Integrating heterogeneity of detection and mark loss to estimate survival and transience in UK grey seal colonies
1. We present new developments in statistical methodology allowing in-depth analysis of realistic, complex biological models for longitudinal data sets. Important biological details such as mark-loss and recapture heterogeneity can be identified.
2. We use a Bayesian hidden process framework for a comparative analysis of long-term (1978–2006) capture–recapture data with various combinations of marking methods for adult female grey seals Halichoerus grypus at two UK colonies.
3. Seals were identified using three different methods: flipper tags, brands, or natural pelage markings. Animals identified by brands or natural markings were re-sighted more effectively than those with tags.
4. Flipper tag-loss rates differed between colonies, and there was evidence for non-independent tag-loss in double-tagged animals. There was also evidence at one colony for the presence of transient animals, which attend the colony for 1 year only. Apparent survival was higher and more consistent at one site, and the differences in survival between the two colonies were able to explain contrasting pup production trends at these sites.
5. Synthesis and applications. Longitudinal studies allow for the estimation of demographic parameters which have important implications for our understanding of population dynamics and for the conservation and management of populations. Using new statistical developments to allow for the analysis of missing/incomplete data and partial observations, we show how survival can be estimated from complex mark–recapture data, allowing for the effects of mark loss. The re-sightability of different marks is estimated, indicating that photo-ID based on natural pelage markings is a very effective method for identifying grey seals. There are notable contrasts in survival estimates between breeding colonies which can explain contrasts in population trends at these sites, confirming the importance of adult survival in driving population dynamics in this long-lived species.
Document Type: Research Article
Affiliations: 1: Centre for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, North Haugh, University ofSt. Andrews, St. Andrews, Fife KY16 9SS, UK 2: Sea Mammal Research Unit, University of St. Andrews, St. Andrews, Fife KY16 8LB, UK
Publication date: April 1, 2011