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Record W6980031991

Are eastern grey squirrels a big problem for bigleaf maple?

2022· other· en· W6980031991 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace · 2022
Typeother
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlaggingMapleForesterNational parkAbiotic componentHost (biology)
DOInot available

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.275
Teacher spread0.237 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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