Inferring dispersal: a Bayesian approach to phylogeny‐based island biogeography, with special reference to the Canary Islands

Abstract

Aim Oceanic islands represent a special challenge to historical biogeographers because dispersal is typically the dominant process while most existing methods are based on vicariance. Here, we describe a new Bayesian approach to island biogeography that estimates island carrying capacities and dispersal rates based on simple Markov models of biogeographical processes. This is done in the context of simultaneous analysis of phylogenetic and distributional data across groups, accommodating phylogenetic uncertainty and making parameter estimates more robust. We test our models on an empirical data set of published phylogenies of Canary Island organisms to examine overall dispersal rates and correlation of rates with explanatory factors such as geographic proximity and area size.Location Oceanic archipelagos with special reference to the Atlantic Canary Islands.Methods The Canary Islands were divided into three island-groups, corresponding to the main magmatism periods in the formation of the archipelago, while non-Canarian distributions were grouped into a fourth 'mainland-island'. Dispersal between island groups, which were assumed constant through time, was modelled as a homogeneous, time-reversible Markov process, analogous to the standard models of DNA evolution. The stationary state frequencies in these models reflect the relative carrying capacity of the islands, while the exchangeability (rate) parameters reflect the relative dispersal rates between islands. We examined models of increasing complexity: Jukes-Cantor (JC), Equal-in, and General Time Reversible (GTR), with or without the assumption of stepping-stone dispersal. The data consisted of 13 Canarian phylogenies: 954 individuals representing 393 taxonomic (morphological) entities. Each group was allowed to evolve under its own DNA model, with the island-model shared across groups. Posterior distributions on island model parameters were estimated using Markov Chain Monte Carlo (MCMC) sampling, as implemented in MrBayes 4.0, and Bayes Factors were used to compare models.Results The Equal-in step, the GTR, and the GTR step dispersal models showed the best fit to the data. In the Equal-in and GTR models, the largest carrying capacity was estimated for the mainland, followed by the central islands and the western islands, with the eastern islands having the smallest carrying capacity. The relative dispersal rate was highest between the central and eastern islands, and between the central and western islands. The exchange with the mainland was rare in comparison. INTRODUCT IONAfter being relegated to a simple 'footnote acknowledgment' (Lynch, 1989) in biogeographical papers for a long time, dispersal is again receiving increased attention as a fundamental process explaining the distribution of organisms (de Queiroz, 2005;McGlone, 2005;Riddle, 2005;Cowie & Holland, 2006). This trend can be observed in the large number of phylogeny-oriented articles published since 2004 in this journal and in Systematic Biology with the word 'dispersal' in their abst...

Historical Biogeography: Evolution in Time and Space

“…Funk and Wagner (1995) found that patterns of island colonization in these islands were highly congruent across multiple lineages, proceeding from one island to the next along the island chain. Similarly, Sanmartín et al (2008) inferred highly concordant, non-random colonization patterns across numerous organisms endemic to the Atlantic Canary Islands.…”

“…Biogeographical models, however, are best seen not as constraints over the data but as alternative hypotheses to explain the data (Sanmartín et al 2008). A more important limitation is how to balance the complexity of biogeographic models with the inferential power of the method (Ree and Sanmartín 2009).…”

“…This can be done by either adding new parameters to the transition matrix, or by scaling a global dispersal or extinction rate according to abiotic factors like geographic distance, the availability of land connections, or the strength of wind and ocean currents (Buerki et al 2011). For example, in island systems like the Canary Islands, one may wish to constrain dispersal to follow the island chain by making the rate of dispersal between non-adjacent islands in the chain equal to zero (Sanmartín et al 2008). Whether these constraints are biologically realistic or not depends on each particular scenario.…”

“…This allows estimating by maximum likelihood rates of range expansion (dispersal) and contraction (extinction), and range inheritance scenarios at cladogenetic events from a time-calibrated phylogeny with terminal lineage distributions (Ree and Smith 2008). The second method is the Bayesian island biogeography (BIB) model developed by Sanmartín et al (2008), which uses Bayesian inference to estimate areas' carrying capacities and rates of dispersal between islands from phylogenetic and distributional data of multiple co-distributed groups. It can be used to infer general biogeographic patterns by using a Bayesian approach that accommodates for differences in age, evolutionary rate, and dispersal capability across lineages (Sanmartín 2010).…”

Graph hierarchies for phylogeography

“…Alternatively, Sanmartín et al [13], in a similar data analysis, model all strata jointly by imposing the same migration graph and rates to all strata of the analysis. This method effectively combines information from all strata to improve rate estimates.…”