Including trait-based early warning signals helps predict population collapse

Christopher F. Clements () and Arpat Ozgul
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Christopher F. Clements: University of Zurich
Arpat Ozgul: University of Zurich

Nature Communications, 2016, vol. 7, issue 1, 1-8

Abstract: Abstract Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait information alongside traditional abundance-based early warning signals in a single metric of risk, our generalizable approach provides a powerful new way to assess what populations may be on the verge of collapse.

Date: 2016
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DOI: 10.1038/ncomms10984

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