Dealing with detection error in site occupancy surveys: what can we do with a single survey?
Bibliographic record
Abstract
Site occupancy probabilities of target species are commonly used in various ecological studies, e.g. to monitor current status and trends in biodiversity. Detection error introduces bias in the estimators of site occupancy. Existing methods for estimating occupancy probability in the presence of detection error use replicate surveys. These methods assume population closure, i.e. the site occupancy status remains constant across surveys, and independence between surveys. We present an approach for estimating site occupancy probability in the presence of detection error that requires only a single survey and does not require assumption of population closure or independence. In place of the closure assumption, this method requires covariates that affect detection and occupancy. Penalized maximum-likelihood method was used to estimate the parameters. Estimability of the parameters was checked using data cloning. Parametric boostrapping method was used for computing confidence intervals. The single-survey approach facilitates analysis of historical datasets where replicate surveys are unavailable, situations where replicate surveys are expensive to conduct and when the assumptions of closure or independence are not met. This method saves significant amounts of time, energy and money in ecological surveys without sacrificing statistical validity. Further, we show that occupancy and habitat suitability are not synonymous and suggest a method to estimate habitat suitability using single-survey data.
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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 | 0.190 | 0.544 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.021 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| 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".