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Peer-reviewedCC BY 4.0PLOS One

Identifying knowledge gaps in a biodiversity database and their spatial determinants

  • Anaïs Guery1
  • Didier Alard1,2
  1. 1University of Bordeaux, US FAUNA, Pessac, France
  2. 2University of Bordeaux, INRAE, BIOGECO, Pessac, France
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Abstract

Open-access biodiversity databases are valuable resources in the structuring and accessibility of species occurrence data. By compiling different data sources, they reveal the uneven spatial distribution of knowledge, with certain areas and taxonomic groups being better represented. Understanding the determinants of spatial and taxonomic knowledge gaps helps in informing the use of open-access data. Here, we identified determinants of knowledge gaps within a French regional biodiversity database, in the largest administrative region in France. Knowledge gaps were assessed using two metrics, completeness and ignorance scores, for 8 taxonomic groups covering five vertebrate and three insect groups. The data were analyzed for the entire region, but also at the level of the three former sub-regions, to identify the potential drivers that may account for knowledge gaps’ determinants locally. Our findings show that insects were characterized by higher knowledge gaps than vertebrates. Overall, knowledge gaps are influenced by variables related to sites’ accessibility rather than ecological appeal across both metrics. All groups shared similar determinants of gaps, except for the impact of agricultural pressure which is found to be more significant for insects than vertebrates. Ultimately, our study emphasizes the impact of biodiversity governance, through local funding and regional political decisions, on knowledge distribution in open-access databases. We recommend limiting these biases by redirecting biodiversity funding towards under-sampled taxonomic groups and under-prospected areas. When not possible, users of data extracted from these databases should correct for spatial-sampling biases (SSP) using maps of knowledge gaps in order to get a more accurate understanding of species occurrence.

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Cite

BibTeXRIS
BibTeX
@article{guery2026identifying,
  title = {{Identifying knowledge gaps in a biodiversity database and their spatial determinants}},
  author = {Anaïs Guery and Didier Alard},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0360523},
  url = {https://doi.org/10.1371/journal.pone.0360523},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Identifying knowledge gaps in a biodiversity database and their spatial determinants
AU  - Anaïs Guery
AU  - Didier Alard
PY  - 2026
DA  - 2026/10/27
JO  - PLOS One
DO  - 10.1371/journal.pone.0360523
UR  - https://doi.org/10.1371/journal.pone.0360523
AB  - Open-access biodiversity databases are valuable resources in the structuring and accessibility of species occurrence data. By compiling different data sources, they reveal the uneven spatial distribution of knowledge, with certain areas and taxonomic groups being better represented. Understanding the determinants of spatial and taxonomic knowledge gaps helps in informing the use of open-access data. Here, we identified determinants of knowledge gaps within a French regional biodiversity database, in the largest administrative region in France. Knowledge gaps were assessed using two metrics, completeness and ignorance scores, for 8 taxonomic groups covering five vertebrate and three insect groups. The data were analyzed for the entire region, but also at the level of the three former sub-regions, to identify the potential drivers that may account for knowledge gaps’ determinants locally. Our findings show that insects were characterized by higher knowledge gaps than vertebrates. Overall, knowledge gaps are influenced by variables related to sites’ accessibility rather than ecological appeal across both metrics. All groups shared similar determinants of gaps, except for the impact of agricultural pressure which is found to be more significant for insects than vertebrates. Ultimately, our study emphasizes the impact of biodiversity governance, through local funding and regional political decisions, on knowledge distribution in open-access databases. We recommend limiting these biases by redirecting biodiversity funding towards under-sampled taxonomic groups and under-prospected areas. When not possible, users of data extracted from these databases should correct for spatial-sampling biases (SSP) using maps of knowledge gaps in order to get a more accurate understanding of species occurrence.
SN  - 1932-6203
ER  -