Autologistic regression model for the distribution of vegetation

Modeling the contagious distribution of vegetation and species in ecology and biogeography has been a challenging issue. Previous studies have demonstrated that the autologistic regression model is a useful approach for describing the distribution because spatial correlation can readily be accounted for in the model. So far studies have been mainly restrained to the first-order autologistic model. However, the first-order correlation model may sometimes be insufficient as long-range dispersal/migration can play a significant role in species distribution. In this study, we used the second-order autologistic regression model to model the distributions of the subarctic evergreen woodland and the boreal evergreen forest in British Columbia, Canada, in terms of climate covariates. We investigated and compared three estimation methods for the second-order model-the maximum pseudo-likelihood method, the Monte Carlo likelihood method, and the Markov chain Monte Carlo stochastic approximation. Detailed procedures for these methods were developed and their performances were evaluated through simulations. The study demonstrates the importance for including the second-order correlation in the autologistic model for modeling vegetation distribution at the large geographical scale; each of the two vegetations studied was strongly autocorrelated not only in the south-north direction but also in the northwest-southeast direction. The study further concluded that the assessment of climate change should be performed on the basis of individual vegetation or species because different vegetation or species likely respond differently to different sets of climate variables.