Examples gallery
Click any figure for the code that produces it. Every example simulates with ZOMBI2 and plots with Phylustrator, a separate package — so to run one you need both:
pip install zombi2 phylustrator
Species trees
5Forward birth–death trees — the whole history, survivors and extinctions, with the diversification model made visible.
Yule tree
Pure birth, no extinction — a forward tree of 100 lineages.
pure birth (Yule)
Extinct lineages
The full history behind 100 survivors — their branches solid, extinct lineages dashed.
birth–death
Mass extinction
A pulse at t = 3 culls 75% of lineages — the skyline drops sharply at the dashed line, then recovers.
mass extinction · + skyline
Rate shifts
Speciation runs slow, then fast, then slow — the burst packs branches between the two dashed regime lines.
time-varying birth
Diversity-dependent
Speciation slows as diversity fills up; the skyline rises and plateaus at the cap of 100.
birth–death · + skylineGenomes
7Genes on chromosomes — a genome as a ring, synteny between genomes, and gene-family events and copy number read against the species tree.
Circular genome (ordered)
A genome as a ring — genes evenly spaced by rank, coloured by family, arrows by strand. plot(g, layout="circular") + genes().
Synteny between two genomes
Two genomes, one per row; ribbons link same-family genes and cross where the order was rearranged. stack([a,b]) + synteny().
Gene-family events on the tree
One family's history on the species tree: duplications (squares), losses (crosses) and transfers (arrows, donor→recipient). plot(tree) + branch_events(…).
Profile copy-number
A family × genome copy-number heatmap, its rows locked to the tips. beside(tree, heatmap(profiles)).
Real genome (Mycoplasma)
A real bacterium — Mycoplasma genitalium, 546 genes at their true base positions, coloured by strand; the forward/reverse switch marks the replication origin. read_gff(…).
An inversion, before → after
One inversion on a circular genome: the affected segment is reversed and its strands flip (the arrows turn round). The band marks the segment in both rings. highlight(g, start, end) + genes().
A transfer highway between clades
Transfers steered to run between two clades (a Clades + Between kernel — topology, not a trait). Tree coloured by clade; the barplot counts transfers by clade pair, so A↔B towers over within-clade.
Sequences
4The dated tree the sequences evolve down, and an alignment lined up row-for-row with its tips.
Sequence phylogram
The clock tree the sequences evolve down — branch lengths are substitutions/site under an uncorrelated relaxed clock, so the tips are not level.
phylustrator · phylogram
Autocorrelated-clock phylogram
The other clock we ship: under the autocorrelated clock the rate drifts parent→child, so related lineages share a rate — branches coloured by lineage rate move in blocks, not salt-and-pepper. substitution = FromParent(spread).
Ancestral sequences at the nodes
A small tree with its internal nodes numbered (0 = initial genome, 1 = crown, …); beside it the reconstructed sequence at each — one free-floating row per node, not aligned to the tips. seqs.ancestral.
Alignment beside the tree
A single-copy family across 20 species, residues coloured (with a nucleotide key), each row locked to its tip. beside(tree, alignment(aln)).
Trait evolution
5A trait evolving down the tree — branches coloured by its value; some paired with a companion panel.
Brownian motion
Free diffusion — sister lineages drift apart with time.
continuous
Ornstein–Uhlenbeck
Pulled to an optimum: a high start (yellow) converges to blue.
continuous
Discrete states
A two-state trait hops between habitats; each branch is painted by its state history.
discrete · Mk
Dependent continuous traits
Two traits evolve together (r = 0.9) — two trees, coloured by each trait, and the tip scatter.
continuous · + scatter
Dependent discrete traits
Two binary characters where one's flip rate depends on the other's state. Two trees, coloured by each character (X green, Y purple), so you can see Y is present where X is; the 2×2 chain (arrow width = rate) is the model. simulate_discrete(states=("00",…), switch={…}).
Joining and conditioning
6One level drives another through one mechanism: a trait drives diversification so its state shapes the tree, or a trait conditions the genome — driving gene loss — so its state shapes genome size.
BiSSE
A two-state trait drives speciation — the fast state's clades take over; the inset is the state Markov chain.
trait → speciation
State-dependent extinction
One state dies far faster; the doomed lineages (dashed) drop out.
trait → extinction
MuSSE
Three graded speciation rates with constant death — the fastest state fills the tree, extinct lineages dashed.
trait → speciation
Genome reduction
The same coupling, aimed at the genome instead of the tree: an irreversible endosymbiont lifestyle drives fast gene loss and near-zero gene gain, so those lineages' genomes collapse. Tree coloured by lifestyle; bars are per-tip genome size.
trait → loss
Genome expansion
The mirror image: a trait conditions the duplication rate, so under relaxed selection duplicates pile up and those genomes grow. Same figure as reduction, run the other way. duplication = base * DrivenBy(sel, {…}).
HGT uptake by competence
A trait conditions who receives a transfer, not a rate — competent lineages take up DNA more readily and their genomes swell. The trait-driven twin of the topological clade highway. transfer_to = DrivenBy(competence, {…}).