Ecology Letters

Knowledge about the per capita interactions between organisms and their intrinsic growth rates, and how these vary over environmental gradients, allows understanding and predicting species coexistence and community dynamics. Estimating these crucial ecological parameters requires tedious experimental work, with isolation of organisms from their natural context. Here, we provide a novel approach for inferring these key parameters from time-series data by using weighted multivariate regression on the per capita growth rates of populations. Beyond the validation of our approach on synthetic data, we reveal from experimental data an expected allocative trade-off between grazing resistance and rapid growth in algae. Application of observational data suggests facilitation between cyanobacteria and chrysophyte, indicating a possible explanation for cyanobacteria bloom. Our approach offers a way forward for inferring per capita interactions and intrinsic growth rates directly from natural communities, providing realism, mechanistic understanding of eco-evolutionary dynamics, and key parameters to develop predictive models.

This preprint of a book chapter presents the newly proposed Equilibrium Theory of Biodiversity Dynamics (ETBD), whose aim is to conceptualize large-scale dynamics of species richness via addressing the population size-dependence of speciation and extinction rates, the resulting diversity-dependence of these rates, and their modulation by the environment. It provides the most general framework for understanding large-scale biodiversity patterns such as the latitudinal diversity gradient (LDG) and temporal patterns of biodiversity changes. The theory has been published elsewhere in its full form that includes all the derivations (Okie & Storch 2004, American Naturalist https://doi.org/10.1086/733103), but here it is presented in a simpler and user-friendly way, focusing on its major implications comprising macroecological scaling relationships between energy (or resource) availability, species richness and community abundance.

Classic niche theory assumes that species-level functional traits affect species relative fitness and thus community structuring, but empirical tests of this assumption are scarce. Moreover, recent evidence shows increasing functional over-redundancy towards the tropics, suggesting that the extent to which functional traits confer species fitness and thus impact community structuring differs across latitudes. Here, we develop a new method: comparing the frequencies of trait categories in the species-rank abundance distributions of local communities versus their frequencies in the regional average species pool. We contrasted subarctic versus subtropical copepod communities for six important traits. In subarctic communities, medium-sized and cold-water species are selected to dominate, thus traits affect relative fitness as predicted by classic niche theory. In subtropical communities, most species are small and warm-water, but these categories are not selected to dominate, suggesting that greater diversity towards the tropics results from lesser trait-based fitness differences allowing more species to coexist.

O_LIUnderstanding how ecologically similar species coexist remains a central challenge in ecology, particularly in small, spatially constrained systems where opportunities for segregation may be limited. Classical niche theory predicts that coexistence is facilitated when species partition resources across multiple niche axes, yet few empirical studies quantify how spatial, temporal, and environmental dimensions jointly structure realized niches in natural systems. C_LIO_LIWe examined spatiotemporal niche partitioning between two coexisting top predators--the eastern red-spotted newt (Notophthalmus viridescens) and bluegill sunfish (Lepomis macrochirus)--in a pond lacking piscivorous fish. Using year-round trapping data collected across depth strata, diel periods, and seasons, we combined hierarchical count models, temperature-informed partial effect analyses, and null-model tests of niche overlap. C_LIO_LINewts and sunfish exhibited strongly contrasting patterns of habitat use across multiple axes. Newt capture rates were highest during cooler periods, in deeper habitats, and during morning sampling, whereas sunfish capture rates peaked during warmer periods, in shallow habitats, and during afternoon sampling. Model-based analyses revealed opposing responses to temperature, with predicted newt captures declining and sunfish captures increasing as temperature rose, even after accounting for seasonal effects. As a result, niche overlap across combined season-by-depth-by-time states was consistently lower than expected under randomized null models. C_LIO_LIAcross all analyses, newts and sunfish exhibited strong and consistent spatiotemporal niche partitioning, with opposing seasonal trajectories, contrasting depth and diel activity patterns, divergent thermal responses, and niche overlap significantly lower than expected under null models. These results demonstrate that fine-scale spatiotemporal structure across interacting niche axes can generate pronounced segregation among coexisting top predators, even in small and physically constrained ecosystems. Rather than reflecting partitioning along a single dominant axis, niche differentiation in this system appears to emerge from the coordinated interaction of season, habitat, diel activity, and temperature, highlighting how multi-axis dynamics shape realized niches in natural communities. C_LI Significance StatementCoexisting predators often exploit the same prey and habitats, raising the question of how overlap is reduced in spatially constrained ecosystems such as ponds. By integrating seasonal, diel, habitat, and thermal dimensions, this study demonstrates that two ecologically similar top predators--newts and sunfish--exhibit strong spatiotemporal niche partitioning that substantially lowers overlap relative to random expectations. Our results show that fine-scale temporal and habitat structure can play a major role in organizing predator assemblages, even in small freshwater systems, and highlight the importance of multi-axis niche frameworks for understanding species interactions and persistence in natural communities.

Analysis of long-term trends in abundance provide insights into population dynamics. Population growth rates are the emergent interplay of fertility, survival, and dispersal, but the density feedbacks on some vital rates (component) can be decoupled from density feedback on population growth rates (ensemble). However, the mechanisms responsible for this decoupling are poorly understood. We simulated component density feedbacks on survival in age-structured populations of long-living vertebrates and quantified how imposed nonstationarity (density-independent mortality and variation in carrying-capacity) modified the ensemble feedback signal estimated from logistic-growth models to the simulated abundance time series. The statistical detection of ensemble density feedback was largely unaffected by density-independent processes, but catastrophic and proportional mortality eroded the effect of density-dependent survival on ensemble-feedback strength more strongly than variation in carrying capacity. Thus, phenomenological models offer a robust approach to capture density feedbacks from nonstationary census data when density-independent mortality is low.

O_LILife history strategies emerge from trade-offs between growth, survival and reproduction and are key predictors of how populations respond to environmental disturbances. However, estimating these strategies typically requires detailed demographic data, which are unavailable for many species. Because morphological traits govern whole-organism performance, selection on ecological performance can link morphology with life history strategies. Morphological traits can thus be practical proxies for life history strategies, offering a scalable approach for data-deficient populations. C_LIO_LITo test the hypothesis that morphological traits predict major life-history axes--and, by extension, population performance and resilience--via shared performance trade-offs, we parameterised dynamic energy budget integral projection models for 290 marine and freshwater fish species to quantify life history strategies and measured eight morphological traits from lateral-view photographs for each species. We used phylogenetically corrected principal component analysis to summarise life history strategies and morphological traits, and tested whether morphology predicts life history strategies, and whether either predicts population growth rate or demographic resilience. C_LIO_LILateral size morphology, comprising body elongation, relative eye size and oral gape position predicted generation turnover depending on water column position, and predicted reproductive output depending on clade. Generation turnover, reproductive output and lateral size morphology predicted population growth rate and resilience, but population growth rate and resilience were not directly aligned, challenging common assumptions in fisheries management that treat them as interchangeable. C_LIO_LIOur results support the hypothesis that morphologies linked to ecological performance scale up to shape demographic strategies, providing proof of concept that morphology can predict life-history strategies. They also highlight the potential to develop performance-based trait proxies for rapid, low-cost estimations of demographic vulnerability and recovery potential across data-poor fish populations--expanding the scope of life-history frameworks for fisheries management and conservation under increasing pressures from overfishing, habitat loss and climate change. C_LI

A recent study by Van Dyke et al.1 paired experimental drought manipulations with demographic models and trait measurements to project major shifts in coexistence among a number of annual plant taxa. However, re-analysis of the data under alternative, more predictive competition models reveals that the authors original conclusions are very sensitive to slight variations in model form. Furthermore, propagating model parameter error into coexistence predictions results in relatively weak support for the majority of coexistence shifts predicted by the authors original model. These results highlight the need for increased statistical rigor when treating binary predictions of species coexistence as observed experimental outcomes, as is commonly practiced in empirical coexistence studies.

Understanding the drivers of species coexistence is an important objective in ecology. Yet, the multitude of methods to study coexistence hampers cross-community comparisons. Here, we standardized niche and fitness differences (i.e how species limit themselves compared to others and their competitive ability, respectively) across 1018 species pairs to investigate species coexistence across ecological groups and methodological settings (experimental setup, natural co-occurrence, population model used, and growth method). We find that, first, coexistence is driven by large niche differences, not by small fitness differences. Second, species group into clear clusters of coexisting and non-coexisting species along the niche axis. Finally, these clusters are not driven by ecological or methodological settings. This suggests differences between coexisting and non-coexisting communities transcending those measured in our empirical systems. Overall, our results show that species coexistence is mainly influenced by mechanisms acting on niche differences.

Community disassembly examines how species extinction alters ecological communities. Sometimes, the extinction of one species can trigger the loss of others, known as secondary extinction. These secondary extinctions often result from complex species interactions, complicating the identification of underlying mechanisms. Here, we leverage Modern Coexistence Theory to identify when and why secondary extinctions occur. To identify when secondary extinctions occur, we introduce the Community Disassembly Graph, that uses invasion growth rates to identify transitions between coexisting communities due to extinction. When a secondary extinction is identified, we decompose the invasion growth rates associated with the secondary extinction to understand why it occurs. We demonstrate the utility of this framework by applying it to models in which different species interactions - competition, facilitation, and predation - contribute significantly to secondary extinctions. Our results show Modern Coexistence Theory offers a flexible and interpretable approach to understanding when and why secondary extinctions occur.

Theory and experiments show that diverse ecosystems often have higher levels of function (for instance, biomass production), yet it remains challenging to identify the biological mechanisms responsible. We synthesize developments in coexistence theory into a general theoretical framework linking community coexistence to ecosystem function. Our framework, which we term functional coexistence theory, identifies three components determining the total function of a community of coexisting species. The first component directly corresponds to the niche differences that enable pairwise species coexistence, and to the complementarity component from the additive partition of biodiversity effects. The second component measures whether higher functioning species also have higher competitive fitness, providing a missing link between the additive partitions selection effect and modern coexistence theorys concept of equalization. The third component is least well-studied: reducing functional imbalances between species increases niche differences positive effect on function. Using a mechanistic model of resource competition, we show that our framework can identify how traits drive the effect of competition on productivity, and confirm our theoretical expectations by fitting this model to data from a classic plant competition experiment. Furthermore, we apply our framework to simulations of communities with multiple ecosystem functions or more than two species, demonstrating that relationships between niche, fitness, and function also predict total function beyond the case studied by classical theory. Taken together, our results highlight fundamental links between species coexistence and its consequences for ecosystem function, providing an avenue towards a predictive theory of community-ecosystem feedbacks.

Although natural populations are typically subjected to multiple stressors, most past research has focused on single stressors and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 151 ecological three-stressor interactions from 45 published papers. We found that 89% (n=134) of the three-stressor combinations resulted in new or different interactions than previously reported. We also found substantial levels of emergent properties-- interactions that are only revealed when all three stressors are present. Antagonism was the most prevalent net interaction whereas synergy was the most prevalent emergent interaction. Understanding multiple stressor interactions is crucial for fundamental questions in ecology and also has implications for conservation biology and population management.

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.

The prevalence of rare species in ecosystems begs the question of how they persist. In a recent paper, Calatayuda et al. (CEA) provided a new hypothesis that rare species, in contrast to common species, share unique microhabitats and/or preferentially engage in mutualistic interactions. CEA support this hypotheses by reconstructing association networks from spatially replicated abundance data finding that rare species are over-representing in positive association networks while common species are over-representing in negative association networks. However, the use of abundance and co-occurrence data to infer true species associations is difficult and often inaccurate. Here, I show that the finding of rare species being more represented in positive association networks can be explained by statistical artifacts in the inference of species associations from abundance data. I caution against the inference of ecological association networks from abundance data alone.

Large ecological communities are underpinned by a complex web of interactions whose exact structure, strength and signs are tremendously elusive. While many large-scale patterns in such communities are well-understood and predictable, the outcomes of processes such as invasions are still difficult to analyze. Given that the assembly history of communities is marked by invasion events at many points in time, it is useful to identify those aspects of invasions that can be reliably predicted even if the exact invasion outcomes cannot be determined. We introduce the notion of proximate uninvadable systems and use these to develop a framework for predicting the structural outcomes of invasion events, i.e., what species are present/absent in the eventual equilibrium. The method is particularly illuminating in large ecological communities and applies even when the invading species is initially abundant. We test this method on a broad class of settings and also demonstrate its robustness against imperfect knowledge of species interactions. Using an example of a large food-web from peri-Alpine lakes, we show how this framework can be applied to systems with fluctuating species abundances. Given that these systems exhibit large fluctuations for prolonged periods of time, we make forecasts for extinction risk in the short term thereby extending the purview of our predictive apparatus.

Predicting the outcome and strength of species interactions is a central goal of community ecology. Researchers have proposed that outcomes of species interactions (competitive exclusion and coexistence) are a function of both phylogenetic relatedness and functional similarity. Studies relating phylogenetic distance to competition strength have shown conflicting results. Work investigating the role of phylogenetic relatedness and functional similarity in driving competitive outcomes has been limited in terms of the breadth of taxa and ecological contexts examined, which makes the generality of these studies unclear. Consequently, we gathered 1,748 pairwise competition effect sizes from 269 species and 424 unique species pairs with divergence times ranging from 1.14 to 1,275 million years and used meta-regression and model selection approaches to investigate the importance of phylogenetic relatedness and functional similarity to competition across ecological contexts. We revealed that functional similarity, but not phylogenetic relatedness, predicted the relative strength of interspecific competition (defined as the strength of interspecific competition relative to intraspecific competition). Further, we found that the presence of predators, certain habitats, increasing density of competitors, and decreasing spatial grain of experiments were all associated with more intense interspecific competition relative to intraspecific competition. Our results demonstrate that functional similarity, not phylogenetic relatedness, may explain patterns of competition-associated community assembly, highlighting the value of trait-based approaches in clarifying biotic assembly dynamics.

Climate change is altering the structure and functioning of communities 1. Trait-based approaches are powerful predictive tools that allow consideration of changes in structure and functioning simultaneously 2, 3. The realised biomass-weighted trait distribution of a community rests on the ecophysiology of individuals, but integrates local species interactions and spatial dynamics that feed back to ecosystem functioning. Consider a response trait that determines species performance (e.g. growth rate) as a function of an environmental variable (e.g. temperature). The change in this response traits distribution following directional environmental change integrates all factors contributing to the communitys response and directly reflects the communitys response capacity 3. Here we introduce the average regional community trait-lag (TLMC) as a novel measure of whole-metacommunity response to warming. We show that functional compensation (shifts in resident species relative abundances) confers initial response capacity to communities by reducing and delaying the initial development of a trait-lag. Metacommunity adaptive capacity in the long-term, however, was dependent on dispersal and species tracking of their climate niche by incremental traversal of the landscape. With increasing inter-patch distances, network properties of the functional connectivity network became increasingly more important, and may guide prioritisation of habitat for conservation.

A fundamental assumption of functional ecology is that functional traits determine life-histories. Yet correlations between traits and life-history components are often weak, especially for long lived plants. This is because trade-offs, constraints, dynamic resource budgets and the scaling from single organs to entire plants cause complex relationships between traits and life-history. To elucidate these relationships, we present an integrated Trait-Resource-Life-History (TRL) framework that infers how functional traits affect organ-level costs and benefits of different life history components, how these costs and benefits shape the dynamics of whole-plant resource acquisition and allocation, and how these dynamics translate into life history. We illustrate this framework by developing a TRL model for a functionally diverse group of woody plants (22 species of the genus Protea from the South African Greater Cape Floristic Region). Using hierarchical Bayesian latent state-space modelling, we statistically parameterise this model from data on year-to-year variation in growth, reproduction and maternal care (serotiny) for 600 individuals. The parameterised model reveals that higher resource acquisition translates into both larger absolute resource pools and greater proportional resource allocation to reproduction. Accordingly, specific leaf area, a key trait increasing resource acquisition, is associated with larger resource pools, an earlier age of maturity as well as increased vegetative and reproductive performance at young to intermediate ages. In contrast, seed nitrogen content has opposing effects on the benefits of different organs and thus only shows weak correlations with life-history components. Importantly, the TRL model identifies trait and resource-mediated trade-offs at the level of organs, whole-plant resource budgets and life-histories. It can thus quantify key components of life-history theory that are so far largely inaccessible for long-lived plants. This permits novel insights into ecological and evolutionary mechanisms shaping life-histories. Application of the proposed framework to a broad range of plant systems should be facilitated by the increasing availability of trait and demographic data, whole-plant phenotyping and high resolution remote sensing. The integration of the TRL framework with models of biotic interactions further holds promise for a resource-based understanding of community dynamics across trophic levels and a closer integration of functional ecology, evolutionary ecology, community ecology and ecosystem science.

The problem of pattern and scale remains a central problem in ecology, bridging fundamental and applied questions. Marine microbial communities are a case in point. For instance, to understand the role of zooplankton in oceanic biogeochemistry, their response to changes in environmental conditions, and the implications for ecosystem services (e.g., fisheries), it is critical to understand zooplankton trophic interactions and how they change in a rapidly changing climate. This understanding, however, remains elusive because, unlike for phytoplankton, for which remote sensing of macroscale patterns can provide insight into their microscale dynamics and community composition, obtaining this information for zooplankton largely rests on quantifying the difficult-to-monitor microscale interactions among millions of individuals with different behaviors, and between individuals and their environment. Here, we investigate whether it is possible to obtain indirect information on zooplankton from the macroscale spatial distribution of their prey. To tackle this "problem of scale", we develop a rigorous coarse-graining methodology that connects individual-level properties with macroscale spatial patterns. We demonstrate that the shape of the prey spatial distribution can indeed encode information about zooplankton feeding behavior and community dynamics. Specifically, we predict a change in dominant feeding behavior--from non-motile to motile feeding--as one moves from areas of high to areas of low prey density. These results thus suggest a novel application for remote sensing approaches: the potential tracking of consumer behavioral signatures in the large-scale patterns of the resource. Importantly, the scaling-up methodology that we developed to check whether those signatures exist is general, and can be used to link scales rigorously and systematically in any system in which the complexity of individual dynamics makes connecting scales intractable.

Many facets of ecological theory rely on the analysis of invasion processes, and general approaches exist to understand the early stages of an invasion. However, predicting the long-term transformations of communities following an invasion remains a challenging endeavour. We propose an analytical method that uses community structure and invader dynamical features to predict when these impacts can be large, and show it to be applicable across a wide class of dynamical models. Our approach reveals that short-term invasion success and long-term consequences are two distinct axes of variation controlled by different properties of both invader and resident community. Whether a species can invade is controlled by its invasion fitness, which depends on environmental conditions and direct interactions with resident species. But whether this invasion will cause significant transformations, such as extinctions or a regime shift, depends on a specific measure of indirect feedbacks that may involve the entire resident community. Our approach applies to arbitrarily complex communities, from few competing phenotypes in adaptive dynamics to large nonlinear food webs. It hints at new questions to ask as part of any invasion analysis, and suggests that long-term indirect interactions are key determinants of invasion outcomes.

Beta diversity--the variation among community compositions in a region--is a fundamental measure of biodiversity. Despite a diverse set of measures to quantify beta diversity, most measures have posited that beta diversity is maximized when each community has a single distinct species. However, this assumption overlooks the ecological significance of species interactions and non-additivity in ecological systems, where the function and behaviour of species depend on other species in a community. Here, we introduce a geometric approach to measure beta diversity as the hypervolume of the geometric embedding of a metacommunity. This approach explicitly accounts for non-additivity and captures the idea that introducing a unique, species-rich community composition to a metacommunity increases beta diversity. We show that our hypervolume measure is closely linked to and naturally extends previous information- and variation-based measures while providing a unifying geometric framework for widely adopted extensions of beta diversity. Applying our geometric measures to empirical data, we address two long-standing questions in beta diversity research--the latitudinal pattern of beta diversity and the effect of sampling effort--and present novel ecological insights that were previously obscured by the limitations of traditional approaches. In sum, our geometric approach reconceptualizes beta diversity, offering an alternative and complementary perspective to previous measures, with immediate applicability to existing data.