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DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS CSG 15Research and Development

Final Project Report(Not to be used for LINK projects)

Two hard copies of this form should be returned to:Research Policy and International Division, Final Reports UnitDEFRA, Area 301Cromwell House, Dean Stanley Street, London, SW1P 3JH.

An electronic version should be e-mailed to [email protected]

Project title Improved assessment of eutrophication effects in coastal waters

DEFRA project code AE1020

Contractor organisation and location

CEFAS,Pakefield Road,Lowestoft, NR33 0HT

Total DEFRA project costs £

Project start date Project end date

Executive summary (maximum 2 sides A4)

The purpose of this project was to assess, evaluate and develop new tools for the assessment of eutrophication status in coastal waters. The work was required because of increasing concern over the nutrient status and ecosystem response in coastal waters of England and Wales. The project addressed a technical and a scientific question; the technical concerned the evaluation of a new bio-optical instrument for measurement of primary productivity and the scientific question addressed the need to scale of up discrete and small scale measurements to water column and basin scale estimates of primary production. Evaluation of the bio-optical instrument was carried out through a programme of field work carried out at sea in a range of contrasting environmental conditions. Development of an existing model (COHERENS) was carried out in order to better represent the processes of photosynthesis and plant growth and to provide the mechanism for basis scale assessments of ecosystem response.

OSPAR and European Directives (esp. UWWT Directive, Nitrates Directive) provide the policy background against which improved data is required to support more comprehensive and robust assessments of the health of the oceans. Concerns expressed by the EU over the eutrophic status of UK coastal waters have been difficult to allay due to lack of good evidence for some areas and for some assessment criteria. Current monitoring of coastal waters is limited in scope in terms of frequency and the range of measured variables and have focussed on one aspect of the eutrophication issue – nutrients. Developing a more effective monitoring programme requires the development of tools that allows further useful, predominantly biological, variables to be measured.

The development and commercial availability of a new generation of fluorometer (Fast Repetition Rate Fluorometer, FRRF) with the claimed capability of instantaneously measuring primary productivity has the potential to greatly improve our knowledge of this key measure of ecosystem function and an indicator of eutrophic status. Although there is evidence in the literature to support this view (Sugget et al., 2001) few direct comparison between the standard methods and the FRRF have been reported. Fieldwork was undertaken in a range of different location in UK coastal waters to carry out comparisons with standard methods and to evaluate performance of the FRRF in contrasting hydrographic conditions in terms of light, nutrients and vertical mixing.

Development of an existing model was successfully undertaken to enable simulation of variables describing photosynthesis and algal growth in a comparable manner to the FRRF. In addition, the need to scale up observations required the model to be run in a 3-D form that resulted in over-heating due to biological feedback to the physical model.

CSG 15 (9/01) 1

Projecttitle

Improved assessment of eutrophication effects in coastalwaters

DEFRAproject code

AE1020

To optimise the work programme certain aspects of the work were carried out in collaboration with scientists from three UK universities.

The principal outcomes of the project were;

The FRRF has provided estimates of photosynthetic parameters (from which primary productivity can be derived) from a range of sites, that are generally higher but significantly correlated with the value of those derived from the standard 14-C technique,

Instantaneous measures of the quantum yield of photosynthesis made by the FRRF can be used to map the physiological status of phytoplankton and provide an indication of their likely response to the addition of nutrients,

Field work carried out in the summer stratified North Sea using the FRRF showed that photosynthetic efficiency was highest in the “thin layers” of phytoplankton that are found at the base of the pycnocline, in the bottom front and in the bottom mixed layers in the shallow water overlying the Dogger Bank;

Further work is required before the results obtained from the FRRF could be used to replace standard techniques for the determination of primary productivity,

An improved representation of the underwater light field and the growth of phytoplankton has been incorporated into an existing ecological model to allow comparison with the bio-optical method,

A realistic simulation of a complete seasonal cycle of phytoplankton was achieved in a 1-D mode, Problems were encountered due to biological feedback to the physical model resulting in over-heating of the

modelled water column, Comparison of hourly modelled and observed data on algal growth and production over a daily cycle showed

good agreement Further work is required to improve the biological-physical coupling to take account of the feedback of biology to

the physics.

This work has demonstrated the utility of the FRRF for mapping the physiological status and the likely response of phytoplankton to additional (anthropogenic) nutrient input but has also shown that it is not capable of replacing standard techniques, at this stage, for determination of primary productivity. Further work is recommended with next generation instruments in well defined experimental set-ups for more comparisons with standard methods. Such an approach would have the potential to address uncertainties in the analysis of FRRF derived data and provide more robust estimates productivity.

The COHERENS model has been successfully updated to better represent the underwater light field and the growth of phytoplankton. This an important step forward in the development of scientifically sound models that may form the basis for future assessments of ecosystem response to nutrient inputs in UK coastal waters. Feedback of the biological model to the physical model within COHERENS has identified a need for further development of the physical model. The findings of this work will continue to be publicised through written papers and oral submissions in appropriate journals and for a to ensure maximum uptake of results.

CSG 15 (9/01) 2

Projecttitle


DEFRAproject code

AE1020

Scientific report (maximum 20 sides A4)IntroductionThere is growing concern within Europe over the issue of eutrophication. As defined by OSPAR it involves:

“the enrichment of water by nutrients causing an accelerated growth of algae and higher forms of plant life to produce an undesirable disturbance to the balance of organisms present in the water and to the quality of the water concerned, and therefore refers to the undesirable effects resulting from anthropogenic enrichment by nutrients.”

It is important to distinguish between eutrophication, a process and eutrophic a trophic state (Tett & Edwards, 2002). Eutrophication can be regarded as a human-influenced process leading to undesirable consequences whereas eutrophic that can be regarded as a state and objectively assessed against agreed criteria. In UK waters, identification of eutrophic state has typically involved the measurement of winter nutrient and summer chlorophyll concentrations, which are then compared with defined standards. Currently, no measurements that relate to accelerated growth of algae are carried out. However, some international standards (Rodhe, 1969; Nixon, 1995) have been proposed that use annual primary production as a measure of accelerated growth, hence trophic status, but carry no value in terms of good or bad. A practical difficulty with such a measure is that primary production measurements are costly and subject to controversy (Colijn et al., 1983). Traditional methods involve the incubation of small volumes of sea water in bottles, and apart from ‘bottle effects’ are also subject to the need to scale up from sample volumes of fractions of a litre to the volume of a coastal sea, which is of order 1015 larger. This project concerns two approaches to circumventing these difficulties.

The first approach used a ‘Fast Repetition Rate Fluorometer’ (Kolber & Falkowski, 1992, 1993), a submersible electronic instrument that can, it is claimed, estimate photosynthetic properties of phytoplankton in the sea. It therefore offers the possibility of mapping the distribution of these properties and of instantaneous productivity. The instrument works by delivering a series of flashes of blue light and measuring the red fluorescence emitted in response by phytoplankters. Instruments of this type are regarded as measuring variable fluorescence in contrast to standard fluorometers that measure non-variable fluorescence. The theory used to convert the fluorescence measurements into estimates of photosynthesis is complex and may be an oversimplification of the underlying processes. One aim of the project was to deploy an FRRF during a series of cruises in UK coastal waters, to interpret its output in relation to hydrography, nutrients and light, and to compare FRRF estimates of photosynthetic properties with those obtained from conventional means.

The second approach uses mathematical models for scaling up measurements made on discrete water samples at specific locations, with key model parameters taken from small-scale conventional or FFRF estimates of photosynthetic parameters. The initial model is that developed by the EU COHERENS project (Luyten et al., 1999), which was, during this project, implemented on a 3-dimensional grid for the North Sea. During the present project we have mainly used a 1-D version of the model, resolving depth variation at a give position, and improved the model’s algorithms relating to photosynthetic properties.

In order to understand the link between the observational and modeling work it is important to appreciate the common framework that underpins the estimation of primary productivity (growth of algae) from observed or modeled data. This framework is explained in more detail below.

An important part of the modelling work, and of interpreting observations, is to take account of physical transports of water, nutrient and microplankton. Assuming that this has been done, then the biomass of microplankton observed, or simulated for a given time, is the sum of the difference between production and losses over a preceding period. At a given time, microplankton production is growth rate multiplied by biomass, and growth rate is rate of photosynthesis minus rate of respiration. Rate of photosynthesis depends on photosynthetic efficiency (, mmol C (mg chl)-1 s-1 per unit of irradiance), chlorophyll concentration (X, mg chl m-3) and photosynthetically effective downwelling irradiance (or PAR, ED, µEinstein m-2 s-1, an Einstein being a mole of photons):

(1) P = .ED.X mmol C m-3 s-1

Chlorophyll concentration and PAR can be measured in situ using standard techniques. Photosynthetic efficiency is the key variable on which the present study is focused. It is commonly estimated in practice from the initial slope of a graph of photosynthesis against irradiance, called a P-E curve (e.g. Fig. 2). It has in theory two main components: f, the quantum yield (mmol C fixed per µE of photons absorbed), and a*, the absorption cross-section of photosynthetic pigments (m2 (mg chl)-1):

(2) a = m-1.a*.f mmol C (mg chl)-1 (µE m-2)-1

CSG 15 (9/01) 3

Projecttitle


DEFRAproject code

AE1020

where m is a correction factor.Absorption cross-section is both a property of phytoplankters, indicative of physiological status, and of the

submarine light field.The body of the report now divides into two parts, once dealing with observations and one with modelling. Each

part draws on and expands some of the underpinning theory outlined above. The report concludes by comparing observations and simulations for one of the sites studied in detail (in the Firth of Clyde) and uses this comparison to evaluate the utility and reliability of the FRRF and of models as tools for investigating the trophic status of coastal waters.

MethodsAssessment of the performance of the Fast Repetition Rate Fluorometer

STUDY SITES

The observations carried out at sea during the present study were carried out in parallel with complementary programmes funded by DEFRA (A1219, A1221) in the North Sea and by NERC in the Irish and Clyde Seas. Fieldwork was carried out in three areas as shown in Figure 1 where measurements were carried out in a range of conditions from hypernutrified shallow coastal locations (e.g. Thames estuary, Liverpool Bay) to deep seasonally thermally stratified waters (e.g. North Sea, north western Irish Sea). Measurements were made using standard ship lowered packages fitted with rosette bottle sampler and CTD together with other bio-optical instruments described in more detail below. Positions of stations where samples were collected are shown in Figure 1.

MEASUREMENT OF PRIMARY PRODUCTIVITY

The conventional method chosen for measuring phytoplankton production was based on the 14C uptake procedure introduced by Steeman-Nielson (1952). At each station measurements of primary productivity were made using water collected from the surface mixed layer or the sub-surface phytoplankton peak if present. Water was first filtered through a coarse filter to remove mesozooplankton and then incubated after an addition of C-14 isotope as H14CO3. Samples were then placed in a light gradient incubator for up to 1 hour and temperature maintained at the same level as that of the original water sample. Care was taken to avoid ‘light shock’ and samples collected before midday if possible. After incubation samples were chemically fixed and returned to the laboratory for counting in a liquid scintillation counter. The incubator design and experimental procedure is essentially that described by Lewis & Smith (1983). For each sample, photosynthesis-irradiance (P-E) relationships were determined. Photosynthetic parameters Pbmax (the light saturated photosynthetic potential, (g C (g chl)-1 h-1)), α (the light-limited photosynthetic efficiency, (g C (g chl)-1 h-1 (E m-2 s-

1)-1)) and Ek (=Pbmax/α; an estimate of the minimum irradiance required for the onset of light-saturated photosynthesis, (E m-2 s-1)) were derived from the P-E relationships and then used to calculate primary production for each discrete sample.

Figure 1. Study areas sampled during the programme. FRRF PRINCIPLES OF OPERATION

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Projecttitle


DEFRAproject code

AE1020

Photosynthetic pigments can be regarded as a series of traps for light When exposed to high levels of light the traps close and under these condition then much of the further light falling upon the photosynthetic units (chloroplasts) is re-emitted as fluorescence. When exposed to low levels of light the traps remain open allowing photosynthesis to proceed and fluorescence occurs at a minimal level. The fluorescence emitted with traps closed is termed the “maximum” fluorescence or Fm; the fluorescence minimum is termed Fo; and the difference between the two is called variable fluorescence Fv.

Methods developed earlier to study environmental influences on fluorescence include “pump and probe” fluorometers (Mauzerall, 1972; Falkowski et al., 1988) and PAM fluorometry (Pulse Amplitude Modulated) described by Schreiber and Shliwa (1986). A particular advantage of the FRR fluorometer compared to other techniques is that it provides additional information on the photosynthetic units important to productivity studies. This information is gained from the slope of the rise in fluorescence yields from which functional absorption cross-section (σPS2, m2 quanta-1) can be derived which is a measure of the light harvesting ability of photosystem 2 (PS2). From these properties of photosynthesis quantum yields can be calculated (Fv/Fm) and related to productivity and also provide information on the physiological state of cells (Kolber & Falkowski, 1993)

The details of the approach to estimating productivity from variable fluorescence measurements are now given. A modified biophysical model that relates fluorescence to photosynthesis was used to derive productivity estimates (Kolber & Falkowski, 1993; Gorbunov et al., 2000). The rate of electron transport through PS2 is described by:

(3) (e-1s-1)

where, ETR is the electron transport rate (electrons per second), E is measured ambient irradiance (μ Einsteins m2 s-1), σPS2 is as described above, f is the proportion of functional reaction centres under ambient irradiance (calculated as (Fv/Fm)/0.65), and RC is the quantum yield of photochemistry in PS2 (mol e-1(mol quanta)-1 =1. Converting to biomass normalised carbon fixation rates required knowledge of the ratio of PS2 reaction centres to chlorophyll a (nPS2, mol e-

1(mol Chl a)-1, and assumed to be 0.002), the quantum yield of electron transport in PS2 (e, mol O2(mol e-1)-1, (and PQ , the photosynthetic quotient (mol O2 (mol C) –1), and assumed to be 1 in the present study.

Productivity estimates from the FRRF (and C-14 technique) were fitted to a hyperbolic-tangent function that describes the dependence of photosynthesis on irradiance. The FRRF provided a composite P/E curve, with the rate estimates and corresponding measured irradiance level at each depth throughout the water column used to construct the curve. Additionally, the quantum yield of carbon fixation (, mol C(mol quanta)-1) was estimated for both techniques. For the C-14 method, c = /a*, where is as described above and is derived from the P/E curve, and a* is measured spectrophotometrically on discrete water samples (see below). FRRF derived is obtained using the method of Babin et al., (1993):

(4) f = (PS2 x e x f x RC x nPS2) .(a* x PQ)-1 .(6.74 x 10-3) mol C(mol quanta)-1

where f is the FRRF derived quantum yield, 6.74 x 10-3 is a conversion factor to molar ratios, all other parameters are as described above. The value of a* was estimated at several depths and appropriate values used to derive a water column profile of f .

In the field the FRRF was used to obtain vertical profiles of variable fluorescence by a attaching to a rosette water sampler frame equipped with a CTD and lowering over the side of the vessel at each sample station. Observations were carried out in one of two modes; vertical profiles were obtained at set intervals along a horizontal transect (eg Irish Sea, Thames estuary, Dogger Bank) or repeated observations were obtained at a fixed point over a 25 hour period (eg Clyde). Additional data was gathered on water column salinity and temperature and water samples were collected from selected depths and used for measurement of chlorophyll, and dissolved inorganic nutrients. For chlorophyll, known volumes were filtered through Whatman GF/F glass fibre filters. Pigments were extracted in acetone and analysis was carried out on board ship. A Turner Designs Model 10 filter fluorometer was used to measure extracted pigment fluorescence following the method described by Tett (1987). The fluorometer was calibrated using a solution of pure chlorophyll (Sigma Chemical Co.) with concentration being determined spectrophotometrically. Samples were either preserved or frozen for subsequent analysis or analysed on board for nitrate, nitrite, phosphate and ammonia using a Skalar Auto analyser (Kirkwood, 1996).

At some stations and some depths additional water samples were collected for determination of suspended load and the specific absorption coefficient of chlorophyll. Suspended load was determined on discrete samples, by filtering on to pre-weighed and combusted GF/F filters. The difference between the initial and loaded filter weights gave suspended load weight. Absorption spectra were determined according the method of Kishino et al., (1985). In brief, known volumes of seawater were passed through GF/F filters, and the filters frozen until analysis. The light absorption of particles on

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Projecttitle


DEFRAproject code

AE1020

filter is measured using a spectrophotometer over the visible spectrum (350-700 nm). The filters were then placed in pure methanol, which bleaches any photosynthetic pigments, and re-scanned in the spectrophotometer. The difference between absorption by particles from the initial scan, and absorption from the second scan where particle are without pigments, is considered to be light absorption due to phytoplankton pigments. Normalising to chlorophyll a concentration gives a*, the chlorophyll specific absorption coefficient (m2 (mg Chl a)-1.

Results

COMPARISON OF PHOTOSYNTHETIC PARAMETERS A total of 30 successful FRRF deployments were carried out with accompanying measurements of C-14 uptake

available at 12 stations providing directly comparable results for , Pbm, and Ek. Direct comparison of estimates of phytoplankton photosynthetic rates as derived from the two techniques were qualitatively, if not quantitatively similar. Reasons for such discrepancies are discussed later. Table 1 shows a comparison of photosynthetic parameters as derived from the two techniques.

Values of Pbm and α estimated by the FRRF are higher for all stations while Ek values are lower. This trend in

measured photosynthetic parameters was apparent for almost all comparable P/E curve data. On average, FRRF estimates of Pb

m and α were 1.70 and 2.07 times greater than those generated by C-14 incubations, while Ek was lower by a factor of 0.88. Quantum yield estimates by both techniques, however, showed an almost one to one relationship. Individual photosynthetic parameters were significantly correlated; Pb

m, 0.878 (p = >0.001, n = 12), Ek, 0.809 (p >0.000, n = 12), , 0.734 (p = > 0.000, n = 22). Results from both techniques for contrasting study sites are shown in Figure 2. Highest estimates of light saturated photosynthesis (Pb

m) are found at the coastal sites (Figures 2a – 2d) with lowest values offshore. .

Figure 2. Photosynthesis-irradiance (P/E) curves fitted to FRRF and C-14 estimated photosynthetic rates and irradiance for three different sites. P/E curves are fitted with a hyperbolic-tangent function model describing the dependence of photosynthesis on irradiance (Jassby & Platt, 1976). Sampling sites: a-b = station WK1, Clyde Sea, April 2000; c-d = station C12, North Sea (Thames Estuary), 2001; e-f = C34, Dogger Bank, 2001. C-14 incubations carried out on discrete water samples collected from surface waters (~ 4 m) for the North Sea stations, and 15 m for the Clyde Sea station.

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Pbm = 3.65 alpha = 0.0245Ek = 149

Pbm = 6.48 alpha = 0.484Ek = 134

a

b

c e

d f Pb = 4.24 alpha = 0.0293Ek = 145

Pbm = 5.81 alpha = 0.022Ek = 260

Pbm = 10.4 alpha = 0.0464Ek = 225

Pb = 2.44 alpha = 0.011Ek = 219

CSG 15 (9/01) 6

Projecttitle


DEFRAproject code

AE1020

Table 1. Summary of photosynthetic parameters measured using C-14 and FRRF methods where Pbm = light saturated

photosynthetic rate (mg C (mg Chl a)-1 h-1), α = initial slope of the P/E curve (mg C (mg Chl a) -1 h-1/μ Einstein (m2) s-1), Ek

light saturation onset irradiance (μ Einstein (m2) s-1), f and c = quantum yield of carbon fixation (mol C (mol quanta)-1) for the FRRF and C-14 methods respectively. C-14 values were determined from P/E curves after uptake experiments on discrete water samples. FRRF values from in situ production estimates were also fitted to P/E curves. Values in parenthesis are ± standard errors of the mean. Where FRRF Pb

m values are suffixed with an asterix (*), measured in situ PAR levels were insufficient for light saturated photosynthesis or FRRF data was excluded from analysis due to degradation of variable fluorescence in surface waters. The latter problem results from interference of measured fluorescence parameters by red photons (see text for details). In such cases, value quoted is the highest in situ rate as measured by the FRRF. A symbol indicates that a corresponding FRRF quantum yield value is not available for the depth at which the C-14 quantum yield is given. Also indicated is the ratio of FRRF to C-14 derived photosynthetic parameters.

CSG 15 (9/01) 7

StudyArea/station

Photosynthetic parametersFRRF C-14

Pbm α Ek f Pb

m α Ek c

Clyde Sea: Shallow (5 m)LS2 1.14* N/A N/A 0.044 2.67 (0.07) 0.017 157 (12.9) 0.043LS3 0.03* N/A N/A 0.072 3.36 (0.07) 0.019 186 (7.90) 0.055CE2 6.68* N/A N/A 0.096 6.17 (0.11) 0.030 207 (10.0) 0.078

ULF1 N/A N/A N/A N/A 3.08 (0.04) 0.023 133 (5.21) 0.053Intermediate (7-15m)

CE4 1.36* N/A N/A 0.055 3.72 (0.05) 0.026 141 (6.61) 0.053CE5 2.39* N/A N/A 0.035 5.24 (0.11) 0.032 164 (10.7) 0.043IW2 3.15 (0.02) 0.042 103 (8.09) 0.081 1.49 (0.05) 0.012 126 (17.3) 0.030LR1 5.54 (0.10) 0.037 149 (4.97) 0.087 3.79 (0.08) 0.027 136 (10.6) 0.081LS1 5.70(0.01) 0.038 151 (5.21) 0.092 4.63 (0.13) 0.029 156 (11.5) 0.083RB1 2.11* N/A N/A 0.057 2.70 (0.07) 0.016 176 (12.0) 0.031

ULF2 N/A N/A N/A N/A 3.36 (0.07) 0.019 108 (6.46) 0.044WK1 6.48 (0.10) 0.048 134 (1.79) 0.160 3.65 (0.05) 0.025 149 (7.86) 0.122BS1 1.8(0.054) 0.039 60.2 (2.03) 0.019 1.42 (0.03) 0.013 107 (8.36) 0.018

North Sea:Thames Estuary Shallow (≥ 5 m)

5 1.49* N/A N/A N/A 4.97 (0.21) 0.021 242 (29.1) 0.04112 10.4 (0.11) 0.034 225 (5.58) 0.049 5.81 (0.22) 0.022 260 (28.8) 0.05514 3.7 (0.017) 0.036 99.3 (1.11) 0.036 3.54 (0.14) 0.019 191 (24.3) 0.04315 4.21 (0.08) 0.043 95.7 (5.57) 0.042 N/A N/A N/A N/A16 7.65 (0.11) 0.034 220 (6.56) 0.031 5.0 (0.147) 0.019 275 (27.8) 0.03917 7.5 (0.15) 0.039 191 (6.06) 0.029 4.14 (0.14) 0.022 191 (18.5) 0.040

Dogger BankShallow (≥ 5 m)

34 5.7 (0.14) 0.024 172 (8.75) N/A 2.36 (0.13) 0.010 269 (44.8) 0.01636 3.12 (0.11) 0.036 86.4 (5.76) N/A N/A N/A N/A N/A39 2.41* N/A N/A N/A 3.86 (0.09) 0.016 235 (16.3) 0.02242 3.11* N/A N/A N/A 3.7 (0.07) 0.014 269 (16.7) 0.02855 0.9* N/A N/A 0.045 2.4 (0.11) 0.016 151 (24.8) 0.035

Deep (≤27m)45 2.89* N/A N/A 0.059 2.25 (0.11) 0.030 62.4 (11.8) 0.08649 2.37* N/A N/A 0.066 2.34 (0.18) 0.023 100 (26.6) 0.06851 2.62* N/A N/A 0.048 2.5 (0.16) 0.030 84 (18.7) 0.08553 3.63 (0.07) 0.060 60.3 (3.28) 0.049 1.85 (0.09) 0.021 88.4 (15.1) 0.06054 3.0* N/A N/A 0.067 2.06 (0.14) 0.032 64.5 (15.4) 0.091

Station 85 3.93 (0.15) 0.046 113 (9.27) 0.062 1.89 (0.138) 0.030 63.3 (16.6) 0.084

Ratio of FRRF/C-14 α = 2.07 Pbm = 1.70 Ek = 0.88 = 1.07

FRRF/CTD DEPLOYMENTS

The spatial variability in the vicinity of the Dogger Bank in FRRF derived photosynthetic parameter estimates together with chlorophyll and total nitrogen (ToXN) is shown in Figure 3.

Highest chlorophyll concentrations are associated with the base of the thermocline, the bottom front and in deeper layers on the Dogger Bank. Throughout the transect low chlorophyll and ToXN concentrations were recorded in the surface mixed layer. Beneath the thermocline in deeper water off the Bank, chlorophyll concentrations were also low, however ToXN concentrations were highest beneath and lowest above the thermocline (Figure 3a). . Highest productivity rates of up to 7 mg C(m-3) h-1 (Figure 3b) were recorded at the thermocline and within the euphotic zone (Figure 3a). In the surface mixed layer, productivity rates were generally lower and associated with the lowest chlorophyll and nutrient concentrations. There is also evidence of elevated levels of productivity in the vicinity of the bottom front and on the Dogger Bank. Figure 3c shows the FRRF measured quantum yield of carbon fixation (mol C (mol quanta) -1 with highest values were recorded at the base of the thermocline, in a region of sharp gradients in ToXN and at the base of the euphotic zone.

FRRF FLUORESCENCE PARAMETERS

Both σPS2 and Fv/Fm are used to derive FRRF based estimates of phytoplankton productivity. It has been noted previously that these parameters are sensitive to environmental forcing such as nutrient limitation, irradiance history, and mixing rates throughout the water column (Babin et al., 1996). During the present study, both parameters have been seen to respond to ambient physical and environmental conditions, spatially and temporally.

In order to highlight the role of the role of the vertical density structure on photosynthetic parameters table 2 shows value of Fv/Fm and σPS2 derived from observations at well mixed stations from the Thames region and stratified waters in the vicinity of the Dogger Bank. There is little change in depth in the value of these parameters from well mixed sites in the Thames.

Table 2. Fv/Fm (dimensionless) and σPS2 (m2 quanta-1) distribution throughout the water column in stratified and well mixed waters in the North Sea. The letters S, M, and D designate shallow, mid, and deep respectively. indicates the thermocline depth.

Stratified (Dogger Bank) Mixed (Thames Estuary)Depth (m) St.55 St.38 Depth (m) St. 5 St.17

Fv/Fm S 10 0.20 0.25 5 0.48 0.47(Dimensionless) M 35 0.54 0.60 15 0.54 0.54

D 40 0.52 0.58 20 0.55 0.52σPS2 S 10 200 800 5 485 500

(m2 quanta-1) M ~35 500 500 15 557 480D 40 550 500 20 560 539

To investigate high frequency temporal variability in photosynthetic parameters hourly observations were made at three 25-hour anchor stations. Results from work carried out in April 2000 in the Clyde Sea are shown to illustrate the time and depth varying changes in σPS2 and Fv/Fm during over complete daily cycle.

Projecttitle


MAFFproject code AE1020

There is diel variation in both Fv/Fm and σPS2 over the 25-hour period. In surface waters, minima in both parameters were measured at times of peak irradiance levels around midday. Fv/Fm recovered either side of midday in surface waters reaching highest values at night with a similar pattern evident for σPS2.. A pronounced thermocline (not indicated) was evident at between 10-15 m, and both Fv/Fm and σPS2 levels were observed to change in response to the apparent physical structure of the water column, again indicating adaptation of phytoplankton physiology. Maximal values of Fv/Fm are evident at night, below the thermocline, while highest σPS2 were found above the thermocline for the same period.

MODELLING

MODEL - IMPROVEMENTS, PARAMETERS, TEST CASES AND CLYDE SET-UP

The model described in this document is based on COHERENS - a Coupled Hydrodynamical-Ecological Model for Regional and Shelf Seas (Luyten et al., 1999), version 8.4 September 1999. In the original model the microplankton growth rate was calculated as a daily mean and implemented within a climate of 24 hour mean irradiance. Under this regime it was not possible to simulate diurnal behaviour or realistically model the processes of photosynthesis and respiration for comparison with short incubation primary production and FRRF (fast repetition rate fluorometer) observations. Three improvements were therefore made:

the introduction of a new model state variable of microplankton ‘energy’ or 'carbon biomass potential' to represent the primary products of photosynthesis;

improved parameterisation of microplankton absorption of PAR (photosynthetically active irradiance); the introduction of diurnally varying spectrally resolved irradiance with improved parameterisations for

attenuation by dissolved and particulate material.

The improved model was implemented initially for a laboratory batch culture, using the COHERENS ‘batch test case’. Subsequently the model was set-up for a 1-D water column to simulate the generalised seasonal evolution of physical, optical and biological parameters in the central North Sea. The changes were implemented in a realistically-forced 3-D simulation of the North Sea in 1988-89, but caused numerical problems. Finally, a detailed comparison was made between a 1-D simulation and observations made in Spring in the Clyde Sea, Scotland. This comparison will be found in the final part of this report.

In general, model equations have the form:Yt

Y Y , where Y is any state variable (for example,

carbon biomass), the first right-hand term refers to the divergence of the physical transport of the substance in 1, 2 or 3

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dimensions, and the second right-hand term gives 'nonconservative' or biological processes, which are those dealt with here.

1. Addition of 'carbon potential' state variable

The new model currency of microplankton ‘energy’ was introduced to represent the primary products of photosynthesis. These photosynthetically produced simple carbohydrates (e.g. starch) enter a pool from which they can be drawn on for the manufacture of biomass and which is also depleted by respiration required amongst other purposes for the maintenance of existing biomass that includes, for example, cell walls and membranes.. The pool may also be re-supplied, in adverse conditions such as light or nutrient limitation, by degrading biomass.

If the simulated microplankton are visualised as a mixed soup of chloroplasts and mitochondria (fig. 5), the stored energy pool is in fact a series of pools, each with a different timescale. These pools include the transient pool of sugars resulting directly from photosynthesis, excreted labile DOM (dissolved organic matter) available to heterotrophic bacteria, and new organic carbon in protozoa. The timescale of interest for the present model is that which sustains microplankton metabolism during a diurnal cycle, allowing change in biomass to be seen as a process that is averaged over 24 hours. 'Biomass', in this account, therefore refers to the more permanent parts of the organisms.

The modifications add a new state variable equation to the model, and a change to the growth equation.

New equation for stored energy, CP, in mmol of carbon equivalent m-3

(1) CP Ed f Ed X–

r BByCP

– GCP–

BByCP

+ f CPQ mmol m-3 s-1

-i- -ii- -iii- -iv- -v-

where the terms are:i: the rate of manufacture of stored energy as a result of trapping photons; photosynthetic efficiency m 1 a* is

as explained in the Introduction, the symbol being a quantum yield (in mmol CP E-1) and a* being the absorption cross-section of the photosynthetic pigment system, corrected to scalar irradiance (in m2 mg Chl-1); X

is chlorophyll concentration (in mg m-3); the function f Ed 1Ed

kE

2

(Tett, 1990, derived from Talling

1957) ensures saturation at high irradiances; ii: the loss of stored energy as a result of respiration (per unit biomass B, mmol C m-3) at rate r (s-1); see below

concerning the factor ByCP ;iii: the loss of stored energy as a result of mesozooplankton grazing with pressure G (s-1);iv: the loss due to conversion to organic carbon biomass B with efficiency ByCP (mmol C (mmol CP)-1) in

microplankton growing at rate (s-1);

v: the rate of reconversion from biomass to stored energy f CPQ CPy B rmn

B B0

:CPQCPQmin

:CPQCPQmin

, at rate rmnB s-1;

CP yB gives the efficiency of conversion from biomass to energy, not necessarily the same as that of the reverse conversion.

Term (v) is needed because at low illuminations the rate of energy gain can be less than that lost in growth, grazing and respiration. In this case, there must be some plundering of biomass to provide for basic metabolic needs. This re-conversion presumably occurs through reversal of synthetic pathways in phytoplankton and free-living heterotrophic bacteria. In the case of protozoa, however, the re-conversion is also the result of digestion of prey.

Equation for microplankton carbon biomass, B, in mmol carbon m-3

(2) B B – G B mmol m-3 d-1

-i- -ii-

where the terms are:

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i: gain due to growth, controlled by the availability, relative to biomass, of stored energy CPQ CP

B(dimensionless) and nutrient-nitrogen

NQ NB (mmol N (mmol C)-1); the growth equation is:

max f min f CPQ , f N Q , where f Q 1Qmin

Q for Q Qmin and f is an Arrhenius function

of temperature; the function f(Q) may be repeated to take account of other potentially limiting nutrients;ii: loss due to mesozooplankton grazing.

Term (i) in each equation is relevant to primary production. Term (i) in equation (1) corresponds to gross photosynthetic fixation and in principle to productivity as estimated by the FRRF. Term (i) in equation (2) is 'net microplankton primary production'.

The following table lists the parameters that are relevant to the energy-storage model. The column headed 'type' distinguishes parameters of type 'a' that are a property of the phytoplankton (autotroph) component - i.e. linked to chloroplasts or cyanobacterial cells - from those of type 'mp' that are a property of the microplankton as a whole. The category 'o' refers to (apparent) optical properties of the water or the light field. Estimation of values for the new parameters is discussed at length by Wild-Allen & Tett (2002).

Table of energy-storage model parameters

symbol description type (typical value) units quantum yield of stored energy from absorbed

photonsa (0.04) µmol C µE-1

m mean cosine of downwards PAR o (0.85)

a* absorption cross-section: see 'optical improvements'

o-a (0.3) m2 (mg chl)-1

chlorophyll to (microplankton biomass) carbon ratio: prescribed by standard microplankton model: XqN Qmax a

1

mp (0.2 - 0.5) mg chl (mmol C)-1

r energy pool decay rate = microplankton basal respiration when illuminated

mp (0.03-0.04) d-1

ByCP Efficiency with which stored energy is converted to biomass during growth

mp (0.46) mol C (mol C)-1

CP yB efficiency with which biomass is rendered into stored energy under low-light conditions


CPQminstored energy content (relative to microplankton biomass) for zero growth


CPQmaxupper limit to stored energy content (relative to microplankton biomass)


rmnB rate at which biomass is degraded to stored

energy under low-light conditionsmp 0.1 ?? s-1

kE 'saturation' coefficient in f Ed a 100 µE m-2 s-1

µmax maximum (microplankton) growth rate (at infinite Q etc)

mp 2.3 d-1 = 2.5E-5 s-1

Xq N yield of chlorophyll from nitrogen in phytoplankton

a 3 mg chl (mmol N)-1

Qmax amaximum autotroph nitrogen quota a (0.2) mmol N-1 (mmol C)-1

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heterotroph fraction (of microplankton biomass) mp 0.13 - 0.6

2. Optical improvements - spectral irradiance

In COHERENS, sea surface solar irradiance was supplied to the model as a single value for all wavelength solar radiation (QSOL in W m-2). This was partly reflected at the sea surface, partly absorbed as heat in the near surface layer and partly transmitted and attenuated with depth. The in-water light field was defined at each grid point and depth by a single value for spectrally integrated (400-700 nm), 24 hour mean, photosynthetically active radiation (PAR in W m-2). The absence of a diurnal cycle of illumination made detailed comparisons with biological observations difficult. The model was therefore updated to give spectral resolution and diel variation to the in-water light field and microplankton absorption of PAR. Several realistic optical models already exist (eg. Anderson 1993, Bowers, Harker et al. 1996). For this work the spectrum of in-water irradiance was evaluated more simply from spectrally resolved, incident solar radiation and diffuse attenuation.

A normalised solar spectrum was defined in 61 x 5 nm wavebands based on observations made on 5 May 1998 (a clear day) in Loch Etive (by Peter Wood, Strathclyde University). For a given day and latitude this spectrum was multiplied by incident noonday radiance which was either calculated from a sine wave of climatic data (Clarke 1986) or from the solar model included in the COHERENS subroutine SOLRAD. The noon-day solar radiance spectrum (QRAD) was adjusted to a daily cycle (QSOL) between sunrise (0600) and sunset (1800) using a sine wave correction.

QSOL = 0.95.QRAD.sin(2.(HOUR-5.5)/24)

The spectrum of attenuation at each depth (Kd()), was evaluated by summing the absorption spectra of each optically active substance in water, and applying a mean cosine () to take account of scattering, which in effect increases the length of the path traveled by photons.

Kd ( ) 1.(aw () ay() amss () adet( ) aph( ))

A model bandwidth of 5 nm between 400 and 700 nm resolved the spectrum of photosynthetically active radiation (PAR) into 61 wavebands.

The spectrum of absorption aw ( ) by pure sea water was taken from Pope and Fry (1997). Absorption due to yellow (humic) substance was calculated as an exponential function, which declined with increasing wavelength as:

ay() = ay440.e(-0.014.(-440))

where ay440 is the observed absorption of yellow substance at 440 nm. As humic substances tend to be most abundant in river water, ay440 was allowed to vary with salinity:

ay440 = 1 - 0.9*S/35

Absorption due to inorganic sediment was evaluated from observations made by Caren Binding (University of Wales, Bangor) in the Clyde Sea in May 2000. The specific absorption spectra of mineral suspended particles [retained on a filter paper after combustion at 500oC] was evaluated and fitted with a quadratic function, which declined with increasing wavelength:

amss()* = 6.0E-7. 2 - 0.0009. + 0.36

The absorption due to inorganic sediment was evaluated by multiplying the specific absorption spectrum (m2 g-1) by

sediment concentration (g m-3).Absorption due to phytoplankton pigments was evaluated from observations made by Karen Wild-Allen in the

Clyde Sea in May 2000. The absorption spectra of phytoplankton pigments contained in particles retained on a filter paper was evaluated by comparing the spectra of original and methanol bleached filters. By this method only methanol soluble pigments are resolved, but these include the most important chlorophylls, carotenoids and phaeopigments. The specific absorption spectra was calculated in units of m2(mg Chl)-1 by dividing by the ambient chlorophyll concentration. For comparison with observations the photosynthetic efficiency, 1 aph

* , was calculated using the realised

phytoplankton absorption cross-section aph* which varied with the spectral quality of the ambient light:

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Near surface heat absorption by PAR was previously, in COHERENS, governed by a single attenuation coefficient for pure sea water. This neglected the absorption potential of dissolved and particulate substances. To include these factors heat absorption was calculated using the mean value of the total attenuation spectrum (including sea water, yellow substance, inorganic sediment, organic detritus, and phytoplankton pigments).

Data from only 6 wavebands (centered on 412, 443, 490, 510, 555 and 665 nm) plus an integrated value for PAR (400-700 nm) were routinely saved during simulations. These wavelengths are similar to those at the centre of the narrow-spectral-band channels of the PR600 in-situ irradiance meter and correspond to channels 1-6 of the SeaWifs (Sea-viewing Wide Field of view Sensor) satellite colour sensor launched in 1997.

3. Batch test case

In this COHERENS test case with the improved model (Fig. 6), the aim was to simulate an experiment in which microplankton are grown in a mesocosm under controlled conditions with simplified physical processes. At the start of the experiment the mesocosm was inoculated with various concentrations of nutrients and a low biomass of microplankton. During the first phase of 30 days there was a repetitive diurnal cycle of illumination but no mesozooplankton grazing. Microplankton biomass increased rapidly, preferentially utilising ammonium and then nitrate until these nutrients were exhausted. Microplankton energy increased similarly but lagged the accumulation of biomass as energy was utilised in growth. Initially growth was controlled by energy, varying diurnally with the energy content of the microplankton which was topped up each day by photon capture. Overnight, stored energy reserves were depleted, causing the growth rate to fall, and, during the exponential growth phase (between days 15 and 23), some slight re-conversion of biomass to energy was necessary to maintain respiration. During this period when the growth rate declined overnight, there were slight oscillations in the nutrient quota at diurnal frequency. After free nutrients were exhausted, microplankton stored nitrogen was progressively depleted and growth rate switched between nutrient-limited during the day and energy-limited overnight, eventually becoming fully nutrient-limited and tending to zero. During this latter period, microplankton energy accumulated, as day-time energy capture exceeded respiratory loss.

The second phase of the experiment commenced on simulated day 30, when the light was turned off and zooplankton grazing was turned on. Microplankton biomass and energy were immediately depleted by grazing and respiration. Part of the grazed material directly returned to the water column as ammonium and was immediately taken up by the microplankton, briefly relieving nutrient limitation and allowing growth to continue until curtailed by dwindling energy reserves. To maintain the population, biomass was re-converted to energy, accelerating the depletion of microplankton which tended to zero by day 63. During this period zooplankton released a fraction of the grazed microplankton as organic detritus, and this organic nitrogen was slowly remineralised to ammonium and subsequently oxidised to nitrate. Eventually, given sufficient time, all the organic detritus would be remineralised and the system would revert nearly to its original condition, although part of the initial dissolved nitrogen would be contained in the zooplankton, and much of the remaining nitrogen would be oxidised to nitrate.

Optical conditions during the experiment were dominated by the microplankton. Attenuation increased with biomass and progressively reduced the available PAR. The maximum attenuation was achieved when all the free nutrient had been taken up (day 19), as attenuation due to microplankton depends on its chlorophyll content which is calculated by a fixed ratio with microplankton nitrogen. Spectral resolution showed rapid attenuation of short wave light at high microplankton concentrations. Attenuation remained high until grazing reduced the microplankton biomass, although organic detritus then made a small additional contribution to light absorption.

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4. NSBIO test case - simplified 1-D seasonal cycle in the North Sea

The modified model was used with the COHERENS NSBIO test case to simulate a seasonal cycle of depth-varying physics, optics, nutrients, and biology at station CS of the 1988-89 North Sea project (Fig. 7). This case ignores advection and is forced by annual sine-wave variation in meteorological terms including QRAD and a M2 (only) tidal cycle.

With the onset of spring stratification the near surface water warmed excessively when the new optical algorithms were implemented, and the surface mixed layer became unrealistically shallow (~10 m), restricting vertical mixing throughout the summer period. The problem was initially thought to relate to an overestimation of diffuse attenuation coefficient and heat absorption in surface water. However, scrutiny of the optical parameter values confirmed that they were realistic. Subsequently the problem was recognised to relate to the dispersion of heat in the surface layer, with the model underestimating near surface vertical mixing. An empirical solution was found by increasing the sea surface roughness lengthscale (x 3). This increased wind induced mixing and dispersed the surface heating through a deeper surface mixed layer (~20 m). Although observations at station CS indicate a mixed layer depth of ~40 m in late summer, this cannot be reproduced by a climatologically forced model, which lacks short timescale weather induced fluctuations. The simulation including enhanced sea surface roughness was in good agreement with the standard COHERENS test case simulation of station CS (Luyten et al., 1999).

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The simulation of microplankton by the improved model was also similar to that of Luyten et al., although the spring surface bloom was initiated (in May), about a month later than in the COHERENS NSBIO test case. This was probably caused by the limited availability of microplankton energy during early spring, constraining the growth rate. With the seasonal increase in PAR, microplankton energy accumulated and when the population was nutrient and energy replete a bloom began. Under strong stratification nutrients in the surface waters were rapidly exhausted and the surface bloom quickly ceased. As attenuation increased the quantity and spectral quality of light available for absorption was reduced, and the value of fell. For a short period sinking microplankton continued to grow at depth, making use of increasingly deeper nutrients and utilising stored energy captured in shallower water. Elevated attenuation resulting from particles associated with the bloom restricted the availability of PAR at depth and limited microplankton capture of photons. With the further assault of seasonally high mesozooplankton grazing the energy-depleted population declined rapidly. Organic detritus, generated as a by-product of grazing, continued to be remineralised following the microplankton bloom leading to a concentration of ammonium at depth which was subsequently oxidised to nitrate.

5. Model set-up for Firth of Clyde

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The improved model was set-up in 1-D to simulate a 25 hour time-series at station C1 in the Clyde Sea during 17-18 May 2000, for comparison with observations. For realistic representation of the tidal currents the physical model was spun-up for 3 days prior to the simulation. The implementation was similar to that of the NSBIO test case, except that the initial values of state variables were derived from conditions observed early on the 17 May. Time-dependent meteorological input used observed wind and dew point temperature and climatological solar radiation adjusted for 50% cloud cover at all times.

A detailed comparison (Fig. 8) between the observed and modelled water column properties over at a 25-hour anchor station in the Firth of Clyde allows a detailed assessment of the 1-D model performance. Observations of temperature (Fig 8a) show a thermocline at between 10 – 20 m that oscillates during the course of the anchor station suggesting either advection or the passage of an internal wave which have been observed in this region (F.Cottier, M.Inall & C.Griffiths, Challenger Centenary Conference, Marine Science 2002, Plymouth). Increased near surface concentrations of chlorophyll at night also suggest advection of water with different properties through the site. The observed variability was not reproduced by the model which remained stably stratified with a more uniform temperature, nutrient and chlorophyll structure throughout the anchor station.

Depth profiles (Fig 8b) of temperature, chlorophyll, nitrogen and PAR irradiance show that the model reproduced the range of observed values for all parameters but did not resolve the structure and variability in the thermocline. Modelled microplankton production shown in figure 8c represents the accumulation of carbon biomass, whilst photosynthetic production is the manufacture of simple carbon molecules (by absorption of PAR) which are stored in the transient 'energy' pool. The observed range of values lie in between the modelled values where microplankton and photosynthetic production are something like the minimum and maximum theoretical values for primary production. The high initial values of microplankton production throughout the water column likely result from an imbalance in the initialisation of microplankton nutrient and content.

6. 3-D simulation of the North Sea

This part of the work involved collaboration with Patrick Luyten of MUMM in Brussels, with whom KWA and PT had collaborated in the production of the COHERENS model (Luyten et al., 1999). The improved microplankton model (as described above) was incorporated into the COHERENS code during a visit by KWA to MUMM (Apr 2002). However, the improved, and more realistic, optics of the revised model resulted in excessive warming of the surface layer during a 1D simulation of the seasonal cycle at the North Sea station CS, as described above and empirically solved for the 1D test

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case. A proper solution of this problem requires further investigation, which was not possible within the time-frame of the present project. The over-warming, which was a result of absorption of solar energy by living and non-living particulates in the surface mixed layer, is an interesting example of feedback from biology to physics. It also illustrates a general difficult in physical-biological modelling, in that physical models that are parameterised to provide physically realistic simulations, may need rethinking to accommodate realistic biological feedback. Advancing coupling can thus be time-consuming. Meanwhile, the standard COHERENS code (in FORTRAN 77) has been installed on a P3-800 PC with 256 Mb RAM, compiled with GCC (GNU freeware) and run under Windows 95 to simulate conditions in the southern and central North Sea during 1998 and 1999 with realistic forcing and boundary conditions. A run takes about 5 days real time for 2 simulated months.

Discussion and SummaryModelling

A general conclusion emerging from a recent comparison and analysis of ecosystem model capability according to Moll and Radach (2001) is a view that increasing complexity in model formulation is required. In their opinion ERSEM (Baretta-Bekker et al., 1995) alone stands out as an example of a model encompassing the degree of required complexity for future needs. However, other schools of thought (Tett and Wilson, 2000) employ a different approach where simplicity is seen as the desirable attribute and increasing complexity only required when simple models fail. In this work we have introduced an increased level of complexity into a pre-existing model (COHERENS) in order to more realistically represent the way in which phytoplankton capture and store energy derived from photosynthesis and by more realistic representation of the underwater light field. New rate variables introduced into the model simulated at sub-daily timescales allow a more meaningful comparison of the observations obtained with the FRRF.

Work in batch test mode demonstrated that the new bio-optical model components behaved in a stable manner in a physically simple environment. In addition, rapid (sub-daily) changes in growth rate in response to time varying changes in the supply of light and nutrients demonstrated the enhanced capability of the model to simulate diurnal changes in physiological properties.

The next stage in model development and testing focused on a simulation of a complete seasonal cycle of phytoplankton with more realistic physics. Although good agreement between the new model simulation of the seasonal cycle and that obtained using a standard version of COHERENS was possible it was only achievable after tuning the model empirically by increasing wind mixing efficiency. The problem identified here was also found when the new model was incorporated in to a full COHERENS 3-D format used to simulate the southern North Sea. Most physical-biological coupling involves physical forcing and/or constraining of biological processes. In the present work there was also a biological feedback from biology to the physics through the more realistic representation of the underwater light field and specifically the attenuation of light and heat. Many physical models employ unrealistic values for attenuation of heat in order to disperse solar radiation through a layer sufficiently thick to simulate the development of a realistic density structure. Improving bio-optical realism led to poorer simulation of the density structure. This finding is significant in identifying a need for further work in this area and in illustrating a key philosophical issue in modelling. The issue concerns whether models are miniature numerical representations of the real world, or testable hypothesis about it. If the former, then ERSEM-like models of increasing complexity can be constructed by simple addition of model building

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blocks. If the latter, then each new parameterisation can have a holistic effect, leading to disproof of a modelling construct previously considered valid.

Finally, a comparison of the observations and simulations during a 25 hour time series demonstrated strengths of the model development. The realistic simulation of key aspects of primary productivity over a daily cycle at the station represents a successful outcome of the modelling component of this work. It is an important step forward towards a more realistic representation of the growth of phytoplankton in shelf-seas. The results also demonstrate that for the Clyde study site that local conditions and vertical properties, with regard to nutrient and light regime, are more important than horizontal variability in limiting and controlling rates of primary productivity. The implication is that the bio-optical improvements in the model, which can now simulate growth of phytoplankton in terms of rapidly-changing photosynthesis, have been successful.

Although we have identified specific problems with biological feedback that need to be addressed we have demonstrated that the improved model realistically simulated photosynthesis at the test site. The addition of such complexity addresses criticisms raised by Moll and Radach (2001) and in particular the need to better determine influence of light regime on algal growth and with regard to evaluating regional differences in light and nutrient limitation. The promise of improved regional scale estimates of production is yet to be delivered but an important step forward has been achieved and the problem preventing progress better defined.

Assessment of FRRF as a tool Here an overall assessment of the performance of the FRRF is given with an assessment of its capability as a tool to

allow rapid estimation of primary productivity and physiological status. There is contradictory evidence concerning the measurement of primary productivity and few direct comparisons with conventional methods. Our results confirm that the FRRF can be used to determine physiological status but that direct estimation of primary productivity while possible leads to overestimates and further work is required to address these current shortcomings. Comparison of photosynthetic parameters derived from the novel bio-optical (FRRF) and conventional (14-C) techniques has provided evidence of the potential effectiveness of the FRRF method. A significant correlations was found between estimates of maximum photosynthetic rate, Pbm, by each method, between both estimates of saturation irradiance, Ek, and both estimates of quantum yield, , but not between the different estimates of . The absolute values returned by the two techniques were different, with FRRF derived Pbm and being overestimated by 1.7 and 2.07 respectively, and Ek values underestimated by 0.8, relative to the C-14 method. The key parameter, the quantum yield of photosynthesis (), at some sites showed almost identical values when estimated by the two techniques, for example in the Cldye sea.. However, this finding does depend on the, values used as input variables for the calculation of the quantum yield.

Where we observed significant correlations between photosynthetic parameters then the use of a systematic offset to “correct” or calibrate the FRRF parameter estimates may be possible. However, this may prove difficult because of uncertainties in the parameterisation of the biophysical models use to link variable fluorescence measurements of the FRRF to photosynthesis. For example, the ratio of PS2 reaction centres to chlorophyll a (nPS2, mol e-1(mol Chl a)-1) and the quantum yield of electron transport in PS2 (e) are assumed constant in the biophysical model and a value of 0.002 for nPS2 is assumed in all calculations of FRRF productivity. However, algal cells tend to change nPS2 in response to different irradiance levels, as well as there being variation due to phytoplankton species (Dubinski et al., 1986; Falkowski & Kolber 1993). Since the FRRF measures fluorescence throughout the water column, where differences in vertical distribution of phytoplankton species composition may occur that in turn may be adapted to different sub-surface light regimes then nPS2 is unlikely to be constant. However, nPS2 is very difficult to measure and there are few reported field estimates (Falkowski and Raven, 1997; Raateoja and Seppala, 2001). Furthermore, the degree of variability of these properties in the vertical is likely to reflect the vertical density structure and the rate of mixing within each layer. So when the rate of vertical mixing is greater that the rate of adaptation of phytoplankton cells to the external light and nutrient regime then vertical differences in the biophysical model parameters (nPS2, e) are likely to be reduced.

This study has confirmed that some aspects of instrument performance need to be improved and that the factory calibration of the FRRF should not be automatically assumed to apply to any particular water. In optically clear waters where higher levels of irradiance penetrate deeper than more turbid coastal waters, there was often clear degradation of near surface fluorescence parameters, resulting in some cases, of exclusion of data from the upper 5-10 m of the water column. Problems also arise due to penetration of red photons present in a greater proportion in surface waters. The FRRF is particularly sensitive to red light as the fluorescence emission spectra is measured in the red end of the spectrum. A re-design of the optical head of the FRRF should eliminate this problem in future instruments.

The FRRF is pre-configured by the manufacturer for an expected range of chlorophyll concentrations based on a laboratory calibration which is quoted as extending to 30 mg m-3, the FRRF was found to saturate at chlorophyll

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concentrations of around ~15 mg m-3 in the sea. It is important to be aware of these limitations but in the present work levels above 15 mg m-3 were only encountered in the Clyde Sea during the spring bloom.

Reliance on manufacturer supplied post-processing software is also a potential source of error highlighted during discussion with other FRRF users at the Challenger 2002 Conference in held in September at the University of Plymouth. In particular the post-processing software supplied with each instrument to retrieve fluorescence parameters was regarded as not very robust especially when the instrument has been used at the limits of detection. This results from the non-trivial problem of fitting lines to fluorescence saturation curves. New software is being developed within the user community rather than by the manufacturer, and is now becoming freely available offering the possibility of re-analysis of the results from the present study. Preliminary results (pers. comm.) gathered elsewhere indicate a significant improvement in extraction of FRRF fluorescence parameters with the new software. The problems of extracting fluorescence parameters become important when the FRRF is used where chlorophyll concentrations are very low. Additionally, such software improvements would contribute to better data gathering, in waters with high suspended load concentrations, where a reduction in the signal to noise ratio may occur. This problem arises in part from increased scattering of photons by suspended particulate material.

The biophysical model linking fluorescence to photosynthesis uses FRRF fluorescence parameters, Fv/Fm and σPS2, to estimate production rates. Clear temporal (Clyde Sea, April 2000) and spatial (Irish Sea, July 2000; North Sea, August 2001) variation in these parameters were observed and appeared to respond either to the hydrographic regime, particularly vertical density structure and mixing, or variations in environmental variables such as nutrient supply or irradiance history. The evidence confirms the FRRF’s role as an ecophysiological tool with demonstrated potential for highlighting the relationships between biological productivity and chemical and physical process in marine ecosystems. For example figure 4 shows a clear diel cycle in Fv/Fm and σPS2 over a 25-hour period at an anchor station in the Clyde Sea that reflects changes daily irradiance. Changes in Fv/Fm and σPS2 with depth also relate to the vertical density structure and the differences in mixed layer depth properties with regard to the supply of nutrients and light.

Spatial variability in these parameters was also apparent for the Dogger Bank. Highest Fv/Fm values were associated with the chlorophyll maxima at the base of the thermocline and in the frontal region, with lowest values in the upper mixed layer and in the bottom mixed layers off the Bank. Interpretation of σ PS2 is complicated due to the range of factors that may affect its distribution. It has been demonstrated that σPS2 increases with decreasing irradiance (Dubinski et al., 1986; Sukenik et al., 1987) in which case highest values would be expected in deeper waters. The latter phenomenon is associated with photoadaptation. This pattern was observed for some stations over the Dogger Bank (see table 2). However, nutrient limitation can sometimes have a modifying effect, tending to increase σPS2 (Kolber, 1990). This was seen over the Dogger Bank where values increased sharply from around 500 m2 quanta-1 below the thermocline, to over 800 m2 quanta-1 in very low nutrient surface waters, even though irradiance levels were high. Distribution of both fluorescence parameters was more homogeneous in well-mixed waters with a high chlorophyll, nutrient, and suspended load content such as the Thames Estuary.

CONCLUSIONThe COHERENS model has been successfully updated to allow diurnal cycles of in-water optics and

microplankton growth to be simulated. Spectral resolution of incident solar radiation and its attenuation by dissolved and particulate substances now enables a more realistic description of the in-water irradiance field which drives algal photosynthesis. Improvements to the algorithms for microplankton light absorption and growth more accurately represent algal photochemistry. The addition of a microplankton stored energy pool distinguishes carbon used in metabolic processes (growth and respiration) from carbon forming the structural biomass of the micro-organisms. Derived parameter values give an indication of likely transfer efficiencies between the two carbon pools during periods of microplankton growth and of biomass to energy conversion (when microplankton energy content is limited).

Simulations of laboratory conditions were consistent with our understanding of algal physiology and microplankton ecologythese. Detailed examination of the results showed that microplankton accumulated energy during the latter part of the day which then sustained growth during short periods of darkness. Overnight, progressive energy depletion slowed growth rate and allowed luxury uptake of nutrients. Pre-dawn energy reserves were minimal and at times necessitated the re-conversion of biomass to energy to sustain the basic metabolic requirements.

Simulation of field conditions successfully demonstrated the evolution of a seasonal cycle of mixing and stratification at North Sea station CS. Some difficulties were found with the rapid absorption of heat in the near surface layer due in part to the more realistic representation of attenuation by dissolved and particulate substances. Rapid shoaling of the surface mixed layer restricted mixing which compounded the problem. A solution was found by increasing the surface drag coefficient. This effectively increased surface wind induced mixing and distributed the absorbed heat through a greater depth. Simulated nutrient and biological structures reflected the physical structure of the

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water column which promoted a surface microplankton bloom coincident with the onset of stratification in late spring. This rapidly evolved into a subsurface chlorophyll maximum which declined with the depletion of nutrients in the euphotic layer. [The stable structure of the water column and short duration of the microplankton bloom in this simulation resulted primarily from the smooth climatic forcing.]

Comparison of the model simulations with observations made at a 24 hour station in the Clyde Sea show general agreement. The model simulated a diurnal cycle of microplankton absorption and growth but was unable to reproduce the general variability in thermocline depth and the patchiness in the observed chlorophyll field. These were thought to result from advection through the site which would be better represented by a 3D model domain.

The FRRF was used in a number of contrasting hydrographic regions with different optical condition and levels of nutrient input around the UK. Qualitatively similar results were obtained when compared with a conventional method for measuring productivity, indicating the two approaches are measuring the same processes. In addition the FRRF demonstrated its value as an ecophysiological tool. Although reliable estimates of primary productivity cannot be obtained using the FRRF sufficient progress has been achieved to merit further work. The growing International and UK user community confirms the importance of the technique, and the increased level of interest will add momentum to the process of problem solving and development of improved hardware and software.

Similar findings have been reported recently (Suggett et al, 2001). However, improved parameterisation in the biophysical model introduced by Kolber & Falkowski (1993) to estimate FRRF productivity is needed before absolute rates tally. There is some evidence that a different model is required, and other research groups are moving in this direction (Suggett, pers com.). A number of instrument specific problems have been identified and some further development of the instrument is required in order to address these problems. A next generation instrument is now under development by the manufacturers and is expected to address issues identified in this study.

Recommendations for future workFurther work comparing estimates of productivity from the next generation FRRF and conventional methods is

required. These should employ new software and biophysical model improvements. Measurements could be carried out in constrained systems such as mesocosms (natural or artificial) to allow manipulation of environmental conditions so that a systematic assessment of the FRRF derived measurements can be undertaken. Outcomes would include improved parameter values used in the biophysical model employed for productivity estimates. Additional field work would be used to evaluate improvements in hardware and software with the aim of delivering an instrument that could be brought into routine use in monitoring programmes.

Further comparison of the improved model in 1-D form against high frequency observations of primary productivity (from FRRF &/or conventional methods) would enable confirmation of the agreement obtained in the present study. Data sets generated from field studies, such as those identified above, would be suitable for such comparisons. Inter-comparison with other models is required to quantify what improvement in estimates of productivity can be derived from the newly developed model. Additional work is also required to enable incorporation of biological feedback into the physical model component of ecosystem models.

The recommended work will be important in moving the necessary science forward to deliver ecosystem models with the necessary sophistication to to act as effective tools for assessing trophic status, predicting changes therein, and, with assimilation of real-time data from moorings and other sources, for surveillance and operational monitoring.

Actions resulting from the workThe capability of the current version of the FRRF needs to be borne in mind when interpreting estimates of

primary productivity derived from its use in the field. However, its capacity for mapping the physiological status of phytoplankton with the resulting indication of their likely response to nutrient inputs is relevant to future assessments and prediction of any steps to manage the input of anthropogenic nutrients to UK coastal waters. There is a need to ensure limitations and capability are brought to the attention of the wider user community through publication of results and reporting to appropriate scientific bodies such as ICES. A summary of results will be presented to the ICES Working Group on Phytoplankton at its next meeting.

Next generation ecosystem models need to ensure an appropriate level of sophistication in representing the underwater light field and in the way in which algal growth is parameterised and simulated. This is especially important in optically complex coastal waters where the bulk of anthropogenically driven production is likely to occur and where existing models are most likely to struggle in providing realistic estimates of production. Meeting these requirements is essential in models that may be used in future assessment of eutrophic status.

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Collaborative Institutes and projectsProf. Tett at Napier University, Edinburgh and Dr Bowers at University of Wales, Bangor were sub-contractors to

this project. Dr Bowers also collaborated on the basis of NERC grants investigating optical properties of seawater in conjunction with this project. Dr. Karen Wild-Allen was the post-doctoral research scientist for the project based at Aberdeen University while the research student funded by the project was based at University of Wales, Bangor. Dr Cunningham at University of Strathclyde employing the RV Prince Madog through a NERC grant collaborated in field work.

Publications and other reports during the projectWild-Allen, K., Lane, A. & Tett, P. (2002). Plankton, sediment and optical observations in Netherlands coastal water in

spring. Journal of Sea Research, 47, 303-315. Paper accepted for publication in Journal of Sea Research and acknowledges element of support from this contract.

Lee, J.-Y., Tett, P., Jones, K., Jones, S., Luyten, P., Smith, C. & Wild-Allen, K. (2002). The PROWQM physical-biological model with benthic-pelagic coupling applied to the northern North Sea. Journal of Sea Research, 47, in press. Paper accepted for publication in Journal of Sea Research and acknowledges element of support from this contract.

Raymond Delahunty, Joanne Slater, David Mills. 2002. The applicability of Fast Repetition Rate Fluorescence Techniques to Determine in situ Phytoplankton Primary Production and Photosynthetic Parameters in UK Coastal Waters. Poster and abstract presented at the ‘Phytoplankton Productivity’ international conference held at Bangor, March 18th-22nd , 2002,

David Mills, 2002. New Approaches to Detection and Assessment of Eutrophication. Oral paper and abstract presented at ICES International Workshop on Contrasting Approaches to Understanding Eutrophication Effects held in Den Haag, Netherlands 11-13th March 2002. Also a seminar held at the Hong Kong University of Science & Technology, June 21st 2002.

Wild-Allen, Karen, Paul Tett & David K. Mills. 2000. Using a biological model test case to improve the parameterisation of microplankton photosynthesis. Poster presented at 'Challenger Society for Marine Science Conference – UK. Marine Science 2000, September, University of East Anglia, Norwich, UK.

Wild-Allen, Karen, Paul Tett, Ray Delahunty, Dave Bowers, David K. Mills. 2002. Diurnal Cycles in Microplankton Growth: Modelling and Observations in the Clyde Sea. Poster presented at 'Phytoplankton Productivity: an appreciation of 50 years of the study of oceans and lakes', March 18th-22nd, University of Wales Bangor, Wales.

Wild-Allen, Karen, Paul Tett & David K. Mills. 2002. Using COHERENS (A Coupled Hydrodynamical-Ecological Model for Regional and Shelf Seas) to improve our understanding of biological processes and observations. Oral presentation at 'Challenger Society for Marine Science Conference - UK Marine Science 2002, September, University of Plymouth, Plymouth, UK.

Raymond Delahunty, David Mills. 2002. Phytoplankton Photosynthetic Characteristics in the North Sea determined by a Fast Repetition Rate Fluorometer (FRRF). Poster and abstract: 'Challenger Society for Marine Science Conference - UK Marine Science 2002, September, University of Plymouth, Plymouth, UK.

References in the text, but not included aboveBabin, M., Morel, A.., Claustre., H., Bricaud, A.., Kolber, Z., Falkowski, P.G., (1996). Nitrogen and irradiance dependent

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Baretta-Bekker, J.G. and Baretta, J.W., 1977. European Regional Seas Ecosystem Model (ERSEM) II. Journal of Sea Research, 38 (3-4).

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Boyd, P.W., Aiken, J. and Kolber, Z. (1997). Comparison of radiocarbon and fluorescence based (pump and probe) measurements of phytoplankton photosynthetic characteristics in the North East Atlantic Ocean. Marine Ecology Progress Series. 149: 215-22

Colijn, F., Gieskes, W. and Zevenboom, W, 1983. The measurement of primary production: problems and recommendations. Hydrobiological Bulletin, 17(1), 29-51

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Falkowski, P.G., Kolber, Z. (1995). Variations in Chlorophyll Fluorescence Yields in Phytoplankton in the World Oceans. Australian Journal of Plant Physiology. 22, 341-355.

Falkowski, P.G., Raven, J.A. (1997). Aquatic Photosynthesis. Blackwell Science. Cambridge

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Gorbunov, M.Y., Falkowski, P.J., Kolber, Z. (2000) Measurement of photosynthetic parameters in benthic organisms in situ using a SCUBA-based fast repetition rate fluorometer. Limnology and Oceanography. 45(1). 242-245

Kirkwood, D., 1996. Nutrients: Practical notes on their determination in sea water. ICES Techniques in Marine Environmental Sciences, no. 17 (23pp).

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Kolber, Z., Wyman, K.D., Falkowski, P.G. (1990). Natural variability in photosynthetic energy conversion efficiency: A field study in the Gulf of Maine. Limnology and Oceanography. 35:72-79

Kolber, Z. and Falkowski, P.G. (1992). Fast repetition rate (FRR) fluorometer for making in situ measurements of primary productivity. In, Proceedings of the Ocean 1992 Conference. 637-641.

Kolber, Z., Prášil, O. & Falkowski, P.G. (1998). Measurements of variable chlorophyll fluorescence using fast repetition techniques: defining methodology and experimental protocols. Biochemica Et Biophysica Acta. 1367: 88-106.

Kolber, Z., and Falkowski, P.G. (1993).Use of active fluorescence to estimate phytoplankton photosynthesis in situ. Limnology and Oceanography. 38(8): 1646-1665

Lewis, M.R and Smith, J.C. (1984) A small volume, short incubation-time method for measurement of photosynthesis as a function of incident irradiance. Marine Ecology Progress Series. 13: 99-102

Luyten, P. J., J. E. Jones, R. Proctor, A. Tabor, P. Tett and K. Wild-Allen (1999). COHERENS - a coupled hydrodynamical-ecological model for regional and shelf seas: user documentation. Management Unit of the Mathematical Models of the North Sea, Brussels, 911 pp.

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Suggett, D, Kraay, G, Holligan,P., Davey,M., Aiken, J., Geider, R. (2001). Assessment of photosynthesis in a spring cyanobacterial bloom by use of a fast repetition rate fluorometer. Limnology and Oceanography. 46(4). 802-810

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Tett, P. and V. Edwards (2002). Review of Harmful Algal Blooms in Scottish waters. SEPA, Stirling.Tett, P., I. Joint, D. Purdie, M. Baars, S. Oosterhuis, G. Daneri, F. Hannah, D. K. Mills, D. Plummer, A. Pomroy, A. W.

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