Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-7077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7077-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
10 Oct 2025
Model description paper |
| 10 Oct 2025
| 10 Oct 2025
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Straße 2–4, Gartenhaus, 14195 Berlin, Germany
Thibault Moulin
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Straße 2–4, Gartenhaus, 14195 Berlin, Germany
Oksana Buzhdygan
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Straße 2–4, Gartenhaus, 14195 Berlin, Germany
Britta Tietjen
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Straße 2–4, Gartenhaus, 14195 Berlin, Germany
Berlin-Brandenburg Institute of Advanced Biodiversity Research, 14195 Berlin, Germany
Felix May
Freie Universität Berlin, Institute of Biology, Theoretical Ecology, Königin-Luise-Straße 2–4, Gartenhaus, 14195 Berlin, Germany
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Short summary
Short summary
In our simulations increased drought frequencies lead to a drastic reduction in biomass in temperate pine monoculture and mixed forests. Mixed forests eventually recovered as long as drought frequency was not too high. The higher resilience of mixed forests was due to higher adaptive capacity. After adaptation mixed forests were mainly composed of smaller, broadleaved trees with higher wood density and slower growth. This would have strong implications for forestry and other ecosystem services.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
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.
Cited articles
Adler, P. B., Seabloom, E. W., Borer, E. T., Hillebrand, H., Hautier, Y., Hector, A., Harpole, W. S., O’Halloran, L. R., Grace, J. B., Anderson, T. M., Bakker, J. D., Biederman, L. A., Brown, C. S., Buckley, Y. M., Calabrese, L. B., Chu, C.-J., Cleland, E. E., Collins, S. L., Cottingham, K. L., Crawley, M. J., Damschen, E. I., Davies, K. F., DeCrappeo, N. M., Fay, P. A., Firn, J., Frater, P., Gasarch, E. I., Gruner, D. S., Hagenah, N., Hille Ris Lambers, J., Humphries, H., Jin, V. L., Kay, A. D., Kirkman, K. P., Klein, J. A., Knops, J. M. H., La Pierre, K. J., Lambrinos, J. G., Li, W., MacDougall, A. S., McCulley, R. L., Melbourne, B. A., Mitchell, C. E., Moore, J. L., Morgan, J. W., Mortensen, B., Orrock, J. L., Prober, S. M., Pyke, D. A., Risch, A. C., Schuetz, M., Smith, M. D., Stevens, C. J., Sullivan, L. L., Wang, G., Wragg, P. D., Wright, J. P., and Yang, L. H.: Productivity Is a Poor Predictor of Plant Species Richness, Science, 333, 1750–1753, https://doi.org/10.1126/science.1204498, 2011. a
Anten, N. P. R. and Hirose, T.: Interspecific differences in above-ground growth patterns result in spatial and temporal partitioning of light among species in a tall-grass meadow, J. Ecol., 87, 583–597, https://doi.org/10.1046/j.1365-2745.1999.00365.x, 1999. a
Archibald, S., Hempson, G. P., and Lehmann, C.: A unified framework for plant life‐history strategies shaped by fire and herbivory, New Phytol., 224, 1490–1503, https://doi.org/10.1111/nph.15986, 2019. a
Atkinson, J., Gallagher, R., Czyżewski, S., Kerr, M., Trepel, J., Buitenwerf, R., and Svenning, J.: Integrating functional traits into trophic rewilding science, J. Ecol., https://doi.org/10.1111/1365-2745.14307, 2024. a
Barber, S. A. and Silberbush, M.: Plant Root Morphology and Nutrient Uptake, in: Roots, Nutrient and Water Influx, and Plant Growth, ASA Special Publications, 49, 65–87, https://doi.org/10.2134/asaspecpub49.c4, 1984. a
Bergmann, J. and Rillig, M.: Fine root and mycorrhizal traits of 82 grassland species measured in a greenhouse experiment on sand, 2018, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/26546?version=2 (last access: 8 September 2025), 2022. 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, Science Advances, 6, eaba3756, https://doi.org/10.1126/sciadv.aba3756, 2020. a, b, c
Bernton, E., Jacob, P. E., Gerber, M., and Robert, C. P.: On parameter estimation with the Wasserstein distance, Information and Inference: A Journal of the IMA, 8, 657–676, https://doi.org/10.1093/imaiai/iaz003, 2019. a
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A fresh approach to numerical computing, SIAM Rev. Soc. Ind. Appl. Math., 59, 65–98, https://doi.org/10.1137/141000671, 2017. a
Bonanomi, G., Giannino, F., Mazzoleni, S., and Setälä, H.: Negative Plant-Soil Feedback and Species Coexistence, Oikos, 111, 311–321, https://doi.org/10.1111/j.0030-1299.2005.13975.x, 2005. a
Boval, M. and Sauvant, D.: Ingestive behaviour of grazing ruminants: Meta-analysis of the components linking bite mass to daily intake, Anim. Feed Sci. Tech., 278, 115014, https://doi.org/10.1016/j.anifeedsci.2021.115014, 2021. a
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, Nature Ecology & Evolution, 4, 393–405, https://doi.org/10.1038/s41559-020-1123-8, 2020. a
Caldwell, M. M.: Root Structure: The Considerable Cost of Belowground Function, in: Topics in Plant Population Biology, edited by: Solbrig, O. T., Jain, S., Johnson, G. B., and Raven, P. H., Macmillan Education UK, London, 408–427, https://doi.org/10.1007/978-1-349-04627-0_18, ISBN 978-1-349-04627-0, 1979. a
Canarini, A., Kaiser, C., Merchant, A., Richter, A., and Wanek, W.: Root Exudation of Primary Metabolites: Mechanisms and Their Roles in Plant Responses to Environmental Stimuli, Front. Plant Sci., 10, https://doi.org/10.3389/fpls.2019.00157, 2019. 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
Clark, J. S., Carpenter, S. R., Barber, M., Collins, S., Dobson, A., Foley, J. A., Lodge, D. M., Pascual, M., Pielke Jr., R., and Pizer, W.: Ecological forecasts: an emerging imperative, Science, 293, 657–660, https://doi.org/10.1126/science.293.5530.657, 2001. a
Clerx, M., Robinson, M., Lambert, B., Lei, C. L., Ghosh, S., Mirams, G. R., and Gavaghan, D. J.: Probabilistic Inference on Noisy Time Series (PINTS), Journal of Open Research Software, 7, 23, https://doi.org/10.5334/jors.252, 2019. a, b
Dee, L. E., Ferraro, P. J., Severen, C. N., Kimmel, K. A., Borer, E. T., Byrnes, J. E. K., Clark, A. T., Hautier, Y., Hector, A., Raynaud, X., Reich, P. B., Wright, A. J., Arnillas, C. A., Davies, K. F., MacDougall, A., Mori, A. S., Smith, M. D., Adler, P. B., Bakker, J. D., Brauman, K. A., Cowles, J., Komatsu, K., Knops, J. M. H., McCulley, R. L., Moore, J. L., Morgan, J. W., Ohlert, T., Power, S. A., Sullivan, L. L., Stevens, C., and Loreau, M.: Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference, Nat. Commun., 14, 2607, https://doi.org/10.1038/s41467-023-37194-5, 2023. a
Dickson, T. L., Mittelbach, G. G., Reynolds, H. L., and Gross, K. L.: Height and clonality traits determine plant community responses to fertilization, Ecology, 95, 2443–2452, https://doi.org/10.1890/13-1875.1, 2014. a
Díaz, S., Lavorel, S., McIntyre, S., Falczuk, V., Casanoves, F., Milchunas, D. G., Skarpe, C., Rusch, G., Sternberg, M., Noy-Meir, I., Landsberg, J., Zhang, W., Clark, H., and Campbell, B. D.: Plant trait responses to grazing – a global synthesis, Glob. Change Biol., 13, 313–341, https://doi.org/10.1111/j.1365-2486.2006.01288.x, 2007. a
DWD Climate Data Center: Calculated daily values for different characteristic elements of soil and crops, https://opendata.dwd.de/climate_environment/CDC/derived_germany/soil/daily/historical/ (last access: 3 September 2023), 2019. a
European Environment Agency, Kühn, E., Pettersson, L., Strien, A., Õunap, E., Warren, M., Settele, J., Švitra, G., Botham, M., Regan, E., Prokofev, I., Swaay, C., Stefanescu, C., Heliölä, J., Popov, S., Roth, T., Leopold, P., Verovnik, R., Fontaine, B., Musche, M., Julliard, R., Collins, S., Goloshchapova, S., Öberg, S., Cornish, N., Brereton, T., Titeux, N., Harpke, A., and Roy, D.: The European grassland butterfly indicator – 1990–2011, Publications Office of the European Union, https://doi.org/10.2800/89760, 2013. a
Eurostat: Main farm land use by NUTS 2 regions, https://doi.org/10.2908/ef_lus_main, 2020. a
Falster, D. S. and Westoby, M.: Plant height and evolutionary games, Trends Ecol. Evol., 18, 337–343, https://doi.org/10.1016/s0169-5347(03)00061-2, 2003. a
Fartmann, T.: Routledge Handbook of Insect Conservation, Chap. Insect Conservation in Grasslands, Routledge, ISBN 9781003285793, https://doi.org/10.4324/9781003285793, 2024. a
Fischer, M., Bossdorf, O., Gockel, S., Hänsel, F., Hemp, A., Hessenmöller, D., Korte, G., Nieschulze, J., Pfeiffer, S., Prati, D., Renner, S., Schöning, I., Schumacher, U., Wells, K., Buscot, F., Kalko, E. K. V., Linsenmair, K. E., Schulze, E.-D., and Weisser, W. W.: Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories, Basic Appl. Ecol., 11, 473–485, https://doi.org/10.1016/j.baae.2010.07.009, 2010. a
Fort, H.: On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model, Ecol. Model., 387, 154–162, https://doi.org/10.1016/j.ecolmodel.2018.09.009, 2018. a
Funk, J. L., Larson, J. E., Ames, G. M., Butterfield, B. J., Cavender-Bares, J., Firn, J., Laughlin, D. C., Sutton-Grier, A. E., Williams, L., and Wright, J.: Revisiting the Holy Grail: using plant functional traits to understand ecological processes, Biol. Rev., 92, 1156–1173, https://doi.org/10.1111/brv.12275, 2017. a
Geijzendorffer, I. R., van der Werf, W., Bianchi, F. J. J. A., and Schulte, R. P. O.: Sustained dynamic transience in a Lotka-Volterra competition model system for grassland species, Ecol. Model., 222, 2817–2824, https://doi.org/10.1016/j.ecolmodel.2011.05.029, 2011. a
George, E., Marschner, H., and Jakobsen, I.: Role of Arbuscular Mycorrhizal Fungi in Uptake of Phosphorus and Nitrogen From Soil, CRC Cr. Rev. Biotechn., 15, 257–270, https://doi.org/10.3109/07388559509147412, 1995. a
Gillet, F.: Modelling vegetation dynamics in heterogeneous pasture-woodland landscapes, Ecol. Model., 217, 1–18, https://doi.org/10.1016/j.ecolmodel.2008.05.013, 2008. a
Goossens, E. P., Minden, V., Van Poucke, F., and Olde Venterink, H.: Negative plant-soil feedbacks disproportionally affect dominant plants, facilitating coexistence in plant communities, npj Biodiversity, 2, 27, https://doi.org/10.1038/s44185-023-00032-4, 2023. a
Gossner, M. M., Lewinsohn, T. M., Kahl, T., et al.: Land-use intensification causes multitrophic homogenization of grassland communities, Nature, 540, 266–269, https://doi.org/10.1038/nature20575, 2016. a
Gough, L., Gross, K. L., Cleland, E. E., Clark, C. M., Collins, S. L., Fargione, J. E., Pennings, S. C., and Suding, K. N.: Incorporating clonal growth form clarifies the role of plant height in response to nitrogen addition, Oecologia, 169, 1053–1062, https://doi.org/10.1007/s00442-012-2264-5, 2012. a, b
Griffin‐Nolan, R. J., Blumenthal, D. M., Collins, S. L., Farkas, T. E., Hoffman, A. M., Mueller, K. E., Ocheltree, T. W., Smith, M. D., Whitney, K. D., and Knapp, A. K.: Shifts in plant functional composition following long‐term drought in grasslands, J. Ecol., 107, 2133–2148, https://doi.org/10.1111/1365-2745.13252, 2019. a
Grime, J. P.: Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory, The American Naturalist, 111, 1169–1194, https://doi.org/10.1086/283244, 1977. a
Gross, K. L. and Mittelbach, G. G.: Negative effects of fertilization on grassland species richness are stronger when tall clonal species are present, Folia Geobot., 52, 401–409, https://doi.org/10.1007/s12224-017-9300-5, 2017. a
Gubsch, M., Buchmann, N., Schmid, B., Schulze, E.-D., Lipowsky, A., and Roscher, C.: Differential effects of plant diversity on functional trait variation of grass species, Ann. Bot., 107, 157–169, https://doi.org/10.1093/aob/mcq220, 2010. a
Haario, H., Saksman, E., and Tamminen, J.: An Adaptive Metropolis Algorithm, Bernoulli, 7, 223–242, https://doi.org/10.2307/3318737, 2001. a
Heger, T.: Light availability experienced in the field affects ability of following generations to respond to shading in an annual grassland plant, J. Ecol., 104, 1432–1440, https://doi.org/10.1111/1365-2745.12607, 2016. a
Hejcman, M., Klaudisová, M., Schellberg, J., and Honsová, D.: The Rengen Grassland Experiment: Plant species composition after 64 years of fertilizer application, Agr. Ecosyst. Environ., 122, 259–266, https://doi.org/10.1016/j.agee.2006.12.036, 2007. a
Hejcman, M., Hejcmanová, P., Pavlů, V., and Beneš, J.: Origin and history of grasslands in Central Europe – a review, Grass Forage Sci., 68, 345–363, https://doi.org/10.1111/gfs.12066, 2013. a
Herold, N., Schöning, I., and Schrumpf, M.: Soil Survey 2008 Subplot Description, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/4761?version=3 (last access: 8 September 2025), 2021. a
Hilpold, A., Seeber, J., Fontana, V., Niedrist, G., Rief, A., Steinwandter, M., Tasser, E., and Tappeiner, U.: Decline of rare and specialist species across multiple taxonomic groups after grassland intensification and abandonment, Biodivers. Conserv., 27, 3729–3744, https://doi.org/10.1007/s10531-018-1623-x, 2018. a
Hinderling, J. and Keller, S.: Vegetation records for grassland EPs, 2008 – 2022, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/31389?version=7 (last access: 8 September 2025), 2023. a
Hinderling, J., Penone, C., Prati, D., Bolliger, R., Schäfer, D., Boch, S., and Schmitt, B.: Biomass data for grassland EPs, 2009–2023, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/31581?version=5 (last access: 8 September 2025), 2024. a
Hirata, M., Kunieda, E., and Tobisa, M.: Short-term ingestive behaviour of cattle grazing tropical stoloniferous grasses with contrasting growth forms, J. Agr. Sci., 148, 615–624, https://doi.org/10.1017/S0021859610000353, 2010. a, b
Hodgson, J., Clark, D., and Mitchell, R.: Foraging Behavior in Grazing Animals and Its Impact on Plant Communities, in: Forage Quality, Evaluation, and Utilization, John Wiley & Sons, Ltd., 796–827, https://doi.org/10.2134/1994.foragequality.c19, 1994. a
Jäger, E. J., Müller, F., Ritz, C., Welk, E., and Wesche, K. (Eds.): Rothmaler – Exkursionsflora von Deutschland, Gefäßpflanzen: Atlasband, Springer Berlin Heidelberg, ISBN 9783662497104, https://doi.org/10.1007/978-3-662-49710-4, 2017. a
Jeltsch, F., Moloney, K. A., Schurr, F. M., Köchy, M., and Schwager, M.: The state of plant population modelling in light of environmental change, Perspectives in Plant Ecology, Evolution and Systematics, 9, 171–189, https://doi.org/10.1016/j.ppees.2007.11.004, 2008. a, b
Jiang, Y., Li, Y., Nie, G., and Liu, H.: Leaf and Root Growth, Carbon and Nitrogen Contents, and Gene Expression of Perennial Ryegrass to Different Nitrogen Supplies, J. Am. Soc. Hortic. Sci., 141, 555–562, https://doi.org/10.21273/JASHS03883-16, 2016. a
Johnstone, R. H., Chang, E. T., Bardenet, R., de Boer, T. P., Gavaghan, D. J., Pathmanathan, P., Clayton, R. H., and Mirams, G. R.: Uncertainty and variability in models of the cardiac action potential: Can we build trustworthy models?, J. Mol. Cell. Cardiol., 96, 49–62, https://doi.org/10.1016/j.yjmcc.2015.11.018, 2016. a
Jouven, M., Carrère, P., and Baumont, R.: Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description, Grass Forage Sci., 61, 112–124, https://doi.org/10.1111/j.1365-2494.2006.00515.x, 2006. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Kattge, J., Bönisch, G., Díaz, S., et al.: TRY plant trait database – enhanced coverage and open access, Glob. Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020. a, b
Kipling, R. P., Virkajärvi, P., Breitsameter, L., Curnel, Y., De Swaef, T., Gustavsson, A.-M., Hennart, S., Höglind, M., Järvenranta, K., Minet, J., Nendel, C., Persson, T., Picon-Cochard, C., Rolinski, S., Sandars, D. L., Scollan, N. D., Sebek, L., Seddaiu, G., Topp, C. F. E., Twardy, S., Van Middelkoop, J., Wu, L., and Bellocchi, G.: Key challenges and priorities for modelling European grasslands under climate change, Sci. Total Environ., 566-567, 851–864, https://doi.org/10.1016/j.scitotenv.2016.05.144, 2016. a
Klimešová, J., Latzel, V., de Bello, F., and van Groenendael, J. M.: Plant functional traits in studies of vegetation changes in response to grazing and mowing: towards a use of more specific traits, Preslia, 80, 245–253, 2008. a
Konvalinková, T., Püschel, D., Řezáčová, V., Gryndlerová, H., and Jansa, J.: Carbon flow from plant to arbuscular mycorrhizal fungi is reduced under phosphorus fertilization, Plant and Soil, 419, 319–333, https://doi.org/10.1007/s11104-017-3350-6, 2017. a
Kranstauber, B., Smolla, M., and Safi, K.: Similarity in spatial utilization distributions measured by the earth mover's distance, Methods Ecol. Evol., 8, 155–160, https://doi.org/10.1111/2041-210X.12649, 2017. a
Kunrath, T. R., Nunes, P. A. d. A., de Souza Filho, W., Cadenazzi, M., Bremm, C., Martins, A. P., and Carvalho, P. C. d. F.: Sward height determines pasture production and animal performance in a long-term soybean-beef cattle integrated system, Agr. Syst., 177, 102716, https://doi.org/10.1016/j.agsy.2019.102716, 2020. a
Lacasa, J., Hefley, T. J., Otegui, M. E., and Ciampitti, I. A.: A practical guide to estimating the light extinction coefficient with nonlinear models – a case study on maize, Plant Methods, 17, 60, https://doi.org/10.1186/s13007-021-00753-2, 2021. a, b
Lazzarotto, P., Calanca, P., and Fuhrer, J.: (2009). Dynamics of grass–clover mixtures – An analysis of the response to management with the PROductive GRASsland Simulator (PROGRASS), Ecol. Model., 220, 703–724, https://doi.org/10.1016/j.ecolmodel.2008.11.023, 2009. a, b
Liu, F. and Stützel, H.: Biomass partitioning, specific leaf area, and water use efficiency of vegetable amaranth (Amaranthus spp.) in response to drought stress, Sci. Hortic., 102, 15–27, https://doi.org/10.1016/j.scienta.2003.11.014, 2004. a
Liu, X., Parker, I. M., Gilbert, G. S., Lu, Y., Xiao, Y., Zhang, L., Huang, M., Cheng, Y., Zhang, Z., and Zhou, S.: Coexistence is stabilized by conspecific negative density dependence via fungal pathogens more than oomycete pathogens, Ecology, 103, https://doi.org/10.1002/ecy.3841, 2022. a
Long, S. P., Humphries, S., and Falkowski, P. G.: Photoinhibition of Photosynthesis in Nature, Annu. Rev. Plant Phys., 45, 633–662, https://doi.org/10.1146/annurev.pp.45.060194.003221, 1994. a
Lopez, G., Ahmadi, S. H., Amelung, W., Athmann, M., Ewert, F., Gaiser, T., Gocke, M. I., Kautz, T., Postma, J., Rachmilevitch, S., Schaaf, G., Schnepf, A., Stoschus, A., Watt, M., Yu, P., and Seidel, S. J.: Nutrient deficiency effects on root architecture and root-to-shoot ratio in arable crops, Front. Plant Sci., 13, https://doi.org/10.3389/fpls.2022.1067498, 2023. a
Luo, W., Ishii, N. I., Muraina, T. O., Song, L., Te, N., Griffin-Nolan, R. J., Slette, I. J., Ross, S. R. P. J., Sasaki, T., Rudgers, J. A., Smith, M. D., Knapp, A. K., and Collins, S. L.: Extreme Drought Increases the Temporal Variability of Grassland Productivity by Suppressing Dominant Grasses, Ecol. Lett., 28, e70127, https://doi.org/10.1111/ele.70127, 2025. a
Luo, Y., Weng, E., Wu, X., Gao, C., Zhou, X., and Zhang, L.: Parameter Identifiability, Constraint, and Equifinality in Data Assimilation with Ecosystem Models, Ecol. Appl., 19, 571–574, https://doi.org/10.1890/08-0561.1, 2009. a
Marschner, H. and Dell, B.: Nutrient uptake in mycorrhizal symbiosis, Plant Soil, 159, 89–102, https://doi.org/10.1007/bf00000098, 1994. a
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, c, d
Meurer, K. H. E., Bolinder, M. A., Andrén, O., Hansson, A.-C., Pettersson, R., and Kätterer, T.: Shoot and root production in mixed grass ley under daily fertilization and irrigation: validating the N productivity concept under field conditions, Nutr. Cycl. Agroecosys., 115, 85–99, https://doi.org/10.1007/s10705-019-10006-3, 2019. a
Monsi, M.: Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion, Jap. J. Bot., 14, 22–52, 1953. a
Monsi, M. and Saeki, T.: On the Factor Light in Plant Communities and its Importance for Matter Production, Ann. Bot., 95, 549–567, https://doi.org/10.1093/aob/mci052, 2005. a, b, c
Monteith, J. L.: Solar Radiation and Productivity in Tropical Ecosystems, J. Appl. Ecol., 9, 747–766, https://doi.org/10.2307/2401901, 1972. a
Moulin, T., Perasso, A., Calanca, P., and Gillet, F.: DynaGraM: A process-based model to simulate multi-species plant community dynamics in managed grasslands, Ecol. Model., 439, 109345, https://doi.org/10.1016/j.ecolmodel.2020.109345, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad, ae, af, ag
Nagy, S.: Grasslands as a bird habitat, in: Grasslands in Europe: Of High Nature Value, KNNV Publishing, 35–41, ISBN 9789004278103, https://doi.org/10.1163/9789004278103_005, 2009. a
Nößler, F.: Supplementary material (empirical data for calibration and validation, model code, analysis scripts): A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.14011849, 2025. 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
Pakeman, R. J., Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Doležal, J., Eriksson, O., Freitas, H., Golodets, C., Kigel, J., Kleyer, M., Lepš, J., Meier, T., Papadimitriou, M., Papanastasis, V. P., Quested, H., Quétier, F., Rusch, G., Sternberg, M., Theau, J.-P., Thébault, A., and Vile, D.: Impact of abundance weighting on the response of seed traits to climate and land use, J. Ecol., 96, 355–366, https://doi.org/10.1111/j.1365-2745.2007.01336.x, 2008. a
Pärtel, M., Bruun, H., and Sammul, M.: Biodiversity in temperate European grasslands: origin and conservation, in: Integrating efficient grassland farming and biodiversity: Proceedings of the 13th international occasional symposium of the European grassland federation, Vol. 10 of Grassland Science in Europe, 1–14, ISBN: 9985-9611-3-7, 2005. a
Parton, W.: The CENTURY model, in: Evaluation of soil organic matter models: Using existing long-term datasets, Springer, 283–291, https://doi.org/10.1007/978-3-642-61094-3_23, 1996. a
Parton, W. J., Hartman, M., Ojima, D., and Schimel, D.: DAYCENT and its land surface submodel: description and testing, Global Planet. Change, 19, 35–48, https://doi.org/10.1016/S0921-8181(98)00040-X, 1998. a
Pauler, C. M., Isselstein, J., Suter, M., Berard, J., Braunbeck, T., and Schneider, M. K.: Choosy grazers: Influence of plant traits on forage selection by three cattle breeds, Funct. Ecol., 34, 980–992, https://doi.org/10.1111/1365-2435.13542, 2020. a
Pei, Y., Dong, J., Zhang, Y., Yuan, W., Doughty, R., Yang, J., Zhou, D., Zhang, L., and Xiao, X.: Evolution of light use efficiency models: Improvement, uncertainties, and implications, Agr. Forest Meteorol., 317, 108905, https://doi.org/10.1016/j.agrformet.2022.108905, 2022. a
Petermann, J. S. and Buzhdygan, O. Y.: Grassland biodiversity, Curr. Biol., 31, R1195–R1201, https://doi.org/10.1016/j.cub.2021.06.060, 2021. a, b
Piseddu, F., Bellocchi, G., and Picon-Cochard, C.: Mowing and warming effects on grassland species richness and harvested biomass: meta-analyses, Agron. Sustain. Dev., 41, https://doi.org/10.1007/s13593-021-00722-y, 2021. a
Piseddu, F., Martin, R., Movedi, E., Louault, F., Confalonieri, R., and Bellocchi, G.: Simulation of Multi-Species Plant Communities in Perturbed and Nutrient-Limited Grasslands: Development of the Growth Model ModVege, Agronomy, 12, https://doi.org/10.3390/agronomy12102468, 2022. a, b, c, d
Potts, J. R., Auger-Méthé, M., Mokross, K., and Lewis, M. A.: A generalized residual technique for analysing complex movement models using earth mover's distance, Methods Ecol. Evol., 5, 1012–1022, https://doi.org/10.1111/2041-210X.12253, 2014. a
Prati, D., Goßner, M., and Neff, F.: Leaf traits of most abundant plant species from all EPs, 2017/2018, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/24807?version=2 (last access: 8 September 2025), 2021. a
Prieto, I., Roumet, C., Cardinael, R., Dupraz, C., Jourdan, C., Kim, J. H., Maeght, J. L., Mao, Z., Pierret, A., Portillo, N., Roupsard, O., Thammahacksa, C., and Stokes, A.: Root functional parameters along a land-use gradient: evidence of a community-level economics spectrum, J. Ecol., 103, 361–373, https://doi.org/10.1111/1365-2745.12351, 2015. a
Pulungan, M. A., Suzuki, S., Gavina, M. K. A., Tubay, J. M., Ito, H., Nii, M., Ichinose, G., Okabe, T., Ishida, A., Shiyomi, M., Togashi, T., Yoshimura, J., and Morita, S.: Grazing enhances species diversity in grassland communities, Sci. Rep., 9, https://doi.org/10.1038/s41598-019-47635-1, 2019. a
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: 1 October 2024), 2024. a
re3data.org: Biodiversity Exploratories Information System; re3data.org - Registry of Research Data Repositories [data set], https://doi.org/10.17616/R32P9Q, last access: 8 September 2025. a
Reich, P. B.: The world-wide “fast–slow” plant economics spectrum: a traits manifesto, J. Ecol., 102, 275–301, 2014. a
Reich, P. B., Walters, M. B., and Ellsworth, D. S.: Leaf Life-Span in Relation to Leaf, Plant, and Stand Characteristics among Diverse Ecosystems, Ecol. Monogr., 62, 365–392, https://doi.org/10.2307/2937116, 1992. a, b
Riedo, M., Grub, A., Rosset, M., and Fuhrer, J.: A pasture simulation model for dry matter production, and fluxes of carbon, nitrogen, water and energy, Ecol. Model., 105, 141–183, https://doi.org/10.1016/S0304-3800(97)00110-5, 1998. a, b, c
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
Rubner, Y., Tomasi, C., and Guibas, L. J.: The Earth Mover's Distance as a Metric for Image Retrieval, Int. J. Comput. Vision, 40, 99–121, https://doi.org/10.1023/a:1026543900054, 2000. a, b
Schils, R. L. M., Newell Price, P., Klaus, V., Tonn, B., Hejduk, S., Stypinski, P., Hiron, M., Fernández, P., Ravetto Enri, S., Lellei-Kovács, E., Annett, N., Markovic, B., Lively, F., Ten Berge, H., Smith, K., Forster-Brown, C., Jones, M., Buchmann, N., Janicka, M., Fernandez, J., Rankin, J., McConnell, D., Aubry, A., and Korevaar, H.: European permanent grasslands mainly threatened by abandonment, heat and drought, and conversion to temporary grassland, Grassland Science in Europe, 25, 553–555, https://doi.org/10.3929/ETHZ-B-000448642, 2020. a, b
Schroeder-Georgi, T., Wirth, C., Nadrowski, K., Meyer, S. T., Mommer, L., and Weigelt, A.: From pots to plots: hierarchical trait-based prediction of plant performance in a mesic grassland, J. Ecol., 104, 206–218, https://doi.org/10.1111/1365-2745.12489, 2016. a
Schwinning, S. and Weiner, J.: Mechanisms determining the degree of size asymmetry in competition among plants, Oecologia, 113, 447–455, https://doi.org/10.1007/s004420050397, 1998. a
Schöning, I.: Soil carbon and nitrogen concentrations – soil sampling campaign 2021, all experimental plots (EPs), 0–10 cm, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/31210?version=13 (last access: 8 September 2025), 2023. a
Schöning, I., Klötzing, T., Apostolakis, A., Trumbore, S., and Schrumpf, M.: MinSoil 2017 – Soil Carbon and Nitrogen Concentrations, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/23846?version=10 (last access: 8 September 2025), 2021a. a
Schöning, I., Solly, E., Klötzing, T., Trumbore, S., and Schrumpf, M.: MinSoil 2011 – Soil Texture, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/14686?version=10 (last access: 8 September 2025), 2021c. a
Schöning, I., Solly, E., Klötzing, T., Trumbore, S., and Schrumpf, M.: MinSoil 2011 – Soil Bulk Density and Carbon and Nitrogen stocks, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/17086?version=4 (last access: 8 September 2025), 2021d. a
Schöning, I., Trumbore, S., Schrumpf, M., Klötzing, T., and Gan, H. Y.: MinSoil 2014 – Soil Carbon and Nitrogen Concentrations, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/18787?version=6 (last access: 8 September 2025), 2021e. a
Siefert, A., Violle, C., Chalmandrier, L., et al.: A global meta-analysis of the relative extent of intraspecific trait variation in plant communities, Ecol. Lett., 18, 1406–1419, https://doi.org/10.1111/ele.12508, 2015. a
Siehoff, S., Lennartz, G., Heilburg, I. C., Roß-Nickoll, M., Ratte, H. T., and Preuss, T. G.: Process-based modeling of grassland dynamics built on ecological indicator values for land use, Ecol. Model., 222, 3854–3868, https://doi.org/10.1016/j.ecolmodel.2011.10.003, 2011. a, b, c, d
Silva, G. P., Fialho, C. A., Carvalho, L. R., Fonseca, L., Carvalho, P. C. F., Bremm, C., and Da Silva, S. C.: Sward structure and short-term herbage intake in Arachis pintoi cv. Belmonte subjected to varying intensities of grazing, J. Agr. Sci., 156, 92–99, https://doi.org/10.1017/S0021859617000855, 2018. a
Tälle, M., Deák, B., Poschlod, P., Valkó, O., Westerberg, L., and Milberg, P.: Grazing vs. mowing: A meta-analysis of biodiversity benefits for grassland management, Agr. Ecosyst. Environ., 222, 200–212, https://doi.org/10.1016/j.agee.2016.02.008, 2016. 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, 2020. a, b
Van Der Heijden, M. G. A., Martin, F. M., Selosse, M., and Sanders, I. R.: Mycorrhizal ecology and evolution: the past, the present, and the future, New Phytol., 205, 1406–1423, https://doi.org/10.1111/nph.13288, 2015. a
Van Oijen, M., Bellocchi, G., and Höglind, M.: Effects of Climate Change on Grassland Biodiversity and Productivity: The Need for a Diversity of Models, Agronomy, 8, 14, https://doi.org/10.3390/agronomy8020014, 2018. a
Van Oijen, M., Barcza, Z., Confalonieri, R., Korhonen, P., Kröel-Dulay, G., Lellei-Kovács, E., Louarn, G., Louault, F., Martin, R., Moulin, T., Movedi, E., Picon-Cochard, C., Rolinski, S., Viovy, N., Wirth, S. B., and Bellocchi, G.: Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap, Agronomy, 10, 259, https://doi.org/10.3390/agronomy10020259, 2020. a, b
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C.: Rank-normalization, folding, and localization: An improved R̂ for assessing convergence of MCMC (with discussion), Bayesian Anal., 16, 667–718, https://doi.org/10.1214/20-BA1221, 2021. a
Villani, C.: Optimal Transport – Old and New, Springer Berlin Heidelberg, ISBN 9783540710509, https://doi.org/10.1007/978-3-540-71050-9, 2009. a
Violle, C., Enquist, B. J., McGill, B. J., Jiang, L., Albert, C. H., Hulshof, C., Jung, V., and Messier, J.: The return of the variance: intraspecific variability in community ecology, Trends Ecol. Evol., 27, 244–252, https://doi.org/10.1016/j.tree.2011.11.014, 2012. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vogt, J., Weisser, W., Ayasse, M., Fischer, M., Schumacher, U., Schreiber, C., Lauterbach, R., Franke, A., Ostrowski, A., Teuscher, M., and Pompe, S.: Grassland management parameter as input data for a computer model based on interview data of the Biodiversity Exploratories project, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/ddm/data/Showdata/31715?version=9 (last access: 8 September 2025), 2024. a
Wang, D.: MODIS/Terra+Aqua Photosynthetically Active Radiation Daily/3-Hour L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD18C2.061, 2021. a
Watson, D. J.: Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years, Ann. Bot., 11, 41–76, https://doi.org/10.1093/oxfordjournals.aob.a083148, 1947. a
Weiner, J.: Asymmetric competition in plant populations, Trends Ecol. Evol., 5, 360–364, https://doi.org/10.1016/0169-5347(90)90095-U, 1990. a
Went, F.: The effect of temperature on plant growth, Annu. Rev. Plant Physio., 4, 347–362, https://doi.org/10.1146/annurev.pp.04.060153.002023, 1953. a
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A., and Wright, I. J.: Plant ecological strategies: some leading dimensions of variation between species, Annu. Rev. Ecol. Syst., 33, 125–159, https://doi.org/10.1146/annurev.ecolsys.33.010802.150452, 2002. a
Wilson, J. B., Peet, R. K., Dengler, J., and Pärtel, M.: Plant species richness: the world records, J. Veg. Sci., 23, 796–802, https://doi.org/10.1111/j.1654-1103.2012.01400.x, 2012. 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
Wirth, S. B., Poyda, A., Taube, F., Tietjen, B., Müller, C., Thonicke, K., Linstädter, A., Behn, K., Schaphoff, S., von Bloh, W., and Rolinski, S.: 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, Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, 2024. a, b, c, d
Wright, G. C., Hubick, K. T., Farquhar, G. D., and Rao, R. N.: Genetic and environmental variation in transpiration efficiency and its correlation with carbon isotope discrimination and specific leaf area in peanut, in: Stable isotopes and plant carbon-water relations, Elsevier, 247–267, https://doi.org/10.1016/B978-0-08-091801-3.50025-8, 1993. 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, Ü., 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
Wöllauer, S., Hänsel, F., Nauss, T., and Forteva, S.: Climate data – Time Series Web Interface, Biodiversity Exploratories Information System [data set], https://www.bexis.uni-jena.de/tcd/PublicClimateData/ (last access: 1 May 2025), 2023. a
Short summary
To simulate the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure physiological species data. This allows the simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water.
To simulate the response of grassland plant communities to management and climate change, we...
