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Updated: Mar 24, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
Published on: April 18, 2025
Tools and approaches for mapping Marine Animal Forests: A practical overview for researchers and conservationists
Laurence H De Clippele1, Ricardo Aguilar2, Miquel Canals3,4,5
1School of Biodiversity One Health and Veterinary Medicine, University of Glasgow College of Medical Veterinary and Life Sciences, Glasgow, Scotland, G12 8QQ, UK.
Abstract:
Mapping marine animal forests (MAFs) is essential for understanding complex benthic ecosystems and supporting their conservation and management. This review provides a comprehensive overview of the key aspects of MAFs that can be mapped, focusing on both biological and substrate data. We summarise the diverse platforms and technologies used to collect relevant data, including space, air, and sea-based mapping tools. In addition, we highlight the software tools, open-source databases, and modelling approaches that enable researchers to analyse and map MAFs effectively. The modelling approaches include unsupervised mapping techniques, geomorphological classification, species distribution modelling, biomass distribution modelling, and community distribution modelling. Given the variability in habitat types, depths, and spatial scales, we discuss how geophysical data often serve as proxies for environmental conditions that influence the distribution of species and substrates. The increasing use of machine learning and advanced modelling techniques is also addressed as a means to overcome gaps in biological and substrate data and achieve comprehensive spatial predictions. Finally, we present two practical decision-support flow charts to help guide researchers and practitioners in selecting appropriate mapping tools and modelling approaches based on specific project objectives, environmental settings, and data availability. This review offers a practical toolbox for marine scientists, conservationists, and managers aiming to map and understand the structure and distribution of MAFs more effectively.
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