Urban forest vulnerability and its implications for ecosystem service supply at multiple scales
Bibliographic record
Abstract
The urban forest is a valuable ecosystem service provider that is garnering increasing attention in environmental research and municipal planning agendas. However, because of its location in heavily built-up and densely-settled environments, the urban forest is vulnerable. The purpose of this dissertation is to conceptualize, assess, and analyze urban forest ecosystems and their vulnerability at multiple spatial and temporal scales. An urban forest ecosystem classification framework that integrates biophysical, built, and human components is developed. Subsequent classification of ecosystems at the neighbourhood scale reveals the spatial arrangement of several social-ecological interactions. Such information is valuable to ecosystem-based decision support while also informing future vulnerability research. The investigation of ecosystem vulnerability began with the development of a theory-based conceptual framework. Urban forest vulnerability is defined as the likelihood of decline in ecosystem service supply and its associated benefits for human populations, urban infrastructure, and biodiversity. It is comprised of exposure, sensitivity, and adaptive capacity, which describe the built environment and associated stressors, urban forest structure, and the human population, respectively. This framework is applied using empirical field research in Toronto, Canada to explore the processes of vulnerability and their influence on ecological change. Results indicate that there are several significant predictors of urban forest decline and mortality, and emphasize the importance of applying diverse metrics to describe the built environment and urban forest structure at fine spatial scales. Vulnerability assessment and analysis at much broader spatial and temporal scales, using a spatially-explicit assessment approach and ecological modelling of alternative management and disturbance scenarios, is further investigated. This latter research emphasizes the importance of aligning scales of management with ecosystem function and the long-term influence of management intervention on ecological conditions. The multiple scales of investigation and methodological approaches developed in this study provide complementary opportunities to synthesize and apply existing theory in novel settings while also generating new theories pertaining to the processes of urban ecosystem change and decline. The intention of this study is to contribute to the understanding of urban forest ecosystems and their vulnerability, while also providing practical knowledge and tools for the sustainable management of this 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".