| Summary: | The reindeer (Rangifer tarandus L.) is a key species in Fennoscandia, where nearly 40% of the land area is used as reindeer pasture. Reindeer herding is an important source of income for local people and an intrinsic part of the Sami culture In this thesis, the reindeer herding system is studied using a detailed interdisciplinary dynamic model. An age- and sex-structured reindeer-lichen model is developed using findings from previous research and novel data. The model also takes other winter resources, including supplementary food, into account in addition to ground lichens. This ecological model is combined with economic optimization and a description of the herding system with empirically estimated prices, costs, and governmental subsidies. The model is validated and calibrated to describe the reindeer herding system in the northern part of Finnish Lapland. The results for population dynamics without harvesting show that the reindeer-lichen system described by the model is unstable in the absence of predators. However, high availability of arboreal lichens stabilizes the system. In economically optimal solutions increasing the interest rate increases the steady-state reindeer population level, opposite to classical understanding in resource economics. Natural mortality is close to zero in optimal steady-state solutions and harvesting is concentrated on calves. The number of adult males is kept as low as possible without decreasing the reproduction rate of the population. This leads to much higher shadow values for males compared to females. The results show that in order to study sustainable and economically viable reindeer management, both ecological and economic factors must be taken into account, as they strongly affect the solutions and management recommendations. One of the main findings is that the economically optimal steady-state lichen biomass can be surprisingly low. High interest rate, lack of pasture rotation, low growth rate of ground lichen, high availability of arboreal lichens, and government ...
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