Spatial patterns of hyperparasitism along a latitudinal gradient of forest genus diversity: insights from the spruce budworm-parasitoids community
Bibliographic record
Abstract
High-order mobile predators are generally thought to increase ecosystem stability and resilience to natural perturbations. In many insect food-webs, higher trophic positions are occupied by parasitoids, which are themselves hosts for hyperparasitoids that can reduce primary parasitoids' efficiency in controlling insect pests. Hyperparasitoids can thus provide ecosystem disservices by facilitating pest outbreaks, or ecosystem services by stabilizing food web fluctuations over longer time periods. To better understand how hyperparasitism affects multitrophic forest systems, we examined for the first time spatial variations in hyperparasitism associated with the spruce budworm. We examined 2 common primary parasitoids of the spruce budworm during outbreaks (Apanteles fumiferanae and Glypta fumiferanae), and estimated their true and pseudohyperparasitism rates in 2014-2015 from 28 locations across a latitudinal gradient (over 450 km) of forest genus diversity. Hyperparasitoid cryptic diversity was also quantified using DNA-barcoding. We found that A. fumiferanae and G. fumiferanae share at least 2 of 5 common hyperparasitoid species, confirming the connected nature of the spruce budworm-parasitoid food web. Moreover, hyperparasitism is modulated by spatial context as we observed a positive correlation between forest genus diversity and hyperparasitism for A. fumiferanae, but not for G. fumiferanae. Further monitoring hyperparasitism holds significant potential to provide new insights into how forest composition affects multitrophic interactions and spatio-temporal outbreak dynamics.
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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 | 0.001 |
| 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".