# Tools and approaches for mapping Marine Animal Forests: A practical overview for researchers and conservationists

## TL;DR

This paper reviews tools and methods for mapping marine animal forests to better understand and conserve these ecosystems.

## Contribution

The paper provides a practical overview and decision-support tools for selecting appropriate mapping technologies and modeling approaches for marine animal forests.

## Key findings

- A range of mapping technologies, including sonar and underwater cameras, are used to collect data on marine animal forests.
- Machine learning and advanced modeling techniques help overcome data gaps and improve spatial predictions.
- Decision-support flow charts guide researchers in selecting suitable tools based on project goals and data availability.

## 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 (sedimentary) data. We summarise the diverse platforms and technologies used to collect relevant data, including space-based, air-based, and sea-based mapping tools. The latter include active acoustics, side-scan sonar, seismic reflection profiling, multibeam sonar, and underwater cameras. 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.