in

Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna

[adace-ad id="91168"]
  • Rogers, A. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the southern ocean. Annual Review of Marine Science 12, 87–120, https://doi.org/10.1146/annurev-marine-010419-011028 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Tin, T. et al. Impacts of local human activities on the antarctic environment. Antarctic Science 21, 3–33, https://doi.org/10.1017/S0954102009001722 (2009).

    Article 

    Google Scholar 

  • Pineda-Metz, S. E. A., Gerdes, D. & Richter, C. Benthic fauna declined on a whitening antarctic continental shelf. Nature Communications 11, 2226, https://doi.org/10.1038/s41467-020-16093-z (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Convey, P. Antarctic terrestrial biodiversity in a changing world. Polar Biology 34, 1629, https://doi.org/10.1007/s00300-011-1068-0 (2011).

    Article 

    Google Scholar 

  • Kang, Y. H. et al. Composition and structure of the marine benthic community in terra nova bay, antarctica: Responses of the benthic assemblage to disturbances. PLOS ONE 14, 1–16, https://doi.org/10.1371/journal.pone.0225551 (2019).

    Article 

    Google Scholar 

  • Piazza, P. et al. Underwater photogrammetry in antarctica: long-term observations in benthic ecosystems and legacy data rescue. Polar Biology 42, 1061–1079, https://doi.org/10.1007/s00300-019-02480-w (2019).

    Article 

    Google Scholar 

  • SOOS. Southern Ocean Observing System – Report on the 2017 Ross Sea Working Group Meeting. http://www.soos.aq. [Online; accessed 2022/11/15] (2017).

  • SCAR. Scientific Committee on Antarctic Research. https://www.scar.org. [Online; accessed 2022/11/15] (2021).

  • ANTOS. Antarctic near-shore and terrestrial observing system. https://www.scar.org/science/antos/home. [Online; accessed 2022/11/15] (2015).

  • Dayton, P. K. et al. Benthic responses to an antarctic regime shift: food particle size and recruitment biology. Ecological Applications 29, e01823, https://doi.org/10.1002/eap.1823 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Watters, G. M., Hinke, J. T. & Reiss, C. S. Long-term observations from antarctica demonstrate that mismatched scales of fisheries management and predator-prey interaction lead to erroneous conclusions about precaution. Scientific Reports 10, 2314, https://doi.org/10.1038/s41598-020-59223-9 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bolinesi, F. et al. Spatial-related community structure and dynamics in phytoplankton of the ross sea, antarctica. Frontiers in Marine Science 7, https://doi.org/10.3389/fmars.2020.574963 (2020).

  • Stenni, B. et al. Three-year monitoring of stable isotopes of precipitation at concordia station, east antarctica. The Cryosphere 10, 2415–2428, https://doi.org/10.5194/tc-10-2415-2016 (2016).

    Article 

    Google Scholar 

  • Ramesh, K. & Soni, V. Perspectives of antarctic weather monitoring and research efforts. Polar Science 18, 183–188, https://doi.org/10.1016/j.polar.2018.04.005 (2018). Recent Advances in Climate Science of Polar Region (to commemorate the contributions of Late Dr. S.Z. Qasim, a pioneering doyen of the Indian Polar programme).

    Article 

    Google Scholar 

  • Shepherd, A. et al. Mass balance of the antarctic ice sheet from 1992 to 2017. Nature 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y (2018).

    Article 

    Google Scholar 

  • Budge, J. S. & Long, D. G. A comprehensive database for antarctic iceberg tracking using scatterometer data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 434–442, https://doi.org/10.1109/JSTARS.2017.2784186 (2018).

    Article 

    Google Scholar 

  • Rignot, E. et al. Four decades of antarctic ice sheet mass balance from 1979–2017. Proceedings of the National Academy of Sciences of the United States of America 116, 1095–1103, https://doi.org/10.1073/pnas.1812883116 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Barbat, M. M., Rackow, T., Wesche, C., Hellmer, H. H. & Mata, M. M. Automated iceberg tracking with a machine learning approach applied to sar imagery: A weddell sea case study. ISPRS Journal of Photogrammetry and Remote Sensing 172, 189–206, https://doi.org/10.1016/j.isprsjprs.2020.12.006 (2021).

    Article 

    Google Scholar 

  • Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental Science & Technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).

    Article 

    Google Scholar 

  • Piazza, P., Gattone, S., Guzzi, A. & Schiaparelli, S. Towards a robust baseline for long-term monitoring of antarctic coastal benthos. Hydrobiologia 847, 1753–1771, https://doi.org/10.1007/s10750-020-04177-2 (2020).

    Article 

    Google Scholar 

  • Rountree, R. et al. Towards an optimal design for ecosystem-level ocean observatories. Oceanography and Marine Biology 58, 79–105, https://doi.org/10.1201/9780429351495-2 (2020).

    Article 

    Google Scholar 

  • Katsanevakis, S. et al. Monitoring marine populations and communities: Methods dealing with imperfect detectability. Aquatic Biology 16, 31–52, https://doi.org/10.3354/ab00426 (2012).

    Article 

    Google Scholar 

  • Zampoukas, N. et al. Technical guidance on monitoring for the marine strategy framework directive. Tech. Rep., European Commission, Report EUR 26499 (2014).

  • Bicknell, A. W., Godley, B. J., Sheehan, E. V., Votier, S. C. & Witt, M. J. Camera technology for monitoring marine biodiversity and human impact. Frontiers in Ecology and the Environment 14, 424–432, https://doi.org/10.1002/fee.1322 (2016).

    Article 

    Google Scholar 

  • European Marine Board. Working Group on Big Data in Marine Science. https://www.marineboard.eu/publications/big-data-marine-science. [Online; accessed 2022/11/15] (2020).

  • Zurowietz, M. & Nattkemper, T. W. Current trends and future directions of large scale image and video annotation: Observations from four years of biigle 2.0. Frontiers in Marine Science 8, https://doi.org/10.3389/fmars.2021.760036 (2021).

  • Kim, S. L., Thurber, A., Hammerstrom, K. & Conlan, K. Seastar response to organic enrichment in an oligotrophic polar habitat. Journal of Experimental Marine Biology and Ecology 346, 66–75, https://doi.org/10.1016/j.jembe.2007.03.004 (2007).

    Article 

    Google Scholar 

  • Peirano, A., Bordone, A., Marini, S., Piazza, P. & Schiaparelli, S. A simple time-lapse apparatus for monitoring macrozoobenthos activity in antarctica. Antarctic Science 28, 473–474, https://doi.org/10.1017/S0954102016000377 (2016).

    Article 

    Google Scholar 

  • Peirano, A., Marini, S., Bordone, A. & Schiaparelli, S. ICE-LAPSE: Analysis of antarctic benthos dynamics by using non-destructive monitoring devices and permanent stations, pnra 2013/az1.16, funded by the italian national antarctic program (2015-2016).

  • Marini, S. et al. Long-term automated visual monitoring of antarctic benthic fauna. Methods in Ecology and Evolution 13, 1746–1764, https://doi.org/10.1111/2041-210X.13898 (2022).

    Article 

    Google Scholar 

  • Marini, S. et al. EP2863257 (A1) – Underwater images acquisition and processing system. https://data.epo.org/gpi/EP2863257B1. [Online; accessed 2022/11/15] (2013).

  • Corgnati, L. et al. Looking inside the ocean: Toward an autonomous imaging system for monitoring gelatinous zooplankton. Sensors 16, 2124, https://doi.org/10.3390/s16122124 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Marini, S. et al. Automated estimate of fish abundance through the autonomous imaging device guard1. Measurement 126, 72–75, https://doi.org/10.1016/j.measurement.2018.05.035 (2018).

    Article 

    Google Scholar 

  • Pensieri, S. et al. Environmental acoustic noise observations in tethys bay (terra nova bay, ross sea, antarctica). In 2014 Oceans – St. John’s, 1–6, https://doi.org/10.1109/OCEANS.2014.7003196 (2014).

  • Jung, J. et al. Multibeam bathymetry and distribution of clay minerals on surface sediments of a small bay in terra nova bay, antarctica. Minerals 11, https://doi.org/10.3390/min11010072 (2021).

  • Balog, I. et al. Estimation of direct normal irradiance at antarctica for concentrated solar technology. Applied System Innovation 2, https://doi.org/10.3390/asi2030021 (2019).

  • Caputi, S. S. et al. Seasonal food web dynamics in the antarctic benthos of tethys bay (ross sea): Implications for biodiversity persistence under different seasonal sea-ice coverage. Frontiers in Marine Science 7, 1046, https://doi.org/10.3389/fmars.2020.594454 (2020).

    Article 

    Google Scholar 

  • van Leeuwe, M. A. et al. Annual patterns in phytoplankton phenology in antarctic coastal waters explained by environmental drivers. Limnology and Oceanography 65, 1651–1668, https://doi.org/10.1002/lno.11477 (2020).

    Article 

    Google Scholar 

  • OEngineering. OEngineering s.r.l. – GUARD-1, Underwater Autonomous Smart Camera. https://www.oengineering.eu//GUARD-1/. [Online; accessed 2022/11/15] (2021).

  • Magic Lantern. https://magiclantern.fm. [Online; accessed 2022/11/15] (2021).

  • Marini, S. et al. Guard1: An autonomous system for gelatinous zooplankton image-based recognition. In OCEANS 2015 – Genova, 1–7, https://doi.org/10.1109/OCEANS-Genova.2015.7271704 (2015).

  • CR2. The Canon RAW (CRW) File Format. https://exiftool.org/canon_raw.html. [Online; accessed 2022/11/15] (2022).

  • Marini, S. et al. ICE-LAPSE image dataset. Zenodo https://doi.org/10.5281/zenodo.6418163 (2022).

  • LabelImg. A graphical image annotation tool. https://github.com/tzutalin/labelImg. [Online; accessed 2022/11/15] (2021).

  • Schoening, T. et al. Making marine image data fair. Scientific Data 9, 414, https://doi.org/10.1038/s41597-022-01491-3 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cattaneo-Vietti, R., Chiantore, M., Schiaparelli, S. & Albertelli, G. Shallow- and deep-water mollusc distribution at terra nova bay (ross sea, antarctica). Polar Biology 23, 173–182, https://doi.org/10.1007/s003000050024 (2000).

    Article 

    Google Scholar 

  • Cattaneo-Vietti, R. et al. Spatial and Vertical Distribution of Benthic Littoral Communities in Terra Nova Bay, 503–514 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2000).

  • Cummings, V. J. et al. Linking ross sea coastal benthic communities to environmental conditions: Documenting baselines in a spatially variable and changing world. Frontiers in Marine Science 5, 232, https://doi.org/10.3389/fmars.2018.00232 (2018).

    Article 

    Google Scholar 

  • Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788, https://doi.org/10.1109/CVPR.2016.91 (2016).

  • YOLO V5. https://github.com/ultralytics/yolov5. [Online; accessed 2022/11/15] (2022).


  • Source: Ecology - nature.com

    A healthy wind

    Vegetation assessments under the influence of environmental variables from the Yakhtangay Hill of the Hindu-Himalayan range, North Western Pakistan