Note from Jeremy: this is a guest post from Greg Dwyer.
Jeremy invited me to do a guest post because he saw my 2014 ESA Ignite talk, in which I argued that data are almost worthless without some connection to mechanistic models (Jeremy posted interesting comments on the session shortly after it happened). That statement is a little stronger than what I actually believe, but the status of mechanistic models in ecology is so weak that it is hard for me to avoid losing my patience when confronting ecological research that is unconnected to an explicit model. For the purposes of this essay, I am therefore going to stand by my talk title: Trying to understand ecological data without mechanistic models is a waste of time. I think that the only caveat that I would add would be “except in cases where the underlying question is too trivial to be interesting.”
The basis of my argument is that ecological data are almost invariably influenced first by stochasticity, and second by strong nonlinearities, due to interactions between species, or to interactions between species and nutrients. Understanding the effects of stochasticity and nonlinearities is very difficult without explicit mechanistic models. We also need models to know whether a data set includes enough information to characterize a stochastic process, but arguably that statement presupposes that we are using models in the first place.
If you disagree that models are essential for understanding data, maybe I can use other arguments to convince you of the usefulness of mechanistic models. First, there are many ideas and concepts that we cannot learn without models. Theoretical ecology textbooks have many examples (my favorite texts are Mark Kot’s “Elements of Mathematical Ecology”, James D. Murry’s “Mathematical Biology”, and Leah Edelstein-Keshet’s “Mathematical Models in Biology”), but the examples that mean the most to me are the ones that helped me in my own work, or that collaborators and I learned on our own.
For example, a paper by Anderson et al. showed that any variability in a host’s infection risk, due to any mechanism whatsoever, will lower transmission rates and reduce epidemic severity, even if the mean transmission rate is unchanged (Anderson et al. 1986). Likewise, collaborators and I showed that adding stochasticity and a generalist predator to a model of a specialist pathogen produces wildly stochastic population cycles, whereas adding stochasticity alone produces only mild variation, unless the stochasticity is so high that it causes the host to go extinct (Dwyer et al. 2004). Finally, we showed that, in host-pathogen interactions, a tradeoff between average transmission and variation in transmission (as measured by the scale-less C.V.) can allow for the coexistence of pathogen strains (Fleming-Davies et al. 2015). The latter result is new enough that I am not quite sure that I understand it, but we have recently made simpler models of the problem, and those simpler models have helped.
Models can also make it possible for us to see that organisms in disparate taxonomic groups can have very similar ecology, a point first made by Si Levin years ago. Anyone who works on animal diseases can give you examples of how models of human diseases are useful for a wide range of pathogens, but the best example from my own research is that we used models of HIV dynamics in humans to explain virus epidemics in insects (Dwyer et al. 1997). This latter work led to useful approximations, which we in turn used to compare different vaccination strategies in responses to smallpox bioterrorism (Elderd et al. 2006).
Models can also allow us to achieve a mechanistic understanding that is otherwise unavailable. I think a basic idea in most ecological modeling is that we are attempting to explain phenomena at relatively large spatial and temporal scales, in terms of mechanisms acting at relatively small spatial and temporal scales. (Again I learned this idea from Si Levin, but I suspect that every applied mathematician has the same idea in their head. I am often appalled at the number of ecologists who do not understand this point, and for all that I do not want to point fingers, I will say that the empirical literature on animal population cycles often seems to be unaware of this point. Although not Jeremy, but see Brian’s very interesting response). This approach is basic to almost every paper I have ever written, but the example that made the strongest impression on me came from models of spatial spread, which made it possible to use small-scale measurements of insect movement to explain large-scale patterns of virus spread (Dwyer et al. 1994).
In fact, the same models illustrate the usefulness of models for focusing our attention AWAY from complex mechanisms that may have little effect on the phenomenon of interest. For years, there was speculation in the literature that insect viruses are spread in bird poop, or on the feet of spiders, but the models showed that we don’t need those kinds of fancy mechanisms. That’s not to say that there aren’t infectious virus particles in bird poop (there are), but it looks like we don’t need bird poop to explain spatial spread.
My former post-doc David Paez and I will soon submit a paper in which we use models to extrapolate from David’s experimental data to gypsy moth outbreaks. In the process of writing the paper, David asked something like, Won’t the model take over the paper, causing the reader to lose track of the three years I spent doing experiments? My response was, David, you wish! That is, the model is not that interesting without the data, but WITH the data, the model does a beautiful job of showing how a general theory can be used to understand a real problem. The reader may therefore only remember the model, but remembering the model may just cause them to actually remember the basic idea of the paper.
I am always interested in opposing viewpoints, or alternative ways of looking at this problem. I would therefore love to hear what you think.