Archetype

The evolution of the web

Spider web, that is.

This is an excellent example of the way systematic papers should be. In the latest issue of the Proceedings of the National Academy of Sciences (USA), Blackledge and coworkers assembled a comprehensive data set for cladistic analysis of orb web spiders that includes six different molecular loci, 143 morphological characters and behavior in the form of characters derived from web architecture.

Hypothesis of web architecture evolution as optimized in preferred phylogeny (from Blackledge et al. 2009: fig. 2).

The resulting picture supports a single origin of aerial orb webs from irregular webs constructed in the ground. There is a subsequent evolution to more economical, irregular aerial web architectures from the more costly, regular orb types at least three times independently. And there seems to be an instance of evolution towards the simplified aerial web spun by bolas spiders.

However, apart from the nice evolutionary story, the real treat in this paper is hidden in the small text and supplementary information. All the data compiled would have been useless if analysed with poor methods. Instead the authors performed a series of sophisticated phylogenetic techniques that, besides vanilla Bayesian and parsimony analyzes, included implied weighting for the morphology partition and direct optimization for the molecular data. Morphology and molecular data were analyzed separately and in combination for an impressive total of 64 different types of phylogenetic analyzes.

Implied weights and direct optimization analyzes are worth remarking here because they are not well known and still rarely used in flashy high ranked papers. While in a regular parsimony analysis all characters are given equal weights regardless of how well or how poorly they fit a given tree, in implied weights analysis characters are downweighted as a function of the amount of homoplasy (extra steps) that is required to explain their distribution on any given tree topology during the tree-search phase. It is an a posteriori type of character weighting. One of the rationales behind this methods is that one extra step in a character that already performs very poorly (is very homoplasious), should not be counted equally as one extra step in a character with almost perfect fit. The method was developed in the early 1990′s but is has had a resurrection of late for morphological data, curiously because with the rise of molecular analyzes many authors have noticed that the results of analyzes of morphology under implied weights mirror more closely the molecular phylogenies.

For the molecular data, the use of direct optimization techniques is simply a way to push the limits on finding the optimal correspondences among the positions of DNA sequences under comparison. Multiple sequence alignment methods tend to find just one of the many possible optimal solution (if not suboptimal) from which a regular phylogenetic analyzes (parsimony, maximum likelihood or Bayesian) is then performed during a second phase. In contrast, direct optimization side-steps alignment altogether by searching for the optimal correspondences among sequences during tree search in a simultaneous single step process, thus performing a much more aggressive evaluation of the multiple possible alternatives. It is computational intensive, but this is hardly an excuse for good science as exemplified in this paper.

The result of applying these methods is a well supported phylogeny that allow the authors to make a rigorous reconstruction of the evolution of spider silk, bringing together information from silk chemistry, spider morphology and behavioral ecology.

Don’t forget to take a look at the supplementary information. There are lots of nice pictures showing all the types of web architectures. Oh, and its open access, so no subscription required.

Reference

Blackledge, T., Scharff, N., Coddington, J., Szuts, T., Wenzel, J., Hayashi, C., & Agnarsson, I. (2009). Reconstructing web evolution and spider diversification in the molecular era Proceedings of the National Academy of Sciences, 106 (13), 5229-5234 DOI: 10.1073/pnas.0901377106

Tags: Direct optimization, Implied weights