Collaborative Research: Dry cuticles--revealing the physics-driven form of Pinus foliage using phylogenetics, experiment, and modeling

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

Pine trees are vital both commercially and ecologically, with many industries around the world relying on their lumber and pulp wood. In the United States, pine trees make up a large majority of the lumber output, playing a critical role in the forestry industry. Understanding how rainfall affects pine trees is crucial for industry and conserving habitats under a changing climate. This project explores how pine trees have adapted to manage water on their needles. Water on needle surfaces can block tiny pores needed for gas exchange, which is essential for photosynthesis, and make the trees more vulnerable to disease. By combining experiments, ecological data analysis, and predictive modeling, we will decipher the interplay between needle shape, surface properties, and elasticity in their ability to passively shed water. This research will enhance our understanding of how pine trees adapt to different environments and improve our knowledge of ecosystem resilience in the face of a changing climate. Ultimately, this research will revolutionize our understanding of pine needle function and shed light on the physics of fluid interaction with flexible biological structures. Pine needles represent an extreme end of the spectrum of global leaf form and function with highly elongated filament-like foliage. This project will experimentally decompose needle/drop interactions into their fundamental components: fiber elasticity, wettability, surface profile, impact geometry, and needle vibration. We will conduct focused laboratory experiments to define how Pinus traits are tuned against liquid mass retention. Using a phylogenetic comparative approach, we will examine the pertinent test variables to reveal how Pinus traits vary in response to environmental factors before exploring a greater morphological trait space with predictive modeling. In this way, empirical experimentation will provide informative priors for conducting phylogenetic comparative analyses, which will expand our taxonomic and phenotypic scope. Results from these analyses will then be used as inputs for predictive modeling of trait interactions, which will in turn refine our mechanistic hypotheses of trait-trait interactions permitting rigorous examination of trait evolution in response to environmental stressors. This novel approach creates a template for fusing experimental data, new physical insights, and phylogenetic comparisons with multivariable regression to explore optimums, trade-offs, and limitations in Pinus foliage. 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.