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Simple MaxEnt models explain food web degree distributions

机译:简单的MaxEnt模型解释食物网度分布

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Degree distributions are widely used to characterize networks, including food webs, and play a vital role in models of food web structure. To date, there have been no mechanistic or statistical explanations for the form of food web degree distributions. Here, I introduce models for food web degree distributions based on the principle of maximum entropy (MaxEnt) and show that the distributions of the number of consumers and resources in 23 (45%) and 35 (69%) of 51 food webs are not significantly different at a 95% confidence level from the MaxEnt distribution. These findings offer a new null model for the most probable degree distributions in food webs and other networks. They suggest that there is relatively little pressure favoring generalist or specialist consumption strategies but that biological drivers or methodological bias may force the consumer distribution away from the MaxEnt form.
机译:度分布被广泛用于表征包括食物网的网络,并且在食物网结构模型中起着至关重要的作用。迄今为止,还没有关于食物网度分布形式的机械或统计解释。在这里,我介绍了基于最大熵(MaxEnt)原理的食物网度分布模型,并显示了51个食物网中的23个(45%)和35(69%)的消费者和资源数量分布不是与MaxEnt分布在95%的置信度水平上有显着差异。这些发现为食物网和其他网络中最可能的学位分布提供了新的零模型。他们认为,偏向通才或专家消费策略的压力相对较小,但生物学驱动因素或方法偏见可能会迫使消费者分配远离MaxEnt形式。

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