Data and code from: Loss of resource-conservative species affects plant phylogenetic and functional structure under long-term snow addition
Data files
Nov 04, 2025 version files 61.81 KB
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DATAforJE.xlsx
32.93 KB
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README.md
9.09 KB
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SG_Code_forJE.R
17.32 KB
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tree.tre
2.47 KB
Abstract
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.
Dataset DOI: 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
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:
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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/ . 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.
