Species Identification System Research Articles

Deep Learning for Plant Species Classification Using Leaf Vein Morphometric.

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naïve-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.

Integrating a comprehensive DNA barcode reference library with a global map of yews (Taxus L.) for forensic identification.

Rapid and accurate identification of endangered species is a critical component of biosurveillance and conservation management, and potentially policing illegal trades. However, this is often not possible using traditional taxonomy, especially where only small or preprocessed parts of plants are available. Reliable identification can be achieved via a comprehensive DNA barcode reference library, accompanied by precise distribution data. However, these require extensive sampling at spatial and taxonomic scales, which has rarely been achieved for cosmopolitan taxa. Here, we construct a comprehensive DNA barcode reference library and generate distribution maps using species distribution modelling (SDM), for all 15 Taxus species worldwide. We find that trnL-trnF is the ideal barcode for Taxus: It can distinguish all Taxus species and in combination with ITS identify hybrids. Among five analysis methods tested, NJ was the most effective. Among 4,151 individuals screened for trnL-trnF, 73 haplotypes were detected, all species-specific and some population private. Taxonomical, geographical and genetic dimensions of sampling strategy were all found to affect the comprehensiveness of the resulting DNA barcode library. Maps from SDM showed that most species had allopatric distributions, except T. mairei in the Sino-Himalayan region. Using the barcode library and distribution map data, two unknown forensic samples were identified to species (and in one case, population) level and another was determined as a putative interspecific hybrid. This integrated species identification system for Taxus can be used for biosurveillance, conservation management and to monitor and prosecute illegal trade. Similar identification systems are recommended for other IUCN- and CITES-listed taxa.

How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre-ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef-forts on identification of species include specimens' image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179-193).

Establishment of DNA Genetic Marker Identification System for Plant Evidence

To establish a species identification system based on DNA genetic markers for plant evidence. Two hundred common plants in Shanghai were collected and identified by morphological characteristics. The primers of gene segments rbcL, matK, and ITS were designed and amplified. The PCR amplicon was detected by agarose gel electrophoresis. After the sequencing, the universality and the identification capacity of the three markers were evaluated. The success rate of amplification was in order of rbcL （99.5%） > matK （92.5%） > ITS （86.0%）. The identification capacity of the combination of rbcL and matK was better than that of rbcL or matK, by which most plant species could be identified to the genus or higher. ITS was not suitable to be a unique marker because of its unstable result, but it still could be a powerful supplement. The identification capacity of the combination of rbcL, matK and ITS was higher than that of rbcL and matK, by which most plant species could be identified to the genus or lower. The identification system with the combination of rbcL, matK and ITS as markers has excellent universality for plant evidence, which can distinguish most plant species to the genus or lower.

A Methodological Proposal for Collecting and Creating Macroscopic Photograph Collections of Tropical Woods with Potential for Use in Deep Learning

Costa Rica is one of the countries with highest species biodiversity density in the world. More than 2,000 tree species have already been identified, many of which are used in the building, furniture, and packaging industries (Grayum et al. 2003). This rich diversity makes the correct identification of tree species very difficult. As a result, it is common to see in the national market that species are commercialized with mistaken identifications, which makes quality control particularly challenging. In addition, because 90 timber tree species have been classified as “threatened” in Costa Rica, correct identifications are indispensable for law-enforcement. The traditional system for tree species identification is based on macro and microscopic evaluations of the anatomy of the wood. It entails assesing anatomical features such as patterns of vessels, parenchymas, and fibers. Typically, 7.7 x 10 cm pieces of wood cuts are used to identify the tree species (Pan and Kudo 2011, Yusof et al. 2013). However, assessing these features is extremely difficult for taxonomists because properties of the wood can vary considerably due to environmental conditions and intra-specific genetic variability. Deep learning techniques have recently been used to identify plant species (Carranza-Rojas et al. 2017a, Carranza-Rojas et al. 2017b) and are potentially useful to detect subtle differences in patterns of vessels, parenchyma, and other anatomical features of wood. However, it is necessary to have a large collection of macroscopic photographs of individuals from various parts of the country (Pan and Kudo 2011). As a first step in the application of deep learning techniques, we have defined a formal, standard protocol for collecting wood samples, physically processing them, taking pictures, performing data augmentation, and using metadata to provide the primary data necessary for deep learning applications. Unlike traditional xylotheque sampling methods that destroy trees or use wood from fallen trees, we propose a method that extracts small size samples with sufficient quality for anatomical characterization but does not affect the growth and survival of the individual. This study has been developed in three forest permanent plots in Costa Rica, all of which are sites with historical growth data over the last 20 years. We have so far evaluated 40 species (10 individuals per species) with diameters greater than 20 cm. From each individual, a cylindrical sample of 12 mm diameter and 7.5 cm in length was extracted with a cordless drill. Each sample is then cut into five of 8 x 8 x 8 mm cubes and further processed to result in curated xylotheque samples, a dataset with all relevant metadata and original images, and a dataset with images obtained by performing data augmentation on the original images.

Automatic Wood Species Identification of Korean Softwood Based on Convolutional Neural Networks

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

Diagnostic and phylogenetic utility of the first DNA barcode library for longhorn beetles (Coleoptera: Cerambycidae) from the Russian Far East

One hundred forty two specimens representing 56 species of longhorn beetles (Cerambycidae) from the Russian Far East were sequenced for a 658 bp fragment of the 5' end of the mitochondrial cytochrome oxidase subunit I gene (COI, =DNA barcode). The data are publicly available in an open access online library (dx.doi.org/10.5883/DS-CERRF). All analysed species could be differentiated using the standard online DNA barcode identification tool, except for a group of three sympatric colour-defined Menesia species. Seven Menesia records share the same Barcode Identification Number (=BIN), while the single specimen of M. flavotecta shares the same haplotype as some of M. sulphurata. Excluding the Menesia case, the Barcode Index Numbers (BINs) uniquely correspond to the analysed species, in all but the four specimens of Chlorophorus simillimus where they share two BINs. Although DNA barcoding aims to develop species identification systems, some phylogenetic signal was apparent in the data and in the Maximum Likelihood analysis, all four subfamilies were recovered as monophyletic. Notwithstanding the few detected deviations from the absolute taxonomic/phylogenetic match, DNA barcoding is a powerful identification tool with a capacity to place an undocumented record among its closest relatives.

LeafNet: A computer vision system for automatic plant species identification

AimsTaxon identification is an important step in many plant ecological studies. Its efficiency and reproducibility might greatly benefit from partly automating this task. Image-based identification systems exist, but mostly rely on hand-crafted algorithms to extract sets of features chosen a priori to identify species of selected taxa. In consequence, such systems are restricted to these taxa and additionally require involving experts that provide taxonomical knowledge for developing such customized systems. The aim of this study was to develop a deep learning system to learn discriminative features from leaf images along with a classifier for species identification of plants. By comparing our results with customized systems like LeafSnap we can show that learning the features by a convolutional neural network (CNN) can provide better feature representation for leaf images compared to hand-crafted features. MethodsWe developed LeafNet, a CNN-based plant identification system. For evaluation, we utilized the publicly available LeafSnap, Flavia and Foliage datasets. ResultsEvaluating the recognition accuracies of LeafNet on the LeafSnap, Flavia and Foliage datasets reveals a better performance of LeafNet compared to hand-crafted customized systems. ConclusionsGiven the overall species diversity of plants, the goal of a complete automatisation of visual plant species identification is unlikely to be met solely by continually gathering assemblies of customized, specialized and hand-crafted (and therefore expensive) identification systems. Deep Learning CNN approaches offer a self-learning state-of-the-art alternative that allows adaption to different taxa just by presenting new training data instead of developing new software systems.

ANTIBIOTIC RESISTANCE PATTERNS OF POTENTIAL PATHOGENS ISOLATED FROM TWO MAJOR HOSPITALS IN LUSAKA AND NDOLA

BackgroundThis study was conducted as part of an assessment of the effectiveness of existing hygiene and sanitation practices in two first-level hospitals in Lusaka and two central clinics in Ndola...

Evaluation of Three Bacterial Identification Systems for Species Identification of Bacteria Isolated from Bovine Mastitis and Bulk Tank Milk Samples.

A study was conducted to evaluate Sensititre® Automated Reading and Incubation System 2x System (ARIS), API® (API), and Bruker MALDI-TOF MS (MALDI) bacterial species identification systems using 132 diverse bacterial isolates from bovine milk samples and bulk tank milk received at the Penn State Animal Diagnostic Laboratory. The results were compared with 16S rRNA gene sequence analysis, which served as the reference method for species identification. The ARIS, API, and MALDI identified 0%, 40%, and 33.4% of species classified as Gram-positive rod isolates belonging to genera Arthrobacter, Bacillus, Brachybacterium, Brevibacterium, and Corynebacterium, respectively. It was observed that 76.5%, 93.9%, and 96.9% of catalase-negative, Gram-positive cocci (n = 33; Aerococcus, Enterococcus, Lactococcus, Streptococcus) were correctly identified to the species level by ARIS, API, and MALDI, respectively, while 33.4%, 84.5%, and 97.7% of catalase-positive, Gram-positive cocci (n = 45; Kocuria, Staphylococcus) were correctly identified to their species by ARIS, API, and MALDI, respectively. A total of 48 isolates (Acinetobacter, Citrobacter, Enterobacter, Escherichia, Klebsiella, Pantoea, Pasteurella, Providencia, Pseduomonas, Serratia) of Gram-negative bacteria were examined, of which 85.4%, 93.7%, and 95.8% of the isolates were correctly identified to the species level by ARIS, API, and MALDI, respectively. In our laboratory, the MALDI had the least costs associated with consumables and reagents compared to ARIS, API, and 16S rRNA identification methods. Identification of bacterial species was accomplished in <2 h using MALDI and 24 h for ARIS, API, and 16S rRNA identification systems.

Identification of Species in Tripterygium (Celastraceae) Based on DNA Barcoding.

Species of genus Tripterygium (Celastraceae) have attracted much attention owing to their excellent effect on treating autoimmune and inflammatory diseases. However, due to high market demand causing overexploitation, natural populations of genus Tripterygium have rapidly declined. Tripterygium medicinal materials are mainly collected from the wild, making the quality of medicinal materials unstable. Additionally, identification of herbal materials from Tripterygium species and their adulterants is difficult based on morphological characters. Therefore, an accurate, convenient, and stability method is urgently needed. In this wok, we developed a DNA barcoding technique to distinguish T. wilfordii HOOK. f., T. hypoglaucum (LÉVL.) HUTCH, and T. regelii SPRAGUE et TAKEDA and their adulterants based on four uniform and standard DNA regions (internal transcribed spacer 2 (ITS2), matK, rbcL, and psbA-trnH). DNA was extracted from 26 locations of fresh leaves. Phylogenetic tree was constructed with Neighbor-Joining (NJ) method, while barcoding gap was analyzed to assess identification efficiency. Compared with the other DNA barcodes applied individually or in combination, ITS2+psbA-trnH was demonstrated as the optimal barcode. T. hypoglaucum and T. wilfordii can be considered as conspecific, while T. regelii was recognized as a separate species. Furthermore, identification of commercial Tripterygium samples was conducted using BLAST against GenBank and Species Identification System for Traditional Chinese Medicine. Our results indicated that DNA barcoding is a convenient, effective, and stability method to identify and distinguish Tripterygium and its adulterants, and could be applied as the quality control for Tripterygium medicinal preparations and monitoring of the medicinal herb trade in markets.

A software system for automated identification and retrieval of moth images based on wing attributes

Manually collecting, identifying, archiving and retrieving specimen images is an expensive and time-consuming work for entomologists. There is a clear need to introduce fast systems integrated with modern image processing and analysis algorithms to accelerate the process. In this paper, we describe the development of an automated moth species identification and retrieval system (SPIR) using computer vision and pattern recognition techniques. The core of the system is a probabilistic model that infers Semantically Related Visual (SRV) attributes from low-level visual features of moth images in the training set, where moth wings are segmented into information-rich patches from which the local features are extracted, and the SRV attributes are provided by human experts as ground-truth. For the large amount of unlabeled test images in the database or added into the database later on, an automated identification process is evoked to translate the detected salient regions of low-level visual features on the moth wings into meaningful semantic SRV attributes. We further propose a novel network analysis based approach to explore and utilize the co-occurrence patterns of SRV attributes as contextual cues to improve individual attribute detection accuracy. Working with a small set of labeled training images, the approach constructs a network with nodes representing the SRV attributes and weighted edges denoting the co-occurrence correlation. A fast modularity maximization algorithm is proposed to detect the co-occurrence patterns as communities in the network. A random walk process working on the discovered co-occurrence patterns is applied to refine the individual attribute detection results. The effectiveness of the proposed approach is evaluated in automated moth identification and attribute-based image retrieval. In addition, a novel image descriptor called SRV attribute signature is introduced to record the visual and semantic properties of an image and is used to compare image similarity. Experiments are performed on an existing entomology database to illustrate the capabilities of our proposed system. We observed that the system performance is improved by the SRV attribute representation and their co-occurrence patterns.

Statistical analysis of texture in trunk images for biometric identification of tree species.

The identification of tree species is a key step for sustainable management plans of forest resources, as well as for several other applications that are based on such surveys. However, the present available techniques are dependent on the presence of tree structures, such as flowers, fruits, and leaves, limiting the identification process to certain periods of the year. Therefore, this article introduces a study on the application of statistical parameters for texture classification of tree trunk images. For that, 540 samples from five Brazilian native deciduous species were acquired and measures of entropy, uniformity, smoothness, asymmetry (third moment), mean, and standard deviation were obtained from the presented textures. Using a decision tree, a biometric species identification system was constructed and resulted to a 0.84 average precision rate for species classification with 0.83accuracy and 0.79 agreement. Thus, it can be considered that the use of texture presented in trunk images can represent an important advance in tree identification, since the limitations of the current techniques can be overcome.

DNA Barcoding and Elucidation of Cryptic Diversity in Thrips (Thysanoptera)

Accurate and timely identification of invasive insect pests underpins most biological endeavors ranging from biodiversity estimation to insect pest management. In this regard, identification of thrips, an invasive insect pest is important and challenging due to their complex life cycles, parthenogenetic mode of reproduction, sex and color morphs. In the recent years, DNA barcoding employing 5′ region of the mitochondrial Cytochrome Oxidase I (CO-I) gene has become a popular tool for species identification. In this study, we employed CO-I gene sequences for discriminating 151 species of thrips for the first time. Analyses of the intraspecific and intrageneric distances of the CO-I sequences ranged from 0.0 to 7.91% and 8.65% to 31.15% respectively. This study has revealed the existence of cryptic species in Thrips hawaiiensis (Morgan) (Thysanoptera: Thripidae) and Scirtothrips perseae Nakahara (Thysanoptera: Thripidae) for the first time, along with previously reported cryptic species such as Thrips palmi Karny (Thysanoptera: Thripidae), T. tabaci Lindeman, Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae), Scirtothrips dorsalis Hood. We are proposing, the feasibility of hosting an independent integrated taxonomy library for thrips and indicate that it can serve as an effective system for species identification, this approach could potentially play a key role in formulating effective insect pest management strategies.

DNA barcoding for identification of species in mushrooms: A component of product certification

One challenge in the botanical industry is the confirmation of species identification, and this can be compounded by the processing of the materials before reaching the vendor, such as milling or drying. This can be particularly difficult for samples that contain fungal mycelia, where morphological characteristics do not present sufficient variation to differentiate species. However, monitoring the safety and quality of such products is a requirement for the protection of consumer health. DNA barcoding, a genetic-based species identification system, has become popular in the past decade, and it shows great promise for species level identification of fungi. By using DNA barcoding of nuclear ribosomal internal transcribed spacer (ITS) of the rRNA gene with fungal specific ITS primers, we generated ITS barcodes for two representative mushroom fungi, which are being used by consumers for food and dietary supplement purposes. After generating ITS barcodes utilizing standard procedures accepted by the Consortium for the Barcode of Life, we tested the utility of the ITS by performing a BLAST search against both curated and non-curated public databases. Results demonstrated that the ITS region was able to identify both mushrooms used in the present study to species-level. The BLAST search provided 99% similarity of the query sequence to those available in the public database. We anticipate that this presentation will motivate a discussion on DNA barcoding based species identification, particularly as it applies to the verification/certification of mushrooms containing products.

Integrated DNA barcoding datebase for identifying Chinese animal medicine

In order to construct an integrated DNA barcoding database for identifying Chinese animal medicine, the authors and their cooperators have completed a lot of researches for identifying Chinese animal medicines using DNA barcoding technology. Sequences from GenBank have been analyzed simultaneously. Three different methods, BLAST, barcoding gap and Tree building, have been used to confirm the reliabilities of barcode records in the database. The integrated DNA barcoding database for identifying Chinese animal medicine has been constructed using three different parts: specimen, sequence and literature information. This database contained about 800 animal medicines and the adulterants and closely related species. Unknown specimens can be identified by pasting their sequence record into the window on the ID page of species identification system for traditional Chinese medicine (www. tcmbarcode. cn). The integrated DNA barcoding database for identifying Chinese animal medicine is significantly important for animal species identification, rare and endangered species conservation and sustainable utilization of animal resources.

Prolactin I Microsatellite as Genetic Markers for Characterization of Five Oreochromis Tilapia Species and Two Oreochromis Niloticus Strains

Hybrid tilapia is a major aquaculture food fish species in developing countries. The development of molecular markers to characterize and trace tilapia species is necessary to improve tilapia quality and enhance the competitive advantage of the aquaculture industry. Microsatellite markers have been suggested to assist breeding, species identification, and traceability system for tilapia. We used six microsatellites markers located within growth-related genes to discriminate between several tilapia species. We found that a combination of two microsatellites markers located within the proximal promoter of the prolactin I (PRL I) gene, PRL I-MS01 and PRL I-MS02, were able to discriminate between five Oreochromis tilapia species (O. mossambicus, O. aureus, O. niloticus, O. hornorum and O. spilurus) and two O. niloticus strains that exhibit the distinctive growth traits. Furthermore, we found that PRL I-MS01 microsatellite marker was able to trace parental origin of hybrid tilapia. Thus, this marker is a potentially beneficial tool for a tilapia traceability system. We conclude that the GT tandem repeats in PRL I-MS01 and CA tandem repeats in PRL I-MS02 are useful genetic markers to characterize diverse tilapia species, assist in the genetic tracing and conventional breeding of superior strains and strengthen the management of the tilapia aquaculture industry. There was strong relationship between the cold and both temperature at death and cumulative degree hours represented by negative correlation coefficient for all tested fish. There was no correlation between cold tolerance and fish size for all tested fish. The cooling degree hours were significantly different between the selected and non-selected O. niloticus (P<0.005). The selected O. aureus exhibited greater cold tolerance than the non-selected and death began at 14.1°C, while non-selected occurred at 15.2°C.

Molecular identification of Cynomorii Herba using ITS2 DNA barcoding

To identify the Cynomorii Herba and its analogues species using DNA barcoding technique. Total genomic DNA extracted from all materials using the DNA extraction kit. The internal transcribed spacer 2 (ITS2) regions were amplified using polymerase chain reaction (PCR), and purified PCR products were sequenced bi-directionally. Sequence assembly and consensus sequence generation were performed using the CodonCode Aligner 3.7.1. The Kimura 2-Parameter (K2P) distances and GC content were computed using MEGA 5. 0. Species identification analyses were conducted through the species identification system for traditional Chinese medicine and neighbor-joining (NJ) trees. The ITS2 sequence lengths of Cynomorii Herba were 229 bp. The average intra-specific genetic distances of Cynomorii Herba were 0.003. The average inter-specific genetic distances between Cynomorii Herba and its adulterants species were 0.760. The results showed that the minimum inter-specific divergence is larger than the maximum intra-specific divergence. The species identification system for traditional Chinese medicine and NJ trees results indicated that Cynomorii Herba and its adulterants species can be easily identification. The ITS2 region is an efficient barcode for identification of Cynomorii Herba, which provide a new technique to ensure clinical safety in utilization of traditional Chinese medicine.

DNA barcoding of gobiid fishes (Perciformes, Gobioidei)

Gobiids constitute a major proportion of fish population in both tropical and temperate freshwater as well as marine ecosystem. Due to their small size, cryptic ecology and ambiguous morphological characters, gobiids diversity was not documented completely. In this study, DNA barcodes were generated for 11 species of gobiids, collected from the Ashtamudi Lake, India. The mitochondrial COI gene was amplified using universal primers and the resulted 650 bp amplicon was sequenced. The COI barcodes clearly distinguished all the species with high inter-specific genetic distance values than intra-specific values based on K2P (Kimura 2 Parameter) model. The average genetic distance (K2P model) within species, genus and family was 1.2%, 22.2% and 25.3%, respectively. In addition to barcode-based species identification system, Nucleotide Diagnostic (ND) characters specific for species were identified. The Neighbor-Joining tree revealed distinct clusters shared by the species of same genera.

Application of plastid and nuclear markers to DNA barcoding of Euro-Mediterranean oaks (Quercus, Fagaceae): problems, prospects and phylogenetic implications

DNA barcoding, a species identification system based on sequences from a short, standardized DNA region, has emerged recently as a new tool for taxonomists. We investigated the discriminatory power of a subset of highly variable proposed plant barcoding loci (matK, trnH-psbA, ITS2) in Quercus, a taxonomically complex tree genus of global importance. The research included all currently recognized species and some major variants of the Mediterranean region and Europe (32 taxa) and 17 East Asian and North American species used for comparison. Based on sequence character state, we assigned unique plastid haplotypes to 40.8% of the investigated species; ITS2 increased the resolution up to 87.8% of total taxa. Nevertheless, unsuccessful genetic distance-based discrimination questioned the potential efficiency of correct species identification for future studies. Most species appeared to be nonmonophyletic in parallel phylogenetic tests. Three subgeneric groups were outlined, with different rates of within-group variability and geographical differentiation. Members of one of these groups (corresponding to the Eurasian Group Ilex) were paraphyletic to Group Quercus from the New and Old World and the Eurasian Group Cerris. The data gathered indicate that barcoding markers may help to identify closely related species clusters and contribute to the inference of major diversification and evolutionary patterns in oaks, but the methodology per se appears to be of limited efficacy in defining species limits, unless we make a profound revision of traditional Quercus taxonomic categories. © 2013 The Linnean Society of London, Botanical Journal of the Linnean Society, 2013, 172, 478–499.