Taxonomic diversity, Pest Vulnerability, and Carbon Storage of the Urban Forest in Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Canadian prairie cities face a number of challenges when managing urban forests, one of which is reduced tree diversity due to more severe climate constraints to tree survival. This thesis reports on diversity and carbon storage for the city of Winnipeg, Manitoba, Canada. Approximately 24,500 trees were surveyed and measured across 77 Winnipeg neighborhoods, including trees on private lots, which had not been previously reported for the city. Using these data, I evaluated tree species diversity measures for city neighborhoods and compared diversity measures between trees on public and private property. Private properties exhibited higher tree diversity and better health status across all metrics. I also adapted the Pest Vulnerability Matrix (Lacan & McBride, 2008) to environmental conditions found in the city of Winnipeg to identify pests with the most potential to impact city forests and neighborhoods as well as areas most at risk of new pest invasion. Exploring carbon storage in the city, I used methods developed by Wayson et al. (2015) to create prediction intervals (a measure of reliability for the prediction of an observation) around biomass equations used by city foresters. I then estimated carbon storage in residential areas across the city. I found 58% of carbon stored in trees surveyed was in American elm (Ulmus americana), and no other tree species in the survey had an equivalent amount of stored carbon (based on mean DBH). This research incorporates the first large scale private tree inventory within Winnipeg, providing a more comprehensive assessment of tree species diversity and carbon storage values across the city. This study will allow urban forest managers to have a clearer understanding of the existing tree inventory and implications for future urban forest management activities to protect and increase the city’s urban forest resource.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".