Gaps and spatial trends in the accurate data available on mosquitoes (Diptera, Culicidae) in Brazil: Inventory completeness and priority areas

Gaps and trends in species distribution knowledge can negatively influence biodiversity studies, emphasizing the need to map these limitations and assess inventory completeness. This study analyzed spatial inventories of Culicidae, insects with high medical relevance, to identify priority research areas in Brazil. Records from 1900-2021 were collected from digital databases and literature, excluding those without scientific names, coordinates, or sampling year. Sampling effort and inventory completeness were assessed across ecoregions, states, and grid cells at 0.5° and 1° size resolution. Metrics analyzed included record counts, the percentage of observed and expected richness ratio (completeness index, Cc), and accumulation curve slope (Cs). Units were classified as “well-surveyed” based on different thresholds, and priority zones were defined based on the last quartile of cells with the greatest distance and climatic uniqueness. A total of 9,899 records from 22 scientific collections and 356 articles highlight comprehensive datasets in Southeast and Amazonas states, with limited data in the Northeast region. The Atlantic Forest contained the most complete information, yet well-surveyed areas covered less than 1% of Brazil. This scenario shows that Brazilian Culicidae inventories are under construction due to low spatial representativeness and sampling biases for vector species, roads, and urban areas. Filling these gaps with new sampling designs will enhance predictions of epidemiological risks and Culicidae species loss, especially in Acre, Pará, West-Amazon, Northeast-Atlantic, Brazilian Diagonal, and Araucaria-Pampean zones.

Methods
We compiled georeferenced records of Culicidae for Brazil for the years 1900 to 2021 from the GBIF and species link repositories and published articles. These data were evaluated across Brazilian states, ecoregions, and grid cells (1° and 0.5°) to access completeness of inventories, sampling biases, and define priority areas for research.