When can species abundance data reveal non-neutrality?
When can species abundance data reveal non-neutrality?
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DOI：
10.1371/journal.pcbi.1004134
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发表时间：
2015-03
影响因子：
4.3
通讯作者：
Cornell SJ
中科院分区：
文献类型：
--
作者：
Al Hammal O;Alonso D;Etienne RS;Cornell SJ
物种丰度分布(SAD)可能是生态学最广为人知的经验模式，在过去的几十年里，已经提出了许多模型来解释它们的形状。对于哪种模型是正确的，目前还没有达成共识，因为从SAD模式中区分不同过程的程度还没有得到严格的量化。我们提出了一种功率计算，以量化我们使用物种丰度数据检测偏离中性的能力。我们研究了非中立型随机群落模型，并证明了当样本量足够大和/或影响的幅度足够大时，非中立型过程的存在是可检测的。我们的框架可以用于任何可以在计算机上模拟的候选社区模型，并确定区分可选过程所需的抽样努力，以及其模式与中性理论在统计上一致的社区中非中性过程的强度范围。我们发现，即使是巴拿马巴罗科罗拉多岛50Ha森林样地的规模的数据集，也不太可能大到足以检测到仅由竞争相互作用造成的对中性的偏离，尽管可能可以检测到对丰度分布具有相反影响的多个非中性过程的存在。为了预测和缓解生态群落对全球变化的反应，我们需要了解允许多个物种近距离共存的过程。生态学中的一个经典观点是，物种共存是因为它们占据了不同的“生态位”。然而，扩散等随机过程也可以解释物种共存，而不会引发生态位分化。“中性”模型体现了这一思想，省略了生态位分化，并假设所有物种都是相同的。这些模型大多与热带森林树种的相对丰富度在统计上是一致的，但统计程序总是含有不确定因素，许多其他模型也可能与特定的数据集保持一致。我们计算非中性过程需要多强才能在不同大小的数据集中检测到它们的影响。我们发现，目前可用的最大生态数据集，如巴拿马巴罗科罗拉多岛50公顷的地块，不足以区分中性和非中性模型，除非有多个非中性过程在起作用。这意味着需要研究其他类型的模式，或收集更大的数据集，以了解森林生物多样性背后的机制。
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. In order to predict and mitigate the response of ecological communities to global change, we need to understand the processes that allow multiple species to coexist in close proximity. A classic idea in Ecology is that species coexist because they occupy different “niches”. However, random processes such as dispersal could also explain species coocurrence, without invoking niche differentiation. “Neutral” models embody this idea, omitting niche differentiation and assuming all species are identical. Such models are mostly statistically consistent with the relative abundances of tree species in tropical forests, but statistical procedures always contain an element of uncertainty and many other models could also be consistent with a particular data set. We compute how strong the non-neutral processes would need to be in order for their effect to be detectable in data sets of different sizes. We find that the largest ecological data sets currently available, such as the 50 hectare plot on Barro Colorado Island in Panama, are not large enough to distinguish between neutral and non-neutral models, unless multiple non-neutral processes are at work. This means that other types of pattern need to be studied, or larger data sets collected, in order to understand the mechanisms behind forest biodiversity.
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