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| Main Authors: | , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Proceedings. Biological sciences
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/39968621/ |
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| _version_ | 1868266242327969792 |
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| author | Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine |
| author_facet | Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine |
| collection | PubMed - marine biology |
| contents | Phylogenomics of the rarest animals: a second species of Micrognathozoa identified by machine learning. Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine Animals Phylogeny Machine Learning Invertebrates Greenland Transcriptome The latest animal phylum to be discovered, Micrognathozoa, constitutes a rare group of limnic meiofauna. These microscopic 'jaw animals' are among the smallest metazoans yet possess highly complex jaw structures. The single species of Micrognathozoa, Kristensen and Funch, 2000, was first described from Greenland, later reported from a remote Subantarctic island and more recently discovered in the Pyrenees on the European continent. Successful collections of these three known populations facilitated investigations of the intraphylum relationships and species limits within for the first time. Through detailed anatomical comparisons, we substantiate the lack of morphological differences between the three geographically disjunct populations. With transcriptomic data from single specimens, we conducted the first intraphylum phylogenetic analyses and extensively tested species hypotheses using standard approaches and novel machine learning methods. Analyses clearly delimited the Subantarctic population, here described as sp. nov., the second species of Micrognathozoa, but did not definitively split the Greenland and Pyrenees populations as separate species. Divergence dating analysis suggests the disjunct distribution of Micrognathozoa is not human mediated but the result of long-distance dispersal raising questions about their dispersal capabilities and potential undiscovered populations. |
| format | Artículo científico |
| id | pubmed_39968621 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Proceedings. Biological sciences |
| record_format | pubmed |
| spellingShingle | Phylogenomics of the rarest animals: a second species of Micrognathozoa identified by machine learning. Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine Animals Phylogeny Machine Learning Invertebrates Greenland Transcriptome Phylogenomics of the rarest animals: a second species of Micrognathozoa identified by machine learning. Sato, Shoyo Appeldorff, Cecilie Wangensteen, Owen S Garcés-Pastor, Sandra Laumer, Christopher E Herranz, María Giribet, Gonzalo Renault, David Rask Møller, Peter Worsaae, Katrine Animals Phylogeny Machine Learning Invertebrates Greenland Transcriptome The latest animal phylum to be discovered, Micrognathozoa, constitutes a rare group of limnic meiofauna. These microscopic 'jaw animals' are among the smallest metazoans yet possess highly complex jaw structures. The single species of Micrognathozoa, Kristensen and Funch, 2000, was first described from Greenland, later reported from a remote Subantarctic island and more recently discovered in the Pyrenees on the European continent. Successful collections of these three known populations facilitated investigations of the intraphylum relationships and species limits within for the first time. Through detailed anatomical comparisons, we substantiate the lack of morphological differences between the three geographically disjunct populations. With transcriptomic data from single specimens, we conducted the first intraphylum phylogenetic analyses and extensively tested species hypotheses using standard approaches and novel machine learning methods. Analyses clearly delimited the Subantarctic population, here described as sp. nov., the second species of Micrognathozoa, but did not definitively split the Greenland and Pyrenees populations as separate species. Divergence dating analysis suggests the disjunct distribution of Micrognathozoa is not human mediated but the result of long-distance dispersal raising questions about their dispersal capabilities and potential undiscovered populations. |
| title | Phylogenomics of the rarest animals: a second species of Micrognathozoa identified by machine learning. |
| topic | Animals Phylogeny Machine Learning Invertebrates Greenland Transcriptome |
| url | https://pubmed.ncbi.nlm.nih.gov/39968621/ |