Biogeography. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Биология
Год издания: 0
isbn: 9781119882374
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uses Markov-Chain Monte Carlo (MCMC) simulations and BI to estimate ancestral ranges and rates of biogeographic parameters alongside phylogenetic parameters, such as the tree topology, rates of molecular evolution and branch lengths; the input data are DNA sequences and tip distributions of the study species (Sanmartín et al. 2008). The DEC model was originally implemented in an ML framework (Ree and Smith 2008) and later extended to BI (Landis et al. 2013), but it is typically applied to a phylogeny with fixed topology and branch lengths. Though they both use CTMC processes to model range evolution, BIB and DEC models are slightly different (Figure 2.5). BIB (Figure 2.5(a)) implements a simpler character evolutionary model, in which ancestors can only occupy single areas (A or B) and range evolution along the branches if governed by a CTMC process with only one type of parameter equivalent to range switching or instantaneous dispersal; the Q matrix describes the instantaneous transition from one area as a jump dispersal event (p = A to B). At the speciation events in the phylogeny, the single-area ancestral range is inherited entirely and identically by the two descendants (A/A); in other words, there is no need to include a cladogenetic component in the BIB model because the ancestral range is not altered through speciation (Figure 2.5(a)).

      Figure 2.6 illustrates, with a real biogeographic problem, some of the differences between BIB and DEC. In sum, the BIB model allows only anagenetic changes along branches in the phylogeny and constrains ancestors to occur in single areas, whereas DEC includes both anagenetic and cladogenetic range evolution; in fact, DEC is the parametric counterpart of DIVA (Ree et al. 2005). However, this additional level of complexity brings some statistical limitations discussed below. Both models implement different sources of uncertainty (phylogenetic and reconstruction in BIB, and reconstruction in DEC).

      The DEC model is undoubtedly more realistic than BIB. In biogeography, widespread terminals and ancestors are biologically plausible: an extant or extinct taxon could have occupied more than one area, especially if these areas were connected and there was no dispersal barrier between them. However, such complexity comes with a cost: there are 2N possible ancestral ranges for N areas, so the Q instantaneous rate matrix cannot be analytically calculated with more than 10 areas (1,024 states). This can be reduced by removing certain transitions from the Q matrix, for example, disallowing ancestral ranges that involve discrete areas that are non-adjacent in the physical space (Buerki et al. 2011), or using alternative estimation methods, such as data augmentation (Landis et al. 2013). Dispersal in DEC is equivalent to range expansion – the ancestor moves into a new area but keeps the original distribution for some time; in other words, moving between single areas requires going through a widespread state in which the ancestor is present in both areas (Figure 2.5(b)). This type of dispersal may be appropriate for continental settings in which areas are adjacent, that is, share a physical edge, and we expect gene flow to be maintained for some period of time between the allopatric populations (Ree and Sanmartín 2009). Yet, it comes with the necessity of modeling cladogenetic events or range inheritance scenarios (Figure 2.5(b)).

Schematic illustration of parametric biogeographic reconstruction of the spatio-temporal evolution of genus Canarina.