Adriana Rodríguez Santos · 2020. 9. 27. · Adriana Rodríguez Santos Curso 2015/2016 Tutor Javier Arístegui Ruiz Trabajo Fin de Título para la obtención del título de Máster

Ocean Acidification Effects on Phytoplankton Community Structure in

Gran Canaria Island during a Mesocosm

Experiment

Adriana Rodríguez Santos

Curso 2015/2016

Tutor

Javier Arístegui Ruiz

Trabajo Fin de Título para la obtención del título de Máster en Oceanografía

A. Rodríguez-Santos

2

Título

Ocean Acidification Effects on Phytoplankton Community Structure in Gran Canaria Island during a Mesocosm Experiment

Datos personales

Nombre: Adriana

Apellidos: Rodríguez Santos

NIF:

Titulación: Máster en Oceanografía

Datos del trabajo

Tutor: Javier Arístegui Ruiz

Empresa: Instituto de Oceanografía y Cambio Global

Departamento: Grupo de Oceanografía Biológica

Proyecto de Investigación: KOSMOS 2016

Firmas

Estudiante Tutor

Fecha: 20/02/2017


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Index

1. Abstract……………………………………………………………………… 4

2. Introduction…………………………………………………………………. 5

3. Methods

3.1 Set-up of mesocosm experiment and nutrient fertilization ………… 7

3.2 Samples analyses …………………………………………………... 8

3.3 Data analyses and statistics…………………………………………. 9

4. Results

4.1 Carbonate chemistry and nutrient evolution………………………… 9

4.2 Evolution of phytoplankton abundances along the experiment……..12

4.3 Effect of pCO2 on different phytoplankton growth rates before

and after fertilization……………………………………………………15

4.4 Overall community structure trends…………………………………16

5. Discussion

5.1 Effects of pCO2 on different taxonomic groups and

community structure…………………………………………………… 19

5.2 Potential consequences for carbon cycling………………………… 23

6. Conclusions…………………………………………………………………. 24

7. Annex……………………………………………………………………….. 24

8. Acknowledgements ……………………………………………………...…. 24

9. References…………………………………………………………………... 27

10. TFT Report……………………………………………………………….... 34


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1. Abstract The regulation of marine carbon cycling and ocean-atmosphere CO2 exchange is closely related to phytoplankton dynamics. The large increase of atmospheric CO2 levels since the industrial revolution has caused important changes in surface ocean pH and carbonate chemistry. Such changes have been shown to slow down calcification in corals and coralline macroalgae and change competition between phytoplankton groups. Here we investigated whether plankton community structure and net growth rates of phytoplankton groups are affected by pH changes along a pCO2 gradient simulating oligotrophic or eutrophic ocean conditions. The present experiment was performed over 3 March to 3 April 2016 in the eastern coast of Gran Canary island (27º59’22.8” N, -15º22’0.8” Wº) on seven different mesocosms. Our results show how on oligotrophic conditions nanoeukaryotes and dinoflagellates had a positive response to pCO2 increases, while no significant responses were observed on the other phytoplankton groups. Conversely, on high nutrient concentrations, heterotrophic bacteria and diatoms show a positive response on their net growth rates as pCO2 increased, while picoeukaryotes and flagellates presented a negative response. The main changes in the structure of the phytoplankton community occurred at a CO2 threshold of 700-850 ppm. Our data suggest that the combination of an increase in atmospheric CO2 concentrations together with nutrient fertilization would favour the presence of large diatoms and hence the vertical carbon flux in the ocean.


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2. Introduction About half of the cumulative anthropogenic CO2 emissions between 1750 and 2011 have occurred in the last 40 years (IPCC, 2014) and if CO2 emissions continue rising at current rates, atmospheric concentrations could reach between 730 and 1020 ppm levels according to climate-carbon cycle models (IPCC, 2007). Serving as a buffer for global climate change, the ocean has absorbed around 30% of the anthropogenic CO2 since the beginning of the industrial revolution (Sabine et al., 2004) inducing critical changes in the chemistry of seawater, especially in the carbonate system. Projections indicate that the pH will drop 0.3 units from its present value (Caldeira & Witckett, 2003) leading to a 150% increase in H+ and 50% decrease in CO3

2− concentrations (Orr et al., 2005). It has also been suggested that this drop will promote a drastic lowering of calcium carbonate saturation states, with different impacts on shell-forming marine organisms (Doney et al., 2009) as well as a rise in CO2, presumably increasing photosynthetic efficiency at different levels between autotrophic groups (Riebesell 2004) which are responsible of about half of the global primary production on Earth (Field et al., 1998). Indeed, photosynthesis directly depends on the concentration of CO2 at the site of carbon fixation and, as such, it could promote significant implications for marine primary productivity (Rost et al., 2008). Present-day CO2 levels are not saturating the carboxylase reaction of algal RUBISCO, and this potentially sets a limit to algal photosynthesis and growth (Beardall & Raven, 2004). Nearly all marine phytoplankton have evolved concentrating mechanisms (CCMs) to overcome the low CO2 affinity in marine environments, although there are clear evidences that CCMs vary within and among dominant groups and taxa of prokaryotic cyanobacteria and eukaryotic marine phytoplankton (Burkhardt et al., 2001; Rost et al., 2003; Giordano et al., 2005). The different energetic and nutrient costs of CCMs may modulate how CO2 affects primary production, elemental composition, and phytoplankton community structure in the ocean. The fate of phytoplankton community composition and diversity is often difficult to predict, as complex community interactions also depend on the original community composition as well as the intensity and frequency of disturbances. A meta-analysis of published experimental data about phytoplankton growth rates have found that ocean acidification (OA) may impact competition both within and between groups, and appeared to be a stronger driver of functional diversity changes than temperature (Dutkiewicz et al., 2015). This impacts may promote changes on carbon cycle of phytoplankton food web and consequently, it could change how is kidnaped in the seafloor along years. For example, in some mesocosms studies, it has been reported that bacterial abundance may benefit directly and indirectly from decreasing seawater pH (Endres et al., 2014; Thingstad et al., 2008) probably due to the enhancement of production and exudation of carbon-rich components under OA conditions (Engel et al., 2014). At the same time, it is important to emphasise that the responses observed are not always in agreement, probably due to different methodologies and environmental conditions used in studies. For example, Fu et al. (2007) reported that doubling pCO2


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alone had no significant effect on the growth of either Synechococcus or Prochlorococcus in culture experiments while another study had reported lower abundance of Synechococcus at high pCO2 in an enclosed experiment with nutrient addition (Paulino et al., 2008). These responses seem to be controlled by other variables, such as nutrient availability (Lomas et al., 2012). Hence, it seems necessary to study the response of phytoplankton communities to OA under (natural or simulated) oligotrophic (low nutrient) and eutrophic (high nutrient) conditions. Other recent studies have reported that picoeukaryotic cells will benefit from nutrients’ addition at high CO2 (Paulino et al., 2008; Brussard et al 2013; Schulz et al., 2013; Schaum et al., 2012), although at the same time the impact of viral attacks may be intensified (Chen et al., 2015). Diatoms appeared to have neutral or slightly positive responses to increasing CO2 concentrations (Rost et al., 2003; Dutkiewicz et al., 2015), but it has been showed that it could depend on specific-species composition (Kim et al., 2006). On the contrary, coccolithophores are negatively affected due to dissolution of their carbonate shells under high OA (Berry et al, 2002; Rost et al., 2003). At higher trophic levels, there is little information on OA impacts, but Aberle et al. (2013) found almost no direct effects of OA on microzooplankton composition and diversity. All these OA effects at different trophic levels and taxonomic groups of the planktonic community could lead to changes in the pools and fluxes of organic carbon mediated by the nature and size structure of the community, with profound implications on carbon sedimentation and sequestration in the deep ocean. It has been reported that high levels of pCO2 could lead into a counterintuitive cycling of carbon by bacteria, causing a substantial decline of net community production despite higher primary production of organic carbon (Engel et al., 2013; Thingstad et al., 2008). This increase of bacterial activities would exacerbate the competition between phyto- and bacterioplankton for inorganic nutrients inducing a decline of authotrophic productivity and the corresponding carbon flux to the deep ocean.

With the aim of contributing to disentangling the influence of OA in shaping plankton community structure, we ran a series of experiments inside enclosed mesocosms under an acidification gradient. We wanted to test the hypothesis that OA influences the composition and abundances of autotrophic and heterotrophic communities (from bacteria to microplankton) and that nutrient fertilization would exacerbate these differences.

Seven mesocosms with different pCO2 levels (from 368 to 1287 ppm) were installed at the pier side of Taliarte harbour, in Gran Canary Island. The experiment lasted 29 days during early spring 2016. Mesocosms’ experiments are an ideal platform to assess potential effects of changing chemistry on plankton community as they allow for species interaction and competition in quasi natural environment. Most of the previous mesocosms studies published in the literature were carried out in cold or temperate seas (Aberle et al., 2013; Sommer et al., 2015; Brussard et al., 2013; Thoisen et al., 2015; Rossoll et al., 2013). On the contrary, our study took place in subtropical warm coastal


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waters, with low nutrient concentrations similar to open ocean values (González-‐Dávila et al., 2003) and a phytoplankton community dominated by cyanobacteria and small eukaryotes (Arístegui et al., 2001; Arístegui & Montero, 2005; Baltar et al., 2009).

3. Methods

3.1 Set-up of a mesocosm experiment and nutrient addition

The mesocosm study was conducted at the pier side of Taliarte’s harbour, in the eastern coast of Gran Canary island (27º59’22.8” N, -15º22’0.8” Wº), from 3 March to 3 Abril 2016. No specific permission was required to collect water from this area, and the study did not involve endangered or protected species. Eight floating, pelagic mesocosms (volume ~ 1100 m3), made of thermoplastic polyurethane (TPU), were situated on a floating platform. The filling of the mesocosms started after all mesocosms were moored in position with adjacent harbour seawater. There was no risk of differences between enclosed water bodies in terms of seawater chemistry and plankton community abundance and composition, because the mesocosms were all filled simultaneously.

CO2 enrichment was carried out gradually over 3 days (t-3, t-2, t-1) by adding CO2-enriched water aerated with pure CO2 (99.995%) for a minimum of 24h. To reach an even distribution of CO2-enrichement water throughout the mesocosm, a dispensing device (termed “spider”) was slowly moved up and down during the injection. By using this technique total alkalinity remained constant while dissolved inorganic carbon (DIC) increased, mimicking on-going ocean acidification (Dickson et al., 2007). A CO2 gradient was established along mesocosms, with target pCO2 concentrations ranging from 368 to 1287 µatm. This approach involves the use of regression statistics for assessment of possible CO2 effects and increases the chance of detecting a threshold level for any CO2 sensitive processes. Samples were collected by means of a PVC tub with a closure system at the two extremes. The tub’s volume was 5L and its content was gently mixed to ensure a homogeneous depth-integrated water sample.

In order to simulate the upwelling of deeper, nutrient-rich waters, dissolved inorganic nutrients were added on t18 using the same dispersion device as described above, to yield concentrations of 5 µM NO3, 0.32 µM PO4, and 2.5 µM Si. The amount of inorganic nutrients added to each mesocosm was calculated on the basis of volume determinations, depending on mesocosm volume. Water samples from each mesocosm were collected in the morning between 9:00 and 11:00, either every two days (t-3 to t17) or daily (t19 to t25) in order to obtain greater temporal resolution during the fertilization period.


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3.2 Samples analyses

CO2 concentrations were calculated from measured temperature, dissolved inorganic carbon (DIC) and total alkalinity (TA) by members of the GEOMAR group. The samples were pressure-filtered to exclude calcareous particles and particulate organic material because they can influence the precision of the carbonate chemistry measurements. DIC was calculated as the mean of the best three out of four measurements of 2 mL sample volume. According to the open-cell method described in Dickson et al. (2003), TA was determined by potentiometric titration. Nitrate, nitrite, phosphate and silicate were determined from water samples following the methods based on spectrophotometric techniques developed by Murphy and Riley (1962) and Hansen and Grasshoff (1983). Ammonium concentrations were determined fluorometrically following Holmes et al. (1999). The analysis was performed two hours after sample collection by the GEOMAR group.

Heterotrophic bacteria (HB), small photosynthetic eukaryotic cells (picoeukaryotes), and Prochlorococcus and Synechococcus type cyanobateria, were counted by means of a FACScalibur (Becton and Dickinson) cytometer. Picoeukaryotes, Prochlorococcus and Synechococcus samples (4 mL) were analysed in vivo after 30-60min of collecting samples. Heterotrophic bacteria were fixed with 2% final concentration of formaldehyde, incubated for 30 min at 4°C and then stored frozen in liquid nitrogen until analysed. To count HB, 400 µl was stained with DMS-diluted SYTO-13 (Molecular Probes Inc.) stock (10:1) at 2.4µM final concentration. Bacteria were identified by their signatures in a plot of side scatter (SSC) versus green fluorescence (FL1). High DNA (H-DNA) bacteria and low DNA (L-DNA) bacteria were separated in the scatter plot. Small phytoplankton groups were identified by an interactive analysis of multiple bivariate scatter plots of side scatter, red fluorescence and orange fluorescence. Samples were run at low speed for HB (16 µL min-1) and medium speed for the other photosynthetic cells (60 µL min-1). A suspension of yellow–green 1 µm latex beads (∼106beads ml-1) was added as an internal standard (Polysciences, Inc.).

A CytoBuoy cytometer (Dubelaar and Gerritzen, 2000) was used to count nanoeukaryotic cells (2-20 µm). The light scattered from each passing particle is measured at two angles -forward scatter (FWS) and sideward scatter (SWS)- to provide information on size and shape of the particles. The fluorescence emitted by photosynthetic pigments in algal cells is detected at three different wavelengths and these fluorescence signatures of Chl-a, phycoerythrin and phycocyanin, displayed as red, orange and yellow, respectively, assist in determining the algal type of each particle. Samples (about 15-10 mL) were analysed in vivo during 7 minutes at a flow rate of 300 µL min-1). Samples (100 mL) for microphytoplankton (>20 µm) were fixed with alkaline Lugol’s iodine (1 % final concentration), sedimented in Utermöhl chambers and counted by means of an inverted microscope following the Utermöhl method by the GEOMAR group.


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3.3 Data analyses and statistics

To determine potential pCO2 effects on phytoplankton abundances, the daily deviation (𝐴𝐷#) of each mesocosm was calculated by subtracting observations (𝑋#) from the mean of all mesocosms (𝑋) on the specific sampling day 𝐴𝐷# = 𝑋# − 𝑋. These daily deviations were averaged over time, according to (

𝒩(𝐴𝐷𝑖)𝒩

#-( with 𝒩 being the number of sampling days, in order to obtain the mean deviations (MD) of each mesocosm with respect to a particular parameter. Calculated mean deviations were tested against real average pCO2 of the different mesocosms by linear regression. Days -1, 1, 2 were excluded from the analysis because pCO2 levels had not yet reached the correct concentrations. Linear regression analyses were undertaken using R software (http://www.r- project.org/). In order to visualize the level of similarity of all phytoplankton species biomass and check whether there was a change in the community structure, a MDS (Multidimensional Scaling) plot was performed with the software PRIMER v6. Bray-Curtis similarities were computed following the square-root transformation of biomass in order to diminish the importance of the most abundant species and to take the less abundant species into consideration.

4. Results

4.1 Carbonate chemistry and nutrient evolution After 3 days of pCO2 adjustment, all mesocosms reached pCO2 target levels and the average pCO2 concentrations were calculated for each mesocosm during the two periods (Tab.1).

Mesocosm M5 M7 M3 M4 M8 M2 M6 Target pCO2 (µatm) 400 700 850 1000 1150 1300 1450 Average pCO2 (µatm) t3-t17 (BF)

403 663 810 899 1021 1115 1287

Average pCO2 (µatm) t19-t29 (AF)

368 604 690 768 829 974 1021

Symbol

Table 1. Average pCO2 (µatm) concentrations for the two periods, “BF” = before fertilization and “AF” = after fertilization. Symbols and colours indicated here are used in all following figures.

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization

Time (days)

Tota

l Bac

teria

(cel

l/ml)

−3 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+06

3e+0

65e

+06

7e+0

6

Control7008501000115013001450

Fertilization


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The pCO2 concentrations were allowed to vary naturally and treatments remained fairly separate over the entire experiment (Fig. 1). The variability in the highest pCO2

mesocosms was greater than in the others, mostly due to the fact that differences in pCO2 concentrations between atmosphere and seawater were greatest during outgassing processes. The decrease in pCO2 from t20 to t25 was driven by biological uptake, as productive biomass increases after nutrient fertilization (Kim et al., 2006). The pCO2 levels were adjusted to expected values after fertilization.

Nutrient concentrations decreased substantially to low levels during the first period (Fig.2). There was low inorganic nitrogen (~1,46 µmol L-1 NOx (NO2+NO3) and 0,47 µmol L-1 NH4) relative to phosphate (~ 0,17 µmol L-1) in all mesocosms at the start of the study period, compared to the canonical Redfield nutrient stoichiometry (C: N: P = 106:16:1; Redfield, 1958). These concentrations are within the natural range data obtained from European Station for Time Series in the Ocean at the Canary Islands (ESTOC) located in the northeast Atlantic subtropical gyre (González-Dávila et al., 2003) and temporal dynamics between phosphate and nitrates showed decoupling. Nitrate and nitrite concentrations decreased from 1,46 µmol L-1 to 0,011 µmol L-1 during the first three days (-2,4 d-1), whereas phosphate concentrations progressively diminished at an average rate of 0,16 d-1 throughout the nutrient depleted period (BF: Before fertilization).

Time (days)

pCO

2 (u

atm

)

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

050

010

0015

00

Figure 1. Time evolution of pCO2 (µatm) throughout the whole experiment. Dashed line indicates fertilization day, which distinguishes between the period before fertilization “BF”, and after fertilization “AF”. The colours indicate the pCO2 concentrations: high (red), intermediate (grey) and low (blue) pCO2 levels (legend on Tab.1).

pCO

2 (µ

atm

)

BF AF


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After fertilization (AF period) NOx and PO4 increased their concentrations (3,43 µmol L-

1; 0,15 µmol L-1 respectively) and NOx decreased faster than phosphates (Fig.2). An abnormal pattern was observed on M7 treatment, with slower decrease of NOX and PO4, and a delayed peak of NH4. The slower decrease of the silicates indicates that the diatoms are using it in a more progressive way, instead of using it quickly. Moreover, ammonium decreased very quickly in the first three days (-1,2 d-1) and barely reached zero on the BF period (low nutrient concentrations).

Silicate concentration was significantly lower under elevated pCO2 (p<0,05, Fig.3) after fertilization, mainly due to biological uptake of diatoms (p<0,05, Fig. 4) (Bellerby et al., 2008).

Time (days)

SiO

2 (µ

mol

L−1 )

−3 −1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

00.

51

1.5

22.

53

Time (days)

NO

2+N

O3 (µ

mol

L−1 )

−3 −1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

00.

51

1.5

22.

53

3.5

44.

5

Time (days)

NH

4 (µ

mol

L−1 )

−3 −1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

00.

20.

40.

60.

8

Time (days)

PO4 (µ

mol

L−1 )

−3 −1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 290

0.02

0.06

0.1

0.14

0.18

0.22

Figure 2. Temporal development of NOX, PO4, NH4 and SiO2 concentrations (µmol L-1) in the high (red), intermediate (grey) and low (blue) pCO2 treatments during the course of the experiment.


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4.2 Evolution of phytoplankton abundances along the experiment

The abundances of planktonic groups during the course of the experiment are presented in Fig. 5. Heterotrophic bacteria abundance showed a small increase on day 3, which coincides with a little increase in Synechococcus abundances (Fig.5a). Interestingly, five days before nutrient addition, heterotrophic bacterial abundances increased in all mesocosms until t18 with an average concentration of 2x106±3x105 cells mL-1 (Fig. 5a). The average highest abundances of 3,8 x106 cells mL-1 during the phase “AF” were observed for the intermediate and high pCO2 treatments.

R2 = 0.11p = 0.466

R2 = 0.711p = 0.017

1.0e−01

1.5e−01

2.0e−01

2.5e−01

8.0e−01

1.0e+00

1.2e+00

1.4e+00

BF

AF

400 600 800 1000 1200 pCO2

SiO

2 (µ

mol

L−1 )

Figure 3. SiO2 concentrations (µmol L-1) as a function of treatment pCO2. Symbols indicate the mean SiO2 in the high (red), intermediate (grey) and low (blue) pCO2 treatments before and after nutrient addition.

R2 = 0.019p = 0.765

R2 = 0.703p = 0.018

0e+00

2e+02

4e+02

6e+02

0e+00

2e+02

4e+02

6e+02

BFAF

1.41.21.00.8 SiO4 (umol/L)

Diat

oms

(cel

l mL−

1 )

R2 = 0.019p = 0.765

R2 = 0.703p = 0.018

0e+00

2e+02

4e+02

6e+02

0e+00

2e+02

4e+02

6e+02

BFAF

1.41.21.00.8 SiO4 (umol/L)

Diatom

s (ce

ll mL−1

)

R2 = 0.019p = 0.765

R2 = 0.703p = 0.018

0e+00

2e+02

4e+02

6e+02

0e+00

2e+02

4e+02

6e+02

BFAF

1.41.21.00.8 SiO4 (umol/L)

Diat

oms

(cel

l mL−

1 )

Time (days)

SiO2 (µmol L−1)

−3−1

13

57

911

1315

1719

2123

2527

29

0 0.5 1 1.5 2 2.5 3

R2 = 0.019p = 0.765

R2 = 0.703p = 0.018

0e+00

2e+02

4e+02

6e+02

0e+00

2e+02

4e+02

6e+02

BFAF

1.41.21.00.8 SiO4 (umol/L)

Diat

oms

(cel

l mL−

1 )

Figure 4. Diatom abundance (cell L-1) as a function of SiO2 (µmol L-1). Symbols indicate the mean diatom abundance in the high (red), intermediate (grey) and low (blue) pCO2 treatments after the fertilization period (AF).


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We differentiated two subpopulations: High DNA bacteria (high fluorecence ones) and Low DNA bacteria (low fluorecence ones) (Gasol et al., 1999). Our data show how throughout almost the whole experiment LNA bacteria abundance overcame HNA abundances, but not in a significant way (see Annex.a). Synechococcus abundances remained very low (1,9x104 cells mL-1) during the first period in all mesocosms, and peaked at t24 with maximum abundance of 5,3x105 cells mL-1 and 5,1x105 cells mL-1 in M2 (1300µatm) and M4 (1000 µatm) respectively (Fig.5b). Prochlorococcus, one of the most abundant cyanobacteria in the oceans, was absent during all experiment (data not shown). Owing to the fact that this group has been frequently observed in the Canary Islands seawaters (Arístegui & Montero, 2005), we postulate that the enclosure inside the mesocosms is responsible for that disappearance.

Picoeukaryotes showed a sharp drop during first five days in all mesocosms, and only the two treatments with the least pCO2 increased their abundance some days after fertilization (t24) (Fig.5c).

Autotrophic nanoeukaryotes diminished in all mesocosms during the first days in the same way as picoeukaryotes. We differentiated two groups amongst this cluster which showed different behaviours in the two periods. Nanoeukaryotes I, smaller and with less FL3, where the only that predominated during the first period (Fig.5d), while nanoeukaryotes II, larger and with more FL3, increased in the second period, together with nanoeukaryotes I (Fig.5e). The delayed bloom observed on M7 indicates, that this mesocosm behaved differently compared with the others but it has not been excluded from the statistical analysis because it did not cause significant differences in the results obtained.

The last group analysed was the microphytoplankton, formed of diatoms, large flagellates and dinoflagellates. Throughout the entire experiment, diatoms were the most abundant group of microphytoplankton (~87% on BF period, ~72% on AF period) and showed a little bloom at t5, followed by a decrease until nutrient addition (Fig.5g). Diatoms also showed a pronounced increase on high pCO2 treatments during the AF period. Dinoflagellates and flagellates remained practically constant during the BF period and increased on t23, five days after nutrient addition (Fig.5f, h).


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Time (days)

Nan

oeuk

aryo

tes

I Log

10(c

ell m

L−1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

2.8

3.0

3.2

3.4

3.6

3.8

4.0

Time (days)

Pico

euka

ryot

es L

og10

(cel

l mL−

1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

3.0

3.5

4.0

4.5

5.0

Time (days)

Syn

echo

cocc

us (c

ell m

L−1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

01e

+05

3e+0

55e

+05

Time (days)

Tota

l Bac

teria

(cel

l mL−

1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

02e

+06

4e+0

66e

+06

8e+0

6

Time (days)

Flag

ella

tes

(cel

l mL−

1 )

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

010

2030

4050

60

a

b

f

g

Time (days)

Din

ofla

gella

tes

(cel

l mL−

1 )

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

020

4060

8012

016

0 h

Time (days)

Dia

tom

s (c

ell m

L−1 )

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

020

040

060

080

012

00

Figure 5. Temporal development of total heterotrophic bacteria (a), Synechococcus (b), picoeukaryotes c), autotrophic nanoeukaryotes I (d), autotrophic nanoeukaryotes II (e), large flagellates (f), diatoms (g) and dinoflagellates (h) abundances (cell mL-1 or Logcell mL-1) in the high (red), intermediate (grey) and low (blue) pCO2 treatments during the course of the experiment.

c g

d

Time (days)

Nan

oeuk

aryo

tes

II Lo

g10(

cell

mL−

1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

2.5

3.0

3.5

e


15

4.3 Effect of pCO2 on different phytoplankton growth rates before and after fertilization To compare pCO2 effects, we analysed the data of the two phases separately: a) before nutrient addition (BF) (days 3-17) and b) after nutrient addition (AF) (days 19-29). As phytoplankton abundance may also be regulated by grazing and viral lysis, we refer to changes in phytoplankton abundances over time as net phytoplankton growth.

Before nutrient addition, nanoeukaryotes I and dinoflagellates had a positive growth under high pCO2 concentration (p<0,05; Tab.2). The significantly positive response of nanoeukaryotes I+II is due to the contribution of nanoeukaryotes I, which is more abundant in this period. After nutrient addition, differences in heterotrophic bacteria and microphytoplankton abundances between treatments were significant (p<0,05) reporting positive responses on high pCO2 treatments (Tab.2). The microphytoplankton response is mainly due to diatoms response, because they involved a ~72% microphytoplankton abundance in this period. Conversely, picoeukaryotes and flagellates reported negative significant responses on high pCO2 treatments (p<0,05; Tab.2).

Significant codes: * 0.05

**0.01 ***0.001 Table 2. Changes in phytoplankton abundances during the two periods (“before fertilization”,

BF; “after fertilization”, AF) determined by the slope direction of the linear regression. “Pos.” means significantly increasing abundance with increasing pCO2 and “Neg.” means significantly decreasing abundance with increasing pCO2.


16

4.4 Overall community structure trends

Abundance data of each group and species were transformed to biomass (see Annex.b to see the conversion factors used). During the first period, the total biomass in all treatments remained relatively constant (Fig.6). Moreover, after three days of adding inorganic nutrients, the biomass of high pCO2 treatments increased more than the others (Fig.6), although not in a statistically significant way (Fig.7, p>0.05).

Time (days)

Biom

ass

(mg

C m

−3 )

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

020

040

060

080

010

0014

00

Figure 6. Temporal development of total biomass in the high (red), intermediate (grey) and low (blue) pCO2 treatments during the course of the experiment.

R2 = 0.41p = 0.11

R2 = 0.29p = 0.2

57.5

60.0

62.5

65.0

67.5

160

180

200

220

BFAF

400 600 800 1000 1200 pCO2

Tota

l biom

ass (

mg

C m

^−3

)

Figure 7. Total biomass (mg C m-3) versus pCO2 for the seven treatments on the two periods, BF “before fertilization” and “AF” after fertilization. Symbols indicate the mean total abundances in the high (red), intermediate (grey) and low (blue) pCO2 treatments


17

In the second period (AF), some phytoplankton abundances on treatments with less pCO2 (400µatm and 700µatm) were more interrelated (more proximity between dots means more similarity) than the other treatments (Fig 8b, surrounded by a black circle). The availability of a range of pCO2 levels enable us to see the threshold at which the effects of pCO2 begin to be strong enough to affect community structure. In this case, the tipping point was observed between 700 ppm and 850 ppm treatments.

Interestingly this difference was not observed during the first period (Fig 8a), when nutrients, mainly silicates, were practically exhausted and probably limiting the growth of some phytoplankton species.

b)

a)

Figure 8. Bray-Curtis non-parametric MDS plots showing similarities between community structure using days as replicates at (a) before fertilization period (BF) between treatments, (b) after fertilization period (AF) between treatments.


18

The contribution in percentage terms of diatoms’ biomass was the greatest in all treatments during the first period, while after fertilization only in high pCO2 treatments (Fig.9). Moreover, the contribution of nanoplankton’ biomass seems to be more important in low pCO2 treatments after the fertilization period (Fig.9).

0102030405060708090

100

400 700 850 1000 1150 1300 1450

Perc

enta

ge o

f bio

mas

s (%

)

pCO2 treatments

Flagellates

Dinoflagellates

Diatoms

Nanoeukaryotes

Picoeukaryotes

Synechococcus

0102030405060708090100

400 700 850 1000 1150 1300 1450

Perc

enta

ge o

f bio

mas

s (%

)

pCO2 treatments

Flagellates

Dinoflagellates

Diatoms

Nanoeukaryotes

Picoeukaryotes

Synechococcus

b)

Figure 9. Contribution in percentage (%) of each group of phytoplankton to total biomass a) before and b) after fertilization periods.

a)


19

5. Discussion 5.1 Effects of pCO2 on different taxonomic groups and community structure. Predicting the ocean’s role in the global carbon cycle requires an understanding of the coupling between carbon and growth-limiting elements in biogeochemical processes. Therefore, understanding OA effects at ecosystem level is important, as complex communities could either dampen or aggravate the CO2 effects experienced at the single species level. Past studies on ocean acidification have reported significant changes in competitive fitness between phytoplankton types to significantly alter community structure (Dutkiewicz et al., 2015; Bérmudez et al., 2016; Hare et al., 2007). In the present study, shifts in the community structure can be seen in Fig.9, showing a transition from nanoeukaryotes’ to diatoms’ biomass predominance as pCO2 increased during and after the bloom. This shift was related with positive net growth rates on diatoms on high pCO2 treatments (Tab.2), so an apparent CO2 limitation was affecting them or giving them an advantage over other phytoplankton species. More than half of the organic carbon flux to the deep ocean is responsible for diatoms (Nelson et al., 1995, Dugdale & Wilkerson 1998), so the impact of pCO2 on this group has to be carefully studied. Riebesell et al. (1993) reported how the CO2 supply can limit growth rate but not the total biomass attained by a diatom bloom after exhaustion of nitrate and phosphate. The non-significant response observed for the first period suggests that nutrients may be playing an important role on the success of diatoms. During the two periods, nutrient concentrations were depleted rapidly, but the silicate concentrations were much scarcer in the first period than in the second. The strong correlation between silicates and diatoms (Fig.4) suggests that when silicates were limiting diatom’s growth, no apparent effects of pCO2 could be seen in their net growth rates or biomass. In fact, Egge & Aksnes, (1992) confirmed that diatoms tend to predominate in the phytoplankton community except when silicate is scarce, although other studies have reported the opposite result (Hare et al., 2007). Therefore, it is not easy to establish whether this positive pCO2-response is due to changes in the use of nutrient ratios, carbon limitation or both simultaneously. Tortell et al. (2002) associated the CO2-dependent shift to diatoms predominance with a significant change in the use of nutrients, with higher ratios of nitrate:silicate (N:Si) and nitrate:phosphate (N:P) consumption by phytoplankton in the low CO2 treatment. In the present study, the slower decrease of nitrates and silicates on low pCO2 treatments in the bloom period (Fig.2) could support that hypothesis. Field experiments have demonstrated that they are capable of indirect or direct HCO3

− uptake (Tortell & Morel, 2002; Cassar et al., 2004; Martin & Tortell 2006) and for that reason, in general, their growth is not limited by carbon (Tortell et al. 1997, Goldman 1999). However, high-efficiency concentrating mechanisms have been reported (CCMs) on some diatom species (Reinfelder, 2011). The inorganic carbon acquisition strategies are likely to vary among diatom species (Kim et al., 2006; Tortell et al., 2008), and for that reason, the initial community composition could have a significantly greater impact than high CO2 on phytoplankton biomass (Egger et al., 2014). In the first period, the most important diatom genus in terms of biomass and


20

abundance was Dactyliosolen, while in the second Ginardia was the most important genus on high pCO2 treatments. These two genus have been reported in the Canary waters (Moro et al., 2003), and it seems that Guinardia benefited from high pCO2 concentrations during bloom and immediately thereafter. In future studies, Guinardia’s CCMs should be studied to clarify the results obtained in the present study. The other group which also reported positive net growth by increasing pCO2 on rich-nutrient period was heterotrophic bacteria (LNA+HNA) (Tab. 2, p<0,05). Some studies have attributed this response to accumulation of gel particles (TEPs), such as substrate, to attach to and accelerate nutrient availability on rich pCO2 treatments (Endres et al., 2014; Engel et al., 2014). Grossart et al. (2006) also reported an increase in bacterial abundances of particle-attached bacteria at high pCO2 during a bloom decline when the release of algal-derived organic matter was high. Bacterial abundance and bacterial growth would be very different, depending on whether bacterial growth is controlled by the system’s production of labile DOC, or by the parameters determining bacterial success in algal–bacterial competition for nutrients (Thingstat et al., 2008). Endres et al., (2014) showed increased bacteria abundances associated with increased extracellular release of organic compounds by autotrophs under excess availability of CO2 during the nutrient limitation period. In our case, the non-significant correlation during the nutrient depleted period (Tab. 2, p<0,05) does not correlate with the study mentioned above, although we do not have organic compounds data to determine whether the extracellular release of organic compounds was high or not. Another explanation for the bacteria’s behaviour observed could be related with silicates. Thingstad et al. (2008) showed how silicate would seem to have a large potential for changing the ratio between availability of organic carbon and nitrogen to the heterotrophic prokaryotic community. In treatments with silicates and low carbon concentrations where diatoms predominated, a high proportion of the material produced autochthonously was unavailable to bacteria possibly due to chemical form, or because of physical protection (for example, inside diatoms). In the present study, diatoms biomass predominated equally in all treatments during the first period (Fig.4), so the immobilization of autochthonous material could be the responsible for the lack of a positive increase in bacterial abundance. However, in order to test these hypotheses, the different forms of carbon should be collected in future studies of ocean acidification. For bacteria, direct uptake of substrate from seawater is restricted to simple molecules such as mono- and disaccharides and amino acids (LMW-DOM), which are present in very low concentrations in seawater. In contrast, the high- molecular weight (HMW-DOM) fraction needs to be hydrolysed prior to microbial uptake. The increase observed on day 3 (Fig. 5a) was probably due to the initial consumption of LMW-DOM fraction, and the second increase, before fertilization (day 15), could be associated with the remineralization of more refractory organic material fraction. In oligotrophic environments, LNA bacteria may represent the most active and predominating bacterioplankton community (Zubkov et al., 2001; Jochem et al., 2004; Longnecker et al., 2006), although in the present study the two heterotrophic bacteria groups showed


21

similar behaviours on different treatments during the course of the experiment (See Annex.a).

Synechococcus is ubiquitously distributed throughout the world and recognized as one of the main components of picophytoplankton in the ocean (Johnson & Sieburth, 1979; Waterbury et al., 1986). As observed in other studies, no significant correlation on net growth rates between pCO2 treatments were observed before or after fertilization (Fu et al., 2007; Lomas et al., 2012). Similar results have been reported under nutrient-depleted conditions on Maugendre et al. (2014), suggesting that ocean acidification has a very limited impact on this plankton community. Conversely, there are studies reporting negative responses in growth rates on high pCO2 levels (Traving et al., 2014; Paulino et al., 2008) and positive responses (Dutkiewicz et al., 2015, Bermúdez et al., 2016). These differing results highlight the need to perform ocean acidification studies as close as possible to real natural conditions, since the results can vary if they are carried out in isolated cultures or in mesocosm studies. Suffrian et al. (2008) showed that the effects of elevated CO2 on single plankton species observed in laboratory studies are not comparable to those obtained in experiments simulating close to natural conditions, since such complex systems seem to have a higher capacity to buffer changes in pCO2. The apparent photosynthetic affinity for inorganic carbon of Synechococcus ranges between a low-affinity and high-affinity (Kaplan and Reinhold, 1999; Price et al., 2002; Badger and Price, 2003), and unresolved oxygen-depend process could affect the functioning of CCMs (Woodger et al., 2005). The lack of response observed in this group between treatments in the two periods could be associated with the lack of a direct effect of pCO2 on this group or with indirect effects, such as a possible dependency of growth upon viral lysis of heterotrophic bacteria (Weinbauer et al., 2011) or high grazing rates observed in the two periods (Armengol et al., in progress).

Prochlorococcus disappeared since the beginning of the experiment in all mesocosms, so no possible effect of pCO2 could be tested (data not shown). There is evidence to suggest that Prochlorococcus was virtually unaffected by elevated pCO2 (Fu et al., 2007), probably due to the lack of differences in CCM physiology or gene expression observed under different pCO2 conditions (Hopkinson et al., 2014).

Recent studies on ocean acidification are consistent with small-sized phytoplankton benefits from high pCO2 levels in terms of growth rate and biomass accumulation (Bermúdez et al., 2016; Newbold et al., 2012; Brussaard et al., 2013). Some authors suggest that the picoplankton success on high pCO2 environments could be associated with a decrease in microzooplankton predation or in viral production (Larsen et al., 2008; Meakin et al., 2011). In contrast, during our study, picoeukaryotes’ net growth rates decreased in high pCO2 treatments during the bloom period (Tab.2, p < 0.05), while in the nutrient-depleted period, no apparent effects were observed, as pointed out by Newbold et al. (2012). It has been suggested that phytoplankton that lack an efficient carbon concentrating mechanism (CCM) might be favoured in a high CO2 environment (Riebesell 2004). The operation of CCMs is energetically expensive and, because cell


22

membranes are freely permeable to CO2, additional metabolic costs are incurred in limiting the efflux of CO2 from the cell (Giordano et al., 2005). It may be that the energetic savings made by downregulating CCMs are significant under elevated pCO2 in at least some picoplankton species (Meakin et al., 2011), although these benefits may be offset to some degree by the increased costs of other metabolic processes (Raven and Johnston, 1991). The high-efficiency evidences CCMs reported in this group (Iglesias-Rodríguez et al., 1998) could incur an expense in terms of additional energy on high pCO2 treatments, although as we have seen previously, CCMs can vary between species, making it difficult to compare results between studies with different initial phytoplankton communities. Therefore, future work should examine the increase of pCO2 on grazing dynamics, as well as focusing on the CCMs of different picoeukaryotes groups and their sensitivities to pCO2 and nutrient availability.

The positive growth rates observed during the nutrient-poor period in the nanoeukaryotes I group as pCO2 increased (Tab.2, p<0,05), tally with the findings of another study conducted in Bering Sea, where community composition shifted from diatoms and towards nanophytoplankton on high pCO2 (Hare et al., 2007). Before fertilization, the predominant population of nanoeukaryotes was nanoeukaryotes I, smaller and with less FL3, whereas during the second period it was nanoeukayotes II, larger and with more FL3. It is known that the size of cells is important for a variety of processes in seawater, such as diffusion-limited uptake of substrates, resource allocation, predator-prey interactions, and gravitational settling. The relative surface area can be of greater importance than cell diameter, mass or volume with respect to processes such as nutrient uptake, because it is the surface that interferes with the outer medium containing the substrate reservoir. The relative surface area of a phytoplankton cell increases with decreasing size, but also with increasing eccentricity of the cells. Consequently, the small nanoeukaryotes I should be better competitors for resources during the nutrient-poor period (Grover, 1989) but the negative significant correlation of their size as pCO2 increased (see Annex.c) is not still resolved. Reinfelder (2011) put forward a theoretical relationship between cell size, bulk seawater concentration of CO2aq, and cell carbon-specific reaction-diffusion CO2 supply rate. For cells with radii smaller than approximately 10 μm, CO2 reaction-diffusion could support high specific carbon fixation rates (>1 d−1) at bulk seawater CO2aq concentrations >10 μM. At CO2aq concentrations from 3 to 20 μM, reaction-diffusion-supported specific carbon fixation rates drop sharply as cell radius increases from 10 μm to 20 μm. The size of the nanophytoplankton is normally between 2 and 20 µm, but in this study the exact size of the nanoeukaryotes is not available. If the size of nanoeukaryotes I had been smaller than 10 μm, our results might be in agreement with the above-mentioned study. In any case, the relationship between size and pCO2 is still not well known, and therefore further studies should explore this area further. Another possible explanation for the increase of nanoeukaryotes I during the first period could be that their small size gives them an advantage in decreasing the risk of being depredated by other organisms. In fact, Hansen et al. (1997) reported that heterotrophic


23

dinoflagellates preferably ingest prey near to their own size rather than smaller. Aberle et al., (2013) reported a positive CO2 effect on dinoflagellates, as in the present study, during the nutrient-depleted period (Tab.2, p <0.05). The dinoglagellates’s form II RubisCO has the lowest carboxylation: oxygenation specificity factor among eukaryotic phytoplankton (Badger et al. 1998, Whitney & Andrews 1998), giving them a disadvantage with regard to carbon fixation in the high-O2, low-CO2 modern ocean. As a consequence, dinoflagellates are likely to require a CCM to sustain even their relatively slow rates of growth and photosynthesis, and, consequently, it could give them an advantage in high pCO2 media, as observed in our study. Be that as it may, direct experimental evidence for CCMs in marine dinoflagellates is limited, and results vary with species and experimental conditions (Reinfielder et al., 2011).

5.2 Potential consequences for carbon cycling An apparent shift in phytoplankton community structure was found between 700-850 ppm (Fig.8) and this may explain why we did not observe significant differences in total biomass between the CO2 treatments (Fig.7). This result could be important because this threshold is within the atmospheric pCO2 levels that are predicted in the future ocean models (IPCC, 2007). If the future ocean reaches these pCO2 levels the structure of the phytoplankton community could change with important consequences on the flow of the organic carbon moved by the marine trophic chain. Similar results were observed in another study, showing how CO2-dependent taxonomic shifts could occur in the absence of a detectable difference in total primary productivity or biomass (Tortell et al., 2002). In another mesocosm experiment, Engel et al., (2013) observed that addition of CO2 led to an increase in the competition between bacterioplankton and phytoplankton, thus causing a decrease in autotrophic phytoplankton biomass. These findings were not observed in our study, where it seems that some autotrophic organisms, like diatoms, benefited in treatments with higher pCO2. Thingstad et al., (2008) proposed the “counterintuitive carbon cycle”, particularly pronounced in diatom-dominated systems, where the carbon/mineral nutrient ratio in phytoplankton production is high. When bacterial growth rate is limited by mineral nutrients, excess organic carbon is accumulated in the system, while when bacteria is limited by organic carbon, the addition of labile dissolved organic carbon reduced phytoplankton biomass and activity and also the rate at which total organic carbon accumulates. In our study we did not observe a reduction of phytoplankton biomass, although this could be due to enhanced growth caused by nutrient fertilization. Most of the studies carried out suggest that the export of sinking organic carbon may be lower under future warming scenarios (Laws et al., 2000, Bopp et al. 2001) but more studies are needed to test it.


24

6. Conclusions

Despite the apparent changes in taxonomic composition and community structure observed along the experiment, total biomass did not differ significantly between the CO2

treatments. The main changes in community structure were observed over 700-850ppm of pCO2 after nutrient fertilization. Future studies should consider this range as an important transition threshold in order to perform ocean acidification experiments. In our simulated-bloom situation, the net growth rates of heterotrophic bacteria and diatoms were higher in high pCO2 treatments, while picoeukaryotes and flagellates responded oppositely. The assessment of the impact on specific groups needs to be undertaken with extreme caution, since the complexity of marine ecosystems and different composition communities respond differently to pCO2 perturbations.

7. Acknowledgements I wish to thank Javier Arístegui for enabling me to form part of this project, supervising my work and contributing to the phytoplankton analyses. Also, I am very greaful to GEOMAR for set up the experiment and analyse nutrients, pCO2 and microphytoplankton samples. I would also like to thank Nauzet Hernández for his patience and encouragement and - last but not least- Isabel Baños, Alba Filella, Acorayda and Minerva Espino for being such a very welcoming team and helped me in everything I needed.

8. Annex

a) Temporal development of total heterotrophic bacteria (a), LNA/HNA ratio abundances (cell mL-1) in the high (red), intermediate (grey) and low (blue) pCO2 treatments during the course of the experiment

Time (days)

LNA/

HN

A (c

ell m

L−1 )

−1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

00.

51

1.5

22.

53

3.5

44.

5


25

b) Conversion factors used to pass abundances to biomass

Group FgC/cel LNA bacteria 18 HNA bacteria 46 Synechococcus 100 Picoeukaryotes 444 Nanoeukaryotes I 100 NanoeukaryotesII 220

Groups PgC/cel Flagellates Eutripsiella 33 Cryptonomas 90 Pyramimonas 59 Flagellate redonde 159

Dinoflagellates Amphidinium 815 Karenia.mikimotoi 329 Heteocapsa 353 Gymnodiales 3638 Peridinella 671 Prorocentrum 2020 Gyrodinium 2575 Scripsiella 1006 Protoperidinium.bipes 518 Protoperidinium.ovatum 6920 Mesoporos.perforatus 264 Ceratium.fusus 3635 Katodinium 390 Gymnodinium 3638 Ceratium.lineatum 6682

Diatoms Nitzschia.longissima.min50 251 Nitzschia.longissima.mag50 251 Pseudo-nitzschia.min50 14 Pseudo-nitzschia.mag50 14 Licmophora 703 Navicula 300 Skeletonema.costatum 35 Guinardia 1963 Guinardia.mag20 1963 Dactyliosolen 2043 Thalissiosia.constricta 1057 Hemiliaulus.haukii 334 Rhizosolenia.setigera 2500 Chaetoceros.lorenzianus 430

Change of units (mg C/m3) =(cel/ml)*(FgC/cel)*(10^12mg/1Fg)*(1mL/10^-6m3)

Change of units (mg C/m3) =(cel/ml)*(PgC/cel)*(10^-9mg/1Pg)*(1mL/10^-6m3)


26

c) Regression lines between SSC and pCO2 concentrations

Chaetoceros.affinis 133 Chaetoceros.mag10 272 Chaetoceros.min10 272 Chaetoceros.decipiens 432 Chaetoceros.Didymus 209 Leptocylindrus 68 Asterionellopsis.glacialis 108 Ceratulina 723 Corethron 1322 Thalassiosira.nitzschioide 1057 Cylindrotheca 64 Bacteriastrum.hyalinum 140

R2 = 0.109

p = 0.468

R2 = 0.64p = 0.03

1.0e+02

1.5e+02

2.0e+02

2.5e+02

0e+00

2e+02

4e+02

6e+02

BFAF

400 600 800 1000 1200 pCO2 (ppm)

SSC

Syne

choc

occu

s

R2 = 0.368p = 0.148

R2 = 0.044p = 0.649

3.0e+01

3.5e+01

4.0e+01

3.0e+01

3.5e+01

4.0e+01

BFAF

400 600 800 1000 1200 pCO2

SSC

−Pico

euka

ryot

es

R2 = 0.549p = 0.056

R2 = 0.025p = 0.73

2.9e+02

3.0e+02

3.1e+02

3.2e+02

3.3e+02

3.4e+02

2.4e+02

2.6e+02

2.8e+02

BFAF

400 600 800 1000 1200 pCO2 (ppm)

SSC

Nano

euka

ryot

es 1

R2 = 0.601p = 0.04

R2 = 0.004p = 0.89

1.20e+03

1.25e+03

1.30e+03

1.35e+03

5.0e+02

7.0e+02

9.0e+02

1.1e+03

BFAF

400 600 800 1000 1200 pCO2 (ppm)

SSC

Nano

euka

ryot

es 2


27

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10. TFT Report

Descripción detallada de las actividades realizadas en el TFT Inicialmente, éste trabajo empezó con el procesamiento de unas muestras diferentes a las utilizadas. Durante los primero 4 meses analicé las muestras de un experimento de acidificación oceánica que se realizó en las instalaciones del BEA (Banco Español de Algas) durante 15 días. Estas muestras formaban parte de un estudio para comprobar si una alga nociva encontrada en un proyecto de KOSMOS proliferaba en medios con más pCO2. Al no obtener los resultados deseados estas muestras fueron redirigidas a mí para poder realizar mi TFT. En ese momento, me enseñaron a utilizar el citómetro de flujo para procesar tanto muestras de bacterias como de picoeucariotas, tal y como se explica en el apartado de “Methods”. También me mostraron cómo se realiza el lavado de material de laboratorio y cómo se preparan varios compuestos necesarios para los análisis. A continuación pasé varias semanas filtrando y procesando las muestras de nanofitoplancton con un microscopio invertido de auto-florescencia (estas muestras fueron sin duda, las más difíciles de leer). Encontramos resultados que no se ajustaban a lo esperado y buscamos una posible explicación. Concluimos que la forma en que se añadió el CO2 a las bolsas no fue la adecuada, al observar cómo la mayoría de poblaciones de fitoplancton morían poco después de iniciar el experimento. Estas muestras se desecharon y se me permitió encargarme de algunas muestras de otro experimento, KOSMOS16. El hecho de no poder utilizar los datos que tanto esfuerzo me había costado obtener me enseñó una gran lección sobre el mundo de la ciencia. A veces mucho del esfuerzo que inviertes en algo no acaba llegando a buen puerto, pero hay que aprender de los errores cometidos a lo largo del camino, para no volver a cometerlos cuando lo intentas otra vez. Esos meses de trabajo me sirvieron para dominar el citómetro de flujo, varias técnicas y adquirir soltura a la hora de procesar una gran cantidad de datos. El equipo de GEOMAR, supervisado por el doctor Ulf Riebesell y el “Grupo de Oceanografía Biológica” (GOB) nos reunimos para organizar el muestreo y revisar el diseño del experimento del proyecto KOSMOS16. Gracias a GEOMAR se dispuso del material necesario para montar los mesocosmos y todo su equipo hizo un gran trabajo. Durante prácticamente un mes entero nos dirigíamos cada mañana al muelle del puerto de Taliarte para muestrear los 8 mesocosmos que había instalado el grupo GEOMAR. Aproximadamente se tardaban unas 3 horas en recoger todas las muestras y nos íbamos turnando los días de muestreo. Se muestreaba de 6 a 7 veces cada mesocosmos, ya que había que rellenar dos garrafas de 10 litros con tubos que tenían una capacidad de 5 litros. Los tubos restantes se empleaban para medir gases disueltos y metales. Todo este procedimiento se realizaba con un traje especial para evitar la contaminación y los tubos eran cuidadosamente agitados para garantizar la homogeneidad de la columna de agua. Los equipos de muestreo estaban formados por una mezcla de estudiantes de diferentes lugares del mundo, permitiéndonos conocer otros alumnos que estudiaban lo mismo que nosotros, practicar inglés y entablar nuevas amistades. Una vez que cada grupo tenía sus


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pertinentes garrafas de aguas las trasladábamos a nuestros laboratorios y empezábamos los diferentes análisis, siempre manteniendo estrictos protocolos para no contaminar las muestras. Aproximadamente mis análisis terminaban hacia las 18:30h, aunque algunos días terminaba mucho más tarde. La citometría se pasaba “in vivo”, de modo que las muestras no tenían que ser manipuladas de ninguna forma. Al finalizar el experimento pasé varias semanas pasando las muestras de bacterias, que habían sido almacenadas a -80ºC y tenían que ser debidamente teñidas son SYTO13. Debido a algunos resultados no congruentes, todas las muestras se analizaron dos veces. También me encargué de analizar unas muestras de citometría de un experimento con peróxido de hidrógeno que propuso uno de los miembros de GEOMAR a mitad del estudio. Una vez con los datos en bruto empecé a realizar gráficos y análisis estadísticos. Seguidamente escribí la memoria, complementándola con la lectura de la bibliografía que hablaba sobre el mismo tema. Entrenamiento recibido Tanto el grupo de GEOMAR como el “grupo de oceanografía biológica” me enseñaron pacientemente a realizar los muestreos matutinos. La metodología usada para la citometría de flujo con el FACScalibur y el CytoSense me la enseñó mi tutor Javier Arístegui, mostrándome pacientemente como identificar las diferentes poblaciones en los diagramas X-Y. Para entender estos diagramas tuve que aprender qué tipo de pigmentos son específicos de cada especie del fitoplancton y si se trataban de organismos autótrofos o heterótrofos. Acorayda y Minerva, las técnicos de laboratorio de nuestro grupo, me ayudaron a entender el funcionamiento del laboratorio, dónde se guardaba el material y pequeños trucos cuando el citómetro no funcionaba correctamente. Los datos se trabajaron con Excel y después con R-Studio. Este último programa no lo dominaba en absoluto pero gracias a Nauzet Hernández y tutoriales de YouTube logré aprender satisfactoriamente. Después aprendí a usar el programa estadístico PRIMER con el fin de realizar un análisis PERMANOVA, aunque al final me quedé en un MDS debido a la naturaleza de los datos de los que disponía. Nivel de integración e implicación dentro del departamento y relaciones con el personal La relación con todos los científicos, técnicos, personal de mantenimiento y de seguridad que trabajan en el IOCAG ha sido muy buena. En todo momento me sentí integrada en el equipo y siempre me ayudaron en todo lo necesario. Intenté ayudar al equipo en todo lo posible y aprendí que cada uno tiene una función indispensable cuando se trabaja en equipo.


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Aspectos positivos y negativos más significativos relacionados con el desarrollo del TFG En general, he disfrutado mucho estudiando un tema que me apasiona. Tener la suerte de estudiar el cambio climático y sus repercusiones en la oceanografía biológica ha sido muy enriquecedor. Poder colaborar con el proyecto KOSMOS y el grupo de GEOMAR también ha sido muy satisfactorio. Conocer otros estudiantes internacionales siempre te hace enriquecer tanto como científica como persona. Otros aspecto positivo ha sido aprender a trabajar sin descanso durante muchos días. Teniendo en cuenta el cansancio acumulado durante el muestreo, la alegría con la que muchos de nosotros afrontábamos el día a día me pareció un aspecto muy importante a la hora de realizar nuestro trabajo. El grado de organización que requiere una gran cantidad de datos también me ha sorprendido. Si no hubiera sido por la meticulosa nomenclatura y la profesionalidad a la hora de almacenarla, me hubiera sido imposible realizar los diferentes análisis. Como aspectos negativos, creo que no he aprendido muy bien a administrarme el tiempo. Para otro trabajo, es un aspecto que debo mejorar con mucho ahínco. Valoración personal del aprendizaje conseguido a lo largo del TFG La realización de este trabajo me ha permitido ver en primera persona cómo funciona el mundo de la investigación científica. He aprendido a buscar respuestas a problemas que iban presentándose y a no conformarme con la primera solución que encontraba, mejorando mi visión crítica. El apoyo por parte de todo el equipo me hizo trabajar en un ambiente donde realmente me sentía muy a gusto, provocando que las horas se fueran volando y con ganas de volver al día siguiente.


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Nombre: Adriana

Apellidos: Rodríguez Santos

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Titulación: Grado en Ciencias Ambientales

Dirección: Rbla/ Just Oliveras 14, 2º2ª

Localidad: Hospitalet de Llobregat

CP: 08901

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Correo Electrónico: [email protected]

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Adriana Rodríguez Santos · 2020. 9. 27. · Adriana Rodríguez Santos Curso 2015/2016 Tutor Javier Arístegui Ruiz Trabajo Fin de Título para la obtención del título de Máster