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ESTIMATING GRIZZLY BEAR DISTRIBUTION AND ABUNDANCE RELATIVE TO HABITAT AND HUMAN INFLUENCE

2004· article· en· W2177438531 on OpenAlex
Clayton D. Apps, Bruce N. McLellan, John G. Woods, Michael F. Proctor

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Wildlife Management · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMount Revelstoke National ParkGovernment of British ColumbiaUniversity of CalgaryCochrane
Fundersnot available
KeywordsGrizzly BearsGeographyUrsusVegetation (pathology)Abundance (ecology)HabitatEcologyPhysical geographyContext (archaeology)Disturbance (geology)Environmental sciencePopulationBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.233
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it