Mammals in the Chornobyl Exclusion Zone's Red Forest: a motion-activated camera trap study

Abstract

Abstract. Since the accident at the Chornobyl Nuclear Power Plant in 1986, there have been few studies published on medium and large mammals inhabiting the area from which the human population was removed (now referred to as the Chornobyl Exclusion Zone, CEZ). The dataset presented in this paper describes a motion-activated camera trap study (n=21 cameras) conducted from September 2016 to September 2017 in the Red Forest located within the Chornobyl Exclusion Zone. The Red Forest, which is likely the most anthropogenically contaminated radioactive terrestrial ecosystem on earth, suffered a severe wildfire in July 2016. The motion-activated trap cameras were therefore in place as the Red Forest recovered from the wildfire. A total of 45 859 images were captured, and of these 19 391 contained identifiable species or organism types (e.g. insects). A total of 14 mammal species were positively identified together with 23 species of birds (though birds were not a focus of the study). Weighted absorbed radiation dose rates were estimated for mammals across the different camera trap locations; the number of species observed did not vary with estimated dose rate. We also observed no relationship between estimated weighted absorbed radiation dose rates and the number of triggering events for the four main species observed during the study (brown hare, Eurasian elk, red deer, roe deer). The data presented will be of value to those studying wildlife within the CEZ from the perspectives of the potential effects of radiation on wildlife and also rewilding in this large, abandoned area. They may also have value in any future studies investigating the impacts of the recent Russian military action in the CEZ. The data and supporting documentation are freely available from the Environmental Information Data Centre (EIDC) under the terms and conditions of a Creative Commons Attribution (CC BY) license: https://doi.org/10.5285/bf82cec2-5f8a-407c-bf74-f8689ca35e83 (Barnett et al., 2022a).