Single bin QPAD (SQPAD) approach for robust analysis of point-count data with detection error
Bibliographic record
Abstract
Abstract Bird population monitoring is often conducted using point-count surveys. Accounting for detection errors is a major challenge in analyzing these data. The commonly used methods for correcting detection errors in counts of organisms are distance sampling, removal sampling, N-mixture, and QPAD. These methods rely on multiple surveys or subdivisions within surveys (time/distance bins). The reliability of these approaches depends on the accurate estimation of distance, correct identification of individuals, and the closed population assumption. Errors in distance estimation, double counting, and mortality and migration of individuals within and between survey periods can lead to substantial biases in population density estimation. Furthermore, tracking individuals and estimating distances can be difficult in field conditions. We propose a simple modification of the QPAD method so that field observers are required to collect information only about either the occupancy status or count of individuals within a specified time interval and a specified spatial buffer, a “single bin,” around the observer’s location. We show that population density parameters are identifiable by changing the time interval and the radius of the spatial buffer for each survey location. We show that this variable-effort survey method is robust against errors in distance estimation and double counting. We also show that data collected under current protocols in North America can be analyzed using single bin QPAD. We illustrate our methodology with biologically realistic simulations and a reanalysis of some field data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".