![]() Such an approach treats all the unknown model parameters as random variables and first assigns them a prior distribution that represents our beliefs about the unknown parameters before any experimental data are collected. We advocate the use of a Bayesian framework. We discuss below how one can jointly perform parameter inference and model selection within a coherent probabilistic framework. However, there is also uncertainty around the structure of functional response model (e.g., Type II vs. ![]() Furthermore, although it is important to account and quantify the uncertainty around the model parameters, one should not ignore or forget that this is done under the assumption of a particular functional response model (e.g., Holling's Type II). Novak and Stouffer (2021) have recently highlighted and demonstrated using a large compilation of public datasets that there is systematic bias in the statistical comparison of functional response models and the estimation of their parameters which are rooted in a lack of sufficient replication, or in other words, small sample sizes. However, in many cases an experimentalist will have a dataset of a fixed size and may not always be sufficiently large for these asymptotic results to hold. Furthermore, the methods by which the uncertainty is quantified are typically constructed under parametric assumptions of the MLEs and with increasing accuracy observed as the size of a dataset increases. Furthermore, a frequentist approach to uncertainty quantification (most often using maximum likelihood estimation–MLE) assesses the performance of a statistical estimation procedure on the basis of the expected long-run performance given a hypothetical series of datasets collected under identical conditions. However, such an approach provides no information about the uncertainty around the estimates and it may well be the case that there are other plausible parameter values that offer an equally good fit. In many cases, several functional response models are fitted to experimental data using methods such as non-linear least squares optimization (e.g., Juliano and Williams, 1987 Pervez and Omkar, 2005). ![]() In this paper, we summarize advances related to experimental design, statistical analysis, and the mechanistic interpretation of the predation process that are central to the robust quantification of functional responses and hence should be adopted broadly. Therefore, the estimation, as well as a mechanistic understanding of the parameters that determine predator feeding behavior is of importance. The most frequently observed and widely used functional responses in describing predator-prey relationships are that of the type II and III ( Jeschke et al., 2002, 2004), characterized by a curvilinear and a sigmoidal increase in feeding rate with prey abundance, respectively.Īccurate and robust approaches for quantifying functional responses are critical to the investigation of predator-prey coexistence (e.g., Aldebert and Stouffer, 2018 Uszko et al., 2020 Barraquand and Gimenez, 2021 Coblentz and DeLong, 2021). Holling's approach ( Holling, 1959a, b) has been the base upon which many of the critical aspects of predator-prey interactions can be detected (e.g., Abrams, 1980, 1989). Functional responses describe the predator feeding rate with increasing prey density ( Solomon, 1949) and are central to ecology, quantifying the energy transfer across trophic levels.
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