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

ANTONELA MAROZZI; VERONICA CANTARELLI; FACUNDO GÓMEZ; ANTONELLA PANEBIANCO; LEGGIERI, LEONARDO; PABLO GREGORIO; MARINA PONZIO; PABLO CARMANCHAHI

CANADIAN JOURNAL OF ZOOLOGY

NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS

Lugar: Otawa; Año: 2019 vol. 98 p. 13 - 20

0008-4301

Pregnancy status is usually not included in ecological studies due to the difficulty of its evaluation. The use of non-invasive methods is an opportunity to incorporate pregnancy on population studies as can be used avoiding physical restraint of the individuals. In this study, we evaluated sex steroid hormones in plasma and fecal samples on pregnant and non- pregnant females to develop a pregnancy predictive model on guanacos (Lama guanicoe Müller, 1776). Samples were obtained during the procedures for live shearing management (capture, shear, and release). Enzyme immunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations in plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) on feces. Mean hormonal and fecal metabolites concentrations were significantly higher in pregnant females compared with non-pregnant females. A linear relationship was found between each hormone and its fecal metabolites. Finally, hormone data were combined with an independent source of pregnancy diagnosis such as abdominal ballottement to develop a logistic regression model which will be applicable to diagnose pregnancy in unhandled 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.