Sea-ice communities: structure and composition in Baltic, Antarctic and Arctic seas

Sea ice is an important structural component of polar marine ecosystems but also at lower latitude seas like e.g. the northern Baltic Sea. This study summarises observations on biological, chemical and physical characteristics of sea ice and under-ice water obtained during three expeditions to the Baltic Sea, the Fram Strait area (Arctic) and the Bellingshausen Sea (Antarctica). The study aimed at a better understanding and quantification of different components of the sea ice related food web. The seasonal Baltic sea ice is least studied and therefore the work in this area focused on an inventory determination of the abundance and biomass composition of the sympagic (=ice-associated) community of the Bothnian Bay and Bothnian Sea as well as on the importance of abiotic and biotic factors in the control of ice algal accumulation. The work on the better explored polar sea ice focused on the abundance, distribution and characteristics of transparent exopolymer particles (TEP) in Arctic and Antarctic sea ice. TEP are a recently described class of exopolymeric particles, which are formed abiotically and biotically from polysaccharid-rich precursors. High amounts of TEP-precursors are released by bacteria and algae especially in response to environmental stress. In the pelagic realm TEP are important in the aggregation of diatom blooms, provide the matrix of macroaggregates and serve as substrate and habitat for attached bacteria. High concentrations of TEP have been recently described for Arctic sea ice and may have an important impact on carbon dynamics in sea-ice systems. The present study related TEP concentrations to biotic and abiotic sea ice parameters, potential modes of TEP formation were elucidated and the importance of TEP for the sea-ice habitat was discussed

Characterising the sea ice environment using a newly developed sensor array mounted on an under-ice trawl

One of the most pronounced impacts of climate change is the changing sea ice cover, which has implications for sea ice-associated ecosystems that depend on carbon produced by ice-associated algae. In order to fully understand these ecosystems there is a need to understand both the physical and biological components. We present preliminary results from Polarstern cruises to the Eastern Central Arctic Ocean (summer 2012) and Weddell Sea (fall-winter 2013). Biological samples were acquired from the under-ice environment using the Surface and Under-Ice Trawl (SUIT) and from within the ice by extracting ice cores. Biophysical properties of sea ice and under-ice environments were characterized using a sensor array mounted on the SUIT that measured ice thickness, under-ice light spectra, water properties and chlorophyll a biomass (in- and under-ice). Modal ice thicknesses were between 0.45-1.38 m (Arctic) and 0.23-0.70 m (Weddell Sea). Sea ice properties were related to the distribution of some key under-ice species (e.g. Polar Cod and Antarctic Krill). Previous studies have used under-ice light spectra to derive ice-algal biomass but were limited to local-scale point measurements. We present a new method for calculating ice-algal biomass from under-ice spectra on local- to meso-scales and compare the results using both methods

Antarctic sympagic meiofauna in winter: Comparing diversity, abundance and biomass between perennially and seasonally ice-covered regions

This study of Antarctic sympagic meiofauna in pack ice during late winter compares communities between the perennially ice-covered western Weddell Sea and the seasonally ice-covered southern Indian Ocean. Sympagic meiofauna (proto- and metazoans >20 μm) and eggs >20 μm were studied in terms of diversity, abundance and carbon biomass, and with respect to vertical distribution. Metazoan meiofauna had significantly higher abundance and biomass in the western Weddell Sea (medians: 31.1×103 m−2 and 6.53mg m−2, respectively) than in the southern Indian Ocean (medians: 1.0×10 103 m−2and 0.06 mg m−2, respectively). Metazoan diversity was also significantly higher in the western Weddell Sea. Furthermore, the two regions differed significantly in terms of meiofauna community composition, as revealed through multivariate analyses. The overall diversity of sympagic meiofauna was high, and integrated abundance and biomass of total meiofauna were also high in both regions (0.6–178.6×103 m−2 and 0.02–89.70mg m−2, respectively), mostly exceeding values reported earlier from the western Weddell Sea in winter. We attribute the differences in meiofauna communities between the two regions to the older first-year ice and multi-year ice that is present in the western Weddell Sea, but not in the southern Indian Ocean. Our study indicates the significance of perennially ice-covered regions for the establishment of diverse and abundant meiofauna communities. Furthermore, it highlights the potential importance of sympagic meiofauna for the organic matter pool and trophic interactions in sea ice

Spatial variability in sea-ice algal biomass: an under-ice remote sensing perspective

Sea-ice algae are a paramount feature of polar marine ecosystems and ice algal standing stocks are characterized by a high spatio-temporal variability. Traditional sampling techniques, e.g., ice coring, are labor intensive, spatially limited and invasive, thereby limiting our understanding of ice algal biomass variability patterns. This has consequences for quantifying ice-associated algal biomass distribution, primary production, and detecting responses to changing environmental conditions. Close-range under-ice optical remote sensing techniques have emerged as a capable alternative providing non-invasive estimates of ice algal biomass and its spatial variability. In this review we first summarize observational studies, using both classical and new methods that aim to capture biomass variability at multiple spatial scales and identify the environmental drivers. We introduce the complex multi-disciplinary nature of under-ice spectral radiation profiling techniques and discuss relevant concepts of sea-ice radiative transfer and bio-optics. In addition, we tabulate and discuss advances and limitations of different statistical approaches used to correlate biomass and under-ice light spectral composition. We also explore theoretical and technical aspects of using Unmanned Underwater Vehicles (UUV), and Hyperspectral Imaging (HI) technology in an under-ice remote sensing context. The review concludes with an outlook and way forward to combine platforms and optical sensors to quantify ice algal spatial variability and establish relationships with its environmental drivers

Sea ice CO2 flux in the Southern Ocean during mid-winter and early spring

第4回極域科学シンポジウム個別セッション：[OB] 生物圏11月12日（火）13:00-14:00　国立国語研究所 ２階ラウン

Microalgal community structure and primary production in Arctic and Antarctic sea ice : A synthesis

Sea ice is one the largest biomes on earth, yet it is poorly described by biogeochemical and climate models. In this paper, published and unpublished data on sympagic (ice-associated) algal biodiversity and productivity have been compiled from more than 300 sea-ice cores and organized into a systematic framework. Significant patterns in microalgal community structure emerged from this framework. Autotrophic flagellates characterize surface communities, interior communities consist of mixed microalgal populations and pennate diatoms dominate bottom communities. There is overlap between landfast and pack-ice communities, which supports the hypothesis that sympagic microalgae originate from the pelagic environment. Distribution in the Arctic is sometimes quite different compared to the Antarctic. This difference may be related to the time of sampling or lack of dedicated studies. Seasonality has a significant impact on species distribution, with a potentially greater role for flagellates and centric diatoms in early spring. The role of sea-ice algae in seeding pelagic blooms remains uncertain. Photosynthesis in sea ice is mainly controlled by environmental factors on a small scale and therefore cannot be linked to specific ice types. Overall, sea-ice communities show a high capacity for photoacclimation but low maximum productivity compared to pelagic phytoplankton. Low carbon assimilation rates probably result from adaptation to extreme conditions of reduced light and temperature in winter. We hypothesize that in the near future, bottom communities will develop earlier in the season and develop more biomass over a shorter period of time as light penetration increases due to the thinning of sea ice. The Arctic is already witnessing changes. The shift forward in time of the algal bloom can result in a mismatch in trophic relations, but the biogeochemical consequences are still hard to predict. With this paper we provide a number of parameters required to improve the reliability of sea-ice biogeochemical models.Peer reviewe

Iron biogeochemistry in Antarctic pack ice during SIPEX-2

Our study quantified the spatial and temporal distribution of Fe and ancillary biogeochemical parameters at six stations visited during an interdisciplinary Australian Antarctic marine science voyage (SIPEX-2) within the East Antarctic first-year pack ice zone during September–October 2012. Unlike previous studies in the area, the sea ice Chlorophyll a, Particulate Organic Carbon and Nitrogen (POC and PON) maxima did not occur at the ice/water interface because of the snow loading and dynamic processes under which the sea ice formed. Iron in sea ice ranged from 0.9 to 17.4 nM for the dissolved (<0.2 µm) fraction and 0.04 to 990 nM for the particulate (>0.2 µm) fraction. Our results highlight that the concentration of particulate Fe in sea ice was highest when approaching the continent. The high POC concentration and high particulate iron to aluminium ratio in sea ice samples demonstrate that 71% of the particulate Fe was biogenic in composition. Our estimated Fe flux from melting pack ice to East Antarctic surface waters over a 30 day melting period was 0.2 µmol/m2/d of DFe, 2.7 µmol/m2/d of biogenic PFe and 1.3 µmol/m2/d of lithogenic PFe. These estimates suggest that the fertilization potential of the particulate fraction of Fe may have been previously underestimated due to the assumption that it is primarily lithogenic in composition. Our new measurements and calculated fluxes indicate that a large fraction of the total Fe pool within sea ice may be bioavailable and therefore, effective in promoting primary productivity in the marginal ice zone

SIPEX-2: A study of sea-ice physical, biochemical and ecosystem processes off East Antarctica during spring 2012

This editorial introduces a suite of articles resulting from the second Sea Ice Physics and Ecosystems eXperiment(SIPEX-2) voyage by presenting some background information on the study areaandAntarcticsea-ice conditions,and summarising the key findings from the project.Using the Australian iceb reaker RV Aurora Australis, SIPEX-2 was conducted in the area between 115–125°E and 62–66°S off East Antarctica during September to November 2012. This region had been sampled during two previous experiments,i.e. ARISE in 2003 (Massom etal.,2006a) and SIPEX in 2007(Worbyetal.,2011a). The 2012 voyage combined traditional and newly developed sampling methods with satellite and other data to measure sea-ice physical properties and pro- cesses on large scales,which provided context for bio geochemical and ecological case studies. Thes pecific goals of the SIPEX-2 project were to:(i)measure the spatial variability in sea-ice and snow-cover properties over small-to regional-length scales;(ii) improve understanding of sea-ice kinematic processes;and(iii) advance knowledge of the links between sea-ice physical characteristics,sea-ice biogeochemical cycling and ice-associated food-web dynamics.Our field-based activities were designed to inform modelling approaches and to improve our capability to assess impacts of predicted changes in Antarctic sea ice on Southern Ocean biogeochemical cycles and ecosystem function

Long-term decoding of movement force and direction with a wireless myoelectric implant

Objective. The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. Approach. Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. Main results. In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. Significance. These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).German Federal Ministry for Education and Reseach (BMBF) grant No, 16SV3695, 16SV3699, 16SV3697 and 01GQ1005C, DFG Deutsche Forschungsgemeinschaft grant No. GA1475-C