© 2019 jeffrey b. greenspan
TRANSCRIPT
FACTORS RELATED TO PRESENCE OF BLUE TILAPIA IN FLORIDA LAKES
By
JEFFREY B. GREENSPAN
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2019
© 2019 Jeffrey B. Greenspan
To my wife and children, who graciously supported my return to academia
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ACKNOWLEDGMENTS
I thank Dr. Jeff Hill, chairman of my committee, who had the knowledge,
experience and emotional intelligence to guide me gently through the thesis process.
Dr. Quenton Tuckett’s statistical knowledge, serious work ethic, and thoughtful
comments helped me keep the ball rolling in the right direction. Mr. Mark Hoyer’s deep
knowledge of limnology and the study data provided both focus and insight. And Dr.
Charles Cichra provided both encouragement and broad picture knowledge for the
effort. Without the guidance of these four gentlemen, this thesis could not exist. Thank
you all!
I also thank Allison Durland Donahou, leader of the Aquatic Research Graduate
Organization, who was always available to answer questions about the University of
Florida, its people and its processes. Her encyclopedic knowledge of and connections in
the fisheries community makes things happen. Thanks also go to her husband Scott
Donahou for his GIS skills: the map of Florida lakes in this study is due to his creativity.
The Florida LAKEWATCH program, a citizen science program directed by Mr.
Mark Hoyer, deserves thanks for developing and maintaining the database of Florida
Lakes on which this thesis is based.
I thank Eric Sawyers at the Florida Fish and Wildlife Conservation Commission
for additional data on Blue Tilapia from their Long-Term Monitoring database. Though it
wasn’t used in the study, it helped me to understand some of the dispersal patterns and
strategies for the species.
Finally, I thank Kok Ben Toh for his assistance with graphing in R.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 6
LIST OF FIGURES .......................................................................................................... 7
LIST OF ABBREVIATIONS ............................................................................................. 8
ABSTRACT ..................................................................................................................... 9
CHAPTER
1 INTRODUCTION .................................................................................................... 10
2 METHODS.............................................................................................................. 13
Study Organism ...................................................................................................... 13 Basic Methods ........................................................................................................ 15
Statistical Analysis .................................................................................................. 17
3 RESULTS ............................................................................................................... 24
General Statistics ................................................................................................... 24
Presence/Absence Model for Blue Tilapia .............................................................. 24
Biomass and Abundance Models for Blue Tilapia .................................................. 25 Associations of Blue Tilapia with Three Predators and Gizzard Shad .................... 25 Evaluating the Associations of Blue Tilapia with the Fish Community .................... 26
4 DISCUSSION ......................................................................................................... 38
APPENDIX
A LAKES EXCLUDED FROM THE CURRENT STUDY ............................................ 44
LIST OF REFERENCES ............................................................................................... 45
BIOGRAPHICAL SKETCH ............................................................................................ 50
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LIST OF TABLES
Table page 2-1 Lakes in the Florida LAKEWATCH dataset included in the current study. .......... 22
2-2 Limnological and macrophytic characteristics of the 43 lakes included in this study. .................................................................................................................. 23
3-1 Presence and absence statistics, and Wilcoxon W- and p-values for 27 lake factors and 10 fish community factors in 43 lakes. ............................................. 30
3-2 Impact of Largemouth Bass and Gizzard Shad abundance and biomass data on logistic modeling for Blue Tilapia presence.................................................... 34
3-3 t-values, p-values and residual deviance explained by the parameters for Blue Tilapia biomass modeling. .......................................................................... 35
3-4 Mean electrofishing biomass (gm/hr) and abundance (number/hr) CPUE parameters when Blue Tilapia are present and absent, standard errors of the means (in parentheses), and W-values and p-values for the Wilcoxon test comparing the distributions. ................................................................................ 35
3-5 NMDS coordinates and p-values for each species found in the 43 lakes used in the current study (Table 2-1). P-values were calculated by the envfit function of the vegan package in R, which fits environmental vectors onto an ordination. ........................................................................................................... 36
4-1 Statistical factors affecting modeling of Blue Tilapia abundance and biomass. .. 43
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LIST OF FIGURES
Figure page 2-1 Blue Tilapia Oreochromis aureus. FWC artwork: Duane Raver, Jr., ................... 20
2-2 Lakes in the Florida LAKEWATCH dataset included in the current study. .......... 21
3-1 Blue Tilapia presence and absence as a factor of 27 Florida lake characteristics. Some y-axes have been adjusted for clarity and may exclude
outliers. Blue dots represent statistical means. Significance *** 0.001 **
0.01 * 0.05. .................................................................................................. 27
3-2 Predator and Gizzard Shad CPUE from electrofishing data as a function of Blue Tilapia presence and absence. Blue dots represent statistical means. ...... 31
3-3 NMDS ordination plot of the lakes in the study. Lakes without a symbol are classified as eutrophic. ....................................................................................... 32
3-4 NMDS ordination plot of the fish community by presence/absence of Blue Tilapia. See Table 3-5 for species codes. ........................................................... 33
3-5 Community scaled vector plot of fish species with p-values 0.2. Species with higher p-values were removed for readability. See Table 3-5 for species codes. ................................................................................................................. 34
4-1 There are 778 records for Blue Tilapia in Florida in the USGS Nonindigenous Aquatic Species Database as of 03 February 2019, with the vast majority in peninsular Florida. .............................................................................................. 43
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LIST OF ABBREVIATIONS
AIC Akaike Information Criteria
CPUE Catch Per Unit Effort
NMDS Non-Metric Multidimensional Scaling
PAC Percent lake surface Area Covered with aquatic macrophytes
PVI Percent of lake Volume Infested with aquatic macrophytes
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science
FACTORS RELATED TO PRESENCE OF BLUE TILAPIA IN FLORIDA LAKES
By
Jeffrey B. Greenspan
May 2019
Chair: Jeff Hill Cochair: Quenton Tuckett Major: Fisheries and Aquatic Sciences
Blue Tilapia, Oreochromis aureus, were collected in 15 of 43 Florida lakes
sampled, and logistic regression accurately predicted the presence or absence of Blue
Tilapia in 95% of the lakes using lake surface area, Secchi disk depth, conductivity and
the percent of the lake volume infested with macrophytes. Models for biomass and
abundance of Blue Tilapia could not be developed. Lakes with and without Blue Tilapia
were strongly differentiated based on a suite of limnological and fish community
characteristics. Lakes with Blue Tilapia were more productive and had reduced water
clarity and aquatic macrophyte coverage. Lakes with Blue Tilapia were associated with
trophically similar species, like Gizzard Shad and Threadfin Shad. There were positive
correlations between Blue Tilapia presence and the electrofishing biomass CPUEs of
four native species tested, suggesting that habitat “quality” affected both the native and
non-native fish species alike. These results indicate that limnological and trophic
level/productivity factors are both strong predictors of the local presence of Blue Tilapia
and drivers of the native and non-native fish community structure.
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CHAPTER 1 INTRODUCTION
Understanding the patterns and drivers of freshwater fish invasions is a
fundamental question for invasion biology (Olden 2010). Invasion may be modeled as a
series of stages separated by barriers or filters that must be overcome to allow
progression through each stage (Blackburn et al. 2011). Environmental factors are a
strong filter and must be sufficiently suitable within the invaded region for the species to
successfully establish and spread. For example, climate has a powerful influence on
invasion success of aquatic species in the Laurentian Great Lakes, where successful
species must be able to survive the long, frigid winter conditions (Howeth et al. 2016;
Kramer et al. 2017). Numerous environmental factors have been identified as important
for various taxa and regions and have been incorporated into predictive models of
establishment success (e.g., Pheloung et al. 1999; Lawson et al. 2013; Copp et al.
2016).
Once species successfully establish and begin to spread within a region,
questions shift towards their eventual distribution and abundance across landscapes.
Studies with fish invading aquatic systems have identified factors related to ecosystem
change as important predictors of non-native species success. These factors include
anthropogenic modifications like reservoir construction (Marchetti et al. 2004; Olden et
al. 2006; Lapointe et al. 2012), increased resource availability through eutrophication
(Catford et al. 2009), the role of disturbance (Lake and Leishman 2004), as well as
propagule pressure (Holle and Simberloff 2005; Catford et al. 2009). Underlying these
predictors is the assumption that climate and habitat are suitable for the invaders,
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indeed that the physiology of the fish species matches the abiotic factors present in the
environment (Moyle and Light 1996).
Habitat suitability plays a large role in determining the distribution and abundance
of fish species (Canfield and Hoyer 1988), and habitat suitability models are useful in
understanding the spread and ultimate distribution of non-native species (Hirzel et al.
2006). Limnological factors contribute considerably to habitat suitability but vary by
region due to climatic and geological factors (Bachmann et al. 2012). Likewise, non-
native species differ in physiological tolerances, habitat preferences, life history, and
trophic level and thus their distribution and abundance may vary accordingly across the
landscape. Impacts of non-natives also vary across landscape scales, being a function
of both distribution and abundance, and can be measured from the individual level up to
the ecosystem processes level (Parker et al. 1999). Therefore, detailed studies of
species distribution and abundance, along with associations with native species, are
needed to improve predictive models at the landscape scale.
Blue Tilapia, Oreochromis aureus (Steindachner 1864), in the state of Florida
provides a good study species to examine the landscape level factors that contribute to
localized success. First, this is an “old” invasion: Blue Tilapia have been established in
Florida since 1961 (Buntz and Manooch 1968). Second, they spread rapidly (Buntz and
Manooch 1968; Hale 1995; Lawson 2018), aided in Florida by additional introduction
pathways that included intentional stocking and escape from aquaculture (Hill 2011).
These two factors suggest that adequate time has elapsed for spread and for habitat
and community patterns to emerge. Third, much of peninsular Florida provides suitable
climate for the species, so they are broadly distributed throughout much of the
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peninsula (Nico et al. 2018). Fourth, they can achieve high densities (Noble and
Germany 1986), a factor associated with impact (Parker et al. 1999). Fifth, they are well
studied: we know a lot about the fishes’ biology and life history. Sixth, tilapias are an
important global invader with intentional and unintentional introductions producing feral
populations in at least 114 countries (Deines et al. 2016); and Oreochromis species are
the second most important finfish aquaculture species, with a combined production total
behind only Grass Carp, Ctenopharyngodon idella, (FAO 2018). Lastly, lake monitoring
has been performed on Florida lakes over broad temporal and spatial scales. A large
study performed between June 1986 and June 1990 sampled a diverse group of 60
Florida lakes and included a wide set of limnological characteristics along with fish
community composition (Canfield and Hoyer 1992). Though this dataset is older, Blue
Tilapia have had 30 years and ample opportunity to disperse throughout the study
region prior to the collection of these data.
My goal was to improve prediction regarding this fish invader across landscape
scales and identify specific factors relating to its success in lakes. My specific objectives
were to use a database of Florida lakes (Florida LAKEWATCH database; Canfield and
Hoyer 1992) containing water chemistry, aquatic macrophyte abundance, and fish
sampling data to (1) identify the limnological factors related to the presence or absence
of Blue Tilapia in Florida lakes, (2) identify the limnological factors related to abundance
and biomass of Blue Tilapia in Florida lakes, and (3) evaluate associations with other
fish species hypothesized to interact with Blue Tilapia.
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CHAPTER 2 METHODS
Study Organism
Blue Tilapia is a deep-bodied fish that grows to a maximum total length of 50.8
cm (CABI 2018). Normal adult coloration (Figure 2-1) includes a bluish/grey body with
countershading, a small reddish border on the dorsal fin, and a caudal fin that segues
from blue/grey to a wide reddish/orange border. The caudal fin lacks vertical barring, a
characteristic that distinguishes Blue Tilapia from Nile Tilapia Oreochromis niloticus, the
fish with which it is most commonly confused. Ranging from West Africa to Israel
(Trewavas 1983), Blue Tilapia was introduced to Florida in 1961 for research on its
ability to control aquatic plants, provide forage for Largemouth Bass, and serve as a
sport/food fish for the public (Hale et al. 1995). Subsequently, research fish were
intentionally transferred by anglers to public and private waters (Buntz and Manooch
1968), and Blue Tilapia expanded rapidly throughout the state (Lawson 2018). It is now
regarded as the most widespread foreign species in the state (Nico et al. 2018).
Florida populations of Blue Tilapia often have traits associated with Nile Tilapia
and many are thought to consist at least partly of hybrids (Hill 2017). Blue Tilapia and
Nile Tilapia are classified as a conditional non-native species in Florida (Florida
Administrative Code §68-5.002). Conditional species are thought to represent elevated
risk of impacts to Florida’s natural resources or agriculture and generally require a
permit to import or aquaculture; however, Blue Tilapia, Nile Tilapia, and Blue/Nile
hybrids, are so widespread that permits are only required in Florida’s northwest region
(https://myfwc.com/license/commercial/aquaponics/, accessed 04/13/2019).
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Blue Tilapia exhibits numerous characteristics common to successful invaders
globally. Blue Tilapia grows quickly (Peña Messina et al. 2010) and matures rapidly
(Trewavas 1983; Peña Messina et al. 2010). It is physiologically robust, tolerating
environmental extremes including temperature down to 5°C (Trewavas 1983), a wide
range of salinity levels (Stickney 1986), and low dissolved oxygen (Popma and Masser
1999). Blue Tilapia is an opportunistic feeder whose primary diet consists of algae
(phytoplankton, periphyton, and filamentous), detritus, and zooplankton (Cailteux et al.
1992, Gu et al. 1996, Gu et al. 1997). Blue Tilapia has advanced parental care through
maternal mouthbrooding and breeds throughout warm periods (Trewavas 1983).
Tilapias are recognized to have potentially negative environmental impacts in
their introduced countries (Canonico et al. 2005), though most evidence for ecological
impacts is correlative or anecdotal (De Silva et al. 2004, 2006; Attayde et al. 2011).
Noticable impacts are most likely for dense populations of Blue Tilapia such as the
unusual densities reported in Lake Trinidad, Texas, where >2,600 kg/ha of this species
reportedly eliminated recruitment of a native substrate spawner, Largemouth Bass
Micropterus salmoides (Noble and Germany 1986). In Florida, the most likely
mechanisms of impact would derive from potentially high densities of Blue Tilapia
coupled with their feeding and reproductive behavior (Hill 2011). Overcrowding can
disrupt nesting of native substrate spawners (Shafland 1983) or lead to direct
competition for spawning habitat (Traxler and Murphy 1995; but see Hill 2011).
Largemouth Bass and sunfish Lepomis spp. are the species most likely affected.
Feeding impacts of high densities of Blue Tilapia include the reduction of planktonic or
invertebrate prey with potentially negative effects on native planktivores such as
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Gizzard Shad Dorosoma cepedianum and larvae and juveniles of other species (Zale
1987; Zale and Gregory 1990; Traxler and Murphy 1995). Male Blue Tilapia dig
reproductive bowers into the substrate which can cause localized effects on sediments,
benthic macroinvertebrates, and aquatic macrophytes (Hill 2011). Blue Tilapia feeding
on sediments or from periphyton can have direct and indirect effects on turbidity,
nutrient cycling, and aquatic macrophyte coverage, with their feeding activities
described as akin to “aquatic pigs” (Dr. Charles Cichra, University of Florida, personal
communications).
Basic Methods
I used the Florida LAKEWATCH dataset of 60 lakes from central through
northwest Florida (Canfield and Hoyer 1992). The lakes ranged from oligotrophic to
hypereutrophic, and within each trophic category, macrophyte coverage varied from
<10% to over 75%. For the present study, I used 43 of the lakes (Figure 2-2 and Table
2-1). The remaining 17 lakes (Appendix) were excluded because they were either north
of Gainesville, Florida, the northernmost latitude at which Blue Tilapia can currently
survive without a thermal refuge, or east of Gainesville in Putnam County and too small
to provide thermal protection for Blue Tilapia during extended cold periods.
Each lake was sampled between June 1986 and June 1990. Six water quality
sampling stations were established per lake (three littoral, three open water) and each
was sampled in the summer; the three open water stations were sampled again later on
two additional dates. Water was collected at a depth of 0.5m below the surface. Twenty-
seven lake characteristics (Table 2-2) were measured using in-lake or laboratory
methods as described below. The lake factors in the dataset were reduced to a set of
seven important variables for freshwater lakes prior to analyses to reduce the influence
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of non-independent variables. The seven independent variables represent lake size and
structure (surface area and depth), trophic state (chlorophyll α), vegetation (percent
volume infested with aquatic macrophytes, PVI), and water quality (color, conductivity
and Secchi disk depth). Surface area was reported from the Gazetteer of Florida Lakes
(Shafer et al. 1986). Water depths were recorded using a boat-mounted Raytheon
fathometer. To determine the total chlorophyll α concentration, a measured portion of
water was filtered through a Gelman type A-E glass fiber filter and then the method of
Yentsch and Menzel (1963) and equations of Parson and Strickland (1963) were used.
PVI was calculated with a Raytheon DE-719 fathometer (Maceina and Shireman 1980).
Color was determined after Gelman type A-E glass fiber filtration using the platinum-
cobalt method and matched Nessler tubes (APHA 1985). Conductivity was measured
with a Yellow Springs Instrument Model 31 conductivity bridge. Secchi disk depth was
measured at each water collection station using a 20cm-diameter white/black/white
Secchi disk, and then averaged.
Fish sampling occurred between May and November when the first water sample
was taken. Rotenone sampling with two to twelve 0.08-ha blocknets per lake,
depending on lake size, was used to estimate biomass (kg/ha) and abundance
(number/ha) of each fish species caught; blocknets were equally split between and
weighted by littoral and limnetic habitats to obtain whole-lake estimates. Rotenone
sampling resulted in fourteen lakes with Blue Tilapia. In addition to rotenone sampling,
between two and ten 10-minute electrofishing transects were evenly distributed around
each lake to calculate Catch Per Unit Effort (CPUE) electrofishing biomass (kg/hr) and
electrofishing abundance (number/hr) for each fish species caught. Finally, gillnet
17
sampling added Lake Hartridge to the list of lakes with Blue Tilapia present because two
Blue Tilapia were caught in this lake, but no other gillnet data were used in this study.
Statistical Analysis
Box-plots were generated for all twenty-seven measurements against Blue
Tilapia presence/absence data (Figure 2-1), and basic statistics for each measurement
(Table 2-2) were calculated to assess which lake characteristics were predictive. These
characteristics were also analyzed using an unpaired two-sample Wilcoxon test to
determine whether statistically significant differences existed between lakes with and
without Blue Tilapia.
Biomass (gm/ha) and abundance (number/ha) from rotenone sampling were
summarized for Blue Tilapia as well as a putative strong competitor, Gizzard Shad, and
a predator/competitor depending upon life stage, Largemouth Bass. A factor for
Largemouth Bass presence was excluded from the analysis, because this species was
present in every lake. CPUE data (biomass gm/hr and abundance number/hr) were
calculated from electrofishing data for these three species, though CPUE data were not
used for Blue Tilapia because these fish do not readily stun during electrofishing (Hoyer
and Canfield 1994). These data were used bi-directionally, both to model the biomass
and abundance of Blue Tilapia and to evaluate Blue Tilapia’s potential associations with
select native fishes.
Arcsine-square-root transformation of percentage data and, when needed, base-
e log transformation of data requiring scale adjustment were performed as part of data
modeling.
The stepAIC function in R (version 3.5.1, both forward and backward methods)
was used to identify logistic models for presence/absence from the reduced set of
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seven lake factors. The best model based on Akaike Information Criteria (AIC score)
was augmented with Largemouth Bass and Gizzard Shad biomass and abundance data
from rotenone sampling to see whether predictive ability could be improved. All models
use stepwise logistic regression to identify the factors contributing to (or preventing)
establishment:
Logit(𝑝) = 𝑎 + 𝑏1𝑥1 + 𝑏2𝑥2 + … + 𝑏𝑖𝑥𝑖 (2-1)
where
Logit(p) = the logistic probability of Blue Tilapia being collected in lake i
a = intercept
bi = parameter estimate
xi = independent variable for lake i.
The estimate of logit(p) was used to obtain the predicted probability of Blue
Tilapia occurrence (p) as:
𝑝 = 1 − elogit(𝑝) (1 + elogit(𝑝))−1 (2-2)
For lakes that produced Blue Tilapia data from electrofishing, forward and
backwards stepAIC selection was used on the set of seven lake factors to identify
models that predicted biomass and abundance. Like the presence/absence modeling,
expanded models incorporating rotenone Largemouth Bass and Gizzard Shad biomass
and abundance data were also tested.
Model predictive power was tested using McFadden’s R2 measure, which ranges
from zero to just under one, with values closer to zero indicating that the model has no
predictive power.
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To consider whether Blue Tilapia had an impact on Gizzard Shad and predators
(Largemouth Bass, Florida Gar Lepisosteus platyrhincus, and Bowfin Amia calva), I
compared the electrofishing CPUE of these fishes between lakes with and without Blue
Tilapia.
To understand the relationship between the fish communities in lakes with and
without Blue Tilapia, ordination with non-metric multi-dimensional scaling (NMDS) on
the Bray-Curtis dissimilarity index was performed on a fish abundance community
matrix compiled from the Florida LAKEWATCH rotenone abundance data. The vegan
2.5-2 package in R was used to perform the ordination, and the ordination with the
fewest number of axes below a stress level of 0.2 was selected.
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Figure 2-1. Blue Tilapia Oreochromis aureus. FWC artwork: Duane Raver, Jr.,
https://myfwc.com/wildlifehabitats/profiles/freshwater/blue-tilapia/
21
Figure 2-2. Lakes in the Florida LAKEWATCH dataset included in the current study.
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Table 2-1. Lakes in the Florida LAKEWATCH dataset included in the current study.
Lakes Included
County Lake SA (ha) Trophic Category County Lake SA (ha) Trophic Category
ALACHUA BIVENS-ARM • 76 Hypereutrophic ORANGE SUSANNAH 31 Eutrophic
ALACHUA LOCHLOOSA 2309 Hypereutrophic OSCEOLA FISH • 89 Eutrophic
ALACHUA WAUBERG 100 Hypereutrophic OSCEOLA LIVE-OAK 152 Eutrophic
HERNANDO LINDSEY 55 Eutrophic PASCO BELL * 32 Eutrophic
HERNANDO MOUNTAIN 51 Eutrophic PASCO CLEAR * 64 Eutrophic
LAKE CLAY 4.9 Hypereutrophic PASCO PASADENA 151 Eutrophic
LAKE CROOKED 8.4 Eutrophic PASCO WEST MOODY 39 Eutrophic
LAKE DOUGLAS 16 Eutrophic POLK BONNY • 143 Hypereutrophic
LAKE GRASSHOPPER 59 Oligotrophic POLK CONINE • 96 Hypereutrophic
LAKE HARRIS • 5580 Eutrophic POLK GATE LAKE 7.8 Eutrophic
LAKE LAWBREAKER 4.8 Oligotrophic POLK HARTRIDGE • 176 Eutrophic
MARION CATHERINE 41 Eutrophic POLK HOLLINGSWORTH • 144 Hypereutrophic
MARION MILL DAM 85 Mesotrophic POLK HUNTER • 40 Hypereutrophic
MARION ROUND-POND 4 Hypereutrophic POLK MOUNTAIN 2 55 Oligotrophic
MARION SWIM POND 9 Hypereutrophic POLK PATRICK 159 Eutrophic
MARION TOMAHAWK 15 Oligotrophic POLK SANITARY • 204 Eutrophic
ORANGE APOPKA • 12412 Hypereutrophic POLK THOMAS • 55 Eutrophic
ORANGE BALDWIN * 80 Eutrophic POLK WALES •* 132 Hypereutrophic
ORANGE CARLTON • 155 Hypereutrophic SEMINOLE ORIENTA * 52 Eutrophic
ORANGE HOLDEN •* 102 Hypereutrophic SUMTER MIONA 169 Hypereutrophic
ORANGE KILLARNY •* 96 Eutrophic SUMTER OKAHUMPKA 271 Hypereutrophic
ORANGE PEARL * 24 Eutrophic
Trophic Category is based on the classification system of Forsberg and Ryding (1980). Lakes with a (•) contain Blue Tilapia. Lakes with a (*) were treated with grass carp.
23
Table 2-2. Limnological and macrophytic characteristics of the 43 lakes included in this study.
Factor Mean Median Standard Deviation Minimum to Maximum
Secchi Disk Depth (m) • 1.7 1.5 1.2 0.3 – 5.5
Shoreline (km) 6.86 4.01 11.96 0.88 – 61.26
Mean Depth (m) • 2.9 3.0 1.3 0.6 – 5.9
Surface Area (ha) • 552.7 76.0 2057.5 4 – 12412
pH 7.5 7.8 1.5 4.4 – 9.64 Acidity 2.2 2.2 1.6 0 – 6.1 Total Alkalinity (mg/l) 38.9 27.1 34.2 0 – 131
Conductivity (µS/cm @ 25⁰C) • 161 132.2 96.1 33.11 – 384
Phosphorus (mg/l) 0.06 0.02 0.16 0 – 1.03 Nitrogen (mg/l) 1.02 0.8 0.85 0.11 – 3.72 Chloride (mg/l) 18.43 16.33 10.19 4.61 – 43.56
Chlorophyll α (µg/l) • 32.66 10.98 49.28 0.74 – 206.61
Pheophytin (µg/l) 1.51 0.71 2.33 0.07 – 13.96
Color (PT-CO Units) • 21.49 16.67 20.11 0 – 115
Total Dissolved Solids (mg/l) 7.95 3.28 12.56 0.38 – 62.28 Organic Solids (mg/l) 6.63 2.74 10.46 0.25 – 52.4 Inorganic Solids (mg/l) 1.32 0.47 2.22 0.05 – 10.57 Calcium (mg/l) 13.29 9.79 10.46 0.39 – 39.22 Magnesium (mg/l) 4.48 3.58 4.04 0.59 – 17.67 Sodium (mg/l) 9.38 7.4 6.33 1.3 – 32.78 Potassium (mg/l) 3.19 2.15 3.29 0.05 – 12.78
PVI • 24.01 4.6 32.36 0 – 98.1
PAC 38.75 26.7 39.88 0 – 100 Emergent Vegetation (kg/m2) 4.06 2.36 4.83 0.32 – 26.8 Floating Vegetation (kg/m2) 1.36 0.42 2.43 0 – 11.2 Submersed Vegetation (kg/m2) 1.74 0.79 2.97 0 – 16.58 Littoral Zone Width (m) 28.96 20.3 31.3 0.54 – 162.8
The reduced dataset used for statistical modeling is represented by a (•).
24
CHAPTER 3 RESULTS
General Statistics
Blue Tilapia in Florida tend to occur in lakes that are larger, more productive,
have lower water clarity, higher values for pH, alkalinity, conductivity, and dissolved
minerals, and less coverage of aquatic macrophytes (Table 3-1; Figure 3-1). On
average, lakes with Blue Tilapia were 9 times larger, had over 6 times the chlorophyll- α
and total dissolved solids, had less than half the Secchi disk depth, had twice the
conductivity, had almost 8.5 times less volume infested with macrophytes (PVI), and
had 4.5 times less surface area covered by macrophytes (PAC).
The mean depth and mean color between lakes with Blue Tilapia and lakes
without were not different. The mean abundance from rotenone sampling for Blue
Tilapia in the fifteen lakes in which they were found was 66.3 fish/ha (SD = 80.2), and
the mean biomass was 11,882 gm/ha (SD = 15,642). The mean abundance
(number/ha) for rotenone sampling for Gizzard Shad was over 75 times higher in lakes
with Blue Tilapia, but 4.1 times lower for Largemouth Bass. The mean biomass (gm/ha)
for rotenone sampling for Gizzard Shad was 2.1 times higher in lakes with Blue Tilapia
and 1.4 times lower for Largemouth Bass. Additional comparative statistics between
lakes with and without Blue Tilapia are presented in Table 3-1.
Presence/Absence Model for Blue Tilapia
A logistic regression model, using four of the seven limnological factors, proved
95% accurate in predicting Blue Tilapia presence in Florida lakes:
𝐿𝑜𝑔𝑖𝑡(𝑝) = −43.17 – 1.27(𝑆𝑒𝑐𝑐ℎ𝑖) + 2.31𝑙𝑛(𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝐴𝑟𝑒𝑎)
+ 7.13𝑙𝑛(𝐶𝑜𝑛𝑑𝑢𝑐𝑡𝑖𝑣𝑖𝑡𝑦) – 11.06𝑎𝑟𝑐𝑠𝑖𝑛𝑒𝑠𝑞𝑟𝑡(𝑃𝑉𝐼) (3-1)
25
In the model represented by Equation 3-1, both the intercept and Conductivity proved
statistically significant (p = 0.03 for both). This model reduced the NULL deviance from
55.62 to 13.48 and has a McFadden’s R2 = 0.76.
Adding biomass (gm/ha) and abundance (number/ha) information for
Largemouth Bass improved neither the logistic model’s accuracy nor AIC score; nor did
adding presence, biomass (gm/ha), and abundance (number/ha) information for Gizzard
Shad. Table 3-2 presents the additional factors tested, their impact on the log odds of
Blue Tilapia presence, and their impact on residual deviance.
Biomass and Abundance Models for Blue Tilapia
Predictive models for abundance (number/ha) of Blue Tilapia could not be
developed from the available limnological, macrophytic, or community information. No
model tested could generate an AIC score more than 2.5 points better than a NULL
model.
Modeling for Blue Tilapia biomass (gm/ha) also proved challenging. Forward
selection modeling failed to produce any models better than the NULL model.
Backwards selection resulted in a 10-parameter model with low predictive value
(McFadden’s R2 = 0.16) and with most parameters being statistically significant (p
0.05) (Table 3-3).
Associations of Blue Tilapia with Three Predators and Gizzard Shad
Predators like Largemouth Bass, Florida Gar, and Bowfin had slightly higher
electrofishing biomass CPUE (gm/hr) in lakes where Blue Tilapia were present,
whereas Gizzard Shad, which feed at a similar trophic level, had a much higher
biomass CPUE (Figure 3-2). For predators, the biomass CPUE was 1.5, 2.2, and 1.7
times higher, respectively, but for Gizzard Shad, the biomass CPUE was 11.6 times
26
higher. Electrofishing abundance CPUEs were similar for the three predator species
regardless of Blue Tilapia presence/absence, but Gizzard Shad electrofishing
abundance CPUE was 146 times higher in lakes with Blue Tilapia. Table 3-4 details the
differences in mean biomass and abundance CPUEs for these species.
Evaluating the Associations of Blue Tilapia with the Fish Community
Ordination of the fish community using NMDS resulted in a two-axis solution
(stress = 0.18) with good separation along axis NMDS1. There was a strong signal
between lakes with Blue Tilapia and lakes without. A non-metric fit R2 value of 0.968
indicates that the original dissimilarities were well preserved in the 2-dimensional space.
In the ordination plot for the lakes (Figure 3-3), less productive oligotrophic and
mesotrophic lakes are grouped on the right, with more productive and less macrophyte-
filled lakes associated with Blue Tilapia on the left. The species ordination plot (Figure
3-4, and see Table 3-5) shows how the fish communities are organized. Fish that are
found in eutrophic systems with fewer macrophytes, like Gizzard Shad and Threadfin
Shad Dorosoma petenense, are grouped with Blue Tilapia in the yellow shaded area on
the left side of the figure, and fish that are found in waters with more macrophytes are
grouped at the top right. The community scaled vector plot (Figure 3-5) also shows
groups of fish communities organized by trophic state and macrophyte dominance.
27
Figure 3-1. Blue Tilapia presence and absence as a factor of 27 Florida lake characteristics. Some y-axes have been
adjusted for clarity and may exclude outliers. Blue dots represent statistical means. Significance *** 0.001
** 0.01 * 0.05.
28
Figure 3-1. Continued
29
Figure 3-1. Continued
30
Table 3-1. Presence and absence statistics, and Wilcoxon W- and p-values for 27 lake factors and 10 fish community factors in 43 lakes.
Presence Absence Factor Mean Range Mean Range W-value p-value
Secchi Disk Depth (m) • 0.82 0.3 – 2.3 2.19 0.6 – 5.5 46.5 *** < 0.001
Shoreline (km) 11.77 1.71 – 61.26 4.23 0.88 – 22.57 298.5 * 0.025
Mean Depth (m) • 2.98 1.2 – 4.7 2.83 0.6 – 5.9 233 0.566
Surface Area (ha) • 1311.6 29.2 – 12412 146.2 4 - 2286 322 ** 0.004
pH 8.55 7.58 – 9.64 6.89 4.4 – 9.0 360 *** < 0.001 Acidity 0.95 0 – 3 2.92 0.29 – 6.1 61.5 *** < 0.001 Total Alkalinity (mg/l) 64.65 25.56 – 111.33 25.03 0 - 131 366 *** < 0.001
Conductivity (µS/cm @ 25⁰C) • 238.7 118 – 384 119.4 33.1 – 321.7 363 *** < 0.001
Phosphorus (mg/l) 0.14 0.01 – 1.03 0.02 0 - 0.17 359 *** < 0.001 Nitrogen (mg/l) 1.70 0.5 – 3.72 0.65 0.11 – 1.78 360 *** < 0.001 Chloride (mg/l) 24.10 12.33 – 43.56 15.39 4.61 – 43.56 324 ** 0.004
Chlorophyll α (µg/l) • 72.08 4.34 – 206.61 11.54 0.74 – 102.23 374.5 *** < 0.001
Pheophytin (µg/l) 2.42 0.07 – 13.96 1.05 0.1 – 5.27 254 0.125
Color (PT-CO Units) • 22.55 13 – 43.33 20.93 0 - 115 266 0.157
Total Dissolved Solids (mg/l) 17.61 1.52 – 62.28 2.77 0.38 – 8.71 377 *** < 0.001 Organic Solids (mg/l) 14.67 1.14 – 52.4 2.33 0.25 – 7.77 372 *** < 0.001 Inorganic Solids (mg/l) 2.96 0.38 – 10.57 0.44 0.05 – 1.32 392 *** < 0.001 Calcium (mg/l) 22.16 8.13 – 33.56 8.53 0.39 – 39.22 378 *** < 0.001 Magnesium (mg/l) 6.42 1.96 – 17.67 3.44 0.59 – 15.89 313.5 ** 0.009 Sodium (mg/l) 13.01 6.3 – 32.78 7.44 1.3 – 30.67 355 *** < 0.001 Potassium (mg/l) 5.11 1.19 – 12.78 2.16 0.05 – 10.2 334 ** 0.001
PVI • 4.12 0 – 35.7 34.67 0 – 98.1 87 ** 0.002
PAC 11.56 0 – 60 53.31 0 - 100 92 ** 0.002 Emergent Vegetation (kg/m2) 3.98 0.91 – 10.22 4.11 0.32 – 26.8 252.5 0.285 Floating Vegetation (kg/m2) 0.74 0 – 7.6 1.70 0 – 11.2 116 * 0.016 Submersed Vegetation (kg/m2) 0.97 0 – 8 2.16 0 – 16.58 114 * 0.014 Littoral Zone Width (m) 19.59 0.61 – 52.23 33.97 0.54 – 162.8 164 0.250 Blue Tilapia Abundance (number/ha) 66.3 2 – 268.29 N/A N/A N/A N/A Blue Tilapia Biomass (gm/ha) 11882 0 – 53975 N/A N/A N/A N/A Gizzard Shad Abundance (number/ha) 15515 0 – 199357 205 0 – 5485 372 *** < 0.001 Gizzard Shad Biomass (gm/ha) 44865 0 – 311799 20920 0 – 495693 362 *** < 0.001 Largemouth Bass Abundance (number/ha) 114.7 0.1 – 378.8 472.6 0 – 2430.9 91 ** 0.002 Largemouth Bass Biomass (gm/ha) 10084 0 – 24450 13793 0 – 38045 15 *** < 0.001
31
Table 3-1. Continued. Presence Absence Factor Mean Range Mean Range W-value p-value Gizzard Shad EF Abundance (number/hr) 46.7 0 - 347 0.3 0 – 5 294 ** 0.004 Gizzard Shad EF CPUE (gm/hr) 647 0 – 3950 56 0 – 967 290 ** 0.006 Largemouth Bass EF Abundance (number/hr) 41.9 2 – 87 35.8 0 – 139 254 0.268 Largemouth Bass EF CPUE (gm/hr) 12396 1172 - 32667 7988 0 – 28464 278 0.085
EF = Electrofishing. The reduced dataset used for statistical modeling is represented by a (•). Significance *** 0.001 ** 0.01 * 0.05
Figure 3-2. Predator and Gizzard Shad CPUE from electrofishing data as a function of Blue Tilapia presence and
absence. Blue dots represent statistical means.
32
Figure 3-3. NMDS ordination plot of the lakes in the study. Lakes without a symbol are classified as eutrophic.
33
Figure 3-4. NMDS ordination plot of the fish community by presence/absence of Blue Tilapia. See Table 3-5 for species
codes.
34
Figure 3-5. Community scaled vector plot of fish species with p-values 0.2. Species
with higher p-values were removed for readability. See Table 3-5 for species codes.
Table 3-2. Impact of Largemouth Bass and Gizzard Shad abundance and biomass data
on logistic modeling for Blue Tilapia presence. Factor Change in log odds of
Blue Tilapia presence % Reduction in
Residual Deviance
Gizzard Shad presence 3.00 10.7 Gizzard Shad biomass (gms/ha) 0.05 7.6 Gizzard Shad abundance (number/ha) 0.06 10.4 Largemouth Bass biomass (gms/ha) 0.13 0 Largemouth Bass abundance (number/ha) (Note) -1.97 5.6 Gizzard Shad abundance + Largemouth Bass abundance (gms/ha)
-0.04 0
Note: Based on a warning in R, this coefficient may be inflated.
35
Table 3-3. t-values, p-values and residual deviance explained by the parameters for
Blue Tilapia biomass modeling. Factor t-value p-value % Reduction in Residual Deviance
Secchi Disk Depth (m) 3.021 0.06 5.2 Gizzard Shad Presence -2.996 0.06 8.6 Gizzard Shad biomass (gms/ha) 4.718 0.02 0.9 Gizzard Shad abundance (number/ha) -5.542 0.01 2.4 Largemouth Bass biomass (gms/ha) 2.756 0.07 20.8 Largemouth Bass abundance (number/ha) -1.688 0.19 12.2 Lake Depth (m) -4.790 0.02 1.5 Conductivity(µS/cm@25C) 4.684 0.02 6.7 Color (PT-CO Units) -5.709 0.01 22.3 PVI -3.926 0.03 16.3
Table 3-4. Mean electrofishing biomass (gm/hr) and abundance (number/hr) CPUE
parameters when Blue Tilapia are present and absent, standard errors of the means (in parentheses), and W-values and p-values for the Wilcoxon test comparing the distributions.
Species CPUE when present CPUE when absent
W-value p-value
Biomass (gm/hr) Largemouth Bass 12396 (2264) 7988 (1181) 278 0.085 Florida Gar 4190 (2214 1938 (587) 239 0.447 Bowfin 2961 (1027) 1729 (632) 234 0.504 Gizzard Shad 647 (292) 56 (38) 290 ** 0.006 Abundance (number/hr) Largemouth Bass 41.9 (6.2) 35.8 (6.2) 254 0.268 Florida Gar 4.3 (1.6) 4.9 (1.5) 220 0.800 Bowfin 1.2 (0.4) 1.3 (0.5) 225 0.690 Gizzard Shad 46.7 (25.0) 0.32 (0.21) 294 ** 0.004
Significance *** 0.001 ** 0.01 * 0.05
36
Table 3-5. NMDS coordinates and p-values for each species found in the 43 lakes used in the current study (Table 2-1). P-values were calculated by the envfit function of the vegan package in R, which fits environmental vectors onto an ordination.
NMDS Coordinates
Species Name Species Code NMDS1 NMDS2 p-value
Amia calva AMCA -0.0046 0.2007 0.441
Aphredoderus sayanus APSA 0.0899 0.4013 * 0.030
Centrarchus macropterus CEMA 0.0150 0.2321 0.344
Ctenopharyngodon idella CTID -0.1892 0.4060 ** 0.008
Dorosoma cepedianum DOCE -0.2612 0.1260 0.183
Dorosoma petenense DOPE -0.4008 -0.2219 ** 0.006
Elassoma evergladei ELEV 0.1495 0.1503 0.412
Enneacanthus chaetodon ENCH 0.0846 0.2873 0.147
Enneacanthus gloriosus ENGL 0.0672 0.0680 0.820
Erimyzon sucetta ERSU 0.5125 0.1878 ** 0.001
Esox americanus americanus ESAM 0.1540 0.4570 ** 0.003
Esox niger ESNI 0.0755 0.3334 0.105
Etheostoma fusiforme ETFU 0.1380 0.1069 0.540
Fundulus chrysotus FUCH 0.3180 -0.2229 * 0.039
Fundulus lineolatus FULI 0.4430 -0.0657 ** 0.007
Fundulus seminolis FUSE -0.3476 -0.0206 0.067
Gambusia holbrooki GAHO 0.0261 0.1665 0.599
Heterandria formosa HEFO 0.2571 -0.1828 0.123
Ictalurus catus ICCA -0.0498 0.2549 0.241
Ictalurus natalis ICNA -0.0093 0.3973 * 0.036
Ictalurus nebulosus ICNE 0.0118 0.2171 0.372
Jordanella floridae JOFL 0.0440 -0.0900 0.812
Labidesthes sicculus LASI -0.0335 -0.0636 0.915
Lepisosteus osseus LEOS 0.0029 0.1804 0.573
Lepisosteus platyrhincus LEPL 0.0018 0.1894 0.471
Lepomis auritus LEAU -0.2187 0.2282 0.117
Lepomis gulosus LEGU 0.5065 -0.1269 ** 0.004
Lepomis macrochirus LEMAC -0.0120 -0.0600 0.904
Lepomis marginatus LEMAR 0.2069 0.1452 0.277
Lepomis microlophus LEMI -0.0239 0.0227 0.975
Lepomis punctatus LEPU 0.0090 0.2300 0.339
Lepomis spp. LEspp 0.0101 -0.1094 0.787
Leptolucania ommata LEOM 0.2579 0.0454 0.253
Lucania goodei LUGO 0.0327 0.0551 0.904
Micropterus salmoides MISA 0.1747 -0.0937 0.420
37
Table 3-5. Continued.
NMDS Coordinates
Species Name Species Code NMDS1 NMDS2 p-value
Morone chrysops x Morone saxatilis MOCHxMOSA -0.1683 0.4412 ** 0.002
Notemigonus crysoleucas NOCR 0.0006 0.0719 0.904
Notropis maculatus NOMA -0.2287 0.2704 0.077
Notropis spp. NOspp 0.0055 0.1752 0.496
Noturus gyrinus NOGY 0.0258 0.2126 0.408
Poecilia latipinna POLA -0.0529 0.1122 0.721
Pomoxis nigromaculatus PONI -0.1202 0.0832 0.663
Strongylura marina STMA -0.0745 0.2760 0.147
Significance *** 0.001 ** 0.01 * 0.05
38
CHAPTER 4 DISCUSSION
My results show that lakes with and without Blue Tilapia were strongly divergent
based on a suite of limnological and fish community characteristics. Lakes with Blue
Tilapia were more productive and had reduced water clarity and aquatic macrophyte
coverage. These environmental conditions likely drove associated variation in the native
fish communities, such that lakes with Blue Tilapia were associated with trophically
similar species, like Gizzard Shad and Threadfin Shad. There were positive correlations
between Blue Tilapia presence and the electrofishing biomass CPUEs of all four native
species tested, suggesting that habitat quality, specifically trophic state and macrophyte
dominance, affected both the native and non-native fish species alike. Taken together,
these results indicate that limnological factors are strong predictors of the local
occupancy of Blue Tilapia and drivers of the native and non-native fish community
structure. Neither direct biotic interactions, nor the direct or indirect abiotic effects of
Blue Tilapia, were evident in the study, implying that negative impacts from Blue Tilapia,
if they exist, are being overridden by the strong productivity response of Florida lakes
inhabited by Blue Tilapia.
The establishment and spread of Blue Tilapia is bounded by broad-scale climate
factors and influenced by local limnological factors. The observed distribution and
spread of Blue Tilapia is likely the result of differential colonization related to local
propagule pressure and differential success due to limnological factors. Evidence in
support of this assertion includes first the accuracy of Equation 3-1, second the ability of
the species to rapidly disperse (Lawson 2018), and third the ubiquity of secondary
introduction pathways including anglers (Buntz and Manooch 1968), abundant natural
39
and man-made dispersal corridors, and unintentional aquaculture releases (Hill 2011).
Blue Tilapia had 25 years to disperse throughout peninsular Florida prior to the
collection of data used in this study, and it is currently known to be widespread in lakes
and canals plus portions of rivers, large streams, and impoundments (Figure 4-1). Yet,
the species was present in just 15 of the 43 lakes in the dataset despite its broad
distribution.
I found poor model fits for both the abundance and biomass of Blue Tilapia. This
is most likely due to the large standard deviations for these variables (Table 4-1), which
were larger than both the mean and median values. Other possible reasons include
variability due to sampling (a small number of blocknets gave imprecise estimates) and
the narrow trophic state range of lakes with Blue Tilapia.
Blue Tilapia in Florida was associated with a suite of limnological characteristics
related to lake trophic state and enhanced productivity, likely due to food resource
availability. A previous study using the same dataset found similar results for Gizzard
Shad and Threadfin Shad (Allen et al. 2000), in which abundance and biomass of both
shad species were positively related to chlorophyll. The strong ordination associations
between Blue Tilapia and both shad species further supports the assertion that
enhanced productivity is an important driver for abundance and biomass in these
systems. Though prior literature also notes that the feeding habits of Blue Tilapia
overlap with those of other Florida natives at various life stages (Zale and Gregory
1990, Traxler and Murphy 1995; Allen et al. 2000), leading one to expect competitive
impacts, competitive impacts are not evident in this study. Competitive impacts require
40
resources that are limiting, and resources do not appear limiting in the lakes inhabited
by Blue Tilapia in this study.
A significant implication of this study is that eutrophication may make lakes more
conducive for non-native species like Blue Tilapia, increasing distribution and
abundance, and perhaps impact. Cultural eutrophication has been an ongoing problem
both in Florida (but see Canfield et al. 2018) and nationwide, with potential value losses
in the US estimated at over $2B (Dodds et al. 2008). Florida’s eutrophication problems
can be readily observed in Lake Harris in Central Florida, where a pre-anthropogenic
trajectory toward oligotrophication was interrupted by cultural eutrophication (Kenney et
al. 2016) to create a lake classified by Canfield and Hoyer (1992) as eutrophic. The
trend towards eutrophication in Florida lakes is important because it affects fish
communities. It also affects other terrestrial and aquatic life, property and amenity
values, recreational water usage and drinking water (Dodds et al. 2008). In response to
lake eutrophication, Florida monitors lake water quality through a voluntary lake
sampling program (Florida LAKEWATCH). Numerous federal, state and local agencies
also devote significant time and resources to watershed and in-lake nutrient
management (Hoyer et al. 2008; Knight et al. 2013; Fulton et al. 2015; Radomski and
Carlson 2018; Ramseur and Tiemann 2019). Blue Tilapia can be related to this
important issue because 1) eutrophication might increase the number of lakes exhibiting
suitable habitat allowing it to further spread; and 2) Blue Tilapia might also be
associated with declining water quality through foraging mechanisms that result in the
resuspension of nutrients and organic matter.
41
Recent research suggests that biomanipulation, specifically the removal of
Gizzard Shad, can improve water quality (Schaus et al. 2010; Godwin et al. 2011;
Fulton et al. 2015). Improvements occur due to the removal of nutrients in fish biomass
and reduced nutrient recycling from bioturbation (Fulton et al. 2015). Though it would
require sustained intervention, removal of Blue Tilapia could be tested to determine if
this procedure would provide the same benefit, as both species exhibit similar feeding
strategies. Thus, monitoring Blue Tilapia populations and removing biomass, when
populations exceed a certain threshold, could be studied as a method for improving
water quality.
Identifying the community-level impacts of Blue Tilapia would optimally require
pre- and post-invasion data, but ordination techniques elucidate the habitat associations
between certain fish and Blue Tilapia, particularly the Gizzard Shad and Threadfin
Shad; conversely, they make clear the use of dissimilar habitat by species on the
positive side of axis NMDS1. Ordination techniques further clarify that habitat is the
primary driver in determining fish species composition among the lakes included in this
study. Trophic status has long been recognized as the most important factor influencing
fish populations (Canfield and Hoyer 1992). The existence of a strong positive
correlation between the two shad species and Blue Tilapia suggests that environmental
productivity is outweighing both direct biotic interactions and indirect abiotic effects
between these three species, and the same result holds true for other native species
tested in this study. What is good for the nonnative is also good for the native, and lake
productivity makes competition for algae/detritus a moot point at the densities of fish in
these lakes.
42
The importance of limnological factors at the landscape level is strongly
supported by this research. The logistic model (Equation 3-1) can be used both to
predict where Blue Tilapia are today and to predict which lakes Blue Tilapia might
successfully invade should climate change expand their range northward. For example,
applying the logistic model to the seventeen lakes removed from the current study
indicates that only Lake Rowell is likely to be colonized by Blue Tilapia. Using a logistic
model like Equation 3-1 allows resource managers, who are trying to control invasive
species, to preferentially and cost-effectively target those water bodies most likely to
support the future establishment of the species in question. Future research could focus
on application of the logistic model to current distribution patterns, using limnological
parameters to identify lakes that Blue Tilapia are likely to colonize and then evaluating
these lakes for their presence.
43
Figure 4-1. There are 778 records for Blue Tilapia in Florida in the USGS Nonindigenous Aquatic Species Database as of 03 February 2019, with the vast majority in peninsular Florida.
Table 4-1. Statistical factors affecting modeling of Blue Tilapia abundance and biomass.
Mean Median Standard Deviation
Range
Blue Tilapia Abundance (number/ha) 66.3 39.4 80.2 2 – 268.3 Blue Tilapia Biomass (gm/ha) 11882 5983 15642 0 – 53975
44
APPENDIX A LAKES EXCLUDED FROM THE CURRENT STUDY
Lakes Excluded
County Lake SA (ha) Trophic Category
BRADFORD ROWELL 147 Hypereutrophic
CALHOUN TURKEY-PEN 6 Oligotrophic
COLUMBIA ALLIGATOR 137 Hypereutrophic
COLUMBIA WATERTOWN 19 Eutrophic
LAFAYETTE KOON 44 Eutrophic
LEON CARR 254 Hypereutrophic
LEON LOFTEN 5 Mesotrophic
LEON MOORE 28 Mesotrophic
PUTNAM BARCO 13 Oligotrophic
PUTNAM BRIM-POND 3.2 Eutrophic
PUTNAM BULL POND 11 Mesotrophic
PUTNAM CUE 59 Oligotrophic
PUTNAM DEEP 4 Hypereutrophic
PUTNAM KEYS-POND 5.3 Mesotrophic
PUTNAM LITTLE FISH 1.8 Eutrophic
PUTNAM PICNIC 18 Oligotrophic
PUTNAM SUGGS 73 Mesotrophic
45
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BIOGRAPHICAL SKETCH
Jeff Greenspan graduated from the Johns Hopkins University in 1985 with a BS
in mathematical sciences and a minor in computer science. Within two years, he started
Database & LAN Solutions, Inc., an IT consulting company in the Washington, DC
metropolitan area, which he owned and operated until selling it and retiring in 2013.
During his 28-year career in the DC area, Mr. Greenspan was an active volunteer
in the non-profit community. Mr. Greenspan has served on the boards of directors of
three non-profits, the most substantial of which was Wesley Housing Development
Corporation (WHDC). Providing “Housing with Heart,” WHDC provides affordable
housing and support services to over 1000 families in Northern Virginia. WHDC has
built or renovated over 25 affordable communities in Northern Virginia and has over
$100m in assets. Mr. Greenspan served as a member of the Executive Committee from
2005-15 and as Chairman of the Board from 2013-14. Since moving to Gainesville
Florida in 2016, Mr. Greenspan has continued volunteering and currently serves as
Chairman for the North Central Florida chapter of SCORE, a nationwide nonprofit
whose mission is to foster vibrant small business communities through mentoring and
education.
Mr. Greenspan has been a tropical fish hobbyist since 1971, an interest which
presaged his matriculation into the University of Florida’s Fisheries and Aquatic
Sciences program. He has collected fish in Iquitos, Peru and across Uruguay, and has
bred over 100 species of freshwater tropical fish. He also designed and developed
www.MyGroupAuctions.com, which he subsequently donated to the New England
Cichlid Association, to support the auction-based fund-raising activities of fish clubs and
other non-profits.