Anuran carcass persistence on roads: causes and implications for conservation
Bibliographic record
Abstract
Abstract Roads are pervasive and ubiquitous landscape features that have substantial and predominantly negative effects on wildlife. Conducting road surveys to count animals that have been struck and killed by vehicles is a common method for estimating the impact of roads on wildlife, especially for species at risk and animals with low road avoidance (i.e., herpetofauna). For road surveys to provide accurate animal mortality data, information about carcass persistence in different environmental contexts and in relation to survey frequency is necessary, but few studies have implemented these data into evaluations of road effects. Using road survey data collected in Ontario, Canada, in 2015 and survival analysis, we quantified anuran carcass (n = 91) persistence and determined the effects of carcass characteristics (size, species, condition), road characteristics (lane position, traffic volume), and environmental characteristics (precipitation, temperature) on carcass persistence on the road. Contrary to previous findings, we found that anuran carcasses persisted on roads longer than expected (5.5 ± 4.4 days, mean ± SD), with some carcasses persisting for up to 30 days. Temperature and precipitation had the greatest influence on the duration of anuran carcass persistence. Carcass condition, (i.e., intact versus partially intact carcasses), species, location on the road, and traffic volume had little to no effect on persistence. We recommend incorporating carcass persistence into road ecology studies, especially in the context of evaluating population‐level impacts of road mortality. Failure to do so could alter estimates of population viability and misinform management decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".