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Record W4413152120 · doi:10.36939/ir.202508131517

Advanced Dutch Elm Disease Management in Winnipeg through RPAS-Based Monitoring and Elm Bark Beetle Activity Tracking

2025· dissertation· en· W4413152120 on OpenAlexaboutno aff
Jenelle Ehn

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBark beetleDutch elm diseaseBark (sound)BiologyForestryGeographyBotany

Abstract

fetched live from OpenAlex

This study consisted of two projects designed to provide information to improve Dutch elm disease (DED), (Ophiostoma novo-ulmi), management methods in Winnipeg, Manitoba. The objective of the first project was to test whether a remote piloted aircraft system (RPAS) equipped with a multispectral camera can detect DED symptoms in American elm (Ulmus americana) trees. Three neighbourhoods in Winnipeg were surveyed using this technology in 2022 and 2023, and categorical maps of diseased versus healthy trees were generated for each neighbourhood using a variety of vegetation indices and methods of delineating tree canopies in the imagery. Ground disease detection surveys were conducted in conjunction with the aerial surveys to guide and validate these maps. Results for each survey showed that healthy tree canopies had significantly higher mean normalized difference vegetation index (NDVI) values than DED/hazard trees. Other vegetation indices were also tested, but mean NDVI values generated the most accurate classifications. Manually digitized polygons outlining the shape of each tree canopy also generated more accurate classifications than generic circles or rectangles centred on tree coordinates, but overall success of DED detection was still moderately low with manual polygons at 67.3%. The results from this research indicate that the use of RPAS solely to detect DED will require more refinement to increase accuracy levels to be as reliable as ground survey crews. However, the technology is effective at detecting dead elm trees or trees with advanced DED symptoms with 79.5% overall correct classification, and would therefore be a useful tool to assist with current DED management strategies, particularly in less accessible locations. The native elm bark beetle (Hylurgopinus rufipes) is the primary known insect vector of DED spread in Manitoba, while several other elm bark beetle species can also spread the disease elsewhere in North America. One of these species, the banded elm bark beetle (Scolytus schevyrewi) has been reported in rural Manitoba feeding on Siberian elm (Ulmus pumila). The objectives of the second project were to determine the beginning of emergence of summer brood of elm bark beetles in Winnipeg, and to determine if the banded elm bark beetle is present and attracted to American elm trees in Winnipeg. A pilot experiment was carried out from July 13 to September 18, 2023. American elm and Siberian elm logs were collected and left exposed in a Winnipeg neighbourhood. Logs were partially debarked throughout the study period. Beetles found on the surface of logs, boring into them, or inside the logs, along with larvae in galleries, were collected and preserved. Nearly all adult bark beetles collected were identified as banded elm bark beetles, which was unexpected given that banded elm bark beetles were not found to be attracted to American elm in previous studies in rural Manitoba. DNA bar coding technology was used to confirm that larvae and adults collected in elm logs were banded elm bark beetles. These findings suggest that banded elm bark beetles could potentially be a second significant vector of DED in Winnipeg’s urban forest.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.719

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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
Published2025
Admission routes1
Has abstractyes

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