Motion‐sensitive cameras track population abundance changes in a boreal mammal community in southwestern Yukon, Canada
Bibliographic record
Abstract
Abstract Motion‐sensitive cameras are commonly used to monitor wildlife occupancy rates; however, few studies have assessed whether data from cameras are correlated with density estimates obtained from more traditional labor‐intensive methods such as those based on capture‐mark‐recapture. We used data from a boreal forest community to test whether camera data were correlated with densities estimated from independent monitoring methods. We placed 72 covert cameras in the forest around Lhù'ààn Mân' (Kluane Lake), Yukon, Canada, for 7 years and tracked changes in population densities by camera hit rates. We independently estimated population densities of snowshoe hares (Lepus americanus) and red squirrels (Tamiasciurus hudsonicus) using capture‐mark‐recapture via live trapping, and Canada lynx (Lynx canadensis), coyotes (Canis latrans), and moose (Alces americanus) by snow track transects. Density estimates obtained from conventional aerial surveys were also periodically available for moose. Except for red squirrels, camera hit rates were highly correlated with population density estimates obtained by traditional methods, including across a large range of estimated densities corresponding to cyclic population dynamics in several species. Accordingly, we infer that motion‐sensitive cameras could supplement or replace traditional methods for monitoring key species in boreal forest food webs. Using cameras to monitor population change has several advantages; they require less effort in the field, are non‐invasive compared to live‐trapping, include multiple species at the same time, and rely less on weather than either aerial surveys or snow track transects. Tracking changes across the vast boreal forest is becoming increasingly necessary because of climate and landscape change and our data validate the use of motion‐sensitive cameras to provide a useful quantitative method for state‐of‐the‐environment reporting.
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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 | 0.001 | 0.001 |
| 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".