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

Abstract

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.

Near‐infrared spectroscopy for metabolite quantification and species identification

“…Similarly, the NIRS calibration model for two field‐collected mosquito species also had an accuracy of approximately 80% (Mayagaya et al, ). In comparison, the accuracy of identifying four Tetramorium ant species was lower (13.3%–66.7%; Kinzner et al, ).…”

“…There is a need for developing accurate, effective, low‐cost and efficient approaches that can be used in the field (Falk, Wallace, & Ndoen, ; Nansen & Elliott, ). Increasingly, NIRS is being used by the entomological community and it has been shown to accurately identify a range of species including Anopheles mosquitoes (Mayagaya et al, ), Zootermopsis termites (Aldrich, Maghirang, Dowell, & Kambhampati, ), Calliphoridae blowflies (Voss, Magni, Dadour, & Nansen, ), and Tetramorium ants (Kinzner et al, ). Morphologically, male D. melanogaster can be differentiated from male D. simulans by the shape of the genital arch genitalia, but females are difficult to identify and these taxa are considered sibling species.…”

“…In this study, we investigate the accuracy of NIRS in determining metabolites levels by comparing the results to those obtained from a commercial assay kit. NIRS has been applied in noninvasive measurement of a variety of metabolites including blood glucose of patients with type I diabetes (Robinson et al, 1992), less invasive quantitative curately identify a range of species including Anopheles mosquitoes (Mayagaya et al, 2015), Zootermopsis termites (Aldrich, Maghirang, Dowell, & Kambhampati, 2007), Calliphoridae blowflies (Voss, Magni, Dadour, & Nansen, 2017), and Tetramorium ants (Kinzner et al, 2015). Morphologically, male D. melanogaster can be differentiated from male D. simulans by the shape of the genital arch genitalia, but females are difficult to identify and these taxa are considered sibling species.…”

Near-Infrared Spectroscopy for metabolite quantification and species identification

“…Similarly, the NIRS calibration model for two field collected mosquito species also had an accuracy of approximately 80% (Mayagaya et al, 2009). In comparison, the accuracy of identifying four Tetramorium ant species was lower (13.3% - 66.7%) (Kinzner et al (2015). Future studies should consider how the accuracy of NIRS species identification could be improved.…”

“…Accurate insect species identification is essential for agricultural, ecological, medical, taxonomic, veterinary studies and for biosecurity (Falk et al, 2011; Nansen and Elliott, 2016). Increasingly NIRS is being used by the entomological community and has been shown to accurately identify a range of species including Anopheles mosquitoes (Mayagaya et al, 2015), Zootermopsis termites (Aldrich et al, 2007), Calliphoridae blowflies (Voss et al, 2017), and Tetramorium ants (Kinzner et al, 2015). Morphological identification requires taxonomic expertise and is both labor-intensive and time-consuming.…”