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: This deliverable reports on the achievements of the EuroSea project in developing targeted indicators co-designed with demonstrators (WPs 5–7) and forecasts (WP4). For this, the indicators implemented are expressed in term of Essential Ocean/Climate Variables (EOVs/ECVs) together with their requirements. The co-development undertaken address ocean indicators for all range of scales: from the large, basin scale to the regional and local scales. Such approach as well as the proposed solution to focus, at regional/local scales, on EEZs, represent one of the innovative results of EuroSea that will help to rationalize risks assessments and guide environmental management approaches in European Seas.

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: The European Ocean Observing and Forecasting System (EOOFS) plays a pivotal role in understanding, monitoring, forecasting, and managing the complex dynamics and resources of Europe's Seas. It serves as a critical interdisciplinary system for addressing a myriad of challenges, from climate change impacts to marine resources management. However, to ensure its continued effectiveness, it is essential to identify and address the gaps within this system and provide actionable recommendations for improvements at short- and long-term. Therefore, this document serves as a baseline that can guide the funders and supporters of the EOOFS, as well as the various stakeholders directly or indirectly related to the EOOFS, towards the gaps that hinder better monitoring and prediction of various ocean phenomena, along the ocean observing value chain. The main identified gaps are related to spatial and temporal coverage of data and products of the EOOFS, the data integration and accessibility by various types of users, the uncertainties of projections, the technological challenges, as well as to the engagement of various actors and the communication of results and services to them. The main recommendations to be taken into consideration for addressing all highlighted gaps are detailed in the report for every phenomenon and component of the ocean value chain. These recommendations are not provided just to satisfy the academic interest of the EOOFS community, however, they may have profound implications for multiple sectors and the society as a whole, if taken into consideration. This is due to the fact that the EOOFS is essential for climate change mitigation and adaptation measures, in improving the efficiency of the marine resources’ management, in enhancing the resilience of marine and coastal ecosystems as well as coastal cities and infrastructures against disasters and extreme events, for shipping and navigation safety, and for the scientific advancements and innovations of Europe in the field of marine science that serves the society. We propose a scoring approach that can evaluate the EOOFS readiness level (RL) in monitoring ocean phenomena, on a regular basis and in a systematic way. We have demonstrated the usefulness of this approach by implementing it based on our assessment and the feedback of the EOOFS community. The main results clearly show that the EOOFS has “Fitness for Purpose” readiness levels (RL 7) in the three main pillars of the value chain (Input, Process, and Output) only for one ocean phenomenon, while 83% of ocean phenomena have RLs varying from 1 (Idea) to 4 (Trial). A deeper analysis of the scoring results reflects that the EOOFS major gaps are predominantly concentrated in two of its three pillars: the coordination and observational elements (Process) and data management and information products (Output) (Figure 1). In a changing world that is affecting all aspects of European lives, it is crucial to significantly invest and support the EOOFS to better monitor and accurately predict the European Seas, and provide sustained services that can help businesses and improve the resilience of communities and resources.

Description: There is a strong economic interest in commercial deep‐sea mining of polymetallic nodules and therefore a need to define suitable preservation zones in the abyssal plain of the Clarion Clipperton Fracture Zone (CCZ). However, besides ship‐based multibeam data, only sparse continuous environmental information is available over large geographic scales. We test the potential of modelling meiofauna abundance and diversity on high taxonomic level on large geographic scale using a random forest approach. Ship‐based multibeam bathymetry and backscatter signal are the only sources for 11 predictor variables, as well as the modelled abundance of polymetallic nodules on the seafloor. Continuous meiofauna predictions have been combined with all available environmental variables and classified into classes representing abyssal habitats using k‐means clustering. Results show that ship‐based, multibeam‐derived predictors can be used to calculate predictive models for meiofauna distribution on a large geographic scale. Predicted distribution varies between the different meiofauna response variables. To evaluate predictions, random forest regressions were additionally computed with 1,000 replicates, integrating varying numbers of sampling positions and parallel samples per site. Higher numbers of parallel samples are especially useful to smoothen the influence of the remarkable variability of meiofauna distribution on a small scale. However, a high number of sampling positions is even more important, integrating a greater amount of natural variability of environmental conditions into the model. Synthesis and applications. Polymetallic nodule exploration contractors are required to define potential mining and preservation zones within their licence area. The biodiversity and the environment of preservation zones should be representative of the sites that will be impacted by mining. Our predicted distributions of meiofauna and the derived habitat maps are an essential first step to enable the identification of areas with similar ecological conditions. In this way, it is possible to define preservation zones not only based on expert opinion and environmental proxies but also integrating evidence from the distribution of benthic communities.

Description: The 4th Evolving and Sustaining Ocean Best Practices Workshop was held online during the period 17-30 September 2020, addressing community needs for advanced method development and implementation in ocean observations, data management and application. The proceedings for the subject workshop are provided in 2 volumes. Volume 1 addresses the meeting overview, and Volume 2 - Annexes includes the complete Working Group reports.

Description: Shifts in microbial communities and their functioning in response to environmental change result from contemporary interspecific and intraspecific diversity changes. Interspecific changes are driven by ecological shifts in species composition, while intraspecific changes are here assumed to be dominated by evolutionary shifts in genotype frequency. Quantifying the relative contributions of interspecific and intraspecific diversity shifts to community change thus addresses the essential, yet understudied question as to how important ecological and evolutionary contributions are to total community changes. This debate is to date practically constrained by (a) a lack of studies integrating across organizational levels and (b) a mismatch between data requirements of existing partitioning metrics and the feasibility to collect such data, especially in microscopic organisms like phytoplankton. We experimentally assessed the relative ecological and evolutionary contributions to total phytoplankton community changes using a new design and validated its functionality by comparisons to established partitioning metrics. We used a community of coexisting Emiliania huxleyi and Chaetoceros affinis with initially nine genotypes each. First, we exposed the community to elevated CO2 concentration for 80 days (~50 generations) to induce interspecific and intraspecific diversity changes and a total abundance change. Second, we independently manipulated the induced interspecific and intraspecific diversity changes in an assay to quantify the corresponding ecological and evolutionary contributions to the total change. Third, we applied existing partitioning metrics to our experimental data and compared the outcomes. Total phytoplankton abundance declined to one-fifth in the high CO2 exposed community compared to ambient conditions. Consistently across all applied partitioning metrics, the abundance decline could predominantly be explained by ecological shifts and to a low extent by evolutionary changes. We discuss potential consequences of the observed community changes on ecosystem functioning. Furthermore, we explain that the low evolutionary contributions likely resulted of intraspecific diversity changes that occurred irrespectively of CO2. We discuss how the assay could be upscaled to more realistic settings, including more species and drivers. Overall, the presented calculations of eco-evolutionary contributions to phytoplankton community changes constitute another important step towards understanding future phytoplankton shifts, and eco-evolutionary dynamics in general.