Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State

• Autores: Bravo, Gonzalo; Moity, Nicolas; Londoño-Cruz, Edgardo; Muller-Karger, Frank; Bigatti, Gregorio; Klein, Eduardo; Choi, Francis; Parmalee, Lark; Helmuth, Brian; Montes, Enrique
• Año de publicación: 2021
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: AMERICAS; BIODIVERSITY MONITORING; MACHINE LEARNING; MARINE BIODIVERSITY; ESSENTIAL OCEAN VARIABLES (EOVS); PHOTOQUADRATS; ROCKY INTERTIDAL ZONE; CORALNET
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Standardized methods for effectively and rapidly monitoring changes in the biodiversity of marine ecosystems are critical to assess status and trends in ways that are comparable between locations and over time. In intertidal and subtidal habitats, estimates of fractional cover and abundance of organisms are typically obtained with traditional quadrat-based methods, and collection of photoquadrat imagery is a standard practice. However, visual analysis of quadrats, either in the field or from photographs, can be very time-consuming. Cutting-edge machine learning tools are now being used to annotate species records from photoquadrat imagery automatically, significantly reducing processing time of image collections. However, it is not always clear whether information is lost, and if so to what degree, using automated approaches. In this study, we compared results from visual quadrats versus automated photoquadrat assessments of macroalgae and sessile organisms on rocky shores across the American continent, from Patagonia (Argentina), Galapagos Islands (Ecuador), Gorgona Island (Colombian Pacific), and the northeast coast of the United States (Gulf of Maine) using the automated software CoralNet. Photoquadrat imagery was collected at the same time as visual surveys following a protocol implemented across the Americas by the Marine Biodiversity Observation Network (MBON) Pole to Pole of the Americas program. Our results show that photoquadrat machine learning annotations can estimate percent cover levels of intertidal benthic cover categories and functional groups (algae, bare substrate, and invertebrate cover) nearly identical to those from visual quadrat analysis. We found no statistical differences of cover estimations of dominant groups in photoquadrat images annotated by humans and those processed in CoralNet (binomial generalized linear mixed model or GLMM). Differences between these analyses were not significant, resulting in a Bray-Curtis average distance of 0.13 (sd 0.11) for the full label set, and 0.12 (sd 0.14) for functional groups. This is the first time that CoralNet automated annotation software has been used to monitor “Invertebrate Abundance and Distribution” and “Macroalgal Canopy Cover and Composition” Essential Ocean Variables (EOVs) in intertidal habitats. We recommend its use for rapid, continuous surveys over expanded geographical scales and monitoring of intertidal areas globally.
Fil: Bravo, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina
Fil: Moity, Nicolas. Charles Darwin Foundation Santa Cruz; Ecuador
Fil: Londoño-Cruz, Edgardo. Universidad del Valle; Colombia
Fil: Muller-Karger, Frank. University of South Florida St. Petersburg; Estados Unidos
Fil: Bigatti, Gregorio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina. Universidad Espíritu Santo; Ecuador
Fil: Klein, Eduardo. Universidad Simón Bolívar; Venezuela
Fil: Choi, Francis. Northeastern University; Estados Unidos. University Northeastern; Estados Unidos
Fil: Parmalee, Lark. Northeastern University; Estados Unidos. University Northeastern; Estados Unidos
Fil: Helmuth, Brian. Northeastern University; Estados Unidos. University Northeastern; Estados Unidos
Fil: Montes, Enrique. University of South Florida St. Petersburg; Estados Unidos