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695 results for “Decomposition”
Effects of Nitrogen Fertilization on Litter and Soil Decomposition: Soil pH
The influence of inorganic nitrogen (N) inputs on decomposition is poorly understood. Some prior studies suggest that N may reduce the decomposition of substrates with high concentrations of lignin via inhibitory effects on the activity of lignin-degrading enzymes, although such inhibition has not always been demonstrated. The purpose of E145 was to study the effects of nitrogen (N) addition on decomposition of seven substrates ranging in initial lignin concentrations (from 7.4 - 25.6%) over five years in eight different grassland and forest sites in central Minnesota.
Long-term nitrogen fertilization inhibits carbon and nitrogen loss during late stage fungal necromass decomposition depending on necromass chemistry
Fungal necromass is increasingly recognized as a key component of in soil carbon (C) and nitrogen (N) cycling. However, how C and N loss from fungal necromass during decomposition are impacted by global change factors such as anthropogenic N addition and changes to soil C supply (e.g. via changing root exudation and rhizosphere priming) remains unclear and understudied relative to plant tissues. To address these gaps, we conducted a year-long decomposition experiment with four species of fungal necromass incubated across four forested sites in plots that had received inorganic N and/or labile C fertilization for decades in Minnesota, USA. We found that necromass chemistry was the primary driver of C and N loss from fungal necromass as well as response to fertilization. Specifically, N addition suppressed late-stage decomposition, but this effect was weaker in melanin-rich necromass, contrary to the hypothesis based on plant litter dynamics that N addition should suppress decomposition of more complex organic molecules. Labile C addition had no effect on either the early or late stages of necromass decomposition. Nitrogen release from necromass also varied among species, with N-poor necromass having lower N release after controlling for differences in mass loss via regression. The relatively minor effects of N fertilization on the proportion of initial necromass N released suggests that N demand by decomposers is the primary control on N loss during fungal necromass decomposition. Together, our results stress the importance of the afterlife effects of fungal chemical composition to forest soil C and N cycles. Further, they demonstrate that C and N release from this critical pool can be reduced by ongoing anthropogenic N addition.
Aggregate mesquite litter chemistry following soil-mixing and decomposition in a semi-arid grassland at the Jornada Basin LTER, 2010-2012
This dataset contains litter carbon content, nitrogen content, and associated chemistry data from a litter decomposition experiment at the Jornada Basin LTER in 2010 to 2012. To assess the role of soil-litter mixing (SLM) in aridland litter decomposition, litterbags were deployed in the Chihuahuan Desert and interrelationships between vegetation structure, SLM, and rates of decomposition were quantified. To assess the role of vegetation structure, litterbags were deployed in contrasting vegetation microsites, including grass, shrub, and bare ground microsites. This dataset contains litter chemistry data from the experiment including percent carbon, percent nitrogen, ash corrections, and the carbon to nitrogen ratio of litter in recovered bags. This study is complete.
Aggregate mesquite litter mass-loss following soil-mixing and decomposition in a semi-arid grassland at the Jornada Basin LTER, 2010-2012
This package contains litter mass loss data from a litter decomposition experiment at the Jornada Basin LTER. To assess the role of soil-litter mixing (SLM) in aridland litter decomposition, litterbags were deployed in the Chihuahuan Desert and interrelationships between vegetation structure, SLM, and rates of decomposition were quantified. To assess the role of vegetation structure, litterbags were deployed in contrasting vegetation microsites, including grass, shrub, and bare ground microsites. This dataset contains the mass-loss data (including ash-corrections) from the experiment. This study is complete.
OPD01 Konza Prairie standing dead and litter decomposition (1981-1983)
Standing dead and litter decomposition of big bluestem foliage and flowering stems were measured for two years using litterbag methods. Mass, nitrogen and phosphorus content were measured.
Factors influencing decomposition of leaves for five plant species at El Verde
We evaluated the influences of leaf quality, climate and microsite on the decomposition of leaves of five tropical tree species. Single-species litterbags were used to determine weight loss during the first three months of decomposition in the Luquillo Experimental Forest, Puerto Rico. Significant differences were found in decomposition rates among leaf species (Inga fagifolia < I. vera < Manilkara bidentata < C-roton poecilanthus << Sapium laurocerasus), but only S. laurocerasus differed significantly from the other species. Lignin had a suggestive negative correlation with leaf decomposition while carbon content and the lignin:N ratio were significantly correlated with mass loss. Content of N, P, Ca, and polyphenol were not significantly correlated with mass loss, but several of the litter quality variables were correlated with each other. Leaf species decomposed faster under canopies of their source trees than in a common plot where the source species were absent. Decomposition in two species in the Euphorbiaceae, S. laurocerasus and C. poecilanthus, was significantly affected by microsite. Leaching losses during the first three weeks were greater under source trees than in the common plot, and may have been associated with differences in canopy structure and throughfall. Differences in detrital communities, however, could have contributed to the differences in decomposition between microsites. Leaves of all species decomposed significantly faster in the wet than in the dry period (P = 0.001) despite little climatic variation in this subtropical wet forest type. This suggests that decomposition of tropical leaf litter might be sensitive to microclimatic changes on the forest floor resulting from either global climate change, or from natural or anthropogenic disturbances that open the canopy. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB
Leaf litter decomposition experiment In QPA and QPB - 2017-2019
We ran a leaf litter decomposition experiment over a seven-week period in each of three different years: 2017, 2018, and 2019. To assess leaf decomposition rates, we incubated leaf packs of freshly abscised Tabonuco (Dacryodes excelsa) leaves in pools in each of our two study stream reaches, and retrieved them at intervals over a seven-week period. We attempted to run experiments before onset of heavy rains which often occur in September-December. We chose to use leaves of Tabonuco for experimental leaf packs because it is a dominant riparian tree species along both of our study reaches. This dataset can be used to asses ecosystem function within tropical headwater streams. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Leaf Litter Decomposition Experiment in QPA and QPB – Insects - 2019
Macroinvertebrates collected from Tabonuco leaf packs associated with 50-day in-situ leaf litter decomposition experiment from 2019-07-1 – 2019-08-20 in Quebrada Prieta A and Quebrada Prieta B. This data may be used to examine insect colonization of leaf packs within streams. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
leaf litter decomposition experiment In QPA and QPB - visual shrimp observations 2019
Visual shrimp observations from pools associated with 2019 in-situ leaf litter decomposition experiment. Shrimp abundance was recorded over two-minute intervals within pools in Quebrada Prieta A and Quebrada Prieta B. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
fungal interactions during tropical leaf decomposition
Fungal interactions during leaf decomposition can facilitate or inhibit other fungi. This experiment focused on whether preconditioning of leaf litter by microfungi that were confined to one leaf (Unit-Restricted) made leaf litter less likely to be colonized and decomposed by basidiomycetes that bind litter into mats (Non-Unit- Restricted) than non-preconditioned litter. Leaves of Manilkara bidentata in litterbags were preconditioned by incubating them for 0, 1, 2 or 3 months in flat litter/seed rain baskets 10 centimeters above the forest forest floor to avoid colonization by basidiomycete fungi. Preconditioned and non-preconditioned leaves were transferred to 5 replicate basidiomycete fungal mats of Gymnopus johnstonii for 6 weeks. Both attachment by basidiomycete fungi and percent mass loss after 6 weeks decreased significantly with increasing preconditioning time. In non-preconditioned leaves, gamma irradiation did not affect mass loss or percent white-rot despite having significantly increased numbers of basidiomycete fungal connections as compared to non-irradiated leaves. In non-preconditioned leaves, more basidiomycete attachments to non-irradiated than irradiated leaves suggest facilitation by phyllosphere microfungi. While basidiomycete colonization was initially facilitated by phyllosphere fungi, we inferred that degradation of resource quality led to fewer fungal attachments and less mass loss after 1-3 months of preconditioning by microfungi. There is a 1-month time window for basidiomycete fungi to incorporate fallen leaves into their litter mats. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forest
Litter decomposition of the tabonuco forest before hurricane Hugo
We examined forest structure, tree species composition, litterfall rate, and leaf litter decomposition in a mid-successional forest (MSF) and an adjacent mature tabonuco forest (MTF) in the Luquillo Experimental Forest of Puerto Rico. Whereas the MTF site received limited human disturbance, the MSF site had been cleared for timber production by the beginning of this century and was abandoned after hurricanes struck the Luquillo Mountains in the 1920s and 1930s. We found that the MSF was dominated by successional tree species 50 yrs after secondary succession, and did not differ in tree basal area and litterfall rate from the MTF. Leaf decomposition rate in the MSF was higher than in the MTF, but this difference was small. Our results show that deforestation has long-term (>50 years) influence on tree species composition and that leaf decomposition processes in secondary forest is relatively faster than recovery of tree species composition. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Matlab code for a multipolar decomposition of optical forces
<p>This dataset supplements Figure 5 from the publication "Multipolar Origin of the Unexpected Transverse<br> Force Resulting from Two-Wave Interference*" by Karim Achouri, Andrei Kiselev, and Olivier J. F. Martin. Here, we provide the code for the multipolar analysis of the optical force based on the vector spherical harmonic decomposition. Based on the Mie solution, electric fields can be obtained numerically in the far-field by using the software developed by Dr. Karim Achouri https://github.com/kachourim/MieScatteringPEC. This code analyzes the far-field scattered by a perfect electric conductor sphere placed in vacuum for different sphere radii. In the framework of the Maxwell stress tensor, we find the optical force acting on a sphere along the z-direction in the illumination configuration presented in Figure 1*. The use of the vector spherical decomposition allows to observe contributions from different multipolar interactions. In this code,we analyze the force appearing as a result of the interaction between electric dipole aligned along x, px, and electric quadrupole with components xz, Qexz and compare it with the total force along the z-axis appearing as a result of interaction between all multipoles supported by the sphere with given radius.</p>
Approximate sum of squares decompositions for Adj₅ + k·Op₅ - λΔ₅ ∈ ISAut(F₅)
<p>This is the dataset accompanying <em>On property (T) for Aut(Fₙ) and SLₙ(</em>ℤ<em>) </em>paper (https://arxiv.org/abs/1812.03456). See the appendix thereof and Section 4 of (<a href="https://arxiv.org/abs/1712.07167">Aut(F₅) has property (T)</a>) for more details.</p> <p><strong>Content</strong></p> <ol> <li><code>1812.03456-cf6dee7.zip</code> contains a julia environment specification (<code>Project.toml</code> and <code>Manifest.toml</code>) as well as <code>1812.03456.jl</code> script used for automatic certification and jupyter noteboks in <code>./notebooks</code> directory.</li> <li><code>SAutF5_r2.tar.xz</code> contains the precomputed solutions for expressing <code>Adj₅+2·Op₅-0.28Δ₅</code> and <code>Adj₅+3·Op₅-1.4Δ₅</code> as sum of (hermitian) squares in the group ring of <code>SAut(F₅)</code>. The contents of this archive must be placed inside `<code>1812.03456`</code>directory from the <code>zip</code> file.</li> </ol> <p><strong>Preparation</strong></p> <p>The code needs to be run with <code>julia-1.4.0</code> or higher (tested versions include also versions <code>julia-1.5</code>). In principle any version in <code>[1.4-2.0)</code> should work due to the promise of forward compatibility.</p> <p>While located in the main directory (<code>1812.03456</code>) you should run the following code in <code>julia</code>s <code>REPL</code> console to instantiate the environment for computations:</p> <pre><code class="language-julia">using Pkg Pkg.activate(".") Pkg.instantiate()</code></pre> <p>(this needs to be done once per installation). Then the directory <code>SAutF5_r2</code> (from the <code>SAutF5_r2.tar.xz</code> archive) needs to be placed in <code>1812.03456</code>.</p> <p><strong>Replication: Jupyter notebook</strong></p> <p>A jupyter server may be launched then within the directory <code>1812.03456</code> by issuing from julia command-line (<code>REPL</code>) the following commands.</p> <pre><code>using Pkg Pkg.activate(".") using IJulia notebook(dir=".")</code></pre> <p>During the first run the user may be asked for installation of <code>Jupyter</code> program (a server for running this notebook) within <code>miniconda</code> environment, which will happen automatically after confirmation. To execute the commands in the notebook, one needs to navigate to <code>notebooks</code> subdirectory of <code>1812.03456</code> and click either of the notebooks.</p> <p>One can replicate the main computational results of the paper by executing all the cells in the <code>Positivity of Adj_n + kOp_n in ISAut(F_n)</code> notebook.</p> <p><strong>Replication: script</strong></p> <p>To verify that <em>(Adj₅ + 3.0·Op₅) - 1.4·Δ₅</em> admits an approximate sum of squares decomposition run in <code>1812.03456</code> directory</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 3 -l 1.4</code></p> </blockquote> <p>On a modern laptop computer this should finish in less than 2h.</p> <p>At the end of computations you will see lines such as:</p> <blockquote> <p>┌ Info: λ is certified to be ><br> └ λ_cert.lo = 1.3701131733828074<br> [ Info: i.e Adj_5 + 3.0·Op_5 - (1.3701131733828074)·Δ_5 ∈ Σ²₂ ISAut(F_5)</p> </blockquote> <p>This means that <em>Adj₅ + 3.0·Op₅ - λΔ₅</em> is a sum of Hermitian squares of elements from <em>ISAut(F₅)</em> for every <code>λ < 1.370....</code></p> <p>A similar verification for <em>Adj₅ + 2.0·Op₅ - 0.28·Δ₅</em> can be run by executing</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 2 -l 0.28</code></p> </blockquote> <p><strong>Generating the provided files</strong></p> <p>If you wish to produce the whole certificate on your own (including the generation of group ring and its multiplication table), delete all <code>*.jld</code> files from the <code>SAutF5_r2</code> folder and run one of the above commands with the same (or different) parameters again. Note: To do this you need at least 16GB of RAM and spare 24h of your CPU.</p> <p>This research was supported in part by National Science Center, Poland, grant 2017/26/D/ST1/00103.</p>
Mouse olfactory bulb intrinsic signal imaging for NNMF decomposition
<p>This dataset supplements our recent paper about automatic image segmentation using Non-negative Matrix Factorisation (NMF):</p> <p>Jan Soelter, Jan Schumacher, Hartwig Spors, Michael Schmuker (2014). Automatic segmentation of odor maps in the mouse olfactory bulb using regularized non-negative matrix factorization. <em>NeuroImage</em> 98:279-288.</p> <p>http://dx.doi.org/10.1016/j.neuroimage.2014.04.041</p>
Dataset supporting "Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases"
<p>Dataset supporting the paper:</p> <p>Z. Huang, M. England, J.H. Davenport and L.C. Paulson<br> Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases.<br> Proceedings of the 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC '16), pp. 45--52. IEEE, 2016. Digital Object Identifier: 10.1109/SYNASC.2016.020 </p>
From Lab to Technical CO2 Hydrogenation Catalysts: Understanding PdZn Decomposition
<p>Supplementary material: Additional experimental results, including catalytic performances of the different scaled-up samples, SEM images, and additional XAS data related to the linear combination fit</p>
Data and documentation from: Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape
<p>The zip file contains data and code to reproduce the analysis in the submitted manuscript entitled <i>Microclimate explains little variation in year-round decomposition across an Arctic tundra landscape</i>. Please see the manuscript for further details on background, methodology, results and discussion.</p>
Litter decomposition is moderated by scale-dependent microenvironmental variation in tundra ecosystems
<p><strong>QHI_crop.tiff </strong>= We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b).</p> <p><strong>thermsum.tif </strong>= We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (<a href="https://www.zotero.org/google-docs/?broken=Zl6wgI">Kemp et al., 2012)</a>. We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment.</p> <p><strong>Cited Works:</strong></p> <p> </p> <p>Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk – Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179.</p> <p> </p> <p>Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019.</p> <p> </p> <p><a href="https://www.zotero.org/google-docs/?hjdBYY">Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. <em>Global Change Biology</em>, <em>26</em>(2), 1003–1011.</a></p> <p> </p> <p>Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. <em>Methods in Ecology and Evolution</em>, <em>11</em>(1), 38-43.</p> <p> </p> <p>Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. <em>Methods in Ecology & Evolution</em>, <em>3</em>(1), 65-70.</p> <p><strong>Paper Abstract:</strong></p> <ol> <li> <p><strong>The Arctic tundra is one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle. </strong></p> </li> <li> <p><strong>Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada).</strong></p> </li> <li> <p><strong>We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data.</strong></p> </li> <li> <p><strong>We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness.</strong></p> </li> <li> <p><strong>Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature.</strong></p> </li> <li> <p><strong>Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.</strong></p> </li> </ol>
Soil carbon stock, litter decomposition, and weather data from Ethiopian forests
<p><strong>Introduction</strong></p> <p>100 sampling units (SU) were selected from the total of 631 SUs of the Forest Reference Level submission 2017 (FRL 2017). The sampling was designed unbiased for total growing stock per SU, altitude,and mean litter depth per SU. The actual field sampling succeeded on 98 of the pre-selected SUs due to accessibility restrictions.</p> <p><strong>Soil profile sampling</strong></p> <p>Soil sampling was performed from November 2017 till mid-January 2018. Samples were taken from undisturbed soil from depths of 0-10 cm, 10-20 cm, and 20-30 cm below the organic layer. Volumetric samples of 107.5 cm<sup>3</sup> were taken vertically, using a 10 cm long conically shaped corer with a cutting lower edge diameter of 37 mm and upper diameter of 40 mm. </p> <p>Composite samples were formed by combining the volumetric samples taken from different depths of two parallel soil profiles. The samples were transported to EEFRI Soil Laboratory in Addis Ababa after 1-4 weeks of sampling at distant locations. </p> <p> </p> <p><strong>Soil physical characteristics</strong></p> <p>The soil samples were air-dried, homogenized, and subjected to oven-drying at 105°C until constant mass. Total bulk density was determined using the total dry mass and volume of the composite samples. </p> <p>Organic carbon content (C % by wet oxidation method), and soil physical characteristics: moisture content, bulk density of the total sample, and bulk density of fine fraction (particles passing the 2 mm sieve). The mass of the coarse fraction was weighed. The soil fine fraction was also subjected to laser diffraction for more accurate particle size analysis for proportions of clay, silt, and sand. </p> <p> </p> <p>In addition to this 28 samples were also analyzed for C content in the laboratory of Natural Resources Institute Finland to determine C content by LECO CHN analyzer. This was done to calibrate the bulk of wet digestion-based estimates (Fig. 1). Before analysis, the soils were tested for the presence of inorganic C.</p> <p> </p> <p>For Figure 1. See Soil_C_Ethiopia.pdf</p> <p><strong>Figure 1</strong>. Comparison of results from wet oxidation (Walkley-Black) and dry oxidation (CHN analyzer). The dotted line shows the theoretical 1:1 match between the axis, the solid line shows linear regression (intercept = 0) between the methods. The estimated slope value of 1.165 was used in adjusting the wet digestion results to match those obtained by dry oxidation: OC<sub>adj</sub> = 1.165 * OC<sub>wet</sub>.</p> <p>Based on a linear regression between the wet and dry oxidation analysis results, a correction factor of 1.165 was applied to adjust the organic C% obtained by wet digestion. The adjusted data are shown in the file “SOC_Ethiopia_2017-2018.csv”.</p> <p> </p> <p>SOC stocks were calculated by multiplying the proportion of organic C with BD of fine earth, after which the result was corrected for stoniness, a visually estimated proportion of large stones (S, value from 0 to 1) in the soil profile that could not be included in the volumetric soil samples (FAO VS-FAST).</p> <p><span class="math-tex">\(SOCstock = C_{org} * BD_{fe} * (1-S)\)</span></p> <p><strong>Soil organic carbon stock data</strong></p> <p><strong>Files: “SOC_Ethiopia_2017-2018.csv” and “SOC_Ethiopia_2017-2018.xlsx”</strong></p> <p>The file includes soil characteristics from layers of 0-10 cm, 10-20 cm, and 20-30 cm below the loose organic layer on top of the soil. The data are used for SOC stock estimation in the respective layers as described above.</p> <p>In the .csv file individual columns are for </p> <p><strong>LAT</strong> is the latitude of the sampling site corresponding to <strong>FieldCode</strong> and <strong>SU_nr</strong></p> <p><strong>LON</strong> is the longitude of the sampling site corresponding to <strong>FieldCode</strong> and <strong>SU_nr</strong></p> <ul> <li>The coordinates are expressed as decimal degrees of the WGS84 system</li> </ul> <p><strong>FieldCode </strong>refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) </p> <p><strong>SU_nr </strong>is the Sampling Unit number of the Ethiopian NFI</p> <p><strong>Region </strong>is the name of the administrative region where the sample was taken</p> <p><strong>Biome </strong>is the name of the forest biome type where the sample was collected</p> <p><strong>BiomeSimplified </strong>is the name of a biome with some close types combined</p> <p><strong>DepthRange </strong>is the upper and lower limit of the soil sample in the field, cm</p> <p><strong>StoninessVFAST </strong>is a percentage of stones (VS-FAST by FAO) in the ca. 40 cm deep soil profile exposed during the sampling</p> <p><strong>FreshMassInField </strong>is the mass of the total composite soil sample of the given layer, g, primarily indicative of checking the correct number of subsamples in composite</p> <p><strong>NrComposites </strong>is the number of subsamples included in the composite for each soil layer</p> <p><strong>CorerVolume </strong>is a constant of 107.5 cm<sup>3</sup> because only one type of corer was used for undisturbed, volumetric sampling</p> <p><strong>CompositeVolume </strong>is the volume of the composite sample for each soil depth layer</p> <p><strong>CoarseFractionMass </strong>is the dry mass, g of soil particles > 2mm that did not pass the sieve, but were included in the sample volume</p> <p><strong>FE_DryMass </strong>is oven-dry mass, g of the fine fraction that passed the 2 mm sieve.</p> <p><strong>BDtot </strong>is total bulk density, g m<sup>-3</sup>, calculated for the composite sample</p> <p><strong>BDfe </strong>is the bulk density of the fine earth fraction, g m<sup>-3</sup></p> <p><strong>OC_adj</strong> is organic carbon (OC) content (%) in the composite sample, adjusted according to the comparison between dry and wet oxidation methods (Fig. 1)</p> <p><strong>SOCfe </strong>is SOC stock calculated for soil fine earth fraction, t ha<sup>-1</sup> in the 10 cm deep soil layer</p> <p><strong>SOCfe_stoniness</strong> is SOC stock of the fine earth fraction, t ha<sup>-1</sup> in the 10 cm deep soil layer, adjusted for stoniness. The correction assumes that the volume occupied by larger stones would be void of OC. </p> <p> </p> <p><strong>Litter stock data</strong></p> <p><strong>File: “Litter_Ethiopia_2017-2018.csv”</strong></p> <p>The file includes measurements of litter layer on Ethiopian NFI Sampling Unit (SU) sites where sampling for SOC stock determination was done. The depth of the litter layer was measured in the SU’s of the NFI, and this data contains in addition to depth also a volumetric sample of the litter layer. The dry bulk density was used to calculate the carbon stocks in the litter pool.</p> <p> </p> <p>The depth of the litter layer was measured in the field. Litter from the respective spot was sampled quantitatively from a frame of 0.01m<sup>2</sup> of area for litter dry mass estimate.</p> <p>The organic C stock in a litter (L) was calculated as,</p> <p> </p> <p><span class="math-tex">\(L = {M\over z} * {C_{om}\over A}, \)</span></p> <p> </p> <p>where</p> <p><em>M</em> = Dry mass of the litter sample, g</p> <p><em>z</em> = Depth of the litter layer in the field, m</p> <p><em>C<sub>om</sub></em> = Conversion factor from dry organic matter to carbon (C), 0.5</p> <p><em>A</em> = area of quantitative collection of litter (0.01 m<sup>2</sup>)</p> <p> </p> <p>In the .csv file individual columns are for</p> <p><strong>LAT, LON</strong> is the GPS coordinates (decimal degrees of WGS84) for the Sampling Units (<strong>SU_ID</strong>)</p> <p><strong>SU_ID</strong> is the Sampling Unit identification number of the Ethiopian NFI</p> <p><strong>FieldCode </strong>refers to the Region and Sampling Unit number of the Ethiopian NFI (see below)</p> <p><strong>Region </strong>is the name of the administrative region where the sample was taken</p> <p><strong>Litter_dry</strong> is the dry mass, g of the litter sample</p> <p><strong>Area_m2</strong> is the area, m<sup>2</sup> of litter sampling</p> <p><strong>MeanLitterDepth </strong>is the mean depth of the litter layer at the sampling area</p> <p><strong>CDensityLitter </strong>is the dry bulk density of the litter, g m<sup>-2</sup> multiplied by the assumed organic C proportion of the oven-dry litter materials (0.50)</p> <p><strong>LitterCStock_tha</strong> is the litter stock, t ha<sup>-1</sup> calculated from the C density of the litter layer</p> <p> </p> <p><strong>Litter bag data (decomposition and quality)</strong></p> <p>The leaves and twigs were sampled from 2 species (Juniperus and Podocarpus) and 3 locations of the elevation gradient in the Chilimo forest (Table 1). The forest was considered an old-growth with <em>Juniperus procera</em> and <em>Podocarpus falcatus</em>being the main species forming the tree canopy. The sites form an elevation gradient (Table 1).</p> <p> </p> <p>Table 1. Geographical locations of the study sites in the Chilimo forest.</p> <p> </p> <table> <tbody> <tr> <td> <p>id</p> </td> <td> <p>Latitude (deg.)</p> </td> <td> <p>Longitude (deg.)</p> </td> <td> <p>Elevation</p> <p>(m a.s.l)</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>9.0672</p> </td> <td> <p>38.1443</p> </td> <td> <p>2500</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>9.0712</p> </td> <td> <p>38.1556</p> </td> <td> <p>2670</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>9.0869</p> </td> <td> <p>38.1684</p> </td> <td> <p>2800</p> </td> </tr> </tbody> </table> <p> </p> <p>The dying and dead leaves were sampled directly from the trees later referred to as “fresh” and from the branches found on the ground, referred to as “old”. The old leaves were assumed to be dead for around 3 months. The diameter of the branches/twigs was less than 1 cm in diameter. The samples were first sorted and air-dried in an elevated temperature of the greenhouse and thereafter oven-dried in the oven overnight at 45 °C. The samples were analyzed for acid, water, ethanol dissolved,and undissolved fractions (AWEN) (Table 2) and for the decomposition rates of the litter installed into the litter bags corresponding to each of the Chilimo sites. </p> <p> </p> <p>Table 2. Acid, water, ethanol (A, W, E, respectively) dissolved and undissolved fractions (N) from the litter components of the dominant tree species in the Chilimo forest.</p> <table> <tbody> <tr> <td> <p>Litter type</p> </td> <td> <p>Species</p> </td> <td> <p>A</p> </td> <td> <p>W</p> </td> <td> <p>E</p> </td> <td> <p>N</p> </td> </tr> <tr> <td> <p>leaves fresh</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.45</p> </td> <td> <p>0.13</p> </td> <td> <p>0.1</p> </td> <td> <p>0.33</p> </td> </tr> <tr> <td> <p>leaves fresh</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.42</p> </td> <td> <p>0.28</p> </td> <td> <p>0.05</p> </td> <td> <p>0.25</p> </td> </tr> <tr> <td> <p>leaves old</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.44</p> </td> <td> <p>0.07</p> </td> <td> <p>0.08</p> </td> <td> <p>0.41</p> </td> </tr> <tr> <td> <p>leaves old</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.44</p> </td> <td> <p>0.09</p> </td> <td> <p>0.05</p> </td> <td> <p>0.42</p> </td> </tr> <tr> <td> <p>twigs</p> </td> <td> <p><em>Juniperus </em></p> </td> <td> <p>0.61</p> </td> <td> <p>0.04</p> </td> <td> <p>0.02</p> </td> <td> <p>0.32</p> </td> </tr> <tr> <td> <p>twigs</p> </td> <td> <p><em>Podocarpus </em></p> </td> <td> <p>0.56</p> </td> <td> <p>0.15</p> </td> <td> <p>0.02</p> </td> <td> <p>0.27</p> </td> </tr> </tbody> </table> <p> </p> <p>A sufficient amount of litter was placed into the litter bags (polyurethane mesh 1 mm) and the mesh bags were installed on top of the soil surface under the forest canopy (later referred to as “canopy”) and in the forest gap caused by harvesting (later referred as “open”). The installation of the litter bags (for each species 3 replicates of each litter type for each site and canopy type for the 3 periods, in total 12 litter bags for leaves and 6 bags for twigs) was done on 22.9.2017. The mesh bags were left on the ground, protected from grazing by the fence, and retrieved subsequently on 12.10.2017, 31.10.2017, and 12.12.2017. Despite the efforts took few samples were lost. The retrieved samples were oven-dried and initial mass and mass loss data for each period and litter type with a detailed description of the variables can be found in the file “litter.chilimo_07.02.22.xlsx”.</p> <p> </p> <p><strong>Soil temperature data</strong></p> <p>During the period from 22.9.2017 to 12.12.2017, we monitored the soil temperature at 5 cm depth under the canopy and in the open canopy on all Chilimo sites continuously every 4 hours intervals with the Maxim iButton temperature loggers. However, some sensors were lost. Daily means and their standard deviation of the continuous temperatures can be found in the file “soil.temp.chilimo_07.02.22.xlsx”.</p> <p> </p> <p><strong>Processed weather data</strong></p> <p>The air temperature and precipitation data for 98 sampling units corresponding to soil carbon data originated from 73 weather stations located across Ethiopia and were obtained from Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/). Sampling units were joined with weather data by the closest proximity to their corresponding weather stations. Precipitation was unaltered. The air temperature required correction by elevation is described in more detail in Lehtonen et al. (2020). The monthly values of air temperature and precipitation with an accompanied readme description of the variables can be found for 98 sampling units in the file “sampling.units98_meteo_07.02.22.xlsx” and the Chilimo study sites in the file “monthly.weather.chilimo_07.02.22.xlsx”. The monthly values in the file "sampling.units98_meteo_07.02.22.xlsx" correspond to long-term average over the period from 1986 to 2017.</p> <p> </p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen, A., Ťupek, B., Nieminen, T.M., Balázs, A., Anjulo, A., Teshome, M., Tiruneh, Y. and Alm, J., 2020. Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios. <em>Land Degradation & Development</em>, <em>31</em>(18), pp.2763-2774.</p> <p> </p> <p>FRL 2017. https://redd.unfccc.int/files/ethiopia_frel_3.2_final_modified_submission.pdf</p>
Organic Matter and Decomposition Rate Observational Data from Salt Marshes of North Carolina
<p>This data have been collected from salt marshes in Masonboro Island and Wrightsville Beach in NC, US, in 2015 and 2016.</p> <p>The data include organic matter percent, organic carbon percent, bulk density, decomposition rate, stabilization factor, marsh elevation and vegetation cover all along transects from the channel to the inner marsh.</p>
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