A comprehensive approach to analyzing community dynamics using rank abundance curves
A comprehensive approach to analyzing community dynamics using rank abundance curves
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DOI：
10.1002/ecs2.2881
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发表时间：
2019-10-01
期刊：
影响因子：
2.7
通讯作者：
Wilcox, Kevin R.
中科院分区：
文献类型：
--
作者：
Avolio, Meghan L.;Carroll, Ian T.;Wilcox, Kevin R.
单变量和多变量方法通常用于探索生态群落的时空动态，但每种方法都存在局限性，包括对群落的过度简化或抽象。等级丰度曲线（RACs）通过详细描述物种水平的群落变化，有可能整合这些现有的方法。在这里，我们有三个目标：首先，通过开发一套协调的R函数来简化对社区动态的分析；其次，揭开单变量、多变量和rac测量之间的关系的神秘面纱，并检查每个测量如何受到社区参数和数据收集方法的影响。我们在更新的R包库（“codyn”）中开发了新的功能来研究rac的时间变化和空间差异，以及其他新的功能来计算群落动态的单变量和多变量测量。我们还开发了一种新的方法来研究RAC曲线形状的变化。这里提出的R包更新增加了单变量和多变量测量随时间和空间变化的可及性。接下来，我们使用模拟和真实数据来评估RAC和由我们的新函数输出的多变量度量，研究(1)它们是否受到物种丰富度和均匀度、时间更替和空间变异性的影响；(2)这些度量之间的关系。最后，以长期营养添加试验为例，探讨了这些措施的应用。我们发现RAC和多变量测量对物种丰富度和均匀度不敏感，并且所有测量都详细描述了时间变化或空间差异的独特方面。我们还发现，物种重排序是组成变化的多变量测量的最强相关性，并解释了长期营养添加实验中观察到的大多数群落变化。总的来说，我们表明物种重排序是群落随时间变化或处理差异的潜在未充分研究的决定因素。这里开发的功能应加强rac的使用，以进一步探索生态群落的动态。
Univariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or abstraction of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species-level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library("codyn"), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long-term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long-term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.