scholarly journals A forest model intercomparison framework and application at two temperate forests along the East Coast of the United States

2018 ◽  
Author(s):  
Adam Erickson ◽  
Nikolay Strigul
Keyword(s):  
Net Primary Production ◽  
Model Performance ◽  
The United States ◽  
Climate Mitigation ◽  
Total Biomass ◽  
Ecological Processes ◽  
Organic C ◽  
Forest Biogeochemistry ◽  
Forest Models ◽  
C And N

AbstractForest models often reflect the dominant management paradigm of their time. Until the late 1970s, this meant sustaining yields. Following landmark work in forest ecology, physiology, and biogeochemistry, the current generation of models is further intended to inform ecological and climatic forest management in alignment with national biodiversity and climate mitigation targets. This has greatly increased the complexity of models used to inform management, making them difficult to diagnose and understand. State-of-the-art forest models are often complex, analytically intractable, and computationally-expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available for both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and, species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and 2 static metrics tested (HF-EMS = 0.73, +0.07, = 4.84, −10.02; JERC-RD = 0.76, +0.04, = 2.69, −1.86). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

scholarly journals A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 180 ◽  
Author(s):  
Adam Erickson ◽  
Nikolay Strigul
Keyword(s):  
Net Primary Production ◽  
Model Performance ◽  
Belowground Biomass ◽  
The United States ◽  
Total Biomass ◽  
Ecological Processes ◽  
Organic C ◽  
Forest Biogeochemistry ◽  
Forest Models ◽  
C And N

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Decomposition of plant material in Australian soils. III. Residual organic and microbial biomass C and N from isotope-labelled legume material and soil organic matter, decomposing under field conditions

Soil Research ◽  
1985 ◽  
Vol 23 (4) ◽  
pp. 603 ◽  
Author(s):  
JN Ladd ◽  
M Amato ◽  
JM Oades
Keyword(s):  
Microbial Biomass ◽  
South Australia ◽  
Experimental Period ◽  
Decay Rates ◽  
Microbial Biomass C ◽  
Plant Residues ◽  
Total Biomass ◽  
Organic C ◽  
Air Temperatures ◽  
C And N

After eight years decomposition of 14C, 15N-labelled legume (Medicago littoralis) material previously mixed into topsoils (0-10 cm) at four field sites in South Australia, residual organic 14C and 15N to 30 cm depth accounted for respectively 11-13% of input 14C, and 31-38% of input 15N. About 90% of the residual organic 14C and 70-80% of the residual l15N was recovered in topsoils. For sites in similar rainfall areas, soils of heavier texture retained slightly greater amounts of 14C and15N-labelled residues. Throughout the eight-year experimental period, the rates of decline of residual organic 14C and 15N exceeded those of native soil organic C and N. A comparison of the decline of organic 14C in topsoils, averaged for the four South Australian sites, with the average decline reported for 14C-labelled plant residues in soils at English and Nigerian field sites, suggests that net decomposition rates doubled approximately for an 8-9�C rise in mean annual air temperatures. Microbial biomass 14C and 15N of topsoils with time accounted for decreasing proportions of total biomass C and N, and of residual organic I4C and I5N. The relatively greater retention after eight years of biomass 14C and 15N in soils of heavier texture is consistent with the concept that the net decay of C and N in soils is dependent upon the turnover of biomass C and N, and that decay rates are decreased in soils which have the greater capacity to protect decomposer populations.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

Manipulation of rhizosphere organisms to enhance glomalin production and C sequestration: Pitfalls and promises

2014 ◽  
Vol 94 (6) ◽  
pp. 1025-1032 ◽  
Author(s):  
F. L. Walley ◽  
A. W. Gillespie ◽  
Adekunbi B. Adetona ◽  
J. J. Germida ◽  
R. E. Farrell
Keyword(s):  
Plant Growth ◽  
Mycorrhizal Fungi ◽  
Plant Growth Promoting Rhizobacteria ◽  
C Sequestration ◽  
Ionization Mass ◽  
Edge Structure ◽  
Organic C ◽  
Soil C ◽  
N Storage ◽  
C And N

Walley, F. L., Gillespie, A. W., Adetona, A. B., Germida, J. J. and Farrell, R. E. 2014. Manipulation of rhizosphere organisms to enhance glomalin production and C-sequestration: Pitfalls and promises. Can. J. Plant Sci. 94: 1025–1032. Arbuscular mycorrhizal fungi (AMF) reportedly produce glomalin, a glycoprotein that has the potential to increase soil carbon (C) and nitrogen (N) storage. We hypothesized that interactions between rhizosphere microorganisms, such as plant growth-promoting rhizobacteria (PGPR), and AMF, would influence glomalin production. Our objectives were to determine the effects of AMF/PGPR interactions on plant growth and glomalin production in the rhizosphere of pea (Pisum sativum L.) with the goal of enhancing C and N storage in the rhizosphere. One component of the study focussed on the molecular characterization of glomalin and glomalin-related soil protein (GRSP) using complementary synchrotron-based N and C X-ray absorption near-edge structure (XANES) spectroscopy, pyrolysis field ionization mass spectrometry (Py-FIMS), and proteomics techniques to characterize specific organic C and N fractions associated with glomalin production. Our research ultimately led us to conclude that the proteinaceous material extracted, and characterized in the literature, as GRSP is not exclusively of AMF origin. Our research supports the established concept that GRSP is important to soil quality, and C and N storage, irrespective of origin. However, efforts to manipulate this important soil C pool will remain compromised until we more clearly elucidate the chemical nature and origin of this resource.

scholarly journals Development of a semi-parametric PAR (Photosynthetically Active Radiation) partitioning model for the United States, version 1.0

2014 ◽  
Vol 7 (5) ◽  
pp. 2477-2484 ◽  
Author(s):  
J. C. Kathilankal ◽  
T. L. O'Halloran ◽  
A. Schmidt ◽  
C. V. Hanson ◽  
B. E. Law
Keyword(s):  
United States ◽  
Elevation Angle ◽  
Model Performance ◽  
Diffuse Radiation ◽  
The United States ◽  
Polynomial Model ◽  
Coefficient Of Determination ◽  
Data Set ◽  
Cubic Polynomial ◽  
Solar Elevation Angle

Abstract. A semi-parametric PAR diffuse radiation model was developed using commonly measured climatic variables from 108 site-years of data from 17 AmeriFlux sites. The model has a logistic form and improves upon previous efforts using a larger data set and physically viable climate variables as predictors, including relative humidity, clearness index, surface albedo and solar elevation angle. Model performance was evaluated by comparison with a simple cubic polynomial model developed for the PAR spectral range. The logistic model outperformed the polynomial model with an improved coefficient of determination and slope relative to measured data (logistic: R2 = 0.76; slope = 0.76; cubic: R2 = 0.73; slope = 0.72), making this the most robust PAR-partitioning model for the United States currently available.

Evaluating a model for predicting active crown fire rate of spread using wildfire observations

2006 ◽  
Vol 36 (11) ◽  
pp. 3015-3028 ◽  
Author(s):  
Martin E Alexander ◽  
Miguel G Cruz
Keyword(s):  
Fire Management ◽  
Model Performance ◽  
The United States ◽  
Fire Research ◽  
Management Decision ◽  
Crown Fire ◽  
Predictive Capacity ◽  
Data Set ◽  
Rate Of Spread ◽  
Spread Model

We evaluated the predictive capacity of a rate of spread model for active crown fires (M.G. Cruz, M.E. Alexander, and R.H. Wakimoto. 2005. Can. J. For. Res. 35: 1626–1639) using a relatively large (n = 57) independent data set originating from wildfire observations undertaken in Canada and the United States. The assembled wildfire data were characterized by more severe burning conditions and fire behavior in terms of rate of spread and the degree of crowning activity than the data set used to parameterize the crown fire rate of spread model. The statistics used to evaluate model adequacy showed good fit and a level of uncertainty considered acceptable for a wide variety of fire management and fire research applications. The crown fire rate of spread model predicted 42% of the data with an error lower then ±25%. Mean absolute percent errors of 51% and 60% were obtained for Canadian and American wildfires, respectively. The characteristics of the data set did not allow us to determine where model performance was weaker and consequently identify its shortcomings and areas of future improvement. The level of uncertainty observed suggests that the model can be readily utilized in support of operational fire management decision making and for simulations in fire research studies.

scholarly journals Perennial herbaceous legumes as live soil mulches and their effects on C, N and P of the microbial biomass

2003 ◽  
Vol 60 (1) ◽  
pp. 139-147 ◽  
Author(s):  
Gustavo Pereira Duda ◽  
José Guilherme Marinho Guerra ◽  
Marcela Teixeira Monteiro ◽  
Helvécio De-Polli ◽  
Marcelo Grandi Teixeira
Keyword(s):  
Microbial Biomass ◽  
Soil Depth ◽  
Vegetation Management ◽  
Pueraria Phaseoloides ◽  
Organic C ◽  
Arachis Pintoi ◽  
Factorial Combination ◽  
C And N ◽  
Microbial C ◽  
Macroptilium Atropurpureum

The use of living mulch with legumes is increasing but the impact of this management technique on the soil microbial pool is not well known. In this work, the effect of different live mulches was evaluated in relation to the C, N and P pools of the microbial biomass, in a Typic Alfisol of Seropédica, RJ, Brazil. The field experiment was divided in two parts: the first, consisted of treatments set in a 2 x 2 x 4 factorial combination of the following factors: live mulch species (Arachis pintoi and Macroptilium atropurpureum), vegetation management after cutting (leaving residue as a mulch or residue remotion from the plots) and four soil depths. The second part had treatments set in a 4 x 2 x 2 factorial combination of the following factors: absence of live mulch, A. pintoi, Pueraria phaseoloides, and M. atropurpureum, P levels (0 and 88 kg ha-1) and vegetation management after cutting. Variation of microbial C was not observed in relation to soil depth. However, the amount of microbial P and N, water soluble C, available C, and mineralizable C decreased with soil depth. Among the tested legumes, Arachis pintoi promoted an increase of microbial C and available C content of the soil, when compared to the other legume species (Pueraria phaseoloides and Macroptilium atropurpureum). Keeping the shoot as a mulch promoted an increase on soil content of microbial C and N, total organic C and N, and organic C fractions, indicating the importance of this practice to improve soil fertility.

Soil organic carbon and nitrogen sequestration and turnover in aggregates under subtropical leucaena–grass pastures

Soil Research ◽  
2018 ◽  
Vol 56 (6) ◽  
pp. 632 ◽  
Author(s):  
Kathryn Conrad ◽  
Ram C. Dalal ◽  
Ryosuke Fujinuma ◽  
Neal W. Menzies
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Aggregates ◽  
Soil Mass ◽  
Δ13c And Δ15n ◽  
Organic C ◽  
Carbon And Nitrogen ◽  
Nitrogen Sequestration ◽  
C And N

Stabilisation and protection of soil organic carbon (SOC) in macroaggregates and microaggregates represents an important mechanism for the sequestration of SOC. Legume-based grass pastures have the potential to contribute to aggregate formation and stabilisation, thereby leading to SOC sequestration. However, there is limited research on the C and N dynamics of soil organic matter (SOM) fractions in deep-rooted legume leucaena (Leucaena leucocephala)–grass pastures. We assessed the potential of leucaena to sequester carbon (C) and nitrogen (N) in soil aggregates by estimating the origin, quantity and distribution in the soil profile. We utilised a chronosequence (0–40 years) of seasonally grazed leucaena stands (3–6 m rows), which were sampled to a depth of 0.3 m at 0.1-m intervals. The soil was wet-sieved for different aggregate sizes (large macroaggregates, >2000 µm; small macroaggregates, 250–2000 µm; microaggregates, 53–250 µm; and <53 µm), including occluded particulate organic matter (oPOM) within macroaggregates (>250 µm), and then analysed for organic C, N and δ13C and δ15N. Leucaena promoted aggregation, which increased with the age of the leucaena stands, and in particular the formation of large macroaggregates compared with grass in the upper 0.2 m. Macroaggregates contained a greater SOC stock than microaggregates, principally as a function of the soil mass distribution. The oPOM-C and -N concentrations were highest in macroaggregates at all depths. The acid nonhydrolysable C and N distribution (recalcitrant SOM) provided no clear distinction in stabilisation of SOM between pastures. Leucaena- and possibly other legume-based grass pastures have potential to sequester SOC through stabilisation and protection of oPOM within macroaggregates in soil.

scholarly journals Simulating soil organic C dynamics in managed grasslands under humid temperate climatic conditions

2020 ◽  
Author(s):  
Asma Jebari ◽  
Jorge Álvaro-Fuentes ◽  
Guillermo Pardo ◽  
María Almagro ◽  
Agustin del Prado
Keyword(s):  
Field Experiments ◽  
Model Performance ◽  
Climatic Conditions ◽  
Plant Residue ◽  
Plant Residues ◽  
Organic C ◽  
Natural Grasslands ◽  
Rothc Model ◽  
Below Ground ◽  
Intensively Managed

Abstract. Temperate grasslands are of paramount importance in terms of soil organic carbon (SOC) dynamics. Globally, research on SOC dynamics has largely focused on forests, croplands and natural grasslands, while intensively managed grasslands has received much less attention. In this regard, we aimed to improve the prediction of SOC dynamics in managed grasslands under humid temperate regions. In order to do so, we modified and recalibrated the SOC model RothC, originally developed to model the turnover of SOC in arable topsoils, which requires limited amount of readily available input data. The modifications proposed for the RothC are: (1) water content up to saturation conditions in the soil water function of RothC to fit the humid temperate climatic conditions, (2) entry pools that account for particularity of exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) annual variation in the carbon inputs derived from plant residues considering both above- and below-ground plant residue and rhizodeposits components as well as their quality, and (4) the livestock treading effect (i.e., poaching damage) as a common problem in humid areas with higher annual precipitation. In the paper, we describe the basis of these modifications, carry out a simple sensitivity analysis and validate predictions against data from existing field experiments from four sites in Europe. Model performance showed that modified RothC reasonably captures well the different modifications. However, the model seems to be more sensitive to soil moisture and plant residues modifications than to the other modifications. The applied changes in RothC model could be appropriate to simulate both farm and regional SOC dynamics from managed grassland-based systems under humid temperate conditions.

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