Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea
Abstract
:1. Introduction
2. Materials and Methods
2.1. Input Data
2.2. Time Trends for Input Data
2.3. Ecological Model
2.4. Selection of Model Parameters and Validation
2.5. Relative Contributions
3. Results
3.1. Model Fit
3.2. Phytoplankton Time Trends
3.3. Relative Contributions
4. Discussion
4.1. Generalised Additive Modelling Performance
4.2. Comparison Phyto- and Zooplankton Modelling Results
4.3. Relative Contribution of the Key Determinants
4.4. Modelling with Field Data: Advantages and Limitations
4.5. Future Perspectives and Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. NPZD Model Parameters
Parameter | Minimum Possible Value | Maximum Possible Value | References |
---|---|---|---|
maxUptake | 0.25 (day−1) | 1.5 (day−1) | [19,108,109,110,111] |
excretionRate | 0.1 (day−1) | 0.2 (day−1) | [19,109,111] |
maxGrazing | 0.8 (day−1) | 1 (day−1) | [19,108,109,112,113] |
ksGrazing | 1 (mmol N m−3) | 4 (mmol N m−3) | [19,113,114] |
pFaeces | 0.2 (day−1) | 0.5 (day−1) | [19] |
mortalityRate | 0.25 ((mmol N m−3)−1 day−1) | 0.5 ((mmol N m−3)−1 day−1) | [19,108,109,110,111,113,115] |
ChlNratio | 1 (mg Chla/mmol N) | 8 (mg Chla/mmol N) | [50] |
ksPAR | 30 (µEinst m−2 s−1) | 250 (µEinst m−2 s−1) | [19] |
Tobs | 7 °C | 15 °C | [8,113,114] |
ksDIN | 0.25 (mmol N m−3) | 5 (mmol N m−3) | [8,19,108,109,110,111,112,113,114,115] |
ksP | 0.2 (mmol P m−3) | 0.5 (mmol P m−3) | [8,113] |
ksSi | 0.2 (mmol Si m−3) | 0.8 (mmol Si m−3) | [49] |
Kd * | 0.6 (m−1) | 1 (m−1) | Nearshore station |
0.27 | 0.67 | Midshore station | |
0.21 | 0.44 | Offshore stations |
Region of Interest | RMSE–Median (Q1–Q3) | Number of Simulations |
---|---|---|
Nearshore region | 1.34 (1.32–1.36) | 259 |
Midshore region | 0.44 (0.43–0.45) | 498 |
Offshore region | 0.40 (0.37–0.41) | 499 |
Variable | Nearshore Region | Midshore Region | Offshore Region |
---|---|---|---|
Chlorophyll-a | 39 | 37 | 98 |
Zooplankton | 40 | 37 | 96 |
Appendix B. Zooplankton Conversion
Taxon | Body Mass (mg C ind−1) | Species | Reference |
---|---|---|---|
Calanoida | 0.0006 | Acartia clausi, Temora longicornis, Paracalanus parvus, Centropages hamatus, Pseudocalanus elongatus, Centropages typicus and Calanus helgolandicus | [116] |
Noctiluca | 0.0003 | Noctiluca scintillans | [117] |
Harpacticoida | 0.001 | Euterpina acutifrons | [118] |
Appendicularia | 0.002 to 0.006 | Oikopleura dioica | [119] |
Taxon | C:N Ratio | Species | Reference |
---|---|---|---|
Calanoida | 5.5–7 | Acartia spp., Temora sp., Centropages, Oithona sp., Pseudo/Paracalanus spp. | [120] |
Noctiluca | 2.3–4.4 | Noctiluca scintillans | [121] |
Harpacticoida | 4.26–4.74 7.7–8.1 | Euterpina acutifrons | [122] [123] |
Appendicularia | 4.08 | Oikopleura dioica | [119] |
Appendix C. Smoothers for the Generalised Additive Models (GAMs) in the Three Regions of Interest, i.e., the Near-, Mid- and Offshore Regions
Appendix D. Time Trends of the GAMs in the Three Regions of Interest
Region of Interest | Nutrient and SST | RMSE | R2 |
---|---|---|---|
Nearshore region | DIN (mmol N m−3) | 9.61 | 0.30 |
PO4 (mmol P m−3) | 0.30 | 0.32 | |
SiO3 (mmol Si m−3) | 6.43 | 0.39 | |
SST (°C) | 1.82 | 0.94 | |
Midshore region | DIN (mmol N m−3) | 5.69 | 0.37 |
PO4 (mmol P m−3) | 0.23 | 0.30 | |
SiO3 (mmol Si m−3) | 3.65 | 0.29 | |
SST (°C) | 0.88 | 0.96 | |
Offshore region | DIN (mmol N m−3) | 2.33 | 0.55 |
PO4 (mmol P m−3) | 0.05 | 0.87 | |
SiO3 (mmol Si m−3) | 1.30 | 0.30 |
Nearshore Region | ||||||
---|---|---|---|---|---|---|
Variable | k- | k- | AIC | Adjusted R2 | k Performance | GAM |
s (day) | s (year) | |||||
PO4 | 3 | 3 | 58.1 | 0.19 | p-value < 0.05 for day, and 0.38 for year | PO4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 50.26 | 0.30 | p-value < 0.05 for day, and 0.23 for year | ||
5 | 5 | 46.96 | 0.35 | p-value < 0.05 for day, and 0.07 for year | ||
6 | 6 | 47.6 | 0.36 | p-value < 0.05 for day, and 0.06 for year | ||
NH4 | 3 | 3 | 203.54 | 0.04 | p-values > 0.05 for both smoothers | NH4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 201.27 | 0.12 | p-values > 0.05 for both smoothers | ||
5 | 5 | 202.06 | 0.11 | p-values > 0.05 for both smoothers | ||
NO2 | 3 | 3 | 44.04 | 0.08 | p-value < 0.05 for day, and 0.86 for year | NO2~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 33.44 | 0.25 | p-value < 0.05 for day, and 0.79 for year | ||
5 | 5 | 34.31 | 0.25 | p-value < 0.05 for day, and 0.81 for year | ||
NO3 | 3 | 3 | 336.38 | 0.25 | p-values < 0.05 for day, and 0.56 for year | NO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 334.55 | 0.31 | p-values < 0.05 for day and 0.26 for year | ||
5 | 5 | 335.36 | 0.32 | p-values < 0.05 for day, and 0.24 for year. | ||
SiO3 | 3 | 3 | 481.08 | 0.28 | p-value < 0.05 for day, and 0.22 for year | SiO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 476.2 | 0.35 | p-value < 0.05 for both smoothers | ||
5 | 5 | 471.5 | 0.41 | p-value < 0.05 for both smoothers | ||
6 | 6 | 472.28 | 0.41 | p-value < 0.05 for both smoothers | ||
Midshore Region | ||||||
Variable | k- | k- | AIC | Adjusted R2 | k Performance | GAM |
s(day) | s(year) | |||||
PO4 | 3 | 3 | 10.82 | 0.21 | p-values < 0.05 for day, and 0.67 for year | PO4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 4.53 | 0.30 | p-values < 0.05 for day, and 0.71 for year | ||
5 | 5 | 4.96 | 0.31 | p-values < 0.05 for day, and 0.69 for year | ||
NH4 | 3 | 3 | 136.188 | 0.09 | p-values > 0.05 for both smoothers | NH4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 136.19 | 0.09 | p-values > 0.05 for both smoothers | ||
5 | 5 | 136.188 | 0.09 | p-values > 0.05 for both smoothers | ||
NO2 | 3 | 3 | −27.8 | 0.37 | p-values < 0.05 for day, and 0.68 for year | NO2~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | −26.36 | 0.37 | p-values < 0.05 for day, and 0.72 for year | ||
5 | 5 | −25.81 | 0.37 | p-values < 0.05 for day, and 0.72 for year | ||
NO3 | 3 | 3 | 271.38 | 0.37 | p-values < 0.05 for day, and 0.31 for year | NO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 273.25 | 0.38 | p-values < 0.05 for day, and 0.41 for year | ||
5 | 5 | 274.05 | 0.38 | p-values < 0.05 for day, and 0.39 for year | ||
SiO3 | 3 | 3 | 362.68 | 0.28 | p-value < 0.05 for day, and 0.54 for year | SiO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 363.23 | 0.28 | p-value < 0.05 for day, and 0.56 for year | ||
5 | 5 | 363.47 | 0.28 | p-value < 0.05 for day, and 0.55 for year | ||
Offshore Region | ||||||
Variable | k- | k- | AIC | Adjusted R2 | k Performance | GAM |
s(day) | s(year) | |||||
PO4 | 3 | 3 | −281.69 | 0.80 | p-value < 0.05 for both smoothers | PO4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | −288.22 | 0.82 | p-values < 0.05 for both smoothers | ||
5 | 5 | −306.43 | 0.84 | p-values < 0.05 for both smoothers | ||
6 | 6 | −327.19 | 0.87 | p-values < 0.05 for both smoothers | ||
NH4 | 3 | 3 | 232.78 | 0.05 | p-values ≥ 0.05 for both smoothers | NH4~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 230.73 | 0.09 | p-values > 0.05 for both smoothers | ||
NO2 | 3 | 3 | −46.83 | 0.37 | p-values < 0.05 for both smothers | NO2~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | −72.92 | 0.51 | p-values < 0.05 for both smoothers | ||
5 | 5 | −92.81 | 0.60 | p-values < 0.05 for both smoothers | ||
6 | 6 | −125.85 | 0.70 | p-values < 0.05 for both smoothers | ||
NO3 | 3 | 3 | 430.44 | 0.62 | p-values < 0.05 for day, and 0.10 for year | NO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 431.57 | 0.62 | p-values < 0.05 for day, and 0.14 for year | ||
SiO3 | 3 | 3 | 401.81 | 0.26 | p-values < 0.05 for day, and 0.07 for year | SiO3~s(day, k = ks(day)) + s(year, k = ks(year)) |
4 | 4 | 399.83 | 0.28 | p-value < 0.05 for day, and 0.37 for year | ||
5 | 5 | 396.58 | 0.31 | p-value < 0.05 for day, and 0.47 for year | ||
6 | 6 | 396.99 | 0.31 | p-value < 0.05 for day, and 0.47 for year |
Appendix E. Model Validation: Observation versus Simulation
Appendix F. Simulated Zooplankton Abundances
Appendix G. The Relative Contributions of the Determinants of Phytoplankton Biomass Dynamics in the Near-, Mid-, and Offshore Regions
Appendix H. Diatom Cell Density in 2017
References
- Field, C.B.; Behrenfeld, M.J.; Randerson, J.T.; Falkowski, P. Primary Production of the Biosphere. Science 1998, 281, 237–240. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Carr, M.E.; Friedrichs, M.A.M.; Schmeltz, M.; Noguchi Aita, M.; Antoine, D.; Arrigo, K.R.; Asanuma, I.; Aumont, O.; Barber, R.; Behrenfeld, M.; et al. A Comparison of Global Estimates of Marine Primary Production from Ocean Color. Deep. Sea Res. 2 Top. Stud. Oceanogr. 2006, 53, 741–770. [Google Scholar] [CrossRef] [Green Version]
- de Baar, H. Von Liebig’ s Law of the Minimum and Plankton Ecology. Prog. Oceanogr. 1994, 33, 347–386. [Google Scholar] [CrossRef] [Green Version]
- Harpole, W.S.; Ngai, J.T.; Cleland, E.E.; Seabloom, E.W.; Borer, E.T.; Bracken, M.E.S.; Elser, J.J.; Gruner, D.S.; Hillebrand, H.; Shurin, J.B.; et al. Nutrient Co-Limitation of Primary Producer Communities. Ecol. Lett. 2011, 14, 852–862. [Google Scholar] [CrossRef] [PubMed]
- Price, N.M.; Morel, F.M.M. Colimitation of Phytoplankton Growth by Nickel and Nitrogen. Limnol. Oceanogr. 1991, 36, 1071–1077. [Google Scholar] [CrossRef]
- Irigoien, X.; Flynn, K.J.; Harris, R.P. Phytoplankton Blooms: A “loophole” in Microzooplankton Grazing Impact? J. Plankton Res. 2005, 27, 313–321. [Google Scholar] [CrossRef] [Green Version]
- Belpaeme, K.; Konings, P.; Vanhooren, S. De Kustatlas Vlaanderen/België; Coördinatiepunt Duurzaam Kustbeheer: Oostende, Belgium, 2011. [Google Scholar]
- Arndt, S.; Lacroix, G.; Gypens, N.; Regnier, P.; Lancelot, C. Nutrient Dynamics and Phytoplankton Development along an Estuary-Coastal Zone Continuum: A Model Study. J. Mar. Syst. 2011, 84, 49–66. [Google Scholar] [CrossRef]
- Desmit, X.; Nohe, A.; Borges, A.V.; Prins, T.; De Cauwer, K.; Lagring, R.; Van der Zande, D.; Sabbe, K. Changes in Chlorophyll Concentration and Phenology in the North Sea in Relation to De-Eutrophication and Sea Surface Warming. Limnol. Oceanogr. 2020, 65, 828–847. [Google Scholar] [CrossRef] [Green Version]
- Capuzzo, E.; Lynam, C.P.; Barry, J.; Stephens, D.; Forster, R.M.; Greenwood, N.; McQuatters-Gollop, A.; Silva, T.; van Leeuwen, S.M.; Engelhard, G.H. A Decline in Primary Production in the North Sea over 25 Years, Associated with Reductions in Zooplankton Abundance and Fish Stock Recruitment. Glob. Chang. Biol. 2018, 24, e352–e364. [Google Scholar] [CrossRef]
- Blauw, A.N.; Benincà, E.; Laane, R.W.P.M.; Greenwood, N.; Huisman, J. Predictability and Environmental Drivers of Chlorophyll Fluctuations Vary across Different Time Scales and Regions of the North Sea. Prog. Oceanogr. 2018, 161, 1–18. [Google Scholar] [CrossRef]
- Everaert, G.; De Laender, F.; Goethals, P.L.M.; Janssen, C.R. Relative Contribution of Persistent Organic Pollutants to Marine Phytoplankton Biomass Dynamics in the North Sea and the Kattegat. Chemosphere 2015, 134, 76–83. [Google Scholar] [CrossRef] [PubMed]
- Llope, M.; Chan, K.S.; Ciannelli, L.; Reid, P.C.; Stige, L.C.; Stenseth, N.C. Effects of Environmental Conditions on the Seasonal Distribution of Phytoplankton Biomass in the North Sea. Limnol. Oceanogr. 2009, 54, 512–524. [Google Scholar] [CrossRef]
- McQuatters-Gollop, A.; Raitsos, D.E.; Edwards, M.; Pradhan, Y.; Mee, L.D.; Lavender, S.J.; Attrill, M.J. A Long-Term Chlorophyll Data Set Reveals Regime Shift in North Sea Phytoplankton Biomass Unconnected to Nutrient Trends. Limnol. Oceanogr. 2007, 52, 635–648. [Google Scholar] [CrossRef]
- Van Lancker, V.; Baeye, M.; Evangelinos, D.; Eynde, D. Monitoring of the Impact of the Extraction of Marine Aggregates, in Casu Sand, in the Zone of the Hinder Banks; RBINS-OD Nature: Brussels, Belgium, 2015. [Google Scholar]
- Lacroix, G.; Ruddick, K.; Ozer, J.; Lancelot, C. Modelling the Impact of the Scheldt and Rhine/Meuse Plumes on the Salinity Distribution in Belgian Waters (Southern North Sea). J. Sea Res. 2004, 52, 149–163. [Google Scholar] [CrossRef]
- Turrell, W.R. New Hypotheses Concerning the Circulation of the Northern North Sea and Its Relation to North Sea Fish Stock Recruitment. ICES J. Mar. Sci. 1992, 49, 107–123. [Google Scholar] [CrossRef]
- Lacroix, G.; Ruddick, K.; Gypens, N.; Lancelot, C. Modelling the Relative Impact of Rivers (Scheldt/Rhine/Seine) and Western Channel Waters on the Nutrient and Diatoms/Phaeocystis Distributions in Belgian Waters (Southern North Sea). Cont. Shelf Res. 2007, 27, 1422–1446. [Google Scholar] [CrossRef]
- Soetaert, K.; Herman, P.M.J. A Practical Guide to Ecological Modelling; Springer: Dordrecht, The Netherlands, 2009; ISBN 978-1-4020-8623-6. [Google Scholar]
- Ivanov, E.; Capet, A.; De Borger, E.; Degraer, S.; Delhez, E.J.M.; Soetaert, K.; Vanaverbeke, J.; Grégoire, M. Offshore Wind Farm Footprint on Organic and Mineral Particle Flux to the Bottom. Front. Mar. Sci. 2021, 8, 631799. [Google Scholar] [CrossRef]
- Maes, F.; Schrijvers, J.; Vanhulle, A. Een Zee van Ruimte: Naar Een Ruimtelijk Structuurplan Voor Het Duurzaam Beheer van de Noordzee (GAUFRE); Federaal Wetenschapsbeleid: Brussel, Belgium, 2020. [Google Scholar]
- Ivanov, E.; Capet, A.; Barth, A.; Delhez, E.J.M.; Soetaert, K.; Grégoire, M. Hydrodynamic Variability in the Southern Bight of the North Sea in Response to Typical Atmospheric and Tidal Regimes. Benefit of Using a High Resolution Model. Ocean Model. 2020, 154, 101682. [Google Scholar] [CrossRef]
- Mortelmans, J.; Deneudt, K.; Cattrijsse, A.; Beauchard, O.; Daveloose, I.; Vyverman, W.; Vanaverbeke, J.; Timmermans, K.; Peene, J.; Roose, P.; et al. Nutrient, Pigment, Suspended Matter and Turbidity Measurements in the Belgian Part of the North Sea. Sci. Data 2019, 6, 22. [Google Scholar] [CrossRef] [Green Version]
- Mortelmans, J.; Goossens, J.; Amadei Martínez, L.; Deneudt, K.; Cattrijsse, A.; Hernandez, F. LifeWatch Observatory Data: Zooplankton Observations in the Belgian Part of the North Sea. Geosci. Data J. 2019, 6, 76–84. [Google Scholar] [CrossRef]
- Flanders Marine Institute (VLIZ). LifeWatch Observatory Data: Nutrient, Pigment, Suspended Matter and Secchi Measurements in the Belgian Part of the North Sea; Flanders Marine Institute (VLIZ): Ostend, Belgium, 2021. [Google Scholar] [CrossRef]
- Flanders Marine Institute (VLIZ). LifeWatch Observatory Data: Zooplankton Observations in the Belgian Part of the North Sea; Flanders Marine Institute (VLIZ): Ostend, Belgium, 2021. [Google Scholar] [CrossRef]
- Van Ginderdeuren, K.; Van Hoey, G.; Vincx, M.; Hostens, K. The Mesozooplankton Community of the Belgian Shelf (North Sea). J. Sea Res. 2014, 85, 48–58. [Google Scholar] [CrossRef]
- IVA MDK Flemish Banks Monitoring Network. Available online: https://meetnetvlaamsebanken.be/ (accessed on 30 March 2021).
- Zuur, A.F.; Ieno, E.N.; Walker, N.; Saveliev, A.A.; Smith, G.M. Mixed Effects Models and Extensions in Ecology with R; Springer: New York, NY, USA, 2009; ISBN 978-0-387-87457-9. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018. [Google Scholar]
- Lund-Hansen, L.C. Diffuse Attenuation Coefficients Kd(PAR) at the Estuarine North Sea-Baltic Sea Transition: Time-Series, Partitioning, Absorption, and Scattering. Estuar. Coast. Shelf Sci. 2004, 61, 251–259. [Google Scholar] [CrossRef]
- Kirk, J.T.O. Preface to the Second Edition. In Light and Photosynthesis in Aquatic Ecosystems; Cambridge University Press: Cambridge, UK, 1994; pp. xv–xvi. [Google Scholar]
- Thomann, R.V.; Mueller, J.A. Principles of Surface Water Quality Modeling and Control; Harper-Collins: New York, NY, USA, 1987. [Google Scholar]
- Microsoft Corporation; Weston, S. DoParallel: Foreach Parallel Adaptor for the “Parallel” Package 2020; Microsoft Corporation: Redmond, WA, USA, 2020. [Google Scholar]
- Wickham, H.; François, R.; Henry, L.; Müller, K. Dplyr: A Grammar of Data Manipulation. 2019. R Package Version 0.8.3. Available online: https://CRAN.R-project.org/package=dplyr (accessed on 30 March 2021).
- Microsoft; Weston, S. Foreach: Provides Foreach Looping Construct; Microsoft: Redmond, WA, USA, 2022; R package version 1.5.2; Available online: https://CRAN.R-project.org/package=foreach (accessed on 30 March 2021).
- Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
- Kassambara, A. Ggpubr: “ggplot2” Based Publication Ready Plots. 2019. R Package Version 0.2.4. Available online: https://CRAN.R-project.org/package=ggpubr (accessed on 30 March 2021).
- Grolemund, G.; Wickham, H. Dates and Times Made Easy with {lubridate}. J. Stat. Softw. 2011, 40, 1–25. [Google Scholar] [CrossRef]
- Wickham, H. The Split-Apply-Combine Strategy for Data Analysis. J. Stat. Softw. 2011, 40, 1–29. [Google Scholar] [CrossRef] [Green Version]
- Neuwirth, E. RColorBrewer: ColorBrewer Palettes. 2014. R Package Version 1.1-2. Available online: https://CRAN.R-project.org/package=RColorBrewer (accessed on 30 March 2021).
- Wickham, H. Reshaping Data with the {reshape} Package. J. Stat. Softw. 2007, 21, 1–20. [Google Scholar] [CrossRef]
- Garnier, S. Viridis: Default Color Maps from “Matplotlib”. 2018. R Package Version 0.5.0. Available online: https://github.com/sjmgarnier/viridis (accessed on 30 March 2021).
- Ryan, J.A.; Ulrich, J.M. Xts: EXtensible Time Series. 2020. R Package Version 0.12.1. Available online: https://CRAN.R-project.org/package=xts (accessed on 30 March 2021).
- Devlin, M.J.; Barry, J.; Mills, D.K.; Gowen, R.J.; Foden, J.; Sivyer, D.; Greenwood, N.; Pearce, D.; Tett, P. Estimating the Diffuse Attenuation Coefficient from Optically Active Constituents in UK Marine Waters. Estuar. Coast. Shelf Sci. 2009, 82, 73–83. [Google Scholar] [CrossRef] [Green Version]
- Gastwirth, J.L.; Gel, Y.R.; Hui, W.L.W.; Lyubchich, V.; Miao, W.; Noguchi, K. Lawstat: Tools for Biostatistics, Public Policy, and Law. 2020. R Package Version 3. Available online: https://CRAN.R-project.org/package=lawstat (accessed on 30 March 2021).
- Dinno, A. Dunn.Test: Dunn’s Test of Multiple Comparisons Using Rank Sums. 2017. R Package Version 1.3.5. Available online: https://CRAN.R-project.org/package=dunn.test (accessed on 30 March 2021).
- Muylaert, K.; Gonzales, R.; Franck, M.; Lionard, M.; Van der Zee, C.; Cattrijsse, A.; Sabbe, K.; Chou, L.; Vyverman, W. Spatial Variation in Phytoplankton Dynamics in the Belgian Coastal Zone of the North Sea Studied by Microscopy, HPLC-CHEMTAX and Underway Fluorescence Recordings. J. Sea Res. 2006, 55, 253–265. [Google Scholar] [CrossRef]
- Lancelot, C.; Spitz, Y.; Gypens, N.; Ruddick, K.; Becquevort, S.; Rousseau, V.; Lacroix, G.; Billen, G. Modelling Diatom and Phaeocystis Blooms and Nutrient Cycles in the Southern Bight of the North Sea: The MIRO Model. Mar. Ecol. Prog. Ser. 2005, 289, 63–78. [Google Scholar] [CrossRef]
- Alvarez-Fernandez, S.; Riegman, R. Chlorophyll in North Sea Coastal and Offshore Waters Does Not Reflect Long Term Trends of Phytoplankton Biomass. J. Sea Res. 2014, 91, 35–44. [Google Scholar] [CrossRef]
- European Environment Agency (EEA). Mean Chlorophyll-a (Chla) Concentrations in European Seas, 2013–2017; European Environment Agency (EEA): Copenhagen, Denmark, 2019. [Google Scholar]
- Colella, S.; Falcini, F.; Rinaldi, E.; Sammartino, M.; Santoleri, R. Mediterranean Ocean Colour Chlorophyll Trends. PLoS ONE 2016, 11, e0155756. [Google Scholar] [CrossRef] [Green Version]
- Lundsør, E.; Stige, L.C.; Sørensen, K.; Edvardsen, B. Long-Term Coastal Monitoring Data Show Nutrient-Driven Reduction in Chlorophyll. J. Sea Res. 2020, 164, 101925. [Google Scholar] [CrossRef]
- Xu, X.; Lemmen, C.; Wirtz, K.W. Less Nutrients but More Phytoplankton: Long-Term Ecosystem Dynamics of the Southern North Sea. Front. Mar. Sci. 2020, 7, 662. [Google Scholar] [CrossRef]
- Jiang, L.; Gerkema, T.; Kromkamp, J.C.; Van Der Wal, D.; Manuel Carrasco De La Cruz, P.; Soetaert, K. Drivers of the Spatial Phytoplankton Gradient in Estuarine-Coastal Systems: Generic Implications of a Case Study in a Dutch Tidal Bay. Biogeosciences 2020, 17, 4135–4152. [Google Scholar] [CrossRef]
- Nohe, A.; Goffin, A.; Tyberghein, L.; Lagring, R.; De Cauwer, K.; Vyverman, W.; Sabbe, K. Marked Changes in Diatom and Dinoflagellate Biomass, Composition and Seasonality in the Belgian Part of the North Sea between the 1970s and 2000s. Sci. Total Environ. 2020, 716, 136316. [Google Scholar] [CrossRef]
- Speeckaert, G.; Borges, A.V.; Champenois, W.; Royer, C.; Gypens, N. Annual Cycle of Dimethylsulfoniopropionate (DMSP) and Dimethylsulfoxide (DMSO) Related to Phytoplankton Succession in the Southern North Sea. Sci. Total Environ. 2018, 622–623, 362–372. [Google Scholar] [CrossRef] [Green Version]
- Flanders Marine Institute (VLIZ). LifeWatch Observatory Data: Phytoplankton Observations by Imaging Flow Cytometry (FlowCam) in the Belgian Part of the North Sea; Flanders Marine Institute (VLIZ): Ostend, Belgium, 2021. [Google Scholar] [CrossRef]
- Martínez, L.A.; Mortelmans, J.; Dillen, N.; Debusschere, E.; Deneudt, K. LifeWatch Observatory Data: Phytoplankton Observations in the Belgian Part of the North Sea. Biodivers. Data J. 2020, 8, e57236. [Google Scholar] [CrossRef] [PubMed]
- Deschutter, Y.; Everaert, G.; De Schamphelaere, K.; De Troch, M. Relative Contribution of Multiple Stressors on Copepod Density and Diversity Dynamics in the Belgian Part of the North Sea. Mar. Pollut. Bull. 2017, 125, 350–359. [Google Scholar] [CrossRef]
- Brylinski, J.M. The Pelagic Copepods in the Strait of Dover (Eastern English Channel). A Commented Inventory 120 Years after Eugène Canu. Cah. Biol. Mar. 2009, 50, 251–260. [Google Scholar]
- Wright, J.C. The Limnology of Canyon Ferry Reservoir. I. Phytoplankton-Zooplankton Relationships in the Euphotic Zone During September and October, 1956. Limnol. Oceanogr. 1958, 3, 150–159. [Google Scholar] [CrossRef]
- Behrenfeld, M.J.; Boss, E.S. Student’s Tutorial on Bloom Hypotheses in the Context of Phytoplankton Annual Cycles. Glob. Chang. Biol. 2018, 24, 55–77. [Google Scholar] [CrossRef] [Green Version]
- Mortelmans, J.; Aubert, A.; Reubens, J.; Otero, V.; Deneudt, K.; Mees, J. Copepods (Crustacea: Copepoda) in the Belgian Part of the North Sea: Trends, Dynamics and Anomalies. J. Mar. Syst. 2021, 220, 103558. [Google Scholar] [CrossRef]
- Leitão, S.N.; Junior, M.D.M.; Porto Neto, F.D.F.; Silva, A.P.; Garcia Diaz, X.F.; e Silva, T.D.A.; Vieira, D.A.D.N.; Figueiredo, L.G.P.; da Costa, A.E.S.F.; de Santana, J.R.; et al. Connectivity between Coastal and Oceanic Zooplankton from Rio Grande Do Norte in the Tropical Western Atlantic. Front. Mar. Sci. 2019, 6, 287. [Google Scholar] [CrossRef]
- Moore, E.; Sander, F. A Comparative Study of Zooplankton from Oceanic, Shelf, and Harbor Waters of Jamaica. Assoc. Trop. Biol. Conserv. 1979, 11, 196–206. [Google Scholar] [CrossRef]
- Reece, J.B.; Urry, L.A.; Cain, M.L.; Wasserman, S.A.; Minorsky, P.V.; Jackson, R.B. Photosynthesis. In Campbell Biology, 9th ed.; Benjamin Cummings: San Francisco, CA, USA, 2011; pp. 230–251. [Google Scholar]
- Trombetta, T.; Vidussi, F.; Mas, S.; Parin, D.; Simier, M.; Mostajir, B. Water Temperature Drives Phytoplankton Blooms in Coastal Waters. PLoS ONE 2019, 14, e0214933. [Google Scholar] [CrossRef] [Green Version]
- Edwards, K.F.; Thomas, M.K.; Klausmeier, C.A.; Litchman, E. Phytoplankton Growth and the Interaction of Light and Temperature: A Synthesis at the Species and Community Level. Limnol. Oceanogr. 2016, 61, 1232–1244. [Google Scholar] [CrossRef] [Green Version]
- van der Zee, C.; Chou, L. Seasonal Cycling of Phosphorus in the Southern Bight of the North Sea. Biogeosciences 2005, 2, 27–42. [Google Scholar] [CrossRef] [Green Version]
- Gowen, R.J.; McCullough, G.; Kleppel, G.S.; Houchin, L.; Elliott, P. Are Copepods Important Grazers of the Spring Phytoplankton Bloom in the Western Irish Sea? J. Plankton Res. 1999, 21, 465–483. [Google Scholar] [CrossRef]
- Burson, A.; Stomp, M.; Akil, L.; Brussaard, C.P.D.; Huisman, J. Unbalanced Reduction of Nutrient Loads Has Created an Offshore Gradient from Phosphorus to Nitrogen Limitation in the North Sea. Limnol. Oceanogr. 2016, 61, 869–888. [Google Scholar] [CrossRef] [Green Version]
- Fettweis, M.; Van Den Eynde, D. The Mud Deposits and the High Turbidity in the Belgian-Dutch Coastal Zone, Southern Bight of the North Sea. Cont. Shelf Res. 2003, 23, 669–691. [Google Scholar] [CrossRef]
- Ruddick, K.; Lacroix, G. Hydrodynamics and Meteorology of the Belgian Coastal Zone. In Current Status of Eutrophication in the Belgian Coastal Zone; Rousseau, V., Lancelot, C., Cox, D., Eds.; Presses Universitaires de Bruxelles: Bruxelles, Belgium, 2006. [Google Scholar]
- Belgische Staat Initiële Beoordeling Voor de Belgische Mariene Wateren. Kaderrichtlijn Mariene Strategie—Art 8 Lid 1a & 1b; Belgische Staat Initiële Beoordeling Voor de Belgische Mariene Wateren: Brussels, Belgium, 2012. [Google Scholar]
- Rousseau, V.; Youngje, P.; Ruddick, K.; Vyverman, W.; Parent, J.Y.; Lancelot, C. Phytoplankton Blooms in Response to Nutrient Enrichment. In Current Status of Eutrophication in the Belgian Coastal Zone; Rousseau, V., Lancelot, C., Cox, D., Eds.; Presses Universitaires de Bruxelles: Bruxelles, Belgium, 2006. [Google Scholar]
- van Leeuwen, S.; Tett, P.; Mills, D.; van der Molen, J. Stratified and Nonstratified Areas in the North Sea: Long-term Variability and Biological and Policy Implications. J. Geophys. Res. Oceans 2015, 120, 4670–4686. [Google Scholar] [CrossRef] [Green Version]
- Richard, M.; Archambault, P.; Thouzeau, G.; Desrosiers, G. Influence of Suspended Mussel Lines on the Biogeochemical Fluxes in Adjacent Water in the Îles-de-La-Madeleine (Quebec, Canada). Can. J. Fish. Aquat. Sci. 2006, 63, 1198–1213. [Google Scholar] [CrossRef]
- Nizzoli, D.; Welsh, D.T.; Viaroli, P. Seasonal Nitrogen and Phosphorus Dynamics during Benthic Clam and Suspended Mussel Cultivation. Mar. Pollut. Bull. 2011, 62, 1276–1287. [Google Scholar] [CrossRef]
- Cugier, P.; Struski, C.; Blanchard, M.; Mazurié, J.; Pouvreau, S.; Olivier, F.; Trigui, J.R.; Thiébaut, E. Assessing the Role of Benthic Filter Feeders on Phytoplankton Production in a Shellfish Farming Site: Mont Saint Michel Bay, France. J. Mar. Syst. 2010, 82, 21–34. [Google Scholar] [CrossRef]
- Nielsen, T.G.; Maar, M. Effects of a Blue Mussel Mytilus Edulis Bed on Vertical Distribution and Composition of the Pelagic Food Web. Mar. Ecol. Prog. Ser. 2007, 339, 185–198. [Google Scholar] [CrossRef] [Green Version]
- Forster, R. The Effect of Monopile-Induced Turbulence on Local Suspended Sediment Pattern around UK Wind Farms: Field Survey; The Crown Estate: London, UK, 2018. [Google Scholar]
- Flynn, K.J.; Mitra, A.; Wilson, W.H.; Kimmance, S.A.; Clark, D.R.; Pelusi, A.; Polimene, L. ‘Boom-and-busted’ Dynamics of Phytoplankton–Virus Interactions Explain the Paradox of the Plankton. New Phytol. 2022, 234, 990–1002. [Google Scholar] [CrossRef]
- Brussaard, C.P.D. Viral Control of Phytoplankton Populations—A Review. J. Eukaryot. Microbiol. 2004, 51, 125–138. [Google Scholar] [CrossRef]
- Biggs, T.E.G.; Huisman, J.; Brussaard, C.P.D. Viral Lysis Modifies Seasonal Phytoplankton Dynamics and Carbon Flow in the Southern Ocean. ISME J. 2021, 15, 3615–3622. [Google Scholar] [CrossRef]
- Holmström, K.E.; Järnberg, U.; Bignert, A. Temporal Trends of PFOS and PFOA in Guillemot Eggs from the Baltic Sea, 1968–2003. Environ. Sci. Technol. 2005, 39, 80–84. [Google Scholar] [CrossRef]
- Coull, B.C.; Chandler, G.T. Pollution and Meiofauna—Field, Laboratory, and Mesocosm Studies. Oceanogr. Mar. Biol. 1992, 30, 191–271. [Google Scholar]
- Schlüter, M.H.; Kraberg, A.; Wiltshire, K.H. Long-Term Changes in the Seasonality of Selected Diatoms Related to Grazers and Environmental Conditions. J. Sea Res. 2012, 67, 91–97. [Google Scholar] [CrossRef] [Green Version]
- Berdalet, E.; Peters, F.; Koumandou, V.L.; Roldán, C.; Guadayol, Ò.; Estrada, M. Species-Specific Physiological Response of Dinoflagellates to Quantified Small-Scale Turbulence. J. Phycol. 2007, 43, 965–977. [Google Scholar] [CrossRef] [Green Version]
- Jakobsen, H.H.; Markager, S. Carbon-to-Chlorophyll Ratio for Phytoplankton in Temperate Coastal Waters: Seasonal Patterns and Relationship to Nutrients. Limnol. Oceanogr. 2016, 61, 1853–1868. [Google Scholar] [CrossRef]
- Butenschön, M.; Clark, J.; Aldridge, J.N.; Icarus Allen, J.; Artioli, Y.; Blackford, J.; Bruggeman, J.; Cazenave, P.; Ciavatta, S.; Kay, S.; et al. ERSEM 15.06: A Generic Model for Marine Biogeochemistry and the Ecosystem Dynamics of the Lower Trophic Levels. Geosci. Model. Dev. 2016, 9, 1293–1339. [Google Scholar] [CrossRef] [Green Version]
- Pätsch, J.; Kühn, W. Nitrogen and Carbon Cycling in the North Sea and Exchange with the North Atlantic—A Model Study. Part I. Nitrogen Budget and Fluxes. Cont. Shelf Res. 2008, 28, 767–787. [Google Scholar] [CrossRef]
- Capuzzo, E.; Stephens, D.; Silva, T.; Barry, J.; Forster, R.M. Decrease in Water Clarity of the Southern and Central North Sea during the 20th Century. Glob. Chang. Biol. 2015, 21, 2206–2214. [Google Scholar] [CrossRef] [Green Version]
- Zscheischler, J.; Westra, S.; van den Hurk, B.J.J.M.; Seneviratne, S.I.; Ward, P.J.; Pitman, A.; AghaKouchak, A.; Bresch, D.N.; Leonard, M.; Wahl, T.; et al. Future Climate Risk from Compound Events. Nat. Clim. Chang. 2018, 8, 469–477. [Google Scholar] [CrossRef]
- IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate; Pörtner, H.-O., Roberts, D.C., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., Weyer, N.M., Eds.; Cambridge University Press: Cambridge, UK, 2019. [Google Scholar]
- Paerl, H.W.; Valdes, L.M.; Peierls, B.L.; Adolf, J.E.; Harding, L.W. Part 2: Eutrophication of Freshwater and Marine Ecosystems. Limnol. Oceanogr. 2006, 51, 351–355. [Google Scholar]
- IPCC. Summary for Policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, B.Z., Zhai, V.P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021. [Google Scholar]
- Benedetti, F.; Vogt, M.; Elizondo, U.H.; Righetti, D.; Zimmermann, N.E.; Gruber, N. Major Restructuring of Marine Plankton Assemblages under Global Warming. Nat. Commun. 2021, 12, 5226. [Google Scholar] [CrossRef]
- Ferreira, A.; Costa, R.R.; Dotto, T.S.; Kerr, R.; Tavano, V.M.; Brito, A.C.; Brotas, V.; Secchi, E.R.; Mendes, C.R.B. Changes in Phytoplankton Communities Along the Northern Antarctic Peninsula: Causes, Impacts and Research Priorities. Front. Mar. Sci. 2020, 7, 576254. [Google Scholar] [CrossRef]
- Hays, G.; Richardson, A.; Robinson, C. Climate Change and Marine Plankton. Trends Ecol. Evol. 2005, 20, 337–344. [Google Scholar] [CrossRef]
- Chakraborty, K.; Kumar, N.; Girishkumar, M.S.; Gupta, G.V.M.; Ghosh, J.; Udaya Bhaskar, T.V.S.; Thangaprakash, V.P. Assessment of the Impact of Spatial Resolution on ROMS Simulated Upper-Ocean Biogeochemistry of the Arabian Sea from an Operational Perspective. J. Oper. Oceanogr. 2019, 12, 116–142. [Google Scholar] [CrossRef]
- Latasa, M. Improving Estimations of Phytoplankton Class Abundances Using CHEMTAX. Mar. Ecol. Prog. Ser. 2007, 329, 13–21. [Google Scholar] [CrossRef] [Green Version]
- Mackey, M.D.; Mackey, D.J.; Higgins, H.W.; Wright, S.W. CHEMTAX—A Program for Estimating Class Abundances from Chemical Markers: Application to HPLC Measurements of Phytoplankton. Mar. Ecol. Prog. Ser. 1996, 144, 265–283. [Google Scholar] [CrossRef] [Green Version]
- Pan, H.; Li, A.; Cui, Z.; Ding, D.; Qu, K.; Zheng, Y.; Lu, L.; Jiang, T.; Jiang, T. A Comparative Study of Phytoplankton Community Structure and Biomass Determined by HPLC-CHEMTAX and Microscopic Methods during Summer and Autumn in the Central Bohai Sea, China. Mar. Pollut. Bull. 2020, 155, 111172. [Google Scholar] [CrossRef]
- Wells, M.L.; Karlson, B.; Wulff, A.; Kudela, R.; Trick, C.; Asnaghi, V.; Berdalet, E.; Cochlan, W.; Davidson, K.; De Rijcke, M.; et al. Future HAB Science: Directions and Challenges in a Changing Climate. Harmful Algae 2020, 91, 101632. [Google Scholar] [CrossRef]
- Wiltshire, K.H.; Malzahn, A.M.; Wirtz, K.; Greve, W.; Janisch, S.; Mangelsdorf, P.; Manly, B.F.J.; Boersma, M. Resilience of North Sea Phytoplankton Spring Bloom Dynamics: An Analysis of Long-Term Data at Helgoland Roads. Limnol. Oceanogr. 2008, 53, 1294–1302. [Google Scholar] [CrossRef] [Green Version]
- Assante, M.; Candela, L.; Castelli, D.; Cirillo, R.; Coro, G.; Frosini, L.; Lelii, L.; Mangiacrapa, F.; Pagano, P.; Panichi, G.; et al. Enacting Open Science by D4Science. Future Gener. Comput. Syst. 2019, 101, 555–563. [Google Scholar] [CrossRef]
- Kishi, M.J.; Kashiwai, M.; Ware, D.M.; Megrey, B.A.; Eslinger, D.L.; Werner, F.E.; Noguchi-Aita, M.; Azumaya, T.; Fujii, M.; Hashimoto, S.; et al. NEMURO—A Lower Trophic Level Model for the North Pacific Marine Ecosystem. Ecol. Modell. 2007, 202, 12–25. [Google Scholar] [CrossRef] [Green Version]
- Fan, W.; Lv, X. Data Assimilation in a Simple Marine Ecosystem Model Based on Spatial Biological Parameterizations. Ecol. Modell. 2009, 220, 1997–2008. [Google Scholar] [CrossRef]
- Cropp, R.; Norbury, J. Parameterising Competing Zooplankton for Survival in Plankton Functional Type Models. Ecol. Modell. 2010, 221, 1852–1864. [Google Scholar] [CrossRef]
- Ruzicka, J.J.; Wainwright, T.C.; Peterson, W.T. A Simple Plankton Model for the Oregon Upwelling Ecosystem: Sensitivity and Validation against Time-Series Ocean Data. Ecol. Modell. 2011, 222, 1222–1235. [Google Scholar] [CrossRef]
- Beşiktepe, Ş.T.; Lermusiaux, P.F.J.; Robinson, A.R. Coupled Physical and Biogeochemical Data-Driven Simulations of Massachusetts Bay in Late Summer: Real-Time and Postcruise Data Assimilation. J. Mar. Syst. 2003, 40–41, 171–212. [Google Scholar] [CrossRef]
- Billen, G.; Garnier, J. The Phison River Plume: Coastal Eutrophication in Response to Changes in Land Use and Water Management in the Watershed. Aquat. Microb. Ecol. 1997, 13, 3–17. [Google Scholar] [CrossRef]
- Brandt, G.; Wirtz, K.W. Interannual Variability of Alongshore Spring Bloom Dynamics in a Coastal Sea Caused by the Differential Influence of Hydrodynamics and Light Climate. Biogeosciences 2010, 7, 371–386. [Google Scholar] [CrossRef]
- Xu, J.; Hood, R.R. Modeling Biogeochemical Cycles in Chesapeake Bay with a Coupled Physical-Biological Model. Estuar. Coast. Shelf Sci. 2006, 69, 19–46. [Google Scholar] [CrossRef]
- Brun, P.; Payne, M.R.; Kiørboe, T. A Trait Database for Marine Copepods. Earth Syst. Sci. Data 2016, 9, 99–113. [Google Scholar] [CrossRef] [Green Version]
- Löder, M.G.J.; Kraberg, A.C.; Aberle, N.; Peters, S.; Wiltshire, K.H. Dinoflagellates and Ciliates at Helgoland Roads, North Sea. Helgol. Mar. Res. 2012, 66, 11–23. [Google Scholar] [CrossRef] [Green Version]
- Sautour, B.; Castel, J. Spring Zooplankton Distribution and Production of the Copepod Euterpina Acutifrons in Marennes-Oléron Bay (France). Hydrobiologia 1995, 310, 163–175. [Google Scholar] [CrossRef]
- Lombard, F.; Renaud, F.; Sainsbury, C.; Sciandra, A.; Gorsky, G. Appendicularian Ecophysiology I: Food Concentration Dependent Clearance Rate, Assimilation Efficiency, Growth and Reproduction of Oikopleura Dioica. J. Mar. Syst. 2009, 78, 606–616. [Google Scholar] [CrossRef]
- Van Nieuwerburgh, L.; Wänstrand, I.; Snoeijs, P. Growth and C:N:P Ratios in Copepods Grazing on N- or Si-Limited Phytoplankton Blooms. Hydrobiologia 2004, 514, 57–72. [Google Scholar] [CrossRef]
- Tada, K.; Pithakpol, S.; Yano, R.; Montani, S. Carbon and Nitrogen Content of Noctiluca Scintillans in the Seto Inland Sea, Japan. J. Plankton Res. 2000, 22, 1203–1211. [Google Scholar] [CrossRef]
- Szyper, J.P. Nutritional Depletion of the Aquaculture Feed Organisms Euterpina Acutifrons, Artemia Sp. and Brachionus Plicatilis During Starvation. J. World Aquac. Soc. 1989, 20, 162–169. [Google Scholar] [CrossRef]
- Abdel-Moati, M.A.R.; Atta, M.M.; Khalil, A.N.; Nour-El-Din, N.M. Carbon, Nitrogen and Phosphorus Content of the Copepod Euterpina Acutrifons in the Coastal Waters of Alexandria, Egypt. Bull. Nat. Inst. Ocn. Fish. 1993, 173–190. [Google Scholar]
- Wood, S.N. Generalized Additive Models: An Introduction with R, 2nd ed.; Chapman and Hall/CRC: Boca Raton, FL, USA, 2017. [Google Scholar]
- Wood, S.N. Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models. J. R. Stat. Soc. B 2011, 73, 3–36. [Google Scholar] [CrossRef] [Green Version]
Parameter | Unit | Period | Nearshore Region | Midshore Region | Offshore Region |
---|---|---|---|---|---|
maxUptake | day−1 | Spring | 0.38–0.66 | 0.38–0.61 | 0.50–1.12 |
Autumn | 0.40–0.78 | 0.38–0.80 | 0.38–0.90 | ||
excretionRate | day−1 | Spring | 0.16–0.18 | 0.12–0.16 | 0.11–0.15 |
Autumn | 0.11–0.17 | 0.11–0.15 | 0.11–0.14 | ||
maxGrazing | day−1 | Spring | 0.87–0.96 | 0.85–0.92 | 0.88–0.97 |
Autumn | 0.88–0.96 | 0.85–0.93 | 0.89–0.97 | ||
ksGrazing | mmol N m−3 | Spring | 2.15–3.27 | 1.54–2.15 | 1.48–2.22 |
Autumn | 1.31–2.27 | 1.19–1.59 | 1.25–1.94 | ||
pFaeces | day−1 | Spring | 0.29–0.41 | 0.27–0.40 | 0.27–0.40 |
Autumn | 0.25–0.40 | 0.24–0.32 | 0.25–0.37 | ||
mortalityRate | (mmol N m−3)−1 day−1 | Spring | 0.28–0.39 | 0.28–0.41 | 0.29–0.41 |
Autumn | 0.32–0.44 | 0.35–0.42 | 0.33–0.45 | ||
ChlNratio | mg Chla (mmol N)−1 | Spring | 7.00–7.86 | 6.78–7.60 | 6.65–7.47 |
Autumn | 6.62–7.55 | 5.33–6.84 | 4.33–6.61 | ||
ksPAR | µEinst m−2 s−1 | Spring | 126–227 | 133–224 | 103–210 |
Autumn | 121–205 | 126–200 | 115–210 | ||
Tobs | °C | Spring | 9.86–13.84 | 10.11–13.29 | 9.54–13.62 |
Autumn | 10.41–12.83 | 9.66–13.86 | 10.08–13.62 | ||
ksDIN | mmol N m−3 | Spring | 1.33–4.21 | 1.62–3.94 | 1.92–3.70 |
Autumn | 1.17–3.64 | 2.22–4.11 | 2.07–4.29 | ||
ksP | mmol P m−3 | Spring | 0.30–0.43 | 0.28–0.44 | 0.30–0.44 |
Autumn | 0.33–0.40 | 0.29–0.46 | 0.30–0.44 | ||
ksSi | mmol Si m−3 | Spring | 0.41–0.66 | 0.43–0.67 | 0.34–0.67 |
Autumn | 0.35–0.63 | 0.40– 0.67 | 0.39–0.65 | ||
Kd * | m−1 | Spring | 0.73–0.90 | 0.44–0.60 | 0.28–0.38 |
Autumn | 0.77–0.92 | 0.45–0.60 | 0.28–0.38 |
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Otero, V.; Pint, S.; Deneudt, K.; De Rijcke, M.; Mortelmans, J.; Schepers, L.; Martin-Cabrera, P.; Sabbe, K.; Vyverman, W.; Vandegehuchte, M.; et al. Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea. J. Mar. Sci. Eng. 2023, 11, 1510. https://doi.org/10.3390/jmse11081510
Otero V, Pint S, Deneudt K, De Rijcke M, Mortelmans J, Schepers L, Martin-Cabrera P, Sabbe K, Vyverman W, Vandegehuchte M, et al. Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea. Journal of Marine Science and Engineering. 2023; 11(8):1510. https://doi.org/10.3390/jmse11081510
Chicago/Turabian StyleOtero, Viviana, Steven Pint, Klaas Deneudt, Maarten De Rijcke, Jonas Mortelmans, Lennert Schepers, Patricia Martin-Cabrera, Koen Sabbe, Wim Vyverman, Michiel Vandegehuchte, and et al. 2023. "Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea" Journal of Marine Science and Engineering 11, no. 8: 1510. https://doi.org/10.3390/jmse11081510
APA StyleOtero, V., Pint, S., Deneudt, K., De Rijcke, M., Mortelmans, J., Schepers, L., Martin-Cabrera, P., Sabbe, K., Vyverman, W., Vandegehuchte, M., & Everaert, G. (2023). Pronounced Seasonal and Spatial Variability in Determinants of Phytoplankton Biomass Dynamics along a Near–Offshore Gradient in the Southern North Sea. Journal of Marine Science and Engineering, 11(8), 1510. https://doi.org/10.3390/jmse11081510