| Grant number: | 19/05523-8 |
| Support Opportunities: | BIOTA-FAPESP Program - Young Investigators Grants |
| Start date: | November 01, 2019 |
| End date: | October 31, 2023 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics |
| Principal Investigator: | Ricardo Martinez Garcia |
| Grantee: | Ricardo Martinez Garcia |
| Host Institution: | Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil |
| Associated scholarship(s): | 20/14169-0 - The effect of a chaotic environmental flow on the evolution of microbial social behaviors,
BP.MS 20/15643-8 - Aggregation patterns of living organisms: connecting mathematical modeling to experimental data, BP.MS 19/26736-0 - Population dynamics with nonlocal interactions: from the emergence of patterns to species coexistence, BP.MS 19/24433-0 - Toward a mechanistic theory for biological patterns and their ecosystem-management possibilities, AP.BTA.JP |
Abstract
Complex biological systems are composed by many entities that interact among them and with the environment in an intricate and highly nonlinear way. As a result of these interactions, complex systems often self-organize in spatiotemporal patterns. These patterns have attracted a lot of attention in the last years, initially due to their unquestionable beauty, and later due to the important information that they contain about the state of the system in which they form. For instance, spatial patterns have been hypothesized to inform about the robustness of the ecosystems in which they form, thus constituting an important tool to prevent biodiversity loss. Due to the long timescales in which patterns emerge, mathematical modeling has been a very powerful tool to explore their origin and to speculate about their possible (eco)system level consequences. Existing models for pattern formation, however, focus on reproducingthe observed shape of the pattern and often avoid a detailed description of its underlying interactions. This approach has recently raised important concerns, since patterns that seem to be identical can emerge in very different contexts and from very different underlying processes, which may lead to contradictory system-level consequences. Therefore, in order to exploit biological patterns and extract meaningful conclusions about their potential implications, it is necessary to develop a new theoretical framework that focuses not only on recovering the observed structures, but also on doing so from the right set of individual-level interactions. I propose such new approach to pattern formation, in which self-organized patterns and their ecological and evolutionary consequences arise naturally from a detailed description of the system-specific individual-level interactions. In this way, patterns represent a natural bridge between behavior, ecology, and evolution and thus provide a powerful way of integrating processes occurring at several scales in complex biological systems. (AU)
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