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ABSTRACT Species distribution modeling can be used to predict environmental suitability, and removing areas currently lacking appropriate vegetation can refine range estimates for conservation assessments. However, the uncertainty around geographic coordinates can exceed the fine resolution of remotely sensed habitat data. Here, we present a novel methodological approach to reflect this reality by processing habitat data to maintain its fine resolution, but with new values characterizing a larger surrounding area (the “neighborhood”). We implement its use for a forest‐dwelling species (Handleyomys chapmani) considered threatened by the IUCN. We determined deforestation tolerance threshold values by matching occurrence records with forest cover data using two methods: (1) extracting the exact pixel value where a record fell; and (2) using the neighborhood value (more likely to characterize conditions within the radius of actual sampling). We removed regions below these thresholds from the climatic suitability prediction, identifying areas of inferred habitat loss. We calculated Extent of Occurrence (EOO) and Area of Occupancy (AOO), two metrics used by the IUCN for threat level categorization. The values estimated here suggest removing the species from threatened categories. However, the results highlight spatial patterns of loss throughout the range not reflected in these metrics, illustrating drawbacks of EOO and showing how localized losses largely disappeared when resampling to the 2 × 2 km grid required for AOO. The neighborhood approach can be applied to various data sources (NDVI, soils, marine, etc.) to calculate trends over time and should prove useful to many terrestrial and aquatic species. It is particularly useful for species having high coordinate uncertainty in regions of low spatial autocorrelation (where small georeferencing errors can lead to great differences in habitat, misguiding conservation assessments used in policy decisions). More generally, this study illustrates and enhances the practicality of using habitat‐refined distribution maps for biogeography and conservation. 

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.