  Simplified mechanistic models in ecology have been criticized for the fact
that a good fit to data does not imply the mechanism is true: pattern does not
equal process. In parallel, the maximum entropy principle (MaxEnt) has been
applied in ecology to make predictions constrained by just a handful of state
variables, like total abundance or species richness. But an outstanding
question remains: what principle tells us which state variables to constrain?
Here we attempt to solve both problems simultaneously, by translating a given
set of mechanisms into the state variables to be used in MaxEnt, and then using
this MaxEnt theory as a null model against which to compare mechanistic
predictions. In particular, we identify the sufficient statistics needed to
parametrize a given mechanistic model from data and use them as MaxEnt
constraints. Our approach isolates exactly what mechanism is telling us over
and above the state variables alone.
