Exciting new research questions have recently emerged in aquatic ecology, questions that are related to biological, ecological, and physical processes at multiple scales. Sparsely sampled locations make it difficult to recognize multi-scale patterns, and it is prohibitively costly to collect a continuous sample throughout space. Geostatistical methods use spatial data efficiently, and can be used to investigate spatial patterns in streams, relate patterns to processes, and make predictions throughout stream networks. Geostatistical models are typically based on Euclidean distance, which may not be ecologically relevant for many aquatic processes because of flow direction, flow volume, and the network structure of streams. Functional distances that use flow and hydrologic distance better represent the transfer of organisms, material, and energy through networks. Yet, calculating these functional distances for a stream network remains challenging, and substituting hydrologic distance for Euclidean distance usually violates geostatistical modeling assumptions. The purpose of this workshop is to share new ideas and software to better analyze data from stream networks. We begin by establishing the relationship of spatial statistical models to relevant ecological questions. We also describe a new family of geostatistical models using hydrologic distance that we developed for stream networks. Through several examples, we will then give step-by-step instructions for the analysis of data from stream networks. We will demonstrate GIS tools for calculating hydrologic distance parameters, and statistical tools for fitting geostatistical models for stream networks, including spatial regression, kriging, uncertainty estimation, and visualization techniques. All students should bring their own laptops.