Ecological specialization and local adaptation in sympatric sexual and asexual grass thrips species
Bibliographic record
Abstract
Abstract The maintenance of sex is difficult to explain in the face of the demographic advantages of asexuality, especially when sexual and asexual lineages co-occur and compete. Here, we test if niche divergence and specialization can contribute to the maintenance of sympatric populations of two closely related, sexual and asexual Aptinothrips grass thrips species. In mesocosm experiments, ecological niches and ecological specialization were inferred from thrips performances on different grass species used as hosts in natural populations. Sexual and asexual thrips performed best on different grass hosts, indicating niche differentiation. The asexual species was also characterized by a broader fundamental ecological niche than the sexual one. However, niche differentiation is unlikely to explain the maintenance of the two species in sympatry because the reproductive rate of asexual females generally exceeded that of sexual ones. Surprisingly, the asexual but not sexual species showed geographic variation in the fundamental niche. This geographic variation likely stems from different clonal assemblages at different locations because different asexual genotypes have different ecological niches. Across natural populations, the performance of asexual females on a specific grass species was further positively correlated with the frequency of that grass species, consistent with adaptation to locally abundant grasses. Altogether, our results suggest that niche differentiation contributes little to the maintenance of grass thrips species with different reproductive modes and that asexuality facilitates adaptation to a diversity of co-occurring host plants.
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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".