Restructuring tree provenance test data to conform to reciprocal transplant experiments for detecting local adaptation
Bibliographic record
Abstract
Summary Local adaptation is a fundamental assumption in delineating seed zones and developing seed transfer guidelines to safeguard climatic adaptation of tree and plant species during forest regeneration and ecological restoration. It is also broadly assumed for forest tree species that show genetic differentiation among geographic populations, especially for those with widespread natural distributions that occur in distinct environments. However, due to a scarcity of suitable data, the inference of local adaptation has rarely been validated for forest tree species through reciprocal transplant experiments (RTEs). In this study, we illustrate a novel approach to restructure tree provenance test data to conform to RTEs and use recently proposed statistical models to detect local adaptation, using white spruce Picea glauca (Moench) Voss as an example. Our research demonstrates how similar studies can be conducted to validate local adaptation in other forest species and populations, for which RTEs are lacking, but where large, high‐quality provenance test data sets are available. Contrary to common belief, our results show that local adaptation is absent in survival and height for white spruce populations from Ontario, Canada, although they have evolved in considerably different climatic habitats, and exhibit substantial and clinal genetic differentiation and significant genotype‐by‐environment interactions in these two adaptive traits. Synthesis and applications. Our results show that the common assumption of local adaptation in forest tree species may not necessarily be correct within significant portions of their natural range. In forest genetic studies, population differentiation in adaptive traits has often been attributed to local adaptation without rigorous validation. Our results caution against such interpretation of experimental results. In the absence of local adaptation as shown by reciprocal transplant experiments, assisted migration may be considered as an option for enhancing forest adaptation to climate change.
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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 | 0.064 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".