Use of NIRS in Wild Rodents’ Research: A Review of Timid Beginnings

Abstract

The article summarizes the previous experience and results from the study of wild rodents using the NIRS method. The importance and role of rodents in ecosystems and the specifics associated with their research using the NIRS method are briefly mentioned. The results of previous partial studies are mentioned and discussed. The NIRS method proved to be a useful tool to determine the amount of a particular food ingredient in the diet from faeces or chyme and to estimate the food quality (i.e., diversity of nitrogenous substances in chyme). On this basis, other possible directions of research using the NIRS method in wild rodents are proposed. These could help to better understand both the relationship between rodents and their environment and practical management in sectors where rodents interfere with human interests, especially in agriculture and forestry.

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.…”

Small rodent population cycles and plants – after 70 years, where do we go?

“…Below‐snow ecology of rodents is poorly understood in general, as suitable methodologies have been lacking (Krebs, 2013; Ehrich et al ., 2020). New approaches include below‐snow camera trapping (Mölle et al ., 2021) and non‐invasive diet analyses from faeces, both in terms of diet taxonomic composition (Soininen et al ., 2015 a ; Lopes et al ., 2020) and nutritional quality (Čepelka et al ., 2021). However, new molecular methods may be less informative than traditional methods for some aspects of diet, such as dietary restriction to particular plant organs (Batzli & Henttonen, 1990).…”