The availability of online biodiversity data has increased in recent decades, aiding our understanding of diversity patterns and species richness–environment relationships across temporal and spatial scales. However, even the most exhaustive databases are prone to sampling biases, which create knowledge gaps in species distributions and increase uncertainty in model predictions. Regarding marine environments, intertidal zones are globally distributed and considered early warning systems for climate change impacts and species' range shifts. Owing to their relative accessibility, intertidal records should – supposedly – be less incomplete and biased compared to open-ocean and deep-sea areas. Yet, the extent and coverage of intertidal records available in global biodiversity databases remains unknown. In this study, we used a high-resolution worldwide tidal flat map to identify intertidal records of 11 563 benthic species from the OBIS (Ocean Biodiversity Information System) portal. Following a thorough data-cleaning process, we evaluated geographic patterns in observed species richness, site accessibility, sampling effort, and inventory completeness across latitudes. We demonstrate that observed species richness has mid-latitudinal peaks while the tropics accumulate species with missing records, similar to patterns described for the entire marine realm. These patterns correlate with disproportionate mid-latitude sampling efforts and poor tropical sampling coverage. Sixty-five percent of the mapped intertidal sites are located within 3 hours of a city, but sampling records remain almost absent along African Atlantic, South American Pacific, and Indo-Pacific coasts. Thus, even for the accessible and well-studied intertidal shorelines, database records are not free from geographical biases and their associated implications for biodiversity estimates. Our results highlight the need for a better data-sharing culture, and we hope to encourage initiatives promoting more and better-distributed research efforts on intertidal biodiversity, which could improve global scale detection and prediction of climate change impacts at regional and global scales. |
