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Sea ice reduction drives genetic differentiation among Barents Sea polar bears


Maduna, Simo Njabulo et al. (2021), Sea ice reduction drives genetic differentiation among Barents Sea polar bears, Dryad, Dataset,


Loss of Arctic sea ice due to climate change is predicted to reduce both genetic diversity and gene flow in ice-dependent species, with potential negative consequences for their long-term viability. Here, we tested for the population-genetic impacts of reduced sea ice cover on the polar bear (Ursus maritimus) sampled across two decades (1995–2016) from the Svalbard Archipelago, Norway, an area that is affected by rapid sea ice loss in the Arctic Barents Sea. We analysed genetic variation at 22 microsatellite loci for 626 polar bears from four sampling areas within the archipelago. Our results revealed a 3-10% loss of genetic diversity across the study period, accompanied by a near 200% in genetic differentiation across regions. These effects may best be explained by a decrease in gene flow caused by habitat fragmentation due to loss of sea ice coverage, resulting in increased inbreeding of local polar bears within the focal sampling areas in the Svalbard Archipelago. This study illustrates the importance of genetic monitoring for developing adaptive management strategies for polar bears and other ice-dependent species.


The Norwegian Polar Institute, Tromsø, Norway, collected the samples used in this study during a long-term project on the ecology of polar bears in the Barents Sea. To the best of our knowledge, the sampling protocol remained consistent across time. Polar bears were anesthetised from helicopters by remote injection of the drug Zoletil, and thereafter recovered. Tissue samples (biopsy samples and disks of skin from ear tagging) from these georeferenced bears (based on last sampling for bears captured several times) were frozen. We obtained samples from a total of 626 live captured individuals from the Svalbard Archipelago from 1995 to 2016. We allocated the 626 genotyped polar bears to the above mentioned four areas based on sampling locations: NWS (n = 123), NES (n = 110), SWS (n = 241), and SES (n = 152). We did not distinguish between coastal vs offshore bears based on movement data due to inevitable issues with sample sizes across spatiotemporal groups. Moreover, we included all available bears in our study and therefore (some) family groups are present in the data, including some cubs captured together with their mother. We divided the 22-year sampling period into five temporal groups (female age at primiparity typically 5 years): T1: 1995–1999; T2: 2000–2004; T3: 2005–2009; T4: 2010–2014; T5: 2015–2016 per sampling location but excluded periods with fewer than 10 individuals (Dataset 1: 5, 5, 5, 5 and 2 year groups). Thus, our final dataset comprised 16 spatiotemporal groups and 622 individuals. Polar bears were genotyped using 27 previously-published nuclear microsatellite or short tandem repeat (STR) loci: G1D, G10L, G10B, G10C, G10M, G10P, G10X, MU05, MU10, MU15, MU23, MU50, MU51, MU59, MU61, G10H, G10J, G10O, G10U, MSUT2, MSUT3, MSUT6, MSUT8, CXX20, LIST11016 and LIST11020. We resolved the fluorescently labelled multiplex PCR products together with the GeneScan LIZ500 (Applied Biosystems) internal size standard on an ABI 3730 DNA Analyzer without diluting the amplicons and by using a four-second injection time. Then, we scored individual genotypes based on fragment size via GENEMAPPER v. 4.1 (Applied Biosystems) and then verified the called genotypes manually. We assessed the presence of genotyping errors resulting from allelic dropout, stuttering, and null alleles using MICRO-CHECKER 2.2.3. Please see the article and Appendix S1 for more detail. 

Usage Notes

This repository contains the genotypic data and metadata file used to run the analyses for this paper and a readme file that contains an explanation of the columns in the dataset. Detailed information on how the datasets were generated can be found in the associated manuscript referenced above and its supplements. 


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