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Enregistrement W6950576491 · doi:10.5281/zenodo.8331197

Developing and assessing surveillance methodologies for Agrilus beetles

2023· article· en· W6950576491 sur OpenAlexaffabout

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueOceanographic and Atmospheric Processes
Établissements canadiensCanadian Forest Service
Organismes subventionnairesnon disponible
Mots-clésLonghorn beetleBiological dispersalAgrilusEmerald ash borerInvasive speciesBuprestidaeAbiotic componentLarva

Résumé

récupéré en direct d'OpenAlex

The jewel beetle genus Agrilus (Family Buprestidae) has over 3000 species (Kelnarova et al., 2019), all of which are strictly phytophagous, with adults feeding on leaves, and their larvae feeding on the living subcortical tissues of trees and shrubs. Larval feeding can be sufficient to kill a host, especially when it has already been weakened by other abiotic (e.g. drought), and/or biotic (e.g. defoliation) factors (Kelnarova et al., 2019 and references therein). Furthermore, Agrilus species have a proven invasive potential facilitated by their relatively long-lived larvae that are readily transported within nursery plants and wood products (firewood, wood packaging material etc.), whilst adult beetles have good dispersal ability through active flight periods (Kelnarova et al., 2019). As a consequence, some Agrilus species have become important invasive pests after being accidentally introduced into new geographic areas leading to wide ranging environmental, economic and social impacts. Hence, Agrilus beetles constitute a high-risk group of invasive pests, comparable to both longhorn (Cerambycidae) and bark beetles (Curculionidae: Scolytinae and Platypodinae) and should be considered a priority group when developing early detection and surveillance programmes. With the exception of emerald ash borer (Agrilus planipennis), there is relatively little information published within the scientific literature on surveillance and monitoring protocols for the wood-boring beetles of the Agrilus genus. However, across Europe and North America there have been scattered trials and research projects undertaken in the past decade, along with anecdotal evidence of current ongoing research programmes that have started to investigate methodologies for capturing and assessing Agrilus species in a variety of contexts. This Euphresco project aimed to consolidate the European/North American studies that have been conducted, and with collaboration from North American researchers start to develop monitoring tools for either specific Agrilus species (e.g. A. anxius, A. bilineatus, A. biguttatus, A. auroguttatus), and/or develop a more generic trapping technique for this group of wood-boring insects. As well as gathering together the current knowledge on available trapping/monitoring techniques employed for Agrilus species, we encouraged collaborators to evaluate trap designs with and without volatile lures in a variety of forest/woodland settings to assess the efficiency and species diversity of captures. The main objectives of the project were: Collate and report evidence from previous European and North American Agrilus species surveillance and monitoring studies. Consolidate information on current protocols implemented in national surveillance and monitoring programmes for Agrilus beetles. Contribute to designing and evaluating species-specific and generic Agrilus trapping techniques. Validate detection methods to determine specific Agrilus species presence; potential lures and traps will be deployed and assessed to effectively trap native and invasive Agrilus species and allow early detection by deployment at high-risk sites. There is mounting evidence of introductions of Agrilus beetles into new geographic areas, hence there is a real need to develop early detection and monitoring approaches for intercepting this group of wood-boring beetles. In North America there have been at least 12 non-native Agrilus species that have been accidentally introduced and which have subsequently established (Digirolomo et al., 2019), with emerald ash borer (Agrilus planipennis) being the most infamous. Similarly, emerald ash borer has also invaded and established in Europe, in both Russia (Baranchikov et al., 2008) and Ukraine (Drogvalenko et al., 2019). A North American species of Agrilus, the two-lined chestnut borer (Agrilus bilineatus), has also been introduced, and likely established, in Turkey (Hizal & Arslangündoğdu, 2018; EPPO 2020). The North American bronze birch borer (Agrilus anxius) is – like emerald ash borer – regulated as a priority pest in the EU (Commission Delegated Regulation (EU) 2019/1702). As the Agrilus genus has over 3000 known species there is somewhat of an inevitability that in response to the ever-expanding global trade in resources and commodities, and changing climate patterns, there will be an increase in the frequency with which Agrilus spp. will be intercepted in new locations around the world. Hence, understanding what trapping approaches could be utilised for detecting these wood-boring insects and monitoring their spread is a vital first step in establishing national invasive insect monitoring programmes. Both the USA and Canada, along with several European countries already have ongoing research and monitoring activities underway concerning several species of buprestids with an emphasis on Agrilus beetles, so the current project is an opportunity to consolidate and assess the variety of approaches that may be used to detect and monitor for this large family of wood-boring beetles. With a significant emphasis on conducting fieldwork, the results of the field trials, conducted over the two years of the project, should lead to research outputs that contribute to developing best practice guidelines for early detection methodologies, and surveillance and monitoring strategies for the buprestids as a whole, and for specific Agrilus species.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,001
Études des sciences et des technologies0,0000,000
Communication savante0,0020,002
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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.

Tête enseignante Opus0,102
Tête enseignante GPT0,294
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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