Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-1771-2024
© 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/essd-16-1771-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Apr 2024
Data description paper |
| 11 Apr 2024
| 11 Apr 2024
Spatial mapping of key plant functional traits in terrestrial ecosystems across China
Nannan An
Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Nan Lu
CORRESPONDING AUTHOR
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Weiliang Chen
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Yongzhe Chen
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Department of Geography, The University of Hong Kong, Hong Kong SAR 999077, China
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Fuzhong Wu
CORRESPONDING AUTHOR
Key Laboratory of Humid Subtropical Eco-geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350117, China
Bojie Fu
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
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Wenxiu Zhang, Di Liu, Hanqin Tian, Naiqin Pan, Ruqi Yang, Wenhan Tang, Jia Yang, Fei Lu, Buddhi Dayananda, Han Mei, Siyuan Wang, and Hao Shi
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Yongzhe Chen, Xiaoming Feng, and Bojie Fu
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Cited articles
Ali, A. M., Darvishzadeh, R., Skidmore, A. K., van Duren, I., Heiden, U., and Heurich, M.: Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest, Int. J. Appl. Earth Obs. Geoinf., 45, 66–76, https://doi.org/10.1016/j.jag.2015.11.004, 2016.
An, N. N., Lu, N., Fu, B. J., Wang, M. Y., and He, N. P.: Distinct responses of leaf traits to environment and phylogeny between herbaceous and woody angiosperm species in China, Front. Plant Sci., 12, 799401, https://doi.org/10.3389/fpls.2021.799401, 2021.
An, N. N., Lu, N., Chen, W. L., Chen, Y. Z., Shi, H., Wu, F. Z., and Fu, B. J.: Maps of plant functional traits with 1-km spatial resolution in terrestrial ecosystems across China, figshare [data set], https://doi.org/10.6084/m9.figshare.22351498, 2023.
Bakker, M. A., Carreño-Rocabado, G., and Poorter, L.: Leaf economics traits predict litter decomposition of tropical plants and differ among land use types, Funct. Ecol., 25, 473–483, https://doi.org/10.1111/j.1365-2435.2010.01802.x, 2011.
Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Muratorio, S., Bohn, F. J., Reyer, C. P. O., Sabate, S., Sanders, T. G. M., and Hartig, F.: Towards a new generation of trait-flexible vegetation models, Trends Ecol. Evol., 35, 191–205, https://doi.org/10.1016/j.tree.2019.11.006, 2020.
Blumenthal, D. M., Mueller, K. E., Kray, J. A., Ocheltree, T. W., Augustine, D. J., Wilcox, K. R., and Cornelissen, H.: Traits link drought resistance with herbivore defence and plant economics in semi-arid grasslands: The central roles of phenology and leaf dry matter content, J. Ecol., 108, 2336–2351, https://doi.org/10.1111/1365-2745.13454, 2020.
Bohner, A.: Soil chemical properties as indicators of plant species richness in grassland communities. Integrating efficient grassland farming and biodiversity, Proceedings of the 13th International Occasional Symposium of the European Grassland Federation, Tartu, Estonia, 29–31 August 2005, 48–51, 2005.
Boonman, C. C. F., Benitez-Lopez, 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., Penuelas, 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.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Buchhorn, M., Bertels, L., Smets, B., De Roo, B., Lesiv, M., Tsendbazar, N. E., Masiliunas, D., and Linlin, L.: Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Algorithm Theoretical Basis Document, Zenodo [data set], https://doi.org/10.5281/zenodo.3938968, 2020.
Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., Banerjee, A., Atkin, O. K., Kattge, J., Amiaud, B., Blonder, B., Boenisch, G., Bond-Lamberty, B., Brown, K. A., Byun, C., Campetella, G., Cerabolini, B. E. L., Cornelissen, J. H. C., Craine, J. M., Craven, D., de Vries, F. T., Diaz, S., Domingues, T. F., Forey, E., Gonzalez-Melo, A., Gross, N., Han, W., Hattingh, W. N., Hickler, T., Jansen, S., Kramer, K., Kraft, N. J. B., Kurokawa, H., Laughlin, D. C., Meir, P., Minden, V., Niinemets, U., Onoda, Y., Penuelas, J., Read, Q., Sack, L., Schamp, B., Soudzilovskaia, N. A., Spasojevic, M. J., Sosinski, E., Thornton, P. E., Valladares, F., van Bodegom, P. M., Williams, M., Wirth, C., and Reich, P. B.: Mapping local and global variability in plant trait distributions, P. Natl. Acad. Sci. USA, 114, 10937–10946, https://doi.org/10.1073/pnas.1708984114, 2017.
Cavender-Bares, J., Schneider, F. D., Santos, M. J., Armstrong, A., Carnaval, A., Dahlin, K. M., Fatoyinbo, L., Hurtt, G. C., Schimel, D., Townsend, P. A., Ustin, S. L., Wang, Z. H., and Wilson, A. M.: Integrating remote sensing with ecology and evolution to advance biodiversity conservation, Nat. Ecol. Evol., 6, 506–519, https://doi.org/10.1038/s41559-022-01702-5, 2022.
Clevers, J. G. P. W. and Gitelson, A. A.: Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3, Int. J. Appl. Earth Obs. Geoinf., 23, 344–351, https://doi.org/10.1016/j.jag.2012.10.008, 2013.
Dahlin, K. M., Asner, G. P., and Field, C. B.: Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem, P. Natl. Acad. Sci. USA, 110, 6895–6900, https://doi.org/10.1073/pnas.1215513110, 2013.
Darvishzadeh, R., Skidmore, A., Schlerf, M., and Atzberger, C.: Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland, Remote Sens. Environ., 112, 2592–2604, https://doi.org/10.1016/j.rse.2007.12.003, 2008.
Diaz, S., Hodgson, J. G., Thompson, K., Cabido, M., Cornelissen, J. H. C., Jalili, A., Montserrat-Marti, G., Grime, J. P., Zarrinkamar, F., Asri, Y., Band, S. R., Basconcelo, S., Castro-Diez, P., Funes, G., Hamzehee, B., Khoshnevi, M., Perez-Harguindeguy, N., Perez-Rontome, M. C., Shirvany, F. A., Vendramini, F., Yazdani, S., Abbas-Azimi, R., Bogaard, A., Boustani, S., Charles, M., Dehghan, M., de Torres-Espuny, L., Falczuk, V., Guerrero-Campo, J., Hynd, A., Jones, G., Kowsary, E., Kazemi-Saeed, F., Maestro-Martinez, M., Romo-Diez, A., Shaw, S., Siavash, B., Villar-Salvador, P., and Zak, M. R.: The plant traits that drive ecosystems: Evidence from three continents, J. Veg. Sci., 15, 295–304, https://doi.org/10.1111/j.1654-1103.2004.tb02266.x, 2004.
Diaz, S., Kattge, J., Cornelissen, J. H., Wright, I. J., Lavorel, S., Dray, S., Reu, B., Kleyer, M., Wirth, C., Prentice, I. C., Garnier, E., Bonisch, 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., Gunther, A., Falczuk, V., Ruger, N., Mahecha, M. D., and Gorne, L. D.: The global spectrum of plant form and function, Nature, 529, 167–171, https://doi.org/10.1038/nature16489, 2016.
Dong, N., Dechant, B., Wang, H., Wright, I. J., and Prentice, I. C.: Global leaf-trait mapping based on optimality theory, Global Ecol. Biogeogr., 32, 1152–1162, https://doi.org/10.1111/geb.13680, 2023.
Du, L., Liu, H., Guan, W., Li, J., and Li, J.: Drought affects the coordination of belowground and aboveground resource-related traits in Solidago canadensis in China, Ecol. Evol., 9, 9948–9960, https://doi.org/10.1002/ece3.5536, 2019.
Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberon, J., Williams, S., Wisz, M. S., and Zimmermann, N. E.: Novel methods improve prediction of species' distributions from occurrence data, Ecography, 29, 129–151, https://doi.org/10.1111/j.2006.0906-7590.04596.x, 2006.
Elith, J., Leathwick, J. R., and Hastie, T.: A working guide to boosted regression treesm J. Anim. Ecol., 77, 802–813, https://doi.org/10.1111/j.1365-2656.2008.01390.x, 2008.
Elith, J., Kearney, M., and Phillips, S.: The art of modelling range-shifting species, Methods Ecol. Evol., 1, 330–342, https://doi.org/10.1111/j.2041-210X.2010.00036.x, 2010.
Finzi, A. C., Austin, A. T., Cleland, E. E., Frey, S. D., Houlton, B. Z., and Wallenstein, M. D.: Responses and feedbacks of coupled biogeochemical cycles to climate change: examples from terrestrial ecosystems, Front. Ecol. Environ., 9, 61–67, https://doi.org/10.1890/100001, 2011.
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., and Haxeltine, A.: An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochem. Cy., 10, 603–628, https://doi.org/10.1029/96gb02692, 1996.
Freschet, G. T., Cornelissen, J. H. C., van Logtestijn, R. S. P., and Aerts, R.: Evidence of the “plant economics spectrum” in a subarctic flora, J. Ecol., 98, 362–373, https://doi.org/10.1111/j.1365-2745.2009.01615.x, 2010.
Garnier, E., Cortez, J., Billès, G., Navas, M. L., Roumet, C., Debussche, M., Laurent, G., Blanchard, A., Aubry, D., Bellmann, A., Neill, C., and Toussaint, J. P.: Plant functional markers capture ecosystem properties during secondary succession. Ecology, 85, 2630–2637, https://doi.org/10.1890/03-0799, 2004.
Grime, J. P.: Benefits of plant diversity to ecosystems: immediate, filter and founder effects, J. Ecol., 86, 902–910, https://doi.org/10.1046/j.1365-2745.1998.00306.x, 1998.
He, N. P., Yan, P., Liu, C. C., Xu, L., Li, M. X., Van Meerbeek, K., Zhou, G. S., Zhou, G. Y., Liu, S. R., Zhou, X. H., Li, S. G., Niu, S. L., Han, X. G., Buckley, T. N., Sack, L., and Yu, G. R.: Predicting ecosystem productivity based on plant community traits, Trends Plant Sci., 28, 43–53, https://doi.org/10.1016/j.tplants.2022.08.015, 2023.
Hodgson, J. G., Montserrat-Marti, G., Charles, M., Jones, G., Wilson, P., Shipley, B., Sharafi, M., Cerabolini, B. E. L., Cornelissen, J. H. C., Band, S. R., Bogard, A., Castro-Diez, P., Guerrero-Campo, J., Palmer, C., Perez-Rontome, M. C., Carter, G., Hynd, A., Romo-Diez, A., Espuny, L. D., and Pla, F. R.: Is leaf dry matter content a better predictor of soil fertility than specific leaf area?, Ann. Bot., 108, 1337–1345, https://doi.org/10.1093/aob/mcr225, 2011.
Hoeber, S., Leuschner, C., Köhler, L., Arias-Aguilar, D., and Schuldt, B.: The importance of hydraulic conductivity and wood density to growth performance in eight tree species from a tropical semi-dry climate, Forest Ecol. Manag., 330, 126–136, https://doi.org/10.1016/j.foreco.2014.06.039, 2014.
Jónsdóttir, I. S., Halbritter, A. H., Christiansen, C. T., Althuizen, I. H. J., Haugum, S. V., Henn, J. J., Björnsdóttir, K., Maitner, B. S., Malhi, Y., Michaletz, S. T., Roos, R. E., Klanderud, K., Lee, H., Enquist, B. J., and Vandvik, V.: Intraspecific trait variability is a key feature underlying high Arctic plant community resistance to climate warming, Ecol. Monogr., 93, e1555, https://doi.org/10.1002/ecm.1555, 2022.
Jung, V., Violle, C., Mondy, C., Hoffmann, L., and Muller, S.: Intraspecific variability and trait-based community assembly, J. Ecol., 98, 1134–1140, https://doi.org/10.1111/j.1365-2745.2010.01687.x, 2010.
Kattge, J., Bonisch, G., Diaz, 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., Alcantara, J. M., Alcazar, C. C., Aleixo, I., Ali, H., Amiaud, B., et al.: TRY plant trait database – Enhanced coverage and open access, Global Change Biol., 26, 119–188, https://doi.org/10.1111/gcb.14904, 2020.
Kemppinen, J., Niittynen, P., le Roux, P. C., Momberg, M., Happonen, K., Aalto, J., Rautakoski, H., Enquist, B. J., Vandvik, V., Halbritter, A. H., Maitner, B., and Luoto, M.: Consistent trait-environment relationships within and across tundra plant communities, Nat. Ecol. Evol., 5, 458–467, https://doi.org/10.1038/s41559-021-01396-1, 2021.
King, D. A., Davies, S. J., Tan, S., and Noor, N. S. M.: The role of wood density and stem support costs in the growth and mortality of tropical trees, J. Ecol., 94, 670–680, https://doi.org/10.1111/j.1365-2745.2006.01112.x, 2006.
Kirilenko, A. P., Belotelov, N. V., and Bogatyrev, B. G.: Global model of vegetation migration: incorporation of climatic variability, Ecol. Model., 132, 125–133, https://doi.org/10.1016/S0304-3800(00)00310-0, 2000.
LeBauer, D. S. and Treseder, K. K.: Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed, Ecology, 89, 371–379, https://doi.org/10.1890/06-2057.1, 2008.
Li, C. X., Wulf, H., Schmid, B., He, J. S., and Schaepman, M. E.: Estimating plant traits of alpine grasslands on the Qinghai-Tibetan Plateau using remote sensing, IEEE J. Sel. Top. Appl. Earth Obs., 11, 2263–2275, https://doi.org/10.1109/jstars.2018.2824901, 2018.
Li, D. J., Ives, A. R., and Waller, D. M.: Can functional traits account for phylogenetic signal in community composition?, New Phytol., 214, 607–618, https://doi.org/10.1111/nph.14397, 2017.
Li, Y. Q., Reich, P. B., Schmid, B., Shrestha, N., Feng, X., Lyu, T., Maitner, B. S., Xu, X., Li, Y. C., Zou, D. T., Tan, Z. H., Su, X. Y., Tang, Z. Y., Guo, Q. H., Feng, X. J., Enquist, B. J., and Wang, Z. H.: Leaf size of woody dicots predicts ecosystem primary productivity, Ecol. Lett., 23, 1003–1013, https://doi.org/10.1111/ele.13503, 2020.
Liang, X. Y., Ye, Q., Liu, H., and Brodribb, T. J.: Wood density predicts mortality threshold for diverse trees, New Phytol., 229, 3053–3057, https://doi.org/10.1111/nph.17117, 2021.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, 2002.
Liu, H. Y. and Yin, Y.: Response of forest distribution to past climate change: an insight into future predictions, Chinese Sci. Bull., 58, 4426–4436, https://doi.org/10.1007/s11434-013-6032-7, 2013.
Loozen, Y., Rebel, K. T., Karssenberg, D., Wassen, M. J., Sardans, J., Peñuelas, J., and De Jong, S. M.: Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index, Biogeosciences, 15, 2723–2742, https://doi.org/10.5194/bg-15-2723-2018, 2018.
Loozen, Y., Rebel, K. T., de Jong, S. M., Lu, M., Ollinger, S. V., Wassen, M. J., and Karssenberg, D.: Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method, Remote Sens. Environ., 247, 111933, https://doi.org/10.1016/j.rse.2020.111933, 2020.
Madani, N., Kimball, J. S., Ballantyne, A. P., Affleck, D. L. R., van Bodegom, P. M., Reich, P. B., Kattge, J., Sala, A., Nazeri, M., Jones, M. O., Zhao, M., and Running, S. W.: Future global productivity will be affected by plant trait response to climate, Sci. Rep.-UK, 8, 1–10, https://doi.org/10.1038/s41598-018-21172-9, 2018.
Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van Bodegom, P. M., Cornwell, W. K., Ellsworth, D., Niinemets, U., Ordonez, A., Reich, P. B., and Santiago, L. S.: Global effects of soil and climate on leaf photosynthetic traits and rates, Glob. Ecol. Biogeogr., 24, 706–717, https://doi.org/10.1111/geb.12296, 2015.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., and Thuiller, W.: Evaluation of consensus methods in predictive species distribution modelling, Divers. Distrib., 15, 59–69, https://doi.org/10.1111/j.1472-4642.2008.00491.x, 2009.
Martínez-Vilalta, J., Mencuccini, M., Vayreda, J., and Retana, J.: Interspecific variation in functional traits, not climatic differences among species ranges, determines demographic rates across 44 temperate and Mediterranean tree species, J. Ecol., 98, 1462–1475, https://doi.org/10.1111/j.1365-2745.2010.01718.x, 2010.
Matheny, A. M., Mirfenderesgi, G., and Bohrer, G.: Trait-based representation of hydrological functional properties of plants in weather and ecosystem models, Plant Divers., 39, 1–12, https://doi.org/10.1016/j.pld.2016.10.001, 2017.
Moreno-Martínez, Á., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P., Kramer, K., Cornelissen, J. H. C., Reich, P., Bahn, M., Niinemets, Ü., Peñuelas, J., Craine, J. M., Cerabolini, B. E. L., Minden, V., Laughlin, D. C., Sack, L., Allred, B., Baraloto, C., Byun, C., Soudzilovskaia, N. A., and Running, S. W.: A methodology to derive global maps of leaf traits using remote sensing and climate data, Remote Sens. Environ., 218, 69–88, https://doi.org/10.1016/j.rse.2018.09.006, 2018.
Myers-Smith, I. H., Thomas, H. J. D., and Bjorkman, A. D.: Plant traits inform predictions of tundra responses to global change, New Phytol., 221, 1742–1748, https://doi.org/10.1111/nph.15592, 2019.
NEODC: NEODC – NERC Earth Observation Data Centre, Natural Environment Research Council [data set], http://neodc.nerc.ac.uk/ (last access: 28 May 2021), 2015.
Peng, C. H.: From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics, Ecol. Model., 135, 33–54, https://doi.org/10.1016/S0304-3800(00)00348-3, 2000.
Perez-Harguindeguy, N., Diaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., Bret-Harte, M. S., Cornwell, W. K., Craine, J. M., Gurvich, D. E., Urcelay, C., Veneklaas, E. J., Reich, P. B., Poorter, L., Wright, I. J., Ray, P., Enrico, L., Pausas, J. G., de Vos, A. C., Buchmann, N., Funes, G., Quetier, F., Hodgson, J. G., Thompson, K., Morgan, H. D., ter Steege, H., van der Heijden, M. G. A., Sack, L., Blonder, B., Poschlod, P., Vaieretti, M. V., Conti, G., Staver, A. C., Aquino, S., and Cornelissen, J. H. C.: New handbook for standardised measurement of plant functional traits worldwide, Aust. Bot., 61, 167–234, https://doi.org/10.1071/bt12225, 2013.
Piao, S. L., He, Y., Wang, X. H., and Chen, F. H.: Estimation of China's terrestrial ecosystem carbon sink: Methods, progress and prospects, Sci. China Earth Sci., 65, 641–651, https://doi.org/10.1007/s11430-021-9892-6, 2022.
Qiao, J. J., Zuo, X. A., Yue, P., Wang, S. K., Hu, Y., Guo, X. X., Li, X. Y., Lv, P., Guo, A. X., and Sun, S. S.: High nitrogen addition induces functional trait divergence of plant community in a temperate desert steppe, Plant Soil, 487, 133–156, https://doi.org/10.1007/s11104-023-05910-1, 2023.
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 May 2021), 2020.
Reich, P. B. and Oleksyn, J.: Global patterns of plant leaf N and P in relation to temperature and latitude, P. Natl. Acad. Sci. USA, 101, 11001–11006, https://doi.org/10.1073/pnas.0403588101, 2004.
Reich, P. B., Uhl, C., Waiters, M. B., and Ellsworth, D. S.: Leaf lifespan as a determinant of leaf structure and function among 23 Amazonian tree species, Oeologia, 86, 16–24, https://doi.org/10.1007/BF00317383, 1991.
Ridgeway, G.: Gbm: generalized boosted regression models, R package version 1.5-6, http://cran.r-project.org/web/packages/gbm/index.html (last access: 11 February 2009), 2006.
Roderick, M. L. and Berry, S. L.: Linking wood density with tree growth and environment: a theoretical analysis based on the motion of water, New Phytol., 149, 473–485, https://doi.org/10.1046/j.1469-8137.2001.00054.x, 2002.
Romero, A., Aguado, I., and Yebra, M.: Estimation of dry matter content in leaves using normalized indexes and PROSPECT model inversion, Int. J. Remote Sens., 33, 396–414, https://doi.org/10.1080/01431161.2010.532819, 2012.
Sakschewski, B., von Bloh, W., Boit, A., Rammig, A., Kattge, J., Poorter, L., Penuelas, J., and Thonicke, K.: Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model, Global Change Biol., 21, 2711–2725, https://doi.org/10.1111/gcb.12870, 2015.
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.
Schiller, C., Schmidtlein, S., Boonman, C., Moreno-Martinez, 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.
Shangguan, W., Dai, Y. J., Liu, B. Y., Zhu, A. X., Duan, Q. Y., Wu, L. Z., Ji, D. Y., Ye, A. Z., Yuan, H., Zhang, Q., Chen, D. D., Chen, M., Chu, J. T., Dou, Y. J., Guo, J. X., Li, H. Q., Li, J. J., Liang, L., Liang, X., Liu, H. P., Liu, S. Y., Miao, C. Y., and Zhang, Y. Z.: A China data set of soil properties for land surface modeling, J. Adv. Model. Earth Syst., 5, 212–224, https://doi.org/10.1002/jame.20026, 2013.
Siefert, A., Violle, C., Chalmandrier, L., Albert, C. H., Taudiere, A., Fajardo, A., Aarssen, L. W., Baraloto, C., Carlucci, M. B., Cianciaruso, M. V., de, L. D. V., de Bello, F., Duarte, L. D., Fonseca, C. R., Freschet, G. T., Gaucherand, S., Gross, N., Hikosaka, K., Jackson, B., Jung, V., Kamiyama, C., Katabuchi, M., Kembel, S. W., Kichenin, E., Kraft, N. J., Lagerstrom, A., Bagousse-Pinguet, Y. L., Li, Y., Mason, N., Messier, J., Nakashizuka, T., Overton, J. M., Peltzer, D. A., Perez-Ramos, I. M., Pillar, V. D., Prentice, H. C., Richardson, S., Sasaki, T., Schamp, B. S., Schob, C., Shipley, B., Sundqvist, M., Sykes, M. T., Vandewalle, M., and Wardle, D. A.: 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.
Šímová, I., Sandel, B., Enquist, B. J., Michaletz, S. T., Kattge, J., Violle, C., McGill, B. J., Blonder, B., Engemann, K., Peet, R. K., Wiser, S. K., Morueta-Holme, N., Boyle, B., Kraft, N. J. B., Svenning, J. C., and Hector, A.: The relationship of woody plant size and leaf nutrient content to large-scale productivity for forests across the Americas, J. Ecol., 107, 2278–2290, https://doi.org/10.1111/1365-2745.13163, 2019.
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), Global Change Biol., 14, 2015–2039, https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008.
Smart, S. M., Glanville, H. C., Blanes, M. d. C., Mercado, L. M., Emmett, B. A., Jones, D. L., Cosby, B. J., Marrs, R. H., Butler, A., Marshall, M. R., Reinsch, S., Herrero-Jáuregui, C., Hodgson, J. G., and Field, K.: Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area, Funct. Ecol., 31, 1336–1344, https://doi.org/10.1111/1365-2435.12832, 2017.
Telenius, A.: Biodiversity information goes public: GBIF at your service, Nord. J. Bot., 29, 378–381, https://doi.org/10.1111/j.1756-1051.2011.01167.x, 2011.
Thomas, D. S., Montagu, K. D., and Conroy, J. P.: Changes in wood density of Eucalyptus camaldulensis due to temperature-the physiological link between water viscosity and wood anatomy, Forest Ecol. Manag., 193, 157–165, https://doi.org/10.1016/j.foreco.2004.01.028, 2004.
Thomas, S. C.: Photosynthetic capacity peaks at intermediate size in temperate deciduous trees, Tree Physiol., 30, 555–573, https://doi.org/10.1093/treephys/tpq005, 2010.
Thuiller, W., Lafourcade, B., Engler, R., and Araújo, M. B.: BIOMOD – A platform for ensemble forecasting of species distributions, Ecography, 32, 369–373, https://doi.org/10.1111/j.1600-0587.2008.05742.x, 2009.
Trabucco, A. and Zomer, R. J.: Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2. CGIAR Consortium for Spatial Information (CGIAR-CSI) [data set], https://cgiarcsi.community (last access: 18 March 2021), 2018.
Vallicrosa, H., Sardans, J., Maspons, J., Zuccarini, P., Fernández-Martínez, M., Bauters, M., Goll, D. S., Ciais, P., Obersteiner, M., Janssens, I. A., and Peñuelas, J.: Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history, New Phytol., 233, 169–181, https://doi.org/10.1111/nph.17771, 2022.
van Bodegom, P. M., Douma, J. C., Witte, J. P. M., Ordoñez, J. C., Bartholomeus, R. P., and Aerts, R.: Going beyond limitations of plant functional types when predicting global ecosystem-atmosphere fluxes: exploring the merits of traits-based approaches, Global Ecol. Biogeogr., 21, 625–636, https://doi.org/10.1111/j.1466-8238.2011.00717.x, 2012.
Verheijen, L. M., Aerts, R., Bonisch, 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.
Wang, H., Harrison, S. P., Prentice, I. C., Yang, Y. Z., Bai, F., Togashi, H. F., Wang, M., Zhou, S. X., and Ni, J.: The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants, Ecology, 99, 500, https://doi.org/10.1002/ecy.2091, 2018.
Wang, Z. H., Wang, T. J., Darvishzadeh, R., Skidmore, A. K., Jones, S., Suarez, L., Woodgate, W., Heiden, U., Heurich, M., and Hearne, J.: Vegetation indices for mapping canopy foliarnitrogen in a mixed temperate forest, Remote Sens.-Basel, 8, 491, https://doi.org/10.1111/10.3390/rs8060491, 2016.
Webb, C. T., Hoeting, J. A., Ames, G. M., Pyne, M. I., and LeRoy Poff, N.: A structured and dynamic framework to advance traits-based theory and prediction in ecology, Ecol. Lett., 13, 267–283, https://doi.org/10.1111/j.1461-0248.2010.01444.x, 2010.
Wright, I. J., Dong, N., Maire, V., Prentice, I. C., Westoby, M., Diaz, S., Gallagher, R. V., Jacobs, B. F., Kooyman, R., Law, E. A., Leishman, M. R., Niinemets, U., Reich, P. B., Sack, L., Villar, R., Wang, H., and Wilf, P.: Global climatic drivers of leaf size, Science, 357, 917–921, https://doi.org/10.1126/science.aal4760, 2017.
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.
Wullschleger, S. D., Epstein, H. E., Box, E. O., Euskirchen, E. S., Goswami, S., Iversen, C. M., Kattge, J., Norby, R. J., van Bodegom, P. M., and Xu, X.: Plant functional types in earth system models: past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems, Ann. Bot., 114, 1–16, https://doi.org/10.1093/aob/mcu077, 2014.
Yan, P., He, N. P., Yu, K. L., Xu, L., and Van Meerbeek, K.: Integrating multiple plant functional traits to predict ecosystem productivity, Commun. Biol., 6, 239, https://doi.org/10.1038/s42003-023-04626-3, 2023.
Yang, Y. Z., Zhu, Q. A., Peng, C. H., Wang, H., Xue, W., Lin, G. H., Wen, Z. M., Chang, J., Wang, M., Liu, G. B., and Li, S. Q.: A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China, Sci. Rep.-UK, 6, 24110, https://doi.org/10.1038/srep24110, 2016.
Yang, Y. Z., Wang, H., Harrison, S. P., Prentice, I. C., Wright, I. J., Peng, C. H., and Lin, G. H.: Quantifying leaf-trait covariation and its controls across climates and biomes, New Phytol., 221, 155–168, https://doi.org/10.1111/nph.15422, 2018.
Yang, Y. Z., Zhao, J., Zhao, P. X., Wang, H., Wang, B. H., Su, S. F., Li, M. X., Wang, L. M., Zhu, Q. A., Pang, Z. Y., and Peng, C. H.: Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China, Front. Plant Sci., 10, 908, https://doi.org/10.3389/fpls.2019.00908, 2019.
Yurova, A. Y. and Volodin, E. M.: Coupled simulation of climate and vegetation dynamics, Izv. Atmos. Ocean. Phy., 47, 531–539, https://doi.org/10.1134/s0001433811050124, 2011.
Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates, Global Biogeochem. Cy., 24, GB1005, https://doi.org/10.1029/2009gb003521, 2010.
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in...
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