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Planning for success: Identifying effective and efficient survey designs for monitoring
Authors:Joel H Reynolds  William L Thompson  Brook Russell
Institution:aUS Fish & Wildlife Service, Division of Realty and Natural Resources, 1011 E. Tudor Road, MS 221, Anchorage, AK 99503, United States;bUS National Park Service, Southwest Alaska Network, 240 West 5th Avenue, Anchorage, AK 99501, United States;cCasper College, Dept. of Statistics, 125 College Drive, Casper, WY 82601, United States
Abstract:Selecting a survey design to detect change through time in an ecological resource requires balancing the speed with which a given level of change can be detected against the cost of monitoring. Planning studies allow one to assess these tradeoffs and identify the optimal design choices for a specific scenario of change. However, such studies seldom are conducted. Even worse, they seem least likely to be undertaken when they offer the most insight – when survey methods and monitoring designs are complex and not well captured by simple statistical models. This may be due to limited technical capacity within management agencies. Without such planning, managers risk a potentially severe waste of monitoring resources on ineffective and inefficient monitoring, and institutions will remain ignorant of the true costs of information and the potential efficiency gains afforded by a moderate increase in technical capacity. We discuss the importance of planning studies, outline their main components, and illustrate the process through an investigation of competing designs for monitoring for declining brown bear (Ursus arctos) densities in southwestern Alaska. The results provide guidance on how long monitoring must be sustained before any change is likely to be detected (under a scenario of rather strong true decline), the optimal designs for detecting a change, and a tradeoff where accepting a delay of 2 years in detecting the change could reduce the monitoring cost by almost 50%. This report emphasizes the importance of planning studies for guiding monitoring decisions.
Keywords:Cost analysis  Monte carlo simulation  Population trend  Sample size  Statistical power  Study planning
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