Download Modelling the impact of hydrography and lower trophic production

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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Large Zooplankton
consumption
predation
grazing
Small Zooplankton mortality
grazing
consumption
Large Phytoplankton
(e.g diatoms)
Small Phytoplankton
(e.g. dinoflagellates)
primary
production
excretion
Detritus
mortality
Nutrient cycles:
Nitrogen
(NO3, NO2, NH4),
Phosphorous,
Silica
remineralization
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 3Decosystem model
A: variable (e.g. 0,5
º)
B: 1h
Aumont et al., 2003
Global
PISCES (Pelagic Interaction
Scheme for Carbon and
Ecosystem Studies)
24 Æ 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
Mediterranean Sea
Ecosystem model based on
OGCM (Ocean general
circulation model)
N, PP, D (ZP implicitly
considered)
NPD-model; considers Ncycle only
Indian Ocean
Bio-physical OGCM (Ocean
general circulation model)
9 Æ NO3, NH4, Fe; SP, LP;
SZ, LZ; small D, large D
Iron- and nitrogen-limited
phytoplankton growth
North Pacific
NEMURO (North Pacific
Ecosystem Model for
Understanding Regional
Oceanography)
11 Æ NO3, NH4; SP, LP;
SZ, LZ, PZ; PON, DON;
Opal, Si(OH)4
North Sea
NORWECOM (NORWECOM
II): Norwegian Ecological
Model System
8 Æ NO3, NH4, PO4, SiO2,
O2, D, SP, LP
+ benthic submodel
9 Æ NO3, NH4, SP, LP,
heterotr. Flagellates, ZP, B,
D, DOM
+ benthic submodel
2 Æ DIP, PP (zooplankton
prescribed by observations)
+ coupling of benthos with
phosphorous cycle
GHER: Geo-Hydrodynamics
and Environment Research
Model
ECOHAM: Ecological North
Sea Model
Biomass-based model family!
(3D spatially explicit)
simulating the the nutrientphytoplankton-zoo-plankton
food web but with various
extensions (e.g. hybrid
models, NEMURO-FISH)
Primary production, nutrient
budgets, dispersion of
particles
A: ¼ º (31 vertical
levels)
B: 40min
A: ½º Longitude, 1/3º
Latitude,
Surface layer and 19
σ-layers
Crise et al., 1998
Leonard et al. 1999;
Christian et al., 2002;
Wiggert et al., 2006
A: 1º Latitude and
Longitude; 54
vertical layers (510m in extension)
B: 6h
Werner et al., 2007
Aita et al., 2007
A: 20km
B: 15min
Skogen, 1993; Skogen
and Soiland, 1998;
Soiland & Skogen, 2000
Operates on macroscale
spectral window, describes Nand C-cycles
A: 1/6º Box
B: 69sec
Delhez & Martin, 1994;
Delhez, 1998
Simulates phosphorous related
phytoplankton processes
A: 20km (5m vertical
resolution)
B: 15min
Moll, 1995; Moll, 1997;
Moll, 1999
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Important characteristic /
processes (Model focus)
Resolution
A: Spatially
B: Temporally
Reference to 3Decosystem model
Very complex box model (grid
cell of 1º) Æ 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
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 Æ 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-pelagiccoupling; dynamic
zooplankton; nitrogen,
phosphorous and silicate
cycling
A: 12km
B: 18min
Allen et al., 2001
North Sea/
Baltic Sea
ECOSMO (ECOsystem MOdel)
12 Æ 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 088m 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 Æ 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;
Area
Model name
ERSEM (ERSEM II): European
Regional Seas Ecosystem Model
COHERENS: Coupled
Hydrodynamical Ecological
model for Regional NorthwestEuropean Shelf Seas
No. of NPZD state
variables
ERSEM II: 43 pelagic and
22 benthic state variables Æ
incl. NO3, NH4, PO4, SiO2,
O2, four functional phytopl.
groups, three functional
zoopl. Groups, bacteria,
POM, DOM;
+ benthic submodel
MPD model: 8 state
variables Æ NO3, NH4, O2,
microplankton, detritus and
SPM. New submodels for
micro-plankton-2, sediment,
PO4, SiO2 and copepods are
under development.
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 noncopepod 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
Calanoidea (Copepoda)
Cyclopoidea (Copepoda)
Cladocera
Appendicularia
Macrozooplankton
Meroplankton
North Sea
Pseudocalanus elongatus
(***)
Acartia spp. (*)
Temora longicornis (**)
Calanus spp. (C.
helgolandicus, C.
finmarchicus) (**)
Oithona spp.
Evadne sp.
Podon sp.
Oikopleura diocia (***),
Fritillaria spp.
"Krill" (Meganyctiphanes
norvegica, Thysanoessa spp.)
Sagitta spp.
Mysis spp.
Polychaeta larvae
Decapoda larvae
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Baltic Sea
Pseudocalanus acuspes (**)
Acartia spp. (**)
Temora longicornis (***)
Oithona similis
Evadne sp. (**)
Podon sp. (**)
Bosmina sp. (**)
Fritillaria spp.
Mysis spp. (*)
Zooplankton group
Rotatoria
Scyphozoa
Ctenophora
North Sea
Mollusc larvae (Bivalvia,
Gastropoda) (***)
Aurelia aurita
Pleurobrachia pieleus
Mnemiopsis leidyi
Baltic Sea
Synchaeta sp.
Aurelia aurita
Pleurobrachia pieleus
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).
10
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):
W = 0.08 * L2.1
with L = total length in mm of adult females and W = total mass in mg wet weight
11
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 timeseries 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.
12
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 longterm 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.
13
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-theyear 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.
14
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 Æ produce
meaningful simulations of population dynamics Æ Is this planned within the project and how? Æ
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.
15
Tab. 3: Examples of biological data series suitable for validating ecosystem models
Dataset
Type of data
Continuous
Plankton
Recorder
Survey
(CPR)
Phytoplankton
Colour Index
(PCI),
Phytoplankton/
Zooplankton
species
identification
and counts
L4
Stonehaven
Hydrography,
nutrients,
Chla,
zooplankton
identification
and counts,
Calanus
reproduction
Hydrography,
nutrients,
Chla,
phytoplankton/
zooplankton
identification
and counts
Z-DOVE
Hydrography,
zooplankton
identification
and counts
Helgoland
Roads
Hydrography,
Chla,
nutrients,
phytoplankton/
zooplankton
identification
and counts,
fish larvae
identification
Circulation
and
pollutant
fluxes in
the NS
(ZISCH)
Zooplankton
identification
and
abundance,
total biomass
North Sea
Project
Hydrography,
nutrients,
primary/
bacterial
production,
Chla,
Phytoplankton/
zooplankton
abundance
Area
Spatial
resolution
Temporal
resolution
NA, NS
Standard
routes,
alternate
samples,
each
representing
10nm
~monthly,
1948 –
present,
for phytoplankton
consistent
since 1958
Western
English
Channel
Coastal
station:
50°15'N,
004°13'W
Weekly to
monthly,
1988present
Contact/
Access
Sir Alister
Hardy
Foundation
for Ocean
Science
(SAHFOS),
accessible
through data
licensing
agreement
Plymouth
Marine
Laboratory
(PML),
accessible
through
agreement
Fisheries
Research
Services,
Marine
Laboratory
Aberdeen
(FRS)
Dove Marine
Laboratory,
University of
Newcastle,
UK
Web-link/
Reference
http://www.sahfos.a
c.uk;
Richardson et al.,
2006
http://www.westernc
hannelobservatory.o
rg.uk/l4/;
Southward et al.,
2005
http://www.marlab.a
c.uk/Montoring/Ston
ehaven/Stoneframe.
html
NS,
Scotlan
d
Coastal
station:
56º57.80'N,
002º06.20'W
Weekly,
1997present
NS,
Newcas
tle
6km
offshore:
55º07'N, 01º
20'W
Monthly:
1969present
NS,
German
Bight
Coastal
station:
54°11´18”N,
007°54´E
Three
times per
week:
1975present
Alfred
Wegener
Institute of
Polar
Research
(AWI)
Greve et al., 2004
NS
127 stations
in 1986, 120
in 1987
02.-05.13.06.198
6; 26.01.09.03.
1987
University of
Hamburg
Krause et al., 1995
NS
121 stations
in southern
North Sea –
higher
resolution in
coastal areas
Monthly:
1988-1990
British
Oceanographic data
centre
(BODC),
free access to
data CDROM
http://www.bodc.ac.
uk/projects/uk/north
_sea/;
16
http://www.ncl.ac.u
k/marine/about/facili
ties/dove/;
Clark et al., 2003
Dataset
LIFECO
German
GLOBEC
HELCOM
MONAS COMBINE
Baltic Sea
monitoring
program
(BSM), part
of
HELCOMCOMBINE
LATFRI
Mesozoopl
ankton
database
Historical
Ichthyoplan
kton data
Area
Spatial
resolution
Temporal
resolution
Contact/
Access
Web-link/
Reference
NS
variable (1020nm in
northeastern NS)
4 cruises
in 2001
DTU Aqua
(Peter Munk)
http://www.lifeco.dk
BS/ NS
BS: 45-120
(BB, GB,
DD);
NS: ca. 50
3-14
cruises per
year: BS:
20022006;
NS: 20032005
GLOBEC
GERMANY
BS
Multinational
program:
resolution
variable,
highest for
hydrographi
c variables
Variable:
1979present
HELCOM
http//www.helcom.fi
; HELCOM 1987,
1990, 1996, 2002
Hydrography,
nutrients,
Chla,
phytoplankton/
zooplankton
abundance
BS
24 stations
(Mecklenbur
g Bay – Gulf
of Finland)
At least 4times a
year:
1973present
Institute for
Baltic Sea
Research
(IOW)
http//www.helcom.fi
; http//www.iowarnemuende.de;
Wasmund and
Uhlig, 2003
Hydrography,
zooplankton
identification
and abundance
BS –
Baltic
Proper
Variable, up
to 24
stations
4 times a
year:
1959present
BS
Multinationa
l sampling,
combined
from
literature
and survey
data
Variable,
1902present
with gaps
Type of data
Hydrography,
Chla, (primary
production),
zooplankton
identification,
abundance,
size,
production
Hydrography,
Chla (with
gaps),
zooplankton
identification
and
abundance,
production
Hydrography,
nutrients,
Chla,
phytoplankton/
zooplankton
abundances
Ichthyoplankton
identification
and abundance
17
Latvian Fish
Resources
Agency
(LATFRA)
Atlantic
Research
Institute of
Marine
Fisheries and
Oceanography
(AtlantNIRO),
Leibniz
Institute of
Marine
Sciences
(IFMGEOMAR)
http://www.globecgermany.de/
Möllmann et al.,
2000, 2002
Karasiova and Voss,
2004
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