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  • ASSW 2017  (1)
  • Elsevier  (1)
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Years
  • 1
    Publication Date: 2019-08-19
    Description: Information on current diversity and biogeography of Arctic marine microbes (bacteria, archaea and single cell eukaryotes) with adequate temporal, spatial and taxonomic resolution is urgently needed to better understand natural dynamics of ecosystem states in space and time, and consequences of environmental change by anthropogenic factors. Here, we introduce a standardized molecular-based observation strategy for high resolution assessment of marine microbes in space and time, even in remote areas such as the Arctic Ocean. The observation strategy involves molecular analyses such as Next Generation Sequencing (NGS) and quantitative polymerase chain reaction (qPCR) of diverse environmental samples, collected from sea ice, water column and seafloor with a complementary set of automated and ship-based sampling approaches. This includes newly developed automated under-way sampling, moored sediment traps and year-round water samplers, as well as CTD-casts, multi-corers, bottom landers and in the future seafloor crawlers. An integrated standardized dataset including linked, searchable information on synchronous environmental variables provides comprehensive information on the diversity, abundance and biogeography of Arctic marine microbes, covering all three domains of life. The development of the observation strategy involves a set of coordinated pilot studies testing questions of temporal and spatial resolution, i.e. to assess the impact of sea-ice on Arctic marine single-cell eukaroyte community composition, or of ocean warming in Eastern Fram Strait since the year 2000. In the future, the observation strategy for Arctic marine microbes will be implemented as a distributed Molecular Microbial Observatory in the framework of the Arctic observatory FRAM (Frontiers in Arctic Monitoring) and contributes to the ATLANTOS strategy for an integrated Atlantic observatory including genomic information.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 2
    Publication Date: 2019-02-01
    Description: Time-series studies of arctic marine ecosystems are rare. This is not surprising since polar regions are largely only accessible by means of expensive modern infrastructure and instrumentation. In 1999, the Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research (AWI) established the LTER (Long-Term Ecological Research) observatory HAUSGARTEN crossing the Fram Strait at about 79° N. Multidisciplinary investigations covering all parts of the open-ocean ecosystem are carried out at a total of 21 permanent sampling sites in water depths ranging between 250 and 5500 m. From the outset, repeated sampling in the water column and at the deep seafloor during regular expeditions in summer months was complemented by continuous year-round sampling and sensing using autonomous instruments in anchored devices (i.e., moorings and free-falling systems). The central HAUSGARTEN station at 2500 m water depth in the eastern Fram Strait serves as an experimental area for unique biological in situ experiments at the seafloor, simulating various scenarios in changing environmental settings. Long-term ecological research at the HAUSGARTEN observatory revealed a number of interesting temporal trends in numerous biological variables from the pelagic system to the deep seafloor. Contrary to common intuition, the entire ecosystem responded exceptionally fast to environmental changes in the upper water column. Major variations were associated with a Warm-Water-Anomaly evident in surface waters in eastern parts of the Fram Strait between 2005 and 2008. However, even after 15 years of intense time-series work at HAUSGARTEN, we cannot yet predict with complete certainty whether these trends indicate lasting alterations due to anthropologically-induced global environmental changes of the system, or whether they reflect natural variability on multiyear time-scales, for example, in relation to decadal oscillatory atmospheric processes.
    Type: Article , PeerReviewed
    Format: text
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