Identifying the density-dependent structure underlying ecological time series
Identifying the density-dependent structure underlying ecological time series
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DOI：
10.1034/j.1600-0706.2001.920208.x
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发表时间：
2001-02-01
期刊：
影响因子：
3.4
通讯作者：
Turchin, P
中科院分区：
文献类型：
--
作者：
Berryman, A;Turchin, P
生态学中的一个中心问题是解释种群波动的原因，解决问题的重要步骤是确定调节种群动态的负反馈（密度依赖）过程的结构。确定时间序列中密度依赖的维数或阶数的传统方法是计算部分自相关函数（PACF）。然而，我们认为，PACF的设计没有考虑到生物种群，并有错误的空模型检测密度依赖的结构。我们建议一种替代的诊断，部分率相关函数（PRCF），这是专门为生物种群设计的，并有一个适当的空模型，用于检测其密度相关的结构。用模拟数据进行的检验表明，PRCF在检测两个简单数学模型的密度依赖结构方面优于PACF，具有上级的优势。
A central problem in ecology is explaining the causes of population fluctuations, and an important step in the solution is determining the structure of the negative feedback (density dependent) process regulating population dynamics. The conventional way to determine the dimension or order of density dependence in a time series is to calculate the partial autorcorrelation function (PACF). We maintain, however, that PACF is not designed with biological populations in mind and has the wrong null model for detecting the structure of density dependence. We suggest an alternative diagnostic, the partial rate correlation function (PRCF), which is specifically designed for biological populations and has an appropriate null model for detecting their density dependent structures. Tests with simulated data show PRCF to be superior to PACF in detecting the underlying density dependent structure of two simple mathematical models.