Accurate estimates of kill rates remain a key limitation to addressing many predator—prey questions. Past approaches for identifying kill sites of large predators, such as wolves (Canis lupus), have been limited primarily to areas with abundant winter snowfall and have required intensive ground‐tracking or aerial monitoring. More recently, attempts have been made to identify clusters of locations obtained using Global Positioning System (GPS) collars on predators to identify kill sites. However, because decision rules used in determining clusters have not been consistent across studies, results are not necessarily comparable. We illustrate a space—time clustering approach to statistically define clusters of wolf GPS locations that might be wolf kill sites, and we then use binary and multinomial logistic regression to model the probability of a cluster being a non—kill site, kill site of small‐bodied prey species, or kill site of a large‐bodied prey species. We evaluated our approach using field visits of kills and assessed the accuracy of the models using an independent dataset. The cluster‐scan approach identified 42–100% of wolf‐killed prey, and top logistic regression models correctly classified 100% of kills of large‐bodied prey species, but 40% of small‐bodied prey species were classified as nonkills. Although knowledge of prey distribution and vulnerability may help refine this approach, identifying small‐bodied prey species will likely remain problematic without intensive field efforts. We recommend that our approach be utilized with the understanding that variation in prey body size and handling time by wolves will likely have implications for the success of both the cluster scan and logistic regression components of the technique. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):798–807; 2008)

Quantifying kill rates and sources of variation in kill rates remains an important challenge in linking predators to their prey. We address current approaches to using global positioning system (GPS)-based movement data for quantifying key predation components of large carnivores. We review approaches to identify kill sites from GPS movement data as a means to estimate kill rates and address advantages of using GPS-based data over past approaches. Despite considerable progress, modelling the probability that a cluster of GPS points is a kill site is no substitute for field visits, but can guide our field efforts. Once kill sites are identified, time spent at a kill site (handling time) and time between kills (killing time) can be determined. We show how statistical models can be used to investigate the influence of factors such as animal characteristics (e.g. age, sex, group size) and landscape features on either handling time or killing efficiency. If we know the prey densities along paths to a kill, we can quantify the 'attack success' parameter in functional response models directly. Problems remain in incorporating the behavioural complexity derived from GPS movement paths into functional response models, particularly in multi-prey systems, but we believe that exploring the details of GPS movement data has put us on the right path.

Wolves ( Canis lupus L., 1758) are subject to liberal public harvests throughout most of their range in North America, yet detailed information on populations where sport harvest is the primary source of mortality are limited. We studied a harvested wolf population in west-central Alberta from 2003 to 2008. Demographic data were collected from visits to den sites, 84 collared wolves from 19 packs, and a harvest monitoring program that augmented mandatory reporting for registered traplines. Annual harvest rate of wolves was 0.34, with harvest on registered traplines (0.22 ± 0.03) being twice that of hunters (0.12 ± 0.04). Most wolves harvested (71%) were pre-reproductive. Probability of a pack breeding was 0.83 ± 0.01, litter size averaged 5.6 ±1.4, and these rates and stability of home ranges were unaffected by the number of wolves harvested. Natural mortality (0.04 ± 0.03) and dispersal rates (0.25 ± 0.04) were lower than reported for wolf populations in protected areas. Reproductive rates balanced total wolf mortality, indicating harvest was likely sustainable. We suggest that a high proportion of juveniles harvested and the spatial structure of the registered trapline system contributed to the sustainability of harvests.

In Alberta, Canada, number of cougar (Puma concolor) mortalities caused by humans has increased rapidly over the past 2 decades. Management agencies sometimes use human‐caused mortalities as an index of cougar population trend, which would indicate an increasing cougar population in Alberta, but mortalities may be decoupled from cougar numbers. Some authors suggest that higher human‐caused cougar mortalities (primarily due to sport hunting) are causing cougar populations in North America to decline. We used the distribution of human‐caused cougar mortalities in Alberta to evaluate change in cougar populations during 1991–2010, a period over which human‐caused cougar mortality increased rapidly. We provide evidence that cougars have expanded their range in northern and eastern Alberta in tandem with increasing sport hunting and other sources of human‐caused mortality. © 2013 The Wildlife Society.

Although spatial heterogeneity of prey and landscapes are known to contribute to variation around predator-prey functional response models, few studies have quantified these effects. We illustrate a new approach using data from winter movement paths of GPS-collared wolves in the Rocky Mountains of Canada and time-to-event models with competing risks for measuring the effect of prey and landscape characteristics on the time-to-kill, which is the reciprocal of attack rate (aN) in a Holling's functional response. We evaluated 13 a priori models representing hypothesized mechanisms influencing attack rates in a heterogeneous landscape with two prey types. Models ranged from variants on Holling's disc equation, including search rate and prey density, to a full model including prey density and patchiness, search rates, satiation, and landscape features, which were measured along the wolf 's movement path. Movement rates of wolves while searching explained more of the variation in time-to-kill than prey densities. Wolves did not compensate for low prey density by increasing movement rates and there was little evidence that spatial aggregation of prey influenced attack rates in this multiprey system. The top model for predicting time-to-kill included only search rate and landscape features. Wolves killed prey more quickly in flat terrain, likely due to increased vulnerability from accumulated snow, whereas attack rates were lower when wolves hunted near human-made features presumably due to human disturbance. Understanding the sources of variation in attack rates provides refinements to functional response models that can lead to more effective predator-prey management in human-dominated landscapes.