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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. 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. 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.

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.

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge. El conocimiento de expertos es utilizado ampliamente en la ciencia y práctica de la conservación por la complejidad de los problemas, la falta relativa de datos y la naturaleza inminente de muchas decisiones de conservación. El conocimiento de expertos es información sustancial sobre un tópico particular que no es conocido ampliamente por otros. Un experto es alguien que tiene ese conocimiento y a quien se recurre a menudo para su interpretación. Nos referimos a las predicciones de expertos de lo que puede suceder en un contexto particular como juicio de expertos. En general, un método de obtención de expertos consiste en cinco pasos: decidir como se utilizará la información, determinar que se va a obtener, diseñar el proceso de obtención, llevar a cabo la obtención y traducir la información obtenida en datos cuantitativos que puedan ser utilizados directamente o en un modelo para tomar decisiones. Este último paso es conocido como codificación. Algunas de las consideraciones en la obtención de conocimiento de expertos incluyen determinar como trabajar con múltiples expertos y como combinar múltiples juicios, minimizando el sesgo en la información obtenida, y verificando la precisión de la información de expertos. Resaltamos técnicas estructuradas de obtención que, de ser adoptadas, mejorarán la precisión y contenido de información del juicio de expertos y asegurarán que la incertidumbre sea capturada con precisión. Sugerimos que se examinen cuatro aspectos de un ejercicio de obtención de expertos para determinar su amplitud y efectividad: estudiar el diseño y el contexto, diseño de la obtención, método de obtención y resultado de la obtención. Tal como la confiabilidad de los datos empíricos depende del rigor con que fueron obtenidos, también lo es para el conocimiento de expertos.

Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback.

Aim: Many mangrove communities form bands parallel to the shoreline with each community dominated by a single species. However, the key determinants of mangrove species distribution across the intertidal zone are not well understood. We aimed to quantify the relationship between species' dominance and the hydroperiod (defined as the duration of inundation in a year), soil salinity and the salinity of inundating water for three dominant species, Sonneratia alba, Rhizophora stylosa and Ceriops tagal. Location: An extensive (20,000 ha), largely intact mangrove forest in northern Australia, of some note as mangrove forests are threatened globally. Methods: We related species dominance to the explanatory variables by applying two statistical modelling approaches: generalized linear models (GLMs), where a set of competing models were evaluated; and boosted regression tree models (BRTs), an approach that automatically captures interactions and nonlinear relationships between variables. Results: Both GLM and BRT models achieved strong predictive performance for all species based on cross-validation, with receiver operating characteristics above 0.85 for all species, and 88% of deviance explained for S. alba, 42% for R. stylosa and 35% for C. tagal. All models indicated that the hydroperiod was the key variable influencing distribution, followed by soil salinity. The salinity of inundating water was the least informative variable in the models. Ecological space, determined by gradients in hydroperiod and soil salinity, was partitioned between the three species with little overlap. Main conclusions: As anticipated changes in sea level will alter the hydroperiod, our findings are critical for global forecasting of future distributions of mangrove communities, and for the design of mitigation and adaptation measures.

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.

Aim Climate change is a major threat to the persistence of biodiversity. Global assessments highlight the most climate vulnerable species and geographic regions based on species traits and measures of exposure to climate change. Yet the majority of climate change vulnerability assessments have focused on terrestrial and marine vertebrates and largely ignored the less well‐known freshwater species and invertebrates. We present the first global analysis of 574 species of freshwater crayfish (Families: Astacidae, Parastacidae and Cambaridae) using IUCN's trait‐based vulnerability assessment protocol. Location Global. Methods We collected species‐specific information on sensitivity (eight traits), adaptive capacity (four traits) and exposure (five traits) to climate change and combined those dimensions to assess overall species vulnerability. Results Our results predicted that 87% of freshwater crayfish species are highly sensitive to climate change (primarily due to habitat specialization), 35% have low adaptive capacity and 57% are highly exposed (based on an ensemble mean of four general circulation models for a moderate Intergovernmental Panel on Climate Change (IPCC) scenario, RCP6.0). Combining sensitivity, low adaptive capacity and exposure, we assessed 87 species (15%) as vulnerable to climate change. These species are distributed globally with high concentrations in the south‐eastern USA (36 species), south‐eastern Australia (21 species) and Mexico (10 species), reflecting global patterns of crayfish species richness. Of the 91 species listed as threatened by climate change in the IUCN Red List, we predicted 18 species to be climate change vulnerable. Main conclusions We identified hotspots of species vulnerable to climate change that require further conservation attention. The IUCN trait‐based protocol can help identify data gaps and key traits that should be investigated further and thus can help overcome knowledge shortfalls on the effects of climate change. Our study provides key insights for the application of climate change vulnerability assessment to data‐poor invertebrates, which remain underrepresented in global conservation priorities.

In conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different management decisions may result when uncertainty in utilities and probabilities are considered in decision-making problems. We highlight the importance of a full assessment of uncertainty in conservation management decisions to avoid, as much as possible, undesirable outcomes.

Governments have committed to global targets to slow biodiversity loss and sustain ecosystem services. Biodiversity state indicators that measure progress toward these targets mostly focus on species, while indicators synthesizing ecosystem change are largely lacking. We fill this gap with three indices quantifying past and projected changes in ecosystems using data from the International Union for Conservation of Nature (IUCN) Red List of Ecosystems. Our indices quantify changes in risk of ecosystem collapse, ecosystem area and ecological processes, and capture variation in underlying patterns among ecosystems. We apply the indices to three case studies of regional and national assessments (American/Caribbean forests, terrestrial ecosystems of Colombia, and terrestrial ecosystems of South Africa) to illustrate the indices’ complementarity and versatility in revealing patterns of interest for users across sectors. Our indices have the potential to fill the recognized need for ecosystem indicators to inform conservation targets, guide policy, and prioritize management actions.

Detecting exotic plant species is essential for invasive species management. By accounting for factors likely to affect species' detection rates (e.g. survey conditions, observer experience), detectability models can help choose search methods and allocate search effort. Integrating information on species' traits can refine detectability models, and might be particularly valuable if these traits can help improve estimates of detectability where data on particular species are rare. Analysing data collected during line transect distance sampling surveys in Indonesia, we used a multi-species hierarchical distance sampling model to evaluate how plant height, leaf size, leaf shape, and survey location influenced plant species detectability in secondary tropical rainforests. Detectability of the exotic plant species increased with plant height and leaf size. Detectability varied among the different survey locations. We failed to detect a clear effect of leaf shape on detectability. This study indicates that information on traits might improve predictions about exotic species detection, which can then be used to optimise the allocation of search effort for efficient species management. The innovation of the study lies in the multi-species distance sampling model, where the distance-detection function depends on leaf traits and height. The method can be applied elsewhere, including for different traits that may be relevant in other contexts. Trait-based multispecies distance sampling can be a practical approach for sampling exotic shrubs, herbs, or grasses species in the understorey of tropical forests.