Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State
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 organ...
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2021-09-01
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Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2021.691313/full |
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doaj-1657407a86c64a289421c9d4ce4d66152021-09-23T04:19:37ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452021-09-01810.3389/fmars.2021.691313691313Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat StateGonzalo Bravo0Nicolas Moity1Edgardo Londoño-Cruz2Frank Muller-Karger3Gregorio Bigatti4Gregorio Bigatti5Eduardo Klein6Francis Choi7Lark Parmalee8Brian Helmuth9Enrique Montes10Instituto de Biología de Organismos Marinos, IBIOMAR-CONICET, Puerto Madryn, ArgentinaCharles Darwin Research Station, Charles Darwin Foundation, Puerto Ayora, EcuadorDepartamento de Biología, Universidad del Valle, Cali, ColombiaCollege of Marine Science, University of South Florida St. Petersburg, St. Petersburg, FL, United StatesInstituto de Biología de Organismos Marinos, IBIOMAR-CONICET, Puerto Madryn, ArgentinaCentro de Investigaciones, Universidad Espíritu Santo, Samborondón, EcuadorDepartamento de Estudios Ambientales, Universidad Simón Bolívar, Caracas, VenezuelaCoastal Sustainability Institute, Northeastern University, Boston, MA, United StatesCoastal Sustainability Institute, Northeastern University, Boston, MA, United StatesCoastal Sustainability Institute, Northeastern University, Boston, MA, United StatesCollege of Marine Science, University of South Florida St. Petersburg, St. Petersburg, FL, United StatesStandardized 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.https://www.frontiersin.org/articles/10.3389/fmars.2021.691313/fullAmericasbiodiversity monitoringmachine learningmarine biodiversityEssential Ocean Variables (EOVs)photoquadrats |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gonzalo Bravo Nicolas Moity Edgardo Londoño-Cruz Frank Muller-Karger Gregorio Bigatti Gregorio Bigatti Eduardo Klein Francis Choi Lark Parmalee Brian Helmuth Enrique Montes |
spellingShingle |
Gonzalo Bravo Nicolas Moity Edgardo Londoño-Cruz Frank Muller-Karger Gregorio Bigatti Gregorio Bigatti Eduardo Klein Francis Choi Lark Parmalee Brian Helmuth Enrique Montes Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State Frontiers in Marine Science Americas biodiversity monitoring machine learning marine biodiversity Essential Ocean Variables (EOVs) photoquadrats |
author_facet |
Gonzalo Bravo Nicolas Moity Edgardo Londoño-Cruz Frank Muller-Karger Gregorio Bigatti Gregorio Bigatti Eduardo Klein Francis Choi Lark Parmalee Brian Helmuth Enrique Montes |
author_sort |
Gonzalo Bravo |
title |
Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State |
title_short |
Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State |
title_full |
Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State |
title_fullStr |
Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State |
title_full_unstemmed |
Robots Versus Humans: Automated Annotation Accurately Quantifies Essential Ocean Variables of Rocky Intertidal Functional Groups and Habitat State |
title_sort |
robots versus humans: automated annotation accurately quantifies essential ocean variables of rocky intertidal functional groups and habitat state |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Marine Science |
issn |
2296-7745 |
publishDate |
2021-09-01 |
description |
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. |
topic |
Americas biodiversity monitoring machine learning marine biodiversity Essential Ocean Variables (EOVs) photoquadrats |
url |
https://www.frontiersin.org/articles/10.3389/fmars.2021.691313/full |
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