Accessibility bias and the road-map effect in biodiversity data across Central Asia and Afghanistan

Biodiversity data are essential for understanding species distributions and guiding conservation, yet they are often limited by spatial sampling bias. This study provides the first quantitative assessment of accessibility bias, specifically the road-map effect, in biodiversity records across Central Asia and Afghanistan, one of the least-documented yet ecologically diverse regions on Earth. Using 657,299 unique occurrence records compiled from GBIF and iNaturalist , we analyzed how proximity to roads, elevation, climate, and topography influence sampling intensity. Results show that 66.4% of all records occur within 5 km of major roads, indicating a strong accessibility bias. Sampling intensity peaked at mid-elevations (approximately 500–1200 m), moderate temperatures (around 10 °C), and intermediate precipitation (300–500 mm), while steep and remote areas were severely underrepresented. Species richness in plants, animals, and fungi was concentrated along transport corridors and urban centers, reflecting accessibility rather than true biodiversity patterns. Ecoregional analysis revealed heavy sampling in the Tian Shan montane conifer forests, Gissaro-Alai open woodlands, Tian-Shan foothills arid steppe, and Alai-Western Tian-Shan steppe. In contrast, extensive under-sampling occurred in the Registan–North Pakistan sandy desert, the Badghyz and Karabil semi-deserts, the Central Persian desert basins, the Kazakh semi-desert, the Paropamisus xeric woodlands, and the Western Himalayan subalpine conifer forests. Other neglected regions include the Pamir alpine desert and tundra, the Central Afghan Mountains xeric woodlands, and the Kopet Dag semi-desert. These findings identify Central Asia as a major biodiversity darkspot where high potential diversity is obscured by limited data coverage and digitization. Addressing this imbalance will require targeted field surveys in under-sampled regions, digitization of historical collections, and expansion of citizen science initiatives to achieve a more accurate and equitable representation of biodiversity across the region.

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