Non-photochemical quenching (NPQ) within phytoplankton cells often causes the daytime suppression of chlorophyll fluorescence in the Southern Ocean. This is problematic and requires accurate correction when chlorophyll fluorescence is used as a proxy for chlorophyll-a concentration or phytoplankton abundance. In this study, we reveal that Southern Ocean subsurface chlorophyll maxima (SCMs) are the largest source of uncertainty when correcting for NPQ of chlorophyll fluorescence profiles. A detailed assessment of NPQ correction methods supports this claim by taking advantage of coincident chlorophyll fluorescence and chlorophyll concentration profiles. The best performing NPQ correction methods are conditional methods that consider the mixed layer depth (MLD), subsurface fluorescence maximum (SFM) and depth of 20% surface light. Compared to existing methods, the conditional methods proposed halve the bias in corrected chlorophyll fluorescence profiles and improve the success of replicating a SFM relative to chlorophyll concentration profiles. Of existing methods, the X12 and P18 methods, perform best overall, even when considering methods supplemented by beam attenuation or backscatter data. The widely-used S08 method, is more varied in its performance between profiles and its application introduced on average up to 2% more surface bias. Despite the significant improvement of the conditional method, it still underperformed in the presence of an SCM due to 1) changes in optical properties at the SCM and 2) large gradients of chlorophyll fluorescence across the pycnocline. Additionally, we highlight that conditional methods are best applied when uncertainty in chlorophyll fluorescence yields is within 50%. This highlights the need to better characterize the bio-optics of SCMs and chlorophyll fluorescence yields in the Southern Ocean, so that chlorophyll fluorescence data can be accurately converted to chlorophyll concentration in the absence of in situ water sampling.
Abstract Quantifying the structural complexity provided by biogenic habitat structures is important in ecology, conservation and management, and yet remains a challenging task, particularly in deep sea and polar environments, that current photogrammetry tools can alleviate. In this study, we demonstrate how small remotely operated vehicles and compact underwater GoPro® action cameras can be easily integrated into coastal Antarctic surveys to quantify structural complexity of under‐ice benthos via underwater photogrammetry. Forty‐four pairs of 1 m 2 quadrats at 1 cm resolution, each comprising an orthomosaic and three‐dimensional reconstructions, were analyzed to describe relationships between benthic cover and structural complexity metrics. The study case provided insights into a unique biogenic habitat, highlighting the role of integrating structural complexity metrics in Antarctic benthic surveys. Although no clear relationships between structural complexity and biodiversity were found, high cover of live reef‐building polychaetes was associated with higher levels of structural complexity, particularly fractal dimension ( D ). Further, broken biogenic structures, product of disturbance events retain habitat structural complexity known to be associated with larvae settlement and biogenic reef growth. This suggests that D can be used as a metric for detecting subtle changes in biogenic structural complexity. We build from available open‐source code, a reproducible scientific workflow that is expected to facilitate the acquisition and analysis of structural complexity metrics. The workflow presented aims to encourage and accelerate the use of photogrammetry tools for benthic studies aiming to quantify biogenic structural complexity across depths and latitudes.
Abstract Marine imagery is a comparatively cost-effective way to collect data on seafloor organisms, biodiversity and habitat morphology. However, annotating these images to extract detailed biological information is time-consuming and expensive, and reference libraries of consistently annotated seafloor images are rarely publicly available. Here, we present the Antarctic Seafloor Annotated Imagery Database (AS-AID), a result of a multinational collaboration to collate and annotate regional seafloor imagery datasets from 19 Antarctic research cruises between 1985 and 2019. AS-AID comprises of 3,599 georeferenced downward facing seafloor images that have been labelled with a total of 615,051 expert annotations. Annotations are based on the CATAMI (Collaborative and Automated Tools for Analysis of Marine Imagery) classification scheme and have been reviewed by experts. In addition, because the pixel location of each annotation within each image is available, annotations can be viewed easily and customised to suit individual research priorities. This dataset can be used to investigate species distributions, community patterns, it provides a reference to assess change through time, and can be used to train algorithms to automatically detect and annotate marine fauna.