Monitoring of grizzly bear population trends and demography using DNA mark–recapture methods in the Owikeno Lake area of British Columbia
Bibliographic record
Abstract
We used DNA sampling and mark–recapture modeling to estimate population trend(s), population size, and the demographic response of a coastal British Columbia grizzly bear population (Ursus arctos L., 1758) to low salmon escapement levels from 1998 to 2002. We contrasted the demography of three sampling areas in response to temporal and spatial variation in salmon availability. Population trend (λ) estimates suggested that salmon availability was too low in the first 2 years of the study to sustain grizzly bear populations. One of the sampling areas exhibited higher levels of salmon availability in later years of the study, leading to increased rates of addition. Apparent survival rates increased in all areas potentially as a result of increased salmon availability. Joint interpretation of λ and superpopulation estimates allowed for the assessment of whether salmon availability levels were high enough to sustain current population sizes of grizzly bears on salmon streams. This study illustrates how joint modeling of separate sampling areas can be used to assess spatial variation in population demography and population trends, as well as increase precision of estimates for individual sampling areas. It also illustrates how DNA mark–recapture can be used as a methodology to explore the effects of changes in environmental conditions on population demography and population trend of grizzly bears or of other wildlife 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".