Next Article in Journal
Estimation of the Impact of Climate Change on Spinach Cultivation Areas in Türkiye
Previous Article in Journal
Study on the Dynamic Stability and Spectral Characteristics of a Toppling Dangerous Rock Mass under Seismic Excitation
Previous Article in Special Issue
An Analytical Framework of the Factors Affecting Wildlife–Vehicle Collisions and Barriers to Movement
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 21
10.3390/su152115398
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hotspots and Factors Influencing Vertebrate Roadkill on the Ring Changbai Mountain Scenic Road, China

by
Yangang Yang
1,
Yun Wang
1,*,
Shuangcheng Tao
1,
Guoqiang Shi
2,
Zhuocong Wang
2 and
Yaping Kong
1,*
1
China Academy of Transportation Sciences, Beijing 100029, China
2
Changbai Mountain Academy of Sciences of Jilin Province, Antu 133613, China
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(21), 15398; https://doi.org/10.3390/su152115398
Submission received: 19 June 2023 / Revised: 23 October 2023 / Accepted: 24 October 2023 / Published: 28 October 2023
(This article belongs to the Special Issue Ecologically Sustainable Transport and Other Linear Infrastructure in Asia and Europe)
Browse Figures
Review Reports Versions Notes

Abstract

:
The spatial aggregation patterns of wildlife-vehicle collisions are used to inform where mitigation measures are most needed. Based on 10 years of observations of vertebrate roadkill on the Ring Changbai Mountain Scenic Road, the spatial distribution characteristics of roadkill incidence were analyzed in this study. Using the field survey method to investigate roadkill incidents and their influencing factors, we applied generalized linear mixed modeling (GLMM) for model selection and constructed roadkill models for different taxa groups. The spatial distribution patterns of roadkill hotspots vary among different taxa and exhibit a unimodal or multimodal distribution. The road section along a river and with a minimal distance between the road and the water has a high incidence of roadkill. The density of roadkill for various taxa decreases as the distances from rivers, bridges, and ponds increases. However, there appears to be no correlation between the density of bird roadkill and any factors. Finally, wildlife crossing facilities and guidance measures aimed at reducing the incidence of roadkill and enhancing the selection of routes and wildlife crossing structures in the area are formulated.

1. Introduction

While the development of transportation infrastructure has undoubtedly facilitated the movement of people and goods, it has also resulted in a range of negative environmental impacts [1,2], one of the most notable of which is collisions between wildlife and vehicles [3]. Extensive research focused on roadkill has been carried out in many countries [4,5], and it has been found that the occurrence of roadkill is more likely when vehicles encounter slow-moving animals or scavengers [6,7,8]. Wildlife-vehicle collisions in certain areas have been increasingly recognized as a significant factor contributing to wildlife mortality [5], resulting in a decline in wildlife populations [9].
The use of heatmap analysis to study roadkill characteristics and pinpoint influential variables has been extensively investigated. Various methods, including the use of remote sensing data and citizen science data, have been employed to describe the spatial patterns and influencing factors of amphibian roadkill [10,11], reptile roadkill [12], bird roadkill [13], and mammal roadkill [14]. Research indicates that the road mortality among amphibians and reptiles is largely associated with their breeding sites [15,16], while the cumulative risk of road mortality and bird species richness show statistically significant spatial consistency [13].
Extensive research has examined the effects of roads on wildlife mortality in the Changbai Mountain area of China and has shed light on the impact of roads on animal mortality, the alteration of animal habitats caused by roads, and the frequency of use of road crossing structures [17]. In the area, there has been a specific emphasis on amphibians [18], reptiles [19], and mammals [20]. However, research on the distribution of vertebrate roadkill and its influencing factors is currently lacking. If road fatality hotspots can be identified, and if their relationship with the distribution of road fatality zones can be determined, road construction authorities can be guided to optimize road alignment, the layout of engineering structures, and the enactment of measures to mitigate road fatalities based on severity and location.
This study presents a comprehensive 10-year observation of roadkill incidents along a 40 km specific segment of the Ring Changbai Mountain Scenic Road. The study aimed to achieve the following objectives: (a) utilize GIS spatial analysis to identify the spatial distribution characteristics of roadkill incidents among various wildlife groups, including amphibians, reptiles, birds, and mammals; (b) identify roadkill hotspots and establish predictive models for roadkill incidents among different taxa; (c) provide insights for identifying wildlife corridors and guiding the development of control measures for reducing roadkill.

2. Materials and Methods

2.1. Study Area

The expansion of the Ring Changbai Mountain Scenic Road began at the end of 2007 and mainly involved the utilization of existing forest roads to form an arc around the west and north sides of the Changbai Mountain Nature Reserve (41°41′49″–42°51′18″ N, 127°42′55″–128°16′48″ E) (Figure 1). This study was conducted within a protected area in the broad-leaved Korean pine forest zone. The vegetation in the study area consisted of broad-leaved Korean pine forest, with secondary birch forests dominating within 200 m on both sides of the road, along with some patches of broad-leaved Korean pine forest and open areas. A standard secondary road paved with asphalt and equipped with hard shoulders was constructed with a designated speed limit of 60 km/h. The road accommodates a traffic volume of 15–70 vehicles per day [20] and has a subgrade width of 10 m [21]. The region is a UNESCO Biosphere Reserve and a biodiversity-rich area with 1619 plant species, among which 23 are nationally protected. There are about 1586 wildlife species, including 9 amphibian species, 12 reptile species, over 230 bird species, and more than 56 mammal species, among which 49 are nationally protected [22]. The starting point of the roadkill investigation is located at Erdaobaihe Town, corresponding to milestone K0 of the Ring Changbai Mountain Scenic Road, and the endpoint is located at milestone K40, with a total length of 40 km. Approximately 21 km (K10–K31) of the road coincides with the edge of the Nature Reserve, while about 6 km (K31–K37) crosses the experimental zone of the Nature Reserve, and the other sections are located outside the reserve.

2.2. Data Collection and Pre-Processing

2.2.1. Recording and Entry of Roadkill Locations

During the hibernation period of amphibians in the Changbai Mountain region, which extends from early November to March of the following year [18], no surveys were conducted. However, roadkill incidents on the K0–K40 section of the Ring Changbai Mountain Scenic Road were recorded annually between 2009 and 2019 from April to October. The surveys were carried out during rain-free mornings from 6:00 a.m. to 12:00 p.m., with 3 to 4 irregular surveys conducted monthly along the designated road transects. In May and August, two concentrated surveys were conducted using slow-driving vehicles with speeds below 20 km/h. For the remaining period, a walking or cycling transect survey method was employed to record roadkill incidents. These surveys involved documenting animal carcasses on the road surface and roadside slopes, and recording the species, quantity, and location of the observed deceased animals.
In the initial surveys, we made preliminary assessments of the observed roadkill cases and only recorded fresh carcasses to avoid confounding effects from earlier mortality events. The roadkill wildlife was divided into four groups, including amphibians, reptiles, birds, and mammals, and the recorded information included the species and number of killed animals [23], the coordinates of the killing sites, the road milestones, and the recording time. In addition, the types of human interference, the presence of bridges or drainage culverts, and the roadside vegetation conditions were also recorded. After counting, the carcasses were removed and spray-painted at the location to prevent duplicate counting. A total of 1366 roadkill sites were collected, and the results were collated into a roadkill statistics table.

2.2.2. Data Processing of Factors Contributing to Roadkill

Six predictor variables related to roadkill were evaluated, as shown in Table 1, including the elevation (ELEV) [24], the normalized difference vegetation index (NDVI) [25], the distance to a bridge or culvert (DTB) [26], the distance to a river (DTR), the distance to a pool (DTP) [27], and the distance to a disturbance (DTD) [28].
The elevation data were obtained from the National Basic Science Data Cloud (http://www.gscloud.cn (accessed on 20 February 2022), and the SRTM 90 m resolution product from the year 2000 was used. The “Extract Values to Points” tool in ArcGIS was used to extract the elevation data at the roadkill locations.
The vegetation data in the study area were obtained from the 1:1 million vegetation dataset for China (http://www.ncdc.ac.cn (accessed on 5 February 2020) [29]. Field surveys were conducted to identify the type of vegetation alongside the roads in the study area. The NDVI values were calculated from Landsat ETM+ images from the month of August between 2009 and 2019. The NDVI formula was (B5 − B4)/(B5 + B4), where B4 and B5 are, respectively, the spectral reflectance values of the fourth and fifth bands of the ETM+ sensor. The NDVI values were limited to a range of [−1, 1]. We considered the maximum value of the NDVI in the image as the NDVI value for pure vegetation cover and the minimum value as the NDVI value for pure bare soil cover.
To identify the distribution of human disturbances in the study area, Landsat ETM+ data from August 2019 and location data from the field surveys were used to visually interpret the land-use types on both sides of the road, i.e., cultivated land, construction land, artificial forest land, and ginseng cultivation land.
The spatial coordinate information of the bridges, culverts, and ponds along the road was synchronously collected using a handheld GPS device (Garmin, GPSmap 629sc, Shanghai, China) during the field survey and was entered into SiteSurvey software (v 2.30, 22 August 2019, Kansas US) to generate a point layer for road bridges and culverts.
The river data for the study area were obtained from the 1:250,000 National Topographic Database provided by the National Geomatics Center of China.
The roadkill density was generated by using the point density tool in ArcGIS 10.5 (ESRI inc. US), and the search radius included the other eight pixels, which size is 90 m, in the grid adjacent to the central roadkill point. The distance between each roadkill location and the nearest bridge, culvert, pond, river, or human disturbance was determined using the GenerateNearTable tool [30].

2.2.3. Roadkill Hotspot Identification

The road sections in which the roadkill density frequency was greater than the upper quantile (75%) were defined as roadkill “hotspots.” The roadkill hotspots for amphibians, reptiles, birds, and mammals were further identified based on the previously discussed categorization.

2.2.4. Examining the Factors Affecting Roadkill in Different Taxonomic Groups

Based on the visualizations that demonstrate a linear relationship between the explanatory variables and the response variable, we have included these variables in the analysis. Generalized linear mixed modeling (GLMM) was applied to quantitatively analyze the relationships between the high-density areas and the influencing factors since the roadkill density data is a continuous variable rather than a count variable, and it follows a normal distribution pattern. When constructing the generalized linear model, we selected the Gaussian distribution as the exponential family. We used the dredge function of the MuMIn package to construct various models and assess the significance of different explanatory variables (ELEV, NDVI, DTB, DTR, DTP, and DTD) in predicting roadkill density across different wildlife groups (amphibians, reptiles, birds, and mammals). We selected the model with the lowest AIC value, determined by Akaike’s information criterion (AIC). Moreover, predictive models for roadkill hotspots for each wildlife group were developed using generalized linear mixed modeling. In the model, we used the roadkill density as the response variable, the Gaussian family to assess collinearity among the explanatory variables, and the variance inflation factor (VIF) values. A VIF value greater than 10 indicates strong collinearity. We gradually eliminated variables that exhibited collinearity issues. These models allowed for the analysis of the quantitative relationships between the roadkill density and environmental factors. All analyses were conducted using the lme4 package in R [31,32,33].

3. Results

3.1. Spatial Distributions of Roadkill

During the on-site investigation, we recorded the presence of five amphibian species: Chinese brown frog (Rana chensinensis), oriental fire-bellied toad (Bombina orientalis), Asiatic toad (Bufo gargarizans), Siberian salamander (Salamandrella keyserlingi), and North China treefrog (Dryophytes immaculatus); 10 reptile species affected by roadkill incidents with more than 10 fatalities were observed among steppe ratsnake (Elaphe dione), short-tailed pit viper (Gloydius brevicaudus), Ussuri mamushi (Gloydius ussuriensis), Amur ratsnake (Elaphe schrenckii), tiger keelback (Rhabdophis tigrinus), and northern viper (Vipera berus). Among the bird species, 31 different species were observed to suffer significant mortality, with more than 10 fatalities recorded for certain species such as black-faced bunting (Emberiz spodocephala), yellow-throated bunting (Emberiz elegans), grey-backed thrush (Turdus hortulorum), Tristram’s bunting (Emberiz tristrami), Eurasian nuthatch (Sitta europaea) and grey wagtail (Motacilla cinerea). A total of 17 mammal species were impacted by roadkill, and notable casualties exceeding 10 fatalities were observed among Siberian chipmunk (Tamias sibiricus), grey red-backed vole (Myodes rufocanus), Amur hedgehog (Erinaceus amurensis), large-toothed Siberian shrew (Sorex daphaenodon), Korean field mouse (Apodemus peninsulae), and Manchurian hare (Lepus mandshuricus).
The roadkill incidence were specifically quantified as follows: amphibians totaled 4290 casualties in 642 roadkill spots, reptiles recorded 136 fatalities in 131 roadkill spots, birds experienced 236 deaths in 229 roadkill spots, and mammals accounted for 373 fatalities in 364 roadkill spots.
The spatial distributions of roadkill in different wildlife groups on the Ring Changbai Mountain Scenic Road were not random but rather exhibited a clear unimodal or multimodal spatial pattern, as shown in Figure 2 and Figure 3.
The average density of roadkill points along the entire highway was 89.58 ± 1.47 individuals·km−2, and roadkill points with densities greater than the upper quantile value (160.43 ± 2.59 individuals·km−2) including 198 points were mainly distributed between K9 and K19 + 500. The roadkill density of amphibians exhibited a unimodal distribution pattern (Figure 3a), and the segments with densities above the upper quantile value (80.23 ± 1.15 individuals·km−2) including 166 points were mainly distributed between K16 + 000 and K17 + 000. The roadkill density of reptiles higher than the upper quantile (7.93 individuals·km−2) including 32 points was mainly distributed between K12 + 000 and K13 + 000, and between K18 + 000 and K19 + 000, with no prominent peak (Figure 3b). The roadkill density of birds was relatively evenly distributed along the entire highway, without a clear peak in the distribution (Figure 3c). The roadkill density of mammals exhibited a unimodal distribution pattern (Figure 3d), with higher-density segments above the larger quantile value (13.67 ± 0.29 individuals·km−2), including 73 points mainly distributed between K18 + 000 and K20 + 000.

3.2. Factors and Models for Roadkill Prediction

The vertebrate roadkill density prediction model is presented in Table 2. DTB and DTP were found to have negative contributions, with estimated values of −0.039 and −0.031, respectively. In contrast, ELEV and DTD were found to have positive contributions, with estimated values of 0.448 and 0.037, respectively.
The amphibian roadkill density prediction model included the variables DTB, NDVI, DTP, and DTD. DTB and DTP had negative contributions, with estimated values of −0.027 and −0.007, respectively, while NDVI and DTD had positive contributions, with estimated values of 13.748 and 0.010, respectively.
The reptile roadkill density prediction model included the variables DTR, NDVI, and DTP. DTR and NDVI had negative contributions, with estimated values of −0.013 and −10.537, respectively, while DTP had a positive contribution, with an estimated value of 0.002.
The mammal roadkill density prediction model included the variables DTR, NDVI, DTD, and ELEV. DTR DTD and NDVI had negative contributions, with estimated values of −0.023, −0.002, and −28.041, respectively, while ELEV had a positive contribution, with an estimated value of 0.039.

4. Discussion

4.1. Spatial Distribution of Roadkill

Wildlife-vehicle collisions are the main cause of death for wildlife populations in road environments and can have significant impacts on population levels [34]. Spatial distribution studies of roadkill are crucial for the identification and evaluation of high-risk locations [35]. This study analyzed 10 years of roadkill data for the Ring Changbai Mountain Scenic Road using GIS density analysis to provide insights into the spatial distribution of roadkill hotspots. The results showed that slow-moving amphibians often need to cross roads to get different critical resources on either side of the road during various life stages, such as egg-laying, foraging, and land activities. This suggests that roadkill may threaten the stability and the evolutionary consequences to the amphibian populations [36,37,38]. The spatial distribution patterns of roadkill exhibited unimodal or multimodal distributions, meaning that the distribution is spread along the road from one or several hotspots, similar to the results observed in related studies [39,40]. This also suggests that setting mitigation measures at the center of roadkill hotspots is the most effective and necessary solution for future roadkill prevention and the evolutionary consequences to the population [41].
The spatial distribution patterns of roadkill hotspots were found to vary among different taxa [42]. Some studies have found that the high-frequency locations of amphibian roadkill mainly occur near their breeding sites [43]. In the present study, a high roadkill density was observed along the road section located along a river with a short distance between the road and the river. This may explain the high mortality rate of amphibians in this area. The spatial distribution of roadkill hotspots for reptiles was found to be similar to that of amphibians, as both groups often need to move around their habitats for activities such as hibernation, mating, hunting, and egg-laying. They are also sensitive to the impact of roads on their habitats, which can result in roadkill [44]. The entire section of the Ring Changbai Mountain Scenic Road is a known location for bird roadkill, and this phenomenon was not found to be significantly correlated with environmental factors. This finding suggests that roadkill can impact not only rare or endangered species, but also more common ones.
The mammal roadkill hotspot is situated along a river and is primarily dominated by ginseng cultivation, resulting in frequent human disturbances. On one hand, the river serves as a water source for mammals, facilitating their proximity to the road [45]. This, in turn, increases their accessibility to and ability to cross the road. Additionally, the high human activity in this area leads to increased traffic volume, resulting in a higher incidence of roadkill. This indicates that when the foraging, watering, or habitat areas of mammals overlap with regions of frequent human activities and are in close proximity to roads, we should pay attention to the potential risks of roadkill and implement appropriate measures to mitigate this impact.

4.2. Factors for Roadkill Prediction

The overall density of roadkill decreases as the distances from rivers, bridges, and ponds increase. This implies that the closer the distances to rivers, bridges, and ponds, the higher the roadkill density, while the closer the distance to human disturbances, the lower the roadkill density. The Ring Changbai Mountain Scenic Road is surrounded by rivers, ponds, and other water bodies, which serve as the primary habitats for amphibians. The well-developed water system along the highway contributes to a high density of amphibians that often cross the road, leading to a significant number of roadkill incidents [17]. Moreover, because amphibians are the primary prey of reptiles, the factors that affect the roadkill density of reptiles are consistent with those that affect the roadkill density of amphibians [19,46]. Studies also show that amphibians and reptiles near ponds and wetland sections are greatly affected by road traffic [47], and it is believed that roads near wetlands are the main cause of the high mortality rates among amphibians and reptiles [48]. This finding supports the results of the current study.
The roadkill density of mammals was found to be higher in areas closer to rivers, with less human disturbance and a lower vegetation index. This phenomenon may be attributed to the higher incidence of the mortality of small mammals, such as rodents and insectivores, on the road [20]. Adjacent water bodies facilitate the access of mammals to water, while their alertness allows them to evade traffic disturbances. For many mammalian species, lower roadkill rates may be due to their familiarity with the habitat, which enables them to effectively avoid traffic disruptions [49]. Road segments adjacent to rivers with low vegetation density are often colonized by shrubs and grasses, resulting in improved visibility compared to forested segments [50,51]. This, in turn, may lead drivers to travel at higher speeds, thereby increasing the risk of road fatalities for small wildlife. Additionally, the unsuitability of shrubs or grasses in these areas as habitats for small rodent species has been established [21]. Small mammals like rodents do not exhibit long-term residency in such areas and tend to either avoid roads or rapidly traverse them when approaching these roadside areas. Furthermore, insightful research has revealed that the increased fatality rates among three rodent species in this region can be attributed to their feeding behavior, particularly their frequent consumption of plant seeds along the roadside [20]. Consequently, the presence of lower vegetation density may contribute to an elevated incidence of roadkill. In this study, the highest roadkill rate was observed among nocturnal rodents, which experience less human disturbance during their active hours. This accounts for the observed increase in roadkill density with a decrease in human disturbance.
The relationships between the roadkill density and influencing factors for amphibians and reptiles were found to be similar. The closer to rivers, bridges, culverts, and ponds, the higher the roadkill density. Conversely, the closer the proximity to human activity and the lower the elevation, the lower the overall roadkill density.

5. Conclusions

Based on 10 years of observation data, this study analyzed the spatial distribution characteristics of wildlife roadkill incidents and examined the key factors influencing roadkill incidents involving amphibians, reptiles, birds, and mammals. The spatial distribution patterns of roadkill hotspots vary among different taxa and exhibit a unimodal or multimodal distribution. The road section along a river and with a minimal distance between the road and the water has a high incidence of roadkill.
It is recommended that roads be located as far away from rivers and ponds as possible to avoid cutting through wildlife habitats. For sections where ponds and rivers are located beside the road, fences should be installed to prevent wildlife from crossing the road and guide them towards safe passages, such as bridges or culverts. Warning signs or electronic display boards should be installed, where feasible, in sections with a higher concentration of roadkill incidents. The main group affected by roadkill incidents on the Ring Changbai Mountain Scenic Road is amphibians, and excessive roadkill may lead to changes in their population. Additionally, it is crucial for future research to conduct year-round surveys, examining the impact of roadkill incidents on bird and mammal populations, particularly during the winter season. By gaining a comprehensive understanding of roadkill incidents and identifying hotspots, it is also suggested that further research be carried out on the impact of roadkill on the stability of roadside populations.

Author Contributions

Conceptualization, Y.Y. and Y.W.; methodology, Y.Y.; software, Z.W.; validation, Y.W., Z.W. and Y.K.; formal analysis, Y.Y.; investigation, Z.W. and G.S.; resources, Y.W.; data curation, Y.Y.; writing—original draft preparation, Y.Y., Y. W. and Y.K.; writing—review and editing, Y.W. and S.T.; visualization, Y.Y.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “National Key R & D Program of China, grant number 2021YFB2600100” and “Special Fund for Basic Scientific Research of Central Public Research Institutes, grant number 20220604”.

Institutional Review Board Statement

Our study did not require ethical review and approval, and we did not need permission for fieldwork, because people involved in the field surveys did not come into contact with the animals monitored in any way.

Informed Consent Statement

Not applicable.

Data Availability Statement

The dataset used in this study is available from the first author on reasonable request.

Acknowledgments

We are grateful to all the people involved in the field surveys: Piao Zhengji, Sui Yacheng, Li Yunpeng. All individuals included in this section have consented to the acknowledgment. We acknowledge the helpful comments and suggestions provided by four anonymous reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Anđelković, M.; Bogdanović, N. Amphibian and Reptile Road Mortality in Special Nature Reserve Obedska Bara, Serbia. Animals 2022, 12, 561. [Google Scholar] [CrossRef]
  2. Ibisch, P.L.; Hoffmann, M.T.; Kreft, S.; Pe’er, G.; Kati, V.; Biber-Freudenberger, L.; DellaSala, D.A.; Vale, M.M.; Hobson, P.R.; Selva, N. A global map of roadless areas and their conservation status. Science 2016, 354, 1423–1427. [Google Scholar] [CrossRef]
  3. Fabrizio, M.; Di Febbraro, M.; D’Amico, M.; Frate, L.; Roscioni, F.; Loy, A. Habitat suitability vs landscape connectivity determining roadkill risk at a regional scale: A case study on European badger (Meles meles). Eur. J. Wildl. Res. 2019, 65, 7. [Google Scholar] [CrossRef]
  4. Forman, R.; Sperling, D.; Bissonette, J.; Clevenger, A. Road Ecology: Science and Solutions; Island Press: Washington, DC, USA, 2003. [Google Scholar]
  5. Jones, M. Road upgrade, road mortality and remedial measures: Impacts on a population of eastern quolls and Tasmanian devils. Wildl. Res. 2000, 27, 289–296. [Google Scholar] [CrossRef]
  6. Garrote, G.; Fernández-López, J.; López, G.; Ruiz Jiménez, G.; Simóm, M.A. Prediction of Iberian lynx road–mortality in southern Spain: A new approach using the MaxEnt algorithm. Anim. Biodivers. Conserv. 2018, 41, 217–225. [Google Scholar] [CrossRef]
  7. Trombulak, S.C.; Frissell, C.A. Review of Ecological Effects of Roads on Terrestrial and Aquatic Communities. Conserv. Biol. 2000, 14, 18–30. [Google Scholar] [CrossRef]
  8. Hadad, E.; Kosicki, J.Z.; Yosef, R. Spatial modeling of road collisions of striped hyena (Hyaena hyaena) in Israel. Ecol. Res. 2023, 38, 664–675. [Google Scholar] [CrossRef]
  9. Boarman, W.I.; Sazaki, M. A highway’s road-effect zone for desert tortoises (Gopherus agassizii). J. Arid. Environ. 2006, 65, 94–101. [Google Scholar] [CrossRef]
  10. Sillero, N. Amphibian mortality levels on Spanish country roads: Descriptive and spatial analysis. Amphib.-Reptil. 2008, 29, 337–347. [Google Scholar] [CrossRef]
  11. Langen, T.A.; Ogden, K.M.; Schwarting, L.L. Predicting Hot Spots of Herpetofauna Road Mortality along Highway Networks. J. Wildl. Manag. 2009, 73, 104–114. [Google Scholar] [CrossRef]
  12. Heigl, F.; Horvath, K.; Laaha, G.; Zaller, J.G. Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: Using a citizen science approach with open-access land cover data. BMC Ecol. 2017, 17, 24. [Google Scholar] [CrossRef] [PubMed]
  13. Morelli, F.; Benedetti, Y.; Delgado, J.D. A forecasting map of avian roadkill-risk in Europe: A tool to identify potential hotspots. Biol. Conserv. 2020, 249, 108729. [Google Scholar] [CrossRef]
  14. Filius, J.; van der Hoek, Y.; Jarrín-V, P.; van Hooft, P. Wildlife roadkill patterns in a fragmented landscape of the Western Amazon. Ecol. Evol. 2020, 10, 6623–6635. [Google Scholar] [CrossRef] [PubMed]
  15. Coelho, I.P.; Teixeira, F.Z.; Colombo, P.; Coelho, A.V.; Kindel, A. Anuran road-kills neighboring a peri-urban reserve in the Atlantic Forest, Brazil. J. Environ. Manag. 2012, 112, 17–26. [Google Scholar] [CrossRef] [PubMed]
  16. Seo, C.; Thorne, J.H.; Choi, T.; Kwon, H.; Park, C.-H. Disentangling roadkill: The influence of landscape and season on cumulative vertebrate mortality in South Korea. Landsc. Ecol. Eng. 2013, 11, 87–99. [Google Scholar] [CrossRef]
  17. Wang, Y.; Piao, Z.J.; Guan, L.; Kong, Y.P. Influence of Ring Changbai Mountain Scenic Highway on Wildlife. Chin. J. Ecol. 2013, 32, 425–435. [Google Scholar]
  18. Wang, Z.; Wang, Y.; Wang, C.; Luo, Y.; Huang, L.; Zhang, R.; Tai, Z.; Piao, Z. Study on Road Mortality Characteristic of Amphibian on Tourism Highway of Changbai Mountain National Nature Reserve. Chin. J. Zool. 2015, 50, 866–874. [Google Scholar]
  19. Wang, C.; Wang, Z.; Luo, Y.; Huang, N.; Huang, L.; Zhang, R.; Zou, C.; Piao, Z.; Niu, L. Road Mortality Characteristic Analysis of Reptiles in Changbai Mountain National Nature Reserve. Sichuan J. Zool. 2016, 35, 123–128. [Google Scholar]
  20. Piao, Z.; Jin, Y.; Li, S.; Wang, C.; Piao, J.; Luo, Y.; Wang, Z.; Sui, Y. Mammal mortality caused by highways in the Changbai Mountain National Nature Reserve of Jilin Province, China. Acta Theriol. Sin. 2012, 32, 124–129. [Google Scholar]
  21. Wang, Y.; Piao, Z.; Li, Q.; Kong, Y.; Chen, J.; Liu, L.; Zhao, S. Research on Yellow Weasels Roadside Activities along Changbaishan Mountain Tourist Highway in Jilin Province. Sichuan J. Zool. 2010, 29, 166–169. [Google Scholar]
  22. Chen, X.; Wang, S.X.; Wang, Z.G. Biodiversity Conservation and Sustainable Development in Changbai Mountain Protection and Development Zone; Jilin Science and Technology Press: Changchun, China, 2010; pp. 13–14. [Google Scholar]
  23. Bager, A.; da Rosa, C.A. Influence of Sampling Effort on the Estimated Richness of Road-Killed Vertebrate Wildlife. Environ. Manag. 2011, 47, 851–858. [Google Scholar] [CrossRef] [PubMed]
  24. Yang, T.; Zhang, W.; Dai, R.; Zheng, P.; Shu, G.; Zou, Q.; L, C. Reptile road-kills in Simao District of Pu’er City, Yunnan Province, China. Acta Ecol. Sin. 2022, 42, 2648–2656. [Google Scholar] [CrossRef]
  25. Balčiauskas, L.; Wierzchowski, J.; Kučas, A.; Balčiauskienė, L. Habitat suitability based models for ungulate roadkill prognosis. Animals 2020, 10, 1345. [Google Scholar] [CrossRef]
  26. Ciocheti, G.; de Assis, J.C.; Ribeiro, J.W.; Ribeiro, M.C. Highway widening and underpass effects on vertebrate road mortality. Biotropica 2017, 49, 765–769. [Google Scholar] [CrossRef]
  27. Kalandarishvili, A.; Heltai, M. The colonization of raccoon (Procyon lotor L. 1758) in Georgia—The beginning of the invasion? COLUMELLA–J. Agric. Environ. Sci. 2019, 6, 17–27. [Google Scholar] [CrossRef]
  28. Villalobos-Hoffman, R.; Ewing, J.E.; Mooring, M.S. Do Wildlife Crossings Mitigate the Roadkill Mortality of Tropical Mammals? A Case Study from Costa Rica. Diversity 2022, 14, 665. [Google Scholar] [CrossRef]
  29. China Vegetation Map Editorial Committee of the Chinese Academy of Sciences. 1:1 Million Vegetation Data Set in China. Available online: http://www.ncdc.ac.cn (accessed on 1 May 2022).
  30. Esri. ArcGIS Desktop, Version 10.5; Environmental Systems Research Institute: Redlands, CA, USA, 2016. [Google Scholar]
  31. R Core Team. R: A Language and Environment for Statistical Computing, Version 4.2.2; R Foundation for Statistical Computing: Vienna, Austria, 2019. [Google Scholar]
  32. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016. [Google Scholar]
  33. Bates, D.; Maechler, M.; Bolker, B.; Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 2015, 67, 1–48. [Google Scholar] [CrossRef]
  34. Kent, E.; Schwartz, A.L.; Perkins, S.E. Life in the fast lane: Roadkill risk along an urban–rural gradient. J. Urban. Ecol. 2021, 7, juaa039. [Google Scholar] [CrossRef]
  35. Lin, Y.; Anthony, J.; Lin, W.; Lien, W.; Petway, J.R.; Lin, T. Spatiotemporal identification of roadkill probability and systematic conservation planning. Landsc. Ecol. 2019, 34, 717–735. [Google Scholar] [CrossRef]
  36. Blaustein, A.R.; Wake, D.B.; Sousa, W.P. Amphibian declines: Judging stability, persistence, and susceptibility of populations to local and global extinctions. Conserv. Biol. 1994, 8, 60–71. [Google Scholar] [CrossRef]
  37. Fahrig, L.; Pedlar, J.H.; Pope, S.E.; Taylor, P.D.; Wegner, J.F. Effect of road traffic on amphibian density. Biol. Conserv. 1995, 73, 177–182. [Google Scholar] [CrossRef]
  38. Bujoczek, M.; Ciach, M.; Yosef, R. Road-kills affect avian population quality. Biol. Conserv. 2011, 144, 1036–1039. [Google Scholar] [CrossRef]
  39. Brum, T.R.; Santos-Filho, M.; Canale, G.R.; Ignacio, A.R.A. Effects of roads on the vertebrates diversity of the Indigenous Territory Paresi and its surrounding. Braz. J. Biol. 2018, 78, 125–132. [Google Scholar] [CrossRef] [PubMed]
  40. Vargas-Salinas, F.; Delgado-Ospina, I.; Lopez-Aranda, F. Amphibians and reptiles killed by motor vehicles in a Sub-Andean forest in western Colombia. Caldasia 2011, 33, 121–138. [Google Scholar] [CrossRef]
  41. van der Grift, E.A.; van der Ree, R.; Fahrig, L.; Findlay, S.; Houlahan, J.; Jaeger, J.A.G.; Klar, N.; Madriñan, L.F.; Olson, L. Evaluating the effectiveness of road mitigation measures. Biodivers. Conserv. 2012, 22, 425–448. [Google Scholar] [CrossRef]
  42. Canal, D.; Camacho, C.; Martín, B.; de Lucas, M.; Ferrer, M. Magnitude, composition and spatiotemporal patterns of vertebrate roadkill at regional scales: A study in southern Spain. Anim. Biodivers. Conserv. 2018, 41, 281–300. [Google Scholar] [CrossRef]
  43. Zhang, W.; Shu, G.; Li, Y.; Xiong, S.; Liang, C.; Li, C. Daytime driving decreases amphibian roadkill. PeerJ 2018, 6, e5385. [Google Scholar] [CrossRef] [PubMed]
  44. Ernst, C.H.; Ernst, E.M. Snakes of the United States and Canada; Smithsonian Books: Washington, DC, USA, 2003; Volume 790. [Google Scholar]
  45. Puig, J.; Ariño, A.H.; Sanz, L. The link between roadkills distribution and the surrounding landscape in two highways in navarre, spain. Environ. Eng. Manag. J. 2012, 11, 1171–1178. [Google Scholar] [CrossRef]
  46. Steen, D.A.; Aresco, M.; Beilke, S.; Compton, B.; Condon, E.; Kenneth Dodd, C., Jr.; Forrester, H.; Gibbons, J.; Greene, J.; Johnson, G. Relative vulnerability of female turtles to road mortality. Anim. Conserv. 2006, 9, 269–273. [Google Scholar] [CrossRef]
  47. Glista, D.J.; DeVault, T.L.; DeWoody, J.A. Vertebrate road mortality predominantly impacts amphibians. Herpetol. Conserv. Biol. 2008, 3, 77–87. [Google Scholar]
  48. Ashley, E.P.; Robinson, J.T. Road mortality of amphibians, reptiles and other wildlife on the Long Point Causeway, Lake Erie, Ontario. Can. Field Nat. 1996, 110, 403–412. [Google Scholar]
  49. Barthelmess, E.L.; Brooks, M.S. The influence of body-size and diet on road-kill trends in mammals. Biodivers. Conserv. 2010, 19, 1611–1629. [Google Scholar] [CrossRef]
  50. Stein, R.M.; Lecigne, B.; Eitel, J.U.H.; Johnson, T.R.; McGowan, C.; Rachlow, J.L. Vegetation and vantage point influence visibility across diverse ecosystems: Implications for animal ecology. Front. Ecol. Evol. 2022, 10, 911051. [Google Scholar] [CrossRef]
  51. Coleman, B.T.; Hill, R.A. Living in a landscape of fear: The impact of predation, resource availability and habitat structure on primate range use. Anim. Behav. 2014, 88, 165–173. [Google Scholar] [CrossRef]
Sustainability 15 15398 g001
Figure 1. Segment of the Ring Changbai Mountain Scenic Road, China, monitored in search of wildlife roadkill (2009–2019), from points A (Erdaobaihe Town) to B (40 km).
Figure 1. Segment of the Ring Changbai Mountain Scenic Road, China, monitored in search of wildlife roadkill (2009–2019), from points A (Erdaobaihe Town) to B (40 km).
Sustainability 15 15398 g001
Sustainability 15 15398 g002
Figure 2. The statistical values of the roadkill densities of various groups (individuals·km−2). RKD, roadkill density; a, amphibians; b, birds; m, mammals; r, reptiles.
Figure 2. The statistical values of the roadkill densities of various groups (individuals·km−2). RKD, roadkill density; a, amphibians; b, birds; m, mammals; r, reptiles.
Sustainability 15 15398 g002
Sustainability 15 15398 g003
Figure 3. The spatial distributions of roadkill points for (a) amphibians, (b) reptiles, (c) birds, and (d) mammals.
Figure 3. The spatial distributions of roadkill points for (a) amphibians, (b) reptiles, (c) birds, and (d) mammals.
Sustainability 15 15398 g003
Table 1. The descriptive statistics of six predictor variables.
Table 1. The descriptive statistics of six predictor variables.
DTRDTBDTDELEVNDVIDTP
Mean71125253.18610.369147
Median72629161.98630.37149
Standard deviation57.416637.75.430.0433125
Minimum614008530.313.83
Maximum7805001538690.46357
Table 2. The results of the generalized linear mixed models, accounting for the variation in the values of roadkill density in relation to elevation (ELEV), the normalized difference vegetation index (NDVI), the distance to a bridge or culvert (DTB), the distance to a river (DTR), the distance to a pool (DTP), and the distance to a disturbance (DTD).
Table 2. The results of the generalized linear mixed models, accounting for the variation in the values of roadkill density in relation to elevation (ELEV), the normalized difference vegetation index (NDVI), the distance to a bridge or culvert (DTB), the distance to a river (DTR), the distance to a pool (DTP), and the distance to a disturbance (DTD).
ModelPredictorEstimateSE95% Confidence IntervaltpVIFR2Adjusted R2AIC
LowerUpper
Amphibian(Intercept)91.2801.47888.36194.20061.740<0.001-0.8630.860734
DTB−0.0270.002−0.031−0.023−13.800<0.0013.823
DTD0.0100.0050.0000.0192.0100.0461.079
NDVI13.7484.0905.67121.8243.360<0.0013.681
DTP−0.0070.003−0.013−0.002−2.7900.0061.141
Reptile(Intercept)16.9601.75613.36420.5579.659<0.001-0.7020.67089.4
DTR−0.0130.002−0.016−0.009−7.447<0.0011.384
NDVI−10.5373.883−18.491−2.583−2.7140.0111.452
DTP0.0020.0010.0000.0042.2300.0341.063
Mammal(Intercept)1.6389.191 −16.702 19.977 0.1780.859-0.6540.634348
DTR−0.0230.003 −0.030 −0.016 −6.681<0.0011.379
NDVI−28.0417.65 −43.306 −12.776 −3.666<0.0011.31
ELEV0.0390.009 0.021 0.058 4.245<0.0011.919
DTD−0.0020.001 −0.003 −0.0002 −2.3280.0232.31
Vertebrate (Intercept) −191.589 50.408 −291.165−92.013 −3.800 <0.001 0.8820.879892
DTD 0.037 0.009 0.0200.054 4.250 <0.001 1.13
ELEV 0.448 0.059 0.3320.564 7.640 <0.001 1.09
DTB −0.039 0.004 −0.047−0.031 −9.430 <0.001 5.02
DTP −0.031 0.005 −0.042−0.021 −5.740 <0.001 4.94
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Yang, Y.; Wang, Y.; Tao, S.; Shi, G.; Wang, Z.; Kong, Y. Hotspots and Factors Influencing Vertebrate Roadkill on the Ring Changbai Mountain Scenic Road, China. Sustainability 2023, 15, 15398. https://doi.org/10.3390/su152115398

AMA Style

Yang Y, Wang Y, Tao S, Shi G, Wang Z, Kong Y. Hotspots and Factors Influencing Vertebrate Roadkill on the Ring Changbai Mountain Scenic Road, China. Sustainability. 2023; 15(21):15398. https://doi.org/10.3390/su152115398

Chicago/Turabian Style

Yang, Yangang, Yun Wang, Shuangcheng Tao, Guoqiang Shi, Zhuocong Wang, and Yaping Kong. 2023. "Hotspots and Factors Influencing Vertebrate Roadkill on the Ring Changbai Mountain Scenic Road, China" Sustainability 15, no. 21: 15398. https://doi.org/10.3390/su152115398

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop