Community trait structure in phytoplankton: seasonal dynamics from a method for sparse trait data
Community trait structure in phytoplankton: seasonal dynamics from a method for sparse trait data
复制标题
DOI：
10.1002/ecy.1581
复制
发表时间：
2016-12-01
期刊：
影响因子：
4.8
通讯作者：
Edwards, Kyle F.
中科院分区：
文献类型：
--
作者：
Edwards, Kyle F.
功能性状在群落中的分布，以及性状分布如何随时间和空间的变化，是理解群落结构、多样性维持以及群落对生态系统功能影响的关键信息。通常情况下，与生态性能密切相关的特征，如生理能力，很难测量，而且在物种群落中基本上是未知的;然而，这些特征对于揭示群落结构的机械基础特别重要。在这里，我开发了一种方法，结合稀疏性状的数据与统计生态位模型来推断浮游植物群落的性状分布，以及它们在西英吉利海峡10年内的变化。我发现，社区平均硝酸盐亲和力，光限制的增长率，最大增长率都显示出主要的季节性模式，反映交替限制光与氮。性状多样性表现出与群落性状多样性不同的多样性模式，这表明功能多样性的调控是复杂的。诸如此类的模式对于预测海洋生态系统将如何应对全球变化以及开发基于特征的新兴群落结构模型非常重要。这里使用的统计方法可以适用于任何类型的生物体，如果它表现出很强的关系性状和统计生态位估计。
The distribution of functional traits in communities, and how trait distributions shift over time and space, is critical information for understanding community structure, the maintenance of diversity, and community effects on ecosystem function. It is often the case that traits tightly linked to ecological performance, such as physiological capacities, are laborious to measure and largely unknown for speciose communities; however, these traits are particularly important for unraveling the mechanistic basis of community structure. Here I develop a method combining sparse trait data with a statistical niche model to infer trait distributions for phytoplankton communities and how they vary over 10 yr in the western English Channel. I find that community-average nitrate affinity, light-limited growth rate, and maximum growth rate all show major seasonal patterns, reflecting alternate limitation by light vs. nitrogen. Trait diversity exhibits a variety of patterns distinct from community trait means, which suggests complex regulation of functional diversity. Patterns such as these are important for predicting how ocean ecosystems will respond to global change, and for developing trait-based models of emergent community structure. The statistical approach used here could be applied to any kind of organism, if it exhibits strong relationships between traits and statistical niche estimates.