Information

16.5C: Hydrothermal Vent Microbial Ecosystems - Biology

16.5C: Hydrothermal Vent Microbial Ecosystems - Biology


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Hydrothermal vents are home to chemosynthetic bacteria, which are the basis of a unique ecosystem that thrives in total darkness.

Learning Objectives

  • Describe hydrothermal vent microbial ecosystems

Key Points

  • Hydrothermal vents emit nutrient rich, geothermally heated water. Mats of chemosynthetic bacteria grow around the vents and synthesize carbohydrates from the carbon dioxide ejected by the vent.
  • Many species of crabs, worms, snails, and tube worms depend on these bacterial mats for food. These species are often specially adapted to life in the lightless, high pressure, and hot environment of the vent.
  • Vents are the target of exploitation of the mining industry, which is a cause for concern among marine biologists. Mining could damage these very unique and diverse ecosystems.

Key Terms

  • chemosynthesis: The production of carbohydrates and other compounds from simple compounds such as carbon dioxide, using the oxidation of chemical nutrients as a source of energy rather than sunlight; it is limited to certain bacteria and fungi.
  • geothermal: Pertaining to heat energy extracted from reservoirs in the earth’s interior.

Hydrothermal Vents and Their Microbial Communities

A hydrothermal vent is a fissure in the earth’s surface from which geothermally heated water issues. They are typically found deep below the surface of the ocean. Hydrothermal vents are of interest to microbiologists because they have unique microbial communities found nowhere else on earth.

In most shallow water and terrestrial ecosystems, energy comes from sunlight, but in the deep ocean there is total darkness. However, hydrothermal vents often expel nutrient rich water, containing methane and sulfur compounds. Vent bacteria can synthesize all the compounds they need to live from these nutrients, a process called chemosynthesis. These bacteria form the basis of the entire hydrothermal vent ecosystem.

The chemosynthetic bacteria grow into a thick mat, covering the hydrothermal vent, and this is the first trophic level of the ecosystem. Snails, shrimp crabs, tube worms, and fish feed on the bacterial mat and attract larger organisms such as squid and octopuses. Many of these species are specially adapted to live in the dark and lack eyes. Hydrothermal vents are biodiversity hot spots because they have many species that are uniquely adapted to live in this harsh environment. For example, the Pompeii tube worm Alvinella pompejana can resist temperatures up to 176°F. These ecosystems are almost entirely independent of sunlight (although the dissolved oxygen used by some animals does ultimately come from plants at the surface ).

Despite being some of the most remote ecosystems in the world, hydrothermal vents are under threat from mining companies. As mineral resources on land have become depleted, mining companies have turned to deep sea geothermal vents to extract metals and sulfur. Although the technology for deep sea mining is new, conservation biologists are concerned that mining hydrothermal vents will destroy these fragile and unique ecosystems.


Expression patterns of mRNAs for methanotrophy and thiotrophy in symbionts of the hydrothermal vent mussel Bathymodiolus puteoserpentis

The hydrothermal vent mussel Bathymodiolus puteoserpentis (Mytilidae) from the Mid-Atlantic Ridge hosts symbiotic sulfur- and methane-oxidizing bacteria in its gills. In this study, we investigated the activity and distribution of these two symbionts in juvenile mussels from the Logatchev hydrothermal vent field (14°45′N Mid-Atlantic Ridge). Expression patterns of two key genes for chemosynthesis were examined: pmoA (encoding subunit A of the particulate methane monooxygenase) as an indicator for methanotrophy, and aprA (encoding the subunit A of the dissimilatory adenosine-5′-phosphosulfate reductase) as an indicator for thiotrophy. Using simultaneous fluorescence in situ hybridization (FISH) of rRNA and mRNA we observed highest mRNA FISH signals toward the ciliated epithelium where seawater enters the gills. The levels of mRNA expression differed between individual specimens collected in a single grab from the same sampling site, whereas no obvious differences in symbiont abundance or distribution were observed. We propose that the symbionts respond to the steep temporal and spatial gradients in methane, reduced sulfur compounds and oxygen by modifying gene transcription, whereas changes in symbiont abundance and distribution take much longer than regulation of mRNA expression and may only occur in response to long-term changes in vent fluid geochemistry.


CURRENT EVIDENCE

Trisha Lyn Spanbauer ,1,2* Christian Briseño-Avena,3,4Kathleen Johnson Pitz,5Elizabeth Suter 6,7

1Department of Integrative Biology, University of Texas at Austin, Austin, Texas2Department of Environmental Sciences,

University of Toledo, Toledo, Ohio3Hatfield Marine Science Center, Oregon State University, Newport, Oregon

4Department of Environmental and Ocean Sciences, University of San Diego, San Diego, California5Monterey Bay

Aquarium Research Institute, Moss Landing, California6Biology, Chemistry, and Environmental Studies Department, Molloy College, Rockville Centre, New York7School of Marine and Atmospheric Sciences, Stony Brook University, Stony

Scientific Significance Statement

In situ molecular and imaging instrumentation development has advanced our knowledge of plankton processes in aquatic ecosystems. However, these sensors have only begun to be used in freshwater ecosystems. Through a combination of literature review and interviews with instrument developers, we found that there is little technological barrier to transferring marine in situ molecular and imaging technology to freshwater ecosystems. Identified barriers are largely related to infrastructure and funding. These sensors have the ability to inform fundamental and applied plankton research in all types of aquatic systems.

Abstract

Understanding plankton dynamics in marine ecosystems has been advanced using in situ molecular and imag-ing instrumentation. A range of research objectives have been addressed through high-resolution autonomous sampling, from food web characterization to harmful algal bloom dynamics. When used together, molecular and imaging sensors can cover the full-size range, genetic identity, and life stages of plankton. Here, we briefly review a selection of in situ instrumentation developed for the collection of molecular and imaging information on plankton communities in marine ecosystems. In addition, we interviewed a selection of instrumentation developers to determine if the transfer of sensor technology from marine to freshwater ecosystems is feasible and to describe the process of creating in situ sensors. Finally, we discuss the status of in situ molecular and imaging sensors in freshwater ecosystems and how some of the reviewed sensors could be used to address basic and applied research questions.

*Correspondence: [email protected] [email protected] Associate editor: María González

Author Contribution Statement: T.L.S. led the manuscript preparation. C.B.-A., K.J.P., E.S., and T.L.S. contributed equally to the writing and editing of this article.

Data Availability Statement: Data are available in the Dryad Digital Repository at https://doi.org/10.5061/dryad.29pm4qs.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Scientific interest in the “patchy” distribution of plankton has been ongoing for more than a century it has been over 50 yr since Hutchinson (1961) posited the “Paradox of the Plankton” based on evidence from lake studies. Since that pioneering research, the heterogeneous nature of zooplankton and phytoplankton distributions at the macro scale (submeter to kilometer) has been well accepted for both lakes and ocean basins (Wiebe and Benfield 2003). It has taken longer to appreci-ate the heterogeneous distribution of smaller organisms like bacterioplankton (Stocker 2012). Such distributions are not ran-dom, but are the result of underlying chemical, physical, and biological interactions, which can be heterogeneous even at micrometer scales. Over the last two decades, both the oceano-graphic and limnological communities have explored these dis-tributions and their underlying mechanisms as well as their effects through the food chain (e.g., Brentnall et al. 2003 Franks 2005 Blukacz et al. 2009 McGillicuddy and Franks 2019).

Historically, resolving plankton distributions and abun-dances within lakes was relatively easier than in oceans due to their smaller size. Ocean basins require a much greater expen-diture of resources to gather equivalent samples. In situ molecular and imaging sensors are arguably at the forefront of such efforts in oceanographic studies, because of the capabil-ity of these instruments to resolve plankton distributions at small spatial scales over large areas (up to several kilometers) and across wide depth ranges (hundreds of meters vertically) alongside ancillary measurements (e.g., temperature, salinity, dissolved oxygen, carbonate chemistry, pH, photosynthetic active radiation,fluorescence). Advances in the high through-put sampling capabilities of these instruments has dramati-cally decreased the per sample cost for oceanographic research campaigns and has allowed for unique and highly targeted sampling of communities of plankton and their processes.

Most of the research that has made use of in situ molecular and imaging sensors has taken place in marine ecosystems (Table 1). The goals of these campaigns have ranged from fun-damental research, such as investigating predator-prey dynamics (Brownlee et al. 2016), to applied research objec-tives, such as early harmful algal bloom (HAB) detection (Greenfield et al. 2008 Ryan et al. 2011 Caron et al. 2017). A few in situ molecular and imaging instruments have been applied in limnological studies of plankton (e.g., in situ filtra-tion andfixation sampler [IFFS], Wurzbacher et al. 2012 laser optical plankton counter [LOPC], Yurista et al. 2009 Yurista et al. 2012 Table 1) however, an abundance of lake-based research questions could benefit from their expanded use in freshwater ecosystems. Freshwater and saltwater habitats exhibit overlap in multiple environmental issues, including proliferation of algal blooms, biodiversity loss due to climate change, invasive species, overfishing, and changes in biogeo-chemical cycling due to eutrophication and hypoxia. Much has already been learned from the adoption of in situ molecu-lar and imagining sensors, and the continued transfer of tech-nology between the marine and freshwater sciences will

further develop our knowledge of plankton dynamics in rap-idly changing aquatic ecosystems.

Molecular and imaging sensors designed for marine environ-ments have already been used to address a range of research ques-tions related to plankton dynamics. Results from this research has led to advancements in the study of gene expression, micro-bial responses (Edgcomb et al. 2016 Ottesen 2016), and bloom dynamics (Robidart et al. 2012 Brosnahan et al. 2015 Hunter-Cevera et al. 2016). There are similar needs to better understand plankton dynamics in freshwater ecosystems. For instance, one of the most pressing areas of research in all aquatic sciences is the detection and mitigation of HABs (Anderson et al. 2012 Paerl et al. 2016, 2018). Eutrophication of aquatic environments is predicted to get worse as a result of climate change-induced increases in precipitation, which deliver nutrients from the sur-rounding landscape (Sinha et al. 2017). Remote sensing has been useful in tracking HABs at the macroscale (Clark et al. 2017) while in situ molecular sensors have been useful in characterizing and predicting HABs in coastal waters (Babin et al. 2005 Ryan et al. 2011). Furthermore, molecular in situ sensors have been pro-posed as a method for detecting toxic bloom development in the Laurentian Great Lakes (Bullerjahn et al. 2016). HABs are one issue that illustrates the need to apply marine molecular and imaging sensors to freshwater environments. However, there is a whole range of freshwater plankton research (i.e., biogeochemical cycling, food web dynamics, invasive species, community reorga-nization, climate change, etc.) that could benefit with the imple-mentation of marine molecular and imaging in situ sensors in freshwater ecosystems, problems and paradigms shared by both limnology and oceanography sciences (Downing 2014).

In this current evidence article, we identify and briefly review in situ oceanographic instruments developed for the collection of molecular and imaging data, and we outline how these sen-sors can be applied to environmental issues and research areas in freshwater ecosystems. Molecular and imaging sensors are complementary systems that provide the ability to assess com-munity composition and molecular scale processes of the plank-ton at relatively low cost per sample, allowing for wide coverage in time and space. To determine the suitability of transferring molecular and imaging sensor technology from marine to fresh-water, we interviewed a selection of developers of these instru-ments. In doing so, we show both the challenges in the development of the sensors and the variety of applications of the sensors. Finally, we discuss some of the successes in the pioneering use of imaging and molecular in situ sensors in freshwater systems. We conclude with the ways that these sen-sors could be employed to address basic and applied research questions in lake ecosystems, thereby, using technology transfer to bridge marine and freshwater ecosystem sciences.

Two categories of in situ sensors for plankton

One of the major challenges in studying plankton is making accurate measurements of community composition while

Table

240 mL per frame Towed Mesozooplankton 20 μm to few cm Mar ine and estuarine. 0– 250 m depth Bi et al. (2015) LOKI 2.6 mL per frame Vertical pro filer with attached plankton net Mesozooplankton 0.9 –13 mm Mar ine. 0– 500 m depth Schulz et al. (2010) Schmid et al. (2016) SPC 3 m L per frame Moored Phyt oplankton and Mesozooplankton 100 μm –2.5 cm Mar ine and freshwater. Moored. Years http://spc.ucsd. edu/ Zoocam 250 mL per frame Mounted on a Zooglider Protists to mesozooplankton 0.45 mm – 4.95 cm Mar ine. 0– 400 m depth u p to 50 d per mission Ohman et al. (2019) Molecular se nsors AFIS 2.7 L Mounted on CTD Micr obial co mmunity metatransciptome >0.2 μm Mar ine (surface —

100 m depth) Fieke et al. (2012) AFISsys 250 mL Moored Micr obial co mmunity metatransciptome >0.2 μm Brackish (surface) deployed for several days Charve t e t al. (2019) (Continues)

Table

simultaneously measuring activity or turnover processes. Across almost all size classes of plankton, molecular and imaging tech-niques are being used for in situ observation and classification. For nano-, pico-, and microplankton, sized 0.2–200 μm, molecu-lar sensors and flow cytometry-based imaging solutions exist which can differentiate taxa and functional processes. For meso-plankton, macromeso-plankton, and megaplankton (sized 200 μm– 20 mm, 2–20 cm, and > 20 cm, respectively), imaging solutions play a critical role in differentiating plankton by size, morphol-ogy and behavior, and recent advances in environmental DNA (eDNA) analyses have opened the door to in situ genomic observations of species in these size ranges and even larger (Govindarajan et al. 2015 Djurhuus et al. 2018).

Recent advances in molecular in situ instrumentation resolve problems of collecting samples from heterogeneous communi-ties in difficult-to-reach locations, while capturing true variabil-ity of the plankton. These instruments use samples of DNA, RNA, or other cellular products and perform some processing or preservation step in situ, allowing for their molecular characteri-zation. They remove potential artifacts due to classical sampling methods, such as CTD Niskin bottles (Suter et al. 2017), or from delays in sample processing during transport of samples back to a ship or lab (Feike et al. 2012). Recent reviews discuss the breadth of “ecogenomic” sensors and their abilities to solve these problems (e.g., Ottesen 2016 McQuillan and Robidart 2017). Here, we highlight general capabilities within classes of instruments and how they have increased our ability to under-stand ecological phenomena of the plankton.

Molecular sensors differ in their ability to be deployed for varying periods of time, their ability to analyze samples in real time vs. postdeployment, their capability to preserve or conduct molecular tasks such as polymerase chain reaction (PCR) or incubations, their ability to conduct adaptive sampling, and their mobility to sample different environments (Table 1). Some classes of instruments collect and preserve filtered particulate samples in situ during short deployments (hours to days) until instrument recovery (e.g., the automaticflow injection sampler [AFIS], Feike et al. 2012 the autonomous in situ fixation mul-tisampler, AFISsys, Charvet et al. 2019 the autonomous micro-bial sampler [AMS], Taylor et al. 2006 the in situ autonomous biosampler [IS-ABS], Ribeiro et al. 2019 the IFFS, Wurzbacher et al. 2012 the suspended particulate rosette sampler [SUPR], Breier et al. 2014, and the microbial sampler submersible incuba-tion device [MS-SID], Edgcomb et al. 2016). Similar instruments, such as the Biological OsmoSampling System (BOSS), can pre-serve samples for longer deployments over days to months (Robidart et al. 2013) however, they have lower volume capac-ity per sample. Other instruments are able to collect, preserve, and analyze molecular samples in situ in order to detect gene targets or other cellular products such as HAB-produced toxins in near real-time (e.g., the autonomous microbial genosensor [AMG], Fries et al. 2007 and the environmental sample

processor [ESP], reviewed in Scholin et al. 2017). This capability facilitates long-term deployments (days to weeks), and the col-lection of samples over wide environmental gradients. Some instruments also allow for in situ tracer incubations, and thus the determination of rate measurements concomitant with col-lection of molecular samples (e.g., MS-SID Taylor and Doherty 1990 Taylor and Howes 1994 Taylor et al. 2015 Edgcomb et al. 2016 Pachiadaki et al. 2016 Medina et al. 2017). Deployments of many of these instruments can be adapted to allow for short, high intensity or longer-term time series sampling regimes in order to capture different modes of variability. Furthermore, investigators have developed novel capabilities for adaptive sam-pling in several of these instruments including, for example, transmission of biogeochemical real-time data to the user, which allows for triggered sampling under specific environmen-tal conditions: (MS-SID, Fig. 1a, Edgcomb et al. 2016 moored ESP and 3G [3rdgeneration] ESP, Herfort et al. 2016). Others have been specifically adapted for extreme environments: for example, the AMS and SUPR are capable of collection from hydrothermal plume waters while the Vent-SID (currently in development) will allow for incubation studies of hydrothermal vent fluids in situ at vent fluid temperatures up to

70C (C. Taylor et al. pers. comm.).

Compatibility between molecular sensors and autonomous underwater vehicles (AUVs) or long-range autonomous under-water vehicles (LRAUVs) has resulted in incredible advances in mobility and targeted sampling with some sensors, such as the MS-SID and 3G ESP (Birch et al. 2018). Similarly, the SUPR-REMUS, a cousin of the SUPR, was recently incorporated into the AUV, REMUS 600, and deployed to detect larval dis-tributions by genetic markers in a coastal bay (Govindarajan et al. 2015). Clio, a molecular sensing AUV that is under development will be capable of reaching depths of 6000 m and collecting molecular samples at preset depth intervals (Jakuba et al. 2018). These advances allow the survey of aquatic populations without the expense and burden of ship-board operations, allowing for the increased frequency and flexibility in the environmental sampling of populations of plankton. Using the same techniques, a new generation of molecular sensor technologies is evolving genetic techniques traditionally applied to microbial life are now being adapted to the study of larger organisms through eDNA analyses (reviewed in Deiner et al. 2017). This allows for molecular sen-sors to be used to study larger size classes such as meso-, macro-, and megaplankton. All the above-described capabili-ties are atypical of traditional shipboard sampling, and thus emphasize the utility of in situ collection of molecular sam-ples for the community composition of plankton and their activity, making them increasingly popular for oceanographic studies around the world (e.g., Fig. 2a).

Imaging sensors are another rapidly developing and powerful method to study plankton dynamics. While molecular sensors

rely on the cellular products of organisms for identification and study, imaging sensors allow for direct observation, granting additional types of information often not possible to infer from molecular data. These data can include cell size, shape, life cycle

stage, behavioral patterns, and colocalization of other organisms such as symbionts or parasites. Some basic information such as in situ physical morphology may never have been known previously due to organismal fragility (e.g., cnidarians) or Fig. 1.Images of molecular and imaging in situ sensors. (a) MS-SID image. (b) Imaging Flow Cytobot (IFCB) image. Credit: McLane Research Laborato-ries. (c) SPC image. Credit: Jaffe Lab for Underwater Imaging, Scripps Institution of Oceanography.

extremeness of the environment (e.g., the deep sea). Since some of the earliest experiments in the 1950s with underwater camera and television systems, to mounting cameras onto nets in the 1970s, imaging devices for studying plankton in situ have advanced considerably (reviewed in Wiebe and Benfield 2003), and recently several new modern systems have emerged. No single system was designed with the same questions in mind instruments vary in the size class of organisms they can detect, their mode of deployment, the volume imaged, the duration of deployment, and the image resolution (ultimately determining

the taxonomic resolution of the system). We summarize the characteristics of the major imaging systems currently in use globally for studying each size class of plankton and their distri-butions and processes (Table 1).

Similar to the challenges in understanding microbial and phytoplankton life in the oceans, investigating the dynam-ics of mesoplankton to megaplankton (which includes ichthyoplankton) also requires sampling at spatial and temporal scales that often are not possible through traditional means. Nets, for example, integrate samples over large spatial scales Fig. 2.Approximate sampling deployment locations of in situ sensors. (a) Molecular sensors black circles: AFIS red star: autonomous in situfixation multisampler (AFIS-SYS) blue squares: AMG blue circles: AMS dark yellow inverted triangles: BOSS open inverted triangles: Deep-Sea ESP (D-ESP) blue inverted triangles: ESP blue star: IFFS yellow circle: IS-ABS dark yellow squares: SID green squares: MS-SID green star: SUPR open square: Suspended Particulate Rosette Sampler-Remote Environmental Monitoring UnitS (SUPR-REMUS). (b) Imaging systems. Blue circles: FCB green circles: IFCB black squares: ISIIS black stars: Lightframe On-sight Keyspecies Investigation (LOKI) system cyan squares: LOPC (including the SOLOPC) cyan stars: optical plankton counter (OPC) open stars: shadowed image particle profiling and evaluation recorder (SIPPER) dark yellow circles: SPC red circles: underwater vision profiler (UVP) black inverted triangles: VPR green inverted triangles: ZOOplankton Visualization System (ZOOVIS). Methods on location deploy-ment data mining and respective references available on Dryad along with the data set (Briseño-Avena 2019).

both vertically (tens to hundreds of meters) and horizontally (several meters to kilometers). However, cost and time con-straints of traditional shipboard net sampling limit the spatial and temporal availability of environmental samples. Further-more, many organisms are too fragile to be sampled with nets and are thus missed with traditional sampling (Remsen et al. 2004). A new set of in situ imaging sensors can avoid many of these constraints and have opened new avenues of scientific inquiry (Fig. 2b Table 1). The video plankton recorder (VPR), which was one of the earliest imaging systems, was designed to be towed for kilometers horizontally and profile hundreds of meters vertically while continuously imaging mesozooplankton, such as copepods, euphausiids, and small gelatinous organisms (e.g., Benfield et al. 1996 Ashjian et al. 2008). The extensive use of the VPR has led to the under-standing of physical and biological interactions such as micropatchiness and turbulence (Ross 2014), predator-induced diel vertical migration in Calanus finmarchicus (Baumgartner et al. 2011), and copepod-marine snow associa-tions (Möller et al. 2012 Nishibe et al. 2015). These studies illuminated processes affecting carbon export to the deep ocean. The in situ ichthyoplankton imaging system (ISIIS Cowen and Guigand 2008) was designed to image a large vol-ume of water (70 L s−1) in order to capture images of less abun-dant organisms, such as fish larvae and large gelatinous organisms, while still encompassing images of phytoplankton and mesozooplankton. To our knowledge, the ISIIS is the only imaging system that can quantitatively resolve fish larvae distributions with respect to environmental parameters and prey fields (i.e., phytoplankton and zooplankton). More recently, the Scripps Plankton Camera (SPC Roberts et al. 2014 http://spc.ucsd.edu/), a system consisting of two cameras: one designed for microplankton and phytoplankton, and a second one for mesozooplankton, aims to collect rapid time series data with a resolution of 1 frame s−1(Fig. 1c). The SPC, while in its early stages, has already proven its usefulness by revealing a time-sensitive cryptic phenomenon not observed previously. Using a subset of the SPC images, Briseño-Avena (Briseño-Avena unpubl.) observed the external parasitic expression (a phase that lasts only a few minutes) of the Paradium-like parasite attached to the urosome of the copepod Oithona similis.

Other imaging sensors have been developed to detect picoplankton, nanoplankton, and microplankton (0.2–2 μm, 2–20 μm, and 20–200 μm, respectively). The Imaging FlowCytobot (IFCB Olson and Sosik 2007), for example, is a moored system designed to image microplankton (< 10–150 μm) over time scales from minutes to years. An earlier instrument, the FlowCytobot (FCB Olson et al. 2003), can detect picoplankton and nanoplankton (Table 1) and has been collect-ing data since 2006 at Martha’s Vineyard Observatory (Fig. 1b). Both instruments adaptedflow cytometry methods to a mooring system that allows for high-frequency sampling over long time periods. Time series data generated from both the IFCB and FCB have allowed ecologists to understand phytoplankton dynamics

underlying bloom initiation and evolution (hours to days), spe-cies successions (seasons), and regime shifts (multiple years) (Sosik and Olson 2008 Anglès et al. 2015 Henrichs et al. 2015 Hunter-Cevera et al. 2016). The IFCB has also been used to study ciliates and other microzooplankton, as well as parasitic infec-tions of diatoms (Peacock et al. 2014 Brownlee et al. 2016). Ecosystem factors have largely determined the locations of deployment of in situ imaging systems. Most studies in marine ecosystems have occurred in high latitudes where plankton diversity is low (Fig. 2b). The few studies in lower latitudes have been focused in environments with near oligotrophic conditions where imaging conditions are ideal due to lower particle loads (Fig. 2b). Furthermore, few oceanic deployments have occurred in nearshore areas (hundreds of meters from shore), with the exception of the IFCB, FCB, and SPC systems (Fig. 2b). Turbidity has been a challenge for underwater imaging, where light is already a limiting factor due to attenuation. Highly productive waters with high plankton concentrations are also challenging since image volume must be adapted to avoid overlap of imaged particles and plankton on each image frame. However, within the last decade, attempts have been successful in applying in situ underwater imaging systems in low-visibility waters. For exam-ple, Bi et al. (2013, 2015) successfully deployed the ZOOVIS in the turbid waters of an estuary in the Chesapeake Bay to study gelatinous organisms. The LOPC with its most recent modi fica-tions has increased its operational capacity in waters with parti-cle concentrations of up to 103 particles L−1 (Herman et al. 2004). In a similar fashion, the ISIIS has been deployed within the turbid waters of the Mississippi River plume with positive results (Greer et al. 2016).

The other challenge posed by turbid waters is data processing countless particles are imaged, and manual annota-tion of these images becomes a near-impossible task. Auto-mated processing is being tested by some major research groups, and thus this major roadblock is diminishing (Benfield et al. 2007 Sosik and Olson 2007 Schmid et al. 2016 Orenstein and Beijbom 2017 Robinson et al. 2017 Luo et al. 2018). Very recently two new in situ imaging sensors became available, the Zoocam (Ohman et al. 2019), which is attached to the Zooglider, and the Continuous Particle Image Classi fica-tion System (CPIC www.coastaloceanvision.com), which can be mounted on a CTD frame. The latter incorporates onboard image segmentation and an automated classification system.

While in the past decade underwater imaging systems have been gaining traction within the scientific community, they have had limited deployments in freshwater systems (see “From intellection to instrumentation: How in situ ocean technology becomes a reality” section). The Great Lakes, for example, share some similar environmental problems with coastal marine regions such as HABs, invasive species, and waterborne pathogens of humans and native organisms, among other issues. Moreover, while the marine science com-munity has gained much understanding of ecological phe-nomena such as bloom initiation due in part to imaging

systems such as the FCB (see Hunter-Cevera et al. 2016 as a recent example), there are fewer systematic efforts to deploy imaging sensors in freshwater systems. One major exception is the LOPC (Herman et al. 2004), which was deployed in the Great Lakes (Fig. 2b) with the objective to compare net and imaging system biomass estimates (Yurista et al. 2009). Such an effort was recently conducted over the global ocean using the Underwater Vision Profiler (UVP5 Biard et al. 2016) from data collected on cruises from 2008 to 2013 unlike the LOPC estimates, however, the latter estimates were based on conver-sion factors from the literature, and not compared directly to biological samples. More recently, the SPC was tested in Lake Zürich, Switzerland in order to compare image-based density estimates of phytoplankton against laboratory microscopy counts using water samples (Reyes et al. 2017). However, as mentioned above, one imaging system alone cannot be used to address every phenomenon, as each imaging system focuses on a different size class of organisms. Since underwa-ter imaging sensors can be used in freshwaunderwa-ter systems (a less corrosive environment than saltwater), there are great oppor-tunities for gains in knowledge through the application of multiple imaging technologies in freshwater systems.

From intellection to instrumentation: How in situ

Ocean technology becomes a reality

To understand what is required for the development of in situ instrumentation, and the challenges faced in bringing an idea into a tangible reality, we spoke to four investigators with experience in the development and implementation of these types of technologies in their research: Virginia Edgcomb, Jules Jaffe, Heidi Sosik, and Craig Taylor. Each investigator took part in the development of in situ instruments including the Scripps Plankton (and Phytoplankton) Cameras (SPC), the IFCB and the (microbial sampler) SIDs, among others. In each case, these oceanographic instruments were built with broad scientific needs in mind: to increase sample throughput while minimizing artifacts associated with shipboard measurements and to study the organisms at biologically relevant spatiotem-poral scales. These interviews illustrated several themes com-mon across the researcher’s experience: the importance of institutional benefits, such as local engineering expertise and the support of high-risk projects the importance of collabora-tion, which insures instrument relevance and finally, that novel instrument creation is a lengthy process that requires multiple changing sources of funding and may dominate the careers of the primary investigator during its development.

Edgcomb, Jaffe, Sosik, and Taylor work at institutions in the United States with significant institutional benefits includ-ing internal grant programs, an aspect that greatly enhances technology development. In each case, initial pilot studies were run with small institutional grants in order to develop a proof-of-concept instrument. Sosik emphasized the impor-tance of these small grants for high-risk projects such as

instrument prototype development, which are not typically funded by federal agencies. Taylor also emphasized that these small grants can be used to develop a novel aspect of a larger instrument. Critical institutional support also included techni-cal staff and machine shop facilities, which aided in the design and construction of novel instruments. Both Scripps Institution of Oceanography (SIO) and the Woods Hole Oceanographic Institution (WHOI) employ staff that can build most of the electrical or mechanical components of a larger instrument. Instrument development requires many experts and multiple sources of funding over a sustained period. Therefore, different features of a single instrument may be designed with support from several different agencies over the duration of its development. Once initial proof-of-concept aspects were developed, results from these small insti-tutional grants were used as critical preliminary evidence in larger grant proposals to federal organizations such as the Ocean Technology and Interdisciplinary Coordination (OTIC) program at the National Science Foundation (NSF), the National Oceanographic Partnership Program (NOPP), and programs at the Department of Energy (DOE), and the Office of Naval Research (ONR).

Collaborate across scientific disciplines is among the most important activity to take part in during the development of new technologies. Each of our interviewees has had long-standing research relationships with other scientist(s) with skills that complement their own. Sosik also argues strongly for interdisciplinary collaboration among different lab groups early on in technology development. In this way, the instru-ment developers are forced to adapt the instruinstru-ment to be more user-friendly and flexible in order to answer other scientific questions, promoting broad applicability and com-mercialization. Edgcomb stressed that making the instrument user-friendly should be the ultimate goal, and that federal funding agencies prioritize this aspect in proposals. Early col-laboration can also help in the acceptance of the instrument’s usefulness and validity of results within the researcher’s field. In general, acceptance occurs over years and with sufficient data collection. An instrument that has multiple users across many subdisciplines has a greater chance of becoming widely accepted by the field. Once the in situ instrument is devel-oped and successfully implemented, its design may be pur-chased by a company that can increase the production of the instrument, and further refine user-friendliness. Several such ocean instrumentation companies exist, such as McLane Research Laboratories and Bellamare, which helped manufac-ture the ESP, IFCB, SID, and ISIIS instruments. Edgcomb and especially Taylor have had a long-standing relationship with McLane, for example, and frequently discuss scientific needs and collaborate with engineers at the company. Many employees in such companies were trained in academic, feder-ally funded labs, and so there is a close relationship between the research and development process and the commercializa-tion process. Addicommercializa-tionally, the home institucommercializa-tion itself may be

interested in patenting the design of the instrument. In either case, the principal investigators involved in instrument design are not responsible for mass production or customer service. Despite this, Sosik described the commercialization process as nerve-racking due to a sense of responsibility in the instru-ment’s success even outside of her own research interests.

While each of the scientists we spoke with has had great success with design and application of in situ instruments, they also outlined several challenges. The development of a new instrument can have an “infinite gestation period,” as Jaffe put it, but in general, each of these projects took 6–12 yr from conception to full application in the environment. Fur-thermore, while there were a core group of 2–3 scientists working on the project, a total of 4–12 people were required for full design, including engineers, technicians, and students. An instrument design is not static these instruments are still constantly being upgraded or modified in response to new sci-entific questions or improving ease-of-use. In many cases, the evolution of these technologies included many “cousins” of the same instrument. For example, there are several versions of the SID which have each been adapted to sampling in particular environments, such as high temperatures hydro-thermal vent systems or oxygen minimum zones. The IFCB was developed based in part on the questions left unanswered by its older cousin, the FCB. During the development period, Sosik emphasized the importance of continuing to pursue scientific questions and generating interesting data with the instrument. This allows for continual assessment of what the instrument can do and what practical limitations should be addressed in the next development stage. Meanwhile, publica-tions and conference presentapublica-tions are a good way to verify the instrument is successful and to get other groups interested in adopting the technology.

Most of these projects were started several years or even decades ago, when the interviewees noted that funding for instrumentation was easier to obtain. Taking on technology development is also a long and risky endeavor, particularly for an early career scientist who may have fewer publications as a result. Therefore, it was suggested that successful instrument design should be considered in promotion assessments for ten-ure. Furthermore, a common problem we heard was that there are few options for completion of an instrument once a proto-type is developed while institutions typically fund the initial proof-of-concept instrument and a federal organization typi-cally funds the development and application of a prototype, many of the projects required a second round of engineering to realize the full capabilities of the instrument and ease the trans-fer of technology to other groups. Funding for these issues is hard to come by, however, Sosik suggests that continually modifying the instrument so that it answers novel scientific enquiries with each additional engineering capability is a good way to continue to fund an instrument’s development.

Despite the aforementioned challenges, each of the instru-ments we discussed during these interviews is available to the

scientific community either as commercial products or through open collaboration with the developers. System design poses a nontrivial constraint that might prevent the instrument from being widely adopted in freshwater sciences. While in development mode, most instruments are typically bulky, requiring large, ocean-going ships that can support deployment. It is not until miniaturization takes place that instruments can move into smaller bodies of water or dock-side deployment. However, each interviewee emphasized that there would be no major roadblocks to use of the instrument in freshwater and that they are willing to work alongside freshwater scientists in developing the instruments further for freshwater use. In fact, some of the instruments have already been applied in lakes or rivers (Table 1), but broad adoption in limnological studies is still on the horizon. Collaboration and communication between limnologists and oceanographers are key to this crossover process.

Fresh ideas: Opportunities to forward the use of in

Situ sensors in freshwater research

Lakes provide abundant ecosystem services from vital habitat for aquatic organisms to drinking water supply and recreation. Plankton are the foundation of aquatic food webs, can indicate trophic state, and blooms of certain species can negatively affect the environment. Therefore, understanding plankton commu-nity dynamics is essential to preserving ecosystem health and sustainability. In situ instruments in lake settings are powerful tools for gathering vast amounts of data on biological commu-nities and the changing conditions of lakes (Hampton 2013). To date, much of this effort has focused on chemical and fluores-cence sensors. For instance, water quality has been tracked using fluorescence sensors to detect dissolved organic matter in a shal-low eutrophic lake (Niu et al. 2014). Some in situ instruments have readily been adopted in freshwater systems, for example, the Sequoia Scientific’s Laser In Situ Scattering Transmissometry (LISST) instrument (e.g., Serra et al. 2001). Even further, compre-hensive data sets on water quality have proved especially useful when comparing multiple lakes to generate an understanding about how freshwater ecosystems respond to environmental change. The Global Lake Ecological Observatory Network (GLEON) addresses this through a network of high frequency in situ observatories managed collaboratively by members from over 40 countries (gleon.org Rose et al. 2016). Access to aggre-gated data from multiple lakes has allowed for an improved understanding of regional and global patterns. For example, Brentrup et al. (2016) found that profiling buoys that collect high-frequency chlorophyll fluorescence out-performed con-ventional sampling when identifying subsurface chlorophyll maxima, which helped to clarify food web dynamics and car-bon cycling.

The application of in situ water quality sensors in lake environments has led to several important discoveries and inter-esting observations. For instance, a global data set of summer

water surface temperatures gathered from in situ sensors and/or satellite measurements revealed a rapid warming trend in lakes over the last two decades (O’Reilly et al. 2015). Observations like these are essential for tracking environmental change. In situ molecular and imaging sensors can further this effort by obtaining a more refined understanding of plankton dynamics in freshwater, thereby enhancing our knowledge of food web interactions, trophic state, HABs, and more. However, the opti-cal and molecular sensors we reviewed here are just beginning to be used in freshwater systems, and these deployments often are not yet reflected in the peer-reviewed literature. Few and often negligible engineering barriers exist for moving these instruments from a saltwater to a freshwater environment (see “From intellection to instrumentation: How in situ ocean tech-nology becomes a reality” section). Instead, barriers to transfer-ence may be infrastructural. Many instruments require specific technical equipment and specialized teams for deployment and retrieval that may be available on ocean-going vessels but are not currently widely available in lakes (a notable exception are large vessels on the Laurentian Great Lakes operated by NOAA and the EPA). Some of the most compelling freshwater environ-ments for transferal of this technology are relatively large bodies of water, such as the Great Lakes in the United States or Lake Baikal in Russia. These large lakes share many of the same chal-lenges to sampling as ocean environments and pose similar eco-logical questions regarding species distributions (e.g., Yurista et al. 2009), harmful algae (e.g., Brooks et al. 2016), and the roles of planktonic organisms in biogeochemical cycling (e.g., Wurzbacher et al. 2012). However, continuous presence and the generation of high-resolution long-term data sets, such as those created by the IFCB, would also be valuable in small bodies of water (such as lake or stream systems) to resolve ques-tions of trophic interacques-tions or bloom progression.

When in situ molecular and imaging sensors have been used in lake environments, they have most commonly been applied to large lake systems. For instance, the LOPC was used in Lake Superior to assess zooplankton abundance and size (Yurista et al. 2009). In situ instruments for detecting toxins are of particular interest due to the widespread issues of HABs in freshwater systems (Brooks et al. 2016). In 2016, the first deployment of an ESP occurred in Lake Erie and had the capa-bility to detect microcystin, a toxin produced by cyanobacteria that threatens drinking water supplies and other benefits from lakes (http://www.fondriest.com/news/espniagara-tracks-algal-toxins-lake-erie-protects-drinking-water.htm, 25 June 2018). The SID has also been deployed in the Great Lakes for educa-tional purposes (C. Taylor pers. comm.). Another technology developed by MBARI, the LRAUV Tethys, wasfirst deployed in the Great Lakes in 2016 to test its capability to be used in col-laborative ship-LRAUV deployments. An MBARI LRAUV has recently returned to the Great Lakes in 2018 with the 3G ESP module installed, illustrating how in situ instrumentation that can be miniaturized and adapted to mobile platforms can be more widely used. These recent steps are encouraging and

demonstrate the capability to transfer technology from marine to freshwater ecosystems, and that their deployments can address both basic and applied questions in freshwater sys-tems. These new avenues of research are especially needed in the Great Lakes, since those ecosystems are changing rapidly and have experienced large economic and human health impacts from the increasing threat of HABs (Brooks et al. 2016 Carmichael and Boyer 2016).

Research on gene expression is one example of in situ molecular sensor technology being used in both marine and freshwater systems to address similar types of questions. Using the ESP, coordinated regulation of gene expression was observed for a multispecies complex marine microbial commu-nity, suggesting synchrony among unrelated taxa in response to environmental change (Ottesen et al. 2013). In a similar but targeted gene expression study in a freshwater ecosystem using the IFFS, Wurzbacher et al. (2012) followed the expression of an unknown Actinobacterial rhodopsin gene. The function of this gene, although very abundant, was unknown, but their results allowed for the authors to hypothesize its function based on diurnal activity. This example highlights a major finding in a freshwater system that resulted from in situ molec-ular sensor technology. Instruments such as the 3G ESP which can be used to detect gene expression on broad scales and in high resolution would more than likely bring many more of these discoveries to the forefront for lake researchers.

Another benefit from a wider application of in situ sensor technology to freshwater systems would be the creation of long-term time series of high-frequency sampling of plankton assemblages. Long deployments of imaging sensors (such as the IFCB, FCB, and SPC) have generated data that have advanced our understanding of interannual and seasonal vari-ation in plankton assemblage composition (e.g., Sosik et al. 2003), as well as how relatively cryptic phenomena (such as parasitic infections) may be shaping seasonal dynamics (e.g., Peacock et al. 2014). Although lake systems may be more accessible to sampling than marine environments, the benefit of automated high-frequency observations is still large. Often, it is only through using such datasets that we can detect the importance of episodic events that may remain unobserved through less frequent sampling (such as storm events which may introduce an influx of nutrients to a lake).

The potential applications of in situ molecular and imaging sensors are very broad, from population dynamics that occur over short periods to community and ecosystem processes that are adapting to environmental change over longer time scales. In situ technology can also help inform applied research in the areas of HABs and the detection and monitor-ing of invasive species. For instance, instruments like the ESP are useful for monitoring real time dispersal of invasive or harmful species while instruments like the IFCB and ISIIS are suitable for assessing food web dynamics before, during, and after HABs, and visualizing organisms that may be more dif fi-cult to detect or quantify genetically. These potential

applications do not come without some obstacles. However, through collaboration and ingenuity, the transfer of technol-ogy between freshwater and marine systems is feasible and the promise of scientific advancement is high, a goal shared historically by both disciplines and highlighted by Downing (2014), where he rightly points out that there is“a major con-vergence between limnology and oceanography in paradigms as global change advances.”

Conclusion

Molecular and imagining in situ sensors have revolutionized sampling of plankton populations and communities, from the nano- to the macroscale. These sensors link population pro-cesses to physical and geochemical dynamics at varying spatio-temporal scales, which has been vital to understanding the ecology of plankton. Detailed knowledge of plankton is essen-tial as they form the base of the food web and are responsible for a large portion of carbon cycling. The term “plankton” covers a diverse array of organisms and is reflected in the breadth of technologies that have been applied to their study. It is perhaps only through the application of multiple technolo-gies that we gain a fuller picture of the complexity of interac-tions of plankton and their important effects on both ecosystem biodiversity and human interests.

Molecular and imaging in situ sensors take effort and time to develop. Once developed, they can be applied, through col-laboration or commercialization, in a variety of aquatic eco-systems. Current applications of in situ molecular and imaging sensors are just beginning to be explored in freshwa-ter ecosystems. There is great potential to look at issues such as the threat of HABs and invasive species, and the effects of changing climate on freshwater systems with these instru-ments. Overall, as is apparent from recent freshwater deploy-ments of oceanographic sensors and our discussions with instrument developers, few technological barriers exist and there is a lot to be gained from the transferal of technology from ocean basins and coastal ecosystems to freshwater sys-tems. It is an exciting time to have these expanded capabili-ties as we enter an age of high environmental variability.

References

Anderson, D. M., A. D. Cembella, and G. M. Hallegraeff. 2012. Progress in understanding harmful algal blooms: Paradigm shifts and new technologies for research, monitoring, and management. Ann. Rev. Mar. Sci.4: 143–176. doi:10.1146/ annurev-marine-120308-081121

Anglès, S., A. Jordi, and L. Campbell. 2015. Responses of the coastal phytoplankton community to tropical cyclones rev-ealed by high-frequency imagingflow cytometry. Limnol. Oceanogr.60: 1562–1576. doi:10.1002/lno.10117

Ashjian, C. J., C. S. Davis, S. M. Gallager, P. H. Wiebe, and G. L. Lawson. 2008. Distribution of larval krill and zooplankton in association with hydrography in Marguerite Bay, Antarctic

Peninsula, in austral fall and winter 2001 described using the Video Plankton Recorder. Deep-Sea Res. Part II Top. Stud. Oceanogr.55: 455–471. doi:10.1016/j.dsr2.2007.11.016

Babin, M., and others. 2005. New approaches and technolo-gies for observing harmful algal blooms. Oceanography18: 210–227. doi:10.5670/oceanog.2005.55

Baumgartner, M. F., N. S. J. Lysiak, C. Schuman, J. Urban-Rich, and F. W. Wenzel. 2011. Diel vertical migration behavior of Calanusfinmarchicus and its influence on right and sei whale occurrence. Mar. Ecol. Prog. Ser. 423: 167–184. doi:10.3354/meps08931

Benfield, M., and others. 2007. RAPID: Research on automated plankton identification. Oceanography 20: 172–187. doi:

Benfield, M. C., C. S. Davis, P. H. Wiebe, S. M. Gallager, R. Gregory Loughj, and N. J. Copley. 1996. Video Plankton Recorder estimates of copepod, pteropod and larvacean dis-tributions from a stratified region of Georges Bank with comparative measurements from a MOCNESS sampler. Deep-Sea Res. Part II Top. Stud. Oceanogr.43: 1925–1945.

Bi, H., S. Cook, H. Yu, M. C. Benfield, and E. D. Houde. 2013. Deployment of an imaging system to investigate fine-scale spatial distribution of early life stages of the ctenophore Mnemiopsis leidyi in Chesapeake Bay. J. Plankton Res. 35: 270–280. doi:10.1093/plankt/fbs094

Bi, H., Z. Guo, M. C. Benfield, C. Fan, M. Ford, S. Shahrestani, and J. M. Sieracki. 2015. A semi-automated image analysis procedure for in situ plankton imaging systems. PLoS One 10: e0127121. doi:10.1371/journal.pone.0127121

Biard, T., and others. 2016. In situ imaging reveals the bio-mass of giant protists in the global ocean. Nature 532: 504–507. doi:10.1038/nature17652

Birch, J., and others. 2018. Autonomous targeted sampling of the deep chlorophyll maximum layer in a subtropical North Pacific eddy, p. 1–5. In Oceans 2018 MTS/IEEE Charleston. IEEE. doi:10.1109/OCEANS.2018.8604898

Blukacz, E. A., B. J. Shuter, and W. G. Sprules. 2009. Towards understanding the relationship between wind conditions and plankton patchiness. Limnol. Oceanogr.54: 1530–1540.

Breier, J. A., C. G. Rauch, K. McCartney, B. M. Toner, S. C. Fakra, S. N. White, and C. R. German. 2009. A suspended-particle rosette multi-sampler for discrete biogeochemical sampling in low-particle-density waters. Deep-Sea Res. Part I Oceanogr. Res. Pap.56: 1579–1589. doi:10.1016/j.dsr.2009.04.005

Breier, J. A., and others. 2014. A large volume particulate and water multi-sampler with in situ preservation for microbial and biogeochemical studies. Deep-Sea Res. Part I Oceanogr. Res. Pap.94: 195–206. doi:10.1016/j.dsr.2014.08.008

Brentnall, S., K. Richards, J. Brindley, and E. Murphy. 2003. Plankton patchiness and its effects on larger-scale produc-tivity. J. Plankton Res. 25: 121–140. https://doi.org/10. 1093/plankt/25.2.121

Brentrup, J. A., and others. 2016. The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model. Inland Waters 6: 565–580. doi:10.5268/IW-6.4.890

Briseño-Avena, C., T. Spanbauer, K. Pitz, and E. Suter. 2019. Data from: Salty sensors, fresh ideas: The use of molecular and imaging sensors in understanding plankton dynamics across marine and freshwater ecosystems, v3, Dryad, Dataset.https:// doi.org/10.5061/dryad.29pm4qs

Brooks, B. W., and others. 2016. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ. Toxicol. Chem. 35: 6–13. doi:10.1002/etc.3220

Brosnahan, M. L., and others. 2015. Rapid growth and con-certed sexual transitions by a bloom of the harmful dino fla-gellate Alexandrium fundyense (Dinophyceae). Limnol. Oceanogr.60: 2059–2078. doi:10.1002/lno.10155

Brownlee, E. F., R. J. Olson, and H. M. Sosik. 2016. Microzooplankton community structure investigated with imaging flow cytometry and automated live-cell staining. Mar. Ecol. Prog. Ser.550: 65–81. doi:10.3354/meps11687

Bullerjahn, G. S., and others. 2016. Global solutions to regional problems: Collecting global expertise to address the problem of harmful cyanobacterial blooms. A Lake Erie case study. Harmful Algae 54: 223–238. doi:10.1016/j.hal. 2016.01.003

Carmichael, W. W., and G. L. Boyer. 2016. Health impacts from cyanobacteria harmful algae blooms: Implications for the North American Great Lakes. Harmful Algae 54: 194–212. doi:10.1016/j.hal.2016.02.002

Caron, D. A., and others. 2017. Response of phytoplankton and bacterial biomass during a wastewater effluent diver-sion into nearshore coastal waters. Estuar. Coast. Shelf Sci. 186: 223–236. doi:10.1016/j.ecss.2015.09.013

Charvet, S., L. Riemann, J. Alneberg, A. F. Andersson, J. von Borries, U. Fischer, and M. Labrenz. 2019. AFISsys - an autonomous instrument for the preservation of brackish water samples for microbial metatranscriptome analysis. Water Res.149: 351–361. doi:10.1016/j.watres.2018.11.017

Checkley, D. M., R. E. Davis, A. W. Herman, G. A. Jackson, B. Beanlands, and L. A. Regier. 2008. Assessing plankton and other particles in situ with the SOLOPC. Limnol. Oceanogr. 53: 2123–2136. doi:10.4319/lo.2008.53.5_part_2.2123

Clark, J. M., and others. 2017. Satellite monitoring of cyanobacterial harmful algal bloom frequency in recrea-tional waters and drinking water sources. Ecol. Indic. 80: 84–95. doi:10.1016/j.ecolind.2017.04.046

Cowen, R. K., and C. M. Guigand. 2008. In situ ichthyoplankton imaging system (IS IIS): System design and preliminary results. Limnol. Oceanogr.: Methods 6: 126–132. doi:10.4319/lom.2008.6.126

Davis, C. S., F. T. Thwaites, S. M. Gallager, and Q. Hu. 2005. A three-axis fast-tow digital Video Plankton Recorder for

rapid surveys of plankton taxa and hydrography. Limnol. Oceanogr.: Methods3: 59–74. doi:10.4319/lom.2005.3.59

Deiner, K., and others. 2017. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol.26: 5872–5895. doi:10.1111/ mec.14350

Djurhuus, A., K. Pitz, N. A. Sawaya, J. Rojas-Márquez, B. Michaud, E. Montes, F. Muller-Karger, and M. Breitbart. 2018. Evaluation of marine zooplankton community struc-ture through environmental DNA metabarcoding. Limnol. Oceanogr.: Methods16: 209–221. doi:10.1002/lom3.10237

Downing, J. A. 2014. Limnology and oceanography: Two estranged twins reuniting by global change. Inland Waters 4: 215–232. doi:10.5268/IW-4.2.753

Edgcomb, V. P., C. Taylor, M. G. Pachiadaki, S. Honjo, I. Engstrom, and M. Yakimov. 2016. Comparison of Niskin vs. in situ approaches for analysis of gene expression in deep Mediterranean Sea water samples. Deep-Sea Res. Part II Top. Stud. Oceanogr. 129: 213–222. doi:10.1016/j.dsr2.2014. 10.020

Feike, J., K. Jürgens, J.T. Hollibaugh, S. Krüger, G. Lost, and M. Labrenz. 2012. Measuring unbiased metatranscriptomics in suboxic waters of the central Baltic Sea using a new in situ fixa-tion system. ISME J.6: 461–470. doi:10.1038/ismej.2011.94

Franks, P. J. S. 2005. Plankton patchiness, turbulent transport and spatial spectra. Mar. Ecol. Prog. Ser.294: 295–309. doi:

Fries, D., J. Paul, M. Smith, A. Farmer, E. Casper, and J. Wilson. 2007. The autonomous microbial genosensor, an in situ sensor for marine microbe detection. Microsc. Microanal.13: 514–515. doi:10.1017/S1431927607078816

Govindarajan, A. F., J. Pineda, M. Purcell, and J. A. Breier. 2015. Species- and stage-specific barnacle larval distribu-tions obtained from AUV sampling and genetic analysis in Buzzards Bay, Massachusetts, USA. J. Exp. Mar. Biol. Ecol. 472: 158–165. doi:10.1016/j.jembe.2015.07.012

Greenfield, D., and others. 2008. Field applications of the second-generation Environmental Sample Processor (ESP) for remote detection of harmful algae: 2006–2007. Limnol. Oceanogr.: Methods6: 667–679. doi:10.4319/lom.2008.6.667

Greer, A. T., C. B. Woodson, C. E. Smith, C. M. Guigand, and R. K. Cowen. 2016. Examining mesozooplankton patch structure and its implications for trophic interactions in the northern Gulf of Mexico. J. Plankton Res. 38: 1115–1134. doi:10.1093/plankt/fbw033

Hampton, S. E. 2013. Understanding lakes near and far. Sci-ence342: 815–816. doi:10.1126/science.1244732

Henrichs, D. W., R. D. Hetland, and L. Campbell. 2015. Iden-tifying bloom origins of the toxic dinoflagellate Karenia brevis in the western Gulf of Mexico using a spatially explicit individual-based model. Ecol. Model. 313: 251–258. doi:10.1016/j.ecolmodel.2015.06.038

Herfort, L., and others. 2016. Use of continuous, real-time observations and model simulations to achieve

autonomous, adaptive sampling of microbial processes with a robotic sampler. Limnol. Oceanogr.: Methods 14: 50–67. doi:10.1002/lom3.10069

Herman, A. W., B. Beanlands, and E. F. Phillips. 2004. The next generation of optical plankton counter: The laser-OPC. J. Plankton Res.26: 1135–1145. doi:10.1093/plankt/fbh095

Hunter-Cevera, K. R., M. G. Neubert, R. J. Olson, A. R. Solow, A. Shalapyonok, and H. M. Sosik. 2016. Physiological and eco-logical drivers of early spring blooms of a coastal phytoplank-ter. Science354: 326–329. doi:10.1126/science.aaf8536

Hutchinson, G. E. 1961. The paradox of the plankton. Am. Nat.95: 137–145. doi:10.1086/282171

Jakuba, M. V., J. A. Breier, D. Gomez-Ibanez, K. Tradd, and M. A. Saito. 2018. Clio: An autonomous vertical sampling vehicle for global ocean biogeochemical mapping, p. 1–8. In 2018 IEEE/OES Autonomous Underwater Vehicle Work-shop (AUV). IEEE. doi:10.1109/AUV.2018.8729797

Luo, J. Y., J.-O. Irisson, B. Graham, C. Guigand, A. Sarafraz, C. Mader, and R. K. Cowen. 2018. Automated plankton image analysis using convolutional neural networks. Limnol. Oceanogr.: Methods16: 814–827. doi:10.1002/lom3.10285

McGillicuddy, D. J., and P. J. S. Franks. 2019. Models of plankton patchiness, p. 536–546. In J. K. Cochran, J. H. Bokuniewickz, and L. P. Yager [eds.], Encyclopedia of ocean sciences, v.5, 3rd ed. Elsevier. doi: 10.1016/B978-0-12-409548-9.11610-0

McQuillan, J. S., and J. C. Robidart. 2017. Molecular-biological sensing in aquatic environments: Recent developments and emerging capabilities. Curr. Opin. Biotechnol. 45: 43–50. doi:10.1016/j.copbio.2016.11.022

Medina, L. E., C. D. Taylor, M. G. Pachiadaki, C. Henríquez-Castillo, O. Ulloa, and V. P. Edgcomb. 2017. A review of protist grazing below the photic zone emphasizing studies of oxygen-depleted water columns and recent applications of in situ approaches. Front. Mar. Sci.4: 105. doi:10.3389/fmars.2017. 00105

Möller, K. O., M. S. John, A. Temming, J. Floeter, A. F. Sell, J. P. Herrmann, and C. Möllmann. 2012. Marine snow, zooplank-ton and thin layers: Indications of a trophic link from small-scale sampling with the Video Plankton Recorder. Mar. Ecol. Prog. Ser.468: 57–69. doi:10.3354/meps09984

Nishibe, Y., K. Takahashi, T. Ichikawa, K. Hidaka, H. Kurogi, K. Segawa, and H. Saito. 2015. Degradation of discarded appendicularian houses by oncaeid copepods. Limnol. Oceanogr.60: 967–976. doi:10.1002/lno.10061

Niu, C., Y. Zhang, Y. Zhou, K. Shi, X. Liu, and B. Qin. 2014. The potential applications of real-time monitoring of water quality in a large shallow lake (Lake Taihu, China) using a chromophoric dissolved organic matter fluorescence sen-sor. Sensors14: 11580–11594. doi:10.3390/s140711580

Ohman, M. D., R. E. Davis, J. T. Sherman, K. R. Grindley, B. M. Whitmore, C. F. Nickels, and J. S. Ellen. 2019. Zoo-glider: An autonomous vehicle for optical and acoustic

sensing of zooplankton. Limnol. Oceanogr.: Methods 17: 69–86. doi:10.1002/lom3.10301

Olson, R. J., A. Shalapyonok, and H. M. Sosik. 2003. An auto-mated submersible flow cytometer for analyzing pico- and nanophytoplankton: FlowCytobot. Deep-Sea Res. Part I Oceanogr. Res. Pap. 50: 301–315. doi:10.1016/S0967-0637 (03)00003-7

Olson, R. J., and H. M. Sosik. 2007. A submersible imaging-in-flow instrument to analyze nano-and microplankton: Imaging FlowCytobot. Limnol. Oceanogr.: Methods 5: 195–203. doi:10.4319/lom.2007.5.195

O’Reilly, C. M., R. J. Rowley, P. Schneider, J. D. Lenters, P. B. Mcintyre, and B. M. Kraemer. 2015. Rapid and highly vari-able warming of lake surface waters around the globe. Geophys. Res. Lett.42: 1–9. doi:10.1002/2015GL066235

Orenstein, E. C., and O. Beijbom. 2017. Transfer learning and deep feature extraction for planktonic image data sets, p. 1082–1088. In 2017 IEEE Winter Conference on Applica-tions of Computer Vision (WACV). IEEE. doi:10.1109/ WACV.2017.125

Ottesen, E. A. 2016. Probing the living ocean with ecogenomic sensors. Curr. Opin. Microbiol. 31: 132–139.

Ottesen, E. A., R. Marin, C. M. Preston, C. R. Young, J. P. Ryan, C. A. Scholin, and E. F. DeLong. 2011. Meta-transcriptomic analysis of autonomously collected and pre-served marine bacterioplankton. ISME J.5: 1881–1895. doi:

Ottesen, E. A., C. R. Young, J. M. Eppley, J. P. Ryan, F. P. Chavez, C. A. Scholin, and E. F. DeLong. 2013. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl. Acad. Sci. USA 110: E488–E497. doi:10.1073/pnas.1222099110

Pachiadaki, M. G., C. Taylor, A. Oikonomou, M. M. Yakimov, T. Stoeck, and V. Edgcomb. 2016. In situ grazing experi-ments apply new technology to gain insights into deep-sea microbial food webs. Deep-Sea Res. Part II Top. Stud. Oceanogr.129: 223–231. doi:10.1016/j.dsr2.2014.10.019

Paerl, H. W., W. S. Gardner, K. E. Havens, A. R. Joyner, M. J. McCarthy, S. E. Newell, B. Qin, and J. T. Scott. 2016. Miti-gating cyanobacterial harmful algal blooms in aquatic eco-systems impacted by climate change and anthropogenic nutrients. Harmful Algae 54: 213–222. doi:10.1016/j.hal. 2015.09.009

Paerl, H. W., T. G. Otten, and R. Kudela. 2018. Mitigating the expansion of harmful algal blooms across the freshwater-to-marine continuum. Environ. Sci. Technol. 52: 5519–5529. doi:10.1021/acs.est.7b05950

Paul, J., C. Scholin, G. Van Den Engh, and M. J. Perry. 2007. In situ instrumentation. Oceanography 20: 70–78. doi:10. 5670/oceanog.2007.50

Peacock, E., R. Olson, and H. Sosik. 2014. Parasitic infection of the diatom Guinardia delicatula, a recurrent and

ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Ser. 503: 1–10. doi:10.3354/ meps10784

Picheral, M., L. Guidi, L. Stemmann, D. M. Karl, G. Iddaoud, and G. Gorsky. 2010. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr.: Methods8: 462–473. doi:10.4319/lom.2010.8.462

Preston, C. M., and others. 2009. Near real-time, autonomous detection of marine bacterioplankton on a coastal mooring in Monterey Bay, California, using rRNA-targeted DNA pro-bes. Environ. Microbiol. 11: 1168–1180. doi:10.1111/j. 1462-2920.2009.01848.x

Preston, C. M., and others. 2011. Underwater application of quantitative PCR on an ocean mooring. PLoS One 6: e22522. doi:10.1371/journal.pone.0022522

Remsen, A., T. L. Hopkins, and S. Samson. 2004. What you see is not what you catch: A comparison of concurrently collected net, Optical Plankton Counter, and Shadowed Image Particle Profiling Evaluation Recorder data from the northeast Gulf of Mexico. Deep-Sea Res. Part I Oceanogr. Res. Pap.51: 129–151. doi:10.1016/j.dsr.2003.09.008

Reyes, M., P. Spaak, and F. Pomati. 2017. Test of in situ (underwater) automated imaging, as provided by the Scripps Plankton Camera, for monitoring and analysis of lake phytoplankton. Eawag. URL for publication: https:// www.dora.lib4ri.ch/eawag/islandora/object/eawag:16074

Ribeiro, H., and others. 2019. Development of an autono-mous biosampler to capture in situ aquatic microbiomes. PLoS One 14: e0216882. doi:10.1371/journal.pone. 0216882

Roberts, P. L., J. S. Jaffe, E. C. Orenstein, B. Laxton, P. J. S. Franks, C. Briseno, M. L. Carter, and M. Hilbern. 2014. Pier recognition: An in situ plankton web camera. In Ocean optics XXII. Portland, Maine, USA.

Robidart, J., S. J. Callister, P. Song, C. D. Nicora, C. G. Wheat, and P. R. Girguis. 2013. Characterizing microbial commu-nity and geochemical dynamics at hydrothermal vents using osmotically driven continuous fluid samplers. Envi-ron. Sci. Technol.47: 4399–4407. doi:10.1021/es3037302

Robidart, J. C., C. M. Preston, R. W. Paerl, K. A. Turk, A. C. Mosier, C. A. Francis, C. A. Scholin, and J. P. Zehr. 2012. Seasonal Synechococcus and Thaumarchaeal population dynamics examined with high resolution with remote in situ instrumentation. ISME J. 6: 513–523. doi:10.1038/ ismej.2011.127

Robinson, K. L., J. Y. Luo, S. Sponaugle, C. Guigand, and R. K. Cowen. 2017. A tale of two crowds: Public engagement in plankton classification. Front. Mar. Sci. 4: 82. doi:10.3389/ fmars.2017.00082

Rose, K. C., K. C. Weathers, A. L. Hetherington, and D. P. Hamilton. 2016. Insights from the Global Lake Ecological Observatory Network (GLEON). Inland Waters6: 476–482.

Ross, T. 2014. A video-plankton and microstructure profiler for the exploration of in situ connections between zoo-plankton and turbulence. Deep-Sea Res. Part I Oceanogr. Res. Pap.89: 1–10. doi:10.1016/j.dsr.2014.04.003

Ryan, J., and others. 2011. Harmful phytoplankton ecology studies using an autonomous molecular analytical and ocean observing network. Limnol. Oceanogr. 56: 1255–1272. doi:10.4319/lo.2011.56.4.1255

Saito, M. A., V. V. Bulygin, D. M. Moran, C. Taylor, and C. Scholin. 2011. Examination of microbial proteome preser-vation techniques applicable to autonomous environmen-tal sample collection. Front. Microbiol. 2: 1–10. doi:10. 3389/fmicb.2011.00215

Schmid, M. S., C. Aubry, J. Grigor, and L. Fortier. 2016. The LOKI underwater imaging system and an automatic identi-fication model for the detection of zooplankton taxa in the Arctic Ocean. Methods Oceanogr.15–16: 129–160. doi:10. 1016/j.mio.2016.03.003

Scholin, C., and others. 2006. The Environmental Sample Pro-cessor (ESP) - an autonomous robotic device for detecting microorganisms remotely using molecular probe technol-ogy. Ocean: 3–6. doi:10.1109/OCEANS.2006.306885

Scholin, C., and others. 2017. The quest to develop ecogenomic sensors: A 25-year history of the Environmen-tal Sample Processor (ESP) as a case study. Oceanography 30: 100–113. doi:10.5670/oceanog.2017.427

Schulz, J., K. Barz, P. Ayon, A. Ludtke, O. Zielinski, D. Mengedoht, and H. J. Hirche. 2010. Imaging of plankton specimens with the lightframe on-sight keyspecies investi-gation (LOKI) system. J. Eur. Opt. Soc.5: 10017s-1–10017s-9. doi:10.2971/jeos.2010.10017s

Serra, T., J. Colomer, X. P. Cristina, X. Vila, J. B. Arellano, and X. Casamitjana. 2001. Evaluation of laser in situ scattering instrument for measuring concentration of phytoplankton, purple sulfur bacteria, and suspended inorganic sediments in lakes. J. Environ. Eng. 127: 1023–1030. doi:10.1061/ (ASCE)0733-9372(2001)127:11(1023)

Sheik, C. S., K. Anantharaman, J. A. Breier, J. B. Sylvan, K. J. Edwards, and G. J. Dick. 2015. Spatially resolved sampling reveals dynamic microbial communities in rising hydro-thermal plumes across a back-arc basin. ISME J. 9: 1434–1445. doi:10.1038/ismej.2014.228

Sinha, E., A. M. Michalak, and V. Balaji. 2017. Eutrophication will increase during the 21st century as a result of precipitation changes. Science357: 405–408. doi:10.1126/science.aan2409

Sosik, H. M., R. J. Olson, M. G. Neubert, A. Shalapyonok, and A. R. Solow. 2003. Growth rates of coastal phytoplankton from time-series measurements with submersible flow cytometer. Limnol. Oceanogr.48: 1756–1765. doi:10.4319/ lo.2003.48.5.1756

Sosik, H. M., and R. J. Olson. 2007. Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr.: Methods 5: 204–216.


Zombie Cells Follow a Different Clock

The domain below the seafloor can be divided into two distinct regimes: sediment and rock. The former comprises the mud and detritus that accumulates at the bottom of the ocean. This layer resembles a dense sponge in structure: Although 90 percent of its weight might be water, nothing can flow through it efficiently fluids and chemical compounds slowly diffuse through it instead. Microbial cells essentially get buried there, along with whatever matter they might use for energy.

In shallow areas, particularly near coasts where nutrients are more abundant, this buried life thrives: Millions or even a billion microbial cells may dwell in a cubic centimeter of that sediment. As researchers dig deeper, they find fewer cells.

And yet they always seem to find something. They’ve dug into sediments as far as 2,500 meters below the seafloor — where they’ve uncovered just a few cells per cubic centimeter, approaching the very limits of their detection ability.

These cells barely seem alive, at least by our standards. They live very, very slowly, rarely dividing, their energy consumption at times six orders of magnitude lower than that of cells living in surface habitats. “It might take them 100 years or 1,000 years to divide just once,” said Martin Fisk, an ocean ecologist at Oregon State University. “They’re very slowly keeping themselves going.”

Steve D’Hondt, an oceanographer at the University of Rhode Island, calls them “zombie cells.” But they are, indeed, alive — they’re simply not operating at familiar time scales. Our observations of the cells’ activity might be akin to a mayfly’s experience of the life of a tree the insect dies well before it can gain any true understanding of how the tree develops and sustains itself.

But something different might be happening in the basalt rocks that lie beneath the sediment. Unlike the hard-packed mud, those rocks have pores and cracks and fissures through which seawater circulates — and with it, organic matter that microbes can feed on.

Unfortunately, it’s also much more challenging to penetrate and obtain uncontaminated samples from that rock. While sediment can be sampled by essentially driving a pipe vertically into the mud, the rock is too hard for that. Scientists instead have to use a drill, as well as a fluid mixture of seawater and mud to lubricate it — and that process contaminates the edges and crevices of the samples with biological material from higher up.

So to investigate what might be living inside the rocks, microbiologists have to clean them, rinse them in alcohol, flame their surface and break them open. Even then, these methods tend to uncover very limited numbers of cells, and without accompanying photographic evidence, “it’s always very hard to prove that what is being sampled is really from there, and not contamination,” said William Orsi, a geomicrobiologist at the University of Munich.

A prepared rock sample from a deep-sea drilling expedition. The geoscientist Yohey Suzuki and his colleagues developed a novel way to identify and count the cells living inside these rocks. Caitlin Devor, University of Tokyo, CC BY 4.0

Most of the handful of studies that have examined life in basalts have focused on rocks near hydrothermal vents or newly made seafloor — relatively young rocks that are only up to 8 million years old. That’s because it’s in younger rocks that seawater can most easily circulate and replenish the microbes’ food supply. Those rocks are also more chemically reactive, and certain “rock-eating” microbes can use those reactions as an energy source, too.

But as basalt cools and moves away from where it was formed, further along the ocean floor toward subduction zones, its many cracks and pores fill up with precipitated minerals, preventing this more active fluid flow and leading to a more isolated system. It was unclear whether life could survive under such conditions — until now.


Stable Carbon Isotope Discrimination by Form IC RubisCO from u3cemu3eRhodobacter sphaeroidesu3c/emu3e

Major Professor: Kathleen M. Scott, Ph.D. Valerie J. Harwood, Ph.D.

John H. Paul, Ph.D. Date of Approval:

Keywords: carboxylation, Calvin-Benson-Bassham cycle, alphaproteobacteria, autotroph, fractionation

The funding for this graduate research was made possible by the NSF FGLSAMP Bridge to the Doctorate Program at the University of South Florida (Award HRD # 0217675) and NSF Biological Oceanography (Award# OCE-0327488, to K.M.S). Special thanks are given to the graduate researchers in Dr. Scott’s lab, as well as to Dr. Robert Michener at the Boston University Stable Isotope Lab for analyzing our samples via mass spectrometry, and to Dr. F. Robert Tabita for providing the Form IC RubisCO enzyme.

List of Tables ii

Chapter One: Introduction 1

Other Autotrophic Pathways 3

Elucidating autotrophic pathways from contemporary & ancient

environmental samples 6

Stable carbon isotope ratios and δ13C values 7

Enzymatic isotope discrimination 10

Chapter Two: Isotope Discrimination by Form IC RubisCO 12

Experimental Procedures 16

Chapter Three: Conclusion 25

Experiments with Form ID RubisCO 25

Appendix A: Table of RubisCO ε values from Ralstonia eutropha 32 Appendix B: Manuscript on form ID RubisCO ε value from Emiliania

Appendix C: Manuscript on form ID RubisCO ε value from

Skeletonema costatum 45 Appendix D: Abstract from Publication of Thiomicrospira

crunogena XCL-2 genome 65

Appendix E: Abstract from Publication of Sulfurimonas denitrificans

Table 1 The distribution of autotrophic pathways among the domains of

Table 2 RubisCO ε values determined from nine independent

experiments with the Rhodobacter sphaeroides enzyme 19

Table 3 ε values from form I and II RubisCO enzymes 20

Table 4 δ13CCO2 and δ13Cbiomass values from autotrophic organisms

whose RubisCO ε values have been determined 22

Table A-1 RubisCO ε values determined from two independent

experiments with the IC enzyme from Ralstonia eutropha 33

Table B-1 ε values of different RubisCO forms 37

Table C-1 ε values of different forms of RubisCO that have been

measured with kinetic isotope experiments 61

Figure 1. The RubisCO reaction in the Calvin-Benson-Bassham (CBB)

Figure 2. Zero-point energy diagram of 12C - and 13C - carbon dioxide 8 Figure 3. Minimum evolution tree of RubisCO large subunit (cbbL)

Figure 4. Changes in the concentration (‹) and δ13C („) of dissolved inorganic carbon (DIC) vs. time for Rhodobacter sphaeroides

Figure 5. Natural log-transformed isotope ratios (R= 13C/12C) and concentrations of dissolved inorganic carbon (DIC) during

carbon fixation by form IC RubisCO 19

Figure 6. Whole-organism isotopic discrimination (δ13CCO2 - δ

biomass) versus enzymatic isotopic

discrimination (RubisCO ε values). 22

Figure 7. δ13C values of atmospheric CO2 and biomass from organisms

using different pathways of autotrophy 23

Figure B-1. Isotope fractionation by E. huxleyi RubisCO 39

Figure C-1. Isotope fractionation of DIC as CO2 is consumed by

S. costatum RubisCO and spinach RubisCO 55

Figure C-2. The consumption of DIC over time by S. costatum RubisCO 56

Figure C-3. Radiometric assay of partially purified S. costatum RubisCO 57

Figure C-4. Phylogenetic tree of selected RubisCO large subunit genes (rbcL) 58

Figure D-1. PloS Biology Open Access License 67

Stable Carbon Isotope Discrimination by Form IC RubisCO from Rhodobacter sphaeroides

Phaedra J. Thomas ABSTRACT

Variations in the relative amounts of 12C and 13C in microbial biomass can be used to infer the pathway(s) autotrophs use to fix and assimilate dissolved inorganic carbon. Discrimination against 13C by the enzymes catalyzing autotrophic carbon fixation is a major factor dictating the stable carbon isotopic composition (δ13C = <[13C/12Csample/13C/12Cstandard] – 1>X 1000) of biomass. Six different forms of ribulose

1,5-bisphosphate carboxylase/oxygenase or RubisCO (IA, IB, IC, ID, II, and III), the carboxylase of the Calvin-Benson-Bassham cycle (CBB), are utilized by algae and autotrophic bacteria that rely on the CBB cycle for carbon fixation. To date, isotope discrimination has been measured for form IA, IB, and II RubisCOs. Isotopic

discrimination, expressed as ε values (=<[12k/13k] – 1> X 1000 12k and 13k = rates of 12C

and 13C fixation) range from 18 to 29‰, explaining the variation in biomass δ13C values of autotrophs that utilize these enzymes. Isotope discrimination by form IC RubisCO has not been measured, despite the presence of this enzyme in many proteobacteria of

ecological interest, including marine manganese-oxidizing bacteria, some nitrifying and nitrogen-fixing bacteria, and extremely metabolically versatile organisms such as

form IC RubisCO enzyme from R. sphaeroides. Under standard conditions (pH 7.5 and 5 mM DIC), form IC RubisCO had an ε value of 22.9‰. Sampling the full phylogenetic breadth of RubisCO enzymes for isotopic discrimination makes it possible to constrain the range of δ13C values of organisms fixing carbon via the Calvin-Benson-Bassham cycle. These results are helpful for determining the degree to which CBB cycle carbon fixation contributes to primary and secondary productivity in microbially-dominated food webs.

Chapter One Introduction The Calvin Cycle

The Calvin-Benson-Bassham (CBB) cycle is the most common carbon-fixing pathway for autotrophic organisms. It is present in plants, algae, cyanobacteria, and proteobacteria, and has recently been found in firmicutes (3). The CBB cycle consists of the dark reaction of photosynthesis in which CO2 is incorporated into organic compounds

for the biosynthetic needs of the organism. It consists of three stages: carbon fixation, reduction, and regeneration (1).

RubisCO (Ribulose 1,5-bisphosphate carboxylase/oxygenase) is the carbon-fixing enzyme in the Calvin cycle (48). The RubisCO reaction involves the catalytic conversion of one molecule of ribulose 1,5-bisphosphate (RuBP), via carboxylation, into two

molecules of phosphoglyceric acid (PGA) (Figure 1). This comprises the carbon fixation step of the Calvin cycle. The products of the RubisCO reaction can be shuttled into other metabolic pathways and used to form amino acids and other precursors for biosynthesis. RubisCO is a relatively nonselective enzyme that can utilize both carbon dioxide and oxygen as substrates (18).

There are four main forms of RubisCO, form I, II, III, and IV that differ substantially in their amino acid sequences (see below 48). Form I RubisCO is

present in cyanobacteria, proteobacteria, and most plastids, and can be further

subdivided, based on amino acid sequences, into forms IA, IB, IC, and ID. Form II is found in some proteobacteria and dinoflagellates, and the Form III enzyme is present in some archaea. Form IV RubisCO is widespread in bacteria and is not active as a carboxylase (49).

Figure 1. The RubisCO reaction in the Calvin-Benson-Bassham (CBB) Cycle.

Catalytically active form I RubisCO consists of eight large subunits (encoded by the cbbL gene) and eight small subunits (encoded by the cbbS gene), while form II and III consist of a single type of subunit that is homologous to form I large subunits (48). The amino acid sequences of form I large subunits and II RubisCOs are quite divergent, with only

23% sequence similarity (34). Within the form I RubisCOs, the amino acid sequences of the large subunits of form IA and IB RubisCOs are approximately 80% similar, as are the form IC and ID RubisCOs, while the IA/IB cluster has only

approximately 60% sequence similarity with the IC/ID cluster (34, 48). Consistent with these differences in sequence, each form has different specificities for CO2 and O2. Form

I RubisCOs have a higher specificity for CO2 than O2, compared to the form II enzymes

(48, 49). However, despite these differences in the primary structures of the four main forms, the active site responsible for the carboxylation of CO2 and the oxygenation of O2

is conserved across the various RubisCOs (48).

Other Autotrophic Pathways

There are three other autotrophic pathways present in microorganisms in addition to the CBB cycle. In the reverse citric acid cycle (rTCA), acetyl-CoA is formed by splitting citrate that was produced by reversing the citric acid cycle so that it operates in a carboxylating, reductive direction (20). The acetyl-CoA is reductively carboxylated to pyruvate, which is shuttled into other central metabolic pathways (20). The three key enzymes responsible for the reverse rotation of the oxidative citric acid cycle are ATP citrate lyase, 2-oxoglutarate: ferredoxin oxidoreductase, and fumarate reductase (20).

The acetyl-CoA pathway (AcCoA) produces acetyl-coA via sequential reduction of carbon dioxide. This molecule is reduced to the level of a methyl group while attached to a cofactor, followed by acetyl-CoA synthase-mediated condensation of this methyl group with a second carbon that has been reduced from the level of carbon dioxide to carbon monoxide via carbon monoxide dehydrogenase (CODH) (40). The

3-hydroxypropionate cycle (3-HPP), which is the most recently discovered pathway for carbon fixation, functions by carboxylating acetyl-CoA and converting it to propionyl-CoA with 3-hydroxyproprionate as an intermediate (52). The key enzyme for this cycle

is malonyl-CoA reductase, which reduces malonyl-CoA, an initial product of acetyl-CoA carboxylation, to 3-hydroxypropionate (19). Propionyl-CoA undergoes carboxylation, forming malyl-CoA, which further divides into acetyl-CoA and glyoxylate (52).

Autotrophic pathways cannot be predicted based on the host organism’s

phylogeny (Table 1). The rTCA cycle is found in a variety of autotrophic bacteria and archaea, Chlorobium sp., sulfur-reducing Crenarchaeota (Thermoproteus and

Pyrobaculum), sulfate-reducing bacteria (Desulfobacter), as well as microaerophilic and

hyperthermophilic hydrogen-oxidizing bacteria like Aquifex sp. and Hydrogenobacter sp. (20). The acetyl-CoA pathway occurs in autotrophic sulfate-reducing bacteria,

methanogens, and acetogenic bacteria (11). The acetyl-CoA pathway has also been studied in Spirochaetes like Treponema primitia (14). The 3-hydroxypropionate cycle operates in the green nonsulfur bacterium, Chloroflexus aurantiacus and autotrophic Crenarchaeota (19).

The autotrophic pathways presented in Table 1 differ in two important ways: their sensitivity to oxygen (O2) and their energetic requirements. The rTCA cycle and the

acetyl-CoA pathway are sensitive to oxygen, due to the extreme oxygen sensitivity of pyruvate: ferredoxin oxidoreductase, which is responsible for carboxylating acetyl-CoA (rTCA and acetyl-CoA pathways), as well as CODH (acetyl-CoA pathway), which explains why these alternative methods of fixing CO2 predominate in anaerobic

environments (4, 50). The Calvin cycle and the 3-hydroxyproprionate cycle are both less sensitive to oxygen (Thauer, 2007).

Table 1. The distribution of autotrophic pathways among the domains of life. *Only divisions with autotrophic members are listed. †It has not been determined whether Treponema primitia is an autotroph. Abbreviations: Calvin-Benson-Bassham cycle = CBB, reductive citric acid cycle = rTCA, acetyl-CoA pathway = Ac-CoA, and

3-hydroxypropionate cycle = 3-HPP.

Bacteria Proteobacteria CBB, rTCA

Cyanobacteria (Eukarya - Plants) CBB Chloroflexi 3-HPP Chlorobi rTCA Aquificae rTCA Firmicutes CBB, Ac-CoA Planctomycetes Ac-CoA Spirochaetes Ac-CoA†

Archaea Crenarchaeota rTCA, 3-HPP

All of the carbon fixation pathways discussed require NADPH, ferredoxin, or other intracellular electron-shuttling cofactors as electron donors, except the acetyl-CoA pathway, which uses H2 as its donor and is the only known autotrophic pathway that

yields metabolic energy instead of consuming it. This is because it directly couples the removal of electrons from H2, and transfers these electrons to CO2 for the creation of

membrane potential (11). The Calvin cycle is the most energetically expensive method of fixing carbon, due to multiple dephosphorylation events necessary to regenerate RuBP

from 3-PGA, followed by the rTCA cycle, and acetyl-CoA pathway (28). Autotrophs that are primarily found in low-oxygen environments often use alternatives to the CBB cycle because they demand less energy to synthesize an equivalent amount of biomass. However, in oxic environments, the CBB cycle dominates due to its relative stability under these conditions (28).

Elucidating autotrophic pathways from contemporary and ancient environmental samples

It is interesting to resolve which pathways are in operation in contemporary and ancient environments because it gives us insight into the history of carbon cycling. Identifying the autotrophic pathways operating in ancient environments can help clarify whether the CBB cycle has always been the dominant carbon-fixing pathway.

In order to determine which pathways are operating in a particular contemporary environment, enzyme and nucleic acid assays can be used. If sufficient biomass is present, assays for enzymes diagnostic for the different pathways can be used e.g. RubisCO for the CBB cycle (48), ATP citrate lyase for the rTCA cycle (20), CODH for the acetyl-CoA pathway (40), and malonyl-CoA reductase for the 3-hydroxyproprionate cycle (19). If insufficient biomass is available for enzyme assays, nucleic acid-based approaches (e.g., Southern & Northern blots PCR) can be utilized to identify which autotrophic CO2-fixing pathways are in operation. The cbbL or cbbM genes for form I or

II RubisCO can be used as indicators of Calvin Cycle presence in a target organism genes encoding the enzymes diagnostic for other autotrophic pathways, (see above) can be utilized as markers for the presence of those pathways.

However, enzyme assays and nucleic acid-based techniques do not work for fossil samples, as these macromolecules are not present in appreciable quantities. Instead, isotope measurements can be used, since 12C and 13C contents persist throughout geological time. Stable carbon isotope compositions of organic compounds extracted from fossil sediments can help identify which autotrophic pathway dominated the input of carbon into a particular ecosystem (42, 27).

Stable carbon isotope ratios and δ13C values

Three isotopes of carbon exist: two stable isotopes (12C and 13C), and radioactive

14C. 12C is more abundant in nature than 13C, which is

1‰ of all carbon. The relative

amounts of 12C and 13C in a sample are expressed as δ13C values, in parts per thousand (‰): δ13C = ( – 1) x 1000, where the limestone, PeeDee Belemnite, is

the standard (31, 27), and R = 13C/12C. A more negative δ13C value indicates that there is

less 13C in the sample (it is ‘isotopically depleted’). Conversely, a more positive δ13C indicates that more 13C is present in the sample (it is ‘isotopically enriched’) (31).

δ13C values vary greatly. The δ13C value of atmospheric CO2 is

δ13C of inorganic carbon dissolved in seawater is between

1‰ (41, 21, 23). Biomass δ13C values are more isotopically depleted than these inorganic carbon sources, due to the relative weakness of bonds to 12C, compared to 13C. Since the zero-point energy of bonds to 13C is lower than the zero-point energy of bonds to 12C, compounds containing 12C tend to react more quickly than those containing 13C (Figure 2) (29). As a result, autotrophs fix 12CO2 more rapidly than 13CO2 and their biomass has more negative

so is the biomass of the heterotrophs that consume them (17).

Figure 2. Zero-point energy diagram of 12C - and 13C - carbon dioxide. The δ13C of C3 plants that use the CBB cycle is -18-30‰ this great degree of isotope depletion relative to atmospheric CO2 is largely due to substantial fractionation

by RubisCO during carbon fixation (17). The δ13C of C4 plants is -8‰ to -20‰. These values are more positive than C3 plants because the enzymes responsible for the initial fixation of carbon into C4 compounds do not fractionate to the same degree as RubisCO does (17). The δ13C value of marine photoautotrophs such as algae is between 18 and -28‰, but is generally more toward the isotopically enriched end of this range (13). They can be isotopically enriched due to a variety of factors, including carbon-concentrating mechanisms (CCMs) and diffusive limitation (DL) (22).

In the case of both CCMs and DL, the isotopic enrichment of intracellular biomass is due to isotopic enrichment of intracellular CO2. This isotopic enrichment of

influx and fixation. In the case of a cell with a CCM, the rate of influx is high, due to the activities of multiple bicarbonate transporters. Intracellular bicarbonate is converted to CO2 by carbonic anhydrase. The majority of CO2 is fixed by RubisCO. The CO2

remaining is isotopically enriched. Cells with CCMs typically have mechanisms to prevent the efflux of this pool of intracellular CO2, which prevents the isotopic signature

from CO2 fractionation by RubisCO from being wiped away by rapid exchange of

intracellular CO2 with extracellular dissolved inorganic carbon (22). Likewise, cells

experiencing DL have a very low rate of CO2 efflux, since diffusive limitation of CO2

supply to the cell results in extraordinarily low intracellular concentrations of CO2 (22).

The differences in δ13C values among C3, C4, and marine organisms make it possible to trace carbon through food webs (17), drawing on the adage that you are what you eat. The 13C-content of fossil organic carbon has been used as evidence for biological carbon fixation. In some fossil samples, both organic and inorganic carbon are preserved, providing a means to measure isotope discrimination between inorganic (δ13C =

2‰) and organic carbon (δ13C = -25‰), billions of years ago (42). The level of isotopic discrimination between organic and inorganic carbon in these samples cannot be

achieved by a/biological processes, it is only possible via enzyme discrimination during autotrophic carbon fixation (42). Based on the limited data available from cultures, the level of isotope discrimination observed in these fossil biomass samples is consistent with the CBB cycle and acetyl-CoA pathway. However, hypotheses about which autotrophic pathways may be operating are weakened by limited sampling it is still not possible to predict, with confidence, the δ13C values expected for each pathway.

Enzymatic isotope discrimination

The ε value is a measure of isotope discrimination by an enzyme in this case, RubisCO. Biomass δ13C values from organisms using the CBB cycle are almost always more negative than source CO2 (39), but different RubisCO enzymes fractionate to

varying degrees due to slight differences in the structure of their active site (15). Isotope fractionation is described as a discrimination factor (ε = <[12k/13k] –1>X 1000, where 12k and 13k = the rates of 12C and 13C fixation (15). Epsilon values (ε) for enzymes are

calculated by measuring the change in the isotopic composition of the substrate pool as an enzyme reaction progresses. More isotopically selective enzymes will leave behind relatively more 13CO2, while less isotopically selective enzymes (with smaller ε values)

will leave behind less 13CO2.

For RubisCO enzymes, the isotopic composition of dissolved inorganic carbon (DIC) is monitored changes in the δ13C of DIC can be described using the Rayleigh distillation equation: (R/R1) = (C/C1)1/α –1, where R is the isotope ratio of the DIC, C is the

DIC concentration, and both R1 and C1 represent the corresponding quantities present at

the beginning of the experiment (45). The α is Rr/Rp Rr is the isotope ratio of available

reactant, and Rp is the isotope ratio of the product, and is equal to 12k/13k (45). ε values

should affect the δ13C values of autotroph biomass. Large ε values should result in more negative biomass δ13C values, while small ε values should result in more positive

biomass δ13C values, because a less isotopically selective enzyme should fix more 13CO2.

The ε values for forms IA, IB, and II RubisCO enzymes are currently known (ε = 18-29‰). However, the ε values for forms IC and ID RubisCOs have not been measured,

which makes it impossible to predict the range of δ13C values expected for organisms using the CBB cycle, which really limits the interpretation of the δ13C values from contemporary and ancient samples.

The purpose of this research is to determine ε values for the form IC RubisCO enzymes. Form IC RubisCOs have not been explored yet and knowing their ε values will impact the fields of microbial ecology and biogeochemistry because it will help to

Isotope Discrimination by Form IC RubisCO

It is of interest to ascertain which carbon-fixing pathways are operating in contemporary and ancient microbially-dominated habitats, as the pathways differ in energetic expense and cofactor requirements (11), which in turn influence the ecology of the organisms. It is not possible to collect biochemical and/or nucleic acid-based

evidence for pathway presence in the case of fossil biomass samples. Variations in the relative amounts of 12C and 13C in autotrophic microbial biomass, expressed as δ13C values (= <[Rsample/Rstandard] – 1>X 1000 Rsample = 13C/12Csample, Rstandard =

PeeDeeBelemnite), can be used to gather information about the metabolic pathway(s)

used by these organisms. For autotrophs, δ13C values can also be used to ascertain the rate of CO2 exchange between the cell and the environment, to determine the source of

the CO2, and to elucidate environmental factors influencing carbon fixation (17). Indeed,

the broad range of δ13C values collected for autotrophic microorganisms (δ13C = 8‰ to -35‰) has been utilized as evidence for the interplay of these factors. However, in order for the influence of the above factors to be rigorously evaluated, the baseline

fractionation by the carboxylase(s) responsible for carbon fixation must be measured (8, 11, 13, 17, 33, 43).

Autotrophs that utilize the Calvin-Benson-Bassham cycle (CBB autotrophs), have a diversity of RubisCO (ribulose 1,5-bisphosphate carboxylase/oxygenase) enzymes that catalyze the carboxylation of ribulose 1,5-bisphosphate (RuBP) to form two molecules of phosphoglyceric acid (PGA 47). RubisCO exists in six different forms that are

catalytically active as carboxylases (IA, IB, IC, ID, II, and III Figure 3). Form I RubisCO is present in cyanobacteria (IA, IB), some proteobacteria (IA, IC), most chloroplasts (IB, ID) (48), and the firmicute Sulfobacillus acidophilus (IC/ID) (3).

Catalytically active form I RubisCO consists of eight large subunits (encoded by the cbbL gene) and eight small subunits (encoded by the cbbS gene). Form II is found in

proteobacteria and some dinoflagellates, and form III is present in some archaea (48). Both form II and III enzymes consist of a single type of subunit, evolutionarily related to the large subunits of form I RubisCO (48).

Given the divergent forms of RubisCO, it is not surprising that RubisCO enzymes discriminate against 13CO2 to different degrees. Isotope discrimination (ε = <[12k/13k] – 1>

X 1000 12k and 13k = rates of 12C and 13C fixation) (15) has been measured in a limited number of form IA, IB, and II enzymes, and ranges from 18 to 29‰ (44, 15, 36, 37, 38, 46). Since the ε value is roughly equal to the difference between the δ13C of the CO

source from which the RubisCO is drawing (intracellular CO2) and the δ13C of the CO2

that it fixes (15, 44), heterogeneity in RubisCO ε values is likely to be responsible for at least 10‰ of the δ13C scatter observed in CBB autotrophs. Prior to this study, it was impossible to predict whether form IC RubisCO enzymes would have ε values similar to those measured for form IA, IB, & II RubisCOs.

Our long-term objective is to sample the full phylogenetic breadth of RubisCO enzymes and be able to constrain the δ13C values expected for CBB autotrophs from ancient and contemporary ecosystems. Isotope discrimination by form IC RubisCO has not been measured, despite the presence of this enzyme in many proteobacteria of ecological interest, including marine manganese-oxidizing bacteria (5, 32), some nitrifying and nitrogen-fixing bacteria, and soil microorganisms like the extremely metabolically versatile bacterium, Rhodobacter sphaeroides (48 Fig.3).

Rhodobacter sphaeroides is an α-proteobacterium capable of nonoxygenic

photolithoautotrophic growth and photoheterotrophic growth (35). This organism has two RubisCO enzymes: a form IC RubisCO and a form II RubisCO (12). In this

organism, the two forms of RubisCO are differentially expressed in response to a variety of growth conditions, e.g. CO2 concentration (10). In order to be able to detect the

isotope signature of organisms using form IC RubisCO for carbon fixation, we measured the ε value for R. sphaeroides RubisCO using the high-precision substrate depletion method (15, 36, 43).

IC/ID Rhodobacter sphaeroidesATCC17025 Stappia aggregataIAM12614 Paracoccus denitrificansPD1222 Xanthobacter autotrophicusPy2 Xanthobacter autotrophicus Methylibium petroleiphilumPM1 Ralstonia eutrophaH16 Ralstonia eutropha Burkholderia xenovoransLB400 Burkholderia phymatumSTM815 Bradyrhizobium japonicumUSDA110 Bradyrhizobium japonicum Oligotropha carboxidovorans Bradyrhizobium spBTAi1 Nitrobacter hamburgensisX14 Rhodopseudomonas palustrisCGA009 Rhodopseudomonas palustrisBisB18 Aurantimonas spSI859A1 Mnoxidizing bacteriumSI859A1 Roseovarius spHTCC2601 Acidiphilium cryptumJF5 Nitrosospira sp40KI Emiliania huxleyi Porphyridium aerugineum Phaeodactylum tricornutum Cylindrotheca sp Thalassiosira pseudonana Thalassiosira nordenskioeldii Sulfobacillus acidophilus Nitrobacter winogradskyi Nitrosomonas spENI11 Solemya velum

Prochlorococcus marinus MIT 9313 Synechococcus sp WH 8102 Chromatium vinosum Spinacia oleracea Trichodesmium erythraeum Nostoc punctiforme Synechococcus elongatus PCC 6301 Archaeoglobus fulgidus Methanococcus jannaschii Methanosarcina acetivorans Thiobacillus denitrificans Thiomicrospira crunogena Rhodobacter capsulatus Rhodobacter sphaeroides II 100 99 100 57 99 100 100 98 70 63 72 100 100 100 99 100 74 100 100 100 100 64 100 100 99 98 45 87 58 99 100 59 57 38 98 92 54 77 57 56 92 20 31 0.1

IC

ID

IA

IB

III

II

Figure 3. Minimum evolution tree of RubisCO large subunit (cbbL) nucleotide sequences. The nucleotide sequences obtained from GenBank were translated to amino

acid sequences and aligned based on the amino acid sequences using BIOEDIT (16). The alignments were examined to make certain that active sites and other conserved

regions were properly aligned. Nucleotide sequences were used to assemble a phylogenetic tree with MEGA 3.1 software, using the Kimura 2-parameter nucleotide

model with 1000 replicates for calculating bootstrap values (24).

Experimental Procedures

Form IC enzyme was cloned from Rhodobacter sphaeroides, expressed in

Escherichia coli, and purified using standard HPLC protocols (25, 18). The purity of the

enzyme was monitored by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and stained with coomassie blue (25).

RubisCO ε values were determined by using the substrate depletion method, in which a buffered solution of RubisCO, carbonic anhydrase (CA), ribulose

1,5-bisphosphate (RuBP), and dissolved inorganic carbon is sealed in a gastight syringe (44). The ε value is calculated from the changes in the δ13C value of the dissolved inorganic

carbon pool as it is consumed by RubisCO (44). RuBP was synthesized enzymatically to minimize the concentration of inhibitory isomers present in commercially-available

RuBP (44, 46). Fresh RuBP was stored at –70°C and used within one week of synthesis. Form IC RubisCO was prepared for the reaction by desalting it into RubisCO

buffer (25 mM MgCl2, 10 mM dithiothreitol, 50 mM Bicine, 5 mM NaHCO3, pH 7.5),

using PD-10 columns (Amersham Biosciences, NJ). 20 mL of reaction buffer (RubisCO buffer without NaHCO3)was sparged with N2 and 1 mg of bovine carbonic anhydrase

(CA) was added. This solution was filter-sterilized and loaded into a glass gastight syringe sealed with a septum (44). Filter-sterilized NaHCO3 (final concentration of 5

1 mg/mL) were added to the reaction syringe and activated for 10-15 minutes. The reaction was started by injecting fresh RuBP (

5 mM) into the syringe, and was maintained at 25°C.

Reaction progress was monitored by removing samples and injecting them into a gas chromatograph (9) to measure the dissolved inorganic carbon concentration (DIC, = CO2 + HCO3- + CO3-2). Over the time course of the reaction, samples were removed

from the reaction syringe, acidified with 43% phosphoric acid, and injected into a vacuum line to cryodistill the DIC (44). The cryodistilled DIC samples were sent to the Boston University Stable Isotope Facility for measurement of the δ13C of the DIC.

ε values were derived from the DIC concentrations and the δ13C values of the DIC as calculated in Scott et al. (45). A modified version of the Rayleigh distillation equation was used:

RDIC = 13C/12C of DIC at a particular timepoint

RDIC0 = RDIC at the first timepoint

[DIC] = concentration of DIC at a particular timepoint [DIC]0 = [DIC] at the first timepoint

C = RHCO3-/RCO2 from Mook et al., (30)

α = k12/k13 = kinetic isotope effect for CO2

The ε values were calculated from the slope of this line (ε = (α - 1) ×1000), and data from multiple runs were averaged using Pitman Estimators (45).

As expected, carbon fixation by form IC RubisCO of R. sphaeroides results in isotopic enrichment of the remaining dissolved inorganic carbon (Figure 4, Figure 5). The ε value for this enzyme is 22.9‰ with a 95% confidence interval of 21.4-24.7‰ (Table 2). Rhodobacter sphaeroides RubisCO is less isotopically selective than spinach RubisCO (ε = 27.5‰ when incubated under identical conditions) (2).

Figure 4. Changes in the concentration (‹) and δ13C („) of dissolved inorganic carbon (DIC) vs. time for Rhodobacter sphaeroides RubisCO sealed in a sterile, gastight syringe

in buffer with ribulose 1,5-bisphosphate and carbonic anhydrase (see Experimental Procedures for details).

-4 -2 0 2 4 6 8 10 0 1 2 3 4 5 6 Time (hrs.) δ 13 C 0 1 2 3 4 5 6 [DIC], mM

-4.495 -4.487 -4.479 -4.471 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 lnDIC lnR

Figure 5. Natural log-transformed isotope ratios (R= 13C/12C) and concentrations of dissolved inorganic carbon (DIC) during carbon fixation by form IC RubisCO. Results

from nine independent experiments are shown, and each is depicted with a different symbol. The initial isotope ratio and concentration of DIC varied slightly between experiments for clarity, data from all experiments have been normalized to have the

same initial DIC concentration and isotope ratio.

Table 2. RubisCO ε values determined from nine independent experiments with one enzyme preparation of the R. sphaeroides enzyme

Experiment # ε value (‰) # of Timepoints

1 29.6 7 2 24.4 6 3 24.1 7 4 21.0 7 5 22.7 7 6 24.3 8 7 24.7 8 8 22.6 8 9 24.1 7

This is the first determination of an ε value from a Form IC RubisCO, and it falls within the range of ε values measured for other RubisCO enzymes (Table 3). Its ε value is statistically indistinguishable from form IA enzymes, form IB from the cyanobacterium

Anacystis nidulans, as well as from form II RubisCO from Rhodospirillum rubrum.

However, R. sphaeroides RubisCO has an ε value significantly higher than that of the form II RubisCO from the gammaproteobacterial endosymbiont of the hydrothermal vent tubeworm, Riftia pachyptila, and significantly smaller than the ε value of spinach

Table 3. ε values from form I and II RubisCO enzymes

Species Form of RubisCO ε value (‰)

Solemya velum symbiont IA 24.5

Prochlorococcus marinus MIT 9313 IA 24.0

Spinacia oleracea IB 29

Anacystis nidulans IB 22

Rhodobacter sphaeroides IC 22.9

Riftia pachyptila symbiont II 19.5

Rhodospirillum rubrum II 22

The δ13C value of R. sphaeroides biomass, when it fixes carbon with form IC RubisCO, can be predicted from the RubisCO ε value. Organisms that fix carbon with IC enzymes, should they all fractionate similarly to the R. sphaeroides enzyme, are likely to

have biomass δ13C values similar to other CBB autotrophs whose RubisCO ε values have been determined. When an organisms’ whole-cell isotopic discrimination (δ13CCO2 - δ13Cbiomass) is plotted against its RubisCO ε value, it is apparent that organisms fixing

carbon via RubisCOs with larger ε values fractionate carbon to a greater extent (Table 4, Figure 6). Since the R. sphaeroides RubisCO has an ε value similar to the enzyme from

R. rubrum, it is likely that whole-cell discrimination by R. sphaeroides is comparable to

that observed in R. rubrum when grown autotrophically. Indeed, biomass δ13C values for

R. sphaeroides can be predicted from the R. rubrum and R. sphaeroides ε values, as well

as R. rubrum whole-cell isotope discrimination:

For R. rubrum, δ13CCO2 - δ13Cbiomass = 12.3‰ Since

For R. sphaeroides, predicted εbiomass = δ13CCO2 - δ13Cbiomass ≈12.3 + 0.9 = 13.2‰

Table 4. δ13CCO2 and δ13Cbiomass values from autotrophic organisms whose RubisCO ε values have been determined

biomass (‰) Riftia pachyptila symbiont -6.6 to -7.8 -9 to -16

Solemya velum symbiont -14 to -18 -30 to -35

C3 plants (Spinach) -8 -22 to -30

Rhodospirillum rubrum -11.3 -23.6

Figure 6. Whole-organism isotopic discrimination (δ13CCO2 - δ13Cbiomass) versus enzymatic isotopic discrimination (RubisCO ε values). The predicted εbiomass value for

Rhodobacter sphaeroides assumes whole-cell fractionation similar to R. rubrum (see

Discussion for details), and the predicted range (whiskers on graph) assumes changes in whole-organism isotopic discrimination similar to what has been observed in other

A biomass δ13C value of -21.2‰ would place R. sphaeroides and other form IC autotrophs within the range of δ13C values observed for other CBB autotrophs, but would allow them to be distinguished from organisms using alternative carbon fixation

pathways. Their δ13C values should be more negative than what is expected for

organisms using the rTCA or 3-hydroxypropionate cycles for carbon fixation, but not the acetyl-CoA pathway (17 Figure 7).

Figure 7. δ13C values of atmospheric CO2 and biomass from autotrophic organisms using

the 3-hydroxyproprionate pathway (3-HPP), the reverse citric acid cycle (rTCA), the acetyl-CoA pathway, and the C3-Calvin Benson Bassham cycle (C3-CBB).

Future work with Form IC RubisCO will involve sampling the full phylogenetic breadth of Form IC RubisCOs to determine whether all Form ICs have ε values that fall within the range observed for other RubisCO ε values. In order to sample the full

phylogenetic spectrum of this group, ε values could be measured for RubisCOs from, for example, Burkholderia xenovorans LB400 and Roseovarius sp. HTCC 2601, as these two enzymes are present in IC clades distinct from the clade containing R. sphaeroides

RubisCO (Figure 3). B. xenovorans LB400 is an aerobic soil microbe capable of

degrading polychlorinated biphenyls (7). Burkholderia sp. have a great number of carbon metabolism genes that indicate the presence of different pathways for assimilating

carbon, giving this strain an edge, ecologically (7). Roseovarius sp. HTCC 2601 is an aerobic, alphaproteobacterial isolate found in seawater collected from the Sargasso Sea, and is a member of the Roseobacter clade, which is important in the marine sulfur cycle (26, 51). This clade is responsible for degrading dimethylsulfoniopropionate (DMSP) to methanethiol (51).

It would also be of great interest to measure the ε value of RubisCO from the firmicute, Sulfobacillus acidophilus. S. acidophilus is most peculiar because it is an outlier from the Form IC and ID clades (Figure 3). Sulfobacillus species oxidize mineral sulfides and prefer environments with a high concentration of CO2, and their use of this

RubisCO substrate reveals much about the biogeochemical function of these acidophiles (3). Elucidating the degree of isotopic discrimination by form IC enzymes, broadly sampled, would make it possible to use stable carbon isotope analyses to learn more about the role form IC RubisCOs play in the environment and the global carbon cycle.

Chapter Three Conclusion

Rhodobacter sphaeroides IC RubisCO has an ε value of 22.9‰, which is within

the range measured for other RubisCOs. This implies that some CBB autotrophs using form IC enzymes can be predicted to have δ13C values similar to those previously measured in CBB autotrophs. Preliminary experiments have been conducted with form IC RubisCO from Ralstonia eutropha, and this enzyme appears to have an ε value of 26.6‰ (see Appendix table A-1), though replicate experiments are necessary to verify this measurement.

Experiments with Form ID RubisCO

ε values have also recently been collected for form ID RubisCOs from the phytoplankton species, Skeletonema costatum and Emiliania huxleyi. The ID RubisCOs have ε values that are lower than the ε value from R. sphaeroides RubisCO (ε = 11.1‰ for E. huxleyi ε = 18.6‰ for S. costatum see appendix). The diatom, S. costatum, and coccolithophore, E. huxleyi, are a major part of the phytoplankton community and contribute substantially to primary productivity in the oceans. The small ε values reveal that their ID RubisCOs are less isotopically selective for 13CO2 compared to other

RubisCO enzymes. The ε values also provide a mechanism for the levels of isotopic enrichment observed in these organisms, and marine organic carbon in general.

It is apparent that IC/ID RubisCOs have a broad range of ε values (11.1‰ to 22.9‰). Currently, it is impossible to predict ε values for RubisCO enzymes based on primary, secondary, tertiary, or quaternary structure. At this point, one must measure them, in the hope that at some point it will be possible to correlate ε values with primary structure or other features of these enzymes.

1. Bassham, J. A. 1971. Photosynthetic carbon metabolism. Proceedings of the National Academies of Sciences of the United States of America

2. Boller, A. J., Thomas, P. J., Cavanaugh, C. M., and Scott, K. M. in prep. A

3. Caldwell, P. E., MacLean, M. R., and Norris, P. R. 2007. Ribulose

bisphosphate carboxylase activity and a Calvin cycle gene cluster in Sulfobacillus species. Microbiology 153:2231-2240.

4. Campbell, B. J., Stein, J. L., and Cary, S. C. 2003. Evidence of

chemolithoautotrophy in the bacterial community associated with Alvinella

pompejana, a hydrothermal vent polychaete. Applied and Environmental Microbiology 69(9):5070-5078.

5. Caspi, R., Haygood, M. G., and Tebo, B. M. 1996. Unusual

ribuolose-1,5-bisphosphate carboxylase/oxygenase genes from a marine manganese-oxidizing bacterium. Microbiology 142:2549-2559.

6. Chanton, J. P. and Lewis, F. G. 1999. Plankton and dissolved inorganic carbon

isotopic composition in a river-dominated estuary: Apalachicola Bay, Florida.

Estuaries 22(3A):575-583.

7. Denef, V. J., Park, J., Tsoi, T. V., Rouillard, J. -M., Zhang, H.,

Wibbenmeyer, J. A., Verstraete, W., Gulari, E., Hashsham, S. A., and Tiedje, J. M. 2004. Biphenyl and Benzoate Metabolism in a Genomic Context: Outlining

Genome-Wide Metabolic Networks in Burkholderia xenovorans LB400. Applied

and Environmental Microbiology. 70(8):4961-4970.

8. Des Marais, D. J. 2001. Isotopic Evolution of the Biogeochemical Carbon Cycle

During the Precambrian. In John W. Valley and David R. Cole (eds.) Stable Isotope Geochemistry, Reviews in Microbiology and Geochemistry.

Mineralogical Society of America, Washington, D. C. 43:555-578. 9. Dobrinski, K. P., Longo, D. L., and Scott, K. M. 2005. The

carbon-concentrating mechanism of the hydrothermal vent chemolithoautotroph

Thiomicrospira crunogena. Journal of Bacteriology 187(16):5761-5766.

10. Dubbs, J. M. and Tabita, F. R. 1998. Two functionally distinct regions upstream

of the cbb1 operon of Rhodobacter sphaeroides regulate gene expression. Journal

of Bacteriology 180(18):4903-4911.

11. Fuchs, G. 1989. Alternative Pathways of Autotrophic CO2 Fixation. In H.G.

Schlegel and B. Bowien (eds.) Autotrophic Bacteria, Science & Technology Press. pp.365-382.

12. Gibson, J. L. and Tabita, F. R. 1977. Different molecular forms of D

-ribulose-1,5-bisphosphate carboxylase from Rhodopseudomonas sphaeroides. Journal of

Biological Chemistry 252(3):943-949.

13. Goericke, R., Montoya, J. P., and Fry, B. 1994. Physiology of isotopic

fractionation in algae and cyanobacteria. In Kate Lajtha and Robert H. Michener (eds.) Methods in Ecology: Stable Isotopes in Ecology and Environmental Science. Blackwell Scientific, Cambridge, MA. pp.187-221.

14. Graber, J. R. and Breznak, J. A. 2004. Physiology and nutrition of Treponema primitia, an H2/CO2-acetogenic spirochete from termite hindguts. Applied and Environmental Microbiology 70(3):1307-1314.

15. Guy, R. D., Fogel, M. L., and Berry, J. A. 1993. Photosynthetic Fractionation of

the Stable Isotopes of Oxygen and Carbon. Plant Physiology 101:37-47.

16. Hall, T. 2007. BioEdit version 7.0.9. Ibis Biosciences. Carlsbad, CA. Website:

17. Hayes, J. M. 2001. Fractionation of the isotopes of carbon and hydrogen in

biosynthetic processes. In John W. Valley and David R. Cole (eds.) Stable Isotope Geochemistry, Reviews in Microbiology and Geochemistry. Mineralogical Society of America, Washington, D. C. 43:225-278. 18. Horken, K. M. and Tabita, F. R. 1999. Closely related form I ribulose

bisphosphate carboxylase/oxygenase molecules that possess different CO2/O2

substrate specificities. Archives of Biochemistry and Biophysics. 361(2):183-194. 19. Hügler, M., Menendez, C., Schägger, H., and Fuchs, G. 2002.

Malonyl-Coenzyme A reductase from Chloroflexus aurantiacus, a key enzyme of the 3-hydroxproprionate cycle for autotrophic CO2 fixation. Journal of Bacteriology 184(9):2404-2410.

20. Hügler, M., Wirsen, C.O., Fuchs, G., Taylor, C. D., and Sievert, S. M. 2005.

"Evidence for autotrophic CO2 fixation via the reductive tricarboxylic acid cycle

by members of the ε subdivision of proteobacteria." Journal of Bacteriology

21. Inoue, H. and Sugimura, Y. 1985. Carbon isotopic fractionation during the CO2

exchange process between air and sea water under equilibrium and kinetic conditions. Geochimica et Cosmochimica Acta 49:2453-2460.

22. Kaplan, A. and Reinhold, L. 1999. CO2 concentrating mechanisms in

photosynthetic microorganisms. Annual Review of Plant Physiology and Plant

Molecular Biology 50:539-570.

23. Kroopnick, P. M. 1985. The distribution of 13C of ΣCO2 in the world oceans. Deep-Sea Research 32(1):57-84.

24. Kumar, S., Tamura, K., and Nei, M. 2004. Mega 3: Integrated Software for

Molecular Evolutionary Genetic Analysis and Sequence Alignment. Briefings in

Bioinformatics 5:150-163.

25. Lee, B. and Tabita, F. R. 1990. Purification of recombinant

ribulose-1,5-bisphosphate carboxylase/oxygenase large subunits suitable for reconstitution and assembly of active L8S8 enzyme. Biochemistry 29:9352-9357.

26. Lee, K., Choo, Y. -J., Giovannoni, S. J., and Cho, J. -C. 2007. Maritimibacter alkaliphilus gen. nov., sp. nov., a genome-sequenced marine bacterium of the Roseobacter clade in the order Rhodobacterales. International Journal of Systematic and Evolutionary Microbiology 57:1653-1658.

27. Madigan, M. T., Takigiku, R., Lee, R. G., Gest, H., and Hayes, J. M. 1989.

Carbon isotope fractionation by thermophilic phototrophic sulfur bacteria: evidence for autotrophic growth in natural populations. Applied and

Environmental Microbiology 55(3):639-644.

28. McCollom, T. M. and Amend, J. P. 2005. A thermodynamic assessment of

energy requirements for biomass synthesis by chemolithoautotrophic micro-organisms in oxic and anoxic environments. Geobiology 3:135-144.

29. Meckenstock, R. U., Morasch, B., Griebler, C., and Richnow, H. H. 2004.

Stable isotope fractionation analysis as a tool to monitor biodegradation in contaminated acquifers. Journal of Contaminant Hydrology 75:215-255.

30. Mook, W. G., Bommerson, J. C., and Staverman, W. H. 1974. Carbon isotope

fractionation between dissolved bicarbonate and gaseous carbon dioxide. Earth

and Planetary Science Letters 22:169-176.

31. Pardue, J. W., Scalan, R. S., Van Baalen, C., and Parker, P. L. 1976.

Maximum carbon isotope fractionation in photosynthesis by blue-green algae and a green alga. Geochimica et Cosmochimica Acta 40:309-312.

32. Paul, J. H., Alfreider, A., and Wawrik, B. 2000. Micro- and macrodiversity in rbcL sequences in ambient phytoplankton populations from the southeastern Gulf

of Mexico. Marine Ecology Progress Series 198:9-18.

33. Peterson, B. J. and Fry, B. 1987. Stable Isotopes in Ecosystem Studies. Annual Review of Ecology and Systematics 18:293-320.

34. Pichard, S. L., Campbell, L., and Paul, J. H. 1997. Diversity of the ribulose

bisphosphate carboxylase/oxygenase form I gene (rbcL) in natural phytoplankton communities. Applied and Environmental Microbiology 63(9):3600-3606.

35. Qian, Y. and Tabita, F. R. 1996. A global signal transduction system regulates

aerobic and anaerobic CO2 fixation in Rhodobacter sphaeroides. Journal of Bacteriology 178(1):12-18.

36. Robinson, J. J., Scott, K. M., Swanson, S. T., O'Leary, M. H., Horken, K., Tabita, F. R., and Cavanaugh, C. M. 2003. Kinetic isotope effect and

characterization of form II RubisCO from the chemoautotrophic endosymbionts of the hydrothermal vent tubeworm Riftia pachyptila. Limnology and

Oceanography 48(1):48-54.

37. Roeske, C. A. and O'Leary, M. H. 1984. Carbon isotope effects on the

enzyme-catalyzed carboxylation of ribulose bisphosphate. Biochemistry 23:6275-6284. 38. Roeske, C. A. and O'Leary, M. H. 1985. Carbon isotope effect on

carboxylation of ribulose bisphosphate catalyzed by ribulosebisphosphate carboxylase from Rhodospirillum rubrum. Biochemistry 24:1603-1607.

39. Ruby, E. G., Jannasch, H. W., and Deuser, W. G. 1987. Fractionation of stable

40. Russell, M. J. and Martin, W. 2004. The rocky roots of the acetyl-CoA

pathway. Trends in Biochemical Sciences 29(7):358-363.

41. Saurer, M., Siegwolf, R., Borella, S., and Schweingruber, F. 1998.

Environmental information from stable isotopes in tree rings of Fagus sylvatica. In Beniston, M. and Innes, J. L. (eds). The Impacts of Climate Variability on Forests. Germany, Springer Press Berlin. pp.241–254.

42. Schidlowski, M., Hayes, J. M., and Kaplan, I. R. 1983. Isotopic inferences of

ancient biogeochemistries: carbon, sulfur, hydrogen, and nitrogen. In Schopf, J. W. (ed). Earth’s Earliest Biosphere: Its Origin and Evolution: Princeton, New Jersey, Princeton University Press. pp.149–186.

43. Scott, K. M. 2003. A δ13C-based carbon flux model for the hydrothermal vent chemoautotrophic symbiosis Riftia pachyptila predicts sizeable CO2 gradients at

the host-symbiont interface. Environmental Microbiology 5(5):424-432. 44. Scott, K. M., Schwedock, J., Schrag, D. P., and Cavanaugh, C. M. 2004a.

Influence of form IA RubisCO and environmental dissolved inorganic carbon on the δ13C of the clam-chemoautotroph symbiosis Solemya velum. Environmental

Microbiology 6(12):1210-1219.

45. Scott, K. M., Lu, X., Cavanaugh, C. M., and Liu, J. 2004b. Optimal methods

for estimating kinetic isotope effects from different forms of the Rayleigh distillation equation. Geochimica et Cosmochimica Acta 68(3):433-442.

46. Scott, K. M., Henn-Sax, M., Harmer, T. L., Longo, D. L., Frame, C. H., and Cavanaugh, C. M. 2007. Kinetic isotope effect and biochemical characterization

of form IA RubisCO from the marine cyanobacterium Procholorococcus marinus MIT 9313. The American Society of Limnology and Oceanography 52(5):2199-2204.

47. Tabita, F. R. 1988. Molecular and cellular regulation of autotrophic carbon

dioxide fixation in microorganisms. Microbiological Reviews 52(2):155-189. 48. Tabita, F. R. 1999. Microbial ribulose 1,5-bisphosphate carboxylase/oxygenase:

A different perspective. Photosynthesis Research 60:1-28.

49. Tabita, F. R., Satagopan, S., Hanson, T. E., Kreel, N. E., and Scott, S. S.

2008. Distinct form I, II, III, and IV Rubisco proteins from the three kingdoms of life provide clues about Rubisco evolution and structure/function relationships.

Journal of Experimental Botany 59(7):1515-1524.

50. Thauer, R. K. 2007. A fifth pathway of carbon fixation. Science 318:1732-1733.

51. Tripp, H. J., Kitner, J. B., Schwalbach, M. S., Dacey, J. W. H., Wilhelm, L. J., and Giovannoni, S. J. 2008. SAR11 marine bacteria require exogenous

reduced sulphur for growth. Nature 452:741-744.

52. van der Meer, M. T. J., Schouten, S., van Dongen, B. E., Rijpstra, W. I. C., Fuchs, G., Damsté, J. S. S., de Leeuw, J. W., and Ward, D. M. 2001.

Biosynthetic controls on the 13C contents of organic components in the

photoautotrophic bacterium Chloroflexus aurantiacus. The Journal of Biological

Appendix A: Table of RubisCO ε values from Ralstonia eutropha

Table A-1. RubisCO ε values determined from two independent experiments with the IC enzyme from R. eutropha

Experiment # ε value (‰) # of Timepoints

Appendix B: Manuscript on Form ID RubisCO ε value from Emiliania huxleyi

A New Low for RubisCO

(To be submitted to Science)

Amanda J. Boller1, Phaedra J. Thomas1, Colleen M. Cavanaugh2, and Kathleen M. Scott1*

1Biology Department, University of South Florida, Tampa, Florida 33620, USA. 2Department of Organismic and Evolutionary Biology, Harvard University, Cambridge

*To whom correspondence should be addressed. E-mail: [email protected]

The first high-precision measurement of stable carbon isotopic discrimination by form ID RubisCO, responsible for a substantial portion of marine carbon fixation, is reported here and indicates that this enzyme has a shockingly small fractionation factor, providing a mechanistic explanation for 13C-enrichment in marine organic carbon.

Appendix B (Continued) ABSTRACT

The 13C content of carbon is used to identify sources and sinks in the global carbon cycle. One enduring mystery is why the 13C content of marine organic carbon is relatively high. We tested the hypothesis that marine organic carbon 13C enrichment is due to reduced isotopic discrimination during carbon fixation by form ID RubisCOs (ribulose 1,5-bisphosphate carboxylase/oxygenase), found in a substantial portion of marine algae responsible for oceanic carbon fixation. Here, form ID RubisCO from coccolithophore Emiliania huxleyi discriminated substantially less against 13CO2 than

other RubisCO enzymes (ε=11.1‰). Reduced discrimination by form ID RubisCO may be a major factor dictating the high 13C content of marine organic carbon, necessitating re-evaluation of how biological δ13C values are integrated into global primary

Stable carbon isotope analyses are critical to the identification and modeling of global carbon cycle sources and sinks. One key sink is marine carbon fixation (1), and phytoplankton δ13C values (δ13C = [(R

sample/Rstd)-1] x1000, where R=13C/12C) have been

used to untangle trophic links and to estimate in situ growth rates (2). However,

phytoplankton δ13C values vary widely (-16 to -36‰ (2), and the factors responsible for

this variation, as well as their relatively 13C-enriched δ13C values compared to terrestrial

C3 plants (3), are poorly understood. Given the substantial role that phytoplankton play in the global carbon cycle, this conceptual gap compromises not only the interpretation of phytoplankton δ13C values, but also introduces uncertainty into carbon cycle modeling

While some variation in phytoplankton δ13C values is clearly due to carbon

concentrating mechanisms, C4 pathways, and diffusive limitation (5), the effect of isotopic discrimination by different forms of RubisCO (ribulose 1,5-bisphosphate carboxylase/oxygenase), the CO2 fixing enzyme of the Calvin-Benson-Bassham cycle,

has not been widely considered. Instead, δ13C analyses of oceanic primary productivity

typically assume that isotopic discrimination by phytoplankton RubisCO is similar in extent to that of spinach RubisCO (e.g., (6-10). This assumption is untenable, however, as prior studies have shown that different forms of RubisCO discriminate to a greater or lesser extent against 13C (4, 11-16). There are three known forms of RubisCO (I, II, and III), which share as little as 25% in amino acid sequence identity, vary widely in KCO2

and Vmax values, and display dramatic differences in tertiary and quaternary structure (17,

18). Form I enzymes are further subdivided into four subforms (IA – ID), whose amino acid sequences can differ by as much as 40% (17, 18). Marine algae fix carbon using at least four different RubisCO forms: IA in marine Synechococcus and Prochlorococcus spp., IB in algae with green plastids (and terrestrial plants), ID in ‘non-green’ algae (coccolithophores, diatoms, rhodophytes, and some dinoflagellates), and II in peridinin-containing dinoflagellates (19).

Isotopic discrimination, expressed as ε values (ε = (RCO2/Rfixed – 1) x 1000), have

been measured for only three forms of RubisCO using high-precision methods (Table B-1). While values vary considerably, RubisCO forms IA and B (ε = 22 - 29‰)

discriminate to a greater extent than form II (ε = 18 – 22‰) (4, 11-16). Given the prevalence of form ID RubisCO in dominant marine primary producers, many of which are used as model organisms for study in culture, determination of isotope discrimination by this enzyme is critical for the interpretation of environmental and culture δ13C values,

and in turn for global modeling efforts, paleo-oceanography, and other studies using δ13C

values. Here, we characterized and determined the ε value of form ID RubisCO from a model marine alga, the coccolithophore Emiliania huxleyi CCMP 374.

Coccolithophores, whose massive blooms in the North Atlantic and Pacific oceans are visible from space (20, 21), fix CO2 via form ID RubisCO (22) and play a

minute calcium carbonate plates (coccoliths) that cover coccolithophore cells enhance inorganic and organic carbon export from surface waters by ballasting fecal pellets into which they are packed (24, 25). In culture, the biomass δ13C values of Emiliania huxleyi

vary widely, from -9.71 to -38.6‰ (6, 7, 10, 26, 27). While some of this heterogeneity is likely due to variation in study strains and growth conditions, these δ13C values cannot be

rigorously interpreted without a RubisCO ε value.

Table B-1. ε values of different RubisCO forms, *ε = RCO2/Rfixed – 1) x 1000

Form Organisms ε value (‰)* Reference

IB Terrestrial plants and

freshwater cyanobacteria 21 – 30.3 (11, 13, 15) ID Marine coccolithophore Emiliana huxleyi 11.1 This study II Alpha- and gammaproteobacteria 17.8 – 23.0 (12-15)

RubisCO KCO2 and ε values

In order to obtain sufficient Emiliana huxleyi Form ID RubisCO for determination of kinetic parameters and isotope fractionation (

20 mg per ε value experiment), 100 L of

E. huxleyi CCMP 374 cultures were harvested and RubisCO was partially purified from

due exclusively to RubisCO based on incubations in the presence and absence of the substrate ribulose 1,5-bisphosphate (RuBP (28). To characterize E. huxleyi RubisCO activity, its Michaelis-Menten constants (KCO2 and Vmax) were measured radiometrically

as in (29). The ε value of E. huxleyi RubisCO was measured by the high-precision substrate depletion method, in which the concentration and isotopic composition of dissolved inorganic carbon are measured as they are consumed by RubisCO (4, 16, 28). The ε value was calculated using a modified version of the Rayleigh distillation equation and Pitman estimators to calculate the least-biased average (28, 30).

E. huxleyi RubisCO Michaelis-Menten constants (KCO2 = 111 +/- 40 μM Vmax =

146 +/- 102 nmol/min×mg, calculated from five independent experiments) were quite different than those measured for other form ID enzymes from diatoms and rhodophytes (KCO2 = 5 – 60 μM Vmax = 670 – 1670 nmol/min×mg (31, 32). The larger KCO2 value

for the Emiliania huxleyi RubisCO may necessitate a carbon concentrating mechanism with active transport of dissolved inorganic carbon into the cells, since the concentration of CO2 in seawater is only

20 μM (33). The lower Vmax value reflects the labile nature

E. huxleyi form ID RubisCO had an astonishingly low ε value of 11.1‰ (95% CI:

9.8 - 12.6‰ Figure B-1), substantially smaller than any other RubisCO ε measured to date (Table B-1). ε values from replicate experiments were very consistent, falling within a 3.1‰ range (Figure B-1). Spinach form IB RubisCO, used as a control and

incubated under identical conditions to those used for the E. huxleyi enzyme, had an ε value of 27.5‰ (95% CI: 24.0 – 30.9‰ Figure B-1), similar to previously reported values (4, 11, 13). This is the first high-precision ε value to be measured for any eukaryotic algae of ecosystem-level importance the peculiarity of its ε value highlights how little we know about the RubisCO enzymes responsible for marine primary

Figure B-1. Isotope fractionation by E. huxleyi RubisCO. R is the isotope ratio (13C/12C) of dissolved inorganic carbon (DIC) and [DIC] is its concentration. Solid symbols (▲, ♦, ■, and ●) correspond to independent incubations of E. huxleyi RubisCO, with ε = 10.8‰,

11.1‰, 11.8‰, and 13.9‰, respectively. Open symbols (□, ○, and ∆) correspond to independent incubations of spinach RubisCO, with ε = 28.3‰, 28.2‰, and 27.2‰,

Implications for interpreting δ13C values from environmental samples and phytoplankton cultures

This remarkably small RubisCO ε value (11.1‰) suggests a novel explanation for the isotopically enriched δ13C values typically observed for marine phytoplankton and

the food webs they support. An enzymatic basis for these values must now be

considered. Indeed, phytoplankton collected from environmental samples demonstrate whole-cell isotope discrimination values (εp, = [RCO2/Rbiomass – 1] × 1000) that span this

RubisCO ε value. Samples collected from marine environments worldwide have values of εp ranging from 7 to 19‰ (7, 34, 35). For environmental samples dominated by E. huxleyi, reported εp values are 0-14‰ (calculated directly from biomass (36) and 7-19‰

(calculated from E. huxleyi alkanones, assuming a constant fractionation between alkanones and biomass (7). Using the (incorrect) assumption of a RubisCO ε value of 29‰, these εp values suggest the isotope-enriching effects of carbon concentrating

mechanisms or C4 pathways. However, given the small RubisCO ε value measured here, it is not necessary to invoke these mechanisms to explain 13C-enriched biomass.

Similar to ocean samples, εp values from E. huxleyi culture studies are small and

reasonably consistent with the measured RubisCO ε value. In many of these studies, the δ13Cof dissolved inorganic carbon (δ13C

DIC), and not the δ13CCO2, is reported. Therefore,

to calculate εp from these culture studies, the equilibrium isotope effect between

Appendix B (Continued) used to calculate the δ13C

CO2 from δ13CDIC. Based on these studies, εp ranges from

2-18‰ for Emiliania huxleyi (6, 10, 38, 39). One potential factor that could be influencing the εp value is the form of inorganic carbon transported, since there is an equilibrium

isotope effect between HCO3- and CO2, which causes CO2 to be isotopically depleted

relative to HCO3- (37). It is clear that E. huxleyi alters its pattern of inorganic carbon

uptake in response to growth conditions (40). Perhaps smaller εp values are the result of

reliance on extracellular HCO3-, while larger εp values may result from CO2 utilization.

Deciphering the form(s) of dissolved inorganic carbon taken up by this organism under different growth conditions is instrumental in developing a mechanistic understanding for the changes in dissolved inorganic carbon abundance and composition that accompany E.

huxleyi blooms, which in turn determine whether these blooms are sources or sinks of

atmospheric and dissolved CO2.

The unexpectedly low ε value of E. huxleyi RubisCO highlights the necessity of collecting RubisCO ε values from other organisms, in order to uncover any phylogenetic patterns in isotope discrimination. At this point, given the small numbers of enzymes examined (2 form IA’s 2 form IB’s 1 form ID 2 form II’s) it is irresponsible to suggest ‘typical’ ε values for different forms of RubisCO, or even for RubisCO enzymes in general. Particularly with respect to the interpretation of marine δ13C values, and to

refine models of the global carbon cycle that rely on these δ13C values, it is necessary to

importance (e.g., diatoms, rhodophytes) have low ε values. Factors such as carbon concentrating mechanisms, inorganic carbon supply and demand, and C4 pathways

clearly exert an influence on the δ13C values of algal biomass (38, 41-43), but more

1. W. H. Schlesinger. 1997. in Biogeochemistry: An analysis of global change W.

H. Schlesinger, Ed. (Academic Press, San Diego) pp. 291-341.

2. K. H. Freeman. 2001. in Stable Isotope Geochemistry J. W. Valley, D. R. Cole,

Eds. (Mineralogical Society of America, Washington, DC) pp. 579-605. 3. R. H. Michener, D.M. Schell. 1994. in Stable isotopes in ecology and

environmental science K. Lajtha, R.H. Michener, Ed. (Blackwell Scientific

Publications, Cambridge, MA) pp. 138-157.

4. K. M. Scott, J. Schwedock, D. P. Schrag, C. M. Cavanaugh. 2004. Environ Microbiol 6:1210.

5. R. Goericke, J. P. Montoya, B. Fry. 1994. in Stable Isotopes in Ecology and Environmental Science K. Lajtha, R. H. Michener, Eds. (Blackwell Scientific

Publications, Boston) pp. 187-221.

6. P. A. Thompson, S. E. Calvert. 1995. Limnol Oceanogr 40:673.

7. R. R. Bidigare et al. 1997. Global biogeochemical cycles 11:279.

8. E. A. Laws, P. A. Thompson, B. N. Popp, R. R. Bidigare. 1998. Limnology and oceanography 43:136.

9. E. A. Laws, B. N. Popp, N. Cassar, J. Tanimoto. 2002. Functional Plant Biology 29:323.

10. B. Rost, I. Zondervan, U. Riebesell. 2002. Limnol Oceanogr 47:120.

11. C. A. Roeske, M. H. O'Leary. 1984. Biochemistry 23:6275.

12. C. A. Roeske, M. H. O'Leary. 1985. Biochemistry 24:1603.

13. R. D. Guy, M. L. Fogel, J. A. Berry. 1993. Plant Physiol 101:37.

14. J. J. Robinson et al. 2003. Limnol Oceanogr 48:48.

15. D. B. McNevin, M. R. Badger, H. J. Kane, G. D. Farquhar. 2006. Funct. Plant Biol 33: 1115.

16. K. M. Scott, M. Henn-Sax, D. Longo, C. M. Cavanaugh. 2007. Limnol Oceanogr 52:2199.

17. G. M. Watson, F. R. Tabita. 1997. FEMS Microbiol Lett 146:13.

18. F. R. Tabita et al. 2007. Microbiol and Molec Biol Rev 71:576.

19. F. R. Tabita. 1999. Photosynth Res 60:1.

20. Y. Dandonneau, Y. Montel, J. Blanchot, J. Giraudeau, J. Neveux. 2006. Deep-Sea Res. I 53:689.

21. H. Siegel, T. Ohde, M. Gerth, G. Lavik, T. Leipe. 2007. Continental Shelf Res. 27:258.

22. M. V. S. Puerta, T. R. Bachvaroff, C. F. Delwiche. 2005. DNA Research 12:151.


Inhaltsverzeichnis

Die irdische Biosphäre umschreibt den Raum des Planeten Erde, in dem Leben vorkommt. [4] Dabei ist Leben darauf angewiesen, mit seiner Umwelt zu wechselwirken. Um zu überleben, müssen die Lebewesen Stoffe und Energie mit ihrer unbelebten Umwelt und untereinander austauschen. Sie müssen sogenannte Ökosysteme bilden. Dies ist eine grundsätzliche Eigenschaft von Lebewesen. [5] Ohne ökosystemare Wechselwirkungen wäre Leben nicht möglich. Deshalb verändert das Leben zwingend die Ausstattung des Raums, in dem es sich ansiedelt. Da sich Lebewesen weltweit angesiedelt haben, kann die Biosphäre als der Raum eines weltumspannenden Ökosystems begriffen werden.

  • Die irdische Biosphäre umschreibt den Raum des Planeten Erde, in dem Leben vorkommt: Der Raum zusammen mit der darin vorkommenden Gesamtheit der irdischen Organismen und ihrer unbelebten Umwelt und der Wechselwirkungen der Lebewesen untereinander und mit ihrer unbelebten Umwelt.

Das Vorhandensein eines globalen Ökosystems wurde das erste Mal vom russischen Geowissenschaftler Wladimir Iwanowitsch Wernadski erkannt. Um es zu benennen, verwendete er ein Wort, das zuvor vom österreichischen Geologen Eduard Suess erfunden worden war: Biosphäre. [6]

Die Biosphäre kann in drei große Untereinheiten gegliedert werden. Die Tiefe Biosphäre bezeichnet die Ökosysteme der Lithosphäre unterhalb von Erdoberfläche und Böden. [7] Die Hydrobiosphäre umschreibt die von Lebewesen besiedelten und beeinflussten Anteile der Gewässer. [8] Als dritte Untereinheit bezeichnet die Geobiosphäre die von Lebewesen besiedelten und beeinflussten Anteile der Festländer. [9] [10] Weil mit dem gleichen Wort manchmal auch die gesamte Biosphäre benannt wird, birgt Geobiosphäre eine Missverständnismöglichkeit. [11]

Biosphäre der Mikroorganismen Bearbeiten

Die Biosphäre reicht hinauf bis in den unteren Rand der Mesosphäre. Innerhalb des biosphärischen Raums können die Umweltbedingungen stark unterschiedlich ausfallen. Deshalb können nicht alle Bereiche der Biosphäre von allen Lebewesen gleich gut besiedelt werden. [14] Gerade vielzellige Organismen (Metabionta) können dauerhaft – und natürlich in Gesellschaft mit vielen Mikroorganismen – nur in Regionen gedeihen, in denen verhältnismäßig milde Temperaturen, Drücke, Strahlungswerte, pH-Werte und Ähnliches herrschen und in denen ausreichende Angebote an Wasser und Ernährungsmöglichkeiten bestehen.

Dementgegen werden in den biosphärischen Außenzonen die Umweltbedingungen zunehmend extremer. Dort können ausschließlich Mikroorganismen existieren. Bei noch harscheren Umweltbedingungen können selbst solche widerstandsfähigen Mikroben bloß in Dauerstadien bestehen. Die Dauerstadien markieren die Außengrenzen der Biosphäre.

  • Die irdische Biosphäre umschreibt den Raum des Planeten Erde, in dem Mikroorganismen vorkommen. [15]

Biosphäre der Biome Bearbeiten

Lebewesen bilden miteinander Biozönosen (Lebensgemeinschaften). Unter den Mitgliedern einer Biozönose bestehen vielfältige wechselseitige Beziehungen, die als Biotische Ökofaktoren zusammengefasst werden. [16] Biozönosen bewohnen miteinander Physiotope (stoffliche Orte). Ein Physiotop ist ein kleiner Raumausschnitt mit einem homogenen Aussehen, der sich durch ein bestimmtes, einheitliches Physiosystem (Standort) auszeichnet. [17] Mit Physiosystem wird die Gesamtheit der in einem Physiotop ausgeprägten abiotischen Ökofaktoren bezeichnet. [18] [19] [20]

Die Mitglieder der Biozönose wechselwirken untereinander und mit ihrem Physiosystem. [21] Sie bilden ein gemeinsames Wirkungsgefüge. [5] Dieses Wirkungsgefüge heißt Ökosystem. [4] [22]

Ein Ökosystem ist ein System, ein Verband aus miteinander wechselwirkenden Einheiten. Die Systemeinheiten des Ökosystems bestehen einerseits aus den Lebewesen der Biozönose und andererseits aus den unbelebten Dingen des Physiosystems. [23] Ein Ökosystem ist ein offenes System: [22] Stoffe und Energie dringen von Außen in das Ökosystem ein, zirkulieren für eine gewisse Zeit zwischen den Systemeinheiten und verlassen es schließlich wieder. [24]

Ein bestimmtes Physiosystem lässt nur eine bestimmte Biozönose aus solchen Lebensformen zu, die an es angepasst sind. Allerdings verändert die Biozönose allmählich die Ausprägung der abiotischen Ökofaktoren des Physiosystems. [17] [25] Durch die Biozönose wandelt sich das Physiotop zum Ökotop. [26] Das Ökotop bezeichnet einen echten Ort im realen Raum. Es ist das stoffliche Pendant zum Ökosystem-Begriff, der selbst rein funktional-abstrakt gedacht wird. [27]

Ökotope bilden mit ähnlichen Nachbar-Ökotopen gemeinsame Ökochoren. [28] Ökochoren bilden mit ähnlichen Nachbar-Ökochoren gemeinsame Ökoregionen. Ökoregionen bilden mit ähnlichen Nachbar-Ökoregionen gemeinsame Ökozonen. Der WWF unterscheidet weltweit 825 terrestrische Ökoregionen, die sich auf 14 Haupt-Biome verteilen. [29] Dazu treten 426 Ökoregionen des Süßwassers [30] und 232 Ökoregionen der Meere. [31]

Jedes Lebewesen ist Teil einer Ökoregion. Das gilt auch, selbst wenn zum gegenwärtigen Zeitpunkt noch nicht die Ökoregionen für alle aquatischen und erst recht nicht für die rein mikrobiell besiedelten Bereiche der Biosphäre benannt worden sind. Nach der klassischen Definition formt die Biozönose einer Ökoregion ihr Biom. [33]

Ähnliche Begriffe Bearbeiten

Der Biosphäre-Begriff wird von verschiedenen naturwissenschaftlichen Disziplinen unterschiedlich verstanden. Innerhalb der Biowissenschaften konnte sich zwar der ökologische Biosphäre-Begriff inzwischen weitgehend durchsetzen, der von Wladimir Iwanowitsch Wernadski erfunden worden war. Das gelang ihm allerdings nicht bei den Geowissenschaften. Sie verwenden bis heute mehrheitlich einen Biosphäre-Begriff, der auf den französischen Jesuiten Pierre Teilhard de Chardin zurückgeht. Teilhard de Chardin verstand unter der Biosphäre ausschließlich die Gesamtheit der irdischen Organismen. Demzufolge prägte er einen rein biotischen Biosphäre-Begriff.

Weiterhin existieren neben dem ökologischen Biosphäre-Begriff eine Reihe ähnlicher Begriffe. Einige sind mit ihm inhaltlich deckungsgleich. Sie heißen Biogeosphäre, Geobiosphäre und Ökosphäre. Während die Ausdrücke Biogeosphäre und Geobiosphäre vergleichsweise selten vorkommen, wird das Wort Ökosphäre häufig verwendet. Tatsächlich wird Ökosphäre von einigen Autoren für geeigneter als Biosphäre gehalten, um den Raum des globalen Ökosystems zu bezeichnen. [50] [51]

Darüber hinaus existiert eine weitere Gruppe von Begriffen im Umfeld des ökologischen Biosphäre-Begriffs. Sie sind jedoch inhaltlich mit ihm nicht vollkommen deckungsgleich. Stattdessen gehen sie über ihn hinaus, indem sie weitere Anteile der Erde mit einfassen. Es handelt sich um die Begriffe Gaia, System Erde und Bioplanet Erde.

Die hüllenartige Biosphäre beginnt etwa 60 km über der Erdoberfläche und endet ungefähr 5 km unter der Erdoberfläche. Sie fängt an im unteren Saum der Mesosphäre, durchzieht die übrigen, darunter liegenden Schichten der Erdatmosphäre und die oberen Anteile der Hydrosphäre, durchwirkt die Pedosphäre und endet im oberen Abschnitt der Lithosphäre, nach wenigen Kilometern in der Erdkruste. Zumindest wenn auch auf Mikroorganismen geachtet wird, erstreckt sich die Biosphäre über die gesamte Erdoberfläche, die Meere und Meeresgründe.

Vertikale Erstreckung Bearbeiten

Gemäß dem derzeitigen Kenntnisstand befindet sich die obere Begrenzung der irdischen Biosphäre leicht oberhalb der Stratopause, in der untersten Mesosphäre bei 60 km Höhe. [52] Dort kommen noch immer bestimmte Mikroorganismen in Dauerstadien vor. [53] [54] [12] In diesen atmosphärischen Höhen trotzen sie den geringen Temperaturen, die von etwa −50 °C (untere Stratosphäre) bis ungefähr 0 °C (untere Mesosphäre) reichen, [55] sowie dem fast vollständigen Wassermangel [56] und der starken Ultraviolettstrahlung. Gegenwärtig wird davon ausgegangen, dass die gefundenen Mikroorganismen nicht ihren gesamten Lebenszyklus so fern von der Erdoberfläche durchlaufen. Stattdessen sollen sie nur auf verschiedenen Wegen aus der Erdoberflächennähe hinauf gewirbelt werden und dann einige Zeit in der Stratosphäre und untersten Mesosphäre verbleiben. [57]

Unterhalb der Stratosphäre befindet sich die Troposphäre, die dichteste und unterste Erdatmosphärenschicht. Hier besitzt die Luft dank des natürlichen Treibhauseffekts [58] höhere Lufttemperaturen und ist wegen der darüber liegenden, stratosphärischen Ozonschicht [59] verhältnismäßig strahlungsarm. Aus diesen Gründen befinden sich die Lebensräume der terrestrischen Lebewesen in der Troposphäre, temperaturbedingt meistens sogar bloß unterhalb der nivalen Höhenstufe. [60]

Unterhalb der Troposphäre schließen sich einerseits die Böden der Pedosphäre und andererseits die Gewässer der Hydrosphäre an. Die Böden werden von vielfältigen Bodenlebewesen bewohnt. Ihr Lebensraum wird nach Unten hin begrenzt durch das Angebot von Bodenwasser und Bodenluft, wobei Mikroorganismen am tiefsten vordringen. [61] Intakte, aber eingefrorene Mikroorganismen finden sich selbst noch tief im Permafrost. [62] [63] In den Gewässern existieren Lebensformen bis zum Grund und noch einmal viele Meter in den schlammigen Gewässergrund hinein. [64] Tatsächlich kommt ein größerer Anteil der Gesamtbiomasse der Erde in Form von Archaeen und Bakterien in Ozeansedimenten vor. [65] Die auffälligeren Mitglieder des Wasserlebens halten sich aber in den oberen und lichtdurchfluteten Wasserschichten des Epipelagial auf. Jenseits davon können die Arten- und Individuendichten sehr gering werden. Das gilt insbesondere für die Tiefsee. Ihre kalte Dunkelheit wird allerdings von Vulkaninseln und Atollen unterbrochen, die bis über die Wasseroberfläche aufragen. Unterseeisch bieten Guyots und Seamounts vielen Organismen Lebensräume, [66] [67] einige dieser Unterseeberge können bis in das Epipelagial aufragen. Weltweit gesehen kommen Seamounts sehr häufig vor und nehmen insgesamt eine Fläche von der Größe Europas ein. [68] Zusammen genommen formen sie wahrscheinlich eines der größeren Hauptbiome. [69] Je nach Wassertiefe können sich an Vulkaninseln, Atollen, Seamounts und Guyots vielfältige Lebensgemeinschaften einfinden, die auf diese Weise das Wüstenhafte des tiefen Meeres unterbrechen.

Unterhalb der Böden und schlammigen Gewässergründe schließen sich die Gesteine der Lithosphäre an. Hier wurden in Höhlen einfache Höhlen-Ökosysteme gefunden, die aus Mikroorganismen und einigen mehrzelligen Organismen bestehen. [70] [71] Alle anderen Lebensgemeinschaften der Lithosphäre bestehen ausschließlich aus Mikroorganismen. Einige leben in Erdöllagerstätten, [72] [73] [74] [75] [76] Kohleflözen, [77] Gashydraten, [78] in tiefen Aquiferen [79] [80] oder in feinen Poren direkt im Festgestein. Weiterhin kommen zumindest bestimmte mikrobielle Dauerstadien auch in Salzstöcken vor. [81] [82] Es kann angenommen werden, dass sich die Biosphäre in der Lithosphäre bis zu jener Tiefe hinab zieht, ab der die Umgebungstemperatur geothermisch über 150 °C steigt. Ab dieser Temperatur sollte es selbst für hyperthermophile Mikroben endgültig zu heiß werden. [83] [84] Dabei wird als Faustregel angenommen, dass die Umgebungstemperatur um 3 °C pro 100 Meter Tiefe zunimmt. Demnach müsste die Biosphäre in ungefähr 5 km Lithosphärentiefe enden. [85] Allerdings gibt es von dieser Faustregel starke regionale Abweichungen. [86]

Mikrobielle Ökosysteme können sich auch in subglazialen Seen erhalten, die durch das überlagernde Gletschereis vollständig von der Umgebung abgeschottet wurden. [87] Mikroorganismen werden auch tief im Gletschereis selbst gefunden. Dabei bleibt bisher unklar, inwieweit sie dort nur überdauern oder aktive Lebensprozesse zeigen. [88] Forscher des Deep Carbon Observatory-Teams der Oregon State University schätzten 2018, dass rund 70 Prozent der Gesamtzahl der Bakterien und Archaeen der Erde in der Erdkruste leben. [89] [90]

Horizontale Erstreckung Bearbeiten

Die Lebewesen verteilen sich nicht gleichmäßig über die Biosphäre. Zum einen gibt es Biome mit großen Arten- und Individuendichten. Dazu zählen zum Beispiel die Tropischen Regenwälder und Korallenriffe. Zum anderen gibt es aber auch Bereiche mit sehr spärlichem makroskopischen und eingeschränktem mikroskopischen Leben. Dazu zählen auf dem Land die Kältewüsten und Trockenwüsten und in den Meeren die Meeresböden der lichtlosen und kalten Tiefsee (Bathyal, Abyssal, Hadal). Allerdings sind innerhalb der wüsten Gebiete inselhafte Stellen höherer Biodiversität eingestreut: Wasseroasen in den Trockenwüsten, postvulkanische Erscheinungen (Thermalquellen, Solfataren, Fumarolen, Mofetten) in den Kältewüsten, [91] sowie Hydrothermalquellen (Black Smokers, White Smokers) [92] [93] [94] und Methanquellen (Cold Seeps) [95] [96] auf den Meeresböden der Tiefsee.

Bloß eine dünne Hülle der Erde ist Raum mit Leben. Gemessen am irdischen Gesamtvolumen besitzt die Biosphäre nur einen winzigen Rauminhalt. Denn irdische Organismen haben bestimmte Ansprüche an ihre abiotische Umwelt. Die meisten Bereiche der Erde können den Ansprüchen nicht genügen.

Die Ansprüche der Lebewesen beginnen beim Platzbedarf. Sie können sich nur an Orten aufhalten, die genügend Raum für ihre Körpergrößen bereitstellen. Wenn genügend Platz vorhanden ist, muss der Ort auch noch geeignete Möglichkeiten des Im-Raum-Aufhaltens bieten. Welche Möglichkeiten geeignet sind, unterscheidet sich von Lebensform zu Lebensform. So benötigen Bäume genügend Wurzelraum und Tang Anheftungsstellen am Meeresgrund, während Phytoplankter schon mit dem freien Wasserkörper auskommen. Die Ansprüche an den Aufenthaltsort können sich saisonal und lebensalterabhängig wandeln.

Beispiel: Erwachsene Königsalbatrosse brauchen einigen Platz für ihre drei Meter breiten Flügel. Sie durchstreifen die niedrigen Luftschichten über dem offenen Ozean. Dort erbeuten sie hauptsächlich Tintenschnecken, trinken Meerwasser, schlafen im Flug oder ruhen schwimmend auf der Meeresoberfläche. Erwachsene Königsalbatrosse benötigen keine feste Ansiedlungsmöglichkeit. Das ändert sich allerdings saisonal. Denn sie fliegen alle zwei Jahre das Festland an. Dort balzen sie, besetzen einen Brutplatz, bebrüten ihr eines Ei 79 Tage lang und beschützen die noch sehr wehrlosen Jungvögel in den ersten fünf Lebenswochen. Danach fliegen die Elternvögel wieder hinaus auf das Meer. Sie kehren jedoch in unregelmäßigen Abständen zum Brutplatz zurück, um die Jungvögel zu füttern. Die Jungvögel müssen an Land ausharren, bis sie nach 236 Tagen flügge werden und den Eltern nachfolgen: Die Ansprüche der Königsalbatrosse an ihren Aufenthaltsort in der Biosphäre wechseln saisonal und mit dem Lebensalter. [97]

Weiterhin müssen sich am Aufenthaltsort die abiotischen Ökofaktoren (Physiosystem, Standort) in Bandbreiten bewegen, die irdischen Lebensformen erträglich sind. Dies gilt in herausragender Weise für die Angebote von thermischer Energie und Flüssigwasser und nachgeordnet für die übrigen abiotischen Ökofaktoren. Darüber hinaus muss der Aufenthaltsort auch die Ernährung der Lebewesen gewährleisten. Autotrophe Organismen müssen ausreichend Baustoffe (Nährsalze) und heterotrophe Organismen ausreichend Nährstoffe vorfinden.

Im Lauf der Erdgeschichte haben die Lebensformen sehr unterschiedliche Körpergrößen, Ansiedlungsmethoden, Physiosystemansprüche und Ernährungsweisen evolviert. Nun herrschen innerhalb der Biosphäre nicht überall die gleichen Bedingungen. Deshalb kommt kein Lebewesen an allen Orten der Biosphäre vor. Lebensformen mit ähnlichen oder sich ergänzenden Angepasstheiten finden sich zusammen am gleichen Aufenthaltsort. Gemeinsam bilden sie Ökoregionen (Eu-Biome) und Ökozonen (Zonobiome).

Die Lage der Ökozonen des Festlands richtet sich nach dem Großklima. [38] [98] Das Großklima ist abhängig vom Breitengrad (→ Beleuchtungszonen), von der Entfernung zum Meer (→ Ozeanität / Kontinentalität) und eventuell von hohen Gebirgen, die Niederschläge abhalten (→ Klimascheide). Insgesamt verlaufen die Ökozonen ungefähr breitenkreisparallel. [99]

Die Lage der Ökozonen der Ozeane (realms) richtet sich nach der oberflächennahen Wassertemperatur. Zudem ist zu berücksichtigen, dass für viele Meeresorganismen die Küsten der Kontinente oder die schiere Weite der Ozeane Barrieren darstellen, die sie in ihrer Ausbreitung einschränken. Weltweit werden insgesamt zwölf marine Ökozonen unterschieden. Innerhalb einer marinen Ökozöne befinden sich gleichsam wüstenhafte Ökoregionen neben Ökoregionen großer organismischer Fülle. [31] Das liegt daran, dass nicht überall in den Meeren die gleichen trophischen Bedingungen herrschen: Nur in den Meeresabschnitten mit reichem Baustoffangebot kann Phytoplankton umfangreich gedeihen. Das Phytoplankton steht an der Basis der marinen Nahrungsnetze. Folglich kommen dort auch die übrigen marinen Lebensformen besonders zahlreich vor. Meeresabschnitte mit hohen Baustoffkonzentrationen sind Gebiete des Upwelling, in denen baustoffreiches Tiefenwasser zur Wasseroberfläche aufsteigt. [100] Große Mengen Walkot können einen ähnlichen Effekt erzeugen (whale pump). [101]

Organismischer Aufbau Bearbeiten

Der Umfang der Biosphäre wird in erster Linie durch Mikroorganismen bestimmt. An den Außengrenzen der Biosphäre werden ausschließlich Dauerstadien von Mikroben gefunden, die gegen unwirtliche Bedingungen gefeit sind. Das gilt für Mesosphäre und Stratosphäre [52] genauso wie für Permafrostböden, [63] [102] Salzstöcke [81] und tiefes Gletschereis. [88] Aber auch innerhalb der biosphärischen Grenzen können viele Ökosysteme gefunden werden, die ausschließlich aus Mikroorganismen bestehen. Dies gilt für alle Lebensgemeinschaften innerhalb der Lithosphäre, also für Lagerstätten von Erdöl, [76] Kohle [77] und Gashydrat [78] genauso wie für tiefe Aquifere, [80] tiefere Meeressedimentschichten [64] und für Ökosysteme im schlichten Festgestein. [85] Die Mikroorganismen halten zudem alle Räume besetzt, die auch von Vielzellern bewohnt werden. Sie leben sogar auf und in diesen Metabionten, auf Haut [103] [104] und Rhizosphäre [105] genauso wie auf Blättern [106] und in Verdauungstrakten. [107] [108] Die irdische Biosphäre erweist sich überall und gerade in ihren extremeren Bereichen als Sphäre der Mikroorganismen. Im Vergleich dazu erscheint das Habitat der Metabionten sehr eingeschränkt.

Trophischer Aufbau Bearbeiten

Genau genommen besteht die Biosphäre aus vielen Ökosystemen, die mehr oder weniger eng miteinander verzahnt sind. In jedem Ökosystem erfüllen die Lebewesen eine von drei verschiedenen trophischen Funktionen: Primärproduzenten – auch Autotrophe genannt – bauen Biomasse aus energiearmen Baustoffen auf. Diese Biomasse wird daraufhin von Konsumenten gefressen. Während der Produktion und der Konsumation fällt umfangreich Bestandsabfall an. Der Bestandsabfall wird von Organismen der dritten trophischen Funktion, den Destruenten, abgebaut bis zurück zu den energiearmen Baustoffen. Die Baustoffe können anschließend wieder von den Primärproduzenten zum Aufbau neuer Biomasse verwendet werden.

Die Existenz der Konsumenten und Destruenten ist abhängig vom Vorhandensein der Primärproduzenten. Vollständige Ökosysteme können sich nur an Stellen ausbilden, an denen Primärproduzenten geeignete Lebensbedingungen finden. Das gilt letztlich für die gesamte Biosphäre. Ausdehnung und Existenz der gesamten Biosphäre ist raumzeitlich abhängig vom Vorhandensein der Primärproduzenten.

Die auffälligsten und wichtigsten Primärproduzenten der irdischen Biosphäre sind die photoautotrophen Organismen. Sie betreiben Photosynthese, um mit Hilfe von Licht ihre Biomasse aus energiearmen Baustoffen herzustellen. Zu den bekanntesten photoautotrophen Organismen gehören Landpflanzen und Algen (→ phototrophe Organismen), wobei mehr als 99 % der gesamten pflanzlichen Biomasse von Landpflanzen erarbeitet wird. [109] Die photoautotrophe Primärproduktion der Meere wird hauptsächlich durch nicht-kalkbildende Haptophyten und Cyanobakterien geleistet. [110]

Photoautotrophe Organismen stehen an der Basis vieler irdischer Ökosysteme. Die Biosphäre zeigt ihre art- und individuenreichsten Ökosysteme an Standorten, an denen Pflanzen oder andere photoautotrophe Lebensformen existieren können. Auf dem Land an Orten, zu denen Tageslicht gelangt, die aber außerhalb der Kältewüsten, außerhalb der Trockenwüsten und unterhalb der nivalen Höhenstufe liegen. Im Wasser in der euphotischen Zone des Epipelagials.

Jenseits der Bereiche mit Tageslicht können sich langfristig nur dann Lebensgemeinschaften etablieren, wenn sich ihre phototrophen Primärproduzenten allein mit dem spärlichen Glimmen aus vulkanischen Tätigkeiten begnügen [111] – oder wenn sie vollständig unabhängig von photoautotroph erzeugter Biomasse werden. An der Basis solcher völlig lichtunabhängigen Ökosysteme stehen dann chemoautotrophe Primärproduzenten. Chemoautotrophe Organismen bauen ihre Biomasse ebenfalls aus energiearmen Baustoffen. Sie gewinnen die dazu nötige Energie aber nicht aus Licht, sondern aus bestimmten chemischen Reaktionen. Zu den Ökosystemen, die auf chemoautotrophen Primärproduzenten bauen, gehören Hydrothermalquellen (Black Smokers, White Smokers), Methanquellen (Cold Seeps), subglaziale Seen, vollständig von der Außenwelt abgeschottete Höhlen [112] [71] und verschiedene mikrobielle Ökosysteme tief im Festgestein (→ Endolithe). [113]

Zur Biosphäre zählen aber auch noch Räume, die nicht unmittelbar zu den photoautotroph oder chemoautotroph unterhaltenen Ökosystemen gehören. Sie liegen stattdessen zwischen und außerhalb von ihnen. Wegen ungünstiger Lebensbedingungen können die Räume nicht von Primärproduzenten besiedelt werden. Diese unwirtlichen Bereiche können allerdings zeitweilig von Konsumenten in Besitz genommen werden, die anschließend wieder in autotroph unterhaltene Ökosysteme zurückkehren.

Beispiel: Viele Zugvögel passieren auf ihren jährlichen Wanderungen Erdräume mit äußerst spärlichem autotrophen Leben. So durchfliegen Weißstörche die Trockenwüste Sahara. [114] Streifengänse überqueren den vegetationsfreien Hauptkamm des Himalaya. [115] Beide Vogelarten wählen ihre Winter- und Brutgebiete jedoch wieder in Lebensräumen, die von Pflanzen besiedelt sind. Sie bleiben also nur vorübergehend außerhalb photoautotroph unterhaltener Ökosysteme.

Dem jährlichen Vogelzug ähnelt die diel vertical migration: Tageszeitenabhängig wandern viele Wasserorganismen zwischen Epipelagial und den darunter liegenden, lichtarmen Wasserschichten hin und her. Einige Vertreter des Phytoplanktons wandern des Nachts abwärts, um sich Baustoffe in den tieferen Wasserschichten anzueignen. Zum Tagesanbruch kehren sie zur Wasseroberfläche zurück. [116] [117] Gleichzeitig verläuft eine gegenläufige Bewegung von Zooplankton und einigen größeren Tieren. Sie schwimmen im Schutz der Dunkelheit gen Wasseroberfläche, um dort Beute zu machen, und kehren bei Tagesanbruch in die Tiefe zurück, um selbst vor größeren Beutegreifern sicher zu sein. [118] [119]

Außerdem fließt ständig aus den autotroph unterhaltenen Ökosystemen Bestandsabfall ab. Der Bestandsabfall kann von Destruenten auch jenseits der eigentlichen Grenzen jener Ökosysteme noch verwertet werden. Auf diese Weise können Ökosysteme entstehen – und so die Biosphäre ausweiten – die nicht direkt auf anwesenden Primärproduzenten, sondern auf abgeflossenen Bestandsabfällen basieren. Typische Beispiele für solche Ökosysteme sind die Böden, auf die ständig vielfältige Bestandsabfälle terrestrischer Lebewesen fallen. Aber auch Gewässergründe und tiefere Wasserschichten unterhalb der euphotischen Zone gehören dazu, zu denen Bestandsabfall aus dem Epipelagial und von den Ufern herab rieselt. [120] Besonders erwähnenswert sind hierzu die whale falls: Tote Wale sinken hinab auf den Meeresgrund und liefern umfangreiche Mengen an verwertbarem Bestandsabfall für die Tiefseebewohner. [121] [122] Die Walkadaver dienen dabei auch als Zwischenstationen für Tiefseeorganismen auf ihren Wanderungen zwischen den chemoautotroph basierten Ökosystemen der weit gestreuten Hydrothermalquellen (Smokers) und Methanquellen (Cold Seeps). [123] Der Abbau von Bestandsabfall im Meer geschieht in niedrigeren Raten selbst noch in den sauerstoffarmen Zonen (oxygen minimum zones) durch entsprechend angepasste Organismen. [124] Neben Böden und lichtfernen Gewässergründen zählen auch viele Höhlen zu den Bestandsabfall-basierten Ökosystemen, soweit sie nicht vollständig von der Außenwelt abgeschottet sind. In die Höhlen wird auf vielfältige Weise Bestandsabfall eingetragen, ein prominentes Beispiel ist Fledermausguano. [125]