Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic a
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RESEARCH ARTICLE
Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas Ho Yi Wan
. Samuel A. Cushman . Joseph L. Ganey
Received: 22 May 2018 / Accepted: 18 February 2019 Ó 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
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.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10980-019-00788-w) contains supplementary material, which is available to authorized users.
Keywords Connectivity Corridor Endangered species Fragmentation Habitat loss Habitat selection Landscape resistance Mexican spotted owl Resource selection function Scale
H. Y. Wan (&) School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA e-mail: [email protected]
Introduction
S. A. Cushman J. L. Ganey Rocky Mountain Research Station, USDA Forest Service, 2500 S. Pine Knoll, Flagstaff, AZ 86001, USA
Species extinction is accelerating exponentially as a consequence of intensified anthropogenic activities
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Landscape Ecol
(Barnosky et al. 2011; Ceballos et al. 2015; De Vos et al. 2015). Habitat loss and fragmentation are considered the most pressing threats to biodive
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