ALBERT

Description: Landslide-dams, which are often transient, can strongly affect the geomorphology, and sediment and geochemical fluxes, within subaerial fluvial systems. The potential occurrence and impact of analogous landslide-dams in submarine canyons has, however, been difficult to determine due to a scarcity of sufficiently time-resolved observations. Here we present repeat bathymetric surveys of a major submarine canyon, the Congo Canyon, offshore West Africa, from 2005 and 2019. We show how an ~0.09 km3 canyon-flank landslide dammed the canyon, causing temporary storage of a further ~0.4 km3 of sediment, containing ~5 Mt of primarily terrestrial organic carbon. The trapped sediment was up to 150 m thick and extended 〉26 km up-canyon of the landslide-dam. This sediment has been transported by turbidity currents whose sediment load is trapped by the landslide-dam. Our results suggest canyon-flank collapses can be important controls on canyon morphology as they can generate or contribute to the formation of meander cut-offs, knickpoints and terraces. Flank collapses have the potential to modulate sediment and geochemical fluxes to the deep sea and may impact efficiency of major submarine canyons as transport conduits and locations of organic carbon sequestration. This has potential consequences for deep-sea ecosystems that rely on organic carbon transported through submarine canyons.

Description: The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2022 is an update of the previous version, GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulphur hexafluoride (SF6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF6 data, which are now subjected to secondary quality control, and carbon data measured onboard the RV Knorr in the Indian Ocean in 1994–1995 which are now adjusted using CRM measurements made at the time. GLODAPv2.2022 includes measurements from almost 1.4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality controlled cruises in the GLODAPv2.2021 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2) chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 μmol kg-1 in dissolved inorganic carbon, 4 μmol kg-1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments.

Description: Host-specific microbial communities thrive within sponge tissues and this association between sponge and associated microbiota may be driven by the organohalogen chemistry of the sponge animal. Several sponge species produce diverse organobromine secondary metabolites (e.g. brominated phenolics, indoles, and pyrroles) that may function as a chemical defense against microbial fouling, infection or predation. In this study, anaerobic cultures prepared from marine sponges were amended with 2,6-dibromophenol as the electron acceptor and short chain organic acids as electron donors. We observed reductive dehalogenation from diverse sponge species collected at disparate temperate and tropical waters suggesting that biogenic organohalides appear to enrich for populations of dehalogenating microorganisms in the sponge animal. Further enrichment by successive transfers with 2,6-dibromophenol as the sole electron acceptor demonstrated the presence of dehalogenating bacteria in over 20 sponge species collected from temperate and tropical ecoregions in the Atlantic and Pacific Oceans and the Mediterranean Sea. The enriched dehalogenating strains were closely related to Desulfoluna spongiiphila and Desulfoluna butyratoxydans, suggesting a cosmopolitan association between Desulfoluna spp. and various marine sponges. In vivo reductive dehalogenation in intact sponges was also demonstrated. Organobromide-rich sponges may thus provide a specialized habitat for organohalide-respiring microbes and D. spongiiphila and/or its close relatives are responsible for reductive dehalogenation in geographically widely distributed sponge species.

Description: Here we show how major rivers can efficiently connect to the deep-sea, by analysing the longest runout sediment flows (of any type) yet measured in action on Earth. These seafloor turbidity currents originated from the Congo River-mouth, with one flow travelling 〉1,130 km whilst accelerating from 5.2 to 8.0 m/s. In one year, these turbidity currents eroded 1,338-2,675 [〉535-1,070] Mt of sediment from one submarine canyon, equivalent to 19–37 [〉7–15] % of annual suspended sediment flux from present-day rivers. It was known earthquakes trigger canyon-flushing flows. We show river-floods also generate canyon-flushing flows, primed by rapid sediment-accumulation at the river-mouth, and sometimes triggered by spring tides weeks to months post-flood. It is demonstrated that strongly erosional turbidity currents self-accelerate, thereby travelling much further, validating a long-proposed theory. These observations explain highly-efficient organic carbon transfer, and have important implications for hazards to seabed cables, or deep-sea impacts of terrestrial climate change.

Description: Calcium carbonate hexahydrate (ikaite) is a rare mineral that forms as metastable species in the organic-carbon-rich sediments of the King George Basin, Bransfield Strait, Antarctica, as a consequence of early diagenetic decomposition of organic matter under cold water (−1.4 °C) and high pressure (200 bar) conditions. Large crystals grow in the sediment immediately below the diagenetic transition between microbial sulfate reduction and methanogenesis at ~320 cm below sea floor (bsf). This process is reflected in the dissolved sulfate, total carbon dioxide, and methane concentrations, as well as in the carbon, hydrogen, and oxygen isotope chemistries of the interstitial fluids and dissolved gases of the host sediment. The ikaite crystal faithfully records in its zonal structure the changing carbon isotope ratio of the total dissolved carbon dioxide pool as it gradually diminishes during methanogenesis (δ13Cikaite = −17.5 to −21.4‰). These changes in the crystal’s host environment follow general Rayleigh carbon isotope fractionation. The oxygen isotopes of the ikaite carbonate (δ18Oikaite = 1.46 to 4.45‰) also show a strong zonal distribution, unrelated to temperature of formation, but perhaps controlled by the degree of recrystallization of ikaite to calcite. The crystal water of the ikaite is depleted 11‰ in 2H/1H (VSMOW) relative to the coexisting interstitial water, which is in excellent agreement with the isotope fractionation of other hydrated minerals. In addition to the in situ temperature and pressure, nucleation of the ikaite crystals in the Bransfield Basin sediments may be induced by the high alkalinity, high phosphate concentrations, and dissolved organic compounds. Intense microbial metabolism generates such compounds; of these, aspartic acid and glutamic acid may play an important role, as they do in biological and extracellular carbonate mineral precipitation. All indications are that low temperatures (such as of polar environments), high calcium carbonate supersaturation caused by interstitial methanogenesis, and a sufficiently large supply of dissolved phosphate and amino acids favor metastable ikaite formation. These conditions, modified by recrystallization, may be preserved in calcite glendonites, thinolites, and other calcitic pseudomorphs derived from ikaite and found throughout the ancient sedimentary record.

Description: Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy. A commonly criticised point, however, is that the resulting trees may not necessarily be the best representation of the data in terms of accuracy and size. In recent years, this motivated the development of optimal classification tree algorithms that globally optimise the decision tree in contrast to heuristic methods that perform a sequence of locally optimal decisions. We follow this line of work and provide a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our algorithm supports constraints on the depth of the tree and number of nodes. The success of our approach is attributed to a series of specialised techniques that exploit properties unique to classification trees. Whereas algorithms for optimal classification trees have traditionally been plagued by high runtimes and limited scalability, we show in a detailed experimental study that our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances, providing several orders of magnitude improvements and notably contributing towards the practical use of optimal decision trees.