Automated high-frequency quantification of fish communities and biomass using computer vision

TL;DR

This paper introduces an automated computer vision framework that uses stereo underwater videos to quantify fish community structure, including species, abundance, and biomass, over time.

Contribution

It presents a novel, scalable method combining deep learning, multi-object tracking, and 3D reconstruction for high-frequency fish community monitoring.

Findings

Revealed dynamic fluctuations in fish species richness, abundance, and biomass over 20 days.

Demonstrated that the method complements environmental DNA surveys for community assessment.

Enabled continuous, non-invasive, and quantitative fish monitoring.

Abstract

Quantifying fish community structure is essential for understanding biodiversity and ecosystem responses in a changing environment, yet existing survey methods provide limited high-frequency, quantitative observations. Conventional approaches, including catch-based methods, underwater visual censuses, and environmental DNA metabarcoding, either require intensive labor or lack reliable estimates of abundance and biomass. Here, we develop an automated framework for quantifying fish communities from underwater video using computer vision. Using videos acquired with a custom-made stereo camera system, the framework integrates deep learning-based fish identification, multi-object tracking, and 3D reconstruction to estimate species-level abundance and biomass. We applied the approach to a reef fish community over a 20-day period with hourly daytime observations, revealing dynamic fluctuations in species richness, abundance, and biomass associated with changes in species composition. By comparing fish communities estimated from visual census and environmental DNA surveys, we demonstrate that our method provides complementary strengths for continuous, non-invasive, and quantitative monitoring of consistently observed species. This approach provides a scalable foundation for long-term monitoring and advances the capacity to resolve fine-scale temporal dynamics in fish communities.