Using forest structural diversity to inventory habitat diversity of forest-dwelling wildlife in the West Kootenay region of British Columbia
Bibliographic record
Abstract
Forest planners in British Columbia are being asked to consider wildlife species diversity in forest development plans. Forest ecosystem inventories currently used in British Columbia are inappropriate or inadequate as tools for land management planning because they only document forest composition (Vegetation Resources Inventory) or identify plant communities (Terrestrial Ecosystem Mapping). To assist in the effort to obtain information about a site's potential forest-dwelling wildlife species diversity, we developed a method of using forest structure to identify and evaluate habitat quality for multiple species of vertebrates. Using aerial photos, we delineated six classes of forest structure that have been identified by other researchers as important wildlife habitats. We selected five structural attributes of forest stands—vertical structure (canopy complexity), horizontal structure (forest patchiness), coarse woody debris density, litter and duff layer depth, and tree size—to be measured in the field, and we applied the method in three study areas in southeastern British Columbia. We compared abundance of structural features between structural classes to determine whether the classes were indeed unique. Old forests were found to be more structurally complex than younger forests, and forested and riparian sites were more structurally complex than non-forested and upland sites. We then used this data to index structural diversity within a study area to allow stands to be compared. We suggest that our method can be used by biologists and land managers to guide the conservation of forest-dwelling wildlife species.
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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 | 0.000 |
| 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) | 0.001 | 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".