A predictive model to diagnose pregnancy in guanacos (Lama guanicoe) using non-invasive methods

Abstract

Pregnancy status is usually not included in ecological studies because it is difficult to evaluate. The use of non-invasive methods to determine pregnancy, without physically restraining individuals, would enable pregnancy to be included in population studies. In this study, we evaluated sex steroid hormones in plasma and fecal samples from pregnant and non-pregnant females to develop a pregnancy predictive model for guanacos (Lama guanicoe (Müller, 1776)). Samples were obtained during live-shearing management (i.e., capture, shear, and release) of guanacos. Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) in feces. Mean hormonal and fecal metabolite concentrations were significantly higher in pregnant females than in non-pregnant females. A linear relationship was found between each hormone and its fecal metabolite. Finally, hormonal data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model to diagnose pregnancy in non-handled individuals. The use of predictive models and non-invasive methods might be suitable to incorporate pregnancy information in large-scale population studies on guanaco and other free-ranging ungulates.