Graduate School Part II: Choosing a Supervisor

In a previous post I covered the characteristics that are important for success in graduate school (http://evolbiology.blogspot.com/2012/10/graduate-school-part-i.html). However, deciding to attend graduate school is only the first step.

It is of paramount importance that you be interested in your graduate research project. Hopefully, you will have some idea of the area(s) that interest you from your undergraduate courses. As a fourth year undergraduate, I knew I was interested in functional morphology and evolution (thanks to some of the really awesome zoology courses at the University of Calgary). But it can be difficult to judge whether or not you will enjoy scientific research. It is therefore important for most fourth year undergraduates to undertake a research project. Usually, undergraduate research projects last two semesters and can give you a taste of your future in graduate school. You can also volunteer for different labs in your department. Most PhD or MSc students would be excited to have a helper (it gives them more time to drink their precious coffee!). Ask to volunteer for a variety of labs doing different types of research. In Canada, you can also apply for summer internships through the National Science and Engineering Research Council of Canada (NSERC; deadlines for undergraduate applications are usually around December so check with your department). These scholarships look excellent on a CV and provide you with unparalleled research experience. It is also important to peruse the recent scientific literature and/or join a discussion group or club.

Solidifying your interest in science might seem like a lot of work. But doing the work is worth it. Graduate school requires a lot of time and mental energy. Being interested in what you do, makes your job fun. Hating what you do, can make graduate school a bitter experience.

Once you're certain that you definitely want to attend graduate school, you need to find an appropriate supervisor. The best first step is to ask professors with whom you are familiar about potential supervisors in the field. They likely know many people and can point you in the right direction. You can also search university websites. Most professors will publish a description of their research online along with a list of recent (hopefully!) publications. I recommend reading some of their publications. This will give you a good idea of their research interests and make you look keen when it comes time for meetings/interviews.

If you can, it is also a good idea to speak to graduate students. Most graduate students will give you their honest opinion about their adviser. After all, you don't want to end up working for someone who is never around or treats their students poorly. You'll be working under your adviser for years (2-5 depending on your degree level) so it's important to at least get along with them.

The next step is to contact potential supervisors. To get the ball rolling, an email is usually best. Be sure to make your email sound professional. Avoid spelling mistakes and don't use "LOLspeak" ("I can haz masters degree?" = BAD). Also, DO NOT send mass emails to professors. Many profs receive hundreds of mass emails from prospective students every year and yours is liable to be ignored.

I  also urge you to start contacting people EARLY. If you're emailing professors 2 weeks before the application deadline, you are likely to miss it. Professors are busy people! That being said, I would give them 2 weeks to respond before emailing them again (others might have different rules, so ask around). It is okay to remind a professor of your inquiry. It's possible that they tucked the email away for later and forgot about it. I'd forget emails too if I had several graduate students vying for my time, was teaching courses, and serving on departmental committees (among other things!). It doesn't mean that aren't interested!

Once initial contact has been made there are a few options for how to proceed. If they are at a nearby university, you can suggest a one-on-one meeting. These are great because you can often meet lab members and tour the facilities. If they are far away, it's a good idea to arrange for a phone meeting. They are less personal but will show that you're serious about applying. The professor will usually indicate during the one-on-one or phone interview whether they encourage you to apply.

In the event of a positive response, it's time to apply! University websites usually have good instructions and administrative staff that would be happy to help out.

Professional Development and Networking

Most of us know that the network we build will determine (at least in part) the success of our academic careers. The more people we know, both formally and informally, the more likely we are to be considered for post doctoral and other academic positions (assuming your interactions have been positive). Additionally, a larger network affords us more opportunity for collaboration (something that is very important to have on your academic CV when applying for positions). But how can we build a large and targeted academic network? I am far from an expert in this area and I hope this post will be a learning experience for me as well as other graduate students.

There are numerous resources for academics interested in learning how to build a network (websites, books, blogs). Most universities will also hold at least one seminar every year on the subject. I recommend you attend. Seminar coordinators will often invite people with varying levels of academic experience (professors, post docs, and graduate students) to speak on networking. There is usually something for both new and returning graduate students. I have attended a few seminars on networking and there are some recurring themes.

1) Attend conferences and other academic events.

The only way to meet other scientists in your field is to meet them on common ground. This seems really obvious but to some it isn't. Attending conferences can be expensive, especially if you have limited funding, and some graduate students opt out. However, most societies offer travel grants. I received one from the Society of Vertebrate Paleontology to attend the 2009 conference in Bristol, England. Many universities also offer funding for attending conferences and/or professional development. Make sure to check with your Graduate Student's Association and faculty office. Even if you don't have a lot of funding, attending conferences is the best way to build your network and I recommend going anyway (even if that means giving up your daily cup of coffee to save!). I try to attend 2-3 conferences per year and to present something at every one. There is an added benefit of building the "presentations" section of your CV.

2) Introduce yourself or have someone else introduce you to more senior researchers in your field

In my opinion, this is the most difficult part of the networking process. It is really tough to walk up to a famous researcher (or a not so famous one) especially if they are surrounded by other adoring fans. In fact, I find conferences super awkward and stressful for these reasons. It is a lot easier to hang out with your friends at conferences because it's comfortable (I call it my "safety zone"). But you have to force yourself to do it. It will get slightly (very slightly) easier with time. As your network grows and you know more and more people, it is also easier to set up introductions, which is considerably less difficult than approaching a famous researcher at random. Your adviser is also a good resource. Most advisers are happy to set up introductions. The graduate students of said famous researcher are also good resources. They can set up introductions and help reduce any ensuing awkwardness. I had some help from other graduate students when I was applying for PhD positions (and it worked!).

3) Get your research out there

This follows from point 1. If you don't advertise your research through presentations and publications, you are a lot less likely to be noticed. I love giving presentations at conferences, even if they sometimes go badly (usually because I didn't practice!). I also find publications to be a huge motivating factor when I am sitting in the lab or office. There is nothing like a "shiny" reprint with your name on it! Of course, presenting and publishing is only the first step. Emailing your new papers to colleagues or acquaintances will help them remember you and generate feedback on your work. Publishing titles and links to your papers on your website will also help alert the online community to your work. You should also bring reprints and abstract copies to conferences. This will help people remember you when they get home!

4) Get business cards

It might seem like something out of the movie American Psycho (it's up to you whether your business cards sport a fancy water mark!) but business cards are really important tools. As above, they can help people remember you when they return home from a long and stressful conference. However, I have definitely gone home with business cards and had absolutely no recollection of why I had them. I (and others) recommend writing what you talked about or the title of your poster/talk on the back of your business card before handing it off. I have recently started doing this and it REALLY helps. We can't all remember every face and conversation.

5) Online or email networking

Emailing other researchers is an obvious first step! We have all had "missed connections" at conferences. A well-worded email can help maintain or initiate relationships. There are also numerous other means of online networking including blogging, twitter, and websites such as Linked In. Of course, you should keep all of your online activity as professional as possible. Inappropriate online content can prevent you from building a successful network. You should also be prepared to get feedback from the online community! Not everyone will agree with your blogs or tweets (but you should have a thick skin if you want to succeed in academia!).

This is a very brief introduction to academic networking. I suggest you also speak to other graduate students as well as your adviser. We have all been there!

Professional Development

Dr. Sora Kim gave an excellent seminar on professional development in academia today and you can view the PDF on her website at the following link - http://geofaculty.uwyo.edu/skim11/Updates/Entries/2012/10/11_Building_a_Professional_Network.html.

I'll write a more extensive post about this soon!

Graduate School Part I

I have had a few inquiries from some undergraduate friends about applying for graduate school. I thought I would publish some advice here but also open up the comments to anyone who has further advice or competing views.

Many undergraduates are concerned about the fact that they don't have straight A's. Well, it's no big secret, but neither did I (at least for my entire program)! I did "okay" in first year but just scraped by in my second (by the skin of my teeth! Okay, teeth don't have skin, but you know what I mean). The good news is that biology programs normally only consider your last two years (or equivalent number of courses). You should always check with the program you're interested in, but many seem comparable. So if you're entering your third year and a little disappointed with your first two, you'll be just fine.

I also don't think straight A's necessarily make a good researcher. Does the ability to memorize thousands of facts make you a critical thinker? It might get you good grades but it won't help you design a relevant and interesting research project. This is not to say that all people with straight A's merely memorize facts (please, no angry comments!). All I am saying is that the person with the B+ average and the person with the A+ average may not be so different when it comes time for grad school. It all depends on personal characteristics that are at least partially independent of grades. Some important qualities that successful students possess are self motivation, competitiveness (healthy competitiveness!), and passion (not like a romance novel!).

Firstly, self motivation is important because no one is going to be there prodding you to finish your experiment. When your yearly committee meeting rolls around and you haven't made any progress, there will be no one to blame but yourself. You have to be good at setting goals and meeting them. I use two levels of goal setting. The first are long-term goals. For example, "I will submit paper A by January." The second are short-term goals. For example, "Today, I will write three paragraphs of paper A." Because I am self motivated I usually meet my goals (or at least come close).

Secondly, I want to be clear about what I mean by competitiveness. I define this as the desire to excel when your goals for excellence are set by the people around you. This means feeling motivated by how well others are doing and working hard to match their successes (or maybe to do a bit better!). I am only talking about hard work and not about immoral means of competing with others (purposely scooping their research, "bad mouthing" them, or sabotaging experiments). These are all unacceptable and place you into the category of ruthless rather than competitive.

I suspect there is a near linear relationship of healthy competitiveness with number of publications and number of scholarships. Publications and scholarships (in addition to experience and interesting research) are good determinants of future success (jobs, more scholarships) assuming they are of good quality. Of course, there may be exceptions to this "rule."

Finally, you have to be passionate about your research. If you are passionate you are most likely also self motivated and competitive. I think very positively about my research and am always generating new ideas. I wake up in the morning and immediately start thinking about science. But I don't want to be unrealistic. Every day will not be positive! Experiments fail and equipment breaks, but if it doesn't stop you from wanting to pursue research, then you're on the right track!

Part II will cover choosing a supervisor and a university.

A Brief Introduction to Testing for Phylogenetic Signal in Comparative Data

Phylogenetic comparative methods (PCMs) were the subject of my last post. You can read it here (http://evolbiology.blogspot.ca/2012/09/phylogenetic-comparative-methods-some.html). It was a VERY brief description of two commonly employed PCMs (Phylogenetically Independent Contrasts and Phylogenetic Generalized Least Squares Regression). However, it is important to note that PCMs should not be applied unless their use is justified. It's true that the availability of phylogenies and the array of methods for reconstructing phylogenies has skyrocketed. As a result, reviewers are suggesting PCMs more and more. But it is important to consider whether PCMs are necessary and whether they add to your analysis or aid in the interpretation of your data. But how can you determine if your study needs PCMs?

One of the questions to ask is whether your data (rather the residuals; Revell 2010) are phylogenetically structured. In other words, do your data show phylogenetic signal? Two common methods are the K statistics of Blomberg et al. (2003) and Pagel's lambda (Pagel, 1999). The K statistic compares the observed and expected variance for calculated independent contrasts (Blomberg et al. 2009; Glor, 2009). Pagel's lambda is a multiplier of the off diagonal elements of the covariance matrix that varies between 0 and 1. Lambda transforms the phylogenetic tree with the purpose of comparing a complete lack of phylogenetic structure (lambda = 0; star phylogeny) to the untransformed topology and branch lengths of your original tree (lambda = 1) (Pagel, 1999; Gor, 2009). In other words, Pagel's lambda determines which situation, a star or structured phylogeny, fits your data best.

Here is some basic R code for using Blomberg et al.'s K:

require(picante)

# Help file http://127.0.0.1:17385/library/picante/html/phylosignal.html

kstat<-phylosignal(data,tree) 

# Your data must have matching taxon names or be sorted in the same order as the tip labels of the phylogeny

# This will return the K statistics and p value (as well as the variance of the independent contrasts and the associated z value)

Here is some basic R code for using Pagel's lambda:

require(phytools)
# Help file http://127.0.0.1:17385/library/phytools/html/phylosig.html
lamb<- phylosig(tree,data,method="lambda")
# Your data also require names that match the tip labels on the tree
# This will return a lambda value and log likelihood, values of lambda closer to 1 indicate singificant phylogenetic signal

Using the K statistic and Pagel's lambda, you can justify the use of PCMs or demonstrate that they are not necessary. Although I feel strongly that PCMs are powerful tools in comparative studies, I also feel they should only be used when it is statistically justifiable to do so.

You can follow the instructions of Glor (2009) to further understand Pagel's lambda. There are also other methods for testing for phylogenetic signal that I have not covered here.

References

Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.

Glor. 2009. IV. Testing Phylogenetic Signal in R. Bodega Phylogenetics Wiki.

Pagel, M. 1999. Inferring the historical patterns of biological evolution. Nature, 401, 877–884.

Revell, L. J. 2010. Phylogenetic signal and linear regression on species data. Methods in Ecology and Evolution 1:319-329.

Phylogenetic Comparative Methods: Some Generalities

The comparative method, in one form or another, has been of paramount importance in biology since its inception. It is the basis for understanding how and why organisms differ. Prior to the 1980's the comparative method most often involved simple statistics (regression, correlation). There are several problems with this approach when comparing different taxa (species, genera etc.). Firstly, taxa do not have completely independent evolutionary histories. This is not a new concept. Even Linnaeus' hierarchical classification system indirectly represents the non-independence of species. Secondly, simple statistics such as correlation assume complete independence and the evolutionary process inherently violates this assumption. But comparative biologists need not despair!

There are a few methods for dealing with the issue of phylogenetic non-independence. These include (but are not limited to) Phylogenetically Independent Contrasts (PIC) (Felsenstein 1985) and Phylogenetic Generalized Least Squares Regression (PGLS) (Grafen 1989). The most commonly used method has been PIC.

PIC involves the calculation of contrasts (branch length calibrated differences) between sister taxa. Regressions are then carried out on the contrasts (through the origin) as opposed to the raw data, which effectively removes the influence of relatedness (Felsenstein 1985). PIC can be used for the comparison of one continuous with one categorical trait and for two continuous traits.

R code:
require(ape) # Paradis (2006)
tree <- read.nexus("tree.nex") # A tree with branch lengths
data <- read.csv("data.csv", header=T,row.names=1) # Data with row names as taxon names and two traits
pic1 <-pic(trait1,tree) # Calculate contrasts for each trait
pic2 <-pic(trait2,tree)
piclm <- lm(pic1~pic2 -1) # Regress contrasts through the origin

On the other hand, PGLS is very similar to statistics employed by ecologists concerned about spatial autocorrelation (localities closer to each other are likely to be more similar). PGLS constructs a correlation matrix based on the distance of taxa on the tree. The matrix is then incorporated into a generalized linear model (Grafen, 1989). One advantage of PGLS over PIC is that it can accomodate several models of evolution (e.g. changes in evolutionary rate, stabilizing selection). In contrast, PIC assumes a Brownian Motion (stochastic; BM) process of trait evolution (Felsenstein, 1985). Although PIC and PGLS may be equivalent under a BM model (Blomberg et al. 2012), the comparison cannot be made under other evolutionary models (evolutionary models will be the subject of a coming post). PGLS can be used to compare two continuous traits (for the comparison of a continuous and categorical traits, Phylogenetic Generalized Estimating Equations (Paradis and Claude (2002)) function similarly to PGLS).

R code:
# PGLS assuming BM

require(ape)
tree <- read.nexus("tree.nex")

data <- read.csv("data.csv", header=T,row.names=1) 

gls1<- gls(trait1~trait2,data=data,correlation=corBrownian(phy=tree),method="ML")
# Using the maximum likelihood method enables the comparison of evolutionary models using AIC

This post has been rather brief but I intend to continue posts on this topic.

References

Blomberg, S.P., J.G. Lefevre, J.A. Wells, and M. Waterhouse. 2012. Independent contrasts and PGLS estimators are equivalent. Systematic Biology 61: 1-61.

Felsenstein, J. 1985. Phylogenies and the Comparative Method. The American Naturalist 125:1-15.

Grafen, A. 1989. The Phylogenetic Regression. Philosophical Transactions of the Royal Society of London B 326:119-157.

Paradis, E. and J. Claude. 2002. Analysis of Comparative Data Using Generalized Estimating Equations. Journal of theoretical biology 218:175–185.

Paradis, E. 2006. Analysis of Phylogenetics and Evolution with R. Springer Science+Business Media, LLC, New York.

Scientific Politics Part II

Scientific progress is built on testing hypotheses and refining our ideas about the world. We test and re-test hypotheses until we are satisfied that they are supported (or not). An inner belief or intuition is not enough to convince any scientist that a particular hypothesis is true. In other words, the words "I believe in evolution" are meaningless. It's the bountiful evidence for evolution that has convinced every biologist that it is a fact. The greatest thing about the majority of scientists is also that they're willing to discard even their most beloved hypotheses in the wake of new evidence.

However, I think it is sometimes easy to get involved in personal rivalry over competing hypotheses. I have seen people at conferences very nearly yelling at each other and even heard stories about death threats! This is not appropriate conduct for anyone, let alone an educated scientist. In my opinion, you should never take a disagreement with your hypothesis (no matter how awesome!) personally. It's not an insult. It is on disagreement that scientific progress is built! Every time a reviewer or fellow conference attendee disagrees with me, I take time to remind myself that they are enabling science to move forward. After all, if each of us stuck with our respective hypotheses and worked only to find support for them, we would not know that the earth revolves around the sun or that the big bang really happened!

But how should we disagree with each other in a productive way? If someone disagrees with you, it is their responsibility to demonstrate convincingly that your hypothesis is not supported and not to call you names (the reverse is also true). Probably the best outlet for debate is in the scientific literature. Firstly, publication avoids name calling and death threats (usually!). It is important to avoid personal slurs in print. This does not reflect on the person you are refuting, only on you. It doesn't do anyone any good to gain a reputation as a whiner. But if you are scientific and careful in presenting your evidence, you will gain a reputation as a respectable scientist. Secondly, publication involves other scientists in the debate. Broadening your audience will always bring unexpected insight.

Most importantly, if it is demonstrated that your beloved hypothesis is not supported, you should discard it. There is no merit in clinging to debunked ideas. Of course, I think the VAST majority of us scientists find new hypotheses exhilarating and are therefore unlikely to marry any particular one. That's what I love about science!!!

Scientific Politics Part I

I thought I would write about a very touchy subject tonight! That topic is politics. What I include in the term politics are things like authorship, how to deal with disagreements, and what to publish and when (especially when someone disagrees with you or works on a similar or even the same topic). Of course, being a PhD candidate and not a seasoned professor, I am far from an expert but I have some opinions that may or may not be the same ones that others hold.

I'll start with the subject of authorship. Who should be included on your papers, in what order, and why? These are difficult questions, especially when you're a budding scientist. I definitely want to collaborate and share authorship with people that I admire. For me, it has always been a relatively easy process but I usually have a frank discussion with collaborators about who should be included and in what order. It might seem like a subject that is uncomfortable to talk about, especially if you're worried about upsetting someone. But I guarantee that it is less uncomfortable than having a disagreement when it comes time to submit a publication or even after the paper has been published.

It is important to determine what the contributions of individuals will be to the paper. In my opinion, all authors must make an intellectual contribution. Acceptable contributions usually include analyses, writing, or data collection (or all of the above). There are some cases, however, when it is unclear if someone should be included on the paper. For example, if an individual has given you advice or ideas, do you include them on the paper? I think this depends on the gravity of the advice. Did they simply suggest you use a particular analysis? I would not include this person as an author. Did they come up with the idea? I would include this person. Whenever you feel an individual's contribution might merit inclusion on the paper, you should always proceed by asking them if they wish to be. If they decline, then you've done your political duty and made sure that there will be no hurt feelings. If they say yes, the next step is to determine the order of authorship.

For me, author order has always been obvious. The person who writes the paper and does most of the analyses is first author, the person who did some of the analyses or contributed some data is second author and so on. But I have never been involved in a project with more than three or four authors. Once the number of authors starts to grow, I think an explicit agreement should be made prior to any paper submission. Don't leave the discussion until it is too late or until someone is put off. Ask them outright! "Shall I include you as second/third/fourth/fifth author?"

The situation is a bit different when it comes to your supervisor. Different supervisors have different policies when it comes to authorship. Some prefer to be included on all papers and others make this judgement on a case by case basis. Sometimes supervisors prefer to be included as the last author (this is sometimes reserved for the PI) while others are open to any position depending on their contribution. Regardless, when you move on to a new supervisor you should always ask! That way no one will have any excuse to be angry. You'll also avoid pissing off the person that controls your funding!

I have only heard of people having major problems with authorship. I can imagine there have been situations when someone originally agreed to contribute but then failed to uphold their obligation. This can be politically difficult if this person is an office or lab mate but especially if they are a seasoned professor or famous researcher. I can't offer solid advice on this front but if you have made every effort to elicit a contribution, I think you would be justified in excluding them. A well worded email or phone call is likely to smooth things over.

Unfortunately, graduate students are often afraid to have these conversations or exclude authors that haven't contributed. All I can say is that, you're not at the bottom of the ladder. If you're going to put the effort into a project then you should be satisfied with everyone's contribution.

Leaving Ottawa in Ten Days!

I'm officially leaving Ottawa in ten days to study at the University of Wyoming for 9 months! I recently received a Fulbright Scholarship (http://www.fulbright.ca/ if you're interested in applying) to study under Dr. Mark Clementz. I will be investigating isotopic signatures in the hard tissues of pronghorn (Antilocapra americana) from the USA and Canada. I have to say that I am excited.

I applied to Dr. Clementz' and Dr. Rybczynski's labs in 2010 to start a PhD program. It was a difficult decision but I decided to pursue my PhD at Carleton and the Canadian Museum of Nature with Dr. Rybczynski. Now that I have received the Fulbright scholarship it feels like I am getting the best of both worlds. So in a couple of weeks I will be saying a temporary goodbye to Ottawa and will be heading to Laramie, Wyoming.

Laramie is about 2 hours northwest of Denver, Colorado. According to Wikipedia, Laramie is home to only 31,000 people and the University of Wyoming has approximately 14,000 students. At the University of Wyoming I will have access to a stable isotope laboratory, which is something that Carleton does not have. I hope that the experience will broaden my skill set and, let's face it, get me some more publications!

Aside from research, Laramie is located near several national parks and there is ample opportunity for enjoying the great outdoors. There is even a nearby ski hill (although I can't speak for it's quality, yet). I have been missing snowboarding since moving to Ontario. Skiing in the west is significantly better than in the east and I refuse to lower my standards.

It will be a challenge to be away from Canada for so long. It is somewhat difficult to travel into or out of Laramie as the nearest international airport is in Denver. But I am up for the challenge and look forward to interacting with the students in Laramie!

My new blog is a work in progress

I am (as it says to the right) a current PhD candidate at Carleton University and the Canadian Museum of Nature in beautiful Ottawa, Canada. My dissertation is primarily focused on the effects of climate change on Cenozoic mammals (primarily hoofed mammals). I have traditionally worked in the areas of ungulate dietary morphology and tooth wear but over the last two years or maybe more, I have been dabbling in the areas of macroevolution and phylogenetics. I think my interests are generally broad but I have tended focus on morphology and its effects on macroevolutionary patterns. I'm also interested in the successful combination of fossil and extant animals in studies of both macroevolution and climate change. As with most PhD students, I have a long way to go and want to document the process of discovery!