No abstract

Coral reefs are under increasing threat, and the loss of reef-associated fishes providing valuable ecosystem services is accelerating. The monitoring of such rapid changes has become a challenge for ecologists and ecosystems managers using traditional approaches like scuba divers performing underwater visual censuses (UVC) or diver operated video recording (DOV). However, the use of small, low-cost robots could help tackle the challenge of such monitoring, provided that they perform at least as well as diver-based methods. To address this question, tropical fish assemblages from 13 fringing reefs around Mayotte Island (Indian Ocean) were monitored along 50 m-long transects using stereo videos recorded by a semi-autonomous underwater vehicle (SAUV) and by a scuba diver (Diver Operated stereo Video system, DOV). Differences between the methods were tested for complementary fish assemblage metrics (species richness, total biomass, total density, Shannon diversity and Pielou evenness) and for the number and size of nine targeted species. SAUV recorded on average 35% higher biomass than DOV which in turn recorded on average 12% higher species richness. Biomass differences were found to be due to SAUV monitoring larger fishes than DOV, a potential marker of human-related fish avoidance behaviour. This study demonstrates that SAUV provides accurate metrics of coral reef fish biodiversity compared to diver-based procedures. Given their ability to conduct video transects at high frequency, 100 m depth range and at a moderate cost, SAUV is a promising tool for monitoring fish assemblages in coral reef ecosystems.

This article presents a methodology for generating a realtime mission controller of a submarine robot. The initial description of the mission considers the granularity constraints associated with the actors defining the mission. This methodology incorporates a formal analysis of the different possibilities for success of the mission from the models of each component involved in the description of the mission. This article ends illustrating this methodology with the generation of a real robotic mission for marine biodiversity assessment.