Amphibian breeding phenology and reproductive outcome: an examination using terrestrial and aquatic sampling
Bibliographic record
Abstract
Worldwide amphibian declines highlight the need for programs that monitor species presence and track population trends. We sampled larval amphibians with a box trap at 3-week intervals for 23 months in eight wetlands, and concurrently trapped adults and juveniles with drift fences, to examine spatiotemporal patterns of tadpole occurrence; explore relationships between breeding effort, tadpole abundance, and recruitment; and compare the efficacy of both methods in detecting species presence and reproductive outcome. Intermittent detection of species within and among wetlands suggested high mortality, followed by deposition of new eggs and tadpole cohorts. Breeding effort, tadpole abundance, and juvenile recruitment were generally not correlated. The reasons for this may include differential bias in detecting species or life stages between methods and high incidence of egg or tadpole mortality. Drift fences detected more species than box traps, but each provided insights regarding amphibian presence and recruitment. Our results illustrate shortfalls in the ability of infrequent aquatic sampling to detect local species richness of larval amphibians, as occurrence of many species is spatially and temporally variable. We also show the importance of using different sampling methods to detect species’ presence, as well as difficulties associated with both methods in tracking breeding effort, tadpole occurrence, or reproductive outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".