Early warning signals have limited applicability to empirical lake data

O’Brien, Duncan A.; Deb, Smita; Gal, Gideon; Thackeray, Stephen J. ORCID:; Dutta, Partha S.; Matsuzaki, Shin-ichiro S.; May, Linda ORCID:; Clements, Christopher F.. 2023 Early warning signals have limited applicability to empirical lake data. Nature Communications, 14, 7942. 14, pp.

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Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Water Resources (Science Area 2017-)
ISSN: 2041-1723
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: applied mathematics, conservation biology, ecosystem ecology, freshwater ecology, population dynamics
NORA Subject Terms: Ecology and Environment
Date made live: 13 Dec 2023 17:56 +0 (UTC)

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