An updated perspective on the role of environmental autocorrelation in animal populations

Jake M. Ferguson, Felipe Carvalho, Oscar Murillo-García, Mark L. Taper, José M. Ponciano

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Ecological theory predicts that the presence of temporal autocorrelation in environments can considerably affect population extinction risk. However, empirical estimates of autocorrelation values in animal populations have not decoupled intrinsic growth and density feedback processes from environmental autocorrelation. In this study, we first discuss how the autocorrelation present in environmental covariates can be reduced through nonlinear interactions or by interactions with multiple limiting resources. We then estimated the degree of environmental autocorrelation present in the Global Population Dynamics Database using a robust, model-based approach. Our empirical results indicate that time series of animal populations are affected by low levels of environmental autocorrelation, a result consistent with predictions from our theoretical models. Claims supporting the importance of autocorrelated environments have been largely based on indirect empirical measures and theoretical models seldom anchored in realistic assumptions. It is likely that a more nuanced understanding of the effects of autocorrelated environments is necessary to reconcile our conclusions with previous theory. We anticipate that our findings and other recent results will lead to improvements in understanding how to incorporate fluctuating environments into population risk assessments.

Original languageEnglish
Pages (from-to)129-148
Number of pages20
JournalTheoretical Ecology
Volume9
Issue number2
DOIs
StatePublished - Jun 1 2016

Bibliographical note

Publisher Copyright:
© 2015, Springer Science+Business Media Dordrecht.

Keywords

  • Autocorrelation
  • Environmental tracking
  • Environmental variation
  • Extinction risk
  • Time series

ASJC Scopus subject areas

  • Ecology
  • Ecological Modeling

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