Nesting sites in agricultural landscapes may reduce the reproductive success of populations of Blanding’s Turtles (<i>Emydoidea blandingii</i>)
Bibliographic record
Abstract
Almost all turtle species nest in terrestrial environments and maternal site selection represents a critical component of nest success. Females use cues in the current environment to predict the future conditions for embryo development. However, in disturbed landscapes, current and future conditions may not be correlated. We compared selection of nest sites by Blanding’s Turtles (Emydoidea blandingii (Holbrook, 1838)) in a (relatively undisturbed) park and a (heavily disturbed) agricultural landscape in Ontario, Canada, using field measurements and satellite imagery. Environmental variables were compared using logistic regression and Akaike’s information criterion (AIC) based on data measured at nest (presence) and random (pseudoabsence) locations. Specific environmental variables associated with site selection differed between study areas. Most notably, NDVI (normalized difference vegetation index, a proxy for vegetation cover) increased significantly during the year at the agricultural locale, corresponding with the growth of planted fields. No parallel change was observed at the park locale where canopy cover remained more consistent. An increase in vegetation cover may alter nest temperatures and soil moisture. Combined with the unpredictability in timing of crop sowing and harvesting, findings suggest that nests in agricultural fields may act as ecological sinks and that other species nesting in similarly altered habitats may be subjected to the same threats.
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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 | 0.001 |
| 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 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".