Functional Habitat Connectivity Analysis for the Township of Langley, British Columbia
Bibliographic record
Abstract
In response to the growing concern of loss of biodiversity due to global urbanization, much research has investigated its influence on habitat connectivity, yet little is known about the functional habitat connectivity of the Coastal Douglas-fir (CDF) and the Western Hemlock (CWH) BEC Zone in urban areas. This study aimed to assess the functional habitat connectivity of the Township of Langley in British Columbia, Canada, to facilitate urban planning and urban biodiversity conservation. Using lidar-derived and land cover data, quantified habitat connectivity was quantified with the probability of connectivity index (PC) for three species (the western red-backed salamander, the American red squirrel, and the brown creeper) with different habitat requirements and mobility. Suitable habitat patches were filtered and selected based on the habitat requirements of each species, and habitat networks were generated considering dispersal distance to model the connectivity index in the Conefor software. Key patches, hub patches, and key-hub patches were identified based on the patch importance level generated from Conefor. The study found a large variance in PC and distribution of important patches among species in the landscape. The major connectivity corridor, identified according to the distribution of key-hub patches for all species, showed some important areas for overall connectivity improvement. Despite limitations on timeliness, computational power and consideration of dispersal barriers, this study enhances understanding of local-scale connectivity and offers guidance for urban planners and developers seeking to incorporate biodiversity conservation in decision-making. Identification of key patches, hub patches, and key-hub patches also highlights potential areas for habitat preservation, which is particularly relevant for urban green infrastructure design that supports both human well-being and biodiversity conservation.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".