Assessing forest vulnerability to climate warming using a process-based model of tree growth: bad prospects for rear-edges.

Growth models can be used to assess forest vulnerability to climate warming. If global warming amplifies water deficit in drought-prone areas, tree populations located at the driest and southernmost distribution limits (rear-edges) should be particularly threatened. Here, we address these statements by analyzing and projecting growth responses to climate of three major tree species (silver fir, Abies alba; Scots pine, Pinus sylvestris; and mountain pine, Pinus uncinata) in mountainous areas of NE Spain. This region is subjected to Mediterranean continental conditions, it encompasses wide climatic, topographic and environmental gradients, and, more importantly, it includes rear-edges of the continuous distributions of these tree species. We used tree-ring width data from a network of 110 forests in combination with the process-based Vaganov-Shashkin-Lite growth model and climate-growth analyses to forecast changes in tree growth during the 21st century. Climatic projections were based on four ensembles CO emission scenarios. Warm and dry conditions during the growing season constrain silver fir and Scots pine growth, particularly at the species rear-edge. By contrast, growth of high-elevation mountain pine forests is enhanced by climate warming. The emission scenario (RCP 8.5) corresponding to the most pronounced warming (+1.4 to 4.8 °C) forecasted mean growth reductions of -10.7% and -16.4% in silver fir and Scots pine, respectively, after 2050. This indicates that rising temperatures could amplify drought stress and thus constrain the growth of silver fir and Scots pine rear-edge populations growing at xeric sites. Contrastingly, mountain pine growth is expected to increase by +12.5% due to a longer and warmer growing season. The projections of growth reduction in silver fir and Scots pine portend dieback and a contraction of their species distribution areas through potential local extinctions of the most vulnerable driest rear-edge stands. Our modeling approach provides accessible tools to evaluate forest vulnerability to warmer conditions.