PRRIP Adaptive Management Plan - Laserfiche WebLink

Home Browse Search

management decisions in the future resulting in more rapid and cost-effective attainment of management obj ectives. Adaptive management experiments can be categorized into two types: "passive" and "active" (Walters and Holling 1990, Murray and Marmorek 2003). In passive adaptive management, alternatives are assessed in step 1 of Figure 1 a, and the management action deemed best is designed and implemented in steps 2 and 3. Monitoring and evaluation (steps 4 and 5) then lead to appropriate adjustments (step 6). In active adaptive management, managers explicitly recognize in step 1 that they do not know which activities are best, and then select several alternative activities to design and implement in steps 2 and 3. Monitoring and evaluation of each alternative helps in deciding which was more effective in meeting objectives, and adjustments to the next round of management decisions can be made based on those lessons. Passive adaptive management may be initially less expensive and require fewer people, because only one alternative management technique or strategy is implemented. However, if managers are incorrect in their assumptions, it can take longer to learn which activities are indeed most effective. The absence of a formal comparison of alternatives may mask weaknesses in the approach assumed to be best. As a result, it may prove necessary to go through several iterations of passive adaptive management experiments. Passive adaptive management is also more likely to confound natural environmental change and management effects, hampering managers' ability to draw confident conclusions. Active adaptive management may require a larger initial investment of time, labor, and funds, but since several alternatives are tested (usually including a no-action control), learning happens faster and fewer iterations may be needed to find the best alternative. In the Platte River, active adaptive management can only happen at the System and Program Scale through contrasts in actions over time (e.g. different flows in different years), as there are no control systems (see Section I.E. below, for definition of the various scales). At the System Scale, however, actions in one year may have a continuing effect in subsequent years for some ecosystem components, so the intended contrast is blurred. However, sharp spatial contrasts in actions can be created at the Project Scale (see Section V.B), which will likely provide the most promising opportunity for active adaptive management. A major implication of adaptive management is that learning becomes one of the goals of management; therefore, the collection of useful data through monitoring and research should be an integral part of management decisions and actions. Monitoring and research should be designed to reduce management uncertainty. Typical sources of uncertainty include: Ecological (structural) uncertainty: population, community, or landscape dynamics are not completely known; important biological processes are at work; and, there are competing lines of thought as to how they work. Environmental variation: uncontrollable natural and anthropogenic changes that increase randomness in system dynamics. Partial controllability: management decisions are applied to the system in an unpredictable way and/or by parties not involved in the adaptive management process, and are influenced by overriding forces (e.g., laws, regulations, and September 1, 2006 Adaptive Management Plan