Double-Loop Learning in Adaptive Management: The Need, the Challenge, and the Opportunity

Adaptive management addresses uncertainty about the processes influencing resource dynamics, as well as the elements of decision making itself. The use of management to reduce both kinds of uncertainty is known as double-loop learning. Though much work has been done on the theory and procedures to address structural uncertainty, there has been less progress in developing an explicit approach for institutional learning about decision elements. Our objective is to describe evidence-based learning about the decision elements, as a complement to the formal "learning by doing" framework for reducing structural uncertainties. Adaptive management is described as a multi-phase approach to management and learning, with a set-up phase of identifying stakeholders, objectives, and other decision elements; an iterative phase that uses these elements in an ongoing cycle of technical learning about system structure and management impacts; and an institutional learning phase involving the periodic reconsideration of the decision elements. We describe a framework for institutional learning that is complementary to that of technical learning, including uncertainty metrics, propagation of change, and mechanisms and consequences of change over time. Operational issues include ways to recognize when the decision elements should be revisited, which elements should be adjusted, and how alternatives can be identified and incorporated based on experience and management performance. We discuss the application of this framework in decision making for renewable natural resources. As important as it is to learn about the processes driving resource dynamics, learning about the elements of the decision architecture is equally, if not more, important.

Adaptive Management of Environmental Flows

Adaptive management enables managers to work with complexity and uncertainty, and to respond to changing biophysical and social conditions. Amid considerable uncertainty over the benefits of environmental flows, governments are embracing adaptive management as a means to inform decision making. This Special Issue of Environmental Management presents examples of adaptive management of environmental flows and addresses claims that there are few examples of its successful implementation. It arose from a session at the 11th International Symposium on Ecohydraulics held in Australia, and is consequently dominated by papers from Australia. We classified the papers according to the involvement of researchers, managers and the local community in adaptive management. Five papers report on approaches developed by researchers, and one paper on a community-led program; these case studies currently have little impact on decision making. Six papers provide examples involving water managers and researchers, and two papers provide examples involving water managers and the local community. There are no papers where researchers, managers and local communities all contribute equally to adaptive management. Successful adaptive management of environmental flows occurs more often than is perceived. The final paper explores why successes are rarely reported, suggesting a lack of emphasis on reflection on management practices. One major challenge is to increase the documentation of successful adaptive management, so that benefits of learning extend beyond the project where it takes place. Finally, moving towards greater involvement of all stakeholders is critical if we are to realize the benefits of adaptive management for improving outcomes from environmental flows.

Understanding the demographic drivers of realized population growth rates

Identifying the demographic parameters (e.g., reproduction, survival, dispersal) that most influence population dynamics can increase conservation effectiveness and enhance ecological understanding. Life table response experiments (LTRE) aim to decompose the effects of change in parameters on past demographic outcomes (e.g., population growth rates). But the vast majority of LTREs and other retrospective population analyses have focused on decomposing asymptotic population growth rates, which do not account for the dynamic interplay between population structure and vital rates that shape realized population growth rates (λt=Nt+1/Nt) in time-varying environments. We provide an empirical means to overcome these shortcomings by merging recently developed "transient life-table response experiments" with integrated population models (IPMs). IPMs allow for the estimation of latent population structure and other demographic parameters that are required for transient LTRE analysis, and Bayesian versions additionally allow for complete error propagation from the estimation of demographic parameters to derivations of realized population growth rates and perturbation analyses of growth rates. By integrating available monitoring data for Lesser Scaup over 60 yr, and conducting transient LTREs on IPM estimates, we found that the contribution of juvenile female survival to long-term variation in realized population growth rates was 1.6 and 3.7 times larger than that of adult female survival and fecundity, respectively. But a persistent long-term decline in fecundity explained 92% of the decline in abundance between 1983 and 2006. In contrast, an improvement in adult female survival drove the modest recovery in Lesser Scaup abundance since 2006, indicating that the most important demographic drivers of Lesser Scaup population dynamics are temporally dynamic. In addition to resolving uncertainty about Lesser Scaup population dynamics, the merger of IPMs with transient LTREs will strengthen our understanding of demography for many species as we aim to conserve biodiversity during an era of non-stationary global change.

Evaluating the efficacy of adaptive management approaches: is there a formula for success?

Within the field of natural-resources management, the application of adaptive management is appropriate for complex problems high in uncertainty. Adaptive management is becoming an increasingly popular management-decision tool within the scientific community and has developed into two primary schools of thought: the Resilience-Experimentalist School (with high emphasis on stakeholder involvement, resilience, and highly complex models) and the Decision-Theoretic School (which results in relatively simple models through emphasizing stakeholder involvement for identifying management objectives). Because of these differences, adaptive management plans implemented under each of these schools may yield varying levels of success. We evaluated peer-reviewed literature focused on incorporation of adaptive management to identify components of successful adaptive management plans. Our evaluation included adaptive management elements such as stakeholder involvement, definitions of management objectives and actions, use and complexity of predictive models, and the sequence in which these elements were applied. We also defined a scale of degrees of success to make comparisons between the two adaptive management schools of thought. Our results include the relationship between the adaptive management process documented in the reviewed literature and our defined continuum of successful outcomes. Our data suggest an increase in the number of published articles with substantive discussion of adaptive management from 2000 to 2009 at a mean rate of annual change of 0.92 (r² = 0.56). Additionally, our examination of data for temporal patterns related to each school resulted in an increase in acknowledgement of the Decision-Theoretic School of thought at a mean annual rate of change of 0.02 (r² = 0.6679) and a stable acknowledgement for the Resilience-Experimentalist School of thought (r² = 0.0042; slope = 0.0013). Identifying the elements of successful adaptive management will be advantageous to natural-resources managers considering adaptive management as a decision tool.

Learning and adaptation in the management of waterfowl harvests

A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.