Climate change and forest seed zones: Past trends, future prospects and challenges to ponder
Bibliographic record
Abstract
Canada regenerates more than 400 000 ha of forest land annually through planting and seeding operations. Much of the stock for this effort is selected to be climatically suited to the planting site—a match that is often facilitated through the development of seed zones. However, if climate change proceeds as predicted, stock that is well matched under current climate will be growing in sub-optimal conditions within the next 20 to 50 years—in some parts of the country, trees may already be growing outside their optimal climates. To provide a sense of the magnitude of these changes, we present past and predicted future climate trends for Ontario and British Columbia seed zones. For Ontario, over the period 1950 to 2005, minimum temperature of the coldest month has already increased by up to 4.3°C, growing season has lengthened by up to 6 days, and precipitation during the growing season has increased by up to 26%. Changes were more pronounced across British Columbia’s Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) seed zones, with minimum temperature increasing by up to 8°C, a growing season extension of up to 30 days, and growing season precipitation increases of up to 40%. Projections for the end of the current century include: minimum temperature increase of 5°C to 10°C, growing season extension of 31 to 60 days, and growing season precipitation increases of 3% to 42% across the seed zones in both provinces. These changes are certain to have extensive impacts on forest ecosystems. We briefly discuss 3 forest management adaptation strategies intended to mitigate the negative impacts of climate change in Canada. Key words: climate change, seed zones, British Columbia, Ontario, Douglas-fir, seed transfer, assisted migration
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".