The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden

European ungulate populations are increasing both in number and distributional range, resulting in more ungulate-vehicle collisions (UVC). These UVC cause socio-economic losses and are a growing problem in Sweden. Since 2010, drivers in Sweden are legally obliged to report UVC-accidents to the poli...

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Autor principal: Chibwe, Bwalya
Formato: Second cycle, A2E
Lenguaje:sueco
Inglés
Publicado: 2021
Materias:
Acceso en línea:https://stud.epsilon.slu.se/17448/
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author Chibwe, Bwalya
author_browse Chibwe, Bwalya
author_facet Chibwe, Bwalya
author_sort Chibwe, Bwalya
collection Epsilon Archive for Student Projects
description European ungulate populations are increasing both in number and distributional range, resulting in more ungulate-vehicle collisions (UVC). These UVC cause socio-economic losses and are a growing problem in Sweden. Since 2010, drivers in Sweden are legally obliged to report UVC-accidents to the police. The police usually call upon specially contracted hunters to take care of the killed or injured animal and produce a report. With this information, from police and hunters, it is possible to map the occurrence of UVC and derive predictions on where and when the likelihood for accidents is especially high. The purpose of this study was to build on already existing data and research on UVC in Sweden and develop predictive models for the spatial occurrence of accident hotspots. I explored and analysed which road, traffic, landscape, ecological and behavioural related attributes correlate with the aggregation of UVC involving roe deer, moose, wild boar, reindeer, and fallow deer respectively and collectively. Using these variables, I created models by logistic regression to predict UVC hotspots that I believe will potentially assist in future management and preventive actions My results indicate that a combination of road and landscape variables were good predictors of the occurrence of hotspots in all species except in fallow deer. Nevertheless, road characteristics proved to be the most important parameters for predicting the occurrence of hotspots. Three road parameters i.e., Traffic Volume, Speed and Proportion of Unfenced Road had positive correlation to the occurrence of hotspots in all the species’ models. Other common variables that were present in at least 50% of the models included areas of open land, exploited land, arable land, minor and major roads and the distance to built-up areas.
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institution Swedish University of Agricultural Sciences
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Inglés
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spelling RepoSLU174482022-01-05T02:00:43Z https://stud.epsilon.slu.se/17448/ The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden Chibwe, Bwalya Nature conservation and land resources Landscape architecture European ungulate populations are increasing both in number and distributional range, resulting in more ungulate-vehicle collisions (UVC). These UVC cause socio-economic losses and are a growing problem in Sweden. Since 2010, drivers in Sweden are legally obliged to report UVC-accidents to the police. The police usually call upon specially contracted hunters to take care of the killed or injured animal and produce a report. With this information, from police and hunters, it is possible to map the occurrence of UVC and derive predictions on where and when the likelihood for accidents is especially high. The purpose of this study was to build on already existing data and research on UVC in Sweden and develop predictive models for the spatial occurrence of accident hotspots. I explored and analysed which road, traffic, landscape, ecological and behavioural related attributes correlate with the aggregation of UVC involving roe deer, moose, wild boar, reindeer, and fallow deer respectively and collectively. Using these variables, I created models by logistic regression to predict UVC hotspots that I believe will potentially assist in future management and preventive actions My results indicate that a combination of road and landscape variables were good predictors of the occurrence of hotspots in all species except in fallow deer. Nevertheless, road characteristics proved to be the most important parameters for predicting the occurrence of hotspots. Three road parameters i.e., Traffic Volume, Speed and Proportion of Unfenced Road had positive correlation to the occurrence of hotspots in all the species’ models. Other common variables that were present in at least 50% of the models included areas of open land, exploited land, arable land, minor and major roads and the distance to built-up areas. 2021-12-28 Second cycle, A2E NonPeerReviewed application/pdf sv https://stud.epsilon.slu.se/17448/1/chibwe_b_211228.pdf Chibwe, Bwalya, 2021. The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden. Second cycle, A2E. Grimsö: (S) > Dept. of Ecology <https://stud.epsilon.slu.se/view/divisions/4087.html> urn:nbn:se:slu:epsilon-s-17448 eng
spellingShingle Nature conservation and land resources
Landscape architecture
Chibwe, Bwalya
The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title_full The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title_fullStr The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title_full_unstemmed The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title_short The road and landscape features affecting the occurrence of ungulate-vehicle hotspots in Sweden
title_sort road and landscape features affecting the occurrence of ungulate-vehicle hotspots in sweden
topic Nature conservation and land resources
Landscape architecture
url https://stud.epsilon.slu.se/17448/
https://stud.epsilon.slu.se/17448/