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

the steplength and turnangle distributions for the given puma. We, thus, created
predictions of the puma’s expected next movement based on its own movement data.
We created a large (5000 m) buffer to display areas opposite the potential obstacle from
the puma location points analyzed. The “intersect point tool” was used to generate a
count of the number of newly generated points that fell within this zone, indicating an
expected crossing of the obstacle feature. We calculated the percentage of all generated
points that lay across potential obstacles to determine the expected probability of each
puma crossing each feature. Paired Student’s t-tests were used in JMP 5 ® , to compare the
percent of expected crossings to the percent of observed crossings of each highway, river,
and residential development for all pumas, to determine whether pumas avoided crossing
these features. We used a query to calculate the percentage of puma paths crossing a
highway that occurred within a 300-m buffer area of a creek or river that passed beneath
the roadway, to investigate whether pumas may have crossed using these underpasses.
We also noted whether we saw puma sign in these riparian underpasses during tracking.
Parcel size use
For developed-zone pumas, we asked whether the animals preferentially used or avoided
property parcel size classes in their home range areas representative of various types of
rural development including ranches, ranchettes, and suburban style housing
development. For each developed-zone puma’s GPS collar dataset, we used Hawth’s
“intersect point tool” in ArcGIS 9.2 ® to generate a data field displaying the areas (acres)
of all property parcels containing a puma location point. Because smaller parcel size
classes tended to be located in groups of like-sized parcels, the small spatial error
associated with GPS collar locations was not expected to cause an underestimate of puma
use of small parcel size classes. We calculated the percentage of each puma’s locations
occurring in each of 6 parcel size classes, chosen for relevance to development planning
designations: 0.10 to 5.00 acres (0.04 to 2.02 ha), 5.01 to 10.00 acres (2.03 to 4.05 ha),
10.01 to 20.00 acres (4.05 to 8.09 ha), 20.01 to 40.00 acres (8.10 ha to 16.19 ha), 40.01 to
100.00 acres (16.19 to 40.47 ha), and 100 + acres (40.47 + ha).
We next estimated the spatial coverage of each parcel size class within each puma’s
home range area. We used Hawth’s Tools’ “generate random points” function to create
random points within the polygons of each puma’s home range area, equal to the number
of high-quality location points obtained for each puma. We used “home range areas”,
which included a 1-km buffer around each animal’s 95% kernel home range, to include
areas that pumas might avoid, which we wished to identify. For each home range area,
we documented the property parcel sizes associated with each randomly generated point
using the “intersect point tool”, and calculated the percentages of random points falling
within each parcel size class. Paired Student’s t-tests were used in JMP 5 ® to test for
differences between use of each parcel size class by pumas and the spatial coverage of
those parcel classes in home range areas.
We then asked whether puma use of parcel classes differed between day and nighttime.
We designated all location points occurring between 09:00 hrs and 17:00 hrs PST as
Proceedings of the Ninth Mountain Lion Workshop
131