Microbial Ecology of the Oceans E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1157

Veröffentlichungsjahr: 2018

Beschreibung

The newly revised and updated third edition of the bestselling book on microbial ecology in the oceans The third edition of Microbial Ecology of the Oceans features new topics, as well as different approaches to subjects dealt with in previous editions. The book starts out with a general introduction to the changes in the field, as well as looking at the prospects for the coming years. Chapters cover ecology, diversity, and function of microbes, and of microbial genes in the ocean. The biology and ecology of some model organisms, and how we can model the whole of the marine microbes, are dealt with, and some of the trophic roles that have changed in the last years are discussed. Finally, the role of microbes in the oceanic P cycle are presented. Microbial Ecology of the Oceans, Third Edition offers chapters on The Evolution of Microbial Ecology of the Ocean; Marine Microbial Diversity as Seen by High Throughput Sequencing; Ecological Significance of Microbial Trophic Mixing in the Oligotrophic Ocean; Metatranscritomics and Metaproteomics; Advances in Microbial Ecology from Model Marine Bacteria; Marine Microbes and Nonliving Organic Matter; Microbial Ecology and Biogeochemistry of Oxygen-Deficient Water Columns; The Ocean's Microscale; Ecological Genomics of Marine Viruses; Microbial Physiological Ecology of The Marine Phosphorus Cycle; Phytoplankton Functional Types; and more. * A new and updated edition of a key book in aquatic microbial ecology * Includes widely used methodological approaches * Fully describes the structure of the microbial ecosystem, discussing in particular the sources of carbon for microbial growth * Offers theoretical interpretations of subtropical plankton biogeography Microbial Ecology of the Oceans is an ideal text for advanced undergraduates, beginning graduate students, and colleagues from other fields wishing to learn about microbes and the processes they mediate in marine systems.

Leseprobe

Table of Contents

COVER

TITLE PAGE

PREFACE

CONTRIBUTORS

1 INTRODUCTION

1.1 INTRODUCTION

1.2 A BRIEF HISTORY OF MARINE MICROBIAL ECOLOGY

1.3 AN ASSESSMENT OF CURRENT MARINE MICROBIAL ECOLOGY

1.4 THE FUTURE OF MARINE MICROBIAL ECOLOGY

1.5 SUMMARY

1.6 REFERENCES

2 MARINE MICROBIAL DIVERSITY AS SEEN BY HIGH‐THROUGHPUT SEQUENCING

2.1 DIVERSITY

2.2 THE METHODS

2.3 THE USE OF SEQUENCES AS PROXIES FOR TAXA

2.4 DIVERSITY AFTER HTS

2.5 CONCLUSION

2.6 SUMMARY

2.7 ACKNOWLEDGMENTS

2.8 REFERENCES

3 ECOLOGICAL SIGNIFICANCE OF MICROBIAL TROPHIC MIXING IN THE OLIGOTROPHIC OCEAN

3.1 OLIGOTROPHIC OCEANIC GYRES: THE MOST EXTENSIVE, MICROBE‐DOMINATED BIOME ON EARTH

3.2 MICROBIAL COMPOSITION OF THE SUBTROPICAL GYRES

3.3 PROKARYOTIC PHOTOHETEROTROPHY IN GYRES: THE ABILITY TO USE LIGHT ENERGY AND TO TAKE UP ORGANIC MOLECULES SIMULTANEOUSLY

3.4 EUKARYOTIC MIXOTROPHY IN GYRES: THE ABILITY TO USE LIGHT ENERGY AND SIMULTANEOUSLY PREY ON BACTERIOPLANKTON

3.5 HOW DO PHOTOHETEROTROPHY AND MIXOTROPHY AFFECT THE COEXISTENCE OF BACTERIA AND EUKARYOTES IN GYRES?

3.6 KNOWLEDGE GAPS

3.7 SUMMARY

3.8 ACKNOWLEDGMENTS

3.9 REFERENCES

4 METATRANSCRIPTOMICS AND METAPROTEOMICS

4.1 INTRODUCTION TO MARINE “OMICS” AND BIG DATA

4.2 OVERVIEW OF THE METATRANSCRIPTOMICS APPROACH

4.3 OVERVIEW OF THE METAPROTEOMICS APPROACH

4.4 KEY CONSIDERATIONS IN DETECTING COMMUNITY ECOSYSTEM FUNCTIONS

4.5 IMPORTANCE OF CULTIVATION‐BASED STUDIES, REPLICATION, AND QUANTIFICATION

4.6 MARINE MICROBIAL COMMUNITY TRANSCRIPTOMICS AND PROTEOMICS

4.7 SUMMARY

4.8 ACKNOWLEDGMENTS

4.9 REFERENCES

5 ADVANCES IN MICROBIAL ECOLOGY FROM MODEL MARINE BACTERIA

5.1 INTRODUCTION

5.2 CULTIVATION APPROACHES

5.3 LESSONS LEARNED FROM ECOPHYSIOLOGICAL RESPONSE EXPERIMENTS WITH CULTIVATED BACTERIA

5.4 CONCLUDING REMARKS

5.5 SUMMARY

5.6 ACKNOWLEDGMENTS

5.7 REFERENCES

6 AN INSEPARABLE LIAISON

6.1 AN INSEPARABLE LIAISON: MARINE MICROBES AND NONLIVING ORGANIC MATTER

6.2 MARINE CARBON RESERVOIRS

6.3 BIOGEOCHEMICAL CYCLES AND THEIR MICROBIAL ENGINES

6.4 DRIVING FORCES FOR TURNOVER KINETICS

6.5 SPATIAL AND TEMPORAL CHANGES IN ORGANIC MATTER AND MICROBIAL COMMUNITIES

6.6 THE CHALLENGE FOR FUTURE RESEARCH: UNDERSTANDING THE FUNCTIONAL NETWORK OF MARINE MICROBES AND ORGANIC MOLECULES

6.7 SUMMARY

6.8 ACKNOWLEDGMENTS

6.9 REFERENCES

7 MICROBIAL ECOLOGY AND BIOGEOCHEMISTRY OF OXYGEN‐DEFICIENT WATER COLUMNS

7.1 INTRODUCTION

7.2 CURRENT TRENDS

7.3 CHARACTERIZING OXYGEN DEFICIENCY: TERMS AND DEFINITIONS

7.4 TYPES OF OXYGEN‐DEFICIENT AQUATIC SYSTEMS

7.5 PHYSICO‐CHEMICAL PROFILES AS INDICATORS OF BIOGEOCHEMICAL ZONES

7.6 GENERAL CONSIDERATIONS OF MICROBIAL METABOLISM IN ODWCS

7.7 BIOGEOCHEMICAL CYCLES IN OXYGEN‐DEFICIENT SYSTEMS AND MAJOR PROKARYOTES INVOLVED

7.8 MICROBIAL FOOD WEBS IN ODWCS

7.9 SUMMARY

7.10 ACKNOWLEDGMENTS

7.11 REFERENCES

8 THE OCEAN’S MICROSCALE

8.1 INTRODUCTION

8.2 THE MICROSCALE PHYSICS OF THE PELAGIC OCEAN

8.3 PARTICLES, PATCHES, AND PHYCOSPHERES

8.4 MOTILITY AND CHEMOTAXIS

8.5 MICROSCALE MICROBIAL INTERACTIONS

8.6 MICROBIAL METABOLIC ADAPTIONS TO MICROSCALE HETEROGENEITY IN SEAWATER

8.7 BIOGEOCHEMICAL IMPLICATIONS OF MICROSCALE INTERACTIONS

8.8 SUMMARY

8.9 ACKNOWLEDGMENTS

8.10 REFERENCES

9 ECOLOGICAL GENOMICS OF MARINE VIRUSES

9.1 INTRODUCTION

9.2 GENOMICS OF ISOLATED MARINE VIRUSES

9.3 INVESTIGATING VIRAL COMMUNITY DIVERSITY IN NATURE

9.4 MARINE VIRAL COMMUNITY DIVERSITY AND STRUCTURE

9.5 DEPTH‐RELATED PATTERNS EMERGING FROM ANALYSIS OF MARINE VIRAL METAGENOMIC DATA SETS

9.6 EMERGING TEMPORAL PATTERNS IN MARINE VIRAL COMMUNITIES

9.7 ANNOTATING THE UNKNOWN: THE NEED FOR CREATIVE SOLUTIONS

9.8 INVESTIGATION OF VIRUS‐HOST INTERACTIONS IN THE WILD

9.9 FUTURE CHALLENGES IN MARINE VIRAL ECOLOGY

9.10 SUMMARY

9.11 ACKNOWLEDGMENTS

9.12 REFERENCES

10 MICROBIAL PHYSIOLOGICAL ECOLOGY OF THE MARINE PHOSPHORUS CYCLE

10.1 INTRODUCTION

10.2 METHODOLOGICAL ADVANCES AND CHALLENGES

10.3 PHOSPHORUS BIOGEOCHEMISTRY

10.4 PHOSPHORUS IN THE CELL

10.5 MICROBIAL BIOGEOCHEMISTRY OF PHOSPHORUS BOND TYPES

10.6 INORGANIC PHOSPHORUS UTILIZATION

10.7 ORGANIC PHOSPHORUS UTILIZATION

10.8 PHOSPHORUS STRESS RESPONSES

10.9 CASE STUDIES IN PHOSPHORUS PHYSIOLOGY

10.10 CASE STUDIES WITH DIFFERENT SYSTEMS

10.11 SUMMARY

10.12 ACKNOWLEDGMENTS

10.13 REFERENCES

11 PHYTOPLANKTON FUNCTIONAL TYPES

11.1 WHAT ARE FUNCTIONAL TYPES?

11.2 THE MAJOR FUNCTIONAL TRAITS

11.3 CHALLENGES USING TRAITS TO REPRESENT FUNCTIONAL TYPES

11.4 USING FIELD DATA TO IDENTIFY RELEVANT TRAITS AND ESTIMATE TRAIT VALUES

11.5 Should We Model Functional Types or Individual Species?

11.6 A WAY FORWARD

11.7 SUMMARY

11.8 REFERENCES

12 THEORETICAL INTERPRETATIONS OF SUBTROPICAL PLANKTON BIOGEOGRAPHY

12.1 INTRODUCTION: PHYTOPLANKTON BIOGEOGRAPHY IN THE SUBTROPICAL OCEAN

12.2 RESOURCE COMPETITION, FITNESS, AND CELL SIZE

12.3 COEXISTING SIZE CLASSES: PREDATION LEVELS THE PLAYING FIELD

12.4 NICHE DIFFERENTIATION AND RESOURCE RATIO THEORY

12.5 DISCUSSION AND OUTLOOK

12.6 SUMMARY

12.7 ACKNOWLEDGMENTS

REFERENCES

INDEX

END USER LICENSE AGREEMENT

List of Tables

Chapter 01

TABLE 1.1 History of marine microbial ecology, focusing on the water column

Chapter 02

TABLE FOR BOX 2.2 Microbes from the picoplankton (cells below 2–3 μm) belong to two drastically cellular plans, the prokaryotes and the picoeukaryotes (Picoeuks), which share some properties in virtue of their small size but also have fundamental differences. We list here several of these traits

TABLE 2.1 Richness estimates of Northwest Mediterranean communities with different sequencing techniques and number of reads

TABLE 2.2 Richness estimates for bacteria and eukarya derived from large scale sequencing projects with HTS. In the case of eukarya all metazoan sequences were excluded

Chapter 04

TABLE 4.1 Example of metatranscriptomic results. Transcript reads and inventories identified in replicate samples (1 and 2) and filtered by two methods (on‐deck filter versus

in situ

pump). Plankton samples were collected by sequential filtration (0.2–2.0 µm, 5‐m depth). All samples were from Line P at station P4 (48°39.0 N, 126°40.0 W) in the North Pacific Ocean

TABLE 4.2 Example of metaproteomics results. Proteins identified by one or more peptides with a SEQUEST confidence score >98%. Most studies require detection of at least two peptides with high confidence. Proteins detected by homologous peptides were annotated by BLAST searches of all homologs to identify putative lineages and functions. Only the top BLAST hits are shown

Chapter 05

TABLE 5.1 Work on model marine bacteria that has become milestones in microbial ecology (see also Table 1.1 in Chapter 1)

Chapter 07

TABLE 7.1 Selected functional genes and enzymes used as biomarkers for transformations within the carbon, nitrogen, and sulfur cycles in pelagic redoxclines, and representative groups or taxa of prokaryotes mediating these processes.

Chapter 09

TABLE 9.1 Problems and advances in the viral metagenomic sample‐to‐sequence pipeline

TABLE 9.2 Ecological drivers of marine viral community structure in large‐scale quantitative dsDNA metagenomic data sets

Chapter 10

TABLE 10.1 Examples of molecular ‘omics’ tools that have led to key discoveries in marine microbial phosphorus physiology. These are examples, not a comprehensive list

TABLE 10.2 Phosphorus cycle substrate bioavailability and processes and their distribution in representative marine microbes. This summary does not distinguish between strains or species, and there may be substantial heterogeneity within each genus.

Chapter 11

TABLE 11.1 Common Phytoplankton Functional Types and Taxonomic Groupings. Functional types can be defined on the basis of biogeochemical or ecological function. Both approaches tend to identify types that are largely described by taxonomic groupings. For some types, one or a few taxa are commonly used as essentially equivalentto the entire type. Not all groupings are always used and a taxonomic group can fall into more than one biogeochemical or ecological functional type. Picoeukaryotes and Phytoflagellates are composed of multiple taxonomic groups

TABLE 11.2 Examples of common parameters and equations describing growth and loss processes in phytoplankton (see Fig. 11.1)

TABLE 11.3 Summary of selected trait values from literature surveys and biogeochemical functional type models. Trait values from lab studies shown in bold are from Litchman et al. (2006). Other values (not in bold) are those used in phytoplankton functional type models developed by Dutkiewicz et al. (2009); Gregg et al. (2003); Le Quéré et al. (2005); Merico et al. (2004); J. K. Moore et al. (2002); Sarmiento et al. (2010). Maximum growth rates are reported at 30 °C by Gregg et al. (2003), the highest values, except for d iatoms, and at 0 °C by Le Quéré et al. (2005), the lowest values

Chapter 12

TABLE 12.1 Key to symbols used

TABLE 12.2 Allometric scalings for key traits of marine phytoplankton, assuming that trait

A

scales as

, where

is cell volume

(µm

3

)

List of Illustrations

Chapter 01

Fig. 1.1 The subject of marine microbial ecology. Organisms and communities are studied in the framework set by oceanography, while genes and organisms determine the biogeochemical effect of microbial communities. Genes and organisms also determine the physiological response to the environment. In addition, genes and organisms evolve with time, and communities and their biogeochemical effects are subject to ecosystem dynamics, most notably those forced by global change.

Fig. 1.2 A simplified view of the microbial food web. Mostly heterotrophic organisms on the right side, and mostly autotrophic organisms on the left. Some members of both pico‐ and nanophytoplankton can be at least partially heterotrophic. Similarly, some “heterotrophic” bacteria and the ciliates can use light in some way or another. Flows of carbon are depicted as large solid arrows. Use of solar light is in thin arrows. Flows of carbon through viruses as dashed large arrows. Production of dissolved and particulate organic carbon as dashed arrows. Flows of inorganic nutrients in thin dashed arrows.

Fig. 1.3 The number of citations to papers describing classical microbial ecology methods (the DAPI method, the thymidine, and leucine methods), 16S rRNA clone sequencing and fingerprinting, metagenomics, and high‐throughput sequencing of the 16S rRNA gene. We used the same searches used by Kirchman and Pedrós‐Alió (2007). The figure gives the citations to the following papers and methods: for the abundance and production methods, Fuhrman and Azam (1980) (1145 citations), Porter and Feig (1980) (3839 citations), Kirchman et al. (1985) (697 citations), and Simon and Azam (1989) (1297 citations) for a total of 7007 unique citations; for the initial molecular methods, Amann et al. (1990) (5470 citations), Giovannoni et al. (1990), (1047 citations), and Muyzer et al. (1993) (6846 citations) for a total of 13362 unique citations; for metagenomics, Béjà et al. (2000b), (792 citations), Venter et al. (2004) 2250 citations, and Rusch et al. (2007), 1074 citations, a total of 4116 citations; and for high‐throughput tag sequencing, Sogin et al. (2006). The analysis was done on November 29, 2016, using the ISI Web of science. There was no effort to discriminate the papers about the oceans from those about other environments.

Fig. 1.4 The different approaches used to understand nitrogen fixation in the ocean, as an example of the various methodologies used by microbial oceanographers. Similar processes are used for understanding the P and C cycle.

Fig. 1.5 Different approaches to analyze the community of prokaryotes, together with the type of questions that can be addressed by each type of approach. The whole community can be subdivided into physiological or functional groups, large phylogenetic groups, or “species”‐level groups (16S rRNA clones, reads, or metagenomic 16S rRNAs) or more detailed ecotype or ITS (internal transcribed spacers)‐defined units. *The miTags approach is assumed to be quantitative because it does not use PCR.

Chapter 02

Fig. 2.1 Range of approaches available to microbial ecologists. Some allow examination of single cells (solid arrows), whereas others provide information at the community level (empty arrows). Several of these approaches have experienced a revolution thanks to development of HTS (white letters on dark background), and other approaches rely on different technologies.

Fig. 2.2 Stations sampled for molecular diversity studies in

Tara

Oceans (light circles) and Malaspina 2010 (dark circles). Most areas covered are in the tropical and subtropical oceans.

Fig. 2.3 Effects of discarding low abundance OTUs on bacterial diversity estimates using data from ICoMM. Upper panels: richness of coastal, deep, and surface samples considering all OTUs (a), discarding singletons (b), and discarding OTU with less than 50 sequences (c). Richness is decreased considerably. Lower panels: Comparison of samples using Bray‐Curtis distances among samples in an NMDS diagram using all OTUs (d), discarding singletons (b), and discarding OTUs with less than 50 sequences (f). The overall relationships are retained in all cases.

Fig. 2.4 Comparison of the read abundance of different phylogenetic groups in bacteria (a) and picoeukaryotes (b) as estimated in assays based on the extracted RNA or DNA (RNA and DNA surveys, respectively). The diagonal is the 1 to 1 line. Taxa represented by small black dots give similar results in both surveys (close to the 1:1 line), whereas taxa represented by large triangles are more abundant in DNA surveys and those represented by large circles are more abundant in RNA surveys.

Fig. 2.5 Species accumulation and rarefaction curves for pico‐eukaryotes (0.2‐ to 3‐μm size fraction) collected from 124 surface samples during the Malaspina 2010 expedition (see map in Fig. 2.2). The jagged curve is one of the many possible accumulation curves. The exact shape depends on the order in which sequences are collected and identified. The smooth line is the rarefaction curve. Each point indicates the expected number of taxa that would have been statistically expected if a given number of sequences had been examined. Sixteen million 18S rRNA reads (V4 region, MiSeq) were clustered at 99% similarity. Singletons were excluded.

Fig. 2.6 Rank‐abundance curve for the bacteria in a sample from the Northwestern Mediterranean Sea. The curve plots the number of individuals of each species versus the rank of that species. In this case, OTUs are used as proxies for species. The plot follows the convention of showing relative abundance in a log scale in the

y

‐axis and rank in a linear scale in the

x

‐axis. There is a four‐log difference in the abundance of the most abundant OTU (a SAR11 phylotype) that accounted for 11% of all the sequences, and one of the least abundant ones such as

Leeuwenhoekiella blandensis

MED217 that was found to comprise only 0.003% of the sequences. The former was the most abundant in conventional clone libraries but has not been isolated in pure culture from the Mediterranean. The latter could be isolated in pure culture but was not found in conventional clone libraries. The 50 most‐abundant OTUs made up 75% of all the sequences, but most of the OTUs were very rare, constituting the rare biosphere.

Fig. 2.7 Bacterial richness in two circumnavigation cruises:

Tara

Oceans (a) and Malaspina 2010 (b). The stations sampled are shown in Fig. 2.2. Upper panels show richness, and lower panels show rarefaction curves from

Tara

Oceans epi‐ and mesopelagic samples and from Malaspina 2010 bathypelagic samples, respectively. Upper panels show that richness increased with depth in Tara Oceans and that richness was higher in the free living than in the attached fraction in Malaspina 2010. In

Tara

Oceans, samples have been coded by depth: surface (SUR), deep chlorophyll maximum (DCM), and mesopelagic (MES). Richness increased with depth and this was independent of the number of samples examined. In Malaspina 2010, samples have been coded by size fraction: free‐living (FL; <0.8 μm) or particle attached (PA; >0.8 μm). Richness was higher in the free‐living fraction in individual samples. Notice that, when combined, richness of the attached fraction was lower than that of free‐living for a low number of samples, but it was higher for a larger number of samples.

Fig. 2.8 Global OTU abundance distribution and fit to the Preston log‐normal model with eukarya data from

Tara

Oceans. Most OTUs in the data set were represented by 3 to 16 reads, whereas fewer OTUs presented less or more abundances. Quasi‐Poisson fit to octaves (red curve) and maximized likelihood to log2 abundances (blue curve) approximations were used to fit the OTU abundance distribution to the Preston log‐normal model. Overall, the extensive sampling effort (in terms of spatiotemporal coverage and sequencing depth) uncovered the majority of eukaryotic ribosomal diversity within the photic layer of the world’s tropical to temperate oceans. Calculation of the Preston veil, which infers the number of OTUs that were missed (or were veiled) during sampling (~40,000), confirmed that the study captured most of the protistan richness.

Fig. 2.9 Spatial changes in community composition illustrated with samples from

Tara

Oceans. The graph shows one of the many possible ways to compare diversity of samples using a principal coordinates analysis of Bray‐Curtis distances among 139 prokaryotic samples. The largest differences occurred between deep and surface samples. Within the latter, there were also smaller differences among different regions and between surface and DCM. The lower panel shows the significant difference between deep and surface samples globally.

Fig. 2.10 Latitudinal gradient in marine epipelagic bacterial richness with samples from ICoMM. Pearson correlation r values between natural log‐transformed estimated richness and absolute latitudes. The regression coefficients and p values are: north r = –0.187, p = 0.007; south r = –0.656, p <0.001; global r = –0.417, p <0.001. Note the steeper slope in the Southern Hemisphere.

Fig. 2.11 (a) Bray–Curtis similarity plot f samples collected on a monthly basis at a depth of 5 m for over a decade at the San Pedro Ocean Time‐series, which were analyzed by ARISA (automated ribosomal intergenic spacer analysis). Each data point is the average of pairwise measurements of samples collected “

x

” number of months apart; for example, the first point shows the average similarity of all pairs of samples collected 1 month apart, the second point from all samples taken 2 months apart, and so on, with the last point representing the average similarity of the first and last samples. The sinusoidal pattern, with peaks every 12 months and troughs 6 months after each peak, indicates that community composition follows a seasonal pattern. There is a downward trend over the first 4 years (straight green line with steep slope), which indicates interannual variability, whereas this trend plateaus over time, suggesting long‐term stability of the average community. Error bars indicate 95% confidence intervals. (b) A similar plot to that in part (a) but with samples taken at a depth of 150 m. The absence of a sinusoidal pattern indicates that seasonality in community composition is not demonstrated at this depth, and the gentle downward trend (green line) indicates a degree of long‐term variability. Error bars indicate 95% confidence intervals.

Chapter 03

Fig. 3.1 Microbial community composition in the Atlantic Ocean. Biomass contribution of different microbial picoplankton populations in surface water across the Atlantic Ocean in austral spring. Biomass was calculated based on abundance data from Holland and Zubkov (2014) using conversion factors and biovolumes as presented in (Hartmann et al. 2014). For aplastidic protist a cell diameter of 2.9 μm (Hartmann et al. 2012) was used in conjunction with a carbon conversion factor of 200 fg C μm

−3

(Waterbury et al. 1986). Aplast, aplastidic protists (~3 μm); HNA, high nucleic acid bacteria; LNA, low nucleic acid bacteria, mainly SAR11; Plast, plastidic protists (≤3 μm); Pro,

Prochlorococcus

; Syn,

Synechococcus

.

Fig. 3.2 Oligotrophic gyres defined by the abundance of

Synechococcus

.

Synechococcus

abundance increases sharply in the nutrient replete equatorial and temperate waters and can therefore be used to define gyre boundaries. Different regions are indicated by vertical lines. Abundances were measured by Glen Tarran on an Atlantic Meridional Transect cruise in 2010 using flow cytometry. EQ, equatorial; NG, northern subtropical gyre; NT, northern temperate; SG, southern subtropical gyre; ST, southern temperate.

Fig. 3.3 Pulse‐chase tracer experiment. Typical time course of a pulse‐chase tracer experiment in different regions of the Atlantic Ocean expressed as percentage of isotopic label uptake from the total isotopic label pool in the sample. Data were collected on AMT‐20 in 2010. EQ, equatorial; NG, northern subtropical gyre; NT, northern temperate; SG, southern subtropical gyre; ST, southern temperate.

Fig. 3.4 Cell specific bacterivory rates of mixotrophic and heterotrophic protist predators. Plastidic and aplastidic, small (≤6 μm) protist are shown to be active bacterivores in different regions of the world oceans as determined by pulse‐chase radiotracer labeling (

35

S‐methionine), (a) or retention of fluorescently labeled bacteria (b). Plastidic protists are occasionally as efficient predators as aplastidic protists (e.g., NG, SG, WP). The pulse‐chase method directly determines assimilation of bacterial biomass (a), while fluorescently labeled bacteria measure ingestion of prey by the predator (b). The sampling region were: Arc, Beaufort Sea and Canadian Basin; BA, Bay of Aarhus, Denmark; EQ, equatorial Atlantic; MS, Blanes Bay in the Mediterranean Sea; NG, North Atlantic subtropical gyre; NT, Northern temperate Atlantic; SG, South Atlantic subtropical gyre; ST, Southern temperate Atlantic; WP, West Pacific. MS, BA and WP are coastal oligotrophic (MS, WP) and mesotrophic (BA) sites. Data are from Hartmann et al. (2012, NT, NG, EQ, SG, ST), Unrein et al. (2007, 2014, MS), Havskum and Riemann (1996, BA), Tsai et al. (2011, WP) and Sanders and Gast (2012, Arc). Size classifications varied between the different publications. When ambiguous, protists were placed in the larger size class (e.g., protists <5 μm would be placed in Plast, 3–6 μm. Plast refers to pigmented and Aplast to unpigmented protists.

Fig. 3.5 Contribution of mixotrophic and heterotrophic protists to total bacterivory In the Atlantic Ocean bacterivory is dominated by plastidic protists and in other regions of the world oceans they are responsible for a significant fraction of total bacterivory (≥38%). However, as a result of their higher cellular bacterivory rates aplastidic protists (Aplast) as obligate predators contribute considerably despite their lower abundance (compare with Fig. 3.1). Bacterivory rates were determined using pulse‐chase radiotracer labelling (

35

S‐methionine, a) or fluorescently labeled bacteria (b). The pulse‐chase method directly determines assimilation of bacterial biomass (a), while fluorescently labeled bacteria measure ingestion of prey by the predator (b). Sampling regions, indicated on the

x

‐axis, were Arc, Beaufort Sea and Canadian Basin; BA, Bay of Aarhus, Denmark; EQ, equatorial Atlantic; MS, Blanes Bay in the Mediterranean Sea; NG, North Atlantic subtropical gyre; NT, Northern temperate Atlantic; SG, South Atlantic subtropical gyre; ST, Southern temperate Atlantic; WP, West Pacific. MS, BA and WP are coastal oligotrophic (MS, WP) and mesotrophic (BA) sites. Data was collated from Hartmann et al. (2012, NT, NG, EQ, SG, ST), Unrein et al. (2007, 2014, MS), Havskum and Riemann (1996, BA), Tsai et al. (2011, WP) and Sanders and Gast (2012, Arc). Size classifications varied between the different publications. When ambiguous protists were placed in the larger size class (e.g., protist description of <5 μm would be placed in Plast, 3–6 μm). Plast refers to pigmented and Aplast to unpigmented protists.

Fig. 3.6 Ecological significance of photoheterotrophic and mixotrophic processes as depicted in the early 2000s (e.g., Béjà et al. 2000; Kolber et al. 2001; Unrein et al. 2007; Zubkov and Tarran 2008) compared with today, based on the average biomass contribution of different microbial groups in the Atlantic Ocean. Aplast = aplastidic protists (~3 μm); HNA = high nucleic acid bacteria; LNA = low nucleic acid bacteria, mainly SAR11; Plast, plastidic protists (~3 μm); Pro,

Prochlorococcus

; Syn,

Synechococcus

. Biomass was calculated based on abundance data from Holland and Zubkov (2014) using conversion factors and biovolumes as presented in (Hartmann et al. 2014). For aplastidic protist, a cell diameter of 2.9 μm (Hartmann et al. 2012) was used in conjunction with a carbon conversion factor of 200 fg C μm

−3

(Waterbury et al. 1986).

Chapter 04

Fig. 4.1 Diagram of metatranscriptomic workflow. Steps are divided into field, laboratory, and bioinformatics components (1, 2, and 3, respectively). Intermediate stages and processes are labeled by parentheses and above arrows, respectively. Biomass is typically collected by filtration and mRNA amplification is optional, depending on the amount of starting biomass. Sequences are annotated using available web‐based resources, such as the Basic Local Alignment Search Tool (BLAST) and often curated using a custom bioinformatic pipeline.

Fig. 4.2 Diagram of metaproteomic workflow. Steps are divided into field, laboratory, statistical, and bioinformatics components (1, 2, 3, and 4, respectively). Intermediate stages and processes are labeled by parentheses and above arrows, respectively. Biomass is typically collected by filtration or via centrifugation. A database search engine, such as Comet, is required to match raw spectra with predicted spectra (Eng et al. 2013). Raw spectra are obtained from fragmented peptides and predicted spectra are obtained using a database that contains proteins predicted from relevant nucleotide sequence data. Confidence scores for proteins and peptides are provided for all matches.

Fig. 4.3 Pilot metaproteomics study targeting bacterioplankton in Sargasso Sea (60 m). (a) A putative PstS phosphate transport protein expressed by

Prochlorococcus

. (b) A representative MS/MS spectrum identified by a doubly charged ion (

m/z

503.36). The representative PstS sequence shows all detected residues (grey) and a sample tryptic peptide (framed) with its corresponding MS/MS spectrum.

Fig. 4.4 Timeline marking key metatranscriptomic and metaproteomic studies of microbial communities in seawater. Representative metatranscriptomic and metaproteomic papers were chosen from the primary literature. The information provided includes study location, citation, and key findings from coastal and open ocean marine ecosystems. “*” Indicates papers for metatranscriptomics and metaproteomics studies conducted at the same time and location.

Fig. 4.5 Comparison of the most abundant functions identified by metaproteomics of microbial communities in the North Pacific (black bars) and South Atlantic surface waters (white bars). MS/MS spectral counting was used to obtain estimates of relative abundance.

Chapter 05

Fig. 5.1 Schematic representation of the different topics that have been studied using marine model bacteria and that are covered in this chapter.

Fig. 5.2 Images of marine model bacteria. Colony morphologies of (a)

Ruegeria pomeroyi

DSS‐3 (

Alphaproteobacteria

); (b)

Dokdonia

sp. MED134 (

Flavobacteriaceae, Bacteroidetes

); (c)

Vibrio

sp. AND4 (

Gammaproteobacteria

); and (d)

Polaribacter

sp. MED152 (

Flavobacteriaceae

,

Bacteroidetes

). Bacteria were grown on YTSS agar plates in darkness at room temperature for 1 week. The bright coloring of the flavobacterial isolates is as a result of accumulation of carotenoids.

Fig. 5.3 Schematic model of the oligotrophy‐copiotrophy life strategy continuum. Distances between strains do not correspond to actual differences in life strategy.

Fig. 5.4 Ecology, physiology,and phylogeny of

Prochlorococcus

ecotypes. (a) Scheme of stratified open‐ocean water column illustrating vertical gradients in environmental variables allowing niche differentiation; (b) growth rate (filled symbols) and chlorophyll b:a ratio (open symbols) as a function of growth irradiance for

Prochlorococcus

strains MED4 (high‐light–adapted ecotype; triangles) and MIT9313 (low‐light–adapted ecotype; squares); and (c) Relationships between

Prochlorococcus

and other cyanobacteria from 16S rDNA data. Comparative analysis of the genomes of distinct

Prochlorococcus

ecotypes showed the genetic architecture for their different physiologies (e.g., optimal light intensities for growth, pigment contents, light‐harvesting efficiencies, sensitivities to trace metals, nitrogen usage abilities, and cyanophage specificities) and vertical niche partitioning.

Fig. 5.5 Growth of

Candidatus

Nitrosopumilus maritimus in ammonium‐limited artificial seawater medium. Oxidation of ammonium into nitrite is associated with growth. Entry into stationary phase occurred when ammonium was depleted (below detection limit at 10 nM).

Fig. 5.6 Interactions between

Ruegeria pomeroyi

DSS‐3 and

Thalassiosira pseudonana

CCMP1335. (a) Recovery of growth of the diatom

T. pseudonana

by addition of exogenous vitamin B

12

(filled circles) or by coculturing with

R

.

pomeroyi

DSS‐3 (open circles) compared with the vitamin B

12

–limited control (filled triangles). Inset: cell counts over the first 2 days of the experiment for

T. pseudonana

following addition of exogenous B

12

(open circles) or during cocultivation with

R. pomeroyi

(filled circles). Also shown are cell counts for

R. pomeroyi

in the cocultures (open squares) and in the B

12

–limited control (filled squares). (b) Schematic picture showing potential metabolites produced by the diatom and subsequently used by

R. pomeroyi

. The putative identity of metabolites was inferred from annotation of membrane transporters with defined substrate specificities following whole transcriptome sequencing of

R. pomeroyi

during coculture with the diatom. Fold‐change up‐regulation of transport genes (purple numbers) and chemical structures of the metabolites are also shown.

Fig. 5.7 Growth responses of

Dokdonia

sp. MED134 (

Bacteroidetes

) under light of different wavelengths. Cultures were exposed to white (open circles), blue (solid triangles), green (small diamonds), or red light (solid circles) light, and control cultures were maintained in the dark (black circles). Insert: absorption spectrum of MED134 proterhodopsin purified from

E. coli

membranes. Note that most pronounced stimulation of growth was achieved under wavelengths matching the absorbance maximum of the proteorhodopsin.

Fig. 5.8 Images of plaque assays and virion morphology of marine phages. (a)

Microviridae

‐like, (b)

Myoviridae

, and (c)

Siphoviridae

. Plaque assays were carried out using

Cellulophaga baltica

(

Flavobacteriaceae, Bacteroidetes

) as host (line in panel (b) indicates 1 cm). In electron micrograph images note lack of tails phages in panel (a) compared to panels (b) and (c) (scale bar 100 nm applies to all micrographs).

Chapter 06

Fig. 6.1 The microbial loop (Azam et al. 1983, 1994; Fenchel 1988; Pomeroy 1974). All organisms release CO

2

during respiration, and all aquatic organisms also release DOM during growth and decay. The microbial loop is fueled by DOM, which would otherwise have been lost from the food web. In the ocean, as in any aquatic environment, interactions between organisms and the associated flow of matter and energy are almost entirely mediated via organic molecules and inorganic constituents in solution. The microbial community and natural organic matter are both characterized by a high level of diversity, in terms of genetic potential, function, and molecular composition. From the multitude of interactions between the individual players, specific community functions emerge through which the flux of matter and energy in the ocean is controlled.

Fig. 6.2 The marine carbon cycle in a nutshell. Carbon reservoirs are shown as circles (grey area) whose areas correspond roughly to the respective pool sizes. The amount of carbon stored in fossil deposits and in dissolved inorganic form is so large that only sections of circles are shown. Arrows indicate major fluxes. The width of the arrows corresponds roughly to the fluxes. Pools of carbon are expressed in petamoles of carbon, fluxes in petamoles of carbon per year. Comparably minor fluxes and pools, such as chemoautotrophic production cannot be ignored, but are not included in the figure for simplicity. Note that all major fluxes in the ocean are mediated via biota, while living biomass is insignificant in the ocean in terms of carbon storage. About half of net primary production is funneled via DOM through the microbial loop. The turnover of the major organic carbon pools, especially the accumulation of DOM, appears enigmatic in this simplified view in which the chemical and organismal diversity is ignored. All numbers are from Dittmar and Stubbins (2014), except for the detrital and living carbon pool (Eglinton and Repeta 2013) and the sedimentary pool (Sundquist and Visser Ackerman 2013).

Fig. 6.3 Conceptual scheme illustrating the uncoupling of substrate turnover rates and concentration (inspired by Billen et al. 1980). Bacterial growth often lags behind phytoplankton growth, and excreted organic substrate first accumulates in a period of non‐steady state. In a later phase, the excreted substrates are quickly taken up by bacteria. Very low and stable concentrations of substrate are observed during this period, despite high rates of production and decomposition. This example illustrates that turnover rate and concentration of a substrate compound are not directly related. Much of the matter and energy flux in marine systems is funneled through compounds that are present in almost undetectable concentrations.

Fig. 6.4 Ultrahigh‐resolution mass spectrum of deep‐sea DOM illustrating the molecular diversity of DOM. Each signal (peak) in the mass spectrum represents a different molecular formula. For clarity, only one nominal mass is shown in detail of the broad band spectrum on the left, and only three molecular formulae are assigned. To date >10,000 molecular formulae have been detected in deep‐sea DOM (Riedel and Dittmar 2014). For each molecular formula, multiple isomers exist. Per molecular formula far more than 10 different isomers exist (Zark et al. 2017), that is, DOM consists of >100,000 different compounds. The spectrum was obtained on a Fourier‐transform ion cyclotron resonance mass spectrometer (FT‐ICR‐MS, 15 Tesla Bruker Solarix XR) at the University of Oldenburg (Dittmar et al., unpublished). The analyzed sample is solid‐phase extracted DOM from North Equatorial Pacific Intermediate Water (Green et al. 2014).

Fig. 6.5 The dependency of the rate of an energy‐yielding, microbially catalyzed reaction on the concentration of a substrate, according to the Michaelis‐Menten model (modified after LaRowe et al., 2012, and Dittmar, 2015). Above a certain threshold concentration, a maximum substrate consumption rate (V

max

) is reached. Below that threshold, consumption rate is thermodynamically limited. At extremely low substrate concentrations, consumption may come to a complete halt because uptake and metabolization may not yield enough energy to sustain the basal power requirement of the cell.

Fig. 6.6 Conceptual view of organic matter turnover and budgets, in which each molecule is considered as a separate unit. The number of different molecules in the ocean is unknown. Here, features of one billion (10

9

) different compounds are conceptually displayed, the actual number of compounds in the ocean may be orders of magnitudes higher or lower. Turnover rates and pool sizes for each individual compound are plotted on the

y

‐axis. On the

x

‐axis, the molecules are sorted according to their biological production rate. Based on biological production rates, molecules can be conceptually categorized in an “abundant chemosphere” and a “rare chemosphere.” Common biomolecules, mainly recurring combinations of amino acids (i.e., peptides and proteins), fatty acids, or lignin phenols belong to the “abundant chemosphere” whereas rare products, for example, of enzymatic malfunctions are part of the “rare chemosphere.” Here, only monomeric subunits are considered. Combinations of monomers, for example, into proteins add yet another level of diversity. (a) In a dynamic steady state, the rates of production and decomposition for a given compound are identical. (b) The pool size of a given compound is independent of its production rate. On land, common biomolecules are present in large amounts in living (mainly trees) and decaying biomass (soils, peats, and permafrost). In marine DOM, on the other hand, the lack of recognizable biochemical features and the wide age distribution indicates that the accumulation of individual molecules is rather unselective. In the geosphere, specific compound groups that are inherently stable at given environmental conditions, like many pyrolytic reaction products, can accumulate.

Fig. 6.7 Two hypothetical scenarios of changes in global pool size of organic matter as a result of temporary imbalances caused by disturbances. Conceptual view in which each molecule is considered as a separate unit, in analogy to Fig. 6.6. (a) Global‐scale changes in seawater chemistry (e.g., lack of O

2

, may make certain metabolic functions inefficient for microorganisms). The accumulation of common biochemicals in the water column (Albert et al. 1995), similar to processes in peatlands on the continents, may be the consequence. (b) Another scenario where global carbon pools may change is an increased frequency of wild fires, through which biomass is quickly converted into CO

2

and charcoal. The latter may accumulate in soils, sediments and in dissolved form in marine waters (Santín et al. 2016).

Fig. 6.8 The succession of marine bacterioplankton populations after a phytoplankton bloom (modified after Teeling et al. 2012). Chlorophyll

a

concentration and the relative abundances of selected

Bacteroidetes

(

Ulvibacter

spp.,

Formosa

spp., and

Polaribacter

spp.) and selected

Gammaproteobacteria

(SAR92 clade) are shown. This example shows how algal substrate availability and subsequent decomposition products provides a sequence of ecological niches in which specialized bacterioplankton populations bloom.

Fig. 6.9 Latitudinal gradients in the degradation of DOM in the surface ocean (modified from Arnosti et al. 2011). Summed enzymatic hydrolysis rates for a series of polysaccharides are plotted against latitude. Bar height shows the sum of the maximum enzymatic hydrolysis rate of each substrate at each station.

Fig. 6.10 Linking chemodiversity and biodiversity in the North Sea (Europe): example of a field study where individual DOM molecules are statistically related to individual taxa (operational taxonomic units [OTUs]) of the microbial community (modified from Osterholz et al. 2016). Total and active microbial community compositions were obtained by 16S rRNA genes (rDNA) and rRNA sequencing‐based analysis. A total of 6338 DOM molecular formulae, 599 rDNA based OTUs and 946 rRNA based OTUs were identified. To explore relationships between microbial taxa and DOM molecules a multiple‐step statistical approach was applied. The significance of the statistical relationships between OTUs and DOM molecules is indicated by the shade, black and white indicate highest significance. The relative abundance of phylotypes printed in black are positively related to molecules printed in black, and negatively related to the molecules printed in white (and vice versa). (a) and (b) Relationships between molecules and rDNA (panel a) and between molecules and rRNA (panel b) in van Krevelen diagrams. Each dot represents one molecular formula. (c) and (d) Phylogenetic trees of those 30 bacterial phylotypes that were most significantly associated with changes in DOM composition. Note that many organisms with close phylogenetic relation show opposite trends with respect to DOM.

Chapter 07

Fig. 7.1 Oxygen concentration in the oceans at 200‐m depth and location of major oxygen‐deficient water columns. AS, Arabian Sea; BAL, Baltic Sea (central basins); BB, Bay of Bengal; BS, Black Sea; BU, Benguelan Upwelling; CB, Cariaco Basin; ETNP, Eastern Tropical North Pacific; ETSP, Eastern Tropical South Pacific; NEASP, North‐East Subarctic Pacific.

Fig. 7.2 Simplified geochemical profiles in contrasting ODWCs (modified from Ulloa et al. 2012). (a) Open‐ocean OMZ with a midwater depression in oxygen concentration but without anoxic processes (example: large part of the ocean). (b) Low‐oxygen‐OMZ without nitrite accumulation (examples: BB, NE Pacific). (c) Anoxic OMZ (AMZ) (O

2

 < 50 nM) with wide nitrite peak (examples: ETSP, ETNP, AS); shaded area: N

2

loss processes as a result of denitrification or anammox. (d) Euxinic system with sulfidic bottom water and an interface between sulfide and nitrate (or oxygen), (examples: BS, CB, some fjords and meromictic lakes); shaded area: coupled nitrification/chemoautotrophic denitrification. (e) Euxinic system with an extended “suboxic” zone (SO) without oxygen/nitrate and sulfide (example: BS). Upper shaded area: coupled nitrification/chemoautotrophic denitrification; lower shaded area: sulfide removal zone (as a result of unknown mechanism).

Fig. 7.3 Scheme of the depth distribution of the major electron acceptors, moving from the surface oxygenated zone to increasingly reduced environments. Profiles refer both to water columns (spanning meters) and sediments (spanning mm to cm). Microbial processes consuming the electron acceptors produce reduced components, which can again support microbially driven redox reactions (and chemoautotrophy) when transported upward to another redox zone.

Fig. 7.4 Major nitrogen transformations within ODWCs (modified from Thamdrup 2012). Metabolic transformations are shown as thick arrows, including assimilatory (light grey arrows) and dissimilatory (dark grey arrows) processes. Aerobic and anaerobic processes are vertically separated, and dashed vertical arrows indicate exchange or transport between oxic and anoxic environments. The arrowheads indicate the dominant direction of reactions. DNRA, dissimilatory nitrate reduction to ammonium; Org‐N: organic nitrogen compounds in biomass.

Fig. 7.5 (a) Conceptual model of the Mn–Fe–P shuttle for pelagic redoxclines of anoxic basins (modified from Dellwig et al. 2010). (b) Images of Mn–Fe–P (right) and Fe‐P (left) particles taken by scanning electron microscopy, equipped with energy dispersive X‐ray microanalyses (SEM‐EDX) collected in the redoxclines of the Baltic Sea (Landsort Deep, Gotland Basin). Results from EDX‐analyses: positions of EDX measurements (white arrows) and Mn, Fe, and P content in %weight.

Fig. 7.6 Conceptual model of the microaerophilic/anaerobic microbial food web in redoxclines of ODWCs. Vertical bar represents the gradient in redox potential (Eh) from the shallow microoxic to sulfidic conditions. For clarity, the pathway from nanoflagellates to viruses is omitted. OM, organic matter.

Fig. 7.7 Whole water column profile in the Gotland Deep (Baltic Sea). (a) Temperature and salinity profile. Depth distributions of (b) oxygen and hydrogen sulfide concentrations, and prokaryote abundances, and (c) the abundance of the three protist functional groups (heterotrophic nanoflagellates (HNF), dinoflagellates and ciliates). (d) Vertical biomass distributions of HNF, dinoflagellates, and ciliates. The horizontal dotted line indicates the oxic–anoxic interface.

Fig. 7.8 Examples of anaerobic ciliated protists harboring prokaryotic symbionts. (a) Scanning electron micrograph of the

Cariacotrichea

holotype specimen

Cariacothrix caudata

from the Cariaco Basin, showing densely packed bacteria (Ba) on cell surface (side view). AO, Oral apparatus (mouth); RB, right branch of the archway kinety (a defining

Cariacotrichea

feature). Scale bar = 12 μm. (b) Line drawings of

Cariacothrix caudata

, based on SEM micrographs. Ventral view showing the densely ciliated archway kinety and the unique

Cariacotrichea

apomorphy (left) and side view of cell outline only (right). AK, archway kinety; AO, adoral organelles; CC, caudal cilium; CR, ordinary somatic ciliary row; MA, macronucleus; MI, micronucleus; OC, oral cavity; R, ridge. Scale bar = 10 μm. (c–e) Micrographs of

Metacystis

spp. from the Gotland Deep redoxcline with putative symbiotic bacteria. (c) DAPI‐stained cells with prokaryotes in the cytoplasm (note bacteria on the cell surface are not in focus). (d) FISH signal of

Gammaproteobacteria

(GAM42a) on the cell surface. FV, food vacuole; GAM,

Gammaproteobacteria

; MI, micronucleus, MA, macronucleus. Scale bars = 20 μm. (E)

Metacystis

spp. observed after nitrate silver impregnation in vivo. Scale bar = 50 μm.

Chapter 08

Fig. 8.1 The ocean at the microbial scale. The two views emphasize the heterogeneity of resources and related microbial interactions and distributions. (a) “Hot spots” of microbial activity occur in association with detritus, marine snow particles, and phytoplankton cells. Panel (a) further emphasizes the rich texture of particles, filaments, and polymers of various sizes and origins, which contribute to microscale heterogeneity. (b) Organic substrates diffuse from a range of sources, including zooplankton excretions (left), phytoplankton exudation (the “phycosphere”; top; also, bottom right), phytoplankton lysis (top right), and settling marine snow particles (center bottom). Both panels emphasize the distinction between nonmotile cells and flagellated, motile cells, the latter often able to cluster at hot spots by chemotaxis. We estimate an approximate scale for each image to be 1 cm.

Fig. 8.2 The phycosphere. (a, b) An artist’s view of the diffusion boundary layer around individual phytoplankton cells, which incorporates the phycosphere—where concentrations of DOM, and also often motile bacteria, are enhanced over background levels. Motile bacteria can use chemotaxis to respond to the nutrient gradients associated with the enhanced dissolved organic matter concentrations within the phycosphere. Magnified from Fig. 8.1(b). (c) Experimental observation of the phycosphere. A coastal seawater sample was collected via net tow to concentrate planktonic particles. The particles and entrained seawater were incubated for 24 h at 22 ° C, which enriched the bacterial community and stimulated motility. Settled particles were gently resuspended just before video capture via darkfield microscopy. Swimming trajectories shown here illustrate dense accumulations of chemotactic cells near the surface of an individual

Chaetoceros

diatom (S. Smriga, V. Fernandez and R. Stocker, unpublished). See also Smriga et al. (2016). (d) Trajectories of individual bacteria in the same observation shown in panel (c), obtained using digital image analysis and further highlighting the accumulation of bacteria in the phycosphere. Crosses denote starting points of individual trajectories. (S. Smriga, V. Fernandez, K. Son, and R. Stocker, unpublished). (e) The decay of DOM concentration with distance

r

from the center of a DOM‐exuding phytoplankton. The curves refer to two phytoplankton radii: 10 μm (lower, dark curve) and 50 μm (upper, light curve). The black dashed line provides a reference to a bulk background concentration of 10 nM (typical of many organic solutes in the ocean). The DOM concentration field was obtained by solving the steady diffusion equation for a constant source, following Seymour et al. (2010). The phytoplankton cell was assumed to have a 100‐mM internal concentration and to exude 100% of its daily production of the solute. This upper limit of the exudation rate is most applicable to stressed or senescent cells. The diffusion coefficient for the solute was 7.2 × 10

−10

m

2

/s, consistent with values for many organic substrates, such as DMSP.

Fig. 8.3 Ephemeral nutrient pulses. (a) Artist’s view of the lysis of a phytoplankton cell, resulting in a strong yet ephemeral pulse of dissolved organic matter, to which marine bacteria can respond to by chemotaxis. Magnified from Fig. 8.1(b). (b)The DOM concentration field associated with a lysis event, computed by a mathematical model of diffusion from a pulse source, following that Seymour et al. (2010). The abscissa shows the distance from the center of the lysing cell and the ordinate shows the time after the lysis event. Shadings indicate the concentration in μM. This computation applies to a cell of 25 μm radius, with an internal concentration of 100 mM, and a diffusion coefficient of the solute of 7.2 × 10

−10

m

2

/s. These parameters are appropriate, for example, for a medium‐size, DMSP‐producing phytoplankton.

Fig. 8.4 Marine particles. (a) Artist’s view of the plume of dissolved organic matter emanating from a sinking marine snow particle. Magnified from Fig. 8.1(b). (b) Mathematical model illustrating the large vertical extent these plumes can achieve. Shown is a 0.5‐cm radius particle sinking at 1 mm/s. The plume corresponds to a concentration of amino acids of 30 nM above background. Distances are in units of particle radii. Reproduced from Kiørboe and Jackson (2001), with permission. (c) Chemotactic accumulation of

Pseudoalteromonas haloplanktis

bacteria in the plume of a particle, obtained by video microscopy using a microfluidic experimental model of a marine particle and its plume. Background shading indicates concentration of solutes, black dots are individual bacteria. The particle diameter was 500 μm and its sinking speed 110 μm/s, corresponding to a Peclet number of 110 (see d–f). Reproduced from Stocker et al. (2008), with permission. (d–f) The shape of the plume for different Peclet numbers. The Peclet number is a dimensionless index, obtained as the product of the particle radius, its sinking speed, and the inverse of the molecular diffusivity of the solutes forming the plume. Shown are plumes for Peclet numbers of (d) 0, (e) 100 and (f) 10,000, respectively.

Fig. 8.5 Turbulence can contribute to patchiness. Effect of turbulence on a patch of DOM. The DOM is stretched, folded, and stirred by turbulence to create a tangled web of sheets and filaments as small as the Batchelor scale (30 to 300 μm in the ocean). Shading denotes DOM concentration. The characteristic timescale of this process, for a 2.5‐mm patch in moderately strong turbulence (turbulent dissipation rate = 10

−6

W/kg), is ~1 min. The domain size is 5.65 cm.

Fig. 8.6 Utilization of microscale hotspots can drive ecological differentiation among marine bacteria. Yawata et al. (2014) have recently shown that closely related

Vibrio cyclitrophicus

populations display different spatial behaviors to use marine particles. Both populations (light and dark) swim and can chemotax toward particles, but one population (light) attaches and forms biofilms on particles, whereas the other population (dark) hovers near the surface and can readily exploit fresh particles when they appear. One population is therefore a better competitor on the particle (light), the other is a better disperser (dark). Coexistence of the two groups occurs through a competition‐dispersal trade‐off. Image courtesy of Yutaka Yawata, Glynn Gorick and Roman Stocker (unpublished).

Fig. 8.7 Marine bacteria possess specialized motility. (a) Transmission electron microscopy image of

Vibrio alginolyticus

, showing a single polar flagellum (“monotrichous”; K. Son, J. S. Guasto, and R. Stocker, unpublished). This propulsion system is characteristic of the vast majority (>90%; Leifson et al. 1964) of motile marine bacteria. (b)

V. alginolyticus

has a hybrid swimming pattern that alternates reversals (180‐degree changes in direction) after each forward run (dark segments) and reorientations averaging 90 degrees after each backward run (light segments). The latter, called “flicks,” are caused by a whip‐like deformation (“buckling”) of the flagellum, and reorient the cell into a new swimming direction. Dots are 1/30th of a second apart. Modified from Xie et al. (2011), with permission. (c) Artist’s view of the flick, showing a brief forward motion of the cell before the buckling of the flagellum occurs.

Fig. 8.8 Bacterial accumulation by chemotaxis. (a) Chemotaxis of a natural bacterial community towards a detritus particle. (b) Chemotaxis of

Pseudoalteromonas haloplanktis

toward a pulse of phytoplankton exudates. The original footprint of the pulse corresponds to a band of 300‐μm width, extending across the image from left to right, in correspondence to the black vertical bar. The image was acquired 2 minutes after release of the pulse and shows very strong accumulation of trajectories (white paths) at the center of the pulse.

Chapter 09

Fig. 9.1 Examples of the effects of viruses on marine microbial ecology. (a) Depiction of the “viral shunt” that converts cellular material into dissolved organic matter, where it can be recycled, thus diverting it away from the grazing food web. Modified from Brum et al. (2014). (b) Depiction of community compositional changes brought about from “kill‐the‐winner” dynamics and the “arms race.”

Fig. 9.2 Bioinformatic methods to analyze quantitative viral metagenomes and assess viral ecology.

Fig. 9.3 Map of the stations in spatial and temporal ecological datasets discussed in this chapter. TOV (Brum et al. 2015b), GOV (Roux et al. 2016b), and POV (Hurwitz and Sullivan 2013) are large‐scale spatial data sets with quantitatively produced viral metagenomes. SPOT and BATS are long‐running time series that include viral abundance and single viral gene‐based data (e.g., Parsons et al. 2011; Chow and Fuhrman 2012). Palmer Station is the location of the Palmer, Antarctica Long‐Term Ecological Research program where marine virus research has recently been incorporated for a short‐term study (Brum et al. 2015a).

Fig. 9.4 Some viruses contain auxiliary metabolic genes (AMGs) that, when expressed during infection, alter host functions such as metabolic processes, intracellular transport, and membrane transport. This is hypothesized to provide additional energy and materials that enable production of more viruses during infection.

Fig. 9.5 Mining microbial (meta)genomic datasets for partial and complete viral genomes. (a) Binning sequences based on tetranucleotide frequency to assemble viral genomes. (b) Searching for viral genomes within microbial fosmids, SAGs, and genome sequencing projects. (c) Once a virus‐host association has been identified, their interactions can be examined over spatial and temporal scales. Current infections are those identified by intact viral contigs and past infections are those identified by defective prophages and CRISPR loci.

Chapter 10

Fig. 10.1 Phosphorus cycle overview. Phosphorus inputs to the ocean are dominated by weathering of phosphorus‐containing rocks, relative to inputs from atmospheric sources. In the water column, phosphorus is cycled between dissolved and particulate phases, and inorganic and organic forms. It is also increasingly recognized that it can be rapidly cycled between different bond forms and valence states (inset). Representative profiles from 0–4000 m are shown with a representative solid line for the western North Atlantic (Bermuda Atlantic Time Series) and a representative dashed line for the North Pacific Subtropical Gyre (Hawaii Ocean Time Series). Note surface DIP in the western North Atlantic is ~10 nM, and ~50–100 nM in the North Pacific Subtropical Gyre (Karl 2014; Lomas et al. 2010). DIP, dissolved inorganic phosphate; DOP, dissolved organic phosphorus; POP, Particulate organic phosphorus. Input values summarized from the literature (Benitez‐Nelson 2015).

Fig. 10.2 A diagram of possible phosphorus bond forms and their oxidation states in the ocean. Note that elemental phosphorus (P) and phosphine gas (PH

3

; blue diamond) are not thought to occur in marine environments. C‐O‐P (ester), and C‐P (phosphonate) are noted; see also Fig. 10.1.

Fig. 10.3 Enzymes for the hydrolysis of dissolved organic phosphorus. (a) Microbial enzymes for the hydrolysis of phosphoesters. (b) Microbial enzymes for the hydrolysis of different phosphonate substrates.

Fig. 10.4 The putative systems that control sensing and responding to phosphorus in microbes. (a) The putative sensor response system in marine prokaryotes is thought to be similar to

Escherichia coli

, where PhoR is activated by low phosphorus, and phosphorylates PhoB. PhoB controls transcription of the Pho regulon genes, with some exceptions (Su et al. 2007). In cyanobacteria like

Synechococcus

the PtrA protein has also been shown to have a regulatory role on sensing and responding to phosphorus stress (Ostrowski et al. 2010). (b) The sensor response system for yeast involves the kinase (Pho81), which controls the phosphorylation or dephosphorylation of Pho4, which in turn controls transcription of the Pho genes (Lenburg and O’shea 1996). The degree of phosphorylation on Pho4 can control the degree of transcription (Springer et al. 2003). The extent to which this model applies to marine microeukaryotes is an active source of study.

Fig. 10.5 Relative plots of key phosphorus cycle parameters in a typical (a) high phosphorus system, and (b) low phosphorus system. APase: alkaline phosphatase activity; C:P: the carbon to phosphorus ratio in particulate matter of the upper water column; P gene number: This refers to the increased abundance of phosphorus‐related genes typically found per genome in low phosphorus systems, an example is

pstS

; PolyP: polyphosphate; SRP, soluble reactive phosphate (typically used interchangeably with dissolved inorganic phosphate); TPP, Total particulate phosphorus.

Chapter 11

Fig. 11.1 Common parameterizations of processes affecting growth and loss rates used to define phytoplankton functional traits. (a) Michaelis‐Menten uptake kinetics for nutrients. (b) The Droop model for the effect of internal nutrient quota on growth rate. (c) Photosynthetic rate as a function of irradiance. (d) The effect of temperature on growth rate illustrated as the Eppley curve for maximum growth rate across phytoplankton species (bold line) and three species temperature responses for species with different temperature optima (thin lines). (e) Holling‐type grazing rates as a function of prey density (type II,

k

 = 1, bold line; type III,

k

 = 2, dotted line). (f) Allometric scaling of biomass‐normalized growth rate. Many other formulations of these functions have been used in the literature for each process. See Table 11.3 for a description of equations and symbols.

Fig. 11.2 Mean niches estimated from time‐series data. Mean niches derived from MaxEnt species distribution models for 69 diatom species and 50 dinoflagellate species from the North Atlantic Continuous Plankton Recorder. We summarize the niche for each species by its mean. Left panel: the reciprocal of mixed layer depths versus temperature. Right panel: nitrate concentration versus mean irradiance in the mixed layer. The traits separate the two functional types, diatoms (open squares, light color) and dinoflagellates (filled squares, darker color), although there is a great deal of variation within the functional types. Error bars are 95% confidence intervals on the means estimated by bootstrap resampling. For full details see Irwin et al. (2012).

Chapter 12

Fig. 12.1 Phytoplankton biogeography in the Atlantic basin. (a) Transects: Atlantic Meridional Transect 13 (AMT 13; solid line) and data extracted from Luo et al. (2012) (dashed line). (b) Observed

Prochlorococcus

(crosses) and

Synechococcus

(dots) cell densities (log(cells ml

)) on AMT 13 (data from Johnson et al. 2006). Lines are polynomial fits to guide the eye. (c) Observed nitrate concentration on AMT 13, 0‐50 m (data from Johnson et al. 2006). (d) Observed

Trichodesmium

trichome densities (trichomes l

) extracted from the compilation of Luo et al. (2012) along the dashed transect shown in (a). See Luo et al. (2012) for data sources.

Fig. 12.2 The association of surface ocean chlorophyll with cell size based on data from Marañon et al. (2012). Each panel shows the relationship between total observed chlorophyll (x‐axis) and the contribution from a particular size class (y‐axis). Black dots indicate data from latitudes

and grey dots data from latitudes

. Scales are logarithmic in

. Size classes are defined by Effective Spherical Diameter, ESD: (a) pico‐phytoplankton, ESD

; (b) nano‐phytoplankton, 2

ESD

; (c) micro‐phytoplankton, ESD

. (d) Fits of the semi‐empirical model of Brewin et al. (2010), clearly indicating the increasing contribution from larger size classes as total chlorophyll increases. Dashed line, picoplankton; solid line, nanoplankton; dash‐dot line, microplankton.

Fig. 12.3 (a) Population growth rate (μ, day

) as a function of phosphorus quota

at steady state in a laboratory population of

Monochrysis lutheri

. Notice the non‐zero intercept on the x‐axis at zero growth rate, indicating the minimum phosphorus quota for survival,

. Data from Burmaster (1979). (b) Uptake of phosphate (

) as a function of the ambient phosphate concentration

in a laboratory culture of

Alexandrium tamarense

. The solid line indicates a Michaelis‐Menten curve that is consistent with the data. Data from Yamamoto and Taruntani (1999). (c) Schematic diagraom illustrating the decoupling of the uptake of resource R and population growth. Cells take up resources and build new macro‐molecules until division occurs. Uptake and division may be decoupled if the resource is accumulated in storage compounds. In a culture at steady state, growth and uptake are balanced. See, for example, Verdy et al. (2009).

Fig. 12.4 Monod kinetics model of growth rate,

as a function of environmental resource concentration,

R

. μ

o

is the maximum growth rate and

k

R

is the half‐saturation resource concentration: the concentration of the resource at which growth is half the maximum. Box 12.1 explains how μ

o

and

k

R

can be related to the measured parameters of the internal stores model.

Fig. 12.5 The relative difference between the ambient concentration of the limiting resource and the subsistence resource concentration of the most competitive model phytoplankton in a global ocean simulation

(adapted from Dutkiewicz et al. 2009). Light grey shading indicates regions where this diagnostic has a value close to zero and, in the annual mean, the ambient concentration of the limiting resource is close to the

R

* of the lowest‐

R

* virtual phytoplankton type. In these subtropical and tropical regions, the variability in the environment is sufficiently small such that phytoplankton growth and loss are tightly coupled and the steady state solutions reasonably describe the organization of the modeled plankton community. Dark grey shading indicates regions where the ambient concentration of the resource significantly departs from the lowest

R

*, suggesting that non‐steady state dynamics cannot be neglected. White shading indicates regimes where no model plankton had a positive subsistence concentration according to the definition of

R

*.

Fig. 12.6 Growth rate and nominal

R

* as a function of cell volume for marine phytoplankton. (a) A small compilation of laboratory determined maximum growth rates as a function of cell volume for marine phytoplankton from several taxanomic groups. Open circles, diatoms; closed circles, coccolithophores; x, dinoflagellates; +, picocyanobacteria. While there is significant variability, the eukaryotic alga show a significant decline in maximum growth rate with cell volume, indicated by the power law function depicted in the solid line with exponent empirically determined by Edwards et al. (2012) from a compilation of laboratory data. There are also taxanomic patterns, with diatoms showing fastest maximum growth rates for a given size. Prokaryotic cells have slower maximum growth rates and a different size dependence. Data sources: Tang (1995), Morel et al. (1993), Johnson et al. (2006), Christaki et al. (1999), Moore et al. (1998), Agawin and Agustí (1997), Maranon et al. (2013), Sarthou et al. (2005), Buitenhuis et al (2008). (b) Size dependence of subsistence resource concentration for fixed nitrogen,

R

* (N), estimated using equation (12.14) and empirically determined scalings for the fundamental traits listed in Table 12.1. The loss rate,

m

B

, was assumed to be a constant

.

Fig. 12.7 A simple model for the co‐existence of multiple sizes of phytoplankton. (a) Schematic of the simple food chain model in which several primary producers compete for a single limiting resource and each is consumed by a specific predator. (b) Modeled relationship between biomass,

B

, of several size classes of primary producer (solid lines) and their predators (dashed lines) as a function of the rate of delivery of the single resource,

S

R

(µmol l

day

). (See Armstrong,1994; Ward et al. 2014). The smallest primary producer,

P

1

, has the lowest

R

* and dominates the biomass at the lowest rate of resource supply. At higher supply rates, grazers (

Z

1

) control the abundance of the smallest primary producer and enable the co‐existence of larger phytoplankton (

P

2

, etc.). Here the biomass of primary producers is depicted cumulatively by the stacked solid lines and that of the grazers as dashed lines. The total biomass, indicated by uppermost dashed line, increases with resource supply rate, as does the contribution from larger size classes. (Model details: equations (12.16), (12.17) and (12.18) are discretized and integrated to steady state. Four size classes of phytoplankton with cell volumes of

, 10

2

, 10

3

and 10

5

μ

m

3

are resolved. Model parameters are assigned using the allometric relationships and information from Table 1 and Box 12.1. The clearance rate of zooplankton is size dependent,

with a trophic transfer efficiency,

, and a constant mortality (

m

B

, 

m

z

) of

is imposed for all organisms.)

Fig. 12.8 Resource Ratio Theory perspective on nitrogen fixation. Predicted range of diazotrophy (grey) in the global, open ocean based on the relative rates of delivery of fixed nitrogen and iron by ocean transport processes and atmospheric deposition, following Ward et al. (2013). Superimposed is a compilation of observed diazotroph biomass from Luo et al. (2012): the diameter of the white circles indicate indicates relative biomass inferred from cell counts. Gene‐based estimates are not shown. Crosses indicate noted absences.

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