Estimating seasonal abundance of a central place forager using counts and telemetry data

Marine Ecology Progress Series

Ruth J. Sharples¹  Philip S. Hammond¹  Monique L. Mackenzie¹ 

关键词: Mixed effects models;    Phoca vitulina;    Population size;    Autocorrelation;    Harbour seal;    Haul-out behaviour;    Central place forager;   

DOI  :  10.3354/meps07827

学科分类：海洋学与技术

来源: Inter-Research

【 摘 要 】

ABSTRACT: Obtaining population estimates of species that are not easily observed directly can be problematic. However, central place foragers can often be observed some of the time, e.g. when seals are hauled out. In these instances, population estimates can be derived from counts, combined with information on the proportion of time that animals can be observed. We present a modelling framework to estimate seasonal absolute abundance using counts and information from satellite telemetry data. The method was tested on a harbour seal population in an area of southeast Scotland. Counts were made monthly, between November 2001 and June 2003, when seals were hauled out on land and were corrected for the proportion of time the seals were at sea using satellite telemetry. Harbour seals (n = 25) were tagged with satellite relay data loggers between November 2001 and March 2003. To estimate the proportion of time spent hauled out, time at sea on foraging trips was modelled separately from haul-out behaviour close to haul-out sites because of the different factors affecting these processes. A generalised linear mixed model framework was developed to capture the longitudinal nature of the data and the repeated measures across individuals. Despite seasonal variability in the number of seals counted at haul-out sites, the model generated estimates of abundance, with an overall mean of 846 (95% CI: 767 to 979). The methodology shows the value of using count and telemetry data collected concurrently for estimating absolute abundance, information that is essential to assess interactions between predators, fish stocks and fisheries.

【 授权许可】

Unknown