The ecophysiological effects of CO2

enrichment on the seagrass Halophila

ovalis

Stephanie Wong

Bachelor of Science Honours (Marine Science)

School of Veterinary and Life Sciences

Murdoch University

2016

i

Declaration

I declare that this thesis is my own account of my research and contains as its main content

work which has not previously been submitted for a degree at any tertiary education

institution.

Sze Ki Stephanie Wong

October 2016

ii

Abstract Ocean acidification is one of the biggest challenges happening in the marine environment and

causes a shift in dissolved inorganic carbon (DIC) concentrations and lowers the seawater pH.

This causes negative impacts on many marine organisms and thus affecting the ecosystem.

However, the effect of increasing CO2 concentration dissolved in the ocean can potentially be

beneficial to the growth of seagrass. This effect was examined on the seagrass Halophila

ovalis (R. Br.) Hook f., the dominant seagrass species found in Swan-Canning Estuary, Western

Australia. This study was done with controlled experiments, using CO2-enrichment as a

simulation of ocean acidification. The seagrass was collected and cultured in the laboratory for

15 days while pH and alkalinity of seawater and photochemical efficiency (Fv /Fm) of the

seagrass were monitored. Chlorophyll content, growth (shoot plastochrone interval, leaf and

rhizome elongation) and biomass productivity of the plants were measured at the end of the

experiment. The seagrass was collected in early and mid-winter for two experiment replicates

and a strong seasonal variation was observed. A diurnal pattern was found in pH for both CO2-

enriched and control aquaria, showing a buffering effect by seagrass photosynthesis.

Significant differences were found in the DIC concentrations with decreasing pCO2 and

increasing HCO3- concentrations in the first experiment but opposite results found in the

second experiment. The healthier seagrass in the first experiment showed a decreasing

photochemical efficiency over time while the seagrass in the second experiment showed an

increasing photochemical efficiency potentially due to the recovery from storm stress and

epiphyte load. Significantly higher biomass productivity was found in the seagrass from the

CO2-enriched aquaria of the first experiment but not in the second. It was difficult to

determine whether the increase in biomass productivity was caused by the addition of CO2 or

the seagrass reproduction. It is suggested that more replicates and long term experiments are

needed to study the relationship between seagrass productivity and seasonality along with

the effect of increasing dissolved CO2 in seawater. Field experiments are also needed in the

iii

future to explore the potential of using seagrass in buffering the effect of ocean acidification

which might help the broader marine community at an ecosystem level to survive the ongoing

environmental changes.

iv

Acknowledgement

I wish to thank both of my supervisors Dr Mike van Keulen and Dr Navid Moheimani for your

guidance throughout the year and yet allowing lots of freedom for the design of this project

and giving constructive feedback on my work. I would also like to thank the Environmental

Science and Biological Science technicians, Mark Thiele, Steve Goynich, Ian Dapson, Ian

McKernan and Claudia Mueller for their assistance in the aquarium design and maintenance

and collecting seagrass samples. Additional thanks to Dr Lesley Brain for the invaluable advice

on the statistical analyse of this study, the subject librarian Jean Coleman for the information

on research skills and Dr Cecily Scutt for helping me to build up a good writing habit.

I would like to express great appreciation to Tarryn Coward, Low Yin Lun, Senal Siriwardene,

Joel Cuthbert, Hayley Gamble, Isobel Sewell, Audrey Maseva, James Moss, Daniel Zinetti,

Eashani Haria, Yasmin Rainsford, Tim Mcavan, Rushan Bin Abdul Rahman, Kyle Stewart and

David Juszkiewicz. Thank you so much for your time, no matter how early or how late it was,

for helping with the laboratory experiments. Especially those who helped with the tedious

titrations, it could never be done without you. I would also like to thank Cindy Ribbe, Yvette

Chan and Yvonne Ching for helping with some data entry.

Thank you everyone at the Algae R&D Centre, especially Dr Jason Webb, Chia Lee, Ashiwin

Vadiveloo, Tasneema Ishika, Risa Swandari, Javad Faeisossadati, Sam Lim and Emily Hamley,

for sharing your knowledge with me and teaching me some essential experiment techniques.

Last but not least, thank you all my beloved family and friends, and my partner Joyce, for your

support and encouragement that always keep me motivated.

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Table of Contents Declaration......................................................................................................................................... i

Abstract ............................................................................................................................................. ii

Acknowledgement ........................................................................................................................... iv

Table of Contents .............................................................................................................................. v

List of Figures .................................................................................................................................. vii

List of Abbreviations ........................................................................................................................ ix

1 Introduction .............................................................................................................................. 1

1.1 Climate change ............................................................................................................. 2

1.2 Process of ocean acidification ...................................................................................... 3

1.2.1 Effects of ocean acidification on marine organisms ............................................. 5

1.3 Seagrass photophysiology ............................................................................................ 6

1.3.1 Fluorescence ......................................................................................................... 6

1.3.2 Carbon acquisition ................................................................................................ 7

1.4 Effects of ocean acidification on seagrass .................................................................... 8

1.4.1 Global carbon sink ................................................................................................ 9

1.5 Halophila ovalis ........................................................................................................... 11

1.5.1 The physiology .................................................................................................... 11

1.5.2 H. ovalis in Swan River Estuary and Cockburn Sound ......................................... 12

1.6 In situ vs laboratory experiments ............................................................................... 13

1.7 Aims and Hypotheses ................................................................................................. 14

2 Materials and Methods .......................................................................................................... 15

2.1 Sample collecting ........................................................................................................ 15

2.2 Aquaria setup .............................................................................................................. 16

2.3 Preliminary study ........................................................................................................ 17

2.4 pH, Temperature and Salinity ..................................................................................... 18

2.5 Alkalinity ..................................................................................................................... 18

2.6 Chlorophyll fluorescence ............................................................................................ 19

2.7 Chlorophyll content .................................................................................................... 20

2.8 Growth measurement ................................................................................................ 21

2.9 Statistical analyses ...................................................................................................... 22

3 Results ..................................................................................................................................... 23

3.1 Preliminary study ........................................................................................................ 23

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3.1.1 pH ........................................................................................................................ 23

3.1.2 Seagrass density .................................................................................................. 24

3.2 Temperature ............................................................................................................... 24

3.3 Water chemistry ......................................................................................................... 26

3.3.1 pH ........................................................................................................................ 26

3.3.2 Alkalinity ............................................................................................................. 30

3.4 Chlorophyll fluorescence ............................................................................................ 35

3.5 Chlorophyll content .................................................................................................... 38

3.6 Growth measurements ............................................................................................... 39

3.6.1 Shoots ................................................................................................................. 39

3.6.2 Leaves ................................................................................................................. 40

3.6.3 Rhizomes ............................................................................................................. 41

3.6.4 Seagrass biomass productivity ............................................................................ 42

4 Discussion ............................................................................................................................... 44

4.1 Assumptions and Limitations ...................................................................................... 44

4.2 Seasonality .................................................................................................................. 45

4.3 Temperature ............................................................................................................... 46

4.4 pH ................................................................................................................................ 46

4.5 Alkalinity ..................................................................................................................... 47

4.6 Chlorophyll fluorescence ............................................................................................ 48

4.7 Chlorophyll content .................................................................................................... 50

4.8 Seagrass biomass productivity.................................................................................... 51

4.9 Conclusions ................................................................................................................. 52

References ...................................................................................................................................... 54

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List of Figures

Figure 1.2-1: Bjerrum plot for dissolved inorganic carbon (DIC) is seawater (Zeebe and Wolf-

Gladrow 2001). ............................................................................................................................. 4

Figure 2.1-1: Location of study site for seagrass collection at Point Walter, Western Australia

(Google 2015). ............................................................................................................................ 15

Figure 2.2-1: Aquaria setup for the experiment. Top chamber was enriched with 0.2% CO2 12h

(6am to 6pm) a day and the bottom chamber was control with ambient air supply. ............... 17

Figure 2.8-1: Growth of a Halophila ovalis sample after 14 days of experiment. New growth

was measured from the tag to the meristem. ............................................................................ 22

Figure 3.1-1: pH record over 21h of blank CO2-enriched (black line) and control aquaria (red

line) without seagrass. The pH of both sets of aquaria started low and stabilised after the first

5 hours. The established pH was about 8.1 in the control and 7.9 in the CO2-enriched aquaria.

.................................................................................................................................................... 23

Figure 3.2-1: Temperature of CO2 enriched and control aquaria over 15 days period in (a)

experiment 1 and (b) experiment 2 logged at 15-minute interval. Maximum temperatures for

both aquaria were observed at around 6am and minimum temperatures at around 6pm each

day. ............................................................................................................................................. 25

Figure 3.3-1: pH of control (black line) and CO2-enriched aquaria (red line) with seagrass over

15-day period averaged every hour in experiment 1. ................................................................ 27

Figure 3.3-2: pH of control (black line) and CO2-enriched aquaria (red line) with seagrass over

15-day period averaged every hour in experiment 2. ................................................................ 29

Figure 3.3-3: pH, pCO2 (μatm) (yellow circles), HCO3- concentration (μmol / kg SW) (blue

squares) and CO32- concentration (μmol / kg SW) (red triangles) of the CO2-enriched (top row)

and control (bottom row) aquaria on days 2, 9 and 15 of experiment 1 (mean ± SE). .............. 31

Figure 3.3-4: pH, pCO2 (μatm) (yellow circles), HCO3- concentration (μmol / kg SW) (blue

squares) and CO32- concentration (μmol / kg SW) (red triangles) of the CO2-enriched (top row)

and control (bottom row) aquaria on days 2, 9 and 15 of experiment 2 (mean ± SE). .............. 34

Figure 3.4-1: Fv /Fm (mean ± SE) of H. ovalis in the CO2-enriched (solid circles) and control

aquaria (open circles) on days 2, 9 and 15 of experiment 1 (top row) and experiment 2

(bottom row). ............................................................................................................................. 37

Figure 3.5-1: Concentration (mean ± SE) of chlorophyll a and b (µg mL-1 leaf g-1) under CO2-

enriched and control conditions in experiment 1 (a, b) and experiment 2 (c, d). ..................... 38

Figure 3.6-1: Shoot plastochrone interval (mean ± SE), PS (days), under CO2-enriched (n=30)

and control (n=30) condition measured on day 15 of experiment 1 (top) and experiment 2

(bottom). ..................................................................................................................................... 39

Figure 3.6-2: Leaf plastochrone interval (mean ± SE), PL (days), and leaf elongation (cm shoot-1

day-1) of H. ovalis under CO2-enriched (n=30) and control (n=30) conditions in experiment 1 (a,

b) and experiment 2 (c, d). ......................................................................................................... 40

Figure 3.6-3: Rhizome elongation (cm growing tip-1 day-1) (mean ± SE) of H. ovalis in CO2-

enriched (n=30) and control aquaria (n=30) in experiment 1 (top) and experiment 2 (bottom).

.................................................................................................................................................... 41

Figure 3.6-4: A sample of H. ovalis with 5 meristems (red circles) grown on one main rhizome

after 15 days of experiment. ...................................................................................................... 42

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Figure 3.6-5: Dry weight (mg shoot-1 day-1) (mean ± SE) of new grown H. ovalis after

experiment period under CO2-enriched (n=30) and control (n=30) conditions in experiment 1

(top) and experiment 2 (bottom). .............................................................................................. 43

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List of Abbreviations

AT Total alkalinity

DIC Dissolved inorganic carbon

EPOCA The European Project on Ocean Acidification

Fv/Fm Maximum quantum yield of PSII (dark adapted)

Fv’/Fm’ Maximum quantum yield of PSII (light adapted)

OA Ocean acidification

PL Leaf plastochrone interval

PS Shoot plastochrone interval

PSII Photosystem II

1

1 Introduction Seagrass meadows are some of the most productive communities in the marine ecosystem

(Short and Coles 2001). They serve as an important food source to fisheries and many other

marine organisms, provide nursery grounds and prevent coastal erosion by stabilising

sediments, thus supporting a large part of our economy. However global seagrass abundance

is declining due to anthropogenic activities in coastal areas and climate change. These not

only threaten the global seagrass communities but also have consequences for the overall

marine ecosystems. As the concentration of atmospheric CO2 increases, the gas dissolves in

seawater. More than two-thirds of the Earth’s surface is covered by seawater making the

ocean the largest carbon sink. When CO2 dissolves in seawater, it forms carbonic acid, which is

referred to as ocean acidification. Many marine organisms have been shown to suffer

negative impacts to their growth and reproduction from ocean acidification especially the

calcareous species. However, seagrasses, unique marine flowering plants, are potentially able

to gain advantage of the acidified ocean as the decreasing oceanic pH increases available CO2.

Seagrasses have the ability to use extra dissolved inorganic carbon for photosynthesis thus

enhancing their productivity (Beer and Waisel 1979; Borum et al. 2016; Cox et al. 2016). They

can store fixed carbon in leaves, rhizome and root tissues that are later deposited in

sediments as they die (Touchette and Burkholder 2000; Duarte et al. 2013). Seagrass

meadows have shown substantial buffering capacity and ability to modify the pH of the

surrounding water column (Hendriks et al. 2014), which can potentially help to restore

ecosystems being affected by ocean acidification such as coral reefs (Marubini et al. 2008;

Unsworth et al. 2012). Therefore scientists have been studying the capability of seagrasses to

buffer the effects of ocean acidification.

This project aims to study the effect of dissolved carbon dioxide in seawater on the growth of

the seagrass Halophila ovalis. H. ovalis is a small seagrass species commonly found in the

2

warmer waters of the Indo-Pacific region and extends around the Australian coast to some

cooler temperate waters (Kirkman and Kuo 1996; Short et al. 2007). It was found that the

species has an anhydrase enzyme that can convert bicarbonate into carbon dioxide that can

be used for photosynthesis (Beer et al. 2002). The increasing concentrations of dissolved

carbon dioxide and bicarbonate due to ocean acidification can be an advantage to the

seagrass growth.

1.1 Climate change The Earth’s climate has always been dynamic and now we are facing an era of global warming.

The increase in emissions of greenhouse gases and aerosols due to excessive human activities

has intensified the greenhouse effect which traps heat within the atmosphere, causing an

accelerated rise in temperature at the Earth’s surface (Lashof and Ahuja 1990; Kirschbaum

2014). The global mean surface temperature has increased 0.8oC over the last century and the

rate of warming has been upscaling in the last 50 years (Hansen et al. 2010; IPCC 2013). The

increase in Earth’s temperature is causing a series of environmental changes, such as sea level

rise and more extreme weather events. The warmer atmosphere melts the world’s glaciers

and land based ice sheets, hence increasing the amount of water in the ocean. Climate

records from the last interglacial period showed a rise of 5m of the mean sea level when the

global mean surface temperature increased by 2oC (Church et al. 2013). Such a large rise in

sea level can cause severe flooding of coastal areas and some islands may even be submerged

completely. At the 2015 United Nations Climate Change Conference (COP21), 195 countries

adopted the Paris Agreement to limit temperature rise to less than 2oC relative to the pre-

industrial level (COP21 2015). However, while aggressive greenhouse gas mitigation could

stabilise temperature rise, climate models suggested the sea level would continue to rise for

centuries (Meehl et al. 2012). The changing climate also leads to more frequent extreme

weather events such as heat waves, droughts, hurricanes and tornadoes (Holland 2009;

3

Konisky et al. 2016). These rare and episodic weather events not only destroy manmade

structures but also the natural habitats for flora and fauna.

1.2 Process of ocean acidification Since the start of the industrial revolution, the concentration of atmospheric CO2 has been

increasing from preindustrial levels of approximately 280ppm (Doney et al. 2009) to a global

current level of 400ppm (Dlugokencky and Tans 2016). Carbon dioxide dissolves in water and

dissociates into bicarbonate (HCO3-) and carbonate (CO3

-2) with the release of protons

(Equation 1), or remains as free carbon dioxide, often measured as pCO2 (Zeebe and Wolf-

Gladrow 2001).

CO2 + H2O ⇌ HCO3- + H+ ⇌ CO3

2- + 2H+ (Equation 1)

This equilibrium reaction buffers the pH level of seawater by moderating the concentrations

of each carbon species. Bicarbonate and carbonate are the sources of alkalinity in seawater,

making seawater naturally slightly alkaline. The sum of CO2, HCO3- and CO3

2- gives the value of

total dissolved inorganic carbon (DIC). As the concentration of CO2 increases, the

concentration of HCO3- will also increase but the concentration of CO3

2- will decrease at the

current surface seawater pH (Figure 1.2-1). This overall increase in DIC will result in the

lowering of ocean pH and increasing in total alkalinity (Ilyina et al. 2009). This effect is termed

ocean acidification which is another major environmental impact caused by the increasing CO2

level apart from global warming.

4

Figure 1.2-1: Bjerrum plot for dissolved inorganic carbon (DIC) is seawater (Zeebe and Wolf-Gladrow 2001).

The Intergovernmental Panel on Climate Change (IPCC) publishes climate data such as

atmospheric carbon concentrations and temperature rise in global warming which are factors

of ocean acidification. The IPCC Special Report on Emission Scenarios predicted different

scenarios in year 2100 based on various climate models, with atmospheric CO2 concentrations

ranging between 530 and 970ppm (IPCC 2000). It is difficult to estimate the amount of

atmospheric carbon dioxide, denoted as p(CO2)atm, dissolved in the ocean and numerous

models have been generated to take into account the many variables involved. Most marine

scientists are using a model that predicts an average p(CO2)atm of 750ppm or pH 7.7 for the

end of the century to study the effects of ocean acidification from individual organisms to

ecosystem change (Barry et al. 2010).

5

1.2.1 Effects of ocean acidification on marine organisms

The increase in ocean acidity affects the growth of many marine fauna. Evidence suggests that

ocean acidification poses negative impacts to most marine calcifying species such as corals,

molluscs, echinoderms, coralline algae and coccolithophores (Guinotte and Fabry 2008).

During calcification, CO32- precipitates to form calcium carbonate (CaCO3) leaving a proton

that reattaches to a HCO3- which is eventually released as CO2 and water (Equation 1). Ocean

acidification thus reduces the amount of CO32- for calcification and higher acidity causes

dissolution of the calcium carbonate shell (Bach 2015; Wahl et al. 2016). Deeper and colder

waters naturally hold more dissolved CO2 where dissolution of calcium carbonate shells occurs.

The difference in saturation of CaCO3 stratifies the water column and creates a boundary layer,

calcification therefore occurs more readily in shallower waters (Marubini et al. 2008). Marine

calcifiers produce stable carbonate minerals in the forms of calcite and aragonite. The warmer

conditions and higher concentration of CO2 in seawater is moving the aragonite and calcite

saturation zone to shallower depths thus reducing the rate and availability for calcification

(Orr et al. 2005). Besides calcifying species, marine phytoplankton may also be impacted due

to their inability to maintain internal pH homeostasis when the external pH level exceeds the

historical range they have experienced (Flynn et al. 2012). Such a reduction in phytoplankton

abundance could have serious consequences, as they are important primary producers in the

marine ecosystem, which in turn can cause an imbalance of the ecosystem at a community

level. Although studies have shown that ocean acidification causes negative impacts on many

marine organisms, seagrass could be one of the few that benefits and could be a possible

remedy to some of the symptoms locally.

6

1.3 Seagrass photophysiology

1.3.1 Fluorescence

The carbon balance of a seagrass depends on the rate of photosynthesis, inorganic carbon (Ci)

availability, respiration rate of the leaves and other non-photosynthetic tissues including

stems and below-ground roots and rhizomes (Ralph et al. 2007). The rate of seagrass

photosynthesis is mostly measured by maximum photosynthetic rate (Pmax), photosynthetic

efficiency (α) and saturating irradiance (Ek). The most common method for measuring the

rate of photosynthesis is by plotting oxygen evolution under different irradiances until

photoinhibition to produce a photosynthesis-irradiance (P-I) curve (Harrison et al. 1985).

Chlorophyll fluorescence is another common method in measuring photosynthesis. Photon

particles absorbed by the plant pigment molecules undergo either one of the three pathways:

photosynthesis, dissipated as heat or dissipated as light (fluorescence) (Krause and Weis 1991).

When the plant was transferred from dark to light, the PSII reaction centres are progressively

closed, therefore chlorophyll fluorescence would increase (Maxwell and Johnson 2000). After

the plant adapted to the light conditions, chlorophyll fluorescence starts to fall due to opening

of the PSII reaction centres to pass electrons down the electron transport chain for

photosynthesis (i.e. photochemical quenching) or some photons dissipated as heat (i.e. non-

photochemical quenching). By measuring the level of fluorescence and minimising heat loss

(non-photochemical quenching), it is possible to deduce the efficiency of photosynthesis. Fv

/Fm is the most common photosynthesis measurement used in plant research, which is based

on chlorophyll fluorescence. Fv is the maximum variable fluorescence yield and Fm is the

maximum fluorescence yield. Fv /Fm measures the maximum photochemical efficiency

(quantum yield) of PSII in dark adapted state (Maxwell and Johnson 2000). Similarly Fv’/Fm’

measures the maximum quantum efficiency of PSII photochemistry when the plant is at light-

adapted state (Cosgrove and Borowitzka 2011). A pulse amplitude modulation (PAM)

7

fluorometer is often used which provides a non-destructive method in measuring chlorophyll

fluorescence (Beer et al. 2001).

1.3.2 Carbon acquisition

Carbon is fixed in the Calvin Cycle to convert inorganic carbon into organic compounds that

support the growth of living organisms. There are different carbon fixation pathways such as

C3, C4 and Crassulacean Acid Metabolism (CAM), where seagrasses are C3 plants (Beer 1989).

Carbonic anhydrase (CA) was found in several marine phytoplankton and angiosperms which

catalyses the formation of CO2 from HCO3- (Graham and Smillie 1976). There are extracellular

(within cell wall) and intracellular (within cytoplasm) CA in the HCO3- assimilation mechanisms

(Larkum and James 1996). The extracellular CA facilitates the utilisation of external DIC by

converting HCO3- to CO2 within the diffusion boundary layer of the leaves (Beer et al. 2002). It

also creates an H+ gradient across the plasma membrane and the cell wall with an active

proton pump which allows the co-transport of HCO3- and H+ into the cell (Beer et al. 2002). It

gives an advantage to marine plants to utilise DIC for photosynthesis. With the elevated

concentration of DIC in seawater, seagrasses able to utilise HCO3- will be favoured for growth.

Many studies have been done on the photosynthetic productivity of different seagrass species

under CO2 enriched conditions. For example, three tropical seagrasses Cymodocea serrulata,

Halodule uninervis and Thalassia hemprichii showed an increase in most photosynthetic

parameters including Pmax and α under CO2 enriched conditions (Ow et al. 2015). Subtidal

eelgrass, Zostera marina, had three times higher Pmax after 45 days under CO2 enriched

condition (Zimmerman et al. 1997). The CO2 stimulated improvement in photosynthesis and

reduced light requirements, suggest that globally increasing CO2 may enhance seagrass

survival in eutrophic coastal waters, which is often characterised with lower light

transmittance. Short term (2hr) experiments showed that both Z. marina and bull kelp,

Nereocystis luetkeana, had an approximately 2.5-fold increase in net apparent productivity

(NAP) under doubled ambient CO2 concentration (Thom 1996). These experiments illustrate

8

the positive effect on seagrass productivity of extra DIC in seawater for some seagrass species.

Marine productivity has a substantial role in regulating pH and the concentration of DIC in

seawater which can expand over shallow waters including adjacent unvegetated bottoms. The

increase in pH is ofen accompanied with a corresponding decrease in DIC because of a

temporary disequilibrium with atmospheric CO2 while total alkalinity remains constant

(Buapet 2013). As CO2 is taken up for photosynthesis, the natural equilibrium would favour

the reverse reaction, thus less protons are being produced.

1.4 Effects of ocean acidification on seagrass Seagrass meadows support a vast diversity of marine life by providing a food source and

nursery sites, making them one of the most productive marine habitats. Seagrass meadows

provide a food source to grazers in three main ways: the live leaves, epiphytic algae on the

leaves and planktonic algae from the surrounding waters (Heck and Valentine 2006). The leaf

and stem surface of seagrass provides a substrate for the growth of epiphytic organisms.

These micro- to meso- scale organisms are comprised of different groups of algae and a range

of invertebrates such as molluscs, crustaceans and worms. Epiphytes provide extra nutrients

to grazers such as fish, turtles and dugongs (Preen 1995; Valentine and Heck 1999). Epiphytic

organisms rely largely on nutrients dissolved in the water column (Uku and Björk 2001) and a

small amount of nitrate and phosphate from the leaves and roots of seagrasses (Harlin 1975).

Under natural conditions, seagrasses can tolerate a moderate epiphyte load and the shading

effects it causes. However, thick epiphyte loads on seagrass blades can be a barrier for

nutrient uptake and light absorption thus reducing photosynthetic activity (Sand-Jensen 1977;

Bulthuis and Woelkerling 1983; Cornelisen and Thomas 2004). This is often found in nutrient

rich and light limiting conditions when the epiphytes take up nutrients and outcompete

seagrasses without the presence of grazing epifauna (Howard and Short 1986).

In addition to providing a direct food source, seagrasses also help to sustain coastal habitats

by recycling carbon and nutrients. Individual and epiphytic suspension feeders associated with

9

seagrass meadows are potentially able to filter the overlying water column daily and control

the level of suspended organic matter (Lemmens et al. 1996). It was also found that epiphytic

organisms may benefit from the modification of carbonate system by seagrass meadows for

calcification (Hendriks et al. 2014). Therefore in terms of nutrient cycling, seagrass and

epiphyte have a mutual relationship in a balanced ecosystem. However ocean acidification

may alter species composition in seagrass meadows and reef systems which may result in a

loss of biodiversity. In a long term (11 months) in situ CO2(aq) manipulation study, the

assemblage of epiphytes in a seagrass community was observed to change, with declines in

the abundance of coralline algae, along with increases in filamentous algae under the elevated

carbonate parameter (Campbell and Fourqurean 2014). In another ocean acidification

simulation on coral reefs, although the reef coverage remained constant at a low pH of 7.8,

the composition of coral species shifted and species diversity significantly declined while

seagrass biomass increased (Fabricius et al. 2011). Despite the fact that the modification of

water chemistry by seagrasses depends on the morphology of the meadow (Thomas et al.

2000) and other environmental factors such as hydrodynamic regime (Cornelisen and Thomas

2006), these recent studies addressed the biological and ecological effects of ocean

acidification on seagrass and reef communities. However, additional research is still required

to examine the effect of ocean acidification on the broader ecosystems.

1.4.1 Global carbon sink

Seagrasses play an important role in carbon sequestration for both the marine ecosystem and

the overall carbon cycle: not only do they contribute high biomass and high efficiency in net

productivity; they can also help balance the alkalinity of seawater to buffer the effect of

acidification. Seagrass meadows can be multispecific or monospecific, with their community

including benthos and grazers. Different meadows support a broad range of metabolic rates

and tend to be overall autotrophic and are therefore capable of acting as CO2 sinks in the

ecosystem (Duarte et al. 2010). The carbon stored and sequestered by seagrass meadows,

10

along with other coastal vegetation such as mangrove forests and tidal salt marshes, is often

referred as ‘blue carbon’ (Thomas 2014). As they are mostly net autotrophic, seagrass

meadows in the Indo-Pacific can have an average net sink of 155 g C m-2 yr-1 (Unsworth et al.

2012). In Indonesia, one of the world’s largest seagrass and mangroves reserves was

estimated to have 30,000 km2 of seagrass and 31,894 km2 of mangroves which account for 3.4

Pg C, roughly 17% of the world’s blue carbon reservoir (Alongi et al. 2016). In addition,

seagrass detritus has a slow decomposition rate due to low nutrient content and low oxygen

concentration in seagrass sediments (Duarte et al. 2013). Longer term carbon storage aged

over a thousand years can be found in present- day seagrass meadows by sedimentation of

highly organic deposits (Pergent et al. 2014). These characteristics enhance the capability to

store carbon biomass in the seafloor for an extended period.

The rising level of DIC in seawater can be beneficial to the photosynthetic and growth rates of

many marine macro-autotrophs, including seagrasses (Koch et al. 2013). As some seagrasses

have the ability to convert HCO3- to CO2 as an alternative source of inorganic carbon for

photosynthesis, they can potentially mitigate the extra DIC and buffer the effects of ocean

acidification. The additional carbon available for photosynthesis due to ocean acidification

could increase the global seagrass stock by 94%, leading to an estimation of 71.4 million

tonnes carbon sequestration annually (Garrard and Beaumont 2014). Carbon storage in

seagrass meadows consists of above ground (shoots) and below ground biomasses (rhizomes

and roots) and sediment in organic form (bacteria, microalgae, macroalgae and detritus) and

inorganic form (carbonates) (Macreadie et al. 2014). Seagrass with high below-to-above

ground biomass, such as Cymodocea serrulata, have shown potential to minimise the problem

of carbon leakage from under the seabed which could possibly happen with “carbon capture

storage”, an expanding technology for carbon mitigation (Russell et al. 2013). Seagrass

restoration that has been in practice in several continents with major seagrass communities

showed not only improvement in restoring marine habitats but also the chances of more

11

secure carbon sequestration. Palacios and Zimmerman (2007) found that eelgrass Z. marina

had significantly higher reproductive output, below-ground biomass and vegetative

proliferation of new shoots under laboratory condition with CO2 enriched by direct injection

of industrial flue gases for 45 days. A large scale Z. marina restoration meadow showed

potential in enhancing carbon sequestration in coastal zone, with higher sediment nutrient

and the faster accumulation rates of carbon and organic content in 10-year old meadows

compared to the younger ones (Greiner et al. 2013). The results of field and laboratory based

studies support the potential of secure carbon storage, improvement in sediment nutrient

and water treatment for acute carbon source by restoring seagrass meadows.

1.5 Halophila ovalis

1.5.1 The physiology

The physiological aspect of H. ovalis is very well studied with both field and laboratory

experiments. Ralph (1999) conducted experiments with different combinations of

environmental stresses on the growth of H. ovalis and found that temperature was the

dominating stressor, followed by osmotic condition and elevated light. High temperature (40-

45oC) can cause irreparable structural alterations to the PS II reaction centres and chloroplast

dysfunctions of H. ovalis (Campbell et al. 2006). The detrimental effect of high surface water

temperature was supported by the long term climate modelling study on a H. ovalis meadow

in Queensland (Rasheed and Unsworth 2011), where a positive correlation was found

between elevated sea surface temperature and low seagrass biomass. These suggested that H.

ovalis is less likely to tolerate an acute or episodic temperature rise such as an El Niño event.

H. ovalis is commonly found in the intertidal zone where the leaves lie flat on the moist sand

thus preventing desiccation (Björk et al. 1999). It was found that H. ovalis in the intertidal is

slightly photoinhibited during the day as the electron transport rates (ETRs) decreased toward

noontime when measured in situ (Beer and Björk 2000). It shows that this species can be

sensitive to environmental changes.

12

In terms of carbon acquisition, H. ovalis undergoes the C3 photosynthesis pathway where CO2

is used for carbon fixation. Like other seagrass species, H. ovalis features direct uptake of

HCO3- for photosynthesis when the CO2 concentration drops to 20% of natural seawater at pH

8.8 (Schwarz et al. 2000). It was suggested that H. ovalis may be able to utilise external HCO3-

as an alternative source of inorganic carbon with the presence of CA at the cell wall level (Beer

et al. 2002). The external of conversion from HCO3- to CO2 by CA allows H. ovalis to obtain

inorganic carbon via a proton extrusion dependent uptake system (Uku et al. 2005). Therefore

the lower seawater pH may benefit the growth of H. ovalis as more protons are available in

the surrounding waters. Experiments on an isolated monospecific patch of H. ovalis showed

that the species is able to raise the pH of natural seawater from 8.1 to 8.5 (Beer et al. 2006).

Such values are relatively low compared to other species which makes it difficult for the

survival of H. ovalis in a multispecific meadow as pH can be raised beyond its compensation

point (Beer et al. 2006; Russell et al. 2013), thus restricting its utilisation of inorganic carbon.

Even though the mechanism of carbon fixation pathways by H. ovalis is still uncertain, the fact

that the species can uptake extra DIC in seawater supports that H. ovalis can moderate

seawater pH in an acidified environment.

1.5.2 H. ovalis in Swan River Estuary and Cockburn Sound

Halophila is the most species-diverse seagrass genus and it is also biogeographically diverse

with H. ovalis distributed across several regions in the temperate north Pacific, tropical Indo-

Pacific and temperate Southern Oceans (Short et al. 2007). It is the dominant seagrass species

in the Swan River Estuary, the major estuarine system in Perth (Hillman et al. 1995). This

species is often found in the estuary as monospecific meadows or in mixed-species meadows

with Ruppia megacarpa and Zostera meulleri (Eklöf et al. 2010; Choney et al. 2014). H. ovalis is

a small seagrass and is morphologically more fragile compared to other species, which have

strong fibrous blades and rhizomes; it is most commonly found in sheltered habitats

characterised by high light intensity, low to moderate water movement and little disturbance

13

(Carruthers et al. 2007). H. ovalis produces a seedbank and is usually one of the first species to

colonise on bare sand after a storm event. It is often found coexisting with Heterozostera

tasmanica, which has deeper rhizomes that form a thick stable mat over the substratum

(Kirkman 1985; Kirkman and Kuo 1990); this helps other slower growing species to establish.

In a long term study on two transects on seagrass meadows in Cockburn Sound, large

variation in the average percentage cover was found over 12 years in the same season for H.

ovalis, suggesting that the species has a high turnover rate (Kirkman and Kirkman 2000). H.

ovalis is effective at storing nutrients in shoots for growth and reproduction, which helps the

plant to survive periods of nutrient limitation (Connell and Walker 2001). Based on field

observation, H. ovalis has a shallow (typically 5-10cm deep) rhizome and root system that

readily branches out to form dense small colonies in the Swan River Estuary. Large amounts of

detritus are washed up on the river bank after a storm event but the species recolonise

quickly after the start of finer weather. These r-selection growth characteristics of H. ovalis

allow the species be tolerant to dynamic marine and estuarine ecosystems (Rasheed 2004).

1.6 In situ vs laboratory experiments To study the effects of ocean acidification on seagrass, scientists have attempted both in situ

and laboratory experiments. In situ experiments were often done by comparing current and

historical climate data of the locale, and correspond with any ecological and physiological

changes in the species (Doney et al. 2012). These experiments are usually more expensive and

many environmental factors have to be taken into account which might affect the growth and

distribution of a species. Laboratory experiments are more commonly used to show the

effects of a single factor such as comparing the physiological change of a species under set

pCO2 levels. Although a standard guideline has been proposed for OA studies (Barry et al.

2010), the predicted future pCO2 levels might not be suitable in every situation. McElhany and

Shallin Busch (2013) argued that, pCO2 levels for baseline studies should be chosen according

to the levels that the species usually exposed to. In addition, the effect of OA on a species is

14

not a single factor issue in reality. Temperature and salinity are two other important factors

that affect the survival and growth of seagrass. For example, seagrasses near shallow volcanic

CO2 vent are exposed to a pCO2 level of 2000 μatm and temperature around 20°C which is

much higher than the proposed 750 μatm level as suggested by the EU (Apostolaki et al. 2014).

Scientists have proposed various designs for laboratory experiments to account for these

multi stressors and the mixed air approach is by far the most common for OA simulations

(Bockmon et al. 2013).

1.7 Aims and Hypotheses The aim of this project is to study the physiological effects of ocean acidification on

H. ovalis. CO2 enriched aquaria were used to simulate the effects of ocean acidification. The

hypothesis of this study is that the productivity of H. ovalis will be higher in the CO2

enrichment simulation due to its ability to utilise HCO3- as an alternative carbon source for

photosynthesis. Therefore, the seagrass can buffer the effects of acidification in the aquarium

by more effective carbon consumption.

15

2 Materials and Methods

2.1 Sample collecting Seagrasses for laboratory experiments were collected from Point Walter (32°00’37”S

115°47’11”E), on the Swan River Estuary, Western Australia (Figure 2.1-1). An 800 m sandbar

stretches out to the northwest from the north facing shore. Halophila ovalis grows on both

sides of the sandbar but more grazing activities were observed on the western side compared

to the east. Sixty plants of H. ovalis were collected from the eastern side of the sandbar

haphazardly. Each plant was collected by cutting the rhizome behind the third node (i.e. 6

pairs of leaves) from the meristem (Hillman et al. 1995; Short and Duarte 2001). The plants

were carefully removed from the sediments with intact rhizomes and roots. The samples were

kept cool on ice and isolated from light to prevent light and heat decay.

Figure 2.1-1: Location of study site for seagrass collection at Point Walter, Western Australia (Google 2015).

16

The samples were transported to the Algae R&D Centre at Murdoch University immediately

after collection. Epiphytes were removed by gently rubbing the leaf surface and the plants

were washed with clean seawater in the laboratory. Seawater was collected off Hillarys Boat

Harbour (31°48’55”S 115°43’27”E) and stored in an enclosed tank at the Algae R&D Centre for

experiment use. A preliminary study was conducted prior to the main study to stabilise the

seawater pH in the aquaria and to compare the effects of transplanting density (Section 2.3);

60 plants were collected on 27th April 2016. The plants were marked by clipping the tip of the

youngest shoot on the rhizome (Short and Duarte 2001). Two batches of seagrass were

collected for the main study. The first batch of plants was collected in early winter on 18th

May 2016 and the second batch was collected in late winter on 13th August 2016. Samples

were evenly transplanted to the six aquaria with ten plants in each. Due to the loss of some

clipped leaves in the preliminary study, another marking strategy was used in the main study.

A thin copper wire was twisted around the rhizome behind the meristem as a mark for

measuring plastochrone interval (Short and Duarte 2001) (Section 2.8).

2.2 Aquaria setup The experiments were set up indoors at the Algae R&D Centre, Murdoch University. Air

temperature was controlled between 23 - 25°C. Six 15L (35 cm x 19.5 cm x 22 cm) glass

aquaria were used for the experimental setup. The aquaria were stored inside two gas

chambers with three aquaria in each (Figure 1.2-1). The chambers were made of translucent

plastic containers (70 cm x 51 cm x 43 cm) and covered with 3 mm thick clear PVC board on

top. Approximately 4 cm of coarse aquarium coral sand covering the bottom of the aquaria

was used as substrate (particle size = 1-2 mm). All aquaria were constantly aerated with air

pumps. The treatment chamber was supplied with 12 hours (6am to 6pm) of 0.2% CO2 gas and

12 hours (6pm to 6am) of air supply from the top of the chamber whereas the control

chamber had 24 hours of air supply. Light was supplied to the chambers by five 18W LED

17

tubes in each set, providing approximately 225 μmol photons m-2 s-1 irradiance at the water

surface. Lights were switched on at 6am to 6pm each day.

Figure 2.2-1: Aquaria setup for the experiment. Top chamber was enriched with 0.2% CO2 12h (6am to 6pm) a day and the bottom chamber was control with ambient air supply.

2.3 Preliminary study A preliminary study was carried out to monitor the pH of water in blank aquaria with the CO2

enrichment and to test the effect of transplanting densities before the main study. The

preliminary study setup was the same as the main study under the same conditions. Before

collecting and transplanting seagrass, the systems were run and monitored for one week until

constant pH, temperature and salinity were established in both gas chambers (Section 2.4).

The pH in CO2 enriched aquaria was lower than the control aquaria by approximately 0.2 units.

LED light tubes

CO2 gas chamber

Aquarium

Control gas chamber

pH reader

18

Once the aquarium conditions had stabilised, seagrasses were collected from Point Walter

and transplanted into the aquaria (see also Section 2.1). In each gas chamber, one aquarium

was left without seagrass, and was used to monitor pH (Section 2.4), one aquarium was

planted with 10 H. ovalis plants and one aquarium planted with 20 plants. The growth of

seagrass was monitored by measuring shoot plastochrone interval (Equation 6 in Section 2.8).

The effects of shoot density on the shoot plastochrone interval were compared with a one-

way ANOVA (Section 2.9).

2.4 pH, Temperature and Salinity pH in the aquaria was logged at 15-minute intervals with conductivity probes (Ionode),

connected to custom made pH readers and a data logger (LabJack). The probes were

calibrated once before the experiments began. Since there was only one pH reader for each

chamber, the probes were randomly allocated to aquaria within a chamber during the

experimental period. Temperature was logged at 15-minute intervals with a submerged logger

(TinyTag) throughout the experimental period. The logged pH and temperature data were

averaged by hour and plotted against days over the experimental period to show diel

variations of the water condition. Salinity was measured with a handheld conductivity meter

(EcoSense, YSI) once a week on the same day as alkalinity was measured. The conversion of

conductivity (mS cm-1) to salinity (psu) was calculated with an online Excel spreadsheet

(Douglass 2010). Salinity data was used for the determination of DIC in water and to ensure a

suitable growth condition for the seagrass.

2.5 Alkalinity Alkalinity was measured by titration against standardised HCl (aq). Standardisation of 0.02N

HCl(aq) was carried out following the methods previously described by Clesceri et al. (1998)

section 2320B, with 0.05N Na2CO3(aq) as titrant. Alkalinity was measured on days 2, 9 and 15

of the experimental period. 50mL of water sample was taken from the aquaria with a syringe.

It was then titrated immediately against standardised HCl (aq) solution with a handheld pH

19

meter with 3 significant figures (EcoSense, YSI). A magnetic stirrer was used to maintain

consistency of the solution. Titrations were repeated at 7am, 11am, 3pm and 7pm on the day

of measurement. Calculation of inorganic carbon composition was done with a carbon dioxide

system calculator, CO2 sys (Lewis and Wallace 1998). The constants chosen were K1, K2 from

Millero (2010), KHSO4 from Dickson (1990), seawater pH scale, and [B]T value from Lee et al.

(2010). The input data used were salinity (psu), temperature (°C), pressure= 0 dbar and output

condition of 25°C and 0 dbar. The output pCO2 (µatm), [HCO3-] (µmol / kg SW), [CO3

-] (µmol /

kg SW) were used for statistical analyses comparing the mean between CO2 enriched and

control conditions of both experiments with one-way ANOVA and t-tests (Section 2.9).

2.6 Chlorophyll fluorescence Chlorophyll fluorescence was measured with a diving PAM (Pulse Amplitude Modulation

fluorometer) (Walz) on days 2, 9 and 15 of the experimental period. At least 10 saturation

(SAT) pulses were measured on the same leaf in each aquarium. SAT pulses were measured

dark adapted at 5am, light adapted at 7am, 11am, 3pm and dark adapted at 7pm. Analyses of

SAT pulse measurements were done by the following equations (Cosgrove and Borowitzka

2011):

Maximum quantum yield of PSII (dark adapted)

Fv/Fm = (Fm – Fo) / Fm (Equation 2)

Maximum quantum yield of PSII (light adapted)

Fv’/Fm’ = (Fm’ – Fo’) / Fm’ (Equation 3)

Mean Fv/Fm values of leaf samples from CO2-enriched and control aquaria at each

measurement time were compared statistically (Section 2.9).

20

2.7 Chlorophyll content Three mature leaves were randomly selected from each aquarium. Excess water on the leaf

surface was removed and the wet weights of samples were measured. Samples were then

treated with liquid nitrogen to break down cellulose membrane (Wasmund et al. 2006; Hu et

al. 2013). After the liquid nitrogen was fully evaporated, chlorophyll was extracted by grinding

with a mortar and pestle in 3mL of 90% acetone as solvent for 1 minute under dim light. The

solution was poured into centrifuge tubes after rinsing the grinding apparatus with the solvent

and the tube filled up to 6 mL mark with the solvent. The extract was centrifuged at 4000 RPM

for 12 minutes. All samples were kept in a freezer to prevent chlorophyll degradation by heat

and light. 1mL of the supernatant was transferred into a glass cuvette of 1 cm path length for

spectrophotometric measurement. The absorbance at wavelengths 664.0 nm and 647.0 nm

was measured for each sample (Jeffrey and Humphrey 1975).

Calculations for chlorophyll content (μg Chl g -1) are as follows (Granger and Izumi 2001):

Chlorophyll a = (11.93 E664 – 1.93 E647) x (volume of solvent used for extraction)

/ leaf wet weight (Equation 4)

Chlorophyll b = (20.36 E667 – 4.68 E664) x (volume of solvent used for extraction)

/ leaf wet weight (Equation 5)

Differences of chlorophyll a and chlorophyll b concentrations in the leaf samples were

examined using independent t-test and one-way ANOVAs (Section 2.9). An independent t-test

was used to test the effect between treatment groups, i.e. CO2 enrichment and control in

both experiments. A one-way ANOVA was used to test the effect between experimental

groups, i.e. CO2 enrichment and control in each experiment.

21

2.8 Growth measurement The number of leaves and branches were counted for each whole plant. Then the new growth

after the wire marking was cut off (Figure 2.8-1). The number of new shoots (each shoot

consisted of one pair of leaves), length of the youngest mature leaf from tip to base and the

length of rhizome were measured on the new growth. Dry weight of the new growth was then

determined. Analyses of growth measurements were made using plastochrone methods for

mono-meristematic non-leaf-replacing form in Short and Duarte (2001).

The following calculations were made:

Shoot plastochrone interval, PS (days)

PS = days of experiment / number of new shoot (Equation 6)

Leaf plastochrone interval, PL (days)

PL = 0.5PS (Equation 7)

Rhizome plastochrone interval, PR (days)

PR = PS (Equation 8)

Leaf elongation (cm shoot-1 day-1) = leaf length / PL (Equation 9)

Rhizome elongation (cm growing tip-1 day-1) = rhizome length / PR (Equation 10)

New growth dry weight (mg shoot-1 day-1) = dry weight/ PS (Equation 11)

The effects on the above growth measurements between CO2 treatment and the control were

compared using independent t-tests at P=0.05 significance level (Section 2.9).

22

Figure 2.8-1: Growth of a Halophila ovalis sample after 14 days of experiment. New growth was measured from the tag to the meristem.

2.9 Statistical analyses All statistical analyses were done with SigmaPlot 12.0. Two-tailed independent t-tests at

P=0.05 significance level were used to compare means between the CO2-enriched and control

aquaria. All data was tested for normality with Shapiro-Wilk test and Levene’s test for equal

variances prior to the t-tests. If the data fails the normality or equal variances test, a two-

tailed Mann-Whitney Rank Sum test for group medians would be used instead of a t-test. In

cases when a one-way ANOVA test was used, it was followed by the Holm Sidak post-hoc test.

Similarly, if the data did not have a normal distribution or equal variances, a rank sum test for

median would be used instead. The results for t-tests and ANOVA tests would be presented in

the form of mean ± standard error.

23

3 Results

3.1 Preliminary study

3.1.1 pH

Constant pH was established in the two sets of aquaria after the first 5 hours (Figure 3.1-1).

The stabilised pH in the CO2-enriched aquaria ranged between 7.79 and 7.92. pH established

in control aquaria (no CO2 addition) ranged between 8.03 and 8.12. The mean of stabilised pH

in the CO2-enriched and control aquaria was 7.88 ± 0.00362 and 8.08 ± 0.00285, respectively.

The pH in the CO2-enriched aquaria (n=65) was significantly lower than the control (n=65)

(U=0.000, P

24

3.1.2 Seagrass density

In the CO2 enriched aquaria, the mean shoot plastochrone interval was 6.86 ± 0.704 days

(n=10) and 8.12 ± 0.824 days (n=20) with densities of 10 and 20 shoots respectively. In the

control aquaria, the mean plastochrone interval was 8.54 ± 1.603 days (n=10) and 7.75 ±

0.819 days (n=20) with densities of 10 and 20 shoots respectively. No significant difference

was found in the effect of seagrass densities and CO2 treatment (H=0.588, P=0.899).

3.2 Temperature Consistent temperature variation was recorded in both sets of aquaria in experiment 1 and 2

over the 15 days period (Figure 3.2-1). Maximum temperature was reached each day just

before 6am when the lights switched on followed by a decrease until minimum temperature

was reached. As the lights switched off at 6pm, the temperature increased constantly

throughout the night. In experiment 1, the temperature of the CO2-enriched aquaria ranged

between 23.15 °C and 25.80 °C whereas the control aquaria had a temperature range

between 23.04 °C and 25.09 °C (Figure 3.2-1a). The mean temperature was significantly higher

in the CO2-enriched aquaria (24.47 ± 0.0181 °C, n=1344) than the control (24.04 ± 0.0137 °C,

n=1344) (t2686=-18.937, P

25

Figure 3.2-1: Temperature of CO2 enriched and control aquaria over 15 days period in (a) experiment 1 and (b) experiment 2 logged at 15-minute interval. Maximum temperatures for both aquaria were observed at around 6am and minimum temperatures at around 6pm each day.

(a)

(b)

26

3.3 Water chemistry

3.3.1 pH

A consistent pattern was observed in the pH of experiment 1 in the CO2-enriched and control

aquaria over the 15-day experiment (Figure 3.3-1). For both control and CO2-enriched aquaria,

the pH decreased at 6pm as the lights switched off. The decreasing trend was exponential

where it began with a quick drop then slowed down after the first few hours. The aquaria

were aerated at night but the CO2 supply was off, therefore the decrease in pH showed the

respiration of the seagrass which generates CO2. Minimum pH was reached at 6am for both

sets of aquaria when lights (on both set up) and CO2 gas supply (on CO2-enriched set up) were

switched on. It was followed by a constant increase in pH throughout the day until the

maximum pH was reached just before 6pm.

In experiment 1, the highest pH recorded in CO2 enriched aquaria was 8.13 at 6 pm on day 3

and the lowest was 7.41 at 9 am on day 14 (Figure 3.3-1). The highest pH recorded in the

control aquaria was 8.35 at 2pm on day 3 and the lowest was 7.89 at 5am on day 6 (Figure

3.3-1). The overall (day and night) mean pH was significantly lower in the CO2-enriched (7.75 ±

0.00689, n=336) aquaria than the control (8.10 ± 0.00603, n=336) (t670= -38.8, P

27

Figure 3.3-1: pH of control (black line) and CO2-enriched aquaria (red line) with seagrass over 15-day period averaged every hour in experiment 1.

In experiment 2, the pH in the control aquaria followed a similar pattern to experiment 1

(Figure 3.3-2). The increasing trend happened during the day with a minimum pH at 6am until

a maximum was reached at 6pm followed by a decrease throughout the night (Figure 3.3-2).

However in the CO2-enriched aquaria, the opposite pattern was observed. Maximum pH was

recorded just before 6am, followed by a decrease when the CO2 supply and lights were

switched on. Minimum pH was reached at 6pm when both CO2 supply and lights were both

28

switched off, followed by a constant increase throughout the night. This could be an

experimental error that the concentration of CO2 added was much higher compared to the

first experiment.

In experiment 2, the highest pH recorded in the CO2 enriched aquaria was 8.16 at 5 pm on day

13 and the lowest was 7.36 at 10 am on day 9 (Figure 3.3-2). In the control aquaria the highest

pH recorded was 8.14 at 4 pm on day 11 and the lowest was 7.58 at 5 am on day 9 (Figure

3.3-2). The mean of the overall (day and night) pH was significantly lower in the CO2 enriched

aquaria (7.78 ± 0.00884, n=336) than the control (7.83 ± 0.00683, n=336) (U=44163, P

29

Figure 3.3-2: pH of control (black line) and CO2-enriched aquaria (red line) with seagrass over 15-day period averaged every hour in experiment 2.

30

3.3.2 Alkalinity

Diurnal seawater alkalinities were measured on days 2, 9 and 15 of both experiments to

compare a) the overall water chemistry during the seagrass growth and b) the difference

between the experimental conditions. When it comes to inorganic availability for

photosynthesis, pCO2 and HCO3- are the two most important water chemistry data reported in

this study.

In experiment 1, in both CO2-enriched and control aquaria pCO2 deceased over the cultivation

period (Figure 3.3-3). The pCO2 in the CO2-enriched aquaria significantly decreased from 446 ±

26.6 μatm (n=12) on day 2 to 319 ± 20.0 μatm (n=12) and 302 ± 30.0 μatm (n=11) on days 9

and 15 (F2, 32= 9.48, P

31

Figure 3.3-3: pH, pCO2 (μatm) (yellow circles), HCO3- concentration (μmol / kg SW) (blue squares) and CO3

2- concentration (μmol / kg SW) (red triangles) of the CO2-enriched (top row) and control (bottom row) aquaria on days 2, 9 and 15 of experiment 1 (mean ± SE).

32

Table 3.3-2: Results of pCO2 (μatm), HCO3- concentration (μmol / kg SW), CO3

2- concentration (μmol / kg SW) and total alkalinity (μmol / kg SW) compared between the CO2-enriched and control aquaria on days 2, 9 and 15 of experiment 1 at P=0.05 significance level.

Alkalinity CO2-enriched Control Test

statistics P-value

mean ± SE n mean ± SE n

Day 2

pCO2 446 ± 26.6 12 454 ± 30.3 12 t22 = 0.211 Not significant

[HCO3-] 1172 ± 64.2 12 1153 ± 17.0 12 t22 = -0.737 Not significant

[CO32-] 88.9 ± 3.77 12 84.4 ± 4.53 12 t22 = -0.764 Not significant

AT 1346 ± 18.2 12 1320 ± 16.5 12 t22 = -1.04 Not significant

Day 9

pCO2 319 ± 20.0 12 356 ± 17.4 12 t22 = 1.39 Not significant

[HCO3-] 1046 ± 25.3 12 1007 ± 14.9 12 U = 51.0 Not significant

[CO32-] 100 ± 2.73 12 82.1 ± 3.54 12 t22 = -4.10

33

concentration of the control aquaria decreased significantly from 1186 ± 32.3 μmol / kg SW on

day 2 to 1087 ± 24.3 μmol / kg SW and 773 ± 13.3 μmol / kg SW on days 9 and 15 (F2,33= 77.3,

P

34

Figure 3.3-4: pH, pCO2 (μatm) (yellow circles), HCO3- concentration (μmol / kg SW) (blue squares) and CO3

2- concentration (μmol / kg SW) (red triangles) of the CO2-enriched (top row) and control (bottom row) aquaria on days 2, 9 and 15 of experiment 2 (mean ± SE).

35

3.4 Chlorophyll fluorescence In experiment 1, Fv /Fm was mostly higher in the CO2-enriched aquaria than the control but

both treatments followed a decreasing trend over the 15-day experiment (Figure 3.4-1). Over

time, Fv /Fm decreased significantly in the CO2-enriched aquaria (H=112, P

36

sets of aquaria recovered over time but the seagrass still benefited from the addition of CO2

by showing higher photosynthetic efficiency.

37

Figure 3.4-1: Fv /Fm (mean ± SE) of H. ovalis in the CO2-enriched (solid circles) and control aquaria (open circles) on days 2, 9 and 15 of experiment 1 (top row)

and experiment 2 (bottom row).

38

3.5 Chlorophyll content Chlorophyll a concentration was significantly higher in the CO2-enriched aquaria (1169 ± 125

μg Chl g-1) than the control aquaria (811 ± 59.8 μg Chl g-1) in experiment 1 (U=13.9, P=0.017).

However no significant difference was found in experiment 2 between the two sets of aquaria

(t15=-0.02, P=0.984). Seagrass chlorophyll a concentration in experiment 1 was almost twice of

seagrass chlorophyll a concentration in experiment 2 [CO2-enriched seagrass (U=3.0, P=0.002);

control seagrass (t16=3.56, P=0.003)] (Figure 3.5-1 a, c).

A similar pattern was observed in the chlorophyll b concentrations (Figure 3.5-1 b, d). In

experiment 1, chlorophyll b was significantly higher in the CO2-enriched aquaria (653 ± 70.4 μg

Chl g-1) than the control (429 ± 27.3 μg Chl g-1) (t16=2.97, P=0.009). However, there was no

significant difference in experiment 2 (t15=0.101, P=0.921). The chlorophyll b concentration

was significantly higher in experiment 1 compared experiment 2 with almost double the

amount for both the CO2-enriched (t15=4.13, P

39

3.6 Growth measurements

3.6.1 Shoots

No significant difference was found between the CO2-enriched and control aquaria for both

experiment 1 (U=450, P=1.00) and experiment 2 (U=408, P=0.517) in the shoot plastochrone

interval. The mean shoot plastochrone interval was between 4.8 to 5.1 days across all

experimental groups with a few outliers of 2 and 8.5 days (Figure 3.6-1).

Figure 3.6-1: Shoot plastochrone interval (mean ± SE), PS (days), under CO2-enriched (n=30) and control (n=30) condition measured on day 15 of experiment 1 (top) and experiment 2 (bottom).

40

3.6.2 Leaves

Since the leaf plastochrone interval was derived from the shoot plastochrone interval, the

results followed the same pattern. No significant difference was found between the CO2-

enriched and control aquaria in both experiment 1 (U=450, P=1.00) and experiment 2 (U=408,

P=0.517). The mean leaf plastochrone interval was around 2.5 days across all experimental

groups (Figure 3.6-2a, c).

No significant difference was found in the leaf elongation between the CO2-enriched and

control aquaria in both experiment 1 (U=350, P=0.141) and experiment 2 (t58= 0.671, P=0.505).

The mean leaf elongation ranged between 0.85 and 1.0 cm shoot-1 day-1 across all

experimental groups (Figure 3.6-2 b, d).

Figure 3.6-2: Leaf plastochrone interval (mean ± SE), PL (days), and leaf elongation (cm shoot-1 day-1) of H. ovalis under CO2-enriched (n=30) and control (n=30) conditions in experiment 1 (a, b) and experiment 2 (c, d).

(a) (b)

(c) (d)

41

3.6.3 Rhizomes

No significant difference was found in the rhizome elongation between the CO2-enriched and

control aquaria in both experiment 1 (U=444, P=0.935) and experiment 2 (U=425, P=0.717).

The mean rhizome elongation ranged between 1.6 and 2.0 cm growing tip-1 day-1 across all

experimental groups (Figure 3.6-3). However, some plants produced extensive branching in

the rhizomes. As shown in Figure 3.6-4, after 15 days of experiment, a sample of H. ovalis

produced five new meristems out of the main rhizome (i.e. four branches produced) but none

of these branches were used for growth measurements except for the one directly behind the

wire tag.

Figure 3.6-3: Rhizome elongation (cm growing tip-1 day-1) (mean ± SE) of H. ovalis in CO2-enriched (n=30) and control aquaria (n=30) in experiment 1 (top) and experiment 2 (bottom).

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Figure 3.6-4: A sample of H. ovalis with 5 meristems (red circles) grown on one main rhizome after 15 days of experiment.

3.6.4 Seagrass biomass productivity

Biomass productivity was significantly higher in the CO2-enriched (12.2 ± 1.39 mg shoot-1 day-1)

than the control aquaria (6.29 ± 0.586 mg shoot-1 day-1) in experiment 1(U=195, P

43

Figure 3.6-5: Dry weight (mg shoot-1 day-1) (mean ± SE) of new grown H. ovalis after experiment period under CO2-enriched (n=30) and control (n=30) conditions in experiment 1 (top) and experiment 2 (bottom).

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4 Discussion

4.1 Assumptions and Limitations It was assumed that the two experiment setups had the same conditions to make a fair

replicate for the analyses of the results. However differences were observed in the results of

the two experiments; there were several potential factors that may have caused these

differences. Firstly, assumptions were made on the experimental setup to be identical across

aquaria and between the two experiments. The three aquaria stored inside each gas chamber

were treated as an individual replicate. However, the water condition might not be the same

especially between the CO2-enriched replicate aquaria. The carbon dioxide gas was let to

dissolve in the aquaria by mixing the water but there was no measure in place to ensure the

air in the gas chamber was well mixed. Since CO2 has a higher density than air, there was a

tendency that the aquarium directly under the CO2 gas tube might receive more CO2 than the

two aquaria on the sides. This can lead to an unequal effect in the acidification of water in the

aquaria. The setup could potentially be improved by enhancing air mixing within the gas

chamber to reduce variation in the acidification effect. For the water and sediment conditions

between the two experiments of the main study, it was also uncertain if the conditions were

similar enough to be treated as a replicate. Gas bubbles were observed in the sediment during

the second experiment. Although the sediment did not show other signs of turning anoxic

such as darkening, the gas bubbles could be a sign of decomposition happening in the

sediment. The bacteria decomposing surface detritus requires aerobic respiration which

consumes oxygen and produces carbon dioxide (Blum and Mills 1991; Holmer and Olsen 2002).

During the process of subsurface microbial decomposition, hydrogen sulfide (H2S) gas is

produced by sulfate-reducing bacteria as a result of anaerobic respiration (Pollard and

Moriarty 1991; Muyzer and Stams 2008). As the aquaria used in this study were small, such

changes in the water chemistry could alter the concentration of CO2 dissolved in the seawater.

45

In the preliminary study, the seagrass was tested for the most suitable density in the aquaria.

No significant difference was observed between 10 and 20 shoots per aquarium in both the

CO2-enriched and control aquaria. H. ovalis was observed growing in small dense patches at

the sampling site. In the field, the average shoot density of H. ovalis is around 2000 - 3000

shoots m-2 in a healthy meadow (Preen 1995; Longstaff et al. 1999). The reason for choosing

10 shoots per aquarium (about 300 shoots m-2) in the main study was to minimise the

accumulation of detritus in the small aquaria and yet providing sufficient number of replicates.

4.2 Seasonality The results showed that seasonality had an important effect on the utilisation of inorganic

carbon and potentially other nutrients which were not measured. As the seagrass was

collected in different seasons for the experiment, it was expected that the seagrass at

different life stages would require various levels of inorganic carbon to support growth and

reproduction. The weather immediately before seagrass collection and the stress from

transplantation may also affect the photosynthetic efficiency of the plants. In the preliminary

study, which was conducted in late April (late autumn), the weather conditions had been fine

immediately before collection of samples, and the monospecific H. ovalis meadow was very

dense at the study site. The seagrass was flowering and fruits were produced in the aquaria

during the preliminary experiment.

For the first experiment of the main study, the seagrass was collected in May (early winter).

The water was colder than the preliminary study but the meadow condition was similar with

some flowering. H. ovalis flowers in January to April in Western Australia (McConchie and

Knox 1989); however flowers and fruits were recorded in May in this study. This may due to a

warming climate and lack of mixing in Swan- Canning Estuary so that warm water stays in the

estuary for longer, thus leading to prolonged flowering and fruiting periods.

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For the second experiment, the seagrass was collected in August (mid-winter). Perth had

experienced several winter storms prior to the collection date, and the H. ovalis meadow at

the study site had greatly reduced in density with only small patches remaining. No flowering

was noted but the epiphyte load was much higher than the previous two collections. The

seagrass collected for experiment two was the unhealthiest of all three collections based on

observed colouration. This may result in the plants being less productive and more vulnerable

to stress caused by transplantation and CO2 enrichment in the experiment.

4.3 Temperature Increasing water temperature at night time was observed in the aquaria which could be due

to the fact that respiration releases heat as a by-product (Raven et al. 2013). As the plants

started to photosynthesise once the lights switched on, the temperature stopped increasing

and heat was lost to the air due to mixing of the water. Both experiments showed significant

difference in the mean temperature but it was higher in the CO2-enriched aquaria in the first

experiment and higher in the control in the second experiment. This shows an inconsistency in

the two replicates which can be subject to seasonality, making the plants in the second

experiment more vulnerable to stress. The overall temperature range was higher in the

second experiment than the first. This can be caused by higher rate of decomposition as the

amount of organic matter in the sediment accumulated. Overall the seawater temperature in

the aquaria was higher than the seagrass natural habitat (Water 2016) but still within the

optimal photosynthetic range for cultivation of H. ovalis (Ralph 1998).

4.4 pH Despite the inconsistency between experiments 1 and 2, three out of four records showed the

same pattern of an increasing pH during the day and decreasing at night, with the CO2

enriched aquaria in experiment 2 being the only exception. This result agrees with previous

findings that seagrasses have pH buffering capacity to the surrounding waters (Hendriks et al.

2014). During the day, the seagrass consumes CO2 for photosynthesis, lowering the pCO2 level

47

thus shown in an increase of pH. Respiration occurs at night producing CO2, thus increasing

the pCO2 level again and lowering pH level. This modification of water chemistry in the field is

only limited to within the boundary layer when mixing is insufficient (Madsen and Sand-

Jensen 1991). In this experiment, the plants were sparsely distributed in the small aquaria.

The density of the seagrass was not high enough to create a boundary layer and the water

was constantly well mixed by aeration from above rather than a horizontal laminar flow.

Therefore, the effect of the boundary layer could not be tested in this case. For the only

exception, the CO2-enriched aquarium in experiment 2 that showed an opposite pH pattern,

the reason could be to do with the issue that the plants were unhealthy when collected. Since

the plants were stressed and had lower productivity, they failed to consume the extra CO2

dissolved in the water at day time resulting in a decline in pH and the increase of pH at night

was due to stopping CO2 addition.

The CO2 concentration and the method for acidification simulation used in this study achieved

the guideline proposed by EPOCA, i.e. a pCO2 level of 750 or pH 7.8 (Barry et al. 2010).

Although the 12 hours supply of CO2 gas might not reflect field conditions, it still significantly

lowered the pH by 0.2 compared to the control. However there were still large fluctuations in

the pH records. It was difficult to control and maintain precisely the pH of seawater in

laboratory experiments. Direct injection of premixed air to the aquarium is one of the most

common practices in OA research (Gattuso et al. 2010). This method can achieve pH precision

up to 0.001 units (Bockmon et al. 2013) but the setup can be quite expensive.

4.5 Alkalinity Given that acidification in the CO2-enriched aquaria was established before the seagrasses

were transplanted, the results of rapidly declining pCO2 between days 2 and 9 showed that

the seagrass had the highest productivity at the beginning of the experiment and the most

efficient buffering capacity in experiment 1. The higher HCO3- concentration in the CO2

enriched aquaria showed that the additional CO2 turned into HCO3- to buffer the pH of the

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seawater. The pH record ranged from 7.36 to 8.35, which means the predominant DIC species

in the aquaria was HCO3- at all times. H. ovalis can use HCO3

- as a carbon source for

photosynthesis due to the presence of an anhydrase enzyme (Beer et al. 2002; Uku et al.

2005). However, the seagrass still uses CO2 primarily for photosynthesis even when there is

more HCO3- available. Some of the CO2 was assimilated by the seagrass and turned into

biomass which could also result in the reduction of pCO2 level.

An opposite result was observed in experiment 2. The pCO2 level increased and the HCO3-

concentration decreased over time in both sets of aquaria. It could be due to the seagrass

being stressed after storm activity and therefore less tolerant to transplantation and the

effect of CO2 addition. This was also shown in the stronger effect on pH and lower biomass

productivity. Therefore the seagrass could not consume the CO2 fast enough so the

concentration accumulated over time.

There was also limitation in the titration method for determining alkalinity in this study.

Burette titration was used in this study because of the availability of equipment and lower

cost. However, this method can be very inaccurate because CO2 had a low solubility in water;

therefore it escapes readily to the air. CO2 solubility also depends on temperature; it was

suggested that a thermostat bath should be used during titration to keep the temperature

constant (Dickson et al. 2007). The more common method used in determining seawater

alkalinity is by using potentiometric titration which is done within a confined chamber to

minimise the loss of CO2 (Edmond 1970). Some studies have connected the titration cell to a

computer to log the results instantly for better precision and accuracy (Bradshaw et al. 1981;

Millero et al. 1993).

4.6 Chlorophyll fluorescence In experiment 1, the Fv /Fm showed an overall deceasing trend in both sets of aquaria. This

agreed with the previous hypothesis that the seagrass was more productive at the beginning

49

of the experiment and declined over time. The reason could be to do with the decrease in

pCO2 level which is the primary carbon source for seagrass photosynthesis. The mean Fv /Fm

was 0.72 and 0.69 in the CO2-enriched and control aquaria at the beginning of the experiment,

which was similar to previous studies on healthy H. ovalis that had Fv /Fm between 0.7 and 0.8

(Ralph and Burchett 1995; Longstaff et al. 1999). The general pattern observed in the

measurements was a low fluorescence signal at 5am, followed by a rapid increase at 7am and

then the Fv’/Fm’ gradually decreased throughout the day until the evening. As the plants had

been under constant illumination for one hour at 7am, the PSII reaction centres should be

mostly opened; that contributes to the higher photochemical efficiency compared to 5am

when the plants had been respiring all night. The decreasing trend in Fv’/Fm’ during the day

follows the diurnal pattern of midday photoinhibition which was found in studies on emergent

H. ovalis leaves (Ralph et al. 1998; Beer et al. 2006).

In experiment 2, Fv /Fm started low at the beginning of the experiment. This could be a result

of stress on the seagrass due to heavy epiphyte load that causes shading of the leaf surface.

As the epiphytes were removed before transplanting the seagrass into the aquaria, more leaf

surface could be exposed to light and the seagrass gradually recovered, which was shown in

the increasing Fv/Fm pattern. Several studies have shown substantial increase in

photosynthetic rate (Pmax) and photosynthetic efficiency (α) in seagrass under CO2-enriched

condition (Beer and Waisel 1979; Alexandre et al. 2012; Ow et al. 2015). However, the effect

could be subjected to other factors such as spatial variation and seasonality (Cox et al. 2016).

The potential experimental error in using the diving PAM fluorometer for measuring

chlorophyll fluorescence in this study was the inconsistency in the measuring protocol. The

fluorescence signal can be affected by the distance and angle between the leave surface and

the fibre optic sensor. This variation can be minimised by using a “leaf distance clip” to hold

the leaf and keep the distance and angle consistence (Beer et al. 1998). However, H. ovalis

50

had small leaf surface and thin stems and due to the limited space in the aquaria, the leaves

could hardly be held under the clip. The choice of leaf also affects the fluorescence results. A

healthy leaf could have double the Fv/Fm than an unhealthy leaf. To keep the sampling

consistence, the same leaf was measured in each aquarium throughout the experiment.

However, there were cases when the leaf was dying or completely dead that a replacement

leaf was used for the measurement. This also adds inconsistency and errors to the experiment.

To account for this variability and minimise bias, multiple random leaf samples instead of a

single leaf should be used for measuring chlorophyll fluorescence.

4.7 Chlorophyll content

Inconsistency in the two experiments was shown and this could be caused by seasonality of

the seagrass. Chlor