Open AccessAssessing spatial discreteness of Hudson Bay polar bear populations using telemetry and genetics
Author(s) -
Viengkone Michelle,
Derocher Andrew E.,
Richardson Evan S.,
Obbard Martyn E.,
Dyck Markus G.,
Lunn Nicholas J.,
Sahanatien Vicki,
Robinson Barry G.,
Davis Corey S.
Publication year - 2018
Publication title -
ecosphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.255
H-Index - 57
ISSN - 2150-8925
DOI - 10.1002/ecs2.2364
Subject(s) - ursus maritimus , population , biology , genetic diversity , population genetics , ursus , wildlife management , wildlife , bay , ecology , conservation genetics , genetic structure , genetic monitoring , evolutionary biology , geography , microsatellite , genetics , demography , arctic , sociology , allele , archaeology , gene
Abstract Identifying biologically meaningful populations is essential to the conservation and management of at‐risk species. Natural populations can be delineated using a variety of methods including tag recoveries, telemetry, stable isotopes, and population genetics, but understanding the processes that lead to and maintain the demographic and genetic distinctiveness of populations is also important. We combined telemetric and genetic data from three adjacent polar bear ( Ursus maritimus ) populations in Hudson Bay, Canada, to compare two methods of defining structure. We compared the population structure inferred from utilization distributions (UDs) of 62 adult female polar bears tracked by satellite telemetry during the mating season by grouping individuals in two ways: (1) by the management population in which individuals were sampled (capture location), and (2) by population genetic assignment of individuals using marker data (genetic assignment). We found that space‐use overlap varied depending on how individuals were grouped. We found 19.1–34.4% UD overlap when capture locations were used to group individuals, but there was no UD overlap for bears across different genetic groupings. Wildlife management objectives should include consideration of genetic diversity and differentiation, and we found that using genetic assignment to augment analyses from telemetric data provided additional insights on population delineation.