Standardized ML and XAI Workflow for Presence-Only Species Distribution Modeling: A Case Study in the Amazon

The convergence of Machine Learning (ML) and ecology has provided powerful tools for Species Distribution Modeling (SDM). However, the application of complex algorithms to presence-only data often lacks methodological standardization and interpretability. This study proposes a comprehensive and standardized workflow that integrates robust pseudo-absence generation strategies with Explainable Artificial Intelligence (XAI) to enhance the reliability of SDMs. We evaluated four distinct pseudo-absence selection methods—Random, Spatially Constrained, Environmentally Dissimilar, and a Three-Step Methodology—across different classifiers, utilizing a dual-metric framework that prioritizes the False Negative Rate (FNR) for consistency and the True Skill Statistic (TSS) for discriminatory power. The workflow was validated through a case study involvingTyrannidaeandThraupidaebird families in the Amazon, incorporating anthropogenic variables from the GoAmazon campaign. Our results demonstrate that the choice of pseudo-absence strategy is a critical determinant of model performance, with the Three-Step Methodology combined with Support Vector Machines achieving the optimal balance between sensitivity and specificity. Furthermore, the application of post-hoc XAI techniques, including SHAP and LIME, confirmed the ecological validity of the models by revealing biologically meaningful relationships between species distribution and environmental predictors, such as atmospheric pollutants. This work provides a reproducible pipeline that mitigates the closed box nature of ML models, offering a transparent and effective tool for biodiversity conservation and environmental planning.