Moving on with foraging theory: incorporating movement decisions into the functional response of a gregarious shorebird

Models relating intake rate to food abundance and competitor density (generalized functional response models) can predict forager distributions and movements between patches, but we lack understanding of how distributions and small‐scale movements by the foragers themselves affect intake rates. Usin...

Full description

Saved in:

Bibliographic Details
Published in:	The Journal of animal ecology Vol. 84; no. 2; pp. 554 - 564
Main Authors:	Gils, Jan A, Geest, Matthijs, De Meulenaer, Brecht, Gillis, Hanneke, Piersma, Theunis, Folmer, Eelke O, Bearhop, Stuart
Format:	Journal Article
Language:	English
Published:	England Blackwell Scientific Publ 01-03-2015 John Wiley & Sons Ltd Blackwell Publishing Ltd Wiley
Subjects:	animal ecology Animals Appetitive Behavior Behavioural ecology Biodiversity and Ecology Birds Bivalvia Calidris canutus Charadriiformes - physiology competition continuous‐time Markov chain cryptic interference data collection diet distribution Ecology, environment Ecosystem Ecosystems Environmental Sciences Feeding Behavior foraging Foraging behavior functional response Global Changes habitat choice habitats intake rate Life Sciences Locomotion Markov chain Mauritania Models, Statistical movement ecology optimal nutrition predation Predatory Behavior prediction sampling Theory toxic prey toxicity Mauritania continuous‐time Markov chain movement ecology intake rate predation toxic prey competition habitat choice cryptic interference diet distribution
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Models relating intake rate to food abundance and competitor density (generalized functional response models) can predict forager distributions and movements between patches, but we lack understanding of how distributions and small‐scale movements by the foragers themselves affect intake rates. Using a state‐of‐the‐art approach based on continuous‐time Markov chain dynamics, we add realism to classic functional response models by acknowledging that the chances to encounter food and competitors are influenced by movement decisions, and, vice versa, that movement decisions are influenced by these encounters. We used a multi‐state modelling framework to construct a stochastic functional response model in which foragers alternate between three behavioural states: searching, handling and moving. Using behavioural observations on a molluscivore migrant shorebird (red knot, Calidris canutus canutus), at its main wintering area (Banc d'Arguin, Mauritania), we estimated transition rates between foraging states as a function of conspecific densities and densities of the two main bivalve prey. Intake rate decreased with conspecific density. This interference effect was not due to decreased searching efficiency, but resulted from time lost to avoidance movements. Red knots showed a strong functional response to one prey (Dosinia isocardia), but a weak response to the other prey (Loripes lucinalis). This corroborates predictions from a recently developed optimal diet model that accounts for the mildly toxic effects due to consuming Loripes. Using model averaging across the most plausible multi‐state models, the fully parameterized functional response model was then used to predict intake rate for an independent data set on habitat choice by red knot. Comparison of the sites selected by red knots with random sampling sites showed that the birds fed at sites with higher than average Loripes and Dosinia densities, that is sites for which we predicted higher than average intake rates. We discuss the limitations of Holling's classic functional response model which ignores movement and the limitations of contemporary movement ecological theory that ignores consumer–resource interactions. With the rapid advancement of technologies to track movements of individual foragers at fine spatial scales, the time is ripe to integrate descriptive tracking studies with stochastic movement‐based functional response models.
Bibliography:	http://dx.doi.org/10.1111/1365-2656.12301
ISSN:	0021-8790 1365-2656
DOI:	10.1111/1365-2656.12301