The great escape: charting a career outside of academia (guest post)

Note from Jeremy: This guest post is the first in a series on non-academic career paths for ecologists. Not because non-academic career paths are somehow inferior to academic ones (they’re not). But simply because academic jobs are very scarce relative to the demand for them.

This post is by my friend and former Calgary colleague Carla Davidson, who turned off the academic career path after doing a PhD and a postdoc. She’s now an independent scientific consultant. She’s also a blogger, a reality tv star (no joke), and a mom.

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So, it turns out that the best thing I ever did for my career was fail at what I thought I wanted most.

Like most bright-eyed grad students in their first year, I never doubted that my natural brilliance would mean that I could be one of the roughly 10% of PhDs that go on to faculty jobs. I did a project that melded computer science with microbiology. Grad school was an education in not just science, but a sometimes shocking lesson in humility, a yardstick of my strengths and weaknesses, and a persistent reminder to look at what was most important to me. I finished in a little over six years. With mixed feelings. It never felt like the grand victory that I had hoped for after all that work.  What happened to that single-minded focus that begins this paragraph?  In the end, I was consoled by the words a colleague wrote to me: “In the conflict between the stream and the rock, the stream always wins, not through strength, but by perseverance! – H.J. Brown.”

Here’s what happened to that focus. The truth is that though smart, I hadn’t settled on one thing and become an expert in it. My education and experience until now was a long and winding road. I have worked in forestry and wildlife biology; my PhD was in microbiology, genetics and systems biology; and since I’ve worked in vaccine design, microbiome studies, ecological genetics in Daphnia, and experimental evolution. Could someone could question my focus? Yes, they could.

Moreover, the problem with becoming a prof went beyond this lack of focus (In fairness, ‘lack of focus’ isn’t entirely correct. I am just as comfortable with the thought that it was my curiosity that led me down so many paths.  In any case, I harbour no regrets).  My doubts about whether I wanted to be a prof started with my first postdoc. It was the position of my dreams: I went to Michigan State University and worked in the lab of Richard Lenski, studying experimental evolution. It was a beautiful lab, with incredible people and lots of support. But like many women, I was deeply conflicted between my academic dreams and a growing need to settle down. I left my long term boyfriend and the house we bought in Calgary, and resolved to spend half the year in Michigan and half in Calgary. This was difficult on both the relationship and on my meagre stipend. While in Michigan I worked as hard as I could, but roughly every two months I would burst into tears and search the internet for new jobs. I applied for probably 100 jobs over the combined four years that I was a postdoc. How many interviews did I get from them? Two.

One reason for such a dramatic non-response is that my interest was environmental but my scientific work had swerved medical. I didn’t have the work experience for the jobs I wanted.  Also, most big consulting firms have no interest in hiring PhDs, especially when they’re leaving the fields in which they trained. Rightly or wrongly, they think that PhDs can’t work as a part of a team and are more trouble than they’re worth. If so, their reluctance is a further sign that they see their function as one in which they tell the customer what he wants to hear, not what he needs to hear.

By this time, I was just starting yet another postdoctoral position, this one especially miserable. I was back in the dungeon-like lab where I got my PhD (Actually calling it a dungeon does a disservice to medieval law enforcement. It was prone to floods, chemical spills, bad smells from the nearby central sanitation facilities, and random appearances of dead bodies, or body parts, due to the medical anatomy lab across the hall). My income, such as it was, was partly funded by a technician’s position, so I was also responsible for making a dozen different media types for the lab, a most annoying diffusion of my efforts. It was only tangentially related to things that interested me scientifically and, lastly, this dispiriting lab was filled entirely with women who, though I love each individually, as a group tended towards the dramatic.

Such misery often moves us to desperate measures, and, in this regard, I was abetted by a wonderfully supportive supervisor. I got my faculty teaching certificate, continued writing, started sessional teaching at a neighbouring university, and applied for (and got on) a reality television show. My big break came as a result of chance meeting between my sister and a woman who runs a not-for-profit organization that delivers sustainability learning resources, Lisa Fox, Executive Director of Sustainability Resources, Ltd. I emailed her out of the blue, and asked for a meeting. It was awkward and felt like I was asking for a date. But she accepted and we met; she told me all about different people and projects working in the field of sustainability in Alberta, and I hid my shock that there was such a field in this province. I said: “I think what you need is a course on scientific literacy for policy professionals.” She said: “Sounds great. Put one together in six weeks.”

Tina Fey, one of my personal heroes, says she learned everything she needed to know about success from improv comedy. If someone takes the sketch in a direction you didn’t expect, just go with it. In short: say yes. Always say yes.

This is how I started consulting. After all my scientific experience, I knew enough about various fields of science to be generally useful, but not specialized enough to be an expert. I realized that I needed to find a niche for a scientific generalist, and I truly believe that there’s a big need for this. So many policy decisions include various pieces of scientific evidence, and non-scientists really need some help interpreting it. I decided that what I could do was not only translate science, but teach my clients how to think critically about the scientific evidence that comes across their desk. So I designed a half day workshop that managed to get people to start thinking this way.

My last big realization came while I was doing the reality show (Canada’s Greatest Know It All). Whatever audiences might have thought about the ten contestants (obnoxious nerds might have headed the list), they were, to a person, extraordinarily talented in a startling variety of fields. Several of them were running their own successful businesses. One thing became clear to me: though I seem to be unemployable, I was just as smart, scientifically, emotionally and practically, as those against whom I was competing. There was no reason I couldn’t do the same thing. I registered a domain, bought some business cards, and started my business.

So, today, I find myself on a path I enjoy and am rewarded by. My advice is being sought and I get paid for it. I hope that my career trajectory holds meaning to those thinking of leaving the hallowed halls of academe. Especially for those of you whose talents are being wasted on dreary jobs with poor pay, poor job security, and the appalling realization that you went through all that hard work just to find a closed shop.

Pure research may be the best job in the world, but it’s one of the hardest to get, and even once you’ve got a faculty job, the hard work, uncertainty and drudgery only continues until you get tenure. If it’s truly your passion you’ll find a way. BUT, if, like me, you love it, but not enough, think hard about what you are good at. Graduate school does not do a good job of imparting translatable skills, at least formally. But it does teach you some incredibly important things that are underrated: critical thinking, project management, communication, mentoring, team work, and an ability to jump into the deep end of something new and swim your way out. It does NOT teach you how to recognize or market those skills. In fact, the scientific caution you learn often works against you in this regard. So think about what you do best, and figure out a way to market that. If that means that you don’t get a job with some big company, so be it, think of how to market yourself, and start your own business.  I can’t think of any really successful people who became so by attaching themselves to a large corporation.

The minute I completely left academia and committed myself to consulting some amazing things happened, and it’s a constant surprise to me. I now have one major contract and three minor ones that keep me busy and food in the fridge. I have more confidence. No one’s career path is free of rocks and I expect mine still has a few to get in my way.  The difference now is that I have found that being in control of my path makes it easier to kick those rocks to the side.

Stuart Hurlbert rips Sokal & Rohlf and the state of biostatistical training (UPDATED)

In a recent issue of Limnology and Oceanography, Stuart Hurlbert reviews the new (4th, 2012) edition of Sokal & Rohlf’s classic biostatistical text, Biometry (HT Carl Boettiger). The first sentence of the review gives you the flavor:

Reader be forewarned: were it allowed the title of this review would be “A readable but overblown, incomplete and error-ridden cookbook”.

Tell us how you really feel, Stuart! And to think that sometimes I worry if I’m too tough on other people’s work…

You should click through and read the whole thing. But if you’re not so inclined, here’s a brief summary of Hurlbert’s beefs with Sokal & Rohlf (the book, not the people; I’ll refer to the book as Sokal & Rohlf because that’s what everyone does). Hurlbert says his beefs apply to all editions, not just the most recent one:

• No coverage of experimental design, or sampling design of observational studies. Relatedly, and worse, incorrect or confusing implications about experimental design and sampling design. For instance, there are no formal definitions of key terms like “experiment”, “experimental unit”, “block”, “repeated measures”, etc. Worse, observational studies often are described using experimental terms like “treatment”, “control”, and “randomized block design”. This leads to serious confusion, even about matters as basic as what an experiment is.
• Too much emphasis on “statistical gimmickry” of little or no practical use, such as standardized effect sizes.
• Superficial, cookbook-type treatment of many procedures, with no conceptual framework for understanding why one might want to use those procedures.
• Incorrect, incomplete, and confusing coverage of other matters, from when it’s appropriate to use a one-tailed test, to whether to correct for multiple comparisons (Hurlbert apparently believes you should never do so, and so slams Sokal & Rohlf for insisting on this), and many more.
• Rigid adherence to Neyman-Pearson null hypothesis testing, at the expense of estimation and more refined, quantitative assessment of the evidence for or against any given hypothesis.*

The only value Hurlbert sees in Sokal & Rohlf is as a reference manual for the “recipes” for how to calculate various statistical procedures. He concludes by blaming the popularity of Sokal & Rohlf for what he sees as decades of poor statistical practice in biology. He also laments that no current biostatistical textbook teaches an appropriately-modern philosophy of statistics, in a clear way with a focus on principles, with no errors.

What do you think of all this? I have to say I found it kind of surprising, but not because I revere Sokal & Rohlf. I’ve mostly used it as a reference manual myself. I’d certainly never try to teach from it at any level, if for no other reason than it’s way too voluminous. I guess I always assumed, without really thinking about it, that it was always intended, and mostly used, as a reference manual. Was I wrong to assume that? And while I find Sokal & Rohlf old-fashioned in some ways (e.g., randomization, bootstrapping, and generalized linear models render classical non-parametric tests and data transformations largely irrelevant), that never really bothered me. The first edition came out in 1969; of course it’s going to be old-fashioned. And I don’t know that it’s fair to pick on Sokal & Rohlf and blame it for the purportedly terrible statistical practices of modern biologists, even though the book certainly is popular. Insofar as our statistical practices are terrible (and I don’t know if they are or not), there’s surely plenty of blame to go ’round. And can’t you also give Sokal & Rohlf credit for helping to encourage more biologists to use statistics in the first place? But I’ve never really thought about Sokal & Rohlf all that much, and I actually haven’t cracked it open in years, so I’m sort of a curious bystander here.

As an aside, I found it interesting that such vociferous criticism of Sokal & Rohlf came from someone from basically the same school of statistical thought. Hurlbert isn’t a Bayesian of any stripe, nor is he advocating for computationally-intensive methods, for instance. His criticisms of Sokal & Rohlf mostly aren’t criticisms of what the book sets out to do, they’re mostly criticisms of the book’s execution.

What do you think? Does Sokal & Rohlf deserve the criticism Hurlbert heaps on it? More broadly, what do you see as the biggest problems with how modern biologists teach and use statistics? And what textbook(s) should we be using in our courses in order to fix those problems? (Again, Hurlbert says there’s no biostatistics textbook that’s readable, strong on general principles, and error-free!)

My interest in this isn’t purely academic. I’m not just looking to grab some popcorn and watch proponents and detractors of Sokal & Rohlf argue. As I noted in a previous post, this fall I’m taking over teaching the introductory undergrad biostats course in my department. So for the first time, I need to think seriously and in great detail about exactly what introductory biostatistical material to teach and how to teach it. I’ve settled on a textbook (Whitlock & Schluter), and I have a tentative list of lectures and the major changes I want to make to the existing labs. But nothing beyond that. And even getting that far has required a lot of thought, in particular about precisely the issues Hurlbert raises. How much emphasis to place on general, unifying principles vs. coverage of specific tests. How much emphasis to place on black-and-white rules of good statistical practice vs. equipping students to make informed judgment calls. Etc.

It occurs to me that teaching biostatistics is something like teaching children good behavior. You start out by teaching kids black-and-white rules, like “don’t lie” and “don’t hit your sister.” And it’s only later that kids learn that good behavior often isn’t black-and-white. Sometimes it’s not only ok to lie (or to hit your sister!), it’s positively a good idea, morally. Heck, there are lots of tricky moral situations that you aren’t even taught about at all until you’re older. And that’s without even getting into competing, mutually-incompatible philosophies as to what good behavior consists of, and what makes it good! So you tell me–what should we be teaching our “kids” about biostatistics if we want to start them down the road towards responsible “adulthood”? (“Don’t hit your sister fail to correct for multiple comparisons!”)

*Hurlbert actually thinks Sokal & Rohlf should’ve based their book on what Hurlbert calls the “neoFisherian” approach. I confess I’d never heard the term “neoFisherian”, which is Hurlbert’s own recent coinage. Hurlbert has a 2009 paper if you want to find out what he means by “neoFisherian” and why he thinks Neyman-Pearson hypothesis testing is so outdated that it should no longer be taught (UPDATE: link fixed). As far as I can tell, what Hurlbert means by “neoFisherian” doesn’t sound too far from Deborah Mayo’s notion of “error statistics” (which itself is actually not all that far from Neyman-Pearson, or even from some forms of Bayesianism). But it’s a little hard to tell because much of Hurlbert’s paper focuses on what seem to me to be rather nit-picky details of current practice (like conventions for reporting P values). Anyway, I think it would’ve been helpful for Hurlbert to briefly elaborate his own philosophy in his review, rather than just refer to it using a term of his own recent coinage.

Friday links: rejected classic papers, great interview with Peter Kareiva, crowdfunding=bake sale, and more

From Jeremy:

Think you’re the only one who gets rejected? Think again, it happens to everyone. As illustrated by this paper on how even now-classic articles by Nobel Prize-winning economists initially were rejected. Similar incidents have occurred in ecology, as Meg has attested. In evolution, both of George Price’s hugely important Nature papers initially were rejected. And as The EEB and Flow notes, Joe Felsenstein’s hugely popular PHYLIP software has been repeatedly rejected for funding, both before and after it was first developed, a fact Joe memorializes in PHYLIP’s “No thanks to” list. Anyone know of any other really famous ecology & evolution work that was rejected initially? Which isn’t to say that you should always keep doggedly trying to publish an idea that’s been rejected. But deciding if/when to give up can be a difficult, and even heartbreaking, decision. (HT Paul Krugman)

BioDiverse Perspectives has a great interview with population ecologist and Nature Conservancy Chief Scientist Peter Kareiva. Much of it is on how and why to do policy-relevant science, a topic Brian hit on earlier this week. Much of what Kareiva has to say resonates with Brian’s post. Here’s an extended quote from Kareiva to give you the flavor:

Ecology matters to the general public because ecology is about water, pests and pestilence, recreation, food, resilience and so forth…focusing so narrowly on producing graphs that on the horizontal axis display number of species and on the vertical axis report some dependent ecological function (that is distantly related to human well-being) strikes me as not worth so much research…Our mistake has been to focus too much only on the one narrow dimension of nature that systematic biologists, natural historians, and a portion of ecologists care about: biodiversity.  Understand nature in a way that serves the public, not yourself. And remember, biodiversity as a label didn’t come into fashion until the late 1980s. There was a tremendous amount of conservation ecology that produced a wealth of understanding and useful insight before the biodiversity meme. My prediction is that in 2030, we will not be talking about biodiversity anywhere near as much as we do now—instead we will be asking how nature can make humans more resilient to climate disruptions, and what are the limits we should avoid crossing if we want to maintain a reliable supply of food and water.

Apparently this interview is the first in what will be an ongoing series of interviews with visiting speakers at the University of Washington. That’s a great blogging idea, it’s been done a bit before and I think it could be done a lot more. And kudos to Hillary Burgess and Halley Froelich, the grad students who conducted the interview, for having the guts to interview someone who told them that the entire BioDiverse Perspectives website was dedicated to the wrong thing!

Mike the Mad Biologist finds that there’s nothing new under the sun when it comes to scientific fraud.

Here are data on the length of the average dissertation. I leave it to you to decide if “above average” is a good or bad thing in this context.

Quote of the week, Twitter edition: Terry McGlynn says that “Crowdfunding science is as sad as a bake sale for education.” Discuss.

There may be tribes in science. But not only are there tribes in economics, they’ve been studied by an anthropologist! Just kidding of course, the linked article is a joke–a very funny one, even if you don’t know much about economics (though it’s funnier if you do know some economics). Someone should write something similar for ecology. (HT Worthwhile Canadian Initiative)

I didn’t see this until it was too late to participate, but earlier this week E. O. Wilson did an “ask me anything” on Reddit. Sadly, I don’t think anyone asked him if he reads Dynamic Ecology. And that’s probably for the best. I only skimmed it, it mostly looks like questions from admiring fans. There are a few interesting nuggets, such as Wilson’s answer to a question on the most promising fields of ecology and entomology:

I believe that the greatest leaps will be in ecology. The systems are so complex, depending on mostly little known interactions of many species that we have not begun to understand how the entirety of it works. This is a great subject for young scientists to go into, both to explore the ecosystems and define new ways to analyze them.

(Although arguably, whether ecology is “complex” depends on how you look at it) And I smiled at the anonymous evolutionary biologist who prefaced a question about the Nowak et al. kerfuffle with “You have taken more criticism in the last few years than you did over many previous decades.” To which Wilson surprisingly did not respond, “Umm, you’re aware that I wrote this, right? And that protestors at the AAAS meeting dumped a bucket of water on my head because of it?” (HT Terry McGlynn, via Twitter, and reader Artem, via email)

Species distribution modelers often choose their software based on ease of use, its use in previous publications, or on the recommendation of friends. Which the authors claim is a serious problem, but honestly, their arguments for this claim seem pretty weak. They say that trusting one’s colleagues when it comes to software choice is a risky thing to do–but peer review comes down to trust too! I mean, sure, if there’s a bug in someone’s code then that’s a problem. But there are lots of ways for a scientific study to go wrong, and bugs in code is only one of them. And this study has no data on how often it’s a problem. The authors just take survey data on how people choose their software and then leap to conclusions about the purported negative consequences of their choices. And they just assume that this problem needs solving via big changes to how students are trained and how peer review is conducted, with no attempt to balance the proposed changes against their costs, or against the benefits of the status quo.

The CEO and CFO of Plos left the company on the same day?! That’s really unusual for any organization of any size, for-profit or non-profit. I have no idea what this means. I suppose it might not mean anything, at least not anything important–or it could mean something really bad, or somewhere in between. I’m curious: are readers who admire Plos and support its goals worried by this news? Again, I have no idea if you should be worried; I’m just wondering. I mean, if, say, the Ecological Society of America announced that both the President and Treasurer of the Society were leaving their posts, effective in 10 days, I’d be a little worried. But maybe that’s a bad analogy? (HT Scholarly Kitchen)

Hoisted from the comments:

Brian, Jeremy, and ace commenter Margaret Kosmala discuss how to choose the right postdoc. Starts here.

Zombie ideas are losing the war for the intertubes

Readers of this blog know that the intermediate disturbance hypothesis (IDH) is a zombie idea that deserves to die. And now users of Google and Wikipedia are increasingly likely to learn the same thing. If you search Google for “intermediate disturbance hypothesis” here’s what you see on the first page of hits (click for larger version):

That’s right, my TREE paper attacking the IDH is the first scholarly article to come up in the main list of search results! And a second link to the same paper comes up a bit further down on the first page of results. Thanks, internet! Sadly, my paper is not among the top Google Scholar hits–yet!

And when you go to the Wikipedia page on the IDH, you find this:

Under “support and critiques”, there’s an entire paragraph devoted almost entirely to my TREE paper. I didn’t write it and don’t know who did. So thanks, anonymous Wikipedia contributor!

The notion of local-regional richness relationships is another zombie idea. And look what comes up as the very first hit if you google “local-regional richness”:

Clearly, the defenders of zombie ideas need to start blogging. Because slowly but surely, zombie ideas are losing the war for the intertubes.

p.s. I really hope these results don’t reflect Google personalizing its search results for me. That would be a bummer. I don’t think that’s the case, because my post on the zombie local-regional richness relationship is the top hit on Bing and Duck Duck Go as well, despite my never having used either of those search engines before. But personalization of search results may in part explain why my TREE article is one of Google’s top hits for “intermediate disturbance hypothesis”, since on Bing and Duck Duck Go it doesn’t show up until a bit further down in the results.

Project MOSAIC: tools for teaching mathematical modeling and statistics

Via a commenter over at Small Pond Science, just discovered Project MOSAIC, an NSF-funded project to help teach introductory undergraduate mathematical modelling, statistics, and computation. Among other things, they’ve developed a very handy-looking R package called mosaic, which simplifies the use of R for basic statistical and modeling task, and alters the output in a way designed to be friendly and people new to both statistics and to R.

I’m very interested in this as I’m currently revising our intro biostats course. Have you used the mosaic package or other Project MOSAIC resources in your own teaching? If so, please chime in with advice in the comments, or drop me a line (jefox@ucalgary.ca).

Who’s asked me to review recently, and how I’ve responded

In case anyone’s interested, here’s a list of the journals and funding agencies that have asked me for reviews since July 2011, and how I’ve responded. I don’t count reviews of invited revisions, because that’s really just a matter of completing the review of the initial ms. Nor do I count papers I handled as an editor before I resigned from the editorial board of Oikos. In agreeing to serve on an editorial board, you’re agreeing to handle any mss you’re assigned to handle; you can’t pick and choose the way you can as a peer reviewer.

I’m starting in July 2011 because I track these data for my university’s biennial performance review; the next one is this summer. I’m putting the data up now because I’m short on time to write real posts at the moment.

Journal or funding agency, # of reviews requested, # agreed to

Ecology Letters, 11, 3

American Naturalist, 7, 2

Plos One, 6, 0

Nature Communications, 5, 3

Ecology, 3, 2

Ecography, 2, 0

Theoretical Population Biology, 2, 0

Journal of Theoretical Biology, 2, 0

Methods in Ecology and Evolution, 2, 0

Proc. Roy. Soc. Lond. B, 2, 0

Journal of Ecology, 1, 1

Oikos, 1, 1

Ideas in Ecology and Evolution, 1, 1

Frontiers in Microbiology, 1, 1

Journal of Applied Phycology, 1, 0

Applied Vegetation Science, 1, 0

Global Ecology and Biogeography, 1, 0

BMC Biology, 1, 0

Plant Ecology, 1, 0

Functional Ecology, 1, 0

Microbiology and Molecular Biology Reviews, 1, 0

Landscape Ecology, 1, 0

Ecosphere, 1, 0

Oecologia, 1, 0

Frontiers in Biogeography, 1, 0

Trends in Ecology and Evolution (book review request), 1, 0

National Science Foundation (USA), 2, 1

Natural Science and Engineering Research Council (Canada), 2, 2

Marsden Fund (New Zealand), 1, 0

Some comments:

• If you’re scoring at home, that’s a total of 60 requests to review, of which I accepted 17.
• I’ve done over twice as many reviews as papers I’ve submitted or co-authored since July 2011, and that’s without counting the editorial duties I had during that time. In the past, I used to do reviewing at something like four times the rate I submit papers. My PubCred balance is very positive and growing, albeit growing slower than it once was. I’m lucky to get as many requests to review as I do, as it means I can pick and choose without worrying about whether I’m doing my fair share of reviewing.
• In only a few cases have I turned down reviews that I really wanted to do but just didn’t feel like I had the time to do. More often, I decline because the paper sounds boring, bad, or too far from my area of expertise. Of course, being busy and thinking the paper sounds boring or bad aren’t mutually exclusive. Again, I’m always busy, and I get lots of requests to review, so a paper needs to sound interesting for me to agree to review it. Note that I don’t just assume that anything submitted to a leading ecology journal like Ecology Letters or Am Nat will be interesting. I judge by reading the abstract. More than once (including just the other week…), I’ve declined reviews because it’s pretty obvious from the abstract that the authors should’ve submitted to a highly-specialized or unselective journal, but for whatever reason decided to take a shot at a journal like Am Nat.
• Speaking of far from my area of expertise…several of these review requests came from journals I don’t read, including some that came from journals I’d never heard of before receiving the request to review. I’ll let you guess which ones those were.  I declined them all, because none of those papers were anywhere near my area of expertise. This illustrates why you should always suggest reviewers in the cover letter accompanying your submission. Editors often are obliged to handle mss far from their area of expertise, and so may have a hard time identifying potential reviewers. Which sometimes leads them to send papers to reviewers who aren’t really appropriate. This also illustrates why you need to put a bit of thought into which names you suggest. I’m far from the only established ecologist who gets far more requests to review than I will ever agree to. So before you suggest me, or any established ecologist, as a reviewer, try to honestly consider whether I’m likely to do it. In general, people are very reluctant to review papers that they wouldn’t be interested in reading. So in suggesting referees for your paper, think about the audience of the journal to which you’re submitting, and whether the referees you’re suggesting are likely to be part of that audience. Someone who’s never published in the journal to which you’re submitting, or in any closely-allied journal, is probably not part of the audience for your paper, and so isn’t a good person to suggest as a reviewer.
• Yes, I do sometimes do reviews for granting agencies to which I am ineligible to apply. Basically, if I think the grant sounds interesting, I’ll review it.
• No, I don’t have anything against open access journals. I decide whether to review for Plos One, Ecosphere, and other open access journals on the same basis I decide whether to review for any journal. If Plos One ever sends me a paper that sounds sufficiently interesting, I’ll review it.
• A few of these review requests came to me at least in part because of my blogging, specifically concerning the intermediate disturbance hypothesis. Judging from the abstracts of these papers, let’s just say that there are clearly many ecologists out there who don’t read this blog.
• In the past, I’ve reviewed for a number of journals and funding bodies not on this list, and for various journals on this list for which I haven’t reviewed since July 2011.

“Null” and “neutral” models are overrated

Recently I reviewed an interesting paper proposing a new model of X.* X is an ecological phenomenon that we’d like to understand and predict. X has been modeled before in various ways, with different models making different ecological assumptions about the factors that govern X, and different simplifying assumptions about other things. The main goal of this new paper was to develop a simple model of the effects on X of some factors ignored by previous models. All of which is fine (like I said, I found the new model quite interesting), and none of which is what prompted this post.

What prompted this post was that, in a couple of places, the authors referred to their new model of X as a “neutral” model. I admit that I wasn’t 100% clear on what they meant by this. But I got the impression that the authors felt their model had some sort of special status compared to previous models of X. That they viewed their model as a “limiting” or “baseline” case, perhaps–the factors included in their model are always at work, whereas the factors included in other models might or might not be at work. Or perhaps they felt that their model should be treated as a “null” model, to be tested and rejected before we are entitled to infer that some other process, not included in the model, matters? As I say, I’m not clear exactly what they meant, and the authors didn’t make a big deal of it so it wasn’t a huge concern for me

But this paper is just one example of what seems to me to be a growing trend, although its roots go way back. In the wake of Steve Hubbell’s very influential application of a neutral population genetics model to ecology, ecologists seem increasingly keen to develop “neutral” or “null” models for all sorts of ecological phenomena. In practice, this usually means a simple model which omits, or sets to zero, the effects of one or more ecological factors or processes, while explicitly or implicitly retaining the effects of other factors or processes. Just as neutral models in population genetics set selection to zero, but include (or can include) effects of other evolutionary forces, like mutation, migration, and drift. And then it’s claimed or implied that the resulting model has some sort of special status, that it’s somehow different than other models of the same phenomenon, and so should be treated differently.

This trend kind of bugs me. Developing ecological models that omit or set to zero the effects of some ecological processes often is very useful, I have no problem with that. But I really wish we’d quit calling the resulting models “neutral” or “null” models, and treating them differently than we treat other models on which we haven’t slapped those labels.

The issue here is one of which research strategies are effective in which contexts, or for which purposes. There absolutely are contexts in which it makes sense to treat some particular simple model as a “null” model, which ought to be rejected as a first step, before we are entitled to infer the operation of any processes or factors not included in that particular model. But there are many other contexts in which that research strategy is not only ineffective, but likely to be positively misleading.

To explain why, let’s consider a canonical case in which it really does make sense to start with a null model that you will try to reject before doing anything else. In simple statistical contexts, the null hypothesis describes how you’d expect the data to look if there was nothing going on except sampling error. Sampling error is of no scientific interest. It’s a nuisance, pure and simple. If we could completely and accurately census the statistical populations of interest, we would. But unfortunately, complete and accurate censuses ordinarily are impossible, so sampling error is ubiquitous. Further, its effects aren’t always obvious or easily recognized. So in order to avoid getting fooled into seeing patterns that aren’t really there, it makes sense to first rule out the possibility that any apparent patterns in the data arose from sampling error alone. And in order to do this we need to be as sure as we can be that our null hypothesis correctly describes the effects of sampling error, and doesn’t include the effects of anything else besides sampling error. Because otherwise we will be seriously misled.

Of course, sampling error isn’t the only possible “nuisance” in science. A “nuisance” could be any factor that, for whatever reason, is totally irrelevant to the question being asked. So in general, we can say that a “null” model is one that includes the effects of any “nuisance” processes or factors that are of no scientific interest, but just get in the way of detecting effects that are of scientific interest. Unfortunately, these “nuisances” are ubiquitous or nearly so (otherwise why would we worry about them?), and have non-obvious effects (otherwise why would we need to model them to detect them?) To be useful, the null model must correctly describe the effects of these “nuisances”, and must not include any effects of any non-nuisance factors. Indeed, insofar as the null model doesn’t correctly describe the effects of “nuisances”, or includes effects of non-nuisances, it can be worse than useless. It can be positively misleading. And of course, all of this assumes that we can all agree on what’s a “nuisance”, for purposes of the question asked.

In practice, I think “neutral” models in ecology often are intended to function as “null” models in the sense just described. Which is a big problem, I think. Because can you think of any ecological model (as opposed to a statistical model of sampling error) that actually fits the description I just gave? I can’t.

For instance, all neutral (in the sense of selection-free) models of which I’m aware include the effects of other processes of scientific interest–drift, migration, mutation, etc. These processes are of interest both in their own right, and due to their interactions with selection. And further, those other processes aren’t necessarily ubiquitous; there are real-world situations in which some or all of drift, mutation, and migration are negligible. And further still, different models omitting different processes often can produce similar-looking data. This is a really crucial point. For instance, there are models with selection but no drift, mutation, or migration that produce realistic species-abundance distributions. When the world is overdetermined, it is a very bad research strategy to default to assuming that certain processes matter while others might or might not. And in ecology, the world often is overdetermined, by which I mean simply that many different combinations of processes are sufficient to generate the observed data, with no one of them being necessary. So if you’re trying to understand the processes that generated your data, I don’t see why you’d ordinarily want to confer special “null” status on a model omitting any one of those processes. Not when that “null” model is simply one model among others that might have generated the data.

But at least neutral models in population genetics do in fact omit selection, while retaining drift, migrations, etc. Many other putatively “neutral” or “null” models in ecology don’t even manage that. For instance, randomization-based “null” models for detecting effects of interspecific competition are infamously problematic because it’s totally unclear what effects they actually eliminate and what effects they retain. As a second example, the “mid-domain effect” is a strange “null” model that admittedly nullifies only some of the effects of environmental gradients on species’ geographic ranges. I could keep going, but you get the idea.

I sometimes see ecologists argue that one always has to have a null model. You always have to rule out “noise” before you can claim that there’s a “signal” worth studying. One problem with this argument is that it gets deployed in contexts in which what counts as “noise” is highly debatable. If by “noise” you mean, not “sampling error”, but “ecological processes that I personally happen not to be interested in”, you really should not be deploying this argument. A second problem with this argument is that it’s deployed to defend null models that the users themselves admit are imperfect, e.g., because they include effects of “non-nuisance” processes. Again, having a bad null model often is worse than not having one at all, because it’s positively misleading. In such cases, your best bet is to find some other way of addressing the scientific question of interest. For instance, back in the 1980s community ecologists famously abandoned randomization-based null models and other observational approaches for inferring the operation of competition, in favor of field removal experiments to directly test for competition.

I also sometimes see ecologists giving special status to simple “null” models on grounds of parsimony. I don’t buy that. I wonder if people who make this argument have thought sufficiently carefully about precisely what “parsimony” means and why we might care about it. (There is an extensive philosophical literature on this) Personally, I generally don’t care about simplicity (parsimony) for its own sake. I care about the truth, or at least a good enough approximation to the truth for my purposes. And the truth, or a good enough approximation to it, might well be complicated! For instance, if the truth is that the world is not neutral, so that selection is among the processes that actually generated my data, why should I care if a simple model that omits selection can reproduce certain features of my data? Especially since, thanks to overdetermination, different “null” or “neutral” models that omit different factors often will all be able to reproduce those same features of my data. Which means you can’t argue that the factors omitted from any one of those models are irrelevant (too often, “parsimony” is invoked not as a substantive argument but simply as a way to shift the burden of proof) And if you say that simpler models are to be preferred only when all else is equal, you’ve just admitted that parsimony is irrelevant in practice, since in practice all else is never equal when it comes to comparison of substantive scientific models. Bottom line: the reasons for favoring simple models over complex ones, independent of how close they are to the truth, are extremely limited at best.**

None of the above is intended as an argument against statistical hypothesis testing in ecology. Even in an overdetermined world, it still often makes good scientific sense to start by ruling out the possibility that your data could’ve arisen from pure sampling error. Traditional statistical ideas about sampling error are pretty much always relevant.

Don’t get me wrong, I know as well as anyone that all models are false, are imperfect approximations to the unknown and unknowable truth. And there absolutely are good reasons why, when trying to learn about how the world works, we might want to start by developing and testing simple models rather than starting out with more complex ones. This post is emphatically not an argument that we should aim to develop literally-true models (that’s impossible), or models that are as complex as possible! But the whole point of having a false model, or a bunch of different false models, is to home in on the particular ways in which they’re false, and leverage those falsehoods to get closer to the truth. Too often, that’s not how purportedly “neutral” or “null” ecological models are used. It’s usually a bad research strategy to set up one particular model among others as a “null”, just because it happens to be simpler than the others or just because it omits some particular process that other models include. It’s often far more useful to start with a suite of alternative models, none of them privileged with the label “null”, in order to get a sense of the range of models that might have generated the data (e.g., the recent work of Storch et al., to pick one possible example among many).

*Obviously, I can’t go into any further detail without violating confidentiality.

**As illustrated by the fact that popular statistical methods for model selection, such as AIC, are not methods for choosing “parsimonious” models. They’re not methods for choosing “simple” models, independent of how close they are to the truth. They’re not even methods for choosing models that represent some sort of optimal “compromise” between simplicity and closeness to the truth, though they’re often described that way. Rather, they are methods for choosing the model that’s closest to the truth, period. A model can be false by being simpler than the truth, or by being more complex than the truth (as in cases of “overfitting” the observed data, also known as “fitting the noise”). That, and not “parsimony”, is why AIC includes a penalty term for the number of free parameters a model has. AIC scores for alternative models are estimates of the relative Kullback-Leibler divergence between the alternative models under consideration, and the unknown true model that generated the data.

Friday links: s**t students write, do big name scientists have too much money, and more

From Jeremy:

S**t my students write: a Tumblr compilation of hilariously-bad passages from student essays. My favorite line: “Scientists are well educated and don’t make mistakes because they have their degrees and what not.” Yup, that’s me: Jeremy Fox, Ph.D., A.W.N. I have to say, I’m glad my student days were almost behind me before the internet took off, so that all the amusingly-awful poetry I wrote in high school could only be mocked by my classmates rather than being immortalized on Tumblr.

Canadian government continues to slash and burn basic science: with no warning, world-famous Bamfield Marine Science Centre just had its research budget cut by 1/3. A bunch of other major research facilities in all fields of science are getting cut too, in favor of increased funding for “applied” research relevant to industry. Funny how a conservative government that professes to believe in the power of unfettered free markets also believes that the government ought to intervene in the market by subsidizing the sort of industrial R&D that you’d think private business would pay for itself if it was really so relevant to them. And how they don’t want to pay for the sort of basic research and other public goods that the free market has never provided. Although, as The Monkey Cage notes, they might change their tune if they could be convinced that basic research will save us from giant space rocks. Or if basic research could be pitched as creating jobs in the districts of key legislators. Wonder why so many basic researchers are hypocrites when it comes to justifying public funding for their work? This is why.

Pell Grants for low-income students are actually making private US colleges and universities less affordable for those students. That’s because of the way the majority of US colleges and universities structure their tuition and financial aid packages, with the linked goals of making money and attracting “elite” students. Effectively, many colleges and universities are using Pell Grant money to help subsidize merit scholarships that mostly go to students from high-income families, while pricing themselves so as to avoid admitting too many really needy students. There’s an amazing interactive chart if you want to check the numbers for your own private college or university. I was reassured that my own undergraduate college, Williams, is among the few that do it right. Williams offers sufficiently-generous need-based aid that the neediest students pay a low effective price, and has a relatively high proportion of highly-needy students in the student body. Although I was embarrassed to see that our biggest rival, Amherst, does better than we do. And I do wonder if both schools, and others like them, could be doing better still (further up and to the left in that interactive chart), and without even sacrificing student “merit”, by doing a better job of seeking out needy applicants. Many financially-needy students never even apply to places like Williams and Amherst, assuming (incorrectly) that they couldn’t afford them.

2-3% of NSF grant applications have “actionable plagiarism”, and the rate for applications from young investigators is 10-15%?! Is this true? Anyone have more info on this? (HT Retraction Watch, via Twitter)

Further to my post earlier this week on pseudoscience, here’s the text of physicist Richard Feynman’s classic speech on “cargo cult science”. It’s most famous for Feynman’s line “The first principle is that you must not fool yourself–and you are the easiest person to fool.” But the whole thing is well worth your time (HT to a commenter on Andrew Gelman’s blog).

The Lab and Field crunches the numbers and questions the value of Canada’s Excellence Research Chairs Program (a program to help Canadian universities attract big-name scientists). There are two problems with throwing huge amounts of money at small numbers of scientists. First, it’s risky–it amounts to putting all of your eggs in very few baskets. Second, after a certain point it’s surely inefficient, because those big-name scientists stop being money-limited and become time-limited. You’d get more bang for your buck if you gave that money to people who don’t have as much. And indeed, my former Oikos Blog colleague Chris Lortie compiled evidence that the most elite ecologists really do have more money than they can spend effectively, so that funding agencies would get more bang for their buck by reallocating funding away from the “ecological 1%”. For additional discussion of how funding agencies should allocate their funding, with a focus on the virtues of the Canadian Discovery Grant program, see here and here.

As reported in Nature this week, a Rutgers University report has backed famed evolutionary biologist Robert Trivers’ arguments that one of his co-authors falsified the data in a famous Nature paper of theirs. I’ve discussed this case before (see comment thread here). The guilty party, psychologist William Brown, continues to protest his innocence on grounds that manage to be convenient, vague, and implausible all at once (Despite the fact that you were corresponding author, you lost the original hard copies of the data and your electronic copy inexplicably got corrupted, you say? Uh huh. And how long did it take your dog to recover after eating your homework?) Personally, I find Trivers’ evidence (which he self-published as a free book after failing to convince Nature to issue a detailed retraction) and the Rutgers report overwhelming. Worth noting that this is another case in which a graduate student’s attempts to reproduce published analyses led to discovery of fatal (and in this case, clearly intentional) flaws in the published work. Finally, kudos to Robert Trivers for setting a model example of what to do when you suspect that there’s something wrong with your data (and wrong with your collaborators).

You should’ve gotten your PhD in economics instead of whatever it is you actually studied.

Of course you can use the Price equation to model the evolution and adaptation of the entire universe. Which brings to mind an old Weird Al Yankovic lyric, “You can even cut a tin can with it…but you wouldn’t want to!” And I say that fondly–I’m a fan of the Price equation, and of the lead author of this paper.

And finally, a large brood of 17 year cicadas is about to emerge on the US east coast, and The Onion is on it.

From Meg:

Terry McGlynn has a new post on teaching philosophies. He has written it with someone who will be on the job market in mind, but I think it’s also useful for those of us (myself included) working on the teaching statement portion of a tenure dossier. I am starting mine off with a section on my teaching philosophy. Right now, I sum up my teaching philosophy as “students learn best when they are actively engaged with the material”, followed by examples of how I encourage students to engage with the material in different types of classes.

Evolutionary biologist Jeremy Yoder is surveying LGBT folks working in STEM fields. He says the goal of the survey is “to answer the questions we have about queer folks in STEM: who we are, what we study, and how our identities have shaped our interest in science and our experiences of working in research.” You can find the link to the survey in his blog post.

Here’s a great resource on gendered words and letters of recommendation. Studies indicate that we (and, yes, “we” includes women!) are more likely to use words related to teaching or working hard when describing women, and words related to ability and research when describing men. This page gives examples of these different types of words, and includes this excellent advice: “When writing letters of recommendation for women, it is important to keep these associations in mind and purposefully use standout, ability and research words to describe qualified female candidates.”

Book review: The Pseudoscience Wars by Michael Gordin

A little while back I read The Pseudoscience Wars: Immanuel Velikovsky and the Birth of the Modern Fringe by Michael Gordin, a history professor at Princeton. It’s a case study of how professional scientists react to what they see as “pseudoscience”–something that has many of the trappings of real science but is not real science, at least not in the view of professional scientists. It’s a good read, working well as a narrative about the life and times of an unusual character. The story contrasts in interesting ways with that of other “pseuodsciences” with which I’m more familiar, like “creation science” or opposition to climate change science. And the book has implications for contemporary issues I care about, in particular peer review and scientific publishing. I recommend it highly. If you want to know more about why, read on!

The book tells the story of Immanuel Velikovsky (1895-1979), a Russian born Jew and Freudian psychoanalyst. Velikovsky emigrated to the US in 1939. Velikovsky’s research on ancient myths, originally intended as a project in the psychological interpretation of ancient history, eventually led him to publish Worlds In Collision in 1950. In that book, Velikovsky drew on on purported evidence from ancient texts to argue that in the 15th century BCE, Venus was ejected from Jupiter as a comet and passed close to Earth. This changed Earth’s orbit and axis and resulted in massive global catastrophes recorded in ancient texts. The book became a bestseller and caused a firestorm of controversy. The storm later revived in the 1960s and early ’70s, when Velikovsky (who had continued to pursue and publish his ideas) became a countercultural hero to US college students. But despite Velikovsky’s best efforts, his ideas mostly died with him. He and the massive public controversy surrounding his work are mostly forgotten today, even though only a few decades have passed. Gordin tells the story of Velikovsky and his ideas by drawing heavily on Velikovsky’s correspondence and extensive unpublished writings, as well as on the correspondence of his allies and opponents.

Gordin’s book isn’t about whether Velikovsky’s claims were true. Gordin’s interests lie elsewhere. He uses Velikovsky’s story to argue that calling something “pseudoscience” is an act of boundary drawing–and such acts are always contestable. It’s infamously difficult to unambiguously separate “science” from “non-science”, and Gordin argues that it’s actually impossible. One intriguing way he makes this argument is to talk about Velikovsky’s own difficulties drawing boundaries around his own work, policing his own fringes. Velikovsky attracted followers and supporters–but those followers and supporters brought to the table their own ideas, their own interpretations of Velikovsky’s work, and their own suggestions about how it should be pursued. Any discipline, field, or program of inquiry, in order to exist at all, has to draw boundaries that define what it is and what it isn’t.

One way the boundary between science and non-science gets drawn is via peer review. Gordin talks at length about this, and what he found may surprise you. This was one of the most interesting and thought-provoking aspects of the book for me. Gordin notes that one reason astronomers were so upset by Worlds In Collision is that it was originally published as nonfiction by Macmillan, then the most respected publisher of scientific books (especially textbooks) in the US. Getting published by Macmillan was a stamp of serious approval. Outraged astronomers initially accused Macmillan of failing to have Velikovsky’s book peer reviewed–but in fact, Macmillan had had it peer reviewed, in the usual way. The reviewers were all perfectly reasonable choices, and while they all had serious reservations about the correctness of Velikovsky’s claims, they all recommended that the book be published, so that its claims could be exposed to scrutiny. Velikovsky correctly accused his opponents of organized efforts to suppress his ideas (astronomers organized a boycott of Macmillan, and in fear of losing its textbook market Macmillan sold the rights to Worlds in Collision to another publisher). But Velikovsky was no exemplar of openness. As noted above, he himself spent much effort exercising oversight on anyone who wanted to promote his ideas, and purging those who deviated from orthodoxy.

As a blogger, I’m well aware of widespread concern, mostly among more senior scientists, that blogging is just a way for people to do an end run around peer review and publish crazy ideas. So I was very interested to read senior scientists back in the 1950s and ’60s complaining about how it had become too easy for anyone to publish anything, and quite explicitly longing for the days when science was “aristocratic” rather than “democratic”. And remember: their complaint was prompted by a book that went through peer review! Further back, think of cases like Vestiges of the Natural History of Creation, a bestseller in its time (at the dawn of science as a profession), despite widespread and vociferous criticism from the leading experts of the day. I conclude that scientists have been complaining about how it’s too easy for anyone to publish “pseudoscience” for as long as there have been people who call themselves scientists! Which of course is exactly what you’d expect on Gordin’s thesis that, in order for any distinct discipline or intellectual activity to exist, its practitioners have to police its boundaries. So if you long for the “good old days” when it was difficult or impossible for “pseudoscientists” to publish their work and get the public to notice it, sorry–those days never existed!

Gordin is good on the larger context of the time, arguing fairly persuasively that the reason astronomers chose to attack Worlds In Collision publicly (thereby giving it a lot of free publicity and surely helping to boost its sales) was because of then-fresh memories about the progress of Lysenko’s biology in Russia. It was felt that failure of scientists to speak out sufficiently forcefully and publicly against Lysenko had helped him rise to power. Gordin also draws interesting contrasts between the progress of Velikovsky’s ideas, and those of “creation scientists”. American “creation science” in something like its present form has its roots in the 1960s, and its founders actually had some brief contact with Velikovsky, due to their shared interest in the effects of purported recent global catastrophes. For various reasons “creation science” was better able to police its own boundaries than Velikovsky was able to police his, and so became a movement that outlived its founders and still exists today.

For me, Gordin’s book functioned in part as a companion piece to The Price of Altruism, Oren Harman’s recent biography of George Price. Although he was a chemistry Ph.D. and worked on the Manhattan Project, George Price was very much a fringe scientist, with half-brilliant, half-crazy ideas on all sorts of things (most of which he failed to fully develop or publish). He was also a difficult personality. So, not so different from Velikovsky in many ways. Except that Price eventually came up with some big ideas that did pan out scientifically (the application of game theory to animal behavior, and the Price equation). Although even then, those ideas only gained a foothold with the help of established evolutionary biologists (John Maynard Smith and Bill Hamilton, respectively). I suspect that many scientists reading Gordin’s book might resist his thesis–that there’s no clear, bright line separating science from pseudoscience, or scientists from cranks–on the grounds that Velikovsky’s ideas were just obviously not scientific, and Velikovsky himself obviously a non-scientist. This is the “I may not be able to define pornography pseudoscience, but I know it when I see it” view. But if that’s your view, the example of George Price should give you pause. There are infinitely fine gradations from science to pseudoscience, and scientist to crank. Ecologists and evolutionary biologists are comfortable thinking about such fine gradations in their own work–the boundaries between different ecosystems, or between true “species” and mere “varieties”, are famously fuzzy. We should be “preadapted” to be comfortable with the fine gradations from someone like Maynard Smith, to someone like Price, to someone like Velikovsky.

Finally, a point Gordin doesn’t make, but could have. Anyone who thinks that the reason “pseudoscience” flourishes is because the “general public” isn’t sufficiently well-educated will have a tough time explaining the history of “pseudoscience” over the last 150+ years. The average educational level is higher today than it was in the 1950s, and much higher than it was in the 1850s. But yet “pseudoscience” has been around for all that time, and shows no signs of going away (or getting any worse, as far as I can tell). Anti-vaccine campaigns, anti-GMO campaigns, intelligent design, opposition to climate science, unorthodox ideas about economics, health fads…like Velikovsky’s catastrophism, none of that stuff flourishes because of lack of education, or even lack of the right sort of education. So if you want to oppose that stuff (and I do!), focusing on “better education” is unhelpful. Don’t get me wrong, I’m all for good education! I just don’t think that even the most well-educated society would be free of “pseudoscience”, or even have less than we have right now.

Hoisted from the comments: name the most productive, and unproductive, ecology debates ever

Commenting on my recent post on the need for more “short selling” (i.e. criticism) of ideas in ecology, Jeff Houlahan asks a good question:

When was the last time we had an in print debate that matched the heat and light of Diamond versus Connor & Simberloff? Or Andrewartha and Birch versus Nicholson? I’m sure there are more recent examples but they don’t come to my mind.

Let’s crowdsource the answer. Name some vociferous-but-productive (“heat and light”) debates in ecology, either historical or current. And just for comparative purposes, name some that were unproductive–all heat, no light. In the past I’ve speculated on why heated debates in ecology get started (it’s often not for any obvious scientific reason). But the productivity of the subsequent debate is a separate question.

A few opening bids:

Productive: the debate over whether effects of plant diversity on primary productivity are purely “sampling effects”. A narrow debate, to be sure. But a productive one. An important issue was raised, and then resolved to the satisfaction of pretty much everyone (there are always a few holdouts) through a combination of new data and new analytical techniques.

Unproductive: Tilman vs. Grime on plant competition along productivity gradients. I freely admit I never followed this debate very closely, so maybe lots of people will disagree with me on this and tell me what a rich, interesting, and productive debate it was (is? is it still going?) All I can say is that I didn’t follow it closely because when I looked into it briefly many years ago, my foxy sense* warned me off. The sort of mathematical framework that people like Dave Tilman (and me, and basically everyone I hang out with) use to think about competition is just so different from the sort of primarily-verbal models people like Phil Grime use. There has to be some sort of agreement on basic terms, concepts, and goals in order for a debate to be productive, and I’m not sure the necessary baseline agreement was there in this case (again, please enlighten me in the comments if I’m way off base here…) One offshoot of this debate (well, I think it’s an offshoot; maybe it’s a whole separate thing?) has been what is in my view a pointless side debate over alternative indices of the “strength” or “importance” of competition. In general, I think debates over alternative ways of measuring something that lacks a precise agreed definition run a higher-than-normal risk of being unproductive. See, e.g., the debate over alternative ways of measuring alpha and beta diversity. Such debates are arguments about definitions, disguised as (or mixed up with) arguments about substantive issues, and that’s a recipe for disaster.

Not sure: has the debate that Joan Roughgarden started about sexual conflict theory in evolution been at all productive? Early on, I had the impression that the answer was no, that Joan’s criticisms of established thinking were idiosyncratic and implausible at best. But I’m an ignorant outsider, and I haven’t followed subsequent developments in the field at all. Have any productive new lines of work emerged from Joan’s criticisms?

If we get enough responses, we can attempt a comparative analysis of the features associated with productive vs. unproductive debates in ecology. For instance, just from the examples suggested so far, the breadth of the topic under debate does not seem to predict the productivity of the debate. There are examples of both productive and unproductive debates over narrow issues, and over broad issues. Which is a little surprising. I might’ve thought that narrower, more “technical” debates would be more productive because it’d be more likely for all sides to agree on definitions, goals, background assumptions, etc.

*Foxy sense is like Spiderman’s “spidey sense“, only instead of warning me of physical danger, it warns me of unproductive arguments that would be a waste of time to follow.