Prevention and Management of Aquatic Invasive Plants in Texas
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PREVENTION AND MANAGEMENT OF AQUATIC INVASIVE PLANTS IN TEXAS A Thesis by ELIZABETH ANGELIKA EDGERTON Submitted to the Office of Graduate and Professional Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Chair of Committee, Michael Masser Committee Members, William Grant Allen Knutson Head of Department, Michael Masser December 2014 Major Subject: Wildlife and Fisheries Sciences Copyright 2014 Elizabeth A. Edgerton ABSTRACT Determining which non-native aquatic plants have the greatest potential to invade a new area and prohibiting those species prior to their introduction is the key to preventing future injurious invasions. Once introduced however, prioritization and effective control is important to managing infestations. This study focused on identifying potential new aquatic invasive plant species and prioritizing existing infestations in Texas, via two aquatic plant models. An aquatic plant risk assessment was the first model. While other risk assessments of this type currently exist, a model suited to the varied environmental conditions in the State of Texas was not available. In addition, many existing models cover large geographic areas, leading to decreased accuracy on a more localized scale. This new model, referred to as the Texas Aquatic Plant Risk Assessment, was based on previous aquatic plant risk assessment and serves as a pre-entry screening tool for testing non-native plant species and identifying those which are likely to be invasive and should therefore be excluded. The model uses a series of weighted questions to give a score to each plant species tested; the higher the score, the more likely the plant is to be invasive in the State of Texas. We tested the model against 100 known non-native species within the state and subsequently ran a series of statistical tests on the results to determine the model’s accuracy and find the best threshold to separate major invaders from minor and non-invaders. When model results were compared to known species invasiveness and a threshold of 50 was set between high risk major invaders and non-invaders, 100%, 87%, ii and 94% accuracy was achieved in classifying major invaders, minor invaders, and non- invaders, respectively. Other, more precautionary thresholds were also explored during analysis. The second model, the Lake Conroe Invasion Model, simulates growth and senescence of hydrilla in Lake Conroe, and the plant’s response to control efforts using grass carp (Ctenopharyngodon idella). The model was developed using reported data from previous hydrilla infestations and control attempts at Lake Conroe, and serves as a prototype for future simulated invasion modeling. A series of simulations were run to calibrate the model, based on previously reported data, and to demonstrate the model’s use. Results from the simulations accurately reflected reported growth and senescence rates of hydrilla within the lake; growth rates for grass carp in the model were also comparable to rates reported in the literature. Simulations of various management strategies showed that increasing numbers of grass carp were needed to control a hydrilla infestation as the time lag between initial hydrilla invasion and stocking of grass carp increased. However, the number of grass carp needed to control an infestation decreased as the amount of time allowed for control increased. In addition grass carp mortality rates may be significantly impacted by grass carp stocking rates relative to the number of vegetated hectares. If smaller stocking rates are preferred in order to avoid removing all aquatic vegetation from the lake, higher mortality rates likely need to be accounted for as increased mortality due to a decreased predator to prey ratio may occur. iii ACKNOWLEDGEMENTS I would like to thank my committee chair Dr. Masser, and his wife Julie Masser, for welcoming me into their home when I first arrived to Texas A&M, for mentoring me over these past two years, encouraging me to make the many useful professional connections that I have made, and for being understanding when I decided to spend a summer traveling to the other side of the world and back half way through my thesis work. I would also like to thank my committee members; Dr. Grant for all of his time, effort, and patience in designing a brand new simulated model, and Dr. Knutson for his guidance throughout the course of this research. Thanks also go to Dr. DeWitt for providing very much needed statistical analysis insight and to Dr. Rose for all of her help in designing and programming the simulated model. I want to also extend my gratitude to the United States Geological Survey and the W.G. Mills Memorial Endowment which provided the funding for this study, and to everyone at Texas Water Resources Institute for taking my under their wing and being ever-willing to assist me when I was in need. Special thanks goes to Lucas Gregory for mentoring me through this entire process, for being a very useful sounding board and voice of reason, and for allowing me to take every opportunity to travel to conferences and gain useful experience that will benefit me in my future career endeavors. Special thanks go to my friends and family who have always been there to love and support me through all of the stress, and for always being willing to help me move from city to city over the years (inevitably during the worst of the Texas summer iv weather). Finally, extra special thanks go to Elizabeth McCarthy who has been there for me through every minute of this adventure, constantly encouraging me and reminding me that everything will work out in the end, even when I wasn’t sure it would. v NOMENCLATURE AIP Aquatic Invasive Plants ANOVA Analysis of Variance AWRA Australian Weed Risk Assessment DA Discriminant Analysis LDA Linear Discriminant Analysis LOOCV Leave One Out Cross Validation MANOVA Multivariate Analysis of Variance NLPCA Non-linear Principal Component Analysis NZAqWRA New Zealand Aquatic Weed Risk Assessment NZWRA New Zealand Weed Risk Assessment PC Principal Component PCA Principal Component Analysis TXAqPRA Texas Aquatic Plant Risk Assessment USAqWRA United States Aquatic Weed Risk Assessment vi TABLE OF CONTENTS Page ABSTRACT…………………………………………………………………….. ii ACKNOWLEDGEMENTS……………………….……………………………. iv NOMENCLATURE…………………………………………………………….. vi TABLE OF CONTENTS………………………………………………………... vii LIST OF FIGURES……………………………………………………………… ix LIST OF TABLES………………………………………………………………. x CHAPTER I INTRODUCTION AND LITERATURE REVIEW……………… 1 Defining Non-native Species…………………………………….. 2 Aquatic Invasive Plants………………………………………….. 6 Pathways………………………………………………………..... 7 Impacts…………………………………………………………... 15 Removal and Control Techniques……………………………….. 19 Modeling Aquatic Invasive Plants………………………………. 23 CHAPTER II ADAPTING AN AQUATIC PLANT RISK ASSESSMENT FOR THE STATE OF TEXAS………………………………………………….. 29 Materials and Methods……………………………………………. 29 Results…………………………………………………………….. 36 Discussion…………………………………………………………. 42 CHAPTER III SIMULATING AQUATIC PLANT INVASION AND MANAGEMENT IN A RIVERINE RESERVOIR……………………………….. 47 Model Overview…………………………………………………… 47 Model Description…………………………………………………. 47 Model Calibration and Evaluation…………………………………. 50 Simulations of Hydrilla Invasion and Management……………….. 55 Discussion…………………………………………………………. 60 CHAPTER IV CONCLUSIONS…………………………………………………. 63 vii Page REFERENCES……………………………………………………………………. 65 APPENDIX A…………………………………………………………………… 123 APPENDIX B……………………………………………………………………. 234 viii LIST OF FIGURES FIGURE Page 1 Summary scores by invasive status for raw data where blanks were treated as zeroes……………………………………. 37 2 Canonical ordination of 100 plant species based on 40 imputed question responses…………………………………………. 39 3 Conceptual model representing the growth and senescence of hydrilla, the growth and mortality of grass carp, and the consumption of hydrilla by grass carp…………………………. 49 4 Calibration simulation results…………………………………………... 51 5 Evaluation simulation results……………………………………………. 53 6 Results of simulations exploring the effect of increased grass carp mortality…………………………………………… 55 7 Diagrammatic representation of the two sets of simulations demonstrating use of the model…………………………………………. 56 8 Results from set one of the demonstration simulations…………………. 58 9 Results from set two of the demonstration simulations…………………. 59 ix LIST OF TABLES TABLE Page 1 Confusion matrices detailing DA and LOOCV classification success................................................................ 41 2 Lake Conroe grass carp stocking information for 2006 and 2007…….. 52 x CHAPTER I INTRODUCTION AND LITERATURE REVIEW Plants, animals, and other biota have been transported and allowed to establish in areas outside their native range since ancient times. Records dating back to the Akkadian Empire, circa 2300 BCE, discuss in great detail the exotic monkeys, elephants, and other animals that roamed the capitol city of Agade (1). Exotic flora was also commonly found in the pleasure gardens of ancient kings and other royals throughout early times. The garden of Ramat Rahel, which existed in Israel throughout the seventh to fourth centuries BCE, contained various non-native plant species like citron from India, cedar from Lebanon, and Persian walnut (2). Exotic species during those times were valued for their medicinal and aesthetic values, used as a way to demonstrate status for the upper class, and were often given as gifts from visitors to the royals (1, 2). When the Europeans first arrived