First you get the money, then you get the power: Comparing the cost and power of monitoring programs to detect changes in occupancy of a threatened marsupial predator
Bibliographic record
Abstract
Abstract Ecological monitoring is crucial for tracking changes in the status of species over time. However, ensuring that monitoring programs possess adequate statistical power—capacity to detect changes in populations with a high level of confidence—remains a challenge for many wildlife managers globally. While new monitoring technologies potentially offer cost effective solutions to this problem, transitioning to these methods requires careful calibration with existing techniques, such that differences in power and cost can be measured and assessed accurately. Here, we compare new (camera traps) and conventional (live trapping) survey methods in terms of cost and statistical power in tracking occupancy declines in an endangered marsupial predator, the northern quoll ( Dasyurus hallucatus ). We show that camera trap monitoring designs can detect northern quoll occupancy declines of 30%, 50%, and 80% at reduced cost when compared to live trap designs, without compromising statistical power. Overall, we find support for the cost‐effectiveness of camera traps for species monitoring and its potential to replace existing live trap sampling of species when measuring changes in occupancy. Additionally, we offer a robust framework to compare new monitoring techniques against pre‐existing methods on the basis of statistical power.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".