Treachery pigmentation pattern leads to misidentification: Tapinoma melanocephalum (Fabricius), Tapinoma pygmaeum (Dufour) and Tapinoma jandai sp. nov. (Hymenoptera, Formicidae)

Critical comments on the South Atlantic puzzle of reef fish biogeography [J.‐C. Joyeux, S.R. Floeter, C.E.L. Ferreira &amp; J.L. Gasparini (2001) Biogeography of tropical reef fishes: the South Atlantic puzzle. <i>Journal of Biogeography</i>, 28, 831–841

This paper revises the taxonomy of Tapinoma melanocephalum (Fabricius, 1793) as follows: T. melanocephalum = Tapinoma luffae (Kuriam, 1955) syn. nov., = Tapinoma melanocephalum coronatum Forel, 1908 syn. nov., = Tapinoma melanocephalum malesianum Forel, 1913 syn. nov. A neotype of Tapinoma melanocephalum (Fabricius, 1793) is designed here. Lectotypes of Tapinoma melanocephalum coronatum Forel, 1908 and T. melanocephalum malesianum Forel, 1913 are designated. Formica wallacei is proposed as a replacement name for Formica familiaris (= T. melanocephalum senior synonym). The worker, queen and male are redescribed and diagnosed. The morphological variability of populations is discussed. All castes are included in full color images.

Thermophilic streptococci play an important role in the manufacture of many European cheeses, and a rapid and reliable method for their identification is needed. Randomly amplified polymorphic DNA (RAPD) PCR (RAPD-PCR) with two different primers coupled to hierarchical cluster analysis has proven to be a powerful tool for the classification and typing of Streptococcus thermophilus, Enterococcus faecium, and Enterococcus faecalis (G. Moschetti, G. Blaiotta, M. Aponte, P. Catzeddu, F. Villani, P. Deiana, and S. Coppola, J. Appl. Microbiol. 85:25-36, 1998). In order to develop a fast and inexpensive method for the identification of thermophilic streptococci, RAPD-PCR patterns were generated with a single primer (XD9), and the results were analyzed using artificial neural networks (Multilayer Perceptron, Radial Basis Function network, and Bayesian network) and multivariate statistical techniques (cluster analysis, linear discriminant analysis, and classification trees). Cluster analysis allowed the identification of S. thermophilus but not of enterococci. A Bayesian network proved to be more effective than a Multilayer Perceptron or a Radial Basis Function network for the identification of S. thermophilus, E. faecium, and E. faecalis using simplified RAPD-PCR patterns (obtained by summing the bands in selected areas of the patterns). The Bayesian network also significantly outperformed two multivariate statistical techniques (linear discriminant analysis and classification trees) and proved to be less sensitive to the size of the training set and more robust in the response to patterns belonging to unknown species.

Powellomycetaceae (Spizellomycetales) contain a diverse group of exogenously developing chytrids found by baiting water preparations of soils and manure with pollen. A previous molecular phylogenetic study indicated that some lineages within this family represent undescribed genera and species. Description of genera within the Spizellomycetales traditionally has relied on ultrastructural characters of zoospores, whereas species have been based on thallus development and morphology. We analyzed Powellomycetaceae chytrids that had not yet had ultrastructural and thallus morphologies determined. Because these chytrids vary little morphologically, we used a linear discriminant function analysis of thallus characters to determine features most useful for separating species when grown in pure culture on identical media. Zoosporic ultrastructures of two groups of isolates differed from those of the two described genera in the family, and we describe the new genera Thoreauomyces with one new species and Fimicolochytrium with two new species. Also, we describe three new species within Geranomyces. Linear discriminant function analysis, although helpful for determining more stable morphological characters, was not completely accurate in assigning chytrids to the correct genus or species, thus emphasizing the importance of molecular characters for identifying these taxa.

Bat algorithm for variable selection in multivariate classification modeling using linear discriminant analysis

Tapinoma melanocephalum (Fabricius, 1793), known under the vernacular name Ghost Ant and being a widely distributed cosmopolitan tramp species with significant pest status, has been considered long-since to represent a single species. Yet, investigation of 83 worker samples from the whole globe provided evidence that the Ghost Ant consists of two species: 8.4 % of the samples belonged to the new species T. pithecorum sp. nov. whereas 91.6 % were classified as T. melanocephalum. Absence of mixed samples indicates that T. melanocephalum and T. pithecorum sp. nov. do not represent morphs of the same species. T. pithecorum sp. nov. is known from a greenhouse finding in Germany and outdoor populations from the south of the Arabian Peninsula, Pakistan, India, Malaysia and the Fiji and Christmas Islands. The phenotypical separation of the new species from other minute Tapinoma species of Orientalic, Indo-Australian and Australasian origin is very clear, allowing identification even by evaluation of z-stack photos. A detailed argumentation is presented that Tapinoma pellucida (Smith, 1857), T. coronatum (Forel, 1908), T. malesianum (Forel, 1913), T. australis (Santschi, 1928), T. australe (Santschi, 1928), T. luffae (Kurian, 1955), T. wallacei (Guerrero, 2018) and T. jerdoni nom. nov. should be considered as synonyms of T. melanocephalum. Synomymies of T. melanocephalum and T. pithecorum sp. nov. with T. indicum (Forel, 1895) and Tapinoma minutum (Mayr, 1862) are clearly excluded by morphometric data. Diagrams of principal component analyses, photos, a key as well as a morphometric table to separate these four species are presented. In a concluding section, is argued that the true number of species related to these four species is much larger than presently known and a warning is given regarding careless use of pigmentation characters in ant identification.

Body weight and eight morphometric variables were used to differentiate three colour variants (white, black and lavender) of Nigerian indigenous turkey populations. Data included the following variables: body weight (BW, Kg), shank length (SL), beak length (BL), abdomen circumference (AC), thigh length (TL), snood length (SN), spur length (SP), tail feather (TF) and caruncle (CR). Phenotypic homogeneity and differences were evaluated using linear discriminant analysis. Results indicated that the black and lavender coloured variants were the closest in all the evaluated parameters. The pairwise squared Mahalanobis distance of 6.745 was obtained between black and lavender while the corresponding distances between white and each of black and lavender were 9.242 and 57.595 respectively (P<0.01). The results of predicted group membership showed that 100.00, 96.40 and 95.10 percent of black, white and lavender colour variant turkeys respectively, were correctly assigned into their phenotypic groups. Also, among the colour variants, white turkeys, showed significantly higher means (P<0.05) for BW, SL, SN and SP. Of all the morphometric variables, BW, SL and AC were the most discriminatory variables to separate the three colour variants. These results could indicate inherent within population variation in morphometric traits among indigenous turkey. Further detailed phenotypic and genetic characterization studies will contribute to the development of strategic breeding programmes for the genetic improvement of indigenous population of turkeys in Nigeria.

Different strategies of classification are tested and compared over near infrared (NIR) spectra and carbohydrates content data of genotypes of Eucalyptus globulus. Strategies used for the classification were regularized discriminant analysis (RDA) for carbohydrates content data and for the singular NIR spectral data, partial least squares (PLS) for reduction of variables to the scores matrix of NIR spectra plus RDA for classification (PLS/RDA on scores) and PLS‐discriminant analysis (PLS‐DA). Different types of discriminant functions were tested in PLS/RDA on scores method. Results obtained using NIR scores data outperformed those results obtained with carbohydrate content data. Comparison between specified PLS/RDA on scores strategies showed best classifications of the genotypes when parameters λ and γ pertaining to linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used, with 0% risk of misclassification and 100% of correctly assigned samples in the external validation sets for classification of the genotypes by forest company and by cold chamber while external validation for classification of genotypes by cold resistance degree shows 70 and 90% of correctly assigned samples for sensible and tolerant genotypes. Copyright © 2008 John Wiley &amp; Sons, Ltd.

One hundred and fifteen specimens of Pseudecheneis from the Irrawaddy and Salween drainages were divided into ten groups according to localities. When analyzed by the methods of multivariate morphometrics, twenty-five frame and 14 general characteristics were measured. According to the results of principal components analyzed twice, specimens clustered together by scatter plots of scores were gradually picked up and could be divided into five types. The first type only occurred in the middle and lower Longchuan Jiang, a branch of Irrawaddy. It is distinguished from other types by its slender and elongated caudal peduncle and can be identified as P. stenura. The other four types were un-described species. The second type was distributed in the upper Longchuan Jiang, a branch of the Irrawaddy and they had an extremely elongated body. The third type appears in the Wandian He, a branch of the Salween. And the length of its pectoral fin was longer than others which was the main distinguishing characteristic from the other types. Combined with the anatomical and phylogenic results and molecular biological studies, this type is considered an un-described species. The fourth and fifth types occur respectively in the Nanding He, a branch of the Salween and the Daying Jiang, a branch of the Irrawaddy. They differ from each other by the color pattern on the dorsal surface. Furthermore, some specimens from Qushi of Tengchong County and specimens from the Daying Jiang are not differentiated by morphometrics or external characteristics. They belong to a different geographic population of the same species. Different species, or specimens from Qushi of Tengchong County can be considered a new species. All suppositions should be confirmed by further studies in the future.

Intraspecific colour variation is common in nature and can vary from the coexistence of discrete colour variants in polymorphic species to continuous variation. Whether coloration is continuous or discrete is often ambiguous and many species exhibit a combination of the two. The nature of the variation (discrete or continuous) has implications for both the genetic basis of the colour variation and the evolutionary processes generating and maintaining it. Consequently, it is important to qualify the existence of discrete morphs, particularly in relation to the animal's visual system. In this study, we quantified male throat colour variation in Ctenophorus decresii tawny dragon lizard and tested for morphological and ecological correlates of the colour variants. We confirmed that discrete throat colour morphs can be defined based on colour and pattern analyses independent of the human visual system. We also found that the colour variants differed in their conspicuousness from the background, to the lizard's visual system, which has implications for signalling. However, the morphs did not differ in morphology or microhabitat use, which suggests that these characteristics are not involved in the evolutionary maintenance of the polymorphism.

In this paper, we provide numeric morphology-based evidence that the dark-colored Poneracoarctatavar.lucida Emery, 1898, formerly considered a synonym of P.coarctata (Latreille, 1802), is conspecific with the lighter-colored Poneratestacea Emery, 1895. Species hypotheses are developed via NC-PART clustering, combined with Partitioning Algorithm based on Recursive Thresholding (PART), and via PCA combined with gap statistics. We obtained our results from an extensive dataset from the 10 continuous morphometric traits measured on 165 workers belonging to 73 nest samples. Linear discriminant analysis (LDA) confirmed the grouping of hypotheses generated by exploratory analyses with 100% classification success when all ten morphometric traits were involved. The Anatolian Turkish black and the predominantly European yellow samples, did not separate based on their morphometric characteristics. These two color variations broadly overlap in their geographic range in Anatolian Turkey. The investigated type series of Poneracoarctatavar.lucida Emery, 1898 (collected from Kazakhstan) fell within the P.testacea cluster instead of P.coarctata and is also classified with high certainty as P.testacea by confirmatory LDA. Therefore, we propose the synonymy of Poneracoarctatavar.lucida Emery, 1898 with Poneratestacea Emery, 1895. As no other morphological differences than color patterns were detected between the “black” and “pale” P.testacea samples, we hold that these populations constitute geographically occurring color variations of the same species. Finally, our quantitative morphology-based results show that relying on color patterns is not a robust approach in identifying European Ponera samples, particularly in the east, but using multivariate analyses of morphometric traits is advised instead.

Cryptic species, by definition, appear very similar to each other. In the absence of obvious external morphological differences, quantitative measurements of fine-scale colour pattern differences may be used to distinguish between cryptic species. To demonstrate how this is accomplished, 30 specimens each of two cryptic onchidiid sea slug species in Singapore were collected and identified by sequencing a fragment of the mitochondrial cytochrome c oxidase subunit I gene. Sequences displayed a clear barcode gap: intraspecific distances (0–0.4%) and interspecific distances (4.8–5.5%) were distinct. To quantify colour patterns, eight pattern properties on the animals’ dorsal surface were measured using the PAT-GEOM software. Linear discriminant analysis and classification tree analysis were able to classify specimens with 80% and 81.7% accuracy respectively, and both identified proportion cover and randomness as the most important properties for differentiating the two species. Agreement between the genetic and pattern data is demonstrated by a significant correlation between the pairwise genetic and pattern distance matrices, as well as the significantly greater interspecific than intraspecific distances in both datasets. These results demonstrate that fine-scale pattern differences can be used to differentiate Peronia cryptic species. This approach has potential applications for a range of disciplines, including behaviour and ecology, and as an additional line of evidence for integrative taxonomy.

Credits mostly form a considerable part of banks assets and is one of the most risky types of them. Credits for banks are not only the source of income but also they can be the main reason of loss. The main risk that banks meet with lending money is credit risk. It is risk that debtor will not be able to repay his obligations due to certain reasons. Seeking to reduce potential loss due to crediting not reliable clients banks must be able to measure credit risk of each client properly. One of possible methods is using of internal credit risk estimation models.Due to the importance of credit risk analysis, many methods were widely applied to credit risk measurement tasks: linear discriminant analysis, logit analysis, probit analysis, linear programming, integer programming, k-nearest neighbour, classification tree, artificial neural networks (ANN), genetic algorithm, support vector machine, some hybrid models ant other. An increasing field of research in artificial neural networks is the one mainly concerned with interactions between economics and computer science, studying their potential applications to economics. Artificial neural networks represent an easily customizable tool for modelling learning behaviour of agents and for studying a lot of problems very difficult to analyze with standard economic models (Gallo, 2006).ANN have many advantages over conventional methods of analysis. According to Shachmurove (2002), they have the ability to analyze complex patterns quickly and with a high degree of accuracy. Artificial neural networks make no assumptions about the nature of the distribution of the data. Since time-series data are dynamic in nature, it is necessary to have non-linear tools in order to discern relationships among time-series. ANN are best at discovering these types of relationships. Also neural networks perform well with missing or incomplete data. Compared with an econometric model, it is easier to use ANN where a forecast needs to be obtained in a shorter period of time.The purpose of this research is to define the rates of classification accuracy and to measure the classification accuracy of artificial neural networks credit risk estimation models. The methods of the research are analysis of scientific publications about credit risk estimation models and analysis of artificial neural networks credit risk estimation models classification accuracy.One important component needed to accomplish credit risk evaluation is to seek an accurate classifier in order to categorize new applicants or existing customers as good or bad (Lai, Yu, Wang, Zhou, 2006). In this paper rates of credit risk estimation models accuracy and their calculation were described: correct classification and misclassification rates, false negative and false positive rates, model sensitivity and specificity, positive and negative predictive values, F-measure, ROC analysis. Analysis of scientific publications about credit risk estimation models has shown that the most efficient of the most commonly used methods are logistic regression and artificial neural networks. Less reliable methods are decision trees and discriminant analysis. Also artificial neural networks models (multilayer perceptrons) were constructed for the analysis of Lithuanian enterprises credit risk. Calculation of models accuracy rates has shown that the most efficient model analyses data about clients is of 3 years. Experiments have shown that analyzing data of 4 and 5 years classification accuracy decreases due to high quantity of input information and neural network‘s overlearning. Models accuracy rates allowed to estimate risk of client misclassification and other characteristics. Also they helped to make the decision which model is to be used in practice in order to measure credit risk successfully.

The fulva–rudis–texana complex of the ant genus Aphaenogaster includes A. fulva, A. rudis, A. texana, and morphologically similar species. Morphometric and other morphological investigations were conducted on colony representatives of 10 forms (4 likely representing undescribed species) that were previously identified using cytogenetic and electrophoretic markers. In workers, most qualitative characters exhibit such a high degree of size-associated intraspecific variation relative to interspecific differences that they are not reliable for identification. Linear discriminant analysis and canonical variate analysis on 12 morphometric measurements were used to optimize classification and define a morphometric habitus for each form. Two-variable scatterplots clarify the nature of morphometric variation in the complex and provide simple characters that will reliably separate numerous pairs of forms. Indices appear to be of little taxonomic value in this complex. A preliminary key to the workers of the complex summarizes the most important taxonomic characters. This key substantially improves the ability to recognize morphologically most members of the complex, but sometimes only genetic evidence is definitive.

The Cerrado is one of the richest tropical savannas and is considered a biodiversity “hotspot.” It is estimated that, at the current rate of loss, the ecosystem will disappear by the year 2030. The number of new species being discovered in the Cerrado has increased linearly, especially over the last 50 years. We describe a new species of Cnemidophorus from the Jalapão region, in the northern Cerrado biome, Brazil. Linear discriminant analyses and a naïve Bayesian model indicated that a combination of meristic counts (femoral pores, scales around tail, prefemorals, dorsals, and supralabials) and categorical variables derived from pholidosis and coloration clearly distinguish the new species from its congeners. The new species is apparently parapatric with C. mumbuca, the two species occurring on opposite banks of the Novo River. The two species are ecologically and morphologically similar, sharing a small body size, a fixed clutch size of a single egg, and a small number of femoral pores. The new species is apparently endemic to the Jalapão region, in the northern portion of the Cerrado biome. Its small size and restricted geographic range are consistent with findings from Cerrado anurans that undescribed species tend to be small and have reduced ranges. These results highlight the urgency of biotic surveys in Cerrado in face of its accelerated pace of destruction.