Assisted Migration: Adapting Forest Management to a Changing Climate
Bibliographic record
Abstract
Forestry practitioners are increasingly interested in how to adapt practices to accommodate predicted changes in climate. One forest management option involves helping tree species and seed sources (populations) track the movement of their climates through “assisted migration”: the purposeful movement of species to facilitate or mimic natural population or range expansion. In this paper, we discuss assisted migration as a climate change adaptation strategy within forest management. Substantial evidence suggests that most tree species will not be able to adapt through natural selection or migrate naturally at rates sufficient to keep pace with climate change, leaving forests susceptible to forest health risks and reduced productivity. We argue that assisted migration is a prudent, proactive, inexpensive strategy that exploits finely tuned plant-climate adaptations wrought through millennia of natural selection to help maintain forest resilience, health and productivity in a changing climate. Seed migration distances being considered in operational forestry in British Columbia are much shorter than migration distances being contemplated in many conservation biology efforts and are informed by decades of field provenance testing. Further,only migrations between similar biogeoclimatic units are under discussion. These factors reduce considerably the risk of ecological disturbance associated with assisted migration. To facilitate the discussion of assisted migration, we present three forms of assisted migration, and discuss how assisted migration is being considered internationally, nationally, and provincially. Finally, we summarize policy and research needs and provide links to other resources for further reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".