Abstract: Multi-species functional responses (MSFR) describe the feeding rates of generalist consumers that interact with multiple resource species. They are the backbone of dynamic foodweb models which predict biomass changes of communities over time. However, empirical support for their specific usage and parameterization is lacking, largely due to the rarity of multi-species feeding experiments, which pose logistical challenges and require non-trivial statistical analysis.
One key issue is the depletion of prey over the course of the experiment, which must be statistically accounted for correctly. I have developed a novel approach that uses a population dynamics model to generate predictions (by numerically simulating differential equations) and fits them to data, allowing for the accurate estimation of model parameters such as attack rates, handling times, and prey preferences. This method has already been proven valuable for single species functional responses, and my study shows that it can be successfully extended to MSFR.
To test this approach, I performed an extensive simulation study that crossed several classes of MSFR models, experimental designs (combinations and amount of available prey), and sample sizes. Specifically, I simulated realistic feeding trial data with known parameters using a stochastic algorithm, fit population dynamic MSFR models, and evaluated the accuracy of estimated model parameters. Results showed an overall good performance and the investigated MSFR were identifiable from the data. I found that sample size was more important than experimental design. Notably, analyzing the combination of all 2-species mixtures was sufficient to estimate all model parameters accurately in systems with more than 2 prey species, eliminating the need for trials with more than 2 prey species and simplifying the logistics of such experiments.
I propose a framework for estimating MSFR from data by combining dynamical prediction models with Bayesian inference. This study offers guidelines for empiricists who want to perform MSFR experiments, and opens up new possibilities for testing hypotheses on topics such as prey preference and switching, optimal and adaptive foraging, and foodweb stability.
