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.

Contrasting patterns of diversification in a bird family (Aves: Gruiformes: Rallidae) are revealed by analysis of geospatial distribution of species and phylogenetic diversity

“…The completeness of current sampling available on the GBIF database can be considered a limitation but GBIF bird data are disproportionately better sampled and curated than other taxon groups (Maldonado et al 2015, Amano et al 2016, Troudet et al 2017) and our results are consistent with the distribution and diversity of rails and patterns inferred for other vertebrates. in Africa and parts of central and northern Asia), the power of the approach will continue to increase as GIS records of taxon locations accumulate.…”

A new baseline for countrywide α‐diversity and species distributions: illustration using >6,000 plant species in Panama

“…Yet, it is unfeasible to sample local biodiversity at every location, and while impressive databases have become available, they invariably suffer from biases (Troudet et al. ). Particularly, global databases are necessarily coarse, often with imperfect information and uncertainties, and it is questionable whether their integration would improve predictive power.…”

“…) and species (Troudet et al. ). For instance, areas near roads or population centers may be more accessible and more likely to be sampled; some species may possess visually conspicuous traits or may be more popular, leading to spatial and species‐specific biases in the estimation of species distributions.…”

Assessing the utility of conserving evolutionary history

“…Importantly, neither of these latter relationships was corrected for species richness, which was even more predictive of preference. In many cases though, human preference has strong taxonomic biases (Troudet et al, 2017): humans tend to value top predators (Clucas, McHugh & Caro, 2008), larger species, and mammals over birds or non-avian reptiles [see references in Martin-Lopez, Montes & Benayas (2007)] rather than explicit properties of sets such as PD or TR.…”