Back

Extreme in Every Way: Exceedingly Low Genetic Diversity in Snow Leopards Due to Persistently Small Population Size

Solari, K. A.; Morgan, S. A.; Poyarkov, A. D.; Weckworth, B.; Samelius, G.; Sharma, K.; Ostrowski, S.; Ramakrishnan, U.; Kubanychbekov, Z.; Kachel, S.; Johansson, O.; Lkhagvajav, P.; Hemmingmoore, H.; Aleksandrov, D.; Bayaraa, M.; Grachev, A.; Korablev, M. P.; Hernandez-Blanco, J. A.; Munkhtsog, B.; Rosenbaum, B.; Rozhnov, V. V.; Rajabi, A. M.; Noori, H.; Armstrong, E. E.; Petrov, D.

2023-12-15 evolutionary biology
10.1101/2023.12.14.571340 bioRxiv
Show abstract

Snow leopards (Panthera uncia) serve as an umbrella species whose conservation benefits their high-elevation Asian habitat. Their numbers are believed to be in decline due to numerous Anthropogenic threats; however, their conservation is hindered by numerous knowledge gaps. They are the least studied genetically of all big cat species with more to learn regarding their population structure, historical population size, and current levels of genetic diversity. Here, we use whole-genome sequencing data for 41 snow leopards (37 newly sequenced) to offer new insights into these unresolved questions. Among our samples, we find evidence of a primary genetic divide between the northern and southern part of the range around the Dzungarian Basin, as previously identified, and a secondary divide south of Kyrgyzstan around the Taklamakan Desert. Most noteworthy, we find that snow leopards have the lowest genetic diversity of any big cat species, due to a persistently small population size (relative to other big cat species) throughout their evolutionary history rather than recent inbreeding. Without a large population size or ample standing genetic variation to help buffer them from any forthcoming Anthropogenic challenges, snow leopard persistence may be more tenuous than currently appreciated.

Matching journals

The top 4 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.