# An evolutionary perspective on the response-effect framework

Maria Stefania Przybylska, Lucie Mahaut, Cyrille Violle, Denis Vile, François Munoz, J F Scheepens, Xavier Le Roux, Elena Kazakou, Moises Exposito-alonso, Oliver Bossdorf, et al.

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## To cite this version:

Maria Stefania Przybylska, Lucie Mahaut, Cyrille Violle, Denis Vile, François Munoz, et al. "An evolutionary perspective on the response-effect framework. Functional Ecology, 2026, 40 (2), 10.1111/1365-2435.70236. hal-05453656

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# An evolutionary perspective on the response-effect framework

Maria Stefania Przybylska<fnref n="1" />,²,³ ID | Lucie Mahaut⁴ | Cyrille Violle<fnref n="1" /> ID | Denis Vile² ID |

François Munoz⁵ | J. F. Scheepens³ | Xavier Le Roux⁶ ID | Elena Kazakou⁷ ID |

Moises Exposito-Alonso⁸,⁹ ID | Oliver Bossdorf¹⁰ | Detlef Weigel¹¹,¹² ID | François Vasseur<fnref n="1" />

**Correspondence**

Maria Stefania Przybylska

Email: mariastefaniaribeiro@gmail.com

François Vasseur

Email: francois.vasseur@cefe.cnrs.fr

**Funding information**

Agence Nationale de la Recherche, Grant/
Award Number: ANR-17-CE02-0018-01
and ANR-24-CE02-7222; Deutsche
Forschungsgemeinschaft, Grant/Award
Number: SCHE 1899/2-1; European
Research Council, Grant/Award Number:
ERC-StG-2020-949843 and ERC-StG-
2014-639706-CONSTRAINTS

Handling Editor: Amanda Pettersen

**Abstract**

1. The response-effect framework (REF) has provided a foundational approach in functional ecology, using traits to predict how species respond to environmental factors ('response traits') and influence ecosystem functioning ('effect traits').

2. While highly valuable, the REF has largely overlooked evolutionary dynamics and transgenerational processes, despite growing evidence that these processes significantly shape species' responses to environmental changes and their subsequent ecosystem impacts. Addressing this gap by integrating evolutionary dynamics into the REF can substantially enhance our understanding of both short and long-term biodiversity and ecosystem functioning relationships.

3. Here, we build on recent advances to explore how evolutionary processes and eco-evolutionary feedbacks can refine predictions of trait responses to environmental changes and their ecosystem-level implications. We also highlight experimental evolution as a powerful tool to assess the role of eco-evolutionary dynamics in improving the predictive capacity of the REF.

4. We advocate for greater integration of evolutionary dynamics into the REF to reinforce the applicability of the framework in a rapidly changing world.

**KEYWORDS**

eco-evolutionary feedback, ecological strategy, effect trait, experimental evolution, functional trait, response trait

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Functional Ecology* published by John Wiley & Sons Ltd on behalf of British Ecological Society.

<footnote n="1">¹CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France; ²LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France; ³Plant Evolutionary Ecology, Institute of Ecology, Evolution and Diversity, Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany; ⁴CEFE, Univ Montpellier, CNRS, EPHE, IRD, INRAE, Montpellier, France; ⁵LBBE, Université Lyon 1, Lyon, France; ⁶INRAE, Univ Lyon, Université Claude Bernard Lyon 1, CNRS, VetAgro Sup, UMR 1418 LEM, Ecologie Microbienne, Villeurbanne, France; ⁷CEFE, Univ Montpellier, CNRS, EPHE, IRD, Institut Agro, Montpellier, France; ⁸Department of Integrative Biology, University of California Berkeley, Berkeley, California, USA; ⁹Howard Hughes Medical Institute, University of California Berkeley, Berkeley, California, USA; ¹⁰Plant Evolutionary Ecology, Institute of Evolution & Ecology, University of Tübingen, Tübingen, Germany; ¹¹Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, Germany and ¹²Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany</footnote>
# 1 | INTRODUCTION

Since its establishment as a discipline, functional ecology has aimed to (i) characterize the phenotypes of organisms through the analysis of trait (co)variations (Grime, 1985); (ii) determine the relationships between traits and environmental factors (Calow, 1987; Keddy, 1992); and (iii) predict community and ecosystem processes based on the functional role of organisms (Calow, 1987; Ehleringer & Field, 1993; Smith et al., 1997). The 'response-effect framework' (Lavorel & Garnier, 2002), hereafter abbreviated as REF, has been proposed to integrate the latter two objectives. It has attempted to decompose ecological patterns into underlying processes by describing how environmental conditions filter plant species based on their 'response traits', and how the resulting functional structure of communities affects ecosystem properties (e.g. nutrient cycling and productivity; Funk et al., 2017; Lavorel & Garnier, 2002; Suding et al., 2008) through 'effect traits'.

The REF has guided research questions in functional ecology for the last two decades (Funk et al., 2017; Lavorel et al., 2013; Suding et al., 2008). However, its initial applications have largely overlooked the temporal dynamics that structure biodiversity and ecosystems. Dynamic processes such as changes in population demography (e.g. Garnier et al., 2018; Laughlin et al., 2020), community responses to environmental fluctuations (e.g. McLean et al., 2019) and the resulting temporal stability of ecosystem functioning (e.g. Craven et al., 2018; de Bello et al., 2021; Mahaut et al., 2023) have only recently been incorporated into the REF. Despite these developments, there remains a major gap: transgenerational processes and evolutionary dynamics, which may be crucial to understanding biodiversity and ecosystem functioning relationships, have so far been largely overlooked (Figure 1).

A core hypothesis in functional ecology is that functional traits regulate the fitness of organisms by influencing growth, reproduction and survival (Díaz, 2025; Violle et al., 2007). This hypothesis

is rooted in the idea that functional traits are under strong selection and have predictable relationships with environmental factors across species (response traits). Consequently, many studies have estimated selection on functional traits (reviewed by Caruso et al., 2020), concluding that most of these traits have the potential to change in response to selection imposed by environmental factors such as moisture, light and herbivory (Caruso et al., 2020; Geber & Griffen, 2003). However, these observations are not unequivocal, since high variation has been reported in trait-environment relationships among species, life forms and habitats (Anderegg, 2023). For example, leaf area, a key functional trait related to light and water availability, exhibits contrasting intraspecific responses to climatic gradients across species (Dong et al., 2020). This strong variability results in uncertain traits-to-functions-to-fitness mapping (Carmona, 2023; Sobral, 2021; Streit & Bellwood, 2023). Since fitness is defined within a population, by differential survival and reproduction of genotypes, a deeper understanding of within-species processes is crucial to clarifying trait-environment relationships and ensuring their proper use to forecast species' responses to environmental change.

Within-species processes do not only determine the response of traits to environmental factors, but also their effects on ecosystem processes (Matthews et al., 2011), such as primary productivity (Zhang et al., 2023), nutrient cycling (Przybylska et al., 2024; Van Nuland et al., 2019) and trophic interactions (Crutsinger, 2016; Whitham et al., 2020). For example, in bacterial communities, productivity increased more with species richness when communities were composed of strains that had evolved to exploit different resources, thereby reducing niche overlap (Gravel et al., 2011). Despite such evidence, the REF builds on a historical focus on trait comparisons between species and the application of a mean-field approach, averaging species' contributions (e.g. individual species' growth rate) to predict ecosystem processes (e.g. productivity; Garnier et al., 2004; Vile et al., 2006). More

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[1] Migliavacca et al., 2021; [2] Bruelheide et al., 2018; [3] Violle et al., 2009; [4] Williams et al., 2022; [5] van der Plas et al., 2020; [6] Joly et al., 2020; [7] Semchenko et al., 2018; [8] Cabon et al., 2022

**FIGURE 1** Conceptual framework for the dynamics of response and effect traits. The relationships between environment, response traits and effect traits (which can overlap—grey area—or not), proximal environment and ecosystem functioning have been largely investigated in functional ecology, but rarely using an evolutionary perspective. These relationships are depicted here using arrows whose thickness represents the relative number of studies present in the functional ecology literature. Such studies are exemplified with one or two references (in square brackets) per arrow. There is potential for applying experimental evolutionary approaches to investigate most of the represented relationships (arrows highlighted with an orange outline).
recently, trait variability arising from biotic interactions, such as facilitation, competition, niche complementarity and others, has been recognized for its role in ecosystem functioning and incorporated into the REF (Lavorel et al., 2013). Less attention has been paid to the fact that these interactions themselves are shaped by evolutionary dynamics (Hart et al., 2019). Importantly, genetic variation can affect ecological processes such as population dynamics, and these modified dynamics can, in turn, differentially favour genetic variants within the population (Kokko & López-Sepulcre, 2007). This phenomenon, known as eco-evolutionary feedback (Hendry, 2017; Post & Palkovacs, 2009), is of particular interest as it can affect long-term ecosystem dynamics (Wood et al., 2021).

Here, we demonstrate that incorporating evolutionary dynamics into the analysis of response and effect traits is essential for improving our understanding of biodiversity and ecosystem dynamics, which will likely foster the predictive power of the REF. Additionally, building on a previous claim to harness experimental evolution (Rodrigues et al., 2016)—that is, the study of evolutionary dynamics of populations in response to manipulated conditions (Kawecki et al., 2012)—we propose empirical approaches to test how evolutionary processes drive trait changes and how these changes scale up to impact ecosystem functioning. We specifically address the following questions: (i) Why might evolution matter when predicting trait responses to environmental factors and their effects on ecosystem functions? (ii) How can interactions between response and effect traits generate eco-evolutionary feedbacks that shape long-term biodiversity responses and effects on ecosystem processes? (iii) How can we empirically test whether evolutionary and eco-evolutionary dynamics enhance the predictive power of the REF?

## 2 | EVOLUTIONARY DYNAMICS OF RESPONSE AND EFFECT TRAITS

Strong relationships between response traits and the environment are expected because specific trait values optimize species performance in a given environment (Schemske, 2010; Shipley et al., 2016). Within the REF, this idea is rooted in the concept of ecological strategies, which are defined as sets of covarying (response) traits that match with environmental conditions in a potential adaptive way (Craine, 2009; Garnier et al., 2016; Grime, 1979). Patterns of ecological strategies have been described mostly through observational and experimental studies at the species (e.g. Grime, 1974, 1977, 1979; Westoby, 1998; Wright et al., 2004) and community levels (e.g. Poggiato et al., 2023). However, despite REF expectations, many analyses have revealed weak and/or inconsistent trait-environment relationships in plants (Anderegg, 2023; Messier et al., 2017; Vellend et al., 2014). At the within-species level, expected relationships based on theories of ecological strategies sometimes do not hold (Anderegg, 2023; Fajardo & Siefert, 2018; Zhou et al., 2022; but see Sartori et al., 2019; Vasseur et al., 2018). For response traits and ecological strategies to be adaptive, they must be driven by environmental factors

operating within populations and linked to individual fitness (Shipley et al., 2016).

Many studies have quantified the adaptive value of response traits through quantitative genetics, either by estimating selection coefficients (reviewed by Caruso et al., 2020; Geber & Griffen, 2003) or by identifying genetic signatures of natural selection (e.g. Brouillette et al., 2014; Cavender-Bares, 2019; although the term 'response trait' has not been explicitly referenced). When applied to clades with resolved phylogeny (e.g. *Helianthus*—Mason & Donovan, 2015 and *Quercus*—Cavender-Bares, 2019), these approaches are particularly useful to shed light on trait-environment relationships across levels of organization, from the within- to the between-species level. For example, a phylogenetic comparative approach was applied to the *Helianthus* genus (Mason & Donovan, 2015) to bridge macroevolutionary and microevolutionary processes (previously assessed for *Helianthus anomalus*; Brouillette et al., 2014; Donovan et al., 2010). These analyses have shown varying support for the adaptive basis of ecological strategies, with some trait correlations following selective patterns but not others (Brouillette et al., 2014; Mason & Donovan, 2015).

A reason for differences in trait-environment relationships across levels of organization is that, from populations to communities, both species turnover and intraspecific trait variation (ITV, which results from genetic variation and phenotypic plasticity) can explain variations in response traits (Siefert et al., 2015). For example, Dong et al. (2020) compared trait-environment relationships both between and within 705 plant species distributed along a latitudinal gradient and concluded that ITV was the main factor explaining the observed relationships for a leaf trait associated with a trade-off between carbon gain and water loss. In contrast, species turnover predominantly explained trait-environment relationships for leaf area (Dong et al., 2020). Accordingly, the relevance of ITV for trait-environment prediction varies from trait to trait (Siefert et al., 2015), but they also depend on the species (Westerband et al., 2021).

ITV will likely shape trait-environment relationships when it is subject to natural selection. Although ITV is a condition for selective processes to operate, high ITV does not necessarily indicate that the trait is under strong selection and correlates with environmental variables. The opposite may in fact be true, as natural selection tends to reduce ITV within populations by favouring the fittest genotypes (Bell, 2008). However, a high ITV among populations can lead to strong trait-environment relationships if it reflects local adaptation. Overall, it is not the magnitude of ITV per se that determines the strength of trait-environment relationships, but rather how it relates to selective processes. This highlights the need to study response traits through the lens of evolution.

Like response traits, effect traits have historically been studied with little consideration of ITV and its evolutionary aspects (Lavorel & Garnier, 2002; Figure 1). This is partly due to the prevailing view that interspecific variation is the main factor explaining trait variation across spatial scales (Kazakou et al., 2014). This focus led to the generalized adoption of a mean-field approach based on community weighted means (CWM). CWM is a method that combines species'
## BOX 1 The ecological effects of intraspecific trait variation and the response-effect framework

The response-effect framework (REF) emerged when functional ecology, after its foundation in 1987 (Calow, 1987), had steered research priorities towards uncovering general patterns of trait variation at the interspecific level (Keddy, 1992, 1994). At that time, intraspecific trait variation (ITV) was mainly examined in the discipline to identify traits with greater variation between species (BTV) than within species, as these were considered 'robust' (Albert et al., 2011; Garnier et al., 2001). The REF followed this tradition and originally focused on interspecific variation (Lavorel & Garnier, 2002). Interest in ITV reemerged in the 2010s, when many papers (reviewed by Westerband et al., 2021) called for variance partitioning in trait analyses to quantify the proportion of variance explained by ITV. In this context, the key question became whether and when ITV should be accounted for in trait-based analyses (Albert et al., 2011; Siefert et al., 2015). Albert et al. (2011) proposed that studies of response traits should always account for ITV, whereas studies of effect traits should consider it only when examining ecosystem processes over long timescales. However, subsequent research has shown that ITV can also influence short-term ecosystem processes (Ali et al., 2017; Sanders et al., 2024, 2025). In a meta-analysis, Siefert et al. (2015) found that between 25% and 32% of global community-level trait variation was explained by ITV. They emphasized that the magnitude of ITV seems to be higher at local scales and in species-poor communities. However, consistent with Jung et al. (2010), they cautioned that even relatively low ITV can substantially affect community processes. The substantial role of ITV in the dynamics of communities and ecosystems was later confirmed by a meta-analysis that included plants, fungi and animals (Raffard et al., 2019). Interestingly, this study showed that intraspecific richness (genotypic and/or phenotypic richness) exerts effects on community and ecosystem processes comparable in magnitude to those observed at the between-species level (Raffard et al., 2019).

trait values weighted by their relative abundances in the community (Garnier et al., 2004; Violle et al., 2007). It is rooted in the mass-ratio hypothesis, which assumes that the most abundant species exert the strongest influence on short-term ecosystem functioning, primarily through their biomass contributions (Grime, 1998). While CWM can be a reliable proxy of ecosystem processes in some cases (e.g. Vile et al., 2006), the community and ecosystem effects of ITV have been increasingly recognized as underestimated relative to interspecific processes (Albert et al., 2011; Bolnick et al., 2011; Stump et al., 2022; Violle et al., 2012; Zhang et al., 2023).

ITV can affect ecosystem functioning directly through its ecological effects (on, e.g. species coexistence) or indirectly through the evolutionary dynamics it generates, which, in turn, affect community and ecosystem processes (Bolnick et al., 2011). Direct effects have been increasingly incorporated into functional ecology research since the early 2010s (Box 1). Indirect effects, in contrast, remain mostly studied in the field of evolutionary biology (reviewed by Matthews et al., 2011) and therefore disconnected from the REF. We attempted to integrate some examples of these evolutionary studies into the REF framework to encourage greater interconnection between disciplines (Table 1). It is worth noting that the provided list is not exhaustive and includes only examples in which both response and effect traits could be identified as mechanistic links from the population to the ecosystem level.

The recognition that traits impacting ecosystem processes (i.e. effect traits) can also evolve and, by definition, simultaneously function as response traits leads to the expectation of some degree of covariation between response and effect traits. An example of this covariation is specific leaf area, which has been identified as a trait that not only responds to nutrient gradients, but also influences above-ground primary productivity along these gradients (Lavorel & Garnier, 2002). Possible ecosystem-level consequences of phenotypic covariation between response and effect traits have only recently been theorized (de Bello et al., 2021). de Bello et al. (2021) showed that a greater overlap between response and effect traits is expected to propagate environmental changes from the species level to the ecosystem level, while a lower overlap may buffer the impacts of such changes. However, these response-effect relationships can also be influenced by ITV. Accordingly, the role of genetic variation and phenotypic plasticity has been highlighted by de Bello et al. (2021) as a key gap in our understanding of community and ecosystem processes. Genetic correlation between response and effect traits (observed at the within-population level) can, for example, reinforce or oppose the phenotypic correlation observed at the community level (Figure 2). As a result, even if response and effect traits are positively correlated at the community level, environmental changes may only propagate to the ecosystem level if there is a positive genetic correlation between response and effect traits.

## 3 | ECO-EVOLUTIONARY FEEDBACKS BETWEEN RESPONSE AND EFFECT TRAITS

The evolvability of and interplay between response and effect traits leads to the possibility of eco-evolutionary feedbacks, which have been shown to be pervasive (Govaert et al., 2019; Hendry, 2017; Post & Palkovacs, 2009) and to potentially drive long-term ecosystem modifications (e.g. ecosystem (in)stability; Wood et al., 2021). Eco-evolutionary feedbacks are defined as reciprocal interactions between ecological and evolutionary processes (Hendry, 2017; Post & Palkovacs, 2009). They arise when organisms modify environmental conditions (ecology), which then become modified selective pressures acting on themselves or other organisms (evolution).
<table><caption>TABLE 1 Some examples of how studies investigating the ecosystem-level impacts of trait evolution can be incorporated into the response-effect framework.</caption><thead><tr><td>Study system</td><td>Response trait</td><td>Effect trait</td><td>Ecosystem process</td><td>References</td></tr></thead><tbody><tr><td>Bacteria</td><td>Exploitation of different resources</td><td>Allelopathy with ancestral strain</td><td>Primary productivity</td><td>Houpt and Kassen (2023)</td></tr><tr><td>Rotifers</td><td>Resource allocation</td><td>Biomass</td><td>Trophic dynamics</td><td>Declerck et al. (2015)</td></tr><tr><td>Fish</td><td>Habitat use (benthic or limnetic)</td><td>Foraging behaviour and impact on dissolved organic carbon</td><td>Primary productivity, respiration</td><td>Harmon, Matthews, et al. (2009)</td></tr><tr><td>Fish</td><td>Feeding morphology</td><td>Foraging behaviour and rates of nitrogen excretion</td><td>Trophic dynamics, decomposition rates</td><td>Bassar et al. (2010) and Palkovacs et al. (2009)</td></tr><tr><td>Aphids</td><td>Heat tolerance</td><td>Growth rate</td><td>Trophic dynamics</td><td>Harmon, Moran, and Ives (2009)</td></tr><tr><td>Plant</td><td>Quality of rhizobium mutualism</td><td>Nitrogen fixation</td><td>Soil nitrogen availability</td><td>Lau et al. (2022)</td></tr><tr><td>Plant</td><td>Stem width, root depth distribution, root-to-shoot ratio</td><td>Root depth distribution, root-to-shoot ratio</td><td>Soil surface accretion, carbon sequestration</td><td>Vahsen et al. (2023)</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Intraspecific factors that can determine the long-term consequences of response-effect trait interactions. Within a plant community, positive phenotypic covariations may be observed between response and effect traits. However, the long-term consequences of this relationship may depend on processes at the intraspecific scale (here, intraspecific variability is depicted with individuals of varying colours), such as genetic covariations between these traits and intraspecific plant-soil feedbacks.</figcaption></figure>

Accordingly, when organisms and their effect traits (products of prior evolution and, as such, potential response traits) alter environmental conditions, these ecological changes can feedback as selective pressures that promote evolutionary change (modified response traits), in an eco-evolutionary loop. Eco-evolutionary feedbacks can be positive or negative (Hendry, 2017). Positive feedbacks reinforce the dynamics between ecological and evolutionary processes, such as exotic plant species that are more flammable and fire-resistant than natives, gradually becoming dominant in a community (D'Antonio &

Vitousek, 1992). Negative feedbacks, on the other hand, dampen eco-evolutionary dynamics, as seen in the case of species-specific accumulation of soil pathogens that reduce the species abundance in a community over time (Van der Putten et al., 1993).

Though there has not yet been much integration of the study of eco-evolutionary feedbacks into the REF, Violle and Jiang (2009) were one of the first to conceptually link the REF with related ideas. They suggested that plant height could evolve in response to both light availability (as a response trait) and competition (as a shading
strategy towards competitors, i.e. an effect trait). A possible consequence of this double role would be that plant height increases in response to competition for light and ends up constructing a shaded niche that negatively impacts the performance of neighbouring plants. This constructed niche free of competitors would then favour the performance of tall individuals, completing a positive feedback loop. Such assumptions were later confirmed through modelling applied to crops (Montazeaud et al., 2020) and have received empirical support from mixtures of genotypes with contrasting biomass (Turner et al., 2020).

Feedback involving response and effect traits has also been examined through plant-soil interactions (Figure 2). For instance, decomposability and nutrient cycling have been shown to depend on litter stoichiometry (Bardgett, 2017), and this relationship may be based on plant-microbe coevolution. This is because the microbiome present in plant tissues may have an advantage in decomposing the decaying plant material (Cornelissen et al., 2023). Although there is increasing evidence that plant genetic variation can control leaf (e.g. Brachi et al., 2022) and soil microbiome (e.g. Huang et al., 2019; Przybylska et al., 2024; Van Nuland et al., 2019), few studies have investigated how the evolution of litter stoichiometry impacts ecosystem processes such as decomposability and nutrient cycling. In one of these studies, Fitzpatrick et al. (2015) analysed populations of *Oenothera biennis* that have evolved for 5 years under different herbivory pressures, showing that the evolutionary change in these populations (involving tissue phenolics) reduced leaf decay and soil respiration rates. This highlights how evolutionary dynamics can also affect soil processes such as decomposition.

Other conceptual frameworks that have been used to explore, both theoretically and empirically, eco-evolutionary feedbacks involving plant traits are community genetics (interspecific indirect genetic effects; Crutsinger, 2016; Whitham et al., 2020) and intra-specific plant-soil feedbacks (Przybylska et al., 2024; Schweitzer et al., 2018; Figure 2). Combining field observations with reciprocal transplants and a drought experiment, Gehring et al. (2017) first demonstrated that different genotypes of *Pinus edulis* recruit distinct mutualist ectomycorrhizal fungi. Then, they showed that this plant-microbe interaction explained the contrasting plant growth responses observed both under experimental drought conditions and in the field, during a 10-year period of drier-than-average conditions. Also combining field observations and reciprocal transplants using a tree species (*Populus angustifolia*), Van Nuland et al. (2019) demonstrated that genetic-based variation in leaf nitrogen content and diameter at breast height was associated with soil nitrogen and soil microbial communities, which, in turn, affected plant biomass.

Beyond plants—the organisms for which the REF was originally developed (Lavorel & Garnier, 2002)—studies investigating the real-time evolution of entire microbial and zooplankton communities have provided perspectives for approaching evolutionary processes in complex ecological systems. A striking result obtained in one of these experimental studies was that, when grown in mixtures, certain microbial species evolved the capacity to exploit resources produced by other species, a niche partitioning phenomenon that

engendered more productivity (Lawrence et al., 2012). Studying zooplankton, Vanvelk et al. (2024) showed that the temporal dynamics of community composition and grazing pressure depended on whether none, one or all populations in the community had previously evolved under heatwave conditions. Interestingly, for grazing pressure, the cumulative effect of evolution in multispecies communities diverged from the additive expectation based on single-species effects (Vanvelk et al., 2024). This suggests the emergence of evolutionary properties unique to multispecies contexts, highlighting the need for more evolutionary studies employing complex multispecies designs.

Modelling studies are also powerful tools for examining eco-evolutionary feedbacks (reviewed by Govaert et al., 2019). For instance, Vasseur et al. (2011) demonstrated how the evolution of traits involving a trade-off between intraspecific and interspecific competitive abilities can stabilize species coexistence. However, extending such approaches to more complex communities while simultaneously incorporating ecosystem functioning remains a challenge (Levine et al., 2017; for a detailed overview of modelling approaches to the study of eco-evolutionary dynamics, see Govaert et al., 2019).

4 | IMPROVING THE PREDICTIVE POWER OF THE REF WITH EXPERIMENTAL EVOLUTION

In 2018, the American Society of Naturalists organized a debate on the role of evolutionary dynamics in shaping the functioning of ecological communities. The discussion, later summarized in the form of an article (Mayfield et al., 2023), tackled the question of whether microevolutionary processes can be used to predict future ecosystem function. The consensus was that, although these processes are important, we still lack sufficient evidence on how frequently they influence community and ecosystem processes to provide a definitive answer.

In line with this debate, we have shown that the inclusion of within-species processes and eco-evolutionary dynamics in the REF may allow for refined predictions of the consequences of environmental change on biodiversity and ecosystem functioning. However, precision often comes at the expense of generality (Levins, 1966). Adding complexity increases the experimental and computational load and can generate uncertainty in modelling (Berzaghi et al., 2020). Therefore, we subscribe to the call for more studies to better understand how much complexity is essential for a realistic approach without compromising predictive ability. We specifically highlight experimental evolution as a promising, yet still underexplored, approach to test for eco-evolutionary predictions based on theory and/or modelling.

Experimental evolution allows researchers to assess real-time evolution (i.e. evolution that takes place during the course of the experiments), providing direct evidence of natural selection when specific environmental conditions are manipulated while other factors are controlled for (Garland & Rose, 2009; Kawecki et al., 2012).
**FIGURE 3** Functional ecology can be combined with experimental evolution to investigate the evolution of functional traits, biotic interactions and ecosystem processes. An initial population with standing genetic variation (represented by the pool of white circles) is subjected in replicates to three different experimental conditions. Unfit individuals (genotypes) are filtered over time ($t_0$ to $t_N$), causing the mean trait value of each population to shift from its value at $t_0$ ($\bar{z}_0$) towards new fitness optima at $t_N$ ($\bar{z}_1, \bar{z}_2, \bar{z}_3$). The trait variation from $t_0$ to $t_N$ can be related to fitness (response trait) and primary productivity over time (effect trait). This change may be associated with a decrease in intraspecific plant-soil feedbacks (shifting from positive to negative), as exemplified here for the population evolved under condition 3.

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

In this approach, random variation can be controlled through replication of experimental populations, which are monitored over generations (Figure 3). Genetic variation and phenotypic plasticity can be dissociated as causes of the observed phenotypic variation if a common garden experiment is set-up using material harvested from the experimental mesocosms (e.g. seeds).

To understand which (response) traits are most likely to evolve, a promising start is to study the real-time evolution of traits under selective pressures that are key to ecological strategies, such as resource availability and disturbance (Craine, 2009). Resource availability and disturbance (i.e. events that remove living biomass—e.g. herbivory, pathogens, fire and wind damage; Grime, 1977) have been mostly studied independently and corroborated as drivers of trait evolution in microorganisms (e.g. Hoffmann et al., 2025; Jasmin & Kassen, 2007), plants (e.g. Agrawal et al., 2012; Johnson et al., 2022) and animals (e.g. Dutilleul et al., 2014; Kolss et al., 2009). However, the evolutionary study of these factors in combination, such as proposed by Grime's C-S-R theory (Grime, 1974, 1977, 1979), remains scarce (but see Prochazka et al., 2024). C-S-R is the acronym for three main strategies that are thought to evolve in response to varying levels of resource availability and disturbance. Competitive (C) strategies are associated with traits that maximize resource acquisition,

such as size-related traits (e.g. plant height, leaf area) and nutrient uptake capacity, and are expected to evolve under conditions of high resource availability and low disturbance. Stress-tolerant (S) strategies are associated with traits that favour conservation of resources and are expected to evolve under low resources and low disturbance. Ruderal (R) strategies are associated with traits that favour regeneration after disturbance, such as fast acquisition of resources and reproduction, and are expected to evolve under high resources and strong disturbance (Grime, 1977). The theory assumes that organisms cannot persist in environments where resource availability is low and disturbance is strong (but see Herben et al., 2018). Using experimental evolution, one can track in real time the evolutionary trajectories of traits that define C-S-R strategies (e.g. plant height, leaf area, leaf dry matter content, specific leaf area and flowering time; Hodgson et al., 1999; Pierce et al., 2017). It is then possible to test the effect of selection versus neutral processes, thanks to next-generation sequencing technologies (evolve-and-resequence approach; Schlötterer et al., 2015). Originally developed for plants, the C-S-R classification has been extended to microorganisms (Krause et al., 2014; Romillac & Santorufo, 2021) and other organisms (Grime & Pierce, 2012), enabling testing the evolution of the underlying response traits with distinct study systems. This will ultimately allow
us to determine whether some strategies are more likely to evolve than others in response to the key ecological pressures of resource availability and disturbance.

To demonstrate that not only response traits and genes can change over time, but also other ecologically relevant processes that witness the 'effect' role of traits and their consequences, one can harness long-term biodiversity-ecosystem functioning experimental set-ups. These experiments focus on investigating how variation underlying plant-plant interactions affects primary productivity. One such long-term study, the Jena Experiment, has, for example, shown that coevolution in species mixtures positively affects primary productivity and that this influence is underpinned by (effect) trait divergence (i.e. niche complementarity; Zuppinger-Dingley et al., 2014). Taking advantage of this experimental setup, diverse parallel experiments have been designed to address specific hypotheses. For instance, parallel genetic assessments have helped distinguish between genetic and epigenetic variation and test the role of natural selection in effect traits (van Moorsel et al., 2019). Furthermore, Zuppinger-Dingley et al. (2016) investigated how plant selection interacts with soil legacy to explain the positive effects of biodiversity on primary productivity. They found that biotic soil legacy increased complementarity-driven productivity in mixtures. Although this experiment is insightful in terms of the role plant evolution can have on the biotic component of the soil (and vice versa), genetic variance for the plant effect on the soil was not assessed in this experiment. Adaptation to soil legacy was tested in a follow-up experiment (De Giorgi et al., 2025), but the direct effects of soil and plant community processes could not be fully disentangled. If, during the course of experiments such as the Jena Experiment, seeds and soil samples from each generation and treatment were harvested and stocked (in a cold chamber and a freezer, respectively), it would be possible to track, in a common garden, plant-soil evolution. This would allow us to gain powerful knowledge on how response-effect trait coevolution scales up to affect primary productivity over time. Tracking these interactions could reveal, for example, if plant effects on soil foster niche complementarity and, thereby, productivity. By analysing root traits (which are more likely related to plant-driven soil effects), key mechanisms linking evolutionary history and ecosystem functioning could be identified.

Although promising, experimental evolution can only be performed using species with relatively short generation times, such as across communities of microorganisms, zooplankton or annual plants. Moreover, this approach becomes increasingly challenging as the number of species studied increases. However, manipulating 'artificial' communities of two to eight species should already provide valuable insight on the evolution of response and effect traits, while remaining technically feasible (e.g. van Moorsel et al., 2019). The most powerful insights into the evolution of communities and ecosystems will likely come not from exclusively applying experimental evolution, but from combining it with different approaches (e.g. Hoffmann et al., 2025).

## 5 | CONCLUSIONS

Since the publication of the seminal response-effect framework (REF) by Lavorel and Garnier (2002), our understanding of functional trait evolution and the speed at which evolutionary processes influence and feedback into ecological dynamics has advanced significantly. Here, we have outlined how integrating this knowledge into the REF can enhance the ability of the framework to predict how plant traits will change over time and, in turn, shape ecosystem functioning. Though we still lack evidence to affirm whether within-population processes are a sine qua non condition for the predictive strength of the REF, we aim to stimulate further research on this major question. As a promising step forward, experimental approaches that capture real-time evolution, such as experimental evolution, offer valuable tools for hypothesis testing. Additionally, priority should be given to studies investigating how evolutionary processes shape dynamics at higher levels of biological organization. Applying empirical evolutionary approaches to address the evolution of complex ecological systems may be key to developing a more mechanistic and predictive REF capable of addressing both short- and long-term processes. Such an enhanced framework may significantly broaden the applicability of the REF, particularly in areas such as ecosystem management and agroecology.

### AUTHOR CONTRIBUTIONS

Maria Stefania Przybylska, Lucie Mahaut, Cyrille Violle, Denis Vile and François Vasseur conceived the study. All authors contributed to the discussions and the writing of the initial manuscript draft. Maria Stefania Przybylska, Lucie Mahaut, Cyrille Violle, and François Vasseur wrote the final manuscript draft. Maria Stefania Przybylska produced figures in collaboration with Cyrille Violle and François Vasseur. All authors revised the final version of the manuscript and gave final approval for publication.

### ACKNOWLEDGEMENTS

This work was supported by Agence Nationale de la Recherche (ANR; www.anr.fr) and Deutsche Forschungsgemeinschaft (DFG; www.dfg.de) funding as part of the AraBreed project (Grants ANR-17-CE02-0018-01 and SCHE 1899/2-1). It was also supported by the European Research Council (ERC) Starting Grant Project 'PHENOVIGOUR' (Grant ERC-StG-2020-949843) and the REFRAME project (Grant ANR-24-CE02-7222). C.V. was supported by the European Research Council (ERC) Starting Grant Project 'Ecophysiological and biophysical constraints on domestication in crop plants' (Grant ERC-StG-2014-639706-CONSTRAINTS). We are grateful to the Alexander von Humboldt Foundation, the Max Planck Society and the Carnegie Institution for Science for support to M.S.P., D.W. and M.E.-A., respectively, and to INRAE and the European Joint Programming EJP Soil for support to X.L.R. We thank Jeanne Tonnabel for valuable discussions and feedback on the initial manuscript draft. We also thank Robin Heinen for his constructive feedback on the final version of the manuscript and the anonymous
reviewers for their insightful comments and suggestions. Open Access funding enabled and organized by Projekt DEAL.

## CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflict of interest. Cyrille Violle is an Associate Editor of Functional Ecology, but took no part in the peer review and decision-making processes for this paper.

## DATA AVAILABILITY STATEMENT

No primary data were used in this review.

## ORCID

Maria Stefania Przybylska https://orcid.org/0000-0002-9941-892X

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

Denis Vile https://orcid.org/0000-0002-7948-1462

Xavier Le Roux https://orcid.org/0000-0001-9695-0825

Elena Kazakou https://orcid.org/0000-0001-7188-8367

Moises Exposito-Alonso https://orcid.org/0000-0001-5711-0700

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**How to cite this article:** Przybylska, M. S., Mahaut, L., Violle, C., Vile, D., Munoz, F., Scheepens, J. F., Le Roux, X., Kazakou, E., Exposito-Alonso, M., Bossdorf, O., Weigel, D., & Vasseur, F. (2025). An evolutionary perspective on the response-effect framework. *Functional Ecology, 00*, 1-13. https://doi.org/10.1111/1365-2435.70236