American Woodcock Singing-Ground Survey: Comparison of Four Models for Trend in Population Size
Bibliographic record
Abstract
Abstract Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t-distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t-test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.
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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".