ALBERT

Description: Several species from various zooplankton taxa perform seasonal vertical migrations (SVM) of typically several hundred meters between the surface layer and overwintering depths, particularly in high-latitude regions. We use OPtimality-based PLAnkton (OPPLA) ecosystem model) to simulate SVM behavior in zooplankton in the Labrador Sea. Zooplankton in OPPLA is a generic functional group without life cycle, which facilitates analyzing SVM evolutionary stability and interactions between SVM and the plankton ecosystem. A sensitivity analysis of SVM-related parameters reveals that SVM can amplify the seasonal variations of phytoplankton and zooplankton and enhance the reduction of summer surface nutrient concentrations. SVM is often explained as a strategy to reduce exposure to visual predators during winter. We find that species doing SVM can persist and even dominate the summer-time zooplankton community, even in the presence of Stayers, which have the same traits as the migrators, but do not perform SVM. The advantage of SVM depends strongly on the timing of the seasonal migrations, particularly the day of ascent. The presence of higher (visual) predators tends to suppress the Stayers in our simulations, whereas the SVM strategy can persist in the presence of non-migrating species even without higher predators.

Description: Current global warming results in rising sea-water temperatures, and the loss of sea ice in arctic and subarctic oceans impacts the community composition of primary producers with cascading effects on the food web and potentially on carbon export rates. This study analyzes metagenomic shotgun and diatom rbcL amplicon-sequencing data from sedimentary ancient DNA (sedaDNA) of the subarctic western Bering Sea that records phyto- and zooplankton community changes over the last glacial–interglacial cycle, including the last interglacial period (Eemian). Our data show that interglacial and glacial plankton communities differ, with distinct Eemian and Holocene plankton communities. The generally warm Holocene period is dominated by pico-sized cyanobacteria and bacteria-feeding heterotrophic protists, while the Eemian period is dominated by eukaryotic pico-sized chlorophytes and Triparmaceae. In contrast, the glacial period is characterized by micro-sized phototrophic protists, including sea-ice associated diatoms in the family Bacillariaceae and co-occurring diatom-feeding crustaceous zooplankton. Our deep-time record of plankton community changes reveals a long-term decrease in phytoplankton cell size coeval with increasing temperatures, and resembling community changes in the currently warming Bering Sea. The phytoplankton community in the warmer-than-present Eemian period is distinct from modern communities and limits the use of the Eemian as an analog for future climate scenarios. However, under enhanced future warming, the expected shift towards the dominance of small-sized phytoplankton and heterotrophic protists might result in an increased productivity, whereas the community’s potential of carbon export will be decreased, thereby weakening the subarctic Bering Sea’s function as an effective carbon sink.

Description: Volcano seismology is an essential tool for monitoring volcanic processes in the advent and during eruptions. A variety of seismic signals can be recorded at volcanoes, of which some are thought to be related to the migration of fluids which is of primary importance for the anticipation of imminent eruptions. We investigate the volcanic crises at Villarrica volcano in 2015 and report on a newly discovered very-long-period (VLP) signal that accompanies phases of periodic long period (LP) signal burst. Despite their low amplitude emergent character, we can locate the source region of the 1 Hz LP signals to the close vicinity of the volcano using a network-based correlation method. The source of the VLP signal with a period of about 30–100 s appears to locate in the vicinity of two stations a few kilometres from the summit. Both stations record very similar VLP waveforms that are correlated with the envelope of the LP bursts. A shallow magma reservoir was inferred by Contreras from surface deformation as the source of inflation following the eruption in 2015. Cyclic volume changes of 6 m3 in this reservoir at 3 km depth can explain the observed amplitudes of the vertical VLP signal. We propose that the LP signal is generated by the migration of gas or gas-rich magma that is periodically released from the inflating reservoir through a non-linear valve structure which modulates the flux, and thereby causes bursts of flow-related LP signals and pressure changes observed as VLP deformation. Our model predicts that the correlated occurrence of LP bursts and VLP surface motion depends on the intensity of the fluid flux. A weaker flux of fluids may not exceed the opening pressure of valve structure, and higher rates might maintain pressure above the closing pressure. In both cases, the VLP signal vanishes. Our observation provides constrains for models of fluid transport inside volcanoes. At Villarrica the VLP signal, and its relation to the LP activity, reveal additional information about fluxes in the magmatic reservoir that might aide forecasting of volcanic activity.

Description: Compositional variations of amphibole stratigraphically recovered from multiple eruptions at a given volcano have a great potential to archive long-term magmatic processes in its crustal plumbing system. Calcic amphibole is a ubiquitous yet chemically and texturally diverse mineral at Mount St. Helens (MSH), where it occurs in dacites and in co-magmatic enclaves throughout the Spirit Lake stage (last ~4000 years of eruptive history). It forms three populations with distinct geochemical trends in key major and trace elements, which are subdivided into a high-Al (11–14.5 wt% Al2O3), a medium-Al (10–12.5 wt% Al2O3), and a low-Al (7.5–10 wt% Al2O3) amphibole population. The oldest investigated tephra record (Smith Creek period, 3900–3300 years BP) yields a bimodal amphibole distribution in which lower-crustal, high-Al amphibole cores (crystallized dominantly from basaltic andesite to andesite melts) and upper-crustal, low-Al amphibole rims (crystallized from rhyolitic melt) document occasional recharge of a shallow silicic mush by a more mafic melt from a lower-crustal reservoir. The sudden appearance of medium-Al amphiboles enriched in incompatible trace elements in eruptive periods younger than 2900 years BP is associated with a change in reservoir conditions toward hotter and drier magmas, which indicates recharge of the shallow silicic reservoir by basaltic melt enriched in incompatible elements. Deep-crystallizing, high-Al amphibole, however, appears mostly unaffected by such incompatible-element-enriched basaltic recharge, suggesting that these basalts bypass the lower crustal reservoir. This could be the result of the eastward offset position of the lower crustal reservoir relative to the upper crustal storage zone underneath the MSH edifice. Amphibole has proven to be a sensitive geochemical archive for uncovering storage conditions of magmas at MSH. In agreement with geophysical observations, storage and differentiation have occurred in two main zones: an upper crustal and lower crustal reservoir (the lower one being chemically less evolved). The upper crustal silicic reservoir, offset to the west of the lower crustal reservoir, has captured compositionally unusual mafic recharge (drier, hotter, and enriched in incompatible trace elements in comparison to the typical parental magmas in the region), resulting in an increased chemical diversity of amphiboles and their carrier intermediate magmas, in the last ~3000 years of MSH’s volcanic record.

Description: Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

Description: Full waveform inversion (FWI) is a data-fitting technique capable of generating high-resolution velocity models with a resolution down to half the seismic wavelength. FWI is applied typically to densely sampled seismic data. In this study, we applied FWI to 3D wide-angle seismic data acquired using sparsely spaced ocean bottom seismometers (OBSs) from the Deep Galicia Margin west of Iberia. Our dataset samples the S-reflector, a low-angle detachment present in this area. Here we highlight differences between 2D, 2.5D and 3D-FWI performances using a real sparsely spaced dataset. We performed 3D FWI in the time domain and compared the results with 2D and 2.5D FWI results from a profile through the 3D model. When overlaid on multichannel seismic images, the 3D FWI results constrain better the complex faulting within the pre- and syn-rift sediments and crystalline crust compared to the 2D result. Furthermore, we estimate variable serpentinisation of the upper mantle below the S-reflector along the profile using 3D FWI, reaching a maximum of 45 per cent. Differences in the data residuals of the 2D, 2.5D and 3D inversions suggest that 2D inversion can be prone to overfitting when using a sparse dataset. To validate our results, we performed tests to recover the anomalies introduced by the inversions in the final models using synthetic datasets. Based on our comparison of the velocity models, we conclude that the use of 3D data can partially mitigate the problem of receiver sparsity in FWI.

Description: The seismic receiver function (RF) technique is widely used as an economic method to image earth's deep interior in a large number of seismic experiments. P-wave receiver functions (RFs) constrain crustal thickness and average Vp/Vs in the crust by analysis of the Ps phase and multiples (reflected/converted waves) from the Moho. Regional studies often show significant differences between the Moho depth constrained by RF and by reflection/refraction methods. We compare the results from RF and controlled source seismology for the Baikal Rift Zone by calculating 1480 synthetic RFs for a seismic refraction/reflection velocity model and processing them with two common RF techniques [H–κ and Common Conversion Point (CCP) stacking]. We compare the resulting synthetic RF structure with the velocity model, a density model (derived from gravity and the velocity model), and with observed RFs. Our results demonstrate that the use of different frequency filters, the presence of complex phases from sediments and gradual changes in the properties of crustal layers can lead to erroneous interpretation of RFs and incorrect geological interpretations. We suggest that the interpretation of RFs should be combined with other geophysical methods, in particular in complex tectonic regions and that the long-wavelength Bouguer gravity anomaly signal may provide effective calibration for the determination of the correct Moho depth from RF results. We propose and validate a new automated, efficient method for this calibration.

Description: We analyse the consistency of the delay time data in the most recent version of the ISC-EHB bulletin published by the International Seismological Centre covering the years 1964–2018. Considering that the delays are influenced by the lateral heterogeneity in the Earth’s mantle, we construct a tomographic matrix. We use singular value decomposition of the tomographic matrix for 19 707 dense clusters of earthquakes to compute objective estimates of the standard error from data that project into the null space and should be zero if there were no errors. Using a robust initial estimate of the standard deviation of the clustered delay times, we remove a small fraction of outliers before calculating the ultimate errors. We found that the errors depend on the type of body wave, depth of the earthquake (crust or mantle) and the number of decimals with which the arrival time was reported. Using these parameters, we distinguish 45 different classes of delay times for 11 different types of body waves. The errors of each class so divided generally follow a distribution that is approximately normal with a mean that ranges from 0.32 s for PKPbc waves from mantle earthquakes, to 2.82 s for S waves from shallow earthquakes bottoming in the upper mantle. The widths of the distributions of the errors themselves are small enough to serve in formal statistical quantification of the quality of fit in tomographic experiments.

Description: The neritic-oceanic squid Illex argentinus supports one of the largest fisheries in the Southwest Atlantic. It is characterized by extensive migrations across the Patagonian Shelf and complex population structure comprising distinct seasonal spawning groups. To address uncertainty as to the demographic independence of these groups that may compromise sustainable management, a multidisciplinary approach was applied integrating statolith ageing with genome-wide single-nucleotide polymorphism (SNP) analysis. To obtain complete coverage of the spawning groups, sampling was carried out at multiple times during the 2020 fishing season and covered a large proportion of the species' range across the Patagonian Shelf. Statolith and microstructure analysis revealed three distinct seasonal spawning groups of winter-, spring-, and summer-hatched individuals. Subgroups were identified within each seasonal group, with statolith microstructure indicating differences in environmental conditions during ontogeny. Analysis of 〉10 000 SNPs reported no evidence of neutral or non-neutral genetic structure among the various groups. These findings indicate that I. argentinus across the Patagonian Shelf belong to one genetic population and a collaborative management strategy involving international stakeholders is required. The connectivity among spawning groups may represent a "bet-hedging" mechanism important for population resilience.

Description: The dynamics of marine systems at decadal scales are notoriously hard to predict—hence references to this timescale as the “grey zone” for ocean prediction. Nevertheless, decadal-scale prediction is a rapidly developing field with an increasing number of applications to help guide ocean stewardship and sustainable use of marine environments. Such predictions can provide industry and managers with information more suited to support planning and management over strategic timeframes, as compared to seasonal forecasts or long-term (century-scale) predictions. The most significant advances in capability for decadal-scale prediction over recent years have been for ocean physics and biogeochemistry, with some notable advances in ecological prediction skill. In this paper, we argue that the process of “lighting the grey zone” by providing improved predictions at decadal scales should also focus on including human dimensions in prediction systems to better meet the needs and priorities of end users. Our paper reviews information needs for decision-making at decadal scales and assesses current capabilities for meeting these needs. We identify key gaps in current capabilities, including the particular challenge of integrating human elements into decadal prediction systems. We then suggest approaches for overcoming these challenges and gaps, highlighting the important role of co-production of tools and scenarios, to build trust and ensure uptake with end users of decadal prediction systems. We also highlight opportunities for combining narratives and quantitative predictions to better incorporate the human dimension in future efforts to light the grey zone of decadal-scale prediction.