Declining interspecific competition during character displacement: Summoning the ghost of competition past
Bibliographic record
Abstract
Prevailing theories of biotic diversification incorporate resource competition as a leading cause of divergence between new species. In support of this, many cases of divergent character displacement between close relatives (congeners) are known. Yet, experimental tests of underlying mechanisms are uncommon. In a pond experiment with threespine sticklebacks (Gasterosteus spp.), we tested the prediction that competition between species should decline as character divergence proceeds, yielding descendants whose present-day interaction is a ‘ghost’ of its former strength. Competition’s impact on the marine threespine stickleback (G. aculeatus) was contrasted between two treatments simulating early and late stages of a hypothesized character displacement series that began at the end of the last ice age when marine sticklebacks colonized lakes containing an earlier descendant. Growth rate and niche specialization of marine sticklebacks were higher in the ‘post-displacement’ treatment than in the ‘pre-displacement’ treatment, suggesting a decline in competition strength through time. The result supports the idea that interspecific competition favoured divergence between sympatric sticklebacks, with reduced competition the outcome. The influence of other interactions on divergence between sympatric species may be tested with analogous experimental designs.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".