Urban Landscape Connectivity in Southern Ontario: Evaluating Current Approaches and Exploring the Potential of Climate Connectivity Considerations
Bibliographic record
Abstract
Landscape connectivity facilitates the movement of organisms, is important for the maintenance of ecological integrity, and supports the resilience of ecosystems to withstand the impacts of climate change. Land use change resulting from urbanization increases landscape fragmentation and habitat loss which negatively impacts the foraging, dispersal, and migration capabilities of species which can result in decreases in species abundance, diversity, and overall ecosystem function. At the same time, climate change is driving shifts in the ranges of some species as a result of changes in the suitability of habitat and climate conditions. Southern Ontario is the most densely populated region in Canada and is expected to accommodate significant population growth over the next 20-30 years. As a result of the expected growth in this area, the long-term protection and enhancement of landscape connectivity will be an important consideration in southern Ontario. The objectives of this research were to assess the effectiveness of current approaches to protecting and enhancing landscape connectivity in southern Ontario and to examine ways urban areas can support species movement under climate change. These objectives were explored at two different scales. Finer-scale analysis was undertaken through a case study of Waterloo Region (“the Region”) using a combination of spatial and policy analysis. Using circuit theory, we modelled structural connectivity of forests and wetlands across the Region between 2000-2015. Then, we undertook content analysis of provincial and regional land use policies to examine the trends and evolution of land use policy guiding growth and development in the Region between 1996-2020 focusing on requirements to protect and enhance landscape connectivity. Our results showed that existing corridors have remained stable and land use policies for the protection of landscape connectivity have strengthened over time but also highlighted the need for greater emphasis on enhancing landscape connectivity within urban areas. Coarser-scale analysis was then undertaken to analyze existing climate connectivity literature to understand the potential role of urban areas in supporting broad scale ecosystem function and range shifts under climate change. Our analysis found very few discussions on the potential role of urban areas in supporting climate connectivity. In response, we present a perspective piece on potential opportunities for considering climate connectivity in conjunction with existing approaches to protecting and enhancing landscape connectivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".