Crash Course in Conservation: Predicting and Mitigating Wildlife–Vehicle Collisions in a Savannah Area

Abstract

Temporal patterns in wildlife–vehicle collisions (WVCs) correspond with animal behaviour and biology, predominantly occurring during breeding and dispersion seasons, as well as daily foraging and resting activities of animals. As a result, diverse taxonomic groups worldwide are affected by vehicle collisions, including reptiles, amphibians, mammals and birds. Ecologically, WVC results in population declines and can differentially affect animal populations. Yet, monitoring biodiversity and examining the factors influencing its alterations enable society to make informed decisions on conservation and enhance the management of human–wildlife conflicts. Effective mitigation techniques necessitate knowledge about the location and timing of traffic casualties involving wildlife. The objectives of this study were as follows: (i) to analyse the trends in WVC and (ii) to forecast future scenarios of WVC in the Hurungwe Safari Area (HSA), located in the Mid Zambezi Valley, Zimbabwe. The study aims to develop evidence-based strategies tailored to the local context and feasibility for reducing WVC frequency and severity. We used WVC data for 22 different species collected by the Zimbabwe Parks and Wildlife Management Authority (ZPWMA), Marongora Field Station. This study performed a trend analysis and then forecast future WVC using time series methods. We used K-means to determine clusters in the species data. Time series forecasting was performed using the Autoregressive Integrated Moving Average (ARIMA), a popular statistical method used for time series forecasting. Our results indicated an exponential growth in the number of WVC for some animal species, that is, civet, buffalo, hyena and waterbuck by the year 2030. Modelling trends in WVC is important for protecting wildlife, enhancing road safety and reducing economic costs. It informs conservation efforts, guides effective management strategies like wildlife crossings, and raises public awareness about the impact of driving on ecosystems. This data ultimately promotes coexistence between humans and wildlife.