# Functional trait identity regulates productivity better than tree diversity and structural complexity in subtropical mixed-species forests

## TL;DR

This study shows that specific tree traits, rather than diversity or structure, best predict productivity in subtropical forests.

## Contribution

The study demonstrates that functional trait identity is a stronger predictor of productivity than diversity or structural complexity.

## Key findings

- Fagaceae species were the main contributors to forest productivity.
- Traits like maximum height and low wood density enhanced productivity.
- Functional trait identity outperformed diversity and structural metrics in predicting productivity.

## Abstract

Subtropical forests play an important role in global carbon cycle and in mitigating climate change. Understanding the relationship between multiple diversity and ecosystem function is crucial for protecting and managing forests. Here we used forest inventory data from a 6-hectare sample plot in natural evergreen deciduous broad-leaf mixed forest systems for the years 2016 and 2021. We analyzed the effects of multiple aspects of diversity and topographic factors on forest productivity using multiple causal analyses. We found that Fagaceae was the primary contributor to productivity in the forest stand. Elevation, slope, and convexity showed no significant effects on productivity. Structural complexity (stand density, large-diameter trees and tree-size variation) was significantly positively correlated with productivity. Taxonomic and functional diversity indices were weakly correlated with productivity. Specifically, forest productivity was enhanced by traits associated with greater maximum height and lower wood density. Community-weighted mean traits were the most strongest predictor of productivity relative to other variables. Within this forest stand, the mass-ration hypothesis appeared to be more influential on forest productivity compared to the complementarity and selection effects. By integrating multiple drivers of forest ecosystem functioning, our study provides critical system-level insights needed to predict the potential consequences of regional changes in forest diversity, composition, structure and function.