物种多度分布的期望值和零假设检验
Null expectations and null hypothesis testing for the species abundance distribution
摘要：
从生态系统中取样的元素（N）和类型（S）的数量是对丰度、分布和多样性观测的最有力的限制。N和S一起确定可能形式的集合（即，物种丰度分布（SAD）。有三种方法来描述空SAD（=平均可行SAD）。第一种方法是基于满射的随机均匀抽样。在这种方法下，我计算了给定SAD的概率，给定N和S（等式2）。4）。第二种方法是基于成分的随机抽样。在这种方法下，我计算了给定SAD的概率，给定N和S（等式2）。8）。第三种方法是基于分区的随机均匀采样。我回顾了Locey & White（2013）开发的方法，并提供了一些对生态学家有用的渐近结果。一个可行集的中心是一个零期望，它应该偏离足够的替代模型之前调用的生物或生态机制的SAD。在这里，我将可行集方法与生态学中的典型推理框架（拟合优度，零假设检验，模型比较，零建模）相结合。我描述了如何执行数值模拟来描述期望下的不同方法的可行集。我开发的目标或健身功能，允许估计最有可能的SAD使用数值优化。我提供了工具来比较基于可行集方法的零期望与观察结果，在模型比较和零假设检验的背景下。
英文摘要：
The number of elements (N) and types (S) sampled from an ecological system are among the most powerful constraints on observations of abundance, distribution, and diversity. Together, N and S determine sets of possible forms (i.e., feasible sets) for the species abundance distribution (SAD). There are three approaches to the description of the null SAD (= the average feasible SAD). The first approach is based on the random uniform sampling of surjections. I calculate the probability of a given SAD, given N and S, under this approach (Eq. 4). The second approach is based on the random sampling of compositions. I calculate the probability of a given SAD, given N and S, under this approach (Eq. 8). The third approach is based on the random uniform sampling of partitions. I review the approach, which was developed by Locey & White (2013), and provide some asymptotic results useful for ecologists. The center of a feasible set is a null expectation, which should deviate enough from the alternative models before invoking for biological or ecological mechanisms underlying the SAD. Here, I integrate the feasible set approach with the typical framework of inference in ecology (goodness-of-fit, null hypothesis testing, model comparison, null modelling). I describe how to perform numerical simulations to describe expectations under different approaches to the feasible set. I develop objective or fitness functions to allow the estimation of the most likely SAD using numerical optimization. I provide tools to compare null expectations based on the feasible set approach with the observations, in the context of model comparison and null hypothesis testing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
