Master's Thesis (Open Access)
Master of Science (M.S.)
Holothurians are one of the most abundant megafauna observed in abyssal deep-sea communities and are important in the distribution of nutrients in the deep-sea. Despite their abundance and importance, there is little known about their natural history and population dynamics. These taxa respond to fluctuations in organic carbon supply, which originates as surface primary production and sinks through the water column. Previous studies have estimated population density and spatial ecology based on seasonal or monthly observations, that cannot detect fine-scale temporal changes. This study examines the rapid changes in population of 16 holothurian species observed at Station M, a long-term time series site Station M in the northeast Pacific, over a ten year period (2007 - 2017) using hourly time-lapse imagery and periodic videographic surveys conducted with a remotely operated vehicle (ROV). Holothurian density, mainly driven by the dominance of Peniagone sp. A, peaked from November 2013 to January 2014. Lags between changes in mass flux and rapid holothurian community responses were recorded, with Peniagone sp. A and Peniagone vitrea showing the strongest correlation to in situ measured mass flux (r = 0.40, p=0.015; r=0.41, p<0.0001) with a lag of 149 and 100 weeks, respectively. Spatial distribution of holothurians did not differ with changes in density. Similar population density and spatial distances of holothurians were found in continuous time-lapse and seasonal ROV imagery. The results demonstrate the advantage of using high temporal resolution imagery with long-term presence on the sea floor coupled with periodic videographic surveys to characterize the ecology of deep-sea benthic communities. These foundational community and population data will be vital in quantifying any future changes associated with climate change and increased extractive activities on the seafloor.
Lemon, Larissa, "Population Density and Spatial Distribution of Abyssal Epibenthic Holothurians Using Fine Scale Time Series Imagery (2007-2017)" (2018). SNS Master's Theses. 38.