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

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## 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.

## Keywords

Environmental variation Time series Autocorrelation Extinction risk Environmental tracking## Notes

### Acknowledgments

We thank the associate editor and two anonymous reviewers for their thoughtful reviews which greatly improved the quality of this manuscript. We thank Robert Holt, John Hopkins, and Craig Osenberg for reviewing an earlier version of this manuscript and the class, Zoology 6927, Quantitative Methods in Ecology, at the University of Florida for making this project possible. We would like to the lab of Colette St. Mary for constructive comments on this manuscript. JMP was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Grant R01-GM103604. JMF gratefully acknowledges the support by the National Science Foundation under Grant No. 0801544 in the Quantitative Spatial Ecology, Evolution and Environment Program at the University of Florida and the Global Population Dynamics database: http://www3.imperial.ac.uk/cpb/databases/gpdd.

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