Evaluating the effectiveness of climate-based seed transfer and assisted migration: a case study of lodgepole pine (Pinus contorta var. latifolia Dougl.) and interior spruce (Picea engelmanii x glauca (Moench) Voss and their hybrids) in western Canada
Bibliographic record
Abstract
Abstract Key message Forest assisted migration applied in combination with a climate-based seed transfer system to two North American tree species: lodgepole pine (Pinus contorta var. latifolia Dougl.) and interior spruce (Picea engelmanii x glauca (Moench) Voss and their hybrids), resulted in increased height growth and a substantially expanded seed deployment area, compared to a geographic-based seed transfer system in British Columbia, Canada. Context Forest assisted migration and climate-based seed transfer (CBST) are two recent innovations that have received significant attention as climate change adaptation strategies, but claims regarding their merits have not been well evaluated. Aims We aim to test the claim that CBST, combined with assisted migration, can provide closer matching of seed source and plantation climate, greater height growth, and wider seedlot deployment area than a conventional geographic-based seed transfer system (GBST). Methods Using transfer functions developed from two comprehensive, wide-ranging provenance trials of lodgepole pine and interior spruce, with populations from across western Canada, we estimated relative tree height growth at rotation and seed deployment area for a large set of simulated seed transfers in a CBST system with and without assisted migration and in a GBST system. Results When assisted migration and CBST were used in combination, volume growth was 13% (lodgepole pine) or 6% (interior spruce) greater at rotation age and deployment area was 2.2 times (lodgepole pine) or 7.3 times (interior spruce) greater than was expected in a GBST system. Height growth increases were primarily associated with assisted migration, whereas increases in seed deployment area were primarily associated with the use of CBST rather than GBST. Conclusion Converting from GBST to CBST in conjunction with assisted migration should substantially improve adaptation of lodgepole pine and interior spruce in British Columbia. This approach will also significantly offset the impacts of climate change on growth rates, increase deployment area, reduce seed collection costs, and provide greater flexibility to seed users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".