The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Predicting locations of moose–vehicle collisions in Sweden
Open accessAbstract
Summary: Animal–vehicle collisions are a serious problem for road planners and biologists concerned with traffic safety, species conservation and animal welfare. In Sweden, vehicle collisions with moose (MVC) are an important safety issue. Police records average approximately 4500 incidents year−1, including 10–15 human fatalities. New mitigation policies require improved knowledge of the factors influencing the spatial distribution of MVC. Three logistic regression models were developed to predict MVC risks on public roads for use in strategic and project‐related impact assessment. The models were based on remotely sensed landscape data, road and traffic statistics and estimations of moose density, quantified at 2000 accident and 2000 non‐accident control sites in south‐central Sweden. Model predictions were validated on 2600 1‐km road sections in the county of Örebro, which were classified as either accident or non‐accident roads. Model performances were compared using Akaike's information criterion. Traffic volume, vehicle speed and the occurrence of fences were dominant factors determining MVC risks, identifying 72·7% of all accident sites. Within a given road category, however, the amount of and distance to forest cover, density of intersections between forest edges, private roads and the main accident road, and moose abundance indexed by harvest statistics, significantly distinguished between accident and control sites. In combination, road–traffic and landscape parameters produced an overall concordance in 83·6% of the predicted sites and identified 76·1% of all test road sections correctly. Synthesis and applications. The risk of moose–vehicle collisions in Sweden can be predicted from remotely sensed landscape data in combination with road traffic data. Prediction models suggest that reduced vehicle speed in combination with road fencing and increased roadside clearance may provide effective tools for road planners in counteracting MVC. However, effective mitigation will depend on integrated management of the surrounding landscape and moose population, as well as increased responsibility of individual drivers. Remedying animal–vehicle collisions must involve road authorities as much as landowners and the public.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Cited by
Predicting Hot Spots of Herpetofauna Road Mortality Along Highway Networks
LANGEN, T. A., OGDEN, K. M., & SCHWARTING, L. L. (2009) · The Journal of Wildlife Management · doi:10.2193/2008-017 · Cited by 117
Metadata
- Authors
- SEILER, A.
- Venue
- Journal of Applied Ecology
- Publisher
- Wiley
- Published
- 2005-04
- Volume
- 42
- Issue
- 2
- Pages
- 371-382
- ISSN
- 0021-8901 (ISSN-L)
- 1365-2664
- OpenAlex
- W2168656741
- Cited by
- 336
- References
- 1
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.