Data and code from: Biodiversity–carbon relationships diverge in forests dominated by different species within the same plant functional type

Theoretical and experimental evidence suggests that tree biodiversity enhances forest ecosystem functioning. However, whether these relationships hold within forest types dominated by different species of the same plant functional type remains unclear. We analysed 772 plots in subtropical China, contrasting high- and low-carbon-sequestration plantations of Cunninghamia lanceolata and Pinus massoniana along a chronosequence, to explore causal links among aboveground carbon storage (AGC), biodiversity, stand structure, and environmental factors. We found that the effects of biodiversity on AGC varied across forest types. Specifically, species diversity and functional diversity enhanced AGC in high-carbon-sequestration C. lanceolata plantations, whereas species and functional diversity of co-occurring broadleaf species had negative effects. In high-carbon-sequestration P. massoniana plantations, phylogenetic diversity and diameter at breast height (DBH) variation positively influenced AGC,..., , # Biodiversity-carbon relationships diverge in forests dominated by different species within the same plant functional type

Dataset DOI: [10.5061/dryad.np5hqc08x](https://doi.org/10.5061/dryad.np5hqc08x)

## Description of the data and file structure

The dataset supports analyses of biodiversity–ecosystem functioning relationships and aboveground carbon storage in managed plantation forests, including the generation of figures presented in the associated manuscript.

### Files and variables

#### File: Linear_mixed-effects_models.R

**Description:** R script used to fit linear mixed-effects models (LMMs) to evaluate the effects of biodiversity, stand structure, and environmental variables on aboveground carbon storage (AGC).

#### File: Piecewise_structural_equation_modeling.R

**Description:** R script used to perform piecewise structural equation modelling (PSEM) to quantify direct and indirect relationships among biodiversity, stand structure, environmental variables, and AGC.

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