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.

What is a Good Calibration Question?

Weighted aggregation of expert judgments based on their performance on calibration questions may improve mathematically aggregated judgments relative to equal weights. However, obtaining validated, relevant calibration questions can be difficult. If so, should analysts settle for equal weights? Or should they use calibration questions that are easier to obtain but less relevant? In this article, we examine what happens to the out-of-sample performance of weighted aggregations of the classical model (CM) compared to equal weighted aggregations when the set of calibration questions includes many so-called "irrelevant" questions, those that might ordinarily be considered to be outside the domain of the questions of interest. We find that performance weighted aggregations outperform equal weights on the combined CM score, but not on statistical accuracy (i.e., calibration). Importantly, there was no appreciable difference in performance when weights were developed on relevant versus irrelevant questions. Experts were unable to adapt their knowledge across vastly different domains, and in-sample validation did not accurately predict out-of-sample performance on irrelevant questions. We suggest that if relevant calibration questions cannot be found, then analysts should use equal weights, and draw on alternative techniques to improve judgments. Our study also indicates limits to the predictive accuracy of performance weighted aggregation, and the degree to which expertise can be adapted across domains. We note limitations in our study and urge further research into the effect of question type on the reliability of performance weighted aggregations.

Strengthening conservation science as a crisis discipline by addressing challenges of precaution, privilege, and individualism.

Conservation science deals with crises and supports policy interventions devised to mitigate highly uncertain threats that pose irreversible harm. When conventional policy tools, such as quantitative risk assessments, are insufficient, the precautionary principle provides a practical framework and range of robust heuristics. Yet, precaution is often resisted in many policy arenas, especially those involving powerful self-interests, and this resistance is compounded by structures of privilege and competitive individualism in science. We describe key drivers and effects of such resistance in conservation science. These include a loss of rigor under uncertainty, an erosion of crisis response capabilities, and a further reinforcement of privileged interests in conservation politics. We recommend open acknowledgement of the pressures exerted by power inside science; greater recognition for the value of the precautionary principle under uncertainty; deliberate measures to resist competitive individualism; support for blind review, open science, and data sharing; and a shift from hierarchical multidisciplinarity toward more egalitarian transdisciplinarity to accelerate advances in conservation science. Article impact statement: Precautionary principle, privilege structures among disciplines, and culture of individualism link to effective conservation policy making.

Fortalecimiento de las Ciencias de la Conservación como Disciplinas de Crisis Resumen Las ciencias de la conservación tratan con crisis y respaldan a muchas intervenciones políticas para mitigar las amenazas altamente inciertas que representan un daño irreversible. El principio de precaución proporciona un marco práctico y una gama de heurística sólida cuando son insuficientes las herramientas convencionales de políticas como las evaluaciones cuantitativas de riesgo. Aun así, con frecuencia se niega el uso de la precaución en muchas arenas políticas, especialmente en aquellas que involucran intereses propios de mucho poder, y esta negación se agrava con las estructuras de privilegio y el individualismo competitivo presentes en la ciencia. Describimos los factores y efectos clave de dicha resistencia en las ciencias de la conservación. Estos incluyen la pérdida del rigor bajo la incertidumbre, un desgaste de las capacidades de respuesta a la crisis y un reforzamiento más profundo de los intereses privilegiados en las políticas de conservación. Recomendamos que se realice una aceptación abierta de las presiones ejercidas por el poder dentro de la ciencia; un mayor reconocimiento del valor del principio de precaución bajo la incertidumbre; que se lleven a cabo medidas deliberadas para oponerse al individualismo competitivo; que se apoye a las revisiones a ciegas, la ciencia abierta y la difusión de datos; y que se realice un cambio de la multidisciplinariedad jerárquica a una transdisciplinariedad más igualitaria para acelerar los avances dentro de las ciencias de la 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.

Protecting biodiversity and economic returns in resource-rich tropical forests.

In pursuit of socioeconomic development, many countries are expanding oil and mineral extraction into tropical forests. These activities seed access to remote, biologically rich areas, thereby endangering global biodiversity. Here we demonstrate that conservation solutions that effectively balance the protection of biodiversity and economic revenues are possible in biologically valuable regions. Using spatial data on oil profits and predicted species and ecosystem extents, we optimise the protection of 741 terrestrial species and 20 ecosystems of the Ecuadorian Amazon, across a range of opportunity costs (i.e. sacrifices of extractive profit). For such an optimisation, giving up 5% of a year's oil profits (US$ 221 million) allows for a protected area network that retains of an average of 65% of the extent of each species/ecosystem. This performance far exceeds that of the network produced by simple land area optimisation which requires a sacrifice of approximately 40% of annual oil profits (US$ 1.7 billion), and uses only marginally less land, to achieve equivalent levels of ecological protection. Applying spatial statistics to remotely sensed, historic deforestation data, we further focus the optimisation to areas most threatened by imminent forest loss. We identify Emergency Conservation Targets: areas that are essential to a cost-effective conservation reserve network and at imminent risk of destruction, thus requiring urgent and effective protection. Governments should employ the methods presented here when considering extractive led development options, to responsibly manage the associated ecological-economic trade-offs and protect natural capital. Article Impact Statement: Governments controlling resource extraction from tropical forests can arrange production and conservation to retain biodiversity and profits. This article is protected by copyright. All rights reserved.

Untapped potential of collective intelligence in conservation and environmental decision making.

Environmental decisions are often deferred to groups of experts, committees, or panels to develop climate policy, plan protected areas, or negotiate trade-offs for biodiversity conservation. There is, however, surprisingly little empirical research on the performance of group decision making related to the environment. We examined examples from a range of different disciplines, demonstrating the emergence of collective intelligence (CI) in the elicitation of quantitative estimates, crowdsourcing applications, and small-group problem solving. We explored the extent to which similar tools are used in environmental decision making. This revealed important gaps (e.g., a lack of integration of fundamental research in decision-making practice, absence of systematic evaluation frameworks) that obstruct mainstreaming of CI. By making judicious use of interdisciplinary learning opportunities, CI can be harnessed effectively to improve decision making in conservation and environmental management. To elicit reliable quantitative estimates an understanding of cognitive psychology and to optimize crowdsourcing artificial intelligence tools may need to be incorporated. The business literature offers insights into the importance of soft skills and diversity in team effectiveness. Environmental problems set a challenging and rich testing ground for collective-intelligence tools and frameworks. We argue this creates an opportunity for significant advancement in decision-making research and practice.

Potencial No Explotado de la Inteligencia Colectiva en la Toma de Decisiones Ambientales y de Conservación Resumen Las decisiones ambientales comúnmente se difieren a grupos de expertos, comités, o paneles para desarrollar la política climática, planear las áreas protegidas o negociar compensaciones por la conservación de la biodiversidad. Aun así, sorprendentemente, existen pocas investigaciones empíricas sobre el desempeño de la toma grupal de decisiones en relación con el ambiente. Examinamos los ejemplos de una gama de disciplinas diferentes, demostrando el surgimiento de la inteligencia colectiva en la obtención de estimaciones cuantitativas, las aplicaciones de la colaboración masiva y la resolución de problemas en grupos pequeños. Exploramos el alcance que tienen las herramientas similares que se usan en la toma de decisiones ambientales. Esto último reveló vacíos importantes (p. ej.: la falta de integración de investigaciones fundamentales en la práctica de la toma de decisiones, la ausencia de marcos de trabajo de evaluación sistemática) que obstruyen la popularización de la inteligencia colectiva. Si hacemos un uso juicioso de las oportunidades de aprendizaje interdisciplinario, la inteligencia colectiva puede aprovecharse efectivamente para mejorar la toma de decisiones en el manejo ambiental y de conservación. La incorporación de un entendimiento de la psicología cognitiva y la optimización de las herramientas de IA para la colaboración masiva pueden ser necesarias para obtener estimados cuantitativos confiables. La literatura de los negocios ofrece conocimientos sobre la importancia de las habilidades blandas y la diversidad en la efectividad del equipo. Los problemas ambientales plantean un campo de pruebas rico y desafiante para las herramientas y los marcos de trabajo de inteligencia colectiva. Argumentamos que esto crea una oportunidad para el avance significativo en la investigación y la práctica de la toma de decisiones.

The Value of Performance Weights and Discussion in Aggregated Expert Judgments.

In risky situations characterized by imminent decisions, scarce resources, and insufficient data, policymakers rely on experts to estimate model parameters and their associated uncertainties. Different elicitation and aggregation methods can vary substantially in their efficacy and robustness. While it is generally agreed that biases in expert judgments can be mitigated using structured elicitations involving groups rather than individuals, there is still some disagreement about how to best elicit and aggregate judgments. This mostly concerns the merits of using performance-based weighting schemes to combine judgments of different individuals (rather than assigning equal weights to individual experts), and the way that interaction between experts should be handled. This article aims to contribute to, and complement, the ongoing discussion on these topics.

Metaresearch for Evaluating Reproducibility in Ecology and Evolution.

Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literatures may contain unverifiable claims. The conditions contributing to irreproducibility in other disciplines are also present in ecology. These include a large discrepancy between the proportion of "positive" or "significant" results and the average statistical power of empirical research, incomplete reporting of sampling stopping rules and results, journal policies that discourage replication studies, and a prevailing publish-or-perish research culture that encourages questionable research practices. We argue that these conditions constitute sufficient reason to systematically evaluate the reproducibility of the evidence base in ecology and evolution. In some cases, the direct replication of ecological research is difficult because of strong temporal and spatial dependencies, so here, we propose metaresearch projects that will provide proxy measures of reproducibility.

Expert Elicitation of Population-Level Effects of Disturbance.

Expert elicitation is a rigorous method for synthesizing expert knowledge to inform decision making and is reliable and practical when field data are limited. We evaluated the feasibility of applying expert elicitation to estimate population-level effects of disturbance on marine mammals. Diverse experts estimated parameters related to mortality and sublethal injury of North Atlantic right whales (Eubalaena glacialis). We are now eliciting expert knowledge on the movement of right whales among geographic regions to parameterize a spatial model of health. Expert elicitation complements methods such as simulation models or extrapolations from other species, sometimes with greater accuracy and less uncertainty.

Clarifying values, risk perceptions, and attitudes to resolve or avoid social conflicts in invasive species management.

Decision makers and researchers recognize the need to effectively confront the social dimensions and conflicts inherent to invasive species research and management. Yet, despite numerous contentious situations that have arisen, no systematic evaluation of the literature has examined the commonalities in the patterns and types of these emergent social issues. Using social and ecological keywords, we reviewed trends in the social dimensions of invasive species research and management and the sources and potential solutions to problems and conflicts that arise around invasive species. We integrated components of cognitive hierarchy theory and risk perceptions theory to provide a conceptual framework to identify, distinguish, and provide understanding of the driving factors underlying disputes associated with invasive species. In the ISI Web of Science database, we found 15,915 peer-reviewed publications on biological invasions, 124 of which included social dimensions of this phenomenon. Of these 124, 28 studies described specific contentious situations. Social approaches to biological invasions have emerged largely in the last decade and have focused on both environmental social sciences and resource management. Despite being distributed in a range of journals, these 124 articles were concentrated mostly in ecology and conservation-oriented outlets. We found that conflicts surrounding invasive species arose based largely on differences in value systems and to a lesser extent stakeholder and decision maker's risk perceptions. To confront or avoid such situations, we suggest integrating the plurality of environmental values into invasive species research and management via structured decision making techniques, which enhance effective risk communication that promotes trust and confidence between stakeholders and decision makers.

A novel method for estimating the number of species within a region.

Ecologists are often required to estimate the number of species in a region or designated area. A number of diversity indices are available for this purpose and are based on sampling the area using quadrats or other means, and estimating the total number of species from these samples. In this paper, a novel theory and method for estimating the number of species is developed. The theory involves the use of the Laplace method for approximating asymptotic integrals. The method is shown to be successful by testing random simulated datasets. In addition, several real survey datasets are tested, including forests that contain a large number (tens to hundreds) of tree species, and an aquatic system with a large number of fish species. The method is shown to give accurate results, and in almost all cases found to be superior to existing tools for estimating diversity.

Voting systems for environmental decisions.

Voting systems aggregate preferences efficiently and are often used for deciding conservation priorities. Desirable characteristics of voting systems include transitivity, completeness, and Pareto optimality, among others. Voting systems that are common and potentially useful for environmental decision making include simple majority, approval, and preferential voting. Unfortunately, no voting system can guarantee an outcome, while also satisfying a range of very reasonable performance criteria. Furthermore, voting methods may be manipulated by decision makers and strategic voters if they have knowledge of the voting patterns and alliances of others in the voting populations. The difficult properties of voting systems arise in routine decision making when there are multiple criteria and management alternatives. Because each method has flaws, we do not endorse one method. Instead, we urge organizers to be transparent about the properties of proposed voting systems and to offer participants the opportunity to approve the voting system as part of the ground rules for operation of a group.

General rules for managing and surveying networks of pests, diseases, and endangered species.

The efficient management of diseases, pests, or endangered species is an important global issue faced by agencies constrained by limited resources. The management challenge is even greater when organisms are difficult to detect. We show how to prioritize management and survey effort across time and space for networks of susceptible-infected-susceptible subpopulations. We present simple and robust rules of thumb for protecting desirable, or eradicating undesirable, subpopulations connected in typical network patterns (motifs). We further demonstrate that these rules can be generalized to larger networks when motifs are combined in more complex formations. Results show that the best location to manage or survey a pest or a disease on a network is also the best location to protect or survey an endangered species. The optimal starting point in a network is the fastest motif to manage, where line, star, island, and cluster motifs range from fast to slow. Managing the most connected node at the right time and maintaining the same management direction provide advantages over previously recommended outside-in strategies. When a species or disease is not detected and our belief in persistence decreases, our results recommend shifting resources toward management or surveillance of the most connected nodes. Our analytic approximation provides guidance on how long we should manage or survey networks for hard-to-detect organisms. Our rules take into account management success, dispersal, economic cost, and imperfect detection and offer managers a practical basis for managing networks relevant to many significant environmental, biosecurity, and human health issues.

Addressing a critique of the TEASI framework for invasive species risk assessment.

We address criticism that the Transport, Establishment, Abundance, Spread, Impact (TEASI) framework does not facilitate objective mapping of risk assessment methods nor defines best practice. We explain why TEASI is appropriate for mapping, despite inherent challenges, and how TEASI offers considerations for best practices, rather than suggesting one best practice.