# Integrating dark diversity, functional traits, and diagnostic species: a framework to diagnose bottlenecks in forest recovery

## TL;DR

This paper introduces a new framework combining dark diversity, functional traits, and diagnostic species to assess and guide forest recovery in subtropical China.

## Contribution

The study integrates dark diversity with functional traits and diagnostic species to diagnose recovery bottlenecks in forest ecosystems.

## Key findings

- The Community Completeness Index (CCI) increased during recovery and stabilized as the forest approached a stable state.
- Stage-specific early-warning species were identified, with their absence signaling successful or problematic recovery stages.
- Functional traits like seed mass and mycorrhizal type were stronger predictors of species absence than environmental conditions.

## Abstract

Accurately assessing the natural recovery processes of forest ecosystems remains a key challenge in restoration ecology. The concept of dark diversity—the set of species absent from a site but belonging to its habitat-specific species pool—provides a novel lens for this assessment.

In this study, we developed and applied an integrated diagnostic framework that synthesizes dark diversity, functional traits, and diagnostic species. We applied this framework to a chronosequence of recovering forest ecosystems in subtropical China, representing early, middle, and late recovery stages.

Our results demonstrated that the Community Completeness Index (CCI), derived from dark diversity, increased significantly during recovery, with its stabilization indicating the approach to a stable state. The framework identified stagespecific early-warning species: the absence of light-demanding, acquisitive transitional species in the mid-stage signaled successful progression, while the absence of shade-tolerant, conservative climax species in the late-stage signaled potential degradation. Crucially, analysis using Dark Diversity Affinity (DDA) revealed that the functional traits of species (e.g., seed mass, mycorrhizal type, leaf economics) were the primary filters determining species absence, exhibiting a stronger influence than local environmental conditions. These filters shifted predictably across stages, from dispersal and establishment limitations early on to competitive interactions later.

The proposed framework translates dark diversity theory into an actionable tool for restoration. It moves beyond simple observation to diagnose recovery success, pinpoint specific bottlenecks, and inform targeted interventions such as assisted dispersal or canopy management. This provides a mechanism-based approach for guiding precision restoration in forest ecosystems.