BACKGROUND: Taxonomic bias is a known issue within the field of biology, causing scientific knowledge to be unevenly distributed across species. However, a systematic quantification of the research interest that the scientific community has allocated to individual species remains a big data problem. Scalable approaches are needed to integrate biodiversity data sets and bibliometric methods across large numbers of species. The outputs of these analyses are important for identifying understudied species and directing future research to fill these gaps. FINDINGS: In this study, we used the species h-index to quantity the research interest in 7,521 species of mammals. We tested factors potentially driving species h-index, by using a Bayesian phylogenetic generalized linear mixed model (GLMM). We found that a third of the mammals had a species h-index of zero, while a select few had inflated research interest. Further, mammals with higher species h-index had larger body masses; were found in temperate latitudes; had their humans uses documented, including domestication; and were in lower-risk International Union for Conservation of Nature Red List categories. These results surprisingly suggested that critically endangered mammals are understudied. A higher interest in domesticated species suggested that human use is a major driver and focus in mammalian scientific literature. CONCLUSIONS: Our study has demonstrated a scalable workflow and systematically identified understudied species of mammals, as well as identified the likely drivers of this taxonomic bias in the literature. This case study can become a benchmark for future research that asks similar biological and meta-research questions for other taxa.
BACKGROUND: Taxonomic bias is a known issue within the field of biology, causing scientific knowledge to be unevenly distributed across species. However, a systematic quantification of the research interest that the scientific community has allocated to individual species remains a big data problem. Scalable approaches are needed to integrate biodiversity data sets and bibliometric methods across large numbers of species. The outputs of these analyses are important for identifying understudied species and directing future research to fill these gaps. FINDINGS: In this study, we used the species h-index to quantity the research interest in 7,521 species of mammals. We tested factors potentially driving species h-index, by using a Bayesian phylogenetic generalized linear mixed model (GLMM). We found that a third of the mammals had a species h-index of zero, while a select few had inflated research interest. Further, mammals with higher species h-index had larger body masses; were found in temperate latitudes; had their humans uses documented, including domestication; and were in lower-risk International Union for Conservation of Nature Red List categories. These results surprisingly suggested that critically endangered mammals are understudied. A higher interest in domesticated species suggested that human use is a major driver and focus in mammalian scientific literature. CONCLUSIONS: Our study has demonstrated a scalable workflow and systematically identified understudied species of mammals, as well as identified the likely drivers of this taxonomic bias in the literature. This case study can become a benchmark for future research that asks similar biological and meta-research questions for other taxa.
Authors: William J Ripple; Christopher Wolf; Thomas M Newsome; Michael Hoffmann; Aaron J Wirsing; Douglas J McCauley Journal: Proc Natl Acad Sci U S A Date: 2017-09-18 Impact factor: 11.205