Ecological systems are characterised by uncertainty. With adaptive management (AM), key hypotheses about uncertainties of a system are tested through experimentation, thereby utilizing AM as a tool for both management and learning . The most current, available scientific knowledge informs policy decisions, the advantage being that policies and decisions can be revisited as more knowledge is acquired . AM encompasses certain fundamental components including; respecting the suitable temporal and spatial scales of systems; use of statistical power and controls to inform experimentation outcomes; use of computer modelling to propose alternative outcomes; involving a multi-stakeholder body to develop alternate management strategies and communicating newly acquired scientific information to policy makers .
Phases of an Adaptive Management Plan
Certain vital components of AM could be adopted by the EIA process to leverage it as a more powerful environmental management tool. It has been argued that EIAs do not attempt to incorporate uncertainty into their predictions resulting in less successful environmental management outcomes . One aspect of EIA explored here is worth the consideration of a more adaptive approach; information collection which impinges directly on developing predictions and making decisions.
EIAs are often based on large compilations of information geared to documenting all aspects of the system, the downfall being the inability of such detailed inventories to provide information on system component change over time . AM principles do align with the scoping phase of an EIA as only pertinent variables to a system’s functioning are highlighted and documented. However, AM principles propose that greater effectiveness could be achieved in understanding the system and developing predictions through more integrative descriptions of critical relationships . Typical EIAs tend to rely on descriptive baseline data for impact prediction, a practice that could be substantially improved by measuring environmental variables and proposing testable hypotheses to assess change . An adaptive management approach would employ experimentation to gauge interactions between system components . This approach is no doubt more time and resource-intensive however it provides a more dynamic view of the system, is more adept at proposing potential impacts and is essential where there is little to no data about a system .
An adaptive management system has been embraced to manage the Colorado river, an extensively regulated socio-ecological system in the United States. The Glen Canyon Dam was built on the Colorado River in the 1960s to provide hydroelectricity and regulate flow from the Upper Colorado River Basin to the lower basin, which accounts for approximately 30 million people . This dam has greatly affected the downstream ecosystem due to large fluctuations in flow but is also a vital water source for the Desert Southwest . Much controversy exists on how to share the water resources as well as managing negative impacts on the downstream ecosystem. In response, the Glen Canyon Dam Adaptive Management Program was established in 1997 to provide long-term research and monitoring .
This management program consists of multiple stakeholders representing diverse interests working to identify strategies for dam management . High-flow experimental releases from the Glen Canyon River dam have been proposed as these high-flow events mimic pre-dam natural flooding of the Colorado River that once conserved and redistributed sediment to the downstream habitats . The intention is to evaluate short-duration, high-volume releases during sediment-enriched periods to determine the effects on sandbars and best practices in conserving this landscape . Sand features downstream are important to support integrity of wildlife habitat, to encourage riparian growth and to preserve archaeological features. The first experimental high-flow was conducted in 1996 over a 7-day period to test the hypothesis that controlled floods improved sediment deposition and would modify the downstream ecosystem without adversely affecting other canyon resources . The experiment provided new information to ecosystem managers concerning aquatic and terrestrial life where certain species were adversely affected while others were not. The experiment also proved to be successful in enhancing sandbars . The execution of this experiment was successful in improving the understanding the system, highlighting what is still not well understood and providing feedback that will inform future decisions.The adaptive management principle of defining critical relationships between system components is fully embraced by the Glen Canyon Dam Adaptive Management Program. The on-going cycle of learning from high-flow experimentation is used to refine future actions on the river. Simply collecting baseline data constrains the effectiveness of making predictions and informing decision-making. With an integrative and dynamic approach to data collection, a wealth of scientific knowledge is acquired which assists greatly in improving stewardship of natural resources. In light of the fact that project-level environmental impact assessments are constrained for both time and financial resources, there would no doubt be great resistance to employing experimentation to obtain vital ecosystem information. There is therefore the need to value quality of information over quantity. Ecological systems and their components interact and fluctuate in ways that are not understood, it is only when hypotheses are developed and tested that knowledge can be gleaned. Without this knowledge impact predictions and decisions lack substance thereby diminishing environmental impact assessment effectiveness.
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 Sandbar deposition following the 2013 high-release flow on the Colorado River in the Grand Canyon. Retrieved from http://www.gcmrc.gov/gis/sandbartour2013/index.html on January 19th, 2014.