The estimation of grizzly bear density through hair‐snagging techniques above the tree line
Bibliographic record
Abstract
ABSTRACT Assessing grizzly bears' ( Ursus arctos ) abundance in the Arctic has been challenging because of the large scale of their movements and the remoteness of field locations. We modified a post sampling method used for wolverines ( Gulo gulo ) to allow collection of hair samples from grizzly bears in the Canadian tundra. We deployed 1 post/cell in a sampling grid of 393 10 × 10‐km cells sampled in 2008 and 2009 for two 14‐day sessions in July–August of both years. We then compared density estimates from mark–recapture estimators that used telemetry data from previous years with spatially explicit mark–recapture models that used only genetic detections. Over the 2 years of sampling, we detected 98 female and 81 male grizzly bears. We found that the DNA degradation rate was related to collection interval and the number of days between rainfall events and sample collection. Estimates of density were in the order of 5 bears/1,000 km 2 . The estimates from the 2 methods were statistically similar, but spatially explicit estimates were more precise than those using radiocollar data. Our results provide the first demonstration of the viability of posts as hair‐snagging stations to obtain DNA from grizzly bears, and of spatially explicit mark–recapture methods to estimate population size and density for grizzly bears above the tree line. © 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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".