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Updated: Sep 17, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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A Neighborhood Approach for Using Remotely Sensed Data to Estimate Current Ranges for Conservation Assessments.

Bethany A Johnson1,2, Gonzalo E Pinilla-Buitrago1,3, Robert P Anderson1,3,4

  • 1Department of Biology, City College of New York City University of New York New York New York USA.

Ecology and Evolution
|June 30, 2025
PubMed
Summary
This summary is machine-generated.

A new "neighborhood" approach refines species distribution models by accounting for geographic coordinate uncertainty. This method improves conservation assessments by better reflecting habitat loss, especially for species with uncertain locations.

Keywords:
biogeographyconservationecological niche modelingremote sensingspecies distribution modelinguncertainty

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Area of Science:

  • Ecology
  • Conservation Biology
  • Biogeography

Background:

  • Species distribution models (SDMs) predict environmental suitability but face challenges with geographic coordinate uncertainty.
  • Remotely sensed habitat data often has finer resolution than the uncertainty associated with species occurrence records.
  • Existing methods may misestimate species ranges and conservation status due to these limitations.

Purpose of the Study:

  • To introduce a novel methodological approach that processes habitat data to account for geographic coordinate uncertainty.
  • To refine range estimates for conservation assessments by incorporating a
  • neighborhood
  • concept for habitat data.
  • To evaluate the impact of this approach on threat categorization for a forest-dwelling species, *Handleyomys chapmani*.

Main Methods:

  • Developed a "neighborhood" approach to process habitat data, maintaining fine resolution while characterizing surrounding areas.
  • Determined deforestation tolerance thresholds by comparing exact pixel values versus neighborhood values of occurrence records with forest cover data.
  • Removed areas below thresholds from climatic suitability predictions to identify inferred habitat loss.
  • Calculated Extent of Occurrence (EOO) and Area of Occupancy (AOO) using both traditional and refined methods.

Main Results:

  • The neighborhood approach identified inferred habitat loss not captured by exact pixel extraction.
  • Calculated EOO and AOO values suggested removing *Handleyomys chapmani* from threatened categories.
  • Highlighted spatial patterns of habitat loss not reflected in traditional EOO and AOO metrics, particularly when using coarse grids for AOO.

Conclusions:

  • The neighborhood approach enhances the practicality of habitat-refined distribution maps for biogeography and conservation.
  • It is particularly useful for species with high coordinate uncertainty in areas of low spatial autocorrelation.
  • The study illustrates the limitations of EOO and AOO metrics and the importance of accounting for spatial uncertainty in conservation assessments.