Leveraging citizen science to classify and track benthic habitat states: An unsupervised UMAP-HDBSCAN pipeline applied to the global reef life survey dataset

Abstract

Benthic biogenic habitats are crucial for coastal marine ecosystems, supporting food and shelter for a large range of marine species, but they are increasingly threatened by increasing anthropogenic impacts. While large-scale monitoring data are increasingly available, tools to describe benthic habitat changes in standardised and yet finely resolved manner are still needed. The aim of this study was to define reef benthic habitat states and explore their spatial and temporal variability on a global scale using an innovative clustering pipeline. For this purpose, we used substrate cover data collected along 6554 transects worldwide by citizen scientists contributing to the Reef Life Survey program. We applied an innovative clustering pipeline that combines three algorithms — Uniform Manifold Approximation and Projection (UMAP) for dimension reduction; Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) — to identify benthic habitat states and Shapley values to interpret the clusters identified. This unsupervised pipeline identified 17 distinct clusters worldwide, representing typical temperate and tropical benthic habitats such as large canopy forming algae and branching corals, respectively, as well as transitional states between different habitat states. Temporal site-specific analyses further demonstrated the pipeline's effectiveness in capturing fine-scale habitat dynamics. By providing a standardised, scalable approach, this work enables consistent tracking of benthic habitat changes across spatial and temporal scales worldwide. This study also showcases the potential of integrating the UMAP-HDBSCAN pipeline with Shapley values for clustering noisy ecological data from citizen science initiatives.