History, Population Growth, and Management of Wolves in Wisconsin

Wydeven, Adrian P.; Wiedenhoeft, Jane E.; Schultz, Ronald N.; Thiel, Richard P.; Jurewicz, Randy L.; Kohn, Bruce E.; Deelen, Timothy R. Van

doi:10.1007/978-0-387-85952-1_6

Cited by 60 publications

(101 citation statements)

References 14 publications

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“…The initial colonization of wolves into Wisconsin was along the border with Minnesota where an endemic of Lyme disease existed and high numbers of I . scapularis occur [30,68]. The wolf population in Wisconsin grew slowly initially, but by mid-1990s began displaying rapid growth, despite high prevalence of Lyme and anaplasma exposure [68].…”

Section: Discussionmentioning

confidence: 99%

“…The samples were from wolves captured as part of the annual monitoring program conducted by the Wisconsin Department of Natural Resources (WDNR) [30] or captured for depredation management by the U.S. Department of Agriculture Wildlife Service (USDA-WS) [36]. Wolves captured as non-target species during the coyote trapping season or found dead from various causes (vehicle collision, illegally killed, diseases) were also included.…”

Section: Methodsmentioning

confidence: 99%

“…Gray wolves had become extirpated in Wisconsin in the late 1950s, but began to recolonize the state in the mid-1970s after being listed as federally endangered in 1974, and state endangered in 1975 [30]. The Wisconsin Department of Natural Resources (WDNR) began monitoring the wolf population in winter 1979–1980 when 25 wolves were counted in midwinter.…”

Section: Introductionmentioning

confidence: 99%

“…The Wisconsin Department of Natural Resources (WDNR) began monitoring the wolf population in winter 1979–1980 when 25 wolves were counted in midwinter. Subsequently the population grew to 782 wolves by winter 2011 [30,31]. Sampling animals for disease testing was started in 1981, and archived samples of blood and serum for additional disease testing was initiated in 1985 [32].…”

Section: Introductionmentioning

confidence: 99%

“…Sampling animals for disease testing was started in 1981, and archived samples of blood and serum for additional disease testing was initiated in 1985 [32]. Gray wolves were removed from the state list of endangered and threatened species in 2004 [30]. Wolves were down-listed to federally threatened in 2003, and delisted in 2007, but during recent legal battles they have returned to the federal list of endangered species [33,34].…”

Section: Introductionmentioning

confidence: 99%

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Gray Wolf Exposure to Emerging Vector-Borne Diseases in Wisconsin with Comparison to Domestic Dogs and Humans

2016

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World-wide concern over emerging vector-borne diseases has increased in recent years for both animal and human health. In the United Sates, concern about vector-borne diseases in canines has focused on Lyme disease, anaplasmosis, ehrlichiosis, and heartworm which infect domestic and wild canids. Of these diseases, Lyme and anaplasmosis are also frequently diagnosed in humans. Gray wolves (Canis lupus) recolonized Wisconsin in the 1970s, and we evaluated their temporal and geographic patterns of exposure to these four vector-borne diseases in Wisconsin as the population expanded between 1985 and 2011. A high proportion of the Wisconsin wolves were exposed to the agents that cause Lyme (65.6%) and anaplasma (47.7%), and a smaller proportion to ehrlichiosis (5.7%) and infected with heartworm (9.2%). Wolf exposure to tick borne diseases was consistently higher in older animals. Wolf exposure was markedly higher than domestic dog (Canis familiaris) exposure for all 4 disease agents during 2001–2013. We found a cluster of wolf exposure to Borrelia burgdorferi in northwestern Wisconsin, which overlaps human and domestic dog clusters for the same pathogen. In addition, wolf exposure to Lyme disease in Wisconsin has increased, corresponding with the increasing human incidence of Lyme disease in a similar time period. Despite generally high prevalence of exposure none of these diseases appear to have slowed the growth of the Wisconsin wolf population.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gray Wolf Exposure to Emerging Vector-Borne Diseases in Wisconsin with Comparison to Domestic Dogs and Humans

2016

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Are the numbers adding up? Exploiting discrepancies among complementary population models

Stenglein

Zhu

Clayton

et al. 2014

Ecology and Evolution

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Large carnivores are difficult to monitor because they tend to be sparsely distributed, sensitive to human activity, and associated with complex life histories. Consequently, understanding population trend and viability requires conservationists to cope with uncertainty and bias in population data. Joint analysis of combined data sets using multiple models (i.e., integrated population model) can improve inference about mechanisms (e.g., habitat heterogeneity and food distribution) affecting population dynamics. However, unobserved or unobservable processes can also introduce bias and can be difficult to quantify. We developed a Bayesian hierarchical modeling approach for inference on an integrated population model that reconciles annual population counts with recruitment and survival data (i.e., demographic processes). Our modeling framework is flexible and enables a realistic form of population dynamics by fitting separate density-dependent responses for each demographic process. Discrepancies estimated from shared parameters among different model components represent unobserved additions (i.e., recruitment or immigration) or removals (i.e., death or emigration) when annual population counts are reliable. In a case study of gray wolves in Wisconsin (1980–2011), concordant with policy changes, we estimated that a discrepancy of 0% (1980–1995), −2% (1996–2002), and 4% (2003–2011) in the annual mortality rate was needed to explain annual growth rate. Additional mortality in 2003–2011 may reflect density-dependent mechanisms, changes in illegal killing with shifts in wolf management, and nonindependent censoring in survival data. Integrated population models provide insights into unobserved or unobservable processes by quantifying discrepancies among data sets. Our modeling approach is generalizable to many population analysis needs and allows for identifying dynamic differences due to external drivers, such as management or policy changes.

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Spatially varying density dependence drives a shifting mosaic of survival in a recovering apex predator (Canis lupus)

O’Neil

Bump

Beyer

2017

Ecology and Evolution

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Understanding landscape patterns in mortality risk is crucial for promoting recovery of threatened and endangered species. Humans affect mortality risk in large carnivores such as wolves (Canis lupus), but spatiotemporally varying density dependence can significantly influence the landscape of survival. This potentially occurs when density varies spatially and risk is unevenly distributed. We quantified spatiotemporal sources of variation in survival rates of gray wolves (C. lupus) during a 21‐year period of population recovery in the Upper Peninsula of Michigan, USA. We focused on mapping risk across time using Cox Proportional Hazards (CPH) models with time‐dependent covariates, thus exploring a shifting mosaic of survival. Extended CPH models and time‐dependent covariates revealed influences of seasonality, density dependence and experience, as well as individual‐level factors and landscape predictors of risk. We used results to predict the shifting landscape of risk at the beginning, middle, and end of the wolf recovery time series. Survival rates varied spatially and declined over time. Long‐term change was density‐dependent, with landscape predictors such as agricultural land cover and edge densities contributing negatively to survival. Survival also varied seasonally and depended on individual experience, sex, and resident versus transient status. The shifting landscape of survival suggested that increasing density contributed to greater potential for human conflict and wolf mortality risk. Long‐term spatial variation in key population vital rates is largely unquantified in many threatened, endangered, and recovering species. Variation in risk may indicate potential for source‐sink population dynamics, especially where individuals preemptively occupy suitable territories, which forces new individuals into riskier habitat types as density increases. We encourage managers to explore relationships between adult survival and localized changes in population density. Density‐dependent risk maps can identify increasing conflict areas or potential habitat sinks which may persist due to high recruitment in adjacent habitats.

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History, Population Growth, and Management of Wolves in Wisconsin

Cited by 60 publications

References 14 publications

Gray Wolf Exposure to Emerging Vector-Borne Diseases in Wisconsin with Comparison to Domestic Dogs and Humans

Gray Wolf Exposure to Emerging Vector-Borne Diseases in Wisconsin with Comparison to Domestic Dogs and Humans

Are the numbers adding up? Exploiting discrepancies among complementary population models

Spatially varying density dependence drives a shifting mosaic of survival in a recovering apex predator (Canis lupus)

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