Habitat Suitability Based Models for Ungulate Roadkill Prognosis

Anthropogenic and Environmental Factors Determining Local Favourable Conditions for Wolves during the Cold Season

Simple Summary

Wolves normally howl in response to unfamiliar vocalisations, to defend their territory and the important resources within it (e.g., pups and prey). During the non-rendezvous period (late autumn and winter), the protectiveness of adults towards pups decreases, as well as reactions to unfamiliar vocal stimuli. In the late fall of 2010, we performed a saturation wolf howling design in the Cicolano area (Central Apennines, Italy), aiming to identify environmental and human-related characteristics of locations where wolves are prone to respond to unfamiliar howling and to assess their eventual ability to provide insights into the distribution of valuable resources (aside from pups) during the cold season. We found that winter response sites (WRS) were characterized by diverging conditions, with respect to all available sites, suggesting that they are non-randomly located but, instead, had been selected by wolves for some reason. We recorded a positive role of thermal refuges and the occurrence of wild boar drive hunts, as well as the negative roles of other forms of human presence and activities, including the occurrence of free-ranging dogs. These results could be of interest both for conservation purposes and for assessing interactions with human activities.

Abstract

Winter resources are crucial for wildlife, and, at a local scale, some anthropogenic and environmental factors could affect their availability. In the case of wolves, it is known that vocalisations in response to unfamiliar howls are issued to defend their territory and the important resources within it. Then, we studied the characteristics of winter response sites (WRS) during the cold season, aiming to assess their eventual ability to provide insights into the distribution of valuable resources within their territories. Within this scope, we planned a wolf-howling survey following a standardised approach. The study covered an Apennine (Central Italy) area of 500 km². A hexagonal mesh was imposed on the area, in order to determine the values of different variables at the local scale. A logistic LASSO regression was performed. WRS were positively related to the presence of thermal refuges (odds = 114.485), to patch richness (odds = 1.153), wild boar drive hunting areas (odds = 1.015), and time elapsed since the last hunt (odds = 1.019). Among negative factors, stray dogs reply considerably affects wolves’ responsiveness (odds = 0.207), where odds are the exponentiated coefficients estimated by the logistic lasso regression. These results suggest that WRS are related to anthropogenic and environmental factors favouring the predation process.

Roadkill-Data-Based Identification and Ranking of Mammal Habitats

Wildlife–vehicle collisions, as well as environmental factors that affect collisions and mitigation measures, are usually modelled and analysed in the vicinity of or within roads, while habitat attractiveness to wildlife along with risk to drivers remain mostly underestimated. The main goal of this study was the identification, characterisation, and ranking of mammalian habitats in Lithuania in relation to 2002–2017 roadkill data. We identified habitat patches as areas (varying from 1 to 1488 square kilometres) isolated by neighbouring roads characterised by at least one wildlife–vehicle collision hotspot. We ranked all identified habitats on the basis of land cover, the presence of an ecological corridor, a mammalian pathway, and roadkill hotspot data. A ranking scenario describing both habitat attractiveness to wildlife and the risk to drivers was defined and applied. Ranks for each habitat were calculated using multiple criteria spatial decision support techniques. Multiple regression analyses were used to identify the relationship between habitat ranks, species richness, and land cover classes. Strong relationships were identified and are discussed between the habitat patch ranks in five (out of 28) land cover classes and in eight (out of 28) species (97% of all mammal road kills). We conclude that, along with conventional roadkill hotspot identification, roadkill-based habitat identification and characterisation as well as species richness analysis should be used in road safety infrastructure planning.