Earlier this fall I read Mark Vellend’s The Theory of Ecological Communities. I read it on my own, and also read it in a reading group with several ecology grad students. Here’s my review.*
tl;dr: It’s a very good book that fills a real pedagogical need. Whether it will also shape the direction of future research in community ecology is an open question, I think. Below the fold you’ll find me engaging with the book, which I think and hope Mark will welcome.
Anecdotal observation: ecologists tend to switch from fundamental to applied research as they age. Marc Cadotte used to ask fundamental questions in protist microcosms; now he’s the editor of an applied journal who blogs about the importance of “ecosystem health”. Dave Tilman started out developing resource competition theory and testing it with algae in chemostats; these days he writes a lot about sustainable agriculture. Will Wilson used to do stuff like model Lotka-Volterra species-abundance distributions; now he’s writing a book on stormwater science. Brian started out working on how to test neutral theory, but these days he thinks a lot about how to do policy-relevant science. Meg once said that she was most interested in basic research questions, and she still is, but lately she’s been devoting increasing amounts of time to public education and outreach. Many more examples could be given.
Obviously, many junior people do applied work. Many senior people do fundamental work. And many people do a mix of both. But when somebody switches from doing one to the other, or changes the mix of work they do, it seems like it’s almost always in the direction of more applied work and less fundamental work. I can’t think of anyone who’s gone in the other direction in a big way.
Why is that?
Following up on my recent post noting that in some social science fields, including economics, faculty hiring places heavy (though far from exclusive) weight on one “job market” paper, here are some other aspects of how faculty hiring works in economics. Tweets from @LauraEllenDee were part of my inspiration, and comments on that previous post were a big help too (have I mentioned lately how much I love our commenters?)
I find it interesting to think about which if any of these formal and informal practices could or should be adopted in ecology and other scientific fields (even though I think current practices in ecology are mostly reasonable). Learning about how things work in other fields stops you from taking things for granted* and helps you imagine how things could work in your own field. It also gives you a more realistic sense of what any reforms in your own field might achieve. Learning about how things work in other fields both helps you dream and keeps you grounded.
One challenge in thinking about this is that to some extent these alternative clusters of practices may be “package deals”. You can’t always pick and choose, at least not very easily, because any one practice might well be undesirable or unworkable in isolation from other practices.
So here are some other hiring practices in economics (follow that link for the post from which I’ve gotten much of my information. See also.) This is obviously a broad-brush picture and I’m sure I haven’t gotten all the details right; comments welcome. If all you know about is hiring practices in ecology, get ready to enter the Twilight Zone. A world like ours in many respects, but weirdly different in others… 🙂
If you didn’t know, in economics and political science, people are hired for faculty positions based in large part on their “job market paper”. As in, one paper, ordinarily from their Ph.D. work and often not even published yet. Number of publications matters relatively little (though apparently it matters more in political science than in economics). Economics even has a centralized repository of job market papers; that’s how much they matter.
I am curious to hear what you think of this, and whether you think this approach or something like it could be an improvement on current practices in ecology. Personally, I think current faculty hiring practices in ecology are mostly pretty reasonable (see also), and so don’t think this would be a net improvement on current practices in ecology. But I think it’s not so obviously a bad idea as to be uninteresting to think about. I find it useful to think about the practices of other fields and whether they’d transfer to ecology. It helps me look at standard practice in ecology with fresh eyes. A few thoughts to get the ball rolling:
It’s often said that nobody dies wishing they’d spent more time at the office. The saying encourages you not to spend your time in a way you’ll come to regret in future.
Except that lots of people do die wishing they’d spent more time at the office. That’s the trouble with trying to live so that you won’t look back with regret in the future: you’re trying to anticipate the preferences of a stranger who you’ll never meet: you, in the future. Future-you may want to look back on different things than Now-you wants right now. Future-you might even want to look back on different things than Now-you thinks Future-you will want to look back on. Youth may be wasted on the young–but only in the eyes of the old.
This isn’t an argument that you should just live for the moment or not make long-term plans. It’s just that evaluating your life in retrospect is different than evaluating it in prospect.
Lately I’ve been wondering about this in the context of science. Do scientists ever look back wishing they’d done different science? Not because of 20-20 hindsight. But just because they’ve changed.
This is the third post in my series on the importance of mathematical constraints in ecology and evolution. See also parts 1 and 2.
Today’s example of an important mathematical constraint is from evolutionary biology, though it has implications for community ecology as well. Community ecology, like evolutionary biology, is centrally concerned with the relative abundances and relative fitnesses of different types of organism (Vellend 2016). “Relative” is the key word here. As a matter of mathematical necessity, the relative abundances of all species you’re considering have to sum to 1. And the mean relative fitness (relative per-capita growth rate) of all species you’re considering has to equal 1. This ain’t Lake Wobegon; not everybody can be above average. These constraints on the values of relative abundances and relative fitnesses are purely mathematical, not biological. They’re as true of the relative abundances of rocks, and the relative “fitnesses” of linguistic variants, as they are of the relative abundances and fitnesses of species. But these mathematical constraints aren’t merely mathematical. They turn out to have important biological consequences, as Allen Orr (2007) showed in a wonderful little paper. From which I will now shamelessly steal (nothing below is original to me).
Keep reading even if you’re not an evolutionary biologist. This post is short, non-technical, and it’s about something really deep and cool.
This post is the second in my series on mathematical constraints in ecology. In part 1 we saw an example of a non-obvious mathematical constraint that can’t be removed from one’s data. Today, an example of a mathematical constraint that’s totally obvious, but that has non-obvious consequences. And that fortunately can be removed from one’s data (well, worked around). Today’s topic is one I’ve posted on before, but I’m revisiting it for my ongoing comparative study of mathematical constraints in ecology and what to do about them.
Much of science boils down to putting numbers on things, and then figuring out why we got the numbers we did, as opposed to other numbers we might have gotten. Usually, when we think about the numbers we got, and might have gotten, we think about the biological, measurement, and sampling processes that together generated the data. For instance, a population ecologist tracking changes over time in the abundances of different species at a site would think about the birth, death, and dispersal rates of those species, and about sources of sampling error and bias in her sampling methods.
Sometimes we also think about physical and measurement constraints that make certain data impossible. For instance, negative abundances are physically impossible, so if I discover a negative abundance in my dataset, I know it’s a typo. As an example of a measurement constraint, standard techniques for measuring dissolved phosphate concentration in lake water can’t detect concentrations below a threshold level.
This post is the first in a series about the importance of an often-overlooked class of reasons why you got the numbers you got, as opposed to other numbers: mathematical constraints.
Ecologists, especially community ecologists, are always looking for ways to infer process from pattern, cause from effect. Ideally, they’d like some way to do this that:
- Is based on previously-collected or easily-obtained observational data
- Is “off the shelf”, meaning that it can be implemented in a routine, “crank the handle” way, without the need for much customization or even thought from the user.
- Can be used in any system
Examples of previously- or currently-prominent ways to infer process from pattern in ecology include:
- randomization of species x site matrices to infer interspecific competition
- plotting coexisting species onto a phylogeny to infer contemporary coexistence mechanisms
- plotting local vs. regional species richness to infer whether local communities are closed to invasion, or whether local species richness and composition is just a random draw from the regional “species pool”
- using the shape of the species-abundance distribution to infer whether communities have neutral dynamics
- using ordination to infer the process dominating metacommunity dynamics
- the use of power law distributions of movement lengths to infer whether foraging animals follow Levy walks
- using body size ratios of co-occurring species to test for limiting similarity
- attractor reconstruction and convergent cross-mapping
The above approaches to inferring process from pattern all have something in common: none of them work, either in theory or practice. Which leads to the my question:
Has any widely applicable “off the shelf” method to infer process from pattern in ecology ever worked? Can anyone name one?
A while back we invited you to ask us anything. Here’s the next question, from Margaret Kosmala, who clearly knows Brian and I. Question has been paraphrased, click through for the original.
In light of this opinion piece from a physicist, should ecologists be trying to estimate the values of universal (or even conditional) constants, thereby allowing more severe tests of ecological hypotheses?