Needs and Gaps in Optical Underwater Technologies and Methods for the Investigation of Marine Animal Forest 3D-Structural Complexity

Abstract

Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.

Which Method for Which Purpose? A Comparison of Line Intercept Transect and Underwater Photogrammetry Methods for Coral Reef Surveys

“…Improving the efficiency of conservation is likely to promote the recovery of marine life (if major pressures are relieved) and as such is an ethical and smart economic objective to achieve a sustainable future (Kenchington, 2018;Duarte et al, 2020). To this end, new methods adopting the FAIR principles (findability, accessibility, interoperability, and reusability) for stewardship of reef data can improve the monitoring of these ecosystems (Rossi et al, 2021). Among them the development of artificial intelligence for automated analysis of images (Williams et al, 2019;González-Rivero et al, 2020) and photogrammetry outputs (e.g., Hopkinson et al, 2020;Mohamed et al, 2020), multispectral and hyperspectral imagery (e.g., Parsons et al, 2018;Bajjouk et al, 2019;Li et al, 2019), and the improvement of diver-based or automated data acquisition from Remotely Operated Vehicles and Autonomous Underwater Vehicles (Friedman et al, 2012;Obura et al, 2019;Hatcher et al, 2020;Rossi et al, 2021) will likely revolutionize this field.…”

“…To this end, new methods adopting the FAIR principles (findability, accessibility, interoperability, and reusability) for stewardship of reef data can improve the monitoring of these ecosystems (Rossi et al, 2021). Among them the development of artificial intelligence for automated analysis of images (Williams et al, 2019;González-Rivero et al, 2020) and photogrammetry outputs (e.g., Hopkinson et al, 2020;Mohamed et al, 2020), multispectral and hyperspectral imagery (e.g., Parsons et al, 2018;Bajjouk et al, 2019;Li et al, 2019), and the improvement of diver-based or automated data acquisition from Remotely Operated Vehicles and Autonomous Underwater Vehicles (Friedman et al, 2012;Obura et al, 2019;Hatcher et al, 2020;Rossi et al, 2021) will likely revolutionize this field. Finally the integration of all existing reef habitat mapping data in common and open source data bases could help to overcome the lack of transferability across systems/scale, provide more accurate and representative assessments of habitats, and generate conservation measures and habitat rehabilitation actions (Madin et al, 2019;Rossi et al, 2021).…”

“…The selection of a given ecological monitoring method to assess the status of reefs should optimally be based on three main factors: (1) observer bias along with standardization of protocols and data (e.g., Caldwell et al, 2016;Flower et al, 2017;Rossi et al, 2021), (2) the ability to conduct assessment at varying spatial and temporal scales (e.g., González-Rivero et al, 2014, González-Rivero et al, 2020Wedding et al, 2019), and (3) the technical, financial, and social feasibility of the method (e.g., Gilbert and Quod, 2018;Darling et al, 2019). Overly simple descriptors will not capture enough information while overly complicated ones will not be sustainable.…”

Marine Robotics for Recurrent Morphological Investigations of Micro-Tidal Marine-Coastal Environments. A Point of View

“…Marine robotics has been steadily expanding researchers' possibilities to study and monitor the underwater environment [10]. Robots' capability of operating and exploring challenging and hazardous scenarios has made these technologies essential tools for field specialists, such as archaeologists [11], biologists [12,13], oceanographers or geologists [14], for collecting high-quality data to analyze and understand complex underwater environments. This section will provide an overview of the current marine robotic technologies, presenting the main available solutions and synthetically highlighting strengths, flaws and possible applications for each of them.…”

Bioconstruction and Bioerosion in the Northern Adriatic Coralligenous Reefs Quantified by X-Ray Computed Tomography

“…However, separating and measuring the two processes simultaneously is very challenging. For this purpose, imaging methodologies in 3-dimensions (3D) based on computed tomography (CT) are very promising (Rossi et al, 2021). In the present study, the bioconstruction and bioerosion rates and their net balance on the northern Adriatic coralligenous reefs have been investigated in a field experiment using travertine limestone tiles as recruitment panels with two exposure times: 3 and 12 years.…”