Robust minimum divergence estimation in a spatial Poisson point process.

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, its statistical performance is not robust when confronted with slight deviations due to model misspecification in SDM. We proposed a new robust estimation method based on a novel divergence for the Poisson point process model. The 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 demonstrated that our proposed method improved the predictive performance of maximum likelihood estimation in both simulation studies and the analysis of vascular plant data in Japan. • We introduced a robust method to address model misspecification in species distribution using thinned Poisson processes. • Simulation studies revealed bias in conventional estimates for mixed data, while our method provided appropriate estimates. • Our method improved prediction performance of the likelihood approach in analyzing presence-only data of vascular plants. [ABSTRACT FROM AUTHOR]