How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions

Authors

  • Harry H. Marshall,

    Corresponding author
    1. Division of Ecology and Evolution, Department of Life Sciences, Imperial College London, Ascot, Berkshire, SL5 7PY, UK
    • Institute of Zoology, Zoological Society of London, London, UK
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  • Alecia J. Carter,

    1. Institute of Zoology, Zoological Society of London, London, UK
    2. The Fenner School of Environment and Society, The Australian National University, Acton, Canberra, ACT, Australia
    Current affiliation:
    1. Large Animal Research Group, Department of Zoology, University of Cambridge, Cambridge, UK
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  • Alexandra Ashford,

    1. Institute of Zoology, Zoological Society of London, London, UK
    2. Division of Ecology and Evolution, Department of Life Sciences, Imperial College London, Ascot, Berkshire, SL5 7PY, UK
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  • J. Marcus Rowcliffe,

    1. Institute of Zoology, Zoological Society of London, London, UK
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  • Guy Cowlishaw

    1. Institute of Zoology, Zoological Society of London, London, UK
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Correspondence author. E-mail: harry.marshall04@ic.ac.uk

Summary

  1. A forager's optimal patch-departure time can be predicted by the prescient marginal value theorem (pMVT), which assumes they have perfect knowledge of the environment, or by approaches such as Bayesian updating and learning rules, which avoid this assumption by allowing foragers to use recent experiences to inform their decisions.
  2. In understanding and predicting broader scale ecological patterns, individual-level mechanisms, such as patch-departure decisions, need to be fully elucidated. Unfortunately, there are few empirical studies that compare the performance of patch-departure models that assume perfect knowledge with those that do not, resulting in a limited understanding of how foragers decide when to leave a patch.
  3. We tested the patch-departure rules predicted by fixed rule, pMVT, Bayesian updating and learning models against one another, using patch residency times (PRTs) recorded from 54 chacma baboons (Papio ursinus) across two groups in natural (n = 6175 patch visits) and field experimental (n = 8569) conditions.
  4. We found greater support in the experiment for the model based on Bayesian updating rules, but greater support for the model based on the pMVT in natural foraging conditions. This suggests that foragers may place more importance on recent experiences in predictable environments, like our experiment, where these experiences provide more reliable information about future opportunities.
  5. Furthermore, the effect of a single recent foraging experience on PRTs was uniformly weak across both conditions. This suggests that foragers’ perception of their environment may incorporate many previous experiences, thus approximating the perfect knowledge assumed by the pMVT. Foragers may, therefore, optimize their patch-departure decisions in line with the pMVT through the adoption of rules similar to those predicted by Bayesian updating.

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