Abstract
Tree mortality patterns play a central role in forest health, terrestrial carbon dynamics, and biodiversity conservation. While changes in tree mortality patterns to recent climate anomalies have been documented, the effects of climate change on ambient or “background” mortality rates in the absence of severe disturbance agents remains poorly understood. Here, we applied a machine learning approach to a large dataset of 24,576 forest permanent sample plots distributed across Canada and the United States, spanning 28.1°C in mean annual temperature and 1743.5 mm in annual precipitation, to study and predict the effects of climate on background mortality for nine of the most abundant tree species in eastern North America. We developed species‐specific climate‐mortality models while controlling for the interactive effects of stand development processes, CO 2 , and atmospheric pollutant SO 4 . We found that temperature ranked among the three strongest predictors of mortality rates and that warmer temperatures led to higher background tree mortality for most species. Furthermore, we predicted notable increases in tree mortality rates along the southern portions of species' ranges in response to future warming. On average, mortality rates are predicted to rise by 0.2%–0.7%·year −1 for five of the nine species under SSP2‐4.5 by mid‐century (2041–2070). By examining climate‐driven variation in background tree mortality at the continental scale, our study provides valuable insight on future forest dynamics under climate change.