An introduction to decision science for conservation.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.

Las decisiones sobre la conservación de la biodiversidad son difíciles de tomar, especialmente cuando involucran diferentes valores, objetivos multidimensionales complejos, recursos limitados, urgencia y una incertidumbre considerable. Las ciencias de la decisión incorporan una teoría sobre cómo tomar decisiones difíciles y una variedad extensa de marcos de trabajo y herramientas que transforman esa teoría en práctica. Buscamos mejorar la claridad conceptual y la aplicación práctica de las ciencias de la decisión para ayudar al órgano decisorio a aplicar estas ciencias a los problemas de conservación. Nos enfocamos en las barreras para la aceptación de las ciencias de la decisión, incluyendo la falta de capacitación y de conciencia por estas ciencias; la confusión por la terminología común y cuáles herramientas y marcos de trabajo aplicar; y la impresión errónea de que la aplicación de estas ciencias consume tiempo y debe ser costosa y compleja. Para asistir en la navegación de la literatura extensa y dispar de las ciencias de la decisión, aclaramos el significado de varios términos comunes: ciencias de la decisión, teoría de la decisión, análisis de decisiones, toma estructurada de decisiones y herramientas de apoyo para las decisiones. La aplicación de las ciencias de la decisión no tiene que ser compleja ni debe llevar mucho tiempo; de hecho, todo comienza con saber cómo pensar detenidamente en los componentes de una decisión mediante el análisis de decisiones (es decir, definir el problema, producir objetivos, desarrollar alternativas, estimar consecuencias y realizar compensaciones). Lo anterior se logra de mejor manera mediante la aplicación de una estrategia prototipos rápidos. En cada paso, las herramientas de apoyo para las decisiones pueden proporcionar visión y claridad adicionales, mientras que los marcos de apoyo para las decisiones (p.ej.: gestión de amenazas prioritarias y planeación sistemática de la conservación) pueden asistir en la navegación de los diferentes pasos de un análisis de decisiones para contextos particulares. Resumimos los marcos de trabajo y las herramientas más importantes de apoyo para las decisiones y describimos el paso, y el contexto, del análisis de decisiones para el que es más útil aplicarlos. Nuestra introducción a las ciencias de la decisión apoyará en la contextualización de las estrategias actuales y los nuevos desarrollos, y ayudarán al órgano decisorio a comenzar a aplicar estas ciencias en los problemas de conservación.

Weighting and aggregating expert ecological judgments.

Performance weighted aggregation of expert judgments, using calibration questions, has been advocated to improve pooled quantitative judgments for ecological questions. However, there is little discussion or practical advice in the ecological literature regarding the application, advantages or challenges of performance weighting. In this paper we (1) illustrate how the IDEA protocol with four-step question format can be extended to include performance weighted aggregation from the Classical Model, and (2) explore the extent to which this extension improves pooled judgments for a range of performance measures. Our case study demonstrates that performance weights can improve judgments derived from the IDEA protocol with four-step question format. However, there is no a-priori guarantee of improvement. We conclude that the merits of the method lie in demonstrating that the final aggregation of judgments provides the best representation of uncertainty (i.e., validation), whether that be via equally weighted or performance weighted aggregation. Whether the time and effort entailed in performance weights can be justified is a matter for decision-makers. Our case study outlines the rationale, challenges, and benefits of performance weighted aggregations. It will help to inform decisions about the deployment of performance weighting and avoid common pitfalls in its application.

Eliciting improved quantitative judgements using the IDEA protocol: A case study in natural resource management.

INTRODUCTION: Natural resource management uses expert judgement to estimate facts that inform important decisions. Unfortunately, expert judgement is often derived by informal and largely untested protocols, despite evidence that the quality of judgements can be improved with structured approaches. We attribute the lack of uptake of structured protocols to the dearth of illustrative examples that demonstrate how they can be applied within pressing time and resource constraints, while also improving judgements. AIMS AND METHODS: In this paper, we demonstrate how the IDEA protocol for structured expert elicitation may be deployed to overcome operational challenges while improving the quality of judgements. The protocol was applied to the estimation of 14 future abiotic and biotic events on the Great Barrier Reef, Australia. Seventy-six participants with varying levels of expertise related to the Great Barrier Reef were recruited and allocated randomly to eight groups. Each participant provided their judgements using the four-step question format of the IDEA protocol ('Investigate', 'Discuss', 'Estimate', 'Aggregate') through remote elicitation. When the events were realised, the participant judgements were scored in terms of accuracy, calibration and informativeness. RESULTS AND CONCLUSIONS: The results demonstrate that the IDEA protocol provides a practical, cost-effective, and repeatable approach to the elicitation of quantitative estimates and uncertainty via remote elicitation. We emphasise that i) the aggregation of diverse individual judgements into pooled group judgments almost always outperformed individuals, and ii) use of a modified Delphi approach helped to remove linguistic ambiguity, and further improved individual and group judgements. Importantly, the protocol encourages review, critical appraisal and replication, each of which is required if judgements are to be used in place of data in a scientific context. The results add to the growing body of literature that demonstrates the merit of using structured elicitation protocols. We urge decision-makers and analysts to use insights and examples to improve the evidence base of expert judgement in natural resource management.

Minimizing the cost of keeping options open for conservation in a changing climate.

Policy documents advocate that managers should keep their options open while planning to protect coastal ecosystems from climate-change impacts. However, the actual costs and benefits of maintaining flexibility remain largely unexplored, and alternative approaches for decision making under uncertainty may lead to better joint outcomes for conservation and other societal goals. For example, keeping options open for coastal ecosystems incurs opportunity costs for developers. We devised a decision framework that integrates these costs and benefits with probabilistic forecasts for the extent of sea-level rise to find a balance between coastal ecosystem protection and moderate coastal development. Here, we suggest that instead of keeping their options open managers should incorporate uncertain sea-level rise predictions into a decision-making framework that evaluates the benefits and costs of conservation and development. In our example, based on plausible scenarios for sea-level rise and assuming a risk-neutral decision maker, we found that substantial development could be accommodated with negligible loss of environmental assets. Characterization of the Pareto efficiency of conservation and development outcomes provides valuable insight into the intensity of trade-offs between development and conservation. However, additional work is required to improve understanding of the consequences of alternative spatial plans and the value judgments and risk preferences of decision makers and stakeholders.

Evaluation of potential responses to invasive non-native species with structured decision making.

In managing invasions and colonizations of non-native species, eradication or control efforts must proceed quickly. There are 2 challenges in taking such quick action. First, managers frequently have to choose among complex and often competing environmental, social, and economic objectives. Second, the effects are highly uncertain. We applied participatory structured decision making (SDM) to develop a response plan for the recent invasion of non-native myrtle rust (Uredo rangelii) in Australia. Structured decision making breaks a complex decision process into 5 steps: identify problems (i.e., decisions to be made), formulate objectives, develop management alternatives, estimate consequences of implementing those alternatives, and select preferred alternatives by evaluating trade-offs among alternatives. To determine the preferred mid- to long-term alternatives to managing the rust, we conducted 2 participatory workshops and 18 interviews with individuals to elicit stakeholders' key concerns and convert them into 5 objectives (minimize management cost, minimize economic cost to industry, minimize effects on natural ecosystems and landscape amenities, and minimize environmental effects associated with use of fungicide) and to identify the 5 management alternatives (full eradication, partial eradication, slow spread, live with it [i.e., major effort invested in mitigation of effects], and do nothing). We also developed decision trees to graphically represent the essence of the decision by displaying the relations between uncertainties and decision points. In the short term or before local expansion of myrtle rust, the do-nothing alternative was not preferred, but an eradication alternative was only recommended if the probability of eradication exceeded about 40%. After the expansion of myrtle rust, the slow-the-spread alternative was preferred regardless of which of the short-term management alternatives was selected at an earlier stage. The participatory SDM approach effectively resulted in informed and transparent response plans that incorporated multiple objectives in decision-making processes under high uncertainty.

A framework for assessing and managing risks posed by emerging diseases.

Frameworks for analyzing the risks of emerging diseases and invasive species often have relied on unstructured estimates of likelihoods and consequences. We suggest a flexible alternative that offers more transparent analysis without need for additional data. Its strength lies in explicit and complementary treatment of technical and social judgments. We describe a system in which cognitive maps, Bayes nets, and multicriteria analysis can be used in tandem to structure a problem, identify exposure pathways, combine data and expert judgement to estimate the likelihoods, and assess consequences of alternative decisions. These tools may be employed in participatory settings or as part of standard regulatory practice. We illustrate this approach with an assessment of the management of an emerging disease that poses a hazard to Australia.

A dynamic hydrological Monte Carlo simulation model to inform decision-making at Lake Toolibin, Western Australia.

Lake Toolibin, an ephemeral lake in the agricultural zone of Western Australia, is under threat from secondary salinity due to land clearance throughout the catchment. The lake is extensively covered with native vegetation and is a Ramsar listed wetland, being one of the few remaining significant migratory bird habitats in the region. Currently, inflow with salinity greater than 1000 mg/L TDS is diverted from the lake in an effort to protect sensitive lakebed vegetation. However, this conservative threshold compromises the frequency and extent of lake inundation, which is essential for bird breeding. It is speculated that relaxing the threshold to 5000 mg/L may pose negligible additional risk to the condition of lakebed vegetation. To characterise the magnitude of improvement in the provision of bird breeding habitat that might be generated by relaxing the threshold, a dynamic water and salt balance model of the lake was developed and implemented using Monte Carlo simulation. Results from best estimate model inputs indicate that relaxation of the threshold increases the likelihood of satisfying habitat requirements by a factor of 9.7. A second-order Monte Carlo analysis incorporating incertitude generated plausible bounds of [2.6, 37.5] around the best estimate for the relative likelihood of satisfying habitat requirements. Parameter-specific sensitivity analyses suggest the availability of habitat is most sensitive to pan evaporation, lower than expected inflow volume, and higher than expected inflow salt concentration. The characterisation of uncertainty associated with environmental variation and incertitude allows managers to make informed risk-weighted decisions.