Should the advisor leave the room for part of a student’s committee meeting?

Scrolling through twitter a couple of weekends ago, I saw this tweet:

At first, I misread it and thought it was indicating that the student had been sent out of the room (which is the norm for committees I’ve been on). It took me a second to realize that it was the advisor who had gone out of the room so that the student could have a discussion with their committee without the advisor present. I suspect my misreading wasn’t just a product of quickly scrolling through twitter on the weekend—rather, I think part of the reason why I misread it was because it was such a shift from how things are normally done in departments I’ve been in.*

After realizing what it said, though, I thought it was an interesting idea. I can think of cases where it might have helped to have a discussion without the advisor there to get a better sense of the student’s opinion on things, such as when they would prefer to defend or how excited they are about project 1 vs. project 2 or how they feel about traveling to remote location X to collect samples. And, in the rarer cases where there were major problems, it might have led to those becoming apparent to the committee sooner, which hopefully would lead to the student getting support sooner.

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Guest Post: iPads and digital data collection in the field

From Meghan: This is a guest blog post by ecologists Isla Myers-Smith and Gergana Daskalova from the University of Edinburgh. I loved their comment on my post on our new lab notebook backup system and asked them if they could turn it into a guest post. I was very happy that they agreed! Isla and Gergana are off to the Arctic this summer with the Team Shrub field crew for another year of hopefully successful digital data collection. To find out more about their research check out the Team Shrub website and blog (https://teamshrub.com/).

Guest post:

Two things have really changed my academic life over the past five years: the first is embracing GitHub for version control of code, data, manuscripts and my research group’s individual and combined science, and the other is switching over to digital data collection. For ecologists who haven’t made the switch from paper field books to iPads and digital data collection it is not as scary as you might think!!!

Caption: Collecting plant phenology data – the recorder sitting in the back with an iPad! (photo credit: Jeff Kerby)

The benefits of going digital

Digital data collection can be more rigorous with error checking as data are collected to prevent mistakes. Data can be better backed up. And finally, it forces us to put thought into the structure of data before we collect it (significant digits, continuous or categorical data, are the data unrestricted or constrained to a particular range or particular set of values, etc.), which helps down the road when it comes time for analysis. Digital data collection has saved days, if not months, of data entry each year for my team and has allowed us to go from ecological monitoring in the field to analysis of results within hours instead of days. Our work flows are streamlined and our iPads are waterproof, so data collection can occur under any conditions – and we work in the Arctic, so we experience it all from wet to dry, hot to cold, rain, snow, you name it.

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Social aspects of writing

Intro: this is the second of a series of posts exploring some common themes in three books: Anne Lamott’s Bird by Bird, Helen Sword’s Air & Light & Time & Space: How Successful Academics Write, and Tad Hills’ Rocket Writes a Story. The first post focused on getting started with a new writing project, rough drafts, and the pleasures of writing. This post focuses on social aspects of writing.

 

Writing is inherently social – at a minimum, your article is read by reviewers and, of course, we write hoping that colleagues will read and understand (and maybe even like!) our article once it comes out. But the process of actually doing the writing can sometimes feel very isolated. Certainly my general approach is to hole up in my office and try to crank out some text. I get feedback from coauthors, but that’s done at a distance and with little interaction outside Word.

So, I was interested to see that Helen Sword has social habits as one of the four components of a strong writing practice. She devotes a chapter specifically to writing among others, talking about writing groups, write-on-site boot camps and retreats, and online writing forums. Each chapter of Sword’s book ends with a “Things to Try” section; for the chapter on writing among others, it includes “start a writing group” and “retreat in the company of others” as two of the four suggestions.

Right after reading that section of Sword’s book, I read a Monday Motivator email from NCFDD (written by Kerry Ann Rockquemore) that also emphasized the social aspects of writing. That email also focused on social aspects of writing, including traditional writing groups, writing accountability groups, write-on-site groups, and boot camps.

Reading those back-to-back made me realize that I severely lacked social components in my writing. I have gotten very used to setting my own goals and not sharing them with anyone else, and to holing up in my office to write. But I also don’t feel like writing is generally a problem for me, so wasn’t sure if I really needed to address the lack of social habits. If there isn’t a problem, why try to fix it?

But then, on a solo morning run, I thought about how much further and faster and more enjoyably I can run on the days where I go with a friend. And I thought about how, when I first got into distance running, I would tell some friends and family members about my race plans, which made me more committed to sticking with my training runs. And I’m much less likely to skip a run if I am meeting a running buddy, which explains why I ended up running in a downpour recently. Could these same social habits help with writing?

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#Readinghour: My plan to read more in 2018

A common theme that comes up when talking with other scientists and academics is that we wish we had more time to read. I’ve been trying to figure out how to do a better job of reading for years, and spent 2015 tracking my reading using #365papers. The goal of that was to read a paper every day – I wasn’t planning on reading work papers on weekends, but I thought there would be enough work days where I read more than one paper that it would offset it. I was wrong. I didn’t get anywhere near 365 (I got to 181), but it still motivated me to read more than I would have – at least, until teaching Intro Bio completely took over.

Having just completed another semester of teaching Intro Bio (and having it take over), I was thinking again about how to reprioritize reading. I decided that I would prefer to have a time goal (30 minutes per day) rather than a paper goal, since I felt like having a paper goal was distorting my reading habits in a way that wasn’t useful.

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Is my latest paper a super-cool result? Or merely a “cute” curiosity? You tell me!

My collaborators and I just published “Population extinctions can increase metapopulation persistence“. New Scientist did a piece on it, which is the first time any media outlet other than my local newspaper has written up my work. I’m chuffed about this, because I think this is the coolest paper I’ve ever done by some distance.

Or, maybe it’s just a cute result–a fun curiosity. I could even imagine someone arguing that it’s oversold fluff. So why do I think it’s so cool? And what’s the difference between “cool” and “cute”?

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Introducing #EEBMentorMatch: linking students from minority serving institutions with application mentors

Research has demonstrated that science benefits from diversity, but graduate programs still suffer from a lack of diversity, including in terms of race/ethnicity and the type of undergraduate institutions of applicants. Meanwhile, minority-serving institutions are full of students who are talented and passionate about science. Faculty members at these institutions are dedicated to their students and work to connect them with opportunities. But, at the same time, those faculty members are often overextended (unfortunately, minority serving institutions tend to be underresourced) and simply do not have the time to mentor all of their promising students through the process of applying to graduate schools and fellowship programs, including the National Science Foundation Graduate Research Fellowship Program and the Ford Foundation Predoctoral Fellowship. Moreover, most of these institutions primarily serve undergraduates and there is little access to graduate students and postdocs who can serve as mentors and role models.

In other words: graduate programs are looking to recruit more minority scholars, fellowship programs are looking for bright applicants, and minority serving institutions are full of students who are ready to excel in graduate school and research. But, right now, many of those students from minority-serving institutions don’t apply to graduate programs or for graduate research fellowships.

Therefore, we* have created EEB Mentor Match, with the goal of matching undergraduate students from minority-serving institutions (MSIs) who are interested in ecology and evolutionary biology (EEB) with mentors who can provide feedback on graduate school and fellowship applications. We are looking for:

  1. undergraduate students who are considering applying to graduate schools in ecology and evolutionary biology (defined broadly, including programs in conservation biology, natural resources, etc.) and/or to the National Science Foundation’s Graduate Research Fellowship Program and/or to the Ford Foundation Predoctoral Fellowship;
  2. masters students who are planning to apply to PhD programs in ecology and evolutionary biology (defined broadly, including programs in conservation biology, natural resources, etc.) and/or to the National Science Foundation’s Graduate Research Fellowship Program and/or to the Ford Foundation Predoctoral Fellowship;
  3. graduate students, postdocs, faculty, and others with experience with the graduate school application process and/or NSF’s GRFP and/or Ford Foundation Predoctoral Fellowships who are interested in working an undergraduate student from a minority serving institution as they craft their application materials; and
  4. mentors of students at MSIs who can nominate students who are considering applying to graduate school in EEB and/or for fellowships. We will then contact these students to see if they are interested in being mentored and, if so, pair them with a mentor.

Note that this is focused on students who are interested in ecology & evolutionary biology (defined broadly, including programs in conservation biology and natural resources). Our hope is that, by keeping this more focused, we will be able to do a better job of matching mentors and mentees. (Also, there are only so many hours in the day, unfortunately.) We encourage people in other research areas to develop similar resources for their fields!

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Who should be senior author on papers resulting from collaborations between multiple research groups?

I am pretty much through with revisions to my manuscript on authorship, with one exception. One of the reviewers is (quite reasonably) pushing me to make a stronger recommendation about how authorship decisions should be made in the increasingly common case of collaborations between groups. But, of course, this is a tricky issue, and I’m waffling on what exactly to recommend. This blog post is me trying to work through that, and looking for feedback at the end. I’m quite interested in hearing how others think decisions about authorship should be made when multiple groups collaborate substantially on a project!

I’ll start by recapping some of what my results, since they set up the general question. Then, I’ll give some of my thoughts on what might be a proposed solution. And, as I said above, I’ll end by asking for feedback on what I propose.

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Why functional trait ecology needs population ecology

I have an embarrassing confession: I’m just not that into you trait-based ecology.

Which doesn’t feel like confessing a murder, but does feel like confessing, I dunno, not liking Groundhog Day.* It’s slightly embarrassing. For years now trait-based ecology has been one of the biggest and fastest-growing bandwagons in ecology. Plenty of terrific ecologists whom I really respect are really into it. Which doesn’t mean that I have to be into it too, of course–but which does mean that if I’m not into it, I’d better have a good reason.

Which is a problem, because honestly I’m not sure why I’m not into it. In a field like ecology, where there’s no universal agreement as to what questions are most important to ask or exactly how to go about answering them, I think it becomes more (not less) important that each of us be able to justify our chosen question and approach, in terms that others can appreciate if not necessarily agree with. And also justify not liking any questions or approaches we don’t like. It really bugs me when people object to my own favorite approach for weak reasons that don’t stand up to even casual scrutiny. So I’m embarrassed to admit that there’s lots of trait-based ecology that I just vaguely think of as uninteresting or not likely to go anywhere, even though honestly I don’t know enough about it to really have an informed opinion. It’s embarrassing to not have an informed opinion on what’s probably the most popular current approach to topics that I care a lot about (e.g., species diversity, composition, and coexistence along environmental gradients).

This post is my attempt to do better. I want to think out loud about what I like and don’t like about trait-based ecology. My selfish goal is to clarify my own thinking, and to get comments that will teach me something and help me think better. My less-selfish hope is that buried somewhere within my half-formed thoughts are some useful ideas that trait-based ecology could take on board.

Here’s my plan: I’m going to talk about a body of work in trait-based ecology that I actually do know well and that I do like a lot. Then I’m going to go back to Brian’s old post on where trait-based ecology is at and where it ought to go and see how this body of work stacks up. How do my reasons for liking this particular body of trait-based ecology line up with what an actual trait-based ecologist–Brian–looks for in trait-based ecology?

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Have ecologists ever successfully explained deviations from a baseline “null” model?

In an old post I talked about how the falsehood of our models often is a feature, not a bug. One of the many potential uses of false models is as a baseline. You compare the observed data to data predicted by a baseline model that incorporates some factors, processes, or effects known or thought to be important. Any differences imply that there’s something going on in the observed data that isn’t included in your baseline model.* You can then set out to explain those differences.

Ecologists often recommend this approach to one another. For instance (and this is just the first example that occurred to me off the top of my head), one of the arguments for metabolic theory (Brown 2004) is that it provides a baseline model of how metabolic rates and other key parameters scale with body size:

The residual variation can then be measured as departures from these predictions, and the magnitude and direction of these deviations may provide clues to their causes.

Other examples abound. One of the original arguments for mid-domain effect models was as a baseline model of the distribution of species richness within bounded domains. Only patterns of species richness that differ from those predicted by a mid-domain effect “null” model require any ecological explanation in terms of environmental gradients, or so it was argued. The same argument has been made for neutral theory–we should use neutral theory predictions as a baseline, and focus on explaining any observed deviations from those predictions. Same for MaxEnt. I’m sure many other examples could be given (please share yours in the comments!)

This approach often gets proposed as a sophisticated improvement on treating baseline models like statistical null hypotheses that the data will either reject or fail to reject. Don’t just set out to reject the null hypothesis, it’s said. Instead, use the “null” model as a baseline and explain deviations of the observed data from that baseline.

Which sounds great in theory. But here’s my question: how often do ecologists actually do this in practice? Not merely document deviations of observed data from the predictions of some baseline model (many ecologists have done that), but then go on to explain them? Put another way, when have deviations of observed data from a baseline model ever served as a useful basis for further theoretical and empirical work in ecology? When have they ever given future theoreticians and empiricists a useful “target to shoot at”?

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