DOI: 10.5061/dryad.66t1g1kdv
title: Data and code from: Loss of resource-conservative species affects plant phylogenetic and functional structure under long-term snow addition

description: Climate change and human activities are increasingly influencing
 ecological communities. Within this context, increasing extreme snow
 events and persistent livestock grazing are known to pose significant
 challenges to alpine ecosystems on the Tibetan Plateau. However, the
 mechanisms driving long-term community assembly and structural changes
 under these concurrent pressures remain unclear. Here, we used a 16-year
 field experiment in a Tibetan alpine grassland to investigate the effects
 of spring snow addition and yak grazing on taxonomic, phylogenetic, and
 functional community diversity and structure. We found that snow addition
 was the primary driver of community structure, while the effects of
 grazing were less pronounced. Specifically, snow addition shifted the
 phylogenetic structure from being random to overdispersed. This shift was
 driven by the selective loss of species with conservative resource-use
 strategies (i.e., those with high leaf dry matter content and low specific
 leaf area), which were phylogenetically more closely related to the
 residents than were the gained species. In contrast, communities remained
 functionally clustered under all treatments. This resulted from opposing
 structural shifts in individual traits, where leaf dry matter content
 became more overdispersed, while plant height and leaf nitrogen content
 (LNC) became more clustered, driven by the loss of taller species and the
 gain of species with low LNC. This decoupling between phylogenetic and
 functional responses suggests that environmental filtering selects for
 convergent functional adaptations among phylogenetically distant species.
 Our findings highlight the importance of considering multi-faceted
 diversity metrics when exploring community assembly, and provide the first
 experimental evidence that long-term snow addition reshapes plant
 phylogenetic community structure on the Tibetan Plateau. Importantly, the
 loss of conservative species suggests that altered snow regimes may
 potentially shift key ecosystem functions in alpine grasslands. Our
 findings also demonstrate that integrating species gain and loss is
 essential for a predictive understanding of long-term community dynamics
 under global change.
description: # Data and code from: Loss of resource-conservative species affects plant
 phylogenetic and functional structure under long-term snow addition
 Dataset DOI:
 [10.5061/dryad.66t1g1kdv](https://doi.org/10.5061/dryad.66t1g1kdv) ##
 Description of the data and file structure Dataset Title Data from: Loss
 of resource-conservative species affects plant phylogenetic and functional
 structure under long-term snow addition 
 
 Authors Qianxin Jiang , Gyal Skalsang , Juntao Zhu , Xian Yang , Yunlong
 He , Ge Hou , Yangjian Zhang , Tsechoe Dorji , Marc W. Cadotte , Lin Jiang
 
 
 Citation Jiang, Q., Skalsang, G., Zhu, J., Yang, X., He, Y., Hou, G.,
 Zhang, Y., Dorji, T., Cadotte, M. W., & Jiang, L. (2025). Data from:
 Loss of resource-conservative species affects plant phylogenetic and
 functional structure under long-term snow addition. Dryad Digital
 Repository.
 [https://doi.org/10.5061/dryad.66t1g1kdv](https://doi.org/10.5061/dryad.66t1g1kdv) 
 
 Study Site and Methods The experiment was conducted in an alpine meadow
 in Namtso village, central Tibet, China (30.72°N, 91.05°E, 4875 m a.s.l).
 The experiment was established in June 2009 using a randomized block
 design with eight replicate blocks. Each block contained four plots
 randomly assigned to one of four treatments: control (C), moderate
 growing-season yak grazing (G), spring snow addition (S), and snow
 addition combined with grazing (SG). Vegetation surveys were conducted
 annually from 2009 to 2024 (with exceptions in 2013, 2017, and 2022). Four
 plant functional traits were measured: leaf dry matter content (LDMC),
 leaf nitrogen content (LNC), specific leaf area (SLA), and plant height. 
 
 ### Files and variables #### File: DATAforJE.xlsx **Description:** This
 Excel file contains the analyzed results and summary statistics used for
 the manuscript. It contains four sheets: * Sheet 1: Sheet1 (Indicator
 Averages) Description: Contains the average value and standard error of
 key community indicators, calculated per treatment group (group2) for each
 survey year. Variables: group2: A unique identifier for the treatment and
 year (e.g., "C2009"). Treat: The experimental treatment.
 Categories: C (Control), G (Grazing), S (Snow Addition), SG (Snow Addition
 + Grazing). year: The survey year. SR: Species Richness (average). SR_se:
 Standard error of Species Richness. Phylo_MPDa: Abundance-weighted Mean
 Pairwise Distance (Phylogenetic Diversity). Phylo_MPDa_se: Standard error
 of Phylo_MPDa. Phylo_NRIa: Abundance-weighted Net Relatedness Index
 (Phylogenetic Structure). Phylo_NRIa_se: Standard error of Phylo_NRIa.
 Func_MPDa: Abundance-weighted Mean Pairwise Distance (Functional
 Diversity). Func_MPDa_se: Standard error of Func_MPDa. Func_NRIa:
 Abundance-weighted Net Relatedness Index (Functional Structure).
 Func_NRIa_se: Standard error of Func_NRIa. KP.percent: Percentage cover of
 Kobresia pygmaea (average). KP.percent_se: Standard error of Kobresia
 pygmaea percentage cover. * Sheet 2: Sheet2 (Beta NTI) Description:
 Contains the phylogenetic and functional dissimilarity (βNTI) between
 gained/lost species and the resident species, averaged across replicates
 for each treatment. Variables: treat: The experimental treatment.
 Categories: C, G, S, SG. turnover: The species dynamic being measured.
 Categories: gain (colonist species) or loss (extirpated species) .
 beta_nti_phylogenetic: The phylogenetic dissimilarity (PhyloβNTI) between
 the turnover group and resident species. se (column D): Standard error for
 beta_nti_phylogenetic. beta_nti_functional: The functional dissimilarity
 (FuncβNTI) between the turnover group and resident species. se (column F):
 Standard error for beta_nti_functional. * Sheet 3: Sheet3 (FuncNRI for
 individual traits) Description: Contains the functional net relatedness
 index (FuncNRI) calculated for each of the four individual traits, per
 treatment group and year. Variables: Treat: The experimental treatment.
 Categories: C, G, S, SG. year: The survey year. group2: A unique
 identifier for the treatment and year. Func_NRIa_LDMC: FuncNRI for Leaf
 Dry Matter Content. Func_NRIa_LDMC_se: Standard error for Func_NRIa_LDMC.
 Func_NRIa_LNC: FuncNRI for Leaf Nitrogen Content. Func_NRIa_LNC_se:
 Standard error for Func_NRIa_LNC. Func_NRIa_Height: FuncNRI for Plant
 Height. Func_NRIa_Height_se: Standard error for Func_NRIa_Height.
 Func_NRIa_SLA: FuncNRI for Specific Leaf Area. Func_NRIa_SLA_se: Standard
 error for Func_NRIa_SLA. * Sheet 4: Sheet4 (Taxa Name) Description: A
 taxonomic list of all 59 species observed in the study. Variables:
 species: The species name. genus: The genus name. family: The family name.
 #### File: tree.tre **Description:** The phylogenetic tree file for the 59
 observed species, saved in Newick format. This file is used in the R
 script for all phylogenetic analyses. #### File: Code.R **Description:** R
 script used to perform all statistical analyses and generate figures
 presented in the manuscript. Analyses included: Phylogenetic tree
 construction (V.PhyloMaker). Calculation of phylogenetic and functional
 diversity/structure (MPD, NRI, MNTD) using the picante package.
 Calculation of phylogenetic signal (Blomberg's K). Gower distance
 calculation (FD package). Linear mixed-effect models (LMMs) using lme4 and
 lmerTest. Logistic regressions (glm). Principal Component Analysis (PCA)
 (FactoMineR). Standardized Precipitation Evapotranspiration Index (SPEI)
 calculation (SPEI package). Data visualization using ggplot2 and ggtree.
 ## Code/software The data files can be viewed using common software: .xlsx
 (Excel) files: Can be opened with any modern spreadsheet software, such as
 Microsoft Excel, Google Sheets, Apple Numbers, or the open-source
 LibreOffice Calc. .csv (CSV) files: Can be opened with any spreadsheet
 software (as listed above) or a plain text editor (e.g., Notepad,
 TextEdit, Notepad++). .tre (Newick tree) file: Can be opened with plain
 text editors or specialized phylogenetic tree-viewing software, such as
 FigTree or online viewers. Software for Analysis All analyses were
 conducted using R, a free and open-source language and environment for
 statistical computing. R Version: R version 4.2.2 was used. The following
 R packages must be installed and loaded: V.PhyloMaker: Used to generate
 the phylogenetic tree. picante: Used for calculating phylogenetic and
 functional diversity metrics (e.g., MPD, MNTD, NRI) and Blomberg's
 K-statistic. ape: Used for reading, manipulating, and analyzing
 phylogenetic trees. FD: Used to calculate Gower's distance for
 functional traits. lme4: Used to fit linear mixed-effect models (LMMs) and
 logistic regressions. lmerTest: Used to obtain p-values for LMMs.
 effectsize: Used to calculate Partial Eta-squared (η²p) for effect sizes.
 multcomp: Used for post-hoc multiple comparisons. openxlsx: Used to read
 and write .xlsx files. dplyr: Used for data manipulation and wrangling.
 Rmisc: Used for calculating summary statistics (mean, sd, se). ggplot2:
 Used for creating data visualizations (e.g., line plots, point plots).
 ggtree: Used for visualizing the phylogenetic tree. phytools: Used for
 phylogenetic analysis and visualization. ggnewscale: Used to add multiple
 color scales to ggplot2 plots. tidyr: Used for data tidying (e.g.,
 pivot_longer). FactoMineR: Used to perform Principal Component Analysis
 (PCA). missMDA: Used for imputing missing values in the PCA. SPEI: Used to
 calculate the Standardized Precipitation Evapotranspiration Index.
 Analysis Workflow The SG_Code_forJE.R script perform the following steps:
 Phylogenetic Tree: Constructs the phylogenetic tree (tree.tre). Diversity
 Calculations: Calculates phylogenetic diversity (MPDses, MNTDses) and
 functional diversity (Func_NRI, Func_NTI). Statistical Models: Runs LMMs
 on community metrics (richness, PhyloNRI, FuncNRI) and logistic
 regressions on species gain/loss probabilities. PCA: Performs PCA on
 functional traits. Climate Analysis: Calculates SPEI. Visualization:
 Generates all figures used in the manuscript. ## Access information Other
 publicly accessible locations of the data: * None Data was derived from
 the following sources: * The files in this submission (the .xlsx results,
 .tre file, and .R script) are the original work of the authors and are
 provided under the Creative Commons Zero (CC0) license waiver, as required
 by Dryad. This dataset was produced using methods that relied on external
 data sources, which are cited in the manuscript but are not redistributed
 here. These sources include: Plant Trait Data: Trait data for uncommon
 species were extracted from the TRY database (Kattge et al. 2020). The raw
 TRY data are not included in this submission. Climate Data: Climate data
 (MAT, MAP) were obtained from
 [https://www.geodata.cn/](https://www.geodata.cn/) . The raw climate data
 are not included in this submission. Phylogenetic Data: The mega-tree used
 by the V.PhyloMaker package (Jin & Qian 2019) was used to generate the
 tree.tre file.