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Perturbations such as climate change, invasive species and pollution, impact the functioning and diversity of ecosystems. However diversity has many meanings, and ecosystems provide a plethora of functions. Thus, on top of the various perturbations that global change represents, there are also many ways to measure a perturbation's ecological impact. This leads to an overwhelming response variability, which undermines hopes of prediction. Here, we show that this variability can instead provide insights into hidden features of functions and of species responses to perturbations. By analysing a dataset of global change experiments in microbial soil systems we first show that the variability of functional and diversity responses to perturbations is not random; functions that are mechanistically similar tend to respond coherently. Furthermore, diversity metrics and broad functions (e.g. total biomass) systematically respond in opposite ways. We then formalise these observations to demonstrate, using geometrical arguments, simulations, and a theory-driven analysis of the empirical data, that the response variability of ecosystems is not only predictable, but can also be used to access useful information about species contributions to functions and population-level responses to perturbations. Our research offers a powerful framework for understanding the complexity of ecological responses to global change., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The study of ecological communities often involves detailed simulations of complex networks. However, our empirical knowledge of these networks is typically incomplete and the space of simulation models and parameters is vast, leaving room for uncertainty in theoretical predictions. Here we show that a large fraction of this space of possibilities exhibits generic behaviors that are robust to modeling choices. We consider a wide array of model features, including interaction types and community structures, known to generate different dynamics for a few species. We combine these features in large simulated communities, and show that equilibrium diversity, functioning, and stability can be predicted analytically using a random model parameterized by a few statistical properties of the community. We give an ecological interpretation of this "disordered" limit where structure fails to emerge from complexity. We also demonstrate that some well-studied interaction patterns remain relevant in large ecosystems, but their impact can be encapsulated in a minimal number of additional parameters. Our approach provides a powerful framework for predicting the outcomes of ecosystem assembly and quantifying the added value of more detailed models and measurements., Competing Interests: The authors declare no conflict of interest.

An enduring challenge for ecology is identifying the drivers of ecosystem and population stability. In a spatially explicit context, key features to consider are landscape spatial structure, local interactions, and dispersal. Substantial work has been done on each of these features as a driver of stability, but little is known on the interplay between them. Missing has been a more integrative approach, able to map and identify different dynamical regimes, predicting a system's response to perturbations. Here we first consider a simple scenario, i.e., the recovery of a homogeneous metapopulation from a single localized pulse disturbance. The analysis of this scenario reveals three fundamental recovery regimes: Isolated Regime when dispersal is not significant, Rescue Regime when dispersal mediates recovery, and Mixing Regime when perturbations spread throughout the system. Despite its simplicity, our approach leads to remarkably general predictions. These include the qualitatively different outcomes of various scenarios of habitat fragmentation, the surprising benefits of local extinctions on population persistence at the transition between regimes, and the productivity shifts of metacommunities in a changing environment. This study thus provides context to known results and insight into future directions of research. [ABSTRACT FROM AUTHOR]