Influence of Cladophora sp. on the Composition and Spatial Distribution of
Macroinvertebrate Communities in Streams.

A
THESIS
SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES
of
BLOOMSBURG UNIVERSITY OF PENNSYLVANIA

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF MASTER OF SCIENCE
PROGRAM IN BIOLOGY
DEPARTMENT OF BIOLOGICAL AND ALLIED HEALTH SCIENCES
BY
BENJAMIN R. PAUL
BLOOMSBURG, PENNSYLVANIA
2020

ii
ABSTRACT:
Nitrogen and phosphorus from agricultural runoff cause blooms of the green
alga, Cladophora. This alga provides surface area for epiphytes and invertebrates to
colonize. However, Cladophora is not nutritious and hard to digest for many
invertebrates, which could impact invertebrate communities around this alga. The
purpose of this research was to determine how invertebrate communities differ between
patches in streams where Cladophora is present and absent in the Susquehanna River
watershed. To do this, 13 streams were sampled. Invertebrates were collected using a
Surber sampler by collecting five composites and combining them into one sample per
patch type. In streams where Cladophora was present, patches consisted of areas that
either contained or did not contain Cladophora. In streams where Cladophora was not
present, a single sample was collected. The samples were preserved in ethanol and
macroinvertebrates were identified to the family level. The communities were compared
between patch types from Cladophora streams using four metrics: percent
Ephemeroptera, Plecoptera, and Trichoptera (%EPT), Hilsenhoff Biotic Index (HBI),
Shannon-diversity index and percent Chironomidae (%Chironomidae). Densities were
compared between patch types from Cladophora streams and non-Cladophora streams.
Data loggers were left at sites to collect dissolved oxygen and temperature data every 15
minutes for a week. Percent Chironomidae was greater in Cladophora patches than nonCladophora patches (p<0.005) which decreased Shannon diversity in Cladophora
patches (p<0.05). Densities of invertebrates per m2 were higher in Cladophora patches
than non-Cladophora stream patches (p<0.05). Stream type and percent agriculture
within the stream watershed both directly impacted invertebrate communities (p<0.05).

iii
Logistic regression supported the prediction of Cladophora presence or absence in
streams using alkalinity as a predictor (p=0.0516). This research supports that agriculture
and the alga, Cladophora affect invertebrate communities at both patch and stream-level
spatial scales in the Susquehanna River watershed by increasing invertebrate densities
and decreasing diversity.

v
ACKNOWLEDGEMENTS:
Thank you to Dr. Steven Rier, for showing me how to use and providing access to
research equipment, sharing his knowledge of stream ecology and statistical analyses,
and being a great mentor, without his help and guidance this research project would not
have been possible. Thank you to my thesis committee members, Dr. Lauri Green and Dr.
Thomas Klinger for their input in this project. Thank you to all the individuals who
helped sort macroinvertebrates, including Bobby Paul, Jacob Paul, Randall Paul, Sandra
Paul, Mackenzie Pierce, and Victoria Roper. Thank you to Dr. Stefanie Kroll, Dr.
Matthew McTammany and Dr. Robert (Bob) Smith for their advice during this research
study. Thank you to the Pennsylvania Fish and Boat Commission for allowing me to
collect macroinvertebrates for this project. Thank you to Bloomsburg University for
providing funding for this research project through a 2019/2020 Thesis Research Grant.

vi
TABLE OF CONTENTS:
INTRODUCTION: ............................................................................................................ 1
Hypotheses: .................................................................................................................... 6
MATERIALS AND METHODS:....................................................................................... 6
Stream selection: ............................................................................................................ 6
Macroinvertebrate Processing and Identification: ......................................................... 8
Macroinvertebrate Metrics: ............................................................................................ 9
Percent EPT Taxa: ............................................................................................................. 9
Shannon Diversity Index: ................................................................................................ 10
Hilsenhoff Biotic Index (HBI): ........................................................................................ 10
Percent Chironomidae:..................................................................................................... 11
Water Chemistry: ......................................................................................................... 11
Percent Agriculture Land Cover: ................................................................................. 12
Statistical analyses: ...................................................................................................... 13
RESULTS: ........................................................................................................................ 15
DISCUSSION: .................................................................................................................. 27
LITERATURE CITED: .................................................................................................... 27
APPENDICES: ................................................................................................................ 43

vii
LIST OF TABLES:
Table 1.

Raw data collected from each site including the stream site, patch type,
proportion of the total macroinvertebrate sample that was identified, date the
samples were collected, TP (ug/L), TN (ug/L), total alkalinity (mg CaCO3/L),
coordinates for each site, %Ag in each stream watershed, average diel change
in dissolved oxygen (Average DDO) (mg/L), average diel change in water
temperature in °C (Average DTemperature), and identified macroinvertebrate
data for each stream. ................................................................................... 44

viii
LIST OF FIGURES:
Figure 1.

Map of all sites categorized as Cladophora (yellow) and non-Cladophora
(red) streams within the Susquehanna River watershed. Layers data were
obtained from Pennsylvania Spatial Data Access (PASDA). ....................... 7
Figure 2.
Total alkalinity in mg CaCO3/L from Cladophora streams and nonCladophora streams in central Pennsylvania. Cladophora streams contained
patches of Cladophora that covered at least 50% of the area within a Surber
sampler. Non-Cladophora streams had very little or no Cladophora present.
...................................................................................................................... 16
Figure 3.
Macroinvertebrate densities in non-Cladophora streams and in nonCladophora and Cladophora patches within Cladophora dominated streams
in central Pennsylvania. .............................................................................. 18
Figure 4.
The %Chironomidae in Cladophora patches and non-Cladophora patches in
Cladophora dominated streams in central Pennsylvania. ........................... 19
Figure 5.
Shannon-Weaver diversity (H) scores for Cladophora patches and nonCladophora patches in Cladophora dominated streams in central
Pennsylvania. .............................................................................................. 20
Figure 6.
The %EPT taxa from non-Cladophora patches and Cladophora patches
within Cladophora dominated streams in central Pennsylvania................. 21
Figure 7.
Hilsenhoff Biotic Index (HBI) scores of Cladophora patches and nonCladophora patches within Cladophora dominated streams in central
Pennsylvania ............................................................................................... 23
Figure 8.
Nonmetric multidimensional scaling (NMDS) ordination comparing
Cladophora patch communities (circles) to non-Cladophora patch
communities (triangles) in six Cladophora dominated streams in central
Pennsylvania (stress=0.10571). .................................................................. 24
Figure 9.
Nonmetric multidimensional scaling (NMDS) ordination comparing
Cladophora stream communities (circles) to non-Cladophora stream
communities (triangles) from 12 streams in central Pennsylvania. Briar Creek
was omitted from this NMDS due to low numbers of macroinvertebrates
heavily weighting NMDS analysis (stress=0.14163). ................................ 26
Figure 10. Image serving as an example of a Cladophora patch sample taken within
Penns Creek before a Surber sample from a patch containing Cladophora was
collected. ..................................................................................................... 34
Figure B1. Briar Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 60
Figure B2. Chillisquaque Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 61

ix

LIST OF FIGURES CONTINUED:
Figure B3. Fishing Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 62
Figure B4. Green Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 63
Figure B5. Hemlock Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 64
Figure B6. Huntington Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 65
Figure B7. Little Fishing Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 66
Figure B8. Mahoning Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 67
Figure B9. Mauses Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 68
Figure B10. North Mahantango Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time
the data loggers were deployed. The diel change in dissolved oxygen (c) and
diel change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 69

x
LIST OF FIGURES CONTINUED:
Figure B11. Penns Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 70
Figure B12. Turtle Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 71
Figure B13. Warrior Run data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel
change in water temperature (d) for each day while data loggers were
deployed is also displayed. ......................................................................... 72

xi
LIST OF APPENDICES:
Appendix A. Raw data including the name of each stream site, the patch type, proportion
of the total sample that was identified, date the macroinvertebrate samples
were collected, total phosphorus (TP) in (ug/L), total nitrogen (TN) in (ug/L),
total alkalinity in (mg CaCO3/L), Latitude and Longitude coordinates for each
site, percent agriculture (%Ag) in each stream watershed, average diel change
in dissolved oxygen (Average DDO) (mg/L), average diel change in water
temperature in °C (Average DTemperature), and the identified
macroinvertebrate data for each stream. ...................................................... 43
Appendix B. Dissolved oxygen and water temperature data for each site including the
following variables: dissolved oxygen (a), water temperature (b), diel change
in dissolved oxygen, labeled DDO (c), diel change in water temperature,
labeled DTemperature (d). The figures for each stream are presented in the
following order: Briar Creek (Fig. B1), Chillisquaque Creek (Fig. B2),
Fishing Creek (Fig. B3), Green Creek (Fig. B4), Hemlock Creek (Fig. B5),
Huntington Creek (Fig. B6), Little Fishing Creek (Fig. B7), Mahoning Creek
(Fig. B8), Mauses Creek (Fig. B9), North Mahantango Creek (Fig. B10),
Penns Creek (Fig. B11), Turtle Creek (Fig. B12), and Warrior Run (Fig. B13).
All figures were created by the author using R statistical software (R Core
Team, 2019). ............................................................................................... 59

1

INTRODUCTION:
Nutrients such as nitrogen and phosphorus often enter waterways from anthropogenic
sources like agriculture fields, lawns, septic systems and construction sites (Cole and
Weihe, 2016). Nitrogen concentrations in waterways are often positively associated with
the amount of agricultural land within watersheds (Castillo et al., 2000; Carpenter et al.,
1998). However, phosphorus concentrations are more strongly associated with suspended
particles, like sediment (Jordan et al., 1997). This is likely due to phosphorus binding to
these sediment particles which can be transported into waterways during rainfall events
(McDowell et al., 2001). The addition and overapplication of phosphorus in the form of
fertilizers and manures increases the content of phosphorus in soil and creates a surplus
of phosphorus in many agricultural areas (Sharpley et al., 1994). The greater the
concentration of phosphorus in the soil, the more phosphorus enters waterways through
surface water runoff (Carpenter et al., 1998).
Stream periphyton communities are reactive to environmental variables including
water chemistry, light availability and nutrients found within the watershed of the stream
(Feminella and Hawkins, 1995). Stream periphyton communities are a mixture of
autotrophic algae, heterotrophic microbes and detritus embedded in a matrix of
exopolymeric substances (Cole and Weihe, 2016) covering boulders, cobble, gravel,
sand, silt, and organic detritus (Lamberti and Steinman, 1997; Fairchild and Holomuzki,
2002). Periphytic algae in streams often includes diatoms, cyanobacteria, and filamentous
green algae (Stevenson et al., 2006; Wellnitz and LeRoy Poff, 2012). Cultural
eutrophication is the process of increasing primary productivity, like the abundance of
periphytic algae, in waterways due to anthropogenic inputs of nitrogen and phosphorus

2
(Cole and Weihe, 2016). Whereas nitrogen is often associated with primary productivity,
phosphorus is the limiting nutrient that regulates the biomass of periphytic algae in most
streams (Francoeur, 2001). Therefore, when phosphorus is added to a lotic system, this
can cause a shift in the periphytic algal community to contain predominantly filamentous,
green algae which can grow to levels that can overtake streambeds and harm stream
health (Stevenson et al., 2012).
The genus of filamentous, green alga, Cladophora, is one of the most widespread
taxon of algae on the planet (Higgins et al., 2008) and is a prominent member of
periphytic algal communities (Blum, 1956). The ability to out-compete other algae and
reproduce in massive quantities in nutrient-rich waters are what allow this alga to be so
widely distributed (Whitton, 1970). Cladophora sp., hereafter referred to as Cladophora,
can withstand the high shear stresses that are common in streams, allowing for the
colonization of lentic and lotic systems (Dodds and Gudder 1992). This alga is also
capable of using inorganic carbon sources from the water to undergo photosynthesis and
therefore does not rely solely on carbon dioxide from cellular respiration to
photosynthesize, allowing for large growths of Cladophora to continue to grow without
creating competition for resources between individual filaments (Choo et al., 2002).
Cladophora large biomasses through a variety of reproductive strategies can
overtake nutrient-rich waterways (Dodds and Gudder, 1992; Van den Hoek, 1963;
Zulkifly et al., 2013). In some Cladophora species there is no record of sexual
reproduction (Dodds and Gudder, 1992). Cladophora can reproduce sexually, but the
most common form of reproduction is through the asexual reproduction of biflagellated
spores, called zoospores (Van den Hoek, 1963). The alga can also asexually reproduce

3
through a resting-dormant cell called an akinete (Zulkifly et al., 2013). These methods of
reproduction can cause differences in algal blooms within a year. Cladophora typically
blooms twice annually (Whitton, 1970). In Northern temperate streams and rivers, this
filamentous alga blooms in mid-summer and then dies off in the late-summer months.
The alga then re-blooms in early fall (Higgins et al., 2008; Whitton, 1970).
There is some debate as to which nutrient limits the growth of Cladophora. Some
studies provide evidence for nitrogen limitation (Penick et al., 2012; Lohman and Priscu,
1992), whereas others provide evidence for phosphorus limitation (Zulkifly et al., 2013;
Dodds and Gudder, 1992). In many streams, the limiting nutrient is likely phosphorus,
but it depends on the individual stream and which nutrient is in shorter supply
(Francoeur, 2001).
Cladophora are rarely found in waters below a pH of 7 and are most commonly
found in waters between a pH of 7 and 10 (Whitton, 1970). Research suggested that
Cladophora can withstand a pH of 10.4 and still photosynthesize (Wood, 1975). This
alga is likely to proliferate in waterways with higher alkalinity including areas with
limestone bedrock, rich in CaCO3 (Fairchild and Holomuzki, 2002). During a field
experiment in Lake Erie, McMillan and Verduin (1953) documented Cladophora
glomerata in waters with a total alkalinity of 90 mg CaCO3/L. In a laboratory experiment
in an artificial stream, water alkalinity was controlled at 134 mg CaCO3/ L to study
optimum growth rates of C. glomerata (Robinson and Hawkes, 1986). In Lake Michigan,
Cladophora was collected in areas with total alkalinity between 152 and 179
mg/CaCO3/L (Cheney and Hough, 1983).

4
Cladophora can grow to levels that can alter stream communities and harm
overall stream health (Stevenson et al., 2012) and has been associated with reductions in
the diversity of macroinvertebrate communities (Ellsworth, 2000). A fish kill in the
Illinois River was attributed to large growths of filamentous green algae, including
Cladophora, which caused hypoxic dissolved oxygen concentrations (0.9mg/L)
(Stevenson et al., 2012). Hypoxia is the depletion of oxygen content in the water to about
3 mg/L (Berezina et al., 2007). Decaying Cladophora has also been shown to decrease
dissolved oxygen to hypoxic levels in littoral areas of the Baltic Sea (Berezina et al.,
2007, Berezina, 2008). In the absence of light, cellular respiration by both heterotrophic
and autotrophic organisms removes oxygen from the water. Some macroinvertebrates can
tolerate changes in oxygen better than others and thrive in low oxygen conditions, e.g.
Chironomidae can absorb oxygen directly through their cuticle (Baranov et al., 2016) and
pump water through their cases for irrigation in low flow conditions (Walshe, 1951).
Cladophora can be consumed by several species of macroinvertebrates, including the
caddisfly Gumaga nigricula, Helicopsyche borealis (Feminella and Resh, 1991),
Dicosmoecus gilvipes (Holomuzki et al., 2013), and the mayfly Ephemerella subvaria
(Bird and Kaushik, 1984). However, this alga can be hard to digest for
macroinvertebrates (Dodds and Gudder, 1992). The cell walls of Cladophora are thicker
and the cellulose is more crystalline in structure than that of terrestrial plants (Zulkifly et
al., 2013). There are few amino acids in Cladophora, which gives it a poor nutritional
value (Dodds and Gudder, 1992).
Cladophora grow in filamentous tufts (Blum, 1956), which create a microhabitat
including epiphytes, like diatoms, and the organisms that feed on these epiphytes

5
(Higgins et al., 2008; Ólafsson et al., 2013; Power, 1991). Many macroinvertebrates are
likely ingesting Cladophora for the epiphytes that colonize the surface of this macroalga,
essentially acquiring nutrition from the diatoms and particulate matter collected on the
thalli of Cladophora rather than from the Cladophora itself (Dodds and Gudder, 1992).
Therefore, even with a poor nutritional value, this alga has been associated with increased
densities of macroinvertebrates from the genera Baetis and Simulium and the family
Chironomidae (Ellsworth, 2000). Dudley et al. (1986) found that the genera Baetis,
Hydroptila, Ochrotrichia, and Euparyphus, along with the Chironomidae family, were
positively associated with Cladophora growths in a second order stream in Southern
California. Cladophora has also been positively associated with Hydropsyche,
Rhyacophilla, and Microsema due to the increased surface area this alga provides for
colonization (Dudley et al., 1986). Cladophora has been recorded by Power (1991) to be
used by Chironomidae to make cases or refuges. Therefore, Cladophora is not only a
source of food, but also a source of shelter and building materials for some
macroinvertebrates (Dudley et al., 1986; Power, 1991).
Despite all the research that has been conducted on Cladophora and
macroinvertebrates, no studies have examined the differences in community composition
of macroinvertebrates in relation to Cladophora distribution at varying spatial scales,
both within streams at the patch level and between streams at the stream level. It is
important to gain a better understanding of the interactions between this alga and its
associated macroinvertebrate communities over varying spatial scales, both small and
large. The objectives of this study are to firstly, see if Cladophora is influencing
macroinvertebrate community composition in middle order streams at both a patch-level

6
spatial scale within the same streams, and a between-stream spatial scale; secondly, to
determine the direct and indirect effects of Cladophora on macroinvertebrates as
bioindicators of water quality and to determine what these numbers actually mean for
ecosystem managers; thirdly, to examine the possible mechanisms that could explain the
response of macroinvertebrates to Cladophora; and fourthly, what factors influence the
distribution of Cladophora in streams within central Pennsylvania.
Hypotheses:
1. Cladophora biomass will alter macroinvertebrate community membership at
varying spatial scales both on a scale of patch distribution, within streams, and
between streams.
2. Areas with Cladophora present will have different macroinvertebrate densities,
percent Chironomidae (%Chironomidae), Shannon-diversity Index values,
Hilsenhoff Biotic Index (HBI) values, and percent Ephemeroptera, Plecoptera,
and Trichoptera taxa (%EPT taxa), when compared to areas without Cladophora.
MATERIALS AND METHODS:
Stream selection:
I collected data from thirteen middle order (4-6) streams in the Susquehanna River
watershed, including: Briar Creek, Chillisquaque Creek, Fishing Creek, Green Creek,
Hemlock Creek, Huntington Creek, Little Fishing Creek, Mahoning Creek, Mauses
Creek, North Mahantango Creek, Penns Creek, Turtle Creek, and Warrior Run (Fig. 1).
Streams were selected according to their stream order (4-6), proximity to Bloomsburg
University, and the previous knowledge of Cladophora presence or absence in the
streams from previous field experiments. Streams that contained patches of Cladophora

7
large enough for five Surber samples with at least 50% Cladophora cover within each
Surber sample were classified as Cladophora streams. If a stream did not contain patches
of Cladophora large enough for five Surber samples with at least 50% Cladophora cover
for each Surber sample, they were classified as non-Cladophora streams. Streams in this
study had a pH of 7 or greater because Cladophora tends to be found in streams with
higher pH (Whitton, 1970). A map of the collection sites was created using QGIS
(Version 3.12) (QGIS Development Team, 2020) (see Fig. 1).

Stream type
Non-Cladophora
Cladophora

Figure 1. Map of all sites categorized as Cladophora (yellow) and non-Cladophora (red)
streams within the Susquehanna River watershed. Layers data were obtained from
Pennsylvania Spatial Data Access (PASDA).
Macroinvertebrates were collected when weather conditions allowed for safe access
to streams and when Cladophora was present. Therefore, the sampling dates fell between

8
mid-July and mid-August 2019. The summer months may exhibit decreased diversity
values due to emergence of adult macroinvertebrates (Chalfont, 2015; PADEP, 2012). In
streams where Cladophora was present (Cladophora streams), two macroinvertebrate
collections were taken, one from Cladophora patches and another from non-Cladophora
patches. For each patch type collection, five composites, or grabs, from the Surber
sampler (0.096 m2), were collected and compiled into one composite sample for a given
patch type. Therefore, the total surface area of streambed disturbed per patch type was
0.48 m2. This was be done by placing the Surber sampler in areas within riffles where at
least 50% of the area within the Surber sampler was covered with Cladophora and
disturbing the substrate for 30 seconds, making sure to fully disturb the total area within
the Surber sampler. The same was done where Cladophora was totally absent, in the nonCladophora patches. The macroinvertebrates were temporarily stored in Ziplocâ bags
along with a 95% ethanol solution and then transported to the laboratory to be identified
under a dissecting microscope (Shull and Lookenbill, 2017; Johnson et al., 2013). The
macroinvertebrates were permanently stored in 95% ethanol in scintillation vials in a
repository at Bloomsburg University.
Macroinvertebrate Processing and Identification:
Macroinvertebrates were identified to the family level according to the protocol set
forth by the Pennsylvania Department of Environmental Protection (PADEP, 2012).
Macroinvertebrates were identified according to Merritt et al. (2008). Macroinvertebrates
were identified to the family-level because a majority of individuals were early instar or
immature and did not exhibit the necessary characteristics to confidently identify them to
the genus-level.

9
A sub-sampling protocol was implemented to decrease the time for processing and
identifying the collected macroinvertebrates. I developed this protocol by modifying the
existing Pennsylvania Department of Environmental Protection Division of Water
Quality’s protocol for wadeable freestone riffle-run streams of Pennsylvania (PADEP,
2012) by taking 350 ± 70 individuals from each sample. I recorded the proportion of
macroinvertebrates taken from the whole sample as a fraction, which was modified from
the procedures used by Frost (2006).
Macroinvertebrate Metrics:
After macroinvertebrates were identified to the family level, I calculated the
density of macroinvertebrates per m2 (hereafter referred to as density). I also applied the
taxonomic information to the following metrics:
Percent EPT Taxa:
Richness of Ephemeroptera, Plecoptera, and Trichoptera taxa, hereafter referred to as
EPT taxa, was calculated by adding the total number of these taxa in a sample, modified
from the method used by Chalfont (2015). Percent Ephemeroptera, Plecoptera, and
Trichoptera taxa, hereafter referred to as %EPT taxa, was calculated from the EPT taxa
richness scores (Equation 1). To calculate the %EPT taxa the total EPT richness is
divided by the total number of individuals identified in the sample and multiplied by 100,
to form a percent (Equation 2). The Ephemeroptera, Plecoptera, and Trichoptera are
generally sensitive taxa that are not associated with polluted waterways (Barbour et al.,
1992) and knowing the number of these taxa within a sample can provide insight into the
level of pollution in a waterway. This is a common macroinvertebrate metric used by
many agencies (Chalfont, 2015; PADEP, 2012).

10
Equation 1:
!"# #%&% '()ℎ+,-- = / !0ℎ,1,2304,2% + "6,)304,2% + #2()ℎ304,2%
Equation 2:
%!"# #%&% =

!"# #%&% '()ℎ+,-× 100
8

N= Total number of identified organisms in the sample.
Shannon Diversity Index:
Shannon diversity (1948) scores are a metric that takes into account the family-level
richness and evenness within the sample (Chalfont, 2015). Shannon diversity scores were
calculated for each sample from the community data, excluding the unknown individuals
(Equation 3). I excluded pupa, and damaged individuals that could not be identified from
this calculation. Shannon diversity scores were calculated using the Vegan Package (R
Core Team, 2019; Oksanen, 2019).
Equation 3:
$
!

<ℎ%++3+ =(>,2-(4? @+A,& (C ) = − / 0" ln 0"
"%&

S= the taxa richness, or total number of taxa identified
pi= proportion of taxon i
Hilsenhoff Biotic Index (HBI):
Hilsenhoff Biotic index (1988), hereafter referred to as HBI, was calculated for each
sample according to pollution tolerance values for each family of macroinvertebrate
according to the table provided by the Pennsylvania Department of Environmental
Protection (2012) (Equation 4). The families without pollution tolerance values were

11
excluded from the calculation. The HBI is an index that depicts higher values for areas
that are influenced by anthropogenic sources (Chalfont, 2015). The pollution tolerance
values for each family were originally written to identify organic pollution of raw sewage
and can provide insight into how hypoxic a waterway is due to this organic pollution
(Hilsenhoff, 1988).
Equation 4:
&-

C(6-,+ℎ3HH I(34() @+A,& = /[(( × +"'() *+, " )]/8
"%-

n indv PTV i = number of individuals identified in a sample with PTV i
N= total number of individuals identified in the sample
Percent Chironomidae:
The percent Chironomidae, hereafter referred to as %Chironomidae, is a metric that
was calculated by taking the number of Chironomidae present in each sample and
dividing by the total number of individuals identified in each sample and multiplying by
100 to get the percentage of individuals which belonged to the Chironomidae in each
sample. The number of Chironomidae in samples can be useful to understand why
diversity levels can be lower in some samples due to increased abundances of this taxon.
The numbers of this tolerant taxon in a sample can provide researchers with information
of the water quality and can help identify polluted areas (Pinder, 1986).
Water Chemistry:
Along with collecting macroinvertebrates, water chemistry measurements were also
taken at each site. Data loggers (MiniDOT, PME) were deployed to measure diel
fluctuations in dissolved oxygen and temperature. These data loggers were deployed for

12
at least one week before or after macroinvertebrate samples were collected. Data loggers
measured dissolved oxygen and water temperature every 15 minutes. The diel change in
dissolved oxygen, was calculated by subtracting the minimum value of dissolved oxygen
from the maximum value of dissolved oxygen for each day data were collected. The same
calculation was used for the diel change in water temperature. The diel change in
dissolved oxygen and diel change in water temperature were calculated for all full 24hour days within the dataset of each stream. The average diel change in dissolved oxygen
and average diel change in water temperature were calculated for each stream. A Eureka
Manta Sonde was used to record turbidity, dissolved oxygen, pH, temperature, and
conductivity at the time of macroinvertebrate collection. Total alkalinity was measured in
the field using a Hach digital titrator. Total alkalinity is a measure of the buffer capacity
of the water and is a measure of inorganic carbon sources (Cole and Weihe, 2016). Total
phosphorus, hereafter referred to as TP, and total nitrogen, hereafter referred to as TN,
were analyzed in the laboratory from whole water samples using an alkaline persulfate
oxidation followed by colorimetric determination on a Seal AQ1 Discrete Analyzer. The
TP and TN are the amount of all forms of phosphorus and nitrogen, both organic and
inorganic, respectively, found within the unfiltered water sample collected from a body of
water (Cole and Weihe, 2016).
Percent Agriculture Land Cover:
Percent agriculture (%Ag) for each stream watershed was calculated by
delineating each watershed at the site of field data collection using
modelmywatershed.org (Stroud Research Center, 2020). To calculate the total %Ag land

13
cover the percent pasture/hay was added to the percent cultivated crops to get a total
%Ag land cover within each stream watershed.
Statistical analyses:
All statistical analyses were performed using R statistical software (R Core Team,
2019). A logistic regression is a statistical analysis that can be used to test relationships
between a dependent categorical variable and independent continuous predictor variables
(Peng et al., 2002). A logistic regression was used to determine if environmental
variables could predict the presence or absence of Cladophora in each stream sampled.
The following environmental variables were analyzed in this model: %Ag, total
alkalinity, and TP. These environmental variables were transformed using a log base 10
transformation to improve normality and homoscedasticity. An Akaike information
criterion (AIC) is a way to compare multiple models to ensure the most parsimonious
model is selected (Akaike, 1974 as cited by Akaike 1979). I performed a stepwise AIC
test to select the most parsimonious categorical logistic regression. I carried out this
categorical logistic regression using the generalized linear model (glm) function in base R
(R Core Team, 2019). It is important to note, the non-Cladophora streams may have had
Cladophora present in small amounts.
I used a series of paired t-tests to compare % EPT taxa, Shannon diversity scores,
HBI scores, and %Chironomidae values between Cladophora patches and nonCladophora patches from the same streams. These tests were performed using the t.test
function in base R (R Core Team, 2019).
I used a Kruskal-Wallis rank sum test as a nonparametric equivalent to a one-way
analysis of variance (ANOVA) to compare the densities of macroinvertebrates by patch

14
type. This analysis compared densities from Cladophora patches, non-Cladophora
patches and patches collected from non-Cladophora streams. When a Kruskal-Wallis
rank sum test indicated a significant difference between one or more groups, I used a
Dunn (1964) test as a nonparametric equivalent to a Tukey post hoc test. The KruskalWallis rank sum test was performed using the kruskal.test function in base R (R Core
Team, 2019). The Dunn test was performed using the dunnTest function in the FSA
Package version 0.8.27 in R (Ogle et al., 2020).
Nonmetric multidimensional scaling (NMDS) ordination is a way to take multiple
variables and arrange them in a multidimensional space according to the variable’s
similarity or dissimilarity to each other and then plot them in a specified number of
dimensions (Kruskal, 1964). In this case, macroinvertebrate communities (samples from
different patch types) were the variables arranged according to Bray-Curtis dissimilarity.
Bray-Curtis dissimilarity was calculated from Bray-Curtis similarity (Bray and Curtis,
1957) by the metaMDS function in the Vegan Package version 2.5-6 in R (Oksanen et al.,
2019). This function also plotted the communities in a two-dimensional space, a biplot
(Oksanen et al., 2019; R Core Team, 2019). The data were Wisconsin Double
standardized and square root transformed for normality by the Vegan Package version
2.5-6 (Oksanen et al., 2007).
Two NMDS biplots were created using the (ggplot) function in the ggplot2 package
in R (Wickham, 2016). One biplot compared the macroinvertebrate communities from
Cladophora patch samples to the non-Cladophora patch samples from the same streams.
Another biplot compared the combined samples from Cladophora streams (Cladophora

15
patch samples and non-Cladophora patch samples from the same streams) to the samples
from streams where Cladophora was absent.
The permutational multivariate analysis of variance (PERMANOVA) is a
nonparametric alternative to a multivariate analysis of variance, MANOVA (Anderson,
2001). PERMANOVA can be used to test the null hypothesis that the dependent
variables, each taxon in the community, does not affect the community structure
(Anderson, 2014). Two PERMANOVAs were performed using the macroinvertebrate
community data. One PERMANOVA was used to test the hypothesis macroinvertebrate
community structure was influenced by patch type, Cladophora patch or non-Cladophora
patch within the same stream. Another PERMANOVA was used to test the hypothesis
that macroinvertebrate community structure was influenced by stream type (Cladophora
stream or non-Cladophora stream), %Ag, TP, average diel change in dissolved oxygen,
or average diel change in water temperature. This statistical analysis was performed using
the adonis function in the Vegan Package version 2.5-6 in R (Oksanen et al., 2007; R
Core Team, 2019).
RESULTS:
Cladophora was generally found in streams with higher alkalinity values (see Fig.
2). The stepwise AIC test indicated the best model was one that used the log10 of total
alkalinity values to predict the presence or absence of Cladophora in streams
(AIC=14.73). The second-best model used the log10 of total alkalinity along with the
log10 of TP (AIC=15.82). I used the log10 of total alkalinity values for each stream as
the sole predictor variable for the categorical logistic regression to predict the presence or
absence of Cladophora in streams (p=0.0516).

16

Figure 2. Total alkalinity in mg CaCO3/L from Cladophora streams and non-Cladophora
streams in central Pennsylvania. Cladophora streams contained patches of Cladophora
that covered at least 50% of the area within a Surber sampler. Non-Cladophora streams
had very little or no Cladophora present.
Samples collected from Cladophora patches contained greater numbers of
macroinvertebrates compared to the samples collected from non-Cladophora patches.
The lowest densities were found in samples collected from patches in non-Cladophora

17
streams, then samples from non-Cladophora patches within Cladophora streams had
higher densities, and the highest densities were found in samples collected from
Cladophora patches (see Fig. 3). The Kruskal-Wallis rank sum test indicated a difference
(p<0.05) when comparing densities between all patch types. The Dunn test indicated the
Cladophora patch densities were higher than the non-Cladophora stream densities
(p<0.05). The non-Cladophora patch densities and the non-Cladophora stream densities
exhibited less distinction (p=0.079). The differences in the densities for the Cladophora
patches and the non-Cladophora patches from the same streams were similar (p=0.473).

18

Figure 3. Macroinvertebrate densities in non-Cladophora streams and in non-Cladophora
and Cladophora patches within Cladophora dominated streams in central Pennsylvania.
Samples collected from Cladophora patches contained greater numbers of
Chironomidae and exhibited lower diversity than samples collected from nonCladophora patches (see Fig. 4 and Fig. 5). The %Chironomidae was greater in
Cladophora patches compared to non-Cladophora patches (p<0.005). Therefore,
Shannon diversity scores were lower in Cladophora patches compared to nonCladophora patches (p<0.05).

19

Figure 4. The %Chironomidae in Cladophora patches and non-Cladophora patches in
Cladophora dominated streams in central Pennsylvania.

20

Figure 5. Shannon-Weaver diversity (H) scores for Cladophora patches and nonCladophora patches in Cladophora dominated streams in central Pennsylvania.
Similar abundances of macroinvertebrate from the orders Ephemeroptera,
Plecoptera, and Trichoptera were found in both Cladophora patches and non-Cladophora
patches from the same streams. While the %EPT taxa for Cladophora patches were
generally lower than that of the non-Cladophora patches (see Fig. 6) the differences were
subtle (p=0.3508).

21

Figure 6. The %EPT taxa from non-Cladophora patches and Cladophora patches within
Cladophora dominated streams in central Pennsylvania.
Many of the same families of macroinvertebrates were found in both Cladophora
patches and non-Cladophora patches within the same streams. Therefore, HBI scores
between Cladophora patches and non-Cladophora patches from the same streams were
comparable (p=0.1463) (see Fig. 7). When comparing macroinvertebrate community
structures by patch type using a PERMANOVA, I did not find a clear difference between
the macroinvertebrate community structures of Cladophora patches and non-Cladophora

22
patches within the same streams (p=0.296). It should be noted, in most cases, Cladophora
patch samples (circles) were found to the left of the non-Cladophora patch samples
(triangles) (see Fig. 8). This could indicate a trend that each Cladophora patch sample
was separated from the non-Cladophora patch samples due to a difference in community
structure in a similar direction, the negative direction on the NMDS1 axis. However,
based on the results from the PERMANOVA, this trend is not strong enough to indicate
clear differences in macroinvertebrate community structures between the two patch types.

23

Figure 7. Hilsenhoff Biotic Index (HBI) scores of Cladophora patches and nonCladophora patches within Cladophora dominated streams in central Pennsylvania.

24

Figure 8. Nonmetric multidimensional scaling (NMDS) ordination comparing
Cladophora patch communities (circles) to non-Cladophora patch communities
(triangles) in six Cladophora dominated streams in central Pennsylvania
(stress=0.10571).
Macroinvertebrate communities from Cladophora streams generally had greater
numbers of macroinvertebrates and contained different families of macroinvertebrates
when compared to non-Cladophora streams. Cladophora streams exhibited different
community structure than non-Cladophora streams (p<0.05), indicated by closer
grouping of Cladophora streams (circles) toward the middle of the biplot when compared

25
to non-Cladophora streams (triangles; see Fig. 9). This indicates many of the same taxa
are found at similar abundances in Cladophora streams. Essentially, the Cladophora
streams are more similar in community structure to each other than they are to the nonCladophora streams. The non-Cladophora streams show greater variation in their
community structure and therefore do not group as tightly together as the Cladophora
streams. It should be noted, when visually comparing Cladophora stream communities to
non-Cladophora stream communities using NMDS ordination, an outlier was identified.
This outlier was Briar Creek, which had far fewer individuals in the sample compared to
all other samples. This caused a greater weight on the NMDS analysis and therefore,
Briar Creek data were removed when making the NMDS biplot. Briar Creek data were
not removed from the PERMANOVA test when comparing Cladophora stream and nonCladophora stream macroinvertebrate communities.

26

Figure 9. Nonmetric multidimensional scaling (NMDS) ordination comparing
Cladophora stream communities (circles) to non-Cladophora stream communities
(triangles) from 12 streams in central Pennsylvania. Briar Creek was omitted from this
NMDS due to low numbers of macroinvertebrates heavily weighting NMDS analysis
(stress=0.14163).
The percent agriculture within stream watersheds impacted the macroinvertebrate
community composition (p<0.05). However, my findings suggest that average diel
change in dissolved oxygen (p=0.235), average diel change in water temperature
(p=0.221), total phosphorus (p=0.094), and total alkalinity (p=0.191) along with an
interaction between stream type : average diel change in dissolved oxygen (p=0.676) did

27
not seem to directly impact macroinvertebrate community composition between
Cladophora streams and non-Cladophora streams.
DISCUSSION:
The findings of this study further support the large body of evidence that humans
affect the communities of macroinvertebrates found in streams through agricultural land
use and nutrient enrichment. The alga Cladophora is commonly associated with nutrient
enrichment in streams (Stevenson et al., 2012; Whitton, 1970; Zulkifly et al., 2013). I
found higher macroinvertebrate densities and decreased Shannon diversity in patches
dominated by Cladophora due to higher Chironomidae abundances in these patches.
These findings can help ecosystem managers by providing evidence for agricultural
impact on macroinvertebrate communities, and potentially explain reasons for decreased
diversity in nutrient-rich waters containing the filamentous, green alga, Cladophora.
My findings support the hypothesis that agriculture land use impacts
macroinvertebrate communities in streams. This means %Ag within the upstream
watershed from the site of macroinvertebrate collection impacted the community
structure of macroinvertebrates found within the streams. Hanna et al. (2020) found
agriculture land use was associated with decreased ecosystem services and lower
diversity of macroinvertebrates in streams. In a review by Weijters et al. (2009) many
studies found significant negative relationships between the percent agriculture and
macroinvertebrate diversity scores. It is important to note that the likely reason for seeing
the percent agriculture directly influence macroinvertebrate community structure was
likely due to phosphorus playing a role in Cladophora growth. Agriculture increases
phosphorus content in soil (Sharpley et al., 1994) and phosphorus allows for large

28
growths of filamentous green algae, like Cladophora (Stevenson et al., 2012). Therefore,
the percent agriculture indirectly influences Cladophora biomass which has a direct
impact on macroinvertebrate community structure. I was unable to determine exactly how
the percent agriculture specifically alters the community structure of macroinvertebrates.
However, I suspect it is altering the community by increasing macroinvertebrate densities
as well as the abundance of Chironomidae.
In previous studies, macroinvertebrate densities have been shown to increase with
Cladophora presence (Dudley et al., 1986; Ellsworth, 2000; Fairchild and Holomuzki,
2002; Feminella and Resh, 1991). My findings further support this trend. The higher
densities of macroinvertebrates in Cladophora patches compared to samples from nonCladophora stream patches, suggested the Cladophora provided something to these
macroinvertebrates, allowing for population sizes to increase. The macroinvertebrates
could be benefitting from the increased surface area the Cladophora provides in streams,
essentially providing a substrate for macroinvertebrate colonization (Dudley et al., 1986).
Macroinvertebrates could also be feeding on epiphytic algae found on the Cladophora or
potentially feeding on the Cladophora itself (Dudley et al., 1986; Feminella and Resh,
1991).
The likely reason for increased macroinvertebrate densities is due to much higher
abundances of Chironomidae in samples from Cladophora patches. The association of
this dipteran larvae with Cladophora has been well documented (Berezina, 2008; Dudley
et al., 1986; Higgins et al., 2008; Whitton, 1970). Chironomidae have been found to make
their cases from filaments of this alga (Power, 1991). Though this study did not focus on
what this alga provides for the Chironomidae, a higher %Chironomidae was found in

29
samples collected from patches containing Cladophora compared to patches without
Cladophora from the same streams. Identification of Chironomidae past the family level
would be necessary to determine the functional feeding groups present in this population.
A potential future research project could be identifying the Chironomidae to the genus
level to determine functional feeding groups present in each sample by taking a
subsample of the Chironomidae and mounting head capsules for further identification. It
is unknown exactly, what the Cladophora is providing the Chironomidae. However,
research suggested the Chironomidae are likely feeding upon the diatoms and other
epiphytes that typically colonize Cladophora and could also be using the Cladophora as
a substrate to attach for filter feeding (Dudley et al., 1986; Furey et al., 2012; Pinder,
1986). Therefore, the Cladophora could be supplying the Chironomidae with both shelter
and food, allowing for significant increases in Chironomidae populations in areas
dominated by Cladophora compared to areas that lack this alga. The increase in
Chironomidae in samples from Cladophora patches is likely the driving force for why
Shannon diversity was lower for those samples. Essentially, the greater the abundance of
one taxon in a sample, the lower the diversity for that sample. My research supports the
findings of Ellsworth (2000), who found lower macroinvertebrate diversity associated
with dense Cladophora growths in streams. However, Ellsworth (2000) attributed this
decrease of diversity to increases of Baetis, Simulium and Chironomidae. My findings
suggest Chironomidae are the dominant taxon in Cladophora patches and play a more
significant role in the diversity of these patches than other taxa in central Pennsylvania.
My research indicated macroinvertebrate community composition is likely
determined at the stream-level spatial scale rather than patch-level spatial scale. This

30
claim is supported by finding distinct community composition separation between
Cladophora streams and non-Cladophora streams, and not between Cladophora and nonCladophora patches within the same streams. However, this should be interpreted with
caution. The distinction between macroinvertebrate communities in Cladophora streams
and non-Cladophora streams could be due to geographic separation of these two stream
types. Many of the Cladophora streams are geographically closer together than the nonCladophora streams, potentially causing similar families of macroinvertebrates to be
found due to geographic proximity. The reason for a lack of distinction between
communities at the patch level could be due to lower taxonomic resolution resulting from
family-level identification. Though family level identification can tell us a great deal
about macroinvertebrate communities (Osborne et al, 1980), most agencies recommend
genus-level identification (PADEP, 2012). This is because genus-level identification
would allow for further separation of the communities. It is possible that with greater
taxonomic resolution (i.e. genus-level identification) a difference in community
composition at the patch level could be found.
Total alkalinity did not have a direct impact on macroinvertebrate communities
between streams. However, I found that alkalinity did impact the presence or absence of
Cladophora in streams and that Cladophora can impact macroinvertebrate density and
diversity. Therefore, alkalinity seems to indirectly impact the macroinvertebrate
communities through the influence of Cladophora. Cladophora tends to be found in
waters with alkalinity of at least 90 mg CaCO3/L or higher (McMillan and Verduin 1953;
Cheney and Hough, 1983; Robinson and Hawkes, 1986). Choo et al. (2002) were able to
identify the uptake of inorganic species of carbon, directly related to alkalinity, in the

31
Baltic Sea. It has also been documented that C. glomerata is capable of utilizing HCO3as a carbon source within streams (Raven et al., 1982; Raven et al., 1994). Therefore, it
makes sense for this alga to be present in larger quantities in streams that have greater
amounts of this inorganic carbon form, i.e. waters with higher alkalinity values.
The likely reason %Ag and TP were not identified as key predictors of
Cladophora presence in the streams within this study is due to the limited number of
stream sites examined. Variation within the %Ag and TP of these 13 streams was likely
causing these variables to be excluded as predictors. It is well-known that Cladophora
has a close association with higher levels of phosphorus (Whitton, 1970; Stevenson et al.,
2012; Zulkifly et al., 2013; Dodds and Gudder, 1992). Agriculture is linked to
phosphorus by the application of fertilizers and manures to agricultural lands increasing
concentrations of phosphorus in soil (Sharpley et al., 1994), ultimately increasing the
amount of phosphorus in streams (Carpenter et al., 1998). Therefore, if more sites, of
both Cladophora and non-Cladophora streams, were included in this study then %Ag
and TP would likely be good predictors of Cladophora presence in streams.
The reason for a lack of distinction between some macroinvertebrate metrics at
the patch level could also be due to lower taxonomic resolution resulting from familylevel identification. Many of the same families of macroinvertebrates were found in both
Cladophora and non-Cladophora patches in the same streams, likely influencing the
%EPT taxa and causing it to be similar for both patch types. However, Cladophora
patches generally exhibited lower %EPT taxa, which would suggest the alga is creating
slightly unfavorable conditions for these sensitive taxa. The orders Ephemeroptera,
Plecoptera, and Trichoptera are generally sensitive to environmental variables,

32
specifically lower dissolved oxygen (Connolly et al., 2004), and are often not found in
abundance in waters with pollution (Barbour et al., 1992). Cladophora may be creating
subtle changes in dissolved oxygen that these taxa do not prefer, causing them to be
found less frequently in patches of Cladophora. A valuable future study could examine
how subtle changes in dissolved oxygen could cause EPT taxa to avoid areas of
Cladophora. To do this, a micro-sensor that collects dissolved oxygen data could be
placed in patches of Cladophora before sampling for macroinvertebrates to see diel
changes in dissolved oxygen at a smaller scale. Then the %EPT taxa could be calculated
to determine how these small-scale diel changes in dissolved oxygen impact the EPT taxa
found in Cladophora patches. If more sites were included in this study, a difference of
%EPT taxa between Cladophora patches and non-Cladophora patches may be observed.
The HBI metric was also calculated using family-level identified macroinvertebrates.
Using family-level identified macroinvertebrates for HBI scores is a common practice
(Hilsenhoff, 1988). However, HBI scores can also be calculated using the pollution
tolerance values for macroinvertebrates identified to the genus level (Chalfont, 2015).
Pollution tolerance values for macroinvertebrates identified to the genus level are more
specific, whereas family level identified pollution tolerance values are more conservative
and are typically higher than that of some of the genera within that particular family
(PADEP, 2012). Therefore, if macroinvertebrates were identified to the genus level, a
greater resolution could be found, further distinguishing any differences that may be
found in the HBI values. Another explanation for the lack of difference between HBI
values for Cladophora patches and non-Cladophora patches could be due to the limited
number of sites to compare. In this comparison, six Cladophora patches were compared

33
to six non-Cladophora patches from the same streams. If more sites containing
Cladophora were found and separate HBI scores were calculated to compare Cladophora
patches to non-Cladophora patches, it is possible a difference could be observed.
Large quantities of filamentous green algae, like Cladophora, can decrease
dissolved oxygen to hypoxic levels in lotic systems, which harm fish and invertebrate
communities (Stevenson et al., 2012). It was rare for any of the sites studied in this
project to drop below dissolved oxygen concentrations of about 6 mg/L and so these sites
were not in danger of becoming hypoxic. Hypoxia is when oxygen concentrations drop to
around 3 mg/L (Berezina et al., 2007). Stevenson et al. (2012) reported a diel change in
dissolved oxygen of around 7 mg/L that resulted in a fish kill. However, the reason for
this fish kill was likely due to dissolved oxygen concentrations reaching as low as
0.9mg/L, not the actual diel change in dissolved oxygen. The highest average diel change
in dissolved oxygen I found was from Penns Creek with an average diel change in
dissolved oxygen of 5.45 mg/L. However, the lowest recorded dissolved oxygen for this
site was 5.53 mg/L, well above hypoxic levels. Therefore, it is not surprising that the
average diel change in dissolved oxygen did not have a direct impact on
macroinvertebrate communities because these communities were likely receiving
adequate dissolved oxygen, even at their lowest dissolved oxygen levels. I was unable to
detect a change in the average diel change in dissolved oxygen due to the presence of
Cladophora in streams. This is likely due to Cladophora growths not completely
overtaking the streambeds. The site with the most Cladophora cover was Penns Creek
(see Fig. 10). The Cladophora streams had Cladophora patches large enough to be
considered Cladophora patches, greater than 50% Cladophora cover within a Surber

34
sample, but I would not consider any of the sites in this study to be completely overrun
with Cladophora. Therefore, it is unlikely the Cladophora would be significantly
impacting the average diel change in dissolved oxygen within the streams at a detectable
level. To see significant changes in average diel change in dissolved oxygen due to
Cladophora with this number of sites, the sites in this study would need to have larger
amounts of Cladophora present in the stream, greater than that of Penns Creek.

Figure 10. Image serving as an example of a Cladophora patch sample taken within
Penns Creek before a Surber sample from a patch containing Cladophora was collected.
Low macroinvertebrate diversity in streams has been attributed to large (13°C)
diel changes in water temperature (Jacobsen and Marín, 2008). However, the highest
average diel change in water temperature I found was 5.25°C in Briar Creek. Most of the

35
sites fell within a diel change in water temperature of around 3-4°C. Therefore, it is not
surprising the diel change in water temperature did not have a direct impact on
macroinvertebrate community structure at the stream.
Nutrient enrichment, like phosphorus, can cause shifts in macroinvertebrate
communities, and more specifically, indirectly increase the Chironomidae populations
within macroinvertebrate stream communities (Gafner and Robinson, 2007; Friberg et al.,
2010). My results indicate total phosphorus did not directly impact macroinvertebrate
communities between streams. However, it has been well documented that TP influences
the growth of Cladophora in streams (Whitton, 1970; Stevenson et al., 2012; Zulkifly et
al., 2013; Dodds and Gudder, 1992). Therefore, TP is indirectly influencing
macroinvertebrate communities, and more specifically Chironomidae populations,
through the growth of Cladophora.
My hypotheses were: (1) Cladophora biomass will alter macroinvertebrate
community membership at varying spatial scales both on a scale of patch distribution,
within streams, and between streams, and (2) areas with Cladophora present will have
different macroinvertebrate densities, percent Chironomidae (%Chironomidae), Shannondiversity Index values, Hilsenhoff Biotic Index (HBI) values, and percent
Ephemeroptera, Plecoptera, and Trichoptera taxa (%EPT taxa), when compared to areas
without Cladophora. My first hypothesis is supported by my findings. Though the
community composition may not clearly differ between patches where Cladophora was
present and where it was absent from the same streams, the % Chironomidae was
significantly higher in the patches containing Cladophora. Therefore, at the patch scale, a
greater number of Chironomidae can be found in areas containing Cladophora when

36
compared to areas the alga is absent. Macroinvertebrate communities differed more at the
stream level, than the patch level. The basis for this statement is founded on the
difference between macroinvertebrate community structure when comparing the
macroinvertebrate communities by stream type. My second hypothesis was also
supported by my findings. While not all the macroinvertebrate metrics would support this
claim, both %Chironomidae and Shannon diversity differed between patch types within
the same streams. The Shannon diversity scores were lower in samples from Cladophora
patches, due to the %Chironomidae being greater in these patches. I found supporting
evidence that alkalinity of streams can determine the distribution of Cladophora in
streams of central Pennsylvania. My findings suggest that Cladophora growth in the
streams I examined has not reached the nuisance levels reported by Stevenson et al.
(2012) and is likely not strongly influencing diel changes in dissolved oxygen at a scale
large enough for me perceive with the equipment I used. Cladophora seems to be
influencing the macroinvertebrate community structure to be skewed with a dominant
presence of Chironomidae.
Future research could focus on the impacts of Cladophora on higher trophic
levels. Beneficial questions which could be addressed include the following: What does
the increased densities of macroinvertebrates mean for the organisms that feed on these
macroinvertebrates? How does the presence or absence of Cladophora influence fish
species and aquatic predators feeding on the aquatic life stages of macroinvertebrates?
The results provided in this study lay the groundwork for studying the questions above
and provide insight into how humans are impacting streams and, more specifically, the
macroinvertebrates within streams.

37
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43

APPENDICES:

Appendix A

Raw data including the name of each stream site, the patch type, proportion of the
total sample that was identified, date the macroinvertebrate samples were collected, total
phosphorus (TP) in (ug/L), total nitrogen (TN) in (ug/L), total alkalinity in (mg
CaCO3/L), Latitude and Longitude coordinates for each site, percent agriculture (%Ag) in
each stream watershed, average diel change in dissolved oxygen (Average DDO) (mg/L),
average diel change in water temperature in °C (Average DTemperature), and the
identified macroinvertebrate data for each stream.

44

Table 1. Raw data collected from each site including the stream site, patch type, proportion of the total macroinvertebrate sample that
was identified, date the samples were collected, TP (ug/L), TN (ug/L), total alkalinity (mg CaCO3/L), coordinates for each site, %Ag
in each stream watershed, average diel change in dissolved oxygen (Average DDO) (mg/L), average diel change in water temperature
in °C (Average DTemperature), and identified macroinvertebrate data for each stream.
Stream

Patch Type

Proportion subsampled

Date

TP

TN

Total Alkalinity

Briar Creek

Non-Cladophora Stream

1 (N/A)

7/10/19 61.9779566 1815.336

60

Penns Creek

Cladophora Patch

1/14

8/14/19 66.6792756 1174.874

96

Penns Creek

Non-Cladophora Patch

3/28

8/14/19 66.6792756 1174.874

96

Fishing Creek

Non-Cladophora Stream

3/28

7/16/19 30.0589682 1011.955

58

Green Creek

Non-Cladophora Stream

9/28

7/15/19 15.5895266 2491.359

50

Mahoning Creek

Cladophora Patch

1/28

8/20/19 62.3403174 1619.008

103

Mahoning Creek

Non-Cladophora Patch

3/28

8/20/19 62.3403174 1619.008

103

Chillisquaque Creek

Cladophora Patch

1/7

8/15/19 98.1796748 1982.938

101

Chillisquaque Creek

Non-Cladophora Patch

3/28

8/15/19 98.1796748 1982.938

101

Mauses Creek

Non-Cladophora Patch

3/28

8/20/19 43.3913466 1805.275

134

Mauses Creek

Cladophora Patch

3/28

8/20/19 43.3913466 1805.275

134

Hemlock Creek

Non-Cladophora Stream

9/28

7/15/19

22.580591

2650.922

68

Warrior Run

Cladophora Patch

3/28

8/22/19 29.0968378

731.019

102

Warrior Run

Non-Cladophora Patch

3/28

8/22/19 29.0968378

731.019

102

45

Table 1continued
Stream

Patch Type

Proportion subsampled

Little Fishing Creek

Non-Cladophora Stream

3/28

7/17/19 33.3045964 1591.275

58

Huntington Creek

Non-Cladophora Stream

1/7

7/16/19 26.8352066

720.604

47

Turtle Creek

Cladophora Patch

1/56

8/18/19 46.0340814 5128.933

205

Turtle Creek

Non-Cladophora Patch

1/28

8/18/19 46.0340814 5128.933

205

1/7

8/14/19

154

North Mahantango Creek Non-Cladophora Stream

Date

TP

51.553836

TN

1422.952

Total Alkalinity

46

Table 1 continued
Stream

Patch Type

Latitude

Longitude

%Ag

Briar Creek

Non-Cladophora Stream

41.0679

-76.29013 34.39

Penns Creek

Cladophora Patch

40.826213 -76.87228

Penns Creek

Non-Cladophora Patch

40.826213 -76.87228

Average DDO Average DTemperature
1.702929

5.252929

27

5.45675

3.921875

27

5.45675

3.921875

Fishing Creek

Non-Cladophora Stream 41.048969 -76.42997 28.08

2.199591

3.14

Green Creek

Non-Cladophora Stream 41.109922 -76.41719 52.15

1.261667

2.960667

Mahoning Creek

Cladophora Patch

40.977506 -76.62412

46.1

3.1626

2.908

Mahoning Creek

Non-Cladophora Patch

40.977506 -76.62412

46.1

3.1626

2.908

Chillisquaque Creek

Cladophora Patch

40.960146 -76.81623 65.71

1.433111

1.629444

Chillisquaque Creek

Non-Cladophora Patch

40.960146 -76.81623 65.71

1.433111

1.629444

Mauses Creek

Non-Cladophora Patch

40.984725 -76.63109 45.28

1.7227

2.4646

Mauses Creek

Cladophora Patch

40.984725 -76.63109 45.28

1.7227

2.4646

54.39

1.263313

4.056313

Hemlock Creek

Non-Cladophora Stream 40.995499

-76.4861

Warrior Run

Cladophora Patch

41.101962 -76.79345 39.97

1.41225

1.78

Warrior Run

Non-Cladophora Patch

41.101962 -76.79345 39.97

1.41225

1.78

39.1

1.599444

3.647833

Non-Cladophora Stream 41.113928 -76.33979 26.61

1.394375

3.0385

Little Fishing Creek
Huntington Creek

Non-Cladophora Stream 41.027598 -76.48027

Turtle Creek

Cladophora Patch

40.928275 -76.88129 46.72

4.377

4.170333

Turtle Creek

Non-Cladophora Patch

40.928275 -76.88129 46.72

4.377

4.170333

2.715944

3.301778

North Mahantango Creek Non-Cladophora Stream

40.6495

-76.96636 35.31

47

Table 1 continued
Stream

Patch Type

Chironomidae Psephenidae Elmidae Oligochaeta Bivalvia

Briar Creek

Non-Cladophora Stream

665

2

12

20

4

Penns Creek

Cladophora Patch

138

2

107

2

2

Penns Creek

Non-Cladophora Patch

43

5

70

6

0

Fishing Creek

Non-Cladophora Stream

151

0

2

3

0

Green Creek

Non-Cladophora Stream

94

8

38

9

0

Mahoning Creek

Cladophora Patch

172

2

15

3

0

Mahoning Creek

Non-Cladophora Patch

96

6

47

21

0

Chillisquaque Creek

Cladophora Patch

88

1

26

2

0

Chillisquaque Creek

Non-Cladophora Patch

61

6

115

4

0

Mauses Creek

Non-Cladophora Patch

54

2

13

45

0

Mauses Creek

Cladophora Patch

173

3

18

19

0

Hemlock Creek

Non-Cladophora Stream

32

2

12

5

0

Warrior Run

Cladophora Patch

327

2

5

1

0

Warrior Run

Non-Cladophora Patch

152

2

19

6

0

Little Fishing Creek

Non-Cladophora Stream

48

2

37

65

0

Huntington Creek

Non-Cladophora Stream

117

3

22

18

1

Turtle Creek

Cladophora Patch

169

20

45

4

4

Turtle Creek

Non-Cladophora Patch

111

9

82

5

2

160

10

106

5

5

North Mahantango Creek Non-Cladophora Stream

48

Table 1 continued
Stream

Patch Type

Chloroperlidae Heptageniidae Limoniidae Tabanidae Julida

Briar Creek

Non-Cladophora Stream

1

1

5

1

1

Penns Creek

Cladophora Patch

0

9

0

0

0

Penns Creek

Non-Cladophora Patch

0

7

0

0

0

Fishing Creek

Non-Cladophora Stream

0

8

0

0

0

Green Creek

Non-Cladophora Stream

3

34

2

0

0

Mahoning Creek

Cladophora Patch

0

15

0

0

0

Mahoning Creek

Non-Cladophora Patch

0

17

0

0

0

Chillisquaque Creek

Cladophora Patch

0

7

0

0

0

Chillisquaque Creek

Non-Cladophora Patch

0

21

0

0

0

Mauses Creek

Non-Cladophora Patch

0

11

7

0

0

Mauses Creek

Cladophora Patch

0

10

12

0

0

Hemlock Creek

Non-Cladophora Stream

0

11

2

0

0

Warrior Run

Cladophora Patch

1

4

5

0

0

Warrior Run

Non-Cladophora Patch

0

2

6

0

0

Little Fishing Creek

Non-Cladophora Stream

1

35

1

0

0

Huntington Creek

Non-Cladophora Stream

0

8

12

0

0

Turtle Creek

Cladophora Patch

0

0

7

0

0

Turtle Creek

Non-Cladophora Patch

0

0

8

0

0

0

2

3

0

0

North Mahantango Creek Non-Cladophora Stream

49

Table 1 continued
Stream

Patch Type

Nematoda Isopoda Baetidae Hydropsychidae Arachnida Viviparidae

Briar Creek

Non-Cladophora Stream

2

1

3

1

1

0

Penns Creek

Cladophora Patch

1

0

32

24

0

80

Penns Creek

Non-Cladophora Patch

0

0

48

45

0

39

Fishing Creek

Non-Cladophora Stream

5

0

70

57

0

0

Green Creek

Non-Cladophora Stream

1

0

86

72

0

0

Mahoning Creek

Cladophora Patch

1

0

31

82

0

0

Mahoning Creek

Non-Cladophora Patch

10

0

32

103

0

0

Chillisquaque Creek

Cladophora Patch

0

0

28

131

1

0

Chillisquaque Creek

Non-Cladophora Patch

5

0

25

79

0

0

Mauses Creek

Non-Cladophora Patch

4

0

84

76

0

0

Mauses Creek

Cladophora Patch

0

0

66

55

0

0

Hemlock Creek

Non-Cladophora Stream

6

0

80

103

0

0

Warrior Run

Cladophora Patch

2

0

33

49

0

0

Warrior Run

Non-Cladophora Patch

28

0

51

55

0

0

Little Fishing Creek

Non-Cladophora Stream

16

0

124

67

0

0

Huntington Creek

Non-Cladophora Stream

1

0

25

61

0

0

Turtle Creek

Cladophora Patch

1

3

26

65

0

0

Turtle Creek

Non-Cladophora Patch

7

0

26

46

0

0

8

0

7

62

0

0

North Mahantango Creek Non-Cladophora Stream

50

Table 1 continued
Stream

Patch Type

Lepidostomatidae Perlidae Glossosomatidae Philopotamidae

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

1

5

32

1

Penns Creek

Non-Cladophora Patch

0

4

13

2

Fishing Creek

Non-Cladophora Stream

4

7

0

7

Green Creek

Non-Cladophora Stream

0

2

0

3

Mahoning Creek

Cladophora Patch

0

0

0

7

Mahoning Creek

Non-Cladophora Patch

0

0

0

5

Chillisquaque Creek

Cladophora Patch

0

0

0

9

Chillisquaque Creek

Non-Cladophora Patch

0

0

0

19

Mauses Creek

Non-Cladophora Patch

0

0

23

8

Mauses Creek

Cladophora Patch

0

0

1

4

Hemlock Creek

Non-Cladophora Stream

0

0

4

22

Warrior Run

Cladophora Patch

0

0

2

7

Warrior Run

Non-Cladophora Patch

0

0

1

20

Little Fishing Creek

Non-Cladophora Stream

0

2

0

5

Huntington Creek

Non-Cladophora Stream

0

3

0

0

Turtle Creek

Cladophora Patch

0

0

0

17

Turtle Creek

Non-Cladophora Patch

0

0

0

44

0

0

0

1

North Mahantango Creek Non-Cladophora Stream

51

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

22

10

9

1

Penns Creek

Non-Cladophora Patch

24

12

15

0

Fishing Creek

Non-Cladophora Stream

0

0

0

0

Green Creek

Non-Cladophora Stream

0

0

0

0

Mahoning Creek

Cladophora Patch

0

2

10

0

Mahoning Creek

Non-Cladophora Patch

0

0

13

0

Chillisquaque Creek

Cladophora Patch

0

4

75

0

Chillisquaque Creek

Non-Cladophora Patch

0

0

30

0

Mauses Creek

Non-Cladophora Patch

0

1

1

0

Mauses Creek

Cladophora Patch

0

0

0

0

Hemlock Creek

Non-Cladophora Stream

0

4

0

0

Warrior Run

Cladophora Patch

0

4

6

0

Warrior Run

Non-Cladophora Patch

0

0

0

0

Little Fishing Creek

Non-Cladophora Stream

0

2

0

0

Huntington Creek

Non-Cladophora Stream

0

0

3

0

Turtle Creek

Cladophora Patch

0

4

22

1

Turtle Creek

Non-Cladophora Patch

0

0

1

0

8

0

4

3

North Mahantango Creek Non-Cladophora Stream

Potamanthidae Simuliidae Hydroptilidae Helicopsyche

52

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

7

57

1

1

Penns Creek

Non-Cladophora Patch

5

23

1

0

Fishing Creek

Non-Cladophora Stream

1

13

1

1

Green Creek

Non-Cladophora Stream

0

2

0

0

Mahoning Creek

Cladophora Patch

1

0

0

0

Mahoning Creek

Non-Cladophora Patch

7

0

3

0

Chillisquaque Creek

Cladophora Patch

8

2

0

0

Chillisquaque Creek

Non-Cladophora Patch

9

0

0

0

Mauses Creek

Non-Cladophora Patch

2

0

0

0

Mauses Creek

Cladophora Patch

0

3

0

0

Hemlock Creek

Non-Cladophora Stream

1

0

0

0

Warrior Run

Cladophora Patch

9

0

0

0

Warrior Run

Non-Cladophora Patch

8

0

0

0

Little Fishing Creek

Non-Cladophora Stream

0

0

1

0

Huntington Creek

Non-Cladophora Stream

0

3

0

0

Turtle Creek

Cladophora Patch

22

0

0

0

Turtle Creek

Non-Cladophora Patch

58

0

0

0

2

14

0

9

North Mahantango Creek Non-Cladophora Stream

Turbellaria Ephemerellidae Caenidae Brachycentridae

53

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

1

0

0

0

Penns Creek

Non-Cladophora Patch

0

1

1

0

Fishing Creek

Non-Cladophora Stream

4

13

0

1

Green Creek

Non-Cladophora Stream

15

1

0

2

Mahoning Creek

Cladophora Patch

16

3

0

0

Mahoning Creek

Non-Cladophora Patch

21

4

0

3

Chillisquaque Creek

Cladophora Patch

20

3

0

4

Chillisquaque Creek

Non-Cladophora Patch

13

2

0

6

Mauses Creek

Non-Cladophora Patch

1

0

0

0

Mauses Creek

Cladophora Patch

1

1

4

4

Hemlock Creek

Non-Cladophora Stream

1

0

0

1

Warrior Run

Cladophora Patch

1

0

0

1

Warrior Run

Non-Cladophora Patch

0

0

1

1

Little Fishing Creek

Non-Cladophora Stream

3

12

0

3

Huntington Creek

Non-Cladophora Stream

0

0

1

2

Turtle Creek

Cladophora Patch

2

0

1

1

Turtle Creek

Non-Cladophora Patch

0

0

0

3

0

2

0

2

North Mahantango Creek Non-Cladophora Stream

Athericidae Isonychiidae Ancylidae Hydrachnidia

54

Table 1 continued
Stream

Patch Type

Capniidae Aeshnidae Ceratopogonidae Polycentropodidae Sialis

Briar Creek

Non-Cladophora Stream

6

0

0

0

0

Penns Creek

Cladophora Patch

0

0

0

0

0

Penns Creek

Non-Cladophora Patch

0

0

0

0

0

Fishing Creek

Non-Cladophora Stream

2

0

0

0

0

Green Creek

Non-Cladophora Stream

0

1

1

2

1

Mahoning Creek

Cladophora Patch

0

0

1

3

0

Mahoning Creek

Non-Cladophora Patch

0

0

0

4

1

Chillisquaque Creek

Cladophora Patch

0

0

0

3

1

Chillisquaque Creek

Non-Cladophora Patch

0

0

0

2

0

Mauses Creek

Non-Cladophora Patch

0

0

0

0

0

Mauses Creek

Cladophora Patch

0

0

0

0

0

Hemlock Creek

Non-Cladophora Stream

0

0

0

2

0

Warrior Run

Cladophora Patch

0

0

0

4

0

Warrior Run

Non-Cladophora Patch

0

0

0

4

0

Little Fishing Creek

Non-Cladophora Stream

1

0

1

1

0

Huntington Creek

Non-Cladophora Stream

0

0

0

7

2

Turtle Creek

Cladophora Patch

0

0

0

0

0

Turtle Creek

Non-Cladophora Patch

0

0

0

4

0

0

0

0

0

0

North Mahantango Creek Non-Cladophora Stream

55

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

0

0

0

0

Penns Creek

Non-Cladophora Patch

0

0

0

0

Fishing Creek

Non-Cladophora Stream

0

0

0

0

Green Creek

Non-Cladophora Stream

0

0

0

0

Mahoning Creek

Cladophora Patch

0

0

0

0

Mahoning Creek

Non-Cladophora Patch

4

1

1

0

Chillisquaque Creek

Cladophora Patch

0

0

0

1

Chillisquaque Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Cladophora Patch

0

0

0

0

Hemlock Creek

Non-Cladophora Stream

0

0

0

0

Warrior Run

Cladophora Patch

0

0

0

0

Warrior Run

Non-Cladophora Patch

0

0

0

0

Little Fishing Creek

Non-Cladophora Stream

0

0

0

0

Huntington Creek

Non-Cladophora Stream

0

0

0

0

Turtle Creek

Cladophora Patch

1

0

0

0

Turtle Creek

Non-Cladophora Patch

2

0

0

0

0

0

0

0

North Mahantango Creek Non-Cladophora Stream

Amphipoda Perlodidae Gerridae Formicidae

56

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

0

0

0

0

Penns Creek

Non-Cladophora Patch

0

0

0

0

Fishing Creek

Non-Cladophora Stream

0

0

0

0

Green Creek

Non-Cladophora Stream

0

0

0

0

Mahoning Creek

Cladophora Patch

0

0

0

0

Mahoning Creek

Non-Cladophora Patch

0

0

0

0

Chillisquaque Creek

Cladophora Patch

0

0

0

0

Chillisquaque Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Cladophora Patch

1

0

0

0

Hemlock Creek

Non-Cladophora Stream

0

4

1

0

Warrior Run

Cladophora Patch

0

0

0

0

Warrior Run

Non-Cladophora Patch

0

0

0

1

Little Fishing Creek

Non-Cladophora Stream

0

0

0

0

Huntington Creek

Non-Cladophora Stream

0

0

5

0

Turtle Creek

Cladophora Patch

0

0

0

0

Turtle Creek

Non-Cladophora Patch

0

0

0

0

0

0

0

0

North Mahantango Creek Non-Cladophora Stream

Leptophlebiidae Leuctridae Limnephilidae Tipulidae

57

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

0

0

0

Penns Creek

Cladophora Patch

0

0

0

0

Penns Creek

Non-Cladophora Patch

0

0

0

0

Fishing Creek

Non-Cladophora Stream

0

0

0

0

Green Creek

Non-Cladophora Stream

0

0

0

0

Mahoning Creek

Cladophora Patch

0

0

0

0

Mahoning Creek

Non-Cladophora Patch

0

0

0

0

Chillisquaque Creek

Cladophora Patch

0

0

0

0

Chillisquaque Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Non-Cladophora Patch

0

0

0

0

Mauses Creek

Cladophora Patch

0

0

0

0

Hemlock Creek

Non-Cladophora Stream

0

0

0

0

Warrior Run

Cladophora Patch

0

0

0

0

Warrior Run

Non-Cladophora Patch

2

0

0

0

Little Fishing Creek

Non-Cladophora Stream

0

0

0

0

Huntington Creek

Non-Cladophora Stream

0

10

0

0

Turtle Creek

Cladophora Patch

0

0

0

0

Turtle Creek

Non-Cladophora Patch

0

0

1

0

1

0

6

2

North Mahantango Creek Non-Cladophora Stream

Corydalidae Psychomyiidae Pediciidae Gomphidae

58

Table 1 continued
Stream

Patch Type

Briar Creek

Non-Cladophora Stream

0

4

730

Penns Creek

Cladophora Patch

1

22

568

Penns Creek

Non-Cladophora Patch

0

10

374

Fishing Creek

Non-Cladophora Stream

0

34

384

Green Creek

Non-Cladophora Stream

0

27

404

Mahoning Creek

Cladophora Patch

0

9

373

Mahoning Creek

Non-Cladophora Patch

0

14

413

Chillisquaque Creek

Cladophora Patch

0

15

429

Chillisquaque Creek

Non-Cladophora Patch

0

19

416

Mauses Creek

Non-Cladophora Patch

0

25

357

Mauses Creek

Cladophora Patch

0

30

405

Hemlock Creek

Non-Cladophora Stream

0

15

308

Warrior Run

Cladophora Patch

0

15

478

Warrior Run

Non-Cladophora Patch

0

28

387

Little Fishing Creek

Non-Cladophora Stream

0

17

444

Huntington Creek

Non-Cladophora Stream

0

15

319

Turtle Creek

Cladophora Patch

0

19

434

Turtle Creek

Non-Cladophora Patch

0

15

424

0

12

434

North Mahantango Creek Non-Cladophora Stream

Polymitarcyidae Unknowns Totals

59
Appendix B

Dissolved oxygen and water temperature data for each site including the
following variables: dissolved oxygen (a), water temperature (b), diel change in dissolved
oxygen, labeled DDO (c), diel change in water temperature, labeled DTemperature (d).
The figures for each stream are presented in the following order: Briar Creek (Fig. B1),
Chillisquaque Creek (Fig. B2), Fishing Creek (Fig. B3), Green Creek (Fig. B4), Hemlock
Creek (Fig. B5), Huntington Creek (Fig. B6), Little Fishing Creek (Fig. B7), Mahoning
Creek (Fig. B8), Mauses Creek (Fig. B9), North Mahantango Creek (Fig. B10), Penns
Creek (Fig. B11), Turtle Creek (Fig. B12), and Warrior Run (Fig. B13). All figures were
created by the author using R statistical software (R Core Team, 2019).

60

Figure B1. Briar Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

61

Figure B2. Chillisquaque Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

62

Figure B3. Fishing Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

63

Figure B4. Green Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

64

Figure B5. Hemlock Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

65

Figure B6. Huntington Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

66

Figure B7. Little Fishing Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

67

Figure B8. Mahoning Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

68

Figure B9. Mauses Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

69

Figure B10. North Mahantango Creek data including dissolved oxygen data (a) and water
temperature data (b) collected every 15 minutes for the total amount of time the data
loggers were deployed. The diel change in dissolved oxygen (c) and diel change in water
temperature (d) for each day while data loggers were deployed is also displayed.

70

Figure B11. Penns Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

71

Figure B12. Turtle Creek data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.

72

Figure B13. Warrior Run data including dissolved oxygen data (a) and water temperature
data (b) collected every 15 minutes for the total amount of time the data loggers were
deployed. The diel change in dissolved oxygen (c) and diel change in water temperature
(d) for each day while data loggers were deployed is also displayed.