Replication Data for: Novel frontier in wildlife monitoring: identification of small rodent species from faecal pellets using Near-Infrared Reflectance Spectroscopy (NIRS)

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 non-invasive methods for large-scale, intensive sampling. Fecal pellet counts readily provide relative abundance indices, and given suitable analytical methods, feces could also allow for 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 co-existing 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 development of such calibration models, fNIRS analytics could complement novel genetic methods and greatly support ecosystem- and interaction-based approaches to monitoring.

小型啮齿类动物在全球各类生物群系中分布广泛且功能至关重要，因此对其开展监测对于生态系统管理、生物多样性保护、林业与农业均具有重要意义。当前，学界对于可支撑大规模、集约化采样的低成本非侵入式监测方法的需求日益增长。粪便颗粒计数可便捷获取相对丰度指数，若搭配适宜的分析方法，粪便样本还可用于测定多项生态与生理变量，包括群落组成。在此背景下，本研究利用粪便近红外反射光谱（fecal near-infrared reflectance spectroscopy, fNIRS）构建了用于啮齿类分类鉴定的校准模型。研究结果表明，fNIRS是一种准确且稳健的方法，可精准预测5种共存于亚北极环境的田鼠亚科啮齿类的属与物种归属。本研究发现，样本经自然风化暴露后可提升该方法的识别精度，这表明其适用于野外采集的样本。在本次研究的样本中，饮食并非影响物种预测精度的主要因素，因为不同物种的饮食存在较大的变异与重叠。此外，fNIRS还可跨区域应用：包含两个区域样本的校准模型，可对两个区域的样本均实现良好的预测精度。本研究证实，fNIRS是一种快速、低成本且高通量的啮齿类分类鉴定方法，具备开展跨区域校准以及应用于野外采集样本的潜力。尤为重要的是，fNIRS的多功能性是其核心优势所在。除啮齿类种群普查外，fNIRS还可获取种群统计学信息、粪便营养成分、应激激素甚至疾病相关数据。随着此类校准模型的不断开发完善，fNIRS分析技术可作为新兴遗传学方法的有效补充，极大助力基于生态系统与物种互作的监测研究。