White oak (Quercus alba L.) mortality in response to biotic and abiotic factors and climate change in the eastern United States
No Thumbnail Available
Authors
Meeting name
Sponsors
Date
Journal Title
Format
Thesis
Subject
Abstract
White oak mortality in the eastern United States presents significant challenges for forest conservation efforts. Understanding the spatial patterns and underlying processes, impacts of biotic and abiotic factors, and climate change is crucial for effective management of white oak forest in the eastern United States. White oak mortality (WOM) can have clustered patterns at local scale due to stand development and soil conditions. While there might be a random WOM pattern due to drought events and uniform pattern from soil conditions and moisture availability at broad scale. White oak mortality is also greatly impacted by biotic factors such as basal area, stand density, and abiotic factors such as spring precipitation and winter temperature. Chapter I laid out the overall objectives and justifications for chapter II, III, and IV. It stated ecological importance of white oaks in the eastern US and its conservation for maintaining regional biodiversity. This chapter addressed spatial pattern distribution, role of biotic and abiotic factors, and climate change impacts on WOM rate. In this chapter, there was a specific description on data processing, result analyses, major findings, and summaries from chapters. Chapter II investigated the spatial patterns in southern, central, and northern region of WOM rate across the eastern US. I compiled multicycle Forest Inventory and Analysis data from 1998 to 2019 to capture WOM rate. Results of this chapter found clustered pattern mostly at local scale across the southern and northern regions of WOM rate. However, the central region indicated random patterns at mid scales and uniform pattern at a broad scale across all regions. The observed various spatial pattern of WOM rate explored the underlying factors associated WOM. This study provides an important basis for depicting the broad, general pattern of WOM and understanding the possible factors impacting WOM rate at varying scales. Chapter III identified the most important variables affecting WOM rate by integrating biotic, abiotic factor and forest inventory data. I ranked biotic and abiotic variables in response to WOM rate by integrating forest inventory data and Classification and Regression Tree. I also identified that basal area was first in the hierarchical ranking denoting major impacting variable to WOM rate. I demonstrated that biotic factors were significantly more important than abiotic factors, in which basal area was very important followed by stand density, summer temperature, summer and spring precipitation, and elevation. This research contributes valuable insights into the biotic and abiotic factors impacting white oak forest and highlight the importance of holistic approach in addressing complex environmental challenges. Chapter IV investigated the situation of WOM rate under climate change in the intermediate scenario i.e., SSP2-4.5. I projected WOM rate to the year 2025 to 2099 with break off at three averaged time intervals i.e., early (2025-2049), mid (2050-2074), and late (2075-2099). I analyzed five different climate models by integrating remote sensing and Coupled Model Intercomparison Project Phase 6 climate variables. I focused on four seasonal variables mainly summer and winter temperature and spring and summer precipitation. Results of this chapter showed that WOM rate will increase mostly in the southern region of our study area whereas northern region mostly witness a decrease in WOM rate. I also identified that there will be a increase in few areas as well as very less areas for decrease in WOM rate in the central region of our study area. Rising temperature and shifting precipitation pattern will increase WOM rate while ongoing management efforts will decrease WOM rate at northern region. This research points out important insights into highly impacted as well as less impacting areas of WOM rates under changing climate. Chapter V is the overall conclusions that summarizes major findings. Chapter II identified mainly clustered pattern at local scale associated with stand-level competition and soil characteristics. Random pattern at local and broad scale was associated with stochastic events such as droughts and other climate stressors. The uniform pattern at broad scale was associated with land use practices and forest management efforts. Chapter III pointed out that basal area was the main biotic variable impacting white oaks due to massive competition of resources during natural thinning. Likewise, climate, terrain, and soil characteristics also found important in impacting white oaks in the second and third tier, respectively. Chapter IV demonstrated that WOM rate will increase mainly in the southern region due to rising temperature and shifting precipitation. The central and northern region will experience decrease as well as moderate increase due to possible forest management efforts. However, majority areas will also remain unchanged for WOM rate due to ongoing management efforts.
Table of Contents
PubMed ID
Degree
Ph. D.