Beschreibung: The biomass of zooplankton communities in Arctic shelf regions is dominated by the calanoid copepod Calanus glacialis . This species spends the winter in deep water, and then, metabolic rates are low. In late winter, it migrates to the surface where the spring generation develops. To date, it is not fully understood what regulates the activity of the copepods and how it coincides with food availability. To fill this gap, we sampled C. glacialis, mainly copepodite stage V, in a high-Arctic fjord in monthly intervals for 1 year and determined proteinase and lipase/esterase activities in relation to food availability and depth distribution of the copepods. By substrate SDSPAGE(sodium dodecyl sulfate-polyacrylamide gel electrophoresis),we tackled changes in specific isoforms. We found a clear seasonal enzyme activity pattern. Activities in winter were reduced by at least 75 % as compared to spring. Substrate SDS-PAGE showed high heterogeneity of lipolytic enzymes, which could reflect extensive accumulation and metabolization of internal lipids. Only one band of proteolytic activity was found, and it intensified with the onset of the algal blooms. In late winter/spring, we sampled females and CIV, which also showed high digestive enzyme activities in surface water and low activities in deep water. High enzyme activities were related to the ice algal and phytoplankton blooms in spring. In autumn, the copepods descended although food was still available. C. glacialis could thus benefit from an early ice breakup and early algal blooms, but not from long-lasting phytoplankton availability

Beschreibung: Knowledge on the capability of zooplankton to adapt to the rapidly changing environmental conditions in the Arctic is crucial to predict future ecosystem processes. The key species on the Arctic shelf, the calanoid copepod Calanus glacialis, grows and accumulates lipid reserves in spring and summer in surface waters. The winter is spent in dormancy in deeper water layers with low metabolic activity. As timing and intensity of metabolic changes have been poorly investigated, our study aims to characterize the physiology of C. glacialis over an entire year, from July 2012 to July 2013. We followed anabolic and catabolic enzyme activities and the biochemical composition of this species, taking depth-stratified samples once a month in Billefjorden, a high-Arctic sill fjord. A large part of the population had migrated to depths 〉100 m by July 2012. Only thereafter, anabolic activities decreased slowly, suggesting that low metabolism is related to ceased feeding rather than to endogenous regulation. During overwintering, anabolic enzyme activities were reduced by half as compared to peak activities in spring. The biochemical composition of the copepods changed little from July to December. Then, the lipid catabolic activity increased and the lipid content decreased, likely fuelling moulting and gonad maturation. The protein content did not change significantly during winter, suggesting that proteins are not much catabolized during that time. The relatively high metabolic activity in C. glacialis in winter suggests that this species is not entering a true diapause and should thus be able to respond flexible to changing environmental conditions.

Beschreibung: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Orenstein, E., Ayata, S., Maps, F., Becker, É., Benedetti, F., Biard, T., Garidel‐Thoron, T., Ellen, J., Ferrario, F., Giering, S., Guy‐Haim, T., Hoebeke, L., Iversen, M., Kiørboe, T., Lalonde, J., Lana, A., Laviale, M., Lombard, F., Lorimer, T., Martini, S., Meyer, A., Möller, K.O., Niehoff, B., Ohman, M.D., Pradalier, C., Romagnan, J.-B., Schröder, S.-M., Sonnet, V., Sosik, H.M., Stemmann, L.S., Stock, M., Terbiyik-Kurt, T., Valcárcel-Pérez, N., Vilgrain, L., Wacquet, G., Waite, A.M., & Irisson, J. Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67(8), (2022): 1647-1669, https://doi.org/10.1002/lno.12101.

Beschreibung: Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Beschreibung: SDA acknowledges funding from CNRS for her sabbatical in 2018–2020. Additional support was provided by the Institut des Sciences du Calcul et des Données (ISCD) of Sorbonne Université (SU) through the support of the sponsored junior team FORMAL (From ObseRving to Modeling oceAn Life), especially through the post-doctoral contract of EO. JOI acknowledges funding from the Belmont Forum, grant ANR-18-BELM-0003-01. French co-authors also wish to thank public taxpayers who fund their salaries. This work is a contribution to the scientific program of Québec Océan and the Takuvik Joint International Laboratory (UMI3376; CNRS - Université Laval). FM was supported by an NSERC Discovery Grant (RGPIN-2014-05433). MS is supported by the Research Foundation - Flanders (FWO17/PDO/067). FB received support from ETH Zürich. MDO is supported by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. ECB is supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under the grant agreement no. 88882.438735/2019-01. TB is supported by the French National Research Agency (ANR-19-CE01-0006). NVP is supported by the Spanish State Research Agency, Ministry of Science and Innovation (PTA2016-12822-I). FL is supported by the Institut Universitaire de France (IUF). HMS was supported by the Simons Foundation (561126) and the U.S. National Science Foundation (CCF-1539256, OCE-1655686). Emily Peacock is gratefully acknowledged for expert annotation of IFCB images. LS was supported by the Chair VISION from CNRS/Sorbonne Université.

Beschreibung: In the Arctic Ocean, sea-ice decline will significantly change the structure of biological communities. At the same time, changing nutrient dynamics can have similarly strong and potentially interacting effects. To investigate the response of the taxonomic and trophic structure of planktonic and ice-associated communities to varying sea-ice properties and nutrient concentrations, we analysed four different communities sampled in the Eurasian Basin in summer 2012: (1) protists and (2) metazoans from the under-ice habitat, and (3) protists and (4) metazoans from the epipelagic habitat. The taxonomic composition of protist communities was characterised with 18S meta-barcoding. The taxonomic composition of metazoan communities was determined based on morphology. The analysis of environmental parameters identified (i) a ‘shelf-influenced’ regime with melting sea ice, high-silicate concentrations and low NOx (nitrate + nitrite) concentrations; (ii) a ‘Polar’ regime with low silicate concentrations and low NOx concentrations; and (iii) an ‘Atlantic’ regime with low silicate concentrations and high NOx concentrations. Multivariate analyses of combined bio-environmental datasets showed that taxonomic community structure primarily responded to the variability of sea-ice properties and hydrography across all four communities. Trophic community structure, however, responded significantly to NOx concentrations. In three of the four communities, the most heterotrophic trophic group significantly dominated in the NOx-poor shelf-influenced and Polar regimes compared to the NOx-rich Atlantic regime. The more heterotrophic, NOx-poor regimes were associated with lower productivity and carbon export than the NOx-rich Atlantic regime. For modelling future Arctic ecosystems, it is important to consider that taxonomic diversity can respond to different drivers than trophic diversity.