van Leeuwen et al. 
(built on an agreed baseline and acceptable deviation thereof), 
.ncludes the definition of a so-called “reference status” related to 
marine conditions undisturbed by anthropogenic inputs. As there 
are no suitable areas undisturbed by anthropogenic pressures that 
can serve as a reference area for the North-East Atlantic, an 
alternative method is to define a “historic” reference that 
tepresents a pre-eutrophic state (EC, 2003). Since observations 
{from such a period are lacking, these conditions will have to be 
estimated using ecosystem models. The objective of this study is 
‚herefore to define a common baseline representing pre-eutrophic 
conditions. This is a significant step forward towards science-based 
and coherent thresholds for marine eutrophication management, 
and away from previous thresholds based on expert judgment and 
national modelling efforts, and applied to nationally set 
assessment areas. 
The pre-eutrophic conditions shown in this study are not 
identical to pristine conditions (i.e. complete removal of 
anthropogenic influences and Europe largely covered in woods, 
see Billen & Garnier (1997)). In a previous study, Desmit et al. 
2018) reported that the N:P ratio averaged across the coastal 
North-East Atlantic would be lowered from -35 moIN molP“* 
under current conditions to -11 molN molP* under pristine 
conditions, which would foster diatoms and reduce the impact of 
Phaeocystis globosa in the southern bight of the North Sea. Here, the 
N:P ratio shows a decrease of similar magnitude, with a N:P ratio of 
55-25 molN molP* in the current state and of 30-15 moIlN molP“ 
in the pre-eutrophic state, suggesting that pre-eutrophic conditions 
may be sufficient to induce desirable phytoplankton community 
structures. Although most coastal systems of the North Sea are P- 
limited in the spring rather than N-limited, the high N:P ratios in 
many coastal areas would argue against further P reduction 
measures without accompanying N reductions as the N:P ratio 
shapes the structure of the phytoplankton communities (Cloern, 
2001; Conley et al., 2009). Therefore, any shift in this ratio should be 
applied to improve the phytoplankton community structure, and 
not foster undesirable species (Radach and Moll, 1990; Prins 
et al., 2012). 
10.3389/fmars.2023.1129951 
The observations used in the weighting method were obtained 
(rom the COMPEAT tool built by OSPAR, that draws on the ICES 
marine database (Supplementary Materials Figure S$1). Even though 
N and P are the main driving nutrients of primary production in 
shelf seas and therefore constitute the basis of our understanding of 
eutrophication processes, measurements of N and P concentrations 
in many areas (for example the Channel Well Mixed Tidally 
Influenced; CWMTI, Figure 5) are severely limited in number. 
This complicates comparison with model results offering more 
temporal and spatial resolution, and increases uncertainty. Figure 
51 in the supplementary materials highlights the spatial bias of the 
observations (available mainly in near-coastal zones), while the 
annual results (Figures S9-S15, Appendix F) highlight issues with 
temporal observational coverage. It is therefore not surprising that 
cost function results for the individual models (Appendix E) 
regularly show moderate or poor results. To have a good fit, the 
model results, averaged over all simulated years and the entire 
assessment area, should compare well with limited observations, 
mainly taken in the coastal zone during daylight hours in fair 
weather conditions. This discrepancy does not diminish the validity 
of observations, rather it highlights both their importance and their 
limitations (Skogen et al., 2021). In order to improve the applied 
method more observations in time and space are needed. Thereby, 
models can indicate where measurements are most needed to 
improve spatial and temporal coverage with respect to the 
processes being measured (Ferrarin et al., 2021). 
Within this exercise the added value of the satellite data for Chl 
has been clear, ensuring weighting factors for Chl in all assessment 
areas. The integration of in-situ and EO Chl data is an optimal 
remote sensing approach to provide water surface properties in 
coastal regions with high temporal and spatial resolution (Arabi 
et al., 2020). As such, Chl in-situ measurements are also vital, and 
more are needed. Note that satellites observe wavelength, and that a 
mathematical model is applied to derive surface chlorophyll a data 
from these. By including observations in the weighted results, the 
related observational uncertainties (due to limited data availability) 
are also imported. Improved observational coverage could 
mitigate this. 
4.2 Importance of observational data 
4.3 Level of confidence In model outputs 
Observational data are of prime importance for the applied 
approach: as input data, as validation data and as data used for 
weighting different model results in the ensemble. This immediately 
highlights issues with both avajilability of observations and their 
:‚emporal and spatial resolution. 
Chlorophyll a levels encompass diatom contributions, but these 
phytoplankton can only grow when silicate is available for them to 
build their cell walls. In the applied riverine inputs database 
information on silicate is lacking for Belgian, Danish, Irish and 
Spanish rivers. Total nitrogen data is lacking for British and Spanish 
rivers. Without realistic values for these nutrient inputs the models 
will invariably struggle to reproduce observed concentrations in the 
adjacent coastal zones. More coordinated riverine monitoring 
within Europe and subsequent central storage of sample data for 
zasy access could address these issues. 
“rontiers in Marıne © zie.17e 
All models used in this exercise have been extensively validated, 
and their respective cost functions are shown in Appendix E. Some 
models generally underestimate mean concentrations while others 
averestimate them: the ensemble approach ensures a balanced 
response. For chlorophyll a it is apparent that two models 
overestimate Chl concentrations (Deltares, SMHI) while the 
others show a consistent underestimation. In river plume areas 
Chl tends to be underestimated by nearly every model (Appendix 
P). The scatter plot of modelled weighted-average Chl P90 versus 
winter DIN in each area displays a linear relationship (results not 
shown, r” > 0.81). The slope of this relationship is 0.34 [ıg Chl/umol 
N, which is significantly lower than the slopes obtained from long 
time series on the Belgian and Dutch continental shelves, displaying 
respective values of 0.6 and 1.2 fg Chl/tuumol N (Desmit et al., 2015). 
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