Linking multidimensional functional diversity to quantitative methods: a graphical hypothesis-evaluation framework
Linking multidimensional functional diversity to quantitative methods: a graphical hypothesis-evaluation framework
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DOI：
10.1890/15-0688
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发表时间：
2016-03-01
期刊：
影响因子：
4.8
通讯作者：
Gitelman, Alix I.
中科院分区：
文献类型：
--
作者：
Boersma, Kate S.;Dee, Laura E.;Gitelman, Alix I.
功能性状分析是研究生物群落差异的一种有吸引力的方法，因为性状决定了物种对环境的反应及其对生态系统功能的影响。尽管数量的文献迅速扩大，它仍然具有挑战性的概念化并发变化的多个性状维度（性状空间），并选择定量功能多样性的方法来测试假设分析之前。为了满足这一需求，我们提出了一个广泛适用的框架可视化生态现象的性状空间，以指导选择，应用和解释的定量功能多样性方法。我们描述了五个假设，代表一般模式的功能群落生态学干扰的反应，然后应用一个正式的决策过程，以确定适当的定量方法来测试生态假设。作为这个过程的一部分，我们设计了一个新的统计方法来测试社区之间的功能营业额。我们的假设和指标的组合可以广泛应用于解决生态问题，在一系列的系统和研究设计。我们说明的框架与淡水社区干扰的案例研究。这种假设驱动的方法将增加应用功能性状研究的严谨性和透明度。
Functional trait analysis is an appealing approach to study differences among biological communities because traits determine species' responses to the environment and their impacts on ecosystem functioning. Despite a rapidly expanding quantitative literature, it remains challenging to conceptualize concurrent changes in multiple trait dimensions (trait space) and select quantitative functional diversity methods to test hypotheses prior to analysis. To address this need, we present a widely applicable framework for visualizing ecological phenomena in trait space to guide the selection, application, and interpretation of quantitative functional diversity methods. We describe five hypotheses that represent general patterns of responses to disturbance in functional community ecology and then apply a formal decision process to determine appropriate quantitative methods to test ecological hypotheses. As a part of this process, we devise a new statistical approach to test for functional turnover among communities. Our combination of hypotheses and metrics can be applied broadly to address ecological questions across a range of systems and study designs. We illustrate the framework with a case study of disturbance in freshwater communities. This hypothesis-driven approach will increase the rigor and transparency of applied functional trait studies.