The influence of regional hydrology on nesting behavior and nest fate of the American alligator
Bibliographic record
Abstract
Abstract Hydrologic conditions are critical to the nesting behavior and reproductive success of crocodilians. In South Florida, USA, growing human settlement has led to extensive surface water management and modification of historical water flows in the wetlands, which have affected regional nesting of the American alligator (Alligator mississippiensis). Although both natural and anthropogenic factors are considered to determine hydrologic conditions, the aspects of hydrological patterns that affect alligator nest effort, flooding (partial and complete), and failure (no hatchling) are unclear. We deconstructed annual hydrological patterns using harmonic models that estimated hydrological matrices including mean, amplitude, timing of peak, and periodicity of surface water depth and discharge and examined their effects on alligator nesting using survey data from Shark Slough, Everglades National Park, from 1985 to 2005. Nest effort increased in years with higher mean and lesser periodicity of water depth. A greater proportion of nests were flooded and failed when peak discharge occurred earlier in the year. Also, nest flooding rates were greater in years with greater periodicity of water depth, and nest failure rate was greater when mean discharge was higher. This study guides future water management decisions to mitigate negative impacts on reproduction of alligators and provides wildlife managers with a tool for assessing and modifying annual water management plans to conserve crocodilians and other wetland species. © 2012 The Wildlife Society.
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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 | 0.000 | 0.001 |
| 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".