Many students tend to find the ESA meeting overwhelmingly big. So in the interests of helping you sort through the clutter, I’m going to put up a series of blog posts highlighting some of the talks I plan to see each day.
I’m interested in population, community, and evolutionary ecology, especially work that mixes theory and data, and my choices reflect that. So if your interests are very different than my own (landscape ecology, say, or ecological education), sorry, but I can’t really help you.
Just FYI, I choose my talks in a couple of ways. I go to see talks by friends, and by people who I know give good talks (those are often the same people—I’m fortunate to have many friends who give good talks). I also go to see talks by the students and postdocs of my friends. And I go to see talks with interesting sounding abstracts. I don’t choose particular sessions to attend, because I rarely want to see all the talks in any given session.
I use the online personal scheduler to plan my meeting before I arrive, just because once I’m there I find it impossible to find the time to do any advance planning (and because I’ve found choosing talks based on a quick glance at the titles in the printed program to be unreliable). I bring a printout of my schedule with me, as I don’t have a smartphone, and there may not be free wifi in the convention center. I include every talk I might want to see, and then resolve time conflicts on the fly. Often, later in the meeting I find I have less energy and so I’ll resolve time conflicts in a way that minimizes the amount of room-switching I have to do.
I’m not going to highlight every talk on my schedule, for various reasons. It would take too long, for one thing. So you shouldn’t read anything into the fact that I don’t highlight a given talk.
I’m not going to bother highlighting posters, because in my experience it’s feasible to just walk up and down the aisles at the poster session and stop to read whatever catches your eye.
1:30, room 8: Matt Helmus on phylogenetic species area curves. Matt’s a friend and he’s very good, so this is on my list even though it’s a little far from what I do.
1:30, room 17B: Dalziel and Ellner on commuter movement patterns and flu outbreak dynamics. Mathematical epidemiology has undergone a renaissance, thanks to awesome datasets and increasing modeling sophistication and computing power. Doing mathematical epidemiology seems like a lot of fun, because you get to do cool fundamental stuff that’s also directly relevant to human health, so you get the best of both worlds. I’m often impressed by the very subtle and sometimes counterintuitive effects that mathematical epidemiologists can reveal through sophisticated model fitting and use of “natural” experiments. It tends to make me worry about the mistakes that we might be making in other areas of ecology where the combination of poorer data and less well-developed dynamical theory forces us to do more arm-waving, sometimes without realizing we’re doing it.
1:30, room 15: Diehl et al. on spectral niche complementarity in phytoplankton. Seb Diehl gives a great talk. So even though the experiments described in the abstract sound like a slightly indirect way of testing the hypothesis that phytoplankton coexist by partitioning the light spectrum (why not really nail things down by providing only light of a certain wavelength and seeing if you get competitive exclusion?), I really want to hear this one.
1:50, room 8: Ostling et al. on testing neutral theory with static data. Ostling et al. are going to draw on the experience of evolutionary biology to argue (contrary to my own view, and what I think is a semi-consensus in the literature) that we can in fact distinguish niche and neutral models by looking at “static” data like the species-abundance distribution. Annette Ostling is very smart, so I’m curious whether she can convince me that what I had thought was a dead end is not, in fact, a dead end.
4 pm, room 19A: Cadotte et al. on evolutionary relatedness and stability of ecosystem function. Marc Cadotte seems to have found phylogenetic signal in plant population dynamics: species in communities with distantly-related species exhibit more stable dynamics than species in communities with closely-related species. I’ll be curious to see whether/how effects of evolutionary relatedness can be isolated from effects of species richness and species identity, and whether there are independent lines of evidence (including mathematical modeling?) which allow the evolutionary signal to be interpreted in terms of more proximate mechanisms.
4 pm, room 9C: Rafferty and Ives on pollinator effectiveness, flowering time, and community context. Flowering plants have to answer the question “When should I flower?” In general, the right answer depends on abiotic factors (e.g., flower too early and you risk frost damage) and biotic factors (e.g., flowering when there are no pollinators around, or when lots of other plants are competing for the same pollinators, risks not getting pollinated). And when the climate is changing, what used to be the right answer may become the wrong answer. Rafferty and Ives put plants in the greenhouse to manipulate their flowering time, then put those manipulated plants out in the field and tracked pollinator identity, visitation rates, and other data. I’m interested in this in part because I have a grad student doing related work, so I feel like I need to be up to speed on what’s going on in this area.
4 pm, room 12B: Godoy and Levine on phenology as a coexistence mechanism. I’m a big fan of Chessonian niche theory, and Jon Levine’s group leads the way in putting that theory to work in practice. Their latest work tests how the strength of equalizing and stabilizing coexistence mechanisms varies as the phenological overlap of competing plant species varies. I’ll be curious if they can say anything about the mechanisms underlying their results (e.g., resource competition vs. competition for pollinators?)
4:20 pm, room 4: Allesina and Levine on a network theory of competitive coexistence. Imagine you’ve got a whole bunch of competitors, and you know all their pairwise competitive abilities. It turns out that each species is superior to some of the others, but none is superior to all. What, if anything, can you predict about long-term competitive outcomes just based on that information? I would’ve thought the answer would be “nothing”, but apparently I would be wrong—you can use a network modeling approach to predict what the species’ abundances will be. I’m interested in this because Stefano Allesina and Jon Levine are both really good modelers, and because I have a grad student with some data on competing bean beetles which may provide a real-world test of this modeling exercise.
p.s. I have yet to decide if I’ll do any summary posts about what I’ve seen. Folks who won’t be attending the meeting seem to find these useful and fun, but I doubt I’ll have the energy to do one every day. And I’ve decided to leave the tweeting to others.