Camera trapping for density estimation: comparing the TIFC model to aerial surveys for multiple ungulate populations
Bibliographic record
Abstract
Population density estimates are invaluable to wildlife managers, but difficult to attain. Several methods have been developed to estimate density using camera traps, many of which require further testing. The time-in-front-of-the-camera (TIFC) approach allows for density estimation when "unmarked" individuals are monitored using camera traps. We applied the TIFC model to populations of bison (Bison bison, B. bison athabascae), elk (Cervus elaphus canadensis), and moose (Alces alces) in Elk Island National Park (EINP) and to populations of elk and moose in Cooking Lake - Blackfoot Provincial Recreation Area (BPRA). EINP and BPRA are fully fenced natural areas in the Beaverhills Region of central Alberta, Canada. Our objectives were to (i) use the TIFC model to estimate ungulate densities in EINP and BPRA, and (ii) compare the performance of TIFC density estimates against aerial ungulate survey estimates. Camera trap data were collected from 43 cameras in EINP between December 2016 and October 2020, and 23 cameras in BPRA from April 2019 to August 2020. Annual densities were estimated in EINP north and south (2017-2019) and in BPRA (2019). Moose density estimates had the lowest discrepancy between approaches. Bison TIFC density estimates were lower than AUS densities, and elk TIFC density estimates were higher than AUS densities. In addition to the density estimates evaluated for the three focal species, the TIFC approach also was applied to white-tailed deer (Odocoileus virginianus) and mule deer (O. hemionus) in EINP and BPRA, in the absence of aerial survey data. We conclude that the TIFC model and AUS were complementary, with pros and cons of the two approaches varying based on focal species ecology. Careful consideration is required for several factors related to camera study design for TIFC density estimation that can affect the accuracy and precision of estimates.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".