New indicators for monitoring genetic diversity applied to alpine brown trout populations using whole genome sequence data.
JSON
Kurland S, Saha A, Keehnen N, de la Paz Celorio-Mancera M, Díez-Del-Molino D, Ryman N, Laikre L
Mol. Ecol. 33 (2) e17213 [2024-01-00; online 2023-11-28]
International policy recently adopted commitments to maintain genetic diversity in wild populations to secure their adaptive potential, including metrics to monitor temporal trends in genetic diversity - so-called indicators. A national programme for assessing trends in genetic diversity was recently initiated in Sweden. Relating to this effort, we systematically assess contemporary genome-wide temporal trends (40 years) in wild populations using the newly adopted indicators and whole genome sequencing (WGS). We use pooled and individual WGS data from brown trout (Salmo trutta) in eight alpine lakes in protected areas. Observed temporal trends in diversity metrics (nucleotide diversity, Watterson's ϴ and heterozygosity) lie within proposed acceptable threshold values for six of the lakes, but with consistently low values in lakes above the tree line and declines observed in these northern-most lakes. Local effective population size is low in all lakes, highlighting the importance of continued protection of interconnected systems to allow genetic connectivity for long-term viability of these populations. Inbreeding (FROH ) spans 10%-30% and is mostly represented by ancient (<1 Mb) runs of homozygosity, with observations of little change in mutational load. We also investigate adaptive dynamics over evolutionarily short time frames (a few generations); identifying putative parallel selection across all lakes within a gene pertaining to skin pigmentation as well as candidates of selection unique to specific lakes and lake systems involved in reproduction and immunity. We demonstrate the utility of WGS for systematic monitoring of natural populations, a priority concern if genetic diversity is to be protected.
Bioinformatics Support for Computational Resources [Service]
NGI Stockholm (Genomics Production) [Service]
National Genomics Infrastructure [Service]
PubMed 38014725
DOI 10.1111/mec.17213
Crossref 10.1111/mec.17213