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Data from: The ecological and genomic basis of explosive adaptive radiation

Cite this dataset

McGee, Matt (2020). Data from: The ecological and genomic basis of explosive adaptive radiation [Dataset]. Dryad.


Rates of speciation vary tremendously among evolutionary lineages, with our understanding of what fuels the rapid succession of speciation events within young adaptive radiations remaining particularly incomplete. The cichlid fish family provides the most notable example of such variation among extant metazoans. It includes many slowly speciating lineages as well as the several exceptionally large and rapid adaptive radiations. By reconstructing a large phylogeny of all described cichlid species, we show that explosive speciation is solely concentrated in several large yet young lake species flocks. Across the family, speciation rate increases are associated with absence of top predators, and speciation rate decreases are associated with arid climate, but these factors are not nearly sufficient to explain explosive speciation in lake radiations, in particular the Lake Victoria adaptive radiation. Across lake radiations we observe a positive relationship between speciation rate and enrichment with large indel polymorphisms. Assembly of one hundred Victorian cichlid genomes comprising all extant ecological guilds and taxonomic genera reveals this radiation contains exceptional ‘genomic potential’ - hundreds of ancient haplotypes bearing indel polymorphisms, many of which are strongly associated with specific ecologies and are shared with ecologically similar species from other older lake radiations elsewhere in Africa. Network analysis reveals fundamentally non-treelike evolution through recombining old haplotypes, with origins of ecological guilds concentrated early in the Victoria radiation. Our results suggest that the combination of ecological opportunity, sexual selection and exceptional genomic potential is the key to understanding explosive adaptive radiation.


Swiss National Science Foundation, Award: 31003A_163338

Swiss National Science Foundation, Award: 31003A_163338