Over the last half of the 20 century, the western Antarctic Peninsula has been one of the most rapidly warming regions on Earth, leading to substantial reductions in regional sea ice coverage. These changes are modulated by atmospheric forcing, including the Amundsen Sea Low (ASL) pressure system. We utilized a novel 25-year (1993-2017) time series to model the effects of environmental variability on larvae of a keystone species, the Antarctic Silverfish (Pleuragramma antarctica). Antarctic Silverfish use sea ice as spawning habitat and are important prey for penguins and other predators. We show that warmer sea surface temperature and decreased sea ice are associated with reduced larval abundance. Variability in the ASL modulates both sea surface temperature and sea ice; a strong ASL is associated with reduced larvae. These findings support a narrow sea ice and temperature tolerance for adult and larval fish. Further regional warming predicted to occur during the 21st century could displace populations of Antarctic Silverfish, altering this pelagic ecosystem.
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http://dx.doi.org/10.1038/s42003-022-03042-3 | DOI Listing |
Glob Chang Biol
January 2025
Department of Surface Waters-Research and Management, EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, Switzerland.
The primary production of fjords across the Arctic and Subarctic is undergoing significant transformations due to the climatically driven retreat of glaciers and ice sheets. However, the implications of these changes for upper trophic levels remain largely unknown. In this study, we employ both bulk and compound-specific stable isotope analyses to investigate how shifts at the base of fjord food webs impact the carbon and energy sources of consumers.
View Article and Find Full Text PDFCommun Earth Environ
January 2025
Recherche en Prévision Numérique Environnementale/Environnement et Changement Climatique Canada, Dorval, QC Canada.
The Last Ice Area-located to the north of Greenland and the northern Canadian Arctic Archipelago-is expected to persist as the central Arctic Ocean becomes seasonally ice-free within a few decades. Projections of the Last Ice Area, however, have come from relatively low resolution Global Climate Models that do not resolve sea ice export through the waterways of the Canadian Arctic Archipelago and Nares Strait. Here we revisit Last Ice Area projections using high-resolution numerical simulations from the Community Earth System Model, which resolves these narrow waterways.
View Article and Find Full Text PDFTalanta
January 2025
Center for Multiplatform Metabolomics Studies (CEMM) at the Institute of Chemistry, University of Sao Paulo, Sao Paulo, SP, 05508-000, Brazil. Electronic address:
There is no consensus in the literature regarding the ideal protocol for obtaining and preparing cell samples for untargeted metabolomics. Nevertheless, the procedures must be carefully evaluated for proper and reliable results for each organism under study. This work proposes a novel protocol for determining intracellular metabolites in Leishmania promastigotes and is fully optimized for application in conjunction with gas chromatography-mass spectrometry platforms.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronic and Electrical Engineering, University of Manchester, Manchester M13 9PL, UK.
Frequency-domain electromagnetic induction (EMI) is routinely used to detect the presence of seawater due to the inherent electrical conductivity of the seawater. This approach is used to infer sea-ice thickness (SIT). A time-domain EMI sensor is presented, which demonstrates the potential for correlating the spectroscopic properties of the received signal with the distance to the sea surface.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Faculty of Engineering and Applied Science, Memorial University of Newfoundland (MUN), St. John's, NL A1B 3X5, Canada.
The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system.
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