Data from: 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, linking 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 107 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 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.