Barcode index numbers expedite quarantine inspections and aid the interception of nonindigenous mealybugs (Pseudococcidae)

Assessing arthropod biodiversity with DNA barcoding in Jinnah Garden, Lahore, Pakistan

Previous difficulties in arthropod taxonomy (such as limitations in conventional morphological approaches, the possibility of cryptic species and a shortage of knowledgeable taxonomists) has been overcome by the powerful tool of DNA barcoding. This study presents a thorough analysis of DNA barcoding in regards to Pakistani arthropods, which were collected from Lahore's Jinnah Garden. The 88 % (9,451) of the 10,792 specimens that were examined were able to generate DNA barcodes and 83% (8,974) of specimens were assigned 1,361 barcode index numbers (BINs). However, the success rate differed significantly between the orders of arthropods, from 77% for Thysanoptera to an astounding 93% for Diptera. Through morphological exams, DNA barcoding, and cross-referencing with the Barcode of Life Data system (BOLD), the Barcode Index Numbers (BINs) were assigned with a high degree of accuracy, both at the order (100%) and family (98%) levels. Though, identifications at the genus (37%) and species (15%) levels showed room for improvement. This underscores the ongoing need for enhancing and expanding the DNA barcode reference library. This study identified 324 genera and 191 species, underscoring the advantages of DNA barcoding over traditional morphological identification methods. Among the 17 arthropod orders identified, Coleoptera, Diptera, Hemiptera, Hymenoptera, and Lepidoptera from the class Insecta dominated, collectively constituting 94% of BINs. Expected malaise trap Arthropod fauna in Jinnah Garden could contain approximately 2,785 BINs according to Preston log-normal species distribution, yet the Chao-1 Index predicts 2,389.74 BINs. The Simpson Index of Diversity (1-D) is 0.989, signaling high species diversity, while the Shannon Index is 5.77, indicating significant species richness and evenness. These results demonstrated that in Pakistani arthropods, DNA barcoding and BOLD are an invaluable tool for improving taxonomic understanding and biodiversity assessment, opening the door for further eDNA and metabarcoding research.

Improving DNA barcoding library of armored scale insects (Hemiptera: Diaspididae) in China

DNA barcoding is used to identify cryptic species, survey environmental samples, and estimate phyletic and genetic diversity. Armored scale insects are phytophagous insects and are the most species-rich taxa in the Coccoidea superfamily. This study developed a DNA barcode library for armored scale insect species collected from southern China during 2021-2022. We sequenced a total of 239 specimens, recognized as 50 morphological species, representing two subfamilies and 21 genera. Sequencing analysis revealed that the average G + C content of the cytochrome oxidase subunit I (COI) gene sequence was very low (~18.06%) and that the average interspecific divergence was 10.07% while intraspecific divergence was 3.20%. The intraspecific divergence value was inflated by the high intraspecific divergence in ten taxa, which may indicate novel species overlooked by current taxonomic treatments. All the Automated Barcode Gap Discovery, Assemble Species by Automatic Partitioning, Taxon DNA analysis and Bayesian Poisson Tree Process methods yielded largely consistent results, indicating a robust and credible species delimitation. Based on these results, an intergeneric distance threshold of ≤ 5% was deemed appropriate for the differentiation of armored scale insect species in China. This study establishes a comprehensive barcode library for the identification of armored scale insects, future research, and application.

Molecular Species Delimitation Using COI Barcodes of Mealybugs (Hemiptera: Pseudococcidae) from Coffee Plants in Espírito Santo, Brazil

Mealybugs are insects belonging to the family Pseudococcidae. This family includes many plant-pest species with similar morphologies, which may lead to errors in mealybug identification and delimitation. In the present study, we employed molecular-species-delimitation approaches based on distance (ASAP) and coalescence (GMYC and mPTP) methods to identify mealybugs collected from coffee and other plant hosts in the states of Espírito Santo, Bahia, Minas Gerais, and Pernambuco, Brazil. We obtained 171 new COI sequences, and 565 from the BOLD Systems database, representing 26 candidate species of Pseudococcidae. The MOTUs estimated were not congruent across different methods (ASAP-25; GMYC-30; mPTP-22). Misidentifications were revealed in the sequences from the BOLD Systems database involving Phenacoccus solani × Ph. solenopsis, Ph. tucumanus × Ph. baccharidis, and Planacoccus citri × Pl. minor species. Ten mealybug species were collected from coffee plants in Espírito Santo. Due to the incorrect labeling of the species sequences, the COI barcode library of the dataset from the database needs to be carefully analyzed to avoid the misidentification of species. The systematics and taxonomy of mealybugs may be improved by integrative taxonomy which may facilitate the integrated pest management of these pests.

Using DNA metabarcoding to assess insect diversity in citrus orchards

Background

DNA metabarcoding is rapidly emerging as a cost-effective approach for large-scale biodiversity assessment and pest monitoring. The current study employed metabarcoding to assess insect diversity in citrus orchards in Ganzhou City, Jiangxi, China in both 2018 and 2019. Insects were sampled using Malaise traps deployed in three citrus orchards producing a total of 43 pooled monthly samples.

Methods

The Malaise trap samples were sequenced following DNA metabarcoding workflow. Generated sequences were curated and analyzed using two cloud databases and analytical platforms, the barcode of life data system (BOLD) and multiplex barcode research and visualization environment (mBRAVE).

Results

These platforms assigned the sequences to 2,141 barcode index numbers (BINs), a species proxy. Most (63%) of the BINs were shared among the three sampling sites while BIN sharing between any two sites did not exceed 71%. Shannon diversity index (H') showed a similar pattern of BIN assortment at the three sampling sites. Beta diversity analysis by Jaccard similarity coefficient (J) and Bray-Curtis distance matrix (BC) revealed a high level of BIN similarity among the three sites (J = 0.67-0.68; BC = 0.19-0.20). Comparison of BIN records against all those on BOLD made it possible to identify 40% of the BINs to a species, 57% to a genus, 97% to a family and 99% to an order. BINs which received a species match on BOLD were placed in one of four categories based on this assignment: pest, parasitoid, predator, or pollinator. As this study provides the first baseline data on insect biodiversity in Chinese citrus plantations, it is a valuable resource for research in a broad range of areas such as pest management and monitoring beneficial insects in citrus gardens.

Molecular characterization and phylogenetic relationships among Rhynchophorus sp. haplotypes in Makkah Al-Mukarramah Region-KSA

The study aims at detecting and characterizing haplotypes of red palm weevil (RPW) Rhynchophorus sp. in the Western region of Saudi Arabia based on the cytochrome oxidase subunit I (COI) gene sequence. The results indicated the occurrence of 17 nucleotide substitutions, of which three were nonsynonymous (NS). These three NS substitutions resulted in the variation in amino acid sequence in three positions, out of 133. These amino acids are isoleucine/valine, glycine/serine, and arginine/histidine. Based on the chemical properties of the cytochrome C oxidase (COX) enzyme, it is likely that the change at the first position has no effect, while changes at the other two positions can affect the chemical properties of the enzyme. At the three-dimensional (3D) level, the first two positions exist at the border or inside loop 3–4 of the enzyme, while the third position exists inside loop 4–5. These two loops influence the folding pattern of the enzyme, thus, likely affecting the function of the enzyme. However, it is unlikely that variations in the three positions will affect the binding ability of the heme group, which promotes the action of the COX enzyme in the electron transport chain. Variations in chemical properties and 3D structure of COX enzyme might be an evolutionary process (positive selection) that promotes in-time and in-site adaptation to the insect. In conclusion, this study can be helpful in pest management programs and in tracing RPW geographic spread and migration in Saudi Arabia.

A DNA barcode survey of insect biodiversity in Pakistan

Although Pakistan has rich biodiversity, many groups are poorly known, particularly insects. To address this gap, we employed DNA barcoding to survey its insect diversity. Specimens obtained through diverse collecting methods at 1,858 sites across Pakistan from 2010-2019 were examined for sequence variation in the 658 bp barcode region of the cytochrome c oxidase 1 (COI) gene. Sequences from nearly 49,000 specimens were assigned to 6,590 Barcode Index Numbers (BINs), a proxy for species, and most (88%) also possessed a representative image on the Barcode of Life Data System (BOLD). By coupling morphological inspections with barcode matches on BOLD, every BIN was assigned to an order (19) and most (99.8%) were placed to a family (362). However, just 40% of the BINs were assigned to a genus (1,375) and 21% to a species (1,364). Five orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera) accounted for 92% of the specimens and BINs. More than half of the BINs (59%) are so far only known from Pakistan, but others have also been reported from Bangladesh (13%), India (12%), and China (8%). Representing the first DNA barcode survey of the insect fauna in any South Asian country, this study provides the foundation for a complete inventory of the insect fauna in Pakistan while also contributing to the global DNA barcode reference library.

Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network

In order to explore the application of the image recognition model based on multi-stage convolutional neural network (MS-CNN) in the deep learning neural network in the intelligent recognition of commodity images and the recognition performance of the method, in the study, the features of color, shape, and texture of commodity images are first analyzed, and the basic structure of deep convolutional neural network (CNN) model is analyzed. Then, 50,000 pictures containing different commodities are constructed to verify the recognition effect of the model. Finally, the MS-CNN model is taken as the research object for improvement to explore the influence of label errors (p = 0.03, 0.05, 0.07, 0.09, 0.12) with different parameter settings and different probabilities (size of convolutional kernel, Dropout rate) on the recognition accuracy of MS-CNN model, at the same time, a CIR system platform based on MS-CNN model is built, and the recognition performance of salt and pepper noise images with different SNR (0, 0.03, 0.05, 0.07, 0.1) was compared, then the performance of the algorithm in the actual image recognition test was compared. The results show that the recognition accuracy is the highest (97.8%) when the convolution kernel size in the MS-CNN model is 2*2 and 3*3, and the average recognition accuracy is the highest (97.8%) when the dropout rate is 0.1; when the error probability of picture label is 12%, the recognition accuracy of the model constructed in this study is above 96%. Finally, the commodity image database constructed in this study is used to identify and verify the model. The recognition accuracy of the algorithm in this study is significantly higher than that of the Minitch stochastic gradient descent algorithm under different SNR conditions, and the recognition accuracy is the highest when SNR = 0 (99.3%). The test results show that the model proposed in this study has good recognition effect in the identification of commodity images in scenes of local occlusion, different perspectives, different backgrounds, and different light intensity, and the recognition accuracy is 97.1%. To sum up, the CIR platform based on MS-CNN model constructed in this study has high recognition accuracy and robustness, which can lay a foundation for the realization of subsequent intelligent commodity recognition technology.

BIN overlap confirms transcontinental distribution of pest aphids (Hemiptera: Aphididae)

DNA barcoding is highly effective for identifying specimens once a reference sequence library is available for the species assemblage targeted for analysis. Despite the great need for an improved capacity to identify the insect pests of crops, the use of DNA barcoding is constrained by the lack of a well-parameterized reference library. The current study begins to address this limitation by developing a DNA barcode reference library for the pest aphids of Pakistan. It also examines the affinities of these species with conspecific populations from other geographic regions based on both conventional taxonomy and Barcode Index Numbers (BINs). A total of 809 aphids were collected from a range of plant species at sites across Pakistan. Morphological study and DNA barcoding allowed 774 specimens to be identified to one of 42 species while the others were placed to a genus or subfamily. Sequences obtained from these specimens were assigned to 52 BINs whose monophyly were supported by neighbor-joining (NJ) clustering and Bayesian inference. The 42 species were assigned to 41 BINs with 38 showing BIN concordance. These species were represented on BOLD by 7,870 records from 69 countries. Combining these records with those from Pakistan produced 60 BINs with 12 species showing a BIN split and three a BIN merger. Geo-distance correlations showed that intraspecific divergence values for 49% of the species were not affected by the distance between populations. Forty four of the 52 BINs from Pakistan had counterparts in 73 countries across six continents, documenting the broad distributions of pest aphids.

Molecular evolution of cytochrome C oxidase-I protein of insects living in Saudi Arabia

The study underpins barcode characterization of insect species collected from Saudi Arabia and explored functional constraints during evolution at the DNA and protein levels to expect the possible mechanisms of protein evolution in insects. Codon structure designated AT-biased insect barcode of the cytochrome C oxidase I (COI). In addition, the predicted 3D structure of COI protein indicated tyrosine in close proximity with the heme ligand, depicted substitution to phenylalanine in two Hymenopteran species. This change resulted in the loss of chemical bonding with the heme ligand. The estimated nucleotide substitution matrices in insect COI barcode generally showed a higher probability of transversion compared with the transition. Computations of codon-by-codon nonsynonymous substitutions in Hymenopteran and Hemipteran species indicated that almost half of the codons are under positive evolution. Nevertheless, codons of COI barcode of Coleoptera, Lepidoptera and Diptera are mostly under purifying selection. The results reinforce that codons in helices 2, 5 and 6 and those in loops 2–3 and 5–6 are mostly conserved and approach strong purifying selection. The overall results argue the possible evolutionary position of Hymenopteran species among those of other insects.

Assembling a DNA barcode reference library for the spiders (Arachnida: Araneae) of Pakistan

Morphological study of 1,795 spiders from sites across Pakistan placed these specimens in 27 families and 202 putative species. COI sequences >400 bp recovered from 1,782 specimens were analyzed using neighbor-joining trees, Bayesian inference, barcode gap, and Barcode Index Numbers (BINs). Specimens of 109 morphological species were assigned to 123 BINs with ten species showing BIN splits, while 93 interim species included representatives of 98 BINs. Maximum conspecific divergences ranged from 0-5.3% while congeneric distances varied from 2.8-23.2%. Excepting one species pair (Oxyopes azhari-Oxyopes oryzae), the maximum intraspecific distance was always less than the nearest-neighbor (NN) distance. Intraspecific divergence values were not significantly correlated with geographic distance. Most (75%) BINs detected in this study were new to science, while those shared with other nations mainly derived from India. The discovery of many new, potentially endemic species and the low level of BIN overlap with other nations highlight the importance of constructing regional DNA barcode reference libraries.

Linking morphological and molecular taxonomy for the identification of poultry house, soil, and nest dwelling mites in the Western Palearctic

Because of its ability to expedite specimen identification and species delineation, the barcode index number (BIN) system presents a powerful tool to characterize hyperdiverse invertebrate groups such as the Acari (mites). However, the congruence between BINs and morphologically recognized species has seen limited testing in this taxon. We therefore apply this method towards the development of a barcode reference library for soil, poultry litter, and nest dwelling mites in the Western Palearctic. Through analysis of over 600 specimens, we provide DNA barcode coverage for 35 described species and 70 molecular taxonomic units (BINs). Nearly 80% of the species were accurately identified through this method, but just 60% perfectly matched (1:1) with BINs. High intraspecific divergences were found in 34% of the species examined and likely reflect cryptic diversity, highlighting the need for revision in these taxa. These findings provide a valuable resource for integrative pest management, but also highlight the importance of integrating morphological and molecular methods for fine-scale taxonomic resolution in poorly-known invertebrate lineages.