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Influence of Different Habitat Factors on Creek Chub
(Semotilus atromaculatus) within Channelized Agricultural
Headwater Streams
Peter C. Smiley Jr.1,*, Kevin W. King1, and Norman R. Fausey1
Abstract - Natural history information on habitat relationships of Semotilus atromaculatus
(Creek Chub) in channelized agricultural headwater streams in the northeastern region of
North America is limited. We hypothesized that Creek Chubs within channelized agricultural
headwater streams would be more strongly influenced by instream habitat than other
physical and chemical habitat variables. We sampled Creek Chubs and measured watershed
characteristics, riparian habitat characteristics, geomorphology, instream habitat characteristics,
and water chemistry in 14 channelized agricultural headwater streams in central
Ohio from 2006 to 2011. We found that the abundance, mean length, and biomass of Creek
Chub were most strongly influenced by watershed characteristics (land use, soil type) and
geomorphology (channel shape, channel size). Our results indicate that conservation and
restoration practices designed to mitigate physical habitat degradation are most likely to
benefit Creek Chub within channelized agricultural headwater str eams.
Introduction
Semotilus atromaculatus (Mitchill) (Creek Chub) is a North American fish species
that is one of the most widespread and commonly occurring stream fishes in the
eastern United States and southeastern Canada (Barber and Minckley 1971, McMahon
1982, Page and Burr 1991). The native distribution of this species encompasses
most of eastern North America and extends westward from the Atlantic Coast to
Wyoming and Montana in the United States and to Manitoba in Canada (Page and
Burr 1991). Creek Chubs are found in a wide variety of stream types and sizes
(Barber and Minckley 1971, Fitzgerald et al. 1999), but they exhibit a preference
for small warmwater headwater streams (Copes 1978, Mahon et al. 1979, Shelford
1913, Starrett 1950, Thompson and Hunt 1930). Small Creek Chubs are primarily
insectivorous, but larger individuals within small headwater streams are often the
primary predator feeding on fishes, amphibians, and aquatic insects (Barber and
Minckley 1971, Copes 1978, Quist et al. 2005, Storck and Momot 1989). Creek
Chubs are classified by regulatory agencies in the United States as fish tolerant of
natural and anthropogenic stressors (Barbour et al. 1999) because of their ability to
persist within and adapt to variable hydrologic and chemical conditions (Blevins
et al. 2013, Larimore et al. 1959, Nagrodski et al. 2013, Shelford 1913, Walker
and Adams 2016). Yet, these fishes are often the most abundant fish species found
in small, forested headwater streams (Heithaus and Grame 1997, Lotrich 1973).
1USDA Agricultural Research Service, Soil Drainage Research Unit, 590 Woody Hayes
Drive, Columbus, OH 43210. *Corresponding author - rocky.smiley@ars.usda.gov.
Manuscript Editor: Jay Stauffer
Natural History of Agricultural Landscapes
2017 Northeastern Naturalist 24(Special Issue 8):18–44
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2017 Vol. 24, Special Issue 8
Creek Chubs also exhibit ecological and physiological responses to improvements
in habitat quality and reduced concentrations of chemical pollutants (Dube et al.
2006, Fischer et al. 2010, Henshel et al. 2006, Lyons 2006, Portt et al. 1986, Stair
et al. 1984, Thompson and Hunt 1930).
Creek Chubs and other fishes are commonly found within channelized agricultural
headwater streams (Table 1; Jordan et al. 2013, Smiley and Gillespie 2010).
These small streams are common within agricultural watersheds throughout the
northeastern portion of North America (i.e., the region extending west from Virginia
to Missouri, north to Manitoba, and then back east to the Atlantic coast;
Madramootoo et al. 2007, Needelman et al. 2007, Smiley and Gillespie 2010, Stammler
et al. 2008). Channelized agricultural headwater streams (e.g., agricultural
drainage ditches or open agricultural drains) are first-, second-, and third-order
streams that have been modified or created for draining excess water from adjacent
agricultural fields. Subsequently, these streams exhibit physical and chemical habitat
degradation that includes loss of riparian habitat and vegetation, loss of instream
habitat, altered hydrology, increased nutrient loadings, and pesticide contamination
as a result of channelization, use of subsurface tile drains, and current nutrient and
pesticide application practices.
Developing conservation and restoration strategies for fishes within channelized
agricultural headwater streams requires understanding the natural history of fishes
within this stream type. Particularly, understanding the fish–habitat relationships
within channelized agricultural headwater streams will provide predictions on
what type of conservation and restoration practices will be most effective. Most
agricultural conservation efforts focus on improving water quality by reducing
nutrient, pesticide, and sediment inputs into agricultural watersheds. The current
focus on water quality improvement might be an effective management approach if
community and population structure of fishes are more strongly linked with water
chemistry than physical habitat conditions. If community and population structure
of fishes within these small streams are more strongly correlated with physical
habitat conditions than water chemistry, then conservation efforts focusing on water
chemistry improvements are less likely to benefit these fish compared to efforts
targeting physical habitat improvements.
Past research efforts within agricultural streams have focused on evaluating
the impacts of channelization by comparing the differences in fishes and habitat
between channelized and unchannelized streams and/or evaluating fish–habitat
relationships across a continuum of channel modifications extending from recently
channelized to recovering channelized streams to unchannelized streams (Smiley
and Gillespie 2010). As a result of this emphasis on documenting channelization
impacts, basic natural history information on fish–habitat relationships within
channelized agricultural headwater streams is limited. Our previous research on
fish–habitat relationships within channelized agricultural headwater streams in
northeastern Indiana and central Ohio compared the degree of influence that riparian
habitat, instream habitat, and water chemistry have on fish community structure.
The initial results (Sanders 2012; Smiley et al. 2008, 2009) with the first 5 years
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of data from channelized agricultural headwater streams documented that fish
community structure was most strongly influenced by instream habitat variables
and only exhibited weak relationships with water chemistry (nutrients, pesticides,
physicochemical variables) and riparian habitat variables.
Most previous research in the United States and Canada has documented that
the abundance (number of individuals), density (number of individuals per m2), or
presence/absence of Creek Chubs is influenced by instream habitat (i.e., hydrology,
substrate types, cover types) and watershed habitat variables (i.e., watershed size,
land-use types) within unchannelized headwater streams (Table 1). Only a limited
amount of information is available on Creek Chub–habitat relationships within
channelized agricultural headwater streams and on the relationships of Creek Chub
with riparian habitat, geomorphology, and water chemistry (Table 1). Additionally,
only a few studies (Hubert and Rahel 1989, Magoulik 2000, Quist and Guy 2001,
Wuellner et al. 2013) have compared the relative influence of different types of
habitat variables on the population structure of Creek Chub.
In this manuscript, we expand upon previous research findings (Sanders
2012; Smiley et al. 2008, 2009) by evaluating if the fish–habitat relationships
we observed at the community level also occur at the population level within
these small streams. We selected Creek Chub for this population-level assessment
because they were the most abundant fish species captured within our study
sites in Indiana and Ohio (Sanders 2012; Smiley et al. 2008, 2009). We also seek
to increase the current understanding of Creek Chub–habitat relationships by
comparing the relative influence of different habitat variables on the population
structure of Creek Chub within channelized agricultural headwater streams. We
sampled Creek Chub and measured watershed characteristics, riparian habitat,
geomorphology, instream habitat, and water chemistry from 14 channelized
agricultural headwater stream sites in central Ohio over a 6-year period to evaluate
the relative influence of different habitat factors on the population structure
of this common headwater fish. Specifically, our research hypothesis was that
instream habitat will have the greatest influence on the population structure of
Creek Chub in channelized agricultural headwater streams.
Field-Site Description
Upper Big Walnut Creek (UBWC) watershed is located in central Ohio (Fig. 1)
and is part of the Scioto River watershed, which is one of the most biologically
diverse watersheds in Ohio (Sanders 2001). This watershed is located in the humid
continental climatic region of the United States. Daily temperatures range from an
average minimum of -9.6 °C in January to an average maximum of 33.9 °C in July
(King et al. 2008). Mean annual total precipitation within the watershed during
our study varied from 903 mm to 1334 mm (P.C. Smiley Jr., USDA Agricultural
Research Service, Columbus, Ohio, USA, unpubl. data). Thunderstorms during the
spring and summer produce short-duration intense rainfalls. Moisture from December
to March occurs primarily in the form of frozen precipitation or snow. Cropland
consisting of corn and soybean is the dominant land use in the UBWC watershed
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Table 1. Associations of Creek Chub with different types of habitat variables and individual habitat variables within channelized and unchannelized
headwater streams in the United States and Canada. Abbreviations for types of habitat variables are: wate = watershed, riph = riparian habitat, geom =
geomorphology, insh = instream habitat, watc = water chemistry. Individual habitat variable abbreviations are: wsize = watershed size, lu = land use,
precip = precipitation, elev = elevation, ripht = riparian habitat type, ccov = canopy cover, wva = riparian woody vegetation amount, wd = water depth,
mh = microhabitat type or microhabitat availability, ww = wet width, wv = water velocity, st = substrate type, pa = pool availability, pc = pool complexity,
sa = surface area, disc = discharge, psize = pool size, ct = cover type, nutr = nutrients, herb = herbicides, wtemp = water temperature, cond = conductivity,
do = dissolved oxygen, TSS = total suspended solids, pcb = polycholorinated biphenyl concentration. [Table continued on next page.]
Type habitat
Stream type/location Population variable variable Individual habitat variables Source
Channelized headwater streams
Iowa Length, age, growth riph, insh ripht, ccov, ct Fischer et al. 2010
Michigan Movement geom, insh culvert size, culvert type Briggs and Galarowicz 2013
Ohio Abundance riph wva Smiley et al. 2008
Ohio Abundance watc nutr, herb, wtemp, cond, pH Smiley et al. 2009
Channelized and unchannelized headwater streams
Ontario Biomass, production insh wd Portt et al. 1986
Illinois Abundance insh mh Schlosser 1982
Indiana Growth, morphology watc cattle manure Leet et al. 2012
Iowa Abundance, biomass, length, presence insh ww, wv, st, ct Scarnecchia 1988
Ohio Abundance insh mh Trautman and Gartman 1974
Ontario Abundance wate lu Stammler et al. 2008
Unchannelized headwater streams
Arkansas Density, age class insh ww, st Magoulick 2000
Arkansas Biomass insh ct Mitchell et al. 2012
Arkansas Movement insh pa, pc Walker and Adams 2016
Arkansas Density insh wd, pa Dekar and Magoulik 2007
Arkansas Survival, movement, abundance insh mh Hodges and Magoulick 2011
Illinois Abundance insh mh Schwartz and Herricks 2008
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Table 1, continued.
Type habitat
Stream type/location Population variable variable Individual habitat variables Source
Illinois Physiology wate, watc lu, wtemp, do Blevins et al. 2013
Kansas Growth, age classes insh st, ct Quist and Guy 2001
Kentucky Abundance, biomass wate wsize Lotrich 1973
Maryland Presence wate, insh, watc wsize, lu, elev, mh, st, pH Pinder and Morgan 1995
Minnesota Density wate, insh precip, wd, wv, ct Schlosser 1995
Minnesota Density insh wd, wv, ct Schlosser 1998
North Carolina Presence wate wsize Lemly 1985
Ohio Abundance, biomass wate, insh wsize, sa, mh Storck and Momot 1989
Ohio Abundance, age classes wate, insh wsize, wd, ct Storck and Momot 1981
Ohio Presence watc sewage Katz and Gaufin 1953
Ohio Size, growth wate, watc wsize, sewage Katz and Howard 1955
Pennsylvania Presence insh, watc disc, TSS, wtemp Wohl and Carline 1996
Pennsylvania Abundance wate, insh wsize, lu, psize Butler and Fairchild 2005
Penn. & New Jersey Presence riph ripht Ross et al. 2003
South Carolina Density, age classes insh ww, wd, wv Meffe and Sheldon 1988
Tennessee Abundance, age classes, size, growth insh disc Stair et al. 1984
Headwater stream – undocumented channelization status
Indiana Size, age classes, growth, survivorship watc pcb Henshel et al. 2006
Ohio Abundance insh ct Gatz 2008
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Figure 1. Map depicting the locations of sampling sites (gray circles with black center dots)
within the Upper Big Walnut Creek watershed, OH. The symbols have been sized to ensure
their clarity and do not reflect actual site sizes or distances between sites. The inset map
shows the location of the watershed within Ohio.
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(Smiley et al. 2014). The majority of headwater streams in the UBWC watershed
are impaired by nutrient enrichment, pathogens, and habitat degradation stemming
from current agricultural management practices (Ohio EPA 2005).
We selected 14 sites in 7 channelized agricultural headwater streams in UBWC
as our study sites (Fig. 1). Sampling sites located within the same stream were
separated by a mean distance of 0.6 km (varying from 0.2 to 1.9 km). Each site
was a 125-m-long reach near locations where we collected weekly grab samples of
water for nutrient and pesticide measurements. Our established site lengths were on
average 67 times the mean wet width (varying from 33 to 151 times the mean wet
width) and frequently exceed the minimum site lengths recommended for collecting
fishes via electrofishing within streams in the midwestern United States (i.e., 35
times mean wet width; Lyons 1992). Within each site, we established 6 permanent
transects spaced 25 m apart in each site for sampling riparian habitat, geomorphology,
and instream habitat. All sites possessed riparian habitats consisting mostly
of herbaceous riparian vegetation and exhibited the straightened, over-enlarged,
trapezoidal channel shape typical of channelized agricultural headwater streams in
the region (Table 2; Smiley et al. 2011). We collected fishes and measured riparian
habitat, geomorphology, instream habitat, and water chemistry from each site from
May 2006 to November 2011. We also compiled information on watershed characteristics
from each site.
Methods
Habitat sampling
We obtained watershed characteristics by conducting geographic information
system analyses with digital elevation models, orthophotos, and USDA Natural
Resources Conservation Service (NRCS) soil survey results. Watershed size for
each site was calculated as the total area encompassed by the watershed boundaries
identified with digital elevation models. We determined the percent of 3 primary
land-use types (i.e., agricultural, residential, forest/shrub) within each watershed
from 2010 orthophotos generated by the Delaware County Auditor and based on
0.31-m resolution. We calculated the percent of 4 commonly occurring soil types
(Bennington, Centerburg, Pewamo, Amanda) within the watersheds of our sites
from the NRCS Soil Survey Geographic database.
We established 12 quadrats (1 m x 10 m) in each site to characterize riparian
habitat once annually in the fall (September to November). Two quadrats were
located along each of the 6 permanent transects, with 1 quadrat placed on each
streambank. The quadrats began at the waters edge and extended into the adjacent
riparian habitat and sometimes into the agricultural fields. We identified and enumerated
woody vegetation >1 m tall within each quadrat. The presence and absence
of herbaceous vegetation within 3 height stratas (0–0.5 m, 0.5–2 m, 2–5 m) and
the presence and absence of woody vegetation within 4 height stratas (0–0.5 m,
0.5–2 m, 2–5 m, and >5 m) were also noted in each quadrat. We used a spherical
densionometer to obtain 3 measurements of riparian canopy cover at each transect
(i.e., in the middle of the left quadrat, in the middle of the stream, and in the middle
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of the right quadrat). We measured riparian widths with an electronic total station
or a Real Time Kinematic global positioning system. Riparian widths were determined
by calculating the straight-line distance between coordinate measurements
obtained at the water’s edge and the edge of the agricultural fields. We calculated 15
riparian habitat variables from each site for each year. Herbaceous and woody structural
richness is the number of height strata within a site containing herbaceous or
woody vegetation. Percent frequency of herbaceous and woody vegetation in each
height strata was calculated by dividing the occurrence of herbaceous and woody
vegetation in each quadrat within each height strata by the total possible occurrence
(12) of herbaceous and woody vegetation within all 12 quadrats in a site. The sum
percent frequency of herbaceous and woody vegetation was the sum of the percent
Table 2. Mean, minimum and maximum values of seasonal means of selected watershed, riparian, geomorphology,
instream habitat, and water chemistry variables from channelized agricultural headwater
streams in the Upper Big Walnut Creek watershed, OH, 2006 to 2011.
Mean Minimum Maximum
Watershed
Watershed size (km2) 3.57 0.60 9.67
Percent agriculture 71.25 45.27 95.12
Percent bennington soil type 45.13 26.43 56.34
Percent centerburg soil type 18.19 0.93 56.15
Percent pewamo soil type 32.25 0.00 47.45
Riparian habitat
Riparian width (m) 25.06 4.74 80.26
Woody vegetation density (#/m2) 0.15 0.00 0.80
Percent canopy cover 6.92 0.00 55.10
Sum frequency of woody vegetation 91.70 0.00 308.33
Sum frequency of herbaceous vegetation 214.69 133.33 283.33
Geomorphology
Cross-section area (m2) 8.02 3.63 16.35
Top bank width (m) 8.54 5.95 12.09
Thalweg depth (m) 1.76 1.08 2.50
Sinuosity 1.03 0.99 1.29
Gradient (m/125 m) -0.19 0.01 -0.51
Instream habitat
Water depth (m) 0.13 0.01 0.50
Water velocity (m/s) 0.02 -0.05 0.19
Wet width (m) 1.91 0.43 4.83
Percent clay 32.0 0.0 91.7
Percent gravel 22.0 0.0 71.0
Percent instream wood 1.0 0.0 13.0
Water chemistry
Total nitrogen (mg/L) 4.66 0.36 22.88
Total phosphorus (mg/L) 0.13 0.00 0.75
Atrazine (μg/L) 2.64 0.00 85.26
Metolachlor (μg/L ) 0.73 0.00 15.65
Water temperature (°C) 17.79 3.60 36.21
Dissolved oxygen (mg/L) 87.05 9.70 315.20
Turbidity (NTU) 97.87 0.00 2816.00
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frequency of herbaceous and woody vegetation in each height strata. The woody
vegetation importance value was calculated as the sum of the relative frequency
of woody vegetation (i.e., sum frequency of woody vegetation divided by the sum
frequency of woody and herbaceous vegetation) and the mean woody vegetation
density for each site. We also calculated mean percent canopy cover and mean riparian
width.
We conducted geomorphology surveys within each site in the fall of each year.
Coordinate measurements for calculation of geomorphology variables were collected
from a minimum of 9 points along each transect using either a Real Time
Kinematic global positioning system or an electronic total station. We calculated 7
geomorphology variables from each site for each year. Specifically, we calculated
the mean cross-section area, mean channel depth, mean thalweg depth, mean topbank
width (i.e., the width of the channel at the bankfull elevation that is just below
the elevation where the water would begin spilling over into the floodplains and
agricultural fields), top-bank width to thalweg depth ratio, gra dient, and sinuosity.
We took 1 measurement of wet width and 4 measurements of water velocity and
depth along the 6 permanent transects within each site in the spring (May to June),
summer (July to August), and fall of each year concurrently with fish sampling. One
additional transect was established for the calculation of instantaneous discharge,
and we made 10 equidistant measurements of water depth and velocity along this
transect. We measured water depths with a top-setting wading rod, water velocity
with an electromagnetic velocity meter, and wet widths with a tape measure. The
dominant substrate type and instream cover types were visually identified at each
point. For each site during each season, we calculated mean and SD of water depth,
velocity, and wet width, as well as instantaneous discharge, the percent of each
substrate type, the percent of instream cover types, and the number of substrate and
cover types found. In total, we calculated 17 instream habitat variables from our
seasonal instream habitat measurements.
Our habitat assessment also included 18 water chemistry variables. We collected
weekly grab samples of water from each site from April to December of each
year for the measurement of nutrients, herbicides, and fungicides. Concentrations
of nitrate+nitrite, ammonia, and dissolved reactive phosphorus were determined
colorimetrically. We measured nitrate+nitrite and ammonia by application of the
copperized-cadmium method and dissolved reactive phosphorus by the ascorbic
reduction method (Parsons et al. 1984). We performed total nitrogen and total
phosphorus analyses on unfiltered samples following alkaline persulfate oxidation
(Koroleff 1983) with subsequent determination of nitrate+nitrite and dissolved reactive
phosphorus. We used heated persulfate oxidation with a total organic carbon
analyzer for measurements of dissolved organic carbon concentrations (Menzel and
Vaccaro 1964). We determined concentrations of 4 herbicides (alachlor, atrazine,
metolachlor, simazine), 1 herbicide metabolite (atrazine desethyl), and 2 fungicides
(chlorothalonil, metalaxyl) using gas chromatography following standards protocols
for pesticide analysis (US EPA 1995, Zaugg et al. 1995). We selected these 7
pesticides for measurement because of their known occurrence within agricultural
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streams in the region (Battaglin et al. 2011, Gilliom 2007, Smiley et al. 2014) or
our anticipation that they would be emerging contaminants as a result of known
pest management issues within the UBWC watershed. We calculated mean nutrient
and pesticide concentrations for each site from data collected during a 3-week
period beginning 1 week before and ending 1 week after the week of fish sampling.
Using a multiparameter meter, we obtained in situ measurements of dissolved
oxygen, water temperature, pH, and specific conductivity from each site 3 times a
year concurrently with fish sampling. Grab samples for turbidity were collected in
conjunction with in situ physico-chemical measurements and measured in the lab
with a turbidity meter.
Fish sampling and population assessments
At each site, we sampled fishes 3 times a year, once each in the spring, summer,
and fall. Block nets were set at the upstream and downstream borders of the sites
prior to fish sampling. We collected fishes with a backpack electrofisher (100 V,
60 Hz, DC current). Electrofishing began at the downstream border of a site and
proceeded upstream. Care was taken to ensure that all microhabitat types within
each site were sampled thoroughly during electrofishing. After electrofishing was
completed, we also collected 5 samples with a seine (2 m x 4 m, 0.32-cm mesh)
that were equally distributed throughout each site. Pools and slow-flowing areas
were sampled with a seine haul, and fast-flowing riffle and run areas were sampled
by kick-seining. We identified, measured, counted, and returned to the stream all
Creek Chubs caught. Small fishes that could not be reliably identified in the field
were anesthetized in MS-222, fixed in a 10% formalin solution, and taken to the lab
for identification. We calculated the abundance (number of captures), mean length,
and biomass of Creek Chub for each site during each seasonal collection. We determined
the weight of each fish using published length–weight relationships of Creek
Chub from an Illinois stream (Carlander 1969). Biomass of Creek Chubs at each site
from each season was calculated as the product of the mean weight and abundance.
Statistical analyses
We conducted principal components analysis (PCA) separately for the 8 watershed
variables, 15 riparian habitat variables, 7 geomorphology variables, 17
instream habitat variables, and 18 water chemistry variables to obtain the site scores
from the first 2 PCA axes of each habitat factor that would be used as independent
variables in our linear mixed-effect model analyses. Each PCA axis represents the
underlying gradient in combinations of habitat variables that occurs among our
study sites. Our use of PCA enabled us to objectively reduce the number of independent
variables within our statistical analyses from 65 to 10. Variable-reduction
methods to reduce multicollinearity prior to multiple regression analyses are necessary
to avoid spurious results (Dormann et al. 2013). Principal components analyses
with correlation cross-product matrixes were conducted with PC-ORD 4 for Windows
(McCune and Mefford 1999).
Examining the pairwise correlation coefficients among all 10 PCA axes, we
found that the |r| values (absolute values of the correlation coefficients) were below
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the level that indicates the presence of strong multicollinearity (i.e., |r| = 0.7) among
independent variables (Table 3; Dormann et al. 2013). We observed that 4 pairs of
correlations (i.e., watershed PCA axis 1 and geomorphology PCA axis 1, watershed
PCA axis 2 and riparian habitat PCA axis 2, watershed PCA axis 2 and instream
habitat PCA axis 2, riparian habitat PCA axis 2 and geomorphology PCA axis 2) exhibited
moderate levels of multicollinearity as indicated by |r| values between 0.52
and 0.62 (Table 3). We recognized that even moderate levels of multicollinearity
may potentially influence the conclusions from the statistical analyses we planned
to conduct as part of our mechanistic comparison of the influence of all habitat
factors on the abundance, mean length, and biomass of Creek Chubs. To address
this potential multicollinearity issue, we conducted multimodel inference analysis
(Burnham and Anderson 2002) that involved determining the overall importance
value of each independent variable based on 8 linear mixed-effect models with and
without moderate levels of multicollinearity.
The first step in our multimodel inference analysis was the development of 8 linear
mixed-effect models that included at least 4 of 5 habitat factors (i.e., watershed
characteristics, riparian habitat, geomorphology, instream habitat, water chemistry)
in each model (Table 4). Model 1 is our global model that contains all 10 PCA axes.
Models 2 to 5 represent 4 reduced versions of the global model (Model 1) that
omit selected PCA axes to remove the moderate levels of multicollinearity present
among 4 pairs of our independent variables (Table 2). Model 2 excludes riparian
habitat PCA axis 2, geomorphology PCA axis 1, and instream habitat PCA axis 2.
Model 3 excludes watershed PCA axis 1, riparian PCA axis 2, and instream habitat
PCA axis 2. Model 4 excludes watershed PCA axis 2, geomorphology PCA axis 1,
and geomorphology PCA axis 2. Model 5 excludes watershed PCA axis 1, watershed
PCA axis 2, and geomorphology PCA axis 2. Model 6 is a model containing 7
PCA axes (watershed PCA axis 2, riparian habitat PCA axis 1, geomorphology PCA
Table 3. Pearson correlation coefficients from pairwise correlations among the 10 principal components
analysis (PCA) axes of watershed characteristics (WS), riparian habitat (RH), geomorphology
(GE), instream habitat (IH), and water chemistry (WC) within channelized agricultural headwater
streams in the Upper Big Walnut Creek, OH, 2006 to 2011. * indicate correlation coefficients are those
pairs of principal components axes that exhibited a significant correlation (P < 0.05).
WS PCA RH PCA GE PCA IH PCA WC PCA
Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2
WS PCA axis 1 - - - - - - - - - -
WS PCA axis 2 0.00 - - - - - - - - -
RH PCA axis 1 0.08 -0.11 - - - - - - - -
RH PCA axis 2 -0.04 -0.54* 0.00 - - - - - - -
GE PCA axis 1 -0.62* 0.10 0.05 -0.01 - - - - - -
GE PCA axis 2 -0.16 0.14* -0.04 -0.56* 0.00 - - - - -
IH PCA axis 1 -0.30* 0.15* 0.09 0.12 0.46* -0.31* - - - -
IH PCA axis 2 -0.21* -0.52* -0.17* 0.10 0.08 0.29 0.00 - - -
WC PCA axis 1 0.27* -0.03 0.25* -0.03 -0.05 -0.08 0.13* -0.09 - -
WC PCA axis 2 0.12 -0.22 0.00 0.05 -0.02 -0.05 -0.39* -0.11 0.00 -
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axis 1, instream habitat PCA axis 1, instream habitat PCA axis 2, water chemistry
PCA axis 1, water chemistry PCA axis 2) (Table 4) that contain habitat variables
that have been previously documented to influence the abundance, length, and
biomass of Creek Chub within headwater streams (Table 1). Models 7 and 8 are
reduced versions of Model 6 that exclude either geomorphology PCA axis 2 and
instream habitat PCA axis 2 (Model 7) or watershed PCA axis 2 and geomorphology
PCA axis 1 (Model 8) to remove moderate levels of multicollinearity (Table 4).
The second step of the multimodel inference analysis consisted of conducting
linear mixed-effect model analysis for each model. Specifically, for each model,
we conducted linear mixed-effect model analysis with the 3 response variables as
our dependent variables, the PCA axes as independent variables (fixed effects), and
site as a random effect to address the issue of pseudoreplication that results from
repeatedly sampling the same sites through time.
Linear mixed-effect model analysis is an extension of the traditional linear
regression analysis that incorporates fixed effects and random effects into the
regression formula. We conducted linear mixed-effect model analyses with R (R
Core Team 2014) using the lme function within nlme package (Pinheiro et al.
2014). Inspection of the residuals of the initial mixed-effect model analyses of the
global model of abundance and biomass indicated the residuals were not normally
distributed; therefore these 2 dependent variables were log (x + 1) transformed.
We also accounted for the effect of heteroscedasticity in the variances among sites
(abundance, mean length) or months (biomass) using the weights option (i.e.,
weights=varIdent(form=~1|site) or weights=varIdent(form=~1|months) within the
lme function.
The third step of the multimodel inference analysis involved obtaining the small
sample Akaike information criterion (AICc ) score, ΔAICc (the difference in AICc
between each model and the model with the minimum AICc), and Akaike weight
(Wi) for each of the 8 models (Burnham and Anderson 2002, Johnson and Omland
2004). We based our multimodel inference analysis on the AICc because the ratio
of n/K was less than 40 for all models (Burnham and Anderson 2002, Johnson and Omland
Table 4. Summary of therincipal components analysis (PCA) axes of watershed characteristics (WS),
riparian habitat (RH), geomorphology (GE), instream habitat (IH), and water chemistry (WC) that are
present within the 8 models developed for multimodel inference analysis. A value of 1 indicates the
variable is included in the model, and a value of 0 indicates the variable is not included in the model.
WS PCA RH PCA GE PCA IH PCA WC PCA
Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2
Global 1 1 1 1 1 1 1 1 1 1
model
Model 2 1 1 1 0 0 1 1 0 1 1
Model 3 0 1 1 0 1 1 1 0 1 1
Model 4 1 0 1 1 0 0 1 1 1 1
Model 5 0 0 1 1 1 0 1 1 1 1
Model 6 0 1 1 0 1 0 1 1 1 1
Model 7 0 1 1 0 1 0 1 0 1 1
Model 8 0 0 1 0 0 1 1 1 1 1
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2004). We also calculated the relative importance of each independent variable as
the mean Wi over all models in which the independent variable occurs (Burnham
and Anderson 2002, Johnson and Omland 2004, Kittle et al. 2008). Our importance
value reports the mean Wi instead of the summed Wi because all independent variables
did not occur in the same number of models (Kittle et al. 2008). AICc values
were obtained with the AICc function within the AICcmodavg package (Mazerolle
2016) in R. We also reported the standardized regression coefficients from the best
performing model (i.e., the model with the minimum AICc) for each response variable
as an additional way to evaluate the relative importance of individual habitat
variables in determining the population structure of Creek Chub and to identify
the specific relationships that occurred between the abundance, mean length, and
biomass of Creek Chub with the independent variables.
Results
We captured 10,401 Creek Chubs from our 14 study sites over a 6-year period.
Creek Chub were captured at least once from every site during our study. The mean
number of fish captured from each site per season was 45 and ranged from 0 to 809
fishes. Length of Creek Chub from each site per season averaged 8.4 cm and ranged
from 1.6 to 18.6 cm. The largest fish captured was 22.0 cm in length. Biomass of
Creek Chub from each site per season averaged 288 g and ranged from 0 to 2043 g.
Our sampling occurred under a wide range of hydrological and chemical conditions
that were expected to occur within these small streams (Table 2). Hydrologic
conditions were quite variable, with some sites consisting of small fragmented
pools with no water flow during the summer to some sites with elevated water
depths and water velocities that follow precipitation events in the spring and late
fall (Table 2). Clay and gravel were the most frequently occurring substrate types
and within some sites the dominant substrate types (Table 2). Our sites typically
contained minimal amounts of instream wood (Table 2), but some sites contained
large amounts of aquatic plants that could serve as cover for fishes. The observed
mean and range of nutrient and pesticide concentrations indicated that our sites
exhibited wide variations in concentrations that are to be expected within agricultural
streams that experience peaks in agricultural contaminants following storm
events (Table 2; Smiley et al. 2014). Additionally, mean total nitrogen, maximum
total nitrogen, and total phosphorus concentrations exceeded levels found to be
capable of impacting stream communities (Table 2; Justus et al. 2010). With the
exception of maximum atrazine concentrations, mean and maximum herbicide and
fungicide concentrations were less than chronic and acute toxicity levels for fish
(US EPA 2014). Average physicochemical conditions were within acceptable levels
for aquatic life, yet our sites exhibited a wide range of water temperature, dissolved
oxygen, and turbidity values (Table 2).
Principal components analysis results
The first 2 PCA axes of all habitat factors possessed eigenvalues that were
greater than the broken-stick eigenvalues. Additionally, the cumulative percent of
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2017 Vol. 24, Special Issue 8
the variance explained by the first 2 PCA axes ranged from 37% for water chemistry
to 82% for watershed characteristics with a mean of 62% cumulative percent
variance explained for all 5 habitat factors (Table 5). The first watershed PCA axis
was a soil-type gradient where the percentages of Centerburg and Amanda soil
types increased and percentages of Bennington and Pewamo soil types decreased
with increasing site scores (Table 5). The second watershed PCA axis was a landuse
gradient where percentages of forested and residential land use increased and
Table 5. Loadings for each habitat variable from the first two principal components analysis (PCA)
axes of watershed characteristics, riparian habitat, geomorphology, instream habitat, and water chemistry
from channelized agricultural headwater streams in the Upper Big Walnut Creek watershed, OH,
2006 to 2011. [Table continued on next page.]
Variables Axis 1 Axis 2
Watershed variables
Watershed size 0.303 0.035
Percent agriculture 0.036 -0.605A, B
Percent forested 0.081 0.560A, B
Percent residential -0.153 0.538A, B
Percent Bennington -0.492A, B 0.008
Percent Centerburg 0.486A, B -0.023
Percent Pewamo -0.461A, B 0.020
Percent Amanda 0.432A, B 0.167
% variance explained by axis 48.5 33.7
Riparian habitat variables
Mean riparian width 0.103 -0.259
Percent canopy cover -0.306 0.165
% frequency herbaceous vegetation in height strata 1 0.029 -0.032
% frequency herbaceous vegetation in height strata 2 -0.172 -0.390A
% frequency herbaceous vegetation in height strata 3 -0.030 -0.478A
Sum frequency of herbaceous vegetation -0.101 -0.535A, B
Herbaceous vegetation structural richness -0.137 -0.419A
% frequency woody vegetation in height strata 1 -0.309 -0.007
% frequency woody vegetation in height strata 2 -0.328 -0.025
% frequency woody vegetation in height strata 3 -0.327 0.140
% frequency woody vegetation in height strata 4 -0.291 0.140
Sum frequency of woody vegetation -0.361A, B 0.062
Woody vegetation structural richness -0.300 -0.096
Mean woody vegetation density -0.312 0.079
Importance value of woody vegetation -0.352A, B 0.059
% variance explained by axis 48.7 19.8
Geomorphology variables
Mean cross-sectionArea -0.514A, B 0.011
Mean top bank width -0.481A -0.204
Mean thalweg depth -0.504A, B -0.153
Mean channel depth -0.445A 0.320
Ratio of mean top bank width to mean thalweg depth 0.059 0.633A, B
Sinuosity 0.044 0.519A, B
Gradient -0.217 0.404A
% variance explained by axis 52.3 29.3
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percentages of agricultural land use decreased with increasing site scores (Table 5).
The first riparian habitat PCA axis was a woody vegetation gradient where the sum
frequency of woody vegetation and woody vegetation importance values decreased
with increasing site scores (Table 5). The second riparian habitat PCA axis was a
herbaceous vegetation gradient where the sum frequency of herbaceous vegetation
decreased with increasing site scores (Table 5). The first geomorphology PCA axis
Table 5, continued.
Variables Axis 1 Axis 2
Instream habitat variables
Mean water depth -0.426A, B 0.161
Standard deviation water depth -0.371A -0.136
Mean water velocity -0.352A -0.031
Standard deviation water velocity -0.338 0.001
Mean wet width -0.395A, B -0.104
Standard deviation wet width -0.108 0.322
Discharge -0.401A, B -0.088
Percent clay 0.027 -0.381A, B
Percent sand -0.023 0.327
Percent gravel -0.135 0.487A, B
Percent cobble -0.137 0.273
Percent terrestrial vegetation 0.087 -0.338
Percent leaf litter -0.038 -0.180
Percent instream wood -0.142 -0.088
Percent algae 0.055 0.187
Percent aquatic plants 0.111 0.000
Number of substrate types -0.179 -0.282
% variance explained by axis 23.5 15.9
Water chemistry variables
Nitrate plus nitrite -0.395A 0.236
Ammonia -0.176 -0.329
Total nitrogen -0.428A, B 0.162
Dissolved reactive phosphorus -0.201 -0.374A
Total phosphorus -0.200 -0.455A, B
Dissolved organic carbon -0.191 -0.378A
Alachlor -0.068 0.053
Atrazine -0.328 0.074
Atrazine desethyl -0.414A, B 0.085
Chorothalonil 0.009 0.053
Metalaxyl -0.013 0.044
Metolachlor -0.389A 0.075
Simazine -0.253 0.106
Water temperature -0.044 -0.171
Dissolved oxygen -0.067 0.204
Specific conductivity 0.034 -0.347
pH 0.080 0.055
Turbidity 0.001 -0.297
% variance explained by axis 19.7 17.7
ALoadings that were greater than 0.35.
BLoadings that best characterized the underlying habitat gradients of each PCA axis.
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was a gradient of channel size where channel cross-section area and thalweg depths
decreased with increasing site scores (Table 5). The second geomorphology PCA
axis was a gradient of channel shape where sinuosity and the ratio of mean top-bank
width and mean thalweg depth increased with increasing site scores (Table 5). The
first instream habitat PCA axis was a hydrology gradient where mean water depth,
discharge, and mean wet width decreased with increasing site scores (Table 5).
The second instream habitat PCA axis was a substrate gradient where percent
gravel increased and percent clay decreased with increasing site scores (Table 5).
The first water chemistry PCA axis was a nutrient-pesticide gradient where total
nitrogen and atrazine desethyl concentrations decreased with increasing site scores
(Table 5). The second water chemistry PCA axis was a nutrient gradient where total
phosphorus concentrations decreased with increasing site scores (Table 5).
Multimodel inference analysis results
Our multimodel inference analysis indicated that all of the best models for each
of the 3 response variables were reduced models that did not contain moderate
levels of multicollinearity (Table 6). For abundance, model 3 had the least ΔAICc
and the greatest Wi of all 8 models (Table 6). None of the other abundance models
exhibited a ΔAICc < 2 that would indicate substantial support for another model
(Burnham and Anderson 2002). Model 2 for mean length had the least ΔAICc and
the greatest Wi of all models (Table 6). None of the other mean length models exhibited
a ΔAICc < 2. For biomass, model 3 had the least ΔAICc and the greatest Wi
of all models (Table 6). Model 7 for biomass exhibited a ΔAICc < 1 that indicates
substantial support this model (Burnham and Anderson 2002) and its Wi was similar
to the Wi of model 3. Model 7 differs only from model 3 in that it lacks the second
geomorphology PCA axis. Given that the AICc penalizes for increasing number of
parameters within a model we feel that identifying model 3 as the best model is
justified because it has more parameters than model 7.
Table 6. Summary of the number of parameters (k), small sample Akaike information criterion (AICc),
difference in AICc between each model and the model with the minimum AICc (Δ AICc), and Akaike
weight (Wi) from 8 models containing different combinations of independent variables to determine
which habitat factor had the greatest influence on the abundance, mean length, and biomass of Creek
Chub from channelized agricultural headwater streams within Upper Big Walnut Creek, OH, 2006 to
2011.
Abundance Mean length Biomass
k AICc ΔAICc Wi k AICc ΔAICc Wi k AICc ΔAICc Wi
Global 26 372.43 6.8 0.02 26 761.67 5.8 0.05 30 301.31 6.6 0.02
model
Model 2 23 369.19 3.6 0.11 23 755.87 0.0 0.84 27 299.59 4.9 0.04
Model 3 23 365.64 0.0 0.66 23 769.25 13.4 0.00 27 294.68 0.0 0.41
Model 4 23 379.47 13.8 0.00 23 760.16 4.3 0.10 27 303.96 9.3 0.00
Model 5 23 378.69 13.0 0.00 23 768.52 12.7 0.00 27 300.90 6.2 0.02
Model 6 23 370.79 5.1 0.05 23 768.73 12.9 0.00 27 297.65 3.0 0.09
Model 7 22 368.61 3.0 0.15 22 768.59 12.7 0.00 26 294.95 0.3 0.36
Model 8 22 376.51 10.9 0.00 22 766.28 10.4 0.00 26 298.58 3.9 0.06
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We found that abundance was most strongly influenced by the channel-shape
gradient (geomorphology PCA axis 2), with the land-use gradient (watershed PCA
axis 2) and the channel size gradient (geomorphology PCA axis 1) being secondary
and tertiary factors, respectively, of importance (Fig. 2). We observed that mean
length was most strongly influenced by the soil-type gradient (watershed PCA
axis 1), with the channel-shape gradient and the land-use gradient being secondary
and tertiary factors, respectively, of importance (Fig. 2). We also documented that
biomass was most strongly influenced by the land-use gradient, with the channelsize
and -shape gradients being secondary and tertiary factors, respectively, of
importance (Fig. 2).
The standardized regression coefficients from the models with the lowest AICc
scores also indicated that the abundance, mean length, and biomass of Creek Chub
were most strongly influenced by watershed characteristics and geomorphology (Table
7). The standardized regression coefficients from the models with the lowest AICc
scores also provided information on the types of relationships that occurred between
the response variables and the independent variables (Table 7). Abundance and biomass
exhibited negative correlations with the land-use gradient and decreased with
increasing percentages of forested and urban land use and decreasing percentages of
agriculture in the watershed (Table 7). Mean length was positively correlated with
the soil-type gradient and increased with increasing percentages of Centerburg and
Amanda soil types and decreasing percentages of Bennington and Pewamo soil types
Table 7. Standardized regression coefficients from linear mixed-effect model analyses of abundance,
mean length, and biomass of Creek Chub with habitat factors represented by the site scores from the
first 2 axes from the principal component analysis (PCA) of watershed characteristics (WS), riparian
habitat (RH), geomorphology (GE), instream habitat (IH), and water chemistry (WC) within channelized
agricultural headwater streams in the Upper Big Walnut Creek, OH, 2006 to 2011.
WS PCA RH PCA GE PCA IH PCA WC PCA
Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2
Abundance -0.520A, B 0.079 -0.257B 0.252B -0.004 0.039 0.074
Mean length 0.542A, B 0.055 0.131 0.230B -0.158B -0.014 0.027
Biomass -0.382B -0.108 -0.423A, B 0.229 0.084 0.182B 0.166
AStandardized regression coefficients a having the greatest influence on each population-response
variable.
BStandardized regression coefficients of independent variables that were documented to have significant
effect (P < 0.05) within the linear mixed-effect model analyses.
Figure 2 [following page]. Mean importance values for each independent variable calculated
based on sum of Akaike weights for each habitat factor over 8 models containing
different combinations of independent variables to determine which habitat factor had the
greatest influence on the abundance, mean length, and biomass of Creek Chub from channelized
agricultural headwater streams within Upper Big Walnut Creek, OH, 2006 to 2011.
Independent variable abbreviations are: WS = watershed characteristics, RH = riparian
habitat, GE = geomorphology, IH = instream habitat, WC = water chemistry, a1 = principal
components analysis axis 1, a2 = principal components analysis axis 2.
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Figure 2. [Caption on previous page.]
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(Table 7). Abundance and mean length exhibited positive correlations with the channel-
shape gradient and increased with increasing ratios of mean top-bank width and
mean thalweg depth and increasing sinuosity (Table 7). Abundance and biomass were
negatively correlated with the channel-size gradient and decreased with decreasing
channel cross-section area and thalweg depth (Table 7).
Discussion
Our results did not support our hypothesis that instream habitat would have a
greater effect on the population structure of Creek Chub in channelized agricultural
headwater streams. We instead found that the abundance, mean length, and
biomass of Creek Chub within these small streams were more strongly influenced
by watershed characteristics and geomorphology than by riparian habitat, instream
habitat, and water chemistry. Notably, instream habitat and water chemistry were
not identified as being among the 3 most strongly influential habitat factors on Creek
Chub (Fig. 2). Previous findings related to the habitat relationships of Creek Chub
in headwater streams in North America have frequently documented the influence
of instream habitat on Creek Chub (Table 1). Additionally, most studies (Hubert
and Rahel 1989; Magoulik 2000; Quist and Guy 2001; Sanders 2012; Smiley et al.
2008, 2009) comparing the degree of influence of different habitat variables on the
population structure of Creek Chub in North American streams have documented
that instream habitat had a greater influence on the population structure of Creek
Chub than other habitat factors. Our results related to mean length and biomass
were consistent with those of Wuellner et al. (2013), who found the presence of
Creek Chub was best explained by the watershed characteristic spatial position than
by reach-scale measurements of riparian habitat, instream habitat, and water chemistry.
Our results related to geomorphology were novel because the importance of
channel size and shape on the population structure of Creek Chub had not been
documented previously. Our results and those of Wuellner et al. (2013) highlight
the importance of the larger-scale watershed and geomorphic characteristics in
determining the population structure of Creek Chub in headwater streams in North
America. Our results also represent the first documentation of the relationships of
Creek Chub with land use, soil types, channel shape, and channel size within channelized
agricultural headwater streams in North America (Table 1).
Previous findings from channelized and unchannelized streams having a range of
watershed sizes in North America indicate that Creek Chub exhibit either positive
relationships with increasing amounts of forested land use (Bouska and Whitledge
2014, Nagrodski et al. 2013, Stammler et al. 2008), negative relationships with increasing
amounts of urban land use (Fitzgerald et al. 1999, Horwitz et al. 2008), or
positive relationships with increasing amounts of agriculture (Pinder and Morgan
1995). We documented that the abundance and biomass of Creek Chub decreased
with joint increases in amounts of forested and urban land use and decreases in
the amounts of agriculture in the watershed of channelized agricultural headwater
streams. Based on previous findings (Bouska and Whitledge 2014, Nagrodski et al.
2013, Stammler et al. 2008), we expected that increases in abundance and biomass
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2017 Vol. 24, Special Issue 8
of Creek Chub would occur with increasing amounts of forested land use. Our
results instead suggest that the influence of increasing urban land use may override
the potential benefits of increasing forested land use within the watersheds of
channelized agricultural headwater streams. The variable relationships exhibited
by Creek Chub with land use may be related to the ability of Creek Chub to physiologically
adapt to a wide range of physical and chemical conditions (Blevins et al.
2013) or it could simply represent the site-specific conditions within a study area.
Future research should quantify why Creek Chub exhibit variable relationships
with land-use trends in headwater streams in North America.
Soil type is one of several factors used to distinguish among ecoregions in
the United States (Omerik 1987), and the composition of soil types within the
watershed influences the hydrology, substrate characteristics, and chemical characteristics
of streams (Maloney et al. 2013, Poff et al. 1997, Smith et al. 2003).
Our understanding of the influence of soil types in the watershed on Creek Chub in
headwater streams in North America is limited despite the potential importance of
this watershed characteristic. Maloney et al. (2013) found that the percent of sand in
the watershed was one of the most influential factors on the presence of Creek Chub
in first- to fourth-order streams in Maryland. Specifically, the presence of Creek
Chub decreased in sites within the Southeastern Plains and Mid-Atlantic Coastal
Plains ecoregions as a result of increasing percentages of sand in the watershed
that in turn corresponded with increased sand within the streams (Maloney et al.
2013). In contrast, we found mean length increased with increasing percentages of
Centerburg and Amanda soil types and decreasing percentages of Bennington and
Pewamo soil types. Centerburg and Amanda soil types are well-drained soils having
more sand and less clay than the poorly drained Bennington and Pewamo soil types.
Sites with the greatest percentage of Centerburg and Amanda soil types and having
the greatest mean lengths were sites located in northern part of the watershed on the
mainstem of the Upper Big Walnut Creek. Headwater sites located directly on
the mainstem would be more likely to be colonized by larger-sized individuals than
tributary sites located further away from the mainstem. Future research needs to
examine the influence of soil type in the watershed on Creek Chub in headwater
streams over a larger spatial scale to gain a greater understanding of this potentially
important watershed characteristic.
Watershed size is one watershed characteristic that was frequently documented
to influence the abundance, density, length, biomass, and presence of Creek Chub
within unchannelized headwater streams in the northeastern portion of North
America (Table 1). Abundance, biomass, and occurrence of Creek Chub increases
with decreasing watershed size (Butler and Fairchild 2005, Horwitz et al. 2008,
Lemly 1985, Lotrich 1973, Maloney et al. 2013). These quantitative habitat relationships
concur with the qualitative life-history descriptions of Creek Chub
that indicate their preference for small streams (Copes 1978, Mahon et al. 1979,
Shelford 1913, Starrett 1950, Thompson and Hunt 1930). In contrast, we did not
observe any relationships of abundance, mean length, and biomass of Creek Chub
with watershed size in channelized agricultural headwater streams. Perhaps the
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similarity in watershed sizes among our sites hindered detection of the influence
of this watershed characteristic. Future research needs to quantify the influence of
watershed size on Creek Chub in channelized agricultural headwater streams over
a larger spatial scale and range of watershed sizes to gain a better understanding of
the role of this watershed characteristic within this stream type.
Little is known about the influence of geomorphology on abundance and success
of Creek Chub in headwater streams in North America (Table 1). Creek Chub were
associated with decreasing channel cross-section area in an agricultural watershed
in Ohio (D’Ambrosio et al. 2009). We documented that abundance and biomass of
Creek Chub increased with increasing channel cross-section area and thalweg depth
within channelized agricultural headwater streams. We also documented that abundance
and mean length increased with increasing ratios of mean top-bank width
and mean thalweg depth and increasing sinuosity. These results suggest that stream
channel alterations that modify the channel size and shape will influence success of
Creek Chub within agricultural streams in North America. Our understanding of the
responses of Creek Chub to stream channel alterations is limited despite the large
amount of information available on the influence of stream channelization on fish
community structure in agricultural streams (Smiley and Gillespie 2010). Increasing
the length of stream channel enclosed by culverts installed at road crossings
decreased the probability of occurrence and abundance of Creek Chub upstream
of culverts within channelized agricultural headwater streams in Michigan (Briggs
and Galarowicz 2013). Stream channelization has been found to reduce the number
of larger-sized Creek Chub in a channelized stream site in Iowa (Scarnecchia 1988)
or to have no effect on the occurrence of Creek Chub in agricultural headwater
streams in Ontario (Stammler et al. 2008). Given the recent interest in the use of
natural channel design, 2-stage channel design, and other novel stream channel alteration
practices as part of stream restoration strategies in North America, more research
is needed to identify which of these novel stream channel alteration practices
positively influence Creek Chub in channelized agricultural headwater streams in
the region.
In conclusion, our results provided new information on the importance of
watershed characteristics (land-use and soil-type gradients) and geomorphology
(channel-shape and channel-size gradients) on the abundance, mean length, and
biomass of Creek Chub in channelized agricultural headwater streams in the northeastern
portion of North America. This information can be used to help develop
conservation and restoration strategies for channelized and unchannelized headwater
streams in agricultural watersheds in this region. Specifically, our results related
to the population structure of Creek Chub and past results involving fish community
structure (Sanders 2012; Smiley et al. 2008, 2009) indicate that conservation and
restoration practices designed to mitigate physical habitat degradation within the
watershed, within the channel, and within the streams are most likely to positively
influence the population structure of Creek Chubs and other fishes within agricultural
headwater streams. Our results also suggest that conservation and restoration
practices intended to improve water quality without addressing physical habitat
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2017 Vol. 24, Special Issue 8
degradation within channelized agricultural headwater streams are not likely to
benefit the fishes within these streams.
Acknowledgments
We thank J. Bossley, S. Knight, R. Gillespie, and 2 anonymous reviewers for helpful
comments on an earlier version of this manuscript. D. Gamble, E. Gates, S. Hess, A.
Kemble, A. Rapp, G. Roberts, J. Risley, K. Seger, and R. Shaw assisted with field work. E.
Fischer assisted with laboratory work and provided information on methods and QA/QC
procedures for nutrient and pesticide measurements. K. Stillman assisted with preparing
data summaries. M. Lauer helped with organizing and compiling the references section.
We thank the numerous current and past USDA-ARS Soil Drainage Research Unit personnel
for their assistance with field and laboratory work. B. Bacon provided information on
watershed characteristics. Landowner and site information were provided by Soil and Water
Conservation and NRCS districts in Delaware and Morrow Counties, OH. We are also grateful
to the landowners who provided access to the sites.
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