Dataset Information

Mapping local and global variability in plant trait distributions.

ABSTRACT: Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

SUBMITTER: Butler EE

PROVIDER: S-EPMC5754770 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Mapping local and global variability in plant trait distributions.

Butler Ethan E EE Datta Abhirup A Flores-Moreno Habacuc H Chen Ming M Wythers Kirk R KR Fazayeli Farideh F Banerjee Arindam A Atkin Owen K OK Kattge Jens J Amiaud Bernard B Blonder Benjamin B Boenisch Gerhard G Bond-Lamberty Ben B Brown Kerry A KA Byun Chaeho C Campetella Giandiego G Cerabolini Bruno E L BEL Cornelissen Johannes H C JHC Craine Joseph M JM Craven Dylan D de Vries Franciska T FT Díaz Sandra S Domingues Tomas F TF Forey Estelle E González-Melo Andrés A Gross Nicolas N Han Wenxuan W Hattingh Wesley N WN Hickler Thomas T Jansen Steven S Kramer Koen K Kraft Nathan J B NJB Kurokawa Hiroko H Laughlin Daniel C DC Meir Patrick P Minden Vanessa V Niinemets Ülo Ü Onoda Yusuke Y Peñuelas Josep J Read Quentin Q Sack Lawren L Schamp Brandon B Soudzilovskaia Nadejda A NA Spasojevic Marko J MJ Sosinski Enio E Thornton Peter E PE Valladares Fernando F van Bodegom Peter M PM Williams Mathew M Wirth Christian C Reich Peter B PB

Proceedings of the National Academy of Sciences of the United States of America 20171201 51

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant tr ...[more]

PMID: 29196525

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Project description: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.

Project description:BackgroundSpurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects.ResultsThe expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.ConclusionsThe formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.

Dataset Information

Mapping local and global variability in plant trait distributions.

Publications

Mapping local and global variability in plant trait distributions.

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