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

Dataset DOI: 10.5061/dryad.sxksn03ft
Article DOI: 10.1002/ECE3.71631 

## Description of the data and file structure

Files uploaded here accompany Johnson et al. 2025 \"A neighborhood approach for using remotely sensed data to estimate current ranges for conservation assessments\" and include the input data (occurrence datasets and a shapefile of background extent), four Rmd files to run the analyses in R, and the output evaluation table.

## Files and variables

# R Code

These Rmd files correspond to Appendix S3 in the article.

* #### File: 01_Wallace.rmd

**Description:** R code for species distribution modeling with *Wallace EcoMod* [Step 1].

* #### File: 02_nhood_processing.Rmd

**Description:** R code for neighborhood-processing analysis [Step 2].

* #### File: 03_mRR.Rmd

**Description:** R code for masking distribution maps [Step 3].

* #### File: 04_cRR....,