Classification systems
Artificial vs. natural
Artificial classificationare based on any arbitrary property of interest.
Artificial classifications can be very useful because they specifically focus on properties of interest.
They can also be easy to develop because they are typically based on properties that are easy to measure.
This means that they are also relatively stable; once the classification has been established, it doesn't change a lot.
A major disadvantage of artificial classifications is that they have little predictive value; arbitrarily selected properties do not necessarily correlate well with each other.
This generally means that they are of little use for purposes other than that for which they were designed.
Examples from Bacteria and Archaea:
Morphological classifications
The term "prokaryote" is a description of cell morphology; it is an artificial classification.
Genome size and geometry
Lifestyle (primarily reflecting the biochemical capababilities of the organism)
Human disease relevance
Natural classifications are based on evolutionary relationships
Natural classifications can be used to predict proprties that are not related to the properties used to create the classification.
This is because closely related organisms often share ancestral properties (ancestral properties are called "plesiomorphies")
They are broadly useful because of this predictive value; a classification need not have been designed for a specific purpose to be useful for it.
A disadvantage is that sometimes closely related organisms can differ in important properties.
An example of this is pathogenic bacteria that are very closely related to non-pathogenic strains
Another disadvantage is that it can be quite difficult to determine how organisms are related; consequently natural classification systems tend to change as new information becomes available.
Examples of data types that have been used to develop natural classifications
Morphological and biochemical data
Bergey's Manual of Deterministic Microbiology historically relied on an artificial classification that integrated gross morphology and the biochemical information. This was originally an attempt to develop a natural classification, but that classification is now known to largely be artificial.
Ribosomal RNA (rRNA), or the DNA encoding it.
The small subunit (SSU rDNA) has been most widely used, but other parts of the operon have been used, including the large subunit (LSU rDNA) and the internal transcribed spacer (ITS).
Protein-coding genes.
The ribosomal DNA view
Archaea
Crenarchaeota
Originally described from hot springs, most well-characterized species are hyperthermophiles
However, environmental molecular sampling makes it clear that there are also mesic species
Most lack histones
Euryarchaeota
Korarchaeota
Nanoarchaeota
Bacteria
Thermotogae
Aquificae
Deinococcus/Thermus
Cyanobacteria
Chloroflexi (Green Nonsulfur Bacteria)
Planctomycetes
Bacteroidetes
Cytophagas, Flexibacteria, Flavobacteria, Bacteroides
Chlorobi (Green Sulfur Bacteria)
Chlamydiae
Spirochaetes
Gram positives
Actinobacteria
Firmicutes
Proteobacteria
About 30 newly discovered clades unstudied by culture methods.
Eukarya
Bikonts
Excavates
Rhizaria
Cercozoa
Foraminifera
Radiolaria
Chromalveolates
Chromists (sensu Cavalier-Smith 1986)
Alveolata (sensu Taylor)
Plantae
Glaucocystophyta
Rhodophyta
Viridiplantae
Unikonts
Amoebozoa
Opisthokonta
Animalia
Fungi
Woese, C. R., and G. E. Fox. 1977. Phylogenetic Structure of Prokaryotic Domain - Primary Kingdoms. Proceedings of the National Academy of Sciences of the United States of America 74: 5088-5090.
Barns, S. M., C. F. Delwiche, J. D. Palmer, and N. R. Pace. 1996. Perspectives on archaeal diversity, thermophily and monophyly from environmental rRNA sequences. Proceedings of the National Academy of Sciences of the United States of America 93: 9188-9193.
Baldauf, S. L. 2004.
Cavalier-Smith XXXX