Background & Summary

The study of morphological differences between the sexes in Varanus salvator is ecologically important, especially considering that the species is extensively used for the skin trade and that anthropogenic habitat disturbance is thought to influence the sex ratio1,2. Surprisingly, little attention has been paid to sex determination in V. salvator based on morphometric proportions of the body, although the general body morphology of the species has been extensively studied3,4,5. Within varanids, species show considerable variation in body size, with larger species often exhibiting more pronounced sexual dimorphism6, which is consistent with Rensch’s rule7. Specific features such as variations at the base of the tail (where the male hemipenes are located) and the proportions of the head shape have also been reported8,9.

Reliable sex determination of V. salvator in the field would facilitate the measurement of sex ratios, which is crucial for drawing conclusions about population dynamics in disturbed habitats. Currently, unambiguous sexing requires invasive methods in which the reproductive organs are measured during dissection or genetic analysis9,10. Less invasive methods, such as hemipenis inversion, are unreliable due to the possible for confusion between partially elongated male hemipenis and female hemiclitori but are still used in ecological studies1. Previous studies suggest that tail-to-body ratio, eye-to-ear length, and the extent of the tail base are potential features for sex determination in this species4,9,11. Therefore, there are many research questions that need to be answered by investigating the relationship between the sex of V. salvator and its morphology, and a dataset that allows statistical or machine learning modelling is crucial.

We present a morphometric dataset that provides a non-invasive method for sex prediction and can potentially improve the accuracy of sex determination in the field alongside the commonly practised hemipenes inversion. This dataset is useful for various fields, including machine learning engineers, app developers, data scientists, ecologists, herpetologists, conservationists, and others.



This study sampled a total of 146 individual V. salvator; 83 females, 63 males. Lizards were sampled in a skin factory in Johor (location provided by Department of Wildlife and National Parks Peninsular Malaysia (PERHILITAN)). All lizards were sourced from oil palm plantations in Perak. The sample size was determined by the allocation provided to the researchers by the skin factory.

Dataset formation

Lizard morphological features measured included the following: thigh width (TW), base tail circumference (BTC), skull length (SL), skull width (SW), eye to ear length (EEL), snout-vent length (SVL), snout-tail length (STL), tail length (TL), and weight1,3,5,9,11,12. Length measurements were made using a flexible measuring tape whereas weight measurements were made using a handheld weighing scale. From the measurements made above, TW, BTC, SL, SW, EEL, and TL were divided by STL to derive relative proportions. Similarly, SW and EEL were divided by SL to derive relative head proportions. These variables were used for analysis, as some of the literature suggests relative proportions in body morphology and head dimension could be different between sexes3,4,8. Body condition was made by dividing weight by STL, similar to a body mass index1. Body size assessment involved a principal components analysis (PCA) performed on eight morphometric variables, namely TW, BTC, SL, SW, EEL, SVL, STL, and weight (similar to13). Component number 1 from the resulting PCA output was subsequently utilized as body size (Tables 1 and 2). Definitions of morphometric variables used for sex prediction are provided in Table 3.

Table 1 KMO and Bartlett’s test results indicating variables are suitable for PCA.
Table 2 Eigenvalue and percentage of variance explained for all components.
Table 3 Definition of morphometric variables used for sex prediction.

Ethics statements

All authors confirm that we have complied with all relevant ethical regulations. A permit to conduct research on this species has been secured from PERHILITAN, license number P-00003-15-19; as well as animal ethics approval from Universiti Sains Malaysia, Animal ethics approval number USM/IACUC/2020/(123)(1064).

Data Records

The dataset is publicly available on Figshare at the link: Morphometric measurements were categorised according to sex (83 females, 63 males). The raw data were recorded in a physical data sheet predefined with the attributes and digitised into an Excel file and saved in CSV format. The data were checked, cleaned, and processed into independent variables that can serve as predictors and dependent variables according to the lizards’ sex.

Technical Validation

Pilot testing with basic model construction

A pilot study was conducted to validate the suitability of the dataset in predicting the sex of V. salvator. Six machine learning models were used: logistic regression, random forest, support vector machine, extreme gradient boosting, adaptive boosting, and gaussian naïve bayes. For training and validation, data were split 70% for training, and 30% for validation. Model construction, training and validation was conducted using Python programming in Google Colab workbook. The resulting confusion matrixes and model performances are summarized in Supplementary Table 1.

Usage Notes

This dataset contains morphological measurements form 83 females, 63 male V. salvator. However, it is important to acknowledge several limitations inherent to the dataset. Firstly, the data predominantly represents smaller individuals, as it was collected from individuals captured for the skin trade. Skin factories typically accept individuals weighing ≤5 kg, contributing to this size bias. Additionally, individuals from other habitats like forests and urban areas are notably absent from this dataset, given that the data collection exclusively pertained to animals sourced from oil palm plantations. Moving forward, to enhance the applicability of morphological data analysis, it is recommended to include individuals from wild populations in model training and validation. This inclusion could lead to the development of an app where inputting relevant morphological variables can determine the sex of wild individuals, allowing for easy sex identification in the field. Furthermore, future work could explore image-based means of sex identification, which could prove more time and cost efficient to conduct.