Specialization of trophic position and habitat use by sticklebacks in an adaptive radiation
Bibliographic record
Abstract
Divergence in habitat use among closely related species is a common characteristic of adaptive radiations. Large differences in the size structure of prey between habitats could strengthen disruptive selection on generalist predators and lead to a divergence in trophic position among species in an adaptive radiation. Using threespine stickleback (Gasterosteus aculeatus) in freshwater lakes as a model system, we examined whether divergence in habitat use coincides with shifts in trophic position. We examined the habitat use and trophic position of individual sticklebacks from divergent lake environments that have only one stickleback species (allopatric lakes) and from lakes that have a pair of benthic and limnetic stickleback species (sympatric lakes). In two sympatric lakes, the limnetic species had a higher trophic position than the benthic species, and in both allopatric and sympatric lakes, sticklebacks specializing on pelagic prey had a higher trophic position for a given size than sticklebacks specializing on benthic prey. Furthermore, the trophic position of pelagic specialists was correlated with individual variation in their gill raker length. Our results indicate that gill raker length is an important trait that underlies differentiation in both habitat use and trophic position among stickleback species, populations, and individuals.
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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 | 0.000 | 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".