Mark recently wrote a piece musing on the true fact that many ecologists have evolution envy – wishing to find simply general rules in ecology that match the elegance of evolution, which was itself a play on the more common phrase physics envy. He is certainly right this exists. On the other hand, in the comments, I noted that I had the opposite reaction. As an undergraduate I was a math major looking for a field that I could apply math to. And I instinctively avoided physics or chemistry (or hydrology and other applied versions of physics), instead being attracted to fields like business, economics and ecology. And as a graduate student I ultimately gravitated to ecology over evolution because of its complexity and honesty about that complexity. I think ecology, economics, business, sociology (and evolution although they ignore it too often for my tastes), especially in contrast to a field like physics, have one thing in common. They’re complicated because multicausality rules. And I wanted to go into a field that had that kind of challenge. In short, I thought multicausality was fun!
Few dictionaries define the word “multicausality” although many define “multicausal”, and the noun form multicausality is common enough in the academic literature. Defined simply, multicausality is the condition that the system of interest is subject to many roughly balanced forces acting on the system so that one must pay attention to all the forces rather than focusing on a single dominant force.
It is perhaps easiest to demonstrate what multicausality by returning to physics for a moment. Imagine a steel ball suspended in the air, and then allowed to fall. We want to predict where and how fast it falls. The list of possible forces acting on it is short. Gravity, possibly an electrostatic force if the ball is charged, possibly magnetic forces depending on the magnetic field, and air resistance. We have already ruled out things like the strong and weak nuclear forces because while they exist, they are meaningless at this scale. And we can probably rule out electrostatic forces because it is pretty hard to create and suspend a ball with a large charge to begin with (you wouldn’t want to touch it with your bare hands!) and in any case there aren’t strong electric fields on most of the planet. Similarly there is a magnetic field around the earth but it is weak enough not to influence such motions. So really it is gravity and air resistance. And we know that air resistance is not strong for a round dense object unless we drop it from heights like an airplane. So its just gravity. Now note two things about physics: 1) the list of possible forces is definitive and short; and 2) in many real world scenarios, including the hypothetical dropping ball, most of the forces can be quickly ruled out as having minimal effect due to the scales, the lack of such forces in a “generic place on the planet”, etc. Indeed in this case, we ended up with only one force to worry about. But even if we ended up with two (say gravity and air resistance or gravity and magnetism), that is mathematically tractable.
Now think about your favorite organism (a tree to pick mine) in the middle of a plot of ground. What forces are acting on that system. Well there is weather, but its not just “weather” as a single force but potential cavitation due to freezing, water stress, growing degree days to support cellular processes, wind stress (some of which is necessary for a strong tree but to much of which is fatal), and etc. Then there is nutrients, which would include atmospheric CO2 but soil N, P, K and a dozen micronutrients. But we could turn to shading from other trees, herbivory from insects on leaves, worms and fungi on the roots (some of which are positive and some of which are negative). Endophytic fungi in the leaves, some of which are benficial some of which are negative. And that is just short time scales. To answer more existential questions about why that tree is there we need to look at evolution and dispersal on top of all the shorter term factors. We definitely DO NOT have a short definitive list of causes. And nearly all of those causes are having non-trivial effects on the fitness of that tree. Any one effect might be a few percent change (not zero but not a majority of influence). No one force dominates. This is why ecology (and economics and sociology and business and etc) are fundamentally different from physics and never will be the same as physics.
And as noted 30+ years ago (1983) by Quinn and Dunham, multicausality is why we will never have Plattian strong inference in ecology. Platt argues for what he calls strong inference where there are are alternative hypotheses only one of which can be true. He give the example of does DNA unzip when a cell divides or not. There is no middle ground. It either unzips or it doesn’t. But as Quinn and Dunham point out, how the heck are you supposed to do strong inference when you ask is A or B going on and the answer is both are going on?
Fields that are multicausal require different ways of doing science. One approach is systems thinking, something that worked its way into ecology in the 1970s. I actually take great pleasure in the fact that the cell biology field that Platt held up as an example of strong inference in the 1960s has now had to resort to “systems biology” to deal with the complex multicausal nature of cell processes once they got past the basics of what a protein did, what DNA did, etc. To my thinking, the systems approach boils down to “there is a lot going on but we can simulate it in a computer model”. There are plenty of cases where this approach is effective, and plenty of cases where this is not. I think weather is illustrative because we know all of the physical laws governing the system but still have a hard time. Systems almost always show chaotic dynamics. Which means you need to know the initial conditions of the system with infinite precision to have long term predictability. Maybe ecology will reach this point some day, but I would argue that is pretty far in the future.
I am more interested in other, simpler, methods by which ecologists deal with multicausality. Here is my list:
- Abandon all hope ye who enter here – a not atypical approach to multicausality is to throw up ones arms in hopelessness and use it as an excuse to do lesser science. To be satisfied with collecting data on one tiny piece of the puzzle and claiming nothing generalizes beyond an individual system. I don’t have much sympathy with this approach. Although we will never have the elegance of physics, tools leading to real advances are available in the face of multicausality.
- Box-and-arrow diagrams – One of the simplest approaches is just to draw a diagram showing all of the links of causality with causal factors in boxes and links as arrows. A good example that has been highly influential in the study of bird migration is a figure by Jenni & Schaub showing all the factors influencing a decision to take off and fly.
- Multiple regression – this is surely the weakest quantitative approach, but its a start. Throw in all the possible explanatory variables and do a regression to see which have the biggest impact. But you would be a lot better off comparing the strength of the effect sizes (e.g. coefficients on standardized variables) than doing arbitrary model selection to end up with a falsely binary list of variables that are in vs out.There are a dozen limitations to this approach including collinearity and nonlinearity, that suggest to me that more complicated approaches are probably better, but this is at least a start.
- Variance partitioning – this is a clear step-up from multiple regression. Variance partitioning can be done both on variables (or sets of variables) at the same level as in a multivariate regression or in nested variables (e.g. multiple scales). This at least allows a quantitative statement that in my system 32% of the variance is explained by weather and 29% is explained by competition and 39% is explained by unstudied factors. In a multicausal world, that is a profoundly useful statement.
- Path analysis – path analysis or structured equation modelling (SEM) is increasingly being put forth as an important step towards dealing with multicausality. And very intuitively it combines the visual appeal of box-and-arrow diagrams (#1) with the quantitative rigor of regression (#2) and variance partitioning (#3). It is not perfect – it mostly is used making assumptions of linear relations and it doesn’t totally solve the issue of determining the direction of casuality along the arrows – but it is a big step forward in my book. A great recent example is a paper by Jim Grace and many other authors (including DE guest author Peter Adler) disentangling multicausality in the troublesome productivity-diversity linkage.
- Quantile regression – Another quantitative approach is quantile regression. Instead of fitting a line through the middle of the data, put a line along the boundary. It builds on Liebig’s law of the limiting (only one factor is limiting at a given location) but recognizes that in ecology which factor is limiting varies across space. Thus the boundary of a relationship can show what the relationship is when the factor on the x-axis is limiting. The logic is laid out very clearly in a nice paper by Cade and Noon.
- Controlled experiments – multicausality is of course the reason why ecology is so obsessed with controlled experiments.One way to tease apart the role of a single factor is to control everything else to be constant. The experiments of Gause are found in every textbook because he controlled everything except competition out of the picture and lo and behold the data conform beautiful to the predictions of the Lotka-Volterra competition equations. Note though that this logic probably argues more strongly for laboratory experiments and less strongly for field experiments, the opposite of what is typical in ecology.
- Conditionality – One consequence of multicausality is it can create what seems to be a lack of generality. Competition is really important here. Predation is really important there. Abiotic factors there. But in reality this is just because the relative importance of the causes is shifting between systems. And it ought to be predictable what causes the relative importance to shift. For example, marine systems with its inverted biomass pyarmids and well-mixed systems might be a logical system to expect predation to dominate and immobile trees on land might be a logical system to expect competition to dominate (at least in adults). And lo and behold there is a hypothesis that trophic cascades are stronger in marine than terrestrial systems*. One of my all time favorite quotes about multicausality is from MacArthur, somebody often villified for oversimplifying. But MacArthur said clear in print in his deathbed book :
“[one should erect a] two- or three- way classification of organisms and their geometrical and temporal environments, this classification consuming most of the creative energy of ecologists. The future principles of the ecology of coexistence will then be of the form ‘for organisms of type A, in environments of structure B, such and such relations will hold’ ”
So there you have it. Ecology is fun (and hard) because it is multicausal. Ironically, going back to my first example, if you ask a physicist what would happen if you dropped a feather instead of a steel ball she would instantly recognize it as a multicausal system and refuse to answer the question. (You should hear what engineers say about physicists avoiding real world problems). As ecologists we don’t have the luxury of throwing out studying multicausal systems because every system of interest is multicausal. But multicausality should NOT cause scientists to throw up their hands in despair and do baby science. Instead we should embrace multicausality and the tools that help us root out what is going on. I actually am very optimistic that our toolkit can tackle multicausality. We have been very good at using #3 (multiple regression). But honestly I don’t think we’ve embraced any of the other approaches in full enough depth. #7 really pushes us into the lab, where few want to go. And the statistical techniques of #4, #5, and #6 are nowhere near as hard to use as the mixed models and Bayesian models I see being thrown around these days, but are under-used, probably in part because they require thinking before analyzing. And #8 has been called for by several great scientists, but seems to never be acted upon (although some meta-analyses are starting to move in this direction). But when you read a really good paper like the Grace et al paper that takes an absolute cloud of points and resolves it into a series of separate causal links with r2>0.50, you realize it is definitley possible to deal with multicausality. It is just hard work.
What do you think? Is there no hope in multicausal systems? Am I wrong and ecology can be treated as single cause system, ultimately as simple as physics? Are there tools I listed you would abandon? Are there tools I failed to mention?
*NB: I am aware that none of the pieces of that story are fully nailed down.