Survival of the weakest: increased frond mechanical strength in a wave‐swept kelp inhibits self‐pruning and increases whole‐plant mortality
Bibliographic record
Abstract
Summary Organisms' ability to withstand the physical forces of their environment is a key determinant of their success. Mechanical performance of organisms is often associated with the properties of the tissues that compose them. In mechanically stressful habitats, intraspecific variation in tissue properties may result in differential survivorship and enable natural selection to act on material performance. We tested the hypothesis that tissue mechanical properties affect survivorship (a fitness component) of the perennial kelp, Egregia menziesii, in a mechanically stressful, wave‐swept intertidal habitat. We measured intraspecific variation in strength and flexibility in 38 E. menziesii and tracked their survivorship in the field over the winter storm season to determine whether variation in mechanical properties led to differential survivorship. Significant interindividual variation was found in most mechanical properties, including strength and flexibility. Individuals with increased flexibility and decreased strength were more likely to survive the duration of our study, although this effect was more pronounced in individuals with smaller holdfasts. Increased frond strength was also associated with a reduction in self‐thinning, potentially explaining the observed increase in whole‐plant mortality with increasing frond strength. Results from this study demonstrate that variation in tissue mechanical properties among conspecifics can influence survivorship and this may have important evolutionary implications.
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".