Geographic variation in growth response of Douglas‐fir to interannual climate variability and projected climate change
Bibliographic record
Abstract
Abstract We used 179 tree ring chronologies of Douglas‐fir [Pseudotsuga menziesii(Mirb.) Franco] from the International Tree‐Ring Data Bank to study radial growth response to historical climate variability. For the coastal variety of Douglas‐fir, we found positive correlations of ring width with summer precipitation and temperature of the preceding winter, indicating that growth of coastal populations was limited by summer dryness and that photosynthesis in winter contributed to growth. For the interior variety, low precipitation and high growing season temperatures limited growth. Based on these relationships, we chose a simple heat moisture index (growing season temperature divided by precipitation of the preceding winter and current growing season) to predict growth response for the interior variety. For 105 tree ring chronologies or 81% of the interior samples, we found significant linear correlations with this heat moisture index, and moving correlation functions showed that the response was stable over time (1901–1980). We proceeded to use those relationships to predict regional growth response under 18 climate change scenarios for the 2020s, 2050s, and 2080s with unexpected results: for comparable changes in heat moisture index, the most southern and outlying populations of Douglas‐fir in Mexico showed the least reduction in productivity. Moderate growth reductions were found in the southern United States, and strongly negative response in the central Rocky Mountains. Growth reductions were further more pronounced for high than for low elevation populations. Based on regional differences in the slope of the growth–climate relationship, we propose that southern populations are better adapted to drought conditions and could therefore contain valuable genotypes for reforestation under climate change. The results support the view that climate change may impact species not just at the trailing edges but throughout their range due to genetic adaptation of populations to local environments.
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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".