VI. IMPLICATIONS FOR THE NWFP AND ICBEMP

In the preceding sections, we provide an overview of the conventional approach to monitoring, discuss some of the complications in applying the conventional approach to broad regional plans, and introduce decision analysis techniques as one inroad to explicitly bringing monitoring and decision making together. In our presentation, we have intentionally omitted much of the technical detail for the sake of brevity. By doing so, we run the risk of violating our own standards of rigor. Many of the ideas that we have broached are areas of active research within the statistical and decision analysis arenas. Managing complex systems under uncertainty is not the exclusive purview of land management agencies, nor is monitoring. Our central point is that there are techniques and approaches in the literature and under development in the larger research community that we as a land management agencies can use.

The NWFP and ICBEMP create an environment in which multiple decisions are called for at a variety of scales. Monitoring serves a vital interest in determining whether progress is made towards overall goals, and in improving understanding such that sound, informed decisions are made, regardless of actual outcomes. The difficulties associated with moving and relating information across scales call for a formalized process of hypothesis formulation coupled with and embedded in a decision-making framework where science, management, and policy-making are linked. Too often monitoring fails when scientific and management objectives are poorly matched. Mismatches evolve when the perception of specific monitoring objectives (i.e., hypotheses) and the scales of concern differ among scientists, managers, and policy makers. While many monitoring plans contain both large-scale and to varying degrees, fine-scale goals, what is generally lacking is an explicit delineation of the path and precise steps taken in this scaling down process. Failure may be defined in several ways but most commonly means that it has failed to provide the information needed for judicious, defensible, decision-making. In this context, failure will occur if data are collected at the district level and are unusable for the intended policy objectives mandated at the regional and even national levels. At worst, such an informal articulation leaves the science and logic vulnerable to criticism that it is arbitrary and subjective, therefore indefensible. At best, the lack of rigorous articulation hides critical areas of uncertainty and leaves decision-making decoupled from science. A careful mapping of monitoring objectives from start (i.e., policy or public expectations) to finish (i.e., data collection and analysis) is necessary to ascertain whether specific, testable monitoring objectives executed at the ground level match broad expectations of monitoring. This remains a challenge for large and multi-scale, multi-variate, open systems such as the interior Columbia Basin. As we discuss above, much of the uncertainty can be identified and addressed, if not resolved, within an explicit decision-making framework.

Conclusions

In conclusion, we propose that current monitoring plans based solely on the conventional approach lack two critical features: (1) an explicit decision-making structure linked with the monitoring plan; and (2) no formalized prioritization procedure. Without a decision analysis approach, a framework is lacking in which monitoring results may be used to judge effectiveness and directly linked to management decisions. This leaves monitoring results decoupled from the decision-making process. A lack of articulation between monitoring and decision-analysis can hide areas of uncertainty and the source of uncertainty. In an articulated decision analysis framework, decision-makers at all levels can see the array of possible paths and weigh the relative risk or desirability. We suggest that monitoring plans for the NWFP and ICBEMP be written from a decision analysis perspective and suggest that this approach will help facilitate the step-down process.

There are several benefits to using a formal decision analysis approach:

• This approach allows integration across scales, between research and management, across disciplines, and provides a common channel of communication.
• Because it requires quantification and rigor, the decision analysis framework functions as a permanent repository of scientific knowledge which is accessible to all and future projects.
• The modularity of the decision analysis allows for flexibility linking across scales and geographic locations. As such, it provides a viable tool for comparative studies.
• Assumptions are transparent and risk is quantifiable, leading to more defensible decisions.
• Its explicit articulation helps identify areas of uncertainty and promising research directions.
• The inference diagram method of modeling is theoretically and practically integrated with existing statistical techniques, providing a coherent strategy for data interpretation.
• The decision analysis approach facilitates rigorous adaptive management.