RIVER RESEARCH AND APPLICATIONS

River Res. Applic. 22: 413–427 (2006)

Published online 21 February 2006 in Wiley InterScience(www.interscience.wiley.com). DOI: 10.1002/rra.912

SPATIAL PATTERNS OF FISH COMMUNITIES IN THE UPPER MISSISSIPPIRIVER SYSTEM: ASSESSING FRAGMENTATION BY LOW-HEAD DAMS

JOHN H. CHICK,a* MARK A. PEGGb and TODD M. KOELc

a Illinois Natural History Survey, Great Rivers Field Station, Brighton, IL, USAb Illinois Natural History Survey, Illinois River Biological Survey, Havana, IL, USA

c National Park Service, Yellowstone National Park, WY, USA

ABSTRACT

We assessed the similarity of fish communities among river reaches to assess community-level fragmentation by low-head damsin the Upper Mississippi River System (UMRS). The spatial coverage of standardized electrofishing sampling used in the LongTerm Resource Monitoring Program (LTRMP) was extended for three of the six regional trend areas (RTA; pools 4, 13, and theOpen River Reach) to include river reaches (outpools) immediately upstream and downstream from the standard RTA from 15June to 31 October 2000. Additionally, pools 19 and 20 were sampled in September 2000. Cluster analysis and non-metricmultidimensional scaling of community composition and structure data revealed two major groups, upper and lower reaches,and four (for composition) or five (for structure) sub-groupings of river reaches. In general, all outpools grouped with the nearestRTA for both community composition (no exception) and community structure (one exception). This suggests that fragmenta-tion of fish communities from low-head dams is minimal. Mantel correlations demonstrated strong inverse association betweenthe similarity of fish communities with the distance between reaches. Habitat variables measured during electrofishing collec-tions were significantly correlated with spatial variation of fish composition and community structure, but provided only mar-ginal improvements to correlations with distance between reaches alone. Furthermore, habitat variability among river reachesalso was related to distance between reaches. Determining the extent to which variation of fish communities is related to habitator demographic processes (e.g. migration, larval drift, source-sink dynamics) will be challenging for this system. Althoughlow-head dams on the UMRS may restrict movements for individuals and populations of certain fish species, we found littleevidence that these effects have led to substantial, community-level fragmentation. Copyright# 2006 John Wiley & Sons, Ltd.

key words: fish communities; fragmentation; low-head dams; spatial distribution; Mississippi River; ordination

INTRODUCTION

Large rivers are both extremely productive and extremely altered ecosystems. Perhaps the most obvious and wide-

spread anthropogenic alterations to river ecosystems, particularly in the northern hemisphere, is the construction of

dams (Dynesius and Nilsson, 1994). Alteration of rivers by damming is a major concern for fishes (Ormerod,

2003). Dams can affect fishes through alteration of flow patterns (Gehrke et al., 1995; Galat and Lipkin, 2000;

Lytle and Poff, 2004), changes in connectivity with the floodplain (Aarts et al., 2004; Hirzinger et al., 2004;

Koel, 2004), and by imposing barriers to movements that fragment fish populations (Corbacho and Sanchez,

2001; Morita and Yamamoto, 2002). Despite the fact that a majority of large rivers in the northern hemisphere

have been dammed, and the potential negative impacts of dams on fishes are well recognized, very little is under-

stood about community level responses by large-river fishes to the presence of dams (Dynesius and Nilsson, 1994;

Ormerod, 2003). Indeed, we are only now beginning to understand the importance of the main-channel habitat of

large rivers for fishes (Dettmers et al., 2001; Galat and Zweimuller, 2001).

The degree to which damming fragments fish communities has important implications for management and

monitoring of large rivers. Although large, un-dammed rivers are expected to show some longitudinal variation

Received 15 May 2004

Revised 20 May 2005

Copyright # 2006 John Wiley & Sons, Ltd. Accepted 21 June 2005

*Correspondence to: John H. Chick, Illinois Natural History Survey, Great Rivers Field Station 8450 Montclair Ave. Brighton, IL 62012, USA.E-mail: chick@inhs.uiuc.edu

in fauna and function, they are usually thought of as a continuum (Naiman et al., 1987). On the other hand,

construction of large dams and reservoirs often fragments rivers into a series of distinct lotic and lentic ecosystems

that may have relatively little exchange of fauna (Dynesius and Nilsson, 1994; Andersson et al., 2000; Pegg and

Pierce, 2002; Pegg et al., 2003). Clearly, a fragmented river–reservoir system presents different management

issues than an un-dammed river continuum, and monitoring design should also vary between these two conditions.

For example, systemic-level probabalistic designs (Hunsaker and Carpenter, 1990; Noon, 2003) would probably be

more suitable for un-dammed rivers, whereas fragmented river/reservoir systems would likely require more loca-

lized focus (sensu Olsen and Schreuder, 1997; Fourqurean and Rutten, 2003) based on individual management

units or ecosystem segments.

The Upper Mississippi River is an example of a large-floodplain river that is both extremely productive and

extensively modified, primarily for navigation (Nielsen et al., 1986). To allow for commercial navigation, the

portion of the Mississippi River above Saint Louis Missouri has been divided by 26 locks and dams into a series

of navigation pools. In the remainder of the river, from Saint Louis downstream to the confluence with the Ohio

River, a navigable channel (3m depth) is maintained by wing dams and bank stabilization (Nielsen et al., 1986).

Unlike the large dams that create reservoirs on many rivers, the dams on the Upper Mississippi are low-head dams

with bottom release gates. During high-flow periods, most of these gates are raised above the surface of the water,

allowing free-flow conditions. Under low-flow conditions, the gates are lowered, which likely impedes movement

of fishes and other organisms (Knights et al., 2002; Zigler et al., 2004). Although these low-head locks and dams

do impede movement of fishes, and there is good evidence that the larger and older dams have fragmented popula-

tions of certain species (Nielsen et al., 1986), there is little information on the degree of fragmentation that has

occurred at the community level.

An important source of ecological information on the Upper Mississippi River is the Long Term Resource

Monitoring Program. The Long Term Resource Monitoring Program (LTRMP) was authorized by the Water

Resources Development Acts of 1986 and 1999 as an element of the US Army Corps of Engineers’ Environmental

Management Program (EMP). The primary mission of the LTRMP is to provide resource managers with the

information needed to maintain the Upper Mississippi River System (UMRS) as a viable multiple-use ecosystem

through standardized monitoring of water quality, aquatic vegetation, aquatic invertebrates, and fishes (US Army

Corps of Engineers, 1997). The design of the LTRMP is an attempt to balance the desire to detect large-scale,

systemic trends with the need for adequate resolution at local scales to gain insight into ecosystem function.

The design includes standardized monitoring at six regional trend analysis areas (RTA; Navigation Pools 4, 8,

13, 26, the Open River Reach, and the La Grange Reach of the Illinois River), each of the RTA consisting of

a 48–129 km river reach between two navigation dams (with the exception of the Open River Reach). Lubinski

et al. (2001) conducted a power analysis demonstrating that the present LTRMP design provides adequate

statistical power to detect inter-annual variation for most water quality parameters and many fishes within the

six RTA. Nevertheless, how well these six RTA reflect the overall biotic and abiotic conditions of the entire UMRS

remains unknown and will depend in part on how fragmented the UMRS fish communities are as a result of the

navigation dams.

This study was initiated to expand the spatial coverage of LTRMP fisheries electrofishing monitoring to outpools

immediately upstream and downstream of three RTA in an attempt to provide further insight into the ability of

LTRMP to make ecological inferences to the entire UMRS. The study design also allows for an assessment of

how fragmented fish communities in the UMRS are in relation to the locks and dams. If the locks and dams

severely fragment the UMRS fish community, we would not expect to see strong similarity between adjacent

RTA as would be the case for a river continuum. We present statistical analysis of this study, examining spatial

variation of community composition (the presence or absence of species) and community structure (relative abun-

dance of species) of UMRS fishes. Three major objectives are addressed.

1. Determine similarity of outpools relative to adjacent RTA in terms of community composition and structure of

fishes.

2. To determine groupings of river reaches based on community composition and structure of fishes.

3. Determine how river-reach groupings based on fish communities correspond to spatial variation of important

habitat factors.

414 J. H. CHICK, M. A. PEGG AND T. M. KOEL

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

METHODS

Fish sampling

This study extended the spatial coverage of fish monitoring for three LTRMP field stations. Mississippi River

reaches immediately above and below Pools 4, 12, and the Open River Reach (Figure 1) were sampled using

standard LTRMP electrofishing methodology from 15 June to 31 October 2000. These areas will be referred to

collectively as outpools, and individually as Pools 3, 5, 12, 14, ORU (i.e. Open River upstream), and ORD (Open

River downstream). Standard LTRMP monitoring also occurred in all six RTA (Navigation Pools 4, 8, 13, 26, La

Grange, and the Open River Reach) during 2000, and electrofishing samples were collected from Navigation Pools

19 and 20 by all LTRMP fisheries personnel on 12 and 13 September 2000 (Figure 1). We were interested in

Figure 1. Map of the Upper Mississippi River showing the six regional trend areas monitored by the Long Term Monitoring Program (Pools 4,8, 13 and 26; La Grange and Open River Reaches) and the eight outpools (Pools 3, 5, 12, 15, 19, 20, ORU, ORD) sampled with electrofishing

during 2000

MISSISSIPPI RIVER FISH COMMUNITIES 415

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

examining how similar fish communities in Pools 19 and 20 were compared to other UMRS reaches because Lock

and Dam 19 is believed to present a barrier to migrations of certain fishes (Kelner and Seitman, 2000).

Gutreuter et al. (1995) described standard LTRMP methodology in detail. Sampling locations were selected

using a stratified (by habitat type) random design (Lubinski et al., 2001). Electrofishing was conducted using

pulsed-DC output with two ring anodes and the boat hull serving as the cathode. Voltage and amperage were

adjusted for water temperature and conductivity to achieve a power output of 3000W. Electrofishing was

conducted along shorelines continuously for 15min at each sample collection site and two field staff collected fish

with dip nets. Data on water temperature, depth (average for each collection site), conductivity, and habitat were

collected with each sample (Table I). All fish were identified, measured, and enumerated following standard

LTRMP protocols (Gutreuter et al., 1995).

Statistical analysis

We examined spatial variation in fish community composition and structure among the six RTA and eight out-

pools. Community composition refers to the presence or absence of species, whereas community structure refers to

the abundance of species as measured by mean catch-per-unit-effort (CPUE¼ number per 15min weighted by the

area of each habitat strata). Both of these measures were examined because it is possible for communities to have

identical composition while having substantial and ecologically important differences in community structure.

Alternatively, communities could have some important differences in composition while still maintaining rela-

tively similar community structure (though large differences in composition will necessarily result in differences

in community structure). All analyses were conducted using SAS for Windows (SAS, 1999) and Primer for

Windows (Primer-E, 2001).

For both response variables (presence/absence, CPUE), we used cluster analysis and non-metric multidimen-

sional scaling (NMDS) to identify groupings of river reaches. These analyses were based on a Euclidian distance

matrix for community composition data, and a Bray–Curtis similarity matrix for community structure data.

Catch-per-unit-effort data were square-root transformed to better conform to multivariate normality assumptions.

This transformation dampens the influence of very abundant species for community structure analysis (Clarke and

Warwick, 1994). We also limited the community structure analysis to 16 species for which electrofishing had

power� 0.80 to detect a 20% inter-annual abundance change in at least one habitat strata of an RTA based on

a power analysis of LTRMP components (Lubinski et al., 2001; Table II). This conservative criterion was adopted

to help insure that the patterns of relative abundance used in these analyses reflect true ecological patterns rather

than sampling artifacts. Hybrids and fish not identified to species were omitted from all analyses. For analysis of

community composition, we omitted species that occasionally appear in electrofishing samples, but whose overall

Table I. Habitat variables routinely collected from each electrofishing site for the Long Term Resource Monitoring Program(Gutreuter et al., 1995)

Habitat factor Units Explanation

Secchi cmConductivity mS/cmFlow m/secTemperature �CDepth mEmergent/submersed vegetation 0, 1, 2, 3 0¼ 0% coverage; 1¼ 1–19% coverage; 2¼ 20–49%

coverage; 3¼ � 50% coverageVegetation density 0, 1, 2 0¼ no veg; 1¼ sparse; 2¼ denseSubstrate 1, 2, 3, 4 1¼ silt; 2¼ silt/clay/little sand; 3¼ sand/mostly

sand; 4¼ gravel/rock/hard clayWoody structure pres/abs presence/absence of woody structureRevetment pres/abs presence/absence of shoreline revetmentInlet/outlet pres/abs presence/absence on an inlet/outlet channel to a backwater lakeFlooded terrestrial vegetation pres/abs presence/absence of flooded terrestrial vegetation

416 J. H. CHICK, M. A. PEGG AND T. M. KOEL

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

presence/absence is not reliably determined by this method based on the best professional judgement of LTRMP

field crews and supervisors.

Three criteria were used to determine the sub-grouping level in our cluster analysis. First, we used rarefaction

curves from the six RTA to visually determine the minimum number of individuals needed to reach the asymptote

of the rarefaction curve (i.e. the sampling effort needed to adequately describe species richness). Acceptable

sub-grouping levels should not isolate under-sampled reaches because this isolation could have resulted from

inadequate sampling. Second, we calculated the mean and range of Euclidian distance (community composition)

and Bray–Curtis similarity (community structure) between consecutive years for each RTA using LTRMP data

from 1994 to 2000. Although community composition and structure are expected to change through time, we

should not expect major differences to occur on average from each year to the next (i.e. we do not expect annual

extinction or colonization events to be the norm). Therefore, much of the variation between consecutive years in

community composition and structure data from LTRMP electrofishing is likely to arise from sampling artifacts

(i.e. the failure to capture a rare species in a given year and RTA despite its continued occurrence). Therefore, we

chose to establish the critical Euclidean distance or Bray–Curtis similarity for groupings at a level greater than the

range of year-to-year differences. Finally, we accepted only sub-grouping levels producing groups that were easily

illustrated using NMDS in either two or three dimensions with a stress value less than 0.05. Stress value is a

measure of ‘goodness of fit’ for NMDS, with small values indicating a better fit than large stress values (Clarke

and Warwick, 1994).

Because cluster analysis and NMDS are data exploration techniques, we used analysis of similarity (ANOSIM)

to test for significant variation in fish community composition and structure among groups (Clarke and Warwick,

1994). Analysis of similarity is analogous to univariate ANOVA in that it tests for significant differences among

groups. Unlike ANOVA, however, ANOSIM uses Monte Carlo simulation to determine significance rather than

probability inferences from an assumed statistical distribution. Nonetheless, our use of ANOSIM here is clearly a

post hoc test and results should be interpreted with caution. We used similarity breakdown analysis (the SIMPER

procedure in Primer) to determine the contribution of species to Bray–Curtis dissimilarity between community

structure groupings.

Table II. Mean abundance (square-root No./15min) of the 16 species used in community structure analysis, total catch (allspecies), and species richness from samples collected from 15 June through 31 October 2000. LG¼ the La Grange Reachof the Illinois River. OR¼Open River Reach. ORU¼Open River upstream. ORD¼Open River downstream. Common namesfor fishes follow Robins et al. (1991)

UMRS navigation pool

Species 3 4 5 8 12 13 14 19 20 LG 26 ORU OR ORD

Gizzard shad 7.50 6.30 3.53 2.18 3.45 4.12 2.94 2.61 2.74 6.98 4.51 3.23 5.36 4.14Emerald shiner 7.13 3.99 1.77 2.34 3.93 3.99 1.69 6.85 6.96 1.08 1.49 1.72 2.46 1.26Common carp 2.07 2.23 2.19 1.18 2.23 2.30 2.30 2.11 1.79 2.76 2.46 1.99 1.20 1.71Bluegill 0.52 2.97 3.09 6.28 3.19 3.00 3.76 1.48 0.17 1.75 0.84 0.05 0.09 0.08Freshwater drum 1.44 0.67 0.56 0.34 0.82 0.97 0.91 1.86 1.46 1.50 0.95 1.26 1.22 0.97Largemouth bass 0.05 1.43 1.21 2.66 1.88 2.27 2.24 1.22 0.12 1.23 0.26 0.00 0.04 0.02Spotfin shiner 1.91 1.06 1.12 2.62 1.18 1.14 0.21 1.21 1.53 0.00 0.10 0.08 0.00 0.00Bullhead minnow 1.16 0.75 0.89 3.08 2.03 1.46 0.89 0.81 0.36 0.11 0.13 0.00 0.01 0.02White bass 1.45 0.63 0.39 0.45 0.76 0.76 0.41 0.89 0.72 1.63 0.49 0.69 0.72 0.46Channel catfish 0.26 0.09 0.12 0.09 0.58 0.69 0.38 1.77 0.55 1.17 0.71 0.74 0.83 0.69Black crappie 0.08 0.92 0.90 0.95 0.43 1.14 1.01 0.10 0.08 0.37 0.06 0.02 0.00 0.00Smallmouth buffalo 0.22 0.20 0.15 0.23 0.84 0.24 0.12 0.22 0.63 1.68 0.59 0.08 0.42 0.40Shorthead redhorse 0.82 1.16 1.18 0.91 0.55 0.28 0.12 0.13 0.07 0.12 0.06 0.08 0.04 0.00Smallmouth bass 0.92 0.73 0.98 0.65 0.46 0.06 0.18 0.10 0.05 0.00 0.00 0.01 0.02 0.00Silver redhorse 0.14 0.84 1.09 0.84 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00Bigmouth buffalo 0.02 0.10 0.19 0.00 0.42 0.41 0.15 0.13 0.00 1.05 0.16 0.01 0.17 0.10Total catch 17 905 19 097 10 686 10 855 7296 7418 4058 4357 3228 17 165 6943 1568 4894 2669Species richness 46 52 49 60 56 49 53 43 46 55 44 40 52 38

MISSISSIPPI RIVER FISH COMMUNITIES 417

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

Finally, we used the electrofishing data to examine whether variation in fish community composition and

structure among reaches corresponded with variation in habitat factors. Two sources of habitat data were available

for comparisons: those measured directly in the field with each electrofishing collection (Table I), and a suite of

aquatic/geomorphic and land cover variables quantified for the Habitat Needs Assessment (HNA) query tool that

were available for a subset of reaches (Table III; DeHaan et al., 2000; Koel, 2001). For habitat variables measured

in the field, standardized (mean¼ 0, standard deviation¼ 1) Euclidean distance matrices were calculated and

Mantel tests were used to determine correlations with both the Euclidian matrix from fish community composition

data and the Bray–Curtis similarity matrix from community structure data. Because the aquatic/geomorphic and

land cover measures were all in hectares, a Bray–Curtis similarity matrix was calculated and used in Mantel tests.

A canonical Mantel test (BioEnv procedure in Primer-E, 2001; Clarke and Warwick, 1994) was used to determine

the combination of habitat variables that would provide the greatest correlation with community data. Because

many habitat variables can co-vary with latitude, we included distance between reaches in river miles in both

habitat data sets to help determine whether correlations with habitat reflect covariation with the distance between

reaches. For the La Grange Reach of the Illinois River, distance was the sum of river miles between a given reach

and Pool 26, and river miles between Pool 26 and the La Grange Reach. We also used a Mantel test to examine

whether habitat similarity (standardized Euclidian distance matrix for all habitat features measured in the field)

was correlated with distance between reaches.

RESULTS

Electrofishing

A total of 118 139 fishes were collected, representing 100 species. The species with the greatest overall abun-

dance were gizzard shad, emerald shiner, bluegill, and common carp, which together accounted for over 71% of the

total catch. The mean number of species captured in a river reach was 49, ranging from 38 to 60 (Table III).

Rarefaction curves suggest that sampling sufficient to collect at least 5000 individuals was needed to do an

adequate job of describing community composition of fishes within a given RTA (Figure 2). Because fewer than

5000 fishes were collected from several of the outpools (Table III), we rejected any sub-grouping level that isolated

these under-sampled reaches in our cluster analysis of community composition and community structure data.

Cluster analysis of community composition data revealed two major groupings of river reaches: upper RTA and

outpools and lower RTA and outpools (Figure 3(A)). Twenty-eight species were captured only within lower RTA

and outpools, and 18 species were captured only within upper RTA and outpools. The average Euclidian distance

between consecutive years within an RTAwas 3.60, and ranged from 3.16 to 4.47. Therefore, we chose 5.0 as our

subgrouping distance, resulting in four subgroups: all upper reaches, the La Grange Reach, the Open River Reach

and ORU and ORD, and Pools 19, 20 and 26 (Figure 3(A)). These four groups were illustrated by NMDS in three

dimensions with a stress value¼ 0.02 (Figure 3(B)). Analysis of similarity revealed significant differences

between the two major groupings (global R¼ 0.83; P¼ 0.001), and among the four subgroups (global

R¼ 0.95; P¼ 0.001).

As with community composition, cluster analysis based on community structure of fishes revealed two major

groups, upper and lower RTA and their associated outpools. In this analysis, however, Pools 19 and 20 grouped

with the upper reaches rather than the lower reaches (Figure 4(A)). Six species accounted for more than 70% of the

dissimilarity between upper and lower groupings. Upper reaches were characterized by greater abundance of emer-

ald shiner, bluegill, largemouth bass, bullhead minnow, and spotfin shiner relative to lower reaches (Table III).

Lower reaches had greater abundance of gizzard shad compared with upper reaches.

The mean Bray–Curtis similarity value for consecutive years in a RTA was 85.7 ranging from 74.4 to 92.6

(Figure 4(A)). We chose a Bray–Curtis value of 70 for our sub-grouping distance, producing five subgroups: Pools

26, ORU, ORD, and the Open River Reach (subgroup A) the La Grange Reach (subgroup B); Pools 3, 19 and 20

(subgroup C); Pools 4, 5, and 12–14 (subgroup D); and Pool 8 (subgroup E). Non-metric multidimensional scaling

illustrates these five subgroups in three dimensions with a stress value¼ 0.03 (Figure 4(B)). Eleven species con-

tributed more than 70% to the dissimilarity among subgroups. Pool 8 (subgroup E) had the greatest abundance of

black crappie, bullhead minnow, bluegill, largemouth bass, and spotfin shiner. The La Grange Reach on the Illinois

418 J. H. CHICK, M. A. PEGG AND T. M. KOEL

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

TableIII.

Surfacearea

(hectares)ofaquatic/geomorphichabitatvariablesas

defined

bytheHabitatNeedsAssessm

entQueryToolforUpper

MississippiRiver

Pools4–26

andopen

river

reaches

(OR).Habitatvariableswerethemainchannel(M

NC),tailwater

(TWZ),secondarychannel(SCH),tributary

channel(TRC),contiguousfloodplain

lake(CFL),contiguousfloodplain

shallowaquaticarea

(CFS),contiguousim

pounded

area

(CIM

),terrestrialisland(TIS),contiguousterrestrialfloodplain

(CTF),isolated

terrestrialfloodplain

(ITF),aquaticvegetation(submersedandfloatingleaved;AV),permanentlyflooded

emergentvegetation(PEV),grass

land(G

L),pioneerforest(PF),

floodplain

forest(FF),agriculture

(AG),developed

(DV)

Area(ha)

ofaquatic/geomorphic

habitat

typeandlandcover/use

type

Pool

MC

TWZ

SCH

TRC

CFL

CFS

CIM

IFL

TIS

CTF

ITF

AV

PEV

GL

PF

FF

AG

DV

41093

12

463

97

10320

1567

408

978

1848

8438

155

5676

4177

2310

441

12608

3526

3873

5337

22

278

59

155

733

2178

442

863

6025

81

4226

1713

3049

362

5362

4860

2192

8627

21

510

30

1125

1573

4024

832

2966

3478

281

6363

3408

3253

572

5718

279

4494

12

596

26

740

4401

545

864

435

1446

1645

737

2732

882

1310

117

4747

196

1981

13

1569

20

789

32

1242

1902

3556

1764

2414

8494

4914

10632

3329

3477

305

12915

4353

8084

14

561

22

599

24

668

00

520

1357

3107

3997

988

573

1067

184

7307

1951

4375

19

1350

30

1527

93

868

1282

1069

1537

2297

14033

34558

4175

1593

2700

477

15159

45713

10328

20

574

39

545

200

23

00

547

786

4829

42140

106

59

1152

184

6704

43819

3856

26

1467

28

1483

51

409

0245

1407

2530

18663

12023

1129

803

1784

644

16217

40169

5034

LG

5829

0441

686

13772

255

07026

2748

48689

64556

02009

4516

020809

15334

1017

OR

1509

0261

67

103

00

5829

451

14880

46567

04806

8294

038097

13180

3511

MISSISSIPPI RIVER FISH COMMUNITIES 419

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River (subgroup B) had the greatest abundance of bigmouth buffalo, common carp, smallmouth buffalo, and white

bass. Subgroup C had the greatest abundance of emerald shiner and freshwater drum. Analysis of similarity

revealed significant differences between the two major groupings (global R¼ 0.67; P¼ 0.001) and among the five

subgroups (global R¼ 0.99; P¼ 0.001).

Habitat correlations

Strong correlations between community composition (r¼ 0.80) and structure (r¼ 0.63) matrices with distance

between reaches suggest that fish communities in relatively close reaches are more similar than reaches separated

by larger distances (Figures 5(A), 5(B)). Habitat similarity also was correlated (r¼ 0.74) with distance between

reaches (Figure 5(C)) and most of the habitat variables measured during electrofishing sampling covaried with

river mile (Figure 6). These habitat variables were significantly correlated with both community composition

(r¼ 0.75; P� 0.0001) and community structure (r¼ 0.64; P¼ 0.0010). The canonical Mantel procedure revealed

that the strongest correlations (r¼ 0.90) with community composition were with a habitat matrix composed of

distance between reaches, water depth, conductivity, vegetation density and the frequency of woody structure

presence. The strongest correlations (r¼ 0.73) for community structure were with a habitat matrix comprised

of distance between reaches, flow, vegetation density, frequency of occurrence of woody structure and frequency

of occurrence of flooded terrestrial vegetation. Note that these correlations are only marginal improvements over

correlation with distance between reaches alone.

Mantel tests revealed significant correlations between HNA variables with both community composition

(r¼ 0.72; P¼ 0.0001) and community structure (r¼ 0.54; P¼ 0.0002). The canonical Mantel procedure found

the strongest correlations (r¼ 0.81) for community composition occurred with a land coverage matrix comprised

of side channels, terrestrial islands, aquatic vegetation, floodplain forest and agricultural. Similarly, the strongest

correlations (r¼ 0.63) for community structure occurred for a land coverage matrix comprised of side channels,

contiguous impounded areas, terrestrial islands, aquatic vegetation, floodplain forest and agricultural.

Figure 2. Rarefaction curves from electrofishing data for the six regional trend areas sampled in 2000. It appears that at least 5000 individualsneed to be sampled to adequately describe fish community composition (i.e. to reach the rarefaction curve asymptote)

420 J. H. CHICK, M. A. PEGG AND T. M. KOEL

Copyright # 2006 John Wiley & Sons, Ltd. River Res. Applic. 22: 413–427 (2006)

DISCUSSION

In general, outpools tended to group with adjacent RTAs. Pools 5, 12, 14, ORU and ORD were within the same

sub-groups as adjacent RTAs (Pools 3, 13, and the Open River Reach respectively). The only exception to this

Figure 3. (A) Cluster analysis of fish community composition data collected via electrofishing. The solid line and dashed lines indicate the meanand range of Euclidean distance for consecutive years in a regional trend area. The light-grey line indicates the sub-grouping level (Euclideandistance of 5) resulting in four sub-groups (circled). (B) Non-metric multidimensional scaling plot of Upper Mississippi River pools in three dimen-

sions. The four sub-groups from the cluster analysis are circled

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Figure 4. (A) Cluster analysis of fish community structure data collected via electrofishing. The solid line and dashed lines indicate the meanand range of Bray–Curtis similarity for consecutive years in a regional trend area. The light-grey line indicates the sub-grouping level (Bray–Curtis similarity¼ 70) resulting in five sub-groups (circled). (B) Non-metric multidimensional scaling plot of Upper Mississippi River pools in

three dimensions. The five sub-groups from the cluster analysis are circled

422 J. H. CHICK, M. A. PEGG AND T. M. KOEL

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trend, the sub-grouping of Pools 3, 19 and 20 in our community structure analysis, may have arisen as a result of

similarity in habitat features and/or low sample sizes in Pools 19 and 20. Thus, our study provides little evidence

that the low-head navigation dams on the Upper Mississippi River have fragmented fish communities. Certainly,

the movements and migrations of some species are affected by these dams (Nielsen et al., 1986; Kelner and

Seitman, 2000; Knights et al., 2002; Zigler et al., 2004). These effects, however, apparently are not severe

and/or general enough to have caused substantial shifts in fish communities among adjacent navigation pools.

In fact, our results suggest a strong negative relation between the similarity of fish community composition and

Figure 5. Relationship of distance between reaches (river miles) with (A) community structure of fishes (Bray–Curtis similarity), (B) commu-nity composition of fishes (Euclidian distance) and (C) habitat composition (normalized Euclidian distance). Plotted are all possible pairwise

comparisons among the six regional trend areas and outpools

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structure with distance between reaches. This spatial correlation pattern would not be expected in a severely

fragmented system (Pegg and Pierce, 2002).

Our analysis of both the community composition and community structure of fishes in the UMRS each yielded

two major groups, upper and lower river reaches. Two previous studies also classified the UMRS into upper and

lower reaches based on habitat variables (US Geological Survey, 1999; Koel, 2001). It is likely that geographic

Figure 6. Relationship of water depth, flow, conductivity, vegetation density, occurrence of woody structure and occurrence of flooded terres-trial vegetation with river mile (relative to Pool 3). All habitat measures were collected at the electrofishing sites sampled in 2000

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range limitations of fishes, habitat factors, and possibly historic barriers have all influenced the fish composition

and community structure differences between upper and lower reaches. Our analysis also revealed four or five

subgroups of reaches. The La Grange Reach of the Illinois River was a unique sub-group for both community

composition and structure analyses, which was an expected result for this tributary RTA. Pool 8 was a unique

sub-group in terms of community structure. This RTA had the greatest abundance of centrarchid species, which

may be related to the greater abundance of aquatic vegetation found in this reach (Figure 6). Based on the strength

of our analysis of similarity tests (i.e. global R values) and NMDS plots, the sub-groupings we identified appear to

provide an accurate description of the similarity of community composition and structure among the UMRS

reaches sampled. Although there were clear differences between the upper and lower groups based on community

structure and community composition, it is clear that more spatial structure exists in this system than a simple

dichotomy of upper and lower reaches.

Results from this study should be interpreted with caution because the data available for analysis were limited to

one year (covariation of communities through time cannot be addressed) and one sampling gear—boat electro-

fishing. Lubinski et al. (2001) reported that, among all gears used in the LTRMP, electrofishing generally had the

greatest statistical power to detect trends across all species and habitat types. Nevertheless, boat electrofishing does

not sample all species within the UMRS equally well. For example, electrofishing is conducted near the shoreline

and will not be effective for species that primarily occupy offshore habitats. Given the vast spatial extent of the

UMRS and the great diversity of habitat types and fish species it contains, it is unlikely that any single study could

fully address both the patterns and causes of spatial variation of fish communities within this system. Nevertheless,

we feel the present study provides a useful first step in addressing this issue.

Our attempts to correlate spatial variation of fish communities with habitat data were hindered by the confound-

ing of habitat similarity and distance between reaches. Both the composition and community structure of fishes

should vary as a function of distance between reaches because of zoogeography, immigration and emigration,

source-sink dynamics and similar histories of large-scale disturbances such as major floods and droughts (Drake,

1990, 1991; Hamrick and Nason, 1996; Pulliam, 1996). Because habitat similarity was also correlated with

distance between reaches, it is difficult to determine the influence of habitat on fish communities independent

of the spatial demographic processes listed above. Correlations with HNA variables suggest that floodplain

composition and connectivity may also have important influences on fish communities. As with other habitat

variables, however, there are distinct shifts in floodplain habitat from the upper portion of the system to the lower

reaches. For example, lower reaches of the Upper Mississippi River (i.e. below Pool 17) have a much greater

proportion of their floodplain converted to agriculture and far less submersed and floating-leaved vegetation

compared to upper reaches (Theiling et al., 2000). To gain a better understanding of the influence of habitat

on fish communities, future studies could attempt to account for both spatial proximity and habitat variation by

selecting pairs of study reaches that are relatively close in proximity but differ substantially in specific habitat

measures. Also, future analysis could devise an index of historic habitat alterations for each river reach to assess

whether fish community variation correlates with this index.

This study was not able to completely resolve where Pools 19 and 20 fit within the UMRS. Pools 19 and 20 were

similar to lower reaches in terms of community composition, but similar to upper reaches with regard to commu-

nity structure. Electrofishing collections from Pools 19 and 20 differed from all other reaches in that all data were

collected over a period of two days, rather than over a period of five months. Furthermore, our total catch of fishes

was relatively low in these two pools, suggesting that they may have been under-sampled. It is interesting, how-

ever, that Pools 19 and 20 grouped together in both the community composition and structure analyses because

Lock and Dam 19 is one of the largest and oldest dams on the river, and is known to be a barrier to migratory fishes

such as skipjack herring (Kelner and Seitman, 2000; Nielsen et al., 1986). Despite this barrier, our study suggests

overall fish communities within Pools 19 and 20 are relatively similar. An important caveat to this study is that only

three reaches in the pooled portion of the UMRS were sampled below Pool 14 (i.e. Pools 19, 20 and 26), whereas

seven upper UMRS reaches were sampled (Pools 3, 4, 5, 8, 12, 13 and 14). Studies including a greater number of

lower UMRS reaches might improve our understanding of spatial variation of fish communities.

Results of this study support the concept of maintaining the ability to make inferences at both local (i.e.

individual RTA) and regional scales (i.e. upper versus lower RTA). Our analysis of community similarity indicates

that fish communities in the UMRS exhibit substantial spatial structure. A probabilistic monitoring program

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focused only on regional or systemic inferences about UMRS fish communities would need to stratify in accor-

dance with this spatial structure to match the sampling effort with the spatial distribution of distinct communities.

This will be difficult until future studies better define the exact spatial structure of UMRS fish communities and

populations. Furthermore, the ability to make inferences about the effects of habitat and management projects to

fish communities at local scales is an important attribute of the LTRMP, especially in light of the confounding of

habitat variation and distance between river reaches. These conclusions regarding the value of accommodating

multiple spatial scales in a monitoring program are not unique to the UMRS or aquatic ecosystems in general

(Olsen and Schreuder, 1997; Trexler et al., 2001; Ringold et al., 2003). Monitoring programs may find the

technique of meta-analysis (Arnqvist and Wooster, 1995; Osenberg, Sarnelle, Cooper, and Holt, 1999; Hughes

et al., 2002) useful for making inferences to larger scales from status and trend patterns at smaller scales.

ACKNOWLEDGEMENTS

We thank the LTRMP fish crews from the Minnesota Department of Natural Resources, the Wisconsin Department

of Natural Resources, Iowa Department of Natural Resources, Illinois Natural History Survey, and Missouri

Department of Conservation, who collected the data reported here. This study was funded by the U.S. Army Corps

of Engineers and administered by the U.S. Geological Survey, Upper Midwest Environmental Sciences Center.

Brian Ickes, members of the Long Term Resource Monitoring Program Analysis Team, and several anonymous

reviewers provided valuable comments on earlier versions of this manuscript. This is publication number 8 of the

National Great Rivers Research and Education Center.

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