I was talking with Patrick earlier this week about the effects of large data sets, such as from so-called NextGen sequencing (I dislike that term), on phylogenetic and population genetic methods of analysis. It used to be that the analytical bottleneck was in obtaining sequence data, but that has shifted so that now large amounts of sequence data can be obtained quickly but we are limited by computational power after we have them. I recently read a paper (Tamura et al. 2012, in PNAS) that addressed this problem by bucking the trend for building more and more statistical complexity into Maximum Likelihood and Bayesian analytical packages, and instead introduced a quick and fairly simple method to analyze large phylogenetic datasets with extensive evolutionary rate variation along the branches. The method is implemented in RelTime.

In phylogenetics we try to determine two things, the relationships between taxa and the timing of their divergence, either in relative or absolute time. The most common way to date divergence times is to calibrate a molecular phylogeny using the fossil record. Some form of a molecular clock is then assumed to extrapolate rates of evolution and divergence times to uncalibrated parts of the tree. This extrapolation requires assumptions about if and how rates of sequence evolution vary along different branches of the tree. Phylogenetics programs like BEAST require the user to specify, a priori, a statistical distribution from which to model evolutionary rate heterogeneity among tree branches. This method has been largely successful, but is limited computationally because the complexity increases exponentially as the number of taxa in the tree increases. Thus, the challenge with large datasets is that it can take weeks for even powerful computers to complete each step of the analysis.

RelTime attempts to circumvent this computational limitation by calculating relative divergence times rather than placing them in the context of absolute time. To me, it looks similar to Neighbor Joining methods used for creating tree topology, except that the method assumes a bifurcating topology with branch lengths, then uses distance calculations as it allows for rate heterogeneity along any branches of the tree. RelTime computes branch-specific relative rates from the tree by averaging branch lengths for each pair of descendants at each node. Check out the paper if you want to dig into the math. The result is a tree with relative divergence times, which then can be converted to absolute divergence times using only a few fossil calibration points in a post hoc application. I assume geographic calibrations such as island age could just as easily be used to calibrate the tree.

The biggest advantage of this method is that it is very fast – the authors claim it is 1000 times faster than MCMCTree, which in turn is 1000 times faster than BEAST on their simulated dataset of 446 taxa – yet it performs as well as or better than popular Bayesian methods. It also outperforms Bayesian methods when evolutionary rates vary drastically across the tree.

One exciting aspect of the method is that, according to the authors, “the branch (relative) rates produced by RelTime directly reveal the statistical properties of the distribution of evolutionary rates in a phylogeny, which exposes clades and lineages with significantly slower or faster evolutionary rates.” For a lab studying the radiation of the Hawaiian Drosophila, this is an important application. There are multiple examples in the clade where evolutionary rates probably exploded when a lineage colonized a new island or niche. RelTime looks like it is worth trying as a way to rapidly assess this phenomenon.

Brian

Not many people get paid to be twelve years old, at least not as adults, so I feel I’m one of the lucky ones. I’ve been working on a project that lets me go to so some beautiful rivers and streams, flip over rocks, and look for aquatic insects. It kindles the fun and curiosity that I remember while doing that kind of thing when I was a kid. Now, of course, I have a research question in mind while I’m out there. Our lab has been conducting surveys of aquatic insects in a few representative Northern California watersheds to establish the composition of aquatic insect communities, create a DNA barcoding (see this blog, too) database of Norcal aquatics for more efficient biomonitoring in the future, link taxa to characteristics of the habitat, and, using landscape genetics, make predictions about how global change biology may affect our local rivers and streams.

Aquatic insects have been used in biomonitoring for about a century as a way to assess the health of riparian areas. Biomonitoring adds informative data to chemical testing of water. Chemical testing provides valuable information about a particular component, such as dissolved oxygen or the concentration of a pollutant, at one moment in time. Biomonitoring is a way to assess whether all of the components of a system are such that they support the surveyed organisms over their entire lifespan. Both chemical and biological surveys can be combined to give a fuller picture of ecosystem health. Biomonitoring of aquatic insects is now being used not only to assess current and past ecosystem health, but also to predict future changes, for example in response to climate change.

In recent years, concerns about the effects of human-driven climate change on riparian ecosystem have increased. Climate change is projected to alter precipitation patterns, the timing of seasonal transitions, and extremes of both heat and cold, among other effects. These changes will affect different members of biotic communities differently according to their ability to adapt to changing conditions or disperse to more favorable habitat. We can use species distribution modeling to identify key characteristics of favorable habitat, and use patterns we find today using landscape genetics to identify potential obstacles that could prevent taxa from shifting ranges.

We are fortunate to be doing this as part of a larger consortium on campus, the Berkeley Initiative in Global Change Biology, or BIGCB. With funding from the Vice Chancellor’s Office, the Moore Foundation and the Keck Foundation, the BIGCB is focused on global change forecasting for California ecosystems, using analyses of fossil, historic and current data to better understand California ecosystems responses to environmental change and make predictions of future ecosystem changes.

Brian Ort

Departmental coffee hours are supposed to be cordial affairs, a chance to catch up with colleagues and exchange news and ideas. So why was this graduate student I’d just met getting up in my face about using the “wrong” locus for my work characterizing fungal communities in Hawaii? I’ve been using the D1/D2 domain of 26S rRNA and finding an amazing amount of diversity in plant substrates Hawaiian drosophilids use for reproduction, oviposition, and larval development. The grad student seemed to think if I wasn’t pyrosequencing using ITS, I was just wasting my time. But I’m not pyrosequencing, although I am accepting contributions from benevolent patrons to do so. Instead, I’m focusing on one locus that will be informative over wide phylogenetic space without becoming saturated with mutations. In a recent paper comparing the utility of D1/D2 and ITS for yeast species delimitation, Groenewald et al (2011) found 3 to 4 times greater nucleotide diversity in ITS. That worked out great for their work, in which they combined the sequences from both loci with morphology, mating studies, and physiology to propose three new yeast species in the genus Candida. In another study, Zimmerman and Vitousek used only ITS to identify over 4200 fungal OTUs across 13 sites on Mauna Loa. BLAST searches showed these OTUs spread over a dozen or so fungal classes, but most were either Dothideomycetes (36%) or Not assigned/environmental sample (40%). The problem I see with this is that they identified 4200+ OTUs BLASTing to distant classes using 157 bp of sequence. The experiments were rigorously carried out, but saturation of variable sites, where the same base location is hit with the same mutation in multiple lineages, must be an issue. That can cause errors in defining OTUs, making less related taxa appear to be more related. So in fact Zimmerman and Vitousek may have picked up less diversity than was actually present in their samples. 

Getting back to the grad student’s criticism, is the D1/D2 domain obsolete? I did a quick search on Web of Science to find out. I searched for citations of the paper in which the NL1 and NL4 primers, which amplify the domain, were first described (Kurtzman 1997), and on the paper (Gardes & Bruns 1993) in which one of the primers, ITS1-F, used by Zimmerman & Vitousek (2012) was presented. These searches gave the following hits, broken down by year.

Kurtman’s D1/D2 domain primers have seen pretty steady use over the last decade, with a slight uptick in the late 2000’s. Use of the ITS primer has steadily increased over the same period. Why? I think it’s a combination of one locus becoming accepted as the “standard”, and the fact that there is more phylogenetic information (variation) contained within the ITS locus, which is probably why it became the standard. In addition, the sequence length works quite well with high-throughput pyrosequencing, allowing the generation of very large data sets, as in Zimmerman & Vitousek (2012).

So, which locus would you use? I still think it makes sense to choose a less variable locus when you’re searching across wide phylogenetic space. But to make more refined identifications to the level of species, I’d use ITS, and I’d like to make the switch to pyrosequencing to do it.

Brian Ort

Mike, Brian and I traveled to Angelo Coast Reserve on the 19th to do some collecting.  Our goals were to collect some aquatic insects in the Eel River and look for Scaptomyza species in the surrounding riparian zones.  We didn't see any Scaptomyza but we got a great sample of aquatics, including lots of Dicosmoecus, Neophylax and Calineuria.  

Our paper on "Fungal diversity associated with Hawaiian Drosophila host plants" has been accepted in PLoS ONE.  Congratulations to Brian Ort for leading this effort, along with two undergraduate researchers in the lab, Norma Pantoja and Roxanne Bantay!

This week we made two short field trips to collect aquatic insects.  On Tuesday we worked northwest of Sebastapol. Brian and I visited several sites we had collected last fall. Our first stop was Pyrrington Creek on Graton Road, followed by Dutch Bill Creek along the Occidental Highway.  We stopped for lunch at Stumptown Brewpub in Guerneville.  After lunch we drove north of the Russian River to Austin Creek near the town of Cazadero. Our last stop of the day was Salmon Creek, northwest of the town of Bodega.  

Thursday we did a half day trip to two streams in Marin County, Lagunitas Creek and Pine Gulch Creek in Bolinas.  Thanks to Sarah Hake for allowing us access to Pine Gulch Creek!  We collected Neophylax here, as well as three species of Scaptomyza!!

The lab was just awarded a $3500 grant to do barcoding of aquatic insects in Northern California. Congrats to Brian for all his hard work on this project.

The Drosophila Species Workshop runs from 26-30 October in San Diego.  

The lab has gotten funding to do population genetics on some aquatic insects as part of a larger project trying to understand how climate change will impact species in California.  This proposal is funded through Berkeley's Initiative in Global Change Biology (BIGCB).  Partner labs include Vince Resh, Mary Power and Jonathon Stillman.  More information can be found here: http://ib.berkeley.edu/labs/globalchange/index.html