Assessing Capture Probabilities of Larval Pond-Breeding Anurans in New Brunswick, Canada
Bibliographic record
Abstract
We used removal sampling (RS) and neutral red dye capture–mark–recapture (CMR) methods to estimate capture probabilities for larval Wood Frogs (Lithobates sylvaticus), Spring Peepers (Pseudacris crucifer), and first-year Green Frogs (Lithobates clamitans) in ponds in southern New Brunswick, Canada. We modeled capture probability as a function of environmental variables and tested whether marks were retained between surveys. We also performed simulations to understand the effect of survey effort, capture probability, and abundance on detection probabilities and the number of surveys needed to be confident of absence. Capture probabilities (P ± SD) were low and variable: Wood Frogs, P = 0.262 ± 0.128; Spring Peepers, P = 0.323 ± 0.241; and first-year Green Frogs, P = 0.159 ± 0.106. With the use of AICc, we determined a model with the proportion of non-Typha emergent vegetation to have a positive effect, whereas pond depth had a negative effort on capture probabilities in first-year Green Frog larvae. No covariate models were better than an intercept-only model for Wood Frogs or Spring Peepers. Observers missed the dye mark on 5–24% of the marked larvae. Simulations showed that at observed capture probabilities and low abundances (greater than 100 larvae) all species would be detected on average with a single survey of at least 20% of the pond, but more survey effort/repeat surveys would be required to detect smaller populations. We recommend that capture probabilities be estimated whenever abundance estimates are required.
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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 | 0.000 | 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".