Using a dynamic forest model to predict tree species distributions

Abstract

Aim It has been suggested that predicting species distributions requires a processbased 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. Location Pacific Northwest of North America (PNW). Methods We 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). Results ForClim 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 conclusions Forest 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 should include robust parameterization schemes and additional ecological processes that are important at large spatial scales (e.g. dispersal, disturbances).