Novel frontier in wildlife monitoring: Identification of small rodent species from fecal pellets using near‐infrared reflectance spectroscopy (NIRS)

Abstract

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 fieldcollected 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.

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians

“…Stomach contents, excreta Excreta PLS [63] FDA [64] Amur leopard Sex and species Excreta PLS, MDR [65] Snow leopard Sex and species Excreta PLS, MDR [65] Indian leopard Species Blood traces PLS [62] Bengal tiger Species Blood traces PLS [62] Gorilla Nutrition and food choice Plant leaves MPLS [66] Howler monkey Sex and species Hair PLS [67] Orangutan Estrus Excreta PLS [68] Dugong Nutrition Stomach contents, excreta, plant leaves PLS [38,69,70] Pinniped Nutrition Excreta PLS [71] Koala Nutritional ecology Plant leaves, excreta PLS [72,73] Kangaroo Nutrition Feeding ecology Excreta Plant leaves, excreta PLS [74] PLS [73] Hairy-nosed wombat Nutrition Plant leaves PLS [75] Multi-Class Multiple species of fish, reptile, bird, mammal Taxonomy Skull Bones PLS [76] *Abbreviations for the algorithms applied for calibrating and testing models are as follows: KNN = K-nearest neighbors, LDA = linear discriminant analysis, LMM = linear mixing model, MDR = Mahalanobis distance discrimination based on PCA residuals, MLR = multiple linear regression, OPLS = orthogonal projection to latent structures, PLS = partial least squares, SIMCA = soft independent modeling of class analogies, SMR = stepwise multiple regression, and SVM = support vector machine.…”