The Effects of Road Mortality on Small, Isolated Turtle Populations
Bibliographic record
Abstract
Roads impact wildlife in a variety of direct and indirect ways. Roads may act as barriers to dispersal, lead to decreasing population size and genetic diversity, change animal behavior, result in direct mortality, and increase habitat disturbance. Road mortality is especially detrimental to long-lived species, such as freshwater turtles, whose population persistence relies on high adult and subadult survivorship to counter high egg and hatchling mortality. The Spotted Turtle (Clemmys guttata) is a small-bodied, freshwater turtle species that is listed as endangered in Canada and proposed for federal listing in the United States. We used a population viability analysis to attempt to quantify the impact that road mortality has on two distinct populations of Spotted Turtles. The baseline model for the North Wetland Complex (NWC) population predicted a probability of quasi-extinction within 150 yr of 20%. The baseline model for the South Wetland Complex (SWC) predicted a probability of quasi-extinction within 150 yr of 24%. Including an estimate of road mortality (modeled as a reduction in adult survival through annual catastrophic events) into the models, the probability of quasiextinction within 150 yr changed to 93% for the NWC and 94% for the SWC. Our results highlight the critical importance that anthropogenic additive adult mortality has on small populations of turtles and the necessity of detailed demographic studies to detect potential declines in populations of long-lived species.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".