Centre for Invasion Biology, Department of Botany and Zoology, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa.
Ecol Appl. 2009 Dec;19(8):2038-48. doi: 10.1890/08-2236.1.
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 macroecological 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.
在区域尺度上估计物种丰度需要一种具有成本效益的方法,该方法可以应用于现有的大规模数据。我们比较了 8 种模型在基于南非鸟类存在-缺失图估计物种丰度和群落结构方面的性能。其中 6 种模型基于种内占有-丰度关系(OAR);另外两种基于物种占有度的比例模式(SPO),它量化了当在逐渐更细的尺度上测量时物种范围大小的下降。使用五项测试来检验这些模型的性能:前三项比较了预测的群落结构与有充分记录的宏观生态学模式;最后两项比较了稀有物种的已发表丰度估计值和总区域丰度估计值与预测丰度值。估计大约有 20 亿只鸟类出现在南非、莱索托和斯威士兰。SPO 模型的表现优于 OAR 模型,因为 OAR 模型假设环境均匀,并且产生依赖于尺度的估计。因此,OAR 模型仅适用于小而均匀的区域。相比之下,SPO 模型适用于更大的空间尺度的数据,因为它们基于物种范围大小的尺度依赖性,并包含环境异质性(假设分形栖息地结构或进行占有度的贝叶斯估计)。因此,建议使用 SPO 模型基于空间明确的存在-缺失数据进行集合尺度的区域丰度估计。