Ecological forecasts reveal limitations of common model selection methods: predicting changes in beaver colony densities

Sean M. Johnson-Bice, Jake M. Ferguson, John D. Erb, Thomas D. Gable, Steve K. Windels

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.

Original languageEnglish
Article numbere02198
JournalEcological Applications
Issue number1
StatePublished - Jan 2021

Bibliographical note

Funding Information:
We wish to thank Bill Berg for coordinating the annual beaver colony surveys throughout the duration of our study’s timeframe, and the numerous pilots and observers involved in collecting the survey data. The content of this manuscript was improved by helpful comments from Joseph Bump, Martin Mayer, and two anonymous reviewers. Funding for this project was provided by the Minnesota Department of Natural Resources, University of Minnesota Duluth, University of Minnesota Twin Cities, and the Minnesota Environment and Natural Resources Trust Fund, as recommended by the Legislative‐Citizen Commission on Minnesota Resources (project M.L. 2016, Chp. 186, Sec. 2, Subd.03j).

Publisher Copyright:
© 2020 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of Ecological Society of America


  • Castor canadensis
  • complexity
  • density dependence
  • forecast performance
  • hindcast
  • information criteria
  • long-term data
  • model validation
  • population dynamics
  • prediction
  • time series analysis
  • wolf

ASJC Scopus subject areas

  • Ecology


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