Uneven Spatial and Taxonomic Coverage in the Hyrcanian Forest Biodiversity Data

Citizen science platforms are increasingly utilized for large-scale biodiversity monitoring; however, their coverage in complex and remote forest ecosystems can often be variable. This study explores the spatiotemporal and taxonomic patterns of crowdsourced biodiversity data from 2015 to 2025 within Iran’s globally significant Hyrcanian forest, spanning the provinces of Mazandaran (~53%), Gilan (~26%), and Golestan (~21%). By filtering over 11,000 regional iNaturalist observations down to strict ecological boundaries, the analysis indicates a potential accessibility bias. Despite the forest's extensive geographic spread, 2,249 research-grade records fell within the actual ecosystem boundaries, with a tendency for observations to cluster near urbanized perimeters rather than evenly reflecting the deeper forest distribution. Furthermore, spatiotemporal grid analysis points toward a “spatial plateau” within the forest itself. Despite a rapid increase in annual data volume, particularly between 2023 and 2025, the spatial extent of sampled 10 × 10 km forest grid cells remained relatively stable. This pattern suggests that new observations may frequently be recorded at accessible forest edges and established trails, with slower expansion into unmonitored deep-forest habitats. Temporally, observation patterns shifted from a distinct spring peak to more continuous year-round activity, which appears to be influenced by seasonal taxonomic preferences. Overall, the observed spatial clustering indicates that while citizen science provides substantial and valuable data, relying on it as a standalone tool for comprehensive regional forest monitoring may have limitations. Future Earth observation strategies could benefit from incorporating approaches that gently guide or incentivize volunteer efforts toward under-sampled forest interiors to help enhance broader ecological representativeness.