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On the dimensionality of ecological stability
Author(s) -
Donohue Ian,
Petchey Owen L.,
Montoya José M.,
Jackson Andrew L.,
McNally Luke,
Viana Mafalda,
Healy Kevin,
Lurgi Miguel,
O'Connor Nessa E.,
Emmerson Mark C.
Publication year - 2013
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.12086
Subject(s) - ecological stability , stability (learning theory) , curse of dimensionality , ecology , robustness (evolution) , ecosystem , stability conditions , computer science , mathematics , artificial intelligence , biology , machine learning , statistics , biochemistry , discrete time and continuous time , gene
Ecological stability is touted as a complex and multifaceted concept, including components such as variability, resistance, resilience, persistence and robustness. Even though a complete appreciation of the effects of perturbations on ecosystems requires the simultaneous measurement of these multiple components of stability, most ecological research has focused on one or a few of those components analysed in isolation. Here, we present a new view of ecological stability that recognises explicitly the non‐independence of components of stability. This provides an approach for simplifying the concept of stability. We illustrate the concept and approach using results from a field experiment, and show that the effective dimensionality of ecological stability is considerably lower than if the various components of stability were unrelated. However, strong perturbations can modify, and even decouple, relationships among individual components of stability. Thus, perturbations not only increase the dimensionality of stability but they can also alter the relationships among components of stability in different ways. Studies that focus on single forms of stability in isolation therefore risk underestimating significantly the potential of perturbations to destabilise ecosystems. In contrast, application of the multidimensional stability framework that we propose gives a far richer understanding of how communities respond to perturbations.