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Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics

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  • Holland, E.P.
  • Burrow, J.F.
  • Dytham, C.
  • Aegerter, J.N.

Abstract

Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios.

Suggested Citation

  • Holland, E.P. & Burrow, J.F. & Dytham, C. & Aegerter, J.N., 2009. "Modelling with uncertainty: Introducing a probabilistic framework to predict animal population dynamics," Ecological Modelling, Elsevier, vol. 220(9), pages 1203-1217.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:9:p:1203-1217
    DOI: 10.1016/j.ecolmodel.2009.02.013
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    References listed on IDEAS

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    Cited by:

    1. Hamilton, Serena H. & Pollino, Carmel A. & Jakeman, Anthony J., 2015. "Habitat suitability modelling of rare species using Bayesian networks: Model evaluation under limited data," Ecological Modelling, Elsevier, vol. 299(C), pages 64-78.

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