INVESTIGADORES
CARMANCHAHI Pablo Daniel
artículos
Título:
A predictive model to diagnose pregnancy using noninvasive methods in guanacos (Lama guanicoe)
Autor/es:
MAROZZI, ANTONELA; CANTARELLI, VERÓNICA; GÓMEZ FACUNDO; PANEBIANCO ANTONELLA; LEGGIERI, LEONARDO; GREGORIO PABLO; PONZIO, MARINA; CARMANCHAHI, PABLO
Revista:
CANADIAN JOURNAL OF ZOOLOGY
Editorial:
NATL RESEARCH COUNCIL CANADA-N R C RESEARCH PRESS
Referencias:
Lugar: Otawa; Año: 2019
ISSN:
0008-4301
Resumen:
Pregnancy status is usually not included on 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 on plasma and fecal samples on pregnant and not pregnant females to develop a pregnancy predictive model on guanacos (Lama guanicoe). Samples were obtained during the procedures for live shearing management (capture, shear and release). 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 individuals. The use of predictive models and not invasive methods might be suitable to incorporate pregnancy information in large scale population studies on guanaco and other free- ranging ungulates.