This project is supported by The Swedish Taxonomy Initiative (SLU) through a PhD grant to UJ with Marika Maranzano as a PhD student.
We are currently performing analyses of 58 genomes from microscopic marine animals belonging to the group Acoelomorpha. Assembly and annotation of mitogenomes is in progress. We aim to study mitogenome and nuclear evolutionary rates within Acoelomorpha using the time-tree that we have previously published as calibration. It has been repeatedly suggested that substitution rates in Acoelomorpha are elevated in comparison to other metazoans. We will test this assertion using broad sampling across taxa and genomes. After assembly and phylogenomic analysis of nuclear genomes, robust phylogenetic hypotheses will be generated to resolve some outstanding questions in the Acoelomorpha phylogeny. We also intend to analyse potential cryptic biodiversity in Acoelomorpha and perform introgression analyses in the genus Notocelis. Hypotheses of genome duplication which are based on the occurrence of multiple Hox gene copies in some species and the fact that genome size varies considerably within Acoelomorpha will also be tested. Finally, we will conduct some additional analyses of eDNA where the initial bioinformatics have been carried out elsewhere as well as analyses of metabarcoding data collected during and 2026. 
Acoelomorpha are an abundant and diverse group of microscopic marine animals that are poorly known from a taxonomic and phylogenetic perspective. Our goal here is to resolve difficult phylogenetic problems within Acoela through better taxonomic sampling of genomes, and to determine the staus of some problematic taxa where there are signs of introgression at the family level. After initial assembly steps, we will perform the phylogenomic analyses, tests for introgression, phylogenetic placement of eDNA data etc. We foresee a relatively high demand for processor hours as the parameter rich nucleotide substitution models implemented in e.g. PhyloBayes are computationally very demanding. Furthermore we will need to perform a set of sensitivity analyses where the dataset is modified in various ways and then reanalysed, which multiplies the resource requirements.