Site fidelity of the declining amphibian<i>Rana sierrae</i>(Sierra Nevada yellow-legged frog)
Bibliographic record
Abstract
From 1997 to 2006, we used mark–recapture models to estimate the site fidelity of 1250 Sierra Nevada yellow-legged frogs ( Rana sierrae ) in Kings Canyon National Park, California, USA, during their three main activity periods of overwintering, breeding, and feeding. To quantify site fidelity, the tendency to return to and reuse previously occupied habitats, we used multistrata models (with water bodies as the strata) and potential function analyses. The probability of returning to previously used water bodies during all activity periods was typically greater than 80% and always greater than the probability of moving to other water bodies. Site fidelity models (with lake-specific movement transitions) were favored over those models that held movement transitions equal between lakes. Potential function analyses demonstrated that frogs were most strongly attracted to their original capture lakes rather than moving to the nearest available breeding or overwintering lake. Under current disturbances in high-elevation Sierra Nevada lakes (exotic trout, climate change), site fidelity is problematic because frogs return to lakes subject to drying or those with fish rather than dispersing to other lakes. Future recovery of declining species will need to focus efforts towards restoring habitats when animals maintain strong site fidelity even when their habitats deteriorate.
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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 | 0.002 |
| 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) | 0.001 | 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".