Nationwide climate-sensitive models for stand dynamics and forest scenario simulation

Projecting forest dynamics as a function of alternative management strategies and climatic conditions is key to develop sound forest policy and management planning. In Spain there is a need for a full set of climate-sensitive individual-tree growth and yield models suitable for country-level simulations. In this paper we present environmentally driven models for forest stand dynamics based on distance-independent individual-tree growth and yield. The data for model calibration are based on the second, third and fourth surveys of the Spanish National Forest Inventory, including 50,359 plots and 838,620 trees, representing a broad gradient in forest types and environmental conditions. The results develop a total of 182 models for diameter increment, height increment, total height, survival and ingrowth for 27 species and species groups. The models are integrated into a full forest projection system to perform simulations of forest dynamics and resulting changes in the provision of ecosystem services, including the effects of climate, and forest management. Its potential is illustrated through an array of simulations of forest dynamics in the region of Catalonia under alternative climatic and management scenarios. The resulting models and projections provide a solid basis for the simulation of national or regional climate-sensitive forest scenarios, with the possibility of being applied to other regions, and may be used for future management and planning efforts.This research was supported by structural CTFC funding provided by the Department of Agriculture of the regional Government of Catalonia, the ‘Instituto Nacional de Investigaciones Agrarias’ (INIA), and the Spanish Ministry of Economy and Competitiveness (research project CGL2017–89149-C2–2-R), Ramon y Cajal fellowship (RyC2012–11109). This research also received funding from the European Union’s H2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 101007950 (DecisionES