Ecology of Adaptive Peak Shifts in Alaskan Threespine Stickleback (Gasterosteus aculeatus)
Bibliographic record
Abstract
Divergent natural selection is a major cause of phenotypic differentiation among populations exploiting different environments, but information on the ecological factors contributing to peak shift is largely missing from natural populations. Threespine stickleback (Gasterosteus aculeatus) is an emerging vertebrate model for studying phenotype-environment associations, as ancestral marine populations have adapted independently to postglacial freshwater environments. I characterized antipredator, foraging, and body shape phenotypes of 800+ fish from 16 ecologically diverse sites on the Alaska Peninsula. Gill rakers, antipredator traits, and body shape significantly associated with lake ecology, whereas foraging traits and body shape were influenced by geography. Stickleback from lakes ecologically similar to the ancestral state were more phenotypically similar to marine-influenced populations than fish from ecologically divergent habitats (i.e., small lakes). My study elucidates mechanisms associated with adaptive evolution and is one of relatively few that links ecological features of the adaptive landscape with phenotypic evolution in multiple populations.
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".