A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species

Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of spe...

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Main Authors:	Kinzner, Martin-Carl, Wagner, Herbert C., Peskoller, Andrea, Moder, Karl, Dowell, Floyd E., Arthofer, Wolfgang, Schlick-Steiner, Birgit C., Steiner, Florian M.
Format:	Online
Language:	English
Published:	PeerJ Inc. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4699785/

id	pubmed-4699785
recordtype	oai_dc
spelling	pubmed-46997852016-01-05 A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species Kinzner, Martin-Carl Wagner, Herbert C. Peskoller, Andrea Moder, Karl Dowell, Floyd E. Arthofer, Wolfgang Schlick-Steiner, Birgit C. Steiner, Florian M. Biodiversity Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond. PeerJ Inc. 2015-09-15 /pmc/articles/PMC4699785/ /pubmed/26734510 http://dx.doi.org/10.7717/peerj.991 Text en http://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, made available under the Creative Commons Public Domain Dedication (http://creativecommons.org/publicdomain/zero/1.0/) . This work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kinzner, Martin-Carl Wagner, Herbert C. Peskoller, Andrea Moder, Karl Dowell, Floyd E. Arthofer, Wolfgang Schlick-Steiner, Birgit C. Steiner, Florian M.
spellingShingle	Kinzner, Martin-Carl Wagner, Herbert C. Peskoller, Andrea Moder, Karl Dowell, Floyd E. Arthofer, Wolfgang Schlick-Steiner, Birgit C. Steiner, Florian M. A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
author_facet	Kinzner, Martin-Carl Wagner, Herbert C. Peskoller, Andrea Moder, Karl Dowell, Floyd E. Arthofer, Wolfgang Schlick-Steiner, Birgit C. Steiner, Florian M.
author_sort	Kinzner, Martin-Carl
title	A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
title_short	A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
title_full	A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
title_fullStr	A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
title_full_unstemmed	A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
title_sort	near-infrared spectroscopy routine for unambiguous identification of cryptic ant species
description	Species identification—of importance for most biological disciplines—is not always straightforward as cryptic species hamper traditional identification. Fibre-optic near-infrared spectroscopy (NIRS) is a rapid and inexpensive method of use in various applications, including the identification of species. Despite its efficiency, NIRS has never been tested on a group of more than two cryptic species, and a working routine is still missing. Hence, we tested if the four morphologically highly similar, but genetically distinct ant species Tetramorium alpestre, T. caespitum, T. impurum, and T. sp. B, all four co-occurring above 1,300 m above sea level in the Alps, can be identified unambiguously using NIRS. Furthermore, we evaluated which of our implementations of the three analysis approaches, partial least squares regression (PLS), artificial neural networks (ANN), and random forests (RF), is most efficient in species identification with our data set. We opted for a 100% classification certainty, i.e., a residual risk of misidentification of zero within the available data, at the cost of excluding specimens from identification. Additionally, we examined which strategy among our implementations, one-vs-all, i.e., one species compared with the pooled set of the remaining species, or binary-decision strategies, worked best with our data to reduce a multi-class system to a two-class system, as is necessary for PLS. Our NIRS identification routine, based on a 100% identification certainty, was successful with up to 66.7% of unambiguously identified specimens of a species. In detail, PLS scored best over all species (36.7% of specimens), while RF was much less effective (10.0%) and ANN failed completely (0.0%) with our data and our implementations of the analyses. Moreover, we showed that the one-vs-all strategy is the only acceptable option to reduce multi-class systems because of a minimum expenditure of time. We emphasise our classification routine using fibre-optic NIRS in combination with PLS and the one-vs-all strategy as a highly efficient pre-screening identification method for cryptic ant species and possibly beyond.
publisher	PeerJ Inc.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4699785/
_version_	1613519459967827968

A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species

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