We develop coding strategies for estimation under communication constraints in tree-structured sensor networks. The strategies have a modular and decentralized architecture. This promotes the flexibility, robustness, and scalability that wireless sensor networks need to operate in uncertain, changing, and resource-constrained environments. The strategies are based on a generalization of Wyner-Ziv source coding with decoder side information. We develop solutions for general trees, and illustrate our results in serial (pipeline) and parallel (hub-and-spoke) networks. Additionally, the strategies can be applied to other network information theory problems. They have a successive coding structure that gives an inherently less complex way to attain a number of prior results, as well as some novel results, for the Chief Executive Officer problem, multiterminal source coding, and certain classes of relay channels.