Reduced predation on roadside nests can compensate for road mortality in road‐adjacent turtle populations
Bibliographic record
Abstract
Abstract Turtles are killed on roads, yet there is little evidence of negative road effects on their abundances. We hypothesized that this could be due to reduced predation of turtle nests laid along roadsides, which could compensate for the effects of higher adult mortality on turtle populations near roads. To test this, we quantified the relative differences in predation of artificial painted turtle (Chrysemys picta) nests near and far from roads, in a field experiment controlling for potentially confounding differences between sites. The field experiment tested the predictions that (1) nest predation rates are lower for roadside nests than nonroadside nests, (2) nest predation rates are lower for linearly placed than clumped nests, and (3) nest predation rates are lower for roadside, linearly placed nests than any other road‐adjacency and configuration combinations. We then estimated how much adult roadkill could be compensated for by reduced roadside nest predation using population viability analysis (PVA). Linearly placed roadside nests had a 26% lower predation rate than nonroad nests in a “natural” (clumped) configuration. This result, combined in a PVA with life‐history information for painted turtles, led to an estimate that approximately 3%–6% annual adult roadkill can be compensated for by reduced nest predation near roads. This suggests that compensation for adult roadkill via predation release is a plausible explanation for the previously documented lack of road effects on turtle population abundance. These results highlight the importance of considering species interactions when evaluating the effects of roads on wildlife populations.
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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 | 0.001 |
| 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) | 0.001 | 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".