PHYLOGENETIC-RELATIONSHIPS OF THE GREEN-ALGAE AND BRYOPHYTES

Considerable progress has been made recently, based on classical morphological characters and newly described ultrastructural features, in understanding the phylogenetic relationships of the tracheophytes to the green algae and bryophytes. Recent technological advances in molecular biology, particularly the advent of the polymerase chain reaction (PCR), have allowed nucleotide sequence data relevant to such large-scale phylogenetic questions to accumulate, especially ribosomal RNA gene sequences (both the large and small subunits) from the nucleus and the chloroplast. We present synthetic cladistic analyses of the green plants that combine and compare available morphological and molecular data sets. Although the resulting phylogenies are poorly resolved in some areas at present, certain conclusions are supported: (1) The green plants are composed of two major monophyletic groups, one containing the ''charophyte'' green algae and the land plants (i.e., ''bryophytes'' plus tracheophytes), the other containing the bulk of the classically delimited ''green algae'' (chlorophytes, pleurastrophytes, and ulvophytes). (2) The land plants are a well-supported monophyletic group, but neither the specific outgroup for the land plants nor the precise relationships among basal lineages of land plants are clear. In many analyses (including the combined molecular and morphological analysis) the three major lineages (i.e., liverworts, hornworts, and mosses) appear to be paraphyletic with respect to the tracheophytes, with an indication that the mosses alone may be the sister group of the tracheophytes; however, in other analyses the ''bryophytes'' are supported as a monophyletic group. (3) The ulvophytes, chlorophytes, and pleurastrophytes are each supported as monophyletic (with the exception of a few taxa that may be misplaced in the current classification), with the topology: [ulvophytes [chlorophytes + pleurastrophytes]]. Combined analyses of molecular and morphological data offer the greatest potential for resolving these relationships.