Decades of Road Mortality Cause Severe Decline in a Common Snapping Turtle (Chelydra serpentina) Population from an Urbanized Wetland
Bibliographic record
Abstract
Road networks threaten biodiversity and particularly herpetofauna, including common snapping turtles (Chelydra serpentina), which have an especially slow life history that prevents rapid recovery of populations subjected to road mortality. Cootes Drive is a 2.5-km 4-lane highway that bisects wetland habitat used for nesting and overwintering by snapping turtles. We hypothesized that turtle mortality from collisions with vehicles on Cootes Drive has caused a male bias and a decline in the population as turtles attempt to access habitat on both sides of the road. Capture–mark–recapture studies confirmed a dramatic decline in the turtle population from 941 individuals in 1985 to 177 individuals in 2002, a loss of 764 individuals in only 17 yrs. Using the same data, we also determined that the population has been significantly male-biased since 1985. Using 2009–2016 road mortality data obtained from the Dundas Turtle Watch (a citizen-science program), we completed a population viability analysis using the 2002 population size estimate to isolate the impact of road mortality. We found that this population is at risk of extirpation due to road mortality. The population range overlapped with the Cootes Drive and 7 of the 10 tracked turtles had individual home ranges that overlapped with the road. Our findings support the hypothesis that road mortality has contributed to the dramatic decline in the snapping turtle population in Cootes Paradise Marsh. This population is in jeopardy of extirpation; therefore, exclusion fencing must be installed for an extended distance along both sides of surrounding roads to prevent turtles from crossing the road and to promote their use of existing aquatic culverts.
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 | 0.001 |
| 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) | 0.001 | 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".