GENETIC VARIANCE AND COVARIANCE FOR PHYSIOLOGICAL TRAITS IN LOBELIA: ARE THERE CONSTRAINTS ON ADAPTIVE EVOLUTION?
Bibliographic record
Abstract
Physiological traits that control the uptake of carbon dioxide and loss of water are key determinants of plant growth and reproduction. Variation in these traits is often correlated with environmental gradients of water, light, and nutrients, suggesting that natural selection is the primary evolutionary mechanism responsible for physiological diversification. Responses to selection, however, can be constrained by the amount of standing genetic variation for physiological traits and genetic correlations between these traits. To examine the potential for constraint on adaptive evolution, we estimated the quantitative genetic basis of physiological trait variation in one population of each of two closely related species (Lobelia siphilitica and L. cardinalis). Restricted maximum likelihood analyses of greenhouse-grown half-sib families were used to estimate genetic variances and covariances for seven traits associated with carbon and water relations. We detected significant genetic variation for all traits in L. siphilitica, suggesting that carbon-gain and water-use traits could evolve in response to natural selection in this population. In particular, narrow-sense heritabilities for photosynthetic rate (A), stomatal conductance (gs), and water-use efficiency (WUE) in our L. siphilitica population were high relative to previous studies in other species. Although there was significant narrow-sense heritability for A in L. cardinalis, we detected little genetic variation for traits associated with water use (gs and WUE), suggesting that our population of this species may be unable to adapt to drier environments. Despite being tightly linked functionally, the genetic correlation between A and gs was not strong and significant in either population. Therefore, our L. siphilitica population would not be genetically constrained from evolving high A (and thus fixing more carbon for growth and reproduction) while also decreasing gs to limit water loss. However, a significant negative genetic correlation existed between WUE and plant size in L. siphilitica, suggesting that high WUE may be negatively associated with high fecundity. In contrast, our results suggest that any constraints on the evolution of photosynthetic and stomatal traits of L. cardinalis are caused primarily by a lack of genetic variation, rather than by genetic correlations between these functionally related traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".