The Stochastic Nature of Functional Responses

• Published in: Entropy (Basel, Switzerland) Switzerland), 2021-05, Vol.23 (5), p.575
• Main Authors: Palamara, Gian Marco, Capitán, José A., Alonso, David
• Format: Article
• Language: English
• Subjects: Asymptotic series; Beddington–DeAngelis functional response; Community; Consumers; Consumption; Ecology; Experiments; feeding experiments; Functionals; Holling type II and type III functional responses; Immigration; Linear functions; Ordinary differential equations; Parameters; Per capita; Population; stochastic consumer-resource dynamics; Stochastic systems; system’s size expansion
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23050575

Functional responses are non-linear functions commonly used to describe the variation in the rate of consumption of resources by a consumer. They have been widely used in both theoretical and empirical studies, but a comprehensive understanding of their parameters at different levels of description remains elusive. Here, by depicting consumers and resources as stochastic systems of interacting particles, we present a minimal set of reactions for consumer resource dynamics. We rigorously derived the corresponding system of ODEs, from which we obtained via asymptotic expansions classical 2D consumer-resource dynamics, characterized by different functional responses. We also derived functional responses by focusing on the subset of reactions describing only the feeding process. This involves fixing the total number of consumers and resources, which we call chemostatic conditions. By comparing these two ways of deriving functional responses, we showed that classical functional response parameters in effective 2D consumer-resource dynamics differ from the same parameters obtained by measuring (or deriving) functional responses for typical feeding experiments under chemostatic conditions, which points to potential errors in interpreting empirical data. We finally discuss possible generalizations of our models to systems with multiple consumers and more complex population structures, including spatial dynamics. Our stochastic approach builds on fundamental ecological processes and has natural connections to basic ecological theory.