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Effects of past, present and possible future seawater environments on sea cucumbers and the
sediments they process
Francisco Javier Vidal Ramirez
BSc (Marine Biology)
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in 2016
School of Biological Sciences
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Abstract
Future climate change is predicted to have deleterious impacts on coral reefs, leading to a decline in
the ability of these systems to provide ecological and human services that are both economically
and socially valuable. Much of the projected decline is focused on the negative effects that
increased sea surface temperature and ocean acidification (OA) are expected to have on key
calcifiers (Scleractinian corals), present in the reef ecosystem. There has been significantly less
research effort directed towards the impacts of warming and acidification on other components of
the ecosystem, especially holothurians and the sediment communities with which these
echinoderms interact. Sediments are essential to reefs as the microbes that inhabit them, recycle
nutrients in these otherwise poor nutrient environments; and key to the release of essential nutrients
from the sediments is bioturbation by organisms such as holothurians.
My PhD Thesis aims to investigate: 1) The short-term impact of Holothuria atra (one of the most
abundant Indo-Pacific holothurians) over the seawater carbonate chemistry, nutrient recycling and
OA buffering capacity (AT/DIC) within a sedimentary environment (Chapter 2); 2) The long-term
effects (2 months) of co-varying winter temperature/pCO2 Scenarios over H. atra and sediment
associated biota. Consequently, the aim is to test the impacts of such Scenarios over calcium
carbonate dissolution, AT/DIC, nutrient recycling and O2 flux produced by the animals and
sediment-associated organisms on reef ecosystems (Chapter 3); 3) The long-term effects of summer
temperature/pCO2 Scenarios over the performance of H. atra and other organisms in regards to the
same responses tested in Chapter 3, but tested when more extreme conditions than in winter occur
(e.g., temperatures above MMM and a greater lack of DOM than in winter) (Chapter 4). Seasons
have not been replicated, but each long-term experiment encompasses 67% of the season in terms
of length. The response variables were tested in Chapter 3 and 4 under present day conditions (PD:
+0oC, +0 ppm pCO2), pre-industrial conditions (PI: -1oC, -100 ppm pCO2 below PD, to estimate
potential impacts produced by PD), and two IPCC Scenarios (RCP4.5: +1.8oC, +180 ppm pCO2;
and RCP8.5: +3.6oC, +570 ppm pCO2). All Scenarios included diurnal and seasonal variability.
The results showed that in a short-term period (Chapter 2), in presence of H. atra, there was a
greater CaCO3 dissolution (~290 mg CaCO3 m-2 h-1) and TAN (NH3 + NH4+) production (~45%)
than in sediments without H. atra. However, H. atra was not able to modify most of the carbonate
parameters and AT/DIC, leading to the conclusion that H. atra most likely will not assist reef
calcifiers by the modification of OA buffering capacity.
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In winter (Chapter 3), the only direct effect of H. atra on the system was a 34% increase in net
daytime O2 production and modification of sediment infauna. However, such changes did not
mitigate the observed decreases in O2 production under future climate Scenarios. PI generated a
~62% greater TAN uptake than PD, suggesting that under PD processes associated with nitrogen
such as ANAMMOX may have already been affected in reefs. Calcium carbonate dissolution was
observed always under RCP8.5, regardless the presence of H. atra. Likewise, all other seawater
parameters were influenced either by day/night fluctuations or Scenario, likely assisted by an
observed overall decrease in microbial abundance and change in microbial composition under
RCP8.5 (analysed by qPCR and 16S amplicon sequencing, respectively). Therefore, processes that
may impact calcification rates and AT/DIC may have been affected, such as sulfur-oxidation and
sulfate-reduction, regardless of H. atra.
In summer (Chapter 4), H. atra significantly increased AT/DIC; however, such increase did not
significantly modify the downward trend observed for AT/DIC under RCP8.5. Net CaCO3
calcification rates were highly variable and were not modified by any of the factors tested
(presence/absence of animals, time of day and Scenarios). AT appeared to be the only parameter
that significantly correlated to calcification rates, explaining 10% of the variation observed.
Microbial abundance increased significantly under RCP8.5 compared to PD; however, this increase
was proportional across taxa, yielding no apparent change in microbial composition. Therefore, the
lack of change in microbial composition may help explain the insignificant changes observed for
summer calcification rates and AT/DIC over any potential of the animals at this period of the year.
This study demonstrates that H. atra had a low impact on most of the variables tested compared to
sediment-associated biota (principally prokaryotes). The animals were never able to counter the
downward trends observed for different parameters (e.g., AT/DIC) under future climate Scenarios
linked to current rates of fossil fuel burning. Future business-as-usual Scenarios produced
significant effects on sediment microbes. Therefore, most changes were driven principally by
abiotic factors (PI conditions were generally similar to PD conditions with RCPs producing the
most negative impacts over the response variables), potentially aided by changes to sediment
microbes rather than the action of H. atra.
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Declaration by author
This thesis is composed of my original work, and contains no material previously published or
written by another person except where due reference has been made in the text. I have clearly
stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical
assistance, survey design, data analysis, significant technical procedures, professional editorial
advice, and any other original research work used or reported in my thesis. The content of my thesis
is the result of work I have carried out since the commencement of my research higher degree
candidature and does not include a substantial part of work that has been submitted to qualify for
the award of any other degree or diploma in any university or other tertiary institution. I have
clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and,
subject to the policy and procedures of The University of Queensland, the thesis be made available
for research and study in accordance with the Copyright Act 1968 unless a period of embargo has
been approved by the Dean of the Graduate School.
I acknowledge that copyright of all material contained in my thesis resides with the copyright
holder(s) of that material. Where appropriate I have obtained copyright permission from the
copyright holder to reproduce material in this thesis.
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Publications during candidature
Publications related to this Thesis during candidature
Vidal-Ramirez, F., Dove, S., 2016. Diurnal effects of Holothuria atra on seawater carbonate
chemistry in a sedimentary environment. Journal of Experimental Marine Biology and
Ecology, 474, 156-163. doi: 10.1016/j.jembe.2015.10.007.
Publications unrelated to this Thesis during candidature
Manzur, T., Vidal, F., Pantoja, J. F., Fernández, M., Navarrete, S. A., 2014. Behavioural and
physiological responses of limpet prey to a seastar predator and their transmission to basal
trophic levels. Journal of Animal Ecology, 83, 923–933. doi: 10.1111/1365-2656.12199.
Llabrés, M., Agustí, S., Fernández, M., Canepa, A., Maurin, F., Vidal, F., Duarte, C. M., 2013.
Impact of elevated UVB radiation on marine biota: a meta-analysis. Global Ecology and
Biogeography, 22, 131–144. doi: 10.1111/j.1466-8238.2012.00784.x.
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Publications included in this thesis
Vidal-Ramirez, F., Dove, S., 2016. Diurnal effects of Holothuria atra on seawater carbonate
chemistry in a sedimentary environment. Journal of Experimental Marine Biology and
Ecology. 474, 156-163. doi: 10.1016/j.jembe.2015.10.007. Incorporated as Chapter 2.
Contributor Statement of contribution
Vidal-Ramirez F (Candidate) Designed experiments (80%); conducted
experiments (100%); wrote and edited paper
(70%)
Dove S Funded experiments (100%); designed
experiments (20%); wrote and edited paper
(30%)
Contributions by others to the thesis
Dr. Sophie Dove provided the funding for all experiments and field trips. She was the main
supervisor of the Thesis and was a main contributor in the design of the experiments, analysis and
interpretation of data, and during the writing process and editing of the Thesis.
Dr. Olga Pantos contributed to the processing of sediment samples for microbial communities. She
also contributed to the analysis of the molecular data, writing and editing for the microbial sections
of Chapter 3 and Chapter 4.
Dr. Gene W Tyson contributed to the experimental design concerning to the collection and
processing of sediment samples for microbial communities analysis of the Thesis. He also
contributed to the analysis of the molecular data, writing and editing for the microbial sections of
Chapter 3 and Chapter 4.
Statement of parts of the thesis submitted to qualify for the award of another degree
None
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Acknowledgements
I would like to dedicate this thesis to my wife Marcela and my daughter Antonia. They have been
and endless source of happiness, support and peace. They gave me the extraordinary gift of their
company, and followed me without hesitation to the lands of the Kangaroos and Koalas. Marcela
left aside her career, friends and many things behind to accompany me. My beautiful Antonia left
friends and family to join us in this adventure, without knowing a word of English, but she did it
with great enthusiasm and strength. Thank you for been such great partners and for being there with
me, no matter what. I love you both, more than anything in this world.
I also would like to thank my mother Livia. She has supported me constantly, in every situation and
step of my life. Thank you for being the best mum I could ever ask for. Thanks to my brothers
Pablo and Sebastian, who supported from Chile, gave me good advice when needed and made me
laugh with many jokes and good times during every phone call, WhatsApp text of Skype meeting.
I want to thank Sophie Dove, for being such a great advisor. She taught me, amongst many other
things, how to look the glass half full instead of half empty. In every situation she had an excellent
advice and supported me through this 4 year process. Thanks Sophie.
I want to thank Dr. Maria Byrne for her helpful comments and insights during the candidature.
To my friends in Australia, outside the CRE Lab: Felipe Aguilera, Jorge (Campos and Lizama),
Baillie Le Strange, Daniel Zapata y Paulina, Monica Araya, Karen Bequer, Christian Willig, Karla
Olivares, Carlos and Evie Noronha. Thank you guys for your invaluable support in helping Marcela
and Antonia while I was in the field. However, I especially want to thank you for all the great
memories, parties, movies, and awesome friendship during this long-term process.
Of course, I will also like to the many members of the CRE Lab during my stay for and their help in
many steps of the project. I will like to stop and give special thanks to Anjani Ganese, Manuel
Gonzalez-Rivero, Catalina Reyes-Nivia, Pim Bongaerts, Andreas Kubicek, Michelle Achlatis,
Dominic Bryant, Veronica Radice, Norbert Englebert, Giovanni Bernal Carrillo and Aaron Chai for
their friendship, help and company during many processes like parties, roof drinks, fieldwork and
lab. Thank you all guys.
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Finally I would like to thank Becas Chile scholarship (CONICYT), CRE Lab, the School of
Biological Sciences and Global Change Institute from UQ, and the ARC Centre of Excellence for
Coral Reef Studies for their financial support during my PhD candidature.
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Keywords
Holothuria atra, sea cucumbers, bacteria, microalgae, climate change, IPCC scenarios, Heron
Island
Australian and New Zealand Standard Research Classifications (ANZSRC)
ANZSRC code: 060205, Marine and Estuarine Ecology, 50%
ANZSRC code: 069902, Global Change Biology, 30%
ANZSRC code: 060504, Microbial Ecology, 20%
Fields of Research (FoR) Classification
FoR code: 0602, Ecology, 50%
FoR code: 0699, Other Biological Sciences, 30%
FoR code: 0605, Microbiology, 20%
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TABLE OF CONTENTS
Abstract ii
Declaration by author iv
Publications during candidature v
Publications included in this thesis vi
Contributions by others to the thesis vi
Statement of parts of the thesis submitted to qualify for the award of another degree vi
Acknowledgements vii
Keywords ix
Australian and New Zealand Standard Research Classifications (ANZSRC) ix
Fields of Research (FoR) Classification ix
Table of contents x
List of figures xiii
List of tables xiv
Appendix A: Supplementary information Chapter 3 xv
Appendix B: Supplementary information Chapter 4 xv
Chapter 1: General introduction
1
1.1. Climate change and its impacts on coral reefs, echinoderms and sediment-
associated biota
1
1.2. Specific roles of sea cucumbers on reefs and their interaction with sediment-
associated biota.
4
1.3. Sea cucumber-sediment interactions. Current status of knowledge and
limitations in a climate change context
9
1.4. General aim and overall approach of the thesis 11
1.5. Specific aims and general outline of the thesis 12
1.6. References 15
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Chapter 2: Diurnal effects of Holothuria atra on seawater carbonate chemistry
in a sedimentary environment
26
2.1. Abstract 27
2.2. Introduction 28
2.3 Material and methods 31
2.3.1. Experimental setup 31
2.3.2. Ammonia determination and O2 flux 31
2.3.3. Dissolution rates 32
2.3.4. Carbonate chemistry 33
2.3.5. Grain size analysis 33
2.3.6. Statistical analysis 33
2.4. Results 34
2.4.1. Ammonia estimates 34
2.4.2. Carbonate chemistry 34
2.4.3. O2 Flux 36
2.4.4. Grain size 36
2.5. Discussion 36
2.5.1. General overview 36
2.5.2. CaCO3 dissolution rates and ammonia production by H. atra and sediment-
associated micro-organisms
36
2.5.3. Modification of carbonate parameters during incubation periods 38
2.5.4. Cumulative effect of H. atra on carbonate parameters 39
2.6. Acknowledgments 39
2.7. References
41
Chapter 3: Impacts of winter climate change conditions on decalcification and
ocean acidification buffering capacity are not mitigated by sea cucumbers
57
3.1. Abstract 58
3.2. Introduction 59
3.3. Materials and methods 61
3.3.1. Field collection and general setup 61
3.3.2.Temperature/pCO2 system 61
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3.3.3. Experimental design and incubations periods 62
3.3.4. Nutrient analysis 63
3.3.5. CaCO3 dissolution rates and carbonate chemistry 63
3.3.6. 16S rDNA amplicon sequencing and qPCR of microbial communities 63
3.3.7.Photosynthetic pigment concentrations and infaunal composition of sediments 65
3.3.8. Statistical analysis 66
3.4. Results 67
3.4.1. Cumulative effect of factors on the carbonate chemistry 67
3.4.2. Dissolution rates 68
3.4.3. O2 flux 68
3.4.4. Nutrient production 68
3.4.5. Pigment and infaunal analysis 69
3.4.6. Microbial communities 69
3.5. Discussion 70
3.5.1. General remarks 70
3.5.2. Dissolution rates and carbonate chemistry modification 70
3.5.3. Microbial changes and their role in calcification/dissolution rates 71
3.5.4. O2 Production 73
3.5.5. Ocean acidification buffering capacity 74
3.5.6. Conclusions 75
3.6. Acknowledgments 76
3.7. References 77
Chapter 4: Modification of carbonate chemistry and production under
summer IPCC scenarios in the presence of holothurians and carbonate
sediment associated-organisms
95
4.1. Abstract 96
4.2. Introduction 97
4.3. Materials and methods 99
4.3.1. General setup 99
4.3.2. Temperature/pCO2 system 99
4.3.3. Experimental design and incubations periods 100
4.3.4. Nutrient analysis 101
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4.3.5. CaCO3 dissolution rates and carbonate chemistry 101
4.3.6. Microbial composition and abundance 101
4.3.7. Sediment photosynthetic pigments and infauna 103
4.3.8. Statistical analysis 103
4.4. Results 105
4.4.1. Summer carbonate chemistry 105
4.4.2. Nutrient production 105
4.4.3. Microbial communities 106
4.4.4. Pigment and infaunal analysis 106
4.4.5. Calcification rates 106
4.4.6. O2 flux 107
4.5. Discussion 107
4.5.1. Conclusions 111
4.6. Acknowledgments 111
4.7. References 113
Chapter 5: General Discussion 136
5.1. General outline 136
5.2. Chapter 2 136
5.3. Chapter 3 137
5.4. Chapter 4 138
5.5. Winter versus summer comparisons, and annual estimates of change 139
5.6. Conclusion and future research directions 142
5.7. References 146
LIST OF FIGURES
Figure 1.1. Conceptual representation about main abiotic/biological in presence of
climate change stressors, in which benthic marine invertebrates are involved
2
Figure 1.2. Changes produced by holothurians on total alkalinity (AT), total
ammonia nitrogen (TAN) and production on coral reefs
5
Figure 2.1. Responses on TAN, AT , CaCO3 dissolution rates and DIC in the
presence/absence of H. atra and within different times of the day
48
Figure 2.2. Grain size of sediments at the end of the experiment in the presence and 49
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absence of H. atra
Figure 2.3. Conceptual representation of factors controlling water carbonate
chemistry in the presence of H. atra and sediment-associated organisms
50
Figure 3.1. Differences in pH and the relationship between pH and AT/DIC under
different Scenarios and times of the day
89
Figure 3.2. Winter responses of the system (e.g., calcification rates and O2 flux)
under different Scenarios, times of the day and presence/absence of H. atra
90
Figure 3.3. Changes on density of Nematoda in presence or absence of H. atra 91
Figure 3.4. Changes on microbial abundance and Alpha diversity under different
Scenarios and presence/absence of H. atra
92
Figure 3.5. Microbial composition changes under different Scenarios 93
Figure 4.1. Summer responses of the system (e.g., AT/DIC) under different
Scenarios, times of the day and presence/absence of H. atra
130
Figure 4.2. Relationship between calcification rates and AT for all Scenarios and
PD and RCP8.5 only
131
Fig. 4.3. Summer responses on O2 flux under different Scenarios and times of the
day
132
LIST OF TABLES
Table 2.1. Repeated measures ANOVA for dissolution rates and TAN under
different Condition (+SC and -SC) and sampling Time (mid-day or midnight).
51
Table 2.2. Summary of abiotic parameters (e.g., AT and DIC) for different
Condition (+SC and -SC) and Time (mid-day or midnight)
52
Table 2.3. Repeated measures ANOVA for carbonate parameters at t0, under
different Condition (+SC and -SC) and Time (mid-day or midnight)
54
Table 3.1. PERMANOVA analysis for the carbonate chemistry parameters at t0,
under different Condition, Scenario and Time
94
Table 4.1. Repeated measured ANOVA for the chlorophyll a concentrations under
different Condition, Scenario and Time
133
Table 4.2. PERMANOVA analysis for calcification rates under different
Condition, Scenario and Time. Analysis is presented for: (a) All Scenario; (b) PD
and RCP8.5 only
134
Table 4.3. Distant based linear models (DistLM) with calcification as the response 135
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variable. The analysis is presented for: (a) All Scenario and; (b) PD and RCP8.5
only
APPENDIX A: Supplementary information Chapter 3 151
APPENDIX B: Supplementary information Chapter 4 167
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Chapter 1: General introduction
1.1. Climate change and its impacts on coral reefs, echinoderms and sediment-associated biota
The increasing emissions of greenhouse gases (GHGs) produced by human activities have created
stressful (climate-related) scenarios for different ecosystems. According to the Intergovernmental
Panel on Climate Change, there is evidence that anthropogenic impacts derived from the increasing
emissions of GHGs (especially CO2) are having severe and important effects on different
ecosystems (IPCC, 2014). The result of the enhanced CO2 in the atmosphere is that tropical waters
of Australia have warmed significantly over the past 150 years with increases of +0.73ºC between
1951 and 1990 (Hoegh-Guldberg, 2008). The second stressor produced by current and future CO2
concentrations is ocean acidification (OA). The increased CO2 concentrations in seawater (SW)
lead to a reduction in pH. Currently, the ocean pH has decreased by 0.1 pH units, proposing
deleterious effects for marine calcifying organisms (Hoegh-Guldberg, 2008; Hoegh-Guldberg &
Bruno, 2010; Kleypas & Yates, 2009; Pelejero et al., 2005; Sabine et al., 2004). In this context, the
scenarios of climate change proposed by the IPCC are designated as Representative Concentration
Pathways (RCPs) (IPCC, 2014; Rogelj et al., 2012), which include social, economic and ecological
aspects (amongst others). From these scenarios, RCP8.5 represents the “business-as-usual” CO2
emission scenario, associated with the highest level of ocean warming and acidification (+3.6oC,
+570 ppm pCO2 above present day levels; see Chapter 3 and 4 for present day levels); RCP4.5
represents a medium scenario in which CO2 emissions are significantly reduced by ~2050 according
to the present levels (+1.8oC, +180 ppm pCO2 above present day) (IPCC, 2014; Rogelj et al., 2012).
In the context of climate change, the Great Barrier Reef (GBR) and other coral reefs around the
globe are amongst the ecosystems negatively impacted by GHGs emissions (IPCC, 2014). There is
a great interest to understand the effects of climate change on coral reef environments since they
represent some of the most diverse ecosystems in the world (Bouchet, 2006; Hughes et al., 2002;
Plaisance et al., 2011; Reaka-Kudla, 1997; Small et al., 1998). However, there is an important lack
of knowledge regarding many organisms and their ecological roles within reefs. Many of these
organisms are non-coral macroinvertebrates, which play significant roles and functions that yet
need further understanding (Przeslawski et al., 2008; Fig. 1.1). Moreover, these functions are not
well understood in present day conditions of seawater temperature and pCO2 levels, leading to a
lack of evidence about potential alterations to function due to biotic changes driven by projected
scenarios (IPCC, 2014). In general, research focusing on impacts of climate change on non-coral
macroinvertebrates and their potential effect on the carbonate chemistry of reefs, nutrient
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production, and other roles is very limited when compared to the number of studies conducted on
corals, macroalgae and fish within coral reef environments (Przeslawski et al., 2008). The relative
absence of knowledge in this area raises serious challenges for understanding the ecological
relationships that may be disturbed under climate change conditions. Moreover, due to the limited
knowledge about these potential alterations, problems may develop for future management of
resources and the livelihoods of the people that directly or indirectly depend on reefs (Hernandez-
Delgado, 2015).
Fig. 1.1. Main abiotic processes and their relationships with stressors derived from climate change.
Furthermore, potential associations between these relationships and the biological and ecological
processes, in which benthic marine invertebrates are involved, are shown (taken from Przeslawski
et al., 2008).
In reefs, echinoderms are amongst the invertebrate groups that play important ecological roles
(Byrne, 2008). However, the effects of ocean acidification and increased temperatures on this group
are not well understood (Kleypas et al., 2006; Przeslawski et al., 2015). In general terms, multiple
stressors (e.g., temperature and OA) may have negative consequences for reef echinoderms due to
impacts on the ontogeny of this group, and therefore, in adult populations (Byrne, 2012; Byrne &
Przeslawski, 2013; Byrne et al., 2013; Przeslawski et al., 2015). Sea cucumbers are amongst the
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most conspicuous echinoderms in tropical reef benthic environments. Sea cucumbers are an
economically important group and have been depleted or overfished in many regions (FAO, 2008;
Purcell et al., 2013). Furthermore, they represent a group of ecological relevance (see below), but
information about their role in reef carbonate budgets and their interactions with microbes in the
sediments are scarce (e.g., Hewson & Fuhrman, 2006; Moriarty et al., 1985; Schneider et al., 2011;
Schneider et al., 2013). Although these organisms will face potential stressful conditions due to
emissions of greenhouse gases (GHGs), the impacts of climate change on sea cucumbers have not
been published to date.
Sea cucumbers interact with other organisms in the sediments (such as prokaryotes). Therefore, it
becomes relevant to understand what are the potential impacts for reef microbes under climate
change conditions, not only for their relationship with holothurians, but because nutrient retention is
highly important in coral reef ecosystems, due to typical low nutrient availability. In this regard, the
role played by microorganisms is essential. Bacteria are among the most diverse groups of
organisms in marine environments, and they are important for recycling of nutrients (Charles et al.,
2009). However, bacteria are often not considered in climate change studies (Jones et al., 2014;
Webster & Hill, 2007). Some studies have demonstrated that changes can be produced on microbial
communities by climate change scenarios (Dove et al., 2013; Sultana et al., 2016; Webster el al.,
2011). An example of the impact of elevated pCO2 on microbial communities has been provided by
Raulf et al. (2015). They reported changes in microbial communities for natural environments
(volcanic vents) under elevated pCO2 conditions. Under elevated pCO2, nitrifying bacteria (i.e.
Nitrosococcus) decreased, and ammonia-oxidizing archaea increased their abundance, potentially
altering the nutrient recycling within the system (Raulf et al., 2015). Microbial biofilms are also
relevant components of reefs, because they can be important for reef invertebrate settlement
(Webster et al., 2013). Decreases in pH (from 8.1 to 7.9) can negatively impact these biofilms
(Webster et al., 2016), and therefore, settlement of different invertebrate taxa (e.g., corals) may be
potentially altered. However, whilst there is evidence that biofilms are affected by OA, it is
unknown whether the partnerships between biofilms and invertebrates would vary with either
seasonal, or climate driven changes to temperature.
Other groups of organisms such as microalgae and infauna are constantly in interaction with sea
cucumbers due to their feeding and bioturbation (see below). Hence, their relevance under climate
change conditions gains importance in this context. Microalgae communities in sediments of coral
reef can be abundant (up to 995 mg chlorophyll a m-2) and productive (up to 110 mg O2 m-2 h-1)
(Heil et al., 2004). They also play a significant role in nutrient re-mineralization by interacting with
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sediment-feeders and benthic infauna (Uthicke, 2001a). Changes in composition of infaunal
species, such as nematodes, within marine sediments, can serve as a factor in the recycling of
nutrients. Furthermore, changes in seawater pH have the potential to modify nutrient cycling by
altering the abundance of bioturbators (Sarmento et al., 2015; Widdicombe et al., 2009). Therefore,
it is relevant to understand the impact of climate change (e.g., ocean acidification) on infaunal
organisms, because reductions in pH have deleterious impacts on many infaunal invertebrates
(Cigliano et al., 2010; Kurihara et al., 2007; Sarmento et al., 2015). However, some taxa can benefit
from reductions in pH, such as some crustaceans (Hargrave et al., 2009) and some nematodes (Hale
et al., 2011), likely due to a decrease of ecological constraints (Hale et al., 2011). Differential
responses of this type of biota to pCO2 regimes may produce a modification of food availability for
other organisms (Gaylord et al., 2015; Hargrave et al., 2009; Kroeker et al., 2011) as well as the
quality of food sources (Rossoll et al., 2012).
1.2. Specific roles of sea cucumbers on reefs and their interaction with sediment-associated biota
Sea cucumbers, as previously said, play an important functional role as deposit feeders in the
ecosystems, especially in oligotrophic areas such as coral reefs (Byrne, 2008; Glynn & Enochs,
2011; Hutchings, 2008). Deposit feeders are organisms that obtain nutrients from the organic matter
in the sediments to meet their energetic requirements. Some of these animals pass the sediments
through their digestive systems without any selection, and others, such as some sea cucumbers, use
tentacles to remove the first layer of sediment to reach a greater proportion of living and detrital
material; the latter are sometimes referred as selective deposit feeders (Massin, 1982a). Because of
this mode of feeding, sea cucumbers play two major roles in reef environments: 1) Bioturbation of
the sediments, in which they turnover the sediments and, therefore, affect or modify communities of
microorganisms, releasing trapped nutrients or pockets of acid pore water by re-suspending the
sediments in the water column. In the latter case, this may reduce calcium carbonate dissolution, but
depending on sediment grain size and flow dynamics, may still encourage loss of sediments from
the local reef environment (Barry et al., 2007; Massin, 1982b; Woodroffe, 2002); 2) Digestion of
organic matter, which lead to a potential nitrogen enrichment and enhancement of seawater
alkalinity (Fig. 1.2a) that may in turn increase local production and calcification rates (Glynn &
Enochs 2011; Massin, 1982b; Schneider et al., 2011; Schneider et al., 2013; Uthicke, 2001b)
Furthermore, local increases in seawater ammonium may explain increased production observed
over sediments in contact with sea cucumbers (Uthicke, 2001a) (Fig. 1.2b).
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Fig. 1.2. Observed effects of sea cucumbers on seawater alkalinity and production (determined as
sediment phaeophytin concentration). (a) Mean change in seawater total alkalinity (ΔAT), alkalinity
due to changes in TAN (NH3 + NH4+) secretion (ΔAT(TAN)) and due to CaCO3 dissolution
(ΔAT(CaCO3)), during incubations in the presence of Holothuria atra, Holothuria leucospilota and
Stichopus herrmanni. The incubations were normalized to 24h and presented in units of µmol kg-1
(taken and adapted from Schneider et al., 2013). (b) Microalgal production (defined as the
difference between end and start production) in different sea cucumber treatments (Stichopus
chloronothus, H. atra, control) (taken and adapted from Uthicke, 2001a).
Due to bioturbation, sea cucumbers are in intimate contact with microorganisms, and they can be
selective of certain groups of microorganisms within the sediments (Moriarty, 1982; Moriarty et al.,
1985), but the interaction of these two groups, specially their relative roles in re-mineralization and
the net carbonate budget of reefs are not well elucidated in current literature. Bioturbation refers to
the turnover of sediments by organisms, including sea cucumbers (Massin, 1982b). In some cases,
this turnover can have significant effects on the communities inhabiting the sediments, such as
communities of bacteria and microalgae. For example, in experiments and field observations on
Lizard Island (GBR), the tropical species H. atra and S. chloronotus consumed on average 67 and
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59 grams of wet sediment per individual per day respectively (Uthicke, 1999). Bioturbation by
these animals can modify the stratification of sediments, and increased sediment and pore water
turnover (Massin, 1982b). The grain size may also be reduced by these activities. Some argue that
reductions result from mechanical grinding as sediments pass through their digestive system
(Massin, 1982b). Others argue that the digestive juice in their digestive system is sufficiently acidic
(e.g. pH 5.2-5.6 in H. atra) (Trefz, 1958) to lead to sediment dissolution as a result of passage
through the digestive tract (Schneider et al., 2011; Schneider et al., 2013). Dissolution, as opposed
to mechanical grinding, increases alkalinity through the production of HCO3- or CO3
2-, potentially
providing a local buffer against future acidification (Schneider et al., 2011). Other factors however,
influence this buffering potential, including changes to total DIC (Egleston et al., 2010; Wang et al.,
2013), as well as localised hydrodynamic regimes, with any potential buffering effect likely to be
rapidly diluted in locations other than ponding lagoons (Andersson et al., 2007; Kleypas & Yates,
2009).
Sea cucumbers, due to their catabolic processes, produce nutrients as excretion products (Uthicke,
2001a; Uthicke, 2001b). Ammonium constitutes the main form of inorganic nitrogen excreted by
species like H. atra and S. chloronothus (Uthicke, 2001b), and is released to the water column for
uptake by photosynthetic organisms. Due to inorganic nitrogen excretion, holothurians can enhance
the growth of other organisms such as microalgae. Such enhancement of algal production has been
demonstrated by Uthicke (2001a), in which the author observed that sediment microalgae exposed
to the effluent water from holothurians increased their production, measured as O2 gross production
(Fig. 1.2b).
Marine sediments are colonized by groups of organisms that participate in different and important
ecosystem processes. In oligotrophic tropical and subtropical waters, such as coral reefs, there is a
necessity for an efficient recycling of nitrogen captured by the environment to sustain the biomass
present within the ecosystem. Part of this re-mineralization requires that nutrients are efficiently
recycled back to the water column for uptake by benthic communities. In this context, sediment
microorganisms perform an essential role in the decomposition and re-mineralization of organic
nitrogen that accumulates as detritus (Azam & Malfatti, 2007; Capone et al., 1992; Sorokin, 1973;
Wild et al., 2005). The decomposers (bacteria and archaea) are abundant and the re-mineralization
processes in which they participate vary according to the group of organisms, nutrient availability
and the oxygen conditions of the environment (Gaidos et al., 2011). In general, oligotrophic
carbonate sediments have different gradients of ammonia, nitrate and nitrite concentrations due to
re-mineralization (Haberstroh & Sansone, 1999; Miyajima et al., 2001; Rasheed et al., 2002; Rusch
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et al., 2009; Stimson & Larned, 2000). Bacteria and archaea can contribute to the cycling of
nitrogen in different ways, including the aerobic oxidation of ammonia (produced by ammonia
oxidizing archaea, or AOA; and ammonia oxidizing bacteria, or AOB), nitrification (that can take
place in oxic and anoxic conditions), denitrification (can also be present in oxic and anoxic
conditions and is an important source of energy in suboxic reef sediments), dissimilatory nitrate
reduction to ammonium (DNRA) (reduction of nitrate by nitrate ammonifiers organisms different
than denitrifiers) and anaerobic oxidation of ammonium (ANAMMOX) (Dong et al., 2011, Gaidos
et al., 2011). After such processes, nitrate and ammonia are absorbed by photosynthetic organisms
(such as cyanobacteria and microalgae), or released to the atmosphere in the case of denitrification
and ANAMMOX. The uptake of ammonia or nitrate by O2 producing photo-autotrophs in the
water column or upper layers of the oxic sediments occur in carbonate sediments (Miyajima et al.,
2001). However, ammonia is a preferred source of nitrogen compared to nitrate, because additional
energy is required to convert nitrate into ammonia (nitrate ammonification) for assimilation
(Strohm et al., 2007). In the deeper, less aerobic layers of the sediment, accumulated nitrate is either
denitrified to N2 mainly by heterotrophic bacteria, or ammonified back to ammonia. Since
denitrification produces less energy per unit of body mass than nitrate ammonification (Strohm et
al., 2007), changes in nitrate concentrations may have a greater impact on populations of
ammonifiers.
As bacteria are typically dependent on each other to provide substrates, disturbance of sediments,
either through selective bacterial feeding or the suspension of layers to different depths, can
significantly alter the dominate cycles (Azam & Malfatti, 2007). Finally, within the upper aerobic
layers of the sediment, heterotrophic bacteria are involved in the N-cycle (Capone et al., 1992;
Miyajima et al., 2001). These bacteria appear to play a significant role in the carbonate balance of
reef sediments because they respire O2 and produce CO2, driving down pore-water pH, especially
by night. Therefore, this localised acidification can enhance sediment dissolution even under
present day pCO2 concentrations (Andersson & Gledhill, 2013; and references therein).
In reef sediments, there are a great number of microorganisms (totalling as much as 42 µg C/g of
dry weight on Heron reef sediments) (Moriarty, 1982; Sorokin, 1973; Sorokin, 1993). Sea
cucumbers, due to their feeding processes, have the potential to affect microbial communities in
these sediments. For instance, the tropical and subtropical H. atra can feed on bacteria, detritus and
microalgae (Alongi, 1988; Bakus, 1973; Moriarty, 1982; Moriarty et al., 1985; Uthicke, 1999;
Uthicke, 2001a-b; Yingst, 1976). Furthermore, H. atra is a selective feeder of the sediments, not
only selecting for organic content, but also, for nitrogenous compounds. H. atra have been found to
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avoid N2 fixing cyanobacteria (Moriarty, 1982); and are estimated to acquire 10% of their organic
carbon requirements by preying upon sediment bacteria (Moriarty, 1982). Furthermore, densities of
sea cucumbers (potentially at densities higher than natural: > 1 individual m-2), can significantly
reduce the production of bacteria (carbon per day) and microalgae (Moriarty et al., 1985). In this
regards, detritus appeared to be their main source of energy (which can be enriched and populated
by bacteria, see Wild et al., 2005), with infaunal organisms playing no part in the diet of the species
tested (Moriarty, 1982; Moriarty et al., 1985; Uthicke, 2001a). To date, there are 2 studies using
molecular techniques (applying minimal replication, n=2) that have attempted to elucidate the
relationship of H. atra and microbial communities of reef sediments (Hewson & Fuhrman, 2006;
Ward-Rainey et al., 1996). These studies provided different types of evidence about the effect of H.
atra on the microbial communities. The first shows that the presence of H. atra has no impact on
the microbes within the surrounding sediments (Hewson & Fuhrman, 2006). The second shows that
the numbers of bacteria in the sediments differ from those within different sections of the gut of H.
atra, suggesting the digestion of some types of bacteria and proliferation of others within the
digestive track of the animals (Ward-Rainey et al., 1996).
The effects on the sediment communities also may change if holothurians are in high densities (e.g.
one individual per square meter). At high density, holothurians can deplete microalgae, whilst at
lower concentrations they enhance microalgal growth (Uthicke, 2001a). In this context, if
microalgae are depleted, the sediments may turn suboxic or anoxic due to a reduction in oxygen
production (Uthicke & Klumpp, 1998). This oxygen depletion could adversely affect aerobic
processes such ammonification. Likewise, the removal and possible digestion of microbes in upper
sediments by H. atra (about one cm, inferred from Lee et al., 2008), may also lead to a potential
reduction in nitrate available for anaerobic respiration in deeper layers. Furthermore, the oxygen
content will depend on the metabolism of the organisms in the sediments (photosynthesis and
respiration) and gas exchange at the sediment-seawater interface (Leclercq et al., 1999; Leclercq &
Gattuso, 2002). This may in turn affect whether remnant nitrate is converted to ammonia, by nitrate
ammonification favoured under low nitrate concentrations (Strohm et al., 2007) or lost from the
system through denitrification. Therefore, the effects on the nitrogen cycle are unclear because it
not only depends on the depth to which the sea cucumbers bioturbate the sediments, which
increases oxygen penetration into the sediments and promotes mineralization processes (Adámek &
Maršálek, 2013), but also on its impact on the population of both microbes and microalgae.
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1.3. Sea cucumber-sediment interactions. Current status of knowledge and limitations in a climate
change context
One of the potentially important roles of sea cucumbers in coral reef ecosystems is the improvement
of the deleterious effects of rising CO2, due to their impact to the CaCO3 budget. Schneider et al.
(2011) found that the processing of sediments in the digestive system of sea cucumbers S.
herrmanni and H. leucospilota at One Tree Reef (GBR) can be responsible for up to 50% of the
CaCO3 dissolution at nighttime leading to an increase in seawater alkalinity. They argued that this
could help coral reefs in a scenario of future ocean acidification, in which the animals could serve
as potential buffers against ocean acidification. However, this hypothesis might be relevant only in
reef systems like One Tree Island (OTI), where the elevation of the reef rim above the ocean at low
tide increases water residence times within the reef complex. Such a retention of water could
potential create the necessary conditions for increasing the localised concentration of an
acidification buffer (Cyronak et al., 2013; Silverman et al., 2012). Unfortunately, locations on reefs
(inclusive of OTI) associated with ponding, which include many shallow reef flats and lagoons,
tend to represent zones within reefs where hard coral cover is limited often to less than 10% (Booth
& Beretta, 2002). In the Caribbean, hard coral cover of less than 10% is associated with net erosion
even under present levels of warming and acidification (Jackson, 2014). That is, even if sea
cucumbers are beneficial to calcifiers in these reef-flat locations, the greater impact of this
beneficial role would seem to be compromised by the paucity of corals in these regions. In well-
flushed reefs zones and systems, alkalinity increases due to sediment dissolution are hypothesized
to be rapidly diluted to insignificance (Andersson et al., 2007; Kleypas & Yates, 2009). In these
well-flushed zones, coral growth and percent cover is favoured by rates of observed water flow over
organisms that facilitates gas exchange by minimizing boundary layer effects (Nakamura & van
Woesik, 2001; Nakamura et al., 2003; van Woesik et al., 2011). Finally, the hypothesis detailing a
beneficial role for sea cucumbers on the future carbonate balance of reefs based on present day
experiments, assumes that (1) Sea cucumbers will not be directly or indirectly affected by increases
in temperature or acidification; and (2) That increases in alkalinity may co-occur for example, with
increases in DIC, either due to the new biotic regime or due to the activities of other organisms
within the ecosystem (e.g., sediment-associated biota). Whilst there is no evidence to suggest that
sea cucumbers may be susceptible to increases in temperature and acidification, contrasting
evidence in other echinoderms has been proposed. Adult stages of sea urchins, can be negatively
impacted (e.g., reduced abundance) under conditions of low pH (Hall-Spencer et al., 2008) or can
slightly benefit from high CO2 condition (e.g., increase growth) (Fabricius et al., 2014; Uthicke et
al., 2016). Multiple stressors can have negative effects on the early development of marine
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invertebrates (Byrne, 2012; Byrne & Przeslawski, 2013; Przeslawski et al., 2015). Evidence exists
of such effects (e.g., negative effect of temperature but not effect of pH) on the ontogeny of the sea
urchin Heliocidaris erythrogrammatha (Byrne et al., 2009). Consequently, potential negative
effects of climate change on larval stages of H. atra may produce deleterious impacts on
populations of this species, and therefore, on their ecological roles (e.g., buffering OA), since all
ontogenetic stages must be accomplished in order to sustain adult populations (Byrne, 2012).
Sea cucumbers re-mineralize nutrients to their inorganic forms, and microorganisms allow nutrients
to be efficiently recycled back to the water column for uptake by benthic communities (Azam &
Malfatti, 2007; Capone et al., 1992; Sorokin, 1973; Uthicke, 2001a-b). The relationship between
these deposit feeders and microorganisms is an interesting (and not yet well developed) field of
investigation. To date, there is no clear evidence for microbial selection by sea cucumbers, or for
the relative contributions to nutrient re-mineralization by sea cucumbers and microorganisms under
different seawater environments, predicted as a result of future CO2 emission scenarios.
Furthermore, powerful tools for identification of microbial diversity (such as 16S rRNA gene
amplicon sequencing in Illumina) have not yet been used in other studies within the context of
microbial nutrient recycling interacting with tropical holothurians (Hewson & Fuhrman, 2006;
Ward-Raniey et al., 1996). 16S rRNA gene amplicon sequencing may produce good results for
coral reef sediments associated with sea cucumbers (e.g., characterize microbes interacting with sea
cucumbers), under different climate-related scenarios. For instance, amplicon sequencing has been
proven to be a useful tool to characterize complex microbial communities associated with sponges
in marine environments (Simister et al., 2012; Taylor et al., 2013). Finally, to date, there are no
long-term experiments that take into account the effect of natural fluctuations of the reef system
(such as summer versus winter abiotic factors) on these sea cucumber-microbial interactions.
Evidence of the effects of possible future scenarios, derived by the human release of greenhouse
gases, on the interaction between sea cucumbers and sediments remains unknown. Under such
conditions, feeding on sediments (and the microorganisms therein) by sea cucumbers have not yet
been quantified in terms of possible physiological changes (such as O2 production), which may lead
to different responses in the OA buffering capacity, calcium carbonate budget or re-mineralization
of nutrients. A more detailed composition of the microbial communities and changes through time
(within and between different seasons of the year) has also not been tested.
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1.4. General aim and overall approach of the thesis
Sea cucumbers play important roles in reef systems such as re-cycling of nutrients and bioturbation
(Uthicke, 1999; Uthicke, 2001b). Moreover, according to Schneider et al. (2011) these animals have
the potential to be a natural buffer against ocean acidification due to their feeding process of
sediments, specifically due to calcium carbonate dissolution and increases of seawater alkalinity.
However, attempts to test the buffering role of these animals have been conducted with them
isolated from other organisms of the reefs and for short-term periods of time (Schneider et al., 2011;
Schneider et al., 2013). Therefore, the general aim of this thesis is to test if Holothuria atra, one of
the most abundant holothurian species in Indo-Pacific tropical reefs, will buffer ocean acidification
principally through calcium carbonate dissolution and production of nutrients, over any potential
effect on buffering capacity produced by sediment-associated organisms (principally prokaryotes,
microalgae and benthic infauna) in a context of climate change (i.e., increased seawater
temperatures and ocean acidification). Since the chemistry of the seawater is complex, many
processes can affect OA buffering capacity (AT/DIC). Therefore, the responses of the animals and
sediments need to be tested in terms of production of nutrients, CaCO3 dissolution/calcification
rates, carbonate chemistry, gross metabolism and hence OA buffering capacity. Furthermore, the
purpose of the thesis is to understand how the interaction between these animals and sediment
communities will be potentially modified under different scenarios of combined temperature and
pH (modified through pCO2 manipulation), with consequent implications for buffering capacity
produced by H. atra and sediment biota in future reefs. The scenarios proposed to test these
responses are present day, pre-industrial and two future scenarios (hereafter Scenarios) of
temperature and pCO2 (RCP4.5 and RCP8.5, see IPCC, 2014; Rogelj et al., 2012). Scenarios
represent temperatures that are paired with pCO2 concentrations, in response to the socio-economic
need to understand the roles of these organisms on the ecology of future reefs (Fang et al., 2013;
Harvey et al., 2013). Moreover, there is no information available regarding diurnal and seasonal
environmental variability on these questions, reasons why they have been incorporated to achieve a
more comprehensive approach to answer the research questions. In this regard, I use short-term
experiments and long-term experiments (or seasons). Winter and summer seasons were included in
this thesis. These seasons were not replicated but each long-term experiment was conducted for 2
months, representing 67% of each season in terms of its length.
This thesis targets a specific non-coral invertebrate group that is currently threatened by overfishing
and is functionally significant for reef dynamics. This project represents the first comprehensive
study that will provide a deeper understanding of the impact of climate change on H. atra and the
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environment it inhabits. The thesis will encompass different aspects of the relationship between the
sea cucumbers and the sediments (e.g., microbial communities, quantified by qPCR and 16S
amplicon sequencing), and their interaction in the context of climate change. For the first time, this
study will provide a more developed understanding of how the interactions between holothurians
and sediment-associated biota may change in time and under climate change conditions.
Consequently, this thesis will help increase the knowledge regarding how calcium carbonate
budgets, re-mineralization of nutrients and ultimately OA buffering capacity will be impacted by H.
atra and sediment-associated biota.
1.5. Specific aims and general outline of the thesis
Chapter 2
In this Chapter, I explore the role of a tropical sea cucumber species, H. atra, on calcium carbonate
dissolution, carbonate parameters, O2 production, re-mineralization of ammonia (TAN) and ocean
acidification buffering capacity (measured as the ratio between total alkalinity and DIC: AT/DIC).
The primary goal was to study how the short-term and diurnal influence of H. atra on reefs may
increase CaCO3 dissolution, carbonate parameters, O2 production, production of TAN and AT/DIC
when compared to sediments-associated organisms (infauna, microalgae and microorganisms). This
study showed that the effect of the sea cucumbers over the chemistry of the system is limited and
many of the responses of the animals, such as DIC production, were lost because of the production
of the sediments. Finally, the animals will not assist the ocean acidification buffering capacity and
any potential buffering offered by the animals will be depleted by the local environment.
Chapter 3
In this Chapter, I test if the species H. atra would modify calcium carbonate dissolution, O2
production, nutrient production and AT/DIC and other carbonate parameters (e.g. carbonate ions),
differently than sediment-associated biota in its absence, when exposed to long-term winter
conditions of climate change. Moreover, I test if the animals will help decrease the negative effect
on buffering capacity projected by future climate change conditions over the potential of the
sediments. The primary goal of this study was to evaluate the long-term effect of different winter
temperature/pCO2 Scenarios over H. atra and sediment-associated organisms, and if the exposure to
these conditions would modify the performance of the organisms during a winter period. The
Scenarios proposed were present day (PD: +0oC, +0 ppm pCO2); pre-industrial (PI: -1oC, -100 ppm
pCO2 below PD, to account for potential effects produced by PD in current reefs); RCP4.5 (+1.8oC,
+180 ppm pCO2 above present day) and RCP8.5 (+3.6oC, +570 ppm pCO2 above present day). The
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results showed that the animals played virtually no measurable role over the systems responses. O2
production was the only parameter modified by H. atra. The remaining parameters (e.g., calcium
carbonate dissolution and OA buffering capacity) were mainly modified by bacteria in the
sediments. OA buffering capacity in absence of H. atra under RCP8.5 will significantly decrease
when comparing against present day conditions.
Chapter 4
In this Chapter, I evaluate how austral summer temperature/pCO2 Scenarios can impact the roles of
H. atra and sediment-associated organisms over response variables such as AT/DIC, calcium
carbonate dissolution/calcification rates and nutrient production. The main goal of this study was to
test if H. atra would have a greater role than sediment-associated organisms (e.g. bacteria) over OA
buffering capacity (and other response variables related to it, such as nutrient recycling) during a
long-term summer experiment and under different Scenarios of temperature/pCO2. Summer
represents a more extreme season than winter. For instance, summer has greater temperatures than
winter (e.g., in the study PD Scenario presented most of the time temperatures near or above MMM
of 27oC); and in our simplified reef system, absence of normally increased dissolved organic matter
(DOM, due to mortality of reef organisms), could play a substantial role over the performance of H.
atra and sediment associated biota. The results showed that during summer, calcification was
highly variable and AT was the only parameter explaining the variation observed on those rates by
10%. TAN production significantly increased in the presence of H. atra by at nighttime; however,
TAN production did not significantly modify AT . The lack of change in microbial composition,
potentially driven by low DOM, is in accordance to the lack of significant changes observed for
calcification rates and oxygen flux under all the Scenarios proposed. Finally, under the different
pCO2/Temperature Scenarios proposed, and despite any effect of H. atra on the chemistry of the
system, microbes appear to drive the responses of AT/DIC under RCPs.
Chapter 5
This Chapter represents the general discussion of the thesis and summarizes the main findings of
the project. Changes within different seasons of the year may help explain and better estimate
potential changes produced by the sediments, the animals, RCPs Scenarios or their interactions.
Annual rates produced by H. atra and sediment-associated organisms under the Scenarios described
in Chapter 3 and Chapter 4, are calculated based on the data of 2 long-term experiments (2 months
in winter and 2 months in summer, Chapter 3 and Chapter 4 respectively). Furthermore, I provide a
comprehensive analysis about the effects of climate change on these relationships. Finally, in this
Chapter I explore the potential problems, solutions and future research directions regarding the
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effects of climate change on the interaction between holothurians and the sediments they process in
the context of OA buffering capacity.
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Chapter 2: Diurnal effects of Holothuria atra on seawater carbonate chemistry in a
sedimentary environment
Francisco Vidal-Ramireza,b,, Sophie Dovea, b, c
aSchool of Biological Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
bAustralian Research Council Centre for Excellence in Coral Reef Studies and, The University of
Queensland, St. Lucia, QLD 4072, Australia
cGlobal Change Institute, The University of Queensland, St. Lucia, QLD 4072, Australia
Journal of Experimental Marine Biology and Ecology: 2016. 474, 156-163.
http://dx.doi.org/10.1016/j.jembe.2015.10.007
Corresponding author:
Francisco Vidal-Ramirez
School of Biological Sciences, The University of Queensland, Level 7, Gehrmann Laboratories
(Building #60), St. Lucia, QLD 4072, Australia. Tel.: +61-450704403; fax: +61-7 33651692.
E-mail address: [email protected]
Original Research Article
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2.1. Abstract
Sea cucumbers are important to coral reef ecosystems due to their roles in the recycling of nutrients
and their potential ability to elevate AT/DIC by dissolution of sediments in their gut. The
contribution of the sea cucumber Holothuria atra to the dissolution of sediment CaCO3 was
assessed at mid-day and midnight. The results showed that the presence of H. atra significantly
increase sediment dissolution rates and Total Ammonia Nitrogen (TAN = NH3 + NH4+)
concentrations. Whilst there was a trend for the effect of H. atra on sediment dissolution to be
greater during the day than at night, this trend was not significant. Significantly different day versus
night responses - irrespective of sea cucumber presence - were observed for all carbonate
parameters over the 1-2h incubation periods, reflecting an impact of sediment-associated micro-
organisms in closed recirculating as opposed to open water systems over a period of three days
(cumulative effect). Over three days, the significantly higher daytime DIC concentrations in the
presence of H. atra were driven by elevated bicarbonate (HCO3-). During the incubation periods,
the effects of the animals on DIC concentration were lost by a significant increase in CO2
concentrations arguably by microbial processes within the sediments. The ocean acidification (OA)
buffering capacity of the animals, estimated by changes in AT/DIC ratios, was greater during
nighttime but equivalent to that observed in the sediments over the incubation periods and in the
open water system. The results suggest that H. atra will not assist daytime calcification, due to a
daytime decrease in buffering capacity in their presence. Moreover, H. atra may exacerbate the
impacts of OA due to the dissolution of CaCO3 resulting from their turnover of sediments. Finally,
in areas with seawater with prolonged residence over sediments, such as ponding lagoons, the local
environment is likely to rapidly deplete any pH buffering potential offered by H. atra.
Keywords: Holothuria atra, Sediments, Dissolution, Calcium carbonate, Diurnal.
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2.2. Introduction
Coral reefs are able to rebuild and amass calcium carbonate following destructive storm events
because the rate of calcification exceeds the rate of erosion (Eyre et al., 2014; Perry et al., 2008,
2013; Scoffin et al., 1980) over the long term. Increases in atmospheric CO2 leading to both ocean
warming and ocean acidification have the potential to increase rates of erosion above those of
calcification (Dove et al., 2013). The potential shift to a negative carbonate balance on reefs
threatens the 3D frameworks that provide habitat for the large diversity of organisms found on reefs
today (Bellwood et al., 2004; Connell & Kingsford, 1998), and may reduce coastal protection
offered by reefs through a reduction in wave energy attenuation (Ferrario et al., 2014; Hoegh-
Guldberg et al., 2007). Over millennium timescales, calcium carbonate dissolution in the presence
of water and CO2 will take up much of the CO2 that is currently being vented to the atmosphere as
burnt fossil fuels (Archer et al., 1997). In this reaction, solid carbonates will convert to aqueous
bicarbonates increasing total ocean alkalinity. The reaction time is predicted to occur over
millennia, because it is limited by the dynamics of the ocean carbon cycle (Archer et al., 1997). In
these global models, the contribution of coral reefs to fossil fuel neutralization is considered to be
insignificant in comparison to the role played by abyssal sediments (Archer et al., 1997).
At the local reef scale, however, it has been proposed that the activities of Holothurians (sea
cucumbers) in high population densities may increase the ability of ponding lagoons to buffer pH as
a result of gut sediment dissolution and ammonia production (Schneider et al., 2011; Schneider et
al., 2013). However, that last assumption can be misleading because most reefs (unlike micro-atolls
within the lagoon at One Tree Island) are well flushed. Therefore, any potential effect of sea
cucumbers on pH buffering would be rapidly diluted (Andersson et al. 2007; Kleypas & Langdon
2006; Kleypas & Yates 2009). Regardless of that fact, Schneider et al. (2011) have argued that as a
result of dissolution of CaCO3 due to sea cucumber activity, the rate of increase in local seawater
alkalinity is much greater than that associated with either bioeroding endolithic communities or
microbial dissolution of sediments (Schneider et al., 2011). However, despite the efforts made to
understand the role of sea cucumbers on these processes, information about how these processes
may be modified by these animals in the presence of other organisms, such as microalgae and
bacteria, as well as by daily fluctuations in seawater chemistry is presently lacking. Schneider et al.
(2011) and Schneider et al. (2013) determined changes to seawater chemistry as the result of the
introduction of fecal casts into otherwise empty aquaria (isolated from all other components of the
ecosystem) over 4h daytime incubations with the rate of change in alkalinity measured in filtered
reef water. Large increases in alkalinity observed in their experiment were then used to support the
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case that sea cucumbers may be more effective at buffering localized anthropogenic derived
acidification, than the demonstrated negligible ability associated with CaCO3 dissolution by either
endolithic organisms (Tribollet et al., 2009) or sediment microbial communities (Andersson &
Gledhill, 2013; Andersson et al., 2007; Reyes-Nivia et al., 2013).
The ratio between total alkalinity (AT) and dissolved inorganic carbon (DIC), AT/DIC, can be used
to estimate the buffering capacity of seawater (Wang et al., 2013). The relationships between
buffering capacities and AT/DIC describe 3-degree polynomial functions. For AT/DIC ≥ 1, the
present ocean AT/DIC range (1.05-1.19) provides slightly less than maximal buffering capacity, and
any reduction in AT/DIC result in a decrease in buffering capacity. The buffering capacity, or the
ability of seawater to counteract ocean acidification (OA), reaches a minimum when AT/DIC = 1
(Egleston et al., 2010). The effect of OA on each component (or buffer factors) will depend
ultimately on the complexity of acid-base chemistry dictated by changes in DIC and AT (Egleston et
al., 2010; Wang et al., 2013). Therefore, these changes in DIC and AT can be translated into six
buffering factors (or capacities): buffer of CO2 due to changes in DIC, buffer of H+ (or pH) due to
changes in DIC, and buffer of CaCO3 saturation state (Ω) due to changes in DIC. The other 3 are
represented as buffers of CO2, H+ and Ω due to changes in alkalinity (Egleston et al., 2010). As a
result, an increase in AT does not necessarily mean that seawater will be less sensitive to changes in
CO2, pH or Ω.
In reef sediments, sea cucumbers and sediment-associated microorganisms are able to modify the
concentration of nutrients and carbonate parameters (Andersson & Gledhill, 2013; Andersson et al.,
2007; Capone et al., 1992; Uthicke, 2001a). This complex environment will interact with stressors,
such as ocean acidification (OA), changes to nutrient loads and warming, to affect AT/DIC ratios by
modifying organism metabolism and/or organism community structure. As a next step to
understanding the potential for sea cucumbers to buffer OA, it is fundamentally important to gain
an understanding of their role on the seawater carbonate chemistry in an interacting environment
where other organisms found within the system contribute to dissolution and alkalinity. The
capacity of sea cucumbers, if any, to buffer OA will depend not just on their impact on the water
column, but also on the ability of co-located microbes to alter AT/DIC. The facilitation of reef
calcification by sea cucumber OA buffering is unlikely in reef locations subjected to high rates of
water flow. Most reefs (unlike micro-atolls within the lagoon at One Tree Island) are well flushed
and would dilute the potential effect of sea cucumbers on buffering the pH (Andersson et al., 2007;
Kleypas & Langdon, 2006; Kleypas & Yates, 2009). But likewise, in closed systems, micro-
organisms may rapidly negate the impacts of sea cucumbers on water-column dynamics.
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Schneider et al. (2013) found that sediment gut dissolution by Holothuria atra accounted for
roughly 75% of the increase in alkalinity with the remaining quotient attributed to the production of
ammonia and its protonation to ammonium. By day, however, proton uptake following rapid
oxidation of Total Ammonia Nitrogen (TAN = NH3 + NH4+) by nitrifying photo-autotrophic
bacteria in the sea cucumber aerated sediments, as well as rapid ammonia uptake by benthic micro-
algae living on the surface of the sediments, ought to rapidly counter and/or inhibit TAN induced
alkalinity changes within reef lagoons. Moreover, in reefs other organisms, inclusive of macroalgae,
turfs and phytoplankton, may constrain ammonia induced changes in alkalinity (Dortch, 1990; Haan
et al., 2016; Hopkinson et al., 1987; Larned & Atkinson, 1997; Williams, 1984). Likewise, daytime
increase in alkalinity by gut sediment digestion would have to be significantly greater than net
daytime DIC production by either sea cucumbers or benthic organisms associated with the
sediments to prevent high pCO2 build up in the ponding water column (Sabine et al., 2004; Revelle
& Suess, 1957). Clearly by day, AT/DIC may be elevated by photosynthetic CO2 fixation by local
photoautotrophs, combined with sediment dissolution, leading to a greater proportion of CO32- ions
for the immediate uptake by adjacent calcareous organisms such as corals or, more likely, green
calcareous algae of genus Halimeda (Borowitzka & Larkum, 1976; De Beer & Larkum, 2001) that
prosper in lagoons where sea cucumbers are present at higher densities (Chao et al., 1993; Conand,
1996; Lee et al., 2008). However, by night, respiration produced by reef organisms may
significantly increase DIC (Jokiel et al., 2014; Kline et al., 2012) and thereby reduce AT/DIC ratios.
Furthermore at night, whilst some non-feeding sea cucumbers, such as Stichopus chloronothus
(Uthicke, 1994), may not affect AT/DIC through the dissolution of consumed sediments. However,
they may nonetheless contribute to changes in AT/DIC as a result of the release of respired CO2
(Schneider et al., 2011) and/or the release of ammonium (Uthicke, 2001b). H. atra that feeds by
night and by day (Uthicke, 1994), can however, also influence nighttime AT/DIC via the dissolution
of sediment CaCO3. The feeding behaviour of H. atra implies that the production of CO32- due to
dissolution might be mitigated by the uptake and production of CO2 from different organisms in the
sediments at these different time points. Under these different conditions, the impact of sea
cucumbers on the capacity to buffer pH may be much less than that suggested by previous
observations of their ability to increase seawater alkalinity, as buffering is determined by the
AT/DIC, and sea cucumbers may stimulate other processes in the system to produce DIC.
This study investigates the influence of tropical deposit feeding sea cucumbers on the dissolution of
CaCO3 and local water chemistry, including their pH buffering potential, using H. atra as model
species. The animals were placed in similar conditions to those they might experience on the reef in
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terms of daily changes in production (modulated by fluctuations in natural light, temperature, pCO2,
among others). The potential effect of this species on the sediment grain size, as a direct measure of
dissolution of CaCO3, was also investigated.
2.3 Material and methods
2.3.1. Experimental setup
Six individuals of the species H. atra (weight 237.9 ± 35.71 g and length of 21.22 ± 1.65 cm), along
with sediments from Heron Island reef (23o27'S, 151o55'E) were collected in January (2013) from
the Reef flat (on front of the HIRS, within the Scientific Research Zone) and the Lagoon (23° 26′
550″ S, 151° 56′ 629″E), over a tidally variable depth range of 0.5-5 m. Sediments (~80% of
carbonate content) were composed by coralline algae, coral, foraminifera and molluscs (Jell &
Flood, 1978), ranging in size from ≤2mm (with ~37% to ~48% of the sediments falling into the
125µm and <125µm categories, respectively). Samples were placed in plastic buckets and
immediately transported to the nearby Heron Island Research Station. Each animal was placed into
a separate outdoor flow through aquaria and left for 48 hours to allow for the expulsion of fecal
pellets (which were removed periodically to avoid re-ingestion). Meanwhile, the sediments were
mixed and evenly distributed to create a sediment depth of 3 cm across 12 glass aquaria (58.3 cm x
18 cm x 37.2 cm) lined with Marine Blue light filters (#131; Lee Filters). A pump (Hydor Koralia
Nano 900) with capacity to recirculate 900 L of seawater per hour was placed into each aquarium
and the flow rate within each tank was set to be at 1 L min-1 (achieved by adjusting manually the
flow rate of each tank, 4 times per day, and timing with a chronometer the minute needed to fill a 1
L container), producing a complete turnover of tank seawater every 0.6 h. Animals with empty guts
were randomly assigned to these glass aquaria resulting in a final design of 6 aquaria with
sediments and sea cucumbers (Condition +SC, or +SC tanks), and 6 aquaria with sediments and no
sea cucumbers (Condition -SC, or -SC tanks). Animals were allowed to stay for 1 day in aquaria
with sediments before the measurements started. Seawater was pumped directly from the reef flat to
these aquaria with fluctuating temperatures between 24 oC and 32 oC during incubations periods
(recorded every 1 minute with HOBO pendant water resistant loggers inside the aquaria). The
experiment was conducted through the course of 6 days (3 days acclimation period and 3 days for
incubations).
2.3.2. Ammonia determination and O2 flux
A series of incubations were conducted to measure differences in TAN (NH3 + NH4+). Water
samples for determination of TAN were obtained from these incubations that lasted 1-2 h and were
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performed at mid-day (close to 12:00 pm) and at midnight (close to 12:00 am). These incubations
were achieved by blocking the outlet of the tanks to avoid water leaks and closing the inflow of
seawater during the incubation time. After closing the inflow of seawater, lids (covered with the
same Marine Blue #131 filter) were used to cover the aquaria, which finally allowed an airtight
sealed of the experimental tanks. Before sealing the aquaria, O2 loggers were deployed inside the
tanks to determine O2 flux over the incubation period (RINKO ARO-USB, JFE Advantech;
equipped with a dissolved oxygen sensor coated with photostimulable phosphor (PSP) on the
outside of the pressure-resistant acrylic optical window, measuring the phase difference between
phosphorescent time lengths. Sensors are calibrated at the Coral Reef Ecosystems Laboratory, from
The University of Queensland, every 6 months). Seawater samples (50 mL Falcon tubes) were
taken at the beginning (t0) and the end of each incubation (t1) with sterile syringes and tubes, which
were rinsed 3 times with tanks seawater, before taken the final sample for analysis. This procedure
was conducted for all tanks (Condition and Time). Immediately after collection, samples were
stored at -20 oC for analysis within 2 weeks from collection. The concentration of TAN in seawater
was determined spectrophotometically following the protocol of Parsons et al. (1984). TAN
samples for t0 were damaged during transport. Therefore, the contribution of ΔATAN to seawater
alkalinity (AT) was therefore based on TAN production rates established by Schneider et al. (2013).
To estimate the TAN at t0 and include it in the calculation of calcification rates as ΔTTAN (TANt1-
TANt0), we estimate the concentration of TAN due to the sea cucumbers alone by subtracting the
average TAN produce in the -SC tanks to the +SC tanks by day and night (with this, we also
obtained the contribution of TAN produced only by the sediments within those tanks). After this,
according to Schneider et al. (2013), increases in TAN produced by H. atra correspond to 80%
from t0 to t1. Based on this change, we obtained TAN concentrations for t0 in +SC (only produced
by H. atra) and we estimated TAN concentrations produced by sediments (added together represent
TAN concentration for t0 in tanks +SC which allowed us to obtained subsequently TAN at t0 in
tanks -SC).
2.3.3. Dissolution rates
Total alkalinity (AT) was used as a proxy to estimate the dissolution rates (Yates & Halley, 2006)
produced in the experimental tanks in the presence and absence of sea cucumbers (Condition +SC
and -SC) and different Time (mid-day or midnight). Water samples were taken immediately after
TAN samples through a plastic tube inserted into the lid, which allowed 100 mL of seawater to be
sampled at the beginning (t0) and the end of the incubation (t1). To estimate of the quantity of
CaCO3 dissolved over these incubations, seawater alkalinity (AT) was determined by titration
(Mettler Toledo, model T50), calibrated before each measurement period with Dickson standard
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(Oceanic Carbon Dioxide Quality Control, USA) (Fang et al., 2013; Kline et al., 2012). The
Alkalinity anomaly technique was used to calculate the amount of CaCO3, because under aerobic
conditions and constant salinity, net CaCO3 dissolution, or precipitation, is determined from
observed changes in seawater AT . That is, for every mole of CaCO3 that dissolves or precipitates, 2
moles of AT are produced or consumed, respectively (Chisholm & Gatusso, 1991). Finally,
dissolution rates were determined from the alkalinity due to dissolution of calcium carbonate
(ΔACaCO3) which was calculated from the total change in alkalinity over incubations (ΔAT) minus
the alkalinity change produced by ΔTAN (ΔATAN = TANt1-TANt0) as follows: ΔACaCO3 = ΔAT -
ΔATAN. Final values were converted to mgCaCO3 m-2 h-1 based on the area of sediment at the base
of the tank.
2.3.4. Carbonate chemistry
The carbonate chemistry parameters for the different Condition (+SC or -SC), Time (mid-day and
midnight) and incubation (t0 or t1) were obtained using CO2SYS (Pierrot et al., 2006) with
constants from Mehrbach et al. (1973) as refit by Dickson & Millero (1987). We used as input
conditions: measured salinity (33.92 ± 0.12 SE), temperature (recorded with HOBO loggers), AT
and pH. High quality pH measurements were obtained with the titrator (McMahon et al., 2013),
equipped with a sensor DGi101-SC pH sensor (Mettler Toledo) and calibrated with NBS scale
standard buffers. The output parameters produced were pCO2 (µatm), bicarbonate (HCO3-),
carbonate (CO32-), Dissolve Inorganic Carbon (DIC), calcite (Ωcalc) and aragonite (Ωarag) saturation
states.
2.3.5. Grain size analysis
Samples of 10-15 mL of sediment were taken haphazardly from the surface layer (less than 1 cm) of
each tank at the end of the experiment in order to estimate possible differences in the grain size
driven by different Condition +SC and -SC. The samples were washed with a mild sodium
hypochlorite solution (Hammond, 1981) for 4 hours, then rinsed with water and dried at 60 oC for
24 h. After this time, they were passed through a sieve shaker (Minor M200; Endecotts) for 10
minutes to estimate the weight and percentage of the different size fractions within the samples.
2.3.6. Statistical analysis
Differences in calcium carbonate dissolution rates between Condition (+SC and -SC) and Time
(mid-day and midnight) were analyzed using a two-way repeated measures ANOVA with Time
specified as a within-subject factor. The same approach was used for the analysis of TAN, the
difference between carbonate parameters [Δ(t1-t0) h-1] and the long term effect (tested at t0) over
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the carbonate parameters produced by the Condition (+SC or -SC) on the experimental system.
Transformations to meet the assumptions of the model were found not to be necessary as tested by
Levene’s test for homogeneity of variances and K-S test for normality. To test for significant
differences at the interactions, a post-hoc analysis was performed using Least Significance
Difference (LSD) test.
To identify differences in grain size between Condition (+SC and -SC), the different size fractions
were transformed to percentage from the total dry weight of the samples, then converted to
proportions in order to perform an arcsin transformation to analyze for possible statistical
differences with ANOVA (Quinn & Keough, 2002).
2.4. Results
2.4.1. Ammonia estimates
Analysis of TAN following incubation periods (t1) revealed that seawater from aquaria containing
H. atra (+SC) had significantly higher concentrations than seawater taken from aquaria lacking H.
atra (-SC; Fig. 2.1a, Table 2.1). Specifically, +SC tanks had ~ 36% more TAN than -SC tanks,
irrespective of whether the measurements were made by day or by night (Fig. 2.1a, Table 2.1). The
concentration of TAN across all aquaria (<1.6 µmol kg-1) however, was found to be too low to
significantly affect total alkalinity measurements, even if we assumed that all ammonia produced is
converted to ammonium through the course of the incubation. The contribution of TAN to total
alkalinity (ΔAT) (referred to as ΔATAN) was 2.4-11%. Therefore, calcification rates (ΔAT - ΔATAN =
ΔACaCO3) represented 89-98% of the observed increase in AT over incubations (Fig. 2.1b).
2.4.2. Carbonate chemistry
Dissolution rates of CaCO3 were significantly greater in the presence of the H. atra (Table 2.1)
when determined from changes in alkalinity (ΔACaCO3) over 1-2 h incubation periods [Condition:
F(1,10)=6.34624, P=0.03, Table 2], regardless of Time (mid-day versus midnight). Although not
significantly different from zero, a tendency for positive calcification was observed over mid-day
incubations in the absence of H. atra (mean 84.97 mg CaCO3 m-2
h-1
with a 95% confidence interval
of -44.57 and 214.52 mg CaCO3 m-2
h-1) (Fig. 2.1c). Highest rates of dissolution were observed by
day in the presence of H. atra, leading to average daytime dissolutions that were ~37% and ~38%
greater than nighttime dissolution rates for Condition +SC and -SC respectively (Fig. 2.1c). A
calculation of dissolution rates over 24h, using mid-day and midnight means, suggests that either
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directly or indirectly, the presence of H. atra resulted in the removal of ~3.24 to ~5.10 g CaCO3 m-2
d-1.
Apart from the observed effect of sea cucumbers on total alkalinity (AT), the presence of sea
cucumbers had no impact on changes to other carbonate chemistry parameters over the incubation
periods (t1 – t0) (P > 0.05). However, Time (midnight vs mid-day) had significant effects with
changes, over the incubation periods, observed for: pH [F(1,10)=8.23, P=0.017], pCO2
[F(1,10)=6.94, P=0.025], HCO3- [F(1,10)=10.02, P=0.010], CO3
2- [F(1,10)=10.86, P=0.008], DIC
[F(1,10)=8.49, P=0.015], Ωcalc [F(1,10)=10.58, P=0.009] and Ωarag [F(1,10)=10.52, P=0.009].
Here, pCO2, HCO3- and DIC increased, and, pH, CO3
2-, Ωcalc and Ωarag decreased over daytime
incubations (post-hoc test: P < 0.05 for all comparisons) (Table 2.2). Over the incubations,
Condition (+SC or -SC) had no significant effect on buffering capacity. Time, however, did
significantly affect the buffering capacity of the system with AT/DIC ratios increasing significantly
at mid-night regardless of the presence or absence of H. atra [F(1,10)=11.19, P=0.007] (Table 2.2).
Changes to AT/DIC (average values ranging 1.12-1.19) reflected changes to pH with nighttime
increases observed for both parameters and no effect of sea cucumber presence on either response
variable.
t0 data collected at mid-day and midnight on experimental days 3 through 6 (Table 2.2),
demonstrated that, over a period of three days, H. atra had a cumulative effect on the seawater
chemistry of 35 L tanks that were continuously refreshed with reef-water at a rate of 1 L min-1 (i.e.
a completely turned over every 35 min). The effect potentially resulted from the impact of H. atra
on micro-algae and microbes associated with the sediments. There was a significant interaction
between Condition and Time with the presence of sea cucumbers resulting in a mid-day increase in
HCO3- and DIC (7.6% and 4.1% respectively compared to midnight values), and a midnight
increase in CO32-, Ωcalc and Ωarag (23.1%, 22.4% and 22.3% respectively compared to mid-day
values) (Fig. 2.1d, Table 2.3, post-hoc tests: P < 0.05 for all comparisons). Seawater pH was not
significantly affected by sea cucumbers or Time of measurement. However, there was a non-
significant trend for an interaction between these factors (Table 2.3), with mean pH in +SC tanks
tending to be lower by day (8.03) than by night (8.15), and the reverse in -SC tanks (day = 8.11,
night = 8.07, Table 2.2). AT/DIC at t0 revealed an interaction between Time and Condition (Table
2.3): The post-hoc analysis showed that H. atra tended to reduced mid-day OA buffer capacity, but
increased mid-night OA buffering capacity (P < 0.05; +SC mid-day = 1.14, +SC midnight = 1.19,
-SC mid-day = 1.17, -SC midnight =1.16, with +SC mid-day < +SC mid-night).
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2.4.3. O2 Flux
Net O2 flux was not significantly affected by the presence of H. atra [F(1,9)=0.06, P=0.8], nor by
the interaction of this factor with Time of day [F(1,9)=0.16, P=0.7]. O2 flux was, however, affected
by Time of day (F(1,9)=17.3, P < 0.0025) with a positive flux over mid-day incubations [18.8 ± 6.7
(95% CI) mg O2 h-1], and a zero net flux over mid-night incubations [1.9 ± 2.0 (95% CI) mg O2 h-1].
2.4.4. Grain size
The lagoonal sediments used in this experiment were principally composed of very small grains
given that >84% had a grain size that was less than 0.125 mm in diameter, and over 96% of grains
had a diameter less than 0.5 mm. The statistical analysis of three different size classes (>1 mm, <
0.5 mm, and < 0.25 mm) found no significant difference in the relative abundance of these grains
after 6 days in the presence or absence of sea cucumbers. The analysis of grain size identified three
homogeneous groups [F(1,10)=1.077, P=0.324 for the fraction >1 mm; F(1,10)=0.636, P=0.444 for
the fraction 0.5-0.25 mm; F(1,10)=0.727, P=0.414 for the fraction ≤0.125 mm] (Fig. 2.2)
2.5. Discussion
2.5.1. General overview
The results of the present study demonstrated that: 1) H. atra contributed to the modification of
seawater chemistry of their surrounding environment through the dissolution of CaCO3 and
ammonia production; 2) sediment-associated organisms are relevant and contribute to the
modification of the seawater chemistry, with observed effects of sea cucumbers on water chemistry
lost when the system is closed with water re-circulated for 1-2 h incubations (Fig. 2.3); and 3) over
three days, in simulated slow flowing water, the observed negative effect of H. atra on daytime OA
buffering capacity suggests that they would not assist calcification on the reef, as calcification tends
to be a daytime activity (Albright et al., 2013; Eyre et al., 2014).
2.5.2. CaCO3 dissolution rates and ammonia production by H. atra and sediment-associated micro-
organisms
The presence of the sea cucumber H. atra was associated with a significant increase in TAN
concentration in seawater. This increase is likely to enhance the development of microalgae in the
sediments (Uthicke, 2001a). Increased microalgal biomass can either enhance the uptake of CO2 by
increasing rates of photosynthesis in surface sediments, potentially raising the pH of sediment pore
water hence reducing the potential for sediment decalcification driven by high rates of microbial
respiration (Werner et al., 2008); or, can enhance the production of CO2 based on the
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decomposition of organic matter through a stimulation of denitrification (Satyanarayana et al.,
2012). Using sea cucumber TAN production rates from Schneider et al. (2013), together with end-
point TAN measurements, it was found that even under the assumption that all ammonia produced
protonates to ammonium, this process would have little impact on the changes to alkalinity
observed over the incubations.
Over incubations periods, the presence of H. atra resulted in an increase in AT either caused by the
dissolution of sediment CaCO3 (Schneider et al., 2013) and/or the stimulation of processes such as
denitrification (Paulmier et al., 2009), which is consistent with the production of CO2 observed over
mid-day incubations in the present study. The effect of the presence of H. atra on alkalinity was
more pronounced at mid-day than at midnight, although the relationship was not statistically
significant. Dissolution of sediments could be linked to sea cucumber feeding rates, and is
consistent with studies observing a continuous feeding rhythm by H. atra throughout a 24h
day/night cycle (Uthicke, 1994), however, due to the short-term nature of the experiment, signs of
reduction in grain size between Condition +SC and -SC were not identified. Such a feeding
behavior may therefore explain the lack of apparent diurnal differences in dissolution rates when
sea cucumbers are present. On the other hand, -SC tanks during mid-day incubations showed a
decrease in average AT between t0 and t1 and an increase by night.
Positive calcification has been linked to system net O2 production and is believed to be associated
with resultant increases in pH that facilitate calcification as CO2 is removed and fixed by
photosynthetic organisms (Andersson & Gledhill, 2013). By contrast, the net production of CO2
results in decreases in alkalinity for water in contact with CaCO3 and is promoted by the availability
of organic material for decomposition (Andersson & Gledhill, 2013; Dove et al., 2013; Yates &
Halley, 2006). In the present study, we observed a net increase in O2 flux by day, which is
consistent with positive calcification observed in tanks lacking sea cucumbers. At night, however,
O2 production was balanced by O2 uptake, suggesting that if the accumulation of CO2 within the
closed system was the ultimate driver for decalcification, then the excess of CO2 must be produced
without the uptake of O2. Processes that produce CO2 without the uptake of O2 include calcification
(equivalent to a decrease in alkalinity) from HCO3- (Jury et al., 2010, McConnaughey & Whelan,
1997), and anaerobic denitrification, a process that also takes up H+ resulting in increases in
alkalinity (Paulmier et al., 2009)
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2.5.3. Modification of carbonate parameters during incubation periods
Observing the carbonate parameters through the short-term incubations, DIC increased over mid-
day, irrespective of the presence of sea cucumbers. The results are suggestive of a net metabolic
input of DIC by day to the closed system through a net stimulus of CO2 production from organisms
that do not use O2 as a terminal oxygen acceptor (e.g. denitrifying bacteria, Erler et al., 2013). Such
processes may be active, for instance, either due to the presence of coral mucus in the seawater
flowing into the aquaria, or due to a thin oxic layer established within the sediments (Wild et al.,
2004). In the presence of sea cucumbers, the production of CO32- ions from decalcification in their
intestines must be converted rapidly to HCO3-, that is subsequently transformed to CO2
(McConnaughey & Whelan, 1997). This explains in part the observed reductions in pH (even if
statistical differences were not observed across treatments) and the increase in CO2 and HCO3- by
day in the tanks +SC. The conversion of DIC into HCO3- and later into CO2 is entirely consistent
with the observed reductions in OA buffering capacity (AT/DIC) observed over daytime incubations
and with the very large proportional increases in pCO2 over these incubations (Egleston et al., 2010;
Wang et al., 2013). In the absence of H. atra, during day-time, processes such as sulfate reduction
may be occurring to a greater extent, with a consequent increase in HCO3- and H+ (Morse &
Mackenzie, 1990). This process should increase the alkalinity of the system, however, the produced
HCO3- may be converted into CO2 (McConnaughey & Whelan, 1997), promoting the observed
decrease in alkalinity for -SC tanks during day-time.
By night, relative changes in these carbonate parameters tended to be small across all treatments.
This suggests that the production of DIC as a result of observed nighttime dissolution was
efficiently countered by the metabolic uptake of DIC (e.g. via aerobic respiration or ANAMMOX
activity in low oxygen microenvironments, contributing to the fixation of CO2, see Erler et al., 2013
and Kuypers et al., 2005). At night, AT/DIC increased significantly regardless of the presence of H.
atra. Furthermore, the observed nighttime increase in OA buffering capacity (AT/DIC) during the
incubation periods was correlated with a significant increase in pH (Egleston et al., 2010),
regardless the presence or absence of the animals. In this context, the main drivers for changes in
the concentration of the three principal forms of DIC are metabolic processes from sediment-
associated organisms in the presence and absence of H. atra (Table 2). The results also showed that
the values for AT/DIC are within the ranges of values in the present ocean (Egleston et al., 2010),
however, somewhat larger than those observed by Uthicke et al. (2014) at four different location
across the Great Barrier Reef: Wet-tropics (AT/DIC = ~1.399), Burdekin (AT/DIC = ~1.1416),
Whitsundays (AT/DIC = ~1.1393) and Fitzroy (AT/DIC = ~1.384). Finally, the capacity to buffer
CO2 and pH due to changes in alkalinity will be similar for Condition +SC and -SC.
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2.5.4. Cumulative effect of H. atra on carbonate parameters
The results for the cumulative effect of H. atra on the experimental system over a three day period
showed that the animals are differentially affecting carbonate parameters by day and by night. The
+SC tanks accumulate DIC principally in the form of bicarbonate by day, with no DIC increase,
albeit a shift to carbonate ions by night. Calcite and aragonite saturation states were only greater at
night when an unchanged DIC pool was enriched in carbonate ions in tanks housing H. atra.
Furthermore, by day the animals are likely affecting the abundance of sediment-associated
organisms, which can increase DIC relative to AT in the seawater due to changes in CO2 production
(e.g., respiration) (Schneider et al. 2013). However, these changes in the production of CO2 were
not sufficiently strong to modify the concentration of protons (H+) in the seawater (no significant
differences between conditions in the production/uptake of carbonate parameters such as HCO3- and
CO32-). Therefore, there was a no significant effect on pH by the animals when comparing to tanks
-SC, meaning that the susceptibility of both conditions to uptake CO2 and buffer pH will be
equivalent (Egleston et al., 2010) and similar to the incubation periods. The increase in daytime
DIC explains the observed decrease in AT/DIC as AT is unchanged. In this regard, the daytime
production of HCO3- by H. atra, appears to be decreasing rather than increasing the OA buffering
capacity of the system. By night, no additional DIC is added to the system, and the DIC equilibrium
shifts in favour of carbonate ions as the buffering capacity of the system increases. The results
observed for the open system mirrored those of the incubation periods with few discrepancies.
Importantly, the results showed that any impact that these sea cucumbers are having on the OA
buffering capacity of the system is occurring at the wrong time in the diurnal cycle. Corals and
other reef calcifiers (such as live foraminifera in Heron reef sediments; Mamo, 2011) have higher
calcification rates by day than by night (e.g. Dove et al., 2013). Furthermore, end-of-century
declines in calcification due to increases in OA and/or temperature impact both day and night rates
of calcification (Dove et al., 2013; Kroeker et al., 2010). Therefore, contrary to statements made in
the literature regarding the ability of sea cucumbers to mitigate the effects of OA on reef calcifiers
(Schneider et al., 2011), the present study suggests that H. atra will in fact exacerbate the effects of
future ocean acidification. Not only will they remove calcium carbonate from the system through
the dissolution of sediments, they will also decrease the buffering capacity of the system by day
when it counts most for reef calcifiers.
2.6. Acknowledgments
We thank Dr. Peter Mumby, Dr. Selina Ward, Dr. Maria Byrne and Dr. Dorothea Bender-Champ
for helpful comments on an earlier version of this manuscript. We would also like to thank Aaron
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Chai, Giovanni Bernal Carrillo and Annamieke Van Den Heuvel for assistance in the field. This
research was co-funded by the Australian Research Council (ARC) Centre for Excellence in Coral
Reef Studies (CE0561435), ARC Linkage Grant (LP110200874) (to S.D.), and Becas Chile
scholarship from CONICYT (Chile) (to F. V.-R.)
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Fig. 2.1. Responses of experimental tanks during incubations (t0 and t1) across Condition (+SC and
-SC) and Time (represented as mid-day at the left of each panel and midnight at the right of each
panel). (a) Total Ammonia Nitrogen (TAN) at the end of the incubations (t1) for different Condition
and Time. (b) Changes over the incubation (t1-t0) on total alkalinity (ΔAT), alkalinity driven by
CaCO3 dissolution (ΔACaCO3) and alkalinity by TAN (ΔATAN). (c) Dissolution rates under different
Condition across Time. (d) DIC concentration (HCO3-+ CO3
2-+ CO2) at t0 for the different
Condition and Time. For all panels (A, B, C and D), data plotted are means ± SE from n = 6 for
both Condition and Time.
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Fig. 2.2. Grain sizes of the sediments at the end of the experiment under different Condition (+SC
or -SC). Fractions are represented in black (= or < than 0.125 mm), dark grey (0.5-0.25 mm) and
light grey (> than 1 mm). Data plotted are means ± SE from n = 6 for all Condition.
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Fig. 2.3. Conceptual representation of factors controlling water carbonate chemistry in the presence
of H. atra and sediment-associated organisms. The letters represent different areas of the reef: (a)
Whole reef including the reef slope where most coral species are present; (b) reef flat with
macroalgae such as those of genus Halimeda and; (c) immediate area of influence of H. atra.
Number 1 represents the chemical equation generated from the reaction of CO2 + H2O that produces
carbonic acid (H2CO3) and a further dissociation to a bicarbonate ion and a proton (H+). This
chemical equation is present in a, b and c. In c, increases produced by H. atra and the sediments on
the carbonate parameters are identify for day, night or both. The solid black arrows indicate that
carbonate parameters are available for the uptake by other organisms in (c) or in areas (a) and (b).
The dark grey arrow from (c) to (b) represents the potential of H. atra to affect organisms such as
Halimeda sp. Light grey arrow from (c) to (a) is the potential of H. atra to reach and influence
organisms such as corals in the reef slope (lower probability due to physical distance and currents).
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Table 2.1. Repeated measures ANOVA for dissolution rates and TAN (at t1) under different
Condition (+SC and -SC) and sampling Time (mid-day or midnight, as the within-subject factor).
Bold highlights the factor(s) contributing to significant differences.
Source of variation SS df MS F P
Dissolution rates
Between subjects
Condition 0.0014 1 0.0140 6.0227 0.0340
Error 0.0024 10 0.0002
Within subjects
Time 0.0003 1 0.0003 0.6846 0.4273
Time x Condition 0.0014 1 0.0014 3.0152 0.1131
Error 0.0045 10
TAN
Between subjects
Condition 6.1985 1 6.1985 25.4667 < 0.0001
Error 8.2755 34 0.2434
Within subjects
Time 0.5123 1 0.5123 2.2313 0.1445
Time x Condition 0.0015 1 0.0015 0.0067 0.9352
Error 7.8067 34
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Table 2.2. Summary of temperatures, pH and AT used from the experimental incubations as input parameters, to calculate pCO2, HCO3-, CO3
2-, DIC,
Ωcalc, Ωarag as output results (see Materials and Methods) and the relationship of AT/DIC for different Condition (+SC and -SC). The difference
between t1 and t0 (mean ± SE) is also shown for parameters. Incubations had 1-1.5 hour length at mid-day and 1.5-2 hours length at midnight. All
values are represented as mean ± SE (n = 6).
Carbonate chemistry
Input parameters Output results
Treatment Incubation
T (oC) pH AT
(µmol kg-1)
pCO2
(µatm)
HCO3-
(µmol kg-1)
CO32-
(µmol kg-1)
DIC
(µmol kg-1)
Ωcalc Ωarag
AT/DIC
+SC
mid-day
t0 28.30±0.60
8.03±0.04
2207±10
507±62
1743±37
189±12
1945±26.6 4.6±0.3
3.10±0.20
1.14±0.01
t1 28.60±0.70
7.98±0.02
2220±9
578±42
1797±20
172±6
1984±15.9 4.2±0.2
2.80±0.10
1.12±0.01
Δ (t1-t0) h-1 0.22±0.13 -0.04±0.03 12.35±7.40 57.87±38.14 44.48±27.73 -12.75±10.91 33.20±18.53 -0.31±0.27 -0.20±0.18 -0.01±0.01
+SC
midnight
t0
27.50±0.10
8.15±0.06
2218±14
340±54
1611±50
245±24
1865.30±28.20
6.00±0.60
4.00±0.40
1.19±0.02
t1 27.40±0.10
8.15±0.06
2232±16
341±55
1621±49
248±25
1877.40±25.30 6.00±0.60
4.00±0.40
1.19±0.02
Δ (t1-t0) h-1 -0.10±0.02 0.00±0.01 7.98±2.41 0.66±4.85 6.09±6.20 0.96±2.42 7.09±4.22 0.02±0.06 0.01±0.04 0±0
-SC
t0
28.60±0.60
8.11±0.04
2215±9
400±46
1670±39
222±19
1902.50±22.10
5.40±0.50
3.60±0.30
1.17±0.02
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mid-day
t1 28.50±0.60
8.05±0.04
2211±7
456±38
1718±30
201±13
1930.90±18.90 4.90±0.30
3.30±0.20
1.15±0.01
Δ (t1-t0) h-1 -0.14±0.06 -0.08±0.06 -4.45±3.86 82.92±67.97 67.90±54.14 -29.24±22.83 40.84±33.13 -0.72±0.56 -0.48±0.38 -0.03±0.02
-SC
midnight
t0
27.60±0.10
8.07±0.04
2218±15
421±43
1699±34
211±16
1920.90±21.70
5.10±0.40
3.40±0.30
1.16±0.01
t1 27.50±0.10 8.09±0.05 2232±17 402±53
1687±45
222±21
1918±27.40 5.40±0.50
3.60±0.30
1.16±0.02
Δ (t1-t0) h-1 -0.07±0.01 0.02±0.04 7.71±1.36 -14.24±46.80 -8.85±40.44 6.78±15.84 -2.43±25.83 0.16±0.39 0.11±0.26 0.01±0.02
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Table 2.3. Repeated measures ANOVA for carbonate parameters at t0, under different Condition
(+SC and -SC) and Time (mid-day or midnight, specified as the within-subjects factor). Significant
differences are identified in bold.
Source of variation SS df MS F P
pH
Between subjects
Condition <0.001 1 <0.001 0.0018 0.9666
Error 0.1589 10 0.1589
Within subjects
Time 0.0117 1 0.0117 1.3289 0.2758
Time x Condition 0.0352 1 0.0352 4.0074 0.0732
Error 0.0879 10 0.0088
AT (µmol kg-1)
Between subjects
Condition 105.6315 1 105.6315 0.0864 0.7749
Error 12230.2129 10 1223.0213
Within subjects
Time 275.0635 1 275.0635 0.4247 0.5293
Time x Condition 82.3231 1 82.3231 0.1271 0.7289
Error 6477.0193 10 647.7019
pCO2 (µatm)
Between subjects
Condition 1081.1789 1 1081.1789 0.0582 0.8142
Error 185754.3911 10 18575.4391
Within subjects
Time 31883.7044 1 31883.7044 2.3639 0.1552
Time x Condition 52798.8209 1 52798.8209 3.9146 0.0761
Error 134877.9736 10 13487.7974
CO2 (µmol kg-1)
Between subjects
Condition 0.5442 1 0.5442 0.0418 0.8421
Error 130.2026 10 13. 0203
Within subjects
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Time 17.5566 1 17.5566 2.1008 0.1778
Time x Condition 36.0016 1 36.0016 4.3079 0.0647
Error 83.5701 10 8.3570
HCO3- (µmol kg-1)
Between subjects
Condition 361.3692 1 361.3692 0.026194 0.874650
Error 137957.3224 10 13795.7322
Within subjects
Time 16163.1655 1 16163.1655 2.691688 0.131908
Time x Condition 38816.3241 1 38816.3241 6.464169 0.029239
Error 60048.4377 10 6004.8438
CO32- (µmol kg-1)
Between subjects
Condition 5.3584 1 5.3584 0.001952 0.965630
Error 27451.1990 10 2745.1199
Within subjects
Time 3166.0690 1 3166.0690 2.508987 0.144281
Time x Condition 6862.5621 1 6862.5621 5.438315 0.041906
Error 12618.9135 10 1261.8913
DIC (µmol kg-1)
Between subjects
Condition 254.6326 1 254.6326 0.0493 0.8288
Error 51700.1651 10 5170.0165
Within subjects
Time 5633.5027 1 5633.5027 2.5289 0.1429
Time x Condition 14442.7849 1 14442.7849 6.4837 0.0290
Error 22275.6516 10 2227.5652
Ωcalc
Between subjects
Condition < 0.001 1 < 0.001 < 0.001 0.9991
Error 15.9871 10 1.5987
Within subjects
Time 1.5548 1 1.5548 2.1689 0.1716
Time x Condition 4.0590 1 4.0590 5.6621 0.0386
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Error 7.1686 10 0.7169
Ωarag
Between subjects
Condition <0.001 1 <0.001 <0.001 0.9948
Error 7.2229 10 0.7223
Within subjects
Time 0.6663 1 0.6663 2.1201 0.1760
Time x Condition 1.8131 1 1.8131 5.7688 0.0372
Error 3.1430 10 0.3143
AT/DIC
Between subjects
Condition <0.001 1 <0.001 0.0182 0.8955
Error 0.0241 10 0.0024
Within subjects
Time 0.0031 1 0.0031 2.8019 0.1251
Time x Condition 0.0063 1 0.0063 5.7989 0.0368
Error 0.0109 10 0.0011
.
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Chapter 3: Impacts of winter climate change conditions on decalcification and ocean
acidification buffering capacity are not mitigated by sea cucumbers
Francisco Vidal-Ramireza, Olga Pantosb,c, Gene W. Tysonb.d and Sophie Dovea.c
aSchool of Biological Sciences and Australian Research Council Centre for Excellence in Coral
Reef Studies, The University of Queensland, St. Lucia, Queensland 4072, Australia
bAustralian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, St. Lucia,
Queensland 4072, Australia
cGlobal Change Institute, The University of Queensland, St. Lucia, Queensland 4072, Australia
dAdvanced Water Management Centre, University of Queensland, St. Lucia, Queensland 4072,
Australia
Corresponding author:
Francisco Vidal-Ramirez
School of Biological Sciences, The University of Queensland, Level 7, Gehrmann Laboratories
(Building #60), St. Lucia, QLD 4072, Australia. Tel.: +61-450704403; fax: +61-7 33651692.
E-mail address: [email protected]
Target Journal: Global Change Biology
Original Research Article
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3.1. Abstract
Coral reef ecosystems are under threat from rising seawater temperatures and ocean acidification
(OA) resulting from rising levels of atmospheric carbon dioxide. The role of reef organisms such as
the sea cucumber Holothuria atra, in the reef ecosystem and their interactions with microalgae and
sediment microbes, under future climate change conditions are unknown. To date, contrasting
evidence exists regarding their potential to mitigate OA by increasing pH-buffering capacity. Here,
we examined the potential for sediments and associated organisms, in the presence and absence of
H. atra, to alter the chemistry of seawater (SW) exposed to pre-industrial, present day, RCP4.5 and
RCP8.5 winter Temperature/pCO2 scenarios. We further tested the impact of Scenario and H. atra
on the abundance and composition of sediment-associated organisms. After 8 weeks of exposure,
the only direct effect of H. atra presence on the ecosystem was an increase in net daytime O2
production and modification of sediment infauna, which did not, however, mitigate observed
decreases in production under future climate scenarios. Sediment calcium carbonate dissolution was
greatest at night and under RCP8.5. Likewise, all other SW parameters were influenced either by
day/night fluctuations or Scenario. RCP8.5 led to an observed overall decrease in microbial
abundance and modifications to microbial composition that may lead to changes in the rates of
processes such as sulfur-oxidation, sulfate-reduction and O2 production. Moreover, a ~62% greater
TAN uptake in pre-industrial Scenario (PI) was observed compared to present day (PD), suggesting
that under PD, sediment processes depending on nitrogen may already be perturbed. The present
study suggests that in a sedimentary environment, H. atra will not contribute to the modification of
ocean acidification buffering capacity (AT/DIC) and other carbonate parameters under future winter
pCO2/Temperature Scenarios. Irrespective of the presence of H. atra, pH buffering capacity was
higher under RCP4.5 conditions relative to present day, but was lower under conditions of RCP8.5,
therefore suggesting that H. atra will not mitigate future reef OA, and hence will not assist reef
calcifiers in future winter environments.
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3.2. Introduction
Coral reef ecosystems have experienced long-term environmental stress induced by human
activities. Furthermore, an increase in CO2 emissions associated with the burning of fossil fuel, is
projected to have significant impact on coral reef ecosystems through its tandem effects: ocean
warming and ocean acidification (Hoegh-Guldberg et al., 2007; IPCC, 2014). The combined effect
of these two variables are projected to led to net reef decalcification by the end of the century (Dove
et al., 2013), with non-calcareous algae predicted to replace hermatypic (reef-building) corals on
reefs globally (Ainsworth & Mumby, 2015; Diaz-Pulido et al., 2009). These changes are estimated
to affect millions of people who are dependent on these ecosystems for their livelihoods, food
production, amongst others services (Hernandez-Delgado, 2015).
The negative implications of human induced CO2 emissions for reef ecosystems are mainly due to
projected changes in the chemistry of seawater (OA) and the increase in SST (Dove et al., 2013;
Eyre et al., 2014; Perry et al., 2008; Perry et al., 2013; Scoffin et al., 1980). The capacity for
accretion of calcium carbonate by corals will decrease under unsaturated states of aragonite in
conditions of future ocean acidification and increased SST. Such circumstances could result in coral
reef dissolution exceeding coral reef calcification (Dove et al., 2013; Eyre et al., 2014). These
potential implications highlight the need to understanding how all members of the coral reef
ecosystem will respond to future conditions; and what key roles organisms, other than Scleractinian
corals, play in calcium carbonate accretion and/or dissolution rates, including whether they are able
to buffer the effects of OA.
There is currently a lack of understanding of the ecological role reef invertebrates, other than corals,
will have under future OA and temperature conditions (Przeslawski et al., 2008). Holothurians are
important members of the coral reef community and have ecological-economic importance (Purcell
et al., 2016), yet there is no current information regarding how their survival or ecosystems roles
may change under future projected climate scenarios. Sea cucumbers have been identified as being
important for the reef ecosystem due to their role in the productivity of the sediments (Uthicke,
2001a), the dissolution of calcium carbonate (Chapter 2; Schneider et al., 2011; Schneider et al.,
2013) and physically through bioturbation (Purcell et al., 2016; Uthicke, 1994). Often empirical
studies on the effect of sea cucumbers on carbonate chemistry have been conducted in the absence
of other organisms (such as microalgae/bacterial communities and sediment infaunal organisms)
that may dominate the ecosystem response, or over short durations where the effects of treatment
conditions on the ecosystem may not be fully realized. Overtime, sea cucumbers have the potential
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to modify the density of microalgae (Uthicke, 2001a) and microbes (Hewson & Fuhrman, 2006;
Moriarty et al., 1985) in the sediments they digest, both of which play a critical role in the cycling
of nutrients and energy in the coral reef ecosystem. It is therefore essential that we understand how
these animals may alter the communities in the sediments under future Temperature/pCO2
conditions as well as the implications for the modification of the OA buffering capacity on reefs.
Potential increases in alkalinity mediated by holothurians (Schneider et al., 2011; Schneider et al.,
2013) are not always relevant to the potential of Holothuria to alter the OA buffering capacity of
their local environment (Chapter 2), as the buffering capacity is determined by the ratio of AT to
DIC (Egleston et al., 2010; Wang et al., 2013), such that increases in alkalinity can be negated by
equivalent increases in DIC. In a short-term experiment, it was shown that the presence of H. atra
in the ecosystem does not alter the buffering capacity offered by sediment microorganisms
suggesting that the potential assistance of this species to reef calcification is unlikely (Chapter 2).
The study, however, did not consider the long-term effect of sea cucumbers and/or future scenarios
on ecosystem nutrient or carbonate parameters driven by potential alterations to the abundance and
composition of sediment-associated microorganisms (Andersson & Gledhill, 2013; Andersson et
al., 2007; Capone et al., 1992; Uthicke, 2001a). Over a longer-term, biotic environment factors may
interact with changes to the abiotic factors, such as ocean acidification (OA) and warming, affecting
AT/DIC ratios by modifying organism metabolism and/or organism community structure.
The aim of the present study was to: (1) Estimate the influence of H. atra and sediment-associated
organisms (microalgae, bacterial communities and infaunal organisms) on OA buffering capacity,
carbonate chemistry and productivity of reef sediments under future climate Scenarios proposed by
the Intergovernmental Panel on Climate Change (IPCC, 2014) over a winter period; (2) Test the
effects of projected future Scenarios on ecosystem response variables, where projected future
temperature are paired with projected OA levels associated with the attainment of specific
atmospheric pCO2 concentrations, in response to the socio-economic need to understand the roles of
these organisms on the ecology of future reefs (Fang et al., 2013; Harvey et al., 2013); and (3)
Provide further evidences regarding the interactions between holothurians and sediment biota, to
test whether such long-term interactions, as opposed to short-term interactions (Chapter 2), will
change or not under different winter temperature/pCO2 Scenarios, with potential consequences over
OA buffering capacity and other members of reef ecosystems, such as the corals and macroalgae.
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3.3. Materials and methods
3.3.1. Field collection and general setup
Sediments were collected by SCUBA from the lagoon of Heron Island, Great Barrier Reef
(23o26’550’’S; 151o56’629’’E) in early May 2013 as described in Chapter 2, at a depth of ~ 5 m.
Furthermore, reference samples to investigate microbes in situ were collected (see below for
methodology of collection and processing, n = 3). The location was selected because of its
proximity to a CSIRO buoy that records pCO2 and temperature every two hours providing baseline
present day conditions used in the experimental system (see below). The depth was selected
because in situ light quality and quantity could be experimentally approximated by filtering surface
solar irradiance through marine blue filters. Immediately after collection, sediments were
transferred to experimental tanks at Heron Island Research Station (The University of Queensland).
Sediments were mixed and distributed evenly between 48 outdoor glass aquaria (for more details,
see Chapter 2), creating a 3 cm layer of sediments in each of these aquaria. The sediments were left
with running seawater for 4 days before the addition of the sea cucumbers. Twenty-four individuals
of the sea cucumber species Holothuria atra (weight 230.7 ± 25.4 g and length of 20.7 ± 1.89 cm)
were collected by SCUBA from the initial site of sediment collection. Sea cucumbers were placed
in empty plastic aquaria for 48h to ensure that any gut sediments were excreted prior to their
introduction into the experimental mesocosms.
3.3.2.Temperature/pCO2 system
The Temperature/pCO2 system used to achieve the seawater conditions for the experiment was
reported by Dove et al. (2013). The system was composed of 4 fiberglass sumps (8,000L each), that
constantly received 10 µm filtered seawater pumped from the inner reef flat of Heron Island. Each
sump represented a different future Temperature/pCO2 Scenario (IPCC, 2014). To achieve the
conditions of each Scenario, the temperature of the seawater in the sumps was controlled by a
heater-chiller (HWPO17-1BB; Rheem) that responded to the temperatures measured in an adjacent
mesocosm system (PT100 thermocouples; RS Components, see Dove et al., 2013). The heater-
chillers were set to follow a look-up table established from Harry’s Bommie (data available:
http://www.pmel.noaa.gov/co2/story/Heron+Island), with the assistance of custom-made software
(SCIWARE Software Solutions). pCO2 levels were manipulated by a central sensor (CO2-PRO;
ProOceanus Systems) that received seawater from the sumps. Levels were logged and used to
manipulate the injection air enriched with CO2 (to 30%) or CO2-free. This manipulation allowed the
regulation of pCO2 concentrations at each of the 4 experimental levels according to the look-up
table created for Harry’s Bommie (see Dove et al., 2013 for further details).
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Modified seawater from each sump was delivered into a total of 12 experimental tanks per Scenario
(6 with the Condition +SC and 6 with Condition -SC) at a flow rate of 1 L min−1.
The Scenario regime established for the experiment in this Temperature/pCO2 system were
consistent with those proposed by the IPCC (2014) and allowed us to test the effects of four
Temperature/pCO2 regimes over a 2 month experimental period during a typical present day winter
(2012). The climate Scenarios included: Present day (PD) Temperature/pCO2 (temperature range:
20.4-22.3 °C, pCO2 range: 325-351 ppm); Pre-industrial Scenario (temperature of -1°C and -100
ppm of CO2 below PD); Scenario RCP4.5 (temperature offset of +1.8°C above PD and pCO2 offset
of +180 ppm above PD) and Scenario RCP8.5 (temperature offset of +3.6°C above PD and pCO2
offset of +570 ppm above PD) (IPCC, 2014; Rogelj et al., 2012) (Fig. S1).
3.3.3. Experimental design and incubations periods
After 4 days with running seawater, the 48 experimental tanks (representing an area of sediment of
1049.4 cm2 per tank) were randomly assigned to 2 different conditions: Condition with sea
cucumber (+SC) or without sea cucumber (-SC) (see Chapter 2) and the animals were allocated in
the +SC tanks. A Scenario was randomly assigned to the experimental tanks, with an equal number
of replicates for each combination of Scenario and Condition (e.g., n = 6 for PD Scenario +SC and
n = 6 for PD Scenario -SC, and the same for the remaining Scenarios). Acclimation to these
seawater Scenarios was carried out over a period of 20 days, with a progressive increase in the
proportion of each Scenario water condition (mixed when necessary with non-treated natural
seawater) every 4 days as follows: 20%, 40%, 60% and 80%, until reaching 100% (full Scenario) at
day 16, and maintained under full treatment conditions for 8 weeks (Fig. S1).
At the end 8 weeks, tanks were sealed to carry out a series of incubations (= incubation periods) in
order to obtain samples for different analyses (Chapter 2). Incubations lasted ~1 h and were
performed during daytime (11:00am-3:00pm, hereafter referred to as ‘mid-day’ or ‘day’) and night-
time (11:00pm-3:00am, hereafter referred to as ‘midnight’ or ‘night’). The incubations were
achieved in the tanks using lids (with a plastic tube inserted to allow the collection of water
samples) to produce an airtight seal, water flow was stopped by blocking the outlets (Chapter 2;
Dove et al. 2013). Before the lids were sealed, temperature and O2 loggers (RINKO ARO-USB;
JFE Advantech) were placed into each tank to measure the O2 flux over the incubation. For each
incubation (mid-day and midnight), water samples were collected at the beginning (t0) and end (t1)
for alkalinity and nutrient analysis. In addition, pH measured for t0 and t1 was used to calculate the
carbonate parameters with CO2SYS (Chapter 2; Pierrot et al., 2006).
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3.3.4. Nutrient analysis
Seawater samples for nutrient analysis were collected in 10 mL tubes (SARSTEDT Australia Pty
Ltd.) at t0 and t1 of each incubation period (n = 3). The samples were stored at -20ºC and analysis
was carried out at the Advance Water Management Centre of the University of Queensland.
Analysis of TAN (NH3 + NH4+), nitrite, nitrate and phosphate was carried out on a Lachat
QuikChem8500 Flow Injection Analyzer (Saros et al., 2014). Nutrient net changes (or nutrient
fluxes), were calculated as the change in nutrient concentration over the incubation periods.
3.3.5. CaCO3 dissolution rates and carbonate chemistry
As a proxy for the estimation of calcium carbonate dissolution between Condition (+SC and -SC),
Scenario (PI, PD, RCP4.5 and RCP.8.5) and Time (mid-day and midnight), the changes in total
alkalinity (AT) were estimated for each incubation period from 100 mL seawater samples obtained
at t0 and t1 (n = 5-6). After collection, the samples were treated and analysed following the methods
of Chapter 2 to obtain an estimation of the levels of calcium carbonate dissolution using the
alkalinity anomaly technique (Chisholm & Gatusso, 1991).
To calculate all calcium carbonate dissolution rates, the change in alkalinity due to CaCO 3
(ΔACaCO3) which were derived from the difference between the change in total alkalinity during the
incubations (ΔAT) were used, minus the alkalinity produced by ΔTAN as follows: ΔACaCO3 = ΔAT -
ΔATAN and final values converted to mgCaCO3 h-1 (see Chapter 2) and shown per unit of area (m-2).
The calculation of the carbonate parameters for the experiment were achieved using CO2SYS
(Chapter 2; Pierrot et al., 2006) using constants proposed by Mehrbach et al. (1973) and refitted by
Dickson & Millero (1987). The input conditions used were: salinity (34.10 ± 0.02 SE), pH,
temperature (recorded with HOBO loggers) and AT . The parameters obtained were pCO2 (µatm),
bicarbonate (HCO3-), carbonate (CO3
2-), Dissolve Inorganic Carbon (DIC), calcite (Ωcalc) and
aragonite saturation states (Ωarag).
Finally, to estimate the potential direct impact of H. atra and microorganisms on the dissolve
calcium carbonate, the grain size of sediments in each aquaria were assessed at the end of the 8
week treatment period using methods according to Chapter 2.
3.3.6. 16S rDNA amplicon sequencing and qPCR of microbial communities
To determine potentially greater differences in the microbial communities after 8 weeks, sediments
were collected after the incubations from Scenario PD and RCP8.5 and for each Condition (+SC
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and –SC) during the midnight sampling point. Sediments were collected directly into 5 ml sterile
tubes (Sarsted, Australia Pty. Ltd., see Dove et al., 2013) (n = 3 for each Scenario and Condition)
from the top 1 cm of sediments. The remaining seawater in each tube was discarded and the
sediments immediately resuspended in LifeGuardTM Soil Preservation Solution (MO BIO
Laboratories, Inc.) and stored, according to the manufacturer’s instructions. Once in the laboratory,
samples were thawed on ice for approximately 30 minutes prior to total genomic DNA extraction.
Extractions were performed on ~250 mg of well-mixed sediments using PowerBiofilm® DNA
Isolation Kit (MO BIO Laboratories, Inc.) following the manufacturer’s instructions. Following the
extraction, a Universal Primer pair targeting the V6/V8 regions of the 16S rRNA gene was used for
the PCR amplification: 926F (5’-AAACTYAAAKGAATTGRCGG-3’) and 1392wR (5’-
ACGGGCGGTGWGTRC-3’). The PCR reaction mixture (25 µL final volume) and cycling
conditions were set-up according to Dove et al. (2013). Following initial PCR amplification, DNA
templates of ~5ng/µL and blanks from the extraction kit and PCR mix, were sent to the Australian
Centre for Ecogenomics (ACE) at The University of Queensland, were the 16S rRNA gene was
targeted using the 926F and 1392wR primers modified to contain Illumina specific adapter
sequence
(803F:5’TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGTTAGAKACCCBNGTAGTC3’
and
1392wR:5’GTCTCGTGGGCTCGGGTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGAC
GGGCGGTGWGTRC3’). Preparation of the 16S library was performed using the workflow
outlined by Illumina (#15044223 Rev.B). PCR products of ~466bp were amplified according to the
specified workflow with an alteration in polymerase used to substitute Q5 Hot Start High-Fidelity
2X Master Mix (New England Biolabs) in standard PCR conditions. Resulting PCR amplicons were
purified using Agencourt AMPure XP beads (Beckman Coulter). Purified DNA was indexed with
unique 8bp barcodes using the Illumina Nextera XT 384 sample Index Kit A-D (Illumina FC-131-
1002) in standard PCR conditions with Q5 Hot Start High-Fidelity 2X Master Mix. Indexed
amplicons were pooled together in equimolar concentrations and sequenced on MiSeq Sequencing
System (Illumina) using paired end sequencing with V3 300bp chemistry according to
manufacturer’s protocol.
The Forward read only were processed using a modified version of the QIIME pipeline proposed by
Caporaso et al. (2010). Removal of Illumina sequencing adapters and quality trimming were
performed using Nesoni clip (https://github.com/Victorian-Bioinformatics-Consortium/nesoni, see
Lo et al., 2015). Sequences were assigned to an operational taxonomic unit (OTUs) using
pick_open_reference_otus.py at a 97% sequence identity using default settings. Taxonomy of each
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OTU was assigned using default reference against the Greengenes database October 2013. OTUs
present in the blanks were removed from OTU tables using filter_taxa_from_otu_table.py. The
number of reads was normalised to 10000 and corrected by the 16S rRNA gene copy number
(estimated by qPCR, normalized by the original mass of sediments extracted) using CopyRighter
(Angly et al., 2014). Alpha diversity was assessed by more than one index (Haegeman et al., 2013;
Lemos et al., 2001), therefore described by Richness (Sobs), Shannon-Wiener index and Simpson’s
E.
The qPCR for the estimation of microbial abundance in the sediments was carried out at ACE using
the same DNA templates used for sequencing. The qPCR was performed using 5 µl of 2X SYBR
Green/AmpliTaq Gold DNA Polymerase mix (Life Technologies, Applied Biosystems), 4 µl of
microbial template DNA and 1 µl of primer mix. The 16S 1406F/1525R primer set (0.4 µM) was
designed to amplify bacterial and archaeal 16S rRNA genes: F - GYACWCACCGCCCGT and R -
AAGGAGGTGWTCCARCC. The rpsL F/R primer set (0.2 µM), used for inhibition control,
amplifies Escherichia coli DH10B only: F - GTAAAGTATGCCGTGTTCGT and R -
AGCCTGCTTACGGTCTTTA. Three dilutions 1/50, 1/250 and 1/500 (microbial template DNA,
16S 1406F/1525R primer set) as well as an inhibition control (E. coli DH10B genomic DNA, rpsL
primer set) were run in triplicate for each sample. The PCR was run on the ViiA7 platform (Applied
Biosystems) including a cycle of 10 min at 95C (AmpliTaq activation) and 40 cycles of [15 s at
95oC followed by 20 s at 55oC and 30 s at 72oC]. A melt curve was produced by running a cycle of
2 min at 95oC and a last cycle of 15 s at 60ºC. The cycle threshold (Ct) values were recorded and
analyzed using ViiA7 v1.2.1 software.
3.3.7. Photosynthetic pigment concentrations and infaunal composition of sediments
Sediments were collected at the end of the incubation periods following the same procedure used
for the microbial communities (n =3), and dried using a ScanVac freeze dryer (Labogene). Pigment
extractions were carried out on 0.5 ± 0.01 g of dry sediment according to Buffan-Dubau & Carman
(2000) using cold 100% acetone and sonication (10 min). Extracted sediments and pigments were
incubated in the dark over night at -80oC before HPLC analysis. Following the dark incubations, the
pigments were filtered at 0.22 µm and analysis was conducted following the methods of Dove et al.
(2006) and Zapata et al. (2000) using as solution A: 50% Methanol +25% Acetonitrile + 25%
Ammonium acetate solution (pH 5) and solution B: 20% Methanol + 60% Acetonitrile + 20%
Acetone. The pigments analysed were chlorophyll a, phaeophytin and total chloropigments
(calculated as the sum of chlorophyll a + phaeophytin) (Lee et al., 2008).
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The fauna within the sediments was analysed from 3 samples per tank, collected haphazardly from
the top 1 cm and pooled into 1 sample (Uthicke et al., 2013), from each Scenario and Condition (n
= 3), and were incubated with Rose Bengal (Caulle et al., 2014) for at least 48h prior to analysis
under the microscope (Olympus, SZX10). The major groups identified were Foraminifera,
Crustacea, Nematoda, Polychaeta, Gastropoda and Echinodermata, and their abundances expressed
per gram of sediment. Live Foraminifera such as Elphidium lene, associated with lagoonal
assemblages in Heron Island (Mamo, 2011), were not observed.
3.3.8. Statistical analysis
The long-term cumulative effect of the 3 Factors (Condition, Scenario and Time) on the seawater
carbonate chemistry (Table S1) was based on t0 measurements (since there were not many
discrepancies in carbonate parameters between t0 and the incubations, see Chapter 2 for further
details). A three-way PERMANOVA (PRIMER v6 software and PERMANOVA + add on,
PRIMER-E) was used for this purpose and we included carbonate dissolution rates obtained from
the incubations. For the PERMANOVA analysis, we used Bray Curtis similarity, a Type III sum of
squares, 9999 permutations under a reduced model and a dummy variable (+1, in order to deal with
variability of the data) (Bender et al., 2014). Multivariate differences were visualized using a
canonical analysis of principal coordinates (CAP) based on Bray Curtis distance and Pearson
correlation r > 0.4 (Verges et al., 2011). When statistical differences were detected, an individual
analysis was performed per response variable (Verges et al., 2011), given than PERMANOVA do
not allow establishing the direction of change when the factor (s) produced significant differences.
Individual analyses were conducted through three-way repeated measures ANOVA for each
carbonate parameter and the differences in calcium carbonate dissolution rates [Δ(t1-t0) h-1], with
Time specified as a within-subject factor (Chapter 2; Quinn & Keough, 2002). The relationship
between pH and AT/DIC was analysed by regression (Quinn & Keough, 2002).
For the incubations, three-way repeated measures ANOVAs were used to analyse nutrients (TAN,
NO2-, PO4
3- and NO3-), O2 flux and pigments. The within-subject factor was Time (mid-day and
midnight). For the abundance of the different infaunal groups, a two-way ANOVA was used.
Transformations to meet the assumptions of the model were found not to be necessary when
assumptions were tested using Levene’s test for homogeneity of variances and K-S test for
normality. To test for significant differences at the interactions, a post-hoc analysis was performed
using Least Significance Difference (LSD) test.
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To identify differences in grain size between Condition and Scenario, the different size fractions
were transformed to a percentage of the total dry weight of the samples, and then converted to
proportions in order to perform an arcsin transformation to analyse for possible statistical
differences with ANOVA (Chapter 2; Quinn & Keough, 2002).
Differences in composition of microbial communities between Condition and Scenario were tested
using a two-way PERMANOVA with 9999 permutations (PRIMER-E). For the PERMANOVA
analysis, square root-transformed data and Bray Curtis similarity were used (Hartmann et al., 2015;
Lazarevic et al., 2013; Sacristan-Soriano et al., 2011). A Principal Component Analysis (PCA)
based on a correlation Matrix, Using STATISTICA 12, was performed to present the results. The
alpha diversity indexes and the total microbial abundance estimated from qPCR data were analysed
using a two-way ANOVA, with Condition and Scenario as fixed factors. Assumptions were met
when tested using Levene’s test for homogeneity of variances and K-S test for normality.
3.4. Results
3.4.1. Cumulative effect of factors on the carbonate chemistry
The analysis of the carbonate chemistry at t0 revealed that the Condition (+SC or –SC) had no
significant effect on any of the carbonate parameters (Fig. S2, Table S1). PERMANOVA analysis
showed that the carbonate parameters and the dissolution of CaCO3 significantly varied by the
factor Time, Scenario and the interaction between Time and Scenario (Table 3.1). Further
individual analyses (repeated measures ANOVA) showed significant interactions between Scenario
and Time for the following carbonate parameters: pH (Scenario x Time: P < 0.001, Fig. 3.1a), AT
(Scenario x Time: P = 0.01), pCO2 (Scenario x Time: P < 0.001), HCO3- (Scenario x Time: P <
0.001), CO32- (Scenario x Time: P < 0.001), DIC (Scenario x Time: P < 0.001), Ωcalc (Scenario x
Time: P < 0.001), Ωarag (Scenario x Time: P < 0.001), and AT/DIC (Scenario x Time: P < 0.001).
Most interestingly, DIC was greatest by night under RCP8.5, bicarbonate lowest by day under
RCP4.5, and carbonate ions greatest by day under RCP4.5. The full statistics and post-hoc analyses
for the carbonate parameters are presented in Table S2. A linear regression analysis of the pH and
the buffering capacity (AT/DIC) at t0 between Scenario and Time of the day revealed that there is a
significant relationship between these two variables, were the pH decreases, the buffering capacity
decreases (R2 = 0.26, P < 0.001, Fig. 3.1b), with the exception of RCP4.5 mid-day, that showed a
AT/DIC increased when pH increased (Fig. 3.1b).
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3.4.2. Dissolution rates
Analysis of alkalinity change through the incubation periods revealed that, after an 8 week
treatment period, the sea cucumbers had no direct detectable impact on CaCO3 dissolution (Fig.
3.2a). The main drivers for calcium carbonate dissolution in the experiment were Scenario RCP8.5
[Scenario: F(3,36) = 3.08, P= 0.04] and diurnal changes [Time: F(1,36) = 11.06, P= 0.002]. The
post-hoc test (P < 0.05 for all comparisons) revealed that PI, PD and RCP4.5 have an equal impact
on system CaCO3 dissolution, producing on average of 69%, 93% and 84% less dissolution than
RCP8.5 respectively (Fig. 3.2a). The effects of Scenario and Time were found to be additive for this
variable except for RCP8.5, which dissolved calcium carbonate even by day [Time x Scenario:
F(3,36) = 1.15, P= 0.34]. At night, calcium carbonate dissolved in the system under all Scenarios
(Fig. 3.2a). The effect of alkalinity produced by TAN (ΔTAN) on calcium carbonate dissolution
over the incubation periods was non-significant for all Scenarios.
Finally, dissolution of sediments estimated directly by changes in grain size after the 8 weeks of
experiment was non-significant between any Scenario, Condition or their interaction (P > 0.05),
suggesting that dissolution of CaCO3 was equivalent across all grains sizes, leading to no
proportional change.
3.4.3. O2 flux
The analysis of O2 flux revealed that Time x Scenario [F(3,40)=4.5769, P=0.008] and Time x
Condition [F(1,40)=5.4204, P=0.025] were both significant for this parameter (Fig. 3.2b-c).
Scenario RCP4.5 and RCP8.5 had lower net O2 production at mid-day in comparison with
Scenarios PI and PD (Fig. 3.2b). Furthermore, +SC tanks had significantly greater net O2
production at mid-day than -SC tanks (Fig. 3.2c). At midnight, regardless of the Scenario and
Condition, O2 uptake was not significantly different across all tanks (Fig. 3.2b-c).
3.4.4. Nutrient production
When analysing the rates of TAN production, the interaction between Condition x Scenario
presented significant differences [F(3,16)=4.52, P=0.018], showing that +SC aquaria for the PI
Scenario had greater ammonia uptake than most of the remaining Condition and Scenario and that
PI is the only Scenario capable of producing a difference between Condition +SC and –SC, where
all the remaining Scenario are not (Fig. 3.2d). Furthermore, significant differences between time of
day were observed [F(1,16)=4.5906, P=0.048], with a 3-fold decrease in net uptake between mid-
day and midnight (Fig. 3.2e).
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The analysis of NO2- showed that neither the animals nor time of day had an effect on net NO2
-
uptake and only Scenario produced significant differences after 8 weeks [F(3,16)=8.4074,
P=0.001]. Scenario PI and PD had ~50% greater net uptake of NO2- than Scenario RCP4.5 and
RCP8.5 (Fig. 3.2f). In the case of other nutrients, net production of PO43- and NO3
- was not
significantly different (P > 0.05) for all factors and interactions (Scenario, Condition and Time).
3.4.5. Pigment and infaunal analysis
The HPLC analysis of photosynthetic pigments revealed that the only significant differences in
pigment concentrations were seen for total chloropigments (chlorophyll a + phaeophytin) (Table
S3). When observing total chloropigments, there was a significant interaction between Time x
Scenario [F(1,10)=5.65804, P=0.034] with the concentration of chloropigment being greatest at
mid-day under the PD [post-hoc: PD mid-day ≥ RCP8.5 midnight = RCP8.5 mid-day ≥ PD
midnight] (Table S3). Furthermore, +SC tanks appeared to have ~8.3% more total chloropigments
than –SC tanks, but the difference was not significant (P > 0.05).
Infaunal analysis revealed that the main group present in the sediments was benthic Foraminifera
(ranging between 70% and 93% of the infaunal composition, Fig. S3). Within this group, there were
significant differences in densities produced by Scenario [F(3,16) = 9.12, P = 0.001, post-hoc:
RCP4.5 = PD > RCP8.5=PI] and the interaction between Scenario and Condition was significant
[F(3,16) = 5.64, P = 0.008, post-hoc: RCP4.5-SC ≥ PD+SC = RCP4.5+SC = PI-SC ≥ RCP8.5+SC
= RCP8.5-SC ≥ PI+SC ) (Fig. S3a). However, these changes were not driven by changes to living
taxa of Foraminifera as these appeared to be absent from the sediments (Mamo, 2001).
Furthermore, Crustacea, Polychaeta and Gastropoda were not significantly modified by any
Scenario or Condition (P > 0.05, Fig. S3b). Lastly, in the presence of H. atra, there were
significantly greater densities of Nematoda [Condition: F(1,16) = 6.39, P = 0.02] and
Echinodermata [Condition: F(1,16) = 4.5, P = 0.05]. However, Echinodermata were present in only
3 tanks containing sea cucumbers and were therefore omitted from Fig. 3.3.
3.4.6. Microbial communities
The abundance of microbial communities associated with the sediments determined by qPCR
analysis, showed that the sea cucumbers had no impact on the overall abundance after 8 weeks
(Condition: F(1,8) = 2.8695, P=0.1287). The sole factor accounting for the differences was
Scenario (F(1,8) = 10.3491, P=0.0123), revealing after the post-hoc analysis a significant decrease
in microbial abundance under the Scenario RCP8.5 (Fig. 3.4a). Alpha Diversity was only
significantly modified by Scenario when observing Richness (Sobs) (F(1,8) = 7.4, P=0.0262, Fig.
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3.4b), and Shannon-Wiener index and Simpson’s E, however, were not significant for any factor or
interaction or factors (P > 0.05, Fig. S4a-b). The PERMANOVA analysis on the microbial
communities, based on the OTUs, showed that the relative abundance changed significantly only
with Scenario (Table S4). The analysis of the PCAs showed that the first five Principal Components
explain 82.2% of the variance (Table S5). Taxa such as family Piscirickettsiaceae and order
Sva0725 are positively correlated with Scenario RCP8.5, and families such as Rhodobacteracea are
positively correlated with PD (Fig. 3.5a). The most represented taxa across the different Condition
and Scenario were families Rhodobacteracea (~13-16%), Flavobacteriaceae (~9%) and
Piscirickettsiaceae (~7-8%) (Fig. 3.5b). These taxa were observed in the in situ sediment samples,
and represented some of the most abundant bacterial taxa in Heron Island lagoon (Fig. S5).
3.5. Discussion
3.5.1. General remarks
Mesocosm systems that included sediments and associated microbiota, with and without sea
cucumbers, were found to be significantly affected by end-of-century projected winter warming and
ocean acidification conditions, despite the fact that the business-as-usual (RCP8.5) winter
temperature was well below the maximum monthly mean seawater temperature (27 oC) observed in
the region for 2013 (http://coralreefwatch.noaa.gov/satellite/vs/index.php). RCP8.5 conditions
tended to decrease microbial abundance and modify microbial composition towards Acidobacteria
such as SVA0725 and RB25, a finding that is consistent with previous studies (Stevenson et al.,
2004). Moreover, RCP8.5 was associated with sulfur-oxidizing bacteria (SOB), sulfate-reducing
bacteria (SRB) and led to a reduction in net NO2- uptake, and an increase in daytime rate of CaCO3
dissolution. The presence of sea cucumbers was however, found to have no impact on these
outcomes. Furthermore, sea cucumbers had no impact on the pH buffering capacity (AT/DIC) of the
system, despite the fact that their activities enhanced net daytime O2 production within the
mesocosms.
3.5.2. Dissolution rates and carbonate chemistry modification
After 8 weeks, any initial ability of H. atra (Chapter 2, Table S4) to contribute to the dissolution of
sediments becomes insignificant relative to abiotic and other biotic factors. As with other studies,
end-of-century RCP8.5 temperature and pCO2 conditions were found to have the greatest apparent
effect on the sediments, leading to net calcium carbonate dissolution (e.g. Dove et al., 2013; Eyre et
al., 2014). Furthermore, consistent with other studies (Barnes & Devereux, 1984; Shamberger et al.,
2011; Silvermann et al., 2007), CaCO3 dissolution was significantly greater by night than by day,
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presumably due to lower daytime tank pCO2 and enhanced CO2 uptake by autotrophs in the
sediments (Werner et al., 2008). By day, calcification rates under PI, PD and RCP4.5 were not
significantly different, with biological activity decreasing tank pCO2 (Table S1) below the value
obtained in the upstream sump (Figure S1). Low rates of calcification, observed by day under PI,
PD and RCP4.5 are most likely attributable to a lack of living calcifiers within the mesocosms. In
this regard, genera of Foraminifera found on Heron Island lagoon that are associated with living
assemblages such as Elphidium (Mamo, 2011), were absent in the tank sediments.
There were also heterotrophic microbes such as families Saprospiraceae and Flavobacteriaceae
from the Phylum Bacteroidetes present in the sediments (Raulf et al., 2015). Heterotrophic
microbes are capable of assisting calcium carbonate precipitation through their metabolic activities
when the pH is maintained above 7.9 (Knorre & Krumbein, 2000). Under RCP8.5, tank pH
approximated 7.9 and even by day net calcification was negative suggesting that erosion was
significantly greater than any bacterial assisted inorganic precipitation of CaCO3, despite the
presence of relatively high water column aragonite (2.58 ± 0.2) and calcite (4.11 ± 0.31) saturation
states.
3.5.3. Microbial changes and their role in calcification/dissolution rates
Sea cucumbers played no role in modifying microbial composition and abundance. This finding
might not be surprising given that in the literature, there is contrasting evidence for the effect of sea
cucumbers on bacterial abundance. Some studies argue that species like H. atra can decrease
bacterial abundance (Moriarty et al., 1985), while others state the opposite (Hewson & Fuhrman,
2006). Our findings based on a long-term exposure to winter Temperature/pCO2 conditions,
revealed that the observed changes in microbial abundance were associated with Scenario only, and
showed a decrease associated with RCP8.5 conditions. The finding contrasts with that of Dove et
al. (2013) where RCP8.5 conditions lead to an increase in total microbial abundance over present
day conditions. Importantly, Dove et al. (2013) was performed over the height of the austral
summer, on much coarser reef slope sediments, when seawater temperatures at Heron Island are on
average 6-7oC warmer than in winter. Presently, studies that model environmental changes based on
climate related scenarios, tend to assume that present day benthic bacterial biomass is relatively
constant globally, and is therefore assumed to remain constant under future climate models (Jones
et al., 2014). Clearly, there is a need for more studies detailing the response of sediment-associated
microbes across a broad range of environments and climate scenarios given the importance of these
prokayotes to nutrient cycling in marine ecosystems such as coral reefs (for a review see Arrigo,
2005).
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A decrease in microbial abundance can led to a reduction in the functionality of the microbial taxa
present in the sediments. In this regards, a loss in microbial abundance co-occurred with a change in
microbial composition under RCP8.5. The most abundant taxa in the fine sediments taken from
Heron Island lagoon in winter were bacteria from the families Rhodobacteracea, Flavobacteriaceae
and Piscirickettsiaceae. Interestingly, from the microbes observed in the sediments, taxa from the
phylum Acidobacteria, associated with acidic conditions such as order SVA0725 and class RB25
(Stevenson et al., 2004), were correlated with RCP8.5 as opposed to PD Scenario. Moreover, other
taxa such as family Piscirickettsiaceae and genus Desulfococcus, were principally associated with
the business-as-usual, rather the PD, Scenario. SOB such as Piscirickettsiaceae (Giovannelli et al.,
2012) and SRB like Desulfococcus (Muyzer & Stams, 2008) are known to be present and active in
acidic conditions (~ pH 5) (Kelly et al., 1995; Koschorreck, 2008).
CaCO3 calcification/dissolution rates were estimated through changes in total alkalinity (AT) with
increases in alkalinity assumed to be coincident with CaCO3 dissolution. Therefore, processes that
can interfere with the alkalinity of the system and that do not involve calcification or dissolution
become important to providing accurate estimates. Piscirickettsiaceae (SOB) can transform
hydrogen sulfide (H2S) into sulfate (SO42-), contributing to a decrease in alkalinity by the addition
of H+ (Friedrich et al., 2001). On the other hand, in the presence of sulfate, SRB (Desulfococcus)
can lead to the production of H2S and HCO3-, increasing the alkalinity of the system (Muyzer &
Stams, 2008). However, because of the presence of SOB and SRB under RCP8.5, it is reasonable to
infer that they may cancel their relative contributions to alkalinity. The presence of Desulfococcus
(SBR) may contribute to the observed dissolution under RCP8.5 in another way. Desulfococcus
may acidify seawater, because they degrade organic matter to CO2 (Muyzer & Stams, 2008), and a
significant increase in CO2 was observed by day and by night under RCP8.5 compared to PD.
Furthermore, there are other sulfate-reducing bacteria (i.e. Desulfobulbaceae) in equivalent relative
abundances in the different Scenario (PD = 3.4%, RCP8.5 = 3.5%), which can even be
metabolically active under high CO2 and low pH (6.4 – 7.8) (Yanagawa et al., 2013). It is then
possible that some of the bicarbonate produced could be interconverted into CO2 (McConnaughey
& Whelan, 1997), leading to the reduction observed in CaCO3 production under RCP8.5.
SRB can be potentially inhibited by nitrate-reducing bacteria (NR) or nitrate reducing-sulfide-
oxidizing bacteria (NR-SOB). NR-SOB bacteria such as Thiomicrospira sp. (family
Piscirickettsiaceae), can inhibit SRB due to an increase in the production of NO2- (Haveman et al.,
2005). We observed that under RCP8.5 the production of NO2- was greater than in PD. In case that
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NR-SOB were present in the sediments (taxonomical classification reached only to family level for
this group), SRB may have been metabolically inhibited, contributing to the observed decrease in
alkalinity and calcification under this Scenario. Nevertheless, a combination of abiotic and biotic
factors of the system (i.e. sulfate-oxidation) is contributing to increase acidification and increase
CaCO3 dissolution under RCP8.5.
Finally, ANAMMOX is another metabolic process associated with sediment microbes, in which the
production of N2 occurs by the oxidation of NH4 + with NO2
− (Erler et al., 2013; Kartal et al., 2011),
therefore decreasing NO2- concentrations (Trimmer et al., 2005). ANAMMOX may be favoured
under high CO2 environments (Gazeau et al., 2014; Widdicombe & Needham, 2007). However, the
present study did not appear to support this hypothesis. A high CO2 environment, combined with
high temperature, did not affect TAN uptake compared to PD. Moreover, the results suggest that
PD is affecting TAN uptake by the sediments. Pre-industrial, in the absence of animals, generated a
~62% greater TAN uptake than PD, potentially affecting processes associated with nitrogen such as
ANAMMOX. RCPs did affect other nutrients compared to pre-industrial and PD, such as NO2-,
which uptake decreased with respect to the PD and PI environments. Importantly, key prokaryotes
associated with ANAMMOX such as Planctomyces (Dalsgaard et al., 2005) where equally
abundant across treatments.
3.5.4. O2 Production
Sea cucumbers were able to modify the diurnal production of O2 in the system, regardless the
Scenario. In this regard, sea cucumbers were able to increase net O2 by day, but were unable to
offset loss of daytime net production associated with the future climate scenarios. The lack of
apparent impact of sea cucumbers on either sediment microbes or microalgae (based on total
chloropigment concentrations) suggests that this increase in production is most likely linked to the
release of nutrients through bioturbation of the sediments (Biles et al., 2002), with changes to the
standing stock of microalgal eliminated by feeding. H. atra are sexually active on Heron Island both
at the start of winter and in summer (Harriott, 1982). Moreover, studies on sea cucumbers suggest
that aestivation is more likely to occur at higher rather than lower temperature (Ji et al., 2008). The
lack of impact of H. atra is therefore unlikely to be attributable to a lack of activity, since they need
to feed to reproduce and survive if they are not aestivating.
In terms of the role of bacterial communities on O2 production, there was a positive correlation
between the family Rhodobacteraceae and the PD, but not the RCP8.5 scenario. This could explain
the decrease in O2 production observed under RCP scenarios. Hassenruck et al. (2016) and Witt et
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al. (2011) have provided contrasting evidence concerning the ability of Rhodobacteraceae to thrive
under projected future CO2 ppm. Neither study, however, examined a long-term exposure to
temperature and pCO2 conditions. The present study supports the conclusion that these prokaryotes
are negatively affected, on a low latitude reef, by the combination of business-as-usual ocean winter
temperature and acidification. Rhodobacteraceae has been shown to enhance the primary
productivity on sediments (Raulf et al., 2015), potentially leading to increases in the content of
oxygen in the system. This observation fits well with our results, in which we observed both a
decrease in O2 content and in the abundance of Rhodobacteraceae under temperature and pCO2
conditions associated with future RCP projections. The study suggests that reductions in O2
evolution associated with a depleted pool of Rhodobacteracea are greater than that which can be
counter by H. atra O2 evolution.
3.5.5. Ocean acidification buffering capacity
Sea cucumbers were unable to produce any difference between treatments on the pH buffering
capacity of the system (AT/DIC), as evidenced in Chapter 2. Moreover, as observed in this study, a
significantly lower buffering capacity was observed by night under RCP8.5 (AT/DIC ≈ 1.08), in
accordance to the lower pH values (~ 7.8). The results are consistent with previous observations, in
which buffering capacity presented significantly lower values associated with low pH (~ 7.9)
(Wang et al., 2013)
Carbonate chemistry of the system appeared to be principally modified by organisms in the
sediments and abiotic factors. In this regard, the observed nighttime DIC increases may be
explained by significantly higher concentrations of HCO3- and pCO2 by night, and the greater
overall effect of CO2 in tanks exposed to Scenario RCP8.5, as observed in a previous mesocosm
study (Dove et al., 2013). These results are also supported by an increase in concentrations of
carbonate ions at mid-day which decreased by night. At night, in the experimental system, CO32-
appears to be rapidly converted into bicarbonate, consistent with a decrease in AT by night (Kleypas
& Langdon, 2006). These findings differ in their diurnal pattern with those observed in a short-term
experiment (Chapter 2), in which the possible conversion of carbonate ions to bicarbonate occurred
by day. Nevertheless, the effect of the animals on carbonate parameters and DIC production of the
system remains irrelevant in both studies. These results contrast with evidence provided by
Schneider et al. (2013), in which the H. atra significantly impacted the carbonate chemistry of the
system. For example, the contribution to DIC production by H. atra was significantly greater than
their contribution to AT production (Schneider et al., 2013). However, Schneider et al. (2013)
conducted their measurements in aquaria that lacked other organisms (i.e. prokaryotes), and on a
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single time point of the day (not accounting for diurnal or seasonal fluctuations), making
comparisons with the present study difficult.
3.5.6. Conclusions
In winter, the presence of H. atra did not significantly affect most of the variables tested in this
study with no effect on the abundance of pigments after 8 weeks of experiment. These results
contradict previous studies that found significant effects of these animals on the concentration of
microalgae (Uthicke, 2001a) and bacteria (Moriarty et al., 1985) in the sediments. However, in the
presence of sea cucumbers, there was a greater abundance of Nematoda and Echinodermata (Class
Ophiuroidea), which may compete for the ingestion of bacteria (Moriarty et al., 1985) and/or
microalgae. On the other hand, field studies have shown that H. atra has no significant effect on the
pigment concentrations (as a proxy for microalgae concentrations) (Lee et al., 2008) and the
bacterial composition of the sediments (Hewson & Fuhrman, 2006).
In any case, the study suggests that H. atra would have little effect on the microalgal and bacterial
communities on reefs (as suggested in Chapter 2) under winter season pCO2/Temperature
conditions. The implication of this study is that system processes associated with microalgae (i.e.
photosynthesis) and bacteria (i.e. calcification, ANAMMOX, sulfate-reduction) will have a greater
impact on seawater chemistry (Miyajima et al., 2001) under climate change than any role
attributable to H. atra. The results also suggest that this impact is likely to be driven by changes to
the biomass of specific taxa of sediment associated prokaryotes. The lack of effect on sediment
communities by this holothurian species observed under future climate change conditions, is then of
significant importance for reefs. H. atra represents one of the most abundant holothurian species of
the Indo-Pacific region (Conand, 1996) and the most common in Heron Island (Harriott, 1982).
They have the capacity to rework 24.5 kg of sediment per year per animal in some reefs (e.g.,
Lizard Island; see Uthicke, 1999), even more than other abundant (and larger in size) species like S.
chloronotus (Uthicke, 1999). H. atra is highly abundant and active throughout the year, and it
appears to be unable to modify the sediments. Therefore, it seems unlikely that other less abundant
and/or less active sea cucumber species will have a greater impact in a climate change context.
Finally, winter buffering capacity of the sediments in the presence or absence of sea cucumbers will
only be greater by day, and then only for the reduced emission Scenario (RCP4.5). This is of most
relevance because reef calcification is a process that occurs principally during the day (Albright et
al., 2013; Eyre et al., 2014). Therefore, the sediments during winter periods will contribute to
mitigate the daytime effects of OA only if they are under a reduced CO2 emission Scenario.
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Furthermore, on a diurnal basis, most likely sediments will not be able to buffer OA at a different
rate that they are doing during present day conditions.
3.6. Acknowledgments
We thank Pauline Dusseau for her contribution and assistance in the experiment and in the field
operations at Heron Island. We would also like to thank Aaron Chai, Giovanni Bernal Carrillo and
Annamieke Van Den Heuvel for assistance in the field and in the maintenance of the
pCO2/Temperature system at Heron Island. This research was co-funded by the Australian Research
Council (ARC) Centre for Excellence in Coral Reef Studies (CE0561435), ARC Linkage Grant
(LP110200874) (to S.D.), and Becas Chile Scholarship from CONICYT (Chile) (to F.V.-R.).
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Fig. 3.1. Differences given by Time (Day or Night) and Scenario (PI, PD, RCP4.5 and RCP8.5) on:
(a) pH; and (b) A linear relationship between ocean acidification buffering capacity (AT/DIC) and
pH. (a) and (b) were tested for the different Time and Scenarios. Data are means ± SE (n = 5-6) and
different letters near the bars represent significantly different groups.
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Fig. 3.2. Responses after 8 weeks of experiment in +SC tanks and –SC tanks under different
Scenarios (PI, PD, RCP4.5 and RCP8.5) and Time (Day or Night). (a) CaCO3 dissolution over the
incubations periods under different Scenarios and Time of day, were the gray bar represents mid-
day and the black bar represents midnight. (b) Mid-day (above gray bars) and midnight (above
black bars) O2 flux over the incubation periods under different Scenarios. (c) O2 flux over the
incubation periods under different Condition (+SC or –SC) and Time of day (mid-day = grey bars
and midnight = black bars). (d) TAN flux calculated from change in TAN concentrations over the
incubation periods under different Condition and Scenarios. (e) TAN flux calculated from change in
TAN concentrations over the incubation periods under different Time of day, were the gray bar
represents mid-day and the black bar represents midnight. (f) NO2- flux calculated from change in
NO2- concentrations over the incubation periods under different Scenarios. All data are means ± SE
(n = 5-6) and different letters near the bars represent significantly different groups.
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Fig. 3.3. Changes on densities of Nematoda after 8 weeks of experiment and under different
Condition (+SC or –SC). All data are means ± SE. The letters above the bars represent significantly
different groups.
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Fig. 3.4. Microbial changes after 8 weeks of experiment under different Condition (+SC or –SC)
and Scenarios (PD or RCP8.5). (a) Total microbial abundance based on qPCR data for the different
Condition and Scenarios, showing with asterisks the significant differences between PD and
RCP8.5. (b) The Richness (Sobs) for the different Condition and Scenarios, showing with asterisks
the significant differences between PD and RCP8.5. For all combinations n =3.
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Fig. 3.5. Changes in microbial composition after 8 week of experiment. (a) Principal Component
Analysis (PCA) of microbial composition at genus level under different Scenarios (PD = blue
vector, or RCP8.5 = red vector). (b) Heatmap based on 16S rRNA amplicons showing the bacterial
taxa associated with the different Scenarios (PD = blue or RCP8.5 = red) after 8 weeks of
experiment. Taxonomy was assigned based on the Greengenes database and summarized at the
Phylum level (top) and the genus level (bottom. In bold the most abundant taxa are shown). For all
combinations n =3.
(a)
(b)
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Table 3.1. PERMANOVA analysis for the carbonate chemistry parameters at t0 (including mgCaCO3 during the incubations) for the different
Condition (+SC or –SC), Scenarios (pre-industrial = PI, present day = PD, RCP4.5 = R4 and RCP8.5 = R8) and Time (Day or Night). Bold highlights
the factor(s) contributing to significant differences (n = 5-6).
Carbonate chemistry df SS MS (Pseudo)-F p(perm) Pairwise
Condition 1 3.1528 3.1528 0.5627 0.4735
Scenario 3 378.0000 126.0000 22.4900 0.0001 PI ≠ PD = R4 ≠ R8; PD ≠ R8; R4 ≠ R8
Time 1 107.0600 107.0600 19.1090 0.0001
Day ≠ Night
Condition x Scenario 3 11.0580 3.6861 0.6579 0.5954
Condition x Time 1 0.3238 0.3238 0.0578 0.8837
Scenario x Time 3 100.2500 33.4200 5.9647 0.0005 PI: Day = Night; PD: Day = Night; R4: Day ≠ Night; R8: Day ≠ Night
Condition x Scenario x Time 3 4.2291 1.4097 0.2516 0.8817
Residuals 72 403.3900 5.6027
Total 87 1002.2000
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Chapter 4: Modification of carbonate chemistry and production under summer IPCC
scenarios in the presence of Holothurians and carbonate sediment associated-organisms
Francisco Vidal-Ramirez1*, Olga Pantos2,3, Gene W. Tyson2,4 and Sophie Dove1,3
aSchool of Biological Sciences and Australian Research Council Centre for Excellence in Coral
Reef Studies, The University of Queensland, St. Lucia, Queensland 4072, Australia
bAustralian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, St. Lucia,
Queensland 4072, Australia
cGlobal Change Institute, The University of Queensland, St. Lucia, Queensland 4072, Australia
dAdvanced Water Management Centre, University of Queensland, St. Lucia, Queensland 4072,
Australia
Corresponding author:
Francisco Vidal-Ramirez
School of Biological Sciences, The University of Queensland, Level 7, Gehrmann Laboratories
(Building #60), St. Lucia, QLD 4072, Australia. Tel.: +61-450704403; fax: +61-7 33651692.
E-mail address: [email protected]
Target Journal: PLoS ONE
Original Research Article
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4.1. Abstract
Metabolic activity tends to increase with temperature until it attains a thermal threshold. End-of-
century temperatures projected for summer may exceed this threshold for some coral reef
organisms, but not others. Likewise, ocean acidification may independently or synergistically
impact organism performance. Holothurians such as Holothuria atra and microbes have the
potential to dissolve sediment calcium carbonate. In this regard, the effects of summer climate
change conditions on sea cucumber performance and interactions are unknown. In the present
study, the impacts of a variety of summer climate scenarios on lagoonal mesocosms were
examined. Lagoonal mesocosms were constructed from sediments together with associated biota in
the presence and absence of the sea cucumber H. atra. The ability of the biota to alter seawater
chemistry and the rate of sediment erosion under four Temperature/pCO2 conditions (Scenarios)
was investigated; present day (PD), pre-industrial (PI) and two end-of-century IPCC Scenarios
(RCP4.5 and RCP8.5 = RCPs). Furthermore, we tested the effect of these Scenarios on the
abundance and community structure of the biota. The results showed that H. atra significantly
increased AT due to an increase in TAN by night, leading to a greater ocean acidification buffering
capacity (AT/DIC). However, AT/DIC in the presence of the animals was not sufficiently strong to
overcome the downward trend in buffering capacity produced under the pCO2/Temperature
Scenarios proposed. CaCO3 calcification rates where highly variable especially for PI and PD
samples where replication was reduced due to sample breakage in transport. Tank AT was found to
account for 10% of the net calcification rates observed across all Scenarios and conditions, with all
the other biotic and abiotic variables estimated combining to account for a further ~14%, with no
single other variable accounting for more than 3%. Analysis of the data therefore suggest that water
column AT was the dominant driver for sediment calcification within each tank, irrespective of the
presence of distinctive biotic communities and pH buffering by sea cucumbers. Finally, microbial
abundance increased significantly under RCP8.5, but the increase was proportional across taxa,
suggesting that this lack of change in microbial composition was concomitant with the observed
equivalence for O2 flux, calcification rates and buffering capacity under all Scenarios tested.
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4.2. Introduction
Increased atmospheric CO2 has led to a decrease in seawater pH (ocean acidification = OA) and the
rise in sea surface temperature (SST) in coral reefs (Hoegh-Guldberg et al., 2007; IPCC, 2014).
The effects of OA and increased SST on coral reef building organisms and net ecosystem
calcification have been proposed to be deleterious (Andersson & Gledhill, 2013; Dove et al., 2013;
Eyre et al., 2014; IPCC, 2014). However, our understanding about climate change impact on
calcium carbonate dissolution is limited (Eyre et al., 2014). Calcium carbonate in coral reef
ecosystems is mainly stored in permeable sediments, and under present day conditions of
temperature and CO2, conservative estimations of CaCO3 dissolution for these sediments have been
reported to be 0.09 – 0.50 kg m-2 y-1 (Eyre et al., 2014). However, dissolution rates are inversely
proportional to sediment grain size (van Woesik et al., 2013; Walter & Morse, 1984), likely to be
affected by associated biota (Andersson & Gledhill, 2013), and pore water advection (Cyronak et
al., 2013).
On global scales, calcium carbonate accretion by organisms such as corals will decrease under
future ocean acidification and increased SST conditions, and dissolution will be greater than
calcification for these organisms (Dove et al., 2013; Eyre et al., 2014). However, the impact of
climate change on non-coral invertebrates and protists that play a role in net calcium carbonate
dissolution, such as crustaceans, polychaetes, formanifera and sea cucumbers, is not well
understood (Andersson & Gledhill, 2013; Przeslawski et al., 2008).
On local scales, non-coral invertebrates can modify net rates of CaCO3 dissolution. Holothuria atra
is one of the most conspicuous and abundant holothurian species in the Great Barrier Reef (GBR)
and indo-pacific coral reefs (Conand 1996). This species has the ability to promote calcium
carbonate dissolution over short time scales (hours to days) (Chapter 2; Schneider et al., 2013).
However, the impact of a long-term exposure to climate change conditions that include diurnal and
seasonal fluctuations on H. atra performance and capacity to dissolve CaCO3 have not been tested
over a summer period.
Bioturbation and the release of ammonia are other functions of H. atra in coral reefs (Massin, 1982;
Uthicke, 2001a-b). The impact of the animals on sediments due to their bioturbation and nutrient
production can led to the modification of sediment microalgae and oxygen production (Uthicke
2001). Furthermore, due to their feeding behavior, H. atra can potentially modify bacterial
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abundance and bacterial composition within sediments (Hewson & Fuhrman, 2006; Moriarty et al.,
1985).
The role of other organisms, such as prokaryotes, in the dissolution of sediments under future
summer climate conditions has not been studied in significant detail. Moreover, microbial
communities perform essential nutrient cycling services on coral reefs (Arrigo, 2005). However,
microbes are often not considered in climate change studies (Jones et al., 2014; Webster & Hill,
2007). Although there are suggestions that some taxa of bacteria, such as Acidobacteria, will more
readily prosper under acidification (Stevenson et al., 2004), or high temperatures (e.g., Firmicutes,
thermophilic bacteria, see Muller et al., 2014). It has been shown that microbial communities can
shift under elevated pCO2 and/or temperature conditions similar to those of RCP8.5 (Webster et al.,
2008; Webster et al., 2016). Prokayotes that colonize sediments in coral reefs can increase
dissolution rates through the metabolic release of acids (Andersson & Gledhill, 2013). Specific
prokaryotic processes can also influence alkalinity measurements of calcification. ANAMMOX can
lead to increases in alkalinity because the uptake of NH4+ increases the rate of NH3 protonation
(Erler et al., 2013). Sulfate-reduction (a strictly anaerobic process) can increase HCO3− and hence
alkalinity (Muyzer & Stams, 2008), or sulfur-oxidation can decrease alkalinity though protonation
(Friedrich et al., 2001). Moreover, many sediment associates are important to the carbon cycle and
hence to system DIC concentrations through the uptake and release of CO2. Therefore,
environmentally driven changes to microbial composition can have a significant effect on the
buffering capacity of the system against ocean acidification, measured as the ratio between total
alkalinity (AT) and DIC, irrespective of their role in sediment dissolution/calcification (Egleston et
al., 2010; Wang et al., 2013).
Information to understand long-term dynamics of sedimentary systems under climate change (e.g.,
summer responses) is still needed. Interestingly, other studies have found that over a long-term
experiment during summer, sediments from patch reefs increased their microbial abundance under
RCP8.5 when compared to present day conditions of temperature and CO2 (Dove et al., 2013). The
observed increase in microbial abundance may have been supported by an increased in temperature
and/or in acidification. Alternatively, an increase in organic matter due to high coral mortality under
RCP8.5 may have led to an increase in the abundance of prokaryotes (Patten et al., 2008; Wild et
al., 2004). Increases in detritus may also have spurred sediment pore-water acidification by
increasing the relative abundance of heterotrophic prokaryotes leading to the observed increased
rates of sediment dissolution (O. Pantos pers comm.).
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The aim of this Chapter is to test the relative contributions of H. atra and sediment-associated biota
over OA buffering capacity (AT/DIC), calcification/dissolution rates and related processes (such as
production of oxygen and nutrients), under different austral summer Scenarios of
temperature/pCO2, including projected Scenarios of climate change (IPCC, 2014). In this
experiment, summer represents a season where present day temperatures can increase above MMM
and when DOM may be decreased due to a lack of mortality of other reef organisms (e.g., corals) in
our simplified reef mesocosms. Changes within different seasons of the year (e.g., compared to
winter season; Chapter 3) may help explain and better estimate potential changes produced by the
sediments, the animals, RCPs Scenarios or their interactions in regards to AT/DIC and calcium
carbonate budgets.
4.3. Materials and methods
4.3.1. General setup
Following the methodology of Chapter 3, sediments were collected from Heron Island lagoon
(23o26’550’’S; 151o56’629’’E) in early December 2013, at a depth of ~5 m. Sediments to study
microbes in situ were also collected (see below methodology for collection and processing of
samples for microbial analysis). Twenty-four individuals of Holothuria atra (weight 247.6 ± 8.48 g
and length of 21.8 ± 0.5 cm) were collected by SCUBA at the same location. Sediments and H. atra
were collected 4 days apart.
Sediments and animals were transferred after collection to experimental tanks at Heron Island
Research Station (The University of Queensland). Sediments were mixed and distributed in 48
outdoor glass aquaria as described in Chapter 3, creating a 3 cm layer of sediments in each of these
aquaria. Sediments were maintained with running seawater for 7 days before the addition of the
animals. Sea cucumbers were left in plastic aquaria with running seawater, allowed to eliminate all
the sediments from their intestines during 48h before introducing them into the experimental
aquaria with the sediments. After 7 days with running seawater, the 48 experimental tanks
(representing an area of sediment of 1049.4 cm2 per tank) and animals were randomly assigned to
any of 2 conditions: aquaria with animals (hereafter +SC tanks or Condition +SC) and without
animals (hereafter –SC tanks or Condition –SC), resulting in 24 +SC tanks and 24 -SC tanks.
4.3.2. Temperature/pCO2 system
The Temperature/pCO2 system that was used to achieve the seawater conditions for the experiment
was as reported in Dove et al. (2013). This system allowed us to manipulate and regulate pCO2
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concentrations and seawater temperature that were then delivered to the flow through experimental
tanks (see Chapter 3 and Dove et al., 2013 for further detail).
Seawater at different Temperature/pCO2 conditions (hereafter Scenarios) was delivered to a total of
12 experimental tanks per Scenario (6 with the Condition +SC and 6 with Condition -SC) at a flow
rate of 1 L min−1. The pH levels within the experimental tanks (National Institute of Standards and
Technology scale) were measure for each sampling point, and were used to calculate the carbonate
parameters (see below). Moreover, they were not used to control CO2 dosing in the
Temperature/pCO2 system (Dove et al., 2013).
Four Scenarios were produced and delivered to the experimental tanks. Temperature and pCO2 of
the conditions of the system were as proposed by the IPCC (2014) and Rogelj et al. (2012).
Therefore, we tested the effects of four Scenarios over a 2 month experimental period during
summer 2014: Present day (PD) Scenario (temperature range: 26.6-27.6 °C, pCO2 range: 382-482
ppm), Pre-industrial Scenario (temperature offset of -1°C below PD; and pCO2 offset of 100 ppm
below PD), Scenario RCP4.5 (temperature offset of +1.8°C above PD and pCO2 offset of +180 ppm
above PD) and Scenario RCP8.5 (temperature offset of +3.6°C above PD and pCO2 offset of +570
ppm above PD) (IPCC 2014; Rogelj et al., 2012) (Fig. S1).
4.3.3. Experimental design and incubations periods
Each Scenario was randomly assigned to the experimental tanks ending with n = 6 for each
combination of Scenario and Condition. As for Chapter 3, an acclimation period applied a
progressive increase in the proportion Scenario water to reef water every 4 days as follows: 20%,
40%, 60% and 80%, until reaching 100% (full Scenario) on day 16. The experimental period began
with the conclusion of the acclimatization period, and +SC tanks and -SC tanks were kept exposed
to the full Scenario (100%) for a period of 8 weeks (exposure period).
At the end of the experimental period, incubations were carried out in order to determine changes in
Alkalinity, nutrients (TAN, nitrate, nitrate, phosphates) and O2 flux. Seawater samples for alkalinity
and nutrients were collected at mid-day and midnight for all incubations during t0 and t1 (for
further information on the materials and proceedings used, see Chapter 3 and Vidal-Ramirez &
Dove 2016). These incubations were performed over ~1 h during the day (between11:00am-
3:00pm, hereafter referred to as ‘mid-day’ or ‘day’) and nighttime (between11:00pm-3:00am,
hereafter referred to as ‘midnight’ or ‘night’). Before sealing the aquaria for the incubations,
temperature and O2 loggers (RINKO ARO-USB; JFE Advantech) were inserted in each aquarium to
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measure the O2 flux per incubation. The beginning of the incubation was defined as t0, and the end
as t1. pH measured at t0 and t1 was used to calculate the carbonate parameters with CO2SYS
(Chapter 2, Chapter 3; Pierrot et al. 2006).
4.3.4. Nutrient analysis
Samples for nutrient analysis were collected in 10 mL tubes (SARSTEDT Australia Pty Ltd.) at t0
and t1 (n = 3). The samples stored at -20ºC were analysed at the Advance Management Water
Centre of the University of Queensland. Following the methodology of Chapter 3, analysis of TAN
(NH3 + NH4+), nitrite, nitrate and phosphate were performed with a Lachat QuikChem8500 Flow
Injection Analyzer (Saros et al., 2014).
4.3.5. CaCO3 dissolution rates and carbonate chemistry
Calcium carbonate dissolution was estimated by changes in total alkalinity (AT), between Condition
(+SC and -SC), Scenarios (PI, PD, RCP4.5 and RCP.8.5) and Time (mid-day and midnight). In
order to estimate changes in AT , 100 mL seawater samples were obtained at t0 and t1. Samples
were processed according to Chapter 2 and Chpater3, in order to estimate calcium carbonate
dissolution using the alkalinity anomaly technique (Chisholm & Gatusso, 1991). Calcium carbonate
dissolution rates were then estimated by ΔACaCO3 = ΔAT - ΔATAN, and final values converted to
mgCaCO3 h-1 per unit of area (m-2).
Carbonate parameters after 8 weeks were estimated using CO2SYS (Chapter 2 and 3; Pierrot et al.,
2006), with constants proposed by Mehrbach et al. (1973) and refitted by Dickson and Millero
(1987). Input conditions for CO2SYS were: AT , salinity (34.84 ± 0.08 SE), temperature and pH.
The output parameters were pCO2 (µatm), bicarbonate (HCO3-), carbonate (CO3
2-), Dissolve
Inorganic Carbon (DIC), calcite (Ωcalc) and aragonite (Ωarag) saturation states.
As a proxy of the potential direct impact of the animals and/or sediment-associated organisms on
CaCO3 dissolution, the grain size of sediments was assessed (n = 6) at the end of the experiment
according to Chapter 2, for all Condition and Scenarios.
4.3.6. Microbial composition and abundance
Microbial communities were assessed at the end of the experiment according to Chapter 3.
Sediments from the top 1 cm were collected at midnight after the incubations, from Scenarios PD
and RCP8.5 and for each Condition (+SC and –SC), directly into 5 ml sterile tubes (Sarsted,
Australia Pty. Ltd.) (n = 3). The remaining seawater in each tube was discarded and the sediments
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immediately resuspended in LifeGuardTM Soil Preservation Solution (MO BIO Laboratories, Inc.)
and stored at -20oC, according to the manufacturer’s instructions. In the laboratory, samples were
thawed on ice for approximately 30 minutes prior to total genomic DNA extraction. Extractions
were performed on ~250 mg of well-mixed sediments using PowerBiofilm® DNA Isolation Kit
(MO BIO Laboratories, Inc.) following the manufacturer’s instructions. Following the extraction, a
Universal Primer pair targeting the V6/V8 regions of the 16S rRNA gene was used for the PCR
amplification: 926F (5’-AAACTYAAAKGAATTGRCGG-3’) and 1392wR (5’-
ACGGGCGGTGWGTRC-3’), which are modified to contain an Illumina specific adaptor sequence
(see Chapter 3 for further details). To estimate PCR amplification of Total genomic DNA in the
laboratory prior to the 16S amplicon sequencing, a PCR was carried out with an amplification
mixture (25 µL) including PCR Buffer (2.5 µL), dNTPs (0.5 µL = 10 mM each), MgCl2 (2 µL = 25
mM), Forward primer (0.5 µL = 10 µM), Reverse primer (0.5 µL = 10 µM), BSA (0.75 µL),
Platinum® Taq DNA polymerase (Invitrogen = 0.1 µL), DNA template (1 µL) and DNA/RNA-free
water (17.15 µL). Cycling conditions were set-up according to Dove et al. (2013). Following PCR
amplification, DNA templates of ~5ng/µL (including 1 blank form the extraction kit and 1 from the
PCR mix) were sent for 16S amplicon sequencing to the Australian Centre for Ecogenomics (ACE)
at The University of Queensland (see Chapter 3). The Forward amplicon reads, produced by the
MiSeq Illumina platform, were processed using a modified version of the QIIME pipeline proposed
by Caporaso et al. (2010). Adaptor removal and trimming was achieved using Nesoni clip
(https://github.com/Victorian-Bioinformatics-Consortium/nesoni, see Lo et al., 2015). Sequences
were assigned to an operational taxonomic unit (OTUs) using pick_open_reference_otus.py at a
97% sequence identity using default settings. Taxonomy of each OTU was assigned using default
reference against Greengenes database October 2013 and the OTU table was ultimately rarefied at
10000 reads and corrected by the 16S rRNA gene copy number (estimated by qPCR) using
CopyRighter (Angly et al., 2014). The OTUs present in the blanks were removed in QIIME from
the final OTU table used in the analysis. After Chapter 3, Alpha diversity was described by the
Shannon-Wiener index, Richness (Sobs) and Simpson’s E.
Microbial abundance in the sediments was estimated using qPCR, also carried out at ACE. In
accordance with Chapter 3, DNA templates of ~5 ng/µL were used for sequencing, and for each
sample extracted were corrected the amount of 16S gene copies per gram of sediment used to
perform a quantitative polymerase chain reaction (qPCR) using 5 µl of 2X SYBR Green/AmpliTaq
Gold DNA Polymerase mix (Life Technologies, Applied Biosystems), 4 µl of microbial template
DNA and 1 µl of primer mix. The 16S 1406F/1525R primer set (0.4 µM) was designed to amplify
bacterial and archaeal 16S rRNA genes: F - GYACWCACCGCCCGT and R -
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AAGGAGGTGWTCCARCC. The rpsL F/R primer set (0.2 µM), used for inhibition control,
amplifies Escherichia coli DH10B only: F - GTAAAGTATGCCGTGTTCGT and R -
AGCCTGCTTACGGTCTTTA. Three dilutions 1/50, 1/250 and 1/500 (microbial template DNA,
16S 1406F/1525R primer set) as well as an inhibition control (E. coli DH10B genomic DNA, rpsL
primer set) were run in triplicate for each sample. The PCR was run on the ViiA7 platform (Applied
Biosystems) including a cycle of 10 min at 95C (AmpliTaq activation) and 40 cycles of [15 s at
95oC followed by 20 s at 55oC and 30 s at 72oC]. A melt curve was produced by running a cycle of
2 min at 95oC and a last cycle of 15 s at 60ºC. The cycle threshold (Ct) values were recorded and
analyzed using ViiA7 v1.2.1 software.
4.3.7. Sediment photosynthetic pigments and infauna
Sediments were collected following the methodology of Chapter 3. Extraction of pigments was
conducted on 0.5 ± 0.01 g of dry sediments, in cold 100% acetone according to Buffan-Dubau &
Carman (2000) after 10 min of sonication. Dark incubations at -80oC were conducted on the
extracted sediments before analysis at the HPLC. Following the incubations, pigments were filtered
at 0.22 µm and HPLC analysis conducted following the methods of Dove et al., (2006) and Zapata
et al., (2000). The pigments analysed were chlorophyll a, phaeophytin and total chloropigments
(calculated as the sum of chlorophyll a + pheophytin, see Lee et al., 2008). Replication was n = 3
for all Scenarios, Condition and Time.
Sediment fauna were analysed as in Chapter 3. The major groups identified were Crustacea,
Foraminifera, Nematoda, Polychaeta and Gastropoda, and their abundances expressed per gram (n =
3 for all Scenario and Condition). Echinodermata were not identified in the sediments and live
Foraminifera were assessed following Mamo (2011).
4.3.8. Statistical analysis
The cumulative effect (t0) of the 3 factors tested, Condition (+SC and –SC), Time (mid-day and
midnight) and Scenario (PI, PD, RCP4.5 and RCP8.5) was assessed on the carbonate parameters
using a three-way PERMANOVA (PRIMER v6 software and PERMANOVA + add on, PRIMER-
E). In accordance with Chapter 3, PERMANOVA was based on Bray Curtis similarity, Type III
sum of squares, 9999 permutations applied to a reduced model and a dummy variable (+1) (Bender
et al., 2014). When statistical differences were detected, as per Chapter 3, three-way repeated
measures ANOVAs (factors: Condition, Scenario and Time) were performed on the individual
carbonate parameters to test for differences, with Time specified as the within-subject factor
(Chapter 2 and Chapter 3; Quinn & Keough, 2002). For the analysis of calcium carbonate
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dissolution rates [Δ(t1-t0) h-1], many collected samples were broken during transport from Heron
Island to The University of Queensland prior to the analysis, ending with an n = 3-4. Because of
this, a power analysis based on winter calcification data (Chapter 3) was conducted (Quinn &
Keough, 2002). An n = 3 was found to be sufficient to achieve P < 0.05 between different winter
Scenario (Quinn & Keough, 2002). Therefore, we could have expected to observe significant
differences even with small replication (n = 3), but summer calcification data proved to be very
noisy and significant differences were not observed under any of the treatments proposed using a
PERMANOVA analysis (see above for details). Therefore, an analysis to assess the effects of
different variables on calcification rates was performed using a distant based linear model (DistLM)
(Sawall et al., 2015). Due to the lack of differences in calcification/dissolution rates, and noisiness
of the data especially under PD Scenario, 2 data points (outliers, see Quinn & Keough, 2002) were
removed from the analysis, and we constructed a resemblance matrix for the remaining calcification
data based on Bray Curtis similarity and a step-wise procedure with 9999 permutations. Two
distinct DistLM analyses were then performed; the first was based on all Scenarios and included the
following parameters: light; carbonate parameters; O2; TAN; chlorophyll a concentrations;
phaeophytin concentrations. The second was based only on PD and RCP8.5 data, and included the
same parameters used in the first DistLM analysis plus the potential ability of microbes to explain
calcification responses. Therefore, microbial total abundance, relative abundance of the 3 most
abundant bacterial phyla (Pirellulaceae, Flavobateraceae, Piscirickettsiaceae) and abundance of
phylum Crenarchaeota from Archaea (because of its high variability in PD samples) were included.
Finally, the DistLM analyses were performed using PRIMER v6 software and PERMANOVA +
add on, PRIMER-E, and visualized for the significant predictor with scatter plots.
Nutrients (TAN, NO2-, PO4
3- and NO3-), O2 flux and pigments were analysed, for each of the
incubations, with three-way repeated measures ANOVAs (factors as per calcium carbonate
dissolution). No transformations were needed in order to meet the assumptions when tested with
Levene’s test for homogeneity of variances and K-S test for normality. Significant differences for
the interactions were assessed Least Significance Difference (LSD) test.
Grain size differences between Condition and Scenario were analysed as described in Chapter 3.
Different size fractions were converted to percentage of the total dry weight of the samples, and
then to proportions. An arcsin transformation was conducted and differences analysed with
ANOVA (Chapter 2 and Chapter 3; Quinn & Keough, 2002).
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Microbial communities between Condition and Scenario were analysed as in Chapter 3, with a two-
way PERMANOVA with 9999 permutations (PRIMER-E) using data transformed to square root
and Bray Curtis similarity (Hartmann et al., 2015; Lazarevic et al., 2013; Sacristan-Soriano et al.,
2011). Richness (Sobs), Shannon Wiener index, Simpson’s E and qPCR were analysed with two-
way ANOVAs, using as fixed factors Condition and Scenario.
4.4. Results
4.4.1. Summer carbonate chemistry
Seawater carbonate chemistry, at the start of all incubations (t0, Table S1), was found to respond
interactively only for Condition x Time (PERMANOVA, p(perm) = 0.0272, Table S2). The
PERMANOVA also revealed significant differences due to Condition (PERMANOVA, p(perm) =
0.0022, Table S2) and Scenarios (PERMANOVA, p(perm) = 0.0001, Table S2). Most notably,
individual analysis showed that AT/DIC had lower values under RCP8.5 (1.10 ± 0.01, P < 0.001,
Fig. 4.1a) and greater values in the presence of H. atra (1.17 ± 0.01, P = 0.005, Fig. 4.1b). pH and
CO32- increased in the presence of H. atra (Table S3); pH and CO3
2- decreased by night (Table S3);
pH and CO32- decreased under RCP8.5, and HCO3
- and DIC increased under RCP8.5 (Table S3).
Tank pCO2 was lower by day (406 ± 31 µatm) than by night (459 ± 44 µatm); and whilst the
presence of H. atra reduced tank pCO2, the animals did not reduce pCO2 sufficiently to overcome
the effects of increasing acidic upstream Scenarios on tank pCO2 (Table S3). Tank AT was
significantly greater by day (2257 ± 11 µmol kg-1) than by night (2218 ± 10 µmol kg-1); and H. atra
significantly increased AT (2272 ± 5 µmol kg-1) compared to tanks in their absence (2197 ± 11 µmol
kg-1) (Table S3).
4.4.2. Nutrient production
Total Ammonia Nitrogen (TAN) production was significantly affected by the presence of H. atra,
with TAN production in +SC tanks and TAN uptake in –SC tanks (P = 0.03, Fig. 4.1c). A
significant interaction between Condition and Time (P = 0.045, Fig. 4.1c) revealed that only at
night, was there a decrease in TAN concentrations of ~121% between +SC tanks and –SC tanks.
Therefore, only by night, did H. atra counter the net TAN uptake observed in their absence.
The production or uptake of nitrate, nitrite and phosphate were not significantly modified by any of
the factors (P > 0.05).
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4.4.3. Microbial communities
Total microbial abundance was only affected by Scenario (F(1,8) = 7.2418, P = 0.027), where a
~31% increase in microbial abundance was observed under RCP8.5 relative to PD Scenario (Fig.
4.1d). Microbial abundance was not modified by the presence or absence of H. atra (F(1,8) =
2.0843, P = 0.1868).
Diversity indexes were not significantly affected by any of the factors tested (P > 0.05; Richness,
Shannon-Wiener and Simpsons_E: Fig. S2 a-c respectively). The multivariate analysis of microbial
communities based on OTUs, revealed that no significant effects of H. atra presence
(PERMANOVA, p(perm) = 0.0733), Scenarios (PERMANOVA, p(perm) = 0.0992) or their
interaction (PERMANOVA, p(perm) = 0.8131) on relative microbial abundance (Fig. S3). On
average, the most abundant taxa were families Pirellulaceae (8.7%), Flavobacteriaceae (8.2%) and
Piscirickettsiaceae (7.7%) (Fig. S2), which were also dominant within samples of in situ sediments
(Fig. S5).
4.4.4. Pigment and infaunal analysis
Analysis of photosynthetic pigments revealed significant changes in chlorophyll a concentration for
the interaction between Condition x Scenarios x Time (Table 4.1). Sea cucumbers presence
correlated with decreased chlorophyll a concentrations by day, under PD (0.15 ± 0.02 mg L-1) and
RCP4.5 (0.1 ± 0.03 mg L-1), when compared to PI by night in the absence of the animals (0.53 ±
0.24 mg L-1). Phaeophytin concentrations decreased significantly by ~15.7% in the presence of the
animals (Condition: F(1,24) = 5.34, P= 0.03; +SC: 0.44 ± 0.03 mg L-1 and –SC: 0.51 ± 0.003 mg L-
1), regardless of Scenario or Time. Total chloropigments however, were not significantly affected
by any factor or interaction (P > 0.05).
The analysis of sediment infauna was not modified by any of the factors tested (P > 0.05) (Fig. S4).
Furthermore, Foraminifera of genus Elphidium, associated with live assemblages (Mamo, 2011),
were detected in only 5 tanks (10% of all tanks), with non-significant differences between any
factor and interaction (P > 0.05). Elphidium sp. represented between 1.5% and 3.8% of the total
abundance of foraminifera when present and was never observed under RCP8.5.
4.4.5. Calcification rates
Net calcification was not significantly affected by any of the factors or their interactions, with mid-
day calcification values of 73 ± 37 mgCaCO3 m-2 h-1 and midnight calcification values of 15 ± 58
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mgCaCO3 m-2 h-1 (p(perm) > 0.05 for Condition, Scenarios, Time and their interactions; Table 4.2).
Interestingly, on average there was net positive calcification under all Scenario, and based on
DistLM analysis, calcification rates were positively correlated to AT . Amongst abiotic factors, tank
water column AT was positively correlated (P < 0.0172) and explained ~10% of the variability in
calcification rates observed across all scenarios tested. Other abiotic drivers, however, were only in
combination able to account for a further 14% of the variability in calcification (Fig. 4.2a, Table
4.3a). An analysis of the calcification response limited to PD and RCP8.5 that included microbial
information was able to account for 77% of the variability around observed rates of calcification,
but suggested that only tank alkalinity positively correlated with calcification rates (P < 0.0232),
and explained ~19% of the calcification data. Microbial total abundance and tank O2 flux each
explaining 7% and 5% of the calcification data respectively (Fig. 4.2b, Table 4.3b).
4.4.6. O2 flux
Time was the only factor able to produce significant differences in net O2 flux within the
experimental system (F(1,40) = 281.7; P < 0.001), with all Condition and Scenarios having
equivalent net O2 production by day (40 ± 3.34 mgO2 h-1) and net O2 uptake by night (-20 ± 0.71
mgO2 h-1) (Fig. 4.3). The presence of H. atra had no significant effect on rates of summer O2 flux
(P > 0.05). Scenarios yielded statistically equivalent net O2 production for all levels of the factor (P
> 0.05).
4.5. Discussion
Microbial abundance responded positively to increases in temperature and acidification associated
with RCP8.5 Scenario, however, did not lead to altered microbial community structure. Increases in
the abundance of microbes with increasing temperature and/or acidification are consistent with
previous studies (Dove et al., 2013; Sultana et al., 2016; Webster el al., 2011). Increases in
temperature have the potential to increase rates of microbial metabolism leading to an increase in
microbial biomass as long as essential metabolites are in abundant supply. In summer, PD and
RCP8.5 Scenarios had a maximum temperature of 27oC and 31oC respectively. The most abundant
microbes detected belonged to Bacteroidetes, Planctomycetes and Proteobacteria. In general,
Bacteroidetes have an aerobic heterotrophic metabolism and specialise in the degradation of high
molecular weight compounds derived directly from primary producers (Wilkins et al., 2013). The
dominant Proteobacteria were Gammaproteobacteria, belonging either to the family
Piscirickettsiaceae or OM60, and tend to be involved in Nitrogen, Sulfur and Carbon cycling.
Piscirickettsiaceae tend to be aerobic autotrophic sulphur-oxidizing bacteria (Wilkins et al., 2013),
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but OM60 are aerobic anoxygenic photoheterotrophs that also require an organic carbon source for
growth (Spring et al., 2013). Planctomycetes are ANAMMOX (anaerobic ammonium oxidizing)
prokaryotes that are both slow growing and inhibited by the presence of organic carbon, but are
nonetheless able to grow in environments rich in organic carbon when they form a consortium with
ammonia oxidizing and denitrifying cultures (Keluskar et al., 2013). In such a consortium, nitrite
produced from ammonia oxidation is used as the terminal electron acceptor for the ANAMMOX
reaction and the use of organic carbon by denitrifying prokaryotes prevents the inhibition of
ANAMMOX. Webster et al. (2008) found that Bacteroidetes, Proteobacteria and Planctomycetes
symbiotic with a GBR coral reef sponge responded more positively to an increase in temperature
from 27oC to 33oC than other taxa, potentially suggesting that different optimal temperature ranges
drove community shifts to these taxa. Simister et al. (2012), however, subsequently showed that the
observed change in community composition was driven mostly by the greater provision of sponge
necrotic tissue at high temperature rather than temperature itself. Unfortunately, in neither study
were changes in total microbial abundance reported. Potentially, the absence of a structural change
in the microbial community in the present experiment was limited by the organic biomass present in
the system. In a more complex systems, increased mortality of key reef organisms such as
Scleractinian corals (Anthony et al., 2008; Cantin et al., 2010; Carilli et al., 2009; Donner et al.,
2005; Dove et al., 2013, Glynn, 1993; Hoegh-Guldberg et al., 2007; Rodriguez & Grottoli, 2006),
sponges (Fang et al., 2013; Webster et al., 2008), and even certain taxa of macroalgae (Bender et
al., 2014; Webster et al., 2011) under RCP8.5 would increase the availability of detritus potential
driving community changes towards heterotrophic bacterial communities (O. Pantos pers. comm.).
As a result prokaryotic communities associated with the conversion of ammonia to N2 gas are also
likely to respond positively due to the close proximity of the microbial communities within the
sediments.
Conversion of ammonia to N2 gas is significantly more effective in the presence of oxidisable
detritus (Keluskar et al., 2013). Furthermore, studies have also suggested that the coupling of
nitrification and denitrification is inversely correlated to the algal colonisation of sediments. Here,
observed diurnal patterns in nitrification are argued to be driven by the daytime uptake of ammonia
by the microphytobenthos, limiting its availability for nitrification by bacteria in the oxic sediments
(Risgaard-Petersen, 2003). In an oligotrophic system, the decoupling of nitrification and
denitrification by algal assimilation is essential to the recycling and retention of Nitrogen within the
system (Odum & Odum, 1955). Sea cucumbers consume sediment associated bacteria, microalgae,
and detritus as they feed (Alongi, 1988; Bakus, 1973; Massin, 1982; Moriarty, 1982; Moriarty et
al., 1985; Yingst, 1976; Uthicke, 1999; Uthicke, 2001a-b), excreting TAN to the water column as a
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by-product of their digestive processes (Uthicke, 2001a-b; Uthicke & Klumpp, 1998). As a result,
they have the potential to assist microalgae and/or nitrifying prokaryotes present in the sediments
from ammonia limitation, but also to starve denitrifying microbes of organic matter. Stimulation of
nitrification combined with an inhibition of denitrification should lead to a build-up of nitrite, as
opposed to TAN within the experimental system; or a build-up of neither if ANAMMOX is highly
active. Interestingly, the presence of H. atra converted our experimental system from one in which
water column TAN was depleted by night, to a system in which TAN was produced by night.
Furthermore, the animals had no effect on the production of TAN by day, or on the net flux of
nitrate and nitrite. H. atra did not increase net daytime rates of O2 production, suggesting that any
benefits to microalgal accorded by the release of TAN from the animals had no effect, or that the
animals had negative effects on the standing biomass of algae due to significant grazing. A negative
effect of H. atra on the microalgal biomass is supported by other studies in which sediment
phaeophytin was significantly less in +SC tanks (McTavish et al., 2012), or there was a significant
reduction in microalgal biomass within cages grazed by H. atra (Moriarty et al., 1985). The build-
up of TAN by night in the presence of the animals suggest that neither microbes, nor depleted
microalgae by H. atra are able to match the uptake of TAN to the excess TAN produced by H. atra,
despite the fact that both communities effectively consume TAN in the absence of H. atra. Two
logical explanations arise: (1) Either H. atra consumed the consortium of prokaryotes that led to a
rapid purging of TAN from the system, an explanation that is not supported by our data, or; (2) H.
atra decreased the availability of CO2 within the sediment below a threshold value, inhibiting
chemoautotrophic nitrification and ANAMMOX (Denecke & Liebig, 2003; Jetten et al., 2009). Our
results support a decrease in water column pCO2 associated with the presence of H. atra, but
reduced availability of CO2 at night was the driver. Therefore, we would expect an interaction
between H. atra, Scenario and Time with regards to TAN uptake which was not observed. Such an
interaction, however, is potentially obscured by concurrent changes in temperature that may provide
additional energy for the active transport of bicarbonate across the cell membrane, and hence an
alternative source of inorganic carbon for fixation by autotrophs (Berg et al., 2010; Lliros et al.,
2011).
Calcification rates within the experimental sytem were much more variable in summer than for the
same experiment conducted under winter conditions (Chapter 3). Contrary to other studies on
sediment calcification (e.g., Cyronak et al., 2013), none of the categorical factors (Condition,
Scenario, and Time) appeared to significantly affect calcification rates in summer. Again, the lack
of a significant increase in calcification or dissolution with future Scenarios is potentially driven by
an absence of detritus, that could be provided by increased moratlity of typical reef dwellers. For
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instance, stimulation of bacterial decomposition is likely to increase the ratio of O2 consumption to
production across the surface sediments, potentially enhancing localized acidification and hence
sediment dissolution (McTavish et al., 2012). It has been argued that the presence of sea cucumbers
should affect reef calcification rates in two ways: (1) Negatively for sediments, because the passage
of sediments through their acidic guts promotes dissolution (Schneider et al., 2011); (2) Positively
for reef calcifiers (e.g., corals), because of their potential ability to increase water column AT
(Schneirder et al., 2011; Schneirder et al., 2013) and potential ability to increase water column pH
buffering capacity (Schneirder et al., 2011). Clearly, if HCO3- and CO3
2- were the only components
altering AT , then increases in water column AT driven by H. atra would need to correlate with net
dissolution of sediments in the presence of the animals. Notably, CO3- by day increased
independently of the presence or absence of H. atra, suggesting that calcium carbonate dissolution
by the animals is not accounting for the increase in AT observed in +SC tanks. Moreover, H. atra
were not significanlty asscociated with a decline in calcification rates within the system, however,
they were observed to increase AT and convert net TAN uptake into net TAN production by night.
Despite these observations, +SC changes in TAN were calculated to have an insignificant effect on
AT (similar result observed in Chapter 2 and Chapter 3). This finding is is not surprising because in
seawater and typically within a pH range of 6-8, ammonia represents a very small fraction of TAN
and other processes such as nitrification may as well affect its role on AT (Gieskes, 1974; Wolf-
Gladrow et al., 2007; Zeebe & Wolf-Gladrow, 2001). Furthermore, whilst H. atra had a positive
effect on many parameters of seawater chemistry (pH, AT/DIC, CO3-, and calcite and aragonite
saturation states) that would appear to favor calcification within the system (e.g., Al-Horani et al.,
2003), the scale of these effects was minimal when compared to the negative effects on these
parameters linked with combined increases in temperature and pCO2, (associated with the transition
from past to present and then onto future Scenarios). Water colum AT , was not affected by any
Scenario and was positively correlated with calcification within the system. AT accounted for 10%
of the variation in calcification across all Scenarios, and 19% of the variation when restricted to PD
and RCP8.5. For a 22 day experiment at One Tree Island (GBR, 23° 30’S, 152° 06’E), it has been
showed that a ~7% increase in net community calcification was achieved when carbonate
parameters reached values similar to pre-industrial levels and ~17% of the added AT had been taken
by the community (Albright et al., 2016). However, that study used chemical AT enrichments by
adding NaOH in order to increase alkalinity, carbonate ions and aragonite saturation state to
concentrations similar to those under pre-industrial pCO2 levels, not accounting for other carbonate
parameters that might affect alkalinity, such as bicarbonate. In the present study, AT was however
significantly affected by the diurnal cycle, being greater by day than by night, with reduced rates of
calcification tending to be associatde with low nighttime alkalinity values, as opposed to the
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absence of H. atra. Furthermore, the increased AT by H. atra could neither be linked to microbial
changes in total abundance or composition.
Finally, in terms of infauna, the most abundant group was Foraminifera. However, most
Foraminifera were dead Foraminifera. Live organisms of this group can consume microalgae,
interfering with microalgal abundance hence decreasing the O2 production by day. Moreover,
Foraminifera can carry chloroplast or feed on microalgae (a change into a heterotrophic mode if
environmental conditions change, see Correia & Lee, 2000; Pillet et al., 2011) potentially affecting
the O2 production of the system. However, the lack of differences in this group due to factors, and
the small proportion in the sediments, suggest that most processes, like calcification, O2 flux and
buffering capacity were not affected by them.
4.5.1. Conclusions
Overall, abundance of sediment microbes in our system responded positively to elevations in
temperature and pCO2. Furthermore, the lack of modification to the composition of microbial
communities within the sediments may help explain the lack of change over calcification rates, O2
flux and AT observed between all the Scenarios tested. The experiment lack the addition of detritus
by other organisms, however, the results suggest a very well buffered system, with microbes and H.
atra negating day/night differences in calcification/dissolution rates that are typically observed on
reefs (e.g., Dove et al., 2013). Furthermore, due to the noisiness of the calcification data, larger
replication should be taken into account when trying to explain potential differences in
calcification/dissolution rates across factors in similar summer studies. Finally, the significant
influence of the animals on the chemistry of the system during summer was not sufficiently strong
to produce modifications on the AT/DIC, CaCO3 calcification/dissolution rates or production of
oxygen. Hence, through feeding H. atra most likely did not alter calcification rates through gut
dissolution, through the modification of microbial processes that could lead to changes in
calcification rates via changes in AT , such as sulfate-reduction (Muyzer & Stams, 2008), or
ammonia production.
4.6. Acknowledgments
We thank Eric Beasley and Bar Ayalon for their contribution and assistance in the experiment and
in field operations at Heron Island. We would also like to thank Aaron Chai, Giovanni Bernal
Carrillo and Annamieke Van Den Heuvel for assistance in the field and in the maintenance of the
pCO2/Temperature system at Heron Island during winter and summer experiment. Furthermore, we
would like to thank Dr. Maria Byrne for her helpful comments on the infaunal composition
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analysis, especially for the analysis of foraminifera. This research was co-funded by the Australian
Research Council (ARC) Centre for Excellence in Coral Reef Studies (CE0561435), ARC Linkage
Grant (LP110200874) (to S.D.), and Becas Chile Scholarship from CONICYT (Chile) (to F.V.-R.).
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Fig. 4.1. Responses after 8 weeks of experiment during summer in +SC tanks and –SC tanks under different Scenarios (PI, PD, RCP4.5 and RCP8.5)
and Time (Day or Night). (a) Ocean acidification buffering capacity tested under different Scenarios; (b) Ocean acidification buffering capacity tested
under different Condition; (c) TAN flux calculated from change in TAN concentrations over the incubation periods under different Condition and
Time; (d) Total microbial abundance based on qPCR data for the different Condition and Scenarios. All data are means ± SE (n = 3-6) and different
letters near the bars represent significantly different groups.
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Fig. 4.2. Relationship between calcification rates and total alkalinity (t0). (a) Represents the
analysis for all Scenarios. Differences between Condition and Time are displayed; (b) Represents
the analysis for PD and RCP8.5 only. Differences in Condition are displayed.
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Fig. 4.3. Responses on O2 flux after 8 weeks under different Scenarios (PI, PD, RCP4.5 and
RCP8.5) and Time (Day or Night). Grey bars represent mid-day and the black bars represent
midnight. All data are means ± SE (n = 6).
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Table 4.1. Repeated measures ANOVA for the chlorophyll a concentrations under different
Condition, Scenarios and Time (as the within-subjects factor). Bold highlights the factor(s)
contributing to significant differences.
Source of variation SS df MS F P post-hoc
Chlorophyll a
Between subjects
Condition 0.0896 1 0.0896 2.8490 0.1044
Scenario 0.0610 3 0.0203 0.6464 0.5928
Condition x Scenario 0.0097 3 0.0032 0.1026 0.9577
Error 0.7550 24 0.0315
Within subjects
Time 1.1940 1 0.1940 5.6081 0.0263 Day < Night
Time x Condition 0.0023 1 0.0023 0.0653 0.8004
Time x Scenario 0.0169 3 0.0056 0.1626 0.9205
Time x Condition x Scenario 0.3182 3 0.1061 3.0658 0.0472 PD+SC+Day = RCP4.5+SC+Day
< PI-SC+Night
Error 0.0346
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Table 4.2. PERMANOVA analysis for calcification rates under different Condition (+SC or –SC),
Scenarios (PI, PD, RCP4.5 and RCP8.5) and Time (Day and Night). The analysis is presented for:
(a) All Scenarios; (b) PD and RCP8.5 only, and were time was excluded given that microbial
samples were taken only at midnight sampling (see methods).
Calcification rates df SS MS (Pseudo)-F p(perm)
(a) All Scenarios
Condition 1 136.5400 136.5400 1.7831 0.1788
Time 1 159.9900 159.9900 2.0893 0.1438
Scenario 3 458.6500 152.8800 1.9965 0.1111
Condition x Time 1 32.8880 32.8880 0.4295 0.5752
Condition x Scenario 3 216.7800 72.2600 0.9437 0.4398
Time x Scenario 3 221.0800 73.6940 0.9624 0.4303
Condition x Time x Scenario 3 609.0300 203.0100 2.6511 0.0525
Residuals 38 2909.8000 76.5750
Total 53 4907.9000
(b) PD and RCP8.5 only
Condition 1 57.5870 57.5870 0.7852 0.3931
Scenario 1 102.2000 102.2000 1.3935 0.2543
Condition x Scenario 1 20.0660 20.0660 0.2736 0.6690
Residuals 22 1613.5000 73.3420
Total 25 1820
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Table 4.3. Distant based linear models (DistLM). Calcification was the response variable and only
the 5 top predictor variables are shown. The analysis is presented for: (a) All Scenarios and; (b) PD
and RCP8.5 only. The best solution, R2, for (a) and (b) are presented and the significant predictor
variables are shown in bold.
Predictor variable R2 SS Pseudo-F p Probability cumulative res. df
(a) All Scenarios 0.2433
AT 0.1017 499.2500 5.8887 0.0172 0.1017 0.1017 52
Phaeophytin 0.1366 171.3700 2.0626 0.1425 0.0349 0.1366 51
AT/DIC 0.1527 78.6740 0.9459 0.3426 0.0160 0.1527 50
pH 0.1692 81.2490 0.9764 0.3385 0.0166 0.1692 49
Chlorophyll a 0.1822 63.6420 0.7611 0.3867 0.0129 0.1822 48
(b) PD and RCP8.5 only 0.7679
AT 0.1885 343.1000 5.5757 0.0232 0.1885 0.1885 24
O2 0.2386 91.0570 1.5112 0.2202 0.0500 0.2386 23
pCO2 0.2729 62.5660 1.0402 0.3211 0.0344 0.2729 22
Microbial abundance 0.3448 130.8200 2.3040 0.1344 0.0719 0.3448 21
Temperature 0.3829 69.3670 1.2353 0.2684 0.0381 0.3829 20
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Chapter 5: General Discussion
5.1. General outline
This thesis aimed to investigate the role of the sea cucumber species Holothuria atra over the
production of nutrients, CaCO3 dissolution, carbonate chemistry, gross metabolism and ocean
acidification buffering capacity within a sedimentary environment, including diurnal and seasonal
variability. Moreover, the objective was to test the effects of a variety of climate Scenarios,
involving changes in both temperature and pCO2 projected by the Intergovernmental Panel on
Climate Change (IPCC, 2014), on the performance and responses of H. atra in the presence of other
sediment-associated organisms. The impacts of different climate change Scenarios on the responses
of other sediment organisms like microalgae, infaunal organisms and microbes were also
investigated. Experimental phases of the project were conducted at Heron island reef in the Great
Barrier Reef (Australia), and organisms and sediments collected form Heron island lagoon. The
present thesis shows that H. atra has the ability to modify some carbonate parameters like total
alkalinity and enhance calcium carbonate dissolution in a short-term summer experiment (Chapter
2). Furthermore, a reduced pH and a greater buffering capacity against ocean acidification (AT/DIC)
in the presence of H. atra are observed in a long-term summer experiment (Chapter 4). However,
the relevance of these responses to the overall buffering capacity under different Scenarios of
temperature and pCO2 (pre-industrial, present day, RCP4.5 and RCP8.5) was not reflected during
summer (Chapter 4), concurring with results observed in a long-term winter experiment (Chapter
3). During winter, chemical responses and nutrient production/uptake of the experimental system
were modified by sediment-associated organisms and abiotic factors (e.g., the increased pCO2 and
temperature conditions fed to the system), rather than by H. atra (Chapter 3). Finally, the summer
contribution of sea cucumbers and other organisms to carbonate chemistry, calcification rates and
production were estimated based on a long-term experiment conducted during austral summer
(Chapter 4). It is concluded for summer that the effects of sea cucumbers on calcification rates
would be insignificant under future conditions of pCO2 and seawater temperature. Moreover, the
lack of differences observed in calcification rates are most likely due to the absence of change in
microbial composition, potentially triggered by low contents of dissolved organic matter that other
reef organisms such as corals can produced when die. The main conclusions and findings per
chapter are provided below:
5.2. Chapter 2
The results of a short-term summer experiment showed that H. atra contributed to the modification
of seawater chemistry, and in their presence, there was an increase in calcium carbonate dissolution
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and Total Ammonia Nitrogen (TAN) production. However, the increase in TAN due to the presence
of H. atra accounted for 2.4-11% of the total alkalinity, having a non-significant effect on CaCO3
dissolution. The highest rates of CaCO3 dissolution were observed at mid-day in the presence of H.
atra, with ~38% more dissolution observed in those tanks when comparing to tanks in their absence
by night. The presence of the animals resulted in the dissolution of ~0.34 to ~0.53 g CaCO3 d-1.
Apart from the effect of H. atra on the dissolution rates, other organisms in the sediments
accounted for the significant changes observed in other carbonate parameters during the 1-2h
incubations. When analysing the t0 data (open system or cumulative effect), the results showed that
the increase in daytime DIC resulted in a decrease in the ocean acidification buffering capacity of
the system (AT/DIC), since AT remained unmodified. H. atra increased the concentrations of
bicarbonate by day, which appeared to decrease the buffering capacity of the system. Whilst this
might assist night-time calcification rates by adjacent organisms, the majority of organisms that
contribute to present day net carbonate budgets on reefs do so by day (Albright et al., 2013; Eyre et
al., 2014).
5.3. Chapter 3
A long-term experiment during austral winter 2013 was design to test the effects of different
pCO2/Temperature Scenarios (pre-industrial = PI; present day = PD; RCP4.5; RCP8.5) on the
performance of the holothurian species H. atra and other organisms in the sediments (e.g.,
prokaryotes). The responses tested spanned from chemistry measurements of the seawater (e.g.,
carbonate parameters) to potential changes in sediment communities. Winter responses, after 8
weeks under the different Scenarios, revealed that H. atra had no influence on the carbonate
parameters. Other organisms influence these responses and RCP8.5 produced the highest values of
CaCO3 dissolution. PI, PD and RCP4.5 had on average 69%, 93% and 84% less dissolution than
RCP8.5. Moreover, under RCP8.5 by night, the lowest values of ocean acidification buffering
capacity (AT/DIC), pH and carbonate ions were observed. On the other hand, under RCP8.5 by day,
the largest values pCO2 bicarbonate and DIC were observed. In presence of H. atra the oxygen
production was greater by day, however, this production was not sufficiently strong to modify the
oxygen production under future projected Scenarios. PI and PD had significantly higher oxygen
production by day compared to RCP4.5 and RCP8.5. When observing nutrients, NO2- uptake was
~50% greater in PI and PD when compared to RCP4.5 and RCP8.5. The infaunal analysis revealed
that the abundance of dead foraminifera ranged 70-93% of the sediments and the lowest values of
foraminifera were observed in PI in the presence of H. atra. Microbial communities decreased their
abundance under RCP8.5 and their composition was modified by Scenario. Amongst the most
abundant families, Rhodobacteracea (~13-16%) and Piscirickettsiaceae (~7-8%) were associated
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with PD and RCP8.5 respectively. In conclusion, H. atra plays a minimum role on OA buffering
capacity and other parameters tested during winter, and microbial communities will most likely
drive the changes in seawater chemistry of the system under the pCO2/Temperature conditions
proposed.
5.4. Chapter 4
Conducting a long-term experiment during austral summer 2013-2014, with the same set-up and
response variables than for the winter experiment (Chapter 3), the aim was to observe the responses
when the regional maximum monthly mean for seawater (27oC) was exceeded for a significant
period. Moreover, the objective was to allow the estimation of change in different response
variables under climate change conditions in absence of the increased organic matter (DOM)
produced by the death of common reef dweller (e.g., corals) in summer. The results showed that the
buffering capacity of the system (AT/DIC) was lower under RCP8.5 and greater in the presence of
H. atra. AT , pH and carbonate ions increased in the presence of animals. pH and carbonate ions
decreased under RCP8.5 and bicarbonate and DIC increase under this Scenario. In regards to Total
Ammonia Nitrogen (TAN), there was a ~121% decreased in TAN concentrations in the absence of
H. atra by night. The remaining nutrients remained unchanged under all factors. Microbial
communities increased their abundance under RCP8.5 Scenario compared to PD. However, the
composition of microbial communities in the sediments remained unchanged under the different
Scenarios (unlike during the winter experiment, Chapter 3), suggesting a proportional increase of
the different taxa under RCP8.5 due potentially to the lack of DOM in the system. When observing
pigment concentrations in the sediments as a proxy for microalgal abundance, there was a decrease
in chlorophyll a concentrations by day in the presence of H. atra under PD and RCP4.5 when
compared to PI at night in their absence. Phaeophytin decreased ~15.7% in tanks with sea
cucumbers. Oxygen production was observed at mid-day and respiration at night, suggesting that
the system was net photoautotrophic despite the absence of macroalgae or symbiotic corals. No
significant changes were found in oxygen production by Scenario or due to the presence rather than
absence of H. atra. Infaunal composition of the sediments was not modified by any of the factors
tested. Calcification rates were not modified by any of the categorical factors (Condition, Scenario
and Time), although the ability to detect any changes was influenced by the lack of change in
microbial communities and a high variability amongst replicates (especially under PI and PD).
Furthermore, conducting a distant based linear model (DistLM), it was revealed that only AT was
positively correlated with the calcification rates observed. DisLM explained ~10% of the variation
observed in calcification when including all Scenarios, and ~19% when including PD and RCP8.5
only. In conclusion, the animals appeared to be more active than in winter, based on their ability to
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modify some carbonate parameters and microalgae within the sediment. However, even if the
animals increased OA buffering capacity, these changes were not strong enough to counter the
downward trend observed for AT/DIC under future Scenarios. Moreover, CaCO3
calcification/dissolution rates and oxygen flux were not modified by H. atra under any of the
Scenarios tested. Finally, the results suggest that the lack of change in a functionally dominant
microbial community led to insignificant changes on AT/DIC, calcification rates and O2 flux during
summer conditions of climate change.
5.5. Winter versus summer comparisons, and annual estimates of change
In order to compare seasons (hereafter Seasons), under different Scenarios, Condition and Time,
and estimate annual rates, variables that showed a greater change and/or allow a better
interpretation of the effects of Scenarios over the experimental system were chosen. These variables
were: AT/DIC, calcium carbonate calcification/dissolutions rates, TAN production/uptake rates,
NO2- uptake rates, oxygen flux and microbial abundance. Comparisons for each variable were made
by multifactor analysis of variance for fixed factors (Factors: Condition, Scenario, Time, Season)
(Fang et al., 2014; Quinn & Keoug, 2002). Then, annual rates based on winter (Chapter 3) and
summer (Chapter 4) data were calculated per area of sediment (m-2).
The analysis revealed that calcium carbonate dissolution occurs on winter (-32 mg CaCO3 m-2 h-1)
and calcification occurred during summer (21 mg CaCO3 m-2 h-1). Moreover, the data was highly
variable when comparing winter and summer, and showed that calcification rates were not modified
by any factor (Condition, Scenario, Time and Season) (P > 0.05). Summer calcification was smaller
(~0.18 kg CaCO3 m-2 yr-1) than for other reefs, such as the shelf-edge reef of Cane Bay (~0.71 kg
CaCO3 m-2 yr-1) (Hubbard et al., 1990). However, for Heron Island lagoon, has been estimated that
net ecosystem calcification is 2.25 kg CaCO3 m-2 yr-1 (McMahon et al., 2013) and sediments
contribute from 1% to 3.7% to CaCO3 precipitation (Cyronak et al., 2013). According to such
measurements, sediment CaCO3 precipitation accounts for ~0.03 kg CaCO3 m-2 yr-1 to ~0.07 kg
CaCO3 m-2 yr-1 produced (Cyronak et al., 2013). Such rates are smaller than the rates observed in
our summer study. Moreover, Cyronak et al. (2013) observed that high pCO2 of ~800 µatm (similar
to RCP8.5 pCO2 conditions delivered to our experimental system), CaCO3 dissolution increased in
the sediments, contrasting with our summer results. However, their study was conducted in autumn,
sediments exposed to pCO2 for short-term periods (a day) and without temperature as covariate.
Our annual dissolution rate was -103472 mg CaCO3 m-2 y-1 (~0.1 kg CaCO3 m
-2 yr-1). Similar to our
results, CaCO3 dissolution rates have been estimated for carbonate sediments under present day
conditions of temperature and CO2 between 0.09 – 0.50 kg m-2 y-1 (Eyre et al., 2014). It is important
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to acknowledge that dissolution rates will vary depending on the reef studied, because dissolution
rates vary depending on pore water advection (Cyronak et al., 2013), are inversely correlated to the
grain size of sediments (van Woesik et al., 2013; Walter & Morse, 1984) and are likely to be
affected by sediment-associated organisms (Andersson & Gledhill, 2013). That calculation of
calcification rates in this thesis lacks the variability of the remaining Seasons of the year (autumn
and spring). However, they are based on the most extreme Seasons to account for larger variations
in pCO2 and temperature. Finally, it is important to acknowledge that in this thesis, there was a
great variability unexplained in summer calcification (Chapter 4), probably (amongst others) due to
outliers (relatively low replication in summer; n=3-4), and unaccounted microbial processes (based
on the constant microbial composition across treatments during summer) derived from the potential
lack of dissolve organic matter (DOM). For example, at Lizard Island (GBR, latitude 14o38’S),
Moriarty et al. (1985) observed a ~93% increase in bacterial productivity within sediments from
winter to summer. The increased in bacterial biomass was most likely explained by a greater DOM
content in summer compared to winter (Moriarty et al., 1985). Our experimental tanks simulated
simplified reefs, where organisms such as coral and macroalgae were absent. Therefore, DOM
concentrations are likely lower than in the field and might explain the lack of differences in
microbial taxa, promoted by the decreased nutrients needed for the microbial loop (Charles et al.,
2009). This response is opposed to winter observations (Chapter 3), were DOM concentrations are
likely less than in summer (Moriarty et al., 1985). Therefore, microbes in our experimental system
may cope better under reduced inputs of DOM during winter Season compared to summer Season.
Nutrient production or uptake rates varied depending on Season, Condition and Time. TAN
production was significantly affected by the interaction of Condition, Season and Time (F(1,64) =
7.1483, P = 0.0095). The greatest TAN production was observed in the presence of H. atra, at night
and during summer, with concentrations of 4.65 ± 1.34 µmol kg-1 m-2 h-1. Scenario had no effect on
TAN when comparing winter and summer. Uthicke (2001) has observed that H. atra did not
significantly modified rates of ammonium production between winter and summer (although larger
values were observed for summer). However, the author did not include sediments, therefore
excluding the potential nutrient regeneration due to feeding. When observing NO2- uptake rates,
these were only significantly modified by Season when comparing all factors across winter and
summer (F(1,64) = 4.5360, P = 0.037). Winter showed uptake rates of -0.07 ± 0.01 µmol kg-1 m-2 h-
1, and summer rates represented a ~40 % decrease in NO2- uptake with values of -0.04 ± 0.01 µmol
kg-1 m-2 h-1. In agreement to the findings of Uthicke (2001), H. atra did not modified the production
(therefore the uptake) of NO2-, suggesting that changes in NO2
- concentrations observed for winter
and summer are being produced by microbes in the sediments (see below).
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Oxygen flux was significantly modified by the interaction between Scenario and Time (F(1,160) =
21.9527, P < 0.001) and the interaction between Season and Time (F(3,160) = 4.2899, P = 0.006),
showing a greater O2 production under PD at mid-day (a pattern similar to the results reported for
summer by Dove et al., 2013) and a greater O2 uptake by night during summer. O2 uptake was
never affected by Scenario (neither in winter or summer), concurring with summer O2 uptake
observations made by Dove et al., (2013). Finally, the sea cucumbers did not significantly affect the
overall rates of O2 production/uptake when comparing winter versus summer (P > 0.05), suggesting
that other organisms in the sediments are accounting for the changes observed.
Microbial abundance was significantly affected by the interaction of Season and Scenario (F(1,16)
= 14.7609, P = 0.0014) with the greater abundance of microbes in summer (post-hoc: summer
RCP8.5 > summer PD > winter PD = winter RCP8.5). The summer increase represents ~50% and
~118% more microbes for PD and RCP8.5, respectively, when compared to winter PD.
In summary, when comparing winter versus summer Seasons, across all factors and variables, there
was a clear loss of significance over the response variables in the presence of H. atra, except for
TAN production. PD conditions were associated with greater productions of O2 by day; summer
was associated with a decrease in NO2− uptake when compared to winter and with greater microbial
abundances than in winter. Sediment-associated organisms and abiotic conditions (pCO2 and
temperature Scenarios) were the main drivers for the changes observed in the system. Regardless
the effect on TAN by the sea cucumbers, H. atra had an insignificant effect over most of the annual
rates, based of winter and summer data pooled together. Observing the overall O2 flux between
Seasons, PD presented the greater production by day, regardless of Condition. Moreover, an
increase in the overall O2 flux was observed for summer when compared to winter, regardless of
any factor tested. O2 production may be influenced by bacterial activity. Families such a
Rhodobacteraceae may account for the O2 values observed in winter, since a decrease in the
abundance of that family under RCP8.5 concurred with a decreased in O2 production under that
same Scenario (Chapter 3). Moreover, during summer, bacterial abundance under PD was
significantly greater than PD winter, proposing that the increase in photosynthetic bacteria during
summer time may in fact contribute to enhance the production of oxygen. The change in bacterial
communities may also modify processes such as nitrification, leading to a potential build-up of
nitrite as observed in summer (when compared to winter). It is difficult to estimate if other
processes such as ANAMMOX may be active during summer, given that there were not significant
differences in microbial composition. However, since NO2- uptake decreased during summer when
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compared to winter, it is likely that ANAMMOX was less active that in winter (ANAMMOX, in
order to produce N2, requires uptake of NO2− to combine with NH4
+, see Erler et al., 2013).
Finally, the increase in ocean acidification buffering capacity observed in the presence of the
animals during summer appeared irrelevant when compared to the decreased buffering capacity of
the sediments exposed to RCP Scenarios. Pooling the data of winter and summer, the effect of the
H. atra on AT/DIC was lost (P > 0.05), remarking that any increase of alkalinity and pH produced
by these animals may not suffice to overcome the production of DIC in the system. Moreover,
sediment-associated organisms and abiotic conditions most likely will drive changes in ocean
acidification buffering capacity on an annual basis.
5.6. Conclusion and future research directions
Based on the findings of this study, the implications of the presence or absence of H. atra on
calcium carbonate erosion rates and ocean acidification buffering capacity, differ from short-term
and long-term experiments (Chapter 2 compared to Chapter 3-4) and for different Seasons (winter:
Chapter 3; summer; Chapter 4). This study suggests that the overall responses of the system will be
driven by abiotic factors (e.g., temperature and pCO2), with the assistance of microbial processes
(e.g., sulfur-oxidation) based on changes in composition and abundance of prokaryotes in the
sediments. Furthermore, despite a significant effect of H. atra on some variables (e.g., increased
system net O2 production in their presence during winter, Chapter 3; increased acidification
buffering capacity in their presence during summer, Chapter 4), in neither season the animals were
able to counter the negative impacts observed on these parameters under future climate Scenarios.
Moreover, there was no evidence to support the hypothesis that this species of sea cucumbers, one
of the most abundant on Indo-Pacific reefs, will assist nearby calcifiers on future reefs as proposed
for H. leucospilota and S. herrmanni (Schneider et al., 2011). In accordance to the major findings of
this thesis, four future research directions are proposed (1-4):
1. How CaCO3 erosion of reef sediments would change under climate change scenarios, in
presence and absence of H. atra, during other seasons and with other organisms? It is
known that climate change will have negative impacts on calcium carbonate accretion on
coral reefs (Dove et al., 2013; Eyre et al., 2014). As observed based on two long-term
experiments (Chapter 3 and Chapter 4), the sediments will yield annual calcium carbonate
dissolution rates, regardless the presence or absence of H. atra, or the Scenario. Other
studies proposing similar Scenarios of temperature and pCO2 have demonstrated that in
summer, RCP8.5 will have negative impacts on calcification (Dove et al., 2013), as those
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observed for our winter experiment. Furthermore, it is important to notice that in shallow
reefs, changes in calcium and oxygen budgets are modulated by the biota on diurnal and
seasonal time scales (Kline et al., 2012). Hence, it becomes relevant to test the influence of
H. atra and sediment-associated organisms (e.g., prokaryotes) on calcium carbonate budgets
and seawater chemistry during other Seasons, like autumn or spring. Moreover, including
organisms that can promote the input of dissolved organic matter (DOM) into the system,
such as corals (Anthony et al., 2008; Cantin et al., 2010; Carilli et al., 2009; Donner et al.,
2005; Dove et al., 2013, Glynn, 1993; Hoegh-Guldberg et al., 2007; Rodriguez & Grottoli,
2006), sponges (Fang et al., 2013; Webster et al., 2008) or macroalgae (Bender et al., 2014;
Webster et al., 2011), may be relevant for the carbonate budgets. In our summer experiment,
the lack of change in microbial composition, potentially due to low DOM in the system,
may have been the reason for the insignificant changes observed in calcification rates.
Therefore, DOM inputs may help to account for differences in calcification that otherwise
would be obscured. New Seasons and reef organisms in the system may produce valuable
information in order to obtain more accurate annual estimates about the potential daytime
calcification and nighttime dissolution in the presence and absence of H. atra. Finally, such
information may promote estimates to elucidate whether the animals or microbes will assist
calcifiers or not in future reefs.
2. Effects of climate change on early life stages of sea cucumbers? The results of this thesis
showed that adult stages of H. atra appear to be a resilient under the climate Scenarios
proposed. However, there is no evidence about the potential negative impacts of climate
change Scenarios on the early life stages of H. atra. It is know that multiple stressors can
have deleterious impacts on the ontogeny of marine invertebrates (Byrne, 2012; Byrne &
Przeslawski, 2013; Byrne et al., 2013; Przeslawski et al., 2015). Moreover, evidence of
differential effects of climate change (e.g., negative effect of temperature but not effect of
pH), on early life stages of the sea urchin Heliocidaris erythrogrammatha, has been
provided (Byrne et al., 2009). Such evidence proposes the importance of testing the effects
of elevated temperature and increased pCO2 (combined and independently) on early life
stages of H. atra. In that regard, calcareous structures of the larvae of H. atra
(Laxminarayana, 2005; Rasolofonirina & Jangoux, 2005) may be negatively impacted by
temperature, ocean acidification or their interaction. Such potential negative effects could
have negative consequences for natural populations, that in order to be sustained, need all
ontogenetic stages to be successfully attained (Byrne, 2012). Consequently, the role of these
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animals on CaCO3 dissolution rates, nutrients fluxes, O2 fluxes and OA buffering capacity
may be modified.
3. How climate change will affect other sea cucumber species in the context of CaCO3
erosion? Other studies have suggested that sea cucumbers have the potential to modify the
calcium carbonate budget (Schneider et al., 2011; Schneider et al., 2013). Moreover,
through increases in alkalinity of the surrounding seawater, the animals could provide a
natural buffer against future ocean acidification (Schneider et al., 2011). Both studies rely
on the introduced faecal casts into the experimental aquaria, and metabolic processes of the
animals (e.g., production of TAN), to be responsible for the observed changes in carbonate
chemistry and the increase in alkalinity within the system. However, those studies have
excluded completely the interaction of the animals with sediments (the experimental aquaria
were disposed only with sea cucumbers and seawater). Moreover, Schneider et al. (2011);
Schneider et al. (2013) obtained their samples on a single time point, excluding diurnal and
seasonal variability of the natural reef and diurnal changes in feeding activity of the animals
(e.g. some holothurians feed nocturnally, see Hammond, 1982). Diurnal changes can then
lead to potential modifications over the contributions of the animals to the carbonate
chemistry. Schneider et al. (2011); Schneider et al. (2013) also consider alkalinity to be the
most important variable to estimate effects on the buffering capacity. However, Egleston et
al. (2010) and Wang et al. (2013) have demonstrated that the acidification buffering capacity
is the ratio between total alkalinity and DIC, and in fact, in this thesis DIC proved to be
more relevant to modify this parameter (e.g., Chapter 3). It is then of great importance to
estimate the contribution of sea cucumbers to the carbonate budget, and other processes, in
the presence of sediments and sediment-associated biota. Moreover, It is critical to
understand the contribution of sediment biota to the carbonate budget in the presence of
other species of sea cucumbers (e.g., H. leucospilota), including diurnal and seasonal
variability. Such information could provide more accurate estimates about the relative role
of other sea cucumber species over the ocean acidification buffering capacity and their
potential impact on calcifiers in future reefs.
4. Effects of climate change on reef sediment microbes and microbial metabolism. Microbes
constitute the most diverse and the largest biomass of all marine organisms (Webster & Hill,
2007). Moreover, microbes are critical for nutrient recycling on marine environments
(Arrigo, 2005). However, they are often not considered in climate change studies (Jones et
al., 2014; Webster & Hill, 2007). In this thesis was observed, for instance, that RCP8.5
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promoted the presence of Acidobacteria in winter (see Chapter 3), but not summer (see
Chapter, 4). Then, it becomes interesting to study the potential impacts of climate change on
microbial composition of the sediments during other seasons, and estimate whether the
microbes are metabolically active or not. It would be interesting for example to test if
Acidobacteria observed under RCP8.5 could be metabolically active. This could be achieved
by coupling measurements of microbial metabolism (mesocosms experiments) with rRNA
analysis (Blazewics et al., 2013). Such information would provide more accurate insights
about the role of microbes to different processes (e.g., sulfur-oxidation) in the presence and
absence of sea cucumbers, and their direct role on the modification of carbonate parameters,
nutrient production/uptake, O2 consumption/production that ultimately would lead to
changes in carbonate budgets and OA buffering capacity.
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APPENDIX A: Supplementary information Chapter 3
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Fig. S1. Experimental Temperatures/pCO2 (Scenarios) established and fed to the experimental tanks
during the 8 weeks of experiment. This experimental set-up followed the look-up table established
from Harry’s Bommie data (see Materials and Methods) for winter season. The data for pH, salinity
and other carbonate parameters in the experimental tanks at the time of the incubations (after 8
weeks) is represented at t0 in Table 1. Error bars for temperature represent ± SE.
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Fig. S2. Changes in carbonate chemistry parameters and dissolution rates for different Condition
(+SC or –SC), Time (Day or Night) and Scenarios (PI, PD, RCP4.5 and RCP8.5). The canonical
analysis of principal coordinates (CAP) shows in (a) Condition potentially affecting the parameters,
and the vectors 1-10 are only presented in (a), but in the following panels represent the same
(1=mgCaCO3h-1m-2, 2=AT , 3=DIC, 4=HCO3-, 5=pCO2, 6=pH, 7=AT/DIC, 8=Ωcalcite,
9=Ωaragonite,10=CO32-). (b) Time and Scenario effect on the parameters are presented in (b) and
(c) respectively.
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Fig. S3. Changes on infaunal abundance after 8 weeks of experiment. (a) Density of Foraminifera
per gram of sediment under different Condition (+SC or –SC) and Scenarios (PI, PD, RCP4.5 and
RCP8.5). The letters above the bars represent significantly different groups and error bars represent
± SE. (b) Infaunal abundance and composition within the sediments, under different Condition
(+SC or –SC) and Scenarios (PI, PD, RCP4.5 and RCP8.5). Main groups observed were
Echinodermata, Polychaeta, Nematoda, Crustacea, Foraminifera and Gastropoda.
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Fig. S4. Box-plots presenting the Shannon-Wiener index (a) and Simpson’s E (b), under different
Condition (+SC or –SC) and Scenarios (PD and RCP8.5). Both were non-significantly different
between the factors (n = 3 for all combinations).
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Fig. S5. Heatmap based on 16S rRNA amplicons showing the bacterial taxa associated with Heron
Island lagoon sediments during winter. Taxonomy was assigned based on the Greengenes database
and summarized at the Phylum level (left) and the genus level (right). In bold the some of the most
abundant taxa associated with the lagoon and experimental tank are shown. R1, R2 and R3
represent replicates and scale is in log (x+1).
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Table S1. Carbonate chemistry summary for t0 and t1, showing temperatures, pH and AT used from the experimental incubations as input parameters,
to calculate pCO2, HCO3-, CO3
2-, DIC, Ωcalc, Ωarag (see Materials and Methods) and the relationship of AT/DIC for different Condition, Scenarios and
Time of day tested after 8 week of experiment. All values are represented as mean ± SE (n = 5-6).
T (oC) pH AT
(µmol kg-1)
pCO2
(µatm)
HCO3-
(µmol kg-1)
CO32-
(µmol kg-1)
DIC
(µmol kg-1)
Ωcalc Ωarag
AT/DIC
PI +SC mid-day t0 21.9±0.19 8.14±0.03 2284±5 302±30 1726±34 221±13 1955±22 5.32±0.31 3.34±0.19 1.17±0.01
t1 23.1±0.23 8.24±0.04 2283±6 223±23 1594±53 274±21 1873±33 6.59±0.51 4.14±0.32 1.22±0.02
+SC midnight t0 21.0±0.05 8.14±0.01 2249±5 298±12 1721±12 209±6 1937±7 5.03±014 3.15±0.09 1.16±0.01
t1 20.9±0.05 8.16±0.03 2253±5 285±25 1703±37 218±13 1928±24 5.24±0.32 3.29±0.20 1.17±0.01
PD +SC mid-day t0 22.0±0.15 8.09±0.03 2273±5 344.69±30.19 1766±27 201±10 1975±18 4.83±0.23 3.03±0.15 1.15±0.01
t1 23.7±0.11 8.16±0.03 2273±4 277.85±21.92 1669±27 240±12 1915±16 5.77±0.29 3.62±0.19 1.19±0.01
+SC midnight t0 20.9±0.05 8.07±0.02 2254±7 362.26±25.52 1786±21 185±8 1980±15 4.45±0.18 2.79±0.11 1.14±0.01
t1 20.8±0.07 8.04±0.02 2257±6 391.90±24.87 1813±22 175±7 1999±16 4.22±0.18 2.65±0.11 1.13±0.01
RCP4.5 +SC mid-day t0 24.8±0.18 8.15±0.02 2274±3 291.24±21.00 1672±25 239±10 1917±15 5.76±0.25 3.61±0.16 1.19±0.01
t1 25.7±0.17 8.13±0.03 2267±6 301.14±25.31 1666±38 239±13 1911±26 5.75±0.32 3.60±0.20 1.19±0.01
+SC midnight t0 22.8±0.10 7.95±0.03 2253±7 497.44±31.91 1857±13 156±7 2025±7 3.77±0.18 2.36±0.11 1.11±0.01
t1 22.3±0.05 7.96±0.03 2258±8 493.65±42.76 1861±22 157±11 2030±12 3.78±0.27 2.37±0.17 1.11±0.01
RCP8.5 +SC mid-day t0 25.7±0.19 7.92±0.07 2274±4 574.16±100.36 1854±45 166±23 2032±39 3.99±0.56 2.50±0.35 1.12±0.02
t1 26.8±0.19 7.95±0.05 2277±5 515.23±68.70 1825±45 179±17 2014±30 4.30±0.40 2.70±0.25 1.13±0.02
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+SC midnight t0 24.0±0.27 7.76±0.04 2264±6 856.43±85.28 1989±15 109±8 2117±9 2.62±0.20 1.64±0.12 1.07±0.01
t1 23.4±0.20 7.76±0.04 2269±6 842.80±78.31 1995±17 108±8 2123±10 2.61±0.20 1.64±0.12 1.07±0.01
PI -SC mid-day t0 21.7±0.12 8.18±0.04 2277±5 276.29±31.95 1726±34 235±18 1927±26 5.64±0.43 3.54±0.27 1.18±0.02
t1 23.5±0.22 8.20±0.03 2276±5 246.67±20.46 1594±53 257±11 1891±20 6.18±0.27 3.88±0.17 1.20±0.01
-SC midnight t0 21.0±0.09 8.11±0.03 2252±9 320.44±24.70 1721±12 202±9 1952±19 4.85±0.22 3.05±0.14 1.15±0.01
t1 20.6±0.07 8.13±0.01 2256±10 307.23±10.69 1703±37 206±6 1949±6 4.96±0.15 3.11±0.10 1.16±0.00
PD -SC mid-day t0 23.0±0.33 8.06±0.09 2274±4 428.39±134.06 1771±69 199±28 1980±45 4.80±0.67 3.01±0.42 1.15±0.02
t1 23.7±0.37 8.10±0.02 2271±4 335.89±17.81 1736±24 212±10 1956±15 5.10±0.23 3.20±0.15 1.16±0.01
-SC midnight t0 21.2±0.04 8.09±0.02 2252±9 340.29±24.84 1761±20 194±9 1964±13 4.68±0.22 2.94±0.14 1.15±0.01
t1 21.1±0.04 8.07±0.01 2253±9 363.94±12.25 1788±13 184±4 1981±11 4.43±0.10 2.78±0.06 1.14±0.00
RCP4.5 -SC mid-day t0 24.5±0.10 8.14±0.04 2269±4 301.13±32.51 1672±45 236±18 1915±28 5.69±0.43 3.57±0.27 1.19±0.02
t1 25.4±0.15 8.20±0.03 2268±5 250.29±24.76 1596±39 267±15 1868±24 6.41±0.37 4.02±0.23 1.22±0.02
-SC midnight t0 23.3±0.16 7.92±0.03 2253±5 555.19±39.47 1880±18 147±8 2040±11 3.55±0.20 2.23±0.13 1.10±0.01
t1 22.6±0.11 7.93±0.01 2258±4 526.45±14.91 1882±8 148±3 2043±6 3.57±0.08 2.24±0.05 1.11±0.00
RCP8.5 -SC mid-day t0 25.8±0.25 7.96±0.04 2264±7 501.26±47.06 1822±43 175±16 2007±29 4.20±0.38 2.64±0.24 1.13±0.01
t1 27.0±0.13 8.06±0.03 2269±8 373.53±32.37 1719±35 218±12 1944±24 5.23±0.30 3.28±0.19 1.17±0.01
-SC midnight t0 24.0±0.23 7.84±0.08 2260±8 742.78±171.73 1920±43 134±19 2071±27 3.24±0.46 2.03±0.29 1.09±0.02
t1 23.5±0.18 7.76±0.06 2266±7 867.38±116.19 1983±36 112±16 2115±23 2.69±0.38 1.69±0.24 1.07±0.01
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Table S2. Repeated measures ANOVA for carbonate parameters at t0. Factor Condition (+SC or –SC) was non-significant for all carbonate parameters
(P > 0.05). The post-hoc analysis is determined by: PI-D = Pre-industrial day; PI-N = Pre-industrial night; PD-D = Present day daytime; PD-N =
Present day nighttime; R4-D = RCP4.5 day; R4-N = RCP4.5 night; R8-D = RCP8.5 day; R8-N = RCP8.5 night. Bold highlights the factor(s)
contributing to significant differences. N = 5-6.
Source of variation SS df MS F P post-hoc
pH
Between subjects
Condition 0.0040 1 0.0040 0.2 0.6324
Scenario 0.9510 3 0.3170 20.3 < 0.001 PI ≥ PD ≥ R4 > R8
Condition x Scenario 0.0210 3 0.0070 0.4 0.7190
Error 0.5630 36 0.0160
Within subjects
Time 0.1780 1 0.1780 30.1 < 0.001 D > N
Time x Condition 0.0010 1 0.0010 0.1 0.7214
Time x Scenario 0.1670 3 0.0560 9.4 < 0.001 PI-D = PI-N = PD-D = PD-N = R4-D > R4-N = R8-D > R8-N
Time x Condition x Scenario 0.0090 3 0.0030 0.5 0.6618
Error 0.2130 36 0.0060
AT (µmol kg-1)
Between subjects
Condition 232 1 232 1 0.3447
Scenario 130 3 43 0 0.9149
Condition x Scenario 131 3 44 0 0.9147
Error 9123 36 253
Within subjects
Time 8022 1 8022 63 < 0.001 D > N
Time x Condition 84 1 84 1 0.4209
Time x Scenario 1673 3 558 4 0.0098 PI-D ≥ PD-D ≥ R4-D =R8-D ≥ R8-N ≥ R4-N ≥ PD-N ≥ PI-N
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Time x Condition x Scenario 85 3 28 0 0.8791
Error 4571 36 127
pCO2 (µatm)
Between subjects
Condition 2576 1 2576 0.0720 0.7900
Scenario 1776709 3 592236 12.5437 < 0.001 PI ≤ PD ≤ R4 < R8
Condition x Scenario 56718 3 18906 0.5281 0.6658
Error 1288742 36 35798
Within subjects
Time 293069 1 293069 17.8001 < 0.001 D < N
Time x Condition 1989 1 1989 0.1208 0.7302
Time x Scenario 373066 3 124355 7.5529 < 0.001 PI-D = PI-N = PD-D = PD-N = R4-D < R4-N = R8-D < R8-N
Time x Condition x Scenario 18630 3 6210 0.3772 0.7699
Error 592721 36 16464
HCO3- (µmol kg-1)
Between subjects
Condition 7315 1 7315 0.76 0.3897
Scenario 405820 3 135273 14.02 < 0.001 PI ≤ PD ≤ R4 < R8
Condition x Scenario 11094 3 3698 0.38 0.7657
Error 347434 36 9651
Within subjects
Time 148113 1 148113 33.74 < 0.001 D < N
Time x Condition 29 1 29 0.01 0.9352
Time x Scenario 136995 3 45665 10.40 < 0.001 R4-D ≤ PI-D = PI-N ≤ PD-D = PD-N ≤ R8-D ≤ R4-N < R8-N
Time x Condition x Scenario 5428 3 1809 0.41 0.7453
Error 158057 36 4390
CO32- (µmol kg-1)
Between subjects
Condition 763 1 763 0.4630 0.5005
Scenario 63537 3 21279 12.8570 < 0.001 PI ≥ PD ≥ R4 > R8
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Condition x Scenario 1500 3 500 0.3030 0.8227
Error 59300 36 1647
Within subjects
Time 36158 1 36158 54.3550 < 0.001 D > N
Time x Condition 39 1 39 0.0580 0.8112
Time x Scenario 19218 3 6406 9.6300 < 0.001 R4-D ≥ PI-D ≥ PI-N ≥ PD-N ≥ PD-D ≥ R8-D ≥ R4-N ≥ R8-N
Time x Condition x Scenario 723 3 241 0.3620 0.7806
Error 23948 36 665
DIC (µmol kg-1)
Between subjects
Condition 3519 1 3519 0.91
Scenario 169501 3 56500 14.68 < 0.001 PI = PD =R4 < R8
Condition x Scenario 5141 3 1714 0.45
Error 138516 36 3848
Within subjects
Time 44042 1 44042 23.55 < 0.001 D < N
Time x Condition 0 1 0 0.00
Time x Scenario 60640 3 20213 10.81 < 0.001 R8-N > R4-N ≥ R8-D ≥ PD-D ≥ PD-N ≥ PI-D ≥ PI-D ≥ R4-D
Time x Condition x Scenario 2459 3 820 0.44
Error 67333 36 1870
Ωcalc
Between subjects
Condition 0.4320 1 0.4320 0.4530 0.5052
Scenario 36.8880 3 12.2960 12.8790 < 0.001 PI ≥ PD ≥ R4 > R8
Condition x Scenario 0.8810 3 0.2940 0.3080 0.897
Error 34.3700 36 0.9550
Within subjects
Time 20.7930 1 20.7930 54.0090 < 0.001 D > N
Time x Condition 0.0250 1 0.0250 0.0640 0.8014
Time x Scenario 11.1330 3 3.7110 9.6390 < 0.001 R4-D ≥ PI-D ≥ PI-N ≥ PD-N ≥ PD-D ≥ R8-D ≥ R4-N ≥ R8-N
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Time x Condition x Scenario 0.4130 3 0.1380 0.3570 0.7842
Error 13.8600 36 0.3850
Ωarag
Between subjects
Condition 0.1716 1 0.1716 0.4570 0.5036
Scenario 14.5128 3 4.8376 12.8720 < 0.001 PI ≥ PD ≥ R4 > R8
Condition x Scenario 0.3451 3 0.1150 0.3060 0.8208
Error 13.5301 36 0.3758
Within subjects
Time 8.2086 1 8.2086 54.1340 < 0.001 D > N
Time x Condition 0.0094 1 0.0094 0.0620 0.8049
Time x Scenario 4.3835 3 1.4612 9.6360 < 0.001 R4-D ≥ PI-D ≥ PI-N ≥ PD-N ≥ PD-D ≥ R8-D ≥ R4-N ≥ R8-N
Time x Condition x Scenario 0.1633 3 0.0544 0.3590 0.7829
Error 5.4589 36 0.1516
AT/DIC
Between subjects
Condition 0.0008 1 0.0008 0.56 0.4576
Scenario 0.0520 3 0.0173 12.70 < 0.001 PI = PD =R4 > R8
Condition x Scenario 0.0011 3 0.0004 0.27 0.8444
Error 0.0491 36 0.0014
Within subjects
Time 0.0279 1 0.0279 47.81 < 0.001 D > N
Time x Condition 0.0000 1 0.0000 0.01 0.9176
Time x Scenario 0.0166 3 0.0055 9.48 < 0.001 R4-D ≥ PI-D ≥ PI-N ≥ PD-D ≥ PD-N ≥ R8-D ≥ R4-N ≥ R8-N
Time x Condition x Scenario 0.0006 3 0.0002 0.36 0.7815
Error 0.0210 36 0.0006
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Table S3. Repeated measures ANOVA for sediment pigments (chlorophyll a, phaeophytin, and
total chloropigments) after 8 weeks of experiment and under different Condition (+SC and –SC),
Scenarios (PD and RCP8.5) and Time (mid-day and midnight, as the within-subjects factor). Bold
highlights the factor(s) contributing to significant differences (n= 3 for combinations).
Source of variation SS df MS F P post-hoc
Chlorophyll a
Between subjects
Condition 0.0862 1 0.0862 0.3619 0.5609
Scenario 0.2266 1 0.2266 0.9513 0.3524
Condition x Scenario 0.0454 1 0.0454 0.1905 0.6718
Error 2.3822 10 0.2382
Within subjects
Time 0.3932 1 0.3932 2.0025 0.1874
Time x Condition 0.0470 1 0.0470 0.2394 0.6352
Time x Scenario 0.1268 1 0.1268 0.6458 0.4403
Time x Condition x
Scenario
0.0567 1 0.0567 0.2887 0.6028
Error 1.9634 10 0.1964
Phaeophytin
Between subjects
Condition 0.0006 1 0.0006 0.0034 0.9547
Scenario 0.0002 1 0.0002 0.0012 0.9731
Condition x Scenario 0.0005 1 0.0005 0.0028 0.9592
Error 1.7281 10 0.1781
Within subjects
Time 0.0022 1 0.0022 0.0136 0.9096
Time x Condition 0.0056 1 0.0056 0.0353 0.8547
Time x Scenario 0.5262 1 0.5262 3.3183 0.0985
Time x Condition x
Scenario
0.0008 1 0.0008 0.0051 0.9446
Error 1.5857 10 0.1586
Total chloropigments
Between subjects
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Condition 0.1010 1 0.1010 0.2483 0.6291
Scenario 0.2405 1 0.2405 0.5913 0.4597
Condition x Scenario 0.0366 1 0.0366 0.0899 0.7705
Error 4.0679 10 0.4068
Within subjects
Time 0.3372 1 0.3372 1.1611 0.2304
Time x Condition 0.0202 1 0.0202 0.0975 0.7612
Time x Scenario 1.1696 1 1.1696 5.6580 0.0387 Day PD ≥ Night RCP8.5 = Day
RCP8.5 ≥ Night PD
Time x Condition x
Scenario
0.0710 1 0.0710 0.3435 0.5708
Error 2.0671 10 0.2067
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Table S4. PERMANOVA analysis for microbial composition of the sediments under different
Condition (+SC or –SC) and Scenarios (PD or RCP8.5). Bold highlights the factor(s) contributing
to significant differences (n = 3).
Source of variation df SS MS (Pseudo)-F p(perm) Pairwise
Condition 1 351.8800 351.8800 1.7371 0.1196
Scenario 1 631.4900 631.4900 3.1174 0.0270 PD ≠ RCP8.5
Condition X Scenario 1 177.5000 177.5000 0.8763 0.5448
Residuals 8 1620.6000 202.5700
Total 11 2781.4000
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Table S5. Principal Component Analysis (PCA) of the microbial composition of the sediments
under different Scenarios (PD or RCP8.5) (n = 3).
Eigenvalues % of Variance Cumulative %
PC1 10.07 32.49 32.49
PC2 6.18 19.93 52.42
PC3 4.42 14.24 66.66
PC4 2.65 8.54 75.2
PC5 2.17 7.00 82.2
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APPENDIX B: Supplementary information Chapter 4
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Fig. S1. Experimental Temperatures/pCO2 (Scenarios) established and fed to the experimental tanks
during the course of the summer experiment. The set-up followed a look-up table established from
Harry’s Bommie data (see Materials and Methods) for summer season. Error bars for temperature
represent ± SE.
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Fig. S2. Box-plots for bacterial communities presenting Richness (Sobs) (a), Shannon-Wiener index (b) and Simpson’s E (c), under different
Condition (+SC or –SC) and Scenarios (PD and RCP8.5). Non-significant differences were found by any of the factors (n = 3 for all combinations).
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Fig. S3. Heatmap based on 16S rRNA amplicons showing the bacterial taxa associated with the different Scenarios (PD = blue or RCP8.5 = red) after
8 weeks of experiment during summer. Taxonomy was assigned based on the Greengenes database and summarized at the phylum level (top) and at
the genus level (bottom). In bold some of the most abundant taxa are represented.
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Fig. S4. Abundance of infauna (%) under different Condition (+SC or –SC) and Scenarios (PI, PD, RCP4.5 and RCP8.5). For all combinations n =3.
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Fig. S5. Heatmap of 16S rRNA amplicons for bacterial taxa of Heron Island lagoon sediments
during summer. Taxonomy was assigned based on the Greengenes database and summarized at the
Phylum level (left) and the genus level (right). In bold the some of the most abundant taxa
associated with the lagoon and experimental tank are shown. R1, R2 and R3 represent replicates
and scale is in log (x+1).
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Table S1. Carbonate chemistry summary for t0 and t1, showing temperatures, pH and AT used from the experimental incubations as input parameters,
to calculate pCO2, HCO3-, CO3
2-, DIC, Ωcalc, Ωarag (see Materials and Methods) and the relationship of AT/DIC for different Condition, Scenarios and
Time of day tested after 8 week of experiment. All values are represented as mean ± SE (n = 3-4).
T (oC) pH AT
(µmol kg-1)
pCO2
(µatm)
HCO3-
(µmol kg-1)
CO32-
(µmol kg-1)
DIC
(µmol kg-1)
Ωcalc Ωarag
AT/DIC
PI +SC mid-day t0 27±0.4 8.14±0.03 2296±6 289±26 1622±41 273±14 1903±28 6.56±0.32 4.35±0.21 1.21±0.01
t1 27.2±0.2 8.21±0.03 2287±13 239±22 1542±38 301±13 1850±27 7.25±0.34 4.82±0.22 1.24±0.01
+SC midnight t0 26±0.14 8.22±0.05 2245±15 234±41 1528±70 289±21 1823±49 6.96±0.51 4.59±0.34 1.23±0.02
t1 25.8±0.2 8.25±0.02 2222±5 204±16 1475±31 301±10 1781±21 7.23±0.23 4.78±0.15 1.25±0.01
PD +SC mid-day t0 27.2±0.8 8.11±0.02 2300±5 322±21 1668±36 257±13 1934±24 6.21±0.31 4.12±0.21 1.19±0.01
t1 28.3±0.8 8.19±0.05 2305±2 252±35 1558±66 303±25 1868±41 7.35±0.62 4.89±0.42 1.24±0.03
+SC midnight t0 27.1±0.5 8.16±0.03 2258±15 270±25 1578±43 275±12 1860±33 6.62±0.29 4.40±0.19 1.21±0.01
t1 27±0.4 8.10±0.03 2282±11 330±31 1666±43 250±13 1925±31 6.02±0.30 4.00±0.21 1.19±0.01
RCP4.5 +SC mid-day t0 29.5±0.6 8.07±0.01 2295±10 358±17 1676±22 252±5 1938±18 6.15±0.10 4.11±0.07 1.18±0.01
t1 30.1±0.7 8.14±0.01 2289±9 292±14 1588±22 285±7 1880±17 6.96±0.15 4.66±0.11 1.22±0.01
+SC midnight t0 28.7±0.2 7.99±0.01 2247±8 441±17 1724±19 212±7 1948±13 5.14±0.14 3.43±0.10 1.15±0.01
t1 28.7±0.1 7.98±0.02 2254±4 452±28 1736±21 210±7 1958±15 5.09±0.16 3.39±0.11 1.15±0.01
RCP8.5 +SC mid-day t0 29.4±0.7 7.86±0.02 2296±8 635±30 1859±12 178±5 2053±9 4.30±0.13 2.88±0.09 1.12±0.00
t1 32.7±0.3 7.91±0.05 2284±8 574±93 1765±48 211±19 1989±32 5.15±0.47 3.48±0.32 1.15±0.02
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+SC midnight t0 28.2±0.4 7.80±0.03 2242±1 745±56 1877±23 148±9 2044±16 3.58±0.21 2.38±0.14 1.10±0.01
t1 31.1±0.7 7.77±0.03 2246±3 806±59 1870±16 153±6 2043±12 3.71±0.12 2.50±0.08 1.10±0.01
PI -SC mid-day t0 26±0.4 8.16±0.05 2217±32 274±42 1583±75 256±17 1846±59 6.19±0.43 4.09±0.29 1.20±0.02
t1 26.6±0.6 8.29±0.03 2215±30 179±18 1414±59 323±11 1742±49 7.81±0.30 5.17±0.20 1.27±0.01
-SC midnight t0 26.1±0.3 8.09±0.02 2217±30 324±13 1640±12 233±9 1882±18 5.63±0.21 3.72±0.13 1.18±0.01
t1 26.1±0.3 8.07±0.01 2214±3 348±6 1664±21 222±6 1896±26 5.38±0.12 3.56±0.08 1.17±0.00
PD -SC mid-day t0 28±0.5 8.10±0.03 2167±33 314±32 1575±55 239±11 1822±47 5.82±0.27 3.86±0.18 1.19±0.01
t1 29.1±0.6 8.19±0.02 2164±31 235±14 1457±40 284±5 1747±37 6.94±0.13 4.63±0.09 1.24±0.01
-SC midnight t0 27.2±0.5 8.05±0.02 2145±28 360±21 1616±25 213±12 1839±23 5.18±0.29 3.43±0.20 1.17±0.01
t1 27.2±0.4 7.79±0.22 2112±57 1128±724 1720±61 158±44 1909±40 3.84±1.06 2.55±0.70 1.11±0.04
RCP4.5 -SC mid-day t0 29.2±0.3 8.05±0.03 2236±2 365±27 1651±21 237±9 1898±16 5.80±0.24 3.87±0.16 1.18±0.01
t1 29.9±0.2 8.16±0.02 2226±7 265±20 1520±30 286±10 1813±21 6.98±0.26 4.67±0.09 1.23±0.01
-SC midnight t0 29.1±0.03 8.01±0.02 2167±38 405±16 1645±21 211±10 1866±27 5.14±0.24 3.43±0.16 1.16±0.01
t1 28.9±0.1 7.96±0.01 2166±40 465±14 1691±30 192±6 1895±34 4.68±0.14 3.12±0.10 1.14±0.00
RCP8.5 -SC mid-day t0 33±0.6 7.82±0.03 2239±8 700±53 1850±30 158±9 2026±22 3.86±0.25 2.58±0.17 1.11±0.01
t1 32.6±0.6 7.95±0.03 2237±11 494±46 1704±42 216±13 1932±30 5.31±0.34 3.59±0.23 1.16±0.01
-SC midnight t0 31±0.2 7.72±0.01 2215±41 913±23 1908±34 125±4 2056±38 3.04±0.09 2.03±0.06 1.08±0.00
t1 30.9±0.2 7.72±0.02 2224±41 912±64 1894±47 135±3 2051±47 3.29±0.08 2.21±0.05 1.08±0.01
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Table S2. PERMANOVA analysis for the carbonate chemistry parameters at t0 for the different
Condition (+SC or –SC), Scenarios (pre-industrial = PI, present day = PD, RCP4.5 = R4 and
RCP8.5 = R8) and Time (Day or Night). Bold highlights the factor(s) contributing to significant
differences (n = 3-4).
Source of variation df SS MS (Pseudo)-F p(perm) Pairwise
Condition 1 26.2060 26.2060 7.7709 0.0022 +SC ≠ -SC
Scenario 3 555.1500 185.0500 54.8730 0.0001 PI=PD≠R4≠R8
Time 1 11.4600 11.4600 3.3984 0.0565
Condition x Scenario 3 8.5429 2.8476 0.8444 0.4987
Condition x Time 1 15.7330 15.7333 4.6652 0.0272 Day: +SC ≠ -SC;
Night: +SC ≠ -SC
Scenario x Time 3 12.3640 4.1213 1.2221 0.3091
Condition x Scenario x Time 3 7.4285 2.4762 0.7343 0.5634
Residuals 38 128.1500 3.3723
Total 53 768.8000
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Table S3. Repeated measures ANOVA for carbonate parameters at t0, showing the factor(s) that yield significant differences in bold. The post-hoc
analysis is determined by factors Condition (+SC = with sea cucumber; -SC = without sea cucumber), Scenarios (PI = pre-industrial; PD = present day;
R4= RCP4.5; R8 = RCP8.5) and Time (D= day or N= night, as the whitin-subjects factor). For analysis n = 3-4.
Source of variation SS df MS F P post-hoc
pH
Between subjects
Condition 0.0270 1 0.0270 17 0.0005 +SC > -SC
Scenario 1.0100 3 0.3370 211 < 0.001 PI > PD > R4 > R8
Condition x Scenario 0.0100 3 0.0030 2 0.1419
Error 0.0320 20 0.0020
Within subjects
Time 0.0180 1 0.0180 5 0.0431 D > N
Time x Condition 0.0140 1 0.0140 3 0.0769
Time x Scenario 0.0220 3 0.0070 2 0.1705
Time x Condition x Scenario 0.0140 3 0.0050 1 0.3199
Error 0.0780 20 0.0040
AT (µmol kg-1)
Between subjects
Condition 71383 1 71383 29.5 < 0.001 +SC > -SC
Scenario 7435 3 2478 1.0 0.4027
Condition x Scenario 13319 3 4440 1.8 0.1731
Error 48362 20 2418
Within subjects
Time 20390 1 20390 31.1 < 0.001 D > N
Time x Condition 1285 1 1285 2.0 0.1767
Time x Scenario 2095 3 698 1.1 0.3859
Time x Condition x Scenario 2305 3 768 1.2 0.3451
Error 13107 20 655
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pCO2 (µatm)
Between subjects
Condition 28164 1 28164 11.2980 0.0031 +SC < -SC
Scenario 1893806 3 631269 253.2280 < 0.001 PI=PD < R4 < R8
Condition x Scenario 29991 3 9997 4.0100 0.0219 PI+SC≤PD+SC≤PI-SC≤PD-SC<R4+SC≤R4-SC<R8+SC<R8-SC
Error 49858 20 2493
Within subjects
Time 40207 1 40207 8.3460 0.0091 D < N
Time x Condition 14785 1 14785 3.0690 0.0951
Time x Scenario 61925 3 20642 4.2850 0.0173 PI-N=PI-D≤PD-D≤PD-N≤R4-D≤R4-N<R8-D<R8-N
Time x Condition x Scenario 13403 3 4468 0.9270 0.4457
Error 96354 20 4818
HCO3- (µmol kg-1)
Between subjects
Condition 907 1 907 0.16 0.6908
Scenario 680923 3 226974 40.77 < 0.001 PI = PD < R4 < R8
Condition x Scenario 16112 3 5371 0.96 0.4287
Error 111336 20 5567
Within subjects
Time 217 1 217 0.04 0.8377
Time x Condition 15653 1 15653 3.11 0.0929
Time x Scenario 9417 3 3139 0.62 0.6075
Time x Condition x Scenario 23044 3 7681 1.53 0.2380
Error 100540 20 5027
CO32- (µmol kg-1)
Between subjects
Condition 9681 1 9681 31.2590 < 0.001 +SC > -SC
Scenario 97553 3 32518 104.9940 < 0.001 PI > PD > R4 > R8
Condition x Scenario 2223 3 741 2.3920 0.0988
Error 6194 20 310
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Within subjects
Time 4453 1 4453 7.0160 0.0154 D > N
Time x Condition 1172 1 1172 1.8470 0.1892
Time x Scenario 2860 3 953 1.5020 0.2445
Time x Condition x Scenario 1986 3 662 1.0430 0.3951
Error 12694 20 635
DIC (µmol kg-1)
Between subjects
Condition 15399 1 15399 3.80 0.0655
Scenario 301394 3 100465 24.77 < 0.001 PI < PD < R4 < R8
Condition x Scenario 12804 3 4268 1.05 0.3914
Error 81124 20 4056
Within subjects
Time 2140 1 2140 0.86 0.3657
Time x Condition 8799 1 8799 3.52 0.0752
Time x Scenario 3037 3 1012 0.41 0.7509
Time x Condition x Scenario 12446 3 4149 1.66 0.2074
Error 49968 20 2498
Ωcalc
Between subjects
Condition 5.051 1 5.0510 29.1810 < 0.001 +SC > -SC
Scenario 56.501 3 18.8340 108.8080 < 0.001 PI > PD = R4 > R8
Condition x Scenario 1.212 3 0.4040 2.3340 0.1047
Error 3.462 20 0.1730
Within subjects
Time 2.777 1 2.7770 7.5460 0.0124 D > N
Time x Condition 0.652 1 0.6520 1.7720 0.1982
Time x Scenario 1.724 3 0.5750 1.5620 0.2297
Time x Condition x Scenario 1.190 3 0.3970 1.0780 0.3810
Error 7.359 20 0.3680
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Ωarag
Between subjects
Condition 2.2664 1 2.2664 28.1410 < 0.001 +SC > -SC
Scenario 24.2320 3 8.0773 100.2920 < 0.001 PI > PD = R4 > R8
Condition x Scenario 0.5380 3 0.1793 2.2270 0.1165
Error 1.6108 20 0.0805
Within subjects
Time 1.2825 1 1.2825 7.8360 0.0111 D > N
Time x Condition 0.2701 1 0.2701 1.6500 0.2136
Time x Scenario 0.7721 3 0.2574 1.5720 0.2273
Time x Condition x Scenario 0.5218 3 0.1739 1.0630 0.3871
Error 3.2736 20 0.1637
AT/DIC
Between subjects
Condition 0.0042 1 0.0042 10.2 0.0046 +SC > -SC
Scenario 0.0897 3 0.0299 73.0 < 0.001 PI > PD > R4 > R8
Condition x Scenario 0.0018 3 0.0006 1.4 0.2582
Error 0.0082 20 0.0004
Within subjects
Time 0.0019 1 0.0019 2.8 0.1072
Time x Condition 0.0019 1 0.0019 2.7 0.1134
Time x Scenario 0.0021 3 0.0007 1.1 0.3887
Time x Condition x Scenario 0.0026 3 0.0009 1.3 0.3057
Error 0.0135 20 0.0007