How Molecular Ecologists Work: Daniel Cadena on the eclectic approach and getting the country view

Welcome to “How Molecular Ecologists Work”, the interview series that asks scientists how they get stuff done.

This week, I’m interviewing Carlos Daniel Cadena, Professor at Universidad de los Andes in Colombia. Daniel’s work cuts across a broad swath of evolution and ecology, but mostly focuses on the evolution and systematics of neotropical birds. I asked him how he gets it done.

Location: Universidad de los Andes, Bogotá, Colombia

Current Position: Professor in the Departamento de Ciencias Biológicas

Current mobile device(s): iPhone 6.

Current computer(s):Macbook Pro

What kind of research do you?

Broadly speaking, I am interested in and work on evolutionary biology of birds and (occasionally) other vertebrates. Over the years, I have worked on a broad range of topics, from genetics, physiology and behavior all the way up to diversification and broad-scale patterns of diversity, with a little bit of everything in between. I am most interested in questions in biogeography and I am especially intrigued about the causes of high tropical diversity at various levels. I like to pursue my interests in tropical diversity from a wide variety of perspectives combining field, lab, museum, and computational work.

Can you use one word to describe the way you work?

Multitasking.

What specific strategies do you recommend for running (or establishing) a lab?

My approach to running a lab has been rather different to that of many other researchers in our field. When I first landed my faculty job I often wondered whether I should focus on a single study system in which to address questions at great depth from multiple perspectives and really get to know the system in detail, or rather pursue my curiosity (and allow for student freedom in developing their independent projects) by spreading out quite thinly working on multiple topics and various systems at the same time, moving on from one project/system to the next after learning a few things. I somehow ended up picking the latter option and although I sometimes suffer of the “impostor syndrome” by thinking that much the specifics of my work are rather superficial relative to careful in-depth work done by many colleagues, I believe that knowing relatively little about many different things has been a great career path, if only to maintain my excitement with regards to answering new questions and learning new things.

I say all this only to explain why I believe a strategy that has worked for me is to be open and flexible, and willing to learn from students and colleagues. This has allowed me to mentor a great diversity of students and to collaborate with colleagues with various interests and backgrounds. I also think that being in a lab where people might be working on a bunch of (seemingly) unrelated topics is especially valuable for graduate students, who I think should be exposed to a broad spectrum of ideas and tools. I like to think my students will appreciate my somewhat eclectic approach in the long run.

Where do you work with data (personal computer, lab computers, cluster, etc.)?

Honestly, I am not a big data cruncher these days. Because I have had a rather heavy teaching and administrative burden, over the past few years much of the research I have done has been conducted by supervising students, and it’s really them doing the hard–and fun–work. For the few projects I am currently analyzing data myself, I mostly use my personal computer. In terms of where do I physically work, now that I am relatively free of meetings, etc., I have been exploring working in various places aside from my office including other spots on campus, cafes, and at home. I recently rented a house out in the country outside of the city and working there has been especially fun and productive. I guess the views help.

Besides your phone and computer, what gadget can’t you live without and why?

It’s not really a gadget, but I rely heavily on Slack for communication with members of my team. When I was department chair, my email was total chaos and important and fun things would often get lost, so I basically abolished communication via email with my students and lab associates. Slack allows me to respond quickly and to have all the important communications related to people and projects in the lab properly organized.

What part of your job do you spend the most time on in a week? What part do you wish you had more time for?

Teaching, including preparing and giving lectures, but also reading even if my readings do not immediately get incorporated into classes. Although it is increasingly hard to keep up with the literature, I believe that you really need to know where various fields are going in order to be a good educator. Like almost everyone else, I would like to have more time for research and especially to think about long-term research plans. However, I should say that I was on sabbatical leave recently and while I honestly did not miss teaching a whole lot while I was away, getting back to my classes has been quite enjoyable; I like teaching a lot.

How do you stay organized (to-do lists, digital reminders, etc.)?

I don’t know! I hardly ever write down a to-do list (except when things are getting out of control) and I don’t use a calendar to schedule appointments, etc.. This is probably not good advice for many people, but I guess I (sort of) remember the important things I need to do and they somehow get done. Well, most of the time.

What do you do to recharge outside of science?

Definitively, sports are my escape valve. I started running more than six years ago and very quickly got really into it and became a long-distance runner. I’ve run nine marathons so far and train very hard for this with a team, typically 5-6 days a week. I also picked up road cycling a little over a year ago and have already done a couple of century-mile events. More recently, I started swimming and a few weeks ago completed my first short triathlon; I am now training for my first long-distance event. It will take time, but I think I will end up running ultramarathons and likely ironman triathlons, that’s the kind of person I am.

Some people say they have great ideas for work while running or cycling, but I hardly ever think about work while training. This is my time to get totally disconnected from work and I think this gives me great energy to get me through the day and work effectively. I recently gave what I thought was a quite smooth lecture on a relatively difficult topic at 9 am; my students probably did not notice my day had started very early and I had already run 30 km before showing up to class.

What are you currently reading?

Research manuscripts and thesis drafts! Also, I am little-by-little reading Andrea Wulf’s “The Invention of Nature” on Alexander von Humboldt’s fascinating story.

What is your sleep routine like?

I’m definitively a morning person. I usually go to bed quite early and wake up very early to train. Some of my triathlete friends think I am too much of a wimp because I am not willing to wake up at 3 am to go for a long bike ride, but I am fine with starting my day at 4:30 am to go swim, ride, or run.

Fill in the blank: I’d like to see _______ answer these questions.

Bob Ricklefs. I had the fortune to work with him, but I still do not get how he does it all.

Thanks Daniel! Next week: Dr. Kathryn Hodgins…..

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Molecular ecology, the flowchart

Towards the end of last semester my department’s evolutionary genetics journal club read Rasmus Nielsen’s terrific 2005 review of tests for recent natural selection in genetic data. Nielsen provides figures illustrating the effects of a recent selective sweep and the shape of the site frequency spectrum that you’ve probably seen reproduced in a score of research seminars, and tables that neatly categorize (1) how different evolutionary processes affect diversity within and among species and (2) a selection of widely used test statistics, the patterns they test for, and the kind of data they need. It’s now more than a dozen years old, but it holds up mighty well as an introduction to what we can learn from genetic samples of a population, in no small part because the tests Nielsen describes in human data have only recently become widely accessible for non-human, non-model organisms.

Re-reading the paper and thinking about how I’d approach it as a new grad student, though, it occurred to me that precisely because he was writing in an era when a handful of species were the focus of most real “genomic” research, Nielsen doesn’t really account for the wide variation of genomic research infrastructure we cope with today. Anyone can collect genome-wide SNP data from a tube-rack-full of tissue samples and a RADseq protocol, but what you can do with that data afterwards depends a lot on the species you sampled, the set of individuals in the sample, what other kinds of data you can connect to the samples, and the existence of resources like a reference genome, a linkage map, or “annotations” that might be anything from the results of functional genetic experiments to algorithmic identification of hypothetical gene sequences.

In that journal club discussion we started talking through some of these possibilities in terms of if-then links: if you have SNPs and they’re placed within larger contigs or a whole reference genome then you can look for runs of homozygosity or extended linkage disequilibrium or islands of differentiation … Or if you have SNPs and they’re from multiple species you can estimate a phylogeny and then do ancestral state reconstruction and if you know codon positions you can estimate dN/dS … And so on. I started sketching on the whiteboard, and the result is below:

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Friday action items: End the year on a hopeful note

The March for Science approaches the Capitol. (jby)

On Fridays this year we’ve been posting small, concrete things you can do to help make things better under the current administration. It’s been a tumultuous year, and one in which it was impossible to ignore the politics outside our labs. We pushed back against an EPA funding freeze, organized to march for Science, phoned Congress more than ever before, supported public school science classrooms, and pitched in to help Puerto Rico recover from Hurricane Maria. We didn’t stop the GOP’s massive tax bill, which will endanger funding for everything from healthcare to basic research in the name of a trillion-dollar corporate tax cut — but we did lessen its immediate impact on graduate students.

Now, as 2017 draws to a close, let me suggest a few more things you might do to help make 2018 a better year.

First, more to help science in Puerto Rico and the U.S. Virgin Islands: the American Society of Naturalists is collecting funds for a special grant to scientists affected by this year’s Caribbean hurricanes. You can pitch in using an item on the ASN subscription page — either as a standalone donation or in the course of renewing your membership for 2018. (Whoops, I’m due for that renewal, I think. Make that a first-and-a-half item.)

Second, if you’ve gotten a pile of science-y books as holiday gifts, consider donating copies to your local public library. Science titles can be a little more obscure, especially if you’ve gotten something field-specific, and even in the age of the Internet, libraries are still where children (and adults) can encounter books they’d never find at home.

Third, if you haven’t before, now’s a great time to pay for some of the journalism we’ve all relied on this past year. I’m a member of my local NPR station, and public radio is always a solid choice for both local and national news — but you might also consider your hometown newspaper, or an independent national resource like ProPublica.

Finally, get ready to help change the government. Campaigns are already underway for the 2018 congressional election. Make sure you’re registered to vote in November, and figure out how you can help — either in your own district, or by finding a nearby swing-able seat with SwingLeft. Prospects look good for changes in the House and even the Senate, but it’s up to us to turn good prospects into better government.

Got a suggestion for an Action Item in the new year? E-mail us!

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How Molecular Ecologists Work: Chris Jiggins on organic collaboration and family gardening

Welcome to “How Molecular Ecologists Work”, the interview series that asks scientists how they get stuff done.

This week’s interview is with Dr. Chris Jiggins from the University of Cambridge. Chris and his colleagues broadly study the adaptation and speciation of butterflies and moths. However, I’ve heard that if you look in a mirror and say “Heliconius” three times, he appears with a manuscript in hand.

Location: University of Cambridge, UK

Current Position: Professor of Evolutionary Biology, University of Cambridge

Current mobile device(s): Iphone 7 (just upgraded!) and an iPad

Current computer(s): Macbook Pro (13 inch Retina 2013)

What kind of research do you?

I study the evolutionary biology of Heliconius butterflies. Projects range from studies of wing pattern evolution and diversification, through to speciation genomics and the genetic basis for species differences. We are currently working on pheromone signalling and differences between closely related species in their chemical signals.

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Join the Molecular Ecologist blogging team in 2018!

Update, 22 Jan 2018: We’ve had a bunch of great applications — thanks to everyone who’s applied. Recruiting is closed for now, but keep an eye out for future opportunities.

The Molecular Ecologist is seeking new regular contributors for 2018! Join us in blogging about “ecology, evolution, and everything in between.”

Ideal candidates should have expertise and experience in our core topic, the use of genetic data to understand the past and future of the living world. We’re particularly interested in senior graduate students, postdoctoral researchers, and other working scientists who can discuss basic science on a level that engages our core community of research biologists, as well as explaining fundamental molecular ecology concepts to the general public. New contributors should be ready to commit to posting substantively on at least a monthly basis for the first year of their tenure.

New contributors will receive a small stipend for their first year of regular posting with The Molecular Ecologist, and may continue on a voluntary basis after that. Blogging for The Molecular Ecologist can be an excellent way to hone familiarity with current molecular ecology research, establish connections within the scientific community, and build a portfolio of science writing for a broader audience. In light of this, we are particularly interested in applications from candidates whose racial, ethnic, sexual, or gender identities are underrepresented in science careers.

To apply, please e-mail a brief cover letter explaining why you want to write for The Molecular Ecologist and an appropriate sample of your writing to Jeremy Yoder at jbyoder@gmail.com. Applications should be received by the end of the day on 20 January, 2018 to ensure consideration.

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How Molecular Ecologists Work: Katy Heath on being an expert sleeper and not over-analyzing

Welcome to “How Molecular Ecologists Work”, the interview series that asks scientists how they get stuff done.

This week’s interview is from Dr. Katy Heath from the Department of Plant Biology and the University of Illinois. Katy and her team study the evolutionary ecology of mutualisms using plant, fungal, and bacterial systems.

Location: Urbana-Champaign IL

Current Position: Associate Professor, Plant Biology

Current mobile device(s): iPhone 7

Current computer(s): MacBook Air

What kind of research do you?

Evolutionary ecology and evolutionary genomics focused on legume-rhizobium mutualisms

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Visualize your genome assemblies

Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily), is a program that creates visualisations of sequence assemblies that you can interact with. When assembling a genome with your favorite assembler, you are usually building graphs, from which contigs are then created.

How can visualizing your assemblies help? A simple example is given by Ryan Wick (reprinted with permission):

Imagine a bacterial genome that contains a single repeated element in two separate places (red) in the chromosome.

A researcher (who does not yet know the structure of the genome) sequences it, and the resulting 100 bp reads are assembled with a de novo assembler.

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