Evolution of ontogenetic niches promotes species coexistence in a surprising way
Bibliographic record
Abstract
Animals usually change their trophic niche during their ontogeny, which has fundamental consequences for their population dynamics and interactions with other species. Theory predicts that ontogenetic niche differences between species can influence their ability to coexist. However, we lack empirical evidence for this coexistence mechanism and the role of evolution in shaping species' ontogenetic niches. Here, Anaya-Rojas et al. (2023) show that contemporary evolution of ontogenetic niches likely contributes to the coexistence of two competing fish species (killifish and guppies) in streams on the Caribbean Island of Trinidad. As predicted by coexistence theory, they found that the weaker competitor (killifish) exhibited a relatively large ontogenetic niche shift, feeding at higher trophic levels as it grew, in streams where competition with the stronger competitor (guppies) was intense. Intuition suggests that the weaker competitor should experience strong selection on its ontogenetic niche in a different competitive environment, but this was not the case. Instead, they found that the stronger competitor evolved a more compressed ontogenetic niche, where guppies fed at a low trophic level regardless of their body size, when competition was intense. Although the mechanism underlying this surprising result remains to be determined, this work points to the importance of taking a food web perspective-explicitly accounting for consumer-resource interactions-to understand the outcome of eco-evolutionary dynamics. Given that ontogenetic niche shifts are extremely common in animals, understanding the evolutionary ecology of these niche shifts should be a priority for future research on species coexistence.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".