Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada.
Ecology. 2010 Sep;91(9):2794-805. doi: 10.1890/09-1255.1.
Maximum entropy (maxent) models assign probabilities to states that (1) agree with measured macroscopic constraints on attributes of the states and (2) are otherwise maximally uninformative and are thus as close as possible to a specified prior distribution. Such models have recently become popular in ecology, but classical inferential statistical tests require assumptions of independence during the allocation of entities to states that are rarely fulfilled in ecology. This paper describes a new permutation test for such maxent models that is appropriate for very general prior distributions and for cases in which many states have zero abundance and that can be used to test for conditional relevance of subsets of constraints. Simulations show that the test gives correct probability estimates under the null hypothesis. Power under the alternative hypothesis depends primarily on the number and strength of the constraints and on the number of states in the model; the number of empty states has only a small effect on power. The test is illustrated using two empirical data sets to test the community assembly model of B. Shipley, D. Vile, and E. Garnier and the species abundance distribution models of S. Pueyo, F. He, and T. Zillio.
最大熵 (maxent) 模型为状态分配概率，这些概率（1）与状态属性的测量宏观约束一致，（2）在其他方面信息量最小，因此尽可能接近指定的先验分布。此类模型最近在生态学中变得流行，但经典的推理统计检验要求在将实体分配给状态时假设相互独立，而这种独立性在生态学中很少得到满足。本文描述了一种适用于非常一般的先验分布和许多状态丰度为零的情况的新的最大熵模型排列检验，可用于检验约束子集的条件相关性。模拟表明，在零假设下，该检验给出了正确的概率估计。备择假设下的功效主要取决于约束的数量和强度以及模型中的状态数量；空状态的数量对功效的影响很小。该检验使用两个经验数据集进行说明，以检验 B. Shipley、D. Vile 和 E. Garnier 的群落组装模型和 S. Pueyo、F. He 和 T. Zillio 的物种丰度分布模型。
