Environmental scientists frequently rely on time series of explanatory variables to explain their impact on an important response variable. However, sometimes, researchers are less interested in raw observations of an explanatory variable than in derived indices induced by episodes embedded in its time series. Often these episodes are intermittent, occur within a specific limited memory, persist for varying durations, at varying levels of intensity, and overlap important periods with respect to the response variable. We develop a generic, parametrised, family of weighted indices extracted from an environmental signal called IMPIT indices. To facilitate their construction and calibration, we developed a user friendly app in Shiny R referred to as IMPIT-a. We construct examples of IMPIT indices extracted from the Southern Oscillation Index and sea surface temperature signals. We illustrate their applications to two fished species in Queensland waters (i.e., snapper and saucer scallop) and wheat yield in New South Wales.
翻译:环境科学家常常依赖解释变量的时间序列来解释其对重要响应变量的影响。然而,有时研究人员并不是对解释变量的原始观测数据感兴趣,而是对其时间序列嵌入的情节性指标感兴趣。这些情节通常是间歇性的,发生在特定的受限记忆内,持续的时间不同,强度不同,并在响应变量的重要时期重叠。我们开发了一种从环境信号中提取的通用参数化的加权指标族,称为IMPIT指标。为了方便它们的构建和校准,我们开发了一个用户友好的Shiny R应用程序,称为IMPIT-a。我们构建了从南方涛动指数和海表温度信号中提取的IMPIT指标的示例。我们演示了它们在昆士兰水域两种捕捞物种(鲷鱼和碟形扇贝)和新南威尔士州小麦产量中的应用。