Photosynthetic picoeukaryotes (PPE) are poorly characterized, but appear to be widely distributed and to have a substantial role in carbon fixation. These algae have a reduced cell size that is matched with a reduction in genome size. In an example of convergent evolution, some lineages of oceanic Prochlorococcus cyanobacteria have also undergone a reduction in size. The reason for size reduction in photosynthetic oceanic microbes remains to be established but is key to understanding their role in the environment, both in the food chain and in carbon cycling. There are two hypotheses regarding the size reduction. Firstly, it has been proposed that there has been selection for increased growth rate, which has been facilitated by a reduction in cell/genome size. We propose to test this hypothesis by searching for evidence of increased growth rate of PPE by calculating the changes of their effective population size (Ne) overtime by using the recently developed PSMC method of Li and Durbin, which relies on the distribution of single nucleotide polymorphisms (SNPs) within the genome . Secondly, the Black Queen hypothesis (BQH) proposes that oceanic microbes cooperate with each other by sharing nutrients in a nutrient-poor environment as public goods, and that is a driver for genome reduction. We propose to test this hypothesis by determining which metabolic pathways have been lost or retained during genome reduction due to the black queen effect. Additionally, The Proteomic Constraint Theory (PCT) proposes that proteome/genome size acts as a key factor in determining mutation rate, which is inversely proportional to proteome size and/or the number of DNA repair genes. PCT has been tested for Bacteria, Archaea and DNA viruses but in PPE, it is yet to be established. Hence, PPE genomes will be used to test PCT by screening their complete genome sequence for different DNA repair genes and mutation rate. Furthermore, we aim to estimate the distribution of PPE in global oceans and their potential contribution to carbon fixation and ecological impact by using meta-metabolomics networks derived from marine metagenomics datasets.