Contrasting aerial moose population estimation methods and evaluating sightability in west‐central Alberta, Canada
Bibliographic record
Abstract
ABSTRACT Population assessment is a primary component of ungulate management, but managers are continuously under pressure to reduce survey cost. Another concern in aerial surveys is accounting for undetected animals (i.e., visibility bias). Currently, a stratified random block‐survey design (hereafter, block‐surveys) is used to develop moose ( Alces alces ) population estimates in several regions of North America. In this case study, we evaluated the application of distance sampling as an alternative to block‐surveys in Alberta, Canada. We conducted distance‐sampling surveys in 2010 and 2012 and compared density estimates, precision (coeff. of variation) and flight effort (hr/100 km 2 of survey area) to block‐surveys flown in 2002, 2007, 2009, and 2012. To assess sightability bias and subsequently correct for moose missed on the transect line, we developed a predictive sightability model using 41 sightability trials with 21 radiocollared moose in 2009 and 2010. Without correcting for visibility bias on the transect line, distance sampling was more efficient in terms of flight‐hours than block‐surveys, while providing population estimates with similar or higher precision. Estimated sightability on the transect line was 67% in 2010 and 46% in 2012, which was used to re‐scale the detection functions. Considering that population estimates from block‐surveys as applied in Alberta are based on observable moose, distance sampling with a sightability correction likely provided more accurate estimates. Our results support the application of distance sampling as an alternative to block‐surveys, but we suggest further investigation of methods for correcting visibility bias on the transect line. © 2014 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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".