Dynamic homology – part 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.
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.
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.
The third and likely penultimate session in our “explore cladistic coding” series. A brief primer below; more during our discussions and practices.
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.
This week our weekly lab discussion group shifts gears from the empiricism/realism debate to actual, and mostly still “manual” (as if hands could think), character matrix assembly, Wagner tree construction, and upward-/downward-pass parsimony-based character state optimization. Consistency and retention indices. And WinClada and NONA. Let’s see how far the first session will take us towards understanding the interaction between characters, parsimony, optimizations, and trees.