David J. Curnick

The paper uses passive acoustic telemetry to reveal long-distance migrations of pelagic predators between tropical Atlantic islands and seamounts, showing connectivity across large spatial scales.

The paper uses AI to analyze soundscape data from coral reef restoration sites to assess ecosystem function recovery across multiple regions.

This paper develops a genomic framework to assess shark population health and trace illegal fishing in the Indo-Pacific, revealing genetic diversity patterns and geographic origins of fished individuals with 96% accuracy.

The paper shows density increases network connectedness in wild animal populations, but effects saturate, with stronger spatial than social impacts.

This paper demonstrates that pretrained neural networks and unsupervised learning effectively analyze coral reef soundscapes for ecological insights without manual annotation.

This paper introduces a metric called FIRE to prioritize conservation of functionally irreplaceable species at risk of extinction by integrating trait data and extinction risk.