SAMBA: Super area‐cladogram after resolving multiple biogeographical ambiguities

Abstract

Aim: Cladistic biogeography is all about congruence: when individual area cladograms coincide, they result in a general area cladogram that reveals shared history. However, the complexities of the natural world hamper the reconstruction of fully solved biogeographical patterns. Herein, we present SAMBA (super area-cladogram after resolving multiple biogeographical ambiguities), a pattern-based method combining supertrees and area cladograms to depict the relationships among areas. We also present a prototypical implementation of SAMBA as a web-based framework named iSAMBA. Location: Global.Taxon: Any taxon can be analysed with SAMBA.Methods: SAMBA is based on phylogenetic supertrees, a technique that combines previously calculated phylogenetic trees to produce a general area cladogram representing conciliatory and non-ambiguous patterns of relationships. In our method, the input topologies are individual area cladograms. SAMBA is implemented through a web-based framework named iSAMBA. We analysed a theoretical and a real scenario to compare SAMBA with primary Brooks parsimony analysis (BPA), component analysis, three area statement analysis (TAS) and the transparent method.Results: SAMBA produces area cladograms that converge with the actual history of fragmentations of both hypothetical and real scenarios used as examples of implementation of the method. Primary BPA, component analysis, TAS and the transparent method are much more affected by the "biogeographical noise" (e.g. multiple areas in a single terminal, paralogies and missing areas) than SAMBA.Main Conclusions: SAMBA results in more informative general area cladograms than other pattern-based biogeographical methods. SAMBA reveals shared patterns of biotic distribution without generating multiple unreliable area cladograms. The main advantage of SAMBA is the simplicity of using a single technique to extract biogeographical information from individual area cladograms and combine them to depict a non-ambiguous general pattern of relationships among areas.