GEOG 566

         Advanced spatial statistics and GIScience

June 11, 2017

Final Project: evaluating interspecific competition between wolves and cougar in northeast Oregon

Filed under: Final Project @ 5:48 pm

Background & Research Question

The research I am conducting is focused on understanding and quantifying the competitive interactions between two apex predators, wolves and cougar. Populations of large carnivores have been expanding across portions of their historical range in North America, and sympatric wolves (Canis lupus) and cougars (Puma concolor) share habitat, home ranges, and prey resources (Kunkel et al. 1999, Husseman et al. 2003, Ruth 2004), suggesting these coexisting predators may be subject to the effects of interspecific competition (interference or exploitative). Interspecific competition among carnivores can affect the spatial distribution, demography, and population dynamics of the weaker predator (Lawton and Hassell 1981, Tilman 1986), but demonstrating asymmetric competition affects through quantified measures or experiments has remained difficult for highly mobile terrestrial mammals like large carnivores. Generally, it is expected that cougar are the subordinate competitor in wolf-cougar interactions, but the frequency and strength of agonistic interactions can be system specific and will determine the overall influence either predator has on the population dynamics of the community (Kortello et al. 2007, Atwood et al. 2009, Creel et al. 2001). My goal is to assess the influence wolf recolonization in northeast Oregon has had on cougar spatial dynamics. For this course, I plan to focus on two analyses associated with questions about wolf influence on the distribution of sites where cougar acquire food (prey) resources and the degree to which wolves influence cougar spatial dynamics. More specifically, I hope to address:


How has wolf recolonization and presence influenced the spatial distribution of cougar predation patterns?



The data I used to address my question comes from field data collection in cooperation with the Oregon Department of Fish and Wildlife and available nation-wide dataset GIS coverages. Location points from global positioning system (GPS) collars deployed on a sample of wolves and cougar were used to identify potential predation sites and the study area was a 1, 992km2 game management unit (Mt. Emily WMU) in northeast Oregon (Figure 1).


Cougar kill sites – identified through cluster analysis of GPS location data (see Anderson and Lindsey 2003 and Knopff et al. 2010) and verified through field investigation. This process was done before (2009 – 2012) and after (2014 – 2016) wolves recolonized the area and produced a dataset of kill site locations for cougar (npre-wolf = 1,213; npost-wolf = 541).


Wolf kill sites – identified through cluster analysis of GPS location data (see Sand et al. 2008, Knopff et al. 2010, and DeCesare et al. 2012) and verified through field investigation. This process produced a dataset of kill site locations for wolves (n = 158).


Distance to wolf kill sites and activity– Euclidean distance (m) to nearest wolf kill site; or Euclidean distance (m) to nearest kernel density derived isopleth probability contour edges (25%, 50%, 75%, 95%).


Canopy cover – Continuous percent (%) canopy enclosure where file pixel values range from 0 to 100 percent, with each individual value representing the area or proportion of that 30m cell covered by tree canopy. Available from National Land Cover Database (NLCD


Slope – average change in vertical elevation per unit horizontal distance (i.e. steepness), calculated as degree from 30-m DEM using spatial analyst. (Digital elevation model (DEM) datasets available from USGS National Elevation Dataset


Figure 1. Location of the Mt. Emily Wildlife Management Unit in northeast Oregon and global positioning system (GPS) locations for cougars monitored to determine kill site distribution and spatial dynamics pre- (2009-2012) and post-wolf recolonization (2014-2016).


Based on competition theory and emerging evidence on wolf-cougar interactions in other systems (Alexander et al. 2006, Kortello et al. 2007, Atwood et al. 2009), I expect cougar and wolves in northeastern Oregon to exhibit resource partitioning in foraging niche (resource partitioning hypothesis). Representative evidence for resource partitioning between carnivores could be demonstrated if cougar kill sites occur on the landscape in areas disparate from wolf kill sites. I would also expect cougar to alter their movement and space use relative to pre-wolf recolonization patterns (active avoidance hypothesis). Further, I expect the presence of wolves to affect cougar movement patterns and spatial distribution, which may be evident through a shift in the distribution and space used by cougar between pre- and post-wolf recolonization periods (niche shift [competitive exclusion] hypothesis).


Hypothesis: Niche shift (competitive exclusion) – cougar may demonstrate altered foraging niche (kill sites will begin to occur further from wolf activity centers) and overall distributional shifts between time periods with and without wolf presence.


Analysis Approaches & Methods

I used ArcMap to visualize data, evaluate cougar kill site distribution for differences between pre- and post-wolf time periods, and extract site-specific features related to kill sites. As part of my approach to answer my spatial problem, I also made use of and explored other statistical programs outside Arc, like Geospatial Modelling Environment (GME version 7.2.0, Beyer 2012) and program R to summarize data (dplyr, MASS, car packages), model relationships (lme4 package), and explore spatial relationships (adehabitatHR, ks, stam packages). The primary tool and approaches I explored were:


Ripley’s K – this tool determines spatial clustering or dispersion of features (or values associated with features) over a range of distances and allowed me to statistically measure the overall patterns in kill site data (i.e. distinguish departure from expected random distributions) for both wolves and cougar. I used the ‘multi-distance spatial cluster analysis’ tool in ArcGIS to generate observed/expected K-values, a K-function graph, and confidence envelopes for values. Larger than expected K-values indicate the distribution is more clustered than expected based on a random distribution, and lower than expected K-values indicate the distribution is more dispersed than expected.

Kernel density – This tool calculates the magnitude per unit area for point or polyline features and allowed me to visualize the frequency and distribution of kill sites (points). I used the kde tool in the Geospatial Modeling Environment (GME) to calculate kernel density estimates (KDE), or utilization distributions, for wolf kill sites and for cougar kill sites in each time period. I created density estimates using the PLUGIN bandwidth and a 30m resolution cell size. Multiple bandwidth estimators are available as well as input options for scaling, weighted fields, data subset and/or edge inflation. The sample of kill sites (and sample of cougar) in the pre-wolf time period was higher (45-48%) than the post-wolf sample of kill sites (and cougar), therefore it was necessary to standardize the two cougar kill time period KDE raster datasets so the densities relative to catch (in this case kill events) per sample (cougar) were comparable. I used the raster calculator in ArcGIS to do this by dividing each period kill site KDE raster by that periods’ respective kills/per cougar ‘effort’.

Isopleth probability surfaces –This tool allowed me to relate the proportion of post-wolf cougar kill sites (points) to both the pre-wolf cougar kill site distribution (density raster) and wolf kill site distribution (density raster) using the probability contours (polygon feature) of each density feature. The resulting polygons represent quantiles of interest (programmable) expressed as the proportion of the density surface volume. I specified isopleths for the 25th, 50th, 75th, 95th, and 99th ; 25th and 50th % isopleths are generally thought of as ‘core’ use areas and represent the highest density of the feature of interest, whereby ‘use’ decreases as you move out to 99% contours. I used GME and the ‘isopleth’ command to create polygon probability surfaces. I also used the ‘addarea’ command to calculate and add information to the isopleth shapefiles for each polygons’ area and perimeter, as well as the ‘countpntsinpolys’ command to add a column with the counts of post-wolf cougar kills in each pre-wolf cougar kill or wolf kill isopleth polygon. I also used ‘select by attribute’ and ‘intersect’ features in ArcGIS to subset out summer and winter kill site datasets to explore density feature overlap and seasonal influences on kill distribution.

Latent selection differences – this tool uses a logistic regression framework for direct comparison of selection between two groups of interest to contrast the difference through quantifiable measurements of relationship strengths (Czetwertynski 2007, Latham et al. 2013). This analysis allowed the interpretation of changes in site-specific habitat characteristics as the relative difference in selection between two binary variables, not the selection or use of a given habitat. This regression was performed: 1) between wolves (coded as 1) and cougar (coded as 0) to evaluate the relative difference in selection at kill sites, and 2) between cougar pre- (coded as 0) and post-wolf (coded as 1) to evaluate the relative selection differences in cougar kill sites as it relates periods with and without wolves. Exponentiated coefficients indicate per-unit increase/decrease of habitat or distance to a given variable such that the effect is calculated as [1-exp(coefx)] * 100, and interpreted as the relative risk of selection of variable x by group 1 compared with group 0 increased/decreased by x%.



Ripley’s K

Cougar (pre- and post-wolf) and wolf kill sites showed significant clustering relative to expected random distribution (Figure 2, Table 1). I explored multiple distances and the pattern held for all three kill datasets down to less than 10m (our hand-held GPS units had error ratings from 3-7m when recording kill site coordinates in the field). While this analysis provided irrefutable evidence kill sites for both carnivores exhibited spatial clustering, that is not surprising for animals that exhibit strong territoriality and prey on potentially clumped food resources (herds of ungulates). Thus, the results of this analysis did not help to answer my overarching question.


Figure 2. Example of associated L (d) function for pre-wolf cougar kill sites. Red line is observed spatial point processes (SPP), blue line is expected SPP, and gray dashed lines are confidence envelopes calculated from 99 simulated permutation.


Table 1. Sample of observed and expected K-values for pre-wolf cougar kill sites generated from Ripley’s K analysis.


Kernel density

Visual examination of the cougar pre- and post-wolf kill density rasters (Figure 3) did show what appeared to be a shift in the spatial distribution of where the highest density of cougar kill sites occurred, shifting from the northeast to the south part of the unit. There were several factors, besides the presence of wolves, which could have produced the observed shift in kill density, such as: 1) catch-per-unit effort issues (i.e. larger sample sizes of cougars (and kill sites) in pre-wolf data set), 2) time effects from seasonal distribution shifts (i.e. prey migration patterns). The visual interpretation was useful in guiding further aspects of the analysis that need to be accounted for in my investigation of my spatial problem. The standardized kill density rasters demonstrated that part of the shift observed was related to catch-per-unit effort influences, but a shift between the two highest density areas of kills was still evident from pre- to post-wolf time periods. This suggests other variables, like time effects from seasonal prey distribution changes or wolf presence, could also be factors influencing the distribution of kill density. Standardization of the two kill density rasters improved visual interpretation of the spatial patterns and accounted for one of the other factors (catch-per-unit effort) that might mask distributional shifts related to wolf presence (first two panels of Figure 4). However, this method allowed only an implicit understanding of space-time concepts where I could describe distributional patterns observed through visual interpretation, but the method lacked any inferential measures of significance to quantify the shifts and formally relate the patterns of cougar predation across time.


Figure 3. Comparison of of pre- and post-wolf cougar kill site kernel density estimates (KDE) before and after standardization for catch-per-unit effort (kills/cougar). Dark red indicates high densities (frequency) of kill sites progressing to blue, indicating low (or no) density of kill sites.


Isopleth probability surfaces

Spatial changes in probability – Using the probability contours as a ‘prior’ distribution of kill sites offered a more robust and meaningful measure to quantify the shift in cougar kill distribution (Figure 4). The shift was evident from the proportional changes in post-wolf cougar kills within the pre-wolf cougar kill probability surface. For example, only 28.5% of all post-wolf cougar kills were within the 50% pre-wolf cougar kill probability contour (Table 2). Even if I exclude the kills outside the study area boundary the proportion of kills in each probability contour were 5-15% lower than would be expected based on the pre-wolf kill site distribution. However, the mechanism behind the shift could still be related to factors other than wolf presence like seasonal shifts in prey distribution or density.

Figure 4. Comparison of pre- and post-wolf cougar kill site kernel density estimates (KDE) isopleth probability contours showing 25th, 50th, 75th, 95th, and 99th quartiles. The isopleth contours for pre-wolf cougar kill site distribution are fit with post-wolf cougar kill point locations to demonstrate posterior distributional shift.


Table 2. Pre-wolf cougar kill site probability contour attribute table. Isopleths represent the 25th, 50th, 75th, 95th, and 99th quartiles. ‘Out’ refers to areas outside the probability contour surface. Number of kills (No. Kills) is the number of post-wolf cougar kill sites, % is the proportion of all kills within each polygon ‘donut’, and % Kills is the cumulative proportion of all post-wolf cougar kills within each quartile class.

Wolf activity & seasonal influences – Evaluation of season-specific cougar kills relative to wolf kill activity centers added further evidence toward a spatial shift in the distribution of cougar kills between time periods with and without wolves (Figure 5). The magnitude of the repulsive effect appeared stronger in winter than summer, evident in the uniformly decreased proportions of cougar kills per probability contour of wolf activity, and increased proportion of kills outside any wolf activity between time periods with and without wolves (Table 3). The relationship appeared opposite in summer, with higher proportions of cougar kills observed in the post-wolf period than expected in all but core (25%) and outside (no wolf activity) areas (Table 4). Accounting for seasonal shifts in kill density improved visual interpretation of spatial patterns and reduced another factor that might mask influences of wolf activity. However, the observed shift in winter could still be due to shifts in prey populations as a behaviorally mediated prey response to wolves (i.e. the shift is related to prey responding to wolves, and cougar indirectly shifting distribution to follow prey). The shift could also be related to differences in prey species-specific distributions, as cougar prey predominantly on deer, but show seasonal selection of neonate elk calves in the summer, and wolves prey predominately on elk (i.e. the shift is related to differences in prey species use of habitat). Unfortunately, there isn’t data on prey species at a resolution that will help further tease apart these interactions relative to shifts in prey distributions or density.


Figure 5. Visual comparison of seasonal wolf activity based on kill site density probability contours and the distribution of cougar kill sites across time periods with and without wolves.


Table 3. Winter wolf kill site probability contour attribute table. Isopleths represent the 25th, 50th, 75th, 95th, and 99th quartiles. ‘Out’ refers to areas outside the probability contour surface. Number of kills (No. Kills) is the number of cougar kill sites, and % Kills is the cumulative proportion of all cougar kills within each quartile class.


Table 4. Summer wolf kill site probability contour attribute table. Isopleths show the 25th, 50th, 75th, 95th, and 99th quartiles for pre-wolf cougar kill density and represent the ‘prior’ distribution of cougar kills. ‘Out’ refers to areas outside the probability contour surface. Number of kills (No. Kills) is the number of cougar kill sites, and % Kills is the cumulative proportion of all cougar kills within each quartile class.

Species kill distribution overlap – Wolves consistently allowed for more of their core and total kill distributions to overlap with cougar than cougar allowed their kill range to overlap with wolves (Table 5). This was not surprising, but yielded a helpful visual and metric to compare distribution overlap evident through visual examination (Figure 6).


Figure 6. Seasonal overlap in core (50%) and 95% kill KDE probability contour between wolves (red) and cougar (blue). Dark blue and red lines represent the 50% (core) probability contours while light blue and red lines represent the 95% (total) probability contours for kill distributions.


Table 5. Species-specific proportional overlap between wolf and cougar core (50%) and 95% kill KDE probability contours. Calculated as overlap area (km2) / cougar or wolf 50% or 95% area (km2) * 100 and related to each species core and total kill area usage.


Latent Selection Differences

Cougar pre-post wolf – Relative habitat use at cougar kill sites after wolf recolonization showed little difference from pre-wolf cougar habitat use in the variables evaluated (Table 6). Cougar selected for denser canopy cover (increased by 1.6%) and steeper slopes (increased 2%) at kill after wolves recolonized the study area, but showed little difference in selection for higher elevations or further distances to wolf kill sites (< 1% increase). Conditional density plots for the variables (Figure 7) further demonstrate the ambiguity of the results relative to selection based on the explanatory variable. They also show how the conditional distribution of the dependent variable (pre/post-wolf cougar kill sites) are better determined in the ‘pre-wolf’ time period based on the explanatory variable due to the higher sample sizes of kill sites in that time period.


Wolf-cougar – Relative habitat use at wolf kill sites showed some small differences compared to cougar habitat use at kill sites in the variables evaluated (Table 7). Wolves selected for less dense canopy cover (decreased by 3.6%) and steeper slopes (increased 4.7%) at kill after compared to cougar, but showed little difference in selection of elevations or distances to wolf kill sites (< 1% increase in elevation and decrease in distance to wolf kill sites). Conditional density plots for the variables (Figure 8) further demonstrate the ambiguity of the results relative to selection based on most of the explanatory variable. The plot for slope did show a distinct transition around 55° between cougar and wolf kill sites. However, similar to the pre- and post-wolf cougar analysis, in general the plots show how the conditional distribution of the dependent variable (wolf or cougar kill sites) are likely better determined for cougar based on the explanatory variable due to the higher sample sizes of cougar kill sites.


Table 6. Latent selection differences between cougar kill sites before and after wolf recolonization. Logistic regression coefficient (β) estimates, exponentiated coefficients and associated 95% confidence intervals, and relative selection.


Figure 7. Conditional density plots for cougar pre- and post-wolf kill sites.


Table 7. Latent selection differences between wolf and cougar kill sites. Logistic regression coefficient (β) estimates, exponentiated coefficients and associated 95% confidence intervals, and relative selection.


Figure 8. Conditional density plots for wolf and cougar (post-wolf) kill sites.


The results from the analyses performed for this class begin to improve understanding the spatial aspects of the relationships between these two carnivores. Predation is recognized as a major factor influencing ungulate population dynamics. Predation effects on prey populations are tied to the complexities of intraguild dynamics, as the predation risk for shared prey can vary relative to the nature of predator-predator interactions as well as based on the behavioral responses of prey to predators (Atwood et al. 2009). In addition to addressing key ecological questions regarding predator-predator interactions, results from this research will provide information on the effect of wolves on cougar populations, and potential effects of this expanded predator system on elk and mule deer populations. Knowledge gained from this study will be critical to effective management and conservation of cougar in Oregon and could be useful to other parts of western North America facing similar changes in community dynamics as wolves continue to expand their range.


Learning Outcomes

Software – I gained increased comfort and familiarity with several programs I have used in the past (GME, R, ArcGIS), and began exploring various other packages in R (stam, ks, adhabitatHR), other programs (QGIS) and programming languages (Python), as well as new tools in ArcGIS to compare and contrast results and expand some ‘next steps’ in my analyses of wolf-cougar interactions.


Spatial statistics – Through exploration of the spatial analyst tool box in ArcGIS I learned a great deal about spatial processes and analysis tools as they relate to different data sources and overarching questions. I found that my event (presence only) data did not lend itself to many of the tools well, which forced me to think about my data structure, question(s) of interest, and how I could (or could not) use certain tools to analyze data and answer my spatial questions about wolf-cougar interactions. It was also extremely valuable to have classmates exploring both similar and dissimilar analyses that I could learn from and apply to my own data explorations.


Peer/Instructor Comments – Both my peers in class and Julia were helpful in guiding the progression of these analyses. Some, like investigating prey mediated spatial responses, I cannot address with my data. Feedback about investigating kernel range overlap sparked the species kill overlap analysis above and a next step will be to conduct a similar analysis comparing kill distribution overlap for cougar between time periods with and without wolves.



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