LAND: Spatial economic analyses in wildlife management

In this project I will explore how continuous spatial distributions of habitat may be applied to explain the spatial distribution of a species with high economic value - the moose. Continuous habitat distributions may be estimated from spatial data for in stance by the use of Kernel methods. The Kernel method estimates the density of a given habitat variable at each spatial location given the whole spatial distribution of that habitat. Compared to the traditional use of discrete point- or grid cell estimat es, continuous habitat distributions have some beneficial properties: 1) Smoothing out missing observations; 2) it is partly independent of arbitrary choice of spatial resolution; and 3) continuous habitat distributions may be an index that describes the whole aspect of the landscape mosaic and may therefore give better predictability to the spatial model. Here, I suggest correlating continuous spatial habitat and animal distributions to map the association between habitat and organism. I will further ext rapolate the spatial model into larger areas to study the predictability of the model. The actual landowner configuration will then be used to study the spatial economics of the species and how this may vary through various changes of the landscape. The e conomic profit for each landowner dependent on how the various landowners manage their habitat may then be studied by applying game theory. I will develop these ideas at the CEES at UiO in close collaboration with prof. Nils Chr. Stenseth and his group. In addition I will collaborate with highly competent environments at the Alaska Department of Fish and Game, Fairbanks, where I plan to stay for a 10 month period. The large datasets and the knowledge of the ecological system to be studied will be elabora ted through researchers working on bio-economics at HUC and a large mammal group at NINA in Trondheim.