The genome assembly of Island Oak (Quercus tomentella), a relictual island tree species.

Island oak (Quercus tomentella) is a rare relictual island tree species that exists only on six islands off the coast of California and Mexico, but was once widespread throughout mainland California. Currently, this species is endangered by threats such as non-native plants, grazing animals, and human removal. Efforts for conservation and restoration of island oak currently underway could benefit from information about its range-wide genetic structure and evolutionary history. Here we present a high-quality genome assembly for Q. tomentella, assembled using PacBio HiFi and Omni-C sequencing, developed as part of the California Conservation Genomics Project (CCGP). The resulting assembly has a length of 781 Mb, with a contig N50 of 22.0 Mb and a scaffold N50 of 63.4 Mb. This genome assembly will provide a resource for genomics-informed conservation of this rare oak species. Additionally, this reference genome will be the first one available for a species in Quercus section Protobalanus, a unique oak clade present only in western North America.

Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa.

Local adaptation is a critical evolutionary process that allows plants to grow better in their local compared to non-native habitat and results in species-wide geographic patterns of adaptive genetic variation. For forest tree species with a long generation time, this spatial genetic heterogeneity can shape the ability of trees to respond to rapid climate change. Here, we identify genomic variation that may confer local environmental adaptations and then predict the extent of adaptive mismatch under future climate as a tool for forest restoration or management of the widely distributed high-elevation oak species Quercus rugosa in Mexico. Using genotyping by sequencing, we identified 5,354 single nucleotide polymorphisms (SNPs) genotyped from 103 individuals across 17 sites in the Trans-Mexican Volcanic Belt, and, after controlling for neutral genetic structure, we detected 74 F ST outlier SNPs and 97 SNPs associated with climate variation. Then, we deployed a nonlinear multivariate model, Gradient Forests, to map turnover in allele frequencies along environmental gradients and predict areas most sensitive to climate change. We found that spatial patterns of genetic variation were most strongly associated with precipitation seasonality and geographic distance. We identified regions of contemporary genetic and climatic similarities and predicted regions where future populations of Q. rugosa might be at risk due to high expected rate of climate change. Our findings provide preliminary details for future management strategies of Q. rugosa in Mexico and also illustrate how a landscape genomic approach can provide a useful tool for conservation and resource management strategies.