Role of population genetics in guiding ecological responses to climate
Bibliographic record
Abstract
Abstract Population responses to climate were assessed using 3–7 years height growth data gathered for 266 populations growing in 12 common gardens established in the 1980s as part of five disparate studies ofPinus contortavar.latifolia. Responses are interpreted according to three concepts: the ecological optimum, the climate where a population is competitively exclusive and in which, therefore, it occurs naturally; the physiological optimum, the climate where a population grows best but is most often competitively excluded; and growth potential, the innate capacity for growth at the physiological optimum. Statistical analyses identified winter cold, measured by the square root of negative degree‐days calculated from the daily minimum temperature (MINDD01/2), as the climatic effect most closely related to population growth potential; the colder the winter inhabited by a population, the lower its growth potential, a relationship presumably molded by natural selection. By splitting the data into groups based on populationMINDD01/2and using a function suited to skewed normal distributions, regressions were developed for predicting growth from the distance in climate space (MINDD01/2) populations had been transferred from their native location to a planting site. The regressions were skewed, showing that the ecological optimum of most populations is colder than the physiological optimum and that the discrepancy between the two increases as the ecological optimum becomes colder. Response to climate change is dependent on innate growth potential and the discrepancy between the two optima and, therefore, is population‐specific, developing out of genotype‐environment interactions. Response to warming in the short‐term can be either positive or negative, but long term responses will be negative for all populations, with the timing of the demise dependent on the amount of skew. The results pertain to physiological modeling, species distribution models, and climate‐change adaptation strategies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".