Biomathematics Research Centre, University of Canterbury, Private Bag 4800, Christchurch 8040, New Zealand.
Nature. 2012 Jul 12;487(7406):227-30. doi: 10.1038/nature11214.
Complex networks of interactions are ubiquitous and are particularly important in ecological communities, in which large numbers of species exhibit negative (for example, competition or predation) and positive (for example, mutualism) interactions with one another. Nestedness in mutualistic ecological networks is the tendency for ecological specialists to interact with a subset of species that also interact with more generalist species. Recent mathematical and computational analysis has suggested that such nestedness increases species richness. By examining previous results and applying computational approaches to 59 empirical data sets representing mutualistic plant–pollinator networks, we show that this statement is incorrect. A simpler metric—the number of mutualistic partners a species has—is a much better predictor of individual species survival and hence, community persistence. Nestedness is, at best, a secondary covariate rather than a causative factor for biodiversity in mutualistic communities. Analysis of complex networks should be accompanied by analysis of simpler, underpinning mechanisms that drive multiple higher-order network properties.
相互作用的复杂网络无处不在,在生态群落中尤为重要,其中大量物种之间存在负相互作用(例如竞争或捕食)和正相互作用(例如互利共生)。互利共生生态网络中的嵌套性是指生态专家与一组物种相互作用,而这些物种也与更普遍的物种相互作用。最近的数学和计算分析表明,这种嵌套性会增加物种丰富度。通过检查以前的结果并应用计算方法对 59 个代表互利共生植物-传粉者网络的经验数据集进行分析,我们表明这种说法是不正确的。一个更简单的指标——物种拥有的互利共生伙伴的数量——是更好地预测单个物种生存和因此,群落持续存在的指标。嵌套性充其量只是一个次要的协变量,而不是互利共生群落中生物多样性的因果因素。对复杂网络的分析应该伴随着对驱动多个高阶网络特性的更简单的基础机制的分析。