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Pelagic microorganisms in the northern Baltic Sea

- Ecology, diversity and food web dynamics.

Johnny Berglund

2005

Department of Ecology and Environmental Science

Umeå Marine Sciences Centre

Umeå University

SE-901 87 Umeå

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PhD Thesis

Department of Ecology and Environmental Science Umeå Marine Sciences Centre

Umeå University SE-901 87 Umeå

A doctoral thesis at a university in Sweden is produced as a monograph or as a collection of papers. In the latter case, the introductory part constitutes the formal thesis, which summarizes the accompanying papers. These have either already been published or are manuscripts at various stages (in press, submitted or manuscript).

ISBN: 91-7305-957-9 © Johnny Berglund 2005

Printed by Print & Media

Layout by Ralf Elo, Print & Media: 2001244

Cover: DAPI stained water sample from the experiment in paper III, visualized using epifluorescence microscopy, photo by Johnny Berglund.

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Table of contents

LIST OF PAPERS... 3

INTRODUCTION ... 4

RECENT DEVELOPMENTS WITHIN AQUATIC MICROBIAL ECOLOGY... 4

COMPONENTS OF THE PELAGIC FOOD WEB... 5

FOOD WEB DYNAMICS... 7

FOOD WEB EFFICIENCY... 8

DIVERSITY OF EUKARYOTIC MICROORGANISMS... 9

STUDY AREA: THE NORTHERN BALTIC SEA... 10

OBJECTIVES OF THIS THESIS ... 12

RESULTS & DISCUSSION... 13

LIMITATION AND CONTROL OF HETEROTROPHIC PROTISTS... 13

THE EFFECT OF RESOURCE HETEROGENEITY ON THE PELAGIC FOOD WEB EFFICIENCY... 16

IDENTITY OF NANOFLAGELLATES... 17

CONCLUSIONS AND FUTURE PERSPECTIVES ... 20

ACKNOWLEDGEMENTS... 22

REFERENCES ... 23

POPULÄRVETENSKAPLIG SAMMANFATTNING ... 33

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List of papers

This thesis is based on the following papers, which will be referred to in the text by their Roman numerals.

I. Berglund J., Samuelsson K., Kull T., Müren U. and Andersson A. (2005) Relative strength of resource and predation limitation of heterotrophic nanoflagellates in a low-productive sea area. Journal of Plankton Research, 27: 923-935.

II. Samuelsson K., Berglund J. and Andersson A. Factors structuring the heterotrophic flagellate and ciliate communities along a brackish water primary production gradient. Submitted manuscript.

III. Berglund J., Müren U., Båmstedt U. and Andersson A. Efficiency of a phytoplankton and bacterial-based food web in a pelagic marine ecosystem.

Submitted manuscript.

IV. Berglund J., Jürgens K., Bruchmüller I., Wedin M. and Andersson A. (2005) Use of group-specific PCR primers for identification of chrysophytes by denaturing gradient gel electrophoresis. Aquatic Microbial Ecology, 39: 171-182.

V. Berglund J., Samuelsson K., Kruys Å. and Andersson A. Spatial and temporal variation of chrysomonads in the northern Baltic Sea estimated by DGGE and epifluorescence microscopy. Manuscript.

Paper I and IV have been reproduced with the kind permission from the publishers: Oxford University Press and Inter-Research, respectively.

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Introduction

Recent developments within aquatic microbial ecology

The development of microscopy and chemical techniques in the 1970´s made us realize the importance of microorganisms in the pelagic environment. Epifluorescence microscopy and different staining dyes like Acridine Orange and DAPI made it possible to estimate the true abundance and biovolume of heterotrophic bacteria and heterotrophic flagellates (Hobbie et

al. 1977, Porter and Feig 1980). Sensible measures of bacterial production became

manageable by the tritiated thymidine incorporation method (Fuhrman and Azam 1980). Azam and co-workers (1983) recognized the role of heterotrophic bacteria and that there was a close link between the releases of dissolved organic matter (DOM) from phytoplankton and bacterial growth. They also concluded that tiny flagellates by phagotrophy could control bacterial density and that the flagellates in turn formed a link to microzooplankton, i.e. ciliates, rotifers and mesozooplankton. They called this transfer of matter from DOM produced by phytoplankton via bacteria, flagellates to microzooplankton “the microbial loop”. After the conceptual paper by Azam and coauthors (1983) the microbial food web became a popular study object with increasing number of studies until present day (Fig. 1). Even though the microbial food web paradigm (Azam et al. 1983) has greatly expanded our view about the flow of energy within the plankton community, we are still far from understanding the dynamics of microbial populations and the factors that control them.

The second revolution in techniques for microbial ecologists came in the late 1990´s. The integration of culture-independent molecular biological methods into microbial ecology made it possible to reveal the species composition and richness within the “black-box” of pico- and nano-sized prokaryotes and eukaryotes. Fingerprinting methods like denaturing gradient gel electrophoresis (DGGE) and restriction fragment length polymorphism (RFLP), and clone libraries became popular for analysis of indigenous communities (Muyzer et al. 1993, Laguerre et al. 1994, Schäfer and Muyzer 2001). Fluorescence in-situ hybridization (FISH) with rRNA-targeted probes and real-time quantitative PCR were another set of new techniques that became valuable for quantification of pico- or nano-sized microorganisms (Giovannoni et al. 1988, Amann et al. 1995, Lim et al. 2001b, Massana et al. 2002, Groben and Medlin 2005).

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0 10 20 30 40 50 60 70 80 90 100 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 H its i n W e b of S c ie nc e

"DGGE + (aquatic or pelagic)" "microbial food web" "microbial loop"

Figure 1. The number of publications per year that use the terms ‘microbial loop’, ‘microbial food web’ or DGGE within the title, abstract or keywords during the last 15 years according to Web of Science. The search for the term DGGE was restricted to aquatic and pelagic studies.

However, all these methods have only been used for a few years within the aquatic environment (c.f. DGGE in Fig. 1). The pace of the development is still very rapid, new methods are incorporated or different methods are mixed together, and there is still a lot to improve concerning the confidence of the results and detection limit of the different methods. Nevertheless these methods have the potential to revolutionize microbial ecology, once again.

Components of the pelagic food web

Pelagic food webs are generally strongly size-structured with larger predators feeding on smaller prey (Sheldon et al. 1972, Azam et al. 1983, Wikner and Hagström 1988, Jennings et

al. 2002, Samuelsson and Andersson 2003). Even though there are many examples of pelagic

microorganisms e.g. nanoflagellates or dinoflagellates that may ingest particles of their own size (Havskum and Hansen 1997, Sherr and Sherr 2002), the main flow of particulate matter is towards larger organisms (Fenchel 1988, Wikner and Hagström 1988, Jennings et al. 2002, Samuelsson and Andersson 2003). There is also a minimum size of prey that can be efficiently ingested for any phagotrophic predator of a given size. Predators may ingest prey that is 100 times smaller than them selves, but due to mechanical and physiological constraints the ingestion efficiency or nutritional value normally decreases. In fact, typical

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length ratios between pelagic prey and predator seem to be around 1:10 (Fenchel 1988, Reuman and Cohen 2004). In terms of body mass the predator is usually between one and three order of magnitude larger than the prey (Reuman and Cohen 2004, Woodward et al. 2005).

The pelagic organisms have traditionally been divided by size into pico- (0.2-2 µm), nano- (2-20 µm), micro- (20-200 µm) or mesoplankton (>200 µm) (Fenchel 1988). These size groups have in a very schematic way been considered as separate trophic levels.

Heterotrophic bacteria, unicellular cyanobacteria and small phototrophic eukaryotes are pico-sized organisms at the base of the microbial food web (Fenchel 1988). Heterotrophic protists like flagellates and small ciliates in the nanoplankton size-fraction are important grazers on picoplankton (Sanders et al. 1992, Caron 1999, Sherr and Sherr 2002, Weisse 2002). There is a general consensus from both marine and freshwater systems that heterotrophic nanoflagellates with a size of 2-5 µm are dominating grazers on heterotrophic bacteria (Rassoulzadegan and Sheldon 1986, Wikner and Hagström 1988, Sherr and Sherr 2002). Herbivory among nano-protists has recently been acknowledged (Sherr and Sherr 2002 for review, Weisse 2002). The relative importance of heterotrophic and phototrophic picoplankton as resource for heterotrophic protists may depend on their biomass distribution. Evidently phototrophic prokaryotes like Prochlorococcus and Synechococcus, and picoeukaryotes like prasinophytes may constitute up to 70% of the picoplankton biomass in the open sea (Sherr and Sherr 2002). In the Gulf of Bothnia pico-algae, mainly phototrophic prokaryotes (cyanobacteria) constitute between 35 and 50% of the picoplankton biomass (Sandberg et al. 2004). However, cyanobacteria seem to be a less important food resource for small heterotrophic protists < 5 µm than heterotrophic bacteria (Wikner et al. 1990, Samuelsson and Andersson 2003).

Heterotrophic or mixotrophic dinoflagellates and ciliates in the microplankton size-fraction are efficient grazers on nanoplankton (Weisse 1991, Gasol et al. 1995, Weisse 2002), but also rotifers and mesozooplankton like cladocerans and copepods are recognized predators on nanoplankton (Gifford and Dagg 1988, Dolan and Gallegos 1991, Gasparini and Castel 1997, Merrell and Stoecker 1998, Finlay and Roff 2004). Picoplankton seems to be too small to be effectively ingested by mesozooplankton (Vargas and Gonzalez 2004, Finlay and Roff 2004).

There is of course a myriad of prey and predators of different sizes, and predators of similar size may have different prey-size preferences. To organize the microbial food web into trophic levels by size may be difficult. For example, the nano fraction may contain more

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than four trophic levels (Rassoulzadegan and Sheldon 1986, Wikner and Hagström 1988). However, when considering effective size ratio between predator and prey the main flow of particulate matter should go through size steps of around one order of magnitude in length (Fenchel 1988, Reuman and Cohen 2004, Woodward et al. 2005).

Why all this talk about size and trophic levels? Sorting of organisms into discrete trophic levels has been the basis of major controversy during the 1990´s (Polis 1991, Polis and Strong 1996, Persson et al. 1996). Arguments against the trophic level concept are for example that many organisms are omnivorous (feeding on several trophic levels), and that the numerous interactions (both weak and strong) between organisms are simply too many and too complex to be organized into one major interaction (Polis 1991). Despite the arguments, it certainly increases the possibility to understand and clarify the flow of energy and the dynamic properties of the food web, if the organisms can be divided into tropic levels (Oksanen et al. 1981, Persson 1999). The diversity within the different trophic levels is not neglected, but the interactions between organisms are narrowed down to one dominating link between trophic levels (Oksanen 1992).

Food web dynamics

There are several theoretical frameworks dealing with trophic interactions that have been put forward. The Exploitation Ecosystem Hypothesis model (EEH, Oksanen et al. 1981, Oksanen and Oksanen 2000) originating from the seminal paper “Why is the world green” by Hairston

et al. (1960) has generated an extensive debate, while it at the same time has fostered an

ecological way of thinking (Persson 1999 for review). EEH assumes that the interaction between organisms in the studied system can be “squeezed” down to interactions between trophic levels, creating a linear food chain, and that there is a limited amount of resource for the system. The model predicts that the controlling factor (resource or predation) should alternate between adjacent trophic levels and that the food-chain length would increase with productivity (Oksanen 1981). Empirical studies show, however, that all trophic levels may increase with productivity, indicating that all the model assumptions are not fulfilled in natural systems (McQueen et al. 1986, Brett and Goldman 1997, Steiner 2001). Several factors, such as prey refuges, heterogeneous trophic levels and omnivory, violate the predictions of the linear food web theory (Leibold 1989, Diehl and Feissel 2000, Steiner 2001).

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Another framework of ‘Cascading trophic interactions’ has evolved especially in pelagic systems where it has been found that by removing piscivorous fish, the biomass of planktivorous fish will increase, which will lead to a decreased zooplankton biomass and increased phytoplankton biomass (Carpenter et al. 1985, Persson 1999 for review). By using the framework of trophic cascades, predictions about the abundance and production of organisms at different trophic levels can be made (Carpenter et al. 1985, Persson 1999). The theory assumes predation limitation on all trophic levels (Polis and Strong 1996). This framework requires similar conditions as the EEH. Thus, the prey should be highly edible and the impact of omnivory, should be low (Polis and Strong 1996, Mikola and Setälä 1998). If the model is applied on a community level i.e. trophic levels, there is still a need to “squeeze” all organisms into the same compartment. Due to this Polis and Strong (1996) argue that trophic cascades are restricted to simple food webs with highly edible preys, low spatial heterogeneity and keystone-like grazers or predators.

In this thesis I have adopted some of the ideas about trophic interactions. The main flow of carbon within the microbial food web in northern Baltic Sea according to Samuelsson and Andersson (2003) is through a linear food web of heterotrophic bacteria, nanoflagellates and ciliates. In paper I and II the heterotrophic flagellates and ciliates are classified by size and treated similar as trophic levels.

Food web efficiency

In the initial recognition of the microbial loop the question whether it constituted a “sink” or a “link” in the transfer of energy to higher trophic levels was discussed (Sieburth and Davis 1982, Azam et al. 1983, Ducklow et al. 1986). The microbial loop may be considered as a sink in its original depicted form, since the amount of carbon derived from primary production is severely reduced through respiration if it is directed through the microbial food web instead of transported directly to mesozooplankton (Fenchel 1988 for elucidation). The microbial food web is certainly a link when considering that DOM is made available for higher trophic levels like mesozooplankton. It is evident that allochthonous carbon (originating from outside the ecosystem) can support growth of secondary producers in many freshwater (Tranvik 1992, Karlsson et al. 2001, 2003, Pace at al. 2004, Daniel et al. 2005) and marine systems (Findlay et al. 1991, Zweifel et al. 1995, Rolff and Elmgren 2000, Sandberg et al. 2004). It is evident in these systems that the microbial food web is an important link to mesozooplankton.

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However, the number of trophic steps and the gross growth efficiency of the organisms will affect ecological efficiency of the food web i.e. the proportion of the resource production that is converted to production at the top trophic level. Furthermore, the size of the resource and the size ratio between predators and prey determine which pathway the energy will take, through the “classical food web” or through the “microbial food web” (Fenchel 1988). The number of trophic steps between the producer and the top predator will vary with the main pathway. Hence, the specificity of the resource (e.g. cell-size and quality) may have effects on higher trophic levels (e.g. Persson et al. 2001). In the northern Baltic Sea, the terrestrial derived dissolved organic carbon (TDOC) supply accounts for 40% of the carbon input into the food web via heterotrophic bacteria (Sandberg et al. 2004). The phytoplankton primary production is low in this sea area compared to that in the Baltic Proper (Andersson et

al. 1996). In paper III the food web efficiency of a bacterial-based food web is compared to

that of a phytoplankton-based one. Hence, we return to the “sink” or “link” discussion and actually measure the efficiency of the microbial food web.

Diversity of eukaryotic microorganisms

The diversity of pelagic eukaryotes has traditionally been estimated by light- or electron-microscopy. Morphological characteristics have been used to identify different species. Pico- or nano-sized cells have normally been sorted into unidentified groups depending on cell size, pigment properties or occurrence of flagella. Many of these cells may be identified by light and electron microscopy, but their identification is time-consuming and requires great taxonomic expertise. In addition, some cells (e.g. naked chrysomonads) seem to lack diagnostic morphological features that could be used as taxonomic characteristics (Boenigk et

al. 2005). In such cases the identification is limited to the genus or class level. Recent

advances in aquatic microbial ecology have also recognized problems with the use of morphological characteristic for taxonomic identification. For example, the dinoflagellates

Alexandrium tamarense and A. fundyence have morphologically been separated by the

presence of a single pore on the apical plate, but studies of toxin composition, sexual compatibility, bioluminescence capacity and rRNA gene sequences show that these species are extremely similar and should probably be considered as one single species (Anderson et

al. 1994).

During the last decade molecular methods have been developed to study the diversity of indigenous microbial communities independent of the classical techniques such as

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cultivation and microscopic identification (e.g. Amann et al. 1995, Schäfer and Muyzer 2001). Although most of these molecular methods have been used to study prokaryotes, similar methods have been applied to eukaryotic microorganisms. For example, 18S ribosomal DNA (rDNA) genetic libraries have revealed an astonishing diversity among picoeukaryotes (Díez et al. 2001b, López-García et al. 2001, Moon-van der Staay et al. 2001, Massana et al. 2004). Fluorescent in situ hybridization (FISH) with eukaryotic-specific probes has been used for quantitative studies of heterotrophic marine protists (Caron et al. 1999, Lim

et al. 1999, Massana et al. 2002). Eukaryotic diversity of complex communities has been

illuminated by PCR-based DNA fingerprinting techniques such as DGGE (van Hannen et al. 1998, Díez et al. 2001a, Rasmussen et al. 2001, Gast et al. 2004) and restriction fragment length polymorphism (Lim et al. 2001a, Díez et al. 2001a, Massana and Jürgens 2003). Novel species and even novel phylogenetic lineages of yet uncultured organisms have been found in almost all molecular studies concerning eukaryotic microorganisms (López-García et al. 2001, Moon-van der Stay et al. 2001, Massana et al. 2002, Savin et al. 2004). Comparisons between traditional morphological methods and molecular approaches for identification of phytoplankton show little congruence (Savin et al. 2004). Both methods seem to capture a portion of the total eukaryotic diversity. Paper IV and V in this thesis utilize the PCR-DGGE molecular method to reveal the diversity of chrysomonads, which is an important group of nanoflagellates in the pelagic food web. I also compare the use of the PCR-DGGE method with epifluorescence microscopy for quantitative estimation of chrysomonads (V).

Study area: The northern Baltic Sea

The Baltic Sea consists of a series of basins of varying depth and size (Fig. 2). The northernmost basin, the Bothnian Bay, which is characterized by a large freshwater input of about 98 km3 y-1, has a salinity of only 2-3 ‰ (Sandberg et al. 2004). The adjacent basin, the Bothnian Sea receives a similar load (95 km3 y-1) but due to its 3-fold larger size and closer position to entrance to the North Sea, the salinity remains between 5-6 ‰. The salinity in the Baltic Proper is between 6-8 ‰. Terrestrial dissolved organic matter (TDOM) is also entering the basins with the freshwater input. Hence, there is a gradient with increasing salinity and decreasing TDOM load from north to south. Terrestrial derived dissolved organic carbon (TDOC) has been shown to support secondary production at least within the Bothnian Bay (Zweifel et al. 1995 for bacterial production, Rolff and Elmgren 2000 for mesozooplankton, Sandberg et al. 2004).

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There is also a gradient with a nearly ten-fold increase in primary production from north to south (II). The primary production is approximately 20-30 g C m-2 year-1 in the Bothnian Bay and 50-110 g C m-2 year-1 in the Bothnian Sea (Andersson et al. 1996, Sandberg et al. 2004, II). The Baltic Proper has an annual primary production of ~170 g C m

-2 year-1 (Johansson et al. 2004). For comparison, the average production rate of open oceans is

125 g C m-2 year-1, while in upwelling areas the average production rate is up to 500 g C m-2 year-1 (Valiela 1995). Thus, the Gulf of Bothnia can be considered an oligotrophic low-productive sea area. Secondary production by bacteria is relatively uncoupled from the increase in primary production and varies between 10 and 20 g C m-2 year-1 in the study area (II). The phytoplankton community is generally phosphorous limited in the Bothnian Bay and nitrogen limited in the Bothnian Sea and Baltic Proper (Granéli et al. 1990, Andersson et al. 1996). I have utilized the gradient in productivity in northern Baltic Sea in paper I, II and V to elucidate effects of increased productivity on the microbial food web (Fig. 2).

.

Figure 2. The study area showing the sampling stations in the Bothnian Bay (BB), the Bothnian Sea (BS) and the Baltic Proper (BP) for paper I, II, IV and V. The Bothnian Bay and the Bothnian Sea together constitute the Gulf of Bothnia. Experiments for paper I, II and III were performed close to or at Umeå Marine Sciences Centre (UMSC).

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Objectives of this thesis

The main objective of this thesis was to study how the different components of microbial food webs are regulated. What is limiting growth or controlling biomass of heterotrophic eukaryotes in the northern Baltic Sea (I, II and V)? What happens if the resource for the pelagic food web is changed from being phytoplankton based to bacterial based (III)? Who are the main players within the heterotrophic or mixotrophic nanoflagellate compartment of the northern Baltic Sea food web (II, IV and V)? More specifically:

The aim of paper I was to empirically determine the relative importance of resource and predation limitation of heterotrophic nanoflagellates in the northern Baltic Sea.

The main aim of paper II was to identify factors influencing the abundance and size structure of heterotrophic flagellates and ciliates in the northern Baltic Sea.

The main aim of paper III was to reveal the effects of a change of the basal resource on the ecological efficiency of marine food webs.

The aim of paper IV was to develop and optimize chrysophyte-specific primers that could be utilized in DGGE.

The aim of paper V was to determine the occurrence and seasonal dynamics of heterotrophic or mixotrophic chrysomonads in the open sea in the Gulf of Bothnia.

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Results & Discussion

Limitation and control of heterotrophic protists

Two of the most important factors shaping biological communities are competition for resources and predation. The magnitude of resource and predation limitation of the heterotrophic nanoflagellate (HNF) community was studied in two short-term enclosure experiments performed in June and September 2001 in a coastal area (I). Resource limitation was removed by adding bacteria (Pseudomonas fluorescence) and predation limitation was removed by selective filtration. The change in net growth rate due to resource addition or predator removal was used as a measure of the strength of limitation (Osenberg and Mittelbach 1996). Small heterotrophic flagellates were found to be limited by the amount of resource both in June and September, and addition of bacteria resulted in a net growth increase of 60% and 74%, respectively. Removal of predators had a significant effect only in June. Field data from open sea on abundance of HNF and bacteria were compared to results from a qualitative model by Gasol (1994). Only the bacterivorous flagellates (” 5 µm) and the edible size-fraction of the bacterial community (> 0.07 µm3) was compared to the model data, since it assumes that the flagellates only feed on bacteria and that all bacteria are edible (I for elucidation). The qualitative model showed that the abundance of small HNF was bottom-up controlled during most of the year, except in May and early June in the Bothnian Bay where flagellates seemed to be top-down controlled. Hence, both the net growth rate and the equilibrium abundance of small flagellates were constrained by the amount of resource (I). This was further verified by the significant correlation between temperature and bacterial biovolume concentration, and the biovolume concentration of small heterotrophic nanoflagellates and chrysomonads in the Baltic Sea (II, V). On a large scale the average annual biovolume concentration of small heterotrophic flagellates increased significantly with primary production, but on a smaller temporal scale temperature and bacterial biovolume concentration explained most (46%) of the variation in biovolume concentration. These results were in agreement with other studies from freshwater and marine systems indicating that the seasonal variation of flagellates in general is regulated by temperature and bacteria (Berninger et al. 1991, Hansen and Christoffersen 1995, Weisse 1997, Sestanovic et al. 2004). Temperature per se is directly regulating both HNF grazing rates and bacterial production, and thus indirectly bacterial biomass (Peters 1994, Montagnes et al. 2003). No previous studies that had actually measured the relative importance of resource and predation

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limitation of HNF could be found. Previous studies on HNF limitation have mostly focused on single factors (e.g. Jürgens et al. 1996, Calbet et al. 2001, Samuelsson and Andersson 2003), although some experiments indicate the occurrence of simultaneous resource and predation limitation of HNF (Weisse, 1991, Pace and Funke, 1991, Kivi et al., 1993). Kivi and co-workers (1993), who performed mesocosm experiments in the Baltic Proper, found a positive effect of nutrient addition on HNF net growth, indicating the presence of resource limitation. At the same time a cascade effect was observed due to mesozooplankton exclusion, indicating presence of predation limitation. However, the relative contribution of the limiting factors was not measured.

The annual average biovolume concentration of large heterotrophic flagellates did also increase with productivity, but their seasonal dynamics were explained by temperature and bacterial biovolume concentration by only 20%. These results favor the general assumption that small flagellates with a size ” 5 µm are the main bacterivores, while large flagellates are probably more algivorous, i.e. more dependent on the biovolume concentration of pico- or nano-algae (Rassoulzadegan and Sheldon 1986, Wikner and Hagström 1988, Sherr and Sherr 2002, Samuelsson and Andersson 2003). Small flagellates are also grazed by large flagellates (Wikner and Hagström 1988, Samuelsson and Andersson 2003). However, in the study area large flagellates did not significantly reduce the net growth rate of small flagellates (I).

The annual average biovolume concentration of ciliates did also increase with productivity (II). However, no bottom-up factor (bacterial biovolume concentration, bacterial production, HNF biovolume concentration or amount of chlorophyll a) could explain the variation in ciliates on a smaller seasonal scale. Even though the growth rate of ciliates in June seemed to be mainly limited by the amount of resource in a coastal area (Samuelsson and Andersson 2003, I), the biomass and community composition of ciliates in the northern Baltic Sea was found to be strongly affected by predation of mesozooplankton (Johansson et

al. 2004, II). Studies in the Baltic proper suggest that the ciliate biomass is predation

controlled, while the production may be limited by the resource (Johansson et al. 2004, Kuuppo et al. 2003). Hence, the production of ciliates is cropped down by mesozooplankton. Possibly, mesozooplankton biovolume concentration could have explained the seasonal pattern of ciliates in paper II. There is also a possible shift in the controlling factor of ciliates, from resource control in the Gulf of Bothnia to predation control in the Baltic Proper. The productivity in the Gulf of Bothnia may be too low for significant predation limitation. Empirical data of plankton systems imply that the importance of predation increases with increasing productivity (Sarnelle, 1992, Mazumder, 1994, Steiner, 2001). Similarly, bacterial

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net growth rate was found to be mainly predation limited in the high productive Gotland Basin, central Baltic Sea, while bacterial growth in the Bothnian Sea was mainly resource controlled (Kuuppo et al. 2003, I). However, it is not easy to determine the relative importance of resource and predation limitation for the growth of heterotrophic bacteria, since the predators i.e. HNF may also enhance bacterial growth through nutrient remineralisation (Jumars et al. 1989, Selph et al. 2003). From a strictly cross-factorial experiment performed close to UMSC, it was evident that the community growth rate was enhanced by the presence of predators. The positive effect of nutrient remineralisation by flagellates on bacterial growth was significant even though the nutrient limitation for bacteria was reduced by addition of glucose (Larsson P. et al. unpublished).

The relative importance of resource and predation for the abundance of a population/trophic level is believed to depend on the position in the trophic hierarchy and the productivity of the system (Oksanen et al. 1981). The controlling factor should alternate between adjacent trophic levels and the equilibrium biomass of adjacent trophic levels should respond differently to increasing productivity (Oksanen et al. 1981). It is clear that the biomass of protozoa at different trophic levels increased with increasing productivity in the northern Baltic Sea, but all size groups did not show a clear significant increase. Small bacterivorous flagellates in the northern Baltic Sea increased significantly with productivity, while mid-sized flagellates (5-10 µm) and ciliates (<40 µm) did not. These latter groups were possibly more controlled by predation. Furthermore, the biovolume concentration of large flagellates (>10 µm) and large ciliates (>40 µm) significantly increased with productivity, indicating resource limitation. If these size groups could be considered as adjacent trophic levels, it could be concluded that the microbial food web in the northern Baltic Sea behaves as predicted by the linear food chain theory. Thus, adjacent trophic levels respond differently to the increase in productivity (Oksanen et al. 1981). Small ciliates (<30 µm) have been found to prefer feeding on picoplankton, while larger ciliates (30-50 µm) prefer nanoplankton (Rassoulzadegan et al. 1988). Hence, these groups could be considered as different trophic levels. Similarly small (<5 µm) and large (>5 µm) heterotrophic nanoflagellates seems to graze on different food resources under natural conditions (Rassoulzadegan and Sheldon 1986, Sherr and Sherr 2002, Samuelsson and Andersson 2003). Persson et al. (1996) suggest the use of trophic groups defined as species or groups of species that have similar dynamics because they share the same resource or predator. The different protistan groups could certainly be considered as trophic groups, but the food web is not linear since e.g. larger flagellates and ciliates graze on both heterotrophic bacteria and picoalgae (Sherr and Sherr

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2002, Samuelsson and Andersson 2003). In addition, omnivorous organisms feeding on several size classes are commonly occurring (Havskum and Hansen 1997, Sherr and Sherr 2002). Furthermore, the utilized size groups are just artificially set and may not be relevant for the flow of energy. More work is needed in order to locate relevant trophic groups, although it might not be worth the trouble (Polis and Strong 1996).

The effect of resource heterogeneity on the pelagic food web efficiency

Phytoplankton is considered as the major source of food for zooplankton grazers, with bacteria serving only as a food supplement (Pomeroy 1974, Azam et al. 1983). However, in certain ecosystems bacteria may have an important role in carbon and energy flow. Bacteria can reincorporate up to 50% of the carbon that is released by phytoplankton (Pomeroy 1974, Azam et al. 1983), and they also may take up recalcitrant allochthonous dissolved organic carbon (ADOC) (Tranvik 1992). This recalcitrant DOC represents additional carbon that is available for transfer to higher trophic levels.

Mesocosm experiments were performed to study the transfer of carbon when the resource type was changed from phytoplankton to bacteria (III). A phytoplankton-based food web was attained by incubating in high light with nitrogen and phosphorous (NP)-addition. A bacterial-based food web was obtained by carbon, nitrogen and phosphorous (CNP)-addition and incubating at a lower light level.

The transfer or food web efficiency (FWE) was significantly lower if heterotrophic bacteria constituted the base of the food web instead of phytoplankton (III). This has been discussed in many papers (Azam et al. 1983, Ducklow et al. 1986, Fenchel 1988, Sommer et

al. 2002, Landry and Calbet 2004), although few studies have actually quantified the

difference (Koshikawa et al. 1996, Havens et al. 2000). The FWE defined, as mesozooplankton production per basal production, was 22% in the phytoplankton based food web, while in the bacterial-based food web it was 2%. Hence, the food web efficiency was 11 times lower when heterotrophic bacteria dominated basal production. Somewhat lower differences (6-7 times) have been estimated in the open ocean if picoplankton constitutes the base instead of microalgae (Landry and Calbet 2004). The phytoplankton-based food web seems to have consisted of one trophic link since the gross growth efficiency (GGE) for mesozooplankton is normally between 20 and 30% (c.f. FWE was 22%, Straile 1997). The food web starting with heterotrophic bacteria consisted of ~2.5 trophic links (III). Ducklow et

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al. (1986) also recorded that 2% of bacterial production was transferred to the

mesozooplankton size class. These results should have direct implication for zooplanktivorous fish production. A change from a phytoplankton towards a bacterial-based food web would possibly result in lower fish production. This could be an outcome of a global warming scenario with increased rainfall in the northern Europe and thereby more input of terrestrial derived organic matter (Bergström et al. 2001, Graham 2004). The northern Baltic Sea food web, which is already to some degree based on secondary production by bacteria (Zweifel et al. 1995, Rolff and Elmgren 2000, Sandberg et al. 2004), could become even more bacterial-based due to increased discharge of TDOC. At the same time phytoplankton would turn more light limited due to increased amount of humic substances. It would be interesting to study if a gradually decrease and/or a sudden drop in FWE would be the result of increased dominance of bacterial production.

The studied effect of resource heterogeneity on food web efficiency may also be translated into present food web configuration at different levels of nutrient richness in the sea. In the oligotrophic open ocean picoplankton constitute the base of the food web while nano- or microplankton are dominating in coastal or up-welling areas (Fukuda et al. 1998, Zubkow et al. 2000, Sherr and Sherr 2002, Landry and Calbet 2004). The production at the top trophic level in oligotrophic areas may be un-proportionally low in comparison to moderately nutrient-rich areas due to lower food web efficiency (Sommer et al. 2002).

Identity of nanoflagellates

Due to the scarcity of diagnostic morphological features, the destruction of delicate forms by fixation and the selectivity of culturing efforts we have little knowledge about the diversity, autecology and biogeography of heterotrophic nanoflagellates (Lim et al. 1999, Arndt et al. 2000, Sonntag et al. 2000, Boenigk et al. 2005, Pernthaler 2005).

Chrysomonads or chrysophyceans (class Chrysophyceae) are phototrophic and/or heterotrophic nanoflagellates that comprise a major component of the aquatic food web in both marine and freshwater systems e.g. colorless chrysomonads together with bicosoecids constitute between 20 - 50% of the annual average biomass of pelagic HNF (Arndt et al. 2000, Preisig and Andersen 2002). In the Gulf of Bothnia, chrysomonads were found to constitute between 5 and 70% of HNF, based on cell concentrations (Fig 3, V). Chrysomonads seemed to be relatively more important in Bothnian Bay were they constituted on average 34% of total HNF abundance in comparison to in the Bothnian Sea where they

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constituted on average 15% of total HNF abundance (Fig. 3, V). Assuming an average diameter of 3 µm for the chrysomonads, they would constitute 28 ± 24% of total HNF biovolume in the Gulf of Bothnia. The highest concentrations of chrysomonads (~500 cells ml-1) were recorded in late spring (May-June) and in late autumn (September-November).

0 500 1000 1500 2000 2500 3000 Feb Apr May May Jun Jul Aug Aug Sep Nov Dec Feb Apr Ma y May Jun Ju l Aug Aug Sep Nov Dec C e ll a bun da n c e ( m L -1 ) BS1 BS2 0 500 1000 1500 2000 2500 3000 C e ll a b und a n c e ( m L -1) HNF total Chrysomonads BB1 BB2

Figure 3. Seasonal dynamics of heterotrophic flagellates and heterotrophic/mixotrophic chrysomonads estimated by epifluorescence at the sampling stations in the Bothnian Bay (BB1, BB2) and Bothnian Sea (BS1, BS2) during 2000.

Among larger flagellates Katablepharis cf remigera was recognized at all stations while

Leucocryptos marina was only established in the Baltic Proper. Due to the relative large size

of these flagellates they constituted a significant part of HNF biovolume concentration (II). Several species of choanoflagellates were also recognized in the area (II). Their contribution to the total biovolume concentration of HNF on a yearly basis was only 2 ± 5%, but during spring and late autumn they could constitute between 5-10%.

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Due to the relative importance of chrysomonads, both worldwide and in the northern Baltic Sea, group-specific PCR-primers for amplification of the 18S small sub-unit rRNA gene were optimized for analysis of diversity by DGGE. Two different primer pairs were tested (IV). One primer pair (EukC1-F - Chryso-R) primarily targeted Paraphysomonadaceae and Ochromonadales, which generally have heterotrophic or mixotrophic nutrition (IV, V). The other primer pair (EukC2-F - Chryso-R) which contained one degenerated position (nucleotide A and G) targeted all chrysophytes including class Chrysophyceae and Synurophyceae (IV). Both primer pairs performed well, since PCR products were obtained from corresponding chrysophyte DNA. Analysis of chrysophyte cultures and field samples showed that chrysomonads could selectively be detected by the PCR-DGGE method (IV, V). The primer pair with a degenerated position was not used in the extensive field study that focussed on heterotrophic/mixotrophic chrysomonads (V). A possible disadvantage of the second degenerated primer pair, which was not extensively studied, is that it might form heteroduplex fragments or chimeras (Kowalchuk et al. 1997).

The field study of heterotrophic/mixotrophic chrysomonads in the Gulf of Bothnia showed that there is a handful of chrysomonads present all through the year (V). The mean number of DGGE bands or operational taxonomic units (OTU) during the annual study was 4.4 in the Bothnian Bay and 6.3 in the Bothnian Sea. The highest number of chrysomonad bands recorded in a single sample was 11. These numbers are probably an underestimation of the total species richness since DGGE normally detects species whose abundance is higher than ~1% of total cell counts of the target group (Schäfer & Muyzer 2001). However, it can be assumed that ecologically important chrysomonads was detected by the DGGE method. Prior to the field survey in paper V chrysomonads have been studied in the northern Baltic Sea by light or electron microscopy (Thomsen 1979, Vørs 1992, Ikävalko 1994). These studies have recorded on average 4 (min. 1, max. 8) chrysomonad species per sample, although “naked” forms (without silica-scales or capsules) of chrysomonads have not been distinguished in those studies.

About 20 different chrysomonad bands were detected in the DGGE analysis of samples from the Bothnian Sea (V). A total of 15 chrysomonads species were confirmed by 18S sequences obtained from the DGGE analysis. The discrepancy between the numbers was due to difficulties to obtain sequences from weak DGGE bands (Schäfer and Muyzer 2001). Our numbers are lower but comparable to the twenty-five species belonging to class chrysophyceae that have been recorded by light or electron microscopy in the Gulf of Bothnia (Hällfors 2004). In the list by Hällfors (2004) many species are probably hidden in the lumped

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groups of ´naked` chrysomonads e.g. Ochromonas sp. and Spumella sp. On the other hand, the long tail of less abundant species is not recognized by DGGE (Schäfer and Muyzer 2001). Most of the obtained chrysomonad sequences from the Baltic Sea matched uncultured chrysomonads i.e. environmental clones (V). These results and the fact that novel chrysomonad sequences are accumulating in the GenBank (Boenigk et al. 2005, Countway et

al. 2005, Richards et al. 2005), highlight the growing need of culturing and sequencing work.

The utilized 18S rDNA fragment of ~200 bases was variable enough to separate most chrysomonads in the DGGE, although it might have been too short to reveal the correct species identity (IV, V).

Seasonal dynamics of the total chrysomonad community was estimated by DGGE and epifluorescence microscopy (V). Both methods showed similar seasonal dynamics, although, interestingly, 36% of the dynamic estimated by DGGE was explained by environmental variables like bacterial biomass, bacterial production and temperature, while the seasonal dynamics estimated by epifluorescence could not be explained by any biotic variables (V). Many studies are consistent with the results that the seasonal dynamic of nanoflagellates are mostly dependent on temperature and bacteria (Berninger et al., 1991, Hansen and Christoffersen, 1995, Weisse, 1997, Sestanovic 2004). Despite biases inherent to all PCR-based methods (von Wintzingerode et al. 1997, Schäfer and Muyzer 2001, see IV and V for elucidation) DGGE might be as good as epifluorescence microscopy for quantification of protists. Another independent measure of chrysomonad abundance e.g. quantitative PCR or fluorescence in-situ hybridization (FISH) with rRNA-targeted probes would be needed to confirm the true number of chrysomonads (Lim et al. 1999, Zhu et al. 2005).

Conclusions and future perspectives

Much of the work in this thesis concerned heterotrophic nanoflagellates. The experimental set-up in paper I made it possible to measure the relative importance of resource and predation limitation. The community growth rate of small nanoflagellates was limited by the amount of resource in the low productive northern Baltic Sea. The biomass of heterotrophic nanoflagellates was controlled by the amount of resource i.e. bacteria. The relative importance of resource and predation control for the equilibrium biomass of a population or trophic level is believed to depend on the productivity of the system (Oksanen et al. 1981). This could be tested by performing similar experiments in a gradient of productivity.

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I tried, as Porter (1996), to make the components of the microbial food web easily understood through sorting them into simplified trophic levels and applying food chain theory (Hairston et al. 1960, Oksanen et al. 1981). The organisms were separated into different size groups (I, II). Small heterotrophic nanoflagellates (” 5 µm) in the northern Baltic Sea could be considered a trophic group that utilizes heterotrophic bacteria and is preyed upon by larger protists (Samuelsson and Andersson 2003, II). The other size groups; large flagellates (> 5 µm), small- (< 20 µm), medium- (20-40 µm) and large-sized ciliates (>40 m), were probably more diverse in their feeding behavior, since their seasonal variation could not be explained by any of the included bottom-up factors (II). It might be possible to search for functional groups within the microbial food web by using different size fractionations. The strongest predator-prey link could be separated by the specific filter cut-off that results in strongest grazing pressure on the prey. Furthermore the optimal resource for a certain predator group could be detected by addition of prey of different type or size. The most favorable food should result in the highest growth rate of the predator. However, the importance of particular trophic links is certainly changing with the season and can be very species-specific.

In paper III we recognized low food web efficiency when picoplanktonic bacteria instead of nano-sized phytoplankton dominated the production. The carbon had to pass phagotrophic protists before reaching mesozooplankton. In more productive areas the primary producers are normally dominated by larger cells (II). These cells would constitute a direct link to mesozooplankton and result in higher food web efficiency. Still there are several examples that show equal food web efficiencies in picoplankton- and microplankton-based systems (Koshikawa et al. 1996, Havens et al. 2000). One reason for this is increased occurrence of inedible prey in eutrophic waters. Furthermore, the bacterial based and the phytoplankton-based food web may be very ‘leaky’ in terms of respiration and other loss processes. Nonetheless, there is a need to clarify the relation between productivity and food web efficiency. In paper II we recognized the dominance of smaller cells in the low-productive northern Baltic Sea, possibly resulting in low food web efficiency. Hence, the gradient of productivity in the northern Baltic Sea could in fact be used to study the connection between food web efficiency and productivity.

In paper IV group-specific PCR primers were optimized for studies of chrysophyte diversity by DGGE. The primers performed well, since they selectively amplified chrysophytes from mixed communities (IV, V). Furthermore, the amplified fragments were nicely separated in the DGGE. The field study revealed that at least 4 to 7 heterotrophic or mixotrophic chrysomonads were present year-round in the Gulf of Bothnia. Furthermore,

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about 20 different chrysomonad bands could be detected in the DGGE. It would be interesting to simultaneously analyse samples with the PCR-DGGE method and electron microscopy. The widely used PCR-DGGE molecular method could be nicely evaluated with the narrow target group, chrysomonads, which still to some extent is identifiable by electron microscopy. The usefulness of the molecular approach for species identification relies on sequences of known species that is available for the public. There is a growing need of culturing and sequencing work since the collection of sequences of uncultured clones is increasing (Boenigk et al. 2005, Countway et al 2005, Richards et al. 2005, V). This fact is also recognized in the Baltic Sea since only four out of 18 identified chrysomonads have so far been sequenced. One promising way of improving both the resolution of and the detection by the PCR-DGGE method for the analysis of eukaryote communities is to develop group specific primers (Rasmussen et al. 2001, Gast et al. 2004). The developed chrysomonad primers should be valuable for a wide range of aquatic ecologists studying the diversity of nanoflagellates.

Acknowledgements

I would like to thank Kristina Samuelsson, Pia Haecky, Maano Aunapuu and Agneta Andersson for valuable comments on earlier versions of this thesis. The research presented in this thesis was supported by Research Fellowship grants from the Federation of European Microbiological Societies, the Helge Ax:son Johnson foundation, Ruth and Gunnar Björkman´s Foundation and Umeå Marine Science Centre. Paper I, II and V was supported by grants from EU (BASYS MAS3-CT96- 0058) and paper III from FORMAS (21.0/2003-0214) to A Andersson.

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