Data and code from: Using landscape genomics to define species distributions, delineate seed zones, and predict genomic offset to future climate for the interior spruce hybrid complex (<em>Picea glauca, Picea engelmannii</em>, and their hybrids
Bibliographic record
Abstract
Understanding how tree species adapt to climate is crucial for forest management under climate change. This study employs landscape genomics to investigate climate adaptation in the interior spruce complex (Picea glauca, P. engelmannii, and their hybrids) across western Canada. Using gradient forest modeling with 41,253 SNPs genotyped in 1692 natural interior spruce individuals from 252 populations, we identified winter temperature and moisture-related variables as key drivers of genomic variation. Both adaptive and neutral genetic variation showed similar patterns along climatic gradients, suggesting that population structure largely follows environmental clines. We delineated 11 seed zones based on genomic variation and climate relationships, achieving 88.2% concordance with previous genetic studies in defining species boundaries. Analysis of genetic offsets under future climate scenarios revealed potential risks of maladaptation, particularly in northern and eastern British Columbia. These predictions were validated using fitness-related measurements from common-garden experiments, which showed negative correlations between genetic offsets and traits such as height and diameter at breast height (DBH). Maladaptation predictions based on genetic offsets showed some differences compared with common-garden-based assessments in several regions, offering new perspectives on population vulnerability. Our results demonstrate the effectiveness of landscape genomics as a complementary approach to traditional methods for assessing climate adaptation in tree species. Our findings provide a scientific foundation for climate-smart reforestation strategies and offer practical guidance for forest management in the context of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.071 |
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".