Spatial ecology and multi-scale habitat selection by Western Painted Turtles (<i>Chrysemys picta bellii</i>) in an urban area
Bibliographic record
Abstract
As urban centres expand, knowledge on the habitat and space use of native wildlife, particularly long-lived species, is required for proper management. Our objective was to understand space requirements and key habitat features necessary for long-term persistence of Western Painted Turtles (Chrysemys picta bellii) living in a Canadian urban park. Using radio telemetry, we examined seasonal habitat selection and space use over two years, 2015–2016 (n = 23), and 2016–2017 (n = 29) in Regina, Saskatchewan. Daily movements and home ranges of males and females were smaller during emergence than during nesting or post-nesting phases of the active season. Turtles inhabiting marsh sites had 2- and 4-times larger daily movements and home ranges compared to turtles inhabiting the creek. Turtles selected the shoreline habitat over urban/parkland and open water. Turtles used marsh-shoreline habitats non-randomly, selecting accessible shoreline with large trees in the active season. In contrast, turtles used creek-shoreline habitat according to availability. Overwintering sites selected by turtles were warmer and deeper than random available sites, with no difference in dissolved oxygen level. However, water was hypoxic for most overwintering sites. Our results show that turtles range widely, requiring 20–60 ha throughout the year. Urban park areas should be managed to provide accessible shorelines with a combination of cover and open basking areas. Critically, careful attention needs to be paid to managing water depth so that over-wintering sites remain viable.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".