Henrik Balslev

Aim: To evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change. Location: Amazon rain forest. Methods: We produced species distribution models (SDM) at 5-km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot-based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climate-only versus soil-only models to identify regions where climate and soil might restrict species ranges independently or jointly. Results: For 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought-sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north-west and in the north-east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills. Main conclusions: Our results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species’ abilities to track suitable climate conditions under a drought-increase scenario. Future challenges are to improve the quality of soil data and couple them with process-based models to better predict species range dynamics under climate change.