5 March 2026
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High-throughput phenomics of global ant biodiversity

  • Meineke, E. K., Davies, T. J., Daru, B. H. & Davis, C. C. Biological collections for understanding biodiversity in the Anthropocene. Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2017.0386 (2018).

  • Arnold, S. J. Morphology, performance and fitness. Am. Zool. 23, 347–361 (1983).

    Article 

    Google Scholar
     

  • Wainwright, P. C. Functional versus morphological diversity in macroevolution. Annu. Rev. Ecol. Evol. Syst. 38, 381–401 (2007).

    Article 

    Google Scholar
     

  • Raxworthy, C. J. & Smith, B. T. Mining museums for historical DNA: advances and challenges in museomics. Trends Ecol. Evol. 36, 1049–1060 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Blagoderov, V., Kitching, I. J., Livermore, L., Simonsen, T. J. & Smith, V. S. No specimen left behind: industrial scale digitization of natural history collections. Zookeys 209, 133–146 (2012).

    Article 

    Google Scholar
     

  • Ströbel, B., Schmelzle, S., Blüthgen, N. & Heethoff, M. An automated device for the digitization and 3D modelling of insects, combining extended-depth-of-field and all-side multi-view imaging. Zookeys 759, 1–27 (2018).

    Article 

    Google Scholar
     

  • Plum, F. & Labonte, D. scAnt-an open-source platform for the creation of 3D models of arthropods (and other small objects). PeerJ 9, e11155 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Davies, T. G. et al. Open data and digital morphology. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.0194 (2017).

  • Short, A. E. Z., Dikow, T. & Moreau, C. S. Entomological collections in the age of big data. Annu Rev. Entomol. 63, 513–530 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Nelson, G. & Ellis, S. The history and impact of digitization and digital data mobilization on biodiversity research. Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2017.0391 (2018).

  • Hedrick, B. P. et al. Digitization and the future of natural history collections. BioScience 70, 243–251 (2020).

    Article 

    Google Scholar
     

  • Wipfler, B., Pohl, H., Yavorskaya, M. I. & Beutel, R. G. A review of methods for analysing insect structures – the role of morphology in the age of phylogenomics. Curr. Opin. Insect Sci. 18, 60–68 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Deans, A. R., Mikó, I., Wipfler, B. & Friedrich, F. Evolutionary phenomics and the emerging enlightenment of arthropod systematics. Invertebr. Syst. https://doi.org/10.1071/is12063 (2012).

  • Friedrich, F. et al. Insect morphology in the age of phylogenomics: innovative techniques and its future role in systematics. Entomol. Sci. 17, 1–24 (2014).

    Article 

    Google Scholar
     

  • Blackburn, D. C. et al. Increasing the impact of vertebrate scientific collections through 3D imaging: the openVertebrate (oVert) Thematic Collections Network. Bioscience 74, 169–186 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sombke, A., Lipke, E., Michalik, P., Uhl, G. & Harzsch, S. Potential and limitations of X-Ray micro-computed tomography in arthropod neuroanatomy: a methodological and comparative survey. J. Comp. Neurol. 523, 1281–1295 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Metscher, B. D. MicroCT for comparative morphology: simple staining methods allow high-contrast 3D imaging of diverse non-mineralized animal tissues. BMC Physiol. 9, 11 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gignac, P. M. et al. Diffusible iodine-based contrast-enhanced computed tomography (diceCT): an emerging tool for rapid, high-resolution, 3-D imaging of metazoan soft tissues. J. Anat. 228, 889–909 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pauwels, E., Van Loo, D., Cornillie, P., Brabant, L. & Van Hoorebeke, L. An exploratory study of contrast agents for soft tissue visualization by means of high resolution X-ray computed tomography imaging. J. Microsc. 250, 21–31 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Muñoz, M. M. & Price, S. A. The future is bright for evolutionary morphology and biomechanics in the era of big data. Integr. Comp. Biol. 59, 599–603 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Lösel, P. D. et al. Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning. PLoS Comput. Biol. 19, e1011529 (2023).

  • Toulkeridou, E., Gutierrez, C. E., Baum, D., Doya, K. & Economo, E. P. Automated segmentation of insect anatomy from micro-CT images using deep learning. Nat. Sci. https://doi.org/10.1002/ntls.20230010 (2023).

  • Handschuh, S., Beisser, C. J., Ruthensteiner, B. & Metscher, B. D. Microscopic dual-energy CT (microDECT): a flexible tool for multichannel ex vivo 3D imaging of biological specimens. J. Microsc. 267, 3–26 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • van de Kamp, T. et al. Parasitoid biology preserved in mineralized fossils. Nat. Commun. 9, 3325 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rühr, P. T. et al. Juvenile ecology drives adult morphology in two insect orders. Proc. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rspb.2021.0616 (2021).

  • Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. Phylogeny and biogeography of dolichoderine ants: effects of data partitioning and relict taxa on historical inference. Syst. Biol. 59, 342–362 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ward, P. S., Blaimer, B. B. & Fisher, B. L. A revised phylogenetic classification of the ant subfamily Formicinae (Hymenoptera: Formicidae), with resurrection of the genera Colobopsis and Dinomyrmex. Zootaxa 4072, 343–357 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Ward, P. S., Brady, S. G., Fisher, B. L. & Schultz, T. R. The evolution of myrmicine ants: phylogeny and biogeography of a hyperdiverse ant clade (Hymenoptera: Formicidae). Syst. Entomol. 40, 61–81 (2015).

    Article 

    Google Scholar
     

  • Schmidt, C. A. & Shattuck, S. O. The higher classification of the ant subfamily Ponerinae (Hymenoptera: Formicidae), with a review of ponerine ecology and behavior. Zootaxa 3817, 1–242 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Camacho, G. P. et al. UCE phylogenomics resolves major relationships among ectaheteromorph ants (Hymenoptera: Formicidae: Ectatomminae, Heteroponerinae): a new classification for the subfamilies and the description of a new genus. Insect Syst. Divers. https://doi.org/10.1093/isd/ixab026 (2022).

  • Blaimer, B. B., Ward, P. S., Schultz, T. R., Fisher, B. L. & Brady, S. G. Paleotropical diversification dominates the evolution of the hyperdiverse ant tribe Crematogastrini (Hymenoptera: Formicidae). Insect Syst. Divers. https://doi.org/10.1093/isd/ixy013 (2018).

  • Borowiec, M. L. Convergent evolution of the army ant syndrome and congruence in big-data phylogenetics. Syst. Biol. 68, 642–656 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Economo, E. P., Narula, N., Friedman, N. R., Weiser, M. D. & Guenard, B. Macroecology and macroevolution of the latitudinal diversity gradient in ants. Nat. Commun. 9, 1778 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Parr, C. L. et al. GlobalAnts: a new database on the geography of ant traits (Hymenoptera: Formicidae). Insect Conserv. Divers. 10, 5–20 (2017).

    Article 

    Google Scholar
     

  • Lach, L., Parr, C. L. & Abbott, K. L. Ant Ecology (Oxford Univ. Press, 2010).

  • Boomsma, J. J. et al. The Global Ant Genomics Alliance (GAGA). Myrmecol N. 25, 61–66 (2017).


    Google Scholar
     

  • Vizueta, J. et al. Adaptive radiation and social evolution of the ants. Cell https://doi.org/10.1016/j.cell.2025.05.030 (2025).

  • Bolton, B. An online catalog of the ants of the world. AntCat https://antcat.org (2026).

  • Mokso, R. et al. Advantages of phase retrieval for fast X-ray tomographic microscopy. J. Phys. D Appl. Phys. https://doi.org/10.1088/0022-3727/46/49/494004 (2013).

  • Lösel, P. D. et al. Introducing Biomedisa as an open-source online platform for biomedical image segmentation. Nat. Commun. 11, 5577 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. Biomineral armor in leaf-cutter ants. Nat. Commun. 11, 5792 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Branstetter, M. G. et al. Dry habitats were crucibles of domestication in the evolution of agriculture in ants. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2017.0095 (2017).

  • Hanisch, P. E., Sosa-Calvo, J., Schultz, T. R. & Camacho, G. P. The last piece of the puzzle? Phylogenetic position and natural history of the monotypic fungus-farming ant genus Paramycetophylax (Formicidae: Attini). Insect Syst. Divers. https://doi.org/10.1093/isd/ixab029 (2022).

  • Beal-Neves, M., Mielke, A., Gamarra, S. d. P., Beier, C. & Fontana, C. S. A students’ opinion on the importance of natural history collections and taxonomy in Brazil. Zoologia (Curitiba) https://doi.org/10.1590/s1984-4689.v39.e21045 (2022).

  • Aibekova, L. The skeletomuscular system of the mesosoma of Formica rufa workers (Hymenoptera: Formicidae). Insect Syst. Divers. https://doi.org/10.1093/isd/ixac002 (2022).

  • Koch, S., Tahara, R., Vasquez-Correa, A. & Abouheif, E. Nano-CT imaging of larvae in the ant Pheidole hyatti reveals coordinated growth of a rudimentary organ necessary for soldier development. J. Exp. Zool. B Mol. Dev. Evol. 336, 540–553 (2021).

  • Lürig, M. D., Donoughe, S., Svensson, E. I., Porto, A. & Tsuboi, M. Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2021.642774 (2021).

  • Cotte, M. et al. New opportunities offered by the ESRF to the cultural and natural heritage communities. Synchrotron Radiat. N. 35, 3–9 (2022).

    Article 

    Google Scholar
     

  • Spiecker, R. et al. Dose-efficient in vivo X-ray phase contrast imaging at micrometer resolution by Bragg magnifiers. Optica https://doi.org/10.1364/optica.500978 (2023).

  • Semple, T. L., Peakall, R. & Tatarnic, N. J. A comprehensive and user-friendly framework for 3D-data visualisation in invertebrates and other organisms. J. Morphol. 280, 223–231 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Buser, T. J. et al. The natural historian’s guide to the CT galaxy: step-by-step instructions for preparing and analyzing computed tomographic (CT) data using cross-platform, open access software. Integr. Org. Biol. https://doi.org/10.1093/iob/obaa009 (2020).

  • Poinapen, D. et al. Micro-CT imaging of live insects using carbon dioxide gas-induced hypoxia as anesthetic with minimal impact on certain subsequent life history traits. BMC Zool. https://doi.org/10.1186/s40850-017-0018-x (2017).

  • Alba-Tercedor, J., Hunter, W. B. & Alba-Alejandre, I. Using micro-computed tomography to reveal the anatomy of adult Diaphorina citri Kuwayama (Insecta: Hemiptera, Liviidae) and how it pierces and feeds within a citrus leaf. Sci. Rep. 11, 1358 (2021).

  • van de Kamp, T., dos Santos Rolo, T., Vagovič, P., Baumbach, T. & Riedel, A. Three-dimensional reconstructions come to life–interactive 3D PDF animations in functional morphology. PLoS ONE 9, e102355 (2014).

  • Matte, A. C. et al. The evolution of cheaper workers facilitated larger societies and accelerated diversification in ants. Sci. Adv. 11, eadx8068 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • AntWiki https://www.antwiki.org (2026).

  • Rack, A. et al. The micro-imaging station of the TopoTomo beamline at the ANKA synchrotron light source. Nucl. Instrum. Methods Phys. Res. B 267, 1978–1988 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Cecilia, A. et al. The IMAGE beamline at the KIT Light Source. J. Synchrotron Radiat. 32, 1036–1051 (2025).

  • Douissard, P. A. et al. A versatile indirect detector design for hard X-ray microimaging. J. Instrum. 7, P09016–P09016 (2012).

    Article 

    Google Scholar
     

  • Vogelgesang, M. et al. Real-time image-content-based beamline control for smart 4D X-ray imaging. J. Synchrotron Radiat. 23, 1254–1263 (2016).

    Article 
    PubMed 

    Google Scholar
     

  • Paganin, D., Mayo, S. C., Gureyev, T. E., Miller, P. R. & Wilkins, S. W. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J. Microsc. 206, 33–40 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Faragó, T. et al. syris: a flexible and efficient framework for X-ray imaging experiments simulation. J. Synchrotron Radiat. 24, 1283–1295 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Vogelgesang, M., Chilingaryan, S., dos Santos Rolo, T. & Kopmann, A. UFO: a scalable GPU-based image processing framework for on-line monitoring. In Proc. IEEE 14th International Conference on High Performance Computing and Communication & IEEE 9th International Conference on Embedded Software and Systems 824–829 (IEEE, 2012).

  • Faragó, T. et al. Tofu: a fast, versatile and user-friendly image processing toolkit for computed tomography. J. Synchrotron Radiat. 29, 916–927 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lowekamp, B. C., Chen, D. T., Ibanez, L. & Blezek, D. The design of SimpleITK. Front. Neuroinform. 7, 45 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yaniv, Z., Lowekamp, B. C., Johnson, H. J. & Beare, R. SimpleITK image-analysis notebooks: a collaborative environment for education and reproducible research. J. Digit. Imaging 31, 290–303 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lösel, P. & Heuveline, V. Enhancing a diffusion algorithm for 4D image segmentation using local information. Proc. SPIE Int. Soc. Opt. Eng. 9784, 97842L (2016).


    Google Scholar
     

  • Ayachit, U. The Paraview Guide: A Parallel Visualization Application (Kitware, 2015).

  • Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lösel, P. D. GPU-basierte Verfahren zur Segmentierung biomedizinischer Bilddaten. PhD thesis, Heidelberg Univ. (2022); https://doi.org/10.11588/heidok.00031525

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