Climate-driven decreases in aspen’s distribution and opportunities for future expansion across the Southern Rocky Mountains

Overview
This dataset consists of spatially explicit predictions of aspen habitat suitability made in support of the study "Climate-driven decreases in aspen’s distribution and opportunities for future expansion across the Southern Rocky Mountains". Below, we outlined the aims, methods, results, and conclusions of this study.

Aim
Species distribution models (SDMs) are widely used to understand how climate change may influence habitat suitability and inform climate-adaptive forest management. However, SDMs are often of limited use for understanding the range expansion potential for tree species, which often exhibit slow migration rates. Here, we integrate remotely sensed data products into an SDM to generate predictions of range shifts for quaking aspen (Populus tremuloides Michx), a keystone species in North American forests.

Methods
We related remotely sensed maps of aspen presence with climate, topographic, and soil predictors to generate an ensemble SDM. We then incorporated locations of known populations, information on dispersal distances, and future habitat suitability to forecast areas likely to experience range retraction and expansion.

Results
Our ensemble SDM explained about 78 % of the variation in aspen occurrence. Relative to the 1981-2010 period, we found average aspen habitat suitability across the SRME decreased by 8.4 % for the 2011-2040 period. We also found that large areas of the SRME may be suitable for aspen expansion, but that most of these areas are located more than 100 meters from existing aspen patches.

Conclusions
Our modeling suggests that climate change is likely to decrease habitat suitability for many aspen populations and that the potential expansion will be constrained by dispersal. However, artificial regeneration techniques could be used to facilitate range expansion. Moreover, our work highlights the potential for using remotely-sensed species occurrence datasets in SDMs to provide forest managers with more robust forecasts of how climate change may influence tree species distributions.