Emerging patterns and emerging challenges of comparative phylogeography

Abstract

news and update ISSN 1948-6596 commentary Emerging patterns and emerging challenges of comparative phylogeography Biogeographic studies commonly amass distri- bution datasets for hundreds of species in an attempt to describe biogeographic patterns and their underlying processes (e.g., Keith et al. 2013). In contrast, phylogeographic studies are almost always limited to a small number of species and, while able to detect patterns at a population level, lack the replicative power of biogeography to describe those patterns in terms of relevant processes. This is particularly disadvantageous in phylogeographic studies of marine species, because the ocean contains few conspicuous geographic features that might explain population breaks (Horne 2014). Large, comparative phylogeographic datasets have long been the daydream of mo- lecular ecologists but have previously not been possible because the data didn't exist and rig- orous phylogeographic analyses do not scale well with large datasets (Andrew et al. 2013). For instance, coalescence-based phy- logeographic model fitting is able to accurately assess population patterns amid stochastic sig- nals and other noise in the data (Beaumont et al. 2010) but is computationally intensive and can become overwhelmingly time consuming as datasets grow larger and modelled scenarios become more complex. Even while data remain limited and coa- lescence analyses continue to be computation- ally burdensome, some research groups are expanding the boundaries of comparative phy- logeographic and population genetic research. Specifically, a recent paper, published earlier this year, by Selkoe and colleagues, jointly ana- lyzed population genetic patterns in 35 coral reef-associated species across the main and northwest Hawaiian islands. By biogeography standards this is a tiny dataset, but for a phy- logeographic study 35 species is impressive. Selkoe et al. (2014) did not include coa- lescence-based analyses, instead relying on multivariate analyses (principal components analysis and redundancy analysis) of popula- tion genetic summary statistics, k-means clus- ters, and ecological variables to assess four types of population structuring: long-term pan- mixia across the 2400-km-long archipelago, chaotic genetic heterogeneity, isolation-by- distance (IBD), and regional genetic structure. The multivariate approach used was based, in part, on another comparative study of 27 high- alpine plants (Miermans et al. 2011). Since we can probably expect more studies like these to arise in the future, it seems timely to comment on these comparative methods, their draw- backs and their merits. First, the entire approach rests upon a foundation of summary statistics (e.g., F ST ), which suffer from well-known shortcomings. For example, several of the datasets in Selkoe et al. did not have enough genetic polymor- phism to statistically reject a null hypothesis of panmixia using gene frequencies. At the other extreme, some of the datasets had too much polymorphism to reject panmixia, because if each individual has a unique genetic variant the maximum attainable F ST value is 0. Issues of polymorphism might be addressed with addi- tional sampling, which could uncover additional genetic signal (e.g., Horne et al. 2013). Alterna- tively, one could collect more loci. Regardless, insufficient data is the underlying problem. Ample amounts of genetic variation are the sine qua non of this method; a problem, considering that researchers are generally forced to rely on available datasets from past surveys, many of which were kept small by the costs of Sanger DNA sequencing. Furthermore, species that are too sparsely sampled, or con- tain too few individuals at each location, may not capture enough genetic variation. Selkoe et al. rightly point out that comparative studies demand substantial rigor in sampling coverage frontiers of biogeography 6.4, 2014 — © 2014 the authors; journal compilation © 2014 The International Biogeography Society