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

• Main Author: Tuomi, Maria W.
• Other Authors: Tuomi, Maria, Murguzur, Francisco J.A., Hoset, Katrine S., Soininen, Eeva M., Vesterinen, Eero, Utsi, Tove Aa., Kaino, Sissel, Bråthen, Kari Anne, Climate-Ecological Observatory for Arctic Tundra (COAT)
• Format: Dataset
• Language: English
• Published: DataverseNO 2017
• Subjects: Earth and Environmental Sciences; NIRS; microtine rodent; Arctic; non-invasive sampling; field method; abundance index; multi-species community; diet; metabarcoding; tundra; Subarctic; Tundra
• Online Access: https://doi.org/10.18710/9QKUIQ

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.