Amazonian secondary forests are greatly reducing fragmentation and edge exposure in old-growth forests
Bibliographic record
Abstract
Abstract Restoration of tropical landscapes through the expansion of secondary forests is crucial for climate change mitigation and offers co-benefits for biodiversity. However, the strength of these benefits is influenced by the position of these secondary forests within the landscape. Recovery of both carbon stocks and biodiversity in secondary forests are enhanced by proximity to old-growth forests, and old-growth forests may benefit from secondary forests in return through buffering of edge effects and reduced fragmentation. However, to date there has been no biome-wide assessment of secondary forest location relative to old-growth forests. We mapped Amazonian secondary forests and explored their proximity to old-growth forests of different conditions. We then calculated the extent to which secondary forests buffer old-growth edge forest (<120 m from an edge) and the influence of secondary forests on fragmentation. In 2020, 41.2% of Amazonian secondary forest was directly adjacent to old-growth forest and 94.1% was within a fragment connected to old growth. However, adjacency and connectedness fell to 20.1% and 57.4% respectively when only considering extensive structurally intact old-growth forest. Secondary forests buffered 41.1% of old-growth edge forest and, when acting as corridors, reduced the total number of old-growth fragments by 2 million. Our results reveal the importance of understanding spatial context when examining the potential benefits of increasing secondary forest cover. Improved understanding of the benefits of locating secondary forests next to old-growth forests could support the development of more effective climate change mitigation and restoration strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".