Predicting Gray Wolf Landscape Recolonization: Logistic Regression Models vs. New Field Data

Abstract

Recovery of populations of wolves (Canis lupus) and other large, wideranging carnivores challenges conservation biologists and resource managers because these species are not highly habitat specific, move long distances, and require large home ranges to establish populations successfully. Often, it will be necessary to maintain viable populations of these species within mixed-use landscapes; even the largest parks and reserves are inadequate in area. Spatially delineating suitable habitat for large carnivores within mixed, managed landscapes is beneficial to assessing recovery potentials and managing animals to minimize human conflicts.Here, we test a predictive spatial model of gray wolf habitat suitability. The model is based on logistic regression analysis of regional landscape variables in the upper Midwest, United States, using radiotelemetry data collected on recolonizing wolves in northern Wisconsin since 1979. The model was originally derived from wolf packs radio-collared from 1979 to 1992 and a small test data set of seven packs. The model provided a 0.5 probability cut level that best classified the landscape into favorable (road density Ͻ 0.45 km/km 2 ) and unfavorable habitat (road density Ͼ 0.45 km/km 2 ) and was used to map favorable habitat with the northern Great Lake states of Wisconsin, Minnesota, and Michigan. Our purpose here is to provide a better validation test of the model predictions based on data from new packs colonizing northern Wisconsin from 1993 to 1997. In this test, the model correctly classified 18 of 23 newly established packs into favorable areas. We used compositional analysis to assess use of the original habitat probability classes by wolves in relation to habitat class availability. The overall rank of habitat preference classes (P, the percentage favorability from the original model), based on the new packs, was probability class 2 (P ϭ 75-94%) Ͼ 3 (P ϭ 50-74%) Ͼ 1 (P ϭ 95-100%) Ͼ 4 (P ϭ 25-49%) Ͼ 5 (P ϭ 10-24%) Ͼ 6 (P ϭ 0-9%). As more of the landscape becomes occupied by wolves, classes of lower probability than the 95% class, but above the favorability cut level, are slightly more favored. The 95% class is least abundant on the landscape and is usually associated with larger areas of classes 2 and 3. Wolves may continue to occupy areas of slightly lower habitat probability if adequate population source areas are present to offset the greater mortality in these lower quality areas. The model remains quite robust at predicting areas most likely to be occupied by wolves colonizing new areas based on generally available road network data. The model has also been applied to estimate the amount and spatial configuration of potential habitat in the northeastern United States.

Habitat Use by Adult Red Wolves,Canis rufus, in an Agricultural Landscape, North Carolina, USA

“…Measuring used habitat indices and modeling potential suitable habitat of red wolves provides a glimpse of how the reintroduced red wolf is acclimating to a human-influenced landscape. Red wolf use of areas with low primary road and human population densities are similar to other wolf habitat use results, such as gray wolves in the upper Midwest (Mladenoff et al 1995(Mladenoff et al , 1999. The jackknife estimation of variable importance also supports secondary road density as an important predictor variable, but only below levels of approximately 1.0 km/km 2 .…”

“…We mapped the spatial distribution of land use/land cover (LULC) type, primary and secondary road density, and human population density in order to derive predictor variables of habitat used, based on variables known to influence gray wolf habitat use (Mladenoff et al 1995(Mladenoff et al , 1999. The grid size of each variable was 30 m and we used data from 2000 for all variables in order to remain consistent with the human population census data.…”

“…As there are few natural areas left in the USA untouched by human alteration, these carnivores must either adapt to these altered habitats or face extinction. Gray wolves, Canis lupus, have demonstrated that such large carnivores are capable of persisting in these human-altered landscapes in the western and midwestern USA, although they often avoid areas of high human and road densities (Mladenoff et al 1995(Mladenoff et al , 1999Oakleaf et al 2006). Early studies on red wolf habitat selection suggested they preferred densely vegetated habitats such as hardwood forests and marshes, but this information was based on data collected from the remnant populations and was not thought to be typical (Phillips et al 2003).…”

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

“…Land-cover changes over broad scales have the potential to affect biodiversity through a number of mechanisms such as habitat loss and fragmentation, enabling biological invasions and impairment of ecological processes critical to ecosystem function. Habitat data at a sufficiently fine resolution for most organisms under study (Mladenoff et al, 1999;Bowman et al, 2001;Mitchell et al, 2001;Betts et al, 2006) are rarely available at broad scales. However, this hypothesis has been difficult to test because our ability to predict the effects of land cover and land-cover change on species distributions has been limited by the availability of estimates of land cover at appropriate spatial and temporal scales.…”

Landscape Evaluation In Conservation: Molecular Sampling And Habitat Modeling For The Iberian Lynx

“…To guarantee the conservation of these populations, it is often critical to develop species-specific habitat management strategies that demand comprehensive understanding on their requirements of space and resources. In general, individuals select home ranges in response to landscape patterns related to resource access (such as food or refuges), or to constraints such as human-related disturbance and risk of mortality (e.g., Carey et al 1992, Mladenoff et al 1999, Naves et al 2003. In general, individuals select home ranges in response to landscape patterns related to resource access (such as food or refuges), or to constraints such as human-related disturbance and risk of mortality (e.g., Carey et al 1992, Mladenoff et al 1999, Naves et al 2003.…”