Pacific oyster (Crassostrea gigas) growth modelling and indicators for offshore aquaculture in Europe under climate change uncertainty

Aquaculture development in Europe, while critical to the European Union (EU) Blue Growth strategy, has stagnated over the past decades due largely to high competition for space in the nearshore coastal zone among potential uses and the lack of clear priorities, policy, and planning at EU and national scales. Broad Marine Spatial Planning, including the designation of Allocated Zones for Aquaculture, requires spatial data at the corresponding broad spatial scale, which has not been readily available, as well as model projections to assess potential impacts of climate change. Here, daily chlorophyll-a, water temperature, salinity, and current speed outputs from a marine ecosystem model encompassing the coastal North East Atlantic, the North Sea, and the Mediterranean Sea (the pan-European POLCOMS-ERSEM model configuration) are used to drive a Dynamic Energy Budget growth model of Pacific oyster (Crassostrea gigas). Areas broadly suitable for growth were identified using threshold tolerance range masking applied using the model variables mentioned above, as well as bathymetry data. Oyster growth time series were transformed into simplified indicators that are meaningful to the industry (e.g., time to market weight) and mapped. In addition to early-century indicator maps, modelling and mapping were also carried out for two contrasting late-century climate change projections, following representative concentration pathways 4.5 and 8.5. Areas found to have good oyster growth potential now and into the future were further assessed in terms of their climate robustness (i.e., where oyster growth predictions are comparable between different future climate scenarios). Several areas within Europe were highlighted as priority areas for the development of offshore Pacific oyster cultivation, including coastal waters along the French Atlantic, the southern North Sea, and western Scotland and Ireland. A large potential growth hot spot was also identified along northwestern Africa, associated with a cool, productive upwelling coastal zone. The framework proposed here offers a flexible approach to include a large range of ecological input data, climate and ecosystem model scenarios, aquaculture-related models, species of interest, indicator types, and tolerance thresholds. Such information is suggested to be included in more extensive spatial assessments and planning, along with further socioeconomic and environmental data

Investigating ecosystem connections in the shelf sea environment using complex networks

We use complex network theory to better represent and understand the ecosystem connectivity in a shelf sea environment. The baseline data used for the analysis are obtained from a state-of-the-art coupled marine physics–biogeochemistry model simulating the North West European Shelf (NWES). The complex network built on model outputs is used to identify the functional groups of variables behind the biogeochemistry dynamics, suggesting how to simplify our understanding of the complex web of interactions within the shelf sea ecosystem. We demonstrate that complex networks can also be used to understand spatial ecosystem connectivity, identifying both the (geographically varying) connectivity length-scales and the clusters of spatial locations that are connected. We show that the biogeochemical length-scales vary significantly between variables and are not directly transferable. We also find that the spatial pattern of length-scales is similar across each variable, as long as a specific scaling factor for each variable is taken into account. The clusters indicate geographical regions within which there is a large exchange of information within the ecosystem, while information exchange across the boundaries between these regions is limited. The results of this study describe how information is expected to propagate through the shelf sea ecosystem, and how it can be used in multiple future applications such as stochastic noise modelling, data assimilation, or machine learning

An objective framework to test the quality of candidate indicators of good environmental status

Large efforts are on-going within the EU to prepare the Marine Strategy Framework Directive's (MSFD) assessment of the environmental status of the European seas. This assessment will only be as good as the indicators chosen to monitor the 11 descriptors of good environmental status (GEnS). An objective and transparent framework to determine whether chosen indicators actually support the aims of this policy is, however, not yet in place. Such frameworks are needed to ensure that the limited resources available to this assessment optimize the likelihood of achieving GEnS within collaborating states. Here, we developed a hypothesis-based protocol to evaluate whether candidate indicators meet quality criteria explicit to the MSFD, which the assessment community aspires to. Eight quality criteria are distilled from existing initiatives, and a testing and scoring protocol for each of them is presented. We exemplify its application in three worked examples, covering indicators for three GEnS descriptors (1, 5, and 6), various habitat components (seaweeds, seagrasses, benthic macrofauna, and plankton), and assessment regions (Danish, Lithuanian, and UK waters). We argue that this framework provides a necessary, transparent and standardized structure to support the comparison of candidate indicators, and the decision-making process leading to indicator selection. Its application could help identify potential limitations in currently available candidate metrics and, in such cases, help focus the development of more adequate indicators. Use of such standardized approaches will facilitate the sharing of knowledge gained across the MSFD parties despite context-specificity across assessment regions, and support the evidence-based management of European seas

Modelling mixotrophic functional diversity and implications for ecosystem function

Mixotrophy is widespread among protist plankton displaying diverse functional forms within a wide range of sizes. However, little is known about the niches of different mixotrophs and how they affect nutrient cycling and trophodynamics in marine ecosystems. Here we built a plankton food web model incorporating mixotrophic functional diversity. A distinction was made between mixotrophs with the innate capacity for photosynthesis (constitutive mixotrophs, CMs) and those which acquire phototrophy from their prey (non-constitutive mixotrophs, NCMs). We present the simulations of ecosystems limited by different light and nutrient regimes. Our simulations show that strict autotrophic and heterotrophic competitors increased in relative importance in the transition from nutrient to light limitation, consistent with observed oceanic biomass ratios. Among CMs, cells <20 μm dominate in nutrient-poor conditions while larger cells dominate in light-limited environments. The specificity of the prey from which NCMs acquire their phototrophic potential affects their success, with forms able to exploit diverse prey dominating under nutrient limitation. Overall, mixotrophy decreases the regeneration of inorganics and boosts the trophic transfer efficiency of carbon. Our results show that mixotrophic functional diversity has the potential to radically change our understanding of the ecosystem functioning in the lower trophic levels of food webs

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-color data. There is a growing demand from the ecosystem modeling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeler these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modelers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size [pico- (20 μm)]. The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterize the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.info:eu-repo/semantics/publishedVersio

Occurence and Bioactivities of Funicone-Related Compounds

Studies on production of secondary metabolites by fungi have received a substantial boost lately, particularly with reference to applications of their biological properties in human medicine. Funicones represent a series of related compounds for which there is accumulating evidence supporting their possible use as pharmaceuticals. This paper provides a review on the current status of knowledge on these fungal extrolites, with special reference to aspects concerning their molecular structures and biological activities

The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas

We use modelling and assimilation tools to explore the impact of biogeochemistry on physics in the shelf sea environment, using North-West European Shelf (NWES) as a case study. We demonstrate that such impact is significant: the attenuation of light by biogeochemical substances heats up the upper 20 m of the ocean by up to 1 °C and by a similar margin cools down the ocean within the 20–200 m range of depths. We demonstrate that these changes to sea temperature influence mixing in the upper ocean and feed back into marine biology by influencing the timing of the phytoplankton bloom, as suggested by the critical turbulence hypothesis. We compare different light schemes representing the impact of biogeochemistry on physics, and show that the physics is sensitive to both the spectral resolution of radiances and the represented optically active constituents. We introduce a new development into the research version of the operational model for the NWES, in which we calculate the heat fluxes based on the spectrally resolved attenuation by the simulated biogeochemical tracers, establishing a two-way coupling between biogeochemistry and physics. We demonstrate that in the late spring-summer the two-way coupled model increases heating in the upper oceanic layer compared to the existing model and improves by 1–3 days the timing of the simulated phytoplankton bloom. This improvement is relatively small compared with the existing model bias in bloom timing, but is sufficient to have a visible impact on model skill in the free run. We also validate the skill of the two-way coupling in the context of the weakly coupled physical-biogeochemical assimilation currently used for operational forecasting of the NWES. We show that the change to the skill is negligible for analyses, but it remains to be seen how much it differs for the forecasts

A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts

This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems

Primary-productivity in Upwelling Systems (PRIMUS)

Conferencia sobre los Sistemas de Afloramiento de Borde Oriental (EBUS): Pasado, Presente y Futuro & Segunda Conferencia Internacional sobre el Sistema de Corrientes de Humboldt, 19-23 de Septiembre de 2022, Lima, PerúThe ESA-supported Primary-productivity in Upwelling Systems (PRIMUS) project aims to provide the best possible characterisation of net primary productivity (NPP) and its relationship to upwelling in Atlantic Eastern Boundary Upwelling Systems (EBUS), including the Iberian/Canary and Benguela systems. It will create a 25-year time series of 1-km satellite-derived NPP over the Atlantic, and, experimentally, at higher-resolution (300m) using the unique capabilities of the MERIS and OLCI satellite sensors. PRIMUS will use these data to advance analyses of Atlantic EBUS including temporal and spatial variability in NPP and its statistical relationship to upwelling and climate indices (such as the North Atlantic Oscillation). PRIMUS will also conduct eight further science cases in specific science áreas / regional settings: aquaculture in Galicia; fisheries and eutrophication in the Portuguese upwelling region; potential EBUS impacts on ocean carbón pools; Lagrangian estimates of NPP; and air-sea interaction and acidification impacts. Science cases will make use of EO and in situ data, as well as numerical model outputs (freely available through the EU’s Copernicus and elsewhere) to investigate the 4D character of EBUS, for example linking Lagrangian NPP with sediment traps samples at depth. PRIMUS will also conduct demonstrations that transfer science into solutions for society, working together with scientific, agency, policy and commercial “early-adopters”, building on three science case studies (EBUS and aquaculture; fisheries; and eutrophication monitoring). Furthermore, evaluating transition of data production to operational initiatives such as Copernicus and GMES and Africa and the potential for data exploitation by the European and international ecosystem modelling community. This communication will present initial results from the 25-year NPP time series and high resolution NPP computations as well as selected science casesN