Response of Northern Populations of Black Spruce and Jack Pine to Southward Seed Transfers: Implications for Climate Change
Bibliographic record
Abstract
A variety of responses to climate change have been reported for northern tree populations, primarily from tree-ring and satellite-based studies. Here we employ provenance data to examine growth and survival responses of northern populations (defined here as those occurring north of 52° N) of black spruce (Picea mariana) and jack pine (Pinus banksiana) to southward seed transfers. This space for time substitution affords insights into potential climate change responses by these important northern tree species. Based on previous work, we anticipated relatively flat response curves that peak at much warmer temperatures than those found at seed source origin. These expectations were generally met for growth-related responses, with peak growth associated with seed transfers to environments with mean annual temperatures 2.2 and 3.6 °C warmer than seed source origin for black spruce and jack pine, respectively. These findings imply that northern tree populations harbor a significant amount of resilience to climate warming. However, survival responses told a different story, with both species exhibiting reduced survival rates when moved to warmer and drier environments. Together with the growth-based results, these findings suggest that the warmer and drier conditions expected across much of northern Canada under climate change may reduce survival, but surviving trees may grow at a faster rate up until a certain magnitude of climate warming has been reached. We note that all relationships had high levels of unexplained variation, underlining the many factors that may influence provenance study outcomes and the challenges in predicting tree responses to climate change. Despite certain limitations, we feel that the provenance data employed here provide valuable insights into potential climate change outcomes for northern tree populations.
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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) | 0.000 | 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".