Land preservation decisions: theoretical and empirical analysis
There is a great concern that human actions are leading to increased rates of extinction of species. Ecologists have pointed out that the most important threat to wildlife comes from habitat alteration. The rapidly increasing human population and the resulting pressing demand for food and living space are pushing highly diverse natural areas into agriculture and other alternative uses. Recognizing this, the Federal agencies and private organizations, such as the Nature Conservancy, are becoming increasingly engaged in programs for conservation of species, where habitat restoration is an important component. However, this conservation is costly and there is uncertainty in the efficacy of preservation. Thus, it becomes important to consider these aspects for efficient conservation.;This dissertation examines the effects of uncertainty, ecological and land conversion irreversibility, and endogenous learning on land conversion decisions for species preservation. The preservation decision of three types of policymakers is investigated: active learner, passive learner and non-learner. These policymakers face the same optimization problem but differ in their behavior towards learning. Experimentation effect, which compares the active learner's preservation action to that of a passive learner, is investigated. In addition, the difference between the action of a passive learner and non-learner, termed Learning effect, is also analyzed.;Another important aspect of wildlife conservation policy is to determine which land cover to adopt in order to benefit the species population. The applied work of this dissertation focuses on the direct linkage between land use and species population. Through modeling pheasant population as a function of different habitats we provide guidelines to policymakers as to which land cover is beneficial for pheasants in Iowa. Also, regional variations in pheasant population response to habitat cover are brought to light. The data on pheasants is obtained from the IDNR annual roadside survey and the land use data source is the NRI.