METHODS

ArcGIS 10.1 software with the Advanced license level was used for all spatial analysis. Basic tools from ArcToolbox and tools from the Spatial Analyst Extention were used for geoprocessing (Table 1).

Table 1. ArcGIS tools used for geoprocessing.

Toolbox	Tool	Purpose
Analysis Tools	Buffer	Create an 8km buffer polygon around each route with records of Burrowing Owls
	Clip	Extract the route buffer areas from each of the layers for analysis
	Select	Split routes layer to analyze differences in land ownership and vegetation between the six routes
Data Management Tools	Merge	Merge land use shapefiles when multiple files were needed to cover the buffer area
	Project	Change projections of layers in order to summarize the buffer area and calculate proportion of variables within buffers
Spatial Analyst Tools	Zonal Statistics as Table	Calculate mean temperature and precipitation of PRISM rasters inside route buffers

BBS data are available from the U.S. Geological Survey website (http://www.mbr-pwrc.usgs.gov/bbs/). I obtained the BBS calculated trend estimates and significance values for all routes by navigating through the Burrowing Owl trend links on each of the New Mexico routes. I also downloaded raw data for all routes in New Mexico and extracted all Burrowing Owl data. I downloaded the route spatial data and combined it with the owl count data and organized it in a usable format. I utilized vector data available from the website through shapefiles of relative abundance, trends, and route paths. The route paths layer was used throughout my analysis. This shapefile used an Albers Conical Equal Area projection; therefore I used this projection throughout my project. In addition, this projection preserves area, and this was the quality of highest interest as my objective was to compare proportions of variables within each route area. The other projection distortions of distance, shape, and direction were not important for my analyses. I changed the central meridian to -106 for the cone to open onto NM, and I changed the projection in the other layers I used in order to calculate proportions.

Other data sources I utilized included USGS Land Use/Land Cover 2000 vector data, Bureau of Land Management Surface Land Ownership 2014 vector data, vegetation 2009 vector data, New Mexico Burrowing Owl Working Group vector point data of owl locations from a spreadsheet of UTM projection, Bird Conservation Regions vector data, Physiographic Strata vector data, and PRISM climate raster data (Appendix B).

For analyses, I created an 8km buffer polygon around each 24.5 mile route that had records of Burrowing Owls. I chose a 4km distance from route paths because 4km was the maximum foraging distance I found in the Burrowing Owl literature (Poulin et al. 2011). I used this location data to compare land use, land ownership, and vegetation in the route buffer areas between the routes with significant decreasing and increasing owl trends. In addition, climate variables pertinent to owl physiology and prey availability were compared between routes with decreasing and increasing owl trends.