QUANTIFYING PERIODIC, STOCHASTIC, AND CATASTROPHIC ENVIRONMENTAL VARIATION
QUANTIFYING PERIODIC, STOCHASTIC, AND CATASTROPHIC ENVIRONMENTAL VARIATION
复制标题
量化周期性、随机性和灾难性环境变化
DOI：
10.1890/06-1340.1
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发表时间：
2008
影响因子：
6.1
通讯作者：
D. Post
中科院分区：
文献类型：
--
作者：
J. Sabo;D. Post
环境变化在调节各级生态组织的过程中起着核心作用。环境数据（例如，温度、降雨量、河流流量、水化学）通常很容易大量收集，这是许多数据饥渴型时间序列工具的要求。不幸的是，这些数据很少在生态学中得到有效利用。在这里，我们通过概述一套工具来解决这个问题，这些工具可用于量化环境条件中的周期性、随机性和灾难性变化。我们说明了这些工具的应用程序，使用长期记录的平均日流量在105个溪流和河流保持美国地质调查局的NWIS（国家水信息系统）网站。具体而言，我们应用傅立叶分析估计的周期性（季节性）和随机（年际）的流量变化的组件。然后，我们估计随机变化的时间自相关结构（即，噪声颜色）在每日流量为ea.
Environmental variation plays a central role in regulating processes at all levels of ecological organization. Environmental data (e.g., temperature, rainfall, stream discharge, water chemistry) are typically easy to collect in large quantity, a requirement for many data-hungry time series tools. Unfortunately, these data are very rarely used effectively in ecology. Here we address this problem by outlining a suite of tools that can be used to quantify periodic, stochastic, and catastrophic variation in environmental conditions. We illustrate the application of these tools using long-term records of average daily discharge in 105 streams and rivers maintained by the U.S. Geological Survey on the NWIS (National Water Information System) web site. Specifically, we apply Fourier analysis to estimate the periodic (seasonal) and stochastic (interannual) components of variation in discharge. We then estimate the temporal autocorrelation structure of stochastic variation (i.e., noise color) in daily flows for ea...