DOI: 10.5061/dryad.sxksn03ft
title: Data from: A neighborhood approach for using remotely sensed data to estimate current ranges for conservation assessments

description: 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.
description: # 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.Rmd **Description:** R code for calculating conservation
 metrics [Step 4]. # Input Data * #### File:
 Input_Extent_Hchapmani_bgShp.zip **Description:** Shapefiles (.dbf, .shp,
 .shx) of the background extent for species distribution modeling of
 *Handleyomys chapmani*. * #### File: Input_Hchapmani_OccurrenceData.zip
 **Description:** Occurrence datasets for *Handleyomys chapmani*. This
 corresponds to Appendix S1 in the article. Tables include the scientific
 name of the species; latitude and longitude in decimal degrees; date of
 collection as m/d/y; collection code and catalog number; locality
 information; and year of collection. * occs_full.csv : the full dataset *
 occs_processed.csv : the full dataset after cleaning (spatial thinning +
 removing duplicate & erroneous records) * occs_year.csv : the full
 dataset (minus two erroneous points; see MS) with the year of collection
 as a separate field * occs_recent.csv : subset of occs_year.csv, only
 records collected after the year 2000. # Output Data #### File:
 Output_EvaluationTable.csv **Description:** Table of model performance
 metrics for candidate models from Maxent species distribution modeling via
 *Wallace EcoMod*. Each row represents a model with a unique combination of
 feature classes and regularization multiplier settings. The table reports
 various evaluation metrics used to assess model fit, complexity, and
 predictive performance. This corresponds to Appendix S2 in the article.
 ##### Variables * fc: feature class * rm: regularization multiplier *
 tune.args: combination of arguments that define the complexity settings
 used for tuning * auc.train: Area Under Curve calculated on the full
 dataset * cbi.train: Continuous Boyce Index calculated on the full dataset
 * auc.diff.avg: average difference between auc.train and auc.val *
 auc.diff.sd: standard deviation of the difference between auc.train and
 auc.val * auc.val.avg: average AUC calculated on the validation datasets
 (the data withheld during cross-validation) * auc.val.sd: standard
 deviation for AUC calculated on the validation datasets (the data withheld
 during cross-validation) * cbi.val.avg: average Continuous Boyce Index
 calculated on the validation dataset * cbi.val.sd: standard deviation for
 Continuous Boyce Index calculated on the full dataset * or.10p.avg:
 average omission rate with threshold as the minimum suitability value
 across occurrence records after removing the lowest 10% *
 or.10p.sd: standard deviation for omission rate with threshold as the
 minimum suitability value across occurrence records after removing the
 lowest 10% * or.mtp.avg: average omission rate with threshold as the
 minimum suitability value across occurrence records * or.mtp.sd: standard
 deviation for omission rate with threshold as the minimum suitability
 value across occurrence records * AICc: Akaike Information Criterion
 corrected for small samples sizes * delta.AICc: highest AICc value across
 all models minus this model’s AICc value, where lower values mean higher
 performance and 0 is the highest performing model * w.AIC: Akaike weights
 * ncoef: number of model coefficients * *See
 DOI:10.32614/CRAN.package.ENMeval for more information on evaluation
 metrics, including units, scales, and categorical variables.* #
 Code/software Species distribution modeling analyses run with *Wallace
 EcoMod* v1.9.0. Information on *Wallace EcoMod* can be found here:
 [DOI:10.1111/ecog.06547](https://doi.org/10.1111/ecog.06547)