~ by Aditi Patil
Rapidly growing road networks contribute to a country’s social and economic development, but this contribution comes at a high price that is paid by wildlife. Roads cutting through wild habitat adversely affect animals, with one of the most distinct impacts being Animal-Vehicle Collisions (AVCs). A recent study conducted by researchers at Dr. Bilal Habib's lab at the Wildlife Institute of India examined factors like animal behavior and traffic characteristics that influence AVCs, and how information about these factors can be used to prevent such accidents.
Increased connectivity through roads is critical for development. However, it increases construction of transport infrastructure, often passing through wild habitat. This poses a risk to wildlife. Animals attempting to cross roads often end up in collisions with vehicles. Notably, there are no natural factors of selection governing AVCs, meaning that these accidents occur entirely by chance. Both healthy and unhealthy individuals in animal populations are equally exposed to the risk. This non selective mortality can negatively affect a population through a loss of healthy individuals. Animals may deliberately begin to avoid crossing roads due to AVCs, thus leading to a barrier to movement. Such barriers will result in isolation of animal populations, and in some cases may even lead to the local extinction of species.
In order to successfully reduce the risk posed to animals that use roads, it is important to understand the factors influencing AVCs. Instead of looking at individual species or roads, this study takes a comprehensive look at the bigger picture, taking into consideration all the influencing factors and the relationship between these factors. The result is an empirical model that can be used to identify species and roads that are particularly vulnerable to AVCs.
The scientists used existing information about the physical and behavioral characteristics of animal species. They then simulated different possible road traffic conditions. Combining this information, they developed a model to study the traversability i.e. the probability of an animal successfully crossing the road for six large mammal species - gaur, chital, sambar, wild pig, tiger and leopard. They selected a 60km stretch of the National Highway 44 passing through the Pench Tiger Reserve in the Central Indian states of Maharashtra and Madhya Pradesh. The researchers observed a distinct and direct relationship between animal behavior, traffic conditions and frequency of animal-vehicle collisions.
Herd animals like gaur, chital, sambar and wild pig are more susceptible to collisions with vehicles due to their movement in large numbers, as compared to solitary predators like the tiger and leopard which are swift moving. This effectively demonstrates that animal behavior plays an important role in determining the vulnerability of a species. Similarly, species specific activity patterns also impact the species vulnerability to AVCs. More human tolerant species such as chital and wild pig occupy and are active in road-forest edge habitats i.e. habitats adjoining roads in close proximity. This makes them more likely to encounter collisions with vehicles as compared to species which avoid edge habitats altogether. However, while such avoidance behavior may reduce the risk of death by AVCs, the reduced movement of animals across roads may result in their isolation from other populations. The time of the day during which the animal is most active also plays an important role in determining its vulnerability to AVCs. For example, peak activity of chital coincides with peak traffic activity, resulting in a higher AVC risk for this species.
Features of traffic on the road also have a significant impact on the occurrence of AVCs. A higher number of vehicles will deter animals from crossing the road. While this will reduce the chance of an animal-vehicle collision, it will create a physical barrier to the movement of animals. Conversely, low traffic volume means more distance between consecutive vehicles, increasing the chances of an animal successfully crossing the road. Widening existing roads may seem like a straightforward solution to decrease traffic volume. However, wider roads increase the exposure of a crossing animal to oncoming vehicles, thus increasing the chances of a collision. Along with traffic volume, the type of vehicles also affects the chances of an animal- vehicle collision occurring. The study found that heavy vehicles have a higher frequency of colliding with an animal as compared to fast moving lighter vehicles.
Vulnerable species and collision hotspots on roads need to be identified in order to prioritize and conservation strategies. The empirical model generated in this study, together with existing data on animal and traffic characteristics will help in identifying roads that pose a higher risk to wildlife. Even if there is no current data on animal activity near roads, species specific traits and animal behavior data can be used to predict the vulnerability of species. This information can then be used to inform traffic regulatory measures. Some measures that may be implemented are speed regulations, limitations on heavy vehicle allowance on roads, alternate road networks and construction of animal passages. This will help in maintaining sustainable traffic volume and traffic flow on roads. Developmental plans for roads that are predicted to carry heavy traffic can be prioritized for conservation strategies; particularly roads that will cut across ecologically sensitive habitats.
Wildlife-vehicle collisions are a leading cause of decline in animal population in human dominated landscapes. With a rapidly increasing growth in transport infrastructure, this risk to wildlife is only set to increase in the future. Using the modeling approach to combine information on animal behavior and traffic characteristics will help to efficiently plan and implement conservation strategies for wildlife.
Original Paper: Saxena, Akanksha, et al. "Integrating large mammal behaviour and traffic flow to determine traversability of roads with heterogeneous traffic on a Central Indian Highway." Scientific Reports 10.1 (2020): 1-12.
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