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.

翻译：物种分布建模（SDM）在研究栖息地适宜性和解决各种生态问题中发挥着关键作用。虽然似然分析常用于得出生态结论，但已有研究表明，当SDM因模型设定错误而出现微小偏差时，其统计性能并不稳健。我们提出了一种基于新型散度的泊松点过程模型稳健估计方法。该方法通过对对数似然方程进行加权，以减少仅存在数据中因模型设定错误可能导致的异质性观测值的影响。我们通过模拟研究以及对日本维管植物数据的分析，证明了所提方法能够改进最大似然估计的预测性能。