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Modelling the impact of hydrography and lower trophic production on fish recruitment

- State of the art in ecosystem modelling - Challenges for ecosystem model development

The development of marine ecosystem models requires sound knowledge about physical, chemical and biological processes, the coupling between those and the linkages between the various ecosystem components. Major challenges are the different spatial and temporal scales of processes and related measurements. As an example, processes influencing the state of a population operate on the scale of millimetres to basin-wide distances, from seconds to decades. Therefore, the development of quantitative statements, either of individual vital rates or on the population level, needs special attention. No single modelling approach can incorporate all relevant aspects and thus compromises and simplifications must be found to realistically simulate ecosystem dynamics. Atmospheric forcing is usually used as external forcing and many variables like circulation, topography or boundaries are only approximately resolved. Due to their complexity and non-linearity biological features are most difficult to incorporate into ecosystem models. Mass-balance models are a commonly used ecosystem modelling approach, focussing on the material flow between trophic levels. Species are combined into functional groups and models mostly comprise Nutrient-Phytoplankton-Zooplankton-Detritus components (NPZD-models, e.g. Fasham, 1993). More advanced approaches of intermediate complexity split nutrients, phytoplankton and zooplankton into several subgroups (e.g. Allen et al., 2001; Schrum et al., 2006a). Resolving macronutrient cycles and different phytoplankton groups can take the influence of regional nutrient limitations on primary productivity into account. Dividing zooplankton components into small and large organisms increases the complexity by adding predation and grazing processes to the food web (Fig. 1). NPZD modules can be coupled to 3D-hydrodynamic models that provide information on physical drivers. Furthermore, ecosystem models can be interlinked with other numerical modelling approaches, e.g. with lagrangian transport models, individual based models (IBMs) and size-or age-structured population models (SPMs). The latter two approaches represent the trade-off between taking detailed biological information into account by allowing for the fact, that processes and vital rates in relation to environmental conditions are described for few species only. This means that only dynamics of key species or of species groups that show similar functional and behavioural traits can be resolved. However, the models offer the flexibility to include ontogenetic details and exact information on rates, processes and behavioural patterns. Additionally higher trophic levels beyond the planktonic realm depend to a lesser extend on hydrodynamics as active behaviour and other factors increase in importance. So far they are mainly included in hybrid modelling approaches, integrating higher trophic level IBMs to coupled NPZD-hydrodynamic models. The full life-cycle of certain fish species has rarely been integrated (e.g. NEMURO.FISH, Megrey et al., 2007) and still awaits further development.

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DetritusLarge Phytoplankton(e.g diatoms)

Small Zooplankton

Small Phytoplankton(e.g. dinoflagellates)

Nutrient cycles: Nitrogen

(NO3, NO2, NH4), Phosphorous,

Silica

Large Zooplankton

grazing

grazing

predation

consumption

mortality

remineralization

excretion

primaryproduction

consumption

mortality DetritusLarge Phytoplankton(e.g diatoms)

Small Zooplankton

Small Phytoplankton(e.g. dinoflagellates)

Nutrient cycles: Nitrogen

(NO3, NO2, NH4), Phosphorous,

Silica

Large Zooplankton

grazing

grazing

predation

consumption

mortality

remineralization

excretion

primaryproduction

consumption

mortality

Fig. 1: State-of-the-art conceptual food web model (NPZD) with simplifications concerning number of state variables (e.g. neglecting oxygen and single nutrient components) and pathways of nutrients (after Schrum et al., 2006a - ECOSMO) In the following the current status of ecosystem modelling is described with special reference to North Sea and Baltic Sea initiatives. We focus on NPZD model frameworks within the two systems and provide information about key zooplankton taxa and the parameterisation of population dynamics and vital rates. Finally, we describe how model outputs may be validated with reference to available long-term biological datasets from the two systems. NPZD modelling approaches

To date NPZD-models (Nutrients-Phytoplankton-Zooplankton-Detritus) have been coupled to hydrodynamic models of the global ocean as well as of regional systems including coastal domains. In the latter case the lower trophic pelagic and benthic components need to be coupled and the nutrient fluxes from rivers and other terrigenous sources must be included. The North Sea is one of the best investigated shelf areas with a number of different modelling efforts performed in the past. Moll and Radach (2003) reviewed seven 3D-ecosystem models from that area comparing their complexity in the sense of spatial and temporal resolution, the number and kind of state variables and the processes acting between them. Building up on this exercise we prepared an overview table of 3D-NPZD-models applied in various oceanic areas and systems with an emphasis to our study areas (Tab. 1, incl. ERSEM box model). The number of state variables illustrate the varying complexity of models, and range from two (ECOHAM) up to more than 50 (ERSEM-type models) variables. Many of the less-complex models use relatively simple biological representations (nutrient, phytoplankton, detritus model structure) with zooplankton and the microbial loop only implicitly included. Furthermore, the number of nutrients limiting phytoplankton growth varies, partly considering only Phosphorous and neglecting Nitrogen and Silica (ECOHAM). Due to the fast development of new 3D-ecosystem models or extensions of existing ones, the list of NPZD models (Tab. 1) cannot be comprehensive. For additional information while not necessarily going into the original model descriptions, we refer to the "Model Shopping Tool MoST" provided by the Network of Excellence EUR-OCEANS at www.eur-oceans.eu/models. The database offers fully descriptive data on current ocean ecosystem models (0D-3D), including details on simulated processes and number and descriptions of functional groups or key variables computed. For selected

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ocean ecosystem models, the tool goes even further and shows another level of detail. Users can ‘walk through’ a model’s individual components and appreciate how it is parameterised and where the strengths and weakness are. Within the ModRec project one modelling approach will be based on COHERENS (Luyten et al. 1999), covering the southern North Sea with a horizontal resolution of 7km. It consists of several submodels, like a physical model for simulating the general circulation of the shelf sea, a biological model (microplankton-detritus MPD model), a sediment model, and a contaminant transport model. The biological module is based on Tett (1990) and Tett and Walne (1995) and contains eight state variables: microplankton carbon and nitrogen, detrital carbon and nitrogen, nitrate, ammonium, oxygen, and zooplankton nitrogen. Within the project, the biological model is under development with several new submodels that can be un- or decoupled to the model system depending on the needs and aim of the study (Maar et al. in prep., Timmermann et al. in prep). The new submodels include i) a ‘microplankton-diatom-Si’ model with six state variables (microplankton C, N, P, Si, detritus Si and SiO2), ii) a ‘sediment’ model with seven state variables (organic C, N, P, Si, NH4, PO4 and SiO2), iii) a ‘phosphorus’ model with three state variables (microplankton P, detritus P, PO4) and iiii) a ‘copepod’ model with three state variables (copepod C, N and P (Si is not ingested)). Within the Baltic Sea a biogeochemical ecosystem model has previously been coupled to a circulation model based on MOM2.2 (Modular Ocean Model, Pacanowski et al. 1990) (Neumann, 2000). Within ModRec it is planned to couple the ecosystem model to the circulation model BSHcmod, a two-way nested 3D ocean model originally developed at the Bundesamt für Seeschiffahrt und Hydrographie, Hamburg, Germany and further developed and optimised at the Danish Meteorological Institute. The ocean model covers both the Baltic and North Sea. The resolution is 6 nautical miles in the main area and 1 nautical mile in the nested domain covering the Kattegat and the Danish Straits. The biogeochemical model consists of nine pelagic state variables and a single benthic detritus component. The model is nitrogen-based, but the elements which cycles are modelled include nitrogen, phosporus and oxygen. Three different groups of phytoplankton are modelled, diatoms, flagellates and blue-green algae or cyanobacteria along with a single group of zooplankton (Fig. 2).

Fig. 2: Conceptual sketch of the chemical biological model. Circles are for state variables and rectangles for processes, respectively. In detail, state variables are: ammonium (A), nitrate (N), phosphate (P), flagellates (FL), diatoms (DI), blue-green algae (BA), detritus (D), zooplankton (Z), oxygen (O2)and sediment (SE) (after Neumann, 2000).

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Tab. 1: 3D-ecosystem models (NPZD-type) and their characteristics (state variables, spatial and temporal resolution etc) with special reference to North Sea and Baltic Sea modelling studies.

Area Model name No. of NPZD state variables

Important characteristic / processes (Model focus)

Resolution A: Spatially B: Temporally

Reference to 3D-ecosystem model

Global PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies)

24 e.g. NO3, NH4, PO4, SiO2, Fe; SP, LP, SZ, LZ, DOM, SD, LD

4 plankton functional groups, nutrient co-limitation of phytoplankton growth as a function of N, P, Si and Fe., used in context of climate change

A: variable (e.g. 0,5 º) B: 1h

Aumont et al., 2003

Mediter-ranean Sea

Ecosystem model based on OGCM (Ocean general circulation model)

N, PP, D (ZP implicitly considered)

NPD-model; considers N-cycle only

A: ¼ º (31 vertical levels) B: 40min

Crise et al., 1998

Indian Ocean Bio-physical OGCM (Ocean general circulation model)

9 NO3, NH4, Fe; SP, LP; SZ, LZ; small D, large D

Iron- and nitrogen-limited phytoplankton growth

A: ½º Longitude, 1/3º Latitude, Surface layer and 19 σ-layers

Leonard et al. 1999; Christian et al., 2002; Wiggert et al., 2006

North Pacific

NEMURO (North Pacific Ecosystem Model for Understanding Regional Oceanography)

11 NO3, NH4; SP, LP; SZ, LZ, PZ; PON, DON; Opal, Si(OH)4

Biomass-based model family! (3D spatially explicit) simulating the the nutrient-phytoplankton-zoo-plankton food web but with various extensions (e.g. hybrid models, NEMURO-FISH)

A: 1º Latitude and Longitude; 54 vertical layers (5-10m in extension) B: 6h

Werner et al., 2007 Aita et al., 2007

NORWECOM (NORWECOM II): Norwegian Ecological Model System

8 NO3, NH4, PO4, SiO2, O2, D, SP, LP + benthic submodel

Primary production, nutrient budgets, dispersion of particles

A: 20km B: 15min

Skogen, 1993; Skogen and Soiland, 1998; Soiland & Skogen, 2000

GHER: Geo-Hydrodynamics and Environment Research Model

9 NO3, NH4, SP, LP, heterotr. Flagellates, ZP, B, D, DOM + benthic submodel

Operates on macroscale spectral window, describes N- and C-cycles

A: 1/6º Box B: 69sec

Delhez & Martin, 1994; Delhez, 1998

North Sea

ECOHAM: Ecological North Sea Model

2 DIP, PP (zooplankton prescribed by observations) + coupling of benthos with phosphorous cycle

Simulates phosphorous related phytoplankton processes

A: 20km (5m vertical resolution) B: 15min

Moll, 1995; Moll, 1997; Moll, 1999

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Area Model name No. of NPZD state variables

Important characteristic / processes (Model focus)

Resolution A: Spatially B: Temporally

Reference to 3D-ecosystem model

ERSEM (ERSEM II): European Regional Seas Ecosystem Model

ERSEM II: 43 pelagic and 22 benthic state variables incl. NO3, NH4, PO4, SiO2, O2, four functional phytopl. groups, three functional zoopl. Groups, bacteria, POM, DOM; + benthic submodel

Very complex box model (grid cell of 1º) functional group approach: Biogeochemical cycling of C, N, P Si through both the pelagic and benthic ecosystem and the coupling between them

A: 1º Box (10 surface, 5 deep boxes (85 surface, 45 deep boxes)) B: 24h

Articles in: Baretta, Ebenhöh & Ruardij, 1995; Baretta-Bekker & Baretta, 1997

COHERENS: Coupled Hydrodynamical Ecological model for Regional Northwest-European Shelf Seas

MPD model: 8 state variables NO3, NH4, O2, microplankton, detritus and SPM. New submodels for micro-plankton-2, sediment, PO4, SiO2 and copepods are under development.

Mesopelagic remin., organic production

A: 7km B: 10min Luyten et al., 1999

POL3dERSEM: Proudman Oceanographic Laboratory 3d ERSEM Model

35 pelagic and 18 benthic state variables incl. NO3, NH4, PO4, SiO2, O2, three functional phytopl. groups, three functional zoopl. Groups, three microbial loop groups; + benthic submodel

Higher resolution than ERSEM through coupling with fine-scale POL-3DB baroclinic hydrodynamic model: incl. benthic-pelagic-coupling; dynamic zooplankton; nitrogen, phosphorous and silicate cycling

A: 12km B: 18min Allen et al., 2001

North Sea/ Baltic Sea ECOSMO (ECOsystem MOdel)

12 NO3, NO2, NH4, PO4, SiO2, Opal, D; O2; SP, LP, SZ, LZ

T-dependence only for nitrogen oxidation/ reduction but not for PP or ZP growth

A: 10km (5-8m in 0-88m water depth, coarser resolution in lower layers) B: 20min

Schrum et al., 2006 a, b

Baltic Sea 3D-biogeochemical ecosystem model of the Baltic Sea (based on MOM2.2 circulation model)

10 NO3, NH4 PO4; SP, LP, Cyanobacteria; Z, D; O2; Sediment

Model based on nitrogen cycling (including riverborne N), phosphate linked to nitrogen via Redfield ratio

A: 3nm; 2m for first 12 vertical layers, but increasing with depth

Neumann, 2000;

PP: Phytoplankton (autotroph); SP: Small Phytoplankton (mainly Dinoflagellates); LP: Large Phytoplankton (mainly diatoms); ZP: Zooplankton (heterotroph); SZ: Small Zooplankton; LZ: Large Zooplankton; PZ: Predatory Zooplankton; PON: Particulate organic nitrogen; DON: Dissolved organic nitrogen; DIN: Dissolved inorganic nitrogen; DIP: Dissolved inorganic phosphate; DOM: Dissolved organic matter; Opal: Particulate organic silicate; D: Detritus

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Key species modelling approaches

The coupling of complex ecosystem models in general circulation models is a current trend in ecology (e.g Baretta-Bekker et al., 1997; Gregg et al., 2003; Le Quéré et al., 2005; Moore et al., 2004). Adding complexity beyond simple NPZD models has the difficulty of (i) poorly understood ecology, (ii) lack of experimental or observational data, (iii) aggregating diversity within functional groups into meaningful state variables and constants, (iv) sensitivity of output to the parameterisations into question and their physical and chemical environment (Anderson, 2005). Although all of these aspects are of equal importance, the immediate question when developing ecosystem models is how to make reliable predictions when aggregating the large amount of phytoplankton and zooplankton species into one or few plankton functional types (PFTs). Biogeochemical cycling and seasonal succession is clearly linked to particular plankton groups and sometimes even to individual plankton genera or species. However, building up model complexity does not necessarily improve predictions unless parameterisation is sufficiently robust and accurate. Up to now diatoms appear to be reasonably well simulated in many models (e.g. Baretta-Bekker et al., 1997, Lewis et al., 2006). They have high growth rates and can outcompete other phytoplankton when silica is not a limiting factor, thus making parameterisation relatively straightforward. Other PFTs, belonging to bacteria, phytoplankton, and zooplankton groups, are however more difficult to be parameterised. The prevailing concept to divide e.g. zooplankton groups by size (e.g. micro-/ proto-, meso-, macrozooplankton) seems to be reasonable at first sight, but in reality each of these groups is highly diverse, comprising different trophic levels and life strategies. The key to successfully modelling system behaviour in the Baltic Sea and North Sea can be found in 1) a reasonable aggregation of species into PFTs of similar life history traits and with similar (inter-) relationships with abiotic factors and other PFTs (e.g. trophic interactions), and 2) integration of models of different types, simulating key species/genera only (individual based models – IBMs, structured population models – SPMs), that are capable to represent the link to higher trophic levels. For both approaches it is necessary to get an overview about the key components of the ecosystem and the key processes and vital rates. Here, we will concentrate on zooplankton taxa, representing the direct link to higher trophic levels and thus to fish population dynamics. Commonly, NPZD models are designed to describe and quantify biogeochemical cycling of elements and lower trophic level dynamics, and generally consider the higher trophic levels as an imposed mortality term. In contrast, adult fish bioenergetic models with completely closed life cycles (Rose et al., 1999), fish larvae early life history models (Beyer and Laurence, 1980), and fish individual-based models (Letcher et al., 1996) exist, but generally pay little attention to food-web connections to lower trophic levels. To overcome this shortcoming in the future we firstly define which are the key zooplankton taxa of the two systems and secondly summarise what needs to be considered to successfully parameterise biological processes. Here we are using the link to an already existing meta-database, referencing vital rates obtained from field and laboratory studies world-wide. For key species modelling we could add short descriptions of SPMs and/or IBMs, especially if these approaches will be used in ModRec (e.g. giving examples of the Pseudocalanus SPM model)

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Zooplankton key species in the North Sea and Baltic Sea The definition of key zooplankton taxa can be made on the basis of overall abundance/ biomass (averaged over the whole seasonal cycle), seasonal importance, and their trophic interactions, i.e. their importance as predator and prey. Generally, copepoda are the most widely studied zooplankton group also with outstanding importance as food source for larval and planktivorous fish (e.g. Last, 1980; Möllmann et al., 2004; Nielsen and Munk, 1998). Accordingly, stage structured population models have been developed for copepods and specifically Pseudocalanus spp. (Fennel, 2001; Moll and Stegert, 2007; Neumann and Fennel, 2007; Neumann and Kremp, 2005), but to our knowledge no attempts have been made to simulate the dynamics of other non-copepod taxa. These can be regionally and seasonally of similar or even higher importance (e.g. Simonsen et al., 2006), taking e.g. coastal areas of the Baltic Sea with strong freshwater influences into account (Mehner and Thiel, 1999). Furthermore, invasive species like the recently introduced comb jellyfish Mnemiopsis leydii (Haslob et al., 2007) might gain in importance in structuring the ecosystem in the future. The species list below (Tab. 2) gives a rough overview about important and generally abundant species/ taxa in the North Sea and Baltic Sea (e.g. Colebrook et al., 1972; Fransz et al., 1992; Fransz and Gonzalez, 2001; Krause et al., 1995; Möllmann et al., 2000, 2002; The Continuous Plankton Recorder Survey team, 2004). Furthermore, the importance of these taxa as food source for sprat (Baltic) and sandeel larvae (North Sea) is indicated and relevant references are listed herein. It has to be noted that small sandeel larvae prey additionally on phytoplankton, which is not included in the list. Tab. 2: Key (meso-) zooplankton species in the North Sea and Baltic Sea and their importance as food source for planktivorous fish and their early life stages with special reference to sandeel (NS) and sprat (BS). Copepod life stages are not resolved. Symbol description: * seldom, ** regular, *** regionally/ seasonally dominant food source

Zooplankton group North Sea Baltic Sea

Calanoidea (Copepoda) Pseudocalanus elongatus (***) Pseudocalanus acuspes (**)

Acartia spp. (*) Acartia spp. (**) Temora longicornis (**) Temora longicornis (***)

Calanus spp. (C. helgolandicus, C. finmarchicus) (**)

Cyclopoidea (Copepoda) Oithona spp. Oithona similis Cladocera Evadne sp. Evadne sp. (**) Podon sp. Podon sp. (**) Bosmina sp. (**)

Appendicularia Oikopleura diocia (***), Fritillaria spp. Fritillaria spp.

Macrozooplankton "Krill" (Meganyctiphanes norvegica, Thysanoessa spp.)

Sagitta spp. Mysis spp. Mysis spp. (*) Meroplankton Polychaeta larvae Decapoda larvae

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Zooplankton group North Sea Baltic Sea

Mollusc larvae (Bivalvia, Gastropoda) (***)

Rotatoria Synchaeta sp. Scyphozoa Aurelia aurita Aurelia aurita Ctenophora Pleurobrachia pieleus Pleurobrachia pieleus Mnemiopsis leidyi Mnemiopsis leidyi

Sandeel (North Sea): Last, 1980; Nielsen and Munk, 1998; Ryland, 1964; Simonsen, 2006; Wyatt, 1974 Sprat (Baltic Sea): Bernreuther, 2007; Dickmann et al., 2007; Möllmann and Köster, 1999; Möllmann et al., 2004; Möllmann et al., 2005, Voss et al., 2003 Parameterisation of key processes in marine ecosystem models One major challenge in ecosystem modelling is the parameterisation of biological processes, either on the species or the functional group level. So far, estimates of physiological traits in ecosystem models are often based on single reports or observations. When trait values are obtained from laboratory studies they are usually species specific and not necessarily representative of a functional type. Contrastingly, field observations are relatively scarce and are highly dependent on environmental conditions and the present species assemblages, thus not always representing average responses adequately. Generally, there is still a need to improve our mechanistic understanding of environmental factors that exert control over species or functional groups. All attempts to understand and model the dynamics of ecosystems will remain inconclusive in case of imprecise parameterisations and inadequate spatial and temporal scales for the target organism (De Young et al., 2004). The amount of data on biological rates was limited in the past but in the last 20 years information has been collected regarding growth, mortality or remineralisation processes of different plankton groups (Le Quéré et al., 2005). Most extensive data collections were made for marine copepods in terms of global rates for growth, production and mortality (Hirst and Kiørboe, 2002; Hirst and Bunker, 2005; Huntley and Lopez, 1992). However, organisms at higher trophic levels have complex life histories complicating their coupling to lower trophic levels and the physical environment. As an example relatively little is known about the functional response of copepod reproductive success, i.e. egg production and hatching, to sufficiently wide ranges of temperatures and salinity (Holste et al., 2006), the latter factor being especially important in brackish and estuarine systems like the Baltic Sea. Furthermore, for the parameterisation of regional models, the local or seasonal (mass) occurrence of certain zooplankton taxa needs to be considered as well as the possibility of intra-specific adaptation processes. Here, parameter estimates from the same geographical areas rather than global rates are more appropriate. In many cases physiological traits will need refinements, especially when modelling PFTs. The gathering of available information is a challenge of its own and even more observations will become available in the future. Within the NoE EUR-OCEANS a meta-database has been set up collecting data on key species vital rates from all marine systems and functional groups. The database will be fully available in May 2008 and its information and extensions can be used to improve model parameterisation and PFT traits in the future. Currently it has more than 17600 entries, of which 9400 are for mesozooplankton taxa. An overview about the content of the database and references used can be found at http://www.eur-oceans.eu/integration/wp3.1/. The North Sea and Baltic Sea have been the focus of marine research over decades, so a comparable large amount of field or experimental data is available here. Major experimental efforts for the

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purpose of model parameterisation were carried out recently within the German GLOBEC project (Trophic Interactions between Zooplankton and Fish under the Influence of Physical Processes, 2002-2006) including measurements of in-situ and laboratory egg production, growth and mortality rates of the dominant copepod genera Acartia, Temora and Pseudocalanus. Publication of results is ongoing (e.g. Holste et al., 2006; Peck and Holste, 2006) but basic information about the project and contact information can be found at http://www.globec-germany.de/. Approaches to validating ecosystem models

Validation is a key issue for ecosystem modelling efforts (Arhonditis and Brett, 2004) and needs to go beyond the comparison of bulk properties to the verification of mechanisms and variables and PFTs of interest. So far, examples where model results are explicitly compared to data are still scarce, and many earlier coupled 3D-hydrodynamic-ecosystem models of the North Sea were validated with climatologically monthly mean data only, representing e.g. the annual cycle of primary production (Radach and Moll, 2006). This has to be partly attributed to the frequent lack of observational datasets with adequate spatial and temporal resolution (Kirchner et al., 1996) but also to the fact that model compartments may be unobservable in the environment: in contrast to hydrographic data, nutrients and chlorophyll (e.g. SeaWiFS), biological variables such as plankton species and groups do not always correspond to PFTs, and are measured with less precision and on course temporal and spatial scales. The visual inspection of observed versus predicted data either in space and/ or time is often the first (subjective) step to evaluate model performance, and the calculation of the correlation coefficient between both datasets (usually regionally and seasonally averaged and thus being semi-quantitative) remains the only statistical measure. Previous model validation exercises in the North Sea area (e.g. Lacroix et al., 2007, Moll, 2000; Radach and Moll, 2006) have focused on the use of the OSPAR recommended cost function (OSPAR, 1998). Cost functions give a non-dimensional value which is indicative of the "goodness of fit" between observed and predicted data. They are a measure of ratio of the model data misfit to a measure of the variance in the data. Allen et al (2007a) applied a combination of error statistics and correlations in order to explore relationships between model outputs and observations. They used eight different metrics and evaluated how to successfully benchmark model performance. For this they made a direct model-data comparison, thus testing precision only. This has implications when model and observational data show only small differences in timing, because this can lead to large errors in precision. The results of Allen et al. (2007a) implied that using the OSPAR cost function as only quantitative validation tool is flawed, because other metrics indicate a worse fit. They thus recommended a hierarchy of tests to validate model performance by using (i) the Receiver operator characteristics (ROC, Brown and Davis, 2006), (ii) simplified Taylor plots plotting the ratio between standard deviations of data to model against the square of the correlation coefficient between model and data, (iii) and the combination of model efficiency (Nash and Sutcliffe, 1970) and percentage model bias (the sum of model error normalised by the data). Furthermore, the temporal analysis of error propagation identifies poorly described processes, and the spatio-temporal analysis of variability in errors allows the diagnosis of model errors and defines critical regions and processes (Allen et al., 2007b). In addition to model precision, i.e. comparing model output and data directly in space and time, the reproduction of the inter- and intraannual cycles need to be evaluated. The seasonal dynamics of primary and secondary production are usually validated by either taking the whole model domain or small subareas or selected sites into account (Lewis et al.,2006; Schrum et al., 2006a). Model output

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and observational data are standardised and the magnitude and timing of the behaviour of the biological variables are visually inspected. Smoothing data with running means further highlights existing patterns, and the agreement between observed and modelled dynamics can be assessed using absolute error terms or correlation coefficients. Long-term datasets resolving the annual cycle of phyto- and zooplankton are scarce and are difficult to be compared to ecosystem models for various reasons. The largest multi-decadal plankton monitoring program is the Continuous Plankton Recorder survey (CPR). General approaches as well as difficulties encountered when validating ecosystem models with CPR-data (Lewis et al., 2006; Alekseeva et al., in prep.) can serve as examples for other validation exercises, and we will therefore describe the advantages and drawbacks of the time series in the following. The CPR data and their use for validation The CPR survey started in 1931 and the device and analytical procedure (see e.g. Batten et al., 2003; Richardson et al., 2006) remained relatively unchanged since then. The CPR is towed behind ships of opportunity along standard sampling routes (Fig. 3), visited once a month. Ship speed lies between 15-20 knots and the gear is sampling in a depth of approximately 7m. Plankton is filtered through a square aperture of 1.61cm² onto a constantly moving band of silk with a mesh size of 270µm. Samples represent 10 nautical miles of tow and approximately 3m3 of filtered water. Alternate samples are analysed in the laboratory for plankton abundance and taxonomy. Organismic counts are performed in numerical categories, resulting in semi-quantitative estimates of abundance. Furthermore, a proxy for phytoplankton biomass is generated by the visual inspection of the ‘greenness’ of the silk. This phytoplankton colour index (PCI) is classified into 4 levels with values of 0 (no colour), 1 (very pale green), 2 (pale green), and 6.5 (green). Despite this rough graduation, there is a strong agreement between PCI and chlorophyll content measured fluorometrically from CPR samples, as well as with chlorophyll measured by satellites (Hays and Lindley, 1994, Batten et al., 2003). Each CPR sample is assigned to geographical position (equal to the midpoint of the tow) and local time. In order to calculate the approximate number per m³, phytoplankton as well as zooplankton counts need to be divided by three. However, the CPR most likely underestimates absolute numbers and the degree of underestimation varies between species due to size, shape and behaviour (Batten et al. 2003; Clark et al., 2001; John et al., 2001; Richardson et al., 2004).

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Fig. 3: Map of major routes towed by the CPR. Some routes have been shifted in position but their designation has been retained (from: Richardson et al., 2006). Major problems encountered when using the data for model validation are that (i) samples are representative of upper water layers only (approx. 0-10m); (ii) samples are not collected on a regular grid and not in constant time intervals; (iii) samples are taken during the full diurnal cycle; (iv) subsampling in the analytical process causes a high degree of uncertainty; (v) underestimation of plankton organisms varies between species; and (vi) abundance estimates are semiquantitative and biomass values are missing. As a consequence of the latter two aspects, taxonomic subsets of the CPR data need to be defined that are relevant to the state variables in the model output and that are able to capture the relative patterns in ecosystem dynamics. Taxa need to be selected according to their biological importance but also to their overall representation in the samples (general abundance, catchability, taxonomic resolution). Phytoplankton species can be easily divided into (dino-) flagellates and diatoms, comparing e.g. the onset of the spring bloom to model results (Lewis et al., 2006). PCI values can serve as rough estimation of chlorophyll a content, being generally comparable to satellite measurements (Batten et al., 2003). In contrast to this, zooplankton species can be hardly assigned to one PFT because size, habitat, feeding strategy and trophic level may change during their ontogenetic development. Due to their outstanding ecological importance and their relatively good representation in CPR samples (Batten et al., 2003), (calanoid) copepods were frequently used as plankton indicators to demonstrate seasonal and long-term dynamics of secondary production (e.g., Beaugrand, 2004, 2005; Lewis et al., 2006). For this group, and especially the CV-CVI stages, it is feasible to assume that a consistent fraction of their in-situ abundance is sampled and that their numbers reflect the overall spatial and temporal patterns in the zooplankton community. In spite of the actual non-linear dependencies between organism size and biomass /carbon content, approximations are available to transform copepodite abundances into biomass values. The following standard length-weight relationship for copepods/ zooplankton (Peters, 1983) has been previously applied in the survey (see Richardson et al., 2006):

1.2*08.0 LW = with L = total length in mm of adult females and W = total mass in mg wet weight

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The average wet weight for each species is then multiplied by its abundance (N/m³) and summed up to obtain total (copepod) biomass per m³. Finally, the wet weight can be converted to carbon weight per m³ according to Cushing et al. (1958): 1 mg plankton biomass approximately equals 0.12 mg carbon. This conversion factor was based on 330 µm mesh samples and is consistent between different measures of zooplankton concentrations (see Postel et al., 2000). However, the CPR underestimates overall abundances and the degree of underestimation is difficult to be quantified (Batten et al., 2003; Hays, 1994). Due to the semi-quantitative nature of the data, it is thus more appropriate to normalise them, either using original abundance estimates or derived biomass estimates as a basis. Model and observational data have been compared in different ways. The most straightforward method is to correlate point data from observational datasets to the respective model output, usually the estimate of a grid cell in a specific time window. Due to the above mentioned problems and the relatively imprecise abundance estimates in just one sample, data variability is commonly subsumed by averaging over spatial and/or temporal scales. Spatial interpolation may be additionally necessary as observational data are usually not sufficiently resolved to validate spatial characteristics of the model. However, a threshold value for the number of samples within a grid cell or an area needs to be defined in order to get reliable estimates and reduce the background noise in the data. Seasonal dynamics may be then evaluated either for specific years or by averaging results from a longer time period while taking subareas or the whole model domain into account. Due to the non-regular temporal and spatial sampling scheme in the CPR survey, signals of interest like the onset of the spring bloom may be indiscernible in specific years. Other observational data with a higher temporal and/or spatial resolution are then needed. In the last section we summarise biological data series from the North Sea and Baltic Sea that are suitable for validating various components and aspects of ecosystem models. North Sea and Baltic Sea time-series and survey data available for ecosystem model validation For model validation observational data of different temporal, spatial and taxonomic resolution are needed, depending on the model output. In Tab. 3 we summarise long-term and intensive sampling programs from the North Sea and Baltic Sea, focussing on information about secondary producers. The longest available time-series in the North Sea and North Atlantic is the Continuous Plankton Recorder Survey (CPR) with comparable data available since 1958. In the North Sea some time-series from fixed sampling locations exist (L4, Stonehaven, Z-Dove, Helgoland Roads) with usually a very high temporal and taxonomic resolution from three-times a week up to monthly samples. Within 1986 and 1987 a zooplankton sampling campaign took place (ZISCH) with a high spatial resolution, measuring among other parameters total biomass. This dataset is thus especially suitable to evaluate model performance in terms of the magnitude of total zooplankton biomass. Some additional monitoring studies in the North Sea (North Sea Project) and Baltic Sea (HELCOM MOnAS, BSM, LATFRI) took place, some of them are still ongoing. Due to intensive field sampling in former EU- and national projects additional datasets are available but are not specifically listed here. We only like to point to field sampling during the German GLOBEC project, which was very extensive in space and time and thus seems to be especially suitable to be used for model validation in coastal North Sea areas and the Central Baltic Sea.

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Progress in modelling larval sprat (Sprattus sprattus) Research regarding recruitment of commercially important fish species, such as sprat, has recently received growing attention. One active project is the ‘GLOBEC Germany’-project, which has focused on egg and larval stages of sprat both in the Baltic Sea (e.g. Baumann et al. 2006b; Hinrichsen et al. 2005; Voss et al. 2006) and the North Sea (e.g. Daewel et al. 2008a; Peck and Daewel 2007). Focus has been on basic processes in fish population dynamics, with the aim to uncover controlling physical and biological processes determining recruitment success within the early life stages of sprat. For the Baltic Sea the general drift pattern on spatial scale was evaluated using hydrodynamic drift modelling, hereby identifying potential nursery grounds for sprat originating from different spawning grounds. Due to average, westerly winds over the Baltic Sea, the highest modelled abundances of juvenile sprat were found along the southern and eastern coast lines of the Baltic (Hinrichsen et al. 2005). Also, recruits from Bornholm Basin and Arkona Basin were found to have a higher retention within the basins than recruits from Gotland Basin (Hinrichsen et al. 2005). Years of strong larval displacement towards southern and eastern coasts corresponded to relative recruitment failure, while years of retention within the deep basins were associated with relative recruitment success (e.g., Baumann et al. 2004). Furthermore, strong correlations between long-term surface temperatures, modelled drift patterns and sprat recruitment variability advocate that new year classes of Baltic Sea sprat are mainly composed of individuals born late in the season (Baumann et al. 2006b; Voss et al. 2006). Studies of growth patterns reveal that survivors are found within the fastest growing individuals of the recruits (Baumann et al. 2006c) and also that temperature histories were responsible for large-scale spatial growth variability between young of the year Baltic sprat (Baumann et al. 2006a). Sprat recruitment studies in the North Sea have focused on development and parameterization of individual based models (IBMs) (Daewel et al. 2008b; Peck and Daewel 2007), which in combination with NPZD model will allow for exploring spatial and temporal variability in the interaction between marine organisms and environment (Daewel et al. 2008b; Daewel et al. 2008a; Peck and Daewel 2007). Coupling of the models revealed that the most important factor affecting larval sprat survival was prey availability, which in the setup predicted highest potential larval survival in the vertically mixed areas of the southern North Sea. Further seasonal variability in growth rates was most affected by temperature, while spatial growth rates was estimated to be highest near shore and decrease with increasing distance from the coast following the temperature gradient. Dawel et al. (2008a) concludes that the setup predicts realistic growth rates for North Sea sprat, and is able to reproduce prey selectivity and critical periods. The ‘GLOBEC Germany’-project had the objective to investigate trophic interactions under the influence of physical processes, where the approaches for the Baltic Sean and the North Sea have been different as described previously. Main focus in both areas was on the larval and juvenile stages of sprat, with the aim of determining the ‘window of survival’, i.e. the time interval at which spawning results in the highest recruitment. Both modelling approaches has assumed a uniform horizontally distribution of spawned eggs, further the North Sea case also assumes uniform vertical distribution. The distribution patterns used in both approaches is naturally a simplification and needs attention before the ability to make an end-to-end recruitment model of sprat in the two areas.

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One therefore needs to address the questions of where and when eggs are spawned, as well as the female’s fecundity and how the fecundity is linked to the environment.

References Baumann, H., T. Gröhsler, G. Kornilovs, A. Makarchouk, V. Feldmann, and A. Temming. 2006a.

Temperature-induced regional and temporal growth differences in Baltic young-of-the-year sprat Sprattus sprattus. Marine ecology progress series 317:225-236.

Baumann, H., H. H. Hinrichsen, F. W. Köster, and A. Temming. 2004. A new retention index for the central Baltic Sea: long-term hydrodynamic modelling used to study recruitment variability in central Baltic sprat, Sprattus sprattus. ICES C.M.2004 L:02.

Baumann, H., H. H. Hinrichsen, C. Möllmann, F. W. Köster, A. M. Malzahn, and A. Temming. 2006b. Recruitment variability in Baltic Sea sprat (Sprattus sprattus) is tightly coupled to temperature and transport patterns affecting the larval and early juvenile stages. Canadian journal of aquatic and fisheries science 63:2191-2201.

Baumann, H., H. H. Hinrichsen, R. Voss, D. Stepputtis, W. Grygiel, L. W. Clausen, and A. Temming. 2006c. Linking growth to environmental histories in central Baltic young-of-the-year sprat, Sprattus sprattus: an approach based on otolith microstructure analysis and hydrodynamic modelling. Fisheries oceanography 15:465-476.

Daewel, U., M. A. Peck, W. Kühn, M. A. St.John, I. Alekseeva, and C. Schrum. Coupling ecosystem and individual-based models to simulate the influence of climate variability on potential growth and survival of larval sprat in the North Sea. N/A . 2008a. Ref Type: In Press

Daewel, U., M. A. Peck, C. Schrum, and M. A. St.John. 2008b. How best to include the effects of climate-driven forcing on prey fields in larval fish individual-based models. Journal of plankton research 30:1-5.

Hinrichsen, H. H., G. Kraus, R. Voss, D. Stepputtis, and H. Baumann. 2005. The general distribution pattern and mixing probability of Baltic sprat juvenile populations. Journal of marine systems 58:52-66.

Peck, M. A. and U. Daewel. 2007. Physiologically based limits to food consumption, and individual-based modeling of foraging and growth of larval fishes. Marine ecology progress series 347:171-183.

Voss, R., C. Clemmesen, H. Baumann, and H. H. Hinrichsen. 2006. Baltic sprat larvae: coupling food availability, larval condition and survival. Marine ecology progress series 308:243-254.

15

Conclusions

The development of marine ecosystem models integrating physical, biogeochemical processes up to life cycles of secondary producers and fish cannot make progress by simply increasing complexity. Even though computing power steadily increases parameter richness and biological relevance need to be balanced. Other solutions must be found that should have the greatest functional complexity at the level of the target organism (deYoung et al., 2004). In our current NPZD modelling approaches secondary producers represent the weakest component and to make important steps forward we need to improve our understanding of their responses to climate variability and the interactions with higher trophic levels. Within ModRec we should thus follow two different pathways: First, zooplankton species need to be aggregated to PFTs. Here we need to find reasonable parameterisations for their vital rates and interactions between organisms and trophic levels, if necessary separately for various subareas. Second, structured population models should be coupled to ecosystem models to make reliable predictions of regional single species/ group dynamics, which in turn can be used as prey fields for higher trophic levels. This approach as well as the estimation of mere bulk zooplankton biomass contains some caveats (Daewel et al., 2008) but can be regionally the method of choice when prey composition is dominated by one or few species. Nevertheless, neglected species e.g. in the estuarine coastal parts of the eastern Baltic Sea as well as the occurrence of invasive species (e.g. Mnemiopsis leydii, Cercopagis pengoi) need to be considered. Both can have significant influences on local food web structures and invasive species may even cause long-term changes in the ecosystems of the North Sea and Baltic Sea. This means that their occurrence may hamper the applicability of the model for long-term predictions. At higher trophic levels hybrid modelling approaches seem to be most appropriate integrating structured-population or individual-based models to 3D-coupled NPZD models produce meaningful simulations of population dynamics Is this planned within the project and how? Christina Finally, our model outputs need to be critically evaluated against observational data and the spatial and temporal variability of primary and secondary production. This needs to go beyond the fact that the model makes accurate predictions but also if it does so because of the right reasons, i.e. poorly described processes and target variables need to be identified. For this large, self-consistent datasets are needed and we should propose new monitoring strategies that capture important temporal and spatial scales of variability.

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Tab. 3: Examples of biological data series suitable for validating ecosystem models

Dataset Type of data Area Spatial resolution

Temporal resolution

Contact/ Access

Web-link/ Reference

Continuous Plankton Recorder Survey (CPR)

Phytoplankton Colour Index (PCI), Phytoplankton/ Zooplankton species identification and counts

NA, NS

Standard routes, alternate samples, each representing 10nm

~monthly, 1948 – present, for phyto-plankton consistent since 1958

Sir Alister Hardy Foundation for Ocean Science (SAHFOS), accessible through data licensing agreement

http://www.sahfos.ac.uk; Richardson et al., 2006

L4

Hydrography, nutrients, Chla, zooplankton identification and counts, Calanus reproduction

Western English Channel

Coastal station: 50°15'N, 004°13'W

Weekly to monthly, 1988-present

Plymouth Marine Laboratory (PML), accessible through agreement

http://www.westernchannelobservatory.org.uk/l4/; Southward et al., 2005

Stonehaven

Hydrography, nutrients, Chla, phytoplankton/zooplankton identification and counts

NS, Scotland

Coastal station: 56º57.80'N, 002º06.20'W

Weekly, 1997-present

Fisheries Research Services, Marine Laboratory Aberdeen (FRS)

http://www.marlab.ac.uk/Montoring/Stonehaven/Stoneframe.html

Z-DOVE

Hydrography, zooplankton identification and counts

NS, Newcastle

6km offshore: 55º07'N, 01º 20'W

Monthly: 1969-present

Dove Marine Laboratory, University of Newcastle, UK

http://www.ncl.ac.uk/marine/about/facilities/dove/; Clark et al., 2003

Helgoland Roads

Hydrography, Chla, nutrients, phytoplankton/zooplankton identification and counts, fish larvae identification

NS, German Bight

Coastal station: 54°11´18”N, 007°54´E

Three times per week: 1975-present

Alfred Wegener Institute of Polar Research (AWI)

Greve et al., 2004

Circulation and pollutant fluxes in the NS (ZISCH)

Zooplankton identification and abundance, total biomass

NS 127 stations in 1986, 120 in 1987

02.-05.-13.06.1986; 26.01.-09.03. 1987

University of Hamburg Krause et al., 1995

North Sea Project

Hydrography, nutrients, primary/ bacterial production, Chla, Phytoplankton/ zooplankton abundance

NS

121 stations in southern North Sea – higher resolution in coastal areas

Monthly: 1988-1990

British Oceano-graphic data centre (BODC), free access to data CD-ROM

http://www.bodc.ac.uk/projects/uk/north_sea/;

17

Dataset Type of data Area Spatial resolution

Temporal resolution

Contact/ Access

Web-link/ Reference

LIFECO

Hydrography, Chla, (primary production), zooplankton identification, abundance, size, production

NS

variable (10-20nm in north-eastern NS)

4 cruises in 2001

DTU Aqua (Peter Munk)

http://www.lifeco.dk

German GLOBEC

Hydrography, Chla (with gaps), zooplankton identification and abundance, production

BS/ NS

BS: 45-120 (BB, GB, DD); NS: ca. 50

3-14 cruises per year: BS: 2002-2006; NS: 2003-2005

GLOBEC GERMANY

http://www.globec-germany.de/

HELCOM MONAS - COMBINE

Hydrography, nutrients, Chla, phytoplankton/ zooplankton abundances

BS

Multi-national program: resolution variable, highest for hydrographic variables

Variable: 1979-present

HELCOM http//www.helcom.fi; HELCOM 1987, 1990, 1996, 2002

Baltic Sea monitoring program (BSM), part of HELCOM-COMBINE

Hydrography, nutrients, Chla, phytoplankton/ zooplankton abundance

BS

24 stations (Mecklenburg Bay – Gulf of Finland)

At least 4-times a year: 1973-present

Institute for Baltic Sea Research (IOW)

http//www.helcom.fi; http//www.io-warnemuende.de; Wasmund and Uhlig, 2003

LATFRI Mesozooplankton database

Hydrography, zooplankton identification and abundance

BS – Baltic Proper

Variable, up to 24 stations

4 times a year: 1959-present

Latvian Fish Resources Agency (LATFRA)

Möllmann et al., 2000, 2002

Historical Ichthyoplankton data

Ichthyo-plankton identification and abundance

BS

Multinational sampling, combined from literature and survey data

Variable, 1902-present with gaps

Atlantic Research Institute of Marine Fisheries and Oceano-graphy (AtlantNIRO), Leibniz Institute of Marine Sciences (IFM-GEOMAR)

Karasiova and Voss, 2004

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References Aita, M.N., Yamanaka, Y., and Kishi, M.J. 2007. Interdecadal variation of the lower trophic ecosystem in the

northern Pacific between 1948 and 2002, in a 3-D implementation of the NEMURO model. Ecol. Model. 202: 81-94.

Alekseeva, I., Schrum, C., Diekmann, R., and St. John, M.A. (in prep.) Creation of synergy effects for understanding long-term ecosystem variability: ECOSMO model results vs. Continuous Plankton Recorder data.

Allen, J.I., Blackford, J., Holt, J.T., Proctor, R., Ashworth, M., and Siddorn, J. 2001. A highly spatially resolved ecosystem model for the North West European Continental Shelf. Sarsia 86: 423-440.

Allen, J.I., Holt, J.T., Blackford, J., and Proctor, R. 2007a. Error quantification of a high-resolution coupled hydrodynamic ecosystem coasta-ocean model: Part. 2. Chlorophyll-a, nutrients and SPM. J. Mar. Sys. 68: 381-404.

Allen, J.I., Somerfield, P.J., and Gilbert, F.J. 2007b. Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models. J. Mar. Sys. 64: 3-14.

Anderson, T.R. 2005. Plankton functional type modelling: Running before we can walk? J. Plankton Res. 27: 1073-1081.

Arhonditis, G.B., and Brett, M.T. 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modelling. Mar. Ecol. Prog. Ser 271: 12-26.

Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P. 2003. An ecosystem model of the global ocean including Fe, Si, P colimitations. Global Biogeochem. Cycles, 17(2): Art. 1060.

Baretta, J.W., Ebenöh, W., and Ruardij, P. 1995. The European Regional Seas Ecosystem Model (ERSEM), a complex marine ecosystem model. Neth. J. Sea Res. 33: 233-246.

Baretta-Bekker, J.G., and Baretta, J.W. 1997. European Regional Seas Ecosystem Model (ERSEM) II. J. Sea Res. 38: 169-438.

Batten, S.D., Clark, R.A., Flinkman, J., Hays, G.C., John, E.H., John, A.W.G., Jonas, T., Lindley, A.J., Stevens, D.P., and Walne, A. (2003). CPR sampling: The technical background, materials and methods, consistency and comparability, Progress in Oceanography 58: 193-215.

Beaugrand, G. 2004. The North Sea regime shift: evidence, causes, mechanisms and consequences. Prog. Ocean. 60: 245-262.

Beaugrand, G. 2005. Monitoring pelagic ecosystems using plankton indicators. ICES J. Mar. Sci. 62: 333-338.

Bernreuther, M. 2007. Investigations on the feeding ecology of Baltic Sea herring (Clupea harengus L.) and sprat (Sprattus sprattus L.). Dissertation thesis, University of Hamburg, 176pp.

Beyer, J.E., and Laurence, G.C. 1980. A stochastic model of larval fish growth. Ecol. Model. 8: 109–132. Boltovskoy, D. (1999). South Atlantic Zooplankton (vol. 1). London: Backhuys, 1706 pp. Brown, C., and Davis, H.T. 2006. Receiver operating characteristics curves and related decision measures: A

tutorial. Chemometr. Intell. Lab. Syst. 80: 24-38. Christian, J.R., Verschell, M.A., Murtugudde, R.G., Busalacchi, A.J., McClain, C.R. 2002. Biogeochemical

modelling of the tropical Pacific Ocean II: Seasonal and internannual variability. Deep-Sea Res. II 49: 509-543.

Clark, R.A., Frid, D.J.L., and Batten, S.D. 2001. A critical comparison of two long-term zooplankton time series from the central-west North Sea. Journal of Plankton Research 23: 27-39.

Clark,R.A., Frid, C.L.J., Nicholas, K.R. 2003. Long-term, predation-based control of a central-west North Sea zooplankton community. ICES J. Mar. Sci. 60: 187-197.

Colebrook, J.M. 1972. Changes in the distribution and abundance of zooplankton in the North Sea 1948-1969. Symp. Zool. Soc. Lond. 29: 203-212.

19

Crise, A., Crispi, G., and Mauri, E. 1998. Seasonal three-dimensional study of the nitrogen cycle in the Mediterranean Sea Part I Model implementation and numerical results. Journal of Marine Systems 18: 287–312.

Cushing, D.H., Humphrey, G.F., Banse, K., Laevastu, T. (1958). Report of the Committee on Terms and Equivalents. Rapp. P.V. Reun. Cons. Int. Explor. Mer 144: 15-16.

De Young, B., Heath, M., Werner, F., Chai, F., Megrey, B., and Monfray, P. 2004. Challenges of modeling ocean basin ecosystems. Science 304: 1463-1466.

Delhez, E.J.M. 1998. Macroscale ecohydrodynamic modelling on the northwest European Continental Shelf. J. Mar Sys. 16: 171-190.

Delhez, E.J.M., and Martin, G. 1994. 3D modelling of hydrodynamic and ecohydrodynamic processes on the north-western European continental shelf. Bulletin de la Sociéte Royale des Sciences de Liège 63: 5-64.

Dickmann, M., Möllmann, C., and Voss, R. 2007. Feeding ecology of Central Baltic sprat Sprattus sprattus larvae in relation to zooplankton dynamics: implications for survival. Mar. Ecol. Prog. Ser. 342: 277-289.

Fasham, M.J.R. (1993). Modelling the marine biota. In: Heimann, M. (Ed.), The Global Ocean Cycle. Springer-Verlag, Berlin, pp. 457-504.

Fennel, W. 2001. Modeling copepods with links to circulation models. J. Plankton Res. 23: 1217-1232. Fransz, H.G., and Gonzalez, S.R. 2001. Seasonal and meridional trends in zooplankton diversity of the

Central North Sea. Senckenbergiana maritima 31: 255-261. Fransz, H.G., Colebrook, J.M., Gamble, J.C., and Krause, M. 1991. The zooplankton of the North Sea. Neth.

J. Sea Res. 28: 1-52. Gregg, W.W., Ginoux, P., Schopf, P.S., and Casey, N.W. 2003. Phytoplankton and iron: Validation of a

global three-dimensional ocean biogeochemical model. Deep-Sea Res. II 50: 3143-3169. Greve, W., Reiners, F., Nast, J, and Hoffmann, S. 2004. Helgoland Roads meso- and macrozooplankton

time-series 1974 to 2004: lessons from 30 years of single spot, high frequency sampling at the only off-shore island of the North Sea. Helgol. Mar. Res. 58: 274-288.

Haslob, H., Clemmesen, C., Schaber, M., Hinrichsen, H.-H., Schmidt, J.O., Voss, R., Kraus, G., Köster, F.W. 2007. Invading Mnemiopsis leidyi as a potential threat to Baltic fish. Mar. Ecol. Prog. Ser. 349: 303-306.

Hays, G. C. 1994. Mesh selection and filtration efficiency of the continuous plankton recorder. J. Plankton Res. 16: 403–412.

Hays, G. C., and Lindley, J. A. 1994. Estimating chlorophyll a abundance from phytoplankton colour recorded by the continuous plankton recorder survey: validation with simultaneous fluorometry. J. Plankton Res, 16: 23–34.

HELCOM. 1987. First periodic assessment of the state of the marine environment of the Baltic Sea area, 1980–1985; background document. Baltic Sea Environment Proceedings 17B, 351 pp.

HELCOM. 1990. Second periodic assessment of the state of the marine environment of the Baltic Sea, 1984–1988; background document. Baltic Sea Environment Proceedings 35B, 432 pp.

HELCOM. 1996. Third periodic assessment of the state of the marine environment of the Baltic Sea, 1989–1993; background document. Baltic Sea Environment Proceedings 64B, 252 pp.

HELCOM. 2002. Environment of the Baltic sea area 1994–1998. Baltic Sea Environment Proceedings No. 82B, 215 pp.

Hirst, A.G., and Bunker, A.J. 2005 Growth of marine planktonic copepods: Global rates and patterns in relation to chlorophyll a, temperature, and body weight. Limnol. Oceanogr. 48: 1988-2010.

Hirst, A.G., and Kiørboe, T. 2002. Mortality of marine planktonic copepods: global rates and patterns. Mar. Ecol. Prog. Ser. 230: 195-209.

20

Holste, L., and Peck, M.A. 2006. The effects of temperature and salinity on egg production and hatching success of Baltic Acartia tonsa (Copepoda: Calanoida): a laboratory investigation. Mar. Biol 148: 1061-1070.

Huntley, M.E., and Lopez, M.D.G. 1992. Temperature-dependent production of marine copepods: a global synthesis. Am. Nat. 140: 201-242.

John, E.H., Batten, S.D., Harris, R.P., and Hays, G.C. (2001). Comparison between zooplankton data collected by the Continuous Plankton Recorder survey in the English Channel and by WP-2 nets at station L4, Plymouth (UK). Journal of Sea Research 46:223-232.

Karasiova, E., and Voss, R. 2004. Long-term variability of cod and sprat eggs abundance in ichthyoplankton of the Baltic Sea. ICES CM 2004/L:07.

Kirchner, J.W., Hooper, R.P., Kendall, C., Neal, C., and Leavesley, G. 1996. Testing and validating environmental models. Sci. total Environ. 183: 33-47.

Krause, M., Dippner, J.W., and Beil, J. 1995. A review of hydrographic controls on the distribution of zooplankton biomass and species in the North Sea with particular reference to a survey conducted in January-March 1987. Progr. Oceanogr. 35: 81-152.

Lacroix, G., Ruddick, K., Park, Y., Gupens, N., and Lancelot, C. 2007. Validation of the 3D biogeochemical model MIRO&CO with field nutrient and phytoplankton data and MERIS-derived surface chlorophyll a images. J. Mar. Sys. 64: 66-88.

Last, J.M. 1980. The food of twenty species of fish larvae in the west – central North Sea. Fish. Res. Tech. Rep. 60, 44pp.

Le Quéré, C., Harrison, S.P., Prentice, I.C., Buitenhuis, E.T., Aumonts, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K.E., Legendre, L., Minizza, M., Platt, T., Rivkin, R.B., Sathyendranath, S., Uitz, J., Watson, A., and Wolf-Gladrow, D. 2005. Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models. Global Change Biology 11: 2016-2040.

Leonard, C.L., McClain, C.R., Murtugudde, R., Hofmann, E.E., Harding, L.W., 1999. An iron-based ecosystem model of the central equatorial Pacific. Journal of Geophysical Research 104, 1325–1341.

Letcher, B.H., Rice, J.A., Crowder, L.B., and Rose, K.A. 1996. Variability in survival of larval fish: disentangling components with a generalized individual-based model. Can. J. Fish. Aquat. Sci. 53: 787–801.

Lewis, K., Allen, J.I., Richardson, A.J., and Holt, J.T. 2006. Error quantification of a high resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part3, validation with Continuous Plankton Recorder data. J. Mar. Sys. 63: 209-224.

Luyten, P.J., Jones, J.E., Proctor, R., Tabor, A., Tett, P., and Wild-Allen, K., 1999. COHERENS – a coupled Hydrodynamical–Ecological Model for Regional and Shelf Seas: User Documentation. MUMM Report. Management Unit of the Mathematical Models of the North Sea, 911 pp.

Megrey, B.A., Rose, K.A., Klumb, R.A., Hay, D.E., Werner, F.E., Eslinger, D.L., Smith, S.L. (2007). A bioenergetics-based population dynamic model of Pacific herring (Clupea harengus pallasii) coupled to a lower trophic level nutrient-phytoplankton-zooplankton model: Description, calibration and sensitivity analysis. Ecol. Modell. 202, 144-164.

Mehner, T., and Thiel, R. 1999 . A review of predation impact by 0+fish on zooplankton in fresh and brackish waters of the temperate northern hemisphere. Env. Biol. Fish. 56: 169-181.

Moll, A. 1995. Regionale Differenzierung der Primärproduktion in der Nordsee: Untersuchungen mit einem drei-dimensionalen Modell. Ber. Zentr. Meeres- Kimaf. B: 19, 151pp.

Moll, A. 1997. ECOHAM1 user guide – The ecological North Sea Model, Hamburg, Vers. 1. Tech. Rep. Inst. Meeresk. Univ. Hamburg 97(3), 29pp.

Moll, A. 1999. Variabilität der Primärproduktion aus dreidimensionalen Modellrechnungen für die Nordsee mit ECOHAM1. Deut. Hydrogr. Zeits. – Suppl. 10: 161-164.

21

Moll, A., and Radach, G. 2003. Review of three-dimensional ecological modelling related to the North Sea shelf system. Part I: models and their results. Prog. Oceanogr. 57: 175-217.

Moll, A., and Stegert, C. 2007. Modelling Pseudocalanus elongatus stage-structured population dynamics embedded in a water column ecosystem model for the northern North Sea. J. Mar. Sys. 64: 35-46.

Möllmann, C., and Köster, F.W. 1999. Food consumption by clupeids in the Central Baltic: Evidence for top-down control? ICES J. Mar. Sci. 56 Suppl.: 100-113.

Möllmann, C., Kornilovs, G., and Sidrevics, L. 2000. Long-term dynamics of main mesozooplankton species in the central Baltic Sea. J. Plankton Res. 22: 2015-2038.

Möllmann, C., Kornilovs, G., Fetter, M., and Köster, F.W. 2004. Feeding ecology of central Baltic Sea herring and sprat. J. Fish Biol. 65: 1563-1581.

Möllmann, C., Kornilovs, G., Fetter, M., and Köster, F.W. 2005. Climate, zooplankton, and pelagic fish growth in the central Baltic Sea. ICES J. Mar. Sci. 62: 1270-1280.

Möllmann, C., Köster, F.W., Kornilovs, G. Sidrevics. L. 2002. Long-term trends in abundance of cladocerans in the Central Baltic Sea. Mar. Biol. 141: 343-352.

Moore, K.J., Doney, S.C., Lindsay, K. 2004. Upper ocean ecosystem dynamics and iron cycling in a global three-dimensional model. Global Biogeochemical Cycles, 18: GB4028, doi:10.1029/2004GB002220.

Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models. Part I – a discussion of principles. J. Hydrol. 10: 282-290.

Neumann, T. 2000. Towards a 3D-Ecosystem model of the Baltic Sea. J. Mar. Sys. 25: 405-419. Neumann, T., and Fennel, W. 2007. Stage resolved description of copepods in three dimensional ecosystem

models: GLOBEC Int. Newsl. Oct. 2007, 2pp. Neumann, T., and Kremp, C. 2005. A model study with light-dependent rates of copepod stages. J. Mar Sys.

56: 416-434. Nielsen, T.G., and Munk, P. 1998. Zooplankton diversity and the predatory impact by larval and small

juvenile fish at the Fisher Banks in the North Sea. J. Plankton Res. 20: 2313-1332. OSPAR, Villars, M., Vries, I.D., Bokhorst, M., Ferreira, J., Gellers-Barkman, S., Kelly-Gerreyn, B.,

Lancelot, C., Ménesguen, A., Moll, A., Pätsch, J., Radach, G., Skogen, M., Soiland, H., Svendsen, E., and Vested, H.J. 1998. Report of the ASMO modelling workshop on eutrophication Issues, 5–8 November 1996. The Hague, The Netherlands. OSPAR Commission Report, RIKZ, The Hague, 102pp.

Pacanowski, R.C., Dixon, K., Rosati, A. 1990. The GFDL Modular Ocean Model Users Guide Version 1.0, GFDL Technical Report No. 2., Geophysical Fluid Dynamic Laboratory, NOAA, Princeton University, Princeton.

Peck, M.A., and Holste, L. 2006. Effects of salinity, photoperiod and adult stocking density on egg production and egg hatching success in Acartia tonsa (Copepoda: Calanoida): Optimizing intensive cultures. Aquaculture 255: 341-350.

Peters, R.H. (1983). The ecological implications of body size. Cambridge University Press, Cambridge, 329pp.

Postel, L., Fock, H., and Hagen, W. (2000). Biomass and abundance. In: ICES Zooplankton Methodology Manual. R. Harris et al. (eds.), Elsevier, New York, pp. 83-192.

Radach, G., and Moll, A. 2006. Review of three-dimensional ecological modelling related to the North Sea shelf system – Part 2: Model validation and data needs. Oceanogr. Mar. Biol; Ann. Rev. 44: 1-60.

Richardson, A.J., John , E.H., Irigoien, X., Harris, R.P., Hays, G.C. (2004). How well does the Continuous Plankton Recorder (CPR) sample zooplankton? A comparison with the Longhurst Hardy Plankton Recorder (LHPR) in the northeast Atlantic. Deep Sea Research Part I 51: 1283-1294.

Richardson, A.J., Walne, A.W., John, A.W.G., Jonas, T.D., Lindley, A.J., Sims, D.W., Stevens, D., and Witt, M. (2006). Using continuous plankton recorder data. Progress in Oceanography 68: 27-74.

22

Rose, K.A., Rutherford, E.S., McDermot, D.S., Forney, J.L., and Miles, E.L. 1999. Individual-based model of yellow perch and walleye populations in Oneida Lake. Ecol. Monogr. 69: 127–154.

Ryland, J.S. 1964 The feeding of plaice and sand-eel larvae in the southern North Sea. J. Mar. Biol. Ass. UK 44: 343-364.

Schrum C., Alekseeva, I., and St. John, M. 2006a. Development of a coupled physical–biological ecosystem model ECOSMO: Part I: Model description and validation for the North Sea, J. Mar. Sys. 61: 79-99.

Schrum, C., St. John, M., and Alekseeva, I. 2006b. ECOSMO, a coupled ecosystem model of the North Sea and Baltic Sea: Part II: Spatial-seasonal characteristics in the North Sea as revealed by EOF analysis. J. Mar. Sys. 61: 100-113.

Simonsen, C.S., Munk, P., Folkvord, A., and Pedersen, S.A. 2006. Feeding ecology of Greenland halibut and sandeel larvae off West Greenland. Mar. Biol. 147: 937-952.

Skogen, M.D. 1993. A user's guide to NORWECOM: The NORWegian ECOlogical Model system. Tech. Rep Inst. Mar. Res. 6: 1-23.

Skogen, M.D., and Soiland, H. 1998. A user's guide to NORWECOM V2: The NORWegian ECOlogical Model system. Fisken og Havet 18, 42pp.

Soiland, H. and Skogen, M.D., 2000. Validation of a three-dimensional biophysical model using nutrient observations in the North Sea. ICES J. Mar. Sci. 57: 816-823.

Southward, A.J., Langmead, O., Hardman-Mountford, N.J., Aiken, J., Boalch, G.T., Dando, P.R., Genner, M.J., Joint, I., Kendall, M.A., Halliday, N.C., Harris, R.P., Leaper, R., Mieszkowska, N., Pingree, R.D., Richardson, A.J., Sims, D.W., Smith, T., Walne, A.W., and Hawkins, S.J. 2005. Long-term oceanographic and ecological research in the western English Channel. Advances in Marine Biology 47: 1-105.

Tett, P.B. 1990. A three layer vertical and microbiological processes model for shelf seas. Report - Proudman Oceanographic Laboratory 14, 85pp.

Tett, P.B., and Walne, A. 1995. Observations and simulations of hydrography, nutrients and plankton in the southern North Sea. Ophelia 42: 371–416.

The Continuous Plankton Recorder Survey team. 2004. Continuous Plankton Records: Plankton Atlas of the North Atlantic Ocean (1958–1999). II. Biogeographical charts. Mar. Ecol. Prog. Ser., CPR Plankton Atlas Suppl: 11-75.

Voss, R., Köster, F.W., and Dickmann, M. 2003. Comparing the feeding habits of co-occurring sprat (Sprattus sprattus) and cod (Gadus morhua) larvae in the Bornholm Basin, Baltic Sea. Fish. Res. 63: 97-111.

Wasmund, N., and Uhlig, S. 2003. Phytoplankton trends in the Baltic Sea. ICES J. Mar. Sci. 60: 177-186. Werner, F.E., Ito, S.I., Megrey, B.A., and Kishi, M.J. 2007. Synthesis of the NEMURO model studies and

future directions of marine ecosystem modeling. Ecol. Model. 202: 211-223. Wiggert, J.D, Murtugudde, R.G., Christian, J.R. 2006. Annual ecosystem variability in the tropical Indian

Ocean: Results of a coupled bio-physical general circulation model Wyatt, T. 1974. The feeding of plaice and sand-eel larvae in the Southern Bight in relation to the distribution

of their food organisms. In: The early life history of fish. The Proceedings of an International Symposium held at the Dunstaffnage Marine Research Laboratory of the Scottish Marine Biological Association at Oban, Scotland, May 17-23, 1973. pp. 245-251.