ALBERT

Description: New sedimentological data of facies and diagenesis as well as chronological data including strontium (87Sr/86Sr)-isotope ratios and uranium (U)-series dating, radiocarbon (14C) accelerator mass spectrometry (AMS) dating and biostratigraphy from elevated reef terraces (makatea) in the southern Cook Islands of Mangaia, Rarotonga and Aitutaki contribute to controversial discussions regarding age and sea-level relationships of these occurrences during the Neogene and Quaternary. The oldest limestones of the uplifted makatea island of Mangaia include reef-related facies which are mid-Miocene in age, based on new Sr-isotope and biostratigraphical data. In between these older deposits and the lowest coastal reef terrace of marine isotope stage (MIS) 5e, various older Pleistocene reef-related facies were identified. Based on Sr-isotope ratios, these were deposited during earlier Pleistocene highstands (as old as 2.28 Ma). Rare reef terraces on Rarotonga belong to the Plio-Pleistocene and the late Miocene, according to 87Sr/86Sr ratios. The late Miocene age is enigmatic as it exceeds the age of subaerially exposed volcanic rocks of Rarotonga island. The fossil reef could have formed on an older submarine volcanic high that was later displaced by younger volcanism to its present position, or the Sr-age could be too old due to diagenetic resetting. The Plio-Pleistocene Rarotonga reef terraces are overlain irregularly by Holocene reef deposits that are interpreted as storm rubble. Reef terraces on Aitutaki represent evidence of a higher-than-present (up to 1 m) sea-level during the late Holocene, based on 14C AMS age data. They are very similar to elevated late Holocene reefs of adjacent French Polynesia with regard to composition, elevation and age.

Description: Satellitendaten in einem GIS zu bearbeiten, ist gar nicht so kompliziert. Das zweite Dokument aus der Reihe SAPIENS: Satellitendaten für Planung, Industrie, Energiewirtschaft und Naturschutz ermöglicht den Einstieg in das kostenlose Geoinformationssystem QGIS und beschreibt grundlegende Arbeitsschritte bei der Verarbeitung optischer Satellitendaten. Schritt für Schritt erklären wir, wie man die Daten mit QGIS in verschiedenen Bandkombinationen darstellen und Indizes berechnen kann und wie man sie symbolisieren, zuschneiden und exportieren kann, mit vielen zusätzlichen Tipps, weiterführenden Weblinks und einem Exkurs zu Vektor- und Offenen Geodaten.

Description: Sollen Satellitendaten bearbeitet werden, ist die von der ESA bereitgestellte kostenlose Software SNAP sehr gut geeignet, besonders für Sentineldaten. Aber wie funktioniert das englischsprachige Programm? Das Extra-Handbuch aus der Reihe SAPIENS: Satellitendaten für Planung, Industrie, Energiewirtschaft und Naturschutz erklärt anschaulich die wichtigsten Schritte und Funktionen. Wir zeigen, wie man Satellitenszenen einladen, visualisieren, atmoshärisch korrigieren, mit verschiedensten Werkzeugen bearbeiten und dabei häufige Arbeitsschritte automatisieren kann. Ein ganzes Kapitel ist außerdem der Vielzahl von Indizes gewidmet, die in SNAP zur Verfügung stehen.

Description: © 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.

Description: 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.

Description: 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.

Description: The Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean that uses an arsenal of hydrolases for biomacromolecule decomposition to effectively digest its omnivorous diet. The present study builds on a hybrid-assembled transcriptome (13,671 ORFs) combined with comprehensive proteome profiling. The analysis of individual krill compartments allowed detection of significantly more different proteins compared to that of the entire animal (1,464 vs. 294 proteins). The nearby krill sampling stations in the Bransfield Strait (Antarctic Peninsula) yielded rather uniform proteome datasets. Proteins related to energy production and lipid degradation were particularly abundant in the abdomen, agreeing with the high energy demand of muscle tissue. A total of 378 different biomacromolecule hydrolysing enzymes were detected, including 250 proteases, 99 CAZymes, 14 nucleases and 15 lipases. The large repertoire in proteases is in accord with the protein-rich diet affiliated with E. superba’s omnivorous lifestyle and complex biology. The richness in chitin-degrading enzymes allows not only digestion of zooplankton diet, but also the utilization of the discharged exoskeleton after moulting.