Assisted migration as a climate change adaptation strategy: lessons from restoration and plant reintroductions
Bibliographic record
Abstract
Climate change is thought to threaten between 25% and 50% of global plant biodiversity. In response to this looming crisis, the calls for the translocation, or assisted migration, of species to ensure the survival of this biodiversity have been increasing. The concept has its detractors, and is not without risk, but few studies have documented the success of the approach or veracity of the risk. Here we review both the ecological restoration literature and the plant reintroduction literature to discover insights into the promises and pitfalls of translocating species as an adaptation strategy in the face of our changing climate. Although habitat restoration and the assisted migration of rare plant species have different objectives and goals, they share the practice of translocating species from their site of origin to a new one. It is primarily the scale at which the movement of those species occurs that distinguishes the two.
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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 | 0.001 | 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) | 0.001 | 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 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".