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Enregistrement W6980031991

Are eastern grey squirrels a big problem for bigleaf maple?

2022· other· en· W6980031991 sur OpenAlexaboutno aff

Notice bibliographique

RevueTSpace · 2022
Typeother
Langueen
DomaineEngineering
ThématiqueTree Root and Stability Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFlaggingMapleForesterNational parkAbiotic componentHost (biology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Bigleaf maple (Acer macrophyllum Pursh) is an important tree species on the Pacific coast of North America. It is one of the few hardwoods in the Pacific Northwest and provides several ecological, economic, and social benefits. It supports a large variety of species, is used to make numerous items, and is culturally important to First Nations. However, for about the last ten years, bigleaf maple has been showing signs and symptoms of decline with an unknown cause in the Washington, Oregon, and California range of the species (Betzen, 2018; Christiansen, 2019). Given the important role bigleaf maple plays, its decline could have large-scale impacts. Root disease was suspected by forest professionals in Washington based on symptoms such as partial to entire crown dieback, reduced leaf size, and yellow flagging and dieback of entire branches. Forest professionals tested for and ruled out several potential biotic agents such as Verticillium albo-atrum, Verticillium dahlia, Armillaria, Ganoderma, and Xylella fastidiosa (Omdal & Ramsey-Kroll, 2012; Betzen, 2018). A newly published study in western Washington indicated that the cause of this decline is most likely linked to abiotic agents such as road proximity, increased land development, and escalating summer temperatures (Betzen et. al, 2021). Metro Vancouver Regional District in British Columbia, Canada, had a forest health report completed that identified bigleaf maple flagging in some parks. With little research regarding bigleaf maple decline and few reports on bigleaf maple in general in the British Columbia context, this present study investigates if there is a decline syndrome in Metro Vancouver Regional Parks and what the cause may be. Data were collected for GPS location, Dbh, and several crown health metrics such as dieback percent, canopy openness percent and chlorosis percent. Information was also gathered for the presence or absence of foliar diseases, fruiting bodies, damage, flagging and leaf tip dieback. General linear model regression (Morin et al, 2012) and direct ordination using correspondence analysis was performed using R to determine statistical significance of the data. This study found strong associations to bark-stripping indicating diminishing tree health. With eastern grey squirrels being an introduced species to the area, their known association to bark-stripping hardwoods, including maples (among other species), and their well-documented invasive behaviour of tree destruction in the United Kingdom, makes them the most likely culprit. Kretzschmaria deusta was also found to have strong associations to deteriorating tree health and with larger diameter trees. With strong associations of bark-stripping and Kretzschmaria deusta to declining tree health, this is a management concern for Metro Vancouver Regional District that needs to be addressed to prevent potentially large-scale impacts in the future. Recommendations include initiating management programs for eastern grey squirrels in highly effected parks such as Campbell Valley Regional Park and active monitoring for Kretzschmaria deusta along heavily used trails. It is also recommended to monitor for sooty bark disease (Cryptostroma corticale). Collaboration with the British Columbia Society for the Prevention of Cruelty to Animals (BC SPCA), invasives species councils, the regions’ municipalities and the province to create a unified and effective approach to eastern grey squirrel management in the Metro Vancouver Regional District and the rest of British Columbia is also suggested.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,275
Écart entre enseignants0,237 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2022
Routes d'admission1
Résumé présentoui

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