Theoretical approaches to describe population dynamics tend to focus on a particular scale, balancing the trade-offs of including each type of interaction. For small scales, individual-based models (IBM) can account for an arbitrary level of detail. However, due to its high computational cost, it is limited to small population sizes. For large scales, density-field descriptions (DFD) have been used, but they neglect the dynamics below a cut-off scale. To reconcile both approaches one can perform a coarse-graining procedure that patches together IBMs and DFDs. Since, in general, the emergent properties of the IBMs are out-of-equilibrium and regulated by a complicated set of forces, one should look for computational ways of linking both descriptions. In this talk, I will go through the multiscale framework that collaborators and I have developed to address the large-scale patterns produced by plankton communities.
A fundamental problem in ecology is how individual-level behavior produces macroecological outcomes. The challenges involved in addressing this issue are not only related to the complexity that nature exhibits across scales but also to the fact that theoretical approaches are inevitably scale-constrained, focusing either on the small or large scales. In this seminar, I will present how we applied the multiscale framework that collaborators and I have developed to address the kilometer-scale patterns produced by plankton communities. To further our understanding regarding their bottom-up regulation, we performed a coarse-graining procedure of an agent-based model for a phytoplankton-zooplankton community. The coarse-graining procedure provides a scalable density-field description that maps large-scale features of spatial patterns to individual-level behavior. This powerful connection can guide remote-sensing applications: revealing species' traits and behavior and helping to anticipate regime shifts in the ocean.
