Dynamic seed zones to guide climate-smart seed sourcing for tropical dry forest restoration in Colombia

Tree-based forest landscape restoration interventions require knowledge on the suitability and origin of seed sources and planting material. A common recommendation is to select locally sourced material based on the assumption that it is well adapted to local environmental conditions and to avoid introduction of maladapted genes. However, faced with accelerating climate change, it may be prudent to supplement local provenances with ‘climate-matched’ provenances, i.e. where current climate conditions are similar to those anticipated in the future at the planting site. Restoration practitioners usually do not have access to the necessary information to implement such climate-smart seed sourcing. Here, we combine genetic data of 11 socio-economically important tree species of the tropical dry forests of Colombia with spatial environmental data to inform the delineation of dynamic seed zones for the restoration of this highly threatened ecosystem. Analysis of Molecular Variance (AMOVA) indicates significant population genetic differentiation within all 11 species. We fitted linear mixed effects models to evaluate if the genetic distance between trees was mainly related to geographic distance (i.e. isolation by distance; IBD), environmental distance (i.e. isolation by environment; IBE), or both. Observed scales of genetic differentiation were best explained by the model including both geographic and environmental distance (IBD + IBE) for 6 out of 11 species, and by the IBE model for the remaining species, suggesting that the observed differentiation is at least partly driven by adaptive processes. Aiming at capturing as much as possible of the observed genetic differentiation, we propose a set of 36 provisional seed zones that are applicable across species and dynamic under climate change, based on the clustering of environmental data and geographical coordinates. We project these seed zones to future climate conditions using five general circulation models and two emission scenarios, and discuss how they can be used to implement different climate-smart seed sourcing strategies in a pragmatic way. The seed zone maps are made available in a user-friendly online tool.