Data from: A neighborhood approach for using remotely sensed data to estimate current ranges for conservation assessments

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 km x 2 km grid required for AOO. The neighborhood
approach can be applied to various abiotic 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 (whereby 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.