Ecosystem size shapes antipredator trait evolution in estuarine threespine stickleback
Bibliographic record
Abstract
Ecosystem size is known to influence both community structure and ecosystem processes. Less is known about the evolutionary consequences of ecosystem size. A few studies have shown that ecosystem size shapes the evolution of trophic diversity by shaping habitat heterogeneity, but the effects of ecosystem size on antipredator trait evolution have not been explored. Ecosystem size may impact antipredator trait evolution by shaping predator presence (larger ecosystems have longer food chains) and habitat complexity (larger ecosystems may have more diverse habitat structure). We tested these effects using threespine stickleback from bar‐built estuaries along the Central Coast of California. These stickleback populations are polymorphic for Ectodysplasin‐A ( Eda ), a gene that controls bony lateral plates used as antipredator defense. We inferred Eda genotypes from lateral plate phenotypes and show that the frequency of the complete (C) allele, which is associated with greater number of lateral plates, increases as a function of ecosystem size. Predator presence and habitat complexity are both correlated to ecosystem size. The strongest proximate predictor of Eda allele frequencies was the presence of predatory fishes (steelhead trout and sculpin). Counter to expectations, habitat complexity did not have a strong modifying effect on Eda allele frequencies. Our results point to the importance of ecosystem size for determining predator presence as being the primary pathway to evolutionary effects. Ecosystem size has received much attention in ecology. Our work shows that it may be an important determinant of adaptive evolution in wild populations.
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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 | 0.000 |
| 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".