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2017 SOUTHEASTERN NATURALIST 16(4):546–566
Influence of Lake Surface Area and Total Phosphorus on
Annual Bluegill Growth in Small Impoundments of Central
Georgia
Aaron P. Sundmark1,* and Cecil A. Jennings2
Abstract - The relationships between environmental variables and the growth rates of fishes
are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic
approach to evaluate the influence of lake surface area and total phosphorus on
the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments
in central Georgia. We used model averaging to create composite models and determine
the relative importance of the variables within each model. Results indicated that surface
area was the most important factor in the models predicting growth of Bluegills aged 1–4
years; total phosphorus was also an important predictor for the same age-classes. These
results suggest that managers can use water quality and lake morphometry variables to
create predictive models specific to their waterbody or region to help develop lake-specific
management plans that select for and optimize local-level habitat factors for enhancing
Bluegill growth.
Introduction
Lepomis macrochirus Rafinesque (Bluegill) are aggressive, inquisitive, active,
and brightly colored, all of which have made them more recognizable and
appreciated by the angling and non-angling public than almost any other species
of freshwater fish (Scott and Crossman 1973). Bluegills most likely account for
more individual catches than any other sportfish species in North America (Etnier
and Starnes 1993). As a result, much research has been directed at understanding
Bluegill population structure and the factors that lead to variation in adult body size
(Aday et al. 2008). However, differences in ecological productivity among similar
lakes complicate efforts to manage for common fisheries goals (e.g., quality–size
structure versus abundance). This variation in the productivity of adjacent lakes
and populations has led researchers to suggest that local characteristics (e.g., water
quality, habitat availability, lake morphometry) can be strong drivers of Bluegill
population dynamics (Kratz et al. 1997).
Management strategies for fisheries traditionally focus on 3 variables: fish,
habitat, and people (Hubert and Quist 2010). Within the broad topic of habitat, lake
morphometry is very important in making management decisions, but this aspect
of a fishery typically cannot be actively managed. Managers can, however, select
1Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA
30602. 2US Geological Survey, Georgia Cooperative Fish and Wildlife Research Unit, Warnell
School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602.
*Corresponding author - sundmark@uga.edu.
Manuscript Editor: Andrew Rypel
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water bodies for specific management strategies such as special regulations or
stocking based on water-body morphometry. For example, managers can determine
if an impoundment has suitable habitat requirements, such as expansive littoral areas,
to create a trophy Bluegill fishery. Large littoral areas tend to support a higher
biomass of Bluegill than habitats with limited littoral zones (Barwick 2004).
Common goals for recreational fisheries managers often include providing a
quality fishing experience and sustainable harvest for the public. In a phone survey
of 200 anglers in Missouri, 81% of anglers mentioned the quality of fishing,
76% of anglers mentioned environment, and 41% of anglers mentioned people in
their descriptions of memorable fishing trips (Weithman and Anderson 1978). In a
2013 angler-creel survey conducted in central Georgia, 155 Bluegill anglers were
questioned as to whether they would prefer that Marben Public Fishing Area (PFA)
lakes were managed for more fish, larger fish, or both. Only 12% of the anglers
wanted Marben PFA lakes managed primarily for larger fish, 25% mostly wanted
to catch more fish, and 50% wanted the lakes to be managed for both (Roop 2015).
Weithman and Anderson (1978) and Roop (2015) described the desire for fisheri es
managers to focus their attention on managing for as many fast-growing fish as possible
to improve angler satisfaction. Fisheries managers potentially can improve the
quantity and quality of fish in a population; however, managing for 1 characteristic
typically comes at the expense of the other. In our view, a better understanding of
Bluegill growth may be needed to evaluate management trade-offs between quality
and quantity in populations and the fishery they support.
Georgia Department of Natural Resources (GADNR) fisheries managers at Marben
PFA are interested in the production and management of a trophy Bluegill fishery.
Based on the overall goal of the fisheries managers, the specific objectives of this
project were to: (1) identify and quantify the measurements of surface area and levels
of total phosphorus that have the greatest influence on Bluegill growth at 6 Marben
PFA impoundments, (2) determine the best model for predicting Bluegill growth
based on identified characters, (3) provide the GADNR with an indication of the best
impoundments for growing large Bluegills, and (4) provide fisheries managers with
a framework for developing predictive models based on environmental variables for
selecting their most suitable impoundments for trophy Bluegill fisheries.
Methods
Data collected for each impoundment included: Bluegill biological data, surface
area, mean depth, shoreline-development factor (SDF), impoundment age, total
phosphorus, carbon and nitrogen profiles, water temperature, pH, dissolved oxygen
(DO), conductivity, total dissolved solids (TDS), alkalinity, Secchi-disc depth,
chlorophyll a, and mean total monthly fishing ef fort (angler hours).
Field-site description
Marben PFA is a public outdoor recreation facility located near Mansfield, GA,
that is currently managed by the Georgia Department of Natural Resources’ Wildlife
Resources Division (GADNRWRD). The 2590-ha facility includes campgrounds,
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archery ranges, firearm ranges, a wildlife management area (WMA) for hunting, as
well as ~120 ha of impoundments that constitute Marben PF A (Fig. 1).
We conducted the study in 6 Marben PFA impoundments that varied in size from
1.2 ha to 32 ha and had total phosphorus measurements varied as low as 0.34 mg/L
and as high as 1.76 mg/L. We also concurrently sampled 2 additional impoundments
within Marben PFA for the same variables to be used as post hoc model-assessment
impoundments. These impoundments were 1.9 ha and 5.0 ha in size. The
small reservoirs at Marben PFA are typically shallow basins (1.5–3.6 m deep) with
flooded standing timber and large amounts of woody debris along the shoreline.
Aquatic macrophytes were mostly absent during the sampling period and throughout
most of the year because of the high clay-content of the soil in the region. As
a result excess nutrients, which would typically be utilized for aquatic macrophyte
growth, caused large algal blooms that were present during warm portions of the
summer. All impoundments in this study were near the top of the watershed, and
were surrounded by deciduous forests.
Fish collection
We sampled Bluegills during July and August 2014, presumably after they had
spawned at least once (i.e., in an attempt to obtain an even male–female ratio from
each impoundment). We employed a boat-mounted, pulsed-DC Smith-Root® 6A type
electrofisher with a Wisconsin Ring electrode array and pulsed direct current (30
min pedal time per sample) to sample both adult and juvenile fish. We adjusted pulse
width and voltage to maintain an electrical field (~4–6 amps) that stunned the fish
sufficiently for capture with minimal mortality. Fish sampling occurred from sunset
until up to 4 h after sunset. This period has been previously identified as the optimal
time for Bluegill collection because it is when the species moves to shallow water for
foraging or reproduction (Baumann and Kitchell 1974, Dumont and Dennis 1997,
Malvestuto and Sonski 1990, Pierce et al. 2001, Sanders 1992). During electrofishing,
we measured the total length (TL) of all captured Bluegills to the nearest mm and
sorted the fish into 10-mm–length increments to create length–frequency distributions
of the total catches from each impoundment. We subsampled 5 Bluegills from
each 10-mm–length increment (>50 mm TL) from each impoundment to determine
growth rates (Quist et al. 2012, Tomcko and Pierce 2005). We assigned unique identification
codes to, measured for TL (mm) and total weight (g) of, euthanized (UGA
Animal Use Permit No. A2014-06-023-Y1-A0), placed on ice, and transported the
subsampled fish to the University of Georgia for otolith removal.
Otolith preparation and age determination
We extracted sagittal otoliths by cutting through the ventroanterior surface of
the isthmus and opening the cranial cavity with surgical scissors (Bagenal and
Tesch 1978, Schneidervin and Hubert 1986). Forceps were used to remove the otoliths
from the cranium, and the otoliths were wiped and dried with a paper towel
and stored in 4-mL glass vials labeled with the fish’s individual identification code.
Bluegills have relatively thin otoliths; therefore, otolith preparation for reading
was minimal. We submersed whole otoliths in a water-filled, black-bottomed
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Figure 1. Map of the state of Georgia and the Charlie Elliot Wildlife Center showing the
Marben Public Fishing Area lakes that were sampled during July and August 2014.
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dish placed under a Leica®TM MZ-7 (Leica Microsystems-Wetzlar, Germany)
dissecting microscope at 16X magnification and counted annuli; a fiber-optic light
source was also used for side illumination to facilitate counting annuli. The dissecting
scope was equipped with a Lieca® DFC295 camera that transmitted the image
onto a computer monitor, and we took a photograph of individual otoliths. We began
our counts at the central focus and moved outward towards the edge of the otolith. We
defined the annuli as the outermost margin of the dark, opaque band as in Schramm
(1989). In centrarchids, the annuli of the otoliths tend to be relatively narrow, opaque
bands that accumulate during the winter (Maceina and Betsill 1987, Schramm and
Doerzbacher 1982, Taubert and Coble 1977, Taubert and Tranquilli 1982).
Two readers independently counted the annuli from each whole otolith, recorded
the age of each fish as the total number of annuli counted, and assigned year class
by subtracting the age from the year 2014. Disagreements in age assignments by
the 2 readers were resolved by a consensus recount by both readers. If the 2 readers
could not come to consensus on an age estimate, we eliminated the otolith in
question from the data set. We recorded the percentage of reader agreement after
all otoliths had been analyzed.
We plotted length–frequency distributions for all Bluegills captured and used
age–length keys for all retained Bluegills to estimate size and age structures of the
populations in each lake (Quist et al. 2012). We employed the Fraser–Lee method
to back-calculate length-at-age for specific fish based on the consistent ratio of annular
distances of the hard structure to the total length of the fish, and we derived
and plotted by lake mean length-at-age data and associated standard deviations
for each year class (Quist et al. 2012). A von Bertalanffy growth curve was fitted
to length-at-age data for all 6 study lakes and the 2 assessment lakes (Quist et al.
2012). We calculated annual growth rates of Bluegills in the 6 individual Marben
PFA study impoundments and 2 assessment impoundments by averaging the mean
growth (mm) among years (Quist et al. 2012).
Measuring predictor variables
Initially, we measured many environmental variables as potential predictor variables
in our modeling of Bluegill growth. We eventually abandoned this approach in
favor of a simpler one that included only the predictor variables of lake surface area
and total phosphorus. Specifically, many of the traditional water-quality variables
such as morphoedaphic index, total alkalinity, secchi depth, and total phosphorus are
indicators of lake productivity, and others such as lake surface area and mean depth
are measures of lake morphometry. A small number of impoundments have been used
to create the models predicting lengths-at-ages of Bluegills; thus, over-parameterizing
the models with too many predictor variables became a concern. We addressed
this issue by selecting 1 measure of primary productivity and 1 measure of lake
morphometry for all study impoundments. We ultimately selected total phosphorus
because it is widely considered the largest driver of primary-production dynamics in
lakes (Fee 1979, Smith 1979) and is also a strong correlate of fish production (Downing
et al. 1990, Hansen and Leggett 1982). We selected the surface area of the lakes
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as our metric to describe lake morphometry because it is known to strongly influence
fish production (Bennett 1971, Hoffmann and Dodson 2005, Hubert and Chamberlain
1996, Jenkins and Oglesby 1982, Tomcko and Pierce 1997, Youngs and Heimbuch
1982, Wagner et al. 2007) and is correlated to fish and zooplankton species richness
(Eadie et al. 1986, Kratz et al. 1997).
We used a horizontal Van Dorn water sampler biweekly during July, August, and
September of 2014 from each lake to collect a columnar, euphotic-zone water sample
at a depth of 2 m. From each sample, 250 mL of water was extracted, kept on ice at 4
ºC, and taken to a University of Georgia Lab for environmental analysis. In the lab,
we added 1 mL of sulfuric acid to each for preservation. The water was stored in a
dark refrigerator at 1–4 ºC. We analyzed water samples for total phosphorus concentration
within 60 d of sample collection. We calculated lake surface area from digital
orthophotographs available from Google® Earth (Google, Inc. 2013).
Data analysis
We entered all predictor-variable data from each Marben PFA impoundment
into a Microsoft Excel 2013® spreadsheet for export to the statistical software program
R: Version 3.1.2® for analysis. We examined relationships between Bluegill
growth and lake surface area and total phosphorus by means of multiple-regression
techniques; the analysis was conducted for each age class. To counter skewed predictor-
variable data, we added 0.1 to data on total phosphorus and log10-transformed
the results. We square-root–transformed surface-area data (Sokal and Rohlf 1995).
The response variable was the mean back-calculated length-at-age of Bluegills collected
from each lake. We developed models using variables to represent possible
biological hypotheses predicting Bluegill growth. More specifically, we derived
regression models for predicting mean back-calculated length-at-age from the
transformed lake-surface area and total phosphorus data. We calculated Akaike’s
information criteria (AIC; Akaike 1974), with small sample adjustment (AICc;
Hurvich and Tsai 1989) for each model. We considered the model with the lowest
ΔAICc valueas the most plausible for the age class. Akaike weights (wi, Burnham
and Anderson 2002) were calculated to determine the relative fit of the models, with
the best approximating model for the age class possessing the highest wi value. In
addition, we used a percent maximum wi to identify the confidence model set; the
confidence set of candidate models included models with Akaike weights that were
within 10% of the highest, which is comparable to the minimum cutoff point (i.e., 8
or 1/8) suggested by Royall (1997) as a general guideline for evaluating strength of
evidence. Once we established a confidence set of models for each age class of Bluegill,
we employed model averaging to create a composite model that best described
the factors affecting Bluegill growth within each age class for each study impoundment.
We assigned a relative importance value to each parameter within each of the
age class’ composite models (Burnham and Anderson 2002).
Assessment of model accuracy
We conducted a post hoc analysis of data collected from 2 Marben PFA impoundments
(Greenhouse and Lower Raleigh; separate from the 6 included in the
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original design) to assess the models’ predictive performance. We chose these 2
impoundments for their relatively moderate size and ease of access. To test the
accuracy of the models, we collected 5 Bluegill for each 10-mm–length group
(>50 mm) from the 2 impoundments selected for model-validation analysis. The
Bluegills were collected at about the same time as those collected from the original
6 impoundments and were included in the random selection of lake order when
sampling occurred. We collected sets of predictor-variable data at each of these
impoundments, as described in the previous sections.
We compared the model-validation experimental data to the values predicted
from the models. We entered predictor variables from the 2 model-validation lakes
into the models created from the original 6 lakes and compared the observed responses
from the model-validation lakes with the expected responses to determine
mean-squared prediction error (MSPE) from the 2 test lakes. Prediction error for
the test lakes was then compared to the mean-squared error (MSE) of the 6 model
lakes to assess whether error in predicting new values was significantly larger than
error in fitting the models. MSPE is calculated with the formula :
n* MSPE = Σ ([Yi - Ŷi ]2) / n*,
i = 1
where Yi is the observed mean back-calculated lentgth-at-age for the impoundment,
Ŷi is the predicted mean back-calculated length-at-age for the impoundment, and
n* is the number of samples.
We employed an F-test to compare the MSE and MSPE. If a substantial difference
existed between the values of the MSPE from the 2 test lakes and the MSE from
the 6 model lakes, we would assume that the 2 test lakes had differences in unrelated
characteristics that were causing growth to vary from the original 6 lakes (e.g., competition
for prey resources). Such a result would indicate that the model was not a
good predictor of factors that influence Bluegill growth in the study ponds.
Results
Bluegill catch, size structure, and growth metrics
We captured and enumerated Bluegills of all sizes during the study. Bluegill
total length varied from less than 50 mm in all lakes to 225 mm in Shepherd Lake. We
included a total of 2797 Bluegills, captured in July and August of 2014, to create
length–frequency distributions for the 6 study impoundments (Fig. 2). Total catch
by individual impoundment was lowest in Whitetail Lake (233) and highest in Margery
Lake (733) (Table 1), and the mean total catch = 466.2, SD = 170.6. Bluegill
catch per unit effort (CPUE; 30-min transects) varied from 111.5 in Whitetail Lake
to 480 in Bennett Lake (Table 1), and the mean CPUE = 303.7, SD = 128.7. We
harvested a total of 420 Bluegills from the 6 study impoundments, and the age and
size structures of Bluegills were variable (Fig. 2) among the 6 study impoundments.
We harvested the greatest number of Bluegills in individual impoundments from in
Whitetail Lake (49) and the lowest quantity from Bennett Lake (81) (Table 1) and
the mean number harvested = 70, SD = 12.1.
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Initial agreement between both readers for age assignments was 78.2%; however,
agreement after a consensus read increased agreement to >99%. Only 3
Bluegills were excluded from further analyses because of reader disagreement or
unreadable otoliths. The sample of aged fish was comprised of individuals spanning
5 year-classes (2009–2014), with lake-specific maximum-age classes ranging from
age-3 in Margery Lake to age-5 in Fox, Bennett, Shepherd, and Dairy lakes. Mean
back-calculated lengths-at-age in individual impoundments varied from 132.4 mm
Figure 2. Von Bertalanffy growth models fitted to Bluegill length-at-age data from 8 central
Georgia impoundments with model parameters listed (t0 = length at age-0, K = curvature
parameter, L∞ = length at infinite years). Lower Raleigh and Greenhouse impoundments
were used only for model validation.
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in Dairy Lake to 181.1 mm in Margery Lake for the 3-year-old age class (mean =
151.7 mm, SD = 20.3, n = 6), from 152.2 mm in Dairy Lake to 190.3 mm in Bennett
Lake for the 4-year-old age class (mean = 167.8 mm, SD = 15.4, n = 5), and
from 169.7 mm in Dairy Lake to 190.1 mm in Fox Lake for the 5-year-old age class
(mean = 184.6 mm, SD = 9.9, n = 4) (Table 2).
Annual growth rates were variable across the 6 study impoundments. Minimum
and mazimum values for annual growth for the Bluegill age classes were as follows:
67.7 mm in Lower Raleigh Lake to 84.6 mm in Margery Lake (mean = 75.3 mm, SD
= 5.7) for age-1, 28.7 mm in Shepherd Lake to 48.9 mm in Bennett Lake (mean = 38.5
mm, SD = 7.1) for age-2, 18.3 mm in Shepherd Lake to 31.7 mm in Margery Lake
(mean = 25.9 mm, SD = 4.9) for age-3, 14.0 mm in Lower Raleigh Lake to 28.9 mm
in Fox Lake (mean = 19.0 mm, SD = 4.4) for age-4, and 6.4 mm in Lower Raleigh
Lake to 18.1 mm in Dairy Lake (mean = 15.0 mm, SD = 4.4) for age-5.
Table 1. Fish data: # of transects = number of electrofishing transects, Total catch = total number of
Bluegills caught, CPUE = catch per unit effort (30-min transects; one unit of effort was 30 min of
electrofishing pedal time), and # harvested = the total number of Bluegills harvested. Lake data: Total
phos = total phosphorus (mean ± SD) and lake surface area. Data collected from 8 Marben Farms
Public Fishing Area impoundments in central Georgia during July and August 2014. † signifies impoundments
used for model assessment that were not included during the modeling process.
Fish data Lake data
Total Surface
Impoundment # of transects catch CPUE # harvested Total phos (ppm) area (ha)
Fox 1 367 367.0 78 0.34 ± 0.24 32.13
Bennett 1 480 480.0 81 0.63 ± 0.70 26.52
Margery 2 733 366.5 77 0.67 ± 0.80 15.11
Shepherd 2 537 268.5 72 1.76 ± 2.78 4.54
Dairy 2 457 228.5 63 0.37 ± 0.53 2.84
Whitetail 2 223 111.5 49 0.37 ± 0.51 1.27
Greenhouse† 2 287 143.5 63 0.79 ± 0.52 1.93
Lower Raleigh† 2 375 187.5 63 1.25 ± 0.99 4.99
Table 2. Mean back-calculated lengths-at-age (mm) for Bluegills from 8 Marben Farms Public Fishing
Area impoundments in central Georgia during July and August 2014. Φ = Bluegills of the specified
age-class were not captured in this impoundment. † signifies impoundments used for model assessment
that were not included during the modeling process.
Mean back-calculated length-at-age (mm)
Impoundment Age-1 Age-2 Age-3 Age-4 Age-5 Age-6
Fox 76.6 117.6 150.1 176.2 190.1 Φ
Bennett 79.9 134.4 171.5 190.3 188.5 Φ
Margery 84.6 136.8 181.1 Φ Φ Φ
Shepherd 77.8 111.7 134.8 157.6 190.0 Φ
Dairy 69.3 105.7 132.4 152.2 169.7 Φ
Whitetail 70.8 108.3 140.0 162.5 Φ Φ
Greenhouse† 75.7 113.6 143.6 158.3 176.8 Φ
Lower Raleigh† 67.9 113.1 161.6 170.9 167.2 189.7
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Lake morphometry and primary productivity data
The square-root transformed surface-area data varied from 1.13 at Whitetail
Lake to 5.67 at Fox Lake (mean = 3.27, SD = 1.90), and the log10(TP + 0.1)-transformed
total-phosphorus data varied from -0.50 in Dairy and Whitetail Lakes to
-0.04 in Shepherd Lake (mean = -0.35 ppm, SD = 0.11) (Table 3).
Multiple regression and model averaging
Multiple regression results indicated that the best predictor variables in a model
differed depending on the specific response variable being predicted (i.e., the age
class of the fish and the number of predictor variables in the model). We analyzed
with multiple regression analysis all possible models containing surface area and
total phosphorus (Table 4). The best models for predicting Bluegill lengths at ages
1–4 were single-variable models based on the square root of surface area as the predictor
variable. The single variable models of log10(total phosphorus + 0.1) were the
second-best models in predicting Bluegill lengths at ages for the age 1–4 models.
The log10(total phosphorus + 0.1) and square root of surface-area additive models
had the smallest Akaike weights in the predictions of lengths at ages for Bluegills
ages 1–5 out of the candidate models. The confidence sets of models to be included
in model averaging included both of the 1-variable models for each age class with
the exception of the confidence set from age class 4, which included only the 1-variable
model of the square root of surface area (Table 4). The confidence set created
from the age-1 models included the 1-variable models of square root of surface area
and log10(total phosphorus + 0.1) (wi: 0.64, 0.36; Table 4).
Model averaging revealed that the square root of impoundment surface area
was the best predictor of Bluegill sizes at various age classes (Table 5). Surface
area was positively related and had the greatest relative importance in predicting
growth of Bluegills at ages 1–4. Total phosphorus was positively related and was
also important in predicting growth of Bluegills at ages 1–3. A composite model for
predicting Bluegill length at age-5 could not be calculated because of a low sample
size of impoundments with age-5 Bluegills.
Table 3. Mean log10 (total phosphorus mg/L + 0.1) ± SD and the square root of surface area (Sqrt
of SA; ha). Data collected in 2014 from 8 impoundments at Marben Farms Public Fishing Area in
central Georgia. † signifies impoundments used for model assessment that were not included during
the modeling process.
Impoundment log10 (total phosporus mg/L + 0.1) Sqrt of SA
Fox -0.40 ± 0.22 5.67
Bennett -0.31 ± 0.45 5.15
Margery -0.35 ± 0.51 3.89
Shepherd -0.04 ± 0.54 2.13
Dairy -0.50 ± 0.39 1.69
Whitetail -0.50 ± 0.40 1.13
Greenhouse† -0.13 ± 0.30 1.39
Lower Raleigh† 0.03 ± 0.33 2.23
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Table 4. Candidate models for each Bluegill age-class from selected Marben Farms Public Fishing
Area impoundments in central Georgia during July and August 2014. Data presented for each model
include number of terms within the model (K), Akaike’s information criterion with small sample adjustment
(AICc), delta AICc (Δᵢ) and Akaike weight (wi). * indicates models included in the confidence
set (i.e., had a wᵢ within at least 10% of the wᵢ of the best model).
Model K AICc Δᵢ wᵢ
Age-1 models
sqrt(surface area)* 3 34.89 0.00 0.64
log10(total phosphorus + 0.1)* 3 36.04 1.15 0.36
sqrt(surface area) + log10(total phosphorus + 0.1) 4 62.46 27.58 0.00
Age-2 models
sqrt(surface area)* 3 44.08 0.00 0.87
log10(total phosphorus + 0.1)* 3 47.88 3.10 0.13
sqrt(surface area) + log10(total phosphorus + 0.1) 4 73.93 30.00 0.00
Age-3 models
sqrt(surface area)* 3 49.63 0.00 0.84
log10(total phosphorus + 0.1)* 3 53.01 3.38 0.16
sqrt(surface area) + log10(total phosphorus + 0.1) 4 79.61 29.98 0.00
Age-4 models
sqrt(surface area)* 3 40.71 0.00 0.98
log10(total phosphorus + 0.1) 3 48.40 7.69 0.02
sqrt(surface area) + log10(total phosphorus + 0.1) 4 70.66 29.95 0.00
Age-5 models
sqrt(surface area)* 3 38.45 0.15 0.48
log10(total phosphorus + 0.1)* 3 38.30 0.00 0.51
sqrt(surface area) + log10(total phosphorus + 0.1) 4 47.03 8.72 0.01
Assessment of model accuracy
An F-test comparison between the variances of the 2 impoundment-assessment
models and the 6 impoundment models revealed that the variances were not significant
for ages 1–4, and that they failed to reject the null hypothesis of the F-test
(F-ratios for ages 1–4, 3.29, 0.06, 0.67, and 0.93, respectively; F-crit values for
ages 1–4, 6.61, 6.61, 6.61, 7.71, and 10.13, respectively). This indicates that there
is no evidence of lack of fit of the models that predict the outcome of the assessment
lakes. An F-test could not be performed for age-5 Bluegills because a composite
model could not be created.
Discussion
Models that correctly predict variation in fish growth are important tools in
fisheries management. The results of this study identified specific environmental
variables that were strong predictors of Bluegill growth in small impoundments
in central Georgia. Although the impoundments in this study were relatively
close in proximity, they displayed considerable variation in lake surface area and
total phosphorous concentration. Previous studies elsewhere have linked Bluegill
growth to Daphnia spp. biomass (Shoup et al. 2007) and aquatic vegetation
diversity (Tomcko and Pereira 2006). Tomcko and Pierce (2001, 2005) found that
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Bluegill size structure and growth were positively related to Secchi depth, maximum
depth, total alkalinity, and water temperature.
Our results indicate that Bluegill populations in 6 small impoundments in central
Georgia were influenced by lake surface area and total phosphorus. Consistent with
the results of previous studies that have investigated Bluegills, the surface area of
the impoundments was included in several of our age-specific composite models
and had a pronounced positive effect on growth (Bennett 1971, Hoffmann and
Dodson 2005, Hubert and Chamberlain 1996, Jenkins and Oglesby 1982, Tomcko
and Pierce 1997, Wagner et al. 2007, Youngs and Heimbuch 1982). Surface area explained
a portion of the variation in Bluegill mean back-calculated length and was
incorporated into composite models of ages 1–4. It had the highest value of relative
importance in the predictive models for ages 1–4. Our findings that surface area
was positively related to mean back-calculated lengths-at-age for all 4 age-classes
of Bluegills with sufficient samples sizes for analysis complement those of Tomcko
and Pierce (2001), who found that mean back-calculated lengths-at-ages 5 and 6
were significantly related to surface area. A possible explanation for the profound
effect that surface area had on our models could have been its positive relationship
to littoral habitat availability. For example, annual growth of Bluegills at ages 2 and
3 was significantly greater in Nebraska sandhill lakes with high percentages of littoral
zone area (Porath and Hurley 2005). Model results from our study suggest that
surface area may be the most important environmental variable we measured with
regard to explaining variation in Bluegill growth in Marben PF A impoundments.
Table 5. Composite models and associated parameters created by model averaging for age classes of
Bluegill collected at Marben Farms PFA impoundments, 2014. * indicates that composite model could
not be created because of small sample size of response variable.
Model/model parameter Relative importance β estimate Adjusted SE
Age-1 Bluegill growth composite model
Intercept 74.75 9.02
sqrt(surface area) 0.64 1.20 1.61
log10(total phosporus + 0.1) 0.36 6.19 14.94
Age-2 Bluegill growth composite model
Intercept 105.68 16.21
sqrt(surface area) 0.87 4.28 3.73
log10(total phosporus + 0.1) 0.13 1.80 20.60
Age-3 Bluegill growth composite model
Intercept 132.37 24.82
sqrt(surface area) 0.79 5.91 5.82
log10(total phosphorus + 0.1) 0.21 0.26 33.70
Age-4 Bluegill growth composite model
Intercept 148.04 8.21
sqrt(surface area) 1.00 6.25 2.24
Age-5 Bluegill growth composite model
Intercept * *
sqrt(surface area) * * *
log10(total phosphorus + 0.1) * * *
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Total phosphorus is often considered to be the main indicator of primary productivity
in lentic systems (Downing et al. 1990, Fee 1979, Smith 1979). Elevated
phosphorus levels increase available energy that primary producers can consume
(Brylinsky and Mann 1973, Dejenie et al. 2008); thus, abundance of many
aquatic primary producers, such as phytoplankton, is positively related to total
phosphorus concentrations. Primary producers directly consume nutrients (driving
primary productivity) and are then consumed by secondary producers in both
terrestrial and aquatic systems (Allaby 2004). Our results indicated that the effect
of total phosphorus was incorporated into the predictive models in this study
for ages 1–4 and was the second-most important factor for predicting Bluegill
growth. Overall, we observed the greatest growth in larger lakes with relatively
higher total phosphorus concentrations.
We did not consider biotic factors such as food-web dynamics or macrophyte
coverage in our study because of logistical constraints (e.g., effort and time); however,
Theiling (1990) suggested that biotic factors have a relatively large influence
on Bluegill growth. Theiling (1990) studied variables such as benthic invertebrate
biomass from discrete lake zones, zooplankton density and size distribution,
macrophyte density in the littoral zone and throughout the lake, water chemistry
and nutrient content, chlorophyll-a concentration, secchi-disk transparency, and
lake morphology. He determined that macrophyte density, zooplankton size, and
profundal benthos biomass explained 60% of the variation in a Bluegill growth
index. In contrast, he found that abiotic variables explained none of the variation
in the Bluegill growth he observed. Several other studies considered biotic factors
in seeking to explain variation in growth (Aday et al. 2003, Hoxmeier et al. 2009,
Schultz et al. 2008, Shoup et al. 2007, Snow and Staggs 1994, Tomcko and Pierce
1997, Tomcko and Pierce 2005, Tomcko and Pereira 2006). Aday et al. (2003) considered
the direct and indirect effects of Dorosoma cepedianum (Lesueur) (Gizzard
Shad) on Bluegill growth and population size-structure in 20 Illinois reservoirs
and demonstrated that the presence of Gizzard Shad was associated with reduced
Bluegill growth rates and adult-size structures, and that mechanisms other than
direct competition for food resources might have caused this situation. Dorosoma
spp. (shad) and other species occupy the same ecological niche within several of
the Marben Farms PFA impoundments; thus, the abundance of these species should
be included in future studies that model Bluegill growth in those systems. Intraspecific
competition can also cause variation in growth because fish of the same
species and age generally occupy similar feeding niches. Therefore, high-density
Bluegill populations tend to exhibit relatively slow growth and smaller adult-body
sizes than low-density populations (Beckman 1950, Osenberg et al. 1988, Weiner
and Hanneman 1982). The effect of density dependence on Bluegill growth at all
age-classes is widely reported in the published literature, and would be useful to
include in future growth models. Swingle and Smith (1942) discussed the influence
of macrophyte control, stocking with Micropterus salmoides (Lacepède) (Largemouth
Bass), fertilization, and heavy fishing on a stunted Bluegill population in a
1.2-ha Alabama pond. The pond was producing good fishing within 6 months after
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2017 Vol. 16, No. 4
initiating these management tactics, and after 18 months, seining samples indicated
that 1-year-old Bluegills weighed as much as 5-year-old Bluegills had at the
beginning of the experiment. The presence of Largemouth Bass and other species
should also be documented in future studies that model Bluegill growth because
these species occur as predators of Bluegills within several of the Marben Farms
PFA impoundments. A final consideration for future research efforts is the inclusion
of angler-harvest dynamics. Angling pressure can reduce the number of larger
adult Bluegills in a population and can even induce stunting (Beard et al. 1997).
However, in some instances, the effects of angler harvest were considered to be
negligible (Hoxmeier et al. 2009). This variation in reported effects of angling on
Bluegill size is a fruitful avenue for further investigation.
Difficulties in consistently estimating Bluegill ages and recruitment patterns
were notable challenges to our study. Differences in appearance of otolith centers
(opaque or translucent) are commonly caused by differences in the timing of
hatching relative to annulus formation (Hales and Belk 1992). The variation in appearance
of the otolith centers in this study may have caused the readers to assign
incorrect ages to some samples; however, this error was likely minimized in our
study by the consensus otolith analyses we used to reconcile disagreements in age
estimates when they occurred. During the otolith aging process, we determined that
we had not captured any Bluegills over the age of 3 y in Margery Lake. We do not
know the reason for the absence of older fish, but it reduced the sample size during
the modeling exercise from 6 lakes to 5 lakes for age-4 Bluegills. Also, we did not
assign any ages older than 4 years to Bluegills in Whitetail Lake. The lack of age-4
and age-5 Bluegills in these lakes reduced the sample size during the modeling exercise
from 6 lakes to 4 lakes. The reduction of the sample size of age-4 and age-5
Bluegills may have reduced the precision of the models.
An F-test comparison between the variances of the 2-impoundment assessment
models and the 6-impoundment models for age classes 1–4 revealed that the variances
were not significant. This result suggests the model’s predictions were appropriate
for the data observed from the test lakes.
Marben PFA lakes with a large surface area and a relatively high total-phosphorus
value likely provide the best conditions for Bluegill growth. Surface area had the
highest relative importance among parameters within most of the models; thus, relatively
large impoundments such as Bennett or Margery are excellent candidate lakes
for establishing a trophy Bluegill fishery. Although, Fox Lake has the largest surface
area of all the impoundments within the study, it also had the lowest total phosphorus
concentration. Therefore, Fox Lake is not a good candidate impoundment for fostering
a trophy Bluegill fishery without intensive fertilization. Fish growth rates are
highest in the youngest age classes (including Bluegills); thus, our results on the factors
affecting this early growth in Bluegills have implications in areas well beyond
our study site at Marben PFA. These results may be useful for informing managers
of Bluegill fisheries in small impoundments throughout the species’ range. Similar
approaches may assist managers in creating predictive models that include lake- or
region-specific water quality and lake morphometry data to develop lake-specific
management plans. Such plans would better select for, and optimize, local-level habiSoutheastern
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2017 Vol. 16, No. 4
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tat factors to promote fast-growing Bluegills compared to other generic models. At a
minimum, our modeling technique could be a simple and useful tool for identifying
lakes with potential for trophy Bluegill management in regions that have a surplus of
lentic waterbodies.
Acknowledgments
We thank the Wildlife Resources Division of the Georgia Department of Natural Resources
for their collaboration and funding, as well as allowing the use of the Marben
Public Fishing Area during our research. We are grateful to Steve Zimpfer, Peter Dimmick,
and Hunter Roop at the University of Georgia for providing technical assistance, and Clint
Moore and Douglas Peterson for providing constructive comments that improved the quality
of the manuscript. The Georgia Cooperative Fish and Wildlife Research Unit is sponsored
jointly by the Georgia Department of Natural Resources, the University of Georgia, the US
Fish and Wildlife Service, and the US Geological Survey. This work was conducted under
the auspices of the University of Georgia Animal Use Protocol No. A2014—06-023-Y1-A0.
Any use of trade, firm, or product names is for descriptive purposes only and does not imply
endorsement by the US Government.
Literature Cited
Aday, D.D. 2008. Exploring stunted body size: Where have we been, what do we know, and
where do we go? Pp. 349–367, In M. Allen, S. Sammons and M. Maceina (Eds.). Balancing
Fisheries Management and Water Uses for Impounded River Systems. American
Fisheries Society, Bethesda, MD. 697 pp.
Aday, D.D., R.J.H. Hoxmeier, and D.H. Wahl. 2003. Direct and indirect effects of Gizzard
Shad on Bluegill growth and population size structure. Transactions of the American
Fisheries Society 132:47–56.
Akaike, H. 1974. A new look at the statistical model identification. Automatic Control,
IEEE Transactions 19:716–723.
Allaby, M. 2004. The Concise Oxford Dictionary of Ecology, 3rd Edition. Oxford University
Press, New York, NY. 415 pp.
Bagenal, T.B., and F. W. Tesch. 1978. Age and growth. Pp. 101–136, In T.B. Bagenal (Ed.).
Methods for Assessment of Fish Production in Freshwater, 3rd Edition. Blackwell Scientific
Publication, Oxford, UK. 384 pp.
Barwick, D.H. 2004. Species richness and centrarchid abundance in littoral habitats of three
southern US reservoirs. North American Journal of Fisheries Management, 24(1):76–81.
Baumann, P.C., and J.F. Kitchell. 1974. Diel patterns of distribution and feeding of Bluegill
(Lepomis macrochirus) in Lake Wingra, Wisconsin. Transactions of the American
Fisheries Society 103:255–260.
Beard J.R., T.D., M.T Drake, J.E. Breck, and N.A. Nate. 1997. Effects of simulated angling
regulations on stunting in Bluegill populations. North American Journal of Fisheries
Management 17(2):525–532.
Beckman, W.C. 1950. Changes in growth rates of fishes following reduction in population
densities by winterkill. Transactions of the American Fisheries Society 78:1.
Bennett, G.H. 1971. Management of Lakes and Ponds. Van Nostrand Reinhold, Inc., New
York, NY. 375 pp.
Brylinsky, M., and K. Mann. 1973. An analysis of factors governing productivity in lakes
and reservoirs. American Society of Limnology and Oceanography 18(1):1–14.
Southeastern Naturalist
561
A.P. Sundmark and C.A. Jennings
2017 Vol. 16, No. 4
Burnham, K.P., and D.R. Anderson. 2002. Model Selection and Multimodel Inference: A
Practical Information-Theoretic Approach. Springer Science and Business Media, New
York, NY. 488 pp.
Dejenie, T., T. Asmelash, L. De Meester, A. Mulugeta, A. Gebrekidan, S. Risch, A. Pals,
K. Van derGucht, W. Vyverman, J. Nyssen, and J. Deckers. 2008. Limnological and
ecological characteristics of tropical highland reservoirs in Tigray, Northern Ethiopia.
Hydrobiologia 610(1):193–209.
Downing, J.A., C. Plante, and S. Lalonde. 1990. Fish production correlated with primary
productivity, not the morphoedaphic index. Canadian Journal of Fisheries and Aquatic
Sciences 47:1929–1936.
Dumont, S.C., and J.A. Dennis. 1997. Comparison of day and night electrofishing in Texas
reservoirs. North American Journal of Fisheries Management 17:939–946.
Eadie, J.M., T.A. Hurly, R.D. Montgomerie, and K.L. Teather. 1986. Lakes and rivers as
islands: Species-area relationships in the fish faunas of Ontario. Environmental Biology
of Fishes 15:81–89.
Etnier, D.A., and W.C. Starnes. 1993. The Fishes of Tennessee. The University of Tennessee
Press, Knoxville, TN. 681 pp.
Fee, E.J. 1979. A relation between lake morphometry and primary productivity and its use
in interpreting whole-lake eutrophication experiments. Limnology and Oceanography
24(3):401–416.
Google. 2013. Earth (Version 7.1.2.2041) software., Mountain View, CA. Available online
at https://www.google.com/earth/. Accessed 5 November 2013.
Hales L.S., Jr., and M.C. Belk. 1992. Validation of otolith annuli of Bluegills in a southeastern
thermal reservoir. Transactions of the American Fisheries Society 121(6):823–830.
Hanson, J.M., and W.C. Leggett. 1982. Empirical prediction of fish biomass and yield. Canadian
Journal of Fisheries and Aquatic Sciences: 39(2):257–263.
Hoffmann, M.D., and S.I. Dodson. 2005. Land use, primary productivity, and lake area as
descriptors of zooplankton diversity. Ecology 86:255–261.
Hoxmeier, R.J.H., D.D. Aday, and D.H. Wahl. 2009. Examining interpopulation variation in
Bluegill growth rates and size structure: Effects of harvest, maturation, and environmental
variables. Transactions of the American Fisheries Society 138:423–432.
Hubert, W.A., and C.B. Chamberlain. 1996. Environmental gradients affect Rainbow Trout
populations among lakes and reservoirs in Wyoming. Transactions of the American
Fisheries Society 125:925–932.
Hubert, W.A., and M. Quist. 2010. Inland fisheries management in North America. American
Fisheries Society, Bethesda, MD. 736 pp.
Hurvich, C.M., and C.-L. Tsai. 1989. Regression and time-series model selection in small
samples. Biometrika 76:297–307.
Jenkins, R.M., and R.T. Oglesby. 1982. The morphoedaphic index–concepts and practices:
The Morphoedaphic index and reservoir fish production. Transactions of the American
Fisheries Society 111:133–140.
Kratz, T., K. Webster, C. Bowser, J. Magnuson, and B. Benson. 1997. The influence of
landscape position on lakes in northern Wisconsin. Freshwater Biology 37:209–217.
Maceina, M., and R. Betsill. 1987. Verification and use of whole otoliths to age White Crappie.
Pp. 267–278, In R.C. Summerfelt and G.E. Hall (Eds.). Age and Growth of Fish.
Iowa State University Press, Ames, IA. 544 pp.
Malvestuto, S.P., and A.J. Sonski. 1990. Catch rate and stock structure: A comparison of
daytime versus nighttime electric fishing on West Point Reservoir, Georgia, Alabama.
Pp. 210–218, In I.G. Cowx (Ed.). Developments in Electric Fishing. Blackwell Scientific
Publications, Oxford, UK. 368 pp.
Southeastern Naturalist
A.P. Sundmark and C.A. Jennings
2017 Vol. 16, No. 4
562
Osenberg, C.W., E.E. Werner, G.G. Mittelbach, and B.J. Hall. 1988. Growth patterns in
Bluegill (Lepornis rnacrochirus) and Pumpkinseed (L. gibbosus) sunfish: Environmental
variation and the importance of ontogenetic niche shifts. Canadian Journal of Fisheries
and Aquatic Sciences 45:17–26.
Pierce, C.L., A.M. Corcoran, A.N. Gronbach, S. Hsia, B.J. Mullarkey, and A. Schwartzhoff.
2001. Influence of diel period on electrofishing and beach-seining assessments of littoral
fish assemblages. North American Journal of Fisheries Management 21:918–926.
Porath, M.T., and K.L. Hurley. 2005. Effects of waterbody type and management actions on
Bluegill growth rates. North American Journal of Fisheries Management 25:1041–1050.
Quist, M.C., M.A. Pegg, and D.R. Devries. 2012. Age and growth. Pp. 677–731, In A. Zale,
D. Parrish, and T. Sutton (Eds.). Fisheries Techniques, 3rd Edition. American Fisheries
Society, Bethesda, MD. 1009 pp.
Roop, H.J. 2015. Angling activity (effort, catch, and harvest), use patterns, and anglers’
evaluations of fishery quality and management at the Marben Public Fishing Area. M.Sc.
Thesis. University of Georgia, Athens, GA.
Royall, R. 1997. Statistical Evidence: A Likelihood Paradigm. Volume 71. Chapman and
Hall, London, UK. 191 pp.
Sanders, R.E. 1992. Day versus night electrofishing catches from near-shore waters of the
Ohio and Muskingum Rivers. The Ohio Journal of Science 92:51–59.
Schneider, J.C. 2000. Manual of Fisheries Survey Methods II: With Periodic Updates.
Michigan Department of Natural Resources, Fisheries Division, L ansing, MI. 242 pp.
Schneidervin, R.W., and W.A. Hubert. 1986. A rapid technique for otolith removal from salmonids
and catostomids. North American Journal of Fisheries Management 6:287–287.
Schramm, H.L., Jr. 1989. Formation of annuli in otoliths of Bluegills. Transactions of the
American Fisheries Society 118:546–555.
Schramm, H.L., Jr., and J.F. Doerzbacher. 1982. Use of otoliths to age Black Crappie from
Florida. Proceedings of the Annual Conference of the Southeastern Association of Fish
and Wildlife Agencies 36:95–105.
Schultz, R.D., Z.J. Jackson, and M.C. Quist. 2008. Relating impoundment morphometry
and water quality to Black Crappie, Bluegill, and Largemouth Bass populations in Iowa.
American Fisheries Society Symposium 62:479–491.
Scott, W.B., and E.J. Crossman. 1973. Freshwater fishes of Canada. Fisheries Research
Board of Canada Bulletin 184. 966 pp
Shoup, D., S. Callahan, D. Wahl, and C. Pierce. 2007. Size-specific growth of Bluegill,
Largemouth Bass, and Channel Catfish in relation to prey availability and limnological
variables. Journal of Fish Biology 70:21–34.
Smith, V.H. 1979. Nutrient dependence of primary productivity in lakes. Limnology and
Oceanography 24(6):1051–1064.
Snow, H.E., and M. Staggs. 1994. Factors related to fish growth in northwestern Wisconsin
lakes. Wisconsin Department of Natural Resources Research Report 162. Madison, WI.
Sokal, R.R., and F.J. Rohlf. 1995. Biometry, 3rd Edition. WH Freeman and Company New
York, NY. 880 pp.
Swingle, H.S., and EV. Smith. 1942. The management of ponds with stunted fish populations.
Transactions of the American Fisheries Society 71(1):102–105.
Taubert, B.D., and D.W. Coble. 1977. Daily rings in otoliths of three species of Lepomis and
Tilapia mossambica. Journal of the Fisheries Board of Canada 34:332–340.
Taubert, B.D., and J.A. Tranquilli. 1982. Verification of the formation of annuli in otoliths
of Largemouth Bass. Transactions of the American Fisheries Society 111:531–534.
Southeastern Naturalist
563
A.P. Sundmark and C.A. Jennings
2017 Vol. 16, No. 4
Theiling, C.H. 1990. The relationships between several limnological factors and Bluegill
growth in Michigan lakes. Michigan Department of Natural Resources Fisheries Research
Report 1970. Ann Arbor, MI.
Tomcko, C.M., and D.L. Pereira. 2006. The relationship of Bluegill population dynamics
and submerged aquatic vegetation in Minnesota lakes. Minnesota Department of Natural
Resources Investigational Report 538. St. Paul, MN.
Tomcko, C.M., and R.B. Pierce. 1997. Bluegill growth rates in Minnesota. Minnesota
Department of Natural Resources, Section of Fisheries Investigational Report 458, St.
Paul, MN.
Tomcko, C.M., and R.B. Pierce. 2001. The relationship of Bluegill growth, lake morphometry,
and water quality in Minnesota. Transactions of the American Fisheries Society
130:317–321.
Tomcko, C.M., and R.B. Pierce. 2005. Bluegill recruitment, growth, population size structure,
and associated factors in Minnesota lakes. North American Journal of Fisheries
Management 25:171–179.
Wagner, T., M.T. Bremigan, K.S. Cheruvelil, P.A. Soranno, N.A. Nate, and J.E. Breck.
2007. A multilevel modeling approach to assessing regional and local landscape features
for lake classification and assessment of fish growth rates. Environmental Monitoring
and Assessment 130:437–454.
Weithman, A.S., and R.O. Anderson. 1978. An analysis of memorable fishing trips by Missouri
anglers. Fisheries 3:19–20.
Wetzel, R., and G. Likens. 1979. Limnological Analyses. W.B. Saunders Company, Philadelphia,
PA. 429 pp.
Wiener, J.G., and W.R. Hanneman. 1982. Growth and condition of Bluegills in Wisconsin
Lakes: Effects of population density and lake pH. Transactions of the American Fisheries
Society 111:761–767.
Youngs, W.D., and D.G. Heimbuch. 1982. Another consideration of the morphoedaphic
index. Transactions of the American Fisheries Society 111:151–153.
Zydlewski, J., and S.D. McCormick. 1997. The ontogeny of salinity tolerance in the
American Shad, Alosa sapidissima. Canadian Journal of Fisheries and Aquatic Sciences
54:182–189.
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Appendix A. Morphological data collected in 2014 from impoundments at Marben Farms
Public Fishing Area in central Georgia. Age = impoundment age in 2014 (y), SA = surface
area (ha), SDF = shoreline development factor, and Mean depth = mean impoundment depth
in (m) ± SD. † signifies impoundments used for model assessment that were not included
during the modeling process.
Impoundment Age SA SDF Mean depth
Fox 28 32.13 3.11 3.62 ± 1.99
Bennett 44 26.52 2.09 2.89 ± 1.49
Margery 40 15.11 1.80 2.31 ± 1.21
Shepherd 62 4.54 1.69 1.69 ± 1.07
Dairy 44 2.84 2.16 2.67 ± 1.23
Whitetail 33 1.27 1.56 1.49 ± 0.60
Greenhouse † 56 1.93 1.47 2.46 ± 1.13
Lower Raleigh † 46 4.99 1.69 2.89 ± 1.71
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Appendix B. Mean (± SD) of selected water quality variables collected in 2014 from impoundments at Marben Farms Public Fishing Area
in central Georgia. Measurements for total alkalinity were taken at the surface of the water. Measurements for temperature, dissolved oxygen,
pH, and conductivity were all taken at a 1-m depth. Measurements for total phosphorus and chlorophyll-a were taken at a 2-m depth.
† signifies impoundments used for model assessment that were not included during the modeling process.
Total Total
Conductivity Temperature phosphorus Secchi alkalinity
Impoundment DO (mg/L) pH (μS) (°C) (ppm) Chl-a (ppb) depth (m) (ppm)
Fox 6.58 ± 1.75 6.58 ± 1.75 68.35 ± 1.94 29.18 ± 2.03 0.34 ± 0.24 18.56 ± 16.29 1.23 ± 010 30.00 ± 9.88
Bennett 7.84 ± 1.86 9.12 ± 0.58 90.58 ± 4.72 29.68 ± 2.27 0.63 ± 0.70 34.83 ± 15.41 0.87 ± 0.29 34.50 ± 5.58
Margery 7.40 ± 1.30 9.31 ± 0.60 74.03 ± 4.51 29.02 ± 2.36 0.67 ± 0.80 52.45 ± 37.67 0.47 ± 0.08 32.83 ± 11.03
Shepherd 7.05 ± 1.56 9.04 ± 0.69 82.70 ± 7.53 28.72 ± 1.73 1.76 ± 2.78 59.95 ± 19.56 0.76 ± 0.05 30.50 ± 2.95
Dairy 8.57 ± 1.81 9.18 ± 0.66 67.22 ± 5.33 28.58 ± 1.66 0.37 ± 0.53 14.70 ± 17.19 0.69 ± 0.11 27.00 ± 3.52
Whitetail 7.19 ± 0.85 7.42 ± 0.68 111.45 ± 4.67 28.68 ± 2.51 0.37 ± 0.51 22.45 ± 17.04 0.79 ± 0.17 50.67 ± 9.85
Greenhouse† 8.70 ± 0.89 9.37 ± 0.44 96.60 ± 5.42 28.08 ± 1.68 0.79 ± 0.52 31.15 ± 48.71 0.62 ± 0.17 45.00 ± 8.65
L. Raleigh† 7.65 ± 0.83 8.99 ± 0.53 81.77 ± 4.14 29.10 ± 2.02 1.25 ± 0.99 23.87 ± 8.86 1.30 ± 0.20 34.67 ± 2.73
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Appendix C. Summary statistics for 12 morphological and water-quality predictor variables
measured at 6 Marben Farms Public Fishing Area impoundments in central Georgia
during 2014.
Predictor ariables n Mean SD Minimum Maximum
Age (y) 6 41.8 11.7 28.0 62.0
Mean depth (m) 6 2.4 0.8 1.5 3.6
Surface area (ha) 6 13.7 13.1 1.3 32.1
Shoreline development factor 6 2.1 0.6 1.6 3.1
Dissolved oxygen (mg/L) 6 7.4 0.7 6.6 8.6
pH 6 8.8 0.7 7.4 9.3
Conductivity (μS) 6 82.4 16.8 67.2 111.5
Temperature (°C) 6 29.0 0.4 28.6 29.7
Total Phophorus (ppm) 6 0.7 0.5 0.3 1.8
Chlorophyll-a (ppb) 6 33.8 18.8 14.7 60.0
Secchi depth (m) 6 0.8 0.3 0.5 1.2
Total alkalinity (ppm) 6 34.3 8.4 27.0 50.7
Appendix D. List of Marben Farms Public Fishing Area impoundments in central Georgia
included in the study. X = associated species present and size (ha) are given for each impoundment
sampled during July and August 2014. M.s. = Micropteris salmoides (Lacepède)
(Largemouth Bass), L. mi. = Lepomis microlophus (Günther) (Redear Sunfish), L. ma.=
Lepomis macrochirus (Bluegill), I. p. = Ictalurus punctatus (Rafinesque) (Channel Catfish),
and P. n. = Pomoxis nigromaculatus (Lesueur in Cuvier and Valenciennes) (Black Crappie).
† signifies impoundments used for model assessment that were not included during the
modeling process.
Impoundment name Area (ha) M. s. L. mi. L. ma. I. p. P. n.
Fox 32.13 x x x x x
Bennett 26.52 x x x x x
Margery 15.11 x x x x x
Shepherd 4.54 x x x x x
Dairy 2.84 x x x - -
Whitetail 1.27 x x x - -
Lower Raleigh† 1.93 x x x - x
Greenhouse† 4.99 x x x x -