DOI: 10.5061/dryad.tqjq2bwd0
title: Plant elemental diversity increases ecosystem productivity and temporal stability

description: 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. In theory, elemental diversity captures the community
 functional heterogeneity by quantifying variation in the multidimensional
 elementomes of co-occurring species within a community. However, empirical
 evidence connecting organismal elemental diversity to ecosystem
 functioning and identifying its environmental controls remains scarce. We
 compiled an unprecedented dataset on plant elemental concentrations,
 encompassing more than 2,500 species and 14 analyzed elements (including
 macronutrients, micronutrients, and trace elements) sampled from leaves,
 stems, trunks, and fine roots across eight biomes and 72 sites, covering
 multiple ecosystem types including forests and grasslands. Using these
 data, we investigated the spatial patterns and drivers of plant elemental
 diversity and evaluated its relationship with ecosystem productivity and
 stability. Our results indicate that plant elemental diversity decreased
 with latitude, with interannual variability in temperature and mean annual
 precipitation as the primary controls on its spatial distribution.
 Moreover, ecosystems with higher plant elemental diversity exhibit greater
 efficiency in the use of carbon, water, and light, thereby translating
 into higher productivity and greater temporal stability across and within
 forests and grasslands, and these effects persisted even after accounting
 for climate and soil factors. Taken together, our results support the
 influence of plant elemental diversity as a distinct dimension of
 biodiversity with functional implications. Complementing trait- and
 taxonomy-based measures, plant elemental diversity improves predictions of
 ecosystem productivity and temporal stability under ongoing climatic
 variability, and can substantially advance research on biodiversity and
 ecosystem functioning.
description: # Plant elemental diversity increases ecosystem productivity and temporal
 stability 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. --- ## 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
 &gt; 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 &gt; 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](mailto:yanpu681@gmail.com) for
 specific questions related to the analyses.