Cold-water conditions have excluded durophagous (skeleton-breaking) predators from the Antarctic seafloor for millions of years. Rapidly warming seas off the western Antarctic Peninsula could now facilitate their return to the continental shelf, with profound consequences for the endemic fauna. Among the likely first arrivals are king crabs (Lithodidae), which were discovered recently on the adjacent continental slope. During the austral summer of 2010‒2011, we used underwater imagery to survey a slope-dwelling population of the lithodid Paralomis birsteini off Marguerite Bay, western Antarctic Peninsula for environmental or trophic impediments to shoreward expansion. The population density averaged ∼4.5 individuals × 1,000 m−2 within a depth range of 1,100‒1,500 m (overall observed depth range 841–2,266 m). Images of juveniles, discarded molts, and precopulatory behavior, as well as gravid females in a trapping study, suggested a reproductively viable population on the slope. At the time of the survey, there was no thermal barrier to prevent the lithodids from expanding upward and emerging on the outer shelf (400- to 550-m depth); however, near-surface temperatures remained too cold for them to survive in inner-shelf and coastal environments (<200 m). Ambient salinity, composition of the substrate, and the depth distribution of potential predators likewise indicated no barriers to expansion of lithodids onto the outer shelf. Primary food resources for lithodids—echinoderms and mollusks—were abundant on the upper slope (550–800 m) and outer shelf. As sea temperatures continue to rise, lithodids will likely play an increasingly important role in the trophic structure of subtidal communities closer to shore.
Abstract:We reviewed photographic images of fishes from depths of 381-2282 m in Marguerite Bay and 405-2007 m in the Amundsen Sea. Marguerite Bay fishes were 33% notothenioids and 67% non-notothenioids. Channichthyids (47%) and nototheniids (44%) were the most abundant notothenioids. The deep-living channichthyid Chionobathyscus dewitti (74%) and the nototheniid genus Trematomus (66%) were the most abundant taxa within these two families. The most abundant non-notothenioids were the macrourid Macrourus whitsoni (72%) and zoarcids (18%). Amundsen Sea fishes were 87% notothenioids and 13% non-notothenioids, the latter exclusively Macrourus whitsoni. Bathydraconids (38%) and artedidraconids (30%) were the most abundant notothenioids. We observed that Macrourus whitsoni was benthopelagic and benthic and infested by large ectoparasitic copepods. Juvenile (42 cm) Dissostichus mawsoni was not neutrally buoyant and resided on the substrate at 1277 m. Lepidonotothen squamifrons was seen near and on nests of eggs in early December. A Pogonophryne sp. from 2127 m was not a member of the deep-living unspotted P. albipinna group. Chionobathyscus dewitti inhabited the water column as well as the substrate. The pelagic zoarcid Melanostigma gelatinosum was documented in the water column a few metres above the substrate. The zoogeographic character of the Marguerite Bay fauna was West Antarctic or low-Antarctic and the Amundsen Sea was East Antarctic or high-Antarctic.
A fundamental problem in autonomous underwater robotics is the high latency between the capture of image data and the time at which operators are able to gain a visual understanding of the survey environment. Typical missions can generate imagery at rates hundreds of times greater than highly compressed images can be transmited acoustically, delaying that understanding until after the vehicle has been recovered and the data analyzed. While automated classification algorithms can lessen the burden on human annotators after a mission, most are too computationally expensive or lack the robustness to run in situ on a vehicle. Fast algorithms designed for mission-time performance could lessen the latency of understanding by producing low-bandwidth semantic maps of the survey area that can then be telemetered back to operators during a mission.This thesis presents a lightweight framework for processing imagery in real time aboard a robotic vehicle. We begin with a review of pre-processing techniques for correcting illumination and attenuation artifacts in underwater images, presenting our own approach based on multi-sensor fusion and a strong physical model. Next, we construct a novel image pyramid structure that can reduce the complexity necessary to compute features across multiple scales by an order of magnitude and recommend features which are fast to compute and invariant to underwater artifacts. Finally, we implement our framework on real underwater datasets and demonstrate how it can be used to select summary images for the purpose of creating low-bandwidth semantic maps capable of being transmitted acoustically.
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