Genetic adaptive response: missing issue in climate change assessment studies

Two misconceptions on the adaptive potential of forests occur in climate change impact assessments. The first is that forests would be unable to adapt genetically, as climate change occurs within the lifespan of trees. However, selection takes place continuously in the regeneration phase of the forest when the number of individuals are reduced from many thousands seedlings to several hundred trees per hectare. Thus, although an individual tree might face century or more changing climate, the population where this tree dies may already strongly deviate in its genetic make-up compared to the population in which the tree germinated. The second misconception is that differences between tree species or woody plant functional types are more important for climate change assessments than differences within a tree species. However, there is ample evidence that provenances have adapted to their local environment and consequently differ in their response to climate change. The ForGEM model attempts to accommodate for both misconceptions by combining a classical process-based individual-tree model with a quantitative genetic model. The model parameters can be characterized by the genetic model and result in local adaptation. Key-results of the application of the ForGEM model in climate change assessment are that genetic adaptation is indeed possible within a few generations for important adaptive traits such as phenology and water use, and that the rate of response of adaptive traits to climate change is strongly affected by forest management. We argue that, based on: 1) observational findings of different responses of populations of the same species to climate change due to local adaptation, 2) the simulated findings of adaptive responses within the time frame of climate change, and 3) the vast technological development in genome wide association studies, it is necessary and feasible to include genetic adaptive processes in cross-sectorial climate change assessment studies