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Posts tagged ‘parsimony’

Weekly reading: Intermittent evolution and robustness of phylogenetic models

Models of evolution used in phylogenetic reconstruction make specific assumptions which (in their entirety, and globally applied) are ultimately wrong. They are also approximately right. What does this even mean? This week’s reading gets us into the notion of robustness of phylogenetic models to violations of their inherent assumptions. An important piece of the “which method should I use?” puzzle. Let’s see if we can identify other pieces too.

Nguyen, M.A.T., T. Gesell & A. von Haeseler. 2012. ImOSM: Intermittent evolution and robustness of phylogenetic methods. Molecular Biology and Evolution 29: 663-673. Available on-line here.

Weekly reading: Bayesian analysis outperforms parsimony for morphological data

We had a lively Weekly Discussion of Kumar et al. 2012, and are staying with the general theme (hereby undemocratically coined) of “new insights in statistical phylogenetics/phylogenomics”. Models, biases, assumptions, data. Thus, for next week:

Wright, A.M. & D.M. Hillis. 2014. Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLoS ONE 9(10): e109210. Available on-line here.

Weekly reading: Structural complexity in ancestral ontologies

Next week’s reading in our quickly ending series on coding characters and (most recently) dynamic homology.

Ramírez, M.J. & P. Michalik. 2014. Calculating structural complexity in phylogenies using ancestral ontologies. Cladistics (Early View). Available here.

Update: Wonderful paper! Love the innovative and somewhat irreverent use of ontologies specifically to address and answer a genuine systematic question complex, outside of the “Protégé paradigm” (and in fact without formal reasoning, period). Ramírez and co-authors are onto something novel and impactful.

Weekly reading: Using dynamic homology without parsimony

Dynamic homologypart 1, part2, and now, for behavioral data (not exclusively, one presumes):

Japyassú, H.F. & F.d.A. Machado. 2010. Coding behavioural data for cladistic analysis: using dynamic homology without parsimony. Cladistics 26: 625-642. Available here.

More on this soon, well, hopefully. At least we are caught up now with our weekly reading posts.

Weekly reading: A dynamic homology approach for morphological data

Following up on last week’s wide-ranging explorations of dynamic homology sensu Wheeler, this week’s original, inspiring, and overall excellent paper by Martín Ramírez applies the issue to the challenge of properly (read: parsimoniously) assigning one or two of three potentially available sclerite ‘identities’ to their homologous positions in the complex male spider pedipalps and ranging over variously simultaneous inferred clades. Complex, engaging, and well conceived material for thought and possible application.

Ramírez, M.J. 2007. Homology as a parsimony problem: a dynamic homology approach for morphological data. Cladistics 23: 588-612. Available here.

P.s.: Posted retrospectively for April 04, 2014.

Weekly reading: Dynamic homology and the likelihood criterion

Dynamic homology is an intriguing concept, though getting from the general notion of optimizing character correspondence (inapplicables, indels) and phylogeny simultaneously to a fully realized implementation is not trivial. In this week’s reading we examine a paper by Ward Wheeler who has promoted this approach with a strong emphasis on parsimony optimization.

Wheeler, W.C.  2006. Dynamic homology and the likelihood criterion. Cladistics 22: 157-170. Available here.

P.s.: Posted retrospectively for March 28, 2014.

Weekly reading: Missing values, inapplicable states and polymorphic taxa

The third and likely penultimate session in our “explore cladistic coding” series. A brief primer below; more during our discussions and practices.

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Weekly reading: Character types and coding methods

Second chapter in the “let’s get some practice” series. In this week’s reading practice we will explore the interaction of alternative coding schemes and tree/optimization outcomes, both “by hand” and with WinClada and NONA. In particular, we will apply and compare simple binary, non-additive multi-state, and complex additive character coding schemes. We will assess their effects on cladogram length and on the character state optimizations along the internal cladogram nodes. We will start by learning how to code complex character state hierarchies as additive binary as well as additive multi-state characters. Please do some reading of the handout beforehand.