Field sampling of fig pollinator wasps across host species and host developmental phase: Implications for host recognition and specificity
Bibliographic record
Abstract
Abstract Previous genetic studies of pollinator wasps associated with a community of strangler figs (Ficus subgenus Urostigma, section Americana) in Central Panama suggest that the wasp species exhibit a range in host specificity across their host figs. To better understand factors that might contribute to this observed range of specificity, we used sticky traps to capture fig‐pollinating wasp individuals at 13 Ficus species, sampling at different phases of the reproductive cycle of the host figs (e.g., trees with receptive inflorescences, or vegetative trees, bearing only leaves). We also sampled at other tree species, using them as non‐Ficus controls. DNA barcoding allowed us to identify the wasps to species and therefore assign their presence and abundance to host fig species and the developmental phase of that individual tree. We found: (1) wasps were only very rarely captured at non‐Ficus trees; (2) nonetheless, pollinators were captured often at vegetative individuals of some host species; (3) overwhelmingly, wasp individuals were captured at receptive host fig trees representing the fig species from which they usually emerge. Our results indicate that wasp occurrence is not random either spatially or temporally within the forest and across these hosts, and that wasp specificity is generally high, both at receptive and vegetative host trees. Therefore, in addition to studies that show chemicals produced by receptive fig inflorescences attract pollinator wasps, we suggest that other cues (e.g., chemicals produced by the leaves) can also play a role in host recognition. We discuss our results in the context of recent findings on the role of host shifts in diversification processes in the Ficus genus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".