HOST-RANGE EVOLUTION IN<i>APHIDIUS</i>PARASITOIDS: FIDELITY, VIRULENCE AND FITNESS TRADE-OFFS ON AN ANCESTRAL HOST
Bibliographic record
Abstract
The diversity of parasitic insects remains one of the most conspicuous patterns on the planet. The principal factor thought to contribute to differentiation of populations and ultimately speciation is the intimate relationship parasites share with hosts and the potential for disruptive selection associated with using different host species. Traits that generate this diversity have been an intensely debated topic of central importance to the evolution of specialization and maintenance of ecological diversity. A fundamental hypothesis surrounding the evolution of specialization is that no single genotype is uniformly superior in all environments. This "trade-off" hypothesis suggests that negative fitness correlations can lead to specialization on different hosts as alternative stable strategies. In this study we demonstrate a trade-off in the ability of the parasitoid, Aphidius ervi, to maintain a high level of fitness on an ancestral and novel host, which suggests a genetic basis for host utilization that may limit host-range expansion in parasitoids. Furthermore, behavioral evidence suggests mechanisms that could promote specialization through induced host fidelity. Results are discussed in the context of host-affiliated ecological selection as a potential source driving diversification in parasitoid communities and the influence of host species heterogeneity on population differentiation and local adaptation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".