Modeling the Distribution of Plant Species Using the Autologistic Regression Model Modeling the Distribution of Plant Species Using the Autologistic Regression Model

He obtained his Ph.D. from Stanford University in 1982 and since then has done research in various areas including geometrical probability, multivariate probability inequalities and survival analysis. This work is part of an ongoing project devoted to the modeling the relationship between species distribution and climate variables. Boulder for providing us with species and climate data. Abstract For modeling the distribution of plant species in terms of climate covariates, we consider an autologistic regression model for spatial binary data on a regularly spaced lattice. This model generalizes Besag's (1974) autologistic model by including covariates in the model. Three estimation methods, the coding method, maximum pseudolikelihood method and Markov chain Monte Carlo method are studied and compared via simulation and real data examples. As examples, we use the proposed methodology to model the distributions of two plant species in the state of Florida.