Long-term Macroinvertebrate Assemblages of the West Fork White River, Indiana Improve Following the Clean Water Act

Full text

Long-term Macroinvertebrate Assemblages of the West
Fork White River, Indiana Improve Following the Clean
Water Act
Authors: Artz, Caleb, Pyron, Mark, and Bowley, Laura
Source: The American Midland Naturalist, 184(2) : 233-247
Published By: University of Notre Dame
URL: https://doi.org/10.1637/0003-0031-184.2.233
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Am. Midl. Nat. (2020) 184:233–247
Long-term Macroinvertebrate Assemblages of the West Fork
White River, Indiana Improve Following the Clean Water Act
CALEB ARTZ
Department of Biology, Ball State University, Muncie, Indiana 47306
MARK PYRON
1
Department of Biology, Ball State University, Muncie, Indiana 47306
AND
LAURA BOWLEY
Muncie Sanitary District Bureau of Water Quality, 5150 W. Kilgore Ave, Muncie, Indiana 47304
ABSTRACT.—We tested macroinvertebrate assemblages collected from 1979–2015 for
temporal variation in structure and for impacts of the Clean Water Act of 1974. Collections
were at ten sites on the mainstem of the West Fork White River. We used family-level
taxonomy for macroinvertebrates that resulted in 77 families and 92,477 individuals.
Macroinvertebrate families were further classified by trophic and tolerance traits and tested
for temporal variation. We defined river reaches as upstream, urban, and downstream of
Muncie, Indiana for analyses. Taxonomic richness increased over the study. A nonmetric
multidimensional scaling (NMDS) analysis identified high temporal variation as assemblage
structure differed among decades. Spatial analyses using NMDS indicated significant
differences by river location upstream, urban, and downstream. NMDS and Analysis of
Similarities (ANOSIM) by trophic relationship and tolerance values did not result in
significant temporal or spatial patterns. Our results show the macroinvertebrate assemblages
of the West Fork White River improved, likely due to implementation of the Clean Water Act.
INTRODUCTION
Long-term studies identify unexpected insights, phenomena that are untestable at shorter
time-scales, and allow understanding of ecological concepts (Magnuson, 1990; Dodds et al.,
2012). Long-term time series in ecology are generally longer than 10 y (Magnuson, 1990).
Long-term studies of stream macroinvertebrate communities are useful to understand
effects of land use or other anthropogenic impacts to streams (Jackson and F¨
ureder, 2006).
In addition long-term responses of freshwater macroinvertebrate communities to
contaminants are utilized as a rapid bioassessment tool (Resh et al., 1995). Clements and
Rohr (2009) reviewed basic community ecology concepts that could be applied to
ecotoxicology. Contaminants impact the ecological integrity of macroinvertebrate
assemblages.
Clements and Rohr (2009) identified multiple paradigms that improve the ability to
predict community response to anthropogenic stressors. Contaminants have multiple effects
on communities, including indirect effects that treat contaminants as predators, effects on
resistance and resilience, ecological thresholds, pollution-induced community tolerance,
relationships between diversity and ecosystem functioning, and trophic pathways for
contaminant transport (Clements and Rohr, 2009). Predictions for variation in
macroinvertebrate assemblage structure from contaminant effects are modifications to
1
Corresponding author: Telephone: 765-285-8852; FAX: 765-285-8804; E-mail: mpyron@bsu.edu
233
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trophic structure (contaminants as predators), improvements in aquatic communities
following restoration (resistance and resilience), thresholds that shift communities to
alternative states, changes in community composition with loss of less tolerant taxa
(pollution-induced community tolerance), and taxonomic richness variation (diversity and
ecosystem functioning). Our goal in this study was to identify if these effects could be
identified in a Midwestern U.S. watershed that experienced historical contamination from
industrial and agriculture pollution followed by elimination of the majority of contaminants.
The Clean Water Act (CWA) of 1972 created federal water quality standards to ‘‘restore
and maintain chemical, physical, and biological integrity of the nation’s waters’’ (USEPA,
2002). A further goal was ecological integrity that could be identified by bioassessment (Karr
and Dudley, 1981). The CWA mandated that impaired water bodies are identified and
improved. This resulted in consistent and robust assessment methods, including
bioassessment using biological metrics (Kenney et al., 2009; Kuehne et al., 2017).
Bioassessment of macroinvertebrate assemblages uses multiple metrics that are based on
population and community ecology theory (Kenney et al., 2009). Macroinvertebrate
assemblages respond strongly to multiple common stream disturbances including habitat
alteration, excess sediment, and heavy metal pollution (Mebane, 2001). Fish assemblages
responded similarly to more stringent pollution regulation (Gibson-Reinemer et al., 2017).
Prior to the Clean Water Act (CWA) in 1972, the West Fork White River (WFWR) Indiana
was impacted by high concentrations of ammonia, cyanide, and heavy metals (Conrad,
2017). Post-CWA the Muncie Sanitary District’s Bureau of Water Quality (BWQ) was tasked
with monitoring water quality of the WFWR in Muncie, Indiana. Additional modifications to
the WFWR during this period included reductions in nutrient discharge, industrial
pollutants, and hydrologic alteration (Martin et al., 1996). A major change in socioeconomic
status of citizens in the WFWR watershed occurred simultaneously (Tamney and Johnson,
1983). Muncie was an industrial manufacturing city at the turn of the 20th century, but the
majority of this industry is gone. Impacts from industry were primarily point-source pollution
that included heavy metals and other wastes (Conrad, 2017). Holloway et al. (2018) reported
significant reductions in heavy metal concentrations from 1980–2016 throughout the WFWR
in Muncie, Indiana. Heavy metals, including iron in industrial effluent, alter
macroinvertebrate abundance, species composition, and relative abundances of less
tolerant taxa (Nedeau et al., 2003).
Our objectives were to identify spatial and temporal variation in macroinvertebrate
assemblages using taxonomic composition and functional traits during a 30 y period (1979–
2015). We expected high variation in assemblage structure following the 1972 CWA. Our
specific predictions were increased abundances of invertebrates with specialized trophic
traits, increased abundances of less tolerant taxa, a shift in assemblage structure, and
increased taxonomic richness. In addition we predicted spatial variation in taxon abundance
from upstream to downstream with current land-use modifications and agricultural point/
non-point source pollution. Our expectations were for assemblages to vary from upstream to
downstream with spatial variation in upstream municipal pollutant discharge and
urbanization.
METHODS
SITES AND COLLECTIONS
The White River, Indiana is a predominately agricultural watershed, but historic urban
and industrial impacts resulted in extremely poor water quality (Chauret et al., 2001).
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Throughout the early to mid-1900s a natural gas boom brought large scale industrialization
to Muncie, Indiana (Lynd and Lynd, 1937). Industrial and municipal point source pollution
was discharged from urban areas of the West Fork White River historically (Martin and
Craig, 1990). Although the industrial impacts were primarily eliminated following the CWA,
negative impacts are still present in the watershed. Large volumes of municipal pollution
reach the river through combined sewage overflows (raw sewage release during heavy rain
events, Martin et al., 1996). Agricultural activities in the watershed contribute additional
pollution to the river (Martin et al., 1996). The WFWR, Indiana has high concentrations of
pesticides at urban and agriculture sites (Martin et al., 1996). Trace element pesticide
concentrations are high in streambed sediments but rarely exceed aquatic life criteria
(Martin et al., 1996).
Macroinvertebrates were collected by the Muncie Sanitary District’s Bureau of Water
Quality during monitoring surveys on the WFWR and its tributaries in Delaware and
Randolph County, IN in summer and fall (Fig. 1). The WFWR watershed consists of glacial
deposits and limestone outcrops (White et al., 2005). Our study watershed upstream from
the Delaware County border is 907 km
2
(Hoggatt, 1975). Historically the basin consisted of
prairies and forest, but land use is currently dominated by rowcrop agriculture. The
FIG. 1.—Sites on the mainstem West Fork White River where benthic macroinvertebrate were collected
from 1979–2015. Sites are black dots. The upstream site Lat, Long are 40.165932, -85.182243 and the
downstream Lat, Long are 40.148876, -85.552838. The state of Indiana in the U.S. is depicted at the bottom
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watershed has urban impacts with sporadic intact riparian zones and multiple low-head dams
(Holloway et al., 2018). Ten sites located on the mainstem of the WFWR from rkm 497–518
were sampled annually from 1979–2015 during summers (Fig. 1). Data from 1989, 1990,
2003, and 2008 were omitted due to sampling or data inconsistencies including different
sites, different collection methods, and different identification criteria. From 1979–2008 the
BWQ used an in-house protocol for macroinvertebrate collections (Craddock, 1980). Minor
modifications of the sampling procedure occurred through the period. After 2007, to gain
consistency among local samples, the protocol from the Indiana Department of
Environmental Management (IDEM) was used. We excluded data collected in 2008 as it
was distinctly modified from other collection methods. In 2009 IDEM used the Rapid
Bioassessment Protocol using d-nets, similar to previous sampling. In 2010 IDEM adopted a
Multi-habitat Macroinvertebrate Collection Procedure (mHAB), similar to the BWQ original
method, with the exception of using only one sampler. The mHAB method includes a one-
min riffle kick, if there is no riffle a mid-stream kick is used and a 12 min 50 m bank sample
(macroinvertebrates collected adjacent to the bank; Bowley, 2018). After six separations of
individuals by mixing with water, samples were poured through a #30 United States
Geological Survey (0.6 mm mesh) sieve, and contents were sorted for a 15 min period
(Bowley, 2018). Macroinvertebrates were identified in a BWQ laboratory.
DATA ANALYSIS
We used family-level taxonomy for macroinvertebrates given data were not all to the same
taxonomic resolution. Collections were reported as abundance of taxa collected at all
habitats for each site. Taxon abundance at sites was examined as annual abundance and a
Pearson correlation analysis was used to determine if taxonomic richness or abundance
increased with years. We used nonmetric multidimensional scaling (NMDS) in R (R Core
Team, 2016) using the vegan package version 2.4-2 (Oksanen, 2017; ordiellipse and anosim
functions) to summarize temporal and spatial relationships. Variation in macroinvertebrate
family abundances were represented by pairwise Bray-Curtis distances in the NMDS and
reduced to a two-dimensional configuration. Final configurations were calculated 20 times
from a random starting arrangement, and the lowest stress for permutations was used. Stress
0.20 is considered useful for pattern analysis (Clarke, 1993). NMDS is useful to graphically
represent large ecological datasets with few distribution assumptions (Kenkel and Orlci ´o,
1986).
We defined river reaches as upstream (n ¼3), urban (n ¼5), and downstream (n ¼2) of
Muncie, Indiana for analyses (Fig. 1). Upstream and downstream sites were beyond Muncie
city limits, and urban sites were within city boundaries. We combined annual collections into
decades to detect temporal variation that was not obvious in our pilot analyses of annual
collections. For example, 1980s were grouped and the ordiellipse function created ellipses
around data within the NMDS bi-plot that represented those years. The same process was
used to plot data for spatial analyses. Analysis of Similarities (ANOSIM) was used to test for
statistical differences among decades and spatial trends. We used a two-way ANOVA to test
for significant differences in relative abundance by location and taxa.
Macroinvertebrate families were classified into trophic and tolerance value traits and
tested for temporal variation using the same analyses as above. Five trophic categories as
collectors-gatherers, filterers, predators, scrapers, and shredders were obtained from Merritt
and Cummins (1996) and Hauer and Resh (2011). Tolerance value trait scores were from
Barbour et al. (1999). We used a tolerance scale of 0-10, with 0 the most sensitive and 10 the
least sensitive.
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In long-term literature ‘rarity’ has different meanings and implications, where taxa might
be only temporally rare, but common in long-term analysis (Resh et al., 2005). We selected
not to use the commonly used 5 % rarity cutoff for taxa, because the resulting assemblage
was reduced to only five families. We identified rare taxa as 0.05 % of total abundance and
excluded them from further analyses. We log (x þ1) transformed relative abundance data
prior to analyses as data had three orders of magnitude in variation. Analyses were repeated
with all rare taxa included and we found similar temporal and spatial trends.
RESULTS
Our dataset consisted of 33 years of collections from 1979–2015, with 77 families and
92,477 individuals throughout the study (prior to removal of rare taxa). Taxonomic richness
increased from 40 to 52 families (r¼0.62, P ,0.001, Fig. 2). Taxononmic richness varied
during the 1980s and dropped in the 1990s before increasing gradually. Three phyla were
present continuously throughout the data set. Annelida included Glossiphoniidae, and
Oligochaeta. Oligochaeta was included in analyses based on abundance variation
throughout the time period. Mollusca consisted of two families, Cyrenidae and Physidae.
Arthropoda comprised 21 families. Arthropod taxa were in eight orders, Amphipoda (1),
Coleoptera (3), Diptera (2), Ephemeroptera (6), Hemiptera (5), Isopoda (1), Odonata (2),
and Trichoptera (1). The five taxa (insect families) with highest abundances were
Hydropsychidae (Trichoptera), Chironomidae (Diptera), Elmidae (Coleoptera),
Coenagrionidae (Ephemeroptera), and Caenidae (Ephemeroptera) that together
comprised ~55% relative abundance. Relative abundances of other taxa were 19.7% for
Hydropsychidae, 12.2% for Chironomidae, 11.3% for Elmidae, 6.4% for Coenagrionidae
and 5.4% for Caenidae.
Our NMDS used 27 taxa with 87,569 individuals, after deleting rare taxa. Temporal
variation was observed in the NMDS as assemblage structure differed significantly among
decades (stress: 0.14, ANOSIM R: 0.45, P 0.01) (Fig. 3). Dominant taxa (5% of
collection) varied throughout the study. Dominant taxa during the 1980s were
FIG. 2.—Benthic macroinvertebrate taxonomic richness for all sites with time, as families (r ¼0.62, P ,
0.001)
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Hydropsychidae, Coenagrionidae, Chironomidae, Elmidae, and Baetidae. In the 1990’s
dominant taxa were slightly different with Hydropsychidae, Elmidae, Chironomidae,
Caenidae, and Hydrophilidae in the highest abundance. The 2000 time period resulted
in a change in highest abundance taxa with Elmidae (22% of collection) in higher
abundance over Hydropsychidae. Additional changes in highest abundance taxa through
the 2000’s include Leptohyphidae that replaced Hydrophilidae. Finally, highest abundance
taxa in the 2010’s consisted of Hydropsychidae, Chironomidae, Elmidae, Veliidae, and
Leptohyphidae (Fig. 4). Major changes in abundance of several families occurred from
1980–2000 (e.g., Elmidae 16% increase, and Coenagrionidae 12.7% decrease). However,
Elmidae abundance decreased 10.7% from 2000–2015.
Spatial analyses indicated significant differences by river location (stress: 0.20, ANOSIM R:
0.32, P 0.01) (Fig. 5). Analyses of spatial groups resulted in significant variation of relative
abundance of families at upstream, urban, and downstream sites (Two-way ANOVA,
Location F
2
¼5.3, P ¼0.005, Taxon F
25
¼42.1, P ,0.001) (Fig. 6). Chironomidae had
highest relative abundance in urban reaches, followed by downstream, then upstream
reaches. Elimidae had highest relative abundances in upstream reaches, followed by urban,
then downstream reaches. Caenidae relative abundances were highest in urban reaches,
then upstream and downstream reaches. Most taxa had high variation among river reaches.
The dominant trophic trait was collectors which comprised 53% of individuals, and next
was predators at 33% of individuals. Filterers, scrapers, and shredders comprised ~14% of
individuals collected. Tolerance traits were distributed into seven values (2, 4, 5, 6, 7, 8, and
9). Individuals in families that tended to be less tolerant to disturbance (scores 2–5)
comprised ~52% of total abundance, and more tolerant families (scores 6–9) comprised
~48% of individuals. The highest abundance (42.7%) of individuals had a tolerance score of
4. The relative abundance of the five trophic traits among decades and river location had no
noticeable changes. No major changes were found in sensitivity scores by decade or river
FIG. 3.—Nonmetric multidimensional scaling biplot of temporal trends in macroinvertebrate
assemblages for 1979–2015 West Fork White River. Decades are represented by 95% CI ellipses,
families are indicated by red crosses. Highest loading score taxa are indicated on axes
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FIG. 4.—Decadal time series plot of mean relative abundance of dominant benthic macroinvertebrate
families of the West Fork White River
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FIG. 5.—Nonmetric multidimensional scaling biplot of macroinvertebrate assemblages of the West
Fork White River 1979–2015. Families are indicated by red crosses and ellipses represent different river
locations. Highest loading score taxa are indicated on axes
FIG. 6.—Mean relative abundance of taxa by river reach. Open bars are downstream, filled bars are
urban, and hatched bars are upstream. ASE is Asellidae, BAE is Baetidae, CAE is Caenidae, CAL is
Calopterygidae, CHI is Chironomidae, COE is Coenagrionidae, COR is Corixidae, CYR is Cyrenidae,
ELM is Elmidae, EPH is Ephemeroptera, GER is Gerridae, GLO is Glossophoniidae, HAL is Haliplidae,
HYA is Hyalellidae, HYD is Hydropsychidae, HYO is Hydrophilidae, ISO is Isonychiidae, LEP is
Leptohyphidae, MES is Mesoviliidae, OLI is Oligochaeta, PHY is Physidae, PLA is Planorbidae, PLE is
Pleidae, SIM is Simuliidae, SPH is Sphaeriidae, VEL is Veliidae
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location. NMDS and ANOSIM analyses by trophic relationship and tolerance did not result
in significant temporal or spatial patterns.
Individual river locations by decade resulted in a similar significant decadal variation
pattern for each location in NMDS analyses in Figure 7 (downstream reach: stress: 0.15,
ANOSIM R: 0.42, P 0.01), (urban reach: stress: 0.15, ANOSIM R: 0.39, P 0.01),
(upstream reach: stress: 0.17, R: 0.41, P 0.01). Our NMDS analyses in Figure 8 resulted in
groups by decade that were significantly different with ANOSIM (1980s: stress: 0.14,
ANOSIM R: 0.61, P 0.01), (1990s: stress: 0.13, ANOSIM R: 0.54, P 0.01), (2000s: stress:
0.14, ANOSIM R: 0.32, P 0.01), (2010-2015: stress: 0.16, ANOSIM R: 0.14, P 0.05).
DISCUSSION
Our ordinations depicted distinctive shifts in assemblage structure and taxonomic
richness increased with time, potentially with improved water quality following the CWA of
1972. The shifts we observed are not likely threshold shifts (Andersen et al., 2009), but may
indicate recovery from a prior threshold response to contaminants. Increased taxonomic
richness indicates improvements in community quality, with associated improvements in
ecosystem functions (Clements and Rohr, 2009). We expected the increase in taxonomic
richness to be due to replacement or addition of less tolerant taxa. However, we did not
detect significant variation in macroinvertebrate abundances categorized by trophic traits or
less tolerant taxa.
Significant temporal and spatial trends in macroinvertebrate abundances (Figs. 3 and 5)
occurred simultaneous to changes in water quality practices and upstream-downstream
urban effects. Our ordinations showed that upstream sites and sites in the 1980s decade were
distinctive. Point source discharge was reduced throughout the White River basin after 1980
(Martin and Craig, 1990). The high variation in relative abundances of the dominant taxa
during this period may have contributed to temporal patterns in NMDS analyses. The largest
increase in abundance was for Elmidae from 1980 to 2000, simultaneous with improved
water quality practices with implementation of the CWA, and reduced hydrologic alterations
with stormwater management (Conrad, 2017). Studies of benthic macroinvertebrates in
other locations found that changes in anthropogenic practices directly impacted community
composition (Azrina et al., 2006).
Reductions in pollution (Holloway et al., 2018) potentially provided increased available
habitat (increased stream locations lacking pollution) and resulted in increased relative
abundance of several taxa after 1980. An exception was Coenagrionidae, with decreased
relative abundance during this period. Damselfly communities are strongly influenced by
predator-prey interactions of fish communities (McPeek, 1998). Increased abundances of
fishes that prey on benthic insects (largemouth bass Micropterus salmoides (Lacepede 1802),
golden shiner Notemigonus crysoleucas (Mitchill, 1814), white crappie Pomoxis annularis
Rafinesque, 1818) after 1980 were observed by Holloway et al. (2018) in a long-term study of
the White River, Indiana.
Macroinvertebrate assemblages that are upstream from Muncie likely differ from
urban and downstream sites due to watershed size differences (upstream-downstream
range ¼590–907 km
2
; Hoggatt, 1975), agricultural land use, and urban impacts (Muncie
area is 71 km
2
; wikipedia.org). Watershed land use is predominately agriculture in
Delaware and Randolph Counties: the area of Delaware County is 253,000 acres and
approximately 152,000 acres are corn and soybeans (Tedesco et al., 2011). Agriculturally
influenced streams significantly impact the total number of Ephemeroptera, Plecoptera,
and Trichoptera taxa (the EPT index is a sum of Ephemeroptera, Plecoptera, and
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FIG. 7.—Nonmetric multidimensional scaling biplots of temporal trends by river reach for benthic
macroinvertebrates of the West Fork White River. Ellipses represent decades. Highest loading score taxa
are indicated on axes
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Trichoptera taxa) and community composition (Richards et al., 1993). We found
Chironomidae and Elmidae had high spatial variation in relative abundance.
Chironomidae abundance was higher at urban and downstream sites, likely because
members of this family tend to have high tolerance to changes in water chemistry
parameters (decreased point source pollution; Shimba et al., 2018). Urban and
downstream sites had increased influence from urban land-use, urban stream
channelization, and current impacts from combined sewage overflows. Elmidae had
the highest relative abundance at upstream sites. Suitable riffle-run habitat for Elmidae
may occur with decreased channelization at upstream sites. In addition a natural
hydrologic regime variability provides necessary temporal and spatial variation in
benthic macroinvertebrate assemblages (Resh et al., 1988). It was interesting that
upstream assemblages converged with downstream and urban assemblages at the end of
the period. We do not know if this was an effect of community homogenization or other
phenomena.
We used trophic and tolerance traits as an alternative to taxonomy, because we expected
organisms with similar trophic relationships and tolerance values to react similarly to
human impacts. Floury et al. (2013) showed that long-term macroinvertebrate
communities became dominated by taxa that are generalist for pollution-tolerance. In
addition Vaughan and Ormerod (2012) found that macroinvertebrate communities were
resilient to long-term anthropogenic disturbances. However, we were limited to defining
tolerance and trophic relationships at the family level. Our results may differ if taxa were
categorized by genus or species for tolerance values and trophic traits (Merritt and
FIG. 8.—Nonmetric multidimensional scaling biplots of spatial trends by decade for benthic
macroinvertebrate collections for the West Fork White River. Ellipses represent river locations.
Highest loading score taxa are indicated on axes
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Cummins, 1996). The WFWR fish assemblage in the early 1980’s changed from being
dominated by pollution-tolerant taxa to an assemblage currently dominated by less
tolerant taxa, likely following reductions in heavy metal concentrations (Holloway et al.,
2018). We predict a similar trend in macroinvertebrate assemblages with analyses at lower
taxonomic levels.
We observed increased taxonomic richness that corresponded with increased water quality
practices. However, Bowley (2018) reported that Shannon-Weiner Diversity Index (H’)
scores decreased, Macroinvertebrate Index of Biotic Integrity (mIBI) decreased, intolerant
taxa decreased, and dipteran abundance increased, the percentage of noninsects increased,
and the percentage of collectors/filterers for 2017 sampling increased. Bowley (2018) scores
were for a single year, at the end of our long-term series. The improvements we detected
from 1980–2015 might have reached an upper limit for this watershed. Current major
human impacts to the White River include sewage discharge and agricultural land use
(Martin et al., 1996). The city of Muncie currently treats sewage prior to release, but
nutrients and pharmaceuticals are still present (Bunch and Bernot, 2011; Veach and Bernot,
2011).
We identified significant variation in temporal and spatial assemblages of benthic
macroinvertebrates of the WFWR that included obvious improvements, yet other trends we
could not understand. Further analyses into the mechanisms influencing temporal and
spatial variation might include detailed benthic macroinvertebrate community metrics. We
did not directly examine variation in macroinvertebrate assemblages with heavy metal
concentrations, as Holloway et al. (2018) did for fish assemblages.
We predict future restoration activities for the WFWR have the potential to further
improve the ecosystem as observed elsewhere (Doyle et al., 2005). Removal of low-head
dams is predicted to restore some degree of natural hydrology, and further change the
composition of the benthic macroinvertebrate assemblage (Poff et al., 1997). In addition
rerouting runoff during heavy rain events will mitigate the current impacts of combined
sewage overflows. Reduction in sewage effluent has direct effects on community
composition and distributions of benthic macroinvertebrates (Wright et al., 1995). In
summary we identified variation during a 30-year period in macroinvertebrate
assemblages of the WFWR in Muncie, Indiana that corresponded to improvements in
water quality. We predict similar responses in other rivers where regulations result in
improved water quality.
Acknowledgments.—This research was possible due to the Muncie Sanitary District’s Bureau of Water
Quality and their continual effort to establish quality ecological data for the West Fork White River.
Thank you to Randy Bernot and Paul Venturelli for editorial suggestions and Jeff Robbins for creating a
map.
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