Mexican agricultural frontier communities differ in forest dynamics with consequences for conservation and restoration
Bibliographic record
Abstract
Abstract Forest regrowth is key to achieve restoration commitments, but a general lack of understanding when it occurs and how long secondary forests persist hampers effective upscaling. We quantified spatiotemporal forest dynamics in a recently colonized agricultural frontier in southern Mexico, and tested how temporal variation in climate, and cross‐community variation in land ownership, land quality and accessibility affect forest disturbance, regrowth and secondary forest persistence. We consistently found more forest loss than regrowth, resulting in a net decrease of 45% forest cover (1991–2016) in the study region. Secondary forest cover remained relatively constant while secondary forest persistence increased, suggesting that farmers are moving away from shifting cultivation. Temporal variation in disturbance was explained by annual variation in climatic variables and key policy and market interventions. We found large differences in forest characteristics across communities, and these were explained by differences in land ownership and soil quality. Forests were better conserved on communal land, while secondary forest was more persistent when farms were larger and soil quality is better. At the pixel‐level both old forest and secondary forests were better represented on low‐quality lands indicating agricultural concentration on productive land. Both old forest and secondary forest were less common close to the main road, where secondary forests were also less persistent. We demonstrate the suitability of timeseries analyses to quantify forest disturbance and regrowth and we analyse drivers across time and space. Communities differ in forest dynamics, indicating different possibilities, needs and interests. We warrant that stimulating private land ownership may cause remaining forest patches to be lost and that conservation initiatives should benefit the whole community. Forest regrowth competes with agricultural production and ensuring farmers have access to restoration benefits is key to restoration success.
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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 | 0.000 |
| 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".