Molecular biogeography and host relations of a parasitoid fly
Bibliographic record
Abstract
Abstract Successful geographic range expansion by parasites and parasitoids may also require host range expansion. Thus, the evolutionary advantages of host specialization may trade off against the ability to exploit new host species encountered in new geographic regions. Here, we use molecular techniques and confirmed host records to examine biogeography, population divergence, and host flexibility of the parasitoid fly, Ormia ochracea (Bigot). Gravid females of this fly find their cricket hosts acoustically by eavesdropping on male cricket calling songs; these songs vary greatly among the known host species of crickets. Using both nuclear and mitochondrial genetic markers, we (a) describe the geographical distribution and subdivision of genetic variation in O. ochracea from across the continental United States, the Mexican states of Sonora and Oaxaca, and populations introduced to Hawaii; (b) demonstrate that the distribution of genetic variation among fly populations is consistent with a single widespread species with regional host specialization, rather than locally differentiated cryptic species; (c) identify the more‐probable source populations for the flies introduced to the Hawaiian islands; (d) examine genetic variation and substructure within Hawaii; (e) show that among‐population geographic, genetic, and host song distances are all correlated; and (f) discuss specialization and lability in host‐finding behavior in light of the diversity of cricket songs serving as host cues in different geographically separate 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 | 0.001 | 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".