Golf course living leads to a diet shift for American alligators
Bibliographic record
Abstract
Abstract Human‐driven land use change can fundamentally alter ecological communities, especially the diversity and abundance of large‐bodied predators. Yet, despite the important roles large‐bodied predators play in structuring communities through feeding, there have been only a few investigations of how the feeding patterns of large‐bodied predators change in human‐dominated landscapes. One group of large‐bodied predators that has been largely overlooked in the context of land use change is the crocodilians. To help fill these gaps, we studied the feeding patterns of juvenile American alligators (Alligator mississippiensis) on neighboring barrier islands on the southeast coast of Georgia, USA. Jekyll Island has multiple golf courses and substantial amounts of human activity, while Sapelo Island does not have any golf courses and a much smaller amount of human activity. We found that juvenile alligator populations on both islands ate the same types of prey but in vastly different quantities. Sapelo Island alligators primarily consumed crustaceans while alligators that lived on Jekyll Island's golf courses ate mostly insects/arachnids. Furthermore, the Jekyll Island alligators exhibited a much more generalist feeding pattern (individuals mostly ate the same types of prey in the same quantities) than the more specialized Sapelo Island alligators (diets were more varied across individuals). The most likely explanation for our results is that alligators living on golf courses have different habitat use patterns and have access to different prey communities relative to alligators in more natural habitats. Thus, land use change can strongly alter the feeding patterns of large‐bodied predators and, as a result, may affect their body condition, exposure to human‐made chemicals, and role within ecological communities.
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 | 0.000 | 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 | 0.001 | 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) | 0.003 | 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".