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Searching for genetic evidence of demographic decline in an arctic seabird: beware of overlapping generations.

Emeline CharbonnelClaire Daguin-ThiébautLucille CaradecEléonore MoittiéOlivier GilgMaria V GavriloHallvard StrømMark L MalloryR I Guy MorrisonH Grant GilchristRaphaël LebloisCamille RouxJonathan M YearsleyGlenn YannicThomas Broquet
Published in: Heredity (2022)
Genetic data are useful for detecting sudden population declines in species that are difficult to study in the field. Yet this indirect approach has its own drawbacks, including population structure, mutation patterns, and generation overlap. The ivory gull (Pagophila eburnea), a long-lived Arctic seabird, is currently suffering from rapid alteration of its primary habitat (i.e., sea ice), and dramatic climatic events affecting reproduction and recruitment. However, ivory gulls live in remote areas, and it is difficult to assess the population trend of the species across its distribution. Here we present complementary microsatellite- and SNP-based genetic analyses to test a recent bottleneck genetic signal in ivory gulls over a large portion of their distribution. With attention to the potential effects of population structure, mutation patterns, and sample size, we found no significant signatures of population decline worldwide. At a finer scale, we found a significant bottleneck signal at one location in Canada. These results were compared with predictions from simulations showing how generation time and generation overlap can delay and reduce the bottleneck microsatellite heterozygosity excess signal. The consistency of the results obtained with independent methods strongly indicates that the species shows no genetic evidence of an overall decline in population size. However, drawing conclusions related to the species' population trends will require a better understanding of the effect of age structure in long-lived species. In addition, estimates of the effective global population size of ivory gulls were surprisingly low (~1000 ind.), suggesting that the evolutionary potential of the species is not assured.
Keyphrases
  • genome wide
  • genetic diversity
  • climate change
  • copy number
  • dna methylation
  • machine learning
  • gene expression
  • working memory
  • risk assessment
  • deep learning