Submitted by Benjamin Blonder on
Leaf venation network and its hierarchal traits are crucial to the hydraulic properties of leaves, in that they reflect plant function and life-history strategies. It is not surprising these networks are variable amongst plant species – from a single vascular strand (e.g. pines) to open net patterns (e.g. many ferns) to mostly parallel structures in monocots (e.g. corn) to highly reticulate patterns in many angiosperms (e.g. orange). This variability may reflect evolved solutions for optimizing functionality (e.g. efficiency in water transport) and minimizing costs (e.g. non-living lignified tissues for transport). Other network functions in different context may include mechanical strength, damage resilience and resistance. However, there is an extremely limited understanding of how variation in leaf network architecture relates to hydraulics pathways of the plant. Indeed, veins act as the superhighway for delivery of water to cells/tissues but studies have shown that the efficiency of delivery can be mediated by certain hydraulic properties. For instance, the timing and formation of air bubbles (embolism) in hydraulic pathway in major veins may vary from that of minor vein particularly during extreme dehydration. The overall goal is to link leaf venation networks to leaf hydraulic traits using an innovative approach (machine learning algorithms). Our focus will target how different network features/structure predict certain functions in the leaf especially in relation to their vulnerability to dysfunction (P50; which is indicator of the hydraulic stress tolerance of the plant vascular xylem before it loses integrity)
Better understanding the rules linking network architecture and hydraulic traits will provide insights into ability of leaves to resist embolism formation (a key adaptive axis in plant evolution) under drought stress and will further have implications for agricultural productivity as well as for solving engineering (and other spatial network) problems e.g. solar cells or highway constructions.
Specific role for the undergraduate students (two total) will include working with a large dataset (of high and low-resolution images) showing the leaf venation networks for different plant species across different biomes. They will engage in literature search from peer review journals and data repositories to extract hydraulic traits (e.g. P50, turgor loss point, Kleaf). They will use machine learning algorithms to trace the geometry of the branching and looping patterns of leaves. They will use other algorithms to describe the architecture of these networks across spatial scales. The students will then carry out statistical analyses to understand how and why variation occurs across spatial scales and species.
Additionally, students will develop detailed skills in laboratorial work as well as in machine learning, imaging processing (ImageJ, GIMP) and computational data analysis with R/MATLAB. Students will have the chance to improve their presentation skills through lab presentations/conferences. There is the opportunity to participate in weekly lab meeting activities and social events organized by the Macrosystem Ecology Lab.
The project will be 100% remote unless public health conditions and restrictions change.
Candidates are required to be detailed oriented, to show a strong motivation to work in a laboratorial space and to have a STEM background. Biology major are preferred. Prior experience in acquiring/processing images or in data analysis with R or in laboratorial work is desirable, but not required. Sophomore, Junior or Senior are preferred.
Students with an interest in working with a diverse team (Brazil, Ghana, USA) are encouraged to apply. The lab provides an inclusive and supportive work culture. Current lab members are listed at http://benjaminblonder.org/people/. A $1000 stipend may be available for students who have overcome significant adversity to be present at the university.