Predicting Sex in White Rhinoceroses: A Statistical Model for Conservation Management

Ensuring the effective management of every rhinoceros population is crucial for securing a future for the species, especially considering the escalating global threat of poaching and the challenges faced in captive breeding programs for this endangered species. Steroid hormones play pivotal roles in regulating diverse biological processes, making fecal hormonal determinations a valuable non-invasive tool for monitoring adrenal and gonadal endocrinologies and assessing reproductive status, particularly in endangered species. The purpose of this study was to develop a statistical model for predicting the sex of white rhinoceroses using hormonal determinations obtained from a single fecal sample. To achieve this, 562 fecal samples from 15 individuals of the Ceratotherium simum species were collected, and enzyme immunoassays were conducted to determine the concentrations of fecal cortisol, progesterone, estrone, and testosterone metabolites. The biological validation of the method provided an impressive accuracy rate of nearly 80% in predicting the sex of hypothetically unknown white rhinoceroses. Implementing this statistical model for sex identification in white rhinoceroses would yield significant benefits, including a better understanding of the structure and dynamics of wild populations. Additionally, it would enhance conservation management efforts aimed at protecting this endangered species. By utilizing this innovative approach, we can contribute to the preservation and long-term survival of white rhinoceros populations.

Automated summary

Ensuring effective management of rhinoceros populations is crucial for species' future, given the escalating threat of poaching and challenges in captive breeding. Fecal hormonal determinations can monitor adrenal and gonadal endocrinologies, making them valuable for endangered species. This study developed a statistical model for predicting white rhinoceros sex using hormonal determinations from a single fecal sample, achieving an impressive accuracy rate of nearly 80%. Implementing this model would enhance conservation efforts and increase understanding of wild populations' dynamics and structure.