Snow track counts for density estimation of mammalian predators in the boreal forest
Bibliographic record
Abstract
Context Methods for estimating density of meso-carnivores in northern ecosystems are labour intensive and expensive to implement if mark–recapture and radio collaring are used. One alternative is to count tracks in the snow along transects as an index of density, but this method has been criticised as imprecise and lacking validation. Aims We aimed to examine the utility of track counts along snowmobile trails in the snow for measuring changes in populations of Canada lynx and coyotes in the boreal forest of north-western Canada. Methods We compared winter track counts of Canada lynx at three study sites and of coyotes at one site with concurrent estimates of density based on locations of radio-collared animals and estimates of numbers of uncollared animals from three 7–9-year studies in the Yukon and Northwest Territories, during a period of cyclical population fluctuations. Key results Snow track counts were positively correlated (r2 = 0.83) with density of Canada lynx, estimated by live trapping and radio collaring in the three survey areas. Coyotes also showed a clear relationship (r2 = 0.80) between known density and snow track counts, but track counts were strongly affected by season, with much higher counts in the early winter when snow was shallower. This indicates the need to control for season or snow depth when using track counts as indices of coyote abundance. We recommend sample sizes of at least 10 track counts per winter along a 25-km transect to maximise precision. Conclusions Snow track counts are a relatively simple, inexpensive method of concurrently tracking abundance of multiple species of meso-carnivores. These data indicate that track counts may be reliably used to monitor trends in numbers, but we suggest site-specific validation of the regressions reported here between tracks and animal abundance are required to translate these indices into estimates of density. Implications Track counts have the potential to be an affordable alternative to more intensive methods of monitoring trends in abundance of medium-sized mammals, for purposes such as management of harvest or to measure the success of programs to increase or reduce population abundance.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".