Wolf (Canis lupus) impacts on prey are a central post-wolf-reintroduction issue in the greater Yellowstone ecosystem (GYE) of the western United States. Further, estimates of wolf kill rates, used to ...understand these impacts, can be biased due to unrecovered kills. In Yellowstone National Park (YNP), visibility of wolves allowed us to combine independent aerial and ground observations and use a double-count method to estimate the probability of recovering kills. We consequently used these data to adjust estimates of wolf kill rates. We conducted monitoring annually from 1995 to 2000 during 2 30-day periods in early (15 Nov–14 Dec) and late winter (Mar). Estimated recovery rates of wolf kills for ground and aerial crews were 50% and 45%, respectively, although we determined that this varied by location (distance from road) and possibly age (calf or adult) of the kill. The estimated combined recovery rate was 73%. Estimated wolf kill rates were higher in late winter (2.2 kills/wolf/month) compared to early winter (1.6 kills/wolf/month), with an overall estimated rate of 1.9 kills/wolf/month. The primary prey of wolves in winter was elk (Cervus elaphus; 90%). During our study, 43% of the elk killed were calves, 28% were adult females (cows), 21% were adult males (bulls), and 9% were of unknown age/sex. Comparing prey selection to prey availability, wolf packs residing on the northern range (NR) of the GYE selected for calves, against cows, and approximately proportional to availability for bulls. Prey use was different for wolf packs occupying the NR compared to packs residing in other areas (non-northern range NNR) and varied seasonally for NR packs. Variation in wolf kill rates by season, and the relative stability of the northern Yellowstone elk herd during a series of mild winters despite increases in wolf density, suggest that kill rates and ultimately elk population size are influenced by winter weather. Management of ungulates should reflect the addition of wolves combined with the unpredictability of winter weather in the mountainous terrain of the western United States.
Analysis of global positioning system (GPS) location clusters (GLCs) is becoming increasingly popular in studies of carnivore ecology. While promising, this application of GPS technology is still ...poorly developed for most species. We applied this method to study predation and maternal behavior of the Eurasian lynx (
Lynx lynx
) in the Dinaric Mountains. Low population densities, rugged terrain, dense vegetation, and administrative borders make studies of this endangered population using traditional methods and a limited budget very challenging. We used the geographic information system and linear mixed-effects models to understand the movement of lynx during the consumption process and denning period and estimate lynx kill rates. A total of 99 % of kills were found at GLCs longer than 30 h and with minimum two locations within 300 m. We confirmed 86 % of potential kills and all potential dens that were searched for in the field. High success in predicting kill and den sites showed that the Eurasian lynx is a suitable species for application of the GLC analysis methods. Comparison of field-confirmed kills with model predictions showed the possibility for remote estimation of approximate kill rates in Eurasian lynx. Movements of the lynx were primarily affected by daytime period, time since the last kill/den translocation, lynx category, and their interactions. Based on the empirical data, we programmed simulations of lynx movements and elaborated recommendations for more efficient field procedures and study designs (GPS schedules) for future studies. We believe that our findings and approach will also benefit studies of other species with similar behavior.
Wolf (Canis lupus) kill rates are fundamental to understanding predation, but are not well known at low moose (Alces alces) densities. We investigated kill rates of 6 wolf packs (2–10 wolves/pack) ...during 2 winters on the Yukon Flats, a region of eastern Interior Alaska where moose were the sole ungulate prey of wolves occurring at densities <0.2 moose/km2. Our objectives were to compare kill rates with those from areas of greater moose densities, and to determine potential trends in kill rates across the winter. We located moose killed by wolves in February–March 2009, and November 2009–March 2010 using aerial tracking techniques and global positioning system (GPS) location clusters. Wolves killed more moose in early than late winter (βMONTH = −0.02 moose/pack/day, 95% CI = −0.01 to −0.04), and kill rate estimates (mean, 95% CI) were greatest in November (0.033 moose/wolf/day, 0.011–0.055) and least in February (0.011, 0.002–0.02). Kill rates were similar between February and March 2009 (0.019 moose/wolf/day, 0.01–0.03) and 2010 (0.018, 0.01–0.03). Prey composition was primarily adult females (39%) and young-of-the-year (35%). We attribute an elevated kill rate in early winter to predation on more vulnerable young-of-the-year. Kill rates in our study were similar to those from other studies where moose occurred at greater densities. We suggest that very few, if any, wolf–moose systems in Alaska and the Yukon experience a density-dependent phase in the functional response, and instead wolves respond numerically to changes in moose density or availability in the absence of alternative prey. Through a numerical response, wolf predation rates may approximate the annual growth potential of the moose population, contributing to persistent low densities of moose and wolves on the Yukon Flats. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
We assessed whether use of 2 methods, intensive very high frequency (VHF) radiotelemetry and Global Positioning System (GPS) cluster sampling, yielded similar estimates of cougar (Puma concolor) kill ...rates in Yellowstone National Park, 1998–2005. We additionally determined biases (underestimation or overestimation of rates) resulting from each method. We used modeling to evaluate what characteristics of clusters best predicted a kill versus no kill and further evaluated which predictor(s) minimized effort and the number of missed kills. We conducted 16 VHF ground predation sequences resulting in 37 kill intervals (KIs) and 21 GPS sequences resulting in 84 KIs on 6 solitary adult females, 4 maternal females, and 5 adult males. Kill rates (days/kill and biomass kg killed/day) did not differ between VHF and GPS predation sampling methods for maternal females, solitary adult females, and adult males. Sixteen of 142 (11.3%) kills detected via GPS clusters were missed through VHF ground-based sampling, and the kill rate was underestimated by an average of 5.2 (95% CI = 3.8–6.6) days/kill over all cougar social classes. Five of 142 (3.5%) kills identified by GPS cluster sampling were incorrectly identified as the focal individual's kill from scavenging, and the kill rate was overestimated within the adult male social class by an average of 5.8 (95% CI = 3.0–8.5) days/ungulate kill. The number of nights (locations between 2000 hours and 0500 hours) a cougar spent at a cluster was the most efficient variable at predicting predation, minimizing the missed kills, and minimizing number of extra clusters that needed to be searched. In Yellowstone National Park, where competing carnivores displaced cougars from their kills, it was necessary to search extra sites where a kill may not have been present to ensure we did not miss small, ungulate prey kills or kills with displacement. Using predictions from models to assign unvisited clusters as no kill, small prey kill, or large prey kill can bias downward the number of kills a cougar made and bias upward kills made by competitors that displace cougars or scavenge cougar kills. Our findings emphasize that field visitation is crucial in determining displacement and scavenging events that can result in biases when using GPS cluster methods in multicarnivore systems.
Killing behavior and consumption rate are important components that determine the final predation rate. We studied the predatory behavior of a female jaguar with one offspring in Hato Piñero in ...Venezuelan Los Llanos. Seven carcasses of freshly killed calves were found over a period of 9 days. Automatic video recording was used to document the jaguar’s behavior. Our study revealed a detailed, repetitive sequence of female jaguar behavior while hunting for calves. The sequence started with the female killing a calf by biting through the skull or neck, then she dragged the carcass to concealment, eviscerated it and left it concealed; then, the next evening, the female returned with its cub, fed intermittently for a total time of about 90 min while in the meantime it hunted for new prey. All this sequence seems to have a highly adaptive significance for a female jaguar rearing cubs and utilizing large prey. During the short period of our observations, the estimated kill rate of the female jaguar with one offspring was from 0.67 to 1 calf per day. Proper cattle management is necessary to avoid high losses of calves from predation by jaguars.
Overall mortality rates often are based upon a variety of mortality sources such as predation, disease, and accidents, and each of these sources may influence population dynamics differently. To ...better understand population dynamics or to derive effective conservation plans, it is thus crucial to know the frequency of specific mortality causes as well as their variation over time. However, although the mortality cause of retrieved marked animals is often known, this information cannot be used directly to estimate the frequency of a mortality cause. By calculating the ratio of the number of animals reported dead from a specific cause to the total number of retrieved animals, one does not consider the fact that the probability of finding a dead individual depends on the cause of its death. Although frequently used, such ad hoc estimates can be heavily biased. Here we present a new way of estimating the frequency of a mortality cause from ring-recovery (band-recovery) data without bias. We consider the states "alive," "dead because of mortality cause A," and "dead due to all other causes" and estimate within a multistate capture-recapture framework the transition probabilities as well as the state-specific resighting probabilities. Among the transition probabilities are the overall survival probability and the proportion of animals dying because of A. From these, the probability that an animal dies during a year due to the specific cause of interest (cause A kill rate) can easily be calculated. We illustrate this model using data from White Storks Ciconia ciconia ringed in Switzerland to estimate the proportion of storks that died due to power line collision. Average unbiased estimates of this proportion were 0.37 ± 0.08 (mean ± 1 SE) for juveniles, about 25% lower than ad hoc estimates, and 0.35 ± 0.09 for adults. The annual survival rate of juveniles was 0.33 ± 0.05 and of adults, 0.83 ± 0.02. Power line mortality is thus important for White Storks, with about one in four juveniles and one in 17 adults dying each year because of power line collision. We discuss advantages and disadvantages of the new model and how the results could be used to explore the link between a specific mortality cause and population dynamics.
In North America, brown bears (Ursus arctos) can be a significant predator on moose (Alces alces) calves. Our study in Sweden is the first in which brown bears are the only predator on moose calves. ...Bears and moose occurred at densities of about 30/1,000 km2 and 920/1,000 km2, respectively, and bears killed about 26% of the calves. Ninety-two percent of the predation took place when calves were <1 month old. Bear predation was probably additive to other natural mortality, which was about 10% in areas both with and without bears. Females that lost their calves in spring produced more calves the following year (1.54 calves/F) than females that kept their calves (1.11 calves/F), which reduced the net loss of calves due to predation to about 22%.
: Assessing the impact of large carnivores on ungulate prey has been challenging in part because even basic components of predation are difficult to measure. For cougars (Puma concolor), limited ...field data are available concerning fundamental aspects of predation, such as kill rate, or the influence of season, cougar demography, or prey vulnerability on predation, leading to uncertainty over how best to predict or interpret cougar‐ungulate dynamics. Global Positioning System (GPS) telemetry used to locate predation events in the field is an efficient way to monitor large numbers of cougars over long periods in all seasons. We applied GPS telemetry techniques combined with occasional snow‐tracking to locate 1,509 predation events for 53 marked and an unknown number of unmarked cougars and amassed 9,543 days of continuous predation monitoring for a subset of 42 GPS‐collared cougars in west‐central Alberta, Canada. Cougars killed ungulates at rates near the upper end of the previously recorded range, and demography substantially influenced annual kill rate in terms of both number of ungulates (subad F SAF = 24, subad M SAM = 31, ad M = 35, ad F = 42, ad F with kittens <6 months = 47, ad F with kittens <6 months = 67) and kg of prey (SAF = 1,441, SAM = 2,051, ad M = 4,708, ad F = 2,423, ad F with kittens <6 months = 2,794, ad F with kittens >6 months = 4,280). Demography also influenced prey composition; adult females subsisted primarily on deer (Odocoileus spp.), whereas adult males killed more large ungulates (e.g., moose Alces alces), and subadults incorporated the highest proportion of nonungulate prey. Predation patterns varied by season and cougars killed ungulates 1.5 times more frequently in summer when juveniles dominated the diet. Higher kill rate in summer appeared to be driven primarily by greater vulnerability of juvenile prey and secondarily by reduced handling time for smaller prey. Moreover, in accordance with predictions of the reproductive vulnerability hypothesis, female ungulates made up a higher proportion of cougar diet in spring just prior to and during the birthing period, whereas the proportion of males increased dramatically in autumn during the rut, supporting the notion that prey vulnerability influences cougar predation. Our results have implications for the impact cougars have on ungulate populations and have application for using cougar harvest to manage ungulates.
Several North American studies have reported significant predation rates on moose Alces alces by brown bears Ursus arctos. We documented predation on moose by brown bears in south-central Sweden, ...where brown bears and moose occurred at estimated densities of 10-30 and 400-1,340/1,000 km2, respectively. Bears killed 0.8% of radio-collared adult female moose (i.e. ≥ 1 year old) annually and no male moose (≥ 1 year old). Bear predation was the least important mortality factor we documented. Based on tracking brown bears on snow during spring we recorded one successful hunt for every 372 km of tracks and documented attacks only by adult bears and successful attacks only by adult males. Autopsy of moose older than calves that were killed by brown bears revealed that yearlings were more prone to predation than older moose, and that older (i.e. ≥ 2 years) cows were more vulnerable to predation than older bulls. Our study suggests a lower tendency for Scandinavian brown bears to prey on moose compared to most of the North American studies.
To estimate wolf (Canis lupus) kill rates from fine-scale movement patterns, we followed adult wolves in 3 territories of the Scandinavian wolf population using Global Positioning Systems (GPS) ...during the winters of 2001–2003. The resulting 6 datasets of 62–84 study days gave a total of 8,747 hourly GPS positions. We visited clusters of positions in the field on average 8.8 days after positioning and found moose (Alces alces) killed by wolves during the study period on 74 (8%) of the 953 clusters. The number of positions and visits to a cluster, their interaction, and the proportion of afternoon positions were significant fixed effects in mixed logistic-regression models predicting the probability of a cluster containing a wolf-killed moose. The models, however, displayed a poor goodness-of-fit and were not a suitable tool for estimating kill rates from positioning data alone. They might be used to reduce fieldwork by excluding unlikely clusters, although the reduction was not substantial. We discuss proximate factors (i.e., human disturbance and access to prey) as well as ultimate factors (i.e., social organization, intra-guild dominance, and litter size) as potential causes of the observed high temporal and spatial variation in prey-handling. For similar future kill-rate studies, we recommend increasing field efforts and shortening positioning intervals.
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