Spatial Variation in Foraging Behaviour of a Marine Top Predator (Phoca vitulina) Determined by a Large-Scale Satellite Tagging Program

The harbour seal (Phoca vitulina) is a widespread marine predator in Northern Hemisphere waters. British populations have been subject to rapid declines in recent years. Food supply or inter-specific competition may be implicated but basic ecological data are lacking and there are few studies of harbour seal foraging distribution and habits. In this study, satellite tagging conducted at the major seal haul outs around the British Isles showed both that seal movements were highly variable among individuals and that foraging strategy appears to be specialized within particular regions. We investigated whether these apparent differences could be explained by individual level factors: by modelling measures of trip duration and distance travelled as a function of size, sex and body condition. However, these were not found to be good predictors of foraging trip duration or distance, which instead was best predicted by tagging region, time of year and inter-trip duration. Therefore, we propose that local habitat conditions and the constraints they impose are the major determinants of foraging movements. Specifically the distance to profitable feeding grounds from suitable haul-out locations may dictate foraging strategy and behaviour. Accounting for proximity to productive foraging resources is likely to be an important component of understanding population processes. Despite more extensive offshore movements than expected, there was also marked fidelity to the local haul-out region with limited connectivity between study regions. These empirical observations of regional exchange at short time scales demonstrates the value of large scale electronic tagging programs for robust characterization of at-sea foraging behaviour at a wide spatial scale

Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)