The Effects of Flooding on the Spatial Ecology of Spotted Turtles (Clemmys guttata) in a Partially Mined Peatland
Bibliographic record
Abstract
Many studies have focused on the effects of anthropogenic habitat alterations on animals, but little attention has been given to the effects of natural changes in habitat. The purpose of our study was to examine the effects of flooding caused by Beaver (Castor canadensis) dams on the spatial ecology of the federally endangered Spotted Turtle (Clemmys guttata), in a bog in Ontario that was historically drained for peat extraction. We hypothesized that home range sizes and daily distances traveled would be greater after flooding and that habitat selection would change because turtles would exploit the increase in aquatic habitats post-flooding. Using 12 years of mark–recapture data, radio telemetry, and GIS software, we compared movements and habitat selection before and after flooding. Distances traveled and home range sizes were larger post-flood compared to pre-flood conditions, indicating that turtles were opportunistically exploring the new aquatic habitat. During pre-flooding, turtles primarily selected the drainage ditches created to facilitate peat extraction; these were the only aquatic habitat available. After flooding, there was a strong preference for newly flooded areas and drainage ditches, showing that turtles exploited the increase in available aquatic habitat. Our findings indicate that natural habitat alteration resulting from Beaver dam flooding may be beneficial for Spotted Turtles, although observations also suggest that nesting habitat may be limited due to the flooding, and further research is needed to determine the effect of the flooding on recruitment into the population.
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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 | 0.000 | 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) | 0.000 | 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".