Proceedings of the Ninth Mountain Lion Workshop - Carnivore ...

Using ArcGIS 9.2 ® , we created polyline shapefiles for major highways from USGS
digital line graph road map layers, and for major rivers from USGS National
Hydrography Dataset digital map layers. To identify residential housing developments,
we joined county property parcel map layers from the counties inhabited by the collared
pumas. We created a new polygon shapefile containing only residential parcels less than
2.0 acres (0.8 ha) in size, and used the merge tool to merge adjoining polygons smaller
than 2 acres. Next, we selected only resultant polygons with maximum lengths >1 km to
be investigated as potential puma movement obstacles and created a new “residential
development” shapefile from this selection.
Because highways, major rivers, and residential areas sometimes occurred in association
with each other, we removed the portions of these layers that overlapped or nearly
overlapped, and only analyzed potential obstacles in areas where they did not coincide
with the other 2 features. For each potential obstacle feature we created a 300-m buffer
zone, and selected only areas of that feature and its buffer that did not intersect a different
potential obstacle. We created new shapefiles of highways, rivers and residential
developments that were not immediately proximate to another potential obstacle feature.
We added 300-m buffer zones on either side of the non-overlapping highway and river
features.
Puma data files were filtered to include only locations that occurred at a 6-hour interval
from the next location. We did not include subadult female 901 in these analyses due to
lack of location points. We used a query to create files of locations for each puma that
occurred in highway, river and residential development buffer zones. We included only
locations on the side of the potential obstacle containing most of the puma’s ranging area,
to determine whether pumas were crossing features from one side to the other. Because
residential development polygons had several sides, we manually removed the small
number of puma locations occurring opposite the long side of the polygon proximate to
the larger portion of a puma’s home range area.
We used the Hawth’s Tools’ “point to polyline tool” to connect all successive points in a
puma’s location file in linear paths. Unique paths were constructed for each set of
consecutive 6-hour interval fixes. For each puma we recorded the number of generated
puma paths that crossed rivers, highways, and residential development. We then
determined the expected frequency of potential obstacle feature crossings for each puma,
based on the animal’s movement data. Hawth’s Tools “calculate animal movement
parameters” tool was used to generate a list of distances (steplength) and turn angles
between all successive 6-hr interval locations in each puma’s GPS collar dataset. We
filtered non-successive location points from these tables. We calculated the likelihood of
feature crossings within 6 hours for each puma location point that occurred in the
highway, river or development buffer, on the side of most of the animal’s home range
area. For each puma, we used Hawth’s Tools’ “conditional point sampling tool”, to
generate 1000 points around each collar location occurring in a potential obstacle buffer
zone. The tool allowed us to base the 1000 generated point locations on sampling from
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