Monitoring northern biodiversity: picking the right trap for collecting beetles and spiders

Ecological monitoring is an important endeavour as we seek to understand the effects of environmental change on biodiversity. We need to benchmark the status of our fauna, and check-in on that fauna on a regular basis: in this way we can, for example, better understand how climate change might alter our earth systems. That’s kind of important.

A northern ground beetle, Elaphrus lapponicus. Photo by C. Ernst.

A northern ground beetle, Elaphrus lapponicus. Photo by C. Ernst.

With that backdrop, my lab was involved with a Northern Biodiversity Program a few years ago (a couple of related papers can be found here and here), with a goal of understanding the ecological structure of Arthropods of northern Canada. The project was meant to benchmark where we are now, and one outcome of the work is that we are able to think about a solid framework for ecological monitoring into the future.

A few weeks ago our group published a paper* on how to best monitor ground-dwelling beetles and spiders in northern Canada. The project resulted in over 30,000 beetles and spiders being collected, representing close to 800 species (that’s a LOT of diversity!). My former PhD student Crystal Ernst and MSc student Sarah Loboda looked at the relationship between the different traps we used for collecting these two taxa, to help provide guidelines for future ecological monitoring. For the project, we used both a traditional pitfall trap (essentially a white yogurt container stuck in the ground, with a roof/cover perched above it) and a yellow pan trap (a shallow yellow bowl, also sunk into the ground, but without a cover). Traps were placed in grids, in two different habitats (wet and “more wet”), across 12 sites spanning northern Canada, and in three major biomes (northern boreal, sub-Arctic, and Arctic).

Here’s a video showing pan traps being used in the tundra:

Both of the trap types we used are known to be great at collecting a range of taxa (including beetles and spiders), and since the project was meant to capture a wide array of critters, we used them both. Crystal, Sarah and I were curious whether, in retrospect, both traps were really necessary for beetles and spiders. Practically speaking, it was a lot of work to use multiple traps (and to process the samples afterwards), and we wanted to make recommendations for other researchers looking to monitor beetles and spiders in the north.

The story ends up being a bit complicated… In the high Arctic, if the goal is to best capture the diversity of beetles and spiders, sampling in multiple habitats is more important than using the two trap types. However, the results are different in the northern boreal sites: here, it’s important to have multiple trap types (i.e., the differences among traps were more noticeable) and the differences by habitat were less pronounced. Neither factor (trap type or habitat) was more important than the other when sampling in the subarctic. So, in hindsight, we can be very glad to have used both trap types! It was worth the effort, as characterizing the diversity of beetles and spiders depended on both sampling multiple habitats, and sampling with two trap types. There were enough differences to justify using two trap types, especially when sampling different habitats in different biomes. The interactions between trap types, habitats, and biomes was an unexpected yet important result.

Our results, however, are a little frustrating when thinking about recommendations for future monitoring. Using more than one trap type increases efforts, costs, and time, and these are always limited resources. We therefore recommend that future monitoring in the north, for beetles and spiders, could possibly be done with a trap that’s a mix between the two that we used: a yellow, roof-less pitfall trap. These traps would provide the best of both options: they are deeper than a pan trap (likely a good for collecting some Arthropods), but are yellow and without a cover (other features that are good for capturing many flying insects). These are actually very similar to a design that is already being used with a long-term ecological monitoring program in Greenland. We think they have it right**.

A yellow pitfall trap - the kind used in Greenland, and the one we recommend for future monitoring in Canada's Arctic.

A yellow pitfall trap – the kind used in Greenland, and the one we recommend for future monitoring in Canada’s Arctic.

In sum, this work is really a “methodological” study, which when viewed narrowly may not be that sexy. However, we are optimistic that this work will help guide future ecological monitoring programs in the north. We are faced with increased pressures on our environment, and a pressing need to effectively track these effects on our biodiversity. This requires sound methods that are feasible and provide us with a true picture of faunal diversity and community structure.

It looks to me like we can capture northern beetles and spiders quite efficiently with, um, yellow plastic beer cups. Cheers to that!


Ernst, C, S. Loboda and CM Buddle. 2015. Capturing Northern Biodiversity: diversity of arctic, subarctic and northern boreal beetles and spiders are affected by trap type and habitat. Insect Conservation and Diversity DOI: 10.1111/icad.12143


* The paper isn’t open access. One of the goals of this blog post is to share the results of this work even if everyone can’t access the paper directly. If you want a copy of the paper, please let me know and I’ll be happy to send it to you. I’m afraid I can’t publish all of our work in open access journals because I don’t have enough $ to afford high quality OA journals.

** The big caveat here is that a proper quantitative study that compares pan and and pitfall traps to the “yellow roof-less pitfall” traps is required. We believe it will be the best design, but belief does need to be backed up with data. Unfortunately these kind of trap-comparison papers aren’t usually high on the priority list.

Meet Shaun Turney and Fuzzy Cognitive Mapping

This is another in the series of “Meet the arthropod ecology lab“: Meet PhD student Shaun Turney, and a neat project he’s been working on…

I joined the lab in September and I’ve been really enjoying my first months as a PhD student. I haven’t done any field work yet so that means no specimens to ID or field data to crunch. Instead I’ve been occupying my time very happily playing on the computer. I recently released an R package on CRAN for Fuzzy Cognitive Mapping called “FCMapper”, in collaboration with Michael Bachhofer. It is based on FCMapper for Excel, distributed at, developed by Michael Bachhofer and Martin Wildenberg. Fuzzy Cognitive Mapping is really cool and you should try it out!

Shaun, in the lab, thinking about food-webs.

Shaun, in the lab, thinking about food-webs.

Recently I’ve become interested in graph theory and all that it has to offer to ecology. Anything that can be represented as boxes and arrows (or lines) can be represented as a graph (in the graph theory sense) and can be analyzed using graph theory tools. I LOVE box and arrow diagrams. Like, maybe an inappropriate amount. Any paper that I’ve printed out and read has at least two or three box and arrow diagrams scribbled into the margins. My notebook is filled with box and arrow diagrams from lectures that I’ve attended or random thoughts that have passed through my mind while I’m sitting on the train. Some people think in words, some in pictures, but I think in boxes and arrows. So you can imagine my enthusiasm as I’ve discovered over the past year that there exists a whole body of mathematics that can represent and analyze box and arrow diagrams.

My latest favourite graph theory tool is called Fuzzy Cognitive Mapping. It can be understood by breaking down the term into its component words. A “cognitive map” in this case is when you represent a system as interconnected concepts. Boxes and arrows, in other words. The “fuzzy” part refers to fuzzy logic. Fuzzy logic is logic that deals with approximate rather than exact values. So to make a fuzzy cognitive map, you make a box and arrow diagram and assign approximate values to the arrows (positive vs negative, weak vs strong relationship). The concepts are then allowed to affect each other until they come to an equilibrium. The exciting part is that then you can try out scenarios! For instance, you could fix one (or more!) concept to be a high or low value and see how it affects the rest of the system. In the context of ecology, one use is to explore potential ecosystem management scenarios (ex,

If Fuzzy Cognitive Mapping sounds interesting to you (and it should!), you can download the package from CRAN. Michael Bachhofer and I plan to create a tutorial in the spring, but until then you are welcome to email me if you can’t figure out how to use the package.

Download here:

A graphics output for a toy example I was playing with the other day. It is a cognitive map of things which might affect spotted owl abundance. FCMapper uses igraph for visualization. The thickness of the arrows represents the strength of the relationship and the color represents the direction (red=negative, black=positive), as assigned by me. The size of the circles represents the "size" of each concept at equilibrium, as determined using the nochanges.scenario function in FCMapper. Think of the fun maps you could make for your favourite study system!

A graphics output for a toy example I was playing with the other day. It is a cognitive map of things which might affect spotted owl abundance. FCMapper uses igraph for visualization. The thickness of the arrows represents the strength of the relationship and the color represents the direction (red=negative, black=positive), as assigned by me. The size of the circles represents the “size” of each concept at equilibrium, as determined using the nochanges.scenario function in FCMapper. Think of the fun maps you could make for your favourite study system!

Leading a discussion of a scientific paper

I’m teaching a graduate class in Entomology this term, and part of that class involves students leading discussions about scientific papers in our discipline. These discussions are typically between 60 and 90 minutes, with a small group (4-6 individuals). This post provides some advice and guidelines around how to go about doing this. That being said, this is not a ‘one size fits all’ kind of world, especially when talking about science: you may have better or alternative approaches when discussing scientific papers – please comment, and share your ideas!

1. Provide a (quick) summary of the paper:

In most cases, you want to first provide the audience a brief but accurate overview of the paper. It’s often useful to do a little research about the authors – this provides a context that may be very helpful and may prove insightful later on. For example, do the authors have a publication record that aligns with the current paper? Are the authors graduate students or post-doc (not that it matters, but it does provide context!).

The focus on the summary should be about the Research Questions / Hypothesis, and to explain these you will also need to discuss an overall conceptual framework. This means you need to know this conceptual framework very well. After providing the broader context and framework, you should quickly go over the main methods, and the key results. You should act as a guide for your audience, and take them through the key results. Try not to spend a lot of time on more trivial aspects of a paper. In general, your summary should not delve too deeply in the discussion part of the paper.

Don’t forget: you are assuming everyone in the room has read the paper, so your overall introduction should be relatively short (no more than 10 minutes). More time may be required if a concept or methodological approach is particularly complex. Try not to provide opinions or critiques of the paper at this point in time – save this for the general discussion.

2. Ask for points of clarification:

Before proceeding with detailed discussion of the paper, you should ask the audience if they require clarification on anything in the paper. You are leading a discussion and therefore considered an ‘expert’ on the paper, and as such, should be prepared to handle these points of clarification – this will most likely require you to do a bit of research on areas of the paper that you do not understand!  It’s important you you make it clear that you are not starting a detailed critique (yet); you are first making sure that people all understand the critical ‘nuts and bolts’ of the paper.

3. Leading a discussion:

The majority of the time should be spent on the actual discussion.  There are many ways to do this, but here are some tips:

  • Try not to let your own opinion of the paper distract or take over – your goal is to get other people to reveal their own views; these may or may not agree with your own views! Be welcoming and accommodating to other people’s opinions and viewpoints. Never make anyone feel small or stupid, even if they make a goofy mistake.
  • That being said, make sure that you do have an opinion, and be willing to share it at some point
  • Prepare a list of questions that you could ask other people if the discussion needs help to get started. Always try to find positive points in a paper, even if the paper is, overall, very weak. Similarly, try to bring out negative features even if the paper is strong.  This means you have to sort out strong and negative parts of a paper for yourself (well ahead of time)
  • It’s sometimes a good idea to first go around the room and ask for something that people felt was strong and positive about the paper, and then do this again but ask for points of constructive criticism about the paper.
  • Don’t hesitate to ask people (specifically) for their views on some sections of this paper: a gentle push may be needed to get started on discussing the specifics, but this can be fruitful.
  • Since you are chairing the discussion, don’t be afraid to take control if the discussion wanders too far from where it needs to be, and/or if the discussion gets too trivial or mired in the weeds
  • Related, whenever possible, draw the discussion back to the actual research objectives, and try to broaden the discussion out to the overarching concenptual framework: are the results generalizable to other fields? Does the paper make broad and meaningful conclusions that will be long-lived and significant?
  • Towards the end of the discussion, it may be useful to ask people how they might have done the work differently. Or, stated another way, what could have been improved?

4. Summarize the discussion:

Spend the last five minutes of your time reminding people abou the actual research objectives, and provide a concise summary of the discussion that just wrapped up. Do this in an inclusive way, and give a nod to everyone in the room: make everyone feel that their points of views and opinions are taken seriously.   Try to get an overall consensus about the general quality of the paper, and one litmus test may be whether or not you would cite the paper in your own work, and in what context.

Social media, mobile technology and an outdoor classroom

Last year, my field biology course took part in an amazing project – we used mobile technology in a field setting, and combined that with social media tools.  This was done in collaboration with Teaching and Learning Services at McGill, McGill Libraries, and the tablets were generously provided by Toshiba.  I am immensely thankful for the support and an truly honoured to be able to explore these adventures in teaching and learning.  More specifically, Laura Winer, Adam Finkelstein and PhD student Crystal Ernst helped make this project a success.

One of the ‘products’ of this pilot project is this 5 minute video about using social media to engage students in inquiry-based learning:

We are continuing with these kinds of initiatives, and a Brown-Martlet Foundation grant has allowed my Department to purchase some of the tablets originally used last year. This is terrific, and as the video illustrates, the students end up benefiting.

This term, the course is again using social media, and you can find details in this post, and follow along with twitter using the hashtag #ENVB222.

The art of delegation: Perspectives from Academia

The talented graduate student (and all-around great guy) Morgan Jackson recently posted a question on twitter, asking for advice on the art of delegation, from an Academic perspective. This question really struck me as important, for graduate students who are pursuing academic careers and for tenure-track academics.  The reason why is pretty obvious: without learning how to delegate, burnout is inevitable.

To delegate means to entrust (a task or responsibility) to another person, typically one who is less senior than oneself.

The issue of how to delegate is, of course, common and widespread in the business community but academia is a bit peculiar. Let me explain my perspective: In some cases, delegation is straightforward, especially if a staff member is paid to do a particular job and if roles and responsibilities are well defined. Although these kinds of hierarchies exist in Universities and research institutes, these environments often include a high amount of volunteerism and some aspects of Universities (and research more generally) are run on collegiality and community-minded thinking.  Scientific societies would disintegrate if people didn’t share the work-load, and if society president’s weren’t able to delegate work to (often unpaid!) treasurers, web-masters and scholarship committees.  Universities wouldn’t operate effectively if Professors didn’t agree to sit on committees, often delegated by the Chairperson. Research laboratories would be unhappy places if some of the chores weren’t delegated, from making sure coffee supplies are well stocked, to ordering supplies – sometimes a paid technician does this work, but not always….

Academia is also full of “reverse hierarchies” – sometimes a more junior person has to ask a more senior person do take on a responsibility or task – this happens all the time: from seeking help putting together a symposium at a conference, to getting people to agree to sit on an editorial board.  Bottom line: there are COUNTLESS tasks in Academia that depend on delegation and often the tasks, roles and responsibilities don’t fit neatly into one person’s formal (paid) job description, and often the ‘senior to junior’ hierarchy isn’t straightforward .

And perhaps the most important point of all….  one of the biggest obstacles to delegation is the fact that many Academics are perfectionists. Academics, by in large, like to be in control of ALL THE THINGS, from preparing a CV, to setting up committee meetings, to driving a car to a field site. Professors, in general, have got to their position because of their ability to DO ALL THE THINGS and do them well. You can’t publish good papers without knowing how to write; you can’t publish papers without solid research funding, so you have to perfect the art of writing grants; you can’t get a post-doc position of tenure-track position without being able to put together a top-notch presentation and deliver it with the skills of a seasoned orator; you can’t get good teaching scores without investing time and energy into perfecting Powerpoint slides and learning the content….  etc., etc., etc.

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However, as Peter Adler wrote over at Dynamic Ecology recently, it doesn’t get any easier. In fact, the job gets more demanding on time, expectations on productivity remain, teaching can be time-intensive, and the Academics are expected to do some administration. From a personal perspective, I am far busier now than I have ever been in the past (but I try not to complain about it).  Good time management skills are not enough to get everything done. What’s needed is an ability to delegate. Again, without effective delegation, burnout is inevitable.

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With that backdrop, how do you delegate?

1) Know the players. Delegation requires knowing your community and knowing the skills and abilities of people within your community, whether it be a graduate student secretary, the treasurer of a scientific society, or a colleague down the hall. Before you can even think of delegation, realize that delegating any kind of work has a real, profound affect on somebody and on how they spend their time. It’s about people, so you must get to know these people! This means networking, whether it be around a coffee maker at work, over twitter, or attending a poster session at a conference.  Pay close attention to everyone you interact with, listen to them, learn their passions, learn what they like to spend their time doing.

2) Play nice. In addition to knowing your community, don’t be a jerk to your community! I mentioned Morgan Jackson at the start of this post; he’s an example of someone who is always willing to lend a hand, say a kind word, and be a team player. He plays nice. I am always happy to help Morgan in return, even though I am (in academic terms) his ‘senior’. This seems SO obvious, but I also know that not everyone plays nice. Some people are selfish, ignore those they deem as ‘inferior’, and require you to grovel to get an answer to an email.  It’s a tough world, and there are big personalities in Academia, and everyone has their own agendas.  This can be difficult to navigate, and politics in Academia can be fierce. However, a strategy that always wins is to play nice. Be collegial, polite, and try not to burn bridges. It’s hard to delegate if there’s nobody left that respects you.

3) Prioritize. Delegation is an art, and one of the trickiest parts is learning what to delegate and what to keep on your own plate. It’s also important to avoid delegating everything. Some things are too close to your own expertise, part of your job description and/or are tasks that you just love too much to give up. However, some tasks can be shared effectively among others, and can move away from your to-do list. Write down ALL that you have to do, and put a star beside those that you cannot see anyone else doing (ahem, if there are stars next to all of the tasks, you will burn out. Start again, and see point #6, below). If your are lead-author on a paper, you sure ought to read over those final page proofs! However, maybe your co-author could do a final check over all references, especially if s/he hasn’t contributed as much to the paper..?

4) Have a vision (& communicate it!). Delegation will not be successful if those you delegate to are not sure what role they are playing in the ‘big picture’.  No matter the size of the task, it is being done for some reason. Having someone give a guest lecture is pretty obvious: the guest lecture helps achieve the learning objectives of the class and gives students a new perspective on the content. Sure, that makes sense. But did you communicate that to both the students and the person giving the lecture? EVERYONE involved needs to understand the ‘why’ behind the jobs and tasks at hand. This means effective delegation requires carefully assessing why tasks are being done, and working to communicate this. If people are part of a vision (even one they may not 100% agree with), it’s a lot easier to get them to take part.

5) Ask and Explain. Sometimes it’s as easy as asking (nicely). This goes much smoother if you have a vision and that you have communicated this vision, as mentioned above. In addition to asking, it’s essential that the tasks you are delegating are clearly defined. A volunteer might agree to sort specimens if you ask them. However, a simple ask may result the job getting done, but perhaps with a lot of mistakes. Asking, and then giving someone a 1 hour tutorial and access to resources on-line will result in fewer errors. Preparing a living document that explains your protocols for sorting and letting them refine and improve the document is even better!  All tasks, regardless of their size, need to be defined. Just because you think it’s easy to do, straightforward, and simple doesn’t mean everyone else will.

6) Let go. (TRUST) I have noticed that many Academics (myself included!) don’t delegate because they say “Ah heck, I already know how to do that, it’ll take too much time to explain or show them how to do it…” or “I’ll do that myself, it’ll be faster“. There are a few problems here. First, if you say this about everything, burnout is inevitable. Second, as an Academic / Researchers/ Post-doc, etc, you are responsible for sharing knowledge and training others, and this takes time. In the time it takes you to ‘just do the task’ five times, you could have trained someone else. Third, this may indicate that you don’t ‘trust’ anyone else to do the job. You must let go of this! Be a perfectionist at the right times, but let some things go. There are errors in everything we do, so sharing them around is fine, for some tasks.  Remember, you have developed a network, you are team player, and you have shared your visions and prioritized, and defined the tasks. It’s time to let go.

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7) Verify. Letting go does not mean letting go forever. There must be follow-up and discussion to ensure the job is done well. Accountability is key. Review the job, first on your own, and then with the person to whom the work was delegated. You must provide constructive feedback, but also listen to ideas, complaints and comments. This will help you redefine the task in the future, and they will feel more responsibility and ownership over the task. This also starts the amazing process of creating someone who can later become a delegator of tasks. This is what mentoring is all about… in your laboratory or classroom, you want people to walk away with confidence in what they do, and with an ability to take their skills sets and pass them along to others.

8) Reward. It’s absolutely essential that you reward those to whom you have entrusted a task or responsibility. If people do not feel their work is valued, and that they have not contributed in an important way, you have failed in effective delegation. If you reward, your vision will grow, your team will respect you, your (positive) network will increase. Rewards can be small or big: A few kind words, a big “THANK YOU”, some homemade cookies, a promotion, or a letter of reference. Here’s an example: I often get graduate students to give guest lectures in my courses. This saves me time, and helps me out when I’m overwhelmed. When students do these lectures, I offer to write them letters or recommendation specifically about their abilities in the classroom. Most take me up on this, and it’s a kind of reward. I also ensure to pass along kind words from the students in the class.

9) Get some training. The art of delegation is seldom on an Academic’s CV. It’s often learned by trial and error, and sometimes never really perfected. Like any skill, training is required. In some cases, informal training is enough. This can be via sitting on committees in scientific societies, learning from effective mentors, or just practicing. However, I think that most Academics are not very good at delegation, and more formal training is required. This could be in the form of workshops, perhaps for all incoming Profs at a University, or as part of a research conference. I would like to see these kinds of ‘management’ skills as part of EVERY graduate student’s program, as perhaps part of the seminar/course work often required during a graduate degree. WE MUST DELEGATE ergo WE MUST HAVE TRAINING.

10) Be a leader. Don’t shy away from leadership. Everything mentioned above is about leadership.  Professors are leaders, perhaps a leader in front of the classroom, as a research leader within your institution, a leading expert in an op-ed piece, or a leader on a committee about academic programs. Effective leaders are effective at delegation; in fact, I might argue it’s impossible to be a leader without being effective at delegation.  Behind every good leader is an even better team. It’s so cliché, but also so very true.

In sum, delegation is about empowerment and leadership. It’s about giving someone else ‘ownership’ over a task that is part of something bigger. Delegation will help you work on things that YOU need to work on, and help you avoid burnout. It’s a required skill for success in Academia.

(BIG thanks to twitter-folks to took part in the conversation about delegation, especially Morgan, Terry, Chris, Staffan, and others)

A guide for writing plain language summaries of research papers

Some time ago I wrote a post about the need to have plain language summaries for research papers. That post generated terrific discussions, new collaborations and many ideas, and I am now trying to write plain language summaries of my own research as it gets published. The goal of this current post is to provide some guidance about how to write plain language summaries. This work does not come from just from me, but rather from continued discussions with others, notably Mike Kelly and colleagues over at TechTel. The idea of plain language summaries resonates with so many people, from the business and marketing community, journalists, through to science writers, researchers and academics. I am continuing to work with Mike, and will share more as our ideas and projects develop. For now, however, it’s timely to provide some idea about how to write plain language summaries. As usual, your ideas, opinions, and comments are always welcome!

To revisit, what are plain language summaries?

Plain-language summaries are a way to communicate a scientific research papers to a broad audience, in a jargon-free and clear manner. Jargon is defined as technical terms understood only by specialists in a field of study.  In this post, I am assuming that plain language summaries are aimed at a ‘scientifically literate‘ audience, but an audience that is not specific to a discipline. Most scientists who publish in the peer-reviewed literature are familiar with Abstracts – which are a short synthesis of the research, and which typically highlight the research objectives, method and main findings.  Abstract are typically aimed at the audience that will read a specialized journal, but often contain technical terms, and typically jump into a specialized topic quickly and concisely.  A plain language summary is different because it focuses more broadly, is without jargon, and aims to provide a clear picture about ‘why’ the research was done in additional to ‘how’ the work was done, and the main findings.

Plain language summaries are a valuable contribution as they allow research to be accessed by a broader audience, and because the people who do the research write them, the findings are directly from the source and should capture the proper context for the research. Plain language summaries can provide a means to promote research, whether it is through a publisher, on the blog of a scientific society, or for a University’s Media Relations Office. Department Heads and Deans can take these summaries and both understand and promote the high quality science done by their Professors, research scientists, and students. Journalists could read these summaries and not have to wade through technical terms, and have a higher probability of getting the message right. Colleagues can better understand the work that all scientists do, even when disciplines are quite far apart. Other scientists, journalists, the public, government officials, friends and family, can all better understand science if all research papers were paired with a plain-language summary. Plain language summaries make research available, tangible, and are a way to truly disseminate research findings to all who are interested.

How to write a plain language summary:

The first, and perhaps most essential step, is to explain ‘why’ the research was done. The overarching reason and rational for the research must be explicitly stated in general terms. It’s easy to slip into the habitat of justifying research because “Little is known about x, y or z”.  However, this is not adequate for a plain language summary – ‘something’ is surely known on the topic, it’s just a matter of defining that ‘something’ and explaining how the work is expanding beyond, perhaps to a new research direction, or in a different model system.  Mike Kelly, from his perspective (and background) in marketing, was particularly instrumental in helping recognize that the “why” of research is vitally important, and explaining this should never be taken for granted. Scientists need to start a plain language summaries from a broad, ‘big picture’ and more general framework, and work to place their research paper within this context: they must address and answer the ‘why’.  It takes a lot of time to define the ‘why’ and describe it to a broad audience – take the time – it will make the other steps much easier.

The second step is to state the more specific objectives of the research.   This should flow easily from the first step if there is a clear rationale for the work. The research question is a continual narrowing down to a finer study topic, logically flowing from a big picture overview of the discipline into which the research is nestled. A research objective could be phrased as a question, or goal, and may have several sub-questions.

The third step is to explain ‘what’ you did to answer the research objective. Too much detail will be overwhelming and confusing, too little will not allow the reader to envision how things were done. Try doing a flow-chart that depicts the process of the science, and use this as a guide to writing how the work was done. The goal of a plain language summary is not to allow other scientists to follow your methods, but rather to provide readers with a sense of how you did the work, in broad brushstrokes.

The fourth step is to provide an interpretation of results and make them relevant. Unlike a scientific paper, which typically presents results in a linear fashion and independent of a discussion, plain language summaries should integrate the results with a discussion or interpretation. A plain language summary should show readers how the results to fit together and provide insights into the bigger framework or context of the research. It is not necessary to provide all the results, nor is it necessary to provide specific details about each observation of experiment; rather, the results must tell a story and inform the readers of what you found and why the findings are important relative to your research question. The end of your summary should scope out again, and leave the readers will a strong and positive sense about the contribution of your science to the big-picture that you developed at the start.

The last step is to go through the plain-language summary with a keen eye for meaning and jargon.  Assess each sentence and see that the writing is drawing out the meaning from the research, whether it is a description of the study organism or system, or a rationale for quantitative modeling. Without attention to meaning, at all levels, a plain-language summary will be a re-packaged Abstract, which is to be avoided.  Circle or highlight all terms that could be considered jargon  – have a friend, an uncle or a colleague from a different discipline read over the work to confirm that the jargon is gone.  When jargon is identified, rewrite in non-technical terms – it will take more space, but this is better than having terms that cannot be understood by a general audience.

Then: edit, edit, and edit again.

Some hints….

  • If you are visual person, draw the plain language summary before writing it, this will help draw out the meaning and allow you to understand the flow of the summary and how the different sections fit together.
  • It will likely be helpful to first write your plain language summary with headings.  Use headings such as “Why we did this work”, “How we did this work”, “What were the interesting things that we discovered”, etc. Afterwards, re-work the summary to remove the subheadings.
  • Don’t talk down to your audience. A common mistake is the ‘dumbing down’ of the research and this must be avoided. As mentioned, you are assuming the audience for this summary is scientifically literate, and thus you need to speak to them in this way.
  • Aim for about 500 words – more is too much, fewer can be difficult, especially if your research is highly technical.
  • Have your summaries read by other people outside of your discipline, and then have them explain it back to you. If it’s a good summary, the explanation of your own work should be clear, accurate and precise.  If it’s not, find out the trouble-spots and re-work the summary.
  • Finally, don’t rush the process. Plain language summaries are very difficult to write; they take time, and often draw upon skills that have not been part of a researcher’s typical training. Write the summary, leave it for a day or two, and come back to it. It is very important to get it right, as these summaries have the potential to be read by many more people than would normally read a scientific paper within a journal.

In sum, I hope you find that there is value in plain language summaries, and that this guide provides some ideas about how to write one.

You may have more tips or better ideas – please share! (comments welcome…!)

On the game of Academic Publishing

Back in March, I was asked to present a talk to Professors at Cape Breton University about finding success with Academic publishing.  This was in part because of my own experience with publishing, but also in my role as Editor-in-Chief for The Canadian Entomologist.  This talk took some time to put together, but it was a lot of fun to think in detail about how the publishing ‘game’ is changing, and how’s it is difficult to navigate – especially for early-career Academics.

In this post, I am pleased to share my presentation with you – I hope it is useful to some, and I hope it sparks discussion about finding success in publishing.  I realize some things were missed, and the presentation itself is rather static and cannot capture the dynamic discussion that was part of the seminar given at Cape Breton.

Please share!