Geometric morphometrics and machine learning as tools for the identification of sibling mosquito species of the Maculipennis complex (Anopheles)

Geometric Morphometrics of Astragalus and Shape Variation Analysis in Capra, Ovis, and Bovidae

In the study, it was investigated whether taxonomic differences have an effect on shape using bovine, ovis, and capra astragalus. A total of 142 samples, 32 bovine (Holstein), 71 ovis (Merino ovis), and 39 capra (Hair goat) astragalus, were used as material. The geometric morphometry method was applied in the study. 13 homologous landmarks were marked on the photographed astragalus. Principal component analysis was performed on the new coordinates obtained as a result of Procrustes analysis. In addition, in areas where shape differences were concentrated, allometric effect and grouping features were determined. The MorphoJ program was used for all these analyses. As a result, 22 principal components were calculated for astragalus. In terms of PC1, capra samples were placed between bovine and ovis sample clusters. Most of the shape variations were formed on the medial side of the astragalus. In conclusion, ovis and bovine astragalus samples were grouped taxonomically completely (100%) separately, while capra samples were grouped largely (97.2%) separately between ovis and capras in cross-validation scores. The results are important in terms of contributing to taxonomy, morphology, and zooarcheology studies.

Integrated taxonomy to advance species delimitation of the Anopheles maculipennis complex

The Anopheles maculipennis complex consists of several mosquito species, including some primary malaria vectors. Therefore, the presence of a species in a particular area significantly affects public health. In this study, 1252 mosquitoes were collected in northern Italy, representing four identified species of the Anopheles maculipennis complex (Anopheles daciae sp. inq., Anopheles maculipennis s. s., Anopheles atroparvus and Anopheles melanoon). The sequences of two DNA markers, mitochondrial cytochrome c oxidase I (COI) and nuclear internal transcribed spacer 2 (ITS2), were generated. DNA-based species delimitation analyses were performed, incorporating public sequences, with distance-based and coalescent tree-based methods to confirm the actual species boundaries within the complex. While some morphospecies were unequivocally delimited by all methods and markers, COI analysis splitted An. maculipennis s. s. into two well-supported groups. However, molecular delimitation failed in recognizing An. daciae sp. inq. and An. messeae as two separate evolutionary entities. Species delimitation was further tested with a morphometric approach, which clearly differentiated species collected in the survey area. These findings underscore how challenging the characterization of the taxonomy of the complex is, providing evidence of potential introgression events in An. maculipennis s. s. and suggesting the need for robust evidence to support An. daciae sp. inq. and An. messeae as distinct species.

Smart technology for mosquito control: Recent developments, challenges, and future prospects

Smart technology coupled with digital sensors and deep learning networks have emerging scopes in various fields, including surveillance of mosquitoes. Several studies have been conducted to examine the efficacy of such technologies in the differential identification of mosquitoes with high accuracy. Some smart trap uses computer vision technology and deep learning networks to identify live Aedes aegypti and Culex quinquefasciatus in real time. Implementing such tools integrated with a reliable capture mechanism can be beneficial in identifying live mosquitoes without destroying their morphological features. Such smart traps can correctly differentiates between Cx. quinquefasciatus and Ae. aegypti mosquitoes, and may also help control mosquito-borne diseases and predict their possible outbreak. Smart devices embedded with YOLO V4 Deep Neural Network algorithm has been designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The use of acoustic and optical sensors in combination with machine learning techniques have escalated the automatic classification of mosquitoes based on their flight characteristics, including wing-beat frequency. Thus, such Artificial Intelligence-based tools have promising scopes for surveillance of mosquitoes to control vector-borne diseases. However working efficiency of such technologies requires further evaluation for implementation on a global scale.

Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Considerations for first field trials of low-threshold gene drive for malaria vector control

Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a 'causal pathway' would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.

Detection of Hindwing Landmarks Using Transfer Learning and High-Resolution Networks

Hindwing venation is one of the most important morphological features for the functional and evolutionary analysis of beetles, as it is one of the key features used for the analysis of beetle flight performance and the design of beetle-like flapping wing micro aerial vehicles. However, manual landmark annotation for hindwing morphological analysis is a time-consuming process hindering the development of wing morphology research. In this paper, we present a novel approach for the detection of landmarks on the hindwings of leaf beetles (Coleoptera, Chrysomelidae) using a limited number of samples. The proposed method entails the transfer of a pre-existing model, trained on a large natural image dataset, to the specific domain of leaf beetle hindwings. This is achieved by using a deep high-resolution network as the backbone. The low-stage network parameters are frozen, while the high-stage parameters are re-trained to construct a leaf beetle hindwing landmark detection model. A leaf beetle hindwing landmark dataset was constructed, and the network was trained on varying numbers of randomly selected hindwing samples. The results demonstrate that the average detection normalized mean error for specific landmarks of leaf beetle hindwings (100 samples) remains below 0.02 and only reached 0.045 when using a mere three samples for training. Comparative analyses reveal that the proposed approach out-performs a prevalently used method (i.e., a deep residual network). This study showcases the practicability of employing natural images-specifically, those in ImageNet-for the purpose of pre-training leaf beetle hindwing landmark detection models in particular, providing a promising approach for insect wing venation digitization.

Revisiting the baby schema by a geometric morphometric analysis of infant facial characteristics across great apes

Infants across species are thought to exhibit specific facial features (termed the "baby schema", such as a relatively bigger forehead and eyes, and protruding cheeks), with an adaptive function to induce caretaking behaviour from adults. There is abundant empirical evidence for this in humans, but, surprisingly, the existence of a baby schema in non-human animals has not been scientifically demonstrated. We investigated which facial characteristics are shared across infants in five species of great apes: humans, chimpanzees, bonobos, mountain gorillas, and Bornean orangutans. We analysed eight adult and infant faces for each species (80 images in total) using geometric morphometric analysis and machine learning. We found two principal components characterizing infant faces consistently observed across species. These included (1) relatively bigger eyes located lower in the face, (2) a rounder and vertically shorter face shape, and (3) an inverted triangular face shape. While these features are shared, human infant faces are unique in that the second characteristic (round face shape) is more pronounced, whereas the third (inverted triangular face shape) is less pronounced than other species. We also found some infantile features only found in some species. We discuss future directions to investigate the baby schema using an evolutionary approach.

Geometric Morphometric Versus Genomic Patterns in a Large Polyploid Plant Species Complex

Plant species complexes represent a particularly interesting example of taxonomically complex groups (TCGs), linking hybridization, apomixis, and polyploidy with complex morphological patterns. In such TCGs, mosaic-like character combinations and conflicts of morphological data with molecular phylogenies present a major problem for species classification. Here, we used the large polyploid apomictic European Ranunculus auricomus complex to study relationships among five diploid sexual progenitor species and 75 polyploid apomictic derivate taxa, based on geometric morphometrics using 11,690 landmarked objects (basal and stem leaves, receptacles), genomic data (97,312 RAD-Seq loci, 48 phased target enrichment genes, 71 plastid regions) from 220 populations. We showed that (1) observed genomic clusters correspond to morphological groupings based on basal leaves and concatenated traits, and morphological groups were best resolved with RAD-Seq data; (2) described apomictic taxa usually overlap within trait morphospace except for those taxa at the space edges; (3) apomictic phenotypes are highly influenced by parental subgenome composition and to a lesser extent by climatic factors; and (4) allopolyploid apomictic taxa, compared to their sexual progenitor, resemble a mosaic of ecological and morphological intermediate to transgressive biotypes. The joint evaluation of phylogenomic, phenotypic, reproductive, and ecological data supports a revision of purely descriptive, subjective traditional morphological classifications.

Comparing ant morphology measurements from microscope and online AntWeb.org 2D z-stacked images

Unprecedented technological advances in digitization and the steadily expanding open-access digital repositories are yielding new opportunities to quickly and efficiently measure morphological traits without transportation and advanced/expensive microscope machinery. A prime example is the AntWeb.org database, which allows researchers from all over the world to study taxonomic, ecological, or evolutionary questions on the same ant specimens with ease. However, the reproducibility and reliability of morphometric data deduced from AntWeb compared to traditional microscope measurements has not yet been tested. Here, we compared 12 morphological traits of 46 Temnothorax ant specimens measured either directly by stereomicroscope on physical specimens or via the widely used open-access software tpsDig utilizing AntWeb digital images. We employed a complex statistical framework to test several aspects of reproducibility and reliability between the methods. We estimated (i) the agreement between the measurement methods and (ii) the trait value dependence of the agreement, then (iii) compared the coefficients of variation produced by the different methods, and finally, (iv) tested for systematic bias between the methods in a mixed modeling-based statistical framework. The stereomicroscope measurements were extremely precise. Our comparisons showed that agreement between the two methods was exceptionally high, without trait value dependence. Furthermore, the coefficients of variation did not differ between the methods. However, we found systematic bias in eight traits: apart from one trait where software measurements overestimated the microscopic measurements, the former underestimated the latter. Our results shed light on the fact that relying solely on the level of agreement between methods can be highly misleading. In our case, even though the software measurements predicted microscope measurements very well, replacing traditional microscope measurements with software measurements, and especially mixing data collected by the different methods, might result in erroneous conclusions. We provide guidance on the best way to utilize virtual specimens (2D z-stacked images) as a source of morphometric data, emphasizing the method's limitations in certain fields and applications.

Evaluation of Modern Techniques for Species Identification of Lutzia Mosquitoes (Diptera: Culicidae) in Thailand: Geometric Morphometrics and DNA Barcoding

There are four species of Lutzia mosquitoes in Thailand, including Lutzia chiangmaiensis, Lt. fuscana, Lt. halifaxii, and Lt. vorax. The accurate species identification of adult Lutzia mosquitoes based on morphological features requires many body parts, including the abdominal terga and wing. However, species identification is difficult in the case of damaged specimens when some of their morphological character is missing due to transit or gathering in the field. Thus, we evaluated the efficacy of the landmark-based geometric morphometric (GM) approach for the discrimination of Lutzia species in Thailand. In addition, DNA barcoding was also used in parallel with the GM approach to identify the species. Larvae of Lutzia were collected, raised into adults, and identified based on their morphological characteristics. The validated reclassification test results clearly demonstrated that wing shape resulted in a high level of success in identification (correct identifications ranged from 92.50% to 100%); however, based on the DNA barcoding analyses, our results showed that it was poorly effective in identifying Lt. fuscana and Lt. halifaxii based on an overlap between the intraspecific and interspecific divergence. Moreover, our survey results provide updates on the distribution of Lt. chiangmaiensis and Lt. vorax in Thailand. This research will help medical entomologists more efficiently identify mosquitoes in the genus Lutzia, resulting in more effective mosquito control and surveillance.