An overview of Calanus helgolandicus ecology in European waters

We review current knowledge and understanding of the biology and ecology of the calanoid copepod Calanus helgolandicus in European waters, as well as provide a collaborative synthesis of data from 18 laboratories and 26 sampling stations in areas distributed from the northern North Sea to the Aegean and Levantine Seas. This network of zooplankton time-series stations has enabled us to collect and synthesise seasonal and multi-annual data on abundance, body size, fecundity, hatching success and vertical distribution of C. helgolandicus. An aim was to enable comparison with its congener Calanus finmarchicus, which has been studied intensively as a key component of European and north east Atlantic marine ecosystems. C. finmarchicus is known to over-winter at depth, whereas the life-cycle of C. helgolandicus is less well understood. Overwintering populations of C. helgolandicus have been observed off the Atlantic coast between 400 and 800 m, while in the Mediterranean there is evidence of significant deep-water populations at depths as great as 4200 m. The biogeographical distribution of C. helgolandicus in European coastal waters covers a wide range of habitats, from open ocean to coastal environments, and its contribution to mesozooplankton biomass ranges from 6% to 93%. Highest abundances were recorded in the Adriatic and off the west coast of Spain. C. helgolandicus is generally found in 9-20 C water, with maximum abundances from 13-17 C. In contrast, C. finmarchicus is found in cooler water between 0 and 15 C, with peak abundances from 0 to 9 C. As water has warmed in the North Atlantic over recent decades, the range of C. helgolandicus and its abundance on the fringes of its expanding range have increased. This review will facilitate development of population models of C. helgolandicus. This will not only help answer remaining questions but will improve our ability to forecast future changes, in response to a warming climate, in the abundance and distribution of this important species.

Interregional Biological Responses in the North Atlantic to Hydrometeorological Forcing

Using data from the CPR survey seven case studies are described that document different spatial and temporal responses in the plankton to hydroclimatic events. Long-term trends in the plankton of the eastern Atlantic and the North Sea over the last five decades are examined. Two of the examples revisit correlations that have been described between copepod abundance in the eastern Atlantic and North Sea and indices of atmospheric variability, the North Atlantic Oscillation index and the Gulf Stream North Wall index. Evidence for an increase in levels of Phytoplankton Colour (a visual index of chlorophyll) on the eastern and western sides of the Atlantic is presented. Changes in three trophic levels and in the hydrodynamics and chemistry of the North Sea circa 1988 are outlined as a regime shift. Two of the case studies emphasise the importance of variability in oceanic advection into shelf seas and the role of western and eastern margin currents at the shelf edge. The plankton appear to be integrating hydrometeorological signals and reflecting basin scale changes in circulation of surface, intermediate and deep waters in part associated with the NAO. The extent to which climatic variability may be contributing to the observed changes in the plankton is discussed with a forecast of potential future ecosystem effects in a climate change scenario.

Simple procedures to assess and compare the ecological niche of species

Hutchinson's (1957; Cold Spring Harbour Symp Quant Biol 22:415-427) niche concept is being used increasingly in the context of global change, and is currently applied to many ecological issues including climate change, exotic species invasion and management of endangered species. For both the marine and terrestrial realms, there is a growing need to assess the breadth of the niches of individual species and to make comparisons among them to forecast the species' capabilities to adapt to global change. In this paper, we describe simple non-parametric multivariate procedures derived from a method originally used in climatology to (1) evaluate the breadth of the ecological niche of a species and (2) examine whether the niches are significantly separated. We first applied the statistical procedures to a simple fictive example of 3 species separated by 2 environmental factors in order to describe the technique. We then used it to quantify and compare the ecological niche of 2 key-structural marine zooplankton copepod species, Calanus finmarchicus and C. helgolandicus, in the northern part of the North Atlantic Ocean using 3 environmental factors. The test demonstrates that the niches of both species are significantly separated and that the coldwater species has a niche larger than that of its warmer-water congeneric species.

Macroecological study of Centropages typicus in the North Atlantic Ocean

Centropages typicus is a temperate neritic-coastal species of the North Atlantic Oceans, generally found between the latitudes of the Mediterranean and the Norwegian Sea. Therefore, the species experiences a large number of environments and adjusts its life cycle in response to changes in key abiotic parameters such as temperature. Using data from the Continuous Plankton Recorder (CPR) Survey, we review the macroecology of C. typicus and factors that influence its spatial distribution, phenology and year-to-year to decadal variability. The ecological preferences are identified and quantified. Mechanisms that allow the species to occur in such different environments are discussed and hypotheses are proposed as to how the species adapts to its environment. We show that temperature and both quantity and quality of phytoplankton are important factors explaining the space and time variability of C. typicus. These results show that C. typicus will not respond only to temperature increase in the region but also to changes in phytoplankton abundance, structure and composition and timing of occurrence. Methods such as a decision tree can help to forecast expected changes in the distribution of this species with hydro-climatic forcing. (C) 2007 Elsevier Ltd. All rights reserved.

Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling

In this paper we evaluate whether the assimilation of remotely-sensed optical data into a marine ecosystem model improves the simulation of biogeochemistry in a shelf sea. A localized Ensemble Kalman filter was used to assimilate weekly diffuse light attenuation coefficient data, Kd(443) from SeaWiFs, into an ecosystem model of the western English Channel. The spatial distributions of (unassimilated) surface chlorophyll from satellite, and a multivariate time series of eighteen biogeochemical and optical variables measured in situ at one long-term monitoring site were used to evaluate the system performance for the year 2006. Assimilation reduced the root mean square error and improved the correlation with the assimilated Kd(443) observations, for both the analysis and, to a lesser extent, the forecast estimates, when compared to the reference model simulation. Improvements in the simulation of (unassimilated) ocean colour chlorophyll were less evident, and in some parts of the Channel the simulation of this data deteriorated. The estimation errors for the (unassimilated) in situ data were reduced for most variables with some exceptions, e.g. dissolved nitrogen. Importantly, the assimilation adjusted the balance of ecosystem processes by shifting the simulated food web towards the microbial loop, thus improving the estimation of some properties, e.g. total particulate carbon. Assimilation of Kd(443) outperformed a comparative chlorophyll assimilation experiment, in both the estimation of ocean colour data and in the simulation of independent in situ data. These results are related to relatively low error in Kd(443) data, and because it is a bulk optical property of marine ecosystems. Assimilation of remotely-sensed optical properties is a promising approach to improve the simulation of biogeochemical and optical variables that are relevant for ecosystem functioning and climate change studies.

Operational monitoring and forecasting of bathing water quality through exploiting satellite Earth observation and models: The AlgaRisk demonstration service

Coastal zones and shelf-seas are important for tourism, commercial fishing and aquaculture. As a result the importance of good water quality within these regions to support life is recognised worldwide and a number of international directives for monitoring them now exist. This paper describes the AlgaRisk water quality monitoring demonstration service that was developed and operated for the UK Environment Agency in response to the microbiological monitoring needs within the revised European Union Bathing Waters Directive. The AlgaRisk approach used satellite Earth observation to provide a near-real time monitoring of microbiological water quality and a series of nested operational models (atmospheric and hydrodynamic-ecosystem) provided a forecast capability. For the period of the demonstration service (2008–2013) all monitoring and forecast datasets were processed in near-real time on a daily basis and disseminated through a dedicated web portal, with extracted data automatically emailed to agency staff. Near-real time data processing was achieved using a series of supercomputers and an Open Grid approach. The novel web portal and java-based viewer enabled users to visualise and interrogate current and historical data. The system description, the algorithms employed and example results focussing on a case study of an incidence of the harmful algal bloom Karenia mikimotoi are presented. Recommendations and the potential exploitation of web services for future water quality monitoring services are discussed.

Arctic Oceanography - Oceanography: Atmosphere-Ocean Exchange, Biogeochemistry & Physics

The Arctic Ocean is, on average, the shallowest of Earth’s oceans. Its vast continental shelf areas, which account for approximately half of the Arctic Ocean’s total area, are heavily influenced by the surrounding land masses through river run-off and coastal erosion. As a main area of deep water formation, the Arctic is one of the main «engines» of global ocean circulation, due to large freshwater inputs, it is also strongly stratified. The Arctic Ocean’s complex oceanographic configuration is tightly linked to the atmosphere, the land, and the cryosphere. The physical dynamics not only drive important climate and global circulation patterns, but also control biogeochemical cycles and ecosystem dynamics. Current changes in Arctic sea-ice thickness and distribution, air and water temperatures, and water column stability are resulting in measurable shifts in the properties and functioning of the ocean and its ecosystems. The Arctic Ocean is forecast to shift to a seasonally ice-free ocean resulting in changes to physical, chemical, and biological processes. These include the exchange of gases across the atmosphere-ocean interface, the wind-driven ciruclation and mixing regimes, light and nutrient availability for primary production, food web dynamics, and export of material to the deep ocean. In anticipation of these changes, extending our knowledge of the present Arctic oceanography and these complex changes has never been more urgent.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.