Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33457
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Noninvasive Low-cost Method to Identify Armadillos' Burrows: A Machine Learning Approach
Author(s): Rodrigues, Thiago F
Nogueira, Keiller
Chiarello, Adriano G
Contact Email: keiller.nogueira@stir.ac.uk
Keywords: burrow
conservation
Cabassous unicinctus
Cabassous tatouay
Dasypus novemcinctus
Euphractus sexcinctus
mammal
noninvasive method
Xenarthra
Issue Date: Sep-2021
Date Deposited: 14-Oct-2021
Citation: Rodrigues TF, Nogueira K & Chiarello AG (2021) Noninvasive Low-cost Method to Identify Armadillos' Burrows: A Machine Learning Approach. Wildlife Society Bulletin, 45 (3), pp. 396-401. https://doi.org/10.1002/wsb.1222
Abstract: Having accurate information about population parameters of armadillos (Mammalia, Cingulata) is essential for the conservation and management of the taxon, most species of which remain poorly studied. We investigated whether we could accurately identify 4 armadillo species (Euphractus sexcinctus, Dasypus novemcinctus, Cabassous tatouay, and Cabassous unicinctus) based on burrow morphometry. We first selected published studies that reported measurements of width, height, and angle of the burrows used by the 4 species of armadillos. Then, using such data we simulated burrow measurements for each of the 4 species of armadillos and we created predictive models through supervised machine learning that were capable of correctly identifying the species of armadillos based on their burrows' morphometry. By using classification algorithms such as Random Forest, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, and Decision Tree C5.0, we achieved the overall accuracy for the classification task by about 71%, including an overall Kappa index by about 61%. Euphractus sexcinctus was the most difficult species to discriminate and classify (approximately 68% of accuracy), whereas C. unicinctus was the easiest to discriminate (approximately 93% of accuracy). We found that it was possible to identify similar-sized armadillos based on the measurements of their burrows described in the literature. Finally, we developed an R function (armadilloID) that automatically identified the 4 species of armadillos using burrow morphology. As the data we used represented all studies that reported the morphometry of burrows for the 4 species of armadillos, we can generalize that our function can predict armadillo species beyond our data. © 2021 The Wildlife Society.
DOI Link: 10.1002/wsb.1222
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is the peer reviewed version of the following article: Rodrigues, T.F., Nogueira, K. and Chiarello, A.G. (2021), Noninvasive Low-cost Method to Identify Armadillos' Burrows: A Machine Learning Approach. Wildlife Society Bulletin, 45: 396-401, which has been published in final form at https://doi.org/10.1002/wsb.1222. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for self-archiving.
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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