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In Lotka–Volterra community models, given a set of biotic interactions, recent approaches have analysed the probability of finding a set of species intrinsic growth rates (representing intraspecific demographic features) that will allow coexistence. Several metrics have been used to quantify the fragility of coexistence in the face of variations in those intrinsic growth rates (representing environmental perturbations), thus probing a notion of 'distance' to the edge of coexistence of the community. Here, for any set of interacting species, we derive an analytical expression for the whole distribution of distances to the edge of their coexistence. Remarkably, this distribution is entirely driven by (at most) two characteristic distances that can be directly computed from the matrix of species interactions. We illustrate on data from experimental plant communities that our results offer new ways to study the contextual role of species in maintaining coexistence, and allow us to quantify the extent to which intraspecific features and biotic interactions combine favorably (making coexistence more robust than expected), or unfavourably (making coexistence less robust than expected). Our work synthesizes different study of coexistence and proposes new, easily calculable metrics to enrich research on community persistence in the face of environmental disturbances. [ABSTRACT FROM AUTHOR]

Predicting how ecological communities will respond to disturbances is notoriously challenging, especially given the variability in species' responses within the same community. Focusing solely on aggregate responses may obscure extinction risks for certain species owing to compensatory effects, emphasizing the need to understand the drivers of the response variability at the species level. Yet, these drivers remain poorly understood. Here, we reveal that despite the typical complexity of biotic interaction networks, species' responses follow a discernible pattern. Specifically, we demonstrate that the species whose population abundances are most reduced by biotic interactions—which are not always the rarest species—are those that exhibit the strongest responses to disturbances. This insight enables us to pinpoint sensitive species within communities without requiring precise information about biotic interactions. Our novel approach introduces avenues for future research aimed at identifying sensitive species and elucidating their impacts on entire communities. [ABSTRACT FROM AUTHOR]

Analysis of stable isotopes in consumers is used commonly to study their ecological and/or environmental niche. There is, however, considerable debate regarding how isotopic values relate to diet and how other sources of variation confound this link, which can undermine the utility. From the analysis of a simple, but general, model of isotopic incorporation in consumer organisms, we examine the relationship between isotopic variance among individuals, and diet variability within a consumer population. We show that variance in consumer isotope values is directly proportional to variation in diet (through Simpson indices), to the number of isotopically distinct food sources in the diet, and to the baseline variation within and among the isotope values of the food sources. Additionally, when considering temporal diet variation within a consumer we identify the interplay between diet turnover rates and tissue turnover rates that controls the sensitivity of stable isotopes to detect diet variation. Our work demonstrates that variation in the stable isotope values of consumers reflect variation in their diet. This relationship, however, can be confounded with other factors to the extent that they may mask the signal coming from diet. We show how simple quantitative corrections can recover a direct 1:1 correlation in some situations, and in others we can adjust our interpretation in light of the new understanding arising from our models. Our framework provides guidance for the design and analysis of empirical studies where the goal is to infer niche width from stable isotope data. [ABSTRACT FROM AUTHOR]

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so‐called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well‐understood in few‐species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species‐rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities. [ABSTRACT FROM AUTHOR]

The assessment of relative model performance using information criteria like AIC and BIC has become routine among functional-response studies, reflecting trends in the broader ecological literature. Such information criteria allow comparison across diverse models because they penalize each model's fit by its parametric complexity--in terms of their number of free parameters--which allows simpler models to outperform similarly fitting models of higher parametric complexity. However, criteria like AIC and BIC do not consider an additional form of model complexity, referred to as geometric complexity, which relates specifically to the mathematical form of the model. Models of equivalent parametric complexity can differ in their geometric complexity and thereby in their ability to flexibly fit data. Here we use the Fisher Information Approximation to compare, explain, and contextualize how geometric complexity varies across a large compilation of single-prey functional-response models--including prey-, ratio-, and predator-dependent formulations--reflecting varying apparent degrees and forms of non-linearity. Because a model's geometric complexity varies with the data's underlying experimental design, we also sought to determine which designs are best at leveling the playing field among functional-response models. Our analyses illustrate (1) the large differences in geometric complexity that exist among functional-response models, (2) there is no experimental design that can minimize these differences across all models, and (3) even the qualitative nature by which some models are more or less flexible than others is reversed by changes in experimental design. Failure to appreciate model flexibility in the empirical evaluation of functional-response models may therefore lead to biased inferences for predator-prey ecology, particularly at low experimental sample sizes where its impact is strongest. We conclude by discussing the statistical and epistemological challenges that model flexibility poses for the study of functional responses as it relates to the attainment of biological truth and predictive ability. [ABSTRACT FROM AUTHOR]

Beyond abiotic conditions, do population dynamics mostly depend on a species' direct predators, preys and conspecifics? Or can indirect feedback that ripples across the whole community be equally important? Determining where ecological communities sit on the spectrum between these two characterizations requires a metric able to capture the difference between them. Here we show that the spectral radius of a community's interaction matrix provides such a metric, thus a measure of ecological collectivity, which is accessible from imperfect knowledge of biotic interactions and related to observable signatures. This measure of collectivity integrates existing approaches to complexity, interaction structure and indirect interactions. Our work thus provides an original perspective on the question of to what degree communities are more than loose collections of species or simple interaction motifs and explains when pragmatic reductionist approaches ought to suffice or fail when applied to ecological communities. [ABSTRACT FROM AUTHOR]

Global change encompasses many co‐occurring anthropogenic stressors. Understanding the interactions between these multiple stressors, whether they be additive, antagonistic or synergistic, is critical for ecosystem managers when prioritizing which stressors to mitigate in the face of global change. While such interactions between stressors appear prevalent, it remains unclear if and how these interactions change over time, as the majority of multiple‐stressor studies rarely span multiple generations of study organisms. Although meta‐analyses have reported some intriguing temporal trends in stressor interactions, for example that synergism may take time to emerge, the mechanistic basis for such observations is unknown. In this study, by analysing data from an evolution experiment with the rotifer Brachionus calyciflorus (~35 generations and 31,320 observations), we show that adaptation to multiple stressors shifts stressor interactions towards synergism. We show that trade‐offs, where populations cannot optimally perform multiple tasks (i.e. adapting to multiple stressors), generate this bias towards synergism. We also show that removal of stressors from evolved populations does not necessarily increase fitness and that there is variation in the evolutionary trajectories of populations that experienced the same stressor regimes. Our results highlight outstanding questions at the interface between evolution and global change biology, and illustrate the importance of considering rapid adaptation when managing or restoring ecosystems subjected to multiple stressors under global change. [ABSTRACT FROM AUTHOR]

Theory in ecology and evolution often relies 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 a general analytical method that uses both resident community and invader dynamical features to predict whether an invasion causes large long‐term impacts on the invaded community.This approach reveals a direction in which classic invasion analysis, based on initial invasion growth rate, can be extended. Indeed, we explain how the density dependence of invasion growth, if properly defined, synthetically encodes the long‐term biotic transformations caused by an invasion, and therefore predicts its ultimate outcome. This approach further clarifies how the density dependence of the invasion growth rate is as much a property of the invading population as it is one of the invaded community.Our theory applies to any stable community model, and directs us towards new questions that may enrich the toolset of invasion analysis, and suggests that indirect interactions and dynamical stability are key determinants of invasion outcomes. [ABSTRACT FROM AUTHOR]

Uncertainty is an irreducible part of predictive science, causing us to over‐ or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system‐level properties (e.g. ecosystem function).Here we show that this process of scaling up uncertain predictions to higher levels of organisation has a surprising consequence: it tends to systematically underestimate the magnitude of system‐level change, an effect whose significance grows with the system's dimensionality. This stems from a geometric observation: in high dimensions there are more ways to be more different, than ways to be more similar.We focus on ecosystem‐level predictions generated from the combination of predictions at the species level. In this setting, the ecosystem's relevant dimensionality is a measure of its diversity (and not simply the number of species). We explain why dimensional effects do not play out when predicting change of a single linear aggregate property (e.g. total biomass), yet are revealed when predicting change of nonlinear properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi‐functional ecology.As an application we discuss the consequences of our theory for multiple‐stressor research. This empirical field focuses on interactions between stressors, defined as the error made by a prediction based on their observed individual effects. Our geometric approach can be visualised and explored with a web application (https://doi.org/10.5281/zenodo.4611133), and we provide pseudocode outlining how our theory can be applied. Our findings highlight and describe the counter‐intuitive effects of scaling up uncertain predictions, effects that can occur in any field of science where a reductionist approach is used to generate predictions. ​ [ABSTRACT FROM AUTHOR]

The biomass distribution across trophic levels (biomass pyramid) and cascading responses to perturbations (trophic cascades) are archetypal representatives of the interconnected set of static and dynamical properties of food chains. A vast literature has explored their respective ecological drivers, sometimes generating correlations between them. Here we instead reveal a fundamental connection: both pyramids and cascades reflect the dynamical sensitivity of the food chain to changes in species intrinsic rates. We deduce a direct relationship between cascades and pyramids, modulated by what we call trophic dissipation – a synthetic concept that encodes the contribution of top‐down propagation of consumer losses in the biomass pyramid. Predictable across‐ecosystem patterns emerge when systems are in similar regimes of trophic dissipation. Data from 31 aquatic mesocosm experiments demonstrate how our approach can reveal the causal mechanisms linking trophic cascades and biomass distributions, thus providing a road map to deduce reliable predictions from empirical patterns. [ABSTRACT FROM AUTHOR]

Among plant traits, plant secondary metabolites such as tannins mediate plant–herbivore interactions but also have after‐life effects on litter decomposition and nutrient cycling. We propose that niche construction mechanisms based on positive plant–soil feedback (PSF) could influence the evolution of tannin production.By modelling the flow of nitrogen (N) and carbon (C) through plants and soil in a spatially explicit context, we explored the relative contribution of herbivory and positive PSF as drivers of tannin evolution. We assumed soil N to be contained in labile and recalcitrant compartments, the latter made of tannin–protein complexes from which plants can absorb nutrients via associations with mycorrhizal fungi.In infertile environments and for plants with low biomass turnover rates, we show that when tannins modify soil properties locally, positive PSF alone can drive their evolution. We further predict the existence of positive coevolutionary feedback between associations with mycorrhizal fungi with a decaying ability and tannins, possibly triggered by the evolution of the latter as protection against herbivores. In line with our theoretical results, empirical evidence suggest that tannins are mostly present in plants with low tissue turnover, associated with mycorrhizal fungi able to decay organic matter and inhabiting infertile environments.Synthesis. Our model proposes that the evolution of tannin production can be triggered by positive plant–soil feedback, provided that tannins promote the local N retention and that mycorrhizal fungi associated with plants are able to absorb N from tannin–protein complexes. In our model, tannin production evolves only in infertile ecosystems, in agreement with field observations. Our findings highlight that the strength of niche construction depends on the ecological context, hence that global ecological properties constrain local eco‐evolutionary dynamics. [ABSTRACT FROM AUTHOR]

Empirical knowledge of diversity–stability relationships is mostly based on the analysis of temporal variability. Variability, however, often depends on external factors that act as disturbances, which makes comparisons across systems difficult to interpret. Here, we show how variability can reveal inherent stability properties of ecological communities. This requires that we abandon one‐dimensional representations, in which a single variability measurement is taken as a proxy for how stable a system is, and instead consider the whole set of variability values generated by all possible stochastic perturbations. Despite this complexity, in species‐rich systems, a generic pattern emerges from community assembly, relating variability to the abundance of perturbed species. Strikingly, the contrasting contributions of different species abundance classes to variability, driven by different types of perturbations, can lead to opposite diversity–stability patterns. We conclude that a multidimensional perspective on variability helps reveal the dynamical richness of ecological systems and the underlying meaning of their stability patterns. [ABSTRACT FROM AUTHOR]