (Figure 5). In particular, alTV of leaf area was three times higher in annuals. It was also higher in annuals for leaf dry matter content, leaf nitrogen content, leaf δ¹³C and reproductive height, but values were closer among annuals and perennials for specific leaf area, leaf carbon content and dispersal date.

# 4 | DISCUSSION

In this study, we tested whether changes in trait values with the environment depended on species life history (annual or perennial) in a grassland undergoing two management regimes with contrasted conditions of disturbance and resource stress, where annual and perennial species co-occur. For most traits, we evidenced a significant interaction between environment and life history. In particular, changes in trait values were higher in perennials for leaf traits related to tissue density and growth. These differences were explained by (a) a lower turnover of species for annuals than for perennials, (b) the presence of species with more distinct trait values across environments within the perennial life history, and (c) equivalent amounts of ITV among life histories for most traits (cf. Figure 1, case b1) – with the notable exception of two leaf traits with higher intraspecific variation for annuals. As a result, (a) the overall magnitude of changes in trait values was higher for perennials for most traits, and (b) the contribution of species turnover to changes in trait values was higher for perennials for all traits.

The lower turnover of annuals across management regimes may result from different ecological requirements for annuals and perennials. Disturbance is known to favour plants with fast growth, whatever their life history (Garnier et al., 2016). By contrast, stresses of various natures have been shown to convergently select for fast growth and early phenology in annuals (i.e. stress escape syndromes), but tend to filter for slower growth rates (i.e. stress-tolerance syndromes) in perennials (Stanton et al., 2000). Unlike perennials, annuals would thus display fast growth under both disturbance and stress, probably because both environmental pressures tend to generate open microsites relieved from competition, which favour annual species (Barbaro & Corcket, 2000; Barbaro et al., 2004). Accordingly, the higher richness and abundance of annuals in the intensive regime could result from the strong disturbance due to sheep, possibly generating more gaps suitable to these species than in the moderately stressful conditions characterizing the extensive regime. In the intensive regime, annuals would escape stress, whereas perennials tend to undergo it. However, some annual species specific to the extensive regime, such as *Bupleurum*
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>FIGURE 3 Trait values for species of each life history (circles, annual species; triangles, perennial species) in the two management regimes. Boxplots represent trait values in the intensive (grey boxes) or extensive (white boxes) regime. Numbers of species measured are given below each box. Lines connect points belonging to the same species, measured in the two regimes, and indicate intraspecific trait changes with the environment. Letters above the boxes indicate modalities that differed significantly, identified by a post-hoc test. See Appendix S6 for details of the mixed model for each trait.</figcaption></figure>

baldense, which have recurrently been characterized as stress-tolerant annuals, do not fully escape drought, because they undergo part of the dry season in their vegetative phase (Madon & Medail, 1997; Barbaro et al., 2004; Hodgson et al., 2020). This indicates that at least some annuals experienced temporally similar environmental conditions to those of co-occurring perennials in the extensive regime, and may explain why the turnover of species across the two regimes, although different among the two life histories, remains high for annuals. Differences in species turnover thus do not fully explain why changes in trait values were lower for annuals for most traits, and why it depended on the trait dimension considered.

Perennial species displayed contrasted syndromes of traits across environmental conditions on both the size and the acquisition-conservation dimension, whereas it was only true for the size dimension in annuals. High values of leaf dry matter content and low values of specific leaf area, characteristic of perennial species specialist of the extensive regime (and resulting in high coordinates on the second axis of the PCA), are routinely used as proxies for stress-tolerant strategies (Pierce et al., 2013, 2017; Reich, 2014). Variation in the values of these traits was relatively limited for annuals, in agreement with what has been evidenced on more than 800 annual species (Hodgson

et al., 2020). One of the most characteristic features of annual species is that their life cycle constrains them to grow fast: indeed, they dispersed earlier than perennials, whatever the treatment. This probably impedes annual species to develop dense, slow-growing leaves, even when they are adapted to stress (Kurze et al., 2021), and reduces the range of trait values that annual species can span along the acquisition-conservation trade-off. The most functionally distinct species, i.e. those being the most distant from all other species in the functional space (Violle et al., 2017), were perennials, indicating a wider range of phenotypes covered by species belonging to this life history. However, changes in trait values were of identical magnitude for annuals and perennials along the first phenotypic dimension related to plant and organ size. This was in part because of the presence of annuals having the potential to grow large in the intensive regime (e.g. *Vicia sativa*), which were absent from the extensive regime. This suggests that, at the interspecific level, trait values of annual species are much more constrained along the acquisition-conservation dimension than along the size dimension.

Definitions of ecological strategies usually assume that one combination of traits corresponds to one environment (a "one-to-one mapping" from environment to strategy) (Marks & Lechowicz, 2006;
<figure><img src="image_15.png" /></figure>

**FIGURE 4** First plane of the multivariate trait space for species from both life histories. Annual species are represented by circles, and perennial species are represented by triangles. Open shapes represent species measured in the two management regimes, and plain shapes represent species measured in one regime only. Red, Intensive regime; blue, extensive regime. (a) Projection of traits on the first two dimensions of the principal component analysis (PCA). Trait abbreviations are given in Table 1. (b, c) Position of annuals and perennials, respectively, in the functional space, according to the management regime. Some species present in one treatment, but not in the other, are highlighted (Bupleurum baldense, Minuartia hybrida, Hornungia petraea, Vicia sativa, Hordeum murinum, Crepis vesicaria, for annuals, and Teucrium montanum, Stipa pennata, Festuca christiani-barnardii, Rumex acetosa, Carduus nutans, Bellis perennis, for perennials). (d, e) Shifts of coordinates in the first and second axes of the PCA for annuals and perennials, respectively, indicating species measured in the two regimes (open shapes, connected with lines), and species measured in one regime only (plain shapes). Significance of shifts in coordinates are indicated above the plots (***p < 0.001; ****p < 10⁻⁴).

<figure><img src="image_16.png" /></figure>

Dias et al., 2020). As a consequence, the possibility that local divergence in trait values results from multiple phenotypic optima is still overlooked in functional ecology (Dias et al., 2020; Munoz et al., 2023). One reason is that a complex environment (here, both stress and disturbance at intermediate levels) can be either considered as a single environment, possibly filtering multiple strategies (van der Maarel & Sykes, 1993; Frenette-Dussault et al., 2012), or as multiple micro-environments, each filtering one particular strategy (Barbaro et al., 2004). Recent studies have suggested that trait responses to the environment can differ between groups of species varying in their life history or ecological

strategy (Garnier et al., 2019; Anderegg, 2023). One reason is that some traits may have different adaptive significance among groups of species (Vellend et al., 2014): for annuals species, fast growth can be linked to regeneration in patches with low resource levels and therefore low competition intensity (Barbaro & Corcket, 2000; Barbaro et al., 2004), whereas perennials which perform in these environments are rather slow-growing, with conservative resource strategies (Frenette-Dussault et al., 2012). Yet, concluding that co-occurring plants differing in functional traits represent two distinct optima in the same environment, or that both groups are adapted to distinct micro-environments, ultimately depends on the
<figure><img src="image_17.png" /></figure>

<table><caption>TABLE 2 Tests of intraspecific changes in trait values.</caption><thead><tr><th rowspan="2">Trait</th><th colspan="2">Intraspecific variation</th><th colspan="2">Effect of life history</th></tr><tr><th>p</th><th>W</th><th>p</th><th>W</th></tr></thead><tbody><tr><td>LDMC</td><td>0.001</td><td>56</td><td>0.29</td><td>56</td></tr><tr><td>SLA</td><td>0.007</td><td>94</td><td>0.67</td><td>94</td></tr><tr><td>log_LA</td><td>0.002</td><td>34</td><td>0.02</td><td>34</td></tr><tr><td>LCCm</td><td>0.003</td><td>49</td><td>0.29</td><td>49</td></tr><tr><td>LNCm</td><td>0.001</td><td>49</td><td>0.29</td><td>49</td></tr><tr><td>Lδ13C</td><td>0.001</td><td>2</td><td>0.00</td><td>2</td></tr><tr><td>RPH</td><td>0.089</td><td>7</td><td>0.51</td><td>7</td></tr><tr><td>Disp</td><td>0.104</td><td>34</td><td>0.15</td><td>34</td></tr></tbody></table>

Note: Directional intraspecific variation in response to the environment was tested by computing the average intraspecific difference in trait values across species (see Section 2 for details). “Intraspecific variation” gives the *p*-value obtained for each trait. Differences in intraspecific trait changes according to life history were tested by comparing the distribution average intraspecific difference in trait values across species between annuals and perennials. For each trait, Wilcoxon rank sum test value (W) and *p*-value are given.

Abbreviations: Disp, date of first dispersal; log_LA, log(leaf area); LCCm, leaf carbon content per unit mass; LDMC, leaf dry matter content; LNCm, leaf nitrogen content per unit mass; Lδ13C, leaf delta 13C; RPH: reproductive height; SLA, Specific Leaf Area.

<figure><img src="image_18.png" /><figcaption>FIGURE 5 Contribution of species replacement and intraspecific trait variation (ITV) to changes in trait values, for perennials (x axis) and annuals (y axis). Traits are labelled by their abbreviation. Among-communities trait variance (alTV) <0 indicates a greater contribution of species turnover to the total among-community variance. Boxplots of distribution of alTV for the different traits are represented along the axes. Values of alTV were compared for annuals and perennials using an analysis of variance (F = 10.84, *p* = 0.005).</figcaption></figure>

spatial scale at which the environment is considered, which remains an open question in ecology (cf. Blonder et al., 2018).

In contrast to studies predicting differences in plasticity between annual and perennial species (Moriuchi & Winn, 2005), we detected identical magnitude of ITV between the two life histories for a majority of traits. The contribution of ITV to changes in trait values remained higher for annuals, probably as a consequence of the lower species turnover. Studies on the relationships between ecological strategies and plasticity have shown variable results, including higher plasticity for acquisitive species (Stotz et al., 2022), for conservative species (Streit et al., 2022) or no consistent trend (Freschet et al., 2013; Garnier et al., 2019). One possible reason for these variable observations is that plasticity may be better thought of as a property of the trait considered, rather than of whole organisms. Indeed, plasticity can vary among traits, for instance because phenotypic integration can limit the variation of some traits more than others (Gianoli & Palacio-López, 2009), or as a consequence of traits being involved in different functions (Freschet et al., 2013). Our study evidenced greater intraspecific variation in annuals for two traits only: leaf area and leaf δ¹³C. The patterns we observed for leaf area correspond to our last theoretical prediction (case c2, Figure 1): the higher plasticity of annuals compensated for the higher effect of interspecific change of perennials, leading to identical changes in the values of this trait for both life histories. For leaf δ¹³C, the higher intraspecific variation of annuals resulted in greater changes in trait values with the environment than what we observed for perennials, for which the values remained constant across management regimes despite high species replacement. A low leaf δ¹³C, observed for annuals in the extensive regime, is classically interpreted as indicator of low water-use efficiency (Farquhar et al., 1989). The extensive regime, with superficial soils, is rather characterized by water stress (Bernard-Verdier et al., 2012), and should have favoured high values of leaf δ¹³C. One possibility is that annuals' leaf physiology was not well adapted to undergo water stress, because annuals are likely to escape part of this stress thanks to their early phenology (Volaire, 2018). Another reason can be that carbon isotope discrimination is affected by factors other than water-use efficiency, such as biochemical fractioning (cf. Badeck et al., 2005). This dependence of plasticity on the trait studied questions the ability of a single property of organisms (e.g. being acquisitive or conservative, or being annual or perennial) to predict the magnitude of the plastic response of whole organisms. Altogether, our results suggest that plasticity is better studied at the level of individual traits, rather than considered as a property of species per se.

5 | CONCLUSION

We evidenced that changes in trait values at small scales can critically depend on species life history. Considering patterns of trait variation within species (Anderegg et al., 2018), or within functional groups (Kurze et al., 2021), can reveal mechanisms that tend to be blurred out
<figure><img src="image_19.png" /></figure>

in studies performed at large scales; for example, using worldwide trait databases (Joswig et al., 2022). This emphasizes the strong effect of context-dependence in trait variation, which occurs when “the magnitude or sign of a relation varies due to the conditions under which it is observed” (Catford et al., 2022). Studying trait-environment relationships at different nested organizational levels has the potential to reveal processes that tend to be neglected in larger-scale analyses.

## AUTHOR CONTRIBUTIONS

LD, CV, EG and SC conceived the ideas and designed the methodology. LD collected the data. LD, CV and EG analysed the data. LD led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## ACKNOWLEDGEMENTS

We thank Adeline Fayolle, Ignacio Pérez-Ramos, Iris Bumb, Jules Segrestin, Karim Barkaoui and Maud Bernard-Verdier, for their contribution to trait measurements. We are grateful to the members of La Fage INRAE station, and especially Sebastien Douls and Sara Parisot, for their material support during fieldwork. We thank Karim Barkaoui, Pascal Chapon, Florence Volaire, and Didier Alard for their help in plant identification, and Emmanuel Corcket for his insights into the vegetation of oligotrophic, calcareous grasslands. We thank the pôle Isotopie-Chimie de la plateforme SilvaTech for chemical and isotopic measurements, Aurélien Estarague for his help in statistical analyses, and Julie Tschambser for her help during fieldwork.

## FUNDING INFORMATION

This study was supported by the European Research Council (ERC) ('CONSTRAINTS': grant ERC-StG-2014-639706-CONSTRAINTS to Cyrille Violle).

## DATA AVAILABILITY STATEMENT

Data supporting the results are available on InDoRES (https://doi.org/10.48579/PRO/IYVPFJ). The associated R code is available on GitHub (https://github.com/LDelalandre/DelalandreJVS2023).

## ORCID

Léo Delalandre https://orcid.org/0000-0003-2875-4587

Cyrille Violle https://orcid.org/0000-0002-2471-9226

Sylvain Coq https://orcid.org/0000-0002-7887-0247

Eric Garnier https://orcid.org/0000-0002-9392-5154

## REFERENCES

Anderegg, L.D.L. (2023) Why can't we predict traits from the environment? *New Phytologist*, 237(6), 1998-2004. Available from: https://doi.org/10.1111/nph.18586

Anderegg, L.D.L., Berner, L.T., Badgley, G., Sethi, M.L., Law, B.E. & HilleRisLambers, J. (2018) Within-species patterns challenge our understanding of the leaf economics spectrum. *Ecology Letters*, 21(5), 734-744. Available from: https://doi.org/10.1111/ele.12945

Badeck, F.-W., Tcherkez, G., Nogués, S., Piel, C. & Ghashgaieh, J. (2005) Post-photosynthetic fractionation of stable carbon isotopes between plant organs—a widespread phenomenon. *Rapid Communications in Mass Spectrometry*, 19(11), 1381-1391. Available from: https://doi.org/10.1002/rcm.1912

Barbaro, L. & Corcket, E. (2000) Réponses fonctionnelles des commu- nautés de pelouses calcicoles aux facteurs agro-écologiques dans les Préalpes françaises. *Canadian Journal of Botany*, 78(8), 11-1020. Available from: https://doi.org/10.1139/b00-071

Barbaro, L., Dutoit, T., Anthelme, F. & Corcket, E. (2004) Respective in- fluence of habitat conditions and management regimes on preal- pine calcareous grasslands. *Journal of Environmental Management*, 72(4), 261-275. Available from: https://doi.org/10.1016/j.jenvman. 2004.05.006

Barkaoui, K., Bernard-Verdier, M. & Navas, M.-L. (2013) Questioning the reliability of the point intercept method for assessing com- munity functional structure in low-productive and highly diverse Mediterranean grasslands. *Folia Geobotanica*, 48(3), 393-414. Available from: https://doi.org/10.1007/s12224-013-9172-2

Bazzaz, F.A., Ackerly, D.D. & Reekie, E.G. (2000) Reproductive allocation in plants. In: *Seeds: the ecology of regeneration in plant communities*. Wallingford: Commonwealth Agricultural Bureau International Publishing, pp. 1-30.

Bernard-Verdier, M., Navas, M.-L., Vellend, M., Violle, C., Fayolle, A. & Garnier, E. (2012) Community assembly along a soil depth gra- dient: contrasting patterns of plant trait convergence and diver- gence in a Mediterranean rangeland. *Journal of Ecology*, 100(6), 1422-1433. Available from: https://doi.org/10.1111/1365-2745. 12003

Blonder, B., Kapas, R.E., Dalton, R.M., Graae, B.J., Heiling, J.M. & Opedal, Ø.H. (2018) Microenvironment and functional-trait context depen- dence predict alpine plant community dynamics. *Journal of Ecology*, 106(4), 1323-1337. Available from: https://doi.org/10.1111/1365- 2745.12973

Brouillette, L.C., Mason, C.M., Shirk, R.Y. & Donovan, L.A. (2014) Adaptive differentiation of traits related to resource use in a desert annual along a resource gradient. *New Phytologist*, 201(4), 1316- 1327. Available from: https://doi.org/10.1111/nph.12628

Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S.M. et al. (2018) Global trait-environment re- lationships of plant communities. *Nature Ecology & Evolution*, 2(12), 1906-1917. Available from: https://doi.org/10.1038/s4155 9-018-0699-8

Bumb, I., Garnier, E., Bastianelli, D., Richarte, J., Bonnal, L. & Kazakou, E. (2016) Influence of management regime and harvest date on the forage quality of rangelands plants: the importance of dry matter content. *AoB Plants*, 8, plw045. Available from: https://doi.org/10. 1093/aobpla/plw045

Bumb, I., Garnier, E., Coq, S., Nahmani, J., Del Rey Granado, M., Gimenez, O. et al. (2018) Traits determining the digestibility-decomposability rela- tionships in species from Mediterranean rangelands. *Annals of Botany*, 121(3), 459-469. Available from: https://doi.org/10.1093/aob/mcx175

Catford, J.A., Wilson, J.R.U., Pyšek, P., Hulme, P.E. & Duncan, R.P. (2022) Addressing context dependence in ecology. *Trends in Ecology & Evolution*, 37(2), 158-170. Available from: https://doi.org/10. 1016/j.tree.2021.09.007

Cornwell, W.K. & Ackerly, D.D. (2010) A link between plant traits and abundance: evidence from coastal California woody plants. *Journal of Ecology*, 98(4), 814-821. Available from: https://doi.org/10. 1111/j.1365-2745.2010.01662.x

Dias, A.T.C., Rosado, B.H.P., De Bello, F., Pistón, N. & De Mattos, E.A. (2020) Alternative plant designs: consequences for community as- sembly and ecosystem functioning. *Annals of Botany*, 125(3), 391- 398. Available from: https://doi.org/10.1093/aob/mcz180

Díaz, S., Kattge, J., Cornelissen, J.H.C., Wright, I.J., Lavorel, S., Dray, S. et al. (2016) The global spectrum of plant form and function. *Nature*, 529(7585), 167-171. Available from: https://doi.org/10. 1038/nature16489
<figure><img src="image_20.png" /></figure>

Ducrocq, H. & Duru, M. (1997) In vitro digestibility of green lamina of cocksfoot (*Dactylis glomerata* L.) in relation to water deficit. *Grass and Forage Science*, **52**(4), 432-438. Available from: https://doi.org/10.1111/j.1365-2494.1997.tb02375.x

Farquhar, G.D., Ehleringer, J.R. & Hubick, K.T. (1989) Carbon isotope discrimination and photosynthesis. *Annual Review of Plant Physiology and Plant Molecular Biology*, **40**, 503–537. Available from: https://doi.org/10.1146/annurev.pp.40.060189.002443

Fernández Ales, R., Laffarga, J.M. & Ortega, F. (1993) Strategies in Mediterranean grassland annuals in relation to stress and disturbance. *Journal of Vegetation Science*, **4**(3), 313-322. Available from: https://doi.org/10.2307/3235589

Frenette-Dussault, C., Shipley, B., Léger, J.-F., Meziane, D. & Hingrat, Y. (2012) Functional structure of an arid steppe plant community reveals similarities with Grime's C-S-R theory. *Journal of Vegetation Science*, **23**(2), 208-222. Available from: https://doi.org/10.1111/j.1654-1103.2011.01350.x

Freschet, G.T., Bellingham, P.J., Lyver, P.O., Bonner, K.I. & Wardle, D.A. (2013) Plasticity in above- and belowground resource acquisition traits in response to single and multiple environmental factors in three tree species. *Ecology and Evolution*, **3**(4), 1065-1078. Available from: https://doi.org/10.1002/ece3.520

Gardarin, A., Garnier, É., Carrère, P., Cruz, P., Andueza, D., Bonis, A. et al. (2014) Plant trait-digestibility relationships across management and climate gradients in permanent grasslands. *Journal of Applied Ecology*, **51**(5), 1207-1217. Available from: https://doi.org/10.1111/1365-2664.12293

Garnier, E. (1992) Growth analysis of congeneric annual and perennial grass species. *Journal of Ecology*, **80**(4), 665. Available from: https://doi.org/10.2307/2260858

Garnier, E., Fayolle, A., Navas, M.-L., Damgaard, C., Cruz, P., Hubert, D. et al. (2018) Plant demographic and functional responses to management intensification: a long-term study in a Mediterranean rangeland. *Journal of Ecology*, **106**(4), 1363-1376. Available from: https://doi.org/10.1111/1365-2745.12996

Garnier, E. & Laurent, G. (1994) Leaf anatomy, specific mass and water content in congeneric annual and perennial grass species. *New Phytologist*, **128**(4), 725-736. Available from: https://doi.org/10.1111/j.1469-8137.1994.tb04036.x

Garnier, E., Navas, M.-L. & Grigulis, K. (2016) *Plant functional diversity: organismal traits, community structure, and ecosystem properties*. Oxford: Oxford University Press.

Garnier, E., Stahl, U., Laporte, M.-A., Kattge, J., Mougenot, I., Kühn, I. et al. (2017) Towards a thesaurus of plant characteristics: an ecological contribution. *Journal of Ecology*, **105**(2), 298-309. Available from: https://doi.org/10.1111/1365-2745.12698

Garnier, E., Vile, D., Roumet, C., Lavorel, S., Grigulis, K., Navas, M.-L. et al. (2019) Inter- and intra-specific trait shifts among sites differing in drought conditions at the north western edge of the Mediterranean region. *Flora*, **254**, 147-160. Available from: https://doi.org/10.1016/j.flora.2018.07.009

Gianoli, E. & Palacio-López, K. (2009) Phenotypic integration may constrain phenotypic plasticity in plants. *Oikos*, **118**(12), 1924-1928. Available from: https://doi.org/10.1111/j.1600-0706.2009.17884.x

Grenié, M., Denelle, P., Tucker, C.M., Munoz, F. & Violle, C. (2017) Funrar: an R package to characterize functional rarity. *Diversity and Distributions*, **23**(12), 1365-1371. Available from: https://doi.org/10.1111/ddi.12629

Herron, S.A., Rubin, M.J., Albrecht, M.A., Long, Q.G., Sandoval, M.C. & Miller, A.J. (2021) The role of genus and life span in predicting seed and vegetative trait variation and correlation in Lathyrus, Phaseolus, and Vicia. *American Journal of Botany*, **108**(12), 2388-2404. Available from: https://doi.org/10.1002/ajb2.1773

Hodgson, J.G., Montserrat Marti, G., Šerá, B., Jones, G., Bogaard, A., Charles, M. et al. (2020) Seed size, number and strategies in annual

plants: a comparative functional analysis and synthesis. *Annals of Botany*, **126**(7), 1109-1128. Available from: https://doi.org/10.1093/aob/mca151

Joswig, J.S., Wirth, C., Schuman, M.C., Kattge, J., Reu, B., Wright, I.J. et al. (2022) Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation. *Nature Ecology & Evolution*, **6**(1), 36-50. Available from: https://doi.org/10.1038/s41559-021-01616-8

Jouany, C., Cruz, P., Petibon, P. & Duru, M. (2004) Diagnosing phosphorus status of natural grassland in the presence of white clover. *European Journal of Agronomy*, **21**(3), 273-285. Available from: https://doi.org/10.1016/j.eja.2004.06.001

Klimešová, J., Ottaviani, G., Charles-Dominique, T., Campetella, G., Canullo, R., Chelli, S. et al. (2021) Incorporating clonality into the plant ecology research agenda. *Trends in Plant Science*, **26**(12), 1236-1247. Available from: https://doi.org/10.1016/j.tplants.2021.07.019

Kurze, S., Engelbrecht, B.M.J., Bilton, M.C., Tielbörger, K. & Álvarez-Cansino, L. (2021) Rethinking the plant economics spectrum for annuals: a multi-species study. *Frontiers in Plant Science*, **12**, 640862. Available from: https://doi.org/10.3389/fpls.2021.640862

Laughlin, D.C. (2014) The intrinsic dimensionality of plant traits and its relevance to community assembly. *Journal of Ecology*, **102**(1), 186-193. Available from: https://doi.org/10.1111/1365-2745.12187

Lemaire, G. & Gastal, F. (1997) N uptake and distribution in plant canopies. In: Lemaire, G. (Ed.) *Diagnosis of the nitrogen status in crops*. Cham: Springer, pp. 3-43.

Lepš, J., de Bello, F., Šmilauer, P. & Doležal, J. (2011) Community trait response to environment: disentangling species turnover vs intraspecific trait variability effects. *Ecography*, **34**(5), 856-863. Available from: https://doi.org/10.1111/j.1600-0587.2010.06904.x

Lindberg, C.L., Hanslin, H.M., Schubert, M., Marcussen, T., Trevaskis, B., Preston, J.C. et al. (2020) Increased above-ground resource allocation is a likely precursor for independent evolutionary origins of annuity in the Pooidae grass subfamily. *New Phytologist*, **228**(1), 318-329. Available from: https://doi.org/10.1111/nph.16666

Madon, O. & Medail, F. (1997) The ecological significance of annuals on a Mediterranean grassland (Mt Ventoux, France). *Plant Ecology*, **129**, 189-199. Available from: https://doi.org/10.1023/A:1009759730000

Marks, C.O. & Lechowicz, M.J. (2006) Alternative designs and the evolution of functional diversity. *The American Naturalist*, **167**(167), 55-66. Available from: https://doi.org/10.1086/498276

Moriuchi, K.S. & Winn, A.A. (2005) Relationships among growth, development and plastic response to environment quality in a perennial plant. *New Phytologist*, **166**(1), 149-158. Available from: https://doi.org/10.1111/j.1469-8137.2005.01346.x

Munoz, F., Klausmeier, C.A., Gaüzère, P., Kandlikar, G., Litchman, E., Mouquet, N. et al. (2023) The ecological causes of functional distinctiveness in communities. *Ecology Letters*, **26**(8), 1452-1465. Available from: https://doi.org/10.1111/ele.14265

Muscarella, R. & Uriarte, M. (2016) Do community-weighted mean functional traits reflect optimal strategies? *Proceedings of the Royal Society B: Biological Sciences*, **283**(1827), 20152434. Available from: https://doi.org/10.1098/rspb.2015.2434

Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P. et al. (2013) New handbook for standardised measurement of plant functional traits worldwide. *Australian Journal of Botany*, **61**(3), 167. Available from: https://doi.org/10.1071/BT12225

Pierce, S., Brusa, G., Vagge, I. & Cerabolini, B.E.L. (2013) Allocating CSR plant functional types: the use of leaf economics and size traits to classify woody and herbaceous vascular plants. *Functional Ecology*,
<figure><img src="image_21.png" /></figure>

27(4), 1002-1010. Available from: https://doi.org/10.1111/1365-2435.12095

Pierce, S., Negreiros, D., Cerabolini, B.E.L., Kattge, J., Díaz, S., Kleyer, M. et al. (2017) A global method for calculating plant CSR ecological strategies applied across biomes world-wide. *Functional Ecology*, 31(2), 444-457. Available from: https://doi.org/10.1111/1365-2435.12722

Pigliucci, M. (2003) Phenotypic integration: studying the ecology and evolution of complex phenotypes. *Ecology Letters*, 6(3), 265-272. Available from: https://doi.org/10.1046/j.1461-0248.2003.00428.x

Reich, P.B. (2014) The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. *Journal of Ecology*, 102(2), 275–301. Available from: https://doi.org/10.1111/1365-2745.12211

Sartori, K., Vasseur, F., Violle, C., Baron, E., Gerard, M., Rowe, N. et al. (2019) Leaf economics and slow-fast adaptation across the geographic range of *Arabidopsis thaliana*. *Scientific Reports*, 9(1), 10758. Available from: https://doi.org/10.1038/s41598-019-46878-2

Siefert, A., Violle, C., Chalmandrier, L., Albert, C.H., Taudiere, A., Fajardo, A. et al. (2015) A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. *Ecology Letters*, 18(12), 1406–1419. Available from: https://doi.org/10.1111/ele.12508

Šímová, I., Violle, C., Svenning, J.-C., Kattge, J., Engemann, K., Sandel, B. et al. (2018) Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species. *Journal of Biogeography*, 45(4), 895–916. Available from: https://doi.org/10.1111/jbi.13171

Stanton, M.L., Roy, B.A. & Thiede, D.A. (2000) Evolution in stressful environments. I. Phenotypic variability, phenotypic selection, and response to selection in five distinct environmental stresses. *Evolution*, 54(1), 93–111. Available from: https://doi.org/10.1111/j.0014-3820.2000.tb00011.x

Stotz, G.C., Salgado-Luarte, C., Escobedo, V.M., Valladares, F. & Gianoli, E. (2022) Phenotypic plasticity and the leaf economics spectrum: plasticity is positively associated with specific leaf area. *Oikos*, 2022(11), e09342. Available from: https://doi.org/10.1111/oik.09342

Streit, H., Menezes, L.S., Pillar, V.D. & Overbeck, G.E. (2022) Intraspecific trait variation of grassland species in response to grazing depends on resource acquisition strategy. *Journal of Vegetation Science*, 33(3), e13129. Available from: https://doi.org/10.1111/jvs.13129

van der Maarel, E. & Sykes, M.T. (1993) Small-scale plant species turnover in a limestone grassland: the carousel model and some comments on the niche concept. *Journal of Vegetation Science*, 4(2), 179–188. Available from: https://doi.org/10.2307/3236103

Vellend, M., Lajoie, G., Bourret, A., Múrria, C., Kembel, S.W. & Garant, D. (2014) Drawing ecological inferences from coincident patterns of population- and community-level biodiversity. *Molecular Ecology*, 23(12), 2890–2901. Available from: https://doi.org/10.1111/mec.12756

Violle, C., Thuiller, W., Mouquet, N., Munoz, F., Kraft, N.J.B., Cadotte, M.W. et al. (2017) Functional rarity: the ecology of outliers. *Trends*

in Ecology & Evolution, 32(5), 356–367. Available from: https://doi.org/10.1016/j.tree.2017.02.002

Volaire, F. (2018) A unified framework of plant adaptive strategies to drought: crossing scales and disciplines. *Global Change Biology*, 24(7), 2929–2938. Available from: https://doi.org/10.1111/gcb.14062

Wessinger, C.A. (2021) From pollen dispersal to plant diversification: genetic consequences of pollination mode. *New Phytologist*, 229(6), 3125–3132. Available from: https://doi.org/10.1111/nph.17073

Wüest, R.O., Münkemüller, T., Lavergne, S., Pollock, L.J. & Thuiller, W. (2018) Integrating correlation between traits improves spatial predictions of plant functional composition. *Oikos*, 127(3), 472–481. Available from: https://doi.org/10.1111/oik.04420

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Appendix S1.** Description of the sampling protocols used to measure environmental variables.

**Appendix S2.** List of species.

**Appendix S3.** Trait coverage, measured as the proportion of species present in the releves for which traits were measured.

**Appendix S4.** Cumulated relative abundance of annuals, estimated as the proportion of contacts represented by annual species in each transect.

**Appendix S5.** Species turnover across the two management regimes.

**Appendix S6.** Result of mixed models predicting trait as a function of life history and environment.

**Appendix S7.** Contribution of variables to the first three PCA dimensions.

**Appendix S8.** Coordinates of annuals and perennials, on the first dimension and the second dimension of the PCA, differentiating species present in one and in the two management regimes.

**Appendix S9.** Functional distinctiveness of species, computed relative to all the species.

**How to cite this article:** Delalandre, L., Violle, C., Coq, S. & Garnier, E. (2023) Trait-environment relationships depend on species life history. *Journal of Vegetation Science*, 34, e13211. Available from: https://doi.org/10.1111/jvs.13211