Exploring plant-soil interactions in MIMICS-CN

Slides:



Advertisements
Similar presentations
Chapter 4 Organic Matter Decomposition © 2013 Elsevier, Inc. All rights reserved. From Fundamentals of Ecosystem Science, Weathers, Strayer, and Likens.
Advertisements

– Winter Ecology. Introduction  Global Climate Change  How microbs may be affected by snowpack depth  Temperature/precipitation trends.
Bayesian Deconvolution of Belowground Ecosystem Processes Kiona Ogle University of Wyoming Departments of Botany & Statistics.
CENTURY ECOSYSTEM MODEL Introduction to CENTURY. WHY CENTURY Evaluate Effects of Environmental Change Evaluate Changes in Management.
Diagnostic canopy Prognostic canopy. Offline results: Combined influence of sun/shade and prognostic canopy scheme, compared to CLM3.0. CLM3-CN running.
Mineralization N/P driven by the of the dissolved inorganic compounds N/P in the ocean (“Redfield paradigm”) 7 Redfield-type ratios have been explored.
Integrating plant-microbe interactions to understand soil C stabilization with the MIcrobial-MIneral Carbon Stabilization model (MIMICS) Background Despite.
Soil CO 2 Efflux from a Subalpine Catchment Diego A. Riveros-Iregui 1, Brian L. McGlynn 1, Vincent J. Pacific 1, Howard E. Epstein 2, Daniel L. Welsch,
Carbon and Nitrogen Cycling in Soils Weathering represented processes that mainly deplete soils in elements relative to earth’s crust Biological processes.
“Carbon Isotope Systematics in Soil” -or- “Plant Poo and Microbe Farts” Justin Yeakel, UCSC.
SOIL ORGANIC MATTER. Organic Matter Decomposition: a cyclic view organic matter population sizes, temperature, moisture energy + CO 2 Biomass (more bugs)
Carbon Isotope Systematics in Soil. Soil Pathway Summary Organic matter finds it’s way into soils and decomposes SOM (Soil Organic Matter) is further.
DIFFERENCES IN SOIL RESPIRATION RATES BASED ON VEGETATION TYPE Maggie Vest Winter Ecology 2013 Mountain Research Station.
Roots and arbuscular mycorrhizal fungi influence nitrogen cycling in agricultural soils under contrasting management Amanda B. Daly, A. Stuart Grandy Department.
Biome Productivity Exercise The Long Term Ecological Research (LTER)
Chapter 5 The Biosphere: The Carbon Cycle of Terrestrial Ecosystems
Chapter 5 Element Cycling © 2013 Elsevier, Inc. All rights reserved. From Fundamentals of Ecosystem Science, Weathers, Strayer, and Likens (eds).
Quantifying Uncertainty in Ecosystem Studies (QUEST) John L. Campbell 1, Ruth D. Yanai 2, Mark B. Green 1,3, Carrie Rose Levine 2, Mary Beth Adams 1, Douglas.
Modeling climate change impacts on forest productivity with PnET-CN Emily Peters, Kirk Wythers, Peter Reich NE Landscape Plan Update May 17, 2012.
Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze Harvard University Developing a predictive science of the biosphere.
A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v. 3.0) A. D. Friend, A.K. Stevens, R.G. Knox, M.G.R. Cannell. Ecological Modelling.
Methods Model. The TECOS model is used as forward model to simulate carbon transfer among the carbon pools (Fig.1). In the model, ecosystem is simplified.
MODELING THE IMPACT OF IRRIGATION ON NUTRIENT EXPORT FROM AGRICULTURAL FIELDS IN THE SOUTHEASTERN UNITED STATES W. Lee Ellenburg Graduate Research Assistant.
By: Karl Philippoff Major: Earth Sciences
Introduction: Globally, atmospheric concentrations of CO 2 are rising, and are expected to increase forest productivity and carbon storage. However, forest.
Simulated Interactions of Soil Moisture, Drought Stress, and Regional Climate in the Amazon Basin Scott Denning 1, Jun Liu 1, Ian Baker 1, Maria Assun.
Site Description This research is being conducted as a part of the Detritus Input and Removal Treatments Project (DIRT), a cross-continental experiment.
Nitrogen-use efficiency of a sweetgum forest in elevated CO 2 Richard J. Norby 1 and Colleen M. Iversen 2 1 Oak Ridge National Laboratory, Oak Ridge, TN;
Liebermann R 1, Kraft P 1, Houska T 1, Müller C 2,3, Haas E 4, Kraus D 4, Klatt S 4, Breuer L 1 1 Institute for Landscape Ecology and Resources Management,
The relationship between snow depth and soil respiration in upper montane winter environments Claire Hierseman Winter Ecology Spring 2013 Mountain Research.
Application of Differential Scanning Calorimetry to Determine Enthaplic Character of Composts, Dissolved Organic Carbon (DOC) and Soils Julie Bower, Garrett.
Welcome Grant from National Science Foundation: Fire, Atmospheric pCO 2, and Climate as Alternative Primary Controls of C 4 -Grass Abundance: The Late-Quaternary.
The PILPS-C1 experiment Results of the first phase of the project Complementary simulation to be done Proposition for the future.
ABSTRACT Species in natural communities are linked together by the transfer of energy and nutrients. We investigated the effects of top predators on nutrient.
Ecology.
Nutrient Cycling and Retention
Estimates of Carbon Transfer coefficients Using Probabilistic Inversion for Three Forest Ecosystems in East China Li Zhang 1, Yiqi Luo 2, Guirui Yu 1,
Pre-workshop exercise on SOC stock simulation / calibration of DNDC Steven Sleutel Dept. Soil Management & Soil Care Ghent University.
Arrhenius Kinetics Reaction Velocity = A e -Ea/RT where, A = pre-exponential factor, or y-intercept Ea = activation energy of the substrate R = universal.
MODELLING CARBON FLOWS IN CROP AND SOIL Krisztina R. Végh.
Fundamental Dynamics of the Permafrost Carbon Feedback Schaefer, Kevin 1, Tingjun Zhang 1, Lori Bruhwiler 2, and Andrew Barrett 1 1 National Snow and Ice.
Earth, Ecological, & Environmental Sciences
Soil organic carbon response to harvested crops: a comparison between biogeochemistry model versions Beth Drewniak.
Inorganic Nutrient Research- Bonanza Creek LTER BNZ LTER Global permafrost soil organic C pool: 1,500 Pg 0-3 m depth Warming = 15% loss of pool by 2100.
Greater root carbon storage compared to shoot carbon storage in soil Fig 1 We labeled cereal rye cover crop with 13 CO 2 (left) aboveground biomass was.
Chapter 7 The Nitrogen Cycle © 2013 Elsevier, Inc. All rights reserved. From Fundamentals of Ecosystem Science, Weathers, Strayer, and Likens (eds).
Additions crop residues manures composts losses CO 2 (respiration of soil organisms) erosion soil organic matter Figure 3.1. Additions and losses of organic.
Comparison of Soils and Plants at Prairie Ridge: % C and % N Lori Skidmore.
Above and Below ground decomposition of leaf litter Sukhpreet Sandhu.
Daily net carbon exchange as a mediator of heterotrophic soil respiration across two forest chronosequences Jared L. DeForest, Asko Noormets, and Jiquan.
Samuel T. Dunn 1, 2, Andrew G. Bunn 3, John D. Schade 1
Comparison of GPP from Terra-MODIS and AmeriFlux Network Towers
Dynamics of aggregate stability and biological binding agents
CLM-CN update: Sensitivity to CO2, temperature, and precipitation in C-only vs. C-N mode Peter Thornton, Jean-Francois Lamarque, Mariana Vertenstein, Nan.
We generally think of soil from the ground up
Faith Simitz and Benjamin D. Duval
Does crop rotational diversity increase soil microbial resistance and resilience to drought and flooding? Jörg Schnecker1, A. Stuart Grandy1, D. Boone.
Will Hubbard Brook Soils Be a Source
Biome Productivity Exercise
Opening Activity: Feb. 2, 2016 List examples of burning fossil fuels.
CH19: Carbon Sinks and Sources
CLM-CN update: Sensitivity to CO2, temperature, and precipitation in C-only vs. C-N mode Peter Thornton, Jean-Francois Lamarque, Mariana Vertenstein, Nan.
CH19: Carbon Sinks and Sources
Figure 1. Spatial distribution of pinyon-juniper and ponderosa pine forests is shown for the southwestern United States. Red dots indicate location of.
Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.
Ecology.
Determining the Influence of Winter on Ecosystem
Ecology Biosphere.
Evaluating the Ability to Derive Estimates of Biodiversity from Remote Sensing Kaitlyn Baillargeon Scott Ollinger, Andrew Ouimette,
Examining the Accuracy of Nutrient Cycles in Climate Models
Presentation transcript:

Exploring plant-soil interactions in MIMICS-CN The MIcrobial-MIneral Carbon Stabilization model with coupled N cycling (MIMICS-CN) simulates litter decomposition and soil organic matter dynamics at landscape scales Emily Kyker-Snowman1, Will Wieder2, Stuart Grandy1 1University of New Hampshire, 2National Center for Atmospheric Research Abstract Model evaluation: Simulating C and N losses from litter in the LIDET dataset Model application: Exploring plant-soil interactions in MIMICS-CN Microbes are increasingly recognized as critical mediators of climate change effects on soil carbon (C) and nitrogen (N) transformations, both in field studies and theoretical models. The MIcrobial-MIneral Carbon Stabilization model with coupled nitrogen cycling (MIMICS-CN) uses explicit microbial dynamics and microbe-mineral interactions to simulate measured patterns of C and N cycling at landscape scales. The model simulates C and N losses from litterbags in the LIDET study (6 litter types, 10 years of observations, 14 sites across North America) with reasonable accuracy (C: R2=0.61; N: R2=0.29). MIMICS-CN simulations of litterbag N dynamics are as good or better than DAYCENT simulations of the same data based on root mean square error. Across the 14 simulated LIDET sites, MIMICS-CN produces reasonable equilibrium values for total soil C and N, microbial biomass C and N, respiration, inorganic N, and N mineralization rate. We also simulated several idealized litter addition experiments at one of the LIDET sites (Harvard Forest) to explore plant-soil interactions and transient model behavior across microbial and mineral pools. Unlike first-order models where added litter can only result in increases in soil organic matter (SOM) pools, MIMICS-CN allows fresh litter additions to stimulate microbially-mediated C losses and N release from SOM pools. MIMICS-CN can reproduce site and litter quality effects on litter decomposition C and N dynamics at a landscape scale. MIMICS-CN can produce dynamic non-linear patterns of C and N response to perturbation (i.e. priming) that are impossible in a linear decomposition model. Figure 3. MIMICS-CN simulations of C losses (top) and N remaining (bottom) from litterbags in the LIDET dataset versus observed values across litter types (left) and biomes (right). Model formulation MIMICS-CN represents C and N flow through metabolic and structural litter, oligotrophic and copiotrophic microbes, and physically protected, chemically protected, and available SOM pools. C dynamics are driven by reverse Michaelis-Menten kinetics, while N dynamics are driven by input and microbial C:N. N leaves the model as leaked inorganic N and C leaves the model as respired CO2. MIMICS-CN improves upon DAYCENT simulations. Table 2. MIMICS-CN performs as well or better than the DAYCENT model at simulating N decomposition dynamics in litterbags based on root mean square error (RMSE). Figure 6. MIMICS-CN modeled responses to 4 types of pulse input: metabolic litter (black), structural litter (red), simple C added to the “available” SOM pool (green), and desorption of SOM from the “physically protected” pool to the “available” pool (blue). In the first 3 simulations, enough C was added to equal 1% of the total soil C; in the last simulation, no C was added but a similar about of C was transferred between pools to simulate disruption of organo-mineral associations by plant exudates. The model was spun up to equilibrium at Harvard Forest and run for 15 years following each perturbation. Figure panels show model responses for C (left) and N (right) and show either percent change in instantaneous C or N loss rates (top) or cumulative C or N lost relative to equilibrium values (bottom). Dashed lines in lower plots show the amount of C or N added in each type of pulse input. Figure 4. MIMICS-CN captures immobilization-mineralization thresholds across litters of different quality. Litter quality (in terms of C:N and lignin content) decreases from upper left panel to lower right panel. Red dots show model simulations of C losses vs N losses from litterbags in the LIDET study. Colored dots show observed C vs N losses across biomes. DIN Parameter Value (g/g) CNs 85 CNm 12 CNr 6 CNk 10 NUE 0.85 MIMICS-CN can produce reasonable equilibrium values for a wide range of pools and fluxes. Table 3. Ranges of MIMICS-CN estimates of steady-state values for a variety of soil pools and fluxes, compared against observed ranges from several continent-wide data synthesis studies. The first column of MIMICS-CN values shows values as calculated in the LIDET site simulations, while the second shows a set of simulations in which turnover was increased 3-fold. MIMICS-CN simulates mineral soils to a depth of 30 cm. Conclusions MIMICS-CN can reproduce site and litter quality effects on litter decomposition C and N dynamics at a landscape scale. MIMICS-CN improves upon DAYCENT simulations of the same data. MIMICS-CN can produce reasonable equilibrium values for a wide range of pools and fluxes. MIMICS-CN can produce dynamic non-linear patterns of C and N response to perturbation (i.e. priming) that are impossible in a linear decomposition model. Figure 1. MIMICS-CN model structure and stoichiometric parameters unique to the coupled C-N model. CNs = C:N of structural litter, CNm = C:N of metabolic litter, CNr = C:N of copiotrophs, CNk = C:N of oligotrophs, and NUE = nitrogen use efficiency of both microbial groups. Site MAT (◦C) MAP (mm) ANPP (g C m−2 y−1) Arctic (ARC) -7 327 141 Bonanza Creek (BNZ) -5 403 300 Niwot Ridge (NWT) -3.7 1249 199 Hubbard Brook (HBR) 5 1396 704 Cedar Creek Reserve (CDR) 5.5 823 277 Harvard Forest (HFR) 7.1 1152 744 Andrews Forest (AND) 8.6 2309 800 Shortgrass Steppe (SGS) 8.9 440 116 Kellogg Bio. Station (KBS) 9.7 890 431 Coweeta (CWT) 12.5 1906 1460 Konza Prairie (KNZ) 12.8 791 443 Jornada (JRN) 14.6 298 229 Sevilleta (SEV) 16 254 184 Luquillo (LUQ) 23 3363 1050 Acknowledgements This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1450271 and by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under Project No. 2015-35615-22747. Contact me: ek2002@wildcats.unh.edu Figure 2. LIDET sites included in model simulations. Map borrowed from: Harmon, M. E. et al. (2009). Long-term patterns of mass loss during the decomposition of leaf and fine root litter: an intersite comparison. Global Change Biology, 15: 1320–1338.