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DOI	10.1111/geb.13086
	Assessing the reliability of predicted plant trait distributions at the global scale
	Boonman, Coline C. F.1; Benitez-Lopez, Ana 1,2; Schipper, Aafke M.1,3; Thuiller, Wilfried 4; Anand, Madhur 5; Cerabolini, Bruno E. L.6; Cornelissen, Johannes H. C.7; Gonzalez-Melo, Andres 8; Hattingh, Wesley N.9; Higuchi, Pedro 10; Laughlin, Daniel C.11; Onipchenko, Vladimir G.12; Penuelas, Josep 13,14; Poorter, Lourens 15; Soudzilovskaia, Nadejda A.16; Huijbregts, Mark A. J.1; Santini, Luca 1,17
通讯作者	Boonman, Coline C. F.
来源期刊	GLOBAL ECOLOGY AND BIOGEOGRAPHY
ISSN	1466-822X
EISSN	1466-8238
出版年	2020
卷号	29 期号:6 页码:1034-1051
英文摘要	Aim Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. Location Global. Time period Present. Major taxa studied Vascular plants. Methods We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. Results Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait-environment relationships and trait-trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. Main conclusions Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.
英文关键词	ensemble forecasting environmental filtering intraspecific trait variation leaf nitrogen concentration plant height specific leaf area trait-environment relationships trait model wood density
类型	Article
语种	英语
国家	Netherlands ; Spain ; France ; Canada ; Italy ; Colombia ; South Africa ; Brazil ; USA ; Russia
开放获取类型	Green Published, hybrid
收录类别	SCI-E
WOS记录号	WOS:000520727800001
WOS关键词	LEAF TRAITS ; FUNCTIONAL TRAITS ; SPATIAL-PATTERNS ; CLIMATE-CHANGE ; WOOD DENSITY ; VARIABILITY ; DIVERSITY ; COMMUNITY ; RANGE ; SOIL
WOS类目	Ecology ; Geography, Physical
WOS研究方向	Environmental Sciences & Ecology ; Physical Geography
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314641
作者单位	1.Radboud Univ Nijmegen, Inst Water & Wetland Res, Dept Environm Sci, POB 9010, NL-6500 GL Nijmegen, Netherlands; 2.CSIC, Integrat Ecol Grp, EBD, Seville, Spain; 3.PBL Netherlands Environm Assessment Agcy, The Hague, Netherlands; 4.Univ Savoie Mt Blanc, CNRS, Univ Grenoble Alpes, LECA,Lab Ecol Alpine, Grenoble, France; 5.Univ Guelph, Sch Environm Sci, Guelph, ON, Canada; 6.Univ Insubria, Dept Theoret & Appl Sci, Varese, Italy; 7.Vrije Univ, Dept Ecol Sci, Syst Ecol, Amsterdam, Netherlands; 8.Univ Rosario, Fac Ciencias Nat & Matemat, Bogota, Colombia; 9.Univ Witwatersrand, Sch Anim Plant & Environm Sci, Johannesburg, South Africa; 10.Santa Catarina State Univ, Forestry Dept, Lages, SC, Brazil; 11.Univ Wyoming, Dept Bot, Laramie, WY 82071 USA; 12.Moscow Lomonosov State Univ, Dept Geobot, Moscow, Russia; 13.CREAF, Valles, Catalonia, Spain; 14.UAB, CSIC, Global Ecol Unit CREAF, CEAB, Catalonia, Spain; 15.Wageningen Univ & Res, Forest Ecol & Forest Management Grp, Wageningen, Netherlands; 16.Leiden Univ, Inst Environm Sci, Environm Biol Dept, Leiden, Netherlands; 17.CNR, Inst Res Terr Ecosyst CNR IRET, Monterotondo, Italy
推荐引用方式 GB/T 7714	Boonman, Coline C. F.,Benitez-Lopez, Ana,Schipper, Aafke M.,et al. Assessing the reliability of predicted plant trait distributions at the global scale[J],2020,29(6):1034-1051.
APA	Boonman, Coline C. F..,Benitez-Lopez, Ana.,Schipper, Aafke M..,Thuiller, Wilfried.,Anand, Madhur.,...&Santini, Luca.(2020).Assessing the reliability of predicted plant trait distributions at the global scale.GLOBAL ECOLOGY AND BIOGEOGRAPHY,29(6),1034-1051.
MLA	Boonman, Coline C. F.,et al."Assessing the reliability of predicted plant trait distributions at the global scale".GLOBAL ECOLOGY AND BIOGEOGRAPHY 29.6(2020):1034-1051.