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Turnover of species composition through time is frequently observed in ecosystems. It is often interpreted as indicating the impact of changes in the environment. Continuous turnover due solely to ecological dynamics—species interactions and dispersal—is also known to be theoretically possible; however the prevalence of such autonomous turnover in natural communities remains unclear. Here we demonstrate that observed patterns of compositional turnover and other important macroecological phenomena can be reproduced in large spatially explicit model ecosystems, without external forcing such as environmental change or the invasion of new species into the model. We find that autonomous turnover is triggered by the onset of ecological structural instability—the mechanism that also limits local biodiversity. These results imply that the potential role of autonomous turnover as a widespread and important natural process is underappreciated, challenging assumptions implicit in many observation and management tools. Quantifying the baseline level of compositional change would greatly improve ecological status assessments. Change in ecological communities can be driven by extrinsic forces, but the degree to which intrinsic population dynamics drive turnover has remained unclear. Here the authors use metacommunity modelling to show that biodiversity change previously attributed to external drivers can be explained based on intrinsic ecosystem dynamics.

Conserving biodiversity often requires deciding which sites to prioritise for protection. Predicting the impact of habitat loss is a major challenge, however, since impacts can be distant from the perturbation in both space and time. Here we study the long-term impacts of habitat loss in a mechanistic metacommunity model. We find that site area is a poor predictor of long-term, regional-scale extinctions following localised perturbation. Knowledge of the compositional distinctness (average between-site Bray-Curtis dissimilarity) of the removed community can markedly improve the prediction of impacts on regional assemblages, even when biotic responses play out at substantial spatial or temporal distance from the initial perturbation. Fitting the model to two empirical datasets, we show that this conclusions holds in the empirically relevant parameter range. Our results robustly demonstrate that site area alone is not sufficient to gauge conservation priorities; analysis of compositional distinctness permits improved prioritisation at low cost.

Modern coexistence theory (MCT) offers a conceptually straightforward approach for connecting empirical observations with an elegant theoretical framework, gaining popularity rapidly over the past decade. However, beneath this surface‐level simplicity lie various assumptions and subjective choices made during data analysis. These can lead researchers to draw qualitatively different conclusions from the same set of experiments. As the predictions of MCT studies are often treated as outcomes, and many readers and reviewers may not be familiar with the framework's assumptions, there is a particular risk of ‘researcher degrees of freedom’ inflating the confidence in results, thereby affecting reproducibility and predictive power. To tackle these concerns, we introduce a checklist consisting of statistical best practices to promote more robust empirical applications of MCT. Our recommendations are organised into four categories: presentation and sharing of raw data, testing model assumptions and fits, managing uncertainty associated with model coefficients and incorporating this uncertainty into coexistence predictions. We surveyed empirical MCT studies published over the past 15 years and discovered a high degree of variation in the level of statistical rigour and adherence to best practices. We present case studies to illustrate the dependence of results on seemingly innocuous choices among competition model structure and error distributions, which in some cases reversed the predicted coexistence outcomes. These results demonstrate how different analytical approaches can profoundly alter the interpretation of experimental results, underscoring the importance of carefully considering and thoroughly justifying each step taken in the analysis pathway. Our checklist serves as a resource for authors and reviewers alike, providing guidance to strengthen the empirical foundation of empirical coexistence analyses. As the field of empirical MCT shifts from a descriptive, trailblazing phase to a stage of consolidation, we emphasise the need for caution when building upon the findings of earlier studies. To ensure that progress made in the field of ecological coexistence is based on robust and reliable evidence, it is crucial to subject our predictions, conclusions and generalisability to a more rigorous assessment than is currently the trend.

Recent developments in understanding and predicting species responses to climate change have emphasised the importance of both environmental variability and consideration of the wider biotic community. However, to date, the interaction between the two has received less attention. Both theoretical and empirical results suggest that the combined effect of environmental variability and interspecific interactions can have strong impacts on existing range limits. Here we explore how competitive interactions and temporal variability can interact with the potential to strongly influence range shift dynamics. We highlight the need to understand these between‐process interactions in order to predict how species will respond to global change. In particular, future research will need to move from evaluating possibilities to quantifying their impact. We emphasise the value and utility of empirically parameterised models to determine the direction and relative importance of these forces in natural systems.

The landscape scale response of ecological communities to environmental drivers can be challenging to efficiently summarise and differentiate from expected background turnover through time. Metacommunity structure can be encapsulated by fitting joint species distribution models (JSDMs) and partitioning the variance explained into environmental, spatial and species‐codistribution components. Here we identify how these components respond through time with directed environmental change and propose these changes in metacommunity structure as an indicator of sustained directional pressure. Through simulations, we identify how declines in the variation explained by species codistribution could diagnose ecological disintegration, while increases in the explanatory power of environmental and spatial predictors may indicate losses in peripheral areas and dispersal limitations. We then test these results in two well‐studied systems. Butterflies are known to be strongly responding to climate change, and we show that over 21 years the codistribution component declines for butterfly communities in southern England. By contrast, birds in the same region are thought to be responding less strongly to climatic pressure and, despite high occupancy turnover, do not show clear changes in metacommunity structure as measured by this approach. Our results suggest that these approaches could have a high potential to summarise and compare the impacts of external drivers on whole communities.

The analysis of interaction networks across spatial environmental gradients is a powerful approach to investigate the responses of communities to global change. Using a combination of DNA metabarcoding and traditional molecular methods we built bipartite Drosophila – parasitoid food webs from six Australian rainforest sites across gradients spanning 850 m in elevation and 5°C in mean temperature. Our cost‐effective hierarchical approach to network reconstruction separated the determination of host frequencies from the detection and quantification of interactions. The food webs comprised 5–9 host and 5–11 parasitoid species at each site, and showed a lower incidence of parasitism at high elevation. Despite considerable turnover in the relative abundance of host Drosophila species, and contrary to some previous results, we did not detect significant changes to fundamental metrics of network structure including nestedness and specialisation with elevation. Advances in community ecology depend on data from a combination of methodological approaches. It is therefore especially valuable to develop model study systems for sets of closely‐interacting species that are diverse enough to be representative, yet still amenable to field and laboratory experiments.

Using the authors' original data and models, I used standard model selection approaches to test whether the differentiation of competition treatments by drought treatment is supported (Supplementary Methods 1). Because small changes to individual aterms can have major effects on predicted coexistence, either all uncertainty should be propagated through to the end conclusions, or careful statistical support should be developed for treatment effects. To assess the potential for differential uncertainty to generate artefacts, I then applied the original analysis pipeline: first identifying best-fit parameters for each treatment group; then, for each species pair, comparing whether the ratios between the competition coefficients or demographic potential was higher; and finally conducting a t-test across all species pairs. [...]the original result identifying the dominant role of changes in species interactions over changes to growth rates is likely to be an artefact of greater uncertainty in the competition coefficients.

Although there is some evidence that larger species could be more prone to population declines, the potential role of size traits in determining changes in community composition has been underexplored in global-scale analyses. Here, we combine a large cross-taxon assemblage time series database (BioTIME) with multiple trait databases to show that there is no clear correlation within communities between size traits and changes in abundance over time, suggesting that there is no consistent tendency for larger species to be doing proportionally better or worse than smaller species at local scales. Despite expectations that global anthropogenic pressures on species with communities may be size biased, this relationship has not been tested on a large scale. Here the authors use existing databases to show that larger species have not experienced more declines in abundance within their respective communities than small species.

Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage-deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with mostlikely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties.

Considerable emphasis has been placed recently on the importance of incorporating non‐trophic effects into our understanding of ecological networks. Interaction modifications are well‐established as generating strong non‐trophic impacts by modulating the strength of interspecific interactions. For simplicity and comparison with direct interactions within a network context, the consequences of interaction modifications have often been described as direct pairwise interactions. The consequences of this assumption have not been examined in non‐equilibrium settings where unexpected consequences of interaction modifications are most likely. To test the distinct dynamic nature of these “higher‐order” effects, we directly compare, using dynamic simulations, the robustness to extinctions under perturbation of systems where interaction modifications are either explicitly modelled or represented by corresponding equivalent pairwise non‐trophic interactions. Full, multi‐species representations of interaction modifications resulted in a greater robustness to extinctions compared to equivalent pairwise effects. Explanations for this increased stability despite apparent greater dynamic complexity can be found in additional routes for dynamic feedbacks. Furthermore, interaction modifications changed the relative vulnerability of species to extinction from those trophically connected close to the perturbed species towards those receiving a large number of modifications. Future empirical and theoretical research into non‐trophic effects should distinguish interaction modifications from direct pairwise effects in order to maximize information about the system dynamics. Interaction modifications have the potential to shift expectations of species vulnerability based exclusively on trophic networks. Non‐trophic interactions are increasingly thought to be important, but provide complex challenges. Here the authors show that the higher‐order nature of interaction modifications is important to understand their impact through generalised food web simulations.