Partial refuges from biological control due to intraspecific variation in protective host traits: a case study with the egg parasitoid <i>Trissolcus japonicus</i>
Bibliographic record
Abstract
Abstract Predicting how much of a host or prey population may be attacked by their natural enemies is fundamental to several subfields of applied ecology, particularly biological control of pest organisms. Hosts or prey can occupy refuges from natural enemy attack, but habitat or ecological refuges are challenging or impossible to predict in a laboratory setting – which is often where efficacy and specificity testing of candidate biological control agents is done. Here we explore how intraspecific variation in continuous traits that confers some protection from natural enemy attack – even after the natural enemy has encountered the prey – could provide partial refuges. The size of these trait-based refuges should depend on the relationship between trait values and host/prey susceptibility to natural enemy attack, and on how common different trait values are within a host/prey population. These can be readily estimated in laboratory testing of natural enemy impact on target or non-target prey or hosts as long as sufficient host material is available. We provide a general framework for how intraspecific variation in protective host traits could be integrated into biological control research, specifically with reference to non-target testing as part of classical biological control programs. As a case study, we exposed different host clutch sizes of target (pest) and non-target (native species) stink bug (Hemiptera: Pentatomidae) species to a well-studied exotic biocontrol agent, the egg parasitoid Trissolcus japonicus (Hymenoptera: Scelionidae). Although we observed several behavioural and reproductive responses to variation in host egg mass size by T. japonicus , they did not translate to increases in predicted refuge size (proportion host survival) large enough to change the conclusions of non-target testing. We encourage researchers to investigate intraspecific variation in a wider variety of protective host and prey traits and their consequences for refuge size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".