Coronavirus Information for the UC San Diego Community

Our leaders are working closely with federal and state officials to ensure your ongoing safety at the university. Stay up to date with the latest developments. Learn more.

Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

TitleElevated nonlinearity as an indicator of shifts in the dynamics of populations under stress
Publication TypeJournal Article
Year of Publication2017
AuthorsDakos V., Glaser S.M, Hsieh CH, Sugihara G
JournalJournal of the Royal Society Interface
Date Published2017/03
Type of ArticleArticle
ISBN Number1742-5689
Accession NumberWOS:000398964900002
Keywordscritical transition; early warning signal; early-warning signals; ecological-systems; ecosystems; empirical dynamic modelling; environmental; extinction; fluctuations; generic indicators; phase-shifts; population dynamics; regime shifts; resilience; state-dependence; time-series

Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker- type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom- bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as an additional indicator to infer changes in the dynamics of populations under stress.

Short TitleJ. R. Soc. Interface
Student Publication: