The origin of the adaptive management concept can be traced back to the early concepts of scientific management pioneered by Frederick Taylor in the early 1900’s (Haber 1964). The term ‘adaptive management’ evolved in natural resource management workshops through decision makers, managers and scientists focussing on building simulation models to uncover key assumptions and uncertainties (Bormann et al., 1999). Holling (1978) and Walters (1986) further developed the adaptive management practice.
Adaptive management for resource management purposes has probably been most frequently applied in Australia and North America. Initially applied in fishery management, it received more broad application in the 1990s and 2000s (Biodiversity Support Program of the WWF, The Nature Conservancy (TNC), and World Resources Institute).
Adaptive management is foremost an iterative process of optimal decision making in the face of uncertainty, with an aim to structure and reduce uncertainty over time via system monitoring. In this way, decision making simultaneously maximises one or more resource objectives and accrues information needed to improve future management. In short, adaptive management is a tool that should be used not only to change a system, but also to learn about the system (Holling 1978). And because adaptive management is based on a learning process, it improves long-run management outcomes. The challenge in using adaptive approaches lies in finding the correct balance between gaining knowledge to improve management in the future and achieving the best short-term outcome based on current knowledge (Stankey et al., 2005).
Implementing adaptive monitoring for the execution of a lower-impact marine construction project involves the integration of project design, management, and monitoring to systematically test assumptions in order to adapt and learn.
The three principle components of adaptive management in environmental practice are:
- Testing assumptions, which encompass systematically trying different actions to achieve a desired outcome (contrary to random trial-and-error processes). It involves using knowledge about the specific site to select the best known strategy, laying out the assumptions behind how that strategic approach will work, and then collecting monitoring data to determine if the assumptions hold true.
- Adaptation, which involves changing assumptions and interventions to respond to new or different information obtained through monitoring and project experience.
- Learning, which is about explicitly documenting a team’s planning and implementation processes and its successes and failures for internal learning as well as learning across the stakeholder community.
Adaptive execution cycle as Frame of Reference
When applying the FoR for the adaptive monitoring strategy, the FoR is first designed from the “objective phase” onward. Based on a strategic objective, that is usually an aggregate and project-wide, operational objectives for specific project parts can be identified. These objectives in turn require a management recipe based on the quantitative state concept, benchmark, intervention and evaluation. The necessity to come up with a quantitative state concept is to enable objective and reproducible decision making. Based on knowledge on the ecosystem, the appropriate parameters and indicators should be selected to describe this state.
When applying adaptive execution cycles this process knowledge will of course evolve on-the-go, so the appropriate quantitative state concept is not rigid for the full project duration. From the quantitative state concept, a so-called desired (or reference) state, describing an acceptable (quantitative) state of the ecosystem, can be defined. The benchmark then basically compares that desired state with the current state that should be monitored in sufficient detail on the appropriate spatial and temporal scales. Based on this comparison, action might be required. Within the adaptive approach, the action need can be divided in three categories:
- no action needed (as stress levels are still sufficiently low)
- pro-active action needed (as stress levels are rising, but still at acceptable levels), or
- intervention needed (as the stress levels are above acceptable levels).
Interventions should directly influence the current state in order to avoid the risk for over-stressing the ecosystem. After any intervention (or non-intervention) the process should be evaluated not only whether the decision recipe was successful, but also as feedback on the realism of the pre-defined objectives and to see whether the process knowledge has undergone relevant changes. In this way the process contains feedback loops in diverse directions keeping the pre-defined clear realistic objectives in mind. This makes the scheme useful in all stages of a marine infrastructure development and not only strictly applicable to the execution phase.