Adaptive divergence of lateral plate ultrastructure in threespine stickleback (<i>Gasterosteus aculeatus</i>)
Bibliographic record
Abstract
The lateral plates of threespine stickleback ( Gasterosteus aculeatus Linnaeus, 1758) are well studied for their adaptive morphological responses to predators, yet it is unknown whether habitat influences plate ultrastructure. We investigated using scanning electron microscopy the lateral plate ultrastructure (tubercles and ridges) of stickleback ( N = 61 adult fish) from nine Haida Gwaii (coastal British Columbia, Canada) wild-type populations, two experimental transplants, and two lab-reared cohorts reared from source populations. Tubercle density, but not ridge density, differed significantly across habitat types and populations. Among wild-type fish, tubercle densities were greatest in dystrophic habitats containing predatory fish, and lowest in weakly dystrophic systems featuring bird–invertebrate predation and marine populations with diverse predatory fish. No differences in tubercle density were detected between source and transplant populations, despite major habitat shifts. Lab-reared fish exhibited significantly lower tubercle densities than their source populations (less than one generation). Tubercle density differences across habitat types may reflect adaptation to divergent predation regimes, with tooth-bearing predators selecting for denser tubercles that disperse point forces. Conservation of ridge density across populations suggests an essential function in dispersing forces applied to dorsal spines during predator manipulation. Lateral plate ultrastructure in threespine stickleback thus results from both heritable effects and developmental plasticity.
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 | 0.001 | 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".