In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Coral reefs are both exceptionally biodiverse and threatened by climate change and other
human activities. Here, we review population genomic processes in coral reef taxa and their …

CoralNet is a cloud-based website and platform for manual, semi-automatic and automatic
analysis of coral reef images. Users access CoralNet through optimized web-based …

Substrate complexity is an essential metric of reef health and a strong predictor of several
ecological processes connected to the reef, including disturbance, resilience, and …

Public health initiatives must be made using evidence-based decision-making to have the
greatest impact. Machine learning algorithms are created to gather, store, process, and …

Coral reefs are among the most diverse ecosystems on our planet, and essential to the
livelihood of hundreds of millions of people who depend on them for food security, income …

The choice of ecological monitoring methods and descriptors determines the effectiveness
of a program designed to assess the state of coral reef ecosystems. Here, we comparer the …

In an endeavor to study natural systems at multiple spatial and taxonomic resolutions, there
is an urgent need for automated, high-throughput frameworks that can handle plethora of …

Close-range underwater photogrammetry, hereafter referred to as photogrammetry, is
rapidly emerging as a new standard in measuring and monitoring coral reefs due to its …

Accurate biomass estimates are key to understanding a wide variety of ecological functions.
In marine systems, epibenthic biomass estimates have traditionally relied on either …