Enhancing Predictive Performance for Spectroscopic Studies in Wildlife Science Through a Multi-Model Approach: A Case Study for Species Classification of Live Amphibians
ORCID
AKouba: https://orcid.org/0000-0002-9653-9814; CKouba: https://orcid.org/0000-0001-8466-6077
MSU Affiliation
College of Agriculture and Life Sciences; Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology; College of Forest Resources; Department of Wildlife, Fisheries and Aquaculture
Creation Date
2026-10-30
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 < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed.
Keywords
machine learning algorithms, forecasting, amphibians, near-infrared spectroscopy, salamanders, excretion, toads, wildlife
Publication Date
2-14-2024
Publication Title
Plos Computational Biology
Publisher
Public Library of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© 2024 Chen et al.
Recommended Citation
Chen L-D, Caprio MA, Chen DM, Kouba AJ, Kouba CK (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 Comput Biol 20(2): e1011876. https://doi.org/10.1371/journal.pcbi.1011876
