Rapid health assessment of coral reefs: a standardised, quick, and reliable tool developed and tested during the 2024/25 global bleaching event
Year: 2026
Type: preprint
Abstract: Coral reefs worldwide are experiencing increasingly frequent and severe degradation events driven by rising sea surface temperatures, yet reliable and scientifically comparable data remain sparse for many coral reefs. Park managers, rangers, and community-led groups in areas lacking scientific studies could contribute to data records by collecting relevant data on coral reefs. However, these stakeholders often face logistical constraints that limit their ability to undertake ecological surveys and assess coral reef conditions in ways that are comparable to scientifically collected records. Here, we demonstrate a rapid health assessment protocol that is comparable to traditional, scientific methods, enabling stakeholders other than scientists to contribute robust data on coral bleaching to global-scale analyses. This protocol combines existing citizen science tools with standardised sampling approaches to generate monitoring high-quality data quickly. We compare estimates derived from the proposed methodology with those obtained with the traditional, research-grade, long-term photoquadrat-based coral reef monitoring approach across two distinct and understudied coral reef ecosystems, Lord Howe Island and Norfolk Island. We show that the rapid health assessment methodology provides data comparable to photoquadrats, although some divergences may arise. For example, the rapid health assessment is less likely to detect sparsely distributed conditions, such as partial mortality within coral colonies, compared with methods that assess the benthos in detail. Our study improves accessibility for coral reef stakeholders, allowing them to contribute data to coral reef health monitoring and global monitoring databases.
DOI: 10.32942/x2z412
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223981131