Information Underload: Ecological Complexity, Incomplete Knowledge, and Data Deficits Create Challenges for the Assisted Migration of Forest Trees
Bibliographic record
Abstract
Forest managers are considering assisted migration as a strategy to develop climate-resilient forests. Assisted migration (AM) is the translocation of species or genotypes from their current ranges to areas predicted to have a supportive future climate. Although correlations of species ranges with climate are traditionally used to explore AM potential, successful AM strategies will have to consider many other variables. In this article, we review recent research that informs the complexity of AM decisions. Local microenvironment, herbivory, and intraspecific interactions frequently outweigh climate in determining species establishment and survival. Species adopt several strategies to cope with drought, heat, and cold, but translocated genetic material can display cryptic maladaptation when challenged by extreme, out-of-range weather events. Phenology, photoperiod, and climate are related in complex, often provenance-specific ways. We discuss the implications of our findings for forestry AM and make recommendations for incorporating these insights into future research and AM practice.
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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 | 0.030 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".