A neutral sampling formula for multiple samples and an 'exact' test of neutrality
A neutral sampling formula for multiple samples and an 'exact' test of neutrality
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DOI：
10.1111/j.1461-0248.2007.01052.x
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发表时间：
2007-07-01
期刊：
影响因子：
8.8
通讯作者：
Etienne, Rampal S.
中科院分区：
文献类型：
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作者：
Etienne, Rampal S.
随着生物多样性中性理论的效用日益得到承认，也越来越需要适当的工具来评估中性过程（扩散限制和随机性）的相对重要性。中性理论的关键特征之一是它与数据的密切联系：抽样公式，给出了一组模型参数条件下的数据集的概率，已被开发用于参数估计和模型比较。然而，只有单一的当地样本可以处理与目前可用的采样公式，而数据往往是许多小的空间分离的地块。在这里，我提出了一个采样公式的多个，空间上分离的样本从同一元性，这是一个概括的早期采样公式。我还提供了一个算法来生成数据集的模型，我介绍了一个一般的中立性测试，不需要一个替代模型;这个测试比较观察到的数据（使用新的抽样公式计算）的概率与模型生成的数据集的概率。我说明这与树木丰富的数据从三个大巴拿马新热带森林地块。当使用从三个图估计的模型参数进行检验时，不能拒绝模型;然而，当使用先前报告的BCI参数估计值时，模型被强烈拒绝。这表明，中立性不能同时解释当地（BCI）和区域（巴拿马运河区）规模的三个巴拿马树群落的结构。然而，人们应该意识到，该模式的其他方面而不是中立性可能是其失败的原因。我认为，该模型的空间隐式字符是一个潜在的候选人。
As the utility of the neutral theory of biodiversity is increasingly being recognized, there is also an increasing need for proper tools to evaluate the relative importance of neutral processes (dispersal limitation and stochasticity). One of the key features of neutral theory is its close link to data: sampling formulas, giving the probability of a data set conditional on a set of model parameters, have been developed for parameter estimation and model comparison. However, only single local samples can be handled with the currently available sampling formulas, whereas data are often available for many small spatially separated plots. Here, I present a sampling formula for multiple, spatially separated samples from the same metacommunity, which is a generalization of earlier sampling formulas. I also provide an algorithm to generate data sets with the model and I introduce a general test of neutrality that does not require an alternative model; this test compares the probability of the observed data (calculated using the new sampling formula) with the probability of model-generated data sets. I illustrate this with tree abundance data from three large Panamanian neotropical forest plots. When the test is performed with model parameters estimated from the three plots, the model cannot be rejected; however, when parameter estimates previously reported for BCI are used, the model is strongly rejected. This suggests that neutrality cannot explain the structure of the three Panamanian tree communities on the local (BCI) and regional (Panama Canal Zone) scale simultaneously. One should be aware, however, that aspects of the model other than neutrality may be responsible for its failure. I argue that the spatially implicit character of the model is a potential candidate.