Biodiversity responses to restoration across the Brazilian Atlantic Forest
Bibliographic record
Abstract
The UN Decade on Ecosystem Restoration is focussing attention and resources on restoration globally. Nowhere is this more crucial than in tropical forests that harbor immense biodiversity, but have also undergone widespread deforestation over the past few decades. We performed a meta-analysis to investigate how biodiversity features respond to forest restoration across the Brazilian Atlantic Forest (BAF), one of the most threatened biodiversity hotspots in the world. We assembled biodiversity in different metrics of structure and diversity features of three taxonomic groups (vascular plants, soil microorganisms, and invertebrates), generating a dataset with 2370 observations from 76 primary studies. We quantified the incomplete recovery of biodiversity (i.e., the rate of recovery to a pre-disturbance state) occurring during the restoration process, which we called the 'recovery gap'. Our results revealed that forests undergoing restoration in the BAF show a recovery gap of 34% for structure features and 22% for diversity features in comparison to reference reforests, considering all taxonomic groups investigated. For vascular plants, soil microorganisms, and invertebrates the recovery gap ranged between 46 and 47%, 16-26%, and 4-7%, respectively. Overall, the recovery gap was influenced by the interaction of restoration actions (i.e., the past land use, restoration age and restoration approach - active and passive restoration), however, structure features responded more sensitively to the time elapsed since restoration started, while the recovery gap for diversity features depended more on the past land-use. Our study can help guide the prioritization of the aforenamed taxonomic groups in restoration, the regulation of potential biodiversity offsetting policies in the BAF, and understanding how coupled biodiversity features respond to the interaction of environmental conditions and restoration actions in a high fragmented tropical landscape.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".