Objective 2: determining whether use of parturition habitat by searching predators tracked the phenology of the birth pulse
One strategy for predators to maximize encounters with neonates is to shift habitat use to areas with a high probability of selection by parturient female ungulates. To this end, we constructed a resource selection function (RSF; Manly et al. (2007)) using GPS locations of adult female elk in the 7 days immediately following a parturition event with landscape and vegetative characteristics hypothesized to influence location of parturition sites in our area based on previous research (Johnson et al. 2000, Stewart et al. 2002, Long, Rachlow & Kie 2008). The resource selection function for elk parturition habitat took the form\(w(x)\ \sim\ exp(\beta_{1}\times\text{canopy\ cover}\ +\ \beta_{2}\times ln(\text{distance\ to\ open\ road})+\ \beta_{3}\times ln(\text{distance\ to\ perennial\ water\ source})\ +\ \beta_{4}\times\text{ruggedness}\ +\ \beta_{5}\times\text{shrub\ cover}\ +\ \beta_{6}\times\text{forb\ cover}\ +\ \beta_{7}\times\text{slope}\ +\ \beta_{8}\times\text{aspect}\ +\ \beta_{9}\times\text{elevation}\ +\ \beta_{10}\times\text{potential\ vegetation\ type})\), where w (x ) is the relative probability of selection for adult female elk during the first 7 days following parturition. Percent shrub and forb cover variables were derived from LEMMA’s generalized nearest neighbor model (Ohmann et al. 2011); slope, aspect, and elevation were drawn from a digital elevation model; and other covariates are as described above. We paired each used elk location (coded as 1) with 10 randomly generated locations representing locations available to but not used by elk (coded as 0) and used a generalized linear model with a binomial error distribution and logit link to model the relative probability of selection. Given that our objective was to create a spatially-explicit map of the study area predicting the areas with a high probability of selection by parturient ungulates and not make inference on the specific resources that they selected for, we did not conduct model selection and instead used the global model for predictions. Each 30 x 30 m pixel in the resulting predictive map projected onto the study area represented w (x ), or the RSF score for parturition habitat. We then calculated the mean value of the RSF scores across all GPS locations for each carnivore species that exhibited active search behavior (determined from the previous analysis) on a weekly basis from 15 April to 31 to determine whether predators shifted habitat use toward places likely to be inhabited by ungulate neonates. We predicted that predator use of habitat used for parturition by elk would decline later in the season if predators were attempting to maximize encounters with neonatal elk by shifting habitat use. Weekly mean RSF scores represented each predator’s use of predicted parturition habitat with higher values indicating higher use of parturition habitat, where “use” is a measure of the investment in a set of resource units by an animal during a sampling period (Lele et al. 2013).
We used the weekly average elk parturition RSF score at carnivore GPS relocations as the response variable in a generalized linear mixed model and used Julian week (i.e. the number of weeks elapsing since January 1) as a predictor to assess how carnivore use of elk parturition habitat changed throughout the season. We included a random intercept for animal ID to control for differences in the mean elk parturition RSF score available within individual predator home ranges. We fit models with both linear and quadratic effects of Julian week as predictors. We hypothesized that if a quadratic effect of Julian week on use of parturition habitat tracked the phenology of the birth pulse, it would be indicative of an effort of predators to alter habitat use to maximize encounters with neonates immediately following parturition. Alternatively, lack of a quadratic relationship between predator use of parturition habitat and Julian week could indicate the predator does not spend time in areas where neonates are likely to be immediately following parturition, or that the predator may actively hunt older, more mobile neonates or other age classes of prey. We used likelihood ratio tests to compare whether the quadratic effect of Julian week was supported over a linear effect.