Historical biogeography has a diversity of methods for inferring ancestral geographic ranges on phylogenies, but many of the methods have conflicting assumptions, and there is no common statistical framework by which to judge which models are preferable. Probabilistic modeling of geographic range evolution, pioneered by Ree and Smith (2008, Systematic Biology) in their program LAGRANGE, could provide such a framework, but this potential has not been implemented until now.
I have created an R package, "BioGeoBEARS," described in chapter 1 of the dissertation, that implements in a likelihood framework several commonly used models, such as the LAGRANGE Dispersal-Extinction-Cladogenesis (DEC) model and the Dispersal-Vicariance Analysis (DIVA, Ronquist 1997, Systematic Biology) model. Standard DEC is a model with two free parameters specifying the rate of "dispersal" (range expansion) and "extinction" (range contraction). However, while dispersal and extinction rates are free parameters, the cladogenesis model is fixed, such that the geographic range of the ancestral lineage is inherited by the two daughter lineages through a variety of scenarios fixed to have equal probability. This fixed nature of the cladogenesis model means that it has been indiscriminately applied in all DEC analyses, and has not been subjected to any inference or formal model testing.
BioGeoBEARS also adds a number of features not previously available in most historical biogeography software, such as distance-based dispersal, a model of imperfect detection, and the ability to include fossils either as ancestors or tips on a time-calibrated tree.
Several important conclusions may be drawn from this research. First, formal model selection procedures can be applied in phylogenetic inferences of historical biogeography, and the relative importance of different processes can be measured. These techniques have great potential for strengthening quantitative inference in historical biogeography. No longer are biogeographers forced to simply assume, consciously or not, that some processes (such as vicariance or dispersal) are important and others are not; instead, this can be inferred from the data. Second, founder-event speciation appears to be a crucial explanatory process in most clades, the only exception being some intracontinental taxa showing a large degree of sympatry across widespread ranges. This is not the same thing as claiming that founder-event speciation is the only important process; founder event speciation as the only important process is inferred in only one case (Microlophus lava lizards from the Galapagos). The importance of founder-event speciation will not be surprising to most island biogeographers. However, the results are important nonetheless, as there are still some vocal advocates of vicariance-dominated approaches to biogeography, such as Heads (2012, Molecular Panbiogeography of the Tropics), who allows vicariance and range-expansion to play a role in his historical inferences, but explicitly excludes founder-event speciation a priori. The commonly-used LAGRANGE DEC and DIVA programs actually make assumptions very similar to those of Heads, even though many users of these programs likely consider themselves dispersalists or pluralists. Finally, the inclusion of fossils and imperfect detection within the same likelihood and model-choice framework clears the path for integrating paleobiogeography and neontological biogeography, strengthening inference in both.
Model choice is now standard practice in phylogenetic analysis of DNA sequences: a program such as ModelTest is used to compare models such as Jukes-Cantor, HKY, GTR+I+G, and to select the best model before inferring phylogenies or ancestral states. It is clear that the same should now happen in phylogenetic biogeography. BioGeoBEARS enables this procedure. Perhaps more importantly, however, is the potential for users to create and test new models. Probabilistic modeling of geographic range evolution on phylogenies is still in its infancy, and undoubtedly there are better models out there, waiting to be discovered. It is also undoubtedly true that different clades and different regions will favor different processes, and that further improvements will be had by linking the evolution of organismal traits (e.g., loss of flight) with the evolution of geographic range, within a common inference framework. In a world of rapid climate change and habitat loss, biogeographical methods must maximize both flexibility and statistical rigor if they are to play a role. This research takes several steps in that direction.
BioGeoBEARS is open-source and is freely available at the Comprehensive R Archive Network (http://cran.r-project.org/web/packages/BioGeoBEARS/index.html). A step-by-step tutorial, using the Psychotria dataset, is available at PhyloWiki (http://phylo.wikidot.com/biogeobears).
(Abstract shortened by UMI.)
|Commitee:||Barnosky, Anthony, Byrne, Roger, Jablonski, David|
|School:||University of California, Berkeley|
|School Location:||United States -- California|
|Source:||DAI-B 75/08(E), Dissertation Abstracts International|
|Subjects:||Evolution and Development, Paleontology, Geobiology|
|Keywords:||Biogeobears, Biogeography, Lagrange, Model choice, Paleobiogeography, Phylogenetics|
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