Density estimates of unmarked mammals: comparing two models and assumptions across multiple species and years
Bibliographic record
Abstract
Density estimation is a key goal in ecology, but accurate estimates for unmarked animals remain elusive. Camera trap data can bridge this gap, but accuracy, precision, and concordance varies among estimators. We compared estimates from unmarked spatial capture–recapture (spatial count (SC)) models, and time in front of camera (TIFC) models, for four large mammal species in boreal Canada. Species differed in movement rates, behaviours, and sociality—traits related to model assumptions. TIFC densities typically exceeded SC model estimates for all species. Two- to five-fold differences between estimators were common. SC estimates were annually stable for moose and caribou but not for white-tailed deer. TIFC estimates showed high annual variation in some species, sites, and years, and consistency in others. Both models often produced imprecise estimates. Estimates varied from DNA- and aerial survey-based estimates. We contend models diverge, or implausibly vary, due to violations of model assumptions incurred by animal behaviour. Gregarious animals pose challenges to SC, whereas curious animals pose challenges for TIFC models. Simulations can help unravel the role of assumption violations in affecting accuracy of estimates, but field applications across species and landscapes help interpret the outcomes of estimating density from simulated data.
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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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".