Abstract
Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.
Recent studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can complement such models. We use the framework of fractional calculus to study how the outcomes of a well-characterized interaction model are affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.
Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory slows down the overall dynamics and recovery times after perturbation, thus reducing the system’s resilience. However, it simultaneously mitigates hysteresis and enhances the system’s capacity to resist state shifts. Memory promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states.
Collectively, these results highlight the fundamental role of memory on ecological communities and provide new quantitative tools to analyse its impact under varying conditions.
Author summary An ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms learn from experience, modify their living environment, collect resources, and develop innovative strategies for competition and cooperation. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we combine theory and simulations to investigate how memory can influence community dynamics, stability, and composition. We incorporate in particular memory effects in a multi-species model recently introduced to investigate alternative stable states in microbial communities, and assess the impact of memory on key aspects of model behavior. The approach we propose for modeling memory has the potential to be more broadly applied in microbiome research, thus improving our understanding of microbial community dynamics and ultimately our ability to predict, manipulate and experimentally design microbial ecosystems.
Competing Interest Statement
The authors have declared no competing interest.
