Challenges of pheromone-based mating disruption of Cydia strobilella and Dioryctria abietella in spruce seed orchards
Bibliographic record
Abstract
Seed orchards function as the primary source of high-quality seeds for reforestation in many European countries, but their seed yields can be severely reduced due to seed- and cone-feeding insects. We evaluated various parameters of pheromone-based mating disruption for control of the moths Cydia strobilella and Dioryctria abietella , which are major pests in European Picea abies seed orchards. We applied different types of pheromone dispensers (rubber septa or wax droplets) at different densities and heights, and with different amounts of active components, covering whole orchards or part of an orchard. The efficacy of the treatment was evaluated by analysing male captures in pheromone-baited assessment traps, and presence of larvae in cones. A dramatic decrease (94–100%) in capture of males in traps occurred in all pheromone-treated plots compared to control plots for both moth species. In contrast, a subsequent reduction in larval numbers in cones was only achieved when wax droplets were used as the dispensing formulation at high density and at the highest pheromone dose tested, and only in half of the trials for each pest species. Electrophysiological recordings using antennae of male C. strobilella indicated elevated pheromone concentrations in a treated plot versus a control plot. Our results show that mating disruption has potential to reduce cone damage in spruce seed orchards caused by C. strobilella and D. abietella , but optimisation of the technique is required to achieve consistent and efficient population suppression of these pests.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".