Phytoplankton, and the zooplankton that graze upon them, play a crucial role in the dynamics observed at higher levels of the aquatic food chain and in climate change. Although there is a vast literature of data analysis and modelling work on changes in t he size and composition of plankton populations, there remains much that is poorly understood. While looking at the data alone can give a description of the plankton dynamics, analysing a mathematical model makes it possible to achieve a deep understandin g of the mechanism behind the dynamics. This understanding is a prerequisite for developing a model with predictive power that can be used to estimate, for example, consequences of global warming on plankton populations. In our project, by combining knowl edge of plankton and experience of mathematical modelling applied to biological systems we aim to shed more light into the driving forces of plankton populations while taking into account seasonality in the food availability and predation risk. Our object ive is to develop a model that can be used to test hypotheses on outcomes of plankton populations in environmental conditions that have changes also at a longer than seasonal time-scale. The challenge of the project lies in translating ecological complexi ty into a simplified model that is tractable and includes the most important aspects behind plankton population dynamics. We anticipate to increase the understanding of mechanisms behind plankton dynamics in a seasonal context. In addition, we aim to incr ease dialogue and collaboration between ecologists and mathematicians that play a key role in predicting possible changes in aquatic ecosystems due to climate change.