Quantifying active and passive restoration in Central Mexico from 1986–2012: assessing the evidence of a forest transition
Bibliographic record
Abstract
Recent land cover analysis reveals significant forest recovery around the world, suggesting that some countries may be in a forest transition. However, remotely sensed imagery does not reveal the driving causes of forest recovery, which may be due to active reforestation efforts or natural successional processes (passive reforestation). We aimed to distinguish these two processes in the priority temperate forests surrounding the Monarch Butterfly Biosphere Reserve (342,773 ha) in central Mexico. We combine an analysis of remotely sensed imagery with field interviews ( n = 43) to examine the mechanisms and drivers of observed forest recovery. Our analysis of the satellite imagery revealed a net increase of 3,798 ha of forest between 1986 and 2012, yet the rate of recovery is slowing. Our interview data revealed that the vast majority of the recovered forests are the result of natural regrowth (passive reforestation), and most of this regrowth is observed on previously degraded forest lands. We estimate that between 58 and 429 ha have been recovered from active reforestation efforts in the 1986–2012 period. We find that reduced logging and grazing pressures are important drivers of forest recovery, while agricultural abandonment may be less influential than often believed. Our results suggest that reforestation investments might be wisely spent supporting and maintaining the natural resilience of forests rather than on costly reforestation programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".