Varying effort in capture–recapture studies
Bibliographic record
Abstract
Summary The standard spatial capture–recapture design for sampling animal populations uses a fixed array of detectors, each operated for the same time. However, methods are needed to deal with the unbalanced data that may result from unevenness of effort due to logistical constraints, partial equipment failure or pooling of data for analysis. We describe adjustments for varying effort for three types of data each with a different probability distribution for the number of observations per individual per detector per sampling occasion. A linear adjustment to the expected count is appropriate for P oisson‐distributed counts (e.g. faeces per searched quadrat). A linear adjustment on the hazard scale is appropriate for binary ( B ernoulli‐distributed) observations at either traps or binary proximity detectors (e.g. automatic cameras). Data pooled from varying numbers of binary detectors have a binomial distribution; adjustment is achieved by varying the size parameter of the binomial. We compared a hazard‐based adjustment to a more conventional covariate approach in simulations of one temporal and one spatial scenario for varying effort. The hazard‐based approach was the more parsimonious and appeared more resistant to bias and confounding. We analysed a dataset comprising DNA identifications of female grizzly bears U rsus arctos sampled asynchronously with hair snares in B ritish C olumbia in 2007. Adjustment for variation in sampling interval had negligible effect on density estimates, but unmasked an apparent decline in detection probability over the season. Duration‐dependent decay in sample quality is an alternative explanation for the decline that could be included in future models. Allowing for known variation in effort ensures that estimates of detection probability relate to a consistent unit of effort and improves the fit of detection models. Failure to account for varying effort may result in confounding between effort and density variation in time or space. Adjustment for effort allows rigorous analysis of unbalanced data with little extra cost in terms of precision or processing time. We suggest it should become routine in capture–recapture analyses. The methods have been made available in the R package secr.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".