There is a growing need to address the effects of roadway presence on wildlife. Not only do roads directly impact gene dispersal from a movement perspective, but they limit movement of the individual animal from a habitat perspective by presenting an artificial barrier between one area of viable habitat and another. For this reason it is becoming increasingly important to quantify contact between humans and wildlife and to develop better methods for mitigating these types of conflicts. Studying habitat connectivity and animal mobility in the context of roads can provide actionable information on how, where, and when these encounters might occur in order to minimize the effects transportation networks have on wildlife.
This study uses two different approaches for studying wildlife-road interactions: (1) quantifying habitat fragmentation caused by roads and (2) directly quantifying wildlife interaction with roadways. This was achieved through the development and extension of methods found in the fields of landscape ecology and time geography. First, this study demonstrates the utility of one newly created road-based landscape metric through a detailed case study via the creation of an original ArcGIS toolbox. Second, this study develops a new time-geographic methodology to probabilistically measure and predict where wildlife interactions are most likely to occur on road networks. Additionally, it is important to ensure these methods not only quantify effects of roads from habitat and movement perspectives but can be used to mitigate these conflicts in real world conservation settings. Each of these approaches individually leverages techniques found in the field of spatial optimization to strategically locate wildlife crossing structures.
This study developed two new methodologies to quantify where, when, and how wildlife interactions with roads are most likely to occur: the first using road-based landscape metrics and the second using a probabilistic voxel-based time-geographic approach. To address habitat connectivity issues, one road-based landscape metric was validated on a real world data set and further advanced by developing a GIS-based tool for real world applications. Utilizing landuse and roadway layers in combination with user specified parameters, the script tools developed here readily calculate this road-based landscape metric for a given study area. To address wildlife mobility issues, probabilistic space-time prisms were used to quantify interaction probabilities between wildlife and roads. These prisms were generated for a given set of tracking points and overlaid with an intersecting roads layer in GIS. Summing the probabilities at prism-roadway intersections revealed a pattern in the likelihood of animal-roadway interactions. Finally, each method was expanded to capture habitat fragmentation and animal movement in the presence of roads over large spatial scales using location analysis techniques.
This research also develops and implements new methods that explicitly address wildlife-road interactions and aid in siting potential wildlife crossing structures. Since this study directly addresses effects of roadway presence on wildlife, the techniques developed here offer an alternative approach versus existing methods from a habitat and wildlife movement perspective. These methods can aid planners in the conservation of wildlife whose habitat has been impacted by road development by identifying and targeting areas of high impact.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7067 |
| Date | 01 January 2015 |
| Creators | Loraamm, Rebecca Whitehead |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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