With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.

An Adaptive Environmental Assessment and Management workshop process was used to assist Grand Canyon scientists and managers in developing conceptual and simulation models for the Colorado ecosystem affected by Glen Canyon Dam. This model examines ecosystem variables and processes at multiple scales in space and time, ranging from feet and hours for benthic algal response to diurnal flow changes, to reaches and decades for sediment storage and dynamics of long-lived native fish species. Its aim is to help screen policy options ranging from changes in hourly variation in flow allowed from Glen Canyon Dam, to major structural changes for restoration of more natural temperature regimes. It appears that we can make fairly accurate predictions about some components of ecosystem response to policy change (e.g., autochthonous primary production, insect communities, riparian vegetation, rainbow trout population), but we are moderately or grossly uncertain about others (e.g., long-term sediment storage, response of native and non-native fishes to physical habitat restoration). Further, we do not believe that existing monitoring programs are adequate to detect responses of native fishes or vegetation to anything short of gross habitat changes. Some experimental manipulations (such as controlled floods for beach/habitat- building) should proceed, but most should await development of better monitoring programs and sound temporal baseline information from those programs.

Many case studies in adaptive−management planning for riparian ecosystems have failed to produce useful models for policy comparison or good experimental management plans for resolving key uncertainties. Modeling efforts have been plagued by difficulties in representation of cross−scale effects (from rapid hydrologic change to long−term ecological response), lack of data on key processes that are difficult to study, and confounding of factor effects in validation data. Experimental policies have been seen as too costly or risky, particularly in relation to monitoring costs and risk to sensitive species. Research and management stakeholders have shown deplorable self−interest, seeing adaptive−policy development as a threat to existing research programs and management regimes, rather than as an opportunity for improvement. Proposals for experimental management regimes have exposed and highlighted some really fundamental conflicts in ecological values, particularly in cases in which endangered species have prospered under historical management and would be threatened by ecosystem restoration efforts. There is much potential for adaptive management in the future, if we can find ways around these barriers.

Even unmanaged ecosystems are characterized by combinations of stability and instability and by unexpected shifts in behavior from both internal and external causes. That is even more true of ecosystems managed for the production of food or fiber. Data are sparse, knowledge of processes limited, and the act of management changes the system being managed. Surprise and change is inevitable. Here we review methods to develop, screen, and evaluate alternatives in a process where management itself becomes partner with the science by designing probes that produce updated understanding as well as eco- nomic product.

Renewable natural resources provide important contributions to food, fiber, and recreation in many parts of the world. The economies of some regions a r e heavily dependent on fisheries and forestry, and consumptive use of wildlife (hunting) is a traditional recreational pastime across Europe and North America. The management of renewable resources usually involves public agencies that are responsible for harvest regulation, and often production enhancement, so as to provide sustainable yields into the long-term future (resource husbandry). The track record of such agencies has been spotty: many resources have been mined to low levels before effective harvest regulation could be developed, while others have been managed so conservatively as to miss major harvesting opportunities.

Three key features of renewable resources have made them difficult to manage. First, sustainable production depends on leaving behind a "capital" stock after each harvesting, and there are definite limits to the production rates that this stock can maintain. Second, harvesting is normally undertaken by a community or industry of harvesters whose activities (investment, searching, etc.) are not completely monitored or regulated, so that dynamic responses, such as overcapitalization of fishing fleets, are common. Third, the biological relationships between managed stock size and production rates arises through a complex interplay between the organisms and their surrounding ecosystem; for any particular population, this relationship cannot be predicted in advance from ecological principles and must, instead, be learned through actual management experience.

This book is on the various methods of environmental impact assessment as a guide to design of new environmental development and management projects. This approach surveys the features of the environment likely to be affected by the developments under consideration, analyses the information collected, tries to predict the impact of these developments and lays down guidelines or rules for their management.

This book is concerned with practical problems, e.g. development in Canada, the management of fisheries, pest control, etc. It is devoted to a general understanding of environmental systems through methods that have worked in the real world with its many uncertainties. It does not reject the concept of environmental impact analysis but rather stresses the need for fundamental understanding of the structure and dynamics of ecosystems.