Ecological resilience

What to measure and how

Bibliographic Data

ID15550874
AuthorsVasilis Dakos (0000-0001-8862-718X, Institut de Recherche pour le Développement, corresponding author), Sonia Kéfi (0000-0002-9678-7770, Institut de Recherche pour le Développement)
Year2022
Volume17
Issue4
Pages043003-043003
Publication date2022-02-22
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac5767
OpenAlexW4213386705
LanguageEN
Citations received28
References cited93

The question of what and how to measure ecological resilience has been troubling ecologists since Holling 1973s seminal paper in which he defined resilience as the ability of a system to withstand perturbations without shifting to a different state. This definition moved the focus from studying the local stability of a single attractor to which a system always converges, to the idea that a system may converge to different states when perturbed. These two concepts have later on led to the definitions of engineering (local stability) vs ecological (non-local stability) resilience metrics. While engineering resilience is associated to clear metrics, measuring ecological resilience has remained elusive. As a result, the two notions have been studied largely independently from one another and although several attempts have been devoted to mapping them together in some kind of a coherent framework, the extent to which they overlap or complement each other in quantifying the resilience of a system is not yet fully understood. In this perspective, we focus on metrics that quantify resilience following Holling’s definition based on the concept of the stability landscape. We explore the relationships between different engineering and ecological resilience metrics derived from bistable systems and show that, for low dimensional ecological models, the correlation between engineering and ecological resilience can be high. We also review current approaches for measuring resilience from models and data, and we outline challenges which, if answered, could help us make progress toward a more reliable quantification of resilience in practice

Attractor · Biology · Business · Data mining · Ecological resilience · Ecological systems theory · Environmental resource management · Focus (optics · Machine learning · Measure (data warehouse · Perspective (graphical · Physics · Psychological resilience · Resilience (materials science · Risk analysis (engineering · Stability (learning theory · Computer Science · Ecosystem dynamics and resilience · Environmental Science · Mathematics · Psychology · Sustainability and Ecological Systems Analysis · Zoonotic diseases and public health · Artificial Intelligence · Ecology · Social Psychology

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