Application of OEPA-Produced Biotic Indices and Physical
Stream Measurements to Assess Freshwater Mussel
(Unionidae) Habitat in the Upper Mahoning River, Ohio
Matthew T. Begley and Robert A. Krebs
Northeastern Naturalist, Volume 24, Issue 1 (2017): 1–14
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Northeastern Naturalist Vol. 24, No. 1
M.T. Begley and R.A. Krebs
2017
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2017 NORTHEASTERN NATURALIST 24(1):1–14
Application of OEPA-Produced Biotic Indices and Physical
Stream Measurements to Assess Freshwater Mussel
(Unionidae) Habitat in the Upper Mahoning River, Ohio
Matthew T. Begley1 and Robert A. Krebs1,*
Abstract - Freshwater mussels continue to experience declines in population numbers in
response to changing environments. Identifying aspects of the environment associated with
the presence and abundance of mussels in small streams is challenging where past records
are minimal. Thus, we sought to produce models of habitat favoring mussel species richness
and abundance in the upper Mahoning River using data collected in 2 ways: (1) surveying
sites deemed as suitable habitat via observation and (2) surveying existing Ohio Environmental
Protection Agency (OEPA) sites used to evaluate water quality and aquatic life
through biotic indices. Detailed physical measurements were added at each survey site.
Surveys identified 963 freshwater mussels of 11 species. The more-forested Eagle Creek
contained an abundant mussel assemblage compared to the rest of the Upper Mahoning
River, yet this stream was still dominated by just 1 common species, Lampsilis siliquoidea
(Fatmucket). Drainage area alone correlated with mussel richness and abundance, but a
complex model of multiple characteristics provided equivalent predictive power to assess
how variation in environmental components may enhance the likelihood of mussel
presence. The OEPA composite qualitative habitat evaluation index, which encompasses
substrate, instream cover, channel morphology, riparian zone, pool quality, and map gradient,
also was indicative of greater mussel diversity.
Introduction
Many streams and their associated animal communities have deteriorated in
response to human-driven changes to the surrounding landscape. Impervious surfaces
and agricultural fields left bare of vegetation increase the flashiness and water
temperature of runoff within both urban and agricultural lands (Kaushal et al. 2010,
Weil and Kremen 2007), as well as sediment loads (Allan et al. 1997, Jones et al.
2001), metals (Lenat and Crawford 1994) and nutrients (Duan et al. 2012, Gordon
et al. 2008, Strayer et al. 2003) in neighboring streams. Such changes to surface
cover can reduce diversity in assemblages of macroinvertebrates (Liess et al. 2012,
Roy et al. 2003), fish (Vondracek et al. 2005, Wang et al. 2003), and freshwater
mussels (Atkinson et al. 2014, Gangloff et al. 2009, Gillies et al. 2003), and persistent
species in areas that have experienced such landcover changes are often those
more tolerant of poor water quality (Helms et al. 2005, Peacock et al. 2005, Poole
and Downing 2004).
1Department of Biological, Geological, and Environmental Sciences, Cleveland State
University, 2121 Euclid Avenue SI 214, Cleveland, OH 44115. *Corresponding author -
r.krebs@csuohio.edu.
Manuscript Editor: David Yozzo
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M.T. Begley and R.A. Krebs
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A critical challenge to assessing relationships between stream conditions and a
specific biological assemblage is the collection of key habitat data. For imperiled
mussel species in the family Unionidae, as reviewed by Strayer et al. (2004) and
Haag and Williams (2014), habitat characteristics at multiple spatial scales influence
mussel assemblage composition (Haag 2012, Watters 1992). Impoundments
change habitat (Hardison and Layzer 2001, Vaughn and Taylor 1999), and dams
restrict dispersal (Watters 1996), but an understanding of how variation in stream
morphology and water quality affect diversity in free-flowing streams has remained
elusive, especially within small streams (Layzer and Madison 1995, Lyons et al.
2007, Strayer and Ralley 1993).
Value and efficiency may be added to mussel surveys by aligning them with
stream assessment sites used by state agencies like the Ohio Environmental Protection
Agency (OEPA), which monitor the interaction of chemical, physical, and
biological processes to assess health and well-being of surface waters and their biota
(Karr 1991, OEPA 2011). The OEPA reports drainage area, a qualitative habitat evaluation
index (QHEI; OEPA 2006), a fish community metric (index of biotic integrity
or IBI), and land use of subwatersheds. Biological criteria are one of the principal assessment
tools by which the status of water bodies is determined in Ohio (Yoder and
Rankin 1996), but mussels are not part of any OEPA analyses, which makes contrasts
between mussel diversity and these metrics statistically independent. To assess components
of the habitat associated with greater richness and abundance, we searched
sites in the Upper Mahoning River, a headwater system of the Ohio River, for mussels
in areas never previously examined. We surveyed Eagle Creek in 2013, targeting
easily accessible sites with apparent suitable habitat (determined via observation),
and then sampled across the Upper Mahoning River watershed in 2014 at sites where
OEPA biotic indices were collected in 2006 (OEPA 2011).
Methods
Our surveys encompassed the Upper Mahoning River (UMR) watershed in
northeastern Ohio. This 1500-km2 headwater catchment of the Ohio River lies
adjacent to Ohio’s northern divide, which separates the Lake Erie and Ohio River
watersheds. The UMR composes 4 sub-watersheds (Fig. 1) that in total are 37%
forested, but as land use shifts to more agriculture from north to south, cropland
(23%) and pastureland (17%) combine to comprise a larger proportion than forest
overall, and just 12% of land is considered developed (OEPA 2011). Several large
reservoirs occur on the Mahoning River, another on the West Branch Mahoning
River, and small dams are also present, including in Eagle Creek.
We surveyed 8 sites in Eagle Creek in 2013 and 20 sites throughout the UMR
in 2014 at locations with OEPA data (Fig. 1; http://wwwapp.epa.ohio.gov/dsw/gis/
bio/index.php). Only site 4 was sampled both years. Aquatic life-use attainment categories
were analyzed as discrete values (non attainment [0], partial attainment [1],
and full attainment [2]) based on combined scores from a mix of habitat, fish, and
macroinvertebrate community indices relative to typical community and physical
conditions for similar regions (Yoder and Rankin 1995, 1996).
Northeastern Naturalist Vol. 24, No. 1
M.T. Begley and R.A. Krebs
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Figure 1. Study sites within the Upper Mahoning River. The top shows sites within Eagle
Creek surveyed for mussels in 2013 and encircled numbers indicate the sites surveyed in
2014 that correspond with the Ohio EPA aquatic life-attainment measurements indicated
for each site. The 4 subwatersheds separated for land use were Eagle Creek, West Branch
Mahoning River, Deer Creek, and Mahoning River headwaters. Large impoundments include
the Lake Milton Dam (built 1913), Berlin Lake Dam (built in 1943), the Michael J.
Kirwan Dam (built in 1966), and the smaller Deer Creek Dam (built in 1955) just above
Berlin Lake.
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We conducted timed visual and tactile searches for mussels in 2013 by wading
at each site for 4 person hours in a haphazard pattern within the stream, intensifying
searches to immediate areas where mussels were found. In 2014, two-person-hour
surveys were restricted to a 100-m stretch of the stream bed (upstream of road
crossings, if present), which covered all habitat types within the constrained area.
Turbidity and depth were low enough that visual searches were possible at most sites.
Mussel rakes and tactile methods were used in deeper areas. All live mussels found
were removed from the sediment, identified, measured for length, and returned to the
streambed. We deposited voucher specimens at Cleveland State University.
We recorded physical characteristics at each site in 2014 (Table 1) as an average
value from 5 transects made 20 m apart, starting at the most downstream portion
of the mussel survey. We characterized stream size by measuring channel width,
average depth, bankfull width, bankfull depth, and discharge following protocols in
Gordon et al. (2004), with specific details in Begley (2015). Discharge was calculated
by measuring average flow velocity with a digital flow meter (Hach FH950) at
1-m intervals across each transect. We assessed median grain size of the substrate
using 100 randomly selected samples, measuring all pebbles (>2 mm), while recording
sand, silt, and clay as 0.5 mm, 0.03 mm, and 0.004 mm, respectively (Wolman
1954). We estimated shear stress at baseflow and bankfull levels by multiplying the
density of water (1000 kg/m3) by the gravitational constant (9.8 m/s2), stream slope,
and the average depth at baseflow and bankfull conditions, respectively. Stream
Table 1. Variables recorded to characterize 20 stream sites in the Upper Mahoning River. Summary
statistics (minimum, maximum, mean) are for variables prior to transformation. All sites within a
sub-watershed received the same land-cover values.
Transform
Abbreviation Explanation (units) (if used) Mean Range
n Live mussels found per site 13.9 0–149
R Species richness 1.65 0–6
Drainage Drainage area at survey site (km 2) log10 39.6 7.7–188.4
Pebble Median grain size (mm) x1/2 13.6 0.03–46.5
BaseflowStress Shear stress estimated at baseflow (Pa) x1/2 4.97 1.6–11.7
BankfullStress Shear stress estimated at bankfull (Pa) 23.7 8.9–42.0
Forest Proportion forested land cover 0.39 0.24–0.46
Agriculture Proportion agriculture land cover 0.38 0.30–0.51
Developed Proportion developed land cover 0.11 0.07–0.20
pH pH 8.03 7.47–8.50
Conductivity Specific conductivity (mS/cm) log10+1 780 450–1590
Discharge Discharge measured at each site (m3/s) 0.11 0.00–0.60
BankfullWidth Bankfull channel width (m) log10 10.3 5.6–25.5
Width Baseflow channel width (m) log10 5.9 2.4–14.6
BankfullDepth Bankfull water depth (m) 1.18 0.73–1.60
Depth Baseflow water depth (m) x1/2 0.26 0.10–0.70
Slope Slope of stream reach (m/km) 2.2 0.6–5.3
QHEI Qualitative habitat evaluation index 60.1 42.5–81.5
IBI Index of biotic integrity 36.0 20.0–51.0
Attainment Aquatic life use-attainment status 1.2 0–2
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M.T. Begley and R.A. Krebs
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slope was measured using contour lines within USGS 1:24,000 topographical maps
and expressed as a ratio of elevation change over distance.
We examined physical measurements for normality using the Shapiro-Wilk test
and normalized variables that did not pass the test using log10(x), log10(x+1), or x1/2
transformations (Table 1). Abundance and species richness were not transformed
due to the large number of zeros in the data. We entered land cover in models as
the proportion of forested, agricultural (cropland + pastureland), and developed
land present in each of the 4 main sub-watersheds as reported by the OEPA (2011).
Visually these sub-watershed values appeared to be consistent and indicative of
conditions at each site.
To model physical stream characteristics, we assessed differences in environmental
variables between sites with and without live mussels using MANOVA and
Pearson correlations run in SPSS (Ver. 19, IBM Corp., Armonk, NY) followed
by principle components analysis (PCA) in R (Ver. 3.0.2, R Core Team, Vienna,
Austria). Contrasts were made among 20 sites where OEPA data were available,
both with and without QHEI and IBI, which were unavailable for 1 site. We tested
applicability of PCA components to richness and abundance using backward regression.
We separately applied Poisson and negative binomial regressions to data
on all sites, as these account for the non-normal distribution and excess of zero’s
in the response variables (O’Hara and Kotze 2010). Species richness was modeled
by Poisson regression because the mean and variance were roughly equal (Ramsey
and Schafer 2002), but we used negative binomial regression to model abundance
due to over-dispersed variation (variance >> mean) (Stamey and Beavers 2009).
Our examination of how site-specific environmental variation may impact specific
species differently was limited to the 8 species found in the 20 sites surveyed in
2014. Associations were modeled in canonical correspondence analysis (CCA), a
constrained ordination procedure that examines how much of the variation in one
set of variables explains the variation in another set of variables. The environmental
variables retained were those we found to have low multi-collinearity by using
the “cca” function in the R package “vegan” (Oksanen et al. 2015), which treats the
environmental variables as predictors of the ordination of species.
Results
Diversity and abundance was greater in Eagle Creek (which contained all 12
species [11 live] found, 910 live individuals) than in the remaining Upper Mahoning
watershed (5 species and 53 live individuals) (Table 2). The most widespread
species across both years were Lampsilis siliquoidea (Fatmucket) and Pyganodon
grandis (Giant Floater), while Elliptio dilatata (Spike) occurred at just 2 sites, but
was numerous at both (site 6, n = 117; site 10, n = 27; Fig. 1). Three species, Lasmigona
complanata (White Heelsplitter), Lasmigona compressa (Creek Heelsplitter),
and Strophitus undulatus (Creeper), appeared widely dispersed but never locally
abundant. The remaining 6 species totaled just 14 live individuals, and Amblema
plicata (Threeridge) was represented by only 2 old shells (Table 2). A rarefaction
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analysis suggested nearly complete identification of species presence (11.9 + 0.3
species predicted, 12 observed, including shell data).
Mussel abundance and species richness each correlated significantly and positively
with several measures of stream size, particularly drainage area (r = 0.69,
P = 0.001 and r = 0.85, P < 0.001, respectively). Only 1 live mussel was found at a
site with a drainage area <20 km2. Fish IBI score was also correlated with drainage
area (r = 0.61, P < 0.01), but this index did not differ significantly among sites with
and without mussels (37.9 vs. 34.2, respectively, P > 0.10). Physical measurements
of stream size also correlated with mussel numbers and species richness, which
included stream discharge (r = 0.66, P < 0.01 and r = 0.66, P < 0.01, respectively),
bankfull width (r = 0.53, P < 0.05 and r = 0.59, P < 0.01, respectively), and baseflow
width (r = 0.49, P < 0.05 and r = 0.67, P < 0.01, respectively), as well as QHEI
(r = 0.54, P < 0.05 and r = 0.49, P < 0.05. respectively). The QHEI score from the
OEPA averaged 64.6 where mussels were found and 56.3 for sites lacking mussels.
Mussel species richness associated with higher pH (r = 0.45, P = 0.047), although
no sites were acidic (Table 1). No live mussels were found where specific conductivity
exceeded 900mS/cm, which was a water measurement inversely related to the
proportion of forest (r = -0.70, P = 0.001). Only 4 live mussels were found at sites
in the non-attainment category for aquatic life use: 1 at site 15 and 3 at site 17 (Fig.
1). Four sites that met full attainment lacked mussels, of which 3 sites had small
drainage areas and/or high shear stress (sites 2, 11, and 13) and the other (site 18)
lay in the Mahoning headwaters with the highest agricultural and urban land use.
Table 2. Abundance and presence of mussels in the Upper Mahoning River watershed: 8 sites surveyed
in Eagle Creek (2013) targeting sites based on visible habitat, and 20 sites across the watershed
surveyed (2014) aligned with OEPA water quality sites. Only 2 of 8 Eagle Creek sites were sampled
both years.
West Branch
Eagle Creek and Upper
2013 2014 Mahoning
Species (8 sites) (8 sites) River (12 sites)
Ortmanniana ligamentina (Lamarck) (Mucket) 01 1 0
Amblema plicata (Say) (Threeridge) 01 0 0
Elliptio dilatata (Rafinesque) (Spike) 3 117 27
Lampsilis ovata (Say) (Pocketbook) 0A 0 2B
Lampsilis siliquoidea (Barnes) (Fatmucket) 493 56 19
Lasmigona complanata (Barnes) (White Heelsplitter) 48 8 0
Lasmigona compressa (Lea) (Creek Heelsplitter) 9 12 2
Lasmigona costata (Rafinesque) (Flutedshell) 0 1 0
Pyganodon grandis (Say) (Giant Floater) 80 17 3
Strophitus undulatus (Say) (Creeper) 39 14 0
Toxolasma parvum (Barnes) (Lilliput) 5 0 0
Utterbackia imbecillis (Say) (Paper Pondshell) 7 0 0
Totals 684 226 53
AOne or 2 old shells discovered.
BThese 2 live individuals were found outside of formal surveys on the main branch of the Mahoning
River.
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M.T. Begley and R.A. Krebs
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A PCA reduced 83% of the environmental variation to 4 component sets of
variables that were regressed against mussel abundance and richness (Table 3). No
variable had a loading above |0.50|, but 14 of 15 variables remained in at least 1
component. Applying backward stepwise regression to these PCA variables, components
1 and 3, which together composed 8 different variables with loadings of at
least |0.3| , explained 57% of the variation in species richness (P < 0.001) and 40%
of variation in abundance (P < 0.01). Adding components 2 or 4 did not improve
the model based on the Akaike information criterion (AIC). Poisson and negative
binomial regression suggested that drainage area alone explained most variation,
although a Poisson regression model composed of grain size, baseflow and bankfull
shear stress, and depth, plus 2 interaction terms, bankfull shear stress with grain
size and baseflow shear stress with average depth (a composition similar to that
of PC3) similarly reduced residual deviation and received the same AIC score for
richness (57), while the negative binomial regression for abundance increased the
AIC a small amount (from 99 to 109).
Finally, canonical correspondence analysis (CCA) applied to infer compatibility
or tolerance of specific species along environmental gradients accounted for 68%
of the species variation with respect to environmental variables (Fig. 2). Within this
multivariate construct, the 2 most-dispersed species, Giant Floater and Fatmucket,
associated with sites where shear stress, conductivity, and agricultural land cover
all were higher relative to values associated with the presence of other species in
the study area.
Discussion
Watershed area remains a consistent predictor of abundance and diversity of
mussels (Haag and Warren 1998, Watters 1992), as we observed in the Upper
Table 3. Principal components and loadings (>|0.3|) for all environmental variables assessed at 2014
mussel survey sites across the Upper Mahoning watershed.
Variable Comp 1 Comp 2 Comp 3 Comp 4
Drainage 0.35
Pebble 0.50
BankfullStress -0.37 -0.40
BaseflowStress -0.38 -0.46
Forest -0.41
Agriculture 0.36
Developed 0.32 -0.36
pH
Conductivity 0.30 -0.42
Discharge 0.35
BankfullDepth 0.33
BankfullWidth 0.30 -0.41
Width 0.34 -0.39
Depth -0.47
Slope -0.38
Proportion of variance 0.37 0.21 0.14 0.10
Cumulative proportion 0.37 0.58 0.73 0.83
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2017 Vol. 24, No. 1
Mahoning River watershed. A watershed the size of Eagle Creek (329 km2) should
have 11–12 species based on a model derived from neighboring Lake Erie watersheds
(Krebs et al. 2010a), and this level of richness was observed, although records
for 4 of the 12 species hint at loss (old shells only) or risk of expiration (a single
live individual). In contrast to Eagle Creek, the 1500-km2 Upper Mahoning River
watershed was depauperate, as 24 total species are predicted for a watershed of this
size, but we found only 5 species there, none additional to those we encountered in
Eagle Creek. Swart (1940), summarized later by Dexter et al. (1963) and Hoggarth
(1990), reported 4 additional species just in the West Branch Mahoning River—
Anodontoides ferussacianus (Lea) (Cylindrical Papershell), Obovaria subrotunda
(Rafinesque) (Round Hickorynut), Potamilus alatus (Say) (Pink Heelsplitter), and
Quadrula quadrula (Rafinesque) (Mapleleaf)—none of which have been found
since, and including those only raises the total species recorded to 16. Mussels were
also much more numerous in sand and gravel where the current was described as
swift; these sites (4–8 in Swart 1940) were impounded or isolated by the Michael
J. Kirwan Dam (Fig. 1). Such a deficiency today is perhaps expected for a river
that Amin and Jacobs (2013) characterize as one of the 5 most-contaminated rivers
in the US, with the worst sections beginning at the confluence of Eagle Creek
and the Mahoning River at Leavittsburg, OH. Even above the confluence, many
impoundments built for flood control since the surveys of Swart (1940) create a
predominantly lacustrine river that has isolated streams like Hinkley Creek, which
once contained mussels (Wittine 1969).
Human alterations often explain loss of unionid mussels, but our goal is to
identify what environmental features mussels require, which remains a challenge
Figure 2. A biplot of canonical axes 1 and 2 illustrating the ordination of the 8 species found
live in 2014 and the most-associated environmental variables.
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M.T. Begley and R.A. Krebs
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(Haag 2012). One expected association is that between host fish and mussel presence,
which has received only mixed support (Gagnon et al. 2006, Lyons et al.
2007, Rashleigh 2008). The mussel species present in the Upper Mahoning River
watershed all utilize diverse hosts (Krebs et al. 2010a, Watters et al. 2009), and fish
diversity, as measured by the index of biotic integrity (IBI), did not explain variation
in mussel diversity among sites. One reason may be that the species present
are host generalists, which tend to be widespread (Strayer 2008), and for them, a
complex set of hydraulic factors may be associated with increased mussel numbers
(Hardison and Layzer 2001). Site characteristics as described by the OEPA’s habitat
assessment and aquatic life designation reports (OEPA 2006, 2010) indicated
a greater likelihood of mussel occurrence in sustained versus degraded systems.
Higher QHEI scores, which are a composite index of substrate, instream cover,
channel morphology, riparian zone, pool quality, and map gradient, associate with
a greater likelihood or abundance of mussels. Stream-bed instability, either from
baseflow or bankfull shear stresses, can reduce diversity in both mussels (Allen and
Vaughn 2010, Daraio et al. 2010, Gangloff and Feminella 2007, Howard and Cuffey
2003, Krebs et al. 2010b) and fish (Jellyman et al. 2013).
The designation of full attainment of aquatic life uses appeared necessary, but
not always sufficient, for the presence of mussels. Ortmann (1909) highlighted
that unionid mussels can be the first taxon lost with stream degradation. Assessing
physical habitat at each site also can identify high-impact factors like bankfull
shear stress (Gangloff and Feminella 2007) or specific conductivity, which when
>900 mS may be a limiting threshold character (McRae et al. 2004). High specific
conductivity is linked to stream impairments from agriculture in Oregon (Pan et al.
2004) and urban land in Australia (Hatt et al. 2004), and is associated with fewer
macroinvertebrates (Vander Laan et al. 2013) and mussels (Brown et al. 2010,
Gangloff et al. 2009). In addition, past anthropogenic activities may leave behind
long-term heavy metal contamination of soils (Clark and Benoit 2009, Falfushynska
et al. 2015, Rzymski et al. 2014), imposing diverse effects (Brown et al. 2010,
Gangloff et al. 2009, Gillies et al. 2003). Even agriculture may reduce stream fauna
at neighboring sites for many years after reforestation (Cao et al. 2013, Maloney
and Weller 2011, Poole and Downing 2004).
Within the Upper Mahoning watershed, Eagle Creek may have the fewest
legacies of anthropogenic impacts, yet Fatmucket represented 72% of all mussels,
and 4 species accounted for 96% of individuals. A consistency of species number
and composition, with 4 to 6 species per site (except the one most upstream), suggested
low habitat variation along the length of the stream. No obvious physical
difference occurred even for the single site where Spike was abundant. That surveys
across 2 years produced only 1 individual each of Flutedshell and Mucket and only
old shells of Pocketbook and Threeridge, suggest a richer past assemblage even in
Eagle Creek. Perhaps just Fatmucket and Giant Floater remain widespread as a consequence
of impoundments, extensive agricultural land use, associated increases in
specific conductivity, and decreases in discharge. Tolerance of chemical stressors is
required and reported by Cooper et al. (2010, 2013) for Giant Floater and by Bringolf
et al. (2007) for Fatmucket.
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2017 Vol. 24, No. 1
As landscapes continue to change from human development, connecting elements
of change to abundance and diversity of mussel assemblages is necessary.
Broader-scale watershed characteristics such as stream size (including drainage
area, discharge, width, and depth) and catchment land use remain important considerations,
but knowledge of local site conditions provide the useful addition of
habitat variation. However, the effort required gathering site-specific physical,
chemical, and biotic data rivals that of conducting mussel surveys. Given the
extensive assessments conducted by diverse government agencies, our results
suggest a benefit to surveying established biological and hydrological monitoring
sites.
Acknowledgments
We would like to thank Jennifer Clark, Liz Barkett, Paul Orefice, Erin DePaulo, and
Adam Morris for assistance in field work. Financial support was provided by the Cleveland
State University Engaged Learning Award Program
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