From the Ecological Monographs manuscript.

Title: Plant elemental diversity increases ecosystem productivity and temporal stability
Running title: Elemental diversity and ecosystem functioning
Authors: Pu Yan; Nianpeng He; Kailiang Yu; Lawren Sack; Lin Jiang; Marcos Fernández-Martínez

Overview

This Dryad package provides the analysis-ready datasets and R code used to reproduce all statistical analyses and Figures 2–6 (excluding Figure 1, which is a conceptual schematic) in the manuscript “Plant elemental diversity increases ecosystem productivity and temporal stability.”

The study is motivated by the idea that the elemental composition of organisms (i.e., the elementome) directly constrains metabolic machinery and aligns with functional traits, linking organismal performance to nutrient cycling and energy flow at the ecosystem level. Plant elemental diversity quantifies variation in multidimensional elementomes among co-occurring species and can therefore capture a distinct dimension of biodiversity with functional implications. This package enables reuse and reproducibility of the reported analyses examining (i) spatial/environmental patterns of plant elemental diversity and (ii) relationships between plant elemental diversity and ecosystem functioning, including productivity and temporal stability.

What this package contains

This package contains two analysis-ready datasets (CSV) and two main R scripts, organized into two parts:

• Part 1 (Figures 2–3): Spatial and environmental patterns of plant elemental diversity.
• Part 2 (Figures 4–6): Relationships between plant elemental diversity and ecosystem functioning (productivity, interannual variation, resource-use efficiency), including a piecewise structural equation model (SEM) (with an optional species richness extension).

Together, the datasets contain all variables necessary to reproduce the reported models and figures.

Data files

1) df_ED_pattern.csv (plot-level; used for Figures 2–3)

Description: Plot-level table used to quantify spatial patterns of plant elemental diversity and its relationships with geographic and environmental predictors.

Typical variables included (examples):

• Geography: latitude, longitude, altitude
• Climate summaries: mean and interannual variability of temperature and precipitation (site-level summaries matched to plots, as in Methods)
• Soil predictors: z-standardized soil properties/indices (as used in analyses)
• Response variables: plant elemental diversity metrics used in Part 1 analyses

Unit / interpretation notes:
Climate variables are aggregated summaries (mean and interannual variability) as defined in the manuscript Methods. Soil variables used as predictors are z-standardized in the analysis workflow.

2) df_ED_sub.csv (site-level; one row per site; used for Figures 4–6)

Description: Site-level table used to evaluate relationships between plant elemental diversity and ecosystem functioning and to run the reported piecewise SEM.

Typical variables included (examples):

• Ecosystem productivity and stability:
  • GPP (gross primary productivity)
  • GPPiav (interannual variation in GPP)
• Resource-use efficiency components: WUE, CUE, LUE
• Environmental covariates: climate and soil summaries used in regressions/SEM (as defined in Methods)
• SEM variables: all predictors/mediators/outcomes required to reproduce the piecewise SEM of elemental diversity → RUE pathways, with an optional species richness extension

Unit / interpretation notes:
df_ED_sub.csv is analysis-ready and aligned with the modeling workflow reported in the manuscript (variable definitions and units follow the manuscript Methods and script comments).

R scripts

1) Part_1_main_analysis_figures.R

• Reads: df_ED_pattern.csv
• Reproduces: Part 1 analyses and Figures 2–3 (spatial/environmental patterns of plant elemental diversity)

2) Part_2_main_analysis_figures.R

• Reads: df_ED_sub.csv

• Reproduces: Part 2 analyses and Figures 4–6, including regression models, resource-use efficiency relationships, and piecewise SEM (optionally including species richness extension).

  R data objects (RDS; optional R-native format)

  • df_ED_pattern.rds — R-native version of df_ED_pattern.csv (plot-level table for Part 1 / Figures 2–3).
  • df_ED_sub.rds — R-native version of df_ED_sub.csv (site-level table for Part 2 / Figures 4–6).

  Notes: The .rds files store the same analysis-ready tables in R’s native format (preserving classes/attributes where applicable). Users can load them with readRDS(). The .csv versions are provided for broader interoperability.

Software requirements

R (recommended ≥ 4.1) with packages: dplyr, tidyr, ggplot2, ggpubr, ggprism, piecewiseSEM, performance, semEff.

How to reproduce results

1. Open a fresh R session.
2. Set the working directory to the folder containing the scripts and CSV files (or edit the file paths in the scripts).
3. Run scripts from top to bottom:
   • Part_1_main_analysis_figures.R
   • Part_2_main_analysis_figures.R

The scripts will reproduce the statistical analyses and generate the figures corresponding to Figures 2–6 in the manuscript.

Data dictionary (variable definitions)

A) df_ED_pattern (plot-level)

Use case: Part 1 analyses; Figures 2–3.
Observation unit: plot (site-level predictors may repeat across plots within a site).

Variables

• Site (character) — Site identifier (e.g., “HT01”, “HT02”).
• ED (numeric) — Plant elemental diversity metric used in the manuscript (higher values indicate higher elemental diversity).

Geography

• Latitude (numeric; degrees) — Latitude (decimal degrees).
• Longitude (numeric; degrees) — Longitude (decimal degrees).
• Altitude (numeric; meters) — Elevation above sea level (m).

Soil composite indices (raw and standardized)

• soil_envir (numeric; unitless index) — Composite soil environment/physical index used as an environmental control (raw index).
• soil_nutri (numeric; unitless index) — Composite soil nutrient/chemical index used as a nutrient control (raw index).
• soil_envir_z (numeric; z-score) — z-standardized version of soil_envir (mean 0, SD 1 across the analysis dataset).
• soil_nutri_z (numeric; z-score) — z-standardized version of soil_nutri.

Climate summaries (2013–2019)

• Tmean_mean_1319 (numeric; °C) — Mean annual temperature averaged over 2013–2019.
• Psum_mean_1319 (numeric; mm year⁻¹) — Annual precipitation sum averaged over 2013–2019.
• PV_temp_mean (numeric; unitless index) — Proportional variability index (PV) for temperature, summarized over the same climate period as defined in the analysis pipeline (higher = greater proportional temperature variability).
• PV_precip_mean (numeric; unitless index) — Proportional variability index (PV) for precipitation (higher = greater proportional precipitation variability).

Abbreviation notes

• ED = plant elemental diversity.
• PV = proportional variability index (unitless).
• Tmean = mean annual temperature.
• Psum = annual precipitation sum.
• _1319 = summary computed over 2013–2019.
• _z = z-standardized variable.

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B) df_ED_sub (site-level)

Use case: Part 2 analyses; Figures 4–6; regressions and piecewise SEM.
Observation unit: site (one row per site).

Variables

Identifiers and site descriptors

• Site (character) — Site identifier.
• Year (integer) — Reference year associated with the site-level compilation used for analysis.
• VT (character) — Vegetation type / ecosystem type category (e.g., “forest”, “grassland”).
• Latitude (numeric; degrees) — Site latitude (decimal degrees).
• Longitude (numeric; degrees) — Site longitude (decimal degrees).
• Altitude (numeric; meters) — Site elevation above sea level (m).

Elemental diversity

• ED (numeric) — Plant elemental diversity metric (higher values indicate higher elemental diversity).

Ecosystem functioning

• GPP (numeric; units as in manuscript/processing pipeline) — Gross primary productivity.
• GPPiav (numeric; unitless) — Interannual variability metric of GPP used in the manuscript (definition follows Methods).

Climate / environment predictors

• T (numeric; °C) — Temperature predictor used in Part 2 analyses (site-level; definition/source as in Methods).
• Climpc1_1319 (numeric; unitless PC score) — Climate PC1 score summarizing climate conditions over 2013–2019 (as defined in Methods).
• soil_envir (numeric; unitless index) — Composite soil environment/physical index (raw).
• soil_nutri (numeric; unitless index) — Composite soil nutrient/chemical index (raw).
• soil_envir_z (numeric; z-score) — z-standardized soil_envir.
• soil_nutri_z (numeric; z-score) — z-standardized soil_nutri.

Resource-use efficiency (RUE)

• WUE (numeric) — Water-use efficiency component used in the manuscript.
• CUE (numeric) — Carbon-use efficiency component used in the manuscript.
• LUE (numeric) — Light-use efficiency component used in the manuscript.
• RUEpc1 (numeric; unitless PC score) — PC1 summarizing overall resource-use efficiency (derived from WUE/CUE/LUE as in Methods).

Biodiversity covariate (optional SEM extension)

• SR (numeric) — Species richness (used in the optional richness-extended SEM).

Elemental diversity transformations

• ED_log (numeric) — Natural log of ED (computed as log(ED) for ED > 0).
• ED_ln (numeric) — Natural log of ED (same as ED_log in this dataset snapshot).
• ED_log10 (numeric) — Base-10 log of ED (computed as log10(ED) for ED > 0).

Abbreviation notes

• GPP = gross primary productivity.
• GPPiav = interannual variability of GPP (unitless; see Methods).
• WUE/CUE/LUE = water/carbon/light use efficiency.
• RUEpc1 = PC1 summarizing WUE/CUE/LUE.
• SR = species richness.
• Climpc1_1319 = climate principal component 1 over 2013–2019.
• _z = z-standardized variable.

Contact

For questions about the data or code, please refer to the corresponding author listed in the manuscript. You may also contact Dr. Pu Yan directly at yanpu681@gmail.com for specific questions related to the analyses.