In response to my recent post attacking the phylogenetic community ecology bandwagon, a colleague who works in this broad area wrote to say that he really liked the post, but that he had a question:
But the real problem is a lack of alternative approaches – say you want to know about community assembly, and you have a phylogenetic tree, occurrence data, and maybe some traits. What do you do? We need more methods!
I suspect many readers have the same question. On the one hand, we have all these data, on the other hand, we’ve got this scientific question. How do we bring them together?
Maybe you don’t.
Look, I’m all for learning as much as possible from existing data, and from easy-to-collect data. But I’m against trying to learn more from those data than possible! After all, the reason we have increasing amounts of phylogenetic data, occurrence data, and certain sorts of trait data is because technological advances in gene sequencing, computer hardware and software, and online databases have made those particular sorts of data easy to collect and compile. Those technological advances have had little or no effect on the ease of collecting other sorts of data. Next-generation gene sequencers, multi-core processors, and R packages haven’t reduced the money, time, or physical effort required to conduct field experiments, for instance. So here’s the thing: why should we think we’ve been so lucky that technology just so happens to have advanced in such a way that the particular data we wanted has suddenly become easy to collect? Because that would be really lucky indeed! Isn’t it rather more likely that, now that certain sorts of data just so happen to have become easy to collect or compile, people are now casting about for ways to use those particular data to answer all sorts of questions that those data aren’t really right for addressing?
For instance, it wasn’t until after phylogenetic data became widely available that lots of people started arguing that phylogenetic data could be the key to unlocking the mysteries of community assembly. Which makes me very suspicious that this is a case of having a hammer and looking for a nail. Or having a hammer and trying to figure out how to use it to drive screws. Or having a hammer and then, in order to find some uses for that hammer, trying to come up with arguments as to why things that weren’t previously thought to be nails in fact are nails.*
Further, it’s not as if phylogenetic methods are the only game in town for learning about community assembly or contemporary coexistence mechanisms. As noted in the comments on my recent post, there are lots of other ways to learn about community assembly and contemporary coexistence mechanisms. And if those ways aren’t as easy as building phylogenetic trees or querying trait databases, well, nobody said ecology was easy.
Easy ways to address whatever question you’re interested in do not necessarily exist. When they don’t, it’s incumbent on you to recognize that fact, and do whatever it takes to address whatever question you’re trying to address. You couldn’t figure out the properties of the Higgs boson with existing data and instruments (though Fermilab workers tried to argue otherwise)–so the LHC was built, at great expense. You couldn’t really get at biodiversity-ecosystem function relationships without manipulative experiments–so John Lawton and colleagues went and got millions of dollars to build the Ecotron in order to conduct such experiments, Dave Tilman went and got the money needed to do the Biodiversity I and II experiments, the BIODEPTH team went and got money to conduct that experiment, and the rest is history. And don’t think I’m arguing that ecologists should all go get bazillion dollar grants. The wonderful NutNet experiment was born out of frustration with the limitations of existing data on primary productivity, diversity, and herbivory, and is paid for by a single ordinary NSF grant. I could keep adding examples here, but you get the idea.
I’m absolutely not trying to pick on phylogenetic community ecologists here. They’re just unlucky enough to be the example we happen to have been talking about on this blog lately. An occupational hazard of using any approach, or any source of data, is that you try to make too much of it. Asking “What else can I do with this approach, or with these data?” is a perfectly natural, and perfectly good, question.
Just so long as you recognize that the answer might be “Nothing.”
UPDATE: I see I’m not the only one worried that we’re letting the data that just so happen to be available dictate what questions we ask and how we answer them. Writing in Evolution, Travisano and Shaw (in press; open access) rip the use of genomic data to search for the genetic basis of phenotypic variation as non-explanatory. They argue for a renewed emphasis on process-oriented research: selection, drift, mutation, migration, nonrandom mating, along with the density-dependent ecological processes that underpin many of those evolutionary processes.
*This analogy is deliberately silly and overstated. Nobody who does phylogenetic community ecology would be so foolish as to actually hammer on a screw, or argue that a screw is really a nail. The analogy is merely intended to illustrate and clarify the kind of mistake that I think underpins attempts to overextend any approach. It’s not meant to imply that those who do overextend their preferred approach are foolish or bad scientists or whatever. They’re not. In science, the challenge–and it can be a difficult one–often is to figure out exactly what sort of “tool” you have, and whether it matches the sort of items (“nails”, “screws”, or whatever) you’re trying to “drive”.