A presence–absence map consists of indicators of the occurrence or nonoccurrence of a given species in each cell over a grid, without counting the number of individuals in a cell once it is known it is occupied. They are commonly used to estimate the distribution of a species, but our interest is in using these data to estimate the abundance of the species. In practice, certain types of species (in particular flora types) may be spatially clustered. For example, some plant communities will naturally group together according to similar environmental characteristics within a given area. To estimate abundance, we develop an approach based on clustered negative binomial models with unknown cluster sizes. Our approach uses working clusters of cells to construct an estimator which we show is consistent. We also introduce a new concept called super-clustering used to estimate components of the standard errors and interval estimators. A simulation study is conducted to examine the performance of the estimators and they are applied to real data.

中文翻译：

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使用聚类模型从存在/缺失地图中估计丰度

存在/不存在图由网格上每个单元中给定物种的发生或不存在的指示符组成，而一旦已知某个单元已被占用，就不计算该单元中的个体数量。它们通常用于估计物种的分布，但是我们的兴趣是使用这些数据来估计物种的丰度。实际上，某些类型的物种（尤其是植物类型）可以在空间上聚类。例如，某些植物群落将根据给定区域内的相似环境特征自然地组合在一起。为了估算丰度，我们开发了一种基于未知簇大小的负二项式模型的方法。我们的方法使用工作单元簇构建一个估计量，我们证明它是一致的。我们还引入了一个新概念超级聚类，用于估计标准误差和间隔估计器的成分。进行模拟研究以检查估计器的性能，并将其应用于实际数据。