Natural experiments and meta-analyses in comparative phylogeography
The challenge for phylogeography, like other observational sciences, is to extract general relationships representing causes and effects from complex natural data. I describe comparisons of synchronously diverging co-distributed (SDC) taxa, including sympatric sister species, to help meet this challenge.
As an example, using narrative and best evidence synthesis, I re-evaluate ad hoc aggregate analyses relating population genetic structure (FST) to dispersal potential. I deconstruct an aggregate global analysis to generate a regional subset of data which I compare with datasets describing co-distributed taxa, SDC species and sympatric sister species.
A weak negative relationship between FST and dispersal potential is implied by aggregate global analysis (0.1 ≤ R2 ≤ 0.29). In contrast, regional datasets of co-distributed species show strong correlation between FST and dispersal potential (0.78 ≤ R2 ≤ 0.85). Comparisons between SDC and sympatric sister species consistently evince higher gene flow in species with higher dispersal potential.
Ad hoc aggregate analyses can be compromised by multiple sources of error. Comparisons of SDC taxa, including sympatric sister species, adapt the experimental scientific method to natural situations, providing robust and repeatable tests of phylogeographic hypotheses and accurate estimates of effect sizes. To make strong inferences about phylogeography we should seek out the hidden wealth of natural experiments that provide particularly clear opportunities to study the factors that influence patterns of biodiversity. The SDC framework enables independent tests of existing hypotheses, integration of independent studies in meta-analyses across taxa and regions, and identification of general trends versus location- or taxon-specific phenomena. Coupled with advances in statistical phylogeography, species distribution modelling and palaeoecology, the SDC framework bridges the long-standing gap between observational and experimental sciences.