Whitebark pine (<i>Pinus albicaulis</i>) assisted migration potential: testing establishment north of the species range
Bibliographic record
Abstract
The translocation of species into habitable locations outside of their current ranges, termed assisted migration, has been proposed as a means of saving vulnerable species from extinction as a result of climate change. We explore the use of this controversial technique using a threatened keystone species in western North America, whitebark pine (Pinus albicaulis), as a case study. Species distribution models predict that whitebark pine will be extirpated from most of its current range as temperatures rise over the next 70 years. However, the same models indicate that a large area within northwestern British Columbia, Canada, is climatically suitable for the species under current conditions and will remain so throughout the 21st century. To test the capacity of whitebark pine to establish relative to climatic and habitat features within its predicted climatic range, we planted seeds from seven populations in eight locations spanning from 600 km southeast to 800 km northwest of the northern boundary of the current species range. During the first three growing seasons, germination occurred in all locations. Nearly three times as many treated (induced maturation and broken dormancy) than untreated seeds germinated, and most treated seeds germinated a year earlier than the untreated seeds. Germination, survival, and growth were primarily influenced by seed mass, site climate conditions related to the duration of snow cover, and provenance temperature. Our experiment provides a preliminary test of models predicting the existence of climatically suitable whitebark pine habitat north of the current species ranges. More broadly, our techniques and results inform the development of scientific guidelines for assisting the migration of other species that are highly threatened by climate change. Applied case studies of this kind are critical for assessing the utility of species distribution models as conservation planning tools.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".