The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes
The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes
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DOI：
10.1016/j.ecoinf.2021.101336
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发表时间：
2021-06-05
影响因子：
5.1
通讯作者：
Whitney, Kaitlin Stack
中科院分区：
文献类型：
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作者：
Bahlai, Christie A.;White, Easton R.;Whitney, Kaitlin Stack
时间生态学的一个核心问题是可测性的概念，也就是说，生态学家什么时候能有合理的保证，他们知道一个系统的走向？在本文中，我们描述了一个非随机的resception方法，直接解决的时间方面的缩放生态观测利用现有的数据。来自长期研究地点的发现在生态学中具有巨大的影响力，因为它们具有前所未有的纵向视角，但与典型的资助周期和研究生课程更一致的短期研究仍然是常态。我们使用长期的见解来创建“破窗”，即从短期观察的角度重新分析长期研究，以检查不同时间尺度上趋势的不连续性。破窗算法通过一种自动化、系统化的恢复方法将我们的观察结果在短期和长期之间联系起来：简而言之，我们从现有的长期时间序列中反复“采样”移动窗口的数据，并分析这些采样数据，就好像它们代表了整个数据集一样。然后，我们通过重复采样，汇编用于描述采样数据中关系的典型统计数据，然后使用这些衍生数据来深入了解以下问题：1）在短期数据中观察到的趋势有多经常误导，以及2）这些趋势的特征可以用来预测我们被误导的可能性吗？我们开发了一个系统的恢复方法，"破窗算法，并说明其效用的萤火虫观测的案例研究产生的凯洛格生物站长期生态研究网站（KBS LTER）。通过各种可视化，汇总统计和下游分析，我们提供了一种标准化的方法来评估系统的轨迹，在类似系统中找到有意义的轨迹所需的观察量，以及评估我们对结论的信心的方法。
A core issue in temporal ecology is the concept of trajectory-that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitu-dinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create 'broken windows,' that is, reanalyze long-term studies from short -term observational perspectives to examine discontinuities in trends at differing temporal scales. The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly 'sample' moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the 'broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.