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Extrapolating population size from the occupancy-abundance relationship and the scaling pattern of occupancy

机译：从占用率-丰度关系和占用率缩放模式推断人口规模

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摘要

The estimation of species abundances at regional scales requires a cost-efficient method that can be applied to existing broadscale data. We compared the performance of eight models for estimating species abundance and community structure from presence absence maps of the southern African avifauna. Six models were based on the intraspecific occupancy-abundance relationship (OAR); the other two on the scaling pattern of species occupancy (SPO), which quantifies the decline in species range size when measured across progressively finer scales. The performance of these models was examined using five tests: the first three compared the predicted community structure against well-documented macro-ecological patterns; the final two compared published abundance estimates for rare species and the total regional abundance estimate against predicted abundances. Approximately two billion birds were estimated as occurring in South Africa, Lesotho, and Swaziland. SPO models outperformed the OAR models, due to OAR models assuming environmental homogeneity and yielding scale-dependent estimates. Therefore, OAR models should only be applied across small, homogenous areas. By contrast, SPO models are suitable for data at larger spatial scales because they are based on the scale dependence of species range size and incorporate environmental heterogeneity (assuming fractal habitat structure or performing a Bayesian estimate of occupancy). Therefore, SPO models are recommended for assemblage-scale regional abundance estimation based on spatially explicit presence-absence data.
机译：在区域尺度上估计物种丰度需要一种经济有效的方法，该方法可以应用于现有的大规模数据。我们比较了八个模型的性能，这些模型用于根据南部非洲航空动物的不在场图来估计物种的丰度和群落结构。六个模型基于种内占有率-丰度关系（OAR）;另外两个是物种占有率（SPO）的缩放模式，它在逐步精细的尺度上进行量化时，可以量化物种范围大小的下降。这些模型的性能使用五项测试进行了检验：前三项将预测的社区结构与有据可查的宏观生态模式进行了比较;最后两个将已发布的稀有物种丰度估计值与总区域丰度估计值与预测的丰度进行了比较。据估计，在南非，莱索托和斯威士兰大约有20亿只鸟类。 SPO模型优于OAR模型，这是因为OAR模型假设环境同质性并得出规模依赖的估计值。因此，OAR模型只能应用于较小的同质区域。相比之下，SPO模型适用于较大空间尺度的数据，因为它们基于物种范围大小的尺度依赖性，并结合了环境异质性（假设分形栖息地结构或执行贝叶斯占用估计）。因此，建议将SPO模型用于基于空间显式存在-缺失数据的集合规模区域丰度估计。

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