Pelagic Nitrogen Cycling in Jiaozhou Bay, a Model Study I： the Conceptual Model

A zero-dimensional box model (PNCMjzb) with six state variables (ammonium, nitrate, dissolved organic nitrogen, phytoplankton, zooplankton and detritus) was developed to study nitrogen cycling in the Jiaozhou Bay pelagic ecosystem. The dominant processes within these compartments are considered with nitrogen as flow currency. Phytoplankton and zooplankton are treated as separate state variables, assuming that the species composition was dominated by two or three species the dynamic constants of which are similar and that they represent the entire plankton community. The microbial loop has not been integrated explicitly in the model. The turnover of bacteria is included implicitly in processes such as detritus decomposition, DON remineralization, pelagic nitrification and denitrification. The model is driven by two forcing variables, viz. water temperature and light intensity. Historical data from the1980s and 1990s were compiled and used for model calibration. In this paper (part Ⅰ), the consideration of every main compartment in the model is interpreted in detail. And the applied equations and parameters are presented. The main results from the simulations together with discussion about phytoplankton dynamics and primary production in Jiaozhou Bay are presented in the next paper (part Ⅱ).     

Examination of Daytime Length＇s Influence on Phytoplankton Growth in Jiaozhou Bay, China

This study showed how the daytime length in Jiaozhou Bay affected the water temperature, which in turn affected the phytoplankton growth when solar radiation was sufficient for phytoplankton photosynthesis. Jiaozhou Bay observation data collected from May 1991 to February 1994 were used to analyze the daytime length vs water temperature relationship. Our study showed that daytime length and the variation controlled the cycle of water temperature flunctuation. Should the cyclic variation curve of the daytime length be moved back for two months it would be superimposed with temperature change. The values of daytime length and temperature that calculated in the dynamical model of daytime length lag vs water temperature were consistent with observed values. The light radiation and daytime length in this model determined the photochemistry process and the enzymic catalysis process of phytoplankton photosynthesis. In addition, by considering the effect of the daytime length on water temperature and photosynthesis, we could comprehend the joint effect of daytime length, water temperature, and nutrients, on the spatiotemperal variation of primary production in Jiaozhou Bay.     

EXAMINATION OF SILICATE LIMITATION OF PRIMARY PRODUCTION IN JIAOZHOU BAY, CHINA——I. SILICATE BEING A LIMITING FACTOR OF PHYTOPLANKTON PRIMARY PRODUCTION

Jiaozhou Bay data collected from May 1991 to February 1994, in 12 seasonal investigations, and provided the authors by the Ecological Station of Jiaozhou Bay, were analyzed to determine the spatiotemporal variations in temperature, light, nutrients (NO3^--N, NO2^--N, NH4^ -N, SIO3^2--Si, PO4^3--P), phytoplankton, and primary production in Jiaozhou Bay. The results indicated that only silicate correlated well in time and space with, and had important effects on, the characteristics, dynamic cycles and trends of, primary production in Jiaozhou Bay. The authors developed a corresponding dynamic model of primary production and silicate and water temperature. Eq. ( 1 ) of the model shows that the primary production variation is controlled by the nutrient Si and affected by water temperature; that the main factor controlling the primary production is Si; that water temperature affects the composition of the structure of phytoplankton assemblage; that the different populations of the phytoplankton assemblage occupy different ecological niches for C, the apparent ratio of conversion of silicate in seawater into phytoplankton biomas and D, the coefficient of water temperature‘s effect on phytoplankton biomass. The authors researched the silicon source of Jiaozhou Bay, the biogeochemical sediment process of the silicon, the phytoplankton predominant species and the phytoplankton structure. The authors considered silicate a limiting factor of primary production in Jiaozhou Bay, whose decreasing concentration of silicate from terrestrial source is supposedly due to dilution by current and uptake by phytoplankton; quantified the silicate assimilated by phytoplankton, the intrinsic ratio of conversion of silicon into phytoplankton biomass, the proportion of silicate uptaken by phytoplankton and diluted by current; and found that the primary production of the phytoplankton is determined by the quantity of the silicate assimilated by them. The phenomenon of apparently high plant-nutrient concentTations but low phytoplankton biomass in some waters is reasonably explained in this paper.     

Diagnosis of Physical and Biological Controls on PhytoplanktonDistribution in the Sargasso Sea

The linkage between physical and biological processes is studied by applying a one-dimensional physical-biologicalcoupled model to the Sargasso Sea. The physical model is the Princeton Ocean Model and the biological model is a five-componentsystem including phytoplankton, zooplankton, nitrate, ammonium, and detritus. The coupling between the physical and biologicalmodel is accomplished through vertical mixing which is parameterized by the level 2.5 Mellor and Yamada turbulence closurescheme. The coupled model investigates the annual cycle of ecosystem production and the response to external forcing, such as heatflux, wind stress, and surface salinity, and the relative importance of physical processes in affecting the ecosystem. Sensitivity ex-periments are also carried out, which provide information on how the model bio-chemical parameters affect the biological system.The computed seasonal cycles compare reasonably well with the observations of the Bermuda Atlantic Time-series Study （BATS）.The spring bloom of phytoplankton occurs in March and April, right after the weakening of the winter mixing and before the estab-lishment of the summer stratification. The bloom of zooplankton occurs about two weeks after the bloom of phytoplankton. The sen-sitivity experiments show that zooplankton is more sensitive to the variations of biochemical parameters than phytoplankton.     

Carbon biomass,carbon-to-chlorophyll a ratio and the growth rate of phytoplankton in Jiaozhou Bay,China

Carbon biomass,carbon-to-chlorophyll a ratio(C:Chl a),and the growth rate of phytoplankton cells were studied during four seasonal cruises in 2017 and 2018 in Jiaozhou B ay,China.Water samples were collected from 12 stations,and phytoplankton carbon biomass(phyto-C) was estimated from microscopemeasured cell volumes.The phyto-C ranged from 5.05 to 78.52 μg C/L in the bay,and it constituted a mean of 38.16% of the total particulate organic carbon in the bay.High phyto-C values appeared mostly in the northern or northeastern bay.Diatom carbon was predominant during all four cruises.Dinoflagellate carbon contributed much less(30%) to the total phyto-C,and high values appeared often in the outer bay.The C:Chl a of phytoplankton cells varied from 11.50 to 61.45(mean 3 1.66),and high values appeared in the outer bay during all four seasons.The phyto-C was also used to calculate the intrinsic growth rates of phytoplankton cells in the bay,and phytoplankton growth rates ranged from 0.56 to 1.96/d;the rate was highest in summer(mean 1.79/d),followed by that in fall(mean 1.24/d) and spring(mean 1.17/d),and the rate was lowest in winter(mean 0.77/d).Temperature and silicate concentration were found to be the determining factors of phytoplankton growth rates in the bay.To our knowledge,this study is the first report on phytoplankton carbon biomass and C:Chl a based on water samples in Jiaozhou B ay,and it will provide useful information for studies on carbon-based food web calculations and carbon-based ecosystem models in the bay.     

INTERACTIONS BETWEEN AMMONIUM UPTAKE AND NITRATE UPTAKE BY NATURAL PHYTOPLANKTONASSEMBLAGES

Measurements of ammonium and nitrate uptakes by natural phytoplankton assemblages from Jiaozhou Bay at various combinations of ammonium and nitrate concentrations with 15N trace techniques showed that uptake rate of either nitrogen was influenced by the presence of the other but that the influence of ammonium on nitrate uptake was much greater than, that of nitrate on ammonium uptake. The influence mechanism of ammonium on nitrate uptake manifested as competition at lower concentrations and as inhibition at higher concentrations (ammonium concentration >0.6 umol/L), but no total inhibition appeared within the concentration.range of the experiments (0-10umol/L). The influence of nitrate on ammonium uptake seems to be a result of competition for uptake sites on the cell surface. In view of the in situ nutrient concentration in the given marine . environment, it is believed that both nitrogen sources are utilized by phytoplankton. Nitrate uptake in the presence of ammonium and ammonium uptake in the presen     

Multiple excitation wavelength fluorescence emission spectra technique for discrimination of phytoplankton

In vivo fluorescence methods are efficient tools for studying the distribution of phytoplankton in nature.Different algae species usually have different pigments with different ratios,which results in different fluorescence emission spectra.Based on multiple excitation wavelength fluorescence emission spectra,a discrimination technique is established in this study.The discrimination method,established by multivariate linear regression and weighted least-squares,was used to differentiate the samples cultured in the laboratory and collected from Jiaozhou Bay near Qingdao at the division level.The correctly discriminated samples were ≥ 86% for single algae samples,≥ 88% for simulatively mixed ones,≥ 91% for physically mixed ones and 100% for samples collected from Jiaozhou Bay.The result in this research is more definite for the physically mixed samples in the laboratory.The method described here can be employed to monitor the phytoplankton population in the marine environment.     

A coupled ice-ocean ecosystem model for 1-D and 3-D applications in the Bering and Chukchi Seas

Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments： three phytoplankton （pelagic diatom, flagellates and ice algae： D, F, and Ai） , three zooplankton （copepods, large zooplankton, and microzooplankton ： ZS, ZL, ZP） , three nutrients （ nitrate ＋ nitrite, ammonium, silicon ： NO3 , NH4, Si） and detritus （Det）. The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model.     

Influence of Seawater Temperature on Phytoplankton Growth in Jiaozhou Bay, China

The phytoplankton reproduction capacity (PRC), as a new concept regarding chlorophyll-a and primary production (PP) is described. PRC is different from PP, carbon assimilation number (CAN) or photosynthetic rate ( P^B ) . PRC quantifies phytoplankton growth with a special consideration of the effect of seawater temperature. Observation data in Jiaozhou Bay, Qingdao, China, collected from May 1991 to February 1994 were used to analyze the horizontal distribution and seasonal variation of the PRC in Jiaozhou Bay in order to determine the characteristics, dynamic cycles and trends of phytoplankton growth in Jiaozhou Bay; and to develop a corresponding dynamic model of seawater temperature vs. PRC. Simulation curves showed that seawater temperature has a dual function of limiting and enhancing PRC. PRC‘s periodicity and fluctuation are similar to those of the seawater temperature. Nutrient silicon in Jiaozhou Bay satisfies phytoplankton growth from June 7 to November 3. When nutrients N, P and Si satisfy the phytoplankton growth and solar irradiation is sufficient, the PRC would reflect the influence of seawater temperature on phytoplankton growth. Moreover, the result quantitatively explains the scenario of one-peak or two-peak phytoplankton reproduction in Jiaozhou Bay, and also quantitatively elucidates the internal mechanism of the one- or two-peak phytoplankton reproduction in the global marine areas.     

Numerical simulation of nutrient and phytoplankton dynamics in Guangxi coastal bays,China

The increasing riverine pollutants have resulted in nutrient enrichment and deterioration of water quality in the coastal water of Guangxi Province, China. However, the quantitative relationship between nutrient loads and water quality responses, which is crucial for developing eutrophication control strategies, is not well studied. In this study, the riverine fluxes of nutrients were quantified and integrated with nutrient cycling and phytoplankton dynamics by using box models for Guangxi coastal bays. The model concepts and biogeochemical equations were the same; while most model parameters were specific for each bay. The parameters were calibrated with seasonal observations during 2006–2007, and validated with yearly averaged measurements in 2009. The general features of nutrient and phytoplankton dynamics were reproduced, and the models were proved feasible under a wide range of bay conditions. Dissolved inorganic nitrogen was depleted during the spring algal bloom in Zhenzhu Bay and Fangcheng Bay with relatively less nutrient inputs. Phosphorus concentration was high in spring, which decreased then due to continuous phytoplankton consumption. Chlorophyll-a concentration reached its annual maximum in summer, but was the minimum in winter. Eutrophication was characterized by both an increase in nutrient concentrations and phytoplankton biomass in Lianzhou Bay. Either about 80% reduction of nitrogen or 70% reduction of phosphorus was required to control the algal bloom in Lianzhou Bay. Defects of the models were discussed and suggestions to the environmental protection of Guangxi coastal bays were proposed.     

Examination of Silicate Limitation of Primary Production in Jiaozhou Bay,North China：Ⅲ.Judgment Method ,Rules and Uniqueness of Nutrient Limitation Among N,P,and Si

Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994 (12 seasonal investigations) provided by the Ecological Station of Jiaozhou Bay revealed the characteristic spatiotemporal variation of the ambient concentration Si∶DIN and Si∶16P ratios and the seasonal variation of Jiaozhou Bay Si∶DIN and Si∶16P ratios showing that the Si∶DIN ratios were <1 throughout the year in Jiaozhou Bay; and that the Si∶16P ratios were <1 throughout Jiaozhou Bay in spring, autumn and winter. The results proved that silicate limited phytoplankton growth in spring, autumn and winter in Jiaozhou Bay. Analysis of the Si∶DIN and Si∶P ratios showed that the nutrient Si has been limiting the growth of phytoplankton throughout the year in some Jiaozhou Bay waters; and that the silicate deficiency changed the phytoplankton assemblage structure. Analysis of discontinuous 1962 to 1998 nutrient data showed that there was no N or P limitation of phytoplankton growth in that period. The authors consider that the annual cyclic change of silicate limits phytoplankton growth in spring, autumn and winter every year in Jiaozhou Bay; and that in many Jiaozhou Bay waters where the phytoplankton as the predominant species need a great amount of silicate, analysis of the nutrients N or P limitation of phytoplankton growth relying only on the N and P nutrients and DIN∶P ratio could yield inaccurate conclusions. The results obtained by applying the rules of absolute and relative limitation fully support this view. The authors consider that the main function of nutrient silicon is to regulate and control the mechanism of the phytoplankton growth process in the ecological system in estuaries, bays and the sea. The authors consider that according to the evolution theory of Darwin, continuous environmental pressure gradually changes the phytoplankton assemblage's structure and the physiology of diatoms. Diatoms requiring a great deal of silicon either constantly decrease or reduce their requirement for silicon. This will cause a series of huge changes in the ecosystem so that the whole ecosystem requires continuous renewal, change and balancing. Human beings have to reduce marine pollution and enhance the capacity of continental sources to transport silicon to sustain the continuity and stability in the marine ecosystem. This study was funded by the NSFC (No. 40036010) and subsidized by Special Funds from the National Key Basic Research Program of P. R. China (G199990437), the Postdoctoral Foundation of Ocean University of Qingdao, the Director's Foundation of the Beihai Monitoring Center of the State Oceanic Administration and the Foundation of Shanghai Fisheries University.     

Examination of Silicate Limitation of Primary Production in Jiaozhou Bay, North ChinaⅢ.Judgment Method, Rules and Uniqueness of Nutrient Limitation Among N, P, and Si

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Examination of Silicate Limitation of Primary Production in Jiaozhou Bay,China Ⅱ.Critical Value and Time of Silicate Limitation and Satisfaction of the Phytoplankton Growth

Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994 revealed the spatiotemporal variations of the ambient Si(OH)₄:NO₃ (Si:N) concentration rations and the seasonal variations of (Si:N) ratios in Jiaozhou Bay and showed that the Si:N ratios were <1 throughout Jiaozhou Bay in spring, autumn, and winter. These results provide further evidence that silicate limits the growth of phytoplankton (i.e. diatoms) in spring, autumn and winter. Moreover, comparison of the spatiotemporal variations of the Si:N ratio and primary production in Jiaozhou Bay suggested their close relationship. The spatiotemporal pattern of dissolved silicate matched well that of primary production in Jiaozhou Bay. Along with the environmental change of Jiaozhou Bay in the last thirty years, the N and P concentrations tended to rise, whereas Si concentration showed cyclic seasonal variations. With the variation of nutrient Si limiting the primary production in mind, the authors found that the range of values of primary production is divided into three parts: the basic value of Si limited primary production, the extent of Si limited primary production and the critical value of Si limited primary production, which can be calculated for Jiaozhou Bay by Equations (1), (2) and (3), showing that the time of the critical value of Si limitation of phytoplankton growth in Jiaozhou Bay is around November 3 to November 13 in autumn; and that the time of the critical value of Si satisfaction of phytoplankton growth in Jiaozhou Bay is around May 22 to June 7 in spring. Moreover, the calculated critical value of Si satisfactory for phytoplankton growth is 2.15–0.76 μmol/L and the critical value of Si limitation of phytoplankton growth is 1.42–0.36 μmol/L; so that the time period of Si limitation of phytoplankton growth is around November 13 to May 22 in the next year; the time period of Si satisfactory for phytoplankton growth is around June 7 to November 3. This result also explains why critical values of nutrient silicon affect phytoplankton growth in spring and autumn are different in different waters of Jiaozhou Bay and also indicates how the silicate concentration affects the phytoplankton assemblage structure. The dilution of silicate concentration by seawater exchange affects the growth of phytoplankton so that the primary production of phytoplankton declines outside Jiaozhou Bay earlier than inside Jiaozhou Bay by one and half months. This study showed that Jiaozhou Bay phytoplankton badly need silicon and respond very sensitively and rapidly to the variation of silicon. This study was funded by NSFC (No. 40036010) and subsidized by Special Funds from National Key Basic Research Program of P. R. China (G19990437), the Postdoctoral Foundation of Ocean University of Qingdao, the Director's Foundation of the Beihai Monitoring Center of the State Oceanic Administration and the Foundation of Shanghai Fisheries University.     

EXAMINATION OF SILICATE LIMITATION OF PRIMARY PRODUCTION IN JIAOZHOU BAY, CHINA I. SILICATE BEING A LIMITING FACTOR OF PHYTOPLANKTON PRIMARY PRODUCTION

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EXAMINATION OF SILICATE LIMITATION OF PRIMARY PRODUCTION IN JIAOZHOU BAY, CHINA Ⅰ. SILICATE BEING A LIMITING FACTOR OF PHYTOPLANKTON PRIMARY PRODUCTION

Jiaozhou Bay data collected from May 1991 to February 1994, in 12 seasonal investigations, and provided the authors by the Ecological Station of Jiaozhou B ay, were analyzed to determine the spatiotemporal variations in temperature, light, nutrients (NO-3-N, NO-2-N, NH+4-N, SiO2-3-Si, PO3-4-P), phytoplankton, and primary production in Jiaozhou Bay. The results indicated that only silicate correlated well in time and space with, and had important effects on, the characteristics, dynamic cycles and trends of, primary production in Jiaozhou Bay. The authors developed a corresponding dynamic model of primary production and silicate and water temperature. Eq.(1) of the model shows that the primary production variation is controlled by the nutrient Si and affected by water temp erature; that the main factor controlling the primary production is Si; that water temper ature affects the composition of the structure of phytoplankton assemblage; that the different populations of the phytoplankton assemblage occupy different ecologica l niches for C, the apparent ratio of conversion of silicate in seawater into phytoplankton biomas and D, the coefficient of water temperature's effect on phytoplankton biomass. The authors researched the silicon source of Jiaozhou Bay , the biogeochemical sediment process of the silicon, the phytoplankton predominan t species and the phytoplankton structure. The authors considered silicate a limit ing factor of primary production in Jiaozhou Bay, whose decreasing concentration of silicate from terrestrial source is supposedly due to dilution by current and up take by phytoplankton; quantified the silicate assimilated by phytoplankton, the intrins ic ratio of conversion of silicon into phytoplankton biomass, the proportion of silicate uptaken by phytoplankton and diluted by current; and found that the primary production of the phytoplankton is determined by the quantity of the silicate assimilated by them. The phenomenon of apparently high plant-nutrient concentrations but low phytoplankton biomass in some waters is reasonably explained in this paper.     

Examination of Daytime Length''''s Influence on Phytoplankton Growth in Jiaozhou Bay, China

1　INTRODUCTIONSystematicstudyisusefulforhumanvisualizationandcomprehensionofanetworkofcomplicatedcompo nentsandprocessesinvolvingfrequentenergyflow ,consideringenergyasthebasisofbothstructureandprocess (Automa ,1 993) .Energylanguageisaconceptfordepictingasysteminwhichallphenomenaareac companiedbyenergytransformation .Thefunctionoftheecosystemovertheworlddependsontheenergyfixationbymarineplantphotosynthesis ,mostofthemarefixedbymicrophytoplanktonnearseasurfaceexposedtosunlight (Niebaken …     

Diagnosis of physical and biological controls on phytoplankton distribution in the Sargasso Sea

The linkage between physical and biological processes is studied by applying a one-dimensional physical-biological coupled model to the Sargasso Sea. The physical model is the Princeton Ocean Model and the biological model is a five-component system including phytoplankton, zooplankton, nitrate, ammonium, and detritus. The coupling between the physical and biological model is accomplished through vertical mixing which is parameterized by the level 2.5 Mellor and Yamada turbulence closure scheme. The coupled model investigates the annual cycle of ecosystem production and the response to external forcing, such as heat flux, wind stress, and surface salinity, and the relative importance of physical processes in affecting the ecosystem. Sensitivity experiments are also carried out, which provide information on how the model bio-chemical parameters affect the biological system. The computed seasonal cycles compare reasonably well with the observations of the Bermuda Atlantic Time-series Study (BATS). The spring bloom of phytoplankton occurs in March and April, right after the weakening of the winter mixing and before the establishment of the summer stratification. The bloom of zooplankton occurs about two weeks after the bloom of phytoplankton. The sensitivity experiments show that zooplankton is more sensitive to the variations of biochemical parameters than phytoplankton.     

Silicon limitation on primary production and its destiny in Jiaozhou Bay, China——Ⅳ：Study on cross-bay transect from estuary to ocean

The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m³/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m²·d)/(m³/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m²·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.