Didier Alard | Searcharxiv

arXiv2026

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

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.

cs.DB↗

arXiv2025

Evaluating multi-season occupancy models with autocorrelation fitted to heterogeneous datasets

Predicting species distributions using occupancy models accounting for imperfect detection is now commonplace in ecology. Recently, modeling spatial and temporal autocorrelation was proposed to alleviate the lack of replication in occupancy data, which often prevents model identifiability. However, how such models perform in highly heterogeneous datasets where missing or single-visit data dominates remains an open question. Motivated by a heterogeneous fine-scale butterfly occupancy dataset, we evaluate the performance of a multi-season occupancy model with spatial and temporal random effects to a skewed (Poisson) distribution of the number of surveys per site, overlap of covariates between occupancy and detection submodels, and spatiotemporal clustering of observations. Results showed that the model is robust to heterogeneous data and covariate overlap. However, when spatiotemporal gaps were added, site occupancy was biased towards the average occupancy, itself overestimated. Random effects did not correct the influence of gaps, due to identifiability issues of variance and autocorrelation parameters. Occupancy analysis of two butterfly species further confirmed these results. Overall, multi-season occupancy models with autocorrelation are robust to heterogeneous data and covariate overlap, but still present identifiability issues and are challenged by severe data gaps, which contaminate predictions even in data-rich areas.

stat.AP↗