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Hey Treethinkers!
Just a quick update on some recent work—by
the marvelous Mike May and sensational Sebastian Höhna—that we’re very excited about in the Moore lab.

First, we have a paper in review that describes a new Bayesian approach for detecting mass-extinction events. Briefly, this is a novel method for detecting mass-extinction events from phylogenies estimated from molecular sequence data. We develop our approach in a Bayesian statistical framework, which enables us to harness prior information on the frequency and magnitude of mass-extinction events. The approach is based on an episodic stochastic-branching process model in which rates of speciation and extinction are constant between rate-shift events. We model three types of events: (1) instantaneous tree-wide shifts in speciation rate; (2) instantaneous tree-wide shifts in extinction rate, and; (3) instantaneous tree-wide mass-extinction events.

Each of the events is described by a separate compound Poisson process (CPP) model,
where the waiting times between each event are exponentially distributed with event-specific rate parameters. The magnitude of each event is drawn from an event-specific prior distribution. Parameters of the model are then estimated using a reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm. We demonstrate via simulation that this method has substantial power to detect the number of mass-extinction events, provides unbiased estimates of the timing of mass-extinction events, while exhibiting an appropriate (i.e., below 5%) false discovery rate even in the case of background diversification rate variation. Finally, we provide an empirical application of this approach to conifers, which reveals that this group has experienced two major episodes of mass extinction. This new approach—the CPP on Mass Extinction Times (CoMET) model—provides an effective tool for identifying mass-extinction events from molecular phylogenies, even when the history of those groups includes more prosaic temporal variation in diversification rate.

This paper is available from the bioRxiv here.

We’ve also submitted an application note for our new R package, TESS 2.0, a Bayesian software package implementing the CoMET model and many other tasty methods for inferring rates of lineage diversification. Briefly, TESS implements statistical approaches for estimating rates of lineage diversification (speciation — extinction) from phylogentic trees. The program provides a flexible Bayesian framework for specifying an effectively infinite array of diversification models—where diversification rates are constant, vary continuously, or change episodically through time—and implements numerical methods to estimate parameters of these models from molecular phylogenies.

We provide robust Bayesian methods for assessing the relative fit of these models of lineage diversification to a given study tree–-e.g., where stepping-stone simulation is used to estimate the marginal likelihoods of competing models, which can then be compared using Bayes factors. We also provide Bayesian methods for evaluating the absolute fit of these branching-process models to a given study tree—i.e., where posterior-predictive simulation is used to assess the ability of a candidate model to generate the observed phylogenetic data.

This paper is available from the bioRxiv here.

Finally, all this good stuff is implemented in the newly released TESS 2.0 R package (including the source code, comprehensive user manual, and example files) is available from CRAN here.

Two new workshops on phylogenetics and macroevolution

NESCent Academy will be hosting two workshops this summer that may be of interest to folks reading this blog and the deadline for applications is 1st May 2014.

Paleobiological and Phylogenetic Approaches to Macroevolution, July 22-29 

This course will teach participants to use fossil and phylogenetic data to analyze macroevolutionary patterns using traditional paleobiological stratigraphic methods, phylogenetic comparative methods and combined fossil and tree approaches. Macroevolutionary research is currently split into two quite isolated branches, one based on fossils and the other on extant taxa and phylogenies. Increasingly,evolutionary biologists in both camps are realizing that, only by combining neontological and paleontological data and approaches, can a new, and more powerful integrative macroevolution emerge. Unfortunately, these two disciplines utilize very different data and quantitative methods. Therefore to truly initiate a synthesis of these two approaches we need to train students and researchers to understand the intricacies of both fossil and phylogenetic data, and the methods necessary to integrate them.  APPLY HERE. More information can be found here.

Roger Benson Dept. of Earth Sciences, University of Oxford
Samantha Hopkins Clark Honors College and the Department of Geological Sciences, University of Oregon
Gene Hunt Dept. of Paleobiology, National Museum of Natural History, The Smithsonian Institution, Washington DC 20013-7012, USA.
Samantha Price Dept. Evolution & Ecology, University of California Davis
Daniel Rabosky Dept. of Ecology and Evolutionary Biology, University of Michigan
Lars Schmitz Keck Science Department, Claremont McKenna, Pitzer, and Scripps Colleges
Graham Slater Dept. of Paleobiology, National Museum of Natural History, The Smithsonian Institution

Phylogenetic Analysis Using RevBayes, August 25-31

The Bayesian statistical framework for phylogeny estimation has facilitated the development of models that better capture biological complexity. This course is built around the use of the new, open-source program RevBayes ( RevBayes implements an R-like language (complete with control statements, user-defined functions, and loops) that enables the user to build up phylogenetic models from simple parts. This course cover the basics of probability theory, graphical models, and phylogenetics. Then, building on these concepts, we will provide lectures on statistical methods for phylogenetic inference, macroevolution, and epidemiology. APPLY HERE. More information can be found here.

Bastien Boussau, LBBE, Lyon, France
Tracy Heath, UC Berkeley & U Kansas
Sebastian Höhna, UC Davis & UC Berkeley
John Huelsenbeck, UC Berkeley
Michael Landis, UC Berkeley
Nicolas Lartillot, LBBE, Lyon, France
Brian Moore, UC Davis
Fredrik Ronquist, NRM Stockholm
Tanja Stadler, ETH Zürich

Is There Life After Graduate School?

In an earlier post, I discussed the decision about attending graduate school in the sciences. I argued that graduate school is certainly not the right choice for everyone. For people of a certain mind-set, though, it is the perfect choice. And even if you have all the right attributes for graduate school, you can still be miserable if you pick the wrong advisor or graduate program, so that choice is also important. But let’s assume that you decided that graduate school was the right choice for you, you did the research, found the perfect advisor, happily toiled away long hours discovering things about the natural world that no one else in the world knew about, published lots of exciting papers about those results, finished a dissertation, and successfully completed a Ph.D. Now you have to address the question that friends and family have been asking you for years: What will you do for the rest of your life, and how will you make a living doing it? How can you make a living doing something as specialized and arcane as phylogenetics, for example?

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I’ll Admit It: I Loved Graduate School

At least once a month, I see blog posts from disgruntled current or former graduate students about “The Terrible Experience of Graduate School.” I advise a group of extremely bright undergraduates who are interested in research careers in the sciences, and they get scared to death by all these internet horror stories. The problem is, almost the only people who blog about their graduate school experience are the people who are (or were) extremely unhappy. There are certainly unhappy graduate students, but the truth is that many graduate students love the experience. But no one seems to want to write or read a blog post about the writer’s wonderful experience in graduate school. It sounds like gloating or bragging, and happy people usually are just content to be happy.

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Workshop on Integrating Molecular Phylogenies and the Fossil Record

Last week I attended a workshop organized by Hélène Morlon, Tiago Quental, and Charles Marshall on integrating data from the fossil record into phylogenetic methods. This three-day workshop was sponsored by the the France-Berkeley Fund, a cool program that provides seed grants to build partnerships between UC Berkeley researchers and French collaborators. All of the events took place at the UCMP on the UC Berkeley campus.

Hélène, Charles, and Tiago recognized the increasing interest in methods and analyses that incorporate data from fossil taxa; and since there are several of us working in this area–particularly in methods development–the need for building a collaborative network is critical. Furthermore, as methods become more and more reliant on data from the fossil record, connections between neontologists and paleontologists must be formed. Notably, a similar working group – organized by Sam Price and Lars Schmitz – was held at NESCent this past spring and was made up of an overlapping set of researchers. One result of the NESCent catalysis meeting will be a SSE Symposium at Evolution 2014 on “Reuniting fossil and extant approaches to macroevolution”.
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Motivating/rewarding reviewers

Among other things, researchers are expected to do research, publish the results of this research, and review the research of others. It is this reviewing part that I want to talk about today.

Reviewing is obviously one of the most important responsibilities of a researcher, one that can take a significant amount of time, but one that brings little reward, as it’s usually done for free. All a reviewer can show for it is a line on a CV saying “Reviewer for [put your favorite journal here]”. The purpose of this post is to propose a way to reward researchers who are good reviewers and spend a significant amount of time improving the work of others, often anonymously.

What if journals had awards for “Best reviewer of the year”? The laureate could then add this award on her CV, showing that she is doing a huge amount of service to her field. The award could be based on objective measures, such as the number of reviews returned in time, the number of reviews that concurred with the Editor-In-Chief’s decision, or could be more subjective, based on the Associate Editors and Editor-In-Chief assessments of the quality of the reviews they received. The award could be given with much ceremony at conference banquets, like awards for the best student paper, and perhaps with some money attached to it. Anonymity would not be broken, because all we would know about the laureate is that she reviewed N papers for journal X, not that she reviewed my paper submitted to journal X.

One could also think of a wall-of-fame type of thing, where reviewers would compete for the largest number of reviews returned in time, for instance. Or, to keep high levels of anonymity, give a way for a reviewer to know how her reviewing work compares to others: have I been reviewing more papers than 1%, 50%, 80% of the reviewers of this journal? If I see that I review less that my fellow researchers, perhaps I’ll be willing to accept the next invitation to review a paper. If I see that I review way more than my fellow researchers, perhaps I want to put that on my CV to show how altruistic I am.

Short of paying the reviewers for their reviews, which would perhaps be expensive for the smallest scientific societies, I think some type of reward/award system could be useful to appreciate the amount of time some researchers spend reviewing and improving the work of others. Given that systems for handling submissions and revisions such as “Manuscript Central” have all the stats available, that’s probably not very hard to do.

Bodega, 1 Month Anniversary

cropped-Untitled-6-011.pngI wanted to take advantage of the 1 month anniversary of the 2013 Bodega Bay applied phylogenetics workshop to do a quick debrief of the week, now that we’re all (hopefully) rehydrated and caught up on sleep.

So first, a HUGE thank you to the instructors, the staff at Bodega Bay marine labs (especially our principal contact, Lisa Valentine), the UC Davis EVE administrative staff, and our sponsors, Sierra Nevada Brewing Company and Lagunitas Brewing Company.   And of course, a HUGER thank you to the students of 2013, probably the best class that has ever passed through the course, with the notable exception of the class of 2011, which, incidentally, is when I was a student in the workshop.  Seriously, one sentence can’t hold enough superlatives to describe how exciting your research is.

Second, as part of a continuing effort (the new website, the blog, the tweeting!) to maintain a more cohesive community outside the one-week-a-year workshop, we wanted to encourage this year’s students, and those of workshops past, to stay in touch.  Specifically, it’d be great if you could keep us apprised of any papers you publish that use skills you picked up at the Bodega Bay workshop.  We’d love to profile your work on the blog, both to give you the Treethinkers bump and to advertise the value of the course for future students.  So please, keep us abreast of the latest and greatest in phylogen* research, and send us your papers!

Third, because they aren’t going to thank themselves, I’d like to lead everyone in a round of applause for this year’s faculty: {Peter, Brian, Bob, Luke, Sam, Rich, John, Bruce, Jonathan, Tracy, Jeremy}.  Based on all the comments on the course evaluations, the students really benefitted from all the mental horsepower you brought to the room!

Finally, what I’m sure has been on all of our minds: the Bodega Shake video.  I’m not going to post it, because, you know, some of us probably want to be hired somewhere at some point, but if you need to see it, send me an email or find me at a conference (I’ll be at Snowbird this summer!).

Thanks again to everybody for a great week!  Already excited for #Bodega2014…


Suggestions for a Gentle Bayesian Statistics Tutorial

Last week I was hosted by Mike Palopoli and the Bowdoin College Biology Department, where I gave a departmental seminar on my current work on Bayesian divergence time estimation methods. Bowdoin College is a 4-year liberal arts college with some very bright undergraduates. Several of the honors biology majors attended my talk and after the seminar Mike and I led a discussion of my work and computational evolutionary biology, in general.

During this discussion I got stumped by a question from one of the students. He asked if I could recommend a basic and gentle primer on Bayesian statistics for someone with very little statistics training. (This particular student is currently involved in a population genetics project using Bayesian analysis.) I recommended online teaching materials from the various phylogenetics/molecular evolution workshops, including Bodega and this blog, but I couldn’t point him toward a book that I felt would be suitable for someone without a strong understanding of probability theory. As a graduate student, I took Bill Jefferys’ Bayesian Inference course at the University of Texas, where we used Bayesian Data Analysis by Gelman et al. This book is a great introduction (as was Bill’s awesome course), but might be too advanced even for a bright and motivated undergraduate biology major.

So my goal for my first post on the Treethinkers’ blog is to seek out suggestions from readers: What is a good, introductory primer on Bayesian inference that is suitable for the undergraduate level?

Please add your suggestions in the comments below.