@ARTICLE{TreeBASE2Ref17938,
author = {Alain Vanderpoorten and Bernard Goffinet},
title = {Mapping uncertainty and phylogenetic uncertainty in ancestral character state reconstruction: an example in the moss genus Brachytheciastrum.},
year = {2006},
keywords = {},
doi = {10.1080/10635150601088995},
url = {},
pmid = {},
journal = {Systematic Biology},
volume = {55},
number = {6},
pages = {957--971},
abstract = {The evolution of species traits along a phylogeny can be examined through an increasing number of possible, but not necessarily complementary approaches. In this paper, we assess whether deriving ancestral states of discrete morphological characters from a model whose parameters are (i) optimized by ML on a most likely tree; (ii) optimized by ML onto each of a Bayesian sample of trees; and (iii) sampled by a MCMC visiting the space of a Bayesian sample of trees, affects the reconstruction of ancestral states in the moss genus Brachytheciastrum. In the first two methods, the choice of a single- or two-rate model and of a genetic distance (wherein branch lengths are used to determine the probabilities of change) or speciational (wherein changes are only driven by speciation events) model based upon a likelihood ratio test, strongly depended on the sampled trees. Despite these differences in model selection, reconstructions of ancestral character states were strongly correlated to each others across nodes, often at r>0.9, for all the characters. The Bayesian approach of ancestral character state reconstruction offers, however, a series of advantages over the single tree approach or the ML model optimization on a Bayesian sample of trees because it does not involve restricting model parameters prior to reconstructing ancestral states but rather allows a range of model parameters and ancestral character states to be sampled according to their posterior probabilities. From the distribution of the latter, conclusions on trait evolution can be made in a more satisfactorily way than when a substantial part of the uncertainty of the results is obscured by the focus on a single set of model parameters and associated ancestral states. The reconstructions of ancestral character states in Brachytheciastrum reveal rampant parallel morphological evolution. Most species previously described based on phenetic grounds are thus resolved of polyphyletic origin. Species polyphylly has been increasingly reported among mosses, raising severe reservations regarding current species definition.}
}
Citation for Study 1691
Citation title:
"Mapping uncertainty and phylogenetic uncertainty in ancestral character state reconstruction: an example in the moss genus Brachytheciastrum.".
This study was previously identified under the legacy study ID S1653
(Status: Published).
Citation
Vanderpoorten A., & Goffinet B. 2006. Mapping uncertainty and phylogenetic uncertainty in ancestral character state reconstruction: an example in the moss genus Brachytheciastrum. Systematic Biology, 55(6): 957-971.
Authors
-
Vanderpoorten A.
-
Goffinet B.
Abstract
The evolution of species traits along a phylogeny can be examined through an increasing number of possible, but not necessarily complementary approaches. In this paper, we assess whether deriving ancestral states of discrete morphological characters from a model whose parameters are (i) optimized by ML on a most likely tree; (ii) optimized by ML onto each of a Bayesian sample of trees; and (iii) sampled by a MCMC visiting the space of a Bayesian sample of trees, affects the reconstruction of ancestral states in the moss genus Brachytheciastrum. In the first two methods, the choice of a single- or two-rate model and of a genetic distance (wherein branch lengths are used to determine the probabilities of change) or speciational (wherein changes are only driven by speciation events) model based upon a likelihood ratio test, strongly depended on the sampled trees. Despite these differences in model selection, reconstructions of ancestral character states were strongly correlated to each others across nodes, often at r>0.9, for all the characters. The Bayesian approach of ancestral character state reconstruction offers, however, a series of advantages over the single tree approach or the ML model optimization on a Bayesian sample of trees because it does not involve restricting model parameters prior to reconstructing ancestral states but rather allows a range of model parameters and ancestral character states to be sampled according to their posterior probabilities. From the distribution of the latter, conclusions on trait evolution can be made in a more satisfactorily way than when a substantial part of the uncertainty of the results is obscured by the focus on a single set of model parameters and associated ancestral states. The reconstructions of ancestral character states in Brachytheciastrum reveal rampant parallel morphological evolution. Most species previously described based on phenetic grounds are thus resolved of polyphyletic origin. Species polyphylly has been increasingly reported among mosses, raising severe reservations regarding current species definition.
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http://purl.org/phylo/treebase/phylows/study/TB2:S1691
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@ARTICLE{TreeBASE2Ref17938,
author = {Alain Vanderpoorten and Bernard Goffinet},
title = {Mapping uncertainty and phylogenetic uncertainty in ancestral character state reconstruction: an example in the moss genus Brachytheciastrum.},
year = {2006},
keywords = {},
doi = {10.1080/10635150601088995},
url = {},
pmid = {},
journal = {Systematic Biology},
volume = {55},
number = {6},
pages = {957--971},
abstract = {The evolution of species traits along a phylogeny can be examined through an increasing number of possible, but not necessarily complementary approaches. In this paper, we assess whether deriving ancestral states of discrete morphological characters from a model whose parameters are (i) optimized by ML on a most likely tree; (ii) optimized by ML onto each of a Bayesian sample of trees; and (iii) sampled by a MCMC visiting the space of a Bayesian sample of trees, affects the reconstruction of ancestral states in the moss genus Brachytheciastrum. In the first two methods, the choice of a single- or two-rate model and of a genetic distance (wherein branch lengths are used to determine the probabilities of change) or speciational (wherein changes are only driven by speciation events) model based upon a likelihood ratio test, strongly depended on the sampled trees. Despite these differences in model selection, reconstructions of ancestral character states were strongly correlated to each others across nodes, often at r>0.9, for all the characters. The Bayesian approach of ancestral character state reconstruction offers, however, a series of advantages over the single tree approach or the ML model optimization on a Bayesian sample of trees because it does not involve restricting model parameters prior to reconstructing ancestral states but rather allows a range of model parameters and ancestral character states to be sampled according to their posterior probabilities. From the distribution of the latter, conclusions on trait evolution can be made in a more satisfactorily way than when a substantial part of the uncertainty of the results is obscured by the focus on a single set of model parameters and associated ancestral states. The reconstructions of ancestral character states in Brachytheciastrum reveal rampant parallel morphological evolution. Most species previously described based on phenetic grounds are thus resolved of polyphyletic origin. Species polyphylly has been increasingly reported among mosses, raising severe reservations regarding current species definition.}
}
- Show RIS reference
TY - JOUR
ID - 17938
AU - Vanderpoorten,Alain
AU - Goffinet,Bernard
T1 - Mapping uncertainty and phylogenetic uncertainty in ancestral character state reconstruction: an example in the moss genus Brachytheciastrum.
PY - 2006
KW -
UR - http://dx.doi.org/10.1080/10635150601088995
N2 - The evolution of species traits along a phylogeny can be examined through an increasing number of possible, but not necessarily complementary approaches. In this paper, we assess whether deriving ancestral states of discrete morphological characters from a model whose parameters are (i) optimized by ML on a most likely tree; (ii) optimized by ML onto each of a Bayesian sample of trees; and (iii) sampled by a MCMC visiting the space of a Bayesian sample of trees, affects the reconstruction of ancestral states in the moss genus Brachytheciastrum. In the first two methods, the choice of a single- or two-rate model and of a genetic distance (wherein branch lengths are used to determine the probabilities of change) or speciational (wherein changes are only driven by speciation events) model based upon a likelihood ratio test, strongly depended on the sampled trees. Despite these differences in model selection, reconstructions of ancestral character states were strongly correlated to each others across nodes, often at r>0.9, for all the characters. The Bayesian approach of ancestral character state reconstruction offers, however, a series of advantages over the single tree approach or the ML model optimization on a Bayesian sample of trees because it does not involve restricting model parameters prior to reconstructing ancestral states but rather allows a range of model parameters and ancestral character states to be sampled according to their posterior probabilities. From the distribution of the latter, conclusions on trait evolution can be made in a more satisfactorily way than when a substantial part of the uncertainty of the results is obscured by the focus on a single set of model parameters and associated ancestral states. The reconstructions of ancestral character states in Brachytheciastrum reveal rampant parallel morphological evolution. Most species previously described based on phenetic grounds are thus resolved of polyphyletic origin. Species polyphylly has been increasingly reported among mosses, raising severe reservations regarding current species definition.
L3 - 10.1080/10635150601088995
JF - Systematic Biology
VL - 55
IS - 6
SP - 957
EP - 971
ER -