Appendix E. Analysis of chronic toxicity data and acute chronic 

ratios (ACRs) in support of deriving chronic HC5s: 

Acetylcholinesterase inhibitors

David R. Mount

National Health and Environmental Research Laboratory

Mid-Continent Ecology Division

Duluth, MN

December 20, 2011
1.0 BACKGROUND

The analysis described in this appendix is one of several conducted in support of developing a 

common methodology for assessing chemical effects on aquatic animals for use by the USEPA 

Office of Pesticide Programs (OPP) and the Office of Water (OW). Other appendices describe 

methods for estimating/predicting the acute toxicity values and/or the acute HC5, the 5th

percentile in a species sensitivity distribution for acute toxicity. However, effects assessments 

by both OPP and OW also require estimation of chronic toxicity thresholds. This could include 

predicting chronic thresholds for specific organisms, or the estimation of the chronic HC5, the 5th

percentile of a chronic species sensitivity distribution.

A common approach for extrapolating from acute to chronic toxicity is the Acute-Chronic Ratio 

(ACR), which is calculated as the ratio of the acute effect concentration to a chronic effect 

concentration, generally for the same chemical and organism. Both OPP and OW currently use 

ACRs in some contexts, generally as a “within chemical” extrapolation tool, meaning that an 

ACR developed for a chemical tested with one or more species is used to estimate chronic effect 

thresholds for other organisms exposed to the same chemical. The underlying premise of the 

ACR is that the combination of toxicokinetics and toxicodynamics that determine the separation 

of acute and chronic effect concentrations has some degree of commonality among organisms (or 

sub-groups thereof) and can therefore serve as a basis for extrapolating chronic thresholds for 

organisms having only acute toxicity data.

As an example, EPA’s guidelines for deriving aquatic life criteria (ALC)(USEPA 1985; referred 

to as the “1985 Guidelines”) describe ACRs as one method for deriving the Final Chronic Value 

(FCV), which is intended to represent a theoretical 5th percentile of chronic toxicity for a 

distribution of aquatic species – essentially, the chronic HC5. The method in the 1985 

Guidelines requires that acute and chronic toxicity data are available to calculate ACRs for a 

minimum of one fish, one invertebrate, and one acutely sensitive freshwater organism. A Final 

Acute-Chronic Ratio (FACR) is then calculated as the geometric mean of the available ACRs, or 

by other means as warranted by the data. For example, if the ACRs tend to decrease as acute 

sensitivity decreases, then the FACR may be calculated as the geometric mean of ACRs only for 

acutely sensitivity species. The Final Acute Value (FAV; the theoretical 5th percentile of the 

acute species sensitivity distribution) is then divided by the FACR to derive the FCV.

The FAV and FCV under the 1985 guidelines represent estimates of the acute HC5 and chronic 

HC5, and therefore present a case example of using ACRs to estimate the chronic HC5. To 

generalize this case, we can represent the approach as:

chronic HC5 = (acute HC5) / ACRHC5

where ACRHC5 refers specifically to an ACR derived for the purpose of relating an acute HC5 to 

a chronic HC5. In the case of the 1985 guidelines, the FACR = ACRHC5. As outlined above, 

calculation of the FACR under the 1985 Guidelines requires the availability of paired acute and 

chronic toxicity data for a minimum group of organisms exposed to the same chemical. 

However, cases exist where OPP or OW may wish to estimate chronic effect thresholds for 
chemicals with limited or no chronic toxicity data available; in such cases, an appropriate ACR

must be developed from other data.

As part of the Great Lake Water Quality Initiative (GLI), a method was proposed to use 

“default” values in the calculation of an ACRHC5 (referred to in the methodology as a “Secondary 

Acute-Chronic Ratio” or SACR). The basis for this default value was an analysis by Host et al. 

(1995), who developed a distribution of FACR values from all ALC documents available at the 

time. The 80th percentile of this distribution (18), was then adopted under the GLI as a default 

ACR value to be used in lieu of measured ACRs. Calculation of the SACR uses a combination 

of available measured ACRs and the default value of 18. If two measured ACRs are available, 

the SACR is the geometric mean of 18 and the two measured ACRs; if one measured ACR is 

available, the SACR is the geometric mean of 18, 18, and the measured ACR. If no chronic data 

are available, then the SACR is simply 18.

The FACR distribution compiled by Host et al. (1995) combined FACRs from freshwater ALC 

across all chemical types. TenBrook et al. (2010) took a similar approach, but used FACR data 

only for pesticides (combining pesticides of different types). The 80th percentile of that 

distribution was 12.4, and TenBrook et al. (2010) recommended application of this value in a 

manner parallel to the SACR calculation under the GLI.

In the ACRHC5 estimation methods described above, default ACRs were derived from 

distributions of chemicals with multiple mechanisms of action. In the context of the current 

effort, it was hypothesized that the ACRHC5 approach could be refined by analyzing ACRs in the 

context of MOAs. The working assumption was that the distributional characteristics of ACRs 

would be related to the MOA for a chemical, meaning that particular MOAs might lead to 

consistently higher or lower ACRs compared to averages obtained across multiple MOAs. 

MOA-specific ACRs would logically extend also to the possibility of particular taxa having 

higher or lower ACRs because of the intersection of their physiology and the way in which the 

chemical elicits adverse effects.

This appendix analyzes ACR data for pesticides whose primary MOA is the inhibition of 

acetylcholinesterase (AChE); this includes two primary pesticide groups, organophosphates and 

carbamates. While focusing specifically on AChE-inhibiting chemicals, this analysis is also 

intended as demonstrative of analyses that might be used for developing MOA-specific ACRs 

for other groups of chemicals in the future.
2.0 DATA COMPILATION AND ANALYSIS

Chronic toxicity data were extracted from a database developed by USEPA called 

“AquaChronTox”. This database is a compilation of chronic toxicity data for aquatic animals

obtained from studies that would meet the definition of an acceptable chronic test under the 1985

Guidelines; in general terms, this means life-cycle tests (including reproduction) with 

invertebrates, and life-cycle, partial life-cycle, or early life stage (ELS) tests with fish. Sources 

of data included a previous chronic toxicity data base compiled by Glen Thursby (USEPANarragansett); an existing database of chronic toxicity data compiled and largely generated by 

the USEPA laboratory in Duluth; chronic toxicity data submitted to USEPA under FIFRA 

pesticide registration requirements; and other data from the open literature (compiled in the 

ECOTOX data base and/or evaluated by USEPA as part of ecological risk assessments 

supporting pesticide registration). While the development of AquaChronTox is ongoing, priority 

was given to entry of data specifically for AChE inhibitors to support the analysis described in 

this appendix. Data coverage for AChE inhibitors within AquaChronTox is thought to be robust, 

though not necessarily fully comprehensive.

The AquaChronTox database contains a wide variety of extracted data, including both general 

experimental parameters and treatment level results for individual endpoints related to survival, 

growth, or reproduction. Also captured were statistically determined NOEC and LOEC values 

(where available) from the study authors or later re-analysis (e.g., data evaluation records 

completed as part of USEPA risk assessments). Both freshwater and marine/estuarine organisms 

were included in the data compilation. Both measured (where available) and nominal exposure 

concentrations are contained in the database, but measured concentrations were used for data 

analysis whenever available.

Initially, a total of 119 chronic studies were identified for organophosphate or carbamate 

insecticides. These data were then subject to additional screening wherein studies were 

eliminated if there were test conditions that would disqualify a test from consideration using 

evaluation criteria similar to that used in the development of water quality criteria (e.g., unusual 

dilution water characteristics, use of surfactants in combination with toxicants, poor performance 

of control organisms). Where NOEC and LOEC values were reported for individual endpoints, a 

single NOEC and LOEC value for the study was established based on the most sensitive 

endpoint related to survival, growth, or reproduction (i.e., that endpoint showing significant 

effects at the lowest exposure concentration). As part of this review, exposure response curves 

for those endpoints were evaluated qualitatively to insure that the designation of the NOEC and 

LOEC values was not notably ambiguous and seemed biologically reasonable. Through this 

review, a small number of tests were discarded because of a highly irregular exposure response 

curves. While it is possible that such tests represent true response profiles, they occurred in less 

than two percent of tests, making it seem less likely that the “true” response curve is actually 

non-monotonic. Another characteristic of a small number of tests was that the lowest 

NOEC/LOEC was reported for organism length, but there was no indication of a concomitant 

decrease in organism weight. In general, one expects organism weight to vary with the cube of 

organism length (assuming similar body morphology), so even very small changes in length 

would be expected to cause substantial changes in weight. In cases where small differences in 

length were defining the overall NOEC/LOEC without a corresponding indication of a response 
in weight (or reproduction), the NOEC/LOEC for the test were increased to match the 

NOEC/LOEC for the next most sensitive endpoint. Finally there were a few tests for which the 

spacing of exposure concentrations was large (e.g., greater than 3-fold concentration intervals). 

These tests were discarded because of the associated uncertainties in their NOEC/LOEC values.

Those tests passing the previous filters were then coded into two groups according to whether the 

data yielded “defined” or “unbounded” NOEC or LOEC values. In this context, the term 

“unbounded” means that there were either significant effects at the lowest tested concentration, 

or no effects at the highest tested concentration; in such cases, the NOEC and LOEC can only be 

expressed as inequalities. For most analyses, only those tests with defined NOEC and LOEC 

values were used, because of the innate difficulty in conducting distributional analyses with a 

mixture of defined and unbounded values. However, for some analyses unbounded values were 

included as check to insure that limiting analyses to defined values did not skew the analysis 

(e.g., if eliminating tests with effects noted at the lowest tested concentration had the effect of 

biasing data away from tests indicating high chronic sensitivity). From the original set of 119 

chronic studies, a subset of 73 tests meeting the screening criteria and with defined NOEC and 

LOEC values was established. For these tests, the geometric mean of the NOEC and LOEC was 

also calculated, denoted as “GM” in this document. GM is the equivalent of the “Maximum 

Acceptable Toxicant Concentration” (MATC), a term that appears frequently in the literature. 

MATC is not used here because its name implies a risk management judgement (what is 

“acceptable”) that is not intrinsic to the calculated value itself.

In addition to NOEC, LOEC, and GM, regression analysis was used to estimate EC10, EC20, 

and EC50 concentrations for invertebrate chronic tests meeting all the previous qualification 

criteria. The Toxicity Response Analysis Program (TRAP, Version 1.02; U.S. EPA, MidContinent Ecology Division, Duluth, MN) was used to derive these values. A sigmoid model 

with finite tails was used to fit the exposure response data for individual endpoints, except in a 

very small number of cases where this model did not seem to reflect the shape of the underlying 

data, in which case a piecewise linear model was used. The exposure concentration was 

expressed on a log10 scale while the response variable was expressed on a linear scale. Because a 

log exposure scale prevents the control concentration from being entered as 0, the control 

exposure was assigned a concentration equal to one-tenth of the lowest exposure concentration. 

The test endpoint with the lowest EC20 value was identified from among the reported endpoints, 

and the EC10, EC20, and EC50 values for that endpoint were selected to represent that test. The 

EC20 was used because of its precedent as the chronic effect concentration currently used in the 

derivation of ALC. However, because of differences in the slope of the concentration-response 

curve, it was possible for an endpoint that did not have the lowest EC20 value to have the lowest 

EC10 or EC50 value. In the infrequent cases where this occurred, the differences were small.

Calculating ACRs also requires an acute value for the same species and chemical. Acute values 

were acute LC50 (or immobilization EC50) values (48-h for cladocerans; 96-h for other species) 

selected according to a sequence of priorities as follows:

An LC50 provided by the study author as being paired with the chronic study

An LC50 identified in the USEPA data evaluation record as being paired with the chronic 

study
An LC50 from an acute test conducted by the same laboratory as conducted the chronic 

study, but potentially at a different point in time.

An LC50 (or geometric mean of multiple LC50 values) derived from the data 

compilation underlying Web-ICE (Version 3.1; Raimondo et al. 2010). Values from the 

Web-ICE data were selected based on factors including:

o Must meet minimum data requirements for inclusion in Web-ICE models

o Preference given to values with measured chemistry

o Preference given to values using flow-through methods

o Preference given to values generated using USEPA/OPPTS methods

In the context of developing ALC, ACRs are calculated as the ratio of the acute LC50 (or EC50) 

to the chronic EC20 or GM. For ecological risk assessments for pesticide registration, ACRs are 

calculated as the ratio of the acute LC50 (or EC50) to the chronic NOEC. In this appendix, 

ACR is used when the endpoint is not relevant, such as when speaking conceptually, or to a point 

that applies to ACRs of all types. In cases where the ACR is specific to a particular chronic 

endpoint, subscripts are used to indicate the chronic effect measure used to calculate the ACR. 

For example, ACRGM refers to the ratio of the LC50 to the GM, and the ACRNOEC is the ratio of 

the LC50 to the NOEC. As defined in the introduction, an ACRHC5 is an ACR intended to relate 

the acute HC5 to the chronic HC5.

In some cases, ACR values of less than one were calculated, meaning that the LC50 was actually

lower than the NOEC, GM or EC20. While it is counterintuitive that this could be the case, one 

must remember that the acute value is generally not derived from the same study as the chronic 

endpoints (because of differences in methodology), and since sources of variation exist for both 

the acute and chronic tests that define the ACR, it is possible for ACRs less than one to occur as 

a result of experimental variation. Another possible cause of ACRs less than one is where the 

food added during chronic testing has an effect on the bioavailability or toxicity of the chemical 

causing it to be less toxic than under the acute test conditions (which generally preclude 

feeding); this has been observed with daphnid tests and some metals. For this analysis, ACRs 

less than 1 were retained at their calculated value for purposes of evaluating the distribution of 

ACRs. In a previous analysis of ACRs, Raimondo et al. elected to exclude tests where ACRs 

were less than 1. This was not done here because of concern that it would bias the ACR 

distribution to higher values. This is not to suggest that ACRs less than one would be 

appropriate as an ACRHC5, only that they should be retained during evaluation of the distribution 

of ACRs.

Much of the analysis that follows focuses on the ACRGM, though parallel calculations were done 

using ACRNOEC and ACREC20 (for invertebrates) values. The conclusions reached using the 

ACRNOEC are essentially the same as those obtained from the ACRGM; this is because data were 

only used from tests with similar and relatively close spacing of exposure concentrations. This 

makes the ratio of the ACRGM to the ACRNOEC fairly consistent; for the 73 chronic tests 

evaluated, the median ratio of the ACRGM to the ACRNOEC was 1.40, with 10th and 90th

percentiles of 1.28 and 1.55, and minimum and maximum of 1.11 and 2.00. ACREC20 values 

were only calculated for the 26 chronic tests conducted with invertebrates. Within those data, 

the median ratio of the ACREC20 to the ACRNOEC was 1.41, the 10th and 90th percentiles were 
1.07 and 3.11 and the minimum and maximum were 0.89 and 6.91. Comparing the ACREC20 to 

the ACRGM gave a median ratio of 1.01, the 10th and 90th percentiles were 0.75 and 2.41, and the 

minimum and maximum were 0.63 and 5.12.

Few tests of statistical significance were used to compare groups of ACRs. This is because data 

availability was highly irregular across species and chemicals, so data sets that compare, for 

example, two taxa across the same set of chemicals, are generally limited. Because of the high 

variability in ACRs, statistical comparisons of ACRs with low sample size lack power. 

Accordingly, it was decided that direct inspection of the data was sufficient for most evaluations 

presented.
3.0 RESULTS AND DISCUSSION

Tables 1, 2, and 3 provide a summary of the ACRGM ̧ ACRNOEC, and ACREC20 (respectively) data 

for specific combinations of species and chemicals. Figure 1 shows the cumulative distribution 

of ACRGM values across all taxa and AChE inhibitors, showing that overall ACRs varied widely, 

spanning more that four orders of magnitude. Figure 2 separates the distributions for 

invertebrates and fish and shows that while ACRs for both groups vary considerably, the 

distribution of invertebrate ACRs was consistently lower, and somewhat less variable than that 

for fish.

Figure 3, shows the same distribution as Figure 2, except that data are coded as to whether they 

represent OP or carbamate chemicals. While there are fewer data for carbamate chemicals, their 

ACRs show a fairly wide dispersion across the overall distributions, giving little reason to 

believe that combining data for OP and carbamate chemicals is inappropriate.

Figure 4, shows the same distribution with data coded as to whether the test species was a 

freshwater or marine/estuarine organism. For the fish data, the overlap of freshwater and 

saltwater species is fairly broad. For invertebrates, however, 8 of the 9 saltwater ACRs lie at or 

above the 50th percentile. Figure 5 shows the distributions of invertebrate ACRs by species; as 

indicated in the figure, mysid shrimp (Americamysis bahia) is the only marine invertebrate 

organism, and freshwater data are all for cladocerans, predominately Daphnia magna. To 

provide a more focused evaluation of the comparative sensitivity of mysids and D. magna, a 

subset of the overall data was extracted containing data for only those chemicals that have data 

for both species (Table 4). The ratio of the mysid ACRGM to the daphnid ACRGM was calculated 

for each chemical, then the logs of those ratios were evaluated with a t-test to determine if the 

ratios were significantly different (greater) than 1 (which would indicate that mysids were 

significantly more sensitive). In 6 of 8 direct comparisons, the mysid ACRGM was higher than 

the daphnid ACRGM, with an average ratio of 1.8. However, a one-tailed test (p=0.05) of 

whether this was significantly different from 1 was close to, but not quite, significant (p=0.057).

Some evidence that runs counter to a presumption that mysids are more sensitive than daphnids 

lies in three mysid chronic tests reported by Thursby et al (1990a,b; 1991). These three tests 

(carbaryl, dichlorvos, and propoxur) were excluded because of reduced control survival; 

however, analysis of these data suggest fairly low ACRs (ACRGM = 0.85, 1.21, 5.29, 

respectively). These values would fall in the low end of the overall ACRGM distribution for 

invertebrates, though they cannot be directly compared because ACRGM values for D. magna are 

not available for these same chemicals.

While fish showed high ACRs for AChE inhibitors, this does not translate to high chronic 

sensitivity of fish relative to invertebrates. Because the ACRGM is a ratio of LC50/GM, high 

ACRs can be produced not only by having low chronic effect concentrations, but also by having 

very high LC50 values. Relative to invertebrates, the high ACRs for fish exposed to AChE 

inhibitors appears to be driven largely by a very low acute sensitivity (high LC50 values), rather 

than by having high chronic sensitivity. This low level of acute sensitivity is affirmed by SSD 

analyses for AChE inhibitors conducted by Erickson (see Appendix D).
To help put the chronic sensitivity of fish in perspective, the NOECs from chronic fish tests were 

plotted against the acute LC50 for D. magna for the same chemical (Figure 6). In addition to 

tests with defined NOEC values, this plot also includes tests with unbounded NOECs, and values 

are coded with respect to whether they were derived from ELS tests or life-cycle (or partial lifecycle) tests. As points of reference, three lines were drawn representing:

the D. magna LC50 

an estimate of a median acute HC5, estimated by dividing the D. magna LC50 by 2.89, 

the median acute AChE inhibitor EF derived by Erickson (Appendix D) for data sets 

including D. magna, rainbow trout, and bluegill sunfish;

an estimate of the 90th percentile HC5, estimated by dividing the D. magna LC50 by 16, 

the 90th percentile acute AChE inhibitor EF derived by Erickson (Appendix D) for data 

sets including D. magna, rainbow trout, and bluegill sunfish.

Several inferences may be drawn for Figure 6. First, the vast majority of fish NOEC values are 

at or above the D. magna LC50, meaning that in most cases, the chronic sensitivity of fish to 

AChE inhibitors is less than the acute sensitivity of D. magna to the same chemicals. However 

even in cases where the fish NOEC was lower (more sensitive) than the D. magna LC50, almost 

all were at or above the estimates of acute HC5 values based on EFs calculated by Erickson 

(Appendix D). The conclusion here is that the ACR for fish may not be very important for 

estimating the chronic HC5 for AChE inhibitors, because of the greater sensitivity of 

invertebrates.

Another aspect of Figure 6 is the comparison of the general sensitivity of chronic tests with fish

tested in life-cycle (including reproduction) versus early life stage (ELS) exposures. Based on 

inspection, there is some tendency for life-cycle tests to be toward the lower end of the scatter, 

suggesting they may be more sensitive indicators of chronic toxicity to fish. Using a different 

method of comparison, Raimondo et al., (2007) found that ACRs from fish life-cycle tests were 

significantly higher than those for ELS tests. However, this tendency is not dramatic, as the 

distribution of ELS values encompasses the range of life-cycle values. In regard to estimating a 

chronic HC5, the same conclusion is reached from both tests types, that fish are comparatively 

insensitive to AChE inhibitors.

As outlined in the introduction, the 1985 Guidelines, calculation of the FACR requires ACRs 

(ACRGM or ACREC20) for at least three species, encompassing at least one fish species, at least 

one invertebrate species, and at least one acutely sensitive freshwater species. Depending on the 

variability and distribution of the ACRGM values, the FACR is determined in different ways. If 

the ACRGM values are smaller for organisms with high acute sensitivity (and vice versa), then the 

FACR is determined by the ACRGM for species whose acute sensitivity is near the FAV, rather 

than by the mean of all ACRs. The rationale for this approach is that if organisms with high 

acute sensitivity have smaller ACRs, then applying larger ACRs associated with acutely 

insensitive species would result in a chronic HC5 that was too low. For AChE inhibitors, ACR 

values are related to acute sensitivity, with invertebrates having both higher acute sensitivity and 

lower ACRs. Therefore, using the same logic, it seems appropriate to base the ACRHC5 for 

AChE inhibitors on invertebrate ACRs.
As shown in Figure 5, the invertebrate ACRs for AChE inhibitors are dominated by two species, 

D. magna and mysids. If the ACRHC5 is based on an invertebrate ACR, one must decide whether 

to segregate ACRs by species (e.g., use D. magna ACRs for freshwater ACRHC5 and mysids for 

marine ACRHC5), or to use the joint distribution of all invertebrates. This decision hinges in part 

on the previously discussed issue of whether mysids do in fact have larger ACRs than do 

daphnids and, even if they do, might there be acutely sensitive freshwater organisms that have 

ACRs more similar to mysids than daphnids. Neither of these questions appear easily answered 

from the data in hand. Again drawing from the 1985 Guidelines, that methodology requires a 

minimum of three ACRs to include at least one fish species, one invertebrate species, and one 

acutely sensitive freshwater species. Given the species for which there are chronic data for 

AChE chemicals, the two ways these requirements would be met would be either: 1) D. magna, 

and two fish species, or 2) D. magna, mysids, and a fish. In the former case, the D. magna

ACRGM would be the FACR; in the latter case, the FACR would likely be the mean of the ACR

values for D. magna and mysids, since both are acutely sensitive species.

Given that:

1) it is unclear whether mysids actually have higher ACR values than D. magna;

2) even if they do, they may represent ACRs for other relevant invertebrates; and

3) a FACR under the 1985 Guidelines would be calculated as the geometric mean of the 

ACRs for the two species

it seems reasonable to base an ACRHC5 on an ACR selected from the joint distribution of ACRs 

for all invertebrate species. Figure 7 shows the distribution of invertebrate ACREC20, ACRGM, 

and ACRNOEC combined across all AChE inhibitors and freshwater/saltwater species. Figure 8 

directly compares EC20 and NOEC values, while Figure 9 compares EC20 and GM values. In 

all but one case, the NOEC was lower than the EC20, indicating that the level of effect at the 

NOEC was generally lower than 20%. The EC20 values showed greater similarity to the GM 

concentrations, with only a few GM values falling outside a range of 0.7 to 1.5 of the EC20.

Table 5 provides the 50th, 80th, and 90th percentile values for each of the ACR calculation 

methods along with related values from other ACR analyses. The Host et al. (1995) and

TenBrook et al. (2010) analyses were discussed in the introduction. The Raimondo et al. (2007)

was a compilation of 456 ACRGM values from a broad range of chemicals, the distributions of 

which were analyzed by a variety of sub-groupings. Interestingly, Raimondo et al. (2007) did 

not find a significant difference between ACRs for AChE inhibitors compared to other MOA. 

However, their comparisons across MOA pooled fish and invertebrate ACRs within a MOA, so 

the resulting distribution would have been very large (similar to Figure 1), and would not have 

reflected differences among taxa within an MOA, as has been described here for AChE 

inhibitors. Likewise, the Raimondo et al. comparison of fish and invertebrate ACRs did not 

show the pronounced differences shown in the current analysis, presumably because the 

Raimondo et al. comparison of fish v. invertebrates included all chemicals, and the large 

differences shown for AChE inhibitors likely doesn’t exist for all chemical groups. While 

median ACRGM values are similar between Raimondo et al. and the current analysis, limiting 

selection of the ACRHC5 for AChE inhibitors to invertebrates greatly reduces the ACRs in the 

upper ends of the distributions.
Another difference is that Raimondo et al. discarded all ACRGM values that were less than 1

whereas these values were retained in the current analysis. As explained earlier, this has the 

potential to bias the ACR distribution toward higher values; the same sources of variability that 

create artificially low ACRs presumably create artificially high ACRs also, but these are not 

distinguishable from other values and are therefore left in the analysis. However, the bias 

created by data censoring is probably not high, because Raimondo et al. used similar data 

sources to the current analysis, and the total number of ACRs for AChE inhibitors reported by 

Raimondo et al. (78) is actually slightly higher than the number included in this analysis (73). A 

preliminary comparison of the invertebrate ACRs for AChE inhibitors from both analyses (data 

not shown) showed some differences in the low end of the distribution (which is expected 

because that is where values were excluded), but close agreement in the 50th to 90th percentiles.

The Host et al. (1995) and TenBrook et al. (2010) analyses differ from Raimondo et al. (2007) in

that they are based on distributions of FACR values from ALC documents. Although these 

FACRs are based on ACRGM (or ACREC20) values, they are not directly comparable to raw 

ACRGM values, because FACRs reflect a variety of different derivations – for example, some 

may represent the ACRGM for a single acutely sensitive species, while others may be the average 

of multiple ACRGM values. That difference aside, because the current analysis suggests that 

invertebrate ACR values represent a logical basis for an ACRHC5 for AChE inhibitors, the 

invertebrate ACRGM and/or ACREC20 values from the current analysis are functionally parallel to 

the Host et al. FACR distribution. Invertebrate ACRGM and ACREC20 distributions from this 

analysis range from roughly equal to, to about 2-fold lower than, comparable values from the 

Host et al. (1995) and TenBrook et al. (2010) distributions.

When making this comparison, it is important to note that the degree of similarity between the 

Host et al. (1995) and TenBrook et al. (2010) FACR distributions and the proposed ACRHC5

values for AChE inhibitors is created in part by the previous finding that fish ACRs were not 

important, because the invertebrates have greater chronic sensitivity. If the fish were more 

chronically sensitive to AChE inhibitors, then the conclusions would likely change. As such, it 

is highly uncertain whether the same conclusion would be reached for another mode of action 

with different taxonomic sensitivity.

Because invertebrate ACRs for AChE chemicals show a substantial range, estimation of a 

chronic HC5 using an ACRHC5 will require a risk management decision as to where in the 

distribution the ACRHC5 will be selected. One important consideration is that because ACRs are 

ratios and both the numerator and denominator have uncertainty, variability in the ACRs from 

chance alone will be larger than the variability in either of the component values. In a Monte 

Carlo simulation, a theoretical distribution of ACRGM values was created by assuming true 

values of 5.91 for the LC50 and 1 for the GM – this creates a “true” ACR of 5.91, which was the 

50th percentile for the invertebrate ACRGM for AChE inhibitors. Both the LC50 and GM were 

assumed to vary within 2-fold, meaning that 95% of values would fall between 0.5x and 2x of 

the true value, with a log-normal distribution. This factor of 2 was based on a common “rule of 

thumb” regarding variability in toxicity test results; it is simply illustrative and is not based on 

any specific evaluation of test variability. This simulation gave 50th, 80th, and 90th percentile 

ACRGMs of 5.91 (the true mean), 11.0, and 15.6; the corresponding ACRGM values from the 
current analysis were 5.91, 13.6 and 18.8 (Table 5). Thus, if the true variability in LC50 and 

GM values were on the order of 2-fold, the majority of the observed range in ACRGM values 

could be attributable to random variability alone, even if the “true” ACR were the same for all 

AChE inhibitors.

Less easily evaluated is the possibility that sources of error beyond random variability exist, 

causing extreme values. For example, in the current analysis the highest invertebrate ACRGM is 

255 for D. magna and disulfoton. The reported measured LC50 used to calculate this ACR is 13 

ug/L. However, this value seems inconsistent with the much greater mortality response observed 

in the chronic test (37.5% survival at 0.64 ug/L), and is also inconsistent with a much lower (less 

than 1 ug/L) LC50 predicted by the ECOSAR system. While not certain, these latter 

observations suggest that the measured LC50 and resulting ACR may be too high. Although the 

calculated value of 255 was retained in the analysis, the potential for aberrantly high or low 

values emphasizes the potential inaccuracies that might be introduced by selecting values that are 

in the extreme tails of the distribution. 

Another consideration in selecting an ACR from the distribution is that a chronic HC5 derived 

by dividing the acute HC5 by the ACRHC5 contains the uncertainties from both the acute HC5 

estimation and the ACR distribution. If the acute HC5 derivation contains substantial 

conservatism (e.g., an EF selected from a high percentile of the distribution) and the same is 

done for selecting an ACRHC5, the estimated chronic HC5 may have an undesirably high 

aggregate conservatism.

Based on the experience of developing this analysis, suggestions for future study include the 

following:

1. Determine if additional screening of data to more carefully “match” (e.g., same lab, same 

water, same time frame) the acute and chronic data would decrease variability in ACRs.

2. Consider additional data collection to evaluate specific toxicity values that are 

contributing to extreme values within ACR distributions.

3. Conduct similar analyses on chemicals in other MOAs to explore more fully whether 

consideration of MOA in developing ACRHC5s provides a substantive decrease in 

uncertainty in deriving chronic HC5s as compared to more generalized ACRHC5

distributions.

4. Consider approaches that could be used to identify chemicals or chemical groups for 

which ACRHC5s are likely to exceed default values (e.g., “structural alerts” similar to 

Ahlers et al. 2006), and thereby result in overestimating the chronic HC5.
4.0 CONCLUSIONS

1) When compared across their distributions, ACRs for AChE inhibitors are much higher 

for fish than for invertebrates.

2) Despite having higher ACRs, fish generally have low acute sensitivity to AChE 

inhibitors, such that their chronic effect concentrations are typically higher than the acute

effect concentrations for D. magna exposed to the same chemical, and almost certainly 

higher than an acute HC5 for vertebrates and invertebrates combined; for this reason, 

estimation of fish ACR values is not important to estimating a chronic HC5 for AChE 

inhibitors for all species combined.

3) If the chronic HC5 for AChE inhibitors is estimated by applying an ACRHC5 to the acute 

HC5, it is logical to select the ACRHC5 from the distribution of invertebrate ACR values; 

doing so should produce a chronic HC5 for AChE inhibitors that is protective of both fish

and invertebrate species.

4) The AChE inhibitor-specific distribution of invertebrate ACRs derived here is generally 

lower than those from literature analyses using datasets less specific to the AChE 

inhibition MOA. Accordingly, the same intended level of protection may be achieved 

with a higher chronic HC5 if the MOA-specific ACRHC5 is used.
References

Ahlers, J., C. Riedhammer, M. Vogliano, R. Ebert, R. Kuhne, and G. Schuurman. 2006. Acute to 

chronic ratios in aquatic toxicity -- variation across trophic levels and relationship with chemical 

structure. Environ. Toxicol. Chem. 25:2937-2945.

Host, G.E., R.R. Regal, and C.E. Stephan. 1995. Analyses of acute and chronic data for aquatic

life. Draft report. United States Environmental Protection Agency. Washington, D.C. March 16, 

1995.

Raimondo, S., B.J. Montague and M.G. Barron. 2007. Determinants of variability in acute to 

chronic ratios for aquatic invertebrates and fish. Environ. Toxicol. Chem. 26:2019-2023.

TenBrook, P.L., A.J. Palumbo, and R.S. Tjeerdema. 2009. Methodology for derivation of 

pesticide water quality criteria for the protection of aquatic life. Phase II: Methodology 

development and derivation of chlorpyrifos criteria. University of California Davis, Davis, CA, 

USA.

Thursby, G.B. 1990a. Memo to D.J. Hansen: Flow-through acute and chronic toxicity of 

dichlorvos to Mysidopsis bahia. USEPA, Office of Research and Development, Narragansett, 

RI.

Thursby, G.B. 1990b. Memo to D.J. Hansen: Flow-through acute and chronic toxicity of 

propoxur to Mysidopsis bahia. USEPA, Office of Research and Development, Narragansett, RI.

Thursby, G.B. 1991. Memo to D.J. Hansen: Flow-through acute and chronic toxicity of 

carbaryl to Mysidopsis bahia. USEPA, Office of Research and Development, Narragansett, RI.

USEPA. 1985. Guidelines for Deriving Numerical National Water Quality Criteria for the 

Protection of Aquatic Organisms and their Uses. United States Environmental Protection 

Agency. Stephan, C.E., D.I. Mount, D.J. Hansen, J.H. Gentile, G.A. Chapman and W.A. Brungs.

PB85-227049. National Technical Information Service, Springfield, VA.

USEPA. 2010. Exploration of Methods for Characterizing Effects of Chemical Stressors to 

Aquatic Animals. Draft Report. United States Environmental Protection Agency. November 2, 

2010.
Figure 1: Distribution of ACRGM values, combined across all AChE inhibitors and all species.

Acute Chronic Ratio (LC50/GM)

0.1 0.3 1 3 10 30 100 300 1000 3000 10000

Cumulative Percent

0.2

0.5

1

2

5

10

20

30

50

70

80

90

95

98

99

99.8
Figure 2: Distribution of fish and invertebrate ACRGM values for all AChE inhibitors.

Acute Chronic Ratio (LC50/GM)

0.1 0.3 1 3 10 30 100 300 1000 3000 10000

Cumulative Percent

0.5

1

2

5

10

20

30

50

70

80

90

95

98

99

99.8

Invertebrates

Fish
Figure 3: Distribution of fish and invertebrate ACRGM values for all AChE inhibitors, coded to 

indicate data for carbamate and organophosphate chemicals.

Acute Chronic Ratio (LC50/GM)

0.1 1 10 100 1000 10000

Cumulative Percent

0.5

1

2

5

10

20

30

50

70

80

90

95

98

99

99.8

Invert Carbamate

Invert OP

Fish Carbamate

Fish OP
Figure 4: Distribution of fish and invertebrate ACRGM values for all AChE inhibitors, coded to 

indicate data for freshwater and saltwater species.

Acute Chronic Ratio (LC50/GM)

0.1 0.3 1 3 10 30 100 300 1000 3000 10000

Cumulative Percent

0.5

1

2

5

10

20

30

50

70

80

90

95

98

99

99.8

SW Invertebrates

FW Invertebrates

SW Fish

FW Fish
Figure 5: Distributions of ACREC20 values for individual invertebrate species, combined across 

all AChE inhibitors.

Acute Chronic Ratio (LC50/EC20)

0.1 0.2 0.5 1 2 5 10 20 50 100 200 500 1000

Cumulative Percent

2

5

10

20

30

50

70

80

90

95

98

Americamysis bahia

Daphnia magna

Ceriodaphnia dubia
Figure 6: NOEC values from fish chronic tests with AChE inhibitors plotted as a function of the 

Daphnia magna acute LC50 and estimates of acute HC5s for the same chemical. See text for 

detailed explanation of the lines.

Daphnia magna 48-h LC50 (ug/L)

0.1 1 10 100

NOEC from Fish Chronic Test (ug/L)

0.1

1

10

100

1000

ELS Fish NOEC

ELS Unbounded NOEC

Life Cycle Fish NOEC

Life Cycle Unbounded NOEC

NOEC = LC50

NOEC = Median acute HC5

NOEC = 90th% acute HC5

Fish chronic

less sensitive

Fish chronic

more sensitive
Figure 7: Distribution of ACRNOEC, ACRGM, and ACREC20 values for invertebrates, combined 

across all AChE inhibitors.

ACR

0.1 0.2 0.5 1 2 5 10 20 50 100 200 500

Cumulative Percent

1

2

5

10

20

30

50

70

80

90

95

98

99

ACRNOEC

ACRChV

ACREC20
Figure 8: Comparison of EC20 and NOEC values for invertebrate species combined across all 

AChE inhibitors. Solid line designates unity, dashed line shows 2x deviation from unity, and 

dotted line shows 4x deviation from unity.

NOEC (ug/L)

0.001 0.01 0.1 1 10 100

EC20 (ug/L)

0.001

0.01

0.1

1

10

100

+/- 4x

+/- 2x

1:1
Figure 9: Comparison of EC20 and GM values for invertebrate species combined across all 

AChE inhibitors. Solid line designates unity, dashed line shows 2x deviation from unity, and 

dotted line shows 4x deviation from unity.

ChV (ug/L)

0.001 0.01 0.1 1 10 100

EC20 (ug/L)

0.001

0.01

0.1

1

10

100

+/- 4x

+/- 2x

1:1
Table 1: ACRGM values used in the analysis. Highlighted values represent the geometric mean 

of multiple values.

Americamysis bahia

Ceriodaphnia dubia

Daphnia magna

Cyprinodon variegatus

Menidia beryllina

Menidia peninsulae

Jordanella floridae

Oncorhynchus mykiss

Opsanus beta

Pimephales promelas

Geomean 

Invert

Geomean 

Fish

Geomean 

all

Aminocarb 31.96 31.96 31.96Carbaryl (Sevin) 23.82 23.82 23.82Carbofuran 1.65 8.61 1.65 8.61 3.77Methomyl 5.55 16.14 3.25 25.84 9.46 9.16 9.31Propoxur (Baygon) 9.12 9.12 9.12Azinphos-methyl 9.43 14.08 11.52 11.52Carbophenothion 28.05 28.05 28.05Chlorpyrifos (Dursban) 1.75 3.11 42.25 3.61 1.38 29.88 151.50 2.33 15.71 9.11Diazinon 13.51 4.12 250.63 793.56 7.46 445.97 57.67Dichlorvos (DDVP) 5.53 13.87 21.37 11.79 11.79Disulfoton 255.44 43.32 9.87 28.93 255.44 23.13 42.16Ethoprop 9.66 1.94 12.02 4.33 12.02 6.09Fensulfothion 6.44 6.44 6.44Fenthion 44.52 44.52 44.52Fonofos 6.50 4.49 4.23 7.48 5.40 5.63 5.51Malathion 6.71 5.50 36.05 3.13 6.71 8.53 8.03Methidathion 19.11 8.15 12.48 12.48Methyl parathion 2.59 0.41 15.62 1.03 15.62 2.55Naled 13.89 2.26 324.37 5.60 324.37 21.67Parathion 29.89 29.89 29.89Phosmet 5.01 52.06 5.01 52.06 16.15

Profenofos 8.65 6.27 7.36Terbufos 28.38 28.38Geomean Carbamate 5.55 5.15 3.25 17.29 5.28 13.09 9.67Geomean OP 10.02 2.59 4.97 16.91 3.61 1.38 36.05 13.98 29.88 49.94 6.37 20.72 12.67Geomean All 9.38 2.59 4.99 14.08 3.61 1.38 36.05 13.98 29.88 34.19 6.21 19.11 10.52
Table 2: ACRNOEC values used in the analysis. Highlighted values represent the geometric mean 

of multiple values.

Americamysis bahia

Ceriodaphnia dubia

Daphnia magna

Cyprinodon variegatus

Menidia beryllina

Menidia peninsulae

Jordanella floridae

Oncorhynchus mykiss

Opsanus beta

Pimephales promelas

Geomean 

Invert

Geomean 

Fish

Geomean 

all

Aminocarb 50.13 50.13 50.13Carbaryl (Sevin) 42.86 42.86 42.86Carbofuran 2.51 12.49 2.51 12.49 5.59Methomyl 7.90 19.29 4.46 37.02 12.35 12.85 12.60Propoxur (Baygon) 13.23 13.23 13.23Azinphos-methyl 13.50 17.93 15.56 15.56Carbophenothion 37.86 37.86 37.86Chlorpyrifos (Dursban) 3.50 4.40 60.11 5.60 1.98 48.57 212.69 3.92 23.32 14.01Diazinon 18.26 5.65 341.86 1095.70 10.15 612.03 78.84Dichlorvos (DDVP) 7.66 19.42 28.09 16.10 16.10Disulfoton 351.35 61.73 13.64 44.08 351.35 33.35 60.09Ethoprop 13.41 2.70 16.41 6.02 16.41 8.41Fensulfothion 8.94 8.94 8.94Fenthion 64.15 64.15 64.15Fonofos 9.39 6.45 5.93 10.64 7.78 7.94 7.86Malathion 8.67 8.25 40.58 4.52 8.67 11.48 10.70Methidathion 31.82 11.03 18.73 18.73Methyl parathion 3.05 0.51 17.29 1.25 17.29 3.00Naled 17.81 3.06 478.26 7.38 478.26 29.65Parathion 38.71 38.71 38.71Phosmet 7.18 71.88 7.18 71.88 22.72Profenofos 10.91 8.05 9.37Terbufos 38.19 38.19Geomean Carbamate 7.90 6.95 4.46 26.53 7.26 19.71 14.13Geomean OP 14.47 3.05 6.75 23.86 5.60 1.98 40.58 19.45 48.57 67.94 8.80 28.83 17.58Geomean All 13.53 3.05 6.78 19.81 5.60 1.98 40.58 19.45 48.57 48.56 8.58 26.96 14.69
Table 3: ACREC20 values used in the analysis. Highlighted values represent the geometric mean 

of multiple values.

Americamysis 

bahia

Ceriodaphnia 

dubia

Daphnia 

magna

Geomean

Aminocarb

Carbaryl (Sevin)

Carbofuran 2.04 2.04

Methomyl 8.84 3.67 5.69

Propoxur (Baygon)

Azinphos-methyl

Carbophenothion

Chlorpyrifos 

(Dursban) 1.75 3.84 2.59

Diazinon 2.64 4.97 3.62

Dichlorvos (DDVP)

Disulfoton 121.12 121.12

Ethoprop 6.13 1.56 3.09

Fensulfothion

Fenthion

Fonofos 7.33 2.75 4.49

Malathion 2.79 2.79

Methidathion 26.71 9.19 15.66

Methyl parathion 2.75 0.35 0.98

Naled 6.73 2.38 4.00

Parathion 18.92 18.92

Phosmet 3.92 3.92

Profenofos 8.42 6.45 7.37

Terbufos

Geomean Carbamate 8.84 2.73 4.04

Geomean OP 7.03 2.75 4.09 4.98

Geomean All 7.21 2.75 3.84 4.84
Table 4: ACRGM values for AChE inhibitors with defined values for both Daphnia magna and 

mysids.

Chemical

Mysid 

ACR

D. magna 

ACR Mysid/magna

Log 

mysid/magna

Chlorpyrifos (Dursban) 1.75 3.84 0.456 -0.341

Diazinon 2.64 4.97 0.532 -0.274

Ethoprop 6.13 1.56 3.941 0.596

Fonofos 7.33 2.75 2.664 0.426

Methidathion 26.71 9.19 2.907 0.463

Methomyl 8.84 3.67 2.411 0.382

Naled 6.73 2.38 2.832 0.452

Profenofos 8.42 6.45 1.306 0.116

mean = 0.228

1-tailed t p=0.05 df=7 1.895 SD = 0.357

2-tailed t p=0.05 df=7 2.365 t = 1.803

Not significantly different under 1-tailed test at p=0.05

Not significantly different under 2-tailed test at p=0.05
Table 5: Distributional values for ACRs from the current analysis and literature sources.

ACR Distribution Source/Type

Percentile

50th 80th 90th

Present Analysis:

 AChE inhibitors, fish only ACRNOEC 42.9 118 334

 AChE inhibitors, fish only ACRGM 29.2 79.0 247

 AChE inhibitors, invertebrates only ACRNOEC 7.98 17.9 30.6

 AChE inhibitors, invertebrates only ACRGM 5.91 13.6 18.8

 AChE inhibitors, invertebrates only ACREC20 3.88 8.63 18.0

Raimondo et al. (2007):

 Combined chemicals, all species ACRGM 8.3 NR 79.5

 AChE inhibitors, all species ACRGM 6.4 NR 60.2

 Combined chemicals, fish only ACRGM 9.3 NR 90.0

 Combined chemicals, invertebrates only ACRGM 7.5 NR 68.3

Host et al. (1995):

 Combined chemicals, freshwater FACR 7.1 17.9 29.1

 Combined chemicals, saltwater FACR 4.4 13.5 24.2

TenBrook et al. (2010)

 Combined chemicals, freshwater FACR NR 12.4 NR

NR = Not Reported