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 of each species are modeled as Poisson processes. The number of species can be finite or infinite. Hill numbers are extended to the framework. 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. By extrapolation of the curve in the D1/D2 plot, a species richness estimator that extends Chao1 estimator is introduced. 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. Real data are analyzed to demonstrate the proposed methods. 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:我们建立一个总框架, 建立物种丰度分布( SAD) 和物种积累曲线( SAC) 之间的一对一对应。 物种的外观率和每个物种的外观时间以 Poisson 进程为模型。 物种的数量可以是有限或无限的。 山数可以扩展到框架。 我们引入了模型的线性衍生比率, $\ mathrm{ LDR ⁇ 1$, 其中, 预期 SAC 的第一和第二衍生物的比重是一个线性功能。 提议用 D1/ D2 绘图来探测数据中的线性模式。 通过D1/ D2 绘图中的曲线外推法, 将每个物种的物种丰富度估计值作为扩展到 Chao1 的参数。 $\ mathrm{ LDR ⁇ 1$ 的SADR 是一个线性比例, 并且SAC 包含几种流行的参数, 包括电源法 。 家庭 $\ mathrm { LDR=2$, 其中我们只能以两种方式扩展为直线性模式 。 。, 以美元来显示其直径直径直线性模型显示直径比 。