Learning and adaptation in the management of waterfowl harvests

Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases

Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.

Linking landscape-scale conservation to regional and continental outcomes for a migratory species

Land-use intensification on arable land is expanding and posing a threat to biodiversity and ecosystem services worldwide. We develop methods to link funding for avian breeding habitat conservation and management at landscape scales to equilibrium abundance of a migratory species at the continental scale. We apply this novel approach to a harvested bird valued by birders and hunters in North America, the northern pintail duck (Anas acuta), a species well below its population goal. Based on empirical observations from 2007–2016, habitat conservation investments for waterfowl cost $313 M and affected <2% of the pintail’s primary breeding area in the Prairie Pothole Region of Canada. Realistic scenarios for harvest and habitat conservation costing an estimated $588 M (2016 USD) led to predicted pintail population sizes <3 M when assuming average parameter values. Accounting for parameter uncertainty, converting 70–100% of these croplands to idle grassland (cost: $35.7B–50B) is required to achieve the continental population goal of 4 M individuals under the current harvest policy. Using our work as a starting point, we propose continued development of modeling approaches that link conservation funding, habitat delivery, and population response to better integrate conservation efforts and harvest management of economically important migratory species.

Accumulating evidence in ecology: Once is not enough

Many published studies in ecological science are viewed as stand-alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the "reproducibility crisis" in science. The key step in science is the comparison of hypothesis-based predictions with observations, where the predictions are typically generated by hypothesis-specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta-analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are preadapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.

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.

Evaluating wildlife management by using principles of applied ecology: case studies and implications

Context The broad concepts and generalisations that guide conduct of applied ecology, including wildlife management, have been reviewed and synthesised recently into 22 prescriptive and three empirical principles. Aims The aim of this study was to use these principles to evaluate three on-ground wildlife management programs and assess the utility of the principles themselves. Key results Case studies of long-term management of national park biodiversity impacted by feral pigs (Sus scrofa), and of conservation and harvest of red kangaroos (Macropus rufus) and mallards (Anas platyrhnchos), were selected to provide a representative range of management objectives, spatial scales and land tenures, and to include both native and introduced species. Management documents and a considerable scientific literature were available for all three programs. The results highlight similarities and differences among management activities and demonstrate the 25 principles to differing degrees. Most of the prescriptive principles were demonstrated in both the management and the scientific literature in all three programs, but almost no use was made of the three empirical principles. We propose that use of the prescriptive principles constitutes evidence that these programs meet both societal and scientific expectations. However, the limited use of the empirical principles shows gaps in the three programs. Conclusions The results suggest that evaluating other wildlife management programs against the principles of applied ecology is worthwhile and could highlight aspects of those programs that might otherwise be overlooked. Little use was made of the empirical principles, but the the Effort–outcomes principle in particular provides a framework for evaluating management programs. Implications The effort–outcomes relationship should be a focus of future applied research, and both prescriptive and empirical principles should be integrated into wildlife management programs.

Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.

State-Dependent Resource Harvesting with Lagged Information about System States

Markov decision processes (MDPs), which involve a temporal sequence of actions conditioned on the state of the managed system, are increasingly being applied in natural resource management. This study focuses on the modification of a traditional MDP to account for those cases in which an action must be chosen after a significant time lag in observing system state, but just prior to a new observation. In order to calculate an optimal decision policy under these conditions, possible actions must be conditioned on the previous observed system state and action taken. We show how to solve these problems when the state transition structure is known and when it is uncertain. Our focus is on the latter case, and we show how actions must be conditioned not only on the previous system state and action, but on the probabilities associated with alternative models of system dynamics. To demonstrate this framework, we calculated and simulated optimal, adaptive policies for MDPs with lagged states for the problem of deciding annual harvest regulations for mallards (Anas platyrhynchos) in the United States. In this particular example, changes in harvest policy induced by the use of lagged information about system state were sufficient to maintain expected management performance (e.g. population size, harvest) even in the face of an uncertain system state at the time of a decision.

Practical precautionary resource management using robust optimization

Uncertainties inherent in fisheries motivate a precautionary approach to management, meaning an approach specifically intended to avoid bad outcomes. Stochastic dynamic optimization models, which have been in the fisheries literature for decades, provide a framework for decision making when uncertain outcomes have known probabilities. However, most such models incorporate population dynamics models for which the parameters are assumed known. In this paper, we apply a robust optimization approach to capture a form of uncertainty nearly universal in fisheries, uncertainty regarding the values of model parameters. Our approach, developed by Nilim and El Ghaoui (Oper Res 53(5):780-798, 2005), establishes bounds on parameter values based on the available data and the degree of precaution that the decision maker chooses. To demonstrate the applicability of the method to fisheries management problems, we use a simple example, the Skeena River sockeye salmon fishery. We show that robust optimization offers a structured and computationally tractable approach to formulating precautionary harvest policies. Moreover, as better information about the resource becomes available, less conservative management is possible without reducing the level of precaution.

Adaptive management: from more talk to real action

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.

Adaptive management for a turbulent future

The challenges that face humanity today differ from the past because as the scale of human influence has increased, our biggest challenges have become global in nature, and formerly local problems that could be addressed by shifting populations or switching resources, now aggregate (i.e., "scale up") limiting potential management options. Adaptive management is an approach to natural resource management that emphasizes learning through management based on the philosophy that knowledge is incomplete and much of what we think we know is actually wrong. Adaptive management has explicit structure, including careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. It is evident that adaptive management has matured, but it has also reached a crossroads. Practitioners and scientists have developed adaptive management and structured decision making techniques, and mathematicians have developed methods to reduce the uncertainties encountered in resource management, yet there continues to be misapplication of the method and misunderstanding of its purpose. Ironically, the confusion over the term "adaptive management" may stem from the flexibility inherent in the approach, which has resulted in multiple interpretations of "adaptive management" that fall along a continuum of complexity and a priori design. Adaptive management is not a panacea for the navigation of 'wicked problems' as it does not produce easy answers, and is only appropriate in a subset of natural resource management problems where both uncertainty and controllability are high. Nonetheless, the conceptual underpinnings of adaptive management are simple; there will always be inherent uncertainty and unpredictability in the dynamics and behavior of complex social-ecological systems, but management decisions must still be made, and whenever possible, we should incorporate learning into management.