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Record W7070547292

Plume interaction and odour source spacing of\npheromone and non-host volatiles : : Behavioural\neffects on bark beetles and moths

2009· other· en· W7070547292 on OpenAlexfundno aff

Bibliographic record

VenueEpsilon Archive for Student Projects (University of Southampton) · 2009
Typeother
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
FundersHigher Education Commision, PakistanSvenska Forskningsrådet FormasAlberta Agricultural Research Institute
KeywordsPlumePheromoneSpodoptera littoralisSex pheromoneBark (sound)
DOInot available

Abstract

fetched live from OpenAlex

Insects have a remarkable ability to sense whether odours are released from one point source or from two closely separated ones. Therefore it is of interest to study the interactions of pheromone component odour plumes to affect the insect behaviour when the release points of\ndifferent components are physically separated. It helps us to understand the mechanism that insects use to find their host or any attractive source. We studied the behavioural response of Spodoptera littoralis (Lepidoptera, Noctuidae) males, in a walking bioassay, to two components of the female sex pheromone blend, (9Z,11E-tetradecadienyl acetate) and(9Z,12E-tetradecadienyl acetate), released from two separate dispensers at various distances (0-5 cm) from each other. The main pheromone component, 9Z-11E-14:OAc was tested at 1 and 10 ng in combination with the minor component 9Z-12E-14:OAc, at 1 % of the main\ncomponent. The low dose always showed a lower behavioural response than the high dose at the same separation distances (0-5 cm). When low doses were used, the response was higher at 0 and 3 cm separation than other separations whereas when high doses were used there was not much difference in response at 0, 2, and 3 cm separations. A Photo Ionization Detector(PID) was used to investigate plume width and overlap. The PID showed that 5 cm separation\nof odour sources was enough to totally separate the odour plumes from each other, whereas at 3 cm separation, plumes partially overlapped. By comparing the behavioural results with PID data it is concluded that insects are much more sensitive than the PID, since they responded at\ndistances where the PID showed zero ppb. Male insects may respond well to doses that are 20 times lower than one female equivalent. A field experiment for S. litura was done in Pakistan on cotton crop by spacing sex pheromone components. Pheromone components were spaced\nhorizontally at 0, 3, 5 and 15 cm on plastic moth pheromone traps. The high dose attracted more males than the medium and low dose. Males were highly attracted at 0 cm separation\nbut spacing decreased the catch and at 15 cm spacing there was no catch. The behavioural effect of separating ph. components was further studied on the spruce bark beetle, Ips typographus L., in the field. The attraction of I. typographus to traps baited with the two\naggregation pheromone components (cis-verbenol and 2-metyl-3-buten-2-ol) separated, was investigated in a Norway spruce clear-cut. The pheromone components were separated both vertically (0-112 cm) on extended Lindgren (19 funnel) traps and horizontally (0-80 cm) on modified windvane traps. I. typographus was strongly attracted when the two components were released from the same point but spacing between components decreased the trap catch. However, at 16 cm distance, in both the vertical and horizontal test, the trap catch was not much different from the positive control (0 cm separation). The effect of odour source spacing in the field on I. typographus was also studied with regards to repellent non-host volatiles (NHV). In this test, the aggregation pheromone were separated from a blend of\nrepellents (trans-conopthorin, 1,8-cineol, 3-octanol, 1-octen-3-ol, 1-hexanol and verbenone) using the same experimental design. NHV showed strong inhibitory effect up to 48 cm spacing but there was not much effect at 80 and 112 cm separations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.271
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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