Genetic differentiation for morphological and DNA sequence polymorphisms among species-wide seedling populations of a foundation California oak, Quercus lobata, grown in a common garden

Background/Question/Methods

A major concern about climate change is whether organisms will have the ability to respond or tolerate rapid warming.  This concern is particularly true for trees because the predicted increases in warming will occur on a time scale less than their generation time; thus, adaptation through currently available adaptive genetic variation or the ability to tolerate climate change will be crucial to survival.  This study examines the genetic basis of phenotypic variation in Quercus lobata seedling leaf traits for a collection of 12 seedlings per tree germinated from acorns collected from 96 localities (eight trees each) and grown in a greenhouse at the Institute of Forest Genetics, Placerville, CA. During field collections, we collected leaf tissue for each tree and later extracted DNA for genomic analyses.  Using the genotyping-by-sequencing method, we generated single nucleotide polymorphisms (SNPs) data for a large number of “random” DNA fragments. We then correlated the phenotypic and genotypic regions to test the similarity in geographic patterns between the two datasets. We also compared genetically based phenotypic differentiation and molecular genetic differentiation among sample sites to identify sites where phenotypic differentiation was greater than expected given the genetic background as evidence for local selection on those traits.

Results/Conclusions

Our preliminary analysis of 9000+ first year seedlings in a common garden revealed significant population differentiation using separate ANOVAs and MANOVAs for the all variables:  days until germination, seedling basal diameter, seedling height, leaf thickness, leaf length, and leaf area.  Days until germination and leaf thickness showed the strongest population effects and those values correlated with latitude, elevation, and local precipitation of source sites.  Using a posteriori tests and canonical discriminant function analysis, we identified population clusters with similar phenotypes.  To examine genetic differentiation based on nuclear sequence data, we analyzed over 30,000 SNPs with representation in at least 90% of the 768 adults.  Using F_ST analysis of all SNPs, we found highly significant genetic differentiation among populations.  Using multivariate analysis of all SNPs, we clustered populations together into regional clusters.  We then compared morphological and nuclear genetic clusters to identify the common regions. Our analysis of phenotypic variation using the kinship structure to control for demographic history also distinguished several traits that could be under selection.  These preliminary data suggest that genomic tools can provide useful geographic patterns of adaptive genetic variation.  Such information could make genomic tools useful for resource management of resilient forests facing rapid climate change.