Camera traps offer reliable estimates compared to ground surveys for monitoring duck pairs and broods
Bibliographic record
Abstract
Abstract Monitoring breeding waterfowl populations with ground‐based pair and brood surveys informs management and conservation decisions. However, surveys are often limited temporally and may miss individuals that are not present or available for detection at the time of the survey. Alternative methods to monitor waterfowl such as camera traps may be more appropriate to measure relative abundance, but it is unknown how camera trap surveys compare to ground‐based surveys. We conducted concurrent walk‐up pair and brood surveys on 20 wetlands in Manitoba, Canada and deployed cameras set to take pictures at 10‐min intervals during daylight hours. We compared indices of relative abundance and species richness of ducks and ducklings on small prairie wetlands (<3.8 ha) detected with ground‐based and camera trap surveys and make recommendations regarding the time of day and duration of camera surveys. As predicted, camera surveys detected more ducks, ducklings, and duck species than ground surveys counted. Importantly, camera surveys detected ducks and ducklings at wetlands that ground surveys did not. Both duck and duckling observations were positively associated between survey methods (ducks: R 2 = 0.22, F 1,17 = 4.83, P = 0.04, ducklings: R 2 = 0.49, F 1,15 = 14.16, P = 0.002). We found that cameras are a useful tool to survey relative duck abundance, and the extended temporal surveillance of cameras reduces false negatives.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".