Phylogeographers and paleoecologists have long been interested in the biogeographic histories of the modern temperate forests and especially in their responses to changes in climate. This work has largely taken place within disciplines; each one utilizing its own tools and datasets such as molecular markers or fossil pollen data. However, syntheses of these datasets have largely not been attempted due to their multidisciplinary nature and the lack of accessible analytical tools. We present a new method that quantitatively compares molecular and fossil datasets in an effort to produce a regional multi-species phylogeography. Molecular data are transformed using population genetic summary statistics and interpolated to produce geographic information system (GIS) map layers comparable to interpolated fossil pollen layers. The relationships among datasets are then evaluated by parameterizing spatial linear regression models using these map layers. This method is applied to an eastern North American host-parasite system of the tree, Fagus grandifolia (American beech, Fagaceae), and its parasitic plant, Epifagus virginiana (beechdrop, Orobanchaceae), which consist of the following three datasets: host fossil pollen, host cpDNA, and parasite cpDNA and nuclear microsatellites. The fossil pollen and molecular datasets are used as proxies for different aspects of the host’s migration history. While the fossil pollen record tracks the abundance of host through time, the genetic patterns are more sensitive to low density populations and founder events prevalent in the initial range expansion. Spatial linear models are constructed to understand the relative effects of host density (fossil data) and host range changes (molecular data) on the parasite's migration patterns (molecular data).
Results/Conclusions
The results show that host density played a significant role in structuring parasite populations, while the initial migration routes of the host are uninformative about parasite colonization patterns. Based on these results, we believe this is a promising method for synthesizing disparate datasets, and that this provides a flexible statistical framework for comparing multiple species' phylogeographies.
