106 Assessment of fecal near infrared reflectance spectroscopy to detect and monitor the reproductive status of endangered Amur and Snow leopard females

Abstract

Feces from captive and wild carnivores can yield valuable information about an individuals’ physiological and reproductive status, diet, and ecology. Near infrared spectroscopy (NIRS) is a rapid, noninvasive, cost-efficient technique widely used in the agricultural, pharmaceutical, and chemical industries that has gained traction in diagnostic and ecological field applications for herbivore species, such as wild deer, antelope, and giant panda. The aim of this study was to test the transferability of NIRS to measuring reproductive status in feces from 2 endangered carnivore species, the Snow (Panthera uncia) and Amur (Panthera pardus orientalis) leopards. Fecal near infrared spectra analysed with multivariate statistics were used to generate prediction models for estrone-3-glucuronide (E1G) and progesterone (P4). In the E1G NIRS model, fecal samples (n=93) were obtained from 5 female leopards (3 Amur, 2 Snow) at 5 different zoo facilities, whereas for the P4 NIRS model fecal samples (n=51) from only 1 pregnant Amur leopard was available. The hormones were extracted with methanol and quantified by enzyme-linked immunosorbent assays (C. Munroe), where the sample range for E1G was 0.20-2.17 μg/g and the range for P4 was 0.06-61.89 μg/g. The near infrared spectra (350-2500nm) were acquired with an ASD FieldSpec®3 portable spectrometer (Malvern Panalytical, Malvern, UK), and the chemometric analysis was realised using the Unscrambler® X v.10.4 (CAMO Software AS, Oslo, Norway). Hormone reference values were log transformed before chemometric analysis to account for the heterogeneity of variance. Spectral pretreatment of standard normal variate was applied to the truncated wavelength range 700-240 0nm in order to remove interference from the visible region (350-700nm) due to individual diets that can confer colour variants that alter spectral signatures. Initial principal component analysis for the E1G and P4 datasets models showed >95% of the variation was explained by 4 factors, with no separation of principal component analysis scores between species or reproductive status. Quantitative prediction models using partial least-squares regression on selected wavelength ranges yielded a coefficient of determination for E1G and P4 of 0.10-0.04 and 0.35-0.19 for calibrations and validations, respectively. These near infrared models require further mathematical processing and consideration of sample variation due to diet complexity in carnivores in order to accurately assess hormone levels and monitor reproductive cycles in these species. This work was supported by USDA-ARS Biophotonics Initiative grant #58-6402-3-018.