Identifying knowledge gaps in biodiversity data and their determinants at the regional level

TL;DR

This study analyzes the determinants of spatial and taxonomic knowledge gaps in biodiversity data at a regional level in France, highlighting the influence of accessibility, governance, and funding biases on data completeness.

Contribution

It identifies key drivers of biodiversity knowledge gaps and proposes strategies to mitigate biases in open-access biodiversity databases.

Findings

Invertebrates have higher knowledge gaps than vertebrates.

Accessibility factors influence knowledge gaps more than ecological appeal.

Regional governance and funding significantly impact data distribution.

Abstract

Biodiversity open-access 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 areas or taxonomic groups better prospected than others. Understanding the determinants of spatial and taxonomic knowledge gaps helps in informing the use of open-access data. Here, we identified knowledge gaps' determinants 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 vertebrates and three invertebrates groups. The data was 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. Our findings show that invertebrates 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 invertebrates 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 knowledge gaps' maps in order to get a more accurate understanding of species occurrence.