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Title: Mapping local and global variability in plant trait distributions

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrence ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates ofmore » functional diversity.« less
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Publication Date:
Report Number(s):
Journal ID: ISSN 0027-8424; KP1703020
Grant/Contract Number:
AC05-00OR22725; AC05-76RL01830; SC0012677; CE140100008; DEB-1234162; DEB-1242531; IIS-1563950; 640176; NE/M019160/1; ERC-SyG-2013-610028 IMBALANCE-P; CGL2013-48074-P; SGR 2014-274; FT110100457; NE/F002149/1; 41473068
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 114; Journal Issue: 51; Journal ID: ISSN 0027-8424
National Academy of Sciences, Washington, DC (United States)
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); Australian Research Council; National Science Foundation (NSF); Univ. of Minnesota, Minneapolis, MN (United States); Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry; Univ. of Leipzig (Germany); Natural Environment Research Council (NERC); European Research Council (ERC); Spanish Government; Catalan Government; Wageningen Univ. and Research (Netherlands); National Natural Science Foundation of China (NNSFC); Chinese Academy of Sciences
Country of Publication:
United States
le; gl; plant traits; Bayesian modeling; spatial statistics; global climate; 54 ENVIRONMENTAL SCIENCES; 60 APPLIED LIFE SCIENCES; global; climate
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1415695; OSTI ID: 1427647