Adaptive Resource Management - What is it and how would it be used? Adaptive Resource Management - What is it and how would it be used?
Adaptive resource management (ARM) "Treats management policies as experiments that probe the responses of ecosystems as human behavior changes ( Lee 1999:11). Gunderson (1999:2) explained this further: "Adaptive management … views policy as hypotheses: that is, most policies are really questions masquerading as answers. Because policies are questions, then management actions become treatments, in an experimental sense."
Johnson (1999b:2) states that "The overall goal of adaptive management is not to maintain an optimal condition of the resource, but to develop optimal management capacity. This is accomplished by maintaining ecological resilience that allows the system to react to inevitable stresses, and generating flexibility in institutions and stakeholders that allows managers to react when conditions change. The result is that, rather than managing for a single, optimal state, we manage within a range of acceptable outcomes while avoiding catastrophes and irreversible negative effects."
He went on to say (P 5) that "Adaptive management differs from traditional approaches in that it addresses uncertainty directly by using management as a tool to gain critical knowledge."
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ARM "Promotes learning to high priority in stewardship." (Lee 1999:2) It "… allows managers to agree on policy when they cannot agree on predicted outcomes." (Johnson and Williams 1999:8) It "Does not postpone action until ‘enough’ is known but acknowledges that time and resources are too short to defer some action, particularly actions to address urgent problems." (Lee 1999:5) According to Johnson and Williams (1999:9), "A major advantage of adaptive (harvest) management over the traditional approach is that it makes explicit the role of resource monitoring in the formulation of … strategies."
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Lee (1999:7) believed that "Adaptive management is likely to be worthwhile when laboratory style precision seems infeasible but trial-and-error seem too risky. And that’s much of the time in conservation." He went on to say "The most important uncertainties (should be) tested rigorously and early" (P 5). Johnson (1999b:7) pointed out that "Most experiments will involve some risk of negative effects for stakeholders or the resource.".
Johnson (1999a:3) spoke to another aspect of AHM: "Managers need to find ways to incorporate the nonscientific knowledge and data that stakeholders possess into the adaptive management process." He further explained that "Because adaptive management is more holistic and multidisciplinary than traditional management, it will require more cooperation across disciplines within an agency, and, in some cases, across jurisdictions among agencies and stakeholders. Cooperation entails giving up some control to other agencies or stakeholders." (Johnson 1999b:8).
Shindler and Cheek (1999:1) explained how to foster the involvement and cooperation of partners: "Citizen-agency interactions are more effective when (1) they are open and inclusive, (2) they are built on skilled leadership and interactive forums, (3) they include innovative and flexible methods, (4) involvement is early and continuous, (5) efforts result in action, and (6) they seek to build trust among participants."
Lee (1999:19) provided a word of caution about implementing AHM: "… humans have difficulty distinguishing between message (conflict is healthy) and messenger (adaptive management reveals conflict rather than causing it)." Johnson and Williams (1999:11) added that "… it is these unresolved value judgements, and the lack of an effective structure for organizing debate, that present the greatest threat to adaptive harvest management as a viable means for coping with management uncertainty."
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It is appropriate and necessary that "models" be discussed. It is the explicit description of models that will assist in increasing the learning curve of all management actions. Lee (1999:5) stated "The essence of managing adaptively is having an explicit vision or model of the ecosystem one is trying to guide. That explicit vision provides a baseline for defining surprise. Without surprise, learning does not expand the boundaries of understanding." And Gilmour et al. (1999:2) explained that "… some simplification is involved in all efforts to understand ecosystems: the goal is to use simplified models to assist decision making, not to substitute models for judgement."
Models come in many forms: formal and informal; mental and computer; complex and simple. When an experienced craftsman goes to buy a new power tool, s/he almost certainly uses an informal, mental model that is likely to be complex. Considerations such as the nearness to a power source of the use of the tool, the weight and hand-holds, the actual power available, availability of attachments and price might all play a role in the decision process. The craftsman will draw on a broad range of experiences to decide the value of these variables. A combination of objective and subjective data would be brought into play to decide them. The modeling process will continue after the purchase as the new tool is evaluated for performance and this new data will be used to make future purchases - to further reduce uncertainty. The modeling process, though never really completed, increases in value when the craftsman shares his model with other craftsman to obtain their input or with a novice. Models reduce uncertainty through learning and therefore increase productivity and efficiency. Return to top
One concern that has been expressed by managers and others is that the implementation of ARM will bring with it another time-consuming activity which has the potential to erode their being able to conduct "management." This is legitimate and will be particularly troublesome at first. Further, many administrators expect managers to "manage", not be researchers. This issue is considered below. In many cases, learning can be significantly enhanced by adding two components to current efforts: recording an explicit hypothesis and making a commitment to evaluate the results at least at the same level as the data used to justify the action. This latter concept is demonstrated in the following example. The justification for management is "reed canary grass and cattails have infested the marsh and waterfowl are not observed using it, therefore a controlled burn is prescribed." At least the variables "extent of reed canary grass and cattails" and "an inventory of waterfowl" use would be examined at the end of a period specified in the hypothesis. Learning could be enhanced by also measuring what replaced the reed canary grass and cattails and perhaps follow up monitoring over time.
At the same time, it must be recognized that the values of ARM will only be realized with a change in the way business is conducted. ARM and its attendant benefits are not without cost. A bit more time may need to be spent planning and reporting the plan and detecting and measuring change but perhaps more importantly, a new, broader focus needs to be adopted. Gunderson (1999:7) said the focus needs to be on "adaptive capacity", an ability to take risks, learn and prepare for surprises.
Another concern is in regards to the complexity of the system that might be required to enter and store information about management actions. This too is legitimate but its consideration is beyond the scope of this plan. It should be addressed very early in the implementation of ARM.
The concern that administrators may not provide the time for managers to implement ARM needs to be addressed. Evaluation of actions needs to be institutionalized at all levels within all organizations, partners and stakeholders of any project. An Evaluation coordinator could assist in this effort.