Ecological Connectivity Through UBC – An Omniscape Approach
Bibliographic record
Abstract
Human activities and rapid urbanization have led to habitat destruction and fragmentation, severely impacting biodiversity and ecosystem services globally. This study focuses on the UBC campus, surrounded by the Pacific Spirit Regional Park, to assess and enhance ecological connectivity through the urban landscape. Using advanced remote sensing techniques, including Planet SkySat satellite imagery and British Columbia 2022 LiDAR data, we classified the landscape and identified key habitats. Keystone species were selected based on habitat dependency, observation density, and conservation status, utilizing datasets from the Global Biodiversity Information Facility (GBIF) between 2010 to 2023. The study employed Omniscape, an implementation of Circuitscape 4.0, to model the randomized movement of animals across a resistance-weighted landscape, providing a nuanced understanding of how urban features influence ecological flows. Our findings indicate that urban areas, especially the central UBC campus, act as barriers and funnel species movement, particularly affecting those with small movement ranges and unique habitat requirements, such as the Pacific Tree Frog and Douglas's Squirrel. The analysis highlighted areas of highly channelized flows where predicted movement exceeds landscape capacity, leading from Pacific Spirit Regional Park to UBC's Central core. UBC’s Main Mall was predicted to have highly channelized flow across all species and is a critical corridor for conservation and enhancement. Other recommendations for UBC include creating an East West Ecological Corridor, replicating Main Mall’s design. The existing landscape should be intensified by integrating more water bodies and riparian areas to support amphibian species and interspersing shrub planting between trees to expand habitat availability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".