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Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas

  • Ho Yi WanEmail author
  • Samuel A. Cushman
  • Joseph L. Ganey
Research Article
  • 91 Downloads

Abstract

Context

Habitat loss and fragmentation are the most pressing threats to biodiversity, yet assessing their impacts across broad landscapes is challenging. Information on habitat suitability is sometimes available in the form of a resource selection function model developed from a different geographical area, but its applicability is unknown until tested.

Objectives

We used the Mexican spotted owl as a case study to demonstrate how models developed from different geographic areas affect our predictions for habitat suitability, landscape resistance, and connectivity. We identified the most suitable habitats and core areas for dispersal and movement for the species.

Methods

We applied two multi-scale habitat selection models—a local model and a non-local model—to a broad study area in northern Arizona. We converted the models into landscape resistance surfaces and used simulations to model connectivity corridors for the species, and created composite habitat and connectivity models by averaging the local and non-local models.

Results

While the local and the non-local models both performed well, the local model performed best in the part of the study area where it was built, but performed worse in areas that are beyond the extent of the data used to train it. The composite habitat model improved performances over both models in most cases.

Conclusions

With rigorous testing, multi-scale habitat selection models built on empirical data from other geographical areas can be useful. Averaging predictions of multiple models can improve performance, but the effectiveness is subject to the performance of the reference models.

Keywords

Connectivity Corridor Endangered species Fragmentation Habitat loss Habitat selection Landscape resistance Mexican spotted owl Resource selection function Scale 

Notes

Acknowledgements

This project was funded by Joint Fire Sciences Project and the National Fire Plan. We thank C. Vojta, C. Aslan, and P. Fulé for their constructive comments and feedback on this project. We also thank J. Evans for his speedy response and exceptional assistance on troubleshooting and debugging spatialEco R package.

Supplementary material

10980_2019_788_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 29 kb)
10980_2019_788_MOESM2_ESM.pdf (536 kb)
Supplementary material 2 (PDF 537 kb)

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Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.School of Earth Sciences and Environmental SustainabilityNorthern Arizona UniversityFlagstaffUSA
  2. 2.Rocky Mountain Research StationUSDA Forest ServiceFlagstaffUSA

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