Abstract of Presentation to 2010 Wildlife Society Annual Meeting

A Survey of Wildlife Management Science Decision Making Methods and Tools


Based on an internet survey of public documents, a summary of the management science tools used in policy decision making for mammals and their ecosystems is provided.  Efforts to rely on quantifiable objectives such as biodiversity, maximum game harvest, and public opinion are also identified.  Frequency of use, location, and reported successes and problems are summarized.  This survey of wildlife management science approaches is examined against the more general academic literature on management science and decision making.  One conclusion from this comparison is that a wildlife management approach that has been in some cases given the name Adaptive Resource Management falls under the more general category of Maximum Likelihood strategies in the general management science literature.  The Maximum Likelihood strategy is widely considered to be suboptimal and therefore raises concerns about the use of Adaptive Resource Management as it has been applied in some public policy decisions.

 

 


One promising development has been in the use of volunteers to collect data at dramatically reduced costs in support of quantifiable management objectives. Several efforts illustrate successes related to using volunteers for data collection to support analysis and decision making:  hogwatch, designed to collect data on hedgehogs; the Southern Ute Indian tribe’s online deer corridors map, the U.K. otter survey, the Wisconsin Deer Hunter Wildlife Survey, and a deer density map aggregated by Quality Deer Management.  In addition to providing low cost data, participation can greatly enhance the public’s social capital and awareness of wildlife.  Use of independent, if imperfect sources of data to improve estimation and forecasting is a recurring theme in the general management science literature. A good example is the improved estimates of elk herd size achieved by using input from the Cree Indians.