Work

Assessment of Predictive Habitat Models for Bighorn Sheep in California's Peninsular Ranges

Year: 2009

Type: article

Abstract: ABSTRACT We developed predictive habitat models for a bighorn sheep ( Ovis Canadensis ) population in the Peninsular Ranges of southern California, USA, using 2 Geographic Information System modeling techniques, Ecological Niche Factor Analysis (ENFA) and Genetic Algorithm for Rule‐set Production (GARP). We used >16,000 Global Positioning System locations from 34 animals in 5 subpopulations to develop and test ENFA and GARP models, and we then compared these models to each other and to the expert‐based model presented in the United States Fish and Wildlife Service's Recovery Plan for this population. Based on a suite of evaluation methods, we found both ENFA and GARP to provide useful predictions of habitat; however, models developed with GARP appeared to have higher predictive power. Habitat delineations resulting from GARP models were similar to the expert‐based model, affirming that the expert‐based model provided a useful delineation of bighorn sheep habitat in the Peninsular Ranges. In addition, all 3 models identified continuous bighorn sheep habitat from the northern to southern extent of our study area, indicating that the Recovery Plan's recommendation of maintaining habitat connectivity throughout the range is an appropriate goal.

Source: Journal of Wildlife Management

DOI: 10.2193/2008-240

Authors: ESTHER S. RUBIN, CHRIS J. STERMER, WALTER M. BOYCE, STEVEN G. TORRES

Institutions: Zoological Society of San Diego, California Department of Fish and Wildlife, University of California, Davis

Language: English

Topic: Species Distribution and Climate Change

Subfield: Ecological Modeling

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: green

Funders: Johns Hopkins University, U.S. Fish and Wildlife Service