Systematic Entomology

The preparation of multi panel figures remains a labor intensive step in scientific publication. Albeit there are specific tools available to solve this problem, they are often highly specialized, difficult to install, or time consuming to learn. Griphus is a standalone graphical application designed for rapid composition and experimentation with multi panel figures, developed by and for zoological taxonomists. Functions specifically designed for multi panel composition include automatic figure numbering and placement, aspect ratio operations, spacers, layout rotation, layout suggestions, and automatic generation of figure legends, including scale bar descriptions. The software can perform both spatial interpretation of images on the canvas and work with a simple, editable layout formula. It also enables instant multi panel composition, with numbered images and automatic contrast selection for the numbers, obtained simply by loading images. User defined parameters such as target printable dimensions, resolution, spacing, and color mode are preserved throughout the work. The program produces coordinated outputs consisting of the final composite figure, a readable file describing the layout structure, and a .gri file storing images, transformations, and parameters for exact regeneration. Griphus is intended as a complementary tool to professional image software, providing a simple and efficient environment for constructing high quality multi panel figures.

0.O_LIMorphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator-plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. C_LIO_LIWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. C_LIO_LIWe validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R2 = 0.935, n = 74) under simple-background conditions and (R2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica. Object counts matched manual counts almost exactly for well-separated insects in an insect box (R2 = 0.9997, n = 12). C_LIO_LIBy combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation. C_LI

The tribe Spermacoceae (Rubiaceae) comprises a morphologically diverse assemblage of approximately 1,400 species distributed across the Neotropics, Africa, Asia, Australia, and Pacific region. It remains one of the most taxonomically intractable groups in the family, with generic limits repeatedly redefined for more than two centuries. Previous phylogenetic studies based on a limited number of plastid and nuclear markers left numerous relationships unresolved and provided sparse representation of Neotropical lineages. Here, we present the first phylogenomic study of the tribe based on plastome-scale data and expanded sampling of Neotropical taxa. We sampled 121 species representing 55 genera spanning all major clades and generated 123 new plastomes, including 25 species incorporated into a molecular phylogenetic framework for the first time. Maximum-likelihood and Bayesian analyses recovered a highly resolved and strongly supported phylogeny, with uncertainty restricted to a small number of deep backbone nodes. Pollen and seed micromorphology provided additional evidence for evaluating phylogenetic relationships. The resulting phylogenetic framework clarifies generic boundaries across several problematic lineages and supports multiple taxonomic changes. Pervasive homoplasy in seed and floral characters rendered several traditionally recognized genera non-monophyletic, warranting new combinations, including Edrastima oxycoccoides, Stenotis alexanderae, and S. prostrata, and a reassessment of taxa such as Terrellianthus serpyllaceus and Oldenlandia dusenii. We further identify genera requiring additional study and provide an updated key to the 82 recognized genera of Spermacoceae. Together, these results provide the most robust phylogenetic framework yet available for the tribe and establish a foundation for future systematic, biogeographic, and evolutionary research.

Zingiberales includes eight morphologically distinctive families, but its family-level backbone has remained unstable, especially around Musaceae, Heliconiaceae, Lowiaceae, and Strelitziaceae. We analysed 1566 low-copy nuclear genes from 52 samples, representing all eight families and Pontederia crassipes as outgroup. Concatenated maximum likelihood and multispecies coalescent analyses recovered the same backbone: ((Zingiberaceae, Costaceae), (Cannaceae, Marantaceae)) is sister to (Musaceae, (Heliconiaceae, (Lowiaceae, Strelitziaceae))). Penalized-likelihood dating placed the sampled crown group in the Late Cretaceous, with several deep family-level divergences occurring on short internodes. Analysis of 1248 rerooted gene trees showed that conflict is concentrated on these deep branches and in several shallow clades. HyDe tests of empirical and simulated matrices, each including 62,475 triples, did not support widespread ancient hybridization among the major family-level lineages after filtering against the simulated null model. The nuclear data recover a stable Zingiberales backbone, and the long-standing instability of several deep nodes is best explained by rapid early divergence and extensive incomplete lineage sorting.

Collembola (springtails) are among the most abundant and ecologically important soil arthropods, representing one of the oldest extant terrestrial hexapod lineages, with a fossil record extending to the early Devonian. Despite their relevance, phylogenetic relationships among the four extant orders (Entomobryomorpha, Poduromorpha, Symphypleona, and Neelipleona) have remained unresolved for over two decades. Here, we present the most comprehensive phylogenomic analysis of Collembola to date, comprising 1,127 single-copy orthologues from 145 taxa representing 19 families. To improve orthology inference, we developed a novel HMM-based filtering pipeline that significantly reduced hidden paralogy in BUSCO-derived datasets. Across multiple dataset configurations, gene-jackknife replicates, and various maximum-likelihood analyses, we consistently recovered Poduromorpha as the earliest-diverging lineage. Coalescent-based methods instead highlighted discordant arrangements characterised by extremely short internal branches and low quartet support, a pattern consistent with pervasive incomplete lineage sorting and reticulate evolutionary history. We further dissected the phylogenetic signal by exhaustively evaluating all possible inter-order topological arrangements, both on the full concatenated dataset and gene-by-gene, to identify the most phylogenetically informative loci. These analyses rejected the great majority of previously proposed hypotheses, narrowing support to only two statistically indistinguishable topologies (T11 and T4), with the Poduromorpha-first arrangement consistently favoured across both site-homogeneous and site-heterogeneous substitution models. Finally, with molecular dating, we estimated the origin of crown Collembola in the Early Devonian, with the diversification of the extant orders in the Carboniferous. Several extant genera were estimated to be older than many currently recognized families, highlighting the exceptional evolutionary persistence of springtail lineages and suggesting that lineage longevity should be considered when interpreting higher-level taxonomic diversity.

Monocaul Ardisia (Primulaceae) have a single, vertical, woody stem and spiral phyllotaxy. They range from 30 cm to 100 cm tall. Three species of this architectural group, Ardisia mayumbensis, A. hallei and A. bracteata, have been recorded from Gabon hitherto. In this taxonomic revision we show that A. mayumbensis does not occur in Gabon, and we describe three new species. Two of these, Ardisia doudou sp. nov.and A. mica sp.nov., are endemic to Gabon and one, A. litterbin sp.nov, is found in both Gabon and Republic of the Congo. Ardisia hallei and A. bracteata are redescribed. All five species have a litter-gathering habit with a terminal funnel of leaves, and two of these species, Ardisia doudou and Ardisia litterbin, also possess adventitious roots in the distal part of the stem, a well-established strategy found in litter-gathering forest species of other plant families in tropical Africa. We provisionally assess the conservation status of all five taxa using the 2012 IUCN standard, finding that all monocaul Ardisia in Gabon fall within threatened categories. Two of the species, Ardisia bracteata and A. mica, are known from single collections and have not been seen for 164 and 63 years respectively and are conceivably extinct although further surveys are needed to establish this. We employ new characters in delineating and describing African Ardisia using leaf thickness and oil gland data.

This study presents a systematic revision of the suborder Astrophorina for the temperate Pacific coast of the United States and Canada. Major findings include a reduction in the number of species previously thought to range into the region from Japan; validation of most Geodia species erected by Lendenfeld (1910), which were later synonymized by de Laubenfels (1932); the formal description of 10 new species (Poecillastra alaskensis sp. nov., Vulcanella explorata sp. nov., Vulcanella rupta sp. nov., Stelletta cardenasi sp. nov., Stelletta nicolenya sp. nov., Stelletta limuwensis sp. nov., Dercitus (Stoeba) giveni sp. nov., Penares anyapax sp. nov., Penares foxi sp. nov., and Thenea diastra sp. nov.); and one new combination, Penares orientalis comb. nov. Extensive SCUBA-based collection efforts yielded new samples for 11 of the 26 species identified in the region, which enabled an integrative taxonomic approach that combined field photography, fresh material for DNA sequencing, and improved characterization of species ranges and morphological variability in previously described taxa. Illumina sequencing generated complete nuclear ribosomal haplotypes for five species, while Sanger sequencing of the 28S and cox1 loci placed 20 of the 26 species within molecular phylogenies. The use of very short "mini-barcode" amplicons also enabled sequence recovery from historic type specimens up to 137 years old. This study additionally reports the discovery of sponge grounds of abundant, large Geodia at diving depths in Southern California. Together, these results substantially advance our understanding of global astrophorid diversity and systematics, and the biogeography of sponge diversity in the Northeast Pacific. Note about species names: this pre-print is not intended to be a publication of the associated species names for the purposes of zoological nomenclature.

Biological research often requires information about species traits. Manual literature collation can be time-consuming and miss parts of the literature. To address this gap, we developed trAIt, a publicly available software for the retrieval of characteristics of species from scientific literature catalogued in the Europe PubMed Central (PubMed) database. trAIt provides a graphical user interface (GUI) in which users specify species and characteristics of interest. Leveraging a large language model (LLM), trAIt retrieves relevant papers, combines their content through a consensus-based summarization model, and outputs a species-by-characteristic table. For a case study involving frog species, trAIt recovered 47.1% of trait-species combinations in 2.75 hours, while an expert curator independently recovered 62.4% over months. The consensus-based summarization substantially aids accuracy compared to single-source extraction. Across three case studies of vertebrate taxa, an expert confirmed the accuracy of 70.9% of trait-species entries recovered by trAIt. We observed considerable variation across taxa in trAIts accuracy, which is possibly due to heterogeneity in open-access literature availability and inconsistencies in species and trait terminology. In sum, our analysis suggests that LLM-based tools can accelerate biological data synthesis but should be used to support domain experts research, rather than replace their judgment.

Target enrichment sequencing using probe sets like GoFlag 408 has revolutionized phylogenetics, yet recent genomic data indicate that some probes may be sex-linked, potentially introducing topological conflict while also allowing studies of sex-specific evolutionary processes. To test for sex-linkage across the bryophytes, we developed UVfinder, a pipeline designed to identify sex-linked GoFlag loci across published moss genomes and enable sex-aware downstream analyses. Applying UVfinder to 50 dioicous moss genomes, we identified 93 probes that exhibit sex-linkage in one or more lineages, providing genomic evidence for neo-sex chromosome formation via autosome-sex chromosome fusion and gene translocation. Furthermore, by comparing species trees derived from sex-linked versus autosomal loci in Hypnales and Dicranidae, we demonstrate that sex-linked loci harbor phylogenetic information that is distinct from that in autosomes. We also discovered a pervasive female sampling bias in the genomic data, perhaps reflecting a preference among collectors for plants with sporophytes. Ultimately, our findings highlight the dynamism in sex linkage across bryophytes and suggest that sex-aware phylogenomics can be used to reconstruct ancestral karyotypes and potentially resolve topological conflict. We expect that UVfinder will facilitate the further study of sex-specific evolutionary processes, particularly with improved genome assemblies and increased sampling in males.

Chemosensory systems play a central role in host detection, feeding behavior, and habitat selection in hematophagous insects. Here, we performed a comparative evolutionary analysis of chemosensory gene repertoires across 13 species of the Chagas disease vector genus Rhodnius. While gustatory receptors (GRs), ionotropic receptors (IRs), odorant-binding proteins (OBPs), and chemosensory proteins (CSPs) remained globally conserved, odorant receptors (ORs) displayed extensive lineage-specific expansions, tandem duplications, dynamic transcriptomic regulation, and recurrent signatures of positive selection. Major OR expansions were observed in Rhodnius robustus and Rhodnius colombiensis, suggesting increased sensory diversification in ecologically heterogeneous lineages. In contrast, conserved GR1 expression supports the maintenance of ancestral sugar-detection pathways despite hematophagy lifestyle. We further found no evidence of the canonical insect CO2-associated GRs, suggesting alternative molecular mechanisms for CO2 perception in Triatominae. Several receptors, including Orco, also displayed shifts in selective constraints between sylvatic and domiciliary species, consistent with sensory remodeling associated with adaptation to domestic habitats. Together, our results identify ORs as the most evolutionarily dynamic component of the Rhodnius chemosensory repertoire and highlight contrasting evolutionary trajectories among chemosensory gene families during ecological diversification and vector adaptation.

Automated monitoring of insect pollinators in natural environments with insect camera traps and trained deep learning algorithms provides novel data for insect ecological studies. However, efficient and accurate image recognition analysis of the recorded images or videos is challenging, particularly for images containing small insects against complex backgrounds with diverse vegetation communities. Even when insects can be detected in images, identifying their taxonomy remains difficult, particularly in footage with low image resolution, light conditions, and distances from the plants, and in cases where insects appear blurry or only partially visible. In this work, we present InsectDCT, an AI-based pipeline for automated detection, hierarchical classification, and tracking of insects in footage of natural vegetation tested in different environments. The InsectDCT pipeline consists of three levels: insect Detection and localization, hierarchical taxonomic Classification, and spatio-temporal Tracking. In the first stage, insects are detected in time-lapse images or video recordings using the You Only Look Once (YOLO11) object detection architecture. Detection performance is improved using motion-enhanced images, which improve robustness in cluttered and 3 dimensional environments. The detector is trained on an extensive dataset that contains more than 60,000 images collected using camera traps deployed across a wide range of plant families and floral habitats. In the second stage, detected insects are classified using a hierarchical taxonomy-aware classification framework that covers 80 taxonomic groups. Classification is performed at multiple taxonomic levels, including order, family, and genus/species, allowing coarse and fine-grained ecological analyzes while accounting for varying levels of visual ambiguity. In the third stage, a multi-object tracking module is applied to high temporal-resolution image sequences and video data to associate detections of the same individual across time. InsectDCT code and all datasets are made publicly available. Author summaryInsects are declining worldwide, creating an urgent need for efficient methods to monitor their abundance, activity, and diversity. Traditional insect surveys often require extensive fieldwork and expert taxonomic identification, which limits the scale and frequency of monitoring. In this study, we developed InsectDCT, an artificial intelligence-based pipeline that automatically detects, classifies, and tracks insects in camera-trap recordings collected from natural and semi-natural environments. Our approach combines deep-learning methods for object detection, hierarchical taxonomic classification, and tracking of individual insect observations through time. Unlike many existing systems that are trained for a single habitat or plant species, we designed our framework using images collected across a wide range of flowering plants, camera systems, and insect groups. This makes the system more transferable to new ecological settings. The classifier can identify insects at multiple taxonomic levels and can return higher-level classifications when species-level identification is uncertain. We demonstrate that the pipeline can process large image datasets efficiently, including on low-power edge-computing devices such as Raspberry Pi systems. By providing both the software and the underlying datasets, we aim to support scalable, non-invasive insect monitoring and facilitate future ecological and conservation research.

Remote island systems with small landmasses and reliable estimates of human occupancy are ideal model systems to disentangle the roles of global climatic changes and local human occupation on biota. Here, we used mitochondrial and nuclear genomic data from five endemic Fijian Lasioglossum (Homalictus) bee species to infer changes in effective population size (Ne). These ground-nesting bees are native, with non-specialised floral visitation habits, and distributed across the elevational gradient. All lowland species and populations showed strong signals of increasing Ne that correspond to the timing of human occupation of Fiji, but not Holocene climatic change. Highland populations, with greater isolation and present in regions less affected by anthropogenic impacts, did not show evidence of recent rapid increases in Ne. Population expansion rates across the elevational gradient differed between taxa, with significantly earlier and larger increases in predominantly lowland species than those with more restricted ranges in the highlands. This is consistent with the movement of people inland from coastal regions and into montane elevations of the island, and corresponding landscape changes that benefit the ecology of these bees. Specific life history traits of these bees, combined with substantive clearing of forest cover and floristic changes at lower elevations, has likely increased nesting opportunities and abundance of invasive floral resources. Our findings contrast with recent evidence that human occupation of Fiji has resulted in decreased ant biodiversity and raise the paradoxical possibility that human-mediated environmental changes may benefit some native montane tropical insect faunas.

Reliable species delimitation underpins biodiversity assessment but remains difficult for organisms with plastic morphology and few diagnostic characters. Multispecies coalescent (MSC) methods can delimit species from genomic data, yet they are rarely tested in taxonomically complex, marine invertebrate groups where they are arguably most needed. We used the three Mediterranean species of the genus Tethya, a rare, well-characterised system within the otherwise taxonomically difficult phylum Porifera-distinguished by multiple independent morphological and ecological characters-to evaluate how robust MSC-based delimitation is in such groups. Analysing 64 single-copy nuclear loci in BEAST2 and BPP, we compared constrained, hypothesis-testing approaches (BFD*, BFdriver, A10) with freer, heuristic ones (SPEEDEMON, A11), and examined their sensitivity to data type, clock model, priors, and the species-collapse threshold. All methods recovered the three recognised Mediterranean species, but the resolution of within-lineage structure was method-dependent. The hypothesis-testing approaches consistently supported six lineages, robustly across data types and model assumptions, whereas the heuristic approaches proved less stable. Configurations without a priori species hypotheses often failed to converge or were computationally intractable, a problem compounded by the relaxed clock. In SPEEDEMON the outcome changed with the collapse threshold. Because our system lacks an independent reference point to calibrate this threshold, any delimitation based on it is poorly constrained. We conclude that constrained, hypothesis-testing delimitation is the most robust and reproducible MSC approach, yielding a quantitative, model-based hypothesis that can be weighed against other lines of evidence to inform taxonomic decisions. By clarifying how these methods behave and how their outcomes should be interpreted, our study offers a practical guide for researchers working on comparably complex systems.

Free-living nematodes are among the most abundant animals on Earth and play critical ecological roles in soil ecosystems. However, the global population structure and evolutionary history of most species remain poorly understood. Here, we analyzed genome-wide variation in Oscheius tipulae using whole-genome sequence data from 31 isolates, including 28 publicly available genomes and three newly collected strains from Korea. Population structure analyses, phylogenomic inference, and ancestry estimation consistently identified three deeply divergent lineages. These analyses did not detect admixture among lineages and collectively supported a predominantly tree-like evolutionary history. Notably, the lineages were structured by latitude rather than geographic proximity. Isolates from similar latitudinal zones clustered together regardless of continental origin, forming three major groups: northern mid-latitude (NML), low-latitude (LL), and southern mid-latitude (SML). This pattern indicates that the lineages have maintained largely independent evolutionary trajectories over extended timescales despite the potential for long-distance dispersal. Furthermore, environmentally associated variants showed significant differentiation among lineages, indicating that environmental selection may contribute to the maintenance of this latitudinally structured diversity. Our results reveal unexpectedly deep global divergence within O. tipulae, and highlight the importance of ecological divergence and long-term lineage retention in shaping the global diversity of this group.

The Wangs garden lizard, Calotes wangi, is a widely distributed agamid species in Southern China and Northern Vietnam and exhibits pronounced colour variation and rapid body colour change. Despite increasing interest in the genomic basis of colour variation, chromosome-level genomic resources remain limited in agamid lizards. Here, we generated a chromosome-level reference genome of C. wangi using PacBio HiFi sequencing and Hi-C scaffolding. The final genome assembly was approximately 1.66 Gb in size and comprised 6 macrochromosomes and 11 microchromosomes, with a contig N50 of 110.09 Mb and 98.9% complete BUSCO genes. A total of 20,442 protein-coding genes were annotated. Comparative genomic analyses identified 297 significantly expanded gene families, with enriched functions associated with steroid metabolism, chromatin regulation, and epigenetic processes. This high-quality genome assembly provides an important genomic resource for future studies of colour variation, phenotypic plasticity, and evolutionary diversification in agamid lizards.

Mosquito-borne diseases represent an escalating global health threat, driven by climate change, urbanization, and the spread of invasive vectors into new territories. Effective surveillance is constrained by a critical taxonomic impediment: the rate of specimen collection far outpaces the capacity of expert entomologists to process and identify trap catches. To address this bottleneck we developed MosAICo, an integrated AI-powered ecosystem for automated mosquito species identification designed for real-world, national-scale entomological surveillance. The system combines a standardized benchtop imaging device with MosAICo-Net, a deep learning pipeline enabling efficient and principled open-set recognition and uncertainty quantification. Trained and evaluated on 12, 499 specimens spanning 15 species collected across Italy, the model identifies seven priority vector species while explicitly rejecting out-of-distribution specimens. On a geographically stratified held-out test set, MosAICo-Net achieved over 90% accuracy on target species, and an AUROC of 0.96 for out-of-distribution detection. Field validation across 20 Italian surveillance sites confirmed these results: 94% micro accuracy on 1, 470 field-collected target specimens and strong agreement with expert manual counts ([Formula] = 0.66). To assess cross-geographic generalizability, the system was further evaluated on 118 Aedes albopictus specimens collected at the fringe of the species invasion front in Ghana: a 97.4% accuracy with only a single specimen escalated to expert review, suggests that MosAICo is well-suited for deployment in distant and epidemiologically critical regions. The system processes up to 82 specimens per image, matching expert throughput at constant speed regardless of taxonomic complexity. By embedding uncertainty-aware AI within a standardized hardware-software pipeline, MosAICo acts as a scalable force multiplier for public health entomology, freeing expert attention for rare, invasive, or ambiguous specimens that require human validation.

Waterbirds serve as important indicators of both aquatic and terrestrial ecosystem health, making effective monitoring essential for tracking population health and identifying potential causes of decline. Drones have provided opportunities to overcome historic waterbird monitoring challenges, but the expertise and time required for manual image analysis creates a major bottleneck. Recent advances in deep learning-based object detection have enabled rapid, automatic detection of features in complex ecological imagery, though applications have largely been limited to single-species colonies, and practitioners lack quantitative comparisons of annotation time and accuracy across different levels of automation. We systematically compared four waterbird monitoring approaches using identical survey areas from Chester Island, a mixed-species colony in Matagorda Bay, Texas, in 2025: (1) traditional ground-based counts, (2) manual drone imagery-based counts, (3) computer-assisted counts using pre-annotations from an object detector with manual human verification (Human+ML), and (4) fully automated counts using object detector annotations (ML-only). We trained a YOLOv10 object detection model on manually annotated imagery of Chester Island in 2021 and applied it to the 2025 imagery. Manual drone annotation detected 6,530 birds in 40.5 hr and served as the primary reference standard. Human+ML detected 5,826 birds (89% of manual) in 7.7 hr, an 81% reduction in annotation time. ML-only detected 5,679 birds (87% of manual) in approximately 46 min, a 98% reduction. Ground counts recorded 5,868 birds (90% of manual). Detection generalized well across species while classification depended heavily on training data and morphological distinctiveness. The Human+ML workflow emerged as a practical middle ground, providing practitioners with empirical data to evaluate partial versus full automation strategies based on monitoring objectives.

The impact of projected extinctions on global animal colour diversity remains unknown. Combining citizen science with self-supervised deep learning, we built novel representations of bird plumage colour patterning based on >125,000 museum specimen images covering 9,143 species. We demonstrate that losing currently threatened bird species will drive a disproportionate reduction in avian plumage diversity, with the most severe losses occurring in tropical and subtropical regions. Furthermore, while humans generally find non-typical plumage phenotypes more aesthetically attractive, threatened species are unexpectedly deemed less visually appealing despite their comparatively unusual plumages. Overall, our results highlight severe, imminent threats to the existing avian colourscape and raise critical questions about the future of animal colour diversity in a changing world.

Sharks exhibit extraordinary morphological diversity across a wide range of ecological niches, yet large-scale, high-resolution digital datasets of their internal anatomy remain limited. Here we present an open-access 3D shark anatomical repository derived from published X-ray computed tomography (CT) data, featuring manually segmented and systematically annotated models of the chondrocranium, visceral arches, axial skeleton, musculature, and viscera in standard STL format. The dataset comprises 117 individuals, representing 72 species across 25 families and all nine extant shark orders, with 115 full-body reconstructions and two head-only models. This open-access dataset offers a comprehensive resource for comparative anatomy, biomechanical simulations, evolutionary developmental biology and biomimetics research of extant sharks.

The African clawed frog Xenopus laevis is a widely utilized model organism in biomedical research; however, significant challenges in experimental reproducibility and colony management remain. A major obstacle lies in the reliable identification of individual animals, since frogs are generally housed in large groups and are difficult to distinguish due to their high morphological similarity. Conventional methods, including toe clipping and microchipping, are invasive and cause distress, emphasizing the need for non-invasive methods for accurate documentation and welfare monitoring. In this study, we introduce XIBBIT (Xenopus Image-Based Biometric-pattern Identification Tool), a web-based application integrating computer vision and machine learning to identify individual Xenopus laevis based on their dorsal patterning. By exploiting these natural biometric signatures, the platform achieves reliable identification with up to 95.7% accuracy within three image captures under real life conditions. In addition to identification, XIBBIT provides a centralized colony management system. It archives individual data, including health records and experimental histories, with customizable fields. To demonstrate XIBBITs capabilities, we used the application to track egg quality across repeated egg-laying events, revealing that egg quality is a repeatable, individual-specific trait in Xenopus laevis. Furthermore, we find seasonal effects on egg laying performance with the lowest performance during late-spring and summer months. Ultimately, XIBBIT provides an effective, time-efficient, and non-invasive solution to the problem of individual Xenopus laevis identification, facilitating both experimental reproducibility and high animal welfare standards.