Demography and viability analyses of a diamondback terrapin population
Bibliographic record
Abstract
The diamondback terrapin, Malaclemys terrapin, is a long-lived species with special management requirements but quantitative analyses to support management are lacking. I analyzed markrecapture data and constructed an age-classified matrix population model to determine the status and viability of the only known diamondback terrapin population in Rhode Island. Female diamondback terrapins were captured, marked, and recaptured while nesting during 19902001. Population growth rate (λ) was 1.034 (95% confidence interval = 1.0121.056). For the preceding 5 years, however, abundance had been stable at about 188 breeding females. Adult apparent survival was high but declined slightly by 0.14% per year from 0.959 in 1990 to 0.944 in 2000. Recruitment of breeding females also decreased during the study period; therefore, survival was increasingly a greater component of population growth rate. Juvenile survival was 0.565 at λ = 1.034 and 0.446 at λ = 1. Both retrospective (markrecapture) and prospective (matrix population model) analyses showed a greater influence of survival versus reproduction on population growth. Population- model projections showed that capping nests to improve reproductive success could increase population growth rate, but the magnitude of increase was positively related to pre-reproductive survival, therefore negating nest capping as a remedy for declining populations or poor survival. Extinction attributable to demographic stochasticity is unlikely.
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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 | 0.001 | 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".