Data from: Utilization patterns and optimization suggestions for wildlife passages in Xinjiang nature reserves
Data files
Sep 05, 2024 version files 105.98 KB
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Monitoring_data.xlsx
84.14 KB
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RDA_analysis_data.xlsx
18.25 KB
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README.md
3.59 KB
Abstract
The expansion of linear infrastructure significantly threatens biodiversity, necessitating comprehensive multi-species research and studies on regional differences. We surveyed and monitored wildlife passages in eight nature reserves in Xinjiang, assessing their construction status, utilization patterns, and key factors influencing utilization rates, and proposing optimization suggestions. Our results showed that dedicated wildlife passages were rare, except in large reserves, which mostly relied on small bridges and culverts for water passage. Strengthening passage construction in these areas is recommended. We recorded 32 wildlife species, including 13 bird species and 19 mammal species. The relative abundance index of Cetartiodactyla and Perissodactyla, such as bharal, goitered gazelle, and Asian wild ass, was notably high. Utilization rates of passages were lowest in winter, increased in spring, and peaked in summer and autumn. In July, all monitoring sites showed positive selection, indicating a universal trend. Passage utilization rates were significantly higher at night. At sunrise, 86.96% of monitoring sites showed positive selection, while at sunset, 91.30% did. Seasonal variations and time differences should be considered in passage design and management, particularly by reducing human activities during nighttime and dawn/dusk periods to improve utilization and protect wildlife migration and survival needs. The density of linear infrastructure and human activity intensity were the main factors influencing utilization rates. Lower infrastructure density leads to higher utilization rates, while high-intensity human activity areas, due to more food resources and reduced interspecific competition, increase utilization rates. Minimizing habitat fragmentation and strengthening passage construction and management in high human activity areas is suggested. Our study emphasizes tailored passage design and management based on species-specific and seasonal behaviors, contributing to better conservation practices and policies. These findings can be applied to other regions, offering principles in conservation biology to reduce habitat fragmentation and strategically place passages to facilitate wildlife movement and survival.
Methods
(1) Monitoring data
Infrared camera monitoring technology widely applied in wildlife passage utilization studies was used here. Cameras were set at 0.5-1 meters above the ground, angled slightly downward, avoiding direct sunlight or reflective objects. Camera performance and settings are shown in Table 2. Data was collected for at least three months per monitoring point to account for animal activity variations and ensure comprehensive data collection. Species identification referenced the " List of Biological Species of China (2023 Edition)," " Vertebrate Volume of the Red List of Biodiversity of China," and " List of Birds of China (4th Edition, 2023)." Photos with accurately identifiable species recorded species names and numbers.
(2) RDA analysis data
Traffic volume refers to the annual average traffic volume of motor vehicles, measured in vehicles per day. Underpass and overpass types of passages were assigned values of 1 and 2, respectively. Passage size refers to the volume of the passage (deck width of the bridge × clearance height × span) and the width of the passage reserved in the fence. Linear infrastructure density refers to the ratio of the total length of highways, railways, and fences within a 1-kilometer radius of the sample sites to the area of that region. Human activity intensity refers to the total number of independent valid photos of humans and livestock within the sample site divided by the total number of effective working days at all monitoring points. RAI stands for relative abundance index, and SWI stands for Shannon-Wiener index.