Real Estate

Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates

Description
University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates
Categories
Published
of 148
0
Categories
Published
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Share
Transcript
University of Tennessee, Knoxville Trace: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates Latha Malar Baskaran University of Tennessee, Knoxville, Recommended Citation Baskaran, Latha Malar, Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates. PhD diss., University of Tennessee, This Dissertation is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more information, please contact To the Graduate Council: I am submitting herewith a dissertation written by Latha Malar Baskaran entitled Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates. I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Geography. We have read this dissertation and recommend its acceptance: Virginia Dale, Carol Harden, Shih-Lung Shaw (Original signatures are on file with official student records.) Liem Tran, Major Professor Accepted for the Council: Dixie L. Thompson Vice Provost and Dean of the Graduate School Effects of Switchgrass Related Land-Use Changes on Aquatic Macroinvertebrates A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Latha Malar Baskaran May 2017 Copyright 2017 by Latha Malar Baskaran. All rights reserved. ii DEDICATION I dedicate this dissertation to my mother and mother-in-law, the strongest and most compassionate women in my life, whose endless support made this journey possible. iii ACKNOWLEDGEMENTS I have many people to thank in the path towards my Ph.D. First and foremost, I would like to offer my greatest gratitude to my advisor Dr. Liem Tran who guided and supported my work. His scientific and technical advice ensured my work was technically sound and helped me cross many roadblocks I faced in my research. In spite of my many delays and breaks, he always supported me when I came back to pick up the pieces and continue with my research. Without his support and understanding, I couldn t have made it to this stage. I would like to sincerely thank Dr. Virginia Dale, my mentor, role model, committee member, and supervisor at work. She encouraged me to start my Ph.D., and she was always ready with support and advice when I needed it. I am grateful to have learnt most of my research and scientific approaches from her. I would like to thank my committee members Dr. Carol Harden and Dr. Shih-Lung Shaw for their comments and valuable advice that helped me focus my work early on. The lessons I learnt in their classes and seminars still resonate with me today, My path to this Ph.D. was in conjunction with my work at the Oak Ridge National Laboratory (ORNL), and without the support of my work place and colleagues, this would have not been possible. I am grateful for the educational assistance program at ORNL which provided financial support for my course work. Further, the flexible work hours and ability to work part-time offered by ORNL, and supported by my supervisor, was critical for me to balance work, home and school. I would like to thank Dr. Yetta Jager, a mentor and close colleague, who was very supportive of my Ph.D. work and allowed me to shift project priorities when I had to focus on my dissertation, I would like to acknowledge my colleagues Esther Parish, Jasmine Kreig, Teresa Matthews, and John Smith, who through discussions, reviews and advice supported my work. I could not have sanely made it this far without my friends, who in spite of my lack of phone calls, text messages, and missed social events, were always there for me. I would like to thank Udaya Kalluri, my dear friend who inspired, encouraged and supported me. Chats with her over walks, coffee, or lunch always gave me the boost of energy to move forward. I would like to thank my friends Priya and Giri, who are more like family, and have stood by me from the beginning of this journey and through various life events in between. I am grateful for my friends Geetha, Babu, Amudhan, Praveena, Aarthy, Pauline, Bhargavi, Manasi, Gayatri, Padma, Sriram, Bharat, Kartik and Raj who encouraged me through various ways. My parents and family are a big part of who I am, and I would not be where I am without their love and support. My father, my hero, is my biggest role model and continues to inspire and make me proud today. Though I did not follow his path into physics, I like to believe that I might have inherited a small fraction of his scientific rigor, which helped me get this far in my research. My mother is my biggest supporter to this date. Her unconditional love has helped me sustain through every stage of my life. My sister Ezhil, my best friend, has listened to countless gripes and has always believed in me. My iv brother Satish, though not near, has always been a supportive force while showing his concern for my wellbeing. I am very grateful for my mother-in-law and father-in-law, who treat me like a daughter and have been constant supporters of my Ph.D. I knew I could always rely on them for any help, and without their support, I could not have balanced family and work. I am thankful every day for my greatest blessings, my boys Aadhi, Nilan and Niray, who came into my life at various points in this Ph.D. journey and have been my biggest strengths and joys. They constitute my happy place and give meaning and purpose to everything I do. Last, but certainly not least, my dissertation would not have come this far if not for my husband, Suresh. I am grateful for everything he is and for everything he has done for me. Suresh was truly the force behind my work from day 1. From sharing every chore and responsibility, to encouraging me to work more efficiently, his love and support helped me overcome several obstacles and made this long path to my Ph.D. possible. v ABSTRACT This research examines if switchgrass-based land-management practices have the potential to influence aquatic macroinvertebrates through changes in stream flow and water quality. The number of taxa in Ephemeroptera, Plecoptera, and Trichoptera orders (EPT taxa richness/ept-tr) is analyzed as an aquatic macroinvertebrate bioindicator in the context of regional environmental effects, and changes in stream flow and water quality. This dissertation is structured as three manuscripts that link together to address the overall research question. The first manuscript focuses on identifying regional environmental variables that influence EPT-TR across ecoregions in Tennessee. The influences of temperature, precipitation, geology, soil, stream flow and velocity on EPT-TR differ among ecoregions and also set the context for local-scale factors. The second manuscript uses multilevel regression models to evaluate the effects of stream flow and water quality on EPT-TR in the midst of regional environmental factors in Tennessee. Stream flow is found to be statistically significant in influencing EPT-TR across ecoregions, and total nitrogen, phosphorus and sediment are statistically significant within specific ecoregions. However, the magnitude of these effects is very small in the midst of the effects from regional factors. By testing the significance of EPT-TR in explaining water quality, EPT-TR is not found to be a strong indicator of water-quality changes in Tennessee under the conditions of this study. The third manuscript uses the Soil and Water Assessment Tool (SWAT) to compare stream flow and water quality from a baseline scenario and switchgrass management scenario at the Nolichucky watershed in Tennessee. Stream flow increased and nitrogen and phosphorus concentrations decreased under the switchgrass scenario. Regression models relating EPT-TR and monthly stream flow and water quality from SWAT showed increase in EPT-TR in the switchgrass scenario, but these increases are within the margin of error of monthly estimates. The influence of switchgrass management on EPT-TR cannot be detected under current model assumptions. Overall, results of the whole study show that EPT taxa are affected by factors that operate at different spatial and temporal scales, and impacts due to switchgrassmanagement related stream flow and water quality changes cannot be detected in the current spatial context. vi TABLE OF CONTENTS CHAPTER 1 Introduction... 1 Research objective... 2 Dissertation organization... 3 References... 5 CHAPTER 2 Background... 7 Aquatic macroinvertebrates... 7 Factors affecting aquatic macroinvertebrates... 7 Switchgrass related land-use changes and effects on the stream Models Why SWAT? Modeling framework References CHAPTER 3 Analyzing aquatic macroinvertebrate taxa richness indices across ecoregions in Tennessee Abstract Introduction Study area and methods Study area Methods: Data and analysis Results EPT taxa richness across ecoregions Regional factors among ecoregions Discussion Conclusion References CHAPTER 4 Potential of aquatic macroinvertebrates as water quality indicators: an evaluation across Tennessee, USA Abstract Introduction Methods Macroinvertebrate data Water quality data Environmental variables Stream flow characteristics Multilevel regression model Results Interpretation of sediment variable Multilevel EPT taxa models Multilevel water quality models Discussion Multilevel EPT taxa models Multilevel water quality models Conclusion References vii CHAPTER 5 Assessing effects of switchgrass-based land-management practices on aquatic macroinvertebrate taxa richness Abstract Introduction Study area and data Potential energy crops Methods Step 1: Baseline model setup and calibration Step 2: Simulating bioenergy scenario Step 3: Evaluating EPT taxa richness as a function of stream flow and water quality Step 4: Potential changes in EPT taxa richness under a bioenergy landscape Results Analyzing changes in stream flow and water quality EPT taxa richness as a function of stream flow, N and P Potential changes in EPT taxa richness due to switchgrass-related land management Discussion Conclusion References Appendix CHAPTER 6 Conclusion Summary of results by objectives of study Lessons learned and future research directions References VITA viii LIST OF TABLES Table 1: Landscape and stream features affecting macroinvertebrates, their scales of influence, and the factors by which they affect macroinvertebrate health Table 2: List of regional variables considered to be potentially important in affecting macroinvertebrate taxa richness, with sources of data and statistical tests. NHDplus refers to the hydrologic framework dataset developed by the US Environmental Protection Agency and the US Geological Survey (McKay et al. 2012). The PRISM Climate Group develops spatial climate datasets to reveal short- and long-term climate patterns (PRISM climate group 2013). The SSURGO database contains information about soil collected by the National Cooperative Soil Survey (Soil Survey Staff 2013) Table 3: Chi square values and p-values (in parentheses) of the Kruskal Wallis test to test significance of differences in regional variables across three ecoregion classifications Table 4: Significance of relationship between EPT taxa richness and regional variables by Omernik's ecoregion classification. Significance of the relationship between EPT taxa richness and rock type and relationship between EPT taxa richness and stream order are based on a one-way ANOVA tested at the 0.05 level (indicated with an asterisk). The Pearson s R correlation coefficient significant at the 0.05 level is presented for the rest of the variables Table 5: Landscape and stream features affecting macroinvertebrates, their scales of influence, and the factors by which they affect macroinvertebrate health Table 6: List of regional and stream variables considered to be potentially important in affecting macroinvertebrate taxa richness ( 1 - McKay et al. (2012); 2 - Miller and White (1998); 3 - Soil Survey Staff (2013); 4 - TDEC (2011b); 5 - TDEC (2011a)) Table 7: Recoding the qualitative sediment deposition variable into quantitative and binary variables based on macroinvertebrate and stream assessment protocols (Arnwine et al. 2011) Table 8: Testing the significance of EPT taxa richness models with random slopes and intercepts compared to models with only a random intercept. The Akaike Information Criterion (AIC) of each model and the total variance explained by the random effects are listed Table 9: The coefficient estimates, standard error, and p-statistic of the fixed effects of the multilevel nitrogen model (EPT-N Model) with EPT taxa richness as the dependent variable and regional variables, stream characteristics and N as dependent variables Table 10: The coefficient estimates, standard error, and p-statistic of the fixed effects of the multilevel phosphorus model (EPT-P Model) with EPT taxa richness as the dependent variable and regional variables, stream characteristics and P as dependent variables Table 11: The coefficient estimates, standard error, and p-statistic of the fixed effects of the multilevel sediment model (EPT-S Model) with EPT taxa richness as the dependent variable and regional variables, stream characteristics and sediment as dependent variables ix Table 12: Coefficient estimates of the random effects by ecoregion for the multilevel EPT- N model, with EPT taxa richness as the dependent variable and regional variables, stream characteristics and N as dependent variables. The ecoregions are listed from west to east Table 13: Coefficient estimates of the random effects by ecoregion for the multilevel EPT-P model, with EPT taxa richness as the dependent variable and regional variables, stream characteristics and P as dependent variables. The ecoregions are listed from west to east Table 14: Coefficient estimates of the random effects by ecoregion for the multilevel EPT-S model, with EPT taxa richness as the dependent variable and regional variables, stream characteristics and sediment as dependent variables. The ecoregions are listed from west to east Table 15: Testing significance of water quality models with random slopes and intercepts compared to models with only a random intercept. The AIC of each model and the total variance explained by the random effects is listed. In each model category (N, P and Sed), the better model, based on a chi-square test is indicated with an asterisk Table 16: Coefficient estimates of fixed effects of water quality models with their standard errors Table 17: Coefficient estimates of random effects by ecoregion for N model. The ecoregions are listed from west to east Table 18: Coefficient estimates of random effects by ecoregion for P model. The ecoregions are listed from west to east Table 19: Coefficient estimates of random effects by ecoregion for S model. The ecoregions are listed from west to east Table 20: Results from Poisson regression models with EPT taxa richness as the dependent variable and monthly stream flow, monthly N concentration, monthly P concentration, monthly N loadings, monthly P loadings, monthly sediment concentration, and monthly sediment loadings as independent variables. Model AIC values and regression coefficient and p-values of significant variables (at a 0.05 level) are presented Table 21: SWAT calibrated parameter values Table 22: SWAT calibration and validation results for daily stream flow, monthly stream flow, and residuals of monthly stream flow. Calibration was based on data over the period 2005 to Validation was based on data from Table 23: SWAT calibration and validation results for daily N and P loadings at selected subbasins x LIST OF FIGURES Figure 1: Linkages between switchgrass-based land management and sources of stress to macroinvertebrates (roughly adapted from Nietch et al. 2005) Figure 2: Hierarchical relationships between select landscape and stream features and habitat filters that affect macroinvertebrates (adapted from Poff 1997). The colors from the diagram on the left correspond to various scales and the colors in the table Figure 3: Physical map of Tennessee illustrating elevation gradient, major landforms and rivers Figure 4: Ecoregion classification schemes for Tennessee Figure 5: Box plots of EPT taxa richness (using data collected by TDEC between 2007 and 2010) grouped by Omernik (a), Bailey (b) and Freshwater(c) ecoregion classes of Tennessee (ordered from west to east) and separated by reference stream category (reference streams are light grey boxes and non-reference streams are dark grey boxes). The number of stream samples in each ecoregion class is provided in parenthesis. The boxes represent the interquartile (IQ) range, and the line across the boxes indicates the median value. The whiskers from the upper and lower edge of the box indicate the highest and lowest values within 1.5 times the IQ range. Outliers between 1.5 and 3 times the IQ range are indicated by the closed circles, and outliers with values more than 3 times the IQ range are indicated by the asterisk. Data from the Mississippi Alluvial Plain have been excluded due to insufficient data Figure 6: Silhouette plots for classification of EPT taxa richness using Omenik s (A), Bailey s (B), and Freshwater (C) ecoregion classification schemes. Classes on Omernik s plot (A) are: 1- Blue Ridge, 2-Central Appalachian 3-Interior Plateau, 4- Mississippi Valley Loess Plains, 5-Ridge and Valley, 6-Southeastern Plains, and 7- Southwestern Appalachians. Classes on Bailey s plot (B) are 1-Blue Ridge Mountains, 2-Central Ridge and Valley, 3-Coastal Plains middle section, 4-Interior Low Plateau, Highland Rim Section, 5-Mississippi Alluvial Basin Section, 6- Northern Cumberland Plateau Section, 7-Northern Ridge and Valley Section, 8- Southern Cumberland Mountains Section, and 9-Upper Gulf Coastal Plain Section. Classes on Freshwater ecoregion plot (C) are: 1-Lower Mississippi, 2-Old Ohio, 3- Cumberland, and 4-Tennessee Figure 7: Soil (upper) and climate (lower) gradients across Tennessee based on soil composition and average annual precipitation (Source: SSURGO and PRISM data) Figure 8: Distribution of EPT taxa richness by stream order for ecoregions found to have significant correlation between stream order and EPT taxa richness. The boxes represent the interquartile (IQ) range, and the line across the boxes indicates the median values. The whiskers from the upper and lower edge of the box indicate the highest and lowest values within 1.5 times the IQ range. Outliers between 1.5 and 3 times the IQ range are indicated by the closed circles Figure 9: Physical map of Tennessee illustrating elevation gradients, major landforms and rivers xi Figure 10: Upper panel: Location of the macroinvertebrate stream sampling sites and USGS gages over the ecoregions in Tennessee. Lower panel: Land cover in the study region Figure 11: Relationship between EPT taxa richness and percentage of sediment deposition in stream affecting the bottom substrate by ecoregion. The black line represents the overall linear trend line of the data; the blue lines represent linear trend lines by ecoregion Figure 12: Histograms of water quality variables Figure 13: Land cover within and around the Nolichucky watershed. The counties in and around Nolichucky watershed have been labelled Figure 14: Steps describing the study approach. Grey boxes indicate the input data (1- U.S. Geological Survey 2016), 2 TDEC 2011, 3 Homer et al. 2015, 4 - Wolock 1997, 5 - Jarvis et al. 2008, 6 - Girvetz et al. 2013, 7 Denton et al. 2010). Blue boxes indicate model output and the oval red boxes represent the models Figure 15: Headwater and midway subba
Search
Similar documents
View more...
Related Search
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks
SAVE OUR EARTH

We need your sign to support Project to invent "SMART AND CONTROLLABLE REFLECTIVE BALLOONS" to cover the Sun and Save Our Earth.

More details...

Sign Now!

We are very appreciated for your Prompt Action!

x