Early warning signals have limited applicability to empirical lake data

Duncan A O'Brien, Smita Deb, Gideon Gal, Stephen J. Thackeray, Partha S. Dutta, Shin-ichiro S. Matsuzaki, Linda May, Christopher F. Clements

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
45 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number7942
Number of pages14
JournalNature Communications
Volume14
DOIs
Publication statusPublished - 1 Dec 2023

Bibliographical note

Funding Information:
D.A.O. received funding from the GW4 + FRESH Centre for Doctoral Training in Freshwater Biosciences and Sustainability (NE/R011524/1), S.D. received funding from the Ministry of Education, Government of India (Prime Minister’s Research Fellowship) and CFC received funding Natural Environment Research Council (NE/T003502/1 and NE/T006579/1NE/R016429/1). We thank Tamar Zohary for the long-term phytoplankton dataset from Lake Kinneret, Francesco Pomati for data from the Zurich lakes, the North Temperate Lakes LTER group for data from North American lakes, and Heidrun Feuchtmayr for Windermere data; monitoring at Windermere is currently supported by Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. The Lake Kinneret monitoring programme is funded by the Israeli Water Authority. We also thank the field and laboratory teams who have collected all of the data used in this study. This research was supported in part by the International Centre for Theoretical Sciences (ICTS) for the programme “Tipping Points in Complex Systems” (code: ICTS/tipc2022/9).

Publisher Copyright:
© 2023, The Author(s).

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