Taxonomic bias in biodiversity data and societal preferences

By Julien Troudet, Philippe Grandcolas, A. Blin, Régine Vignes-Lebbe, F. Legendre. Scientific Reports, 2017

732 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Social Psychology

View Paper: https://www.semanticscholar.org/paper/90478017154dd6e4dbcb71895c64c9ddddebfb8c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. E. García‐Berthou
2. A. Almodóvar
3. G. Nicola
4. Anne E. Magurran
5. Jérôme Chave

In Ecology, Evolution, Behavior and Systematics

1. E. García‐Berthou
2. A. Almodóvar
3. G. Nicola
4. Anne E. Magurran
5. S. Carlson
6. Grant E. Brown
7. Jérôme Chave

Citations in the corpus

• Drivers of taxonomic bias in conservation research: a global analysis of terrestrial mammals (2020, unknown): “This is supported by a recent study by Troudet et al. (2017) who showed that societal preferences (as measured by internet searches) were a much better predictor of taxonomic bias in biodiversity information (measured by GBIF records) than was research effort.”
• Sampling biases shape our view of the natural world (2020, unknown): “Whilst these taxonomic biases have been documented previously (Troudet et al. 2017), increasing data availability has actually exacerbated this disparity.”
• Handling missing values in trait data (2020, unknown): “This bias type was not tested by Penone et al. (2014), but it is likely to be common in ecology and evolution, because both trait databases (González-Suárez et al., 2012) and response values are biased (Boakes et al., 2010; Troudet et al., 2017).”
• Effective monitoring of freshwater fish (2019, result): “Despite this and in contrast to other taxonomic groups such as birds, mammals, and many plants, freshwater fish are generally under-represented in contemporary biodiversity studies and monitoring programmes (Mihoub et al., 2017; Troudet et al., 2017).”
• Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, unknown): “Thirdly, participants often have preferences for certain species, which may lead to preferential recording of some species over others (Troudet et al., 2017; Tulloch & Szabo, 2012).”