Department of Biology and the Ecology Center, Utah State University , Logan, UT , USA.
PeerJ. 2013 Nov 21;1:e212. doi: 10.7717/peerj.212. eCollection 2013.
The Maximum Entropy Theory of Ecology (METE) predicts a universal species-area relationship (SAR) that can be fully characterized using only the total abundance (N) and species richness (S) at a single spatial scale. This theory has shown promise for characterizing scale dependence in the SAR. However, there are currently four different approaches to applying METE to predict the SAR and it is unclear which approach should be used due to a lack of empirical comparison. Specifically, METE can be applied recursively or non-recursively and can use either a theoretical or observed species-abundance distribution (SAD). We compared the four different combinations of approaches using empirical data from 16 datasets containing over 1000 species and 300,000 individual trees and herbs. In general, METE accurately downscaled the SAR (R (2) > 0.94), but the recursive approach consistently under-predicted richness. METE's accuracy did not depend strongly on using the observed or predicted SAD. This suggests that the best approach to scaling diversity using METE is to use a combination of non-recursive scaling and the theoretical abundance distribution, which allows predictions to be made across a broad range of spatial scales with only knowledge of the species richness and total abundance at a single scale.
生态最大熵理论（METE）预测了一种普遍的物种-面积关系（SAR），仅使用单一空间尺度的总丰度（N）和物种丰富度（S）就可以完全描述。该理论在描述 SAR 的尺度依赖性方面显示出了一定的前景。然而，目前有四种不同的方法可以应用 METE 来预测 SAR，由于缺乏经验比较，目前还不清楚应该使用哪种方法。具体来说，METE 可以递归或非递归应用，并且可以使用理论或观测到的物种丰度分布（SAD）。我们使用包含超过 1000 个物种和 30 万株树木和草本植物的 16 个数据集的实证数据比较了这四种不同方法的组合。一般来说，METE 可以准确地向下扩展 SAR（R（2）>0.94），但递归方法始终低估了丰富度。METE 的准确性并不强烈依赖于使用观测到的或预测的 SAD。这表明，使用 METE 扩展多样性的最佳方法是使用非递归扩展和理论丰度分布的组合，这允许仅在单一尺度上了解物种丰富度和总丰度的情况下，在广泛的空间尺度上进行预测。
