Maintaining the high diversity of pine and oak species in Mexican temperate forests: a new management approach combining functional zoning and ecosystem adaptability
Bibliographic record
Abstract
Mexican temperate forests, at the southernmost end of the distribution range of this ecosystem, are the world’s centre of diversity of pine and oak, with 55 and 161 species, respectively. Such forests are threatened by land-use change, unsustainable forest management practices, and climate change; these threats reduce their diversity, alter the distribution ranges of species, modify disturbance regimes, and reduce ecosystem adaptability. This paper briefly reviews (i) the ecology of the Mexican temperate forests, (ii) the ecological basis for the unique diversity of pine and oak species, (iii) the main disturbances as well as the main drivers of global changes affecting these forests, in particular climate change, and (iv) the social, economic, and cultural factors to be considered in proposing a new forest management approach. It proposes a new conceptual framework to manage Mexican temperate forests that are in line with (i) their natural dynamics, (ii) the rapidly changing and uncertain global environmental, social, and economic conditions, and (iii) the complex adaptive system approach. This new forest management combines functional zoning, multispecies plantations, and sylvicultural interventions to increase the adaptive capacity of forests as a way to balance the increasing need for timber products with the need for other ecosystem services facing rapidly changing and uncertain future environmental, social, and economic conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".