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Popularized by scientists in the 1970s, adaptive management is an integrative, multi-disciplinary approach to managing landscapes and natural resources. Despite its broad appeal many critics complain that adaptive management rarely works in practice as prescribed in theory. This chapter traces the history and evolution of the concept and assess its implementation challenges. One reason adaptive management has not always delivered on its promise to make natural resource management more “rational” is that in the real world of policymaking scientists and natural resource managers must contend with advocates that have conflicting values and goals. Scientists and managers also operate in the context of institutions that create particular constraints and opportunities, and are generally inflexible and resistant to change. In recognition of these sociopolitical realities, the focus of much adaptive management practice and scholarship has shifted to governance, particularly collaboration with stakeholders, transformation of the institutions responsible for management, and the process of social learning.

Three general steps move civic ecology practices from small local innovations to broader policy innovations: giving a label to the phenomenon (in our case “civic ecology”); becoming more effective as local providers of ecosystem services and contributors to community well-being through partnerships with scientists; and government and larger NGOs formulating policies that allow civic ecology practices to spread. Civic ecology practices are small social or “social-ecological innovations,” whereas larger NGOs and government agencies are policy entrepreneurs who shape the policy environment. Policy entrepreneurs can also bridge between multiple civic ecology practices and larger management initiatives to form regional adaptive and collaborative resource management systems.

The key to wise decision-making in disciplines such as conservation, wildlife management, and epidemiology is the ability to predict consequences of management actions on focal systems. Predicted consequences are evaluated relative to programme objectives in order to select the favoured action. Predictions are typically based on mathematical models developed to represent hypotheses about management effects on system dynamics. For populations ranging from large mammals to plant communities to bacterial pathogens, demographic modelling is often the approach favoured for model development. State variables of such models may be population abundance, density, occupancy, or species richness, with corresponding vital rates such as rates of reproduction, survival, local extinction, and local colonisation. A key source of uncertainty that characterises such modelling efforts is the nature of relationships between management actions and vital rates. Adaptive management is a form of structured decision-making developed for decision problems that are recurrent and characterised by such structural uncertainty. One approach to incorporating this uncertainty is to base decisions on multiple models, each of which makes different predictions according to its underlying hypothesis. An information state of model weights carries information about the relative predictive abilities of the models. Monitoring of system state variables provides information about system responses, and comparison of these responses with model-based predictions provides a basis for updating the information state. Decisions emphasise the better-predicting model(s), leading to better decisions as the process proceeds. Adaptive management can thus produce optimal decisions now, while simultaneously reducing uncertainty for even better management in the future.

As biodiversity, ecosystem function, and ecosystem services become more closely linked with human well-being at all scales, the study of ecology takes on increasing social, economic, and political importance. However, when compared with other disciplines long linked with human well-being, such as medicine, chemistry, and physics, the technical tools and instruments of the ecologist have generally lagged behind those of the others. This disparity is beginning to be overcome with the increasing use of biotelemetric techniques, microtechnologies, satellite and airborne imagery, geographic information systems (GIS), and both regional and global data networks. We believe that the value and efficiency of ecosystem studies can advance significantly with more widespread use of existing technologies, and with the adaptation of technologies currently used in other disciplines to ecosystem studies. More importantly, the broader use of these technologies is critical for contributing to the preservation of biodiversity and the development of sustainable natural resource use by humans. The concept of human management of biodiversity and natural systems is a contentious one. However, we assert that as human population and resource consumption continue to increase, biodiversity and resource sustainability will only be preserved by increasing management efforts—if not of the biodiversity and resources themselves, then of human impacts on them. The technologies described in this chapter will help enable better management efforts. In this context, biodiversity refers not only to numbers of species (i.e., richness) in an arbitrarily defined area, but also to species abundances within that area. Sustainability refers to the maintenance of natural systems, biodiversity, and resources for the benefit of future generations. Arid-land grazing systems support human social systems and economies in regions all over the world, and can be expected to play increasingly critical roles as human populations increase. Further, grazing systems represent a nexus of natural and domesticated systems. In these systems, native biodiversity exists side by side with introduced species and populations, and in fact can benefit from them.