URoL:EN: Emergence of function and dynamics from ecological interaction networks

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Biological systems at virtually all levels of organization are defined by diversity – diversity not only of their constituent units, but also of the interactions among those units. Understanding how the core functions of these systems emerge from such diverse interactions is a fundamental challenge that cuts across fields ranging from physiology and medicine to ecology and environmental engineering. Scientists have long sought to simplify biological interactions by classifying them as either negative (e.g., competitors vying for resources) or positive (e.g., cells cooperating within tissue). Yet, upon close inspection, biological interactions regularly defy this simple dichotomy, and are instead “multivalent”: they involve many different types of interaction occurring simultaneously. Examples include bacteria that exchange genes conferring antibiotic resistance while competing for limiting nutrients, or trees that share carbon through mycorrhizal networks even as they compete for light and water. Multivalent interactions such as these appear to be ubiquitous in biology, but they are not easily incorporated into the conventional network models that are widely used to study system-scale dynamics. This project aims to reveal the Rule of Life that governs how function and dynamics emerge in systems of multivalent interactors. The researchers will decipher this Rule using a model ecosystem: coral reefs. In coral reefs, consumption of algae by fish drives dynamics of diverse fish populations and promotes a coral-dominated ecosystem state. The project team will rigorously measure how multivalent interactions among fish species scale up to influence ecosystem dynamics. In doing so, the team will develop data collection technologies and mathematical tools that will provide a generalizable methodology for measuring and understanding one of the most elusive, yet fundamental aspects of complex biological systems: biological interactions themselves. The project’s findings will inform management of coral reef ecosystems vital to over 500 million people worldwide. Moreover, the project will create a technical undergraduate internship program for students from historically underrepresented backgrounds and an international reef monitoring program that empowers local citizen scientists.Through three aims, the project team will develop methods that discover hidden structure in complex networks of ecological interactions and exploit that structure to understand ecosystem-level function and responses to environmental change. The first phase of the project will use field experiments, new camera technologies, and computer vision to directly measure ecological interactions among species, along with a novel algorithmic modeling framework to describe interaction behaviors based on quantitative behavioral traits. The second phase will use manifold learning methods to search behavioral trait data for “functional clusters” of species and exploit this structure to derive coarse-grained dynamical models of the system. These models will be used to evaluate how the structure of interactions drives emergent ecosystem function. In the third phase, the team will use data-driven dynamical models to project how system function and state will respond to future environmental change. Coarse-grained ecosystem models will be developed to understand long-term, system-scale responses to environmental perturbations. These models will incorporate empirically derived evolutionary rates to evaluate the capacity for evolutionary rescue to influence how ecosystems will respond to change. The high-throughput data-collection technologies, dimension reduction tools, and dynamical modeling methods developed in this research will help provide a novel toolkit for data-driven discovery of the dynamical structure of interactions and their consequences in complex biological systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.