Fine-scale nutrient and carbonate system dynamics around cold-water coral reefs in the northeast Atlantic.

Ocean acidification has been suggested as a serious threat to the future existence of cold-water corals (CWC). However, there are few fine-scale temporal and spatial datasets of carbonate and nutrients conditions available for these reefs, which can provide a baseline definition of extant conditions. Here we provide observational data from four different sites in the northeast Atlantic that are known habitats for CWC. These habitats differ by depth and by the nature of the coral habitat. At depths where CWC are known to occur across these sites the dissolved inorganic carbon ranged from 2088 to 2186 μmol kg−1, alkalinity ranged from 2299 to 2346 μmol kg−1, and aragonite Ω ranged from 1.35 to 2.44. At two sites fine-scale hydrodynamics caused increased variability in the carbonate and nutrient conditions over daily time-scales. The observed high level of variability must be taken into account when assessing CWC sensitivities to future environmental change.

Seasonal dynamics of the carbonate system in the Western English Channel

We present over 900 carbonate system observations collected over four years (2007–2010) in the Western English Channel (WEC). We determined CO2 partial pressure (pCO2), Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC) along a series of 40 km transects, including two oceanographic stations (L4 and E1) within a sustained coastal observatory. Our data follow a seasonal pattern of CO2 undersaturation from January to August, followed by supersaturation in September–October and a return to near-equilibrium thereafter. This pattern is explained by the interplay of thermal and biological sinks in winter and spring–summer, respectively, followed by the breakdown of stratification and mixing with deeper, high-CO2 water in autumn. The drawdown of DIC and inorganic N between March and June with a C:N ratio of 8.7–9.5 was consistent with carbon over-consumption during phytoplankton growth. Monthly mean surface pCO2 was strongly correlated with depth integrated chlorophyll a highlighting the importance of subsurface chlorophyll a maxima in controlling C-fluxes in shelf seas. Mixing of seawater with riverine freshwater in near-shore samples caused a reduction in TA and the saturation state of calcite minerals, particularly in winter. Our data show that the L4 and E1 oceanographic stations were small, net sinks for atmospheric CO2 over an annual cycle (−0.52±0.66 mol C m−2 y−1 and −0.62±0.49 mol C m−2 y−1, respectively).

Towards an integrated forecasting system for pelagic fisheries

First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2–6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

The carbonate system of the NW European shelf: sensitivity and model validation

The ocean plays an important role in regulating the climate, acting as a sink for carbon dioxide, perturbing the carbonate system and resulting in a slow decrease of seawater pH. Understanding the dynamics of the carbonate system in shelf sea regions is necessary to evaluate the impact of Ocean Acidification (OA) in these societally important ecosystems. Complex hydrodynamic and ecosystem coupled models provide a method of capturing the significant heterogeneity of these areas. However rigorous validation is essential to properly assess the reliability of such models. The coupled model POLCOMS–ERSEM has been implemented in the North Western European shelf with a new parameterization for alkalinity explicitly accounting for riverine inputs and the influence of biological processes. The model has been validated in a like with like comparison with North Sea data from the CANOBA dataset. The model shows good to reasonable agreement for the principal variables, physical (temperature and salinity), biogeochemical (nutrients) and carbonate system (dissolved inorganic carbon and total alkalinity), but simulation of the derived variables, pH and pCO2, are not yet fully satisfactory. This high uncertainty is attributed mostly to riverine forcing and primary production. This study suggests that the model is a useful tool to provide information on Ocean Acidification scenarios, but uncertainty on pH and pCO2 needs to be reduced, particularly when impacts of OA on ecosystem functions are included in the model systems.

Impact of zooplankton food selectivity on plankton dynamics and nutrient cycling

Within models, zooplankton grazing is typically defined as being dependent on total prey concentration, with feeding selectivity expressed only as a function of prey size. This behavior ignores taxonomic preferences shown by the preda- tors and the capacity of some zooplankton to actively select or reject individual prey items from mixtures. We carried out two model experiments comparing impacts of zooplankton displaying passive and active selection, which resulted in contrasting dynamics for the pelagic system. Passive selection by the grazer resulted in a top down control on the prey with a fast turn-over of nutrients. Active selection, on the other hand led to a bottom-up control, with slower nutrient turnover constraining primary production by changing the system toward export of particulate matter. Our results suggest that selective feeding behavior is an important trait, and should be considered alongside size and taxonomy when studying the role of zooplankton impact in the ecosystem.

Probabilistic Models to Describe the Dynamics of Migrating Microbial Communities

In all but the most sterile environments bacteria will reside in fluid being transported through conduits and some of these will attach and grow as biofilms on the conduit walls. The concentration and diversity of bacteria in the fluid at the point of delivery will be a mix of those when it entered the conduit and those that have become entrained into the flow due to seeding from biofilms. Examples include fluids through conduits such as drinking water pipe networks, endotracheal tubes, catheters and ventilation systems. Here we present two probabilistic models to describe changes in the composition of bulk fluid microbial communities as they are transported through a conduit whilst exposed to biofilm communities. The first (discrete) model simulates absolute numbers of individual cells, whereas the other (continuous) model simulates the relative abundance of taxa in the bulk fluid. The discrete model is founded on a birth-death process whereby the community changes one individual at a time and the numbers of cells in the system can vary. The continuous model is a stochastic differential equation derived from the discrete model and can also accommodate changes in the carrying capacity of the bulk fluid. These models provide a novel Lagrangian framework to investigate and predict the dynamics of migrating microbial communities. In this paper we compare the two models, discuss their merits, possible applications and present simulation results in the context of drinking water distribution systems. Our results provide novel insight into the effects of stochastic dynamics on the composition of non-stationary microbial communities that are exposed to biofilms and provides a new avenue for modelling microbial dynamics in systems where fluids are being transported.

ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels

The ERSEM model is one of the most established ecosystem models for the lower trophic levels of the marine food-web in the scientific literature. Since its original development in the early nineties it has evolved significantly from a coastal ecosystem model for the North-Sea to a generic tool for ecosystem simulations from shelf seas to the global ocean. The current model release contains all essential elements for the pelagic and benthic part of the marine ecosystem, including the microbial food-web, the carbonate system and calcification. Its distribution is accompanied by a testing framework enabling the analysis of individual parts of the model. Here we provide a detailed mathematical description of all ERSEM components along with case-studies of mesocosm type simulations, water column implementations and a brief example of a full-scale application for the North-West European shelf. Validation against in situ data demonstrates the capability of the model to represent the marine ecosystem in contrasting environments.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels

The European Regional Seas Ecosystem Model (ERSEM) is one of the most established ecosystem models for the lower trophic levels of the marine food web in the scientific literature. Since its original development in the early nineties it has evolved significantly from a coastal ecosystem model for the North Sea to a generic tool for ecosystem simulations from shelf seas to the global ocean. The current model release contains all essential elements for the pelagic and benthic parts of the marine ecosystem, including the microbial food web, the carbonate system, and calcification. Its distribution is accompanied by a testing framework enabling the analysis of individual parts of the model. Here we provide a detailed mathematical description of all ERSEM components along with case studies of mesocosm-type simulations, water column implementations, and a brief example of a full-scale application for the north-western European shelf. Validation against in situ data demonstrates the capability of the model to represent the marine ecosystem in contrasting environments.