Building resiliency of the coastal Douglas-fir zone through ecological linkages: A landscape connectivity analysis of Vancouver Island
Bibliographic record
Abstract
Vancouver Island and Gulf Islands host a diverse landscape consisting of four biogeoclimatic zones (BEC), home to numerous species and ecological communities at risk. One zone of particular biological significance is the Coastal-Douglas fir (CDF) zone, which has experienced extensive development, resulting in the loss of critical habitat essential for supporting biodiversity and ecosystem services. This research aimed to assess the current state of, map landscape connectivity, and identify key ecological linkages across Vancouver Island with a focus on CDF. A gap analysis based on the IUCN’s 30 by 30 conservation goal, revealed that as of 2023, only 8.82% of the CDF zone is protected. Landscape connectivity was modeled using a ‘species agnostic’ approach in Omniscape, applying circuit theory to a resistance surface to map connectivity across the region. Multiple moving window sizes were tested, showing smaller window sizes overrepresented connectivity, while larger sizes led to the loss of important linkages. A 15 km was found to be the most appropriate size for capturing connectivity in the Study Area. A sensitivity analysis highlighted that slope was a limiting factor for connectivity. Model validation, using a Roosevelt elk telemetry data, indicated that current density values at actual elk locations were significantly different from random locations for 12 out of the 15 individuals (Wilcoxon rank-sum test, p<0.05), suggesting that the model accurately represented species movement. A quantile classification of the model outputs identified the top 15% of current density values, which were considered important ecological linkages for conservation. These areas are crucial for facilitating species movements and dispersal, supporting adaptation to climate change, and enhance resilience to environmental disturbances. The results of this study provide insights into landscape connectivity and offer guidance for conservation efforts aimed at meeting the 30 by 30 goal, ensuring the protection and management of critical habitat for biodiversity
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".