ALBERT

Description: With this script, the Meridional Overturning Circulation (MOC) can be computed from NEMO ocean-model output for the whole globe or the Atlantic (AMOC), Indic (IMOC) and Pacific (PMOC) subbasins. The MOC is computable in z- and sigma coordinates. Moreover, for nested configurations, it is possible to combine data from both host and nest grids. Finally, it is possible to take into account of that the ORCA model grid is curvilinear north of 20°N: it is possible to compute the northward velocity component from the velocity field in x- and y- directions and to sum up the meridional flux over latitudional bands instead of in x-direction. When both steps are applied, the resulting MOC shows however strong variability in meridional direction. It needs to be clarified, whether this is realistic or not. The software is provided in the form of the jupyter notebook "MOC.ipynb" which includes more informations on the possibilites of the computations and an extensive appendix section with comparisons to computations with cdftools, as well as with details on the computation of the MOC including nest data and taking the curvilinearity of the grid into account. Necessary python modules are listed at the beginning of the document.

Description: CMSY++ is an advanced state-space Bayesian method for stock assessment that estimates fisheries reference points (MSY, Fmsy, Bmsy) as well as status or relative stock size (B/Bmsy) and fishing pressure or exploitation (F/Fmsy) from catch and (optionally) abundance data, a prior for resilience or productivity (r), and broad priors for the ratio of biomass to unfished biomass (B/k) at the beginning, an intermediate year, and the end of the time series. For the purpose of this User Guide, the whole package is referred to as CMSY++ whereas the part of the method that deals with catch-only data is referred to as CMSY (catch MSY), and the part of the method that requires additional abundance data is referred to as BSM (Bayesian Schaefer Model). Both methods are based on a modified Schaefer surplus production model (see paper cited above for more details). The main advantage of BSM, compared to other implementations of surplus production models, is the focus on informative priors and the acceptance of short and incomplete (i.e., fragmented, with missing years) abundance data. This document provides a simple step-by-step guide for researchers who want to apply CMSY++ to their own data.

Description: MSM89 – Bridgetown/Barbados – Bridgetown/Barbados 2. Wochenbericht – MARIA S. MERIAN - MSM89 20.-26.01.2020

Description: MSM89 – Bridgetown/Barbados – Bridgetown/Barbados 1. Wochenbericht – MARIA S. MERIAN - MSM89 14.-19.01.2020

Description: MSM89 – Bridgetown/Barbados – Bridgetown/Barbados 3. Wochenbericht – MARIA S. MERIAN - MSM89 27.01.-02.02.2020

Description: Summary of Ilse Seibold's vita Ilse Seibold, née Usbeck, was born May 8, 1925 in Breslau, Silesia, and went to school in Halle/Saale during WW2. She started her studies of geology and paleontology at the University of Halle and at the Humboldt University in Berlin, and later at the University of Tübingen, where she received her doctorate as micropaleontologist in 1951 with Otto Schindewolf as her supervisor. She remained active as productive scientist over many decades. In 1952, she married Dr. Eugen Seibold, who in 1958 became professor at Kiel University, founded one of Europe's most important institutes for marine geology, and later became president of the German Science Foundation (DFG), and subsequently of the European Science Foundation (ESF). Being a scientist herself Ilse Seibold soon evolved to a deeply reflective insider of geological sciences. She followed her husband during his scientific career from his appointments in Tübingen, Bonn, Karlsruhe, Kiel, to Bonn and Strasbourg/Freiburg i.Br. She accompanied Eugen on his sabbatical leave at Scripps Institution of Oceanography in La Jolla, CA. She participated in countless international scientific meetings. Together with Eugen she published many papers that document her independence and autonomy as scientist. She gained deep insights into the origins of the geosciences and their historical evolution, up to the ideas of fine arts. We are happy that she documented in her publications a broad range of her scientific and distinguished-humane impressions.

Description: Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator).

Description: Since the sunlight only penetrates a few hundred meters into the ocean, deep-diving robots have to bring their own light sources for imaging the deep sea, e.g., to inspect hydrothermal vent fields. Such co-moving light sources mounted not very far from a camera introduce uneven illumination and dynamic patterns on seafloor structures but also illuminate particles in the water column and create scattered light in the illuminated volume in front of the camera. In this scenario, a key challenge for forward-looking robots inspecting vertical structures in complex terrain is to identify free space (water) for navigation. At the same time, visual SLAM and 3D reconstruction algorithms should only map rigid structures, but not get distracted by apparent patterns in the water, which often resulted in very noisy maps or 3D models with many artefacts. Both challenges, free space detection, and clean mapping could benefit from pre-segmenting the images before maneuvering or 3D reconstruction. We derive a training scheme that exploits depth maps of a reconstructed 3D model of a black smoker field in 1400 m water depth, resulting in a carefully selected, ground-truthed data set of 1000 images. Using this set, we compare the advantages and drawbacks of a classical Markov Random Field-based segmentation solution (graph cut) and a deep learning-based scheme (U-Net) to finding free space in forward-looking cameras in the deep ocean.

Description: Seagrass meadows deliver important ecosystem services such as nutrient cycling, enhanced biodiversity, and contribution to climate change mitigation and adaption through carbon sequestration and coastal protection. Seagrasses, however, are facing the impacts of ocean warming and marine heatwaves, which are altering their ecological structure and function. Shifts in species composition, mass mortality events, and loss of ecosystem complexity after sudden extreme climate events are increasingly common, weakening the ecosystem services they provide. In the west coast of Australia, Shark Bay holds between 0.7 and 2.4% of global seagrass extent (〉4300 km2), but in the austral summer of 2010/2011, the Ningaloo El Niño marine heatwave resulted in the collapse of ~1300 km2 of seagrass ecosystem extent. The loss of the seagrass canopy resulted in the erosion and the likely remineralization of ancient carbon stocks into 2–4 Tg CO2-eq over 6 years following seagrass loss, increasing emissions from land-use change in Australia by 4–8% per annum. Seagrass collapse at Shark Bay also impacted marine food webs, including dugongs, dolphins, cormorants, fish communities, and invertebrates. With increasing recurrence and intensity of marine heatwaves, seagrass resilience is being compromised, underlining the need to implement conservation strategies. Such strategies must precede irreversible climate change-driven tipping points in ecosystem functioning and collapse and result from synchronized efforts involving science, policy, and stakeholders. Management should aim to maintain or enhance the resilience of seagrasses, and using propagation material from heatwave-resistant meadows to restore impacted regions arises as a challenging but promising solution against climate change threats. Although scientific evidence points to severe impacts of extreme climate events on seagrass ecosystems, the occurrence of seagrass assemblages across the planet and the capacity of humans to modify the environment sheds some light on the capability of seagrasses to adapt to changing ecological niches.

Description: FS Alkor Reise 556, Fahrtabschnitt 14.05. - 22.05.2021 Die Ostsee hat im Rahmen des Klimawandels und wachsender anthropogener Nutzung in den letzten 50 Jahren tiefgreifende und im globalen Vergleich besonders schnell ablaufende Veränderungen, wie Erwärmung, Versauerung, Eutrophierung, zunehmenden Sauerstoffmangel, Überfischung, und die Ausbreitung invasiver Arten, erfahren. Die ökologischen und ökonomischen Konsequenzen dieser langfristigen Veränderungen sind durch kurzfristige Projekte nur schwer zu verfolgen. Umso wichtiger sind Langzeitdatenreihen, die auch dekadische Muster abbilden. Das Hauptziel der Ausfahrt AL556 ist es, durch Probennahmen und hydrographische Messungen eine der besten verfügbaren Langzeitdatenreihen für die pelagische Ostsee fortzusetzen. So wurden seit 1986 in den tiefen Becken der Ostsee mit Hauptfokus auf dem Bornholmbecken mit konsistenter Methodik pelagische Schleppnetzfischerei und Fischprobennahmen, Beprobungen des pelagischen Nahrungsnetzes (Phyto- und Zooplankton einschließlich Ichthyo- und gelatinösem („Quallen“) Plankton), ozeanographische/hydrographische Messungen und Hydroakustikaufnahmen durchgeführt. Diese Arbeiten werden während der AL556 weitergeführt, wobei die Ausfahrt aufgrund einer Corona-bedingten Unterbrechung der Langzeitdatenreihe in 2020 von besonderer Bedeutung ist. Die gewonnenen Proben und Daten sind dabei für verschiedene Projekte und internationale Kollaborationen der Abteilung „Marine Evolutionary Ecology“ am GEOMAR relevant. Dazu gehören insbesondere das Projekt "Fischereiindizierte Evolution" im Rahmen der DFG-Graduiertenschule TransEvo (CAU /GEOMAR), und das EU Horizon 2020 Projektes GoJelly. Sonderprojekte in 2021 sind zudem die Isolation von marinen Viren und der Phytoyplanktonart Ostreococcus für das Projekt Marine Mikroben und Viren der Ostsee unter dem Einfluß des Klimawandels und Probennahmen für die Untersuchung der Nahrungsökologie von Fischlarven und planktivoren adulten Fischen mit Hilfe molekularbiologischer Ansätze („Metabarcoding“).