Trees and characters and common ancestors, oh my!

My teaching experience has taken a big step forward this month. I gave my first of two guest lectures to a large undergrad intro bio class (they clapped when I was done; I don't know what's wrong with them!), and am piloting a new module in lab this week. Both of these teaching endeavors are focused on the same topic: phylogenetics. Given that describing evolutionary relationships is one of my specialties, I was both excited and apprehensive to put these teaching materials together.

I've done outreach activities using phylogenetics before, and I've taught classes on computational phylogenetics. I was a teaching assistant in grad school for a semester-long class on plant systematics, which heavily featured phylogenetics. Striking the right balance for a single lecture and lab for an undergrad intro bio class was tricky. I skipped most of the tedious definitions and history of cladistics, as well as in-depth discussions of the attributes of various tree reconstruction methods. I did spent time talking about the practical applications of trees, and possible implementations of these techniques they might find interesting.

Why take that approach? Well, I operated under the assumption that most students wouldn't ever really build trees again, but they might perhaps need to interpret them. I also couched my discussion in the strength of comparative biology: what if we want to breed a better crop? What if we need to know how data about a gene in mouse transfers to a gene family in humans? What can a phylogeny tell us about how traits evolve? In that context, it's way more effective to spend time talking about interpreting phylogenies and deciding how much confidence you should have in them.

At the end of the day, phylogenetics is one of those areas which some students will just understand intuitively without much extra explanation necessary. Other students will struggle, ask questions, and it will take a long time to identify and then break through their misconceptions. The former students will get bored, think my lab is dumb and conclude it to be a waste of time. The latter students will get frustrated, think the lab is dumb and conclude it to be a waste of time.

OK, maybe I hope a few students will like it...and hopefully a few of the other teaching assistants learned something, too.

Data visualization

I've been thinking lots lately about how we visualize data in biological research. As a student of both communication and science, I find the interplay between the two fields to be especially compelling. This interest was reinvigorated last spring by some meetings with a visiting NESCent scholar, Tyler Curtain, who introduced me to some of the preeminent and current literature on the field. Since then, I've noticed the theme popping up repeatedly in my interactions with colleagues. Folks pop in to my office to ask for advice on figures, and some NESCent-associated projects like Open Tree of Life are particularly interested in advancing visualization abilities for evolutionary biology.

As a result of this convergence of events, I jumped at the chance to take a short workshop on data visualization offered by Duke Libraries Data and GIS Services. I was struck by the breadth of options available for representing data visually, and began thinking a bit about how different figures in evolutionary biology are from other fields. I talked a bit with Angela Zoss, the instructor of the course, who concurred that biological disciplines tend to lag behind other sciences in adopting more effective methods of visualizing data (I would extend that claim and imply that sciences in general lag behind other fields of study as well).

Let's face it, folks. Figures representing biological data are notoriously problematic to not only build, but interpret. Dendrograms (which in the data visualization world include many types of tree structures) are very complicated in evolutionary biology, and our tendency is to cram as much information as possible into each figure. A single diagram may include tree topology, branch lengths/divergence times, taxon names (tree tips AND higher taxonomic groupings), color coding for one or more traits, etc. Additionally, I've long thought that effective visualizations for genomics research, especially in the comparative realm, are notoriously convoluted and difficult to understand.

Why are our figures so complicated? I think it's because biology as a science inherently includes many different variables, each of which includes sometimes large margins for error. As scientists, we want to tell our research narrative in as non-biased a manner as possible, which means including (visually) as much data as possible. This impulse is compounded by a push to streamline figures for publications, which is further complicated by lack of availability of color, space, and resolution.

But let's face it. We're never telling an unbiased story with our figures. We make decisions about inclusion of data in a study and methods of analysis even before we get to the publication stage, and cramming as much summary information as possible into a figure doesn't represent those biases. What is the goal of a figure or representation of biological data? It should be interpretable by an audience, which in journals means scientific peers. When visualizations become so specialized that only a handful of people in a field can understand them, we're working counter to the purpose of the visualization. It's not doing its job.

I advocate striking a balance between the goals of the two paragraphs above. Tell a clear story with the data, but include enough information for the audience to understand associated variance and error. As a result, I'm planning a open discussion at NESCent with folks from our informatics and science groups, in addition to folks who work on data visualization, to see how we can improve our methods of building figures.

Possible topics for contemplation include (but aren't limited to) the following:

• tree visualization
• deep time
• visualizing error
• taxonomic levels
• trait mapping