Using a dynamic forest model to predict tree species distributions

作     者：Gutierrez, Alvaro G. Snell, Rebecca S. Bugmann, Harald 

作者机构：ETH Inst Terr Ecosyst Dept Environm Sci Forest Ecol CH-8092 Zurich Switzerland Univ Chile Dept Ciencias Ambientales & Recursos Nat Renovabl Fac Ciencias Agron Santiago Chile 

出 版 物：《GLOBAL ECOLOGY AND BIOGEOGRAPHY》 (全球生态学与生物地理学快报)

年 卷 期：2016年第25卷第3期

页      面：347-358页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 0705[理学-地理学] 0713[理学-生态学] 

基　　金：Marie Curie Intra European Fellowship within 7th European Community Framework Programme (FORECOFUN-SSA) [PIEF-GA-2010-274798] CONICYT-PAI 

主　　题：Dynamic biogeography dynamic vegetation models forest gap models inverse modelling model parameterization prediction accuracy sensitivity analysis temperate rainforests 

摘      要：AimIt has been suggested that predicting species distributions requires a process-based and preferably dynamic approach. If dynamic models are to contribute towards understanding species distributions, uncertainties related to their spatial extrapolation and bioclimatic parameters need to be addressed. Here, we analyse the potential of a forest gap model for predicting species distributions. LocationPacific Northwest of North America (PNW). MethodsWe used the dynamic forest gap model ForClim, which includes climate, competition and demographic processes, to simulate the distribution of 18 tree species outside the domain of the data used for fitting. We explored model accuracy for species distributions at the regional scale by: (1) estimating species climatic tolerances so as to maximize agreement with regional distribution maps versus (2) employing a bioclimatic parameter set that produces high accuracy at the local scale. We then performed the opposite tests and simulated local forest composition in a small area in the PNW, using (3) the local bioclimatic parameters and (4) the bioclimatic parameters that produced the highest accuracy at the regional scale. We also compared the ForClim results with predictions from a standard correlative species distribution model (SDM). ResultsForClim produced regional species distributions with fair to very good agreement for 12 tree species. The optimized bioclimatic parameters consistently improved the accuracy of regional predictions compared with simulations run with the local parameters, and were consistent with SDM results. At the local scale, predictions using the local parameters conformed to descriptions of forest composition, but accuracy decreased strongly when using the regionally calibrated parameters. Main conclusionsForest gap models can predict regional species distributions, but at the cost of reduced accuracy at the local scale. Future applications of gap models to understand regional species distributions shoul