Decision Making in a Human Population Living Sustainably

Abstract: The Tiwi people of northern Australia have managed natural resources continuously for 6000–8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision‐making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long‐term sustainability of environmental use. We examined the Tiwi decision‐making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.     

Combining Structured Decision Making and Value‐of‐Information Analyses to Identify Robust Management Strategies

Abstract: Structured decision making and value‐of‐information analyses can be used to identify robust management strategies even when uncertainty about the response of the system to management is high. We used these methods in a case study of management of the non‐native invasive species gray sallow willow (Salix cinerea) in alpine Australia. Establishment of this species is facilitated by wildfire. Managers are charged with developing a management strategy despite extensive uncertainty regarding the frequency of fires, the willow's demography, and the effectiveness of management actions. We worked with managers in Victoria to conduct a formal decision analysis. We used a dynamic model to identify the best management strategy for a range of budgets. We evaluated the robustness of the strategies to uncertainty with value‐of‐information analyses. Results of the value‐of‐information analysis indicated that reducing uncertainty would not change which management strategy was identified as the best unless budgets increased substantially. This outcome suggests there would be little value in implementing adaptive management for the problem we analyzed. The value‐of‐information analyses also highlighted that the main driver of gray sallow willow invasion (i.e., fire frequency) is not necessarily the same factor that is most important for decision making (i.e., willow seed dispersal distance). Value of‐information analyses enables managers to better target monitoring and research efforts toward factors critical to making the decision and to assess the need for adaptive management.     

A Strategy for Monitoring and Managing Declines in an Amphibian Community

Although many taxa have declined globally, conservation actions are inherently local. Ecosystems degrade even in protected areas, and maintaining natural systems in a desired condition may require active management. Implementing management decisions under uncertainty requires a logical and transparent process to identify objectives, develop management actions, formulate system models to link actions with objectives, monitor to reduce uncertainty and identify system state (i.e., resource condition), and determine an optimal management strategy. We applied one such structured decision‐making approach that incorporates these critical elements to inform management of amphibian populations in a protected area managed by the U.S. National Park Service. Climate change is expected to affect amphibian occupancy of wetlands and to increase uncertainty in management decision making. We used the tools of structured decision making to identify short‐term management solutions that incorporate our current understanding of the effect of climate change on amphibians, emphasizing how management can be undertaken even with incomplete information. Estrategia para Monitorear y Manejar Disminuciones en una Comunidad de Anfibios     

Mapping social–ecological vulnerability to inform local decision making

An overarching challenge of natural resource management and biodiversity conservation is that relationships between people and nature are difficult to integrate into tools that can effectively guide decision making. Social–ecological vulnerability offers a valuable framework for identifying and understanding important social–ecological linkages, and the implications of dependencies and other feedback loops in the system. Unfortunately, its implementation at local scales has hitherto been limited due at least in part to the lack of operational tools for spatial representation of social–ecological vulnerability. We developed a method to map social–ecological vulnerability based on information on human–nature dependencies and ecosystem services at local scales. We applied our method to the small‐scale fishery of Moorea, French Polynesia, by combining spatially explicit indicators of exposure, sensitivity, and adaptive capacity of both the resource (i.e., vulnerability of reef fish assemblages to fishing) and resource users (i.e., vulnerability of fishing households to the loss of fishing opportunity). Our results revealed that both social and ecological vulnerabilities varied considerably through space and highlighted areas where sources of vulnerability were high for both social and ecological subsystems (i.e., social–ecological vulnerability hotspots) and thus of high priority for management intervention. Our approach can be used to inform decisions about where biodiversity conservation strategies are likely to be more effective and how social impacts from policy decisions can be minimized. It provides a new perspective on human–nature linkages that can help guide sustainability management at local scales; delivers insights distinct from those provided by emphasis on a single vulnerability component (e.g., exposure); and demonstrates the feasibility and value of operationalizing the social–ecological vulnerability framework for policy, planning, and participatory management decisions.     

Active Adaptive Management for Conservation

Abstract:  Active adaptive management balances the requirements of management with the need to learn about the system being managed, which leads to better decisions. It is difficult to judge the benefit of management actions that accelerate information gain, relative to the benefit of making the best management decision given what is known at the time. We present a first step in developing methods to optimize management decisions that incorporate both uncertainty and learning via adaptive management. We assumed a manager can allocate effort to discrete units (e.g., areas for revegetation or animals for reintroduction), the outcome can be measured as success or failure (e.g., the revegetation in an area is successful or the animal survives and breeds), and the manager has two possible management options from which to choose. We further assumed that there is an annual budget that may be allocated to one or both of the two options and that the manager must decide on the allocation. We used Bayesian updating of the probability of success of the two options and stochastic dynamic programming to determine the optimal strategy over a specified number of years. The costs, level of certainty about the success of the two options, and the timeframe of management all influenced the optimal allocation of the annual budget. In addition, the choice of management objective had a large influence on the optimal decision. In a case study of Merri Creek, Melbourne, Australia, we applied the approach to determining revegetation strategies. Our approach can be used to determine how best to manage ecological systems in the face of uncertainty.     

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.     

A density projection approach for non-trivial information dynamics: Adaptive management of stochastic natural resources

We demonstrate a density projection approximation method for solving resource management problems with imperfect state information. The method expands the set of partially-observed Markov decision process (POMDP) problems that can be solved with standard dynamic programming tools by addressing dimensionality problems in the decision maker's belief state. Density projection is suitable for uncertainty over both physical states (e.g. resource stock) and process structure (e.g. biophysical parameters). We apply the method to an adaptive management problem under structural uncertainty in which a fishery manager's harvest policy affects both the stock of fish and the belief state about the process governing reproduction. We solve for the optimal endogenous learning policy—the active adaptive management approach—and compare it to passive learning and non-learning strategies. We demonstrate how learning improves efficiency but typically follows a period of costly short-run investment.     

On Valuing Information in Adaptive‐Management Models

Abstract: Active adaptive management looks at the benefit of using strategies that may be suboptimal in the near term but may provide additional information that will facilitate better management in the future. In many adaptive‐management problems that have been studied, the optimal active and passive policies (accounting for learning when designing policies and designing policy on the basis of current best information, respectively) are very similar. This seems paradoxical; when faced with uncertainty about the best course of action, managers should spend very little effort on actively designing programs to learn about the system they are managing. We considered two possible reasons why active and passive adaptive solutions are often similar. First, the benefits of learning are often confined to the particular case study in the modeled scenario, whereas in reality information gained from local studies is often applied more broadly. Second, management objectives that incorporate the variance of an estimate may place greater emphasis on learning than more commonly used objectives that aim to maximize an expected value. We explored these issues in a case study of Merri Creek, Melbourne, Australia, in which the aim was to choose between two options for revegetation. We explicitly incorporated monitoring costs in the model. The value of the terminal rewards and the choice of objective both influenced the difference between active and passive adaptive solutions. Explicitly considering the cost of monitoring provided a different perspective on how the terminal reward and management objective affected learning. The states for which it was optimal to monitor did not always coincide with the states in which active and passive adaptive management differed. Our results emphasize that spending resources on monitoring is only optimal when the expected benefits of the options being considered are similar and when the pay‐off for learning about their benefits is large.     

Improving Peer Review of Listings and Recovery Plans under the Endangered Species Act

Abstract: We discuss several challenges encountered in peer review of Endangered Species Act listings and recovery plans, with particular attention to Meffe et al.'s (1998) statement on independent scientific review in natural resource management. First, Endangered Species Act listing documents and recovery plans pose a diverse array of scientific questions, and we suggest that overall effectiveness of peer review may be increased by segregating the critical issues and identifying specific reviewers for each issue. Some scientific reviewers may be unfamiliar with the decision standards prescribed by the Endangered Species Act and implementing policies. Unnecessary confusion could be prevented by providing reviewers with information about these standards and by requesting that reviewers clearly differentiate their assessment of decisions that must be based on available information from recommendations for future research. Short review periods constitute another constraint on careful review, but tight deadlines are fairly intractable in the context of the Endangered Species Act. We suggest that short time frames could be partially ameliorated by narrowing the scope of issues to be treated by each reviewer, and we discuss the issue of providing monetary compensation for efficient review. Finally, Endangered Species Act listing decisions and recovery planning may profit from more frequent peer review of intermediate analyses that precede publication of formal proposals or complete plans.     

Adaptive Comanagement of a Marine Protected Area Network in Fiji

Adaptive management of natural resources is an iterative process of decision making whereby management strategies are progressively changed or adjusted in response to new information. Despite an increasing focus on the need for adaptive conservation strategies, there remain few applied examples. We describe the 9‐year process of adaptive comanagement of a marine protected area network in Kubulau District, Fiji. In 2011, a review of protected area boundaries and management rules was motivated by the need to enhance management effectiveness and the desire to improve resilience to climate change. Through a series of consultations, with the Wildlife Conservation Society providing scientific input to community decision making, the network of marine protected areas was reconfigured so as to maximize resilience and compliance. Factors identified as contributing to this outcome include well‐defined resource‐access rights; community respect for a flexible system of customary governance; long‐term commitment and presence of comanagement partners; supportive policy environment for comanagement; synthesis of traditional management approaches with systematic monitoring; and district‐wide coordination, which provided a broader spatial context for adaptive‐management decision making. Co‐Manejo Adaptativo de una Red de Áreas Marinas Protegidas en Fiyi     

Accounting for Uncertainty in Making Species Protection Decisions

Abstract:  Uncertainty gives rise to two decision errors in implementing the U.S. Endangered Species Act: listing species that are not in danger of extinction and delisting species that are in danger of extinction. I evaluated four methods (minimum standard, precautionary principle, minimax regret criterion, adaptive management) for deciding whether to list or delist a species when there is uncertainty about how those decisions are likely to influence survival of the species. A safe minimum standard criterion preserves some minimum amount or safe standard (population) of a species unless maintaining that amount generates unacceptable social cost. The precautionary principle favors not delisting a species when there is insufficient evidence on the efficacy of state management plans for protecting them. A minimax regret criterion selects the delisting decision that minimizes the maximum loss likely to occur under alternative ecosystem states. When the cost of making a correct decision is less than the cost of making an incorrect decision, the minimax regret criteria indicates that delisting is the optimal decision. Active adaptive management employs statistically valid experiments to test hypotheses about the likely impacts of delisting decisions. Safe minimum standard and minimax regret criterion are not compatible with the U.S. Endangered Species Act. The precautionary principle comes closest to describing how federal agencies make delisting decisions. Active adaptive management is scientifically superior to the other methods but is costly and time consuming and may not be compatible with the U.S. National Environmental Policy Act.     

The Influence of Model Structure on Conclusions about the Viability and Harvesting of Serengeti Wildebeest

We investigate how the viability and harvestability predicted by population models are affected by details of model construction. Based on this analysis we discuss some of the pitfalls associated with the use of classical statistical techniques for resolving the uncertainties associated with modeling population dynamics. The management of the Serengeti wildebeest (Connochaetes taurinus) is used as a case study. We fitted a collection of age-structured and unstructured models to a common set of available data and compared model predictions in terms of wildebeest viability and harvest. Models that depicted demographic processes in strikingly different ways fitted the data equally well. However, upon further analysis it became clear that models that fit the data equally well could nonetheless have very different management implications. In general, model structure had a much larger effect on viability analysis (e.g., time to collapse) than on optimal harvest analysis (e.g., harvest rate that maximizes harvest). Some modeling decisions, such as including age-dependent fertility rates, did not affect management predictions, but others had a strong effect (e.g., choice of model structure). Because several suitable models of comparable complexity fitted the data equally well, traditional model selection methods based on the parsimony principle were not practical for judging the value of alternative models. Our results stress the need to implement analytical frameworks for population management that explicitly consider the uncertainty about the behavior of natural systems.     

Assessing the components of adaptive capacity to improve conservation and management efforts under global change

Natural‐resource managers and other conservation practitioners are under unprecedented pressure to categorize and quantify the vulnerability of natural systems based on assessment of the exposure, sensitivity, and adaptive capacity of species to climate change. Despite the urgent need for these assessments, neither the theoretical basis of adaptive capacity nor the practical issues underlying its quantification has been articulated in a manner that is directly applicable to natural‐resource management. Both are critical for researchers, managers, and other conservation practitioners to develop reliable strategies for assessing adaptive capacity. Drawing from principles of classical and contemporary research and examples from terrestrial, marine, plant, and animal systems, we examined broadly the theory behind the concept of adaptive capacity. We then considered how interdisciplinary, trait‐ and triage‐based approaches encompassing the oft‐overlooked interactions among components of adaptive capacity can be used to identify species and populations likely to have higher (or lower) adaptive capacity. We identified the challenges and value of such endeavors and argue for a concerted interdisciplinary research approach that combines ecology, ecological genetics, and eco‐physiology to reflect the interacting components of adaptive capacity. We aimed to provide a basis for constructive discussion between natural‐resource managers and researchers, discussions urgently needed to identify research directions that will deliver answers to real‐world questions facing resource managers, other conservation practitioners, and policy makers. Directing research to both seek general patterns and identify ways to facilitate adaptive capacity of key species and populations within species, will enable conservation ecologists and resource managers to maximize returns on research and management investment and arrive at novel and dynamic management and policy decisions.     

Resolving structural uncertainty in natural resources management using POMDP approaches

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems.     

Confronting Uncertainty and Missing Values in Environmental Value Transfer as Applied to Species Conservation

Abstract: The nonuse (or passive) value of nature is important but time‐consuming and costly to quantify with direct surveys. In the absence of estimates of these values, there will likely be less investment in conservation actions that generate substantial nonuse benefits, such as conservation of native species. To help overcome decisions about the allocation of conservation dollars that reflect the lack of estimates of nonuse values, these values can be estimated indirectly by environmental value transfer (EVT). EVT uses existing data or information from a study site such that the estimated monetary value of an environmental good is transferred to another location or policy site. A major challenge in the use of EVT is the uncertainty about the sign and size of the error (i.e., the percentage by which transferred value exceeds the actual value) that results from transferring direct estimates of nonuse values from a study to a policy site, the site where the value is transferred. An EVT is most useful if the decision‐making framework does not require highly accurate information and when the conservation decision is constrained by time and financial resources. To account for uncertainty in the decision‐making process, a decision heuristic that guides the decision process and illustrates the possible decision branches, can be followed. To account for the uncertainty associated with the transfer of values from one site to another, we developed a risk and simulation approach that uses Monte Carlo simulations to evaluate the net benefits of conservation investments and takes into account different possible distributions of transfer error. This method does not reduce transfer error, but it provides a way to account for the effect of transfer error in conservation decision making. Our risk and simulation approach and decision‐based framework on when to use EVT offer better‐informed decision making in conservation.     

Choosing and Using Climate‐Change Scenarios for Ecological‐Impact Assessments and Conservation Decisions

Increased concern over climate change is demonstrated by the many efforts to assess climate effects and develop adaptation strategies. Scientists, resource managers, and decision makers are increasingly expected to use climate information, but they struggle with its uncertainty. With the current proliferation of climate simulations and downscaling methods, scientifically credible strategies for selecting a subset for analysis and decision making are needed. Drawing on a rich literature in climate science and impact assessment and on experience working with natural resource scientists and decision makers, we devised guidelines for choosing climate‐change scenarios for ecological impact assessment that recognize irreducible uncertainty in climate projections and address common misconceptions about this uncertainty. This approach involves identifying primary local climate drivers by climate sensitivity of the biological system of interest; determining appropriate sources of information for future changes in those drivers; considering how well processes controlling local climate are spatially resolved; and selecting scenarios based on considering observed emission trends, relative importance of natural climate variability, and risk tolerance and time horizon of the associated decision. The most appropriate scenarios for a particular analysis will not necessarily be the most appropriate for another due to differences in local climate drivers, biophysical linkages to climate, decision characteristics, and how well a model simulates the climate parameters and processes of interest. Given these complexities, we recommend interaction among climate scientists, natural and physical scientists, and decision makers throughout the process of choosing and using climate‐change scenarios for ecological impact assessment. Selección y Uso de Escenarios de Cambio Climático para Estudios de Impacto Ecológico y Decisiones de Conservación     

Incorporating Uncertainty of Management Costs in Sensitivity Analyses of Matrix Population Models

The importance of accounting for economic costs when making environmental‐management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population‐management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost‐efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making.     

Effect of risk aversion on prioritizing conservation projects

Conservation outcomes are uncertain. Agencies making decisions about what threat mitigation actions to take to save which species frequently face the dilemma of whether to invest in actions with high probability of success and guaranteed benefits or to choose projects with a greater risk of failure that might provide higher benefits if they succeed. The answer to this dilemma lies in the decision maker's aversion to risk—their unwillingness to accept uncertain outcomes. Little guidance exists on how risk preferences affect conservation investment priorities. Using a prioritization approach based on cost effectiveness, we compared 2 approaches: a conservative probability threshold approach that excludes investment in projects with a risk of management failure greater than a fixed level, and a variance‐discounting heuristic used in economics that explicitly accounts for risk tolerance and the probabilities of management success and failure. We applied both approaches to prioritizing projects for 700 of New Zealand's threatened species across 8303 management actions. Both decision makers’ risk tolerance and our choice of approach to dealing with risk preferences drove the prioritization solution (i.e., the species selected for management). Use of a probability threshold minimized uncertainty, but more expensive projects were selected than with variance discounting, which maximized expected benefits by selecting the management of species with higher extinction risk and higher conservation value. Explicitly incorporating risk preferences within the decision making process reduced the number of species expected to be safe from extinction because lower risk tolerance resulted in more species being excluded from management, but the approach allowed decision makers to choose a level of acceptable risk that fit with their ability to accommodate failure. We argue for transparency in risk tolerance and recommend that decision makers accept risk in an adaptive management framework to maximize benefits and avoid potential extinctions due to inefficient allocation of limited resources. El Efecto de la Aversión de Riesgo sobre la Priorización de Proyectos de Conservación     

Social network effects on the adoption of sustainable natural resource management practices in Ethiopia

Soil loss, nutrient depletion and land degradation contribute to the skimpy performance of smallholder agriculture and pose serious policy challenges in developing countries. Surprisingly, natural resource management practices that enhance sustainability while improving productivity have not been fully adopted despite continuous efforts of promotion. Using data collected from 2901 farm households in the Farmers Innovation Fund (FIF) of the World Bank, this study examines factors delaying adoption of resource management and farming practices from the perspective of social learning and network size. Specifically, the study aims at identifying the extent to which differences in network structure matter in providing opportunities to learn about new ways of sustainable resource management practices using regression analysis. The result confirms that social network size plays a significant role in enhancing adoption of natural resource management practices. Moreover, external sources of information such as extension provision play a crucial role in enhancing adoption of resource management practices. Thus, future endeavours should link extension services to informal networks to enhance adoption of sustainable natural resource management practices.