Individual age can be used to design more efficient and suitable management plans in both in situ and ex situ conservation programmes for targeted wildlife species. DNA methylation is a promising marker of epigenetic ageing that can accurately estimate age from small amounts of biological material, which can be collected in a minimally invasive manner. In this study, we sequenced five targeted genetic regions and used 8-23 selected CpG sites to build age estimation models using machine learning methods at only about $3-7 per sample. Blood samples of seven Felidae species were used, ranging from small to big, and domestic to endangered species: domestic cats (Felis catus, 139 samples), Tsushima leopard cats (Prionailurus bengalensis euptilurus, 84 samples) and five Panthera species (96 samples). The models achieved satisfactory accuracy, with the mean absolute error of the most accurate models recorded at 1.966, 1.348 and 1.552 years in domestic cats, Tsushima leopard cats and Panthera spp. respectively. We developed the models in domestic cats and Tsushima leopard cats, which were applicable to individuals regardless of health conditions; therefore, these models are applicable to samples collected from individuals with diverse characteristics, which is often the case in conservation. We also showed the possibility of developing universal age estimation models for the five Panthera spp. using only two of the five genetic regions. We do not recommend building a common age estimation model for all the target species using our markers, because of the degraded performance of models that included all species.
Keywords: Panthera; DNA methylation; Tsushima leopard cat; age estimation; domestic cat; endangered Felidae species.
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