Accurate Species Identification Research Articles

Characterizing the differential susceptibility and resistance to insecticides in populations of Chrysodeixis includens and Rachiplusia nu (Lepidoptera: Noctuidae) in Brazil.

Chrysodeixis includens (Walker) and Rachiplusia nu (Guenée) are major Plusiinae pests of soybean in the Southern Cone region of South America. In recent decades, C. includens was the main defoliator of soybean in Brazil, but from 2021 onwards, R. nu emerged as an important soybean pest in various regions of the country. Here, we characterize the differential susceptibility and resistance to insecticides in these Plusiinae pests from two soybean regions of Brazil. Except for spinetoram and chlorfenapyr (comparable lethality against both species) and a Bt-based biopesticide (more lethal for C. includens), the tested insecticides showed higher lethality against R. nu than against C. includens, but populations of the same species, even separated by long distances, presented similar resistance levels. For both species, the 90% lethal concentration (LC90) values of most insecticides were higher than the field-recommended dose. Nevertheless, the field-recommended doses of spinetoram, metaflumizone, emamectin benzoate, cyclaniliprole and chlorfenapyr showed comparable control efficacy against both species, whereas indoxacarb, chlorantraniliprole, flubendiamide, teflubenzuron and chlorfluazuron were more lethal for R. nu, and methoxyfenozide and the Bt-based insecticide were more lethal for C. includens. Thiodicarb, methomyl and lambda-cyhalothrin showed low lethality against both species. Large interspecific differences in the susceptibility to insecticides was found in major Plusiinae pests of soybean in Brazil. Furthermore, variations in susceptibility to insecticides occurred consistently among species and populations, regardless of the collection site and thus despite unequal temporal and spatial exposure to insecticides. These results demonstrate that accurate species identification is essential for effective control of Plusiinae in soybean. © 2024 Society of Chemical Industry.

Bird Species Detection Using Deep Learning

Bird species classification plays a crucial role in various ecological and conservation endeavors. Leveraging deep learning techniques has shown promise in automating this task, offering the potential for efficient and accurate species identification. This paper presents a comprehensive study on the application of the YOLOv5 architecture, a state-of-the-art object detection framework, for bird species classification from images. We explore the effectiveness of YOLOv5 in comparison to traditional convolutional neural networks (CNNs) for bird species identification. Our study involves extensive experimentation with different configurations of YOLOv5, including variations in model size, training strategies, and dataset preprocessing techniques. We evaluate the performance of the YOLOv5-based classification framework on diverse datasets, ranging from publicly available bird image datasets to custom datasets collected through field observations. Additionally, we compare the classification accuracy of YOLOv5 with other popular CNN architectures, such as ResNet and EfficientNet, to assess its efficacy in bird species recognition tasks. Furthermore, we investigate the transferability of pre-trained YOLOv5 models to different bird species datasets and examine the robustness of the models to variations in image quality, background clutter, and occlusions.Our experimental results demonstrate that YOLOv5 offers competitive performance in bird species classification tasks, achieving high accuracy and efficiency. We discuss the strengths and limitations of using YOLOv5 for this application and provide insights into potential avenues for future research.

The development of multiplex PCR assays for the rapid identification of multiple Saccostrea species, and their practical applications in restoration and aquaculture

BackgroundThe ecology and biology of oysters (Ostreidae) across the tropics is poorly understood. Morphological plasticity and shared characteristics among oysters have resulted in the misidentification of species, creating challenges for understanding basic species-specific biological information that is required for restoration and aquaculture. Genetic barcoding has proven essential for accurate species identification and understanding species geographic ranges. To reduce the costs of molecular species identification we developed multiplex assays using the cytochrome c oxidase subunit I (COI or cox1) barcoding gene for the rapid identification of five species of oysters within the genus Saccostrea that are commonly found in Queensland, Australia: Saccostrea glomerata, Saccostrea lineage B, Saccostrea lineage F, Saccostrea lineage G, and Saccostrea spathulata (lineage J).ResultsMultiplex assays were successful in species-specific amplification of targeted species. The practical application of these primers was tested on wild spat collected from a pilot restoration project in Moreton Bay, Queensland, with identified species (S. glomerata, lineage B and lineage G) validated by Sanger sequencing. DNA sampling by extraction of oyster pallial fluid was also tested on adult oysters collected from the Noosa estuary in Queensland to assess whether oysters were able to be identified non-destructively. DNA concentrations as low as 1 ng/ μL still amplified in most cases, allowing for identification, and mortality at 6 weeks post pallial fluid collection was low (3 out of 104 sampled oysters).ConclusionThese multiplex assays will be essential tools for species identification in future studies, and we successfully demonstrate their practical application in both restoration and aquaculture contexts in Queensland. The multiplex assays developed in this study outline easily replicable methods for the development of additional species-specific primer sets for the rapid identification of other species of Saccostrea found across the Indo-Pacific, which will be instrumental in unravelling the taxonomic ambiguities within this genus in tropical regions.

A DNA barcode reference library of Croatian mosquitoes (Diptera: Culicidae): implications for identification and delimitation of species, with notes on the distribution of potential vector species

BackgroundMosquitoes pose a risk to human health worldwide, and correct species identification and detection of cryptic species are the most important keys for surveillance and control of mosquito vectors. In addition to traditional identification based on morphology, DNA barcoding has recently been widely used as a complementary tool for reliable identification of mosquito species. The main objective of this study was to create a reference DNA barcode library for the Croatian mosquito fauna, which should contribute to more accurate and faster identification of species, including cryptic species, and recognition of relevant vector species.MethodsSampling was carried out in three biogeographical regions of Croatia over six years (2017–2022). The mosquitoes were morphologically identified; molecular identification was based on the standard barcoding region of the mitochondrial COI gene and the nuclear ITS2 region, the latter to identify species within the Anopheles maculipennis complex. The BIN-RESL algorithm assigned the COI sequences to the corresponding BINs (Barcode Index Number clusters) in BOLD, i.e. to putative MOTUs (Molecular Operational Taxonomic Units). The bPTP and ASAP species delimitation methods were applied to the genus datasets in order to verify/confirm the assignment of specimens to specific MOTUs.ResultsA total of 405 mosquito specimens belonging to six genera and 30 morphospecies were collected and processed. Species delimitation methods assigned the samples to 31 (BIN-RESL), 30 (bPTP) and 28 (ASAP) MOTUs, with most delimited MOTUs matching the morphological identification. Some species of the genera Culex, Aedes and Anopheles were assigned to the same MOTUs, especially species that are difficult to distinguish morphologically and/or represent species complexes. In total, COI barcode sequences for 34 mosquito species and ITS2 sequences for three species of the genus Anopheles were added to the mosquito sequence database for Croatia, including one individual from the Intrudens Group, which represents a new record for the Croatian mosquito fauna.ConclusionWe present the results of the first comprehensive study combining morphological and molecular identification of most mosquito species present in Croatia, including several invasive and vector species. With the exception of some closely related species, this study confirmed that DNA barcoding based on COI provides a reliable basis for the identification of mosquito species in Croatia.Graphical

Scanning electron microscopic investigation on Chicory tribe (Compositae) botanical sources and their antimicrobial potential.

This study discusses the micro-level structural details of Cichorieae pollen sources elucidated by scanning electron microscopy (SEM) and explains their symmetry and morphometry. The in-depth knowledge from the electron ultrastructure of Asteraceae pollen has provided insights into enhanced pollen morphology, and the antimicrobial significance of species under study presents novel avenues for their natural defense mechanisms in the development of antimicrobial agents. In this research, both quantitative and qualitative features of pollen were examined. The pollen grains are prolate-spheroidal and oblate-spheroidal in shape, characterized by a maximum polar diameter of 55.6-61.0 μm and a maximum equatorial distance of 68.3-74.4 μm. SEM reveals various configurations such as echinate perforate-tectate, psilate, and echino-lophate perforate. The Cichorieae species have significant antimicrobial efficacy and are promising sources for the development of novel antimicrobial drugs with potential implications in pharmaceutical and healthcare industries. SEM analysis of Cichorieae pollens has provided remarkable insights into their unique structures, revealing diverse shapes and surface ornamentations, which can be used for accurate Asteraceae species identification. RESEARCH HIGHLIGHTS: SEM provides unique pollen surface structures and patterns of Chicory pollen grains. Chemical composition of Chicory botanical sources provides valuable information on their potential as antimicrobial agents. SEM imaging reveals specialized fenestrate grain structures of taxonomic importance.

Remote sensing approaches to identify trees to species-level in the urban forest: A review

Most urban tree inventories depend on resource-intensive, field-based assessments, which are unevenly distributed in space and time. Recently, these inventories have been conducted using field inventories combined with airborne multispectral, hyperspectral, LiDAR, and spaceborne multispectral remote sensing. Significant advances have been made in urban tree GIS databases and remote sensing methods, which include delineating individual tree crowns, extracting tree species metrics, and employing classification techniques. Generally, remote sensing methods distinguish individual urban trees using either pixel-based or object-based methods, while image classification procedures are typically divided into parametric (e.g., regression-based classification, Bayesian, and principal component analysis) and non-parametric approaches such as machine learning (e.g., random forests support vector machines) and deep learning (e.g., convolutional neural networks). Our synthesis of the current state of science suggests sensors with the highest spatial (m), spectral (bands), and temporal (repeat time) resolutions result in the most accurate tree species identification. Combining airborne LiDAR/hyperspectral or airborne LiDAR/spaceborne high-resolution multispectral sensors yields the highest accuracy for the most diverse urban forests. An object-based non-parametric approach, like a fully convolutional neural network, scores higher in accuracy assessments than pixel-based parametric approaches. Future studies can leverage global/regional GIS field inventory databases to expand the scope of studies within and across multiple cities, utilizing LiDAR and spaceborne sensors.

Rapid molecular species identification of mammalian scat samples using nanopore adaptive sampling

Rapid molecular species identification of mammalian scat samples using nanopore adaptive sampling

Biocultural heritage of the Caatinga: a systematic review of Myrtaceae and its multiple uses.

The Caatinga, an exclusively Brazilian biome, stands as a reservoir of remarkable biodiversity. Its significance transcends ecological dimensions, given the direct reliance of the local population on its resources for sustenance and healthcare. While Myrtaceae, a pivotal botanical family within the Brazilian flora, has been extensively explored for its medicinal and nutritional attributes, scant attention has been directed towards its contextual relevance within the Caatinga's local communities. Consequently, this inaugural systematic review addresses the ethnobotanical roles of Myrtaceae within the Caatinga, meticulously anchored in the PRISMA 2020 guidelines. We searched Scopus, MEDLINE/Pubmed, Scielo, and LILIACS. No date-range filter was applied. An initial pool of 203 articles was carefully scrutinized, ultimately yielding 31 pertinent ethnobotanical studies elucidating the utility of Myrtaceae amongst the Caatinga's indigenous populations. Collectively, they revealed seven distinct utilization categories spanning ~54 species and 11 genera. Psidium and Eugenia were the genera with the most applications. The most cited categories of use were food (27 species) and medicinal (22 species). The importance of accurate species identification was highlighted, as many studies did not provide enough information for reliable identification. Additionally, the potential contribution of Myrtaceae fruits to food security and human health was explored. The diversity of uses demonstrates how this family is a valuable resource for local communities, providing sources of food, medicine, energy, and construction materials. This systematic review also highlights the need for more ethnobotanical studies to understand fully the relevance of Myrtaceae species in the Caatinga, promoting biodiversity conservation, as well as support for local populations.

Application of environmental DNA metabarcoding to identify fish community characteristics in subtropical river systems

AbstractFish are vital in river ecosystems; however, traditional investigations of fish usually cause ecological damage. Extracting DNA from aquatic environments and identifying DNA sequences offer an alternative, noninvasive approach for detecting fish species. In this study, the effects of environmental DNA (eDNA), coupled with PCR and next‐generation sequencing, and electrofishing for identifying fish community composition and diversity were compared. In three subtropical rivers of southern China, fish specimens and eDNA in water were collected along the longitudinal (upstream–downstream) gradient of the rivers. Both fish population parameters, including species abundance and biomass, and eDNA OTU richness grouped 38 sampling sites into eight spatial zones with significant differences in local fish community composition. Compared with order‐/family‐level grouping, genus‐/species‐level grouping could more accurately reveal the differences between upstream zones I−III, midstream zones IV−V, and downstream zones VI–VIII. From the headwaters to the estuary, two environmental gradients significantly influenced the longitudinal distribution of the fish species, including the first gradient composed of habitat and physical water parameters and the second gradient composed of chemical water parameters. The high regression coefficient of alpha diversity between eDNA and electrofishing methods as well as the accurate identification of dominant, alien, and biomarker species in each spatial zone indicated that eDNA could characterize fish community attributes at a level similar to that of traditional approaches. Overall, our results demonstrated that eDNA metabarcoding can be used as an effective tool for revealing fish composition and diversity, which is important for using the eDNA technique in aquatic field monitoring.

Weed species classification with UAV imagery and standard CNN models: Assessing the frontiers of training and inference phases

Accurate weed species identification is crucial for effective site-specific weed management (SSWM), enabling targeted and timely control measures for each weed in crop field. This study advanced the current approach to species-level weed identification during the early growth stage by integrating unmanned aerial vehicles (UAVs) imagery with standard convolutional neural networks (CNNs) models such as VGG16, Resnet152 and Inception-Resnet-v2. For this, a robust dataset was created with 33,467 labels of weeds (Atriplex patula, Chenopodium album, Convolvulus arvensis, Cyperus rotundus, Lolium rigidum, Portulaca oleracea, Salsola kali, Solanum nigrum) and crops (maize, tomato), which was subjected to different training, validation and test scenarios. Model inputs were adjusted in order to align them with the information represented by the UAV images. Initially, models were developed in balanced scenarios, gradually increasing label numbers to assess their performance. Inception-ResNet-v2 achieved over 90% accuracy with 400 labels, while ResNet152 and VGG16 required 600 and 800 labels, respectively, for similar accuracy. In a more complex and realistic scenarios with unbalanced datasets, Inception-ResNet-v2 outperformed, likely due to its deeper architecture and enhanced capability to capture intricate features and patterns within UAV images. The study emphasized the importance of the minority-to-majority species ratio in unbalanced datasets, which affects minority species classification. To prevent misclassification, it is crucial to determine the right number of labels for CNN model training and validation. Weed maps were generated after species classification using the Faster R–CNN algorithm as an object detector. This advancement in methodology facilitates the precise and efficient implementation of SSWM techniques.

A double-branch convolutional neural network model for species identification based on multi-modal data

For species identification analysis, methods based on deep learning are becoming prevalent due to their data-driven and task-oriented nature. The most commonly used convolutional neural network (CNN) model has been well applied in Raman spectra recognition. However, when faced with similar molecules or functional groups, the features of overlapping peaks and weak peaks may not be fully extracted using the CNN model, which can potentially hinder accurate species identification. Based on these practical challenges, the fusion of multi-modal data can effectively meet the comprehensive and accurate analysis of actual samples when compared with single-modal data. In this study, we propose a double-branch CNN model by integrating Raman and image multi-modal data, named SI-DBNet. In addition, we have developed a one-dimensional convolutional neural network combining dilated convolutions and efficient channel attention mechanisms for spectral branching. The effectiveness of the model has been demonstrated using the Grad-CAM method to visualize the key regions concerned by the model. When compared to single-modal and multi-modal classification methods, our SI-DBNet model achieved superior performance with a classification accuracy of 98.8%. The proposed method provided a new reference for species identification based on multi-modal data fusion.

Comparative chemical profiling of leaf essential oils from Cinnamomum kanehirae and related species using steam distillation and solvent extraction: Implications for plant-based classification

Cinnamomum kanehirae Hayata, belonging to Lauraceae family, is an indigenous and endangered species of considerable economic importance in Taiwan. It plays a crucial role as the host for the economically valuable saprotrophic fungus, Taiwanofungus camphorates. However, accurate species identification poses a challenge due to the similarity in morphological features and frequent natural hybridization with closely related species. Acquiring high-quality and pure leaf oils becomes imperative for precise species identification and producing superior goods. In this study, our objective was to establish methodologies for analyzing the chemical composition of leaf essential oils and subsequently apply this knowledge to differentiate among three Cinnamomum species. Gas chromatography-mass spectrometry (GC/MS) was employed to scrutinize the chemical makeup of leaf essential oils from three closely related species: C. kanehirae, C. micranthum, and C. camphora. We utilized Steam Distillation (SD) and steam distillation-solvent extraction (SDSE) methods, with the SDSE-Hexane approach chosen for optimization, enhancing extraction efficiency and ensuring essential oil purity. Through the SDSE-Hexane method, we identified seventy-four compounds distributed across three major classes: monoterpenes hydrocarbons (0.0–7.0 %), oxygenated monoterpenes (3.8–90.9 %), sesquiterpenes hydrocarbons (0.0–28.3 %), and oxygenated sesquiterpenes (1.6–88.1 %). Our findings indicated the presence of more than one chemotype in both C. kanehirae and C. camphora, whereas no specific chemotype could be discerned in C. micranthum. Furthermore, clustering based on chemotypes allowed for the differentiation of samples from the three species. Notably, we demonstrated that the chemical compositions of grafted C. kanehirae remained largely unaffected by the rootstock. Conversely, natural hybrids between C. kanehirae and C. camphora exhibited profiles more closely aligned with C. kanehirae. The optimized extraction method and the chemotype-based classification system established in this study present valuable tools for essential oil preparation, species identification, and further exploration into the genetic variation of Cinnamomum.

Erroneous Classification of Brassica juncea L. in South African Studies: Consequences for Agricultural Extension Services

Agricultural extension services play a crucial role by providing expert guidance on enhancing production and processing and facilitating the dissemination of knowledge and scientific findings into practical use. For this reason, researchers must generate accurate information for agricultural extension officers to relay to the ultimate beneficiaries - the farmers. In this paper, we highlight an instance of misidentification by researchers about one of South Africa's extensively researched vegetables, Brassica juncea L. Due to its broad adaptability, this crop has been the focus of numerous South African studies aimed at maximising its production for resource-poor farmers. Regrettably, the accumulated research on B. juncea reveals a prevalent issue of inconsistent naming and classification, leading to confusion with Chinese cabbage (Brassica rapa subs. chinensis). Such inaccurate species identification fosters a disjointed body of knowledge, potentially generating conflicting results and thereby restricting the applicability of the research findings to farmers and other stakeholders. This literature review examines 20 years of South African research to guide future studies and extension efforts on B. juncea. It underscores the necessity for accurate species identification and nomenclature, thereby enhancing the reproducibility and relevance of research outputs.

Meibum Lipidomic Analysis in Evaporative Dry Eye Subjects.

Meibomian Glands (MG) are sebaceous glands responsible for the production of meibum, the main component of the Tear Film Lipid Layer (TFLL). The TFLL facilitates the spread of the tear film over the ocular surface, provides stability and reduces tear evaporation. Alterations in meibum composition lead to different ocular alterations like Meibomian Gland Dysfunction (MGD) and subsequent Evaporative Dry Eye (EDE). The aim of the present study was to investigate the composition and abundance of meibum lipids and their relationship with eyelid margin abnormalities, lipid layer patterns and MG status. The study utilizes a lipidomic approach to identify and quantify lipids in meibum samples using an Elute UHPLC system. This system considered all four dimensions (mass/charge, retention time, ion mobility and intensity) to provide the accurate identification of lipid species. Samples were categorized as healthy or low/no signs of alteration (group 1) or severe signs of alteration or EDE/MGD (group 2). The current investigation found differences in Variable Importance in Projection lipid abundance between both groups for the MGD signs studied. Changes in meibum composition occur and are related to higher scores in eyelid margin hyperaemia, eyelid margin irregularity, MG orifice plugging, MG loss and lipid layer pattern.

Decoding Evolution of Rubioideae: Plastomes Reveal Sweet Secrets of Codon Usage, Diagnostides, and Superbarcoding.

Galium genus belongs to the Rubiaceae family, which consists of approximately 14,000 species. In comparison to its well-known relatives, the plastomes of the Galium genus have not been explored so far. The plastomes of this genus have a typical, quadripartite structure, but differ in gene content, since the infA gene is missing in Galium palustre and Galium trfidum. An evaluation of the effectiveness of using entire chloroplast genome sequences as superbarcodes for accurate plant species identification revealed the high potential of this method for molecular delimitation within the genus and tribe. The trnE-UUC-psbD region showed the biggest number of diagnostides (diagnostic nucleotides) which might be new potential barcodes, not only in Galium, but also in other closely related genera. Relative synonymous codon usage (RSCU) appeared to be connected with the phylogeny of the Rubiaceae family, showing that during evolution, plants started preferring specific codons over others.

Soil and Sediments in Natural Underground Ecosystems as a Source of Culturable Micromycetes: A Case Study of the Brestovská Cave (Western Tatras, Slovakia)

Soil and sediment host microorganisms are able to survive in extremely resource-limited environments. Therefore, more and more attention is being paid to cave sediments as a reservoir of microbiota. The aim of this study is the speleomycological evaluation of the culturable soil and sediment fungal communities in the Brestovská Cave. To explore the origins of fungi, speleomycological studies were conducted both inside and outside the cave under investigation. Additionally, two incubation temperatures (5 and 24 °C) were used to increase the species spectrum of isolated fungi. To achieve the most accurate species identification, we combined an assessment of morphological characteristics of the isolates with molecular sequencing (ITS, internal transcribed spacer). Twenty different species were found and the most frequent was Penicillium commune, followed by Trichosporiella cerebriformis and Pseudogymnoascus pannorum. To our knowledge, our study has enabled the first identification of fungal species such as Penicillium swiecicki, Cephalotrichum hinnuleum, Cosmpospora berkeleyana, Lecythophora hoffmannii, Ambomucor seriatoinflatus, and Mortierella minutissima in underground sites. Our data showed that the abundance and composition of the fungal community varied between the indoor and outdoor samples and thus from the entrance and less visited sites deeper in the cave.

Geometric morphometrics to differentiate species and explore seasonal variation in three Mansonia species (Diptera: Culicidae) in central Thailand and their association with meteorological factors.

Mansonia mosquito species are recognised as a significant vector of human pathogens, primarily transmitting the filarial nematode, Brugia malayi. In central Thailand, the three most prevalent Mansonia species are Mansonia annulifera, Mansonia indiana and Mansonia uniformis. This study explored the influence of seasonal changes on the phenotypic variation of these Mansonia species in central Thailand using the geometric morphometrics (GM). To ensure accurate species identification, we integrated GM techniques with DNA barcoding, examining distinctions in both phenotype and genotype among the species. The intraspecific genetic divergence ranged from 0.00% to 1.69%, whereas the interspecific genetic divergence ranged from 10.52% to 16.36%. The clear distinction between intra- and interspecific distances demonstrated the presence of a barcoding gap, confirming the successful differentiation of the three Mansonia mosquito species through DNA barcoding. Similarly, the interspecies GM assessment for classifying Mansonia species demonstrated a high degree of accuracy, with an overall performance of 98.12%. Exploring seasonal variation in the three Mansonia species revealed wing variations across different seasons, and pronounced variations appearing in the cool season. Regarding their association with meteorological factors, Ma. annulifera and Ma. uniformis showed significant positive correlations with temperature (p < 0.05), and Ma. uniformis also displayed a significant negative correlation with atmospheric pressure (p < 0.05). The insights from this study will deepen our understanding of the adaptive patterns of Mansonia mosquitoes in Thailand's central region, paving the way for enhanced disease surveillance related to these vectors.

Ancylostoma ceylanicum and other zoonotic canine hookworms: neglected public and animal health risks in the Asia–Pacific region

AbstractZoonotic hookworm infections remain a significant public health problem, causing nearly 500 million cases globally and approximately four million disability-adjusted life years lost annually. More than one-fifth of these cases are attributed to Ancylostoma ceylanicum, an emerging zoonotic health issue in the Asia-Pacific region. This review presents key research gaps regarding the epidemiology, diagnosis, control, prevention and elimination of A. ceylanicum and other canine zoonotic hookworms as neglected health threats. A. ceylanicum is the second most prevalent human hookworm in the region; it is the most common hookworm among dogs and cats-reservoirs of zoonotic infections. Previous population genetic and phylogenetic analyses revealed that A. ceylanicum has three possible transmission dynamics: zoonotic, animal-only, and human-only pathways. The actual burden of zoonotic ancylostomiasis in most endemic countries remains unknown due to the use of parasitological techniques (e.g., Kato-Katz thick smear and floatation techniques) that have reduced diagnostic performance and do not allow accurate species identification in helminth surveys. The emergence of benzimidazole resistance in soil-transmitted helminths (STHs), including hookworms, is a concern due to the protracted implementation of mass drug administration (MDA). Resistance is conferred by single nucleotide polymorphisms (SNPs) that occur in the β-tubulin isotype 1 gene. These mutations have been reported in drug-resistant A. caninum but have not been found in A. ceylanicum in the field. A. ceylanicum remains understudied in the Asia-Pacific region. The zoonotic nature of the parasite warrants investigation of its occurrence in human and animal reservoir hosts to understand the dynamics of zoonotic transmission in different endemic foci. The detection of benzimidazole resistance-associated SNPs in zoonotic hookworms from Asia-Pacific countries has yet to be thoroughly explored. Considering the high level of hookworm endemicity in the region, the circulation of resistant isolates between humans and animals potentially presents a significant One Health threat that can undermine current MDA and proposed animal deworming-based control efforts.

The internal transcribed spacer 1 sequence polymorphism brings updates to tsetse species distribution in the northern Cameroon: Importance in planning efficient vector control.

Vector control remains one of the best strategies to prevent the transmission of trypanosome infections in humans and livestock and, thus, a good way to achieve the elimination of human African trypanosomiasis and animal African trypanosomiasis. A key prerequisite for the success of any vector control strategy is the accurate identification and correct mapping of tsetse species. In this work, we updated the tsetse fly species identification and distribution in many geographical areas in Cameroon. Tsetse flies were captured from six localities in Cameroon, and their species were morphologically identified. Thereafter, DNA was extracted from legs of each tsetse fly and the length polymorphism of internal transcribed spacer-1 (ITS1) region of each fly was investigated using PCR. ITS1 DNA fragments of each tsetse species were sequenced. The sequences obtained were analysed and compared to those available in GenBank. This enabled to confirm/infirm results of the morphologic identification and then, to establish the phylogenetic relationships between tsetse species. Morphologic features allowed to clearly distinguish all the tsetse species captured in the South Region of Cameroon, that is, Glossina palpalis palpalis, G. pallicera, G. caliginea and G. nigrofusca. In the northern area, G. morsitans submorsitans could also be distinguished from G. palpalis palpalis, G. tachinoides and G. fuscipes, but these three later could not be distinguished with routine morphological characters. The ITS1 length polymorphism was high among most of the studied species and allowed to identify the following similar species with a single PCR, that is, G. palpalis palpalis with 241 or 242 bp and G. tachinoides with 221 or 222 bp, G. fuscipes with 236 or 237 bp. We also updated the old distribution of tsetse species in the areas assessed, highlighting the presence of G. palpalis palpalis instead of G. fuscipes in Mbakaou, or in sympatry with G. morsitans submorsitans in Dodeo (northern Cameroon). This study confirms the presence of G. palpalis palpalis in the Adamawa Region of Cameroon. It highlights the limits of using morphological criteria to differentiate some tsetse species. Molecular tools based on the polymorphism of ITS1 of tsetse flies can differentiate tsetse species through a simple PCR before downstream analyses or vector control planning.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal.

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.