Background
The creation of a mathematical simulation model of photosynthetic microbial mats is an important stepping stone in our understanding of key biogeochemical cycles that may have altered the atmospheres of early Earth and of other terrestrial planets. A modeling investigation is presented here as a tool to utilize and integrate empirical results from existing and upcoming research in photosynthetic systems into a computational system that can be used to simulate biospheric inputs of trace gases to the atmosphere.
Photosynthetic microbial mats are prokaryotic assemblages that grow from the topmost photic layers of submerged or partially submerged sediment. These microbial communities were abundant in shallow seas surrounding continents during the Precambrian, primarily during the Proterozoic era from about 2.2 billion years onward. (Brock & Madigan 1991; Castenholz 1994). Microbial mats are significant to the field of Astrobiology because of their paleontological importance and because their trace gas emissions to Earth's atmosphere, when modeled, can serve as remote indicators of life (a.k.a. , biosignatures, sensu Hoehler et al. 2001) elsewhere in the solar system. The evolution of cyanobacteria, (the major bacterial guild of these mat ecosystems) with their ability to use water as a source of reducing power, probably caused Earth's biosphere to increase productivity by 2-3 orders of magnitude (Des Marais 1997). Despite their inherently high productivity and dominant role in evolution of the biosphere, many of these cyanobacterial mats are found in environments that today could be considered extreme or stressed, at least with respect to salinity, acidity, temperature, radiation flux, and potential for desiccation.
Microbial mats residing in benthic marine and hypersaline environments may be living analogs to biological communities of early Earth. Thus, they are useful in understanding biotic-atmospheric interactions of early Earth and in identifying biosignatures, particularly for O2, CO2, H2S, and CH4. Notwithstanding recent advances in understanding how hypersaline microbial mats function (Bebout & Garcia-Pichel 1995; Des Marais 1995; Hoehler et al. 2001), many uncertainties remain concerning the importance of physical controls, especially the light limitations, energy fluxes, and the chemical environment. These uncertainties have not yet been addressed fully on an ecosystem scale. In past years, numerous field and laboratory experiments have contributed to the body of knowledge on these communities (e.g. , Castenholz 1994; Nubel et al. 2001; Overmann & van Gemerden 2000; Weiland et al. 2001). Such experiments are, by nature, limited in their applicability to the study of microbial interactions over a variety of time scales (from hours to years to millennia), and in their capacity to explain complex biogeochemical interactions between (aerobic) primary producers and (anaerobic) consumer and fermenter bacterial groups. Simulation modeling can be used with new experimental results to explain complex biochemical interactions of photosynthetic microbial ecosystems at progressively larger temporal and spatial scales. Generally, computer modeling is a highly cost-effective means to create a "virtual laboratory", in which we can expand and test understanding of Astrobiology concepts. Models allow us to explore scenarios and assess implications of experimental findings in an efficient manner that can compliment more expensive field-based measurement activities.
The overall objective of this research investigation is to refine and evaluate simulation models of energy relations, biogeochemical cycling, trace gas exchange, and biodiversity in microbial mat ecosystems with the goals of extrapolating biosignatures of early Earth ecosystems to the scale of a planetary biosphere. We will extend the predicted results from our models over billions of years of ecological change due to environmental forcing conditions.
The significance of the proposed modeling work is to efficiently and comprehensively evaluate the limits of microbial evolution and ecosystem functions in ways that will support NASA missions aimed at explaining biosignatures on distant planets.
Specific Study Objective
- Predict (using simulation modeling) and validate (using experimental measurements) biochemical cycles, and biosignature emissions of O2, CH4, and reduced S gases from the water surface to the atmosphere above photosynthetic microbial mats.
- Investigate the spectral properties and light use efficiency of microbial mat ecosystems using satellite image analysis and in-situ spectroradiometery.
- Extend the generalized model of photosynthetic microbial mats in hypersaline subtidal environments to simulate other prominent microbial ecosystem types (with their peculiar physical and geochemical properties) that may have influenced the atmosphere of early Earth.
- Incorporate results from studies of diversity in microbial mats into the generalized model of microbial ecosystems and biospheres.
- Discern variation in biosignature emissions due to spatial and seasonal environmental fluctuations of a biosphere.
What Are We Doing Now
Currently, we are working towards getting the gases from the diffusive boundary layer to the ocean surface. We are also working with longer simulations (25 days), refining methanogenesis, and adding some key processes in the cycling of organic carbon (e.g. , fermentation and photorespiration).
Results and Accomplishments
We have developed and published a simulation model called MBGC (Microbial BioGeoChemistry) to infer effects of major environmental controllers on microbial community structure and function (Decker et al. , in press). Microbial growth, metabolic reaction rates, and mass flows are represented within and between vertical sediment layers of a microbial mat in the hypersaline environment (Figure 1). In addition, diel cycles are simulated to capture natural variation in environmental boundary conditions, such as light and temperature.
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The fundamental structure of our MBGC simulation model follows that of a previous model (de Wit et al. 1995) of population dynamics and biogeochemistry within benthic mats containing cyanobacteria (CYA), purple sulfur bacteria (PSB) and colorless sulfur bacteria (CSB). Specific additions were incorporated recently into our MBGC model in order to (1) represent CYA photosynthesis metabolism operating as a light-driven quantum efficiency function, (2) complete the microbial sulfur cycle, (3) enable a comparison of the model predictions to hourly field-measured biogeochemical fluxes. More specifically, we have added sulfate-reducing bacteria (SRB) to MBGC, which can consume organic carbon and produce the H2S oxidized by PSB and CSB. In addition, molecular hydrogen (H2), decomposition of dead organic matter (DOM), CO2 fluxes, and realistic (time-varying) temperature and light controls on major metabolic pathways are included in MBGC as extensions of the original de Wit model structure.
Figure 2 - Radiation and temperature data collected June 4-5 (solid lines), and October 10-11 (dotted lines), 2001 at Exportadora del Sal, Guerrero Negro, BCS, Mexico, and used as environmental inputs to MBGC.
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For this paper, we conducted a three day MBGC simulation using field-measured temperature and light data (Figure 2). This data was collected from Exportadora del Sal, Guerrero Negro, Baja, Mexico on June 4-5, 2001 and October 10-11, 2001. This simulation generated O2 (Figure 3, 4) , sulfide (Figure 5, 6), and DIC (Table 1, Figure 7) results that were compared with data collected on the same dates and from the same places as the environmental data.
Figure 3 - Depicts the flow diagram for the O2 portion of MBGC
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Figure 4 - Comparison of MBGC diel O2 and data collected from pond 4 near 5 in the Exportadora del Sal, Guerrero Negro BCS, Mexico. Shown are: a) MBGC simulation results with June 4-5 2001 field-collected light and temperature data b) field-collected data from June 4 and 5, 2001, c) MBGC simulation results with October 10-11, 2001 field-collected light and temperature data, and d) field-collected data from October 10-11, 2001.
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Figure 5
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Figure 6
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Figure 7 - DIC flux across the mat boundary in mmol m-2 hr -1. The figure compares field data (averaged each time period) with MBGC results with various levels of sulfide created per CO2 released (see DIC budget). Positive values are DIC effluxes out of the mat (and into the water column) and negative values represent DIC influxes into the mat (from the water column).
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TABLE 1
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We most recently added the process of methanogenesis with all its associated uncertainties in this ecosystem type, but this addition has yet to be published.
Astrobiology Roadmap Goals
The development and refinement of our MBGC model addresses the following Astrobiology Roadmap Goals: (4) to understand how past life on Earth interacted with its changing planetary and Solar System environment, (5) to understand the evolutionary mechanisms and environmental limits of life (specifically with regards to the biochemical limits), (6) to understand the principles that will shape the future of life, both on Earth and beyond (especially Objective 6.1 - Environmental changes and the cycling of elements by the biota, communities, and ecosystems), and (7) - Determine how to recognize signatures of life on other worlds and on early Earth.
Literature Cited:
Bebout BM and Garcia-Pichel F (1995). UVB-induced vertical migrations of cyanobacteria in a microbial mat. Appl. Environ. Microbiol. Vol. 61: 4215-4222.
Brock TD and Madigan MT (1991). Biology of Microorganisms. Sixth Edition. pp. 677-681.Prentice Hall, New Jersey, p. 874.
Castenholz RW (1994). Microbial Mat Research: The Recent Past and New Perspectives. In: Microbial Mats: Structure, development, and environmental significance, L.J. Stal and P. Caumette (Eds. ) Pp. 3-18. Springer-Verlag Berlin Heidelberg 463 p.
Decker KLM, Potter CS, Bebout BM, Des Marais DJ, Carpenter S, Discipulo M, Hoehler TM, Miller SR, Thamdrup B, Turk KA and Visscher PT (in press). Mathematical simulation of the diel O, S, and C biogeochemistry of a hypersaline microbial mat. FEMS Microbiology Ecology.
Des Marais DJ (1995). The biogeochemistry of hypersaline microbial mats, in Advances in Microbial Ecology, edited by J. G. Jones, pp. 251-274, Plenum, New York.
Des Marais DJ (1997). Long-term evolution of the biogeochemical carbon cycle, in Geomicrobiology, edited by J. Banfield and K. Nealson, pp. 429-445, Mineralogical Society of America, Washington DC.
Hoehler TM, Bebout BM & Des Marais DJ (2001b). The role of microbial mats in the production of reduced gases on the early Earth. Nature, Vol. 412: 324-327.
Nubel U, Bateson MM, Madigan MT, Kuhl M and Ward DM (2001). Diversity and distribution in hypersaline microbial mats of bacteria related to Chloroflexus spp. Applied and Environmental Microbiology, Vol. 67 (9): 4365-4371.
Overmann J and van Gemerden H (2000). Microbial interactions involving sulfur bacteria: implications for the ecology and evolution of bacterial communities. FEMS Microbiology Reviews, Vol. 24: 591-599.
Weiland A, de Beer D, Damgaard LR, Kühl M, van Dusschoten D and van As H (2001). Fine-scale measurement of diffusivity in a microbial mat with nuclear magnetic resonance imaging. Limnology and Oceanograph, Vol. 46(2): 248-259.
Ames Team Members Participating in this Investigation:
For additional information on this investigation, visit the following website: http://geo.arc.nasa.gov/website/geds/mbgc.html
See the following Ames Team research pages:
Formation and Evolution of Habitable Planets
Prebiotic Organics from Space
Origin and Early Evolution of Proteins and Metabolism
Biosignatures in Chemosynthetic and Photosynthetic Systems
Modeling Ecosystems and Biospheres
Hind-Casting Past Environments
Interplanetary Pioneers
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