Two decades of citizen science reveal spatial biases and conservation gaps for elasmobranchs along the Mozambican coast

Citizen science platforms play a crucial role in filling knowledge gaps and documenting global biodiversity trends, especially in under-sampled regions such as the Western Indian Ocean (WIO). Here, we assessed the contribution of citizen science data to elasmobranch records in Mozambique, examining species composition, spatio-temporal patterns, and conservation status. This study helps inform policy revision, targeted monitoring, and national reporting in Mozambique using existing citizen science datasets. Using 408 elasmobranch records from the iNaturalist platform collected between 2007 and 2025, we documented 44 species and noted that species records increased significantly over the last decade, particularly since 2019. Rays dominated the dataset, particularly the Dasyatiids and Mobuliids, whereas sharks were primarily represented by Carcharhiniids and Rhincodontiids. A high proportion of recorded species (71%) were classified as threatened on the IUCN Red List, with 10% listed as Critically endangered, 51% as Endangered, 38% as Vulnerable. Most records (82%) were classified as research grade, supporting the reliability of iNaturalist data for scientific applications. Overall, the iNaturalist dataset accounted for 32% of the 137 elasmobranch species previously reported from past studies in Mozambique. Observations were spatially biased toward southern areas of Mozambique, especially Inhambane and Maputo provinces, reflecting known inconsistencies in sampling effort in central and northern regions. Record density overlapped strongly with Important Shark and Ray Areas (ISRA), which accounted for over 90% of all records, whereas only 17% of records overlapped with Marine Protected Areas (MPAs), revealing a clear mismatch between priority areas and formal protection. Our findings demonstrate that citizen science provides a valuable and cost-effective complementary tool to traditional surveys and can meaningfully inform conservation planning, identify protection gaps, and support evidence-based management in data-limited contexts such as Mozambique and the WIO region.