Experimental test of predation's effect on divergent selection during character displacement in sticklebacks
Bibliographic record
Abstract
Ecological character displacement is common in nature but the mechanisms causing divergence are not well understood. The contributions of ecological interactions other than competition have received little attention. We conducted a pond experiment to explore the contribution of both competition and predation to character divergence in threespine stickleback species. We estimated the strength of divergent selection on a morphologically intermediate target population between competition treatments under two alternate predation treatments. Divergent selection on the target population tended to be stronger in the predator-addition treatment than in the predator-reduction treatment, a difference that approached significance (P = 0.09). This trend occurred even though competition was strongest in the predator-reduction treatment. Overall, the strength of divergent selection was best predicted by stickleback mortality (P = 0.025) being strongest where mortality was highest. These results indicate that predation and other agents of mortality can enhance the rate of change in competition per unit of phenotypic divergence and, thereby, divergent selection, even as they lower the overall strength of competition. In this way, predation and other agents of mortality may facilitate, rather than hinder, character displacement.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".