Institut de Ciències del Mar (CSIC)

Universitat Politècnica de Catalunya

Effects of different allochthonous carbon sources on marine bacterioplankton diversity and function

Memòria presentada per optar al grau de Doctor en Ciències del Mar per la Universitat Politècnica de Catalunya per

Itziar Lekunberri García

Vist-i-plau del director de tesi

Dr. Josep M. Gasol Investigador–CSIC “Marine food web” by Joaquim Buil “ Lo esencial es invisible a los ojos”

“El Principito” (Antoine de Saint-Exup éry)

A mis padres y a mi hermanica, Maitane

Contents

SUMMARY/ RESUM/ RESUMEN/LABURPENA 7

GENERAL INTRODUCTION 11

Chapter I Increased marine bacterioplankton diversity detected by 25 combining culturing and culture-independent techniques

Chapter II Changes in bacterial activity and community composition 61 caused by realistic exposure to oil in mesocosms experiment

Chapter III Effects of different types of phytoplankton blooms caused 95 by nutrient additions on bacterial heterotrophic activity and assemblage composition in sweater mesocososms

Chapter IV Effects of Sahara Dust deposition on marine microbial 119 abundance, bacterial community structure and ecosystem function

Chapter V Transplant experiments show dominance of bottom-up 149 control of prokaryote abundance, heterotrophic production and bacterial community structure in the NE Atlantic Ocean

SYNTHESIS AND DISCUSSION 171

ANNEXES

I: Traditional methods for bacterial culturing 193 II : Protocols for Scanning Electronic Microscopy (SEM) of bacteria 199

REFERENCES (Introduction, discussion and annexes sections) 207

AGRADECIMIENTOS 217

SUMMARY

Bacteria are the most abundant organisms on Earth and mediate many critical ecosystem processes. This indicates the great importance of the knowledge of the identity and population dynamics of the indigenous microorganisms. Initial studies in aquatic microbiology were carried out by traditional cultivation techniques. Later on, cell counts by epiuorescence techniques demonstrated how abundant bacteria were, and molecular techniques were developed and used to detect the most representatives bacteria in the environment. This is mainly because the culture media have nutrient contents (both organic and inorganic) which are several times more concentrated than those in the natural environment. This makes retrieving dominant organisms from an oligotrophic environment not an easy task, and generates selectivity for particular bacterial types, which are not very representative. A similar selection of bacteria also occurs in the natural environment, at another scale, when different allochthonous inputs take place. These can be due to natural processes, such as river run-offs, aerosol deposition, etc., or may have an anthropogenic origin, such as oil spills. In this thesis we evaluate the effect of several of these additions on bacterial community structure and function.

There is an increasing interest in the isolation of naturally dominating organisms since it provides a better information about the metabolic capabilities of the organisms that are growing in the environment. Thus, we decided to evaluate how representative were the isolates obtained in the Mediterrranean Sea as compared to the sequences retrieved by molecular techniques such as cloning and DGGE ngerprinting. Our analysis highlighted the selectivity created by the growth media, but simultaneously allowed to increase our knowledge about the diversity in our study area, and provided a means for looking into the “rare microbial biosphere”, which cannot be analyzed with other techniques.

To address the question of how nutrient addition modies bacterial community structure and affects microbial function, we performed different mesocosm experiments, where we added different types of nutrients to water collected from Blanes Bay (NW Mediterranean Sea). Another mesocosm experiment was carried out to evaluate the effect of the oil spill that took place after the Prestige tanker accident in front of the Galician coast. Mesocosms experiments have been widely used to evaluate addition effects as a means to reproduce of the natural conditions. During these experiments different environmental parameters were measured and also bacterial production and abundance were monitored. Bacterial community structure was determined by the DGGE ngerprinting technique, which allows the comparison of different samples and further analysis of the dominant richness by the Shannon-Weaver index, based on the presence and absence of bands. These analyses showed the inuence of grazers on the bacterial selection after the different additions and, for example, explained the effects of Saharan Dust deposition on microbial function and community metabolism, as an effect of the inorganic phosphorus and organic carbon contributed by the dust. In the oil spill mesocosm experiments, effects on bacterial structure, production and abundance were detected only with additions four times higher than the concentration found after the Prestige tanker accident.

We also include an experimental study carried out in the North Atlantic during two different cruises, in which using dyalisis bags and transplant experiments we tested whether bottom-up conditions were stronger than top-down factors in determining microbial function and community structure. The results showed differences between experiments, but a general higher effect of bottom-up conditions than the effect of predators.

The different experimental work included in this thesis allows for some generalizations, but also show some discrepancies, which we discuss in the last chapter.

7 RESUM

Els bacteris s ón els organismes m és abundants a la Terra i els encarregats de pariticipar multitud de processos que s ón cr ítics per a l´entorn. Aix ò fa que sigui de gran import ància el coneixement de la identitat i din àmica de les poblacions de microorganismes ind ígenes. Els primers estudis en microbiologia es van realitzar mitjan çant tècniques de cultiu tradicional. M és endavant, t ècniques de comptatge per epiuoresc ència van mostrar com n’eren d’abundants els bacteris, i es van emprar també t ècniques moleculars per a detectar els bacteris més representatius del medi. Per ò les concentracions de bacteris en els medis de cultiu (tant org ànics com inorg ànics) s ón diverses vegades superiors a les que es troben en el medi natural. En conseq üè ncia, l’obtenci ó dels cultius dels organismes dominants en un medi oligotr òc no és una tasca f àcil i acaba seleccionant bacteris que no s ón gaire representatius. Una selecci ó similar de bacteris tamb é es d óna en el medi natural, a diferent escala, quan hi ha aports de diferents compostos al ·lòctons. Aquests es poden deure a processos naturals com ara la desc àrregues de rius, deposicions mitjan çant aerosols, etc., o b é poden ser d’origen antropog ènic, com els vessaments de petroli. En aquesta tesi avaluem l’efecte de diverses d’aquestes addicions en la diversitat, l´estructura de les comunitats i la funci ó bacterianes.

Hi ha un inter ès creixent en l’a ïllament d’organismes dominants en el medi, ja que aix ò permet tenir m és informaci ó sobre les capacitats metab òliques dels organismes que hi estan creixent. Per tant, vam decidir mesurar ns a quin punt s ón representatius els a ïllats obtinguts en el mar Mediterrani comparant-los amb les seq üè ncies obtingudes a trav és de t ècniques moleculars com ara les biblioteques de clons i t ècniques coma ara la DGGE ngerprinting . La nostra an àlisi destaca la selectivitat creada pel medi mar í, per ò alhora permet tenir un coneixement m és ampli sobre la diversitat en la nostra àrea d’estudi, i proporciona mitjans per a explorar la “biosfera microbiana rara”, que no pot ésser analitzada mitjan çant altres t ècniques.

Per a abordar la pregunta de com l’addici ó de nutrients selecciona la diversitat bacteriana i afecta la funci ó microbiana, vam realitzar una s èrie d’experiments de mesocosmos, on es van afegir diferents tipus de nutrients a aigua recollida a la Badia de Blanes (Nord-oest del mar Mediterrani). Un altre experiment de mesocosmos va ser realitzat amb l’objectiu d’avaluar l’efecte del vessament de petroli que va donar-se despr és de l’enfonsament del petrolier Prestige davant les costes gallegues. Els experiments de mesocosmos han estat utilitzats àmpliament per a determinar els efectes de les addicions amb l’intenci ó de simular les condicions naturals. Durant aquests experiments es van avaluar diferents par àmetres ambientals i tamb é es van monitoritzar la producci ó i l’abund ància bacterianes. L’estructura de la comunitat bacteriana va ser determinada amb la t ècnica de DGGE ngerprinting , que permet la comparaci ó de diferents mostres i una an àlisi posterior de la diversitat dominant mitjan çant l’ índex de Shannon-Weaver, basat en la pres ència i abs ència de bandes. Aquestes an àlisis mostren la inu ència dels depredadors en la selecci ó bacteriana despr és de les diferents addicions i, per exemple, explica els efectes de la deposici ó de pols del S àhara en la funci ó microbiana i en el metabolisme de la comunitat, especialment per causa del f òsfor inorg ànic i el carboni aportats amb la pols. En els experiments de vessament de petroli, es van poder detectar canvis en l’estructura, producci ó i abund ància bacterianes nom és en el cas d’addicions quatre vegades m és grans que la concentraci ó trobada despr és de l’accident del Prestige .

Tamb é incloem un experiment realitzat a l’Atl àntic Nord durant dues campanyes diferents, en les quals es van utilitzar bosses de di àlisi i experiments de trasplantaments per a demostrar com les condicions de bottom- up van ser m és grans que la regulaci ó top-down en la determinaci ó de la funci ó i estructura de la comunitat microbiana. Els resultats mostren difer ències entre experiments, per ò en general es van detectar efectes m és grans de bottom-up que no pas deguts a depredadors.

Finalment, els diferents treballs experimentals inclosos en aquesta tesi permeten fer algunes generalitzacions per ò tamb é mostren algunes discrep àncies, fet que es discuteix en l’ últim apartat.

8 RESUMEN

Las bacterias son los organismos m ás abundantes de la Tierra y los encargados de mediar en multitud de procesos cr íticos para el medio ambiente. Esto hace que sea de gran importancia el conocimiento de la identidad y din ámica de poblaciones de los microorganismos ind ígenas. Los primeros estudios en microbiolog ía se realizaron mediante t écnicas de cultivo tradicionales. M ás tarde, t écnicas de contaje por epiuorescencia mostraron lo abundantes que eran las bacterias, y t écnicas moleculares se usaron tambi én para detectar las bacterias m ás representativas del medio. Pero la concentraci ón de nutrientes en los medios de cultivo (tanto org ánicos como inorg ánicos), son varias veces superiores a los que se encuentran en el medio natural. En consecuencia, la obtenci ón de los organismos dominantes en un medio oligotr óco no es una tarea f ácil y acaba seleccionando organismos bacterianos, que no son muy representativos. Una selecci ón similar de bacterias ocurre tambi én en el medio natural, a diferente escala, cuando hay aportes de diferentes compuestos al óctonos. Esto puede ser debido a procesos naturales tales como descargas de r íos, deposiciones de aerosoles, etc., o bien pueden ser de origen antropog énico, como los derrames de petr óleo. En esta tesis evaluamos el efecto de varias de estas adiciones en la diversidad y funci ón bacterianas.

Hay un creciente inter és en el aislamiento de organismos dominantes en el medio, ya que esto permite tener mayor informaci ón sobre las capacidades metab ólicas de los organismos que est án creciendo. Por tanto, decidimos evaluar hasta qu é punto son representativos los aislados obtenidos en el mar Mediterr áneo compar ándolos con las secuencias obtenidas mediante t écnicas moleculares como bibliotecas de clones y DGGE ngerprinting . Nuestro an álisis destaca la selectividad creada por el medio de cultivo, pero a la vez permite ampliar el conocimiento sobre la diversidad en nuestra área de estudio, y proporciona medios para explorar la “biosfera microbiana rara”, que no puede ser analizada mediante otras t écnicas.

Para abordar la cuesti ón de c ómo la adici ón de nutrientes selecciona la diversidad bacteriana y afecta a la funci ón microbiana, realizamos diferentes experimentos de mesocosmos, donde a ñadimos diferentes tipos de nutrientes a agua recogida en la Bah ía de Blanes (Noroeste del mar Mediterr áneo). Otro experimento de mesocosmos fue llevado a cabo para evaluar el efecto del derrame de petr óleo que tuvo lugar despu és del hundimiento del petrolero Prestige frente a las costas gallegas. Los experimentos de mesocosmos han sido ampliamente utilizados para determinar los efectos de adiciones con la intenci ón de simular las condiciones naturales. Durante estos experimentos se evaluaron diferentes par ámetros ambientales y tambi én se monitoriz ó la producci ón y la abundancia bacterianas. La estructura de la comunidad bacteriana fue determinada por la técnica de DGGE ngerprinting , que permite la comparaci ón de diferentes muestras y un an álisis posterior de la diversidad dominante mediante el índice de Shannon-Weaver, basado en la presencia y ausencia de bandas. Estos an álisis muestran la inuencia de los depredadores en la selecci ón bacteriana despu és de las diferentes adiciones y, por ejemplo, explica los efectos de la deposici ón de polvo del S áhara en la funci ón microbiana y en el metabolismo de la comunidad, especialmente por el f ósforo inorg ánico y el carbono aportado por el polvo. En los experimentos de derrame de petr óleo, s ólo se detectaron efectos en la estructura, producci ón y abundancia bacterianas en el caso de adiciones cuatro veces mayores que la concentraci ón encontrada despu és del accidente del petrolero Prestige .

Tambi én incluimos un experimento llevado acabo en el Atl ántico Norte durante dos campa ñas diferentes, en las que se usaron bolsas de di álisis y experimentos de trasplantes para demostrar c ómo las condiciones de bottom- up fueron mayores a los factores de top-down en la determinaci ón de la funci ón y estructura de la comunidad microbiana. Los resultados mostraron diferencias entre experimentos, pero en general se detectaron mayores efectos de bottom-up que los debidos a predadores.

Los diferentes trabajos experimentales incluidos en esta tesis permiten hacer algunas generalizaciones, pero tambi én muestran algunas discrepancias, lo que se discute en el último apartado.

9 LABURPENA

Bakterioak Lurreko izakiriki oparoenak dirak eta ingurumeneko prozesu kritiko askotan bitartekari. Guzti honek izaugarrizko garrantzia ematen dio lekuko mikroorganismoen nortasuna eta taldeko dinamikaren berri izateari. Lehen mikrobiologiako ikerketak ohiko kultibo tekniken bidez egin ziren. Ondoren, epiuoreszentzia bidez egindako zenbatze teknikek bakterioen ugaritasuna azaldu zuten, eta teknika molekularrak bakterio adierazgarrienak aurkitzeko erabili izan ziren. Baino elikagai bilketa kultibo baliabideetan (nahiz organiko nahiz inorganikoak) hainbat aldiz haundiagoa da ingurune naturalean aurkitu daitekeena baino. Ondorioz, organismo gainartzaileen lorpena ingurune oligotrokoetan ez da lan erreza eta adierazgarriak ez diren bakterioak aukeratzen bukatzen da. Antzeko aukeraketa bat gertazten da ingurune naturalean, maila ezberdin batean, bertakoak ez diren konposatuen ekarpenak daudenean. Hau prozesu naturalen ondorio izan daiteke hala nola erreken isuria, aerosolen uzteen bidez, e.a. edo gizakiaren jardueraren ondorioz, petrolio isuriak bezela. Tesi honetan hauetako gehitze batzuk bakterioen dibertsitate eta funtzioetan duten eragina aztertzen dugu.

Geroz eta jakin min handiagoa dago bere ingurunean organismo gainartzaileen isolamenduan, honek hazten ari diren organismoen ahalmen metabolikoari buruzko informazioa edukitzen lagunduko baitu. Beraz, aztertzea erabaki duguna zera da, Mediterraneo itsasoan isolatutakoen adierazgarritasuna, klonen liburutegi eta DGGE ngerprinting teknika molekularren bidez lortutako sekuentziekin alderatuz. Gure analisiak kultibo baliabideen bidez sortutako hautaketa nabarmentzen du, baina era berean gure azterketa eremuko dibertsitateari buruzko ezaguera haunditzea laguntzen digu eta beste tekniken bidez aztertu ezin daitekeen “mikrobiar biosfera arraroa” aztertzeko baliabideak ahalbideratzen dizkigu.

Elikagaien gehitzeak nola bakterioen dibertsitatea aukeratzen eta mikrobiar funtzioan eragiten duen aztertzeko, mesokosmosean saiakuntza ezberdinak egin ditugu, non Blaneseko badian (Mediterraneo itsasoko ipar- mendebaldea) hartutako ur laginei elikagai ezberdinak gehitu dizkiogun. Mesokosmoseko beste saiakuntza bat galizar kostaren parean egin zen, Prestige petroliontzia ondoratu ondoren, petrolio isuriak duen eragina aztertzeko. Mesokosmoseko saiakuntzak ugariak izan dira gehitzeen ondorioak zehazteko, egoera naturalak simulatzeko intentzioarekin. Saiakuntza hauetan ingurune parametro ezberdinak aztertu ziren, era berean bakterio produkzioaren eta ugaritasunaren datuak bildu ziren. Bakteriar taldearen estruktura DGGE ngerprinting teknikaren bidez nkatu zen, teknika honek lagin ezberdinak alderatzea eta dibertsitate gainartzailearen geroagoko analisi bat ahalbideratzen ditu Shannon-Weaver indizearen bidez, banden presentzia eta ausentzian oinarrituz. Analisi hauek, gehiketa ezberdinen ondoren, harrapariek bakterioen aukeraketan duten eragina erakusten dute eta, adibidez, Saharako hautsen pilatzeak mikrobiar funtzioetan eta taldeko metabolismoan duen eragina argitzen du, bereziki hautsak ekartzen duen fosforo inorganiko eta karbonoaren ondorioz. Petrolio isurien saiakuntzetan, bakterioen estruktura, produkzio eta ugaritasunean ondorioak antzeman ziren, isuriak Prestige petroliontziaren ezbeharraren ondoren bilatutako kontzentrazioak laukoizten ziren gehitzeetan soilik.

Bi kanpaina ezberdinetan, Atlantiko Iparraldean egindako saiakuntza bat ere gehitzen dugu, non dialisi boltsak eta trasplante saiakuntzak erabili ziren, mikrobiar komunitatearen funtzioa eta estruktura zehazteko garaian, bottom-up egoerak top-down faktoreak baino gehiago izan zirela egiaztatzeko. Emaitzek saiakuntzen arteko ezberdintasunak erakutsi zituzten, baina orokorrean bottom-up faktoreen eragin handiagoak antzeman ziren harrapariei dagokiena baino.

Tesi hontan azaltzen diren saiakuntza ezberdinak jeneralizazio batzuk aurkeztea zilegitzen digu, nahiz eta desadostasun batuk agertu, hauek azken atalean eztabaidatzen dira.

10 INTRODUCTION Los grandes conocimientos engendran las grandes dudas

Arist óteles Introduction

GENERAL INTRODUCTION

BACTERIA IN THE ENVIRONMENT

The ocean is probably the oldest and largest ecosystem on the planet. Microorganisms living there are the closest living descendants of the original forms of life on Earth (Hunter-Cervera et al. 2005). By increasing the oxygen concentration in the atmosphere they shaped a new aerobic environment where plants, animals and all other life forms evolved.

Bacteria are the most abundant and species-rich groups of microorganisms. They mediate many critical ecosystem processes that sustain life on Earth, and other organisms are unable to achieve (Horne-Devine et al. 2003). Some examples of processes carried out exclusively by microorganisms are the key processes that set the pace of the nitrogen cycle. Nitrication, denitrication and half of the microbially-mediated nitrogen xation occur in the oceans. Some bacteria play a role in cloud formation by cycling compounds such as dimethylsulde into the atmosphere. Other prokaryotes adapted to extreme environments are known to participate in the constant efux of methane from the surface of the oceans, although the process is not yet entirely understood. These are only some of the physiological characteristics that have been discovered so far, but the huge microbial diversity indicate that there may be many more metabolic capabilities yet to be discovered.

APPROACHES TO PROKARYOTE DIVERSITY: CULTURING vs MOLECULAR

Early marine microbiologists were convinced that a high percentage of abundant pelagic could be recovered on substrate-amended agar plates (Zobell 1943). Lately, epiuorescense microscopy revealed that the bacteria readily cultivable on standard rich growth media were only about 0.1 percent of the observable bacteria. (Ferguson et al 1984). Jannash & Jones (1959) reported such observations in seawater, although this problem was universal, and was later named “the great plate anomaly” (Staley & Konopka 1985).

13 Introduction

In the mid 1980s molecular methods were introduced in Microbial Ecology. Somehow surprisingly, the results generated by these new techniques did not match the results obtained by culturing. They revealed that some dominant bacteria are not cultivable using standard methodologies, and others that are easily cultivable occur at such low abundance in the natural environment that molecular techniques cannot detect them. Molecular tools made possible to obtain the genetic sequence data of the dominant organisms in spite of not being able to culture them.

While by ngerprinting techniques not more than 100 sequences are commonly detected, it has been shown that with a higher effort in cloning, ca1600 sequences can be obtained (Acinas et al. 2004). Recently appeared different molecular approaches allow massive sequencing of the bacterioplankton community. Some examples are the study by Venter et al. (2004) in the Sargasso Sea that used a brute-force shotgun sequencing approach that permit them to estimate that the sample analyzed contained ca.1800 different bacterial taxa. Sogin et al. (2006), using a more powerful and cheaper sequencing approach to analyze deep-water samples from the North Atlantic, estimating richness to be of thousand taxa. Recent molecular studies thus show that the taxonomic composition of natural bacterial communities comprises a tremendous diversity, although relatively few species appear to form the bulk of the biomass (Pedrós-Alió 2006).

Fig.1 Plots of number of individuals versus taxon rank. The total curve represents biodiversity and is postulate by Pedros-Alió (2006) to be composed of two sections: dominant diversity (red) and to rare taxa (blue), which survive in the ecosystem at low abundances. Molecular techniques can presumably access most taxa in the diversity part of the curve and the biodiversity tail is usually not accessible, however isolation can retrieve some taxa from this part of the curve.

14 Introduction

Margalef (1994) distinguished between ecosystem diversity (a property of the ecosystem) and biodiversity, dened as the total genetic information on Earth (or any part of it) while diversity, in turn, would be the components that are active and abundant at one particular time and place. Based on this statement, Pedrós-Alió (2006) suggested to consider the taxa retrieved by PCR as a proxy for the diversity of an ecosystem. However, rare taxa (the seed bank) that form the long tail of a rank- abundance curve are necessary to complete the biodiversity. Also, it is important to note that the number of representatives of a given group is not necessarily linked to the importance of that group in the functioning to the community (Fig.1).

In relation with this idea, Chapter 1 of this thesis focuses in trying to increase the diversity previously estimated by molecular approaches (DGGE and clone libraries) at the Blanes Bay Microbial Observatory (BBMO) with the diversity that would be retrieved by traditional culturing techniques. Questions at play are not only a (bacterial) group by group comparison of the differences between what is retrieved by culturing and what is retrieved by molecular techniques, but also whether there are new bacterial groups appearing, whether some cultured bacteria are good representatives of groups of sequences appearing in situ, etc. A special attention is given to the microdiversity patterns present in the culture collection vs. in the molecular techniques. Microdiversity can be dened as the diversity measured above the division of species: sequences that are more than 97% similar in their 16S rRNA, but that they are not exactly identical

RELATING BACTERIAL DIVERSITY AND FUNCTION

There is an increasing interest in relating bacterial diversity to their function in the environment. However, bacterial activity has mostly been studied in bulk. There are numerous approaches useful for establishing a functional role for bacterioplankton species and some are used for determining the activity of microorganisms that are uncultured. Some examples are the use of bromodeoxyuridiene uptake to identify growing cells (Urbach et al. 1999), and 13 C incorporation into lipids to identify organisms that assimilate acetate. The MARFISH technique combines autoradiography and in situ hybridation to identify the cells that are able to assimilate specic organic compounds (Cottrell & Kirchman 2000). Another approach that would allow us to investigate the organisms’ physiology is to isolate it and sequence the genome. Indeed, sequencing of complete microbial genomes, now routinely reveals organisms capacities to perform unexpected metabolic functions, that were impossible to identify before.

15 Introduction

Traditional methods (e.g. culturing on ZobBell’s marine growth media) have limitations, because this media has ca. 170-fold more DOC than natural seawater and thus, selects for “eutrophic” bacteria, those that can develop in such a large amount of carbon. Consequently, the isolation of microbes found in oligotrophic environments was a great challenge. Many culture techniques have been used in order to obtain isolates of the most representative members of the bacterial communities. One approach is the “dilution to extinction” culturing method for the isolation of oligotrophic bacteria in seawater (Button et al. 1993) and later modied by Rappé et al. (2002). Other approaches are microencapsulation techniques, and the use of low-nutrient agar plates (Eilers et al. 2001). Relatively new strains that represent many of the most abundant marine clades have been isolated. This is the case of members of the SAR11 clade, which is the most abundant group of bacteria detected in seawater DNA by gene cloning and by FISH (Morris et al. 2002, Rappé et al . 2002).

Cultures are important because they provide complete genomes and the means to test the hypotheses that emerge from genomic data. Cultures supported by predictions from genomic data, together with metagenomics are a powerful combination. The major success of metagenomics was the discovery of the light-driven, proton pump proteorhodopsin in the fragmentary genome data of SAR86 (Bejá et al. 2000). This was the rst report about the presence of such retinaldehyde protein in the domain bacteria, and this report sparked considerable interest in this potentially important mechanism for capturing light energy in marine ecosystems.

The Moore Foundation had the initiative to sequence the whole genome of isolates from different environments, and 9 of our isolates were selected, giving the possibility to learn more about their function in the environment. For example the manual annotation of M152 ( Polaribacter sp .) revealed the presence of proteorhodopsine (Gonzalez et al . 2008). Furthermore inM134 ( Dokdonia sp .) the bacterial light-dependent proton pump was shown to provide M134 with phototrophic potential (Gómez-Consarnau et al. 2007).

MARINE MICROBIAL ECOSYSTEMS

Marine microorganisms are known to live in every corner of the ocean, as they are metabolically versatile to adapt to any environment. The microbial ecologists’ trade is to examine whether bacterial communities differ according to the type of environment. Although there are likely millions of species of bacteria, we are only starting to investigate patterns in their diversity and which are the forces that governs these patterns (Ward et al. 1998). Decades ago it was unclear whether marine

16 Introduction

microbial species are either cosmopolitan, or are more provincial and limited to certain geographical areas. Fenchel & Finlay (2004), after Baas-Becking (1934), proposed that the majority of marine planktonic microorganism were cosmopolitan. The arguments for the “everything is everywhere” idea is that spatial barriers do not occur due to the great capability for dispersal into new locations (Finlay 2002, Fenchel & Finlay 2004, Collins et al. 2001). The Baas-Becking (1934) saying continued as “everything is everywhere, but the environment selects”. This theory argued that spatial differentiation would depend on the capability of each microorganism to succeed in a new environment where it is transported. An example of clear differences in actual community composition are presented by Pommier et al . (2007), showing 16S rRNA gene libraries collected at nine distant localities spread around the world were quite different, and some organisms could not be considered cosmopolitan. Of course, cosmpolitanism could be present in the “rare species” pool (sensu Pedrós-Alió 2006) and the Pommier study would have failed to detect that part of the bacterial biodiversity, because it had used a technique that sampled only the “diversity” (Fig. 2).

A priori, each physical, chemical and biological parameter in the oceans could have an impact on the diversity of microbial communities which would express in a relationship between the observed diversity pattern as a function of the contextual environmental parameter (Ramette et al. 2007).

Fig.2 Global sampling of marine bacterioplankton communities. (Pommier et al. 2007) 17 Introduction

Bacterioplankton composition has been shown to change along temporal (seasonal cycles, Pinhassi & Hagstrom 2000, Morris et al. 2005, Schauer et al. 2003, Eilers et al. 2001) and spatial scales (Pinhassi & Berman et al. 2003, Selje et al. 2004). Since bacteria respond quickly to biotic and abiotic changes of their environment, the vertical distribution of physical and chemical parameters in the marine water column should be the basis for a pronounced stratication of the prominent microbial populations (Rheinheimer & Gocke 1994, Ramsig et al. 1996. Morris et al. 2004, Giovannoni et al. 1996, Wright et al. 1997). Diversity (or bacterial community structure) has also been found to vary following latitudinal gradients (Selje et al. 2004, Pommier et al. 2005). Thus, bacterial species composition in the ocean appears to be determined by factors known to affect the species in culture, such as temperature, light, nutrients, and the quantity and quality of DOM. Those are all parameters that we can consider “bottom-up” forces.

To evaluate the magnitude of that “bottom-up” forcing compared to the effect caused by trophic interactions (predators, “top-down”), we designed a transplant experiment, that appears in Chapter 5 , using dialysis bags to incubate water from different depths in the original conditions and at different depths with their associated ecological conditions. We aimed to address the possibility of bacterial community structure to signicantly vary depending on the environmental conditions and the presence/ absence of bacterial predators.

EFFECTS OF NUTRIENT ADDITIONS ON BACTERIOPLANKTON COMMUNITY STRUCTURE

Changes in inorganic nutrient availability are of decisive importance for dening ocean primary productivity and for regulating phytoplankton community composition, succession and distribution. In response to the increase or decay of different phytoplankton species, bacterioplankton community structure changes relatively fast (Pinhassi et al. 2004, Sapp et al. 2007). Since heterotrophic bacteria are the major consumers of dissolved organic matter in the aquatic environment, limitation of bacteria by organic or inorganic nutrients can have important consequences in biogeochemical Carbon cycling. The specic limiting nutrients for bacterial growth may differ depending on the site. Blanes Bay, and the whole of the Mediterranean show P-limitation (Thingstad et al. 1998, Pinhassi et al. 2006) although not always (Sala et al. 2002).

18 Introduction

The function and diversity of the marine microbial communities is especially affected in coastal areas, which exhibit variability in factors such as salinity, and organic and inorganic nutrients. This variability is consequence of external forcing factors due to natural causes (river-inputs, rainfalls) or anthropogenic effects. Human activities are responsible for most of the allochthonous additions, and industrialization is a component of global change and most of the allochthonous sources of carbon and inorganic nutrients to the ocean are predicted to vary in response to global change. One of the consequences of climate change is a decrease in rainfalls that might lead to desertication of many areas and to an increase in the deposition of dust originated in these areas.

Besides these processes, organic contamination may also cause bacterioplankton community successions (Revilla et al. 2000, Schendel et al. 2004). Coastal eutrophication caused by human activities provides allochthonous sources of nutrients for bacterioplankton communities. Some of the most widespread contaminants in the environments are the petroleum hydrocarbons (Santas et al. 1999) and oil spills are a constant problem in many coastal waters. Bacteria can use the PAHs (Policyclic Aromatic Hidrocarbons, a component of oil) as an allochthonous source of carbon and energy (Castle et al. 2006), but these compounds can also negatively affect bacterial composition and function, as quite a number of studies have shown (Siron et al. 1993, Macnaughton et al. 1999, Kasai et al. 2001, Ogino et al. 2001, Castle et al. 2006, Ohwada et al. 2003, Maruyama et al. 2003, Capello et al . 2006 and 2007, Teira et al. 2007)

The aim of Chapters 2, 3 and 4 of this Thesis is to assess the effects caused by different additions on bacterial function and bacterioplankton community structure. In Chapter 2 we simulated the Prestige oil spill in waters of the Ría de Vigo kept in mesocosm tanks. Chapters 2 and 3, in contrast, were performed in the NW Mediterranean. In Chapter 3 we wanted to determine the effect of different inorganic nutrients on phytoplankton species development and the subsequent effects that they could cause on bacterioplankton. Chapter 4 evaluated the effect of a Sahara dust deposition event on the microbial communities of the coastal NW Mediterranean.

19 Introduction

BACTERIAL DIVERSITY DETERMINED BY ENVIRONMENTAL CONDITIONS

Different environmental conditions (temperature, salinity, nutrient concentrations, light…) are responsible for structuring microbial communities in the ocean. Many past studies have focused on dening the selective role of organic and inorganic nutrient availability on the growth of specic bacteria (Pinhassi et al. 2004, Allers et al. 2007). In some areas the dominant phylogenetic groups differ depending on the season. For example, Eilers et al. (2001) found that Bacteroidetes dominate during spring time and Alphaproteobacteria dominate during summer in the Baltic Sea. However, in the North Sea, the dominant group during spring was Bacteroidetes while Gammaproteobacteria took over during summer time. Therefore, bacterial successions present diverse patterns in different environments.

The Alphaproteobacteria may have special adaptation which allow them to use compounds in low concentrations and may explain why the SAR11 clade is that abundant in open oceans (Morris et al. 2002, Malmstrom et al . 2007). Concretely in the NW Mediterranean sea Alphaproteobacteria was found to account for 40% of total cell counts during summer and spring (Alonso-Sáez et al. 2007). Cytophaga- like bacteria are known to be abundant on particles in estuarines and other marine environments (DeLong et al. 1993, Crump et al. 1999). Indeed, Pinhassi et al. (2004) found that Flavobacteriaceae could be particularly important in the processing of organic matter during algal blooms. Gammaproteobacteria have been shown to have the capacity for fast growth in response to elevated nutrient concentrations (Pinhassi & Berman 2003).

Changes in bacterial composition have also been reported from the Mediterranean Sea (Schauer et al. 2003, Alonso-Sáez et al. 2007) where different studies have shown shifts in bacterial community structure due to inorganic nutrient additions (Schäfer et al . 2001, Lebaron et al. 1999, Pinhassi et al. 2006, Allers et al. 2007). Microbial communities from ecosystems receiving nutrient additions tend to be dominated by those organisms capable of competitively dominate the uptake of the nutrients added. Microbial communities from contaminated ecosystems also tend to be dominated by those organisms capable of utilizing the toxic compounds. As a result, these communities are less diverse in comparison to pristine environments (Torsvik et al. 2002). Examples of this specialization of the marine communities due to the addition of oil spills are reported by Macnaughton et al. (1999), Ogino et al. (2001), Kasai et al. (2002), Teira et al. (2007). After an exposition to petroleum contamination there is a strong selection for hydrocarbon degrading bacteria, such as genus Alcanivorax and Cycloclasticus .

20 Introduction

PREDATION PRESSURE ON BACTERIAL ACTIVITY AND COMMUNITY STRUCTURE

Depending on the type of marine system, the main factors responsible for the regulation of bacterial activity and growth can be temperature (Shiah & Ducklow 1994), grazing (Thingstad et al. 1997) or competition for limiting nutrients (Rivkin & Anderson 1997, Thingstad et al. 1997). Indeed, grazing, especially due to heterotrophic nanoagellates (HNF), has been identied as the main factor leading to bacterial loss (Fenchel et al. 1982, Sherr & Sher 1984, Wright & Cofn 1984, Pace 1988), and can often balance bacterial production. In oligotrophic environments the substrate supply is more relevant than predators (Sanders et al. 1992). Several studies indicate that agellates tend to graze preferentially upon the active component of the bacterioplankton assemblage (del Giorgio et al . 1996), thus selectively exerting grazing pressure upon some specic groups of bacteria, a phenomenon that could modulate assemblage composition (Pernthaler 2005, Pernthaler & Amann 2005).

Although top-down and bottom-up controls are widely known concepts in aquatic microbial ecology (Pace & Cole 1994), there is still disagreement to whether the abundance and productivity of bacteria are determined mainly by predators or nutrients (Pernthaler et al. 1997). Furthermore, from a structural point of view, both predators and nutrients may be major forces shaping the genotypic composition of bacterial communities (Jürgens & Güde 1994). In addition, succession patterns might appear as shaped by selective mortality, since heterotrophic nanoagelates or viral lysis can be phylotype- selective and affect the relative proportions of different microbial populations (Thingstad et al. 2000, Pernthaler 2005). Allers et al. (2007) reported selective agellate grazing on the bacterial population in response to glucose and phosphorus manipulations in a mesocosm. Grazing resulted in a change in bacterial community composition, varing from Alteromonadaceae dominance to Rhodobacteriaceae dominance. Viruses are also important in the control of bacterial abundance (Bratbak et al. 1990)

In all the experiments performed in this thesis we wanted to address the question of which were the bottom-up and top-down factors controlling the bacterial communities. In order to do so we took into consideration the grazing effect, either by measuring the HNF abundance or by controlling predator presence (by ltration).

21 Introduction

EXPERIMENTAL APPROACHES

Biological oceanographers try to understand how marine organisms and biological processes affect and are affected by physical, chemical and geochemical ocean processes. The relative marine microbial activities and their inuences in global oceanic processes are difcult to assess. There are two approaches to address these questions. The rst, and most realistic one, is to perform in situ sampling of different environments. Negative aspects of this approach include that it is time consuming, that it requires economical effort, and that depends on favourable natural conditions. Because of these problems with carrying out in situ studies, laboratory experiments are designed to determine the factors affecting metabolism and diversity of bacteria in the environment. However, this second approach is handicapped by the difculty of simulating natural conditions. As reported by Pinhassi & Berman (2003), growth of bacteria from particular phylogenetic lineages are commonly stimulated by the input of certain labile substrates which might be released during experimental manipulations.

Hence, it is important to restrict unnecessary manipulations. To avoid these laboratory limitations mesocosms experiments are commonly used in aquatic ecology to simulate natural behaviour of communities. These are easy to manipulate and minimize disturbances in the experimental setup by increasing the incubation volume (Odum et al. 1984). Many studies have performed these approaches in order to evaluate the response of marine microorganisms to nutrient additions concretely in the Mediterranean sea: Schäfer et al. (2001), Pinhassi et al. (2006) and Allers et al. (2007). In Chapter 3 and 4 we also used mesocosms experiments to determine the effect of different resources in Blanes Bay (NW Mediterranean) communities. However, this kind of experiments can also mimic other situations in the environment such as the release of contaminants to the environment e.g. oil-spills (Siron et al. 1993, Ohwada et al. 2003, Kasai et al. 2002, Teira et al. 2007). In Chapter 2 we used a mesocosm experiment with a similar set-up as reported by Olsen et al. 2006). It allows generalization of some conclusions to situations other than the one assayed, but also allow to see differences between experiments. A more realistic approach to study the inuences of the environmental conditions to a specic bacterial community is the used of the dyalisis bags, a kind of “cage “ that allows the ux of nutrients. In Chapter 5 we combine mesocosm with transplants using dyalisis bags to assess the bottom-up or top-down factors controlling the microbial communities at different depths.

22 Introduction

Top-down

Bottom-up

RESOURCES

Fig. 3 Conceptual outline of the thesis. Bacterial community composition and functioning are controlled by two different groups of factors (top-down and bottom-up). The aim of the thesis is to assess the effects of substrates from different origins. In Chapter 1, by determining the bacteria thast grow on traditional culturing, media. In Chapter 2, by evaluating inputs of anthropogenic origin (oil-spills). In Chapter 3, by atmospheric depositions (Saharan dust). In Chapter 4, by stimulation of phytoplankton blooms and the subsequent effect on bacteria, and in Chapter 5, by the effect of mixing of waters of different origin along the vertical stratication in the ocean.

23 Introduction

IN SUMMARY, AIMS OF THIS THESIS:

The main goal of this thesis was to determine how the different allochthonous resources affected bacterial community structure, abundance and activity. In order to assess this question we used different approaches, such as traditional culture techniques, mesocosm experiments and transplants: which include a combination of mesocosms and dialysis bags.

Schauer et al. (2003) and Alonso-Sáez et al. (2007) assessed the seasonality of bacterioplankton diversity by clone libraries and DGGE (a ngerprinting technique) in Blanes Bay. As a consequence of the increasing interest for isolation of microorganisms, we put some effort at isolating in plates bacteria from this environment. Chapter 1 was focused on the comparison of the sequences retrieved by culturing with the sequences obtained by culture independent techniques previously reported in other studies.

Mesocosms experiments was an approach used in this thesis to determine the effect of different allochthonous additions, of natural or anthropogenic origin. In Chapter 2 we described the simulation of the Prestige oil spill in front of the Galician coast, in Chapter 3 we analyze bacterial function and community structure after different phytoplankton bloom stimulation experiments using different nutrient sources in the NW Mediterranean sea. In Chapter 4 we mimicked a Saharan dust deposition event in the NW Mediterranean and describe their consequences on the bacterial community structure and on microbial community function, including the metabolic balance.

In Chapter 5 we considered “allochthonous” the water from depths different from those where the resident bacteria developed. We used a transplant experiment to determine the controlling factors of bacterioplankton communities of the epipelagic, the DCM and the mesopelagic in the North Atlantic Ocean.

24 CHAPTER 1

Increased marine bacterioplankton diversity detected by combining culturing and culture-independent techniques

Itziar Lekunberri , Josep M Gasol , Silvia G. Acinas , Laura G ómez-Consarnau , Emilio O. Casamayor , Ramon Massana , Carlos Pedr ós-Ali ó and Jarone Pinhassi

FEMS Microbial Ecology, submitted Per descriure la complexitat d ´un arbre és necessari un poeta

Ram on Margalef Chapter I

ABSTRACT

Despite the recognition that microbial biodiversity is an important characteristic of marine ecosystems, knowledge about the diversity of marine bacteria that can be cultured on traditional media is very limited. We studied the phylogenetic diversity of 136 bacterial isolates obtained from coastal Mediterranean seawater at the Blanes Bay Microbial Observatory (www.icm.csic/bio/projects/ icmicrobis/bbmo). Nearly all isolates belonged to the Gammaproteobacteria, Alphaproteobacteria, or the Bacteroidetes (70, 40 and 20 isolates respectively). The 16S rRNA gene sequences of the isolates were compared to environmental sequences previously obtained by molecular methods, including DGGE (60 sequences) and clone libraries (303 sequences), from samples collected at different seasons. Nine cases of complete or nearly complete identity between isolates and in situ sequences were found: ve in the Gammaproteobacteria, three in the Alphaproteobacteria and one in the Bacteroidetes. This suggests that culturing to some extent provides representative organisms for physiological response experiments for various taxa detected by molecular methods. The limited overlap between sequences obtained by culturing and molecular techniques resulted in a substantial increase in the bacterial diversity (richness) detected at this sampling site by combining the two approaches. Rarefaction curve analysis showed similar patterns for sequences from isolates and molecular approaches, except for Alphaproteobacteria where culturing retrieved a higher diversity per unit effort. Around 30% of the environmental clones and isolates formed microdiversity clusters constrained at 99% 16S rRNA gene sequence similarity. The nding of microdiversity clusters among isolate and clone sequences alike, suggests that this type of microdiversity is not an artifact and that similar evolutionary processes act on cultured and uncultured bacteria. Thus, marine bacterial isolates could serve as models to investigate how microdiversity clusters originate and provide indications of the ecological signicance of such genetic variability.

27 Isolate bacterioplankton diversity in the NW Mediterranean

INTRODUCTION

The application of molecular methods to retrieve small subunit rRNA sequences from natural environments revolutionized microbial ecology (Pace et al. 1985). About 20 years later, environmental sequences are increasingly reported to data bases and have signicantly expanded the known diversity of bacteria (Giovannoni et al . 1989), archaea (DeLong 1994, Fuhrman et al. 1994) and eukarya (Díez et al. 2001, López-García et al. 2001, Moon van der Stay et al. 2001). For most of this period, culturing has been looked upon almost with suspicion, as a technique that provided a biased picture of the “true” diversity of natural communities. In fact, the number of studies of cultured diversity has been extremely low compared to those reporting clone libraries or analyses using different ngerprinting techniques (Donachie et al . 2007).

Ribosomal RNA sequences, however, are a poor substitute for the organisms themselves. The cultured organisms can be used as model organisms for physiological studies (Giovannoni &Stingl 2007, Gómez-Consarnau et al . 2007) and their genomes can be sequenced (Moran et al . 2004, Bauer et al . 2006, González et al . 2008) and used as scaffolds to reassemble genomes from metagenomic analyses (Venter et al . 2004, Rusch et al . 2007). Recently, novel protocols for isolating bacteria have been developed (Zengler et al . 2002, Simu et al . 2005, Giovanonni et al . 2007, Stingl et al . 2007, Nichols et al . 2008) generating new interest in culturing. However, even with the traditional culturing methods, new microorganisms can be easily isolated from any environment (Donachie et al . 2007). Obviously, these microorganisms are present in concentrations that are too low to be retrieved by molecular methods that essentially detect only the most abundant sequences (Pedrós-Alió 2007). These easily cultured organisms may form part of the rare biosphere (Pedrós-Alió 2007): the very large collection of microorganisms present in ecosystems at very low abundance (Sogin et al . 2006). This low abundance protects them from viral lysis and, partially, from predation, and they can persist for very long periods of time with little or no activity. It has been suggested that if conditions change, members of the rare biosphere can quickly multiply and become part of the abundant taxa and participate actively in carbon and energy ow (Pedrós-Alió 2006). Thus, they constitute a “seed-bank” that may become extremely useful for the community to respond to changing conditions, including the appearance of pollutants or other human-made perturbations. The biodiversity of any ecosystem is not completely known until this rare biosphere is characterized (Pedrós-Alió 2006, Donachie et al. 2007). Since molecular methods are generally incapable of retrieving these rare organisms, isolation in pure culture remains one of the best windows into the rare biosphere.

28 Chapter I

Comparisons between molecular and culturing approaches have been attempted in a very limited extent, comparing only one or a few samples and a low number of clones and/or cultures (Suzuki et al . 1997, Eilers et al . 2000, 2001, Hagström et al . 2002, Kisand & Wikner 2003, Stevens et al . 2005). Here, we analyze a collection of bacteria isolated in pure culture from the Blanes Bay Microbial Observatory (BBMO). This is an oligotrophic coastal site in the Northwestern Mediterranean Sea, where extensive molecular data exist (Schauer et al . 2003, Alonso-Sáez et al . 2007) allowing for comparisons between both approaches. Thus, the aim of the present study was to compare the diversity of cultured isolates to the diversity of environmental sequences obtained by cultivation independent techniques. Further, we aimed at comparing the patterns of microdiversity among cultures and clones. In addition, nine of the isolates were selected by the Gordon and Betty Moore Foundation Marine Microbiology initiative (www.moore.org/marine-micro.aspx) for whole genome sequencing. The present study intends to provide the molecular environmental context for these bacteria.

MATERIALS & METHODS

Study area and sampling. The site studied was Blanes Bay, in the NW Mediterranean (The Blanes Bay Microbial Observatory), at 70 km North of Barcelona. Water was sampled at about 1 km offshore (41º40’N, 2º 48’E) from a boat, and immediately ltered through a 200 µm mesh net. Sampling was done in parallel to studies of carbon and sulfur cycling (see details in Alonso-Sáez et al. 2008 and Vila-Costa et al. 2008) through the period 1998-2004 (see details in Table 4). Seawater was kept in 25-l polycarbonate carboys and transported under dim light to the laboratory (less than 2 h).

Origin of bacterial DNA and isolates.

1) Natural seawater. Samples were collected as explained above and inoculated directly on agar plates as detailed below.

2) Whole water enrichments. The effect of nutrient additions on the growth of heterotrophic bacteria was examined in unltered, whole-water samples (Pinhassi et al . 2006a). Briey, nutrients

were added to 250 ml samples in nal concentrations of 40 µM C (as glucose), 2 µM N (NH 4Cl), and 0.6 µM P (NaH 2PO 4), singly and in all different combinations. After incubation for 24 h at in situ temperature in the dark, samples for bacterial production, total bacterial numbers and colony forming units were collected.

29 Isolate bacterioplankton diversity in the NW Mediterranean

3) Dilution cultures. Several times during the period 2001-2004, 1.9 l of seawater were used as growth media after ltration through 0.2 µm pore-size sterivex units (Durapore-Millipore) using a peristaltic pump at pressures <200 mm Hg (for details see Pinhassi et al. 2006a). Inocula consisted in 100 ml seawater prepared by gravity ltration through 0.8 µm pore-size lters (Nuclepore). Bacteria developed in 2 L Nalgene bottles maintained at in situ temperatures (15-22ºC) and in the dark. Cultures were unamended controls or enriched with C and P, alone or in combination. Substrates added were e.g. glucose, dimethylsulfoniopropionate (DMSP), inorganic P, ATP, or DNA, all at a nal concentrations of 20 µM C (Glucose, DMSP) or 0.6 µM P (phosphate, ATP, or DNA).

4) Mesocosm experiments. Two mesocosm (20-100 L) experiments reported in detail in Pinhassi et al. (2004) and Allers et al. (2007) provided additional sequences. For these experiments surface seawater was collected from Blanes Bay and was transferred to the laboratory. Bacterial growth and diversity were monitored in mesocosms enriched with N and P and/or C for approximately one week (see details in the references mentioned above).

Collection of community DNA. To collect in situ microbial biomass, between 5-15 L of seawater were ltered using a peristaltic pump through a 5 µm pore size Durapore lter (Milipore) and a 0.2 µm Sterivex lter (Durapore, Milipore) in succession. The 0.2 µm Sterivex unit was lled with 1.8 ml of lysis buffer (40 mM EDTA, 50 mM Tris-HCl, 0.75 M sucrose) and stored at –70ºC. Microbial biomass from the experiments was collected by ltering 0.5-1 L water onto 0.2 µm-pore-size polycarbonate lters (Milipore) at <200 mm Hg, and lters were stored in lysis buffer at –70ºC. Nucleic acids were extracted by a standard protocol using phenol/chloroform (details in Schauer et al. 2003)

Origin of the sequences used in the phylogenetic comparison.

1) Isolates. A total of 625 colonies were collected from the above-mentioned sources, from which we selected 136 isolates for partial 16S rDNA sequencing based on differences in colony morphology and abundance on agar plates at different sampling occasions or experiments. The isolates were obtained by plating 100 µl of undiluted, or 10x, 100x and 1000x diluted seawater of untreated or experimental samples, in triplicates, onto Zobell agar plates (Zobell 1943). Agar plates were incubated at in situ temperature in the dark for 10-15 days. The 16S rDNA sequences of the 136 bacterial isolates were amplied by means of PCR using Taq polymerase (Boehringer-Mannheim) from DNA preparations using bacterial 16S rDNA primers 27f and 1492r. Isolates were partially sequenced with internal primer 358f, which yielded sequences of approximately 700 - 850 bp length. Accession numbers of the isolates are given in the Table 4.

30 Chapter I

2) DGGE bands. DGGE bands from both environmental samples (27) and enrichment cultures (33) (whole seawater enrichments and dilution cultures) were retrieved. DGGE ngerprints were performed as previously described (Schauer et al . 2003, Sánchez et al. 2007). The 16S fragments (around 550 bp in length) were amplied with bacterial specic primer 358f complementary to position 341 to 358, with GC-clamp and universal primer 907rm complimentary to position 927 to 907 (Muyzer et al. 1997). The PCR products were loaded in a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from 40 to 80%. The gel was run at 100 V for 16 h at 60°C in 1x TAE running buffer. The DGGE bands were excised, reamplied, and veried by a second DGGE. Bands were sequenced using primer 358f without the GC-clamp; with the Big Dye terminator cycle-sequencing kit (Perkin Elmer Corporation) and an ABI PRISM model 377 (v3.3) automated sequencer. These sequences have been reported in Schauer et al. (2003), Alonso-Sáez et al. (2007), Pinhassi et al. (2004, 2006a), and Allers et al. (2007).

3) Clone libraries. Clone libraries were constructed from in situ samples collected on 3 March, 14 May, 14 July, 4 August and 21 October 2003 (Alonso-Sáez et al. 2007). Briey, 16S rDNA sequences were amplied from total community DNA by PCR with universal primers 27f and 1492r, and resulting amplicons were cloned using the TOPO-TA cloning kit. PCR amplicons were digested with the restriction enzyme HaeIII (Invitrogen), and the RFLP patterns were compared to identify unique clones. Clones were partially sequenced with internal primer 358f, which yielded sequences of approximately 700 - 850 bp.

4) Additional sequences included in the analysis. For comparison, eight DGGE sequences from the neighboring Banyuls Bay (ca. 150 km away) were included (Schäfer et al. 2001).

Phylogenetic analysis. The 16S rRNA gene partial sequences of our isolates were compared to existing prokaryotic sequences in GenBank (NCBI) using BLAST (basic local alignment search tool). Comparison of BLAST results from database searches with different regions of the sequences showed that none of our sequences were chimeras. Phylogenetic analyses were performed using the parsimony methods implemented in the ARB sequence analysis package by which sequences were grouped into major phylogenetic groups. The trees were calculated from sequences approximately corresponding to nucleotide positions 420 to 770 ( E. coli numbering). Sequences of close relatives included in GenBank after the BLAST process were included in the trees as references in order to delimitate and name different clusters of sequences.

31 Isolate bacterioplankton diversity in the NW Mediterranean

Comparison of sequence diversity. Rarefaction analysis and diversity estimates by the non- parametric Chao-1 richness estimator were carried out using the online rarefaction calculator software (http://w ww2.biology.ualberta.ca/jbrzusto/rarefact.php). For clustering sequence analysis, the software Clusterer was used (Klepac-Ceraj et al. 2006). This allows grouping of sequences into percent similarity clusters (100, 99% etc.) by the nearest neighbor approach (i.e., sequences are added to clusters if there is at least one sequence which is within a set similarity threshold). These clusters based on 99% similarity cut-off served as the basic units for statistical extrapolation of total sequence diversity using the Chao-1 non-parametric richness estimator and for comparison of sequence composition among the major phylogenetic groups and between molecular and isolation approaches.

RESULTS

General patterns of richness. The 136 bacterial cultures (84 unique) isolated from Blanes Bay and analyzed here are detailed in the Table 4, where their origin, closest sequence, and closest cultured relative are presented. The 16S rRNA sequences of these isolates were compared with 363 sequences (194 unique) obtained from 16S rDNA clone libraries and denaturing gradient gel electrophoresis (DGGE) from the same site, retrieved in previous studies (Schauer et al . 2003, Alonso-Sáez et al . 2007). Fig. 1 shows the main groups of bacteria retrieved from the BBMO indicating whether they were found by culturing, molecular methods or both.

We explored how the percent similarity level chosen would affect the number of OTUs for isolates and clones (Fig. 2A). When the % similarity was increased from 99 to 100% the number of clone OTUs increased substantially while the number of isolate OTUs remained nearly the same. This increase would appear to indicate a larger microdiversity among the clones compared to the cultures. However, when the same exercise was carried out within each of the three main groups of bacteria, signicant differences appeared. In the Alphaproteobacteria there was a large increase in OTUs among the clones, while only minor increases in OTUs among isolates was observed (Fig. 2B). The Gammaproteobacteria showed precisely the opposite trend, with a marked increase in the microdiversity of the cultures and a very small increase in that of clones (Fig. 2C). The Bacteroidetes showed almost no changes either in isolates or in clones (Fig. 2D). Other than this, there was a clear discontinuity above 97%, suggesting that this percentage is a reasonable cutoff point for this data set. Both the Alpha- and Gammaproteobacteria showed the inection point around 97% similarity. Interestingly,

32 Chapter I the Bacteroidetes showed a much more uniform increase of OTUs with increases in percent similarity. We also combined all sequences (isolates, clones, and DGGE bands) for each of the three major groups (Fig. 2E), showing the clear difference in microdiversity between the Proteobacteria, on the one hand, and the Bacteroidetes on the other. The next analysis consisted of calculating rarefaction curves for all the sequences together (Fig. 3A) or for the three groups separating isolates and clones

Bacteroidetes

Sphingobacteria Flavobacteria Fibrobacter Bacteroidetes Chloroflexi

Planctomyces Verrumicrobia Cyanobacteria Algae Archaea

Firmicutes

Marine Actinobacteria

Halomonas

Marinomonas Deltaproteobacteria

Epsilonproteobacteri a Alteromonas Sphingomonas Gammaproteobacteria Pelagibacter

SAR86 Roseobacter Rhodobacter

Betaproteobacteria Alphaproteobacteria

Fig. 1.- Schematic phylogenetic tree displaying the major lineages in the marine prokaryotic plankton. The color of each branch indicates whether only clones (green), isolates (red) or both, clones and isolates (yellow), were retrieved from Blanes Bay. Lack of color indicates that we could not detect this group with any of these techniques. Cyanobacteria and algae are drawn in dark green since they appeared in the clone libraries in this study but no effort was made to isolate them.

33 Isolate bacterioplankton diversity in the NW Mediterranean

tot Clones (B) Isolates (A) All Alphaproteobacteria 80

160 70

140 60 120 50 100 40 80

30 60 Number of OTUs 20 40

20 10

0 0 70 75 80 85 90 95 100 80 85 90 95 100 (C) Gammaproteobacteria (D) Bacteroidetes 60 40

35 50

30

40 25

30 20

15 20

Number of OTUs 10

10 5

0 0 80 85 90 95 100 80 85 90 95 100 Percentage of similarity (%) Percentage of similarity (%) (E) Group comparison Alpha 80 Gamma CFB 70

60

50

40

30 Number of OTUs

20

10

0 80 85 90 95 100 Percentage of similarity (%)

Fig. 2.- Number of OTUs as a function of the percentage of similarity cutoff chosen to group sequences. A) The three major phylogenetic groups of bacteria combined as retrieved by culturing, cloning or both together B) Number of Alphaproteobacteria OTUs obtained with each technique. C) Same for Gammaproteobacteria; D) Same for Bacteroidetes. E) Total number of OTUs obtained using clone libraries and isolation techniques combined for each of the three main groups.

34 Chapter I

(Figs. 3B-D). The pooled data set was used to estimate the total number of OTUs present in Blanes Bay by using the Chao 1 index. When the percent similarity was increased from 97 to 100%, the total richness increased from 125 to 420 OTUs.

Next, we chose a 99% similarity level to compare the estimates of richness provided by isolates, clones or the two combined, for the three main groups (Table 2, Figs. 3B-D). Unexpectedly, the Chao estimates from isolates were similar to (Alphaproteobacteria and Bacteroidetes), or larger than (Gammaproteobacteria), the estimates from clones alone (Table 2). Thus, there was at least as much richness in the isolate collection as in the clone libraries. It must be recalled that all cultures were isolated in Zobell medium under similar conditions, with no particular efforts to provide a variety of culture conditions. Combination of isolates and clones provided Chao estimates similar to, or larger than, those obtained with either isolates or clones separately (Table 2). Thus, the total richness estimate obtained by combining the molecular and the isolation approaches was similar to that obtained for the isolates alone. The rarefaction curves conrmed the differences among the three groups: more OTUs from cultures for the Gammaproteobacteria, and similar estimates for the Alphaproteobacteria and the Bacteroidetes (Fig. 3B-D).

Table 1 .- Number of characterized isolates, clones, and DGGE bands belonging to different taxonomic groups retrieved from Blanes Bay. ______Isolates Clones DGGE bands Total Unique Total Unique In situ Experiments ______Alphaproteobacteria 40 20 219* 80 16 17 Betaproteobacteria 2 2 1 1 0 0 Gammaproteobacteria 70 39 50 26 4 2 Deltaproteobacteria 0 0 0 0 1 0 Epsilonproteobacteria 0 0 1 1 0 0 Bacteroidetes 20 19 18 14 7 13 Firmicutes 3 3 0 0 0 0 Actinobacteria 1 1 4 3 0 0 Verrucomicrobia 0 0 10 9 0 0

All 136 84 303 134 28 32 ______* 95 belonged to the SAR11 group.

35 Isolate bacterioplankton diversity in the NW Mediterranean

All sequences Gammaproteobacteria (A) (C) 200 OTUs Chao1 SD 25 isolates 100% 166 420.3 49.1 clones 99% 116 231.8 28.3 98% 94 159 19.2 97% 74 125.6 17.6 20 150

15

100

10 Number of OTUs observed

50 Number of OTUs observed 5

0 0 0 50 100 150 200 250 300 350 400 0 10 20 30 40 50 60 70 80

Number of sequences screened Number of sequences screened

Alphaproteobacteria (B) (D) Bacteroidetes 35 isolates 20 isolates clones clones

30

15 25

20 10 15 Number of OTUs observed 10 5 Number of OTUs observed

5

0 0 0 50 100 150 200 0 5 10 15 20 25 Number of sequences screened Number of sequences screened

Fig. 3.- Rarefaction analysis for sequences obtained by isolation and by molecular approaches. A) All sequences together. B) Alphaproteobacteria. C) Gammaproteobacteria. D) Bacteroidetes.

36 Chapter I Chao1 richness estimates for the major bacterial groups, based on 99% similarity cut-off using the sequences from either based on 99% similarity cut-off using the sequences from Chao1 richness estimates for the major bacterial groups, Taxon Taxon Alphaproteobacteria 40 Gammaproteobacteria 70 19 All Bacteroidetes 27 OTUs 47 Chao SD 108 Total 16 39 20 All 183 19 OTUs Chao 46 SD 33 34 16 58 11 130 All 36 12 67 OTUs Chao 14 SD 19 246 223 52 16 116 52 44 39 140 248 16 92 31 62 23 125 39 21 32 378 77 116 19 232 28 Table 2.- Table ______Isolates Clones Isolates and clones isolates and clone libraries separately, or all combined. isolates and clone libraries separately,

37 Isolate bacterioplankton diversity in the NW Mediterranean

Identity of the cultures.

Alphaproteobacteria . Phylogenetic analysis of the Alphaproteobacteria revealed that a substantial fraction of the isolates belonged to different clusters within the Roseobacter group (clusters Alpha 1 to 4, Fig. 4). In addition, we found isolates belonging to Erythrobacteraceae (cluster Alpha 5), Hyphomonadaceae and Aurantimonadaceae.

Clusters Alpha 1 and 4 included only sequences obtained by culture independent techniques (Fig. 4, Table 3). Sequences within cluster Alpha 1 (related to clone NAC11-7) and within cluster Alpha 4 (related to clone CHAB-1-5) showed within group similarities >95% and >96% respectively (Table 3). On the other hand, cluster Alpha 2 included only sequences from isolates and P-enrichment experiments (Mes9 and B-260) but none retrieved directly from the in situ samples. Isolate M193 within this cluster has been whole-genome sequenced. In addition, six isolates represented bacterial groups that were not detected by molecular techniques (M588, M464, M441, M624, M623 and M463).

Two clusters containing both isolates and environmental sequences were found. In cluster Alpha 3 the sequences showed within group similarities larger than 97% and belonged to the genus Nereida. The isolate M128 was 99.8% similar to the clone sequence SPR23 and 99.6% to DGGE bands B90 and B94, obtained in P, ATP and DNA addition experiments. Cluster Alpha 5 consisted of the genus Erythrobacter, with four isolates, two clone library sequences, and one in situ DGGE band. Erythrobacter isolate M442 was 99.8% similar to the clone sequence AUT4 and was identical to DGGE band 27. Apart from these clusters, isolate M483 was 99.8% similar to clone SPR41. Thus, isolates M128, M442, and M483 seem good candidates for investigating pure cultures representative of marine bacterioplankton.

All together, 64% of the 16S rRNA sequences grouped into >97% similarity clusters. The Alphaproteobacteria had substantial microdiversity with only 33% of the OTUs grouped at 99% similarity level (Fig. 2B). The rarefaction curves for the isolates were steeper than for the environmental clones (Fig 3B), implying that, with the same effort, there is a potential to recover larger species diversity through isolation than through clone analysis.

38 Chapter I

NAC11-7, AF245635 SPR9, Clone WIN4, Clone BL98-28, DGGE band in situ BL03-35, DGGE band in situ Alpha 1 Roseobacter sp . DQ660384.1 B115, DGGE band experiments B140, DGGE band experiments Roseovarius crassostreae, AF114484 Mes23, DGGE band Mesocosmos Roseoisalinus antarticus , AJ605747.2 SPR10, Clone BL98-31, DGGE band in situ Mes38, DGGE band Mesocosmos M612, Isolate Mes11, DGGE band Mesocosmos Aquamarinicola italica , AM904563 M605, Isolate Mes26, DGGE band Mesocosmos Mes47, DGGE band Mesocosmos Ruegeria gelatinovorans , AF247972 M606, Isolate Mes28, DGGE band Mesocosmos Silicibacter pomeroyi, CF000031 B-230, DGGE band experiments BL98-33, DGGE band in situ M193, Isolate * Mes9, DGGE band Mesocosmos Alpha 2 B-260, DGGE band experiments M165, Isolate M618, Isolate M483, Isolate SPR41, Clone Roseobacter litoralis , X78312 BL03-37, DGGE band in situ B147, DGGE band experiments SPR2, Clone Octadecabacter orientus , DQ167247 Jannashia cystaugens , AB121783 M588, Isolate Nereida ignava, AJ748748 SPR23, Clone B90, DGGE band experiments Alpha 3 B94, DGGE band experiments M128, Isolate M149, Isolate M613, Isolate Mes8, DGGE band Mesocosmos Rhodobacteraceae bacterium , AY962292.1 Mes16, DGGE band Mesocosmos AUT20, Clone CHAB-1-5,AJ240910 SUM32, Clone AY-57, DGGE band Banyuls B79, DGGE band experiments Alpha 4 BL98-34, DGGE band in situ BL03-43, DGGE band in situ BL98-36, DGGE band in situ AY-51, DGGE band Banyuls Mes15, DGGE band Mesocosmos Loktanella hongkongensis ,AY600300.1 M464, Isolate Paracoccus sp .DQ108402.1 M441, Isolate AY-58, DGGE band Banyuls BL03-55, DGGE band in situ Uncultured organism , DQ396058.1 M623, Isolate Hyphomonas oceanitis, F261046 Maricaulis maris , AJ227803.1 M624, Isolate Aurantimonas coralicida , AY065627 M463, Isolate Unculture alphaproteobacteria , AF245619.1 AUT80, Clone Citromicrobium sp. , DQ985045.1 M458, Isolate M443, Isolate Erythrobacter citreus , AF118020 AUT4, Clone Alpha 5 M442, Isolate AUT12, Clone BL03-48, DGGE in situ Erythrobacter aquimaris , AY461443 M155, Isolate Nisaea denitrificans, DQ665839.1 BL98-50, DGGE band in situ BL03-51, DGGE band in situ Candidatus Pelagibacte ubique, CP000084.1

0.10 Fig. 4.- Maximum Parsimony tree illustrating the relationships among the partial 16S rRNA sequences from Blanes Bay for the Alphaproteobacteria. Colors indicate the origin of the sequences: bacteria isolated on solid media (red), DGGE bands from experiments (orange), DGGE bands from in situ samples (green), and clones retrieved from in situ samples (blue). Clusters are indicated by vertical red lines. Identities between isolates and sequences are marked by white arrows. The sequences marked with asterisks have been genome sequenced. The SAR11 sequences are not displayed in this tree. 39 Isolate bacterioplankton diversity in the NW Mediterranean

Gammaproteobacteria. The Gammaproteobacteria was the most diverse group in terms of richness (Table 1, Fig. 5) and could be divided into 6 major clusters. In cluster Gamma 1 (Alteromonas) there were several coincidences of isolates with sequences from in situ clone libraries. This cluster consisted of 9 isolates from different experiments with combined enrichments of inorganic P and C sources like glycerol, glucose and DMSP. In this cluster we found that three isolates (M111, M185 and M292) were identical or nearly identical (>99%) to DGGE bands (labeled as AY and BY) obtained in a mesocosm experiment carried out in 1996 in Banyuls, ca. 150 km northeast of Blanes Bay (Schäffer et al. 2001). Furthermore, isolate M169 was identical to DGGE band B145 from an experiment (isolate M604 was 99.8% similar to this sequence). All the sequences from this Alteromonas group were distantly related to sequence JUL4, which represented 30% of the clones from the July clone library).

Three clusters were formed only by isolates: Gamma 2 (Pseudoalteromonas), Gamma 3 (Vibrio), and Gamma 4 (Marinomonas ). Among the Vibrio sequences there was a substantial phylogenetic diversity (within-cluster similarity 94%, Table 3). The Pseudoalteromonas cluster included isolates obtained from unenriched seawater samples from different seasons (March, June and November). In contrast, most Vibrio sequences were isolated from C+P enrichment experiments. Sequences related to the Marinomonas genus were primarily obtained from enrichments with ATP

Isolate MED266, from unenriched seawater cultures, was 99.3% similar to clone JUL18 that was found in three copies in the July clone library. Isolates afliated with the genus Marinobacter were retrieved from enrichments with three different carbon sources (glucose, pyruvate or DMSP) in combination with phosphate. Cluster Gamma 5 consisted of distantly related sequences containing only clone sequences.

The Gammaproteobacteria, like the Alphaproteobacteria, showed high levels of microdiversity: only 32% of the OTUs were grouped at 99% similarity level. In contrast to the Alphaproteobacteria, this was due to the large microdiversity of isolates but not of clones (Fig. 2C). Moreover, based on the Chao-1 estimator, Gammaprotebacteria had the largest number of OTUs (108). This number of taxa was slightly higher when only isolates were taken into account than with the combination of both molecular and isolation approaches (Table 2).

Six genera were detected by isolation that had not been detected by molecular techniques : Pseudoalteromanoas , Vibrio , Halomonas , Marinomonas , Oleispira and Marinobacter . Isolates M222, M121 and M297 were whole-genome sequenced. MED297 has been formally described as the new species Reinekea blandensis (Pinhassi et al. 2007).

40 Chapter I

M113, Isolate SUM81, Clone M609, Isolate Alteromonas sp. , EF061412.1 M275, Isolate Alteromonas alvinellae , AF288360 B145, DGGE experiments M169, Isolate M604, Isolate Gamma 1 BY-92, DGGE Banyuls M111, Isolate SUM78 , Clone AY-86, DGGE Banyuls M185, Isolate AY-84, DGGE Banyuls M292, Isolate Alteromonas macleodii , Y18231 AY-85, DGGE Banyuls Alteromonas addita , AY682202 M517, Isolate B-227, DGGE experiments JUL4_14, Clone Glaciecola mesoplila , AY771709 M620, Isolate M107, Isolate M290, Isolate Pseudoalteromonas atlantica , X82134 Gamma 2 Alteromonas sp. , AB016267.1 M188, Isolate Pseudoalteromonas citrea , X82137 M246, Isolate Idiomarina sp. , DQ985070.1 M206, Isolate M181, Isolate M428, Isolate M140, Isolate M222, Isolate* Vibrio lentus, AJ278880.1 M511, Isolate M227, Isolate Gamma 3 M241, Isolate Vibrio splendidus, AY227706 Vibrio parahaemolyticus, DQ026024.1 M418, Isolate Vibrio pectenicida , Y13830.1 M535, Isolate M608, Isolate Enterovibrio norvegicus , AJ437193 M126, Isolate Aeromonas sobria , X74683 JUL3, Clone M297, Isolate* Reinekea marinisedimentorum, AJ561121 M121, Isolate* M238, Isolate M254, Isolate M123, Isolate Gamma 4 M119, Isolate M269, Isolate M302, Isolate Marinomonas pontica , AY539835 JUL18_3, Clone M266, Isolate Oleispira antarticam , AJ426421 M498, Isolate Marinobacter aquaeolei , AF173969 M503, Isolate M203, Isolate Halomonas marisflava , AF251143 M543, Isolate AUT70, Clone JUL12, Clone Noriaki katanozaka , AB022713.1 AUT94, Clone WIN38, Clone AUT48_2, Clone Marine gammaproteobacterium , AY3863391 Gamma 5 SPR78, Clone WIN52, Clone WIN63, Clone

0.1 0

Fig. 5.- As previous gure, for the Gammaproteobacteria.

41 Isolate bacterioplankton diversity in the NW Mediterranean

Bacteroidetes. Most of the Bacteroidetes sequences belonged to the Flavobacteriaceae family (Fig. 6, Table 3). Most isolates belonged to clusters Bacteroidetes 1 and 3. Cluster Bacteroidetes 1 contained ve different genera: Gramella , Sufavibacter , Dokdonia (including Krokinobacter ), Salegentibacter and Leeuwenhoekiella (Fig. 6). The last two genera had not been found in Blanes by molecular techniques. There were three subclusters in cluster Bacteroidetes 3: sequences related to Polaribacter including isolates and DGGE sequences from P-enrichment experiments and mesocosms algal blooms; sequences related to Tenacibaculum with a similar origin; and isolate M341 related to two sequences (BL98-5 and BL03-7) obtained by DGGE analysis of winter samples.

Cluster Bacteroidetes 2 included a large number of sequences afliated to the Delaware Bay sequences ZD0403 and CF6 (Kirchman et al. 2003). These sequences were obtained exclusively by molecular techniques. Cluster Bacteroidetes 4 was also composed of uncultivated phylotypes and largely corresponded to the AGG58 clade (O’Sullivan et al. 2004). Isolates M466 and the M571, belonging to the genus Microscilla , had not been found before in Blanes Bay by cultivation-independent techniques.

Only one coincidence between isolates and environmental sequences was found: isolate M134 was very similar to a DGGE band from Banyuls and to the reference strain Dokdonia donghaensis . This isolate has been whole-genome sequenced and is a promising candidate for environmentally relevant physiological studies. In fact, MED134 is the only marine bacterium that has been shown to grow better in the light than in the dark thanks to the presence of proteorhodopsin (Gómez-Consarnau et al. 2007). Isolates M217 ( Leeuwenhoekiella blandensis ) and M152 ( Polaribacter sp. ) have also been whole-genome sequenced. The genome of MED152 has been manually annotated and published (González et al . 2008) and MED217 has been formally described as a new species (Pinhassi et al . 2006b).

42 Chapter I

Gramella echinicolla , AY608409 M454, Isolate M367, Isolate WIN75, Clone Sufflavibacter litoralis , DQ868538.2 M220, Isolate Krokinobacter genikus , AB198086 BY-64, DGGE band Banyuls Dokdonia donghaensis , DQ003277.1 Bacteroidetes 1 M134, Isolate* Dokdonia sp., EU195942.1 M329, Isolate Salegentibacter salinus , EF486353 M532, Isolate M445, Isolate Leeuwenhoekiella aequorea , AJ278780 M217, Isolate* M495, Isolate Croceibacter atlanticus , AY163576.1 B-203.2, DGGE band experiments Maribacter forsetii , AM712900 M381, Isolate Auerimarina marisflavi , EF108215.1 B122, DGGE band experiments SUM25, Clone WIN97, Clone Unculture marine bacterium ZD0403, AJ400347 BL98-14, DGGE band in situ B76-2, DGGE band experiments BL03-28, DGGE band in situ Bacteroidetes 2 SUM18_2, Clone WIN28, Clone AUT25, Clone Unculture Bacteroidetes CF6, AY274860 AUT33, Clone Polaribacter dokdonensis, DQ004686 M152, Isolate* B-240, DGGE band experiments Polaribacter irgensii, AY771712 B98, DGGE band experiments M568, Isolate Tenacibaculum maritimum, M64629 Bacteroidetes 3 M595, Isolate M621, Isolate M622, Isolate B170, DGGE band experiments M341, Isolate BL98-5, DGGE band in situ B100, DGGE band experiments BL03-7, DGGE band in situ Flavobacterium succinicans , AM230494.1 AUT87, Clone Fluviicola taffensis, AF493694.2 B108, DGGE band experiments AUT100, Clone WIN14, Clone B180, DGGE band experiments B131, DGGE band experiments B88, DGGE band experiments B-270, DGGE band experiments Marine Eubacterial AGG58, L10946 Bacteroidetes 4 B175, DGGE band experiments SUM2, Clone AUT19_2, Clone BL03-11, DGGE band in situ SPR33, Clone Flexibacter tractuosus , M58789.1 M599, Isolate AUT46, Clone Cyclobacterium marium , M62788 Microscilla arenaria , AB078078 M466, Isolate M571, Isolate AUT77, Clone Chitinophaga pinensis , AF078775 BL98-40, DGGE band in situ

0.10

Fig. 6.- As previous gures, for the Bacteroidetes.

43 Isolate bacterioplankton diversity in the NW Mediterranean

DISCUSSION

Since the introduction of molecular biology techniques in marine microbial ecology, a great effort to describe the diversity of marine bacterioplankton communities has resulted in a comprehensive inventory of primarily uncultured bacteria (e.g. Giovannoni & Stingl, 2007). As a consequence, a great majority (86%) of clones of our different clone libraries were more than 97% similar to sequences already deposited in GenBank. At the same time, a number of microbial ecology studies have investigated the diversity of cultured isolates (Pinhassi et al. 1997; Eilers et al. 2000, 2001; Hagström et al. 2000; Long & Azam 2001; Pinhassi & Berman, 2003; Stevens et al. 2005; Suzuki et al. 1997; Selje et al. 2005). The increase in information about culturable marine bacteria is evident when one considers that at the time of the pioneering study on the Roseobacter group by González et al. (1997), the group consisted of only 6 described species while in 2005 more than 35 species had been described forming up to 13 different major clusters (Buchan et al. 2005). The potential of culturing to increase the known richness of any environment was also manifest in the present study. The three main groups of bacteria increased the number of OTUs signicantly. Moreover, at least for the Gammaproteobacteria, the shape of the rarefaction curve suggested that potentially more diversity could be uncovered by culturing than by cloning.

The most abundant groups with different techniques. Both culture dependent and independent methods indicated that Alpha- and Gammaproteobacteria and Bacteroidetes were the predominant groups. Together, these three groups accounted for 96% of the isolates, 95% of the clones, and 96% of the DGGE bands (Table 1). A seasonal study carried out at the same sampling site by FISH indicated that these three groups accounted for 75% of the EUB+ count (Alonso-Sáez et al. 2007). On average, Alpha-, Gammaproteobacteria and Bacteroidetes accounted for 30, 4-8, and 11% of the DAPI count respectively. If we take FISH to be the most quantitative technique to identify bacteria in marine waters, Gammaproteobacteria were overrepresented in the three sets of sequences considered here, particularly in the culture collection, where they represented 51% of the isolates, but also in clones (17%) and DGGE bands (14%, Table 1). Alphaproteobacteria, on the other hand, showed the same importance in the culture collection (30%) as in FISH, and were overrepresented by the two molecular approaches (72% of clones and 57% of DGGE bands). Finally, the Bacteroidetes were represented in percentages not too different form FISH by the three approaches (15% in cultures, 6% in clone libraries, and 25% in DGGE bands). These comparisons should be kept in mind to interpret our results.

44 Chapter I

Among the least represented groups, Betaproteobacteria and Actinobacteria were recovered by both culturing and molecular approaches, while Deltaproteobacteria, Epsilonproteobacteria, and Verrucomicrobia were only found by molecular techniques (Fig. 1). Obviously, our sequencing and isolating efforts were not sufcient to retrieve these other groups in signicant numbers, and we can say very little about their diversity. The culturing and sequencing effort would have to be increased at least by one order of magnitude to retrieve signicant numbers of these groups. Recently, a sample from the BBMO collected on September 2007 was analyzed by 454 pyrosequencing within the International Census of Marine Microbes program (http://icomm.mbl.edu). Of the 8605 tag sequences that could be assigned a taxonomic identity and were not cyanobacteria or chloroplasts, sequence tags were distributes as follows: Proteobacteria 84%, Bacteroidetes 9%, Verrucomicrobia 2.5%, Actinobacteria 1.7%, Firmicutes 1.9%, and ten other groups making less than 0.5% each (unpublished data). Therefore, it is clear that Proteobacteria and Bacteroidetes were the major components of the bacterioplankton, while the remaining groups were quantitatively minor components.

Estimates of richness in Blanes Bay. It is well known that bacteria isolated in pure culture frequently do not coincide with sequences retrieved by molecular methods from the same ecosystem. Thus, it is not surprising that the culturing effort contributed signicantly to increase the richness of bacterial taxa known from the BBMO. At least two new genera of Bacteroidetes and six of Gammaproteobacteria were added to the taxa list of Blanes Bay. In terms of OTUs, known richness increased from 62 (clones alone) to 116 (clones plus isolates). And considering the estimates from the Chao 1 indicator, richness increased from 125 to 232 based on clones plus isolates. The degree of novelty provided by cultures was highest for the Gammaproteobacteria and lowest for the Alphaproteobacteria. However, this is still a minor fraction of the total richness in Blanes Bay, as a comparison to the 454 pyrosequencing data mentioned above makes obvious: from a total of 14775 tags, there were 2719 unique ones (representing different OTUs). This is one order of magnitude higher than the highest estimates from the culturing and clone library approaches combined, and increases our knowledge about microbial diversity on the one hand, and exemplify the large efforts needed to completely characterize it. With the used methods we have barely scratched the surface of microbial biodiversity in this site.

45 Isolate bacterioplankton diversity in the NW Mediterranean

Isolates of potential environmental relevance. Both molecular and culturing methods showed the major groups to be Alphaproteobacteria, Gammaproteobacteria and Bacteroidetes, as in most other marine waters (Hagström et al. 2002). However, at the level of specic 16S rRNA gene sequences we found only minimal overlap between cultivated and uncultivated bacteria. This suggested that most of the isolates were of low signicance in the studied environment at the time of sampling. This is a fairly general situation (e.g. Donachie et al . 2007). Thus, Suzuki et al. (1997) compared 127 isolates (37 different) and 58 clones (25 different) from one coastal Pacic seawater sample. They found little overlap between isolate and clone sequences, although a few coincidences occurred in the Gammaproteobacteria (1 Pseudomonas) and in the Alphaproteobacteria (2 Roseobacter, 1 Sphingomonas). In a similar comparison in North Sea tidal ats, Stevens et al. (2005) found four identical clone and isolate sequences among a total of 188 isolates and 77 clones. In Blanes we found 9 cases of identical or nearly identical 16S rRNA gene sequences shared by isolates and in situ sequences (from clones or DGGE bands): 5 in the gammaproteobacteria, 3 in the alphaproteobacteria and 1 in the Bacteroidetes (marked with arrows in Figs. 2-4). Obviously these cultures are good candidates for laboratory studies of environmentally relevant microorganisms.

Expecting the results mentioned above, we reasoned that enrichment cultures would be a good source of isolates of potential importance in the environment. By incubating sea water samples with different carbon sources and/or nutrients we hoped to increase the abundance of bacteria that could respond to such enrichments in nature. It is a frequent observation that novel bands appear in DGGE gels in response to experimental manipulations (e.g. Massana et al. 2001, Allers et al. 2007). We had speculated that these bands could correspond to bacteria that could be relatively easy to isolate in pure culture. Indeed, we found some coincidences between isolates and DGGE bands that appeared in response to enrichment experiments (Figs. 4-6). We envision these enrichment experiments as an opportunity for bacteria in the seed-bank to grow and become members of the diversity of the system, hopefully mimicking episodic enrichments in nature. One particular example is shown in Fig. 7. The DGGE gel shown corresponds to an enrichment experiment with different P-rich compounds. A specic band (band B90) was seen to increase in intensity in the three treatments with P-rich compounds, but it disappeared in the unenriched control. This band was identical to isolate M128, a member of the genus Nereida . The band can be appreciated in the lane corresponding to the natural samples before the beginning of the experiment. This band was too faint for successful sequencing and, thus, would have remained unidentied by conventional molecular techniques. However, under the appropriate conditions, it became one of the dominant members of the assemblage. This suggests that M128 is normally a member of the rare biosphere, but may become important when episodes of

46 Chapter I

P enrichment occur. This also indicates that the composition of the bacterial assemblage likely has a strong dependence on nutrient supply (or bottom-up control), something that gives an explanation for the recurrence of the same bacterial populations year after year at the same season (Fuhrman et al. 2006) .

To PO4 ATP DNA K

in situ as “seed bank” specific for P =M128

Fig. 7.- DGGE gel showing the band patterns from different treatments in a nutrient addition experiment. The in situ sample (labeled T0) showed a larger number of bands than the enrichments. A band increased in intensity in P additions (B90 and B94), and was identical to isolate M128 (see Supplementary Table). This band was also present at time 0, but in a much lower relative abundance.

47 Isolate bacterioplankton diversity in the NW Mediterranean

Microdiversity. We observed that a high fraction of the total diversity for both environmental clones and isolates formed microdiversity clusters, constrained at 99% 16S rRNA gene sequence similarity according to Acinas et al . (2004). While microdiversity clusters are often observed in environmental clone libraries (Field et al . 1997, Acinas et al. 2004, Morris et al. 2005, Pommier et al. 2006), we are not aware of any report on microdiversity for larger collections of marine bacterial isolates. Thirty percent of the total observed OTUs (by both approaches) and 45% of the total species estimated by Chao-1 were grouped as microdiversity clusters (Figs. 2A and 3A). However, this microdiversity fraction was not equally distributed among all taxonomic groups, it was mostly due to the Gammaproteobacteria in the case of isolates and to the Alphaproteobacteria in the case of clones, while Bacteroidetes exhibited the lowest microdiversity. This low microdiversity could be the effect a lower number of Bacteroidetes sequences (45) compared to 223 and 116 sequences from Alpha- and Gammaproteobacteria, respectively. However, the low microdiversity of Bacteroidetes found in our study is a characteristic also observed in other studies (Acinas et al. 2004; Pommie r et al. 2006). The ecological signicance of the relatively high abundance of very divergent lineages for Bacteroidetes remains unresolved. Previous studies show that microdiversity clusters could represent important units of evolutionary differentiation as ecotypes in natural populations of bacteria (Cohan 2006, Thompson et al. 2005, Polz et al. 2007, Cohan & Perry 2007, Zo et al. 2008). The nding of microdiversity clusters among isolate and clone sequences alike, suggests that similar evolutionary processes act on cultured and uncultured bacteria. Thus, marine bacterial isolates could serve as models to investigate how microdiversity clusters arise and provide indications of the ecological signicance of such genetic variability.

ACKNOWLEDGEMENTS

We thank Vanessa Balagu é for her work with the clone libraries, and Laura Alonso-S áez for sharing data. Thanks also to Isabel Ferrera who helped with Figure 1, and to Thomas Pommier for the preliminary 454 data. This work was supported by EU-project BASICS (CTM2005-04795/MAR), Spanish MEC projects Gen µMAR (CTM2004-02586/MAR), MODIVUS (CTM2005-04795/MAR) and GEMMA (CTM2007-63753-C02-01/MAR), and the Swedish Research Council (grant 621-203- 2692 to JP).

48 Chapter I Identity of the bacteria isolated from Blanes The Bay. sequences have different origins and come from different years. different seasons. and - Table 4. Table • DQ681131 (DQ681165) M107 (EU253571) M271 M306 • DQ681132 16/03/01 13/11/01 M111 Control 13/11/01 Control • DQ681133 AJ417594 Pseudoalteromonas agarivorans M113 M107 DQ681131 P 16/03/01 • DQ681134 Control 16/03/01 99.8 M114 M107 DQ681131 AY576689 Alteromonas sp. Control • DQ681136 Gamma (DQ681135) M119 AF288360 Alteromonas alvinellae 16/03/01 M117 Pseudoalteromonadaceae • DQ403809 Control 20/03/01 M121 16/03/01 AJ717299 Thalassobacillus devorans ATP Control 100 100 20/03/01 98.7 DQ681136 M119 • DQ681137 Marinomonas dokdonensis DQ011527.1 ATP Gamma Gamma M123 100 99.8 M234 M252 Alteromonadaceae Alteromonadaceae Marinomonas dokdonensis DQ011526 Firmicutes 95.7 • DQ681138 20/03/01 Bacillaceaea 25/06/01 AJ391191 Alteromonas sp. M126 25/06/01 Gamma DMSP/P ATP M221 M236 Marinomonas ATP 95 AY539835 20/03/01 Marinomonas pontica 23/05/01 25/06/01 ATP Gamma ATP 99 DMSP/P Marinomonas AJ437193 Enterovibrio norvegicus 99.8 AY028196 Marine bacterium 95.5 Gamma Halomonadaceae 99.8 Gamma 98.9 Vibrionaceae AJ316207 sp. Vibrio 100 also from different types of enrichment experiments. Indicated is the % sequence similarity to other isolates or sequences in Origins: GenBank. dd/mm/yy. in isolation of Date GenBank. Similarity in included sequences the for shown values are numbers Accession pairs. base 500 approximately on based are P-amended inorganic from seawater seawater Control. cultures; seawater ATP-amended P. cultures; from in situ. seawater untreated from samples; ATP. cultures without enrichments; etc. When there is more than one GenBank accession number for a given isolate type, only the one without parenthesis is used in the text. Acc# Name Date isol. Origin Most similar organism(s) similarity lineage family 49 Isolate bacterioplankton diversity in the NW Mediterranean • DQ681139 • DQ681139 M128-M130 20/03/01 Control AJ748748 Nereida ignava (DQ681140) M156-M157 M132 20/03/01 M479 • DQ481462 M584-M585 In situ 29/06/04 M592 M134 20/03/01 M625 22/03/04 In situ Control • DQ681142 Control 29/06/04 20/03/01 M140 11/10/04 M128 DQ681139 99.4 In situ Control Control • DQ681143 Alpha 20/03/01 Dokdonia donghaensis DQ003277 M146 Glu/P Rhodobacteraceae • DQ681144 AY612764 Alpha proteobacterium 20/03/01 M149 AF242274 sp. Vibrio • DQ481463 P 99.4 20/03/01 M152 CFB 100 AJ114484 Thalassobacillus devorans • DQ681145 99.8 P 20/03/01 M155 Flavobacteriaceae • DQ681146 AF114484 Roseovarius crassostreae AB198086 Krokinobacter genikus P M165 20/03/01 99.8 99.6 • DQ681147 In situ Polaribacter dokdonensis DQ004686 sp. DQ219366 Vibrio 15/05/01 Firmicutes Gamma M169 Bacillaceae AF465836 Erythrobacter litoralis 98.1 Vibrionaceae 100 P AY553123 Halobacillus sp. 15/05/01 Alpha 99.5 • DQ681148 AY177712 Phaeobacter inhibens P Rhodobacteraceae M209 Uncultured alphaproteobacterium DQ376156 M181 CFB M480 99.0 M496 100 AF288360 Alteromonas alvinellae 23/05/01 100 15/05/01 AY745862 Flavobacteriaceae Marine bacterium Alpha 22/03/04 Glycerol 100 22/03/04 In situ 98.4 P Erythrobacteraceae DMSP Alpha 99.1 AJ514912 tasmaniensis Vibrio AY576690 Roseobacter sp. Rhodobacteraceae Gamma 99.9 Alteromonadaecae Uncultured marine bacterium DQ071162 99.1 100 99.8 Gamma AF022409 sp. Vibrio Vibrionaceae

50 Chapter I • DQ681149 • DQ681149 M185 • DQ681150 15/05/01 M177-M163-M192-M194 M193 P M188 15/05/01 • DQ681151 15/05/01 M203 AY682202 Alteromonas addita P • DQ681152 P 23/05/01 M206 Pyruvate AY177712 Phaeobacter inhibens • DQ681154 AJ000726 Marinobacter aquaeolei 23/05/01 M216 99.6 In situ • DQ294291 23/05/01 Gamma Pseudoalteromonas sp. EU440056 M217 AY635468 Idiomarina seosinensis ASP294361 Alteromonas sp. 97 Alteromonadaceae 100 • DQ681155 P 23/05/01 M219 Alpha Gamma AF500008 Planococcus citreus • DQ681156 P Alteromonadaceae 99.2 23/05/01 Rhodobacteraceae M220 Gamma 100 AJ780980 Leeuwenhoekiella accommodimaris AY669169 M. hydrocarbonoclasticus • DQ681157 P 23/05/01 Idiomarinaceae M222 97 AJ278868 Kocuria polaris Marinobacter sp. DQ270761 P 99.6 • DQ681158 25/06/01 CFB 100 Firmicutes M227 Glu/P AY576653 Salegentibacter mishustinae Planococcaceae Planococcus sp. DQ177334 Flavobacteriaceae M226 AF242274 sp. Vibrio • DQ681159 25/06/01 AY745817 Cytophaga sp. M238 25/06/01 Glu/P 94.1 99.9 M230 99.7 Glu/P AJ582807 gigantis Vibrio CFB 25/06/01 Kocuria rosea DQ176452 Acinobact. DMSP Micrococcaceae 25/06/01 99.9 Flavobacteriaceae Glu/P DQ396362 Uncultured organism Marinomonas blandensis DQ403809 100 99.6 sp. DQ492722 Vibrio Gamma 97.8 100 99.8 Vibrionaceae 99.9 Gamma Gamma AY028201 Marine bacterium Vibrionaceae Oceanospirillales 100 98.5

51 Isolate bacterioplankton diversity in the NW Mediterranean • DQ681160 • DQ681160 M241 (DQ681141) (EU253572) M137 25/06/01 (EU253575) M310 (EU253591) M344 Glu/P M285 M509 20/03/01 M300 28/01/03 • DQ681161 AJ582807 gigantis Vibrio 04/03/03 13/11/01 M246 22/03/04 13/11/01 P • DQ681162 Amino acids Control P M241 DQ681160 M254 25/06/01 P M241 DQ681160 • DQ681163 M241 DQ681160 DNA 25/06/01 M266 M299-M294-M279 AF297958 Pseudoalteromonas avipulchra 100 ATP M278-M268 M319-M324-M326-M327 13/11/01 AY028196 28/01/03 Marine bacterium Gamma 13/11/01 Control 99 • DQ681164 Control Control Vibrionaceae M269 AF055269 Saccharophagus degradans 13/11/01 Gamma 98 Control • DQ681166 Pseudoalteromonadaceae 13/11/01 M275 99 (DQ681153) 99 Control 93.6 M212 99.2 13/11/01 Marinomonas dokdonensis DQ011527 Gamma • DQ681167 Gamma Control 23/05/01 M290 Oceanospirillales AY539835 Marinomonas pontica Marinomonas Glycerol AF288360 Alteromonas alvinellae M614 • DQ681168 M275 DQ681166 97.6 13/11/01 M292 AJ426421 Oleispira antarcticam (EU253590) Gamma In situ 11/10/04 M506 • DQ403810 Marinomonas 13/11/01 95.2 AJ417594 Pseudoalteromonas agarivorans P 99.6 M297 In situ 22/03/04 Gamma DMSP AY028204 Marine bacterium AY682202 Alteromonas addita 99.6 92 13/11/01 Alteromonadaceae M292 DQ681168 In situ Gamma AB010855 Gamma proteobacterium 99 Pseudoalteromonaceae AJ561121 Reinekea marinisedimentorum 99.6 94.8 AY781155 Pseudoalteromonas sp. Gamma Gamma Alteromonadaceae AY664213 Alteromonas sp. Uncultured Reinekea 99 99.8 AY172302 Uncultured bacterium 99.6 96.1

52 Chapter I • DQ681169 • DQ681169 M302 • EU253573 13/11/01 M329 • EU253574.1 M341 P 04/03/03 • EU253577 In situ M367 Marinomonas dokdonensis DQ011527 • EU253579 AB198089 Krokinobacter diaferitkos M381 13/05/03 • EU253580 In situ 98.3 13/05/03 M418 • EU253581 AY608409 Control Gramella echinicola Gamma 96 M428 sp. EU021293 Tenacibaculum Marinomonas AF359541 16/09/03 Marine bacterium • EU253582 CFB Uncultured Marinomonas sp. DQ421663 M441 16/09/03 P • DQ681170 Flavobacteraceae M442 (EU253576) 16/09/03 P 97 AF384142 Gamma proteobacterium (EU253578) 98.4 M365 Control (DQ681172) M380 16/09/03 95 (EU253594) CFB M456 AF384142 Gamma proteobacterium 99 AB264129 Paracoccus sp. (DQ681181) Control M525 13/05/03 M537 M539 13/05/03 CFB Flavobacteraceae AF118020 CFB In situ Erythrobacter citreus 22/03/04 • EU253583 99 In situ 25/05/04 AM71900 25/05/04 Maribacter forsetii subsp. Forsetii In situ M443 25/05/04 M442 DQ681170 Flavobacteraceae Flavobacteraceae 99 In situ Gamma M442 DQ681170 In situ • EU253584 In situ M442 DQ681170 97 16/09/03 Vibrionaceae M442 DQ681170 M445 Gamma M442 DQ681170 • EU253585 Control 100 99 Vibrionaceae M447 16/09/03 Unidentied bacterium DQ985889 • DQ681171 Alpha Alpha In situ M454 16/09/03 Erythrobacteraceae Rhodobacteraceae Salegentibacter sp. EF520007 In situ 22/03/04 100 98 AY926462 Palleronia marisminoris In situ 99 99.4 100 AY608409 Gramella echinicola Alpha 96 Erythrobacteraceae 97 96 AY646157 Erythrobacter sp. CFB Alpha 99.9 Flavobacteraceae Rhodobacteraceae CFB Flavobacteriaceae

53 Isolate bacterioplankton diversity in the NW Mediterranean • DQ681173 • DQ681173 (EU253595) M458 M531 • DQ681174 22/03/04 M463 25/05/04 • EU253587 In situ In situ M464 22/03/04 AY461443 Erythrobacter aquimaris M458 DQ681173 • EU253588 In situ 22/03/04 M466 • DQ681175 AY065627 Aurantimonas coralicida In situ M483-M485 22/03/04 22/03/04 98.1 Unidentied bacterium DQ985896 • DQ681176 Control In situ Alpha M495 100 Antarctobacter heliothermus RS21916 • DQ681177 AB078078 Microscilla arenaria Erythrobacteraceae M498 22/03/04 Alpha 99 100 • DQ681178 DMSP 22/03/04 Aurantimonadaceae 97.3 M503 Alpha AJ780980 Leeuwenhoekiella aequorea Glucose • EU253589 Alpha 98 AY517632 Marinobacter avimaris 22/03/04 Rhodobacteraceae M504 AJ534207 Methylarcula sp. • EU253592 Rhodobacteraceae DMSP CFB 99.6 M511 22/03/04 AY517632 Marinobacter avimaris CFB • EU253593 Flammeovirgaceae DMSP 99.5 22/03/04 M513 AF245634 Amino acids Uncultured Roseobacter AJ289885 • DQ681179 Limnobacter thiooxidans Flavobacteriaceae Gamma sp. DQ923444 Vibrio (EU253598) M517 98 22/03/03 M555 97.0 Alteromonadaceae • EU253596 Acetate Gamma 22/03/03 M532 AY165592 Arctic sea ice bt. Uncultured 97.9 25/05/04 AJ289885 Limnobacter thiooxidans 99.9 • DQ681180 DMSP Alteromonadaceae In situ M535 Beta 25/05/04 AY682202 Alteromonas addita AY687537 Uncultured Marinobacter sp. M517 DQ681179 99.9 In situ 25/05/04 100 Burkholderiaceae 99.9 Salegentibacter slinus EF486353 In situ 100 Gamma Beta Y13830 pectenicida Vibrio AJ874364 splendidus Vibrio Vibrionaceae 99.9 Burkholderiaceae Gamma 99 Alteromonadaceae 97 CFB 100 99.7 Flavobacteriaceae Gamma Vibrionaceae

54 Chapter I (EU253597) M536 • DQ681182 M543 25/05/04 • EU253599 In situ M568 25/05/04 M535 DQ681180 • EU253600 In situ 25/05/04 M571 • DQ681183 AF251143 Halomonas marisava In situ M588 25/05/04 • DQ681184 Polaribacter dokdonensis DQ481463 In situ M595 29/06/04 • EU253601 AB078078 Microscilla arenaria In situ 99.3 29/06/04 M599 96 AB121782 Thalassobacter stenotrophicus In situ Gamma 100 • DQ681185 Halomonadaceae 19/07/04 CFB AF469612 M604 skagerrakense Tenacibaculum • DQ681186 100 In situ M602 99 M605 Flavobacteriaceae 11/10/04 • DQ681187 AB078081 Microscilla sericea AF359539 Alpha Marine bacterium 97.2 11/10/04 M606 CFB P 11/10/04 CFB Rhodobacteraceae Glu/P • DQ681188 Glu/P 11/10/04 Flammeovirgaceae M608 AY664213 Alteromonas sp. Uncultured Flavobacteriaceae AJ878874 Thalassobius mediterraneus Glu/P • DQ681189 99 11/10/04 97 M609 Ruegeria atlantica D88527 AB073587 Cytophaga sp. 100 Glu/P • DQ681190 CFB 99.3 M612 11/10/04 Gamma AJ345063 probioticus Vibrio Alpha • DQ681191 Flexibacteriaceae Alteromonadaceae P 11/10/04 M613 Rhodobacteraceae P AF288360 Alteromonas alvinellae 100 97.9 11/10/04 Alpha Thalassobius gelatinovorus D88523 99.1 AY745859 Roseobacter sp. Rhodobacteraceae Gamma 99.6 AY442178 Thalassobius aestuarii AJ316168 sp. Vibrionaceae Vibrio Gamma 97.5 Alteromonadaceae Alpha 100 Rhodobacteraceae 97.9 Uncultured bacterium DQ117434 Alpha AY663968 Uncultured alpha proteobacterium 99.6 Rhodobacteraceae 100 99.9

55 Isolate bacterioplankton diversity in the NW Mediterranean • DQ681192 • DQ681192 M618 • DQ681193 11/10/04 M620 Glu/P • DQ681194 11/10/04 M621 AY177712 Phaeobacter inhibens Glucose • DQ681195 M622 AJ488501 Glaciecola mesophila 11/10/04 • DQ681196 Glucose M623 11/10/04 AF469612 skagerrakense Tenacibaculum Glucose • DQ681197 97.6 11/10/04 M624 AF469612 skagerrakense Tenacibaculum Alpha Glucose 92.8 98.2 AF082797 Hyphomonas oceanitis 11/10/04 Rhodobacteraceae Gamma AY663966 Uncultured Roseobacter sp. CFB 98.8 Glucose Alteromonadaceae AJ301665 Maricaulis sp. CFB Flavobacteriaceae AF159672 Uncultured marine eubacterium 98 99.1 Flavobacteriaceae 97.3 Alpha Hyphomonadaceae AF359546 Marine bacterium 99.7 Alpha AJ301665 Maricaulis maris Hyphomonadaceae 100 99.9

56 Chapter I

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58 Chapter I

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59 Isolate bacterioplankton diversity in the NW Mediterranean

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60 CHAPTER 2

Changes in bacterial activity and community composition caused by realistic exposure to oil in mesocosm experiments

Itziar Lekunberri , Alejandra Calvo-D íaz , Eva Teira , Xelu A.G. Mor án, Francesc Peters , Mar Nieto-Cid , Oscar Espinoza-Gonz ález , Isabel G. Teixeira and Josep M Gasol La Tierra no es herencia de nuestros padres sino prestamo de nuestros hijos

Proverbio indio Chapter II

ABSTRACT

We studied the effects of the Prestige oil spill on the Ría de Vigo bacterial abundance, production and community structure, by using mesocosms experiments (ca. 3500 L) lled with water from the center of the Ría, to which we added a realistic concentration of polyciclic aromatic hydrocarbons (approximately 20-30 μg L -1 chrysene eq). We followed the changes in bacterial activity by leucine and thymidine incorporation, and changes in bacterial assemblage structure by 16S-rDNA DGGE as well as CARD-FISH, during the four relevant periods of the seasonal cycle: spring bloom, summer stratication, autumn downwelling and winter. In addition, simultaneous to one of the mesocosms experiments, we run microcosms with fuel additions equivalent to 0.5x, 1x, 2x and 4x the treatment imposed to the mesocosms. We detected effects on bacterial diversity (increased richness) and function (more bacteria and more production) only in the summer experiment. In the microcosm experiments we found similar effects at PAH concentrations of ca. 40 µg L -1 (2x 20 µg L -1 ) and clear detrimental effects on phytoplankton at concentrations of ca. 80 µg L -1 , with large development of gammaproteobacteria. We also show that the development of heterotrophic nanoagellate in the mesocosms is what causes the most important changes in bacterial assemblage structure.

63 Effects of oil on bacterial activity and diversity

INTRODUCTION

Bacterioplankton communities inhabiting the plankton of coastal regions use multiple sources of carbon and nutrients (e.g. Moran et al. 1999, Mou et al. 2008). These sources have different origins: autochthonous phytoplankton production, carbon and nutrients coming from the watersheds, or allochthonous nutrient and C sources that have originated because of human activities. Petroleum hydrocarbons are some of these organic carbon sources, and are some of the most widespread contaminants in the environment (Santas et al. 1999). Polyciclic aromatic hydrocarbons (PAHs) are particularly inuential because of their continuous release into the water, their persistence, and their well-known detrimental effects on marine invertebrates (Preston, 2002), which make them subject of public concern.

But bacteria are able to decompose these carbon molecules (e.g. Harayama et al. 1999, Kasai et al. 2002) and may consider chronic PAHs inputs to seawater just one allochthonous source of carbon and energy (Castle et al. 2006). However, immediately after an oil spill, the soluble fraction of polycyclic aromatic hydrocarbons (PAHs) is released into the water column and may represent point concentrations of PAHs much higher than the values measured in chronic exposures and, thus, affect bacterioplankton communities in ways different than just as a regular C source. The effect of oil spills on the natural bacterioplankton communities of coastal ecosystems has been studied using in situ measurements (e.g. Bode et al. 2006), laboratory experiments (Maruyama et al. 2003, Castle et al. 2006, Cappello et al. 2006), and experimental systems such as mesocosms (Siron et al. 1993, Ohwada et al. 2003, Cappello et al. 2007). This last type of approach avoids the effects caused by advection, diffusion and mixing under natural conditions, and it also offers the possibility of comparison with natural populations to which PAHs were not added, thus allowing statistical testing of hypotheses. These types of analyses have reported contradictory results about the effects of the oil spills on plankton communities. Often, no effect on primary producers has been found (e.g. Varela et al. 2006) while simultaneous signicant effects on bacterial variables have been reported (Bode et al. 2006). In several experiments, bacteria have been shown to be stimulated (e.g. Delille & Siron 1993, Ohwada et al. 2003, Bode et al. 2006, Castle et al. 2006, Cappello et al. 2007, Dalby et al. 2008), neutrally affected (Maruyama et al. 2003) or repressed (Garcia et al. 1998, Sargian et al. 2005) by high concentrations of PAHs. In most cases, a shift in bacterial community structure has been observed (Gerdts et al. 2004, Castle et al. 2006, McKew et al. 2007) resulting in decreasing richness

64 Chapter II

(Röling et al. 2002) and elevated contributions of gammaproteobacteria (Yakimov et al. 2004, Gerdts et al. 2004, Cappello et al. 2007), particularly groups such as Alcalinovorax (Cappello et al. 2007) or Cycloclasticus (Kasai et al. 2002, Teira et al. 2007).

The concentration of PAHs used to experimentally simulate oil spills have ranged from 900 mg crude oil L -1 (Cappello et al. 2007) to 4.5 µg L -1 chrysene equivalents (Ohwada et al. 2003), with all possible intermediate values. It might be possible that some of the reported effects of oil are concentration- dependent, so that they are signicant at concentrations which are never experienced by the natural bacteria assemblages. In fact, the lack of effect of oil spills on phytoplankton communities has been attributed to the reduction in exposure concentration related to the dispersion of the oil caused of the hydrographic water movements. Accordingly, it can be hypothesized that if PAHs do not cause effects on microbial diversity and function, it is because the communities are habituated to a certain level of these substances in the water, as parts of their normal carbon pools used for growth.

To explore this hypothesis, we added realistic oil concentrations to mesocosms in order to test the effect of PAHs on plankton communities of the Ría de Vigo. To evaluate the response of bacterioplankton we measured bacterial activity, diversity and community structure in a series of experiments. Bacterial activity and diversity measurements can represent useful tools for characterizing the way in which natural and human stressors drive changes in natural ecosystems. We used relatively low PAHs concentrations (20-30 μg L -1 chrysene eq.) that were, however, sixfold higher than the 90% percentile of the concentrations found along the Galician coast after the accident (González et al. 2006). To assess the changes in bacterial assemblage structure we used the DGGE ngerprinting technique, which serves well to compare microbial assemblages and assess temporal and spatial changes in structure (Casamayor et al. 2002), also providing estimates of diversity. In general, we found little effects with the chosen concentrations and, thus, we also performed a concentration-dependent experiment using a gradient from 0 to 80 µg L -1 chrysene eq. to explore the threshold concentration at which bacterial assemblage composition and metabolism were affected.

The experiments were part of the project IMPRESION, designed to test the effect of the Prestige oil spill on plankton communities of the Ría de Vigo. This coastal embayment is an area affected by marked seasonal cycles of coastal winds (Nogueira et. al. 1997). The annual cycle is divided in an upwelling season (March to September), with short relaxation intervals that enhance productivity (Álvarez-Salgado et al. 2002), and a downwelling season (from October to March), characterized by low phytoplankton biomass and primary production. Microbial plankton composition is different

65 Effects of oil on bacterial activity and diversity in the different situations: upwelling, downwelling or transition (Figueiras et al. 2002, Nogueira et al. 1997, Alonso-Gutiérrez et al . submited, Teira et al. 2008). Thus, we explored the effects of oil on bacterial community structure and activity at the four relevant parts of the seasonal cycle: spring bloom, summer stratication, autumn upwelling and winter.

MATERIALS & METHODS

Experimental setup and sampling. The experimental setup was established in a small harbor of a protected bay, four times during a year. As detailed in Teira et al. (2007), six mesocosms of 1.5 m in diameter and 2 m deep were lled with seawater at the center of the Ría and transported to the harbor to which they were tight. The bags were lled from their bottom through a 200 µm mesh in order to exclude mesozooplankton and facilitate good replication within bags. Two of the mesocosms were used as controls, received no additions and were not treated in any way except for the 200 µm pre- ltration. Two were added a low concentration of soluble PAHs (5-10 µg L -1 chrysene equivalents) and two a high concentration of soluble PAHs (approx. 20-30 µg L -1 chrysene eq.). The experiments lasted 9 days after the addition. Samples were taken every day during the rst 5 days, and thereafter every 2 days. Since during the rst two experiments there was no response in the low-concentration mesocosms, in the subsequent experiments this treatment was eliminated and instead we triplicated the control and the high-PAH concentration treatments.

The PAHs were added as a soluble fraction which was prepared from 15 kg of Prestige-like heavy oil provided by the “Ocina Técnica de Coordinación del Programa de Intervención Cientíca en la Catástrofe del Prestige” mixed with 300 L of 0.2 µm-ltered seawater. The mixture was vigorously stirred during 4 hours and the resulting extract, of approximately 700 µg L -1 soluble PAHs, was separated from the insoluble fuel oil by decantation, collected on 25 litres polyethylene barrels, from which it was added to the mesocosms. PAH concentrations were measured following the MARPOLMON protocol (UNESCO, 1984), with modied volumes, and referred to a chrysene standard (see details in González et al. 2006).

The Prestige oil spill was found to consist of a complex mixture of hydrocarbons, where the aromatic fraction (mainly naphthalene, phenanthrene, and alkyl derivatives) comprised ca. 53% (Alzaga et al. 2004). PAHs represented 99.7% of the water soluble fraction of the Prestige oil and alkanes were almost undetectable in that fraction (J. Albaigés, pers. comm.). Although it was not possible to use exactly the Prestige oil, we used oil with a very similar composition.

66 Chapter II

Addition of oil was done after having taken the time 0 sample, and sampling of the mesocosms was then done daily at sunset with an integrated tube that collected water down to 1 meter above the bottom of the tanks. The experiments were carried out at the 4 periods of the seasonal cycle in the coastal NE Iberian Atlantic waters (Nogueira et al. 1997): spring bloom (March 2005), summer stratication (July 2005), autumn upwelling (September 2005), and winter (January 2006).

Additional microcosm experiments. As described in Teira et al. (2007), a microcosm experiment was run in parallel to the winter mesocosms using the same initial seawater, in order to determine the response of the community to a larger gradient of PAH concentration. For the microcosm experiment ten 5 L-PET bottles were kept opened and refrigerated by circulating surface seawater. A gradient of four concentrations (10, 20, 40 and 80 µg L -1 equivalent chrysene) plus the control were prepared in duplicates. This is equivalent to 0.5x, 1x, 2x, and 4x what was added in the simultaneous mesocosms. The microcosms were kept for 8 days, sampling every 24 h for bacterial abundances, bulk heterotrophic production and bacterial community structure analyses.

Organism abundances. Chlorophyll a concentration was used as an estimator of phytoplankton abundance. Two hundred and fty millilitres of seawater were sequentially ltered through 20, 2 and 0.2 µm polycarbonate lters. The lters were frozen and chlorophyll-a extracted in 90% acetone for 24 h at 4°C. The uorescence of the extracted chlorophyll-a was measured with a Turner-TD- 700 uorometer previously calibrated with pure chlorophyll-a. Total chlorophyll-a concentration was obtained by addition of the concentration in each of the three size classes.

Bacterial abundances were measured by ow cytometry as described in Gasol & del Giorgio (2000). The samples were run in a Becton-Dickinson FACSCalibur cytometer after staining with SybrGreen 2.5 µM nal concentration, Molecular Probes) and bacteria were detected by their signature in a plot of side scatter (SCC) vs FL1 (green uorescence). Calibration of the cytometer, and of the SSC-size relationship was done as described in Calvo-Díaz & Morán (2006).

HNF abundances- Samples for nanoagellate abundance were collected four or ve times in every experiment. Subsamples of 60-80 mL were xed with glutaraldehyde (1%, nal concentration). The samples were ltered through 0.6 µm black polycarbonate lters (Milipore), and stained with DAPI (4, 6 - diamidino-2- phenylindole, Porter and Feig, 1980) at a nal concentration of 5 µg mL -1 (Sieracki et al. 1995). Abundance of these microorganisms was determined at 1000X magnication with an Olympus-BX40-102/E epiuorescence microscope. The heterotrophic and phototrophic nanoagellates were counted under both UV excitation (blue uorescence) and blue excitation (B2

67 Effects of oil on bacterial activity and diversity

lter). Under blue light we discriminated phototrophic nanoagellates (PNF, showing red-orange autouorescence, and/or plastidic structures) from colorless nanoagellates classied as heterotrophic (HNF). With this method we could not distinguish mixotrophic nanoagellates. Random 10 mm transects (100 µm width) were examined and between 20 and 100 HNF, and between 10 and 50 PNF per lter were counted.

Bacterial activity and production. Bacterial heterotrophic production was estimated using both the 3H-leucine (Kirchman et al. 1985) and 3H-thymidine (Fuhrman & Azam 1982) incorporation methods. Triplicate or cuatriplicate aliquots of 1.2 ml were taken for each sample and one or two TCA killed controls. The Leu tracer was used at 40 nM and the TdR tracer at 20 nM nal concentrations in incubations lasting approximately 2 h. The incorporation was stopped with the addition of 120 µl of cold 50% TCA to the samples and, after mixing, they were kept frozen at –20ºC until processing, which was carry out by the centrifugation method of Smith & Azam (1992). The samples were counted on a Beckman scintillation counter, 24 h after addition of 1 ml of scintillation cocktail. We used the standard conversion factors of 3.1 kg C mol Leu -1 and 40 kg C mol TdR -1 , since we had previously determined CFs in the Ría to be on average close to these theoretical ones (Morán et al. 2002). The nal value of heterotrophic bacterial production was computed as the average of the Leucine and Thymidine-based determinations.

Collection of community DNA. Microbial biomass was collected by sequentially ltering around 5 L of seawater through a 3 μm pore size polycarbonate lter (Milipore, 46 mm) and 0.2 μm sterivex lter (Milipore). The Sterivex units were lled with 1.8 ml of lysis buffer (50 mM Tris-HCl pH 8.3, 40 mM EDTA pH 8.0, 0.75 M sucrose) and stored at -80ºC. We used the sterivex units for the analyses. Microbial biomass was treated with lysozime, proteinase K and sodium dodecyl sulphate, and the nucleic acids were extracted with phenol and concentrated in a Centricon-100 (Milipore). Nucleic acids were extracted by a standard protocol using phenol/chloroform (see details in Schauer et al. 2003).

Fingerprinting analysis. DGGE and gel analysis were performed essentially as described before (Schauer et al. 2000; Sánchez et al. 2007). Briey, 16S rRNA gene fragments (around 550 bp in length) were amplied by PCR using the universal primer 907rm and the bacterial specic primer 358f, with a GC-clamp. PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from 40-80%. The gel was run at 100V for 16h at 60ºC in 1x TAE running buffer. DGGE gel images were analyzed using the Diversity Database software (BIO-RAD). A matrix was constructed

68 Chapter II for all lanes taking into account the relative contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix, we obtained a dendogram by UPGMA clustering method (Euclidean distances, software Statistica 6.0).

Bacterial community composition. We used CARDFISH to study the composition of the bacterial communities in the microcosm experiment. Five mililitres were xed with paraformaldehyde (2% nal concentration) and after 12–18 h storage at 4°C in the dark were ltered through a 0.2 µm polycarbonate lter (Millipore, GTTP, 25 mm lter diameter) supported by a cellulose nitrate lter (Millipore, HAWP, 0.45 mm), washed twice with Milli-Q water, dried and stored in a microfuge vial at -20°C until further processing. We used oligonucleotide probes specic for the domain Eubacteria (EUB338) (Amann et al. 1990), the Alpha- (ALF968) (Glöckner et al. 1999) and Gammaproteobacteria (GAM42a) (Manz et al. 1992) subclasses and the class Flavobacteria of phylum Bacteroidetes (CF319a) (Manz et al. 1996). We also used probe CYU829 for Cycloclasticus (Maruyama et al. 2003, Teira et al. 2007). The Eub antisense probe Non 338 probe was used as negative control. Further details in Teira et al . (2008).

Statistical analysis. We used ANOVA to test for the differences between treatments. A repeated measures ANOVA (RMANOVA) with one within-subjects factor (treatment) was conducted to discriminate the treatment from the time effects and all possible interactions. Time is a within-subject factor because the same mesocosms is sampled at sequential time periods (every-24-48 h). One way ANOVA was done to determine differences in the microcosm experiment. We used software Statistica 6.0 and JMP 7 for all analyses.

RESULTS

The initial environmental conditions were very different for each season (Table 1). Inorganic nutrients were very abundant in winter and spring, while chlorophyll was very high particularly in autumn, when dissolved inorganic nitrogen (DIN) -but not silicate- was also high, as our sampling coincided with the decline of an algal bloom (Fig. 1C). Bacterial abundance and heterotrophic production were high in summer and fall.

69 Effects of oil on bacterial activity and diversity 3.4 8.11 0.4 0.31 ± M, ± ± ± μ 0.40 26.25 0.18 3.05 0.72 69.94 0.06 16.88 ± ± ± ± 0.01 6.13 0.01 9.92 0.63 0.01 13.82 5.29 ± ± ± ± M, chlorophyll-a concentration (Chla) in (Chla) concentration chlorophyll-a M, μ Chla PA BHP 4 M, dissolved inorganic phosphorus (DIP) in M, dissolved inorganic 0.04 3.01 0.05 1.50 0.02 0.15 9.22 0.54 μ ± ± ± ± (mean of Leu and TdR with standard conversion factor (CF) 3.1 (CF) factor conversion standard with TdR and Leu of (mean -1 0.00 3.17 0.01 0.59 0.08 0.02 0.41 3.72 ± ± ± ± d -1 g C L C g μ 0.11 0.11 0.15 0.08 0.52 0.73 0.44 0.51 0.48 ± ± ± ± M, particulate organic nitrogen (PON) in (PON) nitrogen organic particulate M, μ and BHP in BHP and -1 0.02 4.40 0.01 0.58 0.01 0.00 5.66 7.70 cell ml cell 5 ± ± ± ± 0.0 35.48 0.0 35.02 0.0 0.0 35.73 35.60 ± ± ± ± M, particulate organic carbon (POC) in (POC) carbon organic particulate M, μ and 40 kg C mol TdR). N=6. -1 ) in ) 4 Initial conditions of the mesocosms experiments. experiment The was microcosm simultaneous to the January’06 mesocosms. Mean , total prokaryotic abundance (PA) in x10 in (PA) abundance prokaryotic total , -3 standard errors. Temperature (T) in ºC, salinity (Sal), dissolved inorganic nitrogen (DIN) in nitrogen (T) in ºC, salinity (Sal), dissolved inorganic Temperature errors. standard Table 1.- Table Experiment Spring Date 02/03/05 T 10.5 Sal DIN DIP SiO mg m mg kg C mol Leu Summer 02/07/05 20.8 ± Autum Winter 22/09/05 24/01/06 15.4 12.4 silicate (SiO silicate

70 Chapter II

The added PAHs disappeared from the water in a similar exponential way in the 4 experiments (Fig. 1). On day 2 of the experiments, only ca. 35% of the added PAHs were found in solution except in summer, where the high ambient temperature caused faster volatilization of the added compounds and only 10% remained in the water (Fig. 1). No PAHs were detected in summer after day 4, while in the other months between 5 and 15% of the added substrates remained in the mesocosms at day 4. The PAHs exponential decay rate was pretty similar in all the experiments (around -0.35 d -1 ) except for the summer experiment in which it was higher -0.7 d -1 (Table 2).

Spring Summer 15 120 15 120 Control PAHs B A 100 100 PAHs concentration ) -3 10 80 10 80

60 60

Chla (mg m 5 40 5 40 % of PAHs remaining % of PAHs 20 20

0 0 0 0 0 2 4 6 8 0 2 4 6 8

Autumn Winter 15 120 15 120 C D 100 100 )

-3 10 80 10 80

60 60

5 40 5 40 Chla (mg m % of PAHs remaining % of PAHs 20 20

0 0 0 0 0 2 4 6 8 0 2 4 6 8 Time (days) Time (days)

Fig 1.- Changes in dissolved PAH concentration and in chlorophyll a concentration in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms. 71 Effects of oil on bacterial activity and diversity

Table 2.- Conditions of the treatments with added PAHs in the different experiments. Average of2 or 3 replicates ±Stdev.

Experiment Date Initial PAHs Conc Exponential decay rate (µg l -1 ) (d -1 )

Spring 02/03/05 14.95 ± 5.59 -0.370 ± 0.024 Summer 02/07/05 29.03 ± 6.08 -0.714 ± 0.015 Autum 22/09/05 23.63 ± 6.11 -0.415 ± 0.045 Winter 24/01/06 18.51 ± 4.68 -0.326 ± 0.014 Microcosms (Winter): 0.5x 9.60 ± 0.99 -0.231 ± 0.009 1x 19.81 ± 0.42 -0.241 ± 0.001 2x 38.85 ± 0.64 -0.234 ± 0.003 4x 73.21 ± 2.97 -0.212 ± 0.009

Chlorophyll a. The evolution of chlorophyll in the mesocosms was different in the different experiments. In spring (Fig 1A, C) water connement caused a bloom (up to 10 mg m -3 ), probably because we had sampled growing healthy algae. In contrast, in autumn, the starting values of Chla were 9 mg m -3 and decreased drastically during the four subsequent days. In summer and winter there were few changes in chlorophyll during the experiments, just small slow increases. In general, no differences were found because of the oil additions. They were only signicant (<0.01) in the summer experiment (Fig 1B, Table 3) when the increase after day four to the end of the experiment, was higher (ca. 2x) in the oil-added mesocosms (Fig. 1B).

Bacterial abundance. The changes in bacterial abundances in the experiments also showed different trends in the different seasons (Fig. 2). Except in the autumn experiment, there was a general initial increase in abundance (day 1) that soon was followed by a decrease in bacterial abundance (days 3-4) and by a second peak, higher in magnitude and at about days 6-8. The autumn experiment showed just one peak. The effect of oil was obvious in the summer experiment (Fig 2b) when the two peaks were higher in magnitude when PAHs had been added. When separating the rst (days 0-4) from the second (days 5-8) part of the experiments, the PAH treatments affected the second part of the experiment in all experiments (Table 3). This suggests that the signicance observed using all the data together is due to the effect in the second part of the experiment.

72 Chapter II

Spring Summer 120 120 Control 6 6 ) 5 10 PAHs 5 10 -1 A B PAHs concentration 100 100 g

6 6 n 4 10 4 10 i n

80 80 i a

6 6 m 3 10 3 10 e r

60 60 s H 6 6 2 10 2 10 A

40 40 P

f o

1 10 6 1 10 6 20 20 %

Bacterial abundance (cell ml 0 0 0 0 0 2 4 6 8 0 2 4 6 8 Autum Winter 120 120

) 5 10 6 5 10 6 -1 C D 100 100 g

4 10 6 6 n 4 10 i

80 n 80 i a

3 10 6 6 m

3 10 e

60 r 60 s H 2 10 6 6

2 10 A 40 40 P

f o 6 1 10 20 1 10 6 20 %

Bacterial abundance (cell ml 0 0 0 0 0 2 4 6 8 0 2 4 6 8 Time (days) Time (days)

Fig 2.- Changes in dissolved PAHs concentration and in bacterial abundances in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms.

73 Effects of oil on bacterial activity and diversity

Table 3.- Results of the repeated measures ANOVA with one within-subject factor (time) and treatment as a between-subjects factor. N = 2-3 for the mesocosms experiments, and N = 4 for the microcosm experiment. Signicant (P< 0.05) effects are in bold.

Treatment Time (treatment) F-value Prob. F-value Prob.

Chlorophyll a Spring 0.43 0.525 68.04 <0.01 Summer 9.81 < 0.01 4.09 < 0.01 Autumn 0.11 0.733 45.55 <0.01 Winter 3.21 0.084 15.63 <0.01 Microcosms 130.85 <0.01 79.49 <0.01

Total prokaryotic abundance Spring 16.10 <0.01 85.07 <0.01 rst half 1.18 0.31 second half 32.91 <0.01 Summer 35.08 <0.01 7.59 <0.01 rst half 1.54 0.24 second half 37.29 <0.01 Autumn 5.95 0.021 4.67 <0.01 rst half 1.34 0.29 second half 60.89 < 0.01 Winter 20.31 <0.01 32.60 <0.01 rst half 2.25 0.15 second half 8.88 0.02 Microcosms 727.79 <0.01 94.81 <0.01

Prokaryotic heterotrophic production Spring 4.71 0.47 55.65 <0.01 Summer 43.54 <0.01 5.44 <0.01 Autumn 2.35 0.136 9.43 <0.01 Winter 0.215 0.646 2.73 0.014 Microcosms 325.44 <0.01 87.15 <0.01

Bacterial growth rate Spring 0.56 0.47 103.09 <0.01 Summer 0.92 0.36 6.55 <0.01 Autumn 3.98 0.06 5.21 <0.01 Winter 5.46 0.03 12.82 <0.01 Microcosms 4.39 <0.01 18.53 <0.01

74 Bacterial production (mg C L-1 d-1) % of PAHs remaining

Chapter II

) Spring Summer

-1 160 120 160 120

d Control -1 140 PAHs A 140 B 100 100 PAHs concentration 120 120 80 80 100 100

80 60 80 60

60 60 40 40 40 40

20 20 % of PAHs remaining 20 20

Bacterial production (mg C L 0 0 0 0 0 2 4 6 8 0 2 4 6 8

Autum Winter 160 120 160 120

140 C 140 100 D 100 120 120 80 80 100 100

80 60 80 60

60 60 40 40 40 40 20 20 20 20

0 0 0 0 0 2 4 6 8 0 2 4 6 8 Times (days) Times (days)

Fig. 3.- Changes in dissolved PAHs concentration and in bacterial production (leucine incorporation) in the control mesocosms (white circles) and the mesocosms to which PAHs had been added (black circles). Spring experiment (A), Summer experiment (B), Autumn experiment (C) and Winter experiment (D). Average plus standard deviation of 2 (spring) or 3 (the other months) replicated mesocosms.

75 Effects of oil on bacterial activity and diversity

5000 6 ) 2.5 10 -1

4000 2 10 6 Bacteria (cells ml

3000 1.5 10 6

2000 1 10 6 -1 )

1000 5 10 5 Heterotrophic nanoflagellates (cells ml 0 0 0 2 4 6 8

Time (days) Fig. 4.- Evolution of heterotrophic nanoagellate abundance in the control (empty symbols) and the oil- added treatment (solid symbols) in the Spring experiment. Averages of 2 replicated mesocosms ± Stdev. Bacterial abundances in the same control (empty squares) and treatment (solid squares) mesocosms are shown for reference (same data of Fig. 2A).

Table 4.- Heterotrophic nanoagellate evolution in the rst three mesocosms with experiments and microcosm. Changes in abundance (growth rates) before and after the abundance peak reached in each experiment. Data are not available for the Winter experiment and for 8x treatment of microcosm.

INITIAL FINAL Time interval (d) GR (d -1 ) Time interval (d) GR (d -1 )

Spring 0-4 0.48 >4 -0.23 Summer 0-2 0.71 >2 -0.59 Autumn 0-2 0.40 >2 -0.76 Winter - - - -

Microcosms 0x 0-3 0.3 >3 0.02 2x 0-3 -0.04 >3 0.06 4x 0-3 -0.07 >3 0.18

76 Chapter II

Bacterial production. Thymidine and leucine incorporations were well correlated throughout the experiments (TdR = 51.9*Leu^0.69, r 2= 0.56, N=165), with an average ratio Leu:TdR of 30.4 ± 2.8. The ratio was lower (average 12.8 ± 1.2) in summer, and larger in spring. It was relatively stable throughout the experiment in summer and winter, but increased throughout the spring experiment (46.0 ± 8.9), and decreased in the autumn experiment. Using repeated measures ANOVA the only signicant differences between the ratio Leu:TdR in PAHs treated vs. control mesocosms was observed in summer (details not shown), when it was signicantly lower in the mesocosms that had received PAHs (9.7 ± 0.7) than in those that had not (13.7 ± 0.9).

Bacterial production was much higher in the autumn experiment than in the rest of the experiments (Fig. 3), in concordance with the fact that this experiment coincided with the end of a phytoplankton bloom that slowly decomposed during our mesocosm experiment (Fig. 1C). In general, bacterial production tended to reproduce the same pattern as bacterial abundances (Fig. 2), with an initial peak and a secondary peak after day 4. This was evident in the spring and winter experiments (Fig. 3A and 3D), less obvious in the summer experiment, and different in the autumn experiment (Fig. 3C). Differences between treatments were only signicant in the summer experiment (Fig. 3B and Table 3), when the mesocosms that had received PAHs had productions about 1.5 fold higher than the controls. The autumn experiment showed a small tendency for production being higher in the mesocosms that had received PAHs (Fig 3).

Bacterial growth rates showed a pattern similar to that of production, with initial values ranging from 0.3 d -1 (spring) to 3 d -1 (summer) (details not shown). They were not signicantly affected by the oil addition, except in the microcosm experiment (see below), where bacterial growth rates at the end of the experiment in the control treatment were twice those of the treatment that had received the maximum amount of oil. There was also a slight effect of oil on bacterial growth rates in the winter experiment, where bacterial growth rates were slightly faster in the oil-treatments than in the controls (ca. 25% decrease in growth rate) between day 3 and day 8.

Heterotrophic nanoagellates. Flagellates were enumerated in the rst three experiments. The evolution was rather similar: they rst increased up to day 3-4, and then decreased until the end of the experiment (see example in Fig. 4, growth rates for the two different phases in Table 4). The peak of HNF abundance was reached at different time in the different seasons, at day 4 in the spring experiment (Fig. 4), at day 2 in the summer and autumn experiments (Table 4). At the peak of agellate development, the abundance was higher in the mesocosms that had received PAHs than in the controls (see example for the spring experiment in Fig. 4).

77 Effects of oil on bacterial activity and diversity

120 2 Control 20 µg L -1 A 40 µg L -1 B 100 80 µg L -1 1.5

80 ) -3

60 1

40 Chla (mg m 0.5 % PAHs remaining % PAHs 20

0 0 01234567 01234567

2 10 6 250 ) ) -1 C -1 D

h 200 1,5 10 6 -1

150

1 10 6 100

5 10 5 50 Bacterial abundance (cells ml

0 Bacterail production (mg C L 0 01234567 01234567 Time (days) Time (days)

Fig. 5.- Time course evolution in the microcosm experiments: PAH concentration (A), Chlorophyl a(B), Bacterial abundance (C) and Prokaryotic production (D). A gradient of four concentrations represented: control (white circle), 20 μg L -1 (black circle), 40 μgL -1 (white square) and 80 μg L -1 (black square). For simplicity, we omit the treatment with ca. 10 µg L -1 .

78 5,700 2,800 2,700 14,000 ± ± ± ± -232,000 -132,000 -104,000 -719,000 5 5 5 5 eq. chrysene (1x), 40 mg -1 10 1.11 0.18 10 0.51 10 0.64 10 5 5 5 5 800 2.50 10 13,000 6,000 1.83 10 44,000 1.68 10 1.83 10 ± ± ± ± -16,000 146,000 88,000 606,000 5 5 5 5 0.76 10 1.97 10 1.29 10 6.87 10 4 4 4 4 6,000 5.09 10 5,300 9.25 10 1,700 5,100 4.07 10 8.14 10 ± ± ± ± -115,000 -126,000 -59,000 -99,000 4 4 4 4 (4x). The “Change in abundance” is the number of cells produced (or lost) during the experiment. (4x). The “Change in abundance” is the number of cells produced -1 3.31 10 0.68 10 1.70 10 6.36 10 5 5 5 5 Composition of the bacterial assemblage in the experiment microcosm determined by CARDFISH: Cells positive with ALF968 probe - (2x) and 80 mgL -1 L Table 5. Table Alphaproteobacteria 0x t0 1.48 10 tf Change in abund. Gammaproteobacteria t0 tf Change in abund. t0 Bacteroidetes tf Change in abund. 1x 2x 1.32 10 4x 0.6 10 1.63 10 for Alphaproteobacteria for Alphaproteobacteria (Alpha), probe GAM42a for Gammaproteobacteria (Gamma) and probe CF319a for Bacteroidetes (CFB). Measures at (0x), 20 mg L conditions: control experiment in the four different the beginning (t0) and at end (tf) of microcosm

79 Effects of oil on bacterial activity and diversity

Sprin g

C2 T8 A C 1 T8 A 2 T8 C 2 T6 C 1 T6 A2 T6 1A T6 C 2 T6 A1 T4 C1 T4 A 2 T2 A 1 T2 T A1 A2 C1 A1 C2 A1 A2 C1 C2 A2 C1 C2 0 5 10 15 20 25 30 T2 T4 T6 T8 Linkage Distance Summer C1 T2 A1 T6 B A1 To A2 T8 C1 To A1 T6 A1 T8 A1 T8 C1 T8 A2 T6 C2 T6 C1 T6 A1 T4 C1 T4 C1 T6 A1 T2 C2 T8 C1 T4 0 C1 T8 C2 T6 C1 T2 A1 T2 A1 T0 C1 A1 C1 A1 C1 A1 C1 C2 A1 A2 C1 C2 A1 A2 C1 T0

To T2 T4 T6 T8 0 50 100 150 200 250 Time (days) Minicosmos Linkage Distance

C control To 20 �g/L To 40 �g/L To control T3 20 �g/L T3 40 �g/L T3 80 �g/L To 80 �g/L T3 control T7 20 �g/L T7 40 �g/L T7 80 �g/L T7 1 1 1 1 1 l 1 1 1 1 - - l l ------o L L o o L L L L L L L r r r t g g g g g t t g g g g n � � � � � n n � � � � 0 5 10 15 20 25 30 o 0 0 0 0 0 o o 0 0 0 0 c 2 4 c c 8 4 4 8 8 2 2

To T3 T7 Linkage Distance Time (days)

Fig 6.- Examples of DGGE gels of bacterial 16S rRNA gene fragments from the Spring (A), Summer (B) and microcosm (C) experiments and dendogram classication (Ward’s Method, Euclidean distances according to the band pattern). DGGE lanes represent the different treatments: control mesocosms (C1 and C2) and PAHs- amended mesocosms (A1 and A2) and the samplings times in days (T0, T2, T4, T6 and T8). In the microcosm experiment the different PAHs concentrations (control, 20 μg L -1 , 40 μg L -1 and 80 μg L -1 ) and the samplings days (T0, T3 and T7). Different grey bands reunite together samples that cluster together.

80 Chapter II

Microcosm experiments. As we have shown before (Teira et al. 2008), the evolution of bacterial communities was similar in the mesocosms and the microcosms that had received the same oil additions. PAHs, however, disappeared more slowly from the microcosms (Fig. 5A) than from the mesocosms (Fig 1D), probably because the water surface in contact with the atmosphere was smaller in the case of the microcosms. PAHs had an effect on chlorophyll a (Fig. 5B, Table 3), which was much stronger at 80 µg L -1 . Bacterial abundance was also affected by the added PAHs, so that the decrease in the peak occurred later the more PAHs had been added, and did not occur when the maximal concentration of 80 µg L -1 was added (Fig. 5C). Bacterial production did not show any signicant differences between treatments until time 4, when the 80 µg L -1 sample increase tremendously (Fig. 5D). HNF developed much more in the control of the microcosms (up to 3500 cells mL -1 ) than in the PAH treatments, which were all rather similar and had few changes in HNF concentration (ca. 1500 mL -1 with a slight increase towards the end of the experiment, details no shown).

Bacterial community structure and estimates of diversity. Figure 6 shows two examples of the ngerprints of bacterial community structure. In three cases (spring, autumn and winter) the band patterns showed no differences between controls and PAHs-added mesoscosms, as exemplied by the March DGGE gel and corresponding dendrogram (Fig. 6A). Only in the case of the summer experiment there was a detectable oil effect (Fig. 6B), the dendrogram showing a cluster of samples labeled “A” (PAH addition) after day 4 onwards. The presence and absence of bands in the DGGE analysis was used to estimate bacterial richness of each sample. Band number varied between 7 and 17 with a global average of 13. Differences in bacterial richness were not signicant for any of the experiments, except for the summer one, where we detected higher diversity in the PAHs-ammended mesocosms, particularly in the rst part of the experiment.

The DGGE analysis and the associated dendrogram of the microcosm experiment showed a clear clustering for the highest concentration treatments, particularly at the start of the experiment (times 0 and 3, Fig. 6C). The samples from time 7 all clustered together, indicating that the time evolution was probably more important than the PAHs additions in determining community structure. The lowest richness was detected with the highest PAHs concentration.

In the microcosm experiments we also followed the changes in bacterial subgroup abundance by CARDFISH. Alphaproteobacteria abundances had similar decreases (1x10 5 cells mL -1 ) in all treatments except in the 2x, when it was lower (5x10 4 cells mL -1 ). Bacteroidetes showed a clearly higher decrease in the tank that was amended with higher concentration of PAHs (Table 5). Somehow different results were observed when comparing the relative contribution of each subgroup to total DAPI-stained

81 Effects of oil on bacterial activity and diversity cells abundance instead of the absolute abundance (Teira et al . 2007). The most relevant changes occurred, however, also in the Gammaproteobacteria, which slightly decreased in the treatment that received no PAHs, but increased a lot in the treatment that had received most PAHs. Community structure in the microcosms that had received PAHs shifted from ca. 50% Bacteroidetes, 35% Alphaproteobacteria and 15% Gammaproteobacteria to 20% Bacteroidetes, 10% Alphaproteobacteria and 73% Gammaproteobacteria. Community structure also shifted in the microcosms that had not received PAHs, but the dominance by Gammaproteobacteria was smaller (55%) and driven by the large decrease in Bacteroidetes (from 50 to 14%). Gammaproteobacteria in the control microcosm changed little, and Alphaproteobacteria lost a large number of cells, but continued to be a relatively large part of the assemblage, ca. 26%.

DISCUSSION

We used a mesocosms approach to test for the effects of realistic additions of oil on bacterial function and assemblage structure. We repeated the experiment four times at different phases of the seasonal cycle of Ría de Vigo to discern whether the assayed levels of PAHs affected microbial communities at all times or only depending on the initial microbial communities or environmental settings. Furthermore we also run a variable-concentration experiment to nd the threshold generating signicant effects. The four experiments started in four different plankton conditions. Spring with a starting phytoplankton bloom, summer with little development of algae, autumn corresponded to a declining algal bloom, and winter had low chlorophyll values. The fact that the algal communities maintained constant populations in the mesocosms in summer and winter indicates that our sampling and bag-lling protocols were accurate and did not disturb too much the communities.

Following the accident of the Prestige tanker in front of the Galician coast in November 2002, several research projects were carried out to determine the effects of the spill on the ecosystem. Our objective was to test to what extend the bacterial assemblages of Ría de Vigo were pre-adapted to the oil concentrations likely to be found in an oil spill accident. We hypothesized that if the assemblages did not change in diversity nor in function after point additions of PAHs, it would indicate that the assemblages were accustomed to the presence of similar PAHs concentrations in the waters, and that they could use and metabolize this type of allochthonous carbon, which would then not dramatically modify the structure and the function of the bacterial community.

82 Chapter II

The concentrations of PAHs measured after similar oil spill accidents are very variable (as are in fact the methodologies used to measure the concentration). After the accident in Paraíso Bay, Antarctica, values of 50-100 µg L -1 were measured, while the values measured after the Exxon Valdez spill were much lower, of ca. 10-30 ng L -1 (González et al. 2006). The values measured in the area of the Prestige oil spill a few weeks after the accident were on average 0.3 µg L -1 (González et al. 2006).

It is somehow difcult to compare different units of PAHs concentrations reported before because not all the units are interchangeable. In any case, the values used in our experiment tend to be much lower than those used in the past in experimental studies. Siron et al. (1993), for example, used a range of oil concentrations of 0.7-4.4 mg L -1 in mesocosms.Some authors used very large crude oil concentrations 900 mg L -1 , Cappello et al. 2006; 100 mg L -1 , Cappello et al. 2007) because they targeted the isolation of hydrocarbon-degrading bacteria, or wanted to detect the specic use of one PAH molecule (naphtalene, 6.4 g L -1, Castle et al. 2006). It is certain that when crude oil is added, the real concentration of PAHs that the microorganisms experience will be much smaller. In our experiments we chose values (Table 2) intermediate between what was detected in the sea, and what had been used in previous similar studies, concentrations that we believed are more realistically close to what in situ bacteria could experience. The fast disappearance of PAHs from the water (Table 1) further assured that PAH concentrations in the water in our experiments were realistic.

Our results indicate that the concentrations of PAHs tested in the mesocosms appeared to affect bacterial abundance in all experiments while, however, chlorophyll, bacterial production and growth rates were only affected signicantly during the summer experiment (Table 2). This contrasting result (effects on abundance and not on the rest of the variables) can be explained by the fact that the effects on bacterial abundance can be seen in the second part of the experiment, after agellates have grazed on the community and have, presumably, affected its composition (see below). That the effects are signicative in summer can be assigned to the fact that in the period with less available inorganic nutrients (summer, Table 1), the increased mineralization activity by bacteria stimulated by the introduced PAHs probably generated more inorganic nutrients, which in turn stimulated algal growth, while in winter the excess of nutrients in the water would mask the increase caused by increased bacterioplankton mineralization. It is interesting to note that we found the highest effect in the season in which PAHs disappeared faster from the water (0.70 d -1 in summer and 0.35 d -1 in the other seasons) probably due to the higher temperatures (Table 1). Temperature also plays a signicant role in controlling the nature and extent of microbial hydrocarbon metabolism (Nedwell et al. 1999), and directly affects both the rate of biodegradation (Brakstad et al . 2006), and the physico-chemical behaviour of oil hydrocarbons, such as viscosity, diffusion and volatilization, which changes oil

83 Effects of oil on bacterial activity and diversity composition, and bioavailability of the water-soluble components (Northcott & Jones, 2000, Rowland et al. 2000). Coulon et al. (2007) showed that the change in temperature had a much more pronounced effect on the oil-degrading microbial communities than nutrient additions, even though biodegradation was possible at all temperatures.

The summer season in the NW Iberian zone is characterized by the upwelling due to the dominance of North winds over the adjacent shelf. Surface water leaves the Ría moving offshore and this water is replaced by subsurface oceanic Eastern North Atlantic Central Water, which enters the Ría and has a profound effect on its hydrography (e.g. Álvarez-Salgado et al. 1993). The bacterial assemblages at this time of the year, previously surviving in deep oceanic waters, are probably less adapted to the amounts of allochthonous organic matter present in the Ría. In fact, we detected using CARD-FISH the presence of some Betaproteobacteria (typically considered “freshwater” groups, Methé et al. 1998) at all seasons except Summer, and the gammaproteobacteria PAH-degrading genus Cycloclasticus was detected in September and January, but not in summer (Alonso-Gutiérrez et al. submitted). SAR11 was also not detected in summer, something which is consistent with a deep-water origin of the bacterial assemblage (SAR11 decreases at mesopelagic depths, e.g. Baltar et al. 2007).

The microcosm experiments run in parallell to the January mesocosms demonstrate that the lack of effect of the added PAH concentrations in the spring, autumn and winter experiments was most probably due to a too low PAH concentration being assayed. In that experiment, ca. 20 µg L -1 of PAHs had no effect, while twice this amount started to be detectable, and 4x this amount had a very relevant effect on the microbial communities, decreasing algal development, increasing bacterial abundance and production, and decreasing bacterial richness. The PAHs addition affected positively bacterial abundance and production both in the summer experiment and in the microcosm experiments (Fig. 2 and Fig. 5), but had contrasting effects on chlorophyll development, which was stimulated in summer, and depressed in the microcosms. Previous studies reported a stimulatory effect of oil addition to autotrophs but a toxic effect at higher concentrations, while bacterial response was however positive at all concentrations (Wang & Koshikawa 2007).

Inspection of the DGGE gels let us conclude that there were signicant changes in bacterial community structure caused by the PAH additions only in the summer experiment where an increase in the number of bands was observed (an estimate of higher richness). This result differs from the previous idea that hydrocarbon polluted areas should have low diversity values and a high dominance of a few bacterial groups (Harayama et al. 1999, MacNaughton et al. 1999, Kasai et al. 2001, Röling et al. 2002), and also differs from Schäfer et al. (2000) that reported that experimental manipulations

84 Chapter II should reduce the diversity of microbial communities. Since the experimentally added PAHs can be considered as a C source additional to other C sources present in the water, it could be speculated that increased richness would indicate the appearance of more niches for bacterial development when the PAHs were present. Interestingly, in the microcosm experiments we observed an increase in richness with the additions of 20 and 40 µg L -1 as compared to the control, but then a decrease with the highest concentration (80 µg L -1 ). The global structure of the community changed little, however, as detected by DGGE (Fig. 6C), and it seemed to do so more at the end of the experiment, when the 80 µg L -1 treatment was more distinct from the control treatment. This is in contrast with the CARD FISH results (Table 5) that indicate a clear shift towards gamma-proteobacteria in the 4x (80 µg L -1 ) treatment, also apparent, but much lower in magnitude, in the 1x and 2x treatments. This discrepancy is maybe due to the fact that the high addition treatment presents much higher abundances respect to the other treatments. Bacteroidetes and alphaproteobacteria decreased in all treatments, but with contrasting patterns: alphaproteobacteria decreased more at low than at high oil additions, while Bacteroidetes were replaced by gammaproteobacteria particularly at the highest PAH addition. These results at times seem to contradict a previous analysis (Teira et al. 2008) which was based on %contribution to each group and not in strict abundance. The differences can be assigned to a different variable being observed.

In previous research, gammaproteobacteria have generally been seen to dominate after oil additions (e.g. Grossman et al. 1990, Kasai et al. 2001, Syutsubo et al. 2001, Röling et al. 2002, McKew et al. 2007, Cappello et al. 2007, etc.). For example, Alcanivorax were shown to dominate in oil-contaminated seawater when nutrients were adequately supplied (Kasai et al . 2002) and to have broad substrate specicity for alkane degradation (Hara et al. 2003). One of the predominant oil degraders after the Nakhodka oil spill was the aromatic hydrocarbon decomposer Cycloclasticus pugetii (23-25%), followed by the aliphatic hydrocarbon decomposer Alcanivorax borkumensis (4- 7%) (Maruyama et al. 2003). Since the type of oil we used was rich in PAHs and poor in aliphatics, we expected to see the development of Cycloclasticus . Indeed, an increase of Cycloclasticus bacteria in the four mesocosms experiments, reaching different levels depending on the season, was reported by Teira et al. (2007) and in the microcosm experiments this organism showed a proportional increase depending on the amount of added PAHs. The Cycloclasticus genus is well known to be implicated in PAH degradation (Kasai et al . 2002, Yakimov et al . 2004). In the microcosm experiments this group was a large fraction of the total gammaproteobacteria specially in the middle of the experiment. As an example, 3, 12, 28, 43 and 89% of the cells positive with the gamma- probe (GAM42a) were also postive with probe CYPU829 for Cycloclasticus in the 0x, 0.5x, 1x, 2x and 4x treatments respectively at day 5. Later on in the experiment, Cycloclasticus disappeared from the microcosms (Teira et al. 2007) and were replaced by other gammaprotebacteria. It is important to note, however, that probe

85 Effects of oil on bacterial activity and diversity

GAM42a has a broad gammaproteobacteria group coverage of only 76% (Amann & Fuchs 2008), and does not target Alcanivorax, nor many other typically hydrocarbon-degrading bacteria, such Cycloclasticus, Oleispira, Thalassolituus , etc. It targets, however, other gammaproteobacteria with capability to degrade hydrocarbons, such as Pseudomonas or Marinobacter hydrocarbonoclasticus (unpublished results).

For the analyses of bacterial community structure in this experiments we used the DGGE approach, as other studies have done in oil polluted marine environments. (Macnaughton et al. 1999, Ogino et al . 2001, Kasai et al. 2001, Castle et al. 2006) Bacterial richness (as estimated by DGGE) or community structure was not affected in the experiments other than in the summer one. Observation of the DGGE band patterns (Fig. 6) and the statistical analyses reported in Table 3 indicates that “Time” is one of the most important factors determining the changes in bacterial community structure in the experiments, and also is one of the main factors determining the changes in chlorophyll, prokaryote abundance and prokaryotic heterotrophic production (Table 3). Since we showed that the oil addition had little effect on bacterial diversity and function, we could speculate that besides the environmental factors (nutrient levels, DOC content, water, temperature, etc.) protist development could be one of the reasons why bacterial function and community structure varied in the different experiments. We expected that changes in bacterial community structure (as detected by DGGE) should be correlated to the changes in bacterial activity.

Indeed, the similarity between bacterial community structure in two consecutive time points was correlated to the total bacterial activity between these two points (Fig. 7A) so that the lower bacterial production, the closer are the two communities, and vice-versa. The relationship seemed to be different in the summer experiment than in the others, again supporting the idea that the summer community developed differently from the bacterial communities of the other experiments. The correlations were signicant but not great. To improve them, we analyzed separately the rst part of the experiments (Fig. 7B), when HNF developed (see example in Fig. 5) with positive growth rates (Table 4) from the second part of the experiment, when HNF declined (Fig. 4) and presented negative growth rates (Table 4). The changes in bacterial community structure were well linked to bacterial production in the rst part of the experiment, when HNF probably induced relatively small changes in the bacterial assemblage. Later on, once HNF had developed and depleted the bacterial population (Fig. 4), bacterial community structure changes were not linked any more to bacterial production.

As in many other published mesocosms experiments (e.g. Lebaron et al. 1999, Pinhassi et al. 2006, Allers et al. 2007) bacterial abundance in our study had a small initial positive response to the

86 Chapter II

mesocosms startup (days 1-2), then they decreased in abundances (days 3-4), and then they increased again towards the end of the experiments. It was this second bacterial peak the one that was most affected by oil.

There is still disagreement as to whether the abundance, productivity and diversity of pelagic bacteria are determined mainly by predators or nutrients (Pernthaler 2005). Grazing has been shown to inuence bacterial community composition in laboratory and eld experiments (van Hannen et al. 1999, Gasol et al. 2002, Simek et al. 2002). Allers et al. (2007), in a mesocosm experiment with Mediterranean waters found a succession of bacterial communities. The initial abundance peak consisted of predominantly Alteromonadaceae, very similar phylotypes in the different treatments. These organisms were depleted by an increase in HNF numbers and afterwards different Rhodobacteraceae phylotypes developed in the different treatments, particularly those that had received P additions. If

Spring, Autumn and Winter experiments From time=0 to time=4 Summer experiment From time=4 to time=8 1 1

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0 0 Similarity of bacterial community structure A B -0.2 -0.2 0 50 100 150 200 250 0 20 40 60 80 100 120 140

Bacterial production between two time points (µgC l-1 d-1) Bacterial production between two time points (µgC l-1 d-1)

Fig. 7. - A) Relationships between integrated bacterial production between samples (X axis) and the similarity in bacterial community structure (Y, Pearson correlation coefcients between two consecutive lanes in a DGGE analysis, considering presence/absence of bands as well as intensity), with separation of experiments run in Spring, Autumn and Winter (solid circles) from the Summer experiment (empty circles). A similitude of 1 indicates that the community did not change. For the Summer experiment, N=8, r=-0.71, P<0.05, for the other experiments, N=22, r=-0.58, P<0.005. B) Same plot for the three experiments of Spring, Autumn and Winter, separating the points before the time= 4 (solid squares) and after that time point (empty squares). This division corresponding to changes before the peak of agellate development, or after that peak (see Fig. 5). See text for further explanations. The relationship is only signicant for the period before agellate development, N=10, r=-0.94, P<0.005. Regression lines are drawn for orientation only

87 Effects of oil on bacterial activity and diversity the bacterial assemblages of coastal waters are composed of rather stable populations with peaks of fast-growing organisms showing rapid temporal uctuations, manipulation of the ecosystem might destabilize this system and promote the growth of these fast-growing populations, that are the ones that will be responsible for the changes in bacterial production and community structure (Pernthaler 2005).

Conclusion

Policyclic aromatic hydrocarbons (PAHs), which are well-known to have toxic effects on organisms also are, however, a signicant allochthonous C source for bacteria. Our microcosm study showed that the addition of PAHSs resulted in an immediate change in bacterial community structure and rapid rate of oil degradation, suggesting the presence of a pre-adapted oil-degrading microbial community assemblage, in agreement with the data presented by Coulon et al. (2007). Our mesocosms experiments showed that, at least in three of the seasons, the natural assemblage seldom reacted to the PAHs additions, did not show any toxic effect, and did not produce any bacterial stimulation, maybe because the concentration was negligible compared to that of the total biovailable DOC. In the Ría de Vigo, we had observed that PAH addition triggered short-lived peaks of Cycloclasticus (Teira et al. 2007) that used the PAHs, and then were virally-lysed or predated by protists. The organisms with the potential to use the allochthonous added substances were present, albeit at very low concentrations, and had the potential to develop immediately after PAHs additions. Our results also indicate that when realistic (i.e. not exagerately large) oil or PAHs concentrations are added in experimental containers, the response of the communities will be more likely to shed light into the realistic behaviour of natural microbial communities.

ACKNOWLEDGEMENTS

This work was supported by project IMPRESION (VEM2003-20021). We thank all the people involved, particularly those who helped with the preparation and sampling of the mesocosms during the four experiments, and F.G. Figueiras and X.A. Álvarez-Salgado who were the driving forces behind the whole project. We also thank J. González (Universidad de Vigo) for access to data and D. Vaqué for help with the HNF counts. We would also like to thank the captain and crew on board RV Mytilus . Financial support to IL and ACD was provided by PhD fellowships from the Spanish government (MEC).

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Castle DM, Montgomery MT, & Kirchman DL (2006) Effects of naphthalene on microbial community composition in the Delaware stuary. FEMS Microb Ecol 56: 55-63 Coulon F, McKew BA, Osborn MA, McGenity TJ & Timmis KN (2007) Effects of temperature and biostimulation on oil-degrading mmicrobial communities in temperatre estuarine waters. Environ Microbiol 9: 177-186. Dalby AP, Konstantinos AK, Christaki U & Karayanni H (2008) Cosmopolitan heterotrophic microeukaryotes are active bacterial grazers in experimental oil-polluted systems. Environ Microbiol 10 : 7-56 Delille B & Siron R (1993) Effect of dispersed oil on heterotrophic bacterial communities in cold marine waters. Microb Ecol 25 : 263-273. Figueiras FG, Labarta U & Reiriz MJF (2002) Coastal upwelling, primary production and mussel growth in the Rias Baixas of Galicia. Hydrobiologia 484 : 121-131 Fuhrman J A & F Azam (1982) Thymidine incorporation as a measure of heterotrophic bacterioplankton production in marine surface waters: Evaluation and eld results. Mar Biol 66 : 109-12 Garcia EM, Siegert IG & Suarez P (1998) Toxicity assays and naphtahalene utlization by natural bacteria selected in marine environments. Bull Environ Contam Toxicol 61 : 0370-0377 Gasol JM, Pedrós-Alió C, & Vaqué D (2002) Regulation of bacterial assemblages in oligotrophic plankton systems: results from experimental and empirical approaches. Ant. van Leeuwenhoek 81 : 435-452. Gasol JM & del Giorgio PA (2000) Using ow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Scientia Marina . 64 : 197- 224 González JJ, Viñas L, Franco MA, Fumega J, Soriano JA, Grueiro G, Muniategui S, López-Mahía P, Prada D, Bayona JM, Alzaga R & Albaigés J (2006) Spatial and temporal distribution of dissolved/dispersed aromatic hydrocarbons in seawater in the area affected by the Prestige oil spill. Mar Pollut Bull 53 : 250-259. Glöckner FO, Fuchs BM & Amann R (1999) Bacterioplankton composition of lakes and oceans: a rst comparison based on uorescence in situ hybridization. Appl Environ Microbiol 65 : 3721- 3726. Gerdts G, Wichels A, Döpke H, Kings K, Gunkel W & Schütt C (2004) 40-year long-term study of microbial parameters near Helgoland(Germand Bight, North Sea): historical view and future perspectives. Helgol Mar Res 58 : 230-242. Hara A, Syutxubo K & Harayama S (2003) Alcanivorax which prevails in oil-contaminated seawater exhibits broad substrate specicity for alkane degradation. Environ Microbio l 5: 746- 753 Harayama S, Kishira H, Kasai Y & Shutsubo K (1999) Petroleum biodegradation inmarine environments. J Mol Microbiol Biotechnol 1: 63-70

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Kasai Y, Kishira, H & Harayama, S (2002) Bacteria Belonging to the Genus Cycloclasticus Play a Primary Role in the Degradation of Aromatic Hydrocarbons Released in a Marine Environment. Appl Environ Microbiol 68 : 5625-5633. Kasai Y, Kishira H, Syutsubo K & Harayama S (2001) Molecular detection of marine bacterial populations on beaches contaminated by the Nakhodka tanker oil-spill accident. Environ Microbiol 3: 246-255. Kirchman DL, K´nees E, & Hodson R (1985) Leucine incorporation and its potential as a measure of protein synthesis by bacteria innatural aquatic ecosystems. Appl Enviro Microbi ol 49 : 1681- 1688 Koshikawa H, Xu DQ, Liu AL, Kohata K, Kawachi M, Maki H, Zhu MY & Watanabe M (2007) Effect of the water-soluble fraction of diesel oil on bacterial and primary production an dthe trophic transfer to mesozooplankton through a microbial food web in Yangtze estuary, China. Est, Coast Shelf Sci 71 : 68-80 Lebaron P, Servais P, Troussellier M, Courties C, Vives-Rego J & Muyzer G (1999) Changes in bacterial community structure in seawater mesocosms differing in their nutrient satatus. Aquatic Microb Ecol 19 : 255-267. Maruyama A, Ishiwata H, Kitamura K, Sunamura M, Fujita T, Matsuo M & Higashihara T (2003) Dynamics of Microbial Populations and Strong Selction for Cycloclasticus pugetii following the Nakhodka Oil Spill. Microb Ecol 46 : 442-453 Macnaughton SJ, Stephen JR, Venosa AD, Davis GA, Chang Y & White DC (1999) Microbial Population Changes during Bioremediation of an Experimental Oil Spill. Appl Environ Microbio l 65 :3 566-3574. Manz W, Amann R, Ludwig, W, Wagner M & Schleifer KH (1992) Phylogenetic oligonucleotide probes for the major subclasses or Proteobacteria: problems and solutions. Syst Appl Microbio l 15 : 593-600. Manz W, Amann R, Ludwig W, Vancanneyt M & Schleifer KH (1996) Application of a suite of 16S r RNA-specic oligonucleotide probes designed to investigat bacteria of the phylum Cytophaga- Flavobacter-Bacteroides in the natural environment. Microbiology-UK 142 : 1097-1106. McKew BA, Coulon F, Yakimov M, Denaro R, Genovese M, Smith CJ, Osborn MA, Timmis KN & McGenity TJ. (2007) Efcacy of intervention strategies for bioremediation of crude oil in marine systems and effects on indigenous dydrocarbonoclstic bacteria. Environ Microbiol. 9: 165-17 Meth é BA, Hiorns WD & Zehr JP (1998) Contrasts between marine and freshwater bacterial communities in Lake George and six other Adirondack lakes. Limn Oceanogr 43 : 368-374 Mor án XAG, Estrada M, Gasol JM & Pedrós-Alió C (2002) Dissolved primary production and the strength of phytoplankton-bacterioplankton coupling in contrasting marine regions Microb Ecol 44 : 217-223 Moran M A, Sheldon WM & Sheldon JE (1999) Biodegradation of riverine dissolved organic carbon in ve estuaries of the Southeastern United States. Estuaries 22 : 55–64.

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Nayar S, GohB PL & Chou LM (2005) Environmental impacts of diessel fuel on bacteria and phytoplankton in a tropical estuary assessed using in situ mesocsms. Ecotoxicology 14: 397-412 Nedwell DB (1999) Effect of low temperature on microbial growth: lowered afnity for substrates limits growth at low temperature, FEMS Microbiol. Ecol. 30 : 101–111 Nogueira E, Ibáñez F, & Figueras FG (1997) Seasonal patterns and long-term trends in an estuarine upwelling ecosystem (Ría de Vigo, NW Spain). Est Coast Shelf Sci 44 : 285-300. Northcott & Jones GL (2000) Experimtntal approaches andanalytical techniques for determining organic compound bound residues in soil and sediment. Environ Poll 108 : 19-43. Ogino A, Koshikawa H, Njahara T & Uchiyama H (2001) Succession of microbial communities during a biostimulation process as evaluated by DGGE and clone library analyses. J Appl Microbiol 91 : 625-635 Ohwada K, Nishimura M, Wada M, Nomura H, Shibata A, Okamoto K, Toyoda K, Yoshida A, Takada H & Yamada M (2003) Study of the effect of water-soluble fractions of heavy-oil on coastal marine organism using enclosed ecosystems, mesocosms. Mar Pollut Bull 47 : 78-84 Pernthaler A, Pernthaler J, Eilers H, & Amann R (2001) Growth patterns of two marine isolates: adaptations to substrate patchiness. Appl Environ Microbiol 67 : 4077-4083 Pinhassi J, Gómez-Consarnau L, Alonso-Sáez L, Sala M, Vidal M, Pedros-Alió C, Gasol JM (2006) Seasonal changes in bacterioplankton nutrient limitaion and their effects on bacterial community composition in the NW Mediterranean Sea Aquat Microb Ecol 44 : 241-252 Porter KG & Feig YS (1980) The use of DAPI for identication and enumartion of bacteria and blu- green algae. Limn Oceano gr 25 : 943-948 Preston BL. (2002) Spatial patterns in benthic biodiversity of Chesapeake Bay,USA (1984-99): association with water quality and sediment toxicity. Environ Tox Chem 21 : 151-162. Rowland AP, Lindley DK, Hall MJ, Rossall MJ, Wilson DR & Bernham DG (2000) Effects of beach sand properties, temperature and rainfall aon the degradation rates of oil in buried oil/beach sand mixtures. Environ Poll 109 : 109-118. Röling WFM, Milner MG, Jones DM, Lee K, Daniel F, Swannell RJP & Head IM (2002) Robust hydrocarbon degradation and dynamics of bacterial communities during nutrient-enhanced oil spill bioremediation. Appl Environ Microbiol 68 : 5537-5548. Sánchez O, Gasol JM, Massana R, Mas J, & Pedrós-Alió C (2007) Comparison of different Denaturing Gradient Gel Electrophoresis primer sets for the study of marine bacterioplankton communities. Applied Environmental Microbiology 73 : 5962-5962. Santas R, Korda A, Tenente A, Buchholz K & Santas PH. (1999) Mesocosm assays of oil spill bioremediation with oleophilic fertilizers: Inipol, F1 or Both? Mar Pollut Bull 38 : 44-48. Sargian P, Mostajir B, Chatila K, Ferreyra GA, Pelletier E & Demers S (2005) Non-synergistic effects of water-soluble crude oil and enhanced ultraviolet-B radiation on a naturual plankton assemblage. Mar Ecol Prog Ser 294 : 63-77

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Schauer M, Massana R, & Pedrós-Alió C (2000) Spatial differences in bacterioplankton composition along the Catalan coast (NW Mediterranean) assessed by molecular ngerprinting. FEMS Microbiol Ecol. 33 : 51-59 Schauer M, Balagué V, Pedrós-Alió C, & Massana R (2003) Seasonal changes in the taxonomic composition of bacterioplankton in a coastal oligotrophic system. Aquat Microb Ecol . 31 : 163- 174 Sieracki ME, Haugen EM & Cucci TL (1995) Overestimation of heterotrophic bacteria in the Sargasso Se: direct evidence by ow and imaging cytometry. Deep-Sea Res 42 : 1399-1409. Simek K, Nedoma J, Pernthaler A, Posch T & Dolan JR (2002) Altering the balance between bacterial production and protistan bacterivory triggers shifts in freshwater bacterial community composition. Antonie van Leewenhoek Int J Gen Mol Microb 81 : 453-463. Siron R, Pelletier E, Delille D & Roy S (1993) Fate and effects of dispersed crude oil under icy conditions simulated in mesocosms. Mar Environ Res 35 : 273-302 Smith DC & Azam F (1992) A simple, economical method for measuring bacteria protein synthesis rates in seawter using 3H-leucine. Mar Microb Food Webs 6: 107-114 Teira E, Lekunberri I, Gasol JM, Nieto-Cid M, Álvarez-Salgado XA & Figueiras F (2007) Dynamics of the hydrocarbon-degrading Cycloclasticus bacteria during mesocosm-simulated oil spills. Environ Microb 9: 165-176 Teira E, Gasol JM, Aranguren-Gassis M, Fernández A, González J, Lekunberri I & Álvarez-Salgado XA (2008) Linkages between bacterioplankton community composition, heterotrophic carbnon cycling and envrionmental conditions in a highly dynamic coastal ecosystem. Environ Microb 10 : 906-917 . UNESCO (IOC) 1984. Manual for monitoring oil and disslved/dispersed hydrocarbons in marine waters and on beaches. Intergovernamental Oceanographic Commission, Manuals and Guides No 13 , 35 p. van Hannen EJ, Zwart G, van Agterveld MP, Gons HJ, Ebert J & Lananbroek HJ (1999) Changes in bacterial and eukaryotic community structure after mass lysis of lamentous Cyanobacteria associated with viruses. Appl Environl Microbiol 65 : 795-801. Varela M, Bode A, Lorenzo J, Álvarez-Ossorio MT, Miranda A, Patrocinio T, Anadón R, Biseca L, Rodríguez N, Valdés L, Cabal J, Urrutia Á, García-Soto C, Rodríguez M, Álvarez-Salgado XA,& Groom S ( 2006). The effect of the “Prestige” oil spill on the plankton of the N-NW Spanish coast. Mar. Pol. Bull. 53 : 272-286g Yakimov M, Gentile G, Bruni V, Cappello S, D´Auria G, Golyshin P & Giuliano L (2004) Crude oil- induced structural shift of coastal bacterial communities of rod bay (Terra Nova Bay, Ross Sea, Antarctica) and characterization of cultured cold-adapted hydrocarbonoclastic bacteria. FEMS Microb Ecol 49: 419-432.

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CHAPTER 3

Different types of phytoplankton blooms caused by different additions in seawater mesocosms, and their effect on bacterial heterotrophic activity and assemblage composition

Itziar Lekunberri , Thomas Lefort ,Clara Ruíz-Gonz ález , Montse Coll, Clara Cardelús, C èlia Marras é and Josep M. Gasol Il est d’un petit esprit, de vouloir ne s’ être jamais trompé

Louis XIV Chapter III

ABSTRACT

Bacterial community structure is thought to be in part related to the type of phytoplankton species dominating during algal blooms. The proliferation of specic organisms in such blooms is due to solar radiation and the presence of specic nutrients. We performed a mesocosms experiment to generate different phytoplankton blooms through the additions of: silica (SiO 4), urea and/or phosphorous to oligotrophic water from the Blanes Bay Microbial Observatory (NW Mediterranean). Over 10 days incubations, bacterial activity, abundance, nutrient composition and bacterial community structure were analyzed. The aims of this experiment were: i) to relate different additions to the phytoplankton and bacterioplankton community composition. ii) to investigate whether the changes in bacterial community structure occur by a bottom-up or top-down control. Our results reveal that the change in the bacterial composition is due to a combination of changes in the phytoplankton communities and grazing pressure. And the differences in abundances and activity of bacteria are due to a shift in the bacterial composition.

97 Bacterial response to different types of phytoplankton

INTRODUCTION

Phytoplankton blooms consist in a marked increase in algal populations, reaching concentrations of up to millions of cells per milliliter, involving typically only one or a few species. These phenomena occur when light and nutrients are available in excess. Cullen et al. (2002) dened 3 types of blooms: upwelling blooms which are induced by deep (nutrient-rich) waters reaching the lit layers of the ocean, entrainment blooms those that appear when the mixed layer deepens due to wind erosion or solar heating reduction, and detraiment blooms, which occur when near-surface stratication is induced in a deep mixed layer of nutrient-rich water. In coastal waters algal blooms are frequently the result of nutrients inputs (Rosenberg et al. 1990, Smayda 1990) or of the start of stratication. And these inputs, even if seasonally recurrent, can be equated with sporadic inputs. Variation in nutrient regimes affects phytoplankton production, diversity and succession, and also inuences the overall structure of the microbial food web (Smayda & Reynolds 2001, 2003, Cullen et al. 2002).

Previous studies have correlated increases in biomass of individual phytoplankton species with nutrient inputs to coastal regions (Smayda 1990, Hallegraeff 1993). Diatom growth has been reported to be dependent on silica availability (e.g. Allen et al. 2005), and urea was found to be preferentially be used as a nitrogen source by some cyanobacteria and dinoagellates (Glibert et al . 2008), thus potentially selecting for these species. Other algae are adapted to low nutrient concentrations, and these species bloom after opportunistic species have done it and exhausted nutrients. For example, blooms of the eukaryotic picoplankter Aureococcus anophagefferens occur mainly after nitrate and ammonium have decreased below detection limits (Keller & Rice 1989, Smayda & Villareal 1989).

Primary producers release DOC that is consumed by bacteria (Hagström et al. 1979, Fuhrman & Azam 1980) and comparative studies show a dependence of bacterial abundances on Chl a suggesting a control on bacterial abundance derived from the availability of resources derived from primary production (Bird & Kalff 1984, Cole et al. 1988, Gasol & Duarte 2000). Thingstad & Lignell (1997) enumerated the 5 factors which inuence growth of heterotrophic bacteria: i) DOC, ii) inorganic phosphate, iii) organic/inorganic nitrogen, iv) protozoan predation and v) lysis by viruses. Control by resources of bacterial abundance and activity is often named “bottom-up control” and control by predation or lysis is called “top-down”.

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While many studies have shown that the specicity of a phytoplankton blooms is a consequence of the specic nutrient added, few have related this selection to shifts in bacterial community structure (but see Schäfer et al . 2001, Pinhassi et al. 2006). Laboratory enrichment experiments show a phytoplankton response followed by changes in bacterioplankton community composition (Pinhassi et al. 2006, Allers et al. 2007) for example diatom blooms tended to be followed by Flavobacteria developments. Besides Sapp et al . (2007a) found strong shifts in the associated bacterial communities (members of the Gammaproteobacteria) to diatom species. However, Sapp et al (2007b) reported that it could not be generalized whether there were bacterial communities strictly microalgal species- specic. Similarly, Garcés et al. (2007) found little correspondance between the type of dinoagellate species developing in blooms and their associated bacterial communities.

Different mesocosms experiments have been used to study the effect of nutrient additions: globally on the phytoplankton communities (Duarte et al. 2000), on the activity and diversity of bacterial assemblages (Schäfer et al. 2001), and on microbial succession after nutrient additions (Allers et al. 2007). In this study we conducted a mesocosms experiment in which we tried to generate three different bloom situations, in order to determine the effect of the different additions on the phytoplankton, and of different phytoplankton on bacterioplankton community structure and function during the summer season in Blanes Bay (NW Mediterranean). Experimental studies have always shown a general phosphorous limitations in the NW Mediterranean ( Thinsgstad et al. 1998, Pinhassi et al. 2006), particularly during the summer (e.g. Pinhassi et al. 2006). To avoid any nutrient limitation phosphorous was added to all the tanks except for the controls. The three enriched treatments included addition of silica, urea, and P alone. We monitored phytoplankton diversity daily and at the same time sampled for bacterial abundance, physiological status, activity and diversity were collected. In addition we also monitored the development of the HNF community to explore whether bottom-up or top-down control led to bacterial population changes.

MATERIALS & METHODS

Experimental setting. The mesocoms experiment was carried out with water collected from Blanes Bay (The Blanes Bay Microbial Observatory, NW Mediterranean). Eight 100 L tanks (2 replicates, 4 treatments) were lled with seawater ltered by 150 µm, in duplicates with different additions, and incubated at in situ temperature (19ºC) with a 12 h photoperiod to simulate natural conditions. The rst sample was taken 1 h after nutrient addition. The experiment was conducted during 10 days from the 5 th to 15 th June 2007.

99 Bacterial response to different types of phytoplankton

Additions . Within the 8 tanks, 2 were left as controls with no nutrient additions (Ka, Kb) and 6 received phosphate in the same proportion (1 µM nal concentration). Of these 6, 4 were enriched with 16 µM N nal concentration, 2 with inorganic N (NO 3) and 2 with organic N as urea (CO(NH 2)2).

The NO 3 amended tanks also received SiO 4. To keep the silicate in excess we added silicate twice (in day 1 and day 2), and obtained a concentration (23-22 µM) similar to a target of 25 µM (thus generating an addition with the ratio P:N:Si of 1:16:25, higher than the standard Redeld’s (1:16:16 to assure that the Si was in excess).To all tanks we added a metal solution in the same proportion to phosphate as in the f/2 medium (Guillard et al. 1975).

Microplankton enumeration. For the identication of microphytoplankton (mainly diatoms, dinoagelates and coccolithophorids), samples were xed with formalin-hexamine solution (0.4% nal concentration). Counts were made with the Utermöhl et al. (1958) methodology using 50 cm 3 settling chambers. One transect of the chamber was observed at 400x magnication to count the smaller (<20 µm) and more frequent organisms. Additionally, 1 transect of half of the chamber was inspected at 200x to enumerate cells of intermediate size (roughly 20 and 50 µm), and the whole chamber was scanned at 200x to count the larger forms.

Pico- and nanoplankton abundances. Determination of algal and bacterial abundance was performed with ow cytometry (Marie et al. 1999, Gasol & del Giorgio 2000). Samples were run in a Becton-Dickinson FACSCalibur ow cytometer with a laser emiting at 488 nm. For phytoplankton, the samples were not xed and were run at high speed (ca. 100 µl min -1 ), Four populations (Prochlorococcus , Synechococcus , picoeukaryotes and nanoeukaryotes) were differentiated according to size and red and orange uorescence. For non-phototrophic bacteria, 1.2 ml samples were xed with a 1% paraformaldehyde + 0.05% glutaraldehyde solution and deep-frozen in liquid N 2. Afterwards, the samples were unfrozen, stained with SybrGreen at 1:10,000 dilution and run at low speed (ca 10 µl min -1 ). Cells were identied in a plot of side scatter vs green uorescence (FL1).

Heterotrophic nanoagellate abundances were measured following the Rose et al . (2004) protocol. From a stock solution of 1 mM Lysotracker Green (Molecular Probes) 1 µl was added to 99 µl of < 0.2 µm MiliQ, and 3.8 µl of the diluted Lysotracker were added to 0.5 ml of the sample, generating a 75 nM Lysotracker nal concentration. We analyzed the samples as in Rose et al. (2004), using a combination of side scatter and green and red uorescence. The samples were run alive, at high (ca. 100 µl min -1 ) speed. Concentrations were obtained from weighting measurement of the volume analyzed.

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Bacterial single-cell activity. Measurements of the physiological status of bacteria were done in two ways: i) Highly respiring prokaryotes, as those able to reduce 5-cyano-2,3-ditiolyl tetrazolium chloride (CTC; Polysciences), CTC turns into a red uorescent formazan that is detectable by epiuorescence and ow cytometry (Sherr et al. 1999, Sieracki et al. 1999). Sample aliquots (0.4 ml) were amended with 5 mM CTC (from a fresh stock solution at 50 mM) immediately following collection and were incubated for 90 min in the dark at room temperature. CTC-positive (CTC +) cells were enumerated by ow cytometry using the FL2-versus-FL3 dot plot (see Gasol & Arístegui 2007). For these analyses, we used a high speed (ca. 100 µl ml -1 ) and a threshold set in red uorescence. ii) Cells with intact membranes were enumerated using the NADS viability protocol, based on the combination of the cell-permanent nucleic acid strain SybrGreen I (Molecular Probes, Eugene, OR) and the cell-impermeant propidium iodine (PI, Sigma Chemical Co.) uorescent probe. We used a 1:10,000 SG1 and 10 µg ml -1 PI concentrations. After simultaneous addition of each stain, the samples were incubated for 20 min in the dark at room temperature and then analyzed by ow cytometry. SG1 and PI uorescence were detected in the green (FL1) and orange-red (FL3) cytometric channels, respectively. A dot plot of red versus green uorescence allowed distinction of the “live” cell cluster (i.e., cells with intact membranes and DNA present) from the “dead” cell one (i.e., with compromised membranes) (Gregori et al. 2003, Falcioni et al. 2008).

Bacterial production. Bacterial heterotrophic production was estimated using the 3H-leucine incorporation method (Kirchman et al. 1985). Quadruplicate aliquots of 1.2 ml and 2 TCA-killed controls were taken daily. The samples were incubated with 40 nM nal concentration for about 2 h in the dark and at in situ temperature. The incorporation was stopped with the addition of 120 μl of cold TCA 50% to each replicate and the samples were kept frozen at –20ºC until processing, which was carried out by the centrifugation method described by of Smith & Azam (1992).

Collection of community DNA. Microbial biomass was collected by sequential ltration of about 5 l of seawater through a 3 µm pore size polycarbonate lter (Millipore, 46 mm) and a 0.2 µm sterivex lter unit (Millipore). Microbial biomass was treated with lysozyme, proteinase K and sodium dodecyl sulphate, and the nucleic acids were extracted with phenol and concentrated in a Centricon- 100 (Milipore). The nucleic acids were extracted by a standard protocol using phenol/chloroform (Schauer et al . 2003).

101 Bacterial response to different types of phytoplankton

Fingerprinting analysis. DGGE and gel analysis were performed essentially as previously described (Schauer et al. 2000). Briey, 16S rRNA gene fragments (around 550 bp in length) were amplied by PCR, using the universal primer 907rm and the bacterial specic primer 358f, with a GC-clamp. The PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from 40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer. DGGE gel images were analyzed using the Diversity Database software (BIO-RAD).

Statistical analysis. A matrix was constructed for all DGGE lanes taking into account the relative contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix, we obtained a dendrogram by the Ward’s clustering method (Euclidean distances, Statistica 6.0), and ordinations of nonmetric multidimensional scaling based on the Bray-Curtis similarity index (MDS, Kruskal & Wish 1979, Clarke & Green 1988). A one-way ANOSIM was computed to test the observe differences between treatments and the control samples, and another to contrast differences among time within treatment samples (software Primer v5). We also used one-way ANOVAs to test for the differences in Chla, bacterial abundance and bacterial activity between treatments at the different times when samples were taken for bacterial diversity.

RESULTS

Evolution of inorganic nutrient concentrations. Variations of the inorganic nutrient concentrations for the different treatments during the experiment are shown in Fig.1. Except from the control, all tanks had received 1 µM phosphate, which was rapidly consumed in the N amended treatments (Si and U) but remained quite high in the P tank. The N source in the Si tank was NO 3, which started to decrease after day 6 (Fig 1C). In the representation of the evolution of silica (Fig 1B) the maximal value in treatment Si was only reached on day 2 (25 µM, after the second nutrient addition), and this concentration was only reduced to ca. 10 µM at the end of the experiment, thus indicating that there was much leftover unused Si in this treatment. Si remained at the initial value of ca 4 µM in the control treatment, but it was used in the Urea treatment after day 3, and in the P treatment after day 5, down to values < 0.1 µM.

102 Ka 1 Kb Pa 100 Pb Sia A B Sib 0.8 Ua 10 Ub

0.6 1 (μM) (μM) -2 4 3+ 4 0.4 0.1 SiO PO

0.2 0.01

0 0.001 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) 100 Time (days) C

10 (μM) 2

1 +NO - 3 NO

0.1

0.01 0 2 4 6 8 10 Time (days) Fig.1.- Nutrient evolution throughout the mesocosms experiment. Phosphate (A), Silicate (B) and nitrate+nitrite (C). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ). The arrow indicates the second addition of silicate.

Table 1.- Concentrations of nutrients (µM) in the 4 different experimental conditions 1 h after the addition. a and b are the replicates.

Experimental Nomenclature PO 4 NO 3 SiO 4 DON conditions Initial water 0.02 0.13 0.21 -

Control Ka, Kb 0.02 ± 0.00 0.34 ± 0.03 0.38 ± 0.01 4.48 ± 0.41 Phosphorous Pa, Pb 0.67 ± 0.03 0.40 ± 0.05 0.34 ± 0.03 6.92 ± 1.05 Urea+P Ua, Ub 0.79 ± 0.01 0.031 ± 0.01 0.31 ± 0.00 16.70 ± 1.09

N+P+SiO 3 Sia, Sib 0.75 ± 0.01 12.5 ± 1.03 22.7 ± 0.79* 17.08 ± 0.70

*The Silicate values correspond to day 2 (after the second addtion of the SiO 3, see text) 103 Bacterial response to different types of phytoplankton

Dynamics of chlorophyll a and phytoplankton composition. The initial value of chlorophyll a (Chl a) was around 0.2 µg L -1 in all tanks (Fig. 2A). Signicant differences (<0.05) between treatments were found after time 3. The Si tank reached concentrations about 16 fold higher than the control and the P tank while the U tanks reached values ca 5-fold those of the K and P tanks. Chl a in all treatments remained high until the end of the experiment. Maximum value of Chl a in the P and K tanks was reached at day 2, in the U tank at day 4, and in the Si tank at day 6.

Ka 6 100 Kb 7 10 Pa Pb A 6 B Sia 5 10 Sib Ua ) Ub -3 6 10 3 10 ) -1

1 (cells ml BA

Chlorophyll (mg m 6 10

8 10 5

5 6 10 0.1 0 2 4 6 8 10 0 2 4 6 8 10

) 4 -1 10

h 4000 -1 C D

) 3000 -1

1000 2000 HNF (cells ml

1000 Leucine incorporation (pmol Leu L

100 0 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days)

Fig. 2 .- Chlorophyll a concentration (A), bacterial abundance (B), leucine incorporation rates (C) and heterotrophic nanoagelate abundance (D) throughout the mesocosms experiment. Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ).Points used for the statistical analyses are indicated with arrows.

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The evolution of phytoplankton composition along the experiment in terms of contribution to biovolume is shown in Fig. 3. The K and P tanks present similar patterns, with alternation between diatoms and dinoagellates. The Si tank showed a large increase in the diatoms comprising ca. 90% of the community. In contrast the U tank had a decrease in the diatom composition compensated by an increased in the dinoagellates from time 4 to the end.

%Dinoflagellates P %Diatoms CONTROL 100 %Cocolitoforales 100 %Nanoflagellates Others A B 80 80 ) 3 m

� 60 60

40 40 % Volume ( % Volume

20 20

0 0 0 2 4 6 8 10 0 2 4 6 8 10 Si U 100 100 C D 80 80 ) 3 m � 60 60

40 40 % Volume ( % Volume

20 20

0 0 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days)

Fig. 3.- Phytoplankton diversity as % of volume. K tank (A), P tank (B), Si tank (C) and U tank (D). Dinoagellates ( ), diatoms ( ), cocolitoforids ( ), nanoagellates (X) and others ( ).

105 Bacterial response to different types of phytoplankton

On the basis of size and pigment content, we identied two different populations of picophytoplankton (Synechococcus and picoeukaryotes). Prochlorococcus, are not present in Blanes Bay at this time of the year (details not shown). Synechococcus in the control and P treatments had a peak at day 3 and then decreased to remain with populations similar to the initial ones. The U and Si treatments had highest values with a peak at day 5 and then a decrease to the disappearance at the end (Fig. 4A). Picoeukaryotes had a clear peak at day 4 with maximal values in the Si and U tanks (Fig. 4B).

Ka Kb Pa Pb Sia A 2 10 5 Sib ) Ua -1 Ub

1.5 10 5

1 10 5 Synechococcus (cells ml 5 10 4

0 0 2 4 6 8 10 3.5 10 4 B 3 10 4 ) -1 2.5 10 4

2 10 4

1.5 10 4

1 10 4 Picoeukaryotes (cells ml 5000

0 0 2 4 6 8 10 Time (days)

Fig. 4.- Evolution of Synechococcus (A) and Picoeukaryotes (B). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ).

106 Chapter III

Bacterial abundance. Initial bacterial cell numbers were 1.2 x 10 6 cells ml -1 (Fig. 2B). There was an initial peak at time 2-3 days in all treatments, which was much larger in treatments P, U and Si than in the control. A second peak at times 6-8 did not occur in the control. This peak was higher in the U treatment than in the Si and P treatments. There were very signicant differences (one-way ANOVA P<0.01, Table 2) between controls and amended tanks at the beginning (time 3) of the experiment

Bacterial production. All the treatments presented an increased at days 2-3 but no signicant differences within the amended tanks were found (Fig 2C), at the beginning they were all very different from the control tank. While the Si treatment maintained a high value al through the experiment, the P treatment decreased at time 3, and the U treatment at time 7.

Heterotrophic nanoagellates. They were determined by Lysotracker staining. The numbers generally followed the changes in bacterial abundances with a time lag, and HNF reached highest densities always at least 1 day after the peak of bacterial abundances (Fig. 2D). The rst maxima occurred at day 3-4, and a second peak at day 9. In the rst peak there were not strong differences between treatments, with a slightly higher value for treatment Si. The second peak was higher for treatments Si and U.

Bacterial single-cell activity. All the amended treatments presented a signicantly (<0.05) higher number of respiring cells compared to the controls (Fig. 5A, Table 2). The U tanks show the highest number of respiring cells, followed by the Si and then the P tanks. The %NADS, which is another indicator of the physiological stage of the bacteria, did not show differences between treatments except for the controls which decreased from time 4 towards the end of the experiments (Fig. 5B). Differences between treatments were also observed by measuring the % of high nucleic acid containing bacteria (Fig. 5C), which showed almost exactly the same pattern as the amount of CTC+ cells.

Bacterial community composition and estimates of diversity. DNA samples were collected at day 0, 3, 6, 8 and 10. The analysis of the bacterial DGGE ngerprints (Fig. 6A) by the Ward’s clustering method, (Fig. 6B) and that of the MDS analyses (Fig. 6C). show similar results: the samples were organized by time rather than by treatment, thus indicating that the internal evolution in the tanks superseded the effects of the additions. The one-way ANOSIM analysis of similarity showed that the

107 Bacterial response to different types of phytoplankton

Ka Kb 95 Pa Pb Sia A B 6 Sib 10 Ua 90 Ub ) -1 85

80 CTC (cells ml % Live cells ( NADS)

10 5 75

70 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) 90 Time (days) C 80

70

60 % HDNA 50

40

30

20 0 2 4 6 8 10 Time (days)

Fig. 5.- Measures of single-cell bacterial activity: CTC-positive cell abundance (A), % of NADS- determined live cells (B) and %HNA (C). Treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( ). Points used for the statistical analyses are indicated with arrows.

108 Chapter III samples from treatments clustered together by timing (R= 0.517, P: 0.003) and were different from the control samples (R= 0.549, P: 0.002). The Shannon-Weaver diversity index for phytoplankton did not change along the experiment in any of the treatments except for the Si tank where it decreased drastically from time 2 towards the end (Fig. 7A). Bacterial diversity (estimated from the DGGE) had a similar magnitude than phytoplankton (ca. 2 bits), and showed non-signicant differences between treatments (Fig. 7B).

Table 2.- Results of one-way ANOVAs done at every time point when DNA samples for the DGGE analysis were taken. N = 2 for all the mesocosm experiments. Signicant (P< 0.05) effects are in bold. Treatments with the same letters are not signicantly different from each other.

T3 T6 T8 T10 Chlorophyll a P 0.011 0.002 <0.0001 0.057

U AB B B A Si A A A A P B A C A K B A C A

Bacterial abundance P <0.001 0.032 0.003 NS

U A A A A Si A AB AB A P A AB BC A K B B C A

Bacterial production P 0.004 0.00 5 0.008 0.012

U A AB B AB Si A A A A P A BC B B K B C B B

CTC + cell abundance P 0.009 0.011 0.001 NS

U A A A A Si A A A A P A AB B A K B B B A 109 Bacterial response to different types of phytoplankton ) -1 ) U (cells L -1 ) Si (cells L -1 ) (cells L P -1 K (cells L Phytoplanktonpopulations at T7 (at the peak of the bloom) In parentesis are the abundance percentage of each group. The “Others” group The “Others” group of each group. the abundance percentage are Phytoplanktonpopulations at T7 (at the peak of bloom) In parentesis Dinoaglelates Diatoms 2290 (5.7%) Cocolithoforids Nanoagellates 3060 (7.7%) 16340 (41.3%) 4350 (2.8%) Others 17820 (45%) 5854 (3.76%) 66708 (43.8%) 10318 (6.8%) 70821 (46.5%) (74%) 114783 40(0.1%) 4852 (3.1%) 8141 (0.04%) 29646 (19%) 20,000,000 (99.5%) 5970 (0.03%) 201722(74.5%) 9861 (3.64%) 180 (0.1%) 82380 (0.41%) 360 (0.2%) 5990 (2.2%) 52155(19.3%) 60 (0.0%) 829 (0.3%) include the rest of phytoplankton populations which are not represented in the previous groups. groups. in the previous not represented of phytoplankton populations which are include the rest Table.3.- In situ

110 Chapter III

A

K P Si U K P Si U U 2º K P Si U K P Si U T0 T3 T6 T8 T10

B C Transform: Square root Resemblance: S17 Bray Curtis similarity

2D Stress: 0.16 Treatment T0 10 K K T3 3 P 3 K T6 Si P T3 U 6 3 K T8 6 T3 6 6 Si T3 8 U T3 K T10 8 P T8 8 U T8 3 Si T8 10 6 P T6 10 Si T6 0 U T6 8 U2º T6 P T10 Si T10 U T10 10

0 50 100 150 200 250 300 Linkage Distance

Fig. 6.- A) Denaturing Gradient Gel Electrophoresis (DGGE) of bacterial 16S rRNA gene fragments from samples of different treatments and at different times of incubation. Two different gels are presented, and one lane (U t6) is presented in both gels. B) Dendogram classication (Ward’s Method, Euclidean distances according to the band pattern) of DNA samples. (C), Nonmetric multidimensional scaling (NMDS) of the bacterial assemblage composition data.

111 Bacterial response to different types of phytoplankton

DISCUSSION

By adding different nutrients to a NW Mediterranean plankton community, we expected to generate differences in phytoplankton biomass levels and in phytoplankton community structure and diversity. And, as a consequence, we expected to generate changes in bacterial biomass, in bacterial function, and in bacterial community structure and diversity. While we did observe the changes in phytoplankton biomass (Fig. 2) and community structure (Fig. 3), we only observed strong changes in bacterial function (Fig 2C), while we observed little changes in bacterial abundances (Fig. 2B) and bacterial diversity (Fig 7). And bacterial community structure was seen to vary mainly following time, and much less following the different nutrient additions (Fig. 6).

Our study area (NW Mediterranean) is known to be phosphorous-limited mainly during the summer (Thingstad et al. 1998, Pinhassi et al . 2006). As the experiment was carried out in June, and to avoid P limitation, all treatments except for the control (K tank) were amended with phosphate

(PO 4) at 1 µM nal concentration. In one tank we added only this phosphorous (the P tank) where we expected the development of picoeukaryotes and small algae. In another treatment urea was added as an organic nitrogen source (U tank). Our interest in urea addition was because it has been suggested that urea enrichment should preferentially lead to dominance by Cyanobacteria , picoeukaryotes and dinoagellates rather than diatoms (Gilbert et al. 2008 and references therein). The last treatment

(Si tank) had NO 3 additions besides the phosphate addition to ensure nutrient sufciency, and also received SiO 4 to promote diatoms dominance, as is well known that they need silica for growth (Allen et al. 2005).

Indeed, diatoms developed in the Si tank, but they also did so in the other treatments specially in the rst part of the experiments. This is clearly seen from the decreases in Si concentration in the tanks

(Fig 1B). Phosphate was rapidly consumed in the tanks, which had some N sources (urea or NO 3) but remained mostly unused in the P tank probably because of the lack of a N source. This indicates that a simple nutrient addition was not enough to promote the selection of a specic algal group as there was co-limitation and the added nutrient was in most cases not fully used (Fig. 1).

The Chla plot shows the dramatic differences between treatments caused mainly by the Si treatment, which reached around 14 fold higher concentrations than the rest of treatments. In this case the sample phytoplankton diversity decreased strongly (Fig. 8). In the U treatment dinoagellates increased towards the end (Fig. 5), and Synechococcus reached the higher values from time 5 to time 9 compared to the other treatments. In contrast, phytoplankton diversity in the K, U and P tanks

112 Chapter III did not show much differences throughout the experiment. In summary, we did induce changes on phytoplankton species by the different additions, but those were particularly clear in the Si treatment, while the P and U treatments evolved rather similarly, with little clear-cut differences.

In terms of bacterial abundance and bacterial activity measured as bacterial production and CTC+ cells, at time 3 there were signicant differences (One way ANOVA P<0.005) between the control tanks respect to the others (Table 2), a further demonstration of bacterioplankton P limitation in the NW Mediterranean. We believe this strong limitation, well known from other studies (Sala et al. 2002, Pinhassi et al. 2006, Thingstad et al. 1998 and references therein) is what drove mainly the initial evolution of the bacterial community, which reached a rather similar peak at times 2-3 (Fig. 2B), and presented changes in community structure that were very similar between treatments from time 0 to time 3 (Fig. 6). Thus, this suggests that the bacterioplankton rst of all responded to the nutrient addition they were mostly dependent, i.e. P. Afterwards, HNF developed (Fig 2C) and depleted bacterial communities which, only after that situation became differentiated, with differences between treatments in abundance and activity. For example, the differences in CTC + cells were caused by the U and Si tanks, but in bacterial production only the Si tank differs from the rest.

The DGGE technique has been widely used in comparative microbial ecology to assess the diversity of microbial communities and their response to changing environments (Labbé et al. 2007, Castle et al. 2006, Macnaughton et al. 1999). The ordination of the samples by non-metric multidimensional scaling (NMDS) based on the DGGE gel band pattern agreed with the clustering results by Ward’s Methods using Euclidean distances. These analyses show that the samples from amended tanks cluster together due to different incubation times. The ANOSIM analyses determined a quite good clustering of samples by the factor of time (R= 0.517, P:0.003). However the control samples had an independent evolution (R=0.549, P:0.002). Thus, in spite of the observed changes in phytoplankton biomass and diversity, bacteria showed much less changes. We believe, mostly driven by the initial P limitation and posterior selection by agellate predation.

In fact, the small changes detected in the community structure analysis at time 3 (Fig. 6) corresponded with some changes in the Shannon diversity index, which were lower in the K and P treatments at time = 3 (the rst bacterial abundance peak), and then also slightly lower in the U treatment during the second bacterial peak (times 6-8). Unfortunately, we had no replicates of these measurements, and no proper statistical tests can be run. But the lower diversity values in the U plot during the second peak suggest some specialization of bacterial composition, in agreement with Schäfer et al. (2001) that found that the DGGE patterns indicated that the addition of nutrients not only had a quantitative

113 Bacterial response to different types of phytoplankton effect on bacterial community dynamics (higher bacterial cell counts and production, Lebaron et al 1999), but also had a qualitative effect on the development of the bacterial composition.

It is interesting to mention here that the maximum Shannon diversity values were seen in the Si treatment, simultaneous to the minimum phytoplankton diversity values (Fig. 7). Although the DGGE- based estimates of richness are subject to polemics (Bent et al. 2007, Danovaro et al. 2007) some authors think they are as good (or as bad) as any other estimate, since accuate richness estimates are impossible (Pedrós-Alió 2006). There are two implications to this observation: one is that in ecology it is commonly believed that diversity is a property that applies to all parts of the ecosystem, and that by measuring diversity of a group of organisms we are estimating diversity of all other groups. Our data boot work againsts that hypothesis. Secondly, it is perfectly possible that an evolving diatom bloom, in which the blooming species age, has different associated bacterial communities, depending on whether the bloom is young and growing, or old and decomposing, each with different bacterial communities of similar richness.

Si K Phytoplankton richness Bacterial richness 2,5 U P 2,5 A B 2 2

1,5 1,5

1 1 Shannon-Weaver Index Shannon-Weaver Shannon-Weaver Index Shannon-Weaver 0,5 0,5

0 0 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days)

Fig.7.- Shanon-Weaver diversity index of the (micro)phytoplankton (A) and bacteria (B) in the different treatments: K tank ( ), P tank ( ), Si tank ( ) and U tank ( )

114 Chapter III

HNF are assumed to be the primary grazers of bacteria. Thus, an important effect of HNF on bacterial community structure is expected. Lebaron et al. (1999) suggested that nutrients and grazing play a key role in shaping the morphologic, genotypic and phenotypic composition of bacterial communities. The pattern of the HNF abundance in our experiment was the same as that of bacterial abundance pattern but with one day lag (Fig. 2D), and also the bacterial biomass increase during the second peak was follow by increases of HNF biomass. Since bacterial abundance and activity parameters showed strong differences between treatments in the second bacterial peak, and even the Shannon index showed differentiation of the samples in the second peak, grazing pressure seemed to be important in the shift of the bacterial assemblage.

Many mesocosms experiments have been performed to investigate the effect of nutrient addition on marine planktonic communities in the Mediterranean Sea (Lebaron et al . 1999, Schäfer et al. 2001, Olsen et al . 2006, Allers et al. 2007) The input of either phosphorus or nitrogen has often been reported to be signicant in the Mediterranean, particularly during stratication periods (Eker-Develi et al . 2006). Recent studies in the NW Mediterranean have found differentiation in the bacterial composition due to different nutrient addition (Schäfer et al . 2001 and Pinhassi et al. 2004) and the effect of grazing on bacterial biomass (Lebaron et al. 1999). Our results concur with Allers et al. (2007) that a combination of bottom-up and top-down factors controls bacterial succession and growth in these type of experiments. We observed that the initial P decit of the bacterial communities strongly determined the evolution of the communities after nutrient additions. After an initial very similar bacterial bloom, depleted by HNF grazing activity, however, we observed that the different nutrients added did modify slightly bacterial community structure and function. The changes observed were more clear in the Silica treatment, as the addition of all potentially limiting nutrients (Si, P, N) allowed the development of a clear phytoplankton bloom, with high chlorophyll and low diversity values. But even during this bloom we observed an evolution of bacterial diversity probably shaped by the evolution of HNF predators. The bacterial community then, seems to be controlled by the top-down factors in contrast to the bottom-up factors that control the initial bacterial bloom as the differences between treatments in bacterial abundances and activity were not detected until after theHNF grazing pressure forced changes.

ACKNOWLEDGMENTS

This work was supported by the NoE Marine Genomics Europe and MARBEF, and by Spanish project MODIVUS (CTM2005-04795/MAR). We thank Vanessa Balagué, Irene Forn, and all the people participating from the Blanes Bay sampling program, particularly Montse Sala, Evaristo Vázquez-Domínguez and Cristina Rodríguez for various help. 115 Bacterial response to different types of phytoplankton

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118 CHAPTER 4

Effects of Saharan Dust deposition on marine microbial abundance, activity and bacterial community structure and ecosystem function

Itziar Lekunberri , Thomas Lefort , Estela Romero, Evaristo V ázquez-Dom ínguez , C èlia Marras é, Francesc Peters , Markus Weinbaue r and Josep M Gasol Nothing in life is to be feared. It is only to be understood

Marie Curie Chapter IV

ABSTRACT

The Mediterranean coast receives large inputs of Saharan dust. Due to its nutrient content, dust has a potential fertilization effect on this oligotrophic environment. The aim of this paper was to evaluate the effect of dust depositions on bacterial abundance, activity and community structure, and on the metabolic balance on the microbial community. In order to assess this goal we performed a 10 day mesocosms experiment with additions of realistic dust concentration (0.05 g L -1 ). Due to the known P limitation of our study area, we included a P amended tank to distinguish the effect of dust alone from that of the phosphorus contained in the dust. To determine the saturating concentration of dust we included a higher dust concentration tank (0.5 g L -1 ). We found that dust increased the initial water phosphorus concentration in 0.3 μM and the DOC concentration in 14 μM. Dust addition increased bacterial abundance (1.8 fold) and bacterial production (5 fold) thus creating higher growth rates at the initial times of the incubation. Primary production and community respiration were stimulated by dust and by P, but the net result of the addition of low amounts of dust was a heterotrophic system, while the net result of the high dust and P additions were net autotrophic communities. Bacterial community structure changed little between the P-addition and the 0.5 g L -1 dust addition, but the resulting communities were different from the control and from the high-dust addition communities.

121 Effects of Saharan dust on bacterial activity and community structure

INTRODUCTION

Coastal microbial communities are subject to external forcing events, such as the inputs of allochthonous nutrient from rivers, coastal runoff, submarine groundwater discharge, resuspension of benthic particles, etc. These allochthonous inputs contain organic and inorganic nutrients, which affect microbial diversity and function (e.g. Wassmann & Olli 2004, Markaki et al . 2003, Bonnet et al. 2005, Pulido-Villena et al. 2008). Among these sources of allochthonous nutrients to the coastal ocean, atmospheric transport is a particularly important type (Herut et al. 1999). Wind-transported particles can settle by wet deposition associated with rain, and this has been shown to be an effective mechanism of dust deposition in the western Mediterranean Sea (Prodi & Fea 1979, Guerzoni et al. 1997, Loyë-Pilot & Martin 1996). The Sahara and the Sahel are, by far, the largest source of dust particles (Prospero et al . 2003): they are responsible for more than half of the world’s mineral dust emission (Perez et al. 2007) and the Mediterranean Sea, because of its closeness to this area, receives large inputs of Saharan Dust (Löye-Pilot & Marin 1996, Guerzoni et al. 1999). Most of the SD (Saharan dust) deposition events in the coastal NW Mediterranean occur during late spring and early summer (Guerzoni et al. 1999, Ridame & Guieu 2002), when “dust rains” are common.

The Mediterranean Sea, and particularly the NW Mediterranean, are known to have phytoplankton and bacterial communities which are P-limited, particularly during the summer season (Thingstad et al . 1998, Pinhassi et al. 2006), and since SD has been seen to contain bioavailable phosphorus (Herut et al. 2002), SD deposition events can stimulate the activity and the production of P-limited microorganisms. In particular, primary production has been seen to respond to SD inputs (Baroli et al. 2005, Bonnet et al. 2005, Lenes et al. 2001, Herut et al. 1999, 2002, 2005, Ridame & Guieu 2002). However also bacterial abundance and activity have been seen to respond to SD enrichments (Herut et al. 2005, Pulido-Villena et al. 2008). A priori, one could speculate that bacteria should be stimulated after algae would be stimulated rst by the nutrients contributed in the deposition.

But SD contains nutrients other than P, particularly N and Fe (Jickells et al. 1995), and is also known to be also a source of organic carbon to the aquatic ecosystems (Pulido-Villena et al. 2008). Heterotrophic bacteria are often the best competitors for phosphorus uptake (Currie 1984), and the simultaneous addition of DOC and P might shift the competition for P to be won by bacteria (Thingstad et al. 1999). Thus, it could be hypothesized that the processes driven by heterotrophic bacteria would be more stimulated by a SD event than the autotrophic processes. SD events would, thus, have the capacity to shift a system towards net heterotrophy by stimulating bacterial respiration more than primary production. Bacterial respiration has been seen to be stimulated by P additions (Obernosterer

122 Chapter IV et al. 2003) and the addition of DOC, presumably of low quality (one can pressume the DOC travelling in the SD is not very labile), in the presence of enough inorganic P but no other sources of labile DOC, could force bacteria to respire most of the low-quality C, thus greatly increasing community respiration. In fact, Pulido-Villena et al. (2008) observed that a natural dust deposition event of 2.6 g m -2 induced a 1.5-fold increase in bacterial abundance and a 2-fold increase in respiration, while experimental dust additions (between 2 and 20 g m -2 ) also stimulated abundance and respiration (1.5-3 fold increases). These authors, however, did not specically compare the heterotrophy stimulation caused by the dust with the simultaneous expected stimulation of primary production (as found by others, e.g. Herut et al. 2005). Thus, while SD inputs are expected to stimulate both bacterial and phytoplanton function, it is uncertain whether the input will shift the metabolic balance of the community.

Addition of P and DOC from the SD, but also changes in phytoplankton community structure caused by the fertilization (e.g. Pinhassi et al. 2004), can affect the structure of the bacterial assemblages. It is particularly interesting to understand whether it is the P or the DOC component of the SD what inuences in a larger degree community structure, and whether there are phylotypes particularly stimulated by the SD pulses. In addition to the inputs of nutrients associated with dust, viable bacteria can be transported long-distances (Kellogg & Grifn 2006), with unknown effects on the resident bacterial community structure.

We designed an experiment to i) conrm the stimulation of primary production (PP) and bacterial function caused by additions of SD to a P-limited oligotrophic plankton community, ii) tell apart the effects of the fertilization by the P contained in the SD, from the effects of the other components of the SD, particularly the bioavailable DOC, iii) test whether SD additions would shift the microbial community to autotrophic or heterotrophic net metabolism, and iv) see whether SD fertilization would alter bacterial community structure in a way fundamentally different from the changes caused by P fertilization alone. We added dust to a series of microcosms at an “experimental” concentration of 50 mg l -1 and used two “controls” on top of a no-addition treatment: one with inorganic P alone at the same concentration that in the SD addition, and one with a much higher SD addition to observe the effects of SD in a saturation pulse of the same magnitude as found sporadically in the Mediterranean (Loyë- Pilot & Martin 1996). Furthermore, as the dust depositions in this area are due to storms (Ridame & Guieu 2002), we added a turbulence treatment to discriminate the effects of the associated water movement from the effects of SD alone.

123 Effects of Saharan dust on bacterial activity and community structure

MATERIALS & METHODS

Experiment setting. The experiment was carried out with water collected in Blanes Bay, NW Mediterranean (The Blanes Bay Microbial Observatory, 41°40’0“ N, 2°48’0“ E) the 16 May 2006. Eight tanks of 7.5 L were used for the experiment lled with seawater ltered by 150 μm. Of the 8 tanks, four were established as controls, four received a low dust concentration (0.05 g L -1 ), two received a high dust concentration (0.5 g L -1 ) and two received an inorganic phosphorus addition. Half of the tanks were incubated with turbulence, and the other half without. While tank replicability was ensured by using two replicates per condition in half of the treatments, space constraints limited the possibility of establishing 2 replicates of all treatments. The incubation was done at in situ temperature (17ºC), and the experiment lasted for 8 days. At the end of the experiment 66% of the volume still remained in the tanks.

The Saharan dust used in the experiment was collected during an intense dust storm over Villefranche-sur-Mer, France (43°42’18“ N, 7°18’45“ E) on February 21, 2004. A hard plastic tray was exposed 1 m above the ground to the rain (during ca. 24 h). The tray had 4 cm high side walls and the dust accumulated at the bottom of the tray. Several hours after the rain had stopped, the supernatant was poured off and the dust was transferred with a plastic spatula into an acid-washed Nalgene high-density polyethylene (HDPE) bottle. The dust was then dried at 60ºC. The dry dust was grinned before a single initial addition to the experimental tanks. Saharan dust deposition events carry between 5 and 8000 mg of dust per litre (Ridame & Guieu 2002). A 0.05 g L -1 dust addition released 3- -1 0.38 ± 0.08 µmol PO 4 L (Romero et al. in prep.), and this value was used as our low addition level (DL) and a 10x higher value (0.5 g L -1 ) as our high addition level (DH). P treatments were enriched 3- with inorganic PO 4 at a concentration of 0.5 µM, again as a single initial addition. This was estimated to be the amount of P that we would add with the Saharan Dust, and would act as a control of the effect of a P addition without the rest of components of the Saharan Dust.

Small-scale turbulence was generated with vertically-oscillating grids with the mechanical device described before (Peters et al. 2002). We used a turbulent kinetic energy dissipation rate of 10 -2 cm 2 s-3 . This value is within the range of turbulence intensities in coastal areas (Kiørboe & Saiz, 1995). We applied turbulence only during the rst three days of the experiment, a typical duration of turbulence events of the applied mean intensity in the Blanes area (Guadayol & Peters, 2006). Thus, containers corresponding to turbulence (T) treatments underwent turbulent conditions for three days and remained still until the end of the experiment, whereas still (S) treatments were kept still all along. Temperature was adjusted to the in situ water temperature (17ºC) and light conditions were set to 225 µmol photons m-2 s -1 inside the containers. The light:dark cycle was also adjusted to that of the time of the year. The experiment started within 3 to 4 hours of water collection.

124 Chapter IV

Chlorophyll a and nutrients. Samples (100 ml) for estimating chlorophyll a concentration (Chla) were ltered through Whatman GF/F lters of 25 mm diameter, which were immediately frozen until extraction. Filters were then placed in 90% acetone at 4ºC for 24 h and the uorescence of the extract measured using a Turner Designs uorometer (Yentsch & Menzel 1963). Total phosphorus was determined by wet oxidation and colorimetry following standard protocols (further details in Romero et al. in prep). For analysis of DOC concentration, ltered samples (20 ml) were acidied with 16 mM HCl (nal conc.) in acid-clean polypropylene tubes and stored at 4°C until analysis. DOC was measured with a high temperature carbon analyzer (Shimadzu TOC 5000).

Organism abundance. Bacterial abundance and the percentage of high nucleic acid cells were measured by ow cytometry (Gasol & del Giorgio 2000). Samples were run in a Becton-Dickinson FACSCalibur cytometer after staining with SybrGreen (1:10,000 nal concentration, Molecular Probes), and bacteria were detected by their signature in a plot of side scatter (SCC) vs FL1 (green uorescence). Regions were established on the SSC versus green uorescence plot in order to discriminate cells with HNA (high nucleic acid) content from cells with LNA (low nucleic acid) content. In the same SybrGreen stained samples, we were able to enumerate the concentration of dust particles (see below). Cell abundance was determined for each subgroup. Bacterial biomass was calculated from abundance assuming a carbon content of 12 fg C cell -1 (Fukuda et al. 1998).

Live samples were analyzed in a FACSCalibur ow cytometer (Becton Dickinson) in unstained aliquots for the enumeration of picophytoplankton populations. Milli-Q water was used as sheath uid and 1-µm yellow-green latex Polysciences beads were used as an internal uorescence standard. The samples were run at the highest possible speed (around 60 µl min -1 ) for 5 min, and the data were acquired in log mode. Abundances were calculated by the ratiometric method from the known amount of added beads, calibrated daily against TrueCount (Becton & Dickinson) beads. Synechococcus were detected by their signature in a plot of orange uorescence (FL2) vs. red uorescence (FL3), picoeukaryotes (Peuk) had higher FL3 signals and no FL2 signals. No Prochlorococcus were present at the time of sampling.

Single-cell bacterial physiology. We used the activity probe CTC (5 cyano-2,3 ditolyl tetrazolium chloride, Polysciences) to enumerate highly actively-respiring prokaryotes, as an indication of the number of very active cells. Two 0.5 ml sample aliquots were spiked with 5 μM nal concentration of CTC from a freshly prepared stock solution (50 mM in Milli-Q water) and incubated for 90 min in the dark, at in situ temperature. The samples were immediately run through the FACSCalibur ow cytometer. An additional 0.5 ml aliquot was xed with paraformaldehyde at time 0, and spiked with 5

125 Effects of Saharan dust on bacterial activity and community structure mM nal concentration of CTC for a background control of CTC uorescence on dead samples. CTC- formazan is excited by wavelengths between 460 and 530 nm and has bright red uorescence. CTC+ particles were those that showed red uorescence (above 630 nm, FL3 in our instrument), higher than the background uorescence level (Gasol & Arístegui 2007). A dual plot of 90° light scatter and red uorescence was used to separate CTC+ cells from background noise. The FL2 (orange uorescence) vs FL3 (red uorescence) plot was used to differentiate the populations of photosynthetic microbes from the CTC+ particles.

Bacterial production. Bacterial heterotrophic production was estimated using the 3H-leucine incorporation method (Kirchman et al. 1985). Triplicate or cuatriplicate aliquots of 1.2 ml were taken for each sample and one or two TCA killed controls. The Leu tracer was used at 40 nM nal concentration in incubations of approximately 2h. The incorporation was stopped with the addition of 120 µl of cold TCA 50% to the samples and, after mixing, they were kept frozen at –20ºC until processing, which was carry out by the centrifugation method of Smith & Azam (1992).

Community metabolism. For primary production, two light and one dark acid-cleaned polycarbonate bottles were lled with 160 ml water samples from each sample, inoculated with ~3.7 10 5 Bq (10 µCi) of 14 C-bicarbonate (VKI, Denmark). Incubations were done in the same chamber where the main samples were incubating, and with the same light levels, and lasted for ~2 h at in situ temperature. Samples were then ltered onto Whatman GF/F lters (25 mm diameter). Filters were placed in 6 ml vials and fumed overnight with HCl 35%. Finally, 4.5 ml of ReadySafe liquid scintillation cocktail were added before determination of disintegrations per minute (dpm) in the laboratory by means of a Beckman LS6500 liquid scintillation counter. Dark bottle dpm were subtracted for correction of non- photosynthetic 14 C xation. No isotope discrimination factor was used. Conversion to carbon units was performed assuming an ambient inorganic C concentration of 25000 mg C m -3 .

For community respiration, we followed the changes in dissolved oxygen during dark incubations of unltered water. Eight BOD bottles were carefully lled, and three replicate bottles were immediately xed with Winkler reagents to determine the initial oxygen concentration. Four replicate bottles were incubated in the darkness at in situ temperature and xed with Winkler reagents after approximately 24 hours. Dissolved oxygen measurements were made with an automatic titrator (DL50 Graphix, Mettler Toledo) based on potentiometric endpoint detection (Outdot et al. 1988). The rate of respiration was determined by regressing the oxygen concentration against the time in which the samples were withdrawn. This estimation assumes that the disappearance of oxygen was linear (Model I regression) and the slope of the regression is equal to the respiration rate. We assumed

126 Chapter IV a respiratory quotient of 0.88 (see further details in Alonso-Sáez et al , 2008). Since, on an annual basis, bacterial respiration at the site is 71±12% of community respiration (Alonso-Sáez et al. 2008), we estimated bacterial respiration from the value of community respiration in order to obtain some estimates of bacterial growth efciency, which is the fraction of bacterial production over bacterial production+respiration.

Collection of community DNA. Microbial biomass was collected by sequentially ltering around 5 L of seawater through a 3 µm pore size polycarbonate lter (Millipore, 46 mm) and a 0.2 µm polycarbonate lter (Millipore). Microbial biomass was treated with lysozime, proteinase K and sodium dodecyl sulphate, and the nucleic acids were extracted with phenol and concentrated in a Centricon- 100 (Millipore). Nucleic acids were extracted by a standard protocol using phenol/chloroform (see details in Schauer et al. 2003).

Fingerprinting analysis. DGGE and gel analysis were performed essentially as previously described (Schauer et al. 2003, Sánchez et al. 2007). Briey, 16S rRNA gene fragments (around 550 bp in length) were amplied by PCR, using the universal primer 907rm and the bacterial specic primer 358f, with a GC-clamp. the PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from 40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer. DGGE gel images were analyzed using the Diversity Database software (BIO-RAD). A matrix was constructed for all lanes taking into account the relative contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix, we obtained a dendogram by UPGMA clustering method (Euclidean distances, Statistica 6.0). DGGE bands were excised, reamplied, and veried by a second DGGE. Bands were sequenced using primer 358f without the GC clamp, with the BigDye terminator cycle-sequencing kit (Perkin Elmer Corporation) and an ABI PRISM model 377 (v3.3) automated sequencer.

Non-metric multidimensional scaling plots (NMDS) were also constructed from the binary matrix. NMDS plots present data in an Euclidean plane (with dimensions of no special signicance) in which similar measurements appear close together. Each band pattern is a point in the plane and, by connecting consecutive points, changes in community structure can be visualized and interpreted (see van Hannen et al. 1999). Dendrograms and NMDS plots are a robust and widely used way of comparison of microbial assemblage structure.

127 Effects of Saharan dust on bacterial activity and community structure

RESULTS

General experimental considerations. Our low SD additions resulted in an increase in P concentration of ca. 0.3 µM above the 0.1 µM present in the water at the time of sampling (Table 1). In the DH (High dust) treatment the increase was ten times higher, up to 3.3 µM. Similarly, the addition of low dust (DL) concentrations enriched the background DOC concentration in ca. 14 µM (from 59 to 73 µM), while the DH treatment supposed an enrichment of slightly more than 10-fold, up to ca. 260 µM (Table 1). Maximum DOC recorded at Blanes Bay is ca. 170 µM (Pinhassi et al. 2006, Alonso-Sáez et al. 2008) and, thus, the DH treatment was well above the common values at the Bay. Total P at the Bay oscillates between 0.01 and 0.2 µM (Guadayol et al. unpublished)

It was possible to detect the Saharan dust particles by ow cytometry. The particles appeared in the cytograms that were used to enumerate bacteria by SybrGreen I (Fig. 1). They appeared as particles with high SSC (light scatter) and FL3 (red uorescence), but low FL1 (green uorescence) as is shown in the plots of FL3 vs FL1. The measured particles were present at ca. 3 x 10 6 particles ml -1 in the DH treatments, and at about 6 x 10 5 ml -1 in the DL treatments. Dust particles slowly settled or disappeared during the rst three days of the experiment and were absent at day 4. Perhaps counterintuitively, dust disappeared faster in the T than in the S treatments (Fig. 1).

Fig.1.- Representation of Dust particles measurements by ow cytometry : concentration (left side) and green uorescence (FL1) vs red uorescence (FL3) plot of a dust sample (upper panel) and control (lower).

128 Chapter IV

Table 1 .- Concentrations of total phosphorus ( µM) and of DOC (µM) at the start of the experiment (t0), but after addition of dust (SD) and inorganic phosphorus (IP), and after three days of incubation (t3). C stands for Control, DL for low dust concentration, DH for High dust concentration, P for Phosphorus addition, T for turbulence treatment, and S for Still treatment. When possible, average ± st.dev. of two replicated tanks.

Target concentrations Measured concentrations SD IP Total phosphorus (µM) DOC(µM) (g L -1 ) (µM) t0 t3 t(18h)

source seawater 0.095 ± 0.007 58.98 ± 0.65 CT 0 0 0.115 ± 0.021 CS 0 0 0.120 ± 0.014 DHT 0.5 0 3.34 264.84 ± 2.55 DHS 0.5 0 3.37 DLT 0.05 0 0.425 ± 0.021 73.17 ± 1.96 DLS 0.05 0 0.445 ± 0.007 PT 0 0.5 0.42 PS 0 0.5 0.47

Microorganism abundance. The experiment started with an initial chlorophyll a (Chla) value of 3 mg m -3 , which is one of the largest values that can be detected in the Bay (see Alonso-Sáez et al. 2008), and that indicates that we had sampled a bloom. The bloom decayed throughout the experiment (Fig. 2A), but did less so in the DL treatment. The P treatment reached values intermediate between those of the C and DL treatments, with a small bloom developing towards the end of the experiment. In the DH treatment, a bloom developed reaching values of up to 10 mg m -3 (Fig. 2B). Treatment was a signicant variable explaining the data (Table 2), particularly between treatments DH and C. Treatment DL maintained on average 1.5x the Chla in the control. Synechococcus , which were initially at 3 x 10 4 cells ml -1 disappeared from all treatments at similar rates (details not shown), while picoeukaryotes responded different to the different treatments: they maintained abundances and even increased slightly at the end of the experiment in the DL treatment (Fig 2C), while they decreased in the control. They reached a peak of 4 x 10 4 cells ml -1 in the DH treatment in days 3-4, and a peak of 2 x 10 5 ml -1 in the P treatments towards the end of the experiment (Fig. 2D). Average Picoeukaryote (Peuk) abundance was 2.7x in the DL treatment as compared to the C treatment (Table 2). Comparison of the evolution of Chla and Peuk abundance suggests that while the Chla peak at the end of the experiment can, in part, be assigned to the Peuk development, the one occurring in the DH treatment needs be assigned to cells larger than picoeukaryotes. Indeed, the responsible for this Chla peak in the DH treatment were nanoagellates and diatoms, mostly of genus Nitzschia (Romero et al. in prep).

129 Effects of Saharan dust on bacterial activity and community structure

Control- Still Dust (high) - Still Control - Turbulence Dust (high) - Turbuence Dust (low) - Still Phosphorus - Still Dust (low) - Turbulence A Phosphorus - Turbulence B 10 10

1 1

01234567 01234567 C D

5 10 5 10

4 10 4 10

01234567 01234567

Time (days) Time (days)

Fig.2.- Upper panels: Changes in chlorophyll a concentrations (mg m -3 ): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In phosphorus addition tank (white squares) and dust high concentration tank (black squares)in still and turbulence conditions (B). Lower panels: Picoeukaryotes abundances (cell ml -1 ) in the same treatments.

130 Chapter IV

Table 2. - Results of a One-way ANOVA with the time-integrated (for production) or time-averaged (for stocks) values for each treatment. Treatments with the same letters are not signicantly different from each other. The Post-hoc tests give the probability of DL treatments being different from the C treatments, and of the P treatmens from the DL treatments. The “factor” is the ratio between the time- integrated value of treatment DL to that of treatment C.

Chla total BA BP Peuk %HNA P/B

Global ANOVA P <0.001 <0.001 0.005 0.002 0.002 NS

Treatment CS D D D C D C A CT C D D C D D B C A DLS B C C A B A A DLT B C C A B B C A A DHS A B A B B A A DHT A A A B C B A A PS C D C D B C D A A B A PT B C C D A B C A A A

Post-Hoc tests

P Difference DL / C 0.001 0.004 0.002 0.007 <0.001 NS Factor (x) 1.5 1.8 2.3 2.7 1.4 - P Difference DL / P NS NS NS 0.01 NS NS

While heterotrophic bacteria decreased in the control tanks until day 3, and then increased towards the end of the experiment (Fig. 3A), they presented an initial peak in treatment DL at day 1, which was suppressed in day 2. The response in the DH treatment was much higher (> 10x in just one day, Fig. 3B), and the high bacterial concentrations lasted for three days after coming back to concentrations similar than those in the control treatment at day 4. In both, P and DH treatments, bacteria increased steadely from day 4 to the end of the experiment. The nal bacterial yield in the P and DH treatments was slightly higher (4-8 10 6 cells ml -1 ) than in the C or DL treatments (3-5 10 6 cells ml -1 ). Addition of dust (DL) increased bacterial abundance by a factor of 1.8 on average (Table 2).

The differences between treatments were higher in the physiological structure of the bacterial assemblage. The %HNA cells increased immediately in all treatments, particularly in the DL, except in the C (Fig 3C-D). Some effects of turbulence were obvious in this parameter, with %HNA being higher in the T replicates of the C, P and DL treatments. The number of actively-respiring bacteria

131 Effects of Saharan dust on bacterial activity and community structure

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Fig.3.- Upper panels: Bacterial abundance (cell ml -1 ): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In the phosphorus addition tank (white squares) and dust high concentration tank (black squares) in still and turbulence conditions (B). Lower panels: Physiological state measured by %HNA in the same treatments.

132 Chapter IV

Table 3. - Average concentrations of CTC+ cells, %CTC (over total bacteria), for days 1 to 3, and for days 4 to 7. Average values ± SE of all replicates and times. In the lower part, results of the Student’s t comparisons between treatments C (control) and LD (Low Dust), or between treatments P (Phosphorus) and LD (Low Dust). In this last case, separating Still from Turbulence treatments.

CTC+ abund %CTC Days 1-3 Days 4-7 Days 1-3 Days 4-7

CS 0.82 10 5 ± 0.20 1.91 10 5 ± 0.28 10.4 ± 2.9 7.7 ± 3.0 CT 0.94 10 5 ± 0.05 0.74 10 5 ± 0.12 10.9 ± 2.3 5.2 ± 0.7 DHS 49.30 10 5 ± 14.7 32.46 10 5 ± 0.05 45.7 ± 22.4 6.3 ± 0.9 DHT 51.22 10 5 ± 14.60 3.55 10 5 ± 2.29 29.3 ± 8.2 26.1 ± 17.1 DLS 3.85 10 5 ± 0.81 3.52 10 5 ± 0.85 26.8 ± 8.5 6.9 ± 1.1 DLT 6.80 10 5 ± 1.82 2.17 10 5 ± 0.25 30.6 ± 6.2 8.5 ± 2.0 PS 0.91 10 5 ± 0.19 1.49 10 5 ± 0.48 9.7 ± 2.1 4.0 ± 0.4 PT 2.24 10 5 ± 0.21 2.76 10 5 ± 1.07 16.2 ± 1.4 8.4 ± 2.4

Prob. diff DL / C P < 0.001 P < 0.001 P < 0.001 NS Factor (X) 6.02 2.15 2.69 1.20 Prob. diff DL / P Still NS NS NS NS Turb P < 0.001 NS NS NS

(CTC+) followed a pattern quite similar to that of the %HNA cells, with a clear difference between the DL and the C treatments (Table 3), a much larger response in the DH, and a relatively higher response in the turbulent replicates of the C, P and DL treatments. While the differences in %HNA between the C and DL treatments (Fig. 3) lasted for the whole incubation, those in the number or proportion of CTC+ cells were mostly apparent during the rst 4 days of incubation. Table 3 was split into two parts (times 1-3 and 4-7 days) to reect better these differences. In any case, the difference in the number of CTC+ cells was signicant between the C and DL treatments for both periods, and it was also signicant for the %CTC+ cells in the rst period (Table 3).

Bacterial activity. Dust stimulated bacterial production by a factor of ca. 5x, from the control treatment to that of the DL treatment (Fig. 4A, Table 2). The effect of the addition of P was more pronounced when turbulence was applied in the incubation (Fig. 4B), but a positive enhancement in the turbulent treatments was evident in almost all additions. Bacterial growth rates were higher

133 Effects of Saharan dust on bacterial activity and community structure

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Fig.4.- Upper panels: Bacterial production ( μg C L -1 ): In the control tank (white circles) and dust low concentration tank (black circles) in still and turbulence conditions (A). In the phosphorus addition tank (white squares) and dust high concentration tank (black squares) in still and turbulence conditions (B). Lower panels: Bacterial growth rate (d -1 ) in the same treatments.

in the DL treatment than in the C at days 2 and 3, but were similar later on (Fig. 4C). Since days 2- 3 are when bacterial abundances decreased, this could indicate the effects of grazing by agellates developing and feeding on bacteria at those times. The addition of a high amount of Dust (DH) did not promote bacterial production above the values created by the addition of lower amounts of dust (DL). It is possible that incorporation at the added 40 nM leucine was actually saturated in these samples that had, at the beginning ca. 10 7 cells ml -1 . The relatively low growth rates measured for the DH treatments at the start of the experiment (Fig. 4D), are not compatible with the large increase in abundances measured (Fig. 3B), supports this possibility, and suggests that the measured values of production and growth rates in the DH treatments might be unreliable.

134 Chapter IV

SD1 SD2 SD3 SD4 SD6 SD7 SD8 SD10

SD 11 SD12 SD13 SD14 SD15

CS CT DLS DLT DHS DHT PS PT DHS DHT PS PT

T0 T3 T7

Resemblance: S17 Bray Curtis similarity

2D Stress: 0.07

Tree Diagram for 13 Cases PS T3 Unweighted pair-group average Euclidean distances DLT T3

PT T7 DLS T3 PS T7 CT T3 DHT T7 DHS T7 PT T3 CS T3 PS T3 DLT T3 DLS T3 PS T7 DHT T7 PT T3 DHS T7 To DHS T3 DHT T3 DHT T3 DHS T3 CT T3 CS T3 PT T7 To

0 10 20 30 40 50 60 Linkage Distance

Fig.5.- Denaturing Gradient Gel Electrophoresis (DGGE) of bacterial 16S rRNA gene fragments from samples of different treatments and at different times of incubation (A). Dendogram classication (Ward’s Method, Euclidean distances according to the band pattern) of DNA samples. (B). Nonmetric multidimensional scaling (MDS) of the bacterial assemblage composition data (C).

Community metabolism. We measured primary production twice during the experiment, and community respiration once. Dust addition increased both, PP and CR (Table 4), the low dust addition by factors ranging from 1.6 to 3.6. The DH addition produced much larger increases, with factors of 6-15. In all cases the increases in metabolism caused by the dust were stronger for PP than for CR, thus turning the communities towards net autotrophy from a near-balanced initial community. There was a clear difference between the DL and the P treatments: P stimulated PP much more than DL, while CR was similarly stimulated by both treatments. The net result of the addition of DL was a heterotrophic system, while the net result of the DH and P additions were autotrophic communities. It is somehow difcult to compare to the controls since CR in the control-Turbulence treatment was highly stimulated.

135 Effects of Saharan dust on bacterial activity and community structure

Bacterial community structure. We ngerprinted bacterial assemblage composition by denaturing gradient gel electrophoresis (DGGE). The Nonmetric multidimensional scaling (MDS) and Ward’s clustering method were used in order to explore the similarities between the samples based on bacterial assemblage structure, and to compare the effect of the dust addition to those of phosphorus and control treatments. The P and DL communities after the addition clustered together and were distant from the C or DH communites. (Fig. 4). The control tanks were similar to the initial sample. There were some effects of Turbulence, more obvious at the end of the experiment (Time 7) than at time 3. We retrieved 13 bands that were dominant in different treatments (Table 5). All the sequences that could be retrieved from the DGGE gel were found to have similarities in GenBank to previously described organisms, with values > 97% (Table 5). Just one phylotype was dominant in all the treatments (SD2), four appeared only in phosphorus treatments, 3 only where Saharan dust had been added, and 3 in both P and Dust treatments.

DISCUSSION

The fertilizing nature of Saharan dust . The amount of SD that we added in our main experimental treatment, 50 mg L -1 was slightly higher than the doses used experimentally in a couple of recent studies (Herut et al. 2005, Pulido-Villena et al. 2008), but were within the limits of what can be expected to occur in the ocean. According to Ridame & Guieu (2002), 60% of ‘Saharan rains’ carry between 5 and 8000 mg of dust per liter. A 0.05 g L -1 dust concentration released, abiotically, 0.38 ± 3- -1 0.08 µmol PO 4 L (Romero et al. in prep) a value which is somewhat lower than the range reported in Ridame & Guieu (2002), where 0.05 g L -1 of dust released anywhere between 0.97 and 3.2 µmol 3- -1 3- PO 4 L . In the experimental tanks, however, we obtained ca. 3 µmol PO 4 per gram of dust (close to the yields reported by Pan et al. 2002, and Herut et al. 2005), which led to nal concentrations of 3- -1 3- -1 1.5 µmol PO 4 L and 0.15 µmol PO 4 L in the DH and DL containers respectively (Romero et al. in prep). This is an amount of P that increased the initial P concentration by a modest factor of 1.3 (Table 1) and, thus, addition of a smaller dust amount would most probably not have affected the microbial communities of the coastal waters we used for the experiment. This might contrast with the experiments of Herut et al. (2005) and Pulido-Villena et al. (2008), which used oceanic waters, initially more oligotrophic than the waters we used.

In spite of the fact that the P addition was relatively modest, it triggered clear effects in Chla development (an average factor of 1.5x, Fig 2), in picoeukaryotes development (an average factor of 2.7x), and in primary production (an average factor of 1.5x at day 3 and 2.7x at day 7, Table 4). That SD enhances primary production had been previously demonstrated in several experimental studies (Blain et al . 2004, Mills et al. 2004, Herut et al. 2005), and the yield we observed (on average 0.5 µg 136 Chapter IV

Chla per 0.05 g. dust, i.e. 50 µg g -1 ) is exactly the same yield observed by Herut et al . (2005) in the Eastern Mediterranean, but smaller than the observations of Blain et al. (2004) and Mills et al. (2004) in the North Atlantic. Enhancement of chlorophyll in the DH treatment was on the order of 10x the enhancement generated in the DL treatment (Fig. 2), which is consistent with the 10-fold difference in the addition. Since SD contains nutrients other than P (Jickells et al. 2005), the hypothesis that other limiting nutrients were the responsible for the primary producers enhancement cannot be disregarded and, in fact, the addition of P alone resulted in a smaller enhancement than the DL addition (Fig 2), although on average the difference was not statistically signicant (Table 2).

Bacteria were also stimulated by the dust addition: abundance (an average factor of 1.8x), production (factor of 2.3x), number of actively respiring cells (factors of 2-6x), and also the single- cell indications of cellular activity, such as the %HNA (factor of 1.4x) and the %CTC+ cells (a factor of 2x). However, bacterial growth rates were not statistically enhanced by the DL addition. The stimulation of bacterial heterotrophic activity resulted also in a stimulation of community respiration (at this site, BR is aprox. 70% of CR, Alonso-Sáez et al. 2008), by a factor of 1.6-2.4 (Table 4). Bacterial abundance and production were, thus, enhanced by a factor higher than the enhancement of chlorophyll a. P enrichment in the DL treatment was of a factor of 1.3x and DOC enrichment was also of a similar factor (1.24x). However, the background DOC concentration at the start of the experiment (59 µM, Table 1) is typically formed by mostly nonreactive C (Hansell & Carlson 1998). The 14 µM added with the DL treatment could indicate a large enrichment factor, although since we do not know the nature of this DOC, we can not conrm this observation except through its effects on bacterial abundance, single-cell activity and heterotrophic production.

A response of the heterotrophic community to the SD addition had already been observed by Bonnet et al. (2005) and Herut et al. (2005), even though they did not measure the DOC added. In these studies, as in our DL treatment, the stimulation of bacterial production was not followed by a proportional stimulation of bacterial abundance, something that they assigned to HNF development cropping the stimulated production. HNF reached a peak at day 3 in our experiment (Romero et al. in prep) and likely cropped most of the new production in the rst days of the experiment, but not after day 4 (Fig. 3), maybe because bacteria then developed grazing-defence strategies. Bacteria seemed to escape HNF checking in the DH treatments (Fig. 3B): the burst of growth caused by the large nutrient dose escaped grazer control.

The bacterial activity “yield” that we obtained was similar to that measured by Herut et al. (2005): they obtained ca. 75 pmol leucine h -1 per mg dust, and we obtained 70 pmol h -1 per mg dust. The bacterial abundance yield we measured (2 x 10 9 cells per 50 mg dust, in the DL treatment and 2 x 137 Effects of Saharan dust on bacterial activity and community structure

10 10 cells per 500 mg in the DH treatment) was consistent between treatments, but lower than the 1 x 10 9 cells per 2 mg measured by Pulido-Villena et al. (2008). In the DH treatment, however, leucine incorporation rates should had been 10x those we measured in the DL treatment and were similar, something that we think was caused because we added too low amount of leucine tracer for the enrichment we had generated.

Biogeochemical effects of SD: autotrophy vs. heterotrophy. Saharan Dust enhanced community respiration in our experiment, as it did in the experiments and natural observations of Pulido-Villena et al. (2008). These authors measured a 2x enhancement of bacterial respiration, while we measured a factor of 1.6-2.4x (Table 4) with a higher SD dose. Our results suggest that this enhancement had to do with the inorganic nutrient fertilization caused by the SD, but also with the DOC that was introduced with the dust. SD contains carbon in amounts that might easily reach 1% dw (Eglinton et al. 2002). This carbon originated in the past in vegetation res accumulated and stored in soil (Eglinton et al. 2002), and is thus, likely not to be very labile. Our results suggest some use of this carbon by the resident (see below) bacterial community.

We only measured respiration at the end of the experiment (Table 4), when respiration in the Still treatment was not signicantly different between the P and DL treatments, but was much higher in the DL treatment under turbulence. A similar result could be observed in the number of actively respiring cells (CTC+, Table 2), which were, under turbulence, much higher in the DL treatment than in the P treatment. In some studies the number of CTC+ bacterial cells have been seen to vary coherently with bacterial respiration (Smith, 1998) and increases in the number of CTC+ cells are usually associated to bursts in growth of bacteria (del Giorgio & Gasol, 2008).

The metabolic balance of the microbial community was altered by the dust addition in a way different from the alteration caused by P addition. P shifted the community to net autotrophic (nal CR/PP values of 0.4-0.5). In the DL treatment the values were net heterotrophic (1.47 ± 0.73 and 1.63 ± 0.52). The control treatment stayed close to 1 (0.95 ± 0.35) in the still treatment but became very heterotrophic under turbulence (see discussion in next section). Interestingly, the high addition of dust (DH, 0.5 g L -1 ) shifted the community towards net autotrophy while the DL treatment shifted it towards heterotrophy. In the DH treatment a large diatom bloom developed with Chla values of ca. 10 mg m -3 (see details in Romero et al. in prep). In contrast, the 1.5x increase in Chla measured in the DL treatment consisted in pico- and nanoeukaryotic cells. Apparently, at least from what can be extrapolated from our few data, a low addition of SD can shift the system to heterotrophy, but a larger input might trigger the development of diatom blooms that would turn the system net autotrophic.

138 Chapter IV

Table 4.- Values of community metabolism in the different treatments. When errors are present, they correspond to the standard error of two replicated microcosms.

Primary Production Community Respiration CR/PP (mgC m -3 h -1 )

Initial 5.02 4.66 0.93 Control – Still day 3 2.32 ± 0.23 day 7 1.24 ± 0.38 1.13 ± 0.10 0.95 ± 0.35 Control – Turbulence day 3 2.40 ± 0.28 day 7 1.04 ± 0.06 3.40 ± 0.11 3.27 ± 0.08 Dust (low) – Still day 3 3.64 ± 0.16 day 7 2.26 ± 0.78 2.74 ± 0.50 1.21 ± 0.73 Dust (low) – Turbulence day 3 3.25 ± 0.12 day 7 3.71 ± 1.16 5.42 ± 0.03 1.46 ± 0.52

FACTOR DL / C day 7-Still 1.82 2.42 day 7-Turbulence 3.56 1.59

Dust (high) – Still day 3 15.76 day 7 39.29 18.27 0.46 Dust (high) – Turbulence day 3 25.20 day 7 21.70 19.70 0.91 Phosphorus – Still day 3 1.55 day 7 6.21 3.08 0.50 Phosphorus – Turbulence day 3 2.13 day 7 8.98 3.62 0.40

FACTOR P/DL day 7-Still 2.75 1.12 day 7-Turbulence 2.42 0.67

139 Effects of Saharan dust on bacterial activity and community structure

The type of algae using the added P might explain this difference, as well as the possibility that some nutrient thresholds exist that shift the “winners” from picoeukaryotes to diatoms. Silica, which was known to be present in SD but with low availability (Bonnet et al . 2005) might be used by the autotrophic plankton when added at some concentrations. Indeed, we observed that the SD that we used contained silica, that this was released into the water, and that it was presumably used up by the microbial community in the DH treatment (Romero et al. in prep).

In this sense it is interesting to refer to the models of the microbial food web developed by Thingstad (in e.g. Thingstad et al. 2007). The models idealize competition for P between bacteria, agellates and diatoms. Addition of labile-DOC shifts the uptake towards the bacteria (and, thus, the system would turn net heterotrophic), while addition of silicate shifts it towards the diatoms (and the system would turn net autotrophic). We hypothesize that our DL treatment would be a situation of the rst type, while the DH treatment would be of the second type. The metabolic response of the plankton community to Saharan Dust additions would be concentration-dependent.

Pulido-Villena et al. (2008) presented evidence that, on top of the fertilizing nature of SD events that could increase primary production and C drawback to the ocean, the stimulation of heterotrophic bacteria could actually reduce the amount of C sequestration caused by these events. These authors calculated that the dust-induced bacterial growth could mineralize up to 70% of the annual bioavailable DOC annually exported to the deep Mediterranean. Our results concur with those of these authors in assigning a very relevant role to bacteria in the use of the nutrients accompanying the dust pulse, but suggest that the real metabolic effects are concentration-dependent, and might depend on the bioavailability of the P, Si and organic C attached to the dust, something that is currently very poorly constrained.

Interactions with turbulence. Our experiment was not designed to be a proper test of the effect of turbulence as an external factor modifying the response of he microbial community. We just wanted to create a situation likely to be close to that encountered by the plankton community at the time when a dust pulse is more probable. Perhaps counterintuitively, the applied turbulence levels facilitated dust sedimentation or dissolution (Fig. 1), instead of retarding it. This result is consistent with experimental work that shows that algae sediment faster under turbulence than under still conditions (Ruiz et al. 2004) and with work showing that at low levels of the shear caused by turbulence, the balance between aggregation and coagulation of particles of sizes 2-10 µm was dominated by coagulation (Colomer et al. 2005). Cytometric detection of dust was likely not complete since very small particles probably escaped detection by the ow cytometer (Fig. 1). In the SSC (a surrogate of particle size)

140 Chapter IV Phylogenetic afliation of 16S rRNA gene sequences from excised DGGE bands obtained in the different treatments. treatments. excised DGGE bands obtained in the different gene sequences from of 16S rRNA Phylogenetic afliation .- Table 5 Table Band ID# P group SD1 D X SD2 T - X SD3 Closest relative X - - SD4 - X - SD6 Uncultured Flavobacteria bacterium clone MS024-1F SD7 EF202335 X X - sp. Tenacibaculum Uncultured X 99% SD8 sp. Tenacibaculum - X sp. Tenacibaculum Uncultured - X Bacteroidetes SD10 X Polaribacter dokdonensis strain MED152 sp. Tenacibaculum Uncultured X X - SD11 sp. Tenacibaculum Access. # Uncultured cytophaga sp. - X - AY573521 SD12 % similarity Polaribacter dokdonensis strain MED152 AY573521 Uncultured Bacteroidetes Phylogenetic X - 100% X 98% AY573521 SD13 Uncultured Bacteroidetes - - Bacteroidetes Lewinella nigricans 99% - SD14 AY573521 Uncultured alphaproteobacterium EF416572 DQ481463 - X Lewinella nigricans - Bacteroidetes 99% 98% Uncultured alphaproteobacterium SD15 98% - AJ487533 X X Marinosulfonomonas methylotropha Bacteroidetes Uncultured alphaproteobacterium AB274770 AB266010 98% X - Roseobacter gallaeciensis 98% 96% Roseobacter sp. EF506911 AB266010 - Bacteroidetes Thalassobius sp. AY922243 99% Rhodobacteraceae bacterium Bacteroidetes 99% AY771769 AB301615 97% Antarctobacter heliothermus Ruegeria algocolus Alphaproteobacteria DQ446171 97% Bacteroidetes 92% AB301615 Ruegeria atlántica Alphaproteobacteria 98% 90% AJ867255 Alphaproteobacteria 96% AJ810845 Y11552 AY258088 99% EF587951 97% 97% Alphaproteobacteria X78315 97% Alphaproteobacteria AF124521 98% 99% Alphaproteobacteria For each phylotype, the closest relative in GenBank are shown, together with their indicate accession whether the phylotype was numbers detected in the P in and treatments, the D or treatments, whether sequence the band tended to similarity. be common more We in also the treatments. Turbulence

141 Effects of Saharan dust on bacterial activity and community structure vs. uorescence plots (cytogram not shown), dust particles appeared to range from sizes smaller than bacteria to >20 µm and, thus, we can expect the dust particles detected by the cytometer to be subject, most likely, to the same aggregation/coagulation regulation shown by Colomer et al. (2005).

We also found a systematic, but weak, positive effect of turbulence on Chla development (Fig. 2A- B). Experimental turbulence has been seen to enhance chlorophyll development in several laboratory studies (e.g. Arin et al. 2002, Pinhassi et al . 2004), and in non-enriched and slightly enriched waters of Blanes Bay particularly in spring (Guadayol et al. unpublish). Turbulence had more effect on the sample receiving P (Fig. 2B) and less effect in the control sample, something that was also seen in Arin et al. (2002). While Chla showed a positive enhancement by turbulence, picoeukaryote development (Fig 2C-D and Table 2) and bacteria (Table 2) were less affected. Shifts in food web structure and agellate feeding rates caused by turbulence could explain these differences (Peters et al. 1998, 2002). The most relevant effects on bacterial activity were again seen in the treatment that had received inorganic nutrients alone (P), which showed a large difference in leucine incorporation (Fig. 4) and in the number of CTC+ cells (Table 3). These effects resulted in relatively similar levels of autotrophic and heterotrophic metabolism at day 7 in the P treatment (Table 4), but in very strong differences in the control treatment, which respired more under turbulence than under still conditions (t-test, P<0.001). The DL treatment also respired more under turbulence than under still conditions (t-test, P<0.001), while the balance CR/PP was signicantly higher for the control under turbulence than for the still control (t-test, P<0.001). The metabolic balance difference was not signicant for treatment DL.

It has to be taken into account that turbulence was applied only from day 1 to day 3 and, thus, the difference reported in Table 4 occurred 4 days after turbulence had stopped. In the only study to date in which microbial community respiration has been studied under turbulence or still conditions, turbulence always generated a higher level of respiration, and a higher heterotrophic metabolic balance (Alcaraz et al. 2002). Other previous studies had not found effects of turbulence on respiration (Peters et al. 1998). Turbulence affected little bacterial community structure (Fg. 5), something that is compatible with the explanation in Pinhassi et al. (2004) that claimed that changes in community structure were linked to the changes in phytoplankton community structure caused by the turbulence and not to the physical stress by itself.

The potential for affecting bacterial community structure. We tested whether bacterial community structure was affected by the different treatments. In particular, we were interested to see whether the DL and P treatments were signicantly different. It has been speculated that airborne

142 Chapter IV bacteria can travel associated to Saharan dust particles (Grifn et al. 2001, Kellogg & Grifn 2006, Grifn 2007). If that were to be a relevant source of allochthonous bacteria able to develop in the ocean, we should see it in our analyses. Of course, if that were only a tiny contribution of inactive bacteria, we would not see it with DGGE, as this technique has a low resolution (picks up only <30 dominant phylotypes, Muyzer et al. 1997).

The main differences observed in community structure were those between the DH treatments and the DL and P treatments (Fig. 5), indicating that the evolution with time of the bacterial community caused by the low addition of SD was very similar to that caused by P alone. Even the high addition of dust didn’t seem to affect the number of bands that were detected (about 12-15 per sample).

Mainly alphaproteobacteria and bacteroidetes could be retrieved from the DGGE and identied. We did not retrieve any gammaproteobacteria, but this might be related to the fact that gammaproteobacteria, particularly Alteromonas , tend to develop at the beginning of these mesocosm experiments (Schäfer et al . 2001, Allers et al. 2007) and tend to be replaced by alphaproteobacteria at the end of these same experiments, particularly by Rhodobacteraceae of types similar to the SD11 and SD13 bands seen in our experiment.

Several of the Bacteroidetes that we retrieved were similar to Tenacibaculum and Polaribacter (Table 5). This group contains the genome-sequenced Polaribacter dokdonensis strain M152 (very similar to band SD2) which has been seen to contain a substantial number of genes for attachment to surfaces or particles, gliding motility, and polymer degradation (in agreement with the currently assumed life strategy of marine Bacteroidetes), but also the proteorhodopsin gene, which together with a remarkable suite of genes to sense and respond to light, may provide a survival advantage in the nutrient-poor sun-lit ocean surface when in search of fresh particles to colonize (González et al . 2008). Organisms of this group have been retrieved in culture, in DGGE bands from enrichment experiments, but are also appearing in situ in Blanes Bay (Lekunberri et al. submitted Chapter 1), something that make them part of the most interesting marine Bacteroidetes, as they grow in culture but at times dominate in situ. Some Tenacibaculum -like organisms (SD3 and SD4) appeared in SD additions, but the sequence closer to M152 (SD2) appeared in almost all experimental treatments.

Bacteroidetes of the Lewinella type (bands SD7 and SD8), relatives of Chitinophaga , are not common in the area (Lekunberri et al. submitted Chapter 1). They appeared only in the P treatment. Within the alphaproteobacteria identied, bacteria of the Ruegeria /Silicibacter cluster appeared in the D and P treatments at the end of the experiment (SD14 and SD15). These are common organisms

143 Effects of Saharan dust on bacterial activity and community structure in Blanes Bay, easy to isolate in plates, and common in mesocosmss experiments (Lekunberri et al. submitted Chapter 1). The other alphaproteobacteria identied (SD10 to SD13) were also commonly developing in mesocosmss experiments (Allers et al. 2007). In that study different types of sequences related to Rhodobacteraceae dominated the different treatments in nutrient-enriched mesocosmsss. Most of the treatments elicited small phytoplankton growth (the mesocosmsss were light-limited), and the development of picoeukaryotes. This might be the reason for the similitude of these sequences to those of Allers et al. (2007) and for the difference between the DL and DH treatments (Fig 5). As pointed out by Pinhassi et al. (2004), the type of algae developing in the system determined bacterial community structure.

Bands 3, 4, 6, 13, 14 and 15 were associated in some way with the dust addition, but most of them appeared also as faint bands in some P treatments (e.g. band SD15). Band SD2, which was very similar to band SD4, appeared in all treatments. It is, thus, unlikely that any of the bands identied can be considered as specic of the SD addition treatments, and the afliation of the bands also do not support this specicity. Bacterial community structure in the DL treatment was likely to be mainly generated by the P pulse, and the associated development of algae. The situation was probably slightly different in the DH treatment.

Conclusion

A small Saharan dust addition affected bacterial abundance, activity and diversity in our mesocosm experiments. It also shifted the metabolic balance of the community to heterotrophy, something that we assign to the DOC addition simultaneous to the P addition with the dust. However, a higher addition of dust caused a shift towards autotrophy. Bacterial community structure was affected simialrly by dust than by P alone. The results reinforce the potential for Sahara Dust inputs to affect bacterial diversity and function, but clearly indicate the need of knowing better the composition of the inputs, as the results encountered were, in part, dependent on the concentration of added nutrients.

ACKNOWLEDGEMENTS

We thank Raffaela Cattaneo and Òscar Guadayol for help during the experiment. We also thank V. Balagué, C. Cardelús and I. Forn for careful organization of sampling at the Microbial Observatory. This work was supported by Spanish projects MODIVUS (CTM2005-04795/MAR) and VARITEC (CTM2004-0442-C02), and is a contribution to the european Networks of Excellence Eur-Oceans and MARBEF. 144 Chapter IV

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Outdot CR, Gerard R, Morin P, Gningue I (1988) Precise shipboard determination of dissolved oxygen (Winkler procedure) for productivity studies with a commuercial system. Limn Oceanogr 33 : 146-150 Pan G, Krom MD, et al. (2002). Adsorption-desorption of phosphate on airborne dust and riverborne particulates in East Mediterranean seawater. Environmental Science & Technology 36 : 3519-3524. Pérez C, Jim énez P, et al. (2007). Long-term trends (1987-2006) of Saharan dust over the Mediterranean and the Canary Islands with the DREAM regional dust model. Geophys Res Abstr 9: 08525. Peters F, Marras é C, Gasol, JM, Sala, MM, Arin L (1998) Effects of turbulence on bacterial growth mediated through food web interactions. Mar Ecol Progr Ser 172 : 293-303. Peters F, Marras é C, Havskum H, Rassoulzadegan F, Dolan J, Alcaraz M, Gasol JM (2002) Turbulence and the microbial food web: effects on bacterial losses to predation and on community structure. J. Plank Res 24 : 321-331. Pinhassi J, Sala M, Havskum H, Peters F, Guadayol O, Malits A & Marras é C. (2004) Changes in bacterioplankton composition under different phytoplankton regismens. Appl Envir Microb 70 : 6753-6766 Pinhassi J, G ómez-Consarnau L, Alonso-S áez L, Sala M, Vidal M, Pedros-Ali ó C, Gasol JM (2006) Seasonal changes in bacterioplankton nutrient limitaion and their effects on bacterial community composition in the NW Mediterranean Sea. Aquat Microb Ecol 44 : 241-252 Prodi F & Fea G (1979). A case of transport and depsition of Sarna dust over the Italian peininsula and southern Europe. J.Gesophys. Res . 84 : 6951-6960 Prospero JM, Barrett K, et al. (1996). Atmospheric deposition of nutrients to the North Atlantic Basin. Biogeochemistry 35 : 27-73. Prospero JM & Lamb PJ (2003). African droughts and dust transport to the Caribbean: climate change implications . Science 302 : 1024-1027 Pulido-Villena, E., T. Wagener, et al. (2008). Bacterial response to dust pulses in the western Mediterranean: Implications for carbon cycling in the oligotrophic ocean. Global Biogeochemical Cycles 22 , doi:10.1029/ 2007GB003091. Ridame C, & Guieu C (2002). Saharan input of phosphate to the oligotrophic water of the open western Mediterranean Sea. Limn Oceanogr 47 : 856-869. Ruiz, J, Mac ías, D., Peters, F. (2004) Turbulence increases the average settling velocity of phytoplankton cells. Proceedings of the National Academy of Science USA 101 : 17720-17724. Sánchez O, Gasol JM, Massana R, Mas J & Pedr ós-Ali ó C (2007) Comparison of different denaturing gradient gel electrophoresis primer sets for the study of marine bacterioplankton communities. Appl Environ Microb 73 : 5962-5962. Sch äfer H , et al. (2001) Microbial community dynamics in Mediterranean nutrient-enriched seawater mesocosms: changes in the genetic diversity of bacterial populations. FEMS Microbiology Ecology 34 : 243-253. Schauer M, Balague V, Pedros-Alio C & Massana R (2003) Seasonal changes in the taxonomic composition of bacterioplankton in a coastal oligotrophic system. Aquatic Microbial Ecology 31 : 163-174. Smith DC & Azam F (1992) A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar Microb Food Webs 6: 107-114.

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Smith EM (1998) Coherence of microbial respiration rate and cell-speicic bacterial activity in a coastal planktonic community. Aquat Microb Ecol 16 :27-35 Thingstad, T.F., Havskum, H., Zweifel, U.L., Berdalet, E., Sala, M.M. Peters, F., Alcaraz, M., Scharek, R., Perez, M., Jacquet, S., Fonnes Flaten, G.A., Dolan, J.R., Marras é, C., Rassoulzadegan, F., Hagstr øm, Å., Vaulot, D. (2007) Ability of a “minimum” microbial food web model to reproduce response patterns observed in mesocosms manipulated with N and P, glucose, and Si. J. Mar Syst 64 : 15-34. Thingstad TF, Zweifel UL, et al. (1998). P limitation of heterotrophic bacteria and phytoplankton in the northwest Mediterranean. Limn Oceanogr 43 : 88-94. Thingstad TF, P érez M, Pelegr í S, Dolan J, Rassoulzadegan F (1999) Trophic control of bacterial growth in microcosms containing a natural community from northwest Mediterranean surface water. Aquat Microb Ecol 18 : 145-156 Thingstad, T. F., M. D. Krom, et al. (2005). Nature of Phosphorus Limitation in the Ultraoligotrophic Eastern Mediterranean. Science 309 : 1068-1071. van Hannen EJ, Zwart G, van Agterveld MP, Gons HJ, Ebert J & Laanbroek HJ (1999) Changes in bacterial and eukaryotic community structure after mass lysis of lamentous cyanobacteria associated with viruses. Appl Environ Microb 65: 795 –801. Wassmann P & Olli K 2004 (eds). Drainage basin nutrient inputs and eutrophication: an integrated approach. http://www.ut.ee/˜olli/eutr. Yentsch CS & Menzel DW (1963) A method for the determination of phytoplankton chlorophyll and phaephytin by uorescene. Deep-Sea Res 10 : 221-231

148 CHAPTER 5

Transplant experiments demonstrate strong bottom- up control of prokaryote abundance, heterotrophic production and bacterial community structure in the NE Atlantic Ocean

Itziar Lekunberri , Federico Baltar , Alejandra Calvo-D íaz , Eva Teira , Javier Ar ístegui and Josep M. Gasol The purpose of science is to create opportunities for unpredictable things to happen.

Freeman Dyson Chapter V

ABSTRACT

Two transplant experiments between different depths were conducted in the eastern North Atlantic Ocean to test whether predators or nutrient supply were more relevant in determining bacterial biomass, production and community structure. To attain this goal, unltered and 0.8 µm-ltered water from surface or from deeper layers (either mesopelagic or DCM) were placed in dialysis bags. These bags were incubated in surface and depth conditions tanks (in tanks containing surface or deep water and the corresponding temperature). The rst experiment, took place during the RODA cruise in August 2006 south of the Canary Islands (26º50´N, 16º18´W), and the second during the CARPOS cruise in October 2006 near Madeira (31º50´N, 16º40´W). Dialysis bags of 12000-14000 Da pore size were used to maintain the microbial communities and let them be inuenced by the surrounding environmental conditions (allowing the free exchange of nutrient salts and dissolved organic matter). We did not observe any strong effect of predators in any of the different conditions. Bacterial activity increased faster when incubated at surface than at depth conditions. Signicant differences were found between incubation conditions in the bacterial community structure analyzed in the rst experiment. Together, our results indicate that bottom-up forces are much more important than top-down regulation in order to determine bacterial community structure and function, at least in the NE Atlantic Ocean.

151 Regulation of bacterial activity and community structure

INTRODUCTION

The microbial loop hypothesis postulates a major role for bacteria in the oceanic carbon cycle (Pomeroy 1974, Williams 1981, Azam et al . 1983), actively growing through the consumption of DOC released by primary producers, C of allochthonous origin, and C originated from trophic interactions between plankton organisms (e.g. Nagata 2000). Bacterioplankton can consume 30-60% of the primary production via dissolved organic matter (Williams 1981, Azam et al . 1983, Cole et al . 1988), reaching close to 100% in very oligotrophic sites (Gasol et al. submitted). Bacterial biomass often comprises a major fraction of particulate organic carbon, in oligotrophic systems at times even surpassing phytoplankton biomass (Cho & Azam 1988, Fuhrman et al . 1989, Simon et al . 1992, Gasol et al. 1997).

Depending on the specic characteristics of each environment, factors that may control prokaryote communities include temperature (Shiah & Ducklow 1994), grazing pressure (Thingstad et al . 1997), and availability of limiting nutrients (Rivkin & Anderson 1997, Thingstad et al . 1997). Bacterial production is considered to be controlled by the rate of nutrient supply (bottom-up), whereas nal standing stocks and the rates of specic growth are considered to be determined by predation pressure (top-down), by substrate supply (bottom-up), or by both mechanisms (Wright & Cofn 1984, Pace & Cole 1994, Thingstad & Lignell 1997). Thus, more nutrients would mean more biomass and production, and more predators could mean less biomass or activity. Grazing is also known to be a selective factor determining bacterial community structure. Predators prefer to predate the largest bacteria (Jürgens & Güde 1994), the most active cells (del Giorgio et al 1996), and prefer some specic bacterial groups over others (Simek et al 1997, Jürgens et al 1999, Suzuki 1999).

It is generally accepted that bacterial communities seem to be rather similar at relatively large spatial scales on the horizontal axis (Acinas et al. 1997, Baldwin et al. 2005), except where different water masses meet (Suzuki et al. 2001, Pinhassi et al. 2003). Since the largest distinction in microbial communities occurs at the boundary of the euphotic and aphotic zones, light and the vertical distribution of autotrophs seem to be an important factor determining bacterial distributions, through their effects on the DOC pool, although vertical clines in the composition of DOC are as yet poorly resolved (Carlson et al . 2002). Furthermore, temperature, nitrogen and phosphorus availability, vary dramatically on the vertical axis, so these factors must be considered together to understand the vertical stratication of microbial communities.

152 Chapter V

The assessment and comprehension of the factors that control bacterial abundance and production will facilitate the understanding of how carbon and nutrients circulate in planktonic food webs. Because of the difculty of naturally replicating nutrient supply in mesocosm, transplant experiments are a research approach of choice for the study of these factors (e.g. Gasol et al. 2002).

Here we describe two transplant experiments designed to assess the factors that control prokaryote abundance, activity, and bacterial community composition at two different depths in the eastern North Atlantic Ocean. To assess these questions, unltered and 0.8 µm-ltered water from surface or from depth (deeper layers, either mesopelagic or DCM) were placed in dialysis bags.. We used dialysis bags that are sort of “microbial cages” that allow exposure of the microbes to ambient nutrient concentrations (Herndl et al . 1993). Samples from the two different depths were incubated in dialysis bags placed in times of in situ conditions and transplanted to another tank with the conditions of the other depth. The experiments lasted 6-8 days and the bags were sampled at 24 h intervals to estimate bacterial abundance and whole community activity.

MATERIALS & METHODS

Study site and water collection. We present data from two experiments conducted in the North Atlantic Ocean in August and October 2006. In the August experiment the water was collected during the RODA cruise near the Canary Islands (26º50´N, 16º18´W), and in the October experiment the water was collected during the CARPOS cruise near the Island of Madeira (31º50´ N, 16º40´ W). In the August experiment, the water used in the experiments came from the surface (ca. 5 m) and the mesopelagic zone (500 m). In October the water for the experiments was collected from the surface (ca. 5 m) and from the depth of the DCM (deep chlorophyll maximum), which was located at 100 m. In both cruises the water was collected with Niskin bottles and the parameters of temperature, salinity and uorescence in each station were recorded by a SeaBird 911 CTD system.

Transplant experiment s. The two experiments started by collecting surface and deep water (from the mesopelagic in the August experiment, and from the DCM in the October experiment). Two acid clean 50 L tanks were lled up with water one from each different depth and placed to incubate in the dark at in situ temperatures. The temperature conditions wer simulated with running supercial water for the supercial water conditions tank and with an incubator for the depth water-conditions tanks (DCM or mesopelagic). In both tanks 4 dialysis bags (of 1 L each) were placed with unltered water from the two different depths and with water that was gently pre-ltered through 0.8 µm polycarbonate

153 Regulation of bacterial activity and community structure

lters, to simultaneously address the effects of grazers and of changes in the natural conditions on the bacterial communities. The dialysis bags (Spectrum laboratories), with a cut-off size of 12000-14000 Da and a maximal width of ~ 8 cm, were cut in lengths of 50 cm to hold ca, 1000 ml. The bags were thoroughly washed in HCl 4% overnight, and then soaked for >3h in Milli-Q water before use. After they were lled, they were closed and placed in the two tanks, the one holding surface water and the other one having deep water. Samples for bacterial abundance and bacterial heterotrophic production were taken daily from the dialysis bags, making sure not to contaminate the inside of the bags with surrounding water, and DNA to analyze bacterial community structure was sampled at the beginning and at the end of the experiments. Unfortunately, the DNA samples of the CARPOS cruise experiment were lost during transport.

Environmental variables. Measurements of chlorophyll a (Chla) and nutrients were done in both cruises (Table 1). Particulate organic carbon (POC) and particulate organic nitrogen (PON) were measured only in the RODA cruise. Organic carbon was determined with a CHN elemental analyzer after ltration through Whatman GF/F lters (POC) or catalytic combustion at high temperature of unltered and acidied samples (TOC). Dissolved organic carbon (DOC) was computed as DOC = TOC-POC. The procedures employed are detailed in previous studies describing the variability of these parameters in the study area (Alonso-Sáez et al. 2007, Arístegui et al. 1997).

Prokaryote abundance and biomass . Heterotrophic prokaryotes were measured by ow cytometry as described in Gasol & del Giorgio (2000). The samples were run in a Becton-Dickinson FACSCalibur cytometer after staining with SybrGreen (1:10,000 nal concentration, Molecular Probes), and bacteria were detected by their signature in a plot of side scatter (SCC) vs FL1 (green uorescence). Calibration of the cytometer, and of the uorescence-size relationship was done as described in Calvo-Díaz & Morán (2006).

Prokaryotic heterotrophic production . Prokaryotic heterotrophic production was estimated using the 3H-leucine incorporation method (Kirchman et al. , 1985). Quadruplicate aliquots of 1.2 ml were taken for each sample plus two TCA killed controls. The Leu tracer was used at 40 nM nal concentrations in incubations lasting 2-3 h. The incorporation was stopped with the addition of 120 µl of cold 50% TCA to the samples and, after mixing, were kept frozen at -20ºC until processing, which was carried out by the centrifugation method of Smith & Azam (1992). The samples were counted on a Beckman scintillation counter, 24 h after addition of 1 ml of scintillation cocktail.

154 Chapter V

Bacterial community structure. Microbial biomass was collected by ltering around 250-500 ml of seawater through 0.2 µm polycarbonate lter (Millipore, 25 mm). The lters were stored at -80ºC. Microbial biomass was treated with lysis buffer (50 mM Tris-HCl pH 8.3, 40 mM EDTA pH 8.0, 0.75 M sucrose), lysozime, proteinase K and sodium dodecyl sulphate. Nucleic acids were extracted by a standard protocol using phenol/chloroform (see details in Schauer et al. 2003).

Fingerprinting analysi s. DGGE and gel analysis were performed essentially as described (Schauer et al. 2000; Sánchez et al., 2007). Briey, 16S rRNA gene fragments (around 550 bp in length) were amplied by PCR using the universal primer 907rm and the bacterial specic primer 358f, with a GC-clamp. PCR products were loaded on a 6% polyacrylamide gel with a DNA-denaturant gradient ranging from 40-80%. The gel was run at 100V for 16 h at 60ºC in 1x TAE running buffer. DGGE gel images were analyzed using the Diversity Database software (BIO-RAD).

Statistical analysis. A matrix was constructed for all DGGE lanes taking into account the relative contribution of each band (in percentage) to the total intensity of the lane. Based on this matrix, we obtained a dendrogram by the Ward’s clustering method (Euclidean distances, Statistica 6.0), and ordinations of nonmetric multidimensional scaling based on Bray-Curtis similarity index (MDS, Kruskal & Wish 1979, Clarke & Green 1988). An statistic test (one way ANOSIM) was used to test for differences between treatments and the control samples, and another to contrast differences among time within treatments (Primer v5).

155 Regulation of bacterial activity and community structure

RESULTS

As expected, we used for our experiments very different source waters (Fig. 2 and Table 1). In cruise RODA, we compared surface with mesopelagic water. This last one was colder (10 degrees lower), nutrient- replete, and with half the concentration of bacteria than to the surface. It was also 20x less active than the surface community (Table 1). Stn E01B of cruise CARPOS was relatively richer (surface Chla was 0.18 as compared to < 0.05 mg m -3 in Stn 64 of cruise RODA), and we Fig.1.- Map to the sampling area showing the two station locations. chose to perform the transplant experiment with water of the deep chlorophyll maximum (DCM), which was very well formed and which reached 0.46 mg m -3 of chlorophyll (Fig. 2). For the interpretation of the results it has to be taken into account that the incubations of the experiments were carried out at dark conditions and at in situ temperature. Since the photosynthetic processes were suppressed, this could affect the original DOC quality during the incubation.

A B Salinity (PSU) 34 34.5 35 35.5 36 36.5 37 Salinity (PSU) Oxigen ( �mol Kg -1 ) 36 36.2 36.4 36.6 36.8 37 Oxigen ( �mol Kg -1 ) 120 140 160 180 200 220 240 260 193.7 200 206.2 212.5 218.7 225 231.2 0 0

F 200 50 O 100 T 400 F O 150 600 S S T 200 800 250

1000 300 5 10 15 20 25 14 16 18 20 22 24 26 Temperature (ºC) Temperature (ºC) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 Fluorescence(vol) Fluorescence (vol)

Fig.2 .- CTD pro les of water temperature (red), oxygen (blue), uorescence (green) and salinity (black) of the stations in RODA (A) and CARPOS (B) from where the experiments were stablished. Arrows indicate the selected depths. 156 Chapter V

Bacterial production was 2 orders of magnitude higher at the DCM than at the surface, while bacterial abundance was higher at the surface and, thus, bacterial growth rates were even higher at depth. That the surface community of RODA was more active than that of the mesopelagic, and that the DCM community of CARPOS was more active than that of the surface was also evidenced by the %HNA cells (Table 1).

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Fig.3.- Estimates of bacterial abundance (left panels, BA) and leucine incorporation rates (right panels, LIR) in the RODA mesopelagic-epipelagic transplant experiment. The upper panels (A, B) correspond to the surface water. The lower panels to the mesopelagic water. In both panels, the control of the tank (outside the bags) is referred with the black round symbol and a continuous line. Water incubated in bags placed in the same type of water are labelled with white symbols (this is Surface water incubated in surface water, or S s, and Mesopelagic water incubated in mesopelagic water, or M m). Transplanted water (S m and M s), stands with dark symbols. Water ltered through 0.8 µm (w/o predators) is plotted with round symbols (e.g. S s 0.8), while untouched water is referred as UF and is plotted with round symbols.

157 Regulation of bacterial activity and community structure

Both, average (and maximal) bacterial abundances were strongly determined by the characteristics of the water where the samples were incubated in the transplant experiment done with meso- and epipelagic waters (cruise RODA experiment, Table 2). For the surface water (Fig. 3A) there was slightly more growth in the incubations at the surface (S s) than in the mesopelagic water (S m).

Bacterial abundance Leucine incorporation ) B 4 7 A -1 10 10 S control h

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Bacterial abundance (cell ml DCM DCM 10 5 01 234 56 1 Leucine incorporation (pmol Leu L 01 234 56 Time (days) Time (days) Fig.4.- Estimates of bacterial abundance (left panels, BA) and leucine incorporation rates (right panels, LIR) in the CARPOS DCM-epipelagic transplant experiment. The upper panels (A, B) correspond to the surface water. The lower panels to the DCM water. In both panels, the control of the tank (outside the bags) is referred with the black round symbol and a continuous line. Water incubated in bags placed in the same type of water are labelled with white symbols (this is Surface water incubated in surface water, or S s, and DCM water incubated in mesopelagic water, or DCM dcm). Transplanted water (S dcm and DCM s), stands with dark symbols. Water ltered through 0.8 µm (w/o predators) is plotted with round symbols (e.g. S s 0.8), while untouched water is referred as UF and is plotted with round symbols.

158 Chapter V

Treatment S-s-UF (unltered) was rather similar to what happened outside the bags, and there was little difference between the unltered and the ltered samples in bacterial abundance, but the statistical tests show signicant effects of ltration on bacterial activity (Table 2, Fig. 5B). In terms of leucine incorporation (Fig 3B), all samples started incorporation at about the same day (day 2), but samples incubated in the mesopelagic had an initial lowering, an then they recovered and increased leucine incorporation at a rate very similar to that of the water incubated at the surface, in spite of the difference in incubation temperature. Bacterial activity was slightly higher in the unltered than in ltered samples. Some more differences were visible when we incubated water originated from the mesopelagic. Bacteria developed strongly when incubated in surface water (Fig. 3C), reaching 4 10 6 cells ml -1 from a start of 1.5 10 5 cells ml -1 in 6 days (growth rate of 4.3 d -1 ). The unltered sample developed a higher yield (Fig. 5) than the ltered sample, suggesting that ltration through 0.8 µm retained some growing bacteria, probably those attached to particles. The rate of growth of the ltered and unltered bacteria was roughly the same. When incubated in the mesopelagic, however, there was a signicant difference between the samples that had been ltered through 0.8 µm, which did not develop until day 5, and those which were unltered, that developed similarly as the control external water (Fig. 3C). Leucine incorporation rates started to be higher earlier in the samples incubated at a higher temperature (i.e. samples M s, Fig 3D). Paired Student’s t-tests indicate that the maximum abundance was reached when incubated at the surface, while the maximum production was achieved in the presence of predators (Table 2).

In the second experiment, with transplanted water from the surface and the depth of the deep chlorophyll maximum (DCM), the starting situation was different: the DCM sample was richer and more active than the surface sample. Surface water incubated at the DCM reached higher abundances than incubated at the surface (Fig. 4A, Fig 5C). The control water was similar to the S-s-UF sample. In terms of activity, however, values were rather similar between treatments with a systematic trend of less production in the UF sample than in the F sample (Table 2, Fig. 5). DCM waters presented a very similar pattern: they developed more when incubated in DCM water, while activity was rather similar between treatments but systematically lower in the UF sample than in the F treatment.

159 Regulation of bacterial activity and community structure

Fig. 5 presents two integrative variables of what happened in the experiment: the average abundance of bacteria in each treatment, and the total integrated bacterial production. The analysis in Table 2 includes two extra variables, the maximal abundance of bacteria and the maximal leucine incorporation rates: these variables show similar trends than the average abundance and the integrated production. Fig. 5 clearly shows the effect on the microbial community due to the incubation water conditions (more abundance and more production in the samples incubated in the surface in RODA ), and a slight, but signicant effect of predators, mainly on activity.

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1 10 e l

d e t

a 2000 5 10 5 r g e t n I Average bacterial abundance (cells ml Average 0 0 MESO MESO S s UF M s UF S s 0.8 S s UF S s 0.8 M s UF M s 0.8 M s 0.8 S m 0.8 S m UF S m UF SUPER S m 0.8 SUPER M m 0.8 M m UF M m UF M m 0.8 C D

) 4000 -1 CARPOS ) l CARPOS 6 M

m 2 10

p s ( l

l n e o c i ( t 3000 a e

6 r c 1,5 10 o n p a r d o n c u n i b

2000 a

6 e 1 10 l n i a i c r u e e t l c

a d 1000 b 5 e

5 10 t e a r g g a r e t e n v I

A 0 0 MESO S s UF MESO M s 0.8 S s 0.8 S m UF M s UF M s UF S s UF S s 0.8 M s 0.8 M m 0.8 SUPER SUPER S m 0.8 S m UF M m UF S m 0.8 M m 0.8 M m UF Fig.5.- Left-side panels, maximum bacterial yield in each treatment of experiment RODA (A) and CARPOS (C), and integrated leucine incorporation (right-side panels) through the whole of the experiment RODA (B) and CARPOS (D). Letters indicate the source water, the incubation water, and the treatment (e.g. M s 0.8 indicates mesopelagic water incubated at surface and after ltration through 0.8 µm).

160 Chapter V

In the RODA experiment, we analyzed community structure by PCR-16S rRNA DGGE ngerprinting. The Ward’s clustering method showed a separation between the tanks, all samples incubated at surface conditions clustered together and all samples incubated in Mesopelagic conditions grouped together (Fig. 6a). The ordination of the samples by NMDS agrees well with the clustering results, and shows separation of the samples incubated at the different depths (Fig. 6b). The ANOSIM analysis determined signicant differences between initial and nal time of incubations (R=0.915, P:0.015) and incubation conditions (R=1, P: 0.008).

DGGE gel A B

Ward`s method Dendogram City-block (Manhattan) distances

SUPER To

MESO To

Ss0.8

SsUF

Ms0.8

MsUF

SUPER Tf

Sm0.8

Mm0.8

SmUF

MmUF

MESO Tf SUPER Ss0.8 SsUF Ms0.8 MsUF SUPER MESO Sm0.8 SmUF Mm0.8 MmUF MESO To Tf To Tf 0 50 100 150 200 250 300 350 400 Linkage Distance

C Resemblance: S17 Bray Curtis similarity

2D Stress: 0.08

SUPER To

MESOTo MESO Tf MmUF SmUF

Sm0.8 Mm0.8 SUPER Tf

Ms0.8 MsUF

Ss0.8

SsUF

Fig.6. - DGGE gel of the RODA experiment (A). Dendogram classication (B)(Ward’s Method, Euclidean distances according to the band pattern). S (water from surface origin), M (water form mesopelagic origin), s (incubation conditions: surface tank), m (incubation conditions: mesopelagic tank). 0.8 (sample ltrated by 0.8 µm) and UF (sample without ltration). Nonmetric multidimensional scaling (NMDS) (C) of the bacterial assemblage composition data (DGGE band pattern). The samples have been clustered in agreement with the Ward’s clustering methods results. 161 Regulation of bacterial activity and community structure

DISCUSSION

Nutrient, light and temperature conditions vary dramatically on the vertical axis in the ocean. Bacterial function, particularly bacterial production, varies also dramatically with depth (with exponential slopes of -0.90 km -1 , Arístegui et al. , submitted). Bacterial community structure is also known to vary dramatically on the vertical axis, more so than in the horizontal plane (Hewson et al. 2006). It is thus interesting to evaluate the effect that an input of surface waters could have in the DCM or mesopelagic zone, as it would occur in an anticyclonic eddy. In this situation DOC accumulated in the surface water masses could be available for the upper mesopelagic microorganisms. Carlson et al. (2004) showed that DOC that could not be degraded by the surface bacterial community could be easily remineralized at depth. Similarly, we were interested in predicting the effects caused by a cyclonic eddy, when deep water masses would move upwards to the surface layer carrying inorganic nutrients.

The objective of this study was to know to which extent bacterioplankton communities were determined by its original conditions (e.g. nutrient concentration, abiotic factors), depending on the depth at which the community had been formed. Hence, we hypothesized that a change in the conditions should reveal the factor that determines a specic level of bacterial metabolism and a specic community structure. As laboratory experiments have the difculty of mimicking natural nutrient conditions, to address this question we performed a transplant experiment using dialysis bags. We used the concept of a “transplant” experiment, because in this approach organisms are allowed to grow at different environmental conditions from their origin, sort of in a “microbial cage” (Herndl et al . 1993).

In addition, we wanted to test whether the growth was nutrient controlled (“bottom-up”) and/ or biomass predator-controlled (“top-down”). A complex interaction exists between the top-down and bottom-up controls of bacteria in plankton environments. Bacterial production is thought to be regulated by the supply of nutrients and nal abundances are either determined both by predation- pressure and substrate supply (Wright & Cofn 1984) or just by predators (Thingstad & Lignell 1997). However in compiling the results of several experimental and empirical studies, Pace & Cole (1994) found little evidence of systems where predators were limiting bacterial abundances. Similarly, we found little evidence of abundances being determined by the presence or absence of predators (Table 2).

162 Chapter V

Table 2.- Results of paired Student’s t-tests (tests whether the average difference is signicantly different from 0) between each paired treatment in the experiments: source water (e.g. surface vs. mesopelagic water), Incubation water, or presence/absence of predators. The variables analyzed are Maximum and experiment- average bacterial abundance, and integrated and maximum leucine incorporation rates. The trend explains whether the Surface (S) value is higher than the mesopelagic (M) or DCM value, or whether the 0.8 µm ltered (F) is higher or lower than the ltered (F) value. The ratio is the average increase in the variable within the experiments (Surface/Mesopelagic, UnFiltered/Filtered or Surface/DCM).

Exp. Variable Effect Prob. Trend Ratio

RODA Max BA Source 0.010 S > M 1.8 ± 0.5 Incubation 0.009 S > M 1.7 ± 0.3 Predators 0.009 UF > F 1.6 ± 0.3 Average BA Source 0.014 S > M 2.7 ± 1.1 Incubation 0.032 S > M 2.2 ± 1.1 Predators 0.041 UF > F 1.6 ± 0.4 Integrated LIR Source 0.055 S > M 1.7 ± 0.5 Incubation NS 1.0 ± 0.3 Predators 0.051 UF > F 0.7 ± 0.2 Max LIR Source NS 1.5 ± 0.7 Incubation 0.058 M > S 0.7 ± 0.3 Predators 0.086 UF > F 0.6 ± 0.3

CARPOS Max BA Source NS 1.2 ± 0.6 Incubation NS 1.3 ± 0.4 Predators NS 1.2 ± 0.4 Average BA Source NS 0.9 ± 0.4 Incubation NS 1.1 ± 0.4 Predators NS 1.0 ± 0.3 Integrated LIR Source NS 1.1 ± 0.1 Incubation NS 1.0 ± 0.1 Predators 0.019 UF > F 1.2 ± 0.1 Max LIR Source NS 1.0 ± 0.2 Incubation 0.002 DCM > S 0.7 ± 0.1 Predators 0.048 UF > F 1.1 ± 0.7

163 Regulation of bacterial activity and community structure

In both of our experiments, the bags which should be similar to the control –the incubation tank itself, (e.g. the S-s-UF sample)- showed a similar pattern in bacterial production and bacterial abundance, thus suggesting that the evolution of bacteria in the bags was not affected further than the global effect caused by the connement in the tank. As a general pattern, there was a clear tendency for the bags with the same origin (e.g. surface water) to diverge depending on whether they were transplanted or incubated in the same water. Furthermore, when e.g. surface water was transplanted into more oligotrophic deep (mesopelagic), growth was lower than in the control, while when the surface water was transplanted into more active (DCM) water, growth was enhanced. In general, the effect of predators was small, and mainly affected leucine incorporation (Fig. 3-5) The abundance was higher in the treatments without ltration that is contrary to what we should expect, an explanation would be that the hot-spots of bacterial activity were excluded in the ltration process.

Grazing, particularly by heterotrophic nanoagellates (HNF), has been identied as the main bacterial loss factor (Fenchel 1982, Sherr & Sherr 1984, Wright & Cofn 1984, Pace et al. 1988) and has been considered to balance more or less bacterial production. In a nutrient addition experiment with waters of Blanes Bay, Allers et al . (2007) detected a shift in bacterial diversity after an increase in agellate abundance in nutrient-enriched mescosms. Thus, this suggests that nutrient supply may favour a bacterial increase which would be followed by a agellate increase, which will act as a selective force on bacterial community structure (Pernthaler 2005). However, as we did not nd any major differences between bags with or without predators, the bottom-up factors seemed to explain (P< 0.01) differences in community structure (Fig. 6). Previous studies have reported that succession within the bacterioplankton in coastal marine systems may be lead by autochthonous processes, or by the input of certain labile organic substrates (nutrient-rich runoff from rivers or organic contamination, Revilla et al. 2000, Schendel et al 2004). This may stimulate the growth of bacteria from particular phylogenetic lineages (e.g. fast-growing-gammaproteobacteria, Pinhassi & Berman 2003). Our data seem to suggest the existence of a shift in bacterioplankton community structure due to the waters in which the samples are incubated.

Gasol et al. (2002) run a transplant experiment in a canyon-shaped reservoir, submitting bacterial communities from the river side to the conditions in the reservoir side. Even with very different growth rates for different bacterial groups, the nal community structure was quite similar to the initial one. In contrast, in our experiments we found signicant differences in the community composition between incubation conditions revealing bottom-up effect on the community structure. This agrees with the results of Carlson et al . (2004), that suggests controls on the bacterial community in space and time during transplant experiments.Fuhrman et al. (2007) showed that the presence of annually reoccurring

164 Chapter V bacterial communities in the Southern California coast, and that the occurrence of each population could be predicted from the ocean environmental conditions. This would imply a large degree of bottom-up control of bacterial community structure, as predators tend to vary more stochastically than the nutrient sources (Tanaka & Taniguchi 1999) that are most likely driven by temperature, light availability, rain and its direct effects on bacteria, or through effects mediated by phytoplankton species succession, all parameters that we consider “bottom-up”. Our results show experimentally this to be the case for surface, mesopelagic and DCM bacterial communities of the North Atlantic Ocean.

ACKNOWLEDGEMENTS

We thank the chief scientist of cruise CARPOS, Dr. Ramiro Varela as well as the rest of coworkers on both cruises for various help. We particularly thank A. Bode for ancillary data and X.A.G. Morán for the opportunity to join cruise CARPOS. The eld work of this study was supported by projects RODA (CTM 2004-06842-C03/MAR) and CARPOS (REN 2003-09532-C03-01), and the lab work was supported by project MODIVUS (CTM2005-04795/MAR). This is a contribution to the EUR- OCEANS and MARBEF European Networks of Excellence.

165 Regulation of bacterial activity and community structure

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170 SYNTHESIS AND D ISCUSSION La armon ía total de este mundo est á formada por una natural aglomeraci ón de discordancias

Séneca Synthesis and dicussion

SYNTHESIS AND DISCUSSION

The aim of this thesis was to describe the effects of different allochthonous carbon sources on the diversity and function of marine bacterioplankton. In some of the chapters (#1 particularly), the focus was on diversity, and in some others it was placed on the link between the changes in diversity and those in bacterial function, in some cases extending beyond the purely bacterial function to that of the whole microbial ecosystem (e.g. Chapter 4). In Chapter 1 we compared the diversity obtained by traditional culture techniques and by molecular approaches in the NW Mediterranean. The culture media can be considered a special type of allochthonous carbon source, and we encountered low similitude between the bacteria present in the environment, and those selected by the media, suggesting a strong effect of the resource types and levels on diversity The diversity detected by cultivation was largely invisible by the molecular PCR-based techniques, and using both methodologies we increased substantially the diversity reported from the area in previous papers. Coastal planktonic communities are subject to the introduction of C and nutrients of anthropogenic origin. We analyzed the effect of one of these sources of C and nutrients of basically human origin in Chapter 2, in which, to determine the effects of the Prestige tanker oil spill tanker in front of the Galician coast, we used realistic additions of oil to mesocosms. We detected little effects of oil on the diversity and function of the authochtonous bacterial microbiota. To evaluate the effect of different nutrient inputs from different origins (terrestrial or athmospheric), two enrichment experiments were carried out in the NW Mediterranean coast. We added different sources to stimulate phytoplankton blooms and determined changes in bacterial community structure, but little differences were found even when a diatom bloom developed ( Chapter 3 ), In the experiment that tried to mimick an event of Saharan dust deposition (Chapter 4) we saw clear effects on bacterial function. In Chapter 5 , however, and we investigated the effects of different types of “nutrient environments” on diversity and function of epipelagic, and mesopelagic bacterioplankton. We did so with transplant experiments, and we found that, in contrast to what we had seen in the NW Mediterranean and partly in the Atlantic coast, the bottom-up factors (nutrient environment) strongly determined the nal results of the experiments.

Several elements were common between the different experiments/chapters. For example, i) we used a mesocosms approach in 3 of the chapters (2, 3 and 4). In these experiments we detected a common pattern of development of bacterial abundance that was also seen in previously published experiments. Below, we analyze the meaning and implications of these patterns, also as they have to do with agellate development. ii) Two of the experiments/chapters were done in Blanes Bay

173 Synthesis and dicussion

PHYTOPLANKTON TRANSPLANTS OIL-SPILL SAHARAN DUST BLOOMS SIMULATION DEPOSITION INDUCTION

+ REALISM -

- CONTROL +

Fig.1.- Summary of different approaches used in this thesis and the associated levels of control, inversely related to the realism levels. From the more realistic and less controlled, to the less realistic and more controlled: transplant experiments (Chapter 5), oil addition (Chapter 2), Saharan dust addition (Chapter 3) and inorganic nutrient addition and phytoplankton bloom induction (Chapter 4). Chapter 1 could somehow be included in the right side of the graph, as the culture media are well dened, and we know well that provides unrealistic views of bacterial community structure.

(NW Mediterranean) waters, but started at different times of the year (chapters 3 and 4). Similarly, in Chapter 2 we considered the variation in effects depending on the time of the year in which the experiment was done. Also, iii) we used a genetic ngerprinting technique (DGGE) in all the chapters. In Chapter 1 we compared the bands appearing in DGGEs done in Blanes Bay at different years with the diversity seen by other techniques (clone libraries and isolation). In other chapters we analyzed changes in bacterial community structure in the different experiments. This allowed us to compare the “amount of change” in structure between different manipulations and in different seasonal studies.

In the following sections we explore some of these general issues that appear different times throughout this thesis.

174 Synthesis and dicussion

i) EXPERIMENTAL APPROCHES

To address the different questions posed in this thesis, we mainly used mesocosms and transplant experiments using dialysis bags. These two kinds of approaches are very useful to experimentally mimic natural situations. Mesocosms are a common tool used in aquatic ecology to simulate natural behaviour of communities, at a scale that can be handled and providing a degree of realism not possible in the laboratory (Odum 1984). They have been widely used to study the effect of nutrient additions on phytoplankton communities (e.g. Duarte et al. 2000), on the activity and diversity of bacterial assemblages (e.g. Schäfer et al. 2001), and on microbial succession (e.g. Allers et al. 2007). This type of experimental approach avoids the effects caused by advection, diffusion and mixing under natural conditions and also offers the possibility of comparison with natural populations where there were no additions, thus allowing statistical testing of hypotheses. We have performed mesocosms experiments to evaluate the effects of oil spills in Chapter 2 , to stimulate different phytoplankton blooms Chapter 3 and to mimic a Saharan dust deposition in Chapter 4 . And we even used in Chapter 5 mesocoms approaches as a way to maintain the transplant experiment with the dialysis bags. These bags are sort of microbial cages that allow nutrients to ow, and are thus, very useful to exposure a determine bacterial community to a particular ambient nutrient concentration (Herndl et al. 1993).

To the enclosures we added different types of substrates, ranging from simple inorganic nutrients, somehow uncharacterized dust originated from the Sahara, or the soluble fraction of Prestige oil. Furthermore, in one case we applied a “black-box” approach to the substrates by incubating samples from some depths into other depths that we expected should have different nutrient elds (DOC and inorganic nutrients, but also temperature). Our target variables (bacterial diversity and function) and our questions (do the target variables change with these additions?) were analyzed in all these different types of approaches. They can be ordered in two types of opposite gradients (Fig. 1) depending on the degree of control we can have over the nutrients added, or on the degree of realism that we can assume each approach has. The dialysis bag experiments are probably more realistic than the other approaches, but we control little the nutrient eld. In contrast, in the nutrient-addition experiments of Chapter 3 we controlled well the nutrients added, but the situation was probably the less realistic one and the one that shed less light into what really occurs in nature. Plate growth media as those used in Chapter 1 are an extreme example of unrealistic, but well controlled, approach.

It is well known that enclosures can either enhance the growth of certain microzooplankton by excluding their predators, or induce their mortality, and this generates the question of whether the mesocosms can also affect the microbial communities. The Mediterranean sea is known to be an oligotrophic ecosystem and, thus, it is likely that the so-called “bottle effect” (Marshall et al. 1971) is more intense than in a relatively more eutrophic system such as Ría de Vigo (Chapter 2). These bottle- effects, maybe related to the dominant P-limitation of Mediterranean Sea bacterioplankton (Thingstad 175 Synthesis and dicussion et al. 1998, Pinhassi et al. 2006) seemed larger in the initial times of the experiments performed in the Mediterranean. Figures 2-4 show a comparison between the changes recorded in the experiments of Chapters 3 and 4 with two other experiments done with waters from the NW Mediterranean, and published in Pinhassi et al. (2004) and Allers et al. (2007). These plots show that the initial start of the these experiments presented shift-ups of 2-3 orders of magnitude in bacterial production, which are much higher than the shift-ups seen in the Ría de Vigo mesocosms experiments (Fig. 3 in Chapter 2).

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Fig.2.- Chlorophyll a values (mg m -3 ) along the incubation of four different mesocosms experiments with NW Mediterranean waters: (A) nutrient addition and turbulence experient (Pinhassi et al. 2004); (B) inorganic and organic addition experiment (Allers et al. 2007); (C) phytoplankton bloom stimulation experiment (Chapter 3) and (D) Saharan dust deposition experiment (Chapter 4). Treatments are K: Control, C: Carbon addition, P, Phosphorus addition, CP: both additions. Si: Silica and P addition, U: Urea and P addition. Low and High Dust are different ammounts of Saharan Dust.

176 Synthesis and dicussion

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0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days) Fig.3.- Bacterial abundance (cells ml -1 ) values along the incubation of four different mesocosms experiments with NW Mediterranean waters. Panels as in Figure 1.

Some mesocosms experiments had been performed before to evaluate the changes of bacterial community structure due to changes in nutrient availability and predators (e.g. Lebaron et al. 1999 and Schäfer et al. 2001). Previous to the studies in this thesis two mesocosms experiments were performed with coastal waters of the NW Mediterranean (Pinhassi et al . 2004, Allers et al. 2007). All of these experiments show differences between treatments and control in Chla concentration, bacterial abundance and bacterial production (Fig 2, 3 and 4) that can be inspected further.

Hence, the utilization of mesocosms as an approach to study the effect of different nutrients additions can be very useful. However, we also show, in the oil-spill simulation experiments in the Galician coast (Chapter 2) that sometimes there may not be any bacterial response because, as shown by the

177 Synthesis and dicussion concentration dependent experiment, the concentrations of the compound added in the experiment were not high enough to generate a response. The complexity of mesocosms experiments implies that not many experimental situations can be analyzed at once. However, the comparison (meta-analysis) of similar mesocosms experiments performed in different environments or settings allows for an increase in the signicance of the conclusions over what would be possible to know from just an experiment alone. It allows generalizing some conclusions to situations other than the one assayed, but also allows seeing differences between experiments (Olsen et al. 2006).

These results indicate a bacterial community uesd to the oil concentration tested. From the transplant experiments done in North Atlantic Ocean, the bags with the same origin as the incubation tank water had the same pattern in bacterial abundance, bacterial production and community structure, suggesting

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1 1 0 2 4 6 8 10 0 2 4 6 8 10 Time (days) Time (days) Fig.4.- Bacterial production (µgC l -1 d -1 ) along the incubation of four different mesocosms experiments with NW Mediterranean waters. Panels as in Figure 1. 178 Synthesis and dicussion no effect of enclosure in the evolution of the bags. All together conrms that enclosure often did not mask the effects on bacterial communities due to changes in the environment.

ii) THE STATUS OF THE INITIAL COMMUNITIES

Inspection of the effect of oil on plankton community structure, as we did in Chapter 2, running similarly designed mesocosms experiments at different times of the seasonal cycle in the planktonic ecosystem of Ría de Vigo allowed us to detect differences in the response of the microbial community depending on the initial situation. In those experiments, we started a mesocosms with a decaying phytoplankton community and another one with a blooming community. However, the community that was more sensitive to oil seemed to be the summer one, something that we assigned to the fact that the water in the Ría at htat time of the year comes mainly from the upwelling of oceanic deep water that enters the Ría and fertilizes the planktonic community. The bacteria in these waters are likely not to be naturally exposed to oil components nor to other allochthonous C sources and might thus be more sensitive to stressors such as the polycyclic aromatic hydrocarbons added.

The response of the NW Mediterranean communities to nutrient additions was also varied depending in part on the time of the year when the experiment was done, but also dependoing on the experimental conditions used. For example, chlorophyll decreased in the control treatment of the experiment reported in Fig. 2B (Allers et al. 2007), but this experiment was run with very low light. Chlorophyll also decreased in the Saharan Dust addition experiment (Chapter 4), but this was due to the fact that the starting community was the one with the largest Chla value (ca. 3 mg m -3 ), during a natural bloom situation, and the experiment evolved during the decline of that bloom.

A few other things are interesting and can be seen in Figs. 2-5. For example, bacteria seem to develop a small initial bloom (except in the Saharan Dust experiment), before there is any noticeable development in Chla. This can be related to the very important P-limitation of bacteria in the area (Pinhassi et al. 2006). The effect of P on bacteria (note that all treatments in Chapters 3 and 4 had received P except the controls) is much stronger than this effect on phytoplankton, as can also be seen comparing Fig. 2 (Chla) and Fig. 2 (bacterial abundance). Finally, the chlorophyll maximum yield in these experiments is ca. 2 orders of magnitude (Chapter 3, addition of Si + P), while the max. bacterial yield did not go over one order of magnitude (in the experiment reported by Pinhassi et al . 2004, and in the addition of a high concentration of Saharan Dust). This can probably be traced back again to the well known lack of proportionality between primary producers and bacteria when their natural changes are compared through regression lines (Gasol & Duarte 2000), but can also be masked by the fact that bacterial numbers might hide increases in bacterial cell size and, thus, in bacterial biomass.

179 Synthesis and dicussion

The role of agellates as grazers of bacteria is very apparent in these experiments. In almost all of them bacteria present an initial peak before algal development, then there is a through, and a second peak of bacteria. In one of these studies (Allers et al. 2007) we could identify most bacteria developing in the rst peak as Alteromonadaceae , while most of the bacteria in the second peak were Rhodobacteraceae . The rst group of sequences were much more homogeneous between treatments than the second group which differed according to the treatment. In fact, it appears as if the initial bacterial peak, particularly when P is added, is rather independent of the treatment, and only the second bacterial peak has to do with the treatment applied (see Chapter 3). The rst bacteria developing would be oportunistic phylotypes that would then be grazed down by agellates.

iii) MEASURING BACTERIAL COMMUNITY STRUCTURE WITH DGGE

For the comparison of bacterial community structure in the different treatments, we used the ngerprinting technique DGGE (denaturing gradient gel electrophoresis, Muyzer et al. 1997). The level of diversity detection of this method is quite low (<30 sequences) compared to other molecular techniques such as clone libraries (which were used in chapter one with a target of 100 sequence types per sample) and much lower compared with the newer shotgun techniques, which are able to detect thousand of sequences. However, this is a very useful methodology for comparative analyses of microbial communities and their response to changing environments. (Kasai et al. 2001, Labbé et al. 2007, Brakstad et al. 2006, Castle et al. 2006, Ogino et al . 2001, Macnaughton et al. 1999, Casamayor et al . 2002, Sánchez et al. 2007).

In Chapter 1 we presented a comparative study of the potential of sequence detection by culturing and culture-independent techniques. It was observed that the molecular techniques differed substantially from the cultivation techniques and, therefore, it was the combination of all the techniques what allowed to enlarge our view of the bacterial biodiversity present at the investigated location (Donachie et al. 2007). In Figure 5 we show a review of the different studies presented in Donachie et al . (2007) with the addition of the results of our study. It is a plot of the percentage of sequences retrieved by clone libraries in comparison with the sequences from isolates and to the sequences detected by both techniques. We found similar overlap as in the other marine study (Eilers et al. 2000), and non- overlapping detection by the two sets of techniques, suggests that the isolate sequences do not represent the diversity of the environment. The concept of biodiversity has been explained by Pedrós-Alió (2006) as a pool of very abundant species and a tail (of unknown length) of rare/occasional species. This author proposes a framework in which each type can be studied with different strategies. PCR primers will predominantly hybridize and represent the common taxa, and the rare ones will only be

180 Synthesis and dicussion retrieved by PCR-independent techniques such as cultivation or metagenomics techniques. Certainly, there is an increasing interest in cultivation (for example, the Moore Foundation initiative http://www. moore.org/marine-micro.aspx) because it provides the complete genome of the isolate and is a way to test the hypotheses that emerge from metagenomic data, since procedures for reconstructing genomes from whole-genome shotgun sequences are not yet very reliable.

Apart from differences from culture and culture independent techniques, differences in the diversity detected between molecular procedures have been described (e.g. Alonso-Sáez et al. 2007). In particular, this study showed that the dominant group present in Blanes Bay (SAR11) was not well detected by the DGGE technique. Castle & Kirchman (2004) carried out a comparative study between DGGE and FISH, and also showed that DGGE failed to detect the most abundant phylogenetic group

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Study Fig. 5.- Comparative analysis of diversity detected by culture-dependent and culture-independent approaches in the same habitat (taken from Donachie et al. 2007 with the addition of our data). Percentages of unique sequence groups reported in libraries done using molecular analyses (PCR-based 16S rRNA clones), in cultivation, or by both techniques (if a phylotype is detected by both methods it is listed separately: ´overlap´). The habitats shown are (1) Blanes Bay Microbial Observatory (Lekunberri et al. chapter 1); (3) endodontic pathogenic oral biolms (Munson et al., 2002); (4) exodontic oral biolms (Munson et al. 2004); (5) Hawaiian lakes (Donaiche et al. 2004); (6) rhizoplane (Kaiser et al. 2001); (7) bird feathers (Shawkey et al. 2005); (8) North Sea (Eilers et al., 2000); (9) hypersaline salterns (Maturrano et al. 2006). 181 Synthesis and dicussion detected by FISH in some samples (Betaproteobacteria in their samples). Besides primer specicity, these authors argued that high richness within groups could lead to an underestimation as compared with FISH, because different sequences would appear as different faint bands, which could be difcult to excise from the gels for sequencing. Indeed, Sánchez et al. (submitted) have shown that the likely reason for the lack of detection of SAR11 in DGGEs of Blanes Bay is the high evenness of multiple microdiverse phyloypes, individually non-dominant in the area. iv) DIFFERENT FERTILIZING EFFECTS OF THE DIFFERENT ADDITIONS

Mesocosms experiments have the difculty of nding the right balance between realistic conditions and controlled changes. The different studies presented in the thesis are in a scale from more control to more realistic approaches (Fig. 1). The transplant experiment ( Chapter 5 ) represent the most realistic and less controlled approach, where the microbial community was transplanted to another conditions that we did not modify. In a second step of complexity, we tried to simulate realistic events occurring to natural communities. The real concentrations of PAHs are very difcult to accurately determine and in addressing the effects of an oil spill, in doing the experiments reported in Chapter 2, we concluded that we should better create a concentration-dependent experiment to determine the threshold concentration that affected the communities. In the experiment mimicking a Saharan dust deposition event, the problem was in the difculty of determining the dust composition ( Chapter 4). We decided to use two different concentrations and we observed very different whole microbial metabolism effects with each concentration. The most controlled approach is the addition of simple compounds, adding well known concentrations as we did in Chapter 3 where we tried to induce different phytoplankton blooms.

In the experiments done in the Ría de Vigo, and using concentrations similar to those measured after the Prestige accident (20-30 μg PAH L -1 ) we did not get an effect on bacteria. Not seeing a signicant response to the realistic addition of oil suggests that the natural communities are used to a certain level of these substances in the water and maybe even the addition remains below the concentration of available DOC. Our results contrasted to the measurements of bacterial production after the accident in the A Coruña area that were higher as compared to previous years (Bode et al . 2006) indicating an effect of oil. In contrast, in the concentration dependent experiment the concentration of 40 μg PAH L-1 indicated a detectable effect and the 80 μg PAH L -1 a clear detrimental effect on phytoplankton, and a large development of Gammaproteobacteria. These results coincided with Nayar et al . (2004) that reported toxicity to the autotrophs and stimulatory effect to the bacteria with the addition of high oil concentrations.

182 Synthesis and dicussion

The addition of Saharan dust only generated signicant increases in bacterial abundance at the highest concentration, suggesting a high contribution of the DOC content of the dust on the bacterial community, and a limitation in phytoplankton growth by other nutrients such as silicate or nitrogen. Previous studies have shown that addition of glucose alone as a C source does not necessarily stimulate bacterial development (Pinhassi et al. 2006, Allers et al. 2007).

Finally, our results indicate a very strong effect of phosphorus on bacterial activity and abundance that seems to supersede the effects of Carbon. Figures 3 and 4 indicate that all the treatments with added P generated similar levels of bacterial biomass and production: treatments P and CP in Allers et al. (2007), treatments P, Si, and U in Chapter 3, and treatments DH, DL and P in the Chapter 4 experiment.

v) FACTORS CONTROLLING BACTERIAL COMMUNITY STRUCTURE

In all ecosystems there is a ux of energy and material from autotrophic to heterotrophic organisms. Phytoplankton organisms are the major producers of new production in the open ocean, while bacteria utilise DOM as an energy source, and this is mainly of phytoplankton origin (5-50%, Nagata 2000). Bacteria also compete for nutrients with phytoplankton. At the same time the heterotrophic nanoagellates are effective bacterivores in the sea (e.g. Sorokin et al. 1979, Sierburth et al . 1982, Unrein et al. 2007). Bacteria have been regarded as remineralizers, responsible for converting organic matter to inorganic and recycling nutrients to primary produces and predation is known to provide a feedback of some material ow to the food webs. Thus, the nutrient ow within the microbial loop is tightly coupled. And the microbial loop concept originated from the idea that bacteria and agellates recirculated the carbon that was previously though to be lost from the system. Grazers are important in regulating bacterial abundance and production, and also seem to be a key factor controlling the recycling of nutrients accumulated as bacteria (Caron & Goldman 1990).

It is also well known that nutrient additions cause changes on phytoplankton and these changes are followed by shifts in bacterial community composition (Pinhassi et al. 2006, Allers et al. 2007), in biomass and in bacterial function (production) at different stages of phytoplankton blooms (Smith et al. 1995). Pernthaler et al. (1997) suggested that there was disagreement to whether the abundance and productivity of bacteria are determined mainly by predators or nutrients. In a recent study Allers

183 Synthesis and dicussion et al. (2007) considered a combination of both factors (bottom-up and top-down) controlling bacterial succession and growth. This was in agreement with Lebaron et al. (1999) that suggested that nutrients and grazing play a key role in shaping the morphologic, genotypic and phenotypic composition of bacterial communities.

In our experiments we focused on the effect of the different additions on the bacterial community. Besides we measure the HNF abundance in all the experiments except in the transplant experiment (Chapter 5 ), where we controlled grazing pressure by ltration. From our results we can conclude the importance of the predators on determining bacterial community structure. In Chapter 2, where we correlated the changes in bacterial community structure with the changes in bacterial production (as no change in structure is possible without production), we observed that this correlation was only signicant before the peak of HNF but not after that peak, suggesting a change in bacterial community structure, maybe towards grazing-resistant forms (Pernthaler 2005). In the phytoplankton bloom stimulation experiment ( Chapter 3 ), the differences in bacterial activity and abundance between treatments were found in the second bacterial peak, after the appearance of the HNF. Hence, there seems to be a shift in bacterial community structure caused by the grazing pressure. In the experiments of transplant of bacterial communities to different depths in the water column ( Chapter 5 ) we observed that the factors that determined a specic community structure were mainly the environmental conditions, although grazing pressure appeared to also play a role in determining bacterial abundance.

When analyzing the relative effects of nutrients or predators on community structure, the problem of comparison between experiments arises. Did bacteria change more in the Sahara dust addition experiment? or when we added nutrients? When we detect that community structure is different in the ltered (no predators) than in the unltered samples because the communities are not placed exactly in the same position in the NMDS plot (Fig 6 in Chapter 5), how can we evaluate the signicance of this difference? To approach answering these questions, we decide to evaluate each of the experiments presented in this thesis by averaging taking each DGGE analysis, and computing the average similitude in each experiment. If, in an experiment, all treatments were to evolve equally, the average Bray- Curtis similarity index would be 100. If the samples were to immediately change and no single band would be repeated from treatment to treatment, then average similarity would be 0. Figure 6 compares the values that we have measured during this thesis, with the typical seasonal evolution recorded in Blanes Bay.

In the experiments where we detected little change, such as those in Ría de Vigo in spring, fall and winter, the value of the average Bray-Curtis was about 65. In the summer experiment when we did see differences, the value was 45. The seasonal changes in Blanes Bay provided values of 35- 45. And the experiments reported values ranging from 35 (transplant experiments, chapter 5) to 65

184 Synthesis and dicussion

(Saharan dust addition, Chapter 4), with intermediate values in the nutrient addition experiment. This comparison allows us to quantitatively say that the changes produced by the transplant were of the same magnitude than those occurring on average along the seasonal cycle in the NW Mediterranean, but that the changes produced by Saharan dust were as modest as those produced by oil additions in Ría de Vigo communities.

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Fig. 6. - Comparison of the average (± SE) similarity indices (Bray-Curtis) in each of the studies for which we had analyzed the variability of bacterial community structure using the DGGE technique. We included the seasonal variability at the Blanes Bay Microbial Observatory during years 1997-1998 (in Schauer et al. 2003), and years 2003-2004 (Alonso-Sáez et al. 2007).

185 Synthesis and dicussion vi) WHICH BACTERIA ARE SELECTED IN THESE TYPE OF ADDITION EXPERIMENTS?

Almost every ecophysiological parameter in the oceans is thought to have an impact on the diversity of microbial communities. Recent studies show that some bacterial taxa exhibit biogeographical patterns, which are traced to the changes in the environmental characteristics (Pace 2003). Previous studies have shown that the different conditions of the different times of the year affect the bacterial assemblage in our study site (Schauer et al. 2003, Alonso-Sáez et al. 2007), i.e. there is seasonality. The bacterial community structure of our study area was also investigated previously by two mesocoms approaches. In the rst one, Pinhassi et al. (2004) found the presence of Roseobacter genera in all treatments and a remarkable predominance of Bacteroidetes (in particular Flavobacteria) following the phytoplankton blooms. Later on Allers et al. (2007) showed a clear contribution of the Roseobacter genera (Alfaproteobacteria subclass) dependent on the Chla concentrations and an until increase in Alteromodaceae (Gammaproteobacteria) at the start of the blooms. Other studies carried out in the NW Mediterranean also found similar patterns, Schäfer et al. (2001), e.g. in an enrichment experiment found particular dominance of genera Ruegeria belonging to the Alphaproteobacteria subclass, and members of the Bacteroidetes. Lebaron et al. (1999) also found that phylotype Alteromonas macleodii was dominant at the end of the incubation.

Langenhender (2005) suggests that bacterial community composition and bacterial functioning do not need to be very tightly linked being however both mostly inuenced by the environment. There are some bacteria that have specic functionalities and only become dominant in specic situations. For example, the Gammaproteobacteria are generally the dominant bacterial group after oil additions (Grossman et al. 1999, Kasai et al. 2001). There are two key organisms identied with the major roles in the degradation in the petroleum hydrocarbons. Alcanivorax is responsible for alkane biodegradation, whereas Cycloclasticus degrades various aromatic hydrocarbons (Harayama et al. 2004). In our oil spill stimulation experiment we found a clear shift towards Gammaproteobacteria in the 80 μg L -1 treatment, where Cycloclasticus ended up being a large faction of the total Gammaproteobacteria.

Not in all experiments we decided to explore phylogenetically the bacteria generating relevant bands in the DGGE. We did so only in the Saharan Dust experiment (Chapter 4), where we obtained bands belonging to Bacteroidetes (known to have a life-style of attachment to particles), Within the Alphaproteobacteria identied, bacteria of the Ruegeria /Silicibacter cluster appeared in several treatments, as well as several Rhodobacteraceae . These are all common organisms in Blanes Bay, easy to isolate in plates, and common in mesocosms experiments (Chapter 1). In fact, addition of sequences retrieved (in the past) from enrichment experiments allowed to nd quite some phylotypes appearing both in culture, and in the natural environment, but when the natural environment had been modied by the different allochthonous enrichments studied here. 186 Conclusions

CONCLUSIONS

1.- Combination of culturing and culture independent approaches has increased substantially the diversity detected in Blanes Bay. We analyzed 136 isolates which belonged mainly to the Gammaproteobacteria, Alphaproteobacteria, or the Bacteroidetes. Addition of these organisms increased the estimates of richness in ca. two times the OTUs richness previously described with molecular tools alone.

2.- Nine cases of complete or nearly complete identity between isolates and in situ sequences were found: ve in the Gammaproteobacteria, three in the Alphaproteobacteria and one in the Bacteroidetes. This suggests that culturing to some extent provides representative organisms for physiological response experiments for various taxa detected by molecular methods.

3.- Around 30% of the environmental clones and isolates formed microdiversity clusters constrained at 99% 16S rRNA gene sequence similarity. The nding of microdiversity clusters among isolate and clone sequences alike, suggests that this type of microdiversity is not an artifact and that similar evolutionary processes act on cultured and uncultured bacteria.

4.- The bacterioplankton communities of coastal areas where there is an intense impact of human activities, such as Ría de Vigo, are used to certain concentrations of PAHs dissolved in the water. However, during the summer when in that area there is an entrance of oceanic water oil additions affected much more the function and community structure of the resident microbial communities.

5.- Development of agellate populations in the experiments performed in diverse marine areas was seen to explain some of the patterns in bacterial function and community structure seen. In the mesocosm experiments performed in Ría de Vigo, for example, we detected a signicant effect of agellates on bacterial community structure.

6.- Addition of different types of inorganic nutrient to NW Mediterranean waters generated responses in bacterial function and community structure mainly because of the P-limited nature of bacterial growth in the area. An initial bacterial peak appeared in all samples to which P was added, independently of the phytoplankton species selected by the addition. A second bacterial peak later on during the experiment was more diverse, showing effects of the different phytoplankton that developed.

7.- Besides the fertilization produced by the phosphorus content of Saharan dust we found that an addition of dust also stimulated directly bacterial activity and community respiration. Dust addition increased bacterial abundance by ca. 2-fold and bacterial production by 5-fold. Primary production and community respiration were stimulated by dust and by P, but the net result of the addition of low amounts of dust was a heterotrophic system, while the net result of the high dust and P additions were net autotrophic communities.

187 Conclusions

8.- Bacterial community structure changed little between the P-addition and a low addition of dust (0.05 g L -1 ), but the resulting communities were very different from the control with no additions, and from the communities that developed following a large dust addition (0.5 g L -1 ),

9.- The incubation of bacterial communities in different environmental conditions (water from surface transplanted to the mesopelagic or the DCM, and viceversa) led to bacterial function and growth to mainly follow the conditions of the incubation tank, with little effects of the initial community and of the presence or absence of predators. Predator presence had a small effect on bacterial production but not on nal bacterial yield.

10.- The incubation of bacterial communities in different environmental conditions (water from surface transplanted to the mesopelagic and viceversa) lead to changes in bacterial community structure, indicating bottom-up forces to be much more important than top-down regulation.

11.- Comparison of different experimental approaches ranging from extremely modied nutrient elds (in Chapter 1) and little touched environmental transplants (chapter 5) show how each approach illustrates different aspects of the dynamic interplay between bacterial community structure, diversity and function in the ocean. The comparison also allows detection of general patterns in bacterial development in experimental mesocosms, which vary depending on the initial conditions of the samples.

12.- Comparison of the changes in bacterial community structure induced by the different experimental treatments presented in this work allows the discrimination of large induced changes (e.g. the transplant experiments of Chapter 5) from low induced changes (Saharan dust addition). These can, in turn, be compared to the seasonal variability do discriminate “large” from “small” effects on bacterial community struture.

188 Future perspectives

FUTURE PERSPECTIVES

In the different enrichment experiments presented, we used the ngerprinting technique DGGE that permits the comparison between samples but, however, has a limitation and seldom ever presents more than 20 bands. We suggest that should promote a technique such as ARISA (Amplied Ribosomal Intergenic Spacer Anlysis, Brown et al. 2005), which is a technique from which a number of around 100 sequences can be retrieved. The RNA section utilized by this technique is from 16S to 23S, including the ITS fragment that has large variability in length and sequence. These products are run on automated sequencer generating an electropherogram where the sequences are differentiated. Standards (e.g. from clones) can be run and used to identify the peaks (Brown et al. 2005, Hewson & Fuhrman 2006).

Besides the culture and molecular techniques used in this thesis to study the diversity of a specic environment, we suggest the use of PCR-independent approaches such as uorescence in situ hybridization (FISH) to complement the analyses of biodiversity of the ecosystem and to follow the temporal dynamics of bacterial populations in enrichment experiments. We used this approach in Teira et al. (2007) to detect the dynamics of small contributors to community structure (such as the Gammaproteobacteria Cycloclasticus ) in the oil-spill simulation experiment. In Chapter 2 we show how, this technique complements well what we can see in DGGE analyses.

There is an increasing interest in relating the ecological function of microbes and diversity. We, thus, suggest that more effort in this issue would provide a better understanding of ecosystem functioning. Different approaches could be used to address this question:

i) One approach would be the isolation of bacteria that allow the sequencing of the complete genome and the subsequent physiological analysis of the capacity of the isolates (e.g. González et al. 2008) specially during enrichment experiment where the concentration of the addition is more or less controlled.

ii) The combination of FISH with microautoradiography provides the information of the single- cell activity of specic bacterial groups on carbon processing. This technique utilizes radio labelled compounds, and thus labelling of the allochthonous compounds to determine the bacteria taking up these substrates isn’t an easy task. We tried hard with 14 C-labelled hydrocarbons, without success.

189 Future perspectives

iii) Another approach to determine which are the organisms growing because of a specic substrate addition, is a recently developed technique that consists in the incubation with compounds labelled with Bromodioxidouridine (BrdU), that is an halogenated nucleoside that works as an analoge to thymidine and as an alternative to [H 3] TdR. It is an alternative to radioactive substrates and can be applied to determine bacterial production if one follows a subsequent molecular analysis. It is used as a specic ngerprinting technique (BUMP-DGGE) that reveals the phylogenetic afliations of the presumably actively growing bacteria that have incorporated this compound (Urbach et al. 1999; Hamasaki et al. 2007). The method has been used simultaneously to FISH (Pernthaler et al. 2002).

190 ANNEXES Non gogoa, han zangoa

Proverbio vasco Annexes

ANNEX I:

Traditional methods for bacterial isolation and cultivation

Bacteria play signicant roles in marine food webs and in most marine biogeochemical cycles (Fenchel 1988). The large biomass of bacteria is believed to be a repository of relatively unknown diversity. Despite the overwhelming bacterial diversity present in the world’s oceans, the majority of marine bacteria can not be easily cultivated in the laboratory (Giovannoni & Rappé 2000).

The discrepancy between direct microscopic enumeration and plate counts of bacteria was rst pointed by Jannasch & Jones (1959). They attributed it to the presence of bacteria in aggregates, to selective effects of the used media, and to the presence of inactive cells. Since values estimated by epiuorescence direct counts are orders of magnitude higher than those estimated by plate counts (the so-called CFU or colony forming units), this has been referred to as “the great plate count anomaly” (Staley & Konopka 1985). Comparison of direct cell counts and classic cultivation approaches, have shown that only a minor fraction, <1% of total bacterial numbers, can be cultivated in common media (Staley & Konopka 1985)

In contrast, based on DNA-DNA hybridation of the genomic DNAs of isolates obtained with the traditional ZoBell medium run against community DNA, it has been suggested that readily culturable bacteria can be abundant in the marine water column (Rehnstam et al . 1993, González et al . 1996, González & Moran 1997, Pinhassi et al . 1997).

The great challenge in microbial ecology is to relate diversity to the ecological function and biogeochemical activities of specic bacteria (Hamasaki et al. 2007). To correlate the activity of specic microorganisms with dened environmental or physiological parameters

Among the different ways for associating diversity and functioning, one possibility is the isolation of relevant members of the assemblage and their study in the laboratory. If we know that a given organism does a given function, we might be able to search for its presence to better know about that linkage. But currently, the general opinion is that the isolates tend not to be representatives of the indigenous bacteria. Lack of knowledge about the dominant uncultured microorganisms can be attributed directly to their low cultivability by standard microbiological techniques.

193 Annexes

Progress has been made in recent years at cultivating members of the numerically abundant clades; for example, the SAR11 clade using sterile marine water supplemented with either phosphate, ammonium and dened mixture of organic carbon compounds (Rappé et al. 2002), the marine group I Crenarchaeota was grown aerobically at 28ºC in Synthetic Crenarchaeota Media amended with autoclaved vitamin solution, KH 2PO 4 solution, selenite-tungstate solution, bicarbonate solution and ammonium chloride per litre of media (Konneke et al. 2005) and the OM43 clade (Connon & Giovannoni, 2002) were obtained in pure cultures. Cultivation allows a comprehensive physiological characterization of relevant members of bacterial communities. Therefore, is important to complement the culture-independent ndings of the microbial communities in aquatic and other environments (Stevens et al. 2005).

The interest on the characterization of new species is increasing, particularly since it is now possible to know their genomes through initiatives such as the Gordon & Betty Moore Foundation Marine Microbial Initiative. Knowing genomic composition allows us to determine the function of microbial organisms in specic environments. Rush et al. (2007) published part of the Sorcereer II Global Ocean Sampling Expedition and suggested the utility of having isolates from a well characterized biogeochemical site to be used as scafolds for reconstruction of the genomes.

Using traditional cultivation techniques we obtained isolates collected offshore at the Blanes Bay Microbial Observatory (32º 80’N 34º 93’E) Some of these isolates were described as novel. Of some, whole-genome sequencing was carried out by the J.Craig Venter Institute (USA) through the Gordon and Betty Moore Foundation initiative in Marine Microbiology (https://www.moore.org/marine- micro.aspx).

194 Annexes

A.- PROTOCOL OF ISOLATION

1.- Petri dish preparation

- 4 g of Peptone (Bacto TM Peptone DIFCO 211677)

- 0.8 g of yeast extract (Bacto Yeast Extract DIFCO 212750)

- 12.5 g of Agar (Bacto TM Agar DIFCO 214010)

- Add 600 ml seawater ltrated by 0.2 μm (sterivex Millipore SVBV010RS

or Membrane lters GVWP02500 or GVWP04700)

- Add 200 ml of sterile Milli Q water

- Mix until is dissolved (with a magnetic stirrer)

- Autoclave 20 minutes at 121ºC (1 kg/cm 2)

- Mix the bottle and autoclave again 20 minutes

- Let it stand until is warm and can be touched

1.1-Prepare Petri dishes

- Leave the Petri dishes in the laminar ux chamber for 30 minutes with ultraviolet light on

- Dispense the liquid into the Petri dishes (40 ml)

- Close the plates

- Leave for 24 h to solidify

- Turn upside-down the Petri dishes (To prevent agar from drying too much)

- Leave standing for 2 days (To completely dry out)

- Close each Petri dish with paralm. (To avoid any contamination)

- Label them with the preparation date.

195 Annexes

1.2- Spread out sample onto the Petri dishes

- Prepare different dilutions of the sample (with sterile seawater): 1x, 10x and 100x

- Add 100 μl with a pipette and spread with a digralsky spreader

- Leave the Petri dishes growing for one week at in situ temperature.

1.3- Isolation of the colonies

- Pick the colonies (with an inoculating loop) and spread them in a new Petri dish.

- Divide the Petri dish in four slices

- Spread each colony in one square of the Petri dish.

- Label each square with the number or name of colony and date of the inoculation.

Fig.1.- Colony forming bacteria from Blanes Bay growing on agar plates .

196 Annexes

1.4- Growth in liquid media

Preparation of 1 L of the Zobell liquid media:

- 4 g of Peptone

- 0.8 g of yeast extract

- Add 600 ml seawater ltrated by 0.2 μm

- Add 200 ml of sterile MilliQ water

- Mix until everything is dissolved

- Dispense 20 ml in 100 ml sterile polycarbonate Nalgene bottles.

- Autoclave for 20 minutes at 121ºC

- Pick the chosen colony from the Petri dish

- Place it in the liquid media

- Leave one or two days until the liquid is opaque.

1.5- DNA collection

- Place all the liquid media in a 15 ml centrifuge tube (DELTALAB) and centrifugate for 10 minutes at 4500 rpm

- Remove the supernatant with a pipette.

- Maintain the pellet at -80ºC

1.6- Check for contamination

- Take 15 μl of the liquid media and spread them on a Petri dish.

- When the drop is dry, spread in the Petri dish with an inoculation loop.

- If the growing colonies are only of one type we can presume that there is no contamination.

197 Annexes

Fig.2.- Two different strains growing on agar plates.

1.7- Glicerination

- Mix 900 μl of sterile glycerol (SIGMA G5516) at 50% with 900 μl of the liquid media

- Keep at –80ºC.

198 Annexes

ANNEX II: Scanning Electronic Microscopy (SEM) images of bacterial cultures

An electron microscope is a type of microscope that uses an electron beam to produce and enlarged an image. Electron microscopes have much greater resolving power and can obtain much higher magnications than light microscopes. The two important contributions to electronic microscopy were the Broglie (1924) theory about the wave proprieties of electron movement and the Bush (1926) discovery of the magnetic or electrostatic elds.

There are two kinds of electron microscopes: the transmission (TEM) and scanning (SEM), or the combination of both (METB). TEM has to be used in ultra-thin cuts (from 10 to 200 nm) depending on the sample. The electrons pass through the sample to form the image. The electron beam scan over the surface, and the signals produced include secondary electrons, backscattered electrons, characteristic X rays, Auger electrons, and photons of various energies. The interactions that the beam electrons undergo in a specimen reveal information on the specimen’s composition, topography, crystallography and other properties.

Samples preparation is a critical process. The main steps are, the selection of the sample, cleaning, xation, deshydratation, placement on the supports and coverage with a conductive product or a metal. All these have to be gently done to avoid the cells to shrink or to break in their surface.

199 Annexes

2- PROTOCOL SEM

2.1- Sample preparation - Counts of bacteria should not exceed 10 6 cells ml -1

- Prepare different dilutions of the liquid media: 1x, 10x and 100x in 10 ml

2.2- Sample xation - Add Glutaraldehyde at 2-3% nal concentration to 10 ml of the different dilutions.

- Leave xing during 2-4 h.

2.3- Poli-L-Lysine treatment: to adhere the sample to the lter - Cover the lter with a drop of Poli-L-Lysine 0.1% w/v solution (SIGMA)

- Wait 1 minute and submerge the lters in Milli Q water for 30 seconds.

2.4- Filtration Filtrate the samples with 0.2 μm polycarbonate lter previously prepared with Poly-L-Lysine (Millipore).

After the ltration, place the lters in a metal net capsule to avoid coiling of the lter.

2.5- Dehydratation Deshydratate in an alcoholic series with Ethanol and esterile MilliQ water:

Leave for 15 minutes at 30%, 15 min. at 50 %, 15 min at 70 %, 15 min at 80 %, 15 minutes at 90 % (two times) and 15 minutes at 100 % (two times)

2.6- Dry by critical point

Critical point dryer (BAL-TEC, CPD 0309). is used to end the dry process without any surface tension on the samples.

1º- Place the sample in a cage of the dryer. 2º- Add ethanol just to cover the sample 3º- Lower temperature until 4-7ºC

4º- Fill the rest of the cage with liquid CO 2

5º-Remove half of the total liquid every 15 minutes around 7-8 times until is all CO 2

6º- Increase temperature until 37ºC to avoid liquifying of the CO 2.( The critical point of CO 2 is reached at a temperature of 31ºC and a pressure of 73,8 bar)

7º- Decrease pressure 6 bar per minute to avoid liquifying of the CO 2

200 Annexes

Images of Blanes Bay Isolates : (check Chapter 1 - Table 4 for details of the strains).

Fig.1 M134 : Dokdonia donghensis

Fig.2 M193: Phaeobacter sp.

Fig3 M222: Vibrio sp.

201 Annexes

Fig.4 M121: Marinomonas blandensis

Fig.5 M217: Leeuwenhoekiella blandensis

Pinhassi P, Bowman JP, Nedashkovskaya OI, Lekunberri I, Gomez-Consarnau L & Pedrós- Alió C (2006). Leeuwenhoekiella blandensis , sp.nov., a novel marine member of the family Flavobacteriaceae. Inter J Syst Evol Microb 56 : 1489-1493.

Pedrós-Alió, C. 2007. Dipping into the rare biosphere. Science 315 :192-193

202 Annexes

Fig.6 M297: Reinekea blandensis

Pinhassi J, Pujalte MJ, Macián MC, Lekunberri I, González JM, Pedros-Alió C & Arahal DR (2007). Reinekea blandensis sp. nov ., a marine genome-sequenced gammaproteobacterium. Inter J Syst and Evol Microb 57 : 2370-2375

Fig.7 M152: Polaribacter dokdonensis

González JM , Fernández-Gómez B, Fernàndez-Guerra A, Sánchez O, Gómez-Consarnau L, Coll-Lladó M, del Campo J, Escudero L, Rodríguez-Martínez R, Alonso-Sáez L, Latasa M, Paulsen I, Nedashkovskaya O, Lekunberri I, Pinhassi J, & Pedrós-Alió C (2008) Genome analysis of the proteorhodopsin-containing marine bacterium Polaribacter sp. MED152 (Flavobacteria): a tale of two environments. PNAS doi: 10.1073/pnas.0712027105

203 Annexes

Fig.8 RED 65: Bermanella marisrubri

Pinhassi J, Pujalte MJ, Pascual J, González JM, Lekunberri I, Pedrós-Alió C & Arahal DR (2008) Bermanella marisrubri gen. nov., sp. nov., a novel genome-sequenced Gammaproteobacterium from the Red Sea. Inter J Syst Evol Microb (in press)

Fig.9 SCB36 Winogradskyella sp.

204 Annexes

Fig.10 MED92: Neuptuniibacter caesariensi

Arahal DA, Lekunberri I, Gonzalez JM, Pascual J, Pujalte MJ, Pedrós-Alió C, & Pinhassi J (2007) Neuptuniibacter caesariensis gen. nov., sp. nov., a novel marine genome-sequenced gammaproteobacterium. Inter J Syst Evol Microb 57 : 1000-1006

205

REFER ENCES (introduction dis cussion and an nexes) Imagination is more im portant t han kno wle dge

Albert Einstein References

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AGRADECIMIENTOS Just can ´t live that negative way…. Make way for the positive day!

Bob Marley AGRADECIMIENTOS

Aunque aún no me lo crea parece que ya llego el nal de esta tesis, donde queda reejado el trabajo que he conseguido realizar gracias al apoyo y ayuda de tantísima gente que día a día han estado ahí para compartir cada momento y ofrecerme la mejor de sus sonrisas, pero también gracias a esa otra parte de mi que a pesar de la distancia siempre han estado tan cerca.

Primero de todo agradecer a mi director de tesis sin quién no habría sido posible este trabajo. Gracias por haberme apoyado desde el primer momento en el que me presenté en tu despacho (con un día de plazo para pedir la beca) y por todo lo que me has enseñado. Sobretodo gracias por tener esa actitud positiva para afrontar cada problema que tanto me ha motivado para realizar mi trabajo. Muchísimas gracias por darme la oportunidad de conocer el mar (tan lejano para mí) desde su punto de vista más pequeñito hasta el más grande en las campañas oceanográcas. Mil gracias en este último periodo por todo el esfuerzo dedicado.

Mis primeros pasitos en la biología molecular fueron dados de la mano de Jarone, a quien agradezco enormemente que me transmitiera su gran pasión por la ciencia, pero también agradezco haber sido siempre tan cercano para mí. Muchas gracias por valorar siempre mi trabajo y seguir mis pasos aún desde Suecia.

En esta tesis han sido cruciales los consejos y correcciones de Carles, muchas gracias por toda la ayuda. A Silvia Acinas agradezco muy especialmente la ayuda y el toque tan especial que dio a mi trabajo. A Emilio por transmitirme sus conocimientos de ARB. A Jose González por contar con mi colaboración y por los varios favores de última hora. A Dolors por los días tan divertidos en Vigo y las aventurillas varias que lo rodearon (con parada en Lerma) y por irradiar siempre alegría. Mila esker horrela izateagatik! A Cèlia por los animos y ayuda especialmente esta última temporada. A Ramon, Cesc y Rafel por sus ánimos y comentarios. Y a todos los que habéis conseguido transmitir tan buen ambiente a todas las generaciones, mil gracias por hacer del ICM un lugar tan especial, de donde cuesta tanto marchar!

Muy especialmente quiero dar las gracias a Andrés que ha sido mi compañero en este viaje desde su inicio, sobre todo en los últimos años, con él que he compartido el estrés, la alegría, las penas, el hambre, el sueño... cada día. Como darte las gracias por haber sido siempre tan compresivo, animarme incluso en tus peores momentos y intentar hacerme ver los problemas de otra manera. Mil gracias por ser el mejor compañero y un gran amigo. Askenian lortu dugu! Orain ospatzeko.. Argentinara!

A Laura Gómez quiero agradecer su gran ayuda en todo momento y disponibilidad. Muchísimas gracias por darme tan buenos consejos y por estar ahí en momentos difíciles y transmitirme tranquilidad. Aunque te marcharas a Suecia (no te lo tendré en cuenta) ha sido un lujo que siempre consiguiéramos el encuentro. Quedan muchos otros!

Gracias a mis chicas por haber compartido conmigo las mejores sobremesas y hacer que volviese mi sonrisa incluso en la peor de las situaciones. A Vane por dar un toque tan especial a cada momento y conseguir sacarme de la monotonía, a Irene por transmitirme siempre mucha energía y optimismo, a Clarita por tantos y tantos favores y por haberme entendido siempre, a Dacha por haber cuidado también de sus niños, a la María que aunque ya marchó siempre alegró nuestras comidas, a Pilar

219 por su agradable compañía y por su puesto no se me olvida el chico Javier que siempre tiene los comentarios más oportunos y me hace reír tanto. A todas y cada uno gracias por tan buenos consejos y por haber compartido conmigo los mejores momentos en el Instituto.

Gracias a Laurita por todo lo que me has enseñado y todo lo que hemos compartido juntas(desde jefe, viajes a piso) y espero que algún día nos volvamos a encontrar, quien sabe quizá Plentzian?

A Imelda que (aunque de Burgos) siempre consiguió hacerme reír y que tanto la eché de menos cuando se marcho, pero a pesar de todo siempre estuvo muy cerquita. Aún nos queda mucho por celebrar!!

A Fer que cuando estaba en Barcelona tanto me ayudo incluso acogiéndome en su casa, que también consiguió hacer más amena mi estancia en Holanda. Preparate boludo que en breve nos volveremos a ver!

A Pati por todos esos momentos compartidos y las interminables charlas que durante este último año tanto me ayudaron.

A Georgios por descubrirme lo fascinante de las profundidades del mar y estar siempre dispuesto a ayudarme. Gracias por dejarme el legado de enseñar el magnico programa de enmaquetación. A Cris por transmitirme siempre alegría y cuidar de mÍ esta última temporada y Lorenzo por sacarme siempre una sonrisa. A mis compis de piso Meri e Inma quienes siempre me hicieron reír. A Ale y Vero por la celebración tan especial del txupinazo en las calles de la Barceloneta. A mis venios italianos Ida y Danielle por las estupendas cenas compartidas.

A Jose Manuel sin quien no habrían dado lugar esas maravillosas fotos del SEM (a pesar de encontrar tan aburridas a las bacterias) Olga por su ayuda en molecular, a Enric por sus consejos de estadística, a Mickhail por su ayuda con las conversión de unidades, a Silvia también por su ayuda con la estadística, a Hugo por resolverme tantas dudas y a muchos otros que en momentos cruciales me echaron un cable. A Susana y Vicky las estudiantes que estuvieron de prácticas y tanto me ayudaron .

El trabajo que se realizó en Vigo fue gracias a la colaboración de mucha gente, gracias a todos por los esfuerzos que hicieron posible que saliera a delante, muy especialmente a Paco y Pepe por todas las sugerencias y ayuda. A todo el resto de gente involucrada Luisa, Belen, Mar, Isabel, Oscar, Pili, Eva, Jose, Maria y muchos otros. Por supuesto fue indispensable la ayuda de los asturianos expertos en citometría Xelu y Ale, muchas gracias a los dos por lo bien que lo pasamos juntos y por haber estado ahí en cada momento para lo que fuera.

A toda la gente que me ha ayudado en mi estancia en Holanda. Eva y Vero que tan bien me cuidaron y hicieron mis estancias más divertidas y emocionantes con la colaboración el de Bilbo y Goa. A mi compañero de bungalow Pedro que consiguió que cada día fuera un aventura en la pequeña isla. A Txetxu que justo marchaba cuando llegue y con quien pasé ratos tan divertidos, gracias lo compartido en Texel y en el Hesperides. Special thanks to Gerhard for all the help in my stays, for looking after me so well in any situation, promise next time I will not drive in Texel!

220 En mi estancia en Kalmar agradecer muy especialmente la hospitalidad de Neus y Jarone y a Nuria y la perrita: Twiggy que tanta compañía me hizo. A Anna por llevarme a conocer un Julefrokost en un granja donde conocí la esencia de la sociedad escandinava.

De las campañas realizadas quisiera agradecer a todos mis compañeros con los que compartí esta aventura tan especial para mí. Especialmente a Javier, Fede y Ale que los volví un poco loquitos con mis experimentos y a Nahiara, Eva, Nacho, Marc, Xavi, Ramón, Kim...mil gracias por tantas risas compartidas. También quiero agradecer al Ocial Alfonso que tan bien me cuido.

A toda la gente que siempre ha tenido una sonrisa que ofrecer y tan bien me han hecho sentir, algunos que ya marcharon, algunas nuevas incorporaciones y a los de siempre MIL GRACIAS: Violeta, Gemma Oscar, Mariona, Ana Sabata, Estela, Hugo, Montse Sala, Albert René, Silvia Anglés, Nagore, Laura Arin, Elisa, Nuria Gómez, Sofía, Sonia, Ester, Montse, Raquel, Lorena, Bea, Elisabet, Arantza, , Clara, Juancho, Thomas, Ero, Mireia, Bego, Lidia, Andrea, Jordi, Xavi Leal, Rodrigo, Julia, Evarist, Albert Calbet, Eva Calvo, Carles, Eva Flo, Enrique Isla, Josep Maria, Cristina Linares, Cova, Sergi Rossi, Elisabeta...

Agradecer a Sara por toda la ayuda que me prestó para que viniese a Barcelona y a Jordi Dach por sus consejos de venir al ICM.

Toda la gente que estuvieron ahí pero más alejados de la ciencia:

A Antonia por buscarme el mejor hogar (“Parallel”) donde encontré una gran familia, y tuve un inversión total en la cultura catalana, gracias a todos y muy especialmente a Miquel, Pau, Jordi, Cunci y Serrano, por haber cuidado también de vuestra “chiqueta” en sus primeras andanzas por la gran ciudad, y por esas riquísimas paellas, arroz al forn y por todos los rincones que descubrimos juntos de Barcelona. Muy especialmente a Alfred por todo su cariño y apoyo en los inicios de esta aventura.

A Blanca y a Mateo por contar conmigo y entenderme, y a todos los del Bitacora, especialmente al Arnau y Juantxo.

A toda la cuadrrrrrrilla de Iruña: Maite, Izaskun, Sandra, Eva, Isa y Nerea, que han seguido toda la vida mis pasos día a día, sin perder detalle, para las que nunca la distancia fue un problema. Gracias por todas la visitas y por la amistad incondicional. Por ese sentimiento de cuadrilla que siempre nos ha mantenido juntas. Mila esker por todo lo vivido por esperar siempre son ansia mi llegada. A Maitetxu especialmente por su gran imaginación, sus buenos consejos de diseño y por las largas conversaciones de Skype que tanto me han ayudado. A todas gracias por escucharme siempre y hacerme sentir tan especial. Mil gracias por pedir que Sanfermin me echara un capote y conseguirlo! A pesar de que no veáis llegar el día de mi vuelta: Benetan laster itzuliko naiz!

A mis primicos: Mikel, Edurne, Amaia, Mirentxu, Lander, Iosu por venirme a visitar con tanta ilusión, por animarme siempre, por tantas conversaciones con las que hemos intentado arreglar el mundo, por

221 hacer que el hecho de estar juntos fuera tan especial. Por incluso alguno veniros a vivir a Barcelona y estar ahí siempre que ha hecho falta.

A mi tío Joaquim que rápidamente captó mi idea, muchas gracias por el diseño.

A Maria que durante todos estos años (y muchos más) estuviera donde estuviera nunca se separó de mí, que siempre me entendió. Por esos ratos compartidos en Leoz, Barcelona, Etxaleku... y por supuesto a Nuria muduaren onen amatxoa! Por comprenderme y mostrarme la vida de otra manera.

A Arantza que siempre estuvo ahí me fuera donde me fuera mil gracias por la amistad y conanza de saber que puedo contar contigo. Gracias a Dani quien me animó a salir de la vieja Iruña, a Felix y Amaia que fueron mi familia danesa y me animaron a venir. A Unai: dudarik gabe lagundu zenidan urte asko pasa izan arren, mila esker denagatik!

A toda la gente de Iruña que cada vez que voy me reciben con tanta ilusión Jon, Maite Aginaga, David, Monica, Mikel Goñi, Mikel de Goñi, Iosu, Maider, Eloisa...y muy especialmente a Aritz por todo el cariño.

Finalmente agradecer a mis padres y Maitanica que siempre conaron en mí y me dieron las energías, la tranquilidad y el cariño incondicional que me ha motivado cada día.

MUCHAS GRACIAS A TODOS!!! MOLTES GRACIES A TOTS!!! MILA ESKER DENORI!!! THANKS TO EVERYONE!!!

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