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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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,
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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.
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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).
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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.
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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
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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
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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.
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