#Evol2012 Nothing in biology makes sense, except in the light of good models.

Mathematical models are a critical part of evolutionary biology. Sometimes they are used to analyze data, sometimes they are used to make theoretical predictions, but in either case, they represent the purest expression of what biologists suppose about the relationship between the patterns they observe in the natural world and the processes that produce them. As a result, they are often at the core of the most important presentations of research in the scientific literature and at professional conferences. Unfortunately they also tend to be very abstract, and can be a stumbling point in those presentations*.

The conference Evolution 2012 has now been raging for three days, and the air is thick with fascinating models, and also incomprehensible ones, so I wanted to take a moment to highlight a couple of speakers who I felt presented a mathematical model in an exceptionally clear and excellent way.

First, Carl Boettiger, in his half hour talk “Detecting evolutionary regime shifts with comparative phylogenetics.” He spoke about developing a model to analyze trait data in a phylogenetic context to ask whether the evolution of the pharyngeal jaw in Labroid fishes freed them from the suction feeding habits of their ancestors and allowed a burst of morphological and ecological diversification in the group. This question, whether a “key innovation” can release a constraint and result in diversification is a long-standing one in evolution, and no explicit mathematical model capable of being applied to real data had been developed to address it. After explaining this, Boettiger launched into a comprehensive, clear explanation of the derivation of his model, what each of the terms meant and then proceeded to analyze it. Perhaps not surprisingly, he found that the pharyngeal jaw did indeed seem to serve as a key innovation, releasing a constraint and resulting in diversification. (Boettiger’s talk is, amazingly, already on-line if you want to check it out)

The second was Gideon Bradburd. Bradburd gave a 15 minute talk “A Bayesian method for estimating genetic differentiation due to isolation by geographic and ecological distance.” Identifying the factors that cause population divergence is a key task in evolution, ecology and conservation biology. One of the ways to go about doing this is to look for correlations between population genetic divergence and geographical or environmental factors. One of the typical ways that people have gone about doing that is by using a statistical method called a partial mantel test. Unfortunately, the partial Mantel test has some undesirable statistical properties that cause it to be misleading under some circumstances. Enter the new model. In just a few slides, Bradburd clearly explained the data required, the math behind how he models genetic divergence, and how the explanatory variables fit in. He then used the model to ask whether elevation influenced genetic divergence in the wild ancestor of corn, teosinte. He found that the model appeared to perform very well and give strong evidence that elevation was important.

Talks like these make the meetings fun to attend, and represent the kind of clarity I strive for in my own presentations.

*See the much discussed recent paper in PNAS (Fawcett and Higginson 2012), in which it is shown that the density of equations in a scientific publication has a negative impact on its citation rate.

#Evol2012 Molecular Ecology Symposium: Your overconfidence is your weakness

I arrived in Ottawa a day before the proper start of the Evolution 2012 meetings so as to attend the symposium hosted by the journal Molecular Ecology, which was almost entirely devoted to the joys of genome-scale data collected from wild populations of our favorite species—and what we can and can’t learn from it. This, readers of this blog will recall, is one of the biggest changes in our field in the last few years.

Yes, your NGS-Star has collected six scrillion SNPs—but do you know how to analyze them?

Alex Buerkle kicked things off with the intersting question of how much data, exactly, do we need? It’s easy (given the funding) to obtain a lot of DNA sequence fragments from next-generation sequencing (NGS) methods—but is it better to collect lots of data from a few individuals (and thereby have high confidence in the data) or collect less data from more individuals and accept that there will be some uncertainty in the data for any one individual? Buerkle argued that the second option is preferable; it’s possible to account for uncertainty in your analysis, but if you don’t sample enough individuals, you can miss rare gene variants.

There was a tension between confidence and uncertainty in these great big genetic datasets running through the whole symposium. Buerkle also noted that patterns of differentiation and diversity across the genomes of related species can be very complex—and in the question and answer session, it was pointed out that complexity and noise can be hard to differentiate.

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What’s changed in evolution and ecology since I started my Ph.D.

The rise of big data is changing ecology and evolutionary biology, along with the rest of the life sciences.

This week’s post is a guest contribution by David Hembry, who recently finished his Ph.D. at the University of California, Berkeley, working on coevolution and diversification of the obligate pollination mutualism between leafflower plants (Phyllantheae) and leafflower moths (Epicephala). He will be starting a postdoctoral fellowship at Kyoto University in the fall.

Last month, I filed my PhD dissertation, bringing to an end an intellectual and personal journey that began seven years ago in the summer of 2005. I know a lot more now than I did then, and I know a lot more about the boundaries of what I don’t know, too. But not only has my knowledge changed—evolution and ecology looks a lot different now than it did seven years ago when I was planning my dissertation research. At some point, and often multiple points, in the process of getting a PhD, everybody wonders whether what they’re doing is already out of date. Some of the transformations in the field I think I could see coming. For instance, it was clear in 2005 that computational power would keep increasing, phylogenetics would be used more and more to ask interesting questions, more and more genomes would be available for analysis, and evolutionary developmental biology was on the rise. It was unfortunately also predictable that it would be possible to study climate change in real time over PhD-length timescales. And although the 2008 global financial crisis didn’t help, it was clear that funding and jobs were going to be more competitive than they had been for our predecessors.

But there were a number of things I didn’t see coming, and which have made the field look radically different than it was back in 2005. Looking back, and looking towards the future, here are the changes I think were most important (from an evolutionist’s perspective), and what I think they mean for young scientists.

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