Soft-sediment surrogacy: acoustic remote sensing relates more strongly to marine sediment eDNA than to infauna

Predicting sediment infaunal biodiversity at ecologically relevant scales remains a major challenge for marine biomonitoring due to the costly and time-intensive nature of direct point sampling. This study evaluates the potential of remotely sensed acoustic variables as surrogates for infaunal biodiversity by modelling abiotic-biotic relationships. Critically, it compares the performance of traditional morphological identification with genomic (eDNA metabarcoding) techniques as an advancement to biological data collection in this context. Sediment grab samples and co-located multibeam acoustic data were collected from Sydney Harbour, Australia, to model biodiversity patterns using both taxonomic approaches. Modelling of the morphological data showed limited relationships with acoustic variables across standard diversity metrics (species richness, Shannon diversity, total abundance), and models were highly driven by sediment grain size. In contrast, diversity metrics from eDNA data revealed more consistent correlations with acoustic variables, particularly, topographic position index, slope, and planar curvature. These findings demonstrate that eDNA methods can improve the detection of abiotic-biotic relationships, likely due to greater taxonomic resolution and sensitivity. The integration of genomic tools with remote sensing offers the potential for a scalable, cost-effective pathway for mapping and monitoring infaunal biodiversity in heterogeneous benthic sedimentary systems. This combined approach enhances our ability to detect ecological patterns and supports more effective environmental assessment and management in estuarine and coastal ecosystems. • We evaluated acoustic and sediment surrogates for morphological and eDNA infauna biodiversity. • Biotic responses were modelled using abiotic predictor variables from sonar mapping and co-located sediment grab samples. • Sonar derived variables alone were insufficient for predicting macrofauna biodiversity. • A combined eDNA and sonar biomonitoring approach is worth further investment.