IMPACT OF TRAP DESIGN FACTORS AND DEPLOYMENT METHODOLOGY ON THE PERFORMANCE OF SEMIOCHEMICAL-BAITED INTERCEPT TRAPS FOR FOREST Coleoptera.
Notice bibliographique
Résumé
Surveys of forest insect pests attempt to monitor populations by sampling the insect or quantifying the damage they cause. Those surveys that target the adult stage of an insect often use semiochemical-baited flight intercept traps. The development of survey and detection programs for forest insects is currently a reactionary trial-and-error process because of unexplained variation in trap design and deployment effects and a lack of consensus in the literature regarding trap performance among taxa and habitats. This approach is costly both in terms of the time required to develop and optimize survey tools and the amount of damage realized before management programs can be implemented. This talk will focus on: 1) the effect of trap design factors on the abundance and diversity of forest insects captured by intercept traps and potential underlying mechanisms; and 2) the impact of trap deployment protocol on intercept trap performance. Field trapping experiments were used to examine the impact of intercept trap design factors on the abundance of target taxa and the diversity of forest Coleoptera captured. A meta-analysis of the available literature of trap design effects observed similar patterns of trap design effects on the capture of forest Coleoptera. It also observed a significant amount of heterogeneity in the effects of these factors that was only partially explained by variation among guilds and families. To begin to develop a mechanistic understanding of trap design effects a field trapping experiment examined the impact of trap silhouette by comparing captures of forest Coleoptera in white, black and clear intercept traps. Trap silhouette effects varied among taxa; more apparent traps captured more individuals in some but not all species. In an attempt to explain variation in the capture of Cerambycidae among four intercept trap designs, CO2 was used as a surrogate semiochemical and the flow of CO2 from each trap design was measured. Although plume structure differed downwind of the four trap designs, the observed differences in plume structure were not consistent with differences in trap captures. Field trapping experiments demonstrated that trap placement along environmental gradients (both edge-interior and canopy-forest floor) effects trap performance and that effects are variable among species. Although considerable progress has been made in recent years, we still have an incomplete understanding of how trap design and deployment effects vary among forest insect taxa and habitats. Future work should continue to document patterns of effects among taxa and habitats and attempt to determine underlying mechanisms. In the absence of a more complete understanding of patterns of effects and the contributing mechanisms, the development and optimization of survey and detection tools for forest insects will remain a costly trial-and-error process.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».