scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (8) ◽  
pp. 6143-6216 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Biogeochemical Cycling ◽  
Laboratory Research ◽  
Biogeochemical Model ◽  
Published Data ◽  
Model Parameters ◽  
Empirical Observation ◽  
Glacier Forefield

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework which is developed as part of an interdisciplinary, iterative, model-data based approach fully integrating fieldwork and laboratory experiments with model development, testing, and application. SHIMMER is designed to simulate the establishment of microbial biomass and associated biogeochemical cycling during the initial stages of ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The model mechanistically describes and predicts transformations in carbon, nitrogen and phosphorus through aggregated components of the microbial community as a set of coupled ordinary differential equations. The rationale for development of the model arises from decades of empirical observation on the initial stages of soil development in glacier forefields. SHIMMER enables a quantitative and process focussed approach to synthesising the existing empirical data and advancing understanding of microbial and biogeochemical dynamics. Here, we provide a detailed description of SHIMMER. The performance of SHIMMER is then tested in two case studies using published data from the Damma Glacier forefield in Switzerland and the Athabasca Glacier in Canada. In addition, a sensitivity analysis helps identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass, and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Simulation results indicate that primary production is responsible for the initial build-up of substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter are identified as important in sustaining this productivity. Microbial production in young soils is supported by labile organic matter, whereas carbon stocks in older soils are more refractory. Nitrogen fixing bacteria are responsible for the initial accumulation of available nitrates in the soil. Biogeochemical rates are highly seasonal, as observed in experimental data. The development and application of SHIMMER not only provides important new insights into forefield dynamics, but also highlights aspects of these systems that require further field and laboratory research. The most pressing advances need to come in quantifying nutrient budgets and biogeochemical rates, in exploring seasonality, the fate of allochthonous deposition in relation to autochthonous production, and empirical studies of microbial growth and cell death, to increase understanding of how glacier forefield development contributes to the global biogeochemical cycling and climate in the future.

scholarly journals SHIMMER (1.0): a novel mathematical model for microbial and biogeochemical dynamics in glacier forefield ecosystems

2015 ◽  
Vol 8 (10) ◽  
pp. 3441-3470 ◽  
Author(s):  
J. A. Bradley ◽  
A. M. Anesio ◽  
J. S. Singarayer ◽  
M. R. Heath ◽  
S. Arndt
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Growth ◽  
Model Development ◽  
Biogeochemical Cycling ◽  
Biogeochemical Model ◽  
Model Parameters ◽  
Glacier Forefield ◽  
Unconstrained Model

Abstract. SHIMMER (Soil biogeocHemIcal Model for Microbial Ecosystem Response) is a new numerical modelling framework designed to simulate microbial dynamics and biogeochemical cycling during initial ecosystem development in glacier forefield soils. However, it is also transferable to other extreme ecosystem types (such as desert soils or the surface of glaciers). The rationale for model development arises from decades of empirical observations in glacier forefields, and enables a quantitative and process focussed approach. Here, we provide a detailed description of SHIMMER, test its performance in two case study forefields: the Damma Glacier (Switzerland) and the Athabasca Glacier (Canada) and analyse sensitivity to identify the most sensitive and unconstrained model parameters. Results show that the accumulation of microbial biomass is highly dependent on variation in microbial growth and death rate constants, Q10 values, the active fraction of microbial biomass and the reactivity of organic matter. The model correctly predicts the rapid accumulation of microbial biomass observed during the initial stages of succession in the forefields of both the case study systems. Primary production is responsible for the initial build-up of labile substrate that subsequently supports heterotrophic growth. However, allochthonous contributions of organic matter, and nitrogen fixation, are important in sustaining this productivity. The development and application of SHIMMER also highlights aspects of these systems that require further empirical research: quantifying nutrient budgets and biogeochemical rates, exploring seasonality and microbial growth and cell death. This will lead to increased understanding of how glacier forefields contribute to global biogeochemical cycling and climate under future ice retreat.

Investigating nutrient controls over microbial activity in tropical soils

2021 ◽  
Author(s):  
Lucia Fuchslueger
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Microbial Communities ◽  
Microbial Activity ◽  
Microbial Growth ◽  
Tropical Soils ◽  
Phosphorus Availability ◽  
Soil P ◽  
Soil Microbial ◽  
Low Phosphorus

<p>The Amazon rainforest is an important sink for atmospheric CO<sub>2</sub> counteracting increased emissions from anthropogenic fossil fuel combustion and land use change storing large amounts of carbon in plant biomass and soils. However, large parts of the Amazon Basin are characterized by highly weathered soils (ultisols and oxisols) with low availability of rock-derived phosphorus (and cations), which are mostly occluded in soil or bound in organic matter. Such low phosphorus availability is thought to be (co-)limiting plant productivity. However, much less is known whether low phosphorus availability influences the activity of heterotrophic microbial communities controlling litter and soil organic matter decomposition and thereby long-term carbon sequestration in tropical soils.</p><p>In tropical soils high temperature and humid conditions allow overall high microbial activity. Over a larger soil phosphorus fertility gradient across several Amazonian rainforest sites, at low P sites almost 40 % of total P was stored in microbial biomass, highlighting the competitive strength of microorganisms and their importance as P reservoir. Across all sites soil microbial biomass was a significant predictor for soil microbial respiration, but mass-specific respiration rates (normalized by microbial biomass C) rather decreased at higher soil P. Using the incorporation of <sup>18</sup>O from labelled water into DNA (i.e., a substrate-independent method) to determine microbial growth, we found significantly lower microbial growth rates per unit of microbial biomass at higher soil P. This resulted in a lower microbial carbon use efficiency, at a narrower C:P stoichiometry in soils with higher P levels, and pointed towards a microbial co-limitation of phosphorus and carbon at low soil P levels. Furthermore, data from a multi-year nutrient manipulation experiment in French Guiana and from short-term lab incubations suggest that microbial communities thriving at low P levels are highly efficient in taking up and storing added P, but do not necessarily respond with increased growth.</p><p>Soil microbial communities play a crucial role in soil carbon and phosphorus cycling in tropical soils as potent competitors for available P. They also play an important role in storing and buffering P losses from highly weathered tropical soils. The potential non-homoeostatic stoichiometric behavior of microbial communities in P cycling is important to consider in soil and ecosystem models based on stoichiometric relationships.</p>

scholarly journals Sensitivity analysis of six soil organic matter models applied to the decomposition of animal manures and crop residues

2016 ◽  
Vol 11 ◽  
Author(s):  
Daniele Cavalli ◽  
Pietro Marino Gallina ◽  
Luca Bechini
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Organic Matter Decomposition ◽  
Model Parameters ◽  
Monod Kinetics ◽  
Decomposition Rates ◽  
Mineral N ◽  
First Order ◽  
Inhibition Hypothesis ◽  
C Decomposition

Two features distinguishing soil organic matter simulation models are the type of kinetics used to calculate pool decomposition rates, and the algorithm used to handle the effects of N shortage on C decomposition. Compared to widely used first-order kinetics, Monod kinetics more realistically represent organic matter decomposition, because they relate decomposition to both substrate and decomposer size. Most models impose a fixed C to N ratio for microbial biomass. When N required by microbial biomass to decompose a given amount of substrate- C is larger than soil available N, carbon decomposition rates are limited proportionally to N deficit (N inhibition hypothesis). Alternatively, C-overflow was proposed as a way of getting rid of excess C, by allocating it to a storage pool of polysaccharides. We built six models to compare the combinations of three decomposition kinetics (first-order, Monod, and reverse Monod), and two ways to simulate the effect of N shortage on C decomposition (N inhibition and C-overflow). We conducted sensitivity analysis to identify model parameters that mostly affected CO<sub>2</sub> emissions and soil mineral N during a simulated 189-day laboratory incubation assuming constant water content and temperature. We evaluated model outputs sensitivity at different stages of organic matter decomposition in a soil amended with three inputs of increasing C to N ratio: liquid manure, solid manure, and low-N crop residue. Only few model parameters and their interactions were responsible for consistent variations of CO<sub>2</sub> and soil mineral N. These parameters were mostly related to microbial biomass and to the partitioning of applied C among input pools, as well as their decomposition constants. In addition, in models with Monod kinetics, CO<sub>2</sub> was also sensitive to a variation of the halfsaturation constants. C-overflow enhanced pool decomposition compared to N inhibition hypothesis when N shortage occurred. Accumulated C in the polysaccharides pool decomposed slowly; therefore model outputs were not sensitive to a variation of its decay constant. Six-month organic matter decomposition was generally higher for models implementing classical Monod kinetics, followed by models with first-order and reverse Monod kinetics, due to the effect of soil microbial biomass growth on decomposition rates. Moreover, models implementing Monod kinetics predicted positive priming effects of native organic matter after soil amendment, according to co-metabolism theory. Thus, priming was proportional to the increase of the microbial biomass and in turn to the decomposability of applied organic matter. We conclude that model calibration should focus only on the few important parameters.

scholarly journals Microbial Necromass in Soils—Linking Microbes to Soil Processes and Carbon Turnover

2021 ◽  
Vol 9 ◽  
Author(s):  
M. Kästner ◽  
A. Miltner ◽  
S. Thiele-Bruhn ◽  
C. Liang
Keyword(s):  
Organic Matter ◽  
Microbial Biomass ◽  
Plant Material ◽  
Microbial Growth ◽  
Tricarboxylic Acid Cycle ◽  
Building Blocks ◽  
Life Strategy ◽  
Soil Processes ◽  
Carbon Turnover ◽  
Molecular Imprint

The organic matter of living plants is the precursor material of the organic matter stored in terrestrial soil ecosystems. Although a great deal of knowledge exists on the carbon turnover processes of plant material, some of the processes of soil organic matter (SOM) formation, in particular from microbial necromass, are still not fully understood. Recent research showed that a larger part of the original plant matter is converted into microbial biomass, while the remaining part in the soil is modified by extracellular enzymes of microbes. At the end of its life, microbial biomass contributes to the microbial molecular imprint of SOM as necromass with specific properties. Next to appropriate environmental conditions, heterotrophic microorganisms require energy-containing substrates with C, H, O, N, S, P, and many other elements for growth, which are provided by the plant material and the nutrients contained in SOM. As easily degradable substrates are often scarce resources in soil, we can hypothesize that microbes optimize their carbon and energy use. Presumably, microorganisms are able to mobilize biomass building blocks (mono and oligomers of fatty acids, amino acids, amino sugars, nucleotides) with the appropriate stoichiometry from microbial necromass in SOM. This is in contrast to mobilizing only nutrients and consuming energy for new synthesis from primary metabolites of the tricarboxylic acid cycle after complete degradation of the substrates. Microbial necromass is thus an important resource in SOM, and microbial mining of building blocks could be a life strategy contributing to priming effects and providing the resources for new microbial growth cycles. Due to the energy needs of microorganisms, we can conclude that the formation of SOM through microbial biomass depends on energy flux. However, specific details and the variability of microbial growth, carbon use and decay cycles in the soil are not yet fully understood and linked to other fields of soil science. Here, we summarize the current knowledge on microbial energy gain, carbon use, growth, decay, and necromass formation for relevant soil processes, e. g. the microbial carbon pump, C storage, and stabilization. We highlight the factors controlling microbial necromass contribution to SOM and the implications for soil carbon use efficiency (CUE) and we identify research needs for process-based SOM turnover modelling and for understanding the variability of these processes in various soil types under different climates.

scholarly journals Nitrogen Immobilisation and Microbial Biomass Build-Up Induced by Miscanthus x giganteus L. Based Fertilisers

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1386
Author(s):  
Michael Stotter ◽  
Florian Wichern ◽  
Ralf Pude ◽  
Martin Hamer
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Microbial Biomass ◽  
Triticum Aestivum L ◽  
Microbial Biomass C ◽  
Microbial Biomass N ◽  
Miscanthus X Giganteus ◽  
Organic Fertilisers ◽  
Biomass N ◽  
Set Up

Cultivation of Miscanthus x giganteus L. (Mis) with annual harvest of biomass could provide an additional C source for farmers. To test the potential of Mis-C for immobilizing inorganic N from slurry or manure and as a C source for soil organic matter build-up in comparison to wheat (Triticum aestivum L.) straw (WS), a greenhouse experiment was performed. Pot experiments with ryegrass (Lolium perenne L.) were set up to investigate the N dynamics of two organic fertilisers based on Mis at Campus Klein-Altendorf, Germany. The two fertilisers, a mixture of cattle slurry and Mis as well as cattle manure from Mis-bedding material resulted in a slightly higher N immobilisation. Especially at the 1st and 2nd harvest, they were partly significantly different compared with the WS treatments. The fertilisers based on Mis resulted in a slightly higher microbial biomass C and microbial biomass N and thus can be identified as an additional C source to prevent nitrogen losses and for the build-up of soil organic matter (SOM) in the long-term.

Microbial and biochemical changes induced by rotation and tillage in a soil under barley production

1993 ◽  
Vol 73 (1) ◽  
pp. 39-50 ◽  
Author(s):  
D. A. Angers ◽  
N. Bissonnette ◽  
A. Légère ◽  
N. Samson
Keyword(s):  
Alkaline Phosphatase ◽  
Organic Matter ◽  
Microbial Biomass ◽  
Phosphatase Activity ◽  
Alkaline Phosphatase Activity ◽  
Soil Layer ◽  
Red Clover ◽  
Organic C ◽  
Top Soil ◽  
Total Organic C

Crop rotations and tillage practices can modify not only the total amount of organic matter (OM) in soils but also its composition. The objective of this study was to determine the changes in total organic C, microbial biomass C (MBC), carbohydrates and alkaline phosphatase activity induced by 4 yr of different rotation and tillage combinations on a Kamouraska clay in La Pocatière, Quebec. Two rotations (continuous barley (Hordeum vulgare L.) versus a 2-yr barley–red clover (Trifolium pratense L.) rotation) and three tillage treatments (moldboard plowing (MP), chisel plowing (CP) and no-tillage (NT)) were compared in a split-plot design. Total organic C was affected by the tillage treatments but not by the rotations. In the top soil layer (0–7.5 cm), NT and CP treatments had C contents 20% higher than the MP treatment. In the same soil layer, MBC averaged 300 mg C kg−1 in the MP treatment and up to 600 mg C kg−1 in the NT soil. Hot-water-extractable and acid-hydrolyzable carbohydrates were on average 40% greater under reduced tillage than under MP. Both carbohydrate fractions were also slightly larger in the rotation than in the soil under continuous barley. The ratios of MBC and carbohydrate C to total organic C suggested that there was a significant enrichment of the OM in labile forms as tillage intensity was reduced. Alkaline phosphatase activity was 50% higher under NT and 20% higher under CP treatments than under MP treatment and, on average, 15% larger in the rotation than in the continuous barley treatment. Overall, the management-induced differences were slightly greater in the top layer (0–7.5 cm) than in the lower layer of the Ap horizon (7.5–15 cm). All the properties measured were highly correlated with one another. They also showed significant temporal variations that were, in most cases, independent of the treatments. Four years of conservation tillage and, to a lesser extent, rotation with red clover resulted in greater OM in the top soil layer compared with the more intensive systems. This organic matter was enriched in labile forms. Key words: Soil management, soil quality, organic matter, carbohydrates, microbial biomass, phosphatase

scholarly journals New Mathematical Modeling Approach for Predicting Microbial Inactivation by High Hydrostatic Pressure

2007 ◽  
Vol 73 (8) ◽  
pp. 2468-2478 ◽  
Author(s):  
Bernadette Klotz ◽  
D. Leo Pyle ◽  
Bernard M. Mackey
Keyword(s):  
Microbial Inactivation ◽  
Inactivation Kinetics ◽  
Published Data ◽  
Model Parameters ◽  
Order Kinetic ◽  
Square Root ◽  
Additional Time ◽  
Decimal Reduction Time ◽  
First Order ◽  
Primary Model

ABSTRACT A new primary model based on a thermodynamically consistent first-order kinetic approach was constructed to describe non-log-linear inactivation kinetics of pressure-treated bacteria. The model assumes a first-order process in which the specific inactivation rate changes inversely with the square root of time. The model gave reasonable fits to experimental data over six to seven orders of magnitude. It was also tested on 138 published data sets and provided good fits in about 70% of cases in which the shape of the curve followed the typical convex upward form. In the remainder of published examples, curves contained additional shoulder regions or extended tail regions. Curves with shoulders could be accommodated by including an additional time delay parameter and curves with tails shoulders could be accommodated by omitting points in the tail beyond the point at which survival levels remained more or less constant. The model parameters varied regularly with pressure, which may reflect a genuine mechanistic basis for the model. This property also allowed the calculation of (a) parameters analogous to the decimal reduction time D and z, the temperature increase needed to change the D value by a factor of 10, in thermal processing, and hence the processing conditions needed to attain a desired level of inactivation; and (b) the apparent thermodynamic volumes of activation associated with the lethal events. The hypothesis that inactivation rates changed as a function of the square root of time would be consistent with a diffusion-limited process.

scholarly journals Impacts of Polarimetric Radar Observations on Hydrologic Simulation

2010 ◽  
Vol 11 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Scott E. Giangrande ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Terry Schuur ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Polarimetric Radar ◽  
Storm Events ◽  
Hydrologic Simulation ◽  
Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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