Modeling Functional Connectivity for Bears Among Spawning Salmon Waterways in Haíɫzaqv (Heiltsuk) Territory, Coastal British Columbia
Bibliographic record
Abstract
ABSTRACT Understanding how functional connectivity can provide mobile consumers access to key resources can inform habitat management. The spatial arrangement of landscape features, for example, can affect movement among resource patches. Guided by the Haíɫzaqv (Heiltsuk) Integrated Resource Management Department ( HIRMD ), and within Haíɫzaqv Territory, coastal British Columbia ( BC ), Canada, our objectives were to (1) estimate functional connectivity for grizzly and black bears ( Ursus arctos and U. americanus , respectively) among aggregations of spawning Pacific salmon ( Oncorhynchus spp.), (2) identify important movement pathways for landscape planning, and (3) contribute to the growing body of functional connectivity research on dynamic ecological systems. Using circuit theory and least cost paths, we predicted movement among salmon spawning reaches within a 5618 km 2 study area. Variables affecting bear movement were parameterized by drawing on the relevant literature and Haíɫzaqv Knowledge. We validated our cumulative resistance surface with observed movements as identified via genetic recapture data. Modeled current from Circuitscape suggested areas of high connectivity between salmon spawns within and among watersheds. Our least cost paths model identified principal routes, which we then ranked to illustrate possible corridors for consideration by HIRMD planners. Understanding movement among salmon spawns, a fitness‐related food, provides key information to inform landscape planning for bears. Further, our work provides an example of connectivity research codeveloped, executed, and applied with an Indigenous government.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".