Suchergebnisse

Population growth (or decline) is influenced by many factors that fall into the broad realms of demographic characteristics, socioeconomic conditions, transportation infrastructure, natural amenities, and land use and development across space and time. This paper adopts an integrated spatial regression approach to investigate the spatial and temporal variations of these factors′ effects on population change. Specifically, we conduct the analysis at the minor civil division level in Wisconsin, USA, from 1970 to 2000. The results suggest that the factors have varying effects on population change over time and across rural, suburban, and urban areas. Their effects depend upon the general trend of population redistribution processes, local dynamics, and areal characteristics. Overall, a systematic examination of population change should consider a variety of factors, temporal and spatial variation of their effects, and spatial spillover effects. The examination should have the flexibility to identify and incorporate influential factors at a given point in time and space, not to adhere to a single set of drivers in all circumstances. The findings have important implications for population predictions used for local and regional planning.

Context In Europe and the United States, wolf–human conflict has increased as wolf populations have recovered and recolonised human-dominated ecosystems. These conflicts may lead to negative attitudes towards wolves and often complicate wolf management. Wolf attacks on bear-hunting hounds (hereafter, hounds) are the second-most common type of depredation on domestic animals in Wisconsin, USA, and, typically, the most costly in terms of compensation per individual animal. Understanding the geospatial patterns in which these depredations occur could promote alternative hunting practices or management strategies that could reduce the number of wolf–human conflicts. Aims We compared variables differentiating between wolf attacks on hounds and non-hounds (e.g., pets), we constructed a spatial, predictive model of wolf attacks on hounds, and we explored how the landscape of risk changed over time. Methods We characterised landscape features of hound depredations using logistic regression. We applied the spatial model to a geographic information system (GIS) to display spatial patterns and to predict areas of risk for wolf attack. Key results Our model correctly classified 84% of sites of past depredations, 1999–2008, and 78% of nearby random-unaffected sites. The model correctly predicted 82% of recent (2009–11) depredation sites not used in model construction, thereby validating its predictive power. Risk of wolf attack on hounds increased with percentage area of public-access land nearby, size of the nearest wolf pack, proximity of the nearest wolf pack, and decreased with percentage of human development. National and county forest lands had significantly (P < 0.001) more hound depredations than did other land-ownership types, whereas private lands had significantly fewer. Conclusions Risk of wolf attacks on hounds had distinctive temporal and spatial signatures, with peak risk occurring during the black bear hound training and hunting seasons and in areas closer to the centre of wolf pack territories, with larger wolf packs and more public access land and less developed land. Implications Our analysis can help bear hunters avoid high-risk areas, and help wildlife managers protect wildlife and recreational use of public lands, and reduce public costs of predator recovery. We present a risk-adjusted compensation equation. If wildlife managers choose, or are required, to provide compensation for hounds attacked by wolves, while hunting on public lands, we suggest that managers consider adjusting compensation payments on the basis of the relative landscape of risk.