Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia.
School of Biological Sciences, The University of Western Australia, Perth, WA, Australia.
Mol Ecol Resour. 2021 Oct;21(7):2316-2323. doi: 10.1111/1755-0998.13437. Epub 2021 Jun 18.
Age is a fundamental parameter in wildlife management as it is used to determine the risk of extinction, manage invasive species, and regulate sustainable harvest. In a broad variety of vertebrates species, age can be determined by measuring DNA methylation. Animals with known ages are initially required during development, calibration, and validation of these epigenetic clocks. However, wild animals with known ages are frequently difficult to obtain. Here, we perform Monte-Carlo simulations to determine the optimal sample size required to create an accurate calibration model for age estimation by elastic net regression modelling of cytosine-phosphate-guanine methylation data. Our results suggest a minimum calibration population size of 70, but ideally 134 individuals or more for accurate and precise models. We also provide estimates to the extent a model can be extrapolated beyond a distribution of ages that was used during calibration. The findings can assist researchers to better design age estimation models and decide if their model is adequate for determining key population attributes.
年龄是野生动物管理中的一个基本参数，因为它被用来确定灭绝的风险、管理入侵物种和调节可持续收获。在广泛的脊椎动物物种中，可以通过测量 DNA 甲基化来确定年龄。在开发、校准和验证这些表观遗传钟时，最初需要具有已知年龄的动物。然而，具有已知年龄的野生动物通常很难获得。在这里，我们进行蒙特卡罗模拟，以确定通过弹性网络回归建模胞嘧啶-磷酸-鸟嘌呤甲基化数据来创建准确的年龄估计校准模型所需的最佳样本量。我们的结果表明，校准人口的最小规模为 70，但理想情况下为 134 人或更多，以获得准确和精确的模型。我们还提供了在用于校准的年龄分布之外进行模型推断的程度的估计。这些发现可以帮助研究人员更好地设计年龄估计模型，并确定他们的模型是否足以确定关键的种群属性。
