Some mechanisms underlying variation in vital rates of grizzly bears on a multiple use landscape
Bibliographic record
Abstract
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.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".