Adapting the patch-cut system to implement forest assisted migration
Bibliographic record
Abstract
As the Anthropocene tightens its grip on the world, forests are facing escalating disturbance rates, tree mortality, degradation and risks of catastrophic collapses. A popular and controversial proposition is to enhance forests’ response diversity by adding novel tree species with missing functional traits through forest assisted migration (FAM). Beyond tests of the survival and growth of southern species or provenances in colder regions and studies of the socio-ecological challenges facing FAM, little interest has been paid to the silvicultural system for FAM implementation. Yet, the topic could influence its biological success, social acceptability, and economic feasibility. For example, southern light-intolerant tree species introduced into northern uneven-aged forests may experience a lack of light availability. Likewise, implementing FAM in clearcuts raises social acceptability issues. The patch-cut system combines advantages of even- and uneven-aged systems useful for FAM; however, perhaps due to the difficulty of its operationalization, it is rarely used. We propose a new way to implement the patch-cut system, enabling from the get-go to plan the location and timing of treatment of each patch in a stand. We discuss the advantages that this revisited patch-cut system presents for FAM: (i) the testing of various planting environments, (ii) easy monitoring in an adaptative management context where each patch is a replicate of a repeated-measure experiment and (iii) low intensity planting for efficient future dispersion of species adapted to a changing climate. We end with a call for the development of an international network of FAM trials within the revised patch-cut system.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".