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粪便近红外光谱分析鉴别大熊猫的生理状态。

Fecal near infrared spectroscopy to discriminate physiological status in giant pandas.

机构信息

Grazingland Animal Nutrition Lab, Blackland Research and Extension Center, Texas A&M University, Temple, Texas, United States of America.

出版信息

PLoS One. 2012;7(6):e38908. doi: 10.1371/journal.pone.0038908. Epub 2012 Jun 13.

Abstract

Giant panda (Ailuropoda melanoleuca) monitoring and research often require accurate estimates of population size and density. However, obtaining these estimates has been challenging. Innovative technologies, such as fecal near infrared reflectance spectroscopy (FNIRS), may be used to differentiate between sex, age class, and reproductive status as has been shown for several other species. The objective of this study was to determine if FNIRS could be similarly used for giant panda physiological discriminations. Based on samples from captive animals in four U.S. zoos, FNIRS calibrations correctly identified 78% of samples from adult males, 81% from adult females, 85% from adults, 89% from juveniles, 75% from pregnant and 70% from non-pregnant females. However, diet had an impact on the success of the calibrations. When diet was controlled for plant part such that "leaf only" feces were evaluated, FNIRS calibrations correctly identified 93% of samples from adult males and 95% from adult females. These data show that FNIRS has the potential to differentiate between the sex, age class, and reproductive status in the giant panda and may be applicable for surveying wild populations.

摘要

大熊猫(Ailuropoda melanoleuca)监测和研究通常需要准确估计种群规模和密度。然而,获得这些估计值一直具有挑战性。创新技术,如粪便近红外反射光谱(FNIRS),可用于区分性别、年龄组和繁殖状态,这已在其他几种物种中得到证实。本研究的目的是确定 FNIRS 是否可以用于大熊猫的生理区分。基于来自美国四个动物园圈养动物的样本,FNIRS 校准正确识别了 78%的成年雄性样本、81%的成年雌性样本、85%的成年样本、89%的幼年样本、75%的怀孕雌性样本和 70%的非怀孕雌性样本。然而,饮食对校准的成功率有影响。当控制饮食中的植物部位,仅评估“叶子”粪便时,FNIRS 校准正确识别了 93%的成年雄性样本和 95%的成年雌性样本。这些数据表明,FNIRS 有可能区分大熊猫的性别、年龄组和繁殖状态,并且可能适用于野生种群的调查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5403/3374779/c4cc250a115c/pone.0038908.g001.jpg

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