A new (quantitative!) method for comparative phylogeography

"I reckon the Rio Juruá has something to do with this widespread phylogeographic pattern!" From Avise (2000)

“I reckon the Rio Juruá has something to do with this widespread phylogeographic pattern!”* From Avise (2000) *not a direct quote

Comparative phylogeographic studies usually involve a) documenting a phylogeographic pattern and b) recognizing that the same pattern is congruent in multiple species.
But what if species histories are only sortof congruent? Perhaps they share one major splitting event but not later events. Or maybe the phylogenies are topologically congruent but on very different timescales. It would be great to measure the degree of phylogeographic discordance among species.
Hickerson et al. (2010), in their review of “Phylogeography’s past, present, and future” said:

“A key challenge for comparative phylogeography is the need for developing analytical tools that can be used to evaluate spatial and temporal congruence or incongruence in phylogeographic patterns across multiple species.”

Satler and Carstens (2016) have answered this call. They present the Phylogeographic Concordance Factor (PCF), a new metric for quantifying the phylogeographic concordance (or discordance) of several codistributed species.
How does it work?
If you understand how BUCKy (Larget et al. 2010) works, then you already have a good grasp of phylogeographic concordance factors. In BUCKy, multiple gene trees are used to calculate the “concordance factor” (CF) of each clade (i.e., the proportion of genes that contain that clade). The “primary concordance tree” is the phylogeny that maximizes the CFs.


The basic idea behind BUCKy. Now just swap the gene trees with species trees and you’ve got yourself a phylogeographic concordance factor!

To calculate PCFs, we use BUCKy’s machinery but instead of estimating a primary concordance tree from multiple gene trees, the concordance tree is estimated from multiple species trees. It’s pretty slick (if slightly difficult to wrap your head around)!
Figure 3 from Satler et al. (2016) showing the phylogeographic concordance tree with PCF values (left) and the individual species trees (right)

Figure 3 from Satler and Carstens (2016) showing the phylogeographic concordance tree with PCF values (left) and the individual species trees (right)

CF values range from 0 to 1.0, but perfect concordance (CF=1.0) is almost never recovered. Using simulations, the authors found that CF values between 0.71 and 1.0 are indicative of high phylogeographic concordance.
In this study, the authors applied the PCF method to seven co-distributed species from the southeastern United States: the pitcher plant Sarracenia alata and six commensal invertebrate species. They found that the species that rely on the pitcher plant for food or shelter have the highest degree of concordance.
One problem with the PCF method is that it only quantifies topological congruence – not temporal congruence. In other words, it can tell you if two species have the same split, but not if that split occurred at the same time. Fortunately, the program msBayes can still help with that question.
Satler, J.D. and B.C. Carstens (2016) Phylogeographic concordance factors quantify phylogeographic congruence among co-distributed species in the Sarracenia alata pitcher plant system. Evolution early release DOI: 10.1111/evo.12924
Avise, J.C. (2000) Phylogeography: the history and formation of species. Harvard university press.
Hickerson et al. (2010) Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol Phylogenet Evol 54:291-301. doi: 10.1016/j.ympev.2009.09.016.
Larget et al. (2010) BUCKy: gene tree/species tree reconciliation with Bayesian concordance analysis. Bioinformatics 26: 2910-2911. doi: 10.1093/bioinformatics/btq539

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