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Biohydrography of eukaryotic microorganisms in a cold-ocean ecosystem Hamilton, Andrew Kent 2006

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B I O H Y D R O G R A P H Y OF E U K A R Y O T I C  MICROORGANISMS  IN A COLD-OCEAN ECOSYSTEM  by ANDREW KENT HAMILTON B . S c , University o f Alberta, 1996  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T OF T H E FOR THE D E G R E E OF M A S T E R OF SCIENCE in T H E F A C U L T Y OF G R A D U A T E STUDIES (Oceanography)  THE UNIVERSITY OF BRITISH C O L U M B I A December 2006 © Andrew Kent Hamilton, 2006  REQUIREMENTS  Abstract Picoeukaryotes (0.2 - 3 urn) dominate the planktonic biomass o f the Arctic Ocean for most o f the year, strongly influencing primary production and carbon and nutrient cycles. Despite their importance i n this cold-ocean ecosystem, little is known about factors controlling picoeukaryote diversity and distribution. Picoeukaryote community composition and distribution i n relation to the physical characteristics o f the water column was investigated, and we introduce the term biohydrography to describe studies of this type. Samples were collected at 6 depths across 9 stations i n the North Water Polynya ( N O W ) , a large recurring polynya in northern Baffin Bay. The hydrography o f the N O W was determined from 52 C T D casts, and several different water masses were identified by their distinct temperature and salinity characteristics. The circulation o f water masses i n the region was reported and evidence o f interleaving and mixing was found along the frontal zone where water masses converged. Picoeukaryote community composition was determined from denaturing gradient gel electrophoresis ( D G G E ) profiles; samples showed 42 distinct band types or operational taxonomic units (OTUs) overall, with 8 to 22 O T U s per sample, and considerable variation i n O T U composition among samples. Similarity analysis o f D G G E profiles showed assemblages from different depths at the same station shared as little as 6% similarity, whereas assemblages from locations hundreds o f kilometers apart shared as much as 90% similarity. Similarity among picoeukaryote communities was most closely related to the origin o f the water mass sampled; for example, Arctic derived waters showed a unique and very different community than those o f Atlantic origin. Separate community assemblages were also identified along the frontal zone, suggesting water masses maintain their signature community until further physical mixing disperses the organisms. Matching o f excised D G G E band sequences identified organisms from taxonomic groups Acantharea, Cercozoa, Chrysophyceae, Bacillariophyceae, Dinophyceae, Prasinophyceae, and Prymnesiophyceae; however, many sequences matched uncultured organisms, whose function i n the environment is unknown, highlighting the need for both culture and ecosystem-based studies. Canonical correspondence analysis ( C C A ) revealed that  latitude, depth, chlorophyll levels, and community size structure were important factors that partially explained 42.6% o f the variability i n assemblages, indicating contemporary environmental conditions influence picoeukaryote community structure. A detailed understanding o f water mass distribution, circulation patterns, and physical mixing processes was required to further explain assemblage relatedness among sites, revealing the importance o f investigating hydrographic processes i n studies o f picoeukaryote community dynamics.  Table of Contents Abstract  ii  Table o f Contents  iv  List o f Tables  •  List o f Figures Acknowledgements Dedication  v vi viii ix  Co-authorship Statement  x  1.  1  Introduction and Background 1.1 1.2 1.3 1.4 1.5  2.  Biohydrography o f picoeukaryotes i n the North Water Polynya 2.1 2.2 2.3 2.4 2.5  3.  Microbial Biohydrography Analysis o f picoeukaryote communities The North Water P o l y n y a Objectives and Hypotheses References  Introduction Material and Methods Results Discussion References  Conclusion 3.1 3.2  Picoeukaryote biohydrography References  Appendix A  1 2 5 6 7 10 10 12 17 21 41 45 45 49 50  iv  List of Tables Table 2.1  Picoeukaryote diversity indices and total Chla concentrations  30  Table 2.2  Sequence matches of excised DGGE bands  31  List of Figures Figure 2.1 Shaded bathymetric map o f the North Water Polynya showing locations o f C T D casts, sample stations, and the average surface circulation (adapted from M e l l i n g et al. 2001). Lines 3 (West to East), 6 (West to East), and 7 (North to South) indicate section transects shown i n F i g . 2.2 and Fig. 2.5 32 Figure 2.2 Transects o f potential temperature and potential density along lines 3, 6, and 7 (see Fig. 2.1. for line locations). Stations are labeled and casts indicated 33 Figure 2.3 0-S diagram indicating water masses present in the North Water Polynya; Arctic surface water (AS), Arctic water ( A W ) , Baffin Bay surface water ( B B S ) , wintertime convection water ( W C W ) , and Atlantic deep water (AtD). Black dots mark 9-S characteristics o f water bottle samples. Black lines are two end member casts that show all water masses present with little mixing. Dotted grey lines show 0-S profiles from all casts 34 Figure 2.4 Relative S i versus salinity diagram for station profiles from the North Water Polynya ( N O W ) . S i is calculated as per Eq. (8) i n Tremblay et al. (2002). H i g h S i values indicate Silicate R i c h Arctic Water ( S R A W ) ; low values indicate Baffin Bay Water ( B B W ) 35 e x  e x  e x  Figure 2.5 Transects o f chlorophyll fluorescence and potential density along lines 3, 6, and 7 (see Fig. 2.1 for line locations). Stations are labeled and casts indicated 36 Figure 2.6 Station profiles showing temperature, salinity, and chlorophyll a concentration (estimated from fluorescence). Sample names are indicated at their corresponding depth 37 Figure 2.7 Histograms showing changes i n picoeukaryote community structure at each station. The intensity o f each D G G E band type, or operational taxonomic unit ( O T U ) , relative to the total band intensity i n each sample is shown. Samples are i n order down the water column at each station. Refer to Figure 2.5 for corresponding depth 38 Figure 2.8 Dendrogram showing similarity o f picoeukaryote assemblages from the North Water polynya. Dendrogram was derived from D G G E intensity band patterns by Agglomerative Hierarchical Clustering ( A H C ) using a Pearson Correlation Coefficient with Unweighted Pair Group Method with Arithmetic mean ( U P G M A ) algorithm. Dotted line indicates cluster branch cutoff similarity o f 50%. W C W wintertime convection water; W G C - West Greenland Current; B B W - Baffin Bay Water 39 Figure 2.9 ( A ) Canonical Correspondence Analysis ( C C A ) ordination diagram relating environmental variables to picoeukaryote assemblages. Each sample represents a community assemblage derived from denaturing gradient gel electrophoresis ( D G G E ) band intensity patterns. (B) C C A ordination with operational taxonomic  vi  units (OTUs). Both C C A s display 18.3% o f the inertia (weighted variance) i n O T U intensity and 42.6% o f the variance i n the weighted averages with respect to the environmental variables. For both diagrams the eigenvalues o f axis F I and F2 are 0.272 and 0.158, respectively; the eigenvalue o f F3 (not displayed) is 0.138 40 Appendix A  Composite denaturing gradient gel electrophoresis image  50  vii  Ackn owledgcm ents This project was made possible with financial support from the Natural Sciences and Engineering Research Council o f Canada and the ArcticNet Network o f Centres o f Excellence o f Canada. The author gratefully acknowledges the assistance o f the crew and scientific personnel aboard the icebreaker C G G S Amundsen during the sampling period o f August 2005. The work reported here is a contribution to ArcticNet under the overall direction o f L . Fortier. The project would not have been possible without the tireless assistance during sample collection and analysis o f M . E . R a i l , E . Pedneault, M . Potvin, R. Terrado, and P. Galand. I am indebted to Y . Gratton, J. E . Tremblay, B . Williams, R. Pawlowicz and E . Carmack for insight into the world o f physical and chemical oceanography at high latitudes. I must extend a heartfelt thank you to my co-supervisor Connie Lovejoy for her support and guidance i n the field and laboratory, during the writing o f this manuscript, and for opening the window into the world o f picoeukaryotes. Thank you also goes to my co-supervisor Grant Ingram for taking me on as a student, allowing me to go wherever this project took me, and giving me the freedom to learn.  vm  Dedication  For the northern lights that have seen strange sights...  ix  Co-authorship Statement I w i l l be the primary author o f the manuscript titled 'Biohydrography picoeukaryotes  of  in the North Water Polynya' that results from Chapter 2.1 devised the  sampling scheme, collected and processed the samples and wrote the manuscript. Samples were collected from the Canadian research icebreaker Amundsen during August 2005. Processing o f C T D data was performed by Yves Gratton's group at the Institut National de la Recherche Scientifique (INRS), and nutrient data was generously provided by the laboratory o f Jean-Eric Tremblay at Universite Laval. The second author on the manuscript is Connie Lovejoy, the primary investigator o f the laboratory at Universite Laval where I conducted my research. She provided the workplace, materials, training i n molecular analysis, and helpful guidance. The final author o f the manuscript is Grant Ingram. H e is the principal investigator o f the physical oceanographic group at University o f British Columbia where I analyzed the data and wrote the manuscript. Connie Lovejoy and Grant Ingram provided impetus and funding for this work. Both co-authors edited the manuscript and they, along with the members o f their respective laboratories, provided insightful discussion on the project. I certify that the above statements about authorship are correct.  1.  Introduction and Background 1.1  Microbial Biohydrography  Life on earth is overwhelmingly microbial, both in biomass and abundance (Nee 2004). Marine microbes are responsible for half of the Earth's primary productivity and are a fundamental component in global carbon and nutrient cycles (Arrigo 2005). Understanding what controls marine microbial distribution and diversity has been highlighted as one of the major challenges facing contemporary oceanographers (Arrigo 2005; Martiny et al. 2006). Growing evidence suggests that microbial composition affects ecosystem processes, and even under similar conditions, communities from different environments might function differently (Martiny et al. 2006). Understanding the diversity and distribution of marine microbes is therefore fundamental to the understanding of carbon and nutrient cycling, and autotrophic and heterotrophic productivity in the world's oceans. Biogeography is the study of the distribution of biodiversity over space and time. Due to the very small size, extremely high abundance and potentially unrestricted dispersal of microorganisms, there is still some debate whether they exhibit any biogeographic patterns (Finlay 2002). If they do, which a large body of recent work supports, there is no conclusion whether they follow the same biogeographic patterns of macroorganisms (Martiny et al. 2006). Environmental microbiology has only recently moved beyond the stage of natural history characterization and into the application and development of ecological and evolutionary theory. Very basic hypotheses of microbial diversity and distribution are only now being tested, enabled to a large degree by advancement in community survey techniques. The basic hypotheses being tested ask fundamental questions and were summarized by Martiny et al. (2006). Are microbial communities non-randomly distributed, (i.e. do they exhibit biogeography)? If so, does distribution reflect the influence of contemporary environmental variation (i.e. the Baas Becking hypothesis (Baas Becking 1934) for microbial taxa which states that "everything is everywhere, but the environment selects")? Or is spatial variation due to historical events (i.e. dispersal limitation or past environmental conditions that led to genetic divergence)? Or finally, is distribution a reflection of both past events and contemporary environmental factors (i.e. similar biogeographic processes as macroorganisms)? 1  In the marine environment, biogeographic provinces have been distinguished for much o f the open ocean (Longhurst 1998). The specification o f ocean provinces by Longhurst was primarily based on the seasonal evolution o f the surface chlorophyll field determined from remote sensing. While a major advancement i n marine biogeography, Longhurst acknowledged the provinces were essentially a two-dimensional description o f surface ecosystems, limited by the ability o f satellite sensors to penetrate into the interior o f the ocean. To move beyond the two-dimensional notion o f biogeography, with terrestrial origins, and into the ocean realm, a three-dimensional approach is required. In the fluid ocean, where one oceanic province and its suite o f biota may overlie an entirely different province, gradients across both horizontal and vertical planes are equally important. This is particularly apparent for marine microorganisms, whose dispersal is largely influenced by hydrography, the physical characteristics o f the water (circulation, tides, mixing, pycnoclines, fronts, eddies, etc.). With this i n mind, the study o f the distribution o f biodiversity i n the dynamic marine environment is termed "biohydrography". Justification could also be made for using the term "hydrobiogeography", but for the purposes o f this thesis the term biohydrography is preferred, and w i l l be used throughout.  1.2  Analysis of picoeukaryote communities  A s a group, marine microbes represent enormous biodiversity, dominating all three domains o f life, the prokaryotic (lacking a nucleus) Archaea and Bacteria, and the Eukarya (having a membrane bound nucleus). Picoeukaryotes, between 0.2 and 3 um i n diameter (Diez et al. 2001a; Massana et al. 2004), both autotrophic and heterotrophic, are the most abundant eukaryotes on Earth. Despite being found i n concentrations between Wto  10 cells per m l i n the world's oceans (Throndsen and Kristiansen 1991; L i 1994), J  and having significant roles i n marine biogeochemical cycles their phylogenetic diversity and distribution have only recently been characterized (Lopez-Garcia 2001, Diez et al. 2004, Massana et al. 2004a; Lovejoy et al. 2006). The capacity o f conventional microscopy to identify these small cells, many o f which have relatively few morphologically distinct features, is limited. Characterizing the phylogenetic diversity and environmental distribution o f marine picoeukaryotes has therefore been greatly facilitated by the development o f molecular techniques.  2  Description o f microbial biodiversity, including the definition o f the three domains o f life, is largely based on comparative analysis o f ribosomal R N A ( r R N A ) sequences (Woese et al. 1990). Ribosomal R N A , found i n all cellular life forms, is comprised o f highly conserved sequence regions interspersed with more variable ones. Conservative portions have sequences that are maintained within a phylogenetically related group o f organisms. Each domain o f life, and even each species, has a unique r R N A signature. Sequence variation i n r R N A has been exploited to determine the genetic diversity o f microbial communities in environmental samples. A s well, the development o f sequence-specific primers allows the amplification and identification o f targeted organisms. Cloning and sequencing o f the eukaryotic-specific small-subunit r R N A (18S r R N A ) genes have revealed the extraordinary diversity o f picoeukaryote communities i n the world's oceans (Lopez-Garcia 2001; Massana 20046). However, to address the fundamental questions o f picoeukaryote diversity and distribution implicit i n biohydrography requires the analysis o f more samples than is practical using a cloning and sequencing approach. These issues can be resolved using community fingerprinting methods, such as denaturing gradient gel electrophoresis ( D G G E ) . D G G E uses the specific melting points o f double stranded D N A to separate different sequences. The melting point (Tm) o f a D N A strand, the point at which 50 % o f the two strands are denatured, is dependent on the sequence o f its nucleotide base-pairs, particularly the proportion o f guanine-cytosine ( G C ) triple H-bond pairs. For D N A fragments o f a particular length, different sequences have different melting points. In general T m i n a solution containing a salt can be estimated as: T m = 81.5 + 16.6(log M [Na+]) + 0.41(%G+C)-0.63(% f) - 600/L Where % f is the percentage o f formamide (HCONH2) which is a denaturant and has the effect o f lowering the T m and L is the length i n base pairs o f the strand o f interest. The migration o f a partially denatured D N A fragment is greatly reduced in a polyacrylamide gel compared to the helical form o f the molecule. A gel with a linear gradient o f increasing concentrations o f denaturant (e.g. urea and formamide) w i l l therefore separate sequences to different positions based on their melting point.  3  Prior to D G G E analysis, extracted D N A from the environment is amplified by polymerase chain reaction ( P C R ) using target-specific primers. In this study, primers targeting the domain Eukarya are based on two conserved regions o f 18S r R N A genes. The primers are also designed to add a G C - r i c h clamp to the end o f the D N A fragment, ensuring the helix does not fully denature in the D G G E gel. The products o f P C R amplification are D N A fragments, all o f the same length, representing each o f the eukaryotic taxa present i n the original environmental sample. These fragments are separated by D G G E , producing a signature band pattern, or community fingerprint, that is dependent upon the taxa present. D G G E does have limitations. A s with all PCR-based techniques, there may be biases i n template-to-amplicon ratios so the intensity o f a band may not reflect the abundance o f the organism in the environment (Suzuki and Giovannoni 1996; Polz and Cavannaugh 1998; Suzuki et al. 1998). A heteroduplex D N A molecule can be formed from two different P C R products during re-annealing, resulting i n multiple bands and an overestimation o f community constituents (Myers et al. 1987). Differences i n the extractability o f D N A among cells (Polz et al. 1999) may cause some band types, called operational taxonomic units (OTUs), to be underrepresented i n D G G E fingerprints. Depending on the resolution o f the denaturant gradient, sequences with minor differences in base-pairs may migrate to the same position (Muyzer et al. 1992). Conversely, a single species may have slight sequence variants and appear as separate bands. Despite these limitations, D G G E has been used successfully to analyze the genetic diversity o f complex microbial populations, able to identify constituents that represent only 1% o f the total population (Muyzer et al. 1992). D G G E has been used to describe the diversity o f prokaryotes i n a variety o f marine environments; the Arctic Ocean (Ferrari and Hollibaugh 1999); along estuarine gradients (Crump et al. 2004; Henriques et al. 2006); and at hydrothermal vents (Muyzer et al. 1995). The application o f D G G E to describe marine picoeukaryotic diversity was first assessed by Diez et al. (200lot), where it was demonstrated to be as revealing as for prokaryotic communities. The utility o f D G G E for evaluation o f spatial distributions o f picoeukaryote assemblages was further demonstrated by Diez et al. (2004) across hyorographic fronts i n the Southern Ocean. That study determined that picoeukaryote assemblages grouped into distinct clusters that were generally consistent with the hydrography o f the region. In this study, D G G E 4  fingerprinting is used to assess picoeukaryote assemblage distribution i n another cold ocean ecosystem, one o f the largest polynyas i n the Arctic.  1.3  The North Water Polynya  The North Water Polynya ( N O W ) is an area o f recurring open water or reduced sea-ice cover i n northern Baffin Bay, between Greenland and Ellesmere Island. The occurrence o f the polynya has been documented since 1616, when W i l l i a m Baffin navigated through heavy sea-ice along the west coast o f Greenland until reaching a large area o f open water (Dunbar and Dunbar 1972). Reduced ice coverage maintains conditions for enhanced primary production over longer periods during the year than surrounding areas. Productive phytoplankton communities support large herbivore populations, concentrating marine mammal and bird populations in a small region. Polynyas are regarded as "oases" i n the ice and as local "hotspots" for biological production and biodiversity (Stirling 1997). The abundant and predictable populations o f wildlife i n the region have attracted Inuit hunters for hundreds, even thousands o f years, and later brought commercial whalers, who called the area the "North Water". The N O W is thought to be one o f the most biologically productive regions i n the Arctic (Dunbar and Dunbar 1972). Scientific interest i n the region increased as the need for assessing environmental change i n the Arctic became apparent. Research cumulated during the International North Water Polynya Study (1997-1999), a focused effort to understand the processes responsible for maintaining the polynya and contributing to conditions favorable for biological production. That project, along with a number o f previous studies, provided an excellent overall understanding o f the N O W and a strong basis for future work i n the region. The research presented here is greatly enhanced by the findings o f these earlier scientists. This project is part o f ArcticNet, a Network o f Centres o f Excellence o f Canada that studies the impacts o f climate change i n the coastal Canadian Arctic. The Earth's climate is warming and global climate models predict the effects w i l l come earliest and most severely at Arctic latitudes ( A C I A 2005). T o predict how these changes w i l l affect northern ecosystems, and the ability o f Arctic biota to survive and adapt, depends on having knowledge o f the current state o f the environment. Picoeukaryotes have been 5  found to dominate the photosynthetic biomass o f cold Arctic waters for most o f the year (Lee and Whitledge 2002; Sherr et al. 2003; Lovejoy et al. i n press) and small heterotrophic populations are both abundant and diverse i n these perennially cold waters (Lovejoy et al. 2006). Despite their obvious importance i n the Arctic marine ecosystem, very little is known about the distribution and diversity o f eukaryotic microorganisms in this region.  1.4  Objectives and Hypotheses  The analysis presented here is the first report on picoeukaryote diversity and distribution i n relation to hydrography in the North Water P o l y n y a The objectives o f the research were: 1) to describe the current hyorographic conditions i n the N O W , focusing on water masses, circulation, and regions o f mixing; 2) to describe the diversity and distribution o f picoeukaryotes i n the N O W ; and 3) to determine i f picoeukaryote diversity and distribution were related to hydrography. Based upon previous work on picoeukaryote distribution i n the Southern Ocean (Diez et al. 2004), and initial studies o f general picoplankton distribution i n the surface waters o f the N O W (Mostajir et al. 2001), it was hypothesized that particular water masses would harbor distinct picoeukaryote communities.  6  1.5  References  A C I A . 2005. Arctic climate impact assessment. Cambridge University Press, Cambridge, United Kingdom. Arrigo, K . R . 2005. Marine microorganisms and global nutrient cycles. Nature 437: 349355. Baas Becking, L . G . M . 1934. Geobiologie ofinleiding and Zoon, The Hague, Netherlands.  tot de mUieukunde. V a n Stockum  Diez, B . , R. Massana, M . Estrada, and C . Pedros-Alio. 2004. Distribution o f eukaryotic picoplankton assemblages across hydrographic fronts i n the Southern Ocean, studied by denaturing gradient gel electrophoresis. Limnology and Oceanography 49: 1022-1034. Diez, B . , C . Pedros-Alio, T. L . Marsh, and R. Massana. 2001a. 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Cunha, and A . Correia. 2006. Seasonal and spatial variability o f free-living bacterial commumty composition along an estuarine gradient ( R i a de Aveiro, Portugal). Estuarine Coastal and Shelf Science 68: 139-148. Lee, S. H , and T. R. Whitledge. 2005. Primary and new production i n the deep Canada Basin during summer 2002. Polar Biology 28: 190-197. L i , W . K . W . 1994. Primary Production o f Prochlorophytes, Cyanobacteria, and Eukaryotic Ultraphytoplankton - Measurements from F l o w Cytometric Sorting. Limnology and Oceanography 39: 169-175.  7  Longhurst, A . 1998. Ecological geography o f the sea. Academic Press, London, United Kingdom. Lopez-Garcia, P., F. Rodriguez-Valera, C . Pedros-Alio, and D . Moreira. 2001. Unexpected diversity o f small eukaryotes i n deep-sea Antarctic plankton. Nature 409: 603-607. Lovejoy, C , E . C . Carmack, L . Legendre, and N . M . Price. 2002a. 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Muyzer, G , E . C . Dewaal, and A . G . Uitterlinden. 1993. Profiling o f Complex MicrobialPopulations by Denaturing Gradient Gel-Electrophoresis Analysis o f Polymerase Chain Reaction-Amplified Genes-Coding for 16s R i b o s o m a l - R N A . Applied and Environmental Microbiology 59: 695-700. Muyzer, G . , and K . Smalla. 1998. Application o f denaturing gradient gel electrophoresis ( D G G E ) and temperature gradient gel electrophoresis ( T G G E ) i n microbial ecology. Antonie V a n Leeuwenhoek International Journal o f General and 8  Molecular Microbiology 73: 127-141. Myers, R. M . , T. Maniatis and L . S. Lerman. 1987. Detection and localization o f single base changes by denaturing gradient gel electrophoresis. Methods i n Enzymology 155: 501-527. Nee, S. 2004. M o r e than meets the eye - Earth's real biodiversity is invisible, whether we like it or not. Nature 429: 804-805. Polz, M . F., and C . M . Cavanaugh. 1998. Bias i n template-to-product ratios in multitemplate P C R . Applied and Environmental Microbiology 64: 3724-3730. Polz, M . F., C . Harbison, and C . M . Cavanaugh. 1999. Diversity and heterogeneity o f epibiotic bacterial communities on the marine nematode Eubostrichus dianae. Applied and Environmental Microbiology 65: 4271-4275. Sherr, E . B . , B . F. Sherr, P. A . Wheeler, and K . Thompson. 2003. Temporal and spatial variation i n stocks o f autotrophic and heterotrophic microbes i n the upper water column o f the central Arctic Ocean. Deep-Sea Research Part I-Oceanographic Research Papers 50: 557-571. Stirling, I. 1997. The importance o f polynyas, ice edges, and leads to marine mammals and birds. Journal o f Marine Systems 10: 9-21. Suzuki, M . T., and S. J. Giovannoni. 1996. Bias caused by template annealing i n the amplification o f mixtures o f 16S r R N A genes by P C R . A p p l i e d and Environmental Microbiology 62: 625-630. Suzuki, M . , M . S. Rappe, and S. J. Giovannoni. 1998. Kinetic bias i n estimates o f coastal picoplankton community structure obtained by measurements o f small-subunit r R N A gene P C R amplicon length heterogeneity. Applied and Environmental Microbiology 64: 4522-4529. Throndsen, J., and S. Kristiansen. 1991. Micromonas-Pusilla (Prasinophyceae) as Part o f Picoplankton and Nanoplankton Communities o f the Barents Sea. Polar Research 10: 201-207. Woese, C . R., O. Kandler, and M . L . Wheelis. 1990. Towards a Natural System o f Organisms - Proposal for the Domains Archaea, Bacteria, and Eucarya. Proceedings o f the National Academy o f Sciences o f the United States o f America 87: 4576-4579.  9  2.  Biohydrography of picoeukaryotes in the North Water Polynya  1  2.1  Introduction  Microscopic life, both eukaryotic and prokaryotic, is responsible for about half o f global primary productivity and most o f the nutrient cycling on the planet. The importance o f microbial community structure on the ultimate fate o f carbon and energy transfer in the ocean has been highlighted recently (Arrigo 2005; L e Quere et al. 2005). Over much o f the world's oceans, picoplankton (0.2- 3 urn; Massana et al. 2004) dominate both photosynthetic and heterotrophic processes. Recently, environmental surveys probing for the eukaryotic 18S r R N A gene have revealed unanticipated diversity o f both small phototrophic and heterotrophic eukaryotes, and that their depth distribution in marine systems is largely unknown (Lopez-Garcia et al. 2001; Massana et al. 2002, 2004a). Picoeukaryotes dominate the photosynthetic biomass o f cold Arctic waters for most o f the year (Lee and Whitledge 2002; Sherr et al. 2003; Lovejoy et al. i n press) and small heterotrophic populations are both abundant and diverse i n these perennially cold waters (Lovejoy et al. 2006). Molecular techniques and community fingerprinting methods, such as denaturing gradient gel electrophoresis ( D G G E ) , have been valuable tools to describe the spatial and temporal variation o f entire assemblages i n other regions of the world's oceans (Diez et al. 2001a). The horizontal and vertical distribution o f picoeukaryote communities in the Arctic, and the environmental factors responsible for their distribution, has not been previously investigated. Picoeukaryotes are an ideal study group for investigating marine species patterns and their distribution i n relation to the physical characteristics o f the water column. Mobility and sinking rates are very low i n picoplankton; the theoretical sinking rate o f a 3 u m cell is 1.7cm day" (as predicted by Stokes L a w assuming 74 k g m" excess density 1  3  over seawater at 0°C with a dynamic viscosity o f 1.88 x 10" k g m" s" ). Empirically 3  1  2  picoplankton have been shown to be basically neutrally buoyant (Takahashi and Bienfang 1983), and the mesoscale spatial distribution o f small cells is determined by lateral advection and vertical mixing. Community structure o f picoeukaryote assemblages (both autotrophic and heterotrophic), while dependent upon biological factors such as resource availability, competition, grazing and viral lysis, w i l l also be strongly influenced by  A version of this chapter will be submitted for publication to Limnology and Oceanography as Hamilton, A. K., C. Lovejoy, and R. G. Ingram. Biohydrography of picoeukaryotes in the North Water Polynya. 1  10  hydrographic factors. These include water mass origin, circulation, and mixing, as well as temperature, salinity, irradiance, and density. With this i n mind, we w i l l refer to the description o f the distribution o f biological particles within dynamic oceanographic systems as biohydrography. The North Water Polynya ( N O W ) , a large region o f perennially ice-free water between Ellesmere Island and Greenland, and one o f the most productive marine ecosystems i n the Arctic (Dunbar and Dunbar .1972), is well suited to investigate the biohydrography o f picoeukaryotes i n polar seas (Fig. 2.1). The hydrography o f the region has been well described (Melling et al. 2001; B a d e et al. 2002; Ingram et al. 2002) , characterized by opposing currents o f cold Arctic water and warmer Atlantic water meeting over complex bathymetry, with evidence o f a dynamic frontal zone showing strong interleaving and mixing features i n the center o f the polynya (Lobb et al. 2003) . The extent and duration o f primary productivity has been described (Klein et al. 2002; M e i et al. 2002; and Odate et al. 2002), with a spring bloom occurring as early as M a r c h and high productivity lasting as late as October. Lovejoy et al. (2002a) described vertically distinct micro- and nano- sized (2 — 20 um and 20 - 200 u m diameter, respectively) protist communities associated with interleaving layers and intrusions along the frontal zone o f the polynya. They proposed that the density differences at the base o f these layers act as traps for particulate organic matter, and that the physical heterogeneity o f the water column likely contributed to the diversity and community structure o f the protist assemblages. The objectives o f the present study were to examine variability o f picoeukaryotes in the N O W and determine factors that affect community diversity and distribution; whether community distribution could be explained by hydrographic patterns, and what environmental factors influence community structure. Here we describe the distribution o f water masses, circulation patterns, and photosynthetic biomass to understand the physical environment o f the N O W at the time o f the study. W e used denaturing gradient gel electrophoresis ( D G G E ) to characterize the community assemblages and relatedness among 57 sites. Multivariate statistics were used to determine factors that may be affecting assemblages - such as temperature, salinity, depth, irradiance, location, chlorophyll concentrations, and community size structure. We also sequenced D N A from extracted D G G E bands to identify the picoeukaryote taxa present i n the N O W . 11  2.2  Material and Methods  Study Area - We sampled the N O W polynya between 16 and 22 August 2005. The N O W is a large recurring region o f low ice cover i n Northern Baffin B a y (between latitudes 76° and 78.5°N) bounded longitudinally by Ellesmere Island and Greenland (Fig. 2.1). The polynya is o f critical importance to marine mammal, bird, and Inuit populations (Dunbar and Dunbar 1972). The region was intensively sampled during the International North Water Polynya ( N O W ) Study from 1997-1999, providing an excellent background to our present work. Hydrography - Our physical oceanographic survey followed 6 E - W transects (Fig. 2.1). The general hydrography, inflowing water masses, and sites o f strong physical interleaving i n the frontal zone where these water masses m i x were identified from 52 C T D casts using a Sea B i r d S B E - 9 1 1 . C T D salinity (S) was calibrated with water samples analyzed by a Guildline Auto-Sal salinometer. Values o f potential temperature (0) and potential density were computed using algorithms from U N E S C O (1983). Fluorescence (Seapoint), transmissivity (WetLabs C-Star Transmissometer), photosynthetically available radiation ( P A R ; Biospherical) and relative nitrate (Satlantic M B A R I I S U S ) were also recorded. Sample collection and analysis - Sampling stations were chosen to maximize hydrographic coverage within logistical constraints o f the overall project and to investigate sites o f strong water-column interleaving. A t nine stations, samples were collected at the following 6 depths, determined by both physical and biological characteristics: the surface (5 m), the subsurface chlorophyll max (10-50 m), the nitricline (40-80 m), the deep water (180 m), and two 'wildcard' temperature related depths (unique subsurface temperature excursions that indicating interleaved layers between 40150 m). Sample depths at each station were determined by viewing the sensor profiles on the C T D downcast. Water samples were collected on the upcast i n 12-liter P V C bottles (Ocean Test Equipment) mounted on a 24-bottle rosette sampler (General Oceanics). To ensure the reproducibility o f the methods, six field replicates (six bottles collected at the same depth on the same cast) were also collected at the chlorophyll max i n the Beaufort Sea (not shown on map) (Station C A 0 4 - 0 5 : 71°03.75 N , 133°36.07 W ) .  12  Nutrient tracer - T o trace water-mass origin we collected samples for silicate and nitrate at most stations. Concentrations were determined using an A L P K E M autoanalyzer with routine colorimetric methods (Grasshoff 1976) and a detection limit o f 0.05 u M . Tremblay et al. (2002) developed a quasi-conservative nutrient tracer ( S i ) for the N O W ex  based on the concentration o f silicate i n excess o f nitrate. S i Si  e x  e x  was calculated as:  = 100(Si - N - 1.06)/(12.75 - 1.06) (Eq. (8); Tremblay et al. 2002)  where Si and N are the concentrations o f silicate and nitrate, respectively. In the upper 200 m, relatively high S i  e x  values indicate the presence o f silicate-rich Arctic water  ( S R A W ) and l o w values indicate Baffin Bay water ( B B W ) of mixed Atlantic and Arctic origin. A t stations along the frontal zone observed by L o b b at al. (2003), where mixing and interleaving occurs (Lovejoy et al. 2002a), this index was useful in determining the water-mass origin o f each sample. Chlorophyll - Size fractionated samples for C h i a were filtered onto Whatman G F / F filters before (total C h i a) and after pre-filtration (pico C h i a) through 3 u m poresize Nucleopore polycarbonate membranes and stored at -80°C until analysis. Pigments were extracted from the filters i n 9 5 % ethanol at 70°C for 5 m i n (Nusch 1980) and concentrations were determined by spectrofluorometry (Cary Eclipse) before and after acidification (Strickland and Parsons 1972). DNA  extraction - Samples for microbial D N A were obtained following the  methods described i n Diez et al. (2001a). Briefly, 6 liters o f water was sequentially filtered through a 53 p m nylon mesh, a 3 p m polycarbonate filter, to remove zooplankton and macro- and nanoplankton, then a 0.2 p m sterivex unit (Millipore). The sterivex was emptied o f seawater and 1.8 m l o f buffer (40 m M E D T A ; 50 m M Tris pH=8.3; 0.75 M Sucrose) was added to maintain the cells. Samples were stored at -80°C until extraction. D N A was extracted by adding lysozyme (final concentration 1 mg m F ) and incubated at 1  45°C with slight agitation. Proteinase K (final concentration 0.2 mg m F ) and sodium 1  dodecyl sulphate (final concentration 1%) were added and incubated at 55°C for 1 h. The lysate was recuperated into a 15 m l falcon tube and the sterivex unit was rinsed with 1 m l of lysis buffer at 55°C for 15 m i n to recover any additional lysate. The lysate was extracted two times with an equal volume o f phenol-chloroform-isoamyl alcohol 13  (25:24:1, pH=8). The aqueous phase was recuperated and extracted once with an equal volume o f chloroform-isoamyl alcohol (24:1). The aqueous phase was recuperated and concentrated i n an A m i c o n tube (Millipore) using a centrifuge (3500 rpm). The concentrate was rinsed and centrifuged 3 times with 2 m l o f T E (or sterile water) to a final volume o f 200 u l . To verify extraction success, 5 u l o f D N A was run on agarose gel electrophoresis stained with ethidium bromide (final concentration 0.5 ng m F ) with a 1  standard (High D N A Mass Ladder). The D N A extract was stored at -80°C until D G G E analysis. PCR - Extracted environmental D N A was amplified by polymerase chain reaction (PCR) with eukaryotic 18S r R N A specific primers E u k A (5'- A A C C T G G T T G A T C C T G C C A G T -3') and Euk516r-GC (5' - A C C A G A C T T G C C C T C C - 3' with a G C clamp) (Diez et al. 2001a). The P C R mixture (25 u L ) contained 1 \i\ (-10 ng) o f extracted D N A as template, each deoxynucleoside triphosphate at 200 u.M, 1.5 m M o f bovine serum albumin ( B S A ; Fermentas), each primer at a concentration o f 0.3 \xM, and 2.5 U o f cTaq D N A polymerase (BioLabs) with buffer. The thermocycler program was optimized from Diez et al. (2001a) and included an initial denaturing cycle at 94°C for 120 s, 30 times amplification cycles o f denaturing at 94°C for 30 s, annealing at 56°C for 45 s, and extension at 72°C for 120 s, with a final extension at 72 °C for 6 m i n then cooled to 4°C. The P C R product was run on agarose gel electrophoresis stained with ethidium bromide to verify amplification o f the 560 base-pair D N A fragment using a standard L o w D N A Mass Ladder. DGGE - The D G G E was run on a 0.75 m m thick 6% polyacrylamide gel with a linear denaturing gradient o f 35-55% (100% denaturant is 7 M urea and 40% deionized formamide) on a B i o - R a d system at 100V for 16 hours submerged i n I X T A E buffer (40 m M Tris [pH 8.0], 20 m M acetic acid, 1 m M E D T A ) at 60°C (Diez et al. 2001a). Each environmental sample lane was loaded with 12 u.1 (-500 ng) o f P C R product and standard lanes were loaded with 5 u l (-100 ng) o f in-lab culture standard. The in-lab standard consisted o f extracted D N A P C R product from 6 picoeukaryote cultures grown i n the lab, including C C M P 2298, 2436, 2296, 2097, 2098, 2099 from taxonomic groups Chrysophyceae, Haptophyceae, Polarella, Pelagophyceae, and Micromonas. Individual gels had 14 lanes, including 2 standards and 12 samples (6 samples each from 2 stations). Analytical replicates (repeated P C R o f a single sample) for all samples were run on a 14  different gel alongside samples from a new station resulting i n an overlapping pattern o f stations on the gels ( A B , B C , C D , etc.). This pattern permitted precise alignment o f gels later during image analysis. Gels were stained with S Y B R G o l d nucleic acid stain (Molecular Probes) for 1 h in the dark and rinsed with 500 m l o f I X T A E buffer before photographed. Exposure was set just below saturation level o f bands. G e l images were acquired with the B i o - R a d G e l D o c imaging system using Quantity One (Bio-Rad v.4.6.0) software and exported as T i f f files. A composite image o f all gels was created with Photoshop (Adobe v8.0). The multiple standard and analytical replicate lanes facilitated precise alignment o f multiple gel images side-by-side on the same vertical scale. The final composite image was saved as a T i f f file for band analysis with Quantity One. We also attempted D G G E image analysis using Gel-Compar software ( A p p l i e d Maths), which permits band matching between separate gels. We found that the automatic band matching performed by Gel-Compar introduced improper scaling o f gels and band-matching errors that required additional manual quality control, with nothing gained compared to the Quantity One T i f f analysis. The composite D G G E image (Appendix A ) consisted o f 107 lanes; 57 unique sample lanes, 17 standard lanes, and 33 replicate lanes. Background was subtracted from the densitometry scan o f all lanes using the rolling disk (size 10) method. Bands contributing >1% o f the total band intensity for each lane were automatically detected and scored as present, all others as absent. A l l 17 standard lanes were manually identified and bands matched. Given the narrow vertical range o f the standard lane band patterns, the replicate lanes were also identified to facilitate automatic band matching. The software then compared all lanes and identified matching bands with identical vertical positions across the image. The banding profile o f the composite D G G E image was converted into two matrices: 1) a binary presence-absence matrix and 2) an intensity matrix, where each band is scored with its intensity relative to the strongest band i n its lane (0-100%). This focused the analysis on relative differences i n band intensity within each sample, and eliminated the variability that may have been introduced by slight differences i n the mass o f D N A loaded. Ecological Statistics - Similarity matrices were generated from the two banding matrices using Pearson product-moment correlation coefficients. The robustness o f each 15  matrix was determined by comparing the average similarity of replicates for both matrices; the intensity matrix showed much higher similarity between field replicates and analytical replicates (0.98 ± 0.01 and 0.93 ± 0.05, respectively) than the presence-absence matrix (0.87 ± 0.03 and 0.73 ± 0 . 1 6 , respectively). The error inherent in presence-absence method is due to the increased emphasis on faint bands, near the 1% intensity minimum, whose presence may differ between replicates. The more robust intensity matrix was therefore used for subsequent ecological analysis, unless otherwise noted. Richness was calculated from the number o f different bands, called operational taxonomic units (OTUs). Where analytical replicates had a different number o f O T U s the higher o f the two was used. The Shannon diversity index (FT; Shannon and Weaver 1963) was calculated as:  H ' = -XMifo) where S is the total number o f O T U s (richness) and p is the proportion o f the r'th O T U in t  the sample. The Simpson index o f diversity (1 - D ; Simpson 1949) was calculated as:  1-D=l-I(p,)  2  Symbols used are the same as those for the Shannon diversity index. Relatedness o f D G G E band patterns was visualized using dendrograms produced by Agglomerative Hierarchical Clustering ( A H C ) using the Unweighted Pair Group Method with Arithmetic mean ( U P G M A ) algorithm. Community composition was directly related to known environmental gradients using the ordination technique Canonical Correspondence Analysis ( C C A ) (ter Braak 1986, 1995) calculated with X L S t a t (Addinsoft). C C A seeks to explain the variance i n community composition under the constraint that gradients are a linear combination o f physical variables. After checking for collinearity among environmental variables, density, salinity, nitrate and fluorescence were eliminated from the analysis. The physical variables included i n the C C A were pressure, total chlorophyll (total C h i a), the percentage o f the pico-sized fraction i n total C h i a (% pico C h i a), latitude, temperature, transmissivity, and P A R . The normality o f all variables except temperature was improved by a log (X+ 1) transformation (Legendre and Legendre 1998). A l l variables were 16  standardized as z-scores by subtracting the mean and dividing by the standard deviation (Legendre and Legendre 1998). One additional qualitative category variable (ter Braak 1995) was added to classify different water masses (see Results). Each sample point was overlain on a 6-S diagram and scored i f present or absent i n a particular water mass using binary notation. The classes o f water masses were not mutually exclusive; i f it was not readily apparent which water mass the sample was from then more than one class was scored as present. This classification was instructive i n that it separated samples from obviously different water masses and yet allowed for samples from mixing regions. Sequencing - The most prominent D G G E bands i n each sample were extracted with a sterile blade, and dissolved i n I X T E at 4 ° C overnight. A 5 p i aliquot o f the dissolute was amplified by P C R as above using E u k l F and Euk516r (no G C clamp). The 25 p i P C R product was sequenced using the forward primer at the Plateforme de Sequencage et de Genotypage des Genomes du Centre de Recherche du C H U L / C H U Q Quebec, Canada using an A p p l i e d Biosystems A B I 3730x1. Nucleotide sequences (-560 bp) were manually edited using F i n c h T V software (Geospiza v. 1.4) and noisy or incomplete sequences were discarded. The closest match to each sequence was found from N C B I Blast (Altschul et al. 1990) search against the GenBank online database.  2.3  Results  Hydrography - Potential temperature sections show the 3-D variability i n the upper water column and the presence o f different water masses (Fig. 2.2). The northern stations ( M B 0 2 , M B 0 7 , M B 0 9 , M B I 1, and M B 2 1 ) , represented by the section along Line 3, show relatively cold water between the freezing point and -1°C throughout most o f the upper 250 m , with some atmospheric warming > 0°C o f the top 25 m. The southern stations ( B A 0 1 , B A 0 2 , B A 0 4 , and L03), represented by the section along Line 6, show a warm surface layer (> 1°C) to 50 m overlying the wintertime convection water ( W C W ) , which extends down to 150-200 m. B e l o w the cold W C W is a warmer (> 1°C) water mass o f Atlantic origin that extends to the bottom. The north-south section (Line 7; F i g . 2.2.) shows the strong frontal zone between the cold Arctic derived waters i n the north and the warm surface waters o f the south, with temperature interleaving layers between 50 and 100 m at station M B 2 1 . We noted the presence o f warm Atlantic waters below 150 m at the southern stations ( B A 0 2 , B A 0 4 , L 0 3 ) .  The 0-S diagram o f all casts showed distinct water masses were present in the N O W (Fig. 2.3). Referencing previous descriptions o f 0-S characteristics (Melling et al. 2001; B a d e et al. 2002), we identified the following five water masses: Arctic surface water (S < 32.5 and 0 < -0.5°C), Arctic water (S > 32.5 and 0 < 0°C), Baffin Bay surface water (S < 32.5 and 0 > 0.5°C), the wintertime convection water (33.5 > S < 33.6 and 0 < -1°C), and Atlantic deep water (S > 33.6 and 0 > 1°C). These are the same water mass categories used i n the C C A . Interleaving layers are detected as strong temperature excursions between salinities o f 31 and 33.2. The S i  tracer calculations distinguished samples with relatively high proportions  e x  of S R A W (Fig. 2.4). S i  e x  is usually expressed as a percentage o f S R A W present, but due  to changes in absolute values between years, here it is expressed in relative nondimensional units (Tremblay, J. E . , pers. comm.). The l o w S i  e x  values and l o w salinities o f  the surface samples from stations M B 0 2 and M B 0 7 (A030, A 0 4 1 , and A042) are associated with ice melt. Other samples show varying proportions, with less S R A W i n more southerly and deeper samples. There was significant variability over depth at different stations: M B 0 7 (A037-42) varied from mostly B B W water at depth, to mostly S R A W i n the mid-depths, to melt water at the surface, while M B 2 1 (A043-48) had relative constant proportions at all depths. Chlorophyll - A t most stations, the near-surface (5 m) C h i a concentration, estimated from relative fluorescence, was l o w (<1 u.g l" ), but increased to a subsurface 1  chlorophyll maximum between 15 and 50 m (Fig. 2.5). Surface values were lowest at M B 0 7 (0.2 ug f ) , and highest at M B 0 9 (3.1 ug l" ). The highest overall C h i a 1  1  concentrations were observed at kilometer 25 along Line 6 (Fig. 2.5), where values peaked at 40.2 p,g l " at 30 m. A t the more northern stations, C h i a concentrations were 1  highest at stations M B 0 9 and M B 0 7 , with values o f 4.8 and 4.7 iig 1"' at 27 m and 45 m , respectively. Overall, concentrations decreased below the subsurface maximum to <2 u.g f . Chlorophyll concentrations were generally higher i n the west and north, consistent 1  with the seasonal northwest migration o f the phytoplankton bloom shown for this region by L e w i s et al. (1996) and Booth et al. (2002).  18  Station profiles (Fig. 2.6) show large variability i n temperature, salinity and chlorophyll concentrations among stations. Interleaving layers were detected between 40 and 60 m at M B 1 1 , M B 2 1 and B A 0 1 . These layers were also associated with small secondary chlorophyll peaks, 0.5 to 1 p.g l " above the background level. M B 0 2 and 1  M B 0 7 show a more diffuse interleaving layer between 100 and 150 m and have a relatively fresh surface layer. B A 0 2 , B A 0 4 , and L 0 3 show a warm mixed surface layer and the presence o f the warm Atlantic layer at depth. DGGE fingerprints - The D G G E fingerprints showed 42 unique O T U s overall, with individual samples containing between 8 and 22 O T U s (Table 2.1). M a x i m u m richness was found at M B 1 1 , minimum at M B 0 2 and L 0 3 . A number o f stations ( B A 0 2 , B A 0 4 , M B 0 7 , M B 1 1 , and M B 2 1 ) showed decreasing richness from the surface to a minimum at about 30m, then an increase with depth, and subsequent variability with increasing depth. Others stations differed; B A 0 1 showed minor variability i n richness from surface to depth, whereas M B 0 2 showed a minimum at the surface, increasing with depth to a maximum at 160m and then decreasing with depth. Surface values were lowest at M B 0 2 and highest at M B 0 7 , having 8 and 20 O T U s , respectively. Richness values i n the chlorophyll maxima were lowest at B A 0 2 , with 10 O T U s , and highest at M B 0 9 and M B 2 1 , both with 18 O T U s . The average richness per station was lowest at M B 0 2 and highest at M B 1 1 , with values o f 12.4 and 18.8 O T U s , respectively. M a x i m a and minima for both diversity indices corresponded to the samples having maximum and minimum richness values. Histograms o f the relative intensity o f O T U s i n each sample show the large variability i n picoeukaryote community composition between stations and over depth (Fig. 2.7). M B 0 2 shows the two surface samples are quite different than the deeper samples, largely influenced by the intensity o f O T U 7. A similar pattern is observed i n M B 0 7 . M B 1 1 and M B 2 1 are not as strongly influenced by changes i n one O T U ; instead these stations show minor changes i n the intensity and composition o f all O T U s . W e found a strong change in community structure at L 0 3 , where O T U 27 was more intense i n the deeper samples. The most ubiquitous O T U s were 28 and 30, found i n 49 different samples. The rarest, O T U 41, was only found i n sample A 0 4 8 , a surface sample from M B 2 1 . Analysis o f the O T U s present indicated that 5 O T U s had cosmopolitan  19  distributions (defined as being present i n > 75% o f all samples). Conversely, 12 O T U s had a metropolitan distribution (defined as being present i n < 2 5 % o f all samples). DGGE fingerprint similarity - W e were able to define 9 major clusters from the similarity dendrogram (Fig. 2.8). The samples generally separated on the basis o f water mass origin, as indicated (see Discussion). Samples from one station were found i n several different clusters (e.g. station B A 0 4 ; samples A049-A054), sharing as little as 15% similarity overall. Surface samples were also found i n multiple clusters, sharing as little as 2 5 % similarity overall. Within each cluster samples shared at least 50% similarity, usually more, yet could be from stations hundreds o f kilometers apart, and from depths ranging from 15 to 180 m. Some samples shared greater than 85% similarity but were from distant sites (e.g. A 0 0 4 and A 0 3 8 from B A 0 1 and M B 0 7 , respectively). CCA - Ordination analysis shows that 42.6% o f the total variation in assemblages can be accounted for by two factors that are a linear combination o f the 7 quantitative physical variables selected (Fig. 2.9). F I alone accounted for 27.2% o f the variation and was correlated with latitude and total C h i a. F 2 accounted for 15.8% o f the variation and was correlated with P A R and pressure. The diagram shows the approximate "centers" o f each sample, and the water mass categories, along the environmental gradients. Projecting each sample orthogonally onto the variable vector permits ranking o f samples along the physical gradient. For example, A 0 4 9 (Fig. 2.9a) falls near the Atlantic water mass category, has a relatively high % pico chl a, is from relatively deep water (high pressure), and a low latitude (extrapolating the latitude vector through the origin). In contrast, A 0 4 2 (Fig. 2.9a) is distinguished as a surface sample with very high P A R values. We note that southern samples from the Atlantic water mass group together with high proportions o f pico chl a, while Arctic surface samples i n the north tend to higher total chl a values. The C C A for environmental variables and O T U s (Fig. 2.9b) is interpreted similarily and shows that certain O T U s are correlated with particular physical conditions and water masses. For example, O T U 27 is closely associated with Atlantic water while O T U 7 is associated with Arctic surface water. O T U s 4, 9, 16, 20, 41 are present i n samples with high total C h l a levels, while O T U s 11, 18, 25, 26, 29, 35, 38 are associated with a high proportion o f pico-sized C h l a. 20  Sequence Identification - W e detected 26 different taxa from the extracted bands (Table 2.2). Many sequences had closest matches to uncultured marine eukaryotes, but most were able to be classified within major taxonomic groups including, Acantharea, Cercozoa, Chrysophyceae, Bacillariophyceae (diatoms), Dinophyceae, Prasinophyceae, Prymnesiophyceae, and uncultured alveolate groups I and II, novel marine stramenopiles ( M A S T ) , as well as a recently discovered putative algal phyla the picobiliphytes (Not et al. i n press). We also found two sequences from Metazoans (Hydrozoa and Appendicularia), organisms obviously larger than our 3 p m mesh (see Discussion).  2.4  Discussion  Analysis of picoeukaryote assemblages - The diversity o f picoeukaryotes i n the world's oceans has been revealed in recent studies (Diez et al. 2001b; Lopez-Garcia et al. 2001; M o o n - v a n der Staay et al. 2001). The spatial distribution o f picoeukaryote assemblages has only very recently been examined in the Antarctic Ocean (Diez et al. 2004). We were interested i n examining the distribution o f picoeukaryote assemblages in relation to the hydrographic environment i n the Arctic. We used D G G E community fingerprinting to describe picoeukaryote distribution, a technique that has been applied previously (Diez et al. 2004). The presence o f a band in a D G G E gel indicates the presence o f a corresponding organism i n the environment. Due to biases inherent i n P C R based studies however, band intensity is not a direct indication o f organism abundance i n the environment, but band intensity changes within a sample set do show changes in the relative community structure i n nature (Casamayor et al. 2002; Diez et al. 2004). Muylaert et al. (2005) found that errors introduced by faint bands i n presence-absence datasets were most likely to obscure relationships between species and the environment, indicating intensity datasets are preferred in studies o f this type. Acknowledging the limitations o f the technique, we used the intensity dataset as the more robust for revealing changes i n community composition, which was the aim o f this study.  Analysis o f distribution patterns across a large region required many samples, which necessitated comparison o f D G G E fingerprints among gels. We ran multiple 21  replicates on different gels to ensure community comparisons were valid. The reliance o f D G G E results on a specific and repeatable protocol, which often varies slightly among labs, and the lack o f a universal standard limited our ability to compare fingerprint patterns among studies. Currently, the only practical method for comparing results among studies is to sequence extracted D G G E bands. To improve confidence in relating sequences to bands, we extracted multiple replicates for each band from each gel. There was however, sequence variability among replicates and we were unable to confidently match a given band with a taxonomic group. Following the same protocol, other studies have reported sequences related to particular bands (Diez et al. 2004; Gast et al. 2004), so we were not expecting such variability between replicates. It is possible that contaminating D N A from adjacent bands may have out-competed our target during the re-amplification P C R , resulting in variable sequences. Similarly, due to the nature o f D G G E and the high diversity o f picoeukaryotes i n this region (Lovejoy et al. 2006), sequence variants could have migrated to the same position in the gel, confusing the results. The method would likely have been improved by rerunning the extracted band P C R product alongside the original sample to confirm a match w i t h the original band (Schafer and M u y z e r 2001; Crump et al. 2004). Thus, we report all organisms identified from the sequences, but do not match taxonomic names to particular bands or O T U s (Table 2.2).  Hydrography - Our aim was to relate the community distribution pattern o f picoeukaryotes to their environment, which requires a detailed understanding o f the p o l y n y a . The creation and persistence o f the N O W p o l y n y a is strongly influenced by the circulation in the region (Fig. 2.1). The p o l y n y a occurs when an ice bridge forms across the northern end o f Smith Sound, preventing Arctic pack ice from flowing south into Northern Baffin B a y (Melling et al. 2001; Ingram et al. 2002). The ice-bridge had brokenup prior to our sampling, bringing scattered ice flows into northern Smith Sound. Land fast and pack ice were present along the coast o f Ellesmere Island whereas the remainder of the N O W was relatively ice-free. The strong southward flow (10-15 c m s" ) o f cold and 1  relatively fresh Arctic water is likely o f Pacific origin, as deeper Atlantic water is 22  restricted by the 230 m sill in Nares Strait (Melling et al. 2001). This water is characterized by relatively high silicate content (Tremblay et al. 2002), likely supporting diatom blooms. The excess silicate enabled us to track the Arctic water as it entered Smith Sound (Tremblay et al. 2002). The Arctic water continues south along the east coast o f Ellesmere and Devon Island (Melling et al. 2001). Surface freshening, observed at M B 0 2 and M B 0 7 (Fig. 2.2 and 2.6), is likely associated with melting o f sea-ice and runoff from the surrounding ice-sheets. Modified Arctic surface water flowing eastward through the Canadian Archipelago enters the N O W through both Lancaster and Jones Sound, near station L 0 3 (Fig 2.1). The combined water forms the Baffin Current flowing along the coast o f Baffin Island. O n the east side o f Baffin Bay, the West Greenland Current ( W G C ) flows north along the coast o f Greenland into the N O W . The W G C is evident as downward sloping isopcynals to the east i n Line 6 at B A 0 1 (Fig. 2.2). The W G C brings warmer and more saline water originating in the N o r t h Atlantic into the N O W at depth. The majority o f the Atlantic water is bathymetrically steered west toward Devon Island in the vicinity o f the 500 m isobath, and is observed at B A 0 4 and L03 (Line 7; Fig. 2.2). A trough to the south o f the Carey Islands (Fig. 2.1) allows some northward excursions o f warm deep Atlantic water further north, possibly as far as M B 0 2 i n Smith Sound (Melling et al. 2001). Surface waters o f the W G C flow parallel to the Greenland coast until veering west i n the vicinity o f the Carey Islands and encountering the southward flowing Arctic water (Bade et al. 2002; Ingram et al. 2002). This can be seen as the sloping isopycnals at M B 2 1 (Line 7, Fig. 2.2), indicating westward circulating water. The lateral mixing o f water masses i n the frontal zone is readily observed as densitycompensating thermohaline intrusions at M B I 1 and M B 2 1 (Fig. 2.6). Given the circulation pattern, it is likely that the source o f these temperature intrusions is the surface waters o f B A 0 1 , which is located i n the W G C . There also appears to be an anticyclonic eddy centered between M B 0 9 and M B 2 1 (Line 7, Fig. 2.2), further evidence o f lateral current shear i n the region. A l l water is eventually re-circulated southward and exits the N O W by the Baffin Current. The general southward flow across most o f the width o f the N O W can be observed by the downward sloping isopycnals to the west i n Line 6 (Fig. 2.2).  23  Picoeukaryote distribution - Patterns i n picoeukaryote community assemblages were strongly related to the hydrography described above. Assemblages located closely i n space, often only a few meters apart, differed substantially, yet others located large distances away revealed very similar communities. The clustering o f assemblages (Fig. 2.8) was best understood i n relation to the circulation o f water masses. Cluster 1 was composed o f the two surface samples from B A 0 1 , representing the warm surface W G C assemblages. The samples i n Cluster 2 share the presence o f rare O T U s (see corresponding samples in Fig. 2.7), but did not have any obvious hydrographic relationship. Cluster 3 was composed o f Baffin Bay surface samples that were related by the presence o f the relatively intense O T U 28 (see corresponding samples in Fig. 2.7). Cluster 4 contained samples from the W C W o f B A 0 4 and L 0 3 , as well as replicates A 1 0 3 - A 1 0 6 collected at 51 m i n the chlorophyll max o f Station C A 0 4 - 0 5 , i n the Beaufort Sea, 2000 k m upstream (through the Canadian Archipelago). The similarity between these distant assemblages could be the result o f circulation o f Beaufort Sea waters through the Canadian Archipelago into the N O W via Lancaster and Jones Sounds. Cluster 5 was comprised o f samples from the northern stations and representative o f Arctic water assemblages. The S i  e x  tracer showed further evidence o f this division, many o f the  samples from Cluster 5 (Fig. 2.8) had a high proportion o f S R A W water (Fig. 2.4). Cluster 6 was also comprised o f samples from the same northern stations as Cluster 5, but i n addition Cluster 6 contained samples from southern B A 0 1 and B A 0 2 . It is likely that Cluster 6 represented assemblages that originated in W G C waters and were circulated throughout the N O W . Clusters 5 and 6 both shared about 30% overall similarity with Cluster 1 ( W G C surface assemblage) on the basis o f O T U intensity fingerprints (Fig. 2.8). However, on the basis o f the O T U presence-absence dataset, we found that 73% o f the O T U s present i n Cluster 1 were also present i n Cluster 6, whereas only 57% were present i n Cluster 3 (data not shown). The higher degree o f identical O T U s between Cluster 1 and Cluster 6 supported the idea that these were W G C derived assemblages. Cluster 7 contained surface samples from northern stations near the pack ice along Ellesmere Island (Fig. 2.1) that had l o w salinity, indicating assemblages likely associated with meltwater. Cluster 7 was strongly defined by the intensity o f O T U 7 (Fig. 2.7).  24  Atlantic deep-water assemblages defined Cluster 8, which had only 20% similarity to any other cluster. This cluster was strongly influenced by the presence o f O T U 27, which was rarely found i n other clusters (Fig. 2.7). Cluster 9, which had the least similarity (6%) to any other cluster, contained samples from the warm halocline between the W C W and deep Atlantic water. Perhaps this can be explained by considering that ecotones, transition zones between two ecological communities that are associated with a strong environmental gradient, may be the habitat o f specialized "edge-effect" species (Odum 1971). Cluster 9 may represent an ecotone community along the environmental gradient between these two water masses, different from either o f the communities associated with the separated water masses. The frontal zone between Arctic water and W G C water is a region o f water mass mixing, and is indicated by the presence o f thermohaline intrusions, or interleaving layers, at M B I 1 and M B 2 1 (Fig. 2.6). Lovejoy et al. (2002) sampled the N O W i n 1998 and observed that stations with interleaving layers had larger changes i n nano- and microplankton community structure through the water column than stations without interleaving. They hypothesized that these layers acted as processing traps for particulate organic matter, contributing to microbial diversity. A t the same stations i n 2005, we found remarkably similar interleaving layers, suggesting the processes responsible for these features are consistent between years. In this study, stations with interleaving layers (e.g. M B 2 1 ) showed consistently high diversity (Table 2.1), but relatively constant picoeukaryote communities across all layers (Fig. 2.7). Lovejoy et al. (2002) focused on nano-and micro-plankton using abundance data as a measure o f community shifts. This study focused only on picoplankton, using community fingerprint patterns as indicators of community shifts. A possible explanation for this difference between size fractions is the mobility o f the biological particles involved. Picoplankton, being neutrally buoyant, are confined to their parent water mass. Larger plankton however, w i l l potentially sink out o f a water mass until reaching a pycnocline. M i n o r density gradients, like those associated with interleaving layers, may be sufficient to act as accumulation sites for these larger plankton, and particulate organic matter, while picoplankton w i l l simply track along with their associated water mass as it circulates. Samples collected specifically from 25  interleaving layers did not show distinct communities compared to samples collected from the surrounding water column. However, samples from these stations, and below the fresher surface layer did separate into two different clusters (Cluster 5 and 6, Fig. 2.8), so there are two distinct picoeukaryote communities present at the interleaving stations. Where two distinct water masses mix, we find two distinct picoeukaryote communities. Environmental  influences on community composition - T o understand how  environmental variables influence community composition we used C C A . A s an ordination technique, C C A is similar to the more commonly used principle component analysis ( P C A ) and multi-dimensional scaling ( M D S ) . It differs however, i n that it calculates ordination coefficients from both the original species and variable datasets and forces the environmental variables to fit the species ordination. Unlike P C A , which assumes a linear species distribution along an environmental gradient, C C A assumes a more realistic unimodal species distribution, where species have a particular niche. From the ordination plot o f environmental variables and samples (Fig. 2.9a), samples can be differentiated by these environmental gradients. Samples were roughly separated by latitude and depth, spatial parameters that separate samples according to water mass. There is also a notable separation o f samples by size-fractionated chlorophyll. Assemblages from northern stations within or above the C h i a max group together, while corresponding assemblages from southern stations are loosely associated with an increase i n the proportion o f pico-sized C h i a. This separation may be due to a strong diatom bloom in the north and west, while southern stations may be i n a postbloom state. Generally, picoeukaryote communities within and above the chl max are distinct from communities below  Ordination o f environmental variables and O T U s (Fig. 2.9b) indicated those taxonomic units that are associated with particular environmental conditions or water masses. For example, O T U 27 was closely associated w i t h Atlantic water, which can be confirmed by the histogram o f deep samples at L 0 3 (Fig. 2.7). O T U 37 was associated with northern samples and primarily found i n samples belonging to Cluster 5 (Arctic  26  water). Attaching taxonomic identification to these O T U s i n the future w i l l provide the next level o f understanding o f the ecosystem. Sequence Identification - The phylogenetic diversity o f picoeukaryotes has been documented recently (Massana et al. 2004a; Lovejoy et al. 2006; N o t et al. in press). M a n y o f the taxonomic groups identified i n this study (Table 2.2) were described i n these previous studies. Sequences from novel marine stramenopiles ( M A S T ) were identified; these are heterotrophic organisms thought to have a cosmopolitan distribution and graze on bacteria (Massana et al. 20046). Other phototrophic stramenopiles found here, such as the diatom Fragilariopsis cylindricus, were originally isolated from the Arctic Ocean (Lovejoy et al. 2006), perhaps linked to the high silicic acid concentrations o f the Pacific layer i n the upper ocean which promotes diatom growth. The alveolates, Group 1 and 2, are likely parasites o f dinoflagellates and zooplankton with picoplankton life stages (Lovejoy et al. 2006). Organisms such as the small green algae prasinophytes, the novel picobiliphytes (Not et al. in press), and the prymnesiophyte Phaeocystis jahnii were present. We also found sequences belonging to metazoans and dinoflagellates, organisms generally larger than our 3 p m filter pore size. This occurrence has been reported i n previous studies (Massana et al. 2004; Lovejoy et al. 2006), and could be due to cell breakage during filtering or sloppy feeding by zooplankton, larger flexible cells that can be forced through the filter pores, or dissolved free D N A adhering to small particles (Lovejoy et al. 2006). Despite identifying numerous sequences belonging to picoeukaryotes, our understanding o f the functioning o f these organisms i n the environment is limited because many o f them remain uncultivated. Massana et al. (2004a) expressed optimism however, as previously "uncultivable" prokaryotes have now been successfully cultured by new techniques (Rappe et al. 2002), and we expect similar developments for difficult eukaryotes.  Biohydrography - Martiny et al. (2006) discussed the different hypotheses concerning microbial biogeography, whether distribution o f very small organisms is due to contemporary environmental influence (i.e. the Baas-Becking hypothesis - "everything is everywhere, but the environment selects"; Baas Becking 1934), or lingering historical 27  effects, or a combination o f past events and environmental conditions. This study has shown that picoeukaryotes are not randomly distributed (to the detection limit o f P C R D G G E methods) in the ocean; there are indeed distinct marine provinces with unique microbial assemblages. O n the scale o f 10s to 100s o f kilometers, the provinces appear to be the result o f historical events; Arctic water has evolved a separate eukaryotic assemblage than Atlantic water. We were able to distinguish separate communities even in regions o f mixing; indicating water masses keep their picoeukaryotic signature. But these findings could also be interpreted as the result o f contemporary environmental influence, as Arctic and Atlantic water had significantly different physical and chemical properties that would have selected for divergent communities. We observed potential evidence o f a novel assemblage along an ecotone between Atlantic water and W G C derived water that had very little similarity to either o f the neighboring water masses, further evidence supporting the hypothesis o f contemporary environmental influence. We can be certain that picoeukaryote community distribution is strongly related to water mass distribution. It is apparent that the "distance effect" o f variation i n microbial assemblages needs to be accounted for in marine biogeographic, or biohydrographic studies. Due to circulation i n the marine environment, the "landscape" is itself moving, so the notion o f a fixed station, as on land, is somewhat o f a misnomer. To truly account for distance effects we need to standardize the sample locations i n reference to circulation. We need to track water masses. T w o sample locations may be very closely situated, but i f they lie in different provinces with opposing flow, then they are effectively very distant from one another. Therefore, the Lagrangian approach to oceanography, utilizing neutrally buoyant floats to follow a given water mass in its trajectory and track changes over time, would be more appropriate than the Eulerian approach described here, which monitors changes at distinct geographic locations.  The results o f this study provide evidence that distinct water masses have an associated unique picoeukaryote community. The large- and small-scale distribution o f a picoeukaryote community, comprised o f small neutrally buoyant cells lacking significant mobility, is determined by the circulation o f the associated water mass. In regions where 28  water masses meet and interleave, the community associated with each individual water mass seems to retain its unique structure and composition, as long as the water mass itself maintains its original physical characteristics. However, where water mass rnixing results in a gradient o f physical conditions between two very different water masses, the new conditions along the gradient may promote the success o f an entirely different community, distinct from those associated with the two separate water masses. Ecological studies o f microbial eukaryotes i n the Arctic are few. This study demonstrates that physical factors strongly influence the community structure o f picoeukaryotes, which w i l l influence understanding o f primary productivity and nutrient cycling in the world ocean. With a rapidly changing Arctic it is imperative that we have a solid understanding o f the structure and diversity o f these key players i n the ecosystem.  29  Table 2.1  Picoeukaryote diversity indices and total C h l a concentrations o f samples  from the N O W . Indices are derived from D G G E intensity band patterns. M a x i m u m index values are indicated i n bold, minimum i n bold italic. Note: Station C A 0 4 - 0 5 is i n the Beaufort Sea (see text). Station  MB02  MB07  MB09  MBit  MB21  BA01  BA02  BA04  L03  CA04-05  Sample  A030 A029 A028 A027 A026 A025 A042 A041 A040 A039 A038 A037 A023 A021 A019 A036 A035 A034 A033 A032 A031 A048 A047 A046 A045 A044 A043 A006 A005 A004 A003 A002 A001 A012 A011 A010 A009 A008 A007 A054 A053 A052 A051 A050 A049 A060 A059 A058 A057 A056 A055 A106  Depth (m) 5 40 75 99 140 180 5 25 45 80 140 180 27 66 180 5 23 45 62 85 180 5 15 32 55 90 180 5 22 48 55 80 150 5 23 40 65 125 180 5 12 23 55 110 200 20 80 200 240 270 400 51  Richness (# of OTUs) 8 11 15 16 17 9 20 11 16 17 17 17 18 19 19 17 15 19 20 22 21 18 18 15 17 16 14 16 17 14 15 17 15 15 10 16 17 17 19 15 13 13 16 14 20 14 18 15 15 17 8 19  Shannon (H) Index 1.20 1.84 2.37 2.36 2.51 1.60 2.53 1.91 2.15 2.55 2.36 2.45 2.61 2.61 2.67 2.47 2.41 2.55 2.68 2.81 2.58 2.55 2.55 2.30 2.44 2.33 2.17 2.55 2.46 2.19 2.22 2.28 2.20 2.48 1.78 2.36 2.48 2.36 2.64 2.28 2.23 2.11 2.40 2.08 2.64 2.06 2.62 2.50 2.35 2.40 1.38 2.60  Simpson (1-D) Index 0.52 0.76 0.87 0.87 0.89 0.68 0.88 0.79 0.82 0.91 0.85 0.88 0.91 0.90 0.91 0.89 0.89 0.89 0.91 0.92 0.90 0.90 0.90 0.85 0.86 0.85 0.83 0.91 0.88 0.83 0.85 0.83 0.84 0.90 0.73 0.87 0.88 0.86 0.91 0.86 0.86 0.83 0.88 0.78 0.90 0.83 0.91 0.91 0.87 0.87 0.60 0.91  Total Chla (ng r ) 1  0.08 1.22 0.49 0.21 0.16 0.08 0.05 0.15 1.91 0.20 0.04 0.02 1.87 0.17 0.07 0.90 0.84 0.55 0.13 0.09 0.03 1.55 1.55 0.63 0.78 0.09 0.01 0.41 0.57 0.40 0.27 0.01 0.01 0.22 1.58 0.03 0.00 0.02 0.01 0.21 0.21 0.79 0.18 0.01 0.02 0.55 0.04 0.01 0.01 0.01 0.01 0.25  30  Table 2.2  Sequence matches o f excised D G G E bands  Closest Match SCM15C12 SCM28C151 OLI11511 SCM27C18 Amoebophrya sp. ex Dinophysis norvegica OLI11023 BB01105 NOR46.14 Spumella sp. GOT220 Fragilariopsis cylindrus strain CCMP1102 Pseudo-nitzschia pungens Thalassiosira aestivalis strain CCMP 975 Gymnodinium sp Gyrodinium spirale SCM37C61 SCM38C58 SCM27C56 Phaeocystis jahnii Stephanomia amphytridis ME 1-21 HE000427.21 UEPACLp5 Oikopleura sp. cf. dioica-TTGS-2 NOR50.52 NW617.02 CCMP2099  BLAST Accession I D AY665098.1 AY665054.1 AJ402343.1 AY665021.1 AY260469.1 AJ402335.1 AY885033.1 DQ314811.1 EF027354.1 AY485467.1 Ul 8240.1 DQ093369.1 AF274260 AB120001.1 AY664880.1 AY664956.1 AY664944.1 AF163148 AY937322.1 AF363190.2 AY381157.1 AY129067.1 AY116613.1 DQ060527.1 DQ060525.1 DQ025753.1  Sequence Similarity %, (bit score) 99.4 97.6 99.8 98.4 89.3 95.8 89.8 100.0 100.4 100.0 100.0 99.2 100.0 99.8 98.0 99.0 99.6 97.8 99.0 99.6 99.0 98.0 95.9 98.2 96.0 100.0  (961) (907) (969) (911) (462) (821) (559) (1025) (916) (908) (969) (950) (989) (967) (916) (954) (987) (910) (955) (973) (955) (914) (821) (914) (809) (967)  Taxonomic Group Acantharea alveolate Group 1 alveolate Group 1 alveolate Group 1 alveolate Group 2 alveolate Group 2 alveolate Group 2 Cercozoa Chrysophyceae Bacillariophyceae Bacillariophyceae Bacillariophyceae Dinophyceae Dinophyceae Dinophyceae Dinophyceae Dinophyceae Prymnesiophyceae Hydrozoa MAST MAST MAST Appendicularia Metazoa picobiliphytes Prasinophyceae  31  Figure 2.1  Shaded bathymetric map o f the North Water Polynya showing locations o f C T D casts, sample stations, and the average surface circulation (adapted from M e l l i n g et al. 2001). Lines 3 (West to East), 6 (West to East), and 7 (North to South) indicate section transects shown i n F i g . 2.2 and Fig. 2.5.  32  Line 6  Line 3 MB09  MB07  50  100  S  25.  J  Pot. Temp (°C)  Pot. Temp (°C) MB11  BA01  BA02  2.5  50  26  h - 1.5  26.  a  100  150  150  200  200  250  A  r i  27  I  0.5  0  -0.5 -1  1-15  50  100  150  East  Distance (km)  Pot. Temp (°C) L03  200  300 Distance (km)  1  South  Figure 2.2 Transects o f potential temperature and potential density along lines 3, 6, and 7 (see Fig. 2.1 for line locations). Stations are labeled and cast locations indicated.  3 3  Salinity  Figure 2.3 9-S diagram indicating water masses present in the North Water Polynya; Arctic surface water (AS), Arctic water ( A W ) , Baffin Bay surface water ( B B S ) , wintertime convection water ( W C W ) , and Atlantic deep water (AtD). Black dots mark 0S characteristics o f water bottle samples. Black lines are two end member casts that show all water masses present with little mixing. Dotted grey lines show 0-S profiles from all casts.  34  28  Figure 2.4  29  30  31  Salinity  32  33  34  35  Relative S i versus salinity diagram for station profiles from the North Water Polynya ( N O W ) . S i is calculated as per E q . (8) i n Tremblay et al. (2002). H i g h S i values indicate silicate-rich Arctic water ( S R A W ) ; l o w values indicate Baffin B a y water ( B B W ) . e x  e x  e x  35  Line 6  Line 3 C h l a (ng T )  C h l a (ug l" )  1  MB07  MB09  10  1  MB11  20  BA02  30  Distance (km)  0 . .  50  _  BA01  100  150  Distance (km)  Line 7 MB02  MB09  MB21  BA02  BA04  C h l a ( u g I') L03  Distance (km)  Figure 2.5  Transects o f chlorophyll fluorescence and potential density along lines 3, 6, and 7 (see F i g . 2.1 for line locations). Stations are labeled and casts indicated.  36  ,  Temperature  ,„ ,„, Salinity Chlorophyll  MB02  MB07  MB09  100 cL 150  200  Figure 2.6  Station profiles showing temperature, salinity, and chlorophyll a concentration (estimated from fluorescence). Sample names are indicated at their corresponding depth.  37  on  MBQ9  MB07  MB02  OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU  A042  "  A023  A041  Tmm  A040  IH  i i mmcm  A021  A039 AO 3 8  A019  A037  M B I A036  A033 A032 A031  MB21 A006  rnmmrn  m i z m i -  A004  ••••••1  BA02  j'  i II  •  •  —  •  i  »  A008 A007  WmmmWkWkmM  WBSl |  Ann 1 MUU 1  • all I • 1  A060  i  •  BR  50 100 Relative Intensity (%)  AO 54  1  • B  mmm  A053  1  A059  A052  1  A058  •  A051  1  A057  •  A050  1  A056  i  A049  I  0  l-  H  H  Hi  ••••  •  4H  OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU  L03  BA04  A m 1 AU 11  A009  WkmmmmmmmmmfWI^^^^'W^^^  A002  A012  A010  Hsnf^.aBBBBi  A003  mm t mm  i m i  mm  A005  • • • • • • •  i•  i i  BA01  KB^Hlfn"  A035 A034  1  I • • • •  •  100 50 Relative Intensity (%)  •a  wmm  1  A055  50 100 Relative Intensity (%)  Figure 2.7  Histograms showing changes i n picoeukaryote community structure at each station. The intensity o f each D G G E band type, or operational taxonomic unit ( O T U ) , relative to the total band intensity in each sample is shown. Samples are in order down the water column at each station. Refer to Figure 2.5 for corresponding depth.  38  A006' A005 A032T A026 A057 _ L A052-T A011 AO 54 A060 A053 A012 A059-r A051 A104 A103 A105 A106A033" A021 A027 A034 A031 A028 A048 A039 A019 A035-L A004-r A003 A044 A038 A025 AOIO A008 A002 A009 A043 AOOl A037 A007 A047 A046 A045 A023-L A042-T A030 A040 A029 A041 A036- A056' A055 A049 - L A058' A050 _ L  Cluster 1 - WGC surface Cluster 2  Cluster 3 - Baffin Bay surface  Cluster 4 - WCW and Beaufort Sea replicates  Cluster 5 - Arctic water  Cluster 6 - WGC and BBW  Cluster 7 - Arctic surface water  1  50 Similarity (%)  Cluster 8 - Atlantic deep water Cluster 9 - Ecotone?  100  Figure 2.8 Dendrogram showing similarity o f picoeukaryote assemblages from the North Water polynya. Dendrogram was derived from D G G E intensity band patterns by Agglomerative Hierarchical Clustering ( A H C ) using a Pearson Correlation Coefficient with Unweighted Pair Group Method with Arithmetic mean ( U P G M A ) algorithm. Dotted line indicates cluster branch cutoff similarity o f 50%. W C W - wintertime convection water; W G C - West Greenland Current; B B W - Baffin Bay Water.  39  A o Variables 0 Categories - - - • - - MB02 - - - D - - -MB07 - - - * - - MB09 - - - A - - MB11 • - - MB21 + "- BAOl - BA02 - - - A - - BA04 - - - • - - L03  •. A 0 5 6 .A0S8 A055 "-A057 a AO 50  B ° Variables o  Categories  » OTUs  N IS » 25 » 29  IN U.  % Pico Chla  FI (27.22 %)  Figure 2.9  ( A ) Canonical Correspondence Analysis ( C C A ) ordination diagram relating environmental variables to picoeukaryote assemblages. Each sample represents a community assemblage derived from denaturing gradient gel electrophoresis ( D G G E ) band intensity patterns. (B) C C A ordination with operational taxonomic units (OTUs). Both C C A s display 18.3% o f the inertia (weighted variance) i n O T U intensity and 42.6% o f the variance i n the weighted averages with respect to the environmental variables. For both diagrams the eigenvalues o f axis F I and F2 are 0.272 and 0.158, respectively; the eigenvalue o f F3 (not displayed) is 0.138. 40  2.5  References  Altschul, S. F., W . Gish, W . M i l l e r , E.W.Meyers and D . J . Lipman. 1990. Basic local alignment search tool. Journal o f Molecular Biology 214: 403. Arrigo, K . R. 2005. Marine microorganisms and global nutrient cycles. Nature 437: 349355. Bacle, J., E . C . Carmack, and R. G . Ingram. 2002. Water column structure and circulation under the North Water during spring transition: April-July 1998. Deep-Sea Research Part II-Topical Studies i n Oceanography 49: 4907-4925. Baas Becking, L . G . M . 1934. Geobiologie ofinleiding and Zoon, The Hague, Netherlands.  tot de milieukunde.  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Distribution o f eukaryotic picoplankton assemblages across hydrographic fronts i n the Southern Ocean, studied by denaturing gradient gel electrophoresis. Limnology and Oceanography 49: 1022-1034. Diez, B . , C . Pedros-Alio, T. L . Marsh, and R. Massana. 2001a. Application o f denaturing gradient gel electrophoresis ( D G G E ) to study the diversity o f marine picoeukaryotic assemblages and comparison o f D G G E with other molecular techniques. Applied and Environmental Microbiology 67: 2942-2951. Diez, B . , C . Pedros-Alio, and R. Massana. 2001b. Study o f genetic diversity o f eukaryotic picoplankton i n different oceanic regions by small-subunit r R N A gene cloning and sequencing. Applied and Environmental Microbiology 67: 29322941. Dunbar, M . and M . J. Dunbar. 1972. The history o f the North Water. Proceedings o f the Royal Society o f Edinburgh B: 231-241. Gast, R. J., M . R. Dennett, and D . A . Caron. 2004. Characterization o f protistan assemblages i n the Ross Sea, Antarctica, by denaturing gradient gel electrophoresis. Applied and Environmental Microbiology 70: 2028-2037. 41  Grasshoff, K . 1976. Methods o f Seawater Analyses. Verlag Chemie, Weinheim, N e w York. Ingram, R. G . , J. Bacle, D . G . Barber, Y . Gratton, and H . M e l l i n g . 2002. A n overview o f physical processes i n the North Water. Deep-Sea Research Part Ii-Topical Studies in Oceanography 49: 4893-4906. K l e i n , B . , B . LeBlanc, Z . P. M e i , R . Beret, J. Michaud, C . J. M u n d y , C . H . von Quillfeldt, M . E . Garneau, S. Roy, Y . Gratton, J. K . Cochran, S. Belanger, P. Larouche, J. D . Pakulski, R. B . R i v k i n and L . Legendre. 2002. Phytoplankton biomass, production and potential export i n the North Water. Deep-Sea Research Part Ii-Topical Studies i n Oceanography 49: 4983-5002. Lee, S. H . , and T. R. Whitledge. 2005. Primary and new production i n the deep Canada Basin during summer 2002. Polar Biology 28: 190-197. Legendre, L . and P. Legendre. 1998. Numerical Ecology, 2nd ed. Elsevier Science, Amsterdam. Lewis, E . L . , D . Ponton, L . Legendre, and B . Leblanc. 1996. Springtime sensible heat, nutrients and phytoplankton i n the Northwater Polynya, Canadian Arctic. Continental Shelf Research 16: 1775-1785. Lobb, J., A . J. Weaver, E . C . Carmack, and R. G . Ingram. 2003. Structure and mixing across an Arctic/Atlantic front in northern Baffin Bay. Geophysical Research Letters 30. [doi:10.1029/2003GL017755] Lopez-Garcia, P., F. Rodriguez-Valera, C . Pedros-Alio, and D . Moreira. 2001. Unexpected diversity o f small eukaryotes i n deep-sea Antarctic plankton. Nature 409: 603-607. Lovejoy, C , E . C . Carmack, L . Legendre, a n d N . M . Price. 2002. Water column interleaving: A new physical mechanism determining protist communities and bacterial states. Limnology and Oceanography 47: 1819-1831. Lovejoy, C , L . Legendre, M . J. Martineau, J. Bacle, and C . H . V o n Quillfeldt. 2002. Distribution o f phytoplankton and other protists i n the North Water. Deep-Sea Research Part Ii-Topical Studies in Oceanography 49: 5027-5047. Lovejoy, C , R. Massana, and C . Pedros-Alio. 2006. Diversity and distribution o f marine microbial eukaryotes in the Arctic Ocean and adjacent seas. Applied and Environmental Microbiology 72: 3085-3095. Lovejoy, C , W . F . Vincent, S. Bonilla, S. R o y , M - J . Martineau, R. Terrado, M . Potvin, R. Massana, and C . Pedros-Alio. In press. Distribution, phylogeny and growth o f cold-adapted picoprasinophytes in arctic seas. Journal o f Phycology L e Quere, C . L . , S. P. Harrison, I. C . Prentice, E . T. Buitenhuis, O. Aumont, L . Bopp, H . Claustre, L . C . D a Cunha, R. Geider, X . Giraud, C . Klaas, K . E . Kohfeld, L . Legendre, M . Manizza, T. Piatt, R. B . R i v k i n , S. Sathyendranath, J. Uitz, A . J. Watson and D . Wolf-Gladrow. 2005. Ecosystem dynamics based on plankton 42  functional types for global ocean biogeochemistry models. Global Change Biology 11: 2016-2040. Martiny, J. B . H . , B . J. M . Bohannan, J. H . Brown, R. K . C o l w e l l , J. A . Fuhrman, J. L . Green, M . C . Horner-Devine, M . Kane, J. A . Krumins, C . R. Kuske, P. J. M o r i n , S. Naeem, L . Ovreas, A . L . Reysenbach, V . H . Smith and J. T. Staley. 2006. Microbial biogeography: putting microorganisms on the map. Nature Reviews Microbiology 4: 102-112. Massana, R., V . Balague, L . Guillou, and C . Pedros-Alio. 2004. Picoeukaryotic diversity in an oligotrophic coastal site studied by molecular and culturing approaches. Ferns Microbiology Ecology 50: 231-243. Massana, R., L . Guillou, B . Diez, and C . Pedros-Alio. 2002. Unveiling the organisms behind novel eukaryotic ribosomal D N A sequences from the ocean. Applied and Environmental Microbiology 68: 4554-4558. Massana, R., R. Terrado, I. Forn, C . Lovejoy, and C . Pedros-Alio. 2006. Distribution and abundance o f uncultured heterotrophic flagellates i n the world oceans. Environmental Microbiology 8: 1515-1522. M e i , Z . P., L . Legendre, Y . Gratton, J. E . Tremblay, B . LeBlanc, C . J. M u n d y , B . K l e i n , M . Gosselin, P. Larouche, T. N . Papakyriakou, C . Lovejoy and C . H . von Quillfeldt. 2002. Physical control o f spring-summer phytoplankton dynamics in the North Water, April-July 1998. Deep-Sea Research Part Ii-Topical Studies in Oceanography 49: 4959-4982. M e l l i n g , H . , Y . Gratton, and G . Ingram. 2001. Ocean circulation within the North Water Polynya o f Baffin Bay. Atmosphere-Ocean 39: 301-325. M o o n - V a n Der Staay, S. Y , R. De Wachter, and D . Vaulot. 2001. Oceanic 18S r D N A sequences from picoplankton reveal unsuspected eukaryotic diversity. Nature 409: 607-610. Mostajir, B . , M . Gosselin, Y . Gratton, B . Booth, C . Vasseur, M . V . Garneau, E . Fouilland, F . Vidussi and S. Demers. 2001. Surface water distribution o f picoand nanophytoplankton in relation to two distinctive water masses i n the North Water, northern Baffin Bay, during fall. Aquatic Microbial Ecology 23: 205-212. Muylaert, K . , K . V a n Der Gucht, N . Vloemans, L . De Meester, M . Gillis, and W . Vyverman. 2002. Relationship between bacterial community composition and bottom-up versus top-down variables i n four eutrophic shallow lakes. Applied and Environmental Microbiology 68: 4740-4750. Not F , K . , Valentin, K . Romari, C . Lovejoy, R. Massana, K . Tobe, D . Vaulot, and L . Medlin. In press. Picobiliphytes: A marine picoplanktonic algal group with unknown affinities to other eukaryotes. Science. Nusch, E . A . 1980. Comparison o f different methods for chlorophyll and phaeopigment determination. A r c h . Hydrobiol. Beih. Ergebn. L i m n o l . 14: 14-36. 43  Odate, T., T. Hirawake, S. Kudoh, B . K l e i n , B . Leblanc, and M . Fukuchi. 2002. Temporal and spatial patterns i n the surface-water biomass o f phytoplankton in the North Water. Deep-Sea Research Part Ii-Topical Studies i n Oceanography 49: 4947-4958. Odum, E . P. 1971. Fundamentals i n Ecology, 3rd ed. W . B . Saunders, London. Rappe , M . S . , S. A . Connon, K . L . Vergin and S. J. Giovannoni. 2002. Cultivation o f the ubiquitous S A R I 1 marine bacterioplankton clade. Nature 418: 630-633. Schafer, H . , and G . Muyzer. 2001. Denaturing gradient gel electrophoresis i n marine microbial ecology, p. 425-468. In J. H . Paul [ed.], Methods i n Microbiology V o l 30. Academic. Shannon, C . E . , and W . Weaver. 1963. The mathematical theory o f communication. University o f Illinois Press, Urbana. Sherr, E . B . , B . F. Sherr, P. A . Wheeler, and K . Thompson. 2003. Temporal and spatial variation i n stocks o f autotrophic and heterotrophic microbes in the upper water column o f the central Arctic Ocean. Deep-Sea Research Part I-Oceanographic Research Papers 50: 557-571. Simpson, E . H . 1949. Measurement o f Diversity. Nature 163: 688. Strickland, J. D . H . and T. R. Parsons. 1972. A practical handbook o f seawater analysis. Bulletin Fisheries Research Board o f Canada 167: 311pp. Takahashi, M . , and P. K . Bienfang. 1983. Size Structure o f Phytoplankton Biomass and Photosynthesis i n Sub-Tropical Hawaiian Waters. Marine Biology 76: 203-211. Ter Braak, C . J. F. 1986. Canonical Correspondence-Analysis - a N e w Eigenvector Technique for Multivariate Direct Gradient Analysis. Ecology 67: 1167-1179. Ter Braak, C . J. F., and P. F. M . Verdonschot. 1995. Canonical Correspondence-Analysis and Related Multivariate Methods i n Aquatic Ecology. Aquatic Sciences 57: 255289. Tremblay, J. E . , Y . Gratton, E . C . Carmack, C . D . Payne, and N . M . Price. 2002. Impact o f the large-scale Arctic circulation and the North Water Polynya on nutrient inventories i n Baffin Bay. Journal o f Geophysical Research-Oceans 107. [doi: 10.1029/2000JC000595] U N E S C O . 1983. Algorithms for computation o f fundamental properties o f sea water. U N E S C O Tech. Pap. Mar. S c i . 44: 58pp.  44  3.  Conclusion 3.1  Picoeukaryote biohydrography  The importance o f picoeukaryotes i n the global ecosystem has become apparent in recent studies. While the significance o f prokaryotic picoplankton species, such as Synechococcus and Prochlorococcus, i n global marine productivity and biomass has been recognized, and substantial advancements have been made i n the understanding o f their spatial distribution and ecosystem functioning (Delong et al. 2006), investigation o f picoeukaryotes is just beginning. Particularly i n the Arctic, the dominant ecosystem role and genetic diversity o f picoeukaryotes is only now being appreciated (Lovejoy et al. 2006). A s global warming accelerates, drastic environmental change i n the Arctic, including increased water temperatures, reduced ice-cover, and altered circulation patterns, w i l l change the physical habitat for picoeukaryotes. H o w these changes w i l l affect their community structure and diversity, and their ultimate functioning i n the ecosystem, is currently unknown. This study provides one o f the first analyses o f the distribution o f picoeukaryotes in the Arctic in relation to the physical environment. We studied the biohydrography o f picoeukaryotes, their distribution and diversity in relation to the physical characteristics o f the water, i n the North Water Polynya ( N O W ) , one o f the largest and most productive polynyas i n the Arctic. The N O W is a region where distinct water masses intersect, Arctic water flowing south through Nares Strait meets Atlantic water flowing north through Baffin Bay. The distinct physical properties o f these two water masses, and the frontal zone at their intersection, provided an excellent study region for biohydrographic investigation. Using molecular techniques to determine community assemblages, we were able to investigate basic hypotheses o f microbial distribution. A s it is virtually impossible to disprove the presence o f a particular microbial species at one location, we used relative changes in D G G E community fingerprint intensity to reflect changes in community structure, a generally accepted method. Contrary to the conclusions o f Finlay (2002), we found that picoeukaryotes are not randomly distributed; they do exhibit biogeographic (referred to as biohydrographic in this study) distribution patterns. Further, we were able to associate community assemblages with particular water masses. Arctic and Atlantic water masses showed different assemblages, as did surface waters with different physical 45  characteristics and different origins; Arctic meltwater, West Greenland Current, and Baffin Bay surface waters all showed distinct assemblages. A t sites o f water mass mixing, assemblages seemed to maintain the composition o f their original water mass. Statistical analysis revealed the relative influence o f certain physical variables on variation i n communities. W e found that spatially related variables, such as depth and latitude, were correlated with picoeukaryote community structure. W e also found that variables related to phytoplankton biomass and overall community size structures were important influences. Samples associated with high total phytoplankton biomass, likely from diatom blooms, had a different picoeukaryote assemblage than samples with l o w phytoplankton biomass, those from depth or non-blooming sites. Using genetic sequencing o f extracted D G G E bands, we were also able to identify many o f the taxa present i n these waters. This study was successful i n providing insight into the factors structuring picoeukaryote community diversity i n Arctic waters. Although we were able to accomplish many o f the original goals o f the project, the study could have been improved by a number o f factors. We were unable to conclusively distinguish between the roles o f historical events versus contemporary environmental influence on the biohyorographic variation in communities. We did find that distinct waters masses had an associated distinct assemblage, and the similarities between assemblages carried over relatively large distances (100s o f kilometers), even i n regions o f mixing, indicating that there were multiple biohydrographic "provinces" differentiated by past events. These provinces however, generally had different physical conditions (temperature, salinity, nutrients), so we cannot rule out the influence o f contemporary environmental factors on community structure. W e also found indications o f ecotone communities, unique assemblages i n the strong physical gradient between separate ecosystems, and an influence o f phytoplankton size structure, evidence that picoeukaryotes are influenced by contemporary conditions. Martiny et al. (2006) outlined some o f the factors that need to be accounted for to differentiate between past history and the contemporary environment. One o f the most important is the "distance effect"; are closer samples more similar than distant ones? This is a non-trivial question for planktonic organisms in the marine environment where the habitat itself is fluid, moving and mixing. T o truly account for the effect o f distance we need to follow water mass trajectories. Depending on the flow o f water, sites closely situated i n space may, due to 46  opposing currents or dispersal boundaries such as pycnoclines, be effectively very distant from one another. T o do so requires the use o f conservative tracers, which was beyond the scope o f this study, but could be utilized i n future studies. A s well, other nonindependent statistical analyses, such as bootstrapped regression and the partial Mantel test (Borcard 1992; Mantel 1967), may be more instructive i n disentangling between the effects o f geographic distance versus the environment on assemblage composition. In the future, the utilization o f neutrally buoyant drifters that could track changes i n a water mass over time would be more appropriate for microbial oceanography, however this requires new technology. Real-time automated molecular sampling probes that could potentially sample the microbial realm at the same resolution as physical data are i n development (Scholin et al. i n press), and this technology would vastly expand our understanding o f the microbial world. A l s o , increasing the depth resolution o f sampling, even by traditional methods, at stations showing interleaving layers or strong physical gradients would provide greater insight into vertical variability o f communities. Despite these approaches that would improve the current study, we were able to discern patterns with the available data, likely indicating that both historical events and contemporary environmental factors contributed to the structure o f picoeukaryote communities. Further areas for improvement center around the data collected and the methods used i n the study. Substantial information would be gained i f the taxonomic identification obtained by sequencing extracted bands could be confidently matched to a particular O T U . This would enable description o f the spatial distribution o f particular species, and investigation o f biohydrographic patterns. Running the re-amplified extracted D G G E band beside the original sample would permit confident matching (Crump et al. 2003). Alternatively, analyzing samples on gels with a narrower range o f denaturant could separate potentially overlapping bands that confused sequencing results. Neither o f these methods is ideal however, as they both require substantial additional investment i n time and resources, conflicting with the very characteristics o f D G G E that make it appropriate for community analysis. Other technologies, such as culture cloning, D N A micro-arrays, and shotgun sequencing would provide the desired taxonomic information, but as with D G G E , each has its strengths and weaknesses. The D G G E technique itself would be improved by development o f an in-lab standard with greater gel separation, permitting  47  more accurate standardization between gels, and potentially allow comparison among future projects utilizing the same protocol. The biohydrographic approach used i n this study acknowledged the influence o f the physical environment on microbial assemblages, and the investigation was structured accordingly. W e attempted to understand the hydrographic environment o f the N O W i n as much detail as we could using previous work and our observations. The interdisciplinary nature o f the project, investigating both physics and biology, is important to understand processes i n the North Water. Utilization o f novel molecular genetic techniques, multivariate statistics, and traditional oceanographic tools, places this project at a unique juncture i n marine research, where exciting advancements i n our knowledge o f the world's oceans w i l l undoubtedly occur i n the near future. This project is an initial contribution to our understanding o f picoeukaryote ecology i n the Arctic. The results described here w i l l provide a framework for designing on-going research i n the N O W , and throughout the Arctic Ocean. Additional information on picoeukaryote communities is currently being obtained by our laboratory through continued sampling and an ever-increasing array o f analytical tools, including cloning and culturing, flow cytometry, real-time polymerase chain reaction ( R T - P C R ) , and fluorescent in-situ hybridization (FISH), all o f which w i l l enhance our understanding o f picoeukaryote abundance, diversity, physiology, and ecosystem functioning. Research is concurrently expanding to include marine Bacteria and Archaea, for a more holistic understanding o f microbial processes i n the rapidly changing Arctic. In collaboration with researchers from other disciplines, from physical oceanographers and climate modelers to chemists and fisheries scientists, this work brings us one step closer to understanding this unique cold ocean ecosystem. The importance o f which cannot be underestimated, as a changing Arctic may have pronounced affects on the state o f the global ecosystem.  48  3.2  References  Borcard, D . , P. Legendre, and P. Drapeau. 1992. Partialling out the Spatial Component o f Ecological Variation. Ecology 73: 1045-1055. Crump, B . C , C . S. Hopkinson, M . L . Sogin, and J. E . Hobbie. 2004. Microbial biogeography along an estuarine salinity gradient: Combined influences o f bacterial growth and residence time. Applied and Environmental Microbiology 70: 1494-1505. Delong, E . F., C . M . Preston, T. Mincer, V . Rich, S. J. Hallam, N . U . Frigaard, A . Martinez, M . B . Sullivan, R. Edwards, B . R. Brito, S. W . Chisholm and D . M . K a r l . 2006. Community genomics among stratified microbial assemblages i n the ocean's interior. Science 311: 496-503. Finlay, B . J. 2002. Global dispersal o f free-living microbial eukaryote species. Science 296: 1061-1063. Lovejoy, C , R. Massana, and C . Pedros-Alio. 2006. Diversity and distribution o f marine microbial eukaryotes i n the Arctic Ocean and adjacent seas. Applied and Environmental Microbiology 72: 3085-3095. Mantel, N . 1967. Detection o f Disease Clustering and a Generalized Regression Approach. Cancer Research 27: 209-220. Martiny, J. B . H . , B . J. M . Bohannan, J. H . Brown, R. K . C o l w e l l , J. A . Fuhrman, J. L . Green, M . C . Horner-Devine, M . Kane, J. A . Krumins, C . R. Kuske, P. J. M o r i n , S. Naeem, L . Ovreas, A . L . Reysenbach, V . H . Smith and J. T. Staley. 2006. Microbial biogeography: putting microorganisms on the map. Nature Reviews Microbiology 4: 102-112. Scholin, C . A . , G . J . Doucette, and A . D . Cembella. In press. Prospects for developing automated systems for i n situ detection o f harmful algae and their toxins. In M . Babin, C . S . Roesler and J.J. Cullen [eds.], Real-Time Coastal Observing Systems for Ecosystem Dynamics and Harmful A l g a l Bloom. U N E S C O Publishing, Paris, France.  49  Appendix A 35% I  <<  m  u-j  ^ O o o © *© ho M o o — — © © o © © ©  333  <<<<<<  t/3  33 < <  m  ©  —  CN  m  m  m m  © <co  d  < © © < o< ©<© < <  © © © © © o o © © © © © o n < < < < < < < < < < < <cn  ©  C/0 <  _  <  ©  VI ©  Tt©  *C  < <<  i c  55%' TJ-  1/3  < < < « 3 < < : < < < < c f l  © © O O O © © & o < < < < < < <  i n so  3 2  a  o © ©  << <  ^  <  <  CV3  ©  ©  t/j < <  m  ( N f*3 TJcn m m  i n so m m  © © © o © ©  < <<< < <  Appendix A Composite denaturing gradient gel electrophoresis image. S - standard lane.  o © o © © © © © © ^<<<<<<<<<<<  © ©  <©  

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