DOI: 10.5061/dryad.4j0zpc8pq
title: Data from: A global synthesis of how plants respond to climate warming from traits to fitness

description: Despite intensive research, our understanding of how plants respond to
 warming by coordinating their full arsenal of traits to adjust fitness is
 lacking. To fill this gap, we applied a trait-based framework with three
 clusters (two functional clusters: “carbon-fixation rate” and
 “carbon-fixation area”; a third cluster: “total carbon fixation”) to a
 global dataset compiled from 572 studies of warming experiments with 677
 species and a comprehensive list of traits and fitness components. The
 pairwise correlation analysis complemented with SEM and PCA showed that
 plants increased biomass (the core variable in the third cluster) under
 warming by coordinating satellite traits in two functional clusters to
 adjust their core traits, net photosynthesis rate, and total leaf area,
 respectively. In particular, the trait coordination was characterized by
 the maintenance of net photosynthesis rate and the increase of total leaf
 area, which was robust across ecological contexts although warming
 responses of the variables per se displayed context-dependences. Moreover,
 the trade-offs between biomass and reproduction (itself bearing mass vs.
 number trade-offs) in their warming responses scaled the coordination to
 enhance fitness except in the contexts where reproduction was reduced.
 These findings could help explain and predict plant form and function in a
 warming world.
description: # A global synthesis of how plants respond to climate warming from traits
 to fitness Dataset DOI: [10.5061/dryad.4j0zpc8pq](10.5061/dryad.4j0zpc8pq)
 Pengyan Jia1†, Rong Zhang1†, Bernhard Schmid2, Han Wang3, Jin-Sheng He1,4,
 Jiaxi Liu1, Sijie Liu1, Sipeng Jian1, Yanhao Feng1* 1State Key Laboratory
 of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral
 Agriculture Science and Technology, Lanzhou University, Lanzhou 730020,
 China 2Remote Sensing Laboratories, Department of Geography, University of
 Zurich, Zurich 8057, Switzerland 3Ministry of Education Key Laboratory for
 Earth System Modeling, Department of Earth System Science, Tsinghua
 University, Beijing 100084, China 4Institute of Ecology, College of Urban
 and Environmental Sciences, Key Laboratory for Earth Surface Processes of
 the Ministry of Education, Peking University, Beijing 100871, China
 *Author of correspondence: Yanhao Feng (Email:
 [fengyh@lzu.edu.cn](mailto:fengyh@lzu.edu.cn)) ## Description of the data
 and file structure ### 1\. WarmingPlants_Data.zip: (1)
 **WarmingPlants_Data.xlsx**: An Excel file containing raw data used in our
 study, along with variable abbreviations for the 45 variables included in
 the raw data. For detailed information on how the raw data was generated,
 please see the data compilation in Materials and Methods. (2)
 **Metadata.csv**: A file that interprets all the 31 columns in the raw
 data (WarmingPlants_Data.xlsx). ### 2\. WarmingPlants_Code.R (Zenodo) The
 codes for generating results of Figures and Tables in the main text and
 supplementary information in our analysis. ### 3\. Supplementary
 information.zip (Zenodo) (1) Estimate: This folder contains four CSV files
 with calculated effect sizes overall and separately for growth forms,
 warming objects, and warming facilities (the calculation of effect sizes
 might take a few minutes, but you can directly read these data tables for
 the visualization of figures) (2) Main: This folder contains all the
 figures and tables in the main text generated by running the R code
 (WarmingPlants_Code.R). (3) Supplementary: This folder contains all the
 figures and tables in supplementary information generated by running the R
 code (WarmingPlants_Code.R). (4) R_functions: This folder contains
 customized R scripts for specific statistical tasks: * get_sig_letter.R:
 Assigns significance marker letters based on p-values. * InDaPCA.R:
 Performs Principal Component Analysis (PCA) on incomplete data. *
 corstarsl.R: Generates correlation matrices with significance stars. ##
 Code/software The codes of R for generating results of Figures and Tables
 in the main text and supplementary information in our analysis are in the
 Software related works.