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Adaptive experimental design produces superior and more efficient estimates of predator functional response

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  • Nikos E Papanikolaou
  • Hayden Moffat
  • Argyro Fantinou
  • Dionysios P Perdikis
  • Michael Bode
  • Christopher Drovandi

Abstract

Ecological dynamics are strongly influenced by the relationship between prey density and predator feeding behavior—that is, the predatory functional response. A useful understanding of this relationship requires us to distinguish between competing models of the functional response, and to robustly estimate the model parameters. Recent advances in this topic have revealed bias in model comparison, as well as in model parameter estimation in functional response studies, mainly attributed to the quality of data. Here, we propose that an adaptive experimental design framework can mitigate these challenges. We then present the first practical demonstration of the improvements it offers over standard experimental design. Our results reveal that adaptive design can efficiently identify the preferred functional response model among the competing models, and can produce much more precise posterior distributions for the estimated functional response parameters. By increasing the efficiency of experimentation, adaptive experimental design will lead to reduced logistical burden.

Suggested Citation

  • Nikos E Papanikolaou & Hayden Moffat & Argyro Fantinou & Dionysios P Perdikis & Michael Bode & Christopher Drovandi, 2023. "Adaptive experimental design produces superior and more efficient estimates of predator functional response," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-14, July.
  • Handle: RePEc:plo:pone00:0288445
    DOI: 10.1371/journal.pone.0288445
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    References listed on IDEAS

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    1. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    2. Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
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