Al Hammal Omar, Alonso David, Etienne Rampal S, Cornell Stephen J
School of Biology, University of Leeds, Leeds, United Kingdom.
School of Biology, University of Leeds, Leeds, United Kingdom; Center for Advanced Studies (CEAB-CSIC), Blanes, Spain.
PLoS Comput Biol. 2015 Mar 20;11(3):e1004134. doi: 10.1371/journal.pcbi.1004134. eCollection 2015 Mar.
Species abundance distributions (SAD) are probably ecology's most well-known empirical pattern, and over the last decades many models have been proposed to explain their shape. There is no consensus over which model is correct, because the degree to which different processes can be discerned from SAD patterns has not yet been rigorously quantified. We present a power calculation to quantify our ability to detect deviations from neutrality using species abundance data. We study non-neutral stochastic community models, and show that the presence of non-neutral processes is detectable if sample size is large enough and/or the amplitude of the effect is strong enough. Our framework can be used for any candidate community model that can be simulated on a computer, and determines both the sampling effort required to distinguish between alternative processes, and a range for the strength of non-neutral processes in communities whose patterns are statistically consistent with neutral theory. We find that even data sets of the scale of the 50 Ha forest plot on Barro Colorado Island, Panama, are unlikely to be large enough to detect deviations from neutrality caused by competitive interactions alone, though the presence of multiple non-neutral processes with contrasting effects on abundance distributions may be detectable.
物种丰度分布（SAD）可能是生态学中最著名的经验模式，在过去几十年里，人们提出了许多模型来解释其形态。对于哪种模型是正确的，目前尚无共识，因为从SAD模式中辨别不同过程的程度尚未得到严格量化。我们提出了一种功效计算方法，以量化我们利用物种丰度数据检测偏离中性的能力。我们研究了非中性随机群落模型，并表明如果样本量足够大且/或效应幅度足够强，非中性过程的存在是可检测的。我们的框架可用于任何可在计算机上模拟的候选群落模型，并确定区分替代过程所需的抽样工作量，以及模式与中性理论统计一致的群落中非中性过程强度的范围。我们发现，即使是巴拿马巴罗科罗拉多岛50公顷森林样地规模的数据集，也不太可能大到足以检测仅由竞争相互作用导致的偏离中性的情况，不过存在对丰度分布有对比效应的多个非中性过程可能是可检测的。
