Forecasting floral futures: leveraging genetic and microenvironmental data to improve seed provenancing under climate change

Revegetation projects seeking to restore degraded ecosystems face a major challenge in sourcing appropriate plant material, as identifying plants adapted to future climates requires knowledge of plant performance under novel conditions. In order to support climate-resilient provenancing efforts, we develop a quantitative trait model that integrates genetic and microenvironmental variation. We train our model with multiple natural plantings of Arabidopsis thaliana and predict days-to-bolting and fecundity across the species’ European range. Model prediction accuracy was high for days-to-bolting and moderate for fecundity, with the majority of trait variation being explained by temperature variation. Concerningly, fecundity was predicted to decline under future conditions, although this response was heterogeneous across regions, and could be offset through the introduction of specific genotypes. Our study highlights the value of predictive models to aid seed provenancing and improve the success of revegetation projects.