Null versus neutral models: what's the difference?
Null versus neutral models: what's the difference?
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DOI：
10.1111/j.2006.0906-7590.04714.x
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发表时间：
2006-10-01
期刊：
影响因子：
5.9
通讯作者：
McGill, Brian J.
中科院分区：
文献类型：
--
作者：
Gotelli, N. J.;McGill, Brian J.
中性模型假定灭绝和物种形成事件的随机变化，加上有限的扩散，可以解释许多社区的属性，包括相对丰度分布。生态学中的这一模型与进化中的三层模型（哈代、魏恩堡、漂移、漂移和选择）之间有着重要的相似之处。由于中性模型调用随机过程并用于经验数据的统计测试，因此它可以被解释为空模型的特殊形式。然而，中性模型的应用和解释在三个重要方面与标准零模型不同：1）大多数零模型都包含物种水平的约束，这些约束通常与生态位差异有关，而中性模型假设所有物种在功能上都是等效的。2)可重构模型通常与直接从数据集本身测量的约束相匹配。相比之下，中性模型需要的物种形成、灭绝和迁移率的参数几乎从来没有直接测量过，所以它们的值必须猜测或拟合。3)最重要的是，零模型被视为简单的统计描述符：未指定的“随机”力量在排除特定生物机制（通常是物种相互作用）的简单模型中产生变化。虽然中性模型最初被认为是一个零模型，但最近中性模型的支持者开始将其视为对社区组装的基于过程的描述，这些差异是最近关于中性模型的许多争议的核心。如果中性模型确实是一个基于过程的模型，那么它的假设应该被直接测试，它的预测应该与一个适当的空模型的预测进行比较。这样的测试很少提供信息，大多数经验数据集可以更简单地拟合到一个简单的对数正态分布。由于中性模型中的未知参数通常必须以特定的方式猜测或拟合，因此经典的频率论测试受到损害，并且可能偏向于找到与模型的良好拟合。在中性模型的统计检验中，对I型和II型错误的可能性的分析很少。中性模型最近被提出作为地理学（中域效应）和群落生态学（物种共现）中更一般的零模型的一种特殊形式。在这两种情况下，中性模型在定性上，但不是定量上，类似于经典零模型的预测。然而，由于中性模型中的重要参数很少能被直接测量，它作为实证检验的零假设的价值可能有限，未来的进展可能来自超越中性与零模型的二分法检验。相反，中性模型可以被看作是一种机制，它与其他过程一起沿着于模式。或者，可以将数据对中性模型的拟合与其他不基于中性假设的基于过程的模型的拟合进行比较。最后，中性模型也可以直接测试，如果它的参数可以独立于测试数据估计。然而，这些方法可能需要比通常可用的更多的数据。由于这些原因，简单的零模型测试在中性模型的评估中仍然很重要。
The neutral model posits that random variation in extinction and speciation events, coupled with limited dispersal, can account for many community properties, including the relative abundance distribution. There are important analogies between this model in ecology and a three-tiered hierarchy of models in evolution (Hardy Weinburg, drift, drift and selection). Because it invokes random processes and is used in statistical tests of empirical data, the neutral model can be interpreted as a specialized form of a null model. However, the application and interpretation of neutral models differs from that of standard null models in three important ways: 1) whereas most null models incorporate species-level constraints that are often associated with niche differences, the neutral model assumes that all species are functionally equivalent. 2) Null models are usually fit with constraints that are measured directly from the data set itself. In contrast, the neutral model requires parameters for speciation, extinction, and migration rates that are almost never measured directly, so their values must be guessed at or fitted. 3) Most important, null models are viewed as simple statistical descriptors: unspecified "random" forces generate variation in a simple model that excludes particular biological mechanisms (usually species interactions). Although the neutral model was originally framed as a null model, recent proponents of the neutral model have begun to treat it as a literal process-based description of community assembly.These differences lie at the heart of much of the recent controversy over the neutral model. If the neutral model is truly a process-based model, then its assumptions should be directly tested, and its predictions should be compared to those of an appropriate null model. Such tests are rarely informative, and most empirical data sets can be fit more parsimoniously to a simple log-normal distribution. Because unknown parameters in the neutral model must usually be guessed at or fit in ad-hoc ways, classical frequentist tests are compromised, and may be biased towards finding a good fit with the model. There has been little analysis of the potential for type I and type II errors in statistical tests of the neutral model.The neutral model has recently been proposed as a specific form of more general null models in biogeography (the mid-domain effect) and community ecology (species co-occurrence). In both cases, the neutral model is qualitatively, but not quantitatively, similar to the predictions of classic null models. However, because the important parameters in the neutral model can rarely be measured directly, it may be of limited value as a null hypothesis for empirical tests.Future progress may come from moving beyond dichotomous tests of neutral versus null models. Instead, the neutral model might be viewed as a mechanism that contributes to pattern along with other processes. Alternatively, the fit of data to the neutral model can be compared to the fit to other process-based models that are not based on neutrality assumptions. Finally, the neutral model can also be tested directly if its parameters can be estimated independently of the test data. However, these approaches may require more data than are often available. For these reasons, simple null model tests will continue to be important in the evaluation of the neutral model.