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The world's natural history collections constitute an enormous evidence base for scientific research on the natural world. To facilitate these studies and improve access to collections, many organisations are embarking on major programmes of digitization. This requires automated approaches to mass-digitization that support rapid imaging of specimens and associated data capture, in order to process the tens of millions of specimens common to most natural history collections. In this paper we present Inselect-a modular, easy-to-use, cross-platform suite of open-source software tools that supports the semi-automated processing of specimen images generated by natural history digitization programmes. The software is made up of a Windows, Mac OS X, and Linux desktop application, together with command-line tools that are designed for unattended operation on batches of images. Blending image visualisation algorithms that automatically recognise specimens together with workflows to support post-processing tasks such as barcode reading, label transcription and metadata capture, Inselect fills a critical gap to increase the rate of specimen digitization.

Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground‐dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine‐tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade‐off between classification accuracy, precision, and recall and taxonomic resolution. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%. Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. Fine‐tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability. This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species. Together, species‐level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change. Species identity can be inferred from simple images of an extensive and highly accurate carabid beetle collection. Such image classification will be instrumental in efforts to increase the rate at which crucial occurrence data for insects are generated in the future.

Chironomids are a useful group for investigating body size responses to warming due to their high local abundance and sensitivity to environmental change. We collected specimens of six species of chironomids every 2 weeks over a 2-year period (2017–2018) from mesocosm experiments using five ponds at ambient temperature and five ponds at 4°C higher than ambient temperature. We investigated (1) wing length responses to temperature within species and between sexes using a regression analysis, (2) interspecific body size responses to test whether the body size of species influences sensitivity to warming, and (3) the correlation between emergence date and wing length. We found a significantly shorter wing length with increasing temperature in both sexes of Procladius crassinervis and Tanytarsus nemorosus, in males of Polypedilum sordens, but no significant relationship in the other three species studied. The average body size of a species affects the magnitude of the temperature-size responses in both sexes, with larger species shrinking disproportionately more with increasing temperature. There was a significant decline in wing length with emergence date across most species studied (excluding Polypedilum nubeculosum and P. sordens), indicating that individuals emerging later in the season tend to be smaller.

Natural history collections are invaluable resources for understanding biotic response to global change. Museums around the world are currently imaging specimens, capturing specimen data and making them freely available online. In parallel to the digitisation effort, there have been great advancements in computer vision: the computer trained automated recognition/detection, and measurement of features in digital images. Applying computer vision to digitised natural history collections has the potential to greatly accelerate the use of these collections for biotic response to global change research. In this paper, we apply computer vision to a very large, digitised collection to test hypotheses in an established area of biotic response to climate change research: temperature‐size responses. We develop a computer vision pipeline (Mothra) and apply it to the NHM collection of British butterflies (>180,000 imaged specimens). Mothra automatically detects the specimen and other objects in the image, sets the scale, measures wing features (e.g. forewing length), determines the orientation of the specimen (pinned ventrally or dorsally) and identifies the sex. We pair these measurements and specimen collection data with temperature records for 17,726 specimens across a subset of 24 species to test how adult size varies with temperature during the immature stages of species. We also assess patterns of sexual size dimorphism across species and families for 32 species trained for automated sex ID. Mothra accurately measures the forewing lengths of butterfly specimens compared to manual measurements and accurately determines the sex of specimens, with females as the larger sex in most species. An increase in adult body size with warmer monthly temperatures during the late larval stages is the most common temperature‐size response. These results confirm suspected patterns and support hypotheses based on recent studies using a smaller dataset of manually measured specimens. We show that computer vision can be a powerful tool to efficiently and accurately extract phenotypic data from a very large collection of digital natural history collections. In the future, computer vision will become widely applied to digital collections to advance ecological and evolutionary research and to accelerate their use to investigate biotic response to global change.

Background The phylogenetic ecology of the Afro-Asian dragonfly genus Trithemis has been investigated previously by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010) and wing ecomorphology by Outomuro et al. (in J Evol Biol 26:1866–1874, 2013). However, the latter investigation employed a somewhat coarse sampling of forewing and hindwing outlines and reported results that were at odds in some ways with expectations given the mapping of landscape and water-body preference over the Trithemis cladogram produced by Damm et al. (in Mol Phylogenet Evol 54:870–882, 2010). To further explore the link between species-specific wing shape variation and habitat we studied a new sample of 27 Trithemis species employing a more robust statistical test for phylogenetic covariation, more comprehensive representations of Trithemis wing morphology and a wider range of morphometric data-analysis procedures. Results Contrary to the Outomuro et al. (in J Evol Biol 26:1866–1874, 2013) report, our results indicate that no statistically significant pattern of phylogenetic covariation exists in our Trithemis forewing and hindwing data and that both male and female wing datasets exhibit substantial shape differences between species that inhabit open and forested landscapes and species that hunt over temporary/standing or running water bodies. Among the morphometric analyses performed, landmark data and geometric morphometric data-analysis methods yielded the worst performance in identifying ecomorphometric shape distinctions between Trithemis habitat guilds. Direct analysis of wing images using an embedded convolution (deep learning) neural network delivered the best performance. Bootstrap and jackknife tests of group separations and discriminant-function stability confirm that our results are not artifacts of overtrained discriminant systems or the “curse of dimensionality” despite the modest size of our sample. Conclusion Our results suggest that Trithemis wing morphology reflects the environment’s “push” to a much greater extent than phylogeny’s “pull”. In addition, they indicate that close attention should be paid to the manner in which morphologies are sampled for morphometric analysis and, if no prior information is available to guide sampling strategy, the sample that most comprehensively represents the morphologies of interest should be obtained. In many cases this will be digital images (2D) or scans (3D) of the entire morphology or morphological feature rather than sparse sets of landmark/semilandmark point locations.

Empirical studies of genotype–phenotype–fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

Freshwater organisms in the Okavango Delta and Lake Ngami (Botswana) provide direct and indirect benefits to people and the economy of the region. However, their existence could be potentially threatened by human activities (primarily, upstream water abstraction and planned hydropower structures) coupled with climate change. For their protection, it is essential to know their distribution, ecology, and status of the ecosystems that they inhabit. Publications that record taxa from the Delta at species level are scarce, particularly aquatic macroinvertebrates. Identifying organisms to species level can provide more accurate information for environmental monitoring and conservation programmes but requires significant training and expertise. Here, we present a comprehensive taxonomical review of 2204 freshwater species from the Okavango Delta and Lake Ngami, with additional 355 species found in other areas of Botswana that are likely to be present in the study region. We also compare the diversity of the Okavango Delta and Lake Ngami with two other tropical wetlands: the Pantanal (Brazil) and the Kakadu Region (Australia). We show that biodiversity in the Okavango Delta and Lake Ngami is higher than in previous estimates, with recorded species richness dominated by phytoplankton and macroinvertebrates. Most species are widespread across the system and southern Africa. The resulting database includes new records (Bryozoa, Porifera), information on species conservation status, habitat, ecology, distribution in continental Africa, site details and taxonomical notes. This will be an essential resource for researchers, conservation managers, policy makers and consultants investigating freshwater biodiversity in tropical wetlands in the region.

Unruptured giant intracranial aneurysms (GIA) are those with diameters of 25 mm or greater. As aneurysm size is correlated with rupture risk, GIA natural history is poor. Parent artery occlusion or trapping plus bypass revascularization should be considered to encourage intra-aneurysmal thrombosis when other treatment options are contraindicated. The mechanistic background of these methods is poorly studied. Thus, we assessed the potential of computational fluid dynamics (CFD) and fluid–structure interaction (FSI) analyses for clinical use in the preoperative stage. A CFD investigation in three patient-specific flexible models of whole arterial brain circulation was performed. A C6 ICA segment GIA model was created based on CT angiography. Two models were then constructed that simulated a virtual bypass in combination with proximal GIA occlusion, but with differing middle cerebral artery (MCA) recipient vessels for the anastomosis. FSI and CFD investigations were performed in three models to assess changes in flow pattern and haemodynamic parameters alternations (wall shear stress (WSS), oscillatory shear index (OSI), maximal time averaged WSS (TAWSS), and pressure). General flow splitting across the entire domain was affected by virtual bypass procedures, and any deficiency was partially compensated by a specific configuration of the circle of Willis. Following the implementation of bypass procedures, a reduction in haemodynamic parameters was observed within the aneurysm in both cases under analysis. In the case of the temporal MCA branch bypass, the decreases in the studied parameters were slightly greater than in the frontal MCA branch bypass. The reduction in the magnitude of the chosen area-averaged parameters (averaged over the aneurysm wall surface) was as follows: WSS 35.7%, OSI 19.0%, TAWSS 94.7%, and pressure 24.2%. FSI CFD investigation based on patient-specific anatomy models with subsequent stimulation of virtual proximal aneurysm occlusion in conjunction with bypass showed that this method creates a pro-thrombotic favourable environment whilst reducing intra-aneurysmal pressure leading to shrinking. MCA branch recipient selection for optimum haemodynamic conditions should be evaluated individually in the preoperative stage.

The Natural History Museum, London (NHM) has embarked on an ambitious programme to digitise its collections. One aim of the programme has been to improve the workflows and infrastructure needed to support high-throughput digitisation and create comprehensive digital inventories of large scientific collections. This paper presents the workflow developed to digitise the entire Phthiraptera (parasitic lice) microscope slide collection (70,663 slides). Here we describe a novel process of semi-automated mass digitisation using both temporary and permanent barcode labels applied before and during slide imaging. By using a series of barcodes encoding information associated with each slide (i.e. unique identifier, location in the collection and taxonomic name), we can run a series of automated processes, including file renaming, image processing and bulk import into the NHM’s collection management system. We provide data on the comparative efficiency of these processes, illustrating how simple activities, like automated file renaming, reduces image post-processing time, minimises human error and can be applied across multiple collection types.

X-ray phase contrast imaging (XPCI) is an innovative imaging technique which extends the contrast capabilities of ‘conventional’ absorption based x-ray systems. However, so far all XPCI implementations have suffered from one or more of the following limitations: low x-ray energies, small field of view (FOV) and long acquisition times. Those limitations relegated XPCI to a ‘research-only’ technique with an uncertain future in terms of large scale, high impact applications. We recently succeeded in designing, realizing and testing an XPCI system, which achieves significant steps toward simultaneously overcoming these limitations. Our system combines, for the first time, large FOV, high energy and fast scanning. Importantly, it is capable of providing high image quality at low x-ray doses, compatible with or even below those currently used in medical imaging. This extends the use of XPCI to areas which were unpractical or even inaccessible to previous XPCI solutions. We expect this will enable a long overdue translation into application fields such as security screening, industrial inspections and large FOV medical radiography – all with the inherent advantages of the XPCI multimodality.