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

Abstract

Background: As global change and anthropogenic pressures continue to increase, conservation and management increasingly needs to consider species’ potential to adapt to novel environmental conditions. Therefore, it is imperative to characterise the main selective forces acting on ecosystems, and how these may influence the evolutionary potential of populations and species. Using a multi-model seascape genomics approach, we compare the dominant environmental drivers of selection in three sympatric southern African marine invertebrates with contrasting ecology and life histories: Cape urchin ( Parechinus angulosus ), Common shore crab ( Cyclograpsus punctatu s), and Granular limpet ( Scutellastra granularis ). Results: Using pooled (Pool-seq), restriction-site associated DNA sequencing (RAD-seq), and seven outlier detection methods, we characterise genomic variation between populations along a strong biogeographical gradient. Of the three species, only S. granularis showed significant isolation-by-distance, and isolation-by-environment driven by sea surface temperatures (SST). In contrast, sea surface salinity (SSS) and range in air temperature correlated more strongly with genomic variation in C. punctatus and P. angulosus . Differences were also found in genomic structuring between the three species, with outlier loci contributing to two clusters in the East and West Coasts for S. granularis and P. angulosus , but not for C. punctatus . Conclusion: The findings illustrate distinct evolutionary potential across species, suggesting that species-specific habitat requirements and responses to environmental stresses better predict evolutionary patterns than the strong environmental gradients within the region. We also found large discrepancies between outlier detection methodologies, and thus offer a novel multi-model approach to identifying the principal environmental selection forces acting on species. Overall, this work highlights how adding a comparative approach to seascape genomics (both with multiple models and species) can elucidate the intricate evolutionary responses of ecosystems to global change.

Neither historical climate nor contemporary range fully explain the extant patterns of molecular diversity in marine species

“…Previous assessments of genetic differentiation in the three study species using mtDNA COI data suggest that each is composed of an east coast and southern/west coast lineage with separate evolutionary histories (Mmonwa et al, 2015; Muller et al, 2012; Wright et al, 2015). Evidence of east and west differentiation was further corroborated by recent genomic work on these species (Nielsen, Henriques, et al, 2020). SDMs including intraspecific population differentiation, by modelling separate lineages, can potentially portray more accurate habitat suitability outputs (Maia‐Carvalho et al, 2018; Zhang et al, 2021).…”

“…Available genomic data from Nielsen, Henriques, et al (2020) were obtained from pooled restriction‐site‐associated DNA sequencing, specifically using the ezRAD approach due to its successful use on non‐model marine invertebrates (Toonen et al, 2013). SNP panels were generated in the R program poolfstat (Hivert et al, 2018), and after testing multiple parameters, the following were used to call SNPs: minimum coverage = 20, maximum coverage = 400 and minimum read count = 4 (Nielsen, Henriques, et al, 2020).…”

“…Species distribution models were used to hindcast species distributions to the LGM, predicting the extent of historical ranges based on statistical relationships between known contemporary species distributions and environmental variation (Guisan & Thuiller, 2005). Models included environmental variables that are thought to influence the genomic variation of the study species (Nielsen, Henriques, et al, 2020) including mean sea surface temperature (SST) and sea surface salinity (SSS) and mean air temperature of the warmest and coolest months ( T max and T min , respectively). Collinearity was assessed from the variance inflation factor (VIF).…”

“…Available genomic data from Nielsen, Henriques, et al (2020) were obtained from pooled restriction‐site‐associated DNA sequencing, specifically using the ezRAD approach due to its successful use on non‐model marine invertebrates (Toonen et al, 2013). SNP panels were generated in the R program poolfstat (Hivert et al, 2018), and after testing multiple parameters, the following were used to call SNPs: minimum coverage = 20, maximum coverage = 400 and minimum read count = 4 (Nielsen, Henriques, et al, 2020). After filtering SNPs for linkage disequilibrium (selecting 1 SNP per 1000 bp), filtering of putatively adaptive alleles, which were those identified by two or more of seven outlier detection methods, was performed (see Nielsen, Henriques, et al, 2020 for details).…”

“…SNP panels were generated in the R program poolfstat (Hivert et al, 2018), and after testing multiple parameters, the following were used to call SNPs: minimum coverage = 20, maximum coverage = 400 and minimum read count = 4 (Nielsen, Henriques, et al, 2020). After filtering SNPs for linkage disequilibrium (selecting 1 SNP per 1000 bp), filtering of putatively adaptive alleles, which were those identified by two or more of seven outlier detection methods, was performed (see Nielsen, Henriques, et al, 2020 for details). Final SNP panels consisted of 1175, 804 and 1628 putatively neutral SNPs for C. punctatus , P. angulosus and S. granularis , respectively.…”