Using temporary dye marks to estimate ungulate population abundance in southwest Yukon, Canada
Bibliographic record
Abstract
We describe the protocols of two mark-resight abundance surveys, using temporary dye-marks, for the Aishihik woodland caribou (Rangifer tarandus caribou) and wood bison (Bison bison athabascae) populations (herds) in the southwest Yukon Territory, Canada. We also provide recommendations based on experiences from these surveys for biologists and managers considering this approach. The Aishihik woodland caribou herd was the focus of intensive management in the 1990s aimed at recovering the herd. Following recovery activities, a target size of 2000 animals was determined and the Champagne-Aishihik Traditional Territory Community-Based Wildlife Management Plan recommended an estimate of the herd’s size be completed before the year 2013. We used an aerial mark-resight approach to estimate the herd’s size in March 2009. Caribou (n = 59) were marked from a helicopter with temporary dye, delivered via a CO2-powered rifle. Two independent resighting sessions were subsequently carried out via helicopter. The herd was estimated at 2044 animals (90% CI: 1768 – 2420) with an overall resighting rate of 0.47. The mean annual growth rate (λ) of the herd from 1997 – 2009 was 1.05 (SE = 0.01). The Aishihik wood bison herd was estimated at 1151 (90% CI: 998 – 1355). Our study suggests that ungulates temporarily marked with dye can be successfully used to obtain statistically sound population estimates.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".