nutrient-diffusing substrate method capabilities in impacted streams with regard to light and
TRANSCRIPT
Nutrient-Diffusing Substrate Method
Capabilities in Impacted Streams with
Regard to Light and Substrate Type
A thesis submitted to the
Graduate School
Of the University of Cincinnati
In partial fulfillment of the
Requirements for the degree of
Master of Science
In the Department of Biomedical, Chemical, and Environmental Engineering
Of the College of Engineering and Applied Science
By
Samantha J. Smith
B.A. University of Cincinnati
June 2007
Committee Members:
Makram Suidan, PhD (Chair)
Christopher T. Nietch, PhD
Lilit Yeghiazarian, PhD
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ABSTRACT
Nutrient-diffusing substrates (NDS) consist of porous material enriched with soluble compounds,
typically nutrients, used to observe the impacts on stream periphyton in situ. This study intended to
evaluate the potential for NDS to test nutrient-specific effects in impacted streams undergoing TMDL
development. A new sampler was designed and tested against a typical sampler in a stream mesocosm.
Inadequate diffusion and premature depletion, respectively, were observed. The new sampler design
was used with substrates of different pore sizes, which were tested for differences in nutrient loss and
assessed for periphyton growth dynamics using a handheld fluorometer. While the larger pore size
substrates stabilized at significantly lower nutrient delivery rates, all appeared to adequately enrich
colonizing periphyton throughout a 21-day deployment. However, periphyton colonized faster on the
larger pore size substrates, which was attributed to higher surface roughness rather than nutrient
delivery rate. The potential importance of these differences was tested using the new sampler design
and two substrate types – porous crucible covers (PCC) and fine fritted glass discs (FGD) – in stream
mesocosms. Field N:P ratio conditions of impacted streams were mimicked, with a low N:P (4.4 ± 0.85
inorganic N:P, consisting of a 421.6 ± 47.1 µg-N/L and 216.2 ± 32.5 µg-P/L, background) and high N:P
(49.25 ± 13.7, 1855.9 ± 136.7 µg-N/L, 90.7 ± 28.5 µg-P/L) treatment, achieved by metering stock NaNO3
or NaH2PO4 solutions continuously to a diluted natural river water supply to approximate a reference
condition for the streams in question. A light treatment was added (low 74.0 ± 3.8 µmol m-2s-1 and high
270.4 ± 28.2 µmol m-2s-1, incident PAR) for a 3-factor experiment. Periphyton growth dynamics were
assessed every other day, and chlorophyll-a, AFDM, and dissolved oxygen metabolism responses were
measured post-deployment. From these measurements, 22 periphyton response metrics were
calculated. These were tested for a response to NDS nutrient-specific enrichment in the expected
direction, based on the assumption that the experimental conditions were, in fact, nutrient-limiting.
Although more expected responses to the NDS-enriched nutrient were observed on PCC (23% N, 32% P)
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than FGD (5% N, 27% P), overall, strong interactions with light availability presently preclude a definitive
answer to the relative importance of substrate type used in NDS studies. It is possible that nutrient
limitation was not actually present in the mesocosms, likely due to high background P. Despite the
improvements made to the NDS deployment method, it needs further study to be applicable. Notably,
the results suggest that increasing replication may help, but the strong interactions with light and the
typically elevated nutrient contents of impacted streams may prove difficult to overcome for attaining a
reliable and standard NDS approach to confirm expected nutrient-specific stress.
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ACKOWLEDGEMENTS
I have many people to thank for their support during this research:
Dr. Christopher Nietch for serving as my advisor, and for his guidance and patience throughout the
development and completion of this project;
Dr. Makram Suidan for serving as my faculty advisor, and Dr. Lilit Yeghiazarian for serving on my
committee;
The staff of the U.S. EPA Experimental Stream Facility – especially Donald Brown, Dr. Balaji
Ramakrishnan, Benjamin Smith, and Elisha Bryan for their assistance with the mesocosms and sampling,
and Maria Maurer, William Wright, and Susanna DeCelles for their assistance in the laboratory;
Pegasus Technical Services, Inc., and my manager Dr. Raghuraman Venkatapathy, for generously
granting me the flexibility to devote such time to this project;
And my colleagues and friends at the U.S. EPA, for their helpful advice and feedback.
I would also like to thank the U.S. Environmental Protection Agency (NRMRL/WSWRD/WQMB) for
funding this research.
Special thanks to my loving husband, Logan, and my parents, for their endless encouragement and
optimism.
I would like to express my sincere appreciation to all of you.
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TABLE OF CONTENTS
INTRODUCTION ..................................................................................................................................... 1
1.1. Nutrient Enrichment & Environmental Consequences ................................................................. 1
1.2. Total Maximum Daily Loads in Lotic Systems ............................................................................... 1
1.3. Periphyton ..................................................................................................................................... 3
1.4. N:P Ratios and Nutrient Limitation ............................................................................................... 5
1.5. Light-nutrient interactions ............................................................................................................ 6
1.6. Nutrient-Diffusing Substrates & Application ................................................................................ 7
NDS SAMPLER DEVELOPMENT ............................................................................................................ 10
2.1. Initial field test ............................................................................................................................ 11
2.2. Initial modifications of NDS sampler design ............................................................................... 12
2.3. First mesocosm substrate test .................................................................................................... 14
2.4. Further modifications of NDS sampler design ............................................................................ 16
2.5. Second mesocosm substrate test ............................................................................................... 17
PURPOSE OF STUDY ............................................................................................................................ 23
MATERIALS AND METHODS ................................................................................................................ 23
4.1. NDS sampler construction .......................................................................................................... 23
4.2. Experimental site ........................................................................................................................ 25
4.2.1. Light treatments .................................................................................................................. 26
4.2.2. N:P ratio treatments ........................................................................................................... 28
4.3. Experimental design .................................................................................................................... 31
4.4. NDS deployment ......................................................................................................................... 32
4.5. NDS retrieval ............................................................................................................................... 33
4.6. Sample analysis ........................................................................................................................... 34
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4.6.1. Periphyton sample processing ............................................................................................ 34
4.6.2. Dissolved oxygen metabolism ............................................................................................. 35
4.6.3. Ash-free dry mass ............................................................................................................... 38
4.6.4. Chlorophyll-a ....................................................................................................................... 39
4.6.5. Benthotorch ........................................................................................................................ 39
4.6.6. Mesocosm nutrients ........................................................................................................... 43
4.6.7. Diffusion rate ...................................................................................................................... 43
4.6.8. Water quality sensors ......................................................................................................... 46
4.6.9. Light sensors ....................................................................................................................... 46
4.7. Statistical analysis ....................................................................................................................... 47
RESULTS............................................................................................................................................... 48
5.1. Mesocosm conditions ................................................................................................................. 48
5.2. Main treatment effects ............................................................................................................... 52
5.3. Nutrient enrichment effects ....................................................................................................... 57
5.4. Diffusion rate effects................................................................................................................... 59
DISCUSSION ......................................................................................................................................... 62
6.1. Light effects ................................................................................................................................. 63
6.2. N:P ratio effects .......................................................................................................................... 64
6.3. Substrate effects ......................................................................................................................... 65
6.4. Deployment method effects ....................................................................................................... 67
6.5. Benthotorch assessment ............................................................................................................ 69
6.6. Implications for NDS application................................................................................................. 71
REFERENCES ........................................................................................................................................ 72
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LIST OF FIGURES
Figure 1. Initial field test at Heiserman stream (Milford, OH) .................................................................... 12
Figure 2. Diffusion from PCC vs. ceramic discs. .......................................................................................... 15
Figure 3. NDS sampler deployment during diffusion study. ....................................................................... 18
Figure 4. Incubation test diffusion results from second mesocosm test. .................................................. 19
Figure 5. Agar analysis diffusion results from second mesocosm test. ...................................................... 21
Figure 6. Benthotorch results from second mesocosm test. ...................................................................... 22
Figure 7. NDS sampler construction for nutrient limitation study. ............................................................ 25
Figure 8. Full view of light treatments for nutrient limitation study. ......................................................... 27
Figure 9. Light treatment isolation in nutrient limitation study. ................................................................ 27
Figure 10. Mesocosm flow and dosing schematic. ..................................................................................... 30
Figure 11. Chemical dosing tanks. .............................................................................................................. 31
Figure 12. Gravel baskets within mesocosm channels. .............................................................................. 33
Figure 13. Periphyton processing on tile substrates. ................................................................................. 35
Figure 14. Sonde setup for DO metabolism measurements. ..................................................................... 37
Figure 15. Explanation of grofit parameters. .............................................................................................. 41
Figure 16. Example growth curves fit with R grofit package, individual replicates .................................... 42
Figure 17. Example growth curves fit with R grofit package, all replicates of a single treatment. ............ 42
Figure 18. Incubation test for nutrient diffusion rate assessment. ............................................................ 45
Figure 19. Water quality parameters during nutrient limitation study. ..................................................... 49
Figure 20. Mesocosm nutrients during nutrient limitation study. ............................................................. 51
Figure 21. Nutrient limitation study control results by light level, N:P ratio, and substrate type. ............ 54
Figure 22. Nutrient limitation study growth parameter results by light level and substrate type. ........... 55
Figure 23. Mean incubation test diffusion rates vs. diatom concentrations for all treatments. ............... 60
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Figure 24. Incubation test results from nutrient limitation study by light level and N:P ratio. ................. 61
Figure 25. Agar analysis diffusion rates from nutrient limitation study. .................................................... 62
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LIST OF TABLES
Table 1. Incubation test, day 16 and day 21 diffusion rates by substrate and nutrient type. ................... 20
Table 2. Nutrient concentrations and N:P ratios observed in EFLMR watershed. ..................................... 28
Table 3. Experimental design for nutrient limitation study........................................................................ 32
Table 4. Mean values of water quality parameters during nutrient limitation study. ............................... 50
Table 5. Nutrient concentrations and flow rates during nutrient limitation study. ................................... 51
Table 6. Nutrient Limitation Study 3-factor ANOVA on control substrates. .............................................. 53
Table 7. Nutrient enrichment effects by N:P ratio and substrate type. ..................................................... 57
Table 8. Significant light interactions with N:P ratio. ................................................................................. 58
Table 9. Types of responses observed in light x N:P ratio interactions. ..................................................... 59
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LIST OF SYMBOLS AND ABBREVIATIONS
µ mu, maximum growth rate (Benthotorch grofit parameter)
λ lambda, lag phase (Benthotorch grofit parameter)
A maximum concentration (Benthotorch grofit parameter)
AFDM ash-free dry mass
AIC Akaike Information Criterion
ANOVA analysis of variance
Chl-a chlorophyll-a
CR community respiration
Cyano cyanobacteria
DIN dissolved inorganic nitrogen
DO dissolved oxygen
DRP dissolved reactive phosphorus
ES ecological stoichiometry
FGD fritted glass disc
GLM general linearized model
GPP gross primary production
N:P nitrogen:phosphorus ratio
NCM net community metabolism
NDS nutrient-diffusing substrate
PAR photosynthetically-active radiation
PCC porous crucible cover
TDI total daily irradiance
1
INTRODUCTION
1.1. Nutrient Enrichment & Environmental Consequences
The U. S. Environmental Protection Agency has named nutrient enrichment among the leading
causes of stream impairment since the 1980’s (Miltner 2010), and nutrients in aquatic ecosystems are
increasingly found to be of anthropogenic origins (Elshorbagy et al. 2005, Iwanyshyn et al. 2008).
Common sources include wastewater and septic system inputs and runoff from agricultural and urban
areas. Nutrients, specifically nitrogen and phosphorus, are responsible for nourishing the inhabitants of
aquatic ecosystems, but the excess of these nutrients also has many negative consequences (Elshorbagy
et al. 2005). Nutrient enrichment is known to cause eutrophication of lakes, reservoirs, and other lentic
water bodies fed by streams – producing nuisance algal growth in both the water column and attached
to substrates. Public awareness of this occurrence is often limited to the detrimental impacts on
aesthetics and recreational uses of water bodies, but the impairments extend beyond these relatively
superficial observations. The growth-decay cycles of algae and the respiration of associated bacteria
lead to an increased consumption of dissolved oxygen, which can deplete the supplies necessary to
sustain other forms of aquatic life, such as fish and macroinvertebrates, ultimately leading to hypoxia of
receiving waters. Extended periods of nutrient enrichment are also responsible for food web
disruptions and decreases in species diversity (Liess and Kahlert 2007, Ferragut and de Campos Bicudo
2010). Due to the negative impacts of excess nutrients in surface waters, it is necessary to identify when
they are the source of impairment in order to support regulation.
1.2. Total Maximum Daily Loads in Lotic Systems
Watershed management has three general objectives: rehabilitation, protection, and
enhancement of watershed resources (Elshorbagy et al. 2005). The implementation of the federal Clean
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Water Act (CWA) in 1972 dramatically improved water quality in the United States and the ability to
assess and attain these watershed management goals (Keller and Cavallaro 2008). A federally
maintained database known as the CWA Section 303(d) list includes information on the nation’s
impaired water bodies and their sources of impairment, including nutrients (Keller and Cavallaro 2008).
The Total Maximum Daily Load (TMDL) program, contained within the CWA Section 303(d), was
originally established to define waste load allocations to point source polluters (Kang et al. 2006).
Today, USEPA’s guidelines define a TMDL as “the sum of allowable pollutant loads from point and
nonpoint sources, added to the natural background” of a receiving water for the maintenance or
improvement of its overall health (Kang et al. 2006, Iwanyshyn et al. 2008). Permits are acquired from
the National Pollutant Discharge Elimination System (NPDES) as discharge limits. This approach to
water quality management has been adopted by many states for water quality management, and states
are now required to develop TMDLs for any water bodies whose engineering controls are insufficient for
meeting designated uses under USEPA Water Quality Planning and Management Regulations (40 CFR
Part 130) (Elshorbagy et al. 2005).
Although it has been in existence since the CWA’s initiation, the TMDL program is only recently
gaining momentum (Elshorbagy et al. 2005). While the program has successfully regulated point source
pollutants for decades, nonpoint source pollutants, e.g. nutrients in runoff, are becoming increasingly
important to control (Elshorbagy et al. 2005). Although this is a step in the right direction for effective
water quality management, many challenges still await those responsible for developing functional
TMDLs (Elshorbagy et al. 2005, Keller and Cavallaro 2008). USEPA has provided only limited guidance to
states, and does not specify which parameters must be assessed (Keller and Cavallaro 2008). This is
problematic, since many states are performing field studies to develop nutrient criteria empirically
(Miltner 2010), and the resulting variations among states have led to difficulties in attaining the nation’s
water quality objectives (Keller and Cavallaro 2008). The use of bioindicators in stream assessments
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may help to alleviate this concern, since these metrics could account for specific ecological conditions
within a region while providing similar endpoints for comparison nationwide. Periphytic algae has been
shown to be particularly useful as a bioindicator (Miltner 2010, Mulholland and Webster 2010), and
research regarding its responses to nutrient enrichment would further support its use in TMDL
development.
1.3. Periphyton
Periphyton is a very important constituent of freshwater systems. It is a symbiotic community
of algae, bacteria and other microbes, and fungi, living within a matrix of excreted exopolymeric
substances. This matrix and its inhabitants form a biofilm attached to benthic substrates of streams and
have been found to contribute significantly to many energy- and nutrient-cycling processes (Battin et al.
2003, Ács et al. 2007). The algal component of periphyton is known to be the dominant primary
producer in streams (Ács et al. 2007, Godwin et al. 2009) and to fulfill a key role in the food web as a link
between consumers and dissolved nutrients (Godwin et al. 2009, Hill et al. 2010). Battin et al. (2003)
referred to periphyton biofilms as “living zones of transient storage”, describing their architectural
advantages for efficient retention and uptake of nutrients and the impact this might have downstream
through longitudinal linkages. Algae had long been assumed the major constituent of periphyton, but
this has recently been disproven (Frost et al. 2005, Danger et al. 2008). Nevertheless, researchers
continue to confirm the significance of algae within the biofilm regarding nutrient uptake and storage
and its impact on overall stream processing (Mulholland et al. 1995, Dodds et al. 2004, Hillebrand et al.
2004, Frost et al. 2005, Danger et al. 2008, Hillebrand et al. 2008, Godwin et al. 2009, Small et al. 2009,
Hill et al. 2010, Schade et al. 2011).
Algal periphyton has many suitable characteristics for studying stream nutrient interactions
(Liess and Kahlert 2007), but there are still many obstacles to understanding even its relatively simple
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functions within a stream (Lambert et al. 2008). Recent studies have focused on periphyton’s responses
to nutrient supply. Schade et al. (2005) described three main mechanisms of response: physiology,
morphology, and behavior. Ferragut and de Campos Bicudo (2010) referred to these mechanisms as
“adaptive strategies” and found that nutrient enrichment correlated with increased algal size, more
firmly-attached growth, and decreased species diversity. Overall, these observations show that under
excess nutrients, mass transfer will no longer limit algal growth and formerly less-competitive forms are
able to dominate (Ferragut and de Campos Bicudo 2010). Other studies have described the effects of
nutrient enrichment on the nutritional quality of periphyton (Qin et al. 2007, Danger et al. 2008,
Hillebrand et al. 2008, Elser et al. 2009). Algal cells can store excess nutrients in internal vacuoles for
“luxury consumption” when in short supply (Hall et al. 2005). This sequestration results in a mismatch
between the elemental ratios in the periphyton’s biomass and that of the water column. The ability of
algae to alter their chemistry in such a way is known as “stoichiometric plasticity” and can negatively
impact its nutritional quality for consumers (Hillebrand et al. 2004, Hall et al. 2005, Hillebrand et al.
2008, Godwin et al. 2009, Schade et al. 2011). Because these changes in periphyton stoichiometry in
response to nutrient supply ultimately affect higher trophic levels, further research is needed on the
subject of nutrient enrichment and the resulting periphytic responses in streams.
Although phytoplankton has long been studied to assess the health and nutrient status of lentic
systems, warning signs observed in these species may come too late for regulation to have a reversible
effect (Lambert et al. 2008). Periphyton, on the other hand, is much more closely-linked to land-derived
inputs and can respond to nutrients before they are diluted in open water (Lambert et al. 2008). Dodds
et al. (2004) described a nutrient “saturation point” that could indicate a level at which the ecosystem is
no longer capable of processing nutrient inputs effectively and therefore is functionally impaired.
Incorporating observations such as these into future TMDL development could improve the predictive
value of models, decrease time and effort involved in monitoring programs, allow greater comparability
5
among regions, and give first-response indication of nutrient impacts compared to the current approach
(Dodds et al. 2004, Elshorbagy et al. 2005, Lambert et al. 2008). For these reasons, the study of
periphyton response to nutrient enrichment is a prudent effort.
1.4. N:P Ratios and Nutrient Limitation
In 2002, Sterner and Elser published the definitive work on an emerging field: “ecological
stoichiometry” (Martinez del Rio 2003). In this book, the authors describe the concept of studying the
ratios of key elements (namely C, N, and P) throughout ecosystems to explore the relationships among
its inhabitants. Of particular interest in the application of ecological stoichiometry (ES) is the ability to
predict nutrient limitation of organisms by measuring the nutrient ratios in surface waters. The well-
known Redfield ratios for C:N:P (106:16:1) were developed for marine phytoplankton in 1967, but more
recent studies have begun to focus on freshwater pelagic and benthic systems (Hillebrand et al. 2004,
Frost et al. 2007, Danger et al. 2008). Although ES typically focuses on C-based ratios (Frost et al. 2005),
it is also important that N:P ratios are studied specifically with regard to stream periphyton (Hillebrand
and Sommer 1999). Nitrogen and phosphorus are the most frequently limiting nutrients in aquatic
systems (Hall et al. 2005) and are also the key drivers of primary production in lotic systems (Irvine and
Jackson 2006). ES defines N-limitation at low N:P ratio, and P-limitation at high N:P ratio. Hillebrand
and Sommer (1999) observed optimal ratios for benthic microalgae at 119:17:1 in laboratory
experiments, but O’Brien and Wehr (2010) found that stream periphyton response to stoichiometry
dramatically deviated from these levels in the natural environment. Irvine and Jackson (2006), however,
found that only about half of the variability they witnessed in periphyton responses could be attributed
to N and P in their field experiments. It is therefore apparent that nutrients alone are not fully
responsible for the periphyton responses observed.
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1.5. Light-nutrient interactions
Periphyton responses to nutrient enrichment have typically been difficult to predict due to the
various other factors within the system – biotic and abiotic – that contribute to these responses
(Scrimgeour and Chambers 1997). Researchers have found several potential interactions in periphyton
nutrient enrichment studies, e.g. heterotrophic content in periphyton, light, herbivory, flow, temporal
effects, land use, and spatial effects. Of considerable import is the effect of light. Light, in fact, is a
quantifiable resource that is limited in similar ways to nutrients – it is inhibited by mass transfer and has
a known saturation point of approximately 100 μmol photons m-2 s-1 (Hill et al. 2011). Although the
independent effects of light and nutrients on periphyton have been studied extensively, less is known
about their combined effects (Hill et al. 2011).
Some researchers have found that light and nutrients can be co-limiting to periphyton, in that
the availability of one resource impacts the utilization of the other (Taulbee et al. 2005, Hill et al. 2011).
Taulbee et al. (2005), for example, found an increasing response to nitrogen enrichment with increasing
light availability in an N-limited stream reach. Hill et al. (2009; 2011) also observed synergy between
light and phosphorus in their experimental streams. Johnson et al. (2009) reported greater magnitude
of nutrient limitation under increased light availability in a large study across ecoregions. These
examples demonstrate how organisms are limited by more than one type of resource, and that growth
is essentially limited by that which is most scarce (Taulbee et al. 2005).
“Photoinhibition” is one mechanism which may impact periphyton responses to light, and refers
to decreased photosynthesis under exposure to high irradiance (Han et al. 2000). This reaction occurs
within hours of high light exposure and damages the electron transport chain in photosystem II (Han et
al. 2000). Since light is limited via mass transfer, photoinhibition is more likely to impact smaller-celled
organisms with greater surface area:volume ratios, and therefore more efficient uptake of photons (Hill
et al. 2011). This effect could be exacerbated by nutrients; Hill et al. (2011) explain that nitrogen, in
7
particular, allows for greater efficiency of photon capture by photosynthetic pigments. Photoinhibition
is more likely to occur at high altitudes, in response to sudden increases in irradiance, or exposure to
high irradiances during early periphyton development (Taulbee et al. 2005). Taulbee et al. (2005) gave
photoinhibition as the possible reason for the lack of response or decreased response to phosphorus
enrichment with increasing light levels in a sub-alpine stream. Hill et al. (2009) reported photoinhibition
in experimental streams, where lower algal biovolume was observed in response to low phosphorus
concentration at the highest light intensity. The effect of photoinhibition is not expected to be observed
in sub-saturating light conditions (< 100 µmol m-2 s-1) and is reported relatively infrequently in streams
(Taulbee et al. 2005), but this mechanism could be partially responsible for light-nutrient interactions in
periphyton studies.
These divergent and intense impacts of light on periphyton underscore the need for thorough
examination of both light and nutrients, since they may be more impactful together than independently
(Taulbee et al. 2005). Considering the effects of light and other compounding factors on periphyton, it is
clear that results from nutrient limitation studies should be assessed for possible interactions. In order
to better understand the effect of nutrient enrichment of surface waters on periphyton, these
interactions must be evaluated or controlled. Some of these factors, such as light, are impossible to
exclude, but efforts to disentangle the effects of these interactions could afford researchers insight into
the core effects of nutrients alone.
1.6. Nutrient-Diffusing Substrates & Application
Studies to assess stream periphyton responses to nutrients generally must employ some form of
artificial enrichment. Nutrient enrichment studies have been performed in streams for decades and
have evolved significantly since their introduction (Mulholland and Webster 2010). Historically, in situ
methods for the direct observation of nutrient impacts consisted of whole-stream enrichment or flow-
8
through systems (Corkum 1996). Nutrient diffusing substrates (NDS) emerged in the mid-1980’s as an
alternative method for artificial enrichment, and have many benefits over whole-stream enrichment
methods – particularly costs savings, decreased time required, and less incidental impacts on other
stream components (Capps et al. 2011). In NDS studies, the source of enrichment is the substrate itself
(Fairchild and Lowe 1984, Mulholland and Webster 2010). The substrates utilized are typically porous
materials that are either affixed atop reservoirs containing nutrients, or are infused with nutrients
themselves. The nutrients are then able to diffuse through the substrate into the boundary layer and
become available for uptake by periphyton at its surface (Corkum 1996). This is particularly
advantageous in the study of benthic communities such as periphyton, since the nutrients can be
available for consumption before becoming diluted in the water column. Due to these benefits and the
usefulness of periphyton as a bioindicator, NDS methods may be well-suited for the assessment of
streams during future TMDL development.
Artificial substrates are an essential component of NDS design, since they must be conducive to
colonization by periphyton and the attachment surface must be porous to allow for the passage of
nutrients. Benefits to using artificial substrates include the simplification of surface area measurement
and consistency of the colonization surface (Bergey and Getty 2006). Previously, researchers have used
a wide variety of materials as substrates in NDS studies: clay pots (Scrimgeour and Chambers 1997,
Capps et al. 2011), clay saucers on bricks (Godwin et al. 2009), porous crucible covers (Tank et al. 2006,
Capps et al. 2011), plankton nets (Sanches et al. 2011), Nitex polyester mesh (Busse et al. 2006),
cellulose sponges (Johnson et al. 2009), fritted glass discs (Johnson et al. 2009, Hoellein et al. 2010),
pressed silica discs (Irvine and Jackson 2006), wood veneers (Tank and Dodds 2003), and glass fiber
filters (Tank and Dodds 2003). The substrates previously employed by other researchers, however, all
suffer from distinct disadvantages. Clay pots protrude too far from the sampler surface – potentially
being sheared off or cracked during high flows, or not remaining submerged during low flows
9
(Scrimgeour and Chambers 1997). Some researchers also claim that clay can bias nutrient limitation
results because it contains metal cations that bind P, but others have found that this interaction is not
significant (Capps et al. 2011). Glass fiber filters are cheap and convenient, but they are subject to
destruction by grazers and provide no control over the nutrient release rate, which is then completely
dependent upon the nutrient medium (Capps et al. 2011). Porous crucible covers, are only available in
one small size, can trap sediment when secured by a cap, cannot be easily reused, and must be
purchased in bulk (see “2.1 Initial field test”). Cellulose sponges and wood veneers act as organic carbon
sources for heterotrophs and would therefore increase C availability to periphyton; while this is a
desirable attribute in studies isolating heterotroph activity, here it would interfere with the ability to
distinguish the effects of N and P alone. For these reasons, it was prudent to pursue other artificial
substrates for this study.
A notable challenge in designing NDS arrays is determining the release rate of nutrients from the
substrate. Release rate tests are extremely important, not only for knowledge of the availability of
nutrients to periphyton, but also as a potential measure for comparison among experiments (Rugenski
et al. 2008, Capps et al. 2011). Many – perhaps the majority of – NDS publications do not even mention
diffusion rate testing. Failure to assess release rates appropriately could cause unfortunate losses in
experimental data; for example: Godwin et al. (2009) decided to exclude P-enrichment from their NDS
study due to insufficient release of P from substrates, likely caused by too-low initial P concentration
(0.05M). Presently there is no standardized method for nutrient release rate determination, nor for the
sampler design itself; this makes inter-experimental comparison of diffusion rates difficult since the
numerous variations in samplers and test methods yield equally diverse results. Nutrient diffusion is
most often tested by submerging enriched substrates in a known volume of water in a sealed container,
and then sampling the water periodically throughout the incubation period. Previous methods for
testing release rates enlisted either deionized or stream water (which may or may not be replaced), no
10
flow or simulated flow (via shakers or magnetic stirrers), and differing sampling frequencies and
incubation times (Capps et al. 2011). Capps et al. (2011) emphasized the need for standardization of
release rate tests in NDS studies in response to these profound differences.
The length of NDS sampler deployment is another variable among NDS studies. The ideal
deployment period for a study is a function of nutrient release rate determined through bench tests
(Corkum 1996) and site-specific knowledge of periphyton accrual (Sanches et al. 2011). Past NDS studies
have incubated samplers at stream sites for periods ranging from 2 to 8 weeks, with a typical length of
14-21 days (Corkum 1996). If nutrient release rates peak early and decline significantly after a relatively
short period of time, it is counterproductive to allow the samplers to remain in the stream, i.e. in the
absence of the intended enrichment effect. Conversely, if periphyton is spontaneously scoured from the
substrates due to storm flows, it may be necessary to allow a longer incubation period for the biomass
to recover. Overall, it is expected that the ideal deployment period will change from study to study, but
diffusion rate and periphyton status are both necessary considerations.
While NDS methods are widely-used, there is still much to consider due to variation in the
sampler design and application. Substrate type, site conditions (e.g. light and background nutrients),
diffusion rate, and deployment period can all potentially affect the usefulness of experimental data.
Capps et al (2011) investigated three commonly-applied NDS methods and found major discrepancies in
the results among the different sampler types. In light of this, it would benefit the field if researchers
could develop a standardized NDS method and provide guidelines for its application; this would also
further support the potential for NDS use during TMDL development.
NDS SAMPLER DEVELOPMENT
The substrate is the primary element of the NDS design and impacts many of the desired
attributes of the NDS sampler – namely durability, cost, and ability to reuse. Multiple substrates were
11
explored prior to inclusion the in NDS nutrient limitation study, including: porous crucible covers,
ceramic discs, and fritted glass discs. These substrates have all been used in published NDS studies, in
some form, and have their own distinct benefits and drawbacks.
2.1. Initial field test
An initial test deployment of NDS samplers employed a typical porous crucible cover (PCC; Cat.
#528-042, LECO Corp., St. Joseph, MI) and snap-lid cup design, introduced by Tank et al. (2006) (Figure
1). The general purpose of this test was to assess this widely-used method for potential use in NDS
studies in the East Fork Little Miami River (EFLMR) watershed. Enrichment treatments for N (0.5 M
NaNO3), P (0.5 M KH2PO4), N+P (0.5 M NaNO3 and 0.5 M KH2PO4), and control (no added nutrient) were
prepared in 2% agar and dispensed into 2-oz. plastic cups with hinged, snap-on lids. Samplers were
deployed in Heiserman Stream, a headwater of the EFLMR near the US EPA Experimental Stream Facility
(Milford, OH). This test shed light on several distinct disadvantages of the use of PCC as a substrate.
First, it has a relatively small surface area for periphyton to colonize – only 6.15 cm2. Second, the discs
are fairly fragile and many arrive chipped or broken and are unsuitable for use. Also, it is difficult to
scrape periphyton from the substrate surface without inadvertently removing particles of the substrate
itself, which can lead to analytical issues and data inaccuracy. This problem leads some researchers to
utilize an entire substrate for analysis rather than first removing the periphyton from its surface, as in
the Tank et al. (2006) method, causing a need for higher replication of samplers since each substrate is
sacrificed for an individual analyte.
12
Figure 1. Initial field test at Heiserman stream (Milford, OH)
Using Tank et al. (2006) NDS method. Sedimentation is visible on substrate surface, and several samplers were dislodged by flow and debris.
The most notable drawback to this design, however, was the issue of sedimentation on the
substrate surface, which occurred shortly after deployment. The PCC discs are quite coarse and the
pores are large enough that sediment particles visibly adhered to the surface. This was exacerbated by
the sampler design, in which a hole was drilled through the lid of the cup to expose the substrate
surface while keeping it secured beneath the rim. The thickness of the lid itself created a depression,
forming a trap for sediment to deposit. Sedimentation was observed to inhibit periphyton colonization
relative to the surrounding natural substrates. Due to this issue, it was practical to explore a modified
NDS design employing a different substrate and another method for mounting it within the sampler.
2.2. Initial modifications of NDS sampler design
An alternative NDS sampler design proposed the use of Mason jars with canning lids as a
substitute agar reservoir. Canning lids were an attractive feature, since the flat lid could be removed
and the ring used to secure a substrate at the mouth of the jar. The canning ring would then be
13
essentially flush with the surface of the underlying substrate, thereby minimizing the opportunity for a
sediment trap. A regular size Mason jar can accommodate a 2.25” substrate at its mouth with a securely
closed canning ring. The smallest jar volume available with a canning lid is 4 fluid ounces (~120 mL).
This volume is approximately twice what is used in the Tank el al (2006) method, but the additional
volume was justified by the expected increase in diffusion due to the greater surface area.
In order to utilize Mason jars as an agar reservoir, a new substrate was needed with the
appropriate diameter. Fritted glass discs large enough to fit the jar mouth were prohibitively expensive,
so ceramic discs were sought as an alternative. Clay-based substrates have been shown to have more
stable nutrient release rates than glass fiber filters (Capps, Booth et al. 2011). They also have the
benefit of being reusable, but with caution as the diffusion rate may change if they are re-combusted
(Scrimgeour and Chambers 1997). Some researchers claim that clay can bias nutrient limitation results
because it contains metal cations that bind P, but others have found that this interaction is not
significant (Capps, Booth et al. 2011).
After unsuccessful attempts to find a suitable commercial product, a procedure was developed
to create custom ceramic discs manually from moist clay, which is readily available in standardized
formulations through ceramic studios and art supply stores. The clay (Standard Ceramics #104) was
shaped into a 2.25”-diameter x 0.25”-thick disc by rolling out with a wooden dowel using 0.25” square
dowels as guides, and cutting apart with a 2.25” circular cookie cutter. The clay discs, once shaped,
were slowly air-dried over 2-3 days to prevent warping, then bisque-fired to maturation in an electric
muffle furnace. The maximum firing temperature was be kept between 900-1000C to ensure cohesion
of particles, yet prevent vitrification (i.e. quartz inversion), which would seal pores and impede inter-
surface flow (Jordan, Montero et al. 2008). During firing, the clay particles sinter together to form
ceramic material, which retains its shape when submerged in water. The dimensions of these discs
provide a relatively large surface area for nutrient diffusion and periphyton colonization (Capps, Booth
14
et al. 2011), are approximately the same thickness as the bottom of clay pots used in other studies, and
after firing, the discs fit snugly into the mouth of a 4-ounce mason jar beneath a canning ring
Since this ceramic disc and Mason jar NDS sampler included several modifications in its design,
and due to the inherent complexity of the field environment, it was deemed necessary to test the new
design under more controlled conditions prior to stream deployment.
2.3. First mesocosm substrate test
In this first test, the modified NDS sampler design was compared to the Tank et al (2006)
sampler design to observe differences in nutrient release rates between the two sampler types and,
therefore, demonstrate how their physical properties can impact the results of an NDS study. The test
was conducted in a stainless steel mesocosm flume at the US EPA Experimental Stream Facility (Milford,
OH; see “4.2 Experimental site”). The water source for the mesocosm was the East Fork River, and the
channel was re-circulated to reduce sediment inputs and fluctuations in background water quality. The
samplers were randomized within the mesocosm channel, and were submerged to allow 1-2” of water
flowing over the surface of the substrate. The diffusion rate for each sampler type was estimated by
agar analysis using a modified method of that described in Corkum (1996). Entire samplers of each type
were removed in triplicate at time 0, hour 3, hour 6, hour 12, and daily until day 14. The agar in each
sampler was then analyzed for remaining nutrients (see “4.6.7 Diffusion rate”). The nutrient mass
remaining in the agar was subtracted from the known initial mass, then converted to the percent that
had been diffused at each time point.
The results from this test are depicted in Figure 2. The percent diffused for each nutrient was
plotted by time, then fit with an exponential rise to maximum regression using SigmaPlot (Systat
Software, Inc.). Nitrogen was shown to diffuse rapidly from PCC, and samplers were approximately 90%
depleted by the end of the test. Some PCC samplers were shown to be effectively depleted by day 10.
15
Phosphorus diffused steadily from PCC samplers and had approximately 60% of the original mass
remaining at the end of 14 days. The PCC method also had high variation among samplers in both N and
P, as shown by the plots. Ceramic discs were not observed to diffuse either nitrogen or phosphorus
appreciably; nitrogen diffused less than 10% during the experiment, and phosphorus none at all.
Figure 2. Diffusion from PCC vs. ceramic discs.
Percent of original mass of nutrient diffused from agar at each time point. Data series were fit with regression of exponential rise to maximum.
This first test showed two undesirable extremes. First, the porous crucible covers allowed
nitrogen to diffuse too quickly, and the agar was effectively depleted before the end of the experiment.
Second, the ceramic discs did not allow nutrients to pass freely enough and diffusion was undetectable
over the course of 14 days. Therefore, neither method was considered appropriate, in its current
format, for use in this study.
PCC discs apparently either did not provide adequate restriction on the flux of nutrients from
the agar into the surface water, or did not have sufficient volume of agar. If agar reservoirs of
16
insufficient volume are used, as is probable in this case, nutrients can become depleted in a shorter
amount of time than is intended for the length of the study. Furthermore, the PCC results were highly
variable among samplers and these differences in diffusion rate could potentially translate to higher
variability of biological responses in an NDS study.
Ceramic discs did not allow nutrients to pass freely enough and diffusion was undetectable over
the course of 14 days. It is apparent that the conditions during firing allowed vitrification to occur within
the discs, sealing the pores and preventing the necessary inter-surface flow. Therefore, future testing
would be required to determine the appropriate conditions to achieve a solid, yet porous, ceramic disc
for use in NDS applications.
2.4. Further modifications of NDS sampler design
Based on the observations from the first mesocosm test, neither NDS design was deemed
suitable for immediate use in field studies. Further modifications were necessary to achieve an NDS
design with the desired attributes. Although the ceramic discs were disregarded as potential substrates,
the Mason jars were still considered an attractive option for an agar reservoir. Since it was difficult to
obtain an affordable substrate to fit the entire mouth of the jar, a method for mounting substrates
within the flat lid was developed. A hole was made in the flat lid using a hydraulic punch, yielding a
diameter only slightly larger than the intended substrate (see “4.1 NDS sampler construction”). This
substrate-mounting method provided for the use of practically any substrate smaller than the diameter
of the jar.
In the first mesocosm test, it was concluded that a larger volume of agar would be required in
order for PCC discs to be used in an NDS sampler. With the substrate-lid assembly method, PCCs were
able to be mounted atop a Mason jar, and a 4 oz. jar provided twice the volume of the snap-lid cups
used in the Tank et al (2006) method. The larger volume was expected to allow PCC samplers to provide
17
nutrients for a longer duration. Furthermore, sealing the agar reservoir was anticipated to help
decrease the variability of diffusion rate among samplers. The concerns over the durability and
reusability of the PCC discs still remained, however. For this reason, fritted glass discs were re-visited as
a potential substrate option.
A fritted glass disc (FGD; Chemglass Life Sciences, Vineland, NJ) is manufactured from glass
particles that have been fused together under high heat to form a solid, yet porous, medium. They were
originally excluded from use in the study due to the high cost of a 2.25” diameter disc needed to fit the
mouth of a Mason jar, but the substrate-lid assembly design allowed for the use of smaller discs at a
more attainable cost. FGDs still cost vastly more than PCCs – $10 each vs. $211 per 1000 – but they
have several advantages that researchers may consider worth the expense. It is useful that these discs
are typically used in filtration applications, since they have known porosity and pore size. They are
available in many different diameters, thicknesses, and pore sizes, and can also be ordered with custom
specifications. Since the discs are made entirely of glass, they are essentially non-reactive and will not
bind nutrients, as is the concern with ceramic. Despite being made from glass, however, they are also
much more durable than PCCs. The substrate can easily be scraped to remove periphyton without
causing damage to the surface, and the disc remains in a suitable condition for reuse in future studies.
Overall, FGDs were deemed the most attractive choice for a substrate.
2.5. Second mesocosm substrate test
The second substrate test utilized the updated sampler design – Mason jars with substrate-lid
assemblies – to compare PCCs to FGDs. The fritted glass discs selected were 40 mm in diameter, which
was small enough to be cost-effective and still provided a large surface area (12.56 cm2) for colonization
relative to that of the PCC (6.15 cm2). The results of this test were used to determine the optimal pore
size for the substrate and the ideal length of the deployment to be used in the main NDS study.
18
This test included five treatments – four enriched substrates and a control – with five replicates
each. The substrates included for comparison were: [1] PCC, [2] coarse FGD (40-60 µm), [3] fine FGD (4-
5.5 µm), and [4] fine FGD + filter. While the pore size of PCC is not reported by the manufacturer, it is
visibly larger than that of the coarse FGD. In the “fine FGD + filter” treatment, a glass fiber filter was
added between the substrate and the agar. Enriched samplers for this test were prepared with 0.5 M of
both nitrate (NaNO3) and phosphate (NaH2PO4). Control samplers contained unenriched agar and were
assembled with only one substrate type (fine FGD) in order to maintain a balanced experimental design.
This was justified in that the main contribution of nutrients from the controls in the incubation test
would be contamination from the sampler surface, which would be essentially the same for all substrate
types. All samplers were deployed in a mesocosm receiving East Form River water, and were
randomized within arrays of gravel baskets (Figure 3).
Figure 3. NDS sampler deployment during diffusion study.
(Left) Mesocosm channels with NDS samplers deployed. (Right) A closer view of a fine FGD and coarse FGD deployed within gravel baskets.
19
Diffusion rates were determined via the incubation test method and agar analysis to
demonstrate each sampler type’s ability to provide nutrient enrichment over a deployment period of at
least 14 days (see “4.6.7 Diffusion rate”). The incubation test (Figure 4) results showed extremely high
diffusion of both nitrate and phosphate initially, which dropped off significantly around 2 days after
deployment. Substrates behaved similarly between the two nutrients, and diffusion progressed from
fastest to slowest as: fine FGD + filter, fine FGD, PCC, and coarse FGD. This trend was maintained over
the course of the experiment.
Figure 4. Incubation test diffusion results from second mesocosm test.
N- and P-diffusion rates by substrate type as observed during incubation testing at each sampling event.
Table 1 gives the incubation test diffusion rates from days 16 and 21. These results show that
although diffusion rates differ greatly among the treatments, each was still able to provide nutrient
enrichment throughout the entire deployment. This is likely due to the increased agar volume in the
Mason jar compared to the snap-lid cups. This may also have been improved by the substrate-lid
assembly method completely sealing the agar reservoir and preventing direct interaction between the
20
surface water and the agar; all nutrient flux, therefore, occurred exclusively through the substrate itself.
The modifications in sampler design also contributed to much lower variation among sample replicates.
In the agar analysis diffusion rates from the first mesocosm substrate test, PCC replicates had 32.13%
and 48.56% CV for TN and TP, respectively. PCC replicates in the second mesocosm test, however,
showed much lower variation – with 7.85% and 10.23% CV for TN and TP, respectively.
Table 1. Incubation test, day 16 and day 21 diffusion rates by substrate and nutrient type.
Diffusion rates (µg cm-2 h-1) of nitrate and phosphate during incubation testing on days 16 and 21.
Substrate type Day 16 Day 21
NO2-3 DRP NO2-3 DRP
PCC 41.69 75.12 23.97 34.11
Coarse FGD 14.42 15.01 12.61 13.11
Fine FGD 105.61 184.03 76.52 117.55
Fine FGD + filter 89.25 144.51 65.61 112.81
The agar analysis results (Figure 5), on the other hand, showed that both nitrate and phosphate
diffusion progressed from fastest to slowest as: PCC, coarse FGD, fine FGD, and fine FGD + filter. These
results may have led to the erroneous conclusion that PCC substrates provided the greatest amount of
nutrient throughout the experiment, but the incubation test results showed this to be incorrect. It is
likely that the rates from the agar analysis were biased by the massive initial losses – as observed via the
incubation test results – since they were calculated as averages over the course of the test from discrete
results post-deployment. Therefore, it appears that the agar analysis does not provide an adequate
measure of the amount of nutrients supplied by the samplers, since it does not distinguish between the
initial losses and what is actually available to periphyton for the duration of the experiment.
It is also worth noting that the trend observed in the agar analysis results is in order of
decreasing pore size. This could indicate that greater initial losses may be expected in substrates with
larger pore sizes. The combined interpretation of results from the agar analysis and the incubation test
21
could, therefore, mean that PCC and coarse FGD substrates lose much more nutrient in the first few
days, resulting in significantly less remaining nutrients by day 2. This would explain why they would
have exhibited lower daily rates for the remainder of the experiment.
Figure 5. Agar analysis diffusion results from second mesocosm test.
Mean N- and P- diffusion rates by substrate type as calculated from remaining nutrients in agar at the end of deployment.
Benthotorch readings were taken to measure periphyton growth (see “4.6.5 Benthotorch”), and
Figure 6 depicts the cyanobacteria and diatom concentrations by substrate type over the course of this
test. In both cyanobacteria and diatoms, the concentrations progressed from highest to lowest as: PCC,
coarse FGD, fine FGD, and fine FGD + filter. This trend was maintained throughout the test. The curves
also show a difference in colonization rate; growth was observed on PCC and coarse FGD substrates
within the first few days, but the fine FGD treatments did not begin to show growth until after day 10.
22
Figure 6. Benthotorch results from second mesocosm test.
Cyanobacteria and diatom concentrations by substrate type at each sampling event.
Since the Benthotorch results showed the same pattern as the agar analysis diffusion results, it
could have been construed that increased diffusion rates yielded increased algal concentrations;
however, the incubation test diffusion results revealed that the real-time rates differed greatly from the
calculated mean rates that were determined through the agar analysis. These real-time diffusion rates
show an almost opposite trend to that observed in the Benthotorch results. Since this implies that
diffusion rate and algal concentrations are inversely related, one could speculate that colonization of the
substrate inhibited the diffusion of nutrients from the agar into the surface water. A more probable
explanation is that the diffusion rate and colonization rate were independently affected by the pore size.
A larger pore size allows for greater initial diffusion, after which the rate slows when it may become
limited by the flux through the agar itself. A larger pore size also creates a rougher surface, which allows
for faster colonization of substrates, and therefore higher accumulated biomass.
The results of this experiment imply that any of the substrate options would be potentially
suitable for future NDS studies, since all were shown to diffuse and colonize appropriately despite the
23
differences in magnitude. Fine FGDs and PCCs substrates were selected for inclusion in the NDS nutrient
limitation study since they were both shown to be functional, yet have very different qualities.
PURPOSE OF STUDY
This study was intended to evaluate the potential for using nutrient diffusing substrates for the
assessment of streams during TMDL development. A nutrient limitation test was conducted in
mesocosm conditions, using two different NDS substrates and two light levels to observe periphyton
responses to nutrient enrichment under imposed N- and P-limitation. The mesocosm surface water was
artificially enriched to achieve the N- and P- limiting conditions and to mimic naturally-occurring N:P
ratios and realistic absolute concentrations. Substrate type and light level were included as factors to
investigate their direct effects on periphyton, and to determine if NDS samplers were capable of
identifying nutrient limitation under the different treatment combinations. Overall, this study aimed to
provide researchers insight into the usefulness and limitations of NDS samplers under various
environmental conditions.
MATERIALS AND METHODS
4.1. NDS sampler construction
Samplers were constructed using both porous crucible covers (PCCs) and fine fritted glass discs (FGDs) as
substrates. Regular mouth, 4-ounce, glass Mason jars with canning lids were used as agar reservoirs.
Substrate-lid assemblies were produced to mount the substrate to the lid of the Mason jar. A hydraulic
punch was used to produce a hole slightly larger than the diameter of the substrate (28mm for PCC,
40mm for FGD) in the flat lid of each Mason jar. The substrate was laid flat on the work surface within
the hole in the lid and temporarily held in place with a piece of tape. The lid and substrate were then
24
turned tape-side down, and a thin bead of silicone caulk was applied to the underside of the void
between the lid and the substrate. After the caulk had cured, the lid was turned right-side up, the tape
was removed, and another bead of caulk was applied to the same void space on the top of the lid.
When the second layer of caulk had cured, the substrate-lid assembly was inspected to ensure that no
gaps or excess caulk were present.
For the nutrient enrichment treatments, 1-L solutions of 0.5 M NO3- (from NaNO3, for N
treatment), 0.5 M PO43- (from NaH2PO4, for P treatment), and 0.5 M each of NO3
- and PO43- (for N+P
treatment) were prepared in volumetric flasks using deionized water. Un-enriched deionized water was
used for the control treatment. Each solution was transferred to a 2-L Erlenmeyer flask with 20 g
granular agar and a stir bar, sealed with aluminum foil, and weighed. The mixture was stirred
continuously and heated to boiling on a magnetic stirrer. When the solution was clear and all agar was
dissolved, the flask was weighed again and water was added to account for any losses due to
evaporation. While hot, the agar solution was poured into the jars designated for its nutrient
enrichment type until a high meniscus formed above the lip of the jar. When the agar had cooled
enough for its surface to become flush with the mouth of the jar, the substrate-lid assemblies were
placed on the jars designated for each type, the canning rings were secured, and the samplers were
inverted onto clean trays to finish cooling (Figure 7). The inversion of the jars was intended to promote
full contact between the agar and the substrate without air gaps. When the agar was fully solidified,
each sampler was rinsed in deionized water to remove any excess agar and the tops were covered with
aluminum foil to prevent evaporation. The assembled NDS samplers were stored refrigerated at 4° C
and deployed within 24 hours.
25
Figure 7. NDS sampler construction for nutrient limitation study.
All mason jars are labelled with identifying information with enamel paint marker. (Top) Substrate lids are placed atop jars when agar has cooled slightly. (Bottom) Canning rings secure the lids, and the samplers are inverted to promote full contact between agar and substrate.
4.2. Experimental site
The study was conducted in May 2014 in the US EPA Experimental Stream Facility in Milford,
OH. This facility allows for experimentation at the mesocosm scale, incorporating elements of both the
field and the laboratory, through the use of artificial stream channels. These stream channels, or
mesocosms, have several advantages over field sites. Field conditions are naturally capricious, which
can lead to difficulties interpreting results, especially during method development. Mesocosms, on the
other hand, offer some level of control and can be fed by river water, RO water, and chemical dosing
tanks in desired proportions. Each flume is separated into two mesocosms, allowing for different water
26
conditions within a single channel. Since the mesocosms are indoors, experiments can be conducted
year-round. These benefits made the facility an ideal location for this study.
Each channel (two mesocosms) had an approximate water capacity of 50 gallons in the
experimental setup and a total discharge of 6 gpm. Mesocosms were recirculated at a rate of 15-20
gpm to maintain near-bed velocities of approximately 20-26 cm/s and a residence time of 8.5 minutes.
This flow regime was intended to provide sufficient mixing to eliminate dead-zone storage while still
maintaining realistic residence times as observed in low-order stream reaches. The recirculation of the
mesocosms allowed for reduced sediment inputs and fluctuations in background water quality, and
reduced chemical additions necessary for dosing. Furthermore, the mesocosms were adjusted to full
recirculation during storm events to exclude elevated levels of suspended sediment from the East Fork
River.
Since the channels were set-up immediately prior to the experiment and the deployment period
was short, macroinvertebrate colonization and grazing were expected to be minimal. Therefore,
herbivory was not considered to be a limiting factor on periphyton biomass.
4.2.1. Light treatments
Light level was incorporated as a factor to observe its effects on periphyton and the results of
the NDS nutrient enrichment. This study included one low light and one high light treatment, and one
full channel was dedicated to each light level (Figure 8). The treatments followed a 13.5 hour light cycle
each day and were expected to approximate shaded and open-canopy conditions, respectively. Full
spectrum grow lights were employed in both treatments. The low light treatment employed 1000 W
Sylvania metal halides installed in the ceiling of the stream facility high bay. The high light treatment
added 1000 W Agrosun (Hydrofarm, Inc.) metal halides in pendant fixtures approximately 4 feet above
the channel. A blackout curtain between the channels isolated the two treatments (Figure 9).
27
Figure 8. Full view of light treatments for nutrient limitation study.
Low light (top) and high-light (bottom) treatment mesocosms. The high light treatment used additional lighting installed above the mesocosm channel. The low light treatment used only the lighting installed in the high bay area.
Figure 9. Light treatment isolation in nutrient limitation study.
A blackout curtain was used between the mesocosms to isolate low-light and high-light treatments.
28
4.2.2. N:P ratio treatments
Based on the ubiquitous Redfield ratios, N-limitation is reportedly likely to occur at molar N:P
ratios < 10, and P-limitation at > 20 (Death et al. 2007). Background N:P ratios of the surface water were
manipulated to simulate N- and P- limiting conditions in the mesocosms, and to reflect the naturally-
occurring ratios and concentrations observed in the EFLMR watershed (Table 2). One treatment of each
nutrient-limiting condition was included in this experiment, and will be referred to as “low N:P” (N-
limiting) and “high N:P” (P-limiting).
Table 2. Nutrient concentrations and N:P ratios observed in EFLMR watershed.
Mean concentrations during March-May at 46 field sites in the EFLMR watershed monitored by US EPA.
# Site ID Nitrate-Nitrite
(µg-N/L) Ammonia (µg-N/L)
DRP (µg-P/L)
N:P Ratio (molar)
Limitation Type
1 143 345 8 34 23 P - Limited
2 506 2308 100 83 64.2 P - Limited
3 890 462 58 218 5.3 N - Limited
4 AVR 273 45 32 22 P - Limited
5 CEC 399 15 19 48.2 P - Limited
6 CLC 368 111 110 9.6 N - Limited
7 CWL 177 62 88 6 N - Limited
8 DAM 750 57 87 20.5 P - Limited
9 DWT 870 23 91 21.7 P - Limited
10 EFB 1252 174 170 18.6 Balanced
11 EFC 1323 48 148 20.5 P - Limited
12 EFG 1436 123 130 26.6 P - Limited
13 EFK 901 41 103 20.3 P - Limited
14 EFM 1351 95 128 25 P - Limited
15 EFY 1420 116 142 24 P - Limited
16 ELI 1120 88 135 19.8 Balanced
17 EUW 1074 93 122 21.2 P - Limited
18 FMR 288 75 79 10.2 Balanced
19 FVC 223 43 275 2.1 N - Limited
20 FVM 766 362 438 5.7 N - Limited
21 GRR 849 164 102 22 P - Limited
29
# Site ID Nitrate-Nitrite
(µg-N/L) Ammonia (µg-N/L)
DRP (µg-P/L)
N:P Ratio (molar)
Limitation Type
22 GRS 1291 152 115 27.8 P - Limited
23 GRT 742 155 70 28.4 P - Limited
24 HLR 262 32 40 16.3 Balanced
25 HST 474 22 40 27.5 P - Limited
26 HWR 811 199 248 9 N - Limited
27 KRT 415 219 148 9.5 N - Limited
28 LRC 296 16 27 25.6 P - Limited
29 LRN 78 23 48 4.7 N - Limited
30 NLT 252 23 43 14.2 Balanced
31 NWT 813 168 196 11.1 Balanced
32 OWT 432 67 181 6.1 N - Limited
33 P04 2295 38 57 90.6 P - Limited
34 S14 1460 128 128 27.5 P - Limited
35 S15 2645 112 95 64.3 P - Limited
36 S50 1065 74 269 9.4 N - Limited
37 S51 746 108 145 13 Balanced
38 SAR 129 20 18 18.3 Balanced
39 SHA 422 66 68 15.9 Balanced
40 SHC 623 60 47 32.2 P - Limited
41 SHR 185 19 22 20.5 P - Limited
42 SLT 292 48 25 30.1 P - Limited
43 STC 405 39 70 14 Balanced
44 TBS 31 16 27 3.9 N - Limited
45 UHL 727 66 44 39.9 P - Limited
46 USR 439 18 19 53.3 P - Limited
Mean: 672 65 88 18.5
Maximum: 2645 362 438 90.6
Minimum: 31 8 18 2.1
Type P - Limited N - Limited Balanced
Occurrences 25 11 10
% 54.35 23.91 21.74
The target concentrations of the non-limiting nutrients were 100 µg-P/L and 1100 µg-N/L, for
the low N:P and high N:P treatments, respectively. These concentrations were based on the
recommended management levels for TP and DIN reported by Miltner (2010). At the time of the
30
experiment, the nutrient concentrations in the East Fork River were 428 ± 70 µg-N/L and 52 ± 14 µg-P/L
for DIN and DRP, respectively, with an average molar N:P ratio of 18.3. To achieve the desired ratios
without exceeding the realistic target concentrations, all channels were fed by a 50:50 mix of East Fork
River water and reverse-osmosis water. The resulting reduction in background concentrations also
decreased the mass of chemicals necessary to achieve the enrichments.
The mesocosm flow diagram and dosing schematic is depicted in Figure 10, and a chemical
dosing tank setup is shown in Figure 11. For the high N:P ratio, or P-limiting, treatment, it was necessary
to elevate NO3- in the source water. The chemical dosing tanks were prepared with 971 mg/L NaNO3
and were dosed into the high N:P ratio mesocosms at 0.1 L/min, with a flow rate of 3 gal/min each. For
the low N:P ratio, or N-limiting, treatment, it was necessary to elevate PO43-- in the source water. The
chemical dosing tanks were prepared with 70 mg/L NaH2PO4 anhydrous and were dosed into the low
N:P ratio mesocosms at 0.1 L/min, with a flow rate of 3 gal/min each.
Figure 10. Mesocosm flow and dosing schematic.
Direction of flow is indicated with arrows. The river water and reverse osmosis water is delivered in 50:50 ratio to the head tank, which is mixed prior to being split between the two channel. Concentrated nutrients are delivered from the dosing tanks into the recirculation lines from each channel.
31
Figure 11. Chemical dosing tanks.
Used to amend mesocosm surface water in low N:P and high N:P ratio treatments. (Top) View of tank setup (dosing pump obscured by lid). (Bottom) View of mixer within tank.
4.3. Experimental design
This experiment employed two complete blocks of a 3-factor design to determine the effects of
light, N:P ratio, substrate type, and nutrient enrichment via NDS methods on periphyton responses. The
design included 3 factors – N:P ratio, substrate type, and NDS type –blocked within a 4th factor, light. All
resulting treatment combinations are summarized in Table 3. Four replicates of each treatment
combination were included in the study. This study therefore included a total of 128 samplers – with 64
samplers of each light level, N:P ratio, and substrate type, and 32 samplers of each NDS type. One full
mesocosm was dedicated to each of the two light treatments. The two channels within each mesocosm
32
were dosed separately for low N:P and high N:P ratio treatments. Samplers of all substrate types and
NDS types were randomized within each channel.
Table 3. Experimental design for nutrient limitation study.
Individual treatment combinations representing a 3-factor design (N:P ratio, substrate type, NDS type) within two treatment blocks (light). Treatment types are represented by the following numeric codes: light: 0 = low, 1 = high; N:P ratio: 0 = low, 1 = high; substrate type: 0 = PCC, 1 = fine FGD; NDS type: 0 = control, 1 = N, 2 = P, 3 = N+P.
4.4. NDS deployment
Gravel baskets were installed in 2 x 12 arrays in each mesocosm channel (Figure 12). The
mesocosms were fed with East Fork River water for 3 days prior to deployment to begin establishing
periphyton colonization on the gravel. Dosing began 2 days prior to deployment. An incubation test
was performed prior to the initial deployment and at intervals throughout the experiment (see “4.6.7
Diffusion rate”). NDS samplers were then deployed in randomized locations within the gravel basket
arrays, as in the second mesocosm test (Figure 3). Ceramic tiles were also deployed within the arrays, 4
per channel, for an additional unenriched control substrate. The channel depth allowed approximately
1-2” of surface water to flow over the surface of the substrates. Benthotorch readings were taken
Light N:P Ratio Substrate Type 0 1 2 3
0 0 0 0000 0001 0002 0003
0 0 1 0010 0011 0012 0013
0 1 0 0100 0101 0102 0103
0 1 1 0110 0111 0112 0113
1 0 0 1000 1001 1002 1003
1 0 1 1010 1011 1012 1013
1 1 0 1100 1101 1102 1103
1 1 1 1110 1111 1112 1113
NDS type
33
directly after deployment and every other day for the remainder of the experiment (see “4.6.5
Benthotorch”).
Figure 12. Gravel baskets within mesocosm channels.
Red outline indicates one gravel basket.
4.5. NDS retrieval
Due to the amount of time required for collection and sample processing, it was not possible to
collect all NDS samplers in a single day; therefore, the high light treatment samplers were retrieved on
day 18 and the low light samplers on day 19. Upon retrieval, the substrate-lid assembly was removed
from each NDS sampler, aluminum foil was placed over the exposed agar, and the canning ring was
replaced to seal it against moisture loss. The sealed jars of agar were then stored in the refrigerator
prior to processing and analysis (see “4.6.7 Diffusion rate”). The substrate-lid assemblies were manually
cleaned to remove excess biomass from the area of the lids surrounding the substrate, i.e. not on the
34
substrate itself. Each lid was each gently rinsed in the channel, then placed individually in a small tub of
stream water from its relevant treatment for subsequent periphyton processing.
4.6. Sample analysis
All sample processing and analyses were performed following standardized methods and quality
assurance standards from U. S. Environmental Protection Agency Standard Operating Procedures (ORD-
NRMRL-WSWRD-WQMB) unless otherwise noted.
4.6.1. Periphyton sample processing
The periphyton processing procedure followed the US EPA standard operation procedure ESF-
SOP-021 with the exception of modified homogenization and tile sub-sampling, as described.
Periphyton was removed from each NDS surface using a toothbrush or razor blade, if needed, and rinsed
into a graduated beaker. Periphyton was subsampled from tiles using a PVC tube fitted with a rubber
gasket to isolate a known surface area (12.56 cm2), and a drill-mounted brush was used to scour the
periphyton from within the isolated area (Figure 13). Each sample was diluted to 140 mL to provide
sufficient volume for subsequent analyses without over-dilution. Since this volume was too small to
accommodate an immersion blender, a milk frother (IKEA, USA) – a small, battery-operated, vibrating
wire whisk – was employed instead to obtain homogeneous periphyton slurries.
35
Figure 13. Periphyton processing on tile substrates.
(Left) Drill with bristle brush attachment. (Right) Removal of periphyton using drill-mounted brush, with known surface area isolated by PVC ring.
4.6.2. Dissolved oxygen metabolism
Dissolved oxygen (DO) metabolism measurements allow for the differentiation between
autotrophic and heterotrophic responses. The method for determining the DO metabolism in the
samples was adapted from Johnson et al (2009) and provides values for net community metabolism
(NCM), community respiration (CR), and gross primary production (GPP). For this analysis, two YSI 600-
OMS V2 sondes with ROX optical DO sensors were used (YSI Environmental, Yellow Springs, OH).
The initial DO concentration was measured on each periphyton slurry (see “4.6.1 Periphyton
sample processing”) on two different YSI sondes (to be used for subsequent “light” and “dark” readings).
The periphyton slurry of each sample was then divided into two 60-mL graduated syringes – one for
“light” incubation and one for “dark” – and the volume of each was recorded. These syringes were
evacuated of air and sealed with rubber plugs. The “light” syringes were submerged in the upstream
section of the relevant mesocosm channel, with the body of the syringe unobscured so that the light
36
was available to the periphyton in the slurry. The “dark” syringes were placed in foil bags to exclude
light before they were placed in the tail tank section of the relevant mesocosm channel. Syringes from
both treatments were allowed to incubate at least one hour. The syringes remained submerged in the
channel throughout the incubation to ensure consistent temperature among samples.
The final DO concentration was measured on the periphyton slurries after incubation, with a
“light” and “dark” reading for each sample. To minimize the flux of DO into or out of the sample during
DO measurements, a flow-cell was fitted over the DO sensor. This measurement setup is shown in
Figure 14. Flow-cells were constructed from a 1.5” PVC elbow, clear acrylic sheeting, and two stopcocks.
The lower stopcock was opened to allow the periphyton slurry to be injected into the flow-cell from the
syringe, while the upper stopcock was opened to allow air to escape from the chamber. A small
magnetic stir bar fit between the probe face and the bottom of the flow-cell chamber, and the assembly
was secured atop a magnetic stirrer to mix the sample as it was being measured. The flow-cell was
flushed with deionized water between readings.
37
Figure 14. Sonde setup for DO metabolism measurements.
(Left) Sonde with attached handheld unit and flow-cell assembly, with sample injected into flow-cell using a syringe. (Right) View of stir bar inside chamber, and outlet valve in flow-cell assembly.
All DO measurements were corrected for drift in the readings since the time of calibration. The
drift correction factor (mg/L·min) was calculated as the [DO value of the calibration check (mg/L) – DO
value at calibration (mg/L)] / difference in time between the two readings (min). The time difference
between each sample reading and the time of calibration was then calculated. The drift-corrected DO
value for each reading was obtained by subtracting the [drift correction factor (mg/L·min) * time
difference for the sample (min)] from the originally reported DO value (mg/L).
38
The NCM and CR values (µg O2/h) were determined as [final DO (mg O2/L) – initial DO (mg O2/L)] /
[(time out of stream – time place in stream) * 24 h * 1000 µg/mg]. NCM was calculated from the drift-
corrected readings of the light-incubated samples; CR was calculated from the drift-corrected readings
of the dark-incubated samples. GPP was calculated as NCM – CR. The “initial” values used in the
calculations were taken from the same sonde that was used to obtain the “final” readings. The NCM,
CR, and GPP were reported with values normalized for surface area of the substrate (µgO2/h·cm2) and
AFDM (µgO2/mgAFDM). GPP was also reported for normalization of chlorophyll-a (µgO2/µgChla).
4.6.3. Ash-free dry mass
Ash-free dry mass (AFDM) is a measurement of organic matter in a sample and was used, along
with chlorophyll-a, to determine the biomass present on each substrate. The procedure followed the US
EPA standard operating procedure ESF-SOP-017. Samples were initially prepared using the periphyton
slurry described above (see “4.6.1 Periphyton sample processing”) and were processed immediately
after they had been measured for dissolved oxygen. A known volume of sample was applied to a pre-
combusted, pre-weighed glass fiber filter (Whatman, GF-C) on a vacuum assembly using a graduated
syringe. Filters were placed in a drying oven at 105° C for 24 hours, cooled, and weighed. The samples
were then placed in a muffle furnace at 550° C for 2 hours, cooled in a desiccator, and weighed again.
The dry weight (g) was calculated as [weight after drying (g)] – [tare weight (g)]. The ash weight
(g) was calculated as [weight after muffle furnace (g)] – [tare weight (g)]. The subsample multiplier is
the [subsample volume (mL)] / [total slurry volume (mL)], where the total slurry volume for all samples
was 140 mL (see “4.6.1 Periphyton sample processing”). From these values, AFDM (mg/cm2) was
calculated as: ([dry weight (g)] – [ash weight (g)]) / [surface area (cm2)] * (1 g / 1000 mg) * [subsample
multiplier].
39
4.6.4. Chlorophyll-a
Chlorophyll-a is a pigment in primary producers used in photosynthesis, and was used as a
measurement of algal biomass on the substrates. The analytical procedure followed US EPA standard
operating procedure ESF-SOP-018, which is based upon EPA Method 445.0. Because algal pigments,
including chlorophyll-a, are photo-sensitive, light exposure of the sample was limited throughout the
processing and analysis processes. Samples were initially prepared using the periphyton slurry
described above (see “4.6.1 Periphyton sample processing”) and were processed immediately after they
had been measured for dissolved oxygen. A known volume of each sample was applied to a glass fiber
filter (Whatman, GF-C) on a vacuum assembly using a graduated syringe. Chlorophyll-a was then
extracted from the filters using 90% acetone and analyzed on a Unicam Spectrophotometer 520.
The chlorophyll-a concentration of the extract was calculated using Jeffrey and Humphrey's
Trichromatic Equations. The corrected absorbance value was obtained by subtracting the value at 750
nm from the result at each of the other wavelengths. The concentration of chlorophyll-a in the extract
was calculated from these corrected absorbance values as follows: 11.85 * (Abs 664) - 1.54 * (Abs 647) -
0.08 * (Abs 630) = mg/L in extract. The whole-water concentration of chlorophyll-a in the slurry was
then calculated as the [concentration in the extract * extract volume (L) * dilution factor] / [sample
volume (L) * cell length (cm)]. This result was then converted from mg/L to µg/L. The final
concentration of chlorophyll-a on the substrate (µg/cm2) was calculated as the slurry concentration
(µg/L) * slurry volume (L) * [1/surface area (cm2)].
4.6.5. Benthotorch
The Benthotorch (bbe Moldaenke GmbH, Germany) is a handheld fluorometer for field
measurement of benthic chlorophyll. This instrument differentiates among classes of algae based on
their specific fluorescence excitation spectra, and determines the concentrations of cyanobacteria,
40
green algae, and diatoms within the sampled area. Benthotorch measurements were taken on each
NDS sampler and tile controls on days 0, 1, 2, 4, 6, 8, 10, 12, 14, and 16 of the experiment. Readings
were also taken from randomly selected tiles at the head of each channel on each of these days and on
day 21.
The Benthotorch reports the concentrations of cyanobacteria, diatoms, and total algal biomass
at the time of measurement. By taking readings at time points throughout the experiment, it was
possible to observe the growth dynamics of periphyton. Growth curves were fit to the data using R
software’s “grofit” package (Kahm et al. 2010). Grofit applies parametric models and a model-free
spline to data to obtain growth parameters. The parametric models applied were logistic, Gompertz,
modified Gompertz, and Richards. The best fit of these models was selected by AIC value. The spline fit
method was used when parametric models were unable to produce reliable results or resulted in errors.
Both methods – models and spline – produced values for the parameters A, mu (µ), lambda (λ), and
integral. The parameter A is the maximum concentration, µ is the maximum growth rate, λ is the length
of the lag phase, and integral is the total accrual throughout the experiment. These parameters are
depicted in Figure 15.
41
Figure 15. Explanation of grofit parameters.
The parameter A describes the maximum concentration, µ the maximum growth rate, λ the length of the lag phase, and integral the total accrual. The parameters are fit using either the best-fit model or a model-free spline. This example is cyanobacteria growth on an individual sampler, and the Gompertz model was used.
Benthotorch data was run through the grofit function in two ways – first as independent
samplers, then as all samplers of a particular treatment type combined. The results are shown in Figure
16 and Figure 17. Data from the independent samplers was chosen for use in this study in order to
preserve replication for analysis, since the replicates were shown to behave similarly within each
treatment.
A
µ
λ
Integral
42
Figure 16. Example growth curves fit with R grofit package, individual replicates
Data depicted is from diatoms on PCC with N enrichment, in high N:P ratio and high light.
Figure 17. Example growth curves fit with R grofit package, all replicates of a single treatment.
Data depicted is from diatoms on PCC with N enrichment, in high N:P ratio and high light.
43
4.6.6. Mesocosm nutrients
Surface water from each channel was grab-sampled daily at the influent, head of channel, and
effluent points. These samples were analyzed for ammonia (NH4+), dissolved reactive phosphorus (DRP),
nitrate-nitrite (NO2-3), total nitrogen (TN), and total phosphorus (TP) on a Lachat Quikchem 8500 series 2
flow-injection colorimeter. Samples were frozen immediately upon collection and sample analysis was
completed within 7 days. Nutrient analyte holding times are established as 28 days for frozen samples.
The NH4+ analysis followed the US EPA standard operating procedure ESF-SOP-027, which is
based upon EPA method 350.1 and Lachat Quikchem Method 10-107-06-1-G.
The DRP analysis followed the US EPA standard operating procedure ESF-SOP-029, which is
based upon EPA method 365.1 and Lachat Quikchem method 10-115-01-1-V.
The NO2-3 analysis followed US EPA standard operating procedure ESF-SOP-026, which is based
upon EPA method 353.2 and Lachat Quikchem method 10-107-04-1-C.
The TN analysis followed US EPA standard operating procedure ESF-SOP-028, which is based
upon EPA method 353.2 and Lachat Quikchem method 10-107-04-1-J.
The TP analysis followed US EPA standard operating procedure ESF-SOP-030, which is based
upon EPA methods 365.1 and 365.3 and Lachat Quikchem method 10-115-01-1-F.
4.6.7. Diffusion rate
The rate of diffusion of N and P from NDS samplers was estimated in two ways: periodic
incubation tests and analysis of nutrients remaining in the agar post-experiment. While neither method
is intended to reflect the true level of nutrients available to the periphyton at the substrate surface, the
results were used to make comparisons between substrate types and among treatment conditions on a
relative basis.
44
Incubation test diffusion method
The incubation test protocol was based on the method described by Capps et al (2011) for
assessing nutrient diffusion. Additional replicates from each substrate type (PCC, fine disc) were
included in the study for dedicated use in this test. These samplers were enriched with both N and P
and were assembled and deployed identically to those used in the remainder of the experiment.
Incubation tests were performed prior to initial deployment on day 0, and on days 1, 2, 4, 8, 12, and 16.
On these days, each substrate was removed from the mesocosm and placed in a dedicated plastic tub
filled with 500mL of water from the mesocosm channel of its relevant treatment. The jar was sealed to
prevent changes in volume due to evaporation during incubation. As a control, a jar of source water
from each treatment was included in the incubation. Samplers were allowed to incubate for 1 hour,
undisturbed (Figure 18). After incubation, each sampler was removed from the jar and re-deployed in
its original mesocosm position. Each incubation tub was mixed on a magnetic stirrer for at least 30
seconds, then subsampled with a 5mL pipette while continuing to mix. Samples were frozen upon
dispensing and were subsequently analyzed for dissolved reactive phosphorus (DRP) and nitrate-nitrite
(NO2-3) using the methods described for mesocosm nutrients (see “4.6.6 Mesocosm nutrients”).
Nutrient concentrations of the control samples for each treatment were subtracted from the
associated incubation test sample results. Diffusion rates were reported as µg/cm2·h to account for
differences in diffusive surface area between PCCs (6.15 cm2) and fine discs (12.56 cm2).
45
Figure 18. Incubation test for nutrient diffusion rate assessment.
From day 2 of diffusion study. Samplers are sealed in tubs individually with deionized water for 1 hour.
Agar analysis diffusion method
A secondary method for assessing nutrient release was employed by analyzing the remaining
nutrients in the agar post-deployment, adapted from the method described in Corkum (1996). Instead
of sub-sampling the agar throughout deployment, however, the entire agar slug was analyzed post-
deployment to obtain the final values. The jars of agar from the NDS samplers were retained upon
retrieval (see “4.5 NDS retrieval”) and stored in the refrigerator at 4° C. The first two replicates of each
treatment combination were processed according to the following protocol for the determination of
remaining nutrients. The agar was removed from the jar, placed into a tared glass beaker, weighed to
the nearest 0.1 g, and the weight was recorded as the total agar weight (g). The beaker was then sealed
with aluminum foil and placed into an oven at 110-130° C to re-liquefy the agar. Once liquefied, the
temperature was reduced to 100° C to prevent the agar from burning while awaiting processing. One
beaker at a time was removed from the oven, stirred on a magnetic stirrer, and sub-sampled with a
pipette for TN and TP analysis in triplicate. Weight was used for samples instead of volume since the
agar was being dispensed hot and the temperature and was expected to vary from sample to sample.
The sub-samples were dispensed into tared glass test tubes, weighed to the nearest 0.0001 g, and the
46
weight was recorded as the sub-sample weight (g). Samples that had been enriched with the nutrient to
be analyzed were pipetted to ~0.1 g, whereas samples that had not been enriched with that nutrient
were pipetted to ~1.0 g. After samples were dispensed, 5 mL Milli-Q water was added to each test tube
prior to digestion for TN and TP (see “4.6.6 Mesocosm nutrients”).
The resulting concentrations were multiplied by the total dilution factor to obtain the corrected
values (µg N or P/L). The concentration of nutrients in the agar (µg N or P/g agar) was calculated from
the peak concentration (µg/L) * (1 L / 1000 mL) * digestion volume (mL) * (1/subsample weight (g)). The
total mass of nutrient remaining in the agar (µg N or P) was determined by the agar concentration (µg N
or P/g agar) * mass of the agar (g). The initial mass of nutrients in the agar prior to deployment (µg N or
P) was calculated as the enriched agar concentration (0.5 mol N or P/L) * molar mass (g N or P/mol) *
mass of the agar (g) * 106 µg/g * 1 L/1000mL. The nutrients diffused from each sampler during the
experiment (µg/sampler) was calculated as the [initial mass of nutrient (µg N or P)] – [the remaining
mass of nutrient (µg N or P)]. The final value for nutrient diffusion rate for each sampler (µg/cm2·d) was
calculated as nutrients diffused (µg/sampler) * (1/surface area (cm2)) * (1/deployment length (d)).
4.6.8. Water quality sensors
Water quality sensors were installed in the tail tanks of each mesocosm channel. Data were
continuously logged throughout the experiment for temperature, turbidity, pH, conductivity, dissolved
oxygen, and discharge.
4.6.9. Light sensors
Photosynthetically-Active Radiation (PAR) was measured using LI-COR quantum sensors (LI-COR,
Inc., Lincoln, NE) in the low-light mesocosm, the high-light mesocosm, and outdoors. Instantaneous PAR
values (µmol m-2 s-1) were converted to total daily irradiance (TDI; mol m-2 d-1) by accounting for the time
47
interval between readings (* 60 s/min * 5 min/interval), taking the sum of all values each day, and
converting from µmoles to moles (* 10-6). The mean TDI values for the low light and high light
treatments were then converted to percentages of the mean TDI outdoors.
4.7. Statistical analysis
The data analysis for this study was generated using SAS software, Version 9.2 of the SAS System
for Windows 7. Copyright © 2012 SAS Institute Inc. SAS and all other SAS Institute Inc. product or
service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA.
The general procedure for data analysis was to first perform Box Cox power transformations
using the SAS software’s Transreg procedure to determine the optimal lambda parameters for the
variables. The homoscedasticity of the transformed data was then verified using the Univariate
procedure. Data were then evaluated with the ANOVA analyses described below.
To evaluate the direct effects of experimental conditions (i.e. light, N:P ratio, and substrate type)
on periphyton, a 3-factor ANOVA was performed using the SAS software’s GLM procedure with Scheffe
analysis for multiple comparisons using control treatments only.
For the identification of nutrient limitation using NDS methods, a 2-factor ANOVA (light and NDS
enrichment type) analyses were performed for each substrate and N:P ratio combination. The SAS
software’s GLM procedure with Dunnett analysis was used to detect increased responses to enrichment
of the limiting nutrient compared to that of the control.
To observe the impact of light on the NDS method’s ability to detect nutrient limitation, a 2-
factor ANOVA (light and N:P ratio) was performed separately by substrate type and N- and P-enrichment
NDS types. This analysis employed the SAS software’s GLM procedure with Scheffe analysis for multiple
comparisons.
48
Diffusion rate analyses were performed on both incubation test data and remaining agar
nutrients data. To observe the impact of substrate type on diffusion rate, a 3-factor ANOVA (light level,
N:P ratio, and substrate type) was performed separately for each type of NDS enrichment (N and P).
These analyses were performed using the SAS software’s GLM procedure with Scheffe analysis for
multiple comparisons.
RESULTS
5.1. Mesocosm conditions
The light conditions in this study yielded mean TDI values of 3.48 mol m-2 d-1 in the low light
treatment and 13.14 mol m-2 d-1 in the high light treatment, which were 13% and 50% of the outdoor TDI
(26.36 mol m-2 d-1), respectively. The mean PAR values were 74.0 ± 3.8 µmol m-2s-1 in the low light
treatment and 270.4 ± 28.2 µmol m-2s-1 in the high light treatment.
The sensors installed in the channels reported that temperature, turbidity, pH, conductivity, and
dissolved oxygen did not differ greatly among the channels, but all were subject to disruptions (Figure
19). In general, these disruptions caused an increase turbidity and dissolved oxygen, and a decrease in
conductivity, pH, and temperature. The disturbances were attributed to both storms and sampler
deployment. Storms occurred on days 0-1 and 17-18 and required the channels to be 100% recirculated
during this time to exclude sediment inputs from the East Fork River influent. A small storm also
occurred on day 14, but the channels only required recirculation for a few hours. Furthermore, the
initial deployment of the samplers in the gravel baskets would have contributed to the large disturbance
at the beginning of the experiment. Periodic removal and replacement of diffusion test samplers would
have likewise created small disturbances on days 2, 4, 8, 12 and 16 (see “4.6.7 Diffusion rate”). One
notable trend is the increase in water temperature over the course of the experiment, which reflected
49
the warming outdoor temperatures. Mean values for the water quality parameters are reported in
Table 4.
Figure 19. Water quality parameters during nutrient limitation study.
Water quality data from sensors installed in the tail tank of each channel. Reference lines indicate recirculation periods due to storm disruptions on days 0, 1, 14, 17, and 18.
50
Table 4. Mean values of water quality parameters during nutrient limitation study.
Mean and standard deviations for each mesocosm channel, labeled by treatment.
Parameter High Light Low N:P
High Light High N:P
Low Light Low N:P
Low Light High N:P
Conductivity (µS/cm) 180.1 ± 49.7 191.8 ± 55.0 187.0 ± 70.3 199.2 ± 49.1
DO (mg/L) 9.9 ± 0.9 10.1 ± 0.9 9.7 ± 1.4 9.6 ± 0.5
pH 7.9 ± 0.6 7.7 ± 0.4 7.6 ± 1.0 7.6 ± 0.3
Temp. (C) 17.4 ± 3.5 17.8 ± 3.6 16.8 ± 3.8 17.5 ± 2.8
Turbidity (NTU) 7.2 ± 10.4 19.7 ± 52.8 4.8 ± 21.8 11.4 ± 4.8
The manipulation of the nutrient concentrations in the mesocosms produced two N:P ratio
treatments: low (4.4 ± 0.85) and high (49.25 ± 13.7). These values were calculated from the molar ratios
of dissolved inorganic nitrogen (NO2-3- and NH4
+) and dissolved reactive phosphorus (PO43-) in the surface
water. The low N:P ratio treatment had mean concentrations of 421.6 ± 47.1 µg-N/L DIN and 216.2 ±
32.5 µg-P/L DRP. The high N:P ratio treatment had mean concentrations of 1855.9 ± 136.7 µg-N/L DIN
and 90.7 ± 28.5 µg-P/L DRP. A summary of all nutrient concentrations and flow observed during the
experiment is given by treatment type in Table 5. The concentrations in both treatments were higher
than the target levels. This was attributed to the accumulation of nutrients contributed by the NDS
samplers from recirculation of the channels, which was especially pronounced during storm events (fully
recirculated). The excess N was not shown to significantly impact the intended treatment levels. The
excess P, however, was observed to negatively affect the desired N:P ratio treatments, so on day 5 of
the experiment, replicates of P-enriched samplers (P, N+P, and diffusion test treatments) were removed
in hopes of mitigating this effect. The impact of these P-enriched samplers is clear in Figure 20 as the
difference between the effluent of the high N:P treatment (not enriched with P) and the unenriched
influent. The flow of the RO water in the influent was also increased from 3.0 gpm to 3.5 gpm on day 5
in order to further dilute the river water influent (3.0 gpm) prior to dosing, for a total of 6.5 gpm.
51
Table 5. Nutrient concentrations and flow rates during nutrient limitation study.
Mean and standard deviations for each mesocosm channel, labeled by treatment.
Parameter High Light Low N:P
High Light High N:P
Low Light Low N:P
Low Light High N:P
Ammonia (µg-N/L) 8.9 ± 3.7 8.2 ± 3.2 9.9 ± 3.7 9.3 ± 3.5
DRP (µg-P/L) 222.1 ± 32.5 83.0 ± 31.4 210.3 ± 32.4 98.4 ± 25.6
Nitrate-nitrite (µg-N/L) 386.2 ± 43.4 1747.9 ± 76.7 438.3 ± 50.3 1946.4 ± 197.2
Total Nitrogen (µg-N/L) 634.2 ± 64.5 1992.1 ± 117.7 679.9 ± 38.5 2331.4 ± 267.6
Total Phosphorus (µg-P/L) 215.0 ± 26.6 78.8 ± 28.4 201.6 ± 27.8 90.2 ± 25.1
DIN (µg-N/L) 395.1 ± 43.8 1756.1 ± 77.0 448.2 ± 50.4 1955.7 ± 196.3
DIN:DRP molar ratio 4.0 ± 0.7 52.2 ± 16.5 4.8 ± 1.0 46.3 ± 10.9
River water flow (gpm) 3.00 ± 0.0 3.00 ± 0.0
RO water flow (gpm) 3.37 ± 0.2 3.37 ± 0.2
Total flow (gpm) 6.37 ± 0.2 6.37 0.2
Figure 20. Mesocosm nutrients during nutrient limitation study.
Daily nutrient concentrations in each dosing treatment. Truncated to exclude storm disruptions on days 0, 1, 17, and 18.
52
5.2. Main treatment effects
To investigate the effects of light, N:P ratio, and substrate type on periphyton responses, the
results from controls (unenriched PCC, fine FGD, tiles, and rocks) were assessed via a 3-factor ANOVA
(see “4.7 Statistical analysis”). Significant results (p < 0.05) are summarized in Table 6. All analytes
produced significant models, and all three factors were observed to have significant effects on
periphyton responses. These results were convoluted by interactions among factors, which were
significant on all light and N:P ratio effects, and all but 2 of the substrate type effects – including a
significant 3-factor interaction (light x N:P ratio x substrate type) in 4 of the analytes. The effects of
these experimental factors on several response variables are depicted in Figure 21 and Figure 22.
53
Table 6. Nutrient Limitation Study 3-factor ANOVA on control substrates.
Analysis of nutrient limitation study experimental effects on control substrates, including tiles and rocks. Results from 3-way ANOVA via SAS software’s GLM procedure with Scheffe adjustment for multiple comparisons. Significant p-values (<0.05) are reported. All data was analyzed via Box Cox, transformed with SAS software’s Transreg procedure for normalization of coefficients, and assessed for normality with Univariate procedure prior to analysis. (LL = light level, NPr = N:P ratio, Subs = substrate, * = interaction)
Analyte Model LL NPr Subs LL*NPr LL*Subs NPr*Subs LL*NPr*Subs
AFDM (mg/cm2) <0.0001 <0.0001 0.0029
Cyano A (µg/cm2) 0.0003 <0.0001 0.001
Cyano Conc. (µg/cm2) <0.0001 <0.0001 0.0293 <0.0001
Cyano Integral (µg/cm2) <0.0001 <0.0001 0.0076 0.0386
Cyano Lambda (d) <0.0001 0.0286 <0.0001 0.0345
Cyano Mu (µg/cm2·d) 0.0056 0.0009 0.0195
Diatom A (µg/cm2) 0.0428 0.0217 0.0021
Diatom Conc. (µg/cm2) 0.0013 <0.0001
Diatom Integral (µg/cm2) <0.0001 <0.0001 0.0002
Diatom Lambda (d) <0.0001 <0.0001 0.0315
Diatom Mu (µg/cm2·d) <0.0001 0.0082 <0.0001 0.0053 <0.0001
Total Conc. (µg/cm2) 0.0017 <0.0001
CR (µgO2/h·mgAFDM) <0.0001 0.0017 0.0003 0.0001 0.0203
CR (µgO2/h·cm2) <0.0001 <0.0001 0.0003 <0.0001 <0.0001
Chl-a (µg/cm2) <0.0001 0.0012 <0.0001 <0.0001
GPP (µgO2/h·mgAFDM) <0.0001 0.0006 0.0043 <0.0001 0.0375 0.0001
GPP (µgO2/h·µgChla) <0.0001 <0.0001 0.0449 0.0293 0.0143
GPP (µgO2/h·cm2) <0.0001 <0.0001 0.0075 0.0001 <0.0001 0.0113 0.0013
Total Significant: 18 8 6 13 8 12 2 4
Total Significant Without Interactions:
0 0 2
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Figure 21. Nutrient limitation study control results by light level, N:P ratio, and substrate type.
Depiction of control substrate responses for select biological variables in the NDS nutrient limitation study. Data are untransformed means and standard errors. Effects with the same letter are not significantly different. Significant light and/or N:P ratio effects are classified with upper case letters. Substrate effects were observed in all variables except chlorophyll-a and were accompanied with significant interactions. In the absence of a significant substrate by N:P ratio interaction, classifications are only given for the high N:P means.
55
Figure 22. Nutrient limitation study growth parameter results by light level and substrate type.
Depiction of control substrate responses for periphyton growth parameters in the NDS nutrient limitation study. Data are untransformed means and standard errors. Effects with the same letter are not significantly different. Significant light effects are classified with upper case letters. N:P ratio differences were largely insignificant and therefore not depicted separately. Significant substrate effects were observed in all variables and were accompanied by significant interactions.
56
Light significantly affected several response variables (8 of the 18), but always showed
significant interaction with another factor. Interactions occurred with both N:P ratio and substrate type,
and in all variables except one (diatom lambda). Of the 8 significant responses to light observed in this
study, high light and low light treatments each produced the greater response on 4 analytes. Notably,
the high light treatment showed greater results for GPP and cyanobacteria concentration (interactions
present), whereas low light elicited greater responses from diatoms (A, µ).
N:P ratio also had a significant effect on several response variables (6 out of 18). Of these 6
significant responses, however, all included an interaction with light. Most frequently, responses
tended to be stronger in high light / high N:P ratio and in low light / low N:P ratio, although the latter
was not significant. CR and GPP both exhibited this trend. In chlorophyll-a, all light/N:P ratio treatment
combinations showed similar results except high light / low N:P ratio, which was lower. Periphyton
growth parameters were also affected by the light-N:P ratio interactions. Cyanobacteria saw an increase
in λ (longer lag phase) under high light compared to low light, and diatoms saw a decrease in µ (slower
growth rate); however, N:P ratio treatments were not assessed separately this data.
Substrate type produced significantly different responses in 13 of the 18 variables, but results
were subject to interactions with both light and N:P ratio. Of these significant responses, PCC discs
produced the greater response in 9 analytes, and fine FGD in 2 analytes. The most compelling
differences were observed in the Benthotorch analytes, which showed that PCCs colonized almost
immediately (low λ) but fine FGDs grew rapidly (high µ) once colonization began. PCCs also showed
greater responses on AFDM, CR, and GPP. Tile responses were typically similar to or lower than fine
FGDs, with the exceptions of GPP and CR normalized by AFDM (tile > fine FGD). Benthotorch
measurements on rocks were included on some experimental days, and these results were significantly
different from artificial substrates in cyanobacteria concentration (lower), diatom concentration
(higher), and total concentration (higher).
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5.3. Nutrient enrichment effects
To detect nutrient limitation from the NDS method, a 2-factor ANOVA model was used to test
for the effects of NDS enrichment relative to control responses for each substrate and N:P ratio
condition (see “4.7 Statistical analysis”). Responses on NDS enriched with the “limiting” nutrient (i.e. N
in low N:P ratio, P in high N:P ratio) were expected to be stronger than those observed on control
substrates. These “expected” responses for the 22 response variables were tallied for each substrate
type and N:P ratio and are summarized in Table 7. Overall, both substrates positively detected nutrient
limitation in very few of the variables tested, and the responses were seldom significant. Chlorophyll-a,
a commonly used variable in NDS methods, only significantly identified N-limitation on PCC discs; it was
unable to significantly detect P-limitation on either substrate type. Fine FGD chlorophyll-a responses
were in the direction expected, but only in high light. AFDM, another popular metric, did not
significantly indicate either type of nutrient limitation; however, non-significant responses were
observed for P-limitation on both PCC and fine FGD (high light only). A paired t-test of expected
responses on PCC and fine FGD, however, did not show the two substrates to be significantly different
from one another. Therefore, despite the many differences observed between PCC and fine FGD in the
ANOVA tests, the t-test implies that one is not more capable than the other at producing the expected
response to enrichment in NDS methods.
Table 7. Nutrient enrichment effects by N:P ratio and substrate type.
Summary of results from 2-factor ANOVA for NDS enrichment effect versus control by N:P ratio and substrate type for 22 variables. Enrichment with the limiting nutrient was expected to induce a greater response. Tallies of the number of significant expected responses were reported, as well as the total (including not significant responses).
Low N:P Ratio Expected: N > Control High N:P Ratio Expected: P > Control
PCC Fine FGD PCC Fine FGD
Significant responses 3 0 Significant responses 3 5
Sig. % of variables 14% 0% Sig. % of variables 14% 23%
Total responses 5 1 Total responses 7 6
Total % of variables 23% 5% Total % of variables 32% 27%
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To assess the impact of light on the ability to detect nutrient limitation in this study, a 2-factor
ANOVA tested for interactions between light and N:P ratio within each substrate and enrichment type
combination. Table 8 shows which analytes were affected by a significant light-N:P ratio interaction. N-
enriched fine fritted glass discs were most often affected, with 9 out of 22 variables showing a
significant interaction.
Table 8. Significant light interactions with N:P ratio.
Significant light * N:P ratio interactions from 2-factor ANOVA by NDS enrichment type and substrate type. A total of 22 variables were tested, and p-values from those with significant light * N:P ratio interactions are reported. Cases where an interaction was present but not significant is denoted by “ns”.
Analyte PCC + N Fine FGD + N PCC + P Fine FGD + P
AFDM (mg/cm2) 0.0206 <0.0001 0.0222
Cyano Lambda (d) ns
Diatom A (µg/cm2) ns
Diatom Conc. (µg/cm2) 0.0053
Diatom Integral (µg/cm2) 0.026
Total Conc. (µg/cm2) 0.0083
CR (µgO2/h·cm2) 0.0343 0.0026
Chl-a (µg/cm2) 0.0002 0.0001
GPP (µgO2/h·mgAFDM) 0.0034
GPP (µgO2/h·µgChla) 0.01 0.0484 0.0019
GPP (µgO2/h·cm2) ns ns 0.0001
NCM (µgO2/h·mgAFDM) 0.0079 0.0109
P:R ratio 0.0018 0.0009
Total Significant: 2 9 4 4
Table 9 depicts the types of light-N:P ratio interactions observed. The number of occurrences
when each light x N:P ratio combination resulted in the stronger response was tallied by substrate and
NDS enrichment type. There were more significant responses to high N:P ratio in high light, whereas
there were more significant responses to low N:P ratio in low light. This interaction occurred regardless
of the substrate or NDS enrichment type.
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Table 9. Types of responses observed in light x N:P ratio interactions.
Results of light x N:P ratio interactions, by NDS enrichment type and substrate. Counts are given for the number of significant and total (including non-significant) expected responses for each light and N:P ratio combination. N-enrichment was expected to produce greater responses in low N:P ratio, and P-enrichment in high N:P ratio. Responses most commonly occurred in High Light/High N:P and Low Light/Low N:P treatments (indicated in blue), regardless of substrate or NDS enrichment type.
N-enrichment Expected: Low N:P ratio P-enrichment Expected: High N:P ratio
PCC Fine PCC Fine
High Light High N:P > Low N:P
2 3 Significant High Light High N:P > Low N:P
0 1 Significant
4 8 Total 4 2 Total
Low Light High N:P > Low N:P
0 1 Significant Low Light High N:P > Low N:P
0 0 Significant
1 3 Total 0 1 Total
High Light Low N:P > High N:P
0 0 Significant High Light Low N:P > High N:P
0 1 Significant
0 1 Total 0 2 Total
Low Light Low N:P > High N:P
0 2 Significant Low Light Low N:P > High N:P
1 2 Significant
2 8 Total 4 2 Total
5.4. Diffusion rate effects
The overall diffusion rates (all treatments) obtained from the incubation test are depicted in
Figure 23, and were plotted against the diatom concentrations for comparison. This figure confirms that
the samplers were able to provide enrichment throughout the length of the experiment and did not
show an obvious change in response to diatom colonization. It also shows that on average, PCCs
diffused nitrate faster whereas fine FGDs diffused phosphate faster. As in the previous experiment (see
“2.5 Second mesocosm substrate test”), the diffusion rates were extremely high initially, then stabilized
around day 2 and tapered off slowly over the remainder of the deployment. The mean day 16 diffusion
rates were 83.5 µg-N/cm2·h and 57.8 µg-P/cm2·h for fine FGDs, and 112.5 µg-N/cm2·h and 52.1 µg-
P/cm2·h for PCCs.
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Figure 23. Mean incubation test diffusion rates vs. diatom concentrations for all treatments.
The incubation test results are segregated by light level and N:P ratio treatments in Figure 24.
Diffusion was generally higher in high N:P ratio than low N:P ratio for both nitrate and phosphate. All
treatments exhibited some degree of interaction between factors. Nitrate diffused faster from PCCs in
the high N:P treatment, but diffused faster from fine FGDs in the low N:P treatment. Samplers generally
diffused faster in low light than high light, but an interaction was observed with substrate type. For both
nitrate and phosphate diffusion, fine FGDs diffused faster in high light, whereas PCCs diffused faster in
low light.
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Figure 24. Incubation test results from nutrient limitation study by light level and N:P ratio.
As in the previous study (see “2.5 Second mesocosm substrate test”), the agar analysis method
shows contradictory diffusion rate results from the incubation test method (Figure 25). PCCs were
always observed to have much higher diffusion rates than fine FGDs. Furthermore, the low N:P
treatments tended to have higher diffusion than the high N:P treatments. The only notable difference
between light levels was greater diffusion from PCC under low light and low N:P ratio. Fine FGDs appear
to have more consistent diffusion than PCCs across treatment conditions for both nitrate and
phosphate.
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Figure 25. Agar analysis diffusion rates from nutrient limitation study.
DISCUSSION
The intention of this study was to evaluate the ability of nutrient-diffusing substrates to assess
streams during TMDL development. NDS methods have been widely used to assess nutrient limitation
in streams, but their performance and applicability need consideration. Experimental conditions at field
sites, e.g. light availability and background nutrients, challenge the capability of NDS as a method in
identifying nutrient limitation when it is present. Furthermore, due to the absence of a standardized
NDS method, the many different sampler designs currently employed in research may prevent
comparison among studies. This study was, therefore, executed in a controlled mesocosm setting to
allow for greater scrutiny of the behavior of NDS under various experimental conditions, particularly
regarding light and substrate effects.
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6.1. Light effects
The irradiances for both light treatments were much weaker than anticipated. According to Hill
et al. (2011), light is known to be “growth-saturating” at 100 µmol m-2 s-1; this PAR value is slightly higher
than the low light condition used in this study (74.0 ± 3.8 µmol m-2s-1). Therefore, it is possible that
some light limitation may have been present. The high light treatment – at 50% of outdoor sunlight –
also fell short of the intended conditions in that it does not approximate open-canopy conditions;
unshaded stream sites are therefore likely to produce stronger responses to light than were observed in
this study. Despite the lower than expected irradiances, the two light levels were still observed to be
sufficiently different from one another to elicit distinct responses from periphyton in low light and high
light. Variables such as GPP and cyanobacteria concentration elicited greater responses in the high light
treatment, whereas diatom parameters gave greater responses in the low light treatment. The diatoms
present in this system likely preferred lower light intensities, as optimal light conditions are known to
vary from species to species (Werner 1977). Since algal communities will vary among field sites,
researchers may encounter more or less of these differential responses to light based on the species
assemblage at a particular site.
The responses to light, however, were subject to strong interactions with other factors –
particularly with N:P ratio. An inverse relationship between light and nutrients was observed in several
analytes, and manifested itself in one of two ways: (1) high light produced an increased response
compared to low light, but only in the high N:P treatment (CR, p < 0.0001; GPP, n.s.; AFDM, n.s.); or (2)
high light did not produce an increased response compared to low light, but the low N:P ratio treatment
decreased under high light (Chl-a, p < 0.0001). These trends were observed in the control treatments
and were maintained despite enrichment with either N or P. If light and nutrients had been co-limited,
one could expect that the response to nutrients would have increased with an increase in light (Taulbee
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et al. 2005, Hill et al. 2009, Johnson et al. 2009, Hill et al. 2011). This may be the case with nitrogen,
since it shows similar responses under low light, yet increases under high light. Phosphorus, on the
other hand, seems to have an antagonistic relationship with light – particularly in the lower observed
chlorophyll-a response under high light versus low light. Photoinhibition would have provided a
convenient explanation, but it is probably unlikely due to the low irradiances and high nutrients in this
study. Taulbee et al. (2005) only observed photoinhibition of algal biovolume at the lowest SRP
concentration (5 µg-P/L) and highest irradiances (~400 µmol m-2 s-1). Overall, these results have poor
implications for the usefulness of NDS methods in impacted streams, since the light x N:P ratio
interaction effectively obscures the responses to nutrient enrichment.
NDS methods are mainly used in pristine, low-nutrient streams, where co-limitation seems to be
common and relatively easy to identify. It is possible that the type of light-phosphorus interaction
observed in this study only becomes significant with elevated nutrient concentrations. Regardless of
site conditions, light is increasingly shown to have a profound impact on results and should therefore be
included as a factor in assessments of periphyton responses to nutrients. Accordingly, it is
recommended to measure light in NDS studies, and to deploy NDS arrays in reaches with light levels
representative of the stream being assessed. It would also be prudent to include algal speciation to
provide support for causality of responses.
6.2. N:P ratio effects
Although the target nutrient levels were exceeded in the respective treatments, the resulting
concentrations were still considered to be representative of the conditions observed in the EFLMR
watershed (Table 2), and the resulting ratios adhered to the prescribed ranges. The altered N:P ratios in
the mesocosm surface water were intended to impose N- and P-limiting conditions in their respective
treatments. Both high N:P and low N:P ratios should theoretically have been limiting to periphyton, and
65
therefore have produced similar responses in the absence of enrichment. Many significant differences,
however, were observed in the controls between the two treatments in this study. Of the variables that
exhibited these differences, though, each also showed a significant interaction with light. This makes it
difficult to determine if the experiment actually achieved limiting conditions, since the observed
differences were not simply in response to nutrients alone. Although the N:P ratios obtained did align
with the predictive thresholds, and the concentrations in the “limiting” treatment fall below the State
recommended thresholds, the absolute concentrations may have been sufficiently high to make the
ratios themselves irrelevant.
6.3. Substrate effects
In this study, substrate had a greater impact on periphyton than either light or N:P ratio, and
was the only factor to produce any significant responses without interactions. Porous crucible covers
generally elicited stronger responses than fine fritted glass discs, and this was particularly true with
regard to colonization rate (low λ). Because colonization began almost immediately, PCCs had more
time than fine FGDs to accumulate biomass during the experiment – resulting in significantly higher
values for A and concentration in cyanobacteria, and integral in both cyanobacteria and diatoms. As
mentioned in the discussion of the second mesocosm substrate test (see “2.5”), the different
colonization rates between PCCs and fine FGDs are likely due to substrate roughness. PCCs provide a
much rougher surface, which could allow for easier adhesion of periphyton.
Despite the drastic differences between substrate types, neither was found to be better able to
detect nutrient limitation using the NDS method. Although PCC more often produced the expected
response, the differences between substrates was not found to be significant. Under this study’s
controlled experimental conditions and among many response variables, neither substrate reliably
identified nutrient limitation in the treatment in which it was intended. Chlorophyll-a and AFDM are the
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most common metrics in NDS studies, and between the two variables the only significant expected
response was by chlorophyll-a for N-limitation on PCCs. This study utilized many more variables than
typical NDS assessments, and nutrient limitation was still not well-identified. This could simply mean
that the experimental conditions were not actually limiting, as previously mentioned. It could also mean
that interacting factors had such conspicuous effects on the responses that they masked the effects of
the NDS enrichments. Furthermore, both PCC and fine FGD substrates showed the expected responses
to enrichment, but the results were not significant. This implies that the treatments may have been
more successful with higher replication. This would be even more important for a field study, in which
environmental conditions would likely introduce additional variation. If the expected responses
observed in this test had been significant, then based on the results given in Table 7, it could have been
determined that PCCs outperformed fine FGDs as an NDS substrate. The significant differences between
substrates – and the potential differences in their ability to identify nutrient limitation – underscore the
need for a standardized NDS method, since substrate type is the most variable aspect of an NDS design.
NDS methods were investigated for use in stream assessments in part to improve comparability among
states’ assessments, so the development of a standard method is imperative if NDS are to serve in this
purpose.
While measurements on rocks (i.e. gravel) in the mesocosms were included as an afterthought,
it would have been beneficial to incorporate them fully into the study to better observe the differences
in responses between the artificial substrates and a natural substrate. Benthotorch measurements
showed that rocks had significantly higher final diatom concentrations than PCCs, fine FGDs, and tiles
(Figure 21). It is unclear whether this difference in magnitude would translate into a difference of
behavior in response to enrichment. Future research could investigate if NDS studies show the same
trends in periphyton responses that would be observed on natural substrates under enriched
conditions.
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6.4. Deployment method effects
The initial field test using the Tank et al. (2006) NDS method revealed the need for modifications
to the sampler design, since sedimentation was observed to interfere with periphyton colonization (see
“2.1 Initial field test”). A proposed alternative design employed a ceramic disc as the substrate, secured
atop a Mason jar with a canning ring (see “2.2 Initial modifications of NDS sampler design”). This design
minimized the depression created between the lid and the substrate surface, thereby eliminating the
“trap” for sediment to collect. While the modifications were expected to improve periphyton
colonization, the sampler’s effectiveness as a diffusing substrate was unknown. Therefore, it was
prudent to assess differences in diffusion rate between the two sampler designs.
Diffusion rate assessments became a central theme in this study after the first mesocosm
substrate test exposed how drastically the NDS sampler design can affect nutrient release (see “2.3 First
mesocosm substrate test”). Not only did the ceramic discs not diffuse nutrients at all, but the PCC
samplers diffused nitrate too quickly. Had the initial designs for PCC and ceramic samplers been used in
field studies prior to testing, the data from the study would probably have been unusable. This
underscores the need to assess diffusion rates in NDS studies, especially prior to using new designs.
The issues regarding diffusion rate were solved with further modifications to the sampler design
(see “2.4”), which incorporated an improved substrate mounting method and increased sampler
volume. The new sampler design also drastically reduced variation in diffusion rate among replicates.
The second mesocosm substrate test (see “2.5”) confirmed that the modified NDS sampler design
enabled all substrate types to provide nutrient enrichment over the entire deployment period and were
able to colonize periphyton. Both diffusion rate and algal concentrations were surmised to have been
impacted by substrate type, however. It was speculated that larger pore size creates a rougher surface,
which allow faster colonization by periphyton. An increase in pore size also appeared to correlate with a
68
greater initial release of nutrients into the surface water, followed by subsequently lower hourly
diffusion rates for the remainder of the deployment. It was unclear whether there was a relationship
between biomass on the substrate and diffusion through the substrate, or if the two were only
connected by the effects of pore size.
The second mesocosm test also revealed that the two diffusion rate methods employed –
incubation testing and agar analysis – gave very different, yet complementary, information. The
incubation test periodically assessed the instantaneous diffusion rates to observe differences among
treatments throughout the experiment. The agar analysis demonstrated that there was little variation
among replicates with the improved sampler design, and also provided the cumulative mass of nutrient
released during the experiment. While both methods proved useful in observing the diffusion dynamics
in this study, it is important to note that the results are not directly comparable. The massive initial
release of nutrients from the samplers, as observed in the incubation test method results, appeared to
bias the mean rates obtained from the agar analysis method. The incubation test, therefore, showed
that the diffusion rates from the agar analysis did not accurately reflect how nutrients were being
supplied throughout the deployment. Agar analysis is still a viable method for assessing diffusion rate
when using multiple sampling events, as in the first mesocosm substrate test (see “2.3”), rather than
calculating mean rates from a single analysis post-deployment. This is an important observation, since
diffusion rate methods in the literature were observed to be highly variable among studies and some
may be inadequate for verifying the functionality of the sampler.
The incubation test diffusion rate assessments in the nutrient limitation study showed that both
substrates – PCCs and fine FGDs – successfully provided enrichment throughout the experiment, but
that diffusion rate was impacted by the experimental conditions (Figure 24). The results showed that
diffusion rate was also subject to interactions among factors, much like periphyton responses. While
there was not a clear link between diffusion rate and periphyton responses in the previous test (see “2.5
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Second mesocosm substrate test”), the two exhibited several of the same patterns in this experiment.
Samplers generally diffused faster in low light; diatom responses were also stronger in the low light
treatment, and cyanobacteria colonized faster (lower λ; Figure 22). High N:P ratio showed greater
diffusion rate than low N:P ratio; periphyton variables were generally higher under high N:P ratio or
showed depressed responses to low N:P ratio, especially under high light (Figure 21). Fine FGDs diffused
faster in high light, whereas PCCs diffused faster in low light – this same pattern was significant in
cyanobacteria A and diatom A responses (Figure 22). The similar patterns of response suggest a
potential link between diffusion rate and biomass on the substrates. Since these observations seem to
imply a direct relationship, it may mean that periphyton was actively up-taking nutrients from the
substrates during this experiment, rather than relying on passive diffusion. One could speculate that
this exacerbates differences among treatment conditions in that increasing biomass could result in
increasing nutrient availability, rather than nutrients remaining equally available among treatments
throughout the experiment. This effect, if it indeed exists, may not significantly complicate NDS studies
compared to the effects of light, nutrient conditions, and substrate type.
6.5. Benthotorch assessment
Because Benthotorch measurements may not have previously been incorporated in nutrient-
diffusing substrate studies, it seems prudent to include an assessment of its usefulness in this
experiment. One distinctive benefit to the Benthotorch is that it allows for measurement of algal
concentrations without sacrificing biomass. This creates the opportunity to take several readings
throughout an experiment, as was done in this study, and thereby produce growth curves from the data.
This data can be analyzed for growth parameters such as mu (growth rate), lambda (lag phase), and A
(total accrual). These parameters give researchers insight into the growth dynamics of the periphyton,
rather than relying only on the typical, discrete, biomass-related analyses, such as AFDM and
70
chlorophyll-a. In fact, the Benthotorch results aided in the identification of several trends in this study
which were not apparent in more typical variables.
The Benthotorch technology is not without its limitations, however. One disappointment was
the exclusion of green algae as an analyte due to the inability of the Benthotorch to detect it among
more dominant algal species. The presence of green algae was frequently reported in the first days of
the study, but then became undetectable as diatom colonization increased. In many cases, a significant
amount of green algae was clearly visible on the substrates, but the Benthotorch would consistently
report zero.
Another shortcoming of the Benthotorch pertains its under-exaggeration of concentrations.
Upon comparing chlorophyll-a results to those of cyanobacteria and diatoms (Figure 21), it became
apparent that the Benthotorch was unable to detect the majority of the biomass on the substrate. This
is thought to stem from the fact that it relies on surface reflectance for its measurements, and therefore
any biomass below the surface layer are unable to be detected. This leads the algal concentrations by
the Benthotorch to be biased low compared to sacrificial methods, such as chlorophyll-a.
Lastly, Benthotorch use is simple and fairly quick (approx. 45 seconds per reading), but the user
must exercise caution while making measurements. The sponge-like cup fitted to the end of the
detector is meant to isolate the periphyton from light, which would interfere with the results. Normal
use tends to leave visible marks on colonized areas, however, so it is advisable to use substrates with a
smaller diameter than the Benthotorch tip in order to prevent disruption. Furthermore, periphyton
produces an especially slippery surface, making it easy for this device to slide across the substrate and
inadvertently remove critical biomass. Care must therefore be taken in the placement of the
Benthotorch, and in stabilizing it during measurement.
Overall, the Benthotorch provides useful insight into periphyton growth dynamics. Researchers
should be aware of its limitations, however, specifically concerning accuracy of concentrations and
71
detection of green algae. The Benthotorch may therefore be best employed as a supplement to other
biomass measurements and algal identification.
6.6. Implications for NDS application
The State of Ohio is currently determining the sources of impairments at sites assessed
throughout the East Fork Little Miami River watershed, which will subsequently be regulated under
TMDLs. Of the 88 sites assessed in 2012, the State reported that 52% were biologically impaired (State
of Ohio Environmental Protection Agency 2014). While organic enrichment and low dissolved oxygen
were the most frequent causes reported, nutrient enrichment was also highly indicated. An apparent
uptake of nitrogen and steadily elevated phosphorus levels were presumed to indicate nitrogen as the
limiting nutrient in this system. Being able to identify which nutrient in excess – nitrogen or phosphorus
– produces the greater response in primary producers is important for stream impact assessment. It
was hoped that NDS methods could be used at EFLMR sites to aid in identifying nutrient limitation
where it is present; in this way, NDS methods would provide support for regulation of the limiting
nutrient. Therefore, the main question this study aimed to answer was: “Can NDS methods be used for
the assessment of impacted streams during TMDL development?” Overall, the results did not strongly
support the usefulness of NDS methods in this capacity. In this study, the observed interaction between
light and N:P ratio tended to obscure periphyton’s response to further enrichment. Increased
replication may have provided the experimental power necessary to identify nutrient limitation – even
in the impacted conditions of this study – but the results were still confounded by interactions among
treatment factors.
Since NDS methods could be expected to give more straightforward results in streams that have
not yet been impacted by nutrient enrichment, it may be most useful to apply them in the assessment
of reference stream reaches within the potentially impacted watershed. This approach would be
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analogous to a positive control in a laboratory study. Based on the results from this study, the
recommended NDS sampler design should employ PCC discs mounted in a substrate-lid assembly (see
“2.4 Further modifications of NDS sampler design”) atop a mason jar. To improve experimental power,
at least 5 replicates should be included per treatment (control, N, P, and N+P). Based on periphyton
growth dynamics, samplers may be retrieved as soon as 14 days after deployment in order to avoid
impending storm flows. Deployment should last no longer than 21 days, however, to ensure adequate
enrichment from the NDS treatments. Light levels should be monitored throughout the experiment, and
deployment sites should include light levels representative of the stream being assessed. Researchers
may also want to consider speciation of the periphyton community, since different algal species are
known to have varying responses to light in particular. To disentangle the effects of light and nutrients,
future studies could determine at what concentrations and irradiances significant interactions – either
synergistic or antagonistic – can be expected. This could inform that sites below certain thresholds may
benefit from NDS assessment.
REFERENCES
Ács, É., A. Borsodi, K. Kröpfl, P. Vladár and G. Záray (2007). "Changes in the algal composition, bacterial metabolic activity and element content of biofilms developed on artificial substrata in the early phase of colonization." Acta Botanica Croatica 66(2): 89-100.
Battin, T. J., L. A. Kaplan, J. D. Newbold and C. M. E. Hansen (2003). "Contributions of microbial biofilms to ecosystem processes in stream mesocosms." Nature 426: 439-442.
Bergey, E. A. and G. M. Getty (2006). "A Review of Methods for Measuring the Surface Area of Stream Substrates." Hydrobiologia 556(1): 7-16.
Busse, L. B., J. C. Simpson and S. D. Cooper (2006). "Relationships among nutrients, algae, and land use in urbanized southern California streams." Canadian Journal of Fisheries and Aquatic Sciences 63(12): 2621-2621.
73
Capps, K. A., M. T. Booth, S. M. Collins, M. A. Davison, J. M. Moslemi, R. W. El-Sabaawi, J. L. Simonis and A. S. Flecker (2011). "Nutrient diffusing substrata: a field comparison of commonly used methods to assess nutrient limitation." Journal of the North American Benthological Society 30(2): 522-532.
Corkum, L. D. (1996). "Patterns of nutrient release from nutrient diffusing substrates in flowing water." Hydrobiologia 333(1): 37-43.
Danger, M., G. Lacroix, C. Ourmarou, D. Benest and J. Meriguet (2008). "Effects of food-web structure on periphyton stoichiometry in eutrophic lakes: a mesocosm study." Freshwater Biology 53: 2089-2100.
Death, R. G., F. Death and O. M. N. Ausseil (2007). "Nutrient limitation of periphyton growth in tributaries and the mainstem of a central North Island river, New Zealand." New Zealand Journal of Marine and Freshwater Research 41(3): 273-281.
Dodds, W. K., E. Martí, J. L. Tank, J. Pontius, S. K. Hamilton, N. B. Grimm, W. B. Bowden, W. H. McDowell, B. J. Peterson, H. M. Valett, J. R. Webster and S. Gregory (2004). "Carbon and nitrogen stoichiometry and nitrogen cycling rates in streams." Oecologia 140: 458-467.
Elser, J. J., T. Andersen, J. S. Baron, A.-K. Bergstrom, M. Jansson, M. Kyle, K. R. Nydick, L. Steger and D. O. Hessen (2009). "Shifts in Lake N:P Stoichiometry and Nutrient Limitation Driven by Atmospheric Nitrogen Deposition." Science 326: 835-837.
Elshorbagy, A., R. S. V. Teegavarapu and L. Ormsbee (2005). "Total maximum daily load (TMDL) approach to surface water quality management: concepts, issues, and applications." Can. J. Civ. Eng. 32: 442-448.
Fairchild, G. W. and R. L. Lowe (1984). "Artificial substrates which release nutrients: effects on periphyton and invertebrate succession." Hydrobiologia 114: 29-37.
Ferragut, C. and D. de Campos Bicudo (2010). "Periphytic algal community adaptive strategies in N and P enriched experiments in a tropical oligotrophic reservoir." Hydrobiologia 646(1): 295-309.
Frost, P. C., C. T. Cherrier, J. H. Larson, S. Bridgham and G. A. Lamberti (2007). "Effects of dissolved organic matter and ultraviolet radiation on the accrual, stoichiometry, and algal taxonomy of stream periphyton." Freshwater Biology 52: 319-330.
Frost, P. C., W. F. Cross and J. P. Benstead (2005). "Ecological stoichiometry in freshwater benthic ecosystems: an introduction." Freshwater Biology 50: 1781-1785.
Godwin, C. M., M. A. Arthur and H. J. Carrick (2009). "Periphyton nutrient status in a temperate stream with mixed land-uses: implications for watershed nitrogen storage." Hydrobiologia 623(1): 141-152.
74
Hall, S. R., V. H. Smith, D. A. Lytle and M. A. Leibold (2005). "Constraints on primary producer N:P stoichiometry along N:P supply ratio gradients." Ecology 86(7): 1894-1904.
Han, B.-P., M. Virtanen, J. Koponen and M. Straskraba (2000). "Effect of photoinhibition on algal photosynthesis: a dynamic model." Journal of Plankton Research 22(5): 865-885.
Hill, B. H., F. H. McCormick, B. C. Harvey, S. L. Johnson, M. L. Warren and C. M. Elonen (2010). "Microbial enzyme activity, nutrient uptake, and nutrient limitation in forested streams." Freshwater Biology 55: 1005-1019.
Hill, W. R., S. E. Fanta and B. J. Roberts (2009). "Quantifying phosphorus and light effects in stream algae." Limnology and Oceanography 54(1): 368-380.
Hill, W. R., B. J. Roberts, S. N. Francouer and S. E. Fanta (2011). "Resource synergy in stream periphyton communities." Journal of Ecology 99: 454-463.
Hillebrand, H., P. Frost and A. Liess (2008). "Ecological stoichiometry of indirect grazer effects on periphyton nutrient content." Oecologia 155: 619-630.
Hillebrand, H., G. d. Montpellier and A. Liess (2004). "Effects of macrograzers and light on periphyton stoichiometry." Oikos 106: 93-104.
Hillebrand, H. and U. Sommer (1999). "The nutrient stoichiometry of benthic microalgal growth: Redfield proportions are optimal." Limnology and Oceanography 44(2): 440-446.
Hoellein, T. J., J. L. Tank, J. J. Kelly and E. J. Rosi-Marshall (2010). "Seasonal variation in nutrient limitation of microbial biofilms colonizing organic and inorganic substrata in streams." Hydrobiologia 649(1): 331-345.
Irvine, R. L. and L. J. Jackson (2006). "Spatial variance of nutrient limitation of periphyton in montane, headwater streams (McLeod River, Alberta, Canada)." Aquatic Ecology 40: 337-348.
Iwanyshyn, M., M. C. Ryan and A. Chu (2008). "Separation of physical loading from photosynthesis/respiration processes in rivers by mass balance." The Science of the Total Environment 390: 205-214.
Johnson, L. T., J. L. Tank and W. K. Dodds (2009). "The influence of land use on stream biofilm nutrient limitation across eight North American ecoregions." Canadian Journal of Fisheries and Aquatic Sciences 66(7): 1081-1081.
75
Kahm, M., G. Hasenbrink, Lichtenberg-Fraté, J. Ludwig and M. Kschischo (2010). "grofit: Fitting biological growth curves with R." Journal of Statistical Software 33(7): 1-21.
Kang, M. S., S. W. Pang, J. J. Lee and K. H. Yoo (2006). "Applying SWAT for TMDL programs to a small watershed containing rice paddy fields." Agricultural Water Management 79: 72-92.
Keller, A. A. and L. Cavallaro (2008). "Assessing the US Clean Water Act 303(d) listing process for determining impairment of a waterbody." Journal of Environmental Management 86: 699-711.
Lambert, D., A. Cattaneo and R. Carignan (2008). "Periphyton as an early indicator of perturbation in recreational lakes." Canadian Journal of Fisheries and Aquatic Sciences 65: 258-265.
Liess, A. and M. Kahlert (2007). "Gastropod grazers and nutrients, but not light, interact in determining periphytic algal diversity." Oecologia 152: 101-111.
Martinez del Rio, C. (2003). "Ecological stoichiometry's proclamation." Ecology 84(8): 2226-2227.
Miltner, R. J. (2010). "A method and rationale for deriving nutrient criteria for small rivers and streams in Ohio." Environmental Management 45: 842-855.
Mulholland, P. J., E. R. Marzolf, S. P. Hendricks, R. V. Wilkerson and A. K. Baybayan (1995). "Longitudinal patterns of nutrient cycling and periphyton characteristics in streams: a test of upstream-downstream linkage." Journal of the North American Benthological Society 14(3): 357-370.
Mulholland, P. J. and J. R. Webster (2010). "Nutrient dynamics in streams and the role of J-NABS." Journal of the North American Benthological Society 29(1): 100-117.
Qin, P., C. M. Mayer, K. L. Schulz, X. Ji and M. E. Ritchie (2007). "Ecological stoichiometry in benthic food webs: effect of light and nutrient on periphyton food quantity and quality in lakes." Limnology and Oceanography 52(4): 1728-1734.
Rugenski, A. T., A. M. Marcarelli, H. A. Bechtold and R. S. Inouye (2008). "Effects of temperature and concentration on nutrient release rates from nutrient diffusing substrates." Journal of the North American Benthological Society 27(1): 52-57.
Sanches, L. F., R. D. Guariento, R. L. Bozelli, A. Caliman and F. A. Esteves (2011). "Effects of nutrients and light on periphytic biomass and nutrient stoichiometry in a tropical black-water aquatic ecosystem." Hydrobiologia 669(1): 35-44.
76
Schade, J. D., K. MacNeill, S. A. Thomas, F. C. McNeely, J. R. Welter, J. Hood, M. Goodrich, M. E. Power and J. C. Finlay (2011). "The stoichiometry of nitrogen and phosphorus spiralling in heterotrophic and autotrophic streams." Freshwater Biology 56: 424-436.
Scrimgeour, G. and P. Chambers (1997). "Development and application of a nutrient-diffusing bioassay for large rivers." Freshwater Biology 38(1): 221-231.
Small, G. E., A. M. Helton and C. Kazanci (2009). "Can consumer stoichiometric regulation control nutrient spiraling in streams?" Journal of the North American Benthological Society 28(4): 747-765.
State of Ohio Environmental Protection Agency (2014). Biological and Water Quality Study of the East Fork Little Miami River and Select Tributaries, 2012. EAS/2014-05-05. Division of Surface Water. Columbus, OH.
Tank, J. L., M. J. Bernot and E. J. Rosi-Marshall (2006). Nitrogen limitation and uptake. Methods in stream ecology. F. R. Hauer and G. A. Lamberti. New York, Elsevier: 213-238.
Tank, J. L. and W. K. Dodds (2003). "Nutrient limitation of epilithic and epixylic biofilms in ten North American streams." Freshwater Biology 48: 1031-1049.
Taulbee, W. K., S. D. Cooper and J. M. Melack (2005). "Effects of nutrient enrichment on algal biomass across a natural light gradient." Archiv fur Hydrobiologie 164(4): 449-464.
Werner, D., Ed. (1977). The Biology of Diatoms. Botanical Monographs 13. Berkley and Los Angeles, CA, University of California Press.