DOI: 10.5061/dryad.gf1vhhn2j
title: Data from: Warming reduces the compositional and biomass stability of alpine meadows via declines in species richness and functional dispersion

description: Understanding the mechanisms that maintain grassland community composition
 and biomass stability is crucial for the functions and
 services provided by ecosystems under climate change, and the
 development of grassland management and restoration
 strategies. However, how the potential mechanisms of warming and
 altered precipitation combine to affect community compositional and
 biomass production stability is still not clear, especially in alpine
 ecosystem. Here, we conducted a 4-year
 field experiment to manipulate
 warming and precipitation alteration in an alpine
 meadow on the eastern Tibetan
 Plateau to investigate
 how climate change affects the
 temporal stability of plant community composition
 and biomass. We showed that warming alone decreased
 community biomass stability, and it combined
 with precipitation alteration decreased compositional stability,
 respectively, while precipitation alteration alone had no significant
 effect. The effect of warming on community
 compositional and biomass stability was influenced by
 precipitation change. Warming reduced community
 compositional and biomass stability by decreasing functional
 dispersion, while precipitation addition and reduction will
 alleviate and exacerbate the negative effects of warming on community
 composition and biomass stability by influencing species
 richness. Moreover, biomass stability is largely determined by
 asynchrony among species. Our results suggest that climate
 warming and combined with precipitation alteration would reduce
 alpine community compositional stability primarily by reducing
 functional dispersion and species richness, and reduce the
 stability of community biomass via decreasing
 compositional stability. Thus, our findings provide valuable information
 for the sustainable functioning of
 grassland maintenance, highlighting that future
 grassland conservation and management should consider
 increasing species and functional diversity to mitigate
 or recover the negative impacts of climate warming on alpine
 plant community composition and biomass stability.
description: # Data from: Warming reduces the compositional and biomass stability of
 alpine meadows via declines in species richness and functional dispersion
 Dataset DOI:
 [10.5061/dryad.gf1vhhn2j](https://doi.org/10.5061/dryad.gf1vhhn2j) ##
 Description of the data and file structure This database comprises two
 components: raw data for analysis and complete analytical code. Below are
 the metadata (describing variables, units, and file structure) and
 annotations for the R code. ### Files and variables #### File:
 raw_data.xlsx **Description:** The all data for this study was selected
 from 2018 to 2021 year. The file (raw data.xlsx) includes 5 sheets: #####
 Variables * Sheet1-climate: The data of soil temperature and moisture
 across 2018-2021 year. * Sheet2-diversity: The data of species richness,
 FDis, community biomass and dominant, common, and rare species biomass
 across 2018-2021 year. * Sheet3-stability: The data of community biomass
 stability and other drivers across 2018-2021 year. * Sheet4-Figure1: The
 mean and se of all respose factors under six treatments for Figure1. *
 Sheet5-Figure2:The mean and se of all respose factors under six treatments
 for Figure2. There are no restrictions on the data. The empty cells in the
 data sheets indicate not available (NA). | **Column name** | **Key** | |
 :---------------------- |
 :---------------------------------------------------------------------------------------------------------------------------------------------------- | | Time | The study time from 2018-2021 | | Treatment | The six treatment combination of warming and precipitation change treatments (including Control, Drought, Wet, Warming, Warming+Drought, Warming+Wet) | | W | Abbreviation for warming treatment (W) in two-factor experiment (Warming and precipitation change treatments) | | P | Abbreviation for precipitation treatment (P) | | Block | Experimental replicate block coding | | Soil moisture | The soil moisture data from 2019-2021(unit: v/v) | | Soil temperature | The soil temperature data from 2019-2021(unit:℃) | | Richness | The number of species in each sample plot | | FDis | Functional dispersion | | Biomass | Community biomass | | Dominant biomass | Dominant species biomass(unit:g/m2) | | Common biomass | Common species biomass(unit:g/m2) | | Rare biomass | Rare species biomass(unit:g/m2) | | Stability | The community biomass stability | | Grass | The grass species biomass stability | | Sedge | The sedge species biomass stability | | Forb | The forb species biomass stability | | Dominant | The dominant species biomass stability | | Common | The common species biomass stability | | Rare | The rare species biomass stability | | Asychrony | The community species asychrony | | Compositional stability | The community compositioanal stability | | Species richness | The mean of species richness across three years | | FDis | The mean of functional dispersion across three years | | CWMheight | The mean of CWM plant height (unit: cm) | | CWMLA | The mean of CWM leaf area (LA, cm2) | | CWMLDMC | The mean of CWM leaf dry matter content (LDMC, mg/g) | | CWMSLA | The mean of CWM specific leaf area (SLA, cm2/g) | | CWMseedmass | The mean of CWM seed mass (mass per 100 seeds) | | Treatment1 | The scale of the X-axis when plotting | | av | The average of the variable | | se | The standard error of the variable | | Group | The group of Ambient and Warming | | Factor | The group of different factors | #### File: code.R **Description:** This R Markdown file contains annotated R code to generate all the figures presented in the manuscript. It loads the necessary libraries and creates all figures using ggplot2 and other relevant R packages. Each figure corresponds to the same figure number and labels as mentioned in the manuscript and can be saved in a designated output directory in high-resolution formats (e.g., PNG, PDF, or JPG).  ## Code/software The specific R analysis code and annotation are shown in the R file.