Vegetative preferences and the impact of Dutch Elm Disease (Ophiostoma ulmi) on bird abundance in Winnipeg, Manitoba
Bibliographic record
Abstract
Since 2016, Winnipeg’s urban forest has lost more than 33,000 American elm (Ulmus americanus) providing habitat to urban birds due to Dutch Elm Disease (DED) (Ophiostoma ulmi) (Trees Winnipeg, 2021). However, the presence of DED in a migratory and breeding stopover site may increase insect abundance for insectivores and urban bird species, such as resident woodpeckers (spp.), warblers (Parulidae spp.), and other insect-eating birds that forage and nest in dead elm trees (Nicholls, 1994). The research investigates the impact of DED and its potential benefit on bird populations, as well as their vegetative preferences by recording 45 bird abundance point counts conducted across 18 neighbourhoods in Winnipeg during spring migration in 2022 and 2023. Bird abundance and vegetative composition was recorded in 50-m² circular plots next to a focal elm tree on public property that was either healthy, diseased with DED, or sites where elms had been recently removed due to DED to assess the potential positive or negative effects of DED on the bird population’s usage of an urban habitat. Results indicated that year-round residents, as generalists in the urban environment, benefit from DED. However, breeding migrant birds, with their more specialized habitat requirements, are significantly and negatively impacted by DED. The fact that breeding migrants and year-round residents were significantly less abundant in sites where elms had been recently removed underscores the urgent need for effective DED management in Winnipeg to preserve elms as bird habitat. Birds were most abundant near non-native deciduous shrubs, coniferous trees and shrubs, and riparian areas like riverbanks and greenspaces. This highlights the importance of restoring riverbanks, cultivating higher diversity in the urban forest, and increasing connectivity between natural areas in Winnipeg.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".