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Assessing the Biological Condition of Maine Streams and Rivers Using Benthic Algal Communities Thomas John Danielson

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Assessing the Biological Condition of Maine Streams and Rivers Using Benthic Algal Communities Thomas John Danielson The University of Maine DigitalCommons@UMaine Electronic Theses and Dissertations Fogler Library 5-2010 Assessing the Biological Condition of Maine Streams and Rivers Using Benthic Algal Communities Thomas John Danielson Follow this and additional works at: http://digitalcommons.library.umaine.edu/etd Part of the Botany Commons, Ecology and Evolutionary Biology Commons, and the Plant Biology Commons Recommended Citation Danielson, Thomas John, "Assessing the Biological Condition of Maine Streams and Rivers Using Benthic Algal Communities" (2010). Electronic Theses and Dissertations. 356. http://digitalcommons.library.umaine.edu/etd/356 This Open-Access Dissertation is brought to you for free and open access by DigitalCommons@UMaine. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DigitalCommons@UMaine. ASSESSING THE BIOLOGICAL CONDITION OF MAINE STREAMS AND RIVERS USING BENTHIC ALGAL COMMUNITIES By Thomas John Danielson B.S., University of Massachusetts, Amherst, 1993 B.B.A., University of Massachusetts, Amherst, 1993 M.E.M., Duke University, 1996 M.P.P., Duke University, 1996 A DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (in Ecology and Environmental Science) The Graduate School The University of Maine May, 2010 Advisory Committee: Dr. Cynthia Loftin, Associate Professor of Wildlife Ecology, Advisor Dr. Susan Brawley, Professor of Biology Dr. David Courtemanch, Director, Division of Environmental Assessment, Maine Department of Environmental Protection Dr. Francis Drummond, Professor of Biology Dr. R. Jan Stevenson, Professor of Biology, Michigan State University ASSESSING THE BIOLOGICAL CONDITION OF MAINE STREAMS AND RIVERS USING BENTHIC ALGAL COMMUNITIES By Thomas John Danielson Dissertation Advisor: Dr. Cynthia Loftin An Abstract of the Dissertation Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (in Ecology and Environmental Science) May, 2010 The purpose of this study was to test and develop algal methods of evaluating the condition of Maine streams and rivers. The primary objective was to develop a statistical model to predict attainment of Maine's aquatic life criteria for water quality classes A, B, and C. I collected 298 samples of algae on rocks from 193 locations across the state. The major pattern in species composition related to conversion of forests to urban, residential, and agricultural land uses. I calculated preferred environmental conditions of 236 algal taxa for 1) concentrations of nitrogen, phosphorus, and dissolved ions in the water, 2) percent of watershed land cover that is not forested, 3) and percent of watershed land cover that is impervious, such as pavement. I then tested and identified algal community metrics that responded to increasing watershed development. Metrics derived from Maine data performed better than metrics developed in other parts of the world. Five biologists with Maine's Department of Environmental Protection (MDEP) grouped samples based on attainment of aquatic life criteria (i.e., A, B, C, and non- attainment) by interpreting algal species abundances and community metrics. I developed a statistical model to replicate biologist assignments, which correctly classified 95% and 91% of samples used to build and test the model. The second objective was to develop models based on algal community composition to estimate concentrations of nitrogen and phosphorus in stream water. A multiple linear regression model and a variation of weighted averaging that weights estimates using localized subsets of data performed the best. The final objective was to use nutrient diffusing substrates to determine if growth of benthic algae in the Sheepscot River was limited by phosphorus or nitrogen. It was co-limited by nitrogen and phosphorus. Although my statistical models have limited transferability to adjacent regions with similar ecological conditions, methods used to build the models have wide transferability. MDEP could use the first model to determine if streams and rivers attain water quality classes A, B, and C. MDEP could then use nutrient inference models and diffusing substrates to better diagnose and manage enrichment of phosphorus and nitrogen. ©Thomas John Danielson All Rights Reserved m ACKNOWLEDGMENTS Funding for this project was from the U.S. Environmental Protection Agency (U.S. EPA), the USGS-Maine Cooperative Fish and Wildlife Research Unit, and the Maine Department of Environmental Protection (MDEP). I would like to thank Jennie Bridge, Al Basile, and Matt Liebman (U.S. EPA), Dr. Cynthia Loftin (USGS), and Dr. David Courtemanch, Norm Marcotte, Tony St. Peter, and Cathy Levesque (MDEP) for helping me to obtain and manage funding for this project. First and foremost, I would like to thank Dr. Cynthia Loftin for her intellectual guidance, support, and good cheer. I could not have asked for a better advisor. She is the best. I am also indebted to my committee members. Dr. David Courtemanch was very generous in allowing me to pursue this dissertation while working for the Department of Environmental Protection. He gave me the freedom and time to learn and explore, but was always there to answer questions and give me guidance. Dr. Frank Drummond is an amazing ecologist and was instrumental in helping me think like a statistician and guide me through the sometimes mystical realm of multivariate statistics. Dr. R. Jan Stevenson and Dr. Susan Brawley are exceptional phycologists who were very generous in sharing their wealth of experience and knowledge of algae. All my committee members were practical, patient, and demanded high quality work for which I am very thankful. I could not have completed this work without the help and support of many of my colleagues at DEP. I would particularly like to thank the other members of the Biological Monitoring Unit, including Leon Tsomides, Jeanne DiFranco, Beth Connors, Susanne Meidel, and Susan Davies. They helped collect, enter, and manage the data, which was an enormous task. They shared their biological expertise and helped me make difficult iv decisions. They were also instrumental in developing the model to predict attainment of biological criteria, which was a long and sometime frustrating process. I must thank them for their hard work, dedication, and friendship. I must also thank the Biological Monitoring Unit's seasonal employees, interns, and AmeriCorps volunteers that participated in the project over the years, including Amanda Roy, Emily Cira, Jessica Palmer, Hannah Wilhelm, Rachel Dicker, Heather Hartley, Caitlin Kersten, Sarah Schacter, Nathaniel Meyer, Leanne Kozur, Erin Pekar, John Heist, Allison Moody, Tracy Kreuger, Allison Piper, and Jeremy Deeds. Mike Smith and Christian Halsted were indispensable for helping me with complex GIS projects, for which I am very thankful. Chris Halsted also wrote visual basic script to calculate many of the algal variables. I must also thank the many MDEP staff that volunteered their time to help collect data. More than 50 people from MDEP and other organizations volunteered to do field work over the course of the project, including many regular volunteers. Not only did they help collect data, they also kept the work interesting and exciting and reminded me to appreciate every day in the field. I would like to thank staff from the following organizations for helping conduct the study: Houlton Band of Maliseet Indians, Penobscot Nation, Passamaquoddy Tribe, State of Maine Health and Ecological Testing Laboratory, Manomet Center for Conservation Sciences, Portland Water District, Union River Watershed Association, Downeast Salmon Federation, Finally, I would like to thank my family for their love, support, and encouragement. My wife, Nicole is my best friend and favorite companion. She has been my greatest supporter over the years and helped me juggle the often competing roles of student, biologist, husband, and father. She is the sunshine in my life. Ben and Kaila v are my greatest joys. I love them immensely and thank them for keeping a smile on my face. I would also like to thank my parents John and Linda, my brother Bill and his wife Susan, my sister Laura, and my mother-in-law Judy for their enduring love and belief in me. I love them very much. VI TABLE OF CONTENTS ACKNOWLEDGMENTS iv LIST OF TABLES xii LIST OF FIGURES xvi LIST OF EQUATIONS xx 1. INTRODUCTION 1 1.1 Biological condition of Maine's streams and rivers 1 1.2 Algae of Maine's wadeable streams and rivers 4 1.2.1 Previous studies 4 1.2.2 Patterns in algal composition in Maine streams and rivers 5 1.2.3 Algal characteristics 10 1.3 Research justification and objectives 14 1.3.1 Nutrient limitation pilot study 14 1.3.2 Nutrient inference models for Maine streams and rivers 14 1.3.3 Algal metrics for evaluating response of lotic algal communities to watershed disturbance 15 1.3.4 Benthic algal model for predicting attainment of biological criteria for Maine streams and rivers 15 vn 2. COMPARISON OF METHODS TO DETERMINE LIMITING NUTRIENTS OF THE SHEEPSCOT RIVER, MAINE 16 2.1 Abstract 16 2.2 Introduction 16 2.3 Methods 19 2.3.1 Study area 19 2.3.2 Samples 21 2.3.3 Analysis 24 2.4 Results 25 2.4.1 Physical characteristics of study site 25 2.4.2 Water chemistry 27 2.4.3 Chlorophyll a, chlorophyll b, and chlorophyll c 28 2.4.4 Species composition 31 2.4.5 Invertebrates on NDS 31 2.5 Discussion 37 2.5.1 Nutrient ratios 37 2.5.2 Chlorophyll a, b, and c 39 2.5.3 Management implications 42 3. ESTIMATING NUTRIENT CONCENTRATIONS IN MAINE STREAMS BASED ON BENTHIC DIATOM ASSEMBLAGES 43 3.1 Abstract 43 3.2 Introduction 44 viii 3.3 Methods 47 3.3.1 Study Sites 47 3.3.2 Field and laboratory procedures 49 3.3.3 Analytical methods 53 3.4 Results 59 3.4.1 Diatom assemblage patterns 59 3.4.2 Diatom response curves, optima, and nutrient indicators 62 3.4.3 Inference models 69 3.5 Discussion 75 3.5.1 Species responses to increasing TP concentrations 75 3.5.2 Inference models 77 3.5.3 Methods of improving performance and removing bias 80 3.5.4 Selecting the best model 82 4.
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