Niche Specialization Influences Adaptive Phenotypic Plasticity in the Threespine Stickleback
Bibliographic record
Abstract
Phenotypic plasticity may be favored in generalist populations if it increases niche width, even in temporally constant environments. Phenotypic plasticity can increase the frequency of extreme phenotypes in a population and thus allow it to make use of a wide resource spectrum. Here we test the prediction that generalist populations should be more plastic than specialists. In a common-garden experiment, we show that solitary, generalist populations of threespine sticklebacks inhabiting small coastal lakes of British Columbia have a higher degree of morphological plasticity than the more specialized sympatric limnetic and benthic species. The ancestral marine stickleback showed low levels of plasticity similar to those of sympatric sticklebacks, implying that the greater plasticity of the generalist population has evolved recently. Measurements of wild populations show that those with mean trait values intermediate between the benthic and limnetic values indeed have higher morphological variation. Our data indicate that plasticity can evolve rapidly after colonization of a new environment in response to changing niche use.
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 | 0.001 |
| 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".