Evaluating the performance of temporal and spatial early warning statistics of algal blooms
Evaluating the performance of temporal and spatial early warning statistics of algal blooms
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DOI：
10.1002/eap.2616
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发表时间：
2022-05-19
影响因子：
5
通讯作者：
Ha, D. T.
中科院分区：
文献类型：
--
作者：
Buelo, C. D.;Pace, M. L.;Ha, D. T.
制度变迁对生态系统及其提供的服务产生重大影响。然而，理解的潜力，原因，接近，和阈值政权转变几乎在所有的设置是困难的。在广泛的生态系统中，已经提出并研究了复原力的通用统计指标，作为一种方法，在没有直接了解基本系统动态或阈值的情况下，检测何时更有可能发生制度转变。这些预警统计数据（EWS）已被单独研究，但有几个例子，直接比较生态系统规模的经验数据的时间和空间的EWS。为了测试这些方法，我们收集了高频率的时间序列和高分辨率的空间数据，在整个湖泊施肥实验，同时也监测相邻的参考湖。我们计算了两个常见的EWS，标准差和自相关，在时间序列和空间数据，以评估其性能之前，所产生的水华。我们还应用最快的检测方法来生成临时EWS弹性变化的二进制警报。一个时间的EWS，滚动窗口标准差，提供了先进的警告，在大多数变量之前的绽放，显示的趋势和湖之间的模式与理论一致。与此相反，时间自相关和空间EWS（空间SD，莫兰的我）的措施提供很少或没有警告。通过编译时间序列数据，从这个和过去的实验，有和没有营养添加剂，我们能够评估时间EWS性能恒定和不断变化的弹性条件。当湖泊被推向水华时，滚动窗口标准差的真阳性警报率比假阳性率高2.5 - 8.3倍。对于滚动窗口自相关，报警率要低得多，没有变量的真阳性报警率高于假阳性报警率。我们的研究结果表明，时间EWS提供藻类水华的提前预警，这种方法可以帮助管理人员准备和/或尽量减少负面的水华影响。
Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem-scale empirical data. To test these methods, we collected high-frequency time series and high-resolution spatial data during a whole-lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between-lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's I) provided little or no warning. By compiling time series data from this and past experiments with and without nutrient additions, we were able to evaluate temporal EWS performance for both constant and changing resilience conditions. True positive alarm rates were 2.5-8.3 times higher for rolling window standard deviation when a lake was being pushed towards a bloom than the rate of false positives when it was not. For rolling window autocorrelation, alarm rates were much lower and no variable had a higher true positive than false positive alarm rate. Our findings suggest temporal EWS provide advanced warning of algal blooms and that this approach could help managers prepare for and/or minimize negative bloom impacts.