Dynamics of Bacterial Endospores, and Microbial Activities in Soils and Sediments

Home , Endospore, Exosporium

exosporium coat

core

Inner membrane Outer cortex membrane

Transmission electron microscopy image of B. atrophaeus spore. After Zhang et al., 2006.

Dynamics of bacterial endospores, and microbial activities in soils and sediments

Paulina Tamez-Hidalgo, PhD dissertation

Hans Røy, supervisor

Faculty of Bioscience, Department for Microbiology, Aarhus University March 2014

Abstract

Prokaryotes (Bacteria and Archaea) are essential for life on Earth. They catalyze unique and necessary chemical transformations, which shape the biogeochemical cycles, develop stable and labile pools of carbon (C) and nitrogen (N), produce essential molecules that multicellular organisms live upon, and together they comprise the largest portion of life’s genetic diversity.

Prokaryotic communities face recurrent nutrient exhaustion periods that can be prolonged up to centuries, until a season with nutrient deposition arrives. Thus, they have developed the ability to lower their metabolism to the limit between being considered slow growers, dormant or even immortal. Despite discussions about minimum energy requirements and metabolic categories, there is a paraphyletic group of Bacteria (the Firmicutes or low-GC Gram positive) able to create the toughest dormant forms on Earth, the endospores. Bacterial endospore is a great advantage to persist in environments in spite of the physicochemical conditions. Bacterial endospores can remained dormant for extraordinarily prolonged periods. Despite their apparent metabolic inactivity, endospores are constantly monitoring the nutritional status of their surroundings. Thus, their ability to react rapidly after an increased presence of monomeric low molecular weight compounds (mLMW) compounds such as amino acids, purines and sugars is a remarkable characteristic.

Studies of endospores abundance and dynamics in the environment have been recently enriched by the analytical quantification of dipicolinic acid (DPA). This endospore biomarker can be quantified using common laboratory-based methods, such as liquid chromatography by measuring the fluorescence of the Tb3+-DPA complex. As any other culture independent approach, this method eliminates underestimation bias due to cultivation. The present investigation was focused on bacterial endospores abundance in different environments, and the factors related. Abundance was estimated through DPA quantification.

The overall results showed that endospore presence is intrinsically related with nutrient limitation. The more nutrient exhausted the environment is, the higher the endospore numbers are found. However, in marine sediments endospores decay rather than accumulate with time, which is an indication of endospore settling from the overlying water and buried into the sediment. Calculated half-life was in the order of hundreds of years. A considerable amount of endospores after the sediment have reached thousands of years was also detected. This was interpreted as a subpopulation able to persist over burial at larger time scales and being in a steady-state to the relevant time scales. Predicted microbial turnover times were in the range of tenths to hundreds of years, which is similar to the endospore half-life.

Abstrakt

Prokaryoter ( Bakterier og Archaea ) er af afgørende betydning i livets planet. De katalyserer unikke og nødvendige kemiske omdannelser, der former de biogeokemiske kredsløb, udvikle stabile og labile puljer af kulstof (C) og kvælstof (N), producerer vigtige molekyler, flercellede organismer lever på, og sammen udgør den største del af livets genetiske mangfoldighed. Prokaryotiske samfund står over for tilbagevendende næringsstoffer udmattelse perioder, der kan forlænges op til århundreder, indtil en sæson med næringsstof aflejring ankommer. Således har de udviklet evnen til at sænke deres stofskifte til grænsen mellem betragtes langsom dyrkere, hvilende eller udødelige. Trods diskussioner om minimumskrav til energi og metaboliske kategorier, der er en paraphyletic gruppe af bakterier (de Firmicutes eller lavt-GC Gram positive) i stand til at skabe de hårdeste hvilende former på jorden, endosporer. Bakteriel endospore er en stor fordel at fortsætte i miljøer på trods af de fysisk-kemiske forhold. Bakterielle endosporer kan forblev hvilende for ekstraordinært lange perioder. På trods af deres tilsyneladende metaboliske inaktivitet er endosporer overvåger konstant ernæringstilstand deres omgivelser. Således deres evne til at reagere hurtigt, når en øget forekomst af mLMW forbindelser, såsom aminosyrer, puriner og sukker er en bemærkelsesværdig egenskab. Undersøgelser af endosporer overflod og dynamik i miljøet er for nylig blevet beriget ved den analytiske kvantificering af dipicolinsyre (DPA). Denne endospore biomarkør kan kvantificeres ved hjælp af fælles laboratorie -baserede metoder, såsom væskekromatografi ved at måle fluorescensen af Tb3 +-DPA kompleks. Som enhver anden kultur uafhængig tilgang , denne metode eliminerer undervurdering bias som følge af dyrkning. Den aktuelle undersøgelse var fokuseret på bakterielle endosporer overflod i forskellige miljøer, og de faktorer relateret. Overflod blev estimeret gennem DPA kvantificering. De overordnede resultater viste, at endospore tilstedeværelse er uløseligt forbundet med næringssaltbegrænsning. Jo mere næringsstof udtømt miljøet er, jo højere endospore numre er fundet. Men i marine sedimenter endosporer henfald i stedet akkumuleres med tiden, hvilket er en indikation af endospore afregning fra det overliggende vand og begravet i sedimentet. Beregnet halveringstiden var i størrelsesordenen hundreder af år. En betydelig mængde af endosporer efter sedimentet har nået tusinder af år blev også opdaget. Dette blev fortolket som en delpopulation i stand til at bestå over nedgravning på større tidsskalaer og at være i en stabil tilstand til det relevante tidsrum. Forudsagte mikrobielle omsætningstal tider var i størrelsesordenen tiendedele for hundreder af år, hvilket svarer til den endospore halveringstid.

Acknowledgments

First of all, I want to thank the Mexican Ministry of Science and Technology in Mexico (CONACyT) for giving the opportunity to study abroad and being part of Aarhus University. Thanks to Per Nørberg for his support my first year. Secondly, I would like to thank all people that were, and are part of the HPLC lab: Alice Langerhuus, Kristoffer Pill, Drew Steen, Stephan Braun and Jonas Hovergaard. Your company, good advices and technical assistance made my life in the PhD really pleasant. Special thanks to Lykke Poulsen, your technical assistance, valuable work and long chats about nothing especial, babies, concerts and bubble tea made this project cooler. Most of all many thanks to Bente A. Lomstein, without your guidance and advices, this project would have never seen light. Thirdly, I would like to thank Hans Røy for taking over supervision and withstanding the toughest part of it with professionalism. Without your emotional support, and envision of my ideas, the chapters would not have been finished the same way. Fourthly, I thank all co-authors of the manuscripts for your scientific contribution, and for helpful assistance during the writing phase: Bent T. Christensen, Mark A. Lever, Oscar Chiang, Mathias Middelboe, Renato Salvatecci, Bente Lomstein and Hans Røy. Also, I would like to thank all my friends and fellows at the department. You all are too many to be mention. I spent very good times with each of you and I will treasure your friendship forever. Finally, but not less importantly, this thesis is dedicated to my family. To my father, he has always boosted my curiosity and supported my decisions. To my mother, she has always been there, supporting everything I do without questioning. To my sister that has always been the compass and the grounds to walk through. Also, I would like to thank my new family. To Torlak and Irene, you have always been supportive and sometimes acted as a parent when I most needed it. To Ane, Rene and little Victor, you have always being caring and fulfilled the missing gap of my family. To Tore, Ketil, you and I have spent a lot of great time together. You have the ability to see things through, especially when we have those cynical discussions about the world. Furthermore, I would like to thank Leonor for being the best friend in the world and taking over Max when I had to go to the lab o spent extra time at the university. Especially, this thesis is dedicated to Ketil, the love of my life. You are my partner, my friend and my direction. Many, many thanks, for all those nights you sat next to me to discuss the manuscripts, when you spent your time read them and for all your proof-reading corrections, suggestions and even checking my calculations. Without you Maxito would have missed his mom tremendously. Maxito, you are my sunshine, this thesis is also dedicated to you.

Preface

This document is the results of 3 years of intensive work, discussions and in the last part writing. The work presented in this PhD dissertation, consisted in the study of dormancy in different environments. Dormancy was investigated through the quantification of endospores and other biogeochemical parameters that are possibly correlating with their presence in the different environments studied. Furthermore, microbial activity was evaluated in deep sediments, and the implications for an extant major non-sporulating dormant community were discussed. Three environments were analized during this PhD project: endospores in soils (Chapter 1) and sediments (Chapter 2), and activity of non-sporulating dormant cells in deep sediments (chapter 3).

The document is divided in a general introduction written as a monograph that comprises the general subject and more particular topics that were involved in the development of the subsequent chapters. Next there are chapters. Each of them is a manuscript draft to be submitted to peer- reviewed scientific journals. The first two are in an almost finish stage being chapter one ready to be submitted after the few last corrections are made. This is a story about the presence of bacterial endospores and the environmental factors promoting them, in different arable soils as evaluated by long-term experimental soil treatments. The name of this chapter is: The prokaryotic community and its bacterial endospores in soil from three long-term agricultural experiments: effect of fertilization, straw incorporation and soil type. The second chapter is close to completion and it will go through a round of co-authorships revisions after the delivery of this thesis. The storyline behind this chapter is the decay of endospores in the shallow subsurface biosphere. The name of the chapter is: The dynamics of endospores in the subsurface of the Peru margin. Finally, the third chapter is a manuscript draft in its first stage. This manuscript will go as well, through a round of co-authorship revisions. This is a study about the microbial activities in the deep subsurface as evaluated in two ways: 1) a recently developed model that estimates the speed at which the total organic carbon (TOC) pool is oxidize and therefore its decrease with time. Based on that, the model estimates the turnover times of biomass and necromass, 2) integrated total carbon oxidation rates based on the observed TOC exponential decay. The name of this chapter is: Microbial activity rates of a Holocene- Pleistocene biosphere. The main results of these three manuscripts are contained in a chapter, subsequent to the introduction, entitled PhD synthesis. So the reader can have a familiar impression of the following manuscripts when reading them.

Contents

Abstract ...... 1 Abstrakt ...... 2 Acknowledgments ...... 3 Preface ...... 4 Introduction ...... 8 Microorganisms, their role as organic matter mineralizers ...... 8 Global estimations of prokaryotic cells ...... 10 Dormancy in nature ...... 11 Dormant or slow growers? ...... 13 Bacterial endospores, a conspicuous fraction of the dormant compartment ...... 15 Endospore enumeration in the field ...... 18 Longevity ...... 23 References ...... 27 PhD synthesis ...... 34 Chapter 1 ...... 35 Chapter 2 ...... 39 Chapter 3 ...... 41 Chapter 1 ...... 47 The prokaryotic community and its bacterial endospores in soil from three long-term agricultural experiments: effect of fertilization, straw incorporation and soil type ...... 47 Abstract ...... 48 1. Introduction ...... 49 2. Materials and Methods ...... 51 2.1. The experimental sites ...... 51 2.2. Soil collection and preparation for analysis ...... 52 2.3. Analyses for soil TOC and TN ...... 53 2.4. HPLC analysis of amino acids and amino sugars ...... 53 2.5. Analysis of dipicolinic acid (DPA) ...... 54 2.6. Cell counts ...... 54 2.7. DNA extraction ...... 55 2.8. Quantitative PCR (qPCR) for 16S rRNA ...... 56

3. Results and Discussion ...... 58 3.1. Chemical analyses ...... 58 3.2. The abundance of cells ...... 58 3.3. The abundance of endospores ...... 59 3.4. Relative abundance of Bacteria and Archaea ...... 60 3.5. Relative proportions of Firmicutes and Bacteria ...... 61 3.6. Ratio of fungal to bacterial residues ...... 61 3.7. Proportions of vegetative versus dormant organisms ...... 61 3.8. Cell and endospore numbers and soil chemical parameters ...... 63 3.9 Determinant factors of bacterial and endospore abundance ...... 63 4. Conclusions...... 66 Acknowledgements ...... 67 Figure legends ...... 68 References ...... 69 Chapter 2 ...... 82 Manuscript: The dynamics of endospores in the subsurface of the Peru margin ...... 82 Abstract ...... 83 1. Introduction ...... 84 2. Materials and Methods ...... 86 2.1. Study site ...... 86 2.2. Sampling and sample processing ...... 86 2.3. Sediment dating by 210Pb ...... 87 2.4. Sediment dating by 14C ...... 87 2.5. Total organic carbon (TOC) ...... 88 2.6. Total hydrolizable amino acids (THAA) ...... 88 2.7. Extraction and enumeration of prokaryotes ...... 88 2.8. Quantification of dipicolinic acid (DPA) and estimation of endospore numbers ...... 89 3. Results ...... 90 4. Discussion ...... 92 4.1. Depth profiles of endospores ...... 92 Acknowledgements ...... 93 References ...... 95 6. Figure legends ...... 100

Chapter 3 ...... 106 Microbial activity rates of a Holocene-Pleistocene biosphere ...... 106 1. Introduction ...... 107 2. Materials and Methods ...... 109 2.1. Study site ...... 109 2.2. Sampling and sample processing ...... 109 2.3. Radiocarbon measurements ...... 110 2.4. Extraction and enumeration of prokaryotes ...... 111 2.5. Total organic carbon (TOC) and total nitrogen (TN) ...... 111 2.6. Total hydrolysable amino acids (THAA) and total hydrolysable amino sugars (THAS) ...... 112 2.7. Analysis of diagenetic indicators ...... 112 2.8. Stereochemical composition (D- L-amino acids isomers) ...... 112 2.9. D:L-Amino acid modelling of bacterial activity ...... 113 3. Results ...... 115 3.1. The two sediment cores at the Peru margin, upwelling system ...... 115 3.2. Abundance of prokaryotic cells ...... 115 3.3. Profiles of TOC, TN, THAA and THAS ...... 115 3.4. Source of OM ...... 117 3.5. D:L-Amino acid measurements ...... 117 4. Discussion ...... 118 4.1. Carbon oxidation under different scenarios ...... 119 4.2. Turnover times of biomass and necromass ...... 120 Acknowledgments ...... 121 References ...... 122 Figure legends ...... 127 Supplementary material ...... 135

Introduction

Microorganisms, their role as organic matter mineralizers

Bacteria and Archaea, popularly simplified as prokaryotes, are an essential component of our planet. What prokaryotes lack in size, they make up in numbers (Parkes et al., 1994; Whitman et al., 1998; Torsvik and Øvreås, 2002; Kallmeyer et al., 2012). They catalyze unique and necessary chemical transformations, which shape the biogeochemical cycles, develop stable and labile pools of carbon (C) and nitrogen (N), produce essential molecules that multicellular organisms live upon, and together they comprise the largest portion of life’s genetic diversity. Mineralization of nutrients carried out by prokaryotes comprise a wide range of biochemical processes ruled both by the accessibility of terminal electron acceptors and the energy yield from the redox reaction involved in the process. By doing so, prokaryotes shape their own surrounding environment in µm3 scale, helping to form biogeochemical interfaces with soil and sediment matrix (Canfield and Thamdrup, 2009; Totsche et al., 2010). Therefore microbial communities are thought to be the architects of soil (Rajendhran and Gunasekaran, 2008), as they consume inorganic compounds to construct biomolecules needed to grow and excrete inorganic waste compounds that are readily used by plants. In the marine environment including sediments, prokaryotes contribute greatly to organic matter (OM) transformation since CO2 is fixed by primary production and enters the water column as organic carbon molecules, defined as organic matter (OM). Degradation of OM is performed through microbial hydrolytic and Figure 1. Molecular composition of organic matter, from sea water traps and sediment, sampled from the central pacific. Molecular fermentative (e.g. Arndt et al., 2013) uncharacterized refers to organic matter that has not been ascribed as amino acids, carbohydrates or lipids. Modified after Wakeham et processes transforming the majority of high al., 1997. molecular weight compounds (HMW),

8 consisting of biopolymers such as proteins and polysaccharides, to monomeric low molecular weight compounds (mLMW) (Burdige and Zheng, 1998).While aerobic respiration mineralizes OM completely to carbon dioxide via the citric acid cycle, mineralization of OM through anaerobic respiration occurs via food chain. The initial breakdown occurs through extracellular and membrane- bound hydrolytic enzymes produced by certain microorganisms. The hydrolytic products are then consumed by fermenting and acetogenic bacteria that produce compounds such as acetate and hydrogen. The terminal step in this anaerobic food chain involves the utilization of these latter compounds by microorganisms that reduce sulfate and oxidized manganese/ iron or produce methane (Arndt et al., 2013). Studies carried out with environmental samples, have demonstrated that essential biomolecules (i.e. amino acids, carbohydrates and lipids) are preferentially degraded compared to bulk OM (Lee and Cronin, 1984; Henrich et al., 1984; Dauwe and Middelburg, 1998; Cowie and Hedges, 1992; Dauwe et al., 1999; Keil et al., 2000). This is considered further In Chapter 3. The decrease of total pools to half the concentration of organic carbon (TOC) and hydrolyzable amino acids (THAA) were found to be in the order of 17 and 13 kyr, respectively. Thus, the fraction of organic carbon present as molecular identifiable material (e.g. THAA) decreases while the molecular uncharacterized fraction (bulk TOC) increases (Wakeham et al., 1997). For example, 85% of the plankton is molecular identifiable whereas only 26% can be identified in deep sea sediments (Fig. 1). In marine sediments, prokaryotes are responsible for most of the OM degradation. There is a vast source of electron acceptors present in sediments that trigger hydrolytic and fermentative respiratory processes (Fig. 2; Canfield and Thamdrup, 2009). Thus, as OM is deposited at the sediment surface, degradation occurs and consequently prokaryotic cell production. As cells die, their necromass becomes available for other cells, and then the original pool of OM will get gradually substituted with local prokaryotic necromass. Organic material in soils, waters and sediments, therefore, persist in transition of higher to lower degradation state. These materials comprised a mix of recently formed plus older and less labile OM (Cowie and Hedges, 1994).

Figure 2 Schematic view of the depth distribution, of commonly found electron acceptors in marine sediments. On the right, a cartoon reflecting the chemical zonations, which typically accompany the respiration processes on the left. After Canfield and Thamdrup, 2009.

Global estimations of prokaryotic cells

Many Prokaryotic abundance estimations are based on direct enumerations with DNA-binding fluorescent dyes in the microscope (Weibauer et al., 1998; Parkes et al., 1994; Torsvik and Øvreås, 2002; Schippers et al., 2005; Kallmeyer et al., 2012; Chapter 1) or with the use of flow cytometry (e.g. Glud and Middelboe, 2004; Duhamel and Jacquet, 2006; Czechowska et al., 2008; Glud et al., 2013; Chapter 2 and Chapter 3). The living fractions of bulk carbon and nitrogen pools, in terrestrial and oceanic subsurfaces, are the single-celled community (Whitman et al., 1998; Fry et al., 2009; Lomstein et al., 2012). Global estimations of prokaryotic cells are in the order > 2 1029 cells in soil and unconsolidated surfaces (Table 1). This corresponds to a considerable amount of carbon (350-550 Pg of C), nitrogen (85-130 Pg of N) and phosphorus (9-14 Pg of P) (Whitman et al., 1998). As they comprise the majority of the biota in such environments, and represent the largest pool of these chemical elements, their role in organic matter decomposition, nutrient cycling and particle aggregation therefore, is essential.

In terms of energy supply, soil, and the terrestrial and marine subsurface become limited and highly selective environments (Torsvik and Øvreås, 2002). In the deep marine sub-surfaces, total cell

10 abundance decreases significantly with depth as the proportion of recalcitrant buried organic matter increase (Parkes et al., 2000; Arndt et al., 2013; Chapter 3). Interestingly, the same trend is not found in terrestrial sub-surfaces (Detmers et al., 2001; Fry et al., 2009).

Terrestrial soils harbor a major fraction of global microbial biomass (Table 1). Due to the complexity of their matrix, these soils have highly compartmentalized microhabitats separated by steep physicochemical gradients (Brözel et al; 2011; Maron et al., 2011). Microorganisms, which inhabit these different microhabitats and gradients, contribute substantially to soil nutrient cycling, water movement (Bisset et al., 2011) and aggregation (Miransari, 2011). In surface soils this natural heterogeneity is disrupted by agricultural practices, e.g. the addition of fertilizers, tillage, and rotation of different crops. This is reflected by the close similarities, found in the prokaryotic diversity, across agricultural lands (Sessitsch et al., 2001; Ogilvie et al., 2008; Bisset et al., 2011; Poulsen et al., 2013). Management promotes seasonal changes in the activity of the microbial community (Girvan et al., 2004). With periods of increase microbial biomass and activity after fertilization, and periods of nutrient exhaustion by the end of the harvest, showing decrease of microbial biomass and activity. Thus, the question arises: Is microbial dormancy playing a role during the starvation periods?

Table 1. Global estimations of single-celled organisms in soil and unconsolidated subsurface calculated as an extrapolation of cell concentration per gram of soil and the total area of the selected environment. Numbers in soil represent the grand total of soil from different environments which are an average of top 1m and top 1-8 m as described in Whitman et al., 1998. Reference Prokaryotes inhabiting Global estimations 29 Whitman et al., 1998 Top soil, < 8m 2.6 10

29 Fry et al., 2009 Terrestrial subsurface, > 8m 6 10

29 Kallmeyer et al., 2012 Oceanic subseafloor 2.9 10

Dormancy in nature

The term dormant has been defined as the state of low metabolic activity where cells are unable to divide or to form a colony on an agar plate without a preceding resuscitation phase (Kell and Young, 2000). Dormant cells can retain viability, but they need to undergo activation (e.g. Mearls, et al., 2012). The extent of dormancy of microbial communities in natural environments has undergone considerable debate (Kaprelyants et al., 1993; Kell and Young, 2000; Price and Sowers,

2004; Lennon and Jones, 2011; Jørgensen, 2011; Makarova et al., 2012). So far, we know that some environments hold the majority of their prokaryotic cells in a dormant state (Fig. 3; Lennon and Jones, 2011). For example, Luna et al., (2002) accounted 26-30% of living bacterial cells in coastal sediments, from which only 4% were identified as actively growing. The percentage increased with increasing sediment organic content. The number of dormant bacteria was estimated as the difference between live bacterial counts and nucleoid-containing cells (actively growing cells). Regardless the method employed, the results are consistent across environments, indicating that the proportion of dormant cells is determined according to the environmental traits.

Although, a proportion of the cells ascribed as dormant, could be at the verge of dying, others can still revive when favourable factors are met (Jones and Lennon, 2010), and after a period of acclimation that includes repair of accumulated cell damage (Price and Sowers, 2004; Mearls et al., 2012). According to Jones and Lennon, (2010), the ability to enter and successfully emerge from dormancy had a strong, positive influence on species richness. Dormancy therefore can influence the persistence of populations and has implications for community dynamics.

The environmental cues that make microorganisms shift from dormant periods to activation periods are still unclear. It may be the lack of nutrients pushing cells to adopt survival strategies (cannibalism, reducing size, become dormant, endospore formation). However, the results from chapter 1 indicate a considerable number of endospores (> 2 107 endospores gdw-1) in soil samples under all the examined agricultural regimes, with as well as without seasonal addition of nutrients.

The results from this investigation (chapter 1) were compared with findings from different environments, including the deep subsurface, an environment which has severely restricted nutritional inputs. The results are synthesized in Table 3, showing that there is a surprisingly small range of endospore abundance regardless the environment.

In general, it is thought that growth is limited by energy (e.i. electron acceptors) and less by the availability of C and N (e.g. Rothfuss et al., 1997; Morono et al., (2011). Nevertheless, survival is a different corollary. In nutrient-limited environments (e.g. the deep biosphere), the issue of debate might only be survival rather than growth. In chapter 3, it is observed that the microbial community is able to persist over larger time scales (Fig. 2, chapter 3). However, as observed in all subsurface biosphere studies, the abundance of cells does not increase. Instead, numbers decrease slowly, following a power law (Parkes et al., 2000).

Figure 3. Abundance of dormant cells in different environments. a) Percentages of cells that were found dormant determined by fluorescent in situ hydridization (FISH) or staining with 5-cyano-2,3-ditolyl tetrazolium (CTC) and compared with total cell counts with DAPI. b) Percentages of OTUs belonging to inactive cells determined by the ribosomal RNA to ribosomal DNA ratio, with the use of terminal restriction fragment length polymorphism (TRFLP). The data shown are for the mean the standard error of the mean. (Taken from Lennon and Jones, 2011)

Dormant or slow growers?

Subseafloor organisms are living at the verge of the minimum energy flux yet populations may persist for millions of years (Jørgensen, 2011). Cells at the subseafloor display metabolic rates far lower than cells in surface environments. Metabolic rate in soil, lake water, and seawater, is typically in the range of 0.1 to 10 fmol C·cell−1·d−1, corresponding to 10−3 to 10−1 g C metabolized per gram cell C per hour. The mean metabolic rate for deep subsurface bacteria is typically four orders of magnitude lower: 10−5 to 10−3 fmol C·cell−1·d−1, corresponding to 10−7 to 10−5 g C·g−1 cell C·h−1 (Jørgensen, 2011). If one compares the definition of dormant cells (Kell and Young, 2000) with these slow metabolic rates, there seem to be only a thin line dividing the concepts of dormant cells and slow growers. Therefore, the question comes in mind: does a cell with detected maintenance metabolic energy only should be considered as dormant? Of course this is hypothetical as this is not yet possible to distinguish with our current laboratory techniques. Thus, if the answer is no, then, what would differentiate a dormant from a dead cell with intact structures? Perhaps the best approximation is the study of molecule motion inside the cell. In recently published research, the motility of several macromolecular structures was screened inside microbial cells during differently nutrition stages. Parry et al. (2014) discovered that cytoplasmic fluidity changes dramatically between well fed and starved cells (Fig. 4). In the case of cell during severe nutrient limitation conditions, all macromolecular structures studied were immobile during the time lapse those cells

13 were screened. The environmental implications for this bacterial physiology behaviour are still not fully known. The authors portrayed as the strategy of non-sporulating dormant naked cells.

Figure 4. The motility of macromolecular structures inside bacterial cells. Time-lapse montages of crescentin-GFP structures acquired under conditions of growth (M2G) and carbon source depletion. C. crescentus cells (CJW1265) were grown and imaged in M2G, a glucose-based medium (top). For carbon starvation, cells were washed into M2 buffer (lacking glucose) and incubated for 3 hr before imaging (bottom). Scale bar, 1 mm. (Taken from Parry et al., 2014).

Price and Sowers, (2004), defined three metabolic rates of living cells, after gathering extensive literature of microorganisms surviving in nutrient-poor ice and permafrost:

1) The metabolic rate necessary for growth. Refers to those cells encountering proper conditions to repair accumulated cell damage and divide. A condition occurring sporadically in nature, the frequency varies according to the environment.

2) The metabolic rate for maintenance. This is enough to maintain vital functions, but inadequate for growth.

3) The rate for survival. Which makes the cell able to only repair molecular damage, thus it is defined as dormant.

The latter category was found to be comparable with the rate of spontaneous molecular damage (Price and Sowers, 2004). Thus cells can remain alive but dormant, in the sense of not growing but repairing cell damage, over long periods of time.

Finally, slow-growers are what it is considered K-strategists (Fontaine et al., 2003; Janssen 2009), which are defined as organisms prepared for a slow but steady existence in nutrient-limiting environments (Janssen, 2009). If there is abrupt flush of energy and substrates, K-strategist might temporarily be overwhelmed by r-strategists which have formerly been present as dormant forms

(e.g. endospores). As r-strategists exhaust the nutrient pool, they will re-enter dormancy (Janssen, 2009). Catabolites and extra-cellular enzymes left behind, however, might be useful for K-strategists, which continue their slow-but-steady life strategy (The priming effect; Fontaine et al., 2003).

Buerger et al., (2012) were able to test cells and endospores, viable but dormant, isolated from soil and marine environments in a nutrient-rich medium. Their results showed a lack of response to nutrients per se from all populations tested. Instead, they observed growth in a stochastic fashion. The majority of the isolates developed colonies in a range of 40-200 days. However, re-growth only took about 24-48 hours, even the isolates that required up to 200 days of initial incubation. This observation was not a result of adaptative mutations. The likely explanation is that slow growth and oligotrophy appears to be rarer than previously thought, wheras stochastic exiting from non-growing state may be more common.

A closer simulatuion of a microbial community, should comprise a mix of cells at different metabolically stages (Price and Sowers, 2004). In nutrient restricted environments dormancy would signify the difference between survival and death and slow growing might as well be an ambiguous interpretation. This discussion is taken further in the chapter 3.

Bacterial endospores, a conspicuous fraction of the dormant compartment

Bacterial endospores are some of the most conspicuous dormant structures representing Earth’s most successful survival strategies of microorganisms, as they resist chemical, physical, radiation and sterilization stresses (Nicholson et al., 2000). Endospores are metabolically inactive and differ structurally from the parental vegetative cell. While in the core they contain the genomic package to form a vegetative cell, the endospore is further covered by the spore cortex, the spore coat and finally the exosporium (Fig. 5). They are formed when members of endospore-forming Firmicutes (EFF) face unfavourable conditions (Slieman and Nicholson, 2001), such as starvation (Lopes da Silva et al., 2005), viral attack (Makarova et al., 2012) or abrupt oxygen changes (Mearls et al., 2012). Endospores can be spread via wind, water, living animal hosts, etc. This ability provides them great advantages in colonizing, thriving and withstanding a wide range of environmental conditions. Consequently, endospores are an important component of many natural microbial communities (Nicholson, 2000).

Laboratory studies have shown that endospore formation is an exosporium coat elaborate and energy intensive process that requires several hours to complete. If during this period nutrients become available, cells in the process of core sporulation would be at a competitive disadvantage relative to cells not committed to sporulation and hence able to resume growth more rapidly. Inner membrane Therefore, cells delay sporulation until Outer cortex membrane forced to do so by prolonged depletion of nutrients (Lopes da Silva et al., 2005; Figure 5. TEM image of B. atrophaeus spores. The scattered fiber-like structure around the spore is the exosporium, a Lennon and Jones, 2011; Mearls et al., glycoprotein layer that can only be observed with ruthenium red stain. After Zhang et al., 2006. 2012). However, microbial communities in natural environments face nutrient exhaustion that can be prolonged up months and years, until a season with nutrient deposition arrives. Thus, endospore formation must be a great advantage for those able to do so.

Bacterial endospores can remain dormant for thousands of years (kyr) (Rothfuss et al., 1996; Vreeland and Rosenzweig, 2002; Nicholson, 2003). Cano and Borucki (1995) may even have revived endospores preserved for 25-40 million years (Ma) in amber, although their results are still controversial (Willerslev et al., 2004). The longevity of the spores is facilitated by their multiple coating-layers which allow them to resist peptidoglycan-lytic enzymes (Fig. 5). Also, they harbour special acid soluble proteins (SASPs) that preserve the DNA (Setlow, 1992). Their ability to germinate is dictated by the multiple receptors anchored to the exosporium, a glycoprotein layer sitting at the exterior of the endospore (Fig. 5).

Despite their apparent metabolic inactivity, endospores are constantly monitoring the nutritional status of their surroundings (Nicholson, 2000). Thus, their ability to react rapidly after an increased presence of mLMW compounds such as amino acids, purines and sugars (Setlow, 2003) is a remarkable characteristic. When such substrates bind to the endospore receptors, enzymatic activity hydrolyses the peptidoglycan content of the endospore cortex, which triggers a biochemical chain reaction that ultimately leads to vegetative outgrowth of the cell. The process of germination can be divided into two stages before outgrowth occurs (Fig. 6.). During those two stages, most of the

16 endospore’s effort concentrate on the hydration of the core by replacement of H+, cations, Zn2+ and 2,6-Pyridinecarboxylic acid (dipicolinic acid, DPA) to increase the core pH and allow enzymatic hydrolysis (Setlow, 2003).

Figure 6. Events in spore germination. After Setlow, 2003.

The endospore dehydration and the associated wet heat resistance are due to a calcium- dipicolinic acid (DPA-Ca++) complex. DPA is an abundant compound residing within the core (5-15% of endospore dry weight; Church and Halvorson, 1959). Powell (1953) showed for the first time that DPA was secreted by B. megaterium after germination, and since then DPA has been isolated from several spore-forming bacteria (Hindle and Hall, 1999). DPA is also linked to the maintenance of dormancy and it is suggested to activate the spore DNA repair system (Slieman and Nicholson, 2001). As free DPA is readily degraded under both oxic (Amador and Taylor, 1990) and anoxic conditions (Seyfried and Schink, 1990), DPA may be used as a biomarker for the presence of bacterial endospores (Hindle and Hall, 1999; Fichtel et al., 2007; Lomstein and Jørgensen, 2012) in environmental complex matrixes. Until now few studies have investigated abundance and dynamics of endospores using detection of DPA (Yung and Ponce, 2007; Fichtel et al., 2008; Ammann et al., 2011; Lomstein et al., 2012; Langerhuus et al., 2012; Chapter 1 and Chapter 2).

Endospore enumeration in the field

Classical estimates of endospores abundance are based on cultivable viable endospore- formers (Rothfuss et al., 1997; Yung et al., 2007; Logan and Halket, 2011; de Rezende et al., 2013). Because there is a big spectrum of physiological diversity of endospore-forming bacteria, these methods may all underestimate the actual in situ numbers of endospores. In addition, this approach selects those EFF that produce large numbers of endospores and those producing endospores that germinate most rapidly according to the growth media (Logan and Halket, 2011). Turnbull et al. (2007) demonstrated that viable counts were lower than microscopic cell counts (10-95% lower) in 13 of 18 analysed strains, because a portion of the population either failed to germinate, were not genuinely viable, or were viable but not cultivable.

Due to their metabolic diversity Bacillus and Clostridia are presumed to be important contributors to the carbon, nitrogen and sulphur cycle (Mandic-Mulec and Prosser, 2011), and as they are often isolated from soil, this is considered a natural reservoir (Kimble-Long and Madigan, 2001; Nicholson, 2002; Logan and Halket, 2011; Hong et al., 2009). However, their actual significance might have been overestimated. A compilation from the literature shows that members from the phyla Firmicutes are diversely abundant in soil and sediment environments (Table 2). Regardless the method employed and their inherent biases, it seems to be that soil does not hold abundant Firmicutes communities.

As mentioned above, only few studies of endospores abundances and dynamics in the environment have been based on the analytical quantification of dipicolinic acid. This endospore biomarker can be quantified using common laboratory-based methods, such as liquid chromatography (Hindle and Hall, 1999), by measuring the fluorescence of the Tb3+-DPA complex. As any other culture independent approach, this method eliminates underestimation bias due to cultivation.

The Tb3+-DPA fluorescence methods usually have a limit of detection in the nM range (Hindle and Hall, 1999; Fichtel et al., 2007; Ammann et al., 2011; Lomstein and Jørgensen, 2012) allowing estimating low concentrations of bacterial endospores. Furthermore, by addition aluminium chloride, one can reduce the interfering effects of the phosphates during fluorescence detection (Ammann et al., 2011; Lomstein and Jørgensen, 2012). In this way, the determination of DPA via the fluorescence of the Tb3+ seems a highly promising approach for investigations in natural samples.

Table 2. Literature compilation of the relative abundance of the phyla Firmicutes in soil and sediments.

Environment sampling depth % Relative of molecular Technique Reference a (mbsf) Firmicutes marker

Soil

Arable soils (long-term experimental top 0.05 0.2-1.7 Firmicutes- qPCR Chapter 1 field, Denmark) taxon specific primers/16S rRNA

Arable soils (long-term experimental top 0.2 6 16S rRNA 455 Poulsen et al., 2013 field, Denmark) Pyrosequencing

Arable soils (Texas High Plains region, top 0.05 15 16S rRNA 456 Acosta-Martinez et al., USA) Pyrosequencing 2008

Several soils (mini review) top 0.05 5 16S rRNA Clone libraries Janssen et al., 2006

Pairie Forest and Desert soils (USA) top 0.05 7 Firmicutes- qPCR, clone Fierer et al., 2005. taxon libraries specific primers

Soils from: a) 3 maize fields, Brazil, top 0.1 5 16S rRNA 456 Roesch et al., 2007 Pyrosequencing b) 1 sugar cane field, Florida, USA, c) 1 experimental field, Illinois, USA, d) 1 boreal forest, Ontario, Canada

Deep terrestrial subsurface (two 31.9 & 133.5 20 16S rRNA Clone libraries, Fry et al., 2009 depths) Isolates,PCR- DGGE for identification from enrichments

Sediments

Lake sediment (Geneva, Switzerland) top 0.09 2.6-59.4 16S rRNA 454 Bueche et al., 2013 Pyrosequencing

Soils and sediments from hypersaline top 0.05 10 16S rRNA qPCR, clone Hollister et al., 2010 lake, La Sal del Rey, Texas, USA libraries, sanger sequencing, 457 Pyrosequencing

Marine sediments, Peru margin (ODP 1-50 16-18 16S rRNA 454 Biddle et al., 2008 site 1229) Pyrosequencing

Marine sediment, India top 0.1 40 50 16S rRNA Illumina Aravindraja et al., 2013 sequencing

Hydrothermal vent field Loki’s Castle 0.16-296 1 16S rRNA 454 Jorgensen et al., 2012 at the Arctic Mid-Ocean Ridge, in the Pyrosequencing Norwegian-Greenland Sea

Oceanic subsurface sediments (Peru > 1 46 16S rRNA Isolates, PCR- Batzke et al., 2007 margin) DGGE for identification

b Subseafloor oceanic crust (rock chip oceanic crust > 52 16S rRNA Clone libraries Orcutt et al., 2011 fragments) 280 mbsf

b Subseafloor oceanic crust (black rust oceanic crust > 86 16S rRNA Clone libraries Nakagawa et al., 2006 formed after seeping of hydrothermal 210 mbsf fluids) a Relative percentage over total gene numbers, clone libraries or culture collection b Incubation chamber in a borehole at eastern flank of the Juan de Fuca Ridge

A number of studies employing DPA-quantification are summarized in Table 3. Overall, it seems endospores abundance in the environment is surprisingly homogeneous in soils and marine sediments (Table 3).

In chapter 1, endospore abundance was estimated in soil samples within an agricultural experimental field. Three experiments were selected: a) Askov-LTE, that investigates the effect of animal manure versus mineral fertilizers and doses rate on a four rotation crop, b) Askov-Maize, that investigates the effect of selected soils from Denmark with different clay content on silage Maize crops, c) Askov-Straw, that investigates different doses of straw incorporation to silage Maize crops. Endospore abundances were found to be in the same order of magnitude in all selected treatments (Table 3). The number of endospores per soil gram of dry weight increased significantly in soils that are nutrient exhausted as they have not been fertilized for more than 100 years (Chapter 1). It was concluded that endospore formation is not affected by common agricultural practices, as the C and N availability are controlled by seasonal inputs of fertilizer or residue incorporation. Thus, it seems there is a permanent pool of endospores in these managed environments, regardless the type of practice.

Ammann et al., (2011) found the highest endospore concentrations in grassland soils (mean 2.4 108 endospores gdw-1), lower concentrations in forest soils (mean 4.6 107 endospore gdw-1) and the lowest concentrations in freshwater sediments (mean 2.5 107 endospores gdw-1) (Table 3). They conclude that endospore abundance was related to soil carbon-to-nitrogen ratio.

In marine sediments, the endospore abundance seems to decay with time, once they are buried into the sediment. Endospores do not seem to have the capacity to germinate as they might never encounter proper conditions before they accumulated enough damage and die. Chapter 2 was focused on investigating endospore abundance in top sediments (0-30 cm). In five stations distributed along a mud-slide in the Peru margin (Fig. 1, chapter 2), endospore numbers showed a

20 narrow variation with sediment depth (Table 3). The interpretations achieved in the chapter are given in the next section.

Likewise, Langerhuus et al., (2012), showed that endospore abundances in marine sediments from Aarhus Bay, Denmark, were in the range 4.5 x 106 to 1.5 x 107 cm-3. The numbers decline in the first 40 cm (only in one station) and then remained relatively constant. However, below that (0.4- 10.9 mbsf), endospore numbers increased nearly double of what was found in the top 0-10 cm interval.

Lomstein et al., (2012) showed that endospore numbers increased with depth in deep sediment layers from the Peru Margin area. Moreover, Fichtel et al. (2008) investigated marine sediments from a tidal flat in the Waden Sea, Germany. In these locations endospore numbers varied considerably, thus abundance was related to lithology (e.i. highest numbers in organic-rich black mud sediments and lowest in sandy sediments).

Table 3. Compiled data of endospore enumeration based on dipicolinic acid-Tb3+ complex detection, determined by reverse phase HPLC from environmental samples. For conversion of DPA concentration to endospore numbers, it was assumed 2.24 x 10-16 mol-DPA endospore-1 (Fichtel et al., 2007). Endospores (DPA) gdw-1 (x108) Std error (x108)

Chapter 3 (soil managed treatments)

Askov-LTE LTE 0 0.61 0.07 1.5 AM 0.38 0.14 1.5 NPK 0.23 0.03 Askov-Maize

RON (18)a 0.25

ROS (14)a 0.36

ASK (11)a 0.41

LUN (5)a 0.25

Askov-Straw

0 straw 0.28 0.05 8 straw 0.43

Fichtel et al., (2008) North Sea, intertidal marine sediment cores 0.13 NSN5 & NSN7b North Sea, intertidal marine sediment core 0.05 NSN10b North Sea, intertidal marine sediment core 0.2 JS11b Ammann et al., (2011) Meadow 3.34 1.13 Meadow 2.70 0.50 Meadow 2.60 0.55 Meadow 1.79 0.41 Bank of river Glatt 1.67 0.28 Forest soil 0.58 0.18 Forest soil 0.50 0.09 Forest soil 0.30 0.26 Bank of canal 0.28 0.02 Sediment of canal 0.22 0.02 Langerhuus et al., (2012)

Aarhus bay, station M1c 0.1-0.05/0.15

Aarhus bay, station M5 0.13-0.1 Lomstein et al., (2012) Peru margin, sediment core, site 1227d, 0.1-0.03 Chapter 2 Peru margin sediments, sites G10d 0.32-0.11 G11d 0.21-0.10 G14d 0.11-0.04 G15d 0.31-0.19 Numbers in Askov experiment are given in average of soil treatment a. Percentage of clay per gdw-1 soil b. Highest concentration c. Top 0-40 cm/highest concentration below 40 cm d. Highest-lowest concentration

Longevity

The longevity of endospores is a matter of discussion. There is no doubt that endospores are far more durable than naked cells, but the inquiry of how long they can last before they are no longer able to germinate becomes problematic. Endospores encountered in natural complex samples are a mix of different anaerobic, aerobic, hetero- and autotrophic organisms. Therefore, their ability to withstand environmental changes will depend on the features of each species.

It is accepted that bacterial endospores can be found still viable after thousands of years (kyr) (Vreeland and Rosenzweig, 2002; Nicholson, 2003) when they have been preserved in embedding samples. Salt crystals (Vreeland et al., 2000), ice cores (Yung et al., 2007) deep sub-surface cores (de Rezende et al., 2013; Rothfuss et al., 1997), and fossilized animals (Cano and Borucki, 1995) are examples of that. The most surprising of them, is the revival of viable endospores of B. sphaericus from the guts of bees trapped in a 25-40 Myr amber (Cano and Borucky, 1995) and from a 250 Myr halite crystal (Vreeland et al., 2000), although their results are still controversial (Nicholson, 2003; Willerslev et al., 2004). Moreover, numerous Bacillus spp. have been isolated from deep-subsurface samples including buried marine sediments (Batzke et al., 2007), ice cores (Zhang et al., 2001; Christner et al., 2003), buried paleosoils (Boone et al, 1995; Balkwill et al., 1997); buried lacustrine sediments (Rothfuss et al., 1997), and oil fields (Cayol et al., 1995). Nevertheless, isolation of bacteria from ancient materials continues to controversial, not only because the isolates are almost identical to modern relatives, but also because the verification of their antiquity as the materials from where they were isolated may be questionable (Maughan et al., 2002).

In chapter 2, the endospore abundance with sediment depth and age was analysed in marine sediments from the Peru margin. As described before, it was found that endospore abundance was influenced by the age of the sediment. Thus, the deeper the sediment, the older it is, and the fewer endospores are found. In a closer inspection, it was realized that in all stations endospores decreased exponentially with sediment age (Fig. 2; chapter 2). Based on this, two pools of endospore longevities were found (chapter 2). The first pool, the labile endospore pool, have half-lives of 175 ( 51.7) years. From the initial bulk of endospores roughly half the concentration will disappear within the first two hundred years. The second pool, the refractory endospores, will endure burial far longer before they disappear (Fig. 4; chapter 2). A somewhat steady-state scenario is hypothesized for the latter pool (Fig. 7). The connotation for disappearance implies: a) certain number of endospores will germinate after some probabilistic nutritional improvement event (e.g. encountering of mLMW attached in clay particles, close proximity of recently dead microbial cell, mLMW catabolites found in

23 sulphate-methane transition zones); b) the rest of endospores will accumulate sufficient cell damage and die. According to Yung et al., (1997), rejuvenation of endospore populations through germination, repair, and sporulation cycles on the time scale of endospore longevity would yield numbers constant with depth. Thus, there is relatively higher endospore dead rate than germination, but still the endospore longevity (from the refractory pool) is large enough to allow detection thousands of years.

Studies based on most probable numbers of viable endospore forming bacteria, have reached similar conclusions. In lake sediments, Rothfuss et al. (1997) found that both endospores from aerobic and anaerobic heterotrophic bacteria decreased exponentially with sediment depth and were below detection limit after 4 m depth. The half-lives calculated for those endospores were 499-546 years.

More recently, endospores from thermophilic sulphate reducer bacteria were found in marine sediments from cold waters, in Aarhus Bay, Denmark (de Rezende et al., 2013). Endospores decreased exponentially with sediment depth, and as endospores of thermophilic sulphate reducers do not have the ability to germinate in those environments, the results indicate that the endospores slowly lose their viability within 250-440 years.

The endospore forming community was studied in a polar ice core from Greenland (Yung et al., 2007), from a depth of 94 m estimated to be 295 years old. From the total endospore concentrations (369 36 endospores per mL), 80% were viable endospores (e.i. able of germination), indicating an endospore longevity older than the estimated age of the sample.

Figure 7. A steady state of endospore abundance in marine sediments from the Peru margin.

Culture experiments of EFF strains have shown that there is an endospore forming subpopulation producing endospores already at the end of exponential/beginning of the steady-state phase (e.g. Lopes da Silva et al., 2005). Then, as starvation increases, the number of endospores will increase.

In general, members of Bacillus and Clostridia are considered opportunistic (r-strategists) due to their versatile physiology and the relative ease with which they are isolated under laboratory conditions. When resources become scarce, they re-enter to a dormant state (endospore formation). This transition requires energy, and if it cannot be completed the cell will die, which could explain the observations formerly described about a EFF sub-set sporulating when there is still some resources to exploit.

Finally, the longevity of an endospore is determined by the time elapsing between its formation and its accumulation of lethal cell damage (Nicholson, 2003). After analysing the kinetics of thermal inactivation of endospores described in scientific reports, Nicholson, (2003), formulated a model to describe survival probabilities of endospores in 25-40°C temperatures. He compared the D- value (e. i. reduction of the population to a one tenth) versus thermal inactivation temperatures of the compiled data and derived best fit lines that extrapolated the data to 0°C. According to his theoretical findings, there might be different endospore longevities that can be divided in three groups: group 1, those endospores that will be able to survive environments with temperatures of 25-40 °C range for 0.19 to 952 years; group 2, the mesophilic endospores that will be able the same environmental temperature range for 571 years to 1.9 million years; group 3, the thermophilic endospores with survival extrapolations that far exceeded credibility (1.9 billion to 1.9 trillion years). Some interesting observations can be taken out from this theoretical analysis. First, endospore survival will depend not only of the environmental characteristics but also on the intrinsic ability of the endospore forming bacteria. And second, the span of endospore longevity is seriously long, thus endospore revival from Myr old samples are not theoretically impossible.

Results from chapter 2, regarding the presence of labile and refractory endospore groups in marine sediments from the Peru margin, are substantiated with this theoretical prediction of different endospore longevities. Figure 8 explains graphical the results from the model obtained by Nicholson, (2003). Assuming that endospore mortality is a probabilistic event, the obtained data was used to calculate the time frame that a determined initial population (divided in the three groups) will survive.

Figure 8. Probability distribution of endospore survival derived from the theoretical observations. On the X-axis is the size of the initial endospore population in log scale, on the Y-axis, the number of years expected for a single endospore to survive from the initial population. Hatched areas denote the survival probabilities for each group of endospores tested within the temperature range of 25-40 °C, indicated by the upper and lower boundaries of each hatched area, respectively (Taken from Nicholson, 2003).

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PhD synthesis

The present PhD project was focused on the study of bacterial endospore abundance in selected environments, and activity of microbial cells in the deep biosphere. Bacterial endospore numbers were compared with the numbers of Bacteria, Archaea and the phylum Firmicutes in different arable soil treatments within a field station (chapter 1). Furthermore, the dynamics of endospore numbers within marine sediments were analyzed and compared with microbial cells

(chapter 2). Finally, the activity of microbial cells was analyzed, in sediments as old as the late

Pleistocene, and contrasted with previous studies. The three chapters summarized the significant results achieved during the project. They are presented in the form of scientific manuscripts.

The fundamental questions behind the project were:

a. What are the distributions of bacterial endospores in those selected environments?

b. Is bacterial endospore abundance dynamic or static on those selected environments?

c. What are the determinant factors for their presence and persistence?

d. How active are the microbial cells found in old sediments?

e. Is dormancy playing a role in those sediments?

Chapter 1

In this chapter, the main goal was to analyze the endospores abundance in arable lands. As little is known about how anthropogenic soil management practices affect microbial communities and their activities. Managed soils comprised near 38% of the total usable land on Earth (mean value from the latest data collection, The World bank, 2014). Managed soils includes land defined by the

FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each

35 harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.

To be more specific, the study in chapter 1 examines the distribution of bacterial endospores in differently long-term managed soils, by comparing the endospore numbers with total prokaryotic community size as well as the fraction of likely endospore-forming bacteria (Firmicutes). The soils are under specific managements for 24 to 118 years at Askov Experimental Station. Samples were from three different experiments involving soil texture (5 to 18 % clay), fertilization (unfertilized, mineral fertilizer, cattle manure) and straw disposal (no straw, 8 t straw ha-1). The microbial community was characterized by the abundance of prokaryotic cells (direct cell counts), endospores (culture- independent dipicolinic acid) and ratios of amino sugars (glucosamine:galactosamine GlcN:GalN, and glucosamine:muramic acid GlcN:MA) indicative for the presence of bacteria or fungi. Bacteria,

Archaea and Firmicutes were characterized by domain- and phylum- specific DNA quantitative polymerase chain reaction (qPCR).

The objective was to isolate and evaluate the effects of soil texture and individual management elements on the structure of the soil prokaryotic community, which includes the presence of Bacteria, Archaea, fungi and bacterial dormant forms (endospores).

The results demonstrated that while conventional agricultural practices have little effect on microbial population size (Fig. 1). endospore numbers proportionally increase in long-term unfertilized soils (LTE 0 treatment), suggesting that nutrient limitation-induced increased sporulation on one hand. Addition of extra carbon residue however, stimulates bacterial growth (8 straw treatment). As organic carbon incorporation might boost the heteroorganotrophic community.

Figure 3. a) Total cell abundances estimated via DAPI cell counts, b) Endospores enumeration calculated as the concentration of DPA gdw-1 and an assumed content of DPA of 2.24 x 10-16 mol endospore-1

Although, managed soils have been identified as the most abundant endospore reservoirs

(Ammann et al., 2011), the present study concluded that the main determinant factor for endospore formation is nutrient availability. The prokaryotic community is significantly reduced with nutrient exhaustion over-long periods (Askov-LTE), and significantly increased with additional amounts energy resources (Askov-Maize). Nevertheless common soil management does not comprise long-extended unfertilized and double addition of organic residues, that correspond to treatments LTE-0 and 8 straw, respectively. In general, endospores were not abundant in the investigated soils, where they comprised less than 1% of the total bacterial community (as bacterial cells occupied the majority of

37 the cell counts). Nevertheless, the Firmicutes:Endospores ratio indicate that the endospore-forming community are as abundant as the endospores (Fig. 2). Further studies could address if the ratio of vegetative cells versus endospores changes drastically with type of environment and with season.

Figure 4. a) Ratios of cell numbers versus endospores, b) Ratio of the calculated Firmicutes fraction from the total cells versus endospores.

This manuscript is in a final stage. It will submit it the highly impacted journal Soil Biology and

Biochemistry, after the manuscript has been formatted following the requirements of the journal.

Chapter 2

The main goal of this chapter was to investigate the presence of bacterial endospores in marine sediments and their dynamics. Bacterial endospores are highly relevant in studies regarding the deep biosphere. As they are tough-bacterial resting life stages for both dispersal to environments and for surviving periods in which nutrients are not available. Marine sediments are characterized by a constant settling of organic matter (OM) and other materials on the surface. Below that, vertical biogeochemical zonations are caused by microbial degradation of organic material and chemical diagenesis (Parkes et al., 2000; Canfield et al., 2009). Underneath the bioturbated surface layer, transport of electron donors and dissolved organic material is generally restricted to molecular diffusion (Orcutt et al., 2011; Arndt et al., 2013). As a consequence, the microorganisms are faced with both constant limitations of energy supply and progressing burial in the sediment (Hoehler and

Jørgensen, 2013). Endospores may constitute a valuable survival state for nutrient-limited microorganisms in such environment (Kaprelyants et al., 1993; Lennon and Jones, 2011), and it is well known that bacterial endospores are present (Lomstein et al., 2012; Langerhuus et al., 2012) and viable in marine sediments (de Rezende et al., 2013).

However, it is not known whether endospores are a result of particle settling or in situ production and whether they fulfil a role down in the sediment. The aim of the present study was to investigate the dynamics of the endospore community relative to the community of microbial cells found in surface and subsurface sediments from the Peru Margin. Abundance was based on the quantification of dipicolinic acid (DPA), a unique biomarker for the presence of endospores.

The overall results demonstrated endospores decay rather than accumulation with time (Fig.

3), which is an indication of endospore settling from the overlying water and buried into the sediment. Calculated endospore half-lives (Table 1) are comparable with previous findings described for specific viable endospore populations. However, a considerable amount of endospores, after the sediment has reached thousands of years, was also detected. This was interpreted in several ways:

a) There is a subpopulation capable to persist over larger time scales.

b) Endospore decay, for example via spontaneous fruitless germination would have decrease

in response to time or environmental conditions, in the deeper sediment layers.

c) Finally, the endospores could have been generated in situ at a rate that balances the

decay.

Because the dynamics of the endospore community approach that of the vegetative community, after the initial die off, this seems like a plausible explanation.

Figure 5. A) Concentration of endospores versus assigned age in a 5 m gravity core in station G10. Endospore numbers are based on DPA quantification and converted assuming 2.24 10-16 mol DPA per endospore. The lines represent the upper and lower 95% confidence interval of the fit in Figure 3, a. The stippled line represents the upper 95% confidence bound of the fit for MUC core with slowest decay rate. B) Concentration of total organic carbon (TOC) and the percentage of carbon bound in amino acids (%TAAC) in the same core as A. C) Cell numbers in the same core as A-B.

Table 4. Calculated endospores half-lives based on their exponential fit decay rates. Stations are organized according to water depth. Decay rates from the literature were obtained via viable endospores quantification.

Water Stations Half-life (yr)* Decay rate depth (m) Endospores G10 313 205 (150-323) -0.0038 G10_gravity core 313 - G15 743 332 (234-570) -0.0021 G11 1015 315 (233-490) -0.0021 Average 284 Other studies Rothfuss et al., (1997) 115 499-546 -0.0013, -0.0025 de Rezende et al., (2013) 28 250-440 -0.002 *95% confidence interval

The results showed interesting assertions about the role of the endospores in the subsurface biosphere. This manuscript is currently under revisions by each of the co-authors, meaning that there will be some feedbacks before this draft is ready to be submitted. The journal of Limnology and

Oceanography has been suggested.

Chapter 3

The storyline of this chapter is still under discussion. The manuscript is presented as a scientific publication draft. However, the part of the discussion is still in a first stage.

The main goal in this chapter was to elucidate the microbial activity in the subsurface biosphere. Until now, it is not yet known the extent of metabolic activity in the microbial cells found in the in sediment layers below the sea bed. Microbial activity is usually evaluated via models focused on cells or biomass turnover times (D´Hondt et al., 2004; Schippers et al., 2005; Holmkvist et al., 2011; Lomstein et al., 2012). The calculations are based on cell counts and by considering the turnover rate of selected metabolic substrates, assumed to be the main electron donors, and the turnover rate of the bulk sediment (Jørgensen, 2011). Previous studies showed bacterial turnover times in the range from hundreds to thousands of years (Schippers et al., 2005; Jørgensen and

D’hondt, 2006). Also bacterial turnover times have been independently determined by the D:L-amino

41 acid modelling (Lomstein et al., 2012), that estimates the rate at which cells rework the amino acid pool, and therefore estimates cells turnover times. This approach has found cells turnover times to be in the order of tenths to hundreds of years in Holocene sediments (Langerhuus et al., 2012) and in the order of hundreds to thousands of years in sediments as old as the Oligocene (Lomstein et al.,

2012).

The results showed that bacterial activities predicted from the model are not in accordance with the observed TOC decay rates obtained by the exponential fit. The TOC decrease with age such that the pool size decreases to the half at approximately every 17 kyr. To test how do the model predictions fit the observed profiles of TOC, we compared the results from the model with the observed TOC decay rate (-0.00004 yr-1; Fig. 4 b). In the sediment core from G10, the TOC pool decrease from 20 to 300 cm depth (calculated ages 0.6-5.2 kyr) amounts to 1.1 mmol-C gdw-1.

Whereas the integrated rates, predicted in the model over the age of the sediment core, the total carbon oxidation amounts to 0.1 mmol-C cm-3 sediment, or 0.34 mmol-C gdw-1 sediment. In the sediment core from G14, the TOC pool decrease from the entire core (calculated ages 13-25.4 kyr) amounts to 1.6 mmol-C gdw-1, and the integrated predicted rates from the model amounts to 0.13 mmol-C cm-3 or 0.07 mmol-C gdw-1 sediment. In both cases, the model predictions were lesser by 3.3 and 23.5 times in G10 and G14 respectively (Fig. 4 a). The differences between the model predictions and the observed TOC decay trend were be interpreted as discordance in the assumption of the steady-state of microbial cell numbers. Therefore, total carbon oxidation rates resulted smaller than the actual observed TOC decrease.

Biomass turnover times estimated for both stations ranged from 124 to 712 and 54 to 294 years at G10 and G14 respectively (Fig. 5). The result was independent of whether Bacteria or

Archaea were assumed to dominate the community. The turnover times estimated in the present study are far longer than generation times estimated for surficial nutrient-rich environments such as

42 soil, lake water or sea water (Jørgensen, 2011), and from results obtained by Schippers et al., (2005), from the ODP Leg 201. However turnover times are similar to what Biddle et al. (2006) found in the sulfate-methane transition zone in sediments from the Peru Margin, and to observations by

Langerhuus et al., (2012) from sediments from Aarhus bay, Denmark. Much longer turnover rates have been found in even older sediment layers from the Peru margin (Lomstein et al., 2012).

Furthermore, the pool of necromass is far larger and cycles in a much slower span (Fig. 2).

-3 -1 -1 a Total C-oxidation (nmol cm yr ) b TOC (µmol gdw )

1 10 100 1000 0 2000 4000 6000 8000 10000 0 0

5000 5000

10000 10000

15000

15000 Assigned Assigned (yr age BP)

20000 20000

25000 25000

30000 30000

Figure 6. a) D:L modelled carbon oxidation rates with sediment age in stations G10 and G14, b) comparison of D:L modelled carbon oxidation rates of 100% respiring bacterial cells, b) Total organic carbon (TOC). Its exponential fit is shown: , R2 0.85.

Turnover time (yr)

1 E+0 1 E+2 1 E+4 1 E+6 0

5000

10000

15000

Assigned (yr BP) age 20000

25000

30000

Figure 7. D:L modeled turnover times of bacterial biomass and necromass with sediment age in stations G14 and G10. Open diamonds and squares biomass and necromass turnover at G10 respectively, close diamonds and squares biomass and necromass turnover times at G14 respectively.

References

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D'Hondt, S., Jørgensen, B.B., Miller, D.J., et al., 2004. Distributions of microbial activities in deep subseafloor sediments. Science 306, 2216–2221.

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Holmkvist, L., Ferdelman, T.G., Jørgensen, B.B., 2011. A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochimica et Cosmochimica Acta 75, 3581–3599.

Jørgensen, B. B. D’Hondt, S. 2006. A starving majority deep beneath the seafloor. Science 314, 932– 934.

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Kaprelyants, A. S., Gottschal, J. C., Kell, D. B. 1993. Dormancy in non-sporulating bacteria. FEMS Microbiology Reviews 104, 271-286.

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Lomstein, B. A., Langerhuus, A. T. D’Hondt, S. Jørgensen, B. B. Spivack, A. J. 2012. Endospore abundance, microbial growth and necromass turnover in deep sub-seafloor sediment. Nature 484, 101-104.

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Chapter 1

The prokaryotic community and its bacterial endospores in soil from three

long-term agricultural experiments: effect of fertilization, straw

incorporation and soil type

For submission to Soil Biology and Biochemistry

Paulina Tamez-Hidalgoa, Bent T. Christensenb, Mark A. Levera,c, Bente Aa. Lomsteina,c

a Section for Microbiology, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-

8000 Aarhus C, Denmark b Department of Agroecology, Aarhus University, AU-Foulum, Blichers Allé 20, DK-8830 Tjele,

Denmark c Center for Geomicrobiology, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-

8000 Aarhus C, Denmark

Abstract

The effects of agricultural management on the structure of the soil microbial community remain obscured. We examined the prokaryotic community in arable soils maintained under specific managements for 24 to 118 years at Askov Experimental Station. Samples were from three different experiments involving soil texture (5 to 18 % clay), fertilization (unfertilized, mineral fertilizer, cattle manure) and straw disposal (no straw, 8 t straw ha-1). The microbial community was characterized by the abundance of prokaryotic cells (direct cell counts), endospores (culture-independent dipicolinic acid) and microbial source markers (glucosamine:galactosamine GlcN:GalN; glucosamine:muramic acid GlcN:MA). Bacteria, Archaea and Firmicutes were characterized by domain- and phylum- specific

DNA quantitative polymerase chain reaction (qPCR).The nutrient-depleted unfertilized soil showed reduced cell numbers and increased endospore abundance, indicating that the prokaryotic community was subject to environmental stress, while straw incorporation had a positive effect on the prokaryotic community. The differently textured soils displayed similar distributions for total cells and endospores but proportions of Firmicutes were reduced in the less clayey soil. Animal manure and mineral fertilizer had similar effect on the relative abundance of Firmicutes, but Firmicutes made up a small fraction of the total bacterial community (0.2 to 1.7 %). Gene analyses showed a clear dominance of Bacteria over Archaea, the last group accounting for 0.2 to 10.9 % of the gene pool.

Determinations of specific microbial biomarkers were less informative, but suggested a predominance of fungal over bacterial residues in all soils. We conclude that the overall structure of the prokaryotic community in long-term arable soils is little affected by conventional management.

1. Introduction

Maintaining soil quality is a critical challenge in modern agriculture (Schjønning et al., 2004).

The concept of soil quality embraces a range of physical, chemical and biological properties, which in turn are defined by climate, soil parent material, land use, and soil management (e.g. crop rotation, tillage, fertilization, and crop residue inputs). The interaction between soil management, microbial community structure, and soil functions is extremely complex and remains a high-priority research area (Brussard et al., 2007; Kibblewhite et al., 2008). Changes in land use (e.g. cultivation of native soils and permanently vegetated soil) can lead to reduced microbial catabolic diversity (Degens et al.,

2000) and changes in community structure (Bissett et al., 2011). Using phospholipid fatty acid analysis, Kaye et al. (2005) reported large effects of land use on the soil microbial biomass but minor effects on the relative abundance of taxonomic groups. The impact of land use on the microbial community may be related to changes in plant residue inputs (Zak et al., 2003; Paterson et al., 2011), and soil properties such as texture and pH have been suggested as key factors for bacterial community composition (Girvan et al., 2003; Johnson et al., 2003; Ulrich and Becker, 2006; Enwall et al., 2007; Yin et al., 2010). Since soil management typically affects several soil properties simultaneously, evaluation of the effect of individual management elements is most often impeded.

For instance, fertilization has generally been found to have little impact on the composition of the soil bacterial community (Sessitsch et al., 2001; Ogilvie et al., 2008; Poulsen et al., 2013). Yet, fertilization may induce indirect effects, e.g. on soil pH, which may mask the impact of fertilization itself (Enwall et al., 2007). Soil management may promote seasonal changes in the activity of the microbial community (Girvan et al., 2004), with periods of increased microbial biomass and activity after residue disposal and nutrient additions and a decrease biomass and activity coinciding with periods of nutrient limitations at the end of the growing season.

The decline in biomass and activity during periods of energy and nutrient depletion may cause microorganisms to enter states of dormancy. Members of two classes of the phylum Firmicutes

(Bacillus, Clostridia) are able to enter a conspicuous state of dormancy termed endospore. The endospore is able to withstand a wide range of environmental conditions for very long periods and endospores are an important component of many natural microbial communities (Nicholson; 2002;

Hong et al., 2009). However, little is known on the abundance of endospore in agricultural soils and how management provokes endospore formation (Ammann et al., 2011). Dipicolinic acid (DPA) is a unique molecule found in bacterial endospores where it may account for of 5-15% of endospore dry weight (Powell, 1953; Church and Halvorson, 1959), and DPA analyses have been applied to quantify endospore abundance in complex natural matrices (Yung et al., 2007; Fichtel et al., 2008; Lomstein et al., 2012; Langerhuus et al., 2012).

Our objectives were to examine effects of soil texture and individual management elements on the structure of the soil prokaryotic community by using soil from experimental plots that had been subject to long-term treatments with plant nutrients (unfertilized, mineral fertilizer, cattle slurry) and straw disposal. All soils were under the same climate and were sampled at the end of the growing season (September) to avoid any effects of recent nutrient inputs and straw disposal. The microbial community was characterized by abundance of prokaryotic cells and endospores and by specific microbial biomarkers. Endospore numbers were estimated by culture-independent quantification of

DPA and the total prokaryotic community was enumerated by direct cell counts. Bacteria, Archaea and Firmicutes were characterized by domain- or phylum-specific DNA quantitative polymerase chain reaction (qPCR). Ratios of vegetative versus dormant cells and ratios of endospore-formers versus endospores were established and potential links between microbial biomarkers, cell numbers and endospore abundance were examined.

2. Materials and Methods

2.1. The experimental sites

This study draws upon soil sampled in September 2012 in three long-term experiments at

Askov Experimental Station. The station is located in the South of Jutland (55°28'N, 09°07'E) and experiences an annual precipitation of 862 mm and a mean annual temperature of 7.7 °C (1961-

1990). The soil pH ranges from 5.5 to 6.5 and is maintained within this range by occasional liming.

Soil from The Askov Long-Term Experiments on Animal Manure and Mineral Fertilizers (Askov-

LTE) was collected in the B3-field of the Lermarken site (Christensen et al., 2006). This site is a terminal morainic deposit with 11 % clay and 13 % silt in the Ap-horizon. The Askov-LTE was initiated in 1894 to test crop responses to different levels (0.5, 1, 1.5 and 2) of nitrogen, phosphorus and potassium, added either in animal manure (AM) or in mineral fertilizers (NPK) and includes unmanured/unfertilized plots as reference treatments (LTE-0). The experiment grows a four course crop rotation of winter wheat (Triticum aestivum), silage maize (Zea mays), spring barley (Hordeum vulgare) and mixed grass-clover crop used for cutting. Averaged across the rotation, 1 AM and 1 NPK correspond to an annual input of 100 kg total-N ha-1, 19 kg P ha-1 and 87 kg K ha-1. For the present study, soil was taken from the treatments LTE-0 (plots no. 322, 344, 351), 1.5 AM (plots no. 334, 341,

363), and 1.5 NPK (plots no. 342, 345, 362). When sampled, silage maize was grown in the B3-field.

Soil was also collected in the Askov Straw experiment (Askov-Straw), a field experiment with different straw disposal initiated in 1981 on the O1-field next to the Askov-LTE and with the same soil type (Thomsen and Christensen, 2004). Spring barley has been grown every year (except for 2000-

2002) and the soil is amended with mineral fertilizers (NPK). At harvest, the straw is baled and removed from the field, leaving only the stubbles behind. Then 0, 4, 8 or 12 t ha-1 of straw are returned and incorporated in main plots. In the present study, soil was sampled from the treatments

0 (plots no. 201, 606, 708) and 8 t ha-1 straw (plots no. 206, 308) given an annual dose of 100 kg N ha-

1, 14 kg P ha-1 and 48 kg K ha-1.

Finally, soil was sampled in a small-plot experiment where silage maize has been grown every year since 1988 (Kristiansen et al., 2005). This experiment (termed Askov-Maize) involves four different soil types collected in 1987 in Ap-horizons (0-20 cm soil) of field experiments at Rønhave

(RON; 18 % clay), Roskilde (ROS; 14 % clay), Askov (ASK; 11 % clay) and Lundgaard (LUN; 5 % clay).

The soils were transported to Askov Experimental Station, sieved to < 4 cm, and placed outdoors in large open-ended cylinders (diameter 0.99 m; area 0.76 m-2; depth 0.5 m) inserted 45 cm into the ground (Table 1). The maize crops receive an annual dose of mineral fertilizers corresponding to 170-

200 kg N ha-1, 36-40 kg P ha-1, and 190 kg K ha-1, and the maize biomass is whole-crop harvested in mid-October leaving only 4 cm of stubbles. For the present study, soil was from LUN (plot no. 50),

ASK (plot no. 49), ROS (plots no. 33, 48) and RON (plots no. 46, 47).

2.2. Soil collection and preparation for analysis

Soil was sampled from the Ap-horizons by removing any surface plant residues and collecting soil from the 0-5 cm layer in sterile bags (Whirl-Pak). Within each experimental plot three replicate samples were collected and immediately stored under cold and dark conditions. In the laboratory, the replicate samples were pooled and mixed in larger sterile bags and stored field-moist overnight before subsamples (ca. 1.5 g) were taken for cell counts and DNA extraction. The remaining soil was oven-dried at 60°C overnight, crushed and sieved to <2 mm, and stored. For cell counts, 1.5 g of field- moist soil was suspended in 4% paraformaldehyde and left to fix overnight at 8°C. Samples were then washed twice with phosphate buffered saline (PBS), re-dissolved in PBS-Methanol solution (1:1 v:v), and stored at 8°C. Other 1.5 g field-moist subsamples were frozen at -20°C for subsequent DNA extraction. Subsamples of dried soil were employed for determination of total organic carbon (TOC)

52 and nitrogen (TN), and for hydrolysable amino acids (THAA), amino sugars (THAS), muramic acid

(MA), and dipicolinic acid (DPA).

2.3. Analyses for soil TOC and TN

Soil TOC and TN contents were determined on dry (< 2 mm) samples. Any inorganic carbon was removed by treating the soil with 5-6% sulfuric acid overnight. Samples were oven-dried overnight at 70°C, and then for an hour at 105°C. Tin cups, treated with 1:1 hexane and acetone solution and heated to 200°C, were used to load the samples into a Thermo Elemental Analyzer,

Flash EA 1112 HT. Empty tin cups were used as blanks. Blanks showed negligible TOC and TN. Fluor powder (% N = 2.31, % C = 44.39) was used to build the standard curve.

2.4. HPLC analysis of amino acids and amino sugars

Freeze-dried soil samples (ca. 0.5 g) were hydrolysed in 10 mL 6 N HCl at 105°C for 24 h under

N2 and then placed in an ice bath to stop any further reactions. Hydrolyzates were stored at -20°C.

Subsamples of hydrolyzate (100 µL) were transferred to glass vials, dried under vacuum at 50°C, re- dissolved in MilliQ water, and dried again. After re-suspension in 4 mL MilliQ water, the hydrolyzate was passed through a 0.2 µm filter (Sartorius) and collected into polycarbonate picovials. Amino acids (THAA) was determined by reverse-phase High Performance Liquid Chromatography (HPLC), of fluorescent o-phthaldialdehyde (OPA)-derivatized products, using the method of Lindroth and

Mopper (1979) as detailed by Langerhuus et al. (2012). Individual amino acids were determined from specific standard curves based on the amino acid standard solution AA-S-18 (Sigma–Aldrich) amended with β-alanine (β-Ala), taurine (Tau) and ornithine (Orn). L-aminobutyric acid (L-Aba) was added to standards and hydrolysed samples and used as internal standard since L-Aba was not present in the original samples. The amino sugars glucosamine (GlcN) and galactosamine (GalN) were

53 measured in the same HPLC analytical run as THAA using previously established standard curves and internal standards. Concentrations were corrected for losses during hydrolysis, which were 25.5% and 21.6% for GlcN and GalN, respectively.

The amino sugar muramic acid (MA) was quantified in a separate HPLC analysis. The method was as for THAA, except that ca. 0.5 g of freeze-dried soil samples were hydrolysed with 5 mL 3 N HCl at 95 °C for 4 h under N2. Total hydrolysable amino sugar (THAS) is the sum of GlcN, GalN and MA.

2.5. Analysis of dipicolinic acid (DPA)

Analysis for DPA was used to estimate endospore abundances. Because free DPA is rapidly decomposed in the soil (Fetzner, 1998), DPA was taken to originate from intact endospores only. DPA was measured according to Lomstein and Jørgensen (2012) using reverse phase-HPLC after complex formation with terbium (Te). In short, samples were prepared as described for MA with three modifications: a) soil samples (ca. 0.5 g) were hydrolysed with 10 mL 3 N HCl at 95 °C for 4 h under

N2, b) two subsamples of each hydrolyzate (300 µL) were dried, re-dissolved in 300 µL Milli-Q, and dried again, and c) the two subsamples were pooled and dissolved in 4 mL of 1 M luminescent grade sodium acetate buffer with 80 µL of 2mM AlCl3 added in order to precipitate phosphates.

Subsamples were run as detailed by Lomstein and Jørgensen (2012). DPA concentrations were determined from 4 to 5 point standard curves, obtained by adding standard to the sample to compensate for matrix effects. To convert DPA concentrations to endospore numbers, 2.24 x 10-16 mol-DPA endospore-1 was assumed (Fichtel et al., 2007).

2.6. Cell counts

Total vegetative and recently dead but still intact cells were enumerated by direct visual inspection using an epifluorescence microscope (Carl Zeiss, Axiovert 200 M) equipped with a Zeiss

Plan-Neofluar x 100 objective (Carl Zeiss, Germany). The extraction of cells followed Kallmeyer et al.

(2008) with two modifications. Firstly, dissolution of carbonate minerals was not implemented because the soils were devoid of pedogenic carbonate. Since lime is added occasionally to keep soil pH in the plots within the range 5.5 to 6.5, the need for removal of lime residues was tested by incubating ca. 0.25 g soil with 0.5 mL of two acid buffers (Na-Acetate-acetic acid, 0.43 M, pH 4.6 and

PBS-Acetic acid 0.43 M, pH 5.5) for 2h. All soils showed negligible bubbling indicating that carbonates were not present. Further, cell separation by density centrifugation involved a second sonication step. When only one sonication was applied, total counts were ca. 5% of that found when applying two sonication steps.

For counting, two dilutions of each sample were prepared (5x and 10x) and poured onto 0.22

µm black polycarbonate (GTBP, Millipore) filters with 0.45 µm cellulose acetate filters placed below for support (Millipore). Filters were pre-wetted with autoclaved 1x PBS solution and dilutions were added along with 5 mL of autoclaved 1x PBS solution and filtered by applying suction up to 15 cm Hg.

After filtration, cells were stained with DAPI-Citifluor:Vectashield solution (4:1 v:v). Filter membranes were cut in halves, mounted on glass slides and the staining solution (< 10 µL) applied directly onto each filter membrane. Membranes were kept in the freezer until they were counted under the microscope. Cells were enumerated with a filter for DAPI. A minimum of 20 view fields were enumerated per filter as recommended by Lunau et al. (2005).

2.7. DNA extraction

Genomic DNA was extracted from soil samples using bead beating (TissueLyzer LT, Qiagen) and the FastDNA™ 2 mL spin kit for soil (MP Biomedicals, LLC) according to the manufacturer’s instructions. After Fastprep and prior to DNA extraction, samples of 0.25 g (wet weight) soil were subjected to enzymatic digestions. In brief, 50 µL of enzyme mixture 1 (1 µg mL-1 of Lysozyme, Lipase,

Pectinase and β-Glucuronidase) were added and incubated for 30 min at 37°C. Then 50 µL of enzyme mixture 2 (1 µg mL-1 of Proteinase K, Protease and Pronase) was added and incubated again for 30 min at 37°C. Finally, 75 µL of sodium dodecyl sulfate (SDS) was added and incubated at 65°C for 2 h.

DNA concentrations were measured with a Qubit 2.0 fluorometer (Invitrogen). Following the instructions provided by the manufacturer, 1 µL of sample was tested for double-stranded DNA

(dsDNA) and measured against instrument standards.

2.8. Quantitative PCR (qPCR) for 16S rRNA

Quantitative PCR was performed to quantify 16S rRNA gene numbers of Bacterial and Archaeal domains and the phylum Firmicutes. Assays were performed on a Roche Light Cycler 480. 16S rRNA universal primers were used to quantify total Bacteria, total Archaea and the phylum Firmicutes. For

Bacteria, the domain-specific primers Bac8Fmod (5´ -3´): AGAGTTTGATYMTGGCTCAG (Loy et al.,

2002; PMID: 12324358) and Bac338Rabc (5´ -3´): GCWGCCWCCCGTAGGWGT (Daims et al., 1999;

PMID: 10553296) were used. For Archaea, the domain-specific primers Arc 915F_mod (5´ -3´) AAT

TGG CGG GGG AGC AC (modified from Stahl and Amann (1991)) and Arc 1059R (5´ -3´) GCC ATG CAC

CWC CTC T (Yu et al., 2005) were used. The primers for the phylum Firmicutes were 928F-Firm (5´ -

3´): TGAAACTYAAAGGAATTGACG and 1040R-Firm (5´ -3´): ACCATGCACCACCTGTC (Bacchetti De

Gregoris et al., 2011). Each reaction mixture contained 2 μL of 3.1-41 ng DNA template, 10 μL Roche

Light cycler master mix containing SYBR-Green, 2 μL of 1 mg mL-1 bovine serum albumin, 1 μL of 50

μM solutions of each primer, and dH2O to complete 20 μL per reaction. Samples were analysed twice at 1:10 and 1:100 dilutions for the detection of Bacteria, and at 1:5 and 1:10 dilutions for the detection of Archaea and Firmicutes. Each sample dilution was run in triplicates. The qPCR protocol included the following steps: 95°C polymerase activation for 5 min, followed by 45 PCR cycles for

Bacteria and Archaea, and 40 PCR cycles for Firmicutes. Individual PCR cycles consisted of

56 denaturation (95°C) for 30 s, annealing for 30 s at 55°C (Bacteria), 56°C (Archaea) and 61.5°C

(Firmicutes), elongation for 30 s at 72°C, and fluorescence measurement after 5 s at 80°C. Each PCR was concluded with a stepwise melting curve from 95°C to 55°C for 1 min. Standard curves (6.4 x 101

– 6.4 x 108) were made in triplicate from pGEM-T plasmids (Promega) with Bacterial or Archaeal

16sRNA insert. Roche Light Cycler software was used to determine the threshold cycle (Ct) of each reaction. In each run, the efficiency of each standard curve was 1.8 and Ct values of negative controls were significantly below the lowest point in the standard curve, indicating minimal DNA contamination. All amplifications were checked for specificity with dsDNA melt curves and any exhibiting multiple products. Bacteria and Archaea have several ribosomal operons including several

16S rRNA genes in their genomes wherefore gene abundance cannot be used directly to estimate cell abundance. Consequently, qPCR estimated gene copy numbers were divided with the average copy number of 16S rRNA operons per genome. The average number of operons per genome is 4.2 for

Bacteria, 1.7 for Archaea domains and 6.78 for the phylum Firmicutes (Lee et al., 2009).

3. Results and Discussion

3.1. Chemical analyses

Table 1 shows the total concentrations of organic carbon (TOC), nitrogen (TN), hydrolysable amino acids (THAA), amino sugars (THAS) and muramic acid (MA) in the three sets of soil. The ranking of the soils from the Askov LTE was similar for all parameters, the lowest concentrations being found in soil from LTE 0 and the highest in soil from the 1.5 AM treatment. Analyses of the differently textured soils from the Askov-Maize experiment did not reveal any consistent trend related to clay content, although the coarse sand soil (LUN) showed the smallest values. Straw incorporation seemed to have little effect on TOC and TN but tended to increase THAA, THAS and MA contents. Hydrolysable amino acids accounted for 8 - 14 % of soil TOC and 34 - 57 % of soil TN. In a previous study of two Danish arable soils with annual straw incorporation, Christensen and Bech-

Andersen (1989) found 32 - 36 % of soil TN to be in hydrolysable amino acids with no effect of straw incorporation.

3.2. The abundance of cells

Cell numbers were determined by microscopy using the fluorescent probe DAPI. Generally, prokaryote numbers were 1 1010 cells gdw-1 (Fig. 1a). The cell numbers align with previous studies using similar methods to detach cells from the soil matrix (Böckelmann et al., 2003; Portillo et al., 2013). Poulsen et al. (2013) found cell numbers ranging from 2 to 4 109 cells gdw-1 in soils from a 4-year old field trial testing effects of high rates of urban waste and cattle manure. One conspicuous exception in the present study, was the unfertilized soil (LTE 0) which showed considerably smaller cell numbers than any other soil. Fertilization and straw incorporation led to significantly higher cell numbers than found in unfertilized soil and soil with straw removal, respectively, when analysed by two-tailed t-tests (LTE 0 versus 1.5 AM, p = 0.04; LTE 0 versus 1.5

NPK, p = 0.02; 0 straw versus 8 t straw, p = 0.007). However, soils amended with mineral fertilizer and animal manure maintained similar levels of prokaryotic cells (Fig. 1a), suggesting that the nutrient source and the organic matter added with manure had little impact on prokaryote abundance. This is in contrast to Poulsen et al. (2013) who found similar cell numbers in unfertilized soil and soil receiving normal rates of mineral fertilizers while numbers were higher in soil taking large loads of manure. The length of the treatment period was much longer in the soils examined here, suggesting that the prokaryote community in arable soils exhibits considerable resilience towards changes in nutrient management. The positive effect of straw incorporation agrees with previous studies in which plant residue inputs were found to increase the microbial community (Zak et al., 2003; Paterson el al., 2011).

3.3. The abundance of endospores

Endospore numbers estimated from DPA measurements were approximately three orders of magnitude smaller than total cell numbers (Fig. 1b). Endospore numbers varied from to 6.1 107 gdw-1 soil and are in accordance with numbers found in forest soils and freshwater sediments (Table

2). Endospore abundance in grassland soils appears to be 5 to 10 times higher while marine sediments show substantially lower numbers. However, the different endospore numbers recorded in samples of different origin reflect the composition, the size as well as the environmental stress imposed on the prokaryotic community. Significant differences in endospore abundance were detected between LTE 0 and 1.5 NPK (p = 0.003) and between no straw and straw incorporation (p =

0.01). The lack of sufficient replicates prevents a proper statistical analysis of effects related to soil type, but we observed no distinct trend related to soil texture.

3.4. Relative abundance of Bacteria and Archaea

Members of the prokaryotic community were ascribed to Bacteria, Archaea and to Firmicutes by domain and phylum-specific DNA quantitative polymerase chain reaction (qPCR). To estimate the proportions of each group, numbers of gene copies were converted to cell numbers as described in

Section 2.8. The sum of qPCR based cell estimates of Bacteria and Archaea averaged 2% of cells enumerated by microscopy and DAPI. This may be due to a low extractability of soil DNA or interference from dissolved soil organic substances during the extraction procedure. Although the qPCR approach underestimates total cell numbers, we assume that the proportions of Bacteria and

Archaea remain representative for the soil community.

Figure 2a shows that most of the 16S rRNA genes were of bacterial origin (89.1-99.8%), while

Archaea accounted for a small fraction (0.2-10.9 %). This is in accordance with other studies (Bates et al., 2011; Bengtson et al., 2012; Poulsen et al., 2013). The different soil types included in the Askov-

Maize experiment exhibited high and similar bacterial dominance (99.5-99.8%). These four soils are under identical management and climate but differ in soil properties including texture (clay contents range 5 to 18 %), suggesting that soil type is not a key factor determining the ratio between Bacteria and Archaea. Previous studies based on soil from different sites have suggested that indigenous soil properties rather than management are a key determinant of microbial community structure in arable soils (Girvan et al., 2003; Johnson et al., 2003; Ulrich and Becker, 2006). However, effects of soil characteristics, climatic conditions and management elements may be confounded in studies based on soils retrieved from different locations.

The treatments without mineral fertilization (LTE 0 and 1.5 AM) showed the highest relative archaeal abundance, suggesting that Archaea may be sensitive to inputs of mineral salts or becomes overruled by fast growing bacterial species. Archaeal diversity appears to be sensitive to changes in soil pH (Roesch et al., 2007; Nicol et al., 2008; Bengtson et al., 2012) and nitrogen availability (Gubry-

Rangin et al., 2010; Rasche et al., 2010; Pereira et al., 2012).

3.5. Relative proportions of Firmicutes and Bacteria

Numbers of vegetative endospore formers were estimated by qPCR specific for the 16S rRNA genes of the phylum Firmicutes. Firmicutes accounted for a small fraction of the 16S rRNA total bacterial community (0.2 to 1.7%; Fig. 2b). This corresponds well with previous studies (Fierer et al.,

2005; Roesch et al., 2007; Acosta-Martínez et al., 2008; Yin et al., 2010; Hollister et al., 2010), although endospore-forming Bacilli and Clostridia has been found to comprise the majority of the

Firmicutes in a Danish arable soil under contrasting management (Poulsen et al., 2013). In the present study at least three management related trends were identified: 1) Firmicutes gene copies varied widely among differently textured soils, the proportion of Firmicutes increased with decreasing clay content, 2) straw disposal had no effect the abundance of Firmicutes, and 3) the nutrient source (manure or NPK) appears to have little effect on the proportion of Firmicutes.

3.6. Ratio of fungal to bacterial residues

According to Glaser et al. (2004), a GlcN:MA ratio 10 in the amino sugar pool of the hydrolysable soil organic matter (SOM) is indicative of bacterial dominance over fungi, whereas

GalN:MA ratio 60 is associated with fungal cell walls. In the present study, the GlcN:MA ratio was above 100 and the GalN:MA ratio above 40 (Table 1). Both source ratios suggest a predominance of fungal over bacterial biomass residues in line with previous observations for cultivated soils (Zhang et al., 1999; Glaser et al., 2004).

3.7. Proportions of vegetative versus dormant organisms

The ratio of total cell number to endospores was relatively similar across soils and treatments with the notable exception of the LTE 0 (Fig. 3a). The smaller number of living plus recently dead cells and the higher number of endospores may indicate that this long-term nutrient-depleted soil

61 harbours a prokaryotic community under environmental stress. The somewhat higher ratio for 1.5

NPK soil than for 1.5 AM soil remains unexplained, but does not support the notion of a pronounced effect of animal manure on the soil bacterial community structure (Hartmann et al., 2006;

Esperschütz et al., 2007). Using a similar endospores and prokaryotic cell quantification approach,

Yung et al., (2007) found that endospores comprised 10% of the total prokaryotic cell numbers in a

295 year old ice core from Greenland. While Bueche et al., (2013) found that the endospore-forming subset of the Firmicutes community represented 0.2 to 6.5% of the total Bacteria in Swiss lake sediments. Those results were achieved by qPCR detection of the spo0A gene coding for sporulation and confined to members of Bacilli and Clostridia and were compared to total Bacteria 16S rRNA gene. In the present study, endospores accounted for 0.2-0.3% regardless of soil type and treatment, the only exception being LTE 0 (1 %).

In the Askov-LTE, the ratio of vegetative Firmicutes to endospores ranged from 0.3 to 2.3 (Fig.

3b), with LTE 0 showing the lowest ratio and 1.5 NPK the highest. Firmicutes seem to be boosted more by inorganic fertilization than by animal manure. When compared to the ratio between total cell numbers and endospores (Fig. 3a), the ratio of Firmicutes to endospores (Fig. 3b) showed a more distinct trend with declining soil clay content (from 6.2 for RON; 18 % clay, to around 1 for ASK; 11 % and LUN; 5 % clay). Recalling the trend in the ratio of Bacteria:Firmicutes (Fig. 2b), the abundance of

Firmicutes appears to be linked to soil clay content. More sandy soils show diminished population size and increased sporulation.

The ratio in Askov-Straw experiment was 2 in both treatments (Fig. 3b). Thus, it seems that the population of Firmicutes is unaffected by long-term annual straw disposal, both in terms of relative abundance and sporulation rate.

3.8. Cell and endospore numbers and soil chemical parameters

Generally the numbers of prokaryotic cells and endospores were only weakly related to the proportions of hydrolysable amino acid-C and amino acid-N (Fig. 4). A Pearson product-moment correlation coefficient was used to analyse all parameters within each experiment. Soils from the

Askov-LTE showed positive correlation of total cells versus contents of TOC (p = 0.036), TN (p =

0.041), THAA (p = 0.01) and MA (p = 0.006). Soil type and straw disposal, on the other hand, did not show any significant correlations. One reason could be the small number of treatment replicates in these two experiments.

Endospore numbers showed no correlation with the soil biogeochemical parameters although endospores have receptors in the outer layer able to detect favourable changes in the environmental conditions (Setlow, 2003). Endospore numbers showed a negative trend when plotted against hydrolysable amino acid-C and -N (Fig. 4), whereas prokaryotic cells numbers seem to increase.

3.9 Determinant factors of bacterial and endospore abundance

In the present study, Askov-LTE showed highest endospore abundance in LTE-0 soils, which have been nutrient exhausted for over 100 years. The use of animal manure and mineral fertilizers, at the same nutrient rate, does not show any significant difference on the relative abundance of

Firmicutes (Fig. 2-b), but decreased the relative abundance of endospores (Fig. 3-b). Therefore, bacterial sporulation seems to be promoted by starvation for nitrogen and phosphorous.

The Askov-Maize experiment, with soils of different origin and clay content, displayed a similar distribution for total cells and endospores (Fig. 1 a-b), and the treatments where clay percentage is highest and lowest showed similar numbers (RON and LUN). It should be noted that a relatively stronger adhesion of cells and endospores to the clay particles in RON soils have biased the results and masked a lower abundance in loamy sand soils (LUN). As a group, members of the Firmicutes in

63 the microbial community were highly affected by the clay content, with lower proportions of

Firmicutes at low clay contents, and an increased tendency to form endospores.

Treatments in Askov-Straw demonstrated that endospore abundances in this experiment are closely correlated with prokaryotic cell abundance and that the increase of the prokaryotic community depends upon energy availability (Fig. 1): The highest abundance was found in soils from treatment 8 straw, where there is an excessive amount of carbon available indicating that the bacterial community, but not the archaeal community (Fig. 2-a), grew as the energy resources increased, followed by sporulation, of the endospore forming community, when the energy resources become limited. The soils from both treatments received the same concentrations of mineral fertilizers as a nitrogen and phosphorous source, and the difference in bacterial growth is induced by the difference in energy availability.

For reference, other studies that were based on quantification of DPA are compared with the results from the present study in Table 2. Ammann et al., (2011), found endospore hot-spots in soils that correspond to grasslands and agricultural regimes and that are govern by energy (organic carbon availability) rather than by nutrient limitation (nitrogen and phosphorous availability) as their C:N ratio is 20. The terrestrial environment (Table 2) displays higher bacterial endospore numbers than the marine sediment (Fichtel et al., 2008; Langerhuus et al., 2012; Lomstein et al., 2012). Perhaps this is due to greater energy limitation as organic carbon resources are limited in marine sediments.

In summary, long-term absence of nutrient addition appears to decrease total prokaryotic community size and increase the number of endospores (LTE 0 soils). Positive effects on the prokaryotic abundance were observed in managed soils with excessive inputs of residue straw (8 straw) where the influential factor is energy availability. However, these two experimental treatments are not common in agricultural practices. Thus, the overall results from the three experiments showed that common soil management practices with different fertilization types, residue incorporation and soil types appear to have little effect on the size of the prokaryotic

64 vegetative community as well as the dormant fraction because the prokaryotic community is not nutrient-limited.

4. Conclusions

We conclude that the general structure of the prokaryotic community in long-term arable soils is resilient to conventional management. Previous studies have also found that soil bacterial diversity remains remarkably stable against changes in management (Sessitsch et al., 2001; Enwall et al., 2007;

Poulsen et al., 2013) whereas the initial cultivation of native soils and radical changes in land use has been shown to affect bacterial and archaeal diversity (Buckley and Schmidt, 2003; Bissett et al.,

2011). Ogilvie et al. (2008) performed a phylogenetic and functional gene analyses on soils treated with mineral fertilizer, farmyard manure or kept unfertilized for 160 years and found that the bacterial community was relatively non-responsive to long-term balanced nutrient inputs. The unfertilized soil from Askov-LTE was comparably high in endospore abundance and low in cell numbers, showing that nutrient exhaustion could be a main factor in endospore formation. The prokaryotic community was significantly reduced but only in extremely nutrient-depleted soil while the relatively high rate of straw incorporation had a positive effect. Animal manure and mineral fertilizer had a similar effect on the relative abundance of Firmicutes. Although endospores comprised less than 1% of the total bacterial community (as bacterial cells occupied the majority of the cell counts), the relation between Firmicutes and endospores abundance indicates that the endospore-forming community are as abundant as the endospores. The differently textured soils displayed similar distribution for total cells and endospores but proportions of Firmicutes were reduced in the less clayey soils.

Acknowledgements

This work was supported by Mexican research council (CONACyT), grant number 213154. The contribution of BTC and field experiments at Askov Experimental Station was financially supported by the EU project SmartSOIL (Grant no. 289694). ). The research underlying this article has been co- funded by the Danish National Research Foundation and from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no

[294200].

Figure legends

Figure 1. a) Total cell abundances estimated via DAPI cell counts, b) Endospores enumeration calculated as the concentration of DPA gdw-1 and an assumed content of DPA of 2.24 x 10-16 mol endospore-1. Experiments are accommodated in the following order: Askov-LTE, Askov-Maize, Askov-

Straw.

Figure 2. a) Ratios of 16S rRNA bacterial gene copies gdw-1 versus 16S rRNA archaeal genes copies gdw-1, b) Ratios of 16S rRNA bacterial gene copies gdw-1 versus 16S rRNA phylum Firmicutes gene copies gdw-1.

Figure 3. a) Ratios of cell numbers versus endospores, b) Ratio of the assigned Firmicutes fraction from the total cells versus endospores.

Figure 4. Total cells and endospores plotted against a-b) Amino acid-C and –N (e.i. the percentage of acid hydrolyzable amino acid identifiable from the TOC and TN). Treatments from each experiment have been assigned different pattern: LTE 0 (solid grey diamonds), 1.5 AM (open diamonds) and 1.5

NPK (solid black diamonds); all treatments from Askov-Maize (open squares); 0 straw (open circles) and 8 straw (black circles).

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Table 1. Summary of the biogeochemical parameters measured in the soil samples. Ratios Amino Amino Treatments TN TOC THAA THAS MA GalN:MA GlcN:MA acid-C (%) acid-N (%) mg N gdw-1 mg C gdw-1 mg AA’s mg AS’s µg C gdw-1 -1 -1 soil soil gdw soil gdw soil soil Askov-LTE LTE 0 0.8 (± 0.1) 9.6 (± 0.8) 1.8 (± 0.2) 1.2 (± 0.04) 11.4 (± 0.8) 8 (± 0.4) 34 (± 3.0) 40.7 110.6 1.5 AM 1.2 (± 0.2) 14.1 (± 2.9) 3.1 (± 0.5) 1.9 (± 0.2) 15.9 (± 3.2) 10 (± 1.4) 39 (± 4.9) 43.1 122.1 1.5 NPK 1.0 (± 0.2) 11.6 (± 2.1) 2.6 (± 0.4) 1.7 (± 0.2) 13.8 (± 1.5) 10 (± 1.2) 42 (± 6.4) 50.7 125.6 Askov-Maize RON 1.6 16.8 3.6 2.4 24.6 9.5 34.8 35.8 100.2 ROS 1.3 14.9 3.1 1.8 17.2 9.3 36.5 48.1 102.4 ASK 1.5 20.4 4.5 2.7 25.1 9.8 45.8 45.1 106.5 LUN 0.7 7.9 2.5 1.5 9.9 14.1 57.4 66.8 151.2 Askov-Straw 1.1 (± 0 straw 12.0 (± 0.6) 2.9 (± 0.1) 1.8 (± 0.05) 14.4 (± 0.8) 11 (± 1.0) 39.7 (± 3.2) 50.7 121.9 0.05) 8 straw 1.1 11.4 3.3 2.2 20.0 12.6 46.3 39.5 108.1 Numbers are the average of measurements. Numbers in brackets represent the standard deviations

Table 2. Compiled data of endospore enumeration based on dipicolinic acid-Tb3+ complex detection, determined by reverse phase HPLC from environmental samples. For conversion of DPA concentration to endospore numbers, it was assumed 2.24 x 10-16 mol-DPA endospore-1 (Fichtel et al., 2007). Endospores (DPA) gdw-1 (x107) Std. error (x107)

Present study

Askov-LTE LTE 0 6.1 0.7 1.5 AM 3.8 1.4 1.5 NPK 2.3 0.3 Askov-Maize

RON (18)a 2.5

ROS (14)a 3.6

ASK (11)a 4.1

LUN (5)a 2.5

Askov-Straw

0 straw 2.8 0.5 8 straw 4.3

Fichtel et al., (2008) North Sea, intertidal marine sediment cores 1.3 NSN5 & NSN7b North Sea, intertidal marine sediment core 0.5 NSN10b North Sea, intertidal marine sediment core 2.0 JS11b Ammann et al., (2011) Grassland Männedorf 1 33.4 11.3 Grassland Männedorf 2 27.0 5.0 Grassland Männedorf 3 26.0 5.5 Grassland Uerikon 17.9 4.1 Grassland Dübendorf 16.7 2.8 Forest soil Stäfa 1 5.8 1.8 Forest soil Stäfa 2 5.0 0.9 Forest soil Stäfa 3 3.0 2.6 Sediment River Glatt 1 2.8 0.2 Sediment River Glatt 2 2.2 0.2 Langerhuus et al., (2012)

Aarhus bay, station M1c 1-0.5/1.5

Aarhus bay, station M5 1.3-1 Lomstein et al., (2012) Peru margin, sediment core, site 1227d, 1-0.3 Numbers in Askov experiment are given in average of soil treatment a. Percentage of clay per gdw-1 soil b. Highest concentration c. Top 0-40 cm/highest concentration below 40 cm

Figure 1.

2.0 E+10

1.8 E+10

1 - 1.6 E+10 1.4 E+10 1.2 E+10 1.0 E+10 8.0 E+9

gdw numbers Cell 6.0 E+9 4.0 E+9 2.0 E+9

0.0 E+0

7 E+7

b LTE 0

6 E+7 1

- 1.5 AM 5 E+7 1.5 NPK

4 E+7 RON

3 E+7 ROS ASK 2 E+7 Endospores gdw Endospores LUN 1 E+7 0 straw 0 E+0 8 straw Askov-LTE Askov-Maize Askov-Straw

Figure 2.

a 450 400 350

300 250

200 150 100

50 Bacteria:Archaea 0 b

350

LTE 0 300 1.5 AM 250 1.5 NPK 200 RON ROS 150 ASK 100 LUN Bacteria:Firmicutes 0 straw 50 8 straw 0 Askov-LTE Askov-Maize Askov-Straw

Figure 3.

600

500

400

300

200

100 numbers:Endospores Cell 0

b 7 LTE 0

6 1.5 AM

1.5 NPK 5 RON 4 ROS 3 ASK

2 LUN Firmicutes :Endospores Firmicutes 1 0 straw

0 8 straw Askov-LTE Askov-Maize Askov-Straw

Figure 4.

3 E+10 a 3 E+10 b

2 E+10 2 E+10 1 - 2 E+10 2 E+10

1 E+10 1 E+10

5 E+9 5 E+9 Cell Cell numbers gdw 0 E+0 0 E+0 0 5 10 15 0 20 40 60 80 c d 7 E+7 7 E+7

6 E+7 6 E+7

1 - 5 E+7 5 E+7

4 E+7 4 E+7

3 E+7 3 E+7

2 E+7 2 E+7

Endosporesgdw 1 E+7 1 E+7

0 E+0 0 E+0 0 5 10 15 0 20 40 60 80 Amino acid-C (%) Amino acid-N (%)

Chapter 2

Manuscript: The dynamics of endospores in the subsurface of the Peru

margin

For submission to Limnology & Oceanography

Paulina Tamez-Hidalgoa, Thorbjørn J. Andersenb, Oscar Chiangc, Mathias Middelboec, and Bente Aa. Lomsteinad and Hans Røyad.

aDepartment of Bioscience, Section for Microbiology, Aarhus University, Aarhus, Denmark bInstitute of Geography, University of Copenhagen, Copenhagen, Denmark cMarine Biological Section, University of Copenhagen, Helsingør, Denmark dCenter for Geomicrobiology, Department of Bioscience, Aarhus University, Aarhus, Denmark,

Abstract

Bacterial endospores in the surface sediments of the Peru Margin decay with a half-life time in the order of 300 years. Endospores in deeper layers, however, did not follow the rapid decay rates found near the surface and their abundance remained nearly constant across sediment layers ranging from 1000 to 8000 years old. Either there was a subpopulation capable to persist over larger time scales or the endospores were generated in situ at a rate that balances the decay. To replenish the endospore pool at the same rate as the decay, and thereby keeping the endospore numbers constant in dynamic equilibrium, then each endospore-former cell must on average form one endospore each 50-800 years. This is similar to the previous estimated microbial cell turnover times for the deep biosphere.

1. Introduction

Endospores are long-lived survival stages formed by low GC, Gram positive bacteria from the genera Bacillus and Clostridia in response to unfavorable conditions. There are examples of endospores, thousands (Kennedy et al., 1994; Rothfuss et al., 1997; Nicholson et al., 2000) to millions of years old that have been able to revive (Cano and Borucki, 1995; Vreeland et al., 2000).

Endospores are important for dispersion of bacteria between habitable niches and they function as seed banks, preserving bacterial diversity during and after perturbations (Lennon and Jones, 2010).

Bacterial endospores have been found in large numbers in aquatic sediments (Rothfuss et al., 1997;

Hubert et al., 2010; de Rezende et al., 2013; Langerhuus et al., 2012; Lomstein et al., 2012), but it is unclear what role they might fulfill.

The environmental conditions below the bioturbated zone in marine sediments are extremely stabile and the availability of harvestable energy is steadily deteriorating as the sediment ages and becomes buried (Middelburg 1989; Røy et al. 2012). There is, therefore, no obvious detrimental condition that bacteria would need to bridge, in the endospore stage, in sediments unless they are obligate oligotrophs. Dispersion of endospores within the sediment column is also an unlikely role of the endospores due to the tight sediment matrix. Some bacterial endospores in permanently cold marine sediments are clearly misplaced as their vegetative stages do not grow below 40°C (Isaksen et al. 2006; Hubert et al., 2010; de Rezende et al. 2013). It is not unlikely that such incidental sedimentation together with other fine grained organic matter is the common fate of all spores in marine sediments, and that they play no role at all in the active community.

Most published estimates of endospore numbers in the environment have been based on cultivable endospore-formers (Widdowson, 1967; Siala et al. 1974; Rothfuss et al., 1997; Hubert et al., 2010; de Rezende et al., 2013), and because of the vast physiological diversity of endospore- forming bacteria, the actual in situ numbers of endospores have most likely been underestimated

(Turnbull et al. 2007). Pyridine-2, 6-dicarboxylic acid (dipicolinic acid, DPA) comprises 5-15% of

84 endospore dry weight and it is found only in substantial amounts in bacterial endospores (Powell,

1953; Church and Halvorson, 1959). DPA can be quantified using common laboratory-based methods such as liquid chromatography by measuring the fluorescence of a Tb3+-DPA complex (Hindle and

Hall, 1999), and a recently developed reverse-phase HPLC method for Tb3+-DPA complex quantification has proven to be an effective and culture independent tool for investigating endospore abundance in environmental samples (Fichtel et al., 2008; Ammann et al., 2011; Lomstein et al., 2012; Langerhuus et al., 2012).

The aim of the present study was to investigate the dynamics of the endospore community relative to the community of microbial cells found in surface and subsurface sediments from the Peru

Margin. Endospore numbers derived from the quantification of DPA in those analyzed sediments.

2. Materials and Methods

2.1. Study site

The study site was on the Peruvian continental shelf and slope (Fig. 1). The waters in this area have the world’s most persistent wind-driven coastal upwelling, supporting intense primary production ( 2700 mg C m-2 day-1; Lee and Cronin 1984; Chavez et al, 1996), which leads to high sedimentation rates (Brodie and Kemp, 1994). The sediments consist of fine-grained, organic -rich

(Suess et al., 1986), diatom-rich laminated mud lenses with intervals of organic-poor and less diatomaceous, structure-less or macroscopically bioturbated, silty muds (Krissek et al., 1980; Brodie and Kemp, 1994). The high input of OM leads to intense oxygen consumption resulting in a pronounced oxygen minimum zone (OMZ) (Wyrtki, 1962). During sampling in February 2007, the

OMZ was located between 50 to 500 m water depths and contained between 2 nmol L-1 and 3 µmol

L-1 of dissolved oxygen (Revsbech et al., 2009). This perennial OMZ maintains organic-rich sediments deposited from the water column, and exclude bioturbation (Schrader, 1992).

2.2. Sampling and sample processing

The sampling was carried out during the Galathea 3 expedition in February 2007. Sediment cores from 4 stations were recovered by multicoring (MUC) and gravity corer. The cores were retrieved from water depths between 313 and 2367 m (Table 1). The cores were sectioned into the following depth intervals: the upper 6 cm was sliced into 1 cm intervals and into 2 cm intervals at 6-

30 cm depth. A separate 1 mL sediment sample was taken from each sampled depth for enumeration of vegetative cells and transferred to 2 mL cryogenic vials, fixed with 1 mL glutaraldehyde (2% final concentration) and frozen at -20°C until further processing. The remaining sediment was transferred into Nasco Whirl-Pack bags and frozen at -20°C immediately after sampling for later analysis of total

86 organic carbon (TOC), total hydrolizable amino acids (THAA) and dipicolinic acid (DPA). A 5 m gravity core retrieve at site 10 was sampled in coarser resolution but otherwise treated identically.

2.3. Sediment dating by 210Pb

Sliced cores in the same intervals as previously described were analysed for the activity of

210Pb, 226Ra and 137Cs via gamma-spectrometry at the Gamma Dating Center, Institute of Geography,

University of Copenhagen. The measurements were carried out on a Canberra low-background

Germanium detector. 210Pb was measured via its gamma-peak at 46,5 keV, 226Ra via the granddaughter 214Pb (peaks at 295 and 352 keV) and 137Cs via its peak at 661 keV. The cores showed surface contents of unsupported 210Pb of about 400 - 1000 Bq kg-1, and the activity generally decreased exponentially with depth although this decrease was not monotonous. The contents of

137Cs were generally below the detection limits. CRS-modeling has been applied on the profiles of unsupported 210Pb using a modified method where the activity below the deepest sample is calculated on the basis of a regression of unsupported 210Pb vs accumulated mass depth (Appleby,

2001). Each section of exponential decrease in unsupported 210Pb in each core was used to assign a sedimentation velocity within that section and the combined results were used to construct age models for each core. In each station, age models were extrapolated to cover the whole core, using the deepest reliably determined sedimentation rate.

2.4. Sediment dating by 14C

The age structure of the gravity core was determined based on radiocarbon in the organic fraction from a parallel core (G10_GC01) at the same station. The age model was transferred to core

G10_GC02, from the present study, by matching TOC profiles, and using the corresponding age to calculate subsequent depths following the sedimentation rate from the linear fits.

2.5. Total organic carbon (TOC)

The TOC in the samples was determined after decarbonating 1 g of sediment samples with

H2SO3 (5-6 v/v %), air-drying and overnight drying at 105°C. The samples were weighted before and after decarbonation to correct for loss of CO2. The analysis was performed in a Carlo Erba NA-1500 elemental analyzer. The concentrations were calculated from individual 4-10 point calibration curves within the respective ranges. Flour containing 44.39% C and 2.31% N was used as standard for the calibration curves.

2.6. Total hydrolizable amino acids (THAA)

THAA were processed and analyzed by high-performance liquid chromatography as described in Lomstein et al. (2006), but the hydrolyzate was dissolved in 5 mL Milli-Q instead of the 4 mL Milli-

Q. In total, 18 amino acids were quantified: aspartic acid (Asp), glutamic acid (Glu), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), arginine (Arg), β-alanine (β-Ala), taurine (Tau), alanine

(Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), ornithine (Orn) and lysine (Lys). The results are given as percentage of amino-acid contained carbon relative to TOC, which is a recognized sensitive indicator of organic matter diagenesis in marine sediments (Keil et al., 2000).

2.7. Extraction and enumeration of prokaryotes

The glutaraldehyde fixed sediment samples were transferred to sterile 50 mL Falcon tubes, diluted in 4 mL autoclaved Milli-Q water, treated with sodium pyrophosphate (Na4P2O7; 5 mM final concentration) and incubated in the dark at room temperature for 15 min. After the incubation, the samples were sonicated (at 20 KHz; Branson, SLPe), two cycles of 30 s on ice, diluted in 30 mL autoclaved Milli-Q water and then centrifuged for 10 min at 1000 RPM to separate the cells in the

88 supernatant from the mineral grains (Danovaro & Middelboe, 2010). The prokaryotes enumeration was performed by flow cytometry (Bencton Dickinson FACSCanto II; equipped with a 488 nm Argon laser). Prior the counting, 5 mL of extract were diluted in 490 mL TE-buffer (Tris- ethylenediaminetetraacetic acid EDTA; pH 7.5), and stained with 5 mL of SYBR Green I (stock 1:200;

Molecular probes; Inc.) for 10 min at 80°C. Counts were calibrated using known densities of 2 and 3 mm fluorescent beads (Bencton Dickinson).

2.8. Quantification of dipicolinic acid (DPA) and estimation of endospore

numbers

Concentrations of the endospore specific compound DPA were determined by reverse phase-

HPLC after complexation with terbium according to Lomstein and Jørgensen (2012). In short, 0.5 g sediment was hydrolysed with 10 mL 3 N HCl at 95 °C for 4 h under N2. Two subsamples of each hydrolizate (ca. 0.3 mL) were dried, re-dissolved in 300 µL Milli-Q, and dried again. Immediately after the two subsamples were combined and dissolved in 4 mL of 1 M luminescent grade sodium acetate buffer, with 80 µL of 2mM AlCl3 added in order to precipitate phosphates. DPA concentrations were determined from 4 to 5 point standard curves, obtained by addition of standard in the sample to compensate for matrix effects. For conversion of DPA concentration to endospore numbers, it was assumed that each endospore contained 2.24 x 10-16 mol-DPA (Fichtel et al., 2007).

3. Results

A total of 3 cores were analyzed for concentrations of DPA down to depths of between 24 and

30 cm (Table 1). In the analysis was also included a 500 cm gravity core retrieved from station G10. In

G16, the deepest station, DPA measurements were unsuccessful despite repeated attempts due to low concentration of DPA and due to matrix effects in the HPLC.

The age of the sediment in different depths was estimated based on the 210Pb activity, a method which is suitable for the time interval of about the past 120 years. All stations have sedimentation rates between 1 and 2.5 mm yr-1 (Fig. 2). The 210Pb dating profiles of surface sediment did not indicate that bioturbation or mixing had any significant effect on sediment profiles at any of the stations.

The DPA analysis showed endospore abundances within the range 1 107 to 3.8 107 endospores gdw-1 in all short cores (Fig. 3, a-c). Overall, endospore abundance decreased with depth and age in the sediments. The half-life of the endospore community in the upper 30 cm of each core was estimated by fitting an exponential decay function to the spore abundance versus the sediment assigned age (Table 1). The decay rates were in the order of 3-2 10-3 yr-1 in all short cores corresponding to endospore half-life between 205-332 years (assuming no formation of new spores).

Total cell counts were performed on sediment cores from stations G15 and G16 (Fig. 4).

Abundance of cells at the sediment surface were two orders of magnitude above the number of endospores, but decayed at a much faster rate (3-5 10-2 yr-1).

The gravity core from G10 was used to compare prokaryotic cells and endospore abundances below 30 cm core depth (Fig. 5). In this core, the sediment dating was based on 14C (see section 2.4).

The abundance of vegetative cells down along the 5 m core followed a power function with a slope near -1 as most other sites on the Peru margin and elsewhere (Whitman et al. 1998; Parkes et al.,

2000; Kallmeyer et al 2012). The exponential decay of endospores down core observed in the short

MUC cores were not sustained down along the 5m gravity core (Fig. 5, a), but instead the progression

90 of the endospore community became much closer coupled to the number of vegetative cells (Fig. 5, c).

4. Discussion

4.1. Depth profiles of endospores

The studied sediments had uniform lithology (Fig. 3, d-f, Fig. 5, b), and with small deviations both the endospore numbers and the TOC content decreased continuously down-core. The decay rates of endospores, calculated in the top 30 cm of sediments from the Peru margin, are surprisingly close to decay rates reported for thermophilic endospores, in permanently cold sediments, with a half-life in the range 250-440 years (de Rezende et al., 2013). Similar values of 499-546 years have also been found in limnic sediments (Rothfuss et al., 1997). This could indicate that the majority of the endospores in the surface sediments of the Peruvian margin constitute a static community under decay, just as the thermophilic endospores found in the arctic must be static because the vegetative stages of those cells do not grow below 50 °C. Such endospores can apparently be dispersed over great distances in the ocean and accumulate together with other fine grained materials in the sediment (Müller et al., 2014). In the sediment, the static endospore community apparently decays at a universal rate in the range of a few hundred years.

If the exponential decay, with a half-life of 300 years, had continued down through the gravity core, then there should have been only 10 endospores gdw-1 left at 2.5 mbsf, which corresponds to 4500 years old sediment. However, the measured number was six orders of magnitude higher. This picture is not changed even if the upper 95% confidence bound to the fit with the slowest decaying endospores is chosen as reference (Fig. 5, a, stippled line). The discrepancy between the decay rate at the surface and the decay rate in the sub-surface can be explained by several processes. One explanation could be the presence of a subpopulation of the endospores that were orders of magnitude more resilient than the majority, so that no substantial decay had occurred across 6000 years (Fig. 5). Another explanation could be if endospore decay, for example via spontaneous fruitless germination (Epstein 2009; Buerger 2012), would decrease in response to

92 time or deprivation of nutritional conditions in the deeper sediment layers. Finally, the endospores could be in dynamic equilibrium with new endospores generated in situ, deep below the sediment surface at a rate that balances the decay. As the dynamics of the endospore community approach that of the vegetative community, after the initial die off (Fig. 5, a-c), this seems like a plausible explanation. The endospore forming bacteria belongs to either two out of the three classes of the phylum Firmicutes (Bacilli and Clostridia). The Firmicutes fraction found in marine sediments constitute in the range of 1-20 % of the total community (Biddle et al., 2008; Jorgensen et al., 2012).

Thus, we expect that the core section between 3 and 4 meters below seafloor (assigned age 6000-

8000 years) that contained 108 cells gdw-1 would contain 106 - 107 potentially endospore forming

Firmicutes per gdw-1. The same core section contains 6 106 endospores gdw-1. If those endospores decay at the same rate as the endospores at the surface, there would be an annual loss of 2 104 endospores gdw-1. To maintain the observed constant trend down-core, they would have needed to be recruited from the vegetative Firmicutes pool (106 - 107 Firmicutes cells gdw-1). Thus, despite the energetic costs that sporulation implicates, each potential endospore former would have to form one new endospore every 50-800 years. This result corresponds well with the estimated turnover times of cells in the deep biosphere (Lomstein et al. 2012; Hoehler and Jørgensen 2013).

Acknowledgements

This study was supported by the Mexican research council (CONACyT), PhD grant number

213154. The research has received funding from the European Research Council under the European

Union’s Seventh Framework Programme (FP/2007-2013)/ERC grant agreement no. 294200 and from the Danish National Research Foundation. Sample collection was carried out during the Galathea 3 expedition under the auspice of the Danish Expedition Foundation. The master and crew on board the Danish Navral vessel ”Vædderen are thanked for their help and hospitality during the cruise. We

93 thank Rikke O Holm, Lykke Poulsen and Preben G. Sørensen for technical support during the laboratory analyses.

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Table 1. Sampling site with positions of stations, water depth, core length and sediment location relative to water column and oxygen minimum zone (OMZ).

Water Bottom water Core Location half-life of Station Station Position depth temperature length relative to endospores code (m) (°C) (cm) OMZ* (yr)** 14°22.956´S G10 G10_GC02 313 11.1 495 Within - 76°23.894´W 14°23.010´S 205 G10 G10_MUC04 313 11.1 30 Within 76°24.069´W (150-323) 13°52.236´S 332 G15 G15_MUC26 743 5.4 30 Below 76°48.349´W (234-570) 14°14.604´S 315 G11 G11_MUC15 1015 4.5 30 Below 76°36.217´W (233-490) 14°16.576´S G16 G16_BR11 2367 >4.5 25 Below no data 76°47.603´W *based on Revsbech et al., (2009) ** with 95% confidence interval

6. Figure legends

Figure 1. Chart of investigated area with bathymetry lines and sampling sites indicated. The map is based on Reinhardt et al., (2002).

Figure 2. Age models of stations G10, G15 and G11 based on the 201Pb dating method.

Figure 3. A-C) Concentration of endospores in MUC cores based on DPA analysis. The lines represent the best exponential fit to the data. The equation of the fit and the derived endospore half-life (T1/2) with 95% confidence interval is printed on each panel. D-F) Concentration of total organic carbon

(TOC) and the percentage of carbon bound in amino acids (%TAAC) in the same cores as A-C. All cores were 30 cm long.

Figure 4. Profiles of cells enumerations versus sediment assigned age. The exponential fits are and in stations G15 and G16 respectively.

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Chapter 3

Microbial activity rates of a Holocene-Pleistocene biosphere

Paulina Tamez-Hidalgoa, Renato Salvattecib, Oscar Chiangc, Mathias Middelboec

Hans Røya and Bente Aa. Lomsteina

aDepartment of Bioscience, Section for Microbiology, University of Aarhus, Aarhus, Denmark, bInstitute of Geosciences, Kiel University, Kiel, Germany cMarine Biological Section, University of Copenhagen, Helsingør, Denmark

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1. Introduction

The subsurface biosphere is constrained by energy limitation, constant burial and the availability of labile organic carbon (Arndt et al., 2013). It is generally agreed that microbial cell abundances decline steeply with depth following a power law (Parkes et al., 2000). Consequently, the average carbon and energy flux per cell is highest near the sediment surface and drops off with depth and age in the sediment (Lomstein et al., 2012).

Nevertheless, little is known regarding the development in microbial activity with depth and age in sediments (Arndt et al., 2013). Prokaryotic activity may be evaluated employing models focused on cells or biomass turnover times (D´Hondt et al., 2004; Schippers et al., 2005; Holmkvist et al., 2011; Lomstein et al., 2012). The calculations are based on cell counts and on turnover rates of selected metabolic substrates that are assumed to be the main electron donors, and the turnover rate of the bulk sediment (Jørgensen, 2011). Previous studies found prokaryotic turnover times in the range from hundreds to thousands of years (Schippers et al., 2005; Jørgensen and D’hondt, 2006).

Prokaryotic turnover times have also been independently determined by the D:L-amino acid modeling (Lomstein et al., 2012), yielding estimates of the rate at which cells rework the amino acid pool, and extrapolates to cells turnover times. This approach has found cell turnover times to be in the order of tens to hundreds of years in Holocene sediments (Langerhuus et al., 2012) and in the order of hundreds to thousands of years in sediments as old as the Oligocene (Lomstein et al., 2012).

According to these models, microbial populations in subsurface environments are adapted to slow but steady existence under constant nutrient limitation, which would correspond to k- strategists (Janssen, 2009). However, it is not known whether the cells are actually dividing or simply turning over cell biomass without cell division (Arndt et al., 2013).

Recent studies have documented the presence of endospores in the subsurface (Lomstein et al., 2012; Langerhuus et al., 2012; Tamez-Hidalgo et al., in revision), illustrating another possible survival strategy. Populations forming endospores may represent r-strategists that grow at higher

107 rates when conditions are suitable, and re-enter a dormant state before nutrients are exhausted

(Janssen, 2009).

The present study investigated the distribution of cells in sediments within the oxygen minimum zone (OMZ) at the Peru Margin. Carbon oxidation rates, and biomass and necromass turnover times are evaluated employing the D:L-amino acid model developed by Lomstein et al.,

(2012). The model results are compared to measurements of profiles of TOC in the sediment.

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2. Materials and Methods

2.1. Study site

The study site is situated on the Peruvian continental shelf and slope (average slope 4°) (Fig. 1).

The waters in this area have the world’s most persistent wind-driven coastal upwelling, supporting intense primary production ( 2700 mg C m-2 day-1; Lee and Cronin 1984; Chavez et al, 1999), which leads to high sedimentation rates (Brodie and Kemp, 1994). The sediments consist of fine-grained, organic-carbon-rich (Suess et al., 1986), diatom-rich laminated mud lenses with intervals of paleo organic-poor and less diatomaceous, structureless or macroscopically bioturbated silty muds (Krissek et al., 1980; Brodie and Kemp, 1994). The high input of OM leads to increase oxygen consumption resulting in an oxygen minimum zone (OMZ) (Wyrtki, 1962). During the Galathea 3 expedition, which was carried out in February, 2007, the OMZ was located between 50 to 500 m water depths and contained between 2 nmol L-1 and 3 µmol L-1 of dissolved oxygen (Revsbech et al., 2009). This perennial OMZ maintains organic-rich sediments deposited from the water column, and exclude bioturbation (Schrader, 1992). According to Suess et al., (1986) the upwelling is most intense in the zones 7°-8°S, 11-12°S and 14°-16°S, which is the zone the sampling was carried out. The OM is mostly of marine origin with high rates of bacterial remineralization (Niggeman and Schubert, 2006). The study sites were located around 14°S at contrasting water depths (Table 1, Fig. 1).

2.2. Sampling and sample processing

The sampling was carried out during the Galathea 3 expedition in February 2007. Two gravity cores were recovered. The cores were retrieved from water depths indicated on table 1. For extraction and enumeration of prokaryotic cells, subsamples for prokaryotic abundance (1 mL) were transferred to 2 mL cryogenic vials, fixed with 1 mL glutaraldehyde (2% final concentration) and frozen at -20ºC until further processing. For biogeochemical analyses including total organic carbon

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(TOC), total nitrogen (TN), total hydrolizable amino acids (THAA), total hydrolizable amino sugars

(THAS) and dipicolinic acid (DPA), the cores were sectioned into the following depth intervals: the upper 6 cm was sliced into 1 cm intervals and into 2 cm intervals at 6-30 cm depth. Each section was transferred into a Nasco Whirl-Pak bags and frozen at -20°C immediately after sampling. The samples were freeze-dried and homogenized by grinding in an agate mortar.

2.3. Radiocarbon measurements

Dating was based on the radiocarbon tracer method. Both stations where dated using neighboring cores (G10-GC01 and G14GC05). Age models for cores in this study were possible by matching TOC profiles, and using the corresponding age to calculate subsequent depths following the sedimentation rate from the linear fits:

2 ; R (Fig. 1). 14C-AMS were analyzed at relatively high resolution (about each 20 cm of sediment depth and/or at interesting stratigraphic features). For the gravity core

G10-GC01, AMS and 32 radiocarbon measurements have been performed on the sedimentary organic fraction, after acidification, at the LMC14 Laboratory Accelerator Mass Spectrometry

Facility (UMS 2572, Gif-sur-Yvette, France). For the gravity core G14-GC05, 30 radiocarbon ages from the same source were determined in the Keck Carbon Cycle AMS facility of the University of California at Irvine. 14C age calibration was done by applying an average local reservoir correction of 200±100 years for the late Holocene (<4000 years BP; Gutiérrez et al., 2009, Ortlieb et al. 2010) and 500±200 years for the early Holocene and Late Pleistocene (Ortlieb et al., 2010).

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2.4. Extraction and enumeration of prokaryotes

Sediment samples were transferred to a sterile 50 mL Falcon tube, diluted in 4 mL autoclaved

Milli-Q water, treated with sodium pyrophosphate (Na4P2O7; 5 mM final concentration) and incubated in the dark at room temperature for 15 min. After the incubation, the samples were sonicated (at 20 KHz; Branson, SLPe), two cycles of 30 s on ice, diluted in 30 mL autoclaved MilliQ water and then, centrifuged for 10 min at 1000 RPM (Danovaro & Middelboe, 2010). The prokaryotes enumeration was performed by flow cytometry (Bencton Dickinson FACSCanto II; equipped with a

488 nm Argon laser). Prior the counting, 5 mL of extract were diluted in 490 mL TE-buffer (Tris- ethylenediaminetetraacetic acid EDTA; pH 7.5), and stained with 5 mL of SYBR Green I (stock 1:200;

Molecular probes; Inc.) for 10 min at 80ºC. Counts were calibrated using known densities of 2 and 3 mm fluorescent beads (Bencton Dickinson).

2.5. Total organic carbon (TOC) and total nitrogen (TN)

The TOC and TN in the samples were determined after decarbonating 1 g of sediment samples with H2SO3 (5-6 w/w %), air-drying and overnight drying at 105°C. The samples were weighted before and after decarbonating in order to correct for any losses or gains in the sample weight due to loss of CO2 or weight gain due to the addition of acid. The analysis was performed in a

Carlo Erba NA-1500 elemental analyser. The concentrations were calculated from individual 4-10 point calibration curves within the respective ranges. Flour containing 44.39% C and 2.31% N was used as standard for the calibration curves.

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2.6. Total hydrolysable amino acids (THAA) and total hydrolysable amino

sugars (THAS)

THAA and THAS were processed and analyzed by high-performance liquid chromatography as described in Lomstein et al. (2006). The only exception was that the dried hydrolyzate was dissolved in 5 mL MilliQ instead of the 4 mL MilliQ given in Lomstein et al. (2006). Blanks were prepared in the same way as samples except that sediment was omitted. Blanks showed negligible concentration of amino acids, from handling and reagents. In total, 18 amino acids were quantified: aspartic acid

(Asp), glutamic acid (Glu), serine (Ser), histidine (His), glycine (Gly), threonine (Thr), arginine (Arg), β- alanine (β-Ala), taurine (Tau), alanine (Ala), γ-aminobutyric acid (γ-Aba), tyrosine (Tyr), valine (Val), phenylalanine (Phe), isoleucine (Ile), leucine (Leu), ornithine (Orn) and lysine (Lys). Two amino sugars were quantified in this order: galactosamine (GalN) and glucosamine (GlcN).

2.7. Analysis of diagenetic indicators

Amino acids are preferentially degraded from the bulk OM (Wakeham, 1997), as they are protein building-block molecules. Thus, as diagenesis progress the amino acid pool will disappear faster over the total OM. Thus, the contribution of amino acid carbon to the total organic carbon

%TAAC (Keil et al, 2000), was calculated taking into account the number of carbon and nitrogen atoms in each individual amino acid relative to TOC.

2.8. Stereochemical composition (D- L-amino acids isomers)

D- and L-amino acid isomers of Asp, Glu, Ser and Ala were measured in aliquots of the hydrolysate used for THAA analysis, following the HPLC method given in Mopper and Furton (1991), with the modifications described in Guldberg et al. (2002) and Lomstein et al. (2009). The concentrations of the isomers of the four amino acids were calculated from individual four-five point

112 calibration curves of the respective amino acids. Blanks were prepared in the same way as samples with the exception that sediment was omitted. Blanks showed negligible concentration of amino acids from handling and reagents. The concentration of each amino acid and their respective isomers were corrected for chemical racemization occurring in the liquid-phase acid hydrolysis as described by Kaiser and Benner (2005). They found that the average percentages of D-enantiomers (

[ ( ] produced during acid hydrolysis of L-enantiomers were 4.4 % for Asp, 2.0 % for

Glu, 0.3 % for Ser and 1.2 % for Ala.

2.9. D:L-Amino acid modelling of bacterial activity

To estimate to what extent the microbial prokaryotic community is remineralizing the pool of amino acid. The D:L-amino acid model developed by Lomstein et al., 2012) was applied. The conceptual model and detailed explanation is found in supplementary material from Lomstein et al.,

(2012). The model conceptualizes the sedimentary amino acid pool (THAA) subdivided into three compartments: amino acids present in vegetative cells, bacterial endospores and necromass. The first two being comprised into biomass and necromass comprises the fraction that cannot be accounted into biomass. The model estimates the speed at which the amino acid-carbon circulate between the different compartments, for that, it assumes that only 11% of the necromass is assimilated into biomass in each cycle (based on a growth yield of 11%; Heijnen and Van Dijken,

1992). It also assumes that bacterial biomass is in steady state meaning that necromass degradation is equal to biomass production. Therefore 89% of the biomass produced must be mobilized from uncharacterized TOC to maintain this steady state. The measured D:L-ratios can be used to model bacterial activity when D:L-Asp ratios deviate from ratios that would otherwise be expected if chemical racemization were the only source of D-Asp in the sediments, and when D:L-Asp ratios deviate from ratios obtained from bacterial cultures. The model is typically performed under two

113 scenarios: a prokaryotic community dominated by Bacteria based on findings by Shippers et al.,

(2005), and prokaryotic community dominated by Archaea (90% Archaea and 10% Bacteria) as found by Lipp et al., (2008).

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3. Results

3.1. The two sediment cores at the Peru margin, upwelling system

Even both gravity cores were nearly the same length, the core from station G10 contained sediments far younger (up to ca.10 kyr) than the core from station G14 (ca. 11-25 kyr). In station G14 from the last 11 kyr were eroded by seismic events (Salvatteci et al., 2012). Although both stations are located close each other (Table 1), in a seismically active mud-lens, the core from G14 belongs to steeper zone than G10, thus being subject to seismic slides producing temporal gaps in the upslope depositions. In the following, the data from both stations are therefore, discussed based on assigned age (yr BP) rather than sediment depth.

3.2. Abundance of prokaryotic cells

A constant decline of prokaryotic cells was observed at both stations (Fig. 2). The numbers of cells at the sediment-water interface were nearly two orders of magnitude higher in station G10 (1

109 9.7 107 cells gdw-1) than in station G14 (9 107 7.4 106 cells gdw-1). Sediments from both cores pooled together followed a power-law function (Fig. 2), that correlates well with previous descriptions (Parkes et al., 2000; Jørgensen et al., 2012).

3.3. Profiles of TOC, TN, THAA and THAS

Bulk pools of OM (TOC and TN) and identifiable pools of OM (THAA and THAS) decreased with sediment age (Fig. 3). However, decrease followed an exponential trend with sediment age in TOC,

TN and THAA only. Calculated decay rates were 17,329 years for TOC (R2 0.85) and TN (R2 0.88) respectively, and 11,552 years for THAA (R2 = 0.92; Fig. 3a-c). The pool of THAA decreases faster with age than total pools of carbon and nitrogen. This corresponds well with previous observations

115

(Wakeham et al., 1997; Keil et al, 2000). The concentrations of THAS, on the other hand, seem to follow a different pattern. Although, the data did not show any significant correlation, it was observed the concentration decreased from 17 to 9.6 µmol gdw-1 and from 18.4 to 8.1 µmol gdw-1 in the first 1000 years in G10 and G14 respectively. After that, a much slower decrease was observed in G10, but G14 remained constant (Fig. 3, d). Thus, both sediments had a similar THAS concentration in the top layers followed by a decrease. The presence of amino sugars reflects bacterial imprint which can interpreted by OM remineralization process occurring at the water-sediment interface and within the first layers of the sediment.

The C:N ratio in station G14 was constant with age ( 11), whereas in station G10, the ratio varied from 9 to 11 (Fig. 1, suppl.). This is likely due to a gradual depletion of N diffusing upwards

(Grundmanis and Murray, 1977; Prokopenko et al., 2013). After 4000 years, G10 reached the same ratio as G14 and remained constant, thus, both stations exhibited a straight line following age of the sediment.

The range of THAA and THAS concentrations found in the present study, correspond well with previous investigations (Henrichs et al., 1984; Niggemann and Schubert, 2006; Lomstein et al., 2009) from the Peru margin.

Cell abundance was plotted against the contribution of amino acid carbon to the total organic carbon (%TAAC; Fig. 4). This ratio has been suggested as a sensitive indicator of diagenesis of OM (Keil et al., 2000). Cell abundances in station G10 have undergone a larger diagenetic span. In comparison, the cells at G14 are sustained by far older and poorer materials (Fig. 4). Therefore, we hypothesized that most of the cells found in G14 are in a dormant state rather than active, which explains their steady abundance down-core.

116

3.4. Source of OM

The source of OM was identified as been primarily of prokaryotic origin (Fig. 2, suppl.). The ratios of glycine versus serine (Gly:Ser) and glucosamine versus galactosamine (GlcN:GalN) were investigated. The Gly:Ser ratio showed a progressive increase with age in station G10. The range varied from 2.1-3.4. In G14, on the other hand, the ratio varied from 2.7-3.7 but the trend was scattered (Fig. 2, suppl.). A ratio around 2.3 indicates the replacement of the phytoplankton sources by bacterial molecules due to OM reworking. Bacterial cells typically have a Gly:Ser ratio of 2.3, while diatoms and phytoplankton have lower ratios, 0.7 and 1.0 respectively (Keil et al., 2000).

The ratio of GlcN:GalN has been shown to decrease as diagenesis proceeds (e.g Langerhuus et al., 2012). At station G10, this ratio varied from 2.8 to 1.8 and at G14 from 1.7 to 1.0. GlcN:GalN ratios below 8.1, indicate again a strong signature of bacterial reworking on sedimented OM.

Both ratios correspond well with previous investigations. The first one with marine sediments studies (Lomstein et al., 2006; Lomstein et al., 2009; Langerhuus et al., 2012), and the latter with sediment studies from rivers (Tremblay and Benner, 2009), lakes (Carstens et al., 2012), estuaries

(Unger et al., 2012; Khodse and Bhosle, 2013), sea and oceanic waters (Benner and Kaiser, 2003;

Davis and Benner, 2005) and marine sediments (Lomstein et al., 2009; Langerhuss et al., 2012).

3.5. D:L-Amino acid measurements

To apply the D:L-amino acid modelling, the occurrence of D-asp was analysed. The D-form was found to increase relatively to its L-isomer as sediment became older (Fig. 3 a suppl.). In other terms, measured D:L-Asp ratios ratios diverged from the D:L-Asp ratios found in bacterial cultures (0.086 mean ratio). Furthermore, total aspartic acid (D- & L-forms) was compared with THAA, and the results indicated that aspartic acid harbours a similar reactivity as the bulk pool of THAA (Fig. 3 b, suppl.). This central assumption of the D:L-amino acid model was thereby fulfilled.

117

4. Discussion

The model assumes that cell abundance is in a steady state (i.e. cell numbers are relatively similar throughout the sediment profile). The implications for a subsurface microbial community in a steady-state can be: 1) the community is actively engaged in respiration at a rate sufficient to maintain the community size, but not to grow, corresponding to a community of K-strategists (e.g.

Janssen 2009); or 2) the majority of the community survive in a dormant state, maintaining a basal metabolism for repairing cell-damage (Price and Sowers, 2004) and permitting slow transport of protein and metabolites to allow reactivation once conditions turn favorable (Parry et al., 2014).

Buerger et al., (2012) were able to test soil and marine isolated cells and endospores, viable but dormant, in a nutrient-rich medium. Their results showed a lack of response to nutrients per se from all populations tested. Instead, they observed growth in a stochastic fashion. The majority of the isolates developed colonies in a range of 40-200 days. However, colony re-growth only took about 24-48 hours, even in the isolates that required up to 200 days of initial incubation. This observation was not a result of adaptative mutations. The likely explanation, the authors claimed, is that slow growth and oligotrophy appears to be rarer than previously thought, and that stochastic exiting from non-growing state may be more common. A microbial community of cells at a different metabolically stages may be a better representation of a natural microbial community (Price and

Sowers, 2004). In nutrient restricted environments, dormancy would signify the difference between survival and death, and slow growth may be an ambiguous interpretation.

In this study, a steady-state situation in terms of microbial abundance was found in the sediment core from station G14. However, at G10, cell numbers only fulfilled this assumption at an interval between 20-300 cm. Therefore, the model was performed only in this section.

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4.1. Carbon oxidation under different scenarios

The D:L-amino acid model predicts that the total carbon oxidation rates at the two stations differed by an order of magnitude from each other (Fig. 5). However, results from both the tested scenarios (100% bacterial and 90% archaeal dominance, see section 2.9), displayed overlapping trends. Analogous results of overlapping scenarios are shown by Lomstein et al., (2012) and

Langerhuus et al., (2012). To avoid clutter, figure 5 is only displaying bacterial dominance.

The predicted carbon oxidation rate at station G10 ranged from 451.5 nmol cm-3 y-1 in the upper part, to 122 nmol cm-3 y-1 in the lower part of the sediment interval. At station G14, on the other hand, considerably lower rates were predicted, as carbon oxidation rates varied from 20 nmol cm-3 y-1 in the top, to 4.9 nmol cm-3 y-1 in the lower part of the sediment. The values obtained from

G14 are similar to ranges obtained in sediments from Aarhus bay (Langerhuus et al., 2012).

The reason for the different carbon oxidation rates at the two stations may be that the sediment layers modelled at G10 contains fresher OM than the layers modelled at G14. Thus it is assumed that what is generated at the top will progressively decay or survive at the expense of the

OM that is buried, therefore carbon oxidation values decrease with sediment depth (Fig. 5).

According to the model, the TOC decrease with age such that the pool size decreases to the half approximately every 17 kyr. To test how this fits the observed profiles of TOC, we compared the results from the model with the observed TOC decay rate in the sediment profiles (-0.00004 yr-1; Fig.

3 a). As a note, the decay rate obtained from the exponential function did not change by plotting the stations separately.

In the sediment core from G10, the decrease in TOC from 20 to 300 cm depth (calculated ages

0.6-5.2 kyr) amounts to 1.1 mmol-C gdw-1. For comparison, the rates predicted in the model were integrated over the age of the sediment core, yielding a total predicted carbon oxidation of 0.1 mmol-C cm-3 sediment, or 0.34 mmol-C gdw-1 sediment. In the sediment core from G14, the decrease in TOC from top to bottom in the core (calculated ages 13-25.4 kyr) amounts to 1.6 mmol-C gdw-1. In

119 contrast, the integrated predicted rates from the model amounts to 0.13 mmol-C cm-3 or 0.07 mmol-

C gdw-1 sediment. Thus, the model predicted an amount of carbon oxidation that was 3.3 and 23.5 times lower than the observed decrease in TOC at station G10 and G14, respectively. The differences between the model predictions and the observed TOC decay trend merits further investigation.

4.2. Turnover times of biomass and necromass

Biomass turnover times estimated for both stations ranged from 124 to 712 and 54 to 294 years at

G10 and G14 respectively. The result was independent of whether Bacteria or Archaea were assumed to dominate the community. The turnover times estimated in the present study are far longer than generation times estimated for surficial nutrient-rich environments such as soil, lake water or sea water (Jørgensen, 2011), and from results obtained by Schippers et al., (2005), from the

ODP Leg 201. However turnover times are similar to what Biddle et al. (2006) found in the sulfate- methane transition zone in sediments from the Peru Margin, and to observations by Langerhuus et al., (2012) from sediments from Aarhus bay, Denmark. Much longer turnover rates have been found in even older sediment layers from the Peru margin (Lomstein et al., 2012).

The pool of necromass is far larger and cycles over a much longer time span (Fig. 6). This implies that whether microbial cells persist in dormant or slow-but-active forms (K-strategists), there is sufficient bulk OM to sustain the community. However, enzymatic hydrolysis becomes increasingly challenging as the pool of OM becomes increasingly refractory (Zonneveld et al., 2010; Arndt et al.,

2013). One can hypothesize that members of a dormant community sporadically enter into an active regime when they are temporarily exposed to labile molecules. For example, this could happen if OM is released from silicate particles, or when a neighbouring cell lyses. Microbial associations are important to optimize energy yields and metabolite exchange (Prokopenko et al., 2013), and a cluster

120 of microbial cells might persist over larger times by maintaining a dormant-like metabolism and exchanging electron donors.

Acknowledgments

This study was supported by the Mexican research council (CONACyT), PhD grant number

213154. We are grateful to Rikke O. Holm and Lykke Poulsen for skilful technical assistance with HPLC analyses. The master and the crew onboard the Danish Naval vessel “Vædderen” are thanked for their help and hospitality. Sample collection was carried out during the Galathea 3 expedition under the auspices of the Danish Expedition Foundation. This is Galathea 3 contribution Px. The research underlying this article has been co-funded by the Danish National Research Foundation and from the

European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-

2013)/ERC grant agreement no [294200].

121

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Figure legends

Figure 1. Sediment profiles versus age model as based on the radio carbon tracer method.

Figure 2. Cell abundance profile of analyzed stations contrasted with sediment age. The plot is shown

-1 in a log10 of cell numbers per gdw versus log10 of the sediment assigned age in years. The y-axis crosses in 1 105 to ease visualization of standard deviations. The pooled data follows a power function ; R2=0.87.

Figure 3. a) Total organic carbon (TOC), b) Total nitrogen (TN), c) Total hydrolysable amino acids

(THAA) and d) amino sugars (THAS) versus sediment age. Exponential fits for TOC, TN and THAA respectively: , R2 0.85; , R2 0.89;

, R2 0.92

Figure 4. Abundance of cells compared with OM diagenesis. The percentage of amino acid carbon to the total organic carbon was used as diagenetic indicator.

Figure 5. a) D:L modelled carbon oxidation rates with sediment age in stations G10 and G14, b) comparison of D:L modelled carbon oxidation rates of 100% respiring bacterial cells.

Figure 6. D:L modeled turnover times of bacterial biomass and necromass with sediment age.

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Table 5. Gravity cores from the Galathea 3 expedition in the Peru margin.

Bottom water Station Core Latitude Longitude Core length (cm) Water depth (m) Location relative to OMZ* Temp. (oC) G10 GC02 14.22.956 S 076.23.894 W 495 313 11.10 Within G14 GC06 14.23.516 S 076.25.507 W 539 390 8.63 Within

128

Figure 1.

G10 G14

30000

vi 25000

v 20000 iv

15000

iii 10000

Calibrated(yr age BP)

5000 ii i 0 0 100 200 300 400 500 600

Sediment depth (cm)

129

Figure 2.

G10 G14

cells gdw-1

1 E+5 1 E+6 1 E+7 1 E+8 1 E+9 1 E+10 1 E+0

1 E+1

1 E+2

assigned(yr age BP) 1 E+3

1 E+4

1 E+5

130

Figure 3. G10 G14

-1 a -1 TOC (µmol gdw ) b TN (µmol gdw )

0 2000 4000 6000 8000 10000 0 200 400 600 800 1000 0 0

5000 5000

10000 10000

15000 15000

assigned(yr age BP) 20000 20000

25000 25000

30000 30000

-1 -1 c THAA (µmol gdw ) d THAS (µmol gdw ) 0 50 100 150 0 5 10 15 20 0 0

5000 5000

10000 10000

15000 15000

20000 20000 assigned(yr age BP)

25000 25000

30000 30000

131

Figure 4.

G10 G14

1 E+10

1 E+9

1 - 1 E+8

Cells gdw Cells

1 E+7

1 E+6 0 2 4 6 8 10 %T C AA %TAAC

132

Figure 5.

G10 G14

Total C-oxidation (nmol cm-3 yr-1)

1 10 100 1000 0

5000

10000

15000

Assigned Assigned (yr age BP)

20000

25000

30000

133

Figure 6. G10 biomass G14 biomass

G10 necromass G14 necromass

Turnover time (yr)

1 E+00 1 E+1 1 E+2 1 E+3 1 E+4 1 E+5 1 E+6 0

5000

10000

15000

Assigned Assigned (yr age BP) 20000

25000

30000

134

Supplementary material

135

Figure 1.

G10 G14

C:N (mol mol-1)

0 2 4 6 8 10 12 14 16 0

5000

10000

15000

Assigned (yr age BP) 20000

25000

30000

Figure 1. Profile of C:N ratio with sediment age.

136

Figure 2.

G10 G14

Gly:Ser GlcN:GalN 0 1 2 3 4 0 1 2 3 4 0 0

5000 5000

10000 10000

15000 15000

20000 20000 Assigned (yr age BP) 25000 25000

30000 30000

Figure 2. Accumulation of prokaryotic remains with age as judged by the Gly:Ser (a) and GlcN:GalN (b) ratios.

137

Figure 3.

G10 G14

a Asp (D:L ratio)

0.00 0.10 0.20 0.30 0

5000

10000

15000

Assigned Assigned (yr age BP) 20000

25000

30000

b y = 0.1094x - 0.3413 16 R² = 0.9696

) 12 1 - 10 8 6 asp (µmol gdw 4 2 0 0 50 100 150

THAA (µmol gdw-1)

Figure 3. D:L ratio of aspartic acid with sediment depth (a). Reactivity profile of aspartic acid as a function of THAA (b).

138