Genetic trials improve the transfer of Douglas-fir distribution models across continents

Abstract

Climate change is likely to result in novel conditions with no analogy to current climate. Therefore, the application of species distribution models (SDMs) based on the correlation between observed species’ occurrence and their environment is questionable and calls for a better understanding of the traits that determine species occurrence. Here, we compared two intraspecific, trait-based SDMs with occurrence-based SDMs, all developed from European data, and analyzed their transferability to the native range of Douglas-fir in North America. With data from 50 provenance trials of Douglas-fir in central Europe multivariate universal response functions (URFs) were developed for two functional traits (dominant tree height and basal area) which are good indicators of growth and vitality under given environmental conditions. These trials included 290 North American provenances of Douglas-fir. The URFs combine genetic effects i.e. the climate of provenance origin and environmental effects, i.e. the climate of planting locations into an integrated model to predict the respective functional trait from climate data. The URFs were applied as SDMs (URF-SDMs) by converting growth performances into occurrence. For comparison, an ensemble occurrence-based SDM was developed and block cross validated with the BIOMOD2 modeling platform utilizing the observed occurrence of Douglas-fir in Europe. The two trait based SDMs and the occurrence-based SDM, all calibrated with data from Europe, were applied to predict the known distribution of Douglas-fir in its introduced and native range in Europe and North America. Both models performed well within their calibration range in Europe, but model transfer to its native range in North America was superior in case of the URF-SDMs showing similar predictive power as SDMs developed in North America itself. The high transferability of the URF-SDMs is a testimony of their applicability under novel climatic conditions highlighting the role of intraspecific trait variation for adaptation planning in climate change.