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. 2020 Jan 30;10(3):1678–1691. doi: 10.1002/ece3.6028

Table 1.

Candidate model sets to test the relative effect of interspecific interactions on predator occurrences

Species Hypothesis—Predator occurrence best explained by Predictor variables
Mesocarnivore 1 Habitat Significant forest cover variables from step 1
Anthropogenic features Linear density (LD) + Habitat
Seasonality Snow + Habitat
Apex predator Wolf + Habitat
Wolf + Snow + Habitat
Wolf × Snow + Habitat
Wolf + LD + Habitat
Wolf × LD + Habitat
Intraguild competition Mesocarnivore2 + Habitat
Mesocarnivore2 + Snow + Habitat
Mesocarnivore2 × Snow + Habitat
Mesocarnivore2 + LD + Habitat
Mesocarnivore2 × LD + Habitat
Predation opportunities Prey + Habitat
Prey + Snow + Habitat
Prey × Snow + Habitat
Prey + LD + Habitat
Prey × LD + Habitat
Black bear Habitat Significant forest cover variables from step 1
Anthropogenic features LD + Habitat
Apex predator Wolf + Habitat
Wolf + LD + Habitat
Wolf × LD + Habitat
Predation opportunities Prey + Habitat
Prey + LD + Habitat
Prey × LD + Habitat

Models were negative binomial GLMs at the spatial‐only scale, and binomial GLMMs at the two spatiotemporal scales. Each model set corresponds to a hypothesized interspecific interaction. We tested models with co‐occurring species as a predictor variable against three base models describing environmental effects. Candidate model sets for mesocarnivores (coyote and lynx) are identical, with mesocarnivore 1 describing the responding predator and mesocarnivore 2 describing the co‐occurring intraguild competitor (e.g., when mesocarnivore 1 is coyote, mesocarnivore 2 is lynx and vice versa). At the spatial‐only scale of analysis, we excluded season models for all species because the response variable aggregated detections across the entire survey period.