Techniques and Options for Modelling

Soilsystems will build on recent developments in methods that are ideally suited for integrated experimental work on the soil system. This asks for techniques such as:

  1. dynamic balances of enthalpy and Gibbs energy during oxidation combined with matter fluxes and modelling (e.g. nano- to megacalorimetry, DSC, thermogravimetry),
  2. biomarker analysis (e.g. proteins, aminosugars, membrane compounds and lipids), and improved quantitative stable isotope probing (SIP) techniques for the determination of compound turnover and metabolic pathways,
  3. DNA, RNA and protein based (meta‑)omics systems biology approaches for assessment of microbial communities including correlation analyses,
  4. chemical high resolution analyses for metabolome identification and transformation pathway mapping (e.g. LC-QTOF-MS, FT-ICR-MS, Py-GC-MS),
  5. high spatial resolution 3D imaging for soil structure analysis and chemical mapping for visualization of compound fluxes and spatial arrangements like XRT, NanoSIMS, XPS, and (H)SEM.

In order to assess the physiology of microorganisms and for ecological modelling, determination of energy contents and fluxes can be performed in different ways, for example by:

  1. balancing turnover of isotope (stable/radioactive) labelled C-compounds including heat and CO2 in defined batch setups (Pausch et al., 2016a, 2016b);
  2. (nano- and micro)calorimetric analysis of heat production rate from biotic activity (Barros et al., 2007) and/or megacalorimetric determination of total heat balance of micro- to mesocosms (Maskow, 2013);
  3. measuring rates of oxygen uptake that are suited to estimate heat production (‘indirect calorimetry’) and, when related to CO2 evolution, provide a real-time measure for biomass related yield coefficients (Hansen et al., 2004; Maskow, 2013);
  4. determination of enthalpies, and theoretical Gibbs energy or the nominal oxidation state of carbon (NOSC) in SOM or major constituents and selected key compounds as well as in developing biomass and remaining necromass (LaRowe and Amend, 2016; Sinsabaugh et al., 2016; Trapp et al., 2018);
  5. information from ecological stoichiometry on (multiple) resource utilization and carbon use efficiency (CUE) (Geyer et al., 2019; Manzoni et al., 2018; Sinsabaugh et al., 2016; Zechmeister-Boltenstern et al., 2015) and from audit of small molecules (Addiscott, 1995).

Besides numerous expedient SOM models, various thermodynamics and flux related modelling approaches can serve to integrate thermodynamic principles into soil systems theory. Models may be based on, e.g.

  1. throughflow analysis such as the Thermodynamics-based Metabolic Flux Analysis (TMFA; Henry et al., 2007), Thermodynamic Feasibility Analysis (TFA; Maskow and Paufler, 2015; Vojinović and Von Stockar, 2009), Flux-Force relations (Beard and Qian, 2007; Noor et al., 2013) and the Ecological Network Analysis (ENA; Matamba et al., 2009);
  2. Dynamic Energy Budget (DEB) models such as the Metabolic Theory (Aoki, 2012; Kooijman et al., 2008; Schramski et al., 2015);
  3. individual-based modelling allowing to capture emergent behaviour of microbial decomposer communities (Kaiser et al., 2015);
  4. ecological network analysis to reveal inter-connection between microbiome diversity and function (Bascompte, 2007);
  5. thermodynamic organizing principles, such as Maximum Entropy Production (MEP, e.g. Ozawa et al., 2003), Maximum Power (Odum and Pinkerton, 1955), Minimum Energy Expenditure (Rodriguez-Iturbe et al., 1992) and Minimum Dissipation (West et al., 1999);
  6. modelling the potential growth yield of microbes feeding on organic compounds (Microbial Turnover to Biomass, MTB; (Brock et al., 2017; Trapp et al., 2018).

Such approaches can be complemented and refined for their application to SOM turnover.

References

Addiscott, T.M., 1995. Entropy and sustainability. European Journal of Soil Science 46(2), 161-168.
Aoki, I., 2012. Entropy Principle for the Development of Complex Biotic Systems. Entropy Principle for the Development of Complex Biotic Systems. Elsevier.
Barros, N., Salgado, J., Feijóo, S., 2007. Calorimetry and soil. Thermochimica Acta 458(1-2), 11-17.
Bascompte, J., 2007. Networks in ecology. Basic and Applied Ecology 8(6), 485-490.
Beard, D.A., Qian, H., 2007. Relationship between thermodynamic driving force and one-way fluxes in reversible processes. PLoS ONE 2(1).
Brock, A.L., Kästner, M., Trapp, S., 2017. Microbial growth yield estimates from thermodynamics and its importance for degradation of pesticides and formation of biogenic non-extractable residues. SAR and QSAR in Environmental Research 28(8), 629-650.
Geyer, K.M., Dijkstra, P., Sinsabaugh, R., Frey, S.D., 2019. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biology and Biochemistry 128, 79-88.
Hansen, L.D., MacFarlane, C., McKinnon, N., Smith, B.N., Criddle, R.S., 2004. Use of calorespirometric ratios, heat per CO 2 and heat per O 2, to quantify metabolic paths and energetics of growing cells. Thermochimica Acta 422(1-2), 55-61.
Henry, C.S., Broadbelt, L.J., Hatzimanikatis, V., 2007. Thermodynamics-based metabolic flux analysis. Biophysical Journal 92(5), 1792-1805.
Kaiser, C., Franklin, O., Richter, A., Dieckmann, U., 2015. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nature Communications 6.
Kooijman, S.A.L.M., Sousa, T., Pecquerie, L., Van Der Meer, J., Jager, T., 2008. From food-dependent statistics to metabolic parameters, a practical guide to the use of dynamic energy budget theory. Biological Reviews 83(4), 533-552.
LaRowe, D.E., Amend, J.P., 2016. The energetics of anabolism in natural settings. ISME J 10(6), 1285-1295.
Manzoni, S., Čapek, P., Porada, P., Thurner, M., Winterdahl, M., Beer, C., Brüchert, V., Frouz, J., Herrmann, A.M., Herrmann, A.M., Lyon, S.W., Šantrůčková, H., Vico, G., Way, D., 2018. Reviews and syntheses: Carbon use efficiency from organisms to ecosystems – Definitions, theories, and empirical evidence. Biogeosci. 15, 5929-5949.
Maskow, T., 2013. Miniaturization of calorimetry: strengths and weaknesses for bioprocess monitoring and control. In: U. von Stockar (Ed.), Biothermodynamics – the Role of Thermodynamics in Biochemical Engineering. EFPL Press, Lausanne, Switzerland, pp. 423-442.
Maskow, T., Paufler, S., 2015. What does calorimetry and thermodynamics of living cells tell us? Methods 76, 3-10.
Matamba, L., Kazanci, C., Schramski, J.R., Blessing, M., Alexander, P., Patten, B.C., 2009. Throughflow analysis: A stochastic approach. Ecological Modelling 220(22), 3174-3181.
Noor, E., Flamholz, A., Liebermeister, W., Bar-Even, A., Milo, R., 2013. A note on the kinetics of enzyme action: A decomposition that highlights thermodynamic effects. FEBS Letters 587(17), 2772-2777.
Odum, H.T., Pinkerton, R.C., 1955. Time’s speed regulator: The optimum efficiency for maximum power output in physical and biological systems. American Scientist 43(2), 331-343.
Ozawa, H., Ohmura, A., Lorenz, R.D., Pujol, T., 2003. The second law of thermodynamics and the global climate system: A review of the maximum entropy production principle. Reviews of Geophysics 41(4), 4-1 – 4-24.
Pausch, J., Hofmann, S., Scharroba, A., Kuzyakov, Y., Ruess, L., 2016a. Fluxes of root-derived carbon into the nematode micro-food web of an arable soil. Food Webs 9, 32-38.
Pausch, J., Kramer, S., Scharroba, A., Scheunemann, N., Butenschoen, O., Kandeler, E., Marhan, S., Riederer, M., Scheu, S., Kuzyakov, Y., Ruess, L., 2016b. Small but active – pool size does not matter for carbon incorporation in below-ground food webs. Functional Ecology 30(3), 479-489.
Rodriguez-Iturbe, I., Rinaldo, A., Rigon, R., Bras, R.L., Ijjasz‐Vasquez, E., Marani, A., 1992. Fractal structures as least energy patterns: The case of river networks. Geophysical Research Letters 19(9), 889-892.
Schramski, J.R., Dell, A.I., Grady, J.M., Sibly, R.M., Brown, J.H., 2015. Metabolic theory predicts whole-ecosystem properties. Proceedings of the National Academy of Sciences of the United States of America 112(8), 2617-2622.
Sinsabaugh, R.L., Turner, B.L., Talbot, J.M., Waring, B.G., Powers, J.S., Kuske, C.R., Moorhead, D.L., Follstad Shah, J.J., 2016. Stoichiometry of microbial carbon use efficiency in soils. Ecological Monographs 86(2), 172-189.
Trapp, S., Brock, A.L., Nowak, K., Kästner, M., 2018. Prediction of the Formation of Biogenic Nonextractable Residues during Degradation of Environmental Chemicals from Biomass Yields. Environmental Science and Technology 52(2), 663-672.
Vojinović, V., Von Stockar, U., 2009. Influence of uncertainties in pH, pMg, activity coefficients, metabolite concentrations, and other factors on the analysis of the thermodynamic feasibility of metabolic pathways. Biotechnology and Bioengineering 103(4), 780-795.
Zechmeister-Boltenstern, S., Keiblinger, K.M., Mooshammer, M., Peñuelas, J., Richter, A., Sardans, J., Wanek, W., 2015. The application of ecological stoichiometry to plant-microbial-soil organic matter transformations. Ecological Monographs 85(2), 133-155.

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