  We discuss species distribution models (SDM) for biodiversity studies in
ecology. SDM plays an important role to estimate abundance of a species based
on environmental variables that are closely related with the habitat of the
species. The resultant habitat map indicates areas where the species is likely
to live, hence it is essential for conservation planning and reserve selection.
We especially focus on a Poisson point process and clarify relations with other
statistical methods. Then we discuss a Poisson point process from a view point
of information divergence, showing the Kullback-Leibler divergence of density
functions reduces to the extended Kullback-Leibler divergence of intensity
functions. This property enables us to extend the Poisson point process to that
derived from other divergence such as $\beta$ and $\gamma$ divergences.
Finally, we discuss integrated SDM and evaluate the estimating performance
based on the Fisher information matrices.
