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Ecological research increasingly considers integrative relationships among phenomena at broad spatial and temporal domains. However, such large-scale inferences are commonly confounded by changing properties in the processes that govern phenomena (termed nonstationarity), which can violate assumptions underlying standard analytical methods. Changing conditions are funda-mental and pervasive features in ecology, but their influence on ecological inference and prediction increases with larger spatial and temporal domains for a host of factors. Fortunately, tools for identifying and accommodating potentially confounding spatial or temporal trends are available, and new methods are being rapidly developed. Here, we provide guidance for gaining a better understanding of nonstationarity, its causes, and how it can be addressed. Acknowledging and addressing non-constant trends in ecological patterns and processes is key to conducting large-scale research and effectively translating findings to local policies and practices. 

To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal land cover derived from MODIS-based IGBP land cover types compiled using Google Earth Engine (GEE).