Stochastic and spatially explicit population viability analyses for an endangered freshwater turtle, <i>Clemmys guttata</i>
Bibliographic record
Abstract
Over two thirds of the world’s turtle species are in decline as a result of habitat destruction and harvesting. Quantitative methods for predicting the risk of extinction of turtle populations are essential for status assessments and recovery planning. Spotted turtles ( Clemmys guttata (Schneider, 1792)) are considered vulnerable internationally, and endangered in Canada. We used population viability analysis to assess the risk of extirpation of a Georgian Bay, Ontario, population that has been under study since 1977 and of nine Ontario populations for which population size is known, and to examine the effects of dispersal between breeding ponds on population persistence. A simple stochastic model for the Georgian Bay population projected a 60% probability of extirpation in 100 years. A metapopulation model for the same study area projected an 18% probability of extirpation within 100 years, suggesting that dispersal between breeding ponds is important for population persistence. Spotted turtles at this relatively pristine site have a relatively high risk of extinction despite the absence of anthropogenic additive mortality. Probability of quasi-extinction as a result of stochasticity for the model simulating nine Ontario populations was low, but the probability of six or more of the nine known Ontario populations becoming extirpated within 100 years was 26%, indicating that recovery action is necessary to prevent decline of spotted turtles within the species’ Canadian range, which is restricted to Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".