Using Resource Selection Functions to Improve Estimation of Elk Population Numbers
Bibliographic record
Abstract
Abstract: Stratification is commonly used to improve sampling efficiency of aerial surveys of ungulate populations with strata typically based on a priori information, such as preflight animal observations or vegetation attributes as surrogates for animal densities. We evaluated the usefulness of stratifying survey units for elk ( Cervus elaphus ) in the Rocky Mountain foothills of Alberta, Canada, using a resource selection function (RSF). We compared precision and design efficiency (DEFF) of population estimates from stratification approaches based on an RSF model to the past approach using amount of forest cover. We used a sample of telemetry relocations taken over a 3‐year period from 165 elk, rarified to times of the day and months of the year when aerial surveys are conducted, to develop the RSF. We then used the top RSF model, based on Akaike's Information Criterion, to derive the average RSF value for an 8‐km 2 survey unit. Using survey data from the first year, we evaluated binning schemes to define RSF‐oriented strata based on poststratification and showed that Jenks natural breaks in the RSF values provided the greatest improvement in DEFF and increased precision, compared to 2 other stratification schemes. We then used this approach with data from 2 additional surveys to find that stratification by RSF consistently improves relative precision and design efficiency of elk population estimates, whether we employ pre‐ or poststratification. Where a RSF is available it could be used as a surrogate for animal densities when conducting stratified sampling for population surveys.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".