FREQUENCY DEPENDENT NATURAL SELECTION DURING CHARACTER DISPLACEMENT IN STICKLEBACKS
Bibliographic record
Abstract
We know little about how natural selection on a species is altered when a closely related species consuming similar resources appears in its environment. In a pond experiment with threespine sticklebacks I tested the prediction that divergent natural selection between competitors is frequency-dependent, changing with the distribution of phenotypes in the environment. Differential growth and survival of phenotypes in a target stickleback population were contrasted between two treatments. In one treatment an offshore zooplankton feeder (the limnetic stickleback species) was added to the same pond as the target. In the other treatment I added the benthic stickleback instead, a species adapted to feeding on invertebrates from sediments and inshore vegetation. The target population was ecologically and morphologically intermediate with phenotypic variance artificially inflated by hybridization. Growth rates of phenotypes within the target population differed between treatments as predicted by character displacement. The impact of adding a second species always fell most heavily on those phenotypes in the target population resembling the added species most closely. However, those individuals in the target population that most resembled the added species did not experience reduced survival. Instead, consistent survival differences between populations suggested the presence of an inshore-offshore gradient in mortality risk. These results provide further support for the hypothesis of character displacement in sympatric sticklebacks. They suggest that displacement along the resource gradient also led to divergence in vulnerability to agents of mortality, probably including predation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".