Habitat Connectivity Using Conefor 2.6 For Salt Spring Island, British Columbia
Bibliographic record
Abstract
Habitat connectivity is fundamental for animal species dispersal and gene flow. However, habitats are becoming increasingly fragmented due to anthropogenic activities. In this study, I analyzed habitat connectivity on Salt Spring Island, British Columbia, from the perspective of the brown creeper bird, the red squirrel, and the red-legged frog. These umbrella species were selected since their survival is at risk with global warming that is altering their fragile habitat. The research objectives were: assessing network connectivity; identifying the most important habitat patches; and identifying the most important disturbed areas for restoration. The methods included: creating habitat patches and links based on specific habitat requirements using the Landsat landcover classes and Light Detection and Ranging data; assigning a quality to each landuse category to calculate the area-weighted quality of each patch; and performing the connectivity analysis in Conefor 2.6 using the Probability of Connectivity index and its associated metrics (sum dPC and dNC) as they were proven to perform the best for modeling habitat connectivity. A Probability of Connectivity value of 0.0000344 was computed for frog compared to 0.1081950 and 0.1175361 for the bird and the squirrel, respectively. These low values suggest that all habitats may require restoration, but efforts may be focus on the frog since it is significantly less connected. In total, 304 patches covering 6.7 km2 were identified as areas of very high functional connectivity that may require protection. Additionally, 31.3% of this total area was found to be disturbed by agriculture, urban area, high soil erosion, and/or major road(s), which may indicate an important need for rehabilitation. The best way to enhance the species survival and dispersal rate may be to buffer existing patches with woodland, and develop corridors and hub patches that are large and rich in resources to provide both more connectivity and more habitat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".