Year of Award

2020

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Environmental Studies

Department or School/College

Environmental Studies

Committee Chair

Len Broberg, PhD

Committee Co-chair

Andrew Jakes, PhD

Commitee Members

Joshua Millspaugh, PhD

Keywords

Fence ecology, fences, migration, connectivity, Centrocercus urophasianus, Yellowstone

Subject Categories

Environmental Studies | Geographic Information Sciences | Natural Resources and Conservation | Nature and Society Relations | Population Biology | Spatial Science

Abstract

Fences pose significant challenges to wildlife movement, but their effects are difficult to quantify because fence location and fence type data are lacking on a global scale. We developed a fence location and density model in southwest Montana, USA to provide data to researchers and managers, and test whether previous models could be applied to a new region and retain suitable levels of statistical accuracy. Our model used local expert opinion to inform how road, land cover, and ownership spatial layers interacted to predict fence locations. We validated the model against fence data collected on random 3.2 km road transects (n = 330). The model predicted 37,687 km of fences across the study area, with a mean fence density of 1.6 km/km2 and a maximum density of 11.3 km/km2. Additionally, we manually digitized fences in Google Earth Pro in a random sample of 50 survey townships (roughly 4,650 km2) within the study area and validated the accuracy of this method to compare results against the fence model predictions.

Our fence model showed lower agreement (Cohen’s Kappa = 0.56) with known samples than manually-digitized fences in Google Earth (Cohen’s Kappa = 0.76), yet had an improved level of accuracy over previous models. The fence model outputs are likely most useful for large scale analyses of ecological influences of fence densities, whereas the Google Earth digitizing method is likely useful to locate individual fences for fine-scale analyses. While the Google Earth approach is highly accurate in open landscapes, it is significantly more time intensive than the modeling approach and so the cost-benefit between methods must be considered. We demonstrate the utility of our Google Earth fence mapping technique using recently collected pronghorn (Antilocapra americana) movement data. The restricted movements of pronghorn interacting with fences support our finding that fences in our study area, regardless of whether they were located on public or private lands, can act as barriers to wildlife. Our results provide options for mapping fences at multiple scales and elucidate a need for fence modifications on both public and private lands to facilitate wildlife movement requirements and improve ecological connectivity.

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© Copyright 2020 Simon Albert Buzzard