Data for: Strong effects of food quality on host life history do not scale to impact parasitoid efficacy or life history
Bibliographic record
Abstract
Abstract Parasitoids are small insects, (e.g., small wasps or flies) that reproduce by laying eggs on or within host arthropods. Parasitoids make up a large proportion of the world’s biodiversity and are popular agents of biological control. Idiobiont parasitoids paralyze their hosts upon attack and thus are expected to only target hosts large enough to support offspring development. Host resources generally impact host attributes and life histories including size, development, and life span. Some argue slow host development in response to resource quality increases parasitoid efficacy (i.e., a parasitoid’s ability to successfully reproduce on or within a host) due to longer host exposure to parasitoids. However, this hypothesis is not always supported and does not consider variation in other host traits in response to resources that may be important for parasitoids (e.g., variation in host size is known to impact parasitoid efficacy). In this study, we test whether trait variation within host developmental stages in response to host resources is more important for parasitoid efficacy and life histories than trait variation across host developmental stages. We exposed seed beetle hosts raised on a food quality gradient to mated female parasitoids and measured the number of hosts parasitized and parasitoid life history traits at the scale of host stage- and age-structure. Our results suggest host food quality does not cascade to impact idiobiont parasitoid life histories despite large food quality effects on host life history. Instead, variation in host life histories across host developmental stages better predicts parasitoid efficacy and life histories, suggesting finding a host in a specific instar is more important for idiobiont parasitoids than finding hosts on or within higher quality resources.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.345 | 0.118 |
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".