Do diets vary over large spatial or temporal ranges? A test using interannual and interpopulation data on Diamondback Terrapin (<i>Malaclemys terrapin</i>) diets
Bibliographic record
Abstract
Animal diets may vary spatially or temporally as resource availability vary. Diets of species with extensive geographic ranges often span multiple habitats, thus their diets may vary accordingly. Temporal diet variation is rarely explored because most diet studies are short term; this is problematic for long-lived species where individuals may persist as prey availability changes. We analyzed diet variation in Diamondback Terrapins (Malaclemys terrapin (Schoepf, 1793)), which inhabits nearly 70 000 km of United States Atlantic coastline, spanning 16.5°N latitude and 27.4°W longitude and four Köppen climatic zones, and Bermuda. We explored spatially or temporally Diamondback Terrapin diet variation, including populations from Atlantic salt marshes, an Everglades mangrove swamp, the Texas Gulf Coast, and a Caribbean golf course pond. We found remarkably high levels of similarity, indicating that although diets vary according to local prey availability, they are broadly similar at lower taxonomic resolution. Even short-term studies may be sufficient to accurately characterize diets of Diamondback Terrapins. These results are surprising given the geographic range sampled in this study and indicate that Diamondback Terrapin diets are conservative, reflecting local prey availability. Such diets apparently allow Diamondback Terrapins to exploit their extensive range and may allow Diamondback terrapin populations to persist as local prey species wax and wane.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".