THE ROLE OF FLUVIAL GEOMORPHOLOGY IN

THE DISTRIBUTION OF FRESHWATER MUSSELS

(BIVALVIA: UNIONIDAE) IN THE KIAMICHI

RIVER, OKLAHOMA

By

SABRINA G. NEGUS

Bachelor of Science

Utah State University

Logan, Utah

2002

Submitted to the Faculty of the Graduate College of the

Oklahoma State University in partial fulfillment of the requirements for

the Degree of MASTER OF SCIENCE

December, 2008

ii

THE ROLE OF FLUVIAL GEOMORPHOLOGY IN

THE DISTRIBUTION OF FRESHWATER MUSSELS

(BIVALVIA: UNIONIDAE) IN THE KIAMICHI RIVER,

OKLAHOMA

Thesis Approved:

William L. Fisher

Thesis Adviser

Anthony A. Echelle

Joseph R. Bidwell

A. Gordon Emslie

Dean of the Graduate College

iii

ACKNOWLEDGMENTS

I would like to thank those who helped in the field, especially Aaron Easley, Nathan

Copeland and Lucas Negus. I would like to thank W. Fisher and R. Marston for help

with project development and J. Bidwell, A. Echelle, D. Shoup and especially W. Fisher

for their comments on this manuscript draft. I need to thank D. Dauwalter for his

statistics help. D you’re my hero. Project funding was provided by a State Wildlife grant

T-19-P through the Oklahoma Department of Wildlife Conservation and the Oklahoma

Cooperative Fish and Wildlife Research Unit. The Oklahoma Cooperative Fish and

Wildlife Research Unit is jointly sponsored by the U.S. Geological Survey; Oklahoma

State University; the Oklahoma Department of Wildlife Conservation; the Wildlife

Management Institute and the U.S. Fish and Wildlife Service. This project would not be

possible without the many landowners who provided river access, their cooperation is

greatly appreciated. I would also like to thank Oklahoma State University and L. Talent

for providing support through a teaching assistantship. And Bill, thank you so much for

your patience. Most important to me, I would like to thank my family for all their love

and support. Mom and Dad your support in everything I do is so very appreciated, I love

you. And Lucas, I couldn’t have done this without you, thank you for all that you are.

iv

TABLE OF CONTENTS

Chapter Page PREFACE ......................................................................................................................x

I. FLUVIAL GEOMORPHIC CHARACTERIZATION OF THE KIAMICHI RIVER,

OKLAHOMA ..........................................................................................................1 Abstract ....................................................................................................................2 Introduction ..............................................................................................................2 Study Area ...............................................................................................................4 Methods....................................................................................................................5 Results ......................................................................................................................8 Discussion ..............................................................................................................10 References ..............................................................................................................15 II. FLUVIAL GEOMORPHOLOGY AND FRESHWATER MUSSEL DISTRIBUTION

................................................................................................................................33 Abstract ..................................................................................................................34 Introduction ............................................................................................................34 Methods..................................................................................................................36 Results ....................................................................................................................39 Discussion ..............................................................................................................41 References ..............................................................................................................45 APPENDIX I ...............................................................................................................58

v

LIST OF TABLES

Table Page 1.1. Simple statistics for morphologic characteristics at bankfull stage (n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below the Jackfork Creek confluence (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold ............................................................18

1.2. Simple statistics for substrate variables (particle size fractions and particle class percentages; n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below Jackfork Creek (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold. ..............................19 1.3. Simple statistics for estimated flow variables at bankfull stage (n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below Jackfork Creek (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold. .......................................................................20 2.1. Simple statistics for morphologic, substrate and estimated flow characteristics at bankfull stage from 131 transects sampled in 2005 in the Kiamichi River, Oklahoma.49 2.2. Percent freshwater mussel occurrence and relative abundance in the 10 sampled reaches (N transects per reach, sampled in 2005) in the Kiamichi River, Oklahoma. Transects with no mussel presence or low mussel relative abundance were combined and classified as low for relative abundance analyses. .......................................................50 2.3. Percent freshwater mussel occurrence and relative abundance by stream channel unit type (N transects per channel unit type) for 131 transects sampled in 2005 in the Kiamichi River, Oklahoma. Transects with no mussel presence or low mussel relative abundance were combined and classified as low for relative abundance analyses. ....51 2.4. Statistics and eigenvalues for eigenvectors from principal components analysis (PCA) of fluvial geomorphic variables from 131 transects in the Kiamichi River, Oklahoma. ....................................................................................................................52

vi

Table Page 2.5. Classification tree analysis (CTA) variable importance for freshwater mussel occurrence in the Kiamichi River, Oklahoma. Width:depth ratio, D84 sediment size fraction and bankfull velocity predicted mussel occurrence correctly 55% of the time. The classification tree (Figure 3) had a tenfold cross-validated relative error of 0.622.53 2.6. Classification tree analysis (CTA) variable importance for freshwater mussel relative abundance in the Kiamichi River, Oklahoma. Bank instability, D16, D50 and D84 sediment size fractions predicted mussel relative abundance class correctly 53% of the time. The classification tree (Figure 4) had a tenfold cross-validated relative error of 0.622.............................................................................................................................54

vii

LIST OF FIGURES

Figure Page 1.1. Daily hydrographs showing discharge of the Kiamichi River in 2007 at the Big Cedar (top), Clayton (middle), and Antlers (bottom), Oklahoma USGS gaging stations, respectively. *Note change in discharge magnitude between graphs. ........................21 1.2. Kiamichi River watershed with major impoundments, USGS gage stations (A, Big Cedar #07335700; B, Clayton #07335790; C, Antlers #07336200), designated reaches (marked at the end of the reach). Lower inset shows Kiamichi River watershed in Oklahoma. Upper inset shows Kiamichi River at the confluence with Jackfork Creek and six closest sample sites. Sites A and B are mussel beds. ............................................22 1.3. Log-linear transport-rate (A), flow-frequency (B), and magnitude-frequency (C) curves for Big Cedar and Antlers, Oklahoma USGS gage stations, respectively, showing effective discharge (Qe) and threshold discharge (Qt) for the Kiamichi River, Oklahoma........................................................................................................................................23 1.4. Linear regression trends for geomorphic variables. Bankfull width, area and width:depth ratio increased significantly longitudinally (p<0.0001, R2=0.25; p<0.0001, R2=0.13; p<0.0001, R2=0.19; respectively). ................................................................24 1.5. Linear regression trends for geomorphic variables. Entrenchment ratio and bank instability decreased significantly longitudinally (p<0.0001, R2=0.11; p=0.0011, R2=0.08; respectively). ................................................................................................................25 1.6. Linear regression trends for sediment variables. Particle size fractions (D35, D50) decreased significantly longitudinally (p=0.0336, R2=0.04; p=0.0066, R2=0.06; respectively). ................................................................................................................26 1.7. Linear regression trends for sediment variables. Particle size fractions (D84, D95) decreased significantly longitudinally (p=0.0179, R2=0.04; p=0.0396, R2=0.03; respectively). ................................................................................................................27 1.8. Linear regression trends for sediment variables. Particle class percentages (sand, cobble, bedrock) decreased significantly longitudinally (p=0.0209, R2=0.04; p=0.0002, R2=0.11; p=0.0034, R2=0.07; respectively). ................................................................28

viii

Figure Page 1.9. Linear regression trends for median particle size (D50) above and below the Jackfork Creek confluence. D50 decreased significantly above Jackfork Creek (p<0.0001, R2=0.39). Linear regression of D50 below Jackfork Creek was not significant. ....................................................................................................................29 1.10. Linear regression trend for gravel percentage. Gravel percentage increased significantly longitudinally (p<0.0001, R2=0.30). .......................................................30 1.11. Linear regression trends for flow variables. Flow variables (velocity, discharge, Reynolds number) increased significantly longitudinally (p=0.0321, R2=0.04; p<0.0001, R2=0.13; p=0.0137, R2=0.05; respectively). ................................................................31 1.12. Kiamichi River, Oklahoma 13.3 km below the confluence with Jackfork Creek showing changes in sediment deposition over time. Aerial photograph from 1979 overlain with digitized channel banks and deposition bars from 1995 (left) and 1995 aerial photograph overlain with digitized banks and deposition bars from 1979 (right).32 1.13. Kiamichi River, Oklahoma confluence with Jackfork Creek showing change in channel shape over time. Aerial photograph from 1979 overlain with digitized channel banks from 1995 (left) and 1995 aerial photograph overlain with digitized channel banks from 1979 (right)..........................................................................................................33 2.1. Kiamichi River watershed with major impoundments, designated reaches (marked at the end of the reach), and sampled channel unit complexes. Inset shows Kiamichi River watershed in Oklahoma................................................................................................55 2.2. Principal components analysis (PCA) biplots of fluvial geomorphic variables of 131 sample transects in the Kiamichi River, Oklahoma labeled by mussel occurrence; absent (A) and present (P). ......................................................................................................56 2.3. Classification tree analysis (CTA) of effects of geomorphic, sediment and flow variables on freshwater mussel occurrence at 131 transects in the Kiamichi River, Oklahoma. Nodes were split using width:depth ratio (WD), bankfull velocity (BKFV) and D84 particle size fraction. The number of cases (i.e. transects) and percent mussel occurrence per class, present (P) or absent (A), is shown per node. Tenfold cross-validated relative error was 0.622. ...............................................................................57

ix

Figure Page 2.4. Classification tree analysis (CTA) of effects of geomorphic, sediment and flow variables on freshwater mussel relative abundance at 131 transects in the Kiamichi River, Oklahoma. Nodes were split using bank stability rating (INSTABILITY), D16, D50 and D84 particle size fractions. The number of cases (i.e. transects) and percent mussel relative abundance per class, low (0), moderate (1) or high (2) abundance is shown per node. Tenfold cross-validated relative error was 0.622. .............................................58

x

PREFACE

The objectives of this thesis were to: 1) characterize current landscape,

geomorphic, flow and sediment regime conditions of the Kiamichi River, Oklahoma

above Hugo Lake, 2) identify and quantify deviations from the morphologic form and

river function in the perturbed portion of the Kiamichi River below Jackfork Creek

compared with the unperturbed portion above Jackfork Creek and 3) determine

relationships between freshwater mussel occurrence and fluvial geomorphology of the

Kiamichi River. These objectives are addressed in two thesis chapters.

Chapter 1 addresses objectives 1 and 2 and presents research that investigates the

physical (geomorphic) characteristics of the Kiamichi River, measured and expected

longitudinal changes, and deviations from these expectations possibly attributable to the

impoundment of the Jackfork Creek tributary.

Chapter 2 addresses objective 3 and presents research that investigates

relationships between freshwater mussel occurrence and relative abundance and fluvial

geomorphic characteristics of the Kiamichi River.

1

CHAPTER I

FLUVIAL GEOMORPHIC CHARACTERIZATION OF THE KIAMICHI RIVER, OKLAHOMA

2

Abstract

Impoundments have a major impact on river form and function. Jackfork Creek,

a major tributary to the Kiamichi River, Oklahoma, was impounded in 1974. An analysis

of the Kiamichi River, Oklahoma was conducted to characterize fluvial geomorphic

condition, flow and sediment regimes and identify potential impacts from the

impoundment of the Jackfork Creek tributary to the morphological form and function of

the river. Twenty-six fluvial geomorphic variables were used to characterize river

condition. Most geomorphic, flow and sediment characteristics changed longitudinally as

expected. However, fluvial geomorphic anomalies were found at the Jackfork Creek

confluence. Fluvial geomorphic characteristics, especially channel dimension and

particle size around the Jackfork Creek confluence were not consistent with longitudinal

river changes or with fluvial geomorphic expectations. The Kiamichi River channel

appears to be adjusting to disturbance at the Jackfork Creek confluence.

Introduction

Dams interrupt and alter most of a river's hydrological and ecological processes

including flow of water, sediment, nutrients, energy and biota. These alterations, habitats

change and the ecology of the river is often significantly altered. The three major

geomorphic responses to stream impoundment are incision, aggradation and changes in

channel pattern (Ligon et al. 1995). Other changes include the streambed becoming finer

or coarser, channel widening or narrowing, change in lateral migration, change in riparian

vegetation and bank collapses. These adjustments can have ecological consequences

such as changes in nutrient and energy flux, riparian vegetation loss, and alteration of

3

periphyton, invertebrate and fish habitats. Human modifications of rivers through water

abstraction, channel modification and impoundments can cause a cascade of events in the

downstream and the upstream direction (Pringle 1997).

The Kiamichi River is at risk from human alterations, specifically water

abstraction and dam operation. In 2002, the Oklahoma Water Resources Board presented

two plans for water diversion from the Kiamichi River, one to provide water to Texas and

another to provide water to Oklahoma City, Oklahoma (OKWRB 2002). A temporary

moratorium by the Oklahoma legislature stopped transfer of water from the basin, so

these proposals have not yet been acted upon. During the past 30 years, the lower half of

the Kiamichi River has experienced a change in flow and sediment regimes resulting

from the construction and operation of Sardis Lake, and the river may be adjusting to

these changes, which can impact river system form and function.

Ligon et al. (1995) developed a five-step protocol for assessing ecological effects

of downstream geomorphic adjustments to impoundments. The first two steps are to

characterize and describe the channel and watershed including aerial photograph

interpretation to determine channel migration and changes in channel morphology, and to

determine existing flow and sediment regimes. This information is needed to quantify

downstream geomorphic adjustments to an impoundment. Our goal was to determine the

extent of impact from the impoundment of Jackfork Creek to the mainstem Kiamichi

River by quantifying longitudinal trends in fluvial geomorphic variables, sediment

deposition patterns and flow regime, and comparing this to longitudinal fluvial

geomorphic expectations.

4

Study Area

The Kiamichi River is a fourth order meandering stream originating in the

Ouachita Mountains of Oklahoma near the Arkansas border. The river flows through the

Ouachita Mountains and South Central Plains ecoregions (Woods et al. 2005) of

southeastern Oklahoma through Le Flore, Pushmataha and Choctaw counties. The

watershed is 4,700 km2 and is confined between the Ouachita and Kiamichi mountains.

Much of the river is contained by a series of narrow valleys with steep, rocky slopes,

ranging from 4 to 20 km wide. Elevation ranges from 480 m (above mean sea level

[MSL]) at the headwaters to 35 m MSL at the confluence with the Red River. The

longitudinal profile for the Kiamichi River shows no obvious knick points, with a basin

relief ratio of 0.00345. The Kiamichi River mainstem is free flowing for the first 248 km

of its length and is impounded by Hugo Lake Dam 29 km upstream from its confluence

with the Red River. Sardis Lake, impounded in 1974, is the only other large

impoundment directly impacting the river and is located on the Jackfork Creek tributary

(105 river km upstream from Lake Hugo and 165 river km upstream from the Kiamichi

River confluence with the Red River). Sardis Lake impounds the Jackfork Creek

tributary 4.4 km upstream from its confluence with the Kiamichi River. Sardis Lake

started operation in 1982, is 58 km2 with 188 km of shoreline and has 3.38–4.90x108 m3

of water storage.

Precipitation is high and uniform in the Kiamichi River Basin, but slightly higher

near the headwaters. Average annual precipitation is 119 cm, ranging from 109 to 144

cm, west to east. Most precipitation occurs in late spring (May), with less rainfall the

remainder of the year. This precipitation pattern contributes to a varied discharge pattern

5

from upstream to downstream and a hydrologically flashy stream system (USGS gaging

stations; Big Cedar #07335700, Clayton #07335790 and Antlers #07336200 [Figure 1]).

Methods

Flow and Sediment Regime

Stream flow and suspended sediment data collected by USGS at the Big Cedar and

Antlers, Oklahoma gaging stations were used to quantify flow and sediment regimes.

Stream discharge (m3 s-1) was used to create a flow-duration curve. Suspended sediment

discharge (tonnes per day) was used to create a transport-rate curve and define threshold

discharge (Qt). The calculated product of these curves is a magnitude-frequency curve

that is used to determine effective discharge (Qe; Wolman and Miller 1960).

Longitudinal Trends

A geomorphic survey was conducted along 212 km of the Kiamichi River, Oklahoma

above Lake Hugo. Channel pattern (braided or meandering), gradient and sinuosity were

determined using USGS topographic maps and used to designate 18 reaches (Appendix

I). The first eight reaches were not sampled because they are intermittent; physical

sampling and stream measurements began in reach 9 (N 34°38’14.701”, W

94°39’13.399”) and ended in reach 17 (N 34°15’17.561”, W 95°37’34.316”; Figure 2).

Meander wavelength and amplitude were measured at the watershed scale for each river

6

meander using 2005 National Agriculture Imagery Program (NAIP) aerial photos in

ArcGIS 8.0 (ESRI, Inc.).

Fluvial geomorphic condition was characterized with a longitudinal survey of the

Kiamichi River. The survey was conducted in early spring 2005 when the river was

navigable by canoe. All channel units were classified as pool, riffle or run. All channel

units from an upstream pool to the next downstream pool (i.e. pool, riffle, pool) were

designated as a channel unit complex. Ten percent of channel unit complexes were

randomly selected and sampled per reach (two minimum per reach; n = 44). Channel

units can be classified differently depending on water levels (Hilderbrand et al. 1999);

channel units were reassessed at the time of transect sampling, reclassified if necessary,

and one transect was placed in each distinct channel unit (n = 131).

Bankfull width and depth, flood-prone width, water surface slope (gradient) and

particle size were measured at one transect in each channel unit. Bankfull stage was

defined as the channel maintaining flow (i.e. highest elevation of deposition features;

Dunne and Leopold 1978). To decrease error, bankfull stage at every transect was

determined by one observer (S. Negus). Transect survey data were entered into

RiverMorph 3.0 software (Rivermorph LLC) to calculate width:depth ratio, entrenchment

ratio, area and hydraulic radius and to estimate Manning’s n, velocity, discharge and

sheer stress for bankfull stage. Froude number and Reynolds number were also calculated

for bankfull stage.

Sediment particles (n = 50 per transect, n = 150 per channel unit complex) were

measured at the b-axis, systematically, from bank to bank to bankfull stage. Particle

counts were entered into RiverMorph software to calculate particle size fractions (D16,

7

D35, D50, D84, D95, D100) and particle class percentages (e.g. bedrock, boulder). Bank

instability was calculated for each channel unit using Pfankuch’s bank stability rating

(Pfankuch 1975).

Fluvial geomorphic variables at each transect were analyzed with linear

regression. Variables were regressed against longitudinal distance downstream to

determine longitudinal (downstream) trends. Linear regression of bankfull area, bankfull

width, maximum bankfull depth and D50 above and below JFC was also analyzed.

Channel depth varies by channel unit type (Rosgen 1996) and gradient may vary by

channel unit, so linear regression of maximum bankfull depth and gradient by channel

unit type was analyzed. All statistics were calculated using SAS 9.1 software (SAS

Institute Inc., Cary, North Carolina) and α = 0.05 unless otherwise noted.

Impoundment Effects

Geomorphic variables upstream and downstream of Jackfork Creek (JFC) were

non-normally distributed and, therefore, analyzed with a Kruskal-Wallis test. We also

tested for differences in geomorphic variables at the JFC confluence, using the six

sampling sites closest to JFC, three above and three below, to assess localized impacts

due to impoundment.

Digital images of aerial photographs, digitalorthoquarterquads (DOQQs), from

1979, pre-operation of Sardis Lake, and 1995, post-operation of Sardis Lake, were

imported into ArcGIS 8.0 for georeferencing, digitization and overlay comparison. The

1979 digital images were georeferenced and then both sets of images were on-screen

digitized, creating a map of both banks of Kiamichi River channel. These maps were

8

then compared to determine what changes may have occurred naturally and due to the

impoundment of JFC. Deposition bars for both years were digitized, and their area and

frequency were calculated. A Student’s t-test was used to assess mean sediment bar area

below JFC before and after impoundment. A Rao-Scott chi-square test was used to

assess deposition bar frequency above and below JFC by year (1979 vs. 1995).

Results

Flow and Sediment Regime

The Kiamichi River is a hydrologically flashy stream (Figure 1). The Big Cedar

log-linear transport rate (SS = 0.02Q1.08, R2 = 0.82, p < 0.0001; Figure 3) had a Qt of

0.003 m3 s-1. The Antlers log-linear transport-rate (SS = 0.04Q1.18, R2 = 0.78, p < 0.0001)

had a Qt of 0.099 m3 s-1. The Big Cedar magnitude-frequency curve peak had a Qe equal

to 127 m3 s-1; the Antlers magnitude-frequency curve peak had a Qe equal to 708 m3s-1.

The Big Cedar flow-duration curve is indicative of the flashy nature of the Kiamichi

River and shows that low flows dominate at this headwater location. The Antlers flow-

duration curve also shows the flashy nature of the stream but at a higher magnitude,

which is expected at this downstream location.

Longitudinal Trends

Significant downstream trends were found in most of the fluvial geomorphic

variables measured. Geomorphic variables (width, area and width:depth ratio) increased

significantly in the downstream direction (Table 1; Figure 4). Width and area increased

9

significantly downstream (longitudinally) above JFC; however, there was no significant

longitudinal change below JFC. Entrenchment ratio and bank instability decreased

significantly in the downstream direction (Figure 5). Sediment variables (D35, D50,

Figure 6; D84, D95, Figure 7; sand, cobble and bedrock, Figure 8) decreased in the

downstream direction. D50 decreased significantly downstream above JFC; however,

there was no significant longitudinal change below JFC (Figure 9). Gravel percentage

increased in the downstream direction (Table 2; Figure 10). Flow variables (velocity,

discharge and Reynolds number) increased significantly in the downstream direction

(Table 3; Figure 11). There was no significant longitudinal change in maximum depth,

gradient, D16, D100, D84:D16 ratio, clay or boulder percentages, Manning’s n, Froude

number or sheer stress. There was no significant longitudinal change in maximum depth

above or below JFC. We also found no longitudinal change in maximum depth or

gradient within channel units.

Impoundment Effects

We found significant changes in geomorphic (Table 1), sediment (Table 2) and

flow variables (Tables 3) above and below JFC. Width, area, width:depth ratio, velocity,

discharge and sheer stress were significantly greater below than above JFC. Bank

instability rating was significantly greater above JFC. Particle size fractions D16, D35

and clay and gravel percentages were significantly greater below JFC. D95 and sand

percentages were significantly greater above JFC. No differences were found in

maximum depth, entrenchment ratio, gradient, Manning’s n, D50, D84, D100, D84:D16

ratio, cobble, boulder, bedrock percentages, Froude number or Reynolds number.

10

We found pronounced differences in geomorphic (Table 1) and sediment (Table

2) variables at the JFC confluence. The area immediately below JFC had significantly

larger D35, D50, D84 and D95 particle size fractions and boulder percentages. Width,

area, maximum depth and sand percentage were significantly lower immediately below

JFC. No differences were found in width:depth ratio, entrenchment ratio, Manning’s n,

bank instability, gradient, D16, D100, D84:D16 ratio, percentages of clay, gravel, cobble

and bedrock, or any flow variables (velocity, discharge, Reynolds, Froude and sheer

stress).

Morphological change was evident below the JFC confluence. Deposition bar

area below JFC was significantly greater in 1995 than 1979 (t = 2.05, df = 304, p =

0.0411). Deposition bar frequency was significantly different than expected (χ2 = 6.12, df

= 1, p = 0.0134); frequencies were highest below JFC in 1995. Deposition patterns and

channel shape have changed below the JFC confluence between 1979 and 1995 (Figure

12 and Figure 13; respectively).

Discussion

Effective discharge levels calculated using magnitude-frequency analyses were

greater than our measured bankfull stage in the Kiamichi River. Effective discharge is

defined as the flow that does the most work or moves the most sediment and is presumed

to equal bankfull stage (Wolman and Miller 1960). Effective discharge is large when

sediment particles (e.g. D50) are large because it takes greater magnitude flows to move

larger particles. Many areas in the Kiamichi River contain large sediment particles; D50

ranged from 1.5-180 mm (sand to large cobble) averaging 30 mm (medium gravel).

11

Stream systems without enough flow competence (stream power) to convey the dominant

sediment particles will experience stream aggradation (i.e. deposition of sediment in the

stream channel; Reiser et al. 1989). We found evidence of an increase in sediment

deposition over time in the stream channel below Jackfork Creek. This may be partially

attributable to the suppression of extreme flood flows by Sardis Lake. Magnitude-

frequency analysis using suspended sediment may not be the most accurate way to

determine effective discharge (Nash 1994; Emmett and Wolman 2001). Including

bedload sediment in magnitude-frequency analysis, in rivers with a high abundance of

large sediment particles (i.e. the Kiamichi River), should increase effective discharge

estimates. Our predictions of channel change and sediment deposition over time are

probably an underestimate. To independently evaluate this conclusion we also looked at

longitudinal stream trends and possible impoundment effects.

Our analysis of fluvial geomorphic variables for longitudinal trends and

impoundment effects revealed geomorphic anomalies. In the Kiamichi River, estimated

bankfull discharge and velocity increased significantly in the downstream direction as

expected. Stream discharge increases downstream with the addition of tributary flows.

With increasing discharge natural channels may increase in dimensions (bankfull area,

channel width and/or depth) to accommodate increased flow (Knighton 1998). Bankfull

cross-sectional area is a function of bankfull width and depth. Bankfull area and bankfull

width significantly increased in the downstream direction; however, no longitudinal

change was found in maximum bankfull depth. At the Jackfork Creek confluence,

bankfull width, bankfull area and maximum bankfull depth were significantly lower at

downstream sites, contrary to longitudinal results and published expectations (Knighton

12

1998). Tributaries have been found to reset longitudinal trends (Knighton 1998);

however, if Jackfork Creek was resetting downstream trends we would expect to see a

different yet significant longitudinal trend below this tributary. This was not the case

with bankfull area, bankfull width or maximum bankfull depth; these variables had

significant longitudinal trends above Jackfork Creek with no longitudinal change below.

Anomalies were also found in sediment variables. Sediment size is expected to

decrease downstream in a river (downstream fining; Knighton 1998), the occurrence of

small diameter particles (sands and gravels) should increase and that of large diameter

particles (cobbles and boulders) should decrease in the downstream direction. Median

particle diameter (D50) and other particle size fractions should also decrease

downstream. Sediment particle size fractions (D35, D50, D84 and D95) decreased

significantly in the downstream direction as expected; however, D35, D50, D84 and D95

were higher in downstream sites at the Jackfork Creek confluence, contrary to

longitudinal expectations. There was no longitudinal change in D16 and D100, which

may be explained by the occurrence of clay and boulders throughout the stream. The

observed smallest particles (D16; clay) and observed largest particles (D100; boulder)

were ubiquitous in the Kiamichi River. Clay was observed throughout the river, settled

out in slack-water areas, in banks and in the interstices of some deposition bars. Boulders

were observed at many sites throughout the river. D84:D16 ratio is a measure of

substrate size variability. Alluvial streams with homogenous sediments will have

D84:D16 ratios approaching one; heterogeneous streams will have much higher values.

There was no longitudinal change in D84:D16 ratio in the Kiamichi River values ranged

from 4-5422 and an average of 345 (i.e. sediment sizes were variable throughout the

13

river, heterogeneous). This is expected with the occurrence of clay and boulders

throughout the river. Extreme variability of sediment sizes upstream to downstream was

found by Splinter (2006) in other Ouachita Mountain streams. Clay percentages did not

change longitudinally; however, clay was significantly higher downstream of Jackfork

Creek. Boulder percentages did not change longitudinally, had no significant difference

above or below JFC, however, boulder percentages were significantly higher below JFC

at the tributary confluence. With the shift in topography leaving the Ouachita Mountains

for the South Central Plains ecoregion (Woods et al. 2005), we would expect boulder

occurrence to decrease longitudinally or to find significantly less boulders downstream

versus upstream. The shift in particle size below Jackfork Creek may be an artifact of

the tributary itself. Tributaries can reset downstream fining trends (Knighton 1998, Rice

et al. 2001); however, reservoirs act as a sediment sink decreasing suspended sediment

load and trapping coarse load (Petts 1982). If Jackfork Creek were resetting downstream

fining we should see a different yet significant longitudinal trend below this tributary.

This was not the case with our median particle size (D50). The larger particle sizes

immediately below the Jackfork Creek confluence may indicate that the tributary is a

source of large size particles, changes in flow competency, or both. Flows from Sardis

Lake may be scouring the tributary, depositing larger particles at the river confluence and

moving smaller sized particles downstream.

A channel that is not changing in dimension, pattern or profile and is neither

aggrading nor degrading is considered stable; channel stability is dependent on a stream's

ability to consistently transport its sediment load (Rosgen 1996). There is little apparent

change in channel shape in the Kiamichi River. Few areas appeared to be affected, with

14

the exception of the channel at the Jackfork Creek confluence where changes in channel

and deposition pattern were evident. The Kiamichi River at the Jackfork Creek

confluence is adjusting in channel dimension and pattern to some disturbance or change

in flow and sediment regime. This instability is apparent in the contrary results found in

fluvial geomorphic variables at the Jackfork Creek tributary. Our results showed that the

impoundment of Jackfork Creek has had an impact on the geomorphology of the

Kiamichi River; however, we can only draw conclusions for the localized area around the

tributary confluence. Fluvial geomorphic differences below the Jackfork Creek

confluence could be influenced by differences in watershed-scale factors (e.g. ecoregion;

Splinter 2006) that were not accounted for in this study.

15

References

Dunne, T. and L.B. Leopold. 1978. Water in environmental planning. W.H. Freeman and Co., San Francisco, CA.

Emmett, W. W., and M. G. Wolman. 2001. Effective discharge and gravel-bed rivers.

Earth Surface Processes and Landforms 26:1369-1380. Hilderbrand, R. H., A. D. Lemly, and C. A. Dolloff. 1999. Habitat sequencing and the

importance of discharge in inferences. North American Journal of Fisheries Management 19:198-202.

Knighton, D., 1998. Fluvial Forms and Processes. Oxford University Press, New York. Ligon, F. K., W. E. Dietrich, and W. J. Thrush. 1995. Downstream ecological effects of

dams. Bioscience 45:183-192. Nash, D. B. 1994. Effective sediment-transporting discharge from magnitude-frequency

analysis. The Journal of Geology 102:79-95. OKWRB. 2002. Southeast Oklahoma Water Resources Development Plan. Oklahoma

Water Resources Board. Oklahoma City, OK. Petts, G. E. 1982. Channel changes in regulated rivers. Pages 117-142 in Papers in earth

studies, Lovatt Lectures, Worcester. Pfankuch, D. J. 1975. Stream reach inventory and channel stability evaluation. U.S.

Forest Service, Northern Region, Missoula, Montana. Pringle, C. M. 1997. Exploring how disturbance is transmitted upstream: going against

the flow. Journal of the North American Benthological Society 16(2):425-438. Reiser, D. W., M. P. Ramey, and T. A. Wesche. 1989. Flushing flows. Pages 91-131 in J.

A. Gore, and G. E. Petts, editors. Alternatives in regulated river management. CRC Press.

Rice, S. P., M. T. Greenwood, and C. B. Joyce. 2001. Tributaries, sediment sources and

the longitudinal organization of macroinvertebrate fauna along river systems. Canadian Journal of Fisheries and Aquatic Sciences 58:824-840.

Rosgen, D. L. 1996. Applied River Morphology, Second edition. Wildland Hydrology,

Pagosa Springs, CO. Splinter, D. K. 2006. Spatial patterns in the fluvial system: comparisons among three

eastern Oklahoma ecoregions. PhD. Dissertaion. Oklahoma State University Stillwater, OK.

16

Wolman M. G. and J. P. Miller. 1960. Magnitude and frequency of forces in geomorphic

processes. Journal of Geology 68:54-74. Woods, A.J., Omernik, J.M., Butler, D.R., Ford, J.G., Henley, J.E., Hoagland, B.W.,

Arndt, D.S., Moran, B.C., 2005. Ecoregions of Oklahoma (color poster with map, descriptive text, summary tables, and photographs). Reston, Virginia, U.S. Geological Survey (map scale 1:1,250,000).

17

Table 1. Simple statistics for morphologic characteristics at bankfull stage (n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below the Jackfork Creek confluence (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold. Linear Regression Kruskal-Wallis Dam Kruskal-Wallis JFC

Variables Mean (±SD) Median (Min-Max) p-value R2 Χ2 (df) p-value Χ

2 (df) p-value Width (m) 36.6 (16.0) 34.0 (13.3-85.2) <0.0001 0.25 23.82 (1) <0.0001 5.95 (1) 0.0147 Depth (m) 1.38 (0.50) 1.25 (0.49-3.90) 0.5165 0.00 0.02 (1) 0.8982 5.06 (1) 0.0244 Area (m2) 36.14 (21.92) 30.06 (3.12-96.17) <0.0001 0.13 10.76 (1) 0.0010 7.47 (1) 0.0063 Width:depth ratio 40.95 (22.26) 35.95 (13.10-135.94) <0.0001 0.19 24.60 (1) <0.0001 1.61 (1) 0.2046 Entrenchment ratio 3.17 (5.87) 1.16 (1-37.79) <0.0001 0.11 3.31 (1) 0.0690 0.35 (1) 0.5567 Bank instability 78 (8) 79 (64-96) 0.0011 0.08 28.96 (1) <0.0001 0.81 (1) 0.3690 Gradient (%) 0.0032 (0.0080) 0.0002 (1x10-7-0.0610) 0.4875 0.00 3.66 (1) 0.0558 0.83 (1) 0.3628 Manning's n 0.0259 (0.0325) 0.0158 (0.0008-0.1623) 0.9519 0.00 1.92 (1) 0.1655 1.39 (1) 0.2393

18

Table 2. Simple statistics for substrate variables (particle size fractions and particle class percentages; n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below Jackfork Creek (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold. Linear Regression Kruskal-Wallis Dam Kruskal-Wallis JFC

Variables Mean (±SD) Median (Min-Max) p-value R2 Χ2 (df) p-value Χ

2 (df) p-value D16 (mm) 5.19 (7.79) 1.60 (0.02-35.25) 0.1239 0.02 4.66 (1) 0.0308 0.86 (1) 0.3533 D35 (mm) 16.21 (16.63) 10.48 (0.05-77.51) 0.0336 0.04 4.83 (1) 0.0279 8.87 (1) 0.0029 D50 (mm) 30.05 (28.11) 21.66 (1.48-180) 0.0066 0.06 1.43 (1) 0.2327 8.30 (1) 0.0040 D84 (mm) 143 (301) 80 (15-2048) 0.0179 0.04 0.47 (1) 0.4925 7.47 (1) 0.0063 D95 (mm) 330 (515) 164 (30-2048) 0.0396 0.03 4.77 (1) 0.0290 6.95 (1) 0.0084 D100 (mm) 554 (599) 362 (56-2048) 0.5681 0.00 3.18 (1) 0.0745 2.83 (1) 0.0928 D84:D16 345 (901) 36 (4-5422) 0.1410 0.02 1.91 (1) 0.1670 0.09 (1) 0.7697 Clay (%) 5 (9) 0 (0-42) 0.4642 0.00 7.03 (1) 0.0080 0.28 (1) 0.5973 Sand (%) 21 (20) 0 (0-76) 0.0209 0.04 10.51 (1) 0.0012 4.42 (1) 0.0355 Gravel (%) 49 (17) 48 (16-90) <0.0001 0.30 23.81 (1) <0.0001 0.06 (1) 0.8067 Cobble (%) 20 (14) 18 (0-62) 0.0002 0.11 0.02 (1) 0.9018 3.14 (1) 0.0766 Boulder (%) 3 (5) 2 (0-20) 0.3131 0.01 2.36 (1) 0.1246 7.28 (1) 0.0070 Bedrock (%) 1 (5) 0 (0-42) 0.0034 0.07 1.82 (1) 0.1778 0.00 (1) 1.0000

19

Table 3. Simple statistics for estimated flow variables at bankfull stage (n = 131) and results for linear regression of longitudinal distance and Kruskal-Wallis (K-W) rank tests above/below Jackfork Creek (Dam) and at the Jackfork Creek confluence (JFC). Significant results at α = 0.05 are bold. Linear Regression Kruskal-Wallis Dam Kruskal-Wallis JFC

Variables Mean (±SD) Median (Min-Max) p-value R2 Χ2 (df) p-value Χ

2 (df) p-value Velocity (ms-1) 1.06 (0.63) 0.81 (0.06-3.23) 0.0321 0.04 5.91 (1) 0.0151 0.04 (1) 0.8452 Discharge (m3s-1) 39 (36) 28 (8x10-4-230) <0.0001 0.13 12.14 (1) 0.0005 2.14 (1) 0.1432 Froude number 0.30 (0.18) 0.20 (0.02-0.81) 0.0911 0.02 3.00 (1) 0.0835 0.15 (1) 0.6963 Reynolds number 1005 (758) 842 (1-4126) 0.0137 0.05 3.67 (1) 0.0553 0.34 (1) 0.5582 Sheer stress 0.02 (0.06) 4x10-4 (2.35x10-7-0.59) 0.6010 0.00 5.08 (1) 0.0242 0.47 (1) 0.4945

20

Figure 1. Daily hydrographs showing discharge of the Kiamichi River in 2007 at the Big Cedar (top), Clayton (middle), and Antlers (bottom), Oklahoma USGS gaging stations, respectively. *Note change in discharge magnitude between graphs.

21

Figure 2. Kiamichi River watershed with major impoundments, USGS gage stations (A, Big Cedar #07335700; B, Clayton #07335790; C, Antlers #07336200), designated reaches (marked at the end of the reach). Lower inset shows Kiamichi River watershed in Oklahoma. Upper inset shows Kiamichi River at the confluence with Jackfork Creek and six closest sample sites. Sites A and B are mussel beds.

22

0.001

0.01

0.1

1

10

100

1000

10000

100000

1000000

0.1 1 10 100 1000 10000

Discharge

A. L

og

-lin

ear

Tra

nsp

ort

Rat

eB

. Fre

qu

ency

of

Flo

w O

ccu

rren

ceC

. Pro

du

ct o

f M

agn

itu

de

and

Fre

qu

ency

A

B

CQe

Qt

0.1

1

10

100

1000

10000

100000

1000000

10000000

100000000

1000000000

1 10 100 1000 10000 100000

Discharge

A. L

og

-lin

ear

Tra

nsp

ort

Rat

eB

. Fre

qu

ency

of

Flo

w O

ccu

rren

ceC

. Pro

du

ct o

f M

agn

itu

de

and

Fre

qu

ency

A

B

C

Qe

Qt

Figure 3. Log-linear transport-rate (A), flow-frequency (B), and magnitude-frequency (C) curves for Big Cedar and Antlers, Oklahoma USGS gage stations, respectively, showing effective discharge (Qe) and threshold discharge (Qt) for the Kiamichi River, Oklahoma.

23

Figure 4. Linear regression trends for geomorphic variables. Bankfull width, area and width:depth ratio increased significantly longitudinally (p<0.0001, R2=0.25; p<0.0001, R2=0.13; p<0.0001, R2=0.19; respectively).

24

Figure 5. Linear regression trends for geomorphic variables. Entrenchment ratio and bank instability decreased significantly longitudinally (p<0.0001, R2=0.11; p=0.0011, R2=0.08; respectively).

25

Figure 6. Linear regression trends for sediment variables. Particle size fractions (D35, D50) decreased significantly longitudinally (p=0.0336, R2=0.04; p=0.0066, R2=0.06; respectively).

26

Figure 7. Linear regression trends for sediment variables. Particle size fractions (D84, D95) decreased significantly longitudinally (p=0.0179, R2=0.04; p=0.0396, R2=0.03; respectively).

27

Figure 8. Linear regression trends for sediment variables. Particle class percentages (sand, cobble, bedrock) decreased significantly longitudinally (p=0.0209, R2=0.04; p=0.0002, R2=0.11; p=0.0034, R2=0.07; respectively).

28

Figure 9. Linear regression trends for median particle size (D50) above and below the Jackfork Creek confluence. D50 decreased significantly above Jackfork Creek (p<0.0001, R2=0.39). Linear regression of D50 below Jackfork Creek was not significant.

29

Figure 10. Linear regression trend for gravel percentage. Gravel percentage increased significantly longitudinally (p<0.0001, R2=0.30).

30

Figure 11. Linear regression trends for flow variables. Flow variables (velocity, discharge, Reynolds number) increased significantly longitudinally (p=0.0321, R2=0.04; p<0.0001, R2=0.13; p=0.0137, R2=0.05; respectively).

31

Figure 12. Kiamichi River, Oklahoma 13.3 km below the confluence with Jackfork Creek showing changes in sediment deposition over time. Aerial photograph from 1979 overlain with digitized channel banks and deposition bars from 1995 (left) and 1995 aerial photograph overlain with digitized banks and deposition bars from 1979 (right).

32

Figure 13. Kiamichi River, Oklahoma confluence with Jackfork Creek showing change in channel shape over time. Aerial photograph from 1979 overlain with digitized channel banks from 1995 (left) and 1995 aerial photograph overlain with digitized channel banks from 1979 (right).

33

CHAPTER II

FLUVIAL GEOMORPHOLOGY AND FRESHWATER MUSSEL DISTRIBUTION

34

Abstract

A geomorphic analysis of the Kiamichi River, Oklahoma, including 26 fluvial

geomorphic (mesohabitat) features, was conducted to characterize the relationship

between fluvial geomorphology and freshwater mussel (Bivalvia: Unionidae) occurrence

and relative abundance. Fine sediment and channel stability, specifically bank stability,

were found to influence freshwater mussel occurrence and relative abundance;

width:depth ratio (a measure of channel stability), D84 (a quantification of sediment

particle size, specifically large particles) and bankfull velocity predicted mussel

occurrence correctly 55% of the time. Bank instability (Pfankuch’s rating), D16, D50 and

D84 (quantifications of small, moderate and large particles; respectively) predicted

mussel relative abundance correctly 53% of the time. Although the predictive power of

mesohabitat features was low (less than 56%), fluvial geomorphic features explained a

small but important portion of variability in freshwater mussel occurrence and relative

abundance at the mesohabitat scale.

Introduction

Freshwater mussels (Bivalvia: Unionidae) inhabit a variety of habitats including

large and small watersheds, high and low gradient streams, all channel unit types and a

large range of flow and sediment regimes (MacMahon and Bogan 2001). Despite this

large range of habitats, unionid mussel species are declining worldwide (Lyedeard et al.

2004). Unionid mussels are critically imperiled in the United States (Strayer et al. 2004);

approximately 20% of recognized unionid species are listed as endangered (USFWS

1999) and 68% of native mussel species are at risk of future extinction (Biggins and

35

Butler 2000). Unionid mussels are declining due to habitat loss; the decline can be

directly attributed to the construction and operation of dams and subsequent changes in

stream geomorphology (Hughes and Parmalee 1999).

Approximately 90% of unionid mussel species in North America occur in the

southeastern United States (Neves et al. 1997). Oklahoma has 55 unionid mussel species

(Galbraith et al. 2008); the Kiamichi-Little River Basin supports approximately 80% of

all unionid mussel species found in the state (Vaughn 2005). The Kiamichi River has 30

unionid mussel species including seven state-designated imperiled or vulnerable species,

two federally endangered species (Leptodea leptodon and Arkansia wheeleri), and one

federally threatened species (Megalonaias nervosa; Galbraith et al. 2008). The Kiamichi

River contains one of the only remaining viable populations of the Ouachita Rock

Pocketbook (Arkansia wheeleri) mussel, one of the rarest mussels in North America

(Vaughn and Pyron 1995). However, a recent survey of the Kiamichi River found only

three live Ouachita Rock Pocketbook individuals (Galbraith et al. 2008). This species

appears to have experienced a serious decline in abundance in recent years.

To preserve native mussel diversity it is important to understand the habitat and

fluvial geomorphic characteristics potentially limiting their occurrence. The fluvial

geomorphic characteristics determining unionid mussel occurrence and distribution have

been widely studied. Early studies focused on macrohabitat characteristics at large

spatial scales (watershed level) and found stream size and surface geology important in

determining the broad-scale distribution of unionid mussel species (van der Schalie 1938,

Strayer 1983, Strayer 1993). However, these characteristics did not account for mussel

spatial patchiness (i.e. mussel beds) nor predict mussel occurrence at smaller spatial

36

scales. Many studies have attempted to identify factors determining unionid mussel

distribution at small spatial scales using microhabitat characteristics such as gravels or

grain size, flow refugia, water depth, current speed and sedimentation (Strayer 1981,

Vannote and Minshall 1982, Salmon and Green 1982, Way et al. 1989, Holland-Bartels

1990, Strayer and Ralley 1993, Layzer and Madison 1995, Strayer 1999, Hardison and

Layzer 2001). Characteristics at this scale had weak predictive power in determining

mussel occurrence (Strayer and Ralley 1993).

In recent studies, emphasis has been placed on mesohabitat (within reach level)

characteristics (such as sheer stress, velocity, habitat quality, flow stability, fine

substratum and specific conductance) because they are easier to measure and as Strayer

and Ralley (1993) hypothesized, may more accurately predict unionid mussel occurrence.

Several studies (Howard and Cuffey 2003, McRae et al. 2004, Gangloff and Feminella

2007) found correlations between mesohabitat characteristics and unionid mussel

distributions and abundance. Our goal was to determine how fluvial geomorphic

(mesohabitat) features influenced unionid mussel occurrence and relative abundance in

the Kiamichi River, Oklahoma.

Methods

A fluvial geomorphic assessment of the Kiamichi River was conducted with a

longitudinal survey and cross-sectional transect sampling. Channel pattern (braided or

meandering), gradient and sinuosity were determined using USGS topographic maps, and

this information was used to designate 18 reaches in the Kiamichi River above Hugo

Lake (Appendix 1). The first eight reaches were not sampled because they were

37

intermittent and therefore lacked freshwater mussels. Physical sampling and stream

measurements began in reach 9 (N 34°38’14.701”, W 94°39’13.399”) and ended in reach

17 (N 34°15’17.561”, W 95°37’34.316”; Figure 1). The longitudinal survey was

conducted in early spring 2005 when the river was navigable by canoe. Channel units

(pool, riffle and run) were classified, and all channel units from upstream pool to the next

downstream pool (e.g. pool, riffle, pool) were designated as one channel unit complex.

We randomly selected and sampled 10% of channel unit complexes per reach (two

minimum per reach; n = 44; Figure 1). Channel units can be classified differently

depending on water levels (Hilderbrand et al. 1999), so we reassessed channel units at the

time of transect sampling, reclassified them if necessary and placed a transect across each

distinct channel unit (n = 131, including 10 known mussel beds).

We measured bankfull width and depth, flood-prone width, water surface slope

(gradient) and particle size at each transect. Bankfull stage is defined as the channel

maintaining flow (Dunne and Leopold 1978; i.e. highest elevation of deposition features).

To decrease error, bankfull stage at every transect was determined by one observer (S.

Negus). Transect survey data was entered into RiverMorph 3.0 software (Rivermorph

LLC.), to calculate width:depth ratio, entrenchment ratio, area and hydraulic radius, and

to estimate Manning’s n, velocity, discharge and sheer stress at bankfull stage. Froude

number and Reynolds number were also calculated for bankfull stage.

Sediment particles, measured at the b-axis, were sampled bank to bank to bankfull

stage (n = 50 per transect, n = 150 per channel unit complex). Particle measurements

were entered into RiverMorph software to calculate sediment size fractions (D16, D35,

D50, D84, D95, D100) and particle class percentages (e.g. bedrock, boulder). Bank

38

instability was calculated for each channel unit using Pfankuch’s bank stability rating

(Pfankuch 1975).

Occurrence (presence or absence) and relative abundance of live native unionid

mussels were determined for each channel unit per site (site). To determine mussel

occurrence and an estimate of abundance, sites were walked bank to bank four times and

longitudinally twice searching for mussels. Deep turbid pools were sampled for mussels

across each transect by touch. Mussel relative abundance was grouped into broad

classes: Low (0) = absent and very low abundance (one or two live mussels found in

channel unit), moderate (1) = moderate (had more than one or two live mussels but less

than a mussel bed) and high (2) = high and very high abundance (i.e. mussel beds).

Logistic regression was used to relate fluvial geomorphic characteristics to mussel

occurrence. The first model tested the probability of mussel presence or absence in

relation to fluvial geomorphic characteristics, using a type 3 likelihood ratio test (Allison

1999). The second model related fluvial geomorphic characteristics to mussel relative

abundance, a surrogate for presence-absence of mussel beds, again using a type 3

likelihood ratio test. Classification tables were calculated to assess model fit, when

possible. Some variables were highly correlated, and to reduce multicollinearity a

dummy linear regression model was run. The variable with the highest variance inflation

was dropped and the model was run without it; this was repeated until variance inflation

dropped below 10 and tolerance approached 0.4 (Allison 1999). The remaining variables

used in the linear regression model were: width, maximum depth, width:depth ratio,

entrenchment ratio, D50, D84:D16, percentages of clay, gravel, cobble and boulder,

velocity, discharge, sheer stress and bank instability. Percentage variables were

39

transformed using a standard arcsine-square root transformation. All regressions were

performed using SAS version 9.1 statistical software (SAS Institute, Inc.) and α =0.05.

Principle components analysis (PCA) was used to determine relationships

between fluvial geomorphic variables from 131 transects in the Kiamichi River.

Principle components analysis was conducted on a correlation matrix containing the

previously reduced set of geomorphic variables (width, maximum depth, width:depth

ratio, entrenchment ratio, D50, D84D16, percentages of clay, gravel, cobble and boulder,

velocity, discharge, sheer stress and bank instability). Eigenvalues greater than those

predicted under the broken-stick model were considered meaningful (Jackson 1993,

Dauwalter et al. 2007). Variable loading per axis greater than 0.3 were considered

important. Principle components analysis was run using Primer version 5.2.9 statistical

software (Primer-E Ltd.).

Classification tree analysis (CTA) was used to determine the influence of fluvial

geomorphic features on freshwater mussel occurrence and relative abundance.

Classification tree analysis was conducted on all variables and on the reduced variable

set. The final tree contained the smallest relative error based on 10-fold cross-validation

(De’ath and Fabricious 2000, Dauwalter et al. 2007). Each CTA was run using CART

version 6.0 software (Salford Systems).

Results

We surveyed channel morphology and sediment, and calculated flow

characteristics at 131 sites in the Kiamichi River, including 10 known mussel beds

(Vaughn and Pryon 1995). Morphological, sediment and flow characteristics (Table 1)

40

were highly variable throughout the river. Mussel presence was observed at 83 sites.

Fifty-seven sites were classified as moderate (n = 37) or high (n = 20) relative abundance,

26 sites had very low relative abundance (i.e. one or two live mussels were found in

channel unit). Mussels were absent (not detected) at 48 sites. Sites with very low

relative abundance and sites with no mussel presence detected were combined for

analysis and classified as low (n = 74). Mussels were found in all sampled reaches; reach

nine, the farthest upstream reach, had very low mussel relative abundance (Table 2).

Mussels were present in all channel unit types but were most abundant in riffles and runs

(Table 3).

Maximum depth and D50 predicted mussel presence (likelihood ratio test; χ2 =

5.57, df = 1, p = 0.0182; χ2 = 4.96, df = 1, p = 0.0260; respectively); however, the model

had low predictability (53.4%). For mussel relative abundance, D50 again was found as

a predictor (likelihood ratio test; χ2 = 9.01, df = 1, p = 0.0027).

Principle components analysis of fluvial geomorphic variables in relation to

mussel occurrence revealed three informative principle components (Table 4). PCA axis

1 was influenced by sediment and channel entrenchment, axis 2 was influenced by fine

sediments and width and axis 3 represented flow variables, velocity, discharge and sheer

stress. Principle components analysis biplots showed some separation between transects

that had mussels present or absent along PCA axis 1 (Figure 2). Three outliers in the

PCA analysis had no mussel presence. These outliers are characterized by large substrate

(high D50), high bank instability and high estimated bankfull velocities.

41

Classification tree analysis of fluvial geomorphic variables in relation to

freshwater mussel occurrence and relative abundance was significantly influenced by

sediment and channel stability. The lowest CTA tree for mussel occurrence with the

reduced set of variables, had a minimum cross-validation relative error of 0.802, 35

nodes, six that were terminal. The lowest tree for mussel occurrence with the full set of

variables, had a minimum cross-validation relative error of 0.622, seven nodes, four that

were terminal (Figure 3). Classification tree analysis with all variables was used because

of its reduced error rate and simplicity of the resulting tree. Predictive value of this

model was low (55%); the influencing variables were channel stability (width:depth

ratio), sediment (D84) and stream flow (estimated bankfull velocity; Table 5). CTAs run

for relative abundance classes had similar results and only the model with all variables

was kept. The lowest tree had a minimum cross-validation relative error of 0.622 and

had nine nodes, five that were terminal (Figure 4). This model had low predictive power

(53%); however it predicted class two (high relative abundance; i.e. mussel beds)

correctly 85% of the time. The influencing variables were bank instability and sediment

(D16, D50 and D84; Table 6).

Discussion

We found freshwater mussel occurrence in the Kiamichi River to be patchy, with

mussels present in low abundance throughout most of the river and in high abundance in

mussel beds. No fluvial geomorphic variables predicted mussel occurrence or relative

abundance with a high probability; however, all models did have significant sediment

components and PCA and CTA models were significantly influenced by channel

42

stability, specifically, bank stability. The model with the highest predictive power

(mussel occurrence CTA; 55%) was only slightly more likely to predict mussel

occurrence than chance; however the relative abundance CTA model predicted high

relative abundance correctly 85% of the time. This model predicts that when bank

instability is low (i.e. stable banks; less than 68) there is a high chance of finding a high

abundance of mussels. When bank instability is high (greater than 68) and there is an

abundance of fine sediment particles (D16 less than 1.9 mm; i.e. clays and fine sands)

there is no chance of finding a high abundance of freshwater mussels. When bank

instability is high, there are less smothering fine sediments and median particle size is

moderate (D50 less than 52mm); there is a high probability of finding mussels in high

abundance. In areas of high bank instability, less fine sediment, but high median particle

sizes (D50 greater than 52mm) there is again no chance of finding a high abundance of

mussels.

The predictive power of all models tested was low. This may be attributable to

our use of mussel occurrence or relative abundance; however, microhabitat studies had

low predictive power determining habitat use for species composition and mussel

occurrence (Tevesz and McCall 1979, Strayer 1981, Holland-Bartels 1990), and Gangloff

and Feminella (2007) found that species richness was related to stream size but that

mussel abundance was related to stream geomorphology. McRae et al. (2004) found that

mussel species richness was significantly related to fluvial geomorphic variables but they

also found that mussel distribution and abundance could be predicted by a combination of

fluvial geomorphic variables.

43

Fluvial geomorphic (mesohabitat) variables were hypothesized by Strayer and

Ralley (1993) to potentially be more useful in predicting freshwater mussel distribution

than previously used micro and macrohabitat variables. Presence of fine sediment (i.e.

silts and clays), is likely negatively affecting freshwater mussel occurrence and

abundance in the Kiamichi River, Oklahoma. Mussel species assemblage richness was

found to increase as fine sediments decreased (McRae et al. 2004). Increasing channel

stability, particularly bank stability and width:depth ratio, also appear to increase the

chance of mussel occurrence. McRae et al. (2004) found visual estimation of habitat,

including bank stability, a good indicator of overall mussel habitat quality. It is also

likely that flow characteristics are influencing mussel occurrence; we found estimated

bankfull velocity to be important. When width:depth ratios were low, mussel occurrence

was more likely in areas with higher bankfull velocities (greater than 1.75 m s-1),

probably riffles and runs. Others using fluvial geomorphic variables have found bankfull

and high flow sheer stress (Howard and Cuffey 2003, Gangloff and Feminella 2007) or

flow stability (McRae et al. 2004) to be important; however the model used by Gangloff

and Feminella (2007) to predict mussel abundance had only 57% predictability, the

model used by McRae et al. (2004) to predict mussel abundance had 63% predictability

and Howard and Cuffey (2003) did not report discriminate power of their flow model, but

their sediment model had 15% predictability. Fluvial geomorphology clearly impacts

freshwater mussel distribution, occurrence and abundance, but predictive value of all

models we tested was low. The factors influencing freshwater mussel lifecycles and

subsequent distributions are likely too complex to be predicted at this scale with fluvial

geomorphic variables alone. A successful model will likely need to incorporate three

44

spatial (macro, meso and microhabitat) and a temporal scale.

45

References

Allison, P. D. 1999. Logistic regression using the SAS system: theory and application. Cary, NC: SAS Institute Inc.

Biggins, R. G. and R. S. Butler. 2000. Bringing mussels back in the Southeast.

Endangered Species Technical Bulletin 25:24-26 Dauwalter, D. C., D. K. Splinter, W. L. Fisher, and R. A. Marston. 2007.

Geomorphology and stream habitat relationships with smallmouth bass (Micropterous dolomieu) abundance at multiple spatial scales in eastern Oklahoma. Canadian Journal of Fisheries and Aquatic Science 64:1116-1129.

De’ath, G. and K. E. Fabricius. 2000. Classification and regression trees: a powerful yet

simple technique for ecological data analysis. Ecology 81:3178-3192. Dunne, T. and L.B. Leopold. 1978. Water in environmental planning. W.H. Freeman

and Co., San Francisco, CA. Galbraith, H. S., D. E. Spooner, and C. C. Vaughn. 2008. Status of rare and endangered

freshwater mussels in Southeastern Oklahoma. The Southwestern Naturalist 53(1):45-50.

Gangloff, M. M., and J. W. Feminella. 2007. Stream channel geomorphology influences

mussel abundance in southern Appalachian streams, U.S.A. Freshwater Biology 52:64-74.

Hardison, B. S., and J. B. Layzer. 2001. Relations between complex hydraulics and the

localized distribution of mussels in three regulated rivers. Regulated Rivers: Research and Management 17:77-84.

Hilderbrand, R. H., A. D. Lemly, and C. A. Dolloff. 1999. Habitat sequencing and the

importance of discharge in inferences. North American Journal of Fisheries Management 19:198-202.

Holland-Bartels, L. E. 1990. Physical factors and their influences on the mussel fauna of

a main channel border habitat of the upper Mississippi River. Journal of the North American Benthological Society 9:327-335.

Howard, J. K., and K. M. Cuffey. 2003. Freshwater mussels in a California North Coast

Range river: occurrence, distribution, and controls. Journal of North American Benthological Society 22(1):63-77.

Hughes, M. H., and P. W. Parmalee. 1999. Prehistoric and modern freshwater mussel

(Mollusca: Bivalvia: Unionoidea) faunas of the Tennessee River: Alabama, Kentucky, and Tennessee. Regulated Rivers 15:25-42.

46

Jackson, D. A. 1993. Stopping rules in PCA: a comparison of heuristical and statistical approaches. Ecology 74:2205-2214.

Layzer, J. B., and L. M. Madison. 1995. Microhabitat use buy freshwater mussels and

recommendations for determining their instream flow needs. Regulated Rivers: Research and Management 10:329-345.

Lyedeard, C. and coauthors. 2004. The global decline of nonmarine mollusks. BioScience

54(4):321-330. MacMahon, R. F., and A. E. Bogan. 2001. Mollusca: Bivalvia, Second Edition edition.

Academic Press, San Diego, CA. McRae, S. E., J. D. Allan, and J. B. Burch. 2004. Reach- and catchment-scale

determinants of the distribution of freshwater mussels (Bivalvia: Unionidae) in south-eastern Michigan, U.S.A. Freshwater Biology 49(2):127-142.

Neves, R. J., A. E. Bogan, J. D. Williams, S. A. Ahlstedt, and P. W. Hartfield. 1997.

Status of aquatic mollusks in the southeastern United States: a downward spiral of diversity. Pages 45-86 in Aquatic fauna in peril: the southeastern perspective. G. W. Benz and D. E. Collins, eds. Southeast Aquatic Research Institute, Decatur, GA.

Pfankuch, D. J. 1975. Stream reach inventory and channel stability evaluation. U.S.

Forest Service, Northern Region, Missoula, Montana. Salmon, A., and R. H. Green. 1982. Environmental determinants of unionid clam

distribution in the Middle Thames River, Ontario. Canadian Journal of Zoology 61:832-838.

Strayer, D. 1981. Notes on the microhabitat of the unionid mussels in some Michigan

streams. American Midland Naturalist 106:411-415. Strayer, D. L. 1983. The effects of surface geology and stream size on freshwater mussel

(Bivalvia, Unionidae) distribution in Southeastern Michigan, U.S.A. Freshwater Biology 13:253-264.

Strayer, D. L. 1993. Macrohabitats of freshwater mussels (Bivalvia: Unionacea) in

streams of the northern Atlantic Slope. Journal of North American Benthological Society 12(3):236-246.

Strayer, D. L. 1999. Use of flow refuges by unionid mussels in rivers. Journal of North

American Benthological Society 18(4):468-476.

47

Strayer, D. L., J. A. Downing, W. R. Haag, T. L. King, J. B. Layzer, T. J. Newton, and S. J. Nichols. 2004. Changing perspectives on pearly mussels: North America’s most imperiled animals. BioScience 25:429-439.

Strayer, D. L., and J. Ralley. 1993. Microhabitat use by an assemblage of stream-

dwelling unionaceans (Bivalvia), including two rare species of Alasmidonta. Journal of North American Benthological Society 12(3):247-258.

Tevesz, M. J. S., and P. L. McCall. 1979. Evolution of substratum preference in bivalves

(Mollusca). Journal of Paleontology 53:112-120. USFWS. 1999. Box score: listings and recovery plans as of June 30, 1999. Endangered

Species Bulletin 23(3):28. van der Schalie, H. 1938. The naiad fauna of the St Joseph River drainage of southeastern

Michigan. Miscellaneous Publications of the University of Michigan Museum of Zoology 40:1-83.

Vannote, R. L., and G. W. Minshall. 1982. Fluvial process and local lithology controlling

abundance, structure, and composition of mussel beds. Proceedings of the National Academy of Science 79:4103-4107.

Vaughn, C. C. 2005. Proceedings of Oklahoma Water 2005, Tulsa, OK, September 27

and 28, Paper #18 Oklahoma Water Resources Research Institute, Stillwater, OK, 12 pgs.

Vaughn, C. C., and M. Pryon. 1995. Population ecology of the endangered Ouachita

rock-pocketbook mussel, Arkansia wheeleri (Bivalvia: Unionidae), in the Kiamichi River, Oklahoma. American Malacological Bulletin 11(2):145-151.

Way, C. M., Miller, Andrew C., and Payne, Barry S. 1989. The influence of physical

factors on the distribution and abundance of freshwater mussels (Bivalvia: Unionidae) in the Lower Tennessee River. The Nautilus 103(3):96-98.

48

Table 1. Simple statistics for morphologic, substrate and estimated flow characteristics at bankfull stage from 131 transects sampled in 2005 in the Kiamichi River, Oklahoma.

Variables Minimum Maximum Mean Standard deviation

Width (m) 13.30 85.20 36.67 16.02 Maximum depth (m) 0.49 3.90 1.38 0.50 Area (m2) 3.12 96.17 36.14 21.92 Width:depth ratio 13.10 135.94 40.95 22.26 Entrenchment ratio 1 37.79 3.17 5.87 Bank instability 64 96 78.32 8.18 Gradient (%) 0 0.06 0.0032 0.0080 Manning's n 0.0008 0.1623 0.0259 0.0325 D16 (mm) 0.02 35 5 7 D35 (mm) 0.05 78 16 17 D50 (mm) 1.48 180 30 28 D84 (mm) 15 2048 143 301 D95 (mm) 30 2048 330 515 D100 (mm) 56 2048 554 599 D84:D16 4 5422 345 901 Clay (%) 0 42 5 9 Sand (%) 0 76 21 20 Gravel (%) 16 90 49 17 Cobble (%) 0 62 20 14 Boulder (%) 0 20 3 5 Bedrock (%) 0 42 1 5 Velocity (ms-1) 0.06 3.23 1.06 0.63 Discharge (m3s-1) 8.00E-04 230.20 38.45 35.76 Froude number 0.02 0.81 0.30 0.18 Reynolds number 1.20 4126.15 1005.20 757.84 Sheer stress 2.35E-07 0.59 0.02 0.06

49

Table 2. Percent freshwater mussel occurrence and relative abundance in the 10 sampled reaches (N transects per reach, sampled in 2005) in the Kiamichi River, Oklahoma. Transects with no mussel presence or low mussel relative abundance were combined and classified as low for relative abundance analyses.

Present Absent Relative Abundance

Reach N High Moderate Low 9 21 0 0 14 86 10 6 0 66 17 17 11 7 0 29 29 42 12 18 6 39 33 22 13 8 13 49 25 13 14a 15 6 27 27 40 14b 10 40 20 0 40 15 19 42 53 0 5 16 15 13 27 40 20 17 12 25 0 25 50

50

Table 3. Percent freshwater mussel occurrence and relative abundance by stream channel unit type (N transects per channel unit type) for 131 transects sampled in 2005 in the Kiamichi River, Oklahoma. Transects with no mussel presence or low mussel relative abundance were combined and classified as low for relative abundance analyses. Present Absent Relative Abundance Channel unit type N High Moderate Low

Pool head 33 3 24 27 46 Pool tail 35 9 17 20 54 Riffle 39 18 41 18 23 Run 24 38 28 17 17

51

Table 4. Statistics and eigenvalues for eigenvectors from principal components analysis (PCA) of fluvial geomorphic variables from 131 transects in the Kiamichi River, Oklahoma. PC1 PC2 PC3 Summary statisics

Observed eigenvalue 3.130 2.630 2.250 Broken-stick criterion 1.000 1.500 1.833 Percent of variance explained 19.6 16.4 14.1

Variables Bankfull width (m) 0.287 0.360 -0.033 Maximum bankfull depth (m) -0.008 0.206 -0.059 Width:depth ratio 0.202 0.241 -0.074 Entrenchment ratio -0.396 0.009 0.123 D50 (mm) -0.447 0.148 -0.025 D84:D16 0.063 0.224 -0.241 Clay 0.125 0.371 -0.227 Sand 0.161 -0.440 0.287 Gravel 0.206 0.132 -0.153 Cobble -0.341 0.183 -0.119 Boulder -0.355 0.196 0.077 Bedrock -0.343 -0.036 0.099 Bankfull velocity (ms-1) 0.053 0.162 0.567 Bankfull discharge (m3s-1) 0.195 0.344 0.429 Sheer stress -0.007 0.213 0.473 Bank instability 0.176 -0.293 0.002

52

Table 5. Classification tree analysis (CTA) variable importance for freshwater mussel occurrence in the Kiamichi River, Oklahoma. Width:depth ratio, D84 sediment size fraction and bankfull velocity predicted mussel occurrence correctly 55% of the time. The classification tree (Figure 3) had a tenfold cross-validated relative error of 0.622.

Variable Score Width:depth ratio 100 D84 (mm) 67.5 Bankfull velocity (ms-1) 62.7 D95 (mm) 54.7 Reynolds number 54.6 Entrenchment ratio 50.1 Rosgen stream classification 39.7 Bankfull width (m) 33.6 Froude number 31.9 D50 (mm) 30.0 Bankfull discharge (m3s-1) 20.9 Sand (%) 20.9 Manning's n 18.9 Maximum bankfull depth (m) 17.9 Bankfull area (m2) 14.0 Bank instability 12.7 D100 (mm) 11.5 Sheer stress 5.0

53

Table 6. Classification tree analysis (CTA) variable importance for freshwater mussel relative abundance in the Kiamichi River, Oklahoma. Bank instability, D16, D50 and D84 sediment size fractions predicted mussel relative abundance class correctly 53% of the time. The classification tree (Figure 4) had a tenfold cross-validated relative error of 0.622.

Variable Score D16 (mm) 100 Instability 85.7 D35 (mm) 81.3 D50 (mm) 70.4 D84 (mm) 53.8 D95 (mm) 43.9 Bankfull width (m) 35.1 D84:D16 34.9 Cobble (%) 30.8 Sand (%) 11.4 D100 (mm) 10.1 Bankfull discharge (m3s-1) 9.6 Bankfull area (m2) 4.1 Width:depth ratio 2.2

54

^

!

Oklahoma City

Tulsa

^

!

Oklahoma City

Tulsa

Figure 1. Kiamichi River watershed with major impoundments, designated reaches (marked at the end of the reach), and sampled channel unit complexes. Inset shows Kiamichi River watershed in Oklahoma.

55

Figure 2. Principal components analysis (PCA) biplots of fluvial geomorphic variables of 131 sample transects in the Kiamichi River, Oklahoma labeled by mussel occurrence; absent (A) and present (P).

56

BKFV <= 1.76

Class Cases %A 35 63.6P 20 36.4

N = 55

BKFV > 1.76

Class Cases %A 1 8.3P 11 91.7

N = 12

WD <= 37.65

Class Cases %A 36 53.7P 31 46.3

N = 67

D84 <= 151.40

Class Cases %A 4 7.7P 48 92.3

N = 52

D84 > 151.40

Class Cases %A 7 58.3P 5 41.7

N = 12

WD > 37.65

Class Cases %A 11 17.2P 53 82.8

N = 64

Class Cases %A 47 35.9P 84 64.1

N = 131

Figure 3. Classification tree analysis (CTA) of effects of geomorphic, sediment and flow variables on freshwater mussel occurrence at 131 transects in the Kiamichi River, Oklahoma. Nodes were split using width:depth ratio (WD), bankfull velocity (BKFV) and D84 particle size fraction. The number of cases (i.e. transects) and percent mussel occurrence per class, present (P) or absent (A), is shown per node. Tenfold cross-validated relative error was 0.622.

57

INSTABILITY <= 68.50

Class Cases %0 3 25.01 0 0.02 9 75.0

N = 12

D84 <= 147.07

Class Cases %0 41 60.31 27 39.72 0 0.0

N = 68

D84 > 147.07

Class Cases %0 11 100.01 0 0.02 0 0.0

N = 11

D16 <= 1.85

Class Cases %0 52 65.81 27 34.22 0 0.0

N = 79

D50 <= 52.25

Class Cases %0 4 16.71 9 37.52 11 45.8

N = 24

D50 > 52.25

Class Cases %0 15 93.81 1 6.32 0 0.0

N = 16

D16 > 1.85

Class Cases %0 19 47.51 10 25.02 11 27.5

N = 40

INSTABILITY > 68.50

Class Cases %0 71 59.71 37 31.12 11 9.2

N = 119

Class Cases %0 74 56.51 37 28.22 20 15.3

N = 131

Figure 4. Classification tree analysis (CTA) of effects of geomorphic, sediment and flow variables on freshwater mussel relative abundance at 131 transects in the Kiamichi River, Oklahoma. Nodes were split using bank stability rating (INSTABILITY), D16, D50 and D84 particle size fractions. The number of cases (i.e. transects) and percent mussel relative abundance per class, low (0), moderate (1) or high (2) abundance is shown per node. Tenfold cross-validated relative error was 0.622.

58

APPENDIX I

GPS points (latitude and longitude in decimal degrees), longitudinal distance downstream

(Long D; km), channel pattern, gradient (%) and sinuosity for 18 reaches in the Kiamichi

River, Oklahoma. Reaches were designated using differences in channel pattern, gradient

and sinuosity. A GPS point was not taken at the start of reach 1, longitudinal distance

was measured from the start of perennial flow on the north headwater fork according to

USGS topographic maps. Reach 14 was spit at the Jackfork Creek confluence.

59

Reach Start Latitude

Start Longitude

End Latitude

End Longitude Long D (km) Pattern Gradient (%) Sinuosity

1 -94.48421 34.67527 2.40 Meander 3.25 1.19 2 -94.48421 34.67527 -94.51324 34.67123 5.47 Meander 1.64 1.09 3 -94.51324 34.67123 -94.52584 34.66863 8.11 Braided 0.89 2.04 4 -94.52584 34.66863 -94.52550 34.66647 9.19 Meander 1.30 1.07 5 -94.52550 34.66647 -94.53565 34.66490 10.77 Braided 0.79 1.40 6 -94.53565 34.66490 -94.53701 34.65564 11.78 Meander 1.16 1.16 7 -94.53701 34.65564 -94.53911 34.64781 14.42 Braided 0.79 1.59 8 -94.53911 34.64781 -94.53901 34.64219 21.58 Meander 0.55 1.11 9 -94.53901 34.64219 -94.62309 34.63703 37.77 Meander 0.35 1.23 10 -94.62309 34.63703 -94.75626 34.65289 46.89 Braided 0.19 1.33 11 -94.75626 34.65289 -94.80739 34.68257 63.31 Braided 0.11 1.49 12 -94.80739 34.68257 -94.89664 34.68270 86.43 Meander 0.07 1.24 13 -94.89664 34.68270 -95.02199 34.65665 95.05 Meander 0.05 1.10 14a -95.02199 34.65665 -95.09933 34.63120 121.30 Meander 0.05 1.12 14b -95.09933 34.63120 -95.33593 34.59513 135.74 Meander 0.06 1.13 15 -95.33593 34.59513 -95.44527 34.54659 168.85 Braided 0.06 1.16 16 -95.44527 34.54659 -95.58800 34.37354 179.02 Meander 0.03 1.09 17 -95.58800 34.37354 -95.64234 34.32209 210.88 Braided 0.05 1.29

VITA

Sabrina G. Negus

Candidate for the Degree of

Master of Science Thesis: THE ROLE OF FLUVIAL GEOMORPHOLGY IN THE DISTRIBUTION OF

FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE) IN THE KIAMICHI RIVER, OKLAHOMA

Major Field: Wildlife and Fisheries Ecology Biographical:

Personal Data: Born in Logan, Utah, 1978 Education: Sky View High School, 1996; Bachelors of Science from Utah

State University in May, 2002; Completed the requirements for the Master of Science in Zoology at Oklahoma State University, Stillwater, Oklahoma in December, 2008.

Experience: Conservation Technician II, Nebraska Game & Parks Commission,

2006-2008; Fluvial Geomorphology Graduate Research Assistant, Oklahoma State University, 2004-2006; Zoology Graduate Teaching Assistant, Oklahoma State University, 2006-2006; Stream Monitoring Technician, U.S. Forest Service, 2004-2004; Fisheries Biological Aide, Utah Division of Wildlife Resources, 2003-2003.

Professional Memberships: Member of the National and Nebraska chapters of

the American Fisheries Society.

ADVISER’S APPROVAL: William L. Fisher

Name: Sabrina G. Negus Date of Degree: December, 2008 Institution: Oklahoma State University Location: Stillwater, Oklahoma Title of Study: THE ROLE OF FLUVIAL GEOMORPHOLOGY IN THE

DISTRIBUTION OF FRESHWATER MUSSELS (BIVALVIA: UNIONIDAE) IN THE KIAMICHI RIVER, OKLAHOMA

Pages in Study: 53 Candidate for the Degree of Master of Science

Major Field: Wildlife and Fisheries Ecology Scope and Method of Study:

Jackfork Creek, a major tributary to the Kiamichi River, Oklahoma, was impounded in 1974. Impoundments have a major impact on river form and function. Unionid mussels are declining worldwide due to habitat loss; the decline can be directly attributed to the construction and operation of dams and subsequent changes in stream geomorphology (Hughes and Parmalee 1999). A comprehensive geomorphic analysis of the Kiamichi River, Oklahoma, including twenty six fluvial geomorphic (mesohabitat) features, was conducted to 1) characterize current landscape, geomorphic, flow and sediment regime conditions of the Kiamichi River, Oklahoma above Hugo Lake, 2) identify and quantify deviations from the morphologic form and river function in the perturbed portion of the Kiamichi River below Jackfork Creek compared with the unperturbed portion above Jackfork Creek and 3) determine relationships between freshwater mussel occurrence and relative abundance and fluvial geomorphology of the Kiamichi River. Findings and Conclusions:

Fluvial geomorphic anomalies were found at the Jackfork Creek confluence. Fluvial geomorphic characteristics, especially channel dimension and particle size around the Jackfork Creek confluence were not consistent with longitudinal river changes or with fluvial geomorphic expectations. The Kiamichi River channel appears to be adjusting to disturbance at the Jackfork Creek confluence. Fine sediment and channel stability, specifically bank stability, were found to influence freshwater mussel occurrence and relative abundance, although the predictive power of mesohabitat features was low (less than 56%). However, classification tree analysis for mussel relative abundance predicted high relative abundance sites correctly 85% of the time. Fluvial geomorphic features alone were not accurate predictors of freshwater mussel occurrence or relative abundance at the mesohabitat scale.