Multiple stressors drive convergent evolution of performance properties in marine macrophytes
Bibliographic record
Abstract
Extreme environments have driven the evolution of some of the most inspiring adaptations in nature. In the intertidal zone of wave-swept shores, organisms face physical forces comparable to hurricanes and must further endure thermal and desiccation stress during low tides, compromising their physiological and biomechanical performance. We examine how these multiple stressors have influenced the evolution of tissue properties during desiccation using eight phylogenetically independent pairs of intertidal and subtidal macrophytes. Intertidal species generally lost water more slowly than their subtidal counterparts, presumably as an adaption to regular emersion. Under partial desiccation, breaking force, strength, and extensibility of intertidal species generally exceeded those of subtidal species, although important differences existed among phylogenetic pairs. This was often true even when subtidal relatives resisted greater forces or were more extensible under full hydration. The interacting effects of mechanical forces and desiccation during low tide are likely a major selective agent in determining macrophyte performance and fitness. Overall, we found that lineages that have independently evolved to occupy the wave-swept intertidal have converged on performance metrics that are likely to be adaptive to the interacting stressors associated with their extreme niches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".