John Lawton’s ‘Patterns in ecology‘ is a minor Oikos classic (cited 73 times since its publication in 1996). As he recognized, John was writing at a time of transition in ecology. Over the previous 15 or so years ecology (especially community ecology) had become increasingly dominated by small-scale field experiments. Many ecologists increasingly felt that this approach missed the big picture. Ricklefs and Schluter’s influential edited volume, Species Diversity in Ecological Communities: Historical and Geographical Perspectives, appeared in 1993. Jim Brown’s Macroecology, calling for ecologists to refocus on large-scale, universal patterns, appeared in 1995. That same year that the National Center for Ecological Analysis and Synthesis (NCEAS) was founded, with a mission to ‘advance the state of ecological knowledge through the search for general patterns and principles in existing data’. John’s article is a nice compact argument in favor of this shift in emphasis from ‘microecology’ to macroecology. The shift took hold, big time–NCEAS in particular was a stunning success, and spawned numerous imitators in various fields. So now seems like a good time to revisit John’s argument, because I think its strengths and weaknesses are both reflected in much of the work which followed it.
The core of John’s article is a very creative and compelling analogy–from which I think John draws a conclusion that’s only half right. Here it is in John’s own words:
‘Imagine you are a fairy, a bit larger than an atom, sitting in a world made up of a mixture of gasses. You would see balls (molecules) of different sizes and colours whizzing about, occasionally colliding, and sometimes combining to yield different sized balls. Being a curious fairy, with an experimental bent, you attempt to manipulate the molecules by building fences in a very local part of this imaginary world, to keep out the big red balls which appear to be attacking the smaller blue ones. The grant application to the king of the fairies says that you wish to: ‘Understand and predict the role of large red balls in structuring the assemblage of other balls.’ Does it sound familiar?‘
What’s right about this argument is that the fairy, the ‘microecologist’, absolutely misses a lot. This gaseous world has macroscopic properties–for instance, temperature, density, and pressure–of which the fairy is not even aware. These macroscopic properties emerge from the essentially random behavior of a large number of individual interacting entities. It’s a compelling analogy for how large-scale patterns in the distribution and abundance of organisms emerge from the essentially random behavior of large numbers of interacting individual organisms.
But what’s wrong with this argument is that it suggests that the macroscopic features of the system are the only features of the system worth paying attention to. I want to undermine this, not by rejecting John’s gaseous analogy or offering a contrary analogy of my own, but by taking John’s analogy more seriously than I think he himself takes it.
In physics, the ideal gas law describes the relationship between the temperature, pressure, and volume of an idealized gas. It’s a good approximation to the behavior of real gases under a wide range of conditions, and so can be regarded as a general, macroscopic pattern in the behavior of gases. This is precisely the sort of macroscopic pattern that John wants ecologists to focus on documenting and explaining. But how do physicists explain the ideal gas law? By appeal to the microscopic properties of gases. Specifically, the ideal gas law is derivable from first principles using the kinetic theory of gases (a theory about how individual gas molecules move and collide with one another), under various simplifying assumptions (e.g., gas molecules are point masses with no volume and undergo only elastic collisions). Importantly, kinetic theory has nothing to do with trying to predict the random trajectories of particular gas molecules, as John’s fairy futilely proposes to attempt. Kinetic theory is all about scaling up: it’s about rigorously deriving the macroscopic consequences of the collective behavior of a large number of microscopic particles. You say you want to focus on the big picture that emerges when one averages away random, microscopic noise? Kinetic theory tells you precisely how to do that, and what the resulting picture looks like.
Further, the approximations used to derive the ideal gas law can be relaxed, allowing derivation of more refined laws which account for subtle microscopic effects omitted from the ideal gas law. For instance, if you incorporate the facts that molecules have a nonzero volume (which matters when the volume occupied by the gas is small), and that they’re attracted to one another, you can derive the Van der Waals equation, a more precise version of the ideal gas law for which Johannes Van der Waals received the Nobel Prize for Physics in 1910. Which just goes to show that, just because the microscopic is random doesn’t mean its details don’t matter.
So if explaining (as opposed to merely describing) large scale patterns is our goal, the example of physics suggests, not that we should ignore the microscopic, but that we should focus on the microscopic–specifically, how to scale up from the microscopic to the macroscopic. If we’re serious about taking the ideal gas law as our model of how to do ecology, then we need an ecological equivalent of kinetic theory on which to base rigorous derivations.
This point of view isn’t original to me (see, e.g., Brian Maurer’s Untangling Ecological Complexity: The Macroscopic Perspective and Lotka’s classic Elements of Mathematical Biology). And I think it’s consistent with the recent history of macroecological research. The macroecological advances that have been most influential, and that have spurred the most productive work, have been advances in microscale theories from which rigorous predictions of macroscale patterns have been derived. Think of the use of detailed metabolic optimization models to predict the 3/4-power scaling of metabolic rate and body size (West et al. 1997). Or think of neutral theory, especially its use to predict the form of the species-abundance distribution. We now know, on the basis of very general arguments, what kinds of microscale assumptions about per-capita birth and death rates can reproduce the commonly-observed forms of the species abundance distribution. It turns out that the class of empirically-viable microscale models is very large–almost any stochastic model in which species are equally fit on average will work. That includes models with and without frequency dependence. This ‘many-to-one’ mapping from microscale processes to macroscale pattern is itself a satisfying explanation for why species-abundance distributions take the shapes they do–there’s no strong reason for them to take on other shapes. Work on John’s favorite macroecological pattern, linear local-regional richness relationships, followed a similar path. The hope of John and others that linear local-regional richness relationships could be used to infer something about the strength of local species interactions (i.e. that they’re weak) were dashed by microscale models, and empirical work directly linking microscale experiments and macroscale patterns. This work, some of it published in Oikos, showed that linear local-regional richness relationships arise from many different combinations of microscale processes.
John’s article concludes with a call for a productive pluralism in our research approaches, because those various approaches complement one another. I heartily agree, and I’m cautiously optimistic that such pluralism is the way of the future. The pendulum swing towards ‘pattern-oriented’ research that began in the mid-90s seems not, as far as I can tell, to have swung so far as to devalue small-scale experiments (in part because NCEAS has supported syntheses of experimental as well as observational data, and also supported theoretical work). With any luck, in years hence calls for future research in ecology won’t need to attack current approaches, and will instead call for ‘more of the same’!