Peer-Reviewed Journal Details
Mandatory Fields
Kane A.;Pirotta E.;Wischnewski S.;Critchley E.J.;Bennison A.;Jessopp M.;Quinn J.L.
2020
July
Marine Ecology Progress Series
Spatio-Temporal patterns of foraging behaviour in a wide-ranging seabird reveal the role of primary productivity in locating prey
Validated
WOS: 4 ()
Optional Fields
Distribution Foraging Hidden Markov model Manx shearwater Productivity Puffinus puffinus
646
175
188
2020 Inter-Research. All rights reserved. Predicting the distribution and behaviour of animals is a fundamental objective in ecology and a cornerstone of conservation biology. Modelling the distribution of ocean-faring species like seabirds remains a significant challenge due to ocean dynamics, colony-specific effects and the vast ranges seabirds can cover. We used a spatial and behavioural approach to model the distribution of the Manx shearwater Puffinus puffinus, a pelagic, central-place forager that can cover great distances while foraging. GPS data from birds tagged in 2 colonies over 3 yr were modelled with a range of environmental predictors of marine productivity. For both colonies, transitions to foraging behaviour correlated with chlorophyll a, and the distribution of foraging behaviour was also associated with areas of high chlorophyll a concentration in coastal but not offshore areas for one colony. Furthermore, there was evidence for colony differences in habitat use, prevalence of nocturnal foraging, and for some competitive exclusion on foraging grounds, even though the colonies were 170 km apart. Despite the extensive dataset, our models had modest predictive power, which we suggest can probably only be improved by including biotic interactions, including more direct measures of food resource distribution. Our results highlight the importance of in cluding spatial complexity and data from multiple sites when predicting the distribution of wideranging predators, because patterns of distribution and habitat use likely differ across the range of a population.
0171-8630
10.3354/meps13386
Grant Details