Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-381-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-381-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jan 2024
Research article |
| 22 Jan 2024
| 22 Jan 2024
Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions
Stephen Björn Wirth
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic Agriculture, Kiel University, Hermann-Rodewald-Str. 9, 24118, Kiel, Germany
Arne Poyda
Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic Agriculture, Kiel University, Hermann-Rodewald-Str. 9, 24118, Kiel, Germany
Friedhelm Taube
Institute of Crop Science and Plant Breeding, Grass and Forage Science/Organic Agriculture, Kiel University, Hermann-Rodewald-Str. 9, 24118, Kiel, Germany
Britta Tietjen
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Str. 2/4, Gartenhaus, 14195 Berlin, Germany
Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany
Christoph Müller
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Kirsten Thonicke
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Anja Linstädter
University of Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany
Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Kai Behn
University of Potsdam, Institute of Biochemistry and Biology, Potsdam, Germany
Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
Sibyll Schaphoff
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Werner von Bloh
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Susanne Rolinski
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
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Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
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Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-6, https://doi.org/10.5194/bg-2022-6, 2022
Manuscript not accepted for further review
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Tobias Herzfeld, Jens Heinke, Susanne Rolinski, and Christoph Müller
Earth Syst. Dynam., 12, 1037–1055, https://doi.org/10.5194/esd-12-1037-2021, https://doi.org/10.5194/esd-12-1037-2021, 2021
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Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
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Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
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Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
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We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
James A. Franke, Christoph Müller, Joshua Elliott, Alex C. Ruane, Jonas Jägermeyr, Abigail Snyder, Marie Dury, Pete D. Falloon, Christian Folberth, Louis François, Tobias Hank, R. Cesar Izaurralde, Ingrid Jacquemin, Curtis Jones, Michelle Li, Wenfeng Liu, Stefan Olin, Meridel Phillips, Thomas A. M. Pugh, Ashwan Reddy, Karina Williams, Ziwei Wang, Florian Zabel, and Elisabeth J. Moyer
Geosci. Model Dev., 13, 3995–4018, https://doi.org/10.5194/gmd-13-3995-2020, https://doi.org/10.5194/gmd-13-3995-2020, 2020
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Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Femke Lutz, Stephen Del Grosso, Stephen Ogle, Stephen Williams, Sara Minoli, Susanne Rolinski, Jens Heinke, Jetse J. Stoorvogel, and Christoph Müller
Geosci. Model Dev., 13, 3905–3923, https://doi.org/10.5194/gmd-13-3905-2020, https://doi.org/10.5194/gmd-13-3905-2020, 2020
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Previous findings have shown deviations between the LPJmL5.0-tillage model and results from meta-analyses on global estimates of tillage effects on N2O emissions. By comparing model results with observational data of four experimental sites and outputs from field-scale DayCent model simulations, we show that advancing information on agricultural management, as well as the representation of soil moisture dynamics, improves LPJmL5.0-tillage and the estimates of tillage effects on N2O emissions.
Cited articles
Degree of Herbage Selection by Grazing Cattle, J. Dairy Sci., 37, 89–102, https://doi.org/10.3168/jds.S0022-0302(54)91236-9, 1954. a
Acocks, J. P. H.: Veld Types of South Africa, 3rd ed, Memoirs of the Botanical Survey of South Africa. Botanical Research Institute, Cape Town, South Africa, 1988. a
Bai, Y. and Cotrufo, M. F.: Grassland Soil Carbon Sequestration: Current Understanding, Challenges, and Solutions, Science, 377, 603–608, https://doi.org/10.1126/science.abo2380, 2022. a
Bai, Y., Han, X., Wu, J., Chen, Z., and Li, L.: Ecosystem Stability and Compensatory Effects in the Inner Mongolia Grassland, Nature, 431, 181–184, https://doi.org/10.1038/nature02850, 2004. a
Bazzaz, F. A.: Habitat Selection in Plants, Am. Nat., 137, 116–130, 1991. a
Bergmann, J., Weigelt, A., van der Plas, F., Laughlin, D. C., Kuyper, T. W., Guerrero-Ramirez, N., Valverde-Barrantes, O. J., Bruelheide, H., Freschet, G. T., Iversen, C. M., Kattge, J., McCormack, M. L., Meier, I. C., Rillig, M. C., Roumet, C., Semchenko, M., Sweeney, C. J., van Ruijven, J., York, L. M., and Mommer, L.: The Fungal Collaboration Gradient Dominates the Root Economics Space in Plants, Sci. Adv., 6, eaba3756, https://doi.org/10.1126/sciadv.aba3756, 2020. a, b
Boote, K. J., Hoogenboom, G., Jones, J. W., and Ingram, K. T.: Modeling Nitrogen Fixation and Its Relationship to Nitrogen Uptake in the CROPGRO Model, in: Quantifying and Understanding Plant Nitrogen Uptake for Systems Modeling, CRC Press, ISBN 978-0-429-14053-2, 2009. a
Branson, F. A.: Two New Factors Affecting Resistance of Grasses to Grazing, J. Range Manage., 6, 165, https://doi.org/10.2307/3893839, 1953. a
Brovkin, V., van Bodegom, P. M., Kleinen, T., Wirth, C., Cornwell, W. K., Cornelissen, J. H. C., and Kattge, J.: Plant-driven variation in decomposition rates improves projections of global litter stock distribution, Biogeosciences, 9, 565–576, https://doi.org/10.5194/bg-9-565-2012, 2012. a
Brown, J. S. and Venable, D. L.: Evolutionary Ecology of Seed-Bank Annuals in Temporally Varying Environments, Am. Nat., 127, 31–47, https://doi.org/10.1086/284465, 1986. a, b
Buzhdygan, O. Y., Meyer, S. T., Weisser, W. W., Eisenhauer, N., Ebeling, A., Borrett, S. R., Buchmann, N., Cortois, R., De Deyn, G. B., de Kroon, H., Gleixner, G., Hertzog, L. R., Hines, J., Lange, M., Mommer, L., Ravenek, J., Scherber, C., Scherer-Lorenzen, M., Scheu, S., Schmid, B., Steinauer, K., Strecker, T., Tietjen, B., Vogel, A., Weigelt, A., and Petermann, J. S.: Biodiversity Increases Multitrophic Energy Use Efficiency, Flow and Storage in Grasslands, Nat. Ecol. Evol., 4, 393–405, https://doi.org/10.1038/s41559-020-1123-8, 2020. a
Caccianiga, M., Luzzaro, A., Pierce, S., Ceriani, R. M., and Cerabolini, B.: The Functional Basis of a Primary Succession Resolved by CSR Classification, Oikos, 112, 10–20, https://doi.org/10.1111/j.0030-1299.2006.14107.x, 2006. a, b
Campbell, B. D. and Grime, J. P.: An Experimental Test of Plant Strategy Theory, Ecology, 73, 15–29, https://doi.org/10.2307/1938717, 1992. a
Cerabolini, B. E. L., Pierce, S., Verginella, A., Brusa, G., Ceriani, R. M., and Armiraglio, S.: Why Are Many Anthropogenic Agroecosystems Particularly Species-Rich?, Plant Biosyst. Int. J. Deal. Asp. Plant Biol., 150, 550–557, https://doi.org/10.1080/11263504.2014.987848, 2016. a
Chang, J., Ciais, P., Gasser, T., Smith, P., Herrero, M., Havlík, P., Obersteiner, M., Guenet, B., Goll, D. S., Li, W., Naipal, V., Peng, S., Qiu, C., Tian, H., Viovy, N., Yue, C., and Zhu, D.: Climate Warming from Managed Grasslands Cancels the Cooling Effect of Carbon Sinks in Sparsely Grazed and Natural Grasslands, Nat. Commun., 12, 118, https://doi.org/10.1038/s41467-020-20406-7, 2021. a
Chaplot, V., Bouahom, B., and Valentin, C.: Soil Organic Carbon Stocks in Laos: Spatial Variations and Controlling Factors, Glob. Change Biol., 16, 1380–1393, https://doi.org/10.1111/j.1365-2486.2009.02013.x, 2010. a
Chen, S., Wang, W., Xu, W., Wang, Y., Wan, H., Chen, D., Tang, Z., Tang, X., Zhou, G., Xie, Z., Zhou, D., Shangguan, Z., Huang, J., He, J.-S., Wang, Y., Sheng, J., Tang, L., Li, X., Dong, M., Wu, Y., Wang, Q., Wang, Z., Wu, J., Chapin, F. S., and Bai, Y.: Plant Diversity Enhances Productivity and Soil Carbon Storage, P. Natl. Acad. Sci. USA, 115, 4027–4032, https://doi.org/10.1073/pnas.1700298114, 2018. a
Chuan, X., Carlyle, C. N., Bork, E. W., Chang, S. X., and Hewins, D. B.: Long-Term Grazing Accelerated Litter Decomposition in Northern Temperate Grasslands, Ecosystems, 21, 1321–1334, https://doi.org/10.1007/s10021-018-0221-9, 2018. a
Conant, R. T., Cerri, C. E. P., Osborne, B. B., and Paustian, K.: Grassland Management Impacts on Soil Carbon Stocks: A New Synthesis, Ecol. Appl., 27, 662–668, https://doi.org/10.1002/eap.1473, 2017. a
Cordova, F. J., Wallace, J. D., and Pieper, R. D.: Forage Intake by Grazing Livestock: A Review, J. Range Manag., 31, 430–438, https://doi.org/10.2307/3897201, 1978. a, b
Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., Garnier, E., Bönisch, G., Westoby, M., Poorter, H., Reich, P. B., Moles, A. T., Dickie, J., Gillison, A. N., Zanne, A. E., Chave, J., Wright, S. J., Sheremet'ev, S. N., Jactel, H., Baraloto, C., Cerabolini, B., Pierce, S., Shipley, B., Kirkup, D., Casanoves, F., Joswig, J. S., Günther, A., Falczuk, V., Rüger, N., Mahecha, M. D., and Gorné, L. D.: The Global Spectrum of Plant Form and Function, Nature, 529, 167–171, https://doi.org/10.1038/nature16489, 2016. a, b, c, d, e, f, g
Doetterl, S., Berhe, A. A., Nadeu, E., Wang, Z., Sommer, M., and Fiener, P.: Erosion, Deposition and Soil Carbon: A Review of Process-Level Controls, Experimental Tools and Models to Address C Cycling in Dynamic Landscapes, Earth-Sci. Rev., 154, 102–122, https://doi.org/10.1016/j.earscirev.2015.12.005, 2016. a
DWD: Wetter Und Klima – Deutscher Wetterdienst – Leistungen – Klimadaten Deutschland – Monats- Und Tageswerte (Archiv), https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivtagmonat.html;jsessionid=A3AB03AA43161688F8D557F88FBF0BF8.live11053?nn=16102 (16 June 2022), 2021. a
Fei, S., Jo, I., Guo, Q., Wardle, D. A., Fang, J., Chen, A., Oswalt, C. M., and Brockerhoff, E. G.: Impacts of Climate on the Biodiversity-Productivity Relationship in Natural Forests, Nat. Commun., 9, 5436, https://doi.org/10.1038/s41467-018-07880-w, 2018. a
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014. a
Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S., Thonicke, K., von Bloh, W., and Carvalhais, N.: Constraining Modelled Global Vegetation Dynamics and Carbon Turnover Using Multiple Satellite Observations, Sci. Rep., 9, 18757, https://doi.org/10.1038/s41598-019-55187-7, 2019. a, b
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora, V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M., Munro, D. R., Nakaoka, S.-I., Niwa, Y., O'Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, 2022. a
Godde, C. M., de Boer, I. J. M., zu Ermgassen, E., Herrero, M., van Middelaar, C. E., Muller, A., Röös, E., Schader, C., Smith, P., van Zanten, H. H. E., and Garnett, T.: Soil Carbon Sequestration in Grazing Systems: Managing Expectations, Clim. Change, 161, 385–391, https://doi.org/10.1007/s10584-020-02673-x, 2020. a
Grime, J. P.: Vegetation Classification by Reference to Strategies, Nature, 250, 26–31, https://doi.org/10.1038/250026a0, 1974. a, b, c
Grime, J. P., Hodgson, J. G., and Hunt, R.: Comparative Plant Ecology: A Functional Approach to Common British Species, Springer Dordrecht, ISBN 978-94-017-1094-7, 1988. a
Guo, Q.: The Diversity–Biomass–Productivity Relationships in Grassland Management and Restoration, Bas. Appl. Ecol., 8, 199–208, https://doi.org/10.1016/j.baae.2006.02.005, 2007. a
Guuroh, R. T., Ruppert, J. C., Ferner, J., Čanak, K., Schmidtlein, S., and Linstädter, A.: Drivers of Forage Provision and Erosion Control in West African Savannas–A Macroecological Perspective, Agr. Ecosyst. Environ., 251, 257–267, https://doi.org/10.1016/j.agee.2017.09.017, 2018. a
Hardin, G.: The Competitive Exclusion Principle, Science, 131, 1292–1297, https://doi.org/10.1126/science.131.3409.1292, 1960. a
Herzfeld, T., Heinke, J., Rolinski, S., and Müller, C.: Soil organic carbon dynamics from agricultural management practices under climate change, Earth Syst. Dynam., 12, 1037–1055, https://doi.org/10.5194/esd-12-1037-2021, 2021. a
Hodgson, J. G., Wilson, P. J., Hunt, R., Grime, J. P., and Thompson, K.: Allocating C-S-R Plant Functional Types: A Soft Approach to a Hard Problem, Oikos, 85, 282–294, https://doi.org/10.2307/3546494, 1999. a
Hoffmann, C., Giese, M., Dickhoefer, U., Wan, H., Bai, Y., Steffens, M., Liu, C., Butterbach-Bahl, K., and Han, X.: Effects of Grazing and Climate Variability on Grassland Ecosystem Functions in Inner Mongolia: Synthesis of a 6-Year Grazing Experiment, J. Arid Environ., 135, 50–63, https://doi.org/10.1016/j.jaridenv.2016.08.003, 2016. a, b, c
Huhtanen, P., Nousiainen, J. I., Rinne, M., Kytölä, K., and Khalili, H.: Utilization and Partition of Dietary Nitrogen in Dairy Cows Fed Grass Silage-Based Diets, J. Dairy Sci., 91, 3589–3599, https://doi.org/10.3168/jds.2008-1181, 2008. a
Hunt, R., Hodgson, J., Thompson, K., Bungener, P., Dunnett, N., and Askew, A.: A New Practical Tool for Deriving a Functional Signature for Herbaceous Vegetation, Appl. Veg. Sci., 7, 163–170, https://doi.org/10.1111/j.1654-109X.2004.tb00607.x, 2004. a
Hyder, D. N.: Defoliation in Relation to Vegetative Growth, in: The Biology and Utilization of Grasses, edited by: Youngner V. B. and McKell, C. M., Academic Press, New York, 302–317, 1972. a
Isbell, F., Craven, D., Connolly, J., Loreau, M., Schmid, B., Beierkuhnlein, C., Bezemer, T. M., Bonin, C., Bruelheide, H., de Luca, E., Ebeling, A., Griffin, J. N., Guo, Q., Hautier, Y., Hector, A., Jentsch, A., Kreyling, J., Lanta, V., Manning, P., Meyer, S. T., Mori, A. S., Naeem, S., Niklaus, P. A., Polley, H. W., Reich, P. B., Roscher, C., Seabloom, E. W., Smith, M. D., Thakur, M. P., Tilman, D., Tracy, B. F., van der Putten, W. H., van Ruijven, J., Weigelt, A., Weisser, W. W., Wilsey, B., and Eisenhauer, N.: Biodiversity Increases the Resistance of Ecosystem Productivity to Climate Extremes, Nature, 526, 574–577, https://doi.org/10.1038/nature15374, 2015. a
Jacobsen, A. L., Pratt, R. B., Venturas, M. D., and Hacke, U. G.: Large Volume Vessels Are Vulnerable to Water-Stress-Induced Embolism in Stems of Poplar, IAWA J., 40, 4-S4, https://doi.org/10.1163/22941932-40190233, 2019. a
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S., Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091, https://doi.org/10.5194/hess-19-3073-2015, 2015. a
Jebari, A., Álvaro-Fuentes, J., Pardo, G., Batalla, I., Martín, J. A. R., and Del Prado, A.: Effect of Dairy Cattle Production Systems on Sustaining Soil Organic Carbon Storage in Grasslands of Northern Spain, Reg. Environ Change, 22, 67, https://doi.org/10.1007/s10113-022-01927-x, 2022. a
Jobbágy, E. G. and Jackson, R. B.: The Vertical Distribution of Soil Organic Carbon and Its Relation to Climate and Vegetation, Ecol. Appl., 10, 423–436, https://doi.org/10.1890/1051-0761(2000)010[0423:TVDOSO]2.0.CO;2, 2000. a
Johnson, H. A. and Biondini, M. E.: Root Morphological Plasticity and Nitrogen Uptake of 59 Plant Species from the Great Plains Grasslands, U.S.A., Basic and Applied Ecology, 2, 127–143, https://doi.org/10.1078/1439-1791-00044, 2001. a
Kaschuk, G., Kuyper, T. W., Leffelaar, P. A., Hungria, M., and Giller, K. E.: Are the Rates of Photosynthesis Stimulated by the Carbon Sink Strength of Rhizobial and Arbuscular Mycorrhizal Symbioses?, Soil Biol. Biochem., 41, 1233–1244, https://doi.org/10.1016/j.soilbio.2009.03.005, 2009. a
Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Van Bodegom, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., Atkin, O., Bahn, M., Baker, T. R., Baldocchi, D., Bekker, R., Blanco, C. C., Blonder, B., Bond, W. J., Bradstock, R., Bunker, D. E., Casanoves, F., Cavender-Bares, J., Chambers, J. Q., Chapin Iii, F. S., Chave, J., Coomes, D., Cornwell, W. K., Craine, J. M., Dobrin, B. H., Duarte, L., Durka, W., Elser, J., Esser, G., Estiarte, M., Fagan, W. F., Fang, J., Fernández-Méndez, F., Fidelis, A., Finegan, B., Flores, O., Ford, H., Frank, D., Freschet, G. T., Fyllas, N. M., Gallagher, R. V., Green, W. A., Gutierrez, A. G., Hickler, T., Higgins, S. I., Hodgson, J. G., Jalili, A., Jansen, S., Joly, C. A., Kerkhoff, A. J., Kirkup, D., Kitajima, K., Kleyer, M., Klotz, S., Knops, J. M. H., Kramer, K., Kühn, I., Kurokawa, H., Laughlin, D., Lee, T. D., Leishman, M., Lens, F., Lenz, T., Lewis, S. L., Lloyd, J., Llusià, J., Louault, F., Ma, S., Mahecha, M. D., Manning, P., Massad, T., Medlyn, B. E., Messier, J., Moles, A. T., Müller, S. C., Nadrowski, K., Naeem, S., Niinemets, Ü., Nöllert, S., Nüske, A., Ogaya, R., Oleksyn, J., Onipchenko, V. G., Onoda, Y., Ordoñez, J., Overbeck, G., Ozinga, W. A., Patiño, S., Paula, S., Pausas, J. G., Peñuelas, J., Phillips, O. L., Pillar, V., Poorter, H., Poorter, L., Poschlod, P., Prinzing, A., Proulx, R., Rammig, A., Reinsch, S., Reu, B., Sack, L., Salgado-Negret, B., Sardans, J., Shiodera, S., Shipley, B., Siefert, A., Sosinski, E., Soussana, J.-F., Swaine, E., Swenson, N., Thompson, K., Thornton, P., Waldram, M., Weiher, E., White, M., White, S., Wright, S. J., Yguel, B., Zaehle, S., Zanne, A. E., and Wirth, C.: TRY – a Global Database of Plant Traits, Glob. Change Biol., 17, 2905–2935, https://doi.org/10.1111/j.1365-2486.2011.02451.x, 2011. a
Kölbl, A., Steffens, M., Wiesmeier, M., Hoffmann, C., Funk, R., Krümmelbein, J., Reszkowska, A., Zhao, Y., Peth, S., Horn, R., Giese, M., and Kögel-Knabner, I.: Grazing Changes Topography-Controlled Topsoil Properties and Their Interaction on Different Spatial Scales in a Semi-Arid Grassland of Inner Mongolia, P.R. China, Plant Soil, 340, 35–58, https://doi.org/10.1007/s11104-010-0473-4, 2011. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the Köppen-Geiger Climate Classification Updated, Metz, 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a, b
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher Climatological Temperature Sensitivity of Soil Carbon in Cold than Warm Climates, Nat. Clim. Change, 7, 817–822, https://doi.org/10.1038/nclimate3421, 2017. a
Kull, O.: Acclimation of Photosynthesis in Canopies: Models and Limitations, Oecologia, 133, 267–279, https://doi.org/10.1007/s00442-002-1042-1, 2002. a
Lange, S. and Büchner, M.: Secondary ISIMIP3b Bias-Adjusted Atmospheric Climate Input Data, https://doi.org/10.48364/ISIMIP.581124.1, 2022. a
Lee, M. A.: A Global Comparison of the Nutritive Values of Forage Plants Grown in Contrasting Environments, J. Plant Res., 131, 641–654, https://doi.org/10.1007/s10265-018-1024-y, 2018. a
Lepš, J., Osbornová-Kosinová, J., and Rejmánek, M.: Community Stability, Complexity and Species Life History Strategies, Vegetatio, 50, 53–63, https://doi.org/10.1007/BF00120678, 1982. a
Li, J., Lin, S., Taube, F., Pan, Q., and Dittert, K.: Above and Belowground Net Primary Productivity of Grassland Influenced by Supplemental Water and Nitrogen in Inner Mongolia, Plant Soil, 340, 253–264, https://doi.org/10.1007/s11104-010-0612-y, 2011. a, b, c
Liu, J., Li, L., Ji, L., Li, Y., Liu, J., and Li, F. Y.: Divergent Effects of Grazing versus Mowing on Plant Nutrients in Typical Steppe Grasslands of Inner Mongolia, J. Plant Ecol., 16, rtac032, https://doi.org/10.1093/jpe/rtac032, 2023. a
Liu, Y., Wu, L., Baddeley, J. A., and Watson, C. A.: Models of Biological Nitrogen Fixation of Legumes. A Review, Agronomy Sust. Developm., 31, 155–172, https://doi.org/10.1051/agro/2010008, 2011. a
Liu, Y. Y., Evans, J. P., McCabe, M. F., de Jeu, R. A. M., van Dijk, A. I. J. M., Dolman, A. J., and Saizen, I.: Changing Climate and Overgrazing Are Decimating Mongolian Steppes, PLOS ONE, 8, e57599, https://doi.org/10.1371/journal.pone.0057599, 2013. a
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019. a
Ma, J., Olin, S., Anthoni, P., Rabin, S. S., Bayer, A. D., Nyawira, S. S., and Arneth, A.: Modeling symbiotic biological nitrogen fixation in grain legumes globally with LPJ-GUESS (v4.0, r10285), Geosci. Model Dev., 15, 815–839, https://doi.org/10.5194/gmd-15-815-2022, 2022. a, b
May, F., Grimm, V., and Jeltsch, F.: Reversed Effects of Grazing on Plant Diversity: The Role of below-Ground Competition and Size Symmetry, Oikos, 118, 1830–1843, https://doi.org/10.1111/j.1600-0706.2009.17724.x, 2009. a, b
McSherry, M. E. and Ritchie, M. E.: Effects of Grazing on Grassland Soil Carbon: A Global Review, Glob. Change Biol., 19, 1347–1357, https://doi.org/10.1111/gcb.12144, 2013. a, b
Meier, I. C. and Leuschner, C.: Variation of Soil and Biomass Carbon Pools in Beech Forests across a Precipitation Gradient, Glob. Change Biol., 16, 1035–1045, https://doi.org/10.1111/j.1365-2486.2009.02074.x, 2010. a
Munjonji, L., Ayisi, K. K., Mudongo, E. I., Mafeo, T. P., Behn, K., Mokoka, M. V., and Linstädter, A.: Disentangling Drought and Grazing Effects on Soil Carbon Stocks and CO2 Fluxes in a Semi-Arid African Savanna, Front. Environ. Sci., 8, https://doi.org/10.3389/fenvs.2020.590665, 2020. a, b, c, d, e
Newman, J. A., Parsons, A. J., Thornley, J. H. M., Penning, P. D., and Krebs, J. R.: Optimal Diet Selection by a Generalist Grazing Herbivore, Funct. Ecol., 9, 255–268, https://doi.org/10.2307/2390572, 1995. a
Noy-Meir, I.: Responses of Two Semiarid Rangeland Communities to Protection from Grazing, Isr. J. Plant Sci., 39, 431–442, https://doi.org/10.1080/0021213X.1990.10677166, 1990. a
Onoda, Y., Wright, I. J., Evans, J. R., Hikosaka, K., Kitajima, K., Niinemets, Ü., Poorter, H., Tosens, T., and Westoby, M.: Physiological and Structural Tradeoffs Underlying the Leaf Economics Spectrum, New Phytol., 214, 1447–1463, https://doi.org/10.1111/nph.14496, 2017. a
Parsons, A., Newman, J., Penning, P., Harvey, A., and Orr, R.: Diet Preference of Sheep: Effects of Recent Diet, Physiological State and Species Abundance, J. Anim. Ecol., 63, 465–478, https://doi.org/10.2307/5563, 1994. a
Patterson, T. G. and Larue, T. A.: Root Respiration Associated with Nitrogenase Activity (C2H2) of Soybean, and a Comparison of Estimates 1, Plant Physiol., 72, 701–705, https://doi.org/10.1104/pp.72.3.701, 1983. a
Pfeiffer, M., Langan, L., Linstädter, A., Martens, C., Gaillard, C., Ruppert, J. C., Higgins, S. I., Mudongo, E. I., and Scheiter, S.: Grazing and Aridity Reduce Perennial Grass Abundance in Semi-Arid Rangelands – Insights from a Trait-Based Dynamic Vegetation Model, Ecol. Model., 395, 11–22, https://doi.org/10.1016/j.ecolmodel.2018.12.013, 2019. a, b
Pierce, S., Brusa, G., Vagge, I., and Cerabolini, B. E. L.: Allocating CSR Plant Functional Types: The Use of Leaf Economics and Size Traits to Classify Woody and Herbaceous Vascular Plants, Funct. Ecol., 27, 1002–1010, https://doi.org/10.1111/1365-2435.12095, 2013. a, b
Pierce, S., Negreiros, D., Cerabolini, B. E. L., Kattge, J., Díaz, S., Kleyer, M., Shipley, B., Wright, S. J., Soudzilovskaia, N. A., Onipchenko, V. G., van Bodegom, P. M., Frenette-Dussault, C., Weiher, E., Pinho, B. X., Cornelissen, J. H. C., Grime, J. P., Thompson, K., Hunt, R., Wilson, P. J., Buffa, G., Nyakunga, O. C., Reich, P. B., Caccianiga, M., Mangili, F., Ceriani, R. M., Luzzaro, A., Brusa, G., Siefert, A., Barbosa, N. P. U., Chapin, F. S., Cornwell, W. K., Fang, J., Fernandes, G. W., Garnier, E., Stradic, S. L., Peñuelas, J., Melo, F. P. L., Slaviero, A., Tabarelli, M., and Tampucci, D.: A Global Method for Calculating Plant CSR Ecological Strategies Applied across Biomes World-Wide, Funct. Ecol., 31, 444–457, https://doi.org/10.1111/1365-2435.12722, 2017. a, b, c
Piñeiro, G., Paruelo, J. M., Oesterheld, M., and Jobbágy, E. G.: Pathways of Grazing Effects on Soil Organic Carbon and Nitrogen, Rangel. Ecol. Manage., 63, 109–119, https://doi.org/10.2111/08-255.1, 2010. a
Quillet, A., Peng, C., and Garneau, M.: Toward Dynamic Global Vegetation Models for Simulating Vegetation–Climate Interactions and Feedbacks: Recent Developments, Limitations, and Future Challenges, Environ. Rev., 18, 333–353, https://doi.org/10.1139/A10-016, 2010. a
R Core Team: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/ (last access: 8 April 2020), 2019. a
Rechenthin, C. A.: Elementary Morphology of Grass Growth and How It Affects Utilization, Range Manage., 9, 167–170, 1956. a
Reinsch, T., Loges, R., Kluß, C., and Taube, F.: Effect of Grassland Ploughing and Reseeding on CO2 Emissions and Soil Carbon Stocks, Agriculture, Ecosyst. Environ., 265, 374–383, https://doi.org/10.1016/j.agee.2018.06.020, 2018a. a, b
Reinsch, T., Loges, R., Kluß, C., and Taube, F.: Renovation and Conversion of Permanent Grass-Clover Swards to Pasture or Crops: Effects on Annual N2O Emissions in the Year after Ploughing, Soil Till. Res., 175, 119–129, https://doi.org/10.1016/j.still.2017.08.009, 2018b. a, b, c, d
Reinsch, T., Malisch, C., Loges, R., and Taube, F.: Nitrous Oxide Emissions from Grass–Clover Swards as Influenced by Sward Age and Biological Nitrogen Fixation, Grass Forage Sci., 75, 372–384, https://doi.org/10.1111/gfs.12496, 2020. a, b
Ren, H., Taube, F., Stein, C., Zhang, Y., Bai, Y., and Hu, S.: Grazing Weakens Temporal Stabilizing Effects of Diversity in the Eurasian Steppe, Ecol. Evol., 8, 231–241, https://doi.org/10.1002/ece3.3669, 2017. a
Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Bodirsky, B. L., Bondeau, A., Boons-Prins, E. R., Bouwman, A. F., Leffelaar, P. A., te Roller, J. A., Schaphoff, S., and Thonicke, K.: Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6, Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, 2018. a, b, c, d, e, f, g, h, i
Rolinski, S., Wirth, S. B., Müller, C., and Tietjen, B.: Strategies for Assessing Grassland Degradation, in: Jt. XXIV Int, Grassl, XI Int, Rangel, Kenya 2021 Virtual Congr, Oral Pap, Proc., vol. 1, Kenya Agricultural and Livestock Research Organisation, Nairobi, Kenia, 383–387, ISBN 978-996-30-093-5, 2021. a
Ruppert, J. C., Harmoney, K., Henkin, Z., Snyman, H. A., Sternberg, M., Willms, W., and Linstädter, A.: Quantifying Drylands' Drought Resistance and Recovery: The Importance of Drought Intensity, Dominant Life History and Grazing Regime, Glob. Change Biol., 21, 1258–1270, https://doi.org/10.1111/gcb.12777, 2015. a
Ryle, G. J. A., Powell, C. E., and Gordon, A. J.: The Respiratory Costs of Nitrogen Fixation in Soyabean, Cowpea, and White Clover: I. Nitrogen Fixation and the Respiration of the Nodulated Root, J. Exp. Bot., 30, 135–144, https://doi.org/10.1093/jxb/30.1.135, 1979. a
Sakschewski, B., von Bloh, W., Boit, A., Rammig, A., Kattge, J., Poorter, L., Peñuelas, J., and Thonicke, K.: Leaf and Stem Economics Spectra Drive Diversity of Functional Plant Traits in a Dynamic Global Vegetation Model, Glob. Change Biol., 21, 2711–2725, https://doi.org/10.1111/gcb.12870, 2015. a, b, c, d, e
Salisbury, E. J.: The Reproductive Capacity of Plants, Nature, 151, 319–320, https://doi.org/10.1038/151319a0, 1943. a
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018. a, b, c, d, e, f, g, h, i
Scheiter, S., Langan, L., and Higgins, S. I.: Next-Generation Dynamic Global Vegetation Models: Learning from Community Ecology, New Phytol., 198, 957–969, https://doi.org/10.1111/nph.12210, 2013. a
Scheiter, S., Pfeiffer, M., Behn, K., Ayisi, K. K., Siebert, F., and Linstädter, A.: Managing Southern African Rangeland Systems in the Face of Drought – a Synthesis of Observation, Experimentation, and Modelling for Policy and Decision Support, in: Sustainability of Southern African Ecosystems under Global Change, edited by: von Maltitz, G. P., Midgley, G. F., Veitch, J., Brümmer, C., Rötter, R. P., Viehberg, F. A., and Veste, M., vol. 248 of Ecological Studies, Springer, ISBN 978-3-031-10948-5, https://doi.org/10.1007/978-3-031-10948-5, 2023. a, b, c
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of Increasing CO2 on the Terrestrial Carbon Cycle, P. Natl. Acad. Sci. USA, 112, 436–441, https://doi.org/10.1073/pnas.1407302112, 2015. a
Schmid, J. S., Huth, A., and Taubert, F.: Influences of Traits and Processes on Productivity and Functional Composition in Grasslands: A Modeling Study, Ecol. Model., 440, 109395, https://doi.org/10.1016/j.ecolmodel.2020.109395, 2021. a
Schmidtlein, S., Feilhauer, H., and Bruelheide, H.: Mapping Plant Strategy Types Using Remote Sensing, J. Veg. Sci., 23, 395–405, https://doi.org/10.1111/j.1654-1103.2011.01370.x, 2012. a
Schönbach, P., Wan, H., Gierus, M., Loges, R., Müller, K., Lin, L., Susenbeth, A., and Taube, F.: Effects of Grazing and Precipitation on Herbage Production, Herbage Nutritive Value and Performance of Sheep in Continental Steppe, Grass Forage Sci., 67, 535–545, https://doi.org/10.1111/j.1365-2494.2012.00874.x, 2012. a, b, c
Shi, Y., Ao, Y., Sun, B., Knops, J. M. H., Zhang, J., Guo, Z., De, X., Han, J., Yang, Y., Jiang, X., Mu, C., and Wang, J.: Productivity of Leymus Chinensis Grassland Is Co-Limited by Water and Nitrogen and Resilient to Climate Change, Plant Soil, 474, 411–422, https://doi.org/10.1007/s11104-022-05344-1, 2022. a
Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L., Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C. D., Prentice, I. C., and Woodward, F. I.: Evaluation of the Terrestrial Carbon Cycle, Future Plant Geography and Climate-Carbon Cycle Feedbacks Using Five Dynamic Global Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039, https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008. a
Sleutel, S., De Neve, S., and Hofman, G.: Assessing Causes of Recent Organic Carbon Losses from Cropland Soils by Means of Regional-Scaled Input Balances for the Case of Flanders (Belgium), Nutr. Cycl. Agroecosyst., 78, 265–278, https://doi.org/10.1007/s10705-007-9090-x, 2007. a
Stuart-Hill, G. and Mentis, M.: Coevolution of African Grasses and Large Herbivores, Proc. Annu. Congr. Grassl. Soc. South. Afr., 17, 122–128, https://doi.org/10.1080/00725560.1982.9648969, 1982. a, b
Taubert, F., Frank, K., and Huth, A.: A Review of Grassland Models in the Biofuel Context, Ecol. Model., 245, 84–93, https://doi.org/10.1016/j.ecolmodel.2012.04.007, 2012. a
Taubert, F., Hetzer, J., Schmid, J. S., and Huth, A.: Confronting an Individual-Based Simulation Model with Empirical Community Patterns of Grasslands, PLOS ONE, 15, e0236546, https://doi.org/10.1371/journal.pone.0236546, 2020a. a
Taubert, F., Hetzer, J., Schmid, J. S., and Huth, A.: The Role of Species Traits for Grassland Productivity, Ecosphere, 11, e03205, https://doi.org/10.1002/ecs2.3205, 2020b. a
Teng, Y., Zhan, J., Agyemang, F. B., and Sun, Y.: The Effects of Degradation on Alpine Grassland Resilience: A Study Based on Meta-Analysis Data, Glob. Ecol. Conserv., 24, e01336, https://doi.org/10.1016/j.gecco.2020.e01336, 2020. a
Thompson, K.: Seeds and Seed Banks, New Phytol., 106, 23–34, https://doi.org/10.1111/j.1469-8137.1987.tb04680.x, 1987. a, b
Thonicke, K., Billing, M., von Bloh, W., Sakschewski, B., Niinemets, Ü., Peñuelas, J., Cornelissen, J. H. C., Onoda, Y., van Bodegom, P., Schaepman, M. E., Schneider, F. D., and Walz, A.: Simulating Functional Diversity of European Natural Forests along Climatic Gradients, J. Biogeogr., 47, 1069–1085, https://doi.org/10.1111/jbi.13809, 2020. a, b
Tilman, D. and El Haddi, A.: Drought and Biodiversity in Grasslands, Oecologia, 89, 257–264, https://doi.org/10.1007/BF00317226, 1992. a
Tribe, D. E. and Gordon, J. G.: An experimental study of palatability, Agric. Progr., 25, 94–101, 1950. a
Tron, S., Bodner, G., Laio, F., Ridolfi, L., and Leitner, D.: Can Diversity in Root Architecture Explain Plant Water Use Efficiency? A Modeling Study, Ecol. Model., 312, 200–210, https://doi.org/10.1016/j.ecolmodel.2015.05.028, 2015. a
Van Oijen, M., Rougier, J., and Smith, R.: Bayesian Calibration of Process-Based Forest Models: Bridging the Gap between Models and Data, Tree Physiol., 25, 915–927, https://doi.org/10.1093/treephys/25.7.915, 2005. a
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018. a, b, c
Wan, H., Bai, Y., Hooper, D. U., Schönbach, P., Gierus, M., Schiborra, A., and Taube, F.: Selective Grazing and Seasonal Precipitation Play Key Roles in Shaping Plant Community Structure of Semi-Arid Grasslands, Landscape Ecol., 30, 1767–1782, https://doi.org/10.1007/s10980-015-0252-y, 2015. a, b
Waring, R. H.: Estimating Forest Growth and Efficiency in Relation to Canopy Leaf Area, in: Advances in Ecological Research, edited by: MacFadyen, A. and Ford, E. D., vol. 13, Academic Press, 327–354, https://doi.org/10.1016/S0065-2504(08)60111-7, 1983. a
Waring, R. H. and Schlesinger, W. H.: Forest Ecosystems: Concepts and Management, Academic Press, Orlando, Florida, ISBN 978-0127354415, 1985. a
Weigelt, A., Mommer, L., Andraczek, K., Iversen, C. M., Bergmann, J., Bruelheide, H., Fan, Y., Freschet, G. T., Guerrero-Ramírez, N. R., Kattge, J., Kuyper, T. W., Laughlin, D. C., Meier, I. C., van der Plas, F., Poorter, H., Roumet, C., van Ruijven, J., Sabatini, F. M., Semchenko, M., Sweeney, C. J., Valverde-Barrantes, O. J., York, L. M., and McCormack, M. L.: An Integrated Framework of Plant Form and Function: The Belowground Perspective, New Phytol., 232, 42–59, https://doi.org/10.1111/nph.17590, 2021. a, b
Weisser, W. W., Roscher, C., Meyer, S. T., Ebeling, A., Luo, G., Allan, E., Beßler, H., Barnard, R. L., Buchmann, N., Buscot, F., Engels, C., Fischer, C., Fischer, M., Gessler, A., Gleixner, G., Halle, S., Hildebrandt, A., Hillebrand, H., de Kroon, H., Lange, M., Leimer, S., Le Roux, X., Milcu, A., Mommer, L., Niklaus, P. A., Oelmann, Y., Proulx, R., Roy, J., Scherber, C., Scherer-Lorenzen, M., Scheu, S., Tscharntke, T., Wachendorf, M., Wagg, C., Weigelt, A., Wilcke, W., Wirth, C., Schulze, E.-D., Schmid, B., and Eisenhauer, N.: Biodiversity Effects on Ecosystem Functioning in a 15-Year Grassland Experiment: Patterns, Mechanisms, and Open Questions, Basic Appl. Ecol., 23, 1–73, https://doi.org/10.1016/j.baae.2017.06.002, 2017. a
Westoby, M., Leishman, M., and Lord, J.: Comparative Ecology of Seed Size and Dispersal, Philos. T. Roy. Soc. B, 351, 1309–1318, https://doi.org/10.1098/rstb.1996.0114, 1996. a
White, R. P., Murray, S., and Rohweder, M.: Pilot Analysis of Global Ecosystems: Grassland Ecosystems, Pilot Anal. Glob. Ecosyst. Grassl. Ecosyst., ISBN 1-56973-461-5, 2000. a
Wiesmeier, M., Barthold, F., Blank, B., and Kögel-Knabner, I.: Digital Mapping of Soil Organic Matter Stocks Using Random Forest Modeling in a Semi-Arid Steppe Ecosystem, Plant Soil, 340, 7–24, https://doi.org/10.1007/s11104-010-0425-z, 2011. a, b, c
Wiesmeier, M., Kreyling, O., Steffens, M., Schoenbach, P., Wan, H., Gierus, M., Taube, F., Kölbl, A., and Kögel-Knabner, I.: Short-Term Degradation of Semiarid Grasslands–Results from a Controlled-Grazing Experiment in Northern China, J. Plant Nutr. Soil Sci., 175, 434–442, https://doi.org/10.1002/jpln.201100327, 2012. a, b
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M., Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco, N., Wollschläger, U., Vogel, H.-J., and Kögel-Knabner, I.: Soil Organic Carbon Storage as a Key Function of Soils – A Review of Drivers and Indicators at Various Scales, Geoderma, 333, 149–162, https://doi.org/10.1016/j.geoderma.2018.07.026, 2019. a
Wirth, S. B., Taubert, F., Tietjen, B., Müller, C., and Rolinski, S.: Do Details Matter?, Disentangling the Processes Related to Plant Species Interactions in Two Grassland Models of Different Complexity, Ecol. Model., 460, 109737, https://doi.org/10.1016/j.ecolmodel.2021.109737, 2021. a, b, c
Wirth, S. B., Müller, C., and Rolinski, S.: Code and Data Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions, Zenodo, https://doi.org/10.5281/zenodo.10217244, 2023. a
Woodward, F. I. and Diament, A. D.: Functional Approaches to Predicting the Ecological Effects of Global Change, Funct. Ecol., 5, 202–212. https://doi.org/10.2307/2389258, 1991. a
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M., Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont, B. B., Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M.-L., Niinemets, U., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V. I., Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and Villar, R.: The Worldwide Leaf Economics Spectrum, Nature, 428, 821–827, https://doi.org/10.1038/nature02403, 2004. a, b, c
Xie, Q., Huete, A., Hall, C. C., Medlyn, B. E., Power, S. A., Davies, J. M., Medek, D. E., and Beggs, P. J.: Satellite-Observed Shifts in C3/C4 Abundance in Australian Grasslands Are Associated with Rainfall Patterns, Remote Sens. Environ., 273, 112983, https://doi.org/10.1016/j.rse.2022.112983, 2022.
Yang, Y., Zhu, Q., Peng, C., Wang, H., and Chen, H.: From Plant Functional Types to Plant Functional Traits: A New Paradigm in Modelling Global Vegetation Dynamics, Prog. Phys. Geogr., 39, 514–535, https://doi.org/10.1177/0309133315582018, 2015. a
Yang, Y., Tilman, D., Furey, G., and Lehman, C.: Soil Carbon Sequestration Accelerated by Restoration of Grassland Biodiversity, Nat. Commun., 10, 718, https://doi.org/10.1038/s41467-019-08636-w, 2019. a, b
Yu, Q., Wu, H., Wang, Z., Flynn, D. F. B., Yang, H., Lü, F., Smith, M., and Han, X.: Long Term Prevention of Disturbance Induces the Collapse of a Dominant Species without Altering Ecosystem Function, Sci. Rep., 5, 14320, https://doi.org/10.1038/srep14320, 2015. a
Yu, T. and Zhuang, Q.: Modeling biological nitrogen fixation in global natural terrestrial ecosystems, Biogeosciences, 17, 3643–3657, https://doi.org/10.5194/bg-17-3643-2020, 2020. a, b, c
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of Parameter Uncertainties on the Modeling of Terrestrial Biosphere Dynamics, Glob. Biogeochem. Cycles, 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005. a
Zimmermann, J., Higgins, S. I., Grimm, V., Hoffmann, J., and Linstädter, A.: Grass Mortality in Semi-Arid Savanna: The Role of Fire, Competition and Self-Shading, Perspectives in Plant Ecology, Evol. Syst., 12, 1–8, https://doi.org/10.1016/j.ppees.2009.09.003, 2010. a, b, c
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply...
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