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Record W6950576491 · doi:10.5281/zenodo.8331197

Developing and assessing surveillance methodologies for Agrilus beetles

2023· article· en· W6950576491 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsCanadian Forest Service
Fundersnot available
KeywordsLonghorn beetleBiological dispersalAgrilusEmerald ash borerInvasive speciesBuprestidaeAbiotic componentLarva

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.102
GPT teacher head0.294
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes2
Has abstractyes

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