Gene tree patterns help answer: vicariance or dispersal?
Nagel, A. A.; Landis, M. J.; Mendes, F. K.
Show abstract
Historical biogeography seeks to understand the drivers of species distributions over space and time. One question of interest is how, out of many possible ways, does geography drive speciation. Vicariance, where geographic barriers arise splitting populations and limiting gene flow, can lead to allopatric speciation. Founder events, where a small number of individuals disperse over a barrier, can similarly lead to allopatric speciation if the individuals remain isolated. Both of these scenarios can lead to identical ranges of and relationships between modern species. Classic biogeographic approaches often focus on the history of populations of one or few species on shallow time scales or multiple species on deep time scales. We argue that focusing exclusively on either end of this time spectrum misses a venue for investigating the biogeographic drivers of speciation, at least for certain speciation events. With simple coalescent simulations with multiple species, we show as a proof of concept that gene tree distributions vary predictably between vicariance and founder event speciation. The existence of predictable patterns warrants the development of new approaches that capitalize the gene trees to distinguish vicariance and dispersal as drivers of speciation.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A quantitative genetics model for the dynamics of phenotypic (co)variances under limited dispersal, with an application to the coevolution of socially synergistic traits 93%
- Eco-Evolutionary Feedbacks between Predator’s Linkage Disequilibrium and Prey Densities Maintain Diversity 93%
- The evolution of local adaptation in long-lived species 93%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.