Eukaryotic life cycles alternate between haploid and diploid phases and in phylogenetically diverse unicellular eukaryotes, expression of paralogous homeodomain genes in gametes primes the haploid-to-diploid transition. In the unicellular Chlorophyte alga Chlamydomonas KNOX and BELL TALE-homeodomain genes mediate this transition. We demonstrate that in the liverwort Marchantia polymorpha paternal (sperm) expression of three of five phylogenetically diverse BELL genes, MpBELL234, and maternal (egg) expression of both MpKNOX1 and MpBELL34 mediate the haploid-to-diploid transition. Loss-of-function alleles of MpKNOX1 result in zygotic arrest, whereas loss of either maternal or paternal MpBELL234 results in variable zygotic and early embryonic arrest. Expression of MpKNOX1 and MpBELL34 during diploid sporophyte development is consistent with a later role for these genes in patterning the sporophyte. These results indicate that the ancestral mechanism to activate diploid gene expression was retained in early diverging land plants and subsequently co-opted during evolution of the diploid sporophyte body.

Although different animal species often exhibit extensive variation in many behaviors, typically scientists examine one or a small number of behaviors in any single study. Here, we propose a new framework to simultaneously study the evolution of many behaviors. We measured the behavioral repertoire of individuals from six species of fruit flies using unsupervised techniques and identified all stereotyped movements exhibited by each species. We then fit a Generalized Linear Mixed Model to estimate the intra- and inter-species behavioral covariances, and, by using the known phylogenetic relationships among species, we estimated the (unobserved) behaviors exhibited by ancestral species. We found that much of intra-specific behavioral variation has a similar covariance structure to previously described long-time scale variation in an individual’s behavior, suggesting that much of the measured variation between individuals of a single species in our assay reflects differences in the status of neural networks, rather than genetic or developmental differences between individuals. We then propose a method to identify groups of behaviors that appear to have evolved in a correlated manner, illustrating how sets of behaviors, rather than individual behaviors, likely evolved. Our approach provides a new framework for identifying co-evolving behaviors and may provide new opportunities to study the mechanistic basis of behavioral evolution.