Wildlife roadkill in the Tsavo Ecosystem, Kenya: identifying hotspots, potential drivers, and affected species

Abstract

Roadkill is one of the highest causes of wildlife mortality and is of global conservation concern. Most roadkill studies have focused on wildlife in developed countries such as the United States of America and temperate biomes, but there are limited data for the impacts of roads on wildlife in the African tropics, where road infrastructure development is projected to grow rapidly in natural environments and conservation areas. The Tsavo Conservation Area is an important biodiversity hotspot in eastern Kenya and is bisected by a major highway and railways that connect the port of Mombasa to the interior. Along this infrastructure corridor, roadkill was recorded for 164 days over an 11-year period (2007–2018). In total, 1,436 roadkill were recorded from 13,008 km driven of a 164.42 km Nairobi-Mombasa road representing 0.11 collisions per kilometer. The majority of roadkill were small to medium sized mammals (<15kg) (53%; n = 756), whereas birds comprised 32% (n = 460), reptiles 10% (n = 143), with the remaining 5% (n = 77) being large mammals (>15kg). Of the 460 birds recorded, 264 were identifiable represented by 62 species. All large mammals comprising 10 species were identified, including the African elephant, Loxodonta africana and the endangered African wild dog, Lycaon pictu s. Thirteen species of small mammal were also identified dominated by Kirk's dik-dik ( Madoqua kirkii ). Reptiles were represented by 11 species which were identified to the species level. Roadkill hotspots were identified using a kernel density method. The spatial distribution of roadkill was associated with adjacent shrub vegetation and proximity to permanent and seasonal rivers, and differences in seasonality and habitats were observed. Roadkill was lowest on road sections that traversed settled areas as opposed to roads adjacent to the protected areas. The results demonstrate that roadkill for two of the taxonomic groups - mammals and birds - appear high with numerous species detected in the Tsavo Conservation Area. These results can be used to focus efforts to reduce wildlife mortality by guiding future mitigation efforts.

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

“…The collection period is from 2006 to 2017. It is worthy to note that time series datasets such as animal-related crash data [ 113 ] and roadkill datasets [ 114 , 115 , 116 ] all focus only on collection but never perform any machine learning techniques on the data. Animal-related crash data [ 113 ] are collected by experts from 2001 to 2007.…”

“…Every year, 0 to 4285 instances are collected in Australia. From 2007 to 2008 in Kenya, data on roadkill [ 114 ] are collected. The collectors record 1436 instances for 164 days over an 11 year period.…”

Influence of infrastructure, ecology, and underpass-dimensions on multi-year use of Standard Gauge Railway underpasses by mammals in Tsavo, Kenya

“…Linear infrastructure is essential for economic growth and development 1 , but it is also widely regarded as a catalyst for biodiversity loss in natural ecosystems 2 – 7 . Moreover, an increase in infrastructure is predicted to occur mostly in the tropics where there are high levels of biodiversity and susceptible ecosystems 8 , 9 .…”

Multi-year Use of the Standard Gauge Railway Underpasses by Mammals in Tsavo, Kenya: Influence of Underpass Dimensions, Infrastructure and Ecology

“…Railway and road infrastructure is essential for economic growth and development 1 , but it is also widely regarded as a catalyst for biodiversity loss in natural ecosystems [2][3][4][5][6][7] . Generally, railway tra c, like road tra c, may negatively impact wildlife and wildlife habitats through increased wildlife mortality from wildlife-train collisions, loss of population connectivity, habitat fragmentation, pollution (e.g., noise, chemical and light), and habitat loss 6,8−12 .…”