A comparative study on the affinities for inorganic carbon uptake, nitrate and phosphate between marine diatoms and dinoflagellates

A comparative study on the affinities for inorganic carbon

uptake, nitrate and phosphate between marine diatoms and

dinoflagellates

Mr. T. (Thomas) Hofman - 11066938

Institute for Biodiversity and Ecosystem Dynamics (IBED) Supervised by: mw. dr. J.H.M. Verspagen

Abstract:

Eutrophication and increasing atmospheric carbon dioxide concentrations are water quality concerns threatening our drinking water and food supply due to a rise in harmful cyanobacterial and harmful algal blooms. Understanding which factors determine the species distribution of phytoplankton could help to prevent the increase of these blooms in the future. Growth is thought to be limited by the scarcest resource available. As eutrophic waters are, by definition, rich in macronutrients such as nitrate and phosphate, inorganic carbon limitation becomes more significant in population dynamics as a limiting factor. Moreover, due to increased growth rates in eutrophied oceans, inorganic carbon depletes faster. An in silico literature research on the the affinity for phosphate, nitrate and inorganic carbon in marine diatom and dinoflagellate species gave insights in species distribution, based on in vivo uptake kinetics, field measurements and uptake mechanisms of both taxonomic groups. The affinity for nitrate and inorganic carbon was significantly higher dinoflagellates. This difference could explain the species composition in marine environments. . According to findings in this research, dinoflagellates are better adapted, based on their affinity for nutrients and inorganic carbon, to oligotrophic and Ci depleted environments.

1. Introduction

Phytoplankton blooms can severely decrease water quality, threatening drinking water and food supply. Anthropogenic increase of atmospheric carbon dioxide (CO2) concentrations and nutrient

enrichment alter hydrological patterns and strongly influence the duration, frequency and intensity of harmful cyanobacterial blooms (HCB’s) (Visser et al, 2016) and harmful algal blooms (HAB’s) (Smith and Schindler, 2009). Eutrophication has become one of the main water quality issues for aquatic ecosystems and despite being extensively researched in the past decades, many interactions remain to be understood (Smith and Schindler, 2009). The main causes for eutrophication are anthropogenic waste waters, manure and fertilizer use, and nitrogen emissions by industries. It is well established that both CO2 increase and nutrient increase positively affect growth in phytoplankton

species (Visser et al, 2016).

Acquisition of macronutrients and resistance to grazers and diseases are fundamental physiological processes in the distribution of phytoplankton (Litchman et al. 2007). Growth and reproduction for instance, are frequently limited by P and N in aquatic ecosystems (Elser et al. 2007). N and P availability are thought to be the major determinant in species composition in oligotrophic waters according to resource competition models (Burson et al. 2018). In eutrophic waters however where N and P availability are high, other factors such as CO2 and light availability are more likely to

limit phytoplankton growth (Smith 1986, Hein 1997) and determine interspecific competition (Huisman et al. 1999, Ji et al. 2017). There is a negative correlation between the nutrient load and the CO2 availability in aquatic systems: at high nutrient load, CO2 concentrations are low, and vice versa

(Balmer and Downing 2011). Therefore, it seems valid that species from oligotrophic systems have a high affinity for N and P, but a low affinity for Ci, while species from eutrophic systems have a low

affinity for N and P, but a high affinity for Ci. Here we will study whether such a difference between

nutrient and Ci affinty exists using published measurements.

Inorganic carbon concentrations are generally not considered a limiting resource as carbon is relatively abundant with millimolar concentrations compared to nanomolar macronutrient concentrations and picomolar micronutrient concentrations (Millero, 2006). During intense phytoplankton blooms however, ocean surface Ci depletion does occur, relative to the deep ocean

(Millero, 2006). Patches of Ci depleted ocean surface can last for several days to weeks during

phytoplankton blooms, although they are replenished by physical exchange ratter quickly. The relatively low diffusion rate of CO limits the accumulation of inorganic carbon by phytoplankton.

Phytoplankton fix Ci as their carbon source as carbon dioxide (CO2) or bicarbonate (HCO3

-) (Verspagen et al. 2014). The uptake of inorganic carbon by phytoplankton can deplete the aquatic Ci

concentrations. Depletion of the CO2 concentration can increase the pH of lakes. Consequently, CO2

depletion combined with high pH is often associated with algal blooms (Verspagen et al. 2014). This change in pH causes a shift in CO2: HCO3

ratio. As CO2 and HCO3

uptake are regulated by different membrane proteins with their own affinity, the pH of the system can greatly alter uptake kinetics for Ci. This study will look at marine species. As marine environments are less susceptible to change in

pH due to a higher buffer capacity of the ocean, marine species are less likely to experience great differences in CO2: HCO3

-ratio, consequently this study looks at the Ci affinity, which is a

combination of both CO2 and HCO3- affinity.

Recent research on the water quality in the Baltic sea uses the diatom: dinoflagellate ratio (Dia/Dino index) as an index for the quality of water (Wasmund et al. 2017). Diatoms and dinoflagellates are the major phytoplankton groups in marine environments. Diatoms are the only major group of phytoplankton that use silicate (Si) for their cell wall structure. Usually, Si is of no concern regarding eutrophic waters. However, high inputs of nitrogen (N) and phosphorus (P) cause the relative amount of Si available for diatoms to decrease (Wasmund et al. 2017). Presumably, according to Wasmund et al. (2017), this makes Si the limiting factor in diatom growth and prevents them from growing in severely eutrophied marine environments. Dinoflagellates will not experience limitation by Si availability as they do not require silicate for their cell structure and grow well in eutrophic waters. The Dia/Dino-index would thus decrease in eutrophic waters, while a high

Dia/Dino-index indicates less eutrophied water (Wasmund et al. 2017). Indeed, dinoflagellates have a relatively low affinity for NO3 compared to other taxonomic groups according to Litchman et al.

(2007), supporting their prevalence in eutrophic ecosystems. In contrast, the affinity for nitrate is high in diatoms (Litchman et al. 2007), which would be more beneficial in less eutrophic environments. Reinfelder (2011) however, reviewed carbon concentrating mechanisms (CCMs) in marine

phytoplankton and found large diatoms prevailed in highly eutrophied water, which is low in Ci

concentration. Reinfelder further suggested the dinoflagellate Ci affinity to be subordinate to that of

diatoms, supported by their CCMs and field experiments.

The absence of diatoms in eutrophied water and the abundance of dinoflagellates in eutrophied waters, as proposed by Wasmund et al. (2017) or the opposite, as argued by Reinfelder (2011), would make these groups relevant to test the for difference in nitrate (NO3), phosphate (PO4) and Ci affinity in highly eutrophied marine environments. Testing groups of organisms instead of species allows data on either the affinity for nitrate, phosphate or the affinity for Ci to be used as the average of the group is

compared. Thus data on both the affinity for NO3/PO4 and Ci affinity per species, which is rarely

available, is not essential. Clearly, affinity for PO4, NO3 and Ci differs among species in the groups.

the major parameters of nitrate uptake and growth, scale with cell size. The general relationship is a power function of the cell volume.

Establishing the relations between PO4, NO3 and Ci affinity increases the

understanding of the mechanisms determining the species composition in marine ecosystems. Affinity for N, P and Ci for many phytoplankton species has already been reported in a number of studies

(Litchman, 2007; Edwards et al 2015, Schwaderer et al 2011, Clement et al 2017). Here, the research question: “Could a trait based approach on the Ci affinity, phosphate and nitrate affinity in marine

diatom and dinoflagellate species explain part of the species distribution in marine environments under eutrophic conditions?” is answered using these data, and additional data collected from additional uptake kinetics experiments. The in silico quantification of Ci affinity using previous studies, could

provide new insights in the phytoplankton uptake dynamics and species distribution. If differences in affinity are observed, this research aims to find the cause for these differences based on cell specific traits, and compare the differences to field observations in order to assess whether affinity is a good indication for species composition or not.

This research predominately focused on constructing a dataset by literature study. After the literature study the findings were analysed using basic statistics. Trait based approaches in community ecology acquired a considerable amount of interest recently. The interaction between environmental gradients, traits, milieu, and performance currencies largely determine the ecological niche of a species in this approach (Litchman et al. 2007). Establish the existence of a trade-off in traits will help to understand marine communities. It forms a basis for further research on trade-offs in nutrient acquisition.

2 Model section:

uptake kinetics in phytoplankton species

The combined net effect of limiting nutrients, major ions, pH, and other physical factors ultimately determines the reproductive rate of algal populations (Tilman et al. 1982). Tilman (1982) suggested a mechanistic, resource-based approach can be expanded to include many important aspects of the physical and biotic habitat. A classic model for resource competition is the model for two nutrient-competition (Tilman, 1982). This model predicted the species with the strongest resource utilization to outcompete other species. The Michaelis-Menten equation is used for nutrient uptake ( eq 1) where Vmax represents the maximum nutrient uptake in (µmol X cell

-1

day-1, where X is the nutrient measured), R represents the nutrient concentration and K1/2 is the half saturation constant, i.e. the

nutrient concentration (µmol, L-1) at which nutrient uptake is half of the maximum uptake rate Vmax.

𝑉𝑉 = 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 ∗_𝐾𝐾 𝑅𝑅

1/2+𝑅𝑅 (eq. 1)

Species competitive ability for resources is determined by resource competition theory (Tilman, 1982). In this theory R* represents the amount of resource for which the birth rate equals the mortality rate. Species with a lower R* will outcompete species with a higher R* in this theory. When death rate (m) approaches zero, the equation can be simplified to equation 2. Here, the competitive ability is largely determined by nutrient affinity (α) in (L cell-1

day-1) which is determined by Aksnes and Egge (1991) and the minimal nutrient concentration at which growth ceases (Qmin) in (µmol X cell-1, where X is the nutrient measured) and described in equation 3. This research will predominantly focus on difference in uptake kinetics between diatoms and dinoflagellates, thus the Qmin part of the

species resource competition theory will not be explored in this research. However, Qmin is positively correlated with cell size (Litchman et al. 2007), and this study will take into account the effects of cell size on species succession.

𝑅𝑅 ∗=

𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉

𝑄𝑄𝑉𝑉𝑄𝑄𝑄𝑄

α =

3. Materials & Method

Data acquisition

The affinity for PO4/NO3 and Ci in diatoms and dinoflagellates is calculated by use of current in vitro

studies and datasets on uptake kinetic in phytoplankton. First, an existing database assembled by Edwards et al. (2015) which contains data on phytoplankton species nutrient uptake for nitrate and phosphate e.g. the Vmax and K1/2 was supplemented with a dataset on Ci uptake kinetics assembled by

Clement et al. (2017). Opposed to other research, this research focusses explicitly on the uptake kinetics of marine diatoms and dinoflagellates, other taxonomic groups and freshwater diatoms and dinoflagellates were thus removed from the dataset. Subsequently, the dataset was complemented with additional research on Ci uptake kinetics in marine diatoms and dinoflagellates (Hu & Gao 2008,

Yamatoho et al. 2017, Rost et al. 2006, Eberlein et al. 2014, Ratti & Giordano 2007, Leggat et al 2007).

Ci K1/2 and Vmax were measured in vitro under Ci limited conditions. Either the O2 production

measured by oxygen evolution (clement et al. 2015) or the Ci consumption under increasing Ci

concentrations was measured by, for example, membrane inlet mass spectrometry(MIMS) (Trimborn et al. 2008), depending on the research. The CO2 consumption: O2 production ratio was assumed to be

1:1 so no alterations are required for the different techniques. PO4 and NO3 K1/2 and Vmax too were

measured in vitro under limited PO4 or NO3 concentrations. The uptake rate for PO4 and NO3 was

calculated under different PO4 and NO3 concentration from the difference between the initial

concentration and the final concentration, thereafter the K1/2 was determined.

Data selection

Affinity is influenced by temperature, light intensity, concentrations of other nutrients (Litchman et al. 2007), pH, cell volume, and chlA concentrations. These influences could be divided in cell specific traits (e.g. volume and chlA) and environmental variables (e.g. nutrient concentration, light intensity, other nutrient concentrations and pH). Cell specific traits cause the difference in affinity among species and do not need to be standardized but could act as explanation for the difference in affinity observed. Environmental variables however, need to be standardized in order to compare different species or taxonomic groups. As not all studies used the same standard conditions, data used in this research might be influenced by the conditions used. The dataset contains information

lower pH effect on the Ci affinity was available. Temperatures and irradiance could not be

standardized as not all measurements contained information on the temperature or irradiance used. All data outside the environmental variable ranges was excluded from the file. Species with multiple measurements for the same uptake kinetics were aggregated, and the mean measurement was taken. The data is made available at :

(https://drive.google.com/file/d/1kqme4usq_0oW7BOM9cSXRb0tuRuEULmp/view?usp=sharing).

Statistical analysis

First, the parametric assumptions were tested to conclude what test should be conducted. Difference in the mean affinity for PO4, NO3 and Ci in diatoms and dinoflagellates was tested by

performing a Mann-Whitney U-test due to unsatisfied parametric assumptions. Subsequently, if a significant difference was found in diatoms and dinoflagellates, the underlying mechanisms were further analysed to find an explanation for different affinities among both groups. The Vmax and K1/2

were compared to determine what caused the difference in affinity; high Ci affinity due to very low

K1/2 but moderate Vmax could imply different cell requirements than high Ci affinity due to a moderate

K1/2 but very high Vmax. Finally, differences in mean volume were determined as the difference in

volume could explain variation in uptake kinetics between diatoms and dinoflagellates. All statistical tests were performed using RStudio (Version 1.1.383). The script used to apply those tests is added in appendix A.

Difference in affinity for Ci was tested by making a subset containing data on the Ci for

diatoms and dinoflagellates. 13 marine diatoms and 6 marine dinoflagellates have been identified for which the Ci uptake kinetics are known. A Shapiro-Wilk test which resulted in rejection of the

null-hypothesis of normally distributed data. The assumption of a reasonably large sample size is violated as only few measurements were found. A Levene test rejected the null-hypothesis of homogeneity in variances, thus this the assumption is not met. A natural log transformation resulted in normally distributed data (Shapiro-Wilk test), thus the diatom population Ci affinity did now meet the

assumption of normality. Due to a small sample size and no homogeneity an non-parametric Mann-Whitney U-test was used to test for difference in mean Ci affinity. This method uses ranks of the

measurements to test if frequency distributions in two populations are the same.

The PO4 affinity was extracted from the data file and subsets for diatoms and dinoflagellates

were made. The PO4 affinity was found for 7 diatom species and 10 dinoflagellate species. Visual

comparison of the affinity in the two populations was done by a boxplot. A Shapiro-Wilk test rejected normal distributed data for diatoms but could not reject normal distribution for dinoflagellates. A natural log transformation normalized the distribution. A Levene test for equal variance could not reject the null hypothesis of equal variances. As the parametric assumption of a reasonably large sample size is violated a Mann-Whitney U-test was conducted to test for difference in mean PO4

The literature research led to data on the NO3 affinity for 10 marine diatom species and 8

marine dinoflagellate species. Both groups species experienced a skewed distribution by visual analysis. A Shapiro-Wilk normality test on the natural log transformed diatom and dinoflagellate NO3

affinity rejected the null-hypothesis of normality for diatom NO3 affinity. A Levene-test for equal

variance could not reject the null-hypothesis of equal variance. Due to the small sample size and no normal distribution, a ranked Mann-Whitney U-test was performed to test for differences in mean NO3

affinity.

The Vmax and K1/2 for Ci, NO3 and PO4 were plotted for all species using the subset containing

the Ci affinity. A Levene test for homogeneity in variance was applied on the Vmax data to test for

different uptake mechanisms within the groups (e.g. diatoms and dinoflagellates). A Pearson’s correlation test was performed of the Vmax on the K1/2 to test if Vmax increases with K1/2.

The average cell volume is known for 10 marine diatoms and 8 marine dinoflagellates. The natural log volume of dinoflagellates is normally distributed but the data for diatoms is not. The natural log transferred data showed homogeneity of variance resulting from the Levene test. Consequently, a Mann-Whitney U-test on the volume of diatoms and dinoflagellates was performed. The mean CO2 and HCO3

affinity were compared. HCO3

data was found for only 5 species as this is most likely a poor representation of the total population, no real comparison with Ci affinity can be

made. Nevertheless, the mean HCO3

affinity for diatoms and dinoflagellates was calculated. CO2

affinity was found for only one diatom and dinoflagellate.

It was not possible to test for correlation between affinity for nutrients and Ci as this requires

4. Results

Difference in mean Ci, NO3 & PO4 affinity

The data analysis revealed significant differences in mean affinity for NO3 and Ci between diatoms and

dinoflagellates (p < 0.05), but could not confirm a difference in mean PO4 affinity. A visual

representation of the different distribution in nutrient and inorganic carbon affinity between dinoflagellates and diatoms is presented as a boxplot in Figure 1. The mean Ci affinity for diatoms and

dinoflagellates is 6.44 L μg chlA-1 day-1 and 41.75 L μg chlA-1 day-1 respectively. Ci affinity in diatoms was significantly lower than in dinoflagellates (Mann-Whitney U-test, U = 15, p = 0.036). The mean PO4 affinity for diatoms and dinoflagellates is 29.68 µL cell-1 day-1 and 7.68 µL cell-1 day-1,

respectively. a Mann-Whitney U-test could not reject the null-hypothesis of equal means (Mann-Whitney U-test, U = 20, p = 0.25) . The mean NO3 affinity for diatoms and dinoflagellates is 48.33

µL cell-1 day-1 and 21.03 µL cell-1 day-1, respectively. The Mann-Whitney U-test rejects the null-hypothesis of equal means (Mann-Whitney U-test, U = 17, p = 0.043).

Figure 1: Boxplots visualizing(A) the affinity for inorganic carbon (Ci) in diatoms (n = 13) and dinoflagellates (n = 6), (B) the affinity for phosphate (PO4) in diatoms (n = 7) and dinoflagellates (n = 10), (C) and affinity for

nitrate (NO3) in diatoms (n = 10) and dinoflagellates (n = 8). Mann-Whitney U-tests were used to compare each

pair, Ci affinity (A) and NO3 affinity (C) differed significantly (P < 0.05). Note: PO4 and NO3 affinity were

natural log transformed.

Traits influencing affinity

The Vmax and K1/2 for Ci uptake was plotted for all diatom and dinoflagellate species (figure 2) as the affinity is a function of both these parameters. Mean Vmax did not differ significantly between diatoms and dinoflagellates (Mann-Whitney U-test, U = 22, p = 0.15) A Levene test for homogeneity did confirm a difference in variance among diatoms and dinoflagellates (p-H0 = 0.016). A Pearson’s

correlation test revealed no significant correlation between Vmax and K1/2 for diatoms (Pearson’s correlation test, r = 0.186 , p = 0.14), dinoflagellates (Pearson’s correlation test, r = -0.062 , p = 0.63) or both species combined(Pearson’s correlation test, r = 0.0001, p = 0.96).

Figure 2: The Ci Vmax and K1/2 for all diatom and dinoflagellate species used in this research. No significant correlation between Vmax, and the K1/2 was observed.

The Vmax and K1/2 for NO3 uptake was plotted in figure 3. A Mann-Whitney u-test was conducted on

the mean Vmax and resulted in a significant difference between diatoms and dinoflagellates

(Mann-Whitney u-test, U= 16 , P = 0.03779). A Levene test for homogeneity in variance could not deny a homogenous variance of the Vmax (p-value 0.3509). A Pearson’s correlation test revealed significant

correlationbetween natural log transformed Vmax and K1/2 for diatoms (Pearson’s correlation test, r =

0.565, p = 0.01) and both species combined, r = 0.354, p = 0.01), but not for dinoflagellates (Pearson’s correlation test, r = 0.044 p = 0.84).

Figure 3: The NO3 Vmax and K1/2 for all diatom and dinoflagellate species used in this research. Significant correlation between Vmax, and the K1/2 was observed for diatoms and both species combined.

The Vmax and K1/2 for P04 was plotted for all diatom and dinoflagellate species figure 4. A

Mann-Whitney u-test resulted in no significant difference (Mann-Mann-Whitney u-test, U= 19 , P = 0.2105). A Levene test for homogeneity in variance did deny a homogenous variance of the Vmax (p-value

0.02965). A Pearson’s correlation test revealed significant correlationbetween natural log transformed Vmax and the natural log transformed K1/2 for diatoms (Pearson’s correlation test, r = 0.802, p = 0.01),

dinoflagellates (Pearson’s correlation test, r = 0.609, p = 0.01) and both species combined (Pearson’s correlation test: r = 0.549, p = 0.001).

Figure 4: The PO4 Vmax and K1/2 for all diatom and dinoflagellate species used in this research. Significant correlation between the natural log transformed Vmax, and the natural log transformed K1/2 was observed for diatoms, dinoflagellates and both species combined.

Volume

The mean volumes for diatoms and dinoflagellates are respectively, 835.53 µm3 and 13140.72 µm3 . A natural log transformed boxplot (Figure 5) shows the distribution of the volume in both diatoms and dinoflagellates. A Mann-Whitney U-test resulted in a rejection of the null hypothesis of equal means (Mann-Whitney U-test, U =10 , P= 0.0062).

Figure 5: Boxplot visualizing the distribution of natural log transformed volume in diatoms and dinoflagellates. A significant difference was observed in the transformed volume between diatoms and dinoflagellates.

CO2 and HCO3

affinity The mean HCO3

affinity for diatom species is 13.682 L chlA-1 day-1. For dinoflagellates the mean HCO3

is 245.361 L chlA-1 day-1. No statistical analysis could be performed due to insufficient data. One dinoflagellate has a 5.638 L chlA-1 day-1 affinity for CO2, although this species is mixotrophic

5. Discussion

The aim of this research was to assess if a trait based approach on the Ci affinity, phosphate and nitrate

affinity in marine diatoms and dinoflagellates, could explain species distribution in marine environments under eutrophic conditions. Species living in eutrophic waters are not expected to suffer from low affinity for NO3 or PO4 but, as Ci concentrations drop in eutrophic waters, they would benefit

from highCi affinity. Species living in non-eutrophic habitats would more likely be limited by NO3 or

PO4 acquisition and high NO3 or PO4 affinity would be make the species more likely to outcompete

others, while Ci affinity would be rather unimportant as Ci concentrations are sufficient for growth.

Diatoms and dinoflagellates are the most abundant harmful phytoplankton species (Smayda & Reynolds, 2003). Diatoms are dependent on the Si concentration of the ocean water. The relative Si concentration is low in eutrophied water which, according to Wasmund et al. (2017), could limit the growth of diatoms in eutrophic areas. Contrastingly, dinoflagellates do not experience this constraint. Moreover, they are motile which allows them to move out of Ci depleted patches. These traits would

allow dinoflagellates to grow in eutrophic areas. Consequently, diatoms were expected to have a low affinity for inorganic carbon and a high affinity for NO3 or PO4, where the contrary was expected for

dinoflagellates. As such, a trade-off between nitrate and phosphate affinity and inorganic carbon was expected. Although, Reinfelder (2011) predicted diatoms would dominate in Ci depleted

environments, based on mesocosm research and the efficient C4 uptake kinetics in diatoms possess,

and the II RubisCO enzyme in dinoflagellates instead of the I RubisCO enzyme in other phytoplankton species.

Nutrient and Ci affinity

This research found the mean Ci affinity in diatom species was significantly lower than in

dinoflagellate species. The mean PO4 affinity did not differ among both groups. While the NO3

affinity was significantly higher in dinoflagellates than in diatoms. This suggests diatoms are poor competitors for nitrate and inorganic carbon. As the mean affinity for NO3, PO4 and Ci was higher

(although not significantly in PO4) in dinoflagellates, a trade-off would be improbable based on the

current affinity data.

The affinity in both diatoms and dinoflagellates collected might not be representative for the taxonomic groups as sample sizes on both nutrient and Ci affinity were small, although measures were

taken to limit the influence of environmental variables. Subsequently, species analysed might originate from different trophic regions, which could influence their affinity for nutrients and inorganic carbon. Nevertheless, further analysis on the uptake mechanisms underlying affinity was conducted to explain the probable absence of a trade-off

Causes for difference in Ci affinity between diatoms and dinoflagellates

The main constraint in carbon uptake was thought to be reduced function of RubisCO (Badger et al. 1998), however CCMs appear to influence this constraint. Two main strategies have evolved to cope with low levels of Ci in the environment; The first strategy is enhanced catalytic ability of RubisCO,

the second is the evolution of biological infrastructure which ensures enough Ci is transferred to the cell (Badger et al. 1998). The latter strategy is referred to as carbon concentrating mechanisms (CCM’s). One of these CCM’s is carbonic anhydrase (CA), which catalyses the dehydration of HCO3

-

to CO2 at the cell surface or at the site of carbon fixation by RubisCO in the cell chloroplast

(Reinfielder, 2011). Substantial leakage of CO2 and Ci of phytoplankton indicates CCM activity is an

active, energy-consuming process. Differences in the CCM’s and the concentration could explain the difference in Ci affinity.

Diatoms experience a wide interspecific variety in CCM’s. CCMs in diatom species are: (I)increased activity of CA, (II) the ability to utilize HCO3

as a source for photosynthesis, and (III) the use of a C4 carbon pump (Reinfelder, 2011). The presence of CCMs is also indicated by the Ci half

saturation constant, which is much higher than that of only RubisCO activity (Reinfelder, 2011). Extracellular CA activity however appears to be low in diatom species. the relatively high K1/2 in

diatoms, does indicate a less effective Ci acquisition far below their Vmax in low Ci environments.

Dinoflagellates likely require CCM’s to sustain even their relatively slow rates of growth and photosynthesis (Reinfelder, 2011). Evidence is found for CCMs by both external CA activity and HCO3

utilization. Different CCMs among dinoflagellate genera are not unlikely given their phylogenetic history. Dinoflagellates are the only oxygenic photoautotrophs with form II RubisCO. The form II RubisCO of dinoflagellates has a significantly lower carboxylation:oxygenation specificity factor, compared to the form I RubisCO in diatoms. Consequently, dinoflagellates require less energy for carboxylation under high CO2 concentrations and are less effective CO2 fixators at low

CO2 concentrations (Reinfelder, 2011)

Ci affinity appears to be higher in dinoflagellates due to the combination of relatively high

Vmax, and the relatively low half saturation constant for Ci combined, as Vmax on its own did not

significantly differ. The combined effect of a relatively high Vmax and Ci could be due to the interaction with RubisCO type II. Type II RubisCO is more effective under high CO2 concentrations,

which is the effect of a higher Vmax. Likewise, type II RubisCO requires a relatively high minimum

concentration of CO2 in the cell to efficiently support CO2 uptake, especially in low Ci environments

HCO3

or CO2

There is some evidence, diatoms use a C4 mechanism (Reinfelder, 2011). This C4 mechanism would

allow diatoms to use HCO3- as their carbon source as it is utilized as carbon source by C4 carboxylase

phosphoenolpyruvate carboxylase (PEPCase). Very limited data is found on the uptake kinetics of HCO3

and CO2. The HCO3

pump, supposedly present in diatoms with C4 carbon fixing mechanisms

according to Reinfelder (2011) would enhance HCO3

affinity in diatoms. However, the mean dinoflagellate HCO3

affinity in dinoflagellates was 17.93 times higher than in diatoms. The findings are thus not in accordance with the results of Reinfelder, who did acknowledge not all diatoms possess a C4 mechanisms, which could alter the outcome of the HCO3

affinity. The CO2 affinity was found for

one diatom species and one dinoflagellate and was higher in the diatom. Yet, the dinoflagellate (Symbiodinium sp.) is a mixotrophic species, indicating it uses both inorganic carbon and organic carbon for fixation. Being a mixotrophic species it would be expected to require less carbon from inorganic sources such as CO2. Being mixotrophic, this species is not a suitable representative for all

dinoflagellates.

Causes for difference in NO3 affinity between diatoms and dinoflagellates

The nitrate affinity was higher in dinoflagellates than in diatoms. This is in contrast with the results from Litchman et. al. (2007) who concluded diatoms would possess a higher affinity for NO3. This

research however differs from this results in that it only uses marine species. The main cause for a higher affinity found in this research is the relatively high Vmax for NO3 in dinoflagellates, which is

significantly higher compared to diatoms. Litchman et al. (2007) also found a positive relation between size (volume), Vmax and K1/2 for nitrate in diatoms and a positive relationship between volume

and Vmax in dinoflagellates. As the average size of dinoflagellates in this experiment is much larger

than the average diatom size, correcting for size might alter the species competitive ability for nitrate (eq. 3) as volume could influence the minimum nutrient requirement (Qmin) of the cell. Unfortunately,

it was impossible to correct for volume due to an absence of data on both NO3 affinity and volume.

Mechanisms determining PO4 affinity in diatoms and dinoflagellates

No significant difference was observed between diatom and dinoflagellate PO4 affinity. This is in

contrast with a field research conducted by Bužančić et al (2016) who found increased dinoflagellate abundance in PO4 rich environments. Further analysis indicated that mean Vmax did not differ

significantly either. However, A Levene test revealed a difference in variance of the Vmax, with

diatoms varying significantly more compared to dinoflagellates. This might indicate interspecific difference in uptake mechanisms in diatoms. It could however be, PO4 is less limiting than NO3 in

Mesocosm and field observations

The comparison with field and mesocosm studies could provide insights in the importance of nutrient and inorganic carbon in species distribution. The main difference between dominant species predictions in Reinfelder (2011) and Wasmund et al (2017) could be explained by the way underlying field measurements were applied. In Tortell et al (2002), field measurements, underlying the Reinfelder predictions, measured species under extreme carbon concentrations by artificially lowering or increasing the CO2 concentrations in open ocean samples. Whereas inorganic carbon concentrations

in samples from Wasmund et al (2017) were naturally lowered, succeeding eutrophication. The preceding nitrate and phosphate fertilizer use causing this eutrophication could induce silicate limitation as the relative concentration of silicate drops more rapidly. Here field and mesocosm studies are compared with findings in this study.

Community composition of phytoplankton in three different bays on the eastern Adriatic coast, with different trophic status (based on PO4 concentration, chlA, and transparency) was

measured by Bužančić et al (2016). In this study, dinoflagellates appeared to be relatively more abundant in PO4 rich waters, whereas diatoms appear to be relatively more abundant in NO3 and Si

rich waters. This supports why Wasmund et al (2017) and Reinfelder (2011) suggested different Dia: Dino ratios under Ci depleted conditions, as the occurrence of diatoms in silicate rich waters is in

agreement with Si as limiting growth factor in diatoms. Nevertheless, diatoms were the most abundant taxonomic group under all conditions. Dinoflagellates were relatively more abundant in areas with greatest human impact e.g. eutrophic areas. This is partially in contrast with the findings in this study as dinoflagellates are better adapted to low nutrient concentrations based on the higher affinity for nitrate, and partially in coherence with the results as dinoflagellates do have higher affinity for inorganic carbon.

Phytoplankton succession under different CO2 concentrations in mesocosms in the Swedish

west coast was measured by Bach et al. (2016). They incorporated nutrient decline and species composition under different CO2 concentrations for 113 days. Two diatom species were most abundant

in the first bloom. The NO3, PO4 and Si(OH)4 concentration dropped rather quickly. Inorganic carbon

concentrations did drop, however after the nutrient concentrations dropped, Ci concentrations rose

back to their original level under natural conditions (under elevated CO2 conditions, the CO2 was

experimentally manipulated to correct for atmospheric exchange losses). The mesocosms were not eutrophied, so as could be expected, nutrient limitations were more important to species succession. No dinoflagellate species were identified in this study. The early blooms of diatoms are in accordance

Species composition

Conclusions based on the species composition in eutrophic marine environment have to be interpreted with care. First, Inorganic carbon affinity was significantly higher in the dinoflagellate species. However, it could be that dinoflagellates are much less efficient with the Ci they acquire e.g. the

minimal nutrient requirement (Qmin) is higher, which supports the high Vmax in two dinoflagellate

species, and would imply that affinity not necessarily increase the carbon fixation. Furthermore, as the affinity for Ci was measured per chlA, perhaps diatoms simply possess more chlA per cell than

dinoflagellates. As the dataset lacked data on the chlA content in diatoms, the chlA concentrations could not be compared. Sathyendranath et al. (2009) however, did not find a difference in chlA per cellular carbon concentration in diatom and dinoflagellates. They did find an increased chlA concentration per cellular carbon in larger species. The volume of dinoflagellates was significantly larger than diatom species in this research. It could thus be that diatoms simply require less inorganic carbon due to their cell size. A relation between volume and affinity for Ci cannot be drawn as

combined data on Ci affinity and volume is very scarce.

Another explanation can be found in the blooming time, Wasmund et al. (2017) found diatoms blooms precede dinoflagellate blooms. If diatoms are indeed more likely to be present in the early stages of succession this could explain their low affinity for nutrients. Furthermore, if dinoflagellates are more likely to be present on the later stages of succession, this could imply dinoflagellates are present in environments lowered nutrient and Ci concentration, which is in line with the higher affinity for

nutrients and inorganic carbon in dinoflagellates found in this research.

Finally, the higher affinity for Ci and the nutrients in dinoflagellates is quite peculiar bearing in mind

the considerable amount of mixotrophic dinoflagellates (Stoecker, 1999), which do not solely rely on autotrophic processes for their carbon and nutrient uptake like in diatoms. Rather, the contrary is expected considering the additional organic nutrient supply from heterotrophic uptake.

For now, it does seem a bit farfetched to use the diatom: dinoflagellate index as an indication for eutrophication, although this research did find a higher Ci affinity in dinoflagellates. According to

findings in this research, dinoflagellates are better adapted, based on their affinity for nutrients and inorganic carbon, to oligotrophic and Ci depleted environments.

Future research

To adequately determine the existence of an affinity trade-off, the species composition in eutrophic and Ci depleted environments, and the implications of Dia: Dino index, in vitro studies are required

that combine the uptake kinetics of multiple nutrients (e.g. nitrate, phosphate, silica) and inorganic carbon. The uptake kinetics should be measured under standardized temperature and irradiance. Furthermore, more field observations of species composition are required to link the in vitro kinetics to the observed species composition. Next, silicate appears to be overlooked in some research on phytoplankton distribution although plenty of research is conducted on silicate uptake kinetics

(Paasche, 1973; Davis et al. 1973). Inclusion of silicate measurements in mesocosm and field studies on phytoplankton growth could increase the understanding of species composition in silicate depleted areas. Next, future research could concentrate on the CCM’s which determine the Ci affinity, as

biochemical properties of the CCM’s are not entirely understood still. Finally, one could argue that due to the significant interspecific differences in NO3 affinity in diatoms and Ci affinity in

dinoflagellates, these groups might not be uniform in their uptake and should not be considered as groups when assessing difference in affinity. Subdividing groups could be done based on original habitat e.g. arctic versus oligotrophic tropical versus temperate estuaries. This would require more research on species origin. Subdividing could also be based on difference in uptake mechanisms. This would require more knowledge on uptake mechanisms for nutrients and carbon concentrating mechanisms.

Acknowledgements:

I wish to thank dr. J.M.H.Verspagen for her help and guidance during the research. Next, I would like to thank Edwards et al. (2015) and Clement et al. (2017) for their data assemblage on uptake kinetics of nutrients and inorganic carbon, which made this research possible.

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Appendix A: R-studio script

setwd("/Users/thomashofman/Desktop/Bachelor Project/R-data") #Packages

library("car")

#Import dataframe DinoDiaMar ####CiAffinity####

#Aggregate means of same species for Ci Affinity CiAffinityvars <- c("species","taxon", "Affinity_Ci") CiAffinityWNA <- DinoDiaMar[CiAffinityvars] CiAffinitynomean <- na.omit(CiAffinityWNA)

CiAffinity3taxon <- aggregate(Affinity_Ci~species+taxon, CiAffinity, mean) summary(CiAffinity3taxon)

#Two spaces made the (dino ) and (dino) not comparable -> write csv and remove spaces in excel write.csv(CiAffinity3taxon, "CiAffinity3taxon.csv")

#Import new CiAffinityAVG.csv

CiAffinityvars2 <- c("taxon", "Affinity_Ci") CiAffinity <- CiAffinityAVG[CiAffinityvars2] #Dinoflagellate and Diatom subsets

DinoCi <- subset(CiAffinity, taxon == "dino") DiatomCi <- subset(CiAffinity, taxon == "diatom") mean(DinoCi$Affinity_Ci)

mean(DiatomCi$Affinity_Ci)

boxplot((Affinity_Ci)~taxon, CiAffinity, ylab = expression(paste("Ci Affinity, ",alpha)), main = "Ci Affinity in diatoms and dinoflagellates")

##t tests for different means in two different populations? #Assumptions CiAffinity

#1Data follows a continuous or ordinal scale: Yes

#2Representative, randomly selected portion of the total population: Yes (sort of..) #3Normal distribution ( e.g. bell-shaped distribution curve) > shapiro wilk test: NO hist(DinoCi$Affinity_Ci, main = "Dinoflagellate Ci Affinity", xlab = "affinity", freq = T) hist(DiatomCi$Affinity_Ci, main = "Diatom Ci Affinity", xlab = "affinity", freq = T) ?hist

shapiro.test(DinoCi$Affinity_Ci) shapiro.test(DiatomCi$Affinity_Ci) #4Reasonably large sample size is used: NO #5Equal variance: NO leveneTest(Affinity_Ci~taxon,CiAffinity) #log-transformation logCiAffinityDino <- c(log(DinoCi$Affinity_Ci)) logCiAffinityDia <- c(log(DiatomCi$Affinity_Ci)) hist(logCiAffinityDia) hist(logCiAffinityDino) shapiro.test(logCiAffinityDia) shapiro.test(logCiAffinityDino) logCiAffinityAll <- CiAffinity

logCiAffinityAll[,2] <- log(logCiAffinityAll[,2])

#Independent Mann-Whitney U test

wilcox.test(Affinity_Ci~taxon, data = CiAffinity) t.test(Affinity_Ci~taxon, data = logCiAffinityAll) ####PO4Affinity####

PO4Affinityvars <- c("species","taxon", "Affinity_PO4") PO4AffinityWNA <- DinoDiaMar[PO4Affinityvars] PO4Affinity3col <- na.omit(PO4AffinityWNA)

PO4Affinity3taxon <- aggregate(Affinity_PO4~species+taxon, PO4Affinity3col, mean) summary(PO4Affinity3taxon)

PO4Affinityvars1 <- c("taxon", "Affinity_PO4") PO4Affinity <- PO4Affinity3taxon[PO4Affinityvars1] write.csv(PO4Affinity, "PO4Aff.csv")

#After checking source of data, one extreme outlier had to be removed as it was wrongly interpreted (citation 131) #Import datafram PO4 affinity

summary(PO4Affinity)

DinoPO4 <- subset(PO4Affinity, taxon == "dino") DiatomPO4 <- subset(PO4Affinity, taxon == "diatom") ##t tests for different means in two different populations? #Assumptions PO4Affinity

#1Data follows a continuous or ordinal scale: Yes

#2Representative, randomly selected portion of the total population: Yes (sort of..) #3Normal distribution ( e.g. bell-shaped distribution curve) > shapiro wilk test: NO

boxplot(log(Affinity_PO4)~taxon, PO4Affinity, ylab = expression(paste("log PO4 Affinity, ",alpha)), main = "PO4 Affinity in diatoms and dinoflagellates")

boxplot(Affinity_PO4*1000~taxon, DiatomPO4)

hist(log(DinoPO4$Affinity_PO4), main = "Dinoflagellate PO4 Affinity", xlab = "affinity", freq = T) hist(log(DiatomPO4$Affinity_PO4), main = "Diatom PO4 Affinity", xlab = "affinity", freq = T) ?hist

shapiro.test(log(DinoPO4$Affinity_PO4)) #W = 0.787, p-value = 0.04475

shapiro.test(log(DiatomPO4$Affinity_PO4)) #W = 0.72651, p-value = 0.001036 #4Reasonably large sample size is used: NO #5Equal variance: NO leveneTest(log(Affinity_PO4)~taxon,PO4Affinity) #log-transformation logPO4AffinityDino <- c(log(DinoPO4$Affinity_PO4)) logPO4AffinityDia <- c(log(DiatomPO4$Affinity_PO4)) hist(logPO4AffinityDia) hist(logPO4AffinityDino) shapiro.test(logPO4AffinityDia) shapiro.test(logPO4AffinityDino)

####NO3Affinity####

NO3Affinityvars <- c("species","taxon", "Affinity_NO3") NO3AffinityWNA <- DinoDiaMar[NO3Affinityvars] NO3Affinity3col <- na.omit(NO3AffinityWNA)

NO3Affinity3taxon <- aggregate(Affinity_NO3~species+taxon, NO3Affinity3col, mean) summary(NO3Affinity3taxon)

NO3Affinityvars1 <- c("taxon", "Affinity_NO3") NO3Affinity <- NO3Affinity3taxon[NO3Affinityvars1] write.csv(NO3Affinity, "NO3Aff.csv")

#Import datafram NO3 Affinity summary(NO3Affinity)

DinoNO3 <- subset(NO3Affinity, taxon == "dino") DiatomNO3 <- subset(NO3Affinity, taxon == "diatom") ##t tests for different means in two different populations? #Assumptions NO3Affinity

#1Data follows a continuous or ordinal scale: Yes

#2Representative, randomly selected portion of the total population: Yes (sort of..) #3Normal distribution ( e.g. bell-shaped distribution curve) > shapiro wilk test: NO

boxplot(log(Affinity_NO3)~taxon, NO3Affinity, ylab = expression(paste("log NO3 Affinity, ",alpha)), main = "NO3 Affinity in diatoms and dinoflagellates")

boxplot(Affinity_NO3*1000~taxon, DiatomNO3)

hist(DinoNO3$Affinity_NO3, main = "Dinoflagellate NO3 Affinity", xlab = "affinity", freq = T) hist((DiatomNO3$Affinity_NO3), main = "Diatom NO3 Affinity", xlab = "affinity", freq = T) ?hist

shapiro.test((DinoNO3$Affinity_NO3)) #W = 0.787, p-value = 0.04475

shapiro.test((DiatomNO3$Affinity_NO3)) #W = 0.72651, p-value = 0.001036 #4Reasonably large sample size is used: NO #5Equal variance: NO leveneTest(log(Affinity_NO3)~taxon,NO3Affinity) #log-transformation logNO3AffinityDino <- c(log(DinoNO3$Affinity_NO3)) logNO3AffinityDia <- c(log(DiatomNO3$Affinity_NO3)) hist(logNO3AffinityDia) hist(logNO3AffinityDino) shapiro.test(logNO3AffinityDia) shapiro.test(logNO3AffinityDino) logNO3AffinityAll <- NO3Affinity logNO3AffinityAll[,2] <- log(logNO3AffinityAll[,2]) logNO3Affinity <- NO3Affinity logNO3Affinity[,3] <- log(logNO3Affinity[,3]) #Independent Mann-Whitney U test

wilcox.test(Affinity_NO3~taxon, data = NO3Affinity) t.test(Affinity_NO3~taxon, data = logNO3Affinity)

####Scatterplot of Vmax vs. K1/2####

CiAffinityplotvars <- c("species","taxon","k_ci","v_max_ci") CiAffinityplotdata1 <- DinoDiaMar[CiAffinityplotvars] CiAffinityplotdata2 <- na.omit(CiAffinityplotdata1)

#CiAffinityplotdata <- aggregate(cbind(k_ci, v_max_ci)~species+taxon, CiAffinityplotdata2 , mean) summary(CiAffinityplotdata)

scatterplot(v_max_ci~k_ci|taxon, data = CiAffinityplotdata, col = c("red","green")[CiAffinityplotdata$taxon])

scatterplot(v_max_ci~k_ci|taxon, data = CiAffinityplotdata, reg.line = T, smoother = F, xlab = "Half saturation constant (µmol Ci L-1)", ylab = "Vmax (µmol Ci μg chlA-1 day-1)",

legend.plot = F)

summary(lm(v_max_ci~k_ci, data = CiAffinityplotdata)) abline(2.727e+01, 9.016e-04, lty = 6)

legend(title = "Taxon", "bottomright", legend=unique(CiAffinityplotdata$taxon), pch = seq_len(length(unique(CiAffinityplotdata$taxon))), pt.cex=0.7, cex=0.7, col = seq_len(length(unique(CiAffinityplotdata$taxon))), ncol = 1)

text(CiAffinityplotdata$v_max_ci~CiAffinityplotdata$k_ci, labels = CiAffinityplotdata$species, pos = 3, cex = 0.7) leveneTest(v_max_ci~taxon, CiAffinityplotdata, center = mean)

summary(lm(v_max_ci~k_ci, data = CiAffinityplotdata, subset = (taxon == "diatom"))) summary(lm(v_max_ci~k_ci, data = CiAffinityplotdata, subset = (taxon == "dino"))) abline(18.091, 0.02254, col = "green", lty = 4)

abline(40.7329, -0.0399, col = "red", lty = 3) ####HCO3-####

HCO3Affinityvars <- c("taxon","species", "Affinity_HCO3") HCO3wNA <- DinoDiaMar[HCO3Affinityvars]

HCO3woNA <- na.omit(HCO3wNA) summary(HCO3woNA)

HCO3Affinity <- aggregate(Affinity_HCO3~species+taxon, HCO3woNA, mean) diaHCO3 <- subset(HCO3Affinity, taxon == "diatom")

mean(diaHCO3$Affinity_HCO3)

dinoHCO3 <- subset(HCO3Affinity , taxon == "dino") mean(dinoHCO3$Affinity_HCO3)

####volume####

volumevars <- c("taxon","species", "volume") volumewNA <- DinoDiaMar[volumevars] volumewoNA <- na.omit(volumewNA) summary(volumewoNA)

volume <- aggregate(volume~species+taxon, volumewoNA, mean) summary(volume)

write.csv(volume, "volume.csv")

diavolume <- subset(volume, taxon == "diatom") mean(diavolume$volume)

dinovolume <- subset(volume , taxon == "dino") mean(dinovolume$volume)

##t tests for different means in two different populations? #Assumptions volume

#1Data follows a continuous or ordinal scale: Yes

#2Representative, randomly selected portion of the total population: Yes (sort of..) #3Normal distribution ( e.g. bell-shaped distribution curve) > shapiro wilk test: NO ?boxplot

boxplot(log(volume)~taxon, volume, ylab = expression(paste("log volume, ",alpha)), main = "volume in diatoms and dinoflagellates") boxplot(log(volume)~taxon, volume, ylab = expression("log volume" ~ (µm^{3})))

hist(log(dinovolume$volume), main = "Dinoflagellate volume", xlab = "volume", freq = T) hist(log(diavolume$volume), main = "Diatom volume", xlab = "affinity", freq = T) ?hist