Interspecific competition alters natural selection on shade avoidance phenotypes in<i> Impatiens capensis</i>
Bibliographic record
Abstract
Shade avoidance syndrome is a known adaptive response for Impatiens capensis growing in dense intraspecific competition. However, I. capensis also grow with dominant interspecific competitors in marshes. Here, we compare the I. capensis shade-avoidance phenotypes produced in the absence and presence of heterospecific competitors, as well as selection on those traits. Two treatments were established in a marsh; in one treatment all heterospecifics were removed, while in the other, all competitors remained. We compared morphological traits, light parameters, seed output and, using phenotypic selection analysis, examined directional and nonlinear selection operating in the different competitive treatments. Average phenotypes, light parameters and seed production all varied depending on competitive treatment. Phenotypic selection analyses revealed different directional, disruptive, stabilizing and correlational selection. The disparities seen in both phenotypes and selection between the treatments related to the important differences in elongation timing depending on the presence of heterospecifics, although environmental covariances between traits and fitness could also contribute. Phenotypes produced by I. capensis depend on their competitive environment, and differing selection on shade-avoidance traits between competitive environments could indirectly select for increased plasticity given gene flow between populations in different competitive contexts.
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 | 0.000 | 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) | 0.001 | 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".