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Record W6967289133 · doi:10.5061/dryad.g79cnp5wv

Data from: One-stage spatial mark-resight analysis reveals an increasing grizzly bear population with declining density near roads

2025· dataset· en· W6967289133 on OpenAlexaffabout

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsParks Canada
Fundersnot available
KeywordsGrizzly BearsAbundance (ecology)WildlifePopulationPopulation densityPopulation modelDistance samplingAerial surveyPopulation size

Abstract

fetched live from OpenAlex

Wildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture-recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark-resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio-collared animals required a two-staged analysis. We developed a one-stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one- and two-stage gSMR models. We then applied the one-stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest one- and two-stage models performed equally well. One-stage models are more dependable as they use exact likelihoods whereas two-stage models have shorter computation times for large datasets. Both methods had > 95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates and > 10 marked animals were required to obtain coefficients of variation < 20% in most scenarios. The grizzly bear population increased slightly (growth rate λmean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km2. Population density was highest within protected areas, within high quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry-based spatial mark-resight models for monitoring spatiotemporal trends in abundance.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.055
GPT teacher head0.331
Teacher spread0.276 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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