Plant porn

I think all these references to porn are going to boost our readership. (From Nicholas Gurewitch's Perry Bible Fellowship.)

Isabella Rossellini stars in invertebrate porn

Isabella Rossellini made several terrific short films, now posted on the Sundance Channel, about the sexual practices and natural history of six insect species, spiders, and earthworms. Botanists are hopeful for a far more interesting and kinky plant porn series, which is perhaps forthcoming. (She fixes the cable?)

Advice for the aspiring phylogeneticist: Part III

Learn new programs. If you’re going to do modern phylogenetic analyses you are going to be learning new programs. All the time. During my ten years in this field I’ve learned to use well over 50 programs. Some of these programs are not be easy to use. Some take weeks, months, or even years to master. You cannot allow this to lead you on a detour of convenience to use of inappropriate analyses. It is your responsibility as a scientist to do the best analyses possible. Don’t be lazy: when you stop learning new programs you stop doing modern phylogenetics.

Dodo for genomes

I love the figure from the Nature news piece about the platypus genome. Of course, I was delighted to see that the "Green anole lizard" was included and noting as one of the species whose genome sequencing has been completed. The funny thing is that the Dodo is included as "Missing/Required." Who has the dodo on their list of the top four bird species for genome sequencing? Let's not forget that the Dodo is just a glorified pigeon. I'm not sure what we'd learn anything from sequencing a dodo genome that we couldn't also learn from sequencing the genome of a rock dove. I suspect it might also be a bit easier to get DNA from a rock dove...

Strange happenings in a strange animal

The platypus genome is out. The coolest result is that the venom produced by the male platypus is derived from some of the same gene families that were coopted for venom production in reptiles. An amazing example of independent evolution, to be sure.

I was really excited to see some phylogenies in this paper and looking forward to learning all about how they were made. After browsing the paper and skimming through the copious supplemental material, however, I have yet to find any information on how the tree in Figure 1 was obtained (see image). A program called NJTree (since renamed TreeBest) is mentioned in the supplemental material, but it's not clear which analyses this program was used for, or which of its algorithms were used (it seems capable of building trees via both neighbor-joining [a distance-based method that is decades old and riddled with problems] and 'extended' maximum likelihood [a method that I've never run across]). Since we can't learn anything from methods that don't exist, we're left to ponder just one question: How can a paper with ~100 authors not include a single meaningful sentence about the methods used to produce at least two of its five figures?

Comparative methods in R

Recently, I participated in a "hackathon" sponsored by NEScent, the National Evolution Synthesis Center. The goal of this weeklong meeting was to gather together programmers who are writing comparative algorithms in the r software language.

If you haven't discovered r yet, it's a free and very powerful platform for carrying out all sorts of statistical analyses. r syntax is a little difficult to master, but it is really worth learning. You can download r here, and find free documentation to learn the language here.

The great thing about r is that people have written all sorts of useful packages to do various things. Most of the phylogenetic comparative approaches available in r are based on Emmanuel Paradis' ape package. My package, geiger, for example, is dependent on the framework provided by ape.

The point of this post, though, is to point out that the hackathon produced a product of great usefulness to the community: the R-phylo wiki. This wiki has detailed instructions for carrying out all sorts of comparative analyses, from independent contrasts to disparity-through-time, in r. Enjoy!

Advice for the aspiring phylogeneticist: Part II

Don’t be intimidated by command line only applications. We all love programs with beautiful graphical user interfaces (GUIs). We should all use a few moments of the time these interfaces have save us to thank the developers who have used many hours of their own time to develop them. For brand-new and highly specialized analyses, developers are justified in making their methods available only in the form of somewhat-more-difficult-to-use text-based analyses. If you are going to do phylogenetics right you must learn to use these applications. Early in this process, you will be doing yourself a favor if learn basic UNIX syntax and file architecture.

Advice for the aspiring phylogeneticist: Part I

Get a text editor and use it. Ironically, the most advanced programs often require the simplest input: ASCII text files. The best way to avoid problems with these types of files is to never use an advanced word processor like Microsoft Office or Mac OSX’s Pages. Don’t even use the simpler text editors that were included in the base install of your operating system (e.g., TextEdit in Mac OSX or Notepad in Windows). Go straight to the your friend the internet and download either TextWrangler (Mac OSX) or TextPad (Windows) (if you're on a UNIX platform you don't need my help!). In addition to sparing you the unbelievable amount of confusion that can result from hidden formatting or invisible extensions, these programs are wonderfully easy to use and full of useful features (It nearly blew my mind to learn that I could use the option key to select columns of text in TextWranger). Use these programs to create, edit, revise, and review all of the files that will be input to or output from text-based applications.

Are we there yet?

Selecting an appropriate burn-in point is critical for Bayesian analyses. Many people continue to do this arbitrarily, by excluding, for example, the first 10% of trees sampled. This seems silly and wrong. Others visualize their posterior scores in programs like Tracer and or Microsoft EXCEL and eliminate the set of initial trees possessing likelihood scores that are obvious outliers. This is also a bit arbitrary. An even more sophisticated approach involves visualization of split posteriors using the on-line application AWTY (Are We There Yet). Seems like a cool idea, leading me to wonder why it isn't more widely used. Probably because its more or less impossible to figure out what its doing!

Density-Dependent Cladogenesis in Birds

A new paper from Ally Phillimore and Trevor Price, Density-Dependent Cladogenesis in Birds, has just been published in PLoS Biology. In this important paper, Phillimore and Price argue that phylogenetic trees of birds show a general pattern of diversification rates that slow through time.

This pattern is revealed by the branches in these trees. If you think about each branch on the tree as an interval between two speciation events, then the lengths of those branches should be related to diversification rates; if diversification rates are high, speciation events will be closer together, and the branches in the trees will be shorter. The authors suggest that one explanation for this pattern is density-dependent cladogenesis. That is, the rate of diversification slows as species accumulate.

I like this paper because this particular tree shape, with short branches near the root, is one that I commonly observe in my own phylogenetic trees. If these results are general, we might be able to confirm something that evolutionary biologists have suspected: species interactions affect diversification rates. We postulated in 2003 (paper here) that such early bursts in diversification rate might be associated with bursts in the rate of morphological evolution, another characteristic pattern of adaptive radiation. This hypothesis has not yet been evaluated across a large enough number of trees to form any conclusions.

This paper reminds me of another general pattern in macroevolution: phylogenetic trees are more imbalanced than one would expect based on most null models (see Mooers and Heard 1997 & Blum and Francois 2006). Such regular patterns at macroevolutionary scales are hard to come by. When we find general patterns at this level, we have learned something deep about the process of evolution. Interestingly, there seems to be some relationship between imbalance and slowdowns in the Phillimore analysis; more imbalanced trees tend to show stronger slowdowns. Perhaps this is because the density dependence sometimes has a lineage-specific component.

Also, don't miss the nifty set of simulations in this paper showing that there is a slight bias in the test the authors are using, but that the pattern in the data is too strong to be explained by that bias.