We build a general framework which establishes a one-to-one correspondence between species abundance distribution (SAD) and species accumulation curve (SAC). The appearance rates of the species and the appearance times of individuals in each species are modeled as Poisson processes. The number of species can be finite or infinite. We introduce a linear derivative ratio family of models, $\mathrm{LDR}_1$, of which the ratio of the first and the second derivatives of the expected SAC is a linear function. A D1/D2 plot is proposed to detect this linear pattern in the data. The SAD of $\mathrm{LDR}_1$ is the Engen's extended negative binomial distribution, and the SAC encompasses several popular parametric forms including the power law. Family $\mathrm{LDR}_1$ is extended in two ways: $\mathrm{LDR}_2$ which allows species with zero detection probability, and $\mathrm{RDR}_1$ where the derivative ratio is a rational function. We also consider the scenario where we record only a few leading appearance times of each species. We show how maximum likelihood inference can be performed when only the empirical SAC is observed, and elucidate its advantages over the traditional curve-fitting method.
翻译:我们建立了一个总框架,在物种丰量分布(SAD)和物种积累曲线(SAC)之间建立一对一的对应关系。物种的外观率和每个物种个体的外观时间以Poisson过程为模型。物种的数量可以是有限或无限的。我们引入了一种线性衍生比率模型的组合,即$\mathrm{LDR ⁇ 1美元,其中第一个和第二个预期SAC衍生物的比重是线性功能。提议了一个D1/D2图案,以探测数据中的线性模式。一个美元/mathrm{LDR ⁇ 1$的SADAD是Egen的扩展负双向分布,而SAC包含几种流行的参数形式,包括权力法。我们从两个方面扩展了:美元/mathrm{LDR ⁇ 2$,允许检测概率为零的物种,而美元/mathrm{RDR ⁇ 1$,其中衍生物比率为合理功能。我们还可以考虑一种假设情景,即我们只记录每个物种的几大前表情时,我们只观察了每个物种的实验方法。我们展示了最高的可能性。