Loyola University Chicago
Loyola eCommons
Master's Theses
Theses and Dissertations
2012
The Impact of Algal Community Succession,
Taxonomic Composition and Biomass Accrual, On
Denitrification Potential of Stream Periphyton
Allison Daley
Loyola University Chicago
Recommended Citation
Daley, Allison, "The Impact of Algal Community Succession, Taxonomic Composition and Biomass Accrual, On Denitrification
Potential of Stream Periphyton" (2012). Master's Theses. Paper 826.
http://ecommons.luc.edu/luc_theses/826
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Copyright © 2012 Allison Daley
LOYOLA UNIVERSITY CHICAGO
THE IMPACT OF ALGAL COMMUNITY SUCCESSION, TAXONOMIC
COMPOSITION AND BIOMASS ACCRUAL, ON DENITRIFICATION POTENTIAL
OF STREAM PERIPHYTON
A THESIS SUBMITTED TO
THE FACULTY OF THE GRADUATE SCHOOL
IN CANDIDACY FOR THE DEGREE OF
MASTER OF SCIENCE
PROGRAM IN BIOLOGY
BY
ALLISON DALEY
CHICAGO, ILLINOIS
DECEMBER 2012
Copyright by Allison Daley, 2012
All rights reserved.
ACKNOWLEDGEMENTS
I would like to, above all, thank my thesis director, Dr. Christopher Peterson for
giving me the opportunity to learn so much and in such a great atmosphere. Dr. Peterson
has been infinitely patient and always encouraging. The former Natural Sciences
Department, with its supportive, friendly faculty and staff, along with a view of Lake
Michigan from the lab in Damen hall, was a haven for me throughout the challenging
years of classes and research. Dr. Nancy Tuchman and Dr. Kimberly Gray have been
amazing role models, combining family and a passionate involvement in demanding
careers. I am grateful to Dr. Jennifer Slate of Northeastern University, who introduced me
to diatoms and aquatic ecology and encouraged me to attend graduate school. I want to
thank Dr. Martin Berg for introducing me to the world of aquatic macroinvertebrates, a
close second in beauty to diatoms. Dr. John Kelly was always very helpful with research
problems and I was always appreciative of his editing and teaching skills. A special
thank you to the late Pam Bradley in the Biology department office, who effortlessly
solved all problems. I will be forever grateful to my Loyola research partner, Shannon
Pechauer, for having the foresight to take quantitative chemistry. Both Shannon and
Katie Kalscheur, our Northwestern research partner, were good companions and great to
work with. I was appreciative for the hard work of Miguel Rojas a graduate student from
Dr. Kelly’s lab who provided further bacterial analyses. We had several willing and able
undergraduates to help in the field, lab and artificial stream facility to whom I am
iii
indebted. My fellow graduate students I thank for their camaraderie and unfailing
willingness to help. I am very grateful to Holly from the Rosi-Marshall lab for sharing
her lab expertise so generously. My family encouraged and supported me throughout the
long process, a very heartfelt thank you to all of them especially my sons, who
contributed a considerable amount of labor to the cause. A warm thank you to my
friends, who have been very patient listeners and never let me give up. I would also like
to thank John Oldenburg for allowing access to study sites within the DuPage County
Forest Preserve system. My assistantship and research were funded by a grant from the
National Science Foundation.
iv
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................... iii
LIST OF FIGURES ........................................................................................................... vi
LIST OF TABLES ........................................................................................................... viii
ABSTRACT ....................................................................................................................... ix
CHAPTER ONE: INTRODUCTION ..................................................................................1
Natural Streams ................................................................................................................5
Artificial Streams ..............................................................................................................8
Hypotheses ........................................................................................................................9
CHAPTER TWO: METHODS AND RESULTS ..............................................................11
Methods...........................................................................................................................11
Results .............................................................................................................................27
CHAPTER THREE: DISCUSSION ..................................................................................72
DNP Patterns ...................................................................................................................76
Algal Species and DNP ...................................................................................................86
CHAPTER FOUR: CONCLUSION ..................................................................................89
APPENDIX A: COMPARISON OF USGS DISCHARGE DATA COLLECTION AND
STUDY COLLECTION SITES .....................................................................................90
APPENDIX B: 2008 ALGAL COMMUNITY SUCCESSION GRAPHS .......................92
APPENDIX C: TABLE OF ALGAL TAXA ....................................................................99
REFERENCE LIST .........................................................................................................104
VITA ................................................................................................................................117
v
LIST OF FIGURES
Figure 1. DuPage County, Illinois forest preserve study site streams ..............................13
Figure 2. Map of DuPage County Illinois with stream sites, wastewater treatment plant
site, land usage and watersheds. ……………………………………………………….14
Figure 3. 2008 Field season discharge measurements. ....................................................31
Figure 4. 2009 Field season discharge measurements. ....................................................32
Figure 5. 2008 (A) and 2009 (B) PCA ordination plots. .................................................35
Figure 6. 2009 PCA ordination plot with 9 variables. .....................................................36
Figure 7. NMDS plots of 2008 (A) and 2009 (B) algal communities. ............................37
Figure 8. 2009 Salt Creek algal taxa and community succession. ...................................40
Figure 9. 2009 EBDPR algal taxa and community succession. .......................................41
Figure 10. 2009 SB 1.1 algal taxa and community succession. .......................................42
Figure 11. 2009 SB 1.2 algal taxa and community succession ........................................43
Figure 12. 2009 SB 2.1 algal taxa and community succession. .......................................44
Figure 13. 2009 SB 2.2 algal taxa and community succession. .......................................45
Figure 14. Shannon Diversity Indices 2009. ....................................................................46
Figure 15. 2009 Biomass means. .....................................................................................48
Figure 16. DNP ................................................................................................................50
Figure 17. Scatter plots (A-F) and linear regression (E), of AFDM and DNP normalized
to area. ............................................................................................................................52
Figure 18. Scatter plots of Chlorophyll a and DNP (normalized to area). ......................53
vi
Figure 19. All stream NMDS ordination plots based on relative abundance of all species,
superimposed with bubble plots of DNP normalized to area. ........................................55
Figure 20. NMDS plots of Artificial stream algal communities and succession, sampling
weeks 2-10. . ...................................................................................................................59
Figure 21. Control streams community succession. . .......................................................61
Figure 22. Low N:P streams community succession. ......................................................62
Figure 23. High N:P streams community succession. .. ...................................................63
Figure 24. Shannon Diversity Indices for the artificial streams ......................................64
Figure 25. Mean AFDM and DNP. ..................................................................................66
Figure 26. DNP (µgN20/hr/cm2) vs biomass factors. ........................................................77
Figure 27. DNP bubble plots superimposed on NMDS ordination plots by sampling
week. ..............................................................................................................................69
Figure 28. Linear regression of DNP vs. N:P molar ratio in the High N:P streams. .......71
Figure 29. 2008 Salt Creek algal taxa and community succession. .................................93
Figure 30. 2008 EBDPR algal taxa and community succession. ....................................94
Figure 31. 2008 SB 1.1 algal taxa and community succession .........................................95
Figure 32. 2008 SB 1.2 algal taxa and community succession. ......................................96
Figure 33. 2008 SB 2.1 algal taxa and community succession .........................................97
Figure 34. 2008 SB 2.2 algal taxa and community succession. .......................................98
vii
LIST OF TABLES
Table 1. Dissolved organic carbon (DOC) and water temperature for 2008 and 2009 field
seasons. Conductivity and salinity for 2009 field seasons. ............................................28
Table 2. Nutrient concentrations and N:P molar ratios for 2008 and 2009 field seasons.29
Table 3. Algal species primarily responsible for NMDS ordinations. Results of
Spearman correlations between NMDS coefficients and relative abundances of all
species greater than 0.05 in any 1 sample. . ....................................................................38
Table 4. Mean nutrient concentrations (lab measurements) and N:P molar ratios, weeks 0
(after nutrient amendments to treated streams), 6 and 12. .............................................56
Table 5. Algal species primarily responsible for NMDS ordinations. Results of
Spearman correlations between NMDS coefficients and relative abundances and
biovolumes of all species >= 0.05 in any one sample. ..................................................60
Table 6. Algal species primarily responsible for NMDS ordinations. Results of
Spearman correlations between NMDS coefficients and relative abundance by
biovolume of all species that are =/> 0.025. P < 0.01. ..................................................70
Table 7. Results of algal monoculture and DNP (2 replicates, positive for DNP +,
negative -, Kalscheur et al. 2012b) and relative abundances of same species in biofilm
samples with the highest DNP by stream. ……………………………………………..87
Table 8. Comparison of geographical coordinates between sample collection sites and
USGS monitoring stations. .............................................................................................91
Table 9. Codes and complete names of all algal taxa encountered in this study ............100
viii
ABSTRACT
We investigated the relationship between algal taxonomy, community succession
biomass accrual and denitrification potential (DNP) within stream periphyton. Field
research was conducted during the summer of 2009, in 6 streams with diverse
environmental characteristics. Additionally, an experiment was conducted in artificial
streams in which 2 sets of streams were amended with nutrients and compared to control
streams. Algal communities in the field study streams formed 4 distinct communities
when analyzed by NMDS and correlations of NMDS axis scores and the abundances of
algal species, identified several taxa that were associated with samples with higher DNP.
The artificial stream algal communities sampled over 10 weeks, formed distinct
communities in the treated streams compared to control streams. Correlations between
NMDS axis scores and algal abundances identified 3 algal taxa that were associated with
higher DNP. Biomass was positively correlated to DNP in less disturbed environments in
both the field and artificial streams
ix
CHAPTER ONE
INTRODUCTION
Land based nitrogen cycling has been dramatically altered by anthropogenic
additions of fixed nitrogen (N), inputs almost doubling since the mid-20th century
(Canfield et al. 2010). The primary sources for these inputs are artificial fertilizers,
first mass-produced in the early 1900’s via the Haber-Bosch process. The use of synthetic
N in agriculture has increased steadily since the 1960’s and is expected to continue
climbing, yet the efficiency of application remains unchanged, with only 50% of the
applied N taken up by crop plants (Mosier 2002). Fertilizer application policies can vary
by country; in China, very excessive application is the norm, resulting in large losses, up
to 20 times greater than in the Midwestern United States (Vitousek et al. 2009). An
unfortunate consequence of such excess application is that even if efficient farming
practices are followed, excess N leaches as nitrate into aquatic ecosystems (Stopes et al.
2002). The negatively charged nitrate ions are highly soluble in water and repelled by
soil particles, thus any excess is readily leached from fields and lawns, entering water
systems via agricultural drainage, runoff and groundwater (Goolsby et al. 2001). High
levels of nitrate in drinking water sources have been implicated in human health
problems such as urothelial cancer (Volkmer et al. 2005), thyroid dysfunction (Gatseva
& Argirova 2005), and bladder and ovarian cancer (Weyer et al. 2001).
1
2
Another significant contributor of excessive nitrates in aquatic ecosystems is the
combustion of fossil fuels by industry, vehicles and power plants (Russell et al. 1998).
Combined, these anthropogenic sources of fixed nitrogen almost equal that produced by
the natural terrestrial nitrogen cycle (Canfield et al. 2010).
Sustained high nutrient levels, can double algal growth (Elsdon & Limburg 2008)
leading to eutrophication in rivers and streams. Elevated nitrates in large watersheds
leads to serious environmental consequences as the nitrates accumulate downstream.
Nitrate pollution from Illinois and Iowa streams is responsible for 35% of the nitrogen
that enters the Gulf of Mexico and is instrumental in the establishment of large seasonal
hypoxic zones (Goolsby et al. 2001).
Removing excess N from aquatic ecosystems is an important step in improving
water quality. The denitrification pathway of the nitrogen cycle that converts nitrate to
molecular nitrogen (N2) is the most advantageous. This pathway:
NO3
NO2
NO
N2O
N2, results in removal from the system (Burgin &
Hamilton 2007). Freshwater ecosystems, including wetlands, streams and rivers, have
higher denitrification potential (DNP) than terrestrial ecosystems, converting anywhere
from 30 to 70 % of the nitrate entering the system to N2 (Galloway et al. 2003). Nitrate
removal via denitrification from streams in 8 regions in the U.S., ranged from 0.5% up to
100% (Mulholland et al. 2009).
Denitrification takes place when heterotrophic bacteria respire under anaerobic
conditions, with nitrate acting as the final electron acceptor (Burgin & Hamilton 2007).
Anoxic zones in lotic systems, streams and rivers, and shallow lentic systems such as
3
wetlands, occur within benthic sediments and periphyton. Sirivedhin & Gray (2006),
found that DNP rates standardized to bacterial cell densities were significantly higher in
periphyton grown on benthic mesh overlying sediment in constructed wetlands, than in
the sediment itself. Similarly, higher DNP (per area) was found in periphyton growing
on macrophytes compared to that in sediments by Toet et al. (2003).
Periphyton, also referred to as biofilm, forms on a variety of benthic substrata and
is composed of algal and bacterial cells, fungal hyphae, protozoa, metazoa, inorganic
mineral particles and detritus embedded within a matrix of extracellular polymeric
substances (EPS) of algal and bacterial origin (Graham & Wilcox 2000, Sutherland
2001). The EPS matrix can form much of the matter within biofilms and allows the
proximal and temporal coexistence of 3 factors that support denitrification, O2 gradients,
NO3-N and carbon substrate for respiration (Canfield et al. 2010, Paerl & Pinckney 1996,
Sutherland 2001, Tiedje 1988).
Increasing microenvironmental heterogeneity as biofilm biomass accrues allows
microzones to form that have been depleted of oxygen (O2) by heterotrophic bacteria that
use available O2 faster than it can be replenished by diffusion (Paerl & Pinckney 1996).
Week-old biofilms examined by confocal microscopy displayed patchiness and
horizontally stratified communities with motile and filamentous species interwoven and
conspicuous voids (Congestri et al. 2006). Interstitial pore water in these voids can
increase temporary retention of water from stream flow by up to 300% (Battin et al.
2003), providing adjacent sources of nitrate ions available for diffusion into the anoxic
4
zones. When sufficient biomass has accumulated to support creation of these O2 depleted
microzones, denitrification is more likely to take place.
Several studies point to a close relationship between algae, bacteria and DNP,
Studying denitrification in periphyton growing on wetland macrophytes, Toet et al
(2003), observed a significant correlation between chlorophyll a (chl a) and higher rates
of DNP, when relatively higher concentrations of nitrate were available in the water
column. Ishida et al. (2008) discovered a significant relationship between the biovolume
of diatoms, in particular the genus Nitzschia, and higher DNP. These studies suggest
functional relationships between algae and bacteria that are mediating denitrification.
Relationships between bacteria and algae in stream biofilms have been examined
extensively in various contexts. Rier & Stevenson (2001) found that when chl a levels in
biofilms surpassed 5µg/cm2, there was a positive relationship between algal and bacterial
biomass. A mutually beneficial relationship has been inferred by studies correlating algal
biomass within benthic periphyton to bacterial density (Carr et al. 2005, Hodoki 2005,
Rier et al. 2007). Other studies have indicated that bacteria are often primary colonizers,
conditioning substrate and facilitating colonization by algal species. Substrate incubated
with bacterial inocula in low light developed a biofilm that significantly shortened
subsequent phototrophic colonization times (Roeselers et al. 2007). Additionally,
bacterial biofilms can mediate the effect of physical factors, increasing algal colonization
rates in faster currents (Peterson & Stevenson 1989). These algal / bacterial
relationships can be very specific, with the taxonomic identity and density of bacteria
associated with algal cells varying by algal species (Grossart et al. 2005). These
5
associations have been shown to be mediated by use of algal photosynthates which are an
important source of organic material for bacteria within biofilms (Bellinger et al. 2009,
Paerl & Pinckney 1996). Algal photosynthates were found to be species-specific in
freshwater phytoplankton (Giroldo & Vieira 2005) and species- or genera-specific in
benthic diatoms (Bahulikar & Kroth 2008). These studies infer species-specific algal/
bacterial relationships based, at least in part, on the production of species-specific algal
carbohydrates. Relating this to the ecosystem process of denitrification, Sirivedhin &
Gray (2006) found a positive correlation between DNP rates and the quality of the
organic carbon available to the bacteria.
The overarching goal of this project was to investigate the functional links, within
stream periphyton, between algal and bacterial community structure and succession, and
algal-derived organic carbon, in the process of denitrification. Research was conducted
over 2 field seasons at 6 streams within DuPage County, Illinois forest preserves and in
the artificial stream facility at Loyola University Chicago, Lakeshore campus.
Natural Streams
Lotic ecosystems, such as the streams and rivers employed as study sites in this
preserve, can be characterized by several physical and chemical variables. The study
streams varied in discharge, nutrient load, riparian canopy, dissolved organic matter,
temperature and watershed landscape use. Influenced by all these factors, periphyton in
each stream should develop distinct benthic algal communities (Bulgakov & Levich
1999, Frost et al. 2007, Leland et al. 2000, Leland et al. 2001, Passy 2007, Peterson &
Stevenson 1990, Potapova & Charles 2003, Stevenson et al. 1991).
6
Factors Influencing Benthic Algal Communities
Several factors influence the composition of algal communities and the patterns of
succession of algal species within periphyton. The taxa that are present in the water
column and are available to colonize benthic substrate can vary among watersheds on
both regional and continental scales (Passy 2008). The patterns of succession are
influenced by factors such as variations in rates of immigration and reproduction and
growth form, which affect access to nutrients and shade tolerance among algal taxa
(Hoagland et al. 1982, McCormick & Stevenson 1991, Stevenson & Peterson 1991).
Algal species assemblages are also influenced by land uses within a watershed
and degrees of anthropogenic disturbances (Porter-Goff et al. 2010, Urrea & Sabater
2009, Leland & Porter 2000). For example, connection to urban storm water drainage
best explained variation in diatom species composition in Newall &Walsh (2005). Munn
et al. (2002) related a wide variation in species representation within communities
influenced by environments that included forest, urban, range and agricultural fields. The
riparian community influences light availability which can affect growth patterns,
taxonomic composition and biomass accumulation within biofilms (Munn et al. 2010
Rier et al. 2006, Stevenson et al. 1991, Zippel & Neu 2005).
The effect of nutrient concentrations and N:P ratios on periphyton communities
and biomass can vary considerably. Carr et al. (2005) reports that benthic algal and
bacterial biomass was positively related to concentrations of phosphate (PO4-P) and
especially NO3-N, in several Canadian streams. AFDM and Chl a increased with nitrate
enrichment in a low nutrient Arizona stream (Peterson & Grimm 1992) and with an
7
increase in both N and P concentrations in a mountain river in Spain (Camargo et al.
2005). Bowes et al. (2007) manipulated P levels in riverside flumes and noted a decrease
in biomass as P concentrations fell below about 90 µg/L. Leland & Porter (2000) were
able to predict benthic algal relative abundances based on observed total P in northern
Illinois rivers. The duration of exposure to higher nutrient levels is also a factor, and
longer exposure can double biomass (Elsdon & Limburg 2008).
Discharge is a measure of both the volume and velocity of the water flow in a
stream or river, varying seasonally and in response to weather. As the dominating
physical factor in streams and rivers, discharge influences benthic community
composition and biomass accrual. Diatom assemblages grouped by Passy (2007) into 3
guilds based on growth patterns and motility, exhibited a strong and significant
relationship with current velocity, and algal biomass increased significantly under slower
flow treatments (Besemer et al. 2007). Significant flooding events can remove
substantial amounts of algal cell biomass (Francoeur & Biggs 2006, Peterson &
Stevenson 1990).
The surface texture of benthic substrata influences both biomass and community
composition (Porter-Goff et al. 2010, Murdock & Dodds 2007). Use of a uniform
artificial substrate in this research facilitated quantification and comparisons within and
among streams by eliminating this source of variation.
Factors Influencing Denitrification
Availability of dissolved inorganic nitrogen (DIN) within a biofilm is related to the
concentration of these nutrients in the water column. The relationship is not entirely
8
linear as uptake can be limited by saturation, overwhelming the adsorption capacity of the
biofilm and/or the ability of taxa to accumulate nutrients (Dodds et al. 2002). The
proportion of the uptake rate that could be attributed to denitrification was positively
related to both NO3-N and NH4-N concentrations in streams influenced by different
levels of anthropogenic disturbance (O’Brien et al. 2007, Mulholland et al. 2009).
Arango et al. (2007) also found a significant positive relationship between water column
NO3-N concentrations and DNP in several streams in Illinois. Chl a and AFDM were
positively correlated to NO3-N uptake in an urban channelized stream (Knapps et al.
2009). A similar Chl a, AFDM and DNP relationship in biofilms associated with wetland
macrophytes in the Netherlands, was reported by Toet et al. (2003). Studying the
relationship of algae and DNP within biofilms, Ishida et al. (2008) discovered a
significant relationship between the biovolume of diatoms, in particular the genus
Nitzschia, and higher DNP.
Artificial Streams
A component of the research was conducted at the artificial stream facility at
Loyola University. The use of artificial streams afforded the opportunity to control and
limit the number of variables and structure communities based on nutrient concentrations
in a replicated experimental design. NO3-N and PO4-P concentrations were manipulated
in 2 sets of streams and compared to another set of streams which were unamended and
served as controls. The goal was to develop 3 distinct sets of algal communities,
differing in species composition, primarily structured by variation in N:P ratios and
nutrient concentrations, in stream ecosystems that varied little in physical and chemical
9
factors. The communities and biofilms should be less diverse than those developed in
natural stream systems that are structured by several environmental factors, allowing
focus on the importance of individual algal species identity, community structure and
biomass accrual to DNP.
Experiments conducted in indoor artificial streams, using uniform substrate for the
development of biofilms, allow control over environmental factors that influence algal
communities. Artificial stream experiments have illustrated the influence on periphytic
biofilms of current velocity (Peterson & Stevenson 1990), substratum complexity (Arnon
et al. 2007), light (Zippel & Neu 2005) and temperature (Villanueva et al. 2011). Holding
these factors constant and varying nutrient concentrations creates conditions whereby
nutrient concentrations are the sole independent environmental variable. Redfield (1958)
identified a cellular ratio of 16 nitrogen (N) atoms to 1 phosphorous (P) atom as optimal
for marine phytoplankton. This ratio represents an average of a wide range of cellular
nutrient ratios of both marine and freshwater phytoplankton (Klausmeier et al. 2004).
Species that have a similar N:P ratio to ambient concentrations in the environment should
not be limited by either N or P concentrations. Variations in the ratio should create
conditions whereby either N or P is limiting, structuring communities that are dominated
by algae with different nutrient optima.
Hypotheses
This thesis addresses 2 hypotheses, exploring the relationship of algal community
taxonomic composition, succession and biomass accrual to DNP that has been
normalized to area and to nirS gene copy number.
10
1. Denitrification will be linked to biofilm thickness/biomass and will take
place in later stages of algal succession and/or when a critical biomass
threshold is reached that allows development of areas of low O2 within.
2. Rates of denitrification within biofilms will vary as a function of the identity of
the dominant algal species.
Results of this research should increase understanding of the biological and
physical attributes that contribute to higher levels of DNP within streams. This was a
collaborative project funded by the National Science Foundation (NSF) led by
Christopher Peterson, (algal stream ecology), John Kelly, (microbial ecology), and
Kimberly Gray, (environmental engineering). My research focused on the algal
component of this project, thus research results reported here are limited to these aspects.
My data and results will be combined with the analysis of the bacterial community from
Dr. Kelly’s lab and the analysis of dissolved organic carbon matter from Dr. Gray’s lab.
CHAPTER TWO
METHODS AND RESULTS
Methods
Natural Streams Site Selection
Several possible stream sites were surveyed in the forest preserves of DuPage
County, Illinois. Sites were selected to represent as much variation among the sites as
possible, including land usage within watersheds and several physical and chemical
variables. Six streams, in 4 watersheds, were chosen for our field sites (Figure 1, 2). Salt
Creek, in the Salt Creek watershed, was located in Salt Creek Preserve, Wood Dale, IL.
A wooded slope led down to the bed of the stream which was heavily silted. A storm
drain emptied into the stream at the downstream sampling site which had a bottom of
primarily cobbles. Next were 2 sites along the Springbrook 1(SB 1) stream, in the
Springbrook 1 watershed. The headwater, site SB1.1, was located in Kelly Park in the
town of Wheaton, IL. This portion of the stream flowed through an incised channel lined
by relatively steep banks covered with shrubs and rimmed with trees, in a primarily
residential area. Storm water drains emptied into the stream at both collection sites. The
second SB 1 site, SB 1.2, was located in the St. James Farm Forest Preserve in
Warrenville, IL. This former farm had been recently acquired by the Forest Preserve
District of DuPage County. The stream runs through a burr oak savannah and was the
most heavily impacted site as it is 3.2 km downstream of a wastewater treatment plant.
11
12
There were 2 sites along Springbrook 2 (SB 2) in the Springbrook 2 watershed. Both sites
are within the Springbrook Prairie Forest Preserve, Naperville, IL. Site SB 2.1 had
formerly been channelized for agriculture and had recently been restored to a meander in
an open prairie also undergoing restoration. Downstream of the restored meander, the
stream flowed through prairie and savannah to the SB 2.2 site. The wooded riparian zone
of SB 2.2 consisted primarily of maple and ash trees and large shrubs. The final site was
the East Branch of the DuPage river (EBDPR), at the edge of the DuPage River Park in
Naperville, IL. The riparian zone consists of a narrow strip of small trees and shrubs.
The East Branch watershed land usage was both urban and commercial and the river
received input from 4 wastewater treatment plants.
13
Salt Creek (downstream site inset)
Springbrook 1.1
East Branch DuPage River
Springbrook 1.2
Springbrook 2.1
Springbrook 2.2
Figure 1. DuPage County, Illinois forest preserve study site streams.
14
Figure 2. Map of DuPage County Illinois with stream sites, wastewater treatment plant
site, land usage and watersheds. Edison Park = SB 1.1, St. James Farm = SB 1.2,
Restored = SB 2.1, Forested = SB 2.2.
Natural Streams: Field Methods Summer/Early Fall 2008
Five sets of unglazed ceramic tiles were tied with 30 lb. test fishing line onto
30.5 x 30.5 x 7.6 cm. concrete blocks. Each set contained sixteen, 2.2 cm by 2.2 cm
attached tiles in a 4 by 4 arrangement. Each stream had two sampling sites with either 1
or 2 blocks placed directly on the streambed to increase the diversity of environmental
conditions under which biofilms developed. If the stream reach had differing levels of
light, blocks were placed in both environments. The Salt Creek site has a storm water
drain inlet and 1 of the blocks was placed in the path of the outlet flow.
15
A set of tiles was collected from each of 2 blocks at each site after 1, 3, 5, 8 and 14
weeks of community development, placed in aplastic container and covered with stream
water for transport back to the lab. The initial sampling schedule had called for sampling
at week 12; however a large flooding event the weekend before the sampling date
necessitated a 2 week delay. As a result of the flood, the blocks from the two streams
with the highest discharge, Salt Creek and East Branch DuPage River were not
recovered, and only 1 block was found at both SB 1.1 and SB 2.2.
Current velocity and depth of the tile set were measured over each set of tiles. Air
and water temperatures were measured at each site. Data for stream discharge
calculations (Gore 2006) was collected by measuring stream velocity with a portable flow
meter (Model 2000 Marsh-McBirney Flo-mateTM ) and depth with a meter ruler at ½ or 1
meter intervals across the stream. Two 250 mL nalgene water bottles were filled with
stream water to be tested in the lab for nutrient concentrations. A set of water samples
was taken from both the upstream and downstream sites in Salt Creek. All water samples
were filtered in the lab. The water bottles and tile sets were placed on ice in coolers and
transported to the labs at Loyola University Chicago. Stream water was collected in two,
1 gallon brown glass jugs to be analyzed for dissolved organic carbon (DOC)
concentrations when the blocks were initially introduced, at week 8, and the last sampling
date. The jugs were placed on ice and transported in coolers to the lab at Northwestern
University. The water samples for nutrient concentration analysis were frozen and
transported to Northwestern University.
16
Light sensors, Odyssey Photosynthetic Irradiance Recorder (Odyssey Data
Recording Systems, Christchurch, New Zealand) were placed at each site between weeks
2 and 3, and readings were taken by the sensors at weeks 4, 5 and 8 and 14. The light
sensors were below water until week 8 after which they were placed above the water
surface due to fouling in the below water position. The light sensors from Salt Creek and
EBDPR were not recovered after the flood so readings for week 14 were not taken.
Natural Streams: Field Methods Late Spring/Summer 2009
Field methods were similar to those followed in the 2008 field season with a few
exceptions. Salinity and conductivity measurements were added in 2009 using a Model
30 Salinity Conductivity and Temperature meter (YSI, Inc.). This meter was also used to
measure water temperature rather than a mercury thermometer as in 2008. The light
sensors were placed above water level at each site and readings taken with a Toshiba
laptop computer with Windows XP operating system at each sampling date. The blocks
were anchored by drilling a hole approximately 1.5 inches wide in the center of the
concrete block and placing a 3 foot section of rebar through the hole and into the
sediment. There was 1 less sampling date in 2009 as we eliminated the week 1 collection.
In addition, the blocks were slightly raised to prevent overturning and silting.
Artificial Streams: Experimental Design and Stream Set Up
The artificial streams are located in the 5th floor greenhouse in the Life Sciences
building at Loyola University Chicago. The oval racetrack shaped, composite fiberglass
streams (4 m. x 15.5 cm. x 15 cm), have a streambed surface area of 0.62 m2. Rotating
stainless steel paddle-wheels connected to a Dayton DC gear motor (model 42129b) and
17
a Dayton DC speed control (model 5X412D, Dayton DC Gear Motor, Niles, Illinois)
recirculate well water which was maintained at a volume of 100 L. A total of 18 streams
were assigned 3 different nutrient regimes, 6 streams each. Two sets of streams were
amended with nitrate and phosphorus to yield target N:P ratios of 8:1 and 30:1 and 1 set
of unamended streams served as controls. The 8:1 and 30:1 ratios were to be maintained
throughout the 12 weeks. Stream bottoms were lined with tile sets, each composed of 36
unglazed square ceramic tiles, each 2.2 cm by 2.2 cm attached to each other and arranged
in a pattern of 6 columns and 6 rows. The area of each tile was 4.84 cm2 with a
cumulative area for each tile set of 174.24 cm2.
Current velocity was set at approximately 7 cm/s, the slowest velocity that could
be maintained without motor strain. Air and water temperature were relatively constant
throughout. After week 3, 50% of the stream water was replaced weekly to maintain
ambient temperature and stream levels and to replenish nutrients in control streams.
Two weeks prior to the first sampling date, each stream was inoculated with 100
mL of a mixture of periphyton collected the same day from each of the 6 streams used in
the field study. Periphyton was removed from natural rock substrata at each site with a
toothbrush and transported in stream water to the artificial stream facility. The samples
were combined and the resulting slurry was passed through a succession of sieves to
remove debris and larger macroinvertebrates. Water filtered by reverse osmosis was
added to bring the volume to 1.8 liters. Each stream had been filled the day before with
100 L of well water and a randomized block design (Hurlbert 1984) was used to assign
18
High N:P ratio, Low N:P ratio or control treatment to each stream. The streams being
amended were dosed with KNO3 and NaPO4 to yield target N:P ratios.
Artificial Streams Nutrient Maintenance
Concentrations of NO3-N and PO4-P in the treated streams were measured every
other day using a Hach DR2700 Spectrophotometer (Hach Company). N and P additions
were made as necessary to maintain ratios. Water samples from all the streams were
collected at weeks 0, 6 and 12 to quantify nutrient levels using more precise methods
identified below. The water was filtered through a 0.45 µm filter (Tuffryn® membrane
filters ) into a 250 mL Nalgene bottle. The bottles were transported to Northwestern
University and samples were frozen until filtered (pre-rinsed 0.45 µm Tuffryn®
membrane filters) for later analysis. As the periphyton biomass increased, concentrated
nutrient solutions were prepared in 10 L nalgene carboys and added to containers that
dripped the solution continually into the streams. These containers, 5 L plastic paint
buckets, had previously been drilled with 4 holes at the base. Adjustable plastic
stopcocks were inserted into ¼” nalgene tubing and sealed with silicon. The tubing was
inserted into the buckets and sealed with silicon. The solute dripped continually into the
streams at each end and in the center on each side.
Artificial Streams Sampling
Every 2 weeks, one tile set was randomly chosen for sampling from each stream.
Four tiles from the set of 36 in each tile set were removed and placed in stream water in a
separate container and transported to Northwestern University to be analyzed for DNP.
19
The remaining 32 tiles were processed for algal, bacterial and biomass analysis. The
experiment was terminated after week 12.
Lab Methods
Nutrient Concentration Measurements
The water samples for nutrient concentration measurements were frozen until
filtered (pre-rinsed 0.45 µm Tuffryn® membrane filters) and analyzed at Northwestern
University for dissolved nitrate (NO3-N) via the ultraviolet second-derivative
spectroscopy method (Crumpton et al. 1992), dissolved ammonia (NH3-N) via the
phenate method (Solorzano1969), and dissolved phosphate (PO4-P) via the ascorbic acid
method (Murphy & Riley 1962).
DOC Analysis
The water for DOC analysis was frozen until filtered (pre-rinsed 0.45 µm
Tuffryn® membrane filters) then analyzed for dissolved organic carbon (DOC) by high
temperature catalytic oxidation (APHA Standard Method 5310 B; Dohrmann Apollo
9000) at Northwestern University.
Characterization of Denitrifying Bacteria
Bacterial characterizations were conducted in the Kelly lab at Loyola University
Chicago. Sub-samples for quantification and characterization of bacterial denitrifiers
were pelleted by centrifugation at 10,000g for 30 sec and stored at -80 ºC until
processing. DNA was extracted from pellets with MoBio Microbial DNA isolation kits
(MoBio Inc, Solana, CA). Successful DNA isolation was confirmed by agarose gel
electrophoresis. The amount of DNA isolated from each sample was determined with the
Quant-iT DNA Assay Kit (Invitrogen, Carlsbad, CA). Densities of denitrifying bacteria
20
were quantified based on copy numbers of nirS genes determined by real-time
quantitative polymerase chain reaction (Q-PCR) following Mincer et al. (2007). Primers
nirS 1F and nirS 6R (Braker et al. 1998) were used to amplify an 890bp fragment of the
nirS gene. The standard used for quantification was genomic DNA isolated from
Pseudomonas stutzeri ATCC 11607, which was assumed to have a genome size of 4 Mbp
and 1 nirS copy per genome (Gruntzig et al. 2001). The P. stutzeri dilution series
included 10-fold dilutions ranging from 1.2 x 105 to 12 copies of nirS. Q-PCR reactions
were run using an MJ Research DNA Engine Opticon1 thermal cycler equipped with
Opticon Monitor software version 3.1 (Biorad, Hercules, CA). Conditions for all Q-PCR
reactions were as follows: 12.5 uL QuantiTect SYBR Green PCR Master Mix (Qiagen,
Valencia, CA), 0.5 uM final concentration of each primer, 1 uL template and water were
added to a final 25 uL volume. All primers were synthesized by Eurofins MWG Operon
(Huntsville, AL). All reactions were performed in low-profile 0.2 mL white strip tubes
with optical ultra-clear strip 154 caps (Bio-Rad). Three analytical replicates were run for
each sample. Specificity of Q-PCR reactions was confirmed by melting curve analysis
and agarose gel electrophoresis. Thermal cycling was conducted as follows: initial
denaturation at 95°C for 10 min; 40 cycles of denaturation at 95°C for 1 min, primer
annealing at 57°C for 1 min, extension at 72°C for 1 min, hold at 78°C for 1 sec, and
plate read. Finally, a melting curve was run from 50–95°C with a read every 1°C and a
hold of 1 sec between reads. Copy numbers were normalized based on the surface area of
the tile supporting the biofilm.
21
DNP
A subset of 4 tiles was cut from each tile set, placed in a container with stream
water and transported to Northwestern University for DNP analysis at weeks 5, 8, 14 in
2008 and weeks 3, 5, 8, 12 in 2009. DNP was measured (the day following collection in
2009 and artificial stream study) by a modified version of the standard acetylene
inhibition method for ecological research (Arnon et al. 2007; Groffman et al. 1999;
Sirivedhin & Gray 2006). Intact biofilms on tiles were placed in 125 mL gas-tight jars (IChem septa jars) and filled with 70 mL of nutrient solution to provide enriched
conditions for denitrification. The nutrient solution contained 40 mg/L NO3-N as KNO3,
100 mg/L carbon as glucose, and 225 mg/L chloramphenicol to inhibit microbial growth.
The incubation started immediately after the jars were flushed with N2 for 3 minutes and
acetylene was added (10% v/v) to inhibit transformation of N2O to N2. During the
incubation the jars were agitated gently at 150 rpm, under dark conditions at room
temperature (23⁰C ± 1⁰C). Headspace samples were measured using a gas chromatograph
(Hewlett Packard 5890) equipped with 63Ni electron capture detector at an operating
temperature of 320⁰C. A stainless steel Porapak Q (80/100 mesh) column was used to
separate the gases at 60⁰C with high purity N2 as a carrier gas (18-20mL/min).
Denitrification rates were calculated from linear regression of N2 accumulation in the
headspace, after the concentrations were corrected for solubility using the Bunsen
coefficient (Venterink et al. 2003). The DNP rates in 2009 were normalized to a per cell
rate, based on nirS copy counts/cm2 from DNA analysis of slurry from the same tiles.
22
Periphyton Analysis
Slurry preparation and sampling
Periphyton analysis took place in the Peterson lab at Loyola University Chicago.
Periphyton was scrubbed from the remaining 12 tiles with toothbrushes and funneled into
graduated 250 mL nalgene® bottles using RO or DI water to rinse. The volume of the
resulting slurry was noted and three, 5 mL sub-samples were pipetted into glass vials.
Lugol’s solution (50 µL) was added to 2 of the vials, 1 for bacterial counts and 1 for algal
analysis. The vial containing unpreserved slurry was retained for bacterial community
analysis. To facilitate diatom identification, an additional unpreserved 5 mL sample vial
was taken at week 1, 8 and 12 to observe live specimens and subsequently oxidize with
30% hydrogen peroxide and potassium dichromate, placing subsamples to dry on
coverslips which were then mounted onto slides using Naphrax refractive medium as a
fixative (Lowe and Laliberte, 2006).
Ash free dry mass (AFDM)
Twenty mL’s of the slurry ( 10 mLs in 2008) were pipetted directly into preweighed aluminum boats for ash-free dry mass (AFDM) analysis. The boats were
placed in a drying oven (Fisher Isotemp® 500 series) and dried for 24 hours at 105⁰C.
The boats were weighed again to measure dry mass (DM) and then ashed in a muffle
furnace (Thermolyne model 62700) for 4 hours at 500⁰C. Boats were then weighed again
to measure AFDM mg/L.
23
Chlorophyll a
Ten mL’s of the slurry were filtered within 24 hours onto a GF/C or F filter
(Whatman) which was placed in an opaque test tube with 10 mL of 95% ethanol. The
test tubes were placed in a 78⁰C water bath (Precision Scientific Model 180) for 5
minutes then refrigerated for 24 to 48 hours. The extraction was measured in a
fluorometer (Aquafluor Turner Designs Model 8000-010) then measured again after the
addition of 1N hydrochloric acid. The post acid measurement was subtracted from the
first measurement to obtain chl a ug/L (adapted from Hauer &Lamberti 2007 by the RosiMarshall lab Loyola University Chicago).
Algal Analysis - Taxonomy, Community Structure and Biovolume
A known quantity of the preserved periphyton slurry was pipetted into a beaker
and mixed with DI water and filtered onto a 0.45 m metricel membrane filter (Pall
Corp., Ann Arbor, MI). The filter was placed on a slide, cleared with immersion oil and
covered with a cover slip for microscopic examination at 1000 x magnification (method
adapted from Rosi-Marshall & Wallace 2002). Diatoms were identified to species; green
algae and cyanobacteria to genus if possible (Collins & Kalinsky1977, Dodd 1987,
Hustedt 1976, Lange-Bertalot 2001, Kociolek & Kingston 1999, Krammer 2002,
Krammer & Lange-Bertalot 1987, 1991,1997, 1998, Patrick & Reimer 1966, 1975,
Prescott 1962, 1978, Tiffany & Britton 1971). Community structure was determined by
counts of 500 live cells per slide in a minimum of 3 and a maximum of 10 transects.
Biovolume was determined for each taxa using formulas from Hillebrand et al. (1999)
and methods from Lowe & LaLiberte (2006).
24
Data Analyses Natural Streams
The physical and chemical variations among streams were assessed with principal
components analysis (PCA) based on Euclidian similarity matrices, using Primer 6
software package (Primer-E Ltd, Plymouth, UK). Variations between the algal
communities by stream and within each stream, among weeks, were determined by nonmetric multi-dimensional scaling (NMDS) also using Primer 6 software package (PrimerE Ltd, Plymouth, UK). The NMDS plots were based on Bray-Curtis similarity matrices
of taxa square root transformed relative abundances.
The relationship between the environmental variables and algal communities was
analyzed with the biological –environmental (Bio-Env) program from the Primer 6
software package (Primer-E Ltd, Plymouth, UK). With this program, the Bray-Curtis
similarity matrix of taxa, square root transformed relative abundances, and a log10
transformed and normalized primary matrix of environmental variables, are searched for
ranked correlations between the matrices.
Comparison of algal assemblages among weeks within each stream was also
analysed using a diversity measure, Shannon Diversity Index (H’loge), calculated via the
Primer 6 software package (Primer-E Ltd, Plymouth, UK).
Associations between DNP and physical, chemical variables, the relationship of
conductivity to DOC and nutrient concentrations, NMDS axis scores and algal relative
abundances, AFDM and discharge, discharge and cell densities, were explored using
Spearman Rank Correlations (Sigma Plot 11.2, Systat Software 2009). Significant
differences in biomass factors, environmental variables and Shannon diversity indices,
25
were assessed using Kruskal-Wallis 1-way ANOVA on ranks and subsequent pairwise
comparisons by Dunn’s method or Holm-Sidak method (Sigma Plot 11.2, Systat
Software 2009). Variation in AFDM among streams was assessed by 1-way ANOVA
(Sigma Plot 11.2, Systat Software 2009) and among weeks by Kruskal-Wallis 1-way
ANOVA on ranks and subsequent pairwise comparisons by Dunn’s method or HolmSidak method (Sigma Plot 11.2, Systat Software 2009). One-way ANOVA with
subsequent pairwise comparisons by Holm-Sidak method was used to assess differences
in DNP among streams (Sigma Plot 11.2, Systat Software 2009). DNP measurements
normalized to nirS gene copy were square root transformed as there were very large
differences in gene copy densities. All other data was log10 transformed as necessary to
approximate normality or visualize graphically.
Linear regressions were performed to explore the relationships between DNP and
the densities of individual algal species as well as chl a, AFDM and nutrient
concentrations. Also explored using linear regressions were the relationships of biomass
factors to nutrient concentrations, discharge and DOC (Sigma Plot 11.2, Systat Software
2009). Relationships between Shannon biodiversity measures and DNP normalized to
area were were explored using Spearman Rank Correlations (Sigma Plot 11.2, Systat
Software 2009).
Data Analyses Artificial Streams
Differences in AFDM among treated and control streams and among weeks
within each set of streams were assessed using Kruskal-Wallis 1-way ANOVA on ranks
and subsequent pairwise comparisons by Dunn’s method or Tukey’ s test (Sigma Plot
26
11.2, Systat Software 2009). Differences in cell size between treatments and weeks was
assessed using 2-way Anova with subsequent pairwise comparisons by Holm-Sidak
method (Sigma Plot 11.2, Systat Software 2009). Average cell size data was normalized
by log10 transformation. Variations in algal communities by time and among streams ,
were determined by non-metric multi-dimensional scaling (NMDS) using the Primer 6
software package (Primer-E Ltd, Plymouth, UK). Additionally, variations in each set of
streams by week were determined by NMDS. NMDS plots were based on Bray-Curtis
similarity matrices of relative abundances of all taxa, square root transformed. The
statistical significance of algal community differences among sets of streams and within
sets of streams by week was analyzed using Anosim, (Primer 6 software package,
Primer-E Ltd, Plymouth, UK). Comparison of algal assemblages between weeks within
each stream was also analysed using Shannon Diversity Index (H’loge), (Primer 6
software package, Primer-E Ltd, Plymouth, UK). Associations between DNP and
AFDM, % Chlorophyta, Shannon diversity measures, algal cell densities, NMDS axis
scores and nutrient levels, as well as AFDM and % Chlorophyta, were explored using
Spearman Rank Correlations (Sigma Plot 11.2, Systat Software 2009). Linear
regressions were performed to explore the relationships between DNP and cell size, algal
cell densities, AFDM, % Chlorophyta and N:P ratio (Sigma Plot 11.2, Systat Software
2009).
27
Results
Natural Streams Results
Environmental Variables and Stream Variation
All of the streams had relatively high conductivity and salinity in 2009 (Table 1),
which was most likely due to the underlying geology as Northern Illinois watersheds
drain carbonate deposits. Mean water temperatures were higher in 2008 than 2009, most
likely due to the summer start date in 2008 and late spring start in 2009. Both years
showed a consistent pattern, with temperatures highest for SB 2.1, the only site with no
riparian canopy and lowest temperatures for SB 1.1 which is spring fed (Table 1). The
three streams most influenced by wastewater effluent, Salt Creek, EBDPR and SB 1.2,
had mean levels of DOC above 8 ppm in 2008, significantly higher than SB 1.1. DOC
levels were lower in 2009, though still in the higher ranges of normal, ranging from about
4 to 8.5 ppm (Table 1).
While there were differences between field seasons, overall, the highest nutrient
concentrations were in SB 1.2 followed by Salt Creek and EBDPR (Table 2), all streams
with significant inputs from wastewater treatment plants.
28
Table 1. Dissolved organic carbon (DOC) and water temperature for 2008 and 2009 field
seasons. Conductivity and salinity for 2009 field seasons.
2009 n = 4, 2008 DOC n = 2 (SC, EBDPR) n = 3 (SB 1, SB 2) 2008 water temp. n = 1
(SB 1) n = 2 (EBDPR, SB 2) n = 3 (SC).
Stream
Salt Creek
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
Mean
SE
Max
Min
Mean
SE
Max
Min
Mean
SE
Max
Min
Mean
SE
Max
Min
Mean
SE
Max
Min
Mean
SE
Max
Min
DOC
(ppmC)
2008/09
8.4 / 7.3
0.3 / 0.2
8.7 / 8.5
8.1 / 6.7
8.2 / 6.8
0.3 / 0.4
8.5 / 7.5
7.9 / 6.1
6.1 / 4.7
0.5 / 0.3
6.7 / 5.1
5.1 / 4.1
8.77 / 6.7
0.62 / 0.4
10 / 7.6
8 / 5.9
7.27 / 5.8
1.01 / 0.2
9.2 / 6.1
5.8 / 5.3
6.77 / 5.5
0.26 / 0.3
7.2 / 6.5
6.3 / 5.1
Conductivity Salinity
(µS/cm)
(ppt)
2009
2009
1122.2
0.56
59.2
0.03
1324
0.7
828
0.4
1138.8
0.56
42.9
0.02
1292
0.6
1051
0.5
1489
0.77
500.45
0.25
2052
1.1
370
0.2
1091.8
0.54
43.8
0.02
1223
0.6
980
0.5
1044.2
0.52
53.6
0.02
1255
0.6
959
0.5
957.2
0.46
37.5
0.02
1087
0.5
894
0.4
Water Temp.
⁰C 2008/09
23.7 / 18.7
0.33 / 0.9
24 / 22
23 / 13.7
23.5 / 20.5
2.5 / 1.4
26 / 23.9
21 / 17.7
17 / 15.13
/ 1.15
17 / 17.7
17 / 13.7
23 / 19
/ 1.3
23 / 21.5
23 / 16
28.5 / 22.9
2.5 / 1.5
31 / 27.6
26 / 19.3
25 / 20.1
3 / 1.2
28 / 23.5
22 / 17.5
29
Table 2. Nutrient concentrations and N:P molar ratios for 2008 and 2009 field seasons.
NO3-N
N:P ratio
PO4-P (µg/L)
NH3-N (µg/L)
(mg/L)
(molar)
Stream
2008/09
2008/09
2008/09
2008/09
2008/09
Salt Creek Mean
233.2 / 718.6
118.9 / 62.6
7.12 / 6.56
716 / 23
SE
84.9 / 97.8
29.8 / 8.9
1.9 / 1.11
397 / 4
n = 9/10
Max
798.6 / 1206.4
294.3 / 100.7 19.32 / 11.22 3519 / 53
Min
2.5 / 17.9
31.7 / 12.1
0.95 / 0.39
13 / 12
EBDPR
Mean
762.4 / 807.8
118.2 / 55.3
10.49/ 8.92
31 / 25
SE
93.5 / 84.5
6.1 / 10.4
2.28 / 1.53
6/3
n = 5/ 5
Max
1048 / 1103.4
140.7 / 77.8
17.08 / 12.45
51 / 31
Min
503.8 / 592.9
107.6 / 28.6
5.31 / 4.90
16 / 14
SB 1.1
Mean
48.3 / 22.5
1207.9 / 391.8
2.53 / 1.75
626 / 654
SE
27.3 / 6.3
845.3 / 146.3
0.15 / 0.43
327 / 462
n = 6/5
2219 /
Max
182.9 / 38.9
5428.3 / 939.2
2.96 / 2.68
2490
Min
2.5 / 2.5
190.4 / 135.8
2.14 / 0.52
31 / 79
SB 1.2
Mean
954.1 / 1168.8
92.2 / 303.3
16.78 / 11.46
42 / 23
SE
122.3 / 110.4
18 / 150.4
2.09 / 1.02
6/3
n = 7/5
Max
1653.4 / 1425.6 141.9 / 876.2 23.80 / 14.35
63 / 32
Min
689 / 856.9
18.6 / 67.4
10.37 / 9.19
14 / 15
SB 2.1
Mean
2.9 / 2.5
62.1 / 46.7
0.09 / 0.09
119 / 122
SE
0.3 / 0
19.5 / 15.1
0.034 / 0.046
30 / 49
n = 6/5
Max
3.9 / 2.5
147.9 / 100.2
0.23 / 0.25
248 / 310
Min
2.2 / 2.5
6.3 / 11
0.01 / 0.01
32 / 38
SB 2.2
Mean
2.7 / 6.2
45.7 / 25.4
0.13 / 0.65
150 / 237
SE
0.22 / 3.7
7.8 / 8.4
0.03 / 0.42
28 / 77
n = 6/5
Max
3.8 / 21
68.1 / 54.1
0.25 / 2.31
259 / 490
Min
2.5 / 2.5
15 / 7.41
0.07 / 0.01
46 / 25
Discharge in Salt Creek and EBDPR was higher than the other streams (Figures 3
A,B and 4 A,B) in both field seasons. SB 1. had the lowest discharge (Figure3C and 4C).
Continuous daily discharge data were available from the United States Geographic
30
Survey (USGS) for locations proximate to 4 sampling sites (Table 8 Appendix A), giving
a more accurate picture of rain events between sampling weeks (Figures 3 H-J and 4 HJ). This is particularly evident in 2008 week 14, when there had been a record setting
rainfall between weeks 8 and 14 which had delayed our sample collection by 2 weeks.
The water levels in Salt Creek and EBDPR were still too high at week 14 to collect
discharge data at the site. Significant rain events in 2009 occurred before sampling week
3 and between sampling weeks 5 and 8. Discharge data is missing just prior to week 3
for Salt Creek so this is more evident in EBDPR, SB 1.2 and SB 2.2 (Figure 4 G-J). The
second event was more evident in Salt Creek, EBDPR and SB 2.2, as discharge was less
variable in SB 1.2 (Figure 4 G-J).
31
2.5
2.0
A
50
30
1.0
20
0.5
10
2.0
H
30
20
0.5
10
0
SB 1.1
C
Field measurement
USGS Daily Discharge
0.03
0.02
0.01
D
SB 1.2
0.3
0.2
0.1
0.0
0.20
EBDPR
40
1.0
0.00
0.6
0.5
0.4
Salt Creek
0
50
EBDPR
B
1.5
0.0
0.06
0.05
0.04
G
40
1.5
0.0
2.5
Discharge (m3/s)
2008
Salt Creek
E
10
8
6
4
2
0
I
10
J
SB 1.2
SB 2.1
0.15
0.10
0.05
0.00
0.20
F
SB 2.2
0.15
8
0.10
6
SB 2.2
4
0.05
2
0.00
0
1
3
5
8
Week
14
6/23/2008 - 9/29/2008
Figure 3. 2008 Field season discharge measurements. Field measurements at sampling
dates (A-F), USGS station daily measurements (G-J) Note scale changes between A,B
and C, and D and E, F, also between the Field measurements and analogous USGS
measurements, A and G, B and H, D and I, F and J.
32
5
Salt Creek
A
2009
30
G
Salt Creek
H
EBDPR
4
20
3
2
10
1
0
5
B
EBDPR
C
SB 1.1
4
3
2
1
0
0.5
0
7
6
5
4
3
2
1
0
0.4
Sampling week
USGS Daily Discharge
3
Discharge (m /s)
0.3
0.2
0.1
0.0
0.5
D
SB 1.2
0.4
0.3
0.2
0.1
0.0
0.5
E
SB 2.1
F
SB 2.2
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
I
SB 1.2
J
SB 2.2
0.4
0.3
0.2
0.1
0.0
0.5
2.0
0.4
1.5
0.3
1.0
0.2
0.5
0.1
0.0
0.0
3
5
8
12
5/12/2009 - 8/4/2009
Week
Figure 4. 2009 Field season discharge measurements. Field measurements at sampling
dates (A-F), USGS station daily measurements (G-J). Note scale changes between A,B
and C-F, also between the field measurements and analogous USGS measurements, A
and G, B and H, D and I, F and J.
A comparison of PCA plots from both field seasons generated from data on
nutrient concentrations, N:P ratio and stream discharge, revealed a pattern of similarities
that separated the streams into 3 groups. The Springbrook 2 sites were grouped together
and distinguished from the others by lower nutrient concentrations and discharge, SB
1.1 samples were less tightly clustered than those from the SB 2 sites, with higher N:P
ratio and NH3-N, a third group included SB 1.2, Salt Creek, EBDPR had the highest
33
discharge, NO3-N and PO4-P (Figure 5). A similar pattern was evident in both years
except for a greater separation of the SB 1.2 site from Salt Creek and EBDPR in 2009
(Figure 5 B). A PCA of the 2009 data, which also included conductivity, salinity, DOC
and water temperature, showed a similar pattern but with a greater separation of SB 1.1
from the other groups due to higher conductivity and salinity and closer clustering of
samples (Figure 6 A). Watershed was not a determining factor on site groupings, as all
sites from a given watershed did not cluster together.
Site Variation in Algal Communities
Samples in the 2008 and 2009 NMDS ordination plots, based on similarities in the
taxonomic structure of the algal communities in each stream, formed 4 clusters based on
45% similarity (Figure 7). Two distinct clusters, clearly separate in ordination space,
were formed by the least similar communities, SB 2.1, the restored prairie site, and SB
1.2, the site closest to a wastewater treatment plant (Figure 7). The SB 1.1 samples
formed a more distinct cluster in 2008 after the first sampling date. SB 2.2, EBDPR and
Salt Creek and weeks 8 and 12 in 2009 SB 1.2, formed another cluster. Correlations of
the NMDS axis scores and algal abundances revealed the species that were most
responsible for separations of samples (Table 3). Watershed did not appear to be
influential in algal community differences as sample clusters in the ordination space of
the NMDS plots did not conform to differences in watersheds.
34
Relationship Between Algal Community Taxonomic Structure and Stream
Environment Variables
When stream environmental characteristics (PCA, Figures 5,6) and algal
taxonomic community (NMDS, Figure 6,7) ordination plots are compared, algal
communities in SB 2.1 and SB 2.2 are separated in the NMDS plots, but are grouped
together in the PCA plots. Similarly SB 1.2 forms a separate algal community group
though it is grouped with Salt Creek, EBDPR in the PCA plot. A statistical analysis of
the relationship between 9 stream environmental variables and algal abundances,
indicated that PO4-P, DOC and water temperature best explained algal community
differences in 2009 (Bio-Env, rho = 0.54, p < 0.01). Further significant correlations
included NO3-N and N:P molar ratios (rho 0.52 = 0.52, p < 0.01) and NH3-N (rho 0.51, p
< 0.01).
35
2008 PCA
4
A
3
Variables
3
PC 2 (24.9 %)
2
0
8
81
5
0
1
5
14
0
1
8
0
3
3
5
14 01
5
51
0Discharge
8
8
1
3
3
14
0
5
0
Salt Creek US
Salt Creek DS
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
PO4-P
0
1
-2
3
14
8
NO3-N
N:P
8
NH3-N
-4
-4
-2
0
2
4
PC 1 (54.6%)
2009 PCA
3
B
5
2
5 5
8
12
PC 2 (23.5%)
1
0
0
8
5
12
Discharge
8
3
N:P
-1
3
8
8
12
012
12
3
PO4-P
3
0
0
0
5
3
03
5
NO3-N
12
0
3
12
5
NH3-N
8
-2
8
-3
-3
-2
-1
0
1
2
3
PC 1 (60.5%)
Figure 5. 2008 (A) and 2009 (B) PCA ordination plots. Stream samples are labeled with
the sampling week. PC 1 and PC 2 combined explain 79.5% (2008) and 84 % (2009)
of the variation among streams.
.
36
2009 PCA
Streams
5
12
4
Salinity
Conductivity
2
PC 2 (25%)
5
3
NH3-N
3
3
N:P
3
0
3
5
5
8
5
38
5
12
8
12
12
NO3-N
PO4-P
Discharge
5
12
8 12
8
8
3
DOC
-2
Temperature
-4
-4
-2
Salt Creek US
Salt Creek DS
EBDPR
0
PC 1 (39.6%)
2
4
Variables
SB 1.1
SB 1.2
SB 2.1
SB 2.2
Figure 6. 2009 PCA ordination plot with 9 variables, streams labeled with sampling
weeks PC 1 and PC 2 combined explain 64.6% of the variation among streams.
37
2008
2
Salt Creek US
Salt Creek DS
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
1
A
1
1
81
8
3 8
1
3 83
3
5
5 5
3
0
8
5
8
5
14
1
3
3
5 14
8
8
1
8
14
5 5
14
1
1
3 5
3
____ 45% similarity
8
3
1
3 14 1
8 3
55
-1
5
1
1
8
MDS 2
-2
Stress 0.18
-2
-1
0
1
2
2009
2
B
3
12
1
3
12
5
0
5
5
12
3
8
12
33
3
5
12
12
8
12 3
8
8
5
5
8
8
5
12
3
12
8
12
12
-1
3
8
5
5
8
12
5
-2
5
8
3
8
5
8
3
3
Stress 0.20
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
MDS 1
Figure 7. NMDS plots of 2008 (A) and 2009 (B) algal communities. (Bubble plot
overlays are based on % similarities derived from cluster analysis). The stream samples
are labeled with the sampling week. The plots are based on the relative abundance of the
taxa in each sample.
38
Table 3. Algal species primarily responsible for sample distribution in NMDS
ordinations. Results of Spearman correlations between NMDS coefficients and relative
abundances of all species greater than 0.05 in any 1 sample. 2008 n = 53, 2009 n = 48.
Species
ACHNMINU
ENCSMICR
CYTEMENE
ACHNLANC
CHLORBAS
AMRAPERP
SELLSEMI
2008
X axis MDS 1
Correlation coefficients
Species
0.78
P = < 0.01
0.72
P = < 0.01
0.59
P = < 0.01
-0.55
-0.59
-0.60
-0.65
P = < 0.01
P = < 0.01
P = < 0.01
P = < 0.01
Y axis MDS 2
Correlation coefficients
CCNEPLeu
0.76
ACHNPLOE
0.73
NAVIANTO
0.71
NAVIRECE
0.66
NAVILANC
0.43
P = < 0.01
P = < 0.01
P = < 0.01
P = < 0.01
P = < 0.01
GONEAFFI
GONEPARV
P = < 0.01
P = < 0.01
-0.63
-0.71
2009
Species
ACHNLANC
AMRAPEDI
CHLORBAS
ENCSMICR
GONESPEC
NAVICATO
ACHNMINU
X axis MDS 1
Correlation coefficients
Species
0.62
P = < 0.01
0.61
P = < 0.01
0.38
P = < 0.01
-0.52
-0.56
-0.58
-0.86
P = < 0.01
P = < 0.01
P = < 0.01
P = < 0.01
Y axis MDS 2
Correlation coefficients
ACHNPLOE
0.78
CCNEPLeu
0.77
CCNEPEDI
0.52
RHSPABBR
0.44
NAVILANC
0.33
P = < 0.01
P = < 0.01
P = < 0.01
P = < 0.01
P = 0.02
GONEPARV
P = < 0.01
-0.51
Algal Communities and Community Succession
Among the 143 algal species encountered in this study, 60 attained relative
abundance and abundance by biovolume equal to or greater than 5 percent in any one
sample in both field seasons (2009 Figures 8-13, 2008 Appendix B, Figures 29-34).
These included 52 diatom species, [Division Bacillariophyta], 7 green algal species,
[Division Chlorophyta] and 1 species within the Division Cyanobacteria. Full names, site
and field season occurrences of all encountered species are listed in Appendix C.
Comparable DNP results were not available for the 2008 field season so the results that
follow are only reported for the 2009 field season as these will be related to the DNP
measurements. The differences between field seasons will not be discussed as the data
39
on the effect of community variation due to season on DNP was not available. One of the
more notable differences in species composition among streams was the presence of
Achnanthidium lanceolatum in all but the SB 2 streams (Figures 8-13 B,C). A.
lanceolatum was a dominant species in all sampling weeks in Salt Creek and SB 1.1 and
to a lesser extent in EBDPR, where Cocconeis placentula var. euglypta averaged about
40% by week 12. Achnanthidium minutissimum was a dominant species in the SB 2.1
samples in all weeks, but, with the exception of the week 8 samples in EBDPR, was
absent from Salt Creek, EBDPR and SB 1.2 (Figures 8-13 B,C). SB 1.1 was the only
stream in which both Achnanthidium species were dominant (Figure 10 B,C). C.
placentula var. euglypta was present in all but SB 1.1 and by week 12 had become a
dominant species in Salt Creek, EBDPR and SB 2.2. Amphora pediculus was the
dominant species in SB 1.2 until week 12 when A. lanceolatum shared abundance
densities (Figure 11 B,C). Green algal basal and filamentous cells were abundant as an
early successional species in Salt Creek and late successional in SB 2.1 (12 B,C).
A graph of the Shannon diversity indices shows the temporal changes in
biodiversity within each stream and the variation among streams over the course of the
study (Figure 14). The Shannon index combines information on both evenness and
richness of sampled communities with values ranging typically between 1.5 and 3.5
(Magurran 2009). The values in our samples ranged from about 1.5 to 3 with SB 2.2
being overall highest. Algal species diversity increased steadily in SB 1.2 over the course
of the study, starting out with the lowest level and reaching levels by week 12 that were
similar to Salt Creek and EBDPR in week 5 (Figure 14). There was a significant
40
relationship between the Shannon index and discharge (USGS), (R = - 0.70, P < 0.01) in
2
SB 1.2 though not in the other streams.
A
6e+6
2
Algal Cell Density (Cells/cm )
3
2
Algal Biovolume Density (um /cm )
2009 Salt Creek
1e+9
5e+6
8e+8
4e+6
6e+8
3e+6
4e+8
2e+6
2e+8
1e+6
0
0
3
5
8
12
OTHER
ACHNLANC
ACHNPLOE
AMRAPEDI
BALAPARA
CCNEPEDI
CCNEPLeu
CHLORBAS
ENTOPALU
MELOVARI
NAVIGREG
NAVILANC
NAVITRIP
NITZINSP
RHSPABBR
SELLSEMI
THSILACU
Week
Relative Abundance
B
Week 3
C
Week 5
Week 8
Week 12
Relative Biovolume
Week 3
Week 5
Week 8
Week 12
Figure 8. 2009 Salt Creek algal taxa and community succession. Biovolume densities
(bar graph) and cell densities, error bars = SE n = 2 (line graph) (A), relative abundance
(B) and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one
sample are included with the exception of cell densities which include all taxa (A).
41
A
4e+8
7e+6
3
3e+8
5e+6
4e+6
2e+8
3e+6
2e+6
1e+8
1e+6
0
2
6e+6
Algal Cell Density (Cells/cm )
2
Algal Biovolume Density (um /cm )
2009 EBDPR
0
3
5
8
12
Week
B
OTHER
ACHNLANC
ACHNMINU
ACHNPLOE
AMRAPEDI
CCNEPEDI
CCNEPLeu
CHLORBAS
CHLORCOS
CYANCHRO
ENTOPALU
MELOVARI
NAVIGREG
NAVILANC
NAVIRECE
NAVITRIP
NITZDUBI
NITZINSP
RHSPABBR
Relative Abundance
Week 3
C
Week 5
Week 8
Week 12
Relative Biovolume
Week 3
Week 5
Week 8
Week 12
Figure 9. 2009 EBDPR algal taxa and community succession. Biovolume densities (bar
graph) and cell densities, error bars = SE n = 2 (line graph) (A), relative abundance (B)
and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one sample
are included with the exception of cell densities which include all taxa (A).
42
A
8e+6
2
Algal Cell Density (Cells/cm )
3
2
Algal Biovolume Density (um /cm )
2009 SB 1.1
1e+9
8e+8
6e+6
6e+8
4e+6
4e+8
2e+6
2e+8
0
0
3
5
8
12
Week
B
OTHER
ACHNLANC
ACHNMINU
AMRAPEDI
CHLORCOS
CHLORFIL
GONEAFFI
GONEPARV
GONETRca
MELOVARI
NAVIGREG
NAVILANC
NITZAMPH
NITZINSP
NITZPALE
RHSPABBR
SYNETABU
TRYBAPIC
TRYBHUNG
Relative Abundance
Week 3
Week 5
Week 8
Week 12
Relative Biovolume
C
Week 3
Week 5
Week 8
Week 12
Figure 10. 2009 SB 1.1 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities, error bars = SE n = 2, (line graph) (A), relative abundance (B)
and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one sample
are included with the exception of cell densities which include all taxa (A).
43
A
2e+6
2.5e+8
2e+6
2.0e+8
1e+6
1e+6
1.5e+8
1e+6
8e+5
1.0e+8
6e+5
4e+5
5.0e+7
2e+5
2
2e+6
Algal Cell Density (Cells/cm )
3
2
Algal Biovolume Density (um /cm )
2009 SB 1.2
0
0.0
3
5
8
12
OTHER
ACHNLANC
AMRAPEDI
CCNEPEDI
CCNEPLeu
CHLORBAS
CHLORCOS
CHLORFIL
CYANCHRO
GONEPARV
NAVIGREG
NAVILANC
NAVISAPR
NAVITRIP
RHSPABBR
SELLSEMI
TRYBAPIC
ULNRULNA
Week
B
Relative Abundance
Week 3
Week 5
Week 8
Week 12
Relative Biovolume
C
Week 3
Week 5
Week 8
Week 12
Figure 11. 2009 SB 1.2 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities, error bars = SE n = 2, (line graph) (A), relative abundance (B)
and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one sample
are included with the exception of cell densities which include all taxa (A).
44
A
3e+8
3e+6
3e+8
3e+6
2e+8
2e+6
2e+8
2e+6
1e+8
1e+6
5e+7
5e+5
2
Algal Cell Density (Cells/cm )
3
2
Algal Biovolume Density (um /cm )
2009 SB 2.1
0
0
3
5
8
12
Week
OTHER
ACHNMINU
AMRAPEDI
CCNEPEDI
CCNEPLeu
CHLORBAS
CHLORCOS
CHLORFIL
CHLORUNI
CYTEMENE
DIATVULG
ENCNPROS
ENCSMICR
GONEMINU
GONESPEC
NAVICATO
NITZFRUS
NITZMICE
RHSPABBR
SURIBREB
Relative Abundance
B
Week 3
Week 5
Week 8
Week 12
Relative Biovolume
C
Week 3
Week 5
Week 8
Week 12
Figure 12. 2009 SB 2.1 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities, error bars = SE n = 2, (line graph) (A), relative abundance (B)
and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one sample
are included with the exception of cell densities which include all taxa (A).
45
A
1.6e+6
1.6e+8
1.4e+6
1.4e+8
1.2e+6
1.2e+8
1.0e+6
1.0e+8
8.0e+5
8.0e+7
6.0e+5
6.0e+7
4.0e+7
4.0e+5
2.0e+7
2.0e+5
0.0
0.0
3
5
8
2
Algal Cell Density (Cells/cm )
3
2
Algal Biovolume Density (um /cm )
2009 SB 2.2
1.8e+8
12
Week
Relative Abundance
B
Week 3
Week 5
Week 8
OTHER
ACHNMINU
ACHNPLOE
AMRAPEDI
CCNEPEDI
CCNEPLeu
CHLORFIL
DIATVULG
ENCNMINU
ENCNPROS
GYSIATTE
NAVIANTO
NAVICATO
NAVICRTE
NAVIGREG
NAVILANC
NAVIRECE
NAVITRIP
NAVIVENE
NITZINSP
PLMADELI
REIMSINU
RHSPABBR
Week 12
Relative Biovolume
C
Week 3
Week 5
Week 8
Week 12
Figure 13. 2009 SB 2.2 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities, error bars = SE n = 2, (line graph) (A), relative abundance (B)
and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one sample
are included with the exception of cell densities which include all taxa (A).
46
3.0
Shannon Diversity Index
2.5
2.0
1.5
Salt Creek US
Salt Creek DS
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
1.0
0.5
0.0
3
5
8
12
Week
Figure 14. Shannon Diversity Indices 2009 Mean +/- SE, n = 2 with the exception of Salt
Creek US, DS n = 1).Based on relative abundance by biovolume of all taxa per sample.
There is a significant difference between SB 2.2 and the SB 1 streams
(P = 0.05). There is no significant difference between weeks among and between the
streams.
Biomass
AFDM accumlation
AFDM accumulation did not vary significantly among streams (1- factor
ANOVA) or weeks (1-factor ANOVA on ranks). The data failed to meet normality and
equal variance tests when performing 2-factor ANOVA and subsequent comparisons
using 1-factor ANOVA on ranks among streams by week and among weeks within each
stream, failed to find significant differences. Salt Creek and EBDPR had the lowest
accumulations of AFDM throughout the field season and the SB 1 streams had the
highest, particularly week 12 in SB 1.2, and weeks 8 and 12 in SB 1.1 (Figure 15 A).
47
When considering all 2009 data, there was a negative correlation between discharge
(field measurements only) and AFDM (correlation coefficient -0.51, p < 0.01) and cell
densities (correlation coefficient -0.35, p = 0.02) but no significant regressions, between
AFDM and discharge, either field measurements or from USGS records. Looking at
individual streams however, there were significant negative linear regressions between
AFDM and discharge in SB 1.2 (R2 = -0.80 field measurements, R2 = -0.74 USGS
records, P > 0.01) and SB 2.1 (R2 = -0.75, P < 0.01 field measurements).
PO4-P and NO3-N were negatively correlated with AFDM when considering all
the 2009 data (correlation coefficient -0.30, p < 0.05). Linear regressions within
individual streams however, showed a positive association (R2 = 0.49, P = 0.03) between
PO4-P and AFDM in SB 1.2.
Chlorophyll a
Chlorophyll a (Chl a) concentrations did not vary significantly among streams
when compared using 1-way ANOVA (Figure 15 B). Chl a levels significantly increased
over time (1-way ANOVA, Holm-Sidak P < 0.01). Linear regressions between discharge
and chl a showed only a slightly negative relationship. Correlations between chl a and
nutrient concentrations were not significant. AFDM and Chl a were significantly
correlated (correlation coefficient 0.78, P < 0.01).
48
2009 AFDM
10
A
9
Salt Creek
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
2
AFDM (mg/cm )
8
7
6
5
4
3
2
1
0
2009 Chlorophyll a
32
B
2
Chl a (ug/cm )
28
24
20
16
12
8
4
0
Streams Arranged by Weeks 3, 5, 8, 12
Figure 15. 2009 Biomass means (+/- SE, n = 2), AFDM (A), Chlorophyll a (B)
DNP
The 2009 DNP results were normalized to substratum area, and to nirS gene copy
abundance per cm2. There were significant differences in DNP by area between SB 1.1
and the SB 2 streams based on 1-way ANOVA and subsequent pairwise comparisons by
the Holm-Sidak method (P = 0.05). There were no significant differences among weeks
in any stream. DNP normalized to nirS gene copy density did not differ significantly
between streams or by week. However, when comparing means on the graphs, it is
apparent that the highest DNP by both measures occurred in Salt Creek, EBDPR and SB
1.1, and only the final sampling week in SB 1.2 had an amount that was comparable
(Figure 16).
49
Biofilms in Salt Creek and EBDPR measured the highest levels of DNP per area
after 5 weeks of development while those in the two SB 1 streams had higher levels after
12 weeks (Figure 16). There seemed to be no particular pattern in the SB 2 streams
(Figure 16).
NirS gene copy densities were not significantly related to DNP normalized to
area. This was particularly evident in EBDPR and SB 1.1 where lower nirS gene copy
densities were paired with high DNP.
50
DNP by area
5
2
Log10+1DNP (ug N2O hr/cm )
A
Salt Creek
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
4
3
2
1
0
DNP per gene copy
B
Mean 0.63
SE 0.61
Mean 0.04
SE 0.03
2
SqrtDNP (ug N2O hr/cell/cm )
0.03
Mean 0.17
SE 0.04
0.02
0.01
0.00
Streams Arranged by Weeks 3, 5, 8, 12
Figure 16. DNP means (+/- SE n = 2) DNP normalized to area (A) DNP normalized to
nirS gene copy abundance (n = 2, exceptions are n = 1 Salt Creek week 12, SB 1.2 week
5) (B). Data was transformed as necessary to visualize graphically. Transformations are
noted on graph.
DNP, NO3-N and N:P molar ratio
Linear regressions between nitrate concentrations and DNP revealed no clear
pattern other than a slight downward trend in DNP as NO3-N concentrations increased.
51
2
This was most notable in Salt Creek (R = - 0.41, P = 0.073), though only with DNP
normalized to nirS gene copy abundance not substratum area. The exceptions were the
SB 1 streams, particularly SB 1.2, which had a marked increase in week 12 in both DNP
and NO3-N, however linear regression did not reveal a significant relationship. There was
also no discernible relationship in any of the streams between N:P ratio and DNP.
DNP and biomass
The relationship between AFDM and DNP varied considerably among the
streams, negative in SB 1.1 and SB 2.2 and generally positive in the others. The only
significant positive regression was in SB 2.1, though the Salt Creek downstream sites did
exhibit a strong positive linear relationship between AFDM and DNP (Figure 17 A,C).
The week 5 samples in Salt Creek had the highest DNP and the highest AFDM and this
was also true of the week 5 samples in EBDPR and one of the week 8 samples. The
minimum (0.05 mg/cm2) and maximum (10 mg/cm2) AFDM encountered in this study
were measured on samples from SB 1.2. SB 1.2 is the only stream in which the
maximum accumulation of biomass was associated with the maximum DNP. The
opposite pattern appeared in SB 2.2, where one of the larger accumulations of AFDM in
the season was not associated with any measurable DNP (Figure 17 F). The highest level
of DNP in SB 1.1 was associated with the lowest level of AFDM (0.22 mg/cm2) in the
stream. The relationship between chl a and DNP followed a similar pattern with the
exception of EBDPR which exhibited a neutral rather than a positive relationship (Figure
18).
52
Salt Creek
3.0
Salt Creek US
Salt Creek DS
2.5
2.0
2.5
2.0
1.5
1.5
Week 3
Week 5
Week 8
Week 12
1.0
0.5
0.0
A
1.0
0.5
0.0
SB 1.1
3.0
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.8
0.0
C
0
1.0
1
2
3
4
5
6
D
0
SB 2.1
1.2
R2 = 0.41
P = 0.05 1,2
2
4
6
8 10 12
SB 2.2
1.0
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
SB 1.2
3.0
2.5
1.2
B
0.0 0.2 0.4 0.6 0.8 1.0 1.2
0.0 0.2 0.4 0.6 0.8 1.0 1.2
Log10+1 DNP (ugN20/hr/cm2)
EBDPR
3.0
0.0
E
F
-0.2
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
AFDM (mg/cm2)
Figure 17. Scatter plots (A-F) and linear regression (E), of AFDM and DNP normalized
to area. DNP was log transformed to visualize graphically. Data for AFDM was not
transformed. Note DNP scale changes (A-D), and (E,F). Note AFDM scale changes
(A,B), C, D and (E,F)
1
data has passed normality , 2 data has passed equal variance
53
Salt Creek
2.5
Salt Creek US
Salt Creek DS
2.0
1.5
2.0
1.5
1.0
0.5
0.0
A
Week 3
Week 5
Week 8
Week 12
1.0
0.5
0.0
2
DNP (ugN2O/hr/cm )
0 2 4 6 8 10 12 14
SB 1.1
3.0
2.5
2.0
1.5
1.0
0.5
0.0
C
0
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
EBDPR
2.5
10
20
0 2 4 6 8 10 12 14
SB 1.2
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
D
0
30
SB 2.1
B
1.2
10
20
30
SB 2.2
1.0
0.8
0.6
0.4
0.2
E
0.0
0 2 4 6 8 10 12 14
F
0 2 4 6 8 10 12 14
2
Chlorophyll a (ug/cm )
Figure 18. Scatter plots of Chlorophyll a and DNP (normalized to area) (A-F). DNP was
log transformed to visualize graphically. Note scale changes between SB 1 (C,D) and the
other streams (A, B, E, F). There were no significant linear regressions.
DNP, individual algal species, community succession and biodiversity
An NMDS ordination plot of all the streams was superimposed with bubble plots
that were indicative of the measurable DNP normalized to area for each sample (Figure
19). The relative abundances of several algal species were significantly correlated with
NMDS coordinates for the ordination space associated with greater DNP (Figure 19).
The taxa listed on the figure all had higher densities in samples with more DNP. There
54
were no significant linear regressions with DNP and these species. Also the density and
DNP data did not meet assumptions of normality and/or equal variance in most cases.
Similar NMDS ordination plots for each stream were performed (data not shown)
DNP did not vary definitively along either NMDS axis, suggesting no clear connection
between algal community composition or community succession and DNP in the
individual streams with the exception SB 1.2, where the highest DNP was measured in
week 12 and the ordination points for the week 12 samples are separated from the
previous weeks. The algal species that was significantly correlated with the NMDS axis
coordinates that define the space was Achnanthidium lanceolatum.
Shannon biodiversity and DNP were not significantly correlated in any of the
streams.
55
2009
2
+ Y Axis
ACHNPLOE (0.78)
CCNEPLeu (0.77)
3
1
12
3
12
12
12
12
3 3
5
3
5
12
8
MDS 2
5
5
0
5
8
8
8
5
8
12
3
3
Salt Creek US
Salt Creek DS
EBDPR
SB 1.1
SB 1.2
SB 2.1
SB 2.2
8
5
5
8
12
3
12
8
12
8
5
5
12
8
12
5
-1
3
8
5
8
3
3
DNP per cm2
3
-2
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
MDS 1
- X Axis
ACHNMINU (-0.86)
+X Axis
ACHNLANC (0.78)
AMRAPEDI (0.61)
CHLORBAS (0.38)
Figure 19. All stream NMDS ordination plots based on relative abundance of all species,
superimposed with bubble plots of DNP normalized to area. Species that were
significantly correlated with NMDS axis coordinates and had higher densities in samples
with higher DNP (differing by stream) are listed on the graph. DNP was log transformed
prior to conversion to bubble size to visualize graphically. Symbols are labeled with
collection week.
Artificial Stream Results
Nutrient levels
The well water that supplied the streams had concentrations of NO3-N well above
what had been expected, leading to higher mean N:P ratios for the control streams than
those of the Low N:P streams (Table 4). Additionally, NH3-N measurements were not
included when streams were amended, resulting in higher N concentrations and N:P
56
ratios. Consistent N: P ratios in the treated streams proved unsustainable, but relative
differences in N:P rates between High N:P and Low N:P streams were maintained.
Table 4. Mean nutrient concentrations (lab measurements) and N:P molar ratios, weeks 0
(after nutrient amendments to treated streams), 6 and 12. (n = 18)
Stream
PO4-P
NH3-N
NO3-N
N:P ratio
Treatment
(µg/L)
(µg/L)
(mg/L)
(molar)
Ambient
Mean
2.5
16.3
0.294
275
SE
0
3.3
0.086
77
Max
2.5
56.3
1.062
957
Min
2.5
0.8
0.003
3
Low
Mean
55.6
29.9
0.325
29
SE
16.8
4.7
0.109
11
Max
290.2
71.6
1.420
185
Min
2.5
2.2
0.003
1
High
Mean
40.6
26.7
6.788
2097
SE
15.9
3.5
0.694
510
Max
239.7
56.0
16.218
6964
Min
2.5
1.5
3.020
52
Algal Communities, Taxonomy and Succession
Algal taxa that were present in relative numerical abundance and relative
abundance by biovolume, equal to or greater than 5 percent in any one sample were
included in analyses of taxonomic structure (see Appendix B for a list of all taxa
encountered). Species meeting these criteria included 31 diatom species, (Division
Bacillariophyta), 7 green algal species, (Division Chlorophyta) and 2 Cyanobacteria
species.
The NMDS ordination plots of samples illustrate divergence of algal community
structure in control stream communities from those in the nutrient amended streams after
week 2 (Figure 20). Algal communities in the High N:P and Low N:P streams followed
similar successional trajectories and diverged in taxonomic content from that in the
57
control streams after week 2, with these communities stabilizing starting in week 6.
Succession in the control streams had a somewhat more directional trajectory. Though 6
and 8 week old communities in control stream replicate samples were similar, half of the
week 10 samples diverged from the rest (Figure 20 B). Analysis of similarity (ANOSIM)
revealed statistically significant (R = 0.38, p < 0.01) differences in algal taxonomic
structure between the control and treated streams but not between those in the High N:P
and Low N:P streams. Community structure in the control streams differed significantly
between weeks 2 and 4-10 of development, also between week 4 and 8,10 (R = 0.43, p =
.001). Succession patterns in the treated streams were similar to each other, and the
higher R values, based on relative abundance by biovolume (Low N:P, R = 0.71, High
N:P, R = 0.66, p = .001) indicated that these communities changed more over time than
those in the control streams. Within week samples in the control streams showed less
variation than in the treated streams (Figure 20). Correlations between NMDS axis
scores and algal relative abundances revealed the species that were primarily responsible
for the positioning of samples on the X and Y axes (Table 5).
The control streams were populated almost exclusively by diatoms throughout the
study, with dominance shifting from larger biovolume species Surirella brebissonii and
Surirella brebissonii var. kuetzingii in weeks 2 and 4, to the much smaller cells of
Achnanthidium minutissimum by week 10 (Figures 21B.C). The mid-size Synedra
rumpens was a prominent species beginning in week 4 and was supplanted by week 10
by the similar sized Synedra tenera. Fragilaria vaucheriae was a prominent species until
week 10, when Encyonopsis microcephala became more abundant (Figures 21B.C).
58
Cell density and biovolume density followed similarly increasing trajectories until week
10 when the taxa with cells of larger biovolume were replaced in dominance by smaller
celled species and the biovolume density decreased (Figures 21A). Average cell size
significantly decreased between week 2 and weeks 6, 8 and 10 and also between week 4
and week 10.
In the treated streams, S. brebissonii. and S. brebissonii var. kuetzingii were also
dominant in earlier weeks but, unlike the control streams, green algal taxa,
predominantly Scenedesmus spp. and basal cells of filamentous green algal species, both
very small celled, became abundant from week 4 onward, and dominant by week 6
(Figures 22,23). The Low N:P ratio streams had higher biovolume densities of
Achnanthidium lanceolatum Bréb. ex Kütz by week 10 than the High N:P ratio streams
which were mostly green algal cells in both biovolume and cell densities, and the small
diatom species Nitzschia inconspicua ,which was dominant in cell densities (Figures 22
B,C, 23 B,C). Cell densities followed similar trajectories in both treated streams,
generally increasing up to week 10 after stabilizing in week 6 and 8. Biovolume densities
increased until week 6 in the High N:P streams and week 4 in the Low N:P streams as the
larger species were succeeded by smaller species (Figures 22 A, 23 A). Average cell
sizes were significantly smaller between week 2 and week 4 collections and continued to
decrease in size. The average cell size was significantly larger in the control streams
compared to the treated streams by week 6.
Samples collected at week 12 were not counted as the tiles had been heavily
grazed by a chironomid infestation and the periphyton was very patchy.
59
Relative Biovolume
2
A
Stress 0.13
Artificial Streams
Control
Low N:P
High N:P
6
8
1
10
4
4
2 2
2 2
2
2
4 4
2
2 2
4
4 22 2
10
2
6 4
2 2 2 6
4
8
2
8
4
4
8
2
6 6 66
8
4
10 1010
4
4
10
8 8 8
4
6
4
10
8
4
10 8 88
6
10
8
64 6 8
6
10
6
6 6
6 868
10
10
8
10
0
10
6
10
8
-1
10
MDS 2
10
10
-2
-2
-1
0
1
2
Relative Abundance
2
B
Stress 0.12
10 10
10
10
1
10
0
6
10
8
66
10 1010
8 8
6
6
10
8 88
1010 88
6
10 8 8
66
88
44
10
64
6 4
4 4
10 6
4
8
4
6
44
4
10
6
2
2 42 2 2
2
2222
2
2 2
10
6
8 6
8 8 810
66
8
64
4
4
4
4
2
2
2
2
2
-1
-2
-2
-1
0
1
2
MDS 1
Figure 20. NMDS ordination plots of artificial stream algal communities and community
succession, sampling weeks 2-10, n = 89. The plots include all taxa. All streams relative
biovolume (A). All streams relative abundance (B).
60
Table 5. Algal species primarily responsible for NMDS ordinations. Results of
Spearman correlations between NMDS coefficients and relative abundances and
biovolumes of all species >= 0.05 in any one sample. P < 0.01, n = 89
Biovolumes
X axis MDS 1
Y axis MDS 2
Species
Correlation coefficients Species
Correlation coefficients
SYNERUMP
0.79
SURIBRku
0.69
SYNETABU
0.71
SURIBRku
0.69
ENCSMICR
0.68
NITZPALE
0.56
SYNETENE
0.67
ACHNMINU
-0.72
SURIBRku
0.56
SURIBREB
0.51
NAVISAPR
-0.69
NITZMICE
-0.77
CHLORBAS
-0.78
ACHNLANC
-0.81
CHLORSCE
-0.85
NITZINSP
-0.90
Counts
ACHNMINU
0.85
NAVISAPR
-0.52
SYNERUMP
0.83
CHLORFIL
-0.58
ENCSMICR
0.82
SURIBREB
-0.61
SYNETENE
0.66
SURIBREB
-0.61
FRAGVAUC
0.53
SELLSEMI
-0.63
GONEOLIV
0.52
NITZDISS
-0.66
SURIBRku
0.51
SURIBRku
-0.74
NAVISAPR
-0.55
SURIBRku
-0.74
NITZMICE
-0.68
NITZINSP
-0.69
CHLORBAS
-0.72
CHLORSCE
-0.82
61
A
Control
2
Biovolume density (um /cm )
1.0e+9
3
2.0e+7
8.0e+8
1.5e+7
6.0e+8
1.0e+7
4.0e+8
5.0e+6
2.0e+8
Algal Cell Density (cells/cm2)
2.5e+7
1.2e+9
OTHER
ACHNMINU
AMRAPEDI
CHLORFIL
CHLORSCE
ENCSMICR
FRAGVAUC
NITZDISS
SURIBREB
SURIBRku
SYNEACUS
SYNERUMP
SYNETENE
0.0
0.0
2
4
6
8
10
Week
B
Week 2
Relative Abundance
Week 4
Week 8
Week 10
Week 8
Week 10
Relative Biovolume
C
Week 2
Week 6
Week 4
Week 6
Figure 21. Control streams community succession. Biovolume density/cm2 and mean
cell density/ cm2 (+/- SE, n = 6)(A). Biovolume density includes all taxa >/= 0.05 in any
one sample. Relative numerical abundance ( B) Relative abundance by biovolume (C).
B and C include only those taxa >/= 0.05 in each sample.
62
A
Low N:P
2.0e+7
3
2
Biovolume density (um /cm )
1.0e+9
8.0e+8
1.5e+7
6.0e+8
1.0e+7
4.0e+8
5.0e+6
2.0e+8
Algal Cell Density (cells/cm2)
2.5e+7
1.2e+9
0.0
0.0
2
4
6
8
10
Week
B
OTHER
ACHNLANC
ACHNMINU
AMRAPEDI
CHLORBAS
CHLORFIL
CHLORSCE
FRAGVAUC
NAVIGREG
NAVISAPR
NITZDISS
NITZINSP
NITZMICE
SELLSEMI
SURIBREB
SURIBRku
SYNERUMP
Relative Abundance
Week 2
Week 4
Week 6
Week 8
Week 10
Week 8
Week 10
Relative Biovolume
C
Week 2
Week 4
Week 6
Figure 22. Low N:P streams community succession. Biovolume density/cm2 and mean
cell density/ cm2 (+/- SE, n = 6)(A). Biovolume density includes all taxa >/= 0.05 in any
one sample. Relative numerical abundance ( B) Relative abundance by biovolume (C).
B and C include only those taxa >/= 0.05 in each sample.
63
A
High N:P
2.0e+7
3
2
Biovolume density (um /cm )
1.0e+9
8.0e+8
1.5e+7
6.0e+8
1.0e+7
4.0e+8
5.0e+6
2.0e+8
Algal Cell Density (cells/cm2)
2.5e+7
1.2e+9
0.0
0.0
2
4
6
8
10
Week
OTHER
ACHNLANC
ACHNMINU
AMRAPEDI
CHLORBAS
CHLORFIL
CHLORSCE
FRAGVAUC
NAVISAPR
NAVITRIP
NITZINSP
NITZMICE
NITZPALE
SELLSEMI
SURIBREB
SURIBRku
SYNERUMP
Relative Abundance
B
Week 2
Week 4
Week 6
Week 8
Week 10
Week 8
Week 10
Relative Biovolume
C
Week 2
Week 4
Week 6
Figure 23. High N:P streams community succession. Biovolume density/cm2 and mean
cell density/ cm2 (+/- SE, n = 6)(A). Biovolume density includes all taxa >/= 0.05 in any
one sample. Relative numerical abundance (B) Relative abundance by biovolume (C). B
and C include only those taxa >/= 0.05 in each sample.
Algal Community Diversity
The Shannon diversity index was calculated using both relative abundance by cell
numbers and by biovolumes of all taxa per sample. The index for the counts followed a
similar downward trend from weeks 2- 10 for all the streams (Figure 24A). The index for
the biovolumes however, started out lower, then peaked in week 4 or 6 before trending
downward to week 10 (Figure 24 B). The difference between the indices can be
64
explained by the dominance in earlier weeks of 2 species that have relatively large
biovolumes, Surirella brebissonii and Surirella brebissonii var. kuetzingii
Relative Abundance
2.8
A
2.6
2.4
2.2
2.0
1.8
1.6
1.4
Shannon Diversity Index
1.2
B
1.0
0.8
2
4
6
8
10
8
10
Control
Low N:P
High N:P
Relative Biovolume
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
2
4
6
Week
Figure 24. Shannon Diversity Indices for the artificial streams ( Mean +/- SE, n = 6).
Based on relative abundance and relative biovolume of all taxa per sample.
Relative abundance (A), Relative biovolume (B).
65
Biomass Development
AFDM increased in all the streams from weeks 2 through 6. Biomass continued
increasing through week 8 in the control streams then decreased in weeks 10 and 12.
Biomass in the treated streams stablilized at week 6 and remained at similar levels
through week 12 (Figure 25). The mean AFDM was significantly lower in the control
streams than the treated streams (P < 0.001). The addition of PO4-P, but not NO3-N, to
the treated streams was likely a significant contributor to this difference in AFDM
(correlation coefficient = 0.43, p = < 0.001).
Biomass and DNP
DNP was measurable in each set of streams in later stages of algal community
succession, after 6 weeks of development in the treated streams and not until week 10 in
the control streams (Figure 25). DNP rates were an order of magnitude lower in the
control streams and present in only 4 samples. There were significant correlations
between DNP and cell density, (p < 0.01, correlation coefficient = 0.70, n = 89) but not
between DNP and biovolume density. AFDM and DNP were also significantly
correlated in the treated streams, (p< 0.01, correlation coefficient 0.47 Low N:P , 0.53
High N:P n = 30), but not in the control streams. Regressions of DNP with AFDM, cell
densities and % Chlorophyta in the treated streams (Figure 26), show that for the most
part, later sampling weeks were most predictive of DNP.
66
Control
2.5
AFDM 0.65 mg/cm
2.0
2
AFDM (mg/cm )
0.8
DNP 0.02 ug N20/hr/cm2
1.5
0.6
1.0
0.4
0.5
0.2
0.0
0.0
0
2
4
6
8
10
12
Low
2.5
10
2
AFDM 1.41 mg/cm
DNP 2.48 ug N2O/hr/cm2
2.0
8
1.5
6
1.0
4
0.5
2
0.0
2
B
1.0
2
DNP (ug N2O/hr/cm )
A
0
0
2
4
6
8
10
12
High
C
2.5
10
2
AFDM 1.40 mg/cm
DNP 2.04 ug N2O/hr/cm2
2.0
8
1.5
6
1.0
4
0.5
2
0.0
0
0
2
4
6
8
10
12
Week
Figure 25. Mean AFDM and DNP (+/- SE n = 6) per sampling week. Note DNP axis
scale change (right side) between ambient and treated streams. Ambient streams ( A),
Low N:P ratio streams ( B), High N:P ratio streams (C). Overall mean (n = 35 A, n = 36
B,C) AFDM and DNP are noted next to the legend symbols and labels on each graph.
67
Low N:P
High N:P
1.2
Week 2
Week 4
Week 6
Week 8
Week 10
1.0
0.8
0.6
2
0.4
1.2
0.8
0.6
0.4
0.2
0.0
A
0.0
1
2
3
R = 0.38
P < 0.01
1.0
R = 0.26
P < 0.01 2
0
2
0.2
D
4
0
2
2
3
4
2
AFDM (mg/cm )
AFDM (mg/cm )
2
Log10+1 DNP (ug/N20/hr/cm )
1
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
6
7
1.0
2
1.2
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
C
0.2
0.4
0.6
0.8
% Chlorophyta
7
8
1.0
2
R2 = 0.25
P = 0.01
1.0
0.0
6
Log10 Cell Density/cm
R2 = 0.28
P < 0.01 1
0.0
E
5
8
Log10 Cell Density/cm
1.2
0.0
B
5
R2 = 0.39
P < 0.01 1
1.2
R2 = 0.24
P < 0.01
1.2
0.0
F
0.0
0.2
0.4
0.6
0.8
1.0
% Chlorophyta
Figure 26. DNP (µgN20/hr/cm2) vs biomass factors. Low N:P ratio streams (A-C), High
N:P ratio streams (D-F). DNP and cell density/cm2 was log10 transformed in order to
visualize graphically.
1
data has passed normality, 2 data has passed equal variance
Algal Communities and DNP
Correlations between the percentage of green algal species and DNP were the
same as the correlation between AFDM and DNP (p = <0.01, correlation coefficient =
68
0.60). This was not surprising, as AFDM and the percentage of green algal species were
highly correlated (p < 0.01, correlation coefficient 0.70).
DNP was related to smaller cell size in that the later successional stages of algal
community development in the treated streams were composed of species with relatively
smaller cell sizes. An NMDS ordination plot of each stream was superimposed with
bubble plots that were indicative of the measurable DNP normalized to area for each
sample each set of streams and further illustrate the association of DNP with later stages
of algal community succession in the treated streams (Figure 27). While DNP is only
measurable after 10 weeks in the control streams, 2 of the 4 samples with DNP have
communities that are more similar to samples collected after 4 and 6 weeks (Figure 27
A). Algal species that were significantly correlated (p < 0.01) with the MDS axis
coordinates are listed in Table 6. The species corresponding to samples with DNP, are
predominately a green algal species Scenedesmus sp. and the diatom species Nitzschia
inconspicua. and Nitzschia microcephala (Table 6).
Correlations between DNP and the Shannon diversity measures revealed negative
relationships in the treated streams (p < 0.01, correlation coefficients = 0.61 for Low N:P
and 0.64 for High N:P) but no relationship in control samples.
69
Control
2
A
Stress 0.14
Week 2
Week 4
Week 6
Week 8
Week 10
1
0
DNP
-1
A
-2
MDS 2
-2
-1
0
1
2
Low N:P
2
B
High N:P
Stress 0.12
1
2
C
Stress 0.10
1
B
C
0
0
-1
-1
-2
-2
-2
-1
0
1
2
-2
-1
0
1
2
MDS 1
Figure 27. DNP bubble plots superimposed on NMDS ordination plots by sampling
week. Control streams (A), Low N:P streams (B). High N:P streams (C). NMDS is based
on relative abundance of all species in a sample. DNP (µgN20/hr/cm2) data was log
transformed prior to conversion to improve graphic visualization.
70
Table 6. Algal species primarily responsible for NMDS ordinations. Results of
Spearman correlations between NMDS coefficients and relative abundance of algal
species that were significantly correlated with the NMDS coefficients (>= +/- 0.50
correlation coefficients). Correlation coefficients in bold signify significant correlations
with cell densities/cm2 and DNP. P < 0.01, Control n = 29, Low and High N:P n = 30
Control
Y axis MDS
X axis MDS 1
2
Correlation
Correlation
Species
coefficients
Species
coefficients
ENCSMICR
0.85
SYNETENE
0.71
ACHNMINU
0.84
SYNERUMP
0.69
CHLORBAS
-0.51
SURIBREB
0.78
AMRAPERP
-0.66
ACHNMINU
-0.52
NITZINSP
-0.67
CHLORFIL
-0.73
SURIBRku
-0.83
ACHNLANC
-0.84
Low N:P
0.77
0.89
CHLORCOL
0.51
CHLORSCE
0.80
0.79
SELLSEMI
-0.58
NITZMICE
0.67
0.50
CHLORBAS
-0.63
NITZINSP
FRAGVAUC
-0.59
NAVISAPR
-0.71
CHLORFIL
-0.63
SYNERUMP
-0.75
NITZDISS
-0.81
SURIBREB
-0.82
SURIBRku
-0.87
High N:P
SURIBRku
0.84
CYANOSCI
0.55
SURIBREB
0.77
FRAGVAUC
0.54
NITZDISS
0.74
NAVISAPR
-0.74
SYNERUMP
0.66
AMRAPERP
0.61
ACHNMINU
0.55
NITZMICE
-0.52
0.78
-0.86
CHLORSCE
71
DNP, Nutrient Concentrations and N:P Molar Ratio
There was little relationship between nutrient concentrations, N:P ratios and DNP
in the treated streams. The one exception was the linear regression of N:P molar ratio vs.
DNP in the High streams and this was relatively weak (Figure 28). However there were
also only 2 collection dates, at week 6 and week 12, where water samples were collected
which could have affected the regressions.
There was no comparative data available for
the control streams as nutrient concentrations were measured at weeks 6 and 12 and DNP
was only measurable in week 10.
Week 6
Week 12
DNP (ugN20 hr/cm2)
High
12
10
8
2
R = 0.30
P = 0.04 1
6
4
2
0
0
3000 6000 9000
N:P molar ratio
Figure 28. Linear regression of DNP vs. N:P molar ratio in the High N:P streams.
1
data passed normality
CHAPTER THREE
DISCUSSION
With this study we researched the relationship between DNP, algal community
succession and biomass accrual over time. We hypothesized that changes in algal
taxonomic composition during biofilm development would influence DNP through
changes in the organic carbon content of the algal exudates released by different species,
which would fuel metabolism of different consortia of denitrifying bacteria. We also
hypothesized that influences of species composition on DNP rates would not be
manifested until sufficient biomass had accrued to allow establishment of zones of
transient anoxia.
The study included field research and an artificial stream experiment. The goal of
the field research component was to develop diverse algal communities within biofilms in
streams influenced by varying environmental characteristics including nutrient
concentrations, discharge, riparian canopy and watershed land use. By studying several
algal communities with variations in taxonomic dominance, by stream and over time, we
hoped to increase opportunities to find relationships between algal species and DNP.
Additionally we were interested in studying the relationships between biofilms and DNP
under a wide variety of environmental conditions to increase our understanding of the
factors involved in variations in DNP.
72
73
The goal of the artificial stream experiment was to limit environmental factors and
develop less diverse algal communities and biofilms in 3 sets of streams that differed
primarily in water column nitrate concentrations and that had N:P molar ratios bracketing
the theoretical optimal structural N:P ratio of 16N:1P (Redfield 1958). With these algal
communities and biofilms we hoped to focus on taxa that were clearly adapted to
different nutrient regimes and with these more homogenous biofilms and communities be
able to test our hypotheses in a replicated experiment.
The algal communities in the 6 field study streams formed 4 distinct groups on an
NMDS ordination plot, and for the most part these groups also had distinct patterns of
biomass accrual and DNP. The exception was SB 2.2, which was grouped with Salt
Creek and EBDPR in the NMDS ordination plot but had a distinctly different DNP
pattern. DNP in the field study streams appeared to be positively related to PO4-P
concentration gradients though less so in SB 1.1, a stream with high N and conditions
that indicated lower oxygen levels. Chénier et al. (2006), studying biofilms inoculated
with river water, saw denitrification and nirS and nirK gene copy numbers increase
markedly with additions of N and P. Graham et al. (2010) found a significant
relationship between PO4-P concentrations and nirS gene copy abundance in the intrinsic
efficiency of denitrification within a stream reach, though not with absolute rates. The
lack of relationship between nirS gene copy densities and DNP that we found in this
study was also evident in a study conducted by Kalscheur et al. (2012b). However, the
specific denitrifying bacterial species containing the nirS gene did prove to be important
to DNP, which highlights the potential importance of specific algal exudates (Kalscheur
74
et al. 2012b). The differences in PO4-P concentrations were also an important factor in
determining DNP in the artificial streams, leading to 2 distinct patterns of DNP. DNP
was higher in the treated streams which had greater biomass accumulations with later
successional algal communities. The control stream samples had lower biomass and very
little DNP in late successional communities.
Interestingly, there were no significant positive associations between NO3-N
concentrations and DNP in the field study streams in contrast to another study in Illinois
streams which found a strong linear relationship (Arango et al. 2007). This could be due
in part to the high NO3-N concentrations in Salt Creek, EBDPR and SB 1.2. Wall et al.
(2005) noted that NO3-N concentrations and DNP within biofilms were positively related
until an apparent saturating effect at 0.88 mg/L and Mulholland et al. (2009) saw nitrate
removal decline in efficiency as concentrations increased. This concentration was well
below those in the field study streams with higher DNP. There was a weak but
significant positive correlation between DNP and N:P ratio in the High N:P artificial
streams which had NO3-N concentrations and N:P ratios well above those in the Low N:P
streams which had mean NO3-N concentrations less than 0.88 mg/L.
Higher DNP was measured in Salt Creek, EBDPR, SB 1.1 and SB 1.2 when there
were several species that had higher relative abundance. This could be due to the
availability of a variety of organic carbon sources, though there were no significant
correlations between biodiversity and DNP. However, a decrease in diversity was
significantly correlated with DNP in treated streams in the artificial stream study which
75
may indicate that denitrifying bacteria populations were less likely to assimilate algal
exudates from the dominant algal species.
The differences among the field study streams were primarily driven by nutrient
concentrations and based on statistical analysis (Bio-env), PO4-P concentrations were the
dominant factor structuring algal communities followed by NO3-N, N:P molar ratio, then
NH3-N. The influence of PO4-P concentrations on algal taxonomic structure has been
noted in other research. Stevenson et al. (2009), studying several Michigan streams,
found P concentrations were more likely than N concentrations to explain species
composition and Leland & Porter (2000) tied the relative abundances of algal taxa in
northern Illinois rivers to P levels.
Nutrient gradients within streams are strongly affected by land use within
watersheds, influencing periphyton biomass and algal community structures, as reported
elsewhere (Carr et al. 2005, Leland & Porter 2000, Leland et al. 2001, Newall & Walsh
2005, Rier & Stevenson 2006, Stevenson et al. 2009, Urrea & Sabater 2009 and Zheng et
al. 2008). Effluent from wastewater treatment plants (WWTP) with subsequent high
nutrient concentrations affected SB 1.2, Salt Creek, and EBDPR. Dissolved organic
carbon (DOC) was also a significant factor (Bio-env) in structuring algal community
groups. The three streams most influenced by wastewater effluent, Salt Creek, EBDPR,
which were grouped together in the NMDS ordination plot, and SB 1.2 which shared
similarities in late successional samples, had high mean levels (7ppm) of DOC. DOC
concentrations in freshwater streams typically vary from 1-8 ppm, with measurements
above 10 ppm not uncommon during flood events (Gremm & Kaplan 1998, Sandford et
76
al. 2010). Frost et al. (2007) found that periphyton biomass and chlorophyll a (chl a)
increased with increases in DOC possibly due to increased carbon uptake by both algae
and bacteria resulting in increases in exudates. Algal community structure also changed
with increases in DOC resulting from increased availability of nutrients (Frost et al.
2007). Other research has noted the influence of nutrient concentrations and organic
carbon levels on river and stream epilithic bacterial communities (Lear & Lewis 2009,
Kobayashi et al. 2009).
Direct influx from storm drains influenced one of the Salt Creek sample sites and
both SB 1.1 sites. Kalscheur et al. (2012a) reported results of an in depth analysis of the
stream water DOC in the 2009 field streams and found an unexpected grouping of
downstream Salt Creek and SB 1.1 , sites most impacted by storm water drains. Though
the algal communities in these sites were not grouped together in the NMDS plots,
common species present in both sites were influenced by the proximity of the storm water
influx.
DNP Patterns
Salt Creek and EBDPR
Salt Creek and EBDPR, though grouped together in the NMDS
ordinations, had several differences in community structure. Acnanthidium lanceolatum
was present throughout the field season in both but was always more abundant in Salt
Creek. Achnanthes ploenensis was absent in late successional biofilms in EBDPR, and
Nitzschia inconspicua was absent in early successional communities in Salt Creek.
77
AFDM accumulation appeared to be limited in both Salt Creek and EBDPR
despite the high nutrient and DOC levels, by high discharge and flooding events during
the course of the study. Salt Creek, in addition had a high sediment load , which adds to
the scouring effect of increased flow velocity (Francoeur & Biggs 2006). The highest
DNP occurred in both streams after 5 weeks of development when the AFDM and Chl a
measures were the highest and coincided with the highest diversity levels in Salt Creek
and high diversity in EBDPR. Strong flooding events occurred prior to week 3 and week
8 so this was a period of an intermediate level of disturbance in both streams. Biggs &
Smith (2002) saw similar higher biodiversity with intermediate flood disturbances
combined with high P levels. Flow velocity as well as biofilm density and structure
affect nutrient absorption into periphyton. Biotic uptake of NO3-N increases as current
velocity increases (Arnon et al. 2007b, Ács & Kiss 1993) and in lower biofilm densities
(Stevenson & Glover 1993). Transient solute storage also changes in response to flow
(Battin et al. 2003). Collectively, these relationships among nutrient transport and uptake,
flow regime, and biofilm biomass likely added to the potential for nutrient gradients
within the biofilms, which appeared to lead to increased biotic community diversity and
availability of NO3-N for DNP.
Algal community diversity not only provides a variety of carbon source niches for
bacteria (Bellinger et al. 2009, Paerl & Pinckney 1996) but also affects the architecture of
the biofilm. As periphyton communities develop, the heterogeneity within algal and
microbial communities over space and time (Hoagland et al. 1982) leads to constantly
changing microenvironments within the extracellular matrix (Paerl & Pinckney 1996,
78
Sutherland 2001). Production of algal exudates can vary, depending on species, (Grossart
et al. 2005) by a factor of 50 (Myklestad 1995) and algal cells and capsular EPS can
provide physical substrate for bacterial growth (Domozych &Domozych 2008, Giraldo &
Vieira 2005, Zakharova et al. 2010). Additionally the biofilm matrix acts as a protected
and transient space for enzyme release and activity (Sutherland 2001) which can be
initiated as rapidly as 4 hours after biofilm initiation (Pohlon et al. 2010). Romani et al.
(2008) noted that the majority of biofilm enzyme activity was found to be most active
within algal EPS in the first 4 days of development. Thus periphyton that is more
heterogeneous and dynamic over space and time may support more activity which
appears to be the case in both Salt Creek and EBDPR.
SB 1.1
SB 1.1 had comparatively high N:P ratios with high NH3-N and lower P
concentrations compared to the other higher nutrient streams. P levels however were still
high compared to less impacted streams (Ponader et al. 2007, Rier & Stevenson 2006,
Snyder et al. 2002) supporting mid to late successional high relative abundance of A.
lanceolatum, which is highly tolerant of eutrophication (Van Dam 1994). The codominance of A. minutissimum which is abundant in high N:P conditions with lower P
concentrations (Stelzer & Lamberti 2001) suggests that internal gradients of P supported
both species. Nitzschia inconspicua and other Nitzschia species were later successional
species in SB 1.1. N. inconspicua and A. lanceolatum are species that in other research
have been correlated with storm drain influx (Newall & Walsh 2005). Low to nonexistent discharge likely contributed to high accumulations of AFDM in some samples
79
though in this stream the highest measure of DNP was associated with the least amount
of AFDM. DOC was not as likely to influence biomass as this site is the headwater of
Springbrook 1 and less influenced by soil runoff and groundwater, major sources of DOC
(Allan & Castillo 2008). High levels of DNP in this stream were likely due to an
increased likelihood of anoxic conditions and readily available NO3-N. The water
source for SB 1.1 was spring water which is typically less oxygenated. NH3-N stream
water concentrations were highest in this stream and nitrification would have not only
have reduced O2 levels within biofilms but also provided additional adjacent NO3-N.
SB 1.2
The close proximity of SB 1.2 to a WWTP led to the highest and probably less
fluctuating P concentrations. The sustained, elevated P concentrations were significantly
related to high levels of AFDM which has been noted by others (Cymbola et al. 2008,
Elsdon & Limberg 2008). The samples in SB 1.2, after 12 weeks of development, had
the highest levels of AFDM among the streams, which could be attributed, in part, to a
controlled release of effluent from the WWTP dampening the scouring effect of storm
events. Additionally there were higher levels of dissolved organic carbon (DOC) in this
stream which also contributes to increased AFDM and Chl a (Frost et al. 2007).
Green algal basal and filamentous cells were an early successional species which
has been found in other research in high nutrient streams (Chételat et al. 1999, Leland &
Porter 2000), and had relatively higher relative abundances in SB 1.2 than the other
streams. Amphora pediculus a widespread species (Patrick & Reimer 1966) with a high
tolerance for organic pollution (Van Dam 1994), was a dominant early and mid-
80
rd
th
successional species in SB 1.2. This stream had the lowest biodiversity in the 3 and 5
week of development which increased as biomass increased when A. lanceolatum and
Cocconeis placentula var. euglypta became more abundant. Urrea & Sabater (2009)
identified similarly low biodiversity with increases in water column P. C. placentula var.
euglypta appears to be a later successional species in the study streams though it was
absent in SB 1.1, and other research has found that intermediate concentrations of P are
preferred by this species (Leland & Porter 2000, Munn et al. 2002). A. lanceolatum was
abundant throughout the field season in SB 1.1, the headwater of SB 1.2, thus would have
been available in the water column in the early weeks of development. It is possible that
the increasingly high AFDM levels allowed the formation of internal gradients of nutrient
concentrations. As biofilms form, dynamic structural heterogeneity increases, variations
in porosity, fragmentation and channel formation can cause shifts between diffusion and
mass transport of solutes (Battin et al 2003, Cook et al. 2007, Sutherland 2001) in concert
with slower diffusion rates as density increases (Renslow et al. 2010). Mulholland et al.
(1994) discovered that higher biomass increases the size of transient storage zones and
decreases the amount of all nutrients incorporated into the biofilm from stream water so
more internal nutrient cycling takes place. Internal gradients may have allowed species
other than A. pediculus, nutrient conditions more suitable for growth.
The DNP rates in SB 1.2 remained low despite consistently high NO3-N and high
biomass until week 12 as algal community biodiversity increased. The availability of
several sources of organic carbon exudates as algal biodiversity increased may have
increased the diversity of denitrifying bacteria leading to higher DNP. Links between
81
algal and bacterial production and/or densities during community succession can became
decoupled with increases in nutrient concentrations (Carr et al. 2005, Peterson et al.
2011, Porubsky et al. 2008, Scott et al. 2008 and Zeigler & Lyon 2010). Peterson et al.
(2011), in a short term (4 week) study within the 2008 field season, noted that taxonomic
changes in bacterial denitrifiers and algal communities during biofilm development were
linked in the SB 2.1 low nutrient site but not in SB 1.2 where both inorganic nutrient
concentrations and organic carbon concentrations were high. Interestingly when SB 1.2
stream water dissolved organic carbon (DOC) was used as a carbon substrate in DNP
incubations (Kalscheur et al. 2012b), measurements were an order of magnitude higher
than with the algal monocultures, suggesting a role for algal community taxonomy or
diversity in DNP that may be more complex than that of carbon source.
SB 2.1 and SB 2.2
The SB 2 streams had lower nutrient levels and relatively high N:P ratios yet algal
communities in SB 2.1 and SB 2.2 were quite different. The main similarity between
these communities was the near absence of A. lanceolatum, common in the more high
nutrient streams, and the high relative abundance of A. minutissimum in SB 2.1
throughout and in early and mid-successional communities in SB 2.2, a species common
in low P, high N:P ratio streams (Stelzer & Lamberti 2001). Photosynthetically active
radiation (PAR) results are not reported in this paper, however SB 2.1 the restored prairie
meander, was the only stream with no riparian canopy. Biomass levels were similar to
those in the shady forested stream SB 2.2, though other researchers have noticed
increased biomass with reduced canopy (Munn et al. 2010). SB 2.1 did have higher
82
relative abundance of green algae which thrives in habitats with higher light levels (Rier
et al. 2006). The forested site downstream, SB 2.2, had consistently higher biodiversity
which has been associated in algal communities in more shaded habitats (Stevenson et al.
1991).
Also contributing to the high biodiversity of algal communities in SB 2.2 were the
availability of habitat niches such as riffles, runs, pools. This environmental variability
can lead to an increase in biodiversity in potential algal colonists (McCabe & Cyr 2006,
Peterson & Grimm 1992). Passy (2007) separated algae into guilds based on differences
in physical profile and motility, which were distributed along a nutrient and flow
gradient. Intermittent higher flow in SB 2.2 may also have contributed to higher species
diversity in this stream as species that fit into these guilds were concurrent in samples
throughout the 2009 season. The NMDS ordination plots indicated that there were
similarities between SB 2.2 algal communities and those in Salt Creek and EBDPR
despite lower DOC and nutrient concentrations. This could be explained by results of an
in depth analysis of stream water in 2009 indicating that DOC from the SB 2.2 stream
water, despite being in a forested preserve, showed signs of wastewater, which the
author attributes to possible septic or combined sewer overflow during high flow events
(Kalscheur et al. 2012a).
Despite high biodiversity and relatively high biomass accumulations, DNP was
very low in SB 2.2. Lower availability of NO3-N was likely to be the major reason .
The streams with higher DNP also had higher relative abundances of A. lanceolatum and
the near absence in SB 2.2 of this species could be attributed to the low P concentrations.
83
DNP was similarly low in SB 2.1 most likely for the same reasons. There was an
interesting difference in this stream in DNP however, as unlike SB 2.2, biomass
accumulation was significantly linked to increased DNP and was accompanied by
increases in algal community diversity. This suggests a coupled increase in bacterial
denitrifier diversity which was noted in the previously mentioned short term study within
the 2008 field season (Peterson et al. 2011).
Artificial Streams
The successional pattern of the algal communities in the treated streams diverged
from those in the control streams after 2 weeks of development. Algal taxonomic
structure in control streams changed in a more directional fashion during community
development and the replicates were more similar. Similar disparities in algal
community succession between streams with large contrasts in nutrient concentrations
were also discovered in a short term study within the 2008 field season (Peterson et al.
2011). Dominant early successional species in all 3 sets of streams were Surirella
brebissonii. and Surirella brebissonii var. kuetzingii. Early successional dominance by
Surirella sp.had also been noted by McCormick & Stevenson (1991) in a study conducted
in streamside mesocosms.
Achnanthidium minutissimum was numerically dominant in
the control streams by 4 weeks of development, along with Synedra rumpens which was
supplanted by S. tenera and S. acus. A similar successional pattern was followed in
experimental stream channels that had equally low P concentrations (Peterson &
Stevenson 1990). A. minutissimum was also more abundant in the field study streams at
low P concentrations and high N:P ratios (Results, Field study: Table 2, Figure 12) and in
84
similar nutrient concentrations in another streamside mesocosm study (Stelzer &
Lamberti 2001).
The treated streams, despite large differences in N:P ratios, developed similar
communities dominated by green algal genera, particularly Scenedesmus which has been
noted in other research under high P concentrations (Cymbola et al. 2008). Ferragut &
Bicudo ( 2012) noticed a shift to Chlorophyta with P additions and an increase in
Scenedesmus in benthic communities when enclosures were amended with both N and P.
The shift to a greater abundance and biomass of Scenedesmus also developed in
experimental fish ponds that received additions of both N and P which raised ratios above
25 N:P (Bulgakov & Levich 1999). It is likely that due to relatively high ambient levels
of N and the higher N:P ratios in all 3 sets of streams, that N was not limiting. Mean
NO3-N concentrations in the Low N:P streams were similar to those in the control
streams, but total N was higher in the treated streams, as NH3-N was almost double that
of the control streams. Further evidence for the dominating influence of nutrient
additions on these algal communities was the presence, in the treated streams on later
sampling dates, of diatom species, including Achnanthidium lanceolatum and Navicula
saprophila and Nitzschia inconspicua that are highly tolerant of eutrophication (Van
Dam 1994). Also, Wyatt et al. (2010) noted an increase in Nitzschia species in stream
that were amended with both N and P. These species were not present in later
communities in the control streams.
DNP was minimal in the control streams and was present at low levels in the
treated streams when biomass peaked and stabilized after 6 weeks of development at
85
levels double those of the control streams. Though late successional stages of algal
community development were clearly associated with DNP in the treated streams, there
were no significant relationships between algal species and DNP. The influence of
biomass accumulation on DNP was evident in the significant correlations between DNP,
AFDM and % chlorophyta.
A slower velocity (7 cm/s) was chosen to minimize biomass loss and develop more
consistent replicates within treatments (Villeneuve et al. 2010), however this
homogeneity and stability may have contributed to the low levels of DNP (0.02 - 20
µgN20/hr/cm2) compared to the natural streams (0.62 - 319 µgN20/hr/cm2). Biofilm
structural heterogeneity which allows a combination of diffusion and advective transport
of solutes, leads to oxygen gradients within biofilms (Cook et al. 2007, Costerton et al.
1995, Paerl & Pinckney 1996) and transient anoxic zones essential to denitrification
(Tiedje 1988). DNP declined in the amended artificial streams after week 8 but this was
not related to biomass as there was no concurrent drop in AFDM. It was likely due to an
infestation of chironomids. Mathieu et al. (2007) noted that oxygen levels doubled in
biofilms with increasing nematode populations particularly at the base of biofilms and it
is possible that chironomid grazing decreased the likelihood of anoxic zones as the
biofilms became patchier. The density of chironomids was not quantified but there were
observably far more individuals on the tiles of the treated streams, with the populations
increasing in later sampling weeks. Research by Maasri et al. (2010) in high nutrient
streams, suggested that green algal species were preferred by chironomids over diatoms,
which may explain the earlier and heavier onset of infestation in treated streams. Later
86
successional average cell biovolume was somewhat higher in the control streams which
could have offered some initial protection from grazing as chironomids can preferentially
graze cells with smaller biovolumes (Maasri et al. 2010). The control streams were
dominated by A. minutissimum by week 10 and AFDM levels decreased after week 8 in
concert with an increase in chironomids. McCormick & Stevenson (1991), suggest that
dense growths of this species may be preferred by herbivorous macroinvertebrates.
Algal Species and DNP
This study identifies several algal species that were abundant in samples with
higher levels of DNP though statistically significant relationships between abundances of
these species and DNP were not evident. Kalscheur et al. (2012b) demonstrated that
dissolved organic carbon produced by monocultures of several algal species had unique
signatures that influenced both species composition and abundance of denitrifying
bacteria. Other research has shown the composition and production of algal exudates to
be species-specific (Bahulikar & Kroth 2008, Girolodo et al. 2007, Myklestad 1995) and
noted changes in bacterial assemblage taxonomy when cultured with algal EPS (Haynes
et al. 2007). The presence or absence of DNP measured from bacterial samples that had
been incubated with dissolved organic carbon isolated from monoalgal cultures were
associated with differences in denitrifier taxonomy and provides insight on our DNP
results (Kalscheur et al. 2012b).
The following table illustrates connections between algal species or genera and
DNP measurements in the study streams compared to the DNP results from algal
monocultures.
87
Table 7. Results of algal monoculture and DNP (2 replicates, positive for DNP +,
negative -, Kalscheur et al. 2012b) and relative abundances of same species in biofilm
samples with the highest DNP by stream. The data in parentheses indicates the relative
abundance of genera rather than species.
Monoculture &
DNP
Relative abundance of species or (genera) in stream samples with
(2 replicates)
highest DNP
Species
Achnanthidium
lanceolatum+/-
SC
US
SC
DS
EBDPR SB1.1 SB1.2 SB2.1 SB2.2
A.S.
High
N:P
0.14
0.42
0.11
0.21
0.42
0
0.02
0.02
Achnanthidium
minutissimum -/-
0.02
0.03
0.02
0.36
0.01
0.52
0.47
0.03
Amphora
pediculus -/-
0.09
0.08
0.02
0.01
0.17
0
0.13
0.01
0
(0.07)
0
(0.17)
0
(0.42)
10
20
Nitzschia
amphibia +/+
0.01
0.02
(0.06) (0.08)
0
(0.22)
Scenedesmus
armatus -/-
(0.01) (0.01)
(0.01)
0
138
318
DNP
(µgN2O/hr/cm2)
189
80
0.11
0
0.01
(0.30) (0.01) (0.03)
(0.06) (0.02)
25
4
The association of Nitzschia species with DNP was also noticed in Ishida et al.
(2008). Interestingly, the Scenedesmus culture replicates were negative for DNP though
there is a correlation between abundances of this species and DNP in the treated artificial
streams. DiPippo et al. (2009) discovered that green algal species generate significantly
less capsular extracellular polysaccharides than diatoms which may have contributed to
the much lower levels of DNP in the artificial streams dominated by Scenedesmus. Also,
the clonal nature of algal reproduction can result in several microenvironments each
associated with a different species population coexisting within the same biofilms
(Hoagland et al. 1982). Under such circumstances, collective DNP within the biofilm
88
would result from multiple aggregates of denitrifying bacteria, each exploiting organic
exudates from different algal species as a carbon source. As such, the abundance of no
single species would be correlated with DNP.
CHAPTER FOUR
CONCLUSION
Higher concentrations of nutrients, particularly PO4-P, accompanied by
intermittent physical disturbance, appear to promote development of more diverse algal
communities and structurally heterogeneous biofilms that generated more DNP. Several
algal species in these biofilms were associated with higher measures of DNP though
significant relationships were not apparent. Other research indicates that denitrifying
bacterial taxa may be specific to algal species and thus biofilms with more niche habitats
may have more potential for this ecosystem process.
However, in more homogenous biofilms, when nutrients become excessive as in
the amended artificial streams or when excessive nutrients are combined with a heavy
load of wastewater effluent as in SB 1.2, this link between algae and bacteria may
become decoupled leading to lower levels of DNP.
Establishing the association of specific algal species and physical and chemical
environments that are conducive to DNP provides direction for further research into the
role of stream benthic communities in excessive nitrate removal from aquatic ecosystems.
89
APPENDIX A
COMPARISON OF USGS DISCHARGE DATA COLLECTION STATIONS AND
STUDY SAMPLING SITES
90
91
Table 8. Comparison of geographical coordinates betweensample collection sites and
USGS monitoring stations.
USGS Site and number
Lat. (N) Long. (W) Collection Sites Lat. (N) Long. (W)
Salt Creek - Elmhurst
05531300
41°54'10" 87°57'33" Salt Creek
41°57'17" 87°59'22"
Spring Brook - Warrenville
05540091
41°50'07" 88°10'58" SB 1.2
41°50'33" 88°10'16"
Spring Brook - Naperville
05540275
41°43'33" 88°09'49" SB 2.2
41°43'33" 88°10'03"
EBDPR - Downers Grove
05540160
41°49'54" 88°02'52" EBDPR
41°45'00" 88°03'50"
APPENDIX B
2008 ALGAL COMMUNITY SUCCESSION GRAPHS
92
93
A
1.4e+8
1.6e+6
1.2e+8
1.4e+6
1.2e+6
1.0e+8
1.0e+6
8.0e+7
8.0e+5
6.0e+7
6.0e+5
4.0e+7
4.0e+5
2.0e+7
2.0e+5
0.0
2
Algal Cell Density (cells/cm )
3
2
Algal Biovolume Density (um /cm )
2008 Salt Creek
0.0
1
3
5
8
OTHER
ACHNDESP
ACHNLANC
ACHNPLOE
AMRAPEDI
CCNEPEDI
CCNEPLeu
CHLORBAS
CYTEMENE
MELOVARI
NAVILANC
NAVIRECE
NITZINSP
NITZPALE
PLMADELI
THSILACU
TRYBHUNG
Week
Relative Abundance
B
Week 1
Week 3
Week 5
Week 8
Relative Biovolume
C
Week 1
Week 3
Week 5
Week 8
Figure 29. 2008 Salt Creek algal taxa and community succession. Biovolume densities
(bar graph) and cell densities, error bars = SE n = 2, (line graph) (A), relative abundance
sample are included with the exception of cell densities which include all taxa (A).
(B) and relative abundance by biovolume (C). All taxa that were >/= 0.05 in any one
93
94
A
1.4e+8
3e+6
1.2e+8
3e+6
1.0e+8
2e+6
8.0e+7
2e+6
6.0e+7
1e+6
4.0e+7
5e+5
2.0e+7
0
Algal Cell Density (cells/cm 2)
3
2
Algal Biovolume Density (um /cm )
2008 EBDPR
0.0
1
3
5
Other
ACHNLANC
ACHNPLOE
CCNEPEDI
CCNEPLeu
CHLORBAS
CHLORFIL
CYANCHRO
CYTEMENE
MELOVARI
NAVILANC
NAVIRECE
NAVISAPR
PLRALAEV
RHSPABBR
SURIBREB
THSILACU
8
Week
Relative Abundance
B
Week 1
Week 3
Week 5
Week 8
Relative Biovolume
C
Week 1
Week 3
Week 5
Week 8
Figure 30. 2008 EBDPR algal taxa and community succession. Biovolume densities (bar
graph) and cell densities (line graph) (A), relative abundance (B) and relative abundance
by biovolume (C). All taxa that were >/= 0.05 in any one sample are included with the
exception of cell densities which include all taxa (A).
95
2008 SB 1.1
1.6e+9
8e+6
3
2
1.4e+9
Algal Cell Density (cells/cm )
2
Algal Biovolume Density (um /cm )
A
6e+6
1.2e+9
1.0e+9
4e+6
8.0e+8
2e+6
6.0e+8
4.0e+8
0
2.0e+8
0.0
1
3
5
8
Other
ACHNLANC
AUSEGRAN
CHLORFIL
GONEAFFI
GONEPARV
GONESPEC
NAVIGREG
NAVILANC
NAVIVENE
NITZAMPH
NITZPALE
RHSPABBR
TRYBHUNG
14
Week
Relative Abundance
B
Week 1
Week 3
Week 5
Week 8
Week 14
Week 8
Week 14
Relative Biovolume
C
Week 1
Week 3
Week 5
Figure 31. 2008 SB 1.1 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities (line graph) (A), relative abundance (B) and relative abundance
by biovolume (C). All taxa that were >/= 0.05 in any one sample are included with the
exception of cell densities which include all taxa (A).
96
2008 SB 1.2
3.5e+8
1e+7
2
Algal Cell Density (cells/cm )
3
2
Algal Biovolume Density (um /cm )
A
3.0e+8
8e+6
2.5e+8
6e+6
2.0e+8
4e+6
1.5e+8
2e+6
1.0e+8
0
5.0e+7
0.0
1
3
5
8
14
Week
Other
ACHNLANC
AMRAPEDI
CCNEPEDI
CHLORBAS
CHLORFIL
CYANCHRO
DIATVULG
GONEPARV
MELOVARI
NAVILANC
NAVISAPR
NAVIVAUC
NITZINSP
RHSPABBR
SELLSEMI
Relative Abundance
B
Week 1
Week 3
Week 5
Week 8
Week 14
Week 8
Week 14
C
Relative Biovolume
Week 1
Week 3
Week 5
Figure 32. 2008 SB 1.2 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities (line graph) (A), relative abundance (B) and relative abundance
by biovolume (C). All taxa that were >/= 0.05 in any one sample are included with the
exception of cell densities which include all taxa (A)
97
A
3e+9
8.0e+6
2e+9
6.0e+6
2e+9
4.0e+6
1e+9
2.0e+6
5e+8
0.0
2
2
3
1.0e+7
Algal Biovolume Density (um /cm )
1.2e+7
Algal Cell Density (cells/cm )
2008 SB 2.1
3e+9
0
1
3
5
8
14
Week
OTHER
ACHNMINU
CCNEPEDI
CCNEPLeu
CHLORSTF
CHLORUNI
CYTEMENE
DINEPUEL
GONEPARV
GONESPEC
NAVILANC
NAVIRECE
NITZINSP
NITZPALE
RHSPABBR
THSIWEIS
TRYBHUNG
Relative Abundance
B
Week 1
Week 3
Week 5
Week 8
Week 14
Week 8
Week 14
Relative Biovolume
C
Week 1
Week 3
Week 5
Figure 33. 2008 SB 2.1 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities (line graph) (A), relative abundance (B) and relative abundance
by biovolume (C). All taxa that were >/= 0.05 in any one sample are included with the
exception of cell densities which include all taxa (A).
98
A
4e+6
3
6e+8
3e+6
3e+6
4e+8
2e+6
2e+6
2e+8
1e+6
5e+5
0
2
4e+6
Algal Cell Density (cells/cm )
2
Algal Biovolume Density (um /cm )
2008 SB 2.2
8e+8
0
1
3
5
8
14
Week
B
Relative Abundance
Week 1
Week 3
Week 5
Week 8
Other
ACHNMINU
AMRAPERP
CCNEPEDI
CCNEPLeu
CHLORBAS
CHLORFIL
CHLORSTB
ENCNMINU
ENCNPROS
GONEPARV
MELOVARI
NAVICATO
NAVILANC
NAVIRECE
NITZDISS
NITZDUBI
NITZINSP
NITZFRUS
PLRALAEV
RHSPABBR
Week 14
C
Relative Biovolume
Week 1
Week 3
Week 5
Week 8
Week 14
Figure 34. 2008 SB 2.2 algal taxa and community succession. Biovolume densities (bar
graph) and cell densities (line graph) (A), relative abundance (B) and relative abundance
by biovolume (C). All taxa that were >/= 0.05 in any one sample are included with the
exception of cell densities which include all taxa (A).
APPENDIX C
TABLE OF ALGAL TAXA
99
100
Table 9. Codes and complete names of all algal taxa encountered in this study.
Division
Bacillariophyta
SPECIES
CODE
ACHNCONS Achnanthes conspicua Mayer
ACHNDESP Achnanthes delicatula spp. Complex
ACHNEXco
Achnanthes exigua var. constricta (Grun.) Hust.
ACHNEXIG Achnanthes exigua Grun.
ACHNHAro
Achnanthes hauckiana var. rostrata Schulz
ACHNHAUC Achnanthes hauckiana Grun.
ACHNLANC Achnanthidium lanceolatum Bréb. ex Kütz.
ACHNMINU Achnanthidium minutissimum (Kütz.) Czarn.
ACHNPLOE Achnanthes ploenensis Hust.
AMRAOVaf Amphora ovalis var. affine (Kütz.) V. H.
AMRAOVAL Amphora ovalis (Kütz.) Kütz
AMRAPEDI Amphora pediculus (Kütz.) Grun. ex A. Schmidt
AMRAPERP Amphora perpusilla (Grun.) Grun.
AMRAVENE Amphora veneta Kütz.
ASRIFORM
Asterionella formosa Hassall
AUSEAMBI Aulacoseira ambigua (Grun.) Simonsen
AUSEGRag
Aulacoseira granulata var. angustissima (O. Müll.) Simonsen
AUSEGRAN Aulacoseira granulata (Ehr.) Simonsen
BALAPARA Bacillaria paradoxa J.F. Gmelin
CANEBACI
Caloneis bacillum (Grun.) Mereschkowsky
CANELEin
Caloneis lewisi var. inflata (Schultze) Patrick
CCNEPEDI
Cocconeis pediculus Kütz.
CCNEPLeu
Cocconeis placentula var. euglypta (Ehr.) Cleve
CCNEPLli
Cocconeis placentula var lineata (Ehr.) Cleve
CRATBUDE Craticula buderi (Hust.) L.-B.
CTENPUla
Ctenophora pulchella var. lanceolata (O'Meara) L. Bukht.
CTENPULC
Ctenophora pulchella (Ralfs ex Kütz.) Will. & Round
CYPHDUBI
Cyclostephanos dubius (Hust.) Round
CYPLSOLE
Cymatopleura solea (Bréb.) W. Sm.
CYTEMENE Cyclotella meneghiniana Kütz.
DIATANCE
Diatoma anceps (Ehr.) Kirchner
DIATTEel
Diatoma tenue var. elongatum Lyngbye
DIATVULG
Diatoma vulgare Bory
DINEPUEL
Diploneis puella (Schumann) Cleve
101
ENCNAUER
ENCNMINU
ENCNPROS
ENCSMICR
ENTOPALU
EUTIPEmi
FALLTERA
FRAGCOEN
FRAGVAUC
FRUSVULG
GONEAFFI
GONEANGU
GONEKOBA
GONEMINU
GONEOLca
GONEOLIV
GONEPARV
GONESPEC
GONETRca
GONETRUN
GYSIACUM
GYSIATTE
HANTAMPH
HIPPCAPI
HIPPHUNG
KARACLEV
LUTIGOEP
LUTIVENT
MELOVARI
NAVIANTO
NAVICATO
NAVICIRC
NAVICRCE
NAVICRTE
NAVIGREG
NAVIGRIM
NAVIINGE
NAVIINTE
Encyonema auerswaldii Rab.
Encyonema minutum (Hilse) Mann
Encyonema prostratum (Berkeley) Kütz.
Encyonopsis microcephala (Grun.) Krammer
Entomoneis paludosa (W. Sm.) Reimer
Eunotia pectinalis var. minor (Kütz.) Rab.
Fallacia tenera (Hust.) Mann
Fragilaria construens (Ehr.) Grun.
Fragilaria vaucheriae (Kütz.) J.B. Petersen
Frustulia vulgaris (Thwaites) De Toni
Gomphonema affine Kütz.
Gomphonema cf. angustatum (Kütz.) Rab.
Gomphonema kobayasii sp.nov.
Gomphonema cf. minutum (Ag.) Ag.
Gomphonema olivaceum var. calcareum (Cleve) Van Heruck
Gomphonema olivaceum (Hornemann) Kütz.
Gomphonema parvulum (Kütz.) Kütz.
Gomphonema spp. Ehr.
Gomphonema truncatum var. capitatum (Ehr.) Patrick
Gomphonema truncatum Ehr.
Gyrosigma acuminatum (Kütz.) Rab.
Gyrosigma attenuatum (Kütz.) Cleve
Hantzschia amphioxys (Ehr.) Grun.
Hippodonta capitata (Ehr.) L.-B., Metzeltin & Witkowski
Hippodonta hungarica (Grun.) L.-B., Metzeltin & Witkowski
Karayevia clevei (Grun.) Round & Bukht.
Luticola goeppertiana (Bleisch) Mann
Luticola ventricosa (Kütz.) Mann
Melosira varians Ag.
Navicula antonii L.-B.
Navicula capitatoradiata Germain
Navicula circumtexta Meist. ex Hust.
Navicula cryptocephala Kütz.
Navicula cryptotenella L.-B.
Navicula gregaria Donkin
Navicula grimmei Krass.
Navicula ingenua Hust.
Navicula integra (W. Sm.) Ralfs
102
NAVILANC
NAVIMELU
NAVIRECE
NAVIRHCE
NAVISANA
NAVISAPR
NAVISYME
NAVITRIP
NAVIVAUC
NAVIVENE
NITZACIC
NITZAMPH
NITZDISS
NITZDUBI
NITZFONT
NITZFRUS
NITZGAND
NITZGRAC
NITZINSP
NITZINTE
NITZMICE
NITZPACE
NITZPALE
NITZRECT
NITZREVE
NITZSIMA
NITZSUAC
PLMADELI
PLMASPEC
PLRALAEV
REIMSINU
RHSPABBR
SELLPUPU
SELLSEMI
SURIANGU
SURIBREB
SURIBRku
SURIOVAL
Navicula lanceolata (Ag.) Kütz.
Navicula menisculus Schumann
Navicula recens (L.-B.) L.-B.
Navicula rhynchocephala Kütz.
Navicula salinarum Grun.
Navicula saprophila L.-B. & Bonik
Navicula symmetrica Patrick
Navicula tripunctata (O. Müll.) Bory
Navicula vaucheriae Petersen
Navicula veneta Kütz.
Nitzschia acicularis (Kütz.) W. Sm.
Nitzschia amphibia Grun.
Nitzschia dissipata (Kütz.) Grun.
Nitzschia dubia W. Sm.
Nitzschia fonticola (Grun.) Grun.
Nitzschia frustulum (Kütz.) Grun.
Nitzschia gandersheimiensis Krass.
Nitzschia gracilis Hantzsch
Nitzschia cf inconspicua Grun.
Nitzschia intermedia Hantzsch ex Cleve & Grun.
Nitzschia microcephala Grun.
Nitzschia paleacea Grun.
Nitzschia palea (Kütz.) W. Sm.
Nitzschia recta Hantzsch ex Rab.
Nitzschia reversa W. Sm.
Nitzschia sigma (Kütz.) W. Sm.
Nitzschia subacicularis Hust.
Pleurosigma delicatulum W. Sm.
Pleurosigma sp. W. Sm.
Pleurosira laevis (Ehr.) Compère
Reimeria sinuata (Greg.) Koc. & Stoermer
Rhoicosphenia abbreviata (Ag.) L.-B.
Sellaphora pupula (Kütz.) Mereschkovsky
Sellaphora seminulum (Grun.) Mann
Surirella angusta Kütz.
Surirella brebissonii Kram. & L.-B.
Surirella brebissonii var. kuetzingii Kram. & L.-B.
Surirella ovalis Bréb.
103
SURISPEC
SURISUEC
SYNEACUS
SYNERUMP
SYNETABU
SYNETENE
THSILACU
THSIWEIS
TRYBAPIC
TRYBHUNG
TRYBLEVI
TRYBVICT
ULNRDELI
ULNRULNA
CHLORANK
CHLORBAS
CHLORCO8
CHLORCOE
CHLORCOL
CHLORCOS
CHLORFIL
CHLORLGB
CHLORPED
CHLORSCE
CHLORSTB
CHLORSTF
CYANCHRO
CYANCOLO
CYANFILA
CYANOSCI
CYANSING
CYANSPIR
Surirella sp. Turpin
Surirella suecica Grun.
Synedra acus Kütz.
Synedra rumpens Kütz.
Synedra tabulata (Ag.) Kütz.
Synedra tenera W. Sm.
Thalassiosira lacustris (Grun.) Hasle
Thalassiosira weissflogii (Grun.) G.Fryxell & Hasle
Tryblionella apiculata Greg.
Tryblionella hungarica (Grun.) Frenguelli
Tryblionella levidensis W. Sm.
Tryblionella victoriae Grun.
Ulnaria delicatissima (W. Sm.) M.Aboal & P.C.Silva
Ulnaria ulna (Nitzsch) Compère
Division
Chlorophyta
Ankistrodesmus Corda
basal cells
8-celled colony
Coelastrum Naegeli
colonial cells
Cosmarium Corda ex Ralfs
filament cells
large basal-type cells
Pediastrum Meyen
Scenedesmus Meyen
Stigeoclonium nanum (basal cell) (Dillwyn) Kütz.
Stigeoclonium nanum (filament cell) (Dillwyn) Kütz.
Division
Cyanobacteria
Chroococcales (Order) R.von Wettstein von Westerheim
colonial cells
filament cells
Oscillatoria Vaucher
single cells
Spirulina (natural unit 10 µm length) Turpin ex Gomont
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VITA
Allison Daley received her Bachelor of Arts degree in Cultural Anthropology
from the University of Maryland in 1976. After working in the business world for
several years, she became interested in a scientific career after earning a naturalist
certificate through the Morton Arboretum in Lisle, Illinois. While taking science and
math prerequisites at Northeastern Illinois University she was introduced to aquatic
ecology and attended a diatom ecology class at the field station of the Iowa State
University in the summer of 2007. She received an assistantship and entered the biology
graduate program at Loyola University Chicago in 2007 and conducted research in algal
ecology. Her research was presented at the North American Benthological Society’s
annual meeting in May 2009. She is interested in a career that involves improving water
quality in natural and manmade aquatic ecosystems in the Chicago regional area.
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