WHAT DO INTERACTION NETWORK METRICS TELL US ABOUT SPECIALIZATION AND BIOLOGICAL TRAITS?
WHAT DO INTERACTION NETWORK METRICS TELL US ABOUT SPECIALIZATION AND BIOLOGICAL TRAITS?
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DOI：
10.1890/07-2121.1
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发表时间：
2008-12-01
期刊：
影响因子：
4.8
通讯作者：
Menzel, Florian
中科院分区：
文献类型：
--
作者：
Bluethgen, Nico;Fruend, Jochen;Menzel, Florian
生态相互作用网络的结构通常被解释为有意义的生态和进化机制的产物，这些机制塑造了社区协会的专业化程度。然而，这里我们表明，未加权的网络指标（连通性、嵌套性和度分布）和加权的网络指标（相互作用均匀性、相互作用强度不对称）都受到观察数量的强烈约束和偏差。很少被观察到的物种不可避免地被认为是“专家”，而不考虑它们的实际联系，从而导致对专业化的有偏见的估计。因此，物种观测记录的倾斜分布（如对数正态），加上生态数据典型的相对较低的采样密度，已经产生了一个“嵌套”和“连接不良”的网络，当相互作用是中性的时，具有“不对称的相互作用强度”。这一点得到了双部网络零模型模拟的证实，该模型假设伴侣在没有任何专业化和相关物种之间生物性状对应（性状匹配）的任何变化的情况下随机结合。频率分布偏度的变化从根本上改变了网络度量的结果。因此，从基本的专业化和特征匹配的角度来解释网络度量，需要对信息缺失所施加的这种严重约束进行适当的控制。当使用控制这些影响的替代方法时，大多数互惠或对抗系统的自然网络显示出比中性条件下预期的更高程度的互惠专业化（排他性）。更高的排他性与更紧密的共同进化是一致的，并且表明比嵌套网络所暗示的更低的生态冗余。
The structure of ecological interaction networks is often interpreted as a product of meaningful ecological and evolutionary mechanisms that shape the degree of specialization in community associations. However, here we show that both unweighted network metrics (connectance, nestedness, and degree distribution) and weighted network metrics (interaction evenness, interaction strength asymmetry) are strongly constrained and biased by the number of observations. Rarely observed species are inevitably regarded as "specialists,'' irrespective of their actual associations, leading to biased estimates of specialization. Consequently, a skewed distribution of species observation records (such as the lognormal), combined with a relatively low sampling density typical for ecological data, already generates a "nested'' and poorly "connected'' network with "asymmetric interaction strengths'' when interactions are neutral. This is confirmed by null model simulations of bipartite networks, assuming that partners associate randomly in the absence of any specialization and any variation in the correspondence of biological traits between associated species (trait matching). Variation in the skewness of the frequency distribution fundamentally changes the outcome of network metrics. Therefore, interpretation of network metrics in terms of fundamental specialization and trait matching requires an appropriate control for such severe constraints imposed by information deficits. When using an alternative approach that controls for these effects, most natural networks of mutualistic or antagonistic systems show a significantly higher degree of reciprocal specialization (exclusiveness) than expected under neutral conditions. A higher exclusiveness is coherent with a tighter coevolution and suggests a lower ecological redundancy than implied by nested networks.