Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33457
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRodrigues, Thiago Fen_UK
dc.contributor.authorNogueira, Keilleren_UK
dc.contributor.authorChiarello, Adriano Gen_UK
dc.date.accessioned2021-10-15T00:00:19Z-
dc.date.available2021-10-15T00:00:19Z-
dc.date.issued2021-09en_UK
dc.identifier.urihttp://hdl.handle.net/1893/33457-
dc.description.abstractHaving 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.en_UK
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.relationRodrigues 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.1222en_UK
dc.rightsThis 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.en_UK
dc.rights.urihttps://storre.stir.ac.uk/STORREEndUserLicence.pdfen_UK
dc.subjectburrowen_UK
dc.subjectconservationen_UK
dc.subjectCabassous unicinctusen_UK
dc.subjectCabassous tatouayen_UK
dc.subjectDasypus novemcinctusen_UK
dc.subjectEuphractus sexcinctusen_UK
dc.subjectmammalen_UK
dc.subjectnoninvasive methoden_UK
dc.subjectXenarthraen_UK
dc.titleNoninvasive Low-cost Method to Identify Armadillos' Burrows: A Machine Learning Approachen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2022-09-28en_UK
dc.rights.embargoreason[armadillo.pdf] Publisher requires embargo of 12 months after publication.en_UK
dc.identifier.doi10.1002/wsb.1222en_UK
dc.citation.jtitleWildlife Society Bulletinen_UK
dc.citation.issn1938-5463en_UK
dc.citation.issn0091-7648en_UK
dc.citation.volume45en_UK
dc.citation.issue3en_UK
dc.citation.spage396en_UK
dc.citation.epage401en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailkeiller.nogueira@stir.ac.uken_UK
dc.citation.date27/09/2021en_UK
dc.contributor.affiliationUniversity of Sao Pauloen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Sao Pauloen_UK
dc.identifier.isiWOS:000700119400001en_UK
dc.identifier.scopusid2-s2.0-85115694047en_UK
dc.identifier.wtid1763654en_UK
dc.contributor.orcid0000-0003-3308-6384en_UK
dc.date.accepted2021-06-16en_UK
dcterms.dateAccepted2021-06-16en_UK
dc.date.filedepositdate2021-10-14en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionAMen_UK
local.rioxx.authorRodrigues, Thiago F|en_UK
local.rioxx.authorNogueira, Keiller|0000-0003-3308-6384en_UK
local.rioxx.authorChiarello, Adriano G|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2022-09-28en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2022-09-27en_UK
local.rioxx.licencehttps://storre.stir.ac.uk/STORREEndUserLicence.pdf|2022-09-28|en_UK
local.rioxx.filenamearmadillo.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source1938-5463en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

Files in This Item:
File Description SizeFormat 
armadillo.pdfFulltext - Accepted Version326.99 kBAdobe PDFUnder Embargo until 2022-09-28    Request a copy


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.