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.

**References**

Axelius,
B. (1991). Areas of distribution and areas of
endemism.

*Cladistics*,*7*(2), 197-199.
Bremer,
K. (1992). Ancestral areas: a cladistic reinterpretation of the
center of origin concept.

*Systematic Biology*,*41*(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.

*Evolution*,*61*(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 Research*,*41*(5), 1003.
Morrone,
J. J. (2001). Homology, biogeography and areas of endemism.

*Diversity and Distributions*,*7*(6), 297-300.
Morrone,
J. J. (2009).

*Evolutionary biogeography: An integrative approach with case studies*. Columbia University Press.
Pagel,
M. (1999). The maximum likelihood approach to reconstructing
ancestral character states of discrete characters on
phylogenies.

*Systematic biology*, 612-622.
Platnick,
N. I. (1991). On areas of endemism.

*Australian Systematic Botany*,*4*(1), 11-12.
Ree,
R. H., Moore, B. R., Webb, C. O., & Donoghue, M. J. (2005). A
likelihood framework for inferring the evolution of geographic range
on phylogenetic trees.

*Evolution*,*59*(11), 2299-2311.
Ree,
R. H., & Smith, S. A. (2008). Maximum likelihood inference of
geographic range evolution by dispersal, local extinction, and
cladogenesis.

*Systematic Biology*,*57*(1), 4-14.
Ronquist,
F. (1995). Ancestral areas revisited.

*Systematic Biology*,*44*(4), 572-575.
Ronquist,
F. (1997). Dispersal-vicariance analysis: a new approach to the
quantification of historical biogeography.

*Systematic Biology*,*46*(1), 195-203.
Ronquist,
F. (1994). Ancestral areas and parsimony.

*Systematic Biology*, 267-274.
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.

*BioScience*,*57*(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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