How to run an online academic book club

Back in August, the first copies of Mark Vellend’s book The Theory of Ecological Communities were released. I got one of them and found out from the publisher and from the Twitter-sphere that the books were in high demand. A couple people on Twitter suggested forming a book club, and being a compulsive organizer, I figured I could gather 6 or 7 or 8 people who wanted to read the book together this fall semester and we could find a time to meet online. So I set up a Google Sheet and sent out a tweet.

More than 40 people responded as word spread on Twitter and beyond. Huh. That would be a bit too many for one group. Instead, I organized everyone into six groups, each of which were to meet once per week online. An additional group formed at UBC of people who met in person, and who kept in touch with the rest of us. I definitely didn’t want to be in charge of 6 or 7 groups and so after setting up the time slots, I asked each group to be autonomous, with a “group organizer” to help keep it on track. Most of the groups have now finished reading the book and I asked the group organizers for feedback on their experiences. Overall, it was successful, and here I’ll talk about some of what worked and what didn’t. I hope this post can help guide anyone else who wants to do something similar in the future.

Since each group was autonomous, the members chose their own schedule and rules. One group set up the reading schedule ahead of time and posted it for group reference online. My group figured out its schedule as we went along; at the end of each meeting, we would look ahead in the book and decide if we wanted to read the next one or two chapters for the following week. Both methods seemed to work fine. 

Attendance is always the key thing for any group meeting on a volunteer basis. The groups comprised 5 to 7 people, and most groups decided upon a quorum of 3 or 4 people to hold the meeting that week and cancelled if not enough people could make it. Not surprisingly, some people who signed up — who really wanted to read the book — found that their schedules were over-full and couldn’t attend regularly. One group stopped meeting because of consistently low attendance. In general, group organizers reported really liking the size of the groups. It seems that a group size of about 6 or 7 works well to ensure that enough people can meet regularly without being too big if most everyone shows up. The group that met in person had many more participants; 10-20 people showed up each week(!) But attendance declined over time, perhaps because it was too big a group for good discussions.

In forming groups, I had to be aware of time zones, as participants hailed from New Zealand, Australia, mainland Europe, the UK, Brazil, and all across North America. This was the trickiest part, because wanted to try to keep groups diverse. The one group that dissolved, however, was the one that had the biggest time differential. It included participants from Australia and New Zealand (who met during their early afternoons) and participants from North America (who met in their evenings). This group’s organizer said that coordinating across such a large time difference proved somewhat difficult. 

Another point that group organizers mentioned was diversity of career stages. I didn’t think to ask about this when forming groups, so there was no attempt at making sure there was a good mix of career stages within groups. My group, for example, was mostly postdocs and ran fine. One group that ended up being all grad students reported wishing that they had had some participants who were postdocs or faculty.

One thing that seemed to work great for most groups was the diversity of areas of expertise among group members. (This was another thing I didn’t think to ask about ahead of time, but most groups seemed to get a good mix by chance.) I think this diversity of expertise is one benefit that can be gleaned from arranging online groups. Groups had people studying things ranging as widely as core community ecology, population genetics, ecosystem ecology, animal behavior, theoretical ecology, and evolutionary ecology. Study systems also ranged widely. Group organizers reported that this diversity of perspectives really contributed to interesting conversations.

Technology-wise, all the online groups used Google Hangouts for their discussions, which seemed to work just fine. One group organizer pointed out that it’s possible to make a permanent link to use each week, which made organizing easier. A couple groups used Google Docs and one made a Google Site to take group notes and share resources (links to papers, TED talks, etc.). I made an open Google Doc that anyone from any group (or no group) could contribute to. These appear to have been used a little bit, especially at the beginning, but there wasn’t a huge interest in using a collaborative tool for notes or continued discussion.

In November, I arranged for Mark Vellend to chat with book club members directly as a nice end-of-book wrap-up. (Thanks again, Mark!) I created a Google Doc ahead of time and asked people to brainstorm questions to ask Mark. We held three one-hour chats during times that three of the groups normally met, but the chats were open to anyone interested. I advertised them directly to all book club members by email and also on Twitter. In total, about 25 people took part. The first one was the most well-attended. Because Google Hangouts only allows ten participants at a time, Mark and I were on Google Hangouts and the stream was broadcast to YouTube, where viewers could post questions via chat feed [1] To do this, go to YouTube’s Live Events page and click the “New live event” button. Quick start instructions. This worked okay, but there wasn’t nearly the interaction that I would have liked for something that was supposedly a “chat”. Instead I asked the majority of the questions, drawing heavily from the questions book club members had brainstormed. For the second two chats, I asked people to join the Hangout directly, and these were much more interactive and lively. If you’re interested in seeing the archived chats, they’re available here: Chat Nov 9 [2] WHAT! This video has 126 views already! (Who ARE you all?) | Chat Nov 18 | Chat Nov 22

All in all, I think it was a successful endeavor. The bulk of my time and effort was upfront in organizing times for all the groups, and that wasn’t actually too much work. Having distributed and autonomous groups definitely made it logistically manageable, although there were several people who were interested in cross-group communication or conversations that just never took off. (Efforts were made by Google Doc as well as on Twitter with hashtag #TOEC.) Initial group sizes of 5-7 worked well, and diversity of career stage and scientific area of expertise seem to be key for engaging conversations. Having an interactive chat with the author at the end was straightforward to organize and was a lot of fun.

So… what book are we all reading next?

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Thoughts on preregistering my research

Last week, I submitted the methods for the project I’ve recently started to the Center for Open Science’s Preregistration Challenge. Briefly, the goal of the challenge is to get more scientists to preregister their research, and it’s got a monetary incentive. The goals of preregistration itself are to increase transparency and reproducibility in scientific research.

I’d never done a preregistration before, but it seemed like a Good Thing to Do in the name of Open Science. And the monetary incentive pushed me over the learning-curve barrier and the fact that it involves a bit more work than usual. I consider my preregistration a bit of an experiment. Having written one now, I have some opinions of the pluses and minuses.

Let’s start with the drawbacks. I found three significant drawbacks, the first of which is simply that preregistration is a foreign concept to most ecologists, and so I had to explain what I was doing — and justify it — a number of times to other people. That was only a slight annoyance in of itself, but it made the other two drawbacks harder.

It took me a few months to put together the preregistration plan. The reason for this is due to the nature of the project. I am using data produced by NEON and doing a series of complex statistical analyses on them. To do a preregistration means thinking about all the parts of analyses in depth: what variables am I going to use, how am I going to transform them, what will be the structure of my equations, and how am I going to do inference from model results to scientific meaning. In addition, I had to think about all the “what ifs”: What if I found that some variable was far from normally distributed? What if the data didn’t have good coverage or the response variables didn’t vary in the way I thought they would? What follow-on tests or modeling was I going to do if I got result A versus result B? Note that I didn’t look at the data while I was doing any of this, as part of the conditions on the preregistration challenge.

These are all very important things to think about, but like most everyone else in ecology, I am accustomed to figuring out many of the answers to these questions when — and if — the situation arises. This classical approach may lead to “researcher degrees of freedom” however, and I understand why it might be a good idea to preregister. On the other hand, having to figure out so many different contingencies might be a waste of time. If I have to figure out a bunch of contingencies that never happen, that’s time I could have been moving forward with analyses. I haven’t yet done the analyses, so we’ll see how much this drawback matters.

The final and probably biggest drawback was that I didn’t have any progress to report for three months. No doubt about it — I was making progress, but I didn’t have anything to show for it. I didn’t have any preliminary analyses or graphs or numbers or anything to show that was doing something. My lab does weekly progress updates and many of mine were feeble sounding: “I worked on some more mathematical modeling.” Blah. Because the NEON staff know I am working with their data, I was also asked by NEON my opinions about some of the data for their annual review. But because I hadn’t performed any analyses yet, I couldn’t provide any useful feedback, other than “ask me next year! I’ll have all the answers.” Pushing all the results to the end of the project can be a real detriment to projects focused on an analysis of existing data and/or applied projects.

Now the advantages of doing a preregistration plan.

Working through the full scope of my analysis without playing with the real data made me think very hard and carefully about the questions I wanted to ask and the kind of results I expected to get. Instead of just plugging data in, I had to ask, “What if the data are like this? What if the data are like that? What would that mean?” It made me figure out my assumptions in a way that I don’t think I usually do when I figure out analyses as I go along. It made me clarify my qualitative thoughts into quantitative predictions. I think the process made me a better scientist.

I think that having scoped out all my analyses in detail at the start will mean that doing the analyses themselves will go really quickly. In fact, if they do, I think figuring out analyses ahead of time will have saved me time in the long run. I remember playing with a big data set as a grad student and trying to figure out all the various questions I could ask of it. Instead of thinking about what questions were important to ask, I tried to ask as many questions as possible. It took a lot of time and left me with many loose threads that were hard to tie together into a coherent story (for a paper). Being super clear about my questions means, I hope, that writing the paper will be fairly straightforward, which would be yet another time-saver. But all of this depends on the analyses working out okay. That is, hopefully I have enough data with enough variation and that at least some of my predictors do actually contribute to predicting the response.

The preregistration queries on the Center for Open Science’s website were super useful in helping me think through my research. I’d recommend using them even if you don’t plan to file an official plan. In particular, when I got to the question about drawing scientific inference from analytical results, I realized I didn’t have a concrete plan. While a p-value of 0.05 is a pretty standard cutoff for a lot of traditional ecology research, I am using Bayesian statistics and am not a fan of arbitrary cutoffs generally. I didn’t have a good answer off the top of my head, so I emailed some colleagues and that turned into an interesting discussion about good/normal/accepted ways to report Bayesian posterior distributions. I don’t think I’d ever have made a conscious effort to figure out how to interpret results otherwise.

Finally, if you do want to take the Preregistration Challenge, I have a couple more notes to recommend it. First, David Mellor has been super responsive and helpful as I waded through my preregistration. Any questions? Ask him. And while the Preregistration Challenge website states that it can take up to two weeks to have your preregistration approved — and that you shouldn’t start your analyses until it is — mine was approved within 24 hours. I’m looking forward to actually putting the data through my models now!

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Some advice on applying for faculty jobs, according to six Harvard assistant professors

This is the year — the first year I’d be applying for faculty jobs, if I wasn’t so adamant about not relocating. I finally have first-author papers, the last checkbox to check on an otherwise pretty decent CV. [1] The trouble with doing too many things is not finishing any of them. I had too many projects plus birthed two babies. (Okay, the birthing does get done, whether it’s your top priority or not!) In 2016, I’ll have had my first three first-author papers published: one from grad school, one from my first postdoc, and one independent. But by the end of 2016, I’ll be three years post-PhD… Even if I was willing to relocate (and I have to say, some of those job announcements are tempting), I’m not entirely sure I’d want to pursue the academic track. Over the years, I’ve befriended a number of junior faculty. And honestly, I’m not sure I want to live like them.

But I think it’s always good to be prepared and keep doors open, if doing so isn’t too costly. So last week, when there was a “how to apply for academic jobs” panel, I went. Just to see. I actually did send a long-shot application for a tenure track position several years ago, and I read a ton of job-seeking advice on the internet at the time. I wanted to see what more this panel could offer. Was there advice that no one was willing to commit to the Internet?

Here are the things I took away that were new to me (someone who has never really been on the job market, but is familiar with the standard set of advice you can find online), with the caveat that the panel consisted of folks who ended up securing tenure track jobs at Harvard [2] which likely means some sort of bias:

  • Applying for jobs is wildly different in different fields. I guess I sort of knew this. But it’s super important that when you find Internet advice, that it makes sense for your field! The panel was six assistant professors in the fields of EEB, EEB, physical anthropology, mathematics, biomed, and applied math. For the latter three, “going on the job market” was a single-year endeavor that entailed sending applications to many tens of institutions. The mathematician and his wife, trying to solve the two-body problem, sent off a combined 100 applications! All three got many interviews and multiple job offers. For ecology and its sister fields, “going on the job market” meant (for 2 of 3 panelists) a multiple-year search consisting of sending out a dozen or two applications per year to targeted positions. (The final panelist applied for just the job at Harvard, because she didn’t think she was ready to be on the job market, but the job description fit her perfectly and some mentors convinced her to apply.)
  • Five out of six said that being completely geographically open and completely open to the idea of institutions you might be skeptical about is necessary to get a good offer. The sixth decided she wanted to be geographically constrained and was happily willing to leave academia if she couldn’t get a job in the right (large) area. I pretty much never hear of this latter perspective, but it makes a lot of sense.
  • All six agreed that you shouldn’t tailor your research or teaching statements to the institutions you’re applying to. They’re about you and not the job opening. The cover letter can be a little bit customized — one panelist suggested a single paragraph that can be changed to talk about how you see yourself fitting in at that institution. (FWIW, a panel of four assistant professors the week before advised the exact opposite: that you should tailor all your materials for each application.)
  • All six agreed that being on the job market can be like a full-time job and is not fun, and that you need to have the support of whoever is paying your paycheck during this time.
  • For interviews, you shouldn’t ask too many questions. That is, sure, ask some questions to show that you’re really interested, but you don’t need to ask about all the little diddly details until you get an offer. And if you’re asking so many questions that the people interviewing you don’t feel like they get a chance to ask you questions, you’re doing it wrong. Related, don’t make it look like you’re interviewing the university. Of course, you want to know if it would be a good fit for you, but if you come across as “I’m interviewing you, too!” then you’re going to be seen as a bit stand-offish. The way you want to be seen is likable. These people aren’t just evaluating your research; they have to decide if they want to be around you for the next couple decades.
  • If you are a member of a group against which there are implicit biases, know these biases and play against them. For example, one panelist mentioned that, as a woman, she would never bring up teaching when interviewing at R1 institutions, although she was ready to talk about teaching if someone else brought it up.

Know of any applying-and-interviewing-for-faculty-jobs tidbits that you don’t regularly see online?

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Making science products Open: an informal guide to copyright and licensing

I grew up a hacker (in the original sense) and thus a True Believer in open knowledge. And so, when it came time to start publishing science, I figured I’d make all my products Open. But it turns out that there’s a bewildering array of things to think about if you want to do so. More recently, I’ve been wanting to incorporate other people’s creations in my own, and have encountered various difficulties in using Open products. I’m writing this post, in part, so I have notes I can easily reference in the future. But I figure if it helps me, it can help others, so here you go.

I have to put a note here that I am not a lawyer, and so this is not legal advice. This is just my good faith understanding of the intersection of U.S. copyright law, licensing, and academic products.

What is copyright, and why do I care?

When you make a Thing, you get to decide how to it’s used and how to distribute it to other people. That’s copyright. The sorts of Things we’re concerned with here are scientific writing (journal papers, reports, dissertations, etc.) and other media (photos, video, audio, etc.), scientific data, and software. You’ll see these Things referred to as “creative works” if you read a lot about copyright. Copyright is a type of intellectual property, and is different from patents, which cover inventions [1] specifically a physical thing or a process, and trademarks, which distinguish products and services from similar ones. And most likely, if you make a scientific Thing, you are automatically granted copyright. [2] There are exceptions, though. If you work for the U.S. government, your Things will automatically be in the public domain. And if you are the employee of a University or other institute, you may have signed away your rights in that flurry of paperwork you got when you were hired; in other words, your institution may own the copyright on Things you make, not you.

What do I do with my copyright?

Whatever you want.

The historical use of copyright goes something like this… I wrote a scientific paper and now Journal of Things (JoT) wants to publish it. I assign a license to JoT saying that they can use my writing to make a new Thing — a journal article — and that this journal article can be disseminated as JoT sees fit. Note that I retain the copyright to my actual writing, but JoT has copyright to the formatted, spiffed-up, published version. Now, let’s say someone else wants to use a figure from the published article, they now need JoT to assign a license to them for the use of that figure.

This model of assignment can work fine if the Thing you make is just used once or twice by others, or if you feel strongly about how your Thing is used and distributed. But otherwise, it can get cumbersome. Instead of (or in addition to) assigning licenses on a case-by-case basis, you can assign a general non-exclusive license that automatically allows people to use and disseminate your Things.

How do I assign one of these general non-exclusive licenses?

The first thing you have to do is pick one. And sadly, there are a lot of options for you out there. I really like the Choose A License site to get a sense of what the possibilities are. But if you just have time for a single blog post, here’s a quick run-down. Answer these questions:

  • Are you willing to let your Thing be distributed to anyone who wants it, free of charge?
  • Are you willing to let your Thing be modified into some other Thing by others? (e.g. If you take a picture that someone else wants to use, is it okay if they crop it differently or change the lighting or include it in a collage?)
  • Are you willing to let your Thing and its modifications be distributed by someone else for commercial purposes? (i.e. They might make money off of it.)
  • Do you require attribution? (i.e. You require that your name be attached to your Thing.)
  • Do you want to make sure everyone who uses or distributes your Thing (or modifications of it) uses the same set of answers to these questions as you do?

This seems straightforward enough until you realize that your answers to these questions might have complicated ramifications. For example, if you decide you do not want your beautiful photo of a rail to be used for commercial purposes without your explicit permission, I would totally understand that. But what that means is that when I want to use it in my Ecology article, I probably still need to contact your for explicit permission. That’s because Ecology, although a publication of the non-profit Ecological Society of America, is published by Wiley, a for-profit publisher. This is, of course, a murky area, but none of us are lawyers, right? So I should ask permission. Now, if you had put an open license on that image that didn’t curtail commercial use, then I could have used it in my article without asking. Even within the Open Source community, there are arguments about which are the best licenses to use. (That’s why there are so many of them.)

Ugh, this all sounds like a lot of effort. What if I just don’t do anything?

If you don’t do anything, you retain the strictest copyright allowable under law. In other words, if you don’t assign a general license to your Thing, then legally, it can’t be used, modified, or disseminated by anyone else without getting explicit permission from you.

Well, huh. I’d like to be more Open than that. What do you suggest?

Here’s where I’m at in my thinking of open licenses, though my thoughts may continue to evolve. For creative things I write, such as blog posts, scientific articles, and so forth, I usually retain full copyright, and don’t assign an open license.

For other media, such as photos, videos, and audio, I typically assign Creative Commons license CC BY. I used to care more about commercial use and so some of my stuff is licensed CC BY-NC. But as someone who’s been stymied by the NC (“non-commercial”) designation when trying to use something for not-for-profit purposes because there’s an awful gray area, I’ve given it up. If there is something that I think might have actual commercial value (such as our Snapshot Serengeti photos), I am more conservative with licensing and will slap on an NC. If anyone does wants to use it for a commercial purpose, they can ask and I can issue a separate non-exclusive commercial license that provides me with some slice of the income (as royalties or a one-time payment).

I also used to be a fan of Creative Commons’ “share alike” (SA) restriction, e.g. CC BY-NC-SA, which forces people who use your Thing to use the same license as you. But I’ve found that such “copylefts” are severely limiting for reuse of material. For example, I am never going to be able to persuade a publisher — even a clearly non-profit one — to make a journal article CC BY-NC-SA, so if you give that license to your rail photo, I’m going to have to ask you for explicit permission if I want to use it in an article. Every. Single. Time. So for me, CC BY is where it’s at, unless I think my Thing has actual commercial value. It essentially mirrors what we do in academia already: reuse and distribute work with attribution.

For data, I make it truly Open. I assign it to the public domain, meaning that anyone can use it for any purpose, without attribution. I do this both because it aligns with standard academic practice and because I don’t want anything to get in the way of anyone using my data. [3] Note: please use my data! (Of course, there are potential ramifications of doing so.)

I divide code into two types: code that I consider “end code” that is very specific to particular scientific study and “general code” that might reasonably be expected to be built upon by others. An example of the former is the specific agent-based model I used for a paper on disease dynamics. And for this sort of code, I tend towards a CC BY license because it’s simple and easy and I don’t have much expectation of reuse. An example of the latter is an R package. For this sort of code, I lean towards GPL-compatible licenses to make sure that my code license meshes easily with the code licenses of others. And since I’m no longer a fan of copyleft, the MIT license works just fine most of the time. It essentially says, “go ahead and use my code as you like, but I’m not providing any guarantees that it’s any good.”

Still seems complicated. Any other thoughts?

I have read a convincing argument [4] that I can’t find now, despite lots of searching. If you know it, can you send me the link? that as academics we might reasonably put everything under a public domain or MIT license (which limits liability). The reasoning is essentially that (1) academic culture already provides for attribution by default; (2) there are lots of murky gray waters in the copyright code such that definitions may vary between people (e.g. my definition of “commercial” may be different than yours), meaning that it’s hard to know what people’s real intentions are when they choose an Open license; and (3) we aren’t prone to go around suing each other over copyright infringement. After all, copyright only really matters if you’re willing to enforce it. And that takes time and money and effort.

I’m still chewing on this argument.

And I’m happy to hear others. How do you license your scientific Things?

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Demands for 48-hour proof turnarounds are unacceptable

Perhaps this sounds familiar… You wrote a manuscript and it got sent out for review. It got generally good reviews, and so you revised the manuscript once or twice. Then it was accepted. Hurrah! Break out the milkshakes. [1] or other beverage of choice Then … crickets … nothing. After a few months, you email the editor, who says, yes, it’s in the queue, just going to be a bit longer. Then one day, out of nowhere, whack! An email appears in your inbox. It’s final edits or proofs and the editor wants it back immediately. Forty-eight hours. Or in one business day.

My immediate reaction to this is always, “I’m sorry, your lack of planning is not my emergency,” and I push back. I really, really don’t get this behavior. It has now happened to me for 3 out of 3 first-author papers. [2] Case 1: proofs sent on a Sunday morning demanding 48-hour turnaround; Case 2: proofs sent on a Wednesday while I was on vacation and without Internet demanding a 48-hour turnaround; Case 3: final edits sent on a Thursday evening, demanding turnaround by end of day on Monday, a holiday. And I find it really, really rude.

I freely admit that I am not an editor nor do I understand the inner workings of academic publishing. But I see no reason for such a short deadline. Academic journals are published on regular schedules with regularly formatted content and with each manuscript on independent pages. It’s not like my article relies on the layout of the article before mine. Proofs and final edits can be prepared weeks in advance of submission to the printer (or posting online).

Dear editors, please understand that my job is busy and that I have a life outside of my job. I cannot just drop everything to attend to the task you want me to do. I have childcare responsibilities and so do not work evenings, weekends, or holidays. Do not expect me to. I have previously scheduled deadlines and meetings that I am not willing to cancel. Do not expect me to. Sometimes I am traveling or on vacation and sometimes I encounter emergencies. If I am away from the Internet for a few days, if my schedule is packed, if I am in the hospital caring for a loved one, you are going to have to wait. And you need to plan for such things, because they are a normal part of life.

Dear editors, I see us as partners in this publishing game. I create content. You publish it. I receive prestige from the deal. You fulfill your organization’s mission and/or receive money from the deal. So let’s treat one another as partners when it comes to final edits and proofs. If possible, please prepare final edits and proofs and send them to me several weeks before you need them. If that’s not possible, then please send me a heads-up email a week or two ahead of time telling me when you expect to need my time. I will put you on my schedule. I value our partnership.

I imagine that by creating this false sense of urgency, editors do tend to get fast turnarounds. But I want to suggest to early career academics that you think about something before you cancel that date to work on proofs, before you stay up all night to do edits, before you stick your kids in front of a screen so you can focus on your work and not them. By the time your paper gets to proof stage, the journal has already invested a lot of time and effort in your manuscript. They’ve even scheduled it for a particular issue. It may be more challenging for them to move articles around than wait a few extra days for you. So do what I do and prioritize the edits or proof, but not to the extent it upends your life. And say so, very politely: [3] Feel free to use my words.

Thank you for sending proofs. Unfortunately, I am unable to return them by [date], but I am prioritizing them, and will get them back to you no later than [date]. Thank you for your understanding.

Then absolutely and without fail, return your proofs or edits by your self-imposed deadline.

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Advice for new postdocs

In case you missed it, last week was National Postdoc Appreciation Week. I almost missed it, but Harvard conveniently put up a huge banner and offered us a bit of free food (Super yummy Mexican food this year!) Good food = appreciation? Sure, why not.


September seems to be a common time for new postdocs to start. “So I’m a postdoc now,” tweeted Allison Barner [1] who is a top contender in the “best personal research website ever”. Seriously. Click through. at the beginning of this month, asking for advice on being a postdoc. Her tweets were quickly rebroadcast as other new postdocs waited for replies.

soimapostdocnowAnd I realized, wow, I’ve been a postdoc for more than 2.5 years. I have advice! So too do many others. Here’s a quick run-down on all the advice offered (with credit to , , , , , , , , , and , for all their advice).

Figure out your relationship with your mentor/boss

If you are on an independent postdoc fellowship, this might be the first time you are truly independent. Talk with your mentor to figure out how they can best assist you to achieve your goals.

If you are a hired postdoc, this might be the first time you really have a boss. Talk with your boss frequently in the first few months. In particular, you want to establish (1) what your boss’s goals for you are; (2) what you have to do to be considered “successful” in your boss’s eyes; (3) your boss’s views on what postdocs are for (which could be anything from “primarily advanced trainee” to “paid worker to get lab research done.”) Put down in writing what your boss’s goals for you are and revisit them periodically. Plan on scheduling 3-month or 6-month check-in meetings with your mentor/boss. DLM said that he found this resource to be useful in guiding those discussions.

Understand your pay and benefits

There are three different points here. The first is related to the points about your boss. If you are on fellowship of more than a year and are a paid postdoc, establish with your boss how your pay will rise. Will there be a simple cost-of-living adjustment once per year? Or will you have to meet certain goal in order to get merit raises? Both? Neither? Talking about this feels uncomfortable, but it’s best to do it early.

The second point is that you may be paid on a different schedule than when you were a grad student. I am paid monthly and so is my husband and it is a royal pain in the neck. We have to be very careful with our boom-and-bust household budget, as we live close to our means. If your payment schedule or your living expenses are changing, keep an eye on your personal finances.

Third, make sure you understand your benefits. They may be very different from what they were when you were a grad student. If your institution offers a ‘new employee orientation,’ go to it, even if it seems very boring. If that sort of thing isn’t offered, schedule an hour to sit down with the appropriate administrator to go over your benefits in detail. Understanding it all at the beginning will save time and headaches and money later on.

Set long-term goals

The postdoc is ideally a transitory job, so figure out where you’re going. What type of job do you want after your postdoc? If you were to apply for that position right now, where would you be lacking? Here are some possibilities:

  • If you aspire to a teaching-oriented academic position, do you have actual teaching experience beyond teaching assistant? Have you taught your own course? Have you done any course design?
  • If you aspire to a research-oriented academic position, do you have a solid set of first-author and collaborative papers? Do you have a (small) reputation beyond the institutions where you’ve done your graduate work? Do you have a “niche”? Do you have a “brand”? If you were to give an elevator speech or put together a tagline on your professional online presence, what would it say?
  • If you aspire to career outside of academia, what additional skills do you want to learn or practice? What sort of people could you connect with during your postdoc to help you find jobs? What experiences could you gain that would make you stand out on a resume or in an interview?

Other long-term goals might be more personal. For example, you may want to publish your dissertation chapters even if you don’t aspire to a research-oriented academic job.

Your goals may not perfectly align with your boss’s. That’s okay and very normal! You need to figure out how to meet your own goals while also meeting your boss’s.

And your goals may change over time. That is also okay and very normal. Revisit your goals regularly, with the help of a mentor if possible.

Realize that the postdoc years can be wonderful or awful and prepare

The other day someone asked me what I thought about my job. Without hesitation, I exclaimed, “I love it!” I surprised myself, as I’ve been doing a lot of thinking about “what next.” On the other hand, these years can be very difficult, lonely, stressful, or heart-wrenching. Learn early on about what sorts of services your university or institution offers for mental health, conflict resolution, and social network development. Some recommendations for maintaining your physical and mental health: [2] If you tend to put these sorts of things off, remember that they will help you achieve your goals.

  • Are you in a new place? Make an effort to meet new people. Develop a social network, preferably one that doesn’t completely overlap your work network.
  • Figure out an exercise regime that works for you. If you can make it a social exercise activity, you’re more likely to stick with it and likely to make new friends.
  • Pay attention to when, what, and where you eat. Try to eat healthily. Try to eat meals with other people. Try not to eat while staring at a screen.
  • Prioritize sleep. When you are in a new place with a new job and new people, life can seem overwhelming. Make sure you get a solid chance to recharge each night. Protip: keeping a regular bedtime makes getting a good night’s sleep easier.

Meet people and collaborate…

As a postdoc, it’s often harder to casually meet people than when you were a grad student. You’re probably going to have to make a bit of an effort. But it’s not all that hard. People love meeting postdocs. Grad students aren’t typically intimidated by you. Professors tend to see you as junior scientists bringing new ideas and approaches to their department. Other postdocs are happy to network. So, attend social functions. Ask your mentor/boss to introduce you around. Invite other postdocs to lunch. Gab with grad students in the hall or lounge. (Grad students know All The Things. Make sure you befriend a few!) Schedule an afternoon coffee with faculty who share your interests. Volunteer to give a department or sub-department talk. Join your university’s postdoc association, if there is one.

SS had several tweets on building a foundation of mentors for career advancement: “Look beyond your immediate advisor for career/research mentors to help get to next stage. Set up meetings with researchers at your university or at conferences to talk science and get career advice. It helps to collaborate and development good working relationships outside of your main lab.”

If you’re an ecologist, consider joining the ESA Early Career Section. This section is made up mostly of postdocs, assistant professors, and non-academic equivalents. The section advocates for early career researchers within ESA, providing a voice for those in this tricky career stage.

… But also say ‘no’ …

One thing that can be challenging about being a postdoc is that you seem to have So Much Time. You’re not taking classes. You typically don’t have teaching responsibilities. You don’t have committee responsibilities. And so you have very little to structure your day at the outset. The trouble with So Much Time is the tendency to fill it up — and to fill it up with requests from other people rather than with the things that will move you towards your goals. So think very carefully before starting new collaborations or agreeing to take on a new responsibility. Think about what things will move you towards your ultimate goal most and what you might have to put off if you take on the new task. Prioritize, prioritize. Because soon you will find that you have Too Little Time.

… But also take some risks

Serendipity can play a large part in life and in careers. [3] e.g. My fun side project as a grad student ended up getting me my postdoc, not my dissertation research. If something sounds fun and exciting, you don’t necessarily have to say ‘no’ just because it doesn’t seem to be working towards one of your career goals. Life is not a flowchart. Experiment.

Invest in skills

Invest time in learning the skills you will want later, whether it’s teaching or coding or taking tree core samples. If you feel you want skills in case academia doesn’t work out, computer skills and communication skills are your best bets for re-use in industry. If you want a research job in academia, consider writing or co-writing a major grant from start to end, including the budget and all the minutia.

Live life and have fun

You may be in a new country, or a new part of a country, or a new institution. There are likely many cool and new things to explore both on-campus and off-campus. Check out the campus museums. See what sort of places are affiliated with your institution and visit them. Explore area restaurants. Be a tourist in your new town or country. Make a bucket list of things you’d like to see/do/experience, because if you’re like me, you’ll put them off if you don’t. Then commit to doing one thing per week or month. Your adventures are the things you’ll remember most about your time as a postdoc, not the many hours you sit at your desk.

A few final nuggets of wisdom

  • “Don’t be afraid of not knowing something. You have a PhD now: you are an expert learner!” – SS
  • “Find a comfortable way of asserting yourself – get credit where it’s due for research & teaching.” – FI

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Avoid using the words “student” and “school” outside of academia

Many, if not most, ecology PhD graduates will go on to jobs outside of academia. One particular area needing improvement in most (all?) graduate departments is on teaching trainees how to market themselves outside of academia. CVs are non-starters outside of academia and resumes are very different beasts. In crafting a resume, you need to show what you’re good for in the future much more so than a CV, which is focused on your past.

Killers in resumes are the words “student” and “school,” which are words people outside of academia use when they think of an 18-year-old undergrad.

I came at academia all backwards, having worked multiple jobs outside of it first, and having crafted a handful of resumes for those jobs. My longest job – and the one I had right out of college – was actually very similar to graduate school: I took classes. I worked on various team and individual projects that spanned many months each. I even developed a class to teach, and co-taught it. All that was seen as professional experience by my employer, and that’s how it appeared on my subsequent resumes.

I mention this because often job ads will be looking for someone with, say, 5 or 10 years of professional experience. If you apply for such a position thinking that your experience as a graduate student should count towards that, you’re right! But you’ve got to frame it that way on your resume and in your cover letter. If you just mention that “as a graduate student,” you created conservation plans for local watersheds, it may not count. As a “student,” you are not considered a professional by those outside of academia.

A PhD friend recently failed to be even considered for a position for which he was well-qualified. The position attracted many resumes, and as a first step, an administrative person scanned resumes to winnow out those who did not have basic qualifications. These included – you guessed it – some number of years of professional experience. Unfortunately, my friend’s resume failed to make it clear that he was doing professional work as part of his dissertation, and so his resume failed this first hurdle. His application wasn’t even seen by the scientists doing the hiring.

This administrative winnowing step is super common, and you don’t want your application tossed out before it’s even considered! So here’s what you do on your resume:

  1. List your PhD in your education section. That’s all the mention of “school” you need.
  2. Where you list your work experience, describe your research projects, and in particular describe your role, the skills you used, and how the experience relates to the job in question. Keep it all short. Do not mention that this research was done as part of your dissertation and do not describe yourself as a “student” anywhere.

As fodder for future blog posts, I’ve been scanning the CVs of ESA Early Career Fellows. The CV of ecosystem ecologist Ariana Sutton-Grier actually incorporates a resume style part-way through. (She’s worked for NOAA, so she’s probably needed a resume at various times.) Her resume-style section on her dissertation research is brilliant. It’s listed under “Professional Experience” and reads:

Wetland Ecology and Biogeochemistry Research Assistant, Instructor, and Mentor, Duke University (2002-2008)

Duties: I designed and conducted interdisciplinary research examining how wetland restoration techniques, including organic matter amendments and plant species diversity, affect the restoration of wetland ecosystem functions.

Major Accomplishments:

  • My research resulted in four first-authored and four co-authored publications.
  • I successfully obtained research grants and fellowships to fund my research and studies including the prestigious National Science Foundation (NSF) Graduate Research Fellowship, the NSF Doctoral Dissertation Improvement Grant, and the American Association of University Women Graduate Fellowship.
  • I supervised over a dozen Masters students as well as one high school student and one undergrad in the lab.
  • I mentored one independent research Master’s project which resulted in a peer-reviewed first-authored publication for the student.
  • I co-designed and co-taught an undergraduate class “Feminism and Ecology” as well as guest lecturing and TAing several courses; received very good teaching evaluations.
  • I mentored three middle school girls for a PBS DragonflyTV “SciGirls” Episode.

What do we learn from this statement? Not only that Dr. Sutton-Grier was a kick-ass grad student (the academic interpretation), but also that she’s gained considerable professional experience in wetland restoration, that she can design and conduct research and produce written reports about it, that she can write grants, and that she has teaching and mentoring skills (the industry interpretation). Importantly, none of these achievements are diluted by calling attention to the fact she was a student in graduate school. Instead, she was a “Research Assistant, Instructor, and Mentor.” [1] If I had written this, I probably would have written “Researcher” instead of “Research Assistant”. Designing, carrying out, and writing up your own research means that you’re not actually an “assistant” in the colloquial meaning of the term.

If you’re a graduate student or recent PhD graduate – and especially if you don’t aspire to an academic career – I encourage you to start practicing seeing yourself as and speaking about yourself as a professional instead of a student right away. When you meet someone at a party or holiday gathering, and they ask you what you do, don’t start off with, “I’m an ecology graduate student,” or “I study ecology in graduate school,” or “I’m a postdoc.” [2] Nobody outside of academia has any real idea what a postdoc is, so it’s best to avoid the term anyway. Instead, whether you’re a grad student or postdoc, say “I’m a researcher at University of State. I study how plants are affected by climate warming,” or “I teach at University of State. I teach a lab on evolution,” or “I’m at University of State. I’m working out ways to avoid roadkill in conservation zones.”

If you get in the habit of viewing yourself as the professional that you are, it will be easier for others to see you that way, too, including in interviews and during networking opportunities. And it will be easier to make it clear in your resume that you have many years of professional experience, regardless of the fact you were a graduate student.

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The thing that pushed me to post a preprint

I have mixed feelings about preprints. On one hand, I like the fact that they allow for the exchange of ideas on pace with the rate that science happens. On the other hand, in ecology, the concept is preprints is all muddled. In the fields where preprints originated and are now standard practice (physics, math, astronomy, computer science), it is typical for authors to post preprint manuscripts to a public site (such as arXiv) as they’re finishing up a final draft or when they submit it to a peer-reviewed journal. Historically, the reason to do so was to have a nicely formatted version of the paper [1] since these fields use the lightweight TeX for writing and formatting that you could then point your colleagues to, enabling rapid dissemination of ideas within your research community. These days, it’s just as easy to email a PDF to colleagues who might be interested in your not-yet-published paper — and I suspect that this is the norm in ecology.

For fields like ecology where preprints are not the cultural norm, the idea of the preprint is getting swept up in the broader Open Science movement. Preprints are billed as a way to get early feedback and a step towards transparency. I rather doubt that the former happens a lot, even in fields where preprints are the norm, and I’m not sure that preprints help that much with transparency. In ecology, where being scooped is usually not a concern, preprints don’t even have the value of establishing first rights to a particular discovery. The only real benefits I see to preprints in ecology are for spreading science more quickly (everyone) and establishing yourself during the long waits while your first papers go through the publishing process (grad students, postdocs).

For me, preprints have always been one of those “I should probably do that because Open Science” things that I never get around to. When I finally finish a manuscript and submit it to a journal, the last thing I feel like doing is spending time on yet another online submission system to post a preprint. So I haven’t.

What has finally pushed me submit a preprint is the ridiculous amount of time it takes for some journals to go from “accept” to “publish.” I am all for peer-review and willing to take the time to do that properly. But it drives me crazy when a paper is accepted, but not actually published until nine months later.

I’m hopeful that as time goes on, all journals will make their way into the 21st century and post manuscripts as soon as they’re accepted. That will help speed up science dissemination. But right now, we’re far from that point. For papers I’ve been on (all in the past couple years), I’ve seen all of the following:

  1. Nothing happens until the paper “goes to press”. When it is published, it appears in print and on the website at about the same time. This can take many months.
  2. The paper is posted as a “preprint” to the journal’s website, but it isn’t considered “published” until a later date, often when it comes out in print.
  3. The paper is quickly posted online as an “online early view” and is considered “published,” but without a journal issue number or page numbers. Later, the paper is put out in print and gets these identifying numbers.
  4. The paper is quickly published online. There is no print version of the journal.

This medley of publishing practices is really confusing, and I very much hope it is just a transitional phase until all manuscripts are posted online shortly after acceptance and considered published right then.

Last week, I pinged the editor of Frontiers in Ecology and the Environment. The journal had accepted a paper of mine [2]with Andrea Wiggins, Ali Swanson, and Brooke Simmons in May, and I wanted to know when it might be published. I was told that it wasn’t even scheduled yet and we were looking at sometime in early 2017. Another nine-month wait! In a journal that is supposed to be at the “frontier.” Ugh. This paper was written in late 2015 and revised in the spring of 2016 (during which time some additional references were added). It will be somewhat out-of-date when it is finally published, as it won’t include literature from 2016.

This paper is on data quality in citizen science, so the content itself won’t be out-of-date, thankfully. But at the same time, I wrote this is paper because the field has needed such a paper for several years. This is a paper that after I spent two years immersing myself in finishing my dissertation, moving cross-country, and having a baby, I was surprised that no one else had written yet. I’ve promised this paper to colleagues to have something to cite — something to point to — to demonstrate due diligence for volunteer-provided data for proposals and in papers.

And so I posted a preprint. Now I can easily send it around to colleagues and they can easily cite it. Just like was done in mathematics back in the 1990’s.

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The Modern Grad Student Paradox

I was sitting in the audience during the discussion of the Hacking Ecology 2.0 Ignite session at ESA this year and Josie Simonis, who was on the panel, said something that really resonated with the grad students in the audience and on Twitter. They said that graduate students face a real paradox: grad students need to learn a lot of modern skills to succeed as scientists, but those who are their teachers (the faculty) don’t have the skills and knowledge to teach them.

What is the purpose of graduate school? It seems like a straightforward question at first, but for those pursuing graduate degrees in the sciences, at least, I think the answer is a lot more complex than it used to be. Because the words “school” and “student” are used, it’s reasonable to suppose the purpose of a graduate education is to learn. For a good chunk of an American PhD program – and for full programs in some other countries – that education doesn’t come in the form of classes, as it does for undergraduate education. Instead, the education is more of an apprenticeship – more like the residency that medical doctors undertake after all their classes are complete.

Let’s pretend that the sole purpose of graduate school in ecology is to create scientists who can fill the shoes of their advisors. [1] This is obviously false, as there are far more PhDs created than R1 faculty jobs that can absorb them. But for the purpose of the post, I want to focus on just the academic path. A newly minted assistant professor today probably spent about 10 years as a graduate student and postdoc. That puts the beginning of their professional training around 2005 (give or take), which is just shortly after the Internet took off as a ubiquitous agent of change. So only the very newest advisors came of (professional) age in what I’m going to call the modern research world. And all the rest – the great majority of tenured and tenure-track faculty – learned how to be scientists during a time when the Internet didn’t exist. (Think about that for a moment…)

I use the Internet as a yardstick, as well as an important driver of research culture. There are a lot of other technologies that have undergone enormous change in the past decade, too. Whatever your particular study system is, it’s likely that there are technological devices, tools, or machines that affect how research in that system has changed over the past decade. And even if your research is bare-bones basic – taxonomy, for example – you have still been affected. The plunging price of computer memory and processing power means that how scientific data is recorded, managed, curated, and accessed has changed.

What all this means is that today’s graduate students need to learn all sorts of things that their advisors can’t teach them. Most advisors haven’t had the time (and in some cases the inclination) to keep up with advances in hardware technology, data standards, software, statistics, and communication. I don’t see this as a shortcoming on the part of the advisors, by the way. Instead I see it as a manifestation of the 12 Hats Problem. But it is a very real conundrum for grad students.

What to do about this paradox? I think the first thing to do is to really assess whether graduate programs are meeting the needs of their students. [2] In this, of course, they need to consider not just those aiming for R1 faculty positions, but also students who will take other types of jobs. My whole time as a grad student – and ever since – I’ve heard a yearning from graduate students for more courses in coding and data management and ecologically relevant statistics. Even if there are teachers for these types of courses (and there often aren’t), there’s always the question of what part of the formal education to drop. My suggestion is to drop or condense requirements that focus on memorization. These days, with the Internet, one can look up a factual piece of information in moments. It simply isn’t worth it for most people to learn how many teeth different mammal skulls hold or to memorize plant families. [3] My emphasis here is on “most people.” There will always be niches of science in which it’s much more useful to have these facts in one’s head than at one’s fingertips. But those niches are quite small – not enough for entire courses. And those who need to memorize this information can do so in the apprentice part of the PhD, rather than the classroom part. I think objections to this come mostly from those who like teaching these sort of (sorry to say it) outdated courses.

One possible solution to the lack of teachers is peer training – that is, grad students (and others) training grad students. The Software Carpentry model is one to consider, in which grad students are trained as teachers and then team teach other students coding skills. Short courses and workshops also fill this gap, but have the downsides of typically being expensive to attend and requiring travel (which disenfranchises some groups of students). Another possibility is to leverage online cross-institution training. Perhaps, for example, there’s a faculty member who is perfect at teaching Needed Skill X. Instead of a class just for students at that teacher’s university, that teacher could open up the class online, allowing participation from students at multiple universities. [4] There exists technology to do this, but I’m relatively unfamiliar with it. For cross-university courses to catch on widely, such technology needs to be rather glitch-free and easy to use. Administrative matters, such as course credits and tuition, need to be addressed too. Perhaps one thing that departments should do is to assign faculty to learn specific skills so they can teach them to students subsequently. As in: “Hey, we’re eliminating your course load this year, but in exchange, we expect you to learn the latest in hierarchical Bayesian Statistics (or R coding or database creation and administration or…) and develop a graduate-level course (or workshop or whatever). You will be teaching it for the next five years, and you will be considered the department expert during that time.”

The apprenticeship part of the PhD still confers many important skills. Being able to read the literature critically, being able to ask a good research question, being able to think logically, being able to write – these are all timeless skills. But for the hard skills, we need a new paradigm – one that doesn’t leave graduate students flailing in a research environment that looks very different from the one their advisors grew up in.

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Online book club forming for “The Theory of Ecological Communities”

If you follow me on Twitter or obsessively read the comments of the Dynamic Ecology blog, you’ll know that I’ve been excited about the publication of Mark Vellend’s new book, The Theory of Ecological Communities, for many months. The book happened to come out just around the time of the ESA meeting, so the publishers rushed a half-dozen copies to the conference, one of which ended up in my suitcase. This year, my summer vacation follows ESA, and so this new book has become my vacation reading. Although I try to avoid working while on vacation [1] and with young children, a vacation is really just a “vacation” in name only – going into an office is way less exhausting than taking care of young ‘uns, I have not done well this time around. [2] A paper got through review faster than I was expecting, so I’ve worked on proofs; I got an unexpected and well-paid very-short-term contract job that I didn’t want to turn down; and I got excited about Mark’s book. Of course, what counts as “work” is a gray area, when you like your job, like most of us do. That’s not to say we like all parts of our jobs, though. So this vacation, I’m trying to not do the parts I don’t like, and allow myself to do some of the parts I do like. Like reading. (And, apparently, organizing a book club.)

Anyway, pre-child, I’d be done with The Theory of Ecological Communities by now. But I’m not because, well, I have little kids. Instead, I’m through the first of three sections of the book. And I’ve been enjoying it. A big fan of Vellend 2010, I found these first chapters went by quickly, mostly reviewing and fleshing out a bit the main tenets of the 2010 paper. They are quite clearly laying the groundwork for the next two sections, which I am very much looking forward to.

One big reason why I have been and continue to be excited about this book is that as a student I hungered for a way to organize my thinking about community ecology, and never felt satisfied. Coming into ecology with a strong math and computer background, but little ecological knowledge, I looked for how to conceptually organize the field. Where do I start? What classes should I take? What are the big questions of our time? I think I even asked my advisors directly about these things in the first couple years. The only organizing structure that I felt was compelling was the distinction among organismal biology, population ecology, community ecology, and ecosystem ecology. (I was unfamiliar with macrosystem ecology back then.) And that didn’t help me think about all the stuff within community ecology.

In the Tilman lab, everything was about competition. But in the Packer lab, behavior mattered. I took on a disease project, and found disease ecology to be almost its own field. When I spent time as a visiting grad student in the Leibold lab, suddenly dispersal was a much bigger deal than competition. I then got interested in food webs and predation. But I couldn’t figure out how to fit everything together into a coherent whole. I took an ecological theory class, but I couldn’t figure out where the forefront of the field was in order to try to contribute. I turned away from theory and did experimental and modeling work. And – to be very frank – my pure community ecology chapters from my dissertation are as yet unpublished because I can’t figure out how to frame them well within the general context of community ecology. The number of ideas and papers and models in community ecology have seemed so numerous and so vaguely connected that I feel like I can’t wrap my mind around them to see where my research fits.

As a result, Mark’s framework for fitting all of community ecology onto a simple scaffold is very appealing. I am actually reading the book with an eye to publishing one community ecology dissertation chapter, and it’s already helping to clarify my thinking. This book is also awesome for its references list. I seriously wish I could have read this book as a second-year grad student. The history chapter is brief, but cites most – if not all – of the big papers in community ecology that you should read as a community ecology grad student. I’ve put a few cited papers that I’ve overlooked on my must-read list. [3] The references list even includes a citation to a blog post, which makes me unreasonably happy. Maybe this is the first blog post citation in a Princeton Press monograph? Jeremy will have to buy the book to find out which post of his it is…

It turns out that I’m not the only one excited about The Theory of Ecological Communities. I handful of us who grabbed the book at ESA and others who ordered it directly decided via Twitter to start a “book club” – a discussion group where we read the book chapter-by-chapter and discuss it. Since we’re all over the place geographically, we decided to do video calls for our discussions. So I set up a sign-up sheet, figuring we might get 6 or 8 or 10 people who were interested – a group or two. But as of now, there are 28 people signed up, ranging from grad students to tenured profs and spanning three continents.

I totally was not planning to organize a large international book club on my vacation, but life is full of surprises. If you want to get in on the crazy experiment that is this book club, sign up and get the book. (You can order from Princeton Press or buy on Amazon. Interlibrary Loan takes longer, but is cheaper.) The first discussion groups will begin next week, but I’m pretty sure there are going to be more groups starting mid-to-late September, as there are several people who haven’t been able to get the book yet who want to talk about it. The idea is to read a chapter per week and meet for an hour to discuss. That’s about it.

I’ve really enjoyed the two book discussion groups I’ve been part of as an ecologist (reading this and this). Those have been in-person, though, so we’ll see how online groups work out. If you’ve got any experience with online book discussion groups – or any pointers in general – please do leave a comment below.

Sign up for The Theory of Ecological Communities Book Club

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