Quality management of bluegill: Factors affecting ...
Transcript of Quality management of bluegill: Factors affecting ...
I L L IN 0UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
PRODUCTION NOTE
University of Illinois atUrbana-Champaign Library
Large-scale Digitization Project, 2007.
S
CAE Natural History Sirv7Libmrry
ILLINOIS NATURAL HISTORY SURVEYCENTER FOR AQUATIC ECOLOGY
ANNUAL PROGRESS REPORT
NOVEMBER 1, 2001 THROUGH SEPTEMBER 30, 2002
QUALITY MANAGEMENT OF BLUEGILL: FACTORSAFFECTING POPULATION SIZE STRUCTUR4
M.J. Diana, J.E. Claussen, J. Stein,D.H. Wahl, D.P. Philipp
Submitted toDivision of Fisheries
Illinois Department of Natural ResourcesFederal Aid Project F-128-R
November 2002
Aquatic Ecology Technical Report 02/09
ANNUAL PROGRESS REPORT
QUALITY MANAGEMENT OF BLUEGILL:
FACTORS AFFECTING POPULATION SIZE STRUCTURE
M.J. Diana, J.E. Claussen, J. Stein, D.H. Wahl, D.P. Philipp
Submitted to
Division of Fisheries
Illinois Department of Natural Resources
Federal Aid Project F-128-R
John Epi fa o DirectorCenter for quatic Ecology
David H. WahlPrincipal Investigator
David P. PhilippCo-Investigator
November 2002
Table of Contents
Executive Summary.....................................
Job 101.1 Categorization of bluegill populations inIllinois impoundments......................
Job 101.2 Evaluation of bluegill life-historyvariation in Illinois impoundments.........
Job 101.3 Pre and post regulation characterization ofexperimental lakes.........................
Job 101.4 Analysis and reporting......................
References .............. ..................
Tables
Figures
Page
i
1-1
2-1
3-1
4-1
Ref-1
Executive Summary
This study contains four jobs, 101.1 Categorization of bluegill
populations in Illiiois impoundments, 101.2 Evaluation of
bluegill life-history variation in Illinois impoundments, 101.3
Pre- and post-r'dgulation characterization of experimental study
lakes, and 101. 4, Analysis and reporting.
In Job 101.1, 60 potential quality and stunted bluegill
populations were chosen for study based upon recommendations
from district biologists. Project electrofishing collections
during the bluegill spawning seasons of 1996 and 1997 were used
to calculate PQM.170 (proportion of mature parental males that
were > 170mm TL). Based upon the PQM.170 values, these 60 study
populations from Illinois were assigned to one of three
categories: quality (populations with many/most adults > 170mm),
stunted (populations with many/most adults <150mm) and
intermediate (populations with a mixture of adult sizes). These
lakes were grouped by predicted size structure (stunted or
quality), by regions (north, central, or south) and by lake size
groups (small = < 100 acres, or large = > 100 acres). From
these lakes, 32 were identified for use in an intensive
management experiment (described in job 101.3). Historical and
present creel data together with statewide FAS electrofishing
data were used to reevaluate the bluegill categorizations based
upon PQM.170. PQBG.170 values (percent quality bluegill over
170 mm in total length) were calculated for each study lake and
compared to those from the FAS electrofishing. The PQBG.170
values for both collections were not significantly different
from each other, indicating that the two different collections
provided similar estimates of bluegill population size
distribution.
In Job 101.2, four factors determine the size structure of
bluegill populations: pre-maturation growth rate, the age at
maturation, post-maturation growth rate, and longevity. Initial
sampling efforts (1996-1997) of the 32 study lakes was conducted
to measure these parameters for each of these bluegill
populations. Resampling of the bluegill population on five
control treatment lakes was performed in 2001 to assess temporal
stability of bluegill size structure. We removed otoliths for
aging and measured lengths, weights, and maturity status on the
samples collected. Analysis suggests that the size structure
(PQM.170) and age-at-maturation (Z-age) of these populations
remains relatively unchanged since initial samples were taken in
1996-1997. Minor alterations observed may be a result of low
sample size in certain critical size classes. Low sample sizes
will result in the need for continued monitoring of these
populations.
In Job 101.3, we continued our monitoring and assessment of
bluegill growth, reproductive characteristics, and age-at-
maturation in response to management manipulations.. These
manipulations consist of four treatments across 32 lakes (8
lakes per treatment): control, restrictive harvest regulations,
predator stocking, and a combination of restrictive harvest
regulations and predator stocking. Treatments have equal
representation from regional, lake size, and bluegill size
structure classifications of lakes. Although changes in
bluegill population size structure were variable within and
among treatments, some increase in relative abundance of larger
sized bluegill was observed in some lakes. In addition, lakes
that were designated quality before the experiment maintained
their quality size structure. Contribution of stocked
largemouth bass to existing predator populations was variable
ii
across lakes. Stocked fish did, however, survive in most of the
16 lakes into which they were stocked. Lakes stocked with
multiple sizes of larger largemouth bass had greater survival, of
stocked fish in the fall than most other lakes. The bluegill
populations in the treatment lakes may require more time for
changes in size structure to be observed. Monitoring of these
lakes should continue to examine changes in the bluegill size
structure as the experiment proceeds.
To assess angler compliance, conservation officers conducted
interviews on lakes with the experimental regulation imposed.
Compliance checks also raise angler awareness of the regulation
and the study purpose on the experimental lakes. Compliance
checks showed that a large majority of anglers were compliant
with the regulation.
We examined biotic and abiotic characteristics of the
experimental lakes, such as prey resources, predation pressure,
and lake-habitat characteristics. To examine the importance of
prey resources to bluegill growth and maturation, we regressed a
variety of bluegill size, density, and maturation variables with
measures of resource availability. We found positive
correlations between macrozooplankton density and CPUE 180 for
all seasons. No other significant correlations were observed.
In addition, we found no overall differences in prey resources
between lakes with stunted and quality populations. These
results suggest that macrozooplankton may be important in
determining the growth of larger bluegill but it is not the main
factor influencing size structure of the bluegill populations in
the study lakes.
iii
Job 101.1 Categorization of bluegill populations in Illinois
impoundments.
Objective
To use existing creel and standardized sampling databases to
categorize bluegill populations based on adult size structure.
Introduction
Bluegill are a key component of Illinois sport fisheries, both
serving as an important prey species and providing anglers with
harvestable size fish. In Illinois lakes where creels of
harvest and release were conducted, bluegill were consistently
caught and harvested in great numbers (Table 1-1). Bluegill are
susceptible to high levels of exploitation, which can shift size
structures toward populations dominated by small fish (Coble
1988). Size structures of bluegill populations have
deteriorated in many lakes within the Midwest over the past 40
years (Drake 1997). Anglers harvest fewer large bluegill from
many exploited lakes that now only support high populations of
small bluegill and the number of trophy-sized bluegill have also
declined across the region (Olsen and Cunningham, 1989).
Before effective management strategies for increasing the size
structure of "stunted" bluegill populations can be developed, we
need more information about the factors controlling growth and
maturation of bluegill. Competition can occur among sunfishes
when high numbers of small fish are forced into refuges to avoid
predation (Mittelbach 1984; Mittelbach 1986). How the effect of
this phenomenon on growth might extend to population structure,
however, likely varies among reservoirs depending upon prey
availability and predator densities. Although the availability
of food resources for bluegill can impact growth, it remains
1-1
unclear as to whether or not density dependent growth rates
(juvenile or post-maturation) affect the ultimate size structure
of bluegill populations.
Bluegill have been shown to exhibit complex reproductive
behaviors such as colonial nest construction, territorial
defense, courtship of females, and defense against brood
predators (Gross and Charnov 1980). Furthermore, male bluegill
exhibit alternative reproductive strategies, whereby some
individuals mature precociously and become cuckolders at a
younger age and a smaller size than their brothers, who delay
maturation to become parental males (Gross and Charnov 1980).
The process of maturation has significant impacts upon growth
trajectories in bluegill and likely all other fish species as
well, because the physiological changes and mating behaviors
associated with reproduction require high energetic investment,
making that energy unavailable for somatic growth (Claussen
1991, Fox and Keast 1991, Jennings 1991, Jennings and Philipp
1992). Although the impact of sexual maturation and spawning
activities on the growth of Lepomis individuals is well
established, little is known about the reverse, i.e., how the
growth and size structure within a population affects age at
maturation and the expression of reproductive behaviors. If we
are to manage bluegill populations effectively, we need to
understand how exploitation and/or various management activities
alter these life-history characteristics. Only by understanding
these complex interactions can the success of bluegill
regulations and management strategies be predicted and realized
effectively.
1-2
Procedures
The current year's efforts were designed to build on previous
project segments, in which historical creel data were used to
assess bluegill population size structure and to select
appropriate study lakes. Following recommendations in previous
reports, metrics to measure size structure using FAS data were
developed, and an initial analysis of study populations was
conducted.
Electrofishing runs were conducted as a part of the current F-
128-R project; length-frequency and sexual maturity data were
determined for these collections (see Job 101.2). From these
data, the metric PQM.170 was calculated to assess the level of
stunting in each bluegill population. PQM.170, the proportion
of quality males in a population, is determined for each lake by
dividing the number of mature males larger than or equal to
170mm TL by the total number of mature (parental, not cuckolder)
males in the sample.
To facilitate comparisons of this population size structure
metric to other collections that do not provide sexual maturity
data, a PQBG.170 metric was developed. PQBG.170, the proportion
of quality fish (of both sexes) in a population, is calculated
by dividing the number of bluegill greater than 170mm by the
total number of bluegill in the collection. PQBG.170 was
calculated based on data collected as a part of the current
project and compared to the PQM.170 metrics calculated based on
the same samples.
Standardized electrofishing runs are also conducted regularly on
Illinois impoundments by district biologists, and fish
population data on multiple species is entered into the
1-3
Fisheries Analysis System databases (supported by F-69-R).
Historical FAS data on bluegill populations in project study
lakes was examined and a PQBG.170 was calculated for each lake.
Findings
PQM.170 and PQBG.170 metrics for 50 study lakes were calculated
using data from our initial project collections, and were highly
correlated (Pearson's coefficient r 0.77, P<.0001, Figure 1-
1). This correlation indicates that PQBG.170 is a viable
substitute for PQM.170 for comparisons with other datasets that
do not contain data on sexual maturity of bluegill males.
PQBG.170 values were also calculated from FAS data on project
bluegill populations and then compared to PQBG.170 values
calculated from our data (Table 1-2). A Kolmogorov-Smirnov two-
sample test for differences in distribution indicated that the
PQBG.170 values for both collections were not significantly
different from each other, indicating that the two different
collections provided similar estimates of bluegill population
size distribution (D=0.109, P>.05, ns).
Information on lake populations and angler activities was
gathered from historical creel survey data; additional data have
been collected over recent years through creel surveys conducted
on bluegill project study lakes. Angler success and harvest
data, as well as overall angling pressure, the proportion of
anglers that target bluegill, and size of bluegill caught and
harvested was used to assess the bluegill fishery in lakes for
which there were data. Preliminary analyses of those data are
summarized in Table 1-3, and indicate that size structure
categorization based on our electrofishing samples agreed fairly
well with size structure predicted from the creel data.
1-4
Recommendations
In previous reports, population estimates using creel data were
used to calculate PCBG.170 values, where the proportion of
creeled bluegill greater than or equal to 170mm in length were
divided by total number of bluegill creeled. In future
examinations of the creel data, actual numbers of bluegill
caught by anglers, rather than extrapolated estimates (with
their associated confidence intervals), will be used to assess
bluegill population structure. Additionally, comparisons of
harvested versus released fish are needed to assess angler
preference for bluegill size in quality and stunted populations.
Continued data entry and analysis of FAS data will be required
for further analysis.
Job 101.2 Evaluation of bluegill life-history variation in
Illinois impoundments.
Objective
To determine the extent of variation in important bluegill life-
history characteristics in selected impoundments throughout
Illinois
Introduction
Bluegill (Lepomis macrochirus) are viewed by many anglers as an
important sportfish. In Illinois, as in many other states, the
demand is growing for populations with quality-sized bluegill.
For management biologists to be able to make sound decisions in
an attempt to provide those quality bluegill populations, we need
to understand the factors that are driving population size
structure.
The size structure of a bluegill population is determined by the
combination of four factors: the growth rate before maturation
(when all energy investment is directed toward somatic growth),
' the age at maturation (which is highly plastic in Lepomis spp.),
growth rate after maturation (when much energy investment is
directed toward reproduction), and longevity. Thus, growth
trajectories for parental male bluegill (and most other fish as
well) follow a pattern in which growth slows significantly
following sexual maturation (Wootton 1985). Given that bluegill
reproduction includes behaviors such as nest construction,
territorial defense, courtship of females, fanning the eggs, and
defense against brood predators, they commit large energetic
investments into reproduction. Because bluegill are also
sexually dimorphic, male and female growth patterns and
2-1
maturation schedules may differ within a population.
In Illinois, a comparison of size structure from 60 populatiohs
of bluegill throughout the state revealed that age at maturation
differed among males and females within a population, sometimes
as much as by two years (F-128-R, Annual Report, 2000).
Furthermore, this comparison also determined that the size
structure of a population is influenced heavily by when males in
the population mature (i.e., stunted populations occur when males
mature at a younger age/smaller size).
Procedures
In the first phase of this project, boat electrofishing was used
to sample 60 target populations between the months of May and
July 1996 (as well as some additional subsampling in 1997) to
determine bluegill abundance, size, age, sex, and maturation
status. Sampling was conducted after bluegill spawning activity
had been initiated in each lake and before it ceased in mid-
summer.
During sampling, type of habitat, weather conditions, secchi disk
readings, water temperatures, and information on other species of
fish found in the lake, e.g., number of sunfish hybrids and
number of largemouth bass shocked were recorded. Electrofishing
runs consisted of an initial run, in which all individuals of all
sizes were collected. These preliminary runs usually were from
30 to 60 minutes in duration. Those bluegill were then measured
quickly to determine the number of individuals in each of eight
specified size classes (<50mm, 50-99mm, 100-149mm, 150-159mm,
160-169mm, 170-179mm, 180-189mm, >189mm). The goal was to obtain
at least 50 individuals from each size class. An additional
2-2
secondary run was then conducted in an attempt to supplement
those size classes in which sample sizes were below 50.
In the second phase of this project, an experimental management
study using 32 of the 60 study lakes was set-up to determine how
various manipulations (predator additions, reduced harvest of
large fish, and a combination of the two) impact the growth rate,
age at maturation, and size structure of a population (see Job
101.3). During the bluegill spawning season of 2001 several
control lakes of the experimental study were sampled using the
same methods as in the phase 1 study lakes to assess temporal
stability in bluegill populations. The control lakes sampled
were: Apple Canyon, Glendale, Hillsboro, Siloam Springs, and
Sterling. Individual fish were processed and data was analyzed
using the same methods as in phase 1.
To analyze the bluegill collected in each lake sampled,
individuals were thawed and total length, weight, and sex
determined. In addition, gonads were identified as to stage of
development, and mature gonads weighed. Scales and otoliths
were removed for age and growth analysis. We used these data to
determine age-specific growth curves, age at maturation, and
abundance of cuckolders. All otoliths were read in whole view
unless there was a disagreement between two readers, or if
crowding of annuli occurred. If so, the otolith was then
sectioned by one of two methods: by either cracking the otolith
in half and reading transverse section with fiber optic light or
by mounting the mid-section on a slide and reading it with
transmitted light. These data were used to determine size at
age, age at maturation (Z-age), longevity, and % of cuckolders
for each population.
2-3
To calculate a descriptor of the size structure that would
distinguish quality from stunted populations, we devised the
Proportion of Quality Males (PQM.170). The PQM.170 for any given
lake is calculated by dividing the number of mature parental
males >170mm by the total. number of mature parental males
collected. The PQM.170 value provides a way to look at the degree
to which a population maybe be classified as stunted or quality.
Findings
Data from the original sampling in 1996 and the five experimental
control lakes sampled in 2001 were analyzed to compare for each
population the following characteristics for both males and
females: average size at each age class, Z-age, and PQM.170
(Tables 2.1 - 2.5). In addition, figures 2.1 - 2.5 show a
comparison of both male and female growth patterns for each lake
between 1996 and 2001.
Apple Canyon showed no significant changes in growth rates or age
at maturation from 1996 to 2001. Although the PQM.170 did
increase from 0.57 in 1996 to 0.90 in 2001, we believe that this
difference is a result of a small sample size in 2001; the
average total length and growth rates of males did not change
from 1996 to 2001.
In the comparison of.the two sampling years for Glendale,
Hillsboro, Siloam Springs, and Sterling lakes few substantive
changes were determined in PQM.170 or in growth rates. No
significant changes of Z-age were seen in Siloam Springs or Lake
Sterling. Although the female Z-age for Glendale increased from
2.9 in 1996 to 3.2 in 2001, we believe this is likely the result
of the low number of age-3 females collected in 2001. The male
Z-age of the Hillsboro population decreased substantially (3.2 in
1996 vs. 2.4 in 2001). With such a dramatic drop in age at
maturity, we would also expect a significant change in PQM.170
and in male growth rates; almost no change, however, was
observed. Further sampling and analysis is needed in the
population.
These data reveal temporal stability in size structure in the
five control bluegill populations. As a result observed changes
in future size structure of the experimental lakes should be the
result of management regulations and not natural fluctuations.
Recommendations
Of the eight control lakes from the 32 experimental populations,
three more need to have their collections processed and data
analyzed (Lincoln Trail, Paris, and Round). Any further
supplemental collections that were performed on the control
lakes should be analyzed as well.
To evaluate the impact of the manipulations and to understand
the mechanisms involved in changing bluegill population size
structure, we need to continue to sample the regulation,
manipulation and regulation/manipulation experimental study
lakes (see table 3.1, Job 3). Bluegill size and age structure,
prey resources (inshore zooplankton, offshore zooplankton, and
benthic invertebrates), and predator populations (especially
largemouth bass) need to be assessed throughout the next several
years to determine the impacts of management manipulations.
Additional experiments are also needed to determine the
mechanism(s) by which bluegill assess their population's social
structure and accordingly make decisions on whether to- mature
early or delay maturation. In addition, it is important to
determine if increased food resources (even through additional
2-5
feeding) can alter size structure patterns and if so, how that
can be accomplished on a large lake scale.
2-6
Job 101.3 Pre- and post-regulation characterization of
experimental study lakes.
Objective
To gather detailed baseline data on bluegill life-history
characteristics as well as the biotic and abiotic variables
that may affect bluegill recruitment, growth, and maturation
,in the chosen experimental study lake6.
Introduction
An important goal of this study is to examine the impact of
various management actions (i.e., harvest regulations and
predator stocking) on bluegill growth rates and size- and age-
at-maturation, and determine how each acts to improve size
structure among stunted bluegill populations in Illinois. Four
aspects of a species' life-history trajectory determine the
ultimate size structure of the adult population in a given water
body: pre-maturation (larval/juvenile) growth rate, age at
maturation, post-maturation (adult) growth rate, and longevity.
These four aspects can be affected by a variety of things within
a water body. Age-at-maturation and longevity are directly
affected by the social relationships among surviving adults and
can be greatly impacted, therefore, by harvest. Both pre- and
post-maturation growth rates are directly affected by density-
dependent processes (i.e., slower growth rates when there is an
overabundance of bluegill or underabundance of prey) at all
bluegill life stages. Additionally, biotic (e.g., interspecific
competition, predation) and abiotic (e.g., temperature,
dissolved oxygen saturation) factors can also influence all four
aspects of a life-history trajectory. This job is designed to
elucidate how these processes may act anQ interact to alter
3-1
bluegill population size structure under different management
options.
Results from Job 101.2 indicate that factors controlling the age-
atrmaturation have the greatest influence in determining size
structure of bluegill populations throughout the state. Quality
populations were characterized by a later age- and larger size-at-
maturity than stunted populations. Manipulative experiments
associated with this project continue to suggest that the social
structure of the population, specifically the presence or absence
of large, mature males, has a direct impact on age-at-maturation of
juvenile male bluegill in the population and, therefore, a direct
impact on population size structure. Management actions designed
to increase the size structure of wild bluegill populations (i.e.,
convert stunted populations to quality populations) need to
increase PQM170. From an evolutionary standpoint, that requires
reaching a new life history state, in which age-at-maturation is
increased; i.e., males delay to older ages and larger sizes prior
to maturing and entering the slower post-maturation growth phase.
Moving a population from a stunted to a quality life history state,
however, might be accomplished by increasing pre-maturation growth
rates, increasing post-maturation growth rates, extending
longevity, or increasing age-at-maturation directly. Which route
successful management actions will use is unclear. As a result, it
is important that we continue to collect juvenile and mature
bluegill from study lakes to monitor size, age, and maturity states
over the next several years.
Both pre- and post-maturation growth rates may be increased by
an underabundance of bluegill .or. mn overaundance of prey. This
density-dependent alteration in growtAo rate can occur at any or
all life stages of the bluegill. Bluegill feed on both
zooplankton and benthic invertebrates throughout their ontogeny.
Competition for food resources (intra- and interspecific) can
occur at each life stage (i.e., larval, juvenile, adult) and
could affect growth. Identifying the importance of altering
competition for limited resources relative to other potential
mechanisms designed to increase growth rates will be important
for evaluating, the success of any management regulation designed
to alleviate stunting. Monitoring prey resources and bluegill
densities in the study lakes is necessary to assess the role
that density-dependent mechanisms may play in altering size
structure of our test bluegill populations.
Procedure
The primary activity in this job was continued monitoring of
experimental populations to determine influences of the
management manipulations on bluegill population size and age
structure. The management experiment, which began in April,
1999, involves 32 lakes across the state of Illinois, divided
into four treatments (8 lakes per treatment): restrictive
harvest regulations (8-inch minimum size limit, 10 fish daily
creel limit); predator stockings (largemouth bass added to
increase predation on juvenile bluegill); restrictive harvest
regulations and predator stockings in combination; control (for
complete details of the management experiment see Claussen et
al. 1999; Table 3-1). Three components of each study lake are
important for current and continued monitoring: 1)bluegill
population parameters (adult abundance, size structure, and age-
at-maturation; larval and juvenile growth and abundance); 2)
biotic variables (e.g., prey availability, predation); and 3)
abiotic variables (e.g., temperature, lake productivity, lake-
habitat characteristics). The sampling protocol that was
established at the initiation of the management experiment (Aday
et al. 1999) was followed during the summer of 2002: all 32
3-3
experimental lakes were sampled for bluegill (juvenile and
adults) and largemouth bass (as a predator) abundance. In
addition, prey resources (zooplankton and macro invertebrates)
were collected in 16 (7.stunted and 9 quality) of the 32
experimental lakes, and larval bluegill were collected in 8 of
them. We will continue to monitor these and other biotic and
abiotic variables in the experimental lakes throughout the
management experiment.
Bluegill Population Parameters
In this segment we continued to monitor changes in bluegill
populations by examining length-frequencies of bluegill collected
in spring and fall electrofishing samples of populations from each
experimental treatment group. We also continued to examine
potential density-dependent mechanisms to understand the role that
they may play in altering population size structure. We determined
larval, juvenile, and adult bluegill abundance in the experimental
study lakes. Larval fish were collected from each offshore site by
pushing an icthyoplankton net (0.5m diameter, 500 mm mesh) for 5
minutes. Volume of water filtered was calculated with a calibrated
flow meter mounted inside the mouth of the net. Inshore bluegill
density (primarily juveniles) was assessed by shoreline seining
(9.2 x 1.2 m bag seine, 3.2 mm mesh) at four fixed sites within
each lake. Effort was calculated as the length of the haul
(nearest m). All fish were counted and a minimum of 50 individuals
of each species collected were measured (total length in mm).
Density (#/m of seine haul) was calculated for bluegill throughout
the study period. Adult bluegill were collected by shoreline
seining (6.7 x 1.2 m bag seine, 3.2 mm mesh) and electrofishing.
Electrofishing samples were performed on each study lake in the
spring in order to compare length frequencies between pre- and
post-regulation populations. A final fall sample was collected in
3-4
September or October from all 32 experimental lakes to examine
population length frequencies.
In previous segmentswe examined correlations between juvenile
bluegill growth rates and prey resources (total zooplankton and
benthic invertebrate densities). We also examined the
relationship between food resources and bluegill growth and
maturity as well as relative abundance of quality sized fish in
the population. In this segment we continued these analyses by
examining the correlations between total zooplankton and total
benthos densities in each study lake with bluegill size-at-age 2
(pre-maturation), CPUE, CPUE 180 (abundance of fish larger than
180 mm) and z-age (maturation). To directly assess the
influence of prey availability on bluegill population size
structure, we correlated total zooplankton and total benthos
densities with PQM180, one variable that is used to define
stunted and quality populations. Across lakes, we determined
differences in prey availability between stunted and quality
populations. In each of these analyses we control more
variation than in past segments through the incorporation of
data from multiple years in each study lake.
3-5
Prey Availability
Prey availability may interact with relative abundance of
bluegill to affect growth at all life stages. Macro
invertebrates and zooplankton are important food items to
larval, juvenile, and adult bluegill. We determined the
abundance of these food resources in 16 of the experimental
lakes. To quantify zooplankton abundance, collections were
taken using vertical tows with a 0.5 m diameter, 64 mm mesh
zooplankton net at four inshore and four offshore sites (one tow
per site). Zooplankton were preserved in a Lugols solution (4%)
for later processing. Inshore macro invertebrates were
collected using a stovepipe sampler (20 cm diameter) at 6 sites
(one sample per site) within each lake. Depth of each sample
collection was measured. Samples were cleaned in a 250 mm mesh
benthos bucket and preserved in an ethanol/rose bengal solution
(70%) for processing.
Predator Abundance
Predator abundance may also influence bluegill size structure
and may be important at each life stage. Largemouth bass, the
primary predator in these centrarchid-dominated experimental
lakes, can consume large numbers of larval and juvenile
bluegill. In addition, bass may compete with bluegill for
available resources at the larval and juvenile stages. To
quantify largemouth bass abundance, spring and fall
electrofishing surveys were conducted on all experimental lakes.
As part of the management experiment, 16 lakes were stocked with
advanced fingerling largemouth bass to increase predator
numbers. Fingerlings were stocked in mid-August 2001 (Table 3-
2), and each bass was given a distinct clip for future
identification. We monitored growth and survival of stocked
3-6
bass through the first fall after they were stocked. Largemouth
bass were collected by day AC electrofishing in the fall by INHS
and Division of Fisheries personnel. All largemouth bass were
examined for marks,.measured, and weighed.
Other biotic and abiotic factors
Abiotic variables may also influence bluegill population
parameters. We measured water transparency, dissolved oxygen,
temperature, total dissolved phosphorous, and chlorophyll a on
16 lakes. Water transparency was measured with a secchi disc.
Temperature and dissolved oxygen profiles were measured at one-
meter intervals. Water samples were collected monthly with an
integrated water sampler for analysis of total phosphorous and
chlorophyll a.
Angler Compliance
To assess compliance of anglers to the experimental regulations,
compliance cards were given to conservation officers at all
lakes with experimental regulations. Conservation officers were
asked to record the number of anglers fishing for bluegill along
with the number of legal and sub-legal length bluegill harvested
by each group of anglers. Conservation officers then completed
these cards each time they performed a bluegill regulation check
on an experimental lake.
3-7
Findings
Bluegill population parameters
Length frequency analysis revealed differing changes among
study lakes in response to experimental regulations (Figures
3-1 to 3-16). Only bluegill over 100 mm were included in these
analyses because we were interested in shifts in adult
bluegill that were both large enough to be effectively sampled
with electroshocking gear and were large enough to be included
in the fishery. Smaller bluegill would also be more strongly
influenced by year-to-year variation in spawning success. In
general, control lakes receiving no treatment had few changes
in the size structure and remained relatively similar through
time for samples taken in both the spring and fall (e.g.,
Lincoln Trail, Figure 3-1, 3-9; Paris, Figure 3-2, 3-10).
Lakes receiving experimental treatments however, had more
highly variable results. In general, lakes that were
designated quality before the experiment maintained their
quality size structure. Regulation treatment lakes with
complete samples showed few changes in size structure, the
exceptions being Pana, which showed an increase in the 140-169
mm size class, and Mermet, which showed an increase in the
170-199 mm size class. Lakes where the regulation is combined
with predator stocking showed an increase in size structure in
two of the seven lakes with complete samples (Bloomington and
Forbes; Figure 3-7). The other lakes showed few changes in
bluegill size structure. Bloomington however suffers from
small sample size in the 100-139mm size range, and may not
accurately represent the bluegill size structure. Lakes
undergoing predator stocking alone did not show any increases
in bluegill size structure. Stocking treatment lakes are
still building supplemental largemouth bass populations and
3-8
will likely require several years to build populations large
enough to influence bluegill populations. Contribution of
stocked largemouth bass was again variable the first fall
after stocking in 2001. All lakes except Spring Lake South
had stocked bass recaptured in the Fall following stocking
(Table 3-3). Lake Mingo had the highest CPUE of stocked bass
(25 stocked largemouth bass per hour of electroshocking).
Lake Mingo also had the highest ratio'of stocked to natural
largemouth bass of all stocked lakes (44%). In general, lakes
where multiple sizes of larger largemouth bass have been
stocked for several years had the greatest numbers of
cumulative stocked bass surviving (Woods, Mingo, and Homer).
Largemouth bass stocked in the initial years of the treatment
are now reaching a size where they can effectively prey on
larger sized bluegill. Continued assessment of the largemouth
bass population is required to evaluate if the stocking of YOY
largemouth bass is increasing the standing stock of predators
that are large enough to feed on multiple sizes of bluegill in
the study lakes.
The bluegill populations in the treatment lakes may require
more time for changes in size structure to be observed.
Monitoring of these lakes should continue to examine changes
in the bluegill size structure as the experiment proceeds.
When increases in size structure were observed, they were in
the percentage of bluegill in the 140-169 and 170-199 mm size
categories, while relatively few lakes showed an increase in
bluegill over 200 mm. This is not surprising given that few
fish of these sizes existed in any of the study populations
prior to initiation of the experiment and the likely targeting
and harvest of bluegill over 200 mm in lakes where the
regulation has been implemented. Low sample numbers in
3-9
certain lakes may result in misleading changes in length
frequency. If sample size is low, the increase of a few
bluegill in a particular size class can greatly influence
interpretation. Future sampling will attempt to correct for
these deficiencies.
Spring samples tended to show shifts in the larger size
classes better than fall samples and seem to be more
consistent with what was expected to be found. Spring may,
therefore, be a preferable season to sample when examining
length frequency distributions to evaluate changes in bluegill
size structure for lakes in this experiment.
Effects of prey availability on bluegill growth and maturity
Multiple years of data (1998-2001) were included from each
population to examine differences in prey resources and
correlations between prey availability and bluegill population
parameters. Incorporating multiple years of data will help
control for high variation among study lakes and was used to
further evaluate effects of prey resources. A repeated measures
ANOVA on prey resources across years in each study lake revealed
no difference in total zooplankton density (F=0.24; P=0.64),
macrozooplankton density(F<0.001; P=0.99) or total benthos
density (F=0.21; P=0.67) between stunted and quality populations
(Figure 3-17). To determine whether there were any
relationships between prey availability and bluegill growth,
density, and age-at-maturity, we regressed macro zooplankton
density and total benthos density with bluegill size-at-age 2,
z-age and PQM180 for the original bluegill samples in 1996 and
1997, as well as spring, fall and total year CPUE (all size
bluegill) and CPUE180 (bluegill over 180 mm) from 2001 samples.
No bluegill parameters were significantly correlated with
3-10
benthos density (Table 3-2). Benthos density may be high enough
that it does not limit bluegill growth or density. There were
also no significant correlations between bluegill size-at-age.
2,PQM180 or CPUE and macrozooplankton density. However,
macrozooplankton» density was significantly correlated to CPUE180
across all seasons as well as z-age for both male and female
bluegill. These results suggest that macrozooplankton density
may have an influence on growth rates and age at maturity of
bluegill. This differs from results found in previous years
where no correlation was found with total zooplankton density.
Bluegill may only be limited by macrozooplankton species and
including nauplii and rotifers in the analysis resulted in no
significant correlations. Continued analyses with additional
years of data will be necessary to validate these conclusions.
In addition, bluegill diet data will help us continue to assess
the importance of prey resources to growth and maturation rates
of bluegill within and among populations. Information on what
prey types bluegill may be feeding on will help understand
limitations to growth and influences on age at maturity.
Compliance
Angler compliance on lakes with implemented regulations was
assessed on eleven lakes during 1999-2002. Across all four years,
compliance by anglers was relatively high, ranging from 43-100%
(Table 3-4). Compliance was lowest on Jacksonville lake (43%)
which had no compliance checks performed on it until 2002.
Throughout the first two years of the study compliance was lower as
anglers were most likely unaware of the new regulation. During
2001, compliance was 100% on all the lakes on which it was
assessed. Each of these lakes had been checked for several years
and likely resulted in high angier awareness oi the regulations.
Average compliance was lower in 2002 than in previous years,
3-11
however, more lakes were checked for compliance and some were being
checked for the first time. Lakes that had been checked throughout
the previous years had higher angler compliance than those that
were checked for the first time in 2002. Monitoring of lakes for
compliance not only allows us to assess the effectiveness of the
regulation, but it helps educate anglers of the regulation and
enforce it. We will need to continue to work with the conservation
officers to assess compliance in future years on all lakes,
including those that were not monitored during 2002.
Recommendations
We need to continue to examine bluegill population parameters, prey
and predator abundances, and fish community variables in the study
populations to determine mechanisms responsible for alteration in
bluegill population size structure expected to result from the
experimental management actions. These assessments will be
critically important to determine the mechanisms by which each
management action alters growth and maturity schedules, and, hence,
size structure of the population.
Length frequency analysis revealed varying changes among study
lakes in response to experimental regulations. Few increases in
size structure were observed this far in most of the study bluegill
populations. We need to continue to monitor population size
structure in each of the experimental study lakes. Our ability to
detect changes in population size structure should increase each
year as the effects of each management action influences new and
existing cohorts. Because fall bluegill collections are
particularly variable, we need to continue to collect bluegill
population data in spring (a procedure that began during the last
two segments) and use these data to evaluate length-frequency and
to determine sex-specific size structures. Results of the gizzard
3-12
shad investigation presented in the last report provide evidence
that fish community variables should be considered as we assess the
impacts of the experimental regulations. Lakes with and without
gizzard shad are distributed fairly equally across experimental
treatment lakesk, so this variable should be easily accounted for in
our analyses.
We also need to continue to measure important biotic and abiotic
variables and their relationship to bluegill abundances at each
life stage. Additional data will help control for high variability
in these analyses. We need to continue to evaluate the importance
of prey resources relative to the initial findings in this segment
and verify relationships between prey availability and bluegill
growth and maturation. Future analyses should focus on community
analyses of both zooplankton and benthic invertebrates to determine
whether individual prey taxa may be disproportionately important to
bluegill growth or maturity. We need to continue to correlate
prey abundance with bluegill densities and size-at-age data to
determine the importance of density-dependent growth on age-at-
maturation and population size structure. Diet data should be
processed and analyzed during the next segments to determine
differences in prey selection by bluegill at each life stage. In
addition, differences in prey selection and prey availability
within populations should be determined to provide insight into
optimal food resources for bluegill in these eutrophic and
hypereutrophic populations.
We will continue to stock fingerling largemouth bass in
manipulation treatment lakes. Stocking bass in the past had varied
success across the 16 study lakes. Continued stocking efforts in
subsequent segments will focus on increasing the success of stocked
bass to increase the numbers of largemouth bass in these systems.
3-13
In addition, predator abundances should continue to be monitored to
determine the effects of predation on bluegill abundance and
growth. By monitoring these various biotic and abiotic variables
before and after implementation of the experimental management
ac tions, we will be able to assess the cause of changes in age-at-
maturation and growth rates that may result. Understanding the
conditions under which changes in bluegill population size
structure occur will be important in determining the future utility
of these management options across a range of lakes.
Based on data collected from conservation officers, compliance was
high across all of the regulation lakes. One lake that had
compliance checks done in the past was not checked in 2002 (Pana).
Five additional lakes have not had compliance checks completed yet
during the study (Tampier, Dolan, Busse South, Kakusha, and
Bullfrog). Efforts should be made to implement checks on these
lakes in the future. Education and enforcement of the bluegill
regulation are imperative to our ability to assess the success of
the regulation. We will work with the conservation officers
responsible for each of these lakes to monitor angler compliance.
3-14
Job 101.4 Analysis and reporting.
Objective
To prepare annual and final reports that'provide guidelines for
bluegill management in Illinois impoundments.
Findings
Relevant data were analyzed and reported in individual jobs of this
report (see Job 101.1-101.3).
4-1
Acknowledgments
The authors of this report would like to acknowledge the help
and input from the current and past staff of the Kaskaskia and
Sam Parr Biological Stations, including, R. Damstra, A. Larsen,
M. Bladock, T. Ranvestel, K. Schnake, J. Morgan, T. Edison, K.
Ostrand, B.J. Bauer and M. Anderson. We would also like to
thank Thomas Harper and all of the conservation police officers
that collected compliance data on bluegill regulations.
A special note of thanks to the regional and district biologists
that assisted in collections, participated in project
discussions, and provided advice on various portions of this
project.
REFERENCES
Aday, D.D., J.E. Claussen, J.H. Hoxmeier, T.E. Edison, D.H.Wahl, D.P. Philipp. (1999). Quality managment of bluegill:facators influencing population size structure. IllinoisNatural History Survey Technical Report.
Aday, D.D., D.H. Wahl, D.P. Philipp (2001a). Genetic andenvironmental contributions to variation in life historystrategies of bluegill. Oecologia. In review.
Aday, D.D., J.H. Hoxmeier, D.H. Wahl. (2001b). Direct andindirect effects of gizzard shad on bluegill growth andpopulation size structure. Transactions of the AmericanFisheries Society. In review.
Bayley P.B., S.T. Sobaski, D.J. Austen (1993). The fisheriesanalysis system (FAS): creel survey and lake analysis. AquaticEcology Technical Report 93/7, Illinois Natural History Survey,Champaign, IL.
Bayley, P.B., S.T. Sobaski, M. Halter, D.J. Austen (1992).Comparison of creel surveys and the precision of theirestimates. American Fisheries Symposium 12:206-211.
Beard, T.D. and T.E. Essington (2000). Effects of angling andlife history processes on bluegill size structure: insights froman individual based model. Transactions of the AmericanFisheries Society 129: 561-568.
Belk, M.C. and L.S. Hales, Jr. (1993). Predation-induceddifferences in growth and reproduction of bluegills (Lepomismacrochirus). Copeia 1993: 1034-1044.
Benjamin, D. M., R. A. Illyes, T. Kassler, and D. P. Philipp.2000. Database management and analysis of fisheries in Illinois.Annual Progress Report, Illinois Natural History Survey,Champaign, IL.
Borowsky, R.L. (1978). Social inhibition of maturation innatural populations of Xiphophoris variatus (Pisces:Poeciliidae). Science 201:933-935.
Callahan, S.P., D.H. Wahl, and C.L. Pierce. 1996. Growth ofbluegill, largemouth bass, and channel catfish in relation tofish abundances, food availability, and other limnologicalvariables. Trans. Am. Fish. Soc. (in review).
Ref-1
Chapman, L.J., D.L. Kramer, and C.A. Chapman. (1991).Population dynamics of the fish Poecilia gillii (Poeciliidae) -inpools of an intermittent tropical stream. J. of An. Ecol.60:441-453.
Claussen, J.C. (1991). Annual variation in the reproductiveactivity of a bluegill population: effect of clutch size andtemperature. M.S. Thesis, U. Toronto.
Claussen, J.E., D.D. Aday, J.H. Hoxmeier, J.L. Kline, D.H. Wahl,and D.P. Philipp. (1999). *Quality management of bluegill:factors affecting population size structure. Aquatic EcologyTechnical Report 99/1, Illinois Department of Natural ResourcesChampaign, IL.
Coble, D.W. (1988). Effects of angling on bluegill populations:management implications. North American Journal of FisheriesManagement 8:277-283.
Dettmers, J. M., and R. A. Stein. 1992. Food consumption bylarval gizzard shad: zooplankton effects and implications forreservoir communities. Transactions of the American FisheriesSociety 121:494-507.
DeVries, D. R., and R. A. Stein. 1992. Complex interactionsbetween fish and zooplankton: quantifying the role of anopen-water planktivore. Canadian Journal of Fisheries andAquatic Sciences 49:1216-1227.
Fox, M.G. and A. Keast. (1991). Effect of overwinter mortalityon reproductive life history characteristics of pumpkinseed(Lepomis gibbosus) populations. Can. J. Fish. Aqu. Sci.48:1792-1799.
Gross, M.R. (1982). Sneakers, satellites and parentals:polymorphic mating strategies in North American sunfishes. Z.Tierpsychol 60: 1-26.
Gross, M.R. and E.L. Charnov (1980). Alternative male lifehistories in bluegill sunfish. Proc. Natl. Acad. Sci. 77:6937-6940.
Ref-2
Jennings, M.J. (1991). Sexual selection, reproductivestrategies and genetic variation in the longear sunfish (Lepomismegalotis). Ph.D. dissertation, University of Illinois,Urbana.
Jennings, M.J. and D.P. Philipp. (1992). Reproductiveinvestment and somatic growth in longear sunfish. Envir. Biol.of Fish 35:257-271.
Jennings, M.J., J.E. Claussen and D.P. Philipp. (1997). Effectof population size structure on reproductive investment amongmale bluegill. North American Journal of Fisheries Management17: 516-524.
Mittelbach, G.G. (1984). Predation and resource partitioning intwo sunfishes (Centrarchidae). Ecology 65:499-513.
Mittelbach, G.G. (1986). Predator-mediated habitat use: someconsequences for species interactions. Env. Bio. Fish.16:159-169.
Philipp, D.P. and M.R. Gross. (1994). Genetic evidence forcuckoldry in bluegill Lepomis macrochirus. Molecular Ecology3:563-569.
Reznick, D. (1983). The structure of guppy life histories: thetradeoff between growth and reproduction. Ecology 64:862-873.
Salant, P. and D. A. Dillman. (1994). How to conduct your own
survey. John Wiley & Sons, Inc. United States of America pp.54-8.
Silverman, H.I. (1978). The effects of visual stimulation uponage at first spawning in the mouth-brooding cichlid fish
Sarotherodaon (Tilapia) mossambicus (Peters). An. Beh. 26:1120-1125.
Sobaski, S.T., P.J. Perea, P.B. Bayley, and D.P. Philipp.(1995). Data Base Management and Analysis of Fisheries in
Illinois Lakes: Optimizing Fisheries Management (F-69-R-8).
Aquatic Ecology Technical Report 95/14, Illinois Natural HistorySurvey, Champaign, IL.
Stearns, S.C. and J. A. Koella. (1986). The evolution of
phenotypic plasticity in life-history traits: predictions of
Ref-3
reaction norms for age and size at maturity. Evolution 40:893-913.
Stein, R.A., D.R. DeVries, and J. M. Dettmers. (1995). Food-web regulation by a planktivore: exploring the generality of thetrophic cascadehypothesis. Canadian Journal of Fisheries andAquatic Sciences 52:2518-2526.
Welker, M.T., C.L. Pierce, and D.H. Wahl. (1994). Growth andsurvival of larval fishes: roles of competition and zooplanktonabundance. Transactions of the American Fisheries Society123:703-717.
Werner, R.G. 1969. Ecology of limnetic bluegill (Lepomismacrochirus) fry in Crane Lake, Indiana. American MidlandNaturalist 81:164-181.
Wootton, R.J. (1985). Energetics of reproduction, pp. 231-254in P.Tyler and P. Callow (eds) Fish Energetics: NewPerspectives. Johns Hopkins University Press, Baltimore, MD.
Ref-4
C C)r-.
r-
(N N
Co o
(N
HM 01
CM4 r4%
m o
C,-. H-r-4 H(n - r
V-1
0
4)N
04.4
U)
U)
0
$.-44
U)
H
0)
0)
$4
0 O>
o £:
»M '0
0
O -)
(0 0-U) 0)
r- M
0 O
0 0
U) (
4 4 4
0
M V
$-4
0
4J -
0 U)
) )
4-) 0
0 NC
44 4
^I 0
04-)
.s 0Q) (0
4I a
4-i -
04 )U) 0-
o -H
(0 (0
0^ (
'.0 (N Co o Co LA
so0)0H-
Co C4co 0
oo) 0)
IRV.^r I
Co
O
r-4
CACoCo
(Y)
r-I0
Cr)
(M1.0
(Y)
(Y)
CAr-)a'H
CArY)rn
NM0Hn
0O0Oa'Ln
0CNr-H
4
H0
E-
01(N "C
0
Q
U.
ot
-(N
CM4
H
--.
k0
oAft Hig r Lo in T- 4 Yo o un oo0
(N (N (NwH H a
0 oDoN 0
H
CC M
a%o•
r-4
LO0)Tr4Hi
CO
U")(NLMLA
H
0")
ro,
o4
CM)(N1H~
0M
ir)oocoLn
o
r)-
roo' LOCr)(N qw
H-
(Nr-CM4
a)
coIV
r- 04
C 0
r - (Nco r-
0 00ro r-o
(04(N
0r)H•
co LO N
O,1 " ' t.r-< QO t.1
C ro ' rqw LAO
H-0
W0oo
r- LACr)•D1.0
N CoLA '.0Cr) ~' 0
44 4 )04 4J- * q04- 0 4-4 F) 04,O U 4. J U) 0
SCH 0 $ 4( ,.-I -A r 0 o () U) (9
0 (0 0) N -H *HH H N a0 0 .
S a u
00LnCM4CM4
00
coM-
4-
' C
Co 00
,--4
W
(WQC
(U4J0
0 -0 C
H N
a,(U
(0a,Hda,
0na)Uo-Ua,0o
Table 1-2. Comparison of PQBG.170 metrics for bluegill populationssampled by electrofishing for the Bluegill Project and standardizedelectrofishing samples collected by district biologists and submitted tothe Illinois Fisheries Analysis System (FAS).
BLG Project FAS Data
Class Lake PQBG.170 PQBG.170
Q Apple Canyon 0.13 --Q Bloomington 0.14 0.11S Bullfrog 0.00 --
Q Busse South 0.07 --
S Dolan 0.04 --Q Forbes 0.11 0.08
Q Glendale 0.22 0.44
S Hillsboro 0.13 0.14
Q Homer 0.03 0.06
S Jacksonville 0.01 0.00Q Kakusha 0.32 0.21S Lake of the Woods 0.01 0.03S Le-Aqua-Na 0.00 --
Q Lincoln Trail 0.11 0.00
S Mcleansboro 0.02 --Q Mermet 0.14 0.06S Mingo 0.02 --
Q Murphysboro 0.06 0.14S Pana 0.01 0.00S Paris East 0.01 0.01S Pierce 0.01 0.00Q Red Hills 0.22 0.17
S Round 0.01 --
Q Sam Parr 0.27 0.08
Q Siloam Spring 0.20 0.30
S Spring Lake North 0.02 --
Q Spring Lake South 0.08 0.01S Sterling 0.00 --
S Tampier 0.01 0.01
Q Walnut Point 0.16 0.04
S Walton Park 0.00 0.00
Q Woods 0.04 --
Table 1-3. Creel data for the 32 experimental bluegill populations.Data includes lake classification, percentage of anglers targetingbluegill, number of angler hours per acre, number of bluegill caught andharvested per hour, average weight of bluegill caught and harvested, andthe proportion of quality fish (PQBG.170) from current projectcollections.
CreelCreel Angler Caught Harvest Data
% BLG Hours/ # Avg TL # Avg TLClass Lake Year Interviews Acre BLG/Hr (mm) BLG/Hr (mm) PCBG.170
Q Apple Canyon 2000 18.6 128. 0.806 173 0.350 206 --Q Bloomington 1996 2.0 63 0.312 167 0.140 182 0.51S Bullfrog 1998 10.2 1045 0.362 122 0.071 142 0.01Q Busse South 1989 0.6 451 0.262 136 0.167 140 --S Dolan 1998 5.2 271 0.388 137 0.235 153 0.05Q Forbes 1999 7.7 84 0.470 153 0.047 193 0.20Q Glendale 1999 2.4 95 0.828 138 0-.308 168 0.55S Hillsboro 1999 6.9 78 0.158 135 0.057 153 0.05Q Homer 1999 3.9 330 0.300 152 0.017 158 0.22S Jacksonville 1999 0.5 48 0.304 129 0.008 154 0.04Q Kakusha 1998 17.6 124 0.137 172 0.098 179 0.52
Lake of theS Woods 1998 5.0 832 0.729 121 0.144 130 0.02S Le-Aqua-Na 1994 6.5 742 0.412 159 0.231 175 0.30
LincolnQ Trail 1996 9.5 112 0.188 182 0.166 186 0.59S Mcleansboro 1999 9.7 60 0.094 167 0.055 172 0.46Q Mermet 1997 11.9 81 0.488 181 0.351 197 0.53S Mingo 1999 9.4 199 0.393 143 0.151 154 0.03
Q Murphysboro 2000 23.9 114 0.642 156 0.182 161 --S Pana 1999 0.1 68 0.226 138 0.011 145 0.14
S Paris East 1999 14.7 87 0.438 143 0.088 157 0.14
S Pierce 1999 4.7 407 0.207 126. 0.016 133 0.07Q Red Hills 1994 18.1 473 0.612 163 0.278 178 0.40S Round 1999 3.8 28 0.227 151 0.042 155 0.24
Q Sam Parr 1997 16.6 217 1.671 154 1.020 173 0.31Siloam
Q Spring 1997 6.6 416 0.279 145 0.076 176 0.17Spring Lake
S North 1999 23.2 64 1.948 136 0.022 168 .050
Spring LakeQ South 1996 31.8 150 1.380 150 0.675 171 0.25S Sterling 2000 1.9 211 0.212 127 0.034 135 --S Tampier 1998 3.2 951 0.118 113 0.022 137 0.02
Q Walnut Point 1997 37.9 199 0.302 151 0.197 173 0.27
S Walton.Park 1999 4.3 107 0.108 130 - -- 0.08
Q Woods 2000 1.0 151 0.473 201 .--..
Table 2.1. Summary data from Apple Canyon (Lake Type: Quality-North-Large- Control) collections for 1996 and 2001.
A. Proportion of Quality males.
1996Number of Mature
Males
Number >170
PQM.170
2001Number of Mature
Males 10
Number >170 9
PQM.170 0.90
B. Average total length and maturity status of individuals at each age.
1996 FEMALES
AGE N # IMM # MAT AVE TL
1 100 100 0 45
2 95 86 9 833 30 1 29 138
4 10 0 10 193
5 3 0 3 215
2001 FEMALESAGE N # IMM # MAT AVE TL1 113 113 0 46
2 59 43 16 903 18 2 16 139
4 8 0 8 1665 1 0 1 206
1996 MALESAGE N # IMM # MAT AVE TL
1 100 100 0 452 75 75 0 793 33 25 8 134
4 68 4 64 174
5 2 0 2 195
6 2 0 2 213
2001 MALESAGE N # IMM # MAT AVE TL
1 113 113 0 46
2 54 54 0 863 13 12 1 1364 7 1 6 1755 3 0 3 205
Males 3 33 25 8 0.76
4 68 4 64 0.06 4.0
SProportion2001 AGE Total f IMM 4, MAT of IMM Z-AGE
2.9
Males 3 13 12 1 0.92
4 7 1 6 0.14 4.0
C.
76430.57
Females 2 59 43 16 0.73
3 18 2 16 0.11
F% I
Table 2.2. Summary data from Glendale (Lake Type: Quality-South-Small-Control) collections for 1996 and 2001.
Proportion of qualit
1996Number of Mature
Males
Number >170
PQM.170
2001Number of Mature
Males 15
Number >170 15
PQM.170 1.0
r males
3432
0.96
B Average total leng e
1996 FEMALES
AGE N # IMM MAT AVE TL1 44 44 0 51
2 40 29 11 106
3 35 2 33 147
4 18 0 18 171
5 11 0 18 176
6 2 0 2 1907 2 0 2 197
2001 ,, FEMALES
AGE N # IMM # MAT AVE TL1 228 228 0 442 20 17 3 853 5 2 3 133
4 4 0 4 172
5 2 0 2 1926 1 0 1 194
1996 MALES __
AGE N # IMM #MAT AVETL1 44 44 0 512 40 40 0 113
' 3 24 23 1 148
4 22 4 18 1785 10 0 10 192
6 3 0 3 183
7 2 0 2 200
2001 MALESAGE N # IMM # MAT AVE TL
1 228 228 0 44
2 12 12 0 85
3 3 3 0 1264 11 0 11 1905 4 0 4 190
C. Z-age for males and females.Proportion
1996 AGE Total # # IMM # MAT of IMM Z-AGE
Females 2 40 29 11 0.73
3 35 2 33 0.06 2.9
Males 3 24 23 1 0.96
4 22 4 18 0.18 4.1
1 2Proportion2001 AGE Total # # IMM # MAT of IMM Z-AGE
Females 2 20 17 3 0.85
1 4 1 ii 1 0 I 11 0.0 4.0
A.
3 5 2 3 0.40 3.2
Males 3 3 3 0 1.04 11 0 11
I I I I I --T-- I
0.0 4.0
AM
2-
Table 2.3. Summary data from Hillsboro (Lake Type: Stunted-Central-Small-Control) collections for 1996 and 2001.
A. Prooortion of Quality males
1996Number of Mature
Males
Nurmber >170
PQM. 170
2001Number of Mature
Males 47
Number >170 14
PQM.170 0.30
B. Average total length and maturity status of individuals at each age
1996 FEMALES
AGE N # IMM # MAT AVE TL
1 22 22 0 70
2 20 3 17 123
3 45 1 44 147
4 45 0 45 159
5 8 0 8 174
2001 FEMALES
AGE N # IMM # MAT AVE TL
1 28 28 0 85
2 46 0 46 134
3 47 0 47 152
4 12 0 12 163
5 9 0 9 166
6 2 0 2 170
7 3 0 3 167
1996 MALESAGE N # IMM # MAT AVE TL
1 22 22 0 70
2 15 15 0 1283 64 12 52 159
4 57 0 57 1645 4 0 4 166
2001 MALES
AGE N # IMM # MAT AVE TL1 28 28 0 85
2 23 9 14 145
3 33 0 33 1664 3 0 3 1705 2 0 2 179
-age for males and females.Proportion
1996 AGE Total # # IMM # MAT of IMM Z-AGE
Females 2 20 2 17 0.15 2.1
Males 2 15 15 0
3 64 12 52 0.19 *3.2
2001 e
Females
AGE Total # # IMMProportion
# MAT of IMM
2 46 0 46 0.00
Males 2 23 9 14 0.39 2.4
3 33 0 33 I I
C. Z
Z-AGE
2.0
I 10926
0.25
...... E• v-- -- .6
Table 2.4. Summary data from Siloam Springs (Lake Type: Quality-Central-Small-Control) collections for 1996 and 2001.
A Proportion of qual
s
1996Number of Mature
Males
Number >170
PQM. 170
54530.98
2001Number of Mature
Males 40
Number >170 37
PQM.170 0.93
B. Average total length and maturity status of individuals at each age
1996 FEMALESAGE N # IMM # MAT AVE TL
1 83 83 0 49
2 55 54 1 88
3 20 11 9 111
4 4 0 4 168
5 11 0 11 176
6 2 0 2 188
2001 FEMALES
AGE N # IMM # MAT AVE TL
1 181 181 0 51
2 99 86 13 89
3 21 4 17 139
4 3 0 3 184
5 1 0 1 212
1996 MALESAGE N # IMM # MAT AVE TL
1 83 83 0 49
2 71 71 0 843 14 14 0 1164 29 6 23 1855 29 0 29 192
6 2 0 2 197
2001 MALESAGE N # IMM # MAT AVE TL
1 181 181 0 51
2 81 81 0 88
3 8 8 0 1344 34 1 33 196
5 7 0 7 209
-age for males and females.Proportion
1996 AGE Total # # IMM # MAT of IMM Z-AGE
[Females 2 55 54 1 0.98
3 20 11 9 0.55 3.3
Males 4 34 1 33 | 0.03 | 4.0
C. Z
[Males 4 s 29 6 23 0.21 4.2
.- -%e w .". -
Table 2.5. Summary data from Sterling Lake (Lake Type: Stunted-North-Small-Control) collections for 1996 and 2001.
Proportion of quali
s
1996Number of Mature
Males
Number >170
PQM.170
2001Number of Mature
Males
Number >170
PQM.1701
24, '0
0.0
00.0
B. Average total length and maturity status of individuals at each age
1996 FEMALES _
AGE N # IMM # MAT AVE TL
1 14 14 0 58
2 36 15 21 86.3 12 0 12 111
4 3 0 3 115
2001 FEMALES_
AGE N # IMM # MAT AVE TL1 16 16 1 0 562 57 19 38 913 73 1 72 117
4 3 0 3 130
1996 MALESAGE N # IMM # MAT AVE TL1 14 14 0 582 22 22 0 823 27, 5 22 1174 2 0 2 134
2001 MALESAGE N # IMM # MAT AVE TL
1 16 16 0 562 17 17 0 863 62 28 34 1234 13 0 13 143
C. Z-age for males and females.Proportion
1996 AGE Total # # IMM # MAT of IMM Z-AGE
Females 2 36 15 21 0.4 2.4
=Males I 3 27 | 5 ] 22 I 0.2 | 3.2
2001Proportion2001 AGE Total # # IMM # MAT of IMM Z-AGE
Females 21 57 19 38 0.33
Males 3 62 28 34 0.45 3.5
A.
2.3
47
46 A- '4w kO'%-o -" %- -ý -a
I4)4 4L ) -44 Uf- N
N-w - 0 4) O O0
r-I '(o 0 00.,-J cC-r. -H (0 -H -H4 020 N 0, 40 r-I 5 ) N U 44 U) 0
1 4-) (0 0 u 0 0 0 NU H -H 4-WN C O0 0 M N 4 0 H U Ho-*4 H" *4 o o (0 0 0 -H (0 (
S(00 0- J 4-J
S0 0I WC CT ) 0 0
0 0 - 4- .0 N I .0U 0 ) N) (0 54O c >1 (0
U--4 S0 O4 0•0 0 * W 4 Cr t O a'0 C -J .*p < 0 0)
o4J- U) (D O N p N I , Hr
o0(M '0 2 O ( I
0) 00 4-i e0 0H (0 .
,-4 E-S0 0 U) 4
p 4•J *H 0 0 41 H0 * *o4- C r- 4-44 (0 4 V- -4H 0) 0
On0)H o Uo o o4.0) (0 . o.§ U) H N * T( H
. - -4..1 -0H4 0 4) m--I C4
0 0
° -4-4 E 0(0 -H (0 C) ) M pH O c rHl 0
0N (0 0 0 '0 H U)
-S4-4 u 4 H) cl 0 -i 0 ) ( N H r-iN - 0 C r -H -H rH 0 -4 (0 -H
4-) N U 4 Cn I O U-41--)OH0
O M OH
$4 4N
(0 W *HN *H
oE a " O )H ) H- ) H0g N 0 ' H C H- mP H ' H-
. N (0 0 N ( N (0
(0 -H * 'O
- CO ^ CQ ^ CO .- I U
C 0 -H 0--i 4-i
-) rH 4.4 4-J 4-1 1 4 -t 4J 4 -)4
:O 00 0 0 0 0 0 0 0 0&Z) Z Cl) Cl) Z C
H (0 0 *HI *-H4-
H0 <1) < Io
Q C0s s ) o (
Table 3-2: Pearson correlation coefficients between benthos andzooplankton density means for all years (1998-2001) and various bluegillmeasures. Bold numbers represent values with P<0.05 and bold andunderline numbers represent values with P<0.01.
C"
Spring 2001 CPUE180
Spring 2001 CPUE
Fall 2001 CPUE180
Fall 2001 CPUE
Total 2001 CPUE180
Total 2001 CPUE
Size at Age 2 (1996 Pre data)
PQM180
z-age Males (1996 Pre data)
z-age Females (1996 Pre data)
Ma crozooplankton
0.68
0.010.60
-0.31
0.65
-0.18
-0.07
-0.09
0.56
0.56
Density Benthos Density
-0.23
-0.27
0.24
0.43
0.05
0.19
0.03
0.06
-0.29
-0.23
I
I
0
,- 0 4J
* .( -H $4H >
i 1 o
e.,H N 0 >
* . 4 W4
S( 4 0-
r.4-i N o -I 4 )
40 • N O. o 0
0 H *H NO E
S4-4 N -H0 0
H0 ) H4- H 0 , r- I -'
c o 04 4 o
O C1 001
to0cC C o
p 0), t V0
0 s '--c I C)
"AH o 0
) 0 0 c0 0
(0 . m' X v - -
r. U 0Q 44 00 -C N (U 0 -H
.Q 0 4-4 4 D0 0 L) 0 H
-H H o o -
S)- H 0 .H
M(0 -H 4 O4)
m(0 - 4 ) 4
44 -rq4 H 4
0 '0 0 0 - 4 J-
4 0CON 00)00 (0 0C P -
8 4 * -r > X o .
0 ( -H g HO
4-) 4-i ( 0
-t 0 ) ) Hd
E-< 4 rNHH-1'0 OH*-
0
0
4-i
rdP
*H(0
0C-4
(0
&1
0
4)4-i
04
00
-,-I
4-i
CO
,-H
4i
.1=
'00
00
Cl)
0)4)(U
()Is
-in qw LO k m
0 T-i in a). -i- oo qw u)') r-L rfl U Hw 0 T-40^ I r-4 r- 4 r r-i T-4 T-4
M rQ r-I koCo A
0 M H r- C 0w 0 O
o -I , N N ( H-4 H
^-1 ,' L) 01 r I H
H
:10
'0
'00 '~Q(U
'0.4)
I-'04
00H
C14
CN
0HOO 00 r-1 r-1 CMr r-4H- H- N-
IO
C 4 N
C) C)
r-4 r-4
L O L o0oN 0 0 U U') ()
He H H
,-40
OCO
H--0
,-.CO
,--0
Co
,-10
0
I"
r--0
CO,--F"
,--I
Co
,-)
co
0% o<N r- 4 H
o •f-4
o rn N q rN
N N LN N
^< ̂- r i) \ Mo r a M w o
H HOHH H
LOIr-Co
'-i
0
Hs
,-i
on
-000o
o Co N4m m TLO
H- 0 0 >- H- 4^00ý%0
0 oa 0 0r4U W 4 Q Z lH
Z CO) U) N4o• ^ o ja • .• .n r2 0 >1 - m m
(d 0 -H . (U4 0z.z0 P ) C) Co (^ e^~ r5 p p;^ccofT1fl) t7» Q0 CO -04 - 04
00'.0'.0H
0
Co
,--
N H C') N0000 ~H H H H
00 N NO00 U') I~ Lfl00 ~ ONN Lfl H ~ H
H0
H
CDr-1
Hr-I0U-lU')
CD
H^0-N-0)
CD
H-0CD00
ccNs
CD
O HN%NIN r -4I
0 - .- ,-0
-H Q) w (a U )C)0 0 - C.) ( 0-U ) C -IH TO ,.0 43 <D uo0 ,.Q O).5 1 4 ,.-io N I 0).5O 0 H
o 0 0 U .-H (r-- 0 0 co (a -r t.CQl Eu t3 -) ý4 134 -
4)
4
4)
*I
3
Table 3-4: Percent compliance for the experimental harvest regulationfor bluegill on study lakes. Compliance data was obtained from regulationfor bluegill. Compliance data was obtained from Illinois conservationofficers and is only presented for lakes with complete data. Lakes notpresented either had low bluegill fishing pressure or infrequentcompliance checks
Lake Number of Bluegill Percent Compliance
Regulation Checks
1999 - 2000
Forbes 40 85
Pana 26 100
Pierce 31 90
Red Hills 46 94
Walnut Point 30 87
Homer 26 96
Lake of the Woods 23 96
2001
Walnut Point 112 100
Homer 42 100
Lake of the Woods 18 100
Bloomington 142 100
2002
Bloomington 168 89
Forbes 103 85
Homer 12 100
Jacksonville 14 43
Lake of the Woods 104 100
Mermet 8 100
Pierce 507 94
Red Hills 107 95
Walnut Point 70 100
Walton Park 20 100
Figure 1-1. Plot comparing PQF.170 and PQM.170 values for 50 Bluegill
project study lakes, showing a significant positive correlation
(Pearson's coefficient r = 0.77, P<.0001).
1
L00CL
0.0 0.1 0.2 0.3 0.4 0.5
PQM.170
Figure 2.1. Comparison of size (average total length) at age for AppleCanyon (Quality-North-Large-Control) in 1996 and 2001.
--- 0--
-0--
FEMALE 1996
FEMALE 2001
0 1 2 3
AGE
I I I
1 2 3
AGE
4 5 6
I I I 7
4 5 6 7
zI--J
H
I
LJ
250-200
150-
100-
50
0-0
-0---
-0--
MALE 1996
MALE 2001
Figure 2.2. Comparison of size (average total length) at age for LakeGlendale (Quality-South-Small-Control) in 1996 and 2001.
-0-- FEMALES 1996
-0-- FEMALES 2001
I U I I U, I U
1 2 3 4 5 6 7
AGE
-0-- MALES 1996
---- MALES 2001
AGE
.220 -200 -180-160 -140-
120-100-
80-60-40-
I
LJ-J
2U0 10
1
11
1
zIi-,J
0H-
0 1 2 3 4 5 6 7
Ap%0%ow
,o-.,, 04
Figure 2.3. Comparison of size (average total length) at age forHillsboro (Stunted-Central-Small-Control) in 1996 and 2001.
i
-- 0--- FEMALE 1996
-0--- FEMALE 2001
0 1 2 3 4 5 6 7
AGE
-- 0--- MALE 1996
-- D--- MALE 2001
AGE
220 -200-
- 180 --- 160-
140 -UJ 120 -- 100-< 80-o 60-I- 40-
200 p I I I
IzLi-J-j
0F-
Figure 2.4. Comparison of size (average total length) at age for SiloamSprings (Quality-Central-Small-Control) in 1996 and 2001.
--- 0--- FEMALE 1996
-- D-- FEMALE 2001
0 1 2 3 4 5 6 7
AGE
-0---- MALE 1996
-- D-- MALE 2001
200 -I 180 -
0 160 -
w 140-J 120... 120 -
I 100-80
60-An -
I I I I
F-0w.J
O-0H-
22U -
200 -180 -160 -140 -120 -100 -
8060-40-20-
n._
0 1 2 3 4 5 6 7
AGE
14 Li ý
ONI I ýUsZ I -' I. - II - i - I -- I -- I
o%00%
Figure 2.5. Comparison of size (average total length) at age forSterling Lake (Stunted-North-Small-Control) in 1996 and 2001.
-- 0-- FEMALE 1996
------ FEMALE 2001
0 1 2 3. 4 5
AGE
-0--- MALE 1996
-- 0-- MALE 2001
II-0zW-J-J
H0H
zWI
0 1 2 3 4
AGE
Vo
Apple Canyon
134-166 167-200
01996-1997IB2001*2002
201+
50-
40-
30-
20-
10-
0-
100 -
80 -
60 -
40 -
20 -0 -
70-60 -50 -40 -a-30-
20 -10-0 -
Siloam
I100-133
I
Springs
134-166 167-200
Lincoln Trail
.1"-
201+
FILEfl 2100-133 134-166 167-200
Glendale
SiH.n
201+
100-133 134-166 167-200 201+
Figure 3-1: Length frequency from spring electrofishing expressed as percent of the total catchfor lakes that were designaled as quality bluegill lakes and are receiving the control treatment.
Samples from 1996 and 1997 were taken before the treatment was implemented.
80 160 -I40-20-
0-1
100-133
1=
0
00.
-r-i
JFA.
J
Round0 1996-1997M 2001* 2002
. .
167-200 201+
100-133 134-166 167-200 201+
Hillsboro60-50-403020-10-0
100-133 134-166 167-200 201+
Figure 3-2: Length frequency from spring electrofishing expressed as percent of the total catchfor lakes that were designated as stunted bluegill lakes and are receiving the control treatment.
Samples from 1996 and 1997 were taken before the treatment was implemented.
706050-40'3020-10-
0 --100-133
I134-166
Paris60-50 . .i ..
q"4.
CL
40-30-20-10-0 -- -
m
Busse South
D1980-1 02C60- ~ n2C201 i I
100-133 134-166 167-200 201+
Mermet
In ni100-133 134-166 167-200
H.'
201+
167-200 201+
Figure 3-3: Length frequency from spring electrofishing expressed as percent of the total catchfor lakes that were designated as quality bluegill lakes and are receiving the regulation treatment.
Samples from 1996 and 1997 were taken before the treatment was implemented.
)96-1997)01)02
60-50-
'c 40o 30. 20-
10-0 -
6050-40 -30-20-10 -0-
Red Hills
I100-133 134-166
__A----L -
Lake of the Woods02000
Smn0i
2002ou
6040200
100-133 134-166 167-200 201+
Panaon'
'i100-133 134-166 167-200 201+
Dolan
o0'ov60-
40-
20-
0-
m1 f"II
I100-133 134-166 167-200 201+
Figure 3-4: Length frequency from spring electrofishing expressed as percent of the total catch
for lakes that were designated as stunted bluegill lakes and are receiving the regulation
treatment. Samples from 1996 and 1997 were taken before the treatment was implemented.
ou40 -
06
0 40 -
2 20-
0 ý .w
9
Spring South
60 140-
20-o -ý00-133 134-166100-133 134-166
0 1996-1997S2001
M 2002
167-200 201+
Murphysboro
i~E
r-j m134-166 167-200 201+
Sam Parr
100-133 134-166 167-200 201+
Figure 3-5: Length frequency from spring electrofishing expressed as percent of the total catch
for lakes that were designated as quality bluegill lakes and are receiving the stocking
treatment. Samples from 1996 and 1997 were taken before the treatment was implemented.
100 1, 80-= 60 -
0 -
100-133
60-50-40 -30-20100 I -I
Spring North
996-1997001002
100-133. 134-166 167-200 201+
Le Aqua Na
I I I
100-133 134-166 167-200
Mingo
100-133 134-166 167-200
201+
McLeansboro80 160-40-
200 -n
100-133 134-166 167-200 201+
Figure 3-6: Length frequency from spring electrofishing expressed as percent of the total catchfor lakes that were designated as stunted bluegill lakes and are receiving the stockingtreatment. Samples from 1996 and 1997 were taken before the treatment was implemented.
80
60
40
20
0
Il-7
120100-80-60-40200 I
e0
CL80 -
60-
40 -
20 I201+
%Aft
I
I =1 --
v •
Bloomington80
LIJ 1I - 199 I
E2001* 2002
4'100-133 134-166 167-200 201+
Kakusha
8060-40 0-133 134-166 167-200 201+
0100-133' 134-166 " 167-200 ...201+
Forbes
100-133 134-166 167-200 201+
Homer
80 160-40-
20-0
100-133 134-166 167-200 201+
Figure 3-7: Length Frequency from sprng electrofishing expressed as percent of the total catchfor lakes that were designated as quality bluegill lakes and are receiving the stocking andregulation treatments. Samples from 1996 and 1997 were taken before the trealmentwas implemented.
60
40
20
0
*0
60 1
40-
20
0 --
ri A r"rnia A n*7
rý".
i
0 1996-1997B2001U 2002
100-133 134-166 167-200 201+
Bullfroa
100-133 134-166 167-200 201+
Inn,
80-
60
40
20-0
100-133
Jacksonville
134-166 167-200 201+
Figure 3-8: Length Frequency from sprng electrofishing expressed as percent of the total catch
for lakes that were designated as stunted bluegill lakes and are receiving the stocking and
regulation treatment. Samples from 1996 and 1997 were taken before the treatment was
implemented.
Pierce
8060
40-20
0-
40
Il
120100806040200
J[
Apple Canyon
g
100-139 140-169 170-199
Siloam Springs
100-139 140-169 170-199 200+
120-100-80-60-40-20-
0-
on
60
40-
20-
0 -
{Lincoln Trail
100-139 140-169 170-199 200+
Glendale
[liii rl100-139 140-169 170-199 200+
Figure 3-9: Length frequency from fall electrofishing expressed as percent of the total catch
for lakes that were designated as quality bluegill lakes and are receiving the control treatment.
100
80-60-
40-20-
0-
I O 1998B 1999B2000* 2001*2002
200+
100806040200
,Id
a..0.a.E
I •
II- ............... • -- •
I I a ý ý -T mmwummý -U- I
. L 1 - I I * -ia II --. .-. --
I
ou
60
40
20
0
100150-
0 --
Round
O 1998IS1999
M*2000
I *2001
l , 02002
100-139 140-169 170-199, 200+
Sterling
150
l 100-139 140-169 170-199 200+
Paris80-60-40-20-
00 I100-139 140-169 170-199 200+
Hillsboro605040302010
100-139 140-169 170-199 200+
Figure 3-10: Length frequency from fall electrofishing expressed as percent of the total catch
for lakes that were designated as stunted bluegill lakes and are receiving the control treatment.
a^
Busse South
01998m 1999M2000M2001M 2002
-I--
100-139 140-169 170-199 200+
Walnut Point
60-
40-20-
0 - . - 1,w M.
100-139 140-169 170-199
onou
60
40
20
0
200+
Mermet
100-139 140-169 170-199 200+
Red Hills
50-4030-20-100
100-139 140-169 170-199 200+
Figure 3-11: Length frequency from fall electrofishing expressed as percent of the total catchfor lakes that were designated as quality bluegill lakes and are receiving the regulation treatment.
80160-
40-
20 -
0-
0
0IL<£
I I N I -I -, Qý L --I
An 8
-ýIEI@ -r6n
IVU -
80-
60-40-
20 -
0-100-139
80-604020-0
Tampier
irn
140-169 170-199 200+
Lake of the Woods
rF~
100-139 140-169 170-199
Pana80
60
40
20- I
100-139 140-169 170-199
Dolan
100-139 140-169 170-199
200+
200+
Figure 3-12: Length frequency from fall electrofishing expressed as percent of the total catchfor lakes that were designated as stunted bluegill lakes and are receiving the regulation treatment.
A ffA
0 1998B 1999M2000M2001m 2002
100
100
CLee%
80.
60-
40 -
20 -
0 -
I200+
v- mm -mi II-
•!|etlll
L
--T-L
Spring South0 1998
O 1999
M 2000
12001
* 2002
140-169 170-199 200+
Woods
d100-139 140-169 170-199 200+
Murphysboro
100-139 140-169 170-199 200+
Sam Parr
-F-
100-139 140-169 170-199 200+
Figure 3-13: Length frequency from fall electrofishing expressed as percent of the total catchfor lakes that were designated as quality bluegill lakes and are receiving the stocking treatment.
120 1100 -80-60-40-20 -
0-
100-139
8060-40-200-
.
h.0
80-
60-
40 -
20 -
O0
80
60-
40-
20-
0 --
*-*-- -
- I
a
Spring North01998B 1999B 2000* 2001*2002
100-139 140-169 170-199 200+
Le Aqua Na
100-139 140-169 170-199 200+
Mingo
n100-139 140-169 170-199 200+
McLeansboro80-
60-
40 -
20-
0- ULM
100-139 140-169 170-199 200+
Figure 3-14: Length frequency from fall electrofishing expressed as percent of the total catch
for lakes that were designated as stunted bluegill lakes and are receiving the stocking treatment.
10080"
60-40-
20-0-
100 180-60-
20-0 -r--
I.
80-
60-
40-
20 -
0-
t"'"l ý
I MAm- * , 1 -
--I mmý- ý
-
-I -
ri
Bloomington80
60
40
20
0100-139 140-169 170-199 200+
Kakusha
100 1au -60-4020-0
100-139 140-169 170-199 200+
Forbes
II
100-139 140-169 170-199 200+
Homer
140-169 170-199 200+
Figure 3-15: Length frequency from fall electrofishing expressed as percent of the total catch
for lakes that were designated as quality bluegill lakes and are receiving the stocking andregulation treatment.
019988 1999S2000* 2001*2002
0
0I.
00o
80-
60-
j40 -
20 -
0
-4 fiIUU -
80-
60-
40-
20 -0- i
100-139
f% ON
I
100 180-60-40-,
20 -04-
Pierce01998El 1999M2000
M2001
*2002
100-139 140-169 170-199 200+
Bullfrog
80-60-
100-139 140-169 170-199 200+
Jacksonville
II rII
100-139 140-169 170-199 200+
Walton Park100 -
80-
60-
40 -
200.a
100-139 140-169 170-199 200+
Figure 3-16: Length frequency from fall electrofishing expressed as percent of the total catch
for lakes that were designated as stunted bluegill lakes and are receiving the stocking and
regulation treatment.
CL
IU2 -
100 -80 -
60-40 -
20 20
1
4l 6nf%
Figure 3-17. Mean zooplankton, macrozooplankton and bethos density byyear in quality and stunted bluegill lakes. Bars indicate standarddeviation.
Total Zooplankton Density0.2000 Quality
1500 - a Stunted-1.j
-0 1000-Ez
500 -
1998. 1999 2000 2001
Macro-Zooplankton Density
300
.- J
o 200
z -100
0
15000
E 10000I-
.0
= 5000z
0
1998 1999 2000 2001
..- A ARBenthos Density
1998 1999 2000 2001