- Sample Size Required to Characterize Area Use of Tracked Seabirds
- Journal of Wildlife Management
- Volume | Issue number
- 81 | 6
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Institute for Biodiversity and Ecosystem Dynamics (IBED)
Conflicts in resource use between humans and wildlife populations are increasingly determined through quantitative approaches. To better understand interactions between birds and human activities in the marine environment, telemetry is routinely used to characterize the area use of species, but evaluations are often based on a small number of individuals taken as representative of a local population studied. Furthermore, the relative importance of the number of animals required and for what duration they should be tracked has received little attention. We examined the central-place foraging movements of 24 lesser black-backed gulls (Larus fuscus) from a protected population from 1 March to 31 August during 2010–2013. Using bootstrapping and non-linear modeling, we investigated whether sample sizes were sufficient to characterize offshore area use by considering the cumulative area use for an increasing number of birds and duration of tracking. Box-and-whisker analysis suggested a minimum of 13 birds and a precautionary upper maximum of 41 birds were needed to describe 95% of the estimated area use of the population (defined by 100% occupancy). Tracking fewer birds for longer was more important than tracking more birds for less time. A period of 145 days was required to characterize area use for 13–41 birds; however, offshore areas were used primarily after May, meaning that a 97-day tracking period from May onwards was also representative. Predicted and observed areas were strongly correlated, and the predicted area of 15 birds for 151 days was 91% of the total estimated for the population. These findings suggest that the data were suitable for determining interaction with offshore developments, and were characteristic of the population. This study has revealed the power of a long-term tracking dataset, and has uncovered further complexities surrounding study design and analysis that may shape conclusions drawn. The method and considerations raised have wider applicability for other datasets where human-wildlife resource use conflicts need to be assessed.
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