A systematic review of transparency in the methods of expert knowledge use

Code sharing in ecology and evolution increases citation rates but remains uncommon

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low-impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open-access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation.

Bridging the gap in fishing effort mapping: a spatially-explicit fisheries dataset for Campanian MPAs, Italy

Recent technological advancements have facilitated the extensive collection of movement data from large-scale fishing vessels, yet a significant data gap remains for small-scale fisheries. This gap hinders the development of consistent exploitation patterns and meeting the information needs for marine spatial planning in fisheries management. This challenge is specifically addressed in the Campania region of Italy, where several Marine Protected Areas support biodiversity conservation and fisheries management. The authors have created a spatially-explicit dataset that encompasses both large-scale (vessels exceeding 12 meters in length) and small-scale (below 12 meters) fishing efforts. This dataset (available at https://doi.org/10.6084/m9.figshare.23592006 ) is derived from vessel tracking data and participatory mapping. It offers insights into potential conflicts between different fishing segments and their interactions with priority species and habitats. The data can assist researchers and coastal management stakeholders in formulating policies that reduce resource competition and promote ecosystem-based fisheries management. Furthermore, the provided mapping approach is adaptable for other regions and decision-making frameworks, as we are committed to sharing the tools and techniques we employed.

The importance of expert selection when identifying threatened ecosystems

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.

A socio-ecological critique on India's local health traditions amidst rising incidence of global pandemics

Introduction

The COVID-19 crisis has exposed inadequacy to deal with such health emergencies. The state of healthcare facilities in India is shambolic, which is further exacerbated by the exclusivity of modern health systems. The authors argue for vertical and horizontal expansion of the existing system to include traditional medicine systems, in favor of an urgently needed holistic and more inclusive healthcare system.

Methods

Secondary data were collected from free online resources, including preprints, reprints and databases, J-gate Plus, PubMed and Web of Science, using keywords such as, “folk medicine”, “folk medicine AND India”, “traditional Indian medicines”, “indigenous Indian medicines AND India”, “Indian ethnomedicines”, “Indian AND folk AND medicine”, “indigenous Indian medicine”.

Results and conclusions

Insufficiently robust public healthcare infrastructure, lack of enough qualified health professionals, and poor use of its traditional medicinal systems, are limiting the access of basic healthcare facilities to a large section of the Indian population. Despite vehement opposition and criticism from modern health system practitioners, indigenous and local healing traditions do offer benefits and share a common global goal of health and healing. The objectives of Universal Health Coverage and Health for All as enshrined in the Sustainable Development Goals (SDGs) cannot be achieved without their involvement, especially in remote and economically disadvantaged regions of the country. Even a sub-optimal utilization of its biological and human resources and related traditional knowledge can not only profoundly change the health, but also the economic landscape of India. Here, we have nuanced the constrains posed by the emerging health challenges, status and prospects of the great and little traditions of the Indian System of Medicines in fulfilling the urgent healthcare needs of the country. The authors based their arguments on the available evidence, rather than emotive ideas or, as a fan of Indian traditional medicines, and suggest for the purposeful inclusion of traditional health systems and practitioners more actively in India’s health care delivery systems. The country must not squander the opportunities offered by its traditional medicinal systems.

Robinson A. Co-designing and building an expert-elicited non-parametric Bayesian network model: demonstrating a methodology using a Bonamia Ostreae spread risk case study

The development and use of probabilistic models, particularly Bayesian networks (BN), to support risk-based decision making is well established. Striking an efficient balance between satisfying model complexity and ease of development requires continuous compromise. Codesign, wherein the structural content of the model is developed hand-in-hand with the experts who will be accountable for the parameter estimates, shows promise, as do so-called nonparametric Bayesian networks (NPBNs), which provide a light-touch approach to capturing complex relationships among nodes. We describe and demonstrate the process of codesigning, building, quantifying, and validating an NPBN model for emerging risks and the consequences of potential management decisions using structured expert judgment (SEJ). We develop a case study of the local spread of a marine pathogen, namely, Bonamia ostreae. The BN was developed through a series of semistructured workshops that incorporated extensive feedback from many experts. The model was then quantified with a combination of field and expert-elicited data. The IDEA protocol for SEJ was used in its hybrid (remote and face-to-face) form to elicit information about more than 100 parameters. This article focuses on the modeling and quantification process, the methodological challenges, and the way these were addressed.

Uncertainty Quantification with Experts: Present Status and Research Needs

Expert elicitation is deployed when data are absent or uninformative and critical decisions must be made. In designing an expert elicitation, most practitioners seek to achieve best practice while balancing practical constraints. The choices made influence the required time and effort investment, the quality of the elicited data, experts' engagement, the defensibility of results, and the acceptability of resulting decisions. This piece outlines some of the common choices practitioners encounter when designing and conducting an elicitation. We discuss the evidence supporting these decisions and identify research gaps. This will hopefully allow practitioners to better navigate the literature, and will inspire the expert judgment research community to conduct well powered, replicable experiments that properly address the research gaps identified.

One Coin, Two Sides: Eliciting Expert Knowledge From Training Participants in a Capacity-Building Program for Veterinary Professionals

Scientific research may include the elicitation of judgment from non-academic subject-matter experts in order to improve the quality and/or impact of research studies. Elicitation of expert knowledge or judgment is used when data are missing, incomplete, or not representative for the specific setting and processes being studied. Rigorous methods are crucial to ensure robust study results, and yet the quality of the elicitation can be affected by a number of practical constraints, including the understanding that subject-matter experts have of the elicitation process itself. In this paper, we present a case of expert elicitation embedded within an extended training course for veterinary professionals as an example of overcoming these constraints. The coupling of the two activities enabled extended opportunities for training and a relationship of mutual respect to be the foundation for the elicitation process. In addition, the participatory research activities reinforced knowledge synthesis objectives of the educational program. Finally, the synergy between the two concurrent objectives may produce benefits which transcend either independent activity: solutions and ideas built by local professionals, evolving collaborative research and training approaches, and a network of diverse academic and practicing professionals. This approach has the versatility to be adapted to many training and research opportunities.

Self-Reporting of Risk Pathways and Parameter Values for Foot-and-Mouth Disease in Slaughter Cattle from Alternative Production Systems by Kenyan and Ugandan Veterinarians

Countries in which foot-and-mouth disease (FMD) is endemic may face bans on the export of FMD-susceptible livestock and products because of the associated risk for transmission of FMD virus. Risk assessment is an essential tool for demonstrating the fitness of one’s goods for the international marketplace and for improving animal health. However, it is difficult to obtain the necessary data for such risk assessments in many countries where FMD is present. This study bridged the gaps of traditional participatory and expert elicitation approaches by partnering with veterinarians from the National Veterinary Services of Kenya (n = 13) and Uganda (n = 10) enrolled in an extended capacity-building program to systematically collect rich, local knowledge in a format appropriate for formal quantitative analysis. Participants mapped risk pathways and quantified variables that determine the risk of infection among cattle at slaughter originating from each of four beef production systems in each country. Findings highlighted that risk processes differ between management systems, that disease and sale are not always independent events, and that events on the risk pathway are influenced by the actions and motivations of value chain actors. The results provide necessary information for evaluating the risk of FMD among cattle pre-harvest in Kenya and Uganda and provide a framework for similar evaluation in other endemic settings.

Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterize currently recognized historical reference states and describe them as Pre-Human, Indigenous Cultural, Pre-Intensification and Hybrid-Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values relative to other locations within the same ecosystem, regardless of the disturbance history. We acknowledge that past processes play an essential role in driving contemporary patterns of diversity. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal-to maximize biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g. native species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This 'road map of reference states' offers perspective needed to define and assess the status and trends in biodiversity and habitats. We demonstrate the contemporary reference state concept with an example from south-eastern Australia. Our framework provides a tractable way for policy-makers and practitioners to navigate biodiversity assessments to maximize conservation and restoration outcomes in contemporary ecosystems.

Untapped potential of collective intelligence in conservation and environmental decision making

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