Novel frontier in wildlife monitoring: Identification of small rodent species from fecal 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 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

Novel frontier in wildlife monitoring : Identification of small rodent species from fecal 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 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.Peer reviewe

One leaf for all: Chemical traits of single leaves measured at the leaf surface using near-infrared reflectance spectroscopy

1. The leaf is an essential unit for measures of plant ecological traits. Yet, measures of plant chemical traits are often achieved by merging several leaves, masking potential foliar variation within and among plant individuals. This is also the case with cost‐effective measures derived using near‐infrared reflectance spectroscopy (NIRS). The calibration models developed for converting NIRS spectral information to chemical traits are typically based on spectra from merged and milled leaves. In this study, we ask whether such calibration models can be applied to spectra derived from whole leaves, providing measures of chemical traits of single leaves. 2. We sampled cohorts of single leaves from different biogeographic regions, growth forms, species and phenological stages to include variation in leaf and chemical traits. For each cohort, we first sampled NIRS spectra from each whole, single leaf, including leaf sizes down to Ø 4 mm (the minimum area of our NIRS application). Next, we merged, milled and tableted the leaves and sampled spectra from the cohort as a tablet. We applied arctic–alpine calibration models to all spectra and derived chemical traits. Finally, we evaluated the performance of the models in predicting chemical traits of whole, single leaves by comparing the traits derived at the level of leaves to that of the tablets. 3. We found that the arctic–alpine calibration models can successfully be applied to single, whole leaves for measures of nitrogen (R2 = 0.88, RMSE = 0.824), phosphorus (R2 = 0.65, RMSE = 0.081) and carbon (R2 = 0.78, RMSE = 2.199) content. For silicon content, we found the method acceptable when applied to silicon‐rich growth forms (R2 = 0.67, RMSE = 0.677). We found a considerable variation in chemical trait values among leaves within the cohorts. 4. This time‐ and cost‐efficient NIRS application provides non‐destructive measures of a set of chemical traits in single, whole leaves, including leaves of small sizes. The application can facilitate research into the scales of variability of chemical traits and include intra‐individual variation. Potential trade‐offs among chemical traits and other traits within the leaf unit can be identified and be related to ecological processes. In sum, this NIRS application can facilitate further ecological understanding of the role of leaf chemical traits