The functional response of a generalist predator

• Published in: PLoS ONE
• Main Authors: Smout, Sophie Caroline, Asseburg, C, Matthiopoulos, Jason, Fernández, Carmen, Redpath, S, Thirgood, S, Harwood, John
• Other Authors: University of St Andrews. Scottish Oceans Institute, University of St Andrews. School of Mathematics and Statistics, University of St Andrews. School of Biology, University of St Andrews. Marine Alliance for Science & Technology Scotland, University of St Andrews. Sea Mammal Research Unit
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2012
• Subjects: QL Zoology; QA Mathematics; QL; QA; Circus cyaneus
• Online Access: https://hdl.handle.net/10023/3269; https://doi.org/10.1371/journal.pone.0010761; http://www.scopus.com/inward/record.url?scp=77956292866&partnerID=8YFLogxK

Background: Predators can have profound impacts on the dynamics of their prey that depend on how predator consumption is affected by prey density (the predator's functional response). Consumption by a generalist predator is expected to depend on the densities of all its major prey species (its multispecies functional response, or MSFR), but most studies of generalists have focussed on their functional response to only one prey species. Methodology and principal findings: Using Bayesian methods, we fit an MSFR to field data from an avian predator (the hen harrier Circus cyaneus) feeding on three different prey species. We use a simple graphical approach to show that ignoring the effects of alternative prey can give a misleading impression of the predator's effect on the prey of interest. For example, in our system, a “predator pit” for one prey species only occurs when the availability of other prey species is low. Conclusions and significance: The Bayesian approach is effective in fitting the MSFR model to field data. It allows flexibility in modelling over-dispersion, incorporates additional biological information into the parameter priors, and generates estimates of uncertainty in the model's predictions. These features of robustness and data efficiency make our approach ideal for the study of long-lived predators, for which data may be sparse and management/conservation priorities pressing. Peer reviewed