Tuomi Maria W, Murguzur Francisco J A, Hoset Katrine S, Soininen Eeva M, Vesterinen Eero J, Utsi Tove Aa, Kaino Sissel, Bråthen Kari Anne
Department of Arctic and Marine Biology UiT The Arctic University of Norway Tromsø Norway.
Section of Ecology Department of Biology University of Turku Turku Finland.
Ecol Evol. 2023 Mar 19;13(3):e9857. doi: 10.1002/ece3.9857. eCollection 2023 Mar.
Small rodents are prevalent and functionally important across the world's biomes, making their monitoring salient for ecosystem management, conservation, forestry, and agriculture. There is a growing need for cost-effective and noninvasive methods for large-scale, intensive sampling. Fecal pellet counts readily provide relative abundance indices, and given suitable analytical methods, feces could also allow for the determination of multiple ecological and physiological variables, including community composition. In this context, we developed calibration models for rodent taxonomic determination using fecal near-infrared reflectance spectroscopy (fNIRS). Our results demonstrate fNIRS as an accurate and robust method for predicting genus and species identity of five coexisting subarctic microtine rodent species. We show that sample exposure to weathering increases the method's accuracy, indicating its suitability for samples collected from the field. Diet was not a major determinant of species prediction accuracy in our samples, as diet exhibited large variation and overlap between species. fNIRS could also be applied across regions, as calibration models including samples from two regions provided a good prediction accuracy for both regions. We show fNIRS as a fast and cost-efficient high-throughput method for rodent taxonomic determination, with the potential for cross-regional calibrations and the use on field-collected samples. Importantly, appeal lies in the versatility of fNIRS. In addition to rodent population censuses, fNIRS can provide information on demography, fecal nutrients, stress hormones, and even disease. Given the development of such calibration models, fNIRS analytics could complement novel genetic methods and greatly support ecosystem- and interaction-based approaches to monitoring.
小型啮齿动物在全球生物群落中普遍存在且具有重要功能,因此对它们的监测对于生态系统管理、保护、林业和农业而言至关重要。对于大规模、密集采样的经济高效且非侵入性方法的需求日益增长。粪便颗粒计数能够轻易提供相对丰度指数,并且如果有合适的分析方法,粪便还可用于确定包括群落组成在内的多个生态和生理变量。在此背景下,我们利用粪便近红外反射光谱法(fNIRS)开发了用于啮齿动物分类鉴定的校准模型。我们的结果表明,fNIRS是预测五种共存的亚北极田鼠类啮齿动物属和种身份的准确且可靠的方法。我们发现,样本暴露于风化环境会提高该方法的准确性,这表明它适用于从野外采集的样本。在我们的样本中,饮食并非物种预测准确性的主要决定因素,因为不同物种之间的饮食差异很大且存在重叠。fNIRS还可跨区域应用,因为包含来自两个区域样本的校准模型对两个区域都具有良好的预测准确性。我们证明fNIRS是一种用于啮齿动物分类鉴定的快速且经济高效的高通量方法,具有进行跨区域校准以及用于野外采集样本的潜力。重要的是,fNIRS的吸引力在于其多功能性。除了啮齿动物种群普查外,fNIRS还可提供有关种群统计学、粪便营养成分、应激激素甚至疾病的信息。鉴于此类校准模型的开发,fNIRS分析可以补充新型遗传方法,并极大地支持基于生态系统和相互作用的监测方法。