Review article

Species delimitation 4.0: integrative taxonomy meets artificial intelligence

Kevin KarbsteinMax Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany. Electronic address: [email protected]Lara M. KöstersMax Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, GermanyLadislav HodačMax Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, GermanyMartin HofmannTechnical University of Ilmenau, Institute for Computer and Systems Engineering, 98693 Ilmenau, GermanyElvira HörandlUniversity of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, GermanySalvatore TomaselloUniversity of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, GermanyNatascha D. WagnerUniversity of Göttingen, Albrecht-von-Haller Institute for Plant Sciences, Department of Systematics, Biodiversity and Evolution of Plants (with Herbarium), 37073 Göttingen, GermanyBrent C. EmersonInstitute of Natural Products and Agrobiology (IPNA-CSIC), Island Ecology and Evolution Research Group, 38206 La Laguna, Tenerife, Canary Islands, SpainDirk C. AlbachCarl von Ossietzky-Universität Oldenburg, Institute of Biology and Environmental Science, 26129 Oldenburg, GermanyStefan ScheuUniversity of Göttingen, Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, 37073 Göttingen, Germany; University of Göttingen, Centre of Biodiversity and Sustainable Land Use (CBL), 37073 Göttingen, GermanySven BradlerUniversity of Göttingen, Johann-Friedrich-Blumenbach Institute of Zoology and Anthropology, 37073 Göttingen, GermanyJan de VriesUniversity of Göttingen, Institute for Microbiology and Genetics, Department of Applied Bioinformatics, 37077 Göttingen, Germany; University of Göttingen, Campus Institute Data Science (CIDAS), 37077 Göttingen, Germany; University of Göttingen, Göttingen Center for Molecular Biosciences (GZMB), Department of Applied Bioinformatics, 37077 Göttingen, GermanyIker IrisarriLeibniz Institute for the Analysis of Biodiversity Change (LIB), Centre for Molecular Biodiversity Research, Phylogenomics Section, Museum of Nature, 20146 Hamburg, GermanyLi HeEastern China Conservation Centre for Wild Endangered Plant Resources, Chenshan Botanical Garden, 201602 Shanghai, ChinaPamela S. SoltisUniversity of Florida, Florida Museum of Natural History, 32611 Gainesville, USAPatrick MäderTechnical University of Ilmenau, Institute for Computer and Systems Engineering, 98693 Ilmenau, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany; Friedrich Schiller University Jena, Faculty of Biological Sciences, Institute of Ecology and Evolution, Philosophenweg 16, 07743 Jena, GermanyJana WäldchenMax Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, 07745 Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany

2024en

ABI

Abstract

Although species are central units for biological research, recent findings in genomics are raising awareness that what we call species can be ill-founded entities due to solely morphology-based, regional species descriptions. This particularly applies to groups characterized by intricate evolutionary processes such as hybridization, polyploidy, or asexuality. Here, challenges of current integrative taxonomy (genetics/genomics + morphology + ecology, etc.) become apparent: different favored species concepts, lack of universal characters/markers, missing appropriate analytical tools for intricate evolutionary processes, and highly subjective ranking and fusion of datasets. Now, integrative taxonomy combined with artificial intelligence under a unified species concept can enable automated feature learning and data integration, and thus reduce subjectivity in species delimitation. This approach will likely accelerate revising and unraveling eukaryotic biodiversity.

Identifiers

DOI: 10.1016/j.tree.2023.11.002

Citations and references

Cited by 20 references

Metrics — AkademScholar