Some forthcoming (in press) Oikos papers that caught my eye. Lots of good stuff in the pipeline!*
Nadeem and Lele introduce a new maximum likelihood-based method of population viability analysis (PVA) and test it on song sparrow time series data. The new method, called “data cloning”, was previously developed by Lele in other contexts. It estimates observation error as well as process error (e.g., demographic stochasticity), and deals gracefully with missing data. The clever thing about it is that it has all the computational advantages of more popular Bayesian estimation methods, but it’s fully frequentist and so doesn’t need priors. Which is a good thing because priors for the rare, poorly-known species for which we often want to construct PVAs often are pretty arbitrary guesses which have strong effect on the outcome of the analysis because there’s not enough data to “swamp” them. You also avoid having to adopt the subjectivist Bayesian interpretation of what your probabilities (e.g., extinction probabilities) mean. In the case of song sparrows, it turns out that incorporating observation error into the analysis really changes the results. The approach is even sensitive enough to detect evidence that the data and associated PVA model omit important biological processes (here, dispersal).
Tielbörger et al. use a massive series of carefully-controlled common garden experiments to reveal strong evidence for “bet-hedging” germination in annual plants. Roughly, bet-hedging is a way of maximizing your expected relative fitness in an uncertain environment. Germinating all your seeds every year (going “all in” in betting parlance) provides a big payoff if the year turns out to be a good one, but it is very risky. If the year turns out to be a bad one, all the resulting plants will die before reproducing (the ecological equivalent of “going bust”). But conversely, if you never germinate any seeds, so that your seeds just sit in the ground, they’ll eventually all die without reproducing (“nothing ventured, nothing gained”). So the optimal germination fraction (the one with the highest expected relative fitness compared to the others) will be some intermediate fraction, the precise value of which depends on the probability distribution of different kinds of years. That’s the theory, anyway. But strong empirical tests are almost non-existent, because they’re really difficult. For instance, you have to control for environmental and genotype x environment variation in germination fraction. The authors went to the trouble of developing inbred lines of each of three annual plant species, growing up their seeds in a common greenhouse environment to eliminate maternal effects, and then planting those seeds into common gardens along a rainfall predictability gradient in the field, and along an artificial rainfall gradient in the greenhouse. As expected, species subject to higher risk of reproductive failure exhibit lower genetically-determined germination fractions. Yes, Virginia, annual plants really do hedge their bets–and those that face more risk do more hedging!
Fraker and Lutbeg develop an individual-based model of mobile predators and prey and show how limitations to the movement rates and perception distances of individuals cause their spatial distributions to deviate from the ideal free distribution. If you have limited information (=limited perception distance) and limited ability to act on that information (=limited movement rate), you can’t attain the ideal free distribution (which assumes that you have perfect information which you’re free and fully able to act upon). Which at that level is kind of obvious, but Fraker and Lutbeg explore precisely how the resulting distributions deviate from ideal free, which is much less obvious. Bailey and McCauley (2009) is one nice experimental paper showing data illustrating some of the predicted consequences of limited information and movement rates. More broadly, I always like stuff that shows the complex and counterintuitive macroscale consequences of different microscale assumptions about the behavior and movement of individual organisms. Maybe if people write enough of these kinds of papers, other people will quit trying to infer the underlying microscale processes directly from inspection of (or some sort of randomization of) macroscale data.
Speaking of starting from microscale assumptions and deriving their macroscale consequences, Casas and McCauley ask: What’s the functional response of a predator that must divide its time between searching for prey, and other activities (broadly denoted as “handling”)? If you said “It’s an increasing saturating function and we’ve known that since Holling (1959),” you’re right–sort of. That is, you’re right only if you’re prepared to make radical simplifying assumptions about the relative timescales of the underlying processes that cause predator individuals to change “states” (here, from the state of “searching for prey” to the state of “handling captured prey” and back again). If you want to avoid such radical (and often unrealistic) assumptions, then you have to be prepared to do much more complicated math, which Casas and McCauley illustrate for both parasitoids and a predator (Mantis, the same predator considered by Holling himself in a classic 1966 study of predator functional responses). One consequence of increased realism is that the predator population never reaches an equilibrium or stationary distribution of individuals in different states, a fact which turns out to have important and testable consequences for predator-prey dynamics.
Finally, I don’t see how I can get away without mentioning Mata et al., an impressively large protist microcosm experiment manipulating disturbance intensity, disturbance frequency, nutrient enrichment, and propagule pressure in factorial fashion and examining their effects on resident community structure and invader success. As you’d expect, such a complicated experiment throws up complicated results, some of which seem to be readily interpretable (e.g., high disturbance intensity creates conditions that favor invaders with high intrinsic rates of increase), others less so. I do think it’s a little unfortunate that the authors frame their experiment as a test of Huston’s “dynamic equilibrium model”, since that “model” shares the same fatal logical flaws as zombie ideas about the intermediate disturbance hypothesis. I suggest that framing the experiment in terms of logically-valid theory might have aided interpretation, and possibly even suggested a somewhat different experimental design.
Many other interesting-looking papers coming out, but I don’t have time to dig into all of them so this’ll have to do for now. Happy reading!
*p.s. Just so you know, no one has ever told me, hinted to me, or implied to me that I should promote the journal’s content. When I highlight Oikos papers that I think are particularly interesting, it’s because I think they’re particularly interesting. It’s not like I ever think “Ok, gotta pick some Oikos papers to talk up now.” I hope you’ll take my word on that, given that I also link to a lot of non-Oikos content and criticize Oikos papers. Don’t get me wrong, I’m an Oikos editor and author, I like the journal, I want to see it do well and continue to fill what I think is an increasingly crucial niche, and I think the blog can help achieve that. And so when I do highlight interesting papers, I highlight interesting Oikos papers. But if I didn’t think there was anything worth highlighting, I wouldn’t. So I hope you find it valuable if I occasionally highlight Oikos papers I find particularly interesting, or invite the authors to do so, just like I hope you find the other posts valuable. But if for whatever reason you don’t, that’s fine.
Note as well that in saying this, I mean no criticism of any other journal blog, many of which focus much more than we do on the content of the associated journal. Different blogs are different.