Landscape Genomic Analyses of Quercus agrifolia Née Predict Patterns of Adaptedness to Future Climate and Provide...

Habitat destruction and climate change are two primary drivers of global biodiversity loss. Many contemporary ecological preserves and preserve networks are designed to protect species and the ecological and evolutionary processes that will maintain future biodiversity. However, it is challenging to identify where to locate preserves and how to manage them to meet these goals. One emerging conservation tool is landscape genomics, which allows us to identify populations that may be maladapted to future climate and thereby helps prioritize areas for acquisition (areas of high adaptedness), restoration (areas of low adaptedness), and management. Here, using whole genome sequence data of 171 adult trees, we studied the climate adaptedness of California populations of coast live oak (Quercus agrifolia), a foundation species that broadly supports biodiversity, to develop a conservation management strategy, including direct acquisition, land management, and restoration that addresses the predicted impact of future climate. Over the range of coast live oak, we find that the northernmost and southernmost stands are predicted to be more at risk for maladaptation to climate change, with the central coast of California containing stands with high adaptedness, including in unprotected areas that might benefit from protection. Using three large preserves (focal sites) managed by The Nature Conservancy (TNC), we illustrate how moving seeds from low to high elevation parts of preserves could benefit future populations range-wide by increasing overall climate adaptedness. We discuss the different levels of risk across focal sites and apply our interpretations into TNC's 400,000 + -acre conservation holdings in California to sustain future populations of this foundational tree species. This study represents a unique collaboration between academic and applied conservation scientists to both assess levels of maladaptedness of a foundational tree to predicted climate change and also develop a genomic-informed seed transfer program to improve adaptedness of future populations.