Natural regeneration increases ecosystem production and functional diversity in an abandoned Afrotropical moist forest landscape
Bibliographic record
Abstract
The growing trend of agricultural abandonment necessitates understanding the development of regrowth forests on old fields in the context of forest restoration. However, the successional patterns of ecosystem functioning and functional diversity of afrotropical regrowth forests are rarely examined. We assessed whether aboveground biomass (AGB) and functional diversity (FD) vary with restoration age and proximity to old-growth forests, compared AGB and FD between regrowth and old-growth forests to measure restoration success and investigated the FD – AGB relationship. We sampled trees in 63 plots (2000 m2 each) in a regrowth forest and 5 plots in an old‐growth forest in 2011, 2014 and 2017. We calculated AGB using diameter, height and wood density. We collated species functional traits (dispersal modes, habitat types, fruit sizes and regeneration guilds) and computed FD measures (richness, evenness, dispersion, divergence and RaoQ’s entropy). AGB and FD measures (richness, dispersion and RaoQ) increased with restoration age. Functional divergence declined with increasing distance to the old-growth forest. Within 22 years, regrowth forests regained 22% of the AGB and recovered all FD measures of the old-growth forest. We found positive, negative and quadratic relationships between AGB and FD depending on the FD measure and forest type. We demonstrate that regrowth forests increase ecosystem production and functional diversity in abandoned areas, however they cannot substitute old-growth forests. Considering multiple measures of functional diversity in different habitats provides a better understanding of the influence of functional diversity on ecosystem functioning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".