Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data

Predicting kill sites of an apex predator from GPS data in different multiprey systems

Kill rates are a central parameter to assess the impact of predation on prey species. An accurate estimation of kill rates requires a correct identification of kill sites, often achieved by field-checking GPS location clusters (GLCs). However, there are potential sources of error included in kill-site identification, such as failing to detect GLCs that are kill sites, and misclassifying the generated GLCs (e.g., kill for nonkill) that were not field checked. Here, we address these two sources of error using a large GPS dataset of collared Eurasian lynx (Lynx lynx), an apex predator of conservation concern in Europe, in three multiprey systems, with different combinations of wild, semidomestic, and domestic prey. We first used a subsampling approach to investigate how different GPS-fix schedules affected the detection of GLC-indicated kill sites. Then, we evaluated the potential of the random forest algorithm to classify GLCs as nonkills, small prey kills, and ungulate kills. We show that the number of fixes can be reduced from seven to three fixes per night without missing more than 5% of the ungulate kills, in a system composed of wild prey. Reducing the number of fixes per 24 h decreased the probability of detecting GLCs connected with kill sites, particularly those of semidomestic or domestic prey, and small prey. Random forest successfully predicted between 73%-90% of ungulate kills, but failed to classify most small prey in all systems, with sensitivity (true positive rate) lower than 65%. Additionally, removing domestic prey improved the algorithm's overall accuracy. We provide a set of recommendations for studies focusing on kill-site detection that can be considered for other large carnivore species in addition to the Eurasian lynx. We recommend caution when working in systems including domestic prey, as the odds of underestimating kill rates are higher.

Variable strategies to solve risk-reward tradeoffs in carnivore communities

Mesopredator release theory suggests that dominant predators suppress subordinate carnivores and ultimately shape community dynamics, but the assumption that subordinate species are only negatively affected ignores the possibility of facilitation through scavenging. We examined the interplay within a carnivore community consisting of cougars, coyotes, black bears, and bobcats using contemporaneous Global Positioning System telemetry data from 51 individuals; diet analysis from 972 DNA-metabarcoded scats; and data from 128 physical investigations of cougar kill sites, 28 of which were monitored with remote cameras. Resource provisioning from competitively dominant cougars to coyotes through scavenging was so prolific as to be an overwhelming determinant of coyote behavior, space use, and resource acquisition. This was evident via the strong attraction of coyotes to cougar kill sites, frequent scavenging of cougar-killed prey, and coyote diets that nearly matched cougars in the magnitude of ungulate consumption. Yet coyotes were often killed by cougars and used space to minimize encounters, complicating the fitness benefits gained from scavenging. We estimated that 23% (95% CI: 8 to 55%) of the coyote population in our study area was killed by cougars annually, suggesting that coyote interactions with cougars are a complex behavioral game of risk and reward. In contrast, we found no indication that bobcat space use or diet was influenced by cougars. Black bears avoided cougars, but there was no evidence of attraction to cougar kill sites and much lower levels of ungulate consumption and carcass visitation than for coyotes. Interspecific interactions among carnivores are multifaceted, encompassing both suppression and facilitation.

Physiological consequences of consuming low-energy foods: herbivory coincides with a stress response in Yellowstone bears

Meat, fruit, seeds and other high-energy bear foods are often highly localized and briefly available and understanding which factors influence bear consumption of these foods is a common focus of bear conservation and ecology. However, the most common bear foods, graminoids and forbs, are more widespread but of lower quality. We poorly understand how herbage consumption impacts bear physiology, such as endocrine system function that regulates homeostasis and stress responses. Here, we described bear diets with a novel approach, measuring the concentration of chlorophyll in bear scats (faecal chlorophyll) to index the proportion of the recent diet that was composed of leaves from graminoids and forbs. We measured faecal chlorophyll and faecal cortisol in 351 grizzly (Ursus arctos, n = 255) and black bear (Ursus americanus, n = 96) scats from Yellowstone National Park in 2008-2009. We compared models of faecal chlorophyll and faecal cortisol concentrations considering the effects of spatial, dietary, scat and bear-specific factors including species. Faecal chlorophyll levels were the strongest predictor of faecal cortisol in a manner that suggested an endocrine response to a low-energy diet. Both compounds were highest during the spring and early summer months, overlapping the breeding season when higher energy foods were less available. Effects of scat composition, scat weathering, bear age, bear sex, species and other factors that have previously been shown to influence faecal cortisol in bears were not important unless faecal chlorophyll was excluded from models. The top models of faecal chlorophyll suggested grazing was primarily influenced by spatial attributes, with greater grazing closer to recreational trails, implying that elevated cortisol with grazing could be a response to anthropogenic activity. Our results confirm that higher stress hormone concentrations correspond with lower quality diets in bears, particularly grazing, and that faecal chlorophyll shows promise as a metric for studying grazing behaviour and its consequences.

Space-time clusters for early detection of grizzly bear predation

Accurate detection and classification of predation events is important to determine predation and consumption rates by predators. However, obtaining this information for large predators is constrained by the speed at which carcasses disappear and the cost of field data collection. To accurately detect predation events, researchers have used GPS collar technology combined with targeted site visits. However, kill sites are often investigated well after the predation event due to limited data retrieval options on GPS collars (VHF or UHF downloading) and to ensure crew safety when working with large predators. This can lead to missing information from small-prey (including young ungulates) kill sites due to scavenging and general site deterioration (e.g., vegetation growth). We used a space-time permutation scan statistic (STPSS) clustering method (SaTScan) to detect predation events of grizzly bears (Ursus arctos) fitted with satellite transmitting GPS collars. We used generalized linear mixed models to verify predation events and the size of carcasses using spatiotemporal characteristics as predictors. STPSS uses a probability model to compare expected cluster size (space and time) with the observed size. We applied this method retrospectively to data from 2006 to 2007 to compare our method to random GPS site selection. In 2013-2014, we applied our detection method to visit sites one week after their occupation. Both datasets were collected in the same study area. Our approach detected 23 of 27 predation sites verified by visiting 464 random grizzly bear locations in 2006-2007, 187 of which were within space-time clusters and 277 outside. Predation site detection increased by 2.75 times (54 predation events of 335 visited clusters) using 2013-2014 data. Our GLMMs showed that cluster size and duration predicted predation events and carcass size with high sensitivity (0.72 and 0.94, respectively). Coupling GPS satellite technology with clusters using a program based on space-time probability models allows for prompt visits to predation sites. This enables accurate identification of the carcass size and increases fieldwork efficiency in predation studies.

Bears napping nearby: daybed selection by brown bears (Ursus arctos) in a human-dominated landscape

Daybeds are essential for the survival of brown bears (Ursus arctos L., 1758) and may represent a population-limiting resource in human-dominated landscapes. In this study, we demonstrate which land-cover types and bear characteristics affect daybed selection in north-central Slovakia. We used the positional and activity data of 21 bears acquired by GPS–GSM telemetry to identify 3864 daybeds. By use of K-select analysis and linear mixed-effects modelling, we explored how bears chose these places for their daytime resting. The most important drivers for daybed selection were the presence of dense regenerating forests and forest–shrubbery belts in farmland. Bears avoided resting in older forests without suitable undergrowth. Females selected daybeds differently depending on the presence of dependent cubs. During spring – early summer, females with cubs of the year avoided other bears by selecting more rugged terrain. These females also selected daybeds significantly closer to human settlements than adult males, possibly to avoid the risk of infanticide. In late summer – autumn, all bears selected daybeds closer to human settlements than in spring, probably because they were attracted by maize (Zea mays) fields and fruit trees. Many daybeds were located outside protected areas in farmland closer to people, which could increase bear–human conflicts.