  Species distribution modeling (SDM) plays a crucial role in investigating
habitat suitability and addressing various ecological issues. While likelihood
analysis is commonly used to draw ecological conclusions, it has been observed
that its statistical performance is not robust when faced with slight
deviations due to misspecification in SDM. We propose a new robust estimation
method based on a novel divergence for the Poisson point process model. The
proposed method is characterized by weighting the log-likelihood equation to
mitigate the impact of heterogeneous observations in the presence-only data,
which can result from model misspecification. We demonstrate that the proposed
method improves the predictive performance of the maximum likelihood estimation
in our simulation studies and in the analysis of vascular plant data in Japan.
