lunes, 21 de septiembre de 2015

Ancestral area reconstruction

Question of interest: Can ancestral geographic distribution be inferred in an ancestral-states reconstruction?

Is legitimate use geographic occurrences, altitudinal or bathymetric range like character states?

Can one infer the origin in this way?

The initial answer to the question is, yes.

The ancestral area reconstruction in its beginning was based on the ancestral-states reconstruction method. Throughout time this approach has been changed in order to minimize mistakes when the ancestral range is estimated. Initially Bremer, in 1992, proposed the Camin & Sokal Optimization and some assumptions (e.g. the ancestral range had to be less than the actual range). Thus, the most probable reconstruction for the ancestral node is simply the state that requires the minimum number of steps to explain the distribution of states among terminal taxa given the tree.

However, according to Ronquist 1994, there are problems at using this method to estimate the ancestral area because the areas can follow fused or hierarchical history. In terms of fused history the Camin & Sokal optimization cannot fit because it is based on irreversible parsimony.

In this way, Ronquist proposed to create a character state matrix with costs to different events, assuming that “the cost of an event should be inversely related to the probability of that particular event occurring.” Another problem encountered is polymorphisms which cannot be solved via Camin & Sokal. Nevertheless, Fitch optimization for unordered states appeared to be the solution. But in this way, the dispersion plays a key role. If there is some evidence of it, the areas have to be ordered in that positions. Thus, the important part of develop an optimization method is finding a reasonable transformation series hypothesis specifying the cost of changing from one combination of distribution areas to any other combination and the cost of retaining a particular distribution (Ronquist 1994). But, if the inference is made assuming that dispersion is irreversible then, ancestral area analysis is flawed.

In 1995, Ronquist developed some ideas that are the base of a nonhierarchical approach to historical biogeography, which combines a basic assumption of vicariance with a minimization of dispersal and extinction. According to Ronquist, the optimal ancestral distributions are those that minimize the number of implied dispersal and extinction events. Thus, this new approach applies costs for the events better than a simple character optimization under assumptions as no vicariance, irreversible dispersion and ancestral range limits.

In the original approaches, vicariance was not taken into account. Instead, when Ronquist, 1997 described his method, he affirmed that allopatric speciation, associated with geographical vicariance, may be accepted as the null model in historical biogeography. The dispersion-vicariance analysis derived from character optimization methods, but in contrast to Fitch optimization allows multiple and reticulate relationships among areas.

In addition, changes of the method used for reconstruction can be observed. From parsimony to maximum likelihood (Pagel, 1999; Ree et al., 2005), where the last, compares local and global likelihood, and with two likelihoods at each node. Then the probability is computed from these. The dispersal, extinction and cladogenesis (DEC) model implemented in LAGRANGE (Ree et al., 2005) is based on a likelihood technique and in contrast of DIVA, it requires branch lengths and the time at the base node, linking the amount of change and to the biogeographic history. Also, new methods have recently been proposed, including pseudo-Bayesian versions of DIVA and LAGRANGE (e.g., Wood et al., 2013). Around the Bayesian technique a new method was developed called BayArea (Landis et al., 2013) which reconstructs geographical histories along phylogenetic branches (Matzke, 2013).

Areas of distribution

The biogeographic studies take into account the geographic distribution of organisms. These distributions are assigned to discrete areas (e.g. areas of endemism), that according to Morrone 2009 are a prerequisite for any biographical analysis. However, there have been studies over the time that use distributional ranges and not areas of endemism. So, what is the meaning of using any of these?, or, what do you have to take into account in the delimitation of the areas?

Axelius 1991, observed a problem with the delimitation of the areas and affirmed: “The result of using areas of distribution instead of separated areas may be fatal,” and one can carry on errors in the resulting area cladogram. To avoid it, the areas have to be separate entities that do not overlap. However, the initial analysis did not use areas of endemism, instead, they used isolated areas and in this way escape from erroneous area cladograms.

Functioning as separate areas, the areas of endemism were proposed for this type of analysis and defined as the congruent distribution of at least two species of restricted range (Platnick, 1991). Also, the areas of endemism represent history and primary homology (Morrone, 2001).

Altitudinal and bathymetric data

Respect to using the altitudinal and bathimetric data, there are studies that use these type of records:

Brumfield and Edwards 2007, assessed the evolution into and out of the Andes, analyzing the historical diversification in Thamnophilus. For this study, they reconstructed the ancestral area and inferred the shift from lowlands to highlands based on the elevations at which each species was most commonly observed in the field. The coding used was lowland-restricted (L, 0–1500 m), lowland-to-highland (LH, 0–3050 m), or highland-restricted (H, 500–2500 m). However, as exposed by the authors, “This discrete coding scheme does not account for variance in habitat distributions, but captures what would be considered the ‘optimal’ elevation for each.” (Brumfield and Edwards 2007). They used two approaches to reconstruct the ancestral area, Parsimony and Bayesian. But they found that the Bayesian reconstructions provided some resolution at nodes where the parsimony and DIVA analyzes were equivocal, although the Bayesian results were not statistically significant.

I think that if you can create a discrete coding for reconstructing the ancestral area ancestral using bathymetric records, you can do an analysis similar to Brumfield and Edwards 2007 but the recommendation is to look for or generate your own areas of endemism.

Miranda et al 2013 did a reanalysis of Southern Ocean areas of endemism that can be used for any study like discrete areas. Nevertheless, theoretical and practical frameworks concerning to areas of endemism are complicated in marine biogeography. This is because the nature of marine realm, the oceanic dynamics, the difficulties in establishing thresholds in ecophysiological continuum and the amazingly diverse strategies of dispersal.

But, if you want to know more about regionalization of the marine zones you can see Spalding et al., 2007, they defined the levels of classification and described how the areas are determined. The problem is that ocean boundaries shift continuously with weather patterns, with seasons, and with longer or more random fluctuations in oceanographic conditions. This study was made with the aim of strategically planning and prioritizing new marine conservation measures but taking into account comprehensive biogeographic system to classify the oceans.

Finally, you can reconstruct your ancestral areas via ancestral-states reconstruction method using any of the methods derived from it and follow different approaches such as parsimony, likelihood or bayesian inference. For your reconstruction, remember that you have to use discrete areas that do not overlap. In terms of areas of endemism, you can use those already available or generate new areas. On the other hand, you can do the reconstruction starting from altitudinal or bathymetric data if you discretize the ranges to use.


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Bremer, K. (1992). Ancestral areas: a cladistic reinterpretation of the center of origin concept. Systematic Biology41(4), 436-445.

Brumfield, R. T., & Edwards, S. V. (2007). Evolution into and out of the Andes: a Bayesian analysis of historical diversification in Thamnophilus antshrikes.Evolution61(2), 346-367.

Landis, M. J., Matzke, N. J., Moore, B. R., & Huelsenbeck, J. P. (2013). Bayesian analysis of biogeography when the number of areas is large.Systematic biology, syt040.

Matzke, N. J. (2013). Probabilistic historical biogeography: new models for founder-event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. University of California, Berkeley.

Miranda, T. P., Cantero, Á. L. P., & Marques, A. C. (2013). Southern Ocean areas of endemism: a reanalysis using benthic hydroids (Cnidaria, Hydrozoa).Latin American Journal of Aquatic Research41(5), 1003.

Morrone, J. J. (2001). Homology, biogeography and areas of endemism.Diversity and Distributions7(6), 297-300.

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Ree, R. H., & Smith, S. A. (2008). Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Systematic Biology57(1), 4-14.

Ronquist, F. (1995). Ancestral areas revisited. Systematic Biology44(4), 572-575.

Ronquist, F. (1997). Dispersal-vicariance analysis: a new approach to the quantification of historical biogeography. Systematic Biology46(1), 195-203.

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Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M. A. X., ... & Robertson, J. (2007). Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience57(7), 573-583.

Wood, H. M., Matzke, N. J., Gillespie, R. G., & Griswold, C. E. (2012). Treating fossils as terminal taxa in divergence time estimation reveals ancient vicariance patterns in the palpimanoid spiders. Systematic Biology, sys092.

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