The understanding of how processes in natural phenomena interact at different scales of time has been a great challenge for humans. How information is transferred across scale is fundamental if one tries to scale up from finer to coarse levels of granularity. Computer simulation has been a powerful tool to determine the appropriate amount of detail one has to impose when developing simulation models of such phenomena. However, it has been proved to be difficult to represent change at many scales of time and subject to cyclical processes. This issue has received little attention in traditional AI work on temporal reasoning but it becomes important in more complex domains, such as ecological modelling. Traditionally, models of ecosystems have been developed in imperative languages. Very few of those temporal logic theories have been used to the specification of simulation models in ecology. The aggregation of processes working at different scales of time is very difficult (sometimes impossible) to do reliably. The reason is because these processes influence each other, and their functionality does not always scale to other levels. Thus the problems to tackle are representing cyclical and interacting processes at many scales and to provide a framework to make the integration of such processes more reliable. We propose a framework for temporal modelling which allows modellers to represent cyclical and interacting processes at many scales. This theory combines both aspects by means of modular temporal classes and an underlying special temporal unification algorithm. To allow integration of different models they are developed as agents to run within a certain range of autonomy in a multi-agent system architecture. This Ecoagency framework is evaluated on ecological modelling problems and compared to a formal language for describing ecological systems.