Research@StAndrews
 
The University of St Andrews

Research@StAndrews:FullText >
University of St Andrews Research >
University of St Andrews Research >
University of St Andrews Research >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/3269
This item has been viewed 1 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
SmoutPLOSone0010761Predator.pdf213.41 kBAdobe PDFView/Open
Title: The functional response of a generalist predator
Authors: Smout, Sophie Caroline
Asseburg, C
Matthiopoulos, Jason
Fernández, Carmen
Redpath, S
Thirgood, S
Harwood, John
Keywords: QL Zoology
QA Mathematics
Issue Date: 27-May-2010
Citation: Smout , S C , Asseburg , C , Matthiopoulos , J , Fernández , C , Redpath , S , Thirgood , S & Harwood , J 2010 , ' The functional response of a generalist predator ' PLoS One , vol 5 , no. 5 , e10761 .
Abstract: 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.
Version: Publisher PDF
Status: Peer reviewed
URI: http://hdl.handle.net/10023/3269
DOI: http://dx.doi.org/10.1371/journal.pone.0010761
ISSN: 1932-6203
Type: Journal article
Rights: © 2010 Smout et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Scottish Oceans Institute Research
Mathematics & Statistics Research
Biology Research
University of St Andrews Research



This item is protected by original copyright

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

DSpace Software Copyright © 2002-2012  Duraspace - Feedback
For help contact: Digital-Repository@st-andrews.ac.uk | Copyright for this page belongs to St Andrews University Library | Terms and Conditions (Cookies)