Impacts of a windfarm and wildfire on the spatial ecology and habitat selection of an endangered freshwater turtle
Bibliographic record
Abstract
Wind is a source of renewable energy, and its use is projected to increase as governments look for solutions to reduce carbon emissions. Although wind energy presents many advantages, windfarms can pose risks to wildlife. I used a post-hoc design to investigate whether body condition, spatial ecology, and multiscale habitat selection by spotted turtles (Clemmys guttata) differed among three treatment sites in central Ontario: Control, windfarm (Wind), and combined post-wildfire and windfarm (Windburn). I outfitted 9-10 turtles per treatment with VHF radio transmitters and tracked them approximately twice per week throughout the active season. Body condition, home range size and minimum daily distances moved by turtles did not differ among treatments, but it is possible that I did not detect acute responses to the habitat modification because turtles may have had sufficient time to adapt their behaviours as my study was conducted 2 years post-construction and 2.5 years post-wildfire. Turtles did not avoid habitats near turbines or roads but also did not cross roads unless a semi-aquatic culvert was present, highlighting the need to maintain habitat connectivity. In Windburn, turtles used wet depressions on rock barrens while Control and Wind turtles did not, possibly because Windburn turtles were exploiting new early successional macrohabitat resulting from the wildfire; however, pre-wildfire movement data would be required to confirm cause. In all treatments, turtles selected microhabitat based on temperature, water depth, available cover, and hummock presence, suggesting that turtles were able to find suitable microsites in the modified landscapes of my study area. My study is one of the first to assess the impacts of windfarms on semi-aquatic turtles, an at-risk and understudied taxon on windfarms, but more research is required to understand the acute and long-term impacts of windfarms and wildfires on turtles to inform data-driven mitigation strategies.
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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 | 0.001 |
| 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".