White-tailed deer sex identification from faecal DNA and pellet morphometry by neural network and fuzzy logic analyses

Abstract

Knowing the sex of white-tailed deer (Odocoileus virginianus) individuals can provide information to set harvesting rates and management activities. Therefore, the aim of this study is to identify the sex through classification function by using faecal pellet morphometry. Faeces were collected for 12 months in Durango, Mexico; their morphometric variables were measured, the faecal DNA was extracted, and the SRY gene marker was amplified to identify sex. A neural network and fuzzy logic sex classification functions were obtained. The outputs were validated with the SRY gene results. Data from adults in the winter were used to obtain the classification functions. Classification functions could accurately classify sex in 94.4% with neural networks and 86.9% with fuzzy logic. The neural network classified more accurately the sex of adult white-tailed deer studied in winter with the faecal pellets morphometry than with the fuzzy logic analysis. This technique can be a tool for non-invasive studies and monitoring of populations.

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