Testing genomic offset with common gardens in genetically structured black spruce ( Picea mariana)

Boreal forests play a crucial role in regulating climate via storage and release of carbon. Anticipated changes in climate are predicted to increase mortality and decrease the biomass of many boreal tree species, putting at risk the functioning of this ecosystem and hence its role in carbon absorption and climate change mitigation. Genomic offset methods leverage spatial distribution of genomic diversity and its association with environmental variables to generate maps of populations vulnerable to projected changes in climate. Here we analyse over 60 populations and more than 1400 individuals of black spruce ( Picea mariana (Mill.) B.S.P), a dominant boreal forest species, to compare population-level genomic offsets calculated with Gradient Forest with multiple fitness traits measured in four long-term (>40 yr) common gardens. Within common gardens, we found that genomic offset predictions were unaffected by the number or type of markers used for model training, with the exception of LFMM climate-associated markers. Most models predicted fitness equally well even when the number of populations in the training set was reduced down to ten or was restricted to a single genetic cluster, suggesting that Gradient Forest can reliably estimate fitness for new populations and novel climates. However, model performances varied among common gardens, with highly accurate fitness predictions in some gardens but contradictory results in others. The accuracy of model predictions was strongly influenced by the choice of climate variables and their relationships with fitness traits. Overall, our results indicate that predicting genomic offsets in genetically structured species across large spatial scales is challenging, because of variation in environmental effects on genotypes and in the interactions among climate variables across the landscape. By capitalizing on our comprehensive validation, we identified the most robust models for projecting black spruce fitness loss in response to future climate change.