The larval pool of coral reef fish has a crucial role in the dynamics of adult fish populations. However, large-scale species-level monitoring of species-rich larval pools has been technically impractical. Here, we use high-throughput metabarcoding to study larval ecology in the Gulf of Aqaba, a region that is inhabited by >500 reef fish species. We analysed 9,933 larvae from 383 samples that were stratified over sites, depth and time. Metagenomic DNA extracted from pooled larvae was matched to a mitochondrial cytochrome c oxidase subunit I barcode database compiled for 77% of known fish species within this region. This yielded species-level reconstruction of the larval community, allowing robust estimation of larval spatio-temporal distributions. We found significant correlations between species abundance in the larval pool and in local adult assemblages, suggesting a major role for larval supply in determining local adult densities. We documented larval flux of species whose adults were never documented in the region, suggesting environmental filtering as the reason for the absence of these species. Larvae of several deep-sea fishes were found in shallow waters, supporting their dispersal over shallow bathymetries, potentially allowing Lessepsian migration into the Mediterranean Sea. Our method is applicable to any larval community and could assist coral reef conservation and fishery management efforts.

Tracking the movement of all individual group members in their natural environment remains a challenging task. Using advances in computer vision and Deep Learning, we developed and tested a semi-automated in situ tracking system to reconstruct simultaneous three-dimensional trajectories of marked individuals in social groups of a coral-reef fish. Our system has a temporal resolution of 10s of milliseconds, allowing for multiple 30-min tracking sessions that have been repeated over weeks to months. We present the technique and illustrate its application for Dascyllus marginatus, a planktivorous damselfish that lives in social groups associated with branching corals. Our technique identified all individuals 85-100% of the time, with a mean spatial error of $ 1.3 cm. It provides a cost-effective semi-automated tool for in situ research on movements and foraging of individuals within small site-attached groups of animals in their natural environment.