For a high resolution copy, click here.

Coyote Ranges

Map and Data Manipulation Steps

ArcGIS Pro – Map Project

Data Manipulation

Added coyotes.csv to map.

Viewed attribute table for imcomplete records and deleted one row with 0 for X and Y.

Displayed XY Date and created coyotes.shp.

Ran Minimum Bounding Geomety tool to create coyotes_mpb and symbolized polygons by animal.

Added field Area_SQ_km and calculated geometry to get area for each animal’s home range.

Ran Kernel Density tool on coyotes and created coyotes_kde.

Range of valued from 0 to 32.3478

Ran Extract Values to Points on coyotes and created coyotes_evp.

Sorted RASTERVALU by descending.

Calculated 50% break = 7.086616 at record 492.

Calculated 95% break = 0.553569 at record 935.

Reclassified coyotes_evp with two classes for both the 50% and 95% breaks and created coyotes_50 and coyotes_95.

Ran Raster to Polygon tool on both creating coyotes_50_poly and coyotes_95_poly.

Switched to ArcMap and ran python script as instructed.

Switched to ArcGIS Pro.

Added us_140evt.tif to map.

Added All_Home_Ranges to map.

Ran Extract by Mask tool to clip evt to All_Home_Ranges and created all_ranges_evt

Added float field PERC_VALUE.

Calcualted field by dividing row COUNT value by total COUNT value (215,273) to get the proportion of each land cover type within the home range.

Exported attributes to table named all_ranges_evt_table then ran Table to Excel and saved Excel of same name.

Ran Clip tool to clip coyotes to coyotes_95_poly in order to only get coyote points that are within the 95% home range and created coyotes_95clip.

Ran Extract Values to Points using coyotes_95clip and us_140evt and created coyotes_95_evp_evt.

Ran Join Field tool and added CLASSNAME to cototes_95_evp_evt using RASTERVALU and VALUE as common field.

Exported attributes to table coyotes_95_evp_evt_table then ran Table to Excel and saved as Excel of same name.

Switched to Excel to complete analysis.

Layout and Map

Added Core and Home range polygons for each coyote.

Ran Merge tool on Core polygons to create core_merge.

Ran Merge tool on Home polygons to create home_merge.

Symbolized both using color ramp ‘set 1 (7 classes)’ so that they would have the same colors for each gridcode (coyote).

Put 1pt gray 30% border for both.

Put core_merge on top of home_merge and set home_merge to 40% transparency.

Added US Letter Landscape layout.

Added Multi-directional Hillshade and National Geographic Style Basemap from Living Atlas

Set Hillshade under basemap.

Set basemap to 30% transparency.

Overlapping coyote home range polygons were not showing well, so moved individual coyote home range polygons for coyotes 13, 14, and 9 between home_merge and core_merge and set each to 50% transparency.

Inserted new map to use as a locater map.

Used same base and hillshade from Living Atlas.

Added Utah shape file from my GIS archive.

Drew polygon around coyote area.

Added other Map elements (legend, north arrow, scale, title, credits, author).

Coyote Home and Core Range Analysis

Tables and Analysis

Both tables show the results of the home range analysis for a subset of tracked coyotes near Dugway, Utah. The point tracking data for the coyotes was analyzed against the expected vegetation type to ascertain their preference and use of the landcover available. The analysis takes each tracking point and associates it with the landcover type associated with that point. The greater the number of points with an landcover type signifies a use preference by the coyotes. The amount of landcover use, by landcover availability, demonstrates a preference of one landcover type versus another given the expected use if all landcover types were used in accordance with their percentage of coverage within the range. I added in the landcover sub-class from the evt data as sometimes the landcover class can be too narrow. From viewing the sub-class, the preference for evergreen shrublands and grasslands is evident. An analysis preference ratio of the top 85% of landcover used within the range shows a preference for evergreen shrubland over grassland, mixed evergreen-deciduous shrubland, and evergreen open tree canopy. Caution must be taken while making inferences about landcover preference ratios in areas where both the available and used areas have limited numbers of tracking points. The only area that stands out here is the lack of preference for barren areas. In Table 1, only the landcover types where coyotes were actually found were included in the available/expected areas. This resulted in slightly less than 100% of the area and an expected count of 931 vs. 934 coyotes. Table 2 adds these available/expected areas in and the coyote counts match up but end up with preference ratios of zero.

For a high resolution copy of the tables, click here.