An updated perspective on the role of environmental autocorrelation in animal populations
- Ferguson, Jake M. [ Department of Biology, University of Florida ] [ National Institute for Mathematical & Biological Synthesis, University of Tennessee ]
- Carvalho, Felipe [ Program of Fisheries and Aquatic Sciences, School of Forest Resources &Conservation, University of Florida ] [ NOAA Pacific Islands Fisheries Science Center ]
- Murillo-Garcia, Oscar [ School of Natural Resources and Environment, Wildlife Ecology & Conservation, University of Florida ] [ Grupo de Investigación en EcologÃa Animal, Departamento de BiologÃa, Universidad del Valle ]
- Taper, Mark L. [ Montana State University: Ecology ]
- Ponciano, Jose M. [ Department of Biology, University of Florida ]
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.