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Record W1924713959 · doi:10.1002/jwmg.896

Some mechanisms underlying variation in vital rates of grizzly bears on a multiple use landscape

2015· article· en· W1924713959 on OpenAlex
Bruce N. McLellan

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.

Bibliographic record

VenueJournal of Wildlife Management · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMinistry of Forests
Fundersnot available
KeywordsUrsusPopulationGrizzly BearsProductivityDemographyEcologyPopulation densityReproductionBiologyMortality rateGeography

Abstract

fetched live from OpenAlex

ABSTRACT Understanding factors that govern the abundance of organisms is fundamental to the science of ecology and important for conservation and management of species. I used temporal and spatial comparisons to test the influence of human industrial activity, huckleberry ( Vaccinium membranaceum ) productivity, and population density on grizzly bear ( Ursus arctos ) vital rates and population trends over a 32‐year period. Survival rates of adult and subadult males were 0.84 and 0.78, respectively, and lower than those of adult (0.93) or subadult females (0.96). Of the 31 bears that died while radio‐collared, 26 (84%) were killed by people. Of those killed by people, 11 (35%) were legally killed by hunters and 84% were deaths that occurred <120 m from a road. In the first decade of study (1979–1988) when salvage logging and gas exploration was intensive, bear density was relatively low, and huckleberry production was generally good, the population increased (λ = 1.074) with high survival rates of cubs (0.84) and yearlings (0.86) plus a high reproductive rate of 0.374. During the second decade (1989–1998) when there was little industrial activity and huckleberry production remained good, the population continued to grow (λ ≈ 1.06–1.08) because survival of all age classes remained high, but the reproductive rate declined to 0.257. Bear density reached its maximum (55.6 bears/1,000 km 2 excluding independent males) at the start of the third decade. During the third decade (1999–2010), there was little industrial activity, but huckleberry production declined dramatically and often completely failed. During the third decade the population declined (λ ≈ 0.955–0.980) as the reproductive rate dropped to 0.192 because of small litters (1.82), extended interbirth intervals (2.93, 3.44, and 4.22 years in decades 1, 2, and 3, respectively) and increased age of primiparity (6.60, 7.09, and 10.46 years in decades 1, 2, and 3, respectively). Adult female survival also declined likely because more females were without offspring and thus vulnerable to hunting. The best model predicting if a parous female would have a small (0 or 1 cub) or large (2 or 3 cub) litter when not encumbered with offspring the previous mating season included both huckleberry abundance the previous year and female bear density. Population inventories during the third decade had approximately twice as many bears detected per DNA hair trap set in the portion of the valley where there had been rapid industrial development, grizzly bear hunting, and large huckleberry fields than in an adjacent portion of the valley that was protected from industry and hunting but with no major huckleberry fields. The abundance of huckleberries growing in mountains above most human activity permitted this population to expand in spite of the industrial development. The population was primarily regulated by the interaction of bear density and the density‐independent production of huckleberries, their major summer‐fall energy food. © 2015 The Wildlife Society.

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)0.001
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 score0.058
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.256
Teacher spread0.213 · 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