Data-driven identification of reliable sensor species to predict regime shifts in ecological networks.

ABSTRACT: Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbat

SUBMITTER: Ghadami A 

PROVIDER: S-EPMC7481725 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature