There are too many overspecialized R packages

I use R. I like it. I especially like the versatility and convenience it gets from add-on packages. I use R packages to do some fairly nonstandard things like fit vector generalized additive models, and simulate ordinary differential equations and fit them to data.

You can probably tell there’s a “but” coming.

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You can’t estimate your odds of getting a faculty job from common quantitative metrics (UPDATED)

The 2016-17 ecology & evolution jobs compilation includes a spreadsheet on which anonymous job seekers can list some common quantitative metrics summarizing their qualifications. Year of PhD, number of years as a postdoc, number of peer-reviewed publications (first-authored and total), h-index, number of major grants held, and number of courses taught (not counting TA positions). Job seekers also can list the number of positions for which they’ve applied this year, the number of interviews they’ve received (phone/skype and on-campus), some personal attributes such as gender, and other information. The purpose presumably is to allow job seekers to determine how competitive they are for faculty positions.

As of Dec. 19, 2016, 73 people had listed their information. Not a massive sample of current ecology & evolution job seekers. Also surely a statistically-biased sample in various ways. But it’s many more current job seekers than anyone not currently sitting on a search committee is likely to have personal knowledge of. So I checked how well quantitative metrics like number of publications and h-index predict the number of interviews job seekers receive. For comparison, I also compiled data on the h-indices of 84 North American ecologists recently hired as assistant professors.

Faculty job seekers understandably want any information they can get on how competitive they are. But how competitive any given individual is for any given position depends on many factors, many of which are only captured coarsely or not at all by common quantitative metrics. You can’t put numbers on fit to the position, quality of your science, strength of your reference letters, and so on. So I suspect that many job seekers tend to overrate the importance to search committees of things you can put numbers on: publication count, h-index, etc. It’s an instance of “looking under the streetlight”. Hence my question: Can you estimate your odds of being interviewed for, or obtaining, a faculty position in ecology and evolution just from common quantitative metrics?

Short answer: No. For the details, read on.

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A bit of good news that most ecologists weren’t expecting: recent ecology hires are gender balanced (updated periodically)

Most recent update: June 20, 2017. To date, all updates have introduced only tiny quantitative changes to the original results, no substantive changes.

Recently I decided to quantify the gender balance of recently-hired ecology faculty in North America. “Recent” being operationally defined as “hired in 2015-16, or in a few cases in 2014”. Data on the gender balance of faculty is widely available only at the level of very broadly-defined fields like “biology”. Current faculty gender balance mostly (not entirely) reflects the long-term legacy of past hiring and tenure practice rather than current hiring practice (Shaw and Stanton 2012; ht Shan Kothari via the comments). And nobody’s anecdotal experience informs them about the outcomes of more than a tiny fraction of all ecology searches in any given year. So this seemed like a topic on which many people would welcome some reasonably comprehensive data. Follow the link for more details on how I compiled the data. In that old post, I also conducted a poll asking readers what they expected me to find.

Here are the answers: what fraction of recently-hired North American ecologists are women, and what do ecologists think that fraction is?

Many of you are going to be pleasantly surprised…

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Poll: guess the gender balance of recently-hired North American ecologists (UPDATED; poll now closed)

Many academic fields are staffed by a male-biased mix of faculty. But the existence and degree of faculty gender imbalance varies among fields. Further, those fields often are quite broadly defined in published datasets (e.g., “biology”), which can leave many people wondering how well published data apply to their own, narrower field (e.g., “ecology”). Gender balance of academic fields also changes over time, but only slowly. Published data therefore only give you an imperfect sense of the gender balance of recent hires in your field. And personal anecdotes and experiences provide only a very small sample. Every year there are many dozens of faculty hired in ecology and closely-allied fields, but nobody hears through the grapevine about the outcomes of more than a small fraction of those hires.

So I decided to quantify the gender balance of recently-hired ecology faculty at North American colleges and universities. I’m doing it by going through this very comprehensive list of all ecology & evolution faculty positions advertised in 2015-16, and checking the university websites to identify who was hired. This turns out to be really easy in many cases, and difficult or impossible in the remaining cases (I therefore remove from the dataset). To keep things manageable, I’m skipping positions outside North America, of which there are very few on the linked list. I’m also skipping non-ecology positions, of which there are many. So not, e.g., biology, anatomy & physiology, genomics, evolution, paleontology, museum curator, science education, etc., even though some of those positions might have been filled by ecologists. But I’m defining “ecology” pretty broadly so as to include fields in which people who self-identify as ecologists often apply for and obtain positions. “Ecology” for purposes of this exercise includes wildlife management, conservation, ecological genetics, ecological physiology, evolutionary ecology, microbial ecology, fisheries, etc. My judgments on what constitutes “ecology” obviously are somewhat subjective and arbitrary, but I don’t see why that would affect the results. To focus on new faculty, I’m only looking at assistant professor positions, so ignoring the (very few) ads for heads of department, program directors, endowed senior chairs, etc. See the footnote (*) at the end for more nitty-gritty details on my procedure. UPDATE: To be clear, I’m including positions at all types of institutions, not just R1 universities. And you should do the same when answering the poll below. I’ll present the results broken down by institution type for anyone who’s curious about that.

I’ll present the results in a future post, in a sufficiently-complete form that you can go back and reproduce my work if you wish.

But before I show the results, I’m very curious what you think I’ll find. So below is a little poll. What do you think is the gender balance of recently hired North American ecology faculty? (UPDATE Nov. 10: responses have slowed to a tiny trickle, so I’ve closed the poll so that I can start analyzing the results. We already have 468 respondents–thanks to everyone who responded!)

p.s. Obviously, these data won’t tell you whether the outcome of any particular search was fair, much less whether every individual applicant for every position was evaluated fairly. And I have no way to collect lots of contextual information that you might want in order to interpret the results, such as the gender mix of the applicant pool for every position. In that future post I’ll talk more about what I think we can and can’t learn from these data.

*Failed searches are among those for which I can’t tell who was hired, so they automatically get dropped from the dataset. The difficulty of identifying who was hired mostly has to do with departmental web page design, so I’m confident that the easily-identifiable hires are a random sample of the population with respect to gender balance. A couple of times, I’ve determined that a position that I thought was ecological wasn’t filled by an ecologist; I’m dropping those cases from the dataset.  I’m being careful to remove duplicate ads from the list so that I don’t double-count anyone. I’m including some other recent (2015-16) hires that weren’t on the linked list. I learned about these either from colleagues, or by stumbling across them while checking on listed positions. A few of the hires I stumbled across might actually be 2014 hires, but I’m fine with that because those are still very recent hires. In every case so far, gender has been obvious from the person’s name and photo.

Ten commandments for good data management

Usually when I am asked to give a few words to describe myself I say macroecologist or large-scale-ecologist. And I might on other days say biodiversity scientist or global change scientist. But a lot of days I would say “ecoinformatician”. Ecoinformatics is the subset of bioinformatics that applies to ecology – that is to say informatic (data) techniques applied to ecology. Some of you may know that I spent 9 years in business before returning to my PhD. But not many know that most of what I was doing was business informatics. Helping companies understand their data. It wasn’t planned. I just have always liked seeing what the data has to tell me. But it turned out to be great training as ecology dived into informatics just as I hit graduate school.

Not surprisingly given my background, I spend a lot of time being asked to make recommendations on how to work with data. I’ve also been involved in some very large data projects like BIEN. Here I don’t want to focus on the large (often social) issues of really big projects (my slides from ESA 2015 on the next 100 years of ecoinformatics are on figshare if you’re interested). Here I want to focus on the much smaller single person or lab-scale project. This post is attempts to summarize what I have learned to be best practices over both my business informatics and ecoinformatics careers. I am intentionally going to stay tool or software agnostic. In this post I really want to emphasize a frame of mind and mental approach that can be implemented in literally dozens of different software packages. In a second post tomorrow, I will give a worked example in R since I know that has the highest popularity in ecology. Continue reading

Does ecology need more criticism of the literature? If so how?

This post has evolved substantially over its writing. It started from a good post over on EEB and Flow by Marc Cadotte arguing that ecology needed a more robust culture of critique to weed out bad papers, and arguing that comments/critiques to the journals that published the original papers was an important way to do this. Despite strongly agreeing with the first part, I instinctively disagreed with the later part. (And have been thinking about critique letters a lot lately in my role as Editor-in-Chief at an ecology journal just as Marc has)*. But unpacking why I don’t like critique letters has led to a lot of musings on how ecology works, how the human mind works, and my own answer to the specific question of how best to steer the field away if you see a bad paper. And just maybe along the way I stumbled on a strategy or two for killing zombie ideas!

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Making waves: can basic ecological research generate headlines? And does it matter?

Note from Jeremy: this is a guest post from Andrew Kleinhesselink, a PhD student at Utah State University, and Peter Adler.


“Gravitational waves: why it’s impossible not to be thrilled by this discovery”, announced the Guardian newspaper after last month’s discovery of gravitational waves by the Laser Inferometer Gravitational Wave Observatory (LIGO). You had to admit that it was pretty thrilling. Even President Obama congratulated the LIGO team. Just like the detection of Higgs bosons by physicists in 2012, or the 1998 discovery of the universe’s accelerating expansion, physicists had somehow attracted massive attention to a scientific result that few members of the public can fully understand and that has little (or at least only indirect) practical significance.

It’s easy to justify basic research when the public celebrates a discovery like this as a pinnacle of cultural and intellectual achievement. Maybe this is the source of ecology’s often diagnosed physics envy: we wish our science sold itself this well. So why doesn’t basic ecological research attract LIGO-levels of public interest? What kinds of ecology stories do attract attention? Should the answers to these questions change how we justify our research—or maybe even the kind of research we do?

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The latest on diversity-productivity relationships: getting past a zombie idea

I’m a bit late to this (sorry, got busy then it slipped my mind), but better late than never. Writing in Nature, Grace et al. 2016 try to slay the zombie idea of a humped diversity-productivity relationship by integrating multiple theories into a single structural equation meta-model that they then fit to data. I don’t ordinarily comment on individual papers from the recent literature. But I’ve been following work on this topic so I thought I’d say a few things. I think there are some important larger lessons here for how to do good ecology.

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We aren’t scientists because of our method – we’re scientists because we count

Scientists still enjoy a fairly high reputation in society as a whole (notwithstanding creationists and climate deniers). It is worth pausing to ask why scientists are still given credibility in this increasingly doubting age. Continue reading

Chill out about Jingmai O’Connor’s criticism of bloggers (UPDATEDx2)

Today in Things the Science Twitterverse is Predictably Upset About: paleontologist Jingmai O’Connor’s interview in Current Biology in which she says that

Those who can, publish. Those who can’t, blog.

I was going to comment on this in the Friday linkfest, but I decided I had enough to say that wasn’t already being said on Twitter that I’d turn it into a post. It’s an experiment–this is the first time I’ve ever tried to use the blog to intervene in a social media firestorm in real time.

tl;dr: Chill out, everybody. Yes, she’s wrong, but it’s not a big deal. She’s probably just overgeneralizing from her own experiences, and you’re being unfair if you’re ripping her, rather than merely disagreeing with her.

(UPDATE 2: Definitely looks like she’s speaking from personal experience in that interview; see the comments. I think this is useful context, but delving further into the personal context here would get us away from a discussion of broader issues. So in the interests of a productive comment thread, I ask that future commenters stick to general issues rather than focusing on O’Connor’s personal experiences.)

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