ESTIMATING GRIZZLY BEAR DISTRIBUTION AND ABUNDANCE RELATIVE TO HABITAT AND HUMAN INFLUENCE
Bibliographic record
Abstract
Understanding factors that influence and predict grizzly bear (Ursus arctos) distribution and abundance is fundamental to their conservation. In southeast British Columbia, Canada, we applied DNA hair-trap sampling (1) to evaluate relationships of grizzly bear detections with landscape variables of habitat and human activity, and (2) to model the spatial distribution and abundance of grizzly bears. During 1996–1998, we sampled grizzly bear occurrence across 5,496 km2 at sites distributed according to grid cells. We compared 244 combinations of sampling sites and sessions where grizzly bears were detected (determined by nDNA analyses) to 845 site–sessions where they were not. We tested for differences in 30 terrain, vegetation, land cover, and human influence variables at 3 spatial scales. Grizzly bears more often were detected in landscapes of relatively high elevation, steep slope, rugged terrain, and low human access and linear disturbance densities. These landscapes also were comprised of more avalanche chutes, alpine tundra, barren surfaces, burned forests, and less young and logged forests. Relationships with forest productivity and some overstory species were positive at broader scales, while associations with forest overstory and productivity were negative at the finest scale. At the finest scale, the strong negative association with very young, logged forests and with increasing values of the Landsat-derived green vegetation index became positive when analyzed in a multivariate context. For multivariate analyses, we considered 2 variables together with 11 principal components that describe ecological gradients among 4 variable groupings. We applied multiple logistic regression and used AIC to rank and weight competing subset models. We derived coefficients for interpretation and prediction using multi-model inference. The resulting function was highly predictive, which we confirmed against an independent dataset. We transformed the output using a multi-annual population estimate for the sampling area, and we applied the resulting grizzly bear density and distribution model across our greater study area as a strategic-level planning tool. We discuss conservation applications and design considerations of this DNA-based approach for grizzly bears and other forest-dwelling 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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".