Effect of paved road density on abundance of white-tailed deer
Bibliographic record
Abstract
Context Although ~3% of white-tailed deer are killed on roads each year, no previous study has tested for an effect of roads on deer abundance. This is difficult to do because road density is generally negatively correlated with deer habitat availability. Aims Our goal was to determine whether roads affect deer abundance. Methods First, we used an existing dataset from Pennsylvania, USA, to determine a range of paved road densities representing a significant range in deer per capita mortality. We then conducted a field study in eastern Ontario, Canada, with sample sites for relative deer abundance selected such that (1) road density in the surrounding landscapes varied over this same range, and (2) there were low correlations across landscapes between road density and deer habitat availability. The latter allowed us to isolate the effects of roads from the effects of habitat on deer abundance. We indexed relative deer abundance using a combination of pellet samples and track counts. Key results Unexpectedly, we observed a positive relationship between relative deer abundance and paved road density. Conclusions We speculate that this positive relationship is due to (1) reduced deer predation and/or perceived predation risk and/or hunting pressure in landscapes with higher road density and/or (2) provision of a resource or service by roads, the benefits of which outweigh the road mortality. Implications We found no evidence that road mortality places deer populations at risk of decline, at least over the range of road density values in our study. Therefore we conclude that road mortality is not a conservation concern for white-tailed deer in ecological contexts similar to our study areas.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| 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".