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Park monitoring: Using decision models to assist vital signs monitoring in National Parks, a prototype using sea otters (Enhydra lutris) in coastal Alaska

Duration

July 2008 - December 2010

Narrative

A fundamental goal of the National Park Service (NPS) is the long-term protection and management of resources in the National Park system. Achievement of this goal requires multiple approaches, including the conservation of essential habitats and the identification and elimination of potential threats to biota and habitats. To accomplish these, the NPS has implemented the Alaska Region Inventory and Monitoring (I&M) Program to monitor key biological, chemical, and physical components (i.e., vital signs) of ecosystems at 270 national parks. By monitoring these vital signs over large spatial and temporal scales, it is thought that park managers will be provided with 1) information on the status and trajectory of park ecosystems and 2) a greater understanding and insight into the ecosystem dynamics. Detecting and quantifying changes is important to conservation efforts. However, if the information provided by the monitoring does not coincide with management objectives, it would be of limited use for formulating remedial actions (Lee 1993). Similarly, monitoring data would provide limited insight into system dynamics if were not collected in such a manner as to resolve key uncertainties (e.g., structural uncertainty; Williams et al. 2002). There remains the need to develop tools to assist managers and their collaborators with current decisions and allow for the formal integration of monitoring data to improve the understanding of system dynamics, thereby improving future decisions.

One such tool is decision analysis, which formalizes complex systems into a straightforward framework consisting of explicit quantitative relationships among management actions, sources of uncertainty, and potential outcomes (Clemen 1996). It allows natural resource managers to examine the expected effects of different management strategies, incorporate multiple objectives and values of stakeholders, determine the relative influence of various sources of uncertainty, and estimate the value of collecting additional data (Possingham 1997; Dorazio and Johnson 2003; Nichols and Williams 2006; Conroy et al. in press). Decision analysis can incorporate structural uncertainty by including more than a single representation of the system (Williams et al. 2002). These alternative representations of the system can differ completely in terms of the factors considered (e.g., density dependent vs. independent). For example, Peterson and Craven (2007) developed a decision model for the NPS Chattahoochee River National Recreation Area that included two alternative representations of streamflow effects, high flow vs. flow variability, on native fish recruitment. Monitoring data can be used to reduce these uncertainties and to learn about how the system works. If done in conjunction with management decision-making, this leads to a formal process that has been defined as adaptive management (Williams et al. 2002).

Thus, the decision models would provide park managers with the means to quantify potential risks to ecosystems so that (1) the effects of different scenarios can be examined a priori, (2) changes park vital signs can be readily interpreted, and (3) monitoring data can be formally incorporated to reduce uncertainty and improve the understanding of system dynamics.


For several important reasons (including many that influenced their selection as a "vital sign"), sea otters are an ideal prototype species for building and testing a decision-modeling approach to management of vital signs in the National Parks.

 

Current Staff

Federal Staff: 4

Masters Students: 12

Phd Students: 11

Post Docs: 3

University Staff: 16

5 Year Summary

Students graduated: 20

Scientific Publications: 71

Presentations: 268

 

Personnel

Funding Agencies

  • Cooperative Research Unit Program

Oregon Cooperative Fish and Wildlife Research Unit Cooperators

  1. Oregon Department of Fish and Wildlife
  2. Oregon State University
  3. U.S. Fish and Wildlife Service
  4. U.S. Geological Survey
  5. Wildlife Management Institute