Using Spatial Approaches to Examine Threats Facing Common Snapping Turtles (Chelydra Serpentina) in the Urbanized Cootes Paradise Marsh, Lake Ontario
Bibliographic record
Abstract
In Ontario, the common snapping turtle, Chelydra serpentina, is listed as Special Concern and is at risk of decline. These long-lived organisms are particularly susceptible to anthropogenic threats owing to delayed sexual maturity, low recruitment and reliance on low adult mortality. Threats facing this species include road mortality and habitat loss and/or destruction. These threats are anecdotally evident within Cootes Paradise Marsh, a highly urbanized river-mouth coastal marsh located in a heavily urbanized region at the western end of Lake Ontario. Straddling Dundas and Hamilton, the wetland is bisected by Cootes Drive, a four-lane highway (maximum speed 80 km/h) that has resulted in fatal collisions with wildlife. We re-analyzed data from previous capture-mark-recapture studies and determined that the snapping turtle population has declined by almost 90% from 1985 to 2002. Using road mortality survey data collected by citizen science group Dundas Turtle Watch, we determined that some of this decline can be attributed to road mortality. Through radio tracking, we also found that the population remains at risk to road mortalities because their home ranges overlap surrounding roads. Besides the direct negative effect of mortality, roads impart broader and additional unintended consequences by hindering turtles’ access to critical nesting and overwintering habitat. We acquired digital orthophotos to assess changes in availability of nesting habitat, which is crucial for recruitment and population maintenance. Potential nesting habitat decreased by almost 50% between 1934 and 2010. Through nest surveys conducted in 2017, we determined that there is currently disproportionate use of artificial gravel mounds for nesting. For overwintering purposes, snapping turtles appear to be using a wide range of habitat types within the upland terrestrial matrix of the wetland ecosystem. We also confirmed that sites used for overwintering did not cool below inhibiting temperatures between early December and end of March. This study illustrates the toll that road mortality can have on urbanized herpetofauna populations and highlights the importance of ensuring that recovery plans focus on restoring both the quantity and quality of nesting and overwintering habitats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".