Detecting population–environmental interactions with mismatched time series data

Jake M. Ferguson, Brian E. Reichert, Robert J. Fletcher, Henriëtte I. Jager

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population–environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.

Original languageEnglish
Pages (from-to)2813-2822
Number of pages10
Issue number11
StatePublished - Nov 2017

Bibliographical note

Funding Information:
We would like to thank Molly Brooks, Leo Polansky, Ken Newman, and two anonymous reviewers for their comments, which greatly improved the quality of this manuscript. JMF would like to thank Trevor Caughlin for suggesting the application of Jensen’s inequality. This majority of this work was conducted while J. M. Ferguson was a Postdoctoral Fellow at the National Institute for Mathematical and Biological Synthesis (NIMBIOS), an Institute sponsored by the National Science Foundation through NSF Award no. DBI-130042,6 with additional support from The University of Tennessee, Knoxville. JMF was also partially supported by the Center for Modeling Complex Interactions through NIH Award no. P20GM104420. H. I. Jager was supported by Joint Faculty Agreement ELA 2015-012 T02 between NIMBioS/University of Tennessee and Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC for the U.S. Department of Energy (DOE) under Contract No. DE-AC05-00OR22725. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( We thank the many people who contributed to the long-term monitoring of Snail kite populations. J. M. Ferguson conceived the study. J. M. Ferguson performed analyses and J. M. Ferguson, H. I. Jager, B. E. Reichert, and R. J. Fletcher wrote the manuscript. B. E. Reichert and R. J. Fletcher provided the Snail kite data.

Publisher Copyright:
© 2017 by the Ecological Society of America


  • ecological memory
  • environmental interaction
  • population dynamics
  • population regulation
  • temporal scale
  • thermal performance

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics


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