Bayesian clustering techniques and progressive partitioning to identify population structuring within a recovering otter population in the UK.
Abstract
After a major decline, the UK otter Lutra lutra population is now recovering in its known strongholds (northern England, Wales and Borders and southwest England) and also in central England where the population had become small, fragmented and was reinforced with captive bred individuals. Bayesian clustering and GIS are used here to identify the genetic structure of the UK otter population and to assess expansion from strongholds and the contribution of introduced otters. Large carnivores have recently started to recolonize landscapes where they were formerly absent, especially in developed countries and understanding the expansion of these populations is essential for informing conservation management, linking fragmented populations and re-establishing gene flow. Three Bayesian clustering techniques were used (STRUCTURE, GENELAND SPATIAL and BAPS4 SPATIAL) to estimate the number of otter populations (K). A novel progressive partitioning approach was tested to identify genetic substructuring at various hierarchical levels using successive partitions at K=2. Four regional populations were identified that reflect known population history. Isolated populations in southwest England and in Wales and its borders showed the lowest levels of genetic diversity. Higher diversity and private alleles in northern and central England reflect the proximity to genetically diverse Scottish populations and the positive effect of reintroductions. Progressive partitioning was used to produce a more detailed analysis, by allowing comparison and combination of clusters identified by different techniques and by avoiding the subjective estimation and choice of K. Synthesis and applications. Although the otter population is increasing, our data show little sign of population expansion from the stronghold regions into central England, instead reflecting the success of population reinforcement in this area. Our progressive partitioning approach allows the identification of fine-scale substructure (11 groups) that enables the prioritization of management effort including identifying barriers to dispersal within and between populations and monitoring of introduced individuals.