A taxonomy of multiple stable states in complex ecological communities

Abstract

Many natural and man-made systems, from financial markets to ecosystems or the human brain, are built from multiple interconnected units. This complex high-dimensionality hinders our capacity to understand and predict the dynamics, functioning and fragility of these systems. One fragility scenario, particularly relevant to ecological communities of interacting species, concerns so-called regime shifts: abrupt and unexpected transitions from healthy, species-rich communities towards states of degraded ecosystem function and services. The accepted explanation for these shifts is that they arise as abrupt transitions between alternative stable states: multiple stable configurations of a system under the same internal and external conditions. These alternative states are well-understood in low-dimensional systems, but how they upscale with system complexity remains a debated question. In the present work we investigate the emergence of multiple stable states in a number of complex system models. We find that high-dimensional models with random interactions can unfold at least four different regimes of multistability, each emerging under a specific interaction scheme. More importantly, each multistability regime leaves a distinct and quantifiable fingerprint, providing a framework to analyze experimental evidence of abrupt shifts. By bridging previous results and studying multistability regimes, their fingerprints and their correlation with empirical evidence in ecology, our study helps define a common ground to understand and classify multiple stable states in complex systems.

Basins of attraction of microbiome structure and soil ecosystem functions

“…The energy landscape analysis allows systematic analyses of taxon-rich community datasets by incorporating the information of multiple environmental factors (Dakos and Kéfi, 2022;Sánchez-Pinillos et al, 2024;Suzuki et al, 2021). While classic studies on community multistability have discussed ecological processes spanning a few intuitively distinguishable community states [high/low tree cover in forest-savanna transitions (Hirota et al, 2011;Staver et al, 2011aStaver et al, , 2011b or macrophyte-/phytoplankton-dominated state in shallow lakes (Ibelings et al, 2007;Scheffer and Carpenter, 2003)], it is now made possible to define basins of attraction based on highdimensional community datasets involving hundreds of species/taxa (Arumugam et al, 2011;Costea et al, 2017;Guim Aguadé-Gorgorió et al, 2023;Hayashi et al, 2024). Application of the general statistical platform will enhance our understanding of how stability landscape properties differ among diverse microbial and non-microbial systems.…”

“…Although the energy landscape analysis enhances our understanding of community stability and functions, its results should be interpreted with caution. First, given that classic empirical studies examined community multistability with system-specific simple criteria [e.g., high/low tree cover (Hirota et al, 2011;Staver et al, 2011aStaver et al, , 2011b], special care should be taken when we extend the approach to species-rich (high-dimensional) community datasets (Guim Aguadé-Gorgorió et al, 2023). In other words, unambiguous and broadly applicable criteria based on statistical evaluation are the prerequisite for comparative analyses of community multistability.…”

Integrating ecological feedbacks across scales and levels of organization

“…With aggregated feedbacks all species experience the same Allee effect, and therefore a single perturbation can drive the collapse of the community simultaneously (Lever et al 2014, Karatayev et al 2023. With positive species-specific feedbacks each species experiences a specific Allee threshold, which thereby does not necessarily cause cascades of extinctions in the community (Aguadé-Gorgorió et al 2023). For example in plant-pollinator communities, because specialist species experience stronger positive feedbacks, they are more vulnerable to perturbations than generalist species (Saavedra et al 2013).…”