Honors Theses and Capstones

Date of Award

Spring 2024

Project Type

Senior Honors Thesis

College or School

COLSA

Department

Natural Resources

Program or Major

Environmental Science: Ecosystems

Degree Name

Bachelor of Science

First Advisor

Scott Ollinger

Second Advisor

Jack Hastings

Abstract

Leaf structural and functional traits are interrelated and can be strong predictors of leaf productivity, and it is important to understand how these traits change across both a temporal and a spatial gradient. The objective of this project is to identify and understand seasonal and height-related variations in red oak (Quercus rubra) leaf traits throughout a forest canopy, while assessing the utility of this knowledge for informing canopy models. Leaf samples were collected from red oak trees using the shotgun method and analyzed in the UNH Terrestrial Ecosystems Analysis Lab. Data were processed in Excel, JMP, and R. Leaf-level modeling and satellite imagery were used to investigate canopy reflectance at various heights throughout the forest canopy.

The key leaf traits investigated in this project are leaf mass per area (LMA), mass-based and area-based nitrogen (Nmass and Narea), and maximum photosynthetic rate (A1500mass and A1500area). LMA, A1500area, and Narea exhibited both seasonal and height-based patterns. Nmass displayed only a seasonal pattern with no discernible change across a canopy height gradient, while A1500mass displayed only a height-based trend with no seasonal pattern. This suggests that LMA is a key driver of height-based leaf trait changes, while remaining relatively uninvolved in the seasonal variability of leaf traits. Canopy reflectance was shown to vary slightly across a height gradient, with satellite imagery best aligning with upper canopy leaves. Seasonality was reflected in both modeled and satellite-based leaf reflectance. This project suggests that seasonality and height gradients are two important regulators of leaf trait changes that should be incorporated into remote sensing techniques to enhance canopy-level productivity estimates.

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