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使用多物种近红外光谱模型预测食草动物粪便样本中的纤维含量。

Predicting fiber content in herbivore fecal samples using a multispecies NIRS model.

作者信息

Rossa Mariana, Serrano Emmanuel, Carvalho João, Fernández Néstor, López-Olvera Jorge R, Garel Mathieu, Santos João P V, Ramanzin Maurizio, Anderwald Pia, Freschi Pierangelo, Bartolomé Jordi, Lavín Santiago, Albanell Elena

机构信息

Departamento de Biologia e Centro de Estudos do Ambiente e do Mar (CESAM), Universidade de Aveiro, Campus Universitário de Santiago, Aveiro, Portugal.

Wildlife Ecology & Health Group (WE&H) and Servei d'Ecopatologia de Fauna Salvatge (SEFaS), Departament de Medicina i Cirurgia Animals, Facultat de Veterinària, Universitat Autònoma de Barcelona (UAB), Bellaterra, Barcelona, Spain.

出版信息

PLoS One. 2025 Jan 8;20(1):e0317145. doi: 10.1371/journal.pone.0317145. eCollection 2025.

Abstract

Fiber is essential for rumen health, microbial fermentation, and the energy supply of herbivores. Even though the study of fecal fiber contents (neutral detergent fiber NDF, acid detergent fiber ADF, and acid detergent lignin ADL) using near-infrared reflectance spectroscopy (NIRS) has allowed investigating nutritional ecology of different herbivore species, NIRS calibrations are species-specific and require a large number of samples for predictions. A multispecies calibration would be an advantage since samples from different herbivores could be used to calibrate a model capable of predicting the fecal fiber content of other herbivores. To date, however, multispecies models have not been developed to predict fiber contents in the feces of herbivores. Here, we fill this gap by calibrating three fiber multispecies models (NDF, ADF and ADL) using fecal samples from domestic and wild herbivore species. We also evaluated the effect of incorporating sodium sulfite in fiber determination protocol. The initial dataset consisting of 445 samples of six herbivore species was used to calibrate (80% of the samples) and validate (20% of the samples) the models. Subsequently, 63 samples of five herbivores not included in the calibration set were used for the external validation of the model. Since sodium sulfite did not significantly improve fecal fiber prediction, our model was developed without this compound. The multispecies models obtained were highly accurate determining NDF, ADF and ADL (R2CAL, coefficient of determination in calibration, ≥ 0.93, R2VAL, coefficient of determination in validation, ≥ 0.91) and independent of external confounders. For external validation, the accuracy in predicting fecal samples in other herbivore species was also satisfactory, with consistently better values for NDF (R2VAL, 0.86-0.94) and ADF (R2VAL, 0.80-0.95) than for ADL (R2VAL, 0.66-0.89). We show that multispecies NIRS calibrations can be used with high accuracy to assess fecal fiber contents across diverse herbivore species. This finding represents a significant advance in the study of the nutritional ecology of herbivores with contrasting foraging patterns. In the future, widening the data range (e.g., species and locations) of the initial dataset could further improve the accuracy of these models.

摘要

纤维对瘤胃健康、微生物发酵以及食草动物的能量供应至关重要。尽管利用近红外反射光谱法(NIRS)对粪便纤维含量(中性洗涤纤维NDF、酸性洗涤纤维ADF和酸性洗涤木质素ADL)进行研究,有助于探究不同食草动物物种的营养生态学,但NIRS校准具有物种特异性,且需要大量样本进行预测。多物种校准将具有优势,因为来自不同食草动物的样本可用于校准一个能够预测其他食草动物粪便纤维含量的模型。然而,迄今为止,尚未开发出用于预测食草动物粪便纤维含量的多物种模型。在此,我们通过使用来自家养和野生食草动物物种的粪便样本校准三个纤维多物种模型(NDF、ADF和ADL)来填补这一空白。我们还评估了在纤维测定方案中加入亚硫酸钠的效果。由六个食草动物物种的445个样本组成的初始数据集用于校准(80%的样本)和验证(20%的样本)模型。随后,将校准集中未包含的五个食草动物的63个样本用于模型的外部验证。由于亚硫酸钠并未显著提高粪便纤维预测能力,我们开发的模型未使用该化合物。所获得的多物种模型在测定NDF、ADF和ADL时具有很高的准确性(R2CAL,校准决定系数,≥0.93;R2VAL,验证决定系数,≥0.91),且不受外部混杂因素影响。对于外部验证,预测其他食草动物物种粪便样本的准确性也令人满意,NDF(R2VAL,0.86 - 0.94)和ADF(R2VAL,0.80 - 0.95)的预测值始终优于ADL(R2VAL,0.66 - 0.89)。我们表明,多物种NIRS校准可高精度用于评估不同食草动物物种的粪便纤维含量。这一发现代表了在具有不同觅食模式的食草动物营养生态学研究方面的重大进展。未来,扩大初始数据集的数据范围(如物种和地点)可能会进一步提高这些模型的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b324/11709307/7627787fe04c/pone.0317145.g001.jpg

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