Articles | Volume 21, issue 21
https://doi.org/10.5194/bg-21-4909-2024
© 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-21-4909-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Nov 2024
Research article |
| 11 Nov 2024
| 11 Nov 2024
Crowd-sourced trait data can be used to delimit global biomes
Simon Scheiter
CORRESPONDING AUTHOR
Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberganlage 25, 60325 Frankfurt am Main, Germany
Sophie Wolf
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Talstr. 35, 04103 Leipzig, Germany
Teja Kattenborn
Sensor-based Geoinformatics (geosense), Faculty of Environment and Natural Resources, University of Freiburg, Tennenbacherstr. 4, 79106 Freiburg, Germany
Related authors
Mirjam Pfeiffer, Munir P. Hoffmann, Simon Scheiter, William Nelson, Johannes Isselstein, Kingsley Ayisi, Jude J. Odhiambo, and Reimund Rötter
Biogeosciences, 19, 3935–3958, https://doi.org/10.5194/bg-19-3935-2022, https://doi.org/10.5194/bg-19-3935-2022, 2022
Short summary
Short summary
Smallholder farmers face challenges due to poor land management and climate change. We linked the APSIM crop model and the aDGVM2 vegetation model to investigate integrated management options that enhance ecosystem functions and services. Sustainable intensification moderately increased yields. Crop residue grazing reduced feed gaps but not for dry-to-wet season transitions. Measures to improve soil water and nutrient status are recommended. Landscape-level ecosystem management is essential.
Dushyant Kumar, Mirjam Pfeiffer, Camille Gaillard, Liam Langan, and Simon Scheiter
Biogeosciences, 18, 2957–2979, https://doi.org/10.5194/bg-18-2957-2021, https://doi.org/10.5194/bg-18-2957-2021, 2021
Short summary
Short summary
In this paper, we investigated the impact of climate change and rising CO2 on biomes using a vegetation model in South Asia, an often neglected region in global modeling studies. Understanding these impacts guides ecosystem management and biodiversity conservation. Our results indicate that savanna regions are at high risk of woody encroachment and transitioning into the forest, and the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and CO2.
Mirjam Pfeiffer, Dushyant Kumar, Carola Martens, and Simon Scheiter
Biogeosciences, 17, 5829–5847, https://doi.org/10.5194/bg-17-5829-2020, https://doi.org/10.5194/bg-17-5829-2020, 2020
Short summary
Short summary
Lags caused by delayed vegetation response to changing environmental conditions can lead to disequilibrium vegetation states. Awareness of this issue is relevant for ecosystem conservation. We used the aDGVM vegetation model to quantify the difference between transient and equilibrium vegetation states in Africa during the 21st century for two potential climate trajectories. Lag times increased over time and vegetation was non-analog to any equilibrium state due to multi-lag composite states.
Simon Lotz, Teja Kattenborn, Julian Frey, Salim Soltani, Anna Göritz, Tom Jakszat, and Negin Katal
EGUsphere, https://doi.org/10.5194/egusphere-2025-1496, https://doi.org/10.5194/egusphere-2025-1496, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Digital hemispherical photography (DHP) is a valuable tool for monitoring leaf area index (LAI), a key factor in ecosystem productivity and climate interactions. We compared DHP with litter traps in a temperate forest and found that at a 20° view angle, both methods aligned well. We applied a calibration model to assess site variability which significantly improved accuracy. Our findings enhance the reliability of ground-based LAI monitoring, supporting better ecosystem assessments.
Eya Cherif, Teja Kattenborn, Luke A. Brown, Michael Ewald, Katja Berger, Phuong D. Dao, Tobias B. Hank, Etienne Laliberté, Bing Lu, and Hannes Feilhauer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1284, https://doi.org/10.5194/egusphere-2025-1284, 2025
Short summary
Short summary
Hyperspectral imagery combined with machine learning enables accurate large-scale mapping of plant traits but struggles with uncertainty when facing unfamiliar environmental conditions. This study introduces a distance-based method that measures dissimilarities between new and training data to reliably quantify uncertainty. Results show it effectively identifies uncertain predictions, greatly improving the reliability of global vegetation monitoring compared to traditional methods.
Salim Soltani, Lauren E. Gillespie, Moises Exposito-Alonso, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn
EGUsphere, https://doi.org/10.5194/egusphere-2025-662, https://doi.org/10.5194/egusphere-2025-662, 2025
Short summary
Short summary
We introduce an automated approach for generating segmentation masks for citizen science plant photos, making them applicable to computer vision models. This framework effectively transforms citizen science data into a data treasure for segmentation models for plant species identification in aerial imagery. Using automatically labeled photos, we train segmentation models for mapping tree species in drone imagery, showcasing their potential for forestry, agriculture, and biodiversity monitoring.
Salim Soltani, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn
Biogeosciences, 21, 2909–2935, https://doi.org/10.5194/bg-21-2909-2024, https://doi.org/10.5194/bg-21-2909-2024, 2024
Short summary
Short summary
In this research, we developed a novel method using citizen science data as alternative training data for computer vision models to map plant species in unoccupied aerial vehicle (UAV) images. We use citizen science plant photographs to train models and apply them to UAV images. We tested our approach on UAV images of a test site with 10 different tree species, yielding accurate results. This research shows the potential of citizen science data to advance our ability to monitor plant species.
Mirjam Pfeiffer, Munir P. Hoffmann, Simon Scheiter, William Nelson, Johannes Isselstein, Kingsley Ayisi, Jude J. Odhiambo, and Reimund Rötter
Biogeosciences, 19, 3935–3958, https://doi.org/10.5194/bg-19-3935-2022, https://doi.org/10.5194/bg-19-3935-2022, 2022
Short summary
Short summary
Smallholder farmers face challenges due to poor land management and climate change. We linked the APSIM crop model and the aDGVM2 vegetation model to investigate integrated management options that enhance ecosystem functions and services. Sustainable intensification moderately increased yields. Crop residue grazing reduced feed gaps but not for dry-to-wet season transitions. Measures to improve soil water and nutrient status are recommended. Landscape-level ecosystem management is essential.
Dushyant Kumar, Mirjam Pfeiffer, Camille Gaillard, Liam Langan, and Simon Scheiter
Biogeosciences, 18, 2957–2979, https://doi.org/10.5194/bg-18-2957-2021, https://doi.org/10.5194/bg-18-2957-2021, 2021
Short summary
Short summary
In this paper, we investigated the impact of climate change and rising CO2 on biomes using a vegetation model in South Asia, an often neglected region in global modeling studies. Understanding these impacts guides ecosystem management and biodiversity conservation. Our results indicate that savanna regions are at high risk of woody encroachment and transitioning into the forest, and the bioclimatic envelopes of biomes need adjustments to account for shifts caused by climate change and CO2.
Mirjam Pfeiffer, Dushyant Kumar, Carola Martens, and Simon Scheiter
Biogeosciences, 17, 5829–5847, https://doi.org/10.5194/bg-17-5829-2020, https://doi.org/10.5194/bg-17-5829-2020, 2020
Short summary
Short summary
Lags caused by delayed vegetation response to changing environmental conditions can lead to disequilibrium vegetation states. Awareness of this issue is relevant for ecosystem conservation. We used the aDGVM vegetation model to quantify the difference between transient and equilibrium vegetation states in Africa during the 21st century for two potential climate trajectories. Lag times increased over time and vegetation was non-analog to any equilibrium state due to multi-lag composite states.
Cited articles
Aguirre-Gutiérrez, J., Rifai, S., Shenkin, A., Oliveras, I., Bentley, L. P., Svátek, M., Girardin, C. A. J., Both, S., Riutta, T., Berenguer, E., Kissling, W. D., Bauman, D., Raab, N., Moore, S., Farfan-Rios, W., Figueiredo, A. E. S., Reis, S. M., Ndong, J. E., Ondo, F. E., N'ssi Bengone, N., Mihindou, V., Moraes de Seixas, M. M., Adu-Bredu, S., Abernethy, K., Asner, G. P., Barlow, J., Burslem, D. F. R. P., Coomes, D. A., Cernusak, L. A., Dargie, G. C., Enquist, B. J., Ewers, R. M., Ferreira, J., Jeffery, K. J., Joly, C. A., Lewis, S. L., Marimon-Junior, B. H., Martin, R. E., Morandi, P. S., Phillips, O. L., Quesada, C. A., Salinas, N., Schwantes Marimon, B., Silman, M., Teh, Y. A., White, L. J. T., and Malhi, Y.: Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data, Remote Sens. Environ., 252, 112122, https://doi.org/10.1016/j.rse.2020.112122, 2021. a
Allen, J. R. M., Forrest, M., Hickler, T., Singarayer, J. S., Valdes, P. J., and Huntley, B.: Global vegetation patterns of the past 140,000years, J. Biogeogr., 47, 2073–2090, 2020. a
Anderegg, W. R. L., Konings, A. G., Trugman, A. T., Yu, K., Bowling, D. R., Gabbitas, R., Karp, D. S., Pacala, S., Sperry, J. S., Sulman, B. N., and Zenes, N.: Hydraulic diversity of forests regulates ecosystem resilience during drought, Nature, 561, 538–541, https://doi.org/10.1038/s41586-018-0539-7, 2018. a
Barnes, R., Spinielli, E., Pebesma, E., and Provoost, P.: r-barnes/dggridR: v3.0.0 (v3.0.0), Zenodo, https://doi.org/10.5281/zenodo.7565922, 2023. a
Beierkuhnlein, C. and Fischer, J.-C.: Global biomes and ecozones – Conceptual and spatial communalities and discrepancies, Erdkunde, 75, 249–270, 2021. a
Boonman, C. C. F., Benítez-López, A., Schipper, A. M., Thuiller, W., Anand, M., Cerabolini, B. E. L., Cornelissen, J. H. C., Gonzalez-Melo, A., Hattingh, W. N., Higuchi, P., Laughlin, D. C., Onipchenko, V. G., Peñuelas, J., Poorter, L., Soudzilovskaia, N. A., Huijbregts, M. A. J., and Santini, L.: Assessing the reliability of predicted plant trait distributions at the global scale, Global Ecol. Biogeogr., 29, 1034–1051, https://doi.org/10.1111/geb.13086, 2020. a, b
Booth, T. H., Nix, H. A., Busby, J. R., and Hutchinson, M. F.: bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies, Divers. Distrib., 20, 1–9, https://doi.org/10.1111/ddi.12144, 2014. a
Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., Botta-Dukát, Z., Chytrý, M., Field, R., Jansen, F., Kattge, J., Pillar, V. D., Schrodt, F., Mahecha, M. D., Peet, R. K., Sandel, B., van Bodegom, P., Altman, J., Alvarez-Dávila, E., Arfin Khan, M. A. S., Attorre, F., Aubin, I., Baraloto, C., Barroso, J. G., Bauters, M., Bergmeier, E., Biurrun, I., Bjorkman, A. D., Blonder, B., Čarni, A., Cayuela, L., Černý, T., Cornelissen, J. H. C., Craven, D., Dainese, M., Derroire, G., De Sanctis, M., Díaz, S., Doležal, J., Farfan-Rios, W., Feldpausch, T. R., Fenton, N. J., Garnier, E., Guerin, G. R., Gutiérrez, A. G., Haider, S., Hattab, T., Henry, G., Hérault, B., Higuchi, P., Hölzel, N., Homeier, J., Jentsch, A., Jürgens, N., Kącki, Z., Karger, D. N., Kessler, M., Kleyer, M., Knollová, I., Korolyuk, A. Y., Kühn, I., Laughlin, D. C., Lens, F., Loos, J., Louault, F., Lyubenova, M. I., Malhi, Y., Marcenò, C., Mencuccini, M., Müller, J. V., Munzinger, J., Myers-Smith, I. H., Neill, D. A., Niinemets, Ü., Orwin, K. H., Ozinga, W. A., Penuelas, J., Pérez-Haase, A., Petřík, P., Phillips, O. L., Pärtel, M., Reich, P. B., Römermann, C., Rodrigues, A. V., Sabatini, F. M., Sardans, J., Schmidt, M., Seidler, G., Silva Espejo, J. E., Silveira, M., Smyth, A., Sporbert, M., Svenning, J.-C., Tang, Z., Thomas, R., Tsiripidis, I., Vassilev, K., Violle, C., Virtanen, R., Weiher, E., Welk, E., Wesche, K., Winter, M., Wirth, C., and Jandt, U.: Global trait–environment relationships of plant communities, Nature Ecology & Evolution, 2, 1906–1917, https://doi.org/10.1038/s41559-018-0699-8, 2018. a
Champreux, A., Saltré, F., Traylor, W., Hickler, T., and Bradshaw, C. J. A.: How to map biomes: Quantitative comparison and review of biome-mapping methods, Ecol. Monogr., 94, e1615, https://doi.org/10.1002/ecm.1615, 2024. a
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., and Zanne, A. E.: Towards a worldwide wood economics spectrum, Ecol. Lett., 12, 351–366, 2009. a
Cherif, E., Feilhauer, H., Berger, K., Dao, P. D., Ewald, M., Hank, T. B., He, Y., Kovach, K. R., Lu, B., Townsend, P. A., and Kattenborn, T.: From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data, Remote Sens. Environ., 292, 113580, https://doi.org/10.1016/j.rse.2023.113580, 2023. a
Dechant, B., Kattge, J., Pavlick, R., Schneider, F. D., Sabatini, F. M., Moreno-Martínez, Á., Butler, E. E., van Bodegom, P. M., Vallicrosa, H., Kattenborn, T., Boonman, C. C. F., Madani, N., Wright, I. J., Dong, N., Feilhauer, H., Peñuelas, J., Sardans, J., Aguirre-Gutiérrez, J., Reich, P. B., Leitão, P. J., Cavender-Bares, J., Myers-Smith, I. H., Durán, S. M., Croft, H., Prentice, I. C., Huth, A., Rebel, K., Zaehle, S., Šímová, I., Díaz, S., Reichstein, M., Schiller, C., Bruelheide, H., Mahecha, M., Wirth, C., Malhi, Y., and Townsend, P. A.: Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches, Remote Sens. Environ., 311, 114276, https://doi.org/10.1016/j.rse.2024.114276, 2024. a, b, c
Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Colin Prentice, I., Garnier, E., Bönisch, G., Westoby, M., Poorter, H., Reich, P. B., Moles, A. T., Dickie, J., Gillison, A. N., Zanne, A. E., Chave, J., Joseph Wright, S., 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
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L., Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Klöser, H., Shennan-Farpón, Y., Kindt, R., Lillesø, J.-P. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F., and Saleem, M.: An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm, BioScience, 67, 534–545, https://doi.org/10.1093/biosci/bix014, 2017. a, b
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017 (data avaialable at: https://www.worldclim.org/data/worldclim21.html, last access: 31 October 2024). a, b
Fischer, J.-C., Walentowitz, A., and Beierkuhnlein, C.: The biome inventory – standardizing global biogeographical land units, Dryad [data set], https://doi.org/10.5061/dryad.hqbzkh1jm, 2022b. a
Franklin, J.: Mapping species distributions. Spatial inference and prediction, Cambridge University Press, ISBN 9780511810602, 2009. a
GBIF.Org User: Occurrence Download, https://doi.org/10.15468/DL.FE2KV3, 2023. a
Grime, J. P.: Plant evolutionary biology, chap. The C-S-R model of primary plant strategies – origins, implications and tests, Chapman and Hall, London and New York, https://doi.org/10.1007/978-94-009-1207-6, 371–393, 1988. a
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, https://CRAN.R-project.org/package=raster (last access: 1 December 2023), R package version version 3.6-26, 2023. a
Hunter, J., Franklin, S., Luxton, S., and Loidi, J.: Terrestrial biomes: a conceptual review, VCS, 2, 73–85, https://doi.org/10.3897/VCS/2021/61463, 2021. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, UK and New York, NY, USA, 2391 pp., https://doi.org/10.1017/9781009157896, 2021. a
Jonard, F., Feldman, A. F., Short Gianotti, D. J., and Entekhabi, D.: Observed water and light limitation across global ecosystems, Biogeosciences, 19, 5575–5590, https://doi.org/10.5194/bg-19-5575-2022, 2022. a
Kattenborn, T., Svidzinska, D., and Wolf, S.: Global trait maps derived from crowd-sourced data (GBIF) (v.0.6), Zenodo [data set], https://doi.org/10.5281/zenodo.10617814, 2024. a, b
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Tautenhahn, S., Werner, G. D. A., Aakala, T., Abedi, M., Acosta, A. T. R., Adamidis, G. C., Adamson, K., Aiba, M., Albert, C. H., Alcántara, J. M., Alcázar C, C., Aleixo, I., Ali, H., Amiaud, B., Ammer, C., Amoroso, M. M., Anand, M., Anderson, C., Anten, N., Antos, J., Apgaua, D. M. G., Ashman, T.-L., Asmara, D. H., Asner, G. P., Aspinwall, M., Atkin, O., Aubin, I., Baastrup-Spohr, L., Bahalkeh, K., Bahn, M., Baker, T., Baker, W. J., Bakker, J. P., Baldocchi, D., Baltzer, J., Banerjee, A., Baranger, A., Barlow, J., Barneche, D. R., Baruch, Z., Bastianelli, D., Battles, J., Bauerle, W., Bauters, M., Bazzato, E., Beckmann, M., Beeckman, H., Beierkuhnlein, C., Bekker, R., Belfry, G., Belluau, M., Beloiu, M., Benavides, R., Benomar, L., Berdugo-Lattke, M. L., Berenguer, E., Bergamin, R., Bergmann, J., Bergmann Carlucci, M., Berner, L., Bernhardt-Römermann, M., Bigler, C., Bjorkman, A. D., Blackman, C., Blanco, C., Blonder, B., Blumenthal, D., Bocanegra-González, K. T., Boeckx, P., Bohlman, S., Böhning-Gaese, K., Boisvert-Marsh, L., Bond, W., Bond-Lamberty, B., Boom, A., Boonman, C. C. F., Bordin, K., Boughton, E. H., Boukili, V., Bowman, D. M. J. 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, c, d, e, f
Kothari, S., Beauchamp-Rioux, R., Blanchard, F., Crofts, A. L., Girard, A., Guilbeault-Mayers, X., Hacker, P. W., Pardo, J., Schweiger, A. K., Demers-Thibeault, S., Bruneau, A., Coops, N. C., Kalacska, M., Vellend, M., and Laliberté, E.: Predicting leaf traits across functional groups using reflectance spectroscopy, New Phytol., 238, 549–566, https://doi.org/10.1111/nph.18713, 2023. a
Kumar, D., Pfeiffer, M., Gaillard, C., Langan, L., and Scheiter, S.: Climate change and elevated CO2 favor forest over savanna under different future scenarios in South Asia, Biogeosciences, 18, 2957–2979, https://doi.org/10.5194/bg-18-2957-2021, 2021. a
Langan, L., Higgins, S. I., and Scheiter, S.: Climate-biomes, pedo-biomes or pyro-biomes: which world view explains the tropical forest – savanna boundary in South America?, J. Biogeogr., 44, 2319–2330, 2017. a
Ludwig, F., Dawson, T. E., Prins, H. H. T., Berendse, F., and de Kroon, H.: Below-ground competition between trees and grasses may overwhelm the facilitative effects of hydraulic lift, Ecol. Lett., 7, 623–631, https://doi.org/10.1111/j.1461-0248.2004.00615.x, 2004. a
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E., and Meyer, H.: Assessing and improving the transferability of current global spatial prediction models, Global Ecol. Biogeogr., 32, 356–368, https://doi.org/10.1111/geb.13635, 2023. a
Martens, C., Hickler, T., Davis-Reddy, C., Engelbrecht, F., Higgins, S. I., von Maltitz, G. P., Midgley, G. F., Pfeiffer, M., and Scheiter, S.: Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies, Glob. Change Biol., 27, 340–358, https://doi.org/10.1111/gcb.15390, 2020. a
Martínez-Vilalta, J., Mencuccini, M., Álvarez, X., Camacho, J., Loepfe, L., and Piñol, J.: Spatial distribution and packing of xylem conduits, Am. J. Bot., 99, 1189–1196, https://doi.org/10.3732/ajb.1100384, 2012. a
Moncrieff, G. R., Scheiter, S., Langan, L., Trabucco, A., and Higgins, S. I.: The future distribution of the savannah biome: model-based and biogeographic contingency, Philos. T. R. Soc. B, 371, 20150311, https://doi.org/10.1098/rstb.2015.0311, 2016. a
Monserud, R. A. and Leemans, R.: Comparing global vegetation maps with the kappa-statistic, Ecol. Model., 62, 275–293, 1992. a
Mucina, L.: Biome: evolution of a crucial ecological and biogeographical concept, New Phytol., 222, 97–114, https://doi.org/10.1111/nph.15609, 2019. a, b
Netzel, P. and Stepinski, T.: On using a clustering approach for global climate classification, J. Climate, 29, 3387–3401, 2016. a
Oliveira, R. S., Eller, C. B., Barros, F. d. V., Hirota, M., Brum, M., and Bittencourt, P.: Linking plant hydraulics and the fast–slow continuum to understand resilience to drought in tropical ecosystems, New Phytol., 230, 904–923, https://doi.org/10.1111/nph.17266, 2021. a
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial Ecoregions of the World: A New Map of Life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity, BioScience, 51, 933–938, https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2, 2001. a, b, c, d
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 III, F. S., Cornwell, W. K., Fang, J., Fernandes, G. W., Garnier, E., Le Stradic, S., 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
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: 20 May 2024), 2024. a
Sabatini, F. M., Lenoir, J., Hattab, T., Arnst, E. A., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S. M., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Levesley, A., Pillar, V. D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A. T. R., Agrillo, E., Alvarez, M., Apostolova, I., Arfin Khan, M. A. S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A. D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., de Gasper, A. L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El-Sheikh, M. A. E.-R. M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán-de Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I. G., Grytnes, J.-A., Guerin, G. R., Gutiérrez, A. G., Haider, S., Hatim, M. Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J. A. M., Jedrzejek, B., Jentsch, A., Jürgens, N., Ka̧cki, Z., Kapfer, J., Karger, D. N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H. S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D. V., Liu, H., Lysenko, T., Mahecha, M. D., Marcenò, C., Martynenko, V., Moeslund, J. E., Monteagudo Mendoza, A., Mucina, L., Müller, J. V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G. E., Pärtel, M., Pauchard, A., Peet, R. K., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O. L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas-Torres, G., Rodwell, J. S., Ruprecht, E., Rūsiu̧a, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M. G., Stančić, Z., Svenning, J.-C., Tang, Z., Tang, C. Q., Tsiripidis, I., Vanselow, K. A., Vásquez Martínez, R., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A. C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D. A., Waller, D. M., Wang, H.-F., Wesche, K., Whitfeld, T. J. S., Willner, W., Wiser, S. K., Wohlgemuth, T., Yamalov, S., Zobel, M., and Bruelheide, H.: sPlotOpen – An environmentally balanced, open-access, global dataset of vegetation plots, Global Ecol. Biogeogr., 30, 1740–1764, https://doi.org/10.1111/geb.13346, 2021. a, b
Scheiter, S. and Savadogo, P.: Ecosystem management can mitigate vegetation shifts induced by climate change in West Africa, Ecol. Model., 332, 19–27, https://doi.org/10.1016/j.ecolmodel.2016.03.022, 2016. a
Scheiter, S., Wolf, S., and Kattenborn, T.: Crowd-sourced trait data can be used to delimit global biomes, Zenodo [data set], https://doi.org/10.5281/zenodo.10526277, 2024b. a
Schiller, C., Schmidtlein, S., Boonman, C., Moreno-Martínez, A., and Kattenborn, T.: Deep learning and citizen science enable automated plant trait predictions from photographs, Sci. Rep.-UK, 11, 16395, https://doi.org/10.1038/s41598-021-95616-0, 2021. a, b
Schleuning, M., Fründ, J., Schweiger, O., Welk, E., Albrecht, J., Albrecht, M., Beil, M., Benadi, G., Blüthgen, N., Bruelheide, H., Böhning-Gaese, K., Dehling, D. M., Dormann, C. F., Exeler, N., Farwig, N., Harpke, A., Hickler, T., Kratochwil, A., Kuhlmann, M., Kühn, I., Michez, D., Mudri-Stojnić, S., Plein, M., Rasmont, P., Schwabe, A., Settele, J., Vujić, A., Weiner, C. N., Wiemers, M., and Hof, C.: Ecological networks are more sensitive to plant than to animal extinction under climate change, Nat. Commun., 7, 13965, https://doi.org/10.1038/ncomms13965, 2016. a
Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., Reichstein, M., Bönisch, G., Díaz, S., Dickie, J., Gillison, A., Karpatne, A., Lavorel, S., Leadley, P., Wirth, C. B., Wright, I. J., Wright, S. J., and Reich, P. B.: BHPMF–a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography, Global Ecol. Biogeogr., 24, 1510–1521, 2015. a
Scrucca, L., Fop, M., Murphy, T. B., and Raftery, A. E.: mclust5: clustering, classification and density estimation using Gaussian finite mixture models, R. J., 8, 289–317, 2016. a
Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 16, 250–256, https://doi.org/10.1038/s41561-023-01125-2, 2023. a
Tateishi, R., Bayaer, U., Al-Bilbisi, H., Aboel Ghar, M., Tsend-Ayush, J., Kobayashi, T., Kasimu, A., Hoan, N. T., Shalaby, A., Alsaaideh, B., Enkhzaya, T., G., and Sato, H. P.: Production of global land cover data – GLCNMO, Int. J. Digit. Earth, 4, 22–49, https://doi.org/10.1080/17538941003777521, 2011. a, b, c, d
Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B., and Patin, R.: biomod2: Ensemble Platform for Species Distribution Modeling, https://CRAN.R-project.org/package=biomod2 (last access: 14 October 2023), R package version 4.2-4, 2023. a
van Bodegom, P. M., Douma, J. C., and Verheijen, L. M.: A fully traits-based approach to modeling global vegetation distribution, P. Natl. Acad. Sci. USA, 111, 13733–13738, https://doi.org/10.1073/pnas.1304551110, 2014. a, b, c
Verheijen, L. M., Aerts, R., Bönisch, G., Kattge, J., and Van Bodegom, P. M.: Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology, New Phytol., 209, 563–575, https://doi.org/10.1111/nph.13623, 2016. a, b
Vu, V. Q. and Friendly, M.: ggbiplot: A Grammar of Graphics Implementation of Biplots, https://CRAN.R-project.org/package=ggbiplot (last access: 10 June 2024), R package version 0.6.2, 2024. a
Walter, H.: Ecology of Tropical and Subtropical Vegetation, Oliver and Boyd, Edinburgh, ISBN 10 0050021303, ISBN 13 9780050021309, 1971. a
Wickham, H.: ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, https://ggplot2.tidyverse.org (last access: 20 May 2024), 2016. 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, 2004. a
Yang, S., Sterck, F. J., Sass-Klaassen, U., Cornelissen, J. H. C., van Logtestijn, R. S. P., Hefting, M., Goudzwaard, L., Zuo, J., and Poorter, L.: Stem Trait Spectra Underpin Multiple Functions of Temperate Tree Species, Front. Plant Sci., 13, 769551, https://doi.org/10.3389/fpls.2022.769551, 2022. a
Zhang, X. and Yan, X.: Spatiotemporal change in geographical distribution of global climate types in the context of climate warming, Clim. Dynam., 43, 595–605, https://doi.org/10.1007/s00382-013-2019-y, 2014. a, b, c
Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., Farooq, H., Herdean, A., Ariza, M., Scharn, R., Svanteson, S., Wengstrom, N., Zizka, V., and Antonelli, A.: CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases, Method. Ecol. Evol., 10, 744–751, https://doi.org/10.1111/2041-210X.13152, R package version 3.0.1, 2019. a
Short summary
Biomes are widely used to map vegetation patterns at large spatial scales and to assess impacts of climate change, yet there is no consensus on a generally valid biome classification scheme. We used crowd-sourced species distribution data and trait data to assess whether trait information is suitable for delimiting biomes. Although the trait data were heterogeneous and had large gaps with respect to the spatial distribution, we found that a global trait-based biome classification was possible.
Biomes are widely used to map vegetation patterns at large spatial scales and to assess impacts...
Altmetrics
Final-revised paper
Preprint