HomePeopleResearchSoftware & DatabasesPublicationsContact


Click to see QuickTime movie


Merging Phylogeography and Paleoecology

Current work in phylogeography involves developing rigorous statistical methods that have the power to test alternative population histories characterized by complex and heavily parameterized models. To this end, the Moritz group is focusing on two approaches: 1) Markov Chain Monte Carlo (MCMC); and 2) approximate Bayesian inference (AB; Estoup et al. 2004). Both approaches are done with the impetus of integrating spatial genetic data with other historical and ecological information into one analysis in order to improve our ability to infer biogeographic histories across taxa within a community. Additionally, such an analysis can test if present ecological and physiological characteristics of a species can correctly predict how it reacts to climate change. A Bayesian framework is a natural way to use such non-genetic information as prior distributions for parameters of interest, while the genetic data can be used to estimate parameters by via posterior distributions, such that alternate histories can be compared by comparing the posterior estimates under alternative prior distributions. Currently, MCMC approaches are enabling us to test spatially explicit phylogeographic models in 2D habitats. Alternatively, the AB approach allows a flexible framework to investigate a wide range of biogeographic hypotheses, while sacrificing some of information used in an MCMC analysis.

Dancing Trees (Stuart Baird)

Motivated by an interest in broader scale evolutionary inference, and in consultation with Ian Wilson, a complementary set of algorithms has recently been developed which allows generalization over a wider class of models of population structure. The approach achieves this generality with a trade-off against computation time. A Markov chain Monte Carlo simulation is created with a state consisting of a tree of paths through discrete space and time. Movement is on a two-dimensional stepping-stone lattice. Between discrete opportunities for movement demes are undisturbed by migration events, and so coalescent probabilities can be described following standard coalescent theory. Proposed transition on the chain state can most succinctly be described as a series of dance steps allowing nodes and paths on the tree to be moved in space and time. The transitions are designed such that change in the tree state is localized, bounded by the nodes connected to the part of the tree being moved and consistent with the stepping-stone paradigm. The process of the Markov chain Monte Carlo simulation can be visualized by iterating the chain and sampling the positions of the lineage paths that make up the tree. Animating the resulting snapshots of the state suggests a label for this approach: the dancing trees algorithm. The dancing trees algorithm can be used to define better the set of lineage trees in nature whose history is well approximated by the population splitting model. In summary the current work has wide implications: the authors' approach and its complements pave the way towards a sounder understanding of population structure and the evolutionary process.


Phylogeographic Experimental Design

Using phylogeographic data to test different biogeographic histories has generally relied on collecting the easily obtained mtDNA in multiple co-distributed taxa. However, the resulting parameter estimates implicit in such biogeographic hypotheses suffer from substantial stochastic error because they are based on a single linked genealogical history per taxon. For instance, mtDNA-based divergence time estimates often show wide variation across co-distributed taxa-pairs that are likely to have diverged nearly simultaneously. Despite the commonly stated limitations of single mtDNA datasets, there has been limited statistical guidance into how researchers should design phylogeographic studies. Therefore we are involved in determining optimal sampling strategies with respect to the number of loci, spatial coverage and number of individuals by conducting Power analyses for different data types (mtDNA, introns, SNPs) using simulations .



Craig Moritz Research Group - Home

Museum of Vertebrate Zoology
Department of Integrative Biology
University of California, Berkeley