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Near infrared spectroscopy for the identification of live anurans: Towards rapid and automated identification of species in the field

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In megadiverse regions, such as the Amazon, the identification of species generally requires specialists that are often not available. Therefore, the use of new species-recognition tools is necessary to streamline… Click to show full abstract

In megadiverse regions, such as the Amazon, the identification of species generally requires specialists that are often not available. Therefore, the use of new species-recognition tools is necessary to streamline surveys and avoid errors in species identification that lead to ineffective decision-making. Near infrared spectroscopy is a quick and non-destructive tool that has been widely used in the recognition of biodiversity. In addition to being used as an indicator group, anurans have species with high morphological diversity, which make them the focus of studies and application of new tools that help in the identification and recognition at the species level. In this study, the viability of recognition of species of live Amazonian frogs under field conditions using the near infrared technique and portable equipment was examined. The performance of classification models based on a linear discriminant analysis, built using spectra obtained from the dorsal and ventral surfaces of four pairs of phylogenetically-close and morphologically-similar species was evaluated. It was possible to distinguish the species of live anurans in five of the eight species studied with hit rates above 80% when using only one spectral reading per individual. The overall mean of correct prediction of the models was below that of previous studies that tested the method with anurans, which are likely to be due to particularities in the acquisition of spectra under field conditions and live species. Therefore, suggestions are made to improve the predictive capacity of the techniques.

Keywords: spectroscopy; identification; near infrared; infrared spectroscopy; field; identification species

Journal Title: Journal of Near Infrared Spectroscopy
Year Published: 2023

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