Long Term Resource Monitoring Program

 Technical Report 

2005-T005

June 2005

Multiyear Synthesis of the Fish Component 

from 1993 to 2002

for the Long Term Resource Monitoring Program 
Long Term Resource Monitoring Program Technical Reports

provide Long Term Resource Monitoring Program 

partners with scientific and technical support.

All reports in this series receive anonymous peer review.

Cover graphic by Mi Ae Lipe-Butterbrodt

Mention of trade names or commercial products does not constitute endorsement

or recommendation for use by the U.S. Department of the Interior, U.S. Geological Survey.
Multiyear Synthesis of the Fish Component 

from 1993 to 2002 for the 

Long Term Resource Monitoring Program

Brian S. Ickes, Melvin C. Bowler, Andrew D. Bartels, Daniel J. Kirby, Steven DeLain, 

John H. Chick, Valerie A. Barko, Kevin S. Irons, and Mark A. Pegg

Final report submitted to 

U.S. Army Corps of Engineer District, Rock Island 

Clock Tower Building 

PO Box 2004

Rock Island, Illinois 61204-2004

June 2005
Suggested citation:

Ickes, B. S., M. C. Bowler, A. D. Bartels, D. J. Kirby, S. DeLain, J. H. Chick, V. A. Barko, K. S. Irons, and M. A. Pegg. 2005. Multiyear Synthesis of the Fish Component from 1993 to 2002 for the Long Term Resource Monitoring Program. U.S. Geological 

Survey, Upper Midwest Environmental Sciences Center, La Crosse, Wisconsin. LTRMP 2005 T005. 60 pp. + Appendixes A–E. 

Additional copies of this report may be obtained from the National Technical Information Service, 5285 Port Royal Road, 

Springfield, VA 22161 (1-800-553-6847 or 703-487-4650). Also available to registered users from the Defense Technical 

Information Center, Attn: Help Desk, 8725 Kingman Road, Suite 0944, Fort Belvoir, VA 22060-6218 (1-800-225-3842 or 

703-767-9050).
iii

Contents

Page

Tables ......................................................................................................... v

Figures ......................................................................................................... vi

Preface ......................................................................................................... vii

Chapter 1: Introduction .................................................................................... 2

Purpose and Scope .................................................................................... 2

Monitoring Rationale ................................................................................. 3

A general argument for monitoring ............................................................ 3

An argument for monitoring fishes within the Upper Mississippi River System ......... 3

Study Area ................................................................................................ 3

Sampling Methods and Databases ..................................................................... 5

History of Methodological Changes ............................................................ 5

Statistical sampling design and general field methods ....................................... 6

Database used in this report ..................................................................... 7

Statistical Methods ....................................................................................... 7

Summary of Collections ................................................................................. 9

Systemic data summary ........................................................................... 9

Study reach summaries ........................................................................... 10

Chapter 2: Community Ecology ........................................................................... 15

Introduction ............................................................................................. 15

Spatial Patterns in Species Richness .................................................................. 15

Community Composition .............................................................................. 15

Community Structure .................................................................................... 16

Community Patterns in Relation to Habitat Factors ................................................ 18

Community Dynamics Relative to Floods ............................................................ 18

Discussion and Management Implications ............................................................ 19

Chapter 3: Single Species Ecology ........................................................................ 21

Introduction ............................................................................................. 21

Species Distribution and Prevalence .................................................................. 21

Spatial Patterns in Length Frequency Distributions ................................................ 22

Temporal and Spatial Trends in Length–Weight Relations ....................................... 23

Temporal and Spatial Trends in Abundance ......................................................... 24

Discussion and Management Implications ............................................................ 26

Chapter 4: Nonnative Species .............................................................................. 28

Introduction ............................................................................................. 28

Detection of Nonnative Species in Long Term Resource Monitoring Program Collections ... 28

Number of Nonnative Species and Their Relative Prominence .................................... 28

Spatial Patterns in Distribution ........................................................................ 29

Spatial Patterns in Numerical Abundance ............................................................ 29

Spatial Patterns in Biomass ........................................................................... 32

Nonnative Fishes of Concern in the Near Future ................................................... 33

Chapter 5: Species with Status ........................................................................... 34

Introduction ............................................................................................. 34

Summary of Observations by the Long Term Resource Monitoring Program .................. 34
iv

Federally listed fishes ........................................................................... 34

State listed fishes ................................................................................. 34

Discussion and Management Implications ............................................................ 34

Chapter 6: Nongame Species .............................................................................. 35

Introduction ............................................................................................. 35

Case study 1 ............................................................................................. 35

Case study 2 ............................................................................................. 35

Case study 3 ............................................................................................. 35

Discussion and Management Implications ............................................................ 39

Chapter 7: Exploited Species .............................................................................. 40

Introduction ............................................................................................. 40

Temporal Trends in Abundance ........................................................................ 40

Spatial and Temporal Patterns in Size Structure ................................................... 40

Establishment of Benchmarks of Resource State ................................................... 41

Chapter 8: Statistical Considerations and Evaluations ................................................ 42

Introduction ............................................................................................. 42

Spatial Inferences ....................................................................................... 42

Temporal Inferences .................................................................................... 42

Methodological Redundancies ........................................................................ 43

Chapter 9: Quality Assurance and Data Serving Objectives .......................................... 44

Introduction ............................................................................................. 44

Evolution of Quality Assurance Procedures ......................................................... 44

Serving Program Data to Partners and the Public ................................................... 44

Chapter 10: Future Directions .............................................................................. 46

A rationale for Continued Standardized Monitoring ................................................ 46

Toward an Integrated Understanding .................................................................. 46

Enhancing Management and Scientific Relevance ................................................... 47

Acknowledgments .......................................................................................... 48

References ................................................................................................... 48

Appendix A .......................................................................................... CD-ROM

Appendix B .......................................................................................... CD-ROM

Appendix C .......................................................................................... CD-ROM

Appendix D .......................................................................................... CD-ROM

Appendix E .......................................................................................... CD-ROM
v

Tables

Number Page

1.1 Key features of the floodplain and aquatic area compositions of the Long Term Resource 

Monitoring Program Mississippi and Illinois River study reaches. .............................. 5

1.2 Area (ha) of different sampling strata within each of the six study areas monitored by the 

Long Term Resources Monitoring Program on the Upper Mississippi River System ......... 7

1.3 Fish species listed by Federal and Upper Mississippi River State agencies .................. 11

4.1 Year and pathways of introduction and effect of nonnative fishes introduced into the Upper 

Mississippi River System arranged from historical to modern records. ........................ 30

4.2 Nonnative species collected for the Long Term Resource Monitoring Program on the Upper 

Mississippi River System .............................................................................. 31
vi

Figures

Number Page

1.1 Location of the six Long Term Resource Monitoring Program field stations on the Upper 

Mississippi River System. ........................................................................... 4

1.2 Graphical depiction of major changes to the sampling design and data continuity of the 

fisheries component of the Long Term Resource Monitoring Program. ........................ 6

1.3 Total number of fish species and mean annual number of species collected by the Long Term 

Resource Monitoring Program Fisheries Component, 1993–2002. ........................... 9

1.4 Percentage of total catch accounted for by the top ten numerically abundant fishes collected 

from each Long Term Resource Monitoring Program study area. .............................. 10

1.5 Total annual catch and number of fish species collected in the Upper Mississippi River by the 

Long Term Resource Monitoring Program Fisheries Component during routine monitoring 

activities over the period 1993–2002. ............................................................... 12

2.1 Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish 

community composition data collected by the Long Term Resources Monitoring Program, 

1994–2002. ............................................................................................. 16

2.2 Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish 

community structure data collected with day electrofishing by the Long Term Resources 

Monitoring Program, 1994–2002. .................................................................. 17

2.3 Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish 

community structure data, indexed by multiple gear and averaged across all Resource Trend 

Areas, collected by the Long Term Resources Monitoring Program, 1994–2002. ............ 19

3.1 Pie charts representing the results of variance decomposition for the relative abundance of 

stock length longnose gar and shovelnose sturgeon captured by electrofishing from five study 

areas in the Upper Mississippi River. ............................................................... 25

3.2 Results of multivariate analysis of variance for relative abundance of 24 selected fish species 

of stock size (see Gablehouse 1984) in the Upper Mississippi River System based on data 

from the Long Term Resource Monitoring Program, 1993-2002. .............................. 26

4.1 Percentage of total annual catch accounted for by nonnative species in the Long Term 

Resource Monitoring Program Fisheries Component, 1993–2002. ........................... 32

4.2 Percentage of total annual biomass accounted for by nonnative species in the catch of the 

Long Term Resource Monitoring Program Fisheries Component, 1994–2002. ............... 32

4.3 Raising a trammel net with bighead carp in Pool 26, Upper Mississippi River. ............... 33

6.1 Contribution of nongame, recreational, and commercial fish species to the total fish catch by 

the Long Term Resource Monitoring Program, 1993–2002. .................................... 35

6.2 Case study 1: Weed shiners in the Upper Mississippi River. .................................... 36

6.3 Case study 2: Habitat specialists versus habitat generalists. .................................... 37

6.4 Case study 3: The effect of habitat upon fish species distributions. ........................... 38

7.1 Heuristic example demonstrating how Long Term Resources Monitoring Program fish data 

can be applied for identifying management relevant changes in resource state. ............... 41
vii

Preface

The Long Term Resource Monitoring Program (LTRMP) was authorized under the Water Resources 

Development Act of 1986 (Public Law 99-662) as an element of the U.S. Army Corps of Engineers 

Environmental Management Program. The LTRMP is being implemented by the Upper Midwest 

Environmental Sciences Center, a U.S. Geological Survey science center, in cooperation with the five 

Upper Mississippi River System (UMRS) states of Illinois, Iowa, Minnesota, Missouri and Wisconsin. 

The U.S. Army Corps of Engineers provides guidance and has overall Program responsibility. The 

mode of operation and respective roles of the agencies are outlined in a 1988 Memorandum of 

Agreement.

The UMRS encompasses the commercially navigable reaches of the Upper Mississippi River, as 

well as the Illinois River and navigable portions of the Kaskaskia, Black, St. Croix, and Minnesota 

Rivers. Congress has declared the UMRS to be both a nationally significant ecosystem and a 

nationally significant commercial navigation system. The mission of the LTRMP is to provide 

decision makers with information for maintaining the UMRS as a sustainable large river ecosystem 

given its multiuse character. The long-term goals of the Program are to understand the system, 

determine resource trends and effects, develop management alternatives, manage information, and 

develop useful products. 

This multiyear report supports Task 2.2.8 as specified in Goal 2, Monitor and Evaluate Fish 

Communities, Guilds, and Populations of the Operating Plan (U.S. Fish and Wildlife Service 1993). 

This report was developed with funding provided by the LTRMP.
Multiyear Synthesis of the Fish Component 

from 1993 to 2002 for the 

Long Term Resource Monitoring Program

Brian S. Ickes1, Melvin C. Bowler2, Andrew D. Bartels3, Daniel J. Kirby2, Steven DeLain4, 

John H. Chick5, Valerie A. Barko6, Kevin S. Irons7, and Mark A. Pegg7

1

US Geological Survey

Upper Midwest Environmental Sciences Center

2630 Fanta Reed Road

La Crosse, Wisconsin 54603

2

Iowa Department of Natural Resources

206 Rose Street

Bellevue, Iowa 52031

3

Wisconsin Department of Natural Resources

2630 Fanta Reed Road

La Crosse, Wisconsin 54603

4

Minnesota Department of Natural Resources

1801 South Oak Street

Lake City, Minnesota 55041

5

Illinois Natural History Survey

8450 Montclair

Brighton, Illinois 62012

6

Missouri Department of Natural Resources

3815 East Jackson Boulevard

Jackson, Missouri 63755

7

Illinois Natural History Survey

704 N. Schrader Ave.

Havana, Illinois 62644

Abstract: Fisheries data are collected in the Upper Mississippi River System (UMRS) under the Long 

Term Resource Monitoring Program (LTRMP). From 1993 to 2002, monitoring activities were conducted 

at six primary study areas in the UMRS, Navigation Pools 4, 8, 13, 26, and the Open River reach in the 

Mississippi River and the La Grange Pool in the Illinois. The monitoring began with LTRMP in 1989 for all 

study areas except the Open River reach, which began in 1991. Since 1993, LTRMP staff have completed 

24,791 samplings in the 6 study areas and collected more than 3 million fish of 134 species. From 1993 to 

2002, the most fish species (98) were collected in the Open River reach and the fewest species (83) collected 

in Pool 13. 

Key words: Fish community structure, habitat, Illinois River, Long Term Resource Monitoring Program, 

Mississippi River, native species, nonnative species, stratified random sampling.
2

Chapter 1: Introduction

Purpose and Scope 

The Environmental Management Program 

(EMP) consists of two principal entities 

managed by the U.S. Army Corps of Engineers 

and implemented in cooperation with the five 

Midwestern states of Illinois, Iowa, Minnesota, 

Missouri, and Wisconsin, as well as several 

additional federal resource agencies. The 

first of these is the Habitat Rehabilitation and 

Enhancement Program (HREP), which is charged 

with physically rehabilitating aquatic habitats 

degraded by navigation development. The second 

is the Long Term Resources Monitoring Program 

(LTRMP), which is charged with monitoring 

key ecological components within the UMRS 

in support of natural resource management 

and science. The LTRMP is conducted by 

the U.S. Geological Survey, Upper Midwest 

Environmental Sciences Center (UMESC), 

through field stations operated by the five UMRS 

states. This report is a product of the LTRMP.

Key ecological components monitored by the 

LTRMP include fisheries, water quality, aquatic 

vegetation, and aquatic macroinvertebrates. 

Four long-term goals have been established for 

the LTRMP: (1) increase understanding of how 

the river ecosystem operates, (2) monitor the 

status and trends of UMRS natural resources, 

(3) assist in the development and evaluation of 

management alternatives, and (4) manage and 

provide access to resulting data, information, 

and products. The LTRMP is the nation’s largest 

river monitoring program and, since its inception 

in 1989, has amassed databases on large river 

ecology unrivaled in North America and perhaps 

the world. 

The purpose of this report is to synthesize and 

present information from the fisheries component 

of the LTRMP for the period 1993–2002. This 

is one of four such reports being developed for 

each of the four LTRMP ecological components. 

The primary audience for the report is natural 

resource managers and scientists within the 

UMRS basin. However, other audiences 

such as the general public, nongovernmental 

organizations, and academia will likely find 

much of this information useful and relevant.

The scope of this report is broad, yet 

necessarily synthetic. At its core, the LTRMP 

fisheries component monitors fish community 

dynamics in the UMRS basin. Reasons for this 

are presented in the following section titled 

“Monitoring Rationale”, and in greater detail 

in Chapter 2. Because of the LTRMP fish 

component’s community focus and large annual 

sample sizes (see “Summary of Collections” 

section in this chapter), however, the program has 

the ability to inform a wider set of management 

and scientific perspectives within the UMRS 

basin. For example, the program provides 

considerable information regarding status of 

individual species and trends in their abundance. 

However, the program cannot provide robust 

information for every species in the system 

because many species are less common and their 

abundance dynamics are consequently less well 

determined. In addition, many natural resource 

groups are presently concerned about invasive 

species within the basin. The community focus 

of the program and the methods used may be 

adequate for detecting new instances of invasive 

species within the system in monitored areas, 

but inadequate for accurately estimating relative 

abundance, determining population size structure, 

and delineating species distribution across the 

entire UMRS. Such situations should not be 

perceived as program deficiencies, but rather 

as benefits derived from a community-focused 

program. At a minimum, the program provides 

baseline information for a host of perspectives 

that can be exploited to develop studies specific 

to these important issues. 

In the following chapters, we highlight several 

perspectives important to resource management 

and scientific interests within the basin. We 

provide data summaries as baseline information 

for a variety of topics, and when possible, we 

present results of in-depth analytical work. For 

several topics in this report, much greater detail 

can be found in additional reports developed in 

parallel with this report (Barko et al. 2005; Chick 

et al. 2005; Irons et al. In press; Kirby and Ickes 

In press). 
3

Finally, it is important to realize the LTRMP 

is still relatively young and is in a transition 

period from critical evaluation and refinement 

of monitoring strategies to enhanced scientific 

understanding through focused analyses of the 

monitoring data. This report presents results 

from both program evaluations and focused 

research. Consequently, results presented from 

focused research represent the beginning of a 

new stage in the history of the program. We hope 

the material presented in this report, as well as 

in several accompanying reports, will provide 

baseline information for research planning and 

prioritization among LTRMP agency partners.

Monitoring Rationale

A general argument for monitoring

In a general sense, long-term monitoring is 

perhaps the most effective means, and in some 

cases the only means, by which large, complex 

ecosystems such as the UMRS can be studied and 

managed. This is because large, complex systems 

such as the UMRS do not have comparable 

ecological analogues and thus, do not easily 

lend themselves to traditional scientific methods 

(e.g., test and control subjects). Moreover, most 

large ecosystems have been modified to such an 

extent by human activity that no effective control 

systems exist. Consequently, changes in the 

state of the ecosystem can only be identified and 

investigated in the context of past observations 

on the same system, as opposed to differences in 

an experimental control. 

An argument for monitoring fishes within the 

Upper Mississippi River System 

The UMRS is probably the most biologically 

productive and economically important large 

floodplain river system in the United States 

(Patrick 1998; U.S. Geological Survey 1999), 

and fish are one of the most important goods 

and services the UMRS provides to humans 

(Carlander 1954). Fishes within the UMRS 

are the subject of commercial and recreational 

fisheries, both of which contribute substantially 

to local economies (Fremling et al. 1989). For 

example, recreation on the Upper Mississippi 

River was estimated to provide 18,000 jobs and 

$1.2 billion annually to the economy and fishing 

is a key component of recreation on the river 

(Carlson et al. 1995; Sparks et al. 1998). 

The UMRS is a nexus of freshwater fish 

diversity in North America. Approximately one 

fourth of the entire North American freshwater 

fish fauna is endemic to the UMRS basin. 

Numerous species are recognized as endangered, 

threatened or of particular conservation 

concern. Notable examples include paddlefish 

(Polyodontidae - Polyodon spathula), one of only 

two extant species of paddlefishes in the world, 

and three species of sturgeons (Acipenseridae), 

perhaps the most threatened family of freshwater 

fishes in the world. 

Scientists and fishery managers also recognize 

fish communities as an integrative index for a 

complex set of physical and biological conditions 

on the UMRS. Thus, fish communities, because 

of their diversity and response to environmental 

variation at multiple scales, are frequently used 

as indicators of ecological integrity for largeriver ecosystems (Gammon and Simon 2000; 

Schiemer 2000; Schmutz et al. 2000). Moreover, 

the general public often perceives environmental 

impacts in the UMRS in terms of changes in the 

fish community or habitat. 

Because of their economic importance, 

conservation potential, and utility for assessing 

the ecological integrity of the UMRS aquatic 

ecosystem, fishes were chosen as a key 

ecological component to be monitored by the 

LTRMP (Jackson et al. 1981; U.S. Fish and 

Wildlife Service 1993). Fisheries data thus 

collected are used to quantify the status and 

trends of fish populations and communities, 

identify relations with various other ecological 

attributes, and address fisheries management 

concerns in a multiuse, large-river resource 

(Gutreuter and Theiling 1999).

Study Area 

The UMRS was defined in the Water 

Resources Development Act as the Mississippi 

River between Minneapolis, Minnesota, and 

the mouth of the Ohio River at Cairo, Illinois 

(approximately 850 miles), and all commercially 
4

navigable tributaries, including all of the Illinois 

River. This definition excludes the Missouri 

River. The UMRS is one of most important 

natural resources in the United States, draining 

one-third of the landmass of the conterminous 

United States (approximately 713,500 mi2), and 

encompassing over 400,000 acres of water (Pitlo 

et al. 1995). To ensure dependable navigation, 

Congress authorized the 9-Foot Channel Project 

on the Upper Mississippi River in 1920. The 

project included 29 lock-and-dam structures 

between Minneapolis, Minnesota, and St. Louis, 

Missouri, with a total fall of approximately 

400 feet. 

The LTRMP conducts standardized monitoring 

activities at six primary study areas within 

the UMRS (Gutreuter et al. 1995). Five are 

located on the Mississippi River and one on 

the Illinois River (Figure 1.1). Study areas are 

referred to by the U.S. Army Corps of Engineers 

navigation pool designations wherein a pool 

name corresponds to the number of the dam 

impounding that pool. River miles on the UMR 

begin at the confluence with the Ohio River and 

on the Illinois River begin at the confluence 

with the Mississippi River. Mississippi River 

navigation pools monitored by the LTRMP 

fisheries component include Pool 4 (river miles 

752–797), Pool 8 (679–703), Pool 13 (523–557), 

Pool 26 (202–242), and an unimpounded, Open 

River reach (29–80, Figure 1.1). The remaining 

study area is La Grange Pool of the Illinois River 

(river miles 80–158; Figure 1.1). The LTRMP 

study areas were chosen, in part, to encompass 

important gradients in geomorphology, 

floodplain features, and navigation management 

strategies existing within the UMRS. 

Pools 4, 8, and 13 are located in an upper 

impounded reach characterized by high 

percentages of open water and aquatic vegetation 

and low agricultural use (Table 1.1). Relatively 

high percentages of the total aquatic area in 

these study areas are contiguous backwaters 

(i.e., connected to the main channel at base flow) 

with relatively low percentages of main 

channel habitat. Pools 4, 8, and 13 are 

geomorphically complex and contain 

braided side channels and backwaters. 

Pool 26, in a lower impounded 

reach, and the Open River study area 

are characterized by relatively low 

percentages of open water and aquatic 

vegetation and a high percentage of 

agriculture in the floodplain (Table 1.1). 

La Grange Pool is similar to Pool 26 in 

floodplain composition, but is similar 

to Pools 8 and 13 in composition of 

the aquatic area and has the greatest 

percentage (52%) of contiguous 

backwaters among the six LTRMP study 

areas (Table 1.1).

Within the LTRMP fisheries 

component, only contiguous bodies 

of water were sampled; there was no 

sampling in isolated water bodies. 

However, not all contiguous aquatic 

areas are sampled in a given pool (e.g., 

Black River mouth in Pool 8, Swan Lake 

in Pool 26) for various reasons decided 

early in the history of the program. The 

spatial sampling frame has remained 

Figure 1.1. Location of the six Long Term Resource Monitoring Program field 

stations on the Upper Mississippi River System.
5

static since established, however, and proper 

inferences are to the sampling frame rather than 

all aquatic areas present in a given study area.

Sampling Methods and Databases

History of Methodological Changes

To be effective and efficient, a monitoring 

program should use scientifically-defensible 

standardized sampling methods and target 

informative characteristics of the ecosystem. 

When designed properly, a long-term monitoring 

program can be a powerful method for 

quantifying the status and trends of key resources 

and for investigating the effects and efficacy 

of management actions. In addition, these data 

can be used to assess the monitoring design 

itself. Such assessments have occurred twice in 

the history of the LTRMP fisheries component 

resulting in the following changes to the 

monitoring protocol. 

The first major programmatic change was 

in 1993. The LTRMP fish component began 

fisheries monitoring in 1989 for all study reaches 

except the Open River reach, which began in 

1991. From 1989 until 1993, monitoring was 

conducted under a fixed-site design with sites 

revisited on an annual rotation. Consequently, 

inferences were site-specific and lack of 

randomization in the site selection process 

injected potential sampling bias into monitoring 

observations. These early years in the program 

were used to test sampling methods and refine 

sampling protocols. In 1993, the LTRMP 

fisheries component adopted a probabilistic 

statistical design for annual site selection known 

as stratified random sampling (SRS). Inferential 

scales shifted from sampling sites to an entire 

study reach, making pre-1993 and post-1993 

data largely incompatible (Figure 1.2). However, 

randomization in the new design ensured that 

any potential bias in sample site selection was 

removed from observed outcomes. This change, 

coupled with refined standardized protocols and 

sampling methods, significantly improved the 

scientific merit of the program (Gutreuter 1993).

The second major programmatic change 

was in 2002. After eight years of sampling 

under the significantly improved stratified 

random sampling design, data were sufficient to 

explore potential sampling redundancies. Ickes 

and Burkhardt (2002) concluded that 4 of 10 

sampling methods employed could be removed 

from the sampling protocols with minor effects 

on the quantity and quality of information 

provided by the fish component. These changes, 

implemented in 2002, represented about a 

33% reduction in sampling effort across the 

program. By redirecting that effort into scientific 

investigations on the monitoring data themselves, 

these changes have enhanced the fiscal and 

scientific efficiency of the program. We discuss 

this change in detail in Chapter 8.

To ensure comparability of the data and 

appropriate inference, the sampling design 

should be statistically rigorous, data collection 

should be continuous through time, and sampling 

should be scaled spatially to the system under 

study. Breaks in data continuity can affect 

assessments of status and trends, hamper efforts 

Table 1.1. Key features of the floodplain and aquatic area compositions of the Long Term Resource Monitoring Program Mississippi and 

Illinois River study reaches.

Floodplain compositiona (%)

Aquatic area 

compositionb (%)

Study reach

Floodplain 

area (ha)

Open 

water

Aquatic 

vegetation Agriculture

Contiguous 

backwater

Main 

channelc

Pool 4 28,358 51 10 12 21 11

Pool 8 19,068 40 14 1 31 14

Pool 13 34,528 30 9 28 29 25

Pool 26 51,688 13 1 65 17 54

Open River 105,244 10 1 72 2 79

La Grange Pool, Illinois River 89,554 16 2 60 52 21 a

 Data on floodplain composition are from Laustrup and Lowenberg (1994).

b

 Aquatic area is that portion of the floodplain that is inundated at normal summer water elevations. Data on the composition 

of aquatic areas are from the Long Term Resource Monitoring Program aquatic areas spatial database.

c

 Main channel includes area in the navigation channel and main channel border areas. 
6

to model ecological responses observed by the 

monitoring network, and reduce the ability to 

assess the efficacy of management alternatives. 

These were serious considerations in each of 

the above assessments. In the 1993 switch from 

fixed sites to SRS, data continuity was almost 

entirely severed (a few fixed sites carried through 

into the SRS era and remain today). Increased 

scientific rigor in the sampling design associated 

with this change, however, outweighed any cost 

of breaking what was yet a very short time series 

of fixed-site data. In the case of the 2002 gear 

reduction, data continuity back to 1993 (SRS 

era) was nearly perfectly preserved (Figure 1.2). 

Notable exceptions are breaks for the four 

gears eliminated and the two strata (impounded 

offshore and backwater contiguous offshore 

strata) sampled exclusively by two of the four 

gears eliminated. Assessments revealed, however, 

that the four gears and two strata did not provide 

any unique information not captured in other 

gears or strata (Ickes and Burkhardt 2002).

Statistical sampling design and general field 

methods

Sampling procedures for the LTRMP fish 

component are standardized and are based on 

commonly accepted methods. Collections are 

stratified over space and season. The basic unit 

of measurement is the individual sample, defined 

as all of the fishes collected during a single 

deployment of a sampling gear at a defined place 

and time. Community sampling requires the use 

of multiple sampling gears, because no single 

gear is effective at characterizing this diverse 

community. Although each gear differs in its 

selectivity for species and size classes (Ickes 

and Burkhardt 2002), each is standardized in its 

physical dimensions and the manner in which it 

is deployed and retrieved over time and across 

space (Gutreuter et al. 1995). A summary of 

LTRMP fisheries component procedures is 

provided below. A complete description can be 

found in Gutreuter et al. (1995).

In 1993, a discrete sampling frame (i.e., the 

aquatic areas making up the sampling universe, 

see Gutreuter 1993) was chosen for each study 

reach. The sampling frame was encoded into a 

spatial database using a Geographic Information 

System (GIS). The sampling frame was then 

stratified into several aquatic area types based on 

the LTRMP aquatic areas database. A database 

of these aquatic areas (termed sampling strata 

in the statistical design) was developed (Owens 

and Ruhser 1996), and each stratum was further 

partitioned into 50-m2 sampling grids using 

GIS software. Annually, sampling sites were 

randomly generated from this grid for three 

time periods: 15 June–31 July, 1 August–

15 September, and 16 September–31 October. A 

separate randomization was conducted for each 

sampling period. In addition, a number of historic 

LTRMP fixed-sites were retained from pre-1993 

sampling for each study area. 

Sampling was conducted in eight strata, 

although not all strata were present in all study 

areas (Table 1.2). These strata include contiguous 

backwater offshore, contiguous backwater 

shoreline, impounded offshore, impounded 

shoreline, main channel border unstructured, 

main channel border wingdam, side channel 

border and tailwater zone (fixed-site sampling 

only). Fish were collected using standardized 

gears described in Gutreuter et al. (1995). These 

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

SRS design with fewer gears

SRS design

Fixed site design

Figure 1.2. Graphical depiction of major changes to the sampling design and data continuity for the fisheries component of the Long Term 

Resources 

continuity relative to the previous design. Stratified random sampling (SRS) was instituted in 1993. To increase efficiency, the number of 

gears was reduced in 2002, but data continuity was maintained for all remaining gears.
7

gear types included day electrofishing, fyke nets, 

gill nets, hoop nets (large and small hoops), minifyke nets, night electrofishing, seines, tandem 

fyke nets, tandem mini-fyke nets, trammel nets, 

and trawls. 

Sampling gears were deployed independently 

within strata. Randomly selected sampling sites 

were generated for each gear type. Because 

some gears could not be deployed under certain 

conditions, not all gears were deployed in each 

stratum. However, each stratum was sampled 

with at least three gears. Because the proportions 

of different strata varied among study reaches, 

gear effort was allocated on a reach-specific basis 

(Gutreuter 1995). 

Optimal allocation schemes were considered 

during initiation of SRS protocols, but were 

abandoned because they required allocating most 

of the samples to the impounded stratum and 

neglected ecologically important strata such as 

side channels and backwaters. Sample allocation 

affects precision of estimates within and across 

strata, but does not bias observations derived 

under a stratified random sampling scheme. 

Since 1993, allocations of sampling effort among 

strata has not remained constant through time or 

among study reaches (Gutreuter 1995), because 

of budgetary constraints, implementation of 

program efficiencies (Ickes and Burkhardt 2002), 

and occasional equipment failure. Summaries 

of annual gear allocation and the number of 

completed samples for each study area are 

contained in Appendix A.1.

Database used in this report

Most of the results in this report are based on a 

10-year subset (1993–2002) of LTRMP fisheries 

database covering the first 10 years of stratified 

random sampling. The database is housed at the 

Upper Midwest Environmental Sciences Center 

(UMESC) and is available at: http://www.umesc.

usgs.gov/data_library/fisheries/fish1_query.html. 

Only individual fishes identified to species were 

included in our analyses. Common and scientific 

names used in this report follow Robins et al. 

(1991) and are listed in Appendix A.2. 

Statistical Methods

Mean catch-per-unit-effort (CPUE) was used 

as an index of population density (Ney 1999). 

The units of effort were specific to particular 

gears. Catch and effort were recorded for each 

species from individual samples. Whenever a 

species was not caught in a sample, the catch 

for that species in that sample was zero. The 

estimates of annual reach-wide mean CPUE were 

obtained from the conventional design-based 

estimator for stratified random samples (Cochran 

1977). The mean CPUE of stratified samples, 

st y , was given by

h

L

h h st N y N y ∑ = = 1

1 (1)

where Nh is the number of sampling units within 

stratum h, L is total number of strata, N = ΣL

h =1

Nh , and yh denotes the estimator of the simple 

Table 1.2. Area (ha) of different sampling strata within each of the six study areas monitored by the Long Term Resources Monitoring 

Program on the Upper Mississippi River System. 

Sampling 

strataa

LTRMP study area

Pool 4 Pool 8 Pool 13 Pool 26

La Grange 

Pool Open River

MCB-O 372 408 882 2508 1207 2500

MCB-S 192 189 228 800 1234 648

SCB 722 1037 690 1418 163 468

BWC-O 1268 495 1469 90 1737 0

BWC-S 965 859 934 191 904 0

IMP-O 0 3301 2501 147 0 0

IMP-S 0 124 110 43 0 0 a

 Strata are main channel border open (MCB-O), main channel border structured (MCB-S), side channel border (SCB), 

backwater contiguous offshore (BWC-O), backwater contiguous shoreline (BWC-S), impounded offshore (IMP-O), and 

impounded offshore (IMP-O). 
8

mean of y for stratum h. The estimator of the 

variance of st y is

( ) ( ) 













 = ∑ − =

h

h

h h

L

h h st n

s

N N n

N y s

2

2 1

2 1

 (2)

where 

 

( )

1

1

2

2

−

∑= − =

h

n

i h hi

h n

y y

s

h

is the usual estimator of the variance of yh , and 

nh is the number of samples taken in stratum h 

(Cochran 1977). The standard error of st y is

therefore s( st y ). For LTRMP fish monitoring, the 

sampling units were 50-m2 sampling grids.

In this report, CPUE statistics are reported 

primarily for stratified random sampling. In 

random samples, equation (1) yields unbiased 

estimates of the pooled means regardless of the 

probability distribution of y (Cochran 1977). An 

unbiased, reach-wide (all strata) mean CPUE 

and variance (standard error; equation 2) were 

calculated for bighead carp, black crappie, blue 

catfish, bluegill, common carp, channel catfish, 

emerald shiner, flathead catfish, freshwater 

drum, gizzard shad, largemouth bass, northern 

pike, sauger, smallmouth buffalo, walleye, white 

bass and white crappie from selected gear types. 

These species were previously identified as of 

specific interest to river managers. Mean CPUE 

and variance were calculated for shovelnose 

sturgeon from tailwater (fixed-site) trawling. For 

all species, a median CPUE was calculated over 

all years with 10% and 90% quartiles. Estimates 

for the species noted above are presented in 

Chapter 7 and in Appendixes B.1–B.26. 

Annual proportional stock densities (PSD; 

Anderson 1976) were calculated for black 

crappie, blue catfish, bluegill, channel catfish, 

common carp, freshwater drum, largemouth 

bass, northern pike, sauger, smallmouth 

buffalo, walleye, white bass, and white crappie 

(Appendixes C.1–C.15 and D.1–D.21) based 

on stock and quality lengths (Gabelhouse 1984) 

for selected study areas and gear combinations. 

PSDs were calculated for flathead catfish 

using size designations proposed by Quinn 

(1991). Eighty-percent confidence intervals 

were constructed for PSD estimates using the 

following formula from Gustafson (1988):

CI ( ) 100

2

1

1

(1 )

, 1

×

















 +



 





−

− = ± − N NP P

t α N

In the above formula CI = confidence interval 

estimate, P = estimated PSD as a decimal 

fraction, N = number of stock-length fish in the 

sample, t = Student’s t for α confidence level 

with N-1 degrees of freedom (two-tailed). 

Annual relative stock density indices 

(Gabelhouse 1984) of preferred-length (RSDP), memorable-length (RSD-M), and trophylength (RSD-T) were used to quantify the 

size distribution of selected fishes, and were 

combined with stock and quality length values to 

plot length-frequency histograms (Appendixes 

E.1–E.191). These length distribution analyses 

were performed for thirteen selected fish 

species and selected gear types: black crappie 

(day electrofishing, fyke nets, and all gears 

combined; all study areas), blue catfish (all 

gears combined; Pool 26 and the Open River),

bluegill (day electrofishing, fyke nets, and 

all gears combined; all study areas), channel 

catfish (small and large hoop nets and all gears 

combined; all study areas), common carp (day 

and night electrofishing and all gears combined; 

all study areas), freshwater drum (day and night 

electrofishing and all gears combined; all study 

areas), largemouth bass (day electrofishing and 

all gears combined; all study areas), northern 

pike (day electrofishing, fyke netting; Pools 4, 

8, and 13), sauger (day and night electrofishing, 

and all gears combined; Pools 4, 8, and 13 and 

La Grange Pool), smallmouth buffalo (large 

hoop nets and all gears combined; all study 
9

areas), walleye (day and night electrofishing 

and all gears combined; Pools 4, 8, and 13 and 

La Grange Pool), white bass (day and night 

electrofishing, and all gears combined; all study 

areas), and white crappie (day electrofishing, 

fyke nets, and all gears combined; all study 

areas). Similarly, relative frequency histograms 

for flathead catfish (large hoop nets and all gears 

combined; all study areas) were derived from 

length designations taken from Quinn (1991). 

Standard-length frequency histograms were 

produced for bighead and silver carp (all gears 

combined; Pool 26, Open River, and La Grange 

Pool) for all years combined. 

Summary of Collections

Systemic data summary

Since 1993, LTRMP staff have completed 

24,791 samples (Appendix A.1) in the six 

study areas and collected more than 3 million 

fish of 134 species (Appendix A.2 and A.3). 

Persistent high water in 1993 significantly 

disrupted sampling in the lower four study areas 

(70% of the allocated samples could not be 

completed in the Open River; Appendix A.1). 

From 1993 to 2002, the most fish species (98) 

were collected in the Open River with the fewest 

species collected in Pool 13 (83; Figure 1.3). 

Forty-seven fish species were common to all 

six study areas. Annual summaries for total 

number of fish species collected, percentage of 

the overall catch, and 10-year numeric ranks 

are contained in Appendixes A.4–A.9 for each 

study area. Emerald shiner was numerically the 

most abundant fish among all study areas and 

accounted for 27% of the total catch (Figure 1.4). 

Gizzard shad, bluegill, freshwater drum, mimic 

shiner, threadfin shad, white bass, common 

carp, spotfin shiner, and river shiner were the 

next most abundant fishes among all study areas 

(Figure 1.4). 

Pool 8 had the highest diversity of fishes 

annually, averaging 74 species per year 

(Figure 1.3). The Open River, which had 

the highest cumulative number of species in 

this 10-year span, had the lowest diversity of 

fishes annually, averaging 62 species per year 

(Figure 1.3). 

Of the 157 fish species listed as existing in the 

UMR by Pitlo et al. (1995), 127 were collected 

for the LTRMP fisheries component. LTRMP 

personnel collected seven fish species not listed 

by Pitlo et al. (1995): bigeye chub, bleeding 

shiner, greenside darter, redspotted sunfish, 

rudd, silver carp, and white perch. Riverine 

species such as Alabama shad, alligator gar, 

flathead chub, greater redhorse, pallid sturgeon, 

and sturgeon chub were listed as present in 

Pool 4 Pool 8 Pool 13 Pool 26 Open River La Grange

50

60

70

80

90

100

Number of species

86

68

89

74

83

65

89

65

98

62

86

67

Figure 1.3. Total number of fish species (solid bar) and mean annual number of species (hatched bar) collected by the 

Long Term Resources Monitoring Program Fisheries Component, 1993–2002.
10

the UMR by Pitlo et al. (1995), but have not 

been observed by the LTRMP. Alligator gar 

probably have been extirpated from the UMRS. 

Seventeen other fish species listed by Pitlo et al. 

(1995) not collected by the LTRMP likely exist 

only occasionally in the UMRS as strays from 

tributaries. Of the fish species collected for the 

LTRMP fisheries component, 39 are listed as 

endangered, threatened, special concern or rare 

by one or more of the five UMR states (Table 1.3, 

discussed in Chapter 5). 

Study reach summaries

In Pool 4, 701,507 fish of 86 species were 

collected from 1993 to 2002 (Appendix A.4). 

The number of species ranged from 58 in 2002 

to 74 in 1994 and averaged 68 species per year 

(Figure 1.5A). Emerald shiner was numerically 

the most abundant fish collected in Pool 4 and 

accounted for 74% of the total catch (Figure 1.4). 

Gizzard shad, bluegill, spotfin shiner, common 

carp, mimic shiner, white bass, black crappie, 

bullhead minnow, and freshwater drum were 

numerically the next most abundant fishes 

(Figure 1.4). 

In Pool 8, 535,275 fish of 89 species were 

collected from 1993 to 2002 (Appendix A.5). 

The number of species ranged from 65 in 2002 

to 77 in 1993 and averaged 74 species per year 

(Figure 1.5B). Bluegill was numerically the most 

abundant fish collected in Pool 8 and accounted 

for 15% of the total catch (Figure 1.4). Emerald 

4

27

23

8

4

4

3

3

3

3

74

5

3

3

2

2

1

1

1

1

15

14

13

7

7

4

4

4

3

3

19

17

15

11

7

6

2

2

2

2

2

2

2

2

2

3

3

3

3

3

4

4

4

4

5

5

5

5

5

5

6

6

7

13

11

27 11

27

41

49

Figure 1.4. Percentage of total catch accounted for by the top ten numerically abundant fishes collected from each Long Term Resources 

Monitoring Program study area and for all study areas combined, 1993–2002. Percentages rounded to nearest whole number.
11

Table 1.3. Fish species with status as listed by Federal and Upper Mississippi River state agencies.

Jurisdictiona

Fish species Federal Minnesota Wisconsin Iowa Illinois Missouri

Alabama shad T

American eel * SC* * *

Bigeye shiner E* *

Black buffalo * T* * * *

Blacknose shiner T E T

Blue catfish SC * *

Blue sucker SC* T* * * SC*

Bluntnose darter SC E E* * *

Brown bullhead * * * SC*

Burbot * * T

Central mudminnow * * * E

Chestnut lamprey * * T* * *

Crystal darter SC* E* X E

Flathead chub E E

Freckled madtom E* * *

Ghost shiner X * T*

Goldeye * E* * * *

Grass pickerel T* * *

Gravel chub SC E

Greater redhorse T E

Highfin carpsucker * * * T

Iowa darter * * E

Lake sturgeon SC* SC* E* E* E*

Longear sunfish T * *

Mississippi silvery minnow * * * * R*

Mooneye * * * * SC*

Mud darter * SC* * * *

Orangethroat darter T * *

Ozark minnow T

Paddlefish T* T* * SC*

Pallid shiner SC* E* X

Pallid sturgeon E E E E

Pearl dace E

Pirate perch SC* SC* SC * *

Pugnose minnow SC* SC* SC* * R*

Pugnose shiner SC T E E

Redfin shiner T

River darter * * * * SC*

River redhorse * T* T* *

Shovelnose sturgeon SC* * * * *

Sicklefin chub C * SC*

Silver chub * SC* * * *

Skipjack herring SC* E* * *

Speckled chub * T* * * *

Starhead topminnow E * T*

Sturgeon chub C E SC

Trout-perch * * * E*

Weed shiner * SC* E E

Western sand darter * SC* T* * T*

Yellow bass SC* * * * * a Listing codes are rare (R), endangered (E), threatened (T), extirpated (X), special concern (SC), or a candidate (C) for 

endangered status in the Mississippi River. An asterisk (*) means the species was collected in that state at some time by the 

Long Term Resource Monitoring Program.

shiner, spotfin shiner, mimic shiner, bullhead 

minnow, river shiner, gizzard shad, white bass, 

black crappie, and shorthead redhorse were 

numerically the next most abundant fishes 

(Figure 1.4). 

In Pool 13, 498,635 fish of 83 species were 

collected from 1993 to 2002 (Appendix A.6). 

The number of species ranged from 59 in 1996 

and 2002 to 69 in 1999 and 2000 and averaged 

65 species per year (Figure 1.5C). Emerald shiner 

was numerically the most abundant fish collected 

in Pool 13 and accounted for 19% of the total 

catch (Figure 1.4). Mimic shiner, bluegill, 

river shiner, gizzard shad, freshwater drum, 
12

12,684

34,099

51,476

35,704

74,179

140,797

61,008

36,788

118,357 136,415

74

60

71 67

72 73

67 66 66

58

0

50,000

100,000

150,000

200,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

80

90

Number of species

N=701,507

28,407

46,710

63,568

54,610

83,187

48,351 52,003

21,486

67,481 69,472

75

65

75

71 74 74 72

77 75 75

0

20,000

40,000

60,000

80,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

80

Number of species

N=535,275

19,408

53,317 49,894

44,431

50,030 53,055 55,914

46,448

89,406

36,732

59

66

69

59 61 61

65 65 67 69

0

20,000

40,000

60,000

80,000

100,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

Number of species

N=498,635

A

Pool 4

C

Pool 13

Figure 1.5. Total annual catch (bars) and number of fish species (line) collected in (A) Pool 4, (B) Pool 8, 

(C) Pool 13, (D) Pool 26), (E) Open River, and (F) La Grange Pool, Upper Mississippi River, by the Long Term 

Resources Monitoring Program’s Fisheries Component during routine monitoring activities, 1993–2002. N 

is total catch over all years. In 2002, sampling was modified by eliminating night electrofishing, seines, 

tandem fyke nets, and tandem mini-fyke nets.

B

Pool 8
13

10,901

23,573

29,281 31,173

25,979

29,392 27,645

19,437

47,248

28,516

66 62 62 63 66 66

63 64

59

70

0

10,000

20,000

30,000

40,000

50,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

80

Number of species

N=273,145

12,070 11,647

18,977

10,695

19,452

13,114

15,186

11,230 10,313 10,337

64 62 60 58 58

66 64

59

63

56

0

5,000

10,000

15,000

20,000

25,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

Number of species

N=133,021

29,072

45,053

102,464 98,507

166,215

77,834

64,519

168,185

187,047

90,130

65

75

64 62

69 68 66

62

71 67

0

50,000

100,000

150,000

200,000

250,000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Total catch

0

10

20

30

40

50

60

70

80

Number of species

N=1,029,026

D

Pool 26

E

Open

River

F

La Grange 

Pool
14

was numerically the most abundant fish collected 

in the Open River and accounted for 27% of the 

total catch (Figure 1.4). Freshwater drum, black 

crappie, channel catfish, emerald shiner, channel 

shiner, common carp, red shiner, bluegill, and 

white bass were numerically the next most 

abundant fishes (Figure 1.4). 

In La Grange Pool of the Illinois River, 

1,029,026 fish of 86 species were collected from 

1993 to 2002 (Appendix A.9). The number of 

species ranged from 62 in 1994 and 2000 to 

75 in 1998 and averaged 67 species per year 

(Figure 1.5F). Gizzard shad was numerically 

the most abundant fish collected in La Grange 

Pool and accounted for 49% of the total catch 

(Figure 1.4). Threadfin shad, emerald shiner, 

bluegill, white bass, common carp, freshwater 

drum, black crappie, smallmouth buffalo, and 

channel catfish were numerically the next most 

abundant fishes (Figure 1.4). 

largemouth bass, bullhead minnow, white bass, 

and black crappie were numerically the next most 

abundant fishes (Figure 1.4). 

In Pool 26, 273,145 fish of 89 species were 

collected from 1993 to 2002 (Appendix A.7). 

The number of species ranged from 59 in 2002 

to 70 in 1995 and averaged 65 species per year 

(Figure 1.5D). Gizzard shad was numerically 

the most abundant fish collected in Pool 26 and 

accounted for 41% of the total catch (Figure 

1.4). Emerald shiner, common carp, channel 

shiner, channel catfish, freshwater drum, bluegill, 

western mosquitofish, white bass, and river 

shiner were numerically the next most abundant 

fishes (Figure 1.4). 

In the Open River reach, 133,021 fish of 

98 species were collected from 1993 to 2002 

(Appendix A.8). The number of species ranged 

from 56 in 2002 to 66 in 1995 and averaged 

62 species per year (Figure 1.5E). Gizzard shad 
15

Chapter 2: Community Ecology

Introduction

Rivers are fundamentally hierarchical in 

their physical organization (Northcote 1988; 

Tarboton et al. 1988), resulting in multiple 

scales of organization. Thus, investigations on 

large rivers must inherently deal with issues 

of spatial and temporal scales coinciding with 

scales of physical organization in the river itself. 

This hierarchy derives from many sources of 

both natural (e.g., geomorphology, latitudinal 

gradients in temperature, regional differences 

in precipitation rates) and anthropogenic 

origin (e.g., changes in land use, mainstem 

impoundment, various river engineering 

structures). Both natural and anthropogenic 

sources are important in the present day physical 

organization of the UMRS.

Fish communities in large-river ecosystems 

respond to environmental variation at multiple 

scales (Gammon and Simon 2000; Schiemer 

2000; Schmutz et al. 2000). Therefore, the 

ability to detect fish community responses at 

multiple scales is a desirable feature of long-term 

monitoring programs in large rivers. The LTRMP 

fish monitoring design permits investigations 

of fish community responses at several spatial 

scales (e.g, the entire system, regional groups 

of study areas, each study area, and sampling 

strata within and among study areas), as well as 

two temporal scales (e.g., annual and seasonal 

scales). Understanding how communities are 

similar or different at these various scales is 

critical for evaluating stressors on fisheries 

resources throughout the UMRS. Understanding 

how communities respond to ecosystem changes 

over time is critical for adaptive management of 

the UMRS. 

In this chapter we present key findings from 

several community-based investigations recently 

completed. These investigations address several 

different spatial and temporal scales and focus 

on several different aspects of fish communities. 

These studies are exploratory in nature, seeking 

to identify patterns in fish communities across 

the UMRS, and to associate observed patterns 

with environmental characteristics. Results from 

these initial analyses can provide direction for 

more detailed modeling and research.

Spatial Patterns in Species Richness

Species richness is defined as the number of 

species in a given area, and thus represents a 

simple measure of diversity. Species richness is 

generally impossible to know precisely because 

rarer species are less likely to be observed than 

more abundant species (Legendre and Legendre 

1998). Thus, species richness is typically 

estimated using a combination of theoretical 

models and field observations. 

Koel (2004) investigated spatial patterns in 

species richness using LTRMP fish component 

data and rarefaction models that estimate 

species richness as a function of sample size. 

The investigation focused on two spatial scales: 

differences in species richness among the six 

LTRMP study reaches and among macrohabitat 

classes (e.g., sampling strata) independent of 

study reach. Koel (2004) found species richness 

to differ significantly among macrohabitat 

classes. Species richness was highest in 

contiguous backwater habitats and lowest in main 

channel border habitats. Among study reaches, 

Koel (2004) found Pools 4, 8, and 13 exhibited 

significantly greater species richness than Pool 

26, La Grange, or the Open River study areas. 

Investigation of species evenness and diversity 

indices demonstrated results similar to species 

richness. Native fish species richness and habitat 

diversity were significantly and positively related. 

Koel (2004) recommended Pools 4, 8, and 13 

should serve as relative reference conditions for 

restoration efforts aimed at enhancing species 

richness throughout the UMRS.

Community Composition

Community composition is measured as the 

presence or absence of a species in a community 

sample. As such, community composition data 

are binary and provide no information on the 

relative importance of individual species in the 

community. However, community composition 

data are useful for describing similarities or 
16

differences in overall assemblage composition 

among locations. 

Chick et al. (2005) investigated patterns in 

UMRS fish community composition using 

a nonparametric form of indirect ordination 

known as nonmetric multidimensional scaling 

(NMDS). They found notable spatial variation 

in community composition at two scales; among 

individual study areas, and among regional 

groupings of study areas. Among year (temporal) 

variation was only marginally significant in 

explaining patterns in community composition 

and its effect was small in comparison to effects 

attributable to spatial differences. Spatially, the 

greatest variation was between upper (Pools 4, 

8, and 13) and lower (Pool 26, Open River, and 

La Grange Pool) regional groups of study areas 

(Figure 2.1). Also, community composition 

was more similar among the three upper study 

areas than among the lower three study areas 

(Figure 2.2). 

These results suggest community composition 

varies most at the largest spatial scales 

investigated in this study (e.g., among regional 

groupings of LTRMP study areas). Moreover, 

the northern study areas share most of the 

species comprising each of their communities. 

Conversely, the southern study reaches differ 

notably from one another and the northern study 

areas in species comprising their respective 

communities. These results are supported by the 

results of spatially expanded sampling conducted 

in 2000 (Chick and Pegg 2004). 

Community Structure 

Community structure is measured as the 

relative abundance of species in a community 

sample (Legendre and Legendre 1998). Thus, 

data are counts (e.g., catch per unit effort) 

used to characterize the relative importance 

of each species in the overall community. 

Species are much more likely to change in 

abundance within the overall community than in 

whether they are present or not. Consequently, 

community structure is generally more variable 

than community composition, both spatially 

and temporally (Legendre and Legendre 

1998). This variability can arise, for example, 

from differential population rate processes, 

environmental variation, and harvest. Thus, 

community structure data tend to be much more 

04

08

13

26

LG

OR

04-1994

04-0404- 1997 -11996 995

04-1998 04-1999

04-2000 04-2001

04-2002

08-1994

08-081995 08-1-1999976 0808-199-1999 8

08-208-002001 0 08-2002

13-1994 13-1995

13-1996 13-13- 19971998 13-1999

13-2000

13- 13-2001 2002

26-1994

26-19926- 6 1997 26-1995

26-1998

26-1999 26-2000

26-2001

26-2002

LG-1994

LG-1995

LG-1996

LG-1997

LG- LG-1999 1998 LG-2000

LG-2001

LG-2002

OR-1994

OR- OR-1995 1996

OR-1997

OR-1998

OR-1999

OROR- -20002001

OR-2002

Stress: 0.06 4

 8

13

26

LG

OR

Stress: 0.06

Figure 2.1. Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish community composition data 

(presence/absence) collected by the Long Term Resources Monitoring Program, 1994–2002. Ecological similarity was measured 

by Euclidean Distance. The upper Resource Trend Areas (Pools 4, 8, and 13) are represented by open symbols whereas the lower 

Resource Trend Areas (Pool 26, Open River, and La Grange Pool) are represented by shaded symbols. (Chick et al. 2005)
17

informative than community composition data 

for scientific investigations into factors that are 

correlated with changes in biotic communities.

Chick et al. (2005) investigated spatial and 

temporal patterns in UMRS fish community 

structure and relations between observed patterns 

and various environmental factors. They focused 

on identifying patterns across the entire sampled 

UMRS, regional groups of study areas, sampling 

strata, and time. They reported that community 

structure of UMRS fishes varies more in 

space than in time, and that results suggested 

a hierarchy of spatial dynamics. Observations 

grouped first according to large scale differences 

between northern (e.g., Pools 4, 8, and 13) and 

southern (e.g. Pool 26, Open River, and La 

Grange Pool) study areas, then at smaller scales 

including individual study areas or sub-groups 

of study areas, and finally according to sampling 

strata. Temporal patterns were largely limited to 

variations within spatial groupings. 

Barko et al. (2005) also investigated patterns 

in UMRS fish community structure and relations 

with environmental factors. However, in contrast 

to Chick et al. (2005), Barko et al. (2005) 

focused mainly on identifying patterns among 

sampling strata and over years within each 

study area independently. Consequently, greater 

detail is provided at these scales than in Chick 

et al. (2005). Barko et al (In press) reported 

that backwater contiguous environments are 

dominated by Centrarchidae throughout most 

of the system, although species composition 

within backwaters changes from north to south. 

Side channel environments differed notably in 

their community structure among study areas 

depending on the condition and connectivity 

of side channels. Community structure also 

differed notably among main channel border 

environments. For example, among the northern 

study areas where substrates are mostly sand and 

the banks are not riprapped, community structure 

was similar. In the southern study areas, where 

main channel border substrates and physical 

composition differ more notably, community 

structure patterns also differed, likely reflecting 

different habitat quality, suitability and selectivity 

for different groups of species. Catostomidae was 

the only family abundant among main channel 

border wingdam environments in the northern 

study areas. However, in the southern study 

areas, where discharge is more variable, and 

especially in the Open River reach where wing 

dikes are usually emergent, green sunfish and 

4

8

13

26

LG

OR

Stress: 0.13

Figure 2.2. Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish community structure data 

(square root catch / 15 minutes) collected with day electrofishing by the Long Term Resources Monitoring Program, 1994–2002. 

Ecological similarity was measured by Bray-Curtis Similarity. The upper Resource Trend Areas (Pools 4, 8, and 13) are represented 

by open symbols whereas the lower Resource Trend Areas (Pool 26, Open River, and La Grange Pool) are represented by shaded 

symbols. (Chick et al. 2005)
18

blue catfish dominated wingdam environments. 

Impounded environments yielded no significant 

or interpretable patterns in fish community 

structure.

The results of Chick et al. (2005) and Barko 

et al. (2005) lead to the overall conclusion that 

community structure patterns in the UMRS are 

largely spatially determined. This conclusion is 

supported by observed similarities among the 

northern study areas and among the southern 

study areas, but notable differences in community 

structure between northern and southern regional 

groupings. Temporal dynamics investigated by 

Barko et al. (2005) were generally study areaspecific, reinforcing the conclusion of Chick et 

al. (2005) that temporal dynamics occur within 

hierarchical spatial constraints. In other words, 

how fish assemblages are structured in different 

portions of the UMRS and how each of these 

assemblages respond to changes in environmental 

conditions over time, is likely a reflection of 

both historical (e.g., regional geomorphology, 

historical dispersal routes, zoogeographic 

constraints) and contemporary (e.g., impediments 

to contemporary dispersal routes, human altered 

environments) factors that have shaped fish 

communities in the UMRS. We discuss the 

implications of these findings in greater detail at 

the end of this chapter.

Community Patterns in Relation to Habitat 

Factors

At the study area scale, Barko et al. (2005) 

investigated associations between fish community 

patterns and several environmental variables 

measured concurrently with LTRMP fisheries 

collections (see Table 3 in Barko et al. 2005). 

Water velocity and transparency were important 

in explaining fish community structure patterns 

among sampling strata classes in each of the 

six study areas, suggesting future work should 

focus on spatial patterns and on the interplay 

between water velocity and transparency 

among contiguous environments, perhaps best 

conceptualized as hydraulic retention differences 

among sampling strata classes. 

Water surface elevation helped to explain 

differences in community structure among 

sampling strata classes in Pools 8, 26, and 

La Grange Pool, but was less important in 

the other three reaches. Temperature was 

important in Pools 8 and 13 and La Grange Pool. 

Associations between fish community patterns 

and several physical features (e.g., closing dams, 

snags, riprap, etc...) within the river environment 

(Gutreuter et al. 1995) were also investigated. 

Generally, in the northern reaches, several 

structures influenced abundance patterns in both 

adult and age-0 fishes. In the lower three study 

areas, a shift towards a predominant structure 

within each reach was observed. Differences in 

structure associations among study areas likely 

reflect differences in structure availability, and 

differences among size classes of fish likely 

result from predator-prey interactions. 

At the UMRS scale, Chick et al. (2005) 

reported that fish community structure patterns 

were well described by several environmental 

variables. The greatest correlation (R = 0.76) 

was observed with a combination of Secchi 

depth (water transparency), water temperature, 

water velocity, and vegetation abundance. The 

northern study areas had greater abundance of 

aquatic vegetation and greater Secchi depths. The 

southern study areas had faster water velocity 

and higher temperature. These differences were 

closely associated with differences in community 

structure between the northern and southern 

study areas.

Community Dynamics in Relation to Floods 

Chick et al. (2005) and Barko et al (2005) also 

speculated on the role of floods in community 

dynamics. Chick et al. (2005) suggested that 

temporal patterns in fish community structure 

observed in their study may reflect an effect of 

the 1993 flood because structure was notably 

different in 1994 and 1995 from all other 

years. Observations made during the 1993 

flood suggest several fishes took advantage 

of increased access to floodplain habitats for 

feeding and reproduction, and many appeared 

to produce exceptional year-classes (National 

Biological Service et al. 1994). Many species 

such as common carp, freshwater drum, and 

black crappie, exhibited their peak abundance in 
19

1994, possibly as a lagged result of successful 

reproduction in 1993 (Figure 2.3). Other species 

had their lowest abundance in 1994, possibly 

as a result of dramatic changes in habitat 

associated with the flood, such as reductions 

in aquatic vegetation (Spink and Rogers 1998) 

and sedimentation effects (National Biological 

Service et al. 1994). Although these annual 

changes were large for some species, the 

overall difference in fish community patterns 

was relatively small, further emphasizing the 

importance of spatial rather than temporal factors 

in structuring fish communities in the UMRS.

In their study area-specific investigation, Barko 

et al. (2005) reported both age-0 and adult fish 

assemblages were affected by flood events of 

varying magnitude in every study area. Results 

suggested age-0 fishes inhabiting the southern 

study areas were more affected by flood events 

in 1993, 1994, 1995, and 2001 than age-0 fishes 

inhabiting the northern study areas. For the 

adult community, a flood year, or a lagged effect 

of a flood year, was associated with changes 

in nearly every study area. Adult assemblages 

within each of the six study areas differed most 

in 1993 or 1994. Barko et al. (2005) reported the 

effects of the 1993 flood resonated through 1994, 

suggesting lag effects as reported by Chick et al. 

(2005). 

Discussion and Management Implications 

Fish communities are frequently regarded 

as indicators of ecological integrity for largeriver ecosystems because of their diversity and 

response to environmental variation at multiple 

scales (Gammon and Simon 2000; Schiemer 

2000; Schmutz et al. 2000). Recent work, 

highlighted above, has attempted to identify 

how fish communities differ across the UMRS, 

Figure 2.3. Nonmetric Multidimensional Scaling ordination for Upper Mississippi River System fish community structure data, indexed 

by multiple gear and averaged across all Resource Trend Areas, collected by the Long Term Resources Monitoring Program, 1994–2002. 

Ecological similarity was measured by Bray-Curtis Similarity. Species scores are overlaid on the ordination for nine species (A = Common 

Carp, B = Black Crappie, C = Freshwater Drum, D = Bluegill, E = Channel Catfish, F = Emerald Shiner, G = Smallmouth Buffalo, H = 

Gizza 

abundance.

A. Common Carp B. Black Crappie C. Freshwater Drum

D. Bluegill E. Channel Catfish F. Emerald Shiner

G. Smallmouth Buffalo H. Gizzard Shad I. White Bass

Stress 0.02 Stress 0.02 Stress 0.02

Stress 0.02

Stress 0.02 Stress 0.02 Stress 0.02

Stress 0.02 Stress 0.02

1994 1994 1994

1994

1994 1994

1994

1994

1994

1996 1996 1996

1996 1996 1996

1996 1996 1996

2000 2000 2000

2000 2000 2000

2000 2000 2000

2001 2001 2001

2001 2001

2001 2001

2001

2001

1998 1998 1998

1998 1998 1998

1998 1998 1998

1997 1997 1997

1997 1997 1997

1997 1997 1997

1995 1995 1995

1995 1995

1995 1995 1995

1995

1999 1999 1999

1999 1999 1999

1999 1999 1999

2002 2002 2002

2002 2002 2002

2002 2002 2002
20

what environmental factors are associated with 

those differences, and how communities respond 

over time. Invariably, investigators have found 

the most important differences in UMRS fish 

communities occur over large spatial scales. 

Koel (2004) demonstrated that greater species 

richness, evenness, and diversity typically occur 

in the northern reaches of the UMRS rather than 

the southern reaches. He also found important 

differences among macrohabitat classes, with 

greater species richness in off-channel classes 

such as backwaters and side channels than in 

main channel environments. The results of 

Chick et al. (2005) and Barko et al. (2005) echo 

these findings, but provide more insight into 

the mechanics underlying observed patterns by 

providing details on how environmental factors 

are associated with patterns in fish communities. 

Clarifying community patterns and dynamics 

in the UMRS is not a trivial task. Several factors 

make analyses such as those presented above 

challenging. First, the multiple gears used in 

LTRMP fish sampling differ in their selectivity 

for different species and size classes of fish. We 

are uncertain how this affects our inferences 

about fish community dynamics. Chick et al. 

(2005) proposed a methodology for using all 

of the information from all of the gears. This 

approach initially appears somewhat robust, 

but additional research is needed in this area. 

In addition, LTRMP gears do not sample deep 

channel habitats. We are uncertain if information 

from these habitats would modify our inferences. 

Second, because of the size of the UMRS, several 

species reach the northern or southern limits of 

their range within the UMRS. Such geographic 

range limitations can potentially confound results 

and must be carefully considered. Alternatively, 

the potential for analyses based on functional 

groups rather than species should be explored. 

Third, habitat factors and possibly contemporary 

or historical barriers to migration likely influence 

differences in fish composition and community 

structure between upper and lower reaches. More 

directed study is needed to assess the significance 

of these features. Finally, results of Barko et 

al. (2005) suggest juvenile fishes differ notably 

from adults in their community level responses. 

Consequently, future studies should consider 

partitioning UMRS fish communities by size 

classes to control for such differences.
21

Chapter 3: Single Species Ecology

Introduction

Fundamentally, communities of organisms 

are composed of numerous single species that 

can interact in complex ways through a variety 

of abiotic and biotic compensatory mechanisms 

(e.g., environmental change, predator–prey 

interactions, inter- and intra-specific competition, 

differential growth, recruitment, and mortality.). 

How species respond to system dynamics over 

time can tell us a lot about the nature of the 

population we are studying and the portion of 

the UMRS within which we are studying it (Van 

Den Avyle and Hayward 1999). For example, 

is abundance largely invariant over time, 

suggesting stable populations, or is it highly 

dynamic, suggesting instability? Moreover, do 

different areas of the UMRS contain populations 

that respond similarly, suggesting that some 

common factors shared among the study areas 

are associated with single species dynamics, 

or do some species differ among study areas, 

suggesting that different factors are operating at 

different scales on each population? Answering 

such questions has important implications for 

identifying and informing rehabilitation efforts 

within the UMRS basin. 

Thus, single species dynamics can be 

powerful indicators of environmental stresses 

in the system, and analysis of these dynamics 

can identify species that would serve as good 

indicators for rehabilitation efforts. Such an 

approach also aids in “weeding out” what factors 

could be operating on different species within the 

system and permits informed modeling efforts 

that attempt to explain what factors are associated 

with changes in abundance. Such models will 

be important components in the development of 

effective management tools.

In the sections below, we present results and 

conclusions from a series of single species 

studies recently conducted by the LTRMP. 

The first section quantifies measures of 

“commonness” for all species observed in 

LTRMP collections. Species “commonness” 

(e.g., prevalence) data are simplistic, but provide 

important and critical insight into systemic 

processes that shape fish communities. The 

second section presents results from an analysis 

of spatial patterns in length frequency distribution 

and proposes competing hypotheses that could 

account for observed differences. The third 

section presents results from an investigation 

into spatiotemporal patterns in length/weight 

relations. Spatial differences in length–weight 

relations may provide insight into mechanisms 

responsible for among area variability in 

additional population parameters, such as 

density dependent resource limitation, whereas 

temporal differences in length–weight relations 

provide insight into environmental conditions 

(e.g., water levels, climate) providing optimum 

conditions for fish growth and health. The fourth 

section presents key results and conclusions 

from an investigation into how single-species 

abundance varies among years, among study 

areas, and among aquatic areas (e.g., backwaters, 

main channel, and side channels) for a host of 

species in the UMRS. This approach provides an 

opportunity to assess the presence and relative 

importance of longitudinal (i.e., among study 

area), lateral (i.e., among aquatic area), and 

temporal (i.e., among year) variation in the 

relative abundance dynamics of numerous UMRS 

fish species. Quantifying how fish populations 

vary across space and time can isolate factors 

responsible for observed patterns in fish 

abundance, aiding future abundance modeling 

efforts, and assisting fisheries managers in the 

identification of species most likely to respond to 

local habitat modifications, climatic variability, 

or degradation of specific types of aquatic areas. 

Our final section discusses the management 

implications of our findings. 

Species Distribution and Prevalence

Where (distribution) and how often 

(prevalence) a species is present can provide 

important insight into processes shaping fish 

communities. Fish communities are, in part, a 

reflection of habitat availability and the habitat 

requirements of individual species. The presence 

of a species in one study area and absence in 

another suggests the species habitat requirements 
22

are being met in the first instance, but perhaps 

not the latter. 

Kirby and Ickes (In press) classified UMRS 

fish species based on quantitative measures 

of their distribution and their prevalence in 

standardized LTRMP monitoring collections. 

Whereas previous attempts have been made to 

develop a similar classification system, they 

were based on disparate data sources developed 

using nonstandardized methods (e.g., Smith et al. 

1971; Rasmussen 1979; Pitlo et al. 1995). Thus, 

Kirby and Ickes (In press) offer an alternative 

classification system not suffering from some 

of the concerns inherent in previous systems, 

but also is somewhat more spatially constrained 

(e.g., based on data from six LTRMP study areas 

rather than all river reaches). 

Kirby and Ickes (In press) classified species 

based on criteria presented in Appendix A.2. 

Detailed information on species distribution and 

prevalence in standardized monitoring collections 

can be found in Kirby and Ickes (In press, 

Table 2).

Of the 134 species detected by LTRMP from 

1993 to 2002, 18 were present in all years 

of sampling in all study areas and classified 

as common and widespread in the UMRS. 

Twenty-nine additional species were captured 

in all study areas, but not in all years, and were 

classified as widespread. The remaining species 

were observed only in particular regions or study 

reaches and considered uncommon, rare, or 

strays (Kirby and Ickes In press).

The 47 species exhibiting a widespread 

distribution in the UMRS represent taxa with 

the ability to survive in a wide variety of habitat 

conditions. The loss of any of these species from 

a reach of the UMR would be indicative of major 

habitat alteration or degradation. Alternatively, 

species particular to certain areas within the 

UMRS will likely serve as important indicator 

species for environmental conditions within 

their area of distribution. Such species tend to be 

more specific in their habitat requirements than 

the widely distributed species, and thus can be 

used to gauge changes in the system that may 

not be reflected in the widely distributed species 

class. This point is important because these less 

common species represent a significant fraction 

of the overall diversity of ichthyofauna in the 

UMRS. 

Spatial Patterns in Length Frequency 

Distributions

Length frequency distributions are sensitive 

empirical indicators of population size structure. 

The form of the length frequency distribution is 

often used as a first order indicator of population 

health. For example, a population with a broad, 

smooth length frequency distribution is indicative 

of a population that is recruiting year classes 

regularly, as well as a population exhibiting 

sufficient survival rates to maintain itself. 

Alternatively, a length frequency distribution 

that is truncated suggests size-selective mortality 

(e.g., as occurs with selective harvest) whereas an 

irregular one (e.g., missing size classes) suggests 

periodic recruitment failure. Such profiles are 

important population characteristics used to 

assess the health of the population under study.

Kirby and Ickes (In press) tested for differences 

among LTRMP study areas in length frequency 

distributions for several UMRS recreationally 

and commercially exploited fish species. Details 

on methods and results can be found in Kirby and 

Ickes (In press). In general, spatial differences 

in length-frequency distributions were most 

pronounced for common carp, flathead 

catfish, shortnose gar, and smallmouth buffalo 

(commercial species) and least pronounced for 

black crappie, bluegill, largemouth bass, sauger, 

and white bass (recreational species). Also, with 

the exception of bowfin, all species examined in 

the most northerly study area (Pool 4) exhibited 

length-frequency distributions composed of a 

higher proportion of large fish than in the other 

study areas. Commercial species in the southern 

reaches, where commercial fishing effort is 

greatest, had lower proportions of large fish than 

commercial species in the northern study areas. 

So why should commercial and recreational 

species differ in their length frequency 

distributions? Two alternative hypotheses are

Ho1: Commercial species generally attain 

a greater maximum length and age than 

recreational species, which magnifies the 
23

effects of subtle environmental differences 

affecting reproduction, recruitment, growth, 

and mortality when comparing among study 

areas, and 

Ho2: Commercial exploitation influences the 

length-frequency distribution of populations 

and harvest is different among study areas, 

but recreational exploitation either has 

comparatively little influence on size structure 

or is relatively similar among study areas. 

One or a combination of both of these 

mechanisms could explain observed differences 

in size structure among study areas. Previous 

studies suggest that commercial fishing has the 

ability to truncate size structures in large river 

fish populations (Mestl 1999; Timmons and 

Hughbanks 2000; Travnichek and Clemons 

2001), however, it is inconclusive whether this 

is the mechanism operating on the results of 

Kirby and Ickes (In press). For example, reachspecific growth rates can also contribute to 

spatial differences is size structure within a river 

system (Kirby 2001). Additional research into the 

reproduction, recruitment, growth, and mortality 

of species exhibiting significant spatial variability 

in size structure will be required to further 

discriminate between the competing hypotheses.

Temporal and Spatial Trends in 

Length–Weight Relations

Length–weight relations of fish populations are 

helpful for determining body condition or general 

“well-being” and can be a robust predictor of 

growth (Anderson and Neumann 1996). Methods 

generally use linear regression to model the 

relation between length and weight. The slope of 

this relation is viewed as the population rate of 

gain (i.e., increase in weight per unit increase in 

length). Populations with a high rate of gain are 

adding weight at a faster rate than populations 

with a low rate of gain. Length–weight regression 

parameters can be used to predict the weight 

of fish, at a given length, for the population. 

Increased predicted weight for a population at a 

given length suggests increased fat reserves are 

present and available for somatic and gonadal 

growth. Factors affecting population length–

weight relations include physiological stress, 

competition for resources, habitat suitability, and 

prey availability. Spatial comparisons of length–

weight relation provide insight into mechanisms 

responsible for differences in population 

dynamics among study areas. Temporal 

comparisons of length–weight relations provide 

insight into environmental conditions (e.g., water 

levels, climate) providing optimum conditions for 

fish growth and health. 

Kirby and Ickes (In press) tested for differences 

in length–weight relations among the six LTRMP 

study areas and among years (1993–2002) for six 

UMRS species (black crappie, channel catfish, 

common carp, highfin carpsucker, sauger, and 

walleye) using analysis of covariance methods. 

Species were chosen based on availability of 

coincident length and weight data in the LTRMP 

database. 

Rate of gain differed nonrandomly among 

study reaches for all species tested, but 

differences were most pronounced in La Grange 

Pool, Illinois River. La Grange Pool was the only 

study area to contain populations with a rate of 

gain significantly higher than the pooled trend for 

a given species across all study areas. Moreover, 

higher rates of gain in La Grange Pool black 

crappie, channel catfish, and sauger were caused 

by a decreased plumpness of small fish and an 

increased plumpness of large fish, suggesting 

the presence of biological factors (e.g., food 

availability, competition, climate, hydrology) in 

La Grange Pool that differ from the mainstem 

UMR. 

The Illinois River became degraded because 

of human influences (levees, pollution, sediment 

contamination) during the 1950s through early 

1970s, coinciding with a decreased condition 

factor in common carp (Theiling 1999). Kirby 

and Ickes’ (In press) findings, based upon fish 

collections from 1993 to 2002, indicate that the 

condition of common carp in the Illinois River 

remains below average. However, for the other 

species studied, condition compared favorably 

between La Grange Pool and mainstem UMR 

sites. Further investigation into bioenergetics and 

growth of these species could identify potential 

causes for the observed spatial patterns in length–

weight relations.
24

The rate of gain was significantly different 

among years for black crappie, channel catfish, 

common carp, and walleye, but not for sauger. 

In 1993, the rates of gain for black crappie and 

common carp were significantly lower than the 

overall rate of gain trend, caused by an increased 

plumpness of small fish. In 1997, the rate of 

gain for black crappie and common carp was 

significantly higher than the overall rate of gain 

trend, caused by an increased plumpness of large 

fish. Black crappie and common carp were the 

only species exhibiting a similar rate of gain 

response to temporal variability (both down in 

1993 and both up in 1997). 

Black crappie and common carp are most 

closely associated with backwater and side 

channel habitats in the Upper Mississippi 

River System, whereas channel catfish, highfin 

carpsucker, walleye, and sauger are most closely 

associated with main channel and side channel 

environments. Previous researchers (i.e., Bartels 

1995; Gutreuter et al. 1999) have tested the flood 

pulse concept (Junk et al. 1989) with respect to 

fish productivity in the Upper Mississippi River 

and have concluded that fish growth is increased 

for littoral fish species in flood years when 

compared to drought years. However, results 

from these investigations were inconclusive for 

fish species with limited use of littoral areas. 

The Mississippi and Illinois River surpassed 

flood stage in all study areas during both 1993 

and 1997, but the timing of the flood events 

differed. The 1993 flood event occurred during 

late June and early July (with a smaller flood in 

April), but the 1997 flood event was primarily 

in April. This suggests that the timing of flood 

events may be as important to fish productivity as 

is the flood itself. Future conceptual models for 

fish productivity in large rivers may be improved 

by incorporating flood frequency and flood 

predictability (as done for community structure 

in smaller rivers by Poff and Ward 1989), as well 

as a measure of flood timing. The ability to test 

such models with LTRMP data will be enhanced 

with additional years of monitoring data and a 

larger sample of observed flood events.

Temporal and Spatial Trends in Abundance

In large river systems, species abundance varies 

temporally (i.e., from year to year), longitudinally 

(i.e., from upstream to downstream), and laterally 

(i.e., among macrohabitats at the same latitude). 

Longitudinal variation typically reflects systemic 

factors (e.g., hydrology, watershed use, degree 

days, floodplain morphology), whereas lateral 

variation reflects differences in local factors 

(e.g., depth, temperature, hydrologic connections, 

dissolved oxygen). Risotto and Turner (1985) 

suggested that factors affecting fish catch on the 

UMR could be divided into short-term factors 

and long-term factors based upon the type of 

influence imposed by the factor. Short-term 

factors, such as rainfall and water temperature, 

affect fish catch on an annual basis, and longterm factors such as latitude and geomorphology 

determine overall productivity, which influences 

long-term trends in abundance (Risotto and 

Turner 1985). Quantifying how fish populations 

vary across space and time can isolate factors 

responsible for observed patterns in fish 

abundance and help group species according 

to their variance signatures. This information 

can help in modeling fish abundance and in 

identifying species most likely to respond to 

local habitat modifications, climatic variability, 

or degradation of specific types of aquatic areas. 

Moreover, indicator species can be identified for 

rehabilitation efforts directed at different spatial 

scales in the Upper Mississippi River System. 

Kirby and Ickes (In press) used a variance 

partitioning method called Multi-factoral 

Analysis of Variance to measure the role that 

temporal (year-to-year), longitudinal (among 

study areas) and lateral (among macrohabitats 

within study areas) factors play in determining 

abundance dynamics for several UMRS species. 

This method partitions the total variation 

in abundance into portions associated with 

each main factor and the interaction terms 

among those factors (e.g., year * longitudinal; 

Figure 3.1). When possible, fish were classified 

by size classes and analyzed separately because 

abundance dynamics often differ by size class. 

In general, Kirby and Ickes (In press) found 

that species differed markedly in the amount 
25

Figure 3.1. Pie charts representing the results of variance 

decomposition for the relative abundance of stock-length longnose 

gar and shovelnose sturgeon captured by electrofishing from five 

study areas in the Upper Mississippi River. Pie charts represent 

the portion of total sum of squares accounted for by error sum of 

squares and seven model factors. The proportion of total sum of 

squares accounted for by model factors was used to ordinate and 

group species based on lateral-spatial (aquatic area), longitudinalspatial (study area), and temporal (year) variance patterns.

of variation in relative abundance caused by 

longitudinal factors, differed to a lesser extent 

in variation caused by lateral factors, and were 

most similar with respect to temporal variation. 

Figure 3.2 portrays the relative roles of temporal, 

longitudinal, and lateral factors in determining 

size-structured species abundance dynamics in 

the UMRS, as measured by the LTRMP. 

Fish management on large rivers is often 

centered on improving the abundance of 

desirable fish populations by minimizing 

temporal variability (e.g., water level 

management and harvest regulations), enhancing 

lateral habitats (e.g., increasing connection 

between channel and off-channel habitats, and 

improving backwater overwintering habitat), 

or addressing longitudinal-spatial factors (e.g., 

optimizing hydrology and land-use practices). 

Thus, the success of a management initiative is 

contingent on addressing factors responsible for 

variation in abundance. Longitudinal variation 

is, in most instances, a reflection of systemic 

factors (e.g., hydrology, water chemistry, 

nutrient dynamics, floodplain morphology) not 

easily controlled through direct management 

intervention for the obvious reasons of scope 

and cost. For this reason, management actions 

typically focus on controlling year-to-year 

variation and lateral habitat improvements in 

an effort to mitigate for undesirable systemic 

factors. 

Those species identified by Kirby and Ickes 

(In press) as exhibiting the highest levels of 

variation due to aquatic area type (lateral 

factors) would be most likely to show a relative 

abundance response to habitat improvements 

focusing on specific types of macrohabitats. 

Generally, such species were predominantly 

Centrarchids. Centrarchids remain the primary 

target of many aquatic habitat rehabilitation 

projects (HREPs) on the UMR, and have 

shown positive abundance responses to HREPs 

focused on backwater habitat (Gent et al. 1995). 

Kirby and Ickes (In press) suggest that bowfin, 

emerald shiner, substock-length flathead catfish, 

golden shiner, stock-length longnose gar, river 

shiner, and yellow perch would also be likely 

to respond positively to HREPs applied to key 

macrohabitats in river reaches at hospitable 

latitudes (Figure 3.2). Conversely, abundance of 

important recreational species such as channel 

catfish, sauger, and walleye was poorly predicted 

by lateral factors, suggesting that HREPs 

focused on single macrohabitats are less likely to 

initiate an abundance response for these species 

(Figure 3.2).

Interactions among lateral, longitudinal, and 

temporal factors were also common for many 
26

species. Interaction terms identify species that 

exhibited different responses to levels of a 

factor dependent upon levels of another factor. 

For example, stock-length channel catfish 

exhibited temporal (i.e., year-to-year) variation 

in macrohabitat use. This suggests that channel 

and off-channel habitat use by some species 

is dependent upon short-term factors (e.g., 

hydrology, rainfall). Such complexity in relative 

abundance patterns is not surprising given 

the size and dynamic nature of the UMR, and 

suggests it is important to consider the influence 

of temporal variability in river conditions when 

assessing lateral-spatial abundance patterns.

Kirby and Ickes (In press) conclude that 

the influence of systemic longitudinal factors 

on species abundance was greatest among 

factors considered. However, when viewed 

from a smaller, pool-level scale (35–100 

river kilometers), lateral-spatial and temporal 

factors are primarily responsible for shortterm abundance patterns. Despite this, river 

managers must remain cognizant that the longterm abundance of fish populations is ultimately 

confined by systemic processes and the 

longitudinal placement of a river reach. 

Discussion and Management Implications

The LTRMP, as a community-centric 

monitoring program, is necessarily constrained 

in how it can approach single species responses 

to important issues such as habitat changes 

and exploitation. This is because only the 

most basic population information is provided 

by the LTRMP design, namely count and 

� Time Longitudinal space �

� Time Lateral space 

�

YLBH

BKBH

WTSK

BKBF

BMBF YWPH

GZSD

SMBF CNCF 

WLYE

NOPK

SHRH

GNSF

SGER FWDM

FHCF LNGR

CARP

WTCP

LMBS

WTBS

RVCS

BKCP

BLGL

Ictaluridae

Cypriniformes

Centrarchidae 

Percidae

Miscellaneous

I 

III IV

II

Figure 3.2. Results of multivariate analysis of variance for relative abundance of 24 selected fish species of stock size (see Gablehouse 

1984) in the Upper Mississippi River System based on data from the Long Term Resource Monitoring Program, 1993-2002. Variance is 

portioned into temporal (year-to-year), longitudinal (among study areas) and lateral (among strata within study areas) factors. Species 

in panels I 

variance -

letter codes for 
27

length observations. Whereas populations are 

certainly affected by abiotic factors that may 

be associated with trends in abundance, biotic 

factors such as density-dependent growth, prey 

density, or habitat availability are also important 

determinants of population responses over time. 

Modeling population responses can benefit 

substantially from rate-dependent process data 

such as growth, mortality, recruitment, and age 

at maturity. However, such data are not presently 

available from LTRMP. 

In the absence of such data, Kirby and Ickes 

(In press) have used available data to identify 

how several different species of ecological, 

recreational, and commercial significance 

respond generally over time and across important 

spatial scales. With more than 130 species in the 

UMRS, this approach provides a foundation for 

identifying species sharing similar abundance 

patterns. This implies that species sharing similar 

abundance responses are likely to be influenced 

by similar environmental factors operating at 

scales revealed in their analysis as important 

in determining abundance dynamics. Knowing 

which scales are important for which species will 

lead to informed model development in the next 

phase of research. Knowing which species share 

similar abundance patterns may also greatly 

simplify future modeling efforts, as one species 

can be chosen to represent other species within a 

similar response group. 

Figure 3.2 also provides a blueprint for 

measuring fish responses to present and future 

habitat rehabilitation efforts within the UMRS. 

For example, Kirby and Ickes (In press) suggests 

that bowfin, emerald shiner, substock-length 

flathead catfish, golden shiner, stock-length 

longnose gar, river shiner, and yellow perch 

would also be likely to respond positively to 

HREPs applied to key macrohabitats in river 

reaches at hospitable latitudes. This assertion 

could be tested by studying the responses of 

these species to future HREP actions, in an 

adaptive management framework. Similarly, 

species exhibiting strong longitudinal 

associations in their abundance responses should 

be biological indicators of rehabilitation efforts at 

the largest scales in the system (e.g., hydrograph 

naturalization, floodplain reconnection). In these 

ways, the results of Kirby and Ickes (In press) 

have important applied management implications 

for adaptive management of the UMRS.
28

Chapter 4: Nonnative Species

Introduction

The effect of nonnative species has emerged as 

one of the most serious environmental problems 

in the 21st century (Mooney and Hobbs 2000; 

Carlton 2001; Mills et al. 1994). The economic 

cost of invasive species to Americans is estimated 

at $137 billion annually (Pimentel et al. 2000), 

but this estimate includes only those damages 

for which market values exist. Nonnative species 

can also negatively affect native species. For 

instance, up to 46% of the plants and animals 

listed as federally endangered species have been 

negatively affected by invasive species (Wilcove 

et al. 1998). Nonnative species and habitat 

degradation are routinely ranked as the top two 

threats to aquatic biodiversity (Tockner and 

Stanford 2002). 

A 1997 survey by the Mississippi Interstate 

Conservation Resources Association (MICRA) 

indicated that 163 nonnative species were 

established in the Mississippi River basin. 

Most of these species (83) were fishes. The 

163 nonnative species in the Mississippi River 

Basin is virtually identical to the 162 species 

established in the Great Lakes, an ecosystem 

often described as highly altered by numerous 

invasions of nonnatives. 

The Nonindigenous Aquatic Species 

information resource, maintained by the U.S. 

Geological Survey at the Center for Aquatic 

Resource Studies in Gainesville, Florida 

(http://nas.er.usgs.gov/fishes/index.html), reports 

that at least 18 nonnative fishes have been 

introduced to the UMRS (Table 4.1). These 

species have entered the UMRS basin through 

numerous dispersal routes and have had different 

effects on the ecology of the system. In this 

chapter, we briefly summarize data on nonnative 

fish species observed by the LTRMP. 

Detection of Nonnative Species in Long Term 

Resource Monitoring Program Collections

As a community monitoring program, the 

LTRMP is not optimized for the detection and 

enumeration of nonnative species. Because of its 

multiple gear design, however, the LTRMP has 

proven effective at documenting the presence and 

distribution of many nonnative species within 

the areas monitored. This information is valuable 

for identifying general patterns in distribution 

and abundance, identifying effective sampling 

methods for focused monitoring or study, and for 

describing general macrohabitat associations of 

established nonnative species. In the following 

sections, we identify nonnative species detected 

by LTRMP during 1999–2002 and provide 

information on their spatial distribution and 

trends in abundance and biomass. More detailed 

information on nonnative fishes in the UMRS can 

be found in Irons et al. (In press).

Number of Nonnative Species and Their 

Relative Prominence

Since 1989, the LTRMP has detected 11 

nonnative species and 3 additional hybrids with 

nonnative species (Table 4.2). Several nonnative 

species detected by LTRMP have only been 

collected once or twice and can be characterized 

as incidental, such as rainbow smelt and brown 

trout. These species are probably strays from 

tributaries because the UMRS does not provide 

appropriate habitat for rainbow smelt or brown 

trout. Two additional species, tiger musky 

and muskellunge, have also been observed 

infrequently and in low abundance. These 

species are considered nonnatives because their 

presence arises from stocking efforts within the 

UMRS. Muskellunge is native to Mississippi 

River reaches north of Minneapolis, Minnesota; 

however, it is not expected to appear in LTRMPmonitored reaches without local or regional 

stocking efforts. Threadfin shad is native to the 

southern regions of the Mississippi River basin, 

but is expanding its range northward because of 

stocking. Thus, it is nonnative to most LTRMP 

study areas. The remaining nonnative species in 

Table 4.2 have achieved some level of abundance 

and maintain populations within some or all of 

the LTRMP study reaches. 

Of particular concern is a recent increase in 

the rate of nonnative introductions to the UMRS. 

Records indicate that common carp, goldfish, 
29

and brown trout were the first nonnative fishes 

established in the UMRS. The introduction and 

establishment of these species throughout North 

America is closely associated with European 

settlement. After these early introductions, only 

three additional fishes became established in the 

UMRS prior to the 1970s: ninespine stickleback 

(Pungitius pungitius), probably from bait bucket 

release, and white catfish (Ameiurus catus) and 

threadfin shad through authorized introductions 

(Table 4.1). None of these species are currently 

considered a nuisance in the UMR. Thus, before 

1970, only six nonnative fish species had become 

established. Since 1970, however, an additional 

nine fishes have become established (Table 4.1). 

With 15 introduced fishes and 137 native 

fishes, nonnative species do not dominate fish 

biodiversity in the UMRS as they do in some 

systems. However, nonnatives can represent 

a sizeable fraction of total fish abundance 

and biomass. The increased rate of invasion 

by nonnative fishes and the ability of some 

nonnative species to achieve high abundance and 

biomass highlights the need to better understand 

the pathway of these introductions and their 

effects on the ecosystem.

Spatial Patterns in Distribution

Notable differences exist in the spatial 

distribution of nonnative species among the six 

LTRMP study areas. The majority of nonnative 

species detected by LTRMP were in the three 

southern study areas with eight nonnatives 

in Pool 26, nine in the Open River, and 10 

in La Grange Pool; Table 4.2). Conversely, 

monitoring in Pool 4, Pool 8, and Pool 13 have 

documented from three to four nonnative species 

per pool. Many nonnatives in Pools 4, 8, and 

13 were incidental species (e.g., brown trout, 

rainbow smelt, rudd), whereas most nonnative 

species in the southern reaches have established 

populations (e.g., threadfin shad, goldfish, 

grass carp, silver carp, bighead carp, white 

perch; Table 4.2). Common carp were abundant 

throughout the system.

Differences in spatial distribution can arise 

from several factors. For example, differences 

may suggest more degraded aquatic environments 

in southern versus northern study areas, resulting 

in greater invasion potential in southern areas. 

Differences may also be partially attributable to 

invasion pathways. Aquaculture operations in 

the southern United States and invasions from 

the Great Lakes (through the Chicago canal to 

the Illinois River) have been the source for many 

recent nonnatives. Thus, the southern LTRMP 

reaches are closer to these sources and would 

be expected to exhibit higher invasion rates. 

Relative absence of nonnatives in the northern 

areas may also be related to dispersal barrier 

effects of dams. The LTRMP data are currently 

too limited to identify the relative roles of each 

of these potential causes in creating differences 

in spatial distribution. Understanding why such 

differences arise, and the mechanisms leading to 

these differences will require additional data and 

directed study. 

La Grange Pool exhibits the highest number 

of established nonnatives among LTRMP study 

areas, likely because of its location between two 

major drainage basins, the Mississippi River 

and the Great Lakes. Thus, La Grange Pool 

is a sentinel point for monitoring nonnative 

exchanges between these connected basins. In 

addition, control measures can be implemented 

on the Illinois River to control movement of 

nonnative species between basins. A good 

example is the electronic barrier established in 

2002 in the Cal-Sag Canal section of the Illinois 

River, near Chicago, Illinois, to control exchange 

of fishes between the Mississippi River and 

Great Lakes basins. This site is an area of active 

research on the control of nonnative species in 

North America.

Spatial Patterns in Numerical Abundance

The percentage of abundance accounted 

for by nonnative species in the LTRMP catch 

ranged from 3 to 12% during 1993–2002 and 

was generally flat or declining (Figure 4.1). One 

notable exception is a sharp increase in 2001. 

This increase was entirely attributable to record 

catches of threadfin shad in La Grange, resulting 

in a nonnative fraction of 27% of the total 

program catch in 2001. 
30

Table 4.1. Year, pathways of introduction, and e 

Common name Scientific name Year introduced into UMRS Pathway Effect in U

Common carp Cyprinus carpio Unknown, but long ago (in Illinois by 

1908; Burr et al. 1996)

Primarily by authorized releases; natural dispersion thereafter (Fuller et al. 1999) 

High—ab

Goldfish Carassius auratus Unknown, but long ago (Burr et al. 

1996)

Primarily by authorized and unauthorized release; natural dispersions thereafter (Fuller et al. 1999)

Low—no

Brown trout Salmo trutta Unknown, but long ago Primarily by authorized releases; natural 

dispersion thereafter (Fuller et al. 1999)

Low--can 

American shad Alosa sapidissima 1872 and 1873 (Smith 1896) Authorized releases (Fuller et al. 1999) Failed intrBlack-banded rainbowfish

Melanotaenia nigrans Collected in 1930s (O’Donnell 1935) Aquarium release or escape from retail 

outlet (O’Donnell 1935)

Failed intr

Ninespine stickleback

Pungitius pungitius Unknown, but prior to 1979 (Smith 

1979)

Unknown, but by bait bucket release elsewhere in U.S.a

Low

White catfish Ameiurus catus Unknown, but prior to 1979 (Smith 

1979)

Authorized release for food and sport (Fuller et al. 1999)

Low—bu(McCarra

Threadfin shad Dorosoma petenense Unknown, but probably in the 1960s 

(Burr and Page 1986)

Authorized release for forage enhancement (Fuller et al. 1999)

Low—bu

Grass carp Ctenopharygodon idella Early 1970s (Pflieger 1975) Authorized and unauthorized releases, 

aquaculture escapes (Fuller et al. 1999)

High—abchange ha

Inland silverside Menidia beryllina First collected in 1977 (Burr and Page 

1986)

Natural dispersal after change in water quality (Burr et al. 1996)

Low—can and Linds

Rainbow smelt Osmerus mordax Late 1970s (Burr and Mayden 1980) Dispersion by canal (Burr and Mayden 

1980) 

Low

Mosquitofish Gambusia affinis Early 1980s (Pflieger 1997) Natural dispersion and unauthorized 

releases (Fuller et al. 1999)

Low—hig

Bighead carp Hypophthalmichthys nobilis Early 1980s (Jennings 1988) Aquaculture escapes High—abSilver carp Hypophthalmichthys molitrix First collected in 1980s Aquaculture escapes High—abPirapatinga Piaractus brachypomus Caught in 1988 (Anonymous 1988) Aquarium release (Fuller et al. 1999) Failed intrWhite perch Morone americana First collected 1990 (Burr et al. 1996) Dispersion by canal High--abuRudd Scardinius erythrophthalmus First collected in the 1990s (not 

established in 1992—Burr et al. 1996) – in Rasmussen 1998

Bait bucket release Low—no

spread far

Striped bass Morone saxatilis Unknown, but recent (LTRMP dataset) Authorized release Low--can 

small fish 

aWalker, P., 1993, A list of the endemic and introduced fishes of Colorado- March 1993. Colorado Division of Wildlife, Aquatic Resources Unit. Unpu
31

Table 4.2. Nonnative species collected for the Long Term Resource Monitoring Program (LTRMP) on the Upper Mississippi River System, including year of first captureareas in which each species has been observed during 1989–2002. 

Species Scientific name

First year captured by LTRMP Total catch

LTRMP study areas where caughtPool 4 Pool 8 Pool 13 Pool 26 La 

Threadfin shad Dorosoma petenense 1989 140,000 X X Goldfish Carassius auratus 1989 930 X X Common carp Cyprinus carpio 1989 135,000 X X X X X Grass carp Ctenopharyngodon idella 1991 900 X X X Silver carp Hypopthalmichthys molitrix 1998 205 X X Bighead carp Hypopthalmichthys nobilis 1991 2500 X X Rudd Scardinius erythrophthalmus 2002 2 XMuskellunge Esox masquinongy 1996 1 Tiger musky E. masquinongy x 

E. lucius

1992 4 X

Rainbow smelt Osmerus mordax 1993 1 XBrown trout Salmo trutta 1992 7 X X XWhite perch Morone americana 1992 237 X XStriped bass Morone saxatilis 1991 59 X Wiper M. saxatilis x 

M. chrysops

1993 139 X X X 
32

Among study areas, abundance can differ 

notably. Generally, abundances tend to be 

greatest in La Grange Pool of the Illinois River, 

followed by Pool 26 and the Open River reach. 

The northern study areas typically exhibit 

very low abundances of nonnative species. 

However, LTRMP methods may not provide 

accurate abundance estimates for some species, 

notably bighead and silver carp. These species 

are pelagic and are not efficiently captured by 

standard LTRMP gears. Thus, abundance indices 

for these species will likely underestimate the 

true abundance but can be useful for determining 

relative abundance between study areas. 

Spatial Patterns in Biomass

In 2003, we developed length–weight 

equations from empirical data sources and the 

open literature for 

100 UMRS fish 

species representing 

> 98% of the total 

catch from 1993 to 

2002. We used these 

models to estimate 

biomass for native 

and nonnative fishes 

to investigate patterns 

in the proportion 

of nonnative fishes 

across the six 

LTRMP study areas 

(Figure 4.2). 

Three general findings are immediately 

apparent. First, whereas nonnatives 

represent only a fraction of the total catch, 

they represent a substantial portion of fish 

biomass in the system. Across study areas 

and years, nonnative species averaged 48% 

of the total biomass observed in LTRMP 

collections, ranging between 23% and 

68%. The majority of this biomass is in 

common carp, a naturalized nonnative 

species. Second, the northern study areas 

tended to exhibit lower proportional 

biomass of nonnatives than the southern 

study areas; specifically, Pool 8 exhibited 

a consistently lower proportion than 

all other study areas. This difference between 

northern and southern reaches may be even 

greater than observed because LTRMP methods 

likely underestimate abundance of bighead and 

silver carp, which have been increasing in the 

three southern reaches. Finally, since 1994 (1993 

data excluded because of sampling difficulties; 

see Appendix A.1), there has been a system-wide 

decline in the proportional biomass of nonnative 

species in LTRMP collections. This trend 

reflects a system-wide decline in common carp 

abundance (Appendix B.10) and size structure 

(Appendix C.7).

This analysis provides a good example of 

how LTRMP data can be used to gain new 

insights into important issues on the UMRS. 

For example, competing hypotheses for why 

Pool 8 would have a lower proportion of its 

fish biomass in nonnative species include (a) 

0%

5%

10%

15%

20%

25%

30%

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

% total catch non-nativ

e

Figure 4.1. Percentage of total annual catch accounted for by nonnative 

species in the Long Term Resource Monitoring Program Fisheries Component, 

1993–2002.

0

10

20

30

40

50

60

70

80

19941995

19961997

1998

1999

20002001

2002

Year

Pool 4

Pool 8

Pool 13

Pool 26

Open

River

La

Grange

Percent Non-native Biomass

Figure 4.2. Percentage of total annual biomass accounted for by nonnative species in the catch of the Long 

Term Resource Monitoring Program Fisheries Component, 1994–2002.
33

Pool 8 has higher standing stocks of predators 

than crop nonnatives at higher rates than other 

areas; (b) nonnative species typically achieve 

their greatest success in impaired systems, thus 

Pool 8 is less impaired in some way than the 

other study reaches; or (c) disease or parasitism 

is greater on Pool 8 nonnatives (principally 

common carp) than other areas, suppressing the 

nonnative component of total biomass. Similar 

hypotheses could be developed for the systemic 

decline in proportional nonnative biomass and 

for spatial contrasts between the northern and 

southern study reaches. LTRMP data are crucial 

for framing these questions and hypotheses, 

and further analyses of these data can begin to 

address which hypotheses seem most likely.

Nonnative Fishes of Concern in the Near 

Future

The ecological and economic costs of 

nonnative species can be substantial, and once 

established, mediating their effects can be both 

difficult and expensive. Recently established 

species, such as bighead and silver carp, are of 

acute concern to natural resource managers, as 

these species have the potential to compete with 

nearly every native fish species and in particular 

other filter-feeding species such as paddlefish 

(Chick and Pegg 2001; Chick 2002; Figure 4.3). 

Unfortunately, the rate of introduction continues 

to increase. 

For example, the black carp (Mylopharyngodon 

piceus) and giant snakehead (Channa 

micropeltes) were collected within the UMRS 

basin for the first time in 2003. Also, the 

round goby (Neogobius melanostomus) has 

been collected in the Calumet and Des Planes 

Rivers near Chicago since 1993. The black 

carp was first imported into the United States 

accidentally in grass carp stocks. Later, it was 

introduced intentionally as a food fish and for 

use in catfish aquaculture in southern states. 

Scientists and natural resource managers 

are concerned that black carp may become 

established in the UMRS and the U.S. Fish and 

Wildlife Service is considering listing black 

carp as an injurious species. Black carp are 

molluscivores and therefore a threat to freshwater 

mussel populations, which are one of the most 

endangered groups of aquatic biota in North 

America. 

The giant snakehead was collected in the Rock 

River, Wisconsin, a tributary of the UMRS by 

a Wisconsin Department of Natural Resources 

(WDNR) survey in September 2003, and from 

the Illinois River in 2004. This large predator, 

if established in the UMRS, could prey on most 

the native fishes. Although the giant snakehead 

would not normally survive winter temperatures 

at UMRS latitudes, abundance of warm water 

discharges may aid in overwintering of this 

species and other potential aquaria releases. 

The round goby has slowly been expanding 

its distribution down the Calumet and Des 

Planes Rivers toward the Illinois and Mississippi 

Rivers since 1993. This benthic fish is likely 

to negatively affect native sculpins and other 

benthic fishes as its range expands (Laird and 

Page 1996).

Figure 4.3. Raising a trammel net with bighead carp in Pool 26, 

Upper Mississippi River. Photo courtesy E. Gittinger, Illinois Natural 

History Survey
34

Chapter 5: Species with Status

Introduction

Species with status are those listed as 

endangered, threatened, special concern, rare, 

or extirpated by state or federal authorities. 

Such species are always relatively rare or have 

limited distributions, but LTRMP fish sampling 

has collected many of these species within the 

UMRS. In some instances, LTRMP data have 

provided the only information within a state on 

the status of such species, and LTRMP data have 

even been used in delisting species. Although 

LTRMP data cannot provide detailed information 

on the distribution and relative abundance of 

these rare species, even presence/absence data 

are valuable to resource management agencies. 

In this chapter, we review basic information on 

current state and federal listings, and summarize 

the catches and locations of listed species during 

1993 to 2002. 

Summary of Observations by the 

Long Term Resource Monitoring Program

Federally listed fishes

Presently only pallid sturgeon are federally 

listed as endangered in the UMRS (Grady et 

al. 2001; Table 1.3). Pallid sturgeon have not 

been observed in routine LTRMP sampling 

from 1993 to 2002. Personnel at the Open River 

field station, however, collected pallid sturgeon 

during sampling for special studies from 1996 to 

2002. Also, in routine monitoring activities, the 

Open River field station personnel collected nine 

sicklefin chub, which had been listed as a federal 

candidate species prior to 2002. 

State listed fishes

Within the UMRS, 50 fish species have some 

form of conservation status in one or more 

states (Table 1.3). Since 1993, the LTRMP has 

collected 39 of those species (Table 1.3).

Nine species of fish with status in Minnesota 

were collected in Pool 4 (Table 1.3 and 

Appendix A.3) and included: four paddlefish 

(threatened); 26 blue sucker, five crystal darter, 

12 lake sturgeon, one pallid shiner, two pirate 

perch, 2,525 pugnose minnow, 127 shovelnose 

sturgeon, and two skipjack herring (special 

concern). Fifteen species of fish with status in 

Wisconsin were collected in Pool 8 (Table 1.3 

and Appendix A.3) and included four crystal 

darter, 17 goldeye, six pallid shiner, and one 

skipjack herring (endangered); two black 

buffalo, 56 blue sucker, 700 river redhorse, and 

12 speckled chub (threatened); two American 

eel, one lake sturgeon, 534 mud darter, five 

pirate perch, 11,485 pugnose minnow, 199 silver 

chub, 3,039 weed shiner, and 868 western sand 

darter (special concern). Seven species of fish 

with status in Iowa were collected in Pool 13 

(Table 1.3 and Appendix A.3) and included 

eight bluntnose darter, one freckled madtom and 

one lake sturgeon (endangered); four chestnut 

lamprey, one grass pickerel, and 39 western sand 

darter (threatened); and 1,087 pugnose minnow 

(special concern). Two species of fish with status 

in Illinois were collected in Pool 26 (Table 1.3 

and Appendix A.3) and included: 6 bigeye shiner, 

and 8 lake sturgeon (endangered). Nine species 

of fish with status in Missouri were collected in 

the Open River (Table 1.3 and Appendix A.3) 

and included: 1 trout perch (endangered); 2 

western sand darter (threatened); 64 blue sucker, 

81 mooneye, 35 paddlefish, 18 river darter, 9 

sicklefin chub (special concern); 355 Mississippi 

silvery minnow and 13 pugnose minnow (rare). 

No species of fish with status in Illinois were 

collected in La Grange Pool (Table 1.3 and 

Appendix A.3). 

Discussion and Management Implications

The multiple gear design and the standardized 

monitoring methods of the LTRMP provide 

benefits beyond community-based assessments. 

Documenting the presence of species of special 

conservation concern is one such benefit. These 

species represent an important fraction of the 

diversity of fishes in the UMRS. The UMRS 

is a nexus of freshwater fish diversity in North 

America, with nearly one third of the entire 

North American fauna endemic to the Mississippi 

River Basin. The conservation of this diversity 

is critical for maintaining healthy, diverse 

ecosystems into the future. 
35

Chapter 6: Nongame Species

Introduction

Nongame species represent more than one 

half of species found in the UMRS (Figure 

6.1), and thus are critical components of faunal 

diversity. Nongame species are often important 

components of food webs supporting species 

valued by humans. Many nongame species are 

fairly specific in their habitat requirements, 

making them excellent candidates for diagnosing 

changes in habitat condition. In addition, 

nongame species, by definition, are not subject to 

exploitation, which may confound interpretation 

of status and trends data for recreational and 

commercial species. 

of information for assessing the status and 

management options for nongame species. We 

demonstrate the potential of the LTRMP data to 

help understand the ecology of nongame fishes 

by presenting three case study examples.

Case study 1

In Chapter 5, we reported that 50 fish species 

in the UMR are listed as endangered, threatened, 

or special concern (Table 1.3), and that the 

LTRMP has collected 39 (78%) of the 50 listed 

species. Because abundance and occurrence 

data collected by LTRMP are geographically 

referenced, we have the opportunity to assess 

changes in population status and gain insight 

into the habitat requirements of rare fish. In Case 

Study 1, we show how LTRMP data can be used 

to understand the habitat requirements of one 

such fish, the weed shiners (Figure 6.2).

Case study 2

The habitat preferences of many common 

nongame fish are not well understood within 

the UMRS. As with the more extensively 

studied recreational and commercial fishes, 

some nongame species are habitat generalists 

whereas other species have specialized habitat 

requirements. Habitat generalists are typically 

more evenly distributed among habitats within 

study areas, when compared to habitat specialists. 

LTRMP data can be used to gain insight into 

the habitat associations of nongame fish, then 

be used to form and test hypotheses concerning 

habitat quality, fish community composition, 

and habitat limitations. In Case Study 2, we 

show how LTRMP data can illustrate the habitat 

associations of three nongame fishes: pugnose 

minnow, river shiner, and bullhead minnow 

(Figure 6.3).

Case study 3

Many nongame fish can serve as indicators 

of aquatic habitat integrity, because of their 

relatively short life spans and close associations 

with specific habitat types. Changes in the 

Non-game 

species

55%

Recreational 

species

21%

Commercial

species

24%

Figure 6.1. Contribution of nongame, recreational, and commercial 

fish species to the total fish catch by the Long Term Resource 

Monitoring Program, 1993–2002. Nongame species have no 

direct use by recreational or commercial fisheries. Any species 

exploited commercially was considered a commercial species, and 

any species not commercially exploited but captured occasionally 

or often with recreational tackle was considered a recreational 

species.

Within the LTRMP, several manuscripts have 

been published regarding nongame species 

during 1993–2002 (Hrabik 1996a, 1996b, 1997; 

Tucker and Cronin 1996; Tucker et al. 1996; 

Bowler 2001, 2003). Also, written summaries of 

nongame species abundance have been provided 

annually (e.g., Burkhardt et al. 2000) and data on 

nongame fishes are served to the public on the 

World Wide Web (see Chapter 9). 

Our goal in this chapter is to make this 

unique information resource known to river 

managers. Within natural resource agencies, 

nongame initiatives are frequently under-funded, 

but LTRMP data can be an important source 
36

Figure 6.2. Case study 1: Weed shiners in the Upper Mississippi River

Weed shiners (Notropis texanus; Figure 6.2.1) 

are listed as a species of special concern by the 

state of Wisconsin, and are listed as endangered by 

the states of Iowa and Illinois. The natural range 

of weed shiners in North America includes the 

entire mainstem Mississippi River. Weed shiners 

are sensitive to environmental changes, although 

the factors affecting populations are not well 

understood (Becker 1983).

Figure 6.2.1. Weed shiner (N otropis texanus). Photo by 

Florida Fish and Wildlife Conservation Commission.

percent of weed shiner collection sites were in 

backwater or side channel border aquatic areas, and 

most collections (70%) were from locations with 

current velocities of 0.10 m/s or less. Backwater 

and side channel areas with low current velocities 

typically have soft substrates, but over half (54%) of 

weed shiner collection sites contained predominately 

hard substrate. Weed shiners appear sensitive to 

local changes in aquatic vegetation. During years 

with reduced levels of submersed aquatic vegetation 

in Pool 8 (i.e., 1994 and 1995), weed shiners were 

confined to mid-pool locations, but have since 

expanded to sites throughout the pool (Figure 6.2.4).

Pool 4 Pool 8

25

20

15

10

5

0

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Weed shiner CPUE (catch/net day)

Figure 6.2.2. Weed shiner catch in mini-fyke nets 1993–2002 in 

Pool 4 and Pool 8 of the Upper Mississippi River.

Figure 6.2.3. Weed shiner habitat preference. The percent 

coverage of aquatic vegetation at sites containing weed shiners 

in the Upper Mississippi River.

Figure 6.2.4. Weed shiner collection sites in Pool 8, Upper 

Mississippi River during years 1993, 1995, 1997, 1999, and 

2001 as part of the Long Term Resource Monitoring Program.

Since 1993, the LTRMP has collected 3,135 weed 

shiners, accounting for 0.1% of fish collected. To 

date, weed shiners have been collected exclusively 

from Pool 4 and Pool 8 study areas. The relative 

abundance of weed shiners in Pool 4 and Pool 8 

has varied considerably across years with few 

specimens captured from either pool in 1994–1998 

(Figure 6.2.2).

Collections containing weed shiners were mostly 

(70%) from sites with some submersed aquatic 

vegetation present (Figure 6.2.3). Seventy-one 
37

Figure 6.3. Case study 2: Habitat Specialists Versus Habitat Generalists

Species comprising the UMRS fish community 

can be split into two basic groups, habitat specialists 

and habitat generalists, based upon habitat use. 

Habitat generalists possess adaptations (e.g., 

high fecundity) that promote success across 

multiple habitats, while habitat specialists possess 

adaptations that provide a competitive advantage 

within specific habitat types (e.g., dorsal-ventral 

flattening, highly developed olfactory organs, 

tolerance to low dissolved oxygen). 

Understanding the habitat-use is an essential 

component of evaluating the significance of 

trends in the abundance and distribution of fish 

species. For example, an increasing trend in 

the abundance of a habitat generalist, such as 

fathead minnows (Pimephales promelas), may 

signal temporal instability or decreased habitat 

heterogeneity. Conversely, an increasing trend in 

a habitat specialist, such as western sand darters 

(Ammocrypta clara), may signal temporal stability 

and the increased presence of a specific habitat 

(western sand darters prefer silt-free channel 

border habitat with sand/cobble substrate and low 

turbidity).

Pugnose minnows, river shiners, and bullhead 

minnows were three of the forty-one nongame 

species collected from Pool 4, during 1993–2002. 

All three species were collected from main channel, 

side channel and backwater aquatic areas, but the 

relative importance of the aquatic areas as habitat 

differed markedly among species (Figure 6.3.1).

Based upon aquatic-area use and the distribution 

of collection sites within the pool, bullhead 

minnows are habitat generalists (Figure 6.3.2). 

Pugnose minnows and river shiners exhibited more 

specific habitat preferences, with pugnose minnow 

collected predominately in backwater areas and 

river shiner collected predominately in channel 

habitats (Figure 6.3.3). This information suggests 

that pugnose minnows, river shiners, and bullhead 

minnows would be expected to respond differently 

to habitat change in channel or backwater areas.

Figure 6.3.1. Frequency of occurrence of three non-game fish 

species in day electrofishing samples from main channel, side 

channel, and backwater shorelines in Pool 4, Upper Mississippi 

River during years 1993–2002.

Figure 6.3.2. Bullhead minnow collection sites in lower Pool 4 

(Lock and Dam 4 to Lake Pepin), Upper Mississippi River during 

years 1993–2002, as part of the Long Term Resource Monitoring 

Program.

Figure 6.3.3. River shiner and pugnose minnow collection 

sites in lower Pool 4 (Lock and Dam 4 to Lake Pepin), Upper 

Mississippi River during years 1993–2002.
38

Figure 6.4. Case study 3: The Effect of Habitat upon Fish Species Distributions

Fish population abundance and distribution within 

the Upper Mississippi River System (UMRS) is 

largely determined by habitat suitability. Habitat 

suitability is a product of two factors, (1) the 

physiology and adaptations of the species, and 

(2) the available aquatic habitats. Aquatic habitat 

conditions in any given area of the UMRS are 

determined by local factors (e.g., current velocity, 

water depth, substrate) and systemic factors (e.g., 

climate, geology, land-use). The relationship between 

fish species distribution and habitat suitability 

provides an opportunity to assess UMRS habitat 

integrity and the relative importance of system and 

local controls. 

Golden shiners (Notemigonus crysoleucas) and 

western mosquitofish (Gambusia affinis) were two 

of the seventy-five nongame fish collected by the 

Long Term Resource Monitoring Program (LTRMP) 

in years 1993-2002. Both western mosquitofish and 

golden shiners are most commonly associated with 

backwater habitats having low current velocities 

and dense vegetation. However, golden shiners are 

typically most abundant in backwaters with low 

turbidities and western mosquitofish are typically 

most abundant in shallow marginal areas (Pflieger 

1997).

Figure 6.4.1. Golden shiner total catch from the six Long Term 

Resource Monitoring Program study areas during years 1993–

2002.

Golden shiners were captured from all six LTRMP 

study areas, but were most abundant in Pools 4, 8, 

and 13 of the UMR and the La Grange Pool of the 

Illinois River (Figure 6.4.1). The study areas with 

the highest total catch of golden shiners were also 

the study areas with the highest portion of aquatic 

area comprised of contiguous backwater (Table 

6.4.1).

Study area

Floodplain 

composition

(%)

Aquatic area 

composition

(%)

Open 

water

Aquatic 

vegetation

Contiguous 

backwater

Main 

channel

Pool 4 51 10 21 11

Pool 8 40 14 31 14

Pool 13 30 9 29 25

Pool 26 13 1 17 54

Open River 10 1 2 79

La Grange Pool 16 2 52 21

Figure 6.4.2. Western mosquitofish total catch from the six Long 

Term Resource Monitoring Program study areas during years 

1993–2002. Western mosquitofish were not captured from Pools 

4, 8, and 13.

Table 6.4.1. Composition of floodplains and aquatic areas within 

Long Term Resource Monitoring Program study areas. Data 

on floodplain composition are from Laustrup and Lowenburg 

(1994). Data on aquatic area composition are from the Long Term 

Resource Monitoring Program aquatic areas spatial database.

Western mosquitofish have a limited distribution 

within the UMRS and were captured only within the 

three study areas located in the southern half of the 

UMRS (Figure 6.4.2). Mosquitofish are intolerant 

of water temperatures found in the northern study 

areas, and were absent from collections there despite 

an abundance of backwater habitat. Mosquitofish 

were most abundant in Pool 26, likely due to a 

combination of adequate amounts of backwater 

habitat and temperatures within thermal limits. 

The contrast between the distribution of golden 

shiners and western mosquitofish within the basin, 

highlights how species physiology and habitat 

dynamics combine to determine species distribution 

patterns and community composition. For example, 

temperature increases associated with global 

warning might allow western mosquito fish to 

expand their range into the northern UMRS. 
39

abundance or distribution of nongame fish can 

be used to assess changes in local or systemic 

habitat features. The LTRMP sampling design 

permits unbiased assessment of reach-specific 

and systemic influences on nongame abundance. 

Coupled with range information, these data can 

be used to identify local and systemic habitat 

conditions responsible for population and 

community spatial patterns. In Case Study 3, we 

illustrate this potential by comparing catches of 

golden shiner and western mosquito fish (Figure 

6.4).

 Discussion and Management Implications

The LTRMP fish sampling design is primarily 

aimed at community assessment, but these 

data can be used to gain a better understanding 

of temporal and spatial patterns of nongame 

populations within the UMRS. Comparisons of 

current and future data will provide a valuable 

tool for assessing ecosystem health and habitat 

change.

The case studies presented illustrate the utility 

of making nongame distinctions in the context of 

a wider community-based monitoring initiative. 

To date most research effort has been directed at 

understanding community patterns and dynamics. 

However, future attempts to develop biological 

indicators of system health within the UMRS 

would likely benefit by focusing on nongame 

species. 
40

Chapter 7: Exploited Species

Introduction

Many fish species within the UMRS are 

subject to recreational or commercial exploitation 

(Appendix A.2). These species represent a 

significant portion of the economic value of 

fishery resources in the UMRS; thus, natural 

resource managers are interested in tracking 

trends in these species to ensure exploitation is 

sustainable over time. Changes in abundance or 

size structure can help diagnose over-exploitation 

and determine the effects of management actions 

initiated to improve populations. 

In the sections below, we present summarized 

data for selected species regarding abundance 

and spatial and temporal patterns in size 

structure. Our interpretation is necessarily limited 

because the large number of species combined 

with multiple river reaches and gears results in 

too many combinations of data to interpret within 

this document. However, we have provided 

graphics showing abundance and size structure 

for most of these combinations in appendixes 

accompanying this report on CD-ROM access 

data for all species, in raw and summarized 

forms, using the LTRMP Web-based data 

browsers (see Chapter 9). 

Temporal Trends in Abundance

Data from stratified random sampling under 

LTRMP protocols are available beginning in 

1993. The ability of the program to assess trends 

with 10 years of data is limited, but to make more 

meaningful interpretations will be substantially 

enhanced by additional years of data (see Chapter 

8). 

Annual abundance data are provided for 15 

exploited species (black crappie, blue catfish, 

bluegill, channel catfish, common carp, flathead 

catfish, freshwater drum, largemouth bass, 

northern pike, sauger, shovelnose sturgeon, 

smallmouth buffalo, walleye, white bass, and 

white crappie) and two keystone forage species 

(emerald shiner and gizzard shad) from by a 

variety of gears in Appendixes B.1–B.26.

In general, no notable systematic trends were 

evident. However, some general trends for 

some species in particular study reaches were 

apparent. For example, common carp appeared to 

decline in abundance in many reaches (Appendix 

B.10), perhaps as a population level response 

to record high abundances recorded following 

the Great Flood of 1993. Both largemouth bass 

(Appendix B.15) and bluegill (Appendix B.5) 

showed notable increases in abundance in Pool 

8, whereas populations show little trend in the 

remaining study areas. Pool 8 has been the site 

for numerous large scale habitat rehabilitation 

projects and whereas the upward trend in 

largemouth bass and bluegill cannot be directly 

attributed to these restoration efforts, trends 

indicate there may be an association. More 

detailed study will be required to determine the 

role of these projects in largemouth bass and 

bluegill responses. Finally, blue catfish have 

increased appreciably since 2000 in Pool 26 

(Appendix B.4) for unknown reasons.

Spatial and Temporal Patterns 

in Size Structure

In Appendixes C.1–C.15 and D.1–D.21, 

we present 10-year trends in proportional 

stock density for bluegill, black crappie, 

largemouth bass, white crappie, common carp, 

channel catfish northern pike, flathead catfish, 

smallmouth buffalo, freshwater drum, sauger, 

walleye, and white bass for each LTRMP 

study area and for selected gears. Also, relative 

length frequency plots are provided for the 

same species for all LTRMP study areas and 

selected gears in Appendixes E.1–E.191. These 

tables and plots show patterns over time, and 

allow for spatial comparisons among LTRMP 

study areas. However, for some study areas and 

species-by-gear combinations, sample sizes 

were small, thus caution should be used when 

interpreting the relative frequency histograms, 

PSDs, and confidence intervals (see Anderson 

and Neumann 1996). In some instances, sample 

size was too small to calculate confidence 

intervals. Results from analyses that tested for 

spatial differences in size structure for several of 
41

the species are summarized in Chapter 3 and in 

Kirby and Ickes (In press).

Establishment of Benchmarks 

of Resource State

Although 10 years of data may be too short to 

reliably detect trends over time, they can be used 

to develop benchmarks for the mean abundance 

and range of variation of many fish species. The 

graphs in Appendix B show median abundance 

of selected fish species from 1993 to 2002 and 

the 10th and 90th percentiles as a measure of the 

observed variation. Data from future years can 

be compared to these baseline conditions to 

determine if they are outside the expected range 

of variation. Such comparisons can provide an 

early warning, or “red flag” system to identify 

situations when abundance of a particular species 

0

5

10

15

20

25

30

35

1995 1997 1999 2001 2003 2005

Catch per unit effort

Year

1993

is outside its normal range. A red flag may not 

require immediate action, but would indicate an 

unusual condition needing further analysis or 

more attention in future years (Figure 7.1). The 

LTRMP data could define benchmark conditions 

for a large number of combinations of species, 

sampling area, strata, and gears. Certainly more 

interaction would be needed between research 

and managers to determine which of these 

combinations are most meaningful and what 

level of change would constitute a red flag. The 

criteria defining a red flag could vary based on 

the sensitivity of the species, but might include 

an absolute level of abundance, degree of 

change among successive years, and number of 

years abundance is outside the expected range. 

Different agencies might propose different 

thresholds for red flags specific to their needs.

Figure 7.1. Heuristic example demonstrating how Long Term Resources Monitoring Program 

(LTRMP) fish data can be applied for identifying management relevant changes in resource state. 

The solid black line is the median catch per unit effort (CPUE) of a hypothetical species from one 

of the LTRMP fish sampling gears. The dashed lines are the 10th and 90th percentiles, defining the 

observed range of natural variation. The black dots are observed CPUE statistics for the 10-year 

benchmark period. The black open circles are hypothetical future observations, which are well 

above the 90th percentile. These observations might indicate the need for management action or 

more focused analyses of the situation. 
42

 Chapter 8: Statistical Considerations and 

Evaluations

Introduction

The primary goal of an ecological monitoring 

program such as the LTRMP is to detect 

changes in a measured response of interest, 

such as abundance of a species. This is typically 

accomplished using inferential statistics to 

determine if a change is significant or could have 

arisen simply by random chance. The ability to 

determine significant change based on statistical 

inferences depends on the statistical methods 

chosen, attributes of the data (e.g., type of data, 

sample size, the magnitude of the change, and 

variance), and the level of probability used to 

define significance. In this chapter we consider 

the ability to determine significant differences 

among various spatial units within the UMRS 

and the ability to detect significant changes over 

time. We also summarize results from a recent 

statistical assessment focusing on methodological 

redundancies. 

Spatial Inferences

Several recent studies used LTRMP data to 

investigate spatial differences in fish species and 

community within the UMRS. Callahan (1998) 

provided the earliest assessment of the ability 

of LTRMP fisheries data to discriminate spatial 

differences. Using cluster analysis methods, 

Callahan (1998) investigated differences in fish 

communities among study areas. He concluded 

that the northern study reaches (Pools 4, 8, and 

13) were more similar in their fish communities 

than the southern study areas (Pool 26, 

La Grange Pool, and Open River) and that fish 

communities in the northern study areas differed 

notably from the southern study areas. However, 

he also concluded that each study area differed 

in some important way from all others and thus 

each area provided unique information on UMRS 

fish communities. Similar findings were reported 

by Chick and Pegg (2004), Chick et al. (2005), 

and Barko et al. (2005). 

Koel (2004) investigated differences in 

species richness estimates among LTRMP study 

areas and among sampling strata within and 

among study areas. Richness measured by day 

electrofishing was significantly higher in the 

northern study areas than the southern study 

areas, and also significantly higher in contiguous 

off-channel habitats than in channel habitats, 

irrespective of study area. Recent studies have 

also demonstrated differences in community 

level responses as a function of spatial patterns in 

habitat among keypools (Chick et al. 2005; Chick 

and Pegg 2004). 

Kirby and Ickes (In press) investigated 

differences in single species abundance among 

study areas and among sampling strata within 

and among study areas. Spatial differences were 

found in the abundance dynamics for 75 different 

species partitioned by size classes. 

Thus, the LTRMP data appear adequate for 

detecting and modeling differences in fish 

communities across space. Also, the data can 

provide sufficient ability to detect and model 

single species responses for many species of 

interest. Data from a community monitoring 

program, however, will probably be insufficient 

for reliably detecting spatial differences of 

species collected in low numbers, such as rare, 

threatened, and endangered species (Lubinski et 

al. 2001).

Temporal Inferences

Lubinski et al. (2002) investigated the 

statistical power of LTRMP fish component data 

to detect changes of 20% and 50% in species 

abundance from one year to the next. Statistical 

power is a function sample size, desired level of 

significance, and the size of the effect one wants 

to detect. 

Lubinski et al. (2002) found that power varied 

considerably among sampling methods and 

species. Electrofishing tended to provide the 

greatest power to detect inter-annual changes 

in abundance. However, for some species, fyke 

nets or hoop nets provided greater power than 

electrofishing. Forty-one species exhibited 

substantial power, defined as greater than 70% 

power to detect a 20% change in abundance from 
43

one year to the next at α = 0.05 in at least one 

study area. Doubling sample size resulted in 54 

species exhibiting substantial power, whereas 

halving sampling size reduced the number to 25 

species. Power to detect change in uncommon 

species was poor at any sample size.

The ability to detect inter-annual change is 

important for the early warning functions of a 

monitoring program, but equally important is 

the ability to detect trends over time. In 2004, 

LTRMP researchers began investigating how 

long it will take to achieve acceptable levels of 

power to detect long-term trends for selected 

species and water quality parameters. 

Preliminary results from these investigations 

can be found on the Web at http://www.umesc.

usgs.gov/ltrmp/power_plots.html (Brian Gray, 

Upper Midwest Environmental Sciences Center, 

personal communication). For fishes, the power 

to detect a trend in bluegill catch-per-unit-effort 

in Pool 4 resulting from an underlying 5% annual 

change was 60% after 14 years and 80% after 

17 years. However, with a 10% annual change, 

power reached 60% in 9 years and 80% in 11 

years. Among LTRMP sampling areas, power to 

detect trends in bluegill CPUE under the standard 

fish sampling design was highest in Pools 4 and 

8 and La Grange Pool, lowest in the Open River, 

and intermediate in Pools 13 and 26. In all cases, 

doubling or halving the sampling effort had 

negligible effects on either power or the time it 

takes to achieve such power. Thus, the general 

conclusion is that annual sample size or effort 

has relatively little effect on the ability to detect 

trends and that power is primarily a function of 

the number of years in the time series. 

Methodological Redundancies

Lubinski et al. (2002) concluded that the 

LTRMP fish sampling design may exhibit 

methodological redundancies because some 

sampling gears provided very low ability to 

detect annual changes in abundance for all 

species. However, detecting annual change is 

only one perspective from which to consider 

the value of multiple gears in a communitybased sampling design. Other criteria would 

include characterizing community composition 

and structure, measuring change in single 

species detection frequencies, and measuring 

species size distributions. Thus, an in-depth 

study was initiated in 2001 to determine if 

different sampling methods provided redundant 

information regarding the biological metrics 

(Ickes and Burkhardt 2002). 

Ickes and Burkhardt (2002) used a 

retrospective simulation analysis, in which 

past observations were used to predict future 

observations under different gear reduction 

scenarios. Differences in community composition 

and structure, and single-species catch per unit 

effort, size structure, and trend detection were 

the criteria for comparing alternative designs. 

Selection of reduction scenarios to consider was 

conducted in concert with program partners. 

Based on statistical analysis of the monitoring 

data and program partner input, Ickes and 

Burkhardt (2002) concluded that 4 of 10 

sampling gears historically used could be 

removed from the sampling protocols with minor 

effects on the quantity and quality of information 

provided by the fish component. These changes, 

implemented in 2002, represented about a 

33% reduction in sampling effort across the 

program. By redirecting that effort into scientific 

investigations on the monitoring data themselves, 

these changes have enhanced the fiscal and 

scientific efficiency of the program. 

In any long-term monitoring program, breaks 

in data continuity can potentially invalidate 

assessments of the status and trends of the 

resource, and significantly hamper efforts to 

model ecological responses and to assess the 

effects of management actions. These were 

serious considerations for Ickes and Burkhardt 

(2002). They reported that data continuity 

back to 1993, when stratified random sampling 

began, was nearly perfectly preserved (Figure 

1.2). Notable exceptions are breaks for the four 

sampling gears removed from the program 

and the elimination of two sampling strata 

(impounded offshore and backwater contiguous 

offshore strata) sampled exclusively by two of 

the four gears eliminated. Assessments revealed, 

however, that these offshore strata did not 

provide any unique information not captured in 

the nearshore counterpart of each stratum (Ickes 

and Burkhardt 2002).
44

Chapter 9: Quality Assurance and Data 

Serving Objectives

Introduction

One of the primary goals of LTRMP is to 

maintain and serve quality data in ways that are 

accessible and useful to a broad community of 

data users. All LTRMP data are subjected to 

well-documented quality control procedures. 

Below we briefly discuss the evolution of quality 

assurance standards for the fisheries component, 

describe how data are served to data users, 

and highlight new approaches that have been 

developed to serve program data in alternative, 

intuitive ways. The LTRMP stands as a national 

leader in serving complex monitoring data to the 

public.

Evolution of Quality Assurance Procedures

Sampling protocols, data standards, and 

quality assurance methods were developed and 

implemented at the earliest stages of the program 

in the late 1980s and have changed little in 

substance or scope since then. These aspects 

of the program are well-described by Gutreuter 

et al. (1995). What has changed is how data 

flow through this process. These changes have 

resulted in significant efficiencies being realized, 

resulting in lower costs, lower error rates, and 

reduced log times for making new data publicity 

available. Below, we briefly contrast the quality 

assurance process in the earliest stages of the 

program against the process used today.

The journey of all data through the LTRMP 

fish component quality assurance process begins 

at the point of collection. Each gear deployment, 

in one place and time, represents a sample. 

Each sample is uniquely identified in the central 

database by a barcode and is also geo-referenced 

to the location where the sample was collected. 

Early in the program, data were collected 

on paper data sheets in the field. Each sample 

had a unique data sheet associated with it. At 

the end of the sampling year, these data sheets 

were copied for archiving and shipped to a data 

entry contractor for conversion to electronic 

files. Upon completion of data entry, databases 

were shipped back to UMESC and computer 

programs were run to flag suspect observations 

based on sampling protocol codes and methods 

(e.g., unrecognized codes, discrepancies in data 

tracking fields, etc.). Flagged records were sent 

back to the field stations for data reconciliation. 

The exchange between UMESC and the field 

stations would continue until each error was 

resolved. This process usually took months to 

complete.

Beginning in 2002, the LTRMP developed 

specialized computer software running on rugged 

laptop computers to enter data in the field, thus 

removing the need for a data entry contractor. 

The software conducts real-time error detection 

based on logic inherent in the sampling protocols 

and in the historical quality control programs. 

The result, therefore, is data quality audits occur 

instantaneously in the field, where they can be 

most accurately addressed. Early in the program, 

it was not uncommon for 50% or more of the 

data sheets to be flagged for data errors. Today, 

that error rate is well below 1%, and data quality 

assurance audits now take about a week to 

complete. 

These changes have produced multiple 

benefits. The evolution of the quality assurance 

process has saved thousands of dollars annually, 

reduced error rates, decreased the time it takes 

to assure the quality of program data, and freed 

human resources for other tasks, such as analysis 

of monitoring data. Data also are now served 

to program partners and the public much more 

quickly, allowing the most recent data to enter 

into management decisions within the UMRS 

basin.

Serving Program Data to Partners 

and the Public

Since its inception, the LTRMP has continually 

improved the methods by which data are 

collected, processed, and served. Users have 

ready access to what is perhaps the world’s best 

source of ecological data on large rivers through 

the World Wide Web. Web-based browsers 

have been developed that provide simple and 

flexible query tools for the entire LTRMP fish 
45

component database housed at UMESC. This 

interface is available at: http://www.umesc.usgs.

gov/data_library/fisheries/fish1_query.html. This 

browser returns raw data to the user. Turning 

these raw data into useful information requires 

summarization, analyses, and interpretation. 

Thus, LTRMP staff also produce annual 

summaries, technical reports, and professional 

papers that synthesize and interpret monitoring 

data in a variety of ways. These resources are 

available at http://www.umesc.usgs.gov/reports_

publications/ltrmp_rep_list.html.

Although raw data are very useful for research 

purposes, analyzing these data is computerintensive and requires a detailed understanding 

of the LTRMP sampling design. Many users 

do not have the time or resources to do this. 

Thus, in 2002, we developed a new approach to 

serving LTRMP data that complements existing 

methods described above. We chose to test the 

new approach on the LTRMP fisheries database 

because this large, complex database provided 

a good test of our concepts. To demonstrate the 

complexity, consider that since 1993 LTRMP 

fish component personnel have made more than 

25,000 samples and collected more than 3 million 

fish of 134 different species. These observations 

are spread across 6 study areas and are collected 

using 10 different gear types deployed within 

eight different sampling strata. 

We developed a new tool for serving 

summarized status and trend data known as 

the Graphical Fish Database Browser. For this 

tool, we summarized the fisheries database to 

derive a suite of population and community 

metrics, generated new databases containing 

these metrics, and then built a Web application to 

search and display information from these new 

databases. The Graphical Fish Database Browser 

features an easy-to-use interface and requires 

only basic computer knowledge. It is accessible 

at http://www.umesc.usgs.gov/data_library/

fisheries/graphical/fish_front.html.

Six population and community metrics are 

available to search. Population metrics focus on 

abundance (catch per unit effort), size structure 

(proportional stock density), and how often a 

species is collected (frequency occurrence). 

Community metrics focus on patterns in the 

individual species collected each year within a 

study reach (species list), comparisons of the 

different species collected across study reaches 

(community composition), and trends in the total 

number of species collected annually within each 

study reach (species richness). 

After the user selects a metric, a more detailed 

search interface is provided that allows the user 

to select data fields from a series of three to 

five drop-down lists. Results of the search are 

provided as an interactive graphic, or a data table, 

depending on the metric selected. The results 

page has many additional features, including 

the ability to print the graphics generated, view 

an interactive map of the study reaches, and 

download a text file of the search results for more 

detailed analysis or presentation quality plotting. 

The Graphical Fish Database Browser helps 

fulfill a primary LTRMP goal of providing ready 

access to monitoring data. This new tool does 

not replace the former browser interface, but 

supplements and enhances it. Users can still 

perform detailed searches on the full dataset 

using the old browser utility. However, the new 

browser allows easy access to summarized data 

that can answer many common questions about 

the status and trends of fishes within the UMRS. 
46

Chapter 10: Future Directions

A rationale for Continued 

Standardized Monitoring

The Upper Mississippi River System is a 

national treasure, both for the social services 

it humanity (e.g., commercial navigation, 

recreation, aesthetics) as well as for its ecological 

significance. Monitoring is a fundamental tool 

in expanding our knowledge of the ecology of 

the UMRS and for understanding and managing 

multiple use demands on its ecosystem. 

The LTRMP continues to stand as a national 

model for ecosystem monitoring. Most 

significant is the role that LTRMP plays in 

the adaptive management of the UMRS. At 

the most basic level, monitoring provides the 

foundational data for understanding of how 

ecological attributes are distributed across space 

and how they vary over time in response to both 

natural and anthropogenic influences. Detection 

of such responses is essential for identifying and 

developing alternative management solutions 

and for measuring the success of management 

actions. 

The capability of a monitoring program to 

provide such functions is entirely dependent 

on its scope, rigor, and the length of time it has 

existed. In the LTRMP, standardized protocols 

are well documented and adhered to, ensuring 

unbiased, scientifically defensible data. The 

distribution of LTRMP study areas across the 

UMRS provides sufficient scope to detect local 

and regional influences on the ecology of the 

UMRS. At 10 years, the LTRMP is transitioning 

from a period of maturation in data collection, 

processing, and serving into a period of enhanced 

understanding through research and analyses 

using the monitoring data. These capabilities 

and the unique insights they provide will only 

continue to enhance the value of the LTRMP to 

managers, scientists, and the public. However, 

realizing these benefits will require continued 

commitment to the role of monitoring in the 

adaptive management of the UMRS.

Toward an Integrated Understanding

As the LTRMP transitions into a period 

of enhanced understanding, we will need 

to integrate knowledge across all of the 

ecological components. The first step toward 

such integration lies in understanding patterns 

and dynamics within each of the ecological 

components LTRMP monitors (fish, aquatic 

vegetation, water quality, aquatic invertebrates). 

Indeed, this report summarizes material from 

several recent studies that investigated patterns 

and dynamics within the fish component (Chick 

et al. 2005; Barko et al. 2005; Kirby and Ickes In 

press; Irons et al. In press). 

Central to nearly all of these studies was 

the relative role that space and time play 

in determining fisheries dynamics at both 

community and population. By understanding 

how fish responses are structured across space 

and how they respond over time, we can begin 

to integrate ecological knowledge. For example, 

if some given fisheries response is spatially 

repeatable, the attributes of this response are 

likely spatially determined. Knowing this has two 

primary benefits. 

First, if other components monitored by 

LTRMP demonstrate similar patterns and 

dynamics and these are also spatially repeatable, 

then such patterns suggest that ecological 

responses are dependent on different aspects of 

physical organization within the system. This is 

a powerful perspective from which to approach 

integrated understanding. Second, if responses 

are spatially repeatable, investigators can choose 

to cautiously ignore temporal dynamics because 

they are a minor component of overall variation 

in the response of interest. The consequence of 

this is that sample size, or data density per unit 

area, increases significantly (e.g., analyzing 10 

years worth of data versus 1 year for any given 

spatial extent), resulting in greater statistical 

power to make inferences. Also, understanding 

spatial scale in ecological responses permits 

informed modeling of processes (see Chapters 2 

and 3). 

Within the fisheries component, greater 

integrated understanding can arise from 

developing new ways to recast LTRMP data 
47

to gain additional insights. As an example, the 

fisheries component is actively developing a 

life history database that describes for each 

fish species in the UMRS, its reproductive and 

feeding strategies, as well as a host of other 

species-specific traits. Species sharing similar 

traits can be classified together into ecological 

guilds (e.g., reproductive guild, feeding guild, 

etc.), and analyses can then focus on functional 

aspects of fish communities. For example, are 

there significant spatial differences in the biomass 

of zooplanktivores and insectivores? Do any 

differences correlate with measures of system 

productivity, or food web structure? In this way, 

additional integrated understanding is achieved. 

Enhancing Management and 

Scientific Relevance

Ultimately, the truest test of a monitoring 

program is whether it is relevant to those who 

use the data and the information it provides; 

namely, natural resource managers and 

scientists. Certainly, the relevance of a 

monitoring program increases over time, as 

changes in status and trends of important 

resources can be more reliably detected 

(see Chapter 8), and this information can be 

directly incorporated into management actions 

and question-driven scientific investigations. 

Program relevance also is enhanced by serving 

data to different audiences in different formats 

(e.g., the Graphical Fish Database browser, see 

Chapter 9). The LTRMP has actively pursued 

alternative, useful ways to serve program data 

over the past 10 years and stands as a national 

leader in developing and implementing such 

methods. Finally, as enhanced understanding 

of the ecology of the UMRS is derived from 

the LTRMP data, the monitoring program will 

become central to inquiry-based scientific 

investigations throughout the basin.
48

Acknowledgments

This report is a result of the efforts and 

contributions of staff and partners of the LTRMP. 

The LTRMP is a cooperative effort by the U.S. 

Geological Service – Biological Resources 

Division, Upper Midwest Environmental Science 

Center (UMESC), the U.S. Army Corps of 

Engineers, the U.S. Fish and Wildlife Service, 

the Illinois Department of Natural Resources, 

the Illinois Natural History Survey, the Iowa 

Department of Natural Resources, the Minnesota 

Department of Natural Resources, the Missouri 

Department of Conservation, and the Wisconsin 

Department of Natural Resources. Monitoring 

is conducted by six field stations operated by 

the participating state resource management 

and research agencies. We especially thank all 

of the field staff at the LTRMP field stations for 

their sampling assistance. Also, we thank Dr. 

Leslie Holland-Bartels, Dr. Carl Korschgen, Dr. 

Steve Gutreuter, Dr. Barry Johnson, Dr. Patricia 

Heglund, Dr. Kenneth Lubinski, and Mr. James 

Rogala, and Dr. James Wiener for program 

development, oversight and support over the 

past 10 years. Finally, we thank members of 

the A-Team, a state and federal consortium that 

oversees technical aspects of the program, and 

staff at the U.S. Army Corps of Engineers for 

reviews of earlier drafts of this report.

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A-1

Appendix A

Annual allocation of fish sampling effort (Appendix A.1); list of fish species collected with 

four-letter codes and exploitation status (Appendix A.2); and number caught of each fish species, 

percentage of total catch, and numerical rank over all years and study areas (Appendix A.3) and 

annually for each study area (Appendixes A.4–A.9) in the Long Term Resource Monitoring Program, 

1993–2002. 

(Appendixes A–E are presented on the accompanying CD-ROM)
B-1

Appendix B

Mean annual catch-per-unit-effort, median catch, and 10% and 90% quartiles for selected 

fish species captured by various gears in each of the six study areas of the Long Term Resource 

Monitoring Program, 1993–2002 (Appendixes B.1–B.26).

(Appendixes A–E are presented on the accompanying CD-ROM)
C-1

Appendix C

Graphs of annual proportional stock density for selected combinations of fish species and gear in 

study areas of the Long Term Resource Monitoring Program, 1993–2002 (Appendixes C.1–C.15). 

(Appendixes A–E are presented on the accompanying CD-ROM)
D-1

Appendix D

Tables of proportional stock density with 80% confidence interval for selected fish species captured 

in various gears in each of the six study areas of the Long Term Resource Monitoring Program, 

1993–2002 (Appendixes D.1–D.21).

(Appendixes A–E are presented on the accompanying CD-ROM)
E-1

Appendix E

Length frequency histograms for selected fish species captured by various gears in each of the six 

study areas of the Long Term Resource Monitoring Program, 1993–2002 (Appendixes E.1–E.191). 

Length intervals are based on sizes used for determining stock density relations. 

(Appendixes A–E are presented on the accompanying CD-ROM)
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June 2005

3. REPORT TYPE AND DATES COVERED

4. TITLE AND SUBTITLE Multiyear Synthesis of the Fish Component from 1993 to 2002 for the 

Long Term Resource Monitoring Program

5. FUNDING NUMBERS

6. AUTHOR(S) Brian S. Ickes1, Melvin C. Bowler2, Andrew D. Bartels3, Daniel J. Kirby2, Steven 

DeLain4, John H. Chick5, Valerie A. Barko6, Kevin S. Irons7, and Mark A. Pegg7

7. PERFORMING ORGANIZATION NAME AND ADDRESS

1

US Geological Survey, Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La 

Crosse, Wisconsin 54603

2

Iowa Department of Natural Resources, 206 Rose Street, Bellevue, Iowa 52031

3

Wisconsin Department of Natural Resources, 2630 Fanta Reed Road, La Crosse, Wisconsin 54603

4

Minnesota Department of Natural Resources, 1801 South Oak Street, Lake City, Minnesota 55041 5

Illinois Natural History Survey, 8450 Montclair, Brighton, Illinois 62012

6

Missouri Department of Natural Resources, 3815 East Jackson Boulevard, Jackson, Missouri 63755 7

Illinois Natural History Survey, 704 N. Schrader Ave., Havana, Illinois 62644

8. PERFORMING ORGANIZATION

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9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

 U.S. Geological Survey 

 Upper Midwest Environmental Sciences Center

 2630 Fanta Reed Road

 La Crosse, Wisconsin, 54603

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3842 or 703-767-9050).

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13. ABSTRACT (Maximum 200 words) 

Abstract: Fisheries data are collected in the Upper Mississippi River System (UMRS) under the Long Term Resource Monitoring 

Program (LTRMP). From 1993 to 2002, monitoring activities were conducted at six primary study areas in the UMRS, Navigation 

Pools 4, 8, 13, 26, and the Open River reach in the Mississippi River and the La Grange Pool in the Illinois. The monitoring began with 

LTRMP in 1989 for all study areas except the Open River reach, which began in 1991. Since 1993, LTRMP staff have completed 24,791 

samplings in the 6 study areas and collected more than 3 million fish of 134 species. From 1993 to 2002, the most fish species (98) were 

collected in the Open River reach and the fewest species (83) collected in Pool 13. 

14. SUBJECT TERMS

 Fish community structure, habitat, Illinois River, Long Term Resource Monitoring Program, 

Mississippi River, native species, nonnative species, stratified random sampling.

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Unclassified

19. SECURITY CLASSIFICATION 

OF ABSTRACT

Unclassified

20. LIMITATION OF ABSTRACT
The Long Term Resource Monitoring Program (LTRMP) for the Upper Mississippi

River System was authorized under the Water Resources Development Act of 1986 as 

an element of the Environmental Management Program. The mission of the LTRMP

is to provide river managers with information for maintaining the Upper Mississippi 

River System as a sustainable large river ecosystem given its multiple-use character.

The LTRMP is a cooperative effort by the U.S. Geological Survey, the U.S. Army Corps

of Engineers, and the States of Illinois, Iowa, Minnesota, Missouri, and Wisconsin.