Challenges in modelling the abundance of 105 tree species in eastern North America using climate, edaphic, and topographic variables

Improving predictions of the location of suitable environmental conditions for species using species distribution models (SDM) is at the core of biodiversity/climate change research, but modelling species abundance, rather than distribution, is proving particularly challenging. Using data from more than 200,000 forest plots in eastern North America and Random Forest, we evaluated the performance of species abundance models (SAM) in predicting the relative abundance (measured as importance value) of each of 105 tree species in relation to climate, edaphic, and topographic variables. We calculated the coefficient of determination R SAM 2 between observed and predicted abundances as a measure of model performance for each species. We also performed multiple linear regressions to explain variation of R SAM 2 among species using five biogeographical or spatial attributes of species as explanatory variables. Predictive performances of SAM R SAM 2 were generally low, ranging from 0.016 to 0.815 (mean = 0.258). Black spruce (Picea mariana) had the best predictive model and Florida maple (Acer barbatum) and American chestnut (Castanea dentata) the worst. Thirty-seven of the 41 best performing species R SAM 2 ⩾ 0.3 had climate ranked as the best and/or second best predictor. Species with the best performance tended to be those that could reach dominance, showed aggregated distribution of abundance, and/or had high latitudinal limits in the study area. Climate change is likely to affect patterns of dominance in communities by altering patterns of co-occurrences, but for many species that constitute the bulk of tree diversity, predictions based solely on the current distribution of relative abundances may not be reliable enough to inform conservation or management decisions. Predicting tree abundance in a warming climate using SAM remains a challenge, but it is only by reporting performances across a range of climate and statistical models, regions and species, as well as by highlighting model limitations and strengths, that we will improve the reliability of predictions and in turn better inform forest conservation and management.