A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly auto-correlated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

The Economics of Managing Wildlife Disease

The spread of infectious disease among and between wild and domesticated animals has become a major problem worldwide. Upon analyzing the dynamics of wildlife growth and infection when the diseased animals cannot be identified separately from healthy wildlife prior to the kill, we find that harvest-based strategies alone have no impact on disease transmission. Other controls that directly influence disease transmission and/or mortality are required. Next, we analyze the socially optimal management of infectious wildlife. The model is applied to the problem of bovine tuberculosis among Michigan white-tailed deer, with non-selective harvests and supplemental feeding being the control variables. Using a two-state linear control model, we find a two-dimensional singular path is optimal (as opposed to a more conventional bang-bang solution) as part of a cycle that results in the disease remaining endemic in the wildlife. This result follows from non-selective harvesting and intermittent wildlife productivity gains from supplemental feeding.

The Change from Red to White Meat: The Role of Technology

Since 1950, the composition of the U.S. meat diet has shifted markedly from red meats to poultry. For example, from 1970 to 1984, on a percapita basis, beef consumption has declined by 6.4 percent, while chicken and turkey consumptions have increased by 37.9, and 42.5 percent respectively (U.S. Department of Agriculture, 1985). The numerous studies of this phenomenon from the demand side (Chavas, 1983; Braschler, 1983; Nyankori and Miller, 1982; Moschini and Meilke, 1984; Wohlgenant, 1985, Thurman, 1987; Chalfant and Alston, 1988) have failed to achieve a consensus as to whether a change in taste contributed to this shift. One reason for the lack of consensus is that the very large price and quantity changes make it difficult to establish whether consumers are on a new indifference map. But there have been no comparable studies of the nature and causes of the technological change that has made these large consumption and price changes possible. A decrease in the relative price of poultry with respect to red meat is in any case a major explanation of recent shifts in meat consumption patterns. The main reason for such a decrease appears to be a higher rate of technical progress in the poultry industry than in the red meat industry. Substantial productivity gains in both the production and marketing of poultry over the last two decades appears to have been translated into lower retail prices for poultry. Although some productivity gains have taken place in the red meat industry, they have not matched the cost reductions in the poultry industry (Chavas, 1987). Thus, a consumption shift from beef to poultry could possibly be interpreted as a response to changing relative prices, the structural change having occurred in the meat industry. This would imply that, if the beef industry desires to maintain or expand its market, it should seek a decrease in the production and marketing costs of beef.