Here’s some data on how many people apply for N. American TT faculty positions in ecology and allied fields!

Alternative clickbait post title: “To a first approximation, there are no fish & wildlife academic ecologists.”

Second alternative clickbait post title: “All ecologists want to live in the Pacific Northwest. Apparently.”

If you want to know the statistical truth underlying those clickbait jokes, read on. 🙂

Recently I invited readers to share their direct knowledge of how many applicants there were for recently-advertised N. American tenure-track faculty positions in ecology and allied fields. I also compiled data provided by the anonymous commenters on from 2014-15 on. I was mostly just curious, and I suspect many of you are too.* These data are a (crude) window into the collective employment preferences of ecology faculty job seekers.

I also compiled, or was provided, various bits of possibly-relevant information about the hiring institutions:

  • geographic location (latitude and longitude)
  • size of the surrounding city/town (>100K people or <100K people)
  • highest degree offered (bachelor’s, master’s, or doctorate)
  • country (USA or Canada)
  • R1 university or not (according to the 2015 Carnegie classification; I classed UBC as an R1 and the other Canadian unis in the dataset as non-R1. If you didn’t know, “R1” basically means “highly research-intensive university”)
  • whether or not it was a fisheries/wildlife/natural resources job

I also looked at the “job season” in which the job was advertised (pre-2015, 2015-16, 2016-17, 2017-18, 2018-19), but there was no obvious effect on number of applicants so I dropped it from further analyses.

I recognize that a few of you may be annoyed that I didn’t collect more/different information. If so, please click this link. 🙂

I ended up with data on 86 positions (48 from, the rest from my survey), with very little missing data. The vast majority of those positions were in ecology or an allied field, but I kept the few in other fields (e.g., evolutionary biology, systems biology) just to increase the sample size. Read the footnote for comments on data accuracy.** This is of course a small sample. It’s large enough to improve on anecdotes. But most of the conclusions I draw below are tentative, and there are other questions I simply can’t address. These data also aren’t a random sample. In particular, I suspect that positions that attracted many applicants may be somewhat overrepresented in this sample. The more people who applied for a position, the more people who received a rejection letter notifying them of how many applicants there were, and thus the more people who might share that information in my survey or on

Before you read any further, take a guess: which of the following choices do you think is closest to the median # of applicants per TT ecology faculty job? (No peeking at the answer below!)

[scroll down for the answer…]






Here’s a histogram of the number of applicants per job:

job applicant histogram

You’ll immediately notice the two most important take-home messages of this post:

  • Number of applicants varies hugely. That’s the one conclusion that definitely wouldn’t change if you compiled additional data, and that is robust to any plausible inaccuracies in the dataset. There’s a TT ecology faculty position in this dataset that only got 12 applicants–and another that got 1000! The middle 50% of the data ranges from 61-175 applicants.
  • The “typical” number of applicants is 100. That’s the median. That’s a lot! But it’s not as many as you sometimes hear (that’s cold comfort to job seekers, I know…). Anybody who tells you that 200+ is common or typical is misinformed. Only 21% of the jobs in this dataset got 200 or more applicants–not rare, but not common either, and definitely not typical. And for reasons I described above, I suspect that 21% is at least a slight overestimate of the true fraction of TT ecology jobs that attract 200+ applicants.

Ok, regarding that 1000 applicant job: that was actually several jobs. It was a broadly-written cluster hire in biology at the University of Washington. Same story for the second most applied-for job in this dataset: it was a broad cluster hire at Notre Dame. Here’s a graph of all the data by longitude of the hiring institution (for reasons that will become clear…), with the two R1 university cluster hires highlighted:

cluster hires

For purposes of further exploratory analysis, I dropped those two cluster hires from the dataset, then ran the remaining data through a regression tree in R using the rpart package. A regression tree seemed like a natural choice. It fits my mental model of how many faculty job seekers decide which jobs to apply for: they apply to any job that ticks certain “boxes”, with different people having different boxes. This was the first regression tree I’d ever run in my life, so I hadn’t the first clue what I was doing and just accepted rpart’s defaults. But the results seem reasonable in light of both my own priors and the exploratory plots I’ll show you in a sec. So here you go: here’s how would-be academic ecologists, collectively, decide which jobs to apply to!

regression tree


The first split is between fisheries/wildlife/natural resources jobs, which tend to get relatively few applicants, and other jobs, which get relatively many. That’s visually obvious in the data:

fisheries applicants

Presumably fisheries/wildlife/natural resources jobs get fewer applicants than others (although still many in an absolute sense) at least in part because people with PhDs in fisheries/wildlife/natural resources often go on to work for government agencies, leaving fewer to pursue academic careers.

The second split on the tree is amusing to us here at Dynamic Ecology, because Brian predicted it. His “all ecologists want to live in the Pacific NW” hypothesis is totally, hilariously true. 🙂 The second split on the regression tree is between “longitude <-122.2 degrees” and points east. Non-fish&wildlife jobs in Berkeley/San Francisco and points west (all of which are also north of Berkeley/San Francisco) get about twice as many applicants on average as non-fish&wildlife jobs anywhere else.*** I suppose I should’ve guessed this, knowing how popular Portland, OR is as an ESA annual meeting location.

The next split in the regression tree is R1 vs. non-R1, with R1 jobs attracting more applicants than non-R1 jobs. I doubt that comes as much surprise. You can see this split in the data, though you have to squint a bit and ignore the Pacific NW jobs:


Finally, the last two splits in the regression tree are by longitude. If you squint at the scatterplots above, you can see ecology faculty job seekers as a group prefer to live near the coasts. The regression tree picks that out, with the aforementioned Pacific NW jobs attracting the most applicants, jobs in the Midwest (operationally defined as “west of Chapel Hill to Kansas”, longitudinally) attracting the fewest applicants, and jobs elsewhere attracting intermediate numbers of applicants. Actually, ecology faculty job seekers’ collective preference for the coasts might even be a bit stronger than the regression tree implies. It looks to me like some of the effect of longitude might be getting absorbed into the R1 vs. non-R1 distinction, because the dataset lacks data from R1 unis in the Great Plains and Mountain West. But it’s a small dataset, so it’s hard to say for sure.

I was slightly surprised that the regression tree didn’t pick out US jobs as getting more applicants than Canadian jobs, and I suspect that it would with a much larger dataset. And I was slightly surprised that the regression tree doesn’t find any effect of city/town size. But a city/town size binary is an extremely crude way to summarize “urban” vs. “rural”, not to mention other geographic factors that commonly enter into faculty job seekers’ decision-making.

Finally, note that there’s a lot of unexplained variation within each of the tips of the regression tree. Number of ecology faculty job applicants varies hugely even among apparently-similar jobs. That’s for all sorts of reasons, only some of them explicable.

Looking forward to your comments, as always.

*I confess I’m not sure what faculty job seekers, or anyone else, could actually do with this information. I mean, why would the likely number of applicants for a given position enter into your decision whether to apply for the position? Plus, it’s not as if knowing the number of applicants really tells you much about how “competitive” the applicant pool is. For instance, jobs that attract large applicant pools do so in part because many non-competitive applicants throw their hats in the ring hoping to get lucky. So if as an applicant you’re just looking for a reason to be optimistic (or pessimistic) about your chances for a given position, I’m sorry, but I don’t think a rough estimate of how many others applied is such a reason. But perhaps the data in this post could be useful to faculty job seekers in other ways I’m not thinking of? I’d welcome comments on this.

**I’m confident it’s accurate enough. I say that for a few reasons. First, there were a couple of cases in which multiple survey respondents gave me information about the same job. In both cases they gave the same number of applicants. Second, the survey emphasized that respondents should only report reliable knowledge, not n-th hand rumors. Third, in a couple of cases a survey respondent who sat on the relevant search committees reported the same number of applicants as was reported on Fourth, the aggregate statistical properties of the data are similar to the survey data. Fifth, I removed from the data a couple of jobs for which it appeared that the reported number of applicants might have been estimated as 10x the number of users who applied themselves. (Aside: The “10x rule”, often mentioned on, is too imprecise to be useful. Sorry. A linear regression of # of applicants on # of users who applied, forced through the origin, gives a slope of 11.3 (i.e. close to 10) and an R^2 of 0.54, with no visually-obvious model misspecification. So if you insist in having a rule of thumb, “number of applicants = 10x number of users who applied” is about the best rule you can come up with. But despite that impressive-sounding R^2 of 0.54, the prediction interval is quite wide. It’s common for the actual number of applicants to differ from that predicted by the 10x rule by several fold, and order-of-magnitude errors are far from unheard of.)

***Sorry Cal State East Bay. According to objective statistical analysis, you are not in the Pacific Northwest. #kiddingobviously


19 thoughts on “Here’s some data on how many people apply for N. American TT faculty positions in ecology and allied fields!

  1. Interestingly (and surprisingly) to me, survey respondents so far are *not* overestimating the median # of applicants/position. The modal guess is the correct one (100), and guesses that are too low and too high are equally common.

    That’s in contrast to many other features of the ecology faculty job market, on which ecologists collectively have pessimistic views that are at odds with the data:

  2. This all very much matches what I would expect from the various stories/data I’ve accumulated over the years.

    To amplify your above point. People should NOT feel freaked out by applying to a job that will receive 100 applications. The reality is a majority of these applications is a reach either in terms of subject area or lacking basic qualifications (which as you’ve noted elsewhere is NOT 15 papers including a Nature/Science paper – more like 5-10 pubs with some in good journals like Ecology/AmNat). In my experience it is probably more like 20-30 applications that really get considered seriously. Thus the odds of making a skype interview shortlist of 10 could be as high as half.

    • Yes to all that.

      Of course, this is consistent with applicants sometimes struggling to judge whether they’re a good fit to a particular position, sometimes getting interviews and offers for positions for which they didn’t think they were good fits, and sometimes failing to get interviews and offers for positions for which they thought they were good fits. What’s not clear to me is whether there’s anything that could be done about that. Given that there are substantially more TT faculty job seekers than there are TT faculty jobs, I don’t know how you’d engineer a world in which no one ever feels surprised at getting an interview, or not getting one. For instance, I think search committees mostly already write their ads as specifically as possible, given the goals of their searches. And there’s often good reason to run a broad search and hence write a broad ad.

      One point that may be underappreciated by many faculty job seekers (who mostly haven’t sat on faculty search committees) is the extent to which the committee often *discovers* what it was looking for by seeing the applications. Search committees don’t–and can’t!–have some fully-formed, very detailed “search image” of The Ideal Applicant and then pick whichever applicant best matches that pre-specified Ideal. This doesn’t mean that search committees are just rolling dice to decide who to hire, of course. It just means that, just as applicants don’t have full information about what the search committee is looking for, well, neither does the search committee (until after they see who applies, that is).

  3. Via Twitter:

    I confess I find it a bit depressing that this question came up. Not *everything* is about politics, not even in the US over the past 5 years. And if you asked ecology faculty job applicants what determines their choices of what jobs to apply to, I doubt that “political orientation of the state in which the hiring institution is located” would be a consideration for more than a small fraction. Would it?

    But I’m procrastinating on writing exams, and so I had a quick look, adding an additional variable to the dataset: for each job, the percent of the total vote that Hilary Clinton got in the state in which the hiring institution is located, in the 2016 US Presidential election. Obviously, there are not data for that variable for the Canadian jobs.

    In a scatterplot, there’s no visually-obvious association between that measure of state political affiliation and # of applicants.

    In a regression tree, the last two splits based on longitude (in the regression tree shown in the post) end up getting replaced by two splits based on Clinton vote share. But those splits don’t really make much sense: they say that R1 jobs get more applicants if the Clinton vote share was >55.2%, and that non-R1 jobs get more applicants if the Clinton vote share was >45.9%. So yes, according to that analysis jobs in “blue” states get more applicants than jobs in “red” states–but with very, and weirdly, different dividing lines between “red” and “blue” for R1 vs. non-R1 jobs. What that tells me is that this is a small dataset lacking any observations from many states, and that Clinton vote share is confounded with other surrogate variables in the dataset, like longitude. For instance, in this dataset, “non-fish&wildlife R1 jobs outside the Pacific NW, in states for which Clinton get >55.2% of the vote” means “SoCal R1s, Cornell, and U of Illinois”. Is it really plausible that appreciable numbers of ecologists are avoiding applying to jobs at Michigan, Minnesota, Rice, UT-Austin, Pittsburgh, Houston, and Ohio State because those R1 institutions are located in states that aren’t “blue” enough?

    Bottom line, I’m sure there are various reasons why, as a group, ecology faculty jobs near the coasts tend to get more applicants than those elsewhere. And I’m sure that, for a few ecology faculty job seekers, state-level political orientation is among those reasons. Obviously, “longitude” is just a crude surrogate for those reasons. But if you want to dig into what longitude is “really” a surrogate for, I confess I have a hard time believing it’s “state-level political orientation”. I just don’t think that state-level political orientation is really what matters to most ecology faculty job seekers. I mean, yes, I bet if you had a bigger dataset you’d find that jobs in rural “red” states get fewer applicants than other jobs on average. But if so, I strongly suspect that’s mostly because those states are rural.

    Probably a much better way to get at this would just be to poll ecology faculty job seekers and ask them how they decide where to apply.

    But I dunno, I’m just going by gut instinct here, and my gut is no more to be relied upon than anyone else’s. So what do others think?

    • Sorry to depress you! Though it bums me out as well, state & local politcs are a real concern for job-seekers whose identities are at odds with the exclusionary, often-dangerous policies being institutionalized in certain places. That said, I’m not too surprised people apply anywhere a TT gig opens up. We just might cross our fingers a little harder for certain applications…

      • I should clarify that I’m absolutely not implying that people ought to apply for jobs in locations where their physical safety would be at heightened risk due to some aspect of their identities, or in locations where they’d find it hard to socialize, or hard to raise their kids as they want to, or etc. As I perhaps should’ve made clearer, what I had in mind was more like someone saying “I would never live in city X because that state always votes in a way I wouldn’t like in federal elections”, even though they’d actually enjoy their day-to-day life in city X. I say this as someone who spent the first 18 years of his life living in and enjoying politically- and culturally-conservative rural Pennsylvania, and the last 15 years living in and enjoying a big politically-moderate city within politically-conservative Alberta. Even though I’m not politically conservative myself. But thanks to aspects of my personal identity (I’m a straight white guy, which of course means I won the birth lottery), and to features of the cities and towns in which I’ve lived during those periods, my day-to-day life has never been dangerous or exhausting or otherwise bad for me due to my membership in a local- or provincial-level political minority. In part because some (not all) of the cities and towns in which I’ve lived were quite different from the state/province as a whole on various political, social, and economic dimensions. It’s probably features of the city/town you live in that matter most for your day-to-day quality of life, rather than features of the state/province (except insofar as those state/provincial features affect the city/town features, of course).

        I also say all this as someone who wasn’t at all sure he wanted to move someplace *very* different than he’d ever lived before for his postdoc (I moved to London, UK, having never lived outside the US or anywhere other than a small town or a suburb), and who ended up loving it. I wouldn’t want to overgeneralize from from my own personal experience, or suggest that such pleasant surprises are the rule. I have no idea if they’re the rule or the exception. But for what little it’s worth, pleasant surprises do happen sometimes when people move to a place they aren’t at all sure they’ll like.

        I do have the anecdotal sense that some (small?) number of ecology faculty job seekers may underrate the extent to which day to day life in *some* (not all) college towns or cities can differ from what one might expect based on consideration of state-level politics and laws. But again, that’s a purely anecdotal sense. And all this is clearly something on which individuals are going to make very personal decisions, that others won’t really be in a position to second-guess.

  4. Via Twitter:

    Funny you should mention how competitive the ecology faculty job market was 30 years ago vs. today. I have a post on that in the queue for Wed. 🙂

    tl;dr: 30 years ago the ecology faculty job market was probably about as competitive as it is today, or at least not too different. You have to go back 40 years, or better yet 50 or 60 years, to find an ecology faculty job market that was definitely *much* less competitive than today’s…

  5. I still think that the number of applicants likely correlates with the number of quality applicants (probably concave/saturating, but I suspect it’s monotonically increasing – I admit that I have no quantitative evidence of this). With this assumption, I think the evidence you present here at least hints at wanting to apply widely. Basically, by applying widely you will surely apply to places with fewer applicants than the median (taking advantage of the high variance). I know you’re sceptical on the number of quality applicants being correlated with the number of applicants, but I’ve heard from search committees for jobs I applied for that roughly 100 of the 150 applicants were excellent and appropriate for the job. Although those two search committees both saying that roughly 2/3 of the applicants were competitive were both in Australia, where folks tend to do more postdocs, which might influence that number.

    Australia is an interesting case study because postdocs here make the same or at least very close to junior TT academic folks. The typical postdoc salary in Australia is equivalent to nearly 55 – 70k USD (80-100k AUD). This means that academics can live very comfortably off a postdoc salary for life (even potentially with children), if they are willing to put up with the short term contracts. So it’s actually less unusual here to see folks applying for TT positions with 4-5 postdocs under their belt [the shorter PhD also probably influences this number]. So I think perhaps there are more folks sticking it out to get those TT positions than in the USA, but that is all anecdotal and not grounded in any evidence. Purely speculation.

    • Oh, there’s probably *some* very noisy and nonlinear but nonetheless monotonic relationship between the total number of applicants and the mean number of well-qualified applicants. But does knowing only that much really give you much information, as an applicant?

      As to whether these data provide a reason to apply widely, I dunno. Personally, I’d advise applicants to just apply for all faculty positions for which they think they could do the job and might take the job if offered.

      But I guess if the total number of applications you were prepared to submit was constrained for some reason, so that you had to triage, yes I suppose you might want to consider how many applicants the job likely will get as one factor among many others in deciding which applications to triage. But even there, I dunno, why not just triage by only applying for the jobs you want the most? Or else find some way to relax the constraint on the total number of jobs you can apply for (e.g., by spending less time customizing each application)? At some level, “which jobs should I apply to?” isn’t really a well-specified optimization problem. So any reasonable heuristic you use to decide which jobs to apply for is probably fine.

      Note as well that the number of applicants who are “excellent” or “well-qualified” in some *absolute* sense is not necessarily tightly related to the number whom the search committee seriously considers for an interview. The latter is a *relative* evaluation, involving comparing the applicants to one another. Basically, the proportion of the applicant pool who exceed some absolute threshold of “well-qualified” (however defined or measured) might, but needn’t, reveal much about how many applicants are sufficiently close to the top 4 (a typical # of people to interview on campus) to merit serious consideration for an interview. I’m happy to believe that, in a 150 applicant pool, 100 of the applicants might be “well-qualified” in some absolute sense. But I’d be surprised if all 100 were sufficiently similar to one another that they all needed a close look from the search committee in order to identify (say) the 4 to be given an on-campus interview.

      Interesting about Australian postdocs, I didn’t know that about the Aussie system.

      • Good point about absolute vs. relative. Yeah my guess is that the committee was suggesting something like “had at least a few 1st author pubs in good journals and were broadly in the same field the job add was targeting.” So they definitely meant the absolute sense. I think they used the more descriptive phrase “We could have hired any of the top 100/150 applicants … but we had to cut the list down to 4”, which implies exactly what you are thinking.

  6. Related to the red state/blue state issue raised above, I was wondering if Deep South vs anywhere else mattered. Take SC, GA, AL, LA, MS, AR, and FL if north of Orlando, maybe also TN and KY (although not Deep). I assume NC and TX are not “academically” Deep South because everyone knows that Austin, Houston, and the Triangle are liberal. My curiosity there is because I’m at Clemson and have had folks tell me they did not apply for some of our jobs because they didn’t want to live in the Deep South with the brand of conservative that they think lives there. Clemson has had a pretty good number of hires in the last few years, both in F&W/For/NatRes and in Ecology, but I haven’t been on those committees to give you data.

    Or maybe ecologists don’t want to live where it is too hot, in which case you can split by latitude. (I jest.)

    Also, I wonder if some of the very low jobs are based at field stations? We definitely see fewer applicants for field station TT faculty than TT positions on main campus. Certainly not enough sample size to tease out…

    • ” I was wondering if Deep South vs anywhere else mattered.”

      Not in a way that the regression tree could detect–it had the option to split by latitude as well as longitude, and it didn’t. But it’s a small dataset without many jobs in the deep south–just 6 in Georgia, 1 in Alabama, 1 in Louisiana. So I wouldn’t expect to detect a “deep south” effect unless it was *huge*–even bigger than the Pacific NW effect (and that Pacific NW effect comes with big error bars and might well be an overestimate, obviously).

      Those 8 deep south jobs got anywhere from 18-250 applicants, and that 250 applicant job was the only one with >100 applicants. But 2 were fish & wildlife jobs and only 3 (including one of the f&w jobs) were at R1 unis. So, eh. It’s a small dataset.

  7. Hi there! I enjoyed this post very much and appreciate your analyses here. I am a recent PhD grad in Ecology looking for post doc and TT positions. I was particularly struck by this comment:
    “I should clarify that I’m absolutely not implying that people ought to apply for jobs in locations where their physical safety would be at heightened risk due to some aspect of their identities, or in locations where they’d find it hard to socialize, or hard to raise their kids as they want to, or etc. ”
    This is an issue I have been thinking about a lot as a person of color with young children. I have seen a number of job postings come up that I put into the box of “right job, wrong place” because I am _specifically_ concerned about moving my family to a place that is potentially unsafe for us in some way. A few years ago, I might not have worried about it quite so much.

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