An evaluation of new parsimony-based versus parametric inference methods in biogeography: a case study using the globally distributed plant family Sapindaceae

Abstract

Aim Recently developed parametric methods in historical biogeography allow researchers to integrate temporal and palaeogeographical information into the reconstruction of biogeographical scenarios, thus overcoming a known bias of parsimony-based approaches. Here, we compare a parametric method, dispersalextinction-cladogenesis (DEC), against a parsimony-based method, dispersalvicariance analysis (DIVA), which does not incorporate branch lengths but accounts for phylogenetic uncertainty through a Bayesian empirical approach (Bayes-DIVA). We analyse the benefits and limitations of each method using the cosmopolitan plant family Sapindaceae as a case study.Location World-wide.Methods Phylogenetic relationships were estimated by Bayesian inference on a large dataset representing generic diversity within Sapindaceae. Lineage divergence times were estimated by penalized likelihood over a sample of trees from the posterior distribution of the phylogeny to account for dating uncertainty in biogeographical reconstructions. We compared biogeographical scenarios between Bayes-DIVA and two different DEC models: one with no geological constraints and another that employed a stratified palaeogeographical model in which dispersal rates were scaled according to area connectivity across four time slices, reflecting the changing continental configuration over the last 110 million years.Results Despite differences in the underlying biogeographical model, Bayes-DIVA and DEC inferred similar biogeographical scenarios. The main differences were: (1) in the timing of dispersal events -which in Bayes-DIVA sometimes conflicts with palaeogeographical information, and (2) in the lower frequency of terminal dispersal events inferred by DEC. Uncertainty in divergence time estimations influenced both the inference of ancestral ranges and the decisiveness with which an area can be assigned to a node.Main conclusions By considering lineage divergence times, the DEC method gives more accurate reconstructions that are in agreement with palaeogeographical evidence. In contrast, Bayes-DIVA showed the highest decisiveness in unequivocally reconstructing ancestral ranges, probably reflecting its ability to integrate phylogenetic uncertainty. Care should be taken in defining the palaeogeographical model in DEC because of the possibility of overestimating the frequency of extinction events, or of inferring ancestral ranges that are outside the extant species ranges, owing to dispersal constraints enforced by the model. The wide-spanning spatial and temporal model proposed here could prove useful for testing large-scale biogeographical patterns in plants.

A well-sampled phylogenetic analysis of the polystichoid ferns (Dryopteridaceae) suggests a complex biogeographical history involving both boreotropical migrations and recent transoceanic dispersals

“…Unlike S-DIVA, DEC likelihood incorporates an explicit model of dispersal through times depending on to paleogeographical connections during different time slices (Buerki et al, 2011;Ree and Smith, 2008;Ree et al, 2005;Sessa et al, 2012a).These probabilities were integrated in four time slices, each of which has a separate Q matrix of dispersal rates between regions (ranging from 0 to 1) according to their relative geographical position, and/or the extent of the geographical barriers. The temporal constraints and the rates of dispersal were determined using a survey of the literature on geological changes through time, as well as biogeographical studies that used the DEC model (e.g., Buerki et al, 2011;Sessa et al, 2012a). The paleogeographical model (i.e., Q-matrix) is presented in Appendix 2.…”

Reconstructing the evolution and biogeographic history of tribe Cardueae (Compositae)

“…To select an optimal level of smoothing, we performed the fossil cross-validation process ( Near and Sanderson, 2004 ). Phylogenetic uncertainty in divergence times was incorporated using a similar approach to Buerki et al (2011) : we performed PL analysis on 1000 trees randomly selected from the Bayesian Markov chain Monte Carlo (MCMC) stationary distribution, Carduoideae , represented by Gochnatia hiriartiana . Finally, a member of family Goodeniaceae ( Scaevola aemula ), and Boopis anthemoides , belonging to family Calyceraceae, were included as external outgroups because these two families have been shown to be closely related to Compositae ( Lundberg, 2009 ), following the relationship: ((Calyceraceae, Compositae), Goodeniaceae).…”

Historical Biogeography: Evolution in Time and Space

“…An advantage of parametric approaches over parsimony-based methods is that they allow external evidence other than the tree topology and lineage distributions to inform the biogeographic model. 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).…”