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Illegal collecting of wild Venus flytraps (Dionaea muscipula) for the horticultural trade represents a persistent threat to populations of the species across their endemic range in the coastal plain of North and South Carolina (United States). Although wild collecting of Venus flytraps is not a novel threat, there has been very little research on the impacts of collecting on the species' conservation to date or why an illegal trade persists alongside a legal one. We drew on qualitative expert stakeholder elicitation to contextualize the threat of illegal collecting to the long‐term conservation of Venus flytraps in relation to other anthropogenic threats. Expert elicitation included botanical and conservation researchers, cognizant state and federal agency staff, land managers, and conservation nonprofit actors. The workshop included mapping of supply chain structures and prioritization of social and environmental harms. Expert consensus determined illegal collecting is an ongoing problem for Venus flytrap conservation, but habitat destruction, degradation, and fire suppression are the most significant threats to flytrap conservation. Supply chain analysis showed that observable social and environmental harms of the trade are focused at the supply stage and that less is known about transit and demand stages. Key research gaps identified include a lack of understanding of plant laundering practices relevant to a range of desirable plant taxa; the role of commercial nurseries in illicit horticultural supply chains; motivations for engaging in Venus flytrap collecting; and the persistent demand for illegally harvested plants when cultivated, legally obtainable plants are readily available. Our findings and methodology are relevant to a range of ornamental plants affected by illegal trade for which robust social data on illegal collecting drivers are lacking. [ABSTRACT FROM AUTHOR]

ABSTRACT Anticipating future biosecurity threats to prevent their occurrence is the most cost‐effective strategy to manage invasive alien species. Yet, biological invasions are complex, highly uncertain processes. High uncertainty drives decision‐making away from strategic preventative measures and towards operational outcomes aimed at post‐invasion management. The limited success of preventative measures in curbing biological invasions reflects this short‐term mindset and decision‐makers should instead apply strategic foresight to imagine futures where biosecurity threats are minimised. Here, four major futures thinking tools (environmental scanning, driver‐mapping, horizon scanning, and scenario planning) that describe probable, possible, plausible and preferable futures are assessed in terms of their potential to support both research and policy addressing biological invasions. Environmental scanning involves surveying existing data sources to detect signals of emerging alien species through knowledge of changes in either the likelihood or consequences of biological invasions. Several approaches are widely used for biosecurity including automated scans of digital media, consensus‐based expert scoring, and prediction markets. Automated systems can be poor at detecting weak signals because of the large volume of ‘noise’ they generate while expert scoring relies on prior knowledge and so fails to identify unknown unknowns which is also true of prediction markets that work well for quite specific known risks. Driver‐mapping uses expert consensus to identify the political, economic, societal, technological, legislative, and environmental forces shaping the future and is a critical component of strategic foresight that has rarely been applied to biological invasions. Considerable potential exists to extend this approach to develop system maps to identify where biosecurity interventions may be most effective and to explore driver complexes to determine megatrends shaping the future of biological invasions. Horizon scanning is a systematic outlook of potential threats and future developments to detect weak signals of emerging issues that exist at the margins of current thinking. Applications have been strongly focused on emerging issues related to research and technological challenges relevant to biosecurity and invasion science. However, most of these emerging issues are already well known in current‐day research. Because horizon scanning is based on expert consensus, it needs to embrace a diversity of cultural, gender, and disciplinary diversity more adequately to ensure participants think intuitively and outside of their own subject boundaries. Scenario planning constructs storylines that describe alternative ways the political, economic, social, technological, legislative, and environmental situation might develop in the future. Biological invasion scenario planning has favoured structured approaches such as standardised archetypes and uncertainty matrices, but scope exists to apply more intuitive thinking by using incasting, backcasting, or causal layered analysis. Futures thinking in biological invasions has not engaged with decision‐makers or other stakeholders adequately and thus outcomes have been light on policy and management priorities. To date, strategic foresight addressing biological invasions has applied each approach in isolation. Yet, an integrated approach to futures thinking that involves a diverse set of stakeholders in exploring the probable, possible, plausible, and preferable futures relating to biological invasions is crucial to the delivery of strategic biosecurity foresight at both national and global scales. [ABSTRACT FROM AUTHOR]

Communities have a strong role in protecting biodiversity. In addition to participation in restoration, a range of actions in the public or private sphere may support biodiversity. Despite this, there is a lack of clarity about what actions should be prioritized for behavior change campaigns. We developed and applied a method to prioritize community actions for biodiversity conservation that incorporates an expert‐based assessment of impact and a community‐informed measure of the likelihood of uptake. In stage 1, experts (n = 143) completed a survey that quantified the relative impact of actions based on best–worst scaling of perceived impact. In stage 2, surveyed community members (n = 3200) ranked the likelihood of adopting actions based on the ease or difficulty of performing each action, and the opportunity for change based on the proportion of respondents not yet engaging in each behavior. Experts gave the following actions the highest ranking for impact: voting for the environment (first), participating in restoration in ecological priority areas (second), and purchasing and protecting remnant bushland (third). When considering the disciplinary background and institutional background of experts, voting and participating in restoration activities remained in the upper ranked options. However, there was some divergence between these groups. For example, reducing beef consumption was ranked third by university‐based experts but ranked 28th by experts based in state government. Overall, community members ranked the following behaviors as most likely to be adopted: following quarantine laws (first), reducing plastic use (second), and managing pets (third). Top likelihood ranking of actions was minimally affected by community characteristics (nature relatedness, gender, location). Integrating these findings, the action ranked most favorably for impact, likelihood, and opportunity was participating in restoration. Choosing actions for behavior change campaigns requires consideration of the entire social–ecological system—from social factors that enable or constrain adoption to the ecological impact of actions across relevant social and ecological contexts. [ABSTRACT FROM AUTHOR]

Expert judgment underpins assessment of threatened ecosystems. However, experts are often narrowly defined, and variability in their judgments may be substantial. Models built from structured elicitation with large diverse expert panels can contribute to more consistent and transparent decision‐making. We conducted a structured elicitation under a broad definition of expertise to examine variation in judgments of ecosystem viability and collapse in a critically endangered ecosystem. We explored whether variation in judgments among 83 experts was related to affiliation and management expertise and assessed performance of an average model based on common ecosystem indicators. There were systematic differences among individuals, much of which were not explained by affiliation or expertise. However, of the individuals affiliated with government, those in conservation and environmental departments were more likely to determine a patch was viable than those in agriculture and rural land management. Classification errors from an average model, in which all individuals were weighted equally, were highest among government agriculture experts (27%) and lowest among government conservation experts (12%). Differences were mostly cases in which the average model predicted a patch was viable but the individual thought it was not. These differences arose primarily for areas that were grazed or cleared of mature trees. These areas are often the target of restoration, but they are also valuable for agriculture. These results highlight the potential for conflicting advice and disagreement about policies and actions for conserving and restoring threatened ecosystems. Although adoption of an average model can improve consistency of ecosystem assessment, it can fail to capture and convey diverse opinions held by experts. Structured elicitation and models of ecosystem viability play an important role in providing data‐driven evidence of where differences arise among experts to support engagement and discussion among stakeholders and decision makers and to improve the management of threatened ecosystems. [ABSTRACT FROM AUTHOR]

Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety evaluation system that incorporates simple qualification degrees, food consumption, project hazard degrees, sales channels, food production regions, and other information obtained from food safety sampling and inspection to reflect the food safety situation accurately, objectively, and comprehensively. This evaluation model combined the statistical method and the machine learning method. The optimal distance method was used to calculate the basic qualification degree, and then expert elicitation via a questionnaire and the factor analysis of mixed data method (FADM) was applied to modify the basic qualification degree so as to obtain the food safety index, which indicates food safety status. Then, the effectiveness of this new method was verified by calculating and analyzing of the food safety index in region X. The results show that this model can clearly distinguish food safety levels in different cities and food categories and identify food safety trends in different years. Thus, this food safety evaluation system based on the FADM quantifies the real food safety level, screens out cities and food categories with high food safety risks, and, finally, helps to optimize the allocation of regulatory resources and provide technical and theoretical support for government decision-making. [ABSTRACT FROM AUTHOR]

Sangay volcano is considered as one of the most active volcanoes worldwide. Nevertheless, due to its remote location and low-impact eruptions, its eruptive history and hazard scenarios are poorly constrained. In this work, we address this issue by combining an analysis of monitoring data and historical chronicles with expert elicitation. During the last 400 years, we recognize periods of quiescence, weak, and enhanced eruptive activity, lasting from several months to several years, punctuated by eruptive pulses, lasting from a few hours to a few days. Sangay volcano has been mainly active since the seventeenth century, with weak eruptive activity as the most common regime, although there have also been several periods of quiescence. During this period, eruptive pulses with VEI 1–3 occurred mainly during enhanced eruptive activity and produced far-reaching impacts due to ash fallout to the west and long-runout lahars to the south-east. Four eruptive pulse scenarios are considered in the expert elicitation: strong ash venting (SAV, VEI 1–2), violent Strombolian (VS, VEI 2–3), sub-Plinian (SPL, VEI 3–4), and Plinian (PL, VEI 4–5). SAV is identified as the most likely scenario, while PL has the smallest probability of occurrence. The elicitation results show high uncertainty about the probability of occurrence of VS and SPL. Large uncertainties are also observed for eruption duration and bulk fallout volume for all eruptive scenarios, while average column height is better characterized, particularly for SAV and VS. We interpret these results as a consequence of the lack of volcano-physical data, which could be reduced with further field studies. This study shows how historical reconstruction and expert elicitation can help to develop hazard scenarios with uncertainty assessment for poorly known volcanoes, representing a first step towards the elaboration of appropriate hazard maps and subsequent planning. [ABSTRACT FROM AUTHOR]

Conservation translocations are widely used to recover threatened species, but can pose risks to recipient ecosystems, particularly in the case of conservation introductions. Because of limited data and uncertainty, risk assessments for such projects often rely on extrapolated evidence and expert opinion, further complicating decision making. The Environmental Impact Classification for Alien Taxa (EICAT) serves to classify the realised impacts of invasive species. We developed a protocol combining EICAT and formal expert elicitation to predict these impacts. We applied our protocol to the extinct-in-the-wild sihek (Guam kingfisher; Todiramphus cinnamominus), for which introduction outside the known historical range is being considered. We elicited from multiple experts probability estimates of impact levels across four impact mechanisms and five candidate release sites. We aggregated estimates using simulation-based and Bayesian approaches, with and without accounting for expert confidence. Experts generally agreed that sihek introduction might impact the recipient ecosystem through predation, competition, and disease, although they disagreed about the likely impact levels. Releases to Palmyra Atoll were considered to pose the lowest risk across candidate sites, so this site was selected for further detailed ecological assessments and risk mitigation efforts. EICAT, with its standardized impact mechanisms and definitions, helped reduce the linguistic uncertainty and subjectivity common to expert-based assessments. Expressing judgments as probabilities allowed us to evaluate uncertainty transparently and to assess the weight of expert confidence on the overall risk estimation. Formal quantitative elicitation and aggregation then allowed a transparent evaluation of results, facilitating communication with stakeholders and decision-makers. [ABSTRACT FROM AUTHOR]

Adversarial Risk Analysis (ARA) allows for much more realistic modeling of game theoretical decision problems than Bayesian game theory. While ARA solutions for various applications have been discussed in the literature, we have not encountered a manuscript that assesses ARA in a real-life case study involving actual decision-makers. In this study, we present an ARA solution for the Parole Board decision problem. To elicit the Parole Board's probabilities and utilities regarding the convict's choices and resulting consequences, as well as their own subjective beliefs about such probabilities and utilities, we conducted a detailed interview with two current members of the New Zealand Parole Board, using a realistic case report. Subsequently, we derived the optimal ARA decision for different scenarios. This study highlights the advantages and challenges of the ARA methodology for real-life decision-making in the presence of an adversary. [ABSTRACT FROM AUTHOR]

Global declines in ecosystem extent and condition mean there is an increasing demand for recovery and conservation plans. Conservation plans for ecological communities require a management framework with measurable, time‐bound objectives. Efficient and structured processes that facilitate timely and comparable conservation plans are essential, especially where resources are constrained. We describe a process to streamline the development of conservation plans by combining functionally similar community sub‐types into a multi‐community State and Transition Model that can be used to guide conservation planning. We demonstrate this approach in a case study using eucalypt dominated woodlands of southern Australia—an ecosystem which occupies a vast geographical range across temperate Australia and includes many distinct vegetation communities, a growing number of which are endangered or threatened. Australian woodland ecologists (grouped according to their knowledge of three broad woodland sub‐types) were asked to develop causal chains to describe all factors associated with transitions among woodland condition states, and estimate the likelihoods associated with each transition at two time‐scales. The resultant State and Transition Model includes a set of eight general condition states that are common to eucalypt dominated woodlands, and 364 unique causal chains describing the drivers of all plausible transitions. We include an example of how the same information can be presented as a series of decision trees aimed at supporting on‐ground management decisions. The case study demonstrates that it is possible to construct a detailed State and Transition Model that synthesizes knowledge across multiple similar vegetation communities. To date, State and Transition Models focused on single communities or a smaller spatial scale, and this is the first attempt to construct a nationally relevant multi‐community State and Transition Model via a structured and participatory process. Synthesis and applications. This approach can be applied at multiple spatial scales to improve and streamline the development of robust conservation plans to improve how we plan for, implement and measure global biodiversity outcomes. [ABSTRACT FROM AUTHOR]

Climate solutions related to mitigation and adaptation vary across the United States and India, given their unique current socio-political–technological abilities and their histories. Here, we discuss results from online face-to-face interviews undertaken with 33 U.S.-based climate experts and 30 India-based climate experts. Using qualitative grounded theory, we explore open-ended responses to questions related to mitigation and adaptation and find the following: (1) there is broad agreement among experts in both countries on the main mitigation solutions focused on the decarbonization of energy systems, but (2) there are a diversity of views between experts on what to prioritize and how to achieve it. Similarly, there is substantial agreement that adaptation solutions are needed to address agriculture, water management, and infrastructure, but there is a wide variety of perspectives on other priorities and how best to proceed. Experts across both countries generally perceived mitigation as needing national policies to succeed, while adaptation is perceived as more local and challenging given the larger number of stakeholders involved in planning and implementation. Our findings indicate that experts agree on the goals of decarbonization, but there was no consensus on how best to accomplish implementation. [ABSTRACT FROM AUTHOR]

Context: Feral cats are responsible for the decline and extinction of species globally. Predation by feral cats is identified in Australian legislation as a key threatening process. However, clear guidance to local land managers on feral cat management techniques and their impacts, limitations and potential costs can be difficult to find. Aims: In this study, feral cat management experts from around Australia identified available management techniques and their average environmental, social, and economic impact for different ecoregions and land-use types. Methods: We convened a 1-day structured elicitation workshop with 19 experts and five facilitators. Experts identified the techniques used for feral cat management; the effectiveness, impact, and cost of each method; and the key knowledge gaps associated with feral cat management. Facilitators aided in the design and format of the workshop, led the discussion at each stage and collated the results. Key results: Experts identified the following 10 techniques currently used in Australia: aerial baiting; ground baiting; leghold trapping; cage trapping; shooting; tracking with detector dogs; tracking by Indigenous Rangers; habitat modification; resource modification; and exclusion fencing. In general, experts highlighted that permits, legislation and scale of application constrained many of these techniques. Aerial baiting was considered the most effective technique for reducing feral cat populations in natural and production systems. Cage trapping, shooting, or tracking with detector dogs were considered more effective in residential areas. For all techniques, efficacy estimates varied according to the following three broad vegetation structural regions: (1) deserts and xeric shrublands; (2) forests and woodlands; and (3) grasslands, savannas and shrublands. Techniques considered to have the lowest social tolerance and highest impact to non-target native species included aerial baiting, ground baiting and leghold trapping. Techniques considered to have high social tolerance and low impact on non-target species included tracking by Rangers, tracking with detector dogs, and habitat and resource modification. Conclusions: Estimates of management action efficacy differ among land-use types and at least three vegetation structural regions. However, social licence, logistic and legislative constraints are the key drivers of the availability of methods for these areas. Implications: Feral cat management programs should consider how program strategy can be prioritised on the basis of technique availability, region of use and expected impact. Feral cats have caused the decline of many native Australian species, but cat management remains challenging with programs being variable in their success. Experts from Australia identified the techniques currently used in cat management and the environmental, economic, and social impacts influencing their success. We summarise the impacts that vegetation, land-use, legislation, and social licence can have on the efficacy of different management techniques. Image by Guy Ballard. [ABSTRACT FROM AUTHOR]

Summary: Vegetation condition metrics are often used as a surrogate of biodiversity to support management decisions, conservation regulations and biodiversity markets. Vegetation condition metrics, which aggregate multiple attributes, are often criticised for simplifying the complexity of biodiversity. A particular challenge is substitution when high‐scoring attributes compensate for those with low scores (e.g. high vegetation cover compensating for low growth from diversity). The geometric mean is often suggested for aggregation to reduce these effects. In New South Wales, Australia, the Vegetation Integrity metric, calculated as the geometric mean of Composition, Structure and Function sub‐indices, measures the losses and gains in biodiversity values within the NSW Biodiversity Offsets Scheme. However, concern has been raised that Vegetation Integrity underestimates conservation values of derived native grasslands when the Function sub‐index (primarily tree‐related attributes) approaches zero. We explore this issue using two datasets and compare the current Vegetation Integrity metric with aggregation using the arithmetic mean, adopting a minimum value of 10/100 for the Function sub‐index and use of a grassland benchmark for derived native grasslands. Our evaluation draws on a large‐scale expert elicitation of conservation values in Critically Endangered Box‐gum Grassy Woodlands and 4018 Vegetation Integrity estimates undertaken during development assessments. We find that Vegetation Integrity underestimates conservation values of derived native grasslands and that the problem is widespread. Although evidence most strongly supports aggregation using the arithmetic mean, this change could be disruptive to the Biodiversity Offsets Scheme. Alternatively, a sub‐index minimum of 10/100 eliminates underestimation of derived native grasslands without substantial impacts in other circumstances. We found little evidence to support the use of a grassland benchmark, which tended to overestimate conservation values. This study highlights the need for sufficient flexibility in biodiversity policies and regulations to accommodate ongoing metric evaluation and revision to support more robust biodiversity outcomes. [ABSTRACT FROM AUTHOR]

The U.S. National Institute of Standards and Technology (NIST) published the Community Resilience Planning Guide in 2016. The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction's resilience against a scenario hazard. The framework centers around tables of expected and desired recovery times for selected community assets, such as electricity, water, and natural gas infrastructures. The NIST Guide does not provide a method for estimating the expected recovery times. However, building high-fidelity computer models for such estimations requires substantial resources that even larger jurisdictions cannot cost-justify. The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the (knowledgeable) crowd. This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data. This work is the first in the literature to examine people's ability to estimate recovery curves and how design influences such estimations. Its main contribution to resilience planning is three-fold: development of a new elicitation tool called Restimate, understanding its potential user base, and providing insights into how it can facilitate resilience planning. Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning. Beyond resilience planning, those who facilitate high-stakes planning activities under large uncertainties (e.g., mission-critical system design and planning) will benefit from a similar research-through-design process.

Physical habitat models represent a widely used tool in river management, yet, there is a growing consensus—particularly for large rivers—that fundamental principles have limits, and it is evident that improved methodologies for assessment and design are needed. Here, we suggest a framework that takes steps towards resolving some of these issues, using changes of fish habitat in a large, regulated river as a case study. First, we propose using hydrodynamic modelling in combination with a fuzzy rule-based classification as a tool to delineate and quantify meso-scale fish habitat. Variability in spatial and temporal extent of mesohabitats can be modelled across a range of flows and under non-wadable conditions when standard mesohabitat surveys become largely unfeasible. Second, research effort and empirical data on habitat use and preference of fishes is typically focused on a small group of species and limited for many imperilled or elusive taxa; we suggest using expert knowledge to expand beyond one or a few species to build the biological models for a community-level assessment until empirical data becomes available. Third, sources of uncertainty that are linked to both fundamental elements of habitat models, namely the biological and hydrodynamic components, need to be quantified and reported in modelling outcomes. The steps described in our modelling framework represent key tools for river managers charged with developing environmental flows guidelines in large, regulated rivers. [ABSTRACT FROM AUTHOR]

The U.S. National Institute of Standards and Technology (NIST) published the Community Resilience Planning Guide in 2016. The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction's resilience against a scenario hazard. The framework centers around tables of expected and desired recovery times for selected community assets, such as electricity, water, and natural gas infrastructures. The NIST Guide does not provide a method for estimating the expected recovery times. However, building high-fidelity computer models for such estimations requires substantial resources that even larger jurisdictions cannot cost-justify. The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the (knowledgeable) crowd. This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data. This work is the first in the literature to examine people's ability to estimate recovery curves and how design influences such estimations. Its main contribution to resilience planning is three-fold: development of a new elicitation tool called Restimate, understanding its potential user base, and providing insights into how it can facilitate resilience planning. Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning. Beyond resilience planning, those who facilitate highstakes planning activities under large uncertainties (e.g., mission-critical system design and planning) will benefit from a similar research-through-design process. [ABSTRACT FROM AUTHOR]

Background To limit the use of antimicrobials without disincentivising the development of novel antimicrobials, there is interest in establishing innovative models that fund antimicrobials based on an evaluation of their value as opposed to the volumes used. The aim of this project was to evaluate the population-level health benefit of cefiderocol in the NHS in England, for the treatment of severe aerobic Gram-negative bacterial infections when used within its licensed indications. The results were used to inform the National Institute for Health and Care Excellence guidance in support of commercial discussions regarding contract value between the manufacturer and NHS England. Methods The health benefit of cefiderocol was first derived for a series of high-value clinical scenarios. These represented uses that were expected to have a significant impact on patients’ mortality risks and health-related quality of life. The clinical effectiveness of cefiderocol relative to its comparators was estimated by synthesising evidence on susceptibility of the pathogens of interest to the antimicrobials in a network meta-analysis. Patient-level costs and health outcomes of cefiderocol under various usage scenarios compared with alternative management strategies were quantified using decision modelling. Results were reported as incremental net health effects expressed in quality-adjusted life-years, which were scaled to 20-year population values using infection number forecasts based on data from Public Health England. The outcomes estimated for the high-value clinical scenarios were extrapolated to other expected uses for cefiderocol. Results Among Enterobacterales isolates with the metallo-beta-lactamase resistance mechanism, the base-case network meta-analysis found that cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.32, 95% credible intervals 0.04 to 2.47), but the result was not statistically significant. The other treatments were also associated with lower susceptibility than colistin, but the results were not statistically significant. In the metallo-beta-lactamase Pseudomonas aeruginosa base-case network meta-analysis, cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.44, 95% credible intervals 0.03 to 3.94), but the result was not statistically significant. The other treatments were associated with no susceptibility. In the base case, patient-level benefit of cefiderocol was between 0.02 and 0.15 quality-adjusted life-years, depending on the site of infection, the pathogen and the usage scenario. There was a high degree of uncertainty surrounding the benefits of cefiderocol across all subgroups. There was substantial uncertainty in the number of infections that are suitable for treatment with cefiderocol, so population-level results are presented for a range of scenarios for the current infection numbers, the expected increases in infections over time and rates of emergence of resistance. The population-level benefits varied substantially across the base-case scenarios, from 896 to 3559 quality-adjusted life-years over 20 years. Conclusion This work has provided quantitative estimates of the value of cefiderocol within its areas of expected usage within the NHS. Limitations Given existing evidence, the estimates of the value of cefiderocol are highly uncertain. Future work Future evaluations of antimicrobials would benefit from improvements to NHS data linkages; research to support appropriate synthesis of susceptibility studies; and application of routine data and decision modelling to assess enablement value. Study registration No registration of this study was undertaken. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment Policy Research Programme (NIHR award ref: NIHR135591), conducted through the Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions, PR-PRU-1217-20401, and is published in full in Health Technology Assessment; Vol. 28, No. 28. See the NIHR Funding and Awards website for further award information. Plain language summary This project tested new methods for estimating the value to the NHS of an antimicrobial, cefiderocol, so its manufacturer could be paid fairly even if very little drug is used in order to reduce the risk of bacteria becoming resistant to the product. Clinicians said that the greatest benefit of cefiderocol is when used for complicated urinary tract infections and pneumonia acquired within hospitals caused by two types of bacteria (called Enterobacterales and Pseudomonas aeruginosa), with a resistance mechanism called metallo-beta-lactamase. Because there were no relevant clinical trial data, we estimated how effective cefiderocol and alternative treatments were by doing a systematic literature review of studies that grew bacteria from infections in the laboratory and tested the drugs on them. We linked this to data estimating the long-term health and survival of patients. Some evidence was obtained by asking clinicians detailed questions about what they thought the effects would be based on their experience and the available evidence. We included the side effects of the alternative treatments, some of which can cause kidney damage. We estimated how many infections there would be in the UK, whether they would increase over time and how resistance to treatments may change over time. Clinicians told us that they would also use cefiderocol to treat intra-abdominal and bloodstream infections, and some infections caused by another bacteria called Stenotrophomonas. We estimated how many of these infections there would be, and assumed the same health benefits as for other types of infections. The total value to the NHS was calculated using these estimates. We also considered whether we had missed any additional elements of value. We estimated that the value to the NHS was £18–71 million over 20 years. This reflects the maximum the NHS could pay for use of cefiderocol if the health lost as a result of making these payments rather than funding other NHS services is not to exceed the health benefits of using this antimicrobial. However, these estimates are uncertain due to limitations with the evidence used to produce them and assumptions that had to be made. Scientific summary This section reproduces and adapts, with permission, content previously published as part of the study protocol (Harnan SE, Woods B, Schmitt L, Kearns B, Srivastava T, Scope A, et al. Protocol for the technology evaluation of cefiderocol for treating severe aerobic Gram-negative bacterial infections. Health Technol Assess 2021.) Background The National Institute for Health and Care Excellence (NICE), NHS England and NHS Improvement are currently undertaking a project to assess the feasibility of innovative models that pay for antimicrobials (AMs) based on an evaluation of their value to the NHS as opposed to the volumes used. This project involves evaluation of two AMs to inform commercial discussions regarding contract value for a period of up to 10 years. This report details the evaluation phase of this project for cefiderocol (Fetcroja), which is manufactured by Shionogi. Cefiderocol is an injectable siderophore cephalosporin and received a marketing authorisation in April 2020 for treating infections due to aerobic Gram-negative organisms in adults with limited treatment options. Aim and objectives The aim of this evaluation is to assess the value of cefiderocol to the NHS in England when used within its licensed indications. Specific objectives are: To identify two high-value clinical scenarios (HVCSs), within its broad licensed indications, for which cefiderocol is expected to have a significant impact on patients’ outcomes in terms of reducing mortality risks and improving health-related quality of life (HRQoL). To undertake an ‘evidence mapping’ exercise and relevant systematic literature reviews to characterise the available clinical effectiveness evidence for the use of cefiderocol in the HVCSs. To establish an appropriate decision-analytic model to quantify the costs and health benefits of the use of cefiderocol under various usage scenarios compared with usage scenarios involving other available AMs in the HVCSs. The decision-analytic model was required to estimate costs and health effects at both the individual level and the aggregate population level, providing population-level incremental net health effects (INHEs). Drawing on the systematic reviews and evidence synthesis, national-level data on healthcare-associated infections and other sources as needed, to identify evidence to populate the decision-analytic models. To use structured expert elicitation as necessary to supplement the available evidence to populate the decision-analytic models. To use available evidence and, where necessary, expert opinion quantitatively to extrapolate estimated population-level INHEs associated with cefiderocol in the HVCSs to other expected uses for the product within the product’s licensed indications. Expected usage and high-value clinical scenarios The licensed indication for cefiderocol is broad. In practice, to control the spread of resistance to cefiderocol and to preserve its long-term viability as an effective treatment option against highly resistant pathogens, cefiderocol may be used in a more restricted group of patients than permitted by its license. Quantifying the health and cost implications of using cefiderocol across anticipated NHS usage, even within this restricted population, remains challenging, as the use is expected in infections which differ in causative organism (pathogen, resistance mechanism), site of the infection, healthcare setting and other patient characteristics. This evaluation characterises the value of cefiderocol across its range of expected uses via a two-step approach. First, decision modelling is used quantitatively to assess the value of cefiderocol in a set of scenarios defined by features of the pathogen, site of infection, healthcare setting and other patient characteristics, considered to represent important uses of cefiderocol; referred to as the HVCSs. Second, rescaling is used to estimate how this evidence can be used to provide quantitative assessments of value in the overall population expected to receive cefiderocol, including patients who fall outside the HVCSs but who are relevant to determining the overall value of cefiderocol to the English NHS. The HVCSs were selected to reflect areas of clinical use where there is a current significant burden from resistant infections, and cefiderocol is expected to offer significant improvements over existing treatments in terms of efficacy and/or safety. The HVCSs were selected based on feedback from the manufacturer, clinical advisors to the Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions (EEPRU) and broader stakeholders involved in the NICE scoping process. The HVCSs focus on the following patient populations: Empiric setting (ES): patients with an infection strongly suspected to be caused by metallo-beta-lactamase (MBL)-producing Enterobacterales or MBL-producing Pseudomonas aeruginosa in patients with hospital-acquired pneumonia or ventilator-associated pneumonia (HAP/VAP). In this patient group, the pathogen, resistance mechanism and antibiotic susceptibility have not yet been established, but treatment is initiated immediately due to the severity of the infection. Microbiology-directed setting (MDS): patients with an infection confirmed to be caused by MBL Enterobacterales or MBL P. aeruginosa, where antibiotic susceptibility testing results are available, and where the site of infection is HAP/VAP or complicated urinary tract infection (cUTI). The resourcing for this project was equivalent to that of a diagnostic assessment review or multiple technology assessment for NICE, but the levels of analysis extend from the typical focus of those evaluations on a single type of patient for one indication and setting. In this evaluation, we also include population-level health effects now and over time, and across several indications and settings. The objective is to use appropriate analyses of the available evidence at every level, but the detail in those analyses is inevitably constrained by the time and resources available for the project. Clinical evidence Methods There are evidential challenges when evaluating the use of new or nearly new AMs to treat infections caused by multidrug-resistant (MDR) pathogens. Randomised controlled trials (RCTs) are of generally low relevance as they tend not to recruit patients with MDR pathogens. Therefore, relative treatment effects between the intervention and comparator cannot be generalised to MDR pathogens, as this may overestimate the efficacy of the comparator. It was anticipated that RCTs were unlikely to be the primary source of evidence. Therefore, a mapping review was undertaken to establish available evidence and ascertain which approach to estimating comparative effectiveness could inform an economic model. Reviews 1 and 2 considered RCTs and observational studies of cefiderocol in patients with HAP/VAP or cUTI infections caused by MBL Enterobacterales or P. aeruginosa or suspected to be caused by carbapenem-resistant Enterobacterales or P. aeruginosa. In Review 3, in vitro susceptibility studies were considered. These studies provide evidence on the proportion of isolates that are susceptible to treatments and comparators as an indication of relative efficacy. Susceptibility studies were considered eligible if they reported susceptibility for MBL Enterobacterales or P. aeruginosa isolates, from any infection site (clinical advisors indicated that infection site was not associated with susceptibility profile), and reported susceptibility to cefiderocol and at least one comparator as defined by the HVCSs (colistin, meropenem, tigecycline, aztreonam, fosfomycin, gentamicin, amikacin, tobramycin). Susceptibility studies test isolates in vitro to ascertain the minimum concentration of any given treatment that is needed to inhibit growth of the microbe [the minimum inhibitory concentration (MIC)]. If this is below the clinical breakpoint published by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [or by the Clinical Laboratory Standards Institute (CLSI) from the USA], the isolate is considered susceptible, and is likely to respond to treatment in vivo. In the UK, the British Society for Antimicrobial Chemotherapy (BSAC) recommends use of EUCAST guidelines. Systematic searches across relevant databases [MEDLINE, EMBASE and Centre for Review and Dissemination (CRD) database] were conducted in March 2021. EEPRU also considered evidence submitted by Shionogi and Public Health England (PHE). After mapping, only Review 3 was pursued, since there was insufficient evidence from Reviews 1 and 2. Risk of bias assessment was performed using a bespoke tool developed for this evaluation. No studies of fosfomycin were identified. An additional rapid review was conducted to identify studies reporting the susceptibility of fosfomycin and at least one other HVCS comparator. A random-effects network meta-analysis (NMA) of susceptibility studies was conducted to allow a comprehensive synthesis of evidence on all relevant treatments. A different network was constructed for each subgroup of pathogens (MBL Enterobacterales and MBL P. aeruginosa), since susceptibility profiles were expected to differ. For each of these, a network was constructed using EUCAST breakpoints and another using CLSI breakpoints. The EUCAST networks were considered the main analysis, while the CLSI networks were considered in sensitivity analyses. Sensitivity analyses were also conducted to test assumptions around the inclusion of comparators in the network, to assess the impact of missed data on the analysis, and prioritising PHE data where possible as these are from English isolates and, therefore, have the highest relevance. Two additional reviews were conducted to provide evidence on the link between susceptibility and clinical outcomes (Review 4) and between susceptibility and long-term outcomes (Review 5) in the sites of interest. A further review (Review 6) was conducted to identify any important safety implications of cefiderocol. Results The reviews of RCT and observational studies identified insufficient evidence to inform an assessment of clinical effectiveness. One RCT did report subgroup data relating to MBLs, but the subgroup was small (n = 16 in the cefiderocol arm, n = 7 in the best available therapy arm) and was, therefore, not used due to the chance of baseline imbalances introducing bias. The key limitations of the observational studies that were identified included small numbers of MBL infections (range n = 2–17 patients), high levels of heterogeneity with respect to prognostic factors, and data not being reported for the sites of interest. Review 3 identified in vitro susceptibility studies in relatively large samples of MBL Enterobacterales or P. aeruginosa isolates obtained from a range of clinical sites of infection. Three studies (SIDERO CR, SIDERO WT and Johnston et al.) reporting data for cefiderocol and at least one comparator met the inclusion criteria and were synthesised. The separate review of fosfomycin identified 10 studies. In the MBL Enterobacterales EUCAST analysis, cefiderocol was associated with a lower susceptibility relative to colistin [odds ratio (OR) 0.32, 95% credible intervals (CrI) 0.04 to 2.47], but the result was not statistically significant. Fosfomycin had a similar OR (OR 0.34, 95% CrI 0.06 to 1.96) compared to colistin as cefiderocol. The remainder of the treatments were also associated with lower susceptibility than colistin, but the results were not statistically significant. In the MBL P. aeruginosa EUCAST analysis, cefiderocol was associated with a lower susceptibility relative to colistin (OR 0.44, 95% CrI 0.03 to 3.94), but the result was not statistically significant. The remainder of the treatments were associated with no susceptibility. Heterogeneity was high in both networks. In the sensitivity analyses using CLSI breakpoints, where the breakpoint for cefiderocol is higher, cefiderocol was associated with a higher susceptibility relative to colistin. However, the results were very uncertain in some of the NMAs due to sparse data and a large number of treatment arms with either zero or 100% susceptibility. Review 4 (link between susceptibility and clinical outcomes) identified two studies that reported mortality or hospital length of stay (LoS) conditional on susceptibility to empiric treatment and were selected for use in the model for the ES. No useful evidence relating to the MDS was identified. Review 5 (link between susceptibility and long-term clinical outcomes) did not identify any relevant literature, but an unpublished study (CARBAR) was submitted by Shionogi that contained useful data. Review 6 indicated that cefiderocol does not appear to increase the risk of acute kidney injury (AKI), Clostridium difficile, or any other serious adverse events, compared to non-toxic comparators (i.e. comparators other than colistin or an aminoglycoside). No study reported a comparison of cefiderocol exclusively to colistin or aminoglycosides. Event rates were generally very low or zero. Discussion of clinical evidence: There were limitations to the approach selected and analyses done. Key limitations include: susceptibility could be considered, at best, a surrogate outcome, but no pre-specified criteria for judging the suitability of the surrogate or the linking evidence were applied; linking data were limited and not specific to the pathogen-mechanism combination, and expert elicitation had to be relied upon to evidence the link in the MDS; uncertainties relating to the determination and application of breakpoints; and the NMAs results were associated with high levels of heterogeneity. Economic evidence Methods No published existing economic evaluations assessed the use of cefiderocol in the HVCSs or areas of expected usage. The manufacturer did not submit a cost-effectiveness model. A de novo decision-analytic model was developed to predict the cost and health consequences of introducing cefiderocol within the HVCSs. These were summarised as INHEs. These are estimates of the quality-adjusted life-years (QALYs) associated with introducing cefiderocol if it was supplied free of charge to the NHS, taking in to account both its health benefits and the health benefits of freeing up NHS resources (e.g. via reduced time in hospital) calculated using an estimate of health opportunity cost of £20,000/QALY. This means that for every £20,000 saved, 1 QALY of health can be generated within the NHS. The estimates of INHE will be used in subsequent negotiations to determine an appropriate payment level for cefiderocol. This quantitative analysis comprises three components: an assessment of the INHEs of introducing cefiderocol within the HVCSs at the patient level; an assessment of INHEs within the HVCSs at the population level; and an assessment of how population-level INHEs within the HVCSs might appropriately be rescaled to reflect expected usage across the English NHS. The patient-level component is structured similarly to decision models developed as part of other NICE processes and characterises the cost, mortality and morbidity consequences of introducing cefiderocol over a patient’s lifetime. Separate but related models are developed for the ES and MDS. In the ES, empiric treatment with cefiderocol is compared to: empiric treatment with a non-colistin/aminoglycoside-based treatment (MBL P. aeruginosa only); empiric treatment with colistin/aminoglycoside-based treatment (considered more toxic). In the MDS, we compare outcomes in the overall microbiology-directed cohort who receive tailored therapy with cefiderocol available as a treatment option, to outcomes when this cohort receive tailored therapy with existing AMs only. In the ES, patients are suspected to have a MDR infection, so it is necessary to model outcomes for both patients in whom this suspicion is confirmed and for those in whom this suspicion turns out to be incorrect. The probability of having the suspected pathogen/resistance mechanism is informed by Second-Generation Surveillance System (SGSS) national surveillance data supplied by PHE for this evaluation. The key driver of effectiveness is in vitro susceptibility as estimated via the evidence syntheses discussed above. Higher susceptibility reduces mortality and LoS in hospital. These relationships are based on a combination of evidence from the literature and structured expert elicitation. Colistin or aminoglycoside-based therapy is expected to be associated with higher rates of AKI than other agents (including cefiderocol), which has significant consequences for patients’ short- and long-term mortality, morbidity and costs. Safety differences between colistin or aminoglycoside-based therapy and other agents are, therefore, modelled using evidence from published systematic reviews. Lifetime costs, quality of life and mortality were predicted accounting for the highly comorbid nature of the patient population and the increased risk of chronic kidney disease (CKD) resulting from AKI. The population-level component uses a forecast-based approach to aggregate the patient-level predictions to the population level accounting for the size of, and growth over time in, the eligible patient population in England within each HVCS. This component also reflects how resistance is likely to develop to cefiderocol and existing AMs over time. Predictions of population-level INHE are presented for patients initiated on treatment over the next 20 years. Current numbers of patients within the HVCSs were based on evidence from SGSS. Future growth in the number of patients in the HVCSs was based on statistical forecasting models fitted to time-series data from a national reference laboratory data set supplied by PHE for this evaluation. A series of scenarios reflects the potential emergence of resistance to cefiderocol. We did not model changes in resistance to existing AMs over time due to the sparsity of evidence available to inform these forecasts. Predicted overall population-level INHEs corresponding to the expected use of cefiderocol in the English NHS were generated by rescaling the population-level INHEs from the HVCSs to reflect additional areas of expected usage. These included patients with bloodstream and intra-abdominal infections (BSIs and IAIs) known or suspected to be caused by MBL Enterobacterales or MBL P. aeruginosa, and patients with infections caused by Stenotrophomonas across a range of sites (HAP/VAP, cUTI, BSIs and IAIs). This rescaling was based on population size estimates from SGSS and the use of expert opinion to inform the similarity in patient-level INHEs between the patients falling within the HVCSs and these additional sites and pathogens of interest. The literature on the economic evaluation of AMs has described a range of elements of value associated with these products that are not relevant to evaluations of other healthcare interventions. We also, therefore, summarise the extent to which these elements of value are captured within the quantitative estimates and, where this has not been possible, whether they are likely to substantively modify the quantitative estimates of value presented. Results Patient-level INHEs in the HVCS For HAP/VAP patients treated empirically, the base-case INHEs (0.15 QALYs) are similar in patients with suspected MBL Enterobacterales, and those with suspected P. aeruginosa. The results were very sensitive to the proportion of patients who have MBL Enterobacterales, use of the CLSI breakpoints, AKI risk, AKI-related mortality and long-term survival (range across scenarios: 0.00–0.23 QALYs). For patients treated in the MDS, the base-case INHEs associated with cefiderocol are much higher for MBL P. aeruginosa (0.15 QALYs for HAP/VAP and cUTI) than MBL Enterobacterales (0.02 QALYs for HAP/VAP and cUTI) as the latter patient group has a higher probability of being susceptible to a non-colistin/aminoglycoside-based treatment, and hence not requiring treatment with cefiderocol. There is a large degree of uncertainty in the patient-level INHE in the MBL P. aeruginosa population (range across scenarios 0.01–0.25). This reflects uncertainties in the susceptibility to non-colistin/aminoglycoside-based treatments. Results in the MBL P. aeruginosa populations are subject to additional uncertainty due to limitations in the evidence base for the comparators (fosfomycin plus meropenem and fosfomycin plus colistin), as fosfomycin does not have an established breakpoint for P. aeruginosa, making links to clinical outcomes more tenuous. Furthermore, the available evidence to inform the relative susceptibility of fosfomycin was based on very small studies (n = 7 in the EUCAST and n = 20 in the CLSI networks). We were unable to select a base case for the population-level results. Population-level results are, therefore, presented for two different approaches to estimating infection numbers (based on different methods to classify infections from clinical specimen sites); two alternative approaches to forecasting increases in infections over time (based on whether observed trends are assumed to persist indefinitely or not); and three different trajectories with respect to resistance emergence to cefiderocol (1%, 5% and 10% at 20 years). Across these scenarios, population-level INHEs range from 896 to 3559 QALYs. The results are particularly sensitive to the assumption about which clinical specimen sites are indicative of HAP/VAP. A substantial part of the value of cefiderocol (20 to 38% depending on scenario) is generated among patients with Stenotrophomonas who were outside of the HVCSs considered by EEPRU. Departures from the base-case assumptions in the patient-level model also had substantial effects on the mean population-level INHEs. The population size estimates used to generate the estimates of population-level INHEs are subject to additional uncertainties relating to the completeness of the national data, whether all tested patients would fall within the HVCS population for empiric treatment, the potential double counting of samples from the same infectious episode and inherent uncertainties in forecasting population size over time. In addition, estimates of population-level INHEs were generated using a number of strong assumptions about how evidence can be generalised between settings. Namely, that patient-level INHE of cefiderocol in patients with BSI can be approximated based on outcomes in HAP/VAP patients, and that the patient-level INHE of cefiderocol in patients with IAI can be proxied by that in patients with cUTI. These assumptions were based on discussions with clinical experts. Based on the available evidence, we concluded that diversity value, transmission value and spectrum value were unlikely to be significant drivers of population-level INHEs for cefiderocol. Insurance value was captured for the HVCS, though the benefits of cefiderocol use in novel future outbreaks were not captured and remain highly uncertain. EEPRU considers that it is possible that cefiderocol use will facilitate additional or at least more prompt receipt of required treatments/procedures for certain groups but that the magnitude of these enablement benefits remains highly uncertain. Conclusion The quantitative assessment of value in this report indicates that cefiderocol is associated with a base-case population-level INHE across its areas of expected usage of 896 to 3559 QALYs over 20 years. T

Wind power technology has changed rapidly in recent years. Technology innovation, evolving power markets, and competing land and ocean uses continue to influence the design and operation of wind turbines and plants. Anticipating these trends and their impact on future facilities can inform commercial strategies and research priorities. Drawing from a recent survey of 140 of the world's foremost wind experts, we identify expectations of future wind plant design in 2035, both for onshore and offshore wind. Experts anticipate continued growth in turbine size, to 5.5 (onshore) and 17 MW (offshore), with plants located in increasingly less favorable wind and siting regimes. They expect plant sizes of 1,100 MW for fixed-bottom and 600 MW for floating offshore wind. Experts forecast enhanced grid-system value from wind through significant to widespread use of larger rotors, hybrid projects with batteries and hydrogen production, and more. To explain experts' perspectives on future plant design and operation, we identify five mechanisms: economies of unit, plant, and resource scale; grid-system value economies; and production efficiencies. We characterize learning effects as a moderating influence on the strength of these mechanisms. In combination, experts predict that these design choices support levelized cost of energy reductions of 27% (onshore) and 17%–35% (floating and fixed-bottom offshore) by 2035 compared to today, while enhancing wind energy's grid service offerings. Our findings provide a much-needed benchmark for representing future wind technologies in power sector models and address a critical research gap by explaining the economics behind wind energy design choices.

Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety evaluation system that incorporates simple qualification degrees, food consumption, project hazard degrees, sales channels, food production regions, and other information obtained from food safety sampling and inspection to reflect the food safety situation accurately, objectively, and comprehensively. This evaluation model combined the statistical method and the machine learning method. The optimal distance method was used to calculate the basic qualification degree, and then expert elicitation via a questionnaire and the factor analysis of mixed data method (FADM) was applied to modify the basic qualification degree so as to obtain the food safety index, which indicates food safety status. Then, the effectiveness of this new method was verified by calculating and analyzing of the food safety index in region X. The results show that this model can clearly distinguish food safety levels in different cities and food categories and identify food safety trends in different years. Thus, this food safety evaluation system based on the FADM quantifies the real food safety level, screens out cities and food categories with high food safety risks, and, finally, helps to optimize the allocation of regulatory resources and provide technical and theoretical support for government decision-making.

Many studies anticipate that carbon capture and sequestration (CCS) will be essential to decarbonizing the U.S. economy. However, prior work has not estimated the time required to develop, approve, and implement a geologic sequestration site in the United States. We generate such an estimate by identifying six clearance points that must be passed before a sequestration site can become operational. For each clearance point (CP), we elicit expert judgments of the time required in the form of probability distributions and then use stochastic simulation to combine and sum the results. We find that, on average, there is a 90% chance that the time required lies between 5.5 and 9.6 y, with an upper bound of 12 y. Even using the most optimistic expert judgements, the lower bound on time is 2.7 y, and the upper bound is 8.3 y. Using the most pessimistic judgements, the lower bound is 3.5 y and the upper bound is 19.2 y. These estimates suggest that strategies must be found to safely accelerate the process. We conclude the paper by discussing seven potential strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: Staff of transport companies and abattoirs who handle live cattle and pigs have a high degree of responsibility for animal welfare. However, there are frequent reports of animal welfare concerns regarding the handling of cattle and pigs during transport and slaughter. Continuous training on the correct and proper handling of animals is suitable for strengthening animal welfare. For that purpose, online training modules are developed at the Freie Universität Berlin, Germany. Experts in this field were asked to rate predetermined action points according to their relevance for animal welfare during transport and slaughter, and their potential for improvement through targeted training in two consecutive survey rounds (=Delphi approach). The rating scale included scores from 0 (='not relevant', respectively 'no possibility of improvement') to 10 (='very relevant', respectively 'very high possibility of improvement'). The experts rated 'Assessment of fitness for transport', 'Unloading at abattoir', 'Handling at stunning' and 'Exsanguination' as the most relevant action points. None of the action points were rated with a median score below 5. The Delphi approach was seen as a valuable method to include external expertise to select the most relevant action points for the online training modules. To improve animal welfare for cattle and pigs during transport and at slaughter, online training modules for all staff including employees in the lairage pen, the slaughter line as well as animal welfare officers are developed at Freie Universität Berlin, Germany. Before starting the development of these modules, an expert elicitation survey using a modified Delphi approach was performed to identify action points considered most relevant for animal welfare during transport and slaughter, and as having the potential for improvement through training. In total, 49 participating experts rated predetermined action points of each step in the transport and slaughter process in two survey rounds. The rating scale included numbers 0 (='not relevant', respectively 'no possibility of improvement') to 10 (='very relevant', respectively 'very high possibility of improvement'). None of the action points were rated with a median score of less than 5. Assessment of fitness for transport, unloading at the abattoir, handling at stunning and exsanguination were amongst the highest rated action points, and were therefore selected to develop online training modules. The Delphi approach was seen as a valuable method to include external expertise to select the most relevant action points for the development of online training modules. [ABSTRACT FROM AUTHOR]

Identifying threatened ecosystem types is fundamental to conservation and management decision‐making. When identification relies on expert judgment, decisions are vulnerable to inconsistent outcomes and can lack transparency. We elicited judgements of the occurrence of a widespread, critically endangered Australian ecosystem from a diverse pool of 83 experts. We asked 4 questions. First, how many experts are required to reliably conclude that the ecosystem is present? Second, how many experts are required to build a reliable model for predicting ecosystem presence? Third, given expert selection can narrow the range opinions, if enough experts are selected, do selection strategies affect model predictions? Finally, does a diverse selection of experts provide better model predictions? We used power and sample size calculations with a finite population of 200 experts to calculate the number of experts required to reliably assess ecosystem presence in a theoretical scenario. We then used boosted regression trees to model expert elicitation of 122 plots based on real‐world data. For a reliable consensus (90% probability of correctly identifying presence and absence) in a relatively certain scenario (85% probability of occurrence), at least 17 experts were required. More experts were required when occurrence was less certain, and fewer were needed if permissible error rates were relaxed. In comparison, only ∼20 experts were required for a reliable model that could predict for a range of scenarios. Expert selection strategies changed modeled outcomes, often overpredicting presence and underestimating uncertainty. However, smaller but diverse pools of experts produced outcomes similar to a model built from all contributing experts. Combining elicited judgements from a diverse pool of experts in a model‐based decision support tool provided an efficient aggregation of a broad range of expertise. Such models can improve the transparency and consistency of conservation and management decision‐making, especially when ecosystems are defined based on complex criteria. [ABSTRACT FROM AUTHOR]

Background: Traditional burning is a practice with social and ecological value used worldwide. However, given the often improper and negligent use of fire, this practice is often associated with rural fire ignitions. Aims: Systematise experts' understanding of traditional burning and identify its challenges in the Portuguese context. Methods: Twenty-eight Portuguese experts from industry, academia, NGOs and public entities with in-depth involvement in fire and forest management were interviewed to create a mental model of traditional burning in Portugal. Key results: Eight dimensions were identified: motivations behind traditional burning, alternative solutions, risks before a traditional burn, risks during a traditional burn, underlying causes of risk, exogenous elements and factors, potential impacts, and activities leading to a successful traditional burn. Conclusions: This study provides a comprehensive understanding of traditional burn practice in the Portuguese context and offers a baseline to support stakeholders and policymakers in managing traditional burning's social and environmental impacts in the future. Implications: This research offers several implications across the eight dimensions identified, including the need to improve regulations on the use of fire and fuel reduction policies, promote fire use education and feasible and affordable alternatives to traditional burning, and increase communities' commitment to mitigation actions. Traditional burning is used worldwide, but the improper and negligent use of fire has led to association of this practice with rural fire ignitions and negative impacts. A mental model approach was used to systematise motivations, alternative solutions, associated risks, potential impacts and activities leading to a successful burn. [ABSTRACT FROM AUTHOR]

Animal welfare is becoming an important consideration in animal health-related decision-making. Integrating considerations of animal welfare into the decision-making process of farmers involves recognising the significance of health disorder impacts in relation to animal welfare. Yet little research quantifies the impact, making it difficult to include animal welfare in the animal health decision-making process. Quantifying the impact of health disorders on animal welfare is incredibly challenging due to empirical animal-based data collection constraints. An approach to circumvent these constraints is to rely on expert knowledge whereby perceived welfare impairment weights are indicative of the negative welfare effect. In this research, we propose an expertise-based method to quantify the perceived impact of sub-optimal mobility (SOM) on the welfare of dairy cows, because of its welfare importance. We first quantified perceived welfare impairment weights of SOM by eliciting expert knowledge using adaptive conjoint analysis (ACA). Second, using the perceived welfare impairment weights, we derived the perceived welfare disutility (i.e., perceived negative welfare effect) of mobility scores 1–5 (1 = optimal mobility, 5 = severely impaired mobility). Third, using the perceived welfare disutility per mobility score, we quantified the perceived welfare impact at case- and herd-level of SOM for different SOM severity. Results showed that perceived welfare disutility increased with each increase in mobility score. However, the perceived welfare impact of SOM cases with lower mobility scores was higher compared to SOM cases with higher mobility scores. This was because of the longer-lasting duration of the SOM cases with lower mobility scores. Moreover, the perceived herd-level welfare impact was largely due to SOM cases with lower mobility scores because of the longer duration and more frequent incidence compared to the SOM cases with higher mobility scores. These results entail that better welfare of dairy cows with respect to SOM can be achieved if lower mobility scores are detected and treated sooner. Our research demonstrates a novel approach that quantifies the perceived impact of health disorders on animal welfare when empirical evidence is limited.

Structured expert judgment is crucial when dealing with significant epistemic and aleatoric uncertainties, particularly in probabilistic hazard assessments, where decisions based on uncertain information are often critical. In structured expert elicitations, participants are asked to quantify their uncertainty judgments by providing their percentile estimates of numerical values for a set of questions. More specifically, performance-based elicitations start with “seed” questions for determining experts’ uncertainty quantification skill. The performance scores are thus used to define each expert’s weight to be applied when considering their judgments on “target” questions, i.e., the actual variables of interest for the case study. In this paper we describe ELICIPY, a new Python tool which allows to perform expert elicitation sessions in a framework that covers both the questionnaire collection and the analysis parts, an approach that simplifies the work normally done by the analyst(s). This is achieved through the automatic generation of online webforms to collect the experts’ answers, their check for consistency and, finally, their analysis using different weighting schemes. The tool automatically produces outputs in different formats and creates a pptx presentation file available just after the collection of the answers.

Abstract The Aso-4 explosive eruption on Kyushu, Japan, 89,500 years ago was one of the biggest eruptions in the last one hundred millennia, with a magnitude of approximately M8. Modern society requires the likelihood of natural events with potentially disastrous consequences to be evaluated, even if probabilities of occurrence are diminishingly small. For some situations, it is not satisfactory to assert an event scenario probability is “negligible” or can be “ignored”. Judicial hearings or litigation may require risk levels to be quantified, in which case, statements of scientific confidence could be decisive. Internationally, e.g., for nuclear site safety evaluations, event likelihoods on order of 10–7/year are often considered for quantitative assessment. At such hazard levels, this might include evaluating the proposition that a particular volcano can deliver a future super-eruption, a supposition that could be attached to Aso volcano. But, simplistically taking the average recurrence interval between past caldera-forming eruptions at a given volcano is an unreliable guide to the likelihood of a future repeat: each past event represented a unique set of tectonic and magmatic conditions within a continually evolving volcanic system. Such processes are not temporally stationary nor statistically uniform. To evaluate the probability of a new M8 event at Aso, within the next 100 years, we performed a comprehensive stochastic probability uncertainty analysis using a model implemented with advanced computational Bayes Net (BN) software. Our eruption process model is informed by multiple strands of evidence from volcanology, petrology, geochemistry and geophysics, together with estimates of epistemic (knowledge) uncertainty, adduced from reviews of published data, modelling and from expert judgement elicitation. Several lines of evidence characterise the likely structure, magmatic composition and eruptive state of the present-day Aso volcano, which has had numerous smaller eruptions since Aso-4. To calculate the probability of another M8 eruption of Aso, we implemented probabilistic ‘Importance Sampling’ in our model. With this approach, we find the chance of an Aso-4 scale eruption (characterised by mean volume 500 km3 DRE and approximate 90% credible interval [210 ‥ 1200] km3 DRE) is less than 1–in–1 billion in the next 100 years (i.e.,

We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.

Conservation practitioners commonly work together in management teams, contributing expertise to the planning and decision‐making associated with threatened species management. These groups may include individuals with different perspectives around if, when and how to implement management. Yet, rarely do we consider the value of discussion in shaping these perspectives, and thus management.Decision triggers can be used to formalise decisions around management and encourage teams to explore these decisions proactively. Previous research has identified how individual experts set decision triggers but there are no accepted approaches for capturing the expertise that exists across management teams when setting decision triggers. To address this gap, we developed an approach to formalise team deliberations about when and how to intervene if a decline in the condition of a threatened species were to be observed.Using a modified Delphi process, individual experts within a threatened species management team were asked to independently set trigger points and assign actions for the species. Then, as a group, experts discussed anonymised responses, enabling us to understand how group dynamics influenced the decision‐making process. Experts had the opportunity to update their responses based on the group discussion, providing a rationale to support their decisions. We tested the approach with experts from teams managing threatened species across conservation organisations in Australia and New Zealand.We found that experts often updated their responses to incorporate issues raised by the group, demonstrating a willingness to integrate the perspectives of other team members. Experts tended to become more cautious after discussing their responses, setting additional trigger points and actions, and triggering actions sooner.Synthesis and applications: Our approach provides a structured process to formally integrate the range of perspectives and experience of groups of experts when setting trigger points and actions. This can help to address common biases that arise during unstructured decision‐making, and ensure decision‐making processes are transparent. [ABSTRACT FROM AUTHOR]

Marine protected area networks (MPANs) are promised as tools for protecting biodiversity and contributing to sustainable development. The variety of expected social‐ecological outcomes associated with MPANs underscores a need to consider ecological, economic, governance, and social dimensions in MPAN design, implementation, monitoring, and evaluation. However, little is known about how these four dimensions are considered or shaped by objectives. We conducted an online survey with MPAN managers, technical staff, and academics from across the globe (77 survey responses that described 48 MPANs located in 59 countries). Our findings confirmed that most MPANs have various co‐occurring, potentially conflicting objectives. MPANs with biodiversity and societal objectives considered attributes (e.g., human well‐being and economic distribution, institutional partnerships, and network‐specific ecological attributes) among all dimensions, with greater frequency than MPANs with only biodiversity objectives. Nonetheless, ecological attributes were always perceived as important irrespective of the MPAN objective. Reaching synergies between the multiple dimensions of MPANs can be challenging if dimensions get overlooked in MPAN evaluations. Identifying the important attributes considered in MPANs offers insight into the practice of MPAN design, implementation, monitoring, and evaluation and can help improve MPAN success. [ABSTRACT FROM AUTHOR]

Abstract Marine protected area networks (MPANs) are promised as tools for protecting biodiversity and contributing to sustainable development. The variety of expected social‐ecological outcomes associated with MPANs underscores a need to consider ecological, economic, governance, and social dimensions in MPAN design, implementation, monitoring, and evaluation. However, little is known about how these four dimensions are considered or shaped by objectives. We conducted an online survey with MPAN managers, technical staff, and academics from across the globe (77 survey responses that described 48 MPANs located in 59 countries). Our findings confirmed that most MPANs have various co‐occurring, potentially conflicting objectives. MPANs with biodiversity and societal objectives considered attributes (e.g., human well‐being and economic distribution, institutional partnerships, and network‐specific ecological attributes) among all dimensions, with greater frequency than MPANs with only biodiversity objectives. Nonetheless, ecological attributes were always perceived as important irrespective of the MPAN objective. Reaching synergies between the multiple dimensions of MPANs can be challenging if dimensions get overlooked in MPAN evaluations. Identifying the important attributes considered in MPANs offers insight into the practice of MPAN design, implementation, monitoring, and evaluation and can help improve MPAN success.

Suppose we have n experts who have their prior opinion about the unknown probability q in the experiment with a binary outcome. It is known that experts' opinions are in conflict with each other. To model "conflicting" experts' opinions a prior distribution based on Selberg's integral is constructed. We prove a theorem regarding the limiting properties of the posterior distribution. Also, differential entropy and Kullback-Leibler (KL) divergence of such posterior are studied. [ABSTRACT FROM AUTHOR]

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Context: Choosing the most effective combinations of actions to manage threatened species is difficult. Aims: This study aimed to identify the most effective combinations of six management actions for reversing population declines of koala populations in three regional landscapes (Coastal, Riverine and Hinterland) in north-east New South Wales, Australia, based on expert-elicited knowledge. Methods: A Bayesian network was used to combine continuous probability distributions representing the impact of specific management actions and combinations of actions. Data were derived from expert elicitation. Variation within and among expert judgements was captured by incorporating submodels of individual responses. We evaluated alternative management solutions to address four mortality factors (disease and injury, vehicle strikes, domestic dogs and wild dogs/dingoes (Canis familiaris), and two habitat-related management actions (restoration and protection). Key results: We show that there are marked differences in the expected response of the koala populations to the various management options in the three regional landscapes over a 20-year period (2019–2039), and that multiple management actions are required to arrest and reverse the decline in koala populations of north-east NSW. Conclusions: Management actions for koala conservation should be based on regional context. Our model, in conjunction with robust expert-elicitation procedures, allows decision makers to distinguish effective from ineffective combinations of management actions for threatened species management, and has been structured so that new data can be incorporated into the model. Implications: Model design could be easily adapted to different species or conservation contexts, and updated as new evidence becomes available, making it valuable in adaptive management for local to regional-scale conservation problems. There are clear differences in the expected response to management within koala populations in the Australian state of New South Wales for different regional landscapes. Bayesian networks were used integrate expert opinion on koala populations under different management scenarios to quantify these differences. This work highlights the importance of designing context-specific management interventions for koala conservation. Photograph by Jonathan Rhodes. [ABSTRACT FROM AUTHOR]

Climate change is altering species' range limits and transforming ecosystems. For example, warming temperatures are leading to the range expansion of tropical, cold-sensitive species at the expense of their cold-tolerant counterparts. In some temperate and subtropical coastal wetlands, warming winters are enabling mangrove forest encroachment into salt marsh, which is a major regime shift that has significant ecological and societal ramifications. Here, we synthesized existing data and expert knowledge to assess the distribution of mangroves near rapidly changing range limits in the southeastern USA. We used expert elicitation to identify data limitations and highlight knowledge gaps for advancing understanding of past, current, and future range dynamics. Mangroves near poleward range limits are often shorter, wider, and more shrublike compared to their tropical counterparts that grow as tall forests in freeze-free, resource-rich environments. The northern range limits of mangroves in the southeastern USA are particularly dynamic and climate sensitive due to abundance of suitable coastal wetland habitat and the exposure of mangroves to winter temperature extremes that are much colder than comparable range limits on other continents. Thus, there is need for methodological refinements and improved spatiotemporal data regarding changes in mangrove structure and abundance near northern range limits in the southeastern USA. Advancing understanding of rapidly changing range limits is critical for foundation plant species such as mangroves, as it provides a basis for anticipating and preparing for the cascading effects of climate-induced species redistribution on ecosystems and the human communities that depend on their ecosystem services. [ABSTRACT FROM AUTHOR]

Experts can provide valuable information to fill knowledge gaps in published research on management effectiveness, particularly for threatened ecosystems, for which there is often limited evidence and the need for prompt intervention to ensure their persistence. One such ecosystem, alpine peatland, is threatened by climate change and other pressures, provides vital ecosystem services, and supports unique biodiversity. In a workshop, we gathered and synthesized into an accessible format information from experts on interventions used, threat context, and intervention effectiveness for Australian alpine peatland and used this knowledge to evaluate local relevance of the global literature for this threatened ecosystem. Experts identified 15 interventions used to conserve Australian peatlands, most of which enhanced or restored peatland condition and effectively addressed diverse threats. Experts' perspectives and global studies were strongly aligned, suggesting that research on peatland management may be broadly relevant across contexts, despite the distinct characteristics of Australian systems. Our workshop‐based expert elicitation approach provided insights into current management practices unavailable in the literature. [ABSTRACT FROM AUTHOR]

The task of this article is to harness the wisdom of a crowd without calibration. We assume each expert to form predictions by linearly combining various information cues inspired by the lens model, and use Gaussian process to account for sampling and judgmental errors in quantile judgments. Without knowing the experts' observed information cues, we develop a three-step estimation algorithm to factor quantile judgments into "variable profiles" (latent cues underlying each variable of interest) and "expert profiles" (each expert's weights over these cues). We can inquire about expert similarity using their weights of the latent cues, which preserve the same clustering results as the actual weights of the observed cues up to a full-rank linear transform. We can then depict the diversity and dependency among experts explicitly and retain a subcrowd by picking delegates from each subgroup of experts based on the estimated weights. Simulation and case studies demonstrate that a subcrowd selected this way can represent the entire expert panel well. [ABSTRACT FROM AUTHOR]

Aim: To develop a causal understanding of the drivers of Species distribution model (SDM) performance. Location: United Kingdom (UK). Methods: We measured the accuracy and variance of SDMs fitted for 518 species of invertebrate and plant in the UK. Our measure of variance reflects variation among replicate model fits, and taxon experts assessed model accuracy. Using directed acyclic graphs, we developed a causal model depicting plausible effects of explanatory variables (e.g. species' prevalence, sample size) on SDM accuracy and variance and quantified those effects using a multilevel piecewise path model. Results: According to our model, sample size and niche completeness (proportion of a species' niche covered by sampling) directly affect SDM accuracy and variance. Prevalence and range completeness have indirect effects mediated by sample size. Challenging conventional wisdom, we found that the effect of prevalence on SDM accuracy is positive. This reflects the facts that sample size has a positive effect on accuracy and larger sample sizes are possible for widespread species. It is possible, however, that the omission of an unobserved confounder biased this effect. Previous studies, which reported negative correlations between prevalence and SDM accuracy, conditioned on sample size. Main conclusions: Our model explicates the causal basis of previously reported correlations between SDM performance and species/data characteristics. It also suggests that niche completeness has similarly large effects on SDM accuracy and variance as sample size. Analysts should consider niche completeness, or proxies thereof, in addition to sample size when deciding whether modelling is worthwhile. [ABSTRACT FROM AUTHOR]

Ex situ ('off‐site') management refers to keeping species in artificial conditions away from their natural habitat and includes captive breeding facilities, botanical gardens and seed banks. There is scope for ex situ programmes to be more commonly used for supplementing or establishing wild populations. However, undertaking ex situ management comes with risks, costs and uncertainties, which must be assessed in the context of available in situ ('on‐site') management options. The PACES (Planning and Assessment for Conservation through Ex situ management) tool tailors the principles of structured decision‐making to the specific problem of assessing and comparing ex situ and in situ management options. We applied the PACES tool to the mahogany glider (Petaurus gracilis), a threatened arboreal marsupial endemic to north Queensland, Australia. Through an expert elicitation process, we predicted the likely benefits of an ex situ and two in situ management options, as compared to a baseline 'do‐nothing' scenario. The 'in situ plus' alternative (where extra resources are dedicated to in situ management) was predicted to result in the largest population increase according to the participants' best estimates. However, this benefit came at a much larger cost than the ex situ alternative, and without the benefit of an ex situ insurance population. The PACES tool assessment allowed the Mahogany Glider Recovery Team to document and plan the financial costs, risks and benefits of potential future management options for the mahogany glider, laying a transparent basis for future assessment and decision‐making. [ABSTRACT FROM AUTHOR]

Introduction: Clinical trials often have short follow-ups, and long-term outcomes such as survival must be extrapolated. Current extrapolation methods often produce a wide range of survival values. To minimize uncertainty in projections, we developed a novel method that incorporates formally elicited expert opinion in a Bayesian analysis and used it to extrapolate survival in the placebo arm of DAPA-CKD, a phase 3 trial of dapagliflozin in patients with chronic kidney disease (NCT03036150). Methods: A summary of mortality data from 13 studies that included DAPA-CKD-like populations and training on elicitation were provided to six experts. An elicitation survey was used to gather the experts' 10- and 20-year survival estimates for patients in the placebo arm of DAPA-CKD. These estimates were combined with DAPA-CKD mortality and general population mortality (GPM) data in a Bayesian analysis to extrapolate long-term survival using seven parametric distributions. Results were compared with those from standard frequentist approaches (with and without GPM data) that do not incorporate expert opinion. Results: The group expert-elicited estimate for 20-year survival was 31% (lower estimate, 10%; upper estimate, 40%). In the Bayesian analysis, the 20-year extrapolated survival across the seven distributions was 14.9–39.1%, a range that was 2.4- and 1.6-fold smaller than those produced by the frequentist methods (0.0–56.9% without and 0.0–39.2% with GPM data). Conclusions: Using expert opinion in a Bayesian analysis provided a robust method for extrapolating long-term survival in the placebo arm of DAPA-CKD. The method could be applied to other populations with limited survival data. [ABSTRACT FROM AUTHOR]

This study presents a method for inferring the potential variabilities that need to be computed in a model developed using the Functional Resonance Analysis Method (FRAM) by means of adapting a questionnaire used in the Resilience Analysis Grid method. The proposed method, called in this study the indirect method, is compared to the technique prescribed in FRAM to acquire variabilities for each system's functions in the specific case of a FRAM model for obtaining a nuclear-powered submarine and its land support facility, hereinafter called the Combined Nuclear Facility (CNF). It should be noted that this model encompasses the design, the nuclear licensing process, and the construction of the CNF and aims to help to point out weaknesses in nuclear safety. The results show that 55.17% of the variability data obtained from both methods was identical (by exploratory data analysis), and a chi-square test of independence, conducted between method type and variability category, displayed that there was not a statistically significant association between method type and variability category. Thus, the null hypothesis cannot be rejected, and variability category and method type are independent of each other. Additionally, a qualitative comparison of a FRAM instantiation is presented using variabilities from the two methods, which resulted in small differences that apparently do not affect the overall result of the FRAM analysis. Therefore, it is concluded that the indirect method used to obtain information on the variability of functions of the model for obtaining the CNF is adequate. [ABSTRACT FROM AUTHOR]

To tackle the twenty-first-century challenges for sustainability, a deeper understanding of their complexity is needed. Hence, interdisciplinary human–environment research integrating knowledge, perspectives, and solutions across scales is crucial. Yet, questions of 'scale' and 'scaling' continue to challenge human–environment research. Despite substantial scholarly attention to scales, no unified definitions and understanding exist, causing confusion among researchers. In this paper, we aim to provide clarity to the challenges and potential solutions to bridging scales in interdisciplinary human–environment research as well as identify leverage points and arenas of change that would enable it. We used a three-step methodology to (a) identify the challenges and solutions based on a survey of 82 interdisciplinary researchers, (b) prioritise the challenges and solutions based on expert elicitation, and (c) organise the solutions according to their potential for leveraging change at three system arenas: individual researchers, institutions and organisations, and the wider academic structures. The main challenges ranged from issues related to differences in research approaches, lack of resources for deeper collaborations, or conflicting perspectives on problem and scale definitions, to miscommunication. The specific solutions linked to these challenges were predominantly related to the potential of improving the research process through open communication, data sharing, or new techniques for upscaling or downscaling results. We argue that bridging scales requires open and humble conversations across disciplines on scale understandings as well as improved collaboration through data sharing, method development, result dissemination, and contribution to theory. Facilitating such collaboration requires enabling spaces of interaction across three systemic arenas: the individual researcher's personal commitment to open communication and reflection, the research institution's capacity to enable interdisciplinary spaces, and the wider academic system valuing and supporting interdisciplinary and cross-scale initiatives. [ABSTRACT FROM AUTHOR]

Abstract Background While African Swine Fever (ASF) virus has historically circulated in wild pigs and in Ornithodoros ticks in parts of South Africa, the virus has spread among domestic pigs throughout the country since 2019. South Africa’s compartment system has been used as a mainstay approach to protecting the swine industry in the face of ASF. However, in 2020, two compartments broke down with ASF. The objectives of this study are to investigate the drivers for ASF introduction into the compartments, to categorize compartments by risk of ASF introduction, and to make corresponding recommendations. The relevance of risk factors for ASF introduction for each compartment were investigated among veterinarians and farm managers. The analysis of risk factors weighted according to an expert elicitation were used to categorize compartments into risk levels. Results Drivers of disease related to human behaviors and to domestic pig management are perceived by farm managers and veterinarians of the compartments to be critical for ASF introduction into compartments in South Africa. Twenty-four units were categorized as high risk, forty-seven as medium risk, and twenty-four as low risk. “Insufficient boot and clothing biosecurity by animal health personnel” was identified as a relevant risk factor in all high risk units. Other prominent risk factors were “insufficient boot and clothing biosecurity by external people,” “underreporting of suspect ASF cases,” “improper hunting/ culling of wild suids inside the compartment,” “un-tested introductions into the herd,” and “entry and contact with free-roaming pigs.” The roles of wild pigs and competent vectors are considered minimal. There is a need for revision of the compartment standards and training of compartment personnel on the standards. The major gaps identified in the standards were absence of a monitoring programme to assess biosecurity implementation and suboptimal surveillance testing and audit strategies. Conclusions The results of our study confirm that ASF is increasingly an anthropogenic problem. Updating the compartment standards and addressing gaps in the knowledge of compartment personnel on ASF are most critical. To enhance compliance with biosecurity measures and thus control the disease, close engagement with all stakeholders linked to the compartments is needed.

Abstract Wind power technology has changed rapidly in recent years. Technology innovation, evolving power markets, and competing land and ocean uses continue to influence the design and operation of wind turbines and plants. Anticipating these trends and their impact on future facilities can inform commercial strategies and research priorities. Drawing from a recent survey of 140 of the world's foremost wind experts, we identify expectations of future wind plant design in 2035, both for onshore and offshore wind. Experts anticipate continued growth in turbine size, to 5.5 (onshore) and 17 MW (offshore), with plants located in increasingly less favorable wind and siting regimes. They expect plant sizes of 1,100 MW for fixed‐bottom and 600 MW for floating offshore wind. Experts forecast enhanced grid‐system value from wind through significant to widespread use of larger rotors, hybrid projects with batteries and hydrogen production, and more. To explain experts' perspectives on future plant design and operation, we identify five mechanisms: economies of unit, plant, and resource scale; grid‐system value economies; and production efficiencies. We characterize learning effects as a moderating influence on the strength of these mechanisms. In combination, experts predict that these design choices support levelized cost of energy reductions of 27% (onshore) and 17%–35% (floating and fixed‐bottom offshore) by 2035 compared to today, while enhancing wind energy's grid service offerings. Our findings provide a much‐needed benchmark for representing future wind technologies in power sector models and address a critical research gap by explaining the economics behind wind energy design choices.

Because the sounds emitted by a managed honeybee colony embrace a wealth of information about the status within and outside the beehive, researchers are interested in developing a beehive sound-based assessment of the colony situation. However, how the global experts rank this approach is unclear. We assessed the importance of beehive sound-based colony monitoring using formal expert elicitation. Our results indicate that policy-making bodies should focus on a non-invasive acoustic approach to monitor swarming, honeybee health, pesticides, and environmental pollution at apiaries, as these were considered very important factors with high confidence by global experts. Moreover, all other factors (pests and pathogens, weather conditions, predators, food availability, and spatiotemporal patterns) are rated as important, but experts' confidence in acoustically monitoring a few of the factors differs. Because experienced forager bees emit bursting sounds during the waggle dance (particularly during the waggle-run phase) at a specific angle on a vertical comb within the hive, we propose an acoustics-based recording setup using a Raspberry Pi and a QuadMic Array to investigate how this sound can predict the spatial and temporal information of the available food sources. In this article, we highlight how the factors falling into the inconclusive category of confidence have the potential to be acoustically monitored. Besides, this paper suggests new and unexplored directions for opening a window for future research in beehive acoustics. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Methods: In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. Results: We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Conclusions: Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. [ABSTRACT FROM AUTHOR]

Tick-borne encephalitis (TBE) is a viral disease endemic in Eurasia. The virus is mainly transmitted to humans via ticks and occasionally via the consumption of unpasteurized milk products. The European Centre for Disease Prevention and Control reported an increase in TBE incidence over the past years in Europe as well as the emergence of the disease in new areas. To better understand this phenomenon, we investigated the drivers of TBE emergence and increase in incidence in humans through an expert knowledge elicitation. We listed 59 possible drivers grouped in eight domains and elicited forty European experts to: (i) allocate a score per driver, (ii) weight this score within each domain, and (iii) weight the different domains and attribute an uncertainty level per domain. An overall weighted score per driver was calculated, and drivers with comparable scores were grouped into three terminal nodes using a regression tree analysis. The drivers with the highest scores were: (i) changes in human behavior/activities; (ii) changes in eating habits or consumer demand; (iii) changes in the landscape; (iv) influence of humidity on the survival and transmission of the pathogen; (v) difficulty to control reservoir(s) and/or vector(s); (vi) influence of temperature on virus survival and transmission; (vii) number of wildlife compartments/groups acting as reservoirs or amplifying hosts; (viii) increase of autochthonous wild mammals; and (ix) number of tick species vectors and their distribution. Our results support researchers in prioritizing studies targeting the most relevant drivers of emergence and increasing TBE incidence. [ABSTRACT FROM AUTHOR]