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


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


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


Males

Number >170

PQM.170


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


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


Males

Nurmber >170

PQM. 170


Males 47

Number >170 14

PQM.170 0.30

B. Average total length and maturity status of individuals at each age

1996 FEMALES


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


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


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


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


Males

Number >170

PQM. 170

54530.98


Males 40

Number >170 37

PQM.170 0.93


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


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


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


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


[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


Males

Number >170

PQM.170


Males

Number >170

PQM.1701

24, '0

0.0

00.0


1996 FEMALES _


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


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


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


Bloomington 142 100

2002

Bloomington 168 89

Forbes 103 85

Homer 12 100

Jacksonville 14 43


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.


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.


)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+


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+


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+


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+


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

quality management of bluegill: factors affecting

Documents