Automatic detection of fish and tracking of movement for ecology.

Animal movement studies are conducted to monitor ecosystem health, understand ecological dynamics, and address management and conservation questions. In marine environments, traditional sampling and monitoring methods to measure animal movement are invasive, labor intensive, costly, and limited in the number of individuals that can be feasibly tracked. Automated detection and tracking of small-scale movements of many animals through cameras are possible but are largely untested in field conditions, hampering applications to ecological questions.

Benthic and coral reef community field data for Heron Reef, Southern Great Barrier Reef, Australia, 2002-2018.

This paper describes benthic coral reef community composition point-based field data sets derived from georeferenced photoquadrats using machine learning. Annually over a 17 year period (2002-2018), data were collected using downward-looking photoquadrats that capture an approximately 1 m footprint along 100 m-1500 m transect surveys distributed along the reef slope and across the reef flat of Heron Reef (28 km), Southern Great Barrier Reef, Australia. Benthic community composition for the photoquadrats was automatically interpreted through deep learning, following initial manual calibration of the algorithm.

A contemporary baseline record of the world's coral reefs.

Addressing the global decline of coral reefs requires effective actions from managers, policymakers and society as a whole. Coral reef scientists are therefore challenged with the task of providing prompt and relevant inputs for science-based decision-making. Here, we provide a baseline dataset, covering 1300 km of tropical coral reef habitats globally, and comprised of over one million geo-referenced, high-resolution photo-quadrats analysed using artificial intelligence to automatically estimate the proportional cover of benthic components.

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats.

Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments.