Development and Application of Aquatic Toxicity Models for Oil and Dispersants

Development and Practical Application of Petroleum and Dispersant
Interspecies Correlation Models for Aquatic Species
Adriana C. Bejarano*,†
and Mace G. Barron‡
†
Research Planning, Inc., 1121 Park Street, Columbia, South Carolina 29201, United States
‡
U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, Florida 32561, United States
*S Supporting Information
ABSTRACT: Assessing the acute toxicity of oil has generally relied on
existing toxicological data for a relatively few standard test species, which has
limited the ability to estimate the impacts of spilled oil on aquatic
communities. Interspecies correlation estimation (ICE) models were
developed for petroleum and dispersant products to facilitate the prediction
of toxicity values to a broader range of species and to better understand
taxonomic differences in species sensitivity. ICE models are log linear
regressions that can be used to estimate toxicity to a diversity of taxa based on
the known toxicity value for a surrogate tested species. ICE models have only
previously been developed for nonpetroleum chemicals. Petroleum and
dispersant ICE models were statistically significant for 93 and 16 unique
surrogate-predicted species pairs, respectively. These models had adjusted
coefficient of determinations (adj-R2
), square errors (MSE) and positive slope
ranging from 0.29 to 0.99, 0.0002 to 0.311, and 0.187 to 2.665, respectively. Based on model cross-validation, predicted toxicity
values for most ICE models (>90%) were within 5-fold of the measured values, with no influence of taxonomic relatedness on
prediction accuracy. A comparison between hazard concentrations (HC) derived from empirical and ICE-based species
sensitivity distributions (SSDs) showed that HC values were within the same order of magnitude of each other. These results
show that ICE-based SSDs provide a statistically valid approach to estimating toxicity to a range of petroleum and dispersant
products with applicability to oil spill assessment.
■ INTRODUCTION
Assessments of the potential toxicological effects of physically
or chemically dispersed oils and dispersants have commonly
relied on relatively few toxicity tests for a limited number of
aquatic species, primarily standard test species. In most cases,
the sensitivity of species of concern is unknown, making
informed decisions challenging. One approach to addressing
uncertainty in species sensitivity has been the development and
application of interspecies correlation estimation models
(ICE),1
which are log−linear regressions between the acute
toxicity of two species for many paired toxicity data. These
models can generate acute toxicity estimates by extrapolating
known toxicity values from a surrogate species to species with
unknown toxicity values. While these models are available for
many chemicals,2−5
these have not been developed for
petroleum or dispersant products. ICE models can be used to
generate species sensitivity distributions (SSDs),2,6,7
which are
cumulative distributions of toxicity data (e.g., median lethal,
LC50, and effects concentrations, EC50) that allow for
comparisons of the relative sensitivities of across species.8
SSDs have been used to establish protective levels to specific
chemicals9
by deriving hazard concentrations (HC) assumed to
protect a wide number of species with varying sensitivities. ICE-
based SSDs have been developed for a number of chemicals,2,6,7
and have been shown to produce HC values similar to those
used to derive water quality criteria.6
Recent research has shown that existing data from stand-
ardized toxicity tests can be used to develop oil-specific
SSDs,10−13
but species diversity is extremely limited compared
to single compounds.2,4,5
The objectives of the current study
were to determine if petroleum and dispersant-specific ICE
models could be developed and used to generate SSDs and
HC5 values with acceptable uncertainty, and to determine
associations between taxonomic relatedness and species
sensitivity.4
Previous studies have documented variability in
test results attributable to differences in test conditions and
exposure media prepared from the same source oil and/or
dispersants.14−17
Therefore, this study focused on two distinct
categories of studies (spiked and continuous exposure) that
reported measured aqueous exposure concentrations of
petroleum products (primarily light and medium crude oils)
and several oil spill dispersants (primarily Corexits). The
models and approach presented here can be used to estimate
toxicity to a broad range of species and assemblages which
Received: October 25, 2013
Revised: March 17, 2014
Accepted: March 21, 2014
Published: March 28, 2014
Article
pubs.acs.org/est
© 2014 American Chemical Society 4564 dx.doi.org/10.1021/es500649v | Environ. Sci. Technol. 2014, 48, 4564−4572

should facilitate the development of protective environmental
concentrations and improved assessment of the potential
consequences of oil spills and dispersant use.
■ EXPERIMENTAL SECTION
Petroleum and Dispersant Toxicity Data Sources. The
primary data source used in the development of ICE models
came from a recently developed toxicity database,18
which
contains quantitative information on the acute toxicity of crude
oils and dispersants from publically available literature. This
database was developed following a quality assurance and
quality control (QA/QC) plan similar to that of a related
database.19
QA/QC procedures included an evaluation of each
original data source, removal of duplicate information, and an
evaluation of currently accepted scientific names.
Two types of data sets were queried and used in the
development of ICE models: petroleum hydrocarbon and
dispersant toxicity data. The petroleum hydrocarbon data set
was comprised of toxicity data (LC50 and EC50) for aquatic
species (primarily fish and crustaceans) derived from aqueous
exposures to physically dispersed (i.e., water accommodated
fractions; WAF) or chemically dispersed (i.e., enhanced water
accommodated fractions; CEWAF) oil. These toxicity data
were generated using a variety of oils under various weathering
stages, but most data were from light (e.g., Chirag, Forties,
South Louisiana, Venezuelan) and medium (e.g., Alaska North
Slope, Forcados, Kuwait, Prudhoe Bay) crude oils (42 oils of 48
total oils). Only studies that reported LC50 and EC50 toxicity
values on the basis of measured concentrations of analytes in
the aqueous exposure media were included in the development
of these models. While rigorous data selection for petroleum
hydrocarbons focused on test results reported on the basis of
measured total hydrocarbon content (THC; including C6−
C36 carbon chains; 72% of the entire data set), other reported
metrics (total polycyclic aromatic hydrocarbons, TPAHs; sum
of PAHs and alkyl homologue groups) were also included. This
data set did not include results from studies reporting effects
concentrations for specific THC carbon chains or PAH
analytes. Petroleum ICE models were developed using over
1500 paired data points for a total of 136 unique species pairs.
The dispersant data set was comprised of toxicity data (LC50
and EC50) for aquatic species exposed to dispersants, almost
exclusively Corexit 9500 and Corexit 9527 (36% and 33% of
the entire data set respectively). Toxicity data for other
dispersants (Corexit 7500; Corexit 7664; Corexit 9552;
Nokomis; SlickAWay) were also included. Only studies
reporting LC50 and EC50 toxicity values on the basis of
measured dispersant concentrations in the exposure media were
included in model development. Dispersant ICE models were
developed using 286 paired data points for a total of 38 unique
species pairs.
The majority of data included here were derived from
standard toxicity tests, including 96 h (65% of the entire data
set) and constant static (76% of the entire data set) laboratory
exposures. To develop petroleum and dispersant ICE models,
surrogate and predicted species were paired from the same
original data source. Pairing of surrogate and predicted species
toxicity data was done only when tests for both species were
performed under the same exposure conditions (i.e., same oil or
dispersant product, exposure regime, analytical methods in
chemical characterization), but with data independently
collected for each species. A complete list of data sources and
core data are provided in the Supporting Information (SI)
material.
Model Development and Verification. Because of the
number of steps involved in the development, selection,
verification and application of ICE models, a diagram is
provided (SI Figure S1) to facilitate the understanding of the
approach presented here. As several statistical methods were
used, the readers are encouraged to refer to key statistical
references20,21
including those describing the development of
ICE models.2,5
Linear regression models were developed for
each pair of species in both, the petroleum hydrocarbon and
dispersant database, containing at least 4 data points. These
linear models are described by Log Pi = β0 + β1 × Log (Si),
where Pi is the acute toxicity of the predicted species, β0 and β1
are the intercept and slope, respectively, and Si is the acute
toxicity of the surrogate species, where both the independent
and dependent variables are random and independent of each
other.22
The slope represents change in the response of toxicity
values of the predicted species per unit change in the toxicity
values of the surrogate species, where a slope of approaching 1
indicates a similar response between two species. Only ICE
models (p-value < 0.05) that passed both the F-test for the
overall fit of the regression equation and the t test for the slope
parameter significantly different from 0, were included in
further analyses. Prior to model validation, statistically
significant models were evaluated for potential influential data
via regression diagnostics analyses.20
The reliability and
predictive power of statistically significant ICE models with at
least 2 degrees of freedom was assessed using a leave-one-out
cross-validation technique.23,24
Briefly, each data set was split
into K subsets, equal to the number of pairs within each data
set, and models fitted K times, each time leaving out one data
subset from the larger training data. For each reduced data set,
toxicity values of the predicted species were calculated and
responses for the deleted subset predicted from the model.
Model uncertainty of the estimated toxicity value was calculated
via the cross-validation success rate (or 1-bias corrected
misclassification error or sum of the square difference between
observed and estimated values), which is an estimate of
generalized error. Taxonomic relatedness for each surrogate
and predicted species was assigned a numeric distance value4
(from same genus = 1 to same domain = 7), and model
prediction accuracy assessed as a function of taxonomic
distance.
Practical Application of ICE Models. Regression
parameters of statistically significant ICE models were used
to estimate toxicity values of several predicted species, using the
known toxicity of petroleum hydrocarbons or dispersants for
one or more surrogate species as the model input. In all
instances, input data for surrogate species were independent of
the data used to develop ICE models.10,18
Surrogate species
included Americamysis bahia (mysid shrimp), Holmesimysis
costata (kelp forest mysid), Menidia beryllina (inland silverside)
and Atherinops affinis (topsmelt). ICE-based SSDs were
generated for each category of petroleum or dispersant product
using the ICE estimated toxicity values, and in all cases the
minimum acceptable number of species on an SSD was set to
five. The petroleum product categories included light crude oils
(>31.1°API) (Forties, South Louisiana, Venezuelan crudes)
and medium crudes (22.3−31.1°API) (Alaska North Slope,
Arabian Medium, Kuwait and Prudhoe Bay crudes), while the
dispersant categories included Corexit 7664, Corexit 9500, and
Corexit 9527. Hazard concentrations (HC) assumed to be
Environmental Science & Technology Article
dx.doi.org/10.1021/es500649v | Environ. Sci. Technol. 2014, 48, 4564−45724565

protective of 99% or 95% of the species in the SSD (i.e., HC1
and HC5, respectively) were computed from the modeled SSD
function. All SSDs and HC values were derived using an
approach described elsewhere.25
Briefly, toxicity values were
fitted to a log-normal distribution function, and SSDs randomly
resampled 2000 times to derive the HC1 and HC5 values and
their associated 95% confidence intervals (95% CI). Only SSDs
that passed goodness of fit tests (α = 0.01) (the Anderson−
Darling for SSDs with >7 species, and the Kolmogorov−
Smirnov test statistics) were included in these analyses. SSDs
were also generated using empirical data for the same categories
of petroleum products and dispersants, as well as using a
combination of empirical data and ICE-based toxicity values.
SSDs with empirical data were generated by calculating the
geometric mean of all toxicity values available for each unique
species. The geometric was used because it gives less weight to
influential data or potentially outliers, leading to conservative
estimates.8
To determine if ICE-based SSDs are a viable
alternative to the estimation of SSDs where toxicity data are
limiting, ICE-based SSDs were compared to empirically based
SSDS using log-likelihood statistics.21
This approach compares
the log-likelihood values of individual SSDs (e.g., M. beryllina
ICE-based and empirical data) with the fitted SSDs of the
combined (pooled) model (e.g., M. beryllina ICE-based plus
empirical data), testing the hypothesis (via the chi-square
statistic) that these SSDs are derived from the same fitted log-
normal curve. The same approach was used to compare ICE-
based SSDs pairs. All the analyses above were performed using
the R statistical platform (v. 2.13.2).22
■ RESULTS
Model Development and Verification. A total of 93
petroleum ICE models for 29 surrogate species, and 16
dispersant ICE models for seven surrogate species were
statistically significant (p-value <0.05 for both, the F-test for
the overall fit and the t test for the slope parameter) (SI Tables
S1−S3, Figures S2 and S3). Models lacking significance
typically had limited paired data (22 petroleum and 5
dispersant ICE models with <5 paired data points) limiting
their use in further analyses.
The validity of statistically significant models was verified
using standard regression diagnostics procedures.20
Visual
examination of the standardized residuals of each significant
model (Figure 1, left panel) showed that all residuals fell within
a horizontal band centered on 0, indicating that the variance of
the error term was constant. No obvious outliers were detected
(±4 standardized residual values). Diagnostic analyses also
indicated that the error term did not depart substantially from a
normal distribution (Figure 1, right panel), as the coefficient of
determination (R2
) between residuals and quantiles for
petroleum and dispersant ICE models were greater (0.997
and 0.991, respectively) than those of the critical R2
value
(0.985) (α = 0.05; n ≥ 100).26
A moderate departure from
normality was noted at the end tails of the distributions,
particularly for petroleum ICE models (14 data points) but was
considered minimal because removal of these data points did
not cause major changes in fitted values. In all cases, the
absolute change between fitted values with and without
potentially influential points ranged from 4 to 11% indicating
no disproportional influence on fitted values.
Statistically significant models had an adjusted coefficient of
determination (adj-R2
) ranging from 0.29 and 0.99, a Mean
Square Error (MSE; a measure of fit) ranging from 0.0002 to
0.311, and a positive slope ranging from 0.187 to 2.665 (SI
Tables S2 and S3). Examination of model parameter viability
shows that 92% of all intercepts were between −0.75 and 0.75,
and that 85% of all slopes were between 0.5 and 1.5. This
relatively narrow variability suggests similarities across most
models. Notable exceptions, where both the slope and intercept
of the same model were outside these ranges included (1) the
petroleum ICE models for the surrogate-predicted pairs
Calanus sinicus- Paracalanus aculeatus (both copepods species),
Cyprinodon variegatus (sheepshead minnow)-A. bahia, and
Pontogammarus maeoticus (amphipod)-Calanipeda aquae dulcis
(copepod); and (2) the dispersant ICE models for the pairs C.
variegatus-A. bahia, A. affinis-H. costata, and A. affinis-
Macrocystis pyrifera (giant kelp). In these deviating cases,
except pairs involving A. affinis, the predicted species was
generally more sensitivity than the surrogate species, but in all
cases MSE were small (below 0.16).
Figure 1. Diagnostic analyses of homogeneity of variances (left) and normality (right) for petroleum and dispersant ICE models.

The reliability and predictive power of statistically significant
models was further assessed by identifying the MSE cutoff
associated with a-priori cross-validation success rates of 85%
and 95%,5
assumed to have moderate and high reliabilities,
respectively (Figure 2). MSE cutoffs for petroleum and
dispersant models were 0.126 and 0.04, respectively. Over
half of the petroleum ICE models (54 models) had MSEs
≤0.04, while over 30% (32 models) had MSEs between 0.04
and 0.126. By comparison, dispersant ICE models were equally
distributed among MSE cutoffs, suggesting that petroleum ICE
models may be more robust.
The high cross-validation success rate (a measure of
predictive power) was consistent with the agreement between
predicted and observed values for both ICE models types
(Figure 3). For petroleum ICE models, 98% of the predicted
values were within 5-fold difference of the observed data, with
most values (92%) being within 2-fold difference of the
observed data. There was no influence of taxonomic relatedness
on prediction accuracy (p-value >0.05), and mean fold-
difference values across taxonomic distances ranged from 1.00
to 1.38 (SI Figure S4). Only 0.5% of predicted values (seven
observations) were >10-fold difference from the observed
values. These outliers occurred only in the M. beryllina- A. bahia
(taxonomic distance = 6), Daphnia magna (water flea)- Artemia
salina (brine shrimp) (taxonomic distance=4), and A. bahia- M.
beryllina surrogate- predicted pairs (3, 3, and 1 observations,
respectively). For dispersant ICE models, 99% of the predicted
values were within 5-fold difference of the observed data, with
most values (88%) being within 2-fold of the observed data.
There was no influence of taxonomic relatedness on prediction
accuracy (p-value >0.05), and mean fold-difference values were
1.29 and 1.14 for taxonomic distances 6 and 7, respectively.
The largest fold difference of 7 occurred in the A. bahia- M.
beryllina surrogate- predicted pair.
Practical Application of ICE Models. Toxicity data for
several petroleum products from an analysis independent of the
research presented here10
were used to verify the applicability
of ICE models. Two sets of SSD were developed using ICE
models for A. bahia and M. beryllina as the surrogate species,
with input surrogate concentrations from Barron et al.10
The
first set of SSDs used all ICE models regardless of their
reliability, while the second set used only ICE models with
MSE <0.12. Statistical comparison of SSDs via (using the log-
likelihood),21
showed that these two types of SSDs for several
petroleum products and for both surrogate species, were not
significantly different from each other (p-value >0.05). This
indicates that exclusion of the least reliable models did not
influence the shape of the SSD. No statistically significant
differences were also found between A. bahia and M. beryllina
ICE-based SSDs (p-value >0.05), or between either of these
two ICE-based SSDs and empirical SSDs10
(p-value >0.05;
Figure 4). Estimated petroleum hydrocarbon HC5s from ICE-
based SSDs were in general agreement with those derived from
SSDs using empirical data10
(SI Table S5), though values from
M. beryllina as the surrogate were generally larger (up to a 6
fold larger) than those calculated from empirical data.
A second verification approach utilized empirical toxicity data
for physically and chemically dispersed combined from
constant and spiked exposures, as well as toxicity data for
Corexit 9500 and Corexit 9727,18
which were used to generate
SSDs (Figure 5). The selection of these data was driven by the
fact that these SSDs shared data for two surrogate species: H.
costata and A. bahia. SSDs were derived using all ICE models
Figure 2. Assessment of model reliability based on the relationship
between model mean square error (MSE) and cross-validation success
rate for both, petroleum and dispersant ICE models.
Figure 3. Comparison of observed and ICE-predicted toxicity values for petroleum hydrocarbons and dispersants. The solid line represents the 1:1
line (equal toxicity), while the dotted lines represent a 5-fold difference between these values. Bar chart on the right, display the fold-difference
between observed and ICE-predicted toxicity values.

for these surrogate species regardless of model reliability.
Because there were insufficient paired surrogate-predicted ICE
models for the same surrogate species to generate SSDs for
dispersants (five species minimum), predicted ICE model
concentrations were combined for H. costata and A. bahia.
Curve comparison of empirically and ICE-based SSDs, using
log-likelihood, showed no statistically significant differences
between A. bahia and H. costata ICE-based SSDs (p-value
>0.05), or between either of these two ICE-based SSDs and
empirical SSDs10,18
(p-value >0.05), with one exception: the H.
costata ICE-based SSDs and the empirical SSDs18
for constant
exposures to dispersed oil (p-value = 0.01; Figure 5). In most
cases, ICE-based models with H. costata and A. bahia as
surrogates, produced smaller HC5s indicative of a slight model
bias toward overprotection of aquatic species. In all cases, HC5s
from ICE-based SSDs were within the same order of magnitude
as HC5s from empirical SSDs. HC5s for Corexit 9500 and
Corexit 9727 from ICE-based SSDs were also similar to those
reported by Barron et al.10
Hazard concentrations (96 h HC1 and HC5) were estimated
for fresh petroleum and dispersant products for which data, on
the basis of measured concentrations, were available from
constant static and spiked flow-through exposures (Table 1).
These hazard concentrations were estimated using ICE-based
SSD with models from each of two surrogate species (A. bahia
and M. beryllina). An additional set of SSDs was also
constructed by combining all available empirical data plus
Figure 4. Comparison between species sensitivity distribution (SSD)
curves for all petroleum products from Barron et al.10
(dashed line),
and SSDs derived using all ICE models, regardless of their reliability,
with A. bahia (black dots and lines) and M. beryllina (blue dots and
lines) as the surrogate species. These curves are not significantly
different (p-value >0.05) from each other.
Figure 5. Comparison between physically and chemically dispersed oil (top), and dispersant (bottom) species sensitivity distribution (SSD) using
empirical18
and ICE-based models (red dots and lines; red dashed line from Barron et al.10
for dispersants only). SSDs using empirical data for
dispersed oil were developed for constant and spiked exposures separately. SSDs were derived using all ICE models regardless of their reliability with
A. bahia (black dots and lines) and H. costata (blue dots and lines) as surrogate species, combining models for these two species for dispersant SSDs
(black lines). Within panels, SSDs are not significantly different (p-value > 0.05) from each other, except for the comparison of H. costata ICE-based
SSDs and the empirical SSDs for constant exposures to dispersed oil (p-value = 0.01; top left).

ICE toxicity values for one or more surrogate species (A. bahia,
H. costata, M. beryllina and A. affinis), keeping the smallest
predicted value for each unique predicted- species (SI Figure
S5). Starting concentrations for surrogate species were those of
studies reporting measured toxicity data,14,15,27−37
and excluded
from model development. HC1s and HC5s for seven fresh oils
and by oil category ranged from 0.13 mg THC/L to 3.72 mg
THC/L, and from 0.16 to 4.92 mg THC/L, respectively.
Venezuelan crude oil had the lowest HC values (more toxic),
while Alaska North Slope oil had the highest values (less toxic).
HC values from ICE-based SSDs with A. bahia as the surrogate
species were similar to those with M. beryllina, and were
generally within the same order of magnitude of each other.
HC values from SSDs that combined empirical data and
predicted toxicity values from one or more surrogate species
were also similar to those derived with either A. bahia or M.
beryllina. In all cases, spiked flow-through exposures produced
HC values up to 12 times greater than HC values from static
exposures, with Venezuelan and Alaska North Slope crude oil
having the largest and smallest differences, respectively,
between these exposure types. SSDs using A. bahia or M.
beryllina as surrogate species, and SSDs with empirical plus ICE
Table 1. Estimated HC1 and HC5 Petroleum Hydrocarbon (THC) Concentrations (mg/L) for Specific Petroleum and
Dispersant Products and Exposure Regimesa
petroleum/dispersant
product
experimental
conditions
surrogate: A. bahia
HC1 (first row) | HC5 (second row)
(95% CI)
surrogate: M. beryllina
(95% CI)
empirical+ ICE-predicted
(95% CI)
Alaska North constant static 0.55 (0.30−0.92) 3.03 (1.96−4.55) 0.45 (0.24−0.77)
Slope 0.69 (0.44−1.02) 3.97 (2.93−5.35) 0.63 (0.37−0.99)
flow-through 1.03 (0.61−1.60) 3.72 (2.44−5.63) 1.27 (0.75−2.06)
1.32 (0.93−1.86) 4.92 (3.69−6.57) 1.72 (1.09−2.58)
Arabian flow-through NA 0.59 (0.34−0.95) NA
Medium NA 0.71 (0.49−1.00) NA
Forties constant static 0.15 (0.07−0.28) 0.14 (0.06−0.29) 0.17 (0.07−0.34)
0.18 (0.10−0.29) 0.16 (0.08−0.26) 0.18 (0.09−0.36)
flow-through 2.32 (1.41−3.66) 2.10 (1.36−3.16) 1.76 (1.05−2.72)
3.06 (2.11−4.29) 2.70 (1.98−3.62) 2.28 (1.50−3.35)
Kuwait constant static 0.23 (0.11−0.41) 0.21 (0.10−0.37) 0.23 (0.12−0.41)
0.28 (0.17−0.42) 0.23 (0.14−0.37) 0.26 (0.15−0.43)
flow-through 2.62 (1.58−4.29) 1.56 (1.01−2.31) 1.26 (0.77−1.9)
3.49 (2.43−4.95) 1.97 (1.45−2.61) 1.57 (1.05−2.23)
Prudhoe Bay constant static NA 1.94 (1.24−2.84) NA
NA 2.48 (1.82−3.31) NA
flow-through 2.40 (1.40−3.90) 3.11 (2.01−4.68) 3.54 (2.24−5.41)
3.19 (2.21−4.50) 4.09 (3.03−5.40) 4.52 (3.14−6.42)
South constant static 0.92 (0.51−1.49) 1.27 (0.80−1.94) 0.75 (0.43−1.20)
Louisiana 1.15 (0.76−1.68) 1.58 (1.16−2.10) 0.97 (0.63−1.49)
Venezuelan constant static 0.13 (0.06−0.24) 0.19 (0.09−0.36) 0.16 (0.07−0.31)
0.16 (0.09−0.25) 0.21 (0.12−0.35) 0.18 (0.09−0.32)
flow-through 2.12 (1.27−3.38) 0.29 (0.14−0.50) 0.36 (0.19−0.61)
2.78 (1.94−3.93) 0.33 (0.20−0.50) 0.42 (0.25−0.66)
Light crudesb
constant static 0.19 (0.09−0.35) 0.44 (0.24−0.74) 0.22 (0.11−0.39)
0.24 (0.13−0.4) 0.53 (0.31−0.82) 0.29 (0.16−0.47)
flow-through 0.84 (0.45−1.44) 0.64 (0.37−1) 0.36 (0.17−0.66)
1.07 (0.61−1.74) 0.78 (0.48−1.2) 0.47 (0.24−0.82)
Medium constant static 0.62 (0.41−0.89) 1.21 (0.74−1.88) 0.27 (0.12−0.49)
crudesc
0.71 (0.50−0.98) 1.50 (0.98−2.21) 0.35 (0.17−0.62)
flow-through 1.74 (1.04−2.85) 1.72 (1.09−2.63) 2.35 (1.52−3.54)
2.25 (1.44−3.55) 2.20 (1.51−3.12) 2.98 (2.08−4.14)
Corexit 7664 flow-through NA NA 375 (182−744)
789 (478−1145)
Corexit 9500 constant static NA NA 2.24 (1.25−3.73)
3.33 (2.15−5.13)
flow-through NA NA 27 (16−42)
40 (27−60)
Corexit 9527 constant static NA NA 1.09 (0.62−1.80)
1.62 (1.02−2.49)
flow-through NA NA 5.51 (3.44−8.60)
7.65 (5.27−11.02)
a
HC Values were generated from ICE-based SSDs using surrogate-predicted ICE Models with at least five pairs per unique surrogate, as well as by
combining empirical and ICE predicted toxicity data from one or more surrogate species. Known surrogate concentrations include 96 h THC
toxicity data from physically dispersed fresh oil and fresh oil chemically dispersed with Corexit 9500, except for Kuwait Oil, which was chemically
dispersed with Corexit 9527. Surrogate toxicity data are the geometric mean of all measured values for the same species.14,15,27−37
NA, data not
available. b
Forties + South Louisiana + Venezuelan. c
Alaska North Slope + Arabian Medium + Kuwait + Prudhoe Bay.

toxicity values were not statistically different (p-value > 0.05)
within petroleum products and by experimental condition.
While there were insufficient dispersant ICE models for A.
bahia or M. beryllina as surrogate species to produce ICE-based
SSDs, combined data from empirical and ICE toxicity values
allowed the estimation HC values for three dispersants.
■ DISCUSSION
One of the greatest challenges in evaluating potential impacts
to aquatic communities during oil spills and impact assessment
continues to be uncertainties in species sensitivity. The
Deepwater Horizon oil spill further highlighted the need to
understand the sensitivity of the diversity of aquatic species in
the deep ocean, pelagic and coastal areas with generally limited
existing toxicity data.38
The current study provides both
petroleum and dispersant-specific toxicity estimation models
that can be applied to a broad range of aquatic species
assemblages using a SSD-based approach.
The practical use of existing oil toxicity data has been limited
by the general lack of standardized laboratory practices,
including differences in media preparation.13,14,39,40
While
these issues may have added uncertainty to the development
of petroleum and dispersant ICE models, emphasis was placed
on rigorously standardizing data selection with the sole intent
of reducing the uncertainty introduced by differences in
experimental procedures across studies. As a result, and despite
inherent limitations with existing data, all statistically significant
ICE models had a relatively small MSE (range 0.0002−0.311),
indicative of a robust model fit. As indicated elsewhere,6
and
applicable here, these robust ICE model relationships suggest
that the same mechanisms of action for each species pair may
be at play. Furthermore, MSE values associated with 85% and
95% cross-validation success rates of ICE models were smaller
(0.126 and 0.04, respectively) than MSE values associated with
the same cutoffs (0.22 and 0.15, respectively) for ICE models
with wildlife species.5
In addition, predicted values from ICE
models were generally within 5-fold difference of the observed
data, with most models being within 2-fold of the observed
data. These predicted-observed differences are well within the
fold difference commonly found across laboratories (fold
difference of 3)41
during optimal interlaboratory comparisons
with the same species. Taxonomic relatedness has been
previously shown to influence model fit and reliability of ICE
models developed from chemicals with mixed modes of
action.4,5
In contrast, petroleum and dispersant-specific ICE
models showed no influence of taxonomic distance on model
accuracy. These results suggest that ICE models developed with
chemicals with a common nonspecific mode of chemical action
such as narcosis can be used to predict toxicity across a broad
range of taxa, and can help improve predictions over ICE
models from with mixed modes of action.
While SSDs have been used in the field of aquatic toxicology
and integrated into the regulatory framework,42
their use in oil
spill research has been limited.10−12
SSDs and derived
benchmarks can be used to protect untested species under
the assumption that their sensitivity is within the range of
sensitivities captured by the species in the SSD. Although SSDs
cannot replace toxicity testing, they can provide additional
information when the costs of toxicity testing are prohibitive or
species-specific testing is restricted or not feasible (e.g.,
endangered, rare, deepwater species). Furthermore, both
empiric and ICE-based SSDs can help inform resource
managers in their assessment of potential acute effects
associated with petroleum or dispersant products, particularly
when data are limited. Moreover, data from concurrent toxicity
testing of rarely investigated taxa (e.g., corals, pelagic fish) and a
surrogate species for which ICE models are available (e.g., A.
bahia or M. beryllina), can be used to construct an ICE-based
SSD allowing for the placement in the curve of the species for
which little toxicity data, facilitating comparisons of relative
sensitivities. As demonstrated here, ICE models could be used
to augment estimates of benchmark concentrations from spiked
flow-through exposures that may be more applicable to short-
duration oil spills, and for which toxicity data are less available
in the scientific literature.13
As shown here and elsewhere,2,6,7
ICE-based SSDs can produce HC5 values similar to those
generated from empirical SSDs, adding reliability to the use of
ICE models to augment toxicity data. Here, HC5s from ICE-
based SSDs for petroleum and Corexit dispersants were within
1 order of magnitude HC5s from SSDs with empirical data.10,18
While previous studies have recommended between 7 and 15
species to develop reliable SSDs and associated benchmarks,2,7
the minimum number of species used here was 5. As a result,
petroleum ICE-based SSDs could only be developed for 9
surrogate species (including A. bahia and M. beryllina), or by
combining data for at least two species as demonstrated with
the dispersant ICE-based SSDs. While A. bahia generally
appears to be more sensitivity than M. beryllina (as shown here
and elsewhere10
), SSDs from ICE models with M. beryllina as
the surrogate species did not result in significantly larger HC
values. Consequently, the surrogate set of models that leads to
smaller HC values may be preferred, particularly when
protection of especially sensitive species is a concern, or
when there are concerns about species in specific microhabitat
(e.g., sheltered salt marshes, mangroves or tidal flats) within
coastal ecosystems.
One of the limitations of the current study is that, because of
the nature of the existing toxicity data, models were developed
under the assumption that petroleum hydrocarbons represent a
single compound with a predominant mode of toxicity
(nonpolar narcosis). In reality, petroleum hydrocarbons are a
complex mixture of chemicals with more than one mode of
toxicity (narcosis, receptor-mediated). Because compounds in
these mixtures have different chemical properties and affinities
for lipids, it is widely recognized that their composition
determine their overall toxicity.43−45
This limitation, however,
could be overcome by using quantitative structure activity
relationships (QSARs),2
which are relationships based on
chemical structure. QSAR toxicity data can provide surrogate
values for the development of hydrocarbon-specific ICE
models,2
or could be used to develop QSAR surrogate-
predicted species ICE models.46
Future refinements of ICE
models are dependent upon greater availability of detailed
analytical chemistry results (e.g., individual PAH analytes),
which are currently lacking in most petroleum and dispersant
toxicity data sources.13
In this study petroleum and dispersant ICE models were
developed and used to generate ICE-based SSDs. While the
development of ICE models was limited by the availability of
toxicity data meeting the rigorous standardization criteria, the
information presented here could facilitate assessments of the
potential toxicological consequences oil and dispersants to
aquatic communities, aid in the estimation of concentration
associated with low or no effects, and allow for comparisons of
the relative sensitivity across test species. ICE-based SSDs
could also be used in conjunction with environmental

concentrations from fate models or environmental monitoring
to help characterize, via joint probability distribution curves, the
fraction of potentially affected species or the magnitude of
adverse effects to aquatic communities.8
The inclusion of
additional paired-empirical data for a wider number of species,
particularly for sensitive life stages, may allow for further
application of ICE models in damage assessments so as to allow
comparisons across communities (e.g., epibenthic vs benthic
effects assessments) and habitats (e.g., tidal flats vs marshes).
While model verification showed promising results, additional
toxicity data could help improve existing ICE models and
facilitate the development of additional ones. Of special interest
is the inclusion of paired standard test-sensitive or rare species
toxicity data, which is essential to refine petroleum and
dispersant benchmark concentrations protective of the most
sensitive or untested species.
■ ASSOCIATED CONTENT
*S Supporting Information
Additional information related to ICE models is available in
Supporting Information 1. This information includes a
complete list of references of the original sources, One table
containing scientific and common names, two tables containing
statistically significant ICE model parameters, 1 Table
comparing empirical and ICE based HC5s, 1 Figure of the
model building scheme, two figures of the fitted models, one
figure on taxonomic relatedness, and one figure with
representative SSDs. Supporting Information 2 contains core
data used in the development of ICE models. This material is
available free of charge via the Internet at http://pubs.acs.org.
■ AUTHOR INFORMATION
Corresponding Author
*Phone: +1 803 254 0278; fax: + 1 803 254 6445; e-mail:
abejarano@researchplanning.com.
Notes
The authors declare no competing financial interest.
■ ACKNOWLEDGMENTS
Special thanks to C. Jackson and S. Raimondo (U.S. EPA) for
comments to an earlier version of this manuscript. This
research was made possible by a grant from NOAA and the
University of New Hampshire’s Coastal Response Research
Center (Contract No. 13-034) to Research Planning, Inc. None
of these results have been reviewed by CRRC and no
endorsement should be inferred. The views expressed in this
article are those of the authors and do not necessarily reflect the
views or policies of the U.S. EPA. This publication does not
constitute an endorsement of any commercial product.
■ ABBREVIATIONS
adj-R2
adjusted coefficient of determination
HC1 and HC5 1st and fifth percentile hazard concentrations
ICE interspecies correlation estimation
LC50 and EC50 median lethal and effects concentrations,
respectively
MSE mean square error
SSD species sensitivity distributions
THC total hydrocarbon content
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