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

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  • Li-Dunn Chen
  • Michael A Caprio
  • Devin M Chen
  • Andrew J Kouba
  • Carrie K Kouba

Abstract

Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p

Suggested Citation

  • Li-Dunn Chen & Michael A Caprio & Devin M Chen & Andrew J Kouba & Carrie K Kouba, 2024. "Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians," PLOS Computational Biology, Public Library of Science, vol. 20(2), pages 1-24, February.
    • Handle: RePEc:plo:pcbi00:1011876
    DOI: 10.1371/journal.pcbi.1011876
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    1. Miriam Villamuelas & Emmanuel Serrano & Johan Espunyes & Néstor Fernández & Jorge R López-Olvera & Mathieu Garel & João Santos & María Ángeles Parra-Aguado & Maurizio Ramanzin & Xavier Fernández-Aguil, 2017. "Predicting herbivore faecal nitrogen using a multispecies near-infrared reflectance spectroscopy calibration," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-15, April.
    2. Andrius Vabalas & Emma Gowen & Ellen Poliakoff & Alexander J Casson, 2019. "Machine learning algorithm validation with a limited sample size," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-20, November.
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