Identifying Suitable Turtle Overwintering Habitat Through the Integration of Thermal, Chemical and Physical Wetland Properties
Bibliographic record
Abstract
Turtles are among one of the most vulnerable vertebrates on the planet and within Canada, all eight native species of freshwater turtle are designated as at-risk under COSEWIC. The primary extinction risk turtles face is the loss of their habitats. At the northern limit of their range, turtles rely on the presence and persistence of aquatic habitats to survive the winter. In the face of unprecedented climate and land-use changes, identification of key overwintering habitat attributes is crucial because relatively little is known about how they interact to optimize overwintering habitat suitability. We measured in-situ thermal, chemical and physical conditions at Blanding’s turtle overwintering sites within wetlands in the Georgian Bay Biosphere Mnidoo Gamii to (1) characterize available overwintering habitat, (2) test for overwintering habitat selection by Blanding’s turtles and (3) quantify the turtle resilience zone (RZ). We measured water and sediment temperatures, dissolved oxygen (DO), water, ice and snow depth at 5 (2020-2021) and 19 (2021-2022) Blanding’s turtle overwintering locations and four paired available microhabitats (floating mat, inflow, lagg, open water) across 5 wetlands. Blanding’s turtles selected average daily temperatures that ranged from 3.58°C to 0.53°C with a mean of 1.08°C, minimum average DO observed was 1.10 mg/L and average water and ice depth ranged from 42cm to 79cm, 12cm to 38cm, respectively. Based on habitat use data, we quantified the RZ as present when aquatic habitats had temperatures between 0 and 4°C, DO ≥1 mg/L and minimum water depth of 40 cm. Using the RZ we identified suitable overwintering habitats within our study region and found that the RZ varied across wetlands and microhabitats. Overall, the RZ is a powerful tool that can be used to identify suitable overwintering sites which can directly contribute to conservation and management strategies for at-risk turtles.
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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) | 0.016 | 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".