Bias in density estimates from avian point-count surveys: Prospects for post-hoc corrections using calibration data
Bibliographic record
Abstract
Abstract Point-count surveys are commonplace in avian monitoring and research but were initially designed to collect data on avian relative abundance rather than densities. However, the increasing realization that detection biases influence conclusions from point-count surveys has given rise to several statistical approaches to correcting such biases. Distance sampling allows for correction of biases in perceptibility and estimation of avian densities. A key assumption is that distance estimation is accurate, but experimental evidence suggests observation error is large, particularly for estimates based on acoustic detections. We had observers estimate distances to 128 singing birds and one mammal while other staff systematically tracked calling individuals and measured the distance from the observer. Log-log regression showed distance estimation errors and uncertainty increased with increasing distance from the observer. We modeled the relationship between true effective detection radius (EDR) and estimated EDR (including distance estimation error). Simulations showed that species with small EDRs (30 m) had densities underestimated by 23% on average (SD = 11), while densities for species with large (130 m) EDRs were biased upward by 59% on average (SD = 25), but these biases could be corrected post-hoc. Applying the same post-hoc corrections in species-habitat regression models for 4 species of boreal forest birds found that our post-hoc corrections increased EDRs by up to 26% and decreased estimated bird densities by up to 38% for highly detectable species, while effects on less detectable species were more subtle. We suggest collection of more known-distance data that may allow post-hoc corrections to reduce bias in avian density estimates from point-count 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 | 0.056 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".