The distribution of sexual reproduction of the geographic parthenogen<i>Eucypris virens</i>(Crustacea: Ostracoda) matches environmental gradients in a temporary lake
Bibliographic record
Abstract
Niche segregation may prevent competitive exclusion and promote local coexistence. This typically results in the occupation of different habitats. In the freshwater ostracod Eucypris virens (Jurine, 1820), the distribution of sexual and parthenogenetic populations in the temporary Lake Caracuel, central Spain, was not homogeneous. Parthenogens were found everywhere including the littoral, whereas sexuals were restricted to the centre. We investigated the hypothesis that spatial distribution responded to ecological differences. We studied the ecological significance of this segregation by linking environmental data to male presence, sexual fraction, ploidy, and genetic structure of our model organism in 12 ponds in the lake basin. Hydro-chemical and biological data indicated that the observed segregation is not only spatial but also ecological, with sexual E. virens occurring in ponds with shorter and probably more unpredictable hydroperiod. The correlations between environmental gradients and E. virens population structure suggest that sexual and parthenogenetic lineages are ecologically segregated. Sexual populations appeared restricted to sufficiently unpredictable environments, while parthenogens dominated environments with longer hydroperiods. Local coexistence seems mediated by spatial heterogeneity in habitat stability.
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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".