Back

Seascape genomics reveals population structure and local adaptation in a widespread coral reef snail, Coralliophila violacea (Kiener, 1836)

Simmonds, S. E.; Cheng, S. H.; Fritts-Penniman, A. L.; Mahardika, G. N.; Barber, P. H.

2021-06-16 evolutionary biology
10.1101/2021.06.15.448144 bioRxiv
Show abstract

Local adaptation to different environments may reinforce neutral evolutionary divergence, especially in populations in the periphery of a species geographic range. Seascape genomics (high-throughput genomics coupled with ocean climate databases) facilitates the exploration of neutral and adaptive variation in concert, developing a clearer picture of processes driving local adaptation in marine populations. This study used a seascape genomics approach to test the relative roles of neutral and adaptive processes shaping population divergence of a widespread coral reef snail, Coralliophila violacea. We collected C. violacea from colonies of their coral host (Porites spp.) at ten locations spanning a large portion of their geographic range. We used RAD-seq to investigate possible local adaptation via genetic-environmental associations with five ocean climate variables. Four genetic partitions were concordant with regions previously observed in mtDNA (Indian Ocean, Coral Triangle, and Hawaiian Islands), with the addition of Vietnam and varying isolation and admixture levels. We identified outlier loci (FDR = 0.10, N = 72) among individual localities and between regions (FDR = 0.10, N = 34), suggesting that some loci are putatively under divergent selection. Association analyses showed that the two strongest drivers of local adaptation were the annual range and mean of sea surface temperature. Populations that experience lower sea surface temperatures at the periphery of C. violaceas geographic range drive these associations. Our results show that local adaptation to different environments likely reinforces neutral divergence, especially in peripheral populations.

Matching journals

The top 3 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.