Classification, Taxonomy & Microbial Diversity

This week is not a list of Latin names to memorise in isolation. It is a story about how we know who is related to whom among organisms too small to see clearly — and why that matters for medicine, ecology, and how we read the history of life on Earth.

1. What “life” means in this course (minimal spine)

Biologists treat living things as cell-based systems that reproduce with heritable variation and carry out metabolism (energy + biosynthesis). Cells are the universal unit; the genome (DNA in cellular life) is the long-term information store. Variation arises from mutation and recombination; evolution is change in inherited traits in populations over generations — including common descent (lineages that share ancestors).

Microbes are not a single taxonomic group: the word covers bacteria, archaea, many unicellular eukaryotes, and (loosely) viruses. Viruses are not classified into the three domains of cellular life — they lack a cellular organisation and are handled with separate rules — so exam questions about “domains” usually mean Bacteria, Archaea, Eukarya only.

2. Prokaryote vs eukaryote — vocabulary before phylogeny

Historically, “prokaryote” meant no membrane-bound nucleus (bacteria + archaea). “Eukaryote” means cells with a nucleus and other membrane-bound organelles. The three-domain model (Woese) showed that Archaea are not a subset of Bacteria — they are a separate domain despite sharing the “no nucleus” layout. So: prokaryote is a useful operational label but evolutionarily incomplete once you know Domains.

3. What is “diversity”?

Microbial diversity can mean:

• Phylogenetic diversity — how many distinct evolutionary lineages (branches on a tree).
• Genotypic diversity — differences in DNA sequences, plasmids, and mobile elements.
• Phenotypic diversity — differences in structure, metabolism, motility, staining, and behaviour we can measure.
• Ecological diversity — different niches (host, soil, ocean vent, etc.).

Classification tries to reflect evolutionary history (who branched from whom) while remaining practical for labs and clinicians. The tension between “perfect tree” and “useful labels” is what this week unpacks.

4. Why we need taxonomy before we argue about trees

Without agreed names and rules, two people can talk past each other even when they mean the same bug. Taxonomy supplies the grammar (binomial nomenclature, ranks, type strains, nomenclatural codes). Classification supplies the hypothesis of relationship — which groups belong together. As sequence data improved, classification shifted from “what it looks like” toward “who it shares ancestry with” — but the need for stable names stayed.

What comes next?

Section 2 states that distinction explicitly; Section 3 adds Linnaean hierarchy and binomial writing. Keep the vocabulary from this page in mind for every section after.

Classification is putting organisms into ordered groups (nested or branching schemes) so we can study, compare, and predict traits. It is a scientific hypothesis about relationships — it can be revised when new fossils or sequences appear.

Taxonomy is the discipline that names those groups and applies formal rules (nomenclatural codes for bacteria, plants, animals, etc.). One named group at any rank = one taxon (plural taxa). Systematics is the broader field: reconstructing evolutionary history and building classifications that reflect it.

Good classification + naming helps communication in the lab, clinic, and literature — everyone must mean the same organism when they say Escherichia coli.

Carl Linnaeus (1707–1778) organised living things by visible traits — hugely influential for plants and animals. Microbiology later added finer splits (e.g. Domain above Kingdom) when sequence data showed how deep the splits really are.

Classic nested ranks (not every group uses every rank — only what is needed):

Binomial nomenclature: each species is written as Genus + specific epithet. Rules you must reproduce in exams:

• Italicise in print (or underline if handwriting).
• Genus capitalised; species epithet lowercase — even if honouring a person (Salmonella enterica, not Enterica).
• After first full mention, genus may abbreviate if unambiguous: E. coli for Escherichia coli; S. aureus for Staphylococcus aureus.
• The word “species” after the binomial is optional in sentences; if used, lowercase (“E. coli is a species …”).

Linnaean / trait-based schemes struggle with microbes:

• Convergence / coevolution: unrelated organisms can look alike in the same environment.
• Hidden diversity: many differences are not visible (physiology, genetics).
• Scale: early systems handled ~10⁴ species and limited ranks — microbes blew past that.
• Linear tree: microbes don’t only inherit “vertically”; horizontal gene transfer (HGT) blurs neat branching.
• Species concept: classical “interbreeding + fertile offspring” fits eukaryotes poorly and bacteria not at all (mostly asexual + DNA uptake).

Life needs energy, bio-elements (CHNOPS etc.), and a viable temperature range. The primordial soup model (Oparin; Haldane) proposes organic building blocks from a reducing atmosphere + energy (e.g. lightning). Miller–Urey-style experiments showed amino acids can form under such conditions.

Limits: soup chemistry explains small molecules well, but not a heritable information system by itself. RNA can store information and (as ribozymes) catalyse reactions — Cech & Altman’s work supports ancient RNA-based life before modern DNA–protein world.

Ribosomes are ancient; comparing ribosomal RNA became the backbone of microbial phylogeny.

Biosignatures: chemical or isotopic patterns in rocks suggesting life (e.g. biologically fractionated carbon). Must be interpreted cautiously — some signals can be abiotic.

Microfossils: tiny preserved structures in non-metamorphic rock (heat/pressure can distort “fossils”). 3D morphology + geochemistry support claims.

Stromatolites: layered microbial mats, often calcium carbonate–bound (e.g. Shark Bay, WA). Layers show oxygenic phototrophs at the surface, sulphur-based metabolism deeper — classic evidence of microbial communities over geological time.

Microbes often reproduce by binary fission; replication errors create variation. Under selective pressure, variants that survive better leave more descendants — natural selection. Neutral changes accumulate; deleterious mutations tend to be lost (reductive / degenerative evolution when functions are no longer needed).

Molecular clock: comparing sequences estimates how long ago lineages diverged — more differences ⇒ more time (assuming roughly constant rate, an idealisation).

Carl Woese compared SSU rRNA sequences and proposed three domains: Bacteria, Archaea, and Eukarya (~1990). This split the old “prokaryote” bag — Archaea are not “just bacteria with odd habitats.”

Phylogenies still use ranks (Domain → … → Species) but branch order comes from sequence, not only morphology.

The “best” universal marker for deep relationships is often small-subunit (SSU) ribosomal RNA: in bacteria and archaea this is 16S rRNA; in eukaryotes 18S rRNA (homologous molecule, different sedimentation name).

It is ancient, functionally constrained, and widely compared — the standard for environmental surveys and taxonomy pipelines.

Tips joined by more recent common ancestors are more closely related. A linear ordering can mislead — rotation around nodes doesn’t change relatedness, but human readers can confuse “left vs right” with “primitive vs advanced.”

Radial (unrooted or rooted) trees are common for overview slides; focus on clades (ancestor + all descendants).

Genes move by vertical inheritance (parent → offspring) and by HGT — between cells of the same generation or across lineages (conjugation, transformation, transduction, etc.).

That is why the “tree of life” is sometimes drawn as a network or with acknowledged transfer edges (e.g. Doolittle’s critiques): prokaryotic evolution is partly reticulate.

Course summary (operational): lineages that are phylogenetically coherent — often ~97% (or 95%+) SSU rRNA identity historically used for “same species” clustering in surveys; modern practice also uses whole-genome metrics (e.g. ANI ~95% across the genome for many bacterial species concepts).

Ecology/pathology: similar metabolism, niche, or disease phenotype supports lumping strains into one named species when genomes agree.