Abstract
Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the management cycle while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM); and hierarchy theory. The SAM Reflexive Learning Framework (SRLF) emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the management cycle, and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive framework and its deployment guides adaptive resource management within and beyond typical single-loop learning, across all governance levels.
Highlights
Adaptive resource management (ARM) is a learning-by-doing approach to managing natural resources (Allan and Stankey 2009, Walker and Salt 2012, Fabricius and Cundill 2014)
Its effective practice involves the activation, completion, and regeneration of the “adaptive management cycle” while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single, double, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance
We focus on building a foundation of reflexive learning to facilitate an appropriate balance within three-mode learning that promotes the activation, completion, and regeneration components of the ARM adaptive management cycle to achieve goals
Summary
Adaptive resource management (ARM) is a learning-by-doing approach to managing natural resources (Allan and Stankey 2009, Walker and Salt 2012, Fabricius and Cundill 2014). At this level of organization they culminate in the Thresholds of Potential Concern (TPC), for example, bedrock-influenced river habitat type coverage is 20 percent or less in rivers of the Crocodile River Catchment, as the explicit end-point goals of the subobjectives at this level of organization (Fig. 2) This hierarchical approach to the setting of objectives within the SRLF has the advantage of providing practitioners, operating at different SRLF levels, the opportunity to pinpoint pertinent end-point goals and the appropriate scales in which to implement ARM processes that are feasible to achieve these end-point goals, and associated objectives. Feedbacks for double-loop learning (green, dotted arrows) give rise to adaptive reflection
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