Assessment of a large number of empirical plant species niche models by elicitation of knowledge from two national experts

Quantitative models play an increasing role in exploring the impact of global change on biodiversity. To win credibility and trust, they need validating. We show how expert knowledge can be used to assess a large number of empirical species niche models constructed for the British vascular plant and bryophyte flora. Key outcomes were (a) scored assessments of each modeled species and niche axis combination, (b) guidance on models needing further development, (c) exploration of the trade‐off between presenting more complex model summaries, which could lead to more thorough validation, versus the longer time these take to evaluate, (d) quantification of the internal consistency of expert opinion based on comparison of assessment scores made on a random subset of models evaluated by both experts. Overall, the experts assessed 39% of species and niche axis combinations to be “poor” and 61% to show a degree of reliability split between “moderate” (30%), “good” (25%), and “excellent” (6%). The two experts agreed in only 43% of cases, reaching greater consensus about poorer models and disagreeing most about models rated as better by either expert. This low agreement rate suggests that a greater number of experts is required to produce reliable assessments and to more fully understand the reasons underlying lack of consensus. While area under curve (AUC) statistics showed generally very good ability of the models to predict random hold‐out samples of the data, there was no correspondence between these and the scores given by the experts and no apparent correlation between AUC and species prevalence. Crowd‐sourcing further assessments by allowing web‐based access to model fits is an obvious next step. To this end, we developed an online application for inspecting and evaluating the fit of each niche surface to its training data.     

Assessing species’ habitat associations from occurrence records,standardised monitoring data and expert opinion: A test with British butterflies

Accurate knowledge of species’ habitat associations is important for conservation planning and policy. Assessing habitat associations is a vital precursor to selecting appropriate indicator species for prioritising sites for conservation or assessing trends in habitat quality. However, much existing knowledge is based on qualitative expert opinion or local scale studies, and may not remain accurate across different spatial scales or geographic locations. Data from biological recording schemes have the potential to provide objective measures of habitat association, with the ability to account for spatial variation. We used data on 50 British butterfly species as a test case to investigate the correspondence of data-derived measures of habitat association with expert opinion, from two different butterfly recording schemes. One scheme collected large quantities of occurrence data (c. 3 million records) and the other, lower quantities of standardised monitoring data (c. 1400 sites). We used general linear mixed effects models to derive scores of association with broad-leaf woodland for both datasets and compared them with scores canvassed from experts.Scores derived from occurrence and abundance data both showed strongly positive correlations with expert opinion. However, only for occurrence data did these fell within the range of correlations between experts. Data-derived scores showed regional spatial variation in the strength of butterfly associations with broad-leaf woodland, with a significant latitudinal trend in 26% of species. Sub-sampling of the data suggested a mean sample size of 5000 occurrence records per species to gain an accurate estimation of habitat association, although habitat specialists are likely to be readily detected using several hundred records. Occurrence data from recording schemes can thus provide easily obtained, objective, quantitative measures of habitat association.     

Four Common Simplifications of Multi-Criteria Decision Analysis do not hold for River Rehabilitation

River rehabilitation aims at alleviating negative effects of human impacts such as loss of biodiversity and reduction of ecosystem services. Such interventions entail difficult trade-offs between different ecological and often socio-economic objectives. Multi-Criteria Decision Analysis (MCDA) is a very suitable approach that helps assessing the current ecological state and prioritizing river rehabilitation measures in a standardized way, based on stakeholder or expert preferences. Applications of MCDA in river rehabilitation projects are often simplified, i.e. using a limited number of objectives and indicators, assuming linear value functions, aggregating individual indicator assessments additively, and/or assuming risk neutrality of experts. Here, we demonstrate an implementation of MCDA expert preference assessments to river rehabilitation and provide ample material for other applications. To test whether the above simplifications reflect common expert opinion, we carried out very detailed interviews with five river ecologists and a hydraulic engineer. We defined essential objectives and measurable quality indicators (attributes), elicited the experts´ preferences for objectives on a standardized scale (value functions) and their risk attitude, and identified suitable aggregation methods. The experts recommended an extensive objectives hierarchy including between 54 and 93 essential objectives and between 37 to 61 essential attributes. For 81% of these, they defined non-linear value functions and in 76% recommended multiplicative aggregation. The experts were risk averse or risk prone (but never risk neutral), depending on the current ecological state of the river, and the experts´ personal importance of objectives. We conclude that the four commonly applied simplifications clearly do not reflect the opinion of river rehabilitation experts. The optimal level of model complexity, however, remains highly case-study specific depending on data and resource availability, the context, and the complexity of the decision problem.     

Likely health outcomes for untreated acute febrile illness in the tropics in decision and economic models; a Delphi survey

Background

Modelling is widely used to inform decisions about management of malaria and acute febrile illnesses. Most models depend on estimates of the probability that untreated patients with malaria or bacterial illnesses will progress to severe disease or death. However, data on these key parameters are lacking and assumptions are frequently made based on expert opinion. Widely diverse opinions can lead to conflicting outcomes in models they inform.

Methods and Findings

A Delphi survey was conducted with malaria experts aiming to reach consensus on key parameters for public health and economic models, relating to the outcome of untreated febrile illnesses. Survey questions were stratified by malaria transmission intensity, patient age, and HIV prevalence. The impact of the variability in opinion on decision models is illustrated with a model previously used to assess the cost-effectiveness of malaria rapid diagnostic tests. Some consensus was reached around the probability that patients from higher transmission settings with untreated malaria would progress to severe disease (median 3%, inter-quartile range (IQR) 1–5%), and the probability that a non-malaria illness required antibiotics in areas of low HIV prevalence (median 20%). Children living in low transmission areas were considered to be at higher risk of progressing to severe malaria (median 30%, IQR 10–58%) than those from higher transmission areas (median 13%, IQR 7–30%). Estimates of the probability of dying from severe malaria were high in all settings (medians 60–73%). However, opinions varied widely for most parameters, and did not converge on resurveying.

Conclusions

This study highlights the uncertainty around potential consequences of untreated malaria and bacterial illnesses. The lack of consensus on most parameters, the wide range of estimates, and the impact of variability in estimates on model outputs, demonstrate the importance of sensitivity analysis for decision models employing expert opinion. Results of such models should be interpreted cautiously. The diversity of expert opinion should be recognised when policy options are debated.     

Predictive Assessment of Fish Health and Fish Kills in the Neuse River Estuary Using Elicited Expert Judgment

Declining fish health and the occurrence of large fish kills are some of the more publicly meaningful indicators of water quality in the impaired Neuse River Estuary, North Carolina. It is generally believed that such problems are caused by the widespread depletion of dissolved oxygen—an indirect result of anthropogenic nutrient pollution. However, the development of scientific simulation models to predict how improvements in oxygen conditions will improve the health of fish and reduce the frequency of fish kills has proven elusive. As a pragmatic solution to this problem, the expert opinion of estuarine fisheries scientists in possession of relevant data and experience was elicited. The relations between joint and conditional probabilities were exploited to translate quantities that are normally hard to assess into quantities that can be drawn more directly from the experiential knowledge of the experts. A combined model of expert opinion was constructed as an influence diagram, and Monte Carlo simulation was used to generate predictions of fish health and fish kills in the Neuse River Estuary under current and improved oxygen conditions. Full model results are expressed as probability distributions, capturing the effects of natural variability and knowledge uncertainty—both contributors to total ecological risk.     

Translating Predictions of Zoonotic Viruses for Policymakers

Recent outbreaks of Ebola virus disease and Zika virus disease highlight the need for disseminating accurate predictions of emerging zoonotic viruses to national governments for disease surveillance and response. Although there are published maps for many emerging zoonotic viruses, it is unknown if there is agreement among different models or if they are concordant with national expert opinion. Therefore, we reviewed existing predictions for five high priority emerging zoonotic viruses with national experts in Cameroon to investigate these issues and determine how to make predictions more useful for national policymakers. Predictive maps relied primarily on environmental parameters and species distribution models. Rift Valley fever virus and Crimean-Congo hemorrhagic fever virus predictions differed from national expert opinion, potentially because of local livestock movements. Our findings reveal that involving national experts could elicit additional data to improve predictions of emerging pathogens as well as help repackage predictions for policymakers.     

Planning for Restoration: A Decision Analysis Approach to Prioritization

Ecological restoration often relies on the use of expert opinion to make management decisions in the face of uncertainty. The quantification of expert opinion can be difficult, especially when more than one expert is consulted and experts are not in agreement. Decision analysis can provide a framework to systematically deconstruct a complex problem and provide greater objectivity to restoration decisions. We utilized decision analysis techniques to identify restoration objectives and to quantify expert opinions to prioritize restoration activities at 112 prairie openings in the Edge of Appalachia Preserve in southern Ohio, U.S.A. We first created an objectives hierarchy to model how decision‐makers decide which prairies to manage. We then determined how to measure each component of the hierarchy and sampled all prairies for percent woody cover, geology, indicator species index (an index of plant species richness), slope, aspect, and distance to nearest prairie. We modeled seven different experts’ preferences for managing prairies with varying values for each of these ecological measures. We then interviewed the same decision‐makers to determine relative weights for each component of the objectives hierarchy using trade‐off analysis. By combining the weights, preference relationships, and sampling data, we were able to rank each prairie and management unit based on its management priority. Experts had similar preferences except for the measure of distance to nearest prairie. We found that decision‐makers gave different weights to each of the different components of the hierarchy. Generally, experts weighted percent woody cover, indicator species index, and geology more highly than slope, aspect, and distance to nearest prairie. Despite these differences, priorities for management, once all factors were weighted and combined, were similar.     

Utility of Expert-Based Knowledge for Predicting Wildlife-Vehicle Collisions

ABSTRACT Wildlife-vehicle collisions have important ecological, economic, and social effects. In North America and across northern Europe, moose (Alces alces) are one of the largest ungulates hit by motor vehicles. The force and increasing frequency of these collisions has resulted in a commitment by wildlife and transportation agencies to limit or reduce causal factors. In an effort to improve these mitigation strategies, we used the most readily available source of knowledge of collision factors, expert opinion, to develop a series of models that explained and predicted location of moose-vehicle collisions (MVC). We developed expert-based models using the Analytical Hierarchy Process (AHP) and we used a structured survey approach where experts could assess criteria relevancy, weight criteria, and review weights for consistency. We hypothesized that collisions were the product of habitat- or driver-related factors and we formulated the survey accordingly. We used the receiver operating characteristic to validate the resulting models and the Kappa index of agreement to quantify differences among spatial predictions originating from the experts. Local and nonlocal experts weighted the moose habitat classification as the most important criterion for identifying MVC. Among driver-related criteria, speed limit was weighted as the most important factor. Overall, habitat-based models were more proficient than driver-based models in predicting MVC within Mount Revelstoke and Glacier National Parks, Canada. Both local and nonlocal expert models were excellent predictors of MVC, with local experts slightly outperforming nonlocal experts. Considering that habitat-related criteria were more powerful for predicting MVC, and that habitat can vary considerably across study areas, we suggest that local experts be used when possible. The AHP is a valuable tool for wildlife, highway, and park managers to better understand why and where wildlife-vehicle collisions occur. Adopting this process, our data suggested that MVC were most strongly correlated with highway attractants associated with habitat. Vegetation management or alternative routing could minimize spatial juxtaposition of moose and motor vehicles.     

Encountering nature through fieldwork: expert knowledge,modes of reasoning,and local creativity

The concept of ‘relation’ has been central to the anthropological reworking of the nature/culture and nature/society dichotomies. However, ecology is relational in a way that has often been ignored or dismissed in contemporary socio‐cultural anthropology. This article shows that there is more to ethnoecology than an ethnocentric form of analysis representing other people's understandings of the natural world through the prejudiced lens of Western scientific classifications. Three ‘fieldwork on fieldwork’ experiments involving encounters between natural scientists and indigenous communities in Amazonian Ecuador and Southern Guyana are discussed to illustrate the heterogeneity of human knowledge, the role of expert knowledge in intercultural communication, and the need to differentiate ecological reasoning from moral reasoning.     

Validating Community-Led Forest Biomass Assessments

The lack of capacity to monitor forest carbon stocks in developing countries is undermining global efforts to reduce carbon emissions. Involving local people in monitoring forest carbon stocks could potentially address this capacity gap. This study conducts a complete expert remeasurement of community-led biomass inventories in remote tropical forests of Papua New Guinea. By fully remeasuring and isolating the effects of 4,481 field measurements, we demonstrate that programmes employing local people (non-experts) can produce forest monitoring data as reliable as those produced by scientists (experts). Overall, non-experts reported lower biomass estimates by an average of 9.1%, equivalent to 55.2 fewer tonnes of biomass ha^-1, which could have important financial implications for communities. However, there were no significant differences between forest biomass estimates of expert and non-expert, nor were there significant differences in some of the components used to calculate these estimates, such as tree diameter at breast height (DBH), tree counts and plot surface area, but were significant differences between tree heights. At the landscape level, the greatest biomass discrepancies resulted from height measurements (41%) and, unexpectedly, a few large missing trees contributing to a third of the overall discrepancies. We show that 85% of the biomass discrepancies at the tree level were caused by measurement taken on large trees (DBH ≥50cm), even though they consisted of only 14% of the stems. We demonstrate that programmes that engage local people can provide high-quality forest carbon data that could help overcome barriers to reducing forest carbon emissions in developing countries. Nonetheless, community-based monitoring programmes should prioritise reducing errors in the field that lead to the most important discrepancies, notably; overcoming challenges to accurately measure large trees.     

Environmentally Conscious Product Design: A Collaborative Internet-based Modeling Approach

This paper proposes a computer-based method for providing product designers with real-time environmental impact assessment. In this concurrent modeling approach, environmental experts build life-cycle models, define their interfaces, and publish them as distributed objects on the Internet. Traditional designers integrating these objects into their design models have access to the impact assessment methods provided by the environmental expert. In this paradigm, the focus shifts from providing techniques that let non-expert designers perform life-cycle impact assessments to tools that facilitate timely communication and information transfer between designers and appropriate environmental experts. Establishing real-time communication between the product design models and the environmental life-cycle models is the primary focus of this paper. Methods for establishing and maintaining the interaction between life-cycle and product design models are described. A beverage container design example illustrates how this collaborative approach can use environmental and traditional design goals to determine effective tradeoffs between design alternatives.     

The Nature Index: a general framework for synthesizing knowledge on the state of biodiversity

The magnitude and urgency of the biodiversity crisis is widely recognized within scientific and political organizations. However, a lack of integrated measures for biodiversity has greatly constrained the national and international response to the biodiversity crisis. Thus, integrated biodiversity indexes will greatly facilitate information transfer from science toward other areas of human society. The Nature Index framework samples scientific information on biodiversity from a variety of sources, synthesizes this information, and then transmits it in a simplified form to environmental managers, policymakers, and the public. The Nature Index optimizes information use by incorporating expert judgment, monitoring-based estimates, and model-based estimates. The index relies on a network of scientific experts, each of whom is responsible for one or more biodiversity indicators. The resulting set of indicators is supposed to represent the best available knowledge on the state of biodiversity and ecosystems in any given area. The value of each indicator is scaled relative to a reference state, i.e., a predicted value assessed by each expert for a hypothetical undisturbed or sustainably managed ecosystem. Scaled indicator values can be aggregated or disaggregated over different axes representing spatiotemporal dimensions or thematic groups. A range of scaling models can be applied to allow for different ways of interpreting the reference states, e.g., optimal situations or minimum sustainable levels. Statistical testing for differences in space or time can be implemented using Monte-Carlo simulations. This study presents the Nature Index framework and details its implementation in Norway. The results suggest that the framework is a functional, efficient, and pragmatic approach for gathering and synthesizing scientific knowledge on the state of biodiversity in any marine or terrestrial ecosystem and has general applicability worldwide.     

Growth sites of polypores from quantitative expert evaluation: Late-stage decayers and saprotrophs fruit closer to ground

Life history traits are key to why species occur when and where they do and how their populations will respond to environmental changes. However, dispersal-related traits of fungi are generally poorly known. We studied how spore release height from the ground, an important determinant of airborne dispersal, is connected to other traits in polypores. We collected expert evaluations of fruit body growth sites for 140 species and found that experts generally provided consistent estimates of height above the ground. Height was correlated with other traits: species fruiting on living trees, earlier decay stages and deciduous hosts tend to fruit higher above the ground. While our data do not allow mechanistic explanations, our study demonstrates the potential of expert knowledge and identifies fruit body height above the ground as one consistent trait relevant to species’ life history strategies. We recommend a more comprehensive expert survey as one cost-efficient way towards a more trait-based fungal ecology.     

Perceptual expertise in forensic facial image comparison

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person''s appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years'' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners'' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.     

‘Illuminating the broader context’: anthropological and historical knowledge at the International Criminal Tribunal for Rwanda

Historians and anthropologists have been among the experts called to enlighten non‐Rwandan judges at the International Criminal Tribunal for Rwanda. Given that the Tribunal's regulations provide little guidance on who qualifies as an expert and that judges are not bound by national rules of evidence, judges exercise considerable discretion on who may provide expert testimony. By considering judges' scepticism towards repetitive expert testimony and that experts have changed their opinion because of information revealed in the course of the trials, this article seeks to convey the distinct characteristics of this innovative context and re‐evaluate the assumption that anthropologists and historians are inevitably engaged in an epistemological contest with law when they act as experts in criminal trials.     

Decline in local ecological knowledge in the Colombian Andes may constrain silvopastoral tree diversity

Local ecological knowledge (LEK) can play an important role in ecological restoration by guiding landholder decision‐making towards more ecologically oriented land management. Silvopastures are a promising option for restoring ecological function to degraded landscapes because they increase tree cover and diversity, moderating the impacts of cattle production on ecological processes. Some silvopastoralists possess considerable LEK that can shape silvopastoral tree communities, but little is known about the temporal trends of this knowledge base. We followed up on a survey of workers, managers, and owners of silvopastoral farms in the Colombian Andes 6 years later and found that the ability to identify native trees and describe their uses, and interest in tree planting, were significantly lower. Importantly, the likelihood that an individual would plant a species was linked directly to the number of uses they listed for that species. Furthermore, the utility of species became more important for influencing the probability of planting native trees over time, as general interest in planting trees declined. We show that knowledge about the uses of native trees is critical for promoting cultivation of diverse tree species in silvopastoral systems, and that multipurpose trees are most likely to be planted. Moreover, our results suggest that the socio‐economic changes driving declines in ecological knowledge elsewhere, including outmigration and limited transmission of knowledge to younger generations, appear to erode LEK among Colombian silvopastoralists. Preserving LEK is therefore a critical factor for ensuring silvopastoral and other agro‐ecosystems can contribute to efforts to restore ecological integrity to degraded landscapes.     

Inference reasoning on fishers' knowledge using Bayesian causal maps

Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individual judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.     

Measures, perceptions and scaling patterns of aggregated species distributions

Non-random (aggregated) species distributions arise from habitat heterogeneity and nonlinear biotic processes. A comprehensive understanding of the concept of aggregation, as well as its measurement, is pivotal to our understanding of species distributions and macroecological patterns. Here, using an individual-based model, we analyzed opinions on the concept of aggregation from the public and experts (trained ecologists), in addition to those calculated from a variety of aggregation indices. Three forms of scaling patterns (logarithmic, power-law and lognormal) and four groups of scaling trajectories emerged. The experts showed no significant difference from the public, although with a much lower deviation. The public opinion was partially influenced by the abundance of individuals in the spatial map, which was not found in the experts. With the increase of resolution (decrease of grain), aggregation indices showed a general trend from significantly different to significantly similar to the expert opinion. The over-dispersion index (i.e. the clumping parameter k in the negative binomial distribution) performed, at certain scales, as the closest index to the expert opinion. Examining performance of aggregation measures from different groups of scaling patterns was proposed as a practical way of analyzing spatial structures. The categorization of the scaling patterns of aggregation measures, as well as their over- and in-sensitivity towards spatial structures, thus not only provides a potential solution to the modifiable areal unit problem, but also unveils the interrelationship among the concept, measures and perceptions of aggregated species distributions.     

Helping farmers to reduce herbicide environmental impacts

While pesticides help to effectively control crop pests, their collateral effects often harm the environment. On the French island of Reunion in the Indian Ocean, over 75% of the pesticides used are herbicides and they are regularly detected in water. Agri-environmental models and pesticide risk indicators can be used to predict and to help pesticide users to reduce environmental impacts. However, while the complexity of models often limits their use to the field of research, pesticide risk indicators, which are easier to implement, do not explicitly identify the technical levers that farmers can act upon to limit such transfers on their scale of action (the field). The aim of this article is to contribute to developing a decision support tool to guide farmers in implementing relevant practices regarding the reduction of pesticide transfers. In this article, we propose a methodology based on classification and regression trees. We applied our methodology to a pesticide risk indicator (I-PHY indicator) for identifying the importance of the variables, their interactions and relative weight in contributing to the score of the indicator. We applied our methodology to the assessment of transfer risks linked to the use of 20 herbicides applied to all soils in Reunion and according to different climate, plot management and product application scenarios (4096 scenarios tested). We constructed regression trees which identified, for each herbicide on each soil type, the contribution made by each input variable to the construction of the indicator score. The tree is represented graphically, and this aids exploration and understanding. The 20 herbicides were divided into 3 groups that differed through the main contributing variable to the indicator score. These variables were all technical levers available to farmers to limit transfer risks. These trees then become decision support tools specific to each pesticide user, enabling them to take appropriate decisions with a view to reducing pesticide environmental impacts.