Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures
Abstract Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) bef...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-10-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.8053 |
| _version_ | 1818931985296392192 |
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| author | Thomas J. Yamashita Trinity D. Livingston Kevin W. Ryer John H. Young Jr. Richard J. Kline |
| author_facet | Thomas J. Yamashita Trinity D. Livingston Kevin W. Ryer John H. Young Jr. Richard J. Kline |
| author_sort | Thomas J. Yamashita |
| collection | DOAJ |
| description | Abstract Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location‐based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing were installed in this area between September 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and land cover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates, should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time. |
| first_indexed | 2024-12-20T04:25:17Z |
| format | Article |
| id | doaj.art-f64d5073d0ff4be587a88d98fdfbfeab |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-12-20T04:25:17Z |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-f64d5073d0ff4be587a88d98fdfbfeab2022-12-21T19:53:32ZengWileyEcology and Evolution2045-77582021-10-011119133051332010.1002/ece3.8053Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structuresThomas J. Yamashita0Trinity D. Livingston1Kevin W. Ryer2John H. Young Jr.3Richard J. Kline4School of Earth, Environmental, and Marine Sciences University of Texas Rio Grande Valley Port Isabel TX USASchool of Earth, Environmental, and Marine Sciences University of Texas Rio Grande Valley Port Isabel TX USASchool of Earth, Environmental, and Marine Sciences University of Texas Rio Grande Valley Port Isabel TX USAEnvironmental Affairs Division Texas Department of Transportation Austin TX USASchool of Earth, Environmental, and Marine Sciences University of Texas Rio Grande Valley Port Isabel TX USAAbstract Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location‐based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing were installed in this area between September 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and land cover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates, should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time.https://doi.org/10.1002/ece3.8053local hot spot analysisMann–Kendall testroad ecologywildlife mitigation structureswildlife road mortality |
| spellingShingle | Thomas J. Yamashita Trinity D. Livingston Kevin W. Ryer John H. Young Jr. Richard J. Kline Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures Ecology and Evolution local hot spot analysis Mann–Kendall test road ecology wildlife mitigation structures wildlife road mortality |
| title | Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| title_full | Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| title_fullStr | Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| title_full_unstemmed | Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| title_short | Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| title_sort | assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures |
| topic | local hot spot analysis Mann–Kendall test road ecology wildlife mitigation structures wildlife road mortality |
| url | https://doi.org/10.1002/ece3.8053 |
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