87 years ago, in ecology

Louis Emberger was an important French plant ecologist in the first half of the last century, known for his work on the assemblages of plants in the mediterranean.

For example, the plot below is his published diagram showing minimum temperature of the coolest month versus a 'pluviometric quotient' capturing several aspects of temperature and precipitation from:

Emberger; La végétation de la région méditerranienne. Rev. Gén. Bot., 42 (1930)

Note this wasn't an unappreciated or ignored paper - it received a couple hundred citations, up until present day. Further, updated versions have appeared in more recent years (see bottom).

So it's fascinating to see the eraser marks and crossed out lines, this visualisation of scientific uncertainty. The final message from this probably depends on your perspective and personality:

• Does it show that plant-environment modelling has changed a lot or that plant environmental modelling is still asking about the same underlying processes in similar ways?
• Does this highlight the value of expert knowledge (still cited) or the limitations of expert knowledge (eraser marks)? 

It's certainly a reminder of how lucky we are to have modern graphical software :)

E.g. updated in Hobbs, Richard J., D. M. Richardson, and G. W. Davis. "Mediterranean-type ecosystems: opportunities and constraints for studying the function of biodiversity." Mediterranean-Type Ecosystems. Springer Berlin Heidelberg, 1995. 1-42.











Thanks to Eric Garnier, for finding and sharing the original Emberger diagram and the more recent versions.

The value of ecology through metaphor

The romanticized view of an untouched, pristine ecosystem is unrealistic; we now live in a world where every major ecosystem has been impacted by human activities. From pollution and deforestation, to the introduction of non-native species, our activity has influenced natural systems around the globe. At the same time, ecologists have largely focused on ‘intact’ or ‘natural’ systems in order to uncover the fundamental operations of nature. Ecological theory abounds with explanations for ecological patterns and processes. However, given that the world is increasingly human dominated and urbanized, we need a better understanding of how biodiversity and ecosystem function can be sustained in the presence of human domination. If our ecological theories provide powerful insights into ecological systems, then human dominated landscapes are where they are desperately needed to solve problems.

From the Spectator

This demand to solve problems is not unique to ecology, other scientific disciplines measure their value in terms of direct contributions to human well-being. The most obvious is human biology. Human biology has transitioned from gross morphology, to physiology, to molecular mechanisms controlling cellular function, and all of these tools provide powerful insights into how humans are put together and how our bodies function. Yet, as much as these tools are used to understand how healthy people function, human biologists often stay focussed on how to cure sick people. That is, the proximate value ascribed to human biology research is in its ability to cure disease and improve peoples’ lives. 

In Ecology, our sick patients are heavily impacted and urbanized landscapes. By understanding how natural systems function can provide insights into strategies to improve degraded ecosystems. This value of ecological science manifests itself in shifts in funding and publishing. We now have synthesis centres that focus on the human-environment interaction (e.g., SESYNC). The journals that publish papers that provide applied solutions to ecological and environmental problems (e.g., Journal of Applied Ecology, Frontiers in Ecology and the Environment, etc.) have gained in prominence over the past decade. But more can be done.

We should keep the ‘sick patient’ metaphor in the back of our minds at all times and ask how our scientific endeavours can help improve the health of ecosystems. I was once a graduate student that pursued purely theoretical tests of how ecosystems are put together, and now I am the executive editor of an applied journal. I think that ecologists should feel like they can develop solutions to environmental problems, and that their underlying science gives them a unique perspective to improving the quality of life for our sick patients. 

What is a community ecologist anyways?

I am organizing a 'community ecology' reading group, and someone asked me whether I didn’t think focusing on communities wasn’t a little restrictive. And no. The thought never crossed my mind. Which I realized is because I internally define community ecology as a large set of things including ‘everything I work on’ :-) When people ask me what I do, I usually say I’m a community ecologist.

Obviously community ecology is the study of ecological communities [“theoretical ideal the complete set of organisms living in a particular place and time as an ecological community sensu lato”, Vellend 2016]. But in practice, it's very difficult to define the boundaries of what a community is (Ricklefs 2008), and the scale of time and space is rather flexible.

So I suppose my working definition has been that a community ecologist researches groups of organisms and understands them in terms of ecological processes. There is flexibility in terms of spatial and temporal scale, number and type of trophic levels, interaction type and number, or response variables of interest. It’s also true that this definition could be encompass much of modern ecology…

On the other hand, a colleague argued that only the specific study of species interactions should be considered as ‘community ecology’: e.g. pollination ecology, predator-prey interactions, competition, probably food web and multi-trophic level interactions. 

Perhaps my definition is so broad as to be uninformative, and my colleague's is too narrow to include all areas. But it is my interest in community ecology that leads me to sometimes think about larger spatial and temporal scales. Maybe that's what community ecologists have in common: the flexibility needed to deal with the complexities of ecological communities.

Reviewing peer review: gender, location and other sources of bias

For academic scientists, publications are the primary currency for success, and so peer review is a central part of scientific life. When discussing peer review, it’s always worth remembering that since it depends on ‘peers’, broader issues across ecology are often reflected in issues with peer review. A series of papers from Charles W. Fox--and coauthors Burns, Muncy, and Meyer--do a great job of illustrating this point, showing how diversity issues in ecology are writ small in the peer review process.

The journal Functional Ecology provided the authors up to 10 years of data on the submission, editorial, and review process (between 2004 and 2014, maximum). This data provides a unique opportunity to explore how factors such as gender and geographic local affects the peer review process and outcomes, and also how this has changed over the past decade.

Author and reviewer gender were assigned using an online database (genderize.io) that includes 200,000 names and an associated probability reflecting the genders for each name. Geographic location of editors and reviewers were also identified based on their profiles. There are some clear limitations to this approach, particularly that Asian names had to be excluded. Still, 97% of names were present in the genderize.io database, and 94% of those names were associated with a single gender >90% of the time.

Many—even most—of Fox et al.’s findings are in line with what has already been shown regarding the causes and effects of gender gaps in academia. But they are interesting, nonetheless. Some of the gender gaps seem to be tied to age: senior editors were all male, and although females make up 43% of first authors on papers submitted to Functional Ecology, they are only 25% of senior authors.

Implicit biases in identifying reviewers are also fairly common: far fewer women were suggested then men, even when female authors or female editors were identifying reviewers. Female editors did invite more female reviewers than male editors. ("Male editors selected less than 25 percent female reviewers even in the year they selected the most women, but female editors consistently selected ~30–35 percent female").  Female authors also suggested slightly more female reviewers than male authors did.

Some of the statistics are great news: there was no effect of author gender or editor gender on how papers were handled and their chances of acceptance, for example. Further, the mean score given to a paper by male and female reviewers did not differ – reviewer gender isn’t affecting your paper’s chance of acceptance. And when the last or senior author on a paper is female, a greater proportion of all the authors on the paper are female too.

The most surprising statistic, to me, was that there was a small (2%) but consistent effect of handling editor gender on the likelihood that male reviewers would respond to review requests. They were less likely to respond and less likely to agree to review, if the editor making the request is female.

That there are still observable effects of gender in peer review despite an increasing awareness of the issue should tell us that the effects of other forms of less-discussed bias are probably similar or greater. Fox et al. hint at this when they show how important the effect of geographic locale is on reviewer choice. Overwhelmingly editors over-selected reviewers from their own geographic locality. This is not surprising, since social and professional networks are geographically driven, but it can have the effect of making science more insular. Other sources of bias – race, country of origin, language – are more difficult to measure from this data, but hopefully the results from these papers are reminders that such biases can have measurable effects.

From Fox et al. 2016a. 

References:

  Fox, C. W., Burns, C. S., Muncy, A. D., Meyer, J. A. (2016), Author-suggested reviewers: gender differences and influences on the peer review process at an ecology journal. Functional Ecology. doi: 10.1111/1365-2435.12665

Fox, C. W., Burns, C. S., Muncy, A. D. and Meyer, J. A. (2016), Gender differences in patterns of authorship do not affect peer review outcomes at an ecology journal. Funct Ecol, 30: 126–139. doi:10.1111/1365-2435.12587

Fox, C. W., Burns, C. S., Meyer, J. A. (2016), Editor and reviewer gender influence the peer review process but not peer review outcomes at an ecology journal. Functional Ecology, 30: 140–153. doi: 10.1111/1365-2435.12529

Examples of pre-interview questions

Last year, several postdocs at my institute (including me) were applying for faculty positions at North American institutions. Frequently, before on campus interviews, a 'long' list of people are asked to take part in phone/Skype interviews before a short list for campus visits is decided on. Since this step is now so common, postdocs put together an informal list of all the questions people had been asked during this initial interview*.

I found the list helpful. The usual caveats apply - different types of institutes and search committees will have different priorities and focus on different types of questions (e.g. teaching vs. research). Thinking about the answers to these questions ahead of time can be helpful for developing a vision of how you approach teaching and research, and being clear in how you communicate that.

(*Thanks to Iris Levin for originally curating this list)

Big picture questions:
Why X institution?
What do the liberal arts mean to you? Why are you interested in a career at a liberal arts college?
Tell us about contributing to XX college’s emphasis on liberal arts in practice, interdisciplinary and/or international aspects of education
How will our Biology Dept enhance your teaching and research?

Teaching focused questions:

General approach

What courses are you best suited to teach and how would you teach it?

What does a typical day in your class look like?

What do you feel you would add to graduate and undergraduate training in the department?

What is the biggest challenge in teaching?

You will teach X course every semester, how would you keep it exciting?

How would you teach a lab differently for introductory, intermediate or advanced students?

Specifics about courses
How would you teach X class?
What sort of interdisciplinary and/or first-year seminar course would you teach?
What sort of non-majors course would you teach? How would you teach it differently for non-majors vs. majors?

What new course(s) would you develop and how?
Tell us about your approach to teaching an XXX course for students who have had one introductory biology course

Tell us about incorporating quantitative and analytical reasoning into an XXX course

Tell us about using open-ended, inquiry-based group work in an introductory biology course

Research focused questions:
Approach and interests
Briefly summarize your most significant research contribution.

Tell us about your research program

You work on xyz – how would you conduct your research here?

How do you see your research complementing that of others in the department, and what do you view as your unique strengths?

Where do you see yourself in 5 years? Where do you see yourself in 10 years?

Who would you collaborate with here? 

How would you collaborate with faculty and bridge different fields?

What sort of projects would you do with graduate students? 

How would undergrads be involved with your research and what would the outcomes be?

Tell us about your approach to mentoring undergraduates in research

Funding
What sources of funding would you pursue to support your research program?
What grants would you apply to? 

Integration with teaching?
What contributions would your research make to these courses?
How would you involve students in your research outside or inside classroom

Misc (what type of colleague would you be?):
How would you contribute to the larger campus community?
How do you address diversity in your teaching and research?
What do you feel you can contribute to efforts to cultivate a wide diversity of people and perspectives at XX College?
Describe what you know about X college, how you would fit in, and any concerns.
How do you deal with conflict?
What has been the biggest obstacle in your professional development?


If you have more to add, please comment!

Science in many languages.

The lingua franca of biology is English, although through history it has variously been Latin, German, or French. Communication is fundamental to the modern scientific landscape, and English dominates the international ecological community. To be indexed by SCOPUS, a journal must be written at least in part in English. All major ecological journals are published in English, and clear, understandable writing is unquestionably an advantage in having work published. Large international conferences are usually conducted in English. Sometimes there is no translation for a key word and the English version is used directly, regardless of the language of the conversation. Even base commands in coding languages like R are in English. There is an undeniable but some times unmentioned advantage to being a native English speaker in science.

A common language is inevitable and necessary to communicate in a time of global connectivity, but it is also necessary to acknowledge that many scientists speak English as a second (or third, or fourth) language and barriers can arise as a result of this. The energy activation to move between languages is high for people, and it can take longer to read and write. But sometimes the costs are more subtle: for example, students may be less likely to give oral talks at conferences as a result of concerns about being understood. Even if they are relatively proficient, the question period after talks is difficult, since questions are often spoken quickly, are not clear, and are expressed in a variety of accents. That’s a difficult situation to address directly, but there are ways to facilitate communication across a variety of English proficiencies. And many of these are simply good practices for communication in any language.

First: slow down. Some of us are guiltier than others, but if you speak too fast, you lose listeners. This is another reason to consciously try to breath and relax during presentations and lectures. Some people speak so quickly that even the native English speakers have trouble following along. Now imagine listening to that talk while needing a little extra processing time.

When you give lectures and presentations, make sure that the slides and the verbal component both provide the overall message. I’ve followed talks in French and Spanish before, because the slides were well-composed (and in English). If someone misses something you say, it should be possible to follow the important points by the slides alone. And vice versa. This is good advice for any talk. Don’t be boring, but also be aware of when overuse of idioms or culture-specific references prevent understanding.

Sometimes fluent English speakers unknowingly dominate conversations because they speak faster and may be more confident in expressing themselves. In group activities like workshops and meetings, allow breaks in the conversation so that non-native speakers (or just less dominating personalities and quieter people) have a chance to express themselves as well.

An ear for accents comes from practice listening. Practice speaking improves accent. It’s a mutually beneficial relationship.

Also, remember that culture and language interact. English is interesting in that we have no pronouns differentiating between formal and informal relationships (we have ‘you’, not ‘tu’/‘vous’, etc.). This can make English speakers seem informal and friendly, or disrespectful, depending on the context. Keep this context in mind when interpreting interactions.

Rebuttal papers don’t work, or citation practices are flawed?

Brian McGill posted an interesting follow up to Marc’s question about whether journals should allow post-publication review in the form of responses to published papers. I don’t know that I have any more clarity as to the answer to that question after reading both (excellent) posts. Being idealistic, I think that when there are clear errors, they should be corrected, and that editors should be invested in identifying and correcting problems in papers in their journals. Based on the discussions I’ve had with co-authors about a response paper we’re working on, I’d also like to believe that rebuttals can produce useful conversations, and ultimately be illuminating for a field. But pragmatically, Brian McGill pointed out that it seems that rebuttals rarely make an impact (citing Banobi et al 2011). Many times this was due to the fact that citations of flawed papers continued, and “were either rather naive or the paper was being cited in a rather generic way”.

Citations are possibly the most human part of writing scientific articles. Citations form a network of connections between research and ideas, and are the written record of progress in science. But they're also one of the clearest points at which biases, laziness, personal relationships (both friendships and feuds), taxonomic biases, and subfield myopia are apparent. So why don't we focus on improving citation practices? 

Ignoring more extreme problems (coercive citations, citation fraud, how to cite supplementary materials, data and software), as the literature grows more rapidly and pressure to publish increases, we have to acknowledge that it is increasingly difficult to know the literature thoroughly enough to cite broadly. A couple of studies found that 60-70% of citations were scored as accurate (Todd et al. 2007; Teixeira et al. 2013) (Whether you can see that as too low or pretty high depends on your personality). Key problems were the tendency to cite 'lazily' (citing reviews or synthetic pieces rather than delve into the literature within) or 'naively' (citing high profile pieces in an offhand way without considering rebuttals and follow ups (a key point of the Banobi et al. piece)). At least one limited analysis (Drake et al. 2013) showed that citations tended to be much more accurate in higher IF journals (>5), perhaps (speculating) due to better peer review or copy editing. 

Todd et al (2007) suggest that journals institute random audits of citations to ensure authors take greater care. This may be a good idea that is difficult to institute in journals where peer reviewers are already in short supply. It may also be useful to have rebuttal papers considered as part of the total communication surrounding a paper - the full text would include them, they would be automatically downloaded in the PDF, there would be a tab (in addition to author information, supplementary material, references, etc) for responses. 

More generally - why don't we learn how to cite well as students? The vast majority of advice on citation practices with a quick google search regards the need to avoiding plagiarism and stylistic concerns. Some of it is philosophical, but I have never heard a deep discussion of questions like, 'What’s an appropriate number of citations – for an idea?'; 'For a manuscript?'; 'How deep do I cite? (Do I need to go to Darwin?)'. It would be great if there were a consensus advice publication, like the sort the BES is so good at on best practices in citation.

Which is to say, that I still hope that rebuttals can work and be valuable.

How to deal with poor science?

Publishing research articles is the bedrock of science. Knowledge advances through testing hypotheses, and the only way such advances are communicated to the broader community of scientists is by writing up the results in a report and sending it to a peer-reviewed journal. The assumption is that papers passing through this review filter report robust and solid science.

Of course this is not always the case. Many papers include questionable methodology and data, or are poorly analyzed. And a small minority actually fabricate or misrepresent data. As Retraction Watch often reminds us, we need to be vigilant against bad science creeping into the published literature.

Why should we care about bad science? Erroneous results or incorrect conclusions in scientific papers can lead other researchers astray and result in bad policy. Take for example the well-flogged Andrew Wakefield, a since discredited researcher who published a paper linking autism to vaccines. The paper is so flawed that it does not stand up to basic scrutiny and was rightly retracted (though how it could have passed through peer review is an astounding mystery). However, this incredibly bad science invigorated an anti-vaccine movement in Europe and North America that is responsible for the re-emergence of childhood diseases that should have been eradicated. This bad science is responsible for hundreds of deaths.

From Huffington Post 

Of course most bad science will not result in death. But bad articles waste time and money if researchers go down blind alleys or work to rebut papers. The important thing is that there are avenues available to researchers to question and criticize published work. Now days this usually means that papers are criticized through two channels. First is through blogs (and other social media). Researchers can communicate their concerns and opinion about a paper to the audience that reads their blog or through social media shares. A classic example was the blog post by Rosie Redfield criticizing a paper published in Science that claimed to have discovered bacteria that used arsenic as a food source.

However, there are a few problems with this avenue. First is that it is not clear that the correct audience is being targeted. For example, if you normally blog about your cat, and your blog followers are fellow cat lovers, then a seemingly random post about a bad paper will likely fall on deaf ears. Secondly, the authors of the original paper may not see your critique and do not have a fair opportunity to rebut your claims. Finally, your criticism is not peer-reviewed and so flaws or misunderstandings in your writing are less likely to be caught.

Unlike the relatively new blog medium, the second option is as old as scientific publication –writing a commentary that is published in the same journal (and often with an opportunity for the authors of the original article to respond). These commentaries are usually reviewed and target the correct audience, namely the scientific community that reads the journal. However, some journals do not have a commentary section and so this avenue is not available to researchers.

Caroline and I experienced this recently when we enquired about the possibility to write a commentary on an article was published and that contained flawed analyses. The Editor responded that they do not publish commentaries on their papers! I am an Editor-in-Chief and I routinely deal with letters sent to me that criticize papers we publish. This is important part of the scientific process. We investigate all claims of error or wrongdoing and if their concerns appear valid, and do not meet the threshold for a retraction, we invite them to write a commentary (and invite the original authors to write a response). This option is so critical to science that it cannot be overstated. Bad science needs to be criticized and the broader community of scientists should to feel like they have opportunities to check and critique publications.

I could perceive that there are many reasons why a journal might not bother with commentaries –to save page space for articles, they’re seen as petty squabbles, etc. but I would argue that scientific journals have important responsibilities to the research community and one of them must be to hold the papers they publish accountable and allow for sound and reasoned criticism of potentially flawed papers.

Looking over the author guidelines of the 40 main ecology and evolution journals (and apologies if I missed statements -author guidelines can be very verbose), only 24 had a clear statement about publishing commentaries on previously published papers. While they all had differing names for these commentary type articles, they all clearly spelled out that there was a set of guidelines to publish a critique of an article and how they handle it. I call these 'Group A' journals. The Group A journals hold peer critique after publication as an important part of their publishing philosophy and should be seen as having a higher ethical standard.

Next are the 'Group B' journals. These five journals had unclear statements about publishing commentaries of previously published papers, but they appeared to have article types that could be used for commentary and critique. It could very well be that these journals do welcome critiques of papers, but they need to clearly state this.

The final class, 'Group C' journals did not have any clear statements about welcoming commentaries or critiques. These 11 journals might accept critiques, but they did not say so. Further, there was no indication of an article type that would allow commentary on previously published material. If these journals do not allow commentary, I would argue that they should re-evaluate their publishing philosophy. A journal that did away with peer-review would be rightly ostracized and seen as not a fully scientific journal and I believe that post publication criticism is just as essential as peer review.

I highlight the differences in journals not to shame specific journals, but rather highlight that we need a set of universal standards to guide all journals. Most journals now adhere to a set of standards for data accessibility and competing interest statements, and I think that they should also feel pressured into accepting a standardized set of protocols to deal with post-publication criticism. 

Thoughts on successful postdoc-ing

Unlike grad school, postdoc positions start and end without much fanfare. If grad students are apprentices, postdocs are the journeymen/women of the trade. (Wikipedia defines journeymen as… “considered competent and authorized to work in that field as a fully qualified employee… [but] they are not yet able to work as a self-employed master craftsman.”) Though short compared to a PhD, postdoc jobs are an important stepping stone towards a 'real' job, be that another postdoc, or a position inside or outside of academia. There’s less advice out there about being successful as a postdoc, and often you are on your own to figure things out. I’m finishing a first postdoc this week, and moving on to a second one, and while I think the last 2 years worked out well, they took their own, unexpected path. Some of this is good advice that I was given, some comes from experience or observation, some I even manage to follow :-) *

Choose carefully. If you have some choice, be strategic in choosing a postdoc job. Decide what the position is going to accomplish for you: that may be expanding your skill set, such as by learning a new experimental system or additional analytical techniques; improving your current skills by working with an expert; being involved in high profile research; or being in a certain locale for various reasons. Beware projects too far from your current skill set – the risk is that the learning curve may be so steep that you will be barely competent at the end, and have little to show for your time. Of course, you might decide to use a postdoc to pursue interdisciplinary work, or move away from your dissertation work, in which case this is a risk worth taking.

Because postdocs are short, it may seem as though having a good fit with your supervisor is less important. Don’t assume that your new supervisor be broadly similar in approach to your previous supervisor (or an improvement). Mismatched expectations between supervisors and postdocs seem pretty common and it’s important to get an understanding of what your role is beforehand. The variation in expectations from supervisor to supervisor is huge - from those that require time sheets and expect strict hours, to those that give you total autonomy. Does your supervisor see postdocs as colleagues? 9-5 employees? Advanced students? Lab managers? Talk to friends, colleagues, and students. This may depend on the source of funding as well - will you be working on a specific existing project with specific timelines (common in the US where many postdocs are funded off of NSF grants), or are you funded by a fellowship and therefore more independent?

Get to know your neighbours. Once you’ve chosen and started your postdoc, the most important thing to do is to establish connections in your lab and department immediately. I cannot emphasize this enough. Don’t wait to settle in, or get on top of some papers, or hope people in the hallway will introduce themselves. Postdoc positions are short, and in many departments postdocs are isolated, not students but not really faculty. This can lead to feelings of disconnection, loneliness, and frustration. Seek out the other postdocs - join or organize postdoc social events, go to lab meetings and journal clubs, get the department to maintain an active postdoc email list. Not only will this give you a sense of belonging, but now you have people to talk to (and sometimes rant to), with whom to navigate administrative issues, and potential collaborators. Postdocs are an invaluable resource for job applications as well: they usually have the most up-to-date experience on the job market, and can provide great feedback on job applications and practice job talks. For example, the postdocs in my current department built an exhaustive list of potential questions asked during academic interviews, and shared interview horror stories over drinks.

Mental health and life balance. Postdocs don’t get the kinder, gentler approach sometimes given to grad students and people expect you to stand on your own. This can reignite imposter syndrome. There is no easy solution to this, but some combination of taking care of yourself, working on that mythical thick skin, and highlighting the positive events in your life can help.

Time management continues to become more important, at least for me. More than in grad school, you have to actively decide how much work you want to be doing. There is always something that you *could* be working on, so start scheduling when things will get done based on priority, energy, etc, is important. In addition, people start inviting you to things or asking for you input on projects. Learn to say no. Be strategic about your time management – it’s flattering to wanted, but time is limited and not all invitations are of equal value towards your specific goals.

Practice professional networking. On the other hand, don’t say no to everything: networking and the opportunities it creates are very helpful. Focus on the professional areas that are of interest to you, but consider joining and being active in ESA sections (including the Early Career section) or other relevant organizations; organize workshops or symposia at conferences; host invited speakers. If your department hosts an external seminar series, take advantage (nicely!) of the revolving cast of scientists. They are a great way to make connections with people whose work you admire, and even speakers you have less in common with are great practice for networking skills. From experience, if you have breakfast with a different visiting speaker every week, you will quickly improve your description of your research and your ability to keep a conversation going (also, you will become an expert on your city’s breakfast places). These are helpful skills to have for faculty interviews, for talking to the media and press, even for telling your family what you do.

Take initiative. You are your own advocate now. If you wish you could learn something, or be invited to a working group, or get teaching experience, look into making it happen yourself. This may include organizing working groups (many provide competitive funding, for example, iDiv/sDiv, CIEE (Canada), the new NCEAS, SESYNC), applying for small grants and other project funding on your own, recruiting undergraduates and mentoring them, organizing or co-teaching courses.

Similarly, don’t stop learning new things. Inertia gets higher the less time you have, and it can be hard find the time to pick up the next skill.

Publish. Focus on publishing (if you are interested in academic jobs)– this may be obvious, but publishing is more important than ever as a postdoc. You need to show that you are independently able to produce work after leaving your PhD lab. This counters the ‘maybe they just had a good supervisor’ concern. It can be hard to find time to work on both current and past projects, but try to. From experience (and illustrated by the periodic emails from my PhD supervisor), the longer your dissertation chapters sit around, the less likely they are to ever be published…

Know what your dream job is, and apply for it if you see it. Be willing to move on if something better comes up. Postdocs usually have to think in the short-term, because most funding is in 1-2 year increments. So keep an eye on new sources of funding/positions. Make decisions based on your needs (be they career-related, family-related, whatever): it’s easy to feel guilty moving on from one unfinished position to another, but the reality is that postdocs are temporary and fleeting.

I was told to start applying for jobs as early as I felt reasonably qualified. The logic was that the best practice for job interviews is doing actual job interviews, and further, it is better to fail when it doesn’t matter, rather than when it is your dream job.

well, sort of...

*Obviously nothing is one-size-fits all, and this is mostly aimed at people who plan to apply for faculty jobs eventually. Other advice, especially for non-academic tracks, would be welcome in the comments!

Debating limits on diversity in class

I wrote a while ago about the debate on whether global diversity has ecological limits, based on two papers from Harmon and Harrison, and Rabosky and Hurlbert. This was in turn based on a debate from the ASN meeting (aside: there should be more formal debates at conferences). I decided to try replicating this debate in the Advanced Ecology class I'm teaching with Kendi Davies, and I was pleasantly impressed with the outcome. The class is mostly upper year students and small (~25 people), and the focus is on reading the primary literature and exploring key topics in ecology using active learning techniques (e.g. 1, 2). Since we're reading about patterns and processes of diversity through space and time, the debate topic was fitting.

The debate was split over two classes - in the first, students were split into two groups and they prepared their opening and closing statements and their supporting arguments. I've tried having students use Google documents and slides for these kind of group collaborative activities, and it seems to work well. (This is in part because there are 'lender laptops' available from the department's IT, which means that all students can participate, even without owning a personal laptop). What is great about Google docs is that when anyone adds or removes or edits text, the other members of the group can see it in real time, which seems to encourage more students to be actively involved than if, say, a single student is taking notes. Each group decided who would present the opening statement, each supporting argument, the rebuttal statement, and the closing statement, and who would take notes and prep the rebuttal.

To raise the stakes a bit, the winning team would get a pass on one homework assignment (the other motivator presumably being fear of letting their group down). What impressed me was how engaged students were during prep and during the actual debate. (For example, during prep, students were watching videos on how to debate, and expressed some concerns about espionage by the other teams ;-) ) More seriously, they took the time to understand the arguments presented in the source literature, and went beyond that to integrate support from other primary literature. I think at times students (okay, most of us) can get away with skimming papers for the key points: this rewarded them for reading carefully and thoughtfully.

Current US political debates provided instruction
on what not to do (from cnn.com).

The judges were a few generous postdocs (motivated by the promise of free food), who not only scored the debates, but gave some feedback to the teams. Ironically, the winning team had argued that “Species Diversity Is Dynamic and Unbounded at Local and Continental Scales” (after Harmon and Susan Harrison), but the class was nearly unanimous that they personally felt that there likely were ecological limits on diversity.

What I would do differently next time:

• Plan some redundancy - a couple of people were sick, etc, who had roles in the debate. This left team members scrambling a bit. 
• Group sizes: 12 people is a bit big for a group and makes coordination difficult. It might be possible to have smaller groups and do 2 sets of debates. Or, alternatively, to assign half the class as judges (or press - another prof here uses students as press who have to prepare questions for the debaters).
• Consider not randomly assigning people to groups - it might be better to try to balance teams.
• Public speaking and argument logic - interestingly, most of the students have little experience in constructing convincing and well supported arguments. We talk a lot about hypothesis construction with STEM students, but persuasive speech and writing receive less attention. Things like 'signposting' important points could use more practice.

Pulling a fast one: getting unscientific nonsense into scientific journals. (or, how PLOS ONE f*#ked up)

The basis of all of science is that we can explain the natural world through observation and experiments. Unanswered questions and unsolved riddles are what drive scientists, and with every observation and hypothesis test, we are that much closer to understanding the universe. However, looking to supernatural causes for Earthly patterns is not science and has no place in scientific inquiry. If we relegate knowledge to divine intervention, then we fundamentally lose the ability to explain phenomena and provide solutions to real world problems.

Publishing in science is about leaping over numerous hurdles. You must satisfy the demands of reviewers and Editors, who usually require that methodologies and inferences satisfy strict and ever evolving criteria -science should be advancing. But sometimes people are able to 'game the system' and get junk science into scientific journals. Usually, this happens by improper use of the peer review systems or inventing data, but papers do not normally get into journals while concluding that simple patterns conform to divine intervention.

Such is the case in a recent paper published in the journal PLOS ONE. This is a fairly pedestrian paper about human hand anatomy and they conclude that anatomical structures provide evidence of a Creator. They conclude that since other primates show a slight difference in tendon connections, a Creator must be responsible for the human hand (well at least the slight, minor modification from earlier shared ancestors). Obviously this lazy science and an embarrassment to anyone that works as an honest scientist. But more importantly, it calls into question the Editor who handled this paper (Renzhi Han, Ohio State University Medical Center), but also PLOS ONE's publishing model. PLOS ONE handles thousands of papers and requires authors to pay for the costs of publishing. This may just be an aberration, a freak one-off, but the implications of this seismic f$@k up, should cause the Editors of PLOS to re-evaluate their publishing model.  

Things to keep in mind when finding a PhD

A wonderful student who worked with me when I was a graduate student is in the midst of applying for graduate school, and has been going through the process of finding a suitable program and advisor. It's been nearly 7 years (!?) since I was first in graduate school and, in my case, I mostly lucked my way from undergraduate to a great lab without nearly enough due diligence (and no one I knew or in my family had been to grad school to provide advice).

If asked during grad school, I had a list of advice I would have liked to have received (admin questions, funding issues, how to get to campus on public transport). But the advice I think is important has actually changed a lot, from just “make sure you love research” (although you should, at least most of the time), to more strategic and practical considerations.

I now think the most important thing is to ask yourself while you consider graduate school is, "Why do I want to get a PhD?" Note that there is absolutely no right answer to this question, but there are some wrongs ones, e.g. "I don’t know what else to do next" or "I have good grades". The problem is that these answers aren’t enough to motivate you through a PhD program. And some people find themselves 5 years later, still not knowing what they’re going to do next or why they got a PhD. It’s okay to answer "I like the research I did as an undergrad" or "I want to develop strong quantitative skills", or "I love working with ideas", because these kind of answers mean you want something from your experience and you've thought about what that is.

Educate yourself about the opportunities that a PhD will bring, both academic and non-academic. Continue this education while you are in graduate school. [Departments, offer more opportunities for students to learn about non-academic jobs.] The reality is that getting the oft-desired research professorship is very difficult (e.g. 200+ applicants for a general ecology position is not unusual). But PhDs produce desirable skill sets and there are other opportunities, so long as you are aware of them. There are many LACs (liberal arts schools) in the US, and thus more teaching oriented professorships advertised every year than there are R1 professorships. There are NGO and government research jobs. And as many of my grad school friends leave academia, it’s a relief to see that their skills – strong quantitative abilities, good data management, a clarity of vision on how to ask questions and answer them with appropriate data – make them employable across a range of professions.

Ask questions ask questions ask questions. Don’t go into a program without knowing what it will entail. Ask the same questions of both faculty and students and see how their answers compare. 

To understand a department, you want to know what the teaching load is on average, how funding works (and for how long!). You should find out the average time to completion of a PhD program, what classwork looks like, whether there are student-lead reading or discussion groups? Is there funding for student travel to conferences or meetings?

If you have a lab in mind, you need to similarly learn about that lab. Find out, from both the PI and their students, how the lab works. What is the supervisory style? Does the PI tend to be hands on, or expect more independent research? How does your personal approach to working mesh with their style? Don't assume that if you like to have structure and feedback and the PI only is around once a month, it will just work out. How often are they physically on campus? How often would you meet? What are other students in the lab working on? Is the lab collaborative? Do students publish together? What skills are emphasized in the group? Has the PI published recently (last 2-3 years, depending on context) and, perhaps most importantly, have they graduated any students? If not, try to figure out why.

Once you’ve found a place, remember that how you feel about your PhD will rise and fall all the time. That’s normal. Avoid the worst of these dips by taking care of your mental health. The sort of unstructured, isolating, often un-rewarded work that goes into a PhD can be draining. But it is also 100% okay to change your mind, to decide a Master’s is sufficient, to hate everything you are doing and quit. Seriously. The sunk-cost fallacy will make you (and people around you) miserable.

Of course, grad school—like life—is stochastic and full of uncertainty. But its possible, with care to increase the probability that you find a supportive, nurturing lab and have a wonderful time as a graduate student.