Density estimates for Canada lynx vary among estimation methods
Bibliographic record
Abstract
Abstract Unbiased population density estimates are critical for ecological research and wildlife management but are often difficult to obtain. Researchers use a variety of sampling and statistical methods to generate estimates of density, but few studies have compared estimates across methods. During 2016–2017, we surveyed Canada lynx ( Lynx canadensis ) in southwestern Yukon Territory, Canada, using track transect counts, hair snares, camera traps, live traps, and Global Positioning System (GPS) collars. From these data, we estimated lynx density with two linearly scaled count methods, one spatial mark–recapture method, three spatial mark–resight methods, and one cumulative‐time method. We found up to fivefold variation in point density estimates despite adhering to method requirements and assumptions in a manner consistent with other studies. Our results highlight the dependency of density estimates on sampling process and model assumptions and demonstrate the value of careful and unbiased sampling design. Further research is needed to fully assess the accuracy and limitations of the many wildlife density estimation methods that are currently in use so that techniques can be appropriately applied to typical study systems and 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 | 0.011 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".