ArticleJournal of the Royal Society, Interface2018
Optimal experimental design for predator-prey functional response experiments.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- Adaptive experimental design produces superior and more efficient estimates of predator functional response.PloS one · 2023Article
- The Stochastic Nature of Functional Responses.Entropy (Basel, Switzerland) · 2021Article
- Predator size affects the intensity of mutual interference in a predatory mirid.Ecology and evolution · 2021Article
- Optimal experimental design for mathematical models of haematopoiesis.Journal of the Royal Society, Interface · 2021Article
- An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infectionsJournal of the Royal Society, Interface · 2020Article
- Sequential experimental design for predator-prey functional response experiments.Journal of the Royal Society, Interface · 2020Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional response models are important in understanding predator-prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs; these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator
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