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Activity Number: 577 - Statistical Models in Ecology
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #305327 Presentation
Title: Bayesian Hierarchical Normal Intrinsic Conditional Autoregressive Model for Stream Networks
Author(s): Yingying Liu* and Kate Cowles
Companies: Biogen and University of Iowa
Keywords: Bayesian Hierarchical Model; ICAR model; Stream Networks

Water quality monitoring and analysis can help researchers predict and learn from natural processes in the environment and determine human impacts on an ecosystem. Stream network models are used to analyze the movement of substances or aquatic life along streams and rivers. In order to model stream network data based on stream distance and water flow, two types of moving average models were developed: tail-up models and tail-down models (Ver Hoef and Peterson, 2010). A stream network could be pure tail-up, pure tail-down, or a mixture of both. In this presentation, a Bayesian hierarchical normal intrinsic conditional autoregressive (HNICAR) model is proposed to approximate stream network models for faster computation. This HNICAR model is a flexible framework for modeling spatially continuous data from stream networks and is applied to a real world example of a stream temperature dataset on the Lewis and Willamette watersheds connected to the Columbia River in the western U.S to help predict the overall thermal suitability for fish in that aquatic ecosystem.

Authors who are presenting talks have a * after their name.

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