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 development and physiology of herring larvae were monitored for individuals reared in control and combined warming-acidification crossed with different food quality treatments. The experiment revealed that warming and acidification triggers a stress response at the molecular level and decrease herring larvae size-at-stage. Global change puts coastal systems under pressure, affecting the ecology and physiology of marine organisms. In particular, fish larvae are sensitive to environmental conditions, and their fitness is an important determinant of fish stock recruitment and fluctuations. To assess the combined effects of warming, acidification and change in food quality, herring larvae were reared in a control scenario (11 & DEG;C*pH 8.0) and a scenario predicted for 2100 (14 & DEG;C*pH 7.6) crossed with two feeding treatments (enriched in phosphorus and docosahexaenoic acid or not). The experiment lasted from hatching to the beginning of the post-flexion stage (i.e. all fins present) corresponding to 47 days post-hatch (dph) at 14 & DEG;C and 60 dph at 11 & DEG;C. Length and stage development were monitored throughout the experiment and the expression of genes involved in growth, metabolic pathways and stress responses were analysed for stage 3 larvae (flexion of the notochord). Although the growth rate was unaffected by acidification and temperature changes, the development was accelerated in the 2100 scenario, where larvae reached the last developmental stage at a smaller size (-8%). We observed no mortality related to treatments and no effect of food quality on the development of herring larvae. However, gene expression analyses revealed that heat shock transcripts expression was higher in the warmer and more acidic treatment. Our findings suggest that the predicted warming and acidification environment are stressful for herring larvae, inducing a decrease in size-at-stage at a precise period of ontogeny. This could either negatively affect survival and recruitment via the extension of the predation window or positively increase the survival by reducing the larval stage duration.

Description: Biological invasions are a global challenge that has received insufficient attention. Recently available cost syntheses have provided policy- and decision makers with reliable and up-to-date information on the economic impacts of biological invasions, aiming to motivate effective management. The resultant InvaCost database is now publicly and freely accessible and enables rapid extraction of monetary cost information. This has facilitated knowledge sharing, developed a more integrated and multidisciplinary network of researchers, and forged multidisciplinary collaborations among diverse organizations and stakeholders. Over 50 scientific publications so far have used the database and have provided detailed assessments of invasion costs across geographic, taxonomic, and spatiotemporal scales. These studies have provided important information that can guide future policy and legislative decisions on the management of biological invasions while simultaneously attracting public and media attention. We provide an overview of the improved availability, reliability, standardization, and defragmentation of monetary costs; discuss how this has enhanced invasion science as a discipline; and outline directions for future development.

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: Eutrophication in marine waters is traditionally assessed by checking if nutrients, algal biomass and oxygen are below/above a given threshold. However, increased biomass, nutrient concentrations and oxygen demand do not lead to undesirable environmental effects if the flow of carbon/energy from primary producers toward high trophic levels is consistently preserved. Consequently, traditional indicators might provide a misleading assessment of the eutrophication risk. To avoid this, we propose to evaluate eutrophication by using a new index based on plankton trophic fluxes instead of biogeochemical concentrations. A preliminary, model-based, assessment suggests that this approach might give a substantially different picture of the eutrophication status of our seas, with potential consequences on marine ecosystem management. Given the difficulties to measure trophic fluxes in the field, the use of numerical simulations is recommended although the uncertainty associated with biogeochemical models inevitably affects the reliability of the index. However, given the effort currently in place to develop refined numerical tools describing the marine environment (Ocean Digital Twins), a reliable, model-based, eutrophication index could be operational in the near future.

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.

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.