Integrating Environmental DNA Metabarcoding and Remote Sensing Reveals Known and Novel Fish Diversity Hotspots in a World Heritage Area

Aim: Shark Bay, a UNESCO World Heritage site in Western Australia, is highly vulnerable to climate change, yet its fish biodiversity remains poorly understood at fine spatial scales. We integrated environmental DNA (eDNA) metabarcoding with high-resolution remote sensing to assess and extrapolate fish diversity patterns, providing a scalable framework for biodiversity monitoring in dynamic coastal ecosystems. Location: Shark Bay, Western Australia. Methods: We analysed 270 water samples across 560 km² using fish-specific 16S and 12S rRNA metabarcoding, comparing our results to earlier studies using conventional methods including seining, trawling, fisheries reports, and fish traps. We linked biodiversity patterns to key environmental variables, including depth, salinity, sea surface temperature, and habitat characteristics derived from high-resolution satellite imagery. To predict fish biodiversity across unsampled areas, we employed machine-learning models, enabling spatial extrapolation of eDNA data across the seascape. Results: eDNA metabarcoding identified 106 fish species across 132 genera and 71 families, with substantial overlap with conventional monitoring but broader coverage at higher taxonomic levels. Fish richness increased with decreasing salinity, high channel habitat coverage, and moderate depths with high seagrass coverage. We delineated five distinct fish communities (A–E): two shallow seagrass communities—one in sparse seagrass (A) and another in dense seagrass (B), one in channel habitats (C) with the greatest fish diversity; one in deep sandy waters (D) and one in medium-depth, seagrass-free areas (E). Additionally, we detected several tropical species, suggesting poleward shifts due to rising water temperatures. Main Conclusions: This study highlights the utility of combining marine eDNA metabarcoding with remote sensing to detect fine-scale biodiversity. The integration of machine learning enables spatial upscaling and timely responses to habitat changes, enhancing marine conservation and management. By identifying key environmental drivers of fish diversity, this approach supports proactive conservation strategies, providing a scalable model for biodiversity monitoring under climate change.