Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models
Quantifying effects of abiotic and biotic drivers on community dynamics with multivariate autoregressive (MAR) models
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DOI：
10.1890/13-0996.1
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发表时间：
2013-12-01
期刊：
影响因子：
4.8
通讯作者：
Ward, Eric J.
中科院分区：
文献类型：
--
作者：
Hampton, Stephanie E.;Holmes, Elizabeth E.;Ward, Eric J.
长期生态数据集提供了机会，以确定社区动态的驱动因素，并通过时间序列分析量化其影响。多元自回归（MAR）模型在许多其他学科中是众所周知的，如计量经济学，但MAR方法在生态学和自然资源管理中的广泛采用要慢得多，尽管有一些广泛引用的生态学例子。在这里，我们回顾了以前的生态应用MAR模型，并强调他们的能力，以确定非生物和生物的驱动程序的人口动态，以及社区水平的稳定性指标，从长期的经验观察。到目前为止，MAR模型主要用于淡水浮游生物群落的数据，我们研究的障碍，可能会阻碍其他系统的采用，并提出实际的修改，将改善MAR模型更广泛的应用。这些修改中的许多在MAR模型常见的其他领域中已经是众所周知的，尽管它们经常以不同的名称描述。在努力使MAR模型更容易获得生态学家，我们包括一个工作的例子，使用最近开发的R包（MAR1和MARSS），免费提供和开放访问的软件。
Long-term ecological data sets present opportunities for identifying drivers of community dynamics and quantifying their effects through time series analysis. Multivariate autoregressive (MAR) models are well known in many other disciplines, such as econometrics, but widespread adoption of MAR methods in ecology and natural resource management has been much slower despite some widely cited ecological examples. Here we review previous ecological applications of MAR models and highlight their ability to identify abiotic and biotic drivers of population dynamics, as well as community-level stability metrics, from long-term empirical observations. Thus far, MAR models have been used mainly with data from freshwater plankton communities; we examine the obstacles that may be hindering adoption in other systems and suggest practical modifications that will improve MAR models for broader application. Many of these modifications are already well known in other fields in which MAR models are common, although they are frequently described under different names. In an effort to make MAR models more accessible to ecologists, we include a worked example using recently developed R packages (MAR1 and MARSS), freely available and open-access software.