Bimodal trait distributions with large variances question the reliability of trait-based aggregate models
Bimodal trait distributions with large variances question the reliability of trait-based aggregate models
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DOI：
10.1007/s12080-016-0297-9
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发表时间：
2016-12-01
影响因子：
1.6
通讯作者：
Gaedke, Ursula
中科院分区：
文献类型：
--
作者：
Coutinho, Renato Mendes;Klauschies, Toni;Gaedke, Ursula
功能多样的社区可以调整其物种组成，以改变环境条件，这可能会影响食物网动态。基于特征的聚合模型通过忽略物种身份的细节而专注于它们的功能特征（特征）来科普这种复杂性。它们描述了整个群落的总体性质的时间变化，包括它们的总生物量、平均性状值和性状方差。聚合模型的适用性取决于其基本假设的有效性，即特质分布是正态的，并且表现出小的方差。我们研究了在何种程度上，这可以预期的工作比较一个创新的模型，占捕食者和猎物社区的完整的性状分布，以相应的聚合模型。我们使用了一个食物网的结构与完善的权衡性状之间的相互调整，促进猎物的可食性和捕食者的选择性选择。我们改变了权衡的形状，以比较两个模型在不同选择制度下的结果，导致性状分布越来越偏离正态性。它们的生物量和性状动态在稳定选择和定向选择中表现得很好，在定向选择中，不同的性状值在不同的时间受到青睐。然而，对于破坏性选择，聚集模型的结果强烈偏离全性状分布模型，表现出大的方差双峰性状分布。因此，聚合模型的结果是可靠的，在理想条件下，但当面对更复杂的选择制度和性状分布，这是在自然界中常见的问题。
Functionally diverse communities can adjust their species composition to altered environmental conditions, which may influence food web dynamics. Trait-based aggregate models cope with this complexity by ignoring details about species identities and focusing on their functional characteristics (traits). They describe the temporal changes of the aggregate properties of entire communities, including their total biomasses, mean trait values, and trait variances. The applicability of aggregate models depends on the validity of their underlying assumptions that trait distributions are normal and exhibit small variances. We investigated to what extent this can be expected to work by comparing an innovative model that accounts for the full trait distributions of predator and prey communities to a corresponding aggregate model. We used a food web structure with well-established trade-offs among traits promoting mutual adjustments between prey edibility and predator selectivity in response to selection. We altered the shape of the trade-offs to compare the outcome of the two models under different selection regimes, leading to trait distributions increasingly deviating from normality. Their biomass and trait dynamics agreed very well for stabilizing selection and reasonably well for directional selection, under which different trait values are favored at different times. However, for disruptive selection, the results of the aggregate model strongly deviated from the full trait distribution model that showed bimodal trait distributions with large variances. Hence, the outcome of aggregate models is reliable under ideal conditions but has to be questioned when confronted with more complex selection regimes and trait distributions, which are commonly observed in nature.