Spatiotemporal microbial community composition in the subsurface sediments of tropical peat- draining rivers in Sarawak, Malaysia

Nastassia Denis 101760198

A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy (by Research) (PhD)

Faculty of Engineering, Computing & Science Swinburne University of Technology, Sarawak Campus

June 2019 ABSTRACT

Our initial study in March 2014 demonstrated high abundance of Acidobacteria, Proteobacte- ria, and Terrabacteria at all sampling stations and advocated the possibility that the microbial diversity and enzyme activity is linked to the low CO2 emissions. In August 2015 and January 2016, we investigated the occurrence of shared and unique core and rare taxa in the pristine peat-draining rivers (Maludam river) and the disturbed peat-draining rivers (Sebuyau and Simunjan rivers) using 16S rRNA amplicon sequencing. Most of the bacterial core taxa found were members of the Proteobacteria, Verrucomicrobia, Actinobacteria, Bacteroidetes, Aci- dobacteria and Planctomyctes, all of which are commonly found in peatland ecosystems and are degraders related to amino acid processes. Alphaproteobacteria usually prevail in meth- ane-emitting wetlands and these bacteria are mainly composed of methanotrophs from Methy- locystaceae. Firmicutes, Bacteroidetes and Actinobacteria are plant-degrading microbes de- graders. Deltaproteobacteria belong to phylogenetic lineages from the genera Syntrophobac- ter and Geobacter which prevail in wetlands with high sulfate levels (Baker, Lazar et al. 2015). The sediment Archaea 16S revealed the core archaea communities composed of Cre- narchaeota, Euryarchaeota and Thaumarchaeota. Crenarchaeota were detected in low se- quence reads and relative abundance in all rivers during both dry and monsoon seasons. The sediments in all the rivers were enriched with Euryarchaeota. Most of the euryarchaeal OTUs detected in the peat-draining rivers were related to Methanosarcinales. Fungi (under the phylum Opisthokonta) were the major group of Eukaryotes for all the three peat-draining rivers. Opistokhonta and Alveolata were the most dominant Eukaryotes. The most dominant lineages under Opistokhonta were Nucletmycea followed by Holozoa. The rare Eukaryotes were Discosea, Gracilipodida, Tubulinea, Chloroplastida, Centrohelida, Discoba, D 1, SAR and Stramenopiles. Both the pristine and anthropogenically influenced peat-draining rivers were dominated by Bacillariophyceae. (consisting of Stramenopiles and Ciliophara). The bacterial abundance and composition increased in the pristine peat-draining river and de- creased in disturbed peat-draining rivers. This pattern is supported by the increased of enzyme activities in the disturbed rivers indicating due to the increase of organic matter from the soil run-off. This study highlights the impact of anthropogenic activities to the microbial commu- nity and activities in the three peat-draining rivers in Sarawak.

!1 ACKNOWLEDGEMENT

Firstly, I would like to express my sincere gratitude to my supervisor Prof. Moritz Mueller for the continuous support of my Ph.D study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my Ph.D study.

Besides my advisor, I would like to thank the rest of my thesis committee: Dr Aazani Mu- jahid, Dr Lik Fong and Dr Daniel Tan for their insightful comments and encouragement, but also for the hard question which incented me to widen my research from various perspectives.

My sincere thanks also goes to Professor Justus Notholt, Dr Thorsten Warnecke and Dr Denise Mueller provided me an opportunity to collaborate this research with them. Dr Aazani Mujahid who gave access to the laboratory facilities in UNIMAS. Dr David Pearce for en- couraging this research. Dr Lim Poh Teen who granted temporary research in IOES, Bachok, Kelantan. Without their precious support it would not be possible to conduct this research.

I thank Prof Wallace Wong, Dr Lim Hong Chang, Dr Tan Toh Hii, Swinburne staff, UNIMAS staff and students, Bremen University staff and students, Sarawak Forestry Coorperation staff and AQUES members for assisting in my research for the last four years. Also, I thank my friends in Inno Biologics Sdn Bhd. In particular, I am grateful to Datuk Dr Mohd Nazlee Ka- mal, Dr. Heilly Chong, Dr Shivraj Dasari and Miss Serena Ganesan for enlightening me the first glance of research.

Last but not the least, I would like to thank my parents, my family and friends for supporting me throughout my PhD journey and my life in general.

Danke!

!2 AUTHOR DECLARATION

I hereby declare that this thesis entitled “Spatiotemporal microbial community composi- tion in the subsurface sediments of tropical peat- draining rivers in Sarawak, Malaysia” is the result of my own research work except for quotations and citations which have been duly acknowledged. I also declare that is, it has not been previously or concurrently submitted for any other Master/PhD students at Swinburne University of Technology (Sarawak Campus).

Name: Nastassia Denis ID No.101760198 Date: 6th June 2019

!3 LISTS OF JOURNAL AND CONFERENCES PUBLICA- TIONS

Work carried out during this PhD project (but not covered in this thesis) has led to the fol- lowing co-authored paper:

Müller, D., T. Warneke, T. Rixen, M. Müller, S. Jamahari, N. Denis, A. Mujahid and J. Notholt (2015). "Lateral carbon fluxes and CO2 outgassing from a tropical peat-draining riv- er." Biogeosciences 12(20): 5967-5979.

List of conference presentations

i. Nastassia Denis, Aazani Mujahid and Moritz Mueller ‘Potential use of oil palm waste for microbial fuel cell’ World Hybrid conference 2013, UNIMAS, Kuching, Malaysia (Poster presentation) ii. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Is Archaea linked to Methane production in Sarawakian rivers?’ 9th International WESTPAC Scientific Conference 2014, Nha Trang, Vietnam (Poster presentation) iii. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Assessment of the microbial diversity in tropical peat-draining rivers in Sarawak, Malaysia using 16s rRNAa pyrosequencing’ 15th International Peat Congress 2016, Kuching, Malaysia (Poster presentation) iv. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Assessment of the microbial diversity in tropical peat-draining rivers in Sarawak, Malaysia using 16s rRNAa pyrosequencing’ 10th International WESTPAC Scientific Conference 2017, Qing Dao, China (Oral presentation) v. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Environmental viral community in the subsurface of peat-drain- ing rivers in Sarawak, Malaysia’ 23rd International Symposium on Environmental Biogeochemistry 2017, Cairns, Australia (Poster presentation)

!4 vi. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Microbial community in the subsurface of peat-draining rivers in Sarawak, Malaysia’ 23rd International Symposium on Environmental Biogeochem- istry 2017 Cairns, Australia (Oral presentation) vii. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Microbial community in the subsurface of peat-draining rivers in Sarawak, Malaysia’ The International Society for Subsurface Microbiology (ISSM) 2017 Conference, Rotorua, New Zealand (Oral presentation) viii. Nastassia Denis, Denise Mueller, Moritz Mueller, Aazani Mujahid,Thorsten War- necke, Justus Notholt ‘Environmental viral community in the subsurface of peat-drain- ing rivers in Sarawak, Malaysia’ The International Society for Subsurface Microbiolo- gy (ISSM) 2017 Conference, Rotorua, New Zealand (Poster presentation)

!5 Table of Contents

!6 ABSTRACT ...... 1 ACKNOWLEDGEMENT ...... 2 AUTHOR DECLARATION ...... 3 LISTS OF JOURNAL AND CONFERENCES PUBLICATIONS ...... 4 Overview ...... 1 Chapter 1 ...... 3 1.0 Introduction ...... 4 1.1 Microbial communities ...... 4 1.2 Rivers ...... 5 1.3 Peatland ...... 5 1.4 Tropical peatland and peat-draining rivers ...... 6 1.5 The microbiology of peatlands ...... 8 Chapter 2 ...... 12 2.0 Aims and Objectives ...... 13 2.1 Thesis outline ...... 14 Chapter 3 ...... 16 3.0 Methodology and geological settings ...... 17 3.1 Maludam River ...... 18 3.2 Sebuyau river ...... 20 3.3 Simunjan river ...... 21 3.4 Sampling approach and station overview ...... 22 3.4.1 Push sediment cores ...... 23 3.5 Microbiome profiling with next gene sequencing ...... 24 3.7 Stoichiometry analysis (H:C, C:N and C:S) ...... 33 Chapter 4 ...... 36 4.0 ...... Seasonal responses of extracellular enzyme activities in the sediments of tropical peat- draining rivers ...... 37 Abstract ...... 37 4.1 ...... Introduction ...... 37 4.2 Results ...... 39 4.2.1 Riverine characteristic ...... 39 4.2.2 Phenol oxidase and peroxidase activities...... 39 4.2.3 Soil stoichiometry data ...... 42 4.3 Discussion ...... 44 4.3.1 Spatial and seasonal patterns of enzymatic activities ...... 44

!7 4.4 Conclusion ...... 46 Chapter 5 ...... 47 5.0 Identifying the core and rare archaeal and bacterial community along the surface sediment of an undisturbed tropical peat-draining river in Sarawak, Malaysia ...... 48 Abstract ...... 48 5.1 ...... Introduction ...... 48 5.2 Results ...... 49 5.2.1 Environmental parameters ...... 49 5.2.2 Community structure, richness, diversity, and activity ...... 50 5.3 Discussion ...... 53 5.3.1 Diversity and spatial variation ...... 53 5.3.2 Microbial core and rare community of surface sediments of an undisturbed tropical peat- draining river...... 56 5.3.3 Potential link between microbes and CO2 uptake and release ...... 59 5.3.4 Hidden importance for methanogens and methanotrophs ? ...... 60 5.4 Conclusion ...... 61 Chapter 6 ...... 62 6.0 Succession of core and rare bacterial taxa in the sediments of the peat-draining rivers in Sarawak ...... 63 Abstract ...... 63 6.1 ...... Introduction ...... 63 6.2 ...... Results ...... 66 6.2.1 Sequences retrieved from sediment samples ...... 66 6.2.3. Microbial diversity in pristine, semi-disturbed and disturbed peat-draining rivers ...... 69 6.2.4. Diversity indices...... 76 6.2.5. Bacterial metabolism ...... 76 6.2.6 Bacterial networks and clustering according to anthropogenic influences ...... 78 6.3 ...... Discussion ...... 81 6.3.1 Core and rare bacterial communities in the peat-draining rivers ...... 81 6.3.2 Composition and sensitivity of core members to exchanging seasons and anthropogenic influences 82 6.4 ...... Conclusion ...... 83 Chapter 7 ...... 85 7.0 ...... Diversity of methanogenic Archaea in three peat-draining rivers in Sarawak ...... 86 Abstract ...... 86 7.1 ...... Introduction

!8 ...... 86 7.2 ...... Results ...... 88 7.2.1 Archaeal community composition and abundance ...... 88 7.2.2 Diversity of archaeal community...... 91 7.3 ...... Discussion ...... 93 7.3.1 Core and rare Archaea and differences in distribution ...... 95 7.3.2 Composition of core methanogenic communities...... 97 7.4 Conclusion ...... 100 Chapter 8 ...... 101 8.0 ...... Core and rare eukaryotes taxa in the sediments of peat-draining rivers in Sarawak ...... 102 Abstract ...... 102 8.1 Introduction ...... 102 8.2 Results ...... 103 8.2.1 Analysis of pyrosequencing data ...... 103 8.2.2 Diversity of microbial eukaryotes...... 104 8.3 Discussion ...... 110 8.3.1 Eukaryotic communities and impact of land use changes ...... 110 8.4 Conclusion ...... 113 Chapter 9 ...... 114 9.0 Conclusion and Outlook ...... 115 Bibliography ...... 119

!9 List of Figures

Figure 1: Estimated oil palm plantation areas in Southeast Asia

Figure 2: Peatlands in Indonesia and Malaysia Figure 3: Sampling stations of surface sediments along Maludam River (March 2014)

Figure 4: Sampling stations of push cores along Maludam Figure 5: Sampling stations of surface sediments along the Sebuyau and Simunjan rivers (August 2015 and January 2016).

Figure 6: The sediment core used for sediment coring. Figure 7: DNA Integrity test performed on all DNA samples prior to DNA sequencing. Figure 8: Overview of ACE pipeline. Figure 9: Aliquots of the supernatant distributed in a 96 well plate. Figure 10: Samples in 1.5mL microcentrifuge tubes. Figure 11: BioTek microplate reader. Figure 12: Blank (underlined) together with positive (+) and negative (-) controls. Figure 13: CHNS Analyzer at UiTM Kota Samarahan, Malaysia. Figure 14: Simplified flowchart for enzyme and CHNS analyses.

Figure 15: Combustion tubes for CHNS analyzer Figure 16: The enzyme activity in cores collected in Maludam Figure 17: The enzyme activity in cores collected in Sebuyau Figure 18: The enzyme activity in cores collected in Simunjan Figure 19: C:H:N:S stoichiometry of the sediment cores during dry season Figure 20: C:H:N:S stoichiometry of the sediment cores during wet season

Figure 21: Number of reads (in percentage) of the Archaea and Bacteria in Maludam Figure 22: Phenol and peroxidase activities. Figure 23: Principal Coordinate Analysis (PCoA) of microbial diversity Figure 24: Heat map produced using MEGAN 6; Bacteria and Archaea Figure 25: Visual representation of the microbial core community in Maludam river, calculated using MEGAN 6

!10 Figure 26: Visual representation of the microbial rare community in Maludam, calculated using MEGAN6 Figure 27: Bacteria rarefraction curves

Figure 28: Relative abundances of bacterial communities (top 10 families) in a pristine river (Maludam), semi-disturbed river (Sebuyau), and disturbed river (Simunjan) during dry and monsoon season

Figure 29: Top 50 sediment bacterial phyla in Maludam, Sebuyau and Simunjan river Figure 30: Venn diagrams (top 20 core, at Phylum level; Bacteria)

Figure 31: Venn diagrams (top 20 rare, at Phylum level; Bacteria) Figure 32: Bacteria core phyla Figure 33: Bacteria rare phyla Figure 34: Bacteria diversity indices according to season and rivers. Figure 35: Top 5 predicted metabolic pathways abundance (Bacteria)

Figure 36: Bacteria network plot Figure 37: PCoA for Bacteria on the basis of pairwise weighted Unifrac distances. Figure 38: Rarefraction plots; Bacteria Figure 39: Phylum level composition of archaeal communities Figure 40: Genus level composition of archaeal communities Figure 41: Archaea diversity indices according to season and rivers. Figure 42: Principal coordinate analysis (PCoA) of the archaeal community. Figure 43: Correlation networks Archaea Figure 44: Venn diagram top 20 core Archaea Figure 45: Venn diagram top 20 rare Archaea Figure 46: Functional categories predicted for Archaea Figure 47: Rarefraction curves; Archaea Figure 48: Bar plots of the top 20 eukaryotic core phyla Figure 49: Bar plots of the rare eukaryotic community (at family level) Figure 50: Eukaryotic diversity indices according to season and rivers. Figure 51: Principal coordinate analysis (PCoA) of eukaryotic communities Figure 52: Co-occurrence network; Eukaryotes Figure 53: Venn diagram top 20 core Eukaryotes

!11 Figure 54: Venn diagram top 20 rare Eukaryotes

List of Tables

Table 1: GPS location for Maludam River in March 2014. Table 2: GPS locations for the Maludam River during the dry and monsoon seasons. Table 3: GPS locations for the anthropogenic rivers (the Sebuyau and Simunjan). Table 4: Environmental parameters measured along Maludam river Table 5: Summary of reads and calculated alpha diversity indices for both Bacteria and Archaea at all 5 stations.

!12 Overview

Microbial ecologists study the relationships between microorganisms and their envi- ronments. All environments on the Earth are populated by microorganisms. Understanding microbial ecology is incredibly important, because the relationships between microorganisms and their environments have a crucial role in the health of the planet and all of its inhabitants. The widening reach of microbial ecology is readily revealed by considering the scope of sev- eral of the leading microbial-ecology journals, which include topics that range from marine biology to population biology, bioremediation and bioenergy and such diverse research areas as the impact of microorganisms on biogeochemical cycles and within-host dynamics of in- fectious diseases.

This thesis aims to study the spatio-temporal microbial diversity in three tropical peat- draining rivers in Sarawak, Malaysia. Although a number of studies have highlighted the im- portance of tropical peatlands and rivers for carbon storage, greenhouse gas release, nitrogen cycling (Müller, Warneke et al. 2015), an assessment of the microbial diversity remains frag- mentary at best. However, without understanding the bacterial, archaeal and eukaryotic com- munity composition and diversity, their potential roles in the control of carbon and other nu- trient fluxes cannot be determined. Besides addressing the knowledge gap in diversity (objec- tive 1), this thesis also addresses changes in microbial diversity (and enzyme activities) due to changes in landuse surrounding the rivers (objective 2), and the impact of seasonality on the microbial diversity (objective 3).

Chapter 1 provides the relevant context highlighting knowledge gaps related to the scope of the thesis. In each of the following chapters (4-7), the underlying hypotheses and ra- tionale are provided in the introduction. Chapter 2 provides an overview of the study site and the methods utilized in the proceeding chapters. Chapter 3 provides a more specialised as- sessment of the environmental conditions for the microbial communities by assessing enzy- matic activities and distribution of dissolved organic carbon in porewaters. Chapter 4 details bacterial and archaeal diversity in sediment samples of a pristine tropical peat-draining river (Maludam) and chapters 5, 6, and 7 dive deeper into the bacterial (5), archaeal (6), and eu- karyotic (7) diversity of pristine (Maludam) and anthropogenically disturbed (Sebuyau and

!1 Simunjan) peat-draining rivers. In chapters 5-7, the impact of seasonality is also assessed (an- swering Objective 2).

!2 Chapter 1

!3 1.0 Introduction

1.1 Microbial communities

Microbial communities in freshwater ecosystems compose of diverse assemblages of microorganisms (Bucci, Szempruch et al. 2014). It was mentioned in several studies that sea- sonal fluctuations have the potential to alter the microbial structure in freshwater streams (Hullar, Kaplan et al. 2006), for example in a river in Japan (Sakami 2008). Although tempo- ral and seasonal differences of bacterioplankton population structures have been observed in response to seasonal fluctuations (Bucci, Szempruch et al. 2014), microbial community struc- ture is often less well understood.

High numbers of Gammaproteobacteria were detected in anthropogenically influ- enced rivers (Baker, Lazar et al. 2015). Alpha-, Beta- and Gammaproteobacteria are common inhabitants in rich nutrient environment owing to their capabilities to decompose nutrient wastewater via denitrification (Matulich, Weihe et al. 2015). Proteobacteria also dominated the microbial communities at oil contaminated industrial sites (Patel, Sharma et al. 2016). Proteobacteria, Firmicutes, Bacteroidetes, Acidobacteria, Actinobacteria, Chloroflexi, Verru- comicrobia and Planctomycetes were often found in contaminated rivers (Xie, Wang et al. 2016). Alpha-, Beta-, and Gammaproteobacteria and a portion of the Bacteroidetes are men- tioned in studies on the Mississippi river (Henson, Hanssen et al. 2018) and estuarine sedi- ments (Baker, Lazar et al. 2015). Nitrospirae, Actinobacteria, Acidobacteria and Chlorobi dominated less anthropogenically influenced sites in the sediments of Lagos Lagoon, Nigeria (Ezirim, Chikezie et al. 2017).

Sediment fungi represent a significant component in decomposition process in the riv- er sediments (Jiang, Zheng et al. 2009). Ascomycota and Basidiomycota were the predominant fungal communities in the Songhua River catchment (Liu, Wang et al. 2015). Chytridiomyco- ta, Glomeromycota and Mucoromycota were detected underneath the reservoir sediment ex- posed to eutrophication at Chinese coasts (Kang and Wang 2018). Other studies found As- comycota and Basidiomycota in subseafloor sediments (Redou, Navarri et al. 2015). Sedimen-

!4 tary fungal communities such as Ascomycota, Basidiomycota, Chytridiomycota, Zygomycota and Glomeromycota were predominant in South American peatlands (Oloo, Valverde et al. 2016).

1.2 Rivers

Rivers are dynamic and continuous systems (Findlay 2010). Each river owns individ- ual characteristics from the landscapes of its drainage basin (Holker, Wurzbacher et al. 2015). Rivers are also susceptible to anthropogenic perturbations (Dorigo, Lefranc et al. 2009). Vari- ous biogeochemical processes ranging from nutrient cycling, decomposition and mineraliza- tion occur in rivers (Matulich, Weihe et al. 2015). This can include processing of end products from the surrounding landscape or elimination of these end products via metabolism.

Tropical riverine ecosystems differs from other freshwater ecosystems based on two fundamentals; movement of the river current and the second is the interaction with its drainage basin; a permanent contribution of allochthonous material (Henson, Hanssen et al. 2018). Rainfall plays an important role in tropical rivers. During the monsoon season, the rivers receive rain water and erosion products by the quick surface runoff as observed in Aus- tria (Jirsa, Neubauer et al. 2013). On the other hand, the dry season will be accentuated and the regime of the running water will be altered (Kaevska, Videnska et al. 2016). Rivers trans- port aquatic and terrestrial carbon from allochthonous and autochthonous sources into the ocean. Riverine ecosystems are net heterotrophic, whereby CO2 is respired into the at- mosphere by the river and not produced (Müller, Warneke et al. 2015).

1.3 Peatland

Peatlands are subsets of wetlands and cover 2.7% of the land surface of the Earth (see Figure 3). There are various types of peatlands such as mires (Figure 7) including fens, bogs (raised bogs and blanket bogs), marsh and swamps (Brouns, Verhoeven et al. 2014, Dieleman, Branfireun et al. 2015, Lamers, Vile et al. 2015, Walker, Garnett et al. 2016, Asemaninejad, Thorn et al. 2017, Weedon, Kowalchuk et al. 2017). Fens, also known as minerogenous peat- lands, have a continuous hydrological input from the groundwater network, with a constant

!5 supply of water and nutrients. Treed minerogenous peatlands are identified as swamp forests in North America (Preston, Smemo et al. 2012). Ombrogenous peatlands, or bogs, are in an irreversible condition compared to fens. Bogs are formed through a deep accumulation of peat with a scarce supply of water and nutrients (Preston, Smemo et al. 2012, Walker, Garnett et al. 2016, Asemaninejad, Thorn et al. 2017, Dean, Billett et al. 2017, Weedon, Kowalchuk et al. 2017). Fens and bogs occur mostly in Northern Europe (see Figure 7 and 8). South African peatlands are formed from volcanic activities (Gorham, Lehman et al. 2012). Amazonian peatlands develop from cessations of rivers whereas the peatlands in Southeast Asia rely on rain precipitation for their nutrient supply and are therefore recognized as ombrotrophic peat- lands (Gorham, Lehman et al. 2012). Compared to minerotrophic fens, ombrotrophic peat- lands contain more net carbon and are known to be susceptible to autogenic changes that could perturb carbon accumulation within the peatland (Brouns, Verhoeven et al. 2014).

1.4 Tropical peatland and peat-draining rivers

Only a few peatlands in Southeast Asia have had their peat structure, age and develop- ment and rates of peat and carbon accumulation investigated (Fitzherbert, Struebig et al. 2008, Carlson, Goodman et al. 2015). Radiocarbon dating of peat materials has revealed a higher carbon concentration in tropical peatlands, compared to boreal and subarctic peatlands (Fitzherbert, Struebig et al. 2008, Carlson, Goodman et al. 2015).

Tropical peat swamp forests can be considered as extreme ecosystems because of their low pH (generally below pH 4), anaerobic and oligotrophic conditions. While water can drain out of peat swamp forests through streams and rivers, the only inputs are from rainfall there- fore nutrient inputs are extremely limited. These conditions have led to the development of highly-specialized plant communities and peat swamp forests have a high proportion of en- demic taxa. To minimize nutrient loss to herbivory, many tropical peatland plants produce toxic secondary compounds or have thick, tough leaves and thorn. Younger, thinner peat typi- cally supports low pole forest or mixed swamp forest plant communities, whereas the thicker, older peat supports taller forests with higher plant diversity (Espenberg, Truu et al. 2018)

!6 Many tropical peatlands are still young, having formed in the last 5,000 years. Re- moval of trees, canals and drained areas for transport of timber material are cut into the peat during logging activities which changes the hydrology and drainage of large areas (Too, Keller et al. 2018). Drying the upper layers of peat can cause large peat fires. Once drained, the peat is exposed to risk of fire with the most degraded peat areas being at greatest risk. These activities also have deep impact on human populations with smoke from the fires creat- ing health problems throughout the south-east Asia region such as in Indonesia, and the pro- duction from fossil fuel burning contribute to 1.3-3.1% of the current global CO2 emission (Leng, Ahmed et al. 2019). Tropical peat swamps are also impacted through conversion to agriculture and reclamation for human inhabitation and industry. Changes in land-use of peat soil have been shown to severely impact the global carbon cycle as high amounts of green- house gases are released during the exposure of old stored carbon (Blodau 2002, Monard, Gantner et al. 2016). Anthropogenic activities (e.g. deforestation, drainage, conversion to agricultural land and forest fires) in the tropical peatlands of Southeast Asia continue to in- crease, with the result that carbon is being transferred to the environment faster than it is be- ing naturally sequestered due to severe drainage (Figure 18), leading to surface peat aeration, decomposition and carbon losses (Lavoie, Paré et al. 2005). Indonesia releases large amounts of CO2 into the atmosphere during forest burning for oil palm plantations and ranks as the world’s No.3 greenhouse gas emitter after the United States and China (Blodau 2002, Billett, Palmer et al. 2004). Scientists need to intensify efforts to prevent the other neighbouring countries from following suit. Indonesia and Malaysia possess 85% of the world’s oil palm production (Bardgett, Freeman et al. 2008,) and protecting the remaining pristine peatlands is an incredibly challenging mission.

!7 !

Figure 1: Estimated oil palm plantation areas in Southeast Asia (Source: Fitzherbet, 2008).

1.5 The microbiology of peatlands

The global survey of 16S rRNA libraries reported Proteobacteria and Acidobacteria out- ranked other bacteria in permafrost soil, representing 40% and 20% of the soil microbial communities in permafrost soil (Yang, Wen et al. 2012). Proteobacteria were the most abun- dant bacteria in permafrost along China-Russia Cruden Oil Pipeline (CRCOP) representing 41.6% of all reads across samples, followed by Acidobacteria (8.56%, 1237 OTUs), Acti- nobacteria (7.86%, 1136 OTUs), Bacteroidetes (7.36%, 1063 OTUs) and Chloroflexi (8.27%, 1195 OTUs) (Yang, Wen et al. 2012). The sediments from Arctic peatland exhibited increased archaeal abundance belonging to the taxa Euryarchaeota and Crenarchaeota (Høj, Olsen et al. 2005).

!8 Bacterial communities in soil from coastal plain wetlands are also dominated by Aci- dobacteria, Gammaproteobacteria and Actinobacteria (Hartman, Richardson et al. 2008, Serkebaeva, Kim et al. 2013, Belova, Kulichevskaya et al. 2014). Other bacterial groups present include the Cytophaga-Flavobacterium-Bacteroidetes (CFB group). The majority of sequences identified in Southern Brazilia Atlantic Forest belonged to Acidobacteria (33.8%) and Proteobacteria (26.2%) (Ibekwe, Andreote et al. 2012). The bog and fen environment were dominated by Acidobacteria, Planctomyctes, Actinobacteroa, Bacteroidetes, Chlorobi and TM7 (Krachler, Krachler et al. 2016). At phylum level, the Sphagnum-bog had high abundances of Acidobacteria, Verrucomicrobia and TM6 (Krachler, Krachler et al. 2016).

The dominance of Ascomycota and Basidiomycota in the eukaryal communities was discovered in many peatland types. A study in the ancient peatlands at Sanjiang Plain, China revealed Ascomycota and Basidiomycota dominated the fungal communities (Zhou, Zhang et al. 2017). The authors also discovered the fungal communities in their study responded differ- ent to environmental parameters (Zhou, Zhang et al. 2017). The fungal community structure in Loess Plateau Grasslands in China was composed of Ascomycota, Chytridiomycota, Basid- iomycota, Glomeromycota and Mucoromycotina (Huhe, Chen et al. 2017). Ascomycetes and Basidiomycetes affiliated members were the most abundant at the tundra zone of Eurasia (Shiryaev 2017).

Tropical forests such as tropical peat forests (a peatland example) are important in ni- trogen cycle involving nitrogen fixing, nitrifying (Vaksmaa, Guerrero-Cruz et al. 2017) and denitrifying microorganisms , methane cycle by methanogens and methanotrophs (Jing, Che- ung et al. 2016) and sulphur cycle by sulfur reducers and oxidisers (Baker, Lazar et al. 2015). Understanding the microbial community ecology involved in biogeochemical processes in forest ecosystem is made possible with the rapid development of metagenomics studies and isotope studies.

Tropical peatlands are commonly dominated by Acidobacteria (roughly 30-50% of the community) and Proteobacteria (20-40% of the community) (Too, Keller et al. 2018). The distribution is quite similar to a general structure of the bacterial community in a typical soil

!9 with Proteobacteria (accounting for 39% of the bacterial community), Acidobacteria (20%), Actinobacteria (13%), Verrucomicrobia (7%), Bacteroidetes (5%) and Chloroflexi (3%) with Planctomycetes, Gemmatimonadetes and Firmicutes (Elliott, Caporn et al. 2015). Other groups commonly reported in tropical peatlands are Actinobacteria, Verrucomicrobia and Planctomycetes (Oloo, Valverde et al. 2016).

1.5.1 Processing of organic matter

The main role of heterotrophic microorganisms in peatland is decomposition and pro- cessing or organic matter in the peat material (Elliott, Caporn et al. 2015). Several decomposi- tion processes are identified such as cellulose decomposition (carbohydrate present in plants), hemicellulose decomposition (water-soluble polysaccharides and consists of hexoses, pen- toses and uronic acids and are the major plant constituents), chitin decomposition (the skin of arthropods and cell walls of fungi), lignin decomposition (plant tissues), lipids decomposition (from plants and microorganisms) and protein decomposition (Elliott, Caporn et al. 2015).

Extracellular enzymes are essential for the decomposition of polysaccharides. Studies have revealed most extracellular enzymes are excreted from Eukaryotes. Fungi, in particular Penicillium and Aspergillus, and bacteria such as Streptomycin and Pseudomonas produce enzymes to degrade cellulose (Lefranc, Thenot et al. 2005). Microorganisms such as fungi, bacteria and actinomycetes are also involved in decomposition of hemicellulose (Matulich, Weihe et al. 2015). The chitin degraders as reported by previous studies are mostly actino- mycetes, Streptomyces, Nocardia, Trichoderma and Verticullum. Basiomycetous fungi are known to degrade lignin (Elliott, Caporn et al. 2015).

The ‘Enzyme Latch theory’ suggests that the decomposition rate deteriorates at low oxygen concentrations as enzymes require oxygen (Novotný, Svobodová et al. 2004). This allows phenolic compounds to accumulate, which, in turn, inhibit those enzymes required for peat decomposition (Andersson, Kjøller et al. 2004). Enzyme activity within the microbial community reflects microbial metabolism and the microbial activity which ensues (Anders-

!10 son, Kjøller et al. 2004). When there is a high level of oxygen concentration in soil, the phe- nolic enzyme concentration increases along with hydrolyzing enzymes, and phenolics become scarce. Hence, decomposition activity may be intensified by elevated temperatures, pH and a low water table, since a low water table provides oxygen (Novotný, Svobodová et al. 2004). During the monsoon season and higher tides, carbon related enzymes latch onto nitrates or sulphates to replace oxygen in order to subsist in the anaerobic conditions (Novotný, Svo- bodová et al. 2004). However, peatlands harbour low levels of nitrates, manganese, ferric ion and sulphates. Therefore, the enzymes exploit other humic substances available in peat to re- place oxygen (Hinojosa, Carreira et al. 2004).

These water soluble enzymes are normally expelled into the soil column and risk be- ing washed out by strong surface water (Hinojosa, Carreira et al. 2004). At low tide, increased enzyme activity together with a higher concentration of DOC, helps bring about an increase in organic matter decomposition by carbon-related enzymes, due to the aerobic conditions (Hi- nojosa, Carreira et al. 2004). This has been contradicted by another study which suggests a lowering of the water table produces a decrease in carbon-related enzyme activity as the activ- ity of the enzymes phenol oxidase and peroxidase requires moisture (Andersson, Kjøller et al.

2004). When the water table in peatland soil is lowered, CO2 is released by oxidation and de- composition (Andersson, Kjøller et al. 2004). These two processes involve two important en- zymes in the carbon cycle balance in soil, i.e. phenol oxidase and peroxidase (Andersson, Kjøller et al. 2004). The enzymes phenol oxidase and peroxidase dissolve easily in water and are highly exposed to sea water intrusion when these enzymes operate on the subsurface of a peat-draining river (Novotný, Svobodová et al. 2004).

!11 Chapter 2

!12 2.0 Aims and Objectives

As outlined above, an assessment of the microbial diversity in tropical peat-draining rivers remains fragmentary at best. This severely limits our ability to assess and predict their poten- tial roles in important geochemical cycles, for example their participation in the carbon cycle and other nutrient fluxes.

This thesis project aims at providing an explanation for seasonal and spatial patterns in the microbial communities of three peat-draining rivers in Sarawak. Research focused on the changes in microbial communities and their relation to pristine (Maludam) and anthropogeni- cally influenced catchments (Sebuyau and Simunjan). In this sense we investigated:

!13 i) to close the major knowledge gap of microbial diversity in tropical peat-draining rivers. Next-generation sequencing was used to assess not only Bacteria but also Archaea and Eukaryotes. ii) the potential indicator species in the microbial communities’ abundance and composition in these peat-draining rivers during two seasons. It was hoped to be able to pinpoint spe- cific patterns in abundance and community composition among the Archaea, Bacteria and Eukaryotes as indicators of landuse change. iii) the rare and core microbial communities. Core taxa contribute greatly in biogeochemical processes whereby rare taxa contribute to species richness. iv) the archaeal and bacterial predictive metabolic profiling using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) to assess their potential involvement in biogeochemical processes. v) the microbial phenol oxidase and peroxidase activities. The enzymatic activity is impor- tant for lignin degradation, carbon mineralization and sequestration, and dissolved organ- ic carbon export. Phenol oxidase and peroxidase were measured using L-DOPA (L-3,4- dihydroxyphenylalanine) which is suited to the acidic peat soils samples.

2.1 Thesis outline

Chapter 1 provides the relevant context highlighting knowledge gaps related to the scope of the thesis. Chapter 2 summarises the aims and outline of the thesis. In each of the following chapters (4-8), the underlying hypotheses and rationale are provided in the intro- duction. Chapter 3 provides an overview of the study site and the methods utilized in the pro- ceeding chapters. Chapter 4 provides a more specialised assessment of the environmental conditions for microbial communities by assessing phenol oxidase and peroxidase and CHNS nutrient stoi- chiometry to maximise our understanding on the dissolved organic matter and carbon turnover in the porewaters of three peat-draining rivers.

!14 Chapter 5 details bacterial and archaeal diversity in sediment samples of a pristine tropical peat-draining river (Maludam) and chapters 6, 7, and 8 dive deeper into the bacterial (6), archaeal (7), and eukaryotic (8 diversity of pristine (Maludam) and anthropogenically dis- turbed (Sebuyau and Simunjan) peat-draining rivers. The core and rare Archaea, Bacteria and Eukaryotes are presented. In chapters 6-8, the impact of seasonality is also assessed (answer- ing Objective 2). The findings are synthesized as a concluding summary in Chapter 9, which also pro- vides recommendations for future research directions.

!15 Chapter 3

!16 3.0 Methodology and geological settings

Our study area consisted of three peat-draining rivers in Sarawak, Malaysia. Sarawak has the largest peatland area in Malaysia, comprising 69.08% of the total peatland area of 1,697,870 ha (Figure 2). Peninsular Malaysia has 642,857 ha of peatland area and Sabah has 116,965 ha (Wetlands International – Malaysia, 2010). However, only 223,277 area of the peatland in Sarawak (13%) is identified as undisturbed, compared with 35% in Peninsular Malaysia and 18% in Sabah. Sarawak has the highest percentage (71%) and area (915,283 ha) of disturbed peatlands, followed by Sabah (34%) and Peninsular Malaysia (18%) (Wetlands International – Malaysia, 2010).

!

Figure 2: Peatlands in Indonesia and Malaysia (highlighted in brown), based on the Harmo- nized World Soil Database (HWSD) Version 1.2 and ArcGIS® software by Esri.

Most peatlands in Sarawak have been converted for agricultural purposes, particularly oil palm plantations (Figure 2), with an alarming increase in oil palm plantation expansion

!17 from 100,000 ha in 2003 to 300,000 ha in 2008. Sarawak will have no peatlands in future should this trend persist (Wetlands International – Malaysia, 2010).

In the following section, the different rivers are introduced.

3.1 Maludam River

Maludam River is located in the heart of Maludam National Park (gazetted as a Na- tional Park on 17th February 2000), the third largest national park in Sarawak, Malaysia. The Maludam National Park covers an area of 43,147 ha and contains the largest undisturbed peat dome in Malaysia (Melling, Hatano et al. 2005). The majority of the vegetation growth along the river consists of Gonstylus bancanus, Dactylocladus stenostachys, Copaifera palustris and unrecorded species of Shorea (Melling, Hatano et al. 2005). Maludam River drains only from that dome, making it an ideal site for studying peat-draining rivers (Müller, Warneke et al. 2015, Wit, Müller et al. 2015). Sample collection of surface sediments started as far upstream as possible and then downstream to the border of the National Park (Figure 3) in March 2014. Samples were col- lected using a grab sampler and preserved in ice during transportation to the laboratory for immediate processing.

!18 ! Figure 3: Sampling stations of surface sediments along Maludam River (March 2014) in Maludam National Park. Subsequent to the first sampling campaign on the Maludam River in March 2014, we continued sampling with push sediment cores on this river, and expanded our sampling to in- clude two anthropogenically influenced peat-draining rivers in the Sadong and Lupar basins, the Sebuyau and Simunjan rivers.

Push sediment cores were used to collect sediment core samples along a transect of the Maludam River (Figure 3); upstream, mid-river and downstream during the dry season (Au- gust 2015) and monsoon season (January 2016). Cores of ~15 cm length were collected dur- ing each sampling trip.

!19 ! Figure 4: Sampling stations of push cores along Maludam River in Maludam National Park. This figure is for orientation purposes only. Please refer to the GPS coordinates in Table 1.

3.2 Sebuyau river

Located on the same peat dome (Figure 5), Sebuyau river is surrounded (but not within close proximity) by both matured and young oil palm plantation but not as many as in Simunjan river. Although minimal damage due to deforestation for logging activities was sighted during the sampling expedition, most of the peat catchment area of the Sebuyau River remained undisturbed. Referring to previous work highlighting the levels of disturbance to each river’s catchment area, the Sebuyau catchment area is classified as “partially disturbed” due to the presence of oil palm plantations covering 2.4% of the catchment area (Pagnone, 2015) (Figure 5).

!20 ! Figure 5: Sampling stations of surface sediments along the Sebuyau and Simunjan rivers (Au- gust 2015 and January 2016). Push sediment corers were used to collect sediment samples along a transect (upstream, mid-river and downstream) during the dry season (August 2015) and the monsoon season (January 2016).

3.3 Simunjan river

The Simunjan River is a large tributary of Batang Sadong. The Simunjan catchment area is classified as “disturbed”, since 29.5% of the catchment area has been converted to oil palm plantations (Pagnone, 2015). Push sediment corers were used to collect sediment samples along a transect (upstream, mid-river and downstream) during the dry season (August 2015) and the monsoon season (January 2016) (Figure 5).

!21 3.4 Sampling approach and station overview

In collaboration with colleagues from the University of Bremen and University Malaysia Sarawak (UNIMAS), we performed physico-chemical analyses both on-site and off-site (lab- oratory analyses). Samples were processed in the respective laboratories and also in external laboratories in UiTM. The following tables summarize all sampling locations for microbio- logical analyses reported in this thesis.

Table 1: GPS location for Maludam River in March 2014.

Station Latitude Longitude

MAL1 1.565 111.097112

MAL2 1.584501 111.083976

MAL3 1.598139 111.073308

MAL4 1.635634 111.049232

MAL5 1.645484 111.043324

!22 Table 2: GPS locations for the three peat-draining rivers in August 2015 and January 2016.

Date Station Latitud Longitu Station Latitude Longitu Station I.D. for Latitud Longitu I.D. for e de I.D. for de S i m u n j a n e de Maluda Sebuya rivers m u rivers

Augu MAL1 1.57834 111.079 SEB1 1.396027 110.99 SIM1 1.2888 110.80 st 2 6 58 2015

Augu MAL2 1.59360 111.078 SEB2 1.399735 110.997 SIM2 1.3093 110.81 st 4 1 75 2015

Augu MAL3 1.64578 111.056 SEB3 1.396886 110.95 SIM3 1.3703 110.75 st 2 1 26 2015

Janua MAL4 1.57361 111.086 SEB4 1.485948 110.95 SIM4 1.2788 110.80 r y 1 2 58 2016

Janua MAL5 1.60277 111.065 SEB5 1.488526 110.93 SIM5 1.2993 110.81 r y 8 4 75 2016

Janua MAL6 1.64591 111.039 SEB6 1.501916 110.92 SIM6 1.3203 110.75 r y 7 9 26 2016

3.4.1 Push sediment cores

Push cores were made rom material (see Figure 6). Holes in the cores were fitted with 8 mm diameter sized screws to fit 5 mL sterile syringes for sucking out pore water at intervals of 1 cm.

!23 !

Figure 6: The sediment core used for sediment coring.

For August 2015 and January 2016, sediment corers were used to collect samples as they create less disturbance by water ripples and collect more highly consolidated deposits. Sediment corers were slowly lowered into the substrate and pushed into the sediment. Both ends of the core were capped with a plastic stopper and the entire segment was preserved at 4oC upon collection. In the laboratory, the cores from the tubes were pushed out with a clean rod and cut into slices 1cm thick. Each slice was stored in an individual clean plastic bag and stored in the university cold room (4oC) for further analysis (e.g. molecular analyses and physio-chemical analyses such as C:H:N:S stoichiometry and enzyme assays). The pore water samples from the sediment cores were transferred into sterile test tubes and stored at 4oC.

3.5 Microbiome profiling with next gene sequencing

Culturing microorganisms is time consuming and not cost effective. To isolate envi- ronmental microorganisms from their natural habitat is equally as difficult. Isolated environ- mental microorganisms are vulnerable and usually respond slowly or negatively to enrich- ment media used. These microorganisms might not able to adapt to the incubator temperature or the environment in the laboratory. Advancements in massively parallel DNA sequencing technologies have resulted in reducing the shortcomings from the culturing method (Matturro, Frascadore et al. 2017). DNA sequencing provide unbiased, comprehensive information but are also faster and more economical to comprehensively evaluate the complexity of microbio- ta in an environment (Matturro, Frascadore et al. 2017). DNA sequencing allows a more com-

!24 plete profiling of the microbial communities in the environment efficiently and promptly without having to address the issues above. This technology helps to provide better insights into microbial specialisation and functions in this the peatland ecosystem that are slowly be- ing converted into agriculture land.

Furthermore, the use of high throughput next-generation sequencing (NGS) technolo- gies, is promising to identify active prokaryotes and eukaryotes in environmental samples (Matturro, Frascadore et al. 2017). The application of DNA sequencing and the movement toward a “molecular taxonomy” for protists, has begun to provide morphology and culture- independent approaches to the investigation of protistan community diversity. Molecular characterisation of protistan communities has focused largely on sequencing the small subunit ribosomal gene (the 18S rRNA gene) rather than other genes more commonly used for bar- coding other eukaryotes. The application of such molecular approaches has lagged behind similar investigations of prokaryotic communities but already led to important discoveries in the eukaryal communities. The identification of active microbial populations within the envi- ronmental samples can be addressed with the availability of substantial databases such as Sil- va, Greengenes and UNITE. The selection of databases depends solely on the compatibility of the OTU clustering platform used.

Metagenomic studies of peatland soil with acidic, high organic accumulation and low mineral content provide insights into microbial specialisation and functions in this unique ter- restrial ecosystem that rapidly diminishing due to conversion into agricultural land or make way for urbanisation. Recent metagenomics studies involving peatland soil are from Narathi- wat peat forest soil in Thailand (Genome Institute, BIOTECH, Pathumethani) (Sripreechasak, Phongsopitanun et al. 2017), Siberia (ABI Prism 277 DNA sequencer) (Hinsa-Leasure, Bhavaraju et al. 2010) and Malaysian peat forests (Promega pGEM-T Easy vector) (Too, Keller et al. 2018).

Modern HTSeq platforms, for instance Illumina MiSeq, deliver unprecedented amounts of clustered PCR fragments, leading to millions of single- or paired end reads at moderate cost per run. Still, several problems remain, such as the relative short read lengths, or non-negligible error rates in PCR and sequencing steps (Li, Hsieh et al. 2015). The latter potentially lead to overestimation and distortion of microbial diversity. Two different kinds of

!25 errors are introduced during PCR amplification (Li, Hsieh et al. 2015). First polymerases used in PCR have error rates of about 1 substitution per 105 to 106 base pairs, depending on the type of polymerase (Li, Hsieh et al. 2015). Second, the PCR process can generate as a byproduct sequence chimeras sequences from overall 10% to more than 70% of sequence reads. Moreover, the sequencing process itself introduces errors, dependent on the sequencing method (Li, Hsieh et al. 2015).

These error and the methods chosen to eliminate them can have a strong impact on the biological interpretation of amplicon HTSeq data, and therefore great efforts have been in- vested in the development of best practice procedures for the analysis of amplicon HTSeq data (Li, Hsieh et al. 2015). Various strategies were devised to remove spurious sequences, such removal of sequences that could not be taxonomically classified. Discarding with an abundance lower than a given threshold and assignment of sequences by specialised cluster- ing strategies. Each of these strategies comes with its own drawbacks, for instance (i) true se- quences might not have been taxonomically assigned so far, (ii) low abundance sequences might correspond to rare species and sequence abundances recontaminated by reads from er- roneous sequences and sequence resolution is decreased (Li, Hsieh et al. 2015).

The analysis of marker-gene data customarily begins with the construction of molecu- lar operational taxonomic units (OTUs): clusters of reads that differ by less than a fixed se- quence dissimilarity threshold, most commonly by 3%. The sample-by-OTU feature table serves as the basis for further analysis, with the observation of an OTU often treated as mak- ing to the observation of a ‘species’ in the taxonomic profiling application (Bokulich, Kaehler et al. 2018). Many methods for defining molecular OTUs have been developed such as the de- novo clustering, the closed-reference method and the open-reference method (Bokulich, Kaehler et al. 2018).

!26 !

Figure 7: DNA Integrity test performed on all DNA samples prior to DNA sequencing. Thick bands are required to ensure the purity of the DNA samples .

In this thesis, sediment river samples were collected from the different rivers and the sediment was harvested for DNA extraction. The genomic DNA of the different samples were directly extracted using MoBio DNA Isolation kit for soil samples according to the protocol of the manufacturer for maximum yields. The genomic DNA purity was determined for the subsequent pyrosequencing analysis of the community, using 1% agarose gel electrophoresis (Figure 7). The DNA was amplified through PCR to analyse the bacterial and archaeal com- munities of the different samples. Amplicons were sent to either the Beijing Genome Institute (BGI), China, or the Australian Centre for Economics (ACE) in the University of Queensland, Brisbane, Australia, for sequencing on the MiSeq platform. Reads obtained were analysed using various bioinformatical solutions. All fastq files were processed with fastqc, trimmed to remove primer sequence with Cutadapt, and quality trimmed to remove poor quality sequence using the software Trimmomatic (Kuczynski, Stombaugh et al. 2011). All reads are then hard trimmed to 250 bases, and any with less than 250 bases excluded (Kuczynski, Stombaugh et al. 2011). Two samples were repeated; sedi- ment river samples from Simunjan river during monsoon season at Station 2 and Station 3 due to low initial sequence reads. Subsequently, sequences were clustered into operational taxo- nomic unit OTUs). For taxonomy-based analysis, the aligned DNA sequences from each OTU were blasted to the UNITED and Greengenes databases with a set confidence threshold of 80% (Kuczynski, Stombaugh et al. 2011). The fasta files are processed using QIIME2’s work- flow (Kuczynski, Stombaugh et al. 2011). Representative features (OTU) sequences are then

!27 BLASTed against reference database (Silva and UNITE) (Kuczynski, Stombaugh et al. 2011). The main output is an OTU table and taxonomy table (in QIIME2 BIOM file; see Figure 8 or an overview). The resulting OTU table contained data from a pristine peat-draining river and two disturbed peat-draining rivers. Each site has 2-3 replicates.

Figure 8: Overview of ACE pipeline.

R is a preferred platform for additional post processing and to perform statistical

analysis after gene sequencing for easier manipulation of data (Pertea, Tian et al. 2018). The phyloseq package in R provides tools to read output files from the OTU clustering (Pertea, Tian et al. 2018). The pyloseq package is dedicated for plotting graphics from phylogenetic sequencing, in combination with the ggplot2 package. In phyloseq, several functions can be used to visualise the data such as the plot_ordination, plot_heatmap, plot_tree, plot_network, plot_bar and plot_richness. These plot_functions offers optional mapping of colour, size and shape that enables the user to edit according to their preferences. Phylogenetic analyses from QIIME were also applied and PICRUSt2 software pack- age was used to infer the potential functional roles of bacterial communities from environ-

!28 mental samples (Bokulich, Kaehler et al. 2018). Metabolic profiles for bacterial community structure of all three peat-draining rivers were annotated using COG and KEGG databases (Kuczynski, Stombaugh et al. 2011). PICRUSt2 inferred pathway abundances from gene fam- ily abundances using MinPath (restricted to prokaryotic pathways only for 16S data) (Kuczynski, Stombaugh et al. 2011). Assembled contigs were analysed by assigning predicted functioning to genes based on COG databases. All MetaCyc pathway are output by default based on KEGG databases (Bokulich, Kaehler et al. 2018).

3.6 Enzyme assay (Phenol oxidase and peroxidase)

Both phenol oxidase and peroxidase aid lignin degradation and are acknowledged as crucial carbon-related enzymes for carbon cycling in peatland ecosystems (Gallo et al., 2004; Allison and Jastrow, 2006; Floch et al., 2007; Bach et al., 2013, Sayo Cork et al., 2002). In previous soil enzyme assays, the activity of phenol oxidase and peroxidase was measured by the oxidation rate when reacted with a substrate such as pyrogallal (PYGL, 1,2,3-trihydroxy- benzene), L-DOPA(L-3,4-dihydroxyphenylalanine) and ABTS (2,2’-azino-bis (3-ethylben- zthiazoline-6-sulfonic acid). For this study, L-DOPA was selected as the substrate to initiate the enzyme activity. Based on the study by Bach et al. (2013), the enzyme activity with the three substrates (pyrogallal, L-DOPA, ABTS) did not differ one from another, but only L- DOPA was suitable for a peat soil sample that ranged from pH 3-pH 4. Peroxidase activity was measured with the presence of hydrogen peroxide. Hydrogen peroxide is used by peroxi- dase to produce catalysts for biological reactions (Martinez 2002; Passardi et al., 2007).

Soil samples weighing 1 g were suspended in 12.5 mL sodium bicarbonate buffer pH 10 and homogenized by vortexing for a few seconds. The soil samples for each station were allocated into three replicates. The suspended soil samples were prorated into 2 x 1.5 mL mi- crocentrifuge tubes (Figure 10) and were centrifuged for 45 seconds at 13,000 rpm to obtain the supernatant. The supernatant was divided into 6 replicates of 150 µL each and added to two sets of sterile flat bottom 96 well plates (Figure 9) with lid.

!29 !

Figure 9: Aliquots of the supernatant distributed in a 96 well plate.

Two sets of 96 well plates were prepared for the analysis of two enzyme assays in which the first set was used to test for phenol reaction in the soil samples and the second set screened for the peroxidase assay. To observe the chromogenic reaction for both enzyme assays, 50 µL of L-DOPA were added to each well, except for the controls. The first sets of 96 well plates designated for the phenoloxidase assay were incubated for 1 hour, at room temperature and shielded from light. The other sets of 96 well plates had 10 µL of 0.3% hydrogen peroxide added to all wells, including the controls, to show the reaction with peroxidase enzyme. The second sets were also incubated for 1 hour, shielded from light and kept at room temperature.

Extracellular phenol oxidase and peroxidase activity was determined using 10-mM dihydroxy phenylalanine (L-DOPA)(Sigma Aldrich Co. Ltd., Dorset) solution as a substrate. The amount of 1 g of soil sample was used for preparation of a soil homogenate in 9 mL ultra- pure water. Two aliquots of 300 µL of the homogenate was transferred to separate 1.5 mL Ep- pendorf tubes. Four hundred and fifty microlitres of ultra-pure water was added to each 300 µL of homogenate and 750uL of 10 mM L-DOPA solution or ultra-pure water (control) was added to each sample and the samples incubated at field temperature for 9 min (mixed by vor- texing at full speed at 0 and 4.5 min). Samples were centrifuging at 7200g (10 min) to termi- nate the reaction. Three analytical replicates of the resulting supernatant (300 µL of ho- mogenate and the tubes cooled to field temperature (measured at each sample collection date). Following cooling 750 µL of 10 mM L-DOPA solution or ultra-pure water (control) at field

!30 temperature for 9 min (mixed by vortexing full speed at 0 and 4.5 min). Samples were cen- trifuged at 7200 g (10 min) to terminate the reaction. Three analytical replicates of the result- ing supernatant (300 µL) were used for absorbance measurements at 460 nm. Both sets of well plates were run in a BioTek microplate reader (Figure 11) to observe the chromogenic reaction between the enzyme and the added substrates. Mean absorbance of control wells was subtracted from sample wells and phenol oxidase activity calculated. Data recorded was ana- lyzed (i.e. one-way ANOVA) using car, dplyr and agricole packages in R Studio (R3.3.3 ver- sion).

!

Figure 10: Samples in 1.5mL microcentrifuge tubes.

!31 !

Figure 11: BioTek microplate reader.

Besides standards and samples being run in the multi-well plate, the blank along with positive and negative controls (Figure 12) are run together with the assay to ensure the reading runs smoothly.

Figure 12: Blank (underlined) together with positive (+) and negative (-) controls.

!32 3.7 Stoichiometry analysis (H:C, C:N and C:S)

C:H:N:S stoichiometry of sediment samples was analyzed on a Perkin Elmer CHNS analyzer (Figure 13) at the Universiti Teknologi Mara, Kota Samarahan, Malaysia, following procedures outlined by de Menezes, Prendergast-Miller et al. (2015) and Tipping, Somerville et al. (2016). The biomass was dried at 80 degree Celsius for 24 hours, weighed, and ground to fine powder using a mortar. Elemental analyzers involve combusting the soil matter in the presence of oxygen (Grebliunas and Perry 2016). This C:H:N:S stoichiometry analysis proto- col has been used for determination of total soil carbon in other studies such as sediment from wetlands (Grebliunas and Perry 2016) and rice field (Ye, Liang et al. 2014).

!

Figure 13: CHNS Analyzer at UiTM Kota Samarahan, Malaysia.

Aluminum foil was folded into weighing boats and weighed on an analytical balance. Approximately 0.2-0.299 g of tungsten was added to the aluminum foil before adding 0.2-0.2999g of the soil sample. The aluminum foil containing the prepared soil sample was folded into a small envelope, weighed to obtain the final weight and placed in the CHNS Analyser.

The samples were left to run in the CHNS Analyser overnight. The CHNS Analyser works on the basis of combustion analysis whereby samples are burnt in excess oxygen and

!33 produce carbon dioxide, oxygen and nitric oxide. Besides carbon, the CHNS Analyser mea- sures other elements such as hydrogen, nitrogen and sulphur.

Figure 14: Combustion tubes for CHNS analyzer (part of calibration) prepared by UiTM staff.

Calibration is run before running the soil samples by running a blank and a calibration verification standard against the current calibration curve, performed by UiTM staff. Figure 15 below provides an overview of the analytical flow for enzyme assays and CHNS analyses.

!34 !

Figure 15: Simplified flowchart for enzyme and CHNS analyses.

!35 Chapter 4

!36 4.0 Seasonal responses of extracellular enzyme activities in the sediments of tropical peat-draining rivers

Abstract

Measurement of activity of extracellular enzymes can reveal rates of ecosystem processes, particularly transformations of carbon and nutrients. Rates of organic matter decomposition can be assessed using colorimetric microplate-based methods. In this study, we examined the phenol oxidase and peroxidase activities in three peat-draining rivers in Sarawak, Malaysia. The results showed that phenol oxidase and peroxidase activities increased during the mon- soon season particularly in the disturbed peat-draining rivers. Seasonal variability of enzymes was linked to environmental factors such as terrestrial inputs. This study provided initial in- sight into organic carbon transformations in these rivers.

4.1 Introduction

Riverine systems are important sites for the production, transport and transformation of organic matters. Most of the organic matter transformation is carried out by heterotrophic microbial communities, whose activities are temporally and spatially variable. Organic matter transformation involves hydrolysis by extracellular enzymes (Jirsa, Neubauer et al. 2013).

The ‘Enzyme Latch theory’ suggests that the decomposition rate deteriorates at low oxygen concentrations as enzymes require oxygen to survive (Novotný, Svobodová et al. 2004). At low oxygen and low pH, phenol oxidase activity is inhibited (Hinojosa, Carreira et al. 2004). This allows phenolic compounds to accumulate, which, in turn, inhibit those en- zymes required for peat decomposition (Andersson, Kjøller et al. 2004) When there is a high level of oxygen concentration in soil, the phenolic enzyme concentration increases along with hydrolyzing enzymes, and phenolics become scarce. Hence, decomposition activity may be intensified by elevated temperature, pH and a low water table, since a low water table pro- vides oxygen (Novotný, Svobodová et al. 2004). During the monsoon season and higher tides,

!37 carbon related enzymes latch onto nitrates or sulphates to replace oxygen in order to subsist in the anaerobic conditions (Tscherko, Hammesfahr et al. 2004, Peacock, Jones et al. 2015). However, peatlands harbour low levels of nitrates, manganese, ferric ion and sulphates. Therefore, the enzymes exploit other humic substances available in peat to replace oxygen (Hinojosa, Carreira et al. 2004).

Soil microorganisms such as fungi (e.g. Basidiomycetes and Ascomycetes) and bacte- ria (e.g. Pseudomonas and Actinobacteria) secrete extracellular enzymes, particularly phenol oxidase and peroxidase, to degrade phenolics (Tscherko, Hammesfahr et al. 2004, Peacock, Jones et al. 2015).

Performing phenol oxidase and peroxidase assays pose challenges when dealing with acidic samples. Assays were for example performed on acidic peat soil from North Selangor Peat Forest, West Malaysia (Wang, He et al. 2013), however, the experiment provided no re- sults because the acidic condition of the peat soil samples interfered with the assay. In previ- ous soil enzyme assays, the activity of phenol oxidase and peroxidase was measured by the oxidation rate when reacted with a substrate such as pyrogallal (PYGL, 1,2,3-trihydroxyben- zene), L-DOPA(L-3,4-dihydroxyphenylalanine) and ABTS (2,2’-azino-bis (3-ethylbenzthia- zoline-6-sulfonic acid). For this study, L-DOPA was selected as the substrate to initiate the enzyme activity. Based on the study by Bach et al. (2013), the enzyme activity with the three substrates (pyrogallal, L-DOPA, ABTS) did not differ one from another, but only L-DOPA was suitable for a peat soil sample that ranged from pH 3-pH 4. Peroxidase activity was mea- sured with the presence of hydrogen peroxide. Hydrogen peroxide is used by peroxidase to produce catalysts for biological reactions (Martinez 2002; Passardi et al., 2007). Both phenol oxidase and peroxidase aid lignin degradation and are acknowledged as crucial carbon-related enzymes for carbon cycling in peatland ecosystems.

Carbon (C), nitrogen (N), hydrogen (H), sulphur (S), all play important roles in litter decomposition (Matulich, Weihe et al. 2015), carbon cycling (Carr, Schubotz et al. 2018) and nutrient cycling (Kerfahi, Tripathi et al. 2014). The nitrogen (N) content in C:N ratio is used to characterise soil organic matter (Grebliunas and Perry 2016). Low nutrient loadings are ex- pected in tropical peat soil (Grebliunas and Perry 2016). In this study, we examine the depth profiles of C:N, C:P and C:S ratios in peat-draining rivers in Sarawak.

!38 We aimed to determine whether the stoichiometry relationships are different between the pristine and disturbed peat-draining rivers. We predicted the C:N and C:H ratios increase in the disturbed peat-draining rivers as they are influenced by the run-off from the anthro- pogenic activities at the disturbed peat-draining rivers.

In an effort to capture and evaluate the variability, we sampled three rivers during both dry and monsoon season. Seasonal changes are often influencing the matter supply, envi- ronmental conditions or microbial community structure which in turn altered the enzymatic activities (Huang, Chen et al. 2017). Anthropogenic perturbations such as additions of chemi- cal nutrients can have similar impact as seasonal changes (Rodriguez, Gallampois et al. 2018).

4.2 Results

4.2.1 Riverine characteristic

The physico-chemical conditions of Maludam have been discussed in detail in Müller et. (2015). Additional relevant information are provided for Sebuyau and Simunjan.

4.2.2 Phenol oxidase and peroxidase activities

Enzyme activity in August 2015 differed significantly at each depth (Station 1: Pr(>F) 0.001948, Station 2: ;Pr(>F) 0.009742 and Station 3: Pr(>F) 0.001869), however, no statisti- cal difference was observed for cores collected in January 2016. Peroxidase ranged from 0-8 and Phenol oxidase ranged from 5-8. There was no significant spatial variation across the three stations in enzymatic activity, with Pr(>0.05).

!39 ! ! !

! !

Figure 16: The enzyme activity in cores collected in Maludam August 2015 (upper row) and January 2016 (lower row). Pink represents phenol oxidase and blue peroxidase.

!40 ! ! !

! ! !

Figure 17: The enzyme activity in cores collected in Sebuyau August 2015 (upper row) and January 2016 (lower row). Pink represents phenol oxidase and blue peroxidase.

Enzyme activity in August 2015 did not differ significantly at each depth (Station 1: Pr(>F) 0.61, Station 2: ;Pr(>F) 0.12 and Station 3: Pr(>F) 0.11), and, no statistical difference was observed for cores collected in January 2016 (Station 1: Pr(>F) 0.16, Station 2: Pr(>F) 0.2 and Station 3: Pr(>F) 0.01). Peroxidase ranged from 0-2.0 and Phenol oxidase ranged from 1.0-2.0. There was no significant spatial variation across the three stations in enzymatic activity, with Pr(>0.05).

!41 ! ! !

! ! !

Figure 18: The enzyme activity in cores collected in Simunjan August 2015 (upper row) and January 2016 (lower row). Pink represents phenol oxidase and blue peroxidase.

Enzyme activity in August 2015 did not differ significantly at each depth (Station 1: Pr(>F) 0.24, Station 2: ;Pr(>F) 0.02 and Station 3: Pr(>F) 0.01), and no statistical difference was observed for cores collected in January 2016 (Station 1: Pr(>F) 0.14, Station 2: Pr(>F) 0.11 and Station 3: Pr(>F) 0.01). Peroxidase ranged from 0.25-0.75 and Phenol oxidase ranged from 0.75-1.25. Phenol oxidase ranged from 1.0-2.0. There was no significant spatial variation across the three stations in enzymatic activity, with Pr(>0.05) .

4.2.3 Soil stoichiometry data

The elements at depth did not differ significantly as calculated by Factorial ANOVA with p>0.24.

!42 !

Figure 19: C:H:N:S stoichiometry of the sediment cores at three main interval depths (top, middle and bottom) during dry season (August 2015) were represented by the bar plots (from left to right; Station 1-3).

!

Figure 20: C:H:N:S stoichiometry of the sediment cores at three main interval depths (top, middle and bottom) during dry season (August 2015) were represented by the barplots.

!43 The elements at depth did not differ significantly as calculated by Factorial ANOVA with 0.10. The similar trend between the enzyme activities and C:H:N:S stoichiometry results could lead to the possibility of high decomposition occurred in both rivers. Based on a differ- entiation of near-surface porewater, there are significant larger C:N in the disturbed peat- draining rivers than in the pristine peat-draining river.

4.3 Discussion

4.3.1 Spatial and seasonal patterns of enzymatic activities

The study presented here includes seasonal and spatial enzyme results over two sea- sons (August 2015 and January 2016). Both phenol oxidase and peroxidase enzymes have been used as indicators for decomposition activity (Oloo, Valverde et al. 2016; Yule 2008, Dieleman, Branfireun et al. 2015, Lv, Ma et al. 2016). Differences among the enzyme activi- ties were expected for the studied peat-draining rivers are due to their different catchment uses, one surrounded by a pristine forest (Maludam river) and the other two by anthropogenic activities such as logging (Sebuyau river) activities and the other is in close proximity to oil palm plantations (Simunjan river).

Erosion soil studies conducted by the Palm Oil and Research Institute Malaysia (PORIM) showed a wide range of soil loss in oil palm fields mainly causing the loss of nutri- ents. The nutrient loss data on all the oil palm plantation in Malaysia released by PORIM suggested nutrients such as N, Mg and K are prone to runoff loses and higher nutrient losses were observed during the rainfall after fertiliser application (Sahat, Yusop et al. 2016). An in- crease in nitrite, phosphate, ammonium, silicate and nitrate can be a result from the usage of fertilizer through leaching and runoff (Westbrook, Devito et al. 2006; Tin, Palaniveloo et al. 2018). Under heavy rainfall or irrigation, these chemical losses are higher in rivers surround- ed by plantations due to the close proximity between the oil palm plantation and the rivers. The higher intensity of the rainfall in the monsoon season leading to higher soil and nutrient runoff from the surrounding plantations could possibly have increased the enzyme activity in Simunjan.

!44 Future seasonal stoichiometry studies are encouraged to capture the temporal outlook of the stoichiometry patterns in these peat-draining rivers. Our one-time sediment pore water study is not representative of peatland sampling and we encouraged for future research focus- ing on the temporal and spatial variation in the sediment pore water profiling. Many studies reported variation existed at depth, lateral and seasons (Dang, Zhou et al. 2013). Nutrient (N.P) and dissolved organic carbon (DOC) concentrations also vary with depth in bogs (Krachler, Krachler et al. 2016). The DOC from the sediment pore water decreased at depth. Our carbon stoichiometry with hydrogen and nitrogen were low similar (as expected from peat soil ) and C:S value increased in comparison to C:N and H:C in all three rivers. Increased nitrogen and phosphorus nutrients in the soil, have negative effects on the activity of phenol oxidase. Evidence of increased enzymatic activities was also observed in domestic waste dump site (Iheme, Ukairo et al. 2017), palm oil mill effluent polluted sites (Sahat, Yusop et al. 2016), in soils polluted with pulp and paper mill effluent from several sampling sites in urban areas (Ezirim, Chikezie et al. 2017). The findings in our study were similar with other oil palm studies (Bah, Husni et al. 2014), suggesting soil enzymatic activities are sensitive to environmental changes. The run- off from the surrounding oil palm plantation contributed to the increase of phenol oxidase and peroxidase activities in the disturbed peat-draining rivers during the monsoon season. Both phenol oxidase and peroxidase activities increased in the Sebuyau and Simunjan rivers (the anthropogenically-influenced rivers) during the monsoon season.

!45 4.4 Conclusion

Our findings revealed that phenol oxidase and peroxidase enzymes increased in asso- ciation with the excess from the soil disintegration from the oil palm plantation due to the rainfall during the monsoon season. Despite the research efforts, this stoichiometry analysis of peat soil data is incomplete. Suggested as future research to improve the current state of knowledge on the C:H:N:S stoichiometry of SOM. Correlational studies and nutrient enrich- ment experiments are encouraged as future research to depict a more accurate information on the relationship between pore water biogeochemistry with the microbial community in these peat-draining rivers. And to find the relative abundance of individual taxa can be correlated directly to variation in geochemical properties.

!46 Chapter 5

!47 5.0 Identifying the core and rare archaeal and bacterial community along the surface sediment of an undisturbed tropical peat-draining river in Sarawak, Malaysia

Abstract

Results revealed a high abundance of Acidobacteria, Proteobacteria (notably from the class Gammaproteobacteria, Deltaproteobacteria and Alphaproteobacteria) and Terrabacteria (particularly from the phylum Firmicutes and the class Actinobacteria) at all sampling sta- tions. Rare families involved in carbon cycling were numerous and included members of the Deferribacteres, Elusimicrobia and Fusobacteria which have been shown to degrade carbon in other settings. Other rare members such as Armatimonadetes, Chloroflexi, Deinococcus- Thermus, Firmicutes and Tenericutes have also been highlighted as active lignin and cellulose degraders. This study provides an important baseline study on microbial communities in sub- surface sediments of an undisturbed tropical peat-draining river and also highlights their po- tential importance in the low CO2 outgassing.

5.1 Introduction

Southeast Asia is rich in tropical peat soils containing around three-quarters of the world tropical peat (Leng, Ahmed et al. 2019). In total, peatlands cover 27.1 million hectares in Southeast Asia (Leng, Ahmed et al. 2019). As natural carbon sinks that keep carbon dioxide out of the atmosphere, intact peatlands are vital to the reduction of global warming (McGuire, D'Angelo et al. 2015). Our previous study on greenhouse gas emissions from an undisturbed tropical peat-draining river in Maludam, Sarawak, Malaysia revealed surprisingly low CO2 outgassing to the atmosphere (Müller, Warneke et al. 2015). Further measurements in In- donesia and Malaysia showed that these rivers exhibit among the highest riverine dissolved organic carbon (DOC) concentrations worldwide (Müller, Warneke et al. 2016) however, re- sults also showed that Southeast Asia might not be the CO2 outgassing hotspot as previously assumed (Müller, Warneke et al. 2015). These findings are important since they change our

!48 current view on the magnitude of river outgassing in Southeast Asia and thereby our budget- ing of the global carbon cycle (Leng, Ahmed et al. 2019).

Changes in land-use of peat soil have, however, shown to severely impact the global carbon cycle as high amounts of greenhouse gases are released during the exposure of old stored carbon (Leng, Ahmed et al. 2019). Peat environments display amazing microbial diver- sity in these extreme ecosystems and it is becoming clear that microorganisms are key players in the turnover of soil organic carbon and in other biogeochemical cycles such as N, S, and Fe (Jirsa, Neubauer et al. 2013). Subsequent studies have mostly been restricted to specific func- tional guilds of microorganisms, such as methanogens, methanotrophs, sulphate reducers or microbes involved in nitrogen cycling (Elshahed, Youssef et al. 2007). Methanotrophs limit the release of methane into the atmosphere (Niu, Fan et al. 2017) a process often carried out by members of the Proteobacteria (Huang, Chen et al. 2017).

Müller et al. (2015) observed differences in the amount of outgassing of CO2 along the Maludam river with higher concentrations as well as outgassing upstream and close to the river mouth. Taking the potential involvement of microbes into consideration, this study aims to (i) examine whether the microbial community composition and activity differed spatially in surface sediments along the transect of this undisturbed tropical peat-draining river, (ii) de- termine the microbial core community, and (iii) to investigate the relationship between the environmental variables and the microbial community composition with a particular focus on carbon-related processes.

5.2 Results

5.2.1 Environmental parameters

The environmental parameters are summarized in Table 4. The upstream part of the river (exhibited increased carbon dioxide partial pressure (pCO2) and oxygen (O2) concentra- tions. Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) were highest at the downstream section of the river. pH was constant around 3.5 and oxygen always under satu- rated.

!49 Table 4: Environmental parameters measured along Maludam river (taken from Müller, Warneke et al. 2015).

Parameter / 1 2 3 4 5 Station

pH 3.52 3.52 3.53 3.50 3.53

O2 (µM) 58.27 43.61 56.65 38.70 36.23

Temperature (oC) 25.15 25.63 26.07 25.96 26.07

SPM (mg/l) 1.75 0.50 0.45 0.36 0.33

POC (mg/L) 0.25 0.27 0.22 0.29 0.21

DOC (mg/L) 44.94 44.04 45.21 46.17 48.17

TDN (mg/L) 0.59 0.72 0.69 0.73 0.69

pCO2 (µatm) 8804.44 7795.35 7073.99 7171.53 7272.13

H:C ratio 1.13 0.33 0.78 0.56 0.35

C:N ratio 7.60 10.46 12.60 18.28 12.21

C:S ratio 1.81 17.66 4.85 7.32 16.79

5.2.2 Community structure, richness, diversity, and activity

A total of 639, 854 reads obtained from the 5 samples (see Table 4). For analyses, each sample was normalised to 35,536 reads which revealed 29 phyla, 60 classes, 121 orders, 236 families and 452 species.

!50 !

! Figure 21: Number of reads (in percentage) of the Archaea and Bacteria in Maludam river. Colors are indicative of taxa (see legend at the bottom) and the size of the boxes is propor- tional to the relative abundance at each station.

Upstream stations 1 and 2 were dominated by Proteobacteria whereas Acidobacteria dominated the lower parts of the river (stations 3-5; Figure 21). Acidobacteria and Proteobac- teria, have also been detected in seacoast permafrost samples (Hinsa-Leasure, Bhavaraju et al. 2010), caves (De Mandal, Chatterjee et al. 2017) and lakes (Huang, Chen et al. 2017) con- firming the wide spatial distribution of these bacteria in peat-dominated settings. Members of the Acidobacteria are often oligotroph with slow growth rates and associated with nitrogen cycling (Kielak, Barreto et al. 2016). Verrucomicrobia were found at all stations, albeit at low abundance. Archaea were found in low abundance in Maludam river, similar to findings in West Malaysia (Too, Keller et al. 2018) Bacterial diversity was highest in upstream stations 1+2 (Shannon index of 4.5 and 4.4, Chao of 1.75 and 1.37, and Simpson of 10.4 and 9.4; Table 5). Lower bacterial species

!51 abundance and richness was found at the downstream stations 4 (Shannon of 3.2, Chao1 of 1.02, and Simpson of 4.9) and 5 (3.4, 1.42, and 5.3; respectively).

Table 5: Summary of reads and calculated alpha diversity indices for both Bacteria and Ar- chaea at all 5 stations.

Station 1 Station 2 Station 3 Station 4 Station 5

Bacteria

No of reads 382,600 68,934 35,536 316,827 178,588

Shannon-Weiner 4.5 4.4 3.2 3.4 3.8

Chao 1.75 1.37 1.02 1.42 1.30

Simpson 10.4 9.4 4.9 5.3 6.9

Archaea

No of reads 4533 2546 350 17275 3215

Shannon-Weiner 2.4 2.4 0 0 2.5

Chao 0 0 0 0 0

Simpson 4.4 4.5 0 0 4.5

The observed higher diversity in upstream of the river mirrored in the enzyme activi- ties (Figure 26). Despite observing higher phenol activity at upstream stations, there was, however, statistically no difference in phenol activity throughout the river. Peroxidase activity, on the other hand, did differ significantly along the stations and was elevated upstream.

!52 ! Figure 22: Phenol and peroxidase activities. Bar plots represents the independent variable for two-way analysis of variance (ANOVA). Error bars on the bar plots indicate standard errors of means. There is significant difference in the enzyme activities between downstream and upstream of the river, with Pr(>F) value of 0.01. Phenol oxidase activity differed from peroxi- dase activity at both locations, with the Pr(>F) value of 0.00.

5.3 Discussion

5.3.1 Diversity and spatial variation

To gain information on the spatial distribution of Archaea and Bacteria, principal component analysis and UPGMA of phylogenetic distances was undertaken (see Figure 23A). Stations 1+2 were grouped together (supporting differences observed in Figures 21); whereas stations 3, 4, and 5 formed the second group (Figure 23B). Heatmap analysis (Figure 24) in- dicated that the relative absence of Acidobacteria and Spirochaetes was a key feature of the more upstream stations. On the contrary, stations 1+2 were also characterized by relatively

!53 higher reads of Deinococcus-Thermus and Chloroflexi. Overall though, the communities were similar across all stations sampled which is not surprising as microbial communities in nature reserves usually consist of more slow-growing organisms and generally display a higher spa- tial heterogeneity than for example agricultural soil (Oloo, Valverde et al. 2016).

!

Figure 23: (A) The Principal Coordinate Analysis (PCoA) projected the dissimilarity of mi- crobial diversity in each station based on Euclidean distance. (B) The grouping of the micro- bial community based on their proximity distinctiveness across the river transect was promul- gated in a form of Unweighted Par Group Method with Arithmetic Mean dendrogram (UPG- MA) using simple agglomerative (bottom-up) hierarchical clustering method. MEGAN 6 was used for both analyses.

!54 ! Figure 24: The heat map produced from MEGAN 6 indicating the measurement of distance of each sampling station. The dark green reflected the high abundance of microbial composition and light green boxes depicted low abundance of the microbial composition.

!55 5.3.2 Microbial core and rare community of surface sediments of an undisturbed tropi- cal peat-draining river.

To the best of our knowledge, there are no other studies published on the microbial community in surface sediments of undisturbed tropical peat-draining rivers. Hence –given the importance of these rivers in the global carbon cycle- our first aim was to characterize the microbial core community in the Maludam river. Acidobacteria, Bacteroidetes, Rhizobiales and Rhodospirillales, Gamma-, Deltaproteobacteria, Verrucomicrobia, Actinobacteria, Bacil- li, and Archaea (unspecified) were all named as part of the core community (Figure 25).

! Figure 25: Visual representation of the microbial core community in surface sediments of the Maludam river, calculated using MEGAN 6 using default settings (sample threshold of 50% and class threshold of 1%).

The observed dominance of Acidobacteria, Proteobacteria and Terrabacteria is also in agreement with findings from similar studies in China (Zhang, Jia et al. 2018), in salt marsh (Angermeyer, Crosby et al. 2018), and tropical stream sediment (Costa, Reis et al. 2015).

!56 Acidobacteria are a prime example given their high abundances in acidic peatlands such as ombrotrophic peatlands (Elliott, Caporn et al. 2015) and in other ecosystem such as trophic lakes (Huang, Chen et al. 2017), often outnumbering other bacterial taxa. However, although Acidobacteria are commonly found in peatlands, their participation in biogeochemi- cal processes such as the carbon cycle in remains elusive and warrants further investigation (Wegner and Liesack 2017). Acidobacteria are metabolically active under anoxic conditions (sulfur metabolism enzymes, high affinity terminal oxidase, group 1 and 3 hydrogenase, alde- hyde-alcohol dehydrogenase AdhE, glycoside hydrolase and other carbon metabolism en- zymes) (Huang, Chen et al. 2017). Glycoside hydrolase, together with other genes encoding diverse carbohydrate-active enzymes such as polysaccharide lyases and carbohydrate es- terase's are involved in degradation of complex sugars and biosynthesis of carbohydrates (Kielak, Barreto et al. 2016). Despite displaying a relatively uniform distribution of the major microbial groups (core microbiome), which underlines the point made above about the relatively uniform com- position across all 5 stations, we observed an astonishing variety in the rare microbiome (Fig- ure 8), with several species likely being hidden players in the local carbon cycle. Rare fami- lies involved in carbon cycling were numerous and included members of the Deferribacteres, Elusimicrobia and Fusobacteria which have been shown to degrade carbon (Huang, Chen et al. 2017). Armatimonadetes, Chloroflexi, Deinococcus-Thermus, Firmicutes and Tenericutes were highlighted to be active in the degradation of lignin and cellulose, akin to the three dom- inant taxa (Klann, McHenry et al. 2016). Bacteroidetes, Chlorobi and Fibrobacteres of Chlorobi/Bacteroidetes increased at the upstream of the river and were known to mineralize organic matter (Hugler, Wirsen et al. 2005). Verrucomicrobia and Synergistetes have also been noted to be great carbon utilizers (e.g. polymers and cellulose) in soil (Baker, Lazar et al. 2015). Sulphur reducing bacteria from Deltaproteobacteria, such as Syntrophobacterales, Desulfuromonadales, and Desulfobacterales involved in sulphur cycling in particular sulphate reduction (Baker, Lazar et al. 2015). A second curious finding was the occurrence of several predatory bacteria such as Vampirovibrio, Bdellovibrio, Peredibacter and Bacteriovorax among the rare microbiome. The link between bacterial activity and their consumption by protozoa as part of the microbial loop is not and none about the role of said predatory bacteria in tropical peat-draining rivers.

!57 This warrants further investigations into their diversity as well as roles in the local microbial loops and biogeochemical controls.

!

!58 Figure 26: Visual representation of the microbial rare community in surface sediments of the Maludam river by MEGAN6 using default settings (sample threshold of 10% and class threshold of 1%).

5.3.3 Potential link between microbes and CO2 uptake and release

CO2 fluxes emitted along Maludam river varied spatially (Müller, Warneke et al.

2015) and, interestingly, at stations with high CO2 outgassing /pCO2, we observed higher se- quencing reads, whereas at the intermediate section of the river which had the lowest pCO2, we observed the lowest sequencing reads (Tables 5). High microbial enzyme activities also coincided with high pCO2 readings (see Müller, Warneke et al. 2015). Processing of DOC to pCO2 has commonly been related to microbial abundance and decomposition activities (Klann, McHenry et al. 2016), and our data points in the same direction.

Nitrospirae, Proteobacteria, Planctomycetes, Gemmatimonadetes, Firmicutes, Deinococcus-Thermus, Deferribacteres, Cyanobacteria, Chloroflexi, Chlorobi, Bacteroidetes, and Candidate_division_WS6 increased with CO2 concentrations. (Table 4). Chlamydiae, Crenarchaeota, Actinobacteria, Verrucomicrobia, Spirochaetes, Euryarchaeota, and Aci- dobacteria displayed little effect on CO2 concentrations(Figure 25). Deinococcus-Thermus are involved in oxidative phosphorylation releasing ATP, discharging CO2 as by product (Theodorakopoulos, Bachar et al. 2013) and the anaerobic ammonia-oxidizing Plancto- mycetes exhale CO2 via anaerobic respiration (Elshahed, Youssef et al. 2007). These microbes are likely contributing to the CO2 emissions in Maludam.

The higher abundance of Deltaproteobacteria particularly Chloroflexi (along with the Gemmatimonadetes, Firmicutes, Deferribacteres, Chlorobi, Bacteroidetes and Candidate_di- vision_WS6) also conformed with the higher CO2 readings as they have been shown to be

CO2 producers during the degradation of cellulose (Webster, Sass et al. 2011). Cellulose can then be further broken down through glycolysis ((Yu, Wu et al. 2018)) which was particularly high at the upstream station which had the highest CO2 readings.

Spirochaetes, Euryarchaeota, and Acidobacteria are likely reducing CO2 emissions from the Maludam river; especially Acidobacteria given their much higher read numbers. Acidobacteria are both degraders of complex plant polymers and CO oxidizers(Kielak, Bar-

!59 reto et al. 2016). Spirochaetes utilize H2 and CO2 for reductive acetogenesis (De Mandal, Chatterjee et al. 2017).

5.3.4 Hidden importance for methanogens and methanotrophs ?

The rare microbiome highlighted a variety of Methanomicrobia such as Methanosarcinales and Methanomicrobiales (Figure 26). Methanogenic Archaea and methan- otropic Bacteria can form symbiotic partnerships essential in transferring carbon from leaf litter to the food chain. Methane produced by methanogens is synthesized by methanotrophs to produce either carbon dioxide or formaldehyde as energy source for other microorganisms in addition to soil organic matter (Jing, Cheung et al. 2016). Archaea such as Methanosarci- nales, Methanomicrobiales and Thermoprotei displayed their highest abundance at the up- stream (station 1) and downstream (station 5) (data not shown) mirroring CO2 emission, how- ever, their involvement is unlikely substantial based on their low counts.

The methanotrophs in this study can be categorized into type I methanotrophs (strive in high concentrations of methane but low concentration of oxygen; members of Betapro- teobacteria and Gammaproteobacteria) and type II methanotrophs which also strive in high concentrations of methane but low concentration of oxygen but are members of Alphapro- teobacteria (Marcial Gomes, Borges et al. 2008). Type II Methylocystaceae dominated all sta- tions. Type II acid-tolerant methanotrophs; Methylocystis and Type I methanotrophs; Methy- lococcaceae and Methylomonas were also prevalent at all sites with the exception of station 1. Type II methanotrophs such as Methylophilales, Methylobacteriaceae and Type I methan- otrophs; Methylococcales, were discovered preeminent at the mouth of the river (station 5; data not shown). While the counts are low, it does seem that further analyses into the activity and roles of methanogens and methanotrophs in tropical peat-draining rivers may reveal some surprising insights.

!60 5.4 Conclusion

The study is to our knowledge the first on the spatial distribution of Archaea and Bac- teria in the surface sediments of a tropical, undisturbed, peat-draining river, providing an im- portant baseline for future studies; particularly considering the anthropogenic stress faced by these ecosystems. The findings from this study advocated the possibility that Proteobacteria and members of the core microbiome could be linked to the unexpectedly low CO2 emissions from Maludam river and again highlight the importance of better understanding these sys- tems. This research found an astonishing diversity in the rare microbiome, with several indi- cations for their importance in the methane cycle in Maludam. Unexpected occurrences of several predatory microbes once again highlights the need to further study these threatened ecosystems. Future research should focus on temporal changes as well as well as comparisons to disturbed sites.

!61 Chapter 6

!62 6.0 Succession of core and rare bacterial taxa in the sedi- ments of the peat-draining rivers in Sarawak

Abstract

This study assessed seasonal and spatial variation of bacterial communities in the sediments of peat-draining rivers in Sarawak, Malaysia. Sediment cores were collected from three rivers representing pristine to disturbed conditions. While the sediment bacterial communities in all three peat-draining rivers were dominated by the same five phyla; Proteobacteria, Plancto- mycetes, Bacteroidetes, Actinobacteria and Acidobacteria, the predominant phyla differed greatly across sites. The bacterial core communities in the pristine river, Maludam, were cha- racterized by Acidobacter; whereas the more disturbed Sebuyau and Simunjan rivers were characterized by Acidobacateriaceae and Acidothermaceae. The bacterial rare communities in Maludam were dominated by Methanosaeta, Anaerolineaceae and Chitinophagaceae; where- as Sebuyau and Simunjan rivers were dominated by DA111 and Solibacteraceae. The rare bacteria tended to be lesser in the disturbed peat-draining rivers compared to the pristine peat- draining river, however, showed an increase in diversity. Seasonality contributed strongly to the dynamics of the bacterial communities in this study area.

6.1 Introduction

Freshwater ecosystems such as peat-draining rivers are vulnerable to land-use and sea- sonal changes. Peat-draining rivers are important grounds for series of biogeochemical trans- formations of dissolved organic matter (carbon, hydrogen, nitrogen and sulphur) (Krachler, Krachler et al. 2015). Sediment microorganisms, especially Bacteria, play dominant roles in these biogeochemical processes (Jirsa, Neubauer et al. 2013). Molecular methods based on 16S rRNA genes have been useful in studies of sediment microbial ecology. Proteobacteria, Acidobacteria, Planctomycetes, Bacteroidetes, Firmicutes and Actinobacteria are typically found in freshwater sediment ecosystems such as in the shal- low Lake Dongping in China (Liu, Wang et al. 2015), in the urban mangroves (Marcial Go-

!63 mes, Borges et al. 2008) and in the freshwater sediments in North Carolina river basin (Bucci, Szempruch et al. 2014). The same phyla have also been reported from sewage polluted river sediments along the Jiaolai river (Huang, Chen et al. 2017), small pesticide-polluted rivers (Dorigo, Lefranc et al. 2009) and oil contaminated industrial estates (Patel, Sharma et al. 2016). Microbial community studies in Sanjiang Plain, China, also revealed Proteobacteria, Actinobacteria and Acidobacteria (Zhou, Zhang et al. 2017) as the abundant / core taxa in tropical peatland. Other ecosystems in the fragile Loess Plateau grasslands in China (Huhe, Chen et al. 2017) and the tropical forest of Tutong White Sands in Brunei Darussalam (Din, Metali et al. 2015) exhibited similar phyla. Beta- and Gammaproteobacteria, Chloroflexi, Bac- teroidetes, Spirochaetes and Firmicutes are well characterized groups in many peatland stu- dies (Kovaleva, Dobrovol’skaya et al. 2007, Lauber, Hamady et al. 2009, Krzmarzick, Crary et al. 2012). Verrucomicrobia and Planctomycetes are still understudied (Kovaleva, Dobro- vol’skaya et al. 2007, Lauber, Hamady et al. 2009, Krzmarzick, Crary et al. 2012).

Many studies of sediment bacterial ecology have been conducted to assess pollution stresses (Rodriguez, Gallampois et al. 2018), clarify biological response and monitor ecologi- cal functions. Sebuyau and Simunjan rivers are peat-draining rivers receiving additional orga- nic matter input from agricultural activities situated along both rivers which prompted us to study the effects of different environmental inputs on the bacterial communities. In this study, we collected sediment samples from pristine and disturbed rivers to test the hypothesis that disturbance impacts microbial community structure and diversity. Disturbance strongly im- pacts patterns of community diversity, yet the shape of the diversity-disturbance relationship remains a matter of debate. Studies on methane-oxidizing bacteria (Yang, Winkel et al. 2017) reported an increase in bacterial abundance which in turn contributed to the increase in bacte- rial diversity. This relationship between abundance and diversity has commonly been reported in many microbial ecological studies (Marcial Gomes, Borges et al. 2008).

While most studies highlight the importance of core bacterial taxa in microbial ecolo- gy, relatively little is known for rare bacterial taxa (Yang, Winkel et al. 2017). Rare bacterial communities have demonstrated high diversity at low abundance as part of their adaptation strategy to restrictive environment (Fernandez, Rasuk et al. 2016) and studies have reported that rare bacterial taxa can change from low abundance to high abundance during interchan-

!64 ging of seasons (Hugoni, Taib et al. 2013). These effects may rise from sensitive species being replaced by more tolerant species or pollutant degrading microorganisms (Jousset, Bienhold et al. 2017). Rare taxa are suggested to be more important for structuring soil communities than abundant taxa, and these rare taxa are more useful as predictors of community structure than environmental factors (Jousset, Bienhold et al. 2017). Abundant taxa contribute greatly in the biogeochemical processes whereby the rare taxa contribute mainly in the species rich- ness. It has been suggested that the rare biosphere is a dormant “seed bank” wherein members become abundant when formerly abundant taxa are vulnerable (Martinez-Abarca, Crespo et al. 2016). As such, rare members are thought to contribute to both functional redundancy and community turnover across many environments in addition to biochemical processes such as nutrient cycling, community assembly, including resistance to disturbance or invasion and

driving the functions of host-associated microbiomes, particularly host immunity (Martinez- Abarca, Crespo et al. 2016).

The main aim of this chapter is to determine the core (or abundant) and rare bacterial taxa in peat-draining rivers in Sarawak. To begin addressing the bacterial ecology, we descri- be the spatiotemporal distribution of bacterial communities across seasons.

!65 6.2 Results

6.2.1 Sequences retrieved from sediment samples

The number of bacterial sequences obtained from the soil samples totalled 388,066 reads. Of these, 187,105 sequence reads were obtained during the dry season, with 125,709 of these sequences reads (67.7% relative abundance) from Maludam, 29,609 (15.1% relative ab- undance) from Sebuyau and 31,787 sequence reads (17% relative abundance) from Simunjan. 200,961 sequence reads were obtained during the monsoon season, with 35,860 sequences reads (18% relative abundance) from Maludam, 95,525 (4% relative abundance) from Sebu- yau and 69,575 sequence reads (35% relative abundance) from Simunjan. Rarefraction cur- ves; (Figure 31) showed that the obtained sequences reasonably represent the overall microbi- al communities. Figure 27: Rarefraction plot observed of the OTU richness in all stations.

QIIME analyses revealed that the Kingdom Bacteria was dominated by Proteobacte- ria (107,675 reads, 35% of relative abundance), Planctomycetes (96,689 reads, 32% of relati- ve abundance), and Bacteroidetes (93,213 reads, 30% of relative abundance). The heterotro- phic Planctomycetes have been reported as carbohydrate fermenters and sulfur reducers, with several known to be involved in CO2 fixation (Elshahed, Youssef et al. 2007).

The top 10 most abundant bacterial families consisted of OTUs belonging to the Aci- dobacteriaceae, Acidothermaceae, Burkholderiaceae, Methylphilaceaea, Mycobacteriaceae, Oxalobacteraceae, Phycisphaeraceae, Sphingomonadaceae, uncultured bacterium and Xant- homonadales Incertae Sedis (Figure 28).

!66

Figure 28: Relative abundances of bacterial communities (top 10 families) in a pristine river (Maludam), semi-disturbed river (Sebuyau), and disturbed river (Simunjan) during dry and monsoon season.

!67 Figure 29: Top 50 sediment bacterial phyla in Maludam river, Sebuyau river and Simunjan river.

!68 6.2.3. Microbial diversity in pristine, semi-disturbed and disturbed peat-draining rivers

All three peat-draining rivers shared OTUs of Acidobacteria, Actinobacteria, Prote- obacteria, Parcubacteria, Peregrinibacteria, Zixibacteria, Saccharibacteria, Spirochaete, Synergistetes, Tectomicrobia, Tenericutes, Thermotogae and Verrucomicrobiae (Figure 29). Abundances depicted seasonal variation with the highest abundance during January 2016. The bacterial communities of the Maludam, Sebuyau and Simunjan rivers were dominated by Pro- teobacteria during dry and monsoon seasons.

The seasonal variation exhibited by Actinobacteria during the monsoon season was in agreement with a study in four lakes in Northeastern Germany (Allgaier and Grossart 2006) whereby Actinobacteria increased during late fall and winter. Maludam river demonstrated a larger proportion of Acidobacteria and Proteobacteria during August 2015 and January 2016. Acidobacteria, Proteobacteria and Actinobacteria are frequently associated with carbon, sulphur and nitrogen cycling (Baker, Lazar et al. 2015). Surprisingly, Plantomycetes were found abundant during the monsoon season contradictory to a few studies reporting an increa- se of Planctomycetes during summer (Elshahed, Youssef et al. 2007). Planctomycetes are re- ported performing anaerobic ammonia oxidation (annamox) (Webster, Sass et al. 2011). Par- cubacteria -also known as Candidate Phylum OD1- obligately ferment simple sugars to orga- nic acid, although some are capable of degrading complex carbon sources such as cellulose and chitin (Nelson and Stegen 2015). Peregrinibacteria have been retrieved from contamina- ted sediment and deep subsurface high CO2 environment (Anantharaman, Brown et al. 2016). Zixibactaeria (also known as RBG-1) have been studied in metal biogeochemistry and have been shown to be able to oxidize many organic compounds including fatty acids, consume hydrogen, and displayed heterotrophic growth in both toxic and nontoxic environments (Cas- telle, Hug et al. 2013). Zixibacteria were found in both pristine and disturbed peat-draining rivers. Tenericutes, Tectomicrobia and Thermotogae strive in anaerobic environments and Tectomicrobia are involved in methane cycling (Kantor, Wrighton et al. 2013). Saccharibacte- ria (also known as Candidate division TM7) are ubiquitous and widely spread in soils, sedi- ments, wastewater and animals (Aziz, Youssef et al. 2015). Spirochaete and Synergestetes were related to sulfate reducing and are commonly found during spring and not in winter as

!69 revealed by a study in the shallow Lake Dongping in China (Song et al., 2015). In this study, Spirochaete and Synergestetes were detected in both seasons with maxima in monsoon sea- son. Verrucomicrobia members including Xanthomonadales Incertae Sedis have been shown to increase during dry season (Cardman, Arnosti et al. 2014). Actinobacteria and Verrucomia- crobia were reported by other authors as major players in cellulose degradation (Cardman, Arnosti et al. 2014). Verrucomicrobia have also performed autotrophic methanotrophy, produ- cing formaldehyde and CO2, from the serine pathway, although many studies refuted metha- notrophs as excellent autotrophs participating in the CO2 fixation pathway by instead produ- cing CO2 owing to their Rubisco genes (Cardman, Arnosti et al. 2014). Alphaproteobacteria, Actinobacteria, Firmicutes and Bacteroidetes exhibited higher abundance during spring or winder in stream habitats (Hullar, Kaplan et al. 2006) and in our study, these phyla demons- trated an increase during monsoon season. These groups are important in organic matter tur- nover and breakdown of cellulose and polycyclic aromatic hydrocarbons (Khan, Takagi et al. 2012). The difference in bacterial communities between the pristine peat-draining river and disturbed peat-draining rivers was due mostly to a higher relative abundance of the Proteob- acteria in the disturbed rivers compared to the pristine rivers. Interestingly, Proteobacteria increased in Sebuyau river in January 2016 and in Simunjan river in August 2015. Conse- quently, the network PCA considering the taxonomy distribution separated clearly the bacteri- al communities of the two soils and the phyloseq analyses considering the Unifac distance matrices showed that the bacterial populations in both soils were highly different (Figure 36 and Figure 37).

!70

Figure 30: Venn diagrams showing the unique and shared OTUs (top 20 core, at Phylum le- vel) of the bacterial communities across different sites and seasons.

Venn diagrams were used to compare similarities and differences among the commu- nities in the different peat-draining rivers (see Figures 30 and Figure 31). Because they were more likely to reflect community function, only high abundant OTUs were used for the calcu- lations (a relative abundance of higher than 1%). Among the top 20 core bacterial phyla, Acidobacteriaceae, Oxalobacteraceae and Sphingomonadaceae, were present in all three peat-draining rivers suggesting a stable core across both seasons (Figure 32). Sphingomonadaceae were involved in polycyclic aromatic hydrocarbon biodegradation as reported in a study in mangrove sediment from Dot Hoi Lot, Samut Songkram Province, Thailand (Muangchinda, Pansri et al. 2013). Oxalobacteraceae are denitrifying bacteria and have been discovered in subsurface sediments affected by mixed- waste contamination (Green, Prakash et al. 2010). Burkholderiaceae were present in Sebuyau and Simunjan rivers in August 2015 and January 2016. Burkholderiaceae have been reported to be involved in methane, nitrogen and sulphur metabolism (Baker, Lazar et al. 2015). Me-

!71 thylophilaceae and Acidothermaceae were present in Sebuyau and Simunjan rivers only du- ring January 2016. Xanthomonadales Incertae Sedis under the ubiquitous Gammaproteobac- teria was present in Sebuyau and Simunjan rivers during January 2016 and have been asso- ciated with carbon fixation and sulfur oxidation (Dyksma, Bischof et al. 2016). Phycispharae (Planctomycetes family) were present in Sebuyau during January 2016. Planctomycetes have been implicated in the oxidation of ammonia under anaerobic conditions in wastewater plants, coastal marine sediments and oceanic and freshwater minimum zones (Elshahed, Youssef et al. 2007).

Figure 31: Venn diagrams showing the unique and shared OTUs (top 20 rare, at Phylum level) of the bacterial communities across different sites and seasons.

Venn diagrams were also created for the top 20 rare phyla (Figure 31). The phylum Mycobacteriaceae was detected in all rivers in August 2015 and January 2016. This pathoge- nic bacterium is capable of growth in low-nutrient environments and of nitrate reduction (Qin, Struewing et al. 2017). Type II methanotrophs (or methane-oxidizing) Methylocystaceae

!72 (Bussman et al. 2004) were present in Sebuyau and Simunjan rivers during August 2015 and January 2016. Anaerolineaceae, Pasteurellaceae, Chitinophagaceae and Acidobacteriaceae were present in Sebuyau and Simunjan river in January 2016. Acidobacteriaecae under Aci- dobacteria have been shown to perform carbohydrate metabolism and nitrogen metabolism (Kielak, Barreto et al. 2016) and were reported as potential indicators for anthropogenic envi- ronment (Rodriguez, Gallampois et al. 2018). Pasteurellaceae (under the Gammaproteobacte- ria) have been isolated from uranium-contaminated subsurface sediments (Akob, Mills et al. 2007). Chitinophagaceae, under the phylum Bacteroidetes, have been isolated from freshwa- ter sediment (Fernández-Gómez, Richter et al. 2013). Solibacteraceae, UASSB-TL25 and DA111 were present in Sebuyau river in January 2016 whereas no rare bacterial community was detected in Maludam river in August 2015. Caulobacteriaceae were present in Maludam river in January 2016.

!73

Figure 32: Identification of Phylum as core (>1% relative sequence abundance) during the dry (March 2015 and August 2015) and monsoon (December 2014 and January 2016) seasons.

!74

Figure 33: Identification of Phylum as rare (<0.01% relative sequence abundance) during the dry (March 2015 and August 2015) and monsoon (December 2014 and January 2016) sea- sons.

!75 6.2.4. Diversity indices

Compare to the species richness (number of species present), the diversity indices provide more information about the microbial community composition. A larger value of Shannon diversity index corresponds to greater diversity. The bacterial Shannon index values in Maludam river were higher in comparison to the Shannon index values in Sebuyau and Si- munjan river with maxima in January 2016. Similar results were obtained from for the Sim- pson index, a smaller value which corresponds to a greater diversity. In the Maludam river, the phylogenetic diversity and the Chao1 index were more elevated than in the disturbed ri- vers and tend to decrease during the monsoon seasons (Figure 34). We found that the overall community composition exhibited considerable variation between the three peat-draining rivers. The diversity does, however, increase in tandem with abundance in the pristine peat-draining river. However, despite the observed differences, Shannon diversity was not statistically significant different between pristine and semi-distur- bed/disturbed peat-draining rivers (p>0.05).

6.2.5. Bacterial metabolism

The top 5 most abundant predicted metabolic pathways were amino acid related pro- cesses such as; valine pathway, updnagsyn pathway (UDP-N-acetylglucosamine pathway), trna changing pathway (aminoacyl-tRNA biosynthesis pathway), thresyn pathway (L-threoni- ne biosynthesis pathway) and TCA (Tricarbocyclic Acid Cycle) cycle (Figure 33).

!76 Figure 34: Diversity indices according to season and rivers.

!77 Figure 35: The barplot summarising the top 5 predicted metabolic pathways abundance (using PICRUSt2).

6.2.6 Bacterial networks and clustering according to anthropogenic influences

As illustrated by recent studies, microbial network patterns can help to unveil ecologi- cally meaningful interactions between taxa. Network properties are useful to study bacterial communities in both pristine and disturbed environments. The network plot (Figure 36) indi- cated that the pristine peat-draining river and semi-disturbed peat-draining river might be clo- sely related.

!78 Figure 36: The network plot of represented the threshold distances between samples or OTUs across the three peat-draining rivers between dry season (August 2015) and monsoon season (January 2016) based on euclidean distances between points on the plot.

The nodes in network referred to the OTUs/samples are coloured by disturbance level of the peat-draining rivers and the shapes represented the sampling date. The threshold de- monstrated the samples from August 2015 and January 2016 shared close relationship (Figure 36).

!79 Figure 37: Similarities between samples were visualised using principal coordinate analysis (PCoA) plot. PCoA was calculated for each season on the basis of pairwise weighted Unifrac distances. Circles represent samples from Maludam, triangles from Sebuyau, and squares from Simunjan, respectively.

The PCoA plot (Figure 37) showed that the bacterial communities in Sebuyau and Simunjan rivers were inclined to cluster together with high similarity. Interestingly, total bac- terial communities were considerably less variable in composition in the sediment of the dis- turbed peat-draining rivers than in the pristine peat-draining river which is interpreted as a sign of biotic homogenization.

!80 6.3 Discussion

6.3.1 Core and rare bacterial communities in the peat-draining rivers

Variations in bacterial communities were observed with dissimilarities during dry and monsoon along the Maludam river. Environmental factors significantly influenced the bacte- rial communities with the organic matter contributing strongly to the spatial distribution. Along the pristine river, the hydrocarbon degraders; Methylobacteria (under the phylum Sphingomonadaceae) and Undibacterium (under the phylum Oxalobacteraceae) increased during the dry season. DOC was recorded high during dry season (Müller, Warneke et al. 2015) showing that these bacteria proliferate and deplete carbon from the elevated allochtho- nous and autochthonous input (Müller, Warneke et al. 2015) during the dry season. In con- trast, Methylobacteria and Unidibacterium decreased along this pristine river and the opposite trend was observed with Acidibacter (under the phylum Xanthomonadales), implying diluted input of allochthonous and autochthonous materials during monsoon season (Müller, Warneke et al. 2015) including other prevailings condition in a conducive environment for the hetero- trophic Acidibacter (Kielak, Barreto et al. 2016). A large proportion of Acidothermaceae and Acidobacteriaceae were observed in Se- buyau and Simunjan river during the monsoon season, suggesting a significant degree of both Actinobacteria and Acidobacteria , similar to an anthropogenically influenced Mediterranean orchard soil (Frenk, Dag et al. 2015). Discerning the input of organic matter in the disturbed rivers, our enzyme study (see Chapter 4) showed an increase in the disturbed rivers with max- ima in the monsoon indicating high organic matter input and microbial activity. This is also consistent with prior enzymatic studies whereby soil enzymatic activity increased in disturbed sites (Iheme, Ukairo et al. 2017). The rare Bacteria abundance and diversity were highest in the pristine peat-draining river. Bacterial rare communities in Maludam river sediments during the monsoon season were dominated by Methanosaeta (under the phylum Proteobacteria), Anaerolineaceae (un- der the phylum Chloroflexi) and Chitinophagaceae (under the phylum Bacteroidetes). Anae- rolineae and Methanoseata were involved in methanogenic degradation of alkanes and likely utilized amino acids as the nitrogen source -based on picrust2 predictions, Figure 39- by in-

!81 corporating nitrogen in ammonium side chain (De Mandal, Chatterjee et al. 2017) . The high utilization of nitrogen may imply the microbial communities are deprived of nitrogen (Costa, Reis et al. 2015) which is supported by previous work highlighting the oligotrophic conditi- ons in Maludam (Mueller et al. 2015).

This study also explored the possible effect of the organic matter provided by soil sur- face runoff from the disturbed rivers to act as trigger in the bacterial abundance and activity (also refer to the enzyme study in Chapter 4). This is turn give the opportunity to detect spe- cific group of bacteria sensitive to environmental disturbance. It was hypothesised that the additional organic matter from soil surface runoff in the disturbed peat-draining rivers affect the composition of bacterial community. As outlined above, rare bacterial communities have been stipulated as potential indicators for environmental changes. The bacterial rare commu- nities in both Sebuyau river and Simunjan river were dominated by DA111 (from the phylum Proteobacteria) and Solibacteraceae (from the phylum Acidobacteriaceae). Despite the dis- tinct pattern, it is likely that the increase in organic matter in the disturbed peat-draining rivers had only a minor effect on the rare bacterial communities given the low abundance and di- versity of the rare bacterial communities compared to the rare communities in the pristine Maludam river (see Figure 33). These results suggested the core bacterial communities play the important role in the biogeochemical cycle in the disturbed peat-draining rivers. The simi- larity of the carbon utilization profiling (Figure 35) could relate to the possible selection of microbial communities adapted to the specific availability of organic compounds. However, the limitation to verify the carbon utilisation patterns in each peat-draining river prompts fur- ther research in future.

6.3.2 Composition and sensitivity of core members to exchanging seasons and anthro- pogenic influences

Acidobacteria were part of the core community as was also observed in caves (De Mandal, Chatterjee et al. 2017), in plain lakes (Huang, Chen et al. 2017) and in industrial soft coal slags (Wegner and Liesack 2017). Acidobacteria are commonly found in acidic peatlands such as in Sphagnum peat bog (Kielak, Barreto et al. 2016), Arctic tundra soils (Rui, Peng et

!82 al. 2009) and in northern wetlands (Serkebaeva, Kim et al. 2013). The research on the Acido- bacteria in Schloppnerbrumen II peatland revealed Acidobacteria are metabolically active under anoxic conditions (sulfur metabolism enzymes and responsible in hydrocarbon degra- dation (Hausmann et al., 2017).

Acidothermaceae under the phylum Actinobacteria and under the bacteria group Ter- rabacteria have been widely isolated from terrestrial environments as well as marine sedi- ments, seawater and marine invertebrates (Khan, Takagi et al. 2012). Actinobacteria are known to degrade both cellulose and hemicellulose (Allgaier and Grossart 2006). It is likely that various species utilised a variety of carbon substrates from the predictive metabolic pa- thway including carbohydrates, polymers, amines and amino acids. Phycisphaeraceae for ex- ample were identified in an anammox study at Baja California Peninsula and Sinaloaas one of the suggested predominant bacterial phylotypes responsible for nitrogen loss in marine envi- ronment via the anaerobic ammonium oxidation process (Lie, Liu et al. 2014). Given that they are part of the core community, they could play a similar role in tropical peat-draining rivers. Methyphilaceae have been found in freshwater lake sediment of a pristine lake, Lake Wa- shington in Seattle, and include members capable of methylotrophy and nitrogen metabolism (Hullar, Kaplan et al. 2006)), Oxalobacteraceae (under Burkholderiaceae) were involved in nitrogen fixation (Hullar, Kaplan et al. 2006) and could be key players in our systems. Prote- obacteria are also known as important decomposers of organic matter (Baker, Lazar et al. 2015).

6.4 Conclusion

This study focused on the changes of the core and rare bacterial communities in tropi- cal peat-draining rivers and their potential responses to anthropogenic impacts. PICRUSt2 metabolic prediction profiling results suggest amino acid related processes; valine pathway, updnagsyn pathway (UDP-N-acetylglucosamine pathway), trna changing pathway (ami- noacyl-tRNA biosynthesis pathway), thresyn pathway (L-threonine biosynthesis pathway) and TCA (Tricarbocyclic Acid Cycle) cycle, play the most important role in all three peat-draining sediments during both seasons. Bacteria likely involved in amino acid and protein degradation

!83 are Acidibacter (Xanthomonadales), Methylobacteria (Sphingomonadaceae), Undibacterium

(Oxalobacteraceae) and Bacteroidetes (Bacteroideceae). Moreover, autotrophic CO2 fixation via TCA cycle is second to the Calvin-Benson-Bassham cycle (Calvin cycle) and likely per- formed by Proteobacteria. This could contribute to the relatively low CO2 emissions in the pristine peat-draining river. The decrease of enzymatic activities in the pristine peat-draining river could be explained by low abundance of bacteria phyla such as Actinobacteria due to lower levels of organic matter. .

!84 Chapter 7

!85 7.0 Diversity of methanogenic Archaea in three peat- draining rivers in Sarawak

Abstract

In this study, the diversity and community composition of archaeal communities were as- sessed in pristine and disturbed peat-draining rivers during dry and monsoon season. 16S rRNA amplicon sequencing was carried out on 20 sediment samples (with 3 replicates each; 60 samples in total) from the three rivers. Our results revealed highly diverse archaeal com- munities in the sediments of the pristine Maludam river during both seasons; represented by members of the Euryarchaeota, Bathryarchaeota and Thaumarchaeota. A decrease in abun- dance and diversity of these three major groups was observed in disturbed peat-draining rivers; Sebuyau and Simunjan rivers. This study provides the first assessment of sediment Ar- chaea in peat-draining rivers of Sarawak.

7.1 Introduction

Increasing evidence reveals widespread presence of Archaea in various non-extreme environments, including soil, ocean and freshwaters. The relative abundance of Archaea in different environments is generally much lower than that of Bacteria, and they usually consti- tute less than 10% of the microbial population in freshwater habitat (Korzhenkov, Toshchakov et al. 2019). Studies suggested the lower density of Archaea may be due to their adaptation strategies to survive in various types of environment (Nelson, Moin et al. 2009). This could also lead to the possibility of non-mutual relationship between Bacteria and Archaea (Nelson, Moin et al. 2009).

Archaeal communities found in freshwater lake sediments were not as diverse as bac- terial communities (Nunoura, Oida et al. 2009). Archaea16S rRNA gene sequences have been detected in freshwater environments such as lake (Liu, Priscu et al. 2016) and salt marshes

!86 (Nelson, Moin et al. 2009). Euryarchaeota and Crenarchaeota are can also be found abundant in extreme environments such as hot springs (Stewart, Houghton et al. 2018), oxic basalt aquifer (O'Connell, Lehman et al. 2003), lake sediment in Tibetan Plateau (Liu, Priscu et al. 2016) and hyper saline lagoons (Fernandez, Rasuk et al. 2016). Members of Crenarchaeota can also be found in moderate environments such as surface coastal waters (Hugoni, Taib et al. 2013). The prokaryotic community in Southern Brazilian Atlantic forest demonstrated dominance of Crenarchaeota in the archaeal community (Vigneron, Cruaud et al. 2014). However, archaeal rRNA diversity studies in northern peatlands reported Crenarchaeota as rare community (Yang, Winkel et al. 2017).

Given the importance of peat-draining rivers in maintaining healthy coastal environ- ment and the role of Archaea in sulphur (Seitz, Lazar et al. 2016), nitrogen and carbon cycling (Dang, Zhou et al. 2013), characterizing archaeal communities is critical to a better under- standing of these habitats. Realising that no data exists to date on Archaea in tropical peat- draining rivers, our study focused on the diversity of Archaea.

The release of CH4 from the anaerobic decomposition of organic matter, also known as methanogenesis, is performed by methanogenic Archaea (Ney, Ahmed et al. 2017). Methanogens are responsible for the mineralization of organic carbon to methane (Vaksmaa, Guerrero-Cruz et al. 2017) and commonly found in anoxic and anaerobic environments such as tropical mangroves (Jing, Cheung et al. 2016), Haima cold seep area in northwest South China Sea (Niu, Fan et al. 2017), lake sediments (Liu, Priscu et al. 2016) and acidic bog (Brauer, Cadillo-Quiroz et al. 2011). Euryarchaeota contain all methanogenic archaea as well as extreme halophiles (O'Connell, Lehman et al. 2003). Due to this phylum’s adaptability, Euryarchaeota appears more widely distributed than Crenarchaeota (Fernandez, Rasuk et al. 2016). A study on archaeal diversity in a methane-emitting river, Sitka stream, revealed three consistent and stable methanogenic groups; Methanosarcinales, Methanomicrobiales and Methanobacteriales (Gilbert, Buriánková et al. 2013).

Given the lack of available information and the global relevance of methane emis- sions, a focus of the discussion will be on methanogens in order to understand better the

!87 methanogenic community composition in pristine peat-draining river compared to disturbed peat-draining rivers.

7.2 Results

7.2.1 Archaeal community composition and abundance

Archaea were represented by 186,571 OTUs (based on 97% similarity, see method chapter). The highest OTU counts for Archaea were by uncultured archaea under the phylum Bath- yarchaeota (27,308 0TUs) and Thaumarchaeota (7,613 OTUs).

Figure 38: Rarefraction plot observed of the OTU richness in all stations.

!88 Euryarchaeota dominance has been reported in other studies on freshwater lakes (Yang, Dai et al. 2016) but was not observed in our study. Bathyarchaeota, Euryarchaeota and Thaumar- chaeota increased in August 2015 and decreased in January 2016 in Maludam river. Opposite trends were demonstrated in Sebuyau river and Simunjan river (see Figure 39).

Figure 39: Phylum level composition of archaeal communities in the three peat-draining rivers during dry (15-Aug) and monsoon (16-Jan) season.

!89 Bathryarchaeota, Euryarchaeota and Thaumarchaeota are all implicated in carbon fixation and have previously been detected in pristine and contaminated environments (Ko- rzhenkov, Toshchakov et al. 2019). Bathryarchaeota include methanogens as revealed by a study in two freshwater lakes (Yang, Dai et al. 2016). Thaumarchaeota contribute to nitrifica- tion as revealed in a study on archaeal community in the sediments of the South China Sea (Dang, Zhou et al. 2013) and in another methanogenic study in Antarctic sediments (Carr, Schubotz et al. 2018). Most of the euryarchaeal OTUs detected in the peat-draining rivers were related to Methanosarcinales. Previous studies reported Methanosarcinales are present in tropical mangroves (Jing, Cheung et al. 2016) and in cold seep area, northwest slope of the South China Sea (Niu, Fan et al. 2017)

Among the rare archaeal communities, Terrestrial Miscellaneous Group (TMEG) is the most abundant group (see Figure 39). Increase in TMEG abundance in Sebuyau river and Simunjan river during monsoon season was observed. Terrestrial Miscellaneous Group (TMEG) is affiliated to oxic sulfate- and methane cycling (Korzhenkov, Toshchakov et al. 2019). Similar to the core communities, the rare communities exhibited an increase in the dis- turbed peat-draining rivers; Sebuyau and Simunjan and with maxima in January 2016. The opposite was observed for Maludam river.

Thaumarchaeota in this study mainly included Nitrososphaerales and Nitrosopumi- lales who are likely playing roles in nitrification and autotrophic carbon fixation (Dang, Zhou et al. 2013). Bathyarchaeota (MCG) comprised a large number of phylotypes from anoxic environments (Yu, Liang et al. 2017). Bathyarcheota was believed to have an organic het- erotrophic lifestyle and can degrade buried organic carbon and detrital proteins in subsurface sediments (Yang, Dai et al. 2016). Bathyarchaeota were also found having methyl-coenzyme M reductase complex and involved in methane metabolism recently and was the only group outside the phylum Euryarchaeota involved in methane metabolism (Yu, Wu et al. 2018). Woesearchaeota were previously reported from saline sediments, but in surface water of some lakes they were also detected (Liu, Li et al. 2018).

!90 Figure 40: Genus level composition (of archaeal communities in the three peat-draining rivers during dry (15-Aug) and monsoon (16-Jan) season.

7.2.2 Diversity of archaeal community

The mean values of species richness (Chao) and diversity indexes (Shannon a) in the Maludam river were higher than those in the Sebuyau and Simunjan rivers, which indicated a decrease in both archaeal richness and diversity in the Sebuyau and Simunjan river. The alpha diversity species richness (Chao), and diversity indexes (Shannon) all confirmed an increas- ing diversity during the dry season for Maludam river. A decline of the diversity and species richness was observed in Sebuyau river and a slight increase of diversity and species richness was observed in Simunjan river. Overall, the diversity and species richness in Sebuyau and

!91 Simunjan rivers were lower in comparison to the diversity and species richness in Maludam river during the dry season. The opposite trend was spotted in all rivers during the monsoon season. The diversity indexes in the disturbed peat-draining rivers were higher in comparison to the diversity indexes in the pristine and semi-disturbed peat draining rivers (see Figure 41). Our archaeal diversity results were similar to a study on archaeal diversity in the tropical for- est in Sabah after conversion to oil palm plantation (Kerfahi, Tripathi et al. 2014).

Figure 41: Measured alpha diversity (Shannon index) and estimated (Chao1 index) species richness are shown for archaeal sequences found in the three peat-draining rivers during Au- gust 2015 and January 2016.

!92 7.3 Discussion

To assess differences across the stations /rivers, principal coordinate analysis (PCoA) of the archaeal community was carried out using the weighted Unifrac distances (Figure 42). The analysis clearly highlighted distinguished communities in each river, with samples from each river clustering together.

Figure 42: Principal coordinate analysis (PCoA) of the archaeal community. Weighted Unifrac distance matrix was used for the PCoA analysis of the archaea community. Circles represent samples from Maludam, triangle from Sebuyau, and square from Simunjan.

!93 We further explored co-occurrence patterns using network inference (Figure 43) based on strong correlations (using non-parametric Spearman’s) and a similar pattern to the PCoA was observed. The correlation networks showed that the archaeal communities in all three peat-draining rivers are more alike during January 2016 which is possibly driven by the thought that the archaeal communities are season dependent. The closest network distance is 0.16 within the archaeal communities in Simunjan river during January 2016 suggesting the archaeal communities share a niche. The disconnected network inference projected niche dif- ferentiation among the archaeal communities.

Figure 43: Correlation networks of co-occurring microorganisms based on the dissimilarity and similarity in the peat-draining rivers and dates, using the maximum distance of 0.3. The colors represent the three peat-draining rivers and the nodes represent the dates of sampling.

!94 7.3.1 Core and rare Archaea and differences in distribution

Maludam river demonstrated the highest Archaea abundance with 68,583 sequence reads during the dry season and 101,326 sequence reads during the monsoon season. The se- quence reads and the relative abundance of Euryarchaeota, Thaumarchaeota and Woe- searchaeota decreased in the disturbed peat-draining rivers; Sebuyau and Simunjan rivers during the monsoon season. Interestingly, Woesearchaeota (DHVEG-6) under the phylum Eu- ryarchaeota increased in all rivers during the dry season. Members of Woesearchaeota might be involved in recalcitrant carbon utilization (Hugoni, Taib et al. 2013). The sequence reads and the relative abundance for Aenigmarchaeota and Candidate division TYNPFFA increased in the disturbed peat-draining rivers during the dry season.

Similarities in the composition of core and rare OTU phylotypes were displayed with Venn diagrams (Figure 44 and Figure 45). Common core phyla across all rivers were Bath- yarchaeota, Euryarchaeota and Thaumarchaeota and common rare phyla were Terrestrial Miscellaneous Group (TMEG), Thaumarachaeota and Bathryarchaeota.

Looking at the top 20 core Archaea, the pristine Maludam river was characterized by a unique phylum (Aigarchaeota) in the dry and 5 unique phyla (Bathyarchaeota, Thaumar- chaeota, Methanscarcinales, Methanomicrobiales and Methanobacteriales) in the wet season. The more disturbed river systems had lesser unique species (Candidatus methanoperedens) but shared several species (Aenigmarchaeota and Lokiarchaeota), suggesting a set of Archaea indicative of disturbance. A similar picture was observed for the rare community, with 5 unique phyla for Maludam during monsoon (Crenarchaeota, Parvarchaeota, Diapherotrites, Hadesarchaea and Lokiarchaeota) and 2 unique phyla for more disturbed rivers (Methanosarcinales and Methanomicrobiales).

!95

Figure 44: Venn diagram showing the shared and specific OTUs for the top 20 abundant (or core) taxa with the mean relative abundance higher than 1% across samples.

Figure 45: Venn diagram showing the shared and specific OTUs for the top 20 rare taxa (threshold of average relative abundance < 0.0.1%).

!96 Thaumarchaeota are key players in carbon, nitrogen and phosphorus cycles in marine sediments due to their involvement in nitrification (Dang, Zhou et al. 2013). Marine Thau- marchaeota, such as Candidates species harbor the genes encoding the Phn phosphate-specif- ic uptake transporter (Park, Kim et al. 2012). This is important in low nutrient environment such as peat-draining rivers as this unique phosphate uptake mechanism may indicate the adaptability of Tharmarchaeota lineages in P-limited conditions.

Bathyarchaeota are wide-spread and abundant in marine sediments (Rissanen, Peura et al. 2019). Previous studies on the diversity of microorganisms in sediments of the South China Sea have frequently detected the presence of Bathyarchaeota in various regions in South China sea (Yu, Liang et al. 2017). The lineages of Bathyarchaeota have been linked with metabolic capability on degradation of detrital proteins, aromatic compounds and plant- derived carbohydrates; acetogenic capability of synthesizing acetate from inorganic carbon and methane metabolism (Yu, Wu et al. 2018).

The methane producer, Methanoregulaceae, was only present during the dry season in Maludam river. Aigarchaeota were present in Simunjan during the dry season and this phyla has been associated with sulphur reduction and carbon monoxide oxidation (Hua, Qu et al. 2018).

7.3.2 Composition of core methanogenic communities

The abundant taxa including Euryarchaeota, Crenarchaeota and Thaumarchaeota were almost entirely shared in core assemblage of methanogenic Archaea among sites. Pre- dicting metabolic functions using PICRUSt2 demonstrated some of the microbial sequences in our study possessed methanogenesis pathway (see Figure 46).

The global biogenic methane budget is directly linked to the activity of methanogenic and methanotrophic microorganisms in the environment (Yuan, Ding et al. 2016). Previous works with methanogens indicated methanogens varied in peatland types such as in Italian paddy field (Vaksmaa, Guerrero-Cruz et al. 2017), in small boreal acid lake (Rissanen, Peura et al. 2019), lake sediment (Zhu, Liu et al. 2011) and along coastal marsh transect (Yuan, Ding et al. 2016).

!97 The pathway of methanogenesis can reflect the community structure of methanogenic Archaea. Characterization of methanogenic Archaea and their association with environmental variables in different types of drained peatlands might help to evaluate or even predict CH4 production potential (Lin, Tfaily et al. 2014). Archaea studies in Arctic wetlands at Spitsber- gen, Norway during two summer seasons revealed Methanomicrobiales, Methanobacteri- acaea, Methanosaeta and Group 1.3b were detected at both sites; Solvatnet and Stuphallet in the Arctic wetlands. Previous archaeal studies on bog methanogen communities have revealed high methanogen diversity and have detected sequences belonging to Methanomicrobiales, Methanobacteriales and Methanosarcinales (Høj, Olsen et al. 2005). Methanobacteriales, Methanosaetaceae, Methanosarcinaceae and Halobacteriaceae were also found across the coastal marsh transect area of China (Yuan, Ding et al. 2016).

From the PICRUSt2 predictive metabolic data, it appears the archaeal community is dominated by methanogens (Figure 46). Methanomicrobioles and Methanobacteriales could contribute to methanogenesis. as these Archaea have been linked to methanogenesis in other studies (Lin, Tfaily et al. 2014). Methanomicrobiales and Methanobacteriales can use H2/CO2 as a substrate for methanogenesis (Yashiro, Sakai et al. 2011), while Methanosarcinales can utilise a number of different substrates (Webster, Sass et al. 2011). Candidatus methanopere- dens increased in numbers in the Simunjan river and this phylum is involved in anaerobic ox- idation of methane (Vaksmaa, Guerrero-Cruz et al. 2017). The most represented species were Methanosaeta, Methanomicrobacterium and Methanomicrobiaceae in a sludge reactor for treatment of palm oil mill effluent (Meesap, Boonapatcharoen et al. 2012).

Methanogens are also syntrophic partners to Bacteria such as Methanosaeta and have been indicated in wastewater improving the metabolism of fermenting bacteria (Buan 2018). Fermentation occurs in anoxic conditions (Li, Hu et al. 2018), supporting the low DO mea- surements (Müller, Warneke et al. 2015).

!98

Figure 46: Results using PICRUSt2 demonstrated functional categories represented by colours. Methanogenesis pathway is represented by green color.

!99 7.4 Conclusion

Highly diverse archaeal communities were found in sediments of the pristine peat- draining river. Euryarchaeota (especially Methanomicrobiales and Methanosarcinales) were the most dominant phylum in the peat-draining sediments. Other prominent Archaea detected in this study included Woesearchaeota, Pacearchaeota, Thaumarchaeota and unclassified Ar- chaea. In the pristine peat-raining rivers, complex organic carbon degradation-related archaeal lineages, such as Woesearchaesta, might be the main contributors to the carbon cycle. Never- theless, more work is needed to investigate the coordination of archaeal functional groups in the crucial processes of matter and element cycling in these peat-draining rivers. The present study does, however, contribute to our understanding on the diversity and potential roles of Archaea in the biogeochemical cycles of sediments in tropical peat-draining rivers.

!100 Chapter 8

!101 8.0 Core and rare eukaryotes taxa in the sediments of peat-draining rivers in Sarawak

Abstract

The aim of the present study was to analyse if spatial and seasonal variation of eukaryotic as- semblages in tropical peat-draining rivers change are due to anthropogenic influences in the river catchment. DNA samples were collected from sediments of a pristine river (Maludam) and two disturbed peat-draining rivers (Sebuyau and Simunjan) and eukaryotic communities analysed by 18S rRNA gene amplicon sequencing using Illumina MiSeq. The taxonomy pro- files showed that peat-draining rivers were dominated by Nuclemyceas, Holozoa, Alveolata, Stamenopiles and SAR 1 lineages. We observed greater richness of eukaryotic organisms in the dry season and also an increase in the eukaryal community richness associated with an- thropogenic activities.

8.1 Introduction

Marine Eukaryotes are important in ecosystem in regards to biogeochemical processes such as the carbon, phosphorus and oxygen cycles (Aguilera, Manrubia et al. 2006). It has been estimated that marine subsurfaces harbour the largest global prokaryotic biomass (Aguilera, Manrubia et al. 2006). However, little is known about the microbial eukaryal communities in peat-draining rivers or their sediments.

Compared with the oceans, information about the molecular diversity of Eukaryotes in freshwater ecosystems is still scarce. Inland waters are more heterogeneous than oceans, and much more sensitive to environmental variation. Given the heterogeneity of this kind of sys- tem on Earth, further microbiological studies on shallow freshwater systems such as rivers should be carried out. Furthermore, eukaryotes are potential biological indicators as they are sensitive to environmental changes such as temperature and moisture.

!102 Recent studies discussed the potential of microeukaryotes as bioindicators for contam- inants because they exhibit the key features to be bioindicators in terms of their abundance and genetic diversity. For example, microeukaryotes (the potential indicator taxa) in man- grove sediment decreased in the presence of pollutants (Santos, Cury et al. 2010). Fungi are important decomposers in peatlands and their roles have been highlighted for nutrient cycling, modulation of plant growth (Mentel and Martin 2008), and maintaining ecosystem function- ing via various processes such as carbon cycling (Shi, Cheng et al. 2017). Opistokhonta and Alveolata were major groups identified in peatlands of Maludam National park, logged over peatland at cremate Ceria, and oil palm plantations at Durafarm and Naman (Maiden et al, 2018). If the same or similar species can be found in the rivers draining from these peatlands is, however, still unknown.

8.2 Results

8.2.1 Analysis of pyrosequencing data

The total sequence reads of eukaryotes in all three peat-draining rivers during the dry season were 9,261 reads with surprisingly the highest reads in Sebuyau river (5,321 reads), dominated by Opisthokonta (88% relative abundance). This is similar with Maludam river and Simunjan river whereby Opisthokonta dominated 70% and 30% of the total sequence ac- cordingly. The total sequence reads of eukaryotes during the monsoon season was 4,622 reads with the highest in Maludam river, 3,423 reads, dominated by Opisthokonta (79% relative abundance; see Figure 50).

The high presence of Opisthokonta in the peat-draining rivers was similar to other stud- ies on anthropogenic environments (Nuy, Lange et al. 2018) such as cleared forest sites (Ker- fahi, Tripathi et al. 2014). By means of rarefraction sampling, all samples were shown to reach saturation, thereby fully representing the eukaryotic microbial diversity (Figure 48).

!103

Figure 47: Rarefraction curve for the samples (archeal communities) created in R. Rarefrac- tion analysis was performed at the most resolved taxonomic level in phyloseq.

8.2.2 Diversity of microbial eukaryotes

Based on previous studies, we define core eukaryotes as those OTUs detectable after normalization in greater than 90% of the sites. The relative abundances of core Eukaryotes was consistent at the upper Maludam and the lower of Maludam river during the monsoon and dry season. The core eukaryotic relative abundances in Sebuyau and Simunjan rivers were consistent from the upper section of the river to the lower section of the river. In general, fungi were the major group of Eukaryotes for all the three peat-draining rivers. The most prevalent were Nucletmycea (53% of the relative abundance; 2,455 reads)

!104 and Holozoa (25% of the relative abundance; 1,171 reads) during the dry season. Nuclet- mycea (51% of the relative abundance; 4,708 reads) and Holozoa (38% of the relative abun- dance; 3,512 reads) dominated all three peat-draining rivers during the monsoon season as well. Nucletmycea is the phylum for most arbuscular mycorrhizal fungi (Liu, Wang et al. 2015), which form symbioses with many herbaceous plants (Nelsen, DiMichele et al. 2016). Exploration of their genome showed CDH genes, which appear to be involved in the degrada- tion of both lignin and cellulose and are not present in genomes of basal fungi (Lefranc, Thenot et al. 2005).

Figure 48: Bar plots of the top 20 eukaryotic core families in the three peat-draining rivers.

It was documented that groups occurring <0.1% are termed as rare or minor (Lopez- Garcia, Philippe et al. 2003). The rare eukaryotes during the dry and monsoon seasons com- prised of Discosea, Gracilipodida, Tubulinea, Chloroplastida, Centrohelida, Discoba, D 1, SAR and Stramenopiles (see Figure 49).

!105 Figure 49: The bar plot of the rare community composition (at family level) in the three peat- draining rivers.

The Discoba derived from the eukaryotic monophyletic supergroup Excavata which are characterised by their shizopyrenid lemix amoebe/amoeboflagellates and acrasid slime moulds (Panek, Ptackova et al. 2014). The term amoeboflagellates refers to their ability to switch from a flagellate to amoeba, and vice versa (Panek, Ptackova et al. 2014). Chloroplas- tida, the photosynthetic and planktonic green algae are core members of marine microbial food webs and play fundamental role in the carbon cycling. The results from a study in the Mediaterranean Sea confirmed the ubiquity of Chloroplastida in marine waters (Viprey, Guil- lou et al. 2008). A study in Southwestern Alberta demonstrated Centrohelids and Chloroplas- tidae dominated the river water during spring with relative abundances of 28.1% and 29.8%, respectively, and both clades decreased during the summer and fall as fungi reigned over the river water (Thomas, Selinger et al. 2012). Their study demonstrated protists were common in the rivers of Southern Alberta and that seasonal changes influenced the protists community

!106 strongly. Centrohelids and Chlorosplastidae were, however, reduced in numbers with in- creased levels of heterotrophic bacteria (Shirokova, Pokrovsky et al. 2009). The Stramenopiles (Heterokonta) form one of the eight major phylogenetic groups of Eukaryotes such as unicellular and multicellular algae, fungus-like cells and parasitic and free-living flagellates (Caamano, Loperena et al. 2017). These phototrophic picoeukaryotes contribute to phytoplankton biomass and primary production. Heterotrophic picoeukaryotes play important roles in microbial food webs and the carbon cycling (Lie, Liu et al. 2014). Stramenopiles are monophyletic in addition to being phototrophic and heteroptrophic (Mas- sana, Castresana et al. 2004) and can be found in the deep sea and in coastal systems, suggest- ing Stramenopiles are cosmopolitan organisms (Massana, Castresana et al. 2004). The most abundant phylum associated with the two disturbed rivers were heterotrophic Ciliaphora (Simon, Lopez-Garcia et al. 2016) Although the Maludam river sediment samples also contained significant Ciliaphora, the dominant phylum was Chlorophyta. In Maludam river, the dominant sequences were closely affiliated with members of the phylum Bacillario- phycaea (43% of total sequences), which are autotrophic, followed by sequences affiliated with Ciliophora (17% of total sequences). Samples from Sebuyau river were dominated by Basidiomycota (79% of sequences) and the second most abundant phylum was Amoebozoa (7% of total sequences). Diversity indices (Figure 50) showed that ranged from approximately 0 to 300 and 0 to 5 for Chao1 and Shannon, respectively. Overall, the eukaryotic biodiversity in Maludam river as measured by these indices had lower range compared to Sebuyau river and Simunjan river during January 2016.

!107

Figure 50: The diversity of the three peat-draining samples was measured by calculating Chao1 and Shannon indices.

We then calculated a set of network parameters including average path length, cluster- ing co-efficient, number of clusters and modularity for observed networks to investigate the eukaryotes interaction pattern in the respective peat-draining rivers during both seasons (Fig- ure 52). The co-occurrence network was inferred based on the Spearman correlation (Figure 52). Network analysis co-occurrence patterns revealing the eukaryotes community interaction were more abundant during January 2016 in all three peat-draining rivers.

!108

Figure 51: Principal coordinate analysis (PCoA) of eukaryotic community in the three peat draining river during dry and monsoon seasons. The PCoA plot was based on Bray-Curtis dis- tance and relative abundances of eukaryal OTUs were used as dataset.

!109 Figure 52: This generated co-occurrence network analysis was performed on all samples to predict the potential interaction among the eukaryotic community in all three peat-draining rivers between August 2015 and January 2016.

8.3 Discussion

8.3.1 Eukaryotic communities and impact of land use changes

The most common fungi found in peatland area are Ascomycota and Basidiomycota (Tedersoo, Bahram et al. 2017). Both Ascomycota and Basidiomycota probably act as potent degraders with a crucial function to break down organic matter (Liu, Wang et al. 2015). In our study, parasitic and saprophytic Basidiomycota were the highest compared to the rest of the groups in the fungal community which is in agreement with other studies (Rousk, Baath et al. 2010). The abundance of Basidiomycota increased in the semi-disturbed river. This suggests that the Basidiomycota are less affected by anthropogenic impact or could be used as indica- tors of landuse change. Previous research has demonstrated similar findings whereby Basid- iomycota increased in anthropogenic site (Kalwasinska, Felfoldi et al. 2017, Fernandes, Kirchman et al. 2014). However, the decrease of Basidiomycota in the disturbed river depict- ed an interesting and contradicting finding.

The PCoA illustrated the separation and seasonality of the eukaryotic community in the three peat-draining rivers (Figure 51). The PCoA plot was used to compare the pristine and disturbed rivers. It was shown that the eukaryotic communities in Sebuyau and Simunjan rivers clustered together. Again, these results supported that the microbial community struc- tures were potentially influenced by anthropogenic consequences. The samples from Sebuyau clustered next to the samples from Simunjan (Figure 51) indicating Eukaryotes from Sebuyau shared the same niche with Eukaryotes from Simunjan. These results indicated that the lan- duse surrounding (and subsequent run off from) the semi-disturbed and disturbed rivers have a major impact on eukaryotic communities in the rivers. Both sites contained high abundances of the phylum Bacillariophyceae (Stramenopiles and Ciliophara). This suggests that Cilio- phara may not only be a feature of high-latitude soils and sediments with low oxygen content

!110 such as in the French Alps (Feret, Bouchez et al. 2017) but can also be found in tropical lati- tudes. Venn analyses (Figure 53 and 54) highlighted unique sequences for Maludam in the dry season which were all shared during the wet season. Looking at the top 20 core Eukaryotes, the pristine Maludam river was characterized by 9 unique phyla (Discosea, Gracilipodida, Tubulinea, Chloroplastida, Centrohelida, Discoba, D 1 SAR and Stramenopiles) in the dry and 1 unique phyla (Amoebozoa) in the wet season. The more disturbed river systems shared 2 unique core phyla (Nucletmycea and Holozoa) with Maludam. A similar picture was ob- served for the rare community, with 1 unique phyla for Maludam during monsoon (Amoebo- zoa) and 1 unique phyla for more disturbed rivers (Bacillariophyceae).

Figure 53 : A Venn diagram with shared and unique core taxa (top 20) was used to depict the similarities and difference between three rivers.

111!

Figure 54: Venn diagram of rare eukaryal communities (top 20) with shared and unique opera- tional taxonomic units (OTUs) among different samples.

Maiden et al. (2018) suggested that the eukaryotic population increased at partially drained logged mixed peat forest, followed by oil palm plantation and lowest in the primary mixed forest at Maludam during the dry season in 2012. However, our study shows the oppo- site trend with the lowest abundance of Opistokhonta in the disturbed peat-draining river, Simunjan and at maxima in Maludam river during the dry season in August 2015. Besides landuse change, an alternative reason for the observed differences could be that the disturbed peat-draining rivers (Sebuyau and Simunjan rivers) are predominantly underlain by mud soils. There is a possibility that Maludam river hosts more aerobic organisms due to the soil condi- tion which was observed to be porous in comparison to Sebuyau and Simunjan rivers. Anaer- obic and hypoxic (oxygen-poor) environments such as Sebuyau and Simunjan rivers could be more replete with eukaryotes as demonstrated by our results. However, given the observed steady decrease in abundance from pristine to semi-disturbed to disturbed, this reasoning seems unlikely and observed changes seem to be driven by changes in landuse.

!112 8.4 Conclusion

Overall, our findings indicate that soil eukaryotic communities and specific taxa are ca- pable of representing changes in landuse. The rare Eukaryotes projected higher diversity and abundance in Maludam river but the core eukaryotes communities were most abundant in Se- buyau river. Furthermore, we showed several phyla with higher sensitivity to anthropogenic influences such as Stramenopiles and Ciliophara as promising bioindicators for anthro- pogenic impacts in peat-draining rivers.

!113 Chapter 9

!114 9.0 Conclusion and Outlook

Next gene sequencing (NGS) enabled many ecological studies exploring microbial diversity in ecosystems such as in estuarine sediments (Baker, Lazar et al. 2015), in aquifer sediment (Castelle, Hug et al. 2013) and in contaminated environments (Akob, Mills et al. 2007). This study aimed to determine and compare the microbial community composition in pristine and disturbed peat-draining rivers during both dry and monsoon seasons. Our hypoth- esis was that there is higher microbial diversity and activity in the pristine peat-draining rivers in comparison to the disturbed peat-draining rivers.

Our microbial data did indeed show lower abundance in the disturbed peat-draining rivers, however, the enzymatic study depicted an increase in the enzyme activities in the dis- turbed peat draining rivers. Studies similar to ours have reported the increase in the enzyme activity in soil and sediments with increased nutrients (carbon and nitrogen) in association with the addition of organic inputs from urban waste (Wakelin, Colloff et al. 2008). The paral- lel increase in the elevated nutrients and enzymatic activity as a response to the increased in- put of carbon and nitrogen in soil and sediments was similar to microbial community studies in response to stress/contamination from industrial sites (Patel, Sharma et al. 2016). The alpha diversity of the fungal community represented by both Shannon indices and Chao richness also increased at Sebuyau and Simunjan river, contrary to the initial hypothesis. More specific studies should be conducted to better understand the functional responses of sediment bacteria in the peat-draining rivers due to anthropogenic impacts.

The majority of bacterial 16S rRNA gene sequences were affiliated with Proteobacte- ria (28% of total), Acidobacteria (10% of total), Firmicutes (1% of total), Verrucomicrobia (1% of total) and Chloroflexi (1% of total). During the dry season, Proteobacteria were even more dominant, representing 35% of the total sequence reads; followed by Acidobacteria (34% of total), Actinobacteria (7% of total), Bacteroidetes (5% of total), Firmicutes (5% of total), Chloroflexi (4% of total) and Nitrospirae (2% of total). Microbial diversity studies in most peatland types reveal that the frequent bacteria in peat are representatives of Proteobac- teria and Acidobacteria, which have good adaptation to acidic environment and exhibit many

!115 different lifestyles, which was similar to our study. These two groups were also most numer- ous found in both forest soil (Kerfahi, Tripathi et al. 2014) and Sphagnum peat bogs (Krach- ler, Krachler et al. 2016). The other important bacterial taxa typical for peatlands are Acti- nobacteria, Verrucomicrobia, Planctomycetes, Chloroflexi, Firmicutes and Chlamydiae (Oloo, Valverde et al. 2016). Results from this thesis show that bacterial communities in rivers draining from peat are similar in composition to bacterial communities found in peat.

Although NGS can reveal high microbial diversity in many ecosystems, functional analyses are needed to understand the role of microbes in the ecosystem. PICRUSt has found widespread application in microbial ecological studies for predictive metabolic profiling (Robinson, Caldwell et al. 2016). PICRUSt does not accurately measure the microbial func- tions but the prediction of micro biome function is based on the marker genes and the refer- ence genome sequence available. Based on 16S rRNA gene copy numbers of detected phylo- types, we predicted the functional profiles of bacterial communities. We observed that amino acid metabolism, carbohydrate metabolism and energy metabolism were enriched in all sam- ples. This predictive metabolic pathway findings supported taxa that are related to amino acid processes such as degraders (for example Acidobacteria, Verrucomicrobia, and Bacteroidetes), fermentation (e.g. Firmicutes) (Baker, Lazar et al. 2015) and methane me- tabolism (e.g. Archaea) (Sakai, Ehara et al. 2012). Alphaproteobacteria usually prevail in methane-emitting wetlands and these bacteria are mainly composed of methanotrophs from Methylocystaceae. Firmicutes, Bacteroidetes and Actinobacteria are plant-degrading mi- crobes degraders. Deltaproteobacteria belong to phylogenetic lineages from the genera Syn- trophobacter and Geobacter which prevail in wetlands with high sulfate levels (Baker, Lazar et al. 2015).

Archaeal communities found in freshwater lake sediments were not as diverse as bac- terial communities (Yang, Dai et al. 2016). Phylogenetic analysis of the sediment Archaea re- vealed high diversity in Maludam river, and the majority of Archaea community belonged to common archaeal groups of river and lake sediments (Yang, Dai et al. 2016). Phylogenetic analysis of the sediment Archaea 16S revealed the core archaea communities composed of Crenarchaeota, Euryarchaeota and Thaumarchaeota. Crenarchaeota were detected in low sequence reads and relative abundance in all rivers during both dry and monsoon seasons. The

!116 sediments in all the rivers were enriched with Euryarchaeota. Most of the euryarchaeal OTUs detected in the peat-draining rivers were related to Methanosarcinales. Previous studies re- ported Methanosarcinales presence in tropical mangroves (Jing, Cheung et al. 2016) and in cold seep area, northwest slope of the South China Sea (Niu, Fan et al. 2017).

Fungi (under the phylum Opisthokonta) were the major group of Eukaryotes for all the three peat-draining rivers. Opistokhonta and Alveolata were the most dominant Eukaryotes. The most dominant lineages under Opistokhonta were Nucletmycea followed by Holozoa. The rare Eukaryotes were Discosea, Gracilipodida, Tubulinea, Chloroplastida, Centrohelida, Discoba, D 1, SAR and Stramenopiles. Both the pristine and anthropogenically influenced peat-draining rivers were dominated by Bacillariophyceae. (consisting of Stramenopiles and Ciliophara). Since the vast majority of molecular studies on Eukaryotes have been conducted in freshwater lakes, information about Eukaryotes in rivers is still lacking. This study pro- vides a baseline for eukaryotic communities in tropical river systems.

There were distinct two factors, spatial and seasonal, influencing the microbial com- munity abundances and composition. The influence of landuse change was clearly reflected in the microbial communities. Growing anthropogenic disturbance in and along tropical peat- draining rivers raises concerns regarding the consequences for the global climate. The find- ings from this study provide vital insights into the microbial community in tropical peat- draining rivers and the impact of anthropogenic activities on these microorganisms. The dras- tic decrease in microbial diversity at the anthropogenically-influenced site as compared to the reference site in this study shows that anthropogenic activities have a negative effect on mi- crobial communities. This study is the platform for future studies in Borneo on the correlation between the microbial community composition interaction with the biogeochemical processes in these rivers.

Land use, ranging from agricultural fields to fallow grasslands, disturbed grasslands and different cultivation practices in agricultural fields, influences bacterial communities. Ir- reversible land-use conversion alters land cover, biota and biogeochemical cycles. Our results demonstrate that land-use conversion affects soil microbial diversity and community compo- sition. Future research could look more into parameters of the soil, such as soil texture and soil stoichiometry, to decipher their role in shaping the microbial communities in these peat-

!117 draining rivers. The implementation of organic biomarkers and stable isotopes are also en- couraged in future research to further entangle the relationships between specific groups of microbes and environmental parameters.

There is a need to raise awareness about the importance of peatlands in the regulation of the climate by predicting how peatlands respond to global change and anthropogenic dis- turbances. A better understanding of the biogeochemical mechanisms controlling microbial diversity and maintaining the function of microbial communities is critical in order to assist in better peatland management. Restoration projects benefit from knowledge of microbes to support fertilization, water table manipulation and re-vegetation of bare degraded forests. In order to strengthen their success, restoration projects should recognize the activities of mi- crobes in terms of e.g. nitrogen fixation, methanotrophy and beneficial plant growth promo- tion, and root symbiotic associations. In order for this to happen, a better understanding of peat microbiology (in all its facets) is required.

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