Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3471-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3471-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Jul 2022
Data description paper |
| 29 Jul 2022
| 29 Jul 2022
Aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Ignacio Andrés Siebenhart
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Amy Theresa Austin
Facultad de Agronomía, Universidad de Buenos Aires,
Buenos Aires, 1417, Argentina
Instituto de Investigaciones Fisiológicas y
Ecológicas Vinculadas a la Agricultura (IFEVA; CONICET-FAUBA), Buenos
Aires, 1417, Argentina
Carlos A. Sierra
Max-Planck-Institut für Biogeochemie, Jena, 07745,
Germany
Swedish University of Agricultural Sciences, Uppsala, Sweden
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Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
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Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Valentina Lara, Carlos A. Sierra, Miguel A. Peña, Sebastián Ramirez, Diego Navarrete, Juan F. Phillips, and Álvaro Duque
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This preprint is open for discussion and under review for Biogeosciences (BG).
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Impacts of deforestation on the soil level are commonly overlooked. Conversion of Amazon rainforest to pastures increases soil compaction and decreases soil carbon storage, with lasting effects over time and across soil depth. After decades, pasture accumulated soil carbon doesn't match the original forest stocks. These changes may worsen climate change by reducing the Amazon basin ability to store carbon, highlighting the need to protect these ecosystems, from canopy to soil.
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We propose an approach to obtain weights for calculating averages of variables from Earth system models (ESM) based on concepts from information theory. It quantifies a relative distance between model output and reality, even though it is impossible to know the absolute distance from model predictions to reality. The relative ranking among models is based on concepts of model selection and multi-model averages previously developed for simple statistical models, but adapted here for ESMs.
Carlos A. Sierra, Ingrid Chanca, Meinrat Andreae, Alessandro Carioca de Araújo, Hella van Asperen, Lars Borchardt, Santiago Botía, Luiz Antonio Candido, Caio S. C. Correa, Cléo Quaresma Dias-Junior, Markus Eritt, Annica Fröhlich, Luciana V. Gatti, Marcus Guderle, Samuel Hammer, Martin Heimann, Viviana Horna, Armin Jordan, Steffen Knabe, Richard Kneißl, Jost Valentin Lavric, Ingeborg Levin, Kita Macario, Juliana Menger, Heiko Moossen, Carlos Alberto Quesada, Michael Rothe, Christian Rödenbeck, Yago Santos, Axel Steinhof, Bruno Takeshi, Susan Trumbore, and Sönke Zaehle
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We present here a unique dataset of atmospheric observations of greenhouse gases and isotopes that provide key information on land-atmosphere interactions for the Amazon forests of central Brazil. The data show a relatively large level of variability, but also important trends in greenhouse gases, and signals from fires as well as seasonal biological activity.
Ingrid Chanca, Ingeborg Levin, Susan Trumbore, Kita Macario, Jost Lavric, Carlos Alberto Quesada, Alessandro Carioca de Araújo, Cléo Quaresma Dias Júnior, Hella van Asperen, Samuel Hammer, and Carlos A. Sierra
Biogeosciences, 22, 455–472, https://doi.org/10.5194/bg-22-455-2025, https://doi.org/10.5194/bg-22-455-2025, 2025
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Assessing the net carbon (C) budget of the Amazon entails considering the magnitude and timing of C absorption and losses through respiration (transit time of C). Radiocarbon-based estimates of the transit time of C in the Amazon Tall Tower Observatory (ATTO) suggest a change in the transit time from 6 ± 2 years and 18 ± 4 years within 2 years (October 2019 and December 2021, respectively). This variability indicates that only a fraction of newly fixed C can be stored for decades or longer.
Maximiliano González-Sosa, Carlos A. Sierra, J. Andrés Quincke, Walter E. Baethgen, Susan Trumbore, and M. Virginia Pravia
SOIL, 10, 467–486, https://doi.org/10.5194/soil-10-467-2024, https://doi.org/10.5194/soil-10-467-2024, 2024
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Based on an approach that involved soil organic carbon (SOC) monitoring, radiocarbon measurement in bulk soil, and incubations from a long-term 60-year experiment, it was concluded that the avoidance of old carbon losses in the integrated crop–pasture systems is the main reason that explains their greater carbon storage capacities compared to continuous cropping. A better understanding of these processes is essential for making agronomic decisions to increase the carbon sequestration capacity.
Andrés Tangarife-Escobar, Georg Guggenberger, Xiaojuan Feng, Guohua Dai, Carolina Urbina-Malo, Mina Azizi-Rad, and Carlos A. Sierra
Biogeosciences, 21, 1277–1299, https://doi.org/10.5194/bg-21-1277-2024, https://doi.org/10.5194/bg-21-1277-2024, 2024
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Soil organic matter stability depends on future temperature and precipitation scenarios. We used radiocarbon (14C) data and model predictions to understand how the transit time of carbon varies under environmental change in grasslands and peatlands. Soil moisture affected the Δ14C of peatlands, while temperature did not have any influence. Our models show the correspondence between Δ14C and transit time and could allow understanding future interactions between terrestrial and atmospheric carbon
Shane W. Stoner, Marion Schrumpf, Alison Hoyt, Carlos A. Sierra, Sebastian Doetterl, Valier Galy, and Susan Trumbore
Biogeosciences, 20, 3151–3163, https://doi.org/10.5194/bg-20-3151-2023, https://doi.org/10.5194/bg-20-3151-2023, 2023
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Soils store more carbon (C) than any other terrestrial C reservoir, but the processes that control how much C stays in soil, and for how long, are very complex. Here, we used a recent method that involves heating soil in the lab to measure the range of C ages in soil. We found that most C in soil is decades to centuries old, while some stays for much shorter times (days to months), and some is thousands of years old. Such detail helps us to estimate how soil C may react to changing climate.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
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Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Song Wang, Carlos Sierra, Yiqi Luo, Jinsong Wang, Weinan Chen, Yahai Zhang, Aizhong Ye, and Shuli Niu
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-33, https://doi.org/10.5194/bg-2023-33, 2023
Manuscript not accepted for further review
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Nitrogen is important for plant growth and carbon uptake, which is uaually limited in nature and can constrain carbon storage and impact efforts to combat climate change. We developed a new method of combining data and models to determine if and how much an ecosystem is nitrogen limited. This new method can help determine if and to what extent an ecosystem is nitrogen-limited, providing insight into nutrient limitations on a global scale and guiding ecosystem management decisions.
Andrea Scheibe, Carlos A. Sierra, and Marie Spohn
Biogeosciences, 20, 827–838, https://doi.org/10.5194/bg-20-827-2023, https://doi.org/10.5194/bg-20-827-2023, 2023
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We explored carbon cycling in soils in three climate zones in Chile down to a depth of 6 m, using carbon isotopes. Our results show that microbial activity several meters below the soil surface is mostly fueled by recently fixed carbon and that strong decomposition of soil organic matter only occurs in the upper decimeters of the soils. The study shows that different layers of the critical zone are tightly connected and that processes in the deep soil depend on recently fixed carbon.
Carlos A. Sierra, Verónika Ceballos-Núñez, Henrik Hartmann, David Herrera-Ramírez, and Holger Metzler
Biogeosciences, 19, 3727–3738, https://doi.org/10.5194/bg-19-3727-2022, https://doi.org/10.5194/bg-19-3727-2022, 2022
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Empirical work that estimates the age of respired CO2 from vegetation tissue shows that it may take from years to decades to respire previously produced photosynthates. However, many ecosystem models represent respiration processes in a form that cannot reproduce these observations. In this contribution, we attempt to provide compelling evidence, based on recent research, with the aim to promote a change in the predominant paradigm implemented in ecosystem models.
Carlos A. Sierra, Susan E. Crow, Martin Heimann, Holger Metzler, and Ernst-Detlef Schulze
Biogeosciences, 18, 1029–1048, https://doi.org/10.5194/bg-18-1029-2021, https://doi.org/10.5194/bg-18-1029-2021, 2021
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The climate benefit of carbon sequestration (CBS) is a metric developed to quantify avoided warming by two separate processes: the amount of carbon drawdown from the atmosphere and the time this carbon is stored in a reservoir. This metric can be useful for quantifying the role of forests and soils for climate change mitigation and to better quantify the benefits of carbon removals by sinks.
Cited articles
Adair, E. C., Parton, W. J., Del Grosso, S. J., Silver, W. L., Harmon, M.
E., Hall, S. A., Burke, I. C., and Hart, S. C.: Simple three-pool model
accurately describes patterns of long-term litter decomposition in diverse
climates, Glob. Chang. Biol., 14, 2636–2660,
https://doi.org/10.1111/j.1365-2486.2008.01674.x, 2008.
Adair, E. C., Parton, W. J., King, J. Y., Brandt, L. A., and Lin, Y.:
Accounting for photodegradation dramatically improves prediction of carbon
losses in dryland systems, Ecosphere, 8, e01892, https://doi.org/10.1002/ecs2.1892,
2017.
Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A.,
Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K.,
Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P.,
Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid
ecosystems in the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015.
Anderson, D. R. and Burnham, K. P.: Model selection and multi-model
inference, 2nd edn., edited by: Burnham, K. P. and Anderson, D. R., Springer-Verlag, NY, https://doi.org/10.1007/b97636, 2004.
Austin, A. T.: Has water limited our imagination for aridland
biogeochemistry, Trends Ecol. Evol., 26, 229–235,
https://doi.org/10.1016/j.tree.2011.02.003, 2011.
Austin, A. T., Méndez, M. S. and Ballaré, C. L.: Photodegradation
alleviates the lignin bottleneck for carbon turnover in terrestrial
ecosystems, P. Natl. Acad. Sci. USA, 113, 4392–4397,
https://doi.org/10.1073/pnas.1516157113, 2016.
Bona, K. A., Hilger, A., Burgess, M., Wozney, N., and Shaw, C.: A peatland
productivity and decomposition parameter database, Ecology, 99,
2406–2406, https://doi.org/10.1002/ecy.2462, 2018.
Bonan, G. B. and Doney, S. C.: Climate, ecosystems, and planetary futures:
The challenge to predict life in Earth system models, Science, 359, eaam8328,
https://doi.org/10.1126/science.aam8328, 2018.
Brun, R., Reichert, P., and Künsch, H. R.: Practical identifiability
analysis of large environmental simulation models, Water Resour. Res.,
37, 1015–1030, https://doi.org/10.1029/2000WR900350, 2001.
Cepeda-Pizarro, J. G.: Litter decomposition in deserts: an overview with an
example from coastal arid Chile, Rev. Chil. Hist. Nat., 66, 323–336, 1993.
Chapin, F. S., Matson, P. A., and Vitousek, P. M. (Eds.): Principles of Terrestrial
Ecosystem Ecology, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4419-9504-9, 2011.
Chen, M., Parton, W. J., Adair, E. C., Asao, S., Hartman, M. D., and Gao, W.:
Simulation of the effects of photodecay on long-term litter decay using
DayCent, Ecosphere, 7, e01631, https://doi.org/10.1002/ecs2.1631, 2016.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., and Paul, E.: The
Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant
litter decomposition with soil organic matter stabilization: Do labile plant
inputs form stable soil organic matter?, Glob. Chang. Biol., 19,
988–995, https://doi.org/10.1111/gcb.12113, 2013.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173,
https://doi.org/10.1038/nature04514, 2006.
Day, T. A., Bliss, M. S., Tomes, A. R., Ruhland, C. T., and Guénon, R.:
Desert leaf litter decay: Coupling of microbial respiration, water-soluble
fractions and photodegradation, Glob. Chang. Biol., 24, 5454–5470,
https://doi.org/10.1111/gcb.14438, 2018.
Feng, S. and Fu, Q.: Expansion of global drylands under a warming climate, Atmos. Chem. Phys., 13, 10081–10094, https://doi.org/10.5194/acp-13-10081-2013, 2013.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37,
4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Foereid, B., Rivero, M. J., Primo, O., and Ortiz, I.: Modelling
photodegradation in the global carbon cycle, Soil Biol. Biochem., 43,
1383–1386, https://doi.org/10.1016/j.soilbio.2011.03.004, 2011.
GBIF.org: GBIF main site, https://www.gbif.org (last access: 18 July 2022),
2022.
GitHub: Homepage, https://github.com/ (last access: 18 July 2022), 2022.
Google LLC: Google Earth Pro, 2020.
Hampton, S. E., Anderson, S. S., Bagby, S. C., Gries, C., Han, X., Hart, E.
M., Jones, M. B., Lenhardt, W. C., MacDonald, A., Michener, W. K., Mudge,
J., Pourmokhtarian, A., Schildhauer, M. P., Woo, K. H., and Zimmerman, N.:
The Tao of open science for ecology, Ecosphere, 6, art120,
https://doi.org/10.1890/ES14-00402.1, 2015.
Harmon, M. E., Nadelhoffer, K. J., and Blair, J. M.: Measuring decomposition,
nutrient turnover, and stores in plant litter, in: Standard Soil Methods for
Long-Term Ecological Research, edited by: Robertson, G., Coleman, D.,
Bledsoe, C., and Sollins, P., 202–240, Oxford University Press, Oxford, ISBN-10 0195120833,
ISBN-13 978-0195120837,
1999.
Huang, J., Ma, J., Guan, X., Li, Y., and He, Y.: Progress in Semi-arid
Climate Change Studies in China, Adv. Atmos. Sci., 36, 922–937,
https://doi.org/10.1007/s00376-018-8200-9, 2019.
Hughes, P., McBratney, A. B., Huang, J., Minasny, B., Micheli, E., and
Hempel, J.: Comparisons between USDA Soil Taxonomy and the Australian Soil
Classification System I: Data harmonization, calculation of taxonomic
distance and inter-taxa variation, Geoderma, 307, 198–209,
https://doi.org/10.1016/j.geoderma.2017.08.009, 2017.
Jafari, M., Tavili, A., Panahi, F., Esfahan, E. Z., and Ghorbani, M.:
Reclamation of Arid Lands, Springer, edited by: Förstner, U.,
Rulkens, W. H., and
Salomons, W.,
https://doi.org/10.1007/978-3-319-54828-9, 2018.
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY plant trait database – enhanced coverage and open access, Glob.
Chang. Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F.,
Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.,
McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra,
C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van
Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J.,
Xu, X., Xu, X., and Zhou, T.: Toward more realistic projections of soil
carbon dynamics by Earth system models, Global Biogeochem. Cycles, 30,
40–56, https://doi.org/10.1002/2015GB005239, 2016.
NASA Langley Research Center (LaRC): NASA Prediction of Worldwide Energy
Resources (POWER) Project, https://power.larc.nasa.gov/, last access: 3 December 2021.
Noy-Meir, I.: Desert Ecosystems: Environment and Producers, Annu. Rev. Ecol.
Syst., 4, 25–51, https://doi.org/10.1146/annurev.es.04.110173.000325, 1973.
Pfeiffer, M., Padarian, J., Osorio, R., Bustamante, N., Olmedo, G. F., Guevara, M., Aburto, F., Albornoz, F., Antilén, M., Araya, E., Arellano, E., Barret, M., Barrera, J., Boeckx, P., Briceño, M., Bunning, S., Cabrol, L., Casanova, M., Cornejo, P., Corradini, F., Curaqueo, G., Doetterl, S., Duran, P., Escudey, M., Espinoza, A., Francke, S., Fuentes, J. P., Fuentes, M., Gajardo, G., García, R., Gallaud, A., Galleguillos, M., Gomez, A., Hidalgo, M., Ivelic-Sáez, J., Mashalaba, L., Matus, F., Meza, F., Mora, M. D. L. L., Mora, J., Muñoz, C., Norambuena, P., Olivera, C., Ovalle, C., Panichini, M., Pauchard, A., Pérez-Quezada, J. F., Radic, S., Ramirez, J., Riveras, N., Ruiz, G., Salazar, O., Salgado, I., Seguel, O., Sepúlveda, M., Sierra, C., Tapia, Y., Tapia, F., Toledo, B., Torrico, J. M., Valle, S., Vargas, R., Wolff, M., and Zagal, E.: CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort, Earth Syst. Sci. Data, 12, 457–468, https://doi.org/10.5194/essd-12-457-2020, 2020.
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W.,
Sitch, S., and Van Der Werf, G. R.: Contribution of semi-arid ecosystems to
interannual variability of the global carbon cycle, Nature, 509,
600–603, https://doi.org/10.1038/nature13376, 2014.
Prescott, C. E. and Vesterdal, L.: Decomposition and transformations along
the continuum from litter to soil organic matter in forest soils, For. Ecol.
Manage., 498, 119522, https://doi.org/10.1016/j.foreco.2021.119522, 2021.
QGIS Development Team: QGIS Geographic Information System, http://www.qgis.org (last access: 18 July 2022), 2021.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 19 July 2022), 2020.
Reynolds, J. F., Smith, D. M. S., Lambin, E. F., Turner, B. L., Mortimore,
M., Batterbury, S. P. J., Downing, T. E., Dowlatabadi, H., Fernandez, R. J.,
Herrick, J. E., Huber-Sannwald, E., Jiang, H., Leemans, R., Lynam, T.,
Maestre, F. T., Ayarza, M., and Walker, B.: Global Desertification: Building
a Science for Dryland Development, Science, 316, 847–851,
https://doi.org/10.1126/science.1131634, 2007.
Rohatgi, A.: WebPlotDigitizer,
Version 4.3,
Pacifica, CA, USA, https://automeris.io/WebPlotDigitizer,
last access: July 2020.
Safriel, U. and Adeel, Z.: Dryland Systems, in: Ecosystems and Human
Well-being: Current State and Trends, vol, 1, edited by: Hassan, R.,
Scholes, R., and Ash, N., 623–662, Island Press, Washington, ISSN 00184888, 2005.
Sarquis, A., Siebenhart, I. A., Austin, A. T., and Sierra, C. A.: aridec, Zenodo [data set],
https://doi.org/10.5281/zenodo.6600345, 2022.
Schädel, C., Beem-Miller, J., Aziz Rad, M., Crow, S. E., Hicks Pries, C. E., Ernakovich, J., Hoyt, A. M., Plante, A., Stoner, S., Treat, C. C., and Sierra, C. A.: Decomposability of soil organic matter over time: the Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures, Earth Syst. Sci. Data, 12, 1511–1524, https://doi.org/10.5194/essd-12-1511-2020, 2020.
Shumway, R. H. and Stoffer, D. S.: Time Series Analysis and Its
Applications, 4th edn., edited by: DeVeaux, R., Fienberg, S. E., and Olkin, I.,
Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-52452-8, 2017.
Sierra, C. A. and Müller, M.: A general mathematical framework for
representing soil organic matter dynamics, Ecol. Monogr., 85, 505–524,
https://doi.org/10.1890/15-0361.1, 2015.
Sierra, C. A., Müller, M., and Trumbore, S. E.: Models of soil organic matter decomposition: the SoilR package, version 1.0, Geosci. Model Dev., 5, 1045–1060, https://doi.org/10.5194/gmd-5-1045-2012, 2012.
Sierra, C. A., Malghani, S., and Müller, M.: Model structure and
parameter identification of soil organic matter models, Soil Biol. Biochem.,
90, 197–203, https://doi.org/10.1016/j.soilbio.2015.08.012, 2015.
Soetaert, K. and Petzoldt, T.: Inverse Modelling, Sensitivity and Monte
Carlo Analysis in R Using Package FME, J. Stat. Softw., 33, 1–28,
https://doi.org/10.18637/jss.v033.i03, 2010.
Tedersoo, L., Laanisto, L., Rahimlou, S., Toussaint, A., Hallikma, T., and
Pärtel, M.: Global database of plants with root-symbiotic nitrogen
fixation: NodDB, edited by: Michalet, R., J. Veg. Sci., 29, 560–568,
https://doi.org/10.1111/jvs.12627, 2018.
Trabucco, A. and Zomer, R.: Global Aridity Index and Potential
Evapotranspiration (ET0) Climate Database v2, CGIAR Consort. Spat. Inf., https://doi.org/10.6084/m9.figshare.7504448.v3,
2019.
United Nations Environment Programme: World atlas of desertification, 2nd
edn., edited by: Middleton, N. and Thomas, D. S. G., Arnold, London, Great
Britain, ISBN 0340691662, 1997.
Verbist, K., Santibañez, F., Gabriels, D., and Soto, G.: Atlas de Zonas
Áridas de América Latina y el Caribe, United Nations Educational,
Scientific and Cultural Organization, edited by: Verbist, K., Santibáñez, Soto,
F.
G.,
Donoso, M. C., and Gabriels,
D., ISBN 9789290891642, 2010.
White, R. P. and Nackoney, J.: Drylands, people and ecosystem goods and
services: a web-based geospatial analysis, http://pdf.wri.org/drylands.pdf (last access: 18 July 2022), 2003.
Yao, J., Liu, H., Huang, J., Gao, Z., Wang, G., Li, D., Yu, H., and Chen, X.:
Accelerated dryland expansion regulates future variability in dryland gross
primary production, Nat. Commun., 11, 1665,
https://doi.org/10.1038/s41467-020-15515-2, 2020.
Zhang, X. and Wang, W.: Control of climate and litter quality on leaf litter
decomposition in different climatic zones, J. Plant Res., 128, 791–802,
https://doi.org/10.1007/s10265-015-0743-6, 2015.
Short summary
Plant litter breakdown in aridlands is driven by processes different from those in more humid ecosystems. A better understanding of these processes will allow us to make better predictions of future carbon cycling. We have compiled aridec, a database of plant litter decomposition studies in aridlands and tested some modeling applications for potential users. Aridec is open for use and collaboration, and we hope it will help answer newer and more important questions as the database develops.
Plant litter breakdown in aridlands is driven by processes different from those in more humid...
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