Assessing the current and future biological control potential of <i>Trichogramma ostriniae</i> on its hosts <i>Ostrinia furnacalis</i> and <scp> <i>Ostrinia nubilalis</i> </scp>
Bibliographic record
Abstract
BACKGROUND: Understanding interactions between biocontrol agents and their pest hosts under climate change should assist implementation of biocontrol strategies, by identifying appropriate biocontrol agents for release or determining the optimal timing of releases. Species distribution models (SDMs) were applied to evaluate the distributions of Trichogramma ostriniae and its native host, Ostrinia furnacalis, in southeastern Asia, and a non-native host, Ostrinia nubilalis, in a novel range, North America, using MAXENT and CLIMEX modelling approaches. RESULTS: The models led to similar predictions about the expected distribution of the two species in Asia, and emphasized likely mismatches between host and natural enemy. Trichogramma ostriniae was predicted to occur in the summer corn region of China, with distribution limits linked to its sensitivity to cold, seasonality of radiation and precipitation. The modelled Ostrinia nubilalis distribution overlapped with the main corn production areas of the northeastern USA and Canada; temporary/seasonal suitable habitat was also predicted across the southeastern USA. Climate change scenarios are predicted to favour T. ostriniae over its hosts in northeastern China and North America. CONCLUSION: The modelling approaches used here proved useful for assessing environmental factors linked to an egg parasitoid and its lepidopteran hosts and identifying areas potentially suitable for inundative releases. © 2017 Society of Chemical Industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".