DOI: 10.5061/dryad.n5tb2rbz5
title: Data From: Advancing fence datasets: Comparing approaches to identify fence locations and specifications in southwest Montana

description: Fencing is a major anthropogenic feature affecting human relationships,
 ecological processes, and wildlife distributions and movements, but its
 impacts are difficult to quantify due to a widespread lack of spatial
 data. We created a fence model and compared outputs to a fence mapping
 approach using satellite imagery in two counties in southwest Montana, USA
 to advance fence data development for use in research and management.
 The model incorporated road, land cover, ownership, and grazing
 boundary spatial layers to predict fence locations. We validated the model
 using data collected on randomized road transects (n = 330). The model
 predicted 34,706.4 km of fences with a mean fence density of 0.93
 km/km2 and a maximum density of 14.9 km/km2. We also digitized
 fences using Google Earth Pro in a random subset of our study area in
 survey townships (n = 50). The Google Earth approach showed greater
 agreement (K = 0.76) with known samples than the fence model
 (K = 0.56) yet was unable to map fences in forests and was
 significantly more time intensive. We also compared fence
 attributes by land ownership and land cover variables to assess factors
 that may influence fence specifications (e.g., wire heights) and types
 (e.g., number of barbed wires). Private lands were more likely to have
 fences with lower bottom wires and higher top wires than those on public
 lands with sample means at 22 cm and 26.4 cm, and 115.2 cm and 110.97,
 respectively. Both bottom wire means were well below recommended heights
 for ungulates navigating underneath fencing (≥ 46 cm), while top wire
 means were closer to the 107 cm maximum fence height recommendation. We
 found that both fence type and land ownership were correlated
 (χ2 = 45.52, df = 5, p = 0.001) as well as fence type and land
 cover type (χ2 = 140.73, df = 15, p = 0.001). We provide tools
 for estimating fence locations, and our novel fence type assessment
 demonstrates an opportunity for updated policy to encourage the adoption
 of “wildlife-friendlier” fencing standards to facilitate wildlife movement
 in the western U.S. while supporting rural livelihoods.
description: For the fence model and fence density layers, the data was
 adapted from publicly available spatial layers informed by local expert
 opinion in Beaverhead and Madison Counties, MT. Data used included Montana
 Department of Transportation road layers, land ownership data from Montana
 State Library cadastral database, land cover data from the 2019 Montana
 Department of Revenue Final Land Unit (FLU), and railroad data from the
 Montana State Library. The data was processed in ArcMap 10.6.1 to form a
 hierarchical predictive fence location and density GIS model.
 For the Google Earth mapped fences, data was collected by
 examining satellite imagery and tracing visible fence lines in Google
 Earth Pro version 7.3.3 (Google 2020) within the bounds of 50 random
 survey township polygons in Beaverhead and Madison Counties.
description: The data can be opened in GIS programs such as Esri ArcGIS, QGIS,
 or GRASS GIS (or others) or in statistical programming software such as R
 (or others).