Impact of urban road characteristics and natural landscapes on animal vehicle collisions: A case study in Nanjing

doi:10.17520/biods.2024141

Abstract

Abstract:

Aims: With the rapid expansion of road networks, the conflict between transportation development and ecological conservation has become increasingly prominent, garnering significant attention from ecologists. Research on road ecology has indicated that roads can fragment animal habitats, reduce connectivity between populations, and severely impact biodiversity. Among these impacts, animal vehicle collisions (i.e., roadkill) represent the most direct and significant threat to wildlife.

Methods: This research investigated the incidence of animal vehicle collisions in Nanjing, a mega city in East China. From November 2020 to October 2021, transect survey method was employed to investigate roadkill incidents across roads of different administrative levels in Nanjing. Regression analysis was used to explore the probability of roadkill incidents, and models were established to further clarify the impact of roads and surrounding environmental factors on the probability of animal roadkill.

Results: Our findings revealed that: (1) A total of 293 roadkill incidents involving 21 species were recorded during the survey period. Mammals accounted for 46.42% of the incidents, with cats (Felis catus) and dogs (Canis lupus familiaris) being the most common, while birds accounted for 48.81%, with the Chinese Blackbird (Turdus mandarinus) and Eurasian Tree Sparrow (Passer montanus) being predominant. (2) The proportion of arable land (dryland and irrigated farmland) and buildings (buildings and structures) in natural landscape significantly influenced the probability of roadkill incidents. Specifically, within a 250 m radius around the road, the coverage proportion of arable land and buildings was significantly negatively correlated with the occurrence probability of roadkill incidents. As cultivated land coverage increased, the probability of roadkill of birds decreased, while the probability of mammals’ roadkill increased, showed completely opposite trends. (3) The road characteristic affected the probability of animal vehicle collisions; roads with isolation belts were associated with an increased probability of roadkill incidents. (4) Linear features (distance to the nearest river, the nearest viaduct and the nearest main road) also affected the incidence of bird’s roadkill events. Birds had fewer vehicle collisions the farther they were from rivers, but more collisions while the farther they were from viaducts and main roads.

Conclusion: This study represents the first periodic investigation into terrestrial vertebrate roadkill in Nanjing. It preliminarily identifies the natural landscape and road factors that influence animal roadkill in Nanjing, thereby providing foundational knowledge to inform strategic decisions regarding regional road network optimization, infrastructure enhancements, ecological corridor establishment, and management strategies.

Key words: animal vehicle collisions, birds, mammals, influence factors

Qiong Wu, Zixi Zhao, Taozhu Sun, Yumeng Zhao, Cong Yu, Qin Zhu, Zhongqiu Li. Impact of urban road characteristics and natural landscapes on animal vehicle collisions: A case study in Nanjing[J]. Biodiv Sci, 2024, 32(8): 24141.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2024141

https://www.biodiversity-science.net/EN/Y2024/V32/I8/24141

Figures/Tables 5

References 61

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响应变量 Response variable	自变量类别 Independent variable category	所选模型 Selected model
每个取样单元内动物/鸟类/兽类路杀事件数量 Number of animal/bird/mammal roadkill incidents in each sampling unit	自然景观 Natural landscape	250 m缓冲区内耕地面积占比 + 250 m缓冲区内建筑面积占比 + 250 m缓冲区内林地面积占比 + 500 m缓冲区内草地面积占比 + 1,000 m缓冲区内草地面积占比 + 1,000 m缓冲区内林地面积占比+ 1,000 m缓冲区内耕地面积占比+ 1,000 m缓冲区内建筑面积占比 + (1 \| 取样单元所在调查样线) P_farm250 + P_build250 + P_forest250 + P_grass500 + P_grass1000 + P_forest1000 + P_farm1000 + P_build1000 + (1 \| ID)
	道路特征 Road features	海拔高度 + 道路曲折度+ 取样单元道路长度 + 车道数目 + 道路最高限速 + 道路隔离带 + 道路类型+ (1 \| 取样单元所在调查样线) Ele + Sinu + Length + Lane + Speed + Iso + Roadtype + (1 \| ID)
	线性特征 Linear features	到最近河流(线状水体)的距离 + 到最近湖泊湿地(面状水体)的距离 + 到最近高架桥的距离 + 到最近城市次干道的距离 + 到最近城市主干道的距离 + 到最近道路的距离+ (1 \| 取样单元所在调查样线) D_river + D_lake + D_trunk + D_subarte + D_arte + D_road + (1 \| ID)

响应变量 Response variable	自变量类别 Independent variable category	所选模型 Selected model
每个取样单元内动物/鸟类/兽类路杀事件数量 Number of animal/bird/mammal roadkill incidents in each sampling unit	自然景观 Natural landscape	250 m缓冲区内耕地面积占比 + 250 m缓冲区内建筑面积占比 + 250 m缓冲区内林地面积占比 + 500 m缓冲区内草地面积占比 + 1,000 m缓冲区内草地面积占比 + 1,000 m缓冲区内林地面积占比+ 1,000 m缓冲区内耕地面积占比+ 1,000 m缓冲区内建筑面积占比 + (1 \| 取样单元所在调查样线) P_farm250 + P_build250 + P_forest250 + P_grass500 + P_grass1000 + P_forest1000 + P_farm1000 + P_build1000 + (1 \| ID)
	道路特征 Road features	海拔高度 + 道路曲折度+ 取样单元道路长度 + 车道数目 + 道路最高限速 + 道路隔离带 + 道路类型+ (1 \| 取样单元所在调查样线) Ele + Sinu + Length + Lane + Speed + Iso + Roadtype + (1 \| ID)
	线性特征 Linear features	到最近河流(线状水体)的距离 + 到最近湖泊湿地(面状水体)的距离 + 到最近高架桥的距离 + 到最近城市次干道的距离 + 到最近城市主干道的距离 + 到最近道路的距离+ (1 \| 取样单元所在调查样线) D_river + D_lake + D_trunk + D_subarte + D_arte + D_road + (1 \| ID)

因变量 Dependent variable	所选模型 Selected model	自然景观变量的AIC AIC of natural landscape variables	道路特征变量的AIC AIC of road features variables	线性特征变量的AIC AIC of linear features variables
动物路杀事件 Animal vehicle collisions	负二项式模型 Negative binomial model	1,107.89	1,120.00	1,100.22
	广义线性混合模型 Generalized linear mixed model	1,194.70	1,227.10	1,162.42
	零膨胀负二项式模型 Zero expansion negative binomial model	1,110.33	1,120.01	1,092.97
鸟类路杀事件 Bird vehicle collisions	负二项式模型 Negative binomial model	693.23	717.34	690.89
	广义线性混合模型 Generalized linear mixed model	723.70	762.30	717.96
	零膨胀负二项式模型 Zero expansion negative binomial model	703.63	717.37	694.33
兽类路杀事件 Mammal vehicle collisions	负二项式模型 Negative binomial model	791.21	789.37	787.95
	广义线性混合模型 Generalized linear mixed model	848.50	843.60	827.92
	零膨胀负二项式模型 Zero expansion negative binomial model	794.04	789.40	780.92

因变量 Dependent variable	所选模型 Selected model	自然景观变量的AIC AIC of natural landscape variables	道路特征变量的AIC AIC of road features variables	线性特征变量的AIC AIC of linear features variables
动物路杀事件 Animal vehicle collisions	负二项式模型 Negative binomial model	1,107.89	1,120.00	1,100.22
	广义线性混合模型 Generalized linear mixed model	1,194.70	1,227.10	1,162.42
	零膨胀负二项式模型 Zero expansion negative binomial model	1,110.33	1,120.01	1,092.97
鸟类路杀事件 Bird vehicle collisions	负二项式模型 Negative binomial model	693.23	717.34	690.89
	广义线性混合模型 Generalized linear mixed model	723.70	762.30	717.96
	零膨胀负二项式模型 Zero expansion negative binomial model	703.63	717.37	694.33
兽类路杀事件 Mammal vehicle collisions	负二项式模型 Negative binomial model	791.21	789.37	787.95
	广义线性混合模型 Generalized linear mixed model	848.50	843.60	827.92
	零膨胀负二项式模型 Zero expansion negative binomial model	794.04	789.40	780.92

因变量 Dependent variable	解释变量 Explanatory variable	参数估计 Parameter estimation	标准误 Standard error	P值 P value
动物路杀事件 Animal vehicle collisions	截距 Intercept	‒0.203	0.138	0.141
	P_farm250	‒0.240	0.082	0.003^**
	P_build250	‒0.219	0.063	< 0.001^***
	P_farm1000	0.236	0.052	< 0.001^***
	Iso1	0.223	0.154	1.452
鸟类路杀事件 Bird vehicle collisions	截距 Intercept	‒1.560	0.252	< 0.001^***
	P_build250	‒0.303	0.100	0.002^**
	P_farm1000	0.272	0.087	0.002^**
	Iso1	0.547	0.227	0.016^*
	D_river	‒0.545	0.144	< 0.001^***
	D_trunk	0.285	0.143	0.046^*
	D_arte	0.462	0.094	< 0.001^***
兽类路杀事件 Mammal vehicle collisions	截距 Intercept	‒0.832	0.163	< 0.001^***
兽类路杀事件 Mammal vehicle collisions	P_farm250	‒0.201	0.103	0.051