DOI: 10.5061/dryad.tqjq2bwcf
title: CSR strategy shifts under biotic resistance and grazing drive invasion success of <em>Solanum rostratum</em> in northern China

description: Biotic resistance and environmental filter are the primary ecological
 barriers to successful invasions. Although the role of functional
 strategies in overcoming these barriers has been extensively investigated,
 whether and how the plasticity of functional strategies facilitates
 successful invasions remains poorly understood. In this study, we explored
 the invasion mechanisms of a widespread invasive plant, Solanum rostratum,
 across a 3,000 km spatial extent in northern China using Grime’s
 competitor, stress tolerator, and ruderal (CSR) framework. Our results
 showed that S. rostratum consistently invested more in C-strategy compared
 to native species, but it exhibited plastic shifts between S- and
 R-strategies. These strategic shifts were primarily influenced by the CSR
 strategy and the taxonomic and functional diversity of native communities,
 which selected for greater strategic divergence between S. rostratum and
 native species. Grazing further modulated these shifts by reducing native
 diversity. By increasing its strategic divergence from native competitors,
 S. rostratum may persist in species-diverse native communities and
 dominate in frequently disturbed and species-limited
 communities. Synthesis: Our findings reveal a dynamic
 strategy-environment matching process across space, underscoring the
 significance of functional plasticity in overcoming varying ecological
 barriers during large-scale invasions.
description: # CSR strategy shifts under biotic resistance and grazing drive invasion
 success of *Solanum rostratum* in northern China Dataset DOI:
 [10.5061/dryad.tqjq2bwcf](https://doi.org/10.5061/dryad.tqjq2bwcf) ##
 Description of the data and file structure We conducted large-scale field
 surveys and trait measurements across40 invaded and native plant
 communities spanning a 3,000 km transect in northern China to investigate
 how the functional strategies of *Solanum rostratum* vary with ecological
 contexts. We recorded species composition, environmental conditions, and
 plant functional traits to calculate CSR strategies for both invasive and
 native species. These data were collected to assess how functional
 strategy plasticity facilitates invasion success across environmental and
 biotic gradients. ### Files and variables ### 1. Best_model_result.csv
 **Description:** Model selection results for candidate linear
 mixed-effects models. **Variables:** * `model_id` — Model identifier
 (character) * `df` — Degrees of freedom * `logLik` — Log-likelihood of the
 model * `AICc` — Corrected Akaike Information Criterion * `delta` —
 Difference in AICc relative to the best model * `weight` — AICc weight *
 `R2m` — Marginal R² (variance explained by fixed effects) * `R2c` —
 Conditional R² (variance explained by fixed + random effects) *
 `variables` — Predictors included in the model (character) * `Moran_I` —
 Moran’s I statistic for spatial autocorrelation * `Expected_I` — Expected
 Moran’s I under the null model * `SD` — Standard deviation of Moran’s I *
 `p_value` — p-value for Moran’s I test ### 2. boot.csv **Description:**
 Bootstrap results of CSR strategic dissimilarity indices (SDI).
 **Variables:** * `SDI_C` — Strategic dissimilarity index for C-strategy *
 `SDI_S` — Strategic dissimilarity index for S-strategy * `SDI_R` —
 Strategic dissimilarity index for R-strategy **Units:** Dimensionless
 indices (0–1) ### 3. cv_dataresult.csv / cvdata.csv **Description:** Trait
 variability data. **Variables:** * `species` — Species name (character) *
 `mean_value` — Mean trait value * `sd_value` — Standard deviation of trait
 value * `cv` — Coefficient of variation * `C`, `S`, `R` — CSR strategy
 scores for individual species (0–100%) ### 4. Mean_CSR_strategies.csv /
 CSR_strategies.csv **Description:** Site-level and species-level CSR
 strategy scores. **Variables:** * `C` — Competitor strategy score (%) *
 `S` — Stress-tolerator strategy score (%) * `R` — Ruderal strategy score
 (%) * `Group` — Species group (e.g., *S. rostratum* vs. native community)
 ### 5. LMMdata.csv **Description:** Predictor and response variables used
 in linear mixed-effects models. **Variables:** * `C_score_SR`,
 `S_score_SR`, `R_score_SR` — CSR scores for *S. rostratum* *
 `C_score_CWM`, `S_score_CWM`, `R_score_CWM` — Community-weighted mean CSR
 scores of native communities * `C_score_Dominant`, `S_score_Dominant`,
 `R_score_Dominant` — CSR scores of dominant native species * `Grazing` —
 Grazing intensity (categorical: grazed/ungrazed) * `Aridity` — Aridity
 index (dimensionless) * `MAT` — Mean annual temperature (°C) *
 `Species_richness` — Number of species per plot * `Simpson` — Simpson
 diversity index * `PD` — Faith’s phylogenetic diversity (dimensionless) *
 `Plant_density` — Plant density (individuals m⁻²) * `Biomass` —
 Aboveground biomass (g m⁻²) * `Vegetation_coverage` — Vegetation cover (%)
 * `MPD_phy`, `MNTD_phy` — Mean pairwise / nearest taxon distance
 (phylogenetic) * `NRI`, `NTI` — Net Relatedness Index and Nearest Taxon
 Index (standardized effect sizes) * `MPD_trait`, `MNTD_trait` — Mean
 pairwise / nearest trait distance * `FD` — Functional diversity index *
 `SDI_C_score`, `SDI_S_score`, `SDI_R_score` — Strategic dissimilarity
 indices for C, S, and R * `III_SR` — Invasion intensity index for *S.
 rostratum* * `Lon`, `Lat` — Longitude and latitude of sampling site
 (decimal degrees) ### 6. random_factors.csv **Description:** Site-level
 random effects used in LMMs. **Variables:** * `Site` — Site identifier
 (character) * `random_effect` — Random effect estimate * `Lon`, `Lat` —
 Site coordinates (decimal degrees) ### 7. model_avg_full_estimates.csv
 **Description:** Model-averaged parameter estimates and importance values.
 **Variables:** * `Variable` — Predictor variable name * `Estimate` —
 Model-averaged estimate * `SE` — Standard error * `Adjusted_SE` — Adjusted
 standard error * `z value` — z statistic * `p` — p-value * `LowerCI`,
 `UpperCI` — 95% confidence interval bounds * `Significance` — Significance
 level (e.g., ‘*’, ‘**’) * `Importance` — Variable importance weight *
 `Frequency` — Frequency of occurrence in candidate models * `Group` —
 Variable group/category ### 8. StrateFy.xlsx **Description:**
 StrateFy.xlsx is an external analytical tool originally published by
 Pierce et al. (2017, Functional ecology) for calculating CSR
 (Competitor–Stress tolerator–Ruderal) strategy scores from plant leaf
 traits. This file is included in the repository solely to ensure full
 reproducibility of the CSR calculations performed in our study. Important:
 This Excel file is not raw data from our research. All raw trait data used
 in our analyses are provided separately in unformatted CSV files.
 **Variables:** Species ID, trait values (e.g., SLA, LDMC, LA), and site
 information. **Units:** Trait-specific (e.g., LA in mm², LDMC in %, SLA in
 mm2 mg-1)). **Reference：** Pierce, S., Negreiros, D., Cerabolini, B. E.
 L., Kattge, J., Díaz, S., Kleyer, M., Shipley, B., Wright, S. J.,
 Soudzilovskaia, N. A., Onipchenko, V. G., van Bodegom, P. M.,
 Frenette-Dussault, C., Weiher, E., Pinho, B. X., Cornelissen, J. H. C.,
 Grime, J. P., Thompson, K., Hunt, R., Wilson, P. J., … Tampucci, D.
 (2017). A global method for calculating plant CSR ecological strategies
 applied across biomes world-wide. Functional Ecology, 31(2), 444–457.
 [https://doi.org/10.1111/1365-2435.12722](https://doi.org/10.1111/1365-2435.12722) ### 9. code_CSR.R **Description:** R script containing code for trait data processing, CSR strategy calculation, SDI computation, and linear mixed-effects model fitting. ### 9. trait_data.csv **Description:** raw leaf trait input file for matted for StrateFy | Variable Description： | 
 
 | 
 
 | | :---------------------------- | :--------------------------------- |
 :------- | | Column name | Description | Unit | | Species binomial | Full
 species scientific name | — | | Site | Sampling site identifier | — | |
 Sample ID | Individual sample identifier | — | | leaf area (mm²) | Leaf
 lamina area | mm² | | leaf dry matter content (%) | LDMC = dry mass /
 fresh mass × 100 | % | | specific leaf area (mm² mg⁻¹) | SLA = leaf area
 per dry mass | mm² mg⁻¹ | ## Code/software All data processing and
 analyses were conducted in the free and open-source statistical
 environment **R (version 4.3.2)**. The workflow is fully contained in the
 provided R script `code_CSR.R`. This script allows users to reproduce the
 entire analytical pipeline from raw trait data to final statistical models
 and figures. ### Required software and packages The following R packages
 were used and must be installed prior to running the script: * `StrateFy`
 – calculation of CSR strategy scores from leaf traits. * `dplyr`, `tidyr`
 – data cleaning and processing. * `ggplot2`, `ggtern` – data visualization
 (e.g., ternary plots, effect plots). * `lme4` – linear mixed-effects
 modeling. * `MuMIn` – model selection and model averaging (AICc-based). *
 `DHARMa` – model diagnostics. * `spdep` – spatial autocorrelation testing
 (Moran’s I). All required packages are open source and available through
 CRAN. ### Analytical workflow 1. **CSR calculation** – Leaf trait data
 from `StrateFy.xlsx` are processed to calculate C, S, and R scores for
 each species. 2. **Trait plasticity** – Within-species variation is
 quantified by calculating the coefficient of variation (CV) of CSR scores
 (`cvdata.csv`). 3. **CSR dissimilarity** – Functional divergence between
 invader and native communities is computed as CSR dissimilarity indices
 (SDI) (`boot.csv`). 4. **Modeling and selection** – Linear mixed-effects
 models are fitted using `lme4` to assess environmental and community
 predictors of invasion intensity. AICc-based model selection and averaging
 are performed using `MuMIn` (`Best_model_result.csv`,
 `model_avg_full_estimates.csv`). 5. **Model diagnostics and spatial
 analysis** – Residual diagnostics are performed with `DHARMa`, and spatial
 autocorrelation of model residuals is tested using Moran’s I (`spdep`). 6.
 **Visualization** – Ternary plots of CSR strategies, CV distributions, and
 model effect plots are generated with `ggplot2` and `ggtern`. ## Variable
 Description Table | 
 
 | 
 
 | 
 
 | 
 
 | | :------------------- |
 :------------------------------------------------------------- |
 :------------------------------ |
 :------------------------------------------------------- | | Variable |
 Definition | Unit / Scale | Notes | | C\_score\_SR | C-strategy score of
 *S. rostratum* | Unitless (0–1) | Calculated from CSR triangle method | |
 S\_score\_SR | S-strategy score of *S. rostratum* | Unitless (0–1) | – | |
 R\_score\_SR | R-strategy score of *S. rostratum* | Unitless (0–1) | – | |
 C\_score\_CWM | Community-weighted mean C-strategy score of native
 communities | Unitless (0–1) | Weighted by species relative abundance | |
 S\_score\_CWM | Community-weighted mean S-strategy score of native
 communities | Unitless (0–1) | – | | R\_score\_CWM | Community-weighted
 mean R-strategy score of native communities | Unitless (0–1) | – | |
 C\_score\_Dominant | C-strategy score of dominant native species |
 Unitless (0–1) | Dominant species identified by relative abundance | |
 S\_score\_Dominant | S-strategy score of dominant native species |
 Unitless (0–1) | – | | R\_score\_Dominant | R-strategy score of dominant
 native species | Unitless (0–1) | – | | Grazing | Grazing intensity |
 Categorical (grazed / ungrazed) | Based on field observation &
 management records | | Aridity | Aridity index | Unitless | Higher = drier
 | | MAT | Mean annual temperature | °C | – | | Species\_richness | Number
 of species per plot | Count | – | | Simpson | Simpson diversity index |
 Unitless (0–1) | Higher = greater diversity | | PD | Faith’s phylogenetic
 diversity | Branch length units | – | | Plant\_density | Plant individual
 density | individuals·m⁻² | – | | Biomass | Aboveground biomass | g·m⁻²
 (dry weight) | – | | Vegetation\_coverage | Vegetation cover | % | – | |
 MPD\_phy | Mean pairwise phylogenetic distance | Branch length units | – |
 | MNTD\_phy | Mean nearest-taxon phylogenetic distance | Branch length
 units | – | | NRI | Net Relatedness Index | Unitless (SES) | Higher =
 stronger clustering | | NTI | Nearest Taxon Index | Unitless (SES) | – | |
 MPD\_trait | Mean pairwise distance based on trait distance | Trait
 distance units | Gower distance | | MNTD\_trait | Mean nearest-taxon trait
 distance | Trait distance units | Gower distance | | FD | Functional
 diversity index | Unitless | Higher = more functionally diverse | |
 SDI\_C\_score | Strategic dissimilarity index for C-strategy | Unitless |
 Dissimilarity between *S. rostratum* CSR-C and community | | SDI\_S\_score
 | Strategic dissimilarity index for S-strategy | Unitless | – | |
 SDI\_R\_score | Strategic dissimilarity index for R-strategy | Unitless |
 – | | III\_SR | Invasion intensity index of *S. rostratum* | Unitless
 index (0–1) | Higher = stronger invasion | | Lon | Longitude of sampling
 plot | Decimal degrees | GPS measured | | Lat | Latitude of sampling plot
 | Decimal degrees | GPS measured | ### Usage Users can open and run the
 script `code_CSR.R` directly in R. The script loads the data files
 automatically, executes the above steps sequentially, and reproduces the
 main analyses and figures reported in the manuscript.