We consider the problem of distributed target tracking in a sensor network under communication constraints between the sensor nodes, a problem that has recently received significant attention. Communication constraints limit sensor data fusion in two ways. It significantly constrains sensor communication across large distances and substantially limits the number of sensors participating in data fusion at any time instant. We explore sensor management policies, i.e., sensor selection under communication constraints, for distributed target tracking. The coupled problem of track estimation and sensor management is generally intractable and significant effort has been devoted towards proposing simple strategies under various performance criteria. In this paper, we adopt a certainty equivalent approach and separate the tasks of track estimation and sensor management. Our approach is an adaptive dynamic strategy for sensor selection that seeks to optimize a tradeoff between tracking error and communications cost. We formulate the sensor management problem for the limiting case of infinite sensor density and derive sensor selection policies for different classes of target dynamics and sensor measurements. Under assumptions of a regular dense network with homogeneous sensors, the optimal strategy is a hybrid switching strategy, where the fusion center location and reporting sensors are held stationary unless the target estimates move outside of a threshold radius around the sensors. We simulate different tracking scenarios to illustrate the performance of our algorithms on sensor networks. We show that the computational as well as the communication costs are constant and do not scale with network size. We also perform different parametric studies to illustrate the validity of our approximations.