O'Dwyer James P, Rominger Andrew, Xiao Xiao
Department of Plant Biology, University of Illinois, Urbana, IL, USA.
Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA.
Ecol Lett. 2017 Jul;20(7):832-841. doi: 10.1111/ele.12788.
Simplified mechanistic models in ecology have been criticised 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 parametrise 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.
生态学中的简化机制模型一直受到批评，因为与数据的良好拟合并不意味着该机制是真实的：模式并不等同于过程。与此同时，最大熵原理（MaxEnt）已应用于生态学中，以仅受少数状态变量（如总丰度或物种丰富度）约束来进行预测。但一个突出的问题仍然存在：什么原理能告诉我们该约束哪些状态变量？在这里，我们试图同时解决这两个问题，方法是将一组给定的机制转化为用于MaxEnt的状态变量，然后将这种MaxEnt理论用作一个零模型，用以比较机制预测。特别是，我们从数据中确定为给定机制模型参数化所需的充分统计量，并将它们用作MaxEnt约束。我们的方法精确地分离出该机制除了单独的状态变量之外还在告诉我们什么。
