Probability of Detection of Nests and Implications for Survey Design
Bibliographic record
Abstract
Surveys based on double sampling include a correction for the probability of detection by assuming complete enumeration of birds in an intensively surveyed subsample of plots. To evaluate this assumption, we calculated the probability of detecting active shorebird nests by using information from observers who searched the same plots independently. Our results demonstrate that this probability varies substantially by species and stage of the nesting cycle but less by site or density of nests. Among the species we studied, the estimated single-visit probability of nest detection during the incubation period varied from 0.21 for the White-rumped Sandpiper (Calidris fuscicollis), the most difficult species to detect, to 0.64 for the Western Sandpiper (Calidris mauri), the most easily detected species, with a mean across species of 0.46. We used these detection probabilities to predict the fraction of persistent nests found over repeated nest searches. For a species with the mean value for detectability, the detection rate exceeded 0.85 after four visits. This level of nest detection was exceeded in only three visits for the Western Sandpiper, but six to nine visits were required for the White-rumped Sandpiper, depending on the type of survey employed. Our results suggest that the double-sampling method's requirement of nearly complete counts of birds in the intensively surveyed plots is likely to be met for birds with nests that survive over several visits of nest searching. Individuals with nests that fail quickly or individuals that do not breed can be detected with high probability only if territorial behavior is used to identify likely nesting pairs.
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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 | 0.342 | 0.749 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".