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Abstract

Habitat modelling presents a challenge due to the variety of data available and their corresponding accuracy. One option is to use Markov random fields as a way to incorporate these distinct types of data for habitat modelling. In this work, I provide a brief overview of the intuition, mathematical theory, and application considerations behind modelling habitat under this framework. In particular, an auto-logistic model is built and applied to modelling sea lion habitat using synthetic data. First, we explore modelling one sample of data. Afterwards, the framework is extended to the multi-sample scenario. Finally, the theory for the methodology is presented, the results of the applied implementation are presented.