Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1111/geb.13086 |
Assessing the reliability of predicted plant trait distributions at the global scale | |
Boonman, Coline C. F.1; Benitez-Lopez, Ana1,2; Schipper, Aafke M.1,3; Thuiller, Wilfried4; Anand, Madhur5; Cerabolini, Bruno E. L.6; Cornelissen, Johannes H. C.7; Gonzalez-Melo, Andres8; Hattingh, Wesley N.9; Higuchi, Pedro10; Laughlin, Daniel C.11; Onipchenko, Vladimir G.12; Penuelas, Josep13,14; Poorter, Lourens15; Soudzilovskaia, Nadejda A.16; Huijbregts, Mark A. J.1; Santini, Luca1,17 | |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。