Seascape genomics of eastern oyster (Crassostrea virginica) along the Atlantic coast of Canada

Abstract

Interactions between environmental factors and complex life‐history characteristics of marine organisms produce the genetic diversity and structure observed within species. Our main goal was to test for genetic differentiation among eastern oyster populations from the coastal region of Canadian Maritimes against expected genetic homogeneity caused by historical events, taking into account spatial and environmental (temperature, salinity, turbidity) variation. This was achieved by genotyping 486 individuals originating from 13 locations using RADSeq. A total of 11,321 filtered SNPs were used in a combination of population genomics and environmental association analyses. We revealed significant neutral genetic differentiation (mean F ST = 0.009) between sampling locations, and the occurrence of six major genetic clusters within the studied system. Redundancy analyses (RDAs) revealed that spatial and environmental variables explained 3.1% and 4.9% of the neutral genetic variation and 38.6% and 12.2% of the putatively adaptive genetic variation, respectively. These results indicate that these environmental factors play a role in the distribution of both neutral and putatively adaptive genetic diversity in the system. Moreover, polygenic selection was suggested by genotype–environment association analysis and significant correlations between additive polygenic scores and temperature and salinity. We discuss our results in the context of their conservation and management implications for the eastern oyster.

Seascape genomics reveals population isolation in the reef-building honeycomb worm, Sabellaria alveolata (L.)

“…Locally collected temperature data provides insight into the conditions that S. alveolata experience at a spatial and temporal scale that cannot be achieved using satellite data [116] and can be used for comparison with genomic data, to test genotype-environment associations [117]. Temperature data were obtained from Seabra et al (2015) [118] who used biomimetic temperature loggers placed in the intertidal zone of exposed shores [116,119].…”

Multi-model seascape genomics identifies distinct environmental drivers of selection among sympatric marine species

“…3). Previous seascape genomic studies in temperate regions have frequently identi ed some measure of SST as the best predictor of genomic variation of marine invertebrates [9,11,12,25,28,63], which is most likely due to SST affecting both cellular processes, and life-history events such as spawning and larval development [59]. However, for P. angulosus, Trange and SSTmean best explained genomic variation, whereas SSSmean best explained the structure of C. punctatus.…”

“…Along with oceanographic patterns and coastal topography, the support for climatic environmental gradients acting as barriers to gene ow is steadily increasing [11][12][13][14]. Uncovering patterns of genetic differentiation and possible local adaptation, and distinguishing which environmental conditions shape such patterns, is critical for effective conservation management in the face of global change [15][16][17][18].…”

“…GEAAs are powerful tools to detect putative adaptive loci (commonly termed 'outlier loci') by directly associating allele frequencies with environmental variables [5,23,24]. Sea surface temperature (SST) is the most common environmental structuring force identi ed among seascape studies to date [25], and has been shown to strongly correlate with genomic variation in abalone [14], mussels [26], oysters [12,27], sea cucumbers [28], and lobsters [11]. As SST is consistently identi ed as one of the prominent drivers of genomic variation in marine invertebrates, it has promise as a proxy for evolutionary processes, such as local selection, in conservation [29].…”

Seascape genomics reveals population isolation in the reef-building honeycomb worm, Sabellaria alveolata (L.)

“…Locally collected temperature data provides insight into the conditions that S. alveolata experience at a spatial and temporal scale that cannot be achieved using satellite data [104] and can be used for comparison with genomic data, to test genotype-environment associations [105]. Temperature data were obtained from Seabra et al (2015) [106] who used biomimetic temperature loggers placed in the intertidal zone of exposed shores [104,107].…”