Using a trait‐based dark diversity approach to evaluate natural recovery potential in forests
Bibliographic record
Abstract
Utilizing ecosystems' natural recovery potential is crucial for enhancing the scale and efficacy of restoration efforts. However, this potential is currently underutilized in restoration ecology, and barriers to natural species recruitment remain poorly understood. In this study, we develop an analytical framework that combines trait‐based and dark diversity approaches to evaluate understory plant community recovery in smelter‐impacted forests and identify factors limiting recovery for absent species. A community completeness index (CCI) was calculated in 18 naturally recovering smelter‐impacted forests and three reference forests in Sudbury, Ontario, Canada. The CCI was used to identify sites that deviate the most from their target communities and was compared to traditionally used indicators of plant recovery. Community weighted means (CWM) of traits for observed and dark diversity species were compared, and principal components analysis was used to identify relationships between traits of observed and missing species among study sites. Species unable to recover passively (i.e. with high dark diversity probabilities) were generally tall and/or large seeded. Understory plant community recovery was structured along two functional gradients, plant economics and size, which both coincided with distance to the smelter. Dark diversity and trait analyses were useful for identifying barriers to natural recovery and, therefore, providing guidance on targeted restoration actions. We conclude by discussing the limitations and opportunities of the dark diversity approach in a restoration context, highlighting the importance of carefully defining habitat‐specific species pools and critically assessing the ecological significance of dark diversity probabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".