在区域层面识别生物多样性数据中的知识缺口及其决定因素
Identifying knowledge gaps in biodiversity data and their determinants at the regional level
AI总结：
本研究在法国区域层面识别生物多样性数据中的知识缺口及其决定因素，发现无脊椎动物知识缺口更大，强调生物多样性治理对知识分布的影响。
摘要：
生物多样性开放获取数据库是构建和获取物种分布数据的重要资源。通过整合不同的数据来源，它们揭示了知识分布的不均衡性，某些区域或分类群比其他区域或分类群更受关注。理解空间和分类学知识缺口的决定因素有助于指导开放获取数据的使用。在此，我们识别了法国区域生物多样性数据库中知识缺口的决定因素，这是法国最大的行政区域。通过完整度和无知评分两个指标，对涵盖五种脊椎动物和三种无脊椎动物类群的8个分类群进行了评估。数据被分析整个区域，也分析了三个前次区域的层面，以识别可能驱动知识缺口决定因素的潜在因素。我们的发现表明，无脊椎动物比脊椎动物具有更高的知识缺口。总体而言，知识缺口受与站点可访问性相关的变量影响，而非生态吸引力，这在两个指标中均是如此。所有类群共享相似的知识缺口决定因素，除了农业压力的影响，该影响在无脊椎动物中比在脊椎动物中更为显著。最终，我们的研究强调了生物多样性治理通过本地资金和区域政治决策对开放获取数据库中知识分布的影响。我们建议通过将生物多样性资金转向未采样类群和未受关注的区域来限制这些偏见。当无法做到时，数据提取者的用户应使用知识缺口地图纠正空间采样偏见（SSP），以获得对物种分布更准确的理解。
英文摘要：
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.
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