Spatiotemporal heterogeneity in prey abundance and vulnerability shapes the foraging tactics of an omnivore.

Prey abundance and prey vulnerability vary across space and time, but we know little about how they mediate predator-prey interactions and predator foraging tactics. To evaluate the interplay between prey abundance, prey vulnerability and predator space use, we examined patterns of black bear (Ursus americanus) predation of caribou (Rangifer tarandus) neonates in Newfoundland, Canada using data from 317 collared individuals (9 bears, 34 adult female caribou, 274 caribou calves). During the caribou calving season, we predicted that landscape features would influence calf vulnerability to bear predation, and that bears would actively hunt calves by selecting areas associated with increased calf vulnerability. Further, we hypothesized that bears would dynamically adjust their foraging tactics in response to spatiotemporal changes in calf abundance and vulnerability (collectively, calf availability). Accordingly, we expected bears to actively hunt calves when they were most abundant and vulnerable, but switch to foraging on other resources as calf availability declined. As predicted, landscape heterogeneity influenced risk of mortality, and bears displayed the strongest selection for areas where they were most likely to kill calves, which suggested they were actively hunting caribou. Initially, the per-capita rate at which bears killed calves followed a type-I functional response, but as the calving season progressed and calf vulnerability declined, kill rates dissociated from calf abundance. In support of our hypothesis, bears adjusted their foraging tactics when they were less efficient at catching calves, highlighting the influence that predation phenology may have on predator space use. Contrary to our expectations, however, bears appeared to continue to hunt caribou as calf availability declined, but switched from a tactic of selecting areas of increased calf vulnerability to a tactic that maximized encounter rates with calves. Our results reveal that generalist predators can dynamically adjust their foraging tactics over short time-scales in response to changing prey abundance and vulnerability. Further, they demonstrate the utility of integrating temporal dynamics of prey availability into investigations of predator-prey interactions, and move towards a mechanistic understanding of the dynamic foraging tactics of a large omnivore.

Integrative use of spatial, genetic, and demographic analyses for investigating genetic connectivity between migratory, montane, and sedentary caribou herds.

Genetic differentiation is generally assumed to be low in highly mobile species, but this simplistic view may obscure the complex conditions and mechanisms allowing genetic exchanges between specific populations. Here, we combined data from satellite-tracked migratory caribou (Rangifer tarandus), microsatellite markers, and demographic simulations to investigate gene flow mechanisms between seven caribou herds of eastern Canada. Our study included one montane, two migratory, and four sedentary herds. Satellite-tracking data indicated possibilities of high gene flow between migratory herds: overlap of their rutting ranges averaged 10% across years and 9.4% of females switched calving sites at least once in their lifetime. Some migratory individuals moved into the range of the sedentary herds, suggesting possibilities of gene flow between these herds. Genetic differentiation between herds was weak but significant (FST=0.015): migratory and montane herds were not significantly distinct (FST all9) than vice-versa (4Nm all<5), which suggests migratory herds had a demographic impact on sedentary herds. Demographic simulations showed that an effective immigration rate of 0.0005 was sufficient to obtain the empirical FST of 0.015, while a null immigration rate increased the simulated FST to >0.6. In conclusion, the weak genetic differentiation between herds cannot be obtained without some genetic exchanges among herds, as demonstrated by genetic and spatial data.