Modeling the Effect of Traffic Calming on Local Animal Population Persistence
Frank van Langevelde, Resource Ecology Group, Wageningen University
Catharinus F. Jaarsma, Land Use Planning Group, Wageningen University
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A steady growth in traffic volumes in industrialized countries with dense human populations is expected, especially on minor roads. As a consequence, the fragmentation of wildlife populations will increase dramatically. In human-dominated landscapes, typically minor roads occur in high densities, and animals encounter them frequently. Traffic calming is a new approach to mitigate negative impacts by reducing traffic volumes and speeds on minor roads at a regional scale. This leads to a distinction between roads with low volumes as being part of the traffic-calmed area, whereas roads with bundled traffic are located around this area. Within the traffic-calmed area, volumes and speeds can be decreased substantially; this is predicted to decrease the disturbance and mortality risk for animals. Thus far, data on the effects of traffic calming on wildlife population persistence remain scarce. Using metapopulation theory, we derived a model to estimate thresholds in the size of traffic-calmed areas and traffic volumes that may allow persistent populations. Our model suggests that traffic calming largely increases the persistence of roe deer in a landscape with a dense road network. Our modeling results show trade-offs between traffic volume on roads within the traffic-calmed area and both the area of habitat available for this species in the traffic-calmed area and the size of the traffic-calmed area. These results suggest ways to mitigate the fragmentation of wildlife habitat by road networks and their expected traffic volumes.
habitat fragmentation; metapopulation theory; mitigation; road ecology; traffic calming; transportation planning
Copyright © 2009 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.