Method For Identification Of Species Research Articles

Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)

Pterocarpus santalinus is considered among the finest luxury woods in the world and has potential commercial and medicinal value. Due to its rich hue and high price, Pterocarpus santalinus has often been substituted and mislabeled with other woods of lower economic value. To maintain the order of the timber market and the interests of consumers, it is necessary to establish a fast and reliable method for Pterocarpus species identification. In this study, wood samples of Pterocarpus santalinus and nine other wood samples commonly used for counterfeiting were analyzed by visible light/near-infrared (Vis/NIR) hyperspectral imaging (HSI). The spectral data were preprocessed with different algorithms. Principal component analysis (PCA) was applied in different spectral ranges: 400~2500 nm, 400~800 nm, and 800~2500 nm. Partial least squares discriminant analysis (PLS-DA) and square support vector machine (SVM) modeling methods were performed for effective discrimination. The best classification model was SVM combined with a normalization preprocessing method in whole spectral range (400~2500 nm), with prediction accuracy higher than 99.8%. The results suggest that the use of Vis/NIR-HSI in combination with chemometric approaches can be used as an effective tool for the discrimination of Pterocarpus santalinus.

High-Resolution Melting (HRM) Analysis for Rapid Molecular Identification of Sparidae Species in the Greek Fish Market.

The red porgy (Pagrus pagrus) and the common dentex (Dentex dentex) are Sparidae species of high commercial value, traded in the Greek market. In some cases, fish species identification from Greek fisheries is difficult for the consumer due to the strong morphological similarities with their imported counterparts or closely related species such as Pagrus major, Pagrus caeroleustictus, Dentex gibbosus and Pagellus erythrinus, especially when specimens are frozen, filleted or cooked. Techniques based on DNA sequencing, such as COI barcoding, accurately identify species substitution incidents; however, they are time consuming and expensive. In this study, regions of mtDNA were analyzed with RFLPs, multiplex PCR and HRM in order to develop a rapid method for species identification within the Sparidae family. HRM analysis of a 113 bp region of cytb and/or a 156 bp region of 16s could discriminate raw or cooked samples of P. pagrus and D. dentex from the aforementioned closely related species and P. pagrus specimens sampled in the Mediterranean Sea when compared to those fished in the eastern Atlantic. HRM analysis exhibited high accuracy and repeatability, revealing incidents of mislabeling. Multiple samples can be analyzed within three hours, rendering this method a useful tool in fish fraud monitoring.

Morphological and molecular characteristics of Donax incarnatus (Bivalvia: Donacidae) from Badur Beach, Madura Island, Indonesia based on the COX1 gene

Abstract. Wijaya CB, Ambarwati R, Rahayu DA. 2023. Morphological and molecular characteristics of Donax incarnatus (Bivalvia: Donacidae) from Badur Beach, Madura Island, Indonesia based on the COX1 gene. Biodiversitas 24: 2805-2813. Donacidae, a family of marine bivalve mollusks, has a high diversity of morphological traits, including shape and color pattern variations. However, these variations often led to errors in morphological identification, necessitating the use of molecular identification methods for accurate species identification and support. This study aims to analyze the morphological and molecular characteristics of Donax incarnatus Gmelin, 1791 collected from Badur Beach of Madura Island, Sumenep District, East Java Province, Indonesia, using the COX1 gene as a molecular marker. Morphological characterization was conducted through qualitative and quantitative observations, while sequencing results were analyzed using bioinformatics software. The results showed that the exterior color of D. incarnatus has three morphological variations, namely purple-black (OQ692130.1), yellowish orange (OQ692131.1), and white (OQ692132.1). Molecular characterization indicated that the similarity value of D. incarnatus with these variations ranged from 96.06% to 99.61%. At the same time, the alignment results of all samples with reference species and outgroups obtained five variations of nucleotide bases. The average genetic distance between D. incarnatus and the in group was 0.01. The phylogenetic tree results obtained using Neighbor-Joining (NJ) and Maximum Likelihood (ML)methods were categorized into two subclusters: D. incarnatus species from Thailand and the study sample from Badur Beach, Madura Island, Indonesia, were placed in the same cluster.

Allergen analysis and authenticity control: Development of mass spectrometry‐based methods for animal‐derived food products

Food allergy and food fraud involving animal‐derived products are two of the most significant issues in food markets. On one hand, immunoglobulin E (IgE)‐mediated allergic reactions after ingestion of fish, crustaceans, eggs, or milk are among the most prevalent and can happen even after ingestion of trace amounts. On the other hand, new rules regarding product commercialization (e.g., novel food regulation) are more and more created while fraudulent species substitution in fishery products is very common. Sensitive and accurate analytical methods for allergen quantification and species identification in commercial food products are therefore urgently required whether to help food industries inform allergic consumers, to ensure the food compliance with new regulations or to combat food fraud.In the past few years, bottom‐up proteomic techniques, which rely on the detection of peptide biomarkers resulting from a tryptic digest of food proteins using liquid chromatography coupled to tandem mass spectrometry (LC‐MS/MS), have been emerging in this field. The selection of reliable allergen‐specific or species‐specific peptide biomarkers is one of the most crucial steps when developing such methods whether for qualitative protein detection (i.e., screening analysis) or protein absolute quantification.The first part of this dissertation relates therefore to the selection of allergen peptide biomarkers for fish, invertebrates, eggs, and milk in an experimental way using a single chaotropic urea extraction buffer. The allergenic proteins responsible for those severe reactions are mainly parvalbumin, tropomyosin, ovalbumin, and caseins.The protein extraction was first assessed via sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) and the major allergens were well extracted. After that, data‐dependent MS/MS spectra which were obtained using digest samples of authentic animals or pure allergenic ingredients were processed against a matching protein database, and identified peptides were filtered according to several criteria such as the sequence length, amino acid composition, specificity, reproducibility, and sensitivity. Myosin proteins were also a target of choice for fish detection due to their high prevalence and sequence homology even if they are non‐allergenic. Contrary to fish and invertebrate databases which included entries for only one specific protein (i.e., parvalbumin, myosin, or tropomyosin), egg and milk databases contained all known allergenic proteins or even additional proteins for egg yolk. Two extra selection steps were achieved for egg and milk markers based on more stringent criteria regarding their sensitivity after targeting them in the corresponding allergenic ingredients. At this point, 17, 13, 10, 10 and 12 potential markers were respectively selected for fish, invertebrates, egg white, egg yolk and milk. An alignment algorithm was used for all those markers to get an idea about their biological specificity. The biological specificity was verified experimentally for fish and invertebrate potential markers by targeted analysis in digest samples of animal species that are relevant in the food industry (vertebrates and invertebrates). This verification was not done for egg and milk markers as it was not regarded as significant due to reported cross‐reactivity among avian eggs and among mammalian milk. Two potential parvalbumin markers were detected in other nonfish vertebrates, while all potential tropomyosin markers except one were specific to at least an invertebrate class belonging to the same phylum. Parvalbumin and myosin markers as well as tropomyosin markers were exclusively found in vertebrates or invertebrates. Marker detectability was checked by analyzing processed fish products as well as cooked fish for potential parvalbumin and myosin markers, while commercial insect‐based food products such as cereals bars or pasta were studied for potential tropomyosin markers. All expected fish and invertebrate markers were detectable in those complex food products. Detectability of egg and milk markers was assessed by analysis of bread, cookies, and chocolate samples contaminated at different stages of the sample preparation with trace amounts (100 pg/g) of eggs and milk (i.e, fortified, spiked, and incurred digest samples). Two egg white markers and seven milk markers were detected in all those samples. The most suitable markers in terms of sensitivity and specificity were finally chosen for each allergenic product. Thus, besides two myosin fish global markers, five parvalbumin markers were retained including at least one of the investigated fish species. In addition, five tropomyosin markers were chosen, their specificity allowing us to distinguish crustacean tropomyosin from that of insects/arachnids, or mollusks. At last, two ovalbumin markers and three casein markers were confirmed to be the most suitable allergen markers respectively for egg white and milk. All those retained markers could be compiled in a single multiplex method. The automation of the sample preparation could also be a promising improvement whether for qualitative or quantitative analysis.

Deep convolutional feature aggregation for fine-grained cultivar recognition

Fine-grained cultivar recognition has recently attracted considerable attention from researchers in pattern recognition and botany. However, this problem is highly challenging because the differences between cultivated species are so subtle that it is difficult to distinguish them effectively. This article presents a novel deep convolutional feature aggregation approach for fine-grained cultivar recognition. First, we propose a description method of the regional convolution covariance feature (RCCF), which describes the subtle changes in cultivated species by accumulating low-level convolution features and has a strong discriminating ability. Second, we also improve the regional maximum activation of convolutions (RMAC) and present a multiresolution RMAC high-level convolutional feature. Finally, we combine complementary RCCF with multiresolution RMAC features for fine-grained cultivar recognition, which significantly improves the recognition accuracy of fine-grained cultivated plants. We have carried out extensive tests on four benchmark cultivar plant datasets. The results show that our approach achieves state-of-the-art recognition performance on four benchmark cultivar plant datasets, surpassing other plant species identification methods.

Genetic Species Identification Using ycf1b, rbcL, and trnH-psbA in the Genus Pinus as a Complementary Method for Anatomical Wood Species Identification

This study proposes the use of genetic analysis as a complementary method for species identification in the genus Pinus, particularly in cases where anatomical identification is challenging. Pinus species were grouped based on anatomical similarities, and the efficacy of using ycf1b, which is the most variable for Pinus species identification, and rbcL, which is a suggested DNA barcode for land plants, was evaluated within each group. Sequences for each species were obtained from the National Center for Biotechnology Information database and were used to perform phylogenetic analysis. Among the species in Group 1 (P. echinata, P. elliottii, P. ponderosa, P. radiata, P. rigida, P. taeda, and P. virginiana), rbcL was only effective in identifying P. radiata and P. ponderosa, while ycf1b classified five species. An additional DNA barcode, trnH-psbA, was needed to identify P. radiata and P. taeda. In Group 2 (P. densiflora, P. sylvestris, and P. thunbergii), most species were identified using both rbcL and ycf1b, with the exception of possible hybrids of P. densiflora and P. sylvestris. In Group 3 (P. koraiensis and P. strobus), two species were identified using rbcL and ycf1b. Combining genetic species identification with anatomical identification can accurately identify species of the genus Pinus.

Automated plant species identification from the stomata images using deep neural network: A study of selected mangrove and freshwater swamp forest tree species of Bangladesh

Stomatal traits of leaves are critical for regulating the exchange of gases between plant tissues and the atmosphere, and thus play a crucial role in the physiological activities of plants. The hypothesis of this study is that distinct stomatal features among different species grown in diverse habitats can serve as a potential marker for species identification. Leaf samples were collected from the mangrove forests of Sundarbans and the freshwater swamp forests of Ratargul in Bangladesh. In total, we examined 11 species from eight different families. We used deep convolutional neural network (DCNN) to automatically identify tree species from microscopic stomatal imprints, as there is currently no established protocol for this task. For model training, 80% (866 images) of the data was used for training the models. Our study observed significant variations in stomatal attributes such as length, width, and density among different species, families, and habitats. These variations could help in accurate species identification by machine learning approaches used in the present study. An empirical comparison was conducted among EfficientNetV2, Xception, VGG16, VGG19, MobileNetV2, ResNet50V2, Resnet152, DenseNet201, and NasNetLarge. We propose a novel approach called the “Normalized Leverage Factor” that utilizes accuracy, precision, recall, and f1-score to select the optimal model. This approach eliminates the non-uniformity of the scores. Although MobileNetV2 achieved an accuracy of 99.06%, our findings indicate that EfficientNetV2 is the optimal model for species identification. This is due to its higher normalized leverage factor (1.92) compared to MobileNetV2 (1.88). The findings demonstrate that plants of diverse habitats show a unique footprint of stomata that offers an innovative method of species identification using DCNN. The study would help to develop a stomatal image-based user interface to identify species even without expert taxonomic knowledge and could be particularly useful in fields such as pharmacology, conservation biology, forestry, and environmental science.

Descriptive analyses of differentially expressed proteins during intrapuparial stage based on the label-free proteomics technique between Chrysomya megacephala and Synthesiomyia nudiseta

The lack of rapid and accurate species identification methods on pupae restricts the practical application of forensic entomology. It is a new idea to construct portable and rapid identification kits based on the principle of antigen/antibody interaction. Screening differentially expressed proteins (DEPs) of fly pupae is a basis of solving the problem. Here, we used the label-free proteomics technique to discover the DEPs and further validate using the parallel reaction monitoring technique (PRM) in the common flies. In this study, we reared the Chrysomya megacephala and Synthesiomyia nudiseta at constanttemperature, and then we sampled at least four pupae at 24 h intervals until the end of the intrapuparial stage. We found 132 DEPs between Ch. megacephala, and S. nudiseta groups, with 68 and 64 proteins being up-regulated and down-regulated between the two groups. Among the 132 DEPs, we selected five proteins having potential for further development and utilization, such as C1-tetrahydrofolate synthase, Malate dehydrogenase, Transferrin, Protein disulfide-isomerase, and Fructose-bisphosphate aldolase, for further validation using PRM-targeted proteomics, with the trends of PRM results being consistent with the label-free data for corresponding proteins. The present study investigated DEPs via the label-free technique during the pupal development in the Ch. megacephala, and S. nudiseta and provided reference data for development of rapid and accurate identification kits.

The re-discovery in Sumatra of a rarely seen moth, Heterosphecia tawonoides, and its identification using citizen science platform iNaturalist.

Traditional methods of species identification involve collection and killing of specimens. The associated costs and expertise required are prohibitory. In this paper we describe the first documented record of Heterosphecia tawonoides in its natural habitat in Sumatra since 1887, reporting its identification using photographs uploaded to citizen science platform iNaturalist. Our findings add valuable information to the small body of work on this species and demonstrate the importance of having freely available high quality tools such as iNaturalist without which this important record would not have been reported and the observation of this rarely seen moth species in Sumatra would have remained unknown.

Diagnostic Utility of a Mycobacterium Multiplex PCR Detection Panel for Tuberculosis and Nontuberculous Mycobacterial Infections.

The prevalence of nontuberculous mycobacterial diseases is increasing worldwide, and tuberculosis remains highly prevalent. Rapid and accurate microbial diagnoses of both tuberculosis and nontuberculous mycobacterial infections are required. A novel PCR-reverse sequence-specific oligonucleotide probe (PCR-rSSO) method-based mycobacterial detection panel (Myco-Panel) test was developed for the rapid identification of 30 mycobacterial species and subspecies. Clinical respiratory samples were collected from patients with suspected or confirmed tuberculosis and nontuberculous mycobacterial lung disease and those with other respiratory diseases. Myco-Panel tests were performed on the samples, and liquid mycobacterial culture and identification were performed for reference according to housekeeping gene sequences of mycobacteria in positive culture tubes. Furthermore, to assess the detection performance for several mycobacterial species rarely recovered in Japan, the accuracy of the Myco-Panel test was investigated using stock mycobacterial type strains and clinical isolates. A total of 178 clinical respiratory samples were analyzed. The Myco-Panel and sequence-based identification results for mycobacterial cultures were 83.1% concordant (kappa coefficient, 0.785 [95% confidence interval, 0.716 to 0.854]). The Myco-Panel correctly identified 281 of the 283 type strains and clinical isolates tested. The Myco-Panel test could accurately detect several mycobacterial species from clinical respiratory samples and mycobacterial suspensions. Rapid and accurate identification of pathogens using respiratory samples is possible using the Myco-Panel. IMPORTANCE Species identification is important for the diagnosis of mycobacterial infections and decisions on treatment regimens. The Myco-Panel test accurately detects clinically common mycobacterial species that cause respiratory infections from clinical respiratory samples and mycobacterial suspensions. The rapid identification of multiple mycobacterial species will provide clinically useful information for the management of patients. Although we understand that the current diagnostic criteria require mycobacterial culture results in general for the diagnosis of nontuberculous mycobacterial infection, mycobacterial culture examination is a time-consuming process. The detection of potentially causative agents directly from clinical samples will aid in practical diagnosis and decision-making for rapid treatment initiation. This is a new laboratory method for species identification, and evaluating its performance is important.

Machine Learning for collagen peptide biomarker determination in the taxonomic identification of archaeological fish remains

Species identification of archaeofaunal remains can be informative of changing local and global ecosystems, as well as how we interacted with them in the past. However, with the vast majority of assemblages being dominated by morphologically indeterminate specimens, methods of biomolecular species identification are becoming more popular, such as the protein fingerprint-based identification approach known as ZooMS (Zooarchaeology by Mass Spectrometry). As larger datasets are being produced, Machine Learning techniques such as those using Random Forest algorithms have been considered to expedite the identification process. However, it has proven difficult to extract meaningful biomarkers from these processes alone and so far only tackled for mammals. Here, we introduce a novel approach based on the principles of the ID3 algorithm for biomarker identification from ZooMS spectral databases, focussing on archaeological fish remains from Europe and the Caribbean. We show that the tool is highly effective at generating comprehensible lists of family- and genus-level biomarkers. At family-level identification, the average sensitivity across five families of fish was ∼0.9, where a specificity of 1 would indicate a greatly effective algorithm that outperforms both traditional Random Forest and Naïve Bayes approaches; at the genus level the mean sensitivity reduced to ∼0.8 across the nine genera tested. However, some anomalous matches were produced, with accuracy dropping when distinguishing between genera of the same family, such as Epinephelus and Mycteroperca belonging to the Serranidae. Therefore, while this tool has value in rapidly producing lists of biomarkers and can efficaciously identify new ZooMS fingerprints based on those markers, it indicates that manual intervention remains a requirement at finer taxonomic resolutions.

Cichlasoma urophthalmus (Günther 1862): discovery of alien predator fish in Kedung Ombo Reservoir, Central Java, Indonesia

The entry of alien predatory fish in Indonesian waters has a negative impact on local ecosystems in Indonesia, foreign predatory fish can spread disease, become main predators and can cause imbalance in the food chain. From this study, we report the presence of alien predatory fish of the type Cichlasoma urophthalmus (Günther 1862) or Mayan Cichlid from tropical waters of North America caught in Kedung Ombo Reservoir, Grobokan Regency, Central Java. The species identification method uses a morphological observation approach based on Miller et al. (2005) and Nico et al. (2007). This finding is the first for the type of Cichlasoma urophthalmus recorded in the Kedung Ombo Reservoir. A description of the morphology of the caught species Cichlasoma urophthalmus is included in the discussion.Keywords: Alien Predatory Fish; Kedung Ombo Reservoir; Mayan Cichlid; Cichlasoma urophthalmus

Pragmatic applications of DNA barcoding markers in identification of fish species – A review

Abstract DNA barcoding and mini-barcoding involve Cytochrome Oxidase Subunit I (COI) gene in mitochondrial genome and are used for accurate identification of species and biodiversity. The basic goal of the current study is to develop a complete reference database of fishes. It also evaluates the applicability of COI gene to identify fish at the species level with other aspects i.e., as Kimura 2 parameter (K2P) distance. The mean observed length of the sequence was ranging from 500 to 700 base pairs for fish species in DNA barcoding and from 80 to 650 base pairs for DNA mini-barcoding. This method describes the status of known to unknown samples but it also facilitates the detection of previously un-sampled species at distinct level. So, mini-barcoding is a method which focuses on the analysis of short-length DNA markers and has been demonstrated to be effective for species identification of processed food containing degraded DNA. While DNA metabarcoding refers to the automated identification of multiple species from a single bulk sample. They may contain entire organisms or a single environmental sample containing degraded DNA. Despite DNA barcoding, mini-barcoding and metabarcoding are efficient methods for species identification which are helpful in conservation and proper management of biodiversity. It aids researchers to take an account of genetic as well as evolutionary relationships by collecting their morphological, distributional and molecular data. Overall, this paper discusses DNA barcoding technology and how it has been used to various fish species, as well as its universality, adaptability, and novel approach to DNA-based species identification.

Assessing the potential use of environmental DNA for multifaceted genetic monitoring of cetaceans: Example of a wandering whale in a highly disturbed bay area

Environmental DNA (eDNA) sampling of water is a powerful method for comprehensive and noninvasive monitoring of aquatic animal species. However, there have been few reports of its application to cetacean species. On June 29, 2021, a whale (nicknamed Xiaobu) appeared in Dapeng Bay off Guangdong Province, China. We used eDNA technology to obtain information related to this whale (e.g., species identification and food resources) and to trace its possible origin. Fragments of four whale mitochondrial sequences (12S rDNA, 16S rDNA, cytochrome c oxidase subunit 1, and control region) were obtained from amplicons of eDNA collected in Dapeng Bay; sequence barcoding showed that this was an Eden’s whale (Balaenoptera edeni edeni Anderson 1879). Analysis of potential prey species (PPS) suggested that this whale might enter Dapeng Bay while tracking prey, mainly sardines (Sardinella lemuru, Sardinella gibbosa and Sardinella jussieui) and anchovies (Thryssa dussumieri, Thryssa vitrirostris and Thryssa kammalensis). Retrieval of eDNA metabarcoding data from samples collected in waters adjacent to Dapeng Bay (i.e. Lingding Bay and Daya Bay) revealed that Eden’s whale had appeared outside Lingding Bay up to 2 months prior to the appearance of this whale in Dapeng Bay (early April 2021). Overall, this study showed that eDNA is a highly effective noninvasive survey method for the accurate identification of target cetacean species and prey composition; it can be used to monitor megafauna that are under strict legal protection or to monitor megafauna with unknown conditions.

Call repertoire of Ptychadena uzungwensis (Anura: Ptychadenidae) to complement molecular and morphological identification of the species from the Soutpansberg, South Africa.

ABSTRACT The identity of adults and tadpoles of Ptychadena uzungwensis from the Soutpansberg range in northern South Africa was confirmed using a combination of morphological characters and molecular analysis. Detailed vocalisation analysis was made and used to complement existing species identification methods. This study confirms the presence of P. uzungwensis in South Africa. Sound recordings obtained by way of directional manual recordings were used to analyse call structure and call type while Passive Acoustic Monitoring with Song Meter® technology were used to survey circadian call patterns. Following confirmation of species identity, the detailed vocalisation analysis indicated a much more diverse repertoire than previously known. Three call types were observed with Type A consisting of two or three compressed notes; Type B consisting of a multi-note vocalisation, containing multiple pulses and notes produced in rapid succession; and Type C being a multi-note vocalisation consisting of rapidly released, randomly-spaced single notes. Detailed descriptions of call repertoires, including patterns and call types are useful for species identification and for use in comparison studies.

Diversity of aquatic plants in the Rote Dead Sea area, East Nusa Tenggara, Indonesia, based on rbcL marker

Abstract. Lonthor DW, Miftahudin, Kayat, Julzarika A, Subehi L, Iswandono E, Dima AOM, Dianto A, Setiawan F, Nugraha MFI. 2023. Diversity of aquatic plants in the Rote Dead Sea area, East Nusa Tenggara, Indonesia, based on rbcL marker. Biodiversitas 24: 810-818. The Rote Dead Sea area in Rote Islands has two freshwater and 24 saltwater lakes. The saltwater lakes have salinity levels varied between 0-100 ppt. Every lake has specific aquatic plant species. Species identification through morphological analysis only has its limitations; therefore, DNA barcoding is a preference due to an effective, accurate, and faster method for species identification. In this study, we used the rbcL marker to identify and analyze the diversity of the aquatic plant species in lakes Ledulu, Oenduy, Oemasapoka, Oesotimori, and Landu Leko. Plants samples' DNA was amplified using rbcL primers, and the amplicons were sequenced. Species identification was based on morphological study and the rbcL sequence database. Diversity analyses were performed based on pairwise genetic distances, analysis of molecular variance, and genetic relationships based on the Kimura 2 parameter. The results showed that all 20 aquatic plants were correctly identified, and high genetic diversity was discovered. AMOVA analysis showed that genetic variations within the population were higher than among the population. All plant species were grouped into four groups based on the phylogenetic tree and fractional component analyses. The species belonging to the certain family were grouped in the same clade and the closely related plant families. The rbcL marker can be used to discriminate aquatic plant species and analyze their genetic diversity and relationships.

Benchmarking and Validation of a Bioinformatics Workflow for Meat Species Identification Using 16S rDNA Metabarcoding

DNA-metabarcoding is becoming more widely used for routine authentication of meat-based food and feed products. Several methods validating species identification methods through amplicon sequencing have already been published. These use a variety of barcodes and analysis workflows, however, no methodical comparison of available algorithms and parameter optimization are published hitherto for meat-based products' authenticity. Additionally, many published methods use very small subsets of the available reference sequences, thereby limiting the potential of the analysis and leading to over-optimistic performance estimates. We here predict and compare the ability of published barcodes to distinguish taxa in the BLAST NT database. We then use a dataset of 79 reference samples, spanning 32 taxa, to benchmark and optimize a metabarcoding analysis workflow for 16S rDNA Illumina sequencing. Furthermore, we provide recommendations as to the parameter choices, sequencing depth, and thresholds that should be used to analyze meat metabarcoding sequencing experiments. The analysis workflow is publicly available, and includes ready-to-use tools for validation and benchmarking.

Conservation-relevant plant species indicate arthropod richness across trophic levels: Habitat quality is more important than habitat amount

The massive declines in terrestrial arthropods reported across Europe call for effective methods to monitor and promote biodiversity in human-dominated landscapes. Previous studies vary in their support for the suitability of plants as indicators of arthropod diversity, while the potential of subsets of conservation-relevant plant species to estimate arthropod richness remains to be tested. Moreover, the relative importance of plant species richness compared to other factors driving arthropod richness, such as land-use intensity, habitat amount and landscape configuration, is poorly understood. We conducted a multi-scale field study in Southern Germany, sampling vascular plants and terrestrial arthropods in four local land-use types (forest, grassland, arable field, settlement) across large-scale gradients of climate and land-use intensity. We obtained an extensive arthropod dataset using Malaise traps as sampling method and DNA metabarcoding for species identification. We compared the correlation of three sets of plant species richness (total, red-listed and biotope-indicator plants) with arthropod richness, including different trophic and taxonomic groups. Using mixed-effect models, we assessed the partial effects of plant species richness, habitat amount and landscape configuration on local arthropod richness while controlling for climate and land-use effects, and explored the environmental drivers of plant species richness. Arthropod species numbers of all trophic groups strongly responded to species numbers of plants that indicate protected habitats, while red-listed plant species richness was a key determinant for the richness of red-listed arthropods and butterflies. In most cases, plant species richness and temperature were stronger drivers of arthropod richness than the amount of surrounding semi-natural habitat and landscape configuration. While total plant species richness was highest in settlements, the richness of biotope-indicator plants was highest in forests and positively influenced by the amount of semi-natural habitat and edge density. The number of red-listed plant species increased with edge density at the landscape scale but was independent of habitat amount. We conclude that the richness of conservation-relevant plant species provides a powerful indicator of arthropod diversity, and underpins the potential of such plant lists for monitoring habitat quality. Our study highlights the importance of openings in forests for conservation of plants and arthropods as well as of preserving and restoring fragments of high-quality biotopes in agricultural and urban areas where the amount of semi-natural habitat is limited.

Successful identification of the species of the semipetrified amber medicinal resin benzoin using molecular diagnostic technology

Benzoin is an incomplete lithified resin secreted from the trunk of the Styrax Linn. that is known as "semipetrified amber" and has been widely used in medicine due to its blood circulation-promoting and pain-relieving properties. However, the lack of an effective species identification method due to the numerous sources of benzoin resin and the difficulty of DNA extraction has led to the uncertainty of species of benzoin in the trade process. Here, we report the successful extraction of DNA from benzoin resin containing bark-like residues and the evaluation of commercially available benzoin species using molecular diagnostic techniques. By performing a BLAST alignment of ITS2 primary sequences and homology prediction analysis of ITS2 secondary structures, we found that commercially available benzoin species were derived from Styrax tonkinensis (Pierre) Craib ex Hart. and Styrax japonicus Sieb. et Zucc. of the genus Styrax Linn. In addition, some of the benzoin samples were mixed with plant tissues from other genera, accounting for 29.6%. Therefore, this study provides a new method to solve the problem of species identification of semipetrified amber benzoin using information from bark residues.

Using a Madurella mycetomatis-specific PCR on grains obtained via non-invasive fine-needle aspirated material is more accurate than cytology.

Eumycetoma is a chronic subcutaneous inflammatory fungal infection most often caused by the fungus Madurella mycetomatis. Using a species-specific PCR on DNA directly isolated from grains is currently the most reliable method for species identification. However, so far, PCR has been performed on grains obtained through deep-seated surgical biopsies, which are invasive procedures. Grains can also be obtained via ultrasound-guided fine-needle aspiration (US-FNA). Here we determined the diagnostic performance of species-specific PCRs performed on samples obtained through US-FNA. From 63 patients, US-FNA was performed to obtain eumycetoma grains; 34 patients also underwent a deep-seated biopsy. From the grains, DNA was isolated, and one pan-fungal and two M. mycetomatis-specific PCRs were performed. The sensitivity and specificity were determined. Of the 63 patients who underwent US-FNA, 78% (49/63) had evidence of eumycetoma based on cytology and 93.7% (59/63) based on species-specific PCRs. In the 34 patients for whom surgical biopsies were performed as well, 31 patients had a positive PCR for M. mycetomatis when DNA was isolated from the deep-seated biopsy, and 30 had a positive PCR when DNA was obtained from the US-FNA material. This resulted in a 96.8% sensitivity, and 100% specificity with 97.1% diagnostic accuracy for PCR performed on US-FNA. PCR performed on the US-FNA material has a similar sensitivity and specificity as PCR performed on deep-seated biopsies. Therefore, when using PCR, a deep-seated biopsy may not be necessary to obtain grains.