Combining spawn egg counts, individual photo‐ID and genetic fingerprinting to estimate the population size and sex ratio of an endangered amphibian
Bibliographic record
Abstract
According to the International Union for Conservation of Nature Red List, 41% of the world's amphibian species are threatened with extinction, making them more threatened than any other vertebrate group nowadays. Given the global amphibian crisis, comprehensive understanding of demographics and population trends of declining and threatened species is essential for effective management and conservation strategies. Counting egg spawns is widely used to assess population abundance in pond breeding anurans. However, it is unknown how such counts translate into robust population size estimations. We monitored the breeding activity of the Natterjack toad (Epidalea calamita), combining egg string counts and individual photo-identification with Capture-Mark-Recapture population size and operational sex ratio estimation. Male Natterjack toads were identified by the pattern of natural markings with repeated ID of the same individual confirmed for 10% of the samples using genetic fingerprinting. We identified 647 unique individuals within a closed study population at Caherdaniel, Co Kerry. Population estimates derived from egg string counts estimated a breeding population of 368 females (95% CI 353-384) and Capture-Mark-Recapture estimated a breeding population of 1698 males (95% CI 1000-2397). The female:male sex ratio was conservatively estimated at 1:5 (95% CI 1:3-1:6) where 62% ± 6% of females were assumed to spawn. These substantially departed from any priori assumption of 1:1 which could have underestimated the breeding population by up to 72%. Where amphibian absolute population size estimation is necessary, methods should include empirical survey data on operational sex ratios and not rely on assumptions or those derived from the literature which may be highly population and/or context-dependent.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".