Spatio‐temporal co‐occurrence of cougars (<i>Felis concolor)</i>, wolves (<i>Canis lupus</i>) and their prey during winter: a comparison of two analytical methods

Abstract Aims To examine the spatio‐temporal co‐occurrence of cougars (Felis concolor), wolves (Canis lupus), and their prey during winter using monthly (November–March) species–environment relationship models. In addition, to contrast predictions across two methods: logistic regression and Geographic Information System (GIS) image correlation. Location The eastern front ranges of the Canadian Rocky Mountains (south‐central Alberta), approximately 100 km west of Calgary, including portions of Banff National Park and Kananaskis Country. Methods Snow‐tracking data were collected simultaneously for cougars, wolves, elk (Cervus elaphus), and deer (Odocoileus virginianus and O. hemionus) between November and March, 1997–2000. Track data were synthesized in a GIS. Logistic regression and Akaike's information criterion (AIC) were used to select optimal environmental relationship models for each species. We first examined co‐occurrence by iteratively using each species as a dependent variable (presence/absence) in a logistic regression analysis and using all other species track‐density estimates as independent variables. We built predictive surfaces in a GIS using the exponent form of the logistic regression models, and assessed model accuracy with a receiver operating characteristic curve. We then re‐examined co‐occurrence using pairwise correlations of species probability surfaces by month. The correlation results were compared with logistic regression results to illuminate mechanisms of co‐occurrence and to investigate predictive consistency across the two methods. Results Cougars showed a trend in distribution from higher elevation and less rugged terrain in December, to lower elevation and more rugged terrain in March. This trend differed from that for wolves, which showed a more stable affinity for low elevation and less rugged valley bottoms across all months. The logistic regression models indicated variable positive and negative associations of cougars with wolves by month, and changes in prey associations over time. Notably, there was a shift in co‐occurrence for both predators from elk to deer in March. We found high predictive accuracy for all probability surfaces, except for the month of January. Our image comparison showed that spatial co‐occurrence amongst all species increased over winter, except that wolves and cougars were negatively correlated in February. Combining the results of each approach we found that cougars and wolves converged spatially over winter at the landscape scale (i.e. the valley), while showing more discrete use of that space over time and by habitat attributes (e.g. forest cover, topographic complexity, and prey track density). Main conclusions In the Rocky Mountains, the spatial distributions of cougars and wolves converged into the valley floor as winter progressed. Cougars were distinct from wolves and prey in the intensity of this shift. We determined that a comparison of predictive surfaces alone fails to explain species co‐occurrence. The surfaces must be coupled with investigation of respective species–environment models to account for temporal changes in associations. We suggest that the two approaches represent different ecological scales: image comparison may be best for landscape‐ (valley) level analysis, while logistic regression is best for site‐level analysis. Ultimately, both approaches were critical to our analysis. Finally, the variability observed over time suggested that annual and seasonal models may obscure important ecological patterns and processes, especially for cougars.

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