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Estimating spatial, temporal and individual variability in dolphin cumulative exposure to boat traffic using spatially explicit capture–recapture methods

Authors

Pirotta, E.; Thompson, P. M.; Cheney, B.; Donovan, C. R.; Lusseau, D.

Year

2015

Journal

Animal Conservation

Volume

18

Issue

1

Pages

20-31

ISBN

1469-1795

Keywords

Bayesian modelling, capture–recapture, disturbance, dolphin, dolphin watching, exposure rate, home range, management, marine traffic, risk analysis, Scotland, tourism, Tursiops truncatus, United Kingdom, whale watching

Abstract

Appropriate management of the effects of human activities on animal populations requires quantification of the rate at which animals encounter stressors. Such activities are heterogeneously distributed in space, as are the individual animals in a population. This will result in a heterogeneous exposure rate, which is also likely to vary over time. A spatially explicit analysis of individual exposure is therefore required. We applied Bayesian spatially explicit capture–recapture models to photo-identification data to estimate the home range of well-marked individuals in a protected coastal population of bottlenose dolphins. Model results were combined with the estimated distribution of boat traffic to quantify how exposure to this disturbance varied in time and space. Variability in exposure between individuals was also investigated using a mixed-effects model. The cumulative individual exposure to boat traffic varied between summers, depending both on the overall area usage and the degree of individual movement around the activity centres. Despite this variability, regions of higher risk could be identified. There were marked inter-individual differences in the predicted amount of time dolphins spent in the presence of boats, and individuals tended to be consistently over- or underexposed across summers. Our study offers a framework to describe the temporal, spatial and individual variation in exposure to anthropogenic stressors when individuals can be repeatedly identified over time. It provides opportunities to map exposure risk and understand how this evolves in time at both individual and population levels. The outcome of such modelling can be used as a robust evidence base to support management decisions.
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