Bacterial and Phytoplankton Inter Relationships in the Kongsfjorden, an Arctic fjord

(1)

BACTERIAL AND PHYTOPLANKTON

INTER RELATIONSHIPS IN THE KONGSFJORDEN, AN ARCTIC FJORD

Thesis submitted for the degree of

DOCTOR OF PHILOSOPHY

In

BIOTECHNOLOGY to the

GOA UNIVERSITY

By

Rupesh Kumar Sinha

National Centre for Antarctic and Ocean Research, Vasco-da-Gama, Goa – 403 804, INDIA

October 2016

(2)

BACTERIAL AND PHYTOPLANKTON

INTER RELATIONSHIPS IN THE KONGSFJORDEN, AN ARCTIC FJORD

A Thesis submitted to Goa University for the award of the degree of DOCTOR OF PHILOSOPHY

In

BIOTECHNOLOGY

By

National Centre for Antarctic and Ocean Research, Vasco-da-Gama, Goa

Work carried out under the guidance of Dr. Savita Kerkar

Professor, Department of Biotechnology, Goa University, Goa

(3)

CONTENTS Statement

Certificate

Acknowledgements

Chapter 1 General Introduction 1

Chapter 2 Pelagic retrievable heterotrophic bacterial diversity of Kongsfjorden

25

2.1 Introduction 26

2.2 Materials and Methods 30

2.2.1 Sampling site 30

2.2.2 Hydrographic profiling and sampling of water column 30 2.2.3 Total counts, culture and isolation of heterotrophic

bacteria

32

2.2.4 PCR amplification of the 16S rDNA, sequencing and phylogenetic analysis

32

2.3 Results 33

2.3.1 Hydrographic properties of Kongsfjorden 33 2.3.2 Retrievable heterotrophic bacterial diversity in the

summer of 2011

37

2.3.3 Retrievable heterotrophic bacterial diversity in the summer of 2012

43

2.4 Discussion 54

2.5 Conclusion 67

Chapter 3 Estimation of bacterial diversity using cloning and next generation sequencing of 16S rDNA

69

(4)

3.2.1 Study site and sampling strategy 73

3.2.2 DNA extraction, 16S rRNA gene cloning and V3 metagenomic library preparation

75

3.3 Results 79

Chapter 4 Phytoplankton abundance, distribution and its influence on bacterial diversity in Kongsfjorden

104

4.2 Materials and methods 108

4.2.1 Study site and sampling strategy 108

4.2.2 Nutrient analysis 110

4.2.2 Pigment analysis by High Performance Liquid Chromatography

110

4.3 Results 113

4.3.1 Distribution of major phytoplankton pigments in Kongsfjorden

113

4.3.2 Distribution of major groups of phytoplankton in Kongsfjorden

123

Chapter 5 Adaptational strategies of Arctic microbes to withstand extreme environmental conditions

136

5.1.1 Bacterial – phytoplankton evolution and role of pigments

137

5.1.2 Role of Carotenoid in microorganisms 143

(5)

5.1.3 Carotenoid as adaptational strategies in Arctic bacteria

144

5.2.1 Phylogenetic characterization of the bacterial isolates

146

5.2.2 Freeze-thaw experiment 147

5.2.3 Pigment extraction and partial characterization 150

5.2.4 Lipid-extraction and analysis 152

5.3 Results 156

5.3.1 Cell count and retrievability 156

5.3.2 HPLC analysis of bacterial pigments 156

5.3.3 Fatty acid profile 169

Chapter 6 Summary and Conclusion 179

References 187

Annexure I List of Publications

Annexure II Published manuscripts from the thesis

(6)

Statement

As required under the University Ordinance OB-9A, I state that the present thesis entitled “BACTERIAL AND PHYTOPLANKTON INTER RELATIONSHIPS IN THE KONGSFJORDEN, AN ARCTIC FJORD” is my original contribution and the same has not been submitted on any previous occasion. To the best of my knowledge, the present study is the first comprehensive work of its kind from the area mentioned.

The literature related to the problem investigated has been cited. Due acknowledgements have been made whenever facilities and suggestions have been availed of.

Rupesh Kumar Sinha Ph.D. student, Department of Biotechnology, Goa University, Goa, India.

(7)

Certificate

This is to certify that the thesis entitled “BACTERIAL AND PHYTOPLANKTON INTER RELATIONSHIPS IN THE KONGSFJORDEN, AN ARCTIC FJORD” submitted by Rupesh Kumar Sinha for the award of the degree of Doctor of Philosophy in Department of Biotechnology is based on his original studies carried out by him under my supervision. The thesis or any part thereof has not been previously submitted for any degree or diploma in any University or Institution.

Place: Vasco-da-Gama Date:

Dr. Savita Kerkar Research Guide, Department of Biotechnology, Goa University, Goa, India.

(8)

…dedicated to my mentor

(9)

Acknowledgements

With heart-felt gratitude, I sincerely thank my guide Dr. Savita Kerkar, for her valuable guidance, patience and encouragement. My sincere thanks to my mentor Dr. K. P. Krishnan for his continuous and invaluable guidance, support and encouragement. I express my sincere thanks to Shri Rasik Ravindra and Dr.

S. Rajan former Directors, NCAOR, and Ravichandran M., Director, NCAOR for their support, facilitation and continuous encouragement. I am thankful to Dr.

Maria Judith, NIO for her kind consideration to be the VC nominee and member of the faculty research committee. I sincerely thank Dr. Shanta Achuthankutty, Dr. P. A. Loka Bharathi and Dr.C.T. Achuthankutty for their kind support and guidance. I acknowledge the support rendered by Dr. Thamban Meloth for providing lab facilities available round the clock. I am thankful to Dr. John Kurian P. for his kind support and patience. My life at NCAOR wouldn’t be have been complete without my friends who were there at any moment of time. I thank Sarun, Shridhar, Manoj, Neelu, Roseline, Ajit, Luvkush and other friends whose names I have not mentioned here for want of space. My colleagues at NCAOR Cyrobiology lab were there always for any help. Femi, Anand, Archana, Divya and Nazira have been very supportive.

I am indebted to my beloved parents and brothers and sisters for everything they have done to help me reach here.

Without ‘His’ blessings nothing would have been possible!

(10)

Chapter 1 General Introduction

(11)

2 The polar regions are the most northerly and southerly areas of the globe – the Arctic and Antarctica. Both the polar regions have low temperatures, long winters with little daylight, and short summers with long days. Snow covers large expanses, sometimes year-round, and vast ice sheets bury the entire Antarctic continent, as well as most of Greenland in the Arctic. The seas are partly covered by sea ice, with more ice in winter. The polar regions are thought to be among the most vulnerable ecosystems impacted by rapid and long-term changes in climate (Metz et al. 2007).

1.1 An introduction to Arctic

Stretching across the northernmost areas of Earth, the Arctic can be defined in several ways. Most commonly, the Arctic Circle (66° 33’ N) is referred to as the starting point of the Arctic, though more liberal definitions include all areas north of 60°N (Fig 1.1). Other definitions rely on an isotherm, the point where the average temperature for the warmest month is 10°C, and the Arctic tree line, the point past which the environment does not support tree growth. Arctic countries include Norway, Sweden, Finland, Russia, United States (the State of Alaska), Canada, Denmark (Greenland and the Faroe Islands), and Iceland. Comprised of land, sea, and ice, the Arctic is a vast area—as large as 40 million km²—and average winter temperatures can range from 0°C to −40°C.

Arctic research is interdisciplinary and international. Areas of Arctic research include the environment and ecosystem, weather, climate, nature and wildlife and indigenous study. The Arctic is of increasing importance to many governments, scientists, industries, businesses, and other internal and external interests.

Contemporary problems in the Arctic such as climate change, pollution and contamination have provided many opportunities for researchers in science to learn more about the roles and relationships between the Arctic and the rest of the world.

(12)

Figure 1.1 Map of Arctic region (source: http://library.arcticportal.org/1338/)Map of Arctic region (source: http://library.arcticportal.org/1338/)Map of Arctic region (source: http://library.arcticportal.org/1338/)

(13)

4 Arctic marine ecosystems have recently received increased attention because they are considered to be sensitive to climate change.

1.2 Arctic marine ecosystem

The Arctic Ocean is the smallest of the world’s oceans (total area c. 10 million km²) and consists of a deep central basin, the Arctic Basin, surrounded by continental shelves. The Arctic Basin is further divided by the Lomonosov Ridge (maximum sill depth: 1,870 m; Jakobsson et al. 2008) into the Eurasian and Amerasian Basins.

Maximum depths (c. 5,260 m) are found near the Gakkel Ridge, an extension of the North Atlantic Mid-Ocean Ridge system that divides the Eurasian Basin along a line from northern Greenland to the East Siberian shelf (Jakobsson et al. 2004). The Arctic Ocean has the most extensive shelves of any ocean, covering about 50% of its total area. The circumpolar marine Arctic comprises the Barents Sea, Kara Sea, Laptev Sea, East Siberian Sea, Chukchi Sea, Beaufort Sea, Canadian Arctic Archipelago and Greenland Sea. The Barents, Kara, Laptev, East Siberian and Chukchi shelves are shallow and broad (400-800 km) while the shelves from Alaska to Greenland are narrow (< 200 km). The very nature of the marine environment makes it difficult to establish ecosystem boundaries, as water masses are modified and displaced seasonally and shift at interannual to interdecadal or longer time scales, causing repositioning of fronts and associated ecological features. The Barents and Chukchi Seas are inflow shelves (Carmack and Wassmann 2006) and are profoundly influenced by the interaction between Arctic and sub-Arctic (Atlantic and Pacific, respectively) waters, as well as by processes associated with the presence of the Marginal Ice Zone (MIZ; Darby et al. 2006). The Barents Sea covers c. 1.4 million km² extending eastwards from the Norwegian Sea to Novaya Zemlya and northwards from the coasts of Norway and Russia into the Arctic Ocean and is the deepest of the

(14)

Arctic shelf seas (average depth 230 m). The complex hydrography and circulation patterns in the Barents Sea strongly influence its biological production. Warm saline Atlantic waters are carried by the Norwegian Atlantic Current into the Barents Sea (Fig 1.2). Inshore of the Atlantic waters is the relatively fresh Norwegian Coastal Current, whereas in the northern part of the Barents Sea, cold low salinity Arctic waters flow in a northeast-southwest direction, separated from Atlantic waters by the Polar Front (Drinkwater 2011, Loeng and Drinkwater 2007).

In the permanently ice-free, Atlantic-water-influenced southwestern Barents Sea (i.e. where surface temperatures > 0 °C), the onset of thermal stratification in spring initiates the development of the phytoplankton bloom. In contrast, the northern Barents Sea, which is influenced by Arctic waters has a highly variable seasonal ice cover (both in duration and extent), and the phytoplankton bloom is typically associated with the retreat of the MIZ (Sakshaug 2004). Production is significantly higher and shows less interannual variability in the Atlantic compared with the Arctic sector of the Barents Sea (Sakshaug et al. 2009, Reigstad et al. 2011). The Arctic Ocean also influences marine ecosystems of the Atlantic Ocean directly, as waters and sea ice exiting the Arctic Ocean affect the physical, chemical and biological characteristics of the North Atlantic. Conversely, the Arctic Ocean receives waters from the Pacific and Atlantic Oceans, and therefore Arctic marine ecosystems are influenced by global changes that influence biodiversity in these oceans.

The Arctic is undergoing major and rapid environmental changes including accelerated warming, decrease in sea ice cover, increase in river runoff and precipitation, and permafrost and glacier melt. These changes together with new opportunities for economic development create multiple stressors and pressures on Arctic marine ecosystems.

(15)

6 Figure 1.2 Surface circulation of the Norwegian and Barents Seas. The red arrows represent the warm, saline Atlantic waters; the white arrow represents cold, fresher Arctic waters and the yellow arrow represents low salinity coastal waters (Source:

Drinkwater 2011).

(16)

The current increase in global temperature is most rapid in the Arctic, with a predicted summer temperature increase of up to 5°C over this century (Metz et al.

2007), and surface water temperature anomalies as high as 5°C recorded in 2007 (Steele et al. 2008). Arctic sea ice, a key defining characteristic of the Arctic Ocean, is declining faster than forecasted by model simulations, with the potential for a summer ice-free Arctic within the next few decades (Stroeve et al. 2007, Wang and Overland 2009). The effects of these and other environmental changes (e.g. changes in freshwater input, shoreline erosion) on Arctic marine ecosystems are already documented (Wassmann et al. 2011, Weslawski et al. 2011). Changes in the distribution and abundance of key species, range extensions and cascading effects on species interactions are taking place, influencing Arctic marine food web architecture.

Unique habitats such as ice shelves and multi-year ice are rapidly shrinking. With continued warming and sea ice decline, measures should be put in place to monitor areas of particular biological significance and uniqueness in support of preservation and protection measures. Moreover, the complexity and regional character of Arctic ecosystem responses to environmental changes calls for the establishment of long- term marine ecosystem observatories across the Arctic, in support of sustainable management and conservation actions.

1.3 Key environmental forcing and community structure in Arctic

Seasonal extremes in photoperiod, river runoff and ice conditions all constitute key forcings to Arctic marine ecosystem functioning and biodiversity. In addition, the structure, functioning and biodiversity of Arctic marine ecosystems is fundamentally linked to the main hydrographic features of the Arctic Ocean, namely the connection to the Pacific and Atlantic Ocean, strong stratification and critical influence of the large continental shelves and riverine input. The Arctic Ocean is connected to the

(17)

8 Pacific and Atlantic Oceans with which it exchanges water and associated physical, chemical and biological properties. Relatively warm and saline (c. 34.8 psu) Atlantic waters enter the Arctic Ocean through Fram Strait and influence the biodiversity of species and ecosystems as they circulate cyclonically, following the bathymetry of the Arctic Ocean (Carmack and Wassmann 2006). There is inherent biodiversity associated with the presence and circulation of Atlantic and Pacific water masses in the Arctic Ocean. In the Barents, Norwegian and Greenland Seas, species associated with warm Atlantic waters such as deep-water shrimps thrive and sustain. The warm Atlantic Waters carry plankton species and planktonic larvae of benthic species into the Kara and Laptev Seas and into the Canadian Basin within the intermediate Atlantic layer (Sirenko 2009). In the Chukchi Sea, the distribution and diversity of zooplankton species is strongly linked to water mass distribution, with different assemblages associated with nearshore, Pacific-origin, and oceanic waters (Hopcroft et al. 2010). Pacific benthic species are mainly found in the Chukchi, Beaufort and the northern part of the East Siberian Seas in areas influenced by Pacific waters entering the Arctic Ocean through Bering Strait (Dunton 1992, Sirenko 2009). The presence of silicious sponge communities reported north of the Queen Elizabeth Islands in the Canadian Archipelago (Van Wagoner et al. 1988) points to the past and present occurrence of Pacific-origin waters in this region. Adding to the stratification originating from the presence of Atlantic and Pacific waters, the large amount of freshwater from rivers and sea ice melt contributes to the formation of a low salinity surface layer, the polar mixed layer, characterizing the Arctic Ocean. This strong stratification also plays an important role in shaping Arctic marine ecosystem biodiversity through its influence on the availability of light and nutrients for primary producers and its effects on the composition of plankton communities. This is because

(18)

different functional groups and species of primary producers thrive under different conditions associated with stratified/well-mixed waters (Cullen et al. 2002). Diatoms, which have high nutritional requirements, thrive in environments where nutrients are periodically replenished through mixing and upwelling. In contrast, small cells have high surface/volume ratios and are better adapted to stratified environments where nutrients can quickly become depleted. The episodic primary production in the Arctic influences the annual cycling of nutrients as surface nutrients drop near detection levels following the spring/summer phytoplankton bloom and remain low until autumn (Aguilera et al. 2002), unless there is a resupply, e.g. via upwelling (Williams and Carmack 2008). Since nutrient supply is essential to sustain primary producers, such seasonality determines potential growth and biomass accumulation at lower trophic levels (Tremblay and Gagnon 2009).

Microbial extremophiles are the dominant life forms of the polar environments. The overall cold temperature in the Arctic may have offered selection for cold-adapted species, low temperatures per se are not related to low biodiversity at lower trophic levels in pelagic (Poulin et al. 2011) or in benthic communities. They are able to survive in the extreme polar environments and have developed mechanisms that allow them to cope with a variety of stressors. These include freezing temperatures and repeated freeze-thaw cycles, desiccation, high or low levels of salinity or pH, and lengthy periods of darkness during winter. Polar life forms must also be able to survive exposure to high levels of solar UV-B (280-314 nm) radiation due to stratospheric ozone depletion over the Arctic regions during the summer (Knudsen et al. 2005). The dominant prokaryotes of polar environments are psychrophilic and psychrotolerant bacteria and archaea and the dominant photosynthetic eukaryotes are algae, primarily diatoms. The lowest temperature at

(19)

10 which active life has been found on Earth is about –20°C, with records of bacteria living in permafrost and sea ice (D’Amico et al. 2006). Psychrophilic species can grow at temperatures < 0°C, have an optimum growth temperature < 15°C and cannot grow above 20°C (Thomas and Dieckmann 2002). The term psychrotrophs (also described as psychrotolerant) refers to microorganisms that have the ability to grow at low temperatures but have optimal and maximal growth temperatures above 15°C and 20°C, respectively (Moyer and Morita 2007). Within Arctic sea ice, extremophile habitat consists of channels or pockets that contain liquid brine. These liquid habitats within the sea ice can persist at temperatures as low as –35°C (Deming 2002).

Extremophile adaptations are primarily associated with cellular proteins (e.g. enzymes of high specific activity; D’Amico et al. 2006), as they control the balance between cellular substrates and production, nutrient fluxes, removal of waste products and the assembly of cellular components including DNA. The production of polyunsaturated fatty acids as well as other changes in the composition of lipid membranes is also critical for maintaining membrane fluidity and proper functioning of the cells (Thomas and Dieckmann 2002). Role of pigment production was also studied in psychrotolerant heterotrophic bacteria as adaptation strategy towards temperature fluctuation and high irradiance (Jagannadham et al. 2000, Dieser et al. 2010). Outside the cell, cryoprotectant materials such as exopolymers have multiple roles that aid the survival of bacterial and algal extremophiles in the sea ice (Krembs and Deming 2008). There is still much to learn about the biodiversity of these extreme habitats and extremophiles as well as the mechanisms they use to survive within the sea ice and extremely cold environments. Thus, Arctic marine ecosystem provide unique environment to study effect of various environmental forcing on microbial community

(20)

structure. A very critical example of Arctic marine environment is coastal ecosystem of Arctic fjords.

The Arctic fjords differ in many aspects from other marine environments, particularly with regard to low temperatures and extreme fluctuations in irradiance due to ice coverage and strong seasonality. The winter season in the Arctic is characterized by little or no sun light, of which only a fraction is able to penetrate the thick sea ice and snow layers to the water column below. This mainly affects photosynthetic organisms like phytoplankton, which require sunlight for carbon fixation. Most pronounced changes for the marine environment occur during the spring, when melting sea ice and rapidly increasing day length allow greater penetration of light to the water column, which together with strong stratification, are followed by increased photosynthetic activity (Sakshaug 2004). During this time, the pelagic microbial community starts to adapt to summer time climate stress such as high irradiance, freeze-thaw etc. (Yager et al. 2001, Seuthe et al. 2011). The fjords of Spitsbergen, the largest island of Svalbard archipelago, may be suitable for observing modifications of the ecosystem structure and functioning as a result of climate change mediated by the variable influence of the Arctic and Atlantic water masses (Cottier et al. 2005). Svalbard archipelago (23,958 mi²/62,051 km²), island group is located in the Arctic Ocean, 640 km north of the Norwegian mainland and between latitude 74°N and 81°N (Fig 1.3). The main islands of the group are Spitsbergen, Nordaustlandet, Edgeøya, Barentsøya, and Prins Karls Forland. Surrounding islands include Hopen, Kong Karls Land, Kvitøya, and Bjørnøya. Permafrost covers the entire landmass of Svalbard, with only the top meter of earth thawing during the summer.

(21)

12 Figure 1.3 Map showing location of Svalbard archipelago (source:

http://europlanet.dlr.de/fileadmin/pictures/sc2/svalbard/).

(22)

Figure 1.4 Map of Kongsfjorden. Four major glaciers viz., Kronebreen, Kongsbreen, Conwaybreen and Blomstrandbreen which feed Kongsfjorden are shown in the figure (Landsat 7 ETM satellite data).

Map of Kongsfjorden. Four major glaciers viz., Kronebreen, Kongsbreen, nwaybreen and Blomstrandbreen which feed Kongsfjorden are shown in the figure (Landsat 7 ETM satellite data).

Map of Kongsfjorden. Four major glaciers viz., Kronebreen, Kongsbreen, nwaybreen and Blomstrandbreen which feed Kongsfjorden are shown in the figure

(23)

14 1.4 Kongsfjorden: An Arctic fjord

Kongsfjorden (Fig 1.4), is a polar fjord situated between 78°04'N-79°05'N and 11°03'E-13°03'E on the west coast of Spitsbergen, Svalbard Archipelago.

Kongsfjorden system is an established reference site for Arctic marine studies with great potential for international, multidisciplinary collaboration due to the presence of the international research platform in Ny-Ålesund. Kongsfjorden also functions as a climate indicator on a local scale because it is directly influenced by variable climate signals in the Arctic. Ny-Ålesund is an ideal site for studies of environmental parameters due to the established research infrastructure. Seasonal observations of ecosystem dynamics in Kongsfjorden have revealed that a strong front was observed in spring between the open sea and the fjord waters which effectively prevented water mass exchange. This front dissolved before the summer and advection of Atlantic water becomes a dominating factor in shaping the biological community (Walkusz et al. 2009). Kongsfjorden has received a lot of research attention in the recent past. The current interest in the fjord is primarily based on the fact that Kongsfjorden is particularly suitable as a site for exploring the impacts of possible climate changes, with Atlantic water influx and melting of tidal glaciers both being linked to climate variability. The Kongsfjorden being influenced by both Atlantic and Arctic water masses harbours a mixture of boreal and Arctic flora and fauna.

Inputs from large tidal glaciers create steep environmental gradients in sedimentation and salinity along the length of this fjord (Fig 1.5). Hence, this environment could also serve as an excellent model to assess the complex and intricate relations between various life forms. The glaciers of western Spitsbergen, Svalbard are highly susceptible to climate fluctuations because of their intermediate position relative to the warm West Spitsbergen Current that transports Atlantic Water

(24)

Figure 1.5 Head of Kongsfjorden fed by surging glaciers. Discharge of meltwater and sediment in Kongsfjorden through these glaciers is pronounced during summer.

(25)

16 from the south and the colder polar water that flows from the North (Trusel et al.

2010, Hald et al. 2004). Changes in freshwater flux from western Svalbard glaciers have many implications including influences on fjord sea ice, local biota, fjord and river hydrology and in extreme climate warming scenarios, a disturbance in deepwater production on the Svalbard shelf (Hagen et al. 2003).

1.5 Bacterial and phytoplankton diversity of Arctic and their adaptational strategies

Planktonic marine bacteria are abundant (typically 10⁸–10⁹ cells l^-1) in the Arctic region (Jiang et al. 2005, Kirchman et al. 2007, Sala et al. 2010). These microorganisms which are an important food source in marine food webs play crucial roles in organic matter cycling (Azam et al. 1983, Naganuma et al. 2006). It is important to determine the dominant groups of marine bacterioplankton as they may be proportionally more influential in carbon cycling and other biogeochemical processes (Cottrell and Kirchman 2000). Prior to the application of molecular techniques, all that was known about the identity of bacteria in the Arctic was from culture based studies of isolates. This approach continues to be valuable and provides unique insights into the metabolic capacity of microorganisms, especially those from extreme environments (Steven et al. 2008, Niederberger et al. 2009a, 2009b). A cumulative and more profound approach along with culture based studies is gene survey, where the taxonomically informative gene coding for 16S ribosomal RNA (rRNA) is amplified by polymerase chain reaction (PCR), cloned and sequenced (Crump et al. 2003, 2009, Hollibaugh et al. 2007, Kellogg and Deming 2009, Jungblut et al. 2005, Harding et al. 2011, Lovejoy 2011). The combination of culture studies and environmental gene surveys could reveal greater diversity than either when used alone (Wilhelm et al. 2011). A big boost to the study of bacterial diversity are the

(26)

recently developed high throughput sequencing technologies that have been widely used to tag samples and enable massive parallel sequencing without cloning. This technique, although now fast becoming the gold standard, was first applied to marine Arctic water samples as part of the International Census of Marine Microbes (ICOMM; Galand et al. 2009, Kirchman et al. 2010). Those results indicate, as with most open ocean systems that bacterial diversity has been underestimated by earlier approaches and showed that biogeography, history and water mass distribution were important determinants in the makeup of pelagic microbial communities. Similar studies of soil systems and freshwater have also indicated that bacterial diversity is much greater than previously perceived (Yergeau et al. 2010, Wilhelm et al. 2011).

The advent of next-generation sequencing (NGS) or high-throughput sequencing has revolutionized the field of microbial ecology and brought classical environmental studies to another level. This type of cutting-edge technology has led to the establishment of the field of “metagenomics”, defined as the direct genetic analysis of genomes contained within an environmental sample without the prior need for cultivating clonal cultures. Initially, the term was only used for functional and sequence-based analysis of the collective microbial genomes contained in an environmental sample. However, it is also widely applied to studies performing PCR amplification of certain genes of interest. The former can be referred to as “full shotgun metagenomics”, and the latter as “marker gene amplification metagenomics”

(i.e., 16S ribosomal RNA gene) or “meta-genetics”. Such methodologies allow a much faster and elaborative genomic/genetic profile generation of an environmental sample at a very acceptable cost. Full shotgun metagenomics has the capacity to fully sequence the majority of available genomes within an environmental sample (or community). This creates a community biodiversity profile that can be further

(27)

18 associated with functional composition analysis of known and unknown lineages (i.e.

genera or taxa) of organism. Shotgun metagenomics has evolved to address the questions of taxonomy and function and thereby their interaction to sustain a balanced ecological niche. It further provides unlimited access to functional gene composition information derived from microbial communities inhabiting practical ecosystems.

Marker gene metagenomics is a fast and gritty way to obtain a community/taxonomic distribution profile or fingerprint using PCR amplification and sequencing of evolutionarily conserved marker genes such as the 16S rRNA gene. This taxonomic distribution can subsequently be associated with environmental data derived from the sampling site under investigation. These techniques have been used to study marine bacterioplankton community of major Arctic fjords like Kongsfjorden and adjacent Barents sea. Pyrosequencing based study by Zeng et al. (2013) reported Gammaproteobacteria and Bacteroidetes as the dominant members of the bacterioplankton community in Kongsfjorden. They also suggested the impact of hydrogeographic conditions in shaping bacterial community structure. However, detailed census of microbial communities in Kongsfjorden using high-throughput metagenomics has not been undertaken yet.

Phytoplankton are free-floating photosynthetic organisms that are found in aquatic environments. These organisms capture energy from sunlight and transform inorganic matter into organic matter. Phytoplankton identification and enumeration is usually done through microscopic examination. This procedure is time-intensive and also requires a high level of taxonomic skill. Moreover, smaller organisms such as picoplankton cannot be identified or counted with this approach. The contribution of autotrophic picoplankton to total primary production may reach 50–90% in oligotrophic lakes and oceans (Goericke and Montoya 1998) and 30–70% in

(28)

meso/eutrophic lakes (Greisberger et al. 2008). Morphological similarities and plasticity have frequently led to erroneous results in species identification (Medlin et al. 2006, Evans et al. 2007). Therefore, much of the species information, including that regarding putative cryptic species, was lost, and the actual species composition of the ecosystem was not reflected in the species lists produced by field surveys. Indeed, DNA sequence information regarding specific target regions of DNA enabled many ambiguous species to be identified and the hidden diversity in environmental samples to be evaluated (López-García et al. 2001, Moon-van der Staay et al. 2001, Medlin et al. 2006, Betournay et al. 2007, Evans et al. 2007). Additionally, single-strand conformational polymorphism (SSCP; Medlin et al. 2006) and denaturing gradient gel electrophoresis (DGGE; Dorigo et al. 2005) have frequently been used to estimate the species diversity in aquatic ecosystems. However, each molecular method has some drawbacks when applied to environmental samples. For example, SSCP and DGGE can only provide a profiling pattern of species composition and can only be used to determine the exact species present in the sample if the DNA band is isolated from the SSCP and DGGE gels and sequenced, which requires additional experimentation. The construction of clone libraries is frequently used to analyze the species composition of environmental samples (López-García et al. 2001; Moon-van der Staay et al. 2001). In this technique, the species composition can be revealed through cloning after PCR amplification of environmental samples. However, the sequence database for phytoplankton 18S rRNA gene is highly confined to the study of particular ecosystem or model organisms. Owing to relatively less diverse 18S rDNA (as compared to 16S rDNA) sequence availability in the databases, the sequence search of environmental clones lead most of the time to highly distant matches. Accordingly, a high level of

(29)

20 unexpected species diversity in aquatic ecosystems has been recovered from 18S rDNA clone libraries (López-García et al. 2001; Moon-Van der Staay et al. 2001).

An advantageous alternative is the use of phytoplankton marker pigments for quantification of phytoplankton groups. Estimation of phytoplankton composition is usually achieved by determining marker pigment using high performance liquid chromatography (HPLC). Unlike morphological, optical, genetic or biochemical methods, the pigment-based method is suitable for regular monitoring as well as ecological studies as: 1) the proportion of autotrophic picoplankton can be determined; 2) the time needed for analysis is relatively short and 3) much of the work can be automated. Chemotaxonomic techniques offer an efficient, high- resolution approach to complement classical micro eukaryotic community analyses.

Photosynthetic pigments can be studied to know the phytoplankton composition and their physiological status. Most of these pigments have chemotaxonomic association.

For example, fucoxanthin is considered to be a marker of diatoms; zeaxanthin, a marker of cyanobacteria; 19'- hexanoyloxyfucoxanthin of Prymnesiophyceae;

alloxanthin and crocoxanthin of Cryptomonads; prasinoxanthin of prasinophytes;

peridinin and chlorophyll c2 of dinoflagellates etc. (Jeffrey and Vesk 1997). However, it should be noted that marker pigments are not exclusive of any one group of algae.

In natural environment, pigment composition may well vary with prevailing light condition and photoadaptive state (Falkowski and LaRoche 1991). Therefore, it has been suggested to use chemotaxonomy together with quick microscopic screening to gain more specific information about dominant species.

In the ocean, organic matter is the foundation of a complex marine food web which relies heavily on microbial transformation: approximately one-half of the carbon that is fixed by marine autotrophs is directly processed by bacteria. The

(30)

remaining carbon either enters the classic marine food web or is transported as sinking particles to the deep ocean for long-term storage via the biological pump.

Localized and transient increases in the abundance of phytoplankton are referred to as blooms and result in a boost in biogeochemical activities, including the assimilation of CO2 and inorganic nutrients such as nitrogen and phosphorus. Studies of phytoplankton distribution in Arctic glacial fjords, like Kongsfjorden and Krossfjorden have shown impact of hydrographic properties on timing and extent of spring bloom (Piquet et al. 2014, Svendsen et al. 2002, Cottier et al. 2005).

Freshwater influx is highest in summer and co-occurs with a strong increase in sediment load, which can strongly limit light penetration (Keck et al. 1999, Svendsen et al. 2002). The observed hydrographic variability in Kongsfjorden leads to a high level of unpredictability in interannual phytoplankton spring bloom timing, biomass and production. For example, enhanced inflow of warm Atlantic water in Kongsfjorden is associated with changes in phytoplankton abundance and composition (Hodal et al. 2012, Hegseth and Tverberg 2013). Therefore, the timing, composition, and biomass of the spring bloom show extensive year-to-year variability (Hegseth and Tverberg 2013). During summer stratification, phytoplankton become nutrient limited and are grazed upon or sink out of the euphotic zone. As a result, a transition occurs towards less-productive, small-sized, but highly diverse plankton communities (Hegseth and Sundfjord 2008, Piquet et al. 2010). In addition to low nutrient availability, high sediment concentrations derived from glacial melt water input limit light availability for phytoplankton growth during summer. The euphotic zone can be restricted to the upper 0.3 m close to the glaciers (Keck et al. 1999), leading to highly unfavourable conditions for phytoplankton growth (Hop et al. 2006).

Thus, the expected increase in magnitude of land-derived meltwater influx may affect

(31)

22 phytoplankton composition and production. A significant decrease in average phytoplankton cell size could be associated with enhanced meltwater input, favoring nanophytoplankton, notably cryptophytes. Summertime freshening is generally found associated with an increase in pico- and bacterioplankton abundance, while altered sea ice conditions causes a spatial shift in phytoplankton distribution as well as an increase in nanoplankton abundance. These processes are partly balanced by a subsequent increase in the activity of heterotrophic bacteria, which transform phytoplankton-derived organic matter. As phytoplankton blooms are often seasonal in nature and are thus transient events, the abundance and activity of heterotrophic bacteria varies accordingly. Indeed, secondary bacterial production typically correlates with the concentration of chlorophyll a, which is a proxy for phytoplankton abundance. This correlation between primary and secondary production is evident on both small and large scales and results in a patchy distribution of bacterial activity throughout the oceans. Copiotrophic bacteria, which swiftly capitalize on increased carbon and nutrient concentrations at both the microscale and macroscale, complement their oligotrophic counterparts, which prefer dilute nutrient concentrations. Together, the heterotrophic bacteria, which use these two distinct trophic strategies balance marine productivity.

Bacteria can be loosely or tightly associated with phytoplankton (Caldwell 1977), leading to a multitude of possible interactions between these organisms in aquatic environments (Cole 1982). Bacterial-phytoplankton interactions in polar regions are also studied for evolution of strategies to withstand environmental stresses. Studies on Antarctic bacteria have shown presence of several molecular adaptations towards high irradiance and freeze-thaw cycles (Vincent and Roy 1993).

Studies by Dieser et al. (2010) and Jagannadham et al. (2000) on Antarctic

(32)

heterotrophic bacteria suggested that pigment production conferred resistance against the effects of UV radiation, photo-oxidation and freeze-thaw which characterizes the polar environment. Very less information is available from the Arctic to determine whether this is a general feature of high latitude microbes. The high frequency of pigment production in bacterial isolates from ice cores (Zhang et al. 2008), marine surface waters (Agogue et al. 2005), glaciers (Foght et al. 2004), or supraglacial fluvial system (Dieser et al. 2010) suggests that pigmentation plays a role in adaptation to cold environments. Moreover, carotenoid pigments may play a role in the modulation of membrane fluidity in bacteria growing under low temperature conditions (Dieser et al. 2010, Jagannadham et al. 2000). However, detail studies to understand the effect of environmental stress on Arctic microbes and their adaptational strategies are lacking.

1.6 Aim and objectives of the present study

The aim of the present study was to analyze the composition of bacterial and phytoplankton communities in Kongsfjorden and to understand adaptational strategies of Arctic microbes to withstand extreme environmental conditions.

Objectives of the study were:

1. To understand bacterial diversity using conventional or molecular methods.

2. To understand the diversity of phytoplankton using conventional or molecular methods.

3. To understand the mutual co-operation between bacteria and phytoplankton in developing adaptational strategies to withstand extreme environmental conditions.

(33)

24 The studies carried out are presented in the following chapters:

Pelagic retrievable heterotrophic bacterial diversity of Kongsfjorden Estimation of bacterial diversity using cloning and next generation sequencing of 16S rDNA

Phytoplankton abundance, distribution and its influence on bacterial diversity in Kongsfjorden

Adaptational strategies of Arctic microbes to withstand extreme environmental conditions

(34)

Chapter 2 Pelagic retrievable heterotrophic bacterial

diversity of Kongsfjorden

(35)

26 2.1 Introduction

It is imperative to have a comprehensive understanding on the tree of life, which is dominated by microbes but still remain mostly uncharted because of the vastness in bacterial diversity. Bacteria have uncanny flexibility and sophistication, allowing it to react fast to the changes in its surrounding environment. Of all the forms of life on Earth, bacteria are the best candidates for negotiating the impact of change in climate over a long term. There may be several reasons for this, including the fact that they have high population size with simple genomes, short generation times and lower overall mutation rates. Many genes that contribute to these adaptations could also be transferred to higher eukaryotes associated closely in the microbial food web through horizontal gene transfer (Amin et al. 2012, Stewart 2013). Hence, long term assessment of bacterial diversity at both taxonomic and functional level is required to understand the basics of sustainability of life in an ever changing environment.

The Arctic is characterized by strong seasonality and the prokaryotes that grow in its coastal shelf environment have to contend with pronounced fluctuations in photosynthetic production, meltwater discharge and temperature. In oligotrophic environment, the prokaryotic heterotrophs can even be the dominant component of the total planktonic biomass (Biddanda et al. 2001, Cotner and Biddanda 2002, Garneau et al. 2008). Seasonal variations in physical and chemical properties of the Arctic water masses are likely to affect and even alter the marine ecosystem and its ecological impact in the same location over a year (Iversen and Seuthe 2011).

The Svalbard archipelago is surrounded by two different water masses: the warm Atlantic water (AW) on the western side and the cold Arctic water on the eastern side. Moored observatories in the fjord since 2001 has revealed the inter-

(36)

seasonal and inter-annual variability of AW inflow (Cottier et al. 2005). It is reported that the fjord normally has a maximum content of AW in late summer, while through the fall and winter, the fjord water masses get gradually replaced by fresher and cold Arctic water (Cottier et al. 2005). Kongsfjorden, a glacial fjord in the Svalbard archipelago, Spitzbergen (79^oN-12^oE), is a key site for Arctic biodiversity monitoring and is used as a model for climate change studies (Hop et al. 2002). The marine ecosystem in Kongsfjorden is well studied with respect to hydrography, mesozooplankton and higher trophic levels, while knowledge on the bacterial diversity is insufficient (Iversen and Seuthe 2011). Knowledge on the bacterial assemblage at a given point of time could convey several vital informations pertaining to the ecological aspects of the region. Several factors have been reported to have an impact on the composition of marine bacterial communities (Buchan et al. 2014).

Phytoplankton composition and thereby substrate composition and concentration seem to play important roles in forming bacterial communities (González et al. 2000, Ray et al. 2012). Heterotrophic bacteria can support the growth of phytoplankton by recycling nutrients and at the same time they also compete with phytoplankton for essential nutrients. Both healthy and dead (or dying) phytoplankton release organic compounds that are consumed by heterotrophic bacteria and these interactions vary with the bacterial species and the physiological status of the phytoplankton (Biddanda and Benner 1997). Indeed, bacterial abundance remain high immediately following the collapse of a bloom, as bacteria continue to use the organic matter that is released from phytoplankton. Despite the variation in phytoplankton composition and environmental conditions, a limited number of taxa are consistently found to dominate bloom-associated bacterial communities. The most frequent bacteria that are identified by 16S ribosomal RNA gene-based surveys are members of the classes

(37)

28 Flavobacteria, -Proteobacteria, including members of Rhodobacteraceae and γ- Proteobacteria (Buchan et al. 2005, Kirchman 2002). Our ability to understand the roles that individual bacterial species have in both the formation of blooms and their eventual collapse, will ultimately lead to a better understanding of the forces that control energy flow in the fjord as well as the cycling of compounds that influence climate change.

Culture-dependent and independent methods using oligonucleotide probes and/or cloning of environmental DNA have revealed exceptionally diverse bacterial communities thriving in Arctic fjord habitats (Zeng et al. 2009, Teske et al. 2011). In a study by Iversen and Seuthe (2011), the heterotrophic bacteria were found as the important contributors to both carbon production and biomass in Kongsfjorden.

Piquet et al. (2010, 2014) have monitored the pelagic microbial communities including diversity of non-culturable bacterial communities, phytoplankton and protists in Kongsfjorden. However, very few attempts (Srinivas et al. 2009, Prasad et al. 2014) have been made to understand the diversity of retrievable heterotrophic bacteria of this ecosystem.

The present study attempts to understand the taxonomy of retrievable heterotrophic bacteria in Kongsfjorden during the summer of 2011 and 2012.

Observation on phytoplankton blooming pattern using mooring data of Scottish Association of Marine Science (SAMS) for the year 2010 (Fig 2.1) indicated that sampling during these summer months maximized the possibility of high heterotrophic bacterial activity and diversity due to collapse of the spring bloom (June-July) and the secondary bloom (August-September). Further, the ecological significance of the various groups of bacteria present in the Kongsfjorden was also discussed.

(38)

Figure 2.1 Daily (converted from hourly) time series fluorescence data measured by fluorometer at 30 m depth in the SAMS mooring at the middle zone (near station 4 in Fig. 2.2) of Kongsfjorden during 2010 (Data courtesy: Scottish Asscoiation of Marine Science).

(39)

30 2.2 Materials and Methods

2.2.1 Sampling site

Kongsfjorden (Fig 2.2), is a polar fjord situated between 78°04'N-79°05'N and 11°03'E-13°03'E on the west coast of Spitsbergen, Svalbard Archipelago. The fjord is characterized by a weak tidal range (~2 m) strongly influenced by topography and the adjacent ocean. Western Svalbard coastal waters are influenced by the northernmost extension of the warm North Atlantic Current (Svendsen et al. 2002). At its inner end, the Kongsfjorden has mainly four glaciers viz. Kronebreen, Kongsbreen, Conwaybreen and Blomstrandbreen draining into it and providing the major source of fresh water. Thus, Kongsfjorden is under the influence of both meltwater of glacial origin as well as by mild temperatures mediated by the inflow of Atlantic water.

2.2.2 Hydrographic profiling and sampling of water column

During the year 2011, water samples were collected from 16 locations (Fig 2.2) at various depths from 5 m to a maximum of 100 m in the month of June, August and September. During the year 2012, water samples were collected from 10 locations (Fig 2.2) at various depths from 5 m to a maximum of 80 m in the month of June, July, August, September and October. Sampling was done following the observations with conductivity-temperature-depth (CTD) profiler (SBE 19 plus V2, Sea Bird Electronics, USA) equipped with a fluorescence sensor (Wet Labs, Philomath, USA).

The water masses were delineated based on the refined classification by Cottier et al.

(2005), i.e., Surface (S < 34.00 and T > 1^oC), Intermediate (S = 34.00 to 34.65 and T

> 1ôC), Transformed Atlantic (S > 34.65 and T = 1 to 3ôC) and Atlantic (S > 34.65 and T > 3ôC). Water samples were collected using Niskin sampler and transferred aseptically in sterile glass bottles (Duran Schott, Stafford, UK). The samples were immediately subjected to bacteriological analysis in the shore laboratory.

(40)

Figure 2.2 Bathymetric map of Kongsfjorden with sampling locations. Samples were collected from 16 stations in 2011 (∆, ). In 2012 samples were collected from 10 stations ( ) in the outer and inner fjord zone.

(41)

32 2.2.3 Total counts, culture and isolation of heterotrophic bacteria

Total cells in the water samples (10 ml each) collected from all the discrete depths were stained with 4', 6-diamidino-2-phenylindole (DAPI) (Hicks et al. 1992) before enumeration. Cells stained with DAPI were first fixed with filter sterilized particle free buffered formaldehyde (final concentration, 3.7%) to preserve the cell morphology and improve the staining efficiency. Stained cells were filtered onto 0.22 µ black polycarbonate membranes (Nucleopore, Whatman) and counted under an

Olympus epifluorescence microscope (BX 53) with the aid of Olympus U-MWU2 filter (Excitation 330-385 nm and Emission 420 nm). Counting was done using a Whipple grid with a 100X objective (Olympus UPLNFLN).

Water samples were spread plated using 100μl aliquot on pre cooled (4^oC) quarter strength Zobell Marine Agar (ZMA) and incubated at 4^oC for 1-2 weeks.

Colonies with unique morphological features were picked and sub-cultured to obtain pure cultures.

2.2.4 PCR amplification of the 16S rDNA, sequencing and phylogenetic analysis Cell biomass for DNA extraction was obtained by growing the culture in quarter strength Zobell marine broth (ZMB). Total bacterial genomic DNA was extracted using ChargeSwitch gDNA mini Bacteria kit (Invitrogen, CA, USA). DNA extracts were verified by gel electrophoresis on 1% agarose. The 16S rRNA gene was amplified using the universal bacterial 16S forward primer (27f) 5’-AGA GTT TGA TCM TGG CTC AG-3’ and bacterial 16S reverse primer (1492r) 5’-GGT TAC CTT GTT ACG ACT T-3’. Amplification of final reaction mixture of 50 µl containing 0.3 pM/μl of each primer, 1.5 mM MgCl₂, 200 µM dNTPs and 2.5 U Taq DNA polymerase (Invitrogen, CA, USA) was carried out in a thermocycler (BioRad CFX 96) with the following program: initial denaturation at 94^oC for 2 min followed by 29

(42)

cycles of 30 s at 95ôC, 30 s at 45ôC and 2 min at 72ôC, with a final extension of 10 min at 72ôC. Amplification was confirmed by electrophoresis in 1% agarose gel. The amplicons were eluted and purified using ChargeSwitch Pro PCR cleanup kit (Invitrogen, CA, USA). The eluted fragment was sequenced using an automated DNA sequencing system (Applied Biosystem, CA, USA). The 16S rDNA sequence of the isolate (~1200 nt) was compared with type strains belonging to the same phylogenetic group obtained from Ribosomal Database Project (http://rdp.cme.msu.edu/) (Cole et al. 2009). CLUSTAL W software was used to align the 16S rRNA gene sequences and phylogenetic trees were constructed using maximum likelihood and Neighbour- joining methods of tree making algorithms in MEGA Version 7 (Kumar et al. 2016).

Isolates having >97% 16S rRNA gene sequence similarity to the type strain were considered as a phylotype. Percentage abundance of the representative species of each phylotype was calculated out of the total number of bacterial species retrieved during each month.

2.3 Result

2.3.1 Hydrographic properties of Kongsfjorden

Details of the geographic location of the stations selected for sampling during summer 2011 and summer 2012 are given in the Fig 2.2. During summer 2011, the water column was warmer (4^oC) as compared to August where temperature decreased gradually with depth, except at the surface which was warmer towards the glacier vicinity as depicted in Fig 2.3. Water temperature was uniform (4^oC) throughout the column during September. Salinity ranged from 31-35 psu during the summer of 2011 (Fig 2.3). Autotrophic biomass (fluorescence) was restricted to shallower depths in September and was higher during June and August as compared to that in September (Fig 2.4).

(43)

34 Figure 2.3 Water masses in Kongsfjorden in summer of 2011 and 2012. Shades and contour lines illustrate variation in temperature (^oC) and salinity, respectively in the fjord.

(44)

Figure 2.4 Fluorescence (RFU) profile in Kongsfjorden in summer of 2011 and 2012.

(45)

36 The autotrophic abundance was confined to the vicinity of Kongsbreen glacier during August 2011, whereas during June and September 2011 it was more towards the mouth of the fjord (Fig 2.4). The average volume of transformed Atlantic water inside Kongsfjorden was 7.9 km³, 27.2 km³ and 12 km³ during June, August and September 2011 respectively.

During the summer of 2012, warmer surface water (SW) (max temperature 7^oC) was observed confined to the inner fjord in June (Fig 2.3). The Intermediate water (IW) and Transformed Atlantic water (TAW) reached till the surface in the central and outer fjord respectively, which co-occurred with the increase in the abundance of autotrophic biomass (fluorescence) (Figs. 2.3 and 2.4). In July, warmer water was present in the outer fjord and cooler water in the inner fjord. The upper 60 m contained SW with a thin layer of IW below it, TAW was present below 75 m in both outer and inner fjord (Fig 2.3). The autotrophic biomass was higher towards the outer fjord in July, while in August it was confined to the shallower depths of the fjord (Fig 2.4).The SW layer deepened towards the inner fjord (~ 80 m) while it shoaled towards the outer fjord (~ 50 m) by August. The IW and TAW also showed the same shoaling feature in the outer fjord. By September, the thickness of SW layer decreased compared to the previous month with a substantial increase in the thickness of IW layer (~15 to 125 m in the inner fjord and ~ 45 to 100 m in the outer fjord) and the Atlantic water (AW) was observed below IW. A well defined watermass structure was observed in October with a sharp temperature inversion (cooler waters at the surface and warmer waters in deeper depths); the SW in the upper 50 m, IW between

~50 and 80 m and AW below 80 m along with concomitant decrease in the autotrophic biomass (Figs 2.3 and 2.4).

(46)

2.3.2 Retrievable heterotrophic bacterial diversity in the summer of 2011

In summer 2011, total bacterial count during June-September ranged from 10⁷-10⁹cells L^-1. During June and August, the cell counts were in the range of 10⁷-10⁸ cells L^-1, while in September the bacterial load was higher (10⁷-10⁹cells L^-1). The lowest bacterial count (2.3 x 10⁷ cells L^-1) was recorded in the vicinity of Kongsbreen glacier during June while during August the abundance was higher (10⁸ cells L^-1) in the same region. The highest bacterial load (2.7 x 10⁹ cells L^-1) was observed during September towards mouth of the Kongsfjorden. Retrievable bacterial load during June ranged from 10³– 10⁶CFU L^-1, while during August and September it was in the range of 10⁴-10⁶CFU L^-1. A total of 117, 332 and 107 bacterial isolates were recovered in June, August and September respectively from the water samples collected from various depths. Isolates having >97% 16S rRNA gene sequence similarity to the type strain were considered as a phylotype. Using representative phylotypes, all the bacterial isolates recovered from water samples of June, August and September 2011 were categorized into 6, 22 and 12 phylotypes respectively and the percentage abundance of each species is illustrated in Figure 2.5. The 16S rRNA gene sequences of the strains isolated during summer 2011 were deposited with accession number HE800807-HE800839, HE815462-HE815463, HG795014- HG795016, HF569156-HF569159 and HF913434-HF913440 in the EMBL database.

Phylogenetic trees illustrating the evolutionary relationships of the bacterial isolates are shown in Figs 2.6A-2.6C.

(47)

38

Figure 2.5 Percentage composition and abundance of heterotrophic bacterial species belonging to the phylumFirmicutes (),Actinobacteria (),α-Proteobacteria(),γ-Proteobacteria() andBacteroidetes() recovered from Kongsfjorden water samples during June, August and September 2011.

(48)

Figure 2.6 Phylogenetic tree based on 16S rRNA gene sequences showing the relationship of bacterial strains (prefix

collected during June (A), August (B) and September (C) 2011 from Kongsfjorden, with their nearest phylogenetic type strains. Accession number of 16s rRNA gene sequence of the strains is denoted next to its nam

values. The bar represents 0.02 substitutions per alignment position.

Phylogenetic tree based on 16S rRNA gene sequences showing the relationship of bacterial strains (prefix with Kongs), obtained from the water samples collected during June (A), August (B) and September (C) 2011 from Kongsfjorden, with their nearest phylogenetic type strains. Accession number of 16s rRNA gene sequence of the strains is denoted next to its name. Numbers at nodes are bootstrap values. The bar represents 0.02 substitutions per alignment position.

Phylogenetic tree based on 16S rRNA gene sequences showing the with Kongs), obtained from the water samples collected during June (A), August (B) and September (C) 2011 from Kongsfjorden, with their nearest phylogenetic type strains. Accession number of 16s rRNA gene e. Numbers at nodes are bootstrap values. The bar represents 0.02 substitutions per alignment position.

(49)

Figure 2.6 (continued)

40

(50)

Figure 2.6 (continued)

(51)

42 Two bacterial strains, Paenibacillus sp. (Kongs-47, HG795014) and Altererythrobacter sp. (Kongs-48, HG795015) recovered from water samples of August and one strain, Brevundimonas sp. (Kongs-49, HG795016), recovered from September could not be assigned species name because of lower percentage (≤97%) of sequence similarity with the type strain and hence could be new species.

The bacterial isolates retrieved during the analyses belonged to five phyla, the Firmicutes, Actinobacteria, -Proteobacteria, γ-Proteobacteria and Bacteroidetes (Fig 2.5). Phylum Bacteroidetes was the most dominant (40%) during June and was entirely represented by the species Leeuwenhoekiella aequorea. It was followed by - Proteobacteria (27%), Actinobacteria (17%), γ-Proteobacteria (9%) and Firmicutes (7%). Sphingopyxis baekryungensis (25%) and Erythrobacter citreus (2%) constituted -Proteobacterial population while Agrococcus baldri, Psychrobacter nivimaris and Bacillus subtilis represented the phyla Actinobacteria, γ-Proteobacteria and Firmicutes, respectively.

Retrievable bacterial diversity was maximum during August which was represented by 22 phylotypes including two unclassified species (Paenibacillus sp.

and Altererythrobacter sp.). The population was dominated by Actinobacteria (39%) and γ-Proteobacteria (23%), while Firmicutes (14%), -Proteobacteria (12%) and Bacteoidetes (12%) shared similar abundance. Abundance of Firmicutes was almost twice as compared to June and was mainly constituted by the genus Staphylococcus which was represented by S. haemolyticus and S. pasteuri (5% each). A well-known phytopathogenic bacterium Rhodococcus fascians constituted most (32%) of the Actinobacterial population which increased by 22% as compared to June. A total of five genera (Brevundimonas, Erythrobacter, Phenylobacterium, Sphingopyxis and Altererythrobacter) were recovered within the class -Proteobacteria where

(52)

Phenylobacterium falsum was major contributor (4%). However, the total abundance of this phylum decreased by 15% as compared to June. γ-Proteobacteria was represented by four genera and seven species. The major fraction was constituted by the genus Psychrobacter (17%) which was represented by P. fozii (2%), P. glacincola (6%), P. nivimaris (4%) and P. okhotskensis (4%). Other genera included Acinetobacter (2%), Halomonas (4%) and Janibacter (1%). As observed in June, Leeuwenhoekiella aequorea was the only species recovered under the phylum Bacteroidetes, however its abundance decreased by 28% during August.

Proteobacteria formed the major phylum in September with -Proteobacteria (31%) and γ-Proteobacteria (33%) being dominant, followed by Actinobacteria (21%), Bacteroidetes (12%) and Firmicutes (3%). Halomonas titanicae (16%) represented the major fraction of γ-Proteobacteria while Brevundimonas variabilis (15%) was dominant -Proteobacteria. Firmicutes was represented by two bacterial species, Bacillus idriensis (2%) and Paenibacillus urinalis (1%). Population of Actinobacteria was reduced by 18% in September as compared to that in August and it was represented by Nocardiodes basaltis (12%) and Rhodococcus fascians (9%).

Leeuwenhoekiella aequorea was the lone species representing Bacteroidetes even in September with almost equal abundance as in August.

2.3.2 Retrievable heterotrophic bacterial diversity in the summer of 2012

In summer 2012, total bacterial counts ranged from 10^7-8 cells L^-1 in both outer and inner fjord (Table 2.1). Bacterial abundance was the lowest during October (~10⁷ cells L^-1), in both inner and outer fjord as compared to the other months (~10⁸ cells L^-

1). Heterotrophic bacterial counts were in the range of 10⁶ CFU L^-1from June to October in the fjord water.