The Dispersal-Extinction-Cladogenesis (DEC) model is a powerful framework for reconstructing the biogeographic history of lineages. By combining phylogenetic trees with species distribution data, DEC analysis allows researchers to infer where ancestral species lived and how they spread across the globe.
What is DEC Analysis?
DEC, introduced by Ree & Smith (2008), models geographic range evolution as a continuous-time process along the branches of a phylogenetic tree. The model includes three key processes:
- Dispersal (d): Range expansion to new areas
- Extinction (e): Local extirpation from areas (range contraction)
- Cladogenesis: Range inheritance at speciation events
Key Components
Geographic Areas
You define discrete geographic areas (e.g., continents, islands, biomes) where species can occur. Common schemes include:
- Continental regions (Nearctic, Neotropical, Palearctic, etc.)
- Island groups (Hawaiian islands, Caribbean islands)
- Biomes or habitat types
- Custom regions relevant to your study group
Species Ranges
For each tip taxon, you code which areas it occupies. A species can occur in one area (endemic) or multiple areas (widespread).
Rate Parameters
DEC estimates two key parameters:
- d (dispersal rate): Rate of range expansion per unit time
- e (extinction rate): Rate of local extirpation per unit time
Cladogenetic Events
At speciation nodes, DEC allows three types of range inheritance:
| Event Type | Description | Example |
|---|---|---|
| Sympatry | One descendant inherits full range, other gets subset | ABC → ABC + A |
| Subset sympatry | Both descendants inherit within ancestral range | AB → A + B |
| Vicariance | Ancestral range splits between descendants | AB → A + B |
DEC+J: Adding Jump Dispersal
The DEC+J model adds a fourth parameter (j) for founder-event speciation - where a lineage disperses to a new area and immediately speciates. This is particularly important for island colonization.
When to Use DEC+J
DEC+J is valuable for island systems and groups where long-distance dispersal followed by rapid speciation is common. However, the +J parameter has been criticized for potential model selection issues - use with caution and report both DEC and DEC+J results.
Running DEC Analysis
Input Requirements
- Time-calibrated phylogeny: Branch lengths in millions of years
- Geographic data: Area coding for each tip taxon
- Area connectivity (optional): Which areas can exchange species
- Time stratification (optional): Changing geography through time
Software Options
- BioGeoBEARS (R): Most flexible, includes DEC, DEC+J, and many other models
- Lagrange: Original DEC implementation (C++ and Python)
- RASP: GUI-based tool with multiple methods
- PhyloVerse: Web-based DEC analysis with visualization
Interpreting Results
Ancestral Range Probabilities
DEC provides probability distributions for ancestral ranges at each node. You might see:
- Node 1: A (0.65), AB (0.25), B (0.10)
- This means 65% probability the ancestor occurred only in area A
Biogeographic Events
From the ancestral reconstructions, you can infer:
- Dispersal events: When lineages expanded to new areas
- Vicariance events: When ranges split at speciation
- Extinction events: When lineages were lost from areas
Best Practices
- Use dated trees: DEC estimates rates per time, so branch lengths must be in time units
- Limit area number: Computational complexity increases exponentially; 6-8 areas is practical
- Compare models: Test DEC vs. DEC+J and other models using AIC
- Consider geology: Use time-stratified models if paleogeography changed significantly
- Validate with fossils: Check if reconstructed ranges match fossil evidence
Run DEC Analysis Online
PhyloVerse provides built-in DEC and DEC+J analysis. Upload your tree, code geographic ranges, and visualize ancestral area reconstructions.
Launch PhyloVerseReferences
- Ree, R.H. & Smith, S.A. (2008). Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Systematic Biology, 57(1), 4-14.
- Matzke, N.J. (2013). BioGeoBEARS: BioGeography with Bayesian (and Likelihood) Evolutionary Analysis in R Scripts. R package.