D.M. Costello et al.
Contaminant exposure substrata (CES)
Appendix S1 – Additional calculations for CES dose
Diffusion rates from CES can be determined empirically by placing a complete CES in a diffusion
chamber consisting of a large beaker with a known
volume of distilled water (e.g., 1 L) that is well mixed
and at a constant temperature close to the expected
stream temperature. After time intervals ranging
from 6-24 h, the water is sampled for a solute concentration and completely replaced with fresh water.
Time intervals must be chosen such that <5% of remaining solute diffuses out of the CES during each
interval, otherwise the rate of diffusion will deviate
from the theoretical release rate (Eq. 1). D is not affected by M0, therefore the initial mass added to the
CES for determination of diffusion rates can be large
to simplify analysis of solute concentrations. Sampling and water replacement should be continued for
the same period of time as expected for field deployment to verify that diffusion remains Fickian
throughout the exposure period. D can be calculated
from equation 1 using the slope of a graph of Mt versus √𝑡. For cases when D at stream temperature is
unavailable, the Stokes-Einstein equation can be used
to estimate D at a given temperature:
𝐷1 = 𝐷2 ∙
𝑇1 𝑈2
∙
𝑇2 𝑈1
(Eq. S1.1)
where D1 is the temperature-specific diffusion coefficient to be calculated, D2 is a known temperaturespecific diffusion coefficient, Ti is temperature (in
Kelvin) for the known and predicted D and Ui is the
dynamic viscosity of water at the respective temperatures.
For soluble compounds, exposure from CES can
be compared to typical exposure from chemicals in
the water column by comparing expected solute flux
rates. A dose rate from overlying water can be estimated using the formula for calculating O2 flux into
sediments (Jørgensen & Revsbech, 1985). The flux
rate (J, in mg m-2 s-1) of a solute is given by:
𝐽=
𝐷 ∙ Δ𝐶
𝑍𝛿
(Eq. S1.2)
where D is the diffusion coefficient in water (m2 s-1),
ΔC is the change in concentration (μg L-1) and Zδ is the
height (m) of the diffusive boundary layer (DBL). The
DBL is a thin region of water above a solid surface
where hydrodynamics are affected by viscous drag.
Depending upon the water velocity, DBL above biofilms is typically in the range of 0.1 to 1.0 mm
(Kuenen, Jørgensen & Revsbech, 1986), with the largest DBL occurring in a static water column and small
DBL in fast-flowing streams. For most contaminants,
ΔC is equivalent to the water column concentration as
biofilms are assumed to provide an infinite sink for
the contaminant.
For users wanting to compare in-stream flux
rates to mean CES flux rates, a modification of equation 2 can be used to calculate an initial mass of solute
needed in a CES to provide a given flux rate:
𝐽𝐶𝐸𝑆 ∙ 𝐴 ∙ 𝑡 ∙ 𝐿
𝑀0 =
2 ∙ (√
𝐷 ∙ 𝑡𝐼𝑁 √𝐷 ∙ 𝑡𝐸𝑄
−
)
𝜋
𝜋
(Eq. S1.3)
where tIN is the time of the expected incubation period (s) and tEQ is the time of the expected equilibration
period (s). This formula can be used to create CES
treatments that closely match expected fluxes from
solutes in streamwater, with the caveat that flux rates
from CES will decline through time.
References
Jørgensen B. & Revsbech N. (1985) Diffusive
boundary layers and the oxygen uptake of
sediments
and
detritus.
Limnology and
Oceanography, 30, 111–122.
Kuenen J., Jørgensen B. & Revsbech N.P. (1986)
Oxygen microprofiles of trickling filter biofilms.
Water Research, 20, 1589–1598.
D.M. Costello et al.
Contaminant exposure substrata (CES)
Appendix S2 –Methods for measuring biofilm response
Biofilm biomass
Measures of biofilm biomass on CES are expressed as a mass of biofilm (or biofilm component)
per unit of surface area of the attachment substratum
exposed to the stream. The most basic measure of
accumulation of mass over the course of the exposure
is ash-free dry mass (AFDM) (Steinman, Lamberti &
Leavitt, 2006). AFDM accounts for all heterotrophic
and autotrophic organic material that accumulates on
the CES substratum over the exposure time while
subtracting the mass of any inorganic material (e.g.,
sand) that might accumulate in the biofilm. The biomass of algae in biofilms can be estimated by extracting and quantifying the mass of photosynthetic pigments, typically chlorophyll a (chl a) (Steinman et al.,
2006). Although chl a is most commonly used to
measure photosynthetic biomass, other photosynthetic pigments may provide mechanistic insights
into algal responses to chemical contaminants
(Guasch & Sabater, 1998; Dorigo et al., 2007; Tlili et
al., 2011). Dividing chl a by AFDM yields an autotrophic index (AI), which is a measure of the relative
contribution of algae to the overall biofilm biomass
(Clark, Dickson & Cairns, 1979). Fungal biomass can
be estimated by quantifying ergosterol concentrations using standard methods (Gulis & Suberkropp,
2006). Bacterial biomass can be estimated by direct
epifluorescence counts (see below) and conversion of
bacterial cell abundance to biomass using conversion
factors (Bratbak, 1985), although this approach does
have significant limitations (Lee & Fuhrman, 1987).
The total biomass of heterotrophs can also be obtained by altering the attachment substratum (i.e.,
organic substrata like cellulose sponge) or exposure
environment (i.e., exclude light).
Biofilm community structure
Microbial community structure has been demonstrated to be a very sensitive endpoint to assess
stress, often being more sensitive to low levels of environmental stressors than responses in biomass or
ecosystem function (Schindler, 1987; Kennedy &
Smith, 1995; Niyogi, Lewis & McKnight, 2002). To
prepare samples from CES for community analysis,
biofilms can be easily removed from fritted glass or
nylon mesh using a stiff-bristled brush and a small
amount of water. The resulting biofilm slurry can be
analyzed by a suite of techniques, including microscopic observation and DNA-based molecular analyses (Molander & Blanck, 1992; Dorigo et al., 2007;
Tlili et al., 2011). To avoid shifts in community composition after sampling, care needs to be taken to 1)
keep biofilms cold prior to processing (e.g., storage on
ice in a cooler for transport), 2) process samples as
soon as possible after sampling and 3) store samples
appropriately. For microscopic observations, subsamples of biofilm slurry are commonly preserved
with formalin or glutaraldehyde (Kepner & Pratt,
1994; APHA, 1999; Barbour et al., 1999). For DNAbased molecular analysis, biofilms should be pelleted
from slurries by centrifugation and stored at -20°C.
Algae (especially diatoms) can be enumerated and
identified by light microscopy using a Palmer counting cell and taxonomic keys (Barbour et al., 1999;
Lowe & LaLiberte, 2006). Bacteria can be enumerated
by epifluorescence microscopy after staining, but due
to their small size and limited morphological variation it is impossible to identify specific bacterial taxa
by direct microscopic observation. More commonly,
DNA-based molecular approaches are used to elucidate the composition of bacterial communities
(Zimmerman et al., 2014).
DNA-based analyses of biofilm
For DNA-based measurement of community
composition, whole community genomic DNA should
be isolated from biofilm pellets. There are several
commercial kits that can be used for this task, with
kits manufactured by MoBio (www.mobio.com) and
MP Biomedicals (http://www.mpbio.com/) being
among the most popular. Once community genomic
DNA has been isolated, a wide range of DNA-based
analytical tools are available to profile biofilm communities (Zimmerman et al., 2014). For bacteria, the
16S rRNA gene is the most common target for analyses of bacterial species composition. There are several profiling techniques that can be used to compare
bacterial communities based on the distribution of
16S rRNA genotypes, including terminal restriction
fragment length polymorphism analysis (T-RFLP),
denaturing gradient gel electrophoresis (DGGE) and
automated ribosomal intergenic spacer-analysis
(Dorigo et al., 2007; Hoellein et al., 2010; Tlili et al.,
2011; Fechner et al., 2012). More recently, nextgeneration sequencing technologies, including the
454 and Illumina sequencing platforms, have been
applied to the analysis of bacterial communities in
environmental samples.
Next-generation sequencing technologies can
provide massive amounts of sequence data from a
large number of samples in parallel, and a variety of
publically available software tools (e.g. Qiime,
MOTHUR) can be used to assign these sequences to
taxonomic groups and to make statistical compari-
D.M. Costello et al.
sons between samples. These approaches allow much
more thorough surveys of bacterial community structure than have been possible with previous techniques. Next-generation sequencing data can be used
to compare samples/treatments based on any available biodiversity metrics, including, but not limited to,
species richness, evenness and Shannon and Simpson
diversity indices. Advances in matrix-based ordination techniques (e.g., nonmetric multidimensional
scaling) can also be used for more comprehensive
exploration of community similarity between treatments (Clarke, 1999; Sparks, Scott & Clarke, 1999).
We have successfully used next-generation sequencing approaches to analyze bacterial communities
from CES and demonstrated significant effects of
pharmaceuticals on the composition of bacterial
communities colonizing CES (Rosi-Marshall et al.,
2013). Recent work by others has demonstrated that
the composition of cyanobacterial and algal communities can also be profiled using next-generation sequencing technology targeting plastid 23S rRNA
genes (Steven, McCann & Ward, 2012), which suggests that this approach can be used to profile primary producers communities colonizing CES. Finally, the
DNA isolated from CES biofilms should also be amenable to analysis using quantitative polymerase chain
reaction (qPCR), which can be used to profile microbial communities by quantifying specific 16S rRNA
gene sequences or the sequences of a variety of functional genes (i.e. gene involved in specific metabolic
pathways, such as nitrification, denitrification and
sulfate reduction). Determining the relative abundances of specific bacterial functional guilds within
CES biofilms using qPCR can be especially valuable
when paired with direct measurement of biofilm
function.
Biofilm function
Biofilms drive many important ecosystem functions in streams and the CES method allows for the
controlled measurement of how pollutants alter critical ecosystem processes. Net primary production
(NPP) and community respiration (CR) can be measured by sealing the biofilm and substrata from the
cup in small, tightly sealed chambers, returning the
chambers to the stream for a known time and measuring the changes in dissolved oxygen (Bott et al.,
1997; Rosi-Marshall et al., 2013; Costello & Burton,
2014). Incubation of the biofilms in transparent
chambers in the stream ensures ambient temperature
and light conditions for measuring NPP. However, for
accurate measurement of CR photosynthesis must be
arrested by using opaque chambers or enclosing
transparent chambers in foil. Photosynthetic activity
Contaminant exposure substrata (CES)
can also be estimated from incubations with isotopically labeled carbon (Molander & Blanck, 1992) or
photosynthesis-irradiance curves (Guasch & Sabater,
1998). Measuring changes in nutrient concentrations
during incubations in sealed containers can also estimate rates of nutrient uptake (Hoellein et al., 2009).
Changes to organic matter decomposition rates can
be estimated by measuring mass loss of organic substratum (e.g., wood veneers; Tank & Dodds, 2003) or
by directly measuring enzyme activities (Fechner,
Dufour & Gourlay-Francé, 2012). Additional assays
measuring biofilm function (e.g., phosphatase activity,
nitrification by nitrapyrin inhibition, N2 fixation) may
be adapted for CES to explore additional ways in
which biofilms are impaired by contaminants.
Trophic interactions
Along with contributing to biogeochemical cycles
in stream ecosystems, biofilms are the primary food
resources for grazing invertebrates. Contaminants
can disrupt the trophic interactions between biofilms
and consumers both directly by reducing biofilm biomass and indirectly by altering biofilm characteristics important for consumption (e.g., microbial community composition, elemental stoichiometry;
Clements & Rohr, 2009). Indirect effects may be
more common than direct effects and may contribute
strongly to how communities and ecosystems respond to contaminants (Fleeger, Carman & Nisbet,
2003; Clements & Rohr, 2009; Clements, Hickey &
Kidd, 2012). Natural biofilms colonizing CES can be
separated from the CES cup and used for food-choice
or no-choice feeding experiments with focal invertebrates. For no-choice experiments, consumers are
provided with only a single food resource (i.e., biofilm
grown on CES) per replicate and feeding rates are
calculated and compared among treatments. For
food-choice experiments, consumers are provided
with two biofilm types (control and contaminantexposed biofilm) within a feeding arena and differences in consumption between the food items are
calculated (c.f., Forrow & Maltby, 2009; Bundschuh et
al., 2009 for detritivore experiments). Care needs to
be taken when conducting feeding assays to account
for non-consumptive biofilm mass loss, and appropriate experimental designs and statistical analyses are
critical (Peterson & Renaud, 1989).
Biofilm chemistry
Biofilms are highly sorptive to many pollutants
(Lock et al., 1984; Arini et al., 2011; Lundqvist,
Bertilsson & Goedkoop, 2012), and pollutants strongly associated with biofilms can cause dietary exposure to consumers. For CES simulating overlying wa-
D.M. Costello et al.
ter exposure (i.e., chemicals in agar) a bioconcentration factor (BCF) is calculated as the ratio of the
chemical concentration in the biofilm (μg g -1 dry
mass) to the average concentration of the chemical in
solution (μg L-1). For CES cups containing sediments,
a bioaccumulation factor (BAF) is calculated as the
ratio of the chemical concentration in the biofilm (μg
g-1 dry mass) to the concentration of the chemical in
the sediment (μg g-1 dry mass). However, the
BCF/BAF approach must be used with caution when
attempting to predict biological effects, since accumulation of a chemical is not equivalent to negative biological effects (Newman & Clements, 2008). We suggest avoiding the use of the BCF/BAF approach in
isolation, but rather using it as a complementary endpoint in combination with direct measurements of
biological effects on biofilms, consumers or both.
References
APHA (1999) Standard Methods for the Examination
of Water and Wastewater, 20th Edn. Port City
Press, Baltimore, MA, USA.
Arini A., Baudrimont M., Feurtet-Mazel A., Coynel A.,
Blanc G., Coste M., et al. (2011) Comparison of
periphytic biofilm and filter-feeding bivalve metal
bioaccumulation (Cd and Zn) to monitor
hydrosystem
restoration
after
industrial
remediation: a year of biomonitoring. Journal of
Environmental Monitoring 13, 3386–3398.
Barbour M., Gerritsen B., Snyder B. & Stribling J.
(1999) Rapid Bioassessment Protocols for Use in
Wadeable Streams and Rivers, 2nd edn. U.S.
Environmental Protection Agency, Office of Water,
Washington, DC.
Bott T.L., Brock J.T., Baattrup-Pederson A., Chambers
P.A., Dodds W.K., Himbeault K.T., et al. (1997) An
evaluation of techniques for measuring
periphyton metabolism in chambers. Canadian
Journal of Fisheries and Aquatic Sciences 54, 715–
725.
Bratbak G. (1985) Bacterial biovolume and biomass
estimations.
Applied
and
Environmental
Microbiology 49, 1488–1493.
Bundschuh M., Hahn T., Gessner M.O. & Schulz R.
(2009) Antibiotics as a chemical stressor affecting
an aquatic decomposer-detritivore system.
Environmental Toxicology and Chemistry 60, 197–
203.
Clark J.R., Dickson K.L. & Cairns J. (1979) Estimating
aufwuchs biomass. In: Methods and Measurements
of Periphyton Communities: A Review. (Ed R.L.
Weitzel), pp. 116–141. American Society for
Testing and Materials, Philadelphia, PA.
Contaminant exposure substrata (CES)
Clarke K. (1999) Nonmetric multivariate analysis in
community-level ecotoxicology. Environmental
Toxicology and Chemistry 18, 118–127.
Clements W.H., Hickey C.W. & Kidd K.A. (2012) How
do aquatic communities respond to contaminants?
It depends on the ecological context.
Environmental Toxicology and Chemistry 31,
1932–1940.
Clements W.H. & Rohr J.R. (2009) Community
responses to contaminants: using basic ecological
principles to predict ecotoxicological effects.
Environmental Toxicology and Chemistry 28,
1789–1800.
Costello D.M. & Burton G.A. (2014) Response of
stream ecosystem function and structure to
sediment metal: Context dependency and
variation among endpoints. Elementa: Science of
the Anthropocene 2, 000030.
Dorigo U., Leboulanger C., Bérard A., Bouchez A.,
Humbert J.-F. & Montuelle B. (2007) Lotic biofilm
community structure and pesticide tolerance
along a contamination gradient in a vineyard area.
Aquatic Microbial Ecology 50, 91–102.
Fechner L.C., Dufour M. & Gourlay-Francé C. (2012)
Pollution-induced community tolerance of
freshwater biofilms: measuring heterotrophic
tolerance to Pb using an enzymatic toxicity test.
Ecotoxicology 21, 2123–2131.
Fleeger J.W., Carman K.R. & Nisbet R.M. (2003)
Indirect effects of contaminants in aquatic
ecosystems. Science of the Total Environment 317,
207–233.
Forrow D.M. & Maltby L. (2009) Toward a
mechanistic understanding of contaminantinduced changes in detritus processing in streams:
Direct and indirect effects on detritivore feeding.
Environmental Toxicology and Chemistry 19,
2100–2106.
Guasch H. & Sabater S. (1998) Light history influences
the sensitivity to atrazine in periphytic algae.
Journal of Phycology 241, 233–241.
Gulis V. & Suberkropp K.F. (2006) Fungi: Biomass,
production,
and
sporulation
of
aquatic
hyphomycetes. In: Methods in Stream Ecology, 2nd
edn. (Eds F.R. Hauer & G.A. Lamberti), pp. 311–
325. Academic Press, San Diego, CA.
Hoellein T.J., Tank J.L., Kelly J.J. & Rosi-Marshall E.J.
(2010) Seasonal variation in nutrient limitation of
microbial biofilms colonizing organic and
inorganic substrata in streams. Hydrobiologia 649,
331–345.
Hoellein T.J., Tank J.L., Rosi-Marshall E.J. & Entrekin
S.A. (2009) Temporal variation in substratumspecific rates of N uptake and metabolism and
their contribution at the stream-reach scale.
D.M. Costello et al.
Journal of the North American Benthological
Society 28, 305–318.
Kennedy A.C. & Smith K.L. (1995) Soil microbial
diversity and the sustainability of agricultural
soils. Plant and Soil 170, 75–86.
Kepner R.L. & Pratt J.R. (1994) Use of fluorochromes
for direct enumeration of total bacteria in
environmental samples: Past and present.
Microbial Reviews 58, 603–615.
Lee S. & Fuhrman J.A. (1987) Relationships between
biovolume and biomass of naturally derived
marine
bacterioplankton.
Applied
and
Environmental Microbiology 53, 1298–1303.
Lock M.A., Wallace R.R., Costerton J.W., Ventuilo R.M.
& Chariton S.E. (1984) River epilithon: Toward a
structural-functional model. Oikos 42, 10–22.
Lowe R.L. & LaLiberte G.D. (2006) Benthic stream
algae: Distribution and structure. In: Methods in
Stream Ecology, 2nd edn. (Eds F.R. Hauer & G.A.
Lamberti), pp. 327–356. Academic Press, San
Diego, CA.
Lundqvist A., Bertilsson S. & Goedkoop W. (2012)
Interactions with DOM and biofilms affect the fate
and bioavailability of insecticides to invertebrate
grazers. Ecotoxicology 21, 2398–2408.
Molander S. & Blanck H. (1992) Detection of
pollution-induced community tolerance (PICT) in
marine periphyton communities established
under diuron exposure. Aquatic Toxicology 22,
129–144.
Newman M.C. & Clements W.H. (2008) Ecotoxicology:
A Comprehensive Treatment. CRC Press, New York.
Niyogi D.K., Lewis W.M. & McKnight D.M. (2002)
Effects of stress from mine drainage on diversity,
biomass, and function of primary producers in
mountain streams. Ecosystems 5, 554–567.
Peterson C.H. & Renaud P.E. (1989) Analysis of
feeding preference experiments. Oecologia 80, 82–
86.
Rosi-Marshall E.J., Kincaid D.W., Bechtold H.A., Royer
T. V., Rojas M. & Kelly J.J. (2013) Pharmaceuticals
suppress algal growth and microbial respiration
and alter bacterial communities in stream
biofilms. Ecological Applications 23, 583–593.
Schindler D. (1987) Detecting ecosystem responses to
anthropogenic stress. Canadian Journal of
Fisheries and Aquatic Sciences 44, 6–25.
Sparks T., Scott W. & Clarke R. (1999) Traditional
multivariate techniques: Potential for use in
ecotoxicology. Environmental Toxicology and
Chemistry 18, 128–137.
Steinman A.D., Lamberti G.A. & Leavitt P.R. (2006)
Biomass and pigments of benthic algae. In:
Methods in Stream Ecology, 2nd edn. (Eds F.R.
Contaminant exposure substrata (CES)
Hauer & G.A. Lamberti), pp. 357–379. Academic
Press, San Diego, CA.
Steven B., McCann S. & Ward N.L. (2012)
Pyrosequencing of plastid 23S rRNA genes reveals
diverse and dynamic cyanobacterial and algal
populations in two eutrophic lakes. FEMS
Microbiology Ecology 82, 607–615.
Tank J. & Dodds W. (2003) Nutrient limitation of
epilithic and epixylic biofilms in ten North
American streams. Freshwater Biology 48, 1031–
1049.
Tlili A., Corcoll N., Bonet B., Morin S., Montuelle B.,
Bérard A., et al. (2011) In situ spatio-temporal
changes
in
pollution-induced
community
tolerance to zinc in autotrophic and heterotrophic
biofilm communities. Ecotoxicology 20, 1823–
1839.
Zimmerman N., Izard J., Klatt C., Zhou J. & Aronson E.
(2014) The unseen world: Environmental
microbial sequencing and identification methods
for ecologists. Frontiers in Ecology and the
Environment 12, 224–231.
D.M. Costello et al.
Contaminant exposure substrata (CES)
Appendix S3 – Biofilm dose-response to diphenhydramine
We conducted an experiment using the CES doseresponse approach to study how autotrophic and
heterotrophic biofilms respond to increasing concentrations of diphenhydramine. CES cups (L = 3.5 cm, A
= 3.8 cm2) were filled with agar and diphenhydramine (3800, 2400, 1300, 630 and 0 mg L-1) and deployed in East Branch Wappinger Creek (Millbrook,
NY, USA) for 18 days. We used both fritted glass and
cellulose substrata (not precolonized), and our endpoints included chl a, GPP and CR. Diphenhydramine
additions significantly reduced the rates of GPP (P <
0.001) and CR (P < 0.001) but had no significant effect
on chl a (P > 0.05) (Fig. S3.1). For both GPP and CR,
we found that all treatments containing diphenhydramine were not different from each other but differed significantly from controls (Fig. S3.1b & c). This
indicates that our lowest concentration of diphenhydramine elicits a toxic response, and the doseresponse function occurs at a concentration lower
than those tested.
To compare our diphenhydramine doses used in
the CES to measured in-stream concentrations we
calculate flux of diphenhydramine from our different
treatments (Eq. 2). Using the previously reported D
for diphenhydramine at 10°C (3.6 × 10-10 m2 s-1) and
equation S1.1, we calculate D at stream temperature
(7°C) of 3.24 × 10-10 m2 s-1. Using this temperaturespecific D and the doses of diphenhydramine in the
agar, we calculate an average flux over the exposure
period (2-18 d) that ranges from 7.5–45.4 μg cm-2 h-1.
These fluxes from our CES are approximately 4 orders of magnitude greater than the modeled instream flux expected in a contaminated stream (0.13
ng cm-2 h-1, Fig. 2). Although these fluxes may not approach expected in-stream fluxes, these data demonstrate that functional endpoints (i.e., GPP and CR) are
sensitive to diphenhydramine whereas our structural
endpoint (i.e., chl a) does not respond to diphenhydramine even at these high concentrations. Additional exposures at lower concentrations of diphenhydramine are needed to resolve the dose-response
relationship between diphenhydramine and biofilm
function.
Figure S3.1 Biofilm response to increasing diphenhydramine doses using contaminant exposure
substrata (CES). Chlorophyll a (a) and gross primary
production (b) were measured on CES with fritted
glass as attachment substratum, whereas community
respiration (c) was measured on CES with cellulose
sponge. Points are means and error bars indicate ±1
SE. Flux of diphenhydramine is calculated as the average flux estimated following the equilibration period until the end of the exposure period (i.e., days 218). Asterisks indicate biofilm responses that differed
significantly from controls but did not differ significantly from each other (ANOVA followed by Tukey’s
post hoc test).