Risk map for wolf threats to livestock still predictive 5 years after construction

Patterns of livestock loss associated with a recolonizing wolf population in Germany

Predation on livestock presents a daunting challenge for human–carnivore coexistence in agricultural landscapes. In Germany, the recolonization of wolves is ongoing and its consequences are insufficiently understood. Knowledge about which livestock species are susceptible to wolf predation, which farm types are predisposed to attacks by wolves, and when predation on livestock occurs is valuable for mitigating stakeholder conflicts. To this end, we analyzed 14 years of monitoring data and assessed the livestock prey spectrum, identified correlates between predation on livestock, farm type and livestock category, and described temporal patterns of livestock loss caused by a recolonizing wolf population in the state of Brandenburg (Germany). Among a total of 1387 recorded cases, 42% were unequivocally attributed to wolves (SCALP criteria C1 and C2) and 12% of cases were not caused by wolves. The number of head of livestock killed during a single wolf attack was mediated by farm type and livestock species; losses per event were greater in full-time farms vs. other farm types and greater in sheep, farmed deer and other livestock species, compared to cattle. While sheep were the most commonly killed livestock species, the increase in wolf territories over the investigation period was associated with a widening of the domestic prey species spectrum. Count regression models provided evidence for the increasing frequency of predation events over the 14-year period, along with an exponential increase in wolf territories. Predation on livestock occurred throughout the year, yet seasonality of events was evident and differed across livestock categories. Predation on sheep peaked in the fall, coinciding with the post-weaning period of wolf offspring. Predation on cattle peaked in the spring, coinciding with the cattle calving period. These results call for renewed investment in the implementation of prevention methods for all susceptible domestic species, particularly during times of elevated predation risk.

Mapping and modeling human-black bear interactions in the Catskills region of New York using resource selection probability functions

Black bears (Ursus americanus) are an iconic and common species throughout much of the United States and people regularly interact with these large predators without conflict. However, negative interactions between people and bears can manifest in conflicts that can hinder conservation efforts. Black bears are highly attracted to anthropogenic sources of food, and negative interactions with people are primarily a product of trash mismanagement. In the Catskills region of New York State, home to a large population of black bears, over 400 such conflicts are reported each year. While the New York Department of Environmental Conservation (DEC) has seen progress recently in educating residents of the region on how to reduce unwanted interactions with bears, they have had less success educating the 12 million tourists that visit the Catskills each year. Understanding where conflict may occur in the future, and the environmental and anthropogenic factors that precede it, may help guide management strategies to reduce these unwanted interactions. Therefore, we designed resource selection probability functions (RSPFs) to examine the relationship between human-black bear conflicts in the Catskills with a suite of landscape and anthropogenic data, using conflicts reported to the DEC across the state of New York in 2018-2019. We found that human-black bear conflicts were more likely to occur in the residential areas of the Catskills on the urban-wildland interface; areas with relatively higher human population densities, away from dense forest, and further from heavily urbanized areas. While future work is needed to continuously validate our model predictions, our results will provide the DEC and other conservation managers in the Catskills the ability to create more targeted plans for mitigating unwanted human-black bear interactions, and provide a better understanding of the mechanisms driving human-carnivore interactions at an urban-wildland interface more generally.

Wolves make roadways safer, generating large economic returns to predator conservation

Recent studies uncover cascading ecological effects resulting from removing and reintroducing predators into a landscape, but little is known about effects on human lives and property. We quantify the effects of restoring wolf populations by evaluating their influence on deer-vehicle collisions (DVCs) in Wisconsin. We show that, for the average county, wolf entry reduced DVCs by 24%, yielding an economic benefit that is 63 times greater than the costs of verified wolf predation on livestock. Most of the reduction is due to a behavioral response of deer to wolves rather than through a deer population decline from wolf predation. This finding supports ecological research emphasizing the role of predators in creating a "landscape of fear." It suggests wolves control economic damages from overabundant deer in ways that human deer hunters cannot.

A landscape of overlapping risks for wolf-human conflict in Wisconsin, USA

Managing risk requires an adequate understanding of risk-factors that influence the likelihood of a particular event occurring in time and space. Risk maps can be valuable tools for natural resource managers, allowing them to better understand spatial characteristics of risk. Risk maps can also support risk-avoidance efforts by identifying which areas are relatively riskier than others. However, risks, such as human-carnivore conflict, can be diverse, multi-faceted, and overlapping in space. Yet, efforts to describe risk typically focus on only one aspect of risk. We examined wolf complaints investigated in Wisconsin, USA for the period of 1999-2011. We described the spatial patterns of four types of wolf-human conflict: livestock depredation, depredation on hunting hounds, depredation on non-hound dogs, and human health and safety concerns (HHSC). Using predictive landscape models and discriminant functions analysis, we visualized the landscape of risk as a continuous surface of overlapping risks. Each type of conflict had its own unique landscape signature; however, the probability of any type of conflict increased closer to the center of wolf pack territories and with increased forest cover. Hunting hound depredations tended to occur in areas considered to be highly suitable wolf habitat, while livestock depredations occurred more regularly in marginal wolf habitat. HHSC and non-hound dog depredations were less predictable spatially but tended to occur in areas with low housing density adjacent to large wildland areas. Similar to other research evaluating the risk of human-carnivore conflict, our data suggests that human-carnivore conflict is most likely to occur where humans or human property and large carnivores co-occur. However, identifying areas of co-occurrence is only marginally valuable from a conservation standpoint and could be described using spatially-explicit human and carnivore data without complex analytical approaches. These results challenge our traditional understanding of risk and the standard approach used in describing risk. We suggest that a more comprehensive understanding of the risk of human-carnivore conflict can be achieved by examining the spatial and non-spatial factors influencing risk within areas of co-occurrence and by describing the landscape of risk as a continuous surface of multiple overlapping risks.