Dataset Information

Taxonomic bias in biodiversity data and societal preferences.

ABSTRACT: Studying and protecting each and every living species on Earth is a major challenge of the 21st century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic bias continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrences from the Global Biodiversity Information Facility (GBIF), the biggest biodiversity data portal, to characterize the taxonomic bias in biodiversity data. We also investigated how societal preferences and taxonomic research relate to biodiversity data gathering. For each species belonging to 24 taxonomic classes, we used the number of publications from Web of Science and the number of web pages from Bing searches to approximate research activity and societal preferences. Our results show that societal preferences, rather than research activity, strongly correlate with taxonomic bias, which lead us to assert that scientists should advertise less charismatic species and develop societal initiatives (e.g. citizen science) that specifically target neglected organisms. Ensuring that biodiversity is representatively sampled while this is still possible is an urgent prerequisite for achieving efficient conservation plans and a global understanding of our surrounding environment.

SUBMITTER: Troudet J

PROVIDER: S-EPMC5567328 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Taxonomic bias in biodiversity data and societal preferences.

Troudet Julien J Grandcolas Philippe P Blin Amandine A Vignes-Lebbe Régine R Legendre Frédéric F

Scientific reports 20170822 1

Studying and protecting each and every living species on Earth is a major challenge of the 21<sup>st</sup> century. Yet, most species remain unknown or unstudied, while others attract most of the public, scientific and government attention. Although known to be detrimental, this taxonomic bias continues to be pervasive in the scientific literature, but is still poorly studied and understood. Here, we used 626 million occurrences from the Global Biodiversity Information Facility (GBIF), the bigge ...[more]

PMID: 28831097

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Project description:BackgroundOpen-access biodiversity databases including mainly citizen science data make temporally and spatially extensive species' observation data available to a wide range of users. Such data have limitations however, which include: sampling bias in favour of recorder distribution, lack of survey effort assessment, and lack of coverage of the distribution of all organisms. These limitations are not always recorded, while any technical assessment or scientific research based on such data should include an evaluation of the uncertainty of its source data and researchers should acknowledge this information in their analysis. The here proposed maps of ignorance are a critical and easy way to implement a tool to not only visually explore the quality of the data, but also to filter out unreliable results.New informationI present simple algorithms to display ignorance maps as a tool to report the spatial distribution of the bias and lack of sampling effort across a study region. Ignorance scores are expressed solely based on raw data in order to rely on the fewest assumptions possible. Therefore there is no prediction or estimation involved. The rationale is based on the assumption that it is appropriate to use species groups as a surrogate for sampling effort because it is likely that an entire group of species observed by similar methods will share similar bias. Simple algorithms are then used to transform raw data into ignorance scores scaled 0-1 that are easily comparable and scalable. Because of the need to perform calculations over big datasets, simplicity is crucial for web-based implementations on infrastructures for biodiversity information. With these algorithms, any infrastructure for biodiversity information can offer a quality report of the observations accessed through them. Users can specify a reference taxonomic group and a time frame according to the research question. The potential of this tool lies in the simplicity of its algorithms and in the lack of assumptions made about the bias distribution, giving the user the freedom to tailor analyses to their specific needs.

Project description:Resources in any healthcare systems are scarce relative to need and therefore choices need to be made which often involve difficult decisions about the best allocation of these resources. One pragmatic and robust tool to aid resource allocation is Programme Budgeting and Marginal Analysis (PBMA), but there is mixed evidence on its uptake and effectiveness. Furthermore, there is also no evidence on the incorporation of the preferences of a large and representative sample of the general public into such a process. The study therefore aims to undertake, evaluate and refine a PBMA process within the exemplar of NHS dentistry in England whilst also using an established methodology (Willingness to Pay (WTP)) to systematically gather views from a representative sample of the public.Stakeholders including service buyers (commissioners), dentists, dental public health representatives and patient representatives will be recruited to participate in a PBMA process involving defining current spend, agreeing criteria to judge services/interventions, defining areas for investment and disinvestment, rating these areas against the criteria and making final recommendations. The process will be refined based on participatory action research principles and evaluated through semi-structured interviews, focus groups and observation of the process by the research team. In parallel a representative sample of English adults will be recruited to complete a series of four surveys including WTP valuations of programmes being considered by the PBMA panel. In addition a methodological experiment comparing two ways of eliciting WTP will be undertaken.The project will allow the PBMA process and particularly the use of WTP within it to be investigated and developed. There will be challenges around engagement with the task by the panel undertaking it and with the outputs by stakeholders but careful relationship building will help to mitigate this. The large volume of data will be managed through careful segmenting of the analysis and the use of the well-established Framework approach to qualitative data analysis. WTP has various potential biases but the elicitation will be carefully designed to minimise these and some methodological investigation will take place.

Dataset Information

Taxonomic bias in biodiversity data and societal preferences.

Publications

Taxonomic bias in biodiversity data and societal preferences.

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