Modeling the effects of road network patterns on population persistence: relative importance of traffic mortality and 'fence effect'
Bibliographic record
Abstract
Roads affect animals in three adverse ways. They act as barriers to movement (âfence effectâ), enhance mortality due to collisions with traffic, and decrease habitat size. We study the relative importance of the first two effects using a spatially explicit individual-based model of population dynamics. We discuss our results with respect to the suitability of fences along roads as a measure to reduce road mortality. The results reveal a much stronger effect of road mortality than of the âfence effectâ; the influence of traffic mortality is always much more significant when the proportions of individuals avoiding the road and those that are killed on the road (in relation to the number of individuals encountering roads) in the two situations compared are the same. The results indicate that putting up fences along roads might be a useful interim mitigation measure until more suitable measures will be applied. However, fences must be used with caution because they could increase extinction risk for species that have large area requirements and small population sizes. In the second part of this paper, we outline a comparison of different configurations of road networks. We ask if different spatial arrangements of the same amount of roads (e.g., âbundlingâ of roads) have consequences for the strength of both the âfence effectâ and road mortality. The model results indicate longer times to extinction in case of a âbundlingâ of roads but the proportion of populations going extinct within 500 time steps does not change significantly.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".