Villamuelas Miriam, Serrano Emmanuel, Espunyes Johan, Fernández Néstor, López-Olvera Jorge R, Garel Mathieu, Santos João, Parra-Aguado María Ángeles, Ramanzin Maurizio, Fernández-Aguilar Xavier, Colom-Cadena Andreu, Marco Ignasi, Lavín Santiago, Bartolomé Jordi, Albanell Elena
Servei d'Ecopatologia de Fauna Salvatge, Departament de Medicina i Cirurgia Animals, Facultat de Veterinária, Universitat Autònoma de Barcelona (UAB), Bellaterra, Barcelona, Spain.
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova (UNIPD), Agripolis, Legnaro, Italy.
PLoS One. 2017 Apr 28;12(4):e0176635. doi: 10.1371/journal.pone.0176635. eCollection 2017.
Optimal management of free-ranging herbivores requires the accurate assessment of an animal's nutritional status. For this purpose 'near-infrared reflectance spectroscopy' (NIRS) is very useful, especially when nutritional assessment is done through faecal indicators such as faecal nitrogen (FN). In order to perform an NIRS calibration, the default protocol recommends starting by generating an initial equation based on at least 50-75 samples from the given species. Although this protocol optimises prediction accuracy, it limits the use of NIRS with rare or endangered species where sample sizes are often small. To overcome this limitation we tested a single NIRS equation (i.e., multispecies calibration) to predict FN in herbivores. Firstly, we used five herbivore species with highly contrasting digestive physiologies to build monospecies and multispecies calibrations, namely horse, sheep, Pyrenean chamois, red deer and European rabbit. Secondly, the equation accuracy was evaluated by two procedures using: (1) an external validation with samples from the same species, which were not used in the calibration process; and (2) samples from different ungulate species, specifically Alpine ibex, domestic goat, European mouflon, roe deer and cattle. The multispecies equation was highly accurate in terms of the coefficient of determination for calibration R2 = 0.98, standard error of validation SECV = 0.10, standard error of external validation SEP = 0.12, ratio of performance to deviation RPD = 5.3, and range error of prediction RER = 28.4. The accuracy of the multispecies equation to predict other herbivore species was also satisfactory (R2 > 0.86, SEP < 0.27, RPD > 2.6, and RER > 8.1). Lastly, the agreement between multi- and monospecies calibrations was also confirmed by the Bland-Altman method. In conclusion, our single multispecies equation can be used as a reliable, cost-effective, easy and powerful analytical method to assess FN in a wide range of herbivore species.
对自由放养的食草动物进行优化管理需要准确评估动物的营养状况。为此，“近红外反射光谱法”（NIRS）非常有用，尤其是当通过粪便指标（如粪便氮（FN））进行营养评估时。为了进行NIRS校准，默认方案建议首先基于给定物种的至少50 - 75个样本生成初始方程。尽管该方案优化了预测准确性，但它限制了NIRS在样本量通常较小的珍稀或濒危物种中的应用。为克服这一限制，我们测试了一个单一的NIRS方程（即多物种校准）来预测食草动物的FN。首先，我们使用了五种消化生理差异极大的食草动物来构建单物种和多物种校准，即马、绵羊、比利牛斯岩羚羊、马鹿和欧洲野兔。其次通过两种程序评估方程准确性：（1）使用校准过程中未使用的来自同一物种的样本进行外部验证；（2）使用来自不同有蹄类物种的样本，具体为高山山羊、家山羊、欧洲盘羊、狍和牛。多物种方程在校准决定系数R2 = 0.98、验证标准误差SECV = 0.10、外部验证标准误差SEP = 0.12、性能与偏差比RPD = 5.3以及预测范围误差RER = 28.4方面具有很高的准确性。多物种方程预测其他食草动物物种的准确性也令人满意（R2 > 0.86，SEP < 0.27，RPD > 2.6，RER > 8.1）。最后，Bland - Altman方法也证实了多物种校准和单物种校准之间的一致性。总之，我们的单一多物种方程可作为一种可靠、经济高效、简便且强大的分析方法，用于评估广泛的食草动物物种中的FN。
