Byron K. Williams

Value of information in natural resource management: technical developments and application to pink‐footed geese

The “value of information” (VOI) is a generic term for the increase in value resulting from better information to guide management, or alternatively, the value foregone under uncertainty about the impacts of management (Yokota and Thompson, Medical Decision Making 2004; 24: 287). The value of information can be characterized in terms of several metrics, including the expected value of perfect information and the expected value of partial information. We extend the technical framework for the value of information by further developing the relationship between value metrics for partial and perfect information and describing patterns of their performance. We use two different expressions for the expected value of partial information to highlight its relationship to the expected value of perfect information. We also develop the expected value of partial information for hierarchical uncertainties. We highlight patterns in the value of information for the Svalbard population of the pink-footed goose (Anser brachyrhynchus), a population that is subject to uncertainty in both reproduction and survival functions. The framework for valuing information is seen as having widespread potential in resource decision making, and serves as a motivation for resource monitoring, assessment, and collaboration.

On formally integrating science and policy: walking the walk

The contribution of science to the development and implementation of policy is typically neither direct nor transparent. In 1995, the U.S. Fish and Wildlife Service (FWS) made a decision that was unprecedented in natural resource management, turning to an unused and unproven decision process to carry out trust responsibilities mandated by an international treaty. The decision process was adopted for the establishment of annual sport hunting regulations for the most economically important duck population in North America, the 6 to 11 million mallards Anas platyrhynchos breeding in the mid-continent region of north-central United States and central Canada. The key idea underlying the adopted decision process was to formally embed within it a scientific process designed to reduce uncertainty (learn) and thus make better decisions in the future. The scientific process entails use of models to develop predictions of competing hypotheses about system response to the selected action at each decision point. These predictions not only are used to select the optimal management action, but also are compared with the subsequent estimates of system state variables, providing evidence for modifying degrees of confidence in, and hence relative influence of, these models at the next decision point. Science and learning in one step are formally and directly incorporated into the next decision, contrasting with the usual ad hoc and indirect use of scientific results in policy development and decision-making. Application of this approach over the last 20 years has led to a substantial reduction in uncertainty, as well as to an increase in transparency and defensibility of annual decisions and a decrease in the contentiousness of the decision process. As resource managers are faced with increased uncertainty associated with various components of global change, this approach provides a roadmap for the future scientific management of natural resources. Science and policy

Resilience and Resource Management

Abstract Resilience is an umbrella concept with many different shades of meaning. The use of the term has grown over the past several decades to the point that by now, many disciplines have their own definitions and metrics. In this paper, we aim to provide a context and focus for linkages of resilience to natural resources management. We consider differences and similarities in resilience as presented in several disciplines relevant to resource management. We present a conceptual framework that includes environmental drivers, management interventions, and system responses cast in terms of system resilience, as well as a process for decision making that allows learning about system resilience through experience and incorporation of that learning into management. We discuss the current state of operational management for resilience, and suggest ways to improve it. Finally, we describe the challenges in managing for resilience and offer some recommendations about the scientific information needs and scientific issues relevant to making resilience a more meaningful component of natural resources management.

Ecological integrity assessment as a metric of biodiversity: are we measuring what we say we are?

As the recognition of the importance of biological diversity in biological conservation grows, an ongoing challenge is to develop metrics that can be used for effective conservation and management. The ecological integrity assessment has been proposed as such a metric. It is held by some to measure species composition, diversity, and habitat quality, as well as ecosystem structure, composition, and function. The methodology relies on proxy variables that include data on landscape characteristics such as patch size, abiotic factors such as hydrology, and some features of vegetation structure and composition. We suggest that the measure is flawed on four levels. First, its putative representation of general ecological form and function, and its lack of specific detail about how it actually represents those attributes, leaves the metric without the focus needed to be useful for measuring ecological features on the ground and testing associated hypotheses and predictions. Second, the proxy variables used to represent biological diversity, such as habitat (vegetation) metrics and vascular plant species diversity, are not empirically correlated with diversity of a range of taxa or of other components of the biota. Third, like other ecological indices that integrate many distinct features, the ecological integrity index is subject to the loss of information in its condensation of multi-dimensional variability into a one-dimensional index, and it may be subject to systematic bias from the conversion of raw data into categorical scores. Fourth, the sampling protocols are at risk of sampling bias, observer bias, and measurement error, any of which can confound the estimation of conservation value. In terms of biological diversity, the methodology produces an unreliable estimate of the number of vascular plant species and their relative percentages of occurrence, and an absence of any protocols for taxa other than plants. For these reasons we believe that ecological integrity assessment is currently of limited value as a measure of site-specific biological diversity and its change over time. A considerable amount of investigation is needed in order to have confidence in the results of an ecological integrity assessment, especially if it is to be used for regulatory purposes. We suggest further refinements and discuss alternative measures of biological diversity that provide reliable metrics for assessing change. A thoughtful choice among measures can help to identify the most appropriate assessment for conservation decisions.

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.

A Decision-Analytic Approach to Adaptive Resource Management

The barriers to implementation, learning, and adaptation in adaptive management have become legendary, and there are serious concerns about the applicability of adaptive management to “wicked problems” in resource conservation. There are, however, hopeful signs that adaptive management may yet live up to its promise. We suggest that the basic concept of adaptive management is helping change the culture of resource management and, thus, is having an impact far broader than any improvements in resource conditions that may or may not have been achieved in particular applications.

Estimating Abundance for Closed Populations with Mark—Recapture Methods

• Estimating N is much more difficult than you might initially expect • A variety of methods can be used: o Census – assume count all animals in the population o Sample plots – assume count all animals on plots o Transect methods – estimate detection probability as a function of distance from a line transect or point to animals o Capture-recapture – estimate capture probability – our focus • Or, can abandon estimation and use an index – often done, seldom tested • Essentially comes down to dealing with counting animals and relating the count to the number in the population somehow.

Frequencies of decision making and monitoring in adaptive resource management

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management implications and extensions.

Optimization and Resilience in Natural Resources Management

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.