Successful teams can adaptively coordinate their members' capabilities, communicate critical information, and are resilient to situational uncertainty, even in changing conditions. In general, teams operate with constraints on resources and therefore must have the ability to determine how best to distribute the team's efforts and capabilities to complete their tasks/mission. As such, the problem of identifying and performing tasks that change in frequency and location is a fundamental challenge to each member of the team. In biology, there are many examples of animal groups or insect collectives that are particularly good at adaptive and robust assignments. For teams of robots, the problem of effectively assigning robots to tasks is known as the Multi Robot Task Allocation (MRTA) problem. Existing MRTA approaches predominantly focus on assigning each robot to a task, which works well if the team is small (fewer than 20 robots) and if the individual task specification does not change (monitoring a static environment). Nevertheless, these solutions require solving a combinatorial optimization problem, which has poor computational scalability as the team and number of tasks increase -- all of which is further exacerbated by changing task or environment conditions. Taking inspiration from biology, instead of posing the problem of assigning robots to tasks (or animals to resources), we allow robots to randomly select a resource weighted by the perceived resource value. Such an approach has been shown to result in beneficial population configurations in biological systems. Towards this end, we propose macroscopic ensemble allocation approaches to solve the MRTA problem and explicitly model the team-wide objectives. These methods can assign robots to stationary task regions, easily control large robot teams (more than 50 robots), and even describe robot team heterogeneity. Despite these advantages, macroscopic ensemble methods have only been effectively used for MRTA problems in static settings. The goal of this thesis is to develop novel adaptive, flexible, collaborative, and resilient macroscopic ensemble methods when environment or task conditions change. The main contributions of this dissertation are the development of online, adaptive, and distributed macroscopic allocation strategies via time-based task transitions and robot-robot collaboration. Our results show coordinated robot teams monitoring spatiotemporal environments using simulation and robot experiments.