Research design considerations to ensure detection of all species in an avian community
Bibliographic record
Abstract
Summary Recent advances in the estimation of species richness from count data have allowed avian ecologists to incorporate incomplete detectability of species when comparing richness across space or time. Raw counts from single or repeated visits to sample point(s) are nonetheless still used for assessing community composition, and the failure to account for detectability when making these evaluations may lead to incorrect inferences about the community. We estimated detection probabilities (P) for a suite of bird species and used these detection probabilities to determine the minimum number of visits at a single point and the minimum number of points in a grid required to confidently (≥90%) detect the full community of birds for rare, moderately rare, and common species. We used occupancy modelling to estimate the detection probabilities for species from two study sites in Nebraska and Saskatchewan. Some common or highly detectable species were confidently detected in a single visit to a point, whereas others with low detection probabilities (P < 0·20) required more than ten visits to be confidently detected at a point. The grid size required to detect a species in an area varied from a single point for a common highly detectable species to over 30 points for a rare species with low detectability. Detection probabilities of the least detectable species in a study area can be used to determine the number of visits to a single point or the number of points in a grid to be confident that the full community is detected. Biologists can conclude that a species is most likely absent from the community if it remains undetected using the appropriate sampling effort.
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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 | 0.293 | 0.292 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".