Michigan

The coyote (Canis latrans), the gray wolf (Canis lupus), and the red fox (Vulpes vulpes) are all known to be ecologically significant predators. All three are widely known canine species ranging in size, but also share similarities due to their close taxonomic relationship. Yet, they have many notable differences as a result of adapting to different drivers of speciation, such as competition or food source availability. Further, it is my goal to determine if there are differences in their daily activity patterns which have previously been observed as crepuscular. Data used for my research was collected using camera traps from the Snapshot USA project located in the Upper Peninsula of Michigan. By running a chi square test, I determined that coyotes are nocturnal, however, the red fox and gray wolf are indeterminant, or cathemeral. Studying daily activity patterns is vital to understanding a species ecological role and impact on their environment. Further research could determine how species activity patterns have impacted various wildlife populations and communities, as well as potential ecological drivers and disturbances that have caused these activity patterns.

Coexistence between humans and large carnivores may depend on carnivore adaptations to use developed landscapes while reducing human encounters. Roads are a widespread form of human development that carnivores may perceive as efficient travel routes or centers of human activity and associated risk. We compared the spatio-temporal responses of carnivores to human road use with high-resolution tracking of a large carnivore guild including American black bears (Ursus americanus), bobcats (Lynx rufus), coyotes (Canis latrans), and wolves (C. lupus) in Michigan, USA. All carnivores selected for roads when traveling at night but avoided roads during the day when human activity was greatest. Human activity explained 90% of temporal variation in road use across carnivore species, with a 3.2–3.7-fold increase in road use at times of low human activity which reduced carnivore activity overlap with humans by 27–42%. Similar but less pronounced activity changes occurred in areas up to 500 m from roads. Bears and wolves increased nocturnal activity with more roads in their home range, but not bobcats or coyotes. Despite increased diurnal activity in areas farther from roads, temporal overlap among carnivores was high regardless of road proximity. Our results suggest that spatio-temporal responses to roads were similar among carnivores and emphasized avoidance of humans over other carnivore species. Further, we provide support that carnivores can be diurnally active while avoiding humans by using areas farther from roads. However, carnivores which are primarily diurnal (e.g., black bears) or have a strong proclivity for using roads (e.g., wolves) likely require greater behavioral changes to avoid humans. Behavioral adaptations allowing multiple species to use and cross roads while avoiding humans are encouraging for human-carnivore coexistence.

According to the ideal‐free distribution (IFD), individuals within a population are free to select habitats that maximize their chances of success. Assuming knowledge of habitat quality, the IFD predicts that average fitness will be approximately equal among individuals and between habitats, while density varies, implying that habitat selection will be density dependent. Populations are often assumed to follow an IFD, although this assumption is rarely tested with empirical data, and may be incorrect when territoriality indicates habitat selection tactics that deviate from the IFD (e.g. ideal despotic distribution or ideal preemptive distribution). When territoriality influences habitat selection, species’ density will not directly reflect components of fitness such as reproductive success or survival. In such cases, assuming an IFD can lead to false conclusions about habitat quality. We tested theoretical models of density‐dependent habitat selection on a species known to exhibit territorial behavior in order to determine whether commonly applied habitat models are appropriate under these circumstances. We combined long‐term radio telemetry and census data from gray wolves (Canis lupus) in the Upper Peninsula of Michigan, USA to relate spatiotemporal variability in wolf density to underlying classifications of habitat within a hierarchical state‐space modeling framework. We then iteratively applied isodar analysis to evaluate which distribution of habitat selection best described this recolonizing wolf population. The wolf population in our study expanded by >1000% during our study (~ 50 to > 600 individuals), and density‐dependent habitat selection was most consistent with the ideal preemptive distribution, as opposed to the ideal‐free or ideal‐despotic alternatives. Population density of terrestrial carnivores may not be positively correlated with the fitness value of their habitats, and density‐dependent habitat selection patterns may help to explain complex predator‐prey dynamics and cascading indirect effects. Source‐sink population dynamics appear likely when species exhibit rapid growth and occupy interspersed habitats of contrasting quality. These conditions are likely and have implications for large carnivores in many systems, such as areas in North America and Europe where large predator species are currently recolonizing their former ranges.

Large carnivore management is among the most important and controversial challenges facing wildlife conservation. Large carnivores generate conflict with neighboring human communities, placing enormous pressure on wildlife managers to balance conservation policies with stakeholder priorities. This can be especially challenging when large carnivores begin to recolonize their former range after extended absences. Over the last quarter century, gray wolves (Canis lupus) have successfully recolonized the entirety of the Michigan’s Upper Peninsula (UP) and on multiple occasions have crossed over to the Northern Lower Peninsula (NLP) of Michigan. Colonization is not yet documented but is likely and will have major ecological and economic implications for the region. To anticipate these implications our objectives are (1) to provide a spectrum of outcomes for wolf population expansion and dynamics and (2) evaluate their sensitivity to initial conditions, environmental variability, and shifting human tolerance. We developed an agent-based model to simulate wolf population expansion and dynamics in Michigan. The model was first applied to the UP, using Michigan DNR annual wolf surveys to validate the spatial and temporal accuracy of the model. Our simulations successfully predicted wolf population dynamics over the first 10 years of recolonization, though the spatial accuracy of local territories varied. We then applied the model to the NLP to simulate potential scenarios of wolf recolonization. Our model suggested that the highest likelihood of recolonization over the first 10 years would occur within the northern and central regions, coinciding with the low human density and high prey abundance. Outcomes were most sensitive to initial conditions, such as founding population size and rate of new dispersers. Nevertheless, in scenarios where initial conditions were strong, our model produced wolf populations of around 40 individuals within 5 years, comparable to outcomes observed in similar, neighboring landscapes.

Following decades of absence, the gray wolf (Canis lupus) has recolonized much of the northern Great Lakes region from Canada and remnant populations in northern Minnesota. The wolf population in Michigan’s Upper Peninsula may now be reaching saturation, with evidence that some dispersing individuals have traversed the Straits of Mackinac during ice-over winter conditions indicating potential recolonization of northern Lower Michigan. While previous research suggests suitable habitat exists in northern Lower Michigan to support a small wolf population, habitat availability at other hierarchical levels, including den habitat and the ability of individuals to disperse successfully among suitable habitat patches, has not been assessed. We evaluated the den habitat availability and landscape connectivity using a multi-scale modeling approach that integrates hierarchical habitat selection theory as well as spatial structure to assess whether corridors exist for wolves to successfully recolonize and raise pups in northern Lower Michigan. We used expert opinion, scientific literature, and geographical information systems to develop models of landscape suitability, resistance, and least-cost path analysis to identify dispersal corridors throughout the Upper and northern Lower Peninsulas of Michigan. Based on our models, the Upper Peninsula was almost entirely amenable to wolves for both denning and dispersing, particularly in the western portion of the peninsula. Our estimates indicate that over 1900 km2 of high quality den habitat exists in northern Lower Michigan, but landscape permeability between these habitat patches appeared relatively low relative to Upper Michigan. We delineated several corridors of high quality habitat in the Upper Peninsula that may facilitate dispersal in to Lower Michigan. Dispersal corridors were of moderate quality in northern Lower Michigan, representing higher mortality risk but potentially capable of promoting recolonization of high-quality habitat areas. Conservation efforts within these identified corridors may further increase the potential for successful recolonization and establishment of viable long-term breeding populations of gray wolves in northern Lower Michigan.

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.

All natural processes are dynamic in space and time. Establishing the links between spatiotemporal patterns and ecological processes is critical for improving our understanding of natural systems. Empirical data representing wildlife populations is accumulating and increasingly involves spatiotemporal components. Wildlife monitoring programs for threatened, endangered, or other species of interest often involve radio-tracking of a sample of individual animals combined with census data. Such data are valuable both for conservation and management of populations and for testing ecological theories about species distribution and what influences patterns over time. We used 20 years of radio telemetry and snow tracking data to evaluate spatiotemporal patterns in gray wolf (Canis lupus) distribution, habitat selection, survival, and mortality in the Upper Peninsula (UP) of Michigan, USA. Wolves recolonized the study area during the early 1990s and exceeded a population size of 600 individuals before the end of the study. In addition, wolves were on the Endangered Species List during the majority of the study. This work therefore explores the spatial ecology of endangered wolves during a period of population recovery. We analyzed winter prey distributions of wolves, evaluated theoretical and modern empirically-driven models of density dependent habitat selection, estimated annual survival, and explored cause-specific mortality. Our methods included isodar analysis, spatiotemporal generalized linear mixed models of habitat selection, proportional hazards models with time-dependent spatial covariates, and competing risks analysis. Winter prey distributions exhibited a habitat functional response depending on winter snow conditions, resulting in a geographic prey limitation that affected wolf territory occupancy within the study area. Density-dependence in habitat selection revealed that wolf selection patterns were more consistent with an ideal-preemptive habitat distribution, as opposed to the ideal-free distribution. Density-dependent habitat selection patterns revealed decreasing selection for prey availability at greater wolf densities, while selection for anthropogenic features such as road density increased. However, selection across time exhibited occupancy-dependence as opposed to density-dependence. Wolf annual survival was ~ 75% and was influenced by sex, age, transient status, agriculture, habitat edge, wolf density, and Julian day, as well as several individual factors. Survival declined as wolf density increased, resulting in a shifting mosaic of wolf survival. Human-caused mortality increased with wolf density and was the primary mortality source of UP wolves, comprising ~ 17% annually. Much of human-caused mortality was attributed to illegal killing. Human-caused mortality was partially compensated for by natural mortality, and negative impacts on population growth rate were most evident when human-caused and natural mortality were both high. The spatial ecology of wolves in this study describes patterns associated with a growing and shifting population. Density-dependent effects population dynamics occurred with expanding wolf range, where later colonizers were forced to utilize habitats closer to human populations. Theoretical tests revealed potential for source-sink population dynamics. Evidence suggested the population had stabilized by the end of the study, and that suitable habitat was saturated. Future conservation of the population will likely depend on preservation of high quality source habitats and managing human conflicts associated with high wolf density areas occurring near population centers.

Wolves (Canis spp.) have recolonized the Great Lakes region and expanded into agricultural areas where there is increasing concern of conflict with livestock. We documented 121 wolf predation events on captive or domestic animals in the Upper Peninsula (UP) of Michigan between April 1996 and April 2009. We investigated the relationship between annual wolf abundance and predation events, seasonality of predations on livestock, and the association between previous winter severity and predations on livestock. The annual number of predations on livestock increased with wolf abundance, and overall, predations on cattle and calves increased during calving season. We observed a direct relationship between the annual number of predations on livestock and previous winter severity. We observed no relationship between the annual number of domestic dogs killed by wolves and wolf abundance. If the observed trends persist, wolf–livestock conflict in the UP will continue to increase, elevating management costs and likely reducing human tolerance for wolves. Managers should be prepared for continued conflicts as wolf populations increase and eventually are delisted in the region.