Barn Owl and Rodent Research Group, School of Biological Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia.
Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400, Kota Kinabalu, Sabah, Malaysia.
Sci Data. 2024 Apr 5;11(1):337. doi: 10.1038/s41597-024-03172-9.
Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures.
传统上，在圆鼻巨蜥（Varanus salvator）中进行可靠的性别鉴定依赖于侵入性方法，如遗传分析或解剖，因为像半阴茎反转这样的侵入性较小的技术不可靠。鉴于该物种的生态重要性以及在受干扰栖息地中出现的性别比例偏斜，一个允许生态学家或动物学家研究蜥蜴性别决定的数据集至关重要。我们提出了一个新的数据集，其中包含了来自皮肤贸易的圆鼻巨蜥个体的形态测量值，通过测量后的解剖来确认性别。该数据集由体重、头骨大小、尾巴长度、状况等主要和次要数据的混合组成，可用于生态和保护研究的建模研究中，以监测该物种的性别比例。通过训练和测试六个机器学习模型来证明了其有效性。该数据集有可能简化性别鉴定，为圆鼻巨蜥研究提供一种非侵入性的替代方法，以补充现有方法，减少对侵入性程序的需求。
