Back

Genetic Erosion at the Edge: Landscape fragmentation and connectivity in sloth bears of the Indian terai

Rana, D.; Samjith, J. C.; Ramakrishnan, U.

2025-09-29 ecology
10.1101/2025.09.27.678944 bioRxiv
Show abstract

Habitat fragmentation is a major global threat to biodiversity, particularly for species at the periphery of their range where populations are smaller and historically more isolated. Fragmented landscapes reduce connectivity, erode genetic diversity, and elevate inbreeding risk, potentially leading to local extinction. Landscape genetics offers a powerful framework to assess how land-use patterns influence gene flow and population viability. We investigated the endemic sloth bear (Melursus ursinus) populations in the Indian Central Terai landscape, a multi-use, high human density region exemplifying fragmentation and range-edge pressures. Using a ddRAD-seq based approach on non-invasively collected faecal samples (n=209), we generated genomic data, and identified 66 individuals using 964 SNP markers. Despite weak spatial genetic structure, with Dudhwa populations showing the highest differentiation (FST = 0.30). The populations exhibited extremely low heterozygosity (HO = 0.073) and high fixation index (FIS = 0.45), indicating recent bottlenecks and isolation. Landscape genetic analyses revealed agriculture density as the primary barrier to gene flow, followed by road density. Compared to sloth bear populations in Central India and Nepal Terai, CTL bears exhibit markedly reduced genetic variation, likely driven by historic fragmentation and peripheral range effects. This study provides the first genomic baseline for sloth bears in the CTL and underscores the urgency of integrating genetic monitoring with habitat management to prevent further erosion of genetic diversity and ensure long-term population persistence.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.