Environmental hazards are distributed in nonrandom patterns; therefore, many biologists work to predict future hazard locations from the locations of past incidents. Predictive spatial models, or risk maps, promise early warning and targeted prevention of nonnative species invasion, disease spread, or wildlife damage. The prevention of hazards safeguards both humans and native biodiversity, especially in the case of conflicts with top predators. Top predators play essential ecological roles and maintain biodiversity, but they can also threaten human life and livelihood, which leads people to eradicate predator populations. In the present article, we present a risk map for gray wolf (Canis lupus) attacks on livestock in Wisconsin between 1999 and 2006 that correctly identified risk in 88% of subsequent attack sites from 2007 to 2009. More-open habitats farther from any forest and closer to wolf pack ranges were the riskiest for livestock. Prediction promotes prevention. We recommend that the next generation of risk mappers employ several criteria for model selection, validate model predictions against data not used in model construction before publication, and integrate predictors from organismal biology alongside human and environmental predictors.
Summary 1.We tested the hypothesis that wolves are reducing local browse intensity by white-tailed deer, thus indirectly mitigating the biotic impoverishment of understorey plant communities in northern Wisconsin. 2. To assess the potential for such a top-down trophic cascade response, we developed a spatially and temporally explicit model of wolf territory occupancy based on three decades of wolf monitoring data. Using a nested multiscale vegetation survey protocol, we compared the understorey plant communities of northern white cedar wetlands found in high wolf areas with control sites found in low wolf areas. 3. We fit species-area curves for plant species grouped by vegetation growth form (based on their predicted response to release from herbivory, i.e. tree, seedling, shrub, forb, grass, sedge or fern) and duration of wolf territory occupancy. 4. As predicted for a trophic cascade response, forb species richness at local scales (10 m 2 ) was significantly higher in high wolf areas (high wolf areas: 10.7 AE 0.9, N = 16, low wolf areas: 7.5 AE 0.9, N = 16, P < 0.001), as was shrub species richness (high wolf areas: 4.4 AE 0.4, N = 16, low wolf areas: 3.2 AE 0.5, N = 16, P < 0.001). Also as predicted, percentage cover of ferns was lower in high wolf areas (high wolf areas: 6.2 AE 2.1, N = 16, low wolf areas: 11.6 AE 5.3, N = 16, P < 0.05). 5. Beta richness was similar between high and low wolf areas, supporting earlier assumptions that deer herbivory impacts plant species richness primarily at local scales. Sampling at multiple spatial scales revealed that changes in species richness were not consistent across scales nor among vegetation growth forms: forbs showed a stronger response at finer scales (1-100 m 2 ), while shrubs showed a response across relatively broader scales (10-1000 m 2 ).6. Synthesis. Our results are consistent with hypothesized trophic effects on understorey plant communities triggered by a keystone predator recovering from regional extinction. In addition, we identified the response variables and spatial scales appropriate for detecting such differences in plant species composition. This study represents the first published evidence of a trophic cascade triggered by wolf recovery in the Great Lakes region.
We developed models and provide computer code to make carcass recovery data more useful to wildlife managers. With these tools, wildlife managers can understand the spatial, temporal (e.g., across time periods, seasons), and demographic patterns in mortality causes from carcass recovery datasets. From datasets of radio-collared and non-collared carcasses, managers can calculate the detection bias by mortality cause in a non-collared carcass dataset compared to a collared carcass dataset. As a first step, we provide a standard procedure to assign mortality causes to carcasses. We provide an example of these methods for radio-collared wolves (n ¼ 208) and non-collared wolves (n ¼ 668) found dead in Wisconsin (1979Wisconsin ( -2012. We analyzed differences in mortality cause relative to season, age and sex classes, wolf harvest zones, and recovery phase (1979-1995: initial recovery, 1996-2002: early growth, 2003-2012: late growth). Seasonally, illegal kills and natural deaths were proportionally higher in winter (Oct-Mar) than summer (Apr-Sep) for collared wolves, whereas vehicle strikes and legal kills were higher in summer than winter. Spatially, more illegally killed collared wolves occurred in eastern wolf harvest zones where wolves reestablished more slowly and in the central forest region where optimal habitat is isolated by agriculture. Natural mortalities of collared wolves (e.g., disease, intraspecific strife, or starvation) were highest in western wolf harvest zones where wolves established earlier and existed at higher densities. Calculating detection bias in the non-collared dataset revealed that more than half of the non-collared carcasses on the landscape are not found. The lowest detection probabilities for non-collared carcasses (0.113-0.176) occurred in winter for natural, illegal, and unknown mortality causes. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
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