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A simulation study of trend detection methods for integrated ecosystem assessment

机译：综合生态系统评估趋势检测方法的仿真研究

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摘要

The identification of trends in ecosystem indicators has become a core component of ecosystem approaches to resource management, although oftentimes assumptions of statistical models are not properly accounted for in the reporting process. To explore the limitations of trend analysis of short times series, we applied three common methods of trend detection, including a generalized least squares model selection approach, the Mann-Kendall test, and Mann-Kendall test with trend-free pre-whitening to simulated time series of varying trend and autocorrelation strengths. Our results suggest that the ability to detect trends in time series is hampered by the influence of autocorrelated residuals in short series lengths. While it is known that tests designed to account for autocorrelation will approach nominal rejection rates as series lengths increase, the results of this study indicate biased rejection rates in the presence of even weak autocorrelation for series lengths often encountered in indicators developed for ecosystem-level reporting (N = 10, 20, 30). This work has broad implications for ecosystemlevel reporting, where indicator time series are often limited in length, maintain a variety of error structures, and are typically assessed using a single statistical method applied uniformly across all time series.
机译：确定生态系统指标的趋势已成为生态系统资源管理方法的核心组成部分，尽管在报告过程中常常没有适当考虑统计模型的假设。为了探索短期序列趋势分析的局限性，我们应用了三种常见的趋势检测方法，包括广义最小二乘模型选择方法，Mann-Kendall检验和具有无趋势预白化的Mann-Kendall检验，以进行仿真。变化趋势和自相关强度的时间序列。我们的结果表明，检测时间序列趋势的能力受到短序列长度中自相关残差的影响。虽然众所周知，随着序列长度的增加，旨在说明自相关的测试将接近标称拒绝率，但这项研究的结果表明，在为生态系统级报告而开发的指标中，对于序列长度即使存在较弱的自相关，拒绝率也存在偏差（N = 10、20、30）。这项工作对生态系统级别的报告具有广泛的意义，在这种情况下，指标时间序列通常长度有限，维护各种错误结构，并且通常使用统一应用于所有时间序列的单一统计方法进行评估。

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