Environmental Biology of Fishes 63: 253–263, 2002.
© 2002 Kluwer Academic Publishers. Printed in the Netherlands.
Structure of littoral-zone fish communities in relation to habitat,
physical, and chemical gradients in a southern reservoir
Keith B. Gidoa,b,f , Chad W. Hargravea,b , William J. Matthewsa,b , Gary D. Schnella,c ,
Darrell W. Poguea,d & Guy W. Sewelle
a
Sam Noble Oklahoma Museum of Natural History, University of Oklahoma, 2401 Chautauqua Avenue,
Norman, OK 73072, U.S.A.
b
University of Oklahoma Biological Station, HC 71, Box 205, Kingston, OK 73439, U.S.A.
c
Department of Zoology, University of Oklahoma, Norman, OK 73019, U.S.A.
d
Department of Biology, University of Texas at Tyler, 3900 University Blvd., Tyler, TX 75799, U.S.A.
e
Environmental Protection Agency, Robert S. Kerr Environmental Research Lab, P.O. Box 1198,
Ada, OK 74821, U.S.A.
f
Present address: Division of Biology, Kansas State University, Ackert Hall, Manhattan, KS 66506, U.S.A.
(e-mail: kgido@ksu.edu)
Received 22 March 2001
Accepted 27 July 2001
Key words: reservoir fishes, environmental gradients, spatial variation, littoral zone, longitudinal zonation
Synopsis
How the distribution and abundance of organisms vary across environmental gradients can reveal factors important
in structuring aquatic communities. We sampled the littoral-zone fish community in a large reservoir (Lake Texoma)
on the Texas–Oklahoma (U.S.A.) border that has pronounced environmental gradients from up- to downlake and
between major tributary arms. Our objective was to evaluate the predictability of the littoral-zone fish-community
structure from a suite of environmental variables. A stepwise multiple-regression model, with environmental factors
at independent variables, explained 64% of the variation in fish species richness across sample sites. The number
of species was positively associated with water-column productivity and total Kjedahl nitrogen, and negatively
associated with Secchi depth and benthic productivity. Canonical correspondence analysis, with environmental
factors as independent variables, explained 63% of the variation in fish-community structure across sites. Equal
proportions of the variation in community structure were explained by variables that have strong gradients within
the reservoir (e.g., Secchi depth and water-column productivity) and those that represent local habitat variables
(e.g., shoreline aspect and substrate type).
Introduction
Species responses to environmental gradients help
explain the relative importance of abiotic and biotic
factors in structuring fish communities. Reservoirs are
ideal systems in which to examine community composition along environmental gradients because the same
regional pool of fishes is subjected to strong physical and chemical gradients from up- to downlake. In
addition, studying the distribution and abundance of
reservoir fishes, which include both native and introduced species, may provide insight into the structuring
of recently invaded communities.
Because of the pronounced environmental gradients of reservoirs, they have been referred to as
‘hybrid’ systems, intermediate in limnological properties between large rivers and lakes (Thornton et al.
1990). Uplake regions of reservoirs are more like
rivers, whereas downlake portions have characteristics
more typical of lakes. In particular, total suspended
254
solids and nutrient concentrations often are greatest
in the uplake regions and lowest in the main basin
(Kennedy & Walker 1990). Given these strong longitudinal gradients, local fish communities might be
predictably arranged across space if individual species
have different optima or tolerance limits that vary
along these gradients (e.g., Henderson 1985, Homgren
& Appelberg 2000). However, local physical habitat,
shoreline structure, and wind exposure also may play
a role in regulating the distribution of fishes within
a reservoir (Matthews 1998, Lienesch & Matthews
2000). If these effects are large, physical habitat rather
than longitudinal position in the reservoir may be the
best predictor of fish-community structure. It is likely
that both habitat structure and limnological parameters
interact to determine local community structure.
Some aspects of the spatial distribution of reservoir fishes are predictable (Gido et al. 2000, Gido &
Matthews 2000). Fish biomass typically increases with
distance from the dam (Siler et al. 1986, Nadirov &
Malinin 1997) and from off- to inshore habitats
(Vanderpuye 1982, Gido & Matthews 2000); this pattern usually is associated with productivity gradients
(e.g., Siler et al. 1986). In addition, the distribution of
fishes can differ vertically in the water column in association with temperature and oxygen gradients (Dendy
1946, Coutant 1985, Gido & Matthews 2000).
In contrast to the above, some species may have
less predictable distributions within a reservoir. For
example, water levels are partly under human control; thus, littoral-zone habitats can be variable across
time (e.g., Ploskey 1986), resulting in a weak association between fish communities and habitat characteristics (e.g., Gelwick & Matthews 1990). In addition,
reservoirs have non-coevolved communities of native
species (i.e., present prior to construction of the reservoir) and those that have been introduced. Thus, species
interactions (e.g., asymmetric competition or predation) within a non-coevolved fish community may
result in a decoupling of the association between fishes
and habitat or environmental gradients.
Our goal was to evaluate the predictability of spatial
variation in littoral-zone fish communities based on a
suite of biotic and abiotic properties of a reservoir. In
addition, we tested the relative importance of variables
that are associated with strong environmental gradients
of the reservoir versus local structural parameters in
influencing the fish community. We expected gradient
variables to vary longitudinally within the reservoir,
whereas structural parameters would be more evenly
distributed across space.
Study area and methods
Study area
Lake Texoma is a 36 000 ha impoundment of the
Washita and Red rivers on the Oklahoma–Texas border.
Reservoir releases and resulting fluctuations in water
levels are primarily for hydropower and flood control. For the two years of this study (1999 and 2000),
reservoir elevation ranged from 186 to 189 m above
sea level (U.S. Army Corps of Engineers unpublished
data), but was at the same elevation (188 m) during our
fish sampling in both years.
Field studies
Sampling was conducted during July at 41 sites in 1999
and 2000 (Figure 1) as part of a larger study to examine effects of human activities on biotic communities
of the reservoir. Twenty sample sites were selected to
represent a variety of habitats associated with potential human disturbances (e.g., agricultural runoff, septic
effluent). Each site was then paired with a physically
similar reference site in the same general vicinity of the
reservoir (one site had two references; hence, 41 total
sites). Although sites near the potential disturbances
Figure 1. Location of Lake Texoma (star) and of 41 study sites
within reservoir. Circles represent sites in the Red River arm,
triangles indicate sites in the main body, and squares mark sites
in the Washita River arm of the reservoir.
255
may influence our results, paired comparisons between
impact and non-impact sites revealed no significant differences between site pairs in the number or composition of fishes. Thus, we omitted potential disturbances
in our predictive models.
During July of 1999 and 2000, fishes were sampled at four adjacent 25 m reaches of shoreline at
each site. Fishes were collected with a 7.62 m × 1.8 m
bag (4.8 mm mesh) and 4.6 m × 1.2 m (3.2 mm mesh)
straight seine. In each reach, the bag seine was hauled
offshore parallel to the shoreline in water 1.0–1.5 m
deep for the length of the reach. Multiple seine hauls
were made with the straight seine along the shoreline in this same 25 m stretch. The number of hauls
depended on the complexity of the shore. Our goal
was to sample all shoreline habitats in a given reach.
Fishes from each of the four reaches were preserved in
separate jars in 10% formalin and returned to the laboratory for enumeration and identification. Large individuals (>200 mm) were identified and released. Thus,
in each year we had four replicate samples at each of
the 41 study sites, so we could assess variance within
sites and efficiency of seining.
Local structural (i.e., habitat) characteristics were
quantified at each site during the July fish sampling in
each year. Major substrate types that occupied >30%
of the area sample were noted for each reach seined.
Substrate categories included silt (<0.12 mm), sand
(0.12–1 mm), gravel/cobble (>1–256 mm) and boulder
(>256 mm). Sites also were ranked subjectively from 0
to 10 based on the complexity of the shoreline habitat.
A silt or sand shoreline with no inundated vegetation
and no large substrates (i.e., no cover for fishes) was
scored as a zero. A score of 10 was assigned for sites
dominated with inundated trees or vegetation, irregular banks, or large substrates (i.e., dense cover for
fishes). During 1999, the slope of the littoral zone was
measured both above and below the shoreline along a
transect perpendicular to the shoreline in the middle
of each site. Shoreline aspect (direction of exposure)
and fetch (distance to nearest southern shoreline) were
determined using USGS maps (1 : 24 000 scale).
Limnological parameters were collected on multiple occasions (Table 1) during the summer and fall of
1999 and 2000. Three 1 l water samples were taken
0.5 m below the surface near shore (ca. 1 m depth) and
analyzed for chlorophyll a. Water samples were placed
immediately on ice and returned to the laboratory,
where they were filtered through Gelman (A/E) filters
that evening. Filters were placed in scintillation vials,
wrapped in aluminum foil and stored at 0◦ C. Within
Table 1. Concordance across sample dates (Kendall’s W ) for limnological parameters measured on ≥3 dates across 41 sample sites
on Lake Texoma. Only sites with complete data across all sample
dates were used in this analysis.
Parameter
Water-column
chlorophyll a
Benthic
productivity
Benthic
productivity
(June and
July)
Benthic ash-free
dry mass
(AFDM)
Benthic AFDM
(June and
July)
Secchi depth
Conductivity
Total
phosphorus
Total Kjedahl
nitrogen
(TKN)
Number of
samples
Number
of sites
Kendall’s
W
p-value
7
40
0.501
<0.001
6
33
0.145
0.639
4
35
0.382
0.033
6
33
0.220
0.182
4
35
0.451
0.003
10
11
3
41
41
28
0.844
0.873
0.711
<0.001
<0.001
0.001
3
28
0.703
0.001
1 week, the samples were analyzed for chlorophyll a
using a spectrophotometer according to the methods
of APHA (1985) with a correction for pheopigments.
Concurrent with the chlorophyll sampling, water transparency was estimated using a Secchi disk and conductivity was measured with a YSI meter. Water samples
were taken on three occasions during the summer and
fall of 2000 and analyzed for total Kjedahl nitrogen and
total phosphorous according to APHA (1985).
Unglazed clay bricks were used to estimate benthic primary productivity in the months of June, July,
and October in both years. For each of these sample months, three bricks were set out on the substrate
(ca. 0.8 m depth) at each site and allowed to accumulate periphyton for a minimum of 21 days. When bricks
were retrieved, the exposed side of each brick (surface
area of one side = 156 cm2 ) was scraped with a razor
blade, scrubbed with a fine brush, and rinsed with distilled water to remove all periphyton. These samples
were immediately placed on ice and returned to the
laboratory for analysis.
In the laboratory, samples were brought to 200 ml
with distilled water and shaken vigorously to homogenize the sample. From this slurry, two 50 ml aliquots
were filtered through Gelman A/E filters. One sample
256
was placed in a drying oven at 60◦ C for 24 h. This sample was weighed, ashed at 550◦ C for 1 h and reweighed
to determine ash-free dry mass (AFDM). The other
sample was frozen and analyzed for chlorophyll a, with
a correction for pheopigments as described above for
the water-column samples. All values were adjusted for
the number of days bricks were left in the reservoir.
During summer 1999 three replicate Ponar dredge
samples (232 cm2 each) were taken at each sample site.
Samples were rinsed through a 0.5 mm sieve, preserved
in 0.5% formalin, and returned to the laboratory for
identification and enumeration. Benthic invertebrates
were identified to genus using identification guides by
Meritt & Cummins (1996) and Pennak (1989).
Herein, we refer to the collective group of variables
measured as environmental parameters. Local structural variables included substrate composition, habitat
complexity, slope, aspect, and fetch, whereas variables associated with longitudinal gradients included
water-column and benthic productivity, Secchi depth,
conductivity, nutrient concentrations, and benthic
invertebrates.
Data analysis
To evaluate the efficiency of our fish sampling we
examined the mean accumulation of species at our
sample sites with increasing number of reaches sampled (i.e., length of shoreline sampled = 25, 50, 75,
and 100 m). If the cumulative number of species averaged across sites, reached an asymptote as we increased
effort (number of 25 m reaches sampled), we considered this to be an adequate sample (e.g., Lohr & Fausch
1997). The mean number of species captured at a site
was calculated for all possible combinations of one
(n = 4), two (n = 6), three (n = 4), and four (n = 1)
reaches sampled. Means and standard deviations were
then generated across the 41 sites for each of the four
levels of sampling effort. The relationship between
mean cumulative number of species and number of
reaches sampled was fitted to a negative exponential
function for each year of sampling using regression
analysis (Angermeier & Smogor 1995).
We only examined spatial variation of environmental
and fish-community data in the reservoir; however, we
visited each station multiple times for all parameters
measured. Thus, to assess the concordance in our measurements across sample dates, we used a Kendall’s W
to test for concordance of parameters measured at
three or more times across sites (e.g., limnological
parameters) and Spearman’s rank correlation (rs ) to
estimate concordance across sites for parameters we
measured on two occasions (e.g., fish-community and
structural data; Sokal & Rohlf 1995). These analyses
were used to evaluate the temporal stability of our measured environmental parameters and their reliability for
characterizing the species–environment relationships.
We predicted that variables that are concordant across
sample dates would be most likely to influence spatial
structure of the fish community across sites.
Our primary goal was to predict fish-community
structure using a combination of environmental variables. Thus, we first used a stepwise multipleregression analysis to select those variables that
significantly contributed to a predictive model of
species richness at each site. Those variables that did
not contribute significantly to the model (p > 0.05)
were excluded. The regression model was generated
using SPSS.1
We used correspondence analysis (CA; Legendre &
Legendre 1998) to ordinate the 41 sites based on community structure. CA is an indirect gradient analysis
useful in analyzing a species × sample data matrix.
Eigenvalues and species loadings for the CA were calculated using PC-ORD.2 Although CA has been criticized for creating a spurious ‘arch’ on the second and
subsequent axes (Hill & Gauch 1980), a similar analysis that corrects this arch (detrended CA) yielded patterns almost identical to those from the CA. Because
rare species can have a strong effect on the position
of samples in multivariate space (ter Braak 1995), we
only included those species that occurred at ≥4 sites.
In addition, species abundances were log(x + 1) transformed prior to analysis to reduce the influence of the
most common species (Legendre & Legendre 1998).
To examine the association of environmental parameters with species composition we used canonical correspondence analysis (CCA; ter Braak 1995). This is a
technique similar to CA; however, it is a direct gradient
analysis in which a species-by-site matrix is ordinated
with the constraint that axes are linear combinations
of variables from an environmental parameter matrix.
In addition, the inertia from the constrained CCA can
be compared to that from the unconstrained CA to
estimate the percent variation in community structure
1
Statistical Package for the Social Sciences. 1996. SPSS base
7.0 for Windows. SPSS inc., Chicago.
2
McCune, B. & M.J. Mefford. 1997. PC-ORD. Multivariate
analysis of ecological data, version 2.0. MjM Software Design,
Gleneden Beach.
257
Table 2. Spearman’s rank correlations between 1999 and 2000 in
number of fish species, abundance, and structural characteristics
measured across the 41 sites on Lake Texoma.
Parameter
rs
p-value
Species richness
Number of individuals
Habitat complexity (HI)
Silt
Sand
Gravel/cobble
Boulder
Cover
0.587
0.439
0.795
0.637
0.539
0.531
0.825
0.653
<0.001
0.004
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
explained by environmental factors (see below). Using
a Monte Carlo procedure, eigenvalues for the first two
axes were compared to those generated from a random
shuffling of the data (1000 iterations) to determine if
they were greater that randomly generated eigenvalues
(i.e., a significant association between the fish community and environmental-parameter matrices). Calculations were performed with PC-ORD.2
In addition, we partitioned the variance in fishcommunity structure into that attributed to variables
associated with major gradients in the reservoir and
that attributed to habitat or structural parameters using
CANOCO (ter Braak 1992). A description of the
variance-partitioning procedure is given in Borcard
et al. (1992). In this analysis we subdivided the environmental matrix into two matrices, one with variables
that represented, or were associated with, longitudinal
gradients in the reservoir (listed in Table 1) and the
other with variables that represented local structural
characteristics (as listed in Table 2 plus aspect, aboveand below-water slope, and fetch).
Results
During our study, the strongest environmental gradients in Lake Texoma were for Secchi depth (transparency) and conductivity (Figure 2). Secchi depth
ranged from 0.1 to 5.5 m and generally was greatest at the dam and least at sites in the upper reaches
of the two river arms. Conductivity was highest near
the inflow of the Red River and diminished towards
the dam and up the Washita River arm of the reservoir. Thus, even though the two main tributary arms
of the reservoir had similar transparency gradients,
they differed in conductivity. In addition, chlorophyll a
concentration varied longitudinally in the reservoir.
A stepwise multiple regression accurately predicted
Figure 2. Variation in mean Secchi depth and conductivity for
41 sample sites on Lake Texoma. Numbers correspond to the site
numbers given in Figure 1, with circles for sites in the Red River
arm (sites 1–12 and 35–41), triangles for those in main body (sites
27–34), and squares for those in Washita River arm (sites 13–26).
mean chlorophyll a concentration across sites using
a combination of total Kjedahl nitrogen, conductivity, and Secchi depth [Chlorophyll a = 1.48 +
0.32(TKN) + 0.006(Conductivity) − 5.77(Secchi);
r2 = 0.861, p < 0.001]. Conductivity had a significant effect in this model because chlorophyll a concentration was generally higher in the Red River arm
than in the Washita River arm of the reservoir. However, the negative association between chlorophyll a
concentration and Secchi depth suggested that chlorophyll a concentration was higher in the two tributary
arms than in the main body near the dam.
Although the mean cumulative number of species
captured at a site increased with sampling effort, the
rate of increase declined asymptotically after three
and four 25-m reaches were sampled (Figure 3). In
both years this relationship strongly matched a negative exponential function (p < 0.001), suggesting our
sampling adequately characterized the fish community
at these sites.
Patterns of variation in environmental parameters
across sites generally were concordant across sample dates. All parameters measured across >2 sample
dates, except benthic productivity, were significantly
concordant across time based on Kendall’s W test of
concordance (Table 1). Benthic productivity did show
a low, but significant degree of concordance across
time when the month of October was removed from
the analysis. All structural parameters, including fish
richness and number of individuals captured, were
significantly concordant between 1999 and 2000 based
258
Figure 3. Increase in mean (±SD) number of species captured as
function of sampling effort (i.e., length of shoreline sampled).
Note the close fit to a negative exponential function, indicating a decreasing rate of species additions after 75 m (three 25 m
reaches) of shoreline sampled.
on Spearman’s rank correlations (Table 2). Thus, with
the exception of benthic productivity, mean values for
these parameters at each site adequately characterized
the environmental gradients across sites.
Fish-community structure varied considerably across
the 41 sites. Of the 46 species captured, inland silverside, Menidia beryllina, and threadfin shad, Dorosoma
petenense, were the first and second most abundant
taxa, respectively (Table 3). Much of this variation is
explained by the location of sites within the reservoir.
For example, sites in the tributary arms of the reservoir
had higher species richness and densities in comparison to sites in the main body. Using multiple regression
we were also able to explain a substantial amount of the
variance (64%) in the number species across sites using
a suite of environmental variables [species richness =
9.2 − 1.69(Secchi) − 20.58(benthic productivity) +
0.18(water—column productivity) + 0.26(benthicinvertebrate richness); r2 = 0.644, p < 0.001].
Species richness was positively associated with watercolumn productivity and benthic-invertebrate richness, and negatively associated with Secchi depth and
benthic productivity.
CA explained 40.5% of this variation in community structure on the first two axes (Figure 4). Much of
the variation on the first axis was explained by species
that typically occur in either small-tributary streams
(positive scores) or large rivers (negative scores) in the
region. Sites with high axis I scores had relatively high
numbers of blackstripe topminnows, Fundulus notatus,
and brook silversides, Labidesthes sicculus, species
typically found in small-tributary streams. Sites with
low axis I scores had relatively high numbers of white
crappie, Pomoxis annularis, ghost shiners, Notropis
buchanani, orangespotted sunfish, Lepomis humilis,
smallmouth buffalo, Ictiobus bubalus, and central
stonerollers, Campostoma anomalum. All but the central stoneroller are characteristic of turbid, silt and sand
bottomed main-stem rivers. The high loading of the
central stoneroller (a tributary species) on this axis is
due to its high abundance at one uplake site (site 20)
that had coarse substrates. Axis II scores generally represented differences between open-water sites near the
dam (low scores) and sites in coves or in upper reaches
of tributary arms. In particular, juvenile striped bass,
Morone saxatilis, and smallmouth bass, Micropterus
dolomieu, were in greatest abundance at exposed sites
near the dam.
CCA also indicated a significant association of fishcommunity composition and environmental factors
(Figure 5). Similar to the CA, the first two axes separated those samples with small-tributary species (positive axis I and negative axis II scores) from those with
large-river species (negative axis I and positive axis II
scores). Tributary species occurrences coincided with
increased transparency (Secchi depth), whereas riverine species were associated with total Kjedahl nitrogen
and water-column productivity. A separate gradient,
orthogonal to the above-mentioned gradient in multivariate space, contrasted sites with a north-facing exposure to those sites with sand substrates and a southern
exposure. This gradient reflected the high abundance
of threadfin shad at sites with a southern exposure.
Environmental variables explained 63.4% of the
overall variation in the unconstrained community structure (i.e., variation from the indirect gradient analysis; CA). Gradient-related and local structural variables
separately accounted for approximately equal proportions (25.7% versus 23.4%, respectively) of the overall variation in fish-community structure, suggesting
both are important in structuring the littoral-zone fish
communities of Lake Texoma.
Discussion
Overall, we found the littoral-zone fish community of
Lake Texoma to be highly predictable across space on
the basis of a combination of structural and gradientrelated environmental parameters. This pattern likely
was the result of a number of interacting processes
including differential responses of individual species
259
Table 3. List of species collected from 41 sample sites on Lake Texoma during summer 1999 and 2000. Values are mean (±SD) number
of individuals captured per site for three sections of the reservoir. Site numbers correspond to those in Figure 1.
Species
Red River arm
(Sites 1–12 & 35–41)
Main body
(Sites 27–34)
Lepisosteus oculatus
L. osseus
L. platostomus
Dorosoma cepedianum
D. petenense
Hiodon alosoides
Campostoma anomalum
Ctenopharyngodon idella
Cyprinella lutrensis
C. venusta
Cyprinus carpio
Extrarius aestivalis
Hybognathus placitus
Macrhybopsis storeriana
Notemigonus chrysoleucas
Notropis atherinoides
N. potteri
N. buchanani
N. stramineus
Pimephales vigilax
Ictiobus bubalus
Carpiodes carpio
Ictalurus punctatus
Pylodictus olivaris
Labidesthes sicculus
Menidia beryllina
Fundulus notatus
Gambusia affinis
Morone saxatilis
M. chrysops
Lepomis cyanellus
L. gulosus
L. humilis
L. macrochirus
L. megalotis
Micropterus dolomieu
M. punctulatus
M. salmoides
Pomoxis annularis
P. nigromaculatus
Etheostoma gracile
E. radiosum
Percina macrolepida
P. sciera
Aplodinotus grunniens
—
—
—
—
Mean number of individuals
Number of species
0.58 (1.18)
0.05 (0.22)
47.47 (1144.78)
483.05 (1230.76)
0.37 (0.93)
—
0.11 (0.31)
42.47 (73.79)
15.05 (17.36)
2.26 (4.62)
1.00 (2.05)
1.11 (2.63)
9.05 (24.21)
0.05 (0.22)
5.05 (9.87)
2.26 (8.26)
0.05 (0.22)
—
24.26 (36.31)
1.68 (5.26)
0.26 (0.55)
0.47 (1.39)
0.05 (0.22)
11.42 (34.31)
2198.00 (1209.84)
—
2.53 (3.79)
82.63 (183.33)
1.47 (2.72)
—
0.11 (0.45)
4.89 (15.48)
10.89 (28.43)
3.37 (5.55)
0.47 (1.35)
1.58 (2.44)
15.53 (22.85)
7.58 (23.80)
0.11 (0.31)
0.05 (.22)
—
1.26 (2.42)
—
3.37 (10.26)
66.27 (329.29)
38
to the physical and chemical gradients in the reservoir. Reservoirs are human-engineered habitats, and the
species that occur there likely will seek those habitats
that are most similar to the preferred habitats in their
1.75 (3.19)
34.88 (41.51)
—
—
—
—
32.75 (29.72)
3.13 (4.29)
—
—
0.13 (0.35)
0.50 (0.71)
0.13 (0.33)
—
—
—
56.13 (99.93)
—
—
1.00 (2.65)
—
17.25 (35.41)
1051.00 (617.05)
8.63 (21.35)
19.25 (34.06)
103.75 (129.02)
0.38 (0.70)
0.13 (0.33)
—
0.13 (0.33)
8.13 (10.29)
2.13 (1.69)
7.00 (3.54)
13.88 (11.99)
61.38 (73.58)
0.13 (0.33)
0.75 (1.98)
0.25 (0.66)
—
5.00 (4.36)
—
0.50 (1.32)
31.78 (154.94)
27
Washita River arm
(Sites 14–26)
0.07 (0.26)
0.07 (0.35)
—
21.71 (36.43)
1106.79 (1973.25)
—
1.76 (6.17)
5.50 (18.46)
55.29 (130.20)
28.29 (36.91)
2.14 (4.57)
—
—
0.57 (0.90)
0.14 (0.52)
—
—
18.50 (66.15)
0.50 (1.80)
45.43 (62.25)
0.64 (2.06)
0.79 (1.57)
0.36 (0.81)
—
3.00 (10.52)
2254.93 (2628.13)
—
155.00 (506.63)
17.79 (33.35)
0.14 (0.35)
0.29 (0.59)
3.21 (11.59)
1.07 (2.68)
5.14 (10.92)
3.43 (11.59)
1.43 (2.69)
2.43 (4.32)
8.07 (9.20)
0.21 (0.80)
—
—
0.07 (0.26)
2.57 (2.26)
0.07 (0.26)
1.07 (1.67)
83.30 (366.02)
35
natural environments. Based on our analyses of the
fish communities using CA, there are at least three
habitat types that are used preferentially by different
suites of species: (1) exposed, sand-bottomed shoreline
260
Figure 4. Correspondence analysis of fish-community data
across 41 sites on Lake Texoma. First and second axes had
eigenvalues of 0.309 and 0.202 and explained 24.5% and
16.0% of the variation in community structure, respectively.
Top panel shows site scores and lower one gives species
scores. Species enclosed in oval had scores that extend beyond
the scale of the graph. Symbols correspond to major regions
in the reservoir as described in Figure 1. Species codes are
first three letters of genus plus first three letters of specific
epithet: APLGRU = Aplodinotus grunniens; CAMANO =
Campostoma anomalum; CTEIDE = Ctenopharyngodon idella;
CYPCAR = Cyprinus carpio; CYPLUT = Cyprinella lutrensis;
CYPVEN = C. venusta; DORCEP = Dorosoma cepedianum;
DORPET = D. petenense; FUNNOT = Fundulus notatus;
GAMAFF = Gambusia affinis; ICTBUB = Ictiobus bubalus;
LABSIC = Labidesthes sicculus; LEPHUM = Lepomis
humilis; LEPMAC = Lepomis macrochirus; LEPMEG =
Lepomis megalotis; MACSTO = Macrhybopsis storeriana;
MENBER = Menidia beryllina; MICDOL = Micropterus
dolomieu; MICPUN = M. punctatus; MICSAL = M. salmoides;
MORCHR = Morone chrysops; MORSAX = M. saxatilis;
NOTATH = Notropis atherinoides; NOTBUC = N. buchanani;
NOTPOT = N. potteri; PERMAC = Percina macrolepida;
PIMVIG = Pimephales vigilax; POMANN = Pomoxis
annularis.
Figure 5. Canonical correspondence analysis of fish community
and associated environmental parameters across 41 sites on Lake
Texoma. First and second axes had eigenvalues of 0.313 and
0.145, respectively. Top graph shows site scores and environmental correlates, whereas lower panel gives species scores. Species
codes are same as those in Figure 4. Species enclosed in oval
had scores that extend beyond the scale of the graph. Abbreviations code: W.C. Prod. = water-column productivity, B. Inv.
Abu. = benthic invertebrate abundance (number of individuals),
Slope BLW = slope below water, and TKN = total Kjedahl
nitrogen.
habitats near the dam; (2) sheltered downlake coves;
and (3) turbid, highly productive shorelines (exposed
and sheltered) near inflows from tributary rivers.
The two species (brook silverside and blackstripe
topminnow) that occupied the sheltered coves near the
dam typically are found in small, clear-water tributary
streams in the region (Robison & Buchanan 1988).
Of the available habitats in the reservoir, these sheltered coves have relatively clear water and potentially
had high input of nutrients from terrestrial sources
261
(e.g., litter and insects), similar to small-tributary
streams in the region (Gido personal observation).
Thus, these coves appear to be suitable habitat for
small-stream fishes. The second major habitat type,
exposed shorelines with relatively high transparency,
typically was occupied by juveniles of both striped
bass and smallmouth bass. These species have been
introduced to the reservoir, with striped bass coming
from landlocked populations in reservoirs and smallmouth bass from clear, cobble-bottomed streams. Both
are visual predators and likely forage most efficiently
in downlake habitats where transparency is greatest.
Also, smallmouth bass are considered to be intolerant of siltation in streams (Robison & Buchanan 1988)
and might avoid silt-bottomed habitats in upper reaches
of the reservoir. Finally, sample sites near the inflows
from tributary rivers were characterized by species that
typically occupy the main channel of those rivers or
small, muddy tributary streams (e.g., ghost shiners,
orangespotted sunfish, and white crappie; Hargrave
2000). Our results agree with those of Fernando &
Holčı́k (1991), who noted that littoral-zone habitats in
the upper reaches of reservoirs are similar to the natural environment for riverine fishes. In addition, these
habitats are in close proximity to the inflowing rivers
and could be colonized by source populations within
the rivers. Moreover, introduced species, adapted to
lentic conditions (e.g., striped bass and smallmouth
bass), can exploit pelagic and downlake reaches of
reservoirs because native-species abundance is typically low (Fernando & Holčı́k 1991, Holčı́k 1998). In
Lake Texoma, it was obvious that in downlake regions,
where limnological conditions are more lacustrine,
large-river species abundance was lower and lacustrine
or small-tributary species abundance was greater.
What factors restrict riverine fishes from lower
reaches of the reservoir? The correlative association between fish-community structure and environmental properties that we have shown provides a
basis for inferring factors that limit the abundance
of fishes to certain habitats within the reservoir. The
intensity of species interactions, for example, may
vary with environmental gradients to limit the distribution of fishes. Small-bodied riverine fishes may
occupy turbid waters in the upper reaches of tributary
arms as a mechanism to help avoid predation. Conversely, visual predators (e.g., striped bass) may forage less efficiently uplake and, thus, prefer downlake
regions of the reservoir. Predation is known to have
a major role in structuring fish communities in northern lakes (Tonn & Magnuson 1982) and is likely an
important regulating factor in reservoirs (e.g., Nobel
1986).
Competition also may influence the distribution of
species in Lake Texoma. Fishes that were found in
sheltered coves downlake (e.g., those typical of smalltributary rivers) may be restricted to those habitats by
superior competitors that occur at exposed sites. For
example, it appears that the brook silverside, which
was found only in coves downlake, is excluded from
the main body of the reservoir by the inland silverside,
a more efficient zooplanktivore (McComas & Drenner
1982, Pratt et al. 2002). Such coves are unique habitats
where spatial gradients that vary longitudinally from
the cove mouth into the tributary creek can be prominent (Kimmel et al. 1990). At least in the lower reaches
of Lake Texoma, coves provide habitats that are occupied by a different fish community than that occurring
at exposed sites.
The length of a CA axes gives insight into the magnitude of species turnover along environmental gradients
(Jongman et al. 1995). An axis length of four standard
deviations (SD) indicates that communities at extremes
of the gradient typically have no species in common. In
our study, the first CA axis (Figure 4) was 3.2 SD, suggesting a high, but not complete, turnover of species
across the environmental gradients in Lake Texoma.
Whereas species composition was markedly different between up- and downlake regions, some species
were widespread and found throughout the reservoir.
The inland silverside, for example, was found at all
sites. This species, which was introduced into this
system, has been very successful in many reservoirs
(T. Buchanan unpublished data) and appears to have a
broad range of tolerance for such environments. Other
species, as mentioned above, appear more specialized
for particular habitats along the reservoir gradient.
Almost all of the environmental and fish-community
parameters were highly concordant across sample dates
(Tables 1 and 2), indicating temporal consistency in the
spatial gradients in Lake Texoma. Thus, it is not surprising that littoral-zone fishes would respond to these temporally stable gradients. The one exception was benthic
productivity, which, as indicated by multiple regression, was inversely correlated with fish-species richness across sites. Although there was a moderate degree
of concordance across sample dates when October samples are excluded, benthic-productivity gradients were
quite variable across time. It appeared that benthic
productivity was strongly influenced by the amount
of light penetrating to benthic surfaces. Thus, benthic
productivity may have been selected as a predictor of
262
fish species richness largely because it covaried with
Secchi depth. A more detailed study or field experiment would be necessary to determine if a mechanistic
relationship existed between benthic productivity and
fish species richness or if these two factors covary with
other environmental parameters.
How important is habitat in structuring the littoralzone fish community? Species richness appeared to
vary across transparency and productivity gradients
within the reservoir, whereas community composition,
as inferred from CCA results, was also influenced by
local habitat or structural variables. In fact, local structural variables accounted for an equal proportion of the
variance in community structure as that explained by
gradient-related variables. Of the local structural factors, aspect and substrate composition appeared to be
important in structuring the fish community. This probably was caused by differences in exposure to wind and
waves that can either be stressful to fishes or sculpture
the habitat (e.g., substrate composition) within a particular reach of shoreline. In 55 consecutive days of
sampling the same shoreline reach of Lake Texoma,
Lienesch & Matthews (2000) found fish-community
structure to vary with wind velocity and wave height.
In addition, Matthews (1998) reported markedly different fish communities among sites within a cove that
differed in their exposure to prevailing south winds.
It seems clear that littoral-zone fish communities in
Lake Texoma are markedly influenced by shoreline
aspect and wind exposure. Moreover, our variance
partitioning suggested there is an interaction between
variables we considered local and those that are associated with longitudinal gradients of the reservoir. This
is likely because local variables such as fetch and wind
exposure can often influence gradient-related variables
such as Secchi depth and water-column productivity.
During the last century, reservoirs have become
a prominent feature of aquatic ecosystems in most
regions of the world, and human populations surrounding these impoundments often are reliant on the recreational and economic benefits of these systems. Thus,
understanding how the biotic communities of reservoirs are structured is crucial to their management.
Results from this and other studies (e.g., Siler et al.
1986, Fernando & Holčı́k 1991) suggest that fishcommunity structure is strongly influenced by both
longitudinal physical and chemical gradients and local
habitats within reservoirs. Ultimately, other factors
such as species interactions and microhabitat preferences may interact with these factors to determine local
community structure. Considering the large degree of
spatial variation in these systems, fisheries biologists
must consider these gradients when sampling or making recommendation to manage reservoir fisheries. In
particular, management scenarios that are appropriate
for a particular portion of the reservoir may not be applicable in other regions with different environmental
conditions.
Acknowledgements
Field and laboratory assistance was provided by
D. Certain, E. Johnson, A. Marsh, K. Pratt, and
R. Ramirez. We are grateful to D. Cobb, R. Page, and
L. Weider for the use and maintenance of equipment
and facilities at the University of Oklahoma Biological
Station. Funding for this project was provided by the
Environmental Protection Agency and the U.S. Army
Corps of Engineers.
References cited
American Public Health Association, American Water Works
Association, and Water Pollution Control Federation. 1985.
Standard methods for the examination of water and wastewater,
16th ed., Washington, D.C.
Angermeier, P.L. & R.A. Smogor. 1995. Estimating number of
species and relative abundances in stream-fish communities:
effects of sampling effort and discontinuous spatial distributions. Can. J. Fish. Aquat. Sci. 52: 936–949.
Borcard, D., P. Legendre & P. Drapeau. 1992. Partialling out
the spatial component of ecological variation. Ecology 73:
1045–1055.
Coutant, C.C. 1985. Striped bass, temperature, and dissolved
oxygen: a speculative hypothesis for environmental risk. Trans.
Amer. Fish. Soc. 114: 31–61.
Dendy, J.S. 1946. Further studies of depth distribution of fish,
Norris Reservoir, Tennessee. J. Tenn. Acad. Sci. 21: 94–102.
Fernando, C.H. & J. Holčı́k. 1991. Fish in reservoirs. Internationale Revue der gesamten Hydrobiologie 76: 149–167.
Gelwick, F.P. & W.J. Matthews. 1990. Temporal and spatial patterns in littoral-zone fish assemblages of a reservoir
(Lake Texoma, Oklahoma–Texas, U.S.A.). Env. Biol. Fish. 27:
107–120.
Gido, K.B., W.J. Matthews & W.C. Wolfinbarger. 2000. Longterm changes in a reservoir fish assemblage: stability in an
unpredictable environment. Ecol. Appl. 10: 1517–1529.
Gido, K.B. & W.J. Matthews. 2000. Dynamics of the offshore
fish assemblage in a southwestern reservoir (Lake Texoma,
Oklahoma–Texas). Copeia 2000: 917–930.
Hargrave, C.W. 2000. Spatial and temporal variation in fish
assemblages of the upper Red River after a summer drought.
M.S. Thesis, University of Oklahoma, Norman. 102 pp.
Henderson, P.A. 1985. An approach to the prediction of temperate freshwater fish communities. J. Fish Biol. 27(Suppl. A):
279–291.
263
Hill, M.O. & H.G. Gauch. 1980. Detrended correspondence analysis, an improved ordination technique. Vegetatio 42: 47–58.
Holčı́k, J. 1998. Lacustrine fishes and the trophic efficiency of
lakes: prelude to the problem. Ital. J. Zool. 65: 411–414.
Holmgren, K. & M. Appelberg. 2000. Size structure of benthic freshwater fish communities in relation to environmental
gradients. J. Fish Biol. 57: 1312–1330.
Jongman, R.H.G., C.J.F ter Braak & O.R.R. Van Tongeren. 1995.
Data analysis in community and landscape ecology. Cambridge
University Press, New York. 299 pp.
Kennedy, R.H. & W.W. Walker. 1990. Reservoir nutrient dynamics. pp. 109–132. In: K.W. Thornton, B.L. Kimmel &
F.E. Payne (ed.) Reservoir Limnology: Ecological Perspectives, John Wiley & Sons, New York.
Kimmel, B.L., O.T. Lind & L.J. Paulson. 1990. Reservoir
ecosystems: conclusions and speculations. pp. 133–194. In:
K.W. Thornton, B.L. Kimmel & F.E. Payne (ed.) Reservoir
Limnology: Ecological Perspectives, John Wiley & Sons,
New York.
Legendre, P. & P. Legendre. 1998. Numerical ecology, 2nd ed.,
Elsevier Science, Amsterdam. 853 pp.
Lienesch, P.W. & W.J. Matthews. 2000. Daily fish and zooplankton abundance in the littoral zone of Lake Texoma, Oklahoma–
Texas, in relation to abiotic variables. Env. Biol. Fish. 59:
271–283.
Lohr, S.C. & K.D. Fausch. 1997. Multiscale analysis of natural
variability in stream fish assemblages of a western Great Plains
watershed. Copeia 1997: 706–724.
Matthews, W.J. 1998. Patterns in freshwater fish ecology.
Chapman and Hall, New York. 756 pp.
McComas, S.R. & R.W. Drenner. 1982. Species replacement in
a reservoir fish community: silverside feeding mechanics and
competition. Can. J. Fish. Aquat. Sci. 39: 815–821.
Merritt, R.W. & K.W. Cummins (ed.). 1996. An introduction to
the aquatic insects of N.A., 3rd ed., Kendall/Hunt Publishing
Co., Dubuque. 862 pp.
Nadirov, S.N. & L.K. Malinin. 1997. Seasonal dynamics of fish
distribution in the piedmont Mingechaur Reservoir. J. Ichthyol.
37: 288–293.
Noble, R.L. 1986. Predator–prey interactions in reservoir communities. pp. 137–143. In: G.E. Hall & M.J. Van Den Ayvle
(ed.) Reservoir Fisheries Management: Strategies for the 80’s,
American Fisheries Society, Bethesda.
Pennak, R.W. 1989. Fresh-water invertebrates of the United
States: Protozoa to Mollusca, 3rd ed., John Wiley & Sons, New
York. 628 pp.
Ploskey, G.R. 1986. Management of the physical and chemical environment: effects of water-level changes on reservoir ecosystems, with implications for fisheries management.
pp. 86–97. In: G.E. Hall & M.J. Van Den Ayvle (ed.) Reservoir
Fisheries Management: Strategies for the 80’s, American
Fisheries Society, Bethesda.
Pratt, K.E., C.W. Hargrave & K.B. Gido. 2002. Rediscovery
of Labidesthes sicculus (Atherinidae) in Lake Texoma
(Oklahoma–Texas). Southwest. Nat. (in press).
Robison, H.W. & T.M. Buchanan. 1988. Fishes of Arkansas.
University of Arkansas Press, Fayetteville. 536 pp.
Siler, J.R., W.J. Foris & M.C. McInerny. 1986. Spatial heterogeneity in fish parameters within a reservoir. pp. 122–136.
In: G.E. Hall & M.J. Van Den Ayvle (ed.) Reservoir Fisheries Management: Strategies for the 80’s, American Fisheries
Society, Bethesda.
Sokal, R.R. & F.J. Rohlf. 1995. Biometry, 3rd ed., W.H. Freeman &
Co., San Francisco. 887 pp.
ter Braak, C.J.F. 1992. CANOCO – a FORTRAN program
for canonical community ordination. Microcomputer Power,
Ithaca. 95 pp.
ter Braak, C.J.F. 1995. Ordination. pp. 91–173. In:
R.H.G. Jongman, C.J.F. ter Braak & O.F.R. Van Tongeren
(ed.) Data Analysis in Community and Landscape Ecology,
Cambridge University Press, New York.
Thornton, K.W., B.L. Kimmel & F.E. Payne (ed.). 1990. Reservoir
limnology: ecological perspectives. John Wiley & Sons,
New York. 246 pp.
Tonn, W.M. & J.J. Magnuson. 1982. Patterns in the species composition and richness of fish assemblages in northern Wisconsin
lakes. Ecology 63: 1149–1166.
Vaughn, C.C. & C.M. Taylor. 2000. Macroecology of a host–
parasite relationship. Ecography 23: 11–20.
Vanderpuye, C.J. 1982. Further observation of the distribution
and abundance of fish stocks in Volta Lake, Ghana. Fish. Res.
1: 319–343.