Management of wolf and lynx conflicts with human interests

In many areas viable populations of large carnivores are political goals. One of the most important factors in order to achieve viable large carnivore populations is human tolerance for presence of large carnivores. Thus, management of large carnivore populations in multi use landscapes will involve mitigating conflicts with human interests. In order to mitigate conflicts in a effective way, managers need tools for predicting likelihood of large carnivore occurrence, knowledge on which conflicts are considered as most important by humans in different areas, and the most efficient ways of mitigating the experienced problems. The aim of this thesis is to contribute to some parts of this toolbox for large carnivore managers. A habitat suitability model, with density of roads and built up areas as the most important variables, classified 79% of Scandinavia outside the reindeer husbandry area as suitable wolf habitat. Human tolerance towards wolves was lowest inside wolf territories and slowly increased amongst residents living up to 200 km from the nearest wolf territory. Human tolerance towards wolves may however be affected by mitigation measures such as subsidising electric fences in order to reduce the risk of wolf depredation on livestock. Management actions as subsidies for pro active measures or predator control should be targeting specific areas or individuals in order to be effective. It is also important to use the “right” management actions at the right time. Therefore it is, among other things, important to know if a reported bold wolf is acting in a way that most wolves would not, given the same circumstances. Wolves moved away from an approaching human on average at a distance of about 100 m. Wind velocity and wind direction influenced the distance heavily and humans may come as close to wolves as 17 meters before the wolves become aware of the human and react

Gray Wolf Biology Questions and Answers

1) Why was the gray wolf listed as endangered? 2) What types of habitat do wolves use? 3) Do wolves need wilderness areas to survive? Can they survive near urban areas? 4) How far do wolves travel? 5) What do wolves eat? 6) If wolf numbers get too high will deer and elk be eliminated? 7) How do wolves in an area affect deer hunting? 8) Do wolves really take the old, young, sick, starving, or injured animals? 9) Do wolves kill more than they can eat? 10) Does the presence of wolves affect the numbers of animals other than their prey? 11) What is a wolf pack? 12) How many wolves are in a pack? 13) Do wolves mate for life? 14) What happens to a pack when the alpha male or female is killed? 15) How does a non-breeding wolf attain breeding status? 16) When do wolves mate? 17) Where do wolves give birth to their young? 18) At what age are wolf pups weaned? 19) How long do wolf pups stay in the den? 20) How long do wolves live? 21) In protected populations, what kills wolves? 22) Are wolves a threat to humans, in particular small children? 23) Is there any danger from wolves to my pets? 25) How big are wolves? 26) How can you tell the difference between a gray wolf and a coyote or a large dog? 27) How can I learn more about wolves and the things that are going on right now that will affect their future

How linear features alter predator movement and the functional\ud response

In areas of oil and gas exploration, seismic lines have been reported to alter the movement patterns of wolves (Canis lupus). We developed a mechanistic first passage time model, based on an anisotropic elliptic partial differential equation, and used this to explore how wolf movement responses to seismic lines influence the encounter rate of the wolves with their prey. The model was parametrized using 5 min GPS location data. These data showed that wolves travelled faster on seismic lines and had a higher probability of staying on a seismic line once they were on it. We simulated wolf movement on a range of seismic line densities and drew implications for the rate of predator–prey interactions as described by the functional response. The functional response exhibited a more than linear increase with respect to prey density (type III) as well as interactions with seismic line density. Encounter rates were significantly higher in landscapes with high seismic line density and were most pronounced at low prey densities. This suggests that prey at low population densities are at higher risk in environments with a high seismic line density unless they learn to avoid them

Group Size Effect on the Success of Wolves Hunting

Social foraging shows unexpected features such as the existence of a group size threshold to accomplish a successful hunt. Above this threshold, additional individuals do not increase the probability of capturing the prey. Recent direct observations of wolves in Yellowstone Park show that the group size threshold when hunting its most formidable prey, bison, is nearly three times greater than when hunting elk, a prey that is considerably less challenging to capture than bison. These observations provide empirical support to a computational particle model of group hunting which was previously shown to be effective in explaining why hunting success peaks at apparently small pack sizes when hunting elk. The model is based on considering two critical distances between wolves and prey: the minimal safe distance at which wolves stand from the prey, and the avoidance distance at which wolves move away from each other when they approach the prey. The minimal safe distance is longer when the prey is more dangerous to hunt. We show that the model explains effectively that the group size threshold is greater when the minimal safe distance is longer. Although both distances are longer when the prey is more dangerous, they contribute oppositely to the value of the group size threshold: the group size threshold is smaller when the avoidance distance is longer. This unexpected mechanism gives rise to a global increase of the group size threshold when considering bison with respect to elk, but other prey more dangerous than elk can lead to specific critical distances that can give rise to the same group size threshold. Our results show that the computational model can guide further research on group size effects, suggesting that more experimental observations should be obtained for other kind of prey as e.g. moose.Comment: 20 pages, 4 figures, 8 references. Other author's papers can be downloaded at http://www.denys-dutykh.com

Bioacoustic Detection of Wolves:Identifying Subspecies and Individuals by Howls

SIMPLE SUMMARY: This study evaluates the use of acoustic devices as a method to monitor wolves by analyzing different variables extracted from wolf howls. By analyzing the wolf howls, we focused on identifying individual wolves, subspecies. We analyzed 170 howls from 16 individuals from the three subspecies: Arctic wolves (Canis lupus arctos), Eurasian wolves (C.l. lupus), and Northwestern wolves (C.l. occidentalis). We assessed the potential for individual recognition and recognition of three subspecies: Arctic, Eurasian, and Northwestern wolves. ABSTRACT: Wolves (Canis lupus) are generally monitored by visual observations, camera traps, and DNA traces. In this study, we evaluated acoustic monitoring of wolf howls as a method for monitoring wolves, which may permit detection of wolves across longer distances than that permitted by camera traps. We analyzed acoustic data of wolves’ howls collected from both wild and captive ones. The analysis focused on individual and subspecies recognition. Furthermore, we aimed to determine the usefulness of acoustic monitoring in the field given the limited data for Eurasian wolves. We analyzed 170 howls from 16 individual wolves from 3 subspecies: Arctic (Canis lupus arctos), Eurasian (C. l. lupus), and Northwestern wolves (C. l. occidentalis). Variables from the fundamental frequency (f0) (lowest frequency band of a sound signal) were extracted and used in discriminant analysis, classification matrix, and pairwise post-hoc Hotelling test. The results indicated that Arctic and Eurasian wolves had subspecies identifiable calls, while Northwestern wolves did not, though this sample size was small. Identification on an individual level was successful for all subspecies. Individuals were correctly classified with 80%–100% accuracy, using discriminant function analysis. Our findings suggest acoustic monitoring could be a valuable and cost-effective tool that complements camera traps, by improving long-distance detection of wolves

WOLVES (\u3ci\u3eCanis lupus\u3c/i\u3e)

Two species of wolves occur in North America, gray wolves (Canis lupus) and red wolves (Canis rufus). During the 1800s, gray wolves ranged over the North American continent as far south as central Mexico. Gray wolves occupy boreal forests and forest/agricultural edge communities in Minnesota, northern Wisconsin, and northern Michigan. Mech (1970) reported that gray wolves prey mainly on large animals including white-tailed deer, mule deer, moose, caribou, elk, Dall sheep, bighorn sheep, and beaver. Gray wolves are highly social, often living in packs of two to eight or more individuals. The ability of wolves to kill cattle, sheep, poultry, and other livestock is well documented (Young and Goldman 1944, Carbyn 1983, Fritts et al. 1992)

WOLVES (\u3ci\u3eCanis lupus\u3c/i\u3e)

Two species of wolves occur in North America, gray wolves (Canis lupus) and red wolves (Canis rufus). During the 1800s, gray wolves ranged over the North American continent as far south as central Mexico. Gray wolves occupy boreal forests and forest/agricultural edge communities in Minnesota, northern Wisconsin, and northern Michigan. Mech (1970) reported that gray wolves prey mainly on large animals including white-tailed deer, mule deer, moose, caribou, elk, Dall sheep, bighorn sheep, and beaver. Gray wolves are highly social, often living in packs of two to eight or more individuals. The ability of wolves to kill cattle, sheep, poultry, and other livestock is well documented (Young and Goldman 1944, Carbyn 1983, Fritts et al. 1992)