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Enregistrement W4229443305 · doi:10.36939/ir.202205091637

Developing Criteria to Prioritize Rapid Removal of American Elm Trees Infected with Dutch Elm Disease

2021· dissertation· en· W4229443305 sur OpenAlexaffabout
Matthew B. Russell

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueForest Insect Ecology and Management
Établissements canadiensUniversity of Winnipeg
Organismes subventionnairesnon disponible
Mots-clésOverwinteringDutch elm diseasePruningBroodBark (sound)CanopyBiologyForestryGeographyEcologyHorticulture

Résumé

récupéré en direct d'OpenAlex

During late summer and early fall in Manitoba, adult native elm bark beetles (NEBB) that carry Dutch Elm Disease (DED) emerge from brood galleries in the canopy and upper trunk of infected elm trees and move to the base and root flares of healthy trees to overwinter. In the spring, DED-carrying beetles disperse from these overwintering sites back to the canopy of healthy elm trees where they feed and construct new brood galleries, thus introducing new DED infections. The current practice after initial DED diagnosis is to remove diseased American and Siberian elm trees prior to emergence of overwintering adult NEBB vectors before the spring. In Manitoba and Saskatchewan, the preferred date for infected tree removal is before the end of March. In Winnipeg, the majority of trees are removed during late fall and winter although infected trees may remain standing into early summer. Infected tree removal remains a vital and primary component of the integrated DED program in the City of Winnipeg, even though other DED management methods are practiced to augment infected tree removal, including insecticidal control of beetles, injection of fungicides for tree protection, sanitation pruning, etc. A significant constraint to this approach is that most infected trees are removed after NEBB adults have emerged in the fall and moved to overwintering sites on healthy trees. Delayed removals due to weather conditions, site accessibility and limitations in resources needed to remove trees have also resulted in infected elm trees remaining in place until the spring. All these issues diminish the success of the elm sanitation program. Removal of all diseased trees before mid-September could potentially reduce NEBB populations and thus, DED incidence, and spread. Logistical limitations are encountered when large numbers of infected trees require immediate removal, and it is impractical to remove that number between July and September. Preliminary research by Holliday (2016) suggested that a small percentage of diseased elm trees may support the majority of maturing NEBB brood. Confirmation of this trend and targeted removal of this small percentage of DED-infected trees carried out prior to the NEBB migration in the fall would greatly reduce DED incidence by decreasing the number of overwintering NEBB. The current project, in collaboration with the University of Winnipeg (UW) and the City of Winnipeg (Forestry Branch), analyzed the correlation between NEBB densities in infected elm trees and the expression of DED symptoms during the summers of 2017, 2018, and 2019. Trunk bark removal and bark removal of upper canopy branches were examined to predict the relationship between canopy NEBB densities and the expression of disease symptoms in the tree crown. A key question was whether specific trees within a larger group of infected trees could be visually confirmed to support large numbers of breeding NEBB during the summer. Surveys were initiated in study neighbourhoods by Forestry Branch DED surveillance staff to confirm the presence of DED in mid-June each year. After DED-infected trees were identified, UW staff assessed a series of external disease symptoms in infected trees. Trees were first assessed in late June, continuing weekly for a minimum of four weeks until the end of August. Once the survey was completed, Forestry Branch sanitation crews removed infected study trees, and branch samples from these trees were taken to determine the number of NEBB brood galleries and percentage of DED staining was present in the canopy. In addition, bark was removed from the lower trunks of infected trees in 2017 to determine whether NEBB colonized this part of the tree during the summer and to examine the level of fungal staining in the lower trunk. During 2018 and 2019, sticky traps on DED-infected study trees were used to capture emerging NEBB and adults searching for overwintering sites. These collected NEBB were then tested for the presence of Ophiostoma novo-ulmi (DED) spores. The relationship between canopy variables recorded during the disease progression survey and NEBB brood gallery density were compared to determine which best predicted high density NEBB trees and could be used to implement a rapid tree removal program. My results indicated that the percentage of dead canopy leaves, dead canopy branches, and DED infection sites were positively correlated with NEBB brood gallery density, whereas overall canopy cover and percentage green canopy leaves were negatively correlated with NEBB brood gallery density. Differences between trees were pronounced when infected trees were placed into two categories (no NEBB brood galleries detected versus NEBB brood galleries detected). Generalized linear models were employed to compare the external canopy variables with NEBB gallery density. Two models predicted which trees had high numbers of NEBB galleries; the first used percentage fungal staining (i.e., proxy for NEBB density) as the response variable while the second model used trees grouped either into detectable or not detectable NEBB density as the response variable. The first model suggested that the percentage of dead leaves in the canopy was a useful predictor of NEBB density, while the second model found the number of initial DED initial infection sites was the most significant predictor of NEBB densities. These findings show that canopy die-back, the percentage of dead leaves in the canopy, and the number of infection sites assessed are the best indicators of NEBB densities. This suggests that if external DED symptoms are tracked during the first month of infection, then they can be used to identify trees and prioritize which need to be removed and disposed of first during July and August in order to prevent NEBB from emerging and dispersing to new trees in the fall.

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)0,011
score de la tête « metaresearch » (Gemma)0,035
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,027
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,035
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0090,003
Études des sciences et des technologies0,0020,001
Communication savante0,0040,002
Science ouverte0,0030,003
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,008
Tête enseignante GPT0,259
Écart entre enseignants0,251 · 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'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

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
Publié2021
Routes d'admission2
Résumé présentoui

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