Taxonomic Accuracy Research Articles

Monitoring Mosquito Abundance: Comparing an Optical Sensor with a Trapping Method.

Optical sensors have shown significant promise in offering additional data to track insect populations. This article presents a comparative study between abundance measurements obtained from a novel near-infrared optical sensor and physical traps. The optical instrument, named an Entomological Bistatic Optical Sensor System, or eBoss, is a non-destructive sensor operating in the near-infrared spectral range and designed to continuously monitor the population of flying insects. The research compares the mosquito aerial density (#/m3) obtained through the eBoss with trap counts from eight physical traps during an eight-month field study. The eBoss recorded over 302,000 insect sightings and assessed the aerial density of all airborne insects as well as male and female mosquitoes specifically with a resolution of one minute. This capability allows for monitoring population trends throughout the season as well as daily activity peaks. The results affirmed the correlation between the two methods. While optical instruments do not match traps in terms of taxonomic accuracy, the eBoss offered greater temporal resolution (one minute versus roughly three days) and statistical significance owing to its much larger sample size. These outcomes further indicate that entomological optical sensors can provide valuable complementary data to more common methods to monitor flying insect populations, such as mosquitoes or pollinators.

Taxonomic accuracy and complementarity between bulk and eDNA metabarcoding provides an alternative to morphology for biological assessment of freshwater macroinvertebrates

Determining biological status of freshwater ecosystems is critical for ensuring ecosystem health and maintaining associated services to such ecosystems. Freshwater macroinvertebrates respond predictably to environmental disturbances and are widely used in biomonitoring programs. However, many freshwater species are difficult to capture and sort from debris or substrate and morphological identification is challenging, especially larval stages, damaged specimens, or hyperdiverse groups such as Diptera. The advent of high throughput sequencing technologies has enhanced DNA barcoding tools to automatise species identification for whole communities, as metabarcoding is increasingly used to monitor biodiversity. However, recent comparisons have revealed little congruence between morphological and molecular-based identifications. Using broad range universal primers for DNA barcode marker cox1, we compare community composition captured between morphological and molecular-based approaches from different sources — tissue-based (bulk benthic and bulk drift samples) and environmental DNA (eDNA, filtered water) metabarcoding — for samples collected along a gradient of anthropogenic disturbances. For comparability, metabarcoding taxonomic assignments were filtered by taxa included in the standardised national biological metric IBMWP. At the family level, bulk benthic metabarcoding showed the highest congruence with morphology, and the most abundant taxa were captured by all techniques. Richness captured by morphology and bulk benthic metabarcoding decreased along the gradient, whereas richness recorded by eDNA remained constant and increased downstream when sequencing bulk drift. Estimates of biological metrics were higher using molecular than morphological identification. At species level, diversity captured by bulk benthic samples were higher than the other techniques. Importantly, bulk benthic and eDNA metabarcoding captured different and complementary portions of the community — benthic versus water column, respectively — and their combined use is recommended. While bulk benthic metabarcoding can likely replace morphology using similar benthic biological indices, water eDNA will require new metrics because this technique sequences a different portion of the community.

Pseudoendemism in Mediterranean black flies (Diptera: Simuliidae): a new record for Africa

The first record of a European black fly, Simulium ichnusae Rivosecchi & Contini, is documented for Africa, where it was found at elevations above 1000 m in the Djurdjura Mountains of Algeria. Considered an endemic species of Sardinia for 60 years, S. ichnusae must now be regarded as pseudoendemic. Larvae, pupae, females, and males from the Djurdjura Mountains compare well with those in the original description of the species. Chromosomally, S. ichnusae differs from the standard banding sequence of the S. vernum group by a transposed nucleolar organizer—the only known member of the group with this condition—and three fixed inversions, all of which are shared by other members of this species group. The addition of S. ichnusae to the Algerian fauna increases the number of nominal species of black flies known from the country to 34. The discovery of S. ichnusae suggests that additional trans-Mediterranean species await discovery in the coastal Mediterranean mountains and emphasizes that claims of endemism depend on thorough surveys and taxonomic accuracy.

Species delimitation, discovery and conservation in a tiger beetle species complex despite discordant genetic data

In an age of species declines, delineating and discovering biodiversity is critical for both taxonomic accuracy and conservation. In recent years, there has been a movement away from using exclusively morphological characters to delineate and describe taxa and an increase in the use of molecular markers to describe diversity or through integrative taxonomy, which employs traditional morphological characters, as well as genetic or other data. Tiger beetles are charismatic, of conservation concern, and much work has been done on the morphological delineation of species and subspecies, but few of these taxa have been tested with genetic analyses. In this study, we tested morphologically based taxonomic hypotheses of polymorphic tiger beetles in the Eunota circumpicta (LaFerté-Sénectère, 1841) species complex using multilocus genomic and mtDNA analyses. We find multiple cryptic species within the previous taxonomic concept of Eunota circumpicta, some of which were historically recognized as subspecies. We found that the mtDNA and genomic datasets did not identify the same taxonomic units and that the mtDNA was most at odds with all other genetic and morphological patterns. Overall, we describe new cryptic diversity, which raises important conservation concerns, and provide a working example for testing species and subspecies validity despite discordant data.

Species delimitation and phylogenetic relationships of the Prionospio complex (Annelida, Spionidae) in the Northeast Atlantic

AbstractThe Prionospio complex comprises the most diverse and complex group within the polychaete family Spionidae. The phylogenetic relationships within the group are still poorly understood, and the generic breakdown is unstable. In this study, we assessed the diversity, relationships, and distribution of species of the Prionospio complex occurring in Norwegian waters. We analysed mitochondrial genomes and nuclear ribosomal DNA assembled via whole‐genome shotgun sequencing, and Sanger sequenced fragments of COI and 16S rDNA. Sanger sequencing proved challenging in the group, where COI was only amplified successfully in 14% of specimens. By molecular species delimitation algorithms, our study revealed the presence of four well‐supported but currently undescribed species of Prionospio in Norwegian waters. We observed a novel distribution pattern of polychaetes in coastal waters, where certain species demonstrated distribution ranges spanning over 7000 km. Such wide distribution parallels patterns of deep‐sea Prionospio species, suggesting that factors beyond recent anthropogenic translocations are involved. Our analysis of 38 mitochondrial genomes and ribosomal nuclear DNA enabled us to hypothesise on the phylogenetic relationships of 14 species of the Prionospio complex. The analysis suggested that two characters previously used to designate genera: the beginning of the branchiae from chaetiger 3 and the presence of pinnules on the branchiae, might have evolved more than one time within the complex. We return Aurospio banyulensis to the genus Prionospio according to the diagnosis of Aurospio resulting tree where this species was nested among Prionospio species. Our findings provide new insights into the diversity and distribution patterns of Prionospio species and contribute to a better understanding of marine benthic biodiversity and the importance of taxonomic accuracy in conservation and management practices.

Metazoan endoparasites of snakes from Argentina: Review and checklist with distributional notes and remarks.

This article presents a checklist of metazoan parasites of snakes from Argentina, along with a comprehensive review of the relevant literature published between 1922 and June 2023, covering various aspects of interest. We compiled 34 species of metazoan endoparasites from 28 studies. The subclass Digenea showed the highest number of species (n = 22 species), followed by the phylum Nematoda (n = 8 species), and the subclass Pentastomida (n = 3 species and 1 taxa inquirenda). Dipsadidae was the family of snakes with the most species examined for metazoan endoparasites (n = 20 species). In contrast, Viperidae had the largest number of specimens surveyed (n = 343). Of 23 provinces, 15 (65.2%) presented at least one report of metazoan endoparasites in snakes. The northeastern provinces showed the highest richness of metazoan endoparasites and host diversity. Many articles focused on taxonomy, but studies on parasite ecology were not found. Although taxonomic accuracy was high in most reports, some records were correctly deposited in zoological collections or geo-referenced. This is the first attempt to include all groups of metazoan endoparasites of snakes from Argentina in a single checklist in the last century.

Quantifying error in occurrence data: Comparing the data quality of iNaturalist and digitized herbarium specimen data in flowering plant families of the southeastern United States

iNaturalist has the potential to be an extremely rich source of organismal occurrence data. Launched in 2008, it now contains over 150 million uploaded observations as of May 2023. Based on the findings of a limited number of past studies assessing the taxonomic accuracy of participatory science-driven sources of occurrence data such as iNaturalist, there has been concern that some portion of these records might be misidentified in certain taxonomic groups. In this case study, we compare Research Grade iNaturalist observations with digitized herbarium specimens, both of which are currently available for combined download from large data aggregators and are therefore the primary sources of occurrence data for large-scale biodiversity/biogeography studies. Our comparisons were confined regionally to the southeastern United States (Florida, Georgia, North Carolina, South Carolina, Texas, Tennessee, Kentucky, and Virginia). Occurrence records from ten plant families (Gentianaceae, Ericaceae, Melanthiaceae, Ulmaceae, Fabaceae, Asteraceae, Fagaceae, Cyperaceae, Juglandaceae, Apocynaceae) were downloaded and scored on taxonomic accuracy. We found a comparable and relatively low rate of misidentification among both digitized herbarium specimens and Research Grade iNaturalist observations within the study area. This finding illustrates the utility and high quality of iNaturalist data for future research in the region, but also points to key differences between data types, giving each a respective advantage, depending on applications of the data.

An updated taxonomic revision of the species of Mourasuchus (Alligatoroidea, Caimaninae)

ABSTRACT Mourasuchus is one of the most well-known components of the notable South American crocodylomorph fauna of the Cenozoic. There has been a recent renewed interest in the taxonomy of Mourasuchus, but some issues remained. This study addressed the taxonomic problems of the genus, concluding that there are four valid species: M. amazonensis (Price 1964), M. atopus , M. arendsi (Bocquentin-Villanueva 1984) and M. pattersoni (Cidade et al. 2017). M. arendsi may be a junior synonym of M. atopus, but this can only be evaluated through an eventual reanalysis of the ventral aspect of the skull of the holotype of M. arendsi. The specimens LACM-160157, UFAC-1424 and UFAC-5883 may either belong to M. amazonensis or represent a new species, which can be evaluated only through an eventual reanalysis of the dorsal aspect of the skull of the holotype of M. amazonensis. Mourasuchus nativus is considered a nomen dubium as its holotype is indistinguishable from at least two other Mourasuchus species: M. arendsi and either M. amazonensis or the possible new species represented by LACM-160157, UFAC-1424 and UFAC-5883. This study stresses the importance of taxonomic accuracy to comprehend fossil faunas, including remarkably diverse ones like South American cenozoic crocodylomorphs.

ELM-based stroke classification using wavelet and empirical mode decomposition techniques

ABSTRACT Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application’s adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%.

Processing Country Centroids at the Global Biodiversity Information Facility

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure that promotes open access to biodiversity data. GBIF serves as a global hub for aggregating and disseminating biodiversity information from diverse sources, including museums, research institutions, and citizen science projects. I will present recent additions to GBIF data quality measures, focusing on the introduction of the country centroid filtering feature. Additionally, I explore the functionality and significance of GBIF data quality flags, which aid in assessing the reliability and usability of the aggregated data. I will also discuss recent changes to the default filtering mechanism employed by GBIF to remove suspicious points. GBIF Data Quality Flags GBIF employs a large set of data quality flags to provide users with an assessment of the reliability and fitness for use of the aggregated data. These flags are assigned based on various quality-related criteria, such as geographic precision, taxonomic accuracy, and completeness of associated metadata. By analyzing these flags, users can make informed decisions regarding the suitability of data for their specific research or conservation needs. GBIF data quality flags serve as valuable indicators that facilitate data filtering and enhance data usability. These flags are assigned to individual occurrence records based on various quality-related criteria and are designed to help data users evaluate the trustworthiness and suitability of the data for their specific needs. Country Centroid filter Retrospective geocoding is a process in which historical occurrence data lacking precise geographic coordinates are assigned approximate mappable locations based on other available information, such as locality descriptions, gazetteers, or administrative boundaries. Retrospective geocoding introduces centroids into occurrence datasets wherever better information than a large named area is unknown. A Country centroid refers to the geographical center point of a country's geographic polygon. Recently, GBIF has implemented country centroid filtering as a feature. By utilizing this filtering technique, GBIF users can identify and exclude occurrences that are country or area centroids. This ensures that data users receive more accurate and reliable information, reducing potential errors resulting from georeferencing issues. The Catalogue of Centroids is a GitHub repository designed to collect and provide a comprehensive list of country centroids for the purpose of occurrence data filtering. The repository serves as a central resource for storing and sharing centroid coordinates associated with each country, area, or province. The main idea is to gather the from sources most often used during retrospective geo-referencing of museum collections. Fig. 1 Default Filtering and Treatment of Suspicious Points GBIF employs default filtering mechanisms to identify suspicious occurrences from its aggregated datasets. For example, the "Country Coordinate Mismatch" data quality flag in GBIF is a quality assessment indicator that highlights occurrences where there is a potential mismatch between the recorded coordinates and the associated country information. Similarly, zero coordinates are often encountered as placeholders or missing data points. GBIF's default filtering process identifies such occurrences from the dataset, as they can introduce inaccuracies when conducting spatial analyses or mapping exercises. This step ensures that only reliable and valid occurrences are available for further analyses and downstream applications. GBIF continues to improve data quality flagging, which significantly enables users to assess the fitness of data for a particular use and increases the utility of the mediated biodiversity information.

Evaluation of the taxonomic accuracy and pathogenicity prediction power of 16 primer sets amplifying single copy marker genes in the Pseudomonas syringae species complex.

The Pseudomonas syringae species complex is composed of several closely related species of bacterial plant pathogens. Here, we used in silico methods to assess 16 PCR primer sets designed for broad identification of isolates throughout the species complex. We evaluated their in silico amplification rate in 2161 publicly available genomes, the correlation between pairwise amplicon sequence distance and whole genome average nucleotide identity, and trained naive Bayes classification models to quantify classification resolution. Furthermore, we show the potential for using single amplicon sequence data to predict type III effector protein repertoires, which are important determinants of host specificity and range.

Convolution neural networks with hybrid feature extraction methods for classification of voice sound signals

The convolutional neural networks (CNNs) lead in the domain of Sound Recognition due to its flexibility and ability with different adjusting parameters. The recognition of spoken English Alphabets by different people with deep learning techniques attracted the research community. In this paper, we are exploring the use of convolutional neural network (CNN), a deep learner that can automatically learn features directly from the dataset while training for the classification of sounds signals of English alphabets. In this proposed work, we consider two CNN architectures. In first architecture, we propose MFCC based features for pretrained two convolutional layer CNN architecture. In the second architecture, we propose a hybrid feature extraction method to train a block-based CNN architecture. The proposed systems consist of two components namely hybrid feature extraction and CNN classifier. The five auditory features log-Mel spectrogram (LM), MFCC, chroma, spectral contrast and Tonnetz features are extracted and then LM & MFCC are combined as one feature set. LM, MFCC, and CST features are aggregated as another for training to the proposed two CNNs, respectively. The different sound samples of English alphabets are collected from different people of different age groups. The feature sets collected from the hybrid feature extraction methods are presented to both the proposed CNNs and the experimental results are collected. The experimental results indicate that the taxonomic accuracy of the proposed architectures can surpass the existing methods of CNNs with single feature extraction methods. The proposed second architecture performs more effectively over the proposed first CNN architecture.

BatMap – authoritative distribution maps for Australian bats

BatMap is a project that has enabled the development of spatial distribution maps for all mainland Australian and Tasmanian bat species. The distribution maps were developed by bat experts and are actively updated and maintained by utilising the collective knowledge of the members of the Australasian Bat Society. Distribution maps are the most accurate maps that are currently available for Australian bat species. The distribution maps are freely available on-line in a variety of formats. The distribution maps have been used for a variety of purposes, including the assessment of the spatial and taxonomic accuracy of bat records from ecological datasets, particularly datasets that are managed by the Atlas of Living Australia, which are considered by many to be the key source of spatial information for bats. BatMap enables better conservation outcomes for Australian bat fauna through the provision of authoritative, readily accessible and usable maps that can be used to increase the accuracy and reliability of bat records that contribute to decision making processes for conservation purposes.

Comprehensive evaluation of shotgun metagenomics, amplicon sequencing, and harmonization of these platforms for epidemiological studies

In a large cohort of 1,772 participants from the Hispanic Community Health Study/Study of Latinos with overlapping 16SV4 rRNA gene (bacterial amplicon), ITS1 (fungal amplicon), and shotgun sequencing data, we demonstrate that 16SV4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. In contrast, for fungal taxa, we did not observe meaningful agreements between shotgun and ITS1 amplicon results. Finally, we show that amplicon and shotgun data can be harmonized and pooled to yield larger microbiome datasets with excellent agreement (<1% effect size variance across three independent outcomes) using pooled amplicon/shotgun data compared to pure shotgun metagenomic analysis. Thus, there are multiple approaches to study the microbiome in epidemiological studies, and we provide a demonstration of a powerful pooling approach that will allow researchers to leverage the massive amount of amplicon sequencing data generated over the last two decades.

DNA Authentication and Chemical Analysis of Psilocybe Mushrooms Reveal Widespread Misdeterminations in Fungaria and Inconsistencies in Metabolites.

The mushroom genus Psilocybe is best known as the core group of psychoactive mushrooms, yet basic information on their diversity, taxonomy, chemistry, and general biology is still largely lacking. In this study, we reexamined 94 Psilocybe fungarium specimens, representing 18 species, by DNA barcoding, evaluated the stability of psilocybin, psilocin, and their related tryptamine alkaloids in 25 specimens across the most commonly vouchered species (Psilocybe cubensis, Psilocybe cyanescens, and Psilocybe semilanceata), and explored the metabolome of cultivated P. cubensis. Our data show that, apart from a few well-known species, the taxonomic accuracy of specimen determinations is largely unreliable, even at the genus level. A substantial quantity of poor-quality and mislabeled sequence data in public repositories, as well as a paucity of sequences derived from types, further exacerbates the problem. Our data also support taxon- and time-dependent decay of psilocybin and psilocin, with some specimens having no detectable quantities of them. We also show that the P. cubensis metabolome possibly contains thousands of uncharacterized compounds, at least some of which may be bioactive. Taken together, our study undermines commonly held assumptions about the accuracy of names and presence of controlled substances in fungarium specimens identified as Psilocybe spp. and reveals that our understanding of the chemical diversity of these mushrooms is largely incomplete. These results have broader implications for regulatory policies pertaining to the storage and sharing of fungarium specimens as well as the use of psychoactive mushrooms for recreation and therapy. IMPORTANCE The therapeutic use of psilocybin, the active ingredient in "magic mushrooms," is revolutionizing mental health care for a number of conditions, including depression, posttraumatic stress disorder (PTSD), and end-of-life care. This has spotlighted the current state of knowledge of psilocybin, including the organisms that endogenously produce it. However, because of international regulation of psilocybin as a controlled substance (often included on the same list as cocaine and heroin), basic research has lagged far behind. Our study highlights how the poor state of knowledge of even the most fundamental scientific information can impact the use of psilocybin-containing mushrooms for recreational or therapeutic applications and undermines critical assumptions that underpin their regulation by legal authorities. Our study shows that currently available chemical studies are mainly inaccurate, irreproducible, and inconsistent, that there exists a high rate of misidentification in museum collections and public databases rendering even names unreliable, and that the concentration of psilocybin and its tryptamine derivatives in three of the most commonly collected Psilocybe species (P. cubensis, P. cyanescens, and P. semilanceata) is highly variable and unstable in museum specimens spanning multiple decades, and our study generates the first-ever insight into the highly complex and largely uncharacterized metabolomic profile for the most commonly cultivated magic mushroom, P. cubensis.

Increasing The Taxonomic Accuracy of Remote Sensing Data Using Traditional Pattern Recognition Methods

The most significant outcomes of remote sensing are maps of Land Use and Land Cover (LULC), which can be controlled by a procedure known as image classification. Using image processing methods, we are developing a system in this effort to categorize satellite photos and extract information. Satellite pictures have been classified into usable and unused areas, as well as sub-classifying each class into numerous further classes. Used satellite photos are further divided into residential, commercial, highway, and agricultural areas, while unused images are divided into forest, river, desert, and beach areas. Since K-means classifiers analyze images features and Otsu's approach for multilevel image thresholding is efficient for automatic classification of satellite images, that is the main topic of this research.

Ethnobotany, Ethnopharmacology, and Phytochemistry of Medicinal Plants Used for Treating Human Diarrheal Cases in Rwanda: A Review.

Diarrhea, often caused by microorganisms, has been associated with high morbidity and mortality in Africa. Increased rates of antimicrobial-resistant pathogens have reignited the quest for alternative therapies. This review aimed at identifying medicinal plants used in the treatment of human diarrheal cases in Rwanda and analyzing their ethnobotany, ethnopharmacology, and phytochemistry. We searched PubMed/Medline, Google Scholar, ScienceDirect, and the Web of Science for published articles on medicinal plants used to treat diarrhea in Rwanda. Additionally, specialized herbarium documents of different institutes were reviewed. Articles were assessed for relevance, quality, and taxonomical accuracy before being included in this review. Overall, 63 species of medicinal plants belonging to 35 families were recorded. Asteraceae was the predominant family with six species, followed by Fabaceae and Lamiaceae, with five species each. The most reported species with anti-diarrheal properties were Vernonia amygdalina Delile, Tetradenia riparia (Hochst.) Codd, Clerodendrum myricoides R. Br. and Chenopodium ugandae (Aellen) Aellen. Leaves (66.7%) and roots (17.5%) were the commonly used plant parts in the preparation of medicine. Phytochemicals from medicinal plants with antidiarrheic activities were sesquiterpene lactones (V. amygdalina); terpene, sterols, saponosides, and flavonoids (C. ugandae); saponins and tannins (T. riparia); and tannins, flavonoids, and alkaloids (C. myricoides). Six studies tested the antimicrobial activities of the plants against bacteria and viruses known to cause diarrhea. Erythrina abyssinica, Euphorbia tirucalli, Dracaena afromontana, and Ficus thonningii are socio-culturally important. Further research on toxicity and posology is needed to ensure the safety of medicinal plants.

A can of worms: Identification issues and morphological conservatism in a large sample of African Green and Bush Snakes (Colubridae: Philothamnus) from Minziro Forest, Tanzania

ABSTRACT The African green snakes of the genus Philothamnus include c. 21 recognised species distributed across sub-Saharan Africa. Many of the species exhibit high morphological conservatism, exist sympatrically, and can have high population densities. This is presumably the reason why the number of species, their systematics, and taxonomy remain debated, and this may compromise the ability to conduct accurate species identifications based on morphological characters. To investigate and quantify this problem, we examined a large sympatric sample of ethanol-preserved Philothamnus specimens from Minziro Forest, Tanzania. Of the 248 specimens examined, 55 were P. heterolepidotus, 49 were P. hoplogaster, 33 were P. angolensis, and one was P. ornatus. A further 110 specimens could not be assigned to any species based on existing identification keys. Thus, this unidentified sample of Philothamnus comprises new forms or undescribed polymorphisms within recognised forms. We examined diagnostic morphological characters using a multivariate approach. Results revealed that if the presence or absence of the white spot character is ignored, many unidentified individuals fall within well-defined clusters based on other characters. These include the heart position, which we explore here as a new diagnostic character for this genus. A thorough revision using integrative taxonomy, for example combining morphological and genetic characteristics from across the spectrum of variability in this genus, could provide a better understanding of species limits, diversity, evolutionary history, and radiation of some of the most commonly encountered snakes in sub-Saharan Africa. This would also improve the taxonomic accuracy of regional herpetological biodiversity assessments.

SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14-week period (17 August-24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian's eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban-wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot-usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species-specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

Neural networks for increased accuracy of allergenic pollen monitoring

Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.