| 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 |
| City of the host institution: | São Paulo |
| 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 interactamong them and with the environment in an intricate and highly nonlinear way. As a result ofthese interactions, complex systems often self-organize in spatiotemporal patterns. Thesepatterns have attracted a lot of attention in the last years, initially due to their unquestionablebeauty, and later due to the important information that they contain about the state of thesystem in which they form. For instance, spatial patterns have been hypothesized to informabout the robustness of the ecosystems in which they form, thus constituting an importanttool to prevent biodiversity loss.Due to the long timescales in which patterns emerge, mathematical modeling has been avery powerful tool to explore their origin and to speculate about their possible (eco)systemlevel consequences. Existing models for pattern formation, however, focus on reproducingthe observed shape of the pattern and often avoid a detailed description of its underlyinginteractions. This approach has recently raised important concerns, since patterns that seem tobe identical can emerge in very different contexts and from very different underlyingprocesses, which may lead to contradictory system-level consequences. Therefore, in order toexploit biological patterns and extract meaningful conclusions about their potentialimplications, it is necessary to develop a new theoretical framework that focuses not only onrecovering the observed structures, but also on doing so from the right set of individual-levelinteractions. I propose such new approach to pattern formation, in which self-organizedpatterns and their ecological and evolutionary consequences arise naturally from a detaileddescription 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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