Prevention and Management of Aquatic Invasive Plants in Texas

264 Pages·2014·4.05 MB·English

by  Edgerton, Elizabeth

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%,
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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.
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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
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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.
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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
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TABLE OF CONTENTS

Page
ABSTRACT…………………………………………………………………….. ii
A(cid:38)KNOWLEDGEMENTS………………………(cid:17)……………………………. 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
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Page
REFERENCES……………………………………………………………………. 65
APPENDIX A…………………………………………………………………… 123
APPENDIX B……………………………………………………………………. 234

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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
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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

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