Many biological systems contain spatial networks that transport resources. Examples include the branches of trees and the circulatory systems of animals. These networks vary widely in their form. Some only branch, while others form loops. Some have multiple levels of hierarchy, while others do not. This variation may reflect evolved solutions for solving diverse environmental and structural challenges. This project will study key network functions in plants, including transport efficiency, resistance to damage, and mechanical strength. There is little theory linking network form to these functions, or for predicting tradeoffs among these functions. Moreover, very few networks have been fully characterized for structure or function due to the difficulty of collecting the data and describing network architecture. Better understanding the rules underlying network architecture will provide insights into the evolution of diverse organismal forms. The principles identified in this research could one day guide the engineering of artificial networks such as solar cells or synthetic organs that could benefit society. The project will also support career development undergraduate researchers via a comprehensive mentoring program aimed at inclusion of underrepresented minority students.
This project will use leaf venation networks are a model empirical system. Leaves are central to plant performance via their roles in carbon gain and water loss, processes mediated by resource transport through their venation networks. These networks have high diversity of form and function and are tractable to phenotyping and functional characterization. This project will 1) quantify network architecture in a phylogenetically broad set of 500 species from temperate forests, desert, and lowland/montane tropical forests, 2) determine how network architecture and functions/costs are linked, 3) develop and test theories for these functions/costs of networks based on multi-scale network statistics, and 4) identify macro-evolutionary drivers of network architecture. Network functionality will be measured in the field with ecophysiology methods. Machine learning methods will be used to extract network architecture from images. The project also will support interdisciplinary training for one postdoctoral researcher and two graduate students, who will gain international fieldwork and collaboration experience.
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.
