Predicting the global fundamental climate niche of lodgepole pine for climate change adaptation

Abstract

The widely used species-occurrence-based models that predict the realized climate niche of plants can be too restrictive and do not reflect among-population variation in assessing climate change impact and guiding assisted migration for adaptation to future climates. To mitigate this deficiency, this study built a fundamental climate niche model for lodgepole pine (Pinus contortaDougl. ex Loud.) based on 20-year tree height from wide-ranging provenance trials as a case study. The model was built through comparisons and optimizations of two candidate models, universal response function (URF) and universal transfer function (UTF), with linear and linear mixed-effect forms, against varying sample sizes based on the comprehensive provenance trials. We found that URF and UTF models had similar performances, while URF models were more straightforward in identifying optimal provenances for planting sites. Linear mixed-effect models did not show clear advantages over linear models in our case but prevented including additional predictors, which are often critical. We selected the linear model of URF and predicted the fundamental climate niche of lodgepole pine on a global scale and revealed a great potential of using this species for climate change adaptation beyond its native distribution, representing a significant step in forest genecology. Our study presented a new approach for assisted migration at the species and the population levels to optimize adaptation and productivity under a changing climate.