Here we describe a new specimen of Archaeopteryx sp. from the lower Tithonian Mörnsheim Formation in the Franconian Alb of Bavaria, Germany. This fossil is the third avialan specimen found in this formation. The skeleton comprises the right forelimb and shoulder as well as fragments of the left forelimb and both hind limbs. The lengths of the humerus and ulna are most similar to those of the Munich specimen of Archaeopteryx. Despite the specimen having been massively altered by late diagenesis, it can be referred to Archaeopteryx sp., based on the morphology of the furcula, coracoid, humerus and radius, as well as a manual ungual, which nests within the morphospace of Archaeopteryx rather than that of Anchiornis. Phylogenetic analyses also support the assignment of the new specimen to Archaeopteryx. Due to the fossil’s state of preservation, as well as the still-unresolved taxonomy of the genus Archaeopteryx on the species level, an identification beyond genus remains impossible.

Anopheles stephensi is an invasive and deadly malaria vector with the ability to use artificial containers as larval habitats. This ability is unique for malaria vectors in Africa and requires distinct surveillance strategies for early detection and rapid response. In this study, we trained a variety of artificial intelligence (AI) image recognition algorithms, using thousands of smartphone photos of laboratory-authenticated An. stephensi and seven endemic mosquito species, to develop a citizen science-friendly tool for An. stephensi detection. In Antananarivo, Madagascar, citizen science observations of >132 Anopheles spp. larvae from multiple artificial containers—including one closeup photo of a larva, from a tire—were submitted via NASA’s GLOBE Observer app in March 2020 and discovered years later. Given that genetic testing was no longer possible, this photo was used as a proof-of-concept to determine whether the AI species identification could be used on citizen science-generated images. The tire larva was classified as An. stephensi by all 11 species models, which yielded high accuracy and confidence (up to 99.34%) and included a false positive rate of <1%. Furthermore, explainable AI (XAI) heat maps led to the discovery of dark spots in abdominal segment VI corresponding to testes, corroborating a separate classification of the tire larva as male by the sex model. All available evidence suggests that AI image identification would have flagged this larva as a suspect An. stephensi, which could have been submitted to a molecular laboratory for further confirmation. Results demonstrate the power of integrating citizen science and AI—for which we provide free online tools—as a low-cost signal for malaria programs to confirm and respond to, and as complementary surveillance to fill the critical knowledge gaps in the distribution of invasive An. stephensi across Africa and beyond.

Recently, a number of studies have emerged that demonstrate the power of integrating AI and citizen science for enhancing mosquito surveillance. Typical R&D has focused on detecting multiple species of mosquitoes from image datasets that are balanced. In our paper, we report findings on using AI methods and citizen-science images to detect a particularly dangerous and invasive mosquito — Anopheles stephensi — which is now a critical vector of malaria in both Asia and Africa. Given the practical significance of detecting this one species among many other mosquitoes in nature, we focused on class imbalance. Specifically, our imbalanced dataset consisted of 1195 images of An. stephensi mosquitoes and 6021 images of non-An. stephensi mosquitoes, all of which were taken via modern smartphones in varying backgrounds. We experimented with multiple state-of-the-art AI models and techniques to address class imbalances. Results reveal that the EfficientNet-B1 architecture performs best for detecting the An. stephensi class. Findings also demonstrate that downsampling the majority class results in very poor accuracies. The best classification results were obtained when the original (non-downsampled) dataset was used, and when the model was trained with focal loss, a loss function specifically designed to handle class imbalances. Grad-CAM visualizations further confirm that the model learned anatomically relevant features for species identification.

Background Anopheles stephensi is an invasive malaria vector in Africa that threatens to put an additional 126 million people at risk of malaria if it continues to spread. The island nation of Mauritius is highly connected to Asia and Africa and is at risk of introduction due to this connectivity. For early detection of An. stephensi, the Vector Biology and Control Division under the Ministry of Health in Mauritius, leveraged a well-established Aedes program, as An. stephensi is known to share Aedes habitats. These efforts triggered multisectoral coordination and cascading benefits of integrated vector and One Health approaches. Methods Beginning June 2021, entomological surveys were conducted at points of entry (seaport, airport) and on ships transporting livestock in collaboration with the Civil Aviation Department, the Mauritian Port Authority and National Veterinary Services. A total of 18, 39, 723 mosquito larval surveys were respectively conducted in the airport, seaport, and other localities in Mauritius while two, 20, and 26 adult mosquito surveys were respectively conducted in the airport, seaport, and twenty-six animal assembly points. Alongside adult mosquito surveys, surveillance of vectors of veterinary importance (e.g.- Culicoides spp.) was also carried out in collaboration with National Parks and Conservation Service and land owners. Results A total of 8,428 adult mosquitoes were collected and 1,844 larval habitats were positive for mosquitoes. All collected mosquitoes were morphologically identified and 151 Anopheles and 339 Aedes mosquitoes were also molecularly characterized. Mosquito species detected were Aedes albopictus, Anopheles arabiensis, An. coustani, An. merus, Culex quinquefasciatus, Cx. thalassius and Lutzia tigripes. Anopheles stephensi was not detected. The One Health approach was shared with the French Agricultural Research Centre for International Development (CIRAD), strengthening collaboration between Mauritius and Réunion Island on vector surveillance at entry points and insecticide resistance monitoring. The Indian Ocean Commission (IOC) was also alerted to the risk of An. stephensi, leading to regional efforts supporting trainings and development of a response strategy to An. stephensi bringing together stakeholders from Comoros, Madagascar, Mauritius, Réunion Island and Seychelles. Conclusions Mauritius is a model system showing how existing public health entomology capabilities can be used to enhance vector surveillance and control and create multisectoral networks to respond to any emerging public and veterinary health vector-borne disease threat.

Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.

Abstract Bombus affinis, commonly known as the rusty-patched bumble bee, is a critically endangered species endemic to North America. Accurate identification of this bee is crucial for monitoring its presence and abundance, and in turn conservation efforts. Here, we design computer vision techniques to identify the rusty-patched bumble bee from images uploaded by citizen scientists on the iNaturalist platform. Our original dataset consists of 200 images of the rusty-patched bumble bee, and 200 images of other bees across six genera. After image augmentation (mirror, rotate) to yield a final dataset of 3, 200 images, we fine-tuned an EfficientNetV2B0 AI model to classify the rusty-patched bumble bee, and were able to achieve accuracies of 90% and 92% for color and grayscale images. However, motivated by a unique V-shaped black band that is present on the thorax of the rusty-patched bumble bee, we implemented an anatomically inspired learning framework wherein we automatically extract and classify only the thorax pixels from an image. Our resulting thorax-only model yielded classification accuracies of 94−95% and sensitivity (recall) values of 95−99%, across color and grayscale versions of manually and AI-cropped thorax images (the improved results presumably coming from removing other sources of noise). Deployment of these techniques could be useful in the automatic and real-time identification of rusty-patched bumble bees, hence aiding their identification and conservation. Our models can be easily extended to other insects, and to more anatomically inspired learning methods for classifying insects in nature.

Living birds (Aves) have bodies substantially modified from the ancestral reptilian condition. The avian pelvis in particular experienced major changes during the transition from early archosaurs to living birds(1,2). This stepwise transformation is well documented by an excellent fossil record(2-4); however, the ontogenetic alterations that underly it are less well understood. We used embryological imaging techniques to examine the morphogenesis of avian pelvic tissues in three dimensions, allowing direct comparison with the fossil record. Many ancestral dinosaurian features(2) (for example, a forward-facing pubis, short ilium and pubic 'boot') are transiently present in the early morphogenesis of birds and arrive at their typical 'avian' form after transitioning through a prenatal developmental sequence that mirrors the phylogenetic sequence of character acquisition. We demonstrate quantitatively that avian pelvic ontogeny parallels the non-avian dinosaur-to-bird transition and provide evidence for phenotypic covariance within the pelvis that is conserved across Archosauria. The presence of ancestral states in avian embryos may stem from this conserved covariant relationship. In sum, our data provide evidence that the avian pelvis, whose early development has been little studied(5-7), evolved through terminal addition-a mechanism(8-10 )whereby new apomorphic states are added to the end of a developmental sequence, resulting in expression(8,11) of ancestral character states earlier in that sequence. The phenotypic integration we detected suggests a previously unrecognized mechanism for terminal addition and hints that retention of ancestral states in development is common during evolutionary transitions.

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases.

The Maluku Province is an underdeveloped region in Indonesia with over 1,340 scattered islands. Due to the limited health facilities and transportation infrastructure, access to healthcare is very challenging. Here, we combined data from various sources to locate the population clusters, health facilities, roads, and ports/docks, and then utilize geographic information systems (GIS) to estimate distances from residents to health facilities. Health workforce distribution data was then integrated to elucidate overall healthcare equity among districts in the province. The average distances to puskesmas (primary health clinics) were 8.89 km (by land) and 18.43 km (by land and water) respectively, and the average distances to hospitals were 56.19 km (by land) and 73.09 km (by land and water), with large disparities within and among districts. Analysis of health workforce data shows that 65% of 207 puskesmas lack physicians, while 49% lack midwives. Ambon, Tual, and Southeast Maluku have the highest health equity, while East Ceram, Buru, and South Buru have the lowest. In general, this study demonstrates the utility of GIS and spatial analyses, which can help identify problem areas in healthcare accessibility and equity in archipelago settings, and provide recommendations to stakeholders such as public health officials and district administrators.

Within the contiguous USA, Florida is unique in having tropical and subtropical climates, a great abundance and diversity of mosquito vectors, and high rates of human travel. These factors contribute to the state being the national ground zero for exotic mosquito-borne diseases, as evidenced by local transmission of viruses spread by Aedes aegypti, including outbreaks of dengue in 2022 and Zika in 2016. Because of limited treatment options, integrated vector management is a key part of mitigating these arboviruses. Practical knowledge of when and where mosquito populations of interest exist is critical for surveillance and control efforts, and habitat predictions at various geographic scales typically rely on ecological niche modeling. However, most of these models, usually created in partnership with academic institutions, demand resources that otherwise may be too time-demanding or difficult for mosquito control programs to replicate and use effectively. Such resources may include intensive computational requirements, high spatiotemporal resolutions of data not regularly available, and/or expert knowledge of statistical analysis. Therefore, our study aims to partner with mosquito control agencies in generating operationally useful mosquito abundance models. Given the increasing threat of mosquito-borne disease transmission in Florida, our analytic approach targets recent Ae. aegypti abundance in the Tampa Bay area. We investigate explanatory variables that: 1) are publicly available, 2) require little to no preprocessing for use, and 3) are known factors associated with Ae. aegypti ecology. Out of our 4 final models, none required more than 5 out of the 36 predictors assessed (13.9%). Similar to previous literature, the strongest predictors were consistently 3- and 4-wk temperature and precipitation lags, followed closely by 1 of 2 environmental predictors: land use/land cover or normalized difference vegetation index. Surprisingly, 3 of our 4 final models included one or more socioeconomic or demographic predictors. In general, larger sample sizes of trap collections and/or citizen science observations should result in greater confidence in model predictions and validation. However, given disparities in trap collections across jurisdictions, individual county models rather than a multicounty conglomerate model would likely yield stronger model fits. Ultimately, we hope that the results of our assessment will enable more accurate and precise mosquito surveillance and control of Ae. aegypti in Florida and beyond.

Several families of neogastropod mollusks independently evolved the ability to drill through mineralized prey skeletons using their own mineralized feeding teeth, sometimes with shell-softening chemical agents produced by an organ in the foot. Teeth with more durable tooth shapes should extend their use and improve predator performance, but past studies have described only the cusped-side of teeth, mostly overlooking morphologies related to functional interactions between teeth. Here, we describe the three-dimensional morphology of the central drilling tooth (rachidian) from four species of the neogastropod family Muricidae using synchrotron tomographic microscopy and assemble a three-dimensional model of a multitooth series in drilling position for two of them to investigate their dynamic form. We find two new types of articulating surfaces, including a saddle joint at either end of the rachidian and a large tongue-and-groove joint in the center. The latter has a shape that maximizes contact surface area between teeth as they rotate away from each other during drilling. Articulating joints have not been described in Neogastropod radula previously, but they are consistent with an earlier hypothesis that impact forces on individual teeth during predatory drilling are dispersed by tooth-tooth interactions.

Background Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide. Methods GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection. Results Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs. Conclusions GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.

AbstractBackgroundAnopheles stephensiis an invasive malaria vector in Africa that threatens to put an additional 126 million people at risk of malaria if it continues to spread. The island nation of Mauritius is highly connected to Asia and Africa and is at risk of introduction due to this connectivity. For early detection ofAn. stephensi,the Vector Biology and Control Division under the Ministry of Health in Mauritius, leveraged a well-establishedAedesprogram, asAn. stephensiis known to shareAedeshabitats. These efforts triggered multisectoral coordination and cascading benefits of integrated vector and One Health approaches.MethodsBeginning June 2021, entomological surveys were conducted at points of entry (seaport, airport) and on ships transporting livestock in collaboration with the Civil Aviation Department, the Mauritian Port Authority and National Veterinary Services.A total of 39, 18, 723 mosquito larval surveys were respectively conducted in the seaport, airport and other localities in Mauritius while 20, two and 26 adult mosquito surveys were respectively conducted in the seaport, airport and twenty-six animal points. Alongside adult mosquito surveys, surveillance of vectors of veterinary importance (e.g.- licoides spp.) was also carried out in collaboration with National Parks and Conservation Service and land owners.ResultsA total of 8,428 adult mosquitoes were collected and 1,844 larval habitats were positive for mosquitoes. All collected mosquitoes were morphologically identified and 151 Anopheles and 339 Aedes mosquitoes were also molecularly characterized. Mosquito species detected were Aedes albopictus, Anopheles arabiensis, An. coustani, An. merus, Culex quinquefasciatus, Cx. thalassius and Lutzia tigripes. Anopheles stephensi was not detected. The One Health approach was shared with the French Agricultural Research Centre for International Development (CIRAD), strengthening collaboration between Mauritius and Réunion Island on vector surveillance at entry points and insecticide resistance monitoring. The Indian Ocean Commission (IOC) was also alerted to the risk of An. stephensi, leading to regional efforts supporting trainings and development of a response strategy to An. stephensi bringing together stakeholders from Comoros, Madagascar, Mauritius, Réunion Island and Seychelles.ConclusionsMauritius is a model system showing how existing public health entomology capabilities can be used to enhance vector surveillance and control and create multisectoral networks to respond to any emerging public and veterinary health vector-borne disease threat.Author summaryThe malaria mosquito,Anopheles stephensi, is invasive in Africa where it threatens to put an additional 126 million people at risk of malaria if it continues to spread throughout the continent. The island nation of Mauritius is highly connected to Asia and Africa through maritime trade and therefore may be at risk ofAn. stephensiintroduction and establishment. Mauritius implemented a One Health approach, enhancing entomological surveillance at entry points and collaborating across sectors (e.g. veterinary services, sea and air port authorities, national parks and conservation, communities, etc.) conducted extensive integrated vector surveillance, inspecting 85,071 larval habitats, and analyzing 8,428 adult mosquitoes morphologically and molecularly. AlthoughAn. stephensiwas not detected, the initiative catalyzed and strengthened multisectoral partnerships nationally and across the Indian Ocean region member states (Comoros, Madagascar, Mauritius, Réunion Island and Seychelles). Leveraging the threat ofAn. stephensi,Mauritius exemplifies utilizing existing capabilities to create multisectoral networks for effective vector surveillance and response.

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well.

Dipteropeltis is a poorly described genus of fish louse endemic to South America. In a small blackwater region within Loreto, Peru, 13 adult and juvenile specimens of an unidentified species of Dipteropeltis Calman, 1912, as well as one adult specimen of D. hirundo Calman, 1912, were observed and collected. Scanning electron and light micrographs were acquired to examine and measure key features of these specimens. Morphological differences from the two known species of Dipteropeltis, D. hirundo and D. campanaformis Neethling et al., 2014, indicate that the collected specimens represent a new species. Dipteropeltis longicaudatus sp. nov. is diagnosed by elongate abdominal lobes, a chevron-shaped carapace, and uniquely shaped maxillae. One specimen represents the longest branchiuran documented to date at 31.5 mm. Additionally, we provide the first sequence data for this genus using DNA barcoding, which corroborates our designation of a new species. Videos were also captured that document behaviors including host attachment, pulsating abdominal lobes, suction disc “walking”, and swimming. Findings have implications for its teleost hosts, Triportheus albus Cope, 1872 and Brycon amazonicus Spix & Agassiz, 1829, the latter being a critical species for aquaculture and commercial fisheries in Amazonia.

Many groups of stingless insects have independently evolved mimicry of bees to fool would-be predators. To investigate this mimicry, we trained artificial intelligence (AI) algorithms-specifically, computer vision-to classify citizen scientist images of bees, bumble bees, and diverse bee mimics. For detecting bees and bumble bees, our models achieved accuracies of 91.71 % and 88.86 % , respectively. As a proxy for a natural predator, our models were poorest in detecting bee mimics that exhibit both aggressive and defensive mimicry. Using the explainable AI method of class activation maps, we validated that our models learn from appropriate components within the image, which in turn provided anatomical insights. Our t-SNE plot yielded perfect within-group clustering, as well as between-group clustering that grossly replicated the phylogeny. Ultimately, the transdisciplinary approaches herein can enhance global citizen science efforts as well as investigations of mimicry and morphology of bees and other insects.

Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

Virtual and augmented reality (VR/AR) are new technologies with the power to revolutionize the study of morphology. Modern imaging approaches such as computed tomography, laser scanning, and photogrammetry have opened up a new digital world, enabling researchers to share and analyze morphological data electronically and in great detail. Because this digital data exists on a computer screen, however, it can remain difficult to understand and unintuitive to interact with. VR/AR technologies bridge the analog-to-digital divide by presenting 3D data to users in a very similar way to how they would interact with actual anatomy, while also providing a more immersive experience and greater possibilities for exploration. This manuscript describes VR/AR hardware, software, and techniques, and is designed to give practicing morphologists and educators a primer on using these technologies in their research, pedagogy, and communication to a wide variety of audiences. We also include a series of case studies from the presentations and workshop given at the 2019 International Congress of Vertebrate Morphology, and suggest best practices for the use of VR/AR in comparative morphology.

The historic fossil feather from the Jurassic Solnhofen has played a pivotal but controversial role in our evolutionary understanding of dinosaurs and birds. Recently, a study confirmed the diagnostic morphology of the feather’s original calamus, but nonetheless challenged the proposed identity as an Archaeopteryx covert. However, there are errors in the results and interpretations presented. Here we show that the feather is most likely an upper major primary covert, based on its long calamus (23.3% total length) and eight other anatomical attributes. Critically, this hypothesis is independently supported by evidence of similar primary coverts in multiple specimens of Archaeopteryx –including from the same fossil site and horizon as the isolated feather. We also provide additional insights, such as an updated colour reconstruction of the entire feather as matte black, with 90% probability. Given the isolated nature of the fossil feather, we can never know the anatomical and taxonomic provenance with 100% certainty. However, based on all available evidence, the most empirical and parsimonious conclusion is that this feather represents a primary covert from the ancient wing of Archaeopteryx .

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25, 867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are especially deadly vectors) is amongst the highest. We also present important lessons learned and practical impact of our techniques in this paper.