A predictive model to diagnose pregnancy using noninvasive methods in guanacos (Lama guanicoe)

MAROZZI A; CANTARELLI VI; GOMEZ F; PANEBIANCO A; LEGGIERI LR; GREGORIO P; PONZIO MF; CARMANCHAHI P

CANADIAN JOURNAL OF ZOOLOGY

NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS

Lugar: Otawa; Año: 2019

0008-4301

Reproductive parameters are an important input on ecological studies but, in general, pregnancy status is not included 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 on plasma and fecal samples on pregnant and not pregnant females in order to develop a pregnancy predictive model on guanacos (Lama guanicoe). Samples were obtained during the procedures for live shearing management (capture, shear and release), in two different sites in 2016 and 2017. Enzimeimmunoassays were used to evaluate progesterone (P4) and estradiol (E2) concentrations on plasma and pregnanediol glucuronides (PdG) and conjugated estrogens (EC) on feces. Mean hormonal and fecal metabolites concentrations were significantly high on pregnant females compared with nonpregnant females. A linear relationship was found between each hormone and its fecal metabolites. Finally, hormone data was 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 guanacos. The use of predictive models and not invasive methods might be suitable to apply on other free-ranging ungulates. Furthermore, this method will be valuable approach to incorporate pregnancy information in large scale population studies.