Abstract
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.
Highlights
Sustained monitoring of the coastal zone is fundamental for the assessment, management, and conservation of living resources over scales ranging from local to global (Miloslavich et al, 2018; Canonico et al, 2019)
Using imagery from diverse rocky intertidal habitats we demonstrate that the CoralNet machine learning system can estimate nearly identical fractional abundances of functional groups as those derived from manual photoquadrat annotation
Image-based biodiversity surveys using automated annotations from CoralNet software were sufficiently robust to characterize the relative abundance of benthic cover categories and functional groups, for “Invertebrate Abundance and Distribution” and “Macroalgal Canopy Cover and Composition” Essential Ocean Variables (EOVs) in rocky intertidal habitat in four countries of the Americas with very different environmental regimes and spanning more than 80◦ of latitude
Summary
Sustained monitoring of the coastal zone is fundamental for the assessment, management, and conservation of living resources over scales ranging from local to global (Miloslavich et al, 2018; Canonico et al, 2019). Machine learning for automated analysis of photoquadrat images can accelerate the flow of information from monitoring programs to decision makers This facilitates early detection of changes in biological communities and rapid responses to mitigate habitat degradation (González-Rivero et al, 2020). Point annotations are typically performed using manual annotation software like pointCount (Porter et al, 2002), Coral Point Count with Excel Extensions (Kohler and Gill, 2006), photoQuad (Trygonis and Sini, 2012), or Biigle (Langenkämper et al, 2017). These facilitate the annotation process through graphical user interfaces and tools for the export of occurrence observations in various digital formats. The CoralNet software is one of these tools, which serves as a collaborative research platform that allows multiple users to interact and analyze large common data sets simultaneously
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