Scientific Presentations: the Dos and Don'ts

 www.pixshark.com

With the ESA submission deadline just passing, the Cadotte Lab decided that it would be helpful to dish out a few tips on how to make a presentation that is both enjoyable for your audience and fun for you to give. Presenting in front of people is never easy; giving a presentation about your own study can be even harder since you have to condense months (or even years) worth of information into a 15 minute time period. So with this in mind here are a few tips for each of the main sections of a presentation:

Note, the percentage by each section heading indicates the relative amount of time you should spend on that section.

Title Slide (5%)

www.nichecartoons.com

This is the first chance you’ll get to catch your audience’s attention, so be interesting!

The title of your presentation depends on the type of audience you’ll be presenting to, so gauge it accordingly. If your audience is a bunch of people with only general biology backgrounds or people that are from completely different fields then don’t complicate things using heavy jargon.

Generally for the title, you want to:

• Be witty and interesting
• Convey the main message or main result from your study

If you’re speaking to a broad audience it could be helpful to have a broad title and then separate it from a more specific title.

Besides the title you’ll also want to include your name and affiliation. Depending on the type of talk, for instance an honors thesis, you should also include your supervisor’s name. If you are collaborating with many people on a study you should also include their names. However, make sure that your name is on the first slide, since you are the presenter, and then on a second slide include a special acknowledgement of the other people involved. It’s also recommended that you acknowledge these people throughout the talk, such as in the methods. 

Introduction (10-15%)

Don’t make this section too long. Give just enough background that the audience can understand the concepts that you’ll be discussing and how it relates to the question you are trying to answer.

Generally for the introduction, you want to:

• Have the background information displayed in a simple to understand way
• You could use info-graphs here to reinforce an idea
• By the 2^nd or 3^rd slide you’ll want to state your study objectives or hypotheses
• You could create ‘toy’ graphs to describe your hypotheses / predictions

Methods (10-15%)

Be very concise with this section. Everyone understands that a lot of work went into performing your study; however, you don’t want to overwhelm your audience with all the nitty-gritty things you had to do. Give enough detail that people understand what you did and if possible try and summarize your methods in a simple figure.

Generally for the methods, you want to talk about:

• The treatments used, sample size, the measurements taken and how they were done, and the statistics that you performed

A note on statistics: try to steer clear of very complicated statistics. Most likely your audience will have a basic understanding of stats, but you may lose people if you get too complicated. When talking about your stats, make sure that you can give an easy to understand explanation of how they work.

Results (50%)

This is the biggest and best section; it’s where you get to show people all the cool and exciting things you’ve done! However, the only way you can convey how awesome your results are is by clearly explaining them.

Generally for the results, you want to:

• Stick to the main results
• You may have a lot different results but always make sure that what you are describing relates directly to the main message of your study
• Don’t overwhelm your audience
• Always thoroughly describe your graphs
• Describe what variables were you examining (the axes)
•  Why is the graph important?
• What is the relationship that the graph is showing?
• The title of the slide could be used to state what the result is
• You’ve spent a lot of time making these graphs and analyzing them - so you know them very well, but your audience doesn’t yet. Take time to walk them through the graphs.
•  If you’re showing several graphs in sequence, make sure to note if the axes are changing
•  If the graphs are very similar it might be helpful to have a break between slides or to use an animation.
•  Don’t show too many stats
•  Just state the p-values and which stats were used
• Avoid tables if possible
•  Summarize all the information in an easy to follow figure
•  If you can’t avoid using a table make it as appealing as possible
•   Highlight key parts or add arrows to show trends if they exist

Discussion (20%)

Now start bringing everything back together. Your audience may have gotten lost during your results section, so now is the time to refocus them so that they can see the big picture.

Generally for the discussion, you want to:

• Restate your hypotheses
• Restate you main results
• Describe how you could improve your study
• Describe the next steps for your work and the field in general

In the end you’ll want to describe the broader implications of your work and give the audience a take home message so that they know that your work is bettering the field in some way.  

Acknowledgements (5%)

Don’t forget to thank everyone who has helped you through this whole process! This includes your supervisor, people who helped you with data analysis or revising your paper, or all the volunteers you helped you conduct your field work or lab work. You’ll also want to acknowledge your institution as well as anyone who provided funding to your project.

General tips

Here’s a quick list of tips to use throughout your presentation:

• Use large text font
• Don’t be flashy, make sure it’s easy to read

• Don’t put too much text on a slide
• This distracts the audience

•  Don’t put any important point (text or an image) at the bottom 1/3^rd of a slide
• Depending on the room you are presenting in it may be very hard for the audience to see it
• In general, try and keep everything within the top 2/3^rd of the slide

• Don’t put too many animations on a slide
• This can be very distracting for the audience

• Don’t read off your slides
•  Use presenter view if you can’t memorize everything

• Including outlines
•  Not necessary in a short talk, but could be helpful in a longer talk

• If you run out of time
• Panic on the inside not the outside!
• Acknowledge that you’re running out of time and start wrapping things up
• Start talking about the broad implications of your work and maybe future directions you plan to take
• If you have more slides, skip over them but tell the audience what you were planning on showing. If they ask questions about what you were going to show you can go back to those slides

• Don’t talk too fast!
• Everyone gets nervous! Take a deep breath and calm yourself down, the calmer you are the easier it is for your audience to follow you

In praise of difficult questions.

There were a lot of people at my graduate institution who weren’t afraid to ask probing, thoughtful, difficult questions. They asked them seemingly without any concern about making the recipient feel bad, although students were more likely to receive kinder versions, and they asked them at departmental talks, committee meetings, student seminars, and at faculty interviews. I’ll admit there were times when this made me uncomfortable, and it certainly contributed no small amount of anxiety before giving talks there (and I’m sure I’m not the only person who felt that way).

These days I find myself missing those tough questions, not because I enjoy confrontation per se, but because they made an important contribution to my education.

To be clear, bullying questions or competitive questioning meant to highlight the questioner’s intelligence are a waste of time (e.g. two minutes of talking about your research followed by "what do you think about that?"). Critical thinking, while one of the most important aspects of a post-graduate education, can't be taught. But tough questions and questioners model critical thinking for students in the most direct way. Being at the front of the room talking does not automatically grant expert status: the speaker's ideas must be clear and robust to debate. 

Difficult questions benefit a speaker too - they are the clearest demonstration that the audience has engaged with their work. The most useful talks are those in which the questions are thought provoking for both the speaker and the audience. 

And finally, it can be refreshing when a questioner holds a person to actually answering the question. Science is built on debate and some times disagreement. Hard questions made me feel that the people asking them were expressing a preference for good science, even if the cost was some discomfort or social unease. And that feels like an important thing to express.

Distilling an ocean of theory and adding a few of your own drops

I recently completed my PhD qualifying exam at the University of Toronto-Scarborough for the Department of Physical and Environmental Science. Prior to going through the process the exam took on a sort of “black box” quality where I’d seen colleagues pass through unscathed but the depth of questioning that took place during the oral examination remained unclear. So I thought it might be of some value to comment on my experience with the process.

The format of these exams is fairly variable across departments and between institutions with some requiring the production of several essays in a short period of time, some based on an extensive readings list, some formatted as a proposal defense and others including some or all of these components. My exam took the form of a proposal defense which required submitting a 9000-word proposal outlining the theoretical framework & justifications for my research questions, hypotheses, objectives, methodologies, preliminary results, discussion and thoughts on the significance of the work, a 25-minute presentation of this proposal followed by an oral examination that lasted about an hour and 30 minutes. These exams are typically meant to be taken at the early stages of one’s PhD, but it seems that they often get kicked further down the road, as was the case with mine which I completed half way into my 3rd year of a 5 year program. This had its advantages and disadvantages where further progress allowed presentation and discussion of some interesting findings and a clearer picture of what my thesis is going to look like, but also came with the colossal challenge of organizing everything into what seemed like a miniscule 25-minute presentation. This was probably the most challenging academic exercise I have faced.

I finalized my presentation a few days before my exam, and felt that it had a nice balance between theory and my contributions, but this only after “throwing away” 100+ slides in the 2 weeks leading up to the exam… And while that might sound like a total waste of time, it actually forced me to distill what seemed like an “ocean of theory” to the essential elements that grounded my work. Further, developing slides that can visually communicate complex theory is a great form of study that can serve you well during the oral exam; even if you can’t show the slides you will know the material. Also, I can’t overstate the importance of peer and supervisory assistance here. I was extremely lucky to have my presentation lovingly torn to shreds by my lab mates. This can be a terrifying process as we know that imposter syndrome is alive and well in academia (http://irblog.eu/impostor-syndrome-phd/). Yet, we of course survive these practice talks and our presentations benefit greatly.

Once I was happy with the content and flow of my talk I decided to inject a little humour by photoshopping some images and spattering in a couple silly animations. This was probably some kind of self-defense mechanism where I was hoping that by putting a smile on the face of an examiner I might be able to ease my own nerves and the general tension that goes along with a comprehensive exam. Of course, whether this succeeds or not will depend on the demeanor of your examiners, your delivery and probably the general quality of the rest of the presentation. In my case, I found that the humour worked and offered a nice lull in the tension. I highly recommend trying this, once you’ve nailed down the meat of the talk of course. Beyond attempts at humour, you should know the talk. You shouldn’t be reading off any notes and should only read out points on the slide that are essential theory items or specific research questions, hypotheses or findings. There will be an upcoming blog post on presentation tips, so I’ll stop there… Just remember that in this exam, your presentation sets the tone. It is your opportunity to articulate your comprehension of the subject and the novelty of your work.

The written component of the proposal, on the other hand, can seem to be propelled by a perpetual motion machine generating an endless sprawl of “conceptual axes”, “synthetic approaches” and “novel perspectives” about your thesis topic. Here, you can definitely produce a fairly comprehensive picture of the subject and your perspectives but you’ll still have to tug the reigns so as not to irritate your readers with a bloated document. If you find yourself delving into the linkages between your thesis and systems theory, and you’re not in physics, odds are you’ve gone too far. Everything in your written proposal is essentially fair-game for the oral examination, so don’t let it disappear from your desktop once you’ve submitted it. You will most certainly get questions about the methods you’ve proposed or have employed, and you will need to be able to justify your choices and situate your studies within the literature.

The oral examination will surely be one of the most unnerving experiences of your academic life, but you can minimize your unease by continually drawing those links between your thesis and the literature in the weeks leading up to the exam. I found the oral exam to be a very fair process where I was tested on the biophysical interactions that I was examining, the measures that I used, and the conceptual links between my thesis components and the trends in the literature. Now, my thesis is fairly atypical in that it takes a multi-disciplinary approach to a larger topic, and this definitely generated some questions about the linkages between the various components. But beyond that challenge I think any questions about the “grand scheme” of your thesis can be addressed by highlighting those initial motivations that you included in your application to your program. In my application, I was required to write a page about why interdisciplinary perspectives are essential in the field of environmental science, and I was able to pull from that motivation to answer these kinds of questions. Odds are that your initial reasons for engaging with a certain research topic will ground a lot of your answers during the oral examination. One question that I didn’t anticipate was essentially “where do you see yourself in 10 years”?  I think in our PhD’s we can easily get tunnel vision and forget that there is an end to the process at which point we’ll move on to something new. So don’t forget about that light at the end of the tunnel during the exam. Think about your future aspirations and how far you’ve come since you became fascinated with your topic. Your examiners want to feel that engagement and passion. And you will get questions about the theory that are right in your wheelhouse, so take advantage when they appear and highlight both your understanding of the unanswered questions and how your work is not just adding to the complexity but is helping to bridge those gaps.

In the end, after all the late nights of writing, pecking at bowls of nuts (because cooking takes too long) and re-arranging your presentation slides for the 100th time, you’ll most likely find that this process has probably been the most constructive thing that you’ve ever been a part of.

Graduate students- employees, scholars, or something in between?

Graduate school has always required that students balance research, classwork, and teaching activities (perhaps with some time for complaining). Though many aspects of graduate school are unchanged, there can be a tension between grad students and their employers driven by a shift in both these groups’ expectations, and the complex nature of STEM graduate school.

This is illustrated well by the current strikes of teaching assistants (primarily graduate students) at University of Toronto and York University – both major Canadian institutions. [And even more extreme cases exist]. The union at U of T has become a defacto union for graduate student issues as well, and the primary sticking point appears to be graduate student stipends, which are far below the poverty line. The students there are striking as teaching assistants (so research work can continue) but their main issue is a holistic “graduate student” issue.

Supposing the components of graduate school have remained similar over the years, why might tension be increasing between what graduate students and faculty/departments expect? Partly because so many other things have changed-–the economy, the workforce, cultural expectations. I think that in the past, it was easier to consider graduate school as a place of passion and intellectual curiosity, where one would make a lousy salary, but consider it “worth it”.  Today, the cost-benefit analysis for getting a PhD is considerably less positive – it takes longer to get a PhD, on average, and the payoff in terms of obtaining a faculty or other job, makes this less clear. The cost of education, particularly in the US, is immense: the possibility of student loan debt from 4-8 years of postgraduate education is fairly unpalatable.

From Nature.

As the realities change, so too do the expectations. That on its own would be the source of some tension. But the dual nature of graduate school compounds the tensions since it is difficult for graduate students, faculty, and department heads to evaluate what reasonable expectations are for things such as pay, hours, vacation time. For most students, graduate school has aspects of both a clear job (usually teaching duties—running labs, marking tests and assignments, sometimes lecture duties) and a clear studentship (class work, appraisal exams, all culminating in a defense). It also includes research, done in a lab or the field, which may vary between being a job (doing tasks primarily for the PI, monitoring undergrads, ordering supplies) and an intense learning experience. Employment involves contracts with expectations and restrictions, set hours and wages; being a student lacks the same expectations but is often associated with greater freedom and personal growth. The extent to which faculty and graduate students see the position as “student” or as “job” may well differ.

The interaction of economic realities with the duality of graduate school is an important issue. Should graduate school be considered the start of one's working life? If so, is it equivalent to an entry-level position? After all, TAs do a lot of grunt work -- marking, marking, and more marking, run simple labs and tutoring sessions -- and many universities hire undergraduates to do similar tasks. On the other hand, graduate students are also high-achievers doing complicated analyses for research, and have reasonably high education levels. Graduate school may come with opportunity costs  - peers with similar educations tend to have jobs and retirement funds. In contrast, the pure academic path usually means you will live frugally for many years before your first "real" position (and you may be in your 30s or later before you get it).

There may be some generational changes as well. It is suggested that Millenials/Generation Y have different priorities than previous generations: they strongly desire fulfilment from their work, but also competitive compensation and job flexibility (e.g.). The downsides of graduate school are greater and perhaps more obvious to this generation: if it is a job, it is poorly paid and entry-level, if it is a studentship, it comes with an opportunity cost. But how to evaluate it when it is both? It is undeniably easier to go through graduate school for those who don't have to deal with the dualities - such as through having a fellowship that allows a student to do research and classes only. Most people are still in graduate school for the same reasons as they always have been - love of science and learning. That hasn't changed. But the meaning of graduate school itself may well have changed. There is no one or easy solution to the issue. But no doubt a recognition by both sides of the realities of being a graduate student (and a supervisor) and honest communication about expectations on both sides (and sometimes, perhaps a little pressure) would go far. 

The real truth about graduate school according to the Simpsons...

**I just want to note that this is inspired by--but not addressing--the U of Toronto situation, and any comments that simply want to debate specific circumstances in particular universities will be deleted...

Larger discussion of the general issue always welcome.

Darwin in images (Darwin Day 2015).

Feb. 12 is the anniversary of Charles Darwin's birthday, a celebration of a man who nearly single-handedly (not to ignore Alfred Russel Wallace and others) laid the foundations for modern ecology and evolution. He championed the idea that evolution was descent with modification, where natural selection was the main means of modification. Darwin's work furthered achievements in science, medicine, and philosophy, perhaps in part because he helped disentangle science, society and religion. One outcome of being such a prominent figure is the frequency with which Darwin ends up in images, cartoons and illustrations, beginning in his own lifetime. So here is a short tour of Darwin and his big idea via cartoons and illustrations. 

A famous Vanity Fair caricature from 1971.

The oft-repeated mantra that evolution means that man evolved directly from a monkey or beast was an early (and still popular) theme. Darwin often took the role of the monkey.

John Tenniel for Fun magazine (1872)

From 1882, Punch’s Almanack, Linley Sambourne

 (Link)

Even in his day, some cartoons supported rather than poked fun. See the speech on the wall, a plea avoid ignorance of Science. (Link)

"Puck Presents Archdeacon Farrar’s New Year’s Hint — A Needed Course of Instruction for Our Religious Instructors"(1890).

The "evolution of man" meme has a long history - what was originally satire is primarily now a visual joke. (Link)

Harper's Bazaar 1871

Modern concerns.

(Link)

Darwin and evolution has been a repeated image in US politics, covering evolution and education, religious tension, science, and social darwinis, among other themes. (Link)

1925 SF Examiner

More recent, by Karl Wimer

Religion and Darwin have been unavoidable companions.

From pro-religion angles: 

1922 Moody's bible institute

And anti-religious:

Darwin pops up on motivational images and posters:

And evolution jokes remain eternally popular:

The Farside providing one of the better ones :)

source unknown

And finally, Darwin's own images have been incredibly influential. He was a talented naturalist and scientist and left many lovely illustrations. His sketches in his books, particularly the "tree of life" image has become an emblem for many scientists - of evolution and the origin of species, of immense intellectual accomplishment, of the birth of modern ecology and evolution.

1983

The famous "I think" image - the tree of life.

To learn more about Darwin in images, this is a great resource.

Predatory open access journals: still keep'n it classy

As most academics are aware, there are hundreds of predatory open access journals that try to trick authors into submitting to their journals, charge exorbitant fees, and do not ensure that articles are peer reviewed or live up to basic scientific standards. The most celebrated cases are journals that embarrassingly publish non-sensical fake papers. I don't know why, but I sometimes go to the journal websites to see what they publish or who is on their editorial boards. I received such an e-mail this morning from SOJ Genetic Science published by Symbiosis, a recognized predatory publisher. This journal, unlike others, actually has a single published issue with an editorial! I thought: "wow, are they trying to be legitimate?"; then I read the editorial. The editorial is probably best described as a nonsensical diatribe about genetics, which lacks any real connection to modern genetic theory. Here is my favourite paragraph:

Predatory open access journals: still keep'n it classy.

What is ecology’s billion dollar brain?

(*The topic of the billion dollar proposal came up with Florian Hartig (@florianhartig), with whom I had an interesting conversation on the idea*)

Last year, the European Commission awarded 1 billion dollars to a hugely ambitious project to recreate the human brain using supercomputers. If successful, the Human Brain Project would revolutionize neuroscience. (Although skepticism remains as to whether this project is a more of a pipe dream than reasonable goal). For ecology and evolution, where infrastructure costs are relatively low (compared to say, a Large Hadron Collider), 1 billion dollars means that there is essentially no financial limitation on your proposal, so nearly any project, experiment, analysis, dataset, or workforce, is within the realm of possibility. The European Commission call was for a proposal for research to occur over 10 years, meaning that the constraints on project length (usually driven by grant terms and graduate student theses) are low. So if you could write a proposal, upon which there are essentially no constraints at all, what would it be for? (*if you think that 10 years is too limiting for a proper long-term study, feel free to assume you can set up the infrastructure in 10 years and run it for as long as you want).

The first thing I recognized was that in proposing the 'ultimate' ecological project, you're implicitly stating how you think ecology should be done. For example, do you could focus on the most general questions and start from the bottom. If this is the case, it might be most effective to ask a single fundamental question. It would not be unreasonable to propose to measure metabolic rates under standardized conditions for every extent species, and develop a database of parameter values for them. This would be the most complete ecological database ever, that certainly seems like an achievement. 

But perhaps you choose something that is still of general importance but less simplistic, and run a standardized experiment in multiple systems. This has been effective for the NutNet project. Propose to run replicate experiments with top-of-the-line warming arrays on plant communities in every major ecosystem. Done for 10 years, over a reasonably large scale, with data recorded on physiology and important life history events, this might provide some ability to predict how warming temperatures are affecting ecosystems. 

The alternative is embrace ecological complexity (and the ability to deal with complexity that 1 billion dollars offers). Given the analytic power, equipment, and man hours that 1 billion dollars can buy, you could record every single variable--biotic, abiotic, weather--in a particular system (say, a wetland) for every second of every day. If you don’t simply drown in the data you’ve gathered, maybe you can reconstruct that wetland, predict every property from the details. While that may seem a bit extreme, if you are a complexity-fatalist, you start to recognize that even the general experiments are quickly muddied by complexity. Even that simple, general list of species' metabolic parameters quickly spirals into complexity. Does it make sense to use only one set of standardized conditions? After all, conditions that are reasonable for a rainforest tree are meaningless for an ocean shark or a tundra shrub. Do you use the mean condition for each ecosystem as the standard, knowing that species may only interact with the variance or extremes in those conditions (such as desert annuals that bloom after rains, or bacteria that use cyst stages to avoid harsh environments). What about ontogenetic or plastic differences? Intraspecific differences?

It's probably best then to realize that there is no perfect ecological experiment. The interesting thing about the Human Brain project is that neuroscience is more like ecology than many scientific fields - it deals with complex organic systems with emergent properties and great variability. What ecology needs, ever so simplistically, is more data and better models. Maybe, like neuroscience, we should request a supercomputer that could located and incorporate all ecological data ever collected, across fields (natural history, forestry, agronomy, etc) and recognize the connections between that data, based on geography, species, or scale. This could both give us the most sophisticated possible data map, showing where the data gaps exist, and where areas are data-rich and ready for model development. Further, it could (like the Human Brain) begin to develop models for the interconnections between data. 

Without too many billion dollar calls going on, this is only a thought experiment, but I have yet to find someone who had an easy answer for what they would propose to do (ecologically) with 1 billion dollars. Why is it so difficult?

Links: Reanalyzing R-squares, NSF pre-proposals, and the difficulties of academia for parents

First, Will Pearse has done a great job of looking at the data behind the recent paper looking at declining R and p-values in ecology, and his reanalysis suggests that there is a much weaker relationship between r2 values and time (only 4% rather than 62% as reported). Because the variance is both very large within-years and also not equal through time, a linear model may not be ideal for capturing this relationship.
Thanks @prairiestopatchreefs for linking this.

From the Sociobiology blog, something that most US ecologists would probably agree on: the NSF pre-proposal program has been around long enough (~3 years) to judge on its merits, and it has not been an improvement. In short, pre-proposals are supposed to use a 5 page proposal to allow NSF to identify the best ideas and then invite those researchers to submit a full proposal similar to the traditional application. Joan Strassman argues that not only is this program more work for applicants (you must write two very different proposals in short order if you are lucky to advance), it offers very few benefits for them.

The reasons for the gender gap in STEM academic careers gets a lot of attention, and rightly so given the continuing underrepresentation of women. The demands of parenthood often receive some of the blame. The Washington Post is reporting on a study that considers parenthood from the perspective of male academics. The study took an interview-based, sociological approach, and found that the "majority of tenured full professors [interviewed] ... have either a full-time spouse at home who handles all caregiving and home duties, or a spouse with a part-time or secondary career who takes primary responsibility for the home." But the majority of these men also said they wanted to be more involved at home. As one author said, “Academic science doesn’t just have a gender problem, but a family problem...men or women, if they want to have families, are likely to face significant challenges.”

On a lighter note, if you've ever joked about PNAS' name, a "satirical journal" has taken that joke and run with it. PNIS (Proceedings of the Natural Institute of Science) looks like the work of bored post-docs, which isn't necessarily a bad thing. The journal has immediately split into two subjournals: PNIS-HARD (Honest and Real Data) and PNIS-SOFD (Satirical or Fake Data), which have rather interesting readership projections:

Edicts for peer reviewing

Reviewing is a right of passage for many academics. But for most graduate students or postdocs, it is also a bit of a trial by fire, since reviewing skills are usually assumed to be gained osmotically, rather than through any specific training. Unfortunately, the reviewing system seems ever more complicated for reviewers and authors alike (slow, poor quality, unpredictable). Concerns about modern reviewing pop up every few months, and different solutions to the difficulties of finding qualified reviewers and the quality of modern reviews (including publishing an instructional guide, taking alternative approaches (PeerJ, etc), or skipping peer review altogether (arXiv)). Still, in the absence of a systematic overhaul of the peer review system, an opinion piece in The Scientist by Matthew A. Mulvey and Dean Tantin provides a rather useful guide for new reviewers and a useful reminder for experienced reviewers. If you are going to do a review (and you should, if you are publishing papers), you should do it well. 

From "An Ecclesiastical Approach to Peer Review" 

"The Golden Rule
Be civil and polite in all your dealings with authors, other reviewers, editors, and so on, even if it is never reciprocated.
As a publishing scientist, you will note that most reviewers break at least a few of the rules that follow. Sometimes that is OK—as reviewers often fail to note, there is more than one way to skin a cat. As an author you will at times feel frustrated by reviews that come across as unnecessarily harsh, nitpicky, or flat-out wrong. Despite the temptation, as a reviewer, never take your frustrations out on others. We call it the “scientific community” for a reason. There is always a chance that you will be rewarded in the long run. 

The Cardinal Rule
If you had to publish your review, would you be comfortable doing so? What if you had to sign it? If the answer to either question is no, start over. (That said, do not make editorial decisions in the written comments to the authors. The decision on suitability is the editors’, not yours. Your task is to provide a balanced assessment of the work in question.) 

The Seven Deadly Sins of sub-par reviews

1. Laundry lists of things the reviewer would have liked to see, but have little bearing on the conclusions.
2. Itemizations of styles or approaches the reviewer would have used if they were the author.
3. Direct statements of suitability for publication in Journal X (leave that to the editor).
4. Vague criticism without specifics as to what, exactly, is being recommended. Specific points are important—especially if the manuscript is rejected.
5. Unclear recommendations, with little sense of priority (what must be done, what would be nice to have but is not required, and what is just a matter of curiosity).
6. Haphazard, grammatically poor writing. This suggests that the reviewer hasn’t bothered to put in much effort.
7. Belligerent or dismissive language. This suggests a hidden agenda. (Back to The Golden Rule: do not abuse the single-blind peer review system in order to exact revenge or waylay a competitor.) 

Vow silence
The information you read is confidential. Don’t mention it in public forums. The consequences to the authors are dire if someone you inform uses the information to gain a competitive advantage in their research. Obviously, don’t use the findings to further your own work (once published, however, they are fair game). Never contact the authors directly.

Be timely
Unless otherwise stated, provide a review within three weeks of receiving a manuscript. This old standard has been eroded in recent years, but nevertheless you should try to stick to this deadline if possible. 

Be thorough
Read the manuscript thoroughly. Conduct any necessary background research. Remember that you have someone’s fate in your hands, so it is not OK to skip over something without attempting to understand it completely. Even if the paper is terrible and in your view has no hope of acceptance, it is your professional duty to develop a complete and constructive review.

Be honest
If there is a technique employed that is beyond your area of expertise, do the best you can, and state to the editor (or in some cases, in your review) that although outside your area, the data look convincing (or if not, explain why). The editor will know to rely more on the other reviewers for this specific item. If the editor has done his or her job correctly, at least one of the other reviewers will have the needed expertise.

Testify
Most manuscript reviews cover about a page or two. Begin writing by briefly summarizing the state of the field and the intended contribution of the study. Outline any major deficits, but refrain from indicating if you think they preclude publication. Keep in mind that most journals employ copy editors, so unless the language completely obstructs understanding, don’t bother criticizing the English. Go on to itemize any additional defects in the manuscript. Don’t just criticize: saying that X is a weakness is not the same as saying the authors should address weakness X by providing additional supporting data. Be clear and provide no loopholes. Keep in mind that you are not an author. No one should care how you would have done things differently in a perfect world. If you think it helpful, provide additional suggestions as minor comments—the editor will understand that the authors are not bound to them.

Judgment Day
Make a decision as to the suitability of the manuscript for the specific journal in question, keeping in mind their expectations. Is it acceptable in its current state? Would a reasonable number of experiments performed in a reasonable amount of time make it so, or not? Answering these questions will allow you to recommend acceptance, rejection, or major/minor revision. 

If the journal allows separate comments to the editor, here is the place to state that in your opinion they should accept and publish the paper as quickly as possible, or that the manuscript falls far below what would be expected for Journal X, or that Y must absolutely be completed to make the manuscript publishable, or that if Z is done you are willing to have it accepted without seeing it again. Good comments here can make the editor’s job easier. The availability of separate comments to the editor does not mean that you should provide only positive comments in the written review and reserve the negative ones for the editor. This approach can result in a rejected manuscript being returned to the authors with glowing reviewer comments. 

Resurrection
A second review is not the same as an initial review. There is rarely any good reason why you should not be able to turn it around in a few days—you are already familiar with the manuscript. Add no new issues—doing so would be the equivalent of tripping someone in a race during the home stretch. Determine whether the authors have adequately addressed your criticisms (and those of the other reviewers, if there was something you missed in the initial review that you think is vital). In some cases, data added to a revised manuscript may raise new questions or concerns, but ask yourself if they really matter before bringing them up in your review. Be willing to give a little if the authors have made reasonable accommodation. Make a decision: up or down. Relay it to the editor. 

Congratulations. You’ve now been baptized, confirmed, and anointed a professional manuscript reviewer."

Researching ecological research

Benjamin Haller. 2014. "Theoretical and Empirical Perspectives in Ecology and Evolution: A Survey". BioScience; doi:10.1093/biosci/biu131.

Etienne Low-Décarie, Corey Chivers, and Monica Granados. 2014. "Rising complexity and falling explanatory power in ecology". Front Ecol Environ 2014; doi:10.1890/130230.

A little navel gazing is good for ecology. Although maybe it seems like it, ecology spends far less time evaluating its approach, compared to simply doing research. Obviously we can't spend all of our time navel-gazing, but the field as a whole would benefit greatly from ongoing conversations about its strength and weaknesses. 

For example, the issue of theory vs. empirical research. Although this issue has received attention and arguments ad nauseum over the years (including here, 1, 2, 3), it never completely goes away. And even though there are arguments that it's not an issue anymore, that everyone recognizes the need for both, if you look closely, the tension continues to exist in subtle ways. If you have participated in a mixed reading group did the common complaint “do we have to read so many math-y papers?" ever arise; or equally “do we have to read so many system specific papers and just critique the methods?” Theory and empirical research don't see eye to eye as closely as we might want to believe.

The good news? Now there is some data. Ben Haller did a survey on this topic that just came out in BioScience. This paper does the probably necessary task of getting some real data beyond the philosophical and argumentative about the theory/data debate. Firstly, he defines empirical research as being involved in the gathering and analysis of real world data, while theoretical research does not gather or analyze real world data, instead involves mathematical models, numerical simulations, and other such work. The survey included 614 scientists from related ecology and evolutionary biology fields, representing a global (rather North American) perspective.

The conclusions are short, sweet and pretty interesting: "(1) Substantial mistrust and tension exists between theorists and empiricists, but despite this, (2) there is an almost universal desire among ecologists and evolutionary biologists for closer interactions between theoretical and empirical work; however, (3) institutions such as journals, funding agencies, and universities often hinder such increased interactions, which points to a need for institutional reforms."

 

For interpreting the plots – the empirical group represents respondents whose research is completely or primarily empirical; the theoretical group's research is mostly or completely related to theory, while the middle group does work that falls equally into both types. Maybe the results don't surprise anyone – scientists still read papers, collaborate, and coauthor papers mostly with others of the same group. What is surprising is that this trend is particularly strong for the empirical group. For example, nearly 80% of theorists have coauthored a paper with someone in the empirical group while only 42% of empiricists have coauthored at least one paper with a theorist. Before we start throwing things at empiricists, it should be noted that this could relate to a relative scarcity of theoretical ecologists, rather than insularity on the part of the empiricists. However, it is interesting that while the responses to the question “how should theory and empiricism coexist together?” across all groups agreed that “theoretical work and empirical work would coexist tightly, driving each other in a continuing feedback loop”, empirical scientists were significantly more likely to say “work would primarily be data-driven; theory would be developed in response to questions raised by empiri­cal findings.”

Most important, and maybe concerning, is that the survey found no real effect of age, stage or gender – i.e. existing attitudes are deeply ingrained and show no sign of changing.

Why is it so important that we reconcile the theoretical/empirical issue? The paper “Rising complexity and falling explanatory power in ecology” offers a pretty compelling reason in its title. Ecological research is getting harder, and we need to marshall all the resources available to us to continue to progress. 

The paper suggests that ecological research is experiencing falling mean R² values. Values in published papers have fallen from above 0.75 prior to 1950 to below 0.5 in today's papers.

The worrying thing is that as a discipline progresses and improves, you might predict that the result is an improving ability to explain ecological phenomenon. For comparison, criminology was found to show no decline in R² values as that matured through time. Why don’t we have that? 

During the same period, however, it is notable that the average complexity of ecological studies also increased – the number of reported p-values is 10x larger on average today compared to the early years (where usually only a single p-value relating to a single question was reported). 

The fall in R² values and the rise in reported p-values could mean a number of things, some worse for ecology than others. The authors suggest that R² values may be declining as a result of exhaustion of “easy” questions (“low hanging fruit”), increased effort in experiments, or a change in publication bias, for example. The low hanging fruit hypothesis may have some merit – after all, studies from before the 1950s were mostly population biology with a focus on a single species in a single place over a single time period. Questions have grown increasingly more complex, involving assemblages of species over a greater range of spatial and temporal scales. For complex sciences, this fits a common pattern of diminishing returns: “For example, large planets, large mammals, and more stable elements were discovered first”.

In some ways, ecologists lack a clear definition of success. No one would argue that ecology is less effective now than it was in the 1920s, for example, and yet a simplistic measure (R²) of success might suggest that ecology is in decline. Any biases between theorists and empiricists is obviously misplaced, in that any definition of success for ecology will require both.  

#ESA2014 : Getting ready for (and surviving) ESA

There is less than one week until ecology's largest meeting. ESA's annual meeting starts August 10th in Sacramento, California, and it can be both exciting and also be overwhelming in its size and scope. Here are a few suggestions for making it a success.

Getting ready for ESA.
Sure, things start in a week and you're scheduled for a talk/poster/meeting with a famous prof, but you haven't started preparing yet.

First off, no point beating yourself up for procrastinating: if you've been thinking about your presentation but doing other projects, you might be in the company of other successful people.

If you're giving a talk, and given it before or are an old hand at this sort of thing, go ahead and put it together the night before your talk. One benefit for the truly experienced or gifted speaker is that this talk will never sound over-rehearsed.

Regardless, all speakers should try for a talk that is focused, with a clear narrative and argument, and within the allotted time. (Nothing is more awkward for everyone involved than watching the moderate have to interrupt a speaker). The good news is that ESA audiences will probably be a) educated to at least a basic level on your topic, and b) are usually generous with their attention and polite with their questions. This blog has some really practical advice on putting together an academic talk.
If at all possible, practice in front of a friendly audience ahead of time.

The questions after your talk will vary, and if you're lucky they will relate to future directions, experimental design, quantitative double-checks, and the truly insightful thoughts. However, there other common questions that you should recognize: the courtesy question (good moderators have a few in hand), the "tell-me-how-it-relates-to-my-work" question, and the wandering unquestion.

Giving a poster is much different than giving a talk, and it has pros and cons. First, you have to have it finished in time to have it printed, so procrastination is less possible. Posters are great if you want one-on-one interactions with a wide range of people. You have to make your poster attractive and interesting: this always means don't put too much text on your poster. The start of this pdf gives some nice advice on getting the most out of your poster presentation.

For both posters and presentations, graphics and visual appeal make a big difference. Check out the blog, DeScience, which has some great suggestions for science communication.

Academic meetings. These run the gamut from collaborators that you're just catching up with, to strangers that you have contacted to meet to discuss common scientific interests. If scientists that you share common research activities and interests with are attending ESA, it never hurts to try to meet with them. Many academics are generous with their time, especially for young researchers. If they say yes, come prepared for the conversation. If necessary, review their work that relates to your own. Come prepared to describe your interests and the project/question/experiment you were looking for advice on. It can be very helpful to have some specific questions in mind, in order facilitate the conversation.

What to wear. Impossible to say. Depending on who you are and wear you work normally, you can wear anything from torn field gear and binos to a nice dress or suit (although not too many people will be in suits).

Surviving ESA.
ESA can be very large and fairly exhausting. The key is to pace yourself and take breaks: you don't need to see talks all day long to get your money's worth from ESA. Prioritize the talks that you want to see based on things like speaker or topic. Sitting in on topics totally different from those you study can be quite energizing as well. In this age of smartphones, the e-program is invaluable.

Social media can help you find popular or interesting sounding talks, or fill you in on highlights you missed. This year the official hashtag on twitter is #ESA2014.

One of the most important things you can do is be open to meeting new people, whether through dinner and lunch invites, mixers, or other organized activities. Introverts might cringe a little, but the longest lasting outcome from big conferences is the connections you make there.

Eat and try to get some sleep.

**The EEB & Flow will be live-blogging during ESA 2014 in Sacramento, as we have for the last few years. See everyone in Sacramento!**

What papers influenced your journey as an ecologist?

For ESA’s centennial year, they are running a pretty cool series called “The Paper Trail”. A variety of ecologists write about the particular paper or papers that catalyzed their research path. Sometimes the papers are valuable for bringing up particular questions, sometimes they facilitated the connection of particular ideas.

William Reiner provides some insight into the value of this exercise: “What are some of the generalizations one can deduce from this paper trail? For me there are five. First, in ecology one cannot take too large a view of the problem one is addressing. Second, it is useful to step out of one's science into others to gain useful new ways of addressing questions. Collaboration with others outside ones field facilitates this complementarity. Third, teaching provides a useful forum for developing one's ideas. Fourth, there is no literature that is too old to have no value for current issues. And fifth, one must take time to read to be a thoughtful, creative scholar.”

In general, people are writing about papers that either specifically related to their own research at the time and opened their eyes to something new, or else broadly inspired or fascinated them at a critical time. (For Lee Frelich, this was reading The Vegetation of Wisconsin, an Ordination of Plant Communities at 12 years old.) I probably fall into the second group. My undergrad degree was in general biology and math, so although I had taken a couple of ecology courses, I knew essentially nothing about the fundamentals of ecological literature. So I was an impressionable PhD student, and I read a lot of papers. When I started, my plan was to do something related to macroecology, and the first paper I remember being excited about was James H. Brown’s 1984 “On the Relationship between Abundance and Distribution of Species”. It is everything a big idea paper should be – confident, persuasive, suggesting that simple tradeoffs may allow us to predict broad ecological patterns. And while with time I feel that some of the logic in the paper is flawed or at least unsupported, it definitely is a reminder of how exciting thinking big can be (and 1870 citations suggests others agree).

The next paper was R.H. Whittaker’s “Gradient analysis of vegetation” (1967). There is a lot of recommend in Whittaker’s work, in particular the fact that it straddles so well modern ecology and traditional ecology. He introduces early multidimensional analyses of plant ecology and asks what an ecological community is, while also having such a clear passion for natural history.

Finally, and perhaps not surprisingly, the biggest influence was probably Chesson (2000) “Mechanisms of the maintenance of species diversity”. The value of the ideas in this paper is that they can (and have) be applied to many modern ecological questions. In many ways, this felt like the most important advance in ecological theory in some time. It is also the sort of paper that you can read many times (and probably have to) and still something new every time.

Of course, there are many other papers that could be on this list, and I’ve probably overlooked something. Also, makes me miss having free time to read lots of papers :)

Guide for writing the ultimate ecology introduction**

**Or find better advice here, here, here.

If only it were so easy...

Guest Post: You teach science, but is your teaching scientific? Part 2: Flipping your class.

The second in a series of guest posts about using scientific teaching, active learning, and flipping the classroom by Sarah Seiter, a teaching fellow at the University of Colorado, Boulder. 

When universities first opened in the middle ages, lecturing was the most cutting edge information technology available to a professor - books were copied by hand so the fastest way to transfer information was to talk at your students (see the awesome TED talk below for a breakdown of how universities can and should change). Lecturing is still the default at most universities, and faculty spend hours developing their lecture skills. But studies have shown over and over again that lecturing is one of the worst possible ways to get students to learn. This means that our most accomplished scientists are working like crazy to master a method of teaching that is straight up medieval.

Lecturing isn’t going away any time soon, but you do a lot for your classes by incorporating active learning techniques, sometimes called “flipping” a class. The main feature of a flipped class is that students do the knowledge acquisition (the lecture-like) part of the course at home, and then do “homework” in the classroom with the instructor and peers to help them apply knowledge.

Flipped Classroom Fears:

Instructors often imagine a Lord of The Flies style scenario when they start flipping their classrooms, but this isn’t usually the case. In fact, most students are actually so conditioned to sit quietly in class that it can be difficult to get them to talk about the material. However, there are a few things you can do to get students in the frame of mind for productive discussion.
Flipping your classroom will probably not result­ in chaos. Nobody is going to smash the conch shell and kill Piggy, but they might learn something.

• Start small: If you’re just getting into transforming your class, it can be helpful to start with something small, like flipping once a week.
• Get extra staff: Since group work key to flipped classroom, it helps to have extra staff to facilitate peer discussions. If you have graduate TAs, consider deputizing them to lead group exercises. If your university has an undergrad TA program, get as many as you can and spend a day training them on how to ask good questions and facilitate conversations.
• Explain to students why flipping works: Students will sometimes complain if they’re used to sitting passively in lecture, and they’re suddenly forced to do homework in class. But flipping builds skills that they’ll need in the workplace or graduate school, so reemphasizing what they’re gaining can help get them to buy in. 

Tools For Flipping: Case Studies

Case studies usually involve taking scientific data or ideas and then applying them to a real world situation (medical, law and business schools have been using them for years). Case studies are all over the internet, although the largest clearing house is the National Case Study Library (the American Museum of Natural History, the National Geographic Society, the Smithsonian, and the Understanding Evolution project at Berkeley also have great resources). The National Case Study Library is the largest and is searchable by topic and age, and includes teaching notes for each case, and can be a great place to get started.

Picking Case Studies: Some case studies are purely hypothetical, but I tend to gravitate to those that use real data from published studies like this one on the evolution of skin color that uses studies from a lot of disciplines to build to a conclusion, or this one on conservation corridors and meta-populations. A lot of case studies open with a fictional story, but this approach is a little corny for me, and I’d rather focus on the real scientists and their questions (the narrative case studies can also get weird (like this paternity case study that could also be a great Maury episode). In general, just pick things that work for you and your students. 

DIY Case Studies: If you have papers that you already like to teach, then consider turning them into a case study. To do this, I usually write an intro briefly framing the problem or question. Then I give students actual graphs from the paper with follow up questions to help them process the information. It is OK if the study has a few confusing elements; while we often want a clear story to present to our classes, there’s great evidence that using “messy” data builds scientific skills. You may have to modify graphs, or remake them for extra readability. This could mean re-labeling axes to remove jargon (e.g. in a paper on insects, “instar” becomes “developmental stage”). It might mean dropping some treatments (you don’t need 10 nitrogen treatments to understand eutrophication). Usually I follow every graph with 2-3 questions that follow a basic format:

• Question 1: Ask students to detect any trends or differences in the graph. 
• Question 2: Have students think of an explanation for the results
• Question 3: Ask students to apply their “findings” to the question or problem posed in the case study

The above formula is just a starting place so add or alter questions to suit your needs. Sometimes I’ll use two or three graphs, and use the formula above. I usually end with a question that ties all the graphs together, like asking them to recommend a policy solution, or contrast the findings of different researchers.

Using Case Studies In Class: You can prepare students for a case either through a short lecture or through a homework assignment or reading quiz (this can be done using classroom management software like Blackboard, or Sakai). Once students have the background, have them break into groups of two to three, and work through the questions. It can be helpful to stop every few minutes to go through the answers (some case studies build on earlier questions, so early feedback is key). A great feature of case studies is that they can take nearly an entire class period, so you can go an entire day without having to lecture.

Clicker Questions 

The other main tool for flipping your classroom is clicker questions. Clickers are basically a real-time poll of your students so you can check how they are learning. Most instructors use them for participation points, rather than grading them for correctness (this encourages students to jump in and grapple with material, and not worry about making mistakes). Your university might have a set of clickers that you can borrow, or you have students use laptops, tablets and smartphones in place of clicker with apps like Poll Everywhere, GoSoapbox, Pinnion, or Socrative (these have different features and price points, so see what works for you). For a more comprehensive list of clicker tools , see this article from a team at Princeton.

Writing Good Clicker Questions: Good clicker questions should encourage discussion, and force students to apply their knowledge, not just test what they remember. This can mean using information to make recommendations, doing a calculation, or making predictions about the outcome of experiment. Standard clickers only allow for multiple choice questions, but other web-based tools will allow your students to do free responses, draw graphs, or give other types of answers. There are lots of great web resources on how to design clicker questions (in appendix). The slide show below shows some clicker questions we used in our flipped evolution class at CU Boulder.

Example Clicker Questions from sarahseiter

Using Clickers In Class: Once you have your clicker questions written, then you’ll need to deploy them in class. Below is a basic blueprint for how to run a clicker question

1. Tell students to break into groups and get ready to discuss a clicker question
2. Give students about a minute to discuss the question, and open whatever clicker software you’re using. You’ll usually hear a 30 second surge in talking that dies down after about a minute. After about a minute give students a warning and tell then close out the clicker question.
3. At this point you can show the results of the clicker poll and start to unpack the question. If your questions are challenging, you should be getting significant amounts of wrong answers, so seeing a wide range of answers means you’re doing it right. Usually if 10% of your students are getting the question wrong, it is worth discussing the question in depth
4. Make students be able to articulate why right answers are right and why wrong answers are wrong. You can call on groups to get them to explain their answers (this is nicer than cold-calling individual students). If nobody wants to talk about wrong answers, say something like “why might someone think that B is a tempting answer?” so that nobody has to admit to being wrong in front of their peers.
5. It can be helpful to follow up with another question asking them to apply the material in a different way.

In conclusion, flipping your classroom can be done pretty cheaply and without that much more work than lecturing. This post is really just a starting place, and there are ton of great resources on the web to take you further. I’ve compiled just a few of them below. Good luck and happy flipping!

By Sarah Seiter

Resources

Videos on Flipped Classrooms:
https://www.youtube.com/watch?v=EMhJcwvmamY

Resources for Clicker Qs:
Clicker Question Guides from University of Colorado Boulder
http://www.colorado.edu/sei/documents/clickeruse_guide0108.pdf
http://www.slideshare.net/stephaniechasteen/writing-great-clicker-questions
Vanderbilt:
http://cft.vanderbilt.edu/guides-sub-pages/clickers/