Parameterizing the random encounter staying time model to generate mean and variance ungulate density and abundance estimates
Bibliographic record
Abstract
Understanding wildlife density and abundance is perhaps the most important and universal concept across facets of wildlife management, conversation, and research. Despite the significance of understanding species densities in ecology, methods for estimating density for large terrestrial mammals in Canada continue to have high levels of inaccuracy in addition to being a costly, exclusive practice. From the mass use of remote camera traps in wildlife life science came a series of camera trap-based density estimation models, known as viewshed density estimators, which could allow practitioners to estimate wildlife density from camera trap data. Despite the cost-effective and accessible framework, viewshed density estimators remain analytically challenging to parameterize and implement. To accurately estimate density, viewshed density estimators require a precise metric of the physical area camera traps monitor, a highly variable number that can be challenging to quantify. Here, I tested a field and analytical framework that can be used to accurately estimate the spatial footprint of camera traps with a 100% capture probability, the Effective Capture Area. Next, I use the Effective Capture Area to parameterize the Random Encounter Staying Time model of density estimation for generating density and abundance estimates for moose (Alces alces) and elk (Cervus canadensis) across camera trap grids in Riding Mountain National Park, Canada. I show that, given adequate spatial and temporal sampling periods, the Random Encounter Staying Time model produces density and abundance estimates that correlate well with historic aerial flight surveys on both fine- and coarse-spatial scales. Finally, I comment on how viewshed density estimators can improve our understanding of wildlife density and abundance estimation, as well as provide novel insights in many areas of ecological study.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".