A range-wide transplant experiment using participatory science and genomic prediction to assess local adaptation in forest trees (MyGardenOfTrees)

How organisms adapt to their environments is the most fundamental question in evolutionary biology and is of utmost importance given climate change threats.

Identifying key traits involved in adaptations and understanding how they interact with each other, and with the environment, is a particularly urgent task for foundation and resource-production species, such as forest trees.

Existing experiments assessing local adaptation lack scalability and predictability in natural environments, especially at the species range margins.

Landscape genomics studies could reveal adaptive loci across environmental gradients, but they are hindered by the assumptions of a neutral model and the highly polygenic nature of most traits.

To address these shortcomings, I will conduct a species range-wide transplant experiment using participatory science and genomics to (i) reveal major patterns and drivers of adaptation and (ii) to build a predictive model for selecting optimal seed sources for a given location that accounts for gene-environment interactions and demography.

I will develop a participatory network of foresters as well as ordinary citizens, who will establish a large number (>2500) of micro gardens (4 to 36 m2).

Seeds source populations of Fagus sylvatica and Abies alba, and their sister species, will be selected from across their ranges.

To evaluate plant performance in novel climate conditions, garden locations will also cover locations beyond the species' current distribution range.

Early survival and growth traits, which are under the highest selection pressure in trees, will be monitored and analyzed herein.

An unprecedented nearly full factorial design transplant data set will be obtained using a genomic prediction (GP) model that exploits the genetic similarity between populations and the environmental similarity between garden locations. Finally, I will implement the GP model for forest managers to aid assisted migration decisions with evoluti