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 and Christopher F. Clements
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Duncan A. O’Brien: University of Bristol
Smita Deb: Indian Institute of Technology Ropar
Gideon Gal: Israel Oceanographic & Limnological Research
Stephen J. Thackeray: UK Centre for Ecology & Hydrology
Partha S. Dutta: Indian Institute of Technology Ropar
Shin-ichiro S. Matsuzaki: National Institute for Environmental Studies
Linda May: Bush Estate, Penicuik
Christopher F. Clements: University of Bristol

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43744-8

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