Ecology Letters

Predicting the stability of ecological communities in changing environments is challenging. Classical theory posits that community stability cannot be understood without considering interspecific interactions. A contrasting view is that species environmental responses and their variation (response diversity) influence stability to the extent that effects of interspecific interactions can be ignored. Surprisingly, few studies have evaluated the relative importance of interactions versus species responses. Moreover, trait-based measures of response diversity often show limited predictability. Here, we introduce community performance curves, the aggregate of species performance curves, as a powerful mechanistic link between community composition and stability. This approach reveals that species responses predict most of the variation in community stability in simulated communities, even when the strength of interspecific interactions varies. An experiment with ciliate communities corroborates these findings, while a literature review reveals how rarely both mechanisms are assessed jointly. By moving from summary traits to community performance curves, we reconcile the two perspectives: while species interactions undeniably shape community dynamics, community performance curves are sufficient to predict stability. This provides the opportunity to predict community stability, even when information about the multitude and diversity of interspecific interactions is unavailable.

O_LIStage-dependent interactions, in which different life cycle stages (e.g., juveniles, adults) exert different per-capita competitive effects, are widespread across ecological communities. However, whether explicitly accounting for such ontogenetic variation improves forecasts of stochastic community dynamics remains unclear. We tested how the strength of stage dependence and species life-history strategy influence the predictive accuracy of community models that either include or ignore stage-specific interactions. C_LIO_LIWe constructed stochastic two-species competition models using stage-structured matrix population models spanning five virtual life histories along the fast-slow continuum. Density dependence was imposed separately on juvenile survival, adult survival, progression, retrogression, or fertility, and the strength of stage dependence varied from adult-driven to juvenile-driven competition. We then fitted deterministic projection models with and without stage-dependent interaction terms to simulated time series and quantified predictive performance over 100 time-step forecasts using mean absolute percentage error (MAPE). C_LIO_LIIncreasing stage dependence consistently reduced the predictive accuracy of models that ignored stage structure. However, absolute prediction errors remained small across all scenarios (MAPE < 0.7%), even under strong stage dependence. The influence of life-history strategy depended on which vital rate was density dependent: when juvenile survival was density dependent, faster life histories showed larger errors; when progression, retrogression, or fertility were density dependent, slower life histories exhibited greater errors; and when adult survival was density dependent, no consistent life-history effect emerged. Across simulations, temporal variation in population structure was low (coefficient of variation < 0.036), and prediction error was strongly associated with the magnitude of structural fluctuations rather than life-history pace per se. C_LIO_LISynthesis. Stage-dependent interactions can, in principle, alter stochastic competitive dynamics, but their practical importance for ecological forecasting depends on the extent to which population stage structure fluctuates through time. When environmental stochasticity dominates and stage structure remains near equilibrium, simpler models that ignore stage dependence provide robust approximations of community dynamics. Our results identify conditions under which demographic detail is necessary for forecasting and highlight the central role of structural variability in linking life-history strategy to community-level dynamics. C_LI

Natural populations exhibit complex class structures that profoundly shape evolutionary trajectories. While evolutionary demography provides a formal framework to predict adaptation using invasion fitness, the high mathematical dimensionality of these models often precludes analytical solutions, obscuring biological interpretation and hindering the analysis of long-term evolutionary outcomes. Because current reduction techniques remain fragmented, a unifying theoretical foundation is critically needed. Here, we introduce "structural evolutionary invasion analysis," a systematic framework that integrates two complementary tools to simplify complex life cycles. First, we formulate the "invasion determinant," an algebraic method that yields a direct scalar condition for mutant invasion. Second, we develop the Projected Next-Generation Matrix (PNGM), which structurally compresses life-cycle graphs by eliminating secondary classes. We demonstrate that this reduction is mathematically equivalent to separating dynamical timescales, explicitly preserving Fishers reproductive values for the retained focal classes. Crucially, under the standard assumption of weak selection, our synthesized framework guarantees that all properties of evolutionary singularities--including their location, convergence stability, and evolutionary stability--are strictly identical to those derived from the full, unreduced model. Illustrated with diverse ecological examples, this framework provides modellers with a rigorous and tractable toolkit for decoding state-dependent selection in high-dimensional populations.

O_LIUnderstanding functional community structure and the niche-based mechanisms that enable coexistence among sympatric species is essential for explaining how biodiversity is maintained in natural systems, and for anticipating how ecological communities will respond to ongoing environmental change. Stable isotope analysis provides a process-oriented perspective on resource use by integrating information across time and space, thereby allowing reconstruction of realised isotopic niches that reflect multiple dimensions of ecological differentiation. C_LIO_LIWe applied this framework to a community of ungulates in the Central-Eastern Italian Alps, including red deer (Cervus elaphus), roe deer (Capreolus capreolus), and Alpine chamois (Rupicapra rupicapra). Using stable isotope ratios in summer-grown hair segments ({delta}13C, {delta}15N, {delta}34S, {delta}18O, {delta}2H), we quantified species-specific n-dimensional niche hypervolumes within a Bayesian framework and estimated niche regions, overlap probabilities, univariate differentiation and multivariate structure. C_LIO_LIDespite broad dietary overlap typically observed among these ungulates, we found clear isotopic niche segregation, with mean pairwise overlap consistently remaining below 40%. Three dimensions emerged as primary drivers of differentiation: water sourcing ({delta}18O), diet quality ({delta}15N), and habitat openness ({delta}13C). Specifically, chamois appeared to derive more water from plants in their diet rather than from drinking, and to consume a higher-quality diet compared to Cervids. Red deer relied more heavily on forested habitats for resource use compared to roe deer and chamois, and additional isotopic differences between red deer and roe deer may stem from fine-scale abiotic conditions like microclimate and topography. We found no isotopic evidence for differential niche breadth among the three ungulate species. C_LIO_LITogether, these patterns highlight functional differentiation across multiple ecological axes, offering mechanistic insight into how these ungulates segregate realised niche space despite substantial potential for resource overlap. This multi-element isotope perspective underscores the value of integrative, process-based approaches for understanding current coexistence as well as improving predictions of how mammal communities may reorganise under accelerating environmental change. C_LI

Plant communities within a metacommunity can vary widely in their degree of invasion by introduced species. Disturbance, propagule pressure, and biotic resistance are common explanations for this variation, but empirical evidence for these hypotheses is mixed. Alternatively, the community assembly framework predicts that local assembly filters determine both native and exotic composition, but lower trait variation in the introduced species pool may exclude them from certain sites. We examined evidence for this framework using observational data from forests and woodlands of Long Island, NY, USA. These forests vary in vegetation composition and invasion along a soil gradient. They are also highly disturbed and fragmented, yet some stands have almost no introduced plants. Using data collected in 1998 and 2021-22, we quantified relationships between community composition, soil characteristics, and functional traits for native and exotic assemblages, as indicators of environmental filtering. We found similar trait-environment relationships in native and introduced species, suggesting that both groups follow the same local assembly rules. Introduced species were predominantly found in sites with more nutrient-rich soils and were absent from sites with nutrient-poor soils. At the regional scale, the exotic species pool was biased toward trait values favored in more nutrient-rich environments, particularly high growth rates and low leaf C:N ratios, which explains their absence from nutrient-poor environments. These patterns were consistent over time, and stands that were uninvaded in 1998 remained so in 2021-22, supporting the robustness and reliability of short-term studies. This study shows that invasion patterns in plant communities can be explained by the assembly rules that govern native species. By linking local environmental filtering with regional species pool characteristics, this work advances our understanding of how some communities remain uninvaded despite high disturbance and propagule pressure. Overall, these results highlight the utility of the community assembly framework, and emphasize the importance of regional processes in constraining the local distribution of introduced species.

Theory suggests that different components of environmental fluctuations, from daily and seasonal cycles to multidecadal trends, can have distinct and even opposing effects on species abundances and community dynamics, depending on their specific adaptations. But empirical research that deconstructs the influence of these different cycles on communities is lacking. Here, we used long-term biological monitoring data together with flow records of rivers across New Zealand to (i) investigate the role of fast, slow, and seasonal river-flow fluctuations in structuring macroinvertebrate communities; and (ii) to assess whether life-history and mobility traits mediate the response. Using joint species distribution models, we found striking differences in taxon and community responses to the different components of river flow variation. Responses to slow fluctuations were generally stronger and better predicted by traits, while responses to seasonal fluctuations were highly heterogeneous. Fast increases in flow, typical of flooding events, had pervasive negative effects on species abundances, but the severity of impact partly depended on mobility traits. Our results suggest that different ecological mechanisms underpin the response to distinct environmental fluctuations, highlighting the value of jointly considering multiple temporal scales of variation and species functional traits to understand and predict how communities reorganise under fluctuating environmental regimes.

Cross-scale integration remains a persistent challenge in ecology. Mechanistic network models have advanced this integration by linking individual behavior to community dynamics. Their complexity, however, often limits exploration to numerical simulations, which tend to be insufficient for fully unveiling the fundamental rules governing system behavior. Extracting these rules requires moving beyond numerical observation to establish exact, analytical constraints. Here, a complete mathematical analysis of a mechanistically detailed plant-pollinator model is presented. This cross-scale analysis decouples transient and equilibrium dynamics, proving that pollination strictly gates plant persistence while recruitment competition caps equilibrium abundance. The precise behavioral mechanisms scaling up to determine network stability are determined: nestedness stabilizes communities by generating floral reward gradients that guide adaptive foraging, whereas connectance destabilizes by eroding these rescue pathways. Additionally, native community persistence and biological invasions are conceptually unified; a single, multi-scale reward threshold (R*) is shown to govern both native survival and alien establishment. These analytical derivations are distilled into conceptual frameworks and visual summaries accessible for empiricists interested in theory and conceptual unification. By translating numerical observations into rigorous, trait-grounded proofs, this analysis demonstrates that complex, cross-scale networks are tractable, revealing the precise conditions under which communities assemble, persist, and collapse.

We provide a general theoretical explanation for a longstanding result in spatial ecology: weak dispersal among habitat patches promotes local biodiversity. Using analytical approximations of spatial Lotka-Volterra competition models, we show that species persistence in heterogeneous landscapes can be expressed as a function of regional abundance and local invasion growth rates. We further demonstrate that local multispecies coexistence is governed by the feasibility domain, linking spatial coexistence to a structural property of nonspatial competitive systems. Together, these results explain why weak dispersal increases local species richness and why this effect strengthens with landscape size. We test these predictions using numerical simulations and find that the theory breaks down only when both dispersal and competitive interactions are very strong, in which case dispersal has a unimodal effect on coexistence. In contrast, landscape size retains a positive effect on coexistence whenever an effect is detectable. We then apply the theory to long-term data from a natural Daphnia metacommunity. We detect strong preemptive competition among species and find no detectable effect of dispersal rate on local coexistence, whereas species co-occurrence increases with local landscape size, as predicted by theory. Together, our results identify how dispersal, interaction strength, and landscape size jointly regulate biodiversity in competitive systems.

Classical niche theory predicts that species with more similar traits experience stronger competition, and thus, trait dimensionality, here defined as the number of independent traits per species in a community, plays a critical role in coexistence. Despite this, current studies often assume that communities are structured by only a few key traits. Here, we leveraged a theoretical framework that integrates phylogenetic information with repeated instances of community assembly to estimate the number of ecologically relevant traits required to support observed species richness in experimental and natural plant communities. We found that the inferred trait dimensionality is surprisingly high, often exceeding species richness, suggesting that many traits contribute to coexistence. Furthermore, we explored drivers of grassland trait dimensionality, and it depends in complex ways on area, species pool size, and latitude. Our findings indicate that local coexistence may rely on a larger number of traits than previously assumed, challenging low-dimensional trait-based views of community structure.

Species tolerating the same environmental conditions can potentially colonize and thrive in the same habitats and eco-regions. Are any pair of those species equally probable to co-occur in the same community? Can we quantify the propensity of two species to co-occur together? Here, we focus on a simple but largely overlooked community-level pattern: the co-occurrence-occupancy curve, which relates the tendency of species to co-occur with others to their total occupancy across sites. We first define this empirical curve and then derive its expected shape under a random null model that assumes site equivalence and species independence. Building on these results, we introduce the Species Association Index (SAI), an occupancy-standardized measure that quantifies the tendency of a species to associate with others independently of its overall frequency of occurrence. The SAI enables meaningful comparisons among species with contrasting occupancies and provides a transparent benchmark against which departures from neutrality can be assessed. We illustrate the approach using two contrasting systems--tropical rain forest trees on Barro Colorado Island and organisms from Mediterranean rocky shores--highlighting both the generality of the co-occurrence-occupancy framework and its limitations.

Projecting species responses to changing temperatures remains a major challenge in ecology. Central to this effort is harnessing our understanding of species thermal physiological traits, which underlie ectotherm fitness. These traits are typically characterized by describing performance across temperatures (thermal performance curve, TPC), and/or tolerance limits, which capture endpoints of biological failure. Despite their importance, we still lack an understanding of the functional relationship between these traits, limiting our ability to integrate them into comprehensive vulnerability assessments. Using a synthesized dataset of >100 ectotherms, we tested how heat tolerance (CTmax) relates to key TPC traits: thermal optima, thermal maxima, and the supra-optimal range of temperatures where performance is positive. Across taxa, TPC traits were positively related to CTmax, demonstrating a link between heat tolerance and temperature-dependent performance at sub-critical temperatures. While acute locomotor performance scaled proportionally with CTmax, metabolic processes and sustained locomotion scaled sub-proportionally, suggesting decoupling of CTmax and performance among high-CTmax species. This suggests that using CTmax as a comparative metric may overestimate thermal safety margins for metabolic processes critical to growth. Our results indicate that while CTmax and TPCs reflect shared underlying constraints--particularly in acute neuro-muscular traits--their relationship is dependent on timescale and the TPC response trait. Our findings connect our understanding of the processes that maintain performance over thermal gradients with those that cause performance to fail, improving our ability to project species persistence in a warming world. SignificanceClimate warming is increasingly reshaping the thermal environments that govern species persistence worldwide. Predicting vulnerability requires integrating multiple aspects of thermal biology, yet relationships among widely used thermal traits remain poorly understood. By synthesizing data from more than 100 ectotherm species, we quantify links between acute heat tolerance and traits describing sustained biological function across temperatures. We show that performance at relatively benign temperatures and performance at thermal extremes are coupled, but this coupling is strongly process and timescale dependent, with close correspondence for short term locomotion but weaker coupling for metabolic processes. Our results link the processes that maintain performance across temperatures with those that cause failure, fundamentally advancing our projections of species performance in a warming world.

Habitat complexity (HC) in part determines the diversity, stability, and behavior of food webs and can influence predation according to a wide variety of functional relationships. Many aquatic species provide habitat complexity and are also consumed by other species (e.g., macrophytes, corals, mussels). However, food web theory does not readily account for these species that act as edible habitat complexity (EHC). Here, we combine existing theory on predator-prey interactions, HC, and prey switching to describe the role of EHC in benthic food web models. We dissect feedback loops in each model to demonstrate how self-regulation of the prey species, mediated by species densities and HC, drives that food webs behavior. HC can stabilize predator-prey interactions by coupling prey self-regulation with HC self-regulation. EHC can further stabilize predator-prey interactions across a wide variety of "HC functions" that relate HC to predation rates. Significance StatementHabitat complexity (HC) plays a critical role in trophic interactions, population dynamics, and food web stability. However, little theory exists to describe edible habitat complexity (EHC), where a species is both consumed and confers habitat complexity for other species. We provide a series of models demonstrating how HC and EHC alter the population dynamics and stability of simple aquatic food webs. HC is strongly stabilizing in food webs by providing safety in rarity for prey. EHC provides safety in rarity for both prey and the EHC species because their predators are omnivorous. Given the prevalence of EHC species in aquatic systems (e.g., macrophytes, corals, mussels), our models demonstrate the importance of maintaining EHC species in aquatic systems for stable food webs.

Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which expresses bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, bistability and tipping points become practically identifiable only when data are collected very close to the tipping point.

A central goal of ecology is understanding how the architecture of food webs, which represent the structural backbone of ecosystems, affects their stability. The analysis of stability in the classical sense of population dynamics (i.e. return to equilibrium) can be successful for a single instance of an empirical food web but ignores the multiplicity of alternative states in which the system could be found as a result of intrinsic variability and fluctuations. Here we propose and test a new methodology to reconstruct, from single empirical observations of a food web, the viable ensemble of alternative realizations respecting the observed resource-consumer linkages and empirical ener-getics. The reconstruction can be handled analytically within a maximum-entropy framework which predicts how empirical food webs access a multitude of alternative states with comparable stability and reactivity. The (measurable) entropy of the reconstructed ensemble directly quantifies this multiplicity and serves as a novel proxy of system resilience, that is the rate of return to equilibrium in response to an external perturbation. We show that the associated ensemble fluctuations provide explicit predictions for the expected response of food webs to external perturbations, such as anthropogenic or climate-induced stresses. We do that by validating the proposed fluctuation-response relation on empirical soil food webs subjected to experimentally controlled perturbations, confirming that intrinsic fluctuations in the unperturbed state predict responses to subsequent stresses. The perturbed states are associated with higher entropy, indicating less likely spontaneous recovery.

Species change their population sizes and distributions in response to fluctuating environments. Predicting such changes across space, particularly distributional shifts under climate change, is a central challenge in ecology and conservation1. Traditionally, static traits such as habitat preferences, or performance traits such as abundance-environment relationships, have been used to characterize species responses to the environment2,3. However, these approaches assume fixed relationships between species and their environments, overlooking the inherently state-dependent nature of ecosystems. Here, we show that population-level, state-dependent responses of species to environments can serve as a novel trait to better represent population dynamics in nature, which we term a dynamic response trait. Using nonlinear time-series analysis4,5 and long-term marine fish community data collected from Kyoto, Japan6, we identify causal influences of water temperature on about one hundred fish species. Species with higher latitudinal centers generally show negative dynamic responses to warming, whereas those with lower latitudinal centers show positive ones. Intriguingly, these dynamic response traits quantified at a single location explain the fish species poleward range shift velocities estimated from public biodiversity records; species with negative dynamic responses to warming shift poleward more rapidly, whereas those with positive ones tend to remain. Our findings establish dynamic response traits as a new dimension of trait-based ecology, capturing state-dependent species responses. By linking local population dynamics to broad-scale distributional shifts, this approach provides a powerful basis for guiding ecology and conservation under climate change.

Species introductions and transplants offer powerful contexts to understand evolutionary patterns and processes, and they are increasingly critical for conservation. However, introduction success varies widely, and predicting outcomes remains challenging. Introducing multiple source populations should increase the chance of success, while also providing an opportunity to explore the factors that predict success of individual source populations in the same environment. We used replicated, mixed-population introductions of >12,000 threespine stickleback (Gasterosteus aculeatus) to test whether source population success could be predicted by environmental matching between source and recipient environments and/or by intrinsic source population characteristics. We introduced four to eight source populations of stickleback into each of nine natural lakes and tracked their relative success over the following two years (up to two generations). Source population success was largely consistent across lakes, despite divergent environmental conditions. These results point to the importance of intrinsic source population characteristics rather than environmental matching in predicting introduction success in natural settings. Source populations that were consistently successful tended to have greater stress tolerance (mortality rate during translocation) and higher genetic diversity, though these relationships were not conclusive. Our study highlights the value of considering factors that generate fitness differences independent of environmental contexts in predicting ecological and evolutionary dynamics and planning conservation programs. Significance StatementPredicting which populations will succeed when introduced to new environments is a central challenge in ecology, evolution, and conservation. Environmental conditions are often assumed to be crucial in determining which populations succeed, given the expectation that populations preadapted to local conditions should perform best. We test this assumption by introducing >12,000 threespine stickleback fish from up to eight source populations into nine natural lakes spanning diverse environmental conditions. We show that the relative success of individual source populations was remarkably consistent across lakes and environmental conditions, indicating that some populations are intrinsically better suited to introductions. Our findings underscore the importance of considering intrinsic population characteristics alongside environmental conditions when predicting ecological and evolutionary outcomes and guiding conservation efforts.

The mid-domain effect (MDE) predicts that geometric constraints drive unimodal species richness patterns within bounded gradients. However, the role of this effect in ecological networks is currently unexplored. Here we evaluate the role of the MDE in structuring interaction networks. We combine null-model simulations and empirical analyses of plant-pollinator and ant-plant networks along elevational gradients to assess whether the MDE can drive systematic variation in network structure. Our simulations demonstrated that the MDE alone can generate unimodal/U-shaped patterns in network metrics such as connectance, generality, and vulnerability. However, empirical networks only partially conformed to MDE predictions, with deviations indicating the likely influence of other ecological processes. MDE-based models best explained patterns in network-level specialization and nestedness, while only partially explaining patterns in connectance and generality. Because MDEs can shape interaction networks, MDE null models should be used when quantifying the influence of other ecological processes on network structure.

Assessing ecosystem resilience at large spatial scales remains a major challenge in ecology and conservation. While resilience is typically inferred from temporal dynamics or perturbation experiments, ecosystems governed by spatial self-organization are thought to encode resilience-related information directly in their spatial structure. Here, we show that the spatial patterns of seagrass meadows can be used to infer ecological deterioration and resilience-related states from a single cartographic snapshot. Using a mechanistic model of Posidonia oceanica self-organization, we generated thousands of synthetic seascapes spanning a mortality-driven gradient from continuous meadows through fragmented and collapsed states and trained deep convolutional neural networks to classify discrete pattern states and estimate continuous levels of deterioration along this gradient. Applied to habitat cartography across the Balearic Islands, the framework revealed ecologically interpretable regional variation in meadow condition, enabling large-scale assessment of seagrass resilience from spatial snapshots alone. Networks trained exclusively on synthetic data generalized effectively to real meadows, showcasing that mechanistic models can substitute for empirical training labels. More broadly, our results establish a transferable strategy for integrating ecological theory and machine learning to monitor the resilience of self-organized ecosystems when direct temporal observations are sparse or unavailable.

O_LISpecies diversity potentially has a dual effect on communities: a generally positive effect on overall community biomass, reflecting the expression of species response and interaction traits, and a poorly characterised effect on mass-specific species contribution to ecosystem functions, reflecting the expression of their effect traits. Disentangling the effects of biodiversity on total biomass from those on effect trait expression would help settle a long-standing debate by clarifying how biodiversity relates to both facets of species effects on ecosystem functioning. C_LIO_LIFollowing the classical BEF approach, we calculate expected ecosystem function based on observed functioning in monoculture. We then derive a net biodiversity effect (NBE) and decompose it into four components: the classical complementarity and selection effects on total community biomass, and complementarity and selection effects on effect trait expression. The latter two reflect, respectively, a complementarity or facilitation in how effect traits influence the function, and how species with the highest potential for increasing the function become dominant in the community. C_LIO_LIWe illustrate this NBE decomposition with three ecosystem functions (nitrogen retention capacity, soil hydraulic conductivity improvement, and forage digestibility) measured in assembled communities under controlled experimental conditions of perennial grassland plants. Regarding nitrogen retention, we find a positive complementary effect via total biomass, but a negative biodiversity effect via effect trait expression. For hydraulic conductivity improvement, biodiversity effects are mostly mediated by total biomass. As for forage digestibility, we found a positive complementarity effect on trait expression, outweighed however by a negative selection effect. This analysis reveals how biodiversity may have contrasting effects on ecosystem functions via its impact on biomass and effect trait expression. C_LI SynthesisSeparating between the effect of biodiversity on plant community biomass and on effect trait expression at the community level is one important step towards understanding the pathways by which diverse plant communities drive ecosystem functioning.

Given the accumulation of evidence that evolution can affect ecological dynamics, especially under global change scenarios, a key question is how such ecoevolutionary dynamics may change the coexistence of species and biodiversity in general. In the present article, we propose a graphical approach allowing to simultaneously discuss ecological coexistence and phenotype evolution. Our graphical approach allows tackling the two aspects in the same parameter space, allowing direct links between ecological and evolutionary perspectives. While evolution is often thought positive for the resilience of ecological systems, we first highlight it does not usually allow for better coexistence for the system as a whole. Even when focusing on the fate of the species that is evolving, evolution often leads to greater vulnerability. The graphical approach we propose is flexible and can be applied to all interaction types and covers variations in trade-off structures. Using this flexibility, we highlight how evolutionary effects can be positive or negative for coexistence, depending on these two components. Finally, we illustrate how the approach can be applied, using empirical examples derived from the literature. We thereby highlight the critical ingredients needed to inform the graphical approach, its potential use for proposing testable scenarios, but also clarify its limits.