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Record 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 on OpenAlexaffabout
Matthew B. Russell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsOverwinteringDutch elm diseasePruningBroodBark (sound)CanopyBiologyForestryGeographyEcologyHorticulture

Abstract

fetched live from 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.

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)0.011
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.035
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0090.003
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2021
Admission routes2
Has abstractyes

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