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User’s Guide and Technical Documentation 

KABAM version 1.0 

(KOW (based) Aquatic BioAccumulation Model) 

April 7, 2009 

Environmental Fate and Effects Division (EFED) 

Office of Pesticide Programs 

U.S. Environmental Protection Agency 

Washington, D.C. 
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Acknowledgements 

Author 

Kristina Garber 

 

 

Technical Reviewers 

Brian Anderson 

Lawrence Burkhard 

Paige Doelling 

Keith Sappington 

Thomas Steeger 

Editorial Reviewer 

Karen McCormack 

QA/QC Officer

Nick Mastrota 

QC Testers 

Oak Ridge National Laboratories 
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Table of Contents 

1. Introduction................................................................................................................................. 5 

1.1. Model Description ............................................................................................................... 5 

1.2. When to use this model........................................................................................................ 6 

1.3. Conceptual model ................................................................................................................ 6 

1.4. Model Application ............................................................................................................... 7 

2. Input parameters.......................................................................................................................... 8 

2.1. Chemical Specific Inputs ..................................................................................................... 8 

2.2. Ecosystem Inputs ............................................................................................................... 13 

3. Parameters & Calculations........................................................................................................ 17 

4. Model Results ........................................................................................................................... 18 

5. Assessing pesticide concentrations in fish tissues for human consumption............................. 24 

6. Model assumptions, limitations, and uncertainties................................................................... 24 

Appendix A. Description of bioaccumulation model ................................................................... 26 

A.1. Calculation of fraction of chemical in the water column that is freely dissolved (Ф)...... 28 

A.2. Calculation of chemical concentration in sediment .......................................................... 30 

A.3. Calculation of respiration uptake (k1) and elimination (k2) rate constants ....................... 30 

A.4. Calculation of growth rate constant .................................................................................. 37 

A.5. Calculation of dietary uptake (kD) rate constant ............................................................... 38 

A.6. Calculation of dietary elimination (kE) rate constant........................................................ 43 

A.7. Overall sensitivity of body concentration of chemical (CB) to individual input parameters

................................................................................................................................................... 57 

Appendix B. Explanation of defaults and alternative values representing abiotic characteristics of 

aquatic ecosystem ......................................................................................................................... 72 

B.1. Particulate organic carbon (XPOC) and dissolved organic carbon (XDOC) ......................... 72 

B.2. Concentration of dissolved oxygen (COX)......................................................................... 72 

B.3. Water temperature (T)....................................................................................................... 73 

B.4. Concentration of suspended solids (C SS).......................................................................... 74 

B.5. Sediment organic carbon (OC).......................................................................................... 74 

Appendix C. Explanation of default values representing biotic characteristics of aquatic 

ecosystem, including food web structure...................................................................................... 75 

C.1. Phytoplankton.................................................................................................................... 75 

C.2. Zooplankton ...................................................................................................................... 78 

C.3. Benthic invertebrates......................................................................................................... 79 

C.4. Filter feeders...................................................................................................................... 84 

C.5. Fish (small, medium and large sizes)................................................................................ 86 
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Appendix D. Selection of mammal species of concern and corresponding biological parameters

....................................................................................................................................................... 91 

D.1. Descriptions of mammal species....................................................................................... 91 

D.2. Determination of mammalian default parameters for KABAM ....................................... 92 

Appendix E. Selection of bird species of concern and corresponding biological parameters...... 97 

E.1. Bird family descriptions .................................................................................................... 97 

E.2. Detailed conceptual model .............................................................................................. 102 

E.3. Determination of daily food intake.................................................................................. 103 

E.4. Definition of default parameters to represent birds in KABAM..................................... 104 

Appendix F. Description of equations used to calculate the BCF, BAF, BMF, and BSAF values.

..................................................................................................................................................... 107 

F.1. Bioconcentration.............................................................................................................. 107 

F.2. Bioaccumulation.............................................................................................................. 108 

F.3. Biomagnification ............................................................................................................. 109 

Appendix G. Description of equations used to calculate dietary-based and dose-based EECs, 

toxicity values, and RQs for mammals and birds consuming contaminated aquatic organisms 110 

G.1. Food ingestion rates ........................................................................................................ 110 

G.2. Drinking water intake rates............................................................................................. 110 

G.3. Dose-based EECs............................................................................................................ 111 

G.4. Dietary-based EECs ........................................................................................................ 111 

G.5. Adjusted dose-based toxicity values............................................................................... 111 

Appendix H. Methods for estimating metabolism rate constant (kM) ........................................ 113 

H.1. Use of Equation A1......................................................................................................... 113 

H.2. Use of Arnot et al. 2008.................................................................................................. 114 

H.3. Assumptions and uncertainties........................................................................................ 114 

Appendix I. References Cited..................................................................................................... 116 
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1. Introduction 

1.1. Model Description 

KABAM (KOW (based) Aquatic BioAccumulation Model) is used to estimate potential 

bioaccumulation of hydrophobic organic pesticides in freshwater aquatic food webs and 

subsequent risks to mammals and birds via consumption of contaminated aquatic prey. This 

model can also be used to estimate pesticide concentrations in fish tissues consumed by humans. 

The model was designed for use by the U. S. Environmental Protection Agency Office of 

Pesticide Programs’ Environmental Fate and Effects Division (EFED) scientists. KABAM is 

composed of two parts: 1) a bioaccumulation model estimating pesticide concentrations in 

aquatic organisms and 2) a risk component translating exposure and toxicological effects of a 

pesticide into risk estimates for mammals and birds consuming contaminated aquatic prey. 

The bioaccumulation portion of KABAM is based on an aquatic food web bioaccumulation 

model published by Arnot and Gobas (2004). This model was originally published in 1993 by 

Gobas (Gobas 1993) and was modified by Arnot and Gobas (2004). The Arnot and Gobas 

(2004) model was selected for estimating pesticide bioaccumulation based on the following 

reasons: 1) the Gobas 1993 model underlying the Arnot and Gobas 2004 version is generally 

accepted by the scientific community as a reasonable approach for estimating bioaccumulation of 

persistent hydrophobic organic compounds in aquatic systems (Burkhard 1998); 2) the 1993 

version of the model has been used by EPA for regulatory purposes (USEPA 1995, 2000, 2003); 

and 3) both Gobas 1993 and Arnot and Gobas 2004 have been published in peer-reviewed 

literature. Although originally developed and applied to the Great Lakes ecosystem for modeling 

PCBs and selected pesticides, this model has been applied and validated for other ecosystems, 

including the Hudson river, Fox river/Green Bay and Bayou D’Indie in Louisiana (USEPA 2003, 

Burkhard 2003). A detailed description of the Arnot and Gobas (2004) model is available in 

Appendix A. 

The bioaccumulation portion of KABAM relies on a pesticide’s octanol-water partition 

coefficient (KOW) to estimate uptake and elimination constants through respiration and diet of 

aquatic organisms in different trophic levels. Pesticide tissue concentrations in aquatic organisms 

are calculated for different trophic levels of a food web through diet and respiration. 

In the risk component of KABAM, pesticide concentrations in aquatic organisms are used to 

estimate dose- and dietary-based exposures and associated risk quotients for mammals and birds 

consuming aquatic organisms. The methods used in the risk component of KABAM are 

consistent with EFED’s current modeling approach for assessing risks to terrestrial mammals and 

birds described in USEPA 2004a, as implemented in the T-REX model (version 1.4.1; USEPA 

2008a). 
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1.2. When to Use this Model 

KABAM should be used for pesticides having all of the following characteristics: 

 The pesticide is a non-ionic, organic chemical. 

 The Log KOW value is between 4 and 8. 

 The pesticide has the potential to reach aquatic habitats. 

1.3. Conceptual Model 

Conceptually, KABAM represents a freshwater aquatic ecosystem. This ecosystem receives 

runoff and spray drift containing pesticides from sites where pesticides are applied. The aquatic 

ecosystem incorporates seven food web components to describe the trophic transfer of a 

pesticide in an aquatic food web. These include, in increasing order of trophic level within the 

food web: phytoplankton, zooplankton, benthic invertebrates, filter feeders, small fish, medium 

fish and large fish. These components are referred to within this User’s Guide as “trophic 

levels.” They are not intended to represent discrete trophic levels, but rather generic levels of an 

aquatic food web (e.g., primary producers, primary consumers, secondary consumers, and 

predators). KABAM also evaluates potential exposures and risks to mammals and birds that feed 

upon aquatic animals containing pesticide residues accumulated through the aquatic food web 

(Figure I). 

Figure I. Conceptual model depicting aquatic food web of KABAM. Arrows depict direction of trophic 

transfer of bioaccumulated pesticides from lower levels to higher levels of the food web. 

Sediment

Benthic invertebrates

Phytoplankton 

Zooplankton

Small fish

Filter feeders

Medium fish 

Large fish Water column 

Mammals Birds
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KABAM can represent a specific ecosystem, as defined by the model user. The ecosystem can 

be defined by abiotic (e.g., water temperature, % organic carbon in sediment) and biotic input 

parameters (e.g., body weights of aquatic animals, feeding preferences of fish, birds, and 

mammals). The model user can modify these parameters to match the characteristics of 

ecosystems relevant to a specific mesocosm study or a field study. 

For general use, the default model ecosystem for KABAM is defined as the EFED standard pond 

scenario for the Exposure Analysis Modeling System (EXAMS). The standard pond has two 

compartments: a water column and a benthic area. The water column is 20,000,000 liters in 

volume and the benthic area has a volume of 500,000 liters. The standard pond receives 

pesticides in runoff (dissolved in water and sorbed onto eroded soil) and spray drift from a 10-ha 

treatment field that is immediately adjacent to the pond. The treatment field is represented by 

various scenarios using the Pesticide Root Zone Model (PRZM). The meteorological data 

corresponding to the selected PRZM scenario can influence the runoff of a pesticide into the 

standard pond and also the water temperature of the pond environment. 

The default biotic portions of KABAM are designed to be representative of organisms from the 

seven trophic levels defined above. Mammals and birds of concern are defined by considering 

species of mammals and birds relevant to the United States which rely upon aquatic ecosystems 

for their food sources. 

 1.4. Model Application 

The application of KABAM is referred to in this User’s Guide as the “KABAM tool.” The 

KABAM tool is implemented in Microsoft® Excel 2003. This software program was chosen as 

an operating platform because it is available to EFED users and to the public. Excel is a 

commonly used spreadsheet program that most scientists are familiar with. Computers suitable 

for running the software programs necessary for this tool require no additional hardware. 

Once the KABAM tool is opened, the “Model Description” worksheet is displayed. This 

worksheet contains the version information, a brief model description, and a list of references. 

Across the bottom of this Excel window are several worksheet tabs indicating the various 

portions of the KABAM tool, including “Chemical Specific Inputs,” “Ecosystem Inputs,” 

“Parameters & Calculations,” and “Results.” The requirements and functions of these 

worksheets are explained in more detail below. 

The overall format of the KABAM tool was developed for ease of use. Tables embedded in the 

worksheets were designed for clarity of information and for eventual cut and paste from Excel 

into a Microsoft® Word document containing a risk assessment. Where necessary, comments are 

provided for guidance on selecting input parameters. For more detailed information than is 

contained in the spreadsheet concerning the model, input parameters, calculations, and results, 

this guidance document should be utilized. 
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2. Input Parameters 

Two types of input parameters are required to run KABAM: those that are specific to the 

pesticide and those that define the aquatic ecosystem, including the mammals and birds of 

concern. These input parameters are distinguished by two worksheets that are titled “Chemical 

Specific Inputs” and “Ecosystem Inputs.” 

To run the model, the user is only required to input chemical specific values since default values 

are already inserted into the appropriate locations for ecosystem input parameters. These default 

values allow the user to run KABAM with reasonable and reliable parameters; however, the user 

can select other parameters to explore bioaccumulation of a chemical and associated potential 

risk to mammals and birds that consume aquatic animals. Guidance for altering input parameters 

from the default values is provided in this User’s Guide. 

2.1. Chemical Specific Inputs 

The “Chemical Specific Inputs” worksheet contains three tables for the user to input data. Tables 

1 and 3 require the user to input chemical-specific values. Table 2 contains default values that 

do not require user inputs, but are designed to allow the user flexibility in the case that chemicalspecific data are available for uptake and depuration rate constants in aquatic organisms. 

Table 1 

Table 1 requires inputs related to the chemistry and estimated environmental concentrations 

(EECs) of the pesticide. Required inputs include: 1) pesticide name, 2) Log KOW, 3) organic 

carbon partition coefficient (KOC), 4) sediment pore water concentrations of pesticide residues 

(Pore water EEC), and 5) aqueous concentration of pesticide residues (water column EEC). This 

table contains no default values. The user should input values for each of these parameters in the 

“Value” column of Table 1. 

The titles of several tables displayed in the KABAM tool are designed to automatically insert the 

pesticide name as entered in Table 1. 

Of all parameters incorporated into KABAM, Log KOW has the greatest influence on 

estimates of bioaccumulation in aquatic organisms (see section A.7 of Appendix A). As a 

result, this parameter is the most important for estimating potential exposures of mammals and 

birds to pesticides through consumption of contaminated aquatic organisms. Estimates of Log 

KOW can be obtained for a pesticide from acceptable or supplemental registrant-submitted studies 

(OPPTS Guidelines 830.7550, 830.7560, 830.7570) and from scientific literature. One useful 

source for locating Log KOW data in the scientific literature is Sangster (2007). Before using data 

from this database, the scientist should review the original citation and determine whether the 

data are acceptable or supplemental. If no measured values of Log KOW are available, this value 

can be estimated using EPI Suite software that includes KOWIN (USEPA 2009), which 

considers contributions of the molecule’s individual fragments to the overall Log KOW. If a range 

of Log KOW values is available, it is suggested that the model user input the high and low Log 

KOW values separately in order to bracket the bioaccumulation potential and its associated risks. 
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Bioaccumulation potential increases as Log KOW increases. General guidance for evaluating 

measured and estimated Log KOW data is available in Appendix B of USEPA 2003. 

In Table 1 of the KABAM tool (reproduced below), KOW is automatically calculated as 10 to the 

power of the Log KOW value that is entered by the model user. The KOW is used to estimate 

uptake and clearance rate constants that define the concentrations of the pesticide in the tissues 

of the aquatic organisms. 

KOC data can be obtained from registrant-submitted studies (OPPTS Guidelines 835.1230, 

835.1240) or from the scientific literature. As the KOC value of a chemical increases, the 

estimated accumulation of a chemical also increases. The user should select the KOC value input 

into PRZM/EXAMS for deriving aquatic and benthic EECs. Input parameter guidance for 

PRZM/EXAMS indicates that the KOC parameter value should be calculated as “the average KOC

from batch experiments” (USEPA 2002). If no scientifically valid estimates of KOC are available, 

this parameter value can be estimated as 0.35*KOW (USEPA 2004b). 

Table 1. Chemical characteristics of Pesticide X. 

Characteristic Value Guidance 

Pesticide Name Pesticide X Required input 

Log KOW 5 

Required input 

Enter value from acceptable or supplemental study submitted by 

registrant or available in scientific literature. 

KOW 100000 No input necessary. This value is calculated automatically from the Log 

KOW value entered above. 

KOC 

(L/kg OC) 25000 

Required input 

Input value used in PRZM/EXAMS to derive EECs. Follow input 

parameter guidance for deriving this parameter value (USEPA 2002). 

Time to steady state 

(TS; days) 30 No input necessary. This value is calculated automatically from the Log KOW value entered above.

Pore water EEC 

(μg/L) 5 

Required input 

Enter value generated by PRZM/EXAMS benthic file. PRZM/EXAMS 

EEC represents the freely dissolved concentration of the pesticide in the 

pore water of the sediment. The appropriate averaging period of the 

EEC is dependent on the specific pesticide being modeled and is based 

on the time it takes for the chemical to reach steady state. Select the 

EEC generated by PRZM/EXAMS which has an averaging period 

closest to the time to steady state calculated above. In cases where the 

time to steady state exceeds 365 days, the user should select the EEC 

representing the average of yearly averages. The peak EEC should not

be used. 

Water Column EEC 

(μg/L) 6 

Required input 

Enter value generated by PRZM/EXAMS water column file. 

PRZM/EXAMS EEC represents the freely dissolved concentration of 

the pesticide in the water column. The appropriate averaging period of 

the EEC is dependent on the specific pesticide being modeled and is 

based on the time it takes for the chemical to reach steady state. The 

averaging period used for the water column EEC should be the same as 

the one selected for the pore water EEC (discussed above). 

Note: Table 1 of this User’s Guide contains example data for chemical specific characteristics. 
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The time to steady state (TS; in days) is also calculated automatically by the KABAM tool 

according to Equation 1 (Hawker and Connell 1988). This equation is consistent with 

recommendations provided in USEPA and OECD guidelines for fish BCF studies for 

determining the time to reach steady state (USEPA 1996, OECD 1996). It should be noted that 

there is uncertainty in using this equation for chemicals with Log KOW >6, since this falls outside 

of the range of data used to derive this relationship. Alternatively, the time to steady state can be 

defined using empirical data from available BCF studies that were sufficient to define steady 

state. This information can be used to supplement the calculated TS value. 

24

(6.54 10 )* 55.31 1.

3 + =−

OW

S

x K Equation T

EECs from PRZM (v3.12.2, May 2005) and EXAMS (v2.98.4.6, April 2005) (coupled with the 

input shell pe5.pl, dated Aug 2007) are used in the KABAM tool. EECs generated by 

PRZM/EXAMS represent the freely dissolved concentration of the pesticide in the surface and 

pore water of the standard pond. The bioaccumulation portion of KABAM assumes that the 

aquatic environment is at steady state. Because the time to reach steady state is pesticide specific, 

the appropriate averaging period of the EEC should be determined on a chemical by chemical 

basis. Generally, the time to reach steady state can be related to the Log KOW of a chemical, with 

increasing time required as the Log KOW increases. Therefore, it is not relevant to use short-term 

(peak) estimates of pesticides in the aquatic environment. The EEC used to represent the 

concentration of the pesticide in the pore and surface waters of the aquatic habitat should be 

selected so that the averaging period (i.e., 4-d, 21-d, 90-d, 1 year), is consistent with the time to 

steady state estimated for that chemical. For example, a chemical with a Log KOW = 5 would 

have an estimated time to steady state value of 30 days. Since the standard output file from 

PRZM/EXAMS does not include a 30-d average, the next closest averaging period would be 

selected (either 21 or 60 days). Therefore, the EEC represented by the 21-day average would be 

selected for this chemical. In cases where the time to steady state exceeds 365 days, the user 

should select the EEC representing the yearly EEC.

In cases where multiple uses of a single pesticide are possible (e.g., cotton, corn, apples), EECs 

from the different uses can be modeled to allow for an understanding of the bioaccumulation and 

associated risks associated with different uses. 

Table 2 

KABAM automatically calculates uptake and elimination constants through respiration (k1 and 

k2, respectively) and diet (kD and kE, respectively). In using the model with its default parameters 

in place, it is assumed that the elimination of the pesticide from aquatic organisms through 

metabolism does not occur (i.e., metabolism rate constant kM = 0). 

In Table 2 (reproduced below) of the KABAM tool, the model user can enter measured rate 

constants for uptake and elimination constants. These data can be obtained from acceptable or 

supplemental studies submitted by the registrant or from the literature. For example, k1 and k2

rate constants for fish can be obtained from pesticide BCF studies submitted for the fish (OPPTS 
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Guideline 850.1730). However, caution should be used when altering rate constants. For 

example, the k2 from a bioconcentration study typically represents a total elimination half life. 

However, the k2 in KABAM represents elimination from the gills. Therefore, incorporation of a 

measured k2 into KABAM without consideration of other elimination pathways may result in 

erroneous results. In order to run the model, it is not necessary for the user to alter the 

default values inserted into Table 2. If the model user alters the parameters in Table 2 of the 

KABAM tool, they will be highlighted yellow. 

Table 2. Input parameters for rate constants. "calculated" indicates that model will calculate rate 

constant. 

Trophic level 

k1 

(L/kg*d) 

k2 

(d-1) 

kD 

(kg-food/kgorg/d) 

kE 

(d-1) 

kM* 

(d-1) 

phytoplankton calculated calculated 0* 0* 0 

zooplankton calculated calculated calculated calculated 0 

benthic invertebrates calculated calculated calculated calculated 0 

filter feeders calculated calculated calculated calculated 0 

small fish calculated calculated calculated calculated 0 

medium fish calculated calculated calculated calculated 0 

large fish calculated calculated calculated calculated 0 

* Default value is 0. 

k1 and k2 represent the uptake and elimination constants respectively, through respiration. 

kD and kE represent the uptake and elimination constants, respectively, through diet. 

kM represents the metabolism rate constant. 

The model user should exercise caution when using a value of kM>0, as this approach will 

decrease predicted EECs and RQs for mammals and birds. Initially, kM should be set to 0 as a 

screen. The assumption that there is no metabolism of the pesticide within aquatic organisms is 

conservative. If no metabolism is observed in available fish BCF studies, then kM should not be 

altered. In cases where metabolism occurs, this assumption can result in overestimates of 

pesticide accumulation in tissues of aquatic organisms. In cases where the model user has 

evidence to indicate that metabolism may occur in fish (i.e., from BCF studies) and RQ values 

exceed LOCs, then the user can estimate kM using approaches described in Appendix H. This 

will allow the model user to characterize effects of metabolism on bioaccumulation in aquatic 

ecosystems and associated risks to mammals and birds consuming aquatic organisms. 

Table 3 

To calculate risk quotients, user-supplied avian and mammalian toxicity endpoints should be 

entered into Table 3 (reproduced below). Acceptable or supplemental registrant-submitted or 

open literature studies should be used to define the effects of the pesticide on birds and 

mammals. Required input data include: avian acute oral LD50 (OPPTS Guideline 850.2100), 

avian subacute dietary LC50 (OPPTS Guideline 850.2200), avian reproduction (expressed as a 

NOAEC or a NOAEL) (OPPTS Guideline 850.2300), mammalian acute oral LD50 (OPPTS 

Guideline 870.1100), or subacute dietary LC50 (if available), and mammalian reproduction 

NOAEC or NOAEL (OPPTS Guideline 870.3800). 
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Table 3. Mammalian and avian toxicity data for Pesticide X. These are required inputs. 

Animal Measure of effect 

(units) Value Species 

If selected species is 

"other," enter body 

weight (in kg) here. 

LD50 (mg/kg-bw) 50 mallard duck 

LC50 (mg/kg-diet) 500 Northern bobwhite quail 

NAOEC (mg/kg-diet) 10 mallard duck Avian 

Mineau Scaling 

Factor 1.15 

Default value for all 

species is 1.15 (for 

chemical specific values, 

see Mineau et al. 1996). 

LD50 (mg/kg-bw) 50 other 1.2 

LC50 (mg/kg-diet) N/A other 

Chronic Endpoint 10 

Mammalian 

 

units of chronic 

endpoint * ppm 

laboratory rat 

*ppm = mg/kg-diet 

Note: Table 3 of this User’s Guide contains example data for chemical specific characteristics. 

In the appropriate cell under the “value” column of Table 3, the user should input the lowest 

(most sensitive) available toxicity data for each toxicity endpoint. If an endpoint value is not 

discrete (i.e., contains a “>” symbol), the whole number should be entered as a discrete value, 

keeping in mind that all resulting risk quotient (RQ) values derived using this endpoint are “<”. 

For the chronic mammalian data, the user must also select the units of the value. The user should 

select units from the drop down menu as either “ppm” or “mg/kg-bw.” 

Under the “species” column, the user should use the drop down menu to select the appropriate 

test species associated with the toxicity value entered in the adjacent cell in the “value” column. 

If the test species is not one of the options available in the drop down list, the model user should 

select “other” as the test species. If “other” is selected, the user must enter the body weight (in 

kg) of the test species. In the case that “other” is selected as the test species, a message will 

appear in the spreadsheet below Table 3 to alert the user of the need to enter the body weight of 

the test species. These data should be obtained from the study report if possible (time weighted 

average of control animals). Alternatively, reference body weight values may be obtained from a 

variety of sources, including U.S. EPA 1993 and Dunning 1984. Failure to enter the body weight 

of the test species when it is entered as “other” will prevent calculation of risk quotients that 

correspond to that endpoint. 

If available, the model user should enter chemical-specific data to represent the avian scaling 

factor (see Mineau et al. 1996). If no chemical specific data are available, the default value of 

1.15 should be entered. This value is used to adjust avian dose-based toxicity values based on the 

weight of the species of concern (e.g., herons) as described in the T-REX User’s Guide (USEPA 

2008a) and in Appendix G. 
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2.2. Ecosystem Inputs 

In order to estimate pesticide concentrations in tissues of aquatic organisms, biotic and abiotic 

characteristics of the model aquatic ecosystem must be defined. In addition, the mammals and 

birds consuming aquatic organisms are also defined as ecosystem inputs. 

To run KABAM, it is not necessary to alter any of the default parameters in the 

“Ecosystem Inputs” worksheet. 

If the model user alters default parameter values, they will be highlighted yellow in the 

KABAM tool. 

It may be necessary for the model user to incorporate alternate ecosystem input values if the 

modeling incorporates EECs from a source other than PRZM/EXAMS (e.g., from a mesocosm 

study). In that case, the model user should enter parameter values that correspond to the specific 

water body used. 

Table 4 

Abiotic characteristics of the aquatic ecosystem that are necessary for KABAM are defined in 

Table 4 (reproduced below) of the model tool. These characteristics include: concentrations of 

particulate organic carbon (XPOC), dissolved organic carbon (XDOC), dissolved oxygen (COX) and 

suspended solids (CSS), water temperature (T), and % organic carbon (OC) content of the 

sediment. The model tool is populated with default values for these parameters, which can be 

altered based on the needs of the model user. Default values relevant to the abiotic 

characteristics of the aquatic ecosystem are designed to be consistent with the OPP standard 

pond scenario used in EXAMS. Brief explanations for these default values as well as guidance 

on selecting alternative values are provided in Appendix B. 

Table 4. Abiotic characteristics of the model aquatic ecosystem. 

Characteristic (symbol; units) Value Guidance* 

Concentration of Particulate Organic 

Carbon (XPOC; kg OC/ L) 0 

Concentration of Dissolved Organic 

Carbon (XDOC; kg OC/L) 0 

When using EECs generated by PRZM/EXAMS, use a 

value of “0” for both POC and DOC. 

Concentration of Dissolved Oxygen 

(COX; mg O2/L) 5.0 Default value is 5.0 mg O2/L when using EECs generated by PRZM/EXAMS. 

Water Temperature 

(T; oC) 15 

Value is defined by the average water temperature of the 

EXAMS pond when using EECs generated by 

PRZM/EXAMS. Model user should consult output file 

of EXAMS to define this value. 

Concentration of Suspended Solids 

(CSS; kg/L) 3.00E-05 Default value is 3.00x10-5 kg/L when using EECs generated by PRZM/EXAMS. 

Sediment Organic Carbon 

(OC; %) 4.0% Default value is 4.0% when using EECs generated by PRZM/EXAMS. 

*When using pesticide concentrations from monitoring data or mesocosm studies, consult Appendix B of the 

User’s Guide for specific guidance on selecting values for these parameters.
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Table 5 

Necessary biotic components of the aquatic ecosystem define characteristics of the sediment and 

water column biota. These include body weights and body compositions, specifically % lipids, % 

NLOM (non-lipid organic matter), and % water. These values are defined for the seven trophic 

levels of the aquatic ecosystem (phytoplankton, zooplankton, benthic invertebrates, filter feeders, 

small fish, medium fish and large fish) modeled by KABAM in Table 5 (reproduced below) of 

the tool. Default values for these biotic parameters are displayed in Table 5 below. A description 

of how these default parameters were selected is available in Appendix C. In addition, Table 5 

allows the model user to define whether organisms within each trophic level respire pore water. 

If yes, it is assumed that 5% of the total respired water is from pore water. Default assumptions 

related to respiration of pore water for each trophic level are depicted in Table 5 below. 

Table 5. Characteristics of aquatic biota of the model ecosystem. 

Trophic level 

Wet 

Weight 

(kg) 

% lipids % NLOM % Water 

Do organisms in 

trophic level respire 

some pore water? 

Sediment* N/A 0.0% 4.0% 96.0% N/A 

Phytoplankton N/A 2.0% 8.0% 90.0% no 

Zooplankton 1.0E-07 3.0% 12.0% 85.0% no 

Benthic invertebrates 1.0E-04 3.0% 21.0% 76.0% yes 

Filter feeders 1.0E-03 2.0% 13.0% 85.0% yes 

Small fish 1.0E-02 4.0% 23.0% 73.0% yes 

Medium fish 1.0E-01 4.0% 23.0% 73.0% yes 

Large fish 1.0E+00 4.0% 23.0% 73.0% no 

*Note that sediment is not a trophic level. It is included in this table because it is consumed by aquatic organisms 

of the KABAM food web. 

N/A = not applicable 

Table 6 

Table 6 (reproduced below) of the KABAM tool allows the model user to define the diet 

composition of each of the trophic levels of the aquatic ecosystem. The aquatic trophic levels are 

assigned a hierarchy, which is relevant to the assignment of diet composition. The order of the 

trophic levels, in increasing hierarchy, is as follows: phytoplankton, zooplankton, benthic 

invertebrates, filter feeders, small fish, medium fish, and large fish. The diet of each aquatic 

trophic level is composed of sediment or water column biota from lower trophic levels. The 

KABAM tool does not allow the model user to assign a portion of the diet of one organism to its 

own trophic level or to trophic levels that are higher. The default values defining the diet of each 

trophic level are in Table 6 below. An explanation of how these default parameters were 

determined is available in Appendix C. 
15 of 123 

Note that the total diet of each organism within the aquatic food web should equal 100%. If the 

total diet ≠ 100%, an error message will appear under Table 6. 

Table 6. Diets of aquatic biota of the model ecosystem. 

 Diet for: 

Trophic Level in diet Zoo 

plankton 

Benthic 

Invertebrates 

Filter 

Feeder 

Small 

Fish 

Medium 

Fish Large Fish 

Sediment* 0.0% 34.0% 34.0% 0.0% 0.0% 0.0% 

Phytoplankton 100.0% 33.0% 33.0% 0.0% 0.0% 0.0% 

Zooplankton 33.0% 33.0% 50.0% 0.0% 0.0% 

Benthic invertebrates 0.0% 50.0% 50.0% 0.0% 

Filter feeders 0% 0% 0.0% 

Small fish 50.0% 0.0% 

Medium fish 100.0% 

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

*Note that sediment is not a trophic level. It is included in this table because it is consumed by aquatic organisms of 

the KABAM food web. 

Table 7 

Table 7 (reproduced below) of the KABAM tool allows the model user to define the mammalian 

and avian species of concern, as well as their body weights. Species are considered to be of 

concern for pesticide exposures through consumption of residues in freshwater aquatic animals 

that serve as prey. 

For mammals, default species include the fog shrew (Sorex sonomae), the water shrew (S. 

palustris), the rice rat (Oryzomys palustris), the star-nosed mole (Condylura cristata), the 

American mink (Neovison vison), and the Northern river otter (Lontra canadensis). For birds, 

default species include sandpipers, rails, herons, kingfisher, ducks, grebes, ibis, rails, cormorants, 

osprey, cranes, bald eagles (Haliaeetus leucocephalus) and pelicans. Descriptions of how 

mammalian and avian species were selected, including their body weights, are provided in 

Appendices D and E, respectively. These appendices also provide descriptions of the species 

themselves as well as justifications for default parameters used to represent the species in 

KABAM (i.e., body weight and diet). 

The selected body weight value influences estimates of pesticide exposure through differential 

consumption of contaminated food items, as well as dose-based toxicity values. Therefore, the 

magnitude of the body weight parameter has an effect on the magnitude of the dose-based RQ. 

For mammals, higher body weight values result in higher dose-based RQs (keeping the diet 

constant). As a result, default body weight values for the fog shrew, water shrew, rice rat, and 

star-nosed mole were selected as higher values of relevant ranges in order to represent size 

classes that would be most vulnerable to exposures through bioaccumulation. In order to bound 

the risk of accumulated residues to mink and river otter, the lowest and highest body weights of 

these species were selected as defaults. For birds, higher body weight results in lower RQs. In 

order to bound the risk of accumulated residues to birds, the lowest and highest body weights of 
16 of 123 

birds with the same diet were selected as defaults. The user can alter the assigned body weights 

to represent the low and high end of possible weights in order to bound the potential RQs for a 

particular species. Additional data on body weights of species of mammals and birds are 

provided in Appendices D and E, respectively. 

Table 7. Identification of mammals and birds feeding on aquatic 

biota of the model ecosystem. 

Mammal/Bird # Name Body 

weight (kg) 

Mammal 1 Fog/Water shrew 0.018 

Mammal 2 Rice Rat/Star-nosed mole 0.085 

Mammal 3 Small mink 0.450 

Mammal 4 Large mink 1.800 

Mammal 5 Small river otter 5.000 

Mammal 6 Large river otter 15.000 

Bird 1 Sandpipers 0.02 

Bird 2 Cranes 6.7 

Bird 3 Rails 0.07 

Bird 4 Herons 2.90 

Bird 5 Small osprey 1.25 

Bird 6 White pelican 7.50 

Tables 8 and 9 

Tables 8 and 9 (reproduced below) of the KABAM tool allow the model user to define the diet 

composition of the mammals and birds of concern that are defined in Table 7. The animal names 

entered in Table 7 will appear at the heads of the columns of Tables 8 and 9. The diet of each 

mammal and bird species is attributed to a portion of each trophic level of the aquatic ecosystem. 

Justifications for the default diets for each mammal and bird species are provided in Appendices 

D and E, respectively. Note that the total diet of each mammal and bird should equal 100%. If 

not, an error message will appear under Table 8 or 9. 

Table 8. Diets of mammals feeding on aquatic biota of the model ecosystem. 

Diet for: 

Trophic level in diet Fog/Water 

Shrew 

Rice 

Rat/Starnosed mole 

Small Mink Large Mink Small River 

Otter 

Large River 

Otter 

Phytoplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Zooplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Benthic invertebrates 100.0% 34.0% 0.0% 0.0% 0.0% 0.0% 

Filter feeders 0.0% 33.0% 0.0% 0.0% 0.0% 0.0% 

Small fish 0.0% 33.0% 0.0% 0.0% 0.0% 0.0% 

Medium fish 0.0% 0.0% 100.0% 100.0% 100.0% 0.0% 

Large fish 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 
17 of 123 

Table 9. Diets of birds feeding on aquatic biota of the model ecosystem. 

 Diet for: 

Trophic level in diet Sandpipers Cranes Rails Herons Small Osprey White pelican

Phytoplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Zooplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

Benthic invertebrates 33.0% 33.0% 50.0% 50.0% 0.0% 0.0% 

Filter feeders 33.0% 33.0% 0.0% 0.0% 0.0% 0.0% 

Small fish 34.0% 0.0% 50.0% 0.0% 0.0% 0.0% 

Medium fish 0.0% 34.0% 0.0% 50.0% 100.0% 0.0% 

Large fish 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 

 

If the model user chooses to alter the default diet of a mammal or bird, the model user should 

consider the daily food intake for determining appropriate aquatic trophic levels to include 

within an animal’s diet. The user should verify that the weight of an individual dietary item does 

not greatly exceed the daily food intake of the mammal or bird. This will prevent the user from 

simulating a bird or mammal that consumes prey that are much larger than could be reasonably 

consumed. This can be determined using allometric equations for estimating daily food intake, as 

described in Appendices D and E. In addition, these appendices contain data defining the daily 

food intake for several species of birds and mammals. 

Pesticide exposures to mammals and birds through consumption of contaminated aquatic 

organisms are determined by weighing the exposure concentration by the contribution of each 

food item to the total diet. While this approach is reasonable for chronic exposures, it may 

underestimate acute exposures resulting from consumption of larger trophic level organisms 

within short periods of time. In order to explore high-end exposure concentrations and 

subsequent risks resulting from acute exposures, the model user can set the highest aquatic 

trophic level consumed by a bird or mammal to 100%. For example, high-end acute exposures of 

cranes (which consume benthic invertebrates, filter feeders, and medium fish) to a pesticide 

could be assessed by setting the crane diet to 100% of medium fish. 

3. Parameters & Calculations 

Also included in the KABAM tool is a tabularized summary of the relevant parameters for the 

bioaccumulation portion of KABAM. This summary is included in a separate worksheet, titled 

“Parameters & Calculations” (Table 10 of the KABAM tool) and represents values used to 

calculate pesticide tissue concentrations for the trophic levels of the aquatic ecosystem. This 

worksheet is locked (read only) in the KABAM tool and cannot be altered by the model user; 

however, this worksheet can be printed by the model user or copied into a risk assessment as a 

model output. A full description of the parameters contained in Table 10 of the KABAM tool as 

well as the equations used to calculate these parameters can be found in Appendix A. 
18 of 123 

4. Model Results 

The final outputs of KABAM include Bioconcentration Factors (BCFs), Bioaccumulation 

Factors (BAFs), Biomagnification Factors (BMFs), Biota-Sediment Accumulation Factors 

(BSAFs), estimates of pesticide concentrations in tissues of aquatic organisms, and RQ values 

for mammals and birds consuming contaminated aquatic organisms. 

Note that the “results” worksheet of KABAM is locked (read only) and cannot be altered by the 

model user, with the exception of format changes (e.g., number of decimal places). Also, the 

KABAM tool does not automatically account for significant figures. The format of numerical 

values in the Tool can be altered by the user to increase or decrease the number of decimal 

places. 

Table 11 and Figure 1 

Table 11 (reproduced below) of the KABAM tool reports pesticide concentrations in tissues of 

aquatic organisms on both a total body weight and lipid normalized basis. The table also reports 

contributions of the pesticide concentration in tissue from respiration and from diet. These 

values are useful for understanding the dominant uptake route of the pesticide that influences 

bioaccumulation. Figure 1 (reproduced below) of the KABAM tool graphically represents the 

relative contributions of pesticide uptake through diet and through respiration to the overall 

concentrations of the pesticide in the tissues of the different aquatic animals. 

Table 11. Estimated concentrations of Pesticide X in ecosystem components. 

Ecosystem Component 

Total 

concentration 

(μg/kg-ww) 

Lipid 

normalized 

concentration 

(μg/kg-lipid) 

Contribution 

due to diet 

(μg/kg-ww) 

Contribution 

due to 

respiration 

(μg/kg-ww) 

Water (total)* 6 N/A N/A N/A 

Water (freely dissolved)* 6 N/A N/A N/A 

Sediment (pore water)* 5 N/A N/A N/A 

Sediment (in solid)** 5,000 N/A N/A N/A 

Phytoplankton 27,298 1364913 N/A 27,298.25 

Zooplankton 21,065 702157 651.72 20,412.98 

Benthic Invertebrates 23,678 789265 1,812.95 21,865.01 

Filter Feeders 15,549 777440 1,167.92 14,380.88 

Small Fish 34,713 867830 7,246.79 27,466.40 

Medium Fish 41,050 1026242 14,492.66 26,557.01 

Large Fish 56,332 1408297 30,795.48 25,536.39 

* Units: μg/L; **Units: μg/kg-dw 

Note: Table 11 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 
19 of 123 

Figure 1. Total Pesticide Concentration 

per trophic level

0

10000

20000

30000

40000

50000

60000

Zooplankton Benthic

Invertebrates

Filter Feeders Small Fish Medium Fish Large Fish

Trophic Level

Concentration (μg/kg)

Contribution due to Respiration (μg/kg-ww)

Contribution due to Diet (μg/kg-ww)

Note: Figure 1 of this User’s Guide contains example results based on example chemical-specific data entered in 

Tables 1 and 3. 

Tables 12 and 13 

BCF, BAF, BMF and BSAF are calculated by KABAM (Tables 12 and 13). These terms are 

intended to provide a relative measure of the pesticide concentration in an organism to the 

pesticide concentration in sources (i.e., the environment and the diet) of that pesticide. Appendix 

F contains the equations used to calculate BCF, BAF, BMF and BSAF. 

Table 12. Total BCF and BAF values of Pesticide X in aquatic trophic levels. 

Trophic Level Total BCF 

(μg/kg-ww)/(μg/L) 

Total BAF 

(μg/kg-ww)/(μg/L) 

Phytoplankton 4801 4550 

Zooplankton 3421 3511 

Benthic Invertebrates 3705 3946 

Filter Feeders 2435 2591 

Small Fish 4766 5786 

Medium Fish 4766 6842 

Large Fish 4806 9389 

Note: Table 12 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 
20 of 123 

Table 13. Lipid-normalized BCF, BAF, BMF and BSAF values of Pesticide X in aquatic trophic 

levels. 

Trophic Level 

BCF 

(μg/kglipid)/(μg/L) 

BAF 

(μg/kglipid)/(μg/L) 

BMF 

(μg/kglipid)/(μg/kglipid) 

BSAF 

(μg/kglipid)/(μg/kgOC) 

Phytoplankton 240045 227485 N/A 11 

Zooplankton 114028 117026 0.51 6 

Benthic Invertebrates 123488 131544 1.16 6 

Filter Feeders 121769 129573 1.14 6 

Small Fish 119142 144638 1.16 7 

Medium Fish 119142 171040 1.24 8 

Large Fish 120143 234716 1.37 11 

Note: Table 13 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 

Tables 14, 15, and 16 

Tables 14, 15, and 16 (reproduced below) of the KABAM tool summarize the estimated 

exposure values, mammal and bird toxicity values and resulting RQ values, respectively, used to 

estimate potential risks to mammals and birds that consume aquatic animals contaminated with 

pesticides accumulated through the aquatic food chain. 

Table 14 uses the mammalian and avian body weights (entered by the model user) to calculate 

the dry food ingestion and drinking water intake rates according to allometric equations specific 

to mammals and birds. The wet food intake is calculated using the dry food intake and the % 

water of the diet. Dose-based EECs represent the sum of pesticide intake through diet and 

through drinking water, accounting for pesticide concentrations in diet items and in water and 

food and water intake rates. Dietary-based EECs represent the sum of pesticide intake through 

diet only, without consideration of species specific intake rates or body weights. Descriptions of 

the equations used to calculate food intake rates, water intake rates, dose-based EECs, and 

dietary-based EECs are available in Appendix G. 
21 of 123 

Table 14. Calculation of EECs for mammals and birds consuming fish contaminated by Pesticide X. 

Biological Parameters EECs (pesticide intake) 

Wildlife Species Body 

Weight 

(kg) 

Dry Food 

Ingestion 

Rate (kg-dry 

food/kgbw/day) 

Wet Food 

Ingestion 

Rate (kg-wet 

food/kgbw/day) 

Drinking 

Water 

Intake 

(L/d) 

Dose Based 

(mg/kgbw/d) 

Dietary 

Based 

(ppm) 

Mammalian 

Fog/water shrew 0.02 0.140 0.585 0.003 13.857 23.68 

Rice rat/star-nosed 

mole 0.1 0.107 0.484 0.011 11.921 24.64 

Small mink 0.5 0.079 0.293 0.048 12.041 41.05 

Large mink 1.8 0.062 0.229 0.168 9.408 41.05 

Small river otter 5.0 0.052 0.191 0.421 7.844 41.05 

Large river otter 15.0 0.042 0.157 1.133 8.852 56.33 

Avian 

Sandpipers 0.0 0.228 1.034 0.004 25.5861 24.75 

Cranes 6.7 0.030 0.136 0.211 3.6561 26.90 

Rails 0.1 0.147 0.577 0.010 16.8571 29.20 

Herons 2.9 0.040 0.157 0.120 5.0943 32.36 

Small osprey 1.3 0.054 0.199 0.069 8.1859 41.05 

White pelican 7.5 0.029 0.107 0.228 6.0108 56.33 

Note: Table 14 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 

Table 15 (reproduced below) of the KABAM tool summarizes the acute and chronic, dose-based 

and dietary-based toxicity values representing effects of a pesticide to mammals and birds. 

Dietary-based toxicity values are taken directly from user inputs in Table 3, without adjustment. 

Available dose-based toxicity values are adjusted for the weights of the animal tested (e.g.,

laboratory rat, mallard duck) and of the animal for which the risks are being assessed (e.g., mink, 

bald eagle). Methods for adjusting toxicity values are consistent with those used by T-REX 

(USEPA 2008a). A full description of the methodology for adjusting dose-based toxicity values 

is provided in Appendix G. 
22 of 123 

Table 15. Calculation of toxicity values for mammals and birds consuming fish contaminated by Pesticide 

X. 

Toxicity Values 

Wildlife Species Acute Chronic 

Dose Based 

(mg/kg-bw) 

Dietary Based 

(mg/kg-diet) 

Dose Based 

(mg/kg-bw) 

Dietary Based 

(mg/kg-diet) 

Mammalian 

Fog/water shrew 142.87 N/A 1.05 10 

Rice rat/star-nosed 

mole 96.92 N/A 0.71 10 

Small mink 63.89 N/A 0.47 10 

Large mink 45.18 N/A 0.33 10 

Small river otter 35.00 N/A 0.26 10 

Large river otter 26.59 N/A 0.20 10 

Avian 

Sandpipers 25.96 500.00 N/A 100 

Cranes 62.10 500.00 N/A 100 

Rails 31.33 500.00 N/A 100 

Herons 54.77 500.00 N/A 100 

Small osprey 48.27 500.00 N/A 100 

White pelican 63.16 500.00 N/A 100 

Note: Table 15 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 

Table 16 (reproduced below) of the KABAM tool presents RQs, which are the ratio of exposure 

concentrates to effects values. RQ values are then compared to Agency levels of concern 

(LOCs) for non-listed and listed mammals and birds. For acute exposures, the LOC is 0.5 for 

(non-listed) birds and mammals and 0.1 for federally-listed threatened and endangered (listed) 

species of mammals and birds. For chronic risk, the LOC is 1.0 for all species (non-listed and 

listed) mammals and birds (USEPA 2004). RQ values that exceed their respective LOC values 

appear in red and bold in Table 16. 

Dose-based and dietary-based RQs are not equivalent. Dietary-based RQs are calculated by 

directly comparing the concentration of a pesticide administered to experimental animals in the 

diet in a toxicity study to the concentration estimated in selected food items. These RQs do not 

account for the fact that smaller-sized animals need to consume more food relative to their body 

weight than larger animals. The dose-based RQs account for these factors by incorporating the 
23 of 123 

ingestion rate-adjusted exposure from the various food items to the different weight classes of 

assessed animals and the weight class-scaled toxicity endpoints. 

Table 16. Calculation of RQ values for mammals and birds consuming fish contaminated by Pesticide X. 

Acute Chronic 

Wildlife Species Dose Based Dietary Based Dose Based Dietary Based 

Mammalian 

Fog/water shrew 0.097 N/A 13.198 2.368 

Rice rat/star-nosed 

mole 0.123 N/A 16.737 2.464 

Small mink 0.188 N/A 25.643 4.105 

Large mink 0.208 N/A 28.335 4.105 

Small river otter 0.224 N/A 30.498 4.105 

Large river otter 0.333 N/A 45.296 5.633 

Avian 

Sandpipers 0.986 0.049 N/A 0.247 

Cranes 0.059 0.054 N/A 0.269 

Rails 0.538 0.058 N/A 0.292 

Herons 0.093 0.065 N/A 0.324 

Small osprey 0.170 0.082 N/A 0.410 

White pelican 0.095 0.113 N/A 0.563 

Note: Table 16 of this User’s Guide contains example results based on example chemical specific data entered in 

Tables 1 and 3. 

EECs and RQs for birds are based on the selected body weight of the bird as well as its diet. 

Default values for birds were designed to represent birds on the low and high end of weights 

with three different diets. Birds consuming benthic invertebrates, filter feeders, and fish include 

sandpipers, ducks and cranes (default birds 1 and 2, which are named sandpipers and cranes, 

respectively). Birds consuming benthic invertebrates and fish include belted kingfisher, rails, 

ibis, grebes, double-breasted cormorants, bitterns, egrets, and herons (default birds 3 and 4, 

which are named rails and herons, respectively). Birds consuming fish include osprey, bald 

eagles, and the white pelican (default birds 5 and 6, which are named small osprey and white 

pelican, respectively). In the case that RQs exceed the LOC for both birds within a feeding 

group, then it can be assumed that RQs would exceed the LOC for all of the birds within the 

feeding category since birds on the low and high end of the weight ranges have RQs of concern. 

In the case that RQs exceed the LOC for the default bird with the high body weight of a feeding 
24 of 123 

category (i.e., birds 2, 4, and 6), the model user can refine the EECs and RQs to be representative 

of specific bird species within a feeding category by entering specific body weights of individual 

species of concern. Appendix E contains species specific data on feeding habits and body 

weights of over 40 species of birds, including some listed species, which consume aquatic 

animals from freshwater habitats. 

5. Assessing Pesticide Concentrations in Fish Tissues for Human Consumption 

It is possible to use KABAM to derive pesticide concentrations in edible tissues of fish that are 

relevant to assessments of pesticide risks to human health. Current default values described 

above for % lipid content of fish applies to the whole fish; however, not all fish tissues are 

consumed by humans. Therefore, it is necessary to modify the output of the pesticide tissue 

concentration to account for a lower % lipid composition of edible tissues. This can be 

accomplished by entering in all the relevant default input parameters for KABAM as defined 

above. It may be necessary to explore different body weights of the large fish, based on those 

that would be expected to be consumed by humans. 

The relevant output is the lipid normalized concentration of the pesticide in the large fish (Table 

11). This value can be converted to the total pesticide concentration in edible tissues by 

multiplying by the % lipid content of the edible tissues. The default value for lipid content in 

edible tissue of the large fish is 3%, based on USEPA 2003. The resulting value represents the 

concentration of pesticide in fish tissue (in μg/kg-ww) potentially consumed by humans. This 

value can then be used in conjunction with fish consumption rates to characterize risks of a 

pesticide to humans consuming contaminated fish. 

6. Model Assumptions, Limitations, and Uncertainties 

There are several key assumptions and resulting uncertainties associated with modeling pesticide 

concentrations in tissues of aquatic organisms. The assumptions involve the equations of the 

model itself and the parameterization of those underlying equations. Appendix A describes the 

assumptions associated with the equations of the bioaccumulation model. In order to explore 

uncertainties associated with specific parameters and their influences on model outputs, a 

sensitivity analysis was conducted (see section A7 of Appendix A). This was used to define the 

parameters that have the greatest influence on model outputs (e.g., KOW, water column, and pore 

water EECs). Appendices B and C describe the parameterization of the model, including the 

associated assumptions. 

In addition, the use of PRZM/EXAMS for deriving EECs in the surface and pore waters of the 

aquatic ecosystem introduces the assumptions and uncertainties associated with PRZM and 

EXAMS to KABAM. 

One major assumption associated with KABAM concerns the model’s assumed steady state. 

Given the episodic nature of pesticide applications, sporadic peak exposures to aquatic 

organisms would be expected. For a chemical with a Log KOW of approximately 5, comparison 
25 of 123 

of the fish tissue EECs predicted using the steady state and dynamic bioaccumulation modeling 

with PRZM/EXAMS/Arnot and Gobas indicates predictions are similar (USEPA 2008b) when a 

60-d average was selected for water and sediment concentrations as input to the steady state 

model. This result suggests that steady-state bioaccumulation modeling can provide useful 

predictions of bioaccumulation potential even with highly dynamic exposures, provided proper 

consideration of the averaging period associated with water and sediment concentrations is 

made. 

As discussed above, in using KABAM with default settings, it is assumed that the elimination of 

the pesticide from aquatic organisms through metabolism does not occur, i.e., the metabolism 

rate constant (kM) = 0. In cases where pesticide metabolism does occur, this could overestimate 

pesticide bioaccumulation. Appendix H of this guide provides methods for estimating kM for fish 

using empirical data provided for specific chemicals (from BCF studies). This approach can be 

used to characterize effects of metabolism, but should be used with caution. 

The Arnot and Gobas (2004) model is generally appropriate for chemicals with Log KOW value 

≥4 to ≤ 8. Uncertainty increases as the value increases above 8 because the model has generally 

been validated using chemicals with Log KOW values within the range of 4 - 8. Making 

predictions for a chemical with a Log KOW > 8 leads to uncertainty in model outputs because 

predictions are based upon extrapolations in its subroutines. 

For chemicals with Log Kow < 4, exposure from food becomes insignificant because uptake and 

depuration across the gills controls the residue in the organism. Thus, there is no need to run a 

food web model for these chemicals. In these cases, available BCF data are sufficient to predict 

residues in the aquatic species. 

It is assumed that there is no predation within a trophic level of the aquatic food web (e.g.,

medium fish cannot prey upon medium fish). It is also assumed that mammals and birds only 

consume organisms from the aquatic system. 
26 of 123 

Appendix A. Description of Bioaccumulation Model 

The bioaccumulation portion of KABAM is based on the model published by Arnot and Gobas 

(2004). The purpose of this model is to estimate chemical concentrations (CB) and BCF and 

BAF values for aquatic ecosystems. Conceptually, each aquatic organism is assumed to be a 

single compartment. Chemicals enter the organism through respiration and diet and leave the 

organism through respiration and fecal egestion. The chemical concentration in the organism 

can also be influenced by the growth of the organism as well as metabolism of the chemical 

within the organism. These processes that define uptake and loss of the chemical from aquatic 

organisms are described by rate constants and are incorporated into one equation that is used to 

define the concentration of the chemical in organism tissues (Equation A1, see Table A1). As 

uptake constants (i.e., k1 and kD) increase, so does the estimated pesticide concentration in an 

organism. As elimination constants increase (i.e., k2, kE, kG and kM), estimated pesticide 

concentrations in an organism decrease. However, for respiration and diet, processes of uptake 

and elimination are linked. Therefore, factors that would influence uptake constants would also 

influence elimination constants, so these cannot be considered independently. In addition, as the 

freely dissolved fraction of pesticide in the water (Ф) decreases, so do estimated pesticide 

concentrations in organisms. Rate constants defining the uptake of a chemical through 

respiration (k1) and diet (kD) and the elimination of a chemical through respiration (k2) and fecal 

excretion (kE) as well as growth dilution of a chemical (kG) are estimated separately using 

equations A5-A9, which are described below. Parameter definitions and abbreviations are 

consistent with those published by Arnot and Gobas (2004) in order to ensure consistency with 

the publication and transparent methodology used in KABAM. 

Use of Equation A1 involves several assumptions. The first assumption is that the organism is at 

steady state. The second assumption is that the pesticide is distributed homogenously throughout 

organisms. The third assumption is that the effects of chemical partitioning into egg and sperm 

cells on chemical mass in parents is not considered as a loss pathway. The fourth assumption is 

that when data are lacking to define the metabolism rate constant for a chemical, it is assumed 

that metabolism does not occur and that the elimination rate constant for metabolism (kM) is 0. 

Uptake and elimination of a chemical from an organism is influenced by the body composition 

of the model organism. Body composition includes lipid, non-lipid organic matter (NLOM; e.g.,

carbohydrates and protein), and water. Chemicals are expected to partition differently to these 

components of an organism. Partitioning of a chemical into these components is related to the 

octanol-water partition coefficient (KOW). It is assumed that octanol is a surrogate for the lipid 

fraction of an organism. It is also assumed that the partitioning of a chemical to NLOM is less 

than to octanol, but that there is a relationship between the partitioning of the chemical to NLOM 

and to octanol and that octanol serves as a reasonable surrogate for estimating this parameter. 

Arnot and Gobas (2004) recommend that equation A1 be applied to an aquatic food web with 

seven trophic levels. In increasing order of hierarchy, these trophic levels include: 1) 

phytoplankton, 2) zooplankton, 3) benthic invertebrates, 4) filter feeders, 5) small (juvenile) fish, 

6) medium sized fish, and 7) large fish. Concentrations in organisms are first calculated at the 

lowest level of the aquatic food chain (phytoplankton). Pesticide concentrations are then 

calculated for zooplankton, including consideration that the diet of zooplankton includes 
27 of 123 

phytoplankton, which contain pesticide residues. Tissue residues are calculated for the next five 

trophic levels based on their diets of organisms from lower trophic levels. 

Table A1. Equation A1, calculation of pesticide tissue residue (CB) for single trophic levels and its associated 

parameters (Arnot and Gobas 2004). 

 

E G M

WTO P WDP D i Di

B k k k k

k m C m C k P C Eq A C

+ + +

Φ + + Σ =

2

1 0

*( * * * ) * ( * ) . 1

Parameters: 

Symbol Definition Value Units 

CB pesticide concentration in the organism calculated g/kg (wet 

weight) 

CBD

pesticide concentration in the organism originating from uptake 

through diet; CBD = CB when k1 = 0 calculated g/kg (wet weight) 

CBR

pesticide concentration in the organism originating from uptake 

through respiration; CBR= CB when kD = 0 calculated g/kg (wet weight) 

CDi concentration of pesticide in i (prey item) calculated g/kg (wet 

weight) 

CS concentration of the chemical in sediment (dry weight of sediment) Equation A4 g/(kg (dry) 

sediment) 

CWDP freely dissolved pesticide concentration in pore water of sediment input parameter (from 

PRZM/EXAMS) g/L 

CWTO total pesticide concentration in water column above the sediment input parameter (from 

PRZM/EXAMS) g/L 

k1

pesticide uptake rate constant through respiratory area (i.e., gills, 

skin) Equation A5 L/kg*d 

k2

rate constant for elimination of the pesticide through the respiratory 

area (i.e., gills, skin) Equation A6 d-1

kD pesticide uptake rate constant for uptake through ingestion of food Animals: Equation A8; 

Phytoplankton: 0 

kg food/ 

(kg org*day)

kE

rate constant for elimination of the pesticide through excretion of 

contaminated feces 

Animals: Equation A9; 

Phytoplankton: 0 d-1

kG organism growth rate constant Animals: Equation A7; 

Phytoplankton: 0.1 d-1

kM rate constant for pesticide metabolic transformation 0 d-1

mo fraction of respiratory ventilation involving overlying water 1 - mp none 

mp

fraction of respiratory ventilation that involves pore-water of 

sediment 

≤5%; 

0 for organisms with 

no contact with pore 

water 

none 

Pi fraction of diet containing i (prey item) user defined none 

Ф fraction of the overlying water concentration of the pesticide that is 

freely dissolved and can be absorbed via membrane diffusion Equation A2 none 
28 of 123 

A.1. Calculation of Fraction of Chemical in the Water Column That Is Freely Dissolved 

(Ф) 

Aquatic ecosystems contain organic matter suspended in the water column. This suspended 

organic matter is defined as dissolved organic carbon (DOC) and particulate organic carbon 

(POC). It is assumed that once chemicals partition to organic carbon, they are no longer 

bioavailable to aquatic organisms. The fraction of chemical in the water column that is freely 

dissolved (Ф), and thus bioavailable for uptake by aquatic animals is estimated according to 

Equation A2 (Table A2). This equation assumes that equilibrium exists between the pesticide 

concentration in the water and in the organic carbon in the water column. Equation A2 assumes 

that partitioning between POC and water and DOC and water can be related to partitioning of the 

chemical between octanol and water. These relationships are defined using proportionality 

constants (αPOC and αDOC) that are defined from the scientific literature. 

Table A2. Equation A2, derivation of available pesticide fraction in water (Ф) and its associated 

parameters (Arnot and Gobas 2004). 

1 ( * * ) ( * * )

1 . 2

X POC POC KOW X DOC DOC KOW

Eq A

+ α + α

Φ =

Parameters: 

Symbol Definition Value Units 

XPOC concentration of particulate organic carbon in water user defined kg/L 

XDOC concentration of dissolved organic carbon in water user defined kg/L 

KOW octanol water partition coefficient user defined none 

Ф fraction of the overlying water concentration of the pesticide that is 

freely dissolved and can be absorbed via membrane diffusion calculated none 

αPOC

Proportionality constant to describe the similarity of phase partitioning 

of POC in relation to octanol 0.35 none 

αDOC

Proportionality constant to describe the similarity of phase partitioning 

of DOC in relation to octanol 0.08 none 

The Arnot and Gobas (2004) approach for calculating the fraction of bioavailable pesticide in the 

water column (Equation A2) is different from the approach used in EPA’s Exposure Analysis 

Modeling System (EXAMS) (Equation A3, Table A3). The major difference is that the approach 

employed by EXAMS accounts for decreases in bioavailable pesticide in the water column due 

to sorption to biota. The values for αDOC for the two approaches are also slightly different. 

Despite these different approaches, for chemicals with Log KOW values 4-8, the fractions of 

bioavailable pesticide in the water column estimated by the two approaches differ by <0.02 

(Figure A1). Therefore, utilizing water column EECs generated by EXAMS is still consistent 

with the results that are generated using the approach described by Arnot and Gobas (2004). 
29 of 123 

Table A3. Equation A3, derivation of available pesticide fraction in water (F) by EXAMS and its associated 

parameters. 

( ) 0.907 1 ( * * ) ( * * ) *0.436*

1 . 3

X POC POC KOW X DOC DOC KOW Xbiota KOW

Eq A F

+ + + = α α

Parameters: 

Symbol Definition Value Units 

F fraction of the overlying water concentration of the pesticide that is 

freely dissolved and can be absorbed via membrane diffusion calculated none 

KOW octanol water partition coefficient user defined none 

OCSS percent organic carbon in suspended sediment user defined % 

XSS concentration of suspended sediments in water column user defined kg/m3

XPOC concentration of particulate organic carbon in water XSS*OCSS kg/m3

XDOC concentration of dissolved organic carbon in water user defined kg/m3

Xbiota concentration of biota in water user defined kg/m3

αPOC

Proportionality constant to describe the similarity of phase partitioning 

of POC in relation to octanol 0.35 none 

αDOC

Proportionality constant to describe the similarity of phase partitioning 

of DOC in relation to octanol 0.074 none 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

Log Kow

bioavailable fraction of pesticide in water column

Arnot and Gobas (2004)

EXAMS

Figure A1. Fraction of bioavailable pesticide in water column estimated using approaches of Arnot and 

Gobas (2004) and EXAMS. Values for OCSS, XSS, XDOC and Xbiota are consistent with OPP standard pond 

scenario used in EXAMS.
30 of 123 

A.2. Calculation of Chemical Concentration in Sediment 

Since it is possible for aquatic organisms to be exposed to chemicals through consumption of 

contaminated sediment, it is necessary to estimate the concentration of the chemical of concern 

in the sediment. This is accomplished using Equation A4 (Table A4), which uses the 

concentration of the chemical in the pore water, the chemical KOC, and the organic carbon 

content of the sediment. This approach is consistent with EXAMS for calculating the fraction of 

pesticide sorbed to sediment in the benthic column. 

Table A4. Derivation of pesticide concentration in the solid portion of the sediment (CS) (Arnot and Gobas 

2004). 

Eq.A4 CS = CSOC*OC

Where CSOC CWDP* KOC : =

Parameters: 

Symbol Definition Value Units 

CS

concentration of the chemical in sediment (dry weight of 

sediment) calculated g/(kg (dry) sediment) 

CSOC normalized (for OC content) pesticide concentration in sediment calculated g/(kg OC) 

CWDP freely dissolved pesticide concentration in pore water 

input parameter 

(from 

PRZM/EXAMS) 

g/L 

KOC organic carbon partition coefficient user defined L/kg OC 

OC percent organic carbon in sediment user defined % 

A.3. Calculation of Respiration Uptake (k1) and Elimination (k2) Rate Constants 

The respiratory uptake constant (k1) is calculated differently for phytoplankton (Equation A5.1) 

and for animals (Equation A5.2). For phytoplankton, k1 is dependent upon the KOW of the 

chemical as well as 2 constants (A and B) that describe chemical uptake resistance through the 

aqueous and organic phases (respectively) of the plant. If A and B are kept constant at 6x10-5

and 5.5, respectively as recommended by Arnot and Gobas (2004), k1 for phytoplankton 

increases with increasing KOW, ranging by a factor of 10 from Log KOW 4-8 (Figure A2). 

For animals, k1 is dependent upon the chemical uptake efficiency of the gills, the ventilation rate, 

and the body weight of the organism. The uptake efficiency of the gills is determined by the 

KOW of the chemical, while the ventilation rate of the organism is determined by an allometric 

equation that is influenced by the body weight of that organism and the concentration of oxygen 

in the water column (COX) (Table A5). When COX and organism body weight are kept constant, 

Log KOW has little effect on the k1 for animals (0.8% change from Log KOW 4-8). When Log 

KOW and body weight are kept constant, COX strongly affects k1. The value of k1 decreases by 

50%, as the COX value increases from 5 to 10 mg/L (Figure A3). The value of k1 is also strongly 

influenced by the body weight of the organism, with decreasing k1 observed with increasing 
31 of 123 

body weight. As the body weight of the organism increases from 1x10-7 to 10 kg (a relevant 

range for aquatic organisms, see Appendix C), the k1 value spans 3 orders of magnitude (Figure 

A3). 

Table A5. Equations associated with the derivation of pesticide clearance through the respiratory (gill) 

system (k1) and associated parameters (Arnot and Gobas 2004). 

KOW

A B Eq A For phytoplankton k+ = 1 . 5.1 : 1

B

W V

W

E G Eq A For animals k* . 5.2 : 1 =

1

155

: 1.85

−

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = +

OW

W K

Where E

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

OX

B

V C

W G

0.65

1400 *

Parameters 

Symbol Definition Value Units 

A constant related to the resistance to pesticide uptake through the 

aqueous phase of plant 6.0x10-5 (default) days 

B constant related to the resistance to pesticide uptake through the 

organic phase of plant 5.5 (default) days 

Cox concentration of dissolved oxygen User input (mg O2)/L 

EW pesticide uptake efficiency by gills (fraction) calculated none 

GV ventilation rate of fish, invertebrates, zooplankton calculated L/d 

k1 pesticide uptake rate constant through respiratory area (i.e., gills, skin) calculated L/kg*d 

KOW octanol water partition coefficient user defined none 

WB wet weight of the organism user defined kg 
32 of 123 

1.E+00

1.E+01

1.E+02

1.E+03

1.E+04

1.E+05

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

LogKow

k1

Figure A2. Relationship between Kow and k1 for phytoplankton.

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

1.0E+05

1.0E+06

1.0E+07

0 2 4 6 8 10

Cox (mg 02/L)

k1

WB = 1 kg

WB = 1e-1 kg

WB = 1e-2 kg

WB = 1e-3 kg

WB = 1e-4 kg

WB = 1e-5 kg

WB = 1e-6 kg

WB = 1e-7 kg

Figure A3. Relationship between COX, WB and k1 for aquatic animals. 
33 of 123 

The elimination rate constant for the respiratory system (k2) is related to the respiratory uptake 

constants (k1). This is because both constants are influenced by the same processes related to 

respiration. The value of k2 is also influenced by the partitioning of the chemical between the 

organisms and the water. The organism-water partition coefficient (KBW) is determined by the 

body composition of the organism (i.e., lipid, NLOM, and water) and the KOW of the chemical. It 

is assumed that the partitioning of the chemical between lipid and water is directly related to the 

octanol-water partition coefficient. It is also assumed that the chemical partitioning between 

NLOM and water can be related to the octanol-water partition coefficient using a proportionality 

constant (β) (Equation A6, Table A6). 

Table A6. Equations involved in the derivation of the respiratory elimination rate constant (k2) and 

associated parameters (Arnot and Gobas 2004). 

BW k

k Eq A k 1 6 2 . =

Where KBW =VLB*KOW +VNB* *KOW +VWB : β

Parameters: 

Symbol Definition Value Units 

k1

pesticide uptake rate constant for chemical uptake through 

respiratory area (i.e., gills, skin, membrane permeation) 

calculated 

(Equation 5) L/kg*d 

k2

rate constant for elimination of the pesticide through the 

respiratory area (i.e., gills, skin, membrane permeation) calculated d-1

KBW organism-water partition coefficient (based on wet weight) calculated none 

KOW octanol water partition coefficient user defined none 

VLB lipid fraction of organism user defined 

(kg lipid)/ 

(kg organism 

wet weight) 

VNB

NLOM (Non Lipid Organic Matter) fraction of animals, NLOC 

(Non Lipid Organic Carbon) of plants user defined 

kg NLOM/ 

(kg organism 

wet weight) 

VWB water content of the organism user defined 

kg water/ 

(kg organism 

wet weight) 

β proportionality constant expressing the sorption capacity of 

NLOM or NLOC to that of octanol 

Phytoplankton: 

0.35; 

Animals:0.035 

none 

Elimination rate constants for phytoplankton (k2) are calculated using seven parameters, 

including: KOW, VLB, VNB, VWB, A, B, and β (defined above in Table A6). The parameters A, B 

and β are all constants. If the other parameters are considered in terms of ranges applicable to 

KABAM, KOW has the greatest influence on the determination of k2 for phytoplankton. When 

Log KOW values are changed from 4 to 8, the k2 value decreases by three orders of magnitude 

(Figure A4). The lipid fraction of the organism (VLB) and the non-lipid organic carbon fraction 

(VNB) influence k2, with decreases in k2 observed as VLB and VNB increase. These two parameters 

are related. As VLB decreases, an increase in VNB has a greater effect on k2. Likewise, as VNB
34 of 123 

decreases, an increase in VLB has a greater effect on k2 (Figure A4). When the lipid fraction of 

organism (VLB) is increased from 0.5 to 3%, the elimination rate constant through respiration, 

i.e., k2, decreases by >30% (Figure A5). A change in VNB from 5% to 20% results in a decrease 

in the value of k2 that is >50% (Figure A5). The water content (VWB) of an organism has a 

negligible (<0.5%) effect on k2. 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

LogKow

k2

VLB=2.5%

VLB=2.0%

VLB=1.5%

VLB=1.0%

VLB=0.5%

Figure A4. Influence of Log KOW on k2 (for phytoplankton) at different % lipid (VLB ) (with VNB = 8%). 
35 of 123 

1.0E+00

1.0E+01

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

% lipid

k2

VNB=20%

VNB=15%

VNB = 10%

VNB=5%

Figure A5. Influence of % lipid (VLB) on k2 (for phytoplankton) at different % NLOM (VNB) at Log Kow 4. 

Note that for all Log Kow values from 4-8, the curves follow a similar trend for different VLB and VNB, but 

differ in magnitude of k2. 

To determine elimination constant from respiration (k2) for animals, seven input parameters are 

required: KOW, WB, VLB, VNB, VWB, COX, and β (all of which are defined in Table A6). The 

parameter β is a constant representing the proportionality of the sorption of a chemical to NLOM 

to the KOW of that chemical. If the other parameters are considered in terms of ranges applicable 

to KABAM, the octanol-water partition coefficient (KOW) and the water content of the organism 

(VWB) have the greatest influence on the determination of k2. When Log KOW values are 

changed from 4 to 8, the k2 value decreases by 4 orders of magnitude (Figures A6 and A7). 

Body weight also influences k2, with k2 values decreasing by 2 orders of magnitude as the body 

weight is increased from 1x10-7 to 1 kg (this is a range considered relevant to aquatic animals, 

see Appendix C for more information) (Figure A6). The lipid fraction of the organism (VLB) and 

the non-lipid organic matter fraction (VNB) influence k2, with decreases in k2 observed as these 

two values increase. When VLB is increased from 1 to 5%, k2 decreases by 84% (Figure A7). 

When VNB is increased from 15 to 40%, a 14% decrease in k2 is observed. The water content 

(VWB) of an organism has a negligible (<0.5%) effect on k2. Increasing the concentration of the 

dissolved oxygen (COX) value from 5 to 10 mg/L results in a decrease of 50% in the value of k2. 
36 of 123 

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

4.0 5.0 6.0 7.0 8.0

Log Kow

k2

WB = 10 kg

WB = 1 kg

WB = 1e-1 kg

WB = 1e-2 kg

WB = 1e-3 kg

WB = 1e-4 kg

WB = 1e-5 kg

WB = 1e-6

WB = 1e-7 kg

Figure A6. Influence of Log KOW on k2 (for animals) at different body weights (WB) (with VLB = 5%, VNB = 

20%, VWB = 75% and COX = 10mg/L). 

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

4.0 5.0 6.0 7.0 8.0

Log Kow

k2

VLB = 5%

VLB = 4%

VLB = 3%

VLB = 2%

VLB = 1%

Figure A7. Influence of Log KOW on k2 (for animals) at different % lipid composition (VLB) (with WB = 1 kg, 

VNB = 20%, VWB = 75% and COX = 10 mg/L). 
37 of 123 

A.4. Calculation of Growth Rate Constant 

Equations A7.1 and A7.2 provide an approximation of growth of aquatic organisms based on 

weight and temperature (Table A7). Comparing the results of the two equations indicates that 

higher temperatures result in higher growth rate constant (kG) values (Figure A8). With both 

equations, as body weight increases, kG decreases. There is some uncertainty associated with 

these equations, since growth rate can be influenced by additional factors, including species and 

prey availability. For KABAM, it is assumed that if the water temperature (T) < 17.5 oC 

(midpoint between 10 and 25o

C), equation A.7.1 is used and if T ≥ 17.5 oC, equation A.7.2 is 

used. 

Table A7. Equations involving the derivation of the growth rate constant (kG) and associated parameters 

(Arnot and Gobas 2004). 

. 7.1 0.0005* ( 10 ) 0.2 Eq A k W T C

o

G = B ≈ −

. 7.2 0.00251* ( 25 ) 0.2 Eq A k W T C

o

G = B ≈ −

Parameters: 

Symbol Definition Value Units 

kG organism growth rate constant calculated d-1

T temperature user defined oC 

WB wet weight of the organism user defined kg 

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

-7 -6 -5 -4 -3 -2 -1 0 1

Log WB (in kg)

k

G

T=10oC

T=20oC

Figure A8. Relationship between body weight (WB) and kG at 2 different temperatures. 
38 of 123 

A.5. Calculation of Dietary Uptake (kD) Rate Constant 

In determining uptake and elimination rate constants related to dietary sources of chemicals (kD

and kE, respectively), it is assumed that aquatic organisms are represented by a 2-phase model 

that includes the gastrointestinal tract (GIT) of the organisms and the organism itself. Since 

phytoplankton do not consume other organisms, a dietary uptake constant (kD) is not a relevant 

rate constant, and the elimination rate constant due to fecal elimination (kE) is considered 

insignificant in plants. Therefore, kD and kE are only calculated for animals. 

The dietary uptake constant for a chemical in animals is influenced by the weight of the 

organism, the feeding rate of the organism (GD), and the dietary pesticide transfer efficiency 

(ED). The feeding rate is different for filter feeders compared to other aquatic organisms. 

Empirical dietary pesticide transfer efficiency (ED) values vary from 0-100%. Variability in ED

has been attributed to various factors, including sorption coefficients of chemicals, composition 

of diet, and digestibility of diet. Based on several different observations, it is assumed by Arnot 

and Gobas (2004) that this value can be related to KOW (Equation A8, Table A8). 
39 of 123 

Table A8. Equations involving the derivation of the pesticide clearance rate constant through diet (kD) and 

associated parameters (Arnot and Gobas 2004). 

B

D

D D W

G Eq.A8 k = E *

7 1 : (3.0 10 * 2.0) − − Where ED = x KOW +

( ) : 0.022 * * exp(0.06 * ) 0.85 For animals except filter feeders GD = WB T

For filter feeders: GD = GV*CSS*σ

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

OX

B

V C

W G

0.65

1400 *

Parameters: 

Symbol Definition Value Units 

Cox concentration of dissolved oxygen user defined (mg O2)/L 

CSS concentration of suspended solids user defined kg/L 

ED dietary pesticide transfer efficiency calculated % 

GD feeding rate of organism calculated kg/d 

GV ventilation rate of gills calculated L/d 

kD

pesticide uptake rate constant for uptake through 

ingestion of food calculated kg food/(kg org*day) 

KOW octanol water partition coefficient user defined none 

T temperature user defined oC 

WB wet weight of the organism user defined kg 

σ efficiency of scavenging of particles absorbed from water 100 % 

For aquatic organisms (non-filter feeders), the dietary uptake constant (kD) is derived using 3 

input parameters: octanol-water partition coefficient (KOW), the weight of the organism (WB), 

and temperature (T). For chemicals with Log KOW ranging 4-5.5, changes in Log KOW cause 

little (<4%) effects to the value of kD; however, for chemicals with Log KOW <5.5, increases in 

Log KOW can result in decreases in this value up to an order of magnitude (Figure A9). Increases 

in WB from 1x10-7 to 1 kg result in an order of magnitude decrease in kD (Figure A9). 

Temperature also affects kD, with an observed increase in kD of 65% when the temperature is 

increased from 2.5 to 20o

C (Figure A10). 
40 of 123 

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

4.0 5.0 6.0 7.0 8.0

Log Kow

kD

WB=10 kg

WB=1 kg

WB=1e-1 kg

WB=1e-2 kg

WB=1e-3 kg

WB=1e-4 kg

WB=1e-5 kg

WB=1e-6 kg

WB=1e-7 kg

Figure A9. Relationship between Log KOW and kD (for non-filter feeders) at different body weights (WB) 

(T=10o

C). 
41 of 123 

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

Log Kow

kD

T=20oC

T=15oC

T=10oC

T=5oC

Figure A10. Relationship between Log KOW and kD (for non-filter feeders) at different water temperatures 

(WB=1 kg). 

For filter feeders, the dietary uptake constant (kD) is derived using five input parameters: KOW, 

WB, COX, CSS and σ (all of which are defined in Table A.8). For chemicals with Log KOW values 

ranging 4-5.5, changes in Log KOW cause little (<5%) effects to the value of kD; however, as the 

Log KOW increases from 6 to 8, the kD for filter feeders decreases by an order of magnitude 

(Figure A11). Available National Water Quality Assessment (NAWQA) data for streams 

indicate that suspended sediment concentrations range 1-281 mg/L (USGS 2008b). If the 

concentration of suspended sediments (CSS) is increased from 1x10-6 to 1x10-4 kg/L, kD for filter 

feeders increases by 2 orders of magnitude (Figure A12). Increases in WB from 1x10-4 to 1x10-2

kg (which is a reasonable range of weights for filter feeders; see Appendix C) result in an 80% 

decrease in kD (Figure A11). Increases in oxygen concentration (COX) from 2 to 8 mg/L results 

in a decrease in kD of 80% for filter feeders. Changes in the scavenging efficiency (σ) of filter 

feeders result in proportional changes to the kD value. For example, a 25% decrease in σ results 

in a 25% decrease in kD. 
42 of 123 

1.0E-04

1.0E-03

1.0E-02

1.0E-01

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

Log Kow

kD

WB = 1e-4 kg

WB = 1e-3 kg

WB = 1e-2

Figure A11. Relationship between Log KOW and filter feeder kD with different WB values. 

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0

Log Kow

kD

Css = 1e-6 kg/L

Css = 1e-5 kg/L

Css = 1e-4 kg/L

Figure A12. Relationship between Log KOW and filter feeder kD with different Css values. 
43 of 123 

A.6. Calculation of Dietary Elimination (kE) Rate Constant 

The rate constant for elimination of the pesticide through excretion of contaminated feces (kE) is 

calculated using the fecal egestion rate (GF), the dietary pesticide transfer efficiency (ED), the 

partition coefficient of the pesticide between the gastro-intestinal tract and the organism (KGB), 

and the body weight of the organism (WB) (Equation A9, Table A9). For filter-feeding and nonfilter feeding aquatic animals, kE is calculated in a similar manner, with the exception of the 

method of calculating the feeding rate of an organism (GD). An order of magnitude increase in 

either GF, ED, or KGB results in an order of magnitude increase in fecal egestion rate constant 

(kE). An order of magnitude increase in WB results in an order of magnitude decrease in kE. 

Effects of changes in individual input parameters used to derive GF, ED, and KGB are explored 

below. 

Table A9. Equations involving the derivation of the fecal elimination rate constant (kE) and associated 

parameters (Arnot and Gobas 2004). 

B

GB

E F D W

K Eq.A9 k = G *E *

7 1 : (3.0 10 * 2.0) − − Where ED = x KOW +

( )

( ) LB OW NB OW WB

LG OW NG OW WG

GB V K V K V

V K V K V K

+ +

+ + = * * *

* * *

β

β

( )

() ( ) ( ) L LD N ND W WD

L LD

LG V V V

V V

1 * 1 * 1 *

1 *

ε ε ε

ε

− + − + −

− =

( )

() ( ) ( ) L LD N ND W WD

N ND

NG V V V

V V

1 * 1 * 1 *

1 *

ε ε ε

ε

− + − + −

− =

( )

() ( ) ( ) L LD N ND W WD

W WD

WG V V V

V V

1 * 1 * 1 *

1 *

ε ε ε

ε

− + − + −

− =

[ ] ( ) ( ) ( ) GF 1 L*VLD 1 N*VND 1 W*VWD*GD = − ε + − ε + − ε

( ) : 0.022* *exp(0.06* ) 0.85 For animals except filter feeders GD = WB T

For filter feeders: GD = GV*CSS*σ

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

OX

B

V C

W G

0.65

1400 *

Parameters: 

Symbol Definition Value Units 

Cox concentration of dissolved oxygen calculated (mg O2)/L 

CSS concentration of suspended solids user defined kg/L 
44 of 123 

ED dietary pesticide transfer efficiency calculated % 

GD feeding rate of organism calculated kg/d 

GF egestion rate of fecal matter calculated (kg feces)/(kg 

organism)*d 

GV ventilation rate of gills calculated L/d 

kE

rate constant for elimination of the pesticide through excretion 

of contaminated feces 

for animals: 

calculated 

for plants: 0 

d-1

KGB

partition coefficient of the pesticide between the gastrointestinal tract and the organism calculated none 

KOW octanol water partition coefficient user defined none 

T temperature user defined oC 

VLB lipid fraction of organism user defined 

(kg lipid)/ 

(kg organism 

wet weight) 

VLD overall lipid content of diet user defined kg/kg 

VLG lipid content in the gut calculated 

(kg lipid)/(kg 

digesta wet 

weight) 

VNB

NLOM (Non Lipid Organic Matter) fraction of animals, 

NLOC (Non Lipid Organic Carbon) of plants user defined 

kg NLOM/ 

(kg organism 

wet weight) 

VND overall NLOM content of diet user defined kg/kg 

VNG NLOM content in the gut calculated 

(kg 

NLOM)/(kg 

digesta wet 

weight) 

VWB water content of the organism user defined 

kg water/ 

(kg organism 

wet weight) 

VWD overall water content of diet user defined kg/kg 

VWG water content in the gut calculated 

(kg water)/(kg 

digesta wet 

weight) 

WB wet weight of the organism user defined kg 

β proportionality constant expressing the sorption capacity of 

NLOM to that of octanol 0.035 for animals none 

εL dietary assimilation rate of lipids 

fish: 92%; 

aquatic inverts: 75%; 

zooplankton: 72% 

% 

εN dietary assimilation rate of NLOM 

fish: 60%; 

aquatic inverts: 75%; 

zooplankton: 72% 

% 

εW dietary assimilation rate of water freshwater 

organisms: 25% % 

σ efficiency of scavenging of particles absorbed from water 100 % 
45 of 123 

A.6.1. Parameters Affecting GF

The fecal egestion rate GF is calculated using the feeding rate of the organism (GD; kg/day), the 

dietary assimilation rates for lipids, NLOM, and water (εL, εN and εW, respectively) as well as the 

contents of the diet (VLD, VND, and VWD). 

For non-filter feeders, the feeding rate (GD) is calculated using body weight and temperature. 

Generally, as body weight and temperature increase, so does the feeding rate of the aquatic 

animal (Figure A.13). An order of magnitude increase in body weight leads to an order of 

magnitude increase in the feeding rate of the organism. An order of magnitude increase in 

temperature leads to a 40% increase in the feeding rate of non-filter feeders. 

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 5 10 15 20 25 30

Temperature (oC)

G

D (kg/d)

WB = 1e-6 kg

WB = 1e-3 kg

WB = 1 e2 kg

Figure A13. Relationship between temperature and GD (non-filter feeders) with different WB values. 

For filter feeders, the feeding rate (GD) is calculated using four parameters: the concentration of 

dissolved oxygen in the water (COX), body weight (WB), the concentration of suspended solids 

(CSS), and the scavenging efficiency of particles absorbed from water (σ). As with non-filter 

feeders, increases in body weight of filter feeders leads to increases in GD (Figures A14-A16). 

An order of magnitude increase in body weight leads to an 80% increase in feeding rate (GD). An 

increase in dissolved oxygen in the water (COX) from 2 to 10 mg/L results in a decrease in GD of 

80% (Figure A14). Decreases in scavenging efficiency lead to proportional decreases in GD, with 

every 10% decrease in scavenging efficiency (σ), leading to a 10% decrease in GD (Figure A15). 

An order of magnitude increase in the concentration of suspended solids (CSS) leads to an order 

of magnitude increase in GD (Figure A16). 
46 of 123 

1.0E-07

1.0E-06

1.0E-05

1.0E-04

2 3 4 5 6 7 8 9 10

Cox (mg/L)

GD (kg/d)

WB = 1e-4 kg

WB = 1e-3 kg

WB = 1e-2 kg

Figure A14. Relationship between Cox and GD (filter feeders) with different WB values. 

1.0E-07

1.0E-06

1.0E-05

1.0E-04

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

scavenging efficiency

GD (kg/d)

WB = 1e-4 kg

WB = 1e-3 kg

WB = 1e-2 kg

Figure A15. Relationship between scavenging efficiency and GD (filter feeders) with different WB values. 
47 of 123 

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.00E-06 1.00E-05 1.00E-04

Css (kg/L)

GD (kg/d)

WB = 1e-4 kg

WB = 1e-3 kg

WB = 1e-2 kg

Figure A16. Relationship between concentration of suspended solids (Css) and GD (filter feeders) with 

different WB values. 

The fecal egestion rate (GF) is calculated using the following parameters: εL, εN, εW, VLD , VND, 

VWD as well as GD, which is discussed above. When the default dietary assimilation rates for 

lipids, NLOM, and water (εL, εN, and εW, respectively) are used, changes in lipid, NLOM and 

water composition of the diet (VLD, VND and VWD, respectively) have little effect on GF, when 

compared to effects of GD on GF (Figure A.17). This indicates that as the feeding rate of an 

animal (GD) increases so does its fecal egestion rate (GF), while the composition of the animal’s 

diet has little effect on the fecal egestion rate. 
48 of 123 

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

GD (kg/day)

GF (kg feces/kg organism)

VLD 1%, VND 10%, VWD 89%

VLD 1%, VND 20%, VWD 79%

VLD 10%, VND 10%, VWD 80%

VLD 10%, VND 20%, VWD 70%

Figure A17. Relationship between GD and GF with different lipid, NLOM, and water compositions in the diet 

(VLD, VND, and VWD, respectively). Dietary assimilation rates for lipids, NLOM, and water are set to default 

values used to represent fish (see Table A9). 

When setting the lipid, NLOM, and water composition (VLD, VND and VWD, respectively) of diet 

equal for fish, aquatic invertebrates, and zooplankton, the differences in dietary assimilation rates 

for lipids, NLOM, and water (εL, εN, and εW, respectively) of these three groups of animals have 

little effect on the fecal egestion rate (GF), when compared to effects of the feeding rate (GD) on 

GF (Figure A.18). As with the composition of the diet, changes in GD result in greater effects on 

GF when compared to changes in the assimilation efficiencies of lipid, NLOM, and water. 
49 of 123 

1.0E-08

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1.0E-07 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

GD

GF

Fish

Aquatic invertebrates

Zooplankton

Figure A18. Relationship between GD and GF with dietary assimilation rates for lipids, NLOM, and water set 

to default values for fish, aquatic invertebrates, and zooplankton (see Table A9). Lipid, NLOM, and water 

compositions in the diet (VLD, VND, and VWD, respectively) are set to 1, 20, and 79%, respectively. 

When VLD, VND, VWD are equal (i.e., 33.33%) and when GD is set to a constant number, variations 

of εL, εN, and εW result in equivalent effects to GF, with GF decreasing as assimilation efficiency 

increases (i.e., the organism eliminates less as its digestion [assimilation efficiency] becomes 

more efficient). If assimilation efficiency is set to 1 for lipid, NLOM, and water, the fecal 

elimination rate of the organism is 0 kg feces/kg org (Figure A.19). 
50 of 123 

0

0.0001

0.0002

0.0003

0.0004

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Ex

GF

change in EL

change in EN

change in EW

Figure A19. Relationship between dietary assimilation efficiencies (εL, εN, and εW) and GF

(VLD=VND=VWD=33.33%; GD = 1.0x10-3 kg/day). Each dietary efficiency rate was altered independent of the 

others, with the others set to 1. 

In summary, changes in the feeding rate of the organism (GD) have the greatest effect on the 

fecal egestion rate of the organism (GF). GD is calculated for non-filter feeders using body weight 

and temperature. GD is calculated for filter feeders, using the concentration of dissolved oxygen 

in the water (COX), body weight (WB), the concentration of suspended solids (CSS), and the 

efficiency of scavenging of particles absorbed from water (σ). Changes in these parameter values 

would be expected to have the greatest influence on GF, which would result in influences on rate 

constant for pesticide elimination through excretion (kE). Changes in the composition of the diet 

and the dietary assimilation rates are expected to have less of an influence on GF when compared 

to GD and the parameters used to derive GD. 

A.6.2. Parameters Affecting ED

The efficiency of dietary pesticide transfer (ED) is based only upon the KOW of the pesticide. As 

KOW increases, ED decreases. For chemicals with Log KOW 4-8, the efficiency of dietary 

pesticide transfer is 50-3% (Figure A.20). 
51 of 123 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

4 4.5 5 5.5 6 6.5 7 7.5 8

Log Kow

ED

Figure A.20. Dietary transfer efficiency (ED) vs. Log Kow. 

A.6.3. Parameters Affecting KGB

The equations used to calculate the contents of the gut (i.e., VLG, VNG, and VWG) listed in Table 

A.9 can be simplified as follows, using the equation for GF: 

( )

F

L LD D

LG G

V G V 1−ε * * =

( )

F

N ND D

NG G

V G V 1−ε * * =

( )

F

W WD D

WG G

V G V 1−ε * * =

Using these simplified equations, changes in the feeding rate of the organism (GD), the fecal 

egestion rate (GF), diet composition, and dietary assimilation of lipid, NLOM, and water can be 

explored to understand effects on these parameters on estimations of the lipid composition of the 

gut. If εL, εN, and εW are all equal and VLD, VND, and VWD are all equal, VLG, VNG, and VWG are 

equal. Changes in feeding rate (GD) and fecal egestion rate (GF) do not affect VLG, VNG or VWG

(Figure A.21). As would be expected, changes in VLD result in effects to VLG, with an order of 

magnitude increase in VLD, resulting in an order of magnitude increase in VLG (Figure A.22). 

Also, changes in VND result in effects to VNG, with an increase in VND from 10 to 20%, resulting 
52 of 123 

in a 50% increase in VNG (Figure A.23). An order of magnitude increase in VWD (keeping VLD

and VND constant) results in slight (approximately 10%) decreases in VLG and VNG and slight 

(2%) increases in VWG (Figure A.24). A decrease in εL from 0.9 to 0.1, results in an order of 

magnitude increase in the lipid content of the gut (VLG) (Figure A.25), but only slight (<2%) 

changes to the NLOM and water contents of the gut (VNG and VWG, respectively). A decrease in 

εN from 0.9 to 0.1 results in an order of magnitude increase in the NLOM content of the gut 

(VNG) (Figure A.26), as well as decreases in the gut composition attributed to lipid and water. A 

decrease in εW from 0.9 to 0.1 results in a 50% increase in the water content of the gut (VWG), as 

well as an 80% decrease in the gut composition attributed to lipid and NLOM (Figure A.27). For 

invertebrates, dietary assimilation efficiencies vary significantly, leading to uncertainty in 

assigning one value to this parameter. Since hydrophobic chemicals are not likely to be stored in 

the water of organism tissues, it is assumed that this route is not significant to bioaccumulation. 

1.00E-03

1.00E-02

1.00E-01

1.00E+00

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00

GD

VXG

VLG

VNG

VWG

Figure A21. Relationship between gut contents (VLG, VNG, and VWG) and GD. εL, εN, and εW are set to defaults 

for fish (Table A9). VLD = 1%, VND = 20%, and VWD = 79%. 
53 of 123 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

VLD

VXG

VLG

VNG

VWG

Figure A22. Relationship between gut contents (VLG, VNG, and VWG) and VLD. εL, εN, and εW are set to 

defaults for fish (Table A9). VND = 20%, VWD = 1-VND-VLD. 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3

VND

VXG

VLG

VNG

VWG

Figure A23. Relationship between gut contents (VLG, VNG, and VWG) and VND. εL, εN, and εW are set to 

defaults for fish (Table A9). VLD = 1%, VWD = 1-VND-VLD. 
54 of 123 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83 0.84 0.85

VWD

VXG

VLG

VNG

VWG

Figure A24. Relationship between gut contents (VLG, VNG, and VWG) and VWD. εL, εN, and εW are set to 

defaults for fish (Table A9). VLD = 1%, VND = 20%. Note that VLD+VND+VWD does not equal 100%, except 

when VWD = 0.79. 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EL

VXG

VLG

VNG

VWG

Figure A25. Relationship between gut contents (VLG, VNG, and VWG) and εL. εN, and εW are set to defaults for 

fish (Table A9). VLD = 1%, VND = 20%, VWD = 79%. 
55 of 123 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EN

VXG

VLG

VNG

VWG

Figure A26. Relationship between gut contents (VLG, VNG, and VWG) and εN. εL, and εW are set to defaults for 

fish (Table A9). VLD = 1%, VND = 20%, VWD = 79%. 

1.0E-03

1.0E-02

1.0E-01

1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EW

VXG

VLG

VNG

VWG

Figure A27. Relationship between gut contents (VLG, VNG, and VWG) and εW. εL, and εN are set to defaults for 

fish (Table A9). VLD = 1%, VND = 20%, VWD = 79%. 
56 of 123 

The partitioning of a chemical between the gastrointestinal tract (GIT) and the organism is 

described by KGB. This partition coefficient is determined using the contents of the gut (VLG, 

VNG, and VWG), the contents of the organism’s body (VLB, VNB and VWB) as well as the octanol 

water partition coefficient (KOW) (Table A.9). An order of magnitude increase in the lipid content 

of the body (VLB) results in an order of magnitude decrease in KGB (Figures A.28 and A.29). An 

order of magnitude increase in the NLOM content of the body (VNB) results in a decrease in KGB

of approximately 20%. An order of magnitude increase in the lipid content of the gut (VLG) 

results in a 50% increase in KGB (Figure A.28). An order of magnitude increase in the NLOM 

content of the gut (VNG), results in an increase in KGB of 70% (Figure A.29). Changes in the Log 

KOW of a chemical from 4 to 8 do not alter KGB. 

1.0E-02

1.0E-01

1.0E+00

1.0E+01

0 0.002 0.004 0.006 0.008 0.01 0.012

VLG

kGB

VLB=0.01,VNB=0.20, VWB=0.79

VLB=0.10, VNB=0.20, VWB=0.70

VLB=0.01, VNB=0.30, VWB=0.69

Figure A28. Relationship between lipid content of the gut (VLG) and KGB, with different body compositions 

(VLB, VNB, and VWB). 
57 of 123 

1.0E-02

1.0E-01

1.0E+00

1.0E+01

0 0.1 0.2 0.3 0.4 0.5 0.6

VNG

kGB

VLB=0.01,VNB=0.20, VWB=0.79

VLB=0.10, VNB=0.20, VWB=0.70

VLB=0.01, VNB=0.30, VWB=0.69

Figure A29. Relationship between NLOM content of the gut (VNG) and KGB, with different body compositions 

(VLB, VNB, and VWB). 

A.7. Overall Sensitivity of Body Concentration of Chemical (CB) to Individual Input 

Parameters 

 

A.7.1. First Sensitivity Analysis 

In order to understand the influence of input parameters on model predictions of pesticide 

concentrations in tissue of aquatic organisms (CB), a sensitivity analysis was conducted. 

Parameters were assigned uniform distributions and assumptions of ranges based on data in the 

scientific literature. The range for each parameter is defined in Table A10. Diets of each 

trophic level were varied according to the definitions in Table A11. Uniform distributions were 

used to allow unbiased selection of values from set ranges. Once parameter assumptions were 

assigned, a Monte Carlo simulation was carried out using Crystal Ball 2000. In this simulation, 

10,000 trials were conducted with randomly selected parameter values resulting in predicted 

pesticide concentrations in each of the seven trophic levels. The sensitivity of the model to 

specific parameters was defined by the contribution of each parameter to the variance of the 

estimation of pesticide concentrations in each of the trophic levels. 

The results of this analysis indicate that of all the variables in the model, the Log KOW

contributes the most to variability (<75% of total) in estimates of CB for all animal trophic levels. 

For phytoplankton, the water column EEC, concentration of POC in the water column (XPOC) 
58 of 123 

and Log KOW contribute the greatest variability in the predicted CB values (38, 28, and 22%, 

respectively). 

A.7.2. Second Sensitivity Analysis 

Based on the results of the first sensitivity analysis, a second analysis was conducted where the 

influence of individual parameters on variability in CB was examined, with fixed Log KOW

values. In the second sensitivity analysis, the Log KOW was set to values of 4, 5, 6, 7, and 8 and a 

Monte Carlo simulation (10,000 trials) was run for each Log KOW value. Parameters were 

assigned uniform distributions and assumptions of ranges based on data in the scientific 

literature. The range for each parameter is defined in Table A10 (with the exception of Log 

KOW). Diets of each trophic level were varied according to the definitions in Table A11. 

The contributions of individual parameters at Log KOW values of 4, 5, 6, 7 and 8 to the variability 

in the pesticide tissue concentration (CB) of the seven aquatic trophic levels of KABAM are 

provided in Tables A12-A18. The results of this sensitivity analysis indicate that parameters 

have different relative importance in estimating CB for the seven trophic levels (e.g., the water 

column EEC contributes the most variability to the phytoplankton CB, while the pore water EEC 

and fraction of respiratory ventilation that involves pore-water of sediment (mP) value contribute 

the most variance to the zooplankton CB). In addition, these tables indicate that the relative 

importance of individual parameters to estimates of CB change with Log KOW. It should be 

noted that several parameters in the Arnot and Gobas (2004) model are linked (e.g., mP and mO, 

diet composition, VLB, VNB, and VWB). Therefore, sensitivity of CB predictions to one parameter 

implies sensitivity of the predictions to the linked parameters. 

This sensitivity analysis also indicates that some parameters that are fixed in KABAM, including 

the constant related to the resistance to pesticide uptake through the aqueous phase of plant (A), 

proportionality constant expressing the sorption capacity of NLOM to that of octanol (β), and mP

(set to either 0 or 0.05), can contribute >10% of total variability in estimates of CB. 
59 of 123 

Table A10. Parameters and associated assumptions used for first and second sensitivity analysis of KABAM. 

Parameter Parameter Description Trophic Level Minimum of Range Maximum of Range Source/Comments 

A 

Constant related to the resistance to 

pesticide uptake through the aqueous 

phase of plant 

Phytoplankton 1x10-5 1x10-4

In Arnot and Gobas 2004, this value is days. This value was varied by an ordereported constant value to understand ton estimates of bioaccumulation. The rthis parameter is unknown. 

B 

Constant related to the resistance to 

pesticide uptake through the organic 

phase of plant 

Phytoplankton 1 10 

In Arnot and Gobas 2004, this value is This value was varied by an order of mconstant value to understand the influeestimates of bioaccumulation. The reasthis parameter is unknown. 

COX Concentration of dissolved oxygen (mg O2/L) All 4 12 Minimum is based on 60% of saturatio

saturation (in 30oC water). Maximum ioxygen in cold water (5oC; see USGS 

CSS Concentration of suspended solids (kg/L) All 2.0x10-6 5.0x10-4 Based on 5th and 95th percentiles of app

measurements of suspended sediment waters of the US provided by NAWQA 

CWTO Total pesticide concentration in water column above the sediment All 0.1 100 Assumed to be reasonable range for EE

PRZM/EXAMS modeling. 

CWTP

Freely dissolved pesticide concentration in pore water of sediment 

All 0.1 100 Assumed to be reasonable range for EE

PRZM/EXAMS modeling. 

Log Kow Log of octanol-water partition coefficient All 4 8 Assumption that bioaccumulation mod

with Log Kow 4-8. 

Koc Organic carbon partition coefficient All 3.5x103 3.5 x107

Determined based on assumption that K0.35*Kow. In sensitivity analysis, Koc order to avoid error in selection of incoparameters. 

Zooplankton 0 1 Based on full range of parameter value

Benthic Inv. 0 1 Based on full range of parameter value

Filter Feeders 0 1 Based on full range of parameter value

Small Fish 0 1 Based on full range of parameter value

Medium Fish 0 1 Based on full range of parameter value

mp Fraction of respiratory ventilation that involves pore-water of sediment 

Large Fish 0 1 Based on full range of parameter value

OC Percent organic carbon in sediment All 1% 10% In the OPP standard pond used in EXA

parameter is 4%. This parameter value 
60 of 123 

Table A10. Parameters and associated assumptions used for first and second sensitivity analysis of KABAM. 

Parameter Parameter Description Trophic Level Minimum of Range Maximum of Range Source/Comments 

magnitude around the OPP standard po

T Temperature (oC) All 1 30 Reasonable range of values for this par

Phytoplankton 0.5 2.0 See Table C1 of Appendix C. 

Zooplankton 1.0 4.0 See Table C2 of Appendix C. 

Benthic Inv. 0.5 12 See Tables C4-C9 of Appendix C. 

Filter Feeders 0.4 4 See Tables C13-C15 of Appendix C. 

VLB Lipid fraction of organism 

Fish 0.5 8 See Table C19 of Appendix C. 

VNB

NLOM (Non Lipid Organic Matter) 

fraction of animals, NLOC (Non 

Lipid Organic Carbon) of plants 

All - - Set to equal 1-VLB-VWB

Phytoplankton 0.85 0.95 Assume 5% deviation from mean (i.e., 

Zooplankton 0.74 0.96 See Section C.2. of Appendix C. 

Benthic Inv. 0.69 0.83 See Table C3 of Appendix C. 

Filter Feeders 0.78 0.93 See Table C12 of Appendix C. 

VWB Water content of the organism 

Fish 0.71 0.80 See Table C18 of Appendix C. 

Phytoplankton - - Not a necessary parameter for phytopl

Zooplankton 1x10-9 1x10-7 See Section C.2. of Appendix C. 

Benthic Inv. 5x10-6 2x10-3 See Table C11 of Appendix C. 

Filter Feeders 2x10-4 1x10-2 See Section C.5 of Appendix C. 

Small Fish 1x10-3 5x10-2 See Table C16 of Appendix C. 

Medium Fish 5x10-3 0.6 See Table C17 of Appendix C. 

WB Wet weight (kg) of the organism 

Large Fish 0.25 3.6 See Section C.5 of Appendix C. 

XPOC Concentration of particulate organic carbon in water (kg/L) All 2.0x10-6 5.0x10-4 Based on 5th and 95th percentiles of app

measurements of suspended sediment waters of the US provided by NAWQA 

XDOC Concentration of dissolved organic carbon in water (kg/L) All 5.0x10-7 5.0x10-5 In the OPP standard pond used in EXA

parameter is 5.0x10-6. This parameter vmagnitude around the OPP standard po

β

Proportionality constant expressing 

the sorption capacity of NLOM or 

NLOC to that of octanol 

All 0 1 Designed to represent all values equal 

of a chemical between octanol and wat
61 of 123 

Table A10. Parameters and associated assumptions used for first and second sensitivity analysis of KABAM. 

Parameter Parameter Description Trophic Level Minimum of Range Maximum of Range Source/Comments 

εL Dietary assimilation rate of lipids Animals 0 1 Based on full range of parameter value

εN Dietary assimilation rate of NLOM Animals 0 1 Based on full range of parameter value

εW Dietary assimilation rate of water Animals 0 1 Based on full range of parameter value

σ Efficiency of scavenging of particles absorbed from water Filter Feeders 0 1 Based on full range of parameter value

Table A11. Dietary assumptions of aquatic trophic levels used for sensitivity analysis of KABAM. 

Trophic Level Organism in diet Minimum Value Maximum Value Comments 

Zooplankton Phytoplankton 100% 100% - 

Sediment 0 50% - 

Phytoplankton 0 50% - Benthic Invertebrates Zooplankton - - Set to 1- (% diet attributed to sediment + % diet attri

Sediment 0 33% - 

Phytoplankton 0 33% - 

Filter Feeder Zooplankton 0 33% - 

Benthic 

invertebrates - - 

Set to 1- (% diet attributed to sediment + % diet attridiet attributed to zooplankton) 

Phytoplankton 0 50% - 

Zooplankton 0 50% - Small Fish Benthic 

invertebrates - - Set to 1- (% diet attributed to phytoplankton+% diet 

Zooplankton 0 50% - 

Benthic 

invertebrates 0 50% - Medium Fish 

Small fish - - Set to 1- (% diet attributed to zooplankton + % diet a

invertebrates) 

Small fish 0 100% It is assumed that large fish consume only smaller fis

Large Fish 

Medium Fish - - Set to 1- (% diet attributed to small fish) 
62 of 123 

Table A12. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of phytoplankton at different Log Kow values. 

Variable 4 5 6 7 8 

A ≤0.1% ≤0.1% 0.7% 9.3% 18.2% 

VLB ≤0.1% ≤0.1% ≤0.1% 0.2% ≤0.1% 

VWB 5.9% 3.4% 2.3% 0.2% ≤0.1% 

Water Column EEC 59.6% 46.1% 44.9% 45.7% 43.0% 

XPOC 7.0% 30.9% 39.1% 41.7% 38.0% 

β 26.7% 18.6% 12.0% 2.1% ≤0.1% 

Total 99.2% 99.0% 99.0% 99.2% 99.2% 

Table A13. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of zooplankton at different Log Kow values. 

Variable 4 5 6 7 8 

COX ≤0.1% ≤0.1% 0.2% 2.5% 4.3% 

mP 4.3% 23.0% 37.4% 39.5% 36.1% 

Pore Water EEC 27.6% 37.2% 40.8% 40.6% 37.4% 

T ≤0.1% ≤0.1% 1.3% 7.7% 19.6% 

VLB 1.2% 0.4% 0.6% 0.6% ≤0.1% 

VWB 20.8% 14.1% 8.8% 0.6% 0.8% 

Water Column EEC 11.2% 2.2% 0.3% ≤0.1% ≤0.1% 

WB ≤0.1% ≤0.1% ≤0.1% 0.5% 0.6% 

XPOC 1.7% 2.5% 0.8% ≤0.1% ≤0.1% 

β 32.5% 20.2% 8.4% 1.7% ≤0.1% 

εL ≤0.1% ≤0.1% ≤0.1% 0.2% ≤0.1% 

εN ≤0.1% ≤0.1% 0.5% 1.1% 0.3% 

Total 99.3% 99.6% 99.1% 95.0% 99.1% 
63 of 123 

Table A14. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of benthic invertebrates at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* ≤0.1% 1.2% 12.0% 12.0% 6.0% 

COX ≤0.1% ≤0.1% ≤0.1% 1.3% 1.6% 

Diet composition ≤0.1% 0.2% 1.2% 1.0% 8.6% 

mP 4.9% 16.0% 5.3% ≤0.1% ≤0.1% 

OC ≤0.1% ≤0.1% 0.9% ≤0.1% ≤0.1% 

Pore Water EEC 37.2% 51.1% 61.4% 61.1% 59.4% 

T ≤0.1% ≤0.1% 1.0% 8.2% 16.1% 

VLB 4.0% 2.5% 1.6% 1.0% ≤0.1% 

VWB 3.6% 1.5% 1.1% 0.6% ≤0.1% 

Water Column EEC 14.4% 1.9% ≤0.1% ≤0.1% ≤0.1% 

XPOC 2.5% 2.7% 0.5% ≤0.1% ≤0.1% 

β 32.6% 21.5% 7.6% 0.4% ≤0.1% 

εL ≤0.1% ≤0.1% 0.7% 1.2% ≤0.1% 

εN ≤0.1% 0.5% 5.8% 5.6% ≤0.1% 

Total 99.2% 99.1% 99.1% 92.4% 91.7% 

*mP, body composition, etc. 

Table A15. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of filter feeders at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* ≤0.1% 1.5% 11.8% 11.7% 2.5% 

COX ≤0.1% ≤0.1% ≤0.1% 2.0% 3.8% 

CSS 0.2% 1.4% 2.4% 3.2% 8.1% 

Diet composition ≤0.1% ≤0.1% 0.3% 0.6% 1.6% 

mP 3.1% 6.9% ≤0.1% 0.5% 0.7% 

OC ≤0.1% ≤0.1% 0.9% 2.0% 4.1% 

Pore Water EEC 28.6% 42.0% 52.2% 50.5% 42.3% 

T ≤0.1% ≤0.1% 1.9% 13.0% 25.1% 

VLB 1.5% 1.5% 1.3% 0.4% ≤0.1% 

VWB 11.1% 7.0% 4.3% 2.8% 0.4% 

Water Column EEC 10.6% 1.8% ≤0.1% ≤0.1% ≤0.1% 

WB ≤0.1% ≤0.1% ≤0.1% ≤0.1% 0.3% 

XPOC 2.0% 2.2% 0.2% ≤0.1% ≤0.1% 

β 42.0% 30.7% 13.7% 1.5% ≤0.1% 

εL ≤0.1% 0.4% 2.6% 2.7% 0.5% 

εN ≤0.1% 1.8% 5.3% 4.6% 1.2% 

σ ≤0.1% 1.9% 2.1% 3.5% 7.8% 

Total 99.1% 99.1% 99.0% 99.0% 98.4% 

*mP, body composition, etc. 
64 of 123 

Table A16. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of small fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* ≤0.1% 3.3% 16.1% 18.2% 9.2% 

COX 0.2% 0.2% ≤0.1% 1.3% 2.3% 

Diet composition ≤0.1% 0.6% 2.2% 2.9% 2.7% 

mP 3.9% 4.4% 0.5% ≤0.1% 0.6% 

OC ≤0.1% ≤0.1% 0.5% 1.5% 2.6% 

Pore Water EEC 31.8% 45.3% 52.1% 53.0% 51.6% 

T ≤0.1% 0.4% 0.8% 9.8% 27.7% 

VLB 2.5% 1.3% 1.3% 0.8% 0.3% 

VWB 1.5% 1.0% 0.3% 0.2% ≤0.1% 

Water Column EEC 11.6% 1.5% 0.2% ≤0.1% ≤0.1% 

XPOC 2.0% 2.6% 0.5% ≤0.1% ≤0.1% 

β 45.7% 34.3% 13.8% 1.7% 0.2% 

εL ≤0.1% 1.2% 4.4% 3.4% 0.9% 

εN ≤0.1% 2.9% 6.8% 6.1% 0.8% 

Total 99.2% 99.0% 99.5% 98.9% 98.9% 

*mP, body composition, etc. 

Table A17. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of medium fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* ≤0.1% 2.7% 17.3% 21.0% 10.0% 

COX 0.3% ≤0.1% ≤0.1% 1.1% 2.4% 

Diet composition ≤0.1% 0.2% 0.8% 0.6% ≤0.1% 

mP 1.5% 1.5% ≤0.1% ≤0.1% 0.2% 

OC ≤0.1% ≤0.1% 0.4% 1.3% 1.9% 

Pore Water EEC 20.5% 33.1% 43.3% 48.5% 49.0% 

T 0.2% ≤0.1% 1.0% 11.1% 33.1% 

VLB 0.4% 0.2% 0.2% 0.4% ≤0.1% 

VWB 15.7% 9.1% 5.0% 4.2% 0.5% 

Water Column EEC 7.7% 1.1% 0.2% ≤0.1% ≤0.1% 

WB ≤0.1% 0.2% ≤0.1% ≤0.1% ≤0.1% 

XPOC 1.2% 1.7% 0.5% ≤0.1% ≤0.1% 

β 51.2% 43.5% 20.9% 4.3% 0.5% 

εL 0.2% 1.4% 2.2% 1.9% 0.3% 

εN 0.6% 3.4% 7.4% 4.7% 0.7% 

Total 99.5% 98.1% 99.2% 99.1% 98.6% 

*mP, body composition, etc. 
65 of 123 

Table A18. Second sensitivity analysis results: Contribution to variance of specific 

variables to CB values of large fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* ≤0.1% 5.8% 22.8% 23.1% 9.7% 

COX 0.9% 0.9% ≤0.1% 0.9% 2.1% 

Diet composition 0.3% 0.8% 0.9% 0.3% ≤0.1% 

mP 1.4% 0.3% ≤0.1% ≤0.1% ≤0.1% 

OC ≤0.1% ≤0.1% 0.4% 1.1% 1.7% 

Pore Water EEC 27.0% 35.1% 41.3% 45.1% 44.9% 

T 1.4% 1.5% 0.7% 10.1% 35.1% 

VLB 1.8% 0.9% 1.3% 1.1% ≤0.1% 

VWB 1.1% 0.5% 0.5% 0.6% 0.2% 

Water Column EEC 9.3% 1.2% ≤0.1% ≤0.1% ≤0.1% 

XPOC 1.4% 1.7% 0.3% ≤0.1% ≤0.1% 

β 52.0% 38.8% 13.8% 1.7% ≤0.1% 

εL ≤0.1% 0.9% 1.7% 2.2% 0.4% 

εN 2.0% 10.5% 15.3% 12.8% 4.3% 

Total 98.6% 98.9% 99.0% 99.0% 98.4% 

*mP, body composition, etc. 

A.7.3. Third Sensitivity Analysis 

A third sensitivity analysis was conducted to explore the influences of KABAM input parameters 

that are controlled by the user (including chemical specific inputs and ecosystem inputs), with 

the fixed parameters unchanged. In this sensitivity analysis, the Log KOW was set to values of 4, 

5, 6, 7, and 8, and a Monte Carlo simulation (10,000 trials) was run for each Log KOW value. 

Parameters were assigned uniform distributions and assumptions of ranges based on data in the 

scientific literature. The range for each parameter is defined in Table A19. Diets of each 

trophic level were varied according to the definitions in Table A11. 

The contributions of individual chemical specific and ecosystem input parameters at Log KOW

values of 4, 5, 6, 7, and 8 to the variability in the pesticide tissue concentration (CB) of the seven 

aquatic trophic levels of KABAM are provided in Tables A20-A26. As with the second 

sensitivity analysis, the results of this analysis indicate that parameters have different relative 

importance in estimating CB for the seven trophic levels. In addition, these tables indicate that 

the relative importance of individual parameters to estimates of CB change with Log KOW. 

This sensitivity analysis indicates that several parameters contribute >10% of variance in CB of 

one or more trophic levels. These include: water column EEC, pore water EEC, particulate 

organic carbon (XPOC), sediment organic carbon (OC), concentration of suspended solids (CSS), 

water temperature (T), lipid composition (VLB), diet composition, and characteristics of prey 

(including body composition, diet composition and mP). Several of these parameters, including 

XPOC, OC, and CSS have default values that were selected to be consistent with the standard pond 

used in EXAMS. 
66 of 123 

One notable observation resulting from this sensitivity analysis is that at Log KOW 7 and 8, 

benthic invertebrate diet composed of sediment contributes ≥25% of the variance in CB of all 

three size classes of fish. This indicates that the proportion of the benthic invertebrate diet 

attributed to sediment can influence the estimated pesticide concentrations in fish tissues. 

As indicated above, several parameters in the Arnot and Gobas (2004) model are linked (e.g., mP

and mO, diet composition, VLB, VNB and VWB). Therefore, sensitivity of CB predictions to one 

parameter implies sensitivity of the predictions to the linked parameters. 
67 of 123 

Table A19. Parameters and associated assumptions used for third sensitivity analysis of KABAM. 

Parameter Parameter Description Trophic Level Minimum of Range Maximum of Range Source/Comm

COX Concentration of dissolved oxygen (mg O2/L) All 4 12 Minimum is based on 60% of satura

saturation (in 30oC water). Maximum 

oxygen in cold water (5oC; 

CSS Concentration of suspended solids (kg/L) All 2.0x10-6 5.0x10-4 Based on 5th and 95th percentiles 

measurements of suspended sedime

waters of the US provided by N

CWTO Total pesticide concentration in water column above the sediment All 0.1 100 Assumed to be reasonable range 

PRZM/EXAMS m

CWTP

Freely dissolved pesticide 

concentration in pore water of 

sediment 

All 0.1 100 Assumed to be reasonable range 

PRZM/EXAMS m

Koc Organic carbon partition coefficient All 3.5x103 3.5 x107

Determined based on assumption th

0.35*Kow. In sensitivity analysis, Ko

order to avoid error in selection of i

parameter

Zooplankton 0 0.05 Based on default parameter 

Benthic Inv. 0 0.05 Based on default parameter 

Filter Feeders 0 0.05 Based on default parameter 

Small Fish 0 0.05 Based on default parameter 

Medium Fish 0 0.05 Based on default parameter 

mp Fraction of respiratory ventilation that involves pore-water of sediment 

Large Fish 0 0.05 Based on default parameter 

OC Percent organic carbon in sediment All 1% 10% 

In the OPP standard pond used in EXA

parameter is 4%. This parameter val

magnitude around the OPP s

T Temperature (oC) All 1 30 Reasonable range of values for this p

Phytoplankton 0.5 2.0 See Table C1 of A

Zooplankton 1.0 4.0 See Table C2 of A

Benthic Inv. 0.5 12 See Tables C4-C9 of 

Filter Feeders 0.4 4 See Tables C13-C15 o

VLB Lipid fraction of organism 

Fish 0.5 8 See Table C19 of A

VNB

NLOM (Non Lipid Organic Matter) 

fraction of animals, NLOC (Non 

Lipid Organic Carbon) of plants 

All - - Set to equal 1-V
68 of 123 

Table A19. Parameters and associated assumptions used for third sensitivity analysis of KABAM. 

Parameter Parameter Description Trophic Level Minimum of Range Maximum of Range Source/Comm

Phytoplankton 0.85 0.95 Assume 5% deviation from 

Zooplankton 0.74 0.96 See Section C.2. of A

Benthic Inv. 0.69 0.83 See Table C3 of A

Filter Feeders 0.78 0.93 See Table C12 of A

VWB Water content of the organism 

Fish 0.71 0.80 See Table C18 of A

Phytoplankton - - Not a necessary parameter 

Zooplankton 1x10-9 1x10-7 See Section C.2. of A

Benthic Inv. 5x10-6 2x10-3 See Table C11 of A

Filter Feeders 2x10-4 1x10-2 See Section C.5 of A

Small Fish 1x10-3 5x10-2 See Table C16 of A

Medium Fish 5x10-3 0.6 See Table C17 of A

WB Wet weight (kg) of the organism at t 

Large Fish 0.25 3.6 See Section C.5 of A

XPOC Concentration of particulate organic carbon in water (kg/L) All 2.0x10-6 5.0x10-4 Based on 5th and 95th percentiles 

measurements of suspended sedime

waters of the US provided by N

XDOC Concentration of dissolved organic carbon in water (kg/L) All 5.0x10-7 5.0x10-5 In the OPP standard pond used in EXA

parameter is 5.0x10-6. This parameter 

of magnitude around the OPP 
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Table A20. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of phytoplankton at different Log Kow values. 

Variable 4 5 6 7 8 

VLB 0.7% 0.4% 0.3% <0.1% <0.1% 

VWB 7.6% 5.1% 3.3% 0.5% <0.1% 

Water Column EEC 80.1% 55.9% 51.8% 52.9% 52.8% 

XPOC 11.3% 38.0% 44.2% 46.1% 46.8% 

Total 99.7% 99.4% 99.6% 99.5% 99.6% 

Table A21. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of zooplankton at different Log Kow values. 

Variable 4 5 6 7 8 

COX <0.1% <0.1% <0.1% 0.6% 3.1% 

mP <0.1% 2.2% 22.2% 44.0% 40.2% 

Pore Water EEC <0.1% 2.2% 22.8% 42.0% 38.8% 

T <0.1% <0.1% <0.1% 3.0% 15.5% 

VLB 12.3% 12.7% 13.3% 7.2% 1.1% 

VWB 1.3% 0.8% 0.9% 0.4% <0.1% 

Water Column EEC 75.0% 48.0% 16.0% 0.9% <0.1% 

WB <0.1% <0.1% <0.1% <0.1% 0.6% 

XPOC 10.7% 33.5% 24.2% 1.5% <0.1% 

Total 99.3% 99.4% 99.4% 99.6% 99.3% 

Table A22. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of benthic invertebrates at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* <0.1% <0.1% <0.1% 0.2% <0.1% 

COX <0.1% 0.5% 2.2% 0.2% <0.1% 

Diet composition <0.1% 1.2% 21.1% 34.4% 36.5% 

mP <0.1% 0.8% 0.3% <0.1% <0.1% 

OC <0.1% 0.8% 11.6% 16.6% 18.6% 

Pore Water EEC 0.2% 6.0% 35.1% 41.8% 42.0% 

T <0.1% 1.3% 4.0% <0.1% 1.7% 

VLB 31.1% 38.5% 20.0% 6.3% 0.8% 

VWB <0.1% <0.1% 0.5% 0.2% <0.1% 

Water Column EEC 57.8% 27.9% 1.7% <0.1% <0.1% 

XPOC 7.4% 22.3% 3.0% <0.1% <0.1% 

Total 96.5% 99.3% 99.5% 99.7% 99.6% 

*mP, body composition, etc. 
70 of 123 

Table A23. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of filter feeders at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* <0.1% 2.1% 7.7% 14.8% 5.3% 

COX <0.1% <0.1% 0.5% <0.1% 1.3% 

CSS 0.3% 12.6% 12.5% 6.3% 13.0% 

Diet composition <0.1% 0.8% 2.9% 2.5% 7.6% 

mP 0.2% 0.3% <0.1% <0.1% <0.1% 

OC <0.1% 1.9% 15.6% 19.7% 18.4% 

Pore Water EEC 0.2% 8.2% 39.5% 45.4% 39.2% 

T <0.1% <0.1% 0.9% 1.0% 10.8% 

VLB 24.2% 24.0% 16.5% 9.5% 4.0% 

VWB 0.6% <0.1% 0.4% 0.4% <0.1% 

Water Column EEC 64.8% 26.7% 1.4% <0.1% <0.1% 

WB <0.1% <0.1% <0.1% 0.2% <0.1% 

XPOC 9.2% 22.6% 1.6% <0.1% <0.1% 

Total 99.5% 99.2% 99.5% 99.8% 99.6% 

*mP, body composition, etc. 

Table A24. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of small fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* <0.1% 3.1% 21.0% 32.6% 28.6% 

COX <0.1% 2.1% 5.6% 0.6% <0.1% 

Diet composition <0.1% 1.1% 6.8% 9.4% 10.0% 

mP <0.1% 0.7% <0.1% <0.1% <0.1% 

OC <0.1% <0.1% 6.6% 13.3% 14.5% 

Pore Water EEC <0.1% 2.4% 27.4% 38.6% 39.4% 

T <0.1% 4.2% 11.2% <0.1% 6.5% 

VLB 28.7% 25.3% 13.4% 4.8% 0.5% 

VWB <0.1% 0.2% <0.1% <0.1% <0.1% 

Water Column EEC 61.8% 34.4% 2.7% <0.1% <0.1% 

WB <0.1% 0.4% 0.7% 0.2% <0.1% 

XPOC 8.7% 25.4% 4.3% <0.1% <0.1% 

Total 99.2% 99.3% 99.7% 99.5% 99.5% 

*mP, body composition, etc. 
71 of 123 

Table A25. Third sensitivity analysis results: Contributions to variance of specific variables to CB

values of medium fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* <0.1% 4.9% 25.2% 38.4% 30.7% 

COX <0.1% 3.9% 8.6% 0.9% <0.1% 

Diet composition <0.1% 0.3% 2.7% 2.6% 1.4% 

mP <0.1% 0.2% <0.1% <0.1% <0.1% 

OC <0.1% <0.1% 6.7% 14.0% 14.6% 

Pore Water EEC 0.2% 3.0% 27.0% 39.7% 41.2% 

T 0.2% 9.7% 14.8% 0.2% 11.1% 

VLB 19.3% 14.0% 6.6% 3.0% 0.3% 

VWB 2.4% 1.8% 1.4% 0.4% <0.1% 

Water Column EEC 68.4% 35.5% 2.4% <0.1% <0.1% 

WB <0.1% 0.4% 0.4% <0.1% <0.1% 

XPOC 8.8% 25.8% 3.8% <0.1% <0.1% 

Total 99.3% 99.5% 99.6% 99.2% 99.3% 

*mP, body composition, etc. 

Table A26. Third sensitivity analysis results: Contribution to variance of specific variables to CB

values of large fish at different Log Kow values. 

Variable 4 5 6 7 8 

Characteristics of prey* 0.2% 5.3% 24.5% 38.0% 30.7% 

COX <0.1% 6.6% 9.7% 1.1% <0.1% 

Diet composition <0.1% 0.4% 2.4% 2.4% <0.1% 

OC <0.1% <0.1% 5.5% 13.3% 13.0% 

Pore Water EEC <0.1% 2.3% 22.7% 36.4% 38.1% 

T 0.2% 15.9% 17.2% 0.3% 16.0% 

VLB 27.5% 19.3% 11.6% 8.0% 1.5% 

VWB <0.1% 0.2% <0.1% <0.1% <0.1% 

Water Column EEC 62.6% 28.8% 2.2% <0.1% <0.1% 

WB <0.1% 0.4% <0.1% <0.1% <0.1% 

XPOC 7.9% 19.9% 3.6% <0.1% <0.1% 

Total 98.4% 99.1% 99.4% 99.5% 99.3% 

*mP, body composition, etc. 
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Appendix B. Explanation of Defaults and Alternative Values Representing Abiotic 

Characteristics of Aquatic Ecosystem 

Abiotic characteristics of the aquatic ecosystem that are necessary for KABAM are defined in 

Table 4 of the model tool. These characteristics include concentrations of particulate organic 

carbon (XPOC), dissolved organic carbon (XDOC), dissolved oxygen (COX), suspended solids 

(CSS), water temperature (T), and % organic carbon (OC) content of the sediment. The model 

tool is populated with default values for these parameters, which can be altered based on the 

needs of the model user. Default values are based on the abiotic characteristics of the aquatic 

ecosystem and are designed to be consistent with the OPP standard pond scenario used in 

EXAMS. Brief explanations for these default values as well as guidance on selecting alternative 

values are provided below for each parameter. 

B.1. Particulate Organic Carbon (XPOC) and Dissolved Organic Carbon (XDOC) 

XPOC and XDOC are entered by the model user in units of kg OC/L. These parameters are relevant 

to estimating the available pesticide fraction in water (Ф). The greater the value of either of these 

parameters, the less pesticide is available in water. Less available pesticide results in lower 

concentrations of pesticide in tissues of aquatic organisms. 

The estimated environmental concentrations (EECs) generated by PRZM/EXAMS in the water 

column and pore water represent the available concentration of the pesticide in water. Therefore, 

a default value of “0” is assumed for both XPOC and XDOC. As a result, the pesticide concentration 

available in the water is equal to the PRZM/EXAMS EEC input in Table 1. 

It may be necessary for the model user to incorporate alternate values for the XPOC and XDOC

parameters if the modeling incorporates EECs from a source other than PRZM/EXAMS. For 

example, if the exposure concentrations are available from monitoring data or mesocosm studies, 

XPOC and XDOC specific to the monitoring study may be used. In that case, if the empirical 

exposure concentrations correspond to the total water column (i.e., unfiltered), the model user 

would want to enter XPOC and XDOC values that correspond to the specific water body used. If 

given a range of available values, the user should consider that use of lower XPOC and XDOC 

values will result in more conservative estimates of pesticide accumulation in the aquatic food 

web. 

B.2. Concentration of Dissolved Oxygen (COX) 

The COX parameter influences the ventilation rate of aquatic animals. As COX decreases, the gill 

ventilation rate of aquatic animals increases (because animals need to take in more water to 

acquire the amount of oxygen they require). With an increase in the gill ventilation rate, the rate 

constants for pesticide uptake (k1) and elimination (k2) through respiration both increase. 

Although the increase in k1 leads to an increase in pesticide uptake, the increase in k2 also leads 

to an increase in pesticide elimination. The net effect is a decrease in pesticide concentration in 
73 of 123 

aquatic organism tissues. Therefore, a decrease in the value of COX results in a decrease in 

pesticide concentrations in tissues of aquatic organisms. 

COX is entered by the model user in units of mg O2/L. The default value for this parameter is 5.0 

mg O2/L, based on the OPP standard pond. This concentration does not represent the highest 

possible value for COX (i.e., the limit of solubility of oxygen) and is not expected to result in the 

most conservative estimates of pesticide in aquatic animal tissues. However, it is consistent with 

the OPP standard pond which is used to derive EECs. 

The model user could explore the influence of COX on the predictions of pesticide tissue 

concentrations in aquatic organisms by selecting a higher value, for example the solubility of 

oxygen (potential range: 6-12 mg O2/L). To determine the solubility of oxygen in water at 

specific temperatures and pressures, see USGS 2008a. 

It may be necessary for the model user to incorporate an alternate COX value if the modeling 

incorporates EECs from a source other than PRZM/EXAMS. In that case, the model user should 

enter a COX value that corresponds to the specific water body used. 

B.3. Water Temperature (T) 

The water temperature parameter influences calculation of the growth dilution rate constant (kG), 

the pesticide uptake rate constant through diet (kD), and the pesticide elimination rate constant 

through excretion of feces (kE). The growth dilution rate constant (kG) is dependent on whether 

the temperature is above or below 17.5o

C. The growth dilution rate constant is higher when the 

temperature is above 17.5o

C compared to when the temperature is below 17.5oC (Figure A.8). 

Temperature affects the pesticide uptake rate through the dietary uptake rate constant (kD) by 

changing the feeding rate of the animal (GD). An increase in temperature results in an increase in 

the feeding rate, and with that, an increase in the pesticide uptake constant for the diet (Figure 

A.10). The fecal egestion rate constant (kE) is affected by temperature by changing the feeding 

rate (GD) as well as the fecal egestion rate (GF) of the animal. An increase in temperature results 

in an increase in the feeding rate (Figure A.13), and with that, an increase in the fecal egestion 

rate. The increase in the fecal egestion rate results in an increase in the pesticide rate constant for 

pesticide elimination through excretion. In summary, increase in temperature results in an 

increase in kD, kE, and kG. Although kG and kE represent processes (i.e., pesticide 

elimination/dilution) that compete with kD (i.e., pesticide uptake), the net increase in the two 

processes (uptake and elimination/dilution) does not cancel each other out. 

The water temperature of the EXAMS’ pond varies based on the selected PRZM scenario. 

Therefore, the model user should select the water temperature based on the PRZM scenario used 

for deriving EECs. If the modeling incorporates EECs from a source other than PRZM/EXAMS, 

the water temperature relevant to the other EECs should be utilized. 
74 of 123 

B.4. Concentration of Suspended Solids (C SS)

The concentration of suspended solids (CSS) is relevant to filter feeders only. CSS influences the 

calculation of the rate constants for pesticide uptake through diet (kD) and pesticide elimination 

through excretion of feces (kE). An increase in CSS leads to an increase in the feeding rate of 

filter feeders (GD) which in turn results in an increase in the pesticide uptake through diet (kD). 

An increase in CSS also leads to an increase in the fecal egestion rate of filter feeders (GF) and an 

increase in the pesticide elimination through excretion of fecal matter (kE). Although kD and kE

represent competing processes, the net increase in the two does not cancel each other out. 

CSS is entered by the model user in units of kg/L. The default value for this parameter is 

3.00x10-5 kg/L, based on the OPP standard pond. If the modeling incorporates EECs from a 

source other than PRZM/EXAMS, a CSS value relevant to the other EECs should be utilized. 

B.5. Sediment Organic Carbon (OC) 

Sediment organic carbon (OC) is a parameter that influences organisms that consume sediment. 

As OC increases, the concentration of the pesticide in the solid component of the sediment 

increases to the extent that the pesticide sorbs to organic matter. As the pesticide concentration in 

sediment increases, the pesticide concentration in organisms that consume sediment also 

increases. 

OC is entered by the model user as % of the dry weight of the sediment. The default value for 

this parameter is 4.0%, based on the OPP standard pond. If the modeling incorporates EECs 

from a source other than PRZM/EXAMS, an OC value relevant to the other EECs should be 

utilized.
75 of 123 

 

 Appendix C. Explanation of Default Values Representing Biotic Characteristics of the 

Aquatic Ecosystem, Including Food Web Structure 

The seven trophic levels of the aquatic ecosystem of KABAM are phytoplankton, zooplankton, 

benthic invertebrates, filter feeders, small fish, medium fish, and large fish. In KABAM, each 

trophic level is defined by its % lipid, % Non Lipid Organic Matter (NLOM), % water, body 

weight, and diet. Each of these trophic levels is described within this Appendix, with emphasis 

on the information relevant to KABAM and explanations of default parameters used to define 

these trophic levels (in Tables 5 and 6 of the KABAM tool). If the model user wishes to explore 

the influences of changes in parameter values representing the aquatic food web on EECs and 

RQs for birds and mammals, this can be accomplished by altering parameter values within the 

range of reported values for a specific parameter. 

Although the % water composition of an aquatic organism does not influence the 

bioaccumulation of a chemical in that organism (see Appendix A), it is an important 

consideration for the definition of % lipid and the percent non-lipid organic matter (% NLOM). 

Often, tissue analysis results and body weight data in the scientific literature are reported on a 

dry weight basis. For KABAM, input parameters for body composition are entered on a wet 

weight basis. Therefore, % water composition is discussed in the sections below since it is 

necessary to understand the water composition of an organism in order to translate the reported 

data into input parameters for KABAM. 

Lipid composition of an organism can influence the bioaccumulation of a chemical (See 

Appendix A), with higher lipid composition leading to higher accumulation. Since KABAM is 

intended for use in ecological risk assessments of pesticides with the potential to bioaccumulate 

in aquatic ecosystems, it is necessary for this tool to serve as a conservative representation of 

bioaccumulation. Default parameter values for % lipid were selected from the open literature 

and are intended to represent the high-end of available data (75th-90th percentiles). 

C.1. Phytoplankton 

Phytoplankton are microscopic autotrophic aquatic organisms that derive their nutrition from 

photosynthesis. Groups of freshwater phytoplankton include algae (green, yellow-green and 

golden-brown), cyanobacteria (blue-green algae), diatoms and dinoflagellates. Phytoplankton 

can be unicellular, colonial, or filamentous. These organisms have limited mobility that is based 

on water movements; however, some are able to move via flagella. An aquatic habitat will 

generally contain an assemblage of phytoplanktonic species that vary in proportion over time and 

space (Wetzel 1983). 

For parameterization of KABAM, it is necessary to define the % water, % lipid and % NLOM 

contents of phytoplankton. The body weight is not a necessary input for phytoplankton, nor is 

the diet composition since these organisms do not consume other organisms. 
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Since it is assumed that phytoplankton are present in the water column of the aquatic ecosystem 

where photosynthesis can occur, it is assumed that phytoplankton do not reside in benthic 

sediment and do not respire pore water. This should be indicated in Table 5 of the KABAM tool 

(i.e., “no” should be entered in the column titled: “Do organisms in trophic level respire some 

pore water?”). 

Aquatic plant tissues are composed of approximately 90% water by weight (Hannan and Dorris 

1970; Raven et al. 1999, Sladecek and Sladeckova 1963). The default parameter for the water 

composition of phytoplankton is 90%. 

Reported % lipid values for phytoplankton vary from 2-27% of dry weight. If it is assumed that 

phytoplankton are composed of 90% water, then this range of lipid compositions is equivalent to 

0.2-2.7% on a wet weight basis (Table C1). For KABAM, the default parameter for % lipid 

of phytoplankton was selected as 2% to represent a high-end estimate (75th to 90th

percentile of data in Table C1) of this parameter.

The wet weight of an organism is the sum of the water, lipid, and NLOM content. If the water 

content of phytoplankton is 90% of the wet weight, and the % lipid is known (2%), the NLOM 

content of phytoplankton is the % remaining after subtracting the water and lipid content from 

100%. Therefore, the default parameter for the NLOM composition of phytoplankton is 

8%.
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Table C1. Percent lipid composition of freshwater phytoplankton (under culture conditions) reported in the 

scientific literature. 

Species 

Mean% 

Lipid (dry 

weight basis) 

Mean% 

Lipid (wet 

weight basis) 

Source 

Not stated Not stated 0.5 Oliver and Niimi 1988 

Anabaena sp. 6.8 (±0.4) 0.68* Stange and Swackhamer 1994 

Anabaena sp. 5.3 (±2.4) 0.53* Stange and Swackhamer 1994 

Anabaena sp. 2.2 (±0.2) 0.22* Stange and Swackhamer 1994 

Chamydomonas reinhardtii 10.8 (±6.2) 1.08* Lürling and Van Donk 1997 

Chlamydomonas applanata 18.2 1.82* Shifrin and Chisholm 1981 

Chlamydomonas applanata 16 1.60* Shifrin and Chisholm 1981 

Chlorella ellipsoidea 13.5 1.35* Shifrin and Chisholm 1981 

Chlorella pyrenoidosa 13.4 1.34* Shifrin and Chisholm 1981 

Chlorella pyrenoidosa 14.4 1.44* Shifrin and Chisholm 1981 

Chlorella pyrenoidosa 16.4 1.64* Shifrin and Chisholm 1981 

Chlorella pyrenoidosa 16 1.60* Shifrin and Chisholm 1981 

Chlorella vulgaris 12.5 1.25* Shifrin and Chisholm 1981 

Chlorella vulgaris 13 1.30* Shifrin and Chisholm 1981 

Cryptomonas pyrenoidifera 8.5 (±5.1) 0.85* Lürling and Van Donk 1997 

Microcystis aeruginosa 5.8 (±2.3) 0.58* Lürling and Van Donk 1997 

Nannochloris sp. 20.2 2.02* Shifrin and Chisholm 1981 

Nitzschia palea 22.2 2.22* Shifrin and Chisholm 1981 

Oocystis polymorpha 12.6 1.26* Shifrin and Chisholm 1981 

Ourococcus sp. 27 2.70* Shifrin and Chisholm 1981 

Scenedesmus acutus 6.4 (±2.5) 0.64* Lürling and Van Donk 1997 

Scenedesmus obliquus 19 1.90* Shifrin and Chisholm 1981 

Selanastrum gracile 20.8 2.08* Shifrin and Chisholm 1981 

Selenasrum capricornutum 19.5 (±0.2) 1.95* Stange and Swackhamer 1994 

Selenasrum capricornutum 16.0 (±0.3) 1.60* Stange and Swackhamer 1994 

Selenasrum capricornutum 8.0 (±0.9) 0.80* Stange and Swackhamer 1994 

Synedra sp. 7.5 (±1.6) 0.75* Stange and Swackhamer 1994 

Synedra sp. 13.7 (±0.7) 1.37* Stange and Swackhamer 1994 

Synedra sp. 11.7 (±4.5) 1.17* Stange and Swackhamer 1994 

Synedra ulna 23 2.30* Shifrin and Chisholm 1981 

Average 1.4 

75th percentile 1.8 

90th percentile 2.1 

*Calculated from reported % lipid based on dry weight and assumption that algae wet weight is 90% water. 
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C.2. Zooplankton 

Zooplankton are aquatic animals that are suspended in water. This group is primarily composed 

of rotifers, cladocera and copepods, but also includes protozoa and insects at immature life 

stages. Species of zooplankton primarily consume phytoplankton but also consume detritus, 

bacteria, yeast, and other (smaller) zooplankton (Wetzel 1983). For parameterization of 

KABAM, zooplankton is represented by herbivorous species that have a diet composed 

100% of phytoplankton. 

Since it is assumed that zooplankton are present in the water column of the aquatic ecosystem 

and do not reside in the benthic sediment, it is assumed that zooplankton do not respire pore 

water. This should be indicated in Table 5 of the KABAM tool (i.e., “no” should be entered in 

the column titled: “Do organisms in trophic level respire some pore water?”).

Beers (1966) reported water compositions of several groups of marine zooplankton inhabiting 

the Atlantic Ocean. Average % water composition of these groups ranged 74-96%, with an 

average % water composition of 86% corresponding to copepods. Based on this information, the 

default % water composition of zooplankton is 85%. This value is used to translate dry 

weight data into equivalent wet weight values. 

Reported mean % lipid values for zooplankton vary from 6.4-24.3% of dry weight. If it is 

assumed that zooplankton are composed of 85% water, then this range of lipid compositions is 

equivalent to 0.96-3.6% on a wet weight basis (Table C2). Based on this information, the 

default % lipid for zooplankton is set to 3% to represent a high-end (75th to 90th percentile) 

estimate of this parameter.

The wet weight of an organism is the sum of the water, lipid, and NLOM content. If the water 

content of zooplankton is 85% of the wet weight, and the % lipid is known (3%), then NLOM 

content of zooplankton is the % remaining after subtracting the water and lipid content from 

100%. Therefore, the default parameter for the NLOM composition of zooplankton is 12%.

Wright (1958) provided biomass data for two species of zooplankton (Daphnia longispina and 

D. pulex) in a reservoir in Montana, where the average body weight of zooplankton was 1.3x10-7 

kg-wet weight (assuming 85% water content; range 0.9-1.6x10-7 kg-wet weight). Acharya et al. 

(2005) provided dry body weights for Bosmina freyi that translate to approximately 0.3-3x10-8

kg-wet weight (assuming 85% water content). Jeppesen et al. (2004) provided body weight data 

for Daphnia sp. that translate to approximately 0.67-3.3x10-7 kg-wet weight (assuming 85% 

water content). Based on this information, the default weight for zooplankton is set to 1x10-7 

kg-wet weight, with the intention of being a representative weight of species of zooplankton. 
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Table C2. Percent lipid composition of freshwater zooplankton reported in the scientific literature. 

Zooplankton identification 

Mean% Lipid 

(dry weight 

basis) 

Mean% Lipid 

(wet weight 

basis) 

Source 

Daphnia magna (cladoceran) 6.4-19.7 0.96-3.0* McKee and Knowles 1987 

Unspecified 6.7* 1.0±0.33 Morrison et al. 1997 

Mostly cladocerans, also copepods and 

rotifers 

10.8 (±3.6) 1.6* Mitra et al. 2007 

Mostly cladocerans, also copepods and 

rotifers 

12.1 (±3.0) 1.8* Mitra et al. 2007 

Mostly cladocerans, also copepods and 

rotifers 

12.2 (±2.4) 1.8* Mitra et al. 2007 

Leptodora kindtii 13.1 (±1.0) ** 2.0 Vijverberg and Frank 1976 

Mostly cladocerans, also copepods and 

rotifers 

13.7 (±1.9) 2.1* Mitra et al. 2007 

Mostly cladocerans, also copepods and 

rotifers 

13.9 (±1.9) 2.1* Mitra et al. 2007 

Mostly cladocerans, also copepods and 

rotifers 

14.6 (±1.0) 2.2* Mitra et al. 2007 

Cyclopodia 15.9 (±1.8) ** 2.4 Vijverberg and Frank 1976 

Chydorus sphaericus 18.5 (±2.8) ** 2.8 Vijverberg and Frank 1976 

Bosmina coregoni 20.5 (±1.9) ** 3.1 Vijverberg and Frank 1976 

Eurytemora affinus 23.6 (±2.7) ** 3.5 Vijverberg and Frank 1976 

Daphnia hyalina 24.3 (±5.3)** 3.6 Vijverberg and Frank 1976 

Average 2.3 

75th percentile 2.9 

90th percentile 3.3 

*Calculated from reported % lipid based on dry weight and assumption that zooplankton wet weight is 85% 

water. 

**Expressed as % of total organic matter attributed to lipid. It is assumed that this is equivalent to a dry weight 

basis. 

C.3. Benthic invertebrates 

The benthic invertebrate trophic level includes animals that inhabit the sediments of aquatic 

habitats. Benthic invertebrates include a diverse group of animals, including crustaceans (e.g.,

crayfish, amphipods), aquatic worms (e.g., oligochaetes), aquatic insect larvae (e.g., Diptera, 

caddisflies, beetles, mayflies and dragonflies), protozoa, snails, and nematodes. Different species 

of benthic invertebrates have a variety of feeding strategies, including herbiovory, detritivory, 

and predation upon other benthic invertebrates (Covich et al. 1999). In order to represent all of 

these feeding strategies with the benthic invertebrate trophic level of KABAM, it is assumed that 

benthic invertebrates consume organic matter from sediment, phytoplankton, and zooplankton in 

equal quantities. Therefore, the default diet composition of benthic invertebrates is 34% 

sediment, 33% phytoplankton, and 33% zooplankton.

Since it is assumed that benthic invertebrates are present in the benthic compartment of the 

aquatic ecosystem, it is assumed that benthic invertebrates respire sediment pore water. This 
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should be indicated in Table 5 of the KABAM tool (i.e., “yes” should be entered in the column 

titled: “Do organisms in trophic level respire some pore water?”).

Available water composition data for benthic invertebrates include a range of 69-83% water 

(Table C3). The average value of the available data is 76%. Based on this average, the default 

value for KABAM representing the % water of benthic invertebrates is 76%. 

Table C3. Water composition (%) of benthic invertebrates reported in the scientific literature. 

Organism Mean% Water Source 

Crayfish (Orconectus propinquus) 69 Gewurtz et al. 2000 

Hyalella azteca 72.5 Lotufo et al. 2001 

Mayfly larvae 73 Gewurtz et al. 2000 

Diporeia sp., 73.1 Lotufo et al. 2001 

Crayfish (Astacus fluviatilis) 80.0 Sidwell 1981 

Lumbriculus variegatus (oligochaete) 81 Liebig et al. 2005 

Crayfish (Astacus, Orconectus and Procambarus) 82.5* USDA 2005 

*excluding the shell 

Lipid data are available for various freshwater crustaceans, oligochaetes, and insect larvae. 

These values indicate a wide range (approximately 1 to 10% of wet weight) of lipid composition 

of benthic invertebrates (Tables C4-C9). The default lipid composition for benthic 

invertebrates is 3%. This value was selected to be representative of a high-end value (75th

percentile) of available lipid compositions for freshwater benthic invertebrates (Table C10). 

Table C4. Lipid composition (%) of Hyalella azteca (a freshwater crustacean) reported in the scientific 

literature. 

Source Mean% Lipid 

(dry weight basis) 

Mean% Lipid 

(wet weight basis) 

Lotufo et al. 2001 2.4* 0.66±0.03 

Lotufo et al. 2001 3.2* 0.88±0.04 

Lotufo et al. 2001 6.3* 1.73±0.25 

Lotufo et al. 2001 6.5* 1.79±0.41 

Lotufo et al. 2000 6.9 (±0.9) 1.9* 

Lotufo et al. 2000 7.0 (±1.1) 1.9* 

Lotufo et al. 2000 7.2 (±0.8) 2.0* 

Kane Driscoll and Landrum 1997 7.5 (±1.5) 2.1* 

Lotufo et al. 2000 7.5 (±0.9) 2.1* 

Lotufo et al. 2000 7.7 (±1.5) 2.1* 

Kane Driscoll et al. 1997 8.2 (±0.7) 2.3* 

Kane Driscoll et al. 1997 8.4(±0.7) 2.3* 

Average 6.6 1.8 

75th percentile 7.6 2.1 

90th percentile 8.2 2.3 

*Calculated from reported % lipid and assumption that dry:wet weight ratio for 

H. azteca is 0.275 (based on Lotufo et al. 2001). 
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Table C5. Lipid composition (%) of freshwater crayfish (crustaceans) reported in the scientific literature. 

Genus 

Mean% 

Lipid (wet 

weight basis) 

Source 

Astacus fluviatilis 0.5 Sidwell 1981 

Orconectus 0.86 (±0.11) White et al. 1998 

Astacus, Orconectus and Procambarus 1.0 USDA 2005 

Undefined 1.9 (±0.47) Morrison et al. 1997 

Orconectes propinquus 2.4 (±0.26) Morrison et al. 2000 

Orconectes 2.52 (±0.16) Gewurtz et al. 2000 

Procambarus 2.95 (±1.25) Lin et al. 2004 

Procambarus 3.02 (±1.29) Lin et al. 2004 

Average 1.9 

75th percentile 2.6 

90th percentile 3.0 

Table C6. Lipid composition (%) of Diporeia sp. (freshwater crustaceans) reported in the scientific literature. 

Source Mean% Lipid 

(dry weight basis) 

Mean% Lipid 

(wet weight basis) 

Landrum et al. 2007 10.78 (±1.5) 2.9* 

Landrum et al. 2007 11.97 (±0.38) 3.2* 

Landrum et al. 2007 17.1 (±0.64) 4.6* 

Kane Driscoll et al. 1997 20.1 (±4.6) 5.4* 

Kukkonen et al. 2004 20.4* 5.5±0.7 

Kane Driscoll et al. 1997 21.3 (±6.7) 5.7* 

Lotufo et al. 2001 22.2* 5.97±0.75 

Kukkonen et al. 2004 23.0* 6.2±1.4 

Lotufo et al. 2001 23.3* 6.27±1.21 

Lotufo et al. 2000 23.7 (±8.5) 6.4* 

Lotufo et al. 2000 23.9 (±6.3) 6.4* 

Kane Driscoll and Landrum 1997 27.2 (±1.3) 7.3* 

Lotufo et al. 2001 40.3* 10.85±0.62 

Lotufo et al. 2001 43.1* 11.59±1.18 

Average 23.5 6.3 

75th percentile 23.9 6.4 

90th percentile 36.4 9.8 

*Calculated from reported % lipid and assumption that dry:wet weight ratio for 

Diporeia sp. is 0.269 (based on Lotufo et al. 2001). 
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Table C7. Lipid composition (%) of Lumbriculus variegatus (a freshwater oligochaete) reported in the 

scientific literature. 

Source Mean% Lipid 

(dry weight basis) 

Mean% Lipid 

(wet weight basis) 

Croce et al. 2005 5.8* 1.1±0.1 

Kukkonen et al. 2004 6.3* 1.2±0.13 

Liebig et al. 2005 8 (±0.4) 1.5* 

Kukkonen et al. 2004 7.9 1.5±0.19 

Kukkonen and Landrum 1994 9.2 (±0.9) 1.7* 

Fisk et al. 1998 10.5* 2.0±0.2 

Kukkonen and Landrum 1994 11.1 (±1.4) 2.1* 

Fisk et al. 1998 12.1* 2.3±0.2 

Fisk et al. 1998 13.2* 2.5±0.3 

Kukkonen and Landrum 1994 13.2 (±4.3) 2.5* 

Fisk et al. 1998 15.3* 2.9±0.3 

Fisk et al. 1998 17.9* 3.4±0.8 

Fisk et al. 1998 18.9* 3.6±0.8 

Fisk et al. 1998 19.5* 3.7±0.6 

Average 12.1 2.3 

75th percentile 14.8 2.8 

90th percentile 18.6 3.5 

*Calculated from reported % lipid and assumption that water composition of L. 

variegatus is 81% (Liebig et al. 2005). 

Table C8. Lipid composition (%) of other freshwater oligochaetes reported in the scientific literature. 

Organism Identification Mean% Lipid 

(dry weight basis) 

Mean% Lipid 

(wet weight basis) Source 

Tubifex tubifex and

Limnodrilus hoffmeisteri 5.3* 1* Oliver and Niimi 1988 

Ilyodrilus templetoni** 5.85 (±2.28) 1.1* Lu et al. 2003 

Ilyodrilus templetoni** 6.11 (±0.55) 1.2* Lu et al. 2003

Ilyodrilus templetoni** 6.72 (±1.59) 1.3* Lu et al. 2003

Ilyodrilus templetoni** 7.44 (±1.33) 1.4* Lu et al. 2003

Ilyodrilus templetoni** 7.35 (±1.26) 1.4* Lu et al. 2003

Ilyodrilus templetoni** 8.82 (±1.60) 1.7* Lu et al. 2003

Oligochaete 9.5 (±1.0) 1.8* Landrum et al. 2007 

Limnodrilus sp. 11.93 (±0.16) 2.3* Jonker et al. 2004 

Oligochaete 12.8 (±1.8) 2.4* Landrum et al. 2007 

Average 8.2 1.6 

75th percentile 9.3 1.8 

90th percentile 12.0 2.3 

*Calculated from reported % lipid and assumption that water composition of L. variegatus is 81% (Liebig et 

al. 2005). 

**Mean of values for I. templetoni is 1.4% (wet weight). When this value is used in calculating the mean and 

percentile values for the group of oligochaetes, the mean is 1.8% (wet weight). The 75th and 90th percentiles 

are 2.2 and 2.4, respectively. 
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Table C9. Lipid composition (%) of freshwater insect larvae reported in the scientific literature. 

Organism Identification 

Mean% 

Lipid (wet 

weight basis) 

Source 

Chironomus riparius 0.6 Leonards et al. 1997 

Hexagenia limbata (mayfly larvae) 1.5 (±0.05) Morrison et al. 2000 

H. limbata and H. rigida 1.50 (±0.052) Gewurtz et al. 2000 

Caddisfly larvae 1.7 Morrison et al. 1997 

Mayfly larvae 2.0 (±0.25) Morrison et al. 1997 

Average 1.5 

75th percentile 1.7 

90th percentile 1.9 

Table C10. Mean lipid composition (%, wet weight basis) of freshwater benthic invertebrates from data in 

Tables C4-C9. 

Benthic Invertebrate Mean 75th

Percentile 

90th

Percentile 

Insect Larvae 1.5 1.7 1.9 

Hyalella azteca (a freshwater crustacean) 1.8 2.1 2.3 

Freshwater oligochaetes (excluding L. variegatus) 1.8 2.2 2.4 

Crayfish (freshwater crustaceans) 1.9 2.6 3.0 

Lumbriculus variegatus (a freshwater oligochaete) 2.3 2.8 3.5 

Diporeia sp. (freshwater crustaceans) 6.3 6.4 9.8 

Mean 2.6 3.0 3.8 

The wet weight of an organism is the sum of the water, lipid, and NLOM content. By default, if 

the water content of benthic invertebrates is 76% of the wet weight, and the % lipid is known 

(default = 3%), the NLOM content of benthic invertebrates is the % remaining after subtracting 

the water and lipid content from 100%. Therefore, the default parameter for the NLOM 

composition of benthic invertebrates is 21%.

The benthic invertebrate trophic level is composed of a wide variety of taxonomic groups. The 

body weights of organisms within this group can vary by orders of magnitude (Table C11). The 

default weight for benthic invertebrates is set to 1x10-4 kg-wet weight, with the intention of 

being representative of a midpoint weight of species of benthic invertebrates. 

Table C11. Body weights (wet) of freshwater benthic invertebrates reported in the scientific literature. 

Benthic Invertebrate Weight (kg) Source 

Amphipods 0.05x10-4 Leonards et al. 1997 

Mayfly larvae 0.16x10-4 * Morrison et al. 1997 

Chironomids 0.24x10-4 Leonards et al. 1997 

Caddisfly larvae 0.32x10-4 * Morrison et al. 1997 

Snails 0.82x10-4 Leonards et al. 1997 

Crayfish 18.0x10-4 Morrison et al. 1997 

*converted from reported dry weight to wet weight assuming 75% water content. 
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C.4. Filter Feeders 

Filter feeders are benthic invertebrates that are distinguished by their feeding habits. These 

organisms feed by straining water and extracting organic material such as detritus and plankton. 

Examples of freshwater filter feeders include mollusks. For KABAM, it is assumed that filter 

feeders consume materials suspended in the water column, including phytoplankton, 

zooplankton, and detritus. It is also assumed that filter feeders consume suspended sediment 

incidentally. The default composition of the diet of this trophic level is 34% sediment, 33% 

phytoplankton, and 33% zooplankton. 

Since it is assumed that filter feeders are present in the benthic sediment compartment of the 

aquatic ecosystem, it is also assumed that filter feeders respire sediment pore water. This should 

be indicated in Table 5 of the KABAM tool (i.e., “yes” should be entered in the column titled: 

“Do organisms in trophic level respire some pore water?”).

According to available data, water composition of freshwater mollusks ranges 78-93% (Table 

C12). The default water content of filter feeders is set to 85%, based on the midpoint of the 

range of available data. 

Table C12. Water composition (%) of freshwater mollusks reported in the scientific literature. 

Identification Mean % water* Source 

Corbicula strata (freshwater clam) 77.6 Sidwell 1981

Corbicula japonica (freshwater clam) 79.8 Sidwell 1981

Corbicula sandai (freshwater clam) 80.0 Sidwell 1981

Corbicula fluminea (freshwater clam) 81.4 Sidwell 1981

lamellibrancha clams (subclass) 82 USDA 2005 

Corbicula leana (freshwater clam) 82.1 Sidwell 1981

Dreissena polymorpha (zebra mussel) 87 Bervoets et al. 2005 

D. polymorpha 88-93 Hendriks et al. 1998 

Anodonta anatine (mussels) 90.6-92.8 Hyötyläinen et al. 2002 

*It is assumed that this does not include the shell. 

Data on lipid content are available for several species of freshwater mollusks. These values range 

0.4-4% of wet weight (Tables C13 - C15). The default lipid composition for filter feeders is 

2%. This value was selected to be representative of a high end (75th percentile of Dreissena sp.

and Corbicula sp.) value of available lipid compositions for freshwater mollusks. 
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Table C13. Percent lipid composition of Dreissena sp. (freshwater mollusks) reported in the scientific 

literature. 

Species Mean % Lipid 

(dry weight basis) 

Mean % Lipid 

(wet weight basis) Source 

D. polymorpha 4.3* 0.55 Bervoets et al. 2005 

D. polymorpha 14 1 Hendriks et al. 1998 

D. polymorpha 8.5* 1.1 Kwon et al. 2006 

D. polymorpha 9.1 1.2* Becker van Slooten and Tarradellas 1994 

D. bugensis 9.0 (±1.4) 1.2* Marvin et al. 2002 

D. bugensis 10 (±0.5) 1.3 Marvin et al. 2002 

D. polymorpha 11 (±0.6) 1.4* Marvin et al. 2002 

D. polymorpha 10.8* 1.4 (±0.1) Kwon et al. 2006 

D. polymorpha 11.5* 1.5 (±0.1) Kwon et al. 2006 

D. polymorpha 12.3* 1.6 (±0.1) Kwon et al. 2006 

D. polymorpha 12 (±4.4) 1.6* Marvin et al. 2002 

D. polymorpha 17 2 Hendriks et al. 1998 

D. polymorpha 18 2 Hendriks et al. 1998 

Average 11.3 1.4 

75th percentile 12.3 1.6 

90th percentile 16.4 1.9 

* Calculated from reported % lipid and assumption that water composition of D. polymorpha is 87% (Bervoets 

et al. 2005).

Table C14. Percent lipid composition of Corbicula sp. (freshwater clams) reported in the scientific literature. 

Species Mean % Lipid (wet 

weight basis) Source 

C. leana 1.1 Sidwell 1981

C. japonica 1.2* Kang et al. 2002 

C. japonica 1.2 Sidwell 1981

C. fluminea 1.5 Sidwell 1981

C. sandai 2.4 Sidwell 1981

C. strata 4.0 Sidwell 1981

Average 1.9 

75th percentile 2.2 

90th percentile 3.2 

*Based on reported mean lipid content of 5.8% dry weight and 80% moisture content reported for 

this species by Sidwell 1981.
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Table C15. Percent lipid composition of other freshwater filter feeders reported in the scientific literature. 

Identification Mean % Lipid 

(dry weight basis) 

Mean % Lipid 

(wet weight basis) Source 

Sphaerium striantium (fingernail clam) 8.7 0.36 Rice and White 1987 

Elliptio complanata 3.2 (±1.2) 0.48* Marvin et al. 2002 

Anodonta anatine (mussels) 11.2 (±0.8) 0.81 Hyötyläinen et al. 2002

Anodonta anatine (mussels) 12.2 (±0.7) 0.98 Hyötyläinen et al. 2002

Lamellibrancha (clams) 5.5 1.0 USDA 2005 

Anodonta anatine (mussels) 10.9 (±0.6) 1.02 Hyötyläinen et al. 2002

Anodonta anatine (mussels) 11.3 (±0.9) 1.05 Hyötyläinen et al. 2002 

*Calculated using assumption that filter feeders are 85% water. 

The wet weight of an organism is the sum of the water, lipid, and NLOM content. By default, if 

the water content of filter feeders is 85% of the wet weight and the % lipid is known (default = 

2%), the NLOM content of filter feeders is the % remaining after subtracting the water and lipid 

content from 100%. Therefore, the default parameter for the NLOM composition of filter 

feeders is 13%.

Reported wet weights of various species of mollusks range 0.2-12 x10-3 kg. Mean wet weights of 

Dreissena polymorpha have been reported as 0.41 ± 0.26 x10-3 kg (Van Haelst et al. 1996). Wet 

weights of C. fluminea ranged approximately 0.2-2 x10-3 kg (Andrès et al. 1999, Vidal et al. 

2002). Hyötyläinen et al. (2002) reported wet weights of Anodonta anatine tissue as ranging 4.5-

12.1 x10-3 kg. Based on this information, the default weight of filter feeders is set to 1 x10-3

kg, with the intention of being a representative weight of mollusks. 

C.5. Fish (Small, Medium and Large Sizes) 

There are hundreds of species of fish inhabiting fresh waters of the United States and Canada, 

including ponds, lakes, streams, and rivers. Species of bluegill and other sunfish (Lepomis spp.), 

bass (Micropterus spp.), and crappie (Pomoxis spp.) are common inhabitants of fresh warm 

water ponds, lakes, and streams distributed throughout the continental United States (Page and 

Burr 1991, Carlander 1977). As described below, these species were used to define default 

parameters for the small, medium, and large fish in KABAM. Although there are many other 

species of fish in ponds of the U.S. (e.g., perch, minnows), sunfish, crappie, and bass were 

considered representative of fish that are found in freshwaters of the U.S., and thus suitable for 

models for defining input parameters for use in KABAM. 

Several bird and mammal species (e.g., belted kingfisher [Megaceryle alcyon], northern river 

otter) consume amphibians, in addition to fish. For KABAM, it is assumed that the default fish 

also represent, i.e., serve as surrogates for, aquatic-phase amphibians, such as salamanders and 

frogs. This assumption is consistent with OPP’s policy in which exposure and effects data for 

fish are assumed to be representative of aquatic-phase amphibians (USEPA 2004). 

Default parameters for small fish in KABAM are designed to represent the young-of-year 

(YOY), i.e., fish that have hatched within the year, before January 1 of the next year, of sunfish, 

bass and crappie. YOY of these species consume copepods, cladocerans, rotifers (i.e.,
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zooplankton), chironomid larvae, and mayfly larvae (i.e., benthic invertebrates) (Carlander 

1977). Average body weights of YOY of sunfish, bass, and crappie are provided in Table C16. 

For KABAM, it is assumed that the small fish weighs 0.01 kg and its diet is 50% 

zooplankton and 50% benthic invertebrates. 

Table C16. Average body weights for young of the year fish (Source: Carlander 1977). 

Species (scientific name) Average body weight 

(kg) 

Green sunfish (Lepomis cyanellus) 0.001-0.01 

Pumpkinseed (L. gibbosus) 0.002 

Warmouth (L. gulosus) <0.011 

Bluegill (L. macrochirus) 0.0001-0.05 

Redear sunfish (L. microlophus) 0.0006-0.04 

Largemouth bass (Micropterus salmoides) 0.0002-0.02 

White crappie (Pomoxis annularis) 0.0002-0.01 

Black crappie (P. nigromaculatus) 0.0005-0.02 

The medium fish in KABAM is designed to represent adult sunfish and crappie. These fish reach 

sexual maturity between ages 1 and 3, with lifespans ≥6 years. Their diets include insects, insect 

larvae, crustaceans, snails, and other fish (Carlander 1977). Mature fish range in weight, 0.005-

0.579 kg, depending upon their age (Table C17; data from Carlander 1977). Although mature 

fish display a wide range of weights, most species weigh approximately 0.1 kg as adults. For 

KABAM, it is assumed that the medium-sized fish weighs 0.1 kg and its diet is 50% benthic 

invertebrates and 50% small fish.

Table C17. Average body weights (in kg) of medium fish at different ages. 

Species (scientific name) 1 yr 2 yr 3 yr 4 yr 5 yr 6 yr 7 yr 8 yr 

Green sunfish (Lepomis cyanellus) 0.01 0.024 0.048 0.086 0.086 0.132 - - 

Pumpkinseed (L. gibbosus) 0.005 0.034 0.034 0.063 0.099 0.157 0.157 0.157 

Warmouth (L. gulosus) 0.011 0.046 0.046 0.085 0.163 0.163 - - 

Bluegill (L. macrochirus) 0.014 0.052 0.052 0.090 0.141 0.141 0.208 0.208 

Redear sunfish (L. microlophus) 0.026 0.081 0.125 0.187 0.187 0.265 - - 

White crappie (Pomoxis annularis) 0.031 0.085 0.123 0.181 0.346 0.346 0.579 0.579 

Black crappie (P. nigromaculatus) 0.037 0.097 0.143 0.210 0.289 0.363 0.468 0.468

- Indicates data were not available 

The large fish in KABAM is designed to represent the largemouth bass (Micropterus salmoides), 

which is a predatory fish commonly found in warm waters throughout the continental United 

States. It is also designed to be representative of large predatory fish that are consumed by 

mammals and birds. The diet of largemouth bass is composed primarily of fish, including 

sunfish, crappie, perch, shad and smaller-sized largemouth bass. Largemouth bass will also 

consume crayfish, especially when no other fish are available. Largemouth bass become 

sexually mature between ages 2-5, with a lifespan reaching beyond 10 years. Adult largemouth 

bass weigh 0.25-3.6 kg, depending upon their age (Carlander 1977). For KABAM, it is 

assumed that the large fish weighs 1 kg, and consumes 100% medium-sized fish.
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Since small and medium fish consume benthic invertebrates, it is assumed that these fish are 

sometimes present in the benthic compartment of the aquatic ecosystem. Therefore, it is assumed 

that small and medium fish respire some pore water. It is assumed that medium fish are 

predominantly present in the water column of the aquatic ecosystem, where they are consumed 

by large fish. It is assumed that large fish do not respire pore water. This should be indicated in 

Table 5 of the KABAM tool (i.e., “yes” should be entered for small and medium fish and “no” 

should be entered for large fish in the column titled: “Do organisms in trophic level respire some 

pore water?”).

Available water composition data for Lepomis sp., Pomoxis sp., and Micropterus sp. include a 

range of 71-80% water (Table C18). Although water composition data were not available for 

largemouth bass, data do exist for smallmouth bass (M. dolomieu) and are used as a surrogate for 

largemouth bass. The average value of the available data is 73%. Based on this average, the 

default value for KABAM representing the % water of all fish is 73%.

Table C18. Water composition data for fish relevant to small, medium, and large default fish of KABAM. 

Species (scientific name) Reported Body 

Weight (kg) 

Corresponding 

Default fish % water Source 

Black crappie (Pomoxis 

nigromaculatus) 0.102 (±0.007) Medium 70.7 (±0.29) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) 0.277 (±0.0901) Medium-Large 71.1 (±1.26) Sethajintanin et al. 2004

Smallmouth bass 

(Micropterus dolomieu) 0.870 (±0.0685) Medium-Large 71.3 (±1.76) Sethajintanin et al. 2004

Smallmouth bass 

(Micropterus dolomieu) 0.326 (±0.177) Medium-Large 71.9 (±1.44) Sethajintanin et al. 2004

Black crappie (Pomoxis 

nigromaculatus) 0.148 (±0.021) Medium 71.9 (±0.84) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) 0.395 (±0.222) Medium-Large 72.0 (±0.99) Sethajintanin et al. 2004 

Black crappie (Pomoxis 

nigromaculatus) 0.114 (±0.016) Medium 72.1 (±0.87) Sethajintanin et al. 2004 

Black crappie (Pomoxis 

nigromaculatus) 0.111 (±0.015) Medium 72.6 (±0.38) Sethajintanin et al. 2004 

Black crappie (Pomoxis 

nigromaculatus) 0.0798 (±0.012) Medium 73.2 (±0.59) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) 0.154 (±0.0647) Medium 73.4 (±2.11) Sethajintanin et al. 2004 

Bluegill (L. macrochirus) Not reported unknown 79.5 Sidwell 1981 

Lipid content of fish reported in the literature varies widely for Lepomis sp., Pomoxis sp., and 

Micropterus sp. from 0.5-8% on a wet weight basis, with an average value of 2.9% and a 75th

percentile of 4.0% (Table C19). Table C19 includes lipid composition data for wild-caught and 

laboratory-reared Lepomis sp., Pomoxis sp., and Micropterus sp. Several lipid content values 

available in the literature cannot be related to the weights of the fish analyzed due to a lack of 

information included in the individual studies. Thus, these lipid contents cannot be related to 

one of KABAM’s default fish. Based on this and the data in Table C19, the default lipid 

composition for all three fish is set to 4%, to be representative of a high-end value. 
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Table C19. Lipid composition data for fish relevant to small, medium, and large default fish of KABAM. 

Species (scientific name) Reported Body 

Weight (kg) 

Corresponding 

Default fish 

% Lipid 

(wet weight) Source 

Green sunfish 

(Lepomis cyanellus) Not reported Unknown 0.5-2 Price and Birge 2006 

Bluegill (L. macrochirus) 0.012 (±0.0012) Small 0.72 (±0.46) Liber et al. 1999 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data) 

0.89 (±0.19)* Miranda and Hubbard 

1994 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data) 

0.95(±0.26)* Miranda and Hubbard 

1994 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data) 

0.97 (±0.18)* Miranda and Hubbard 

1994 

White crappie 

(Pomoxis annularis) Not Reported Assume medium (spawning fish) 1 Neuman and Murphy 1992 

Longear sunfish 

(L. megalotis) Not reported Unknown 1-2 Price and Birge 2006 

Bluegill (L. macrochirus) Not reported Unknown 1-3 Price and Birge 2006 

Largemouth bass 

(Micropterus salmoides) Not reported Unknown 1-5 Price and Birge 2006 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data)

1.3 (±0.29)* Miranda and Hubbard 

1994 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data)

1.3 (±0.24)* Miranda and Hubbard 

1994 

Largemouth bass 

(Micropterus salmoides) Not reported 

Small 

(defined based on 

length data) 

1.6 (±0.47)* Miranda and Hubbard 

1994 

Bluegill (L. macrochirus) Not reported 

(juveniles) Presume small 1.7* Fischer et al. 1998 

Bluegill (L. macrochirus) Not reported 

(adult males) Presume medium 1.8* Fischer et al. 1998 

Smallmouth bass 

(Micropterus dolomieu) Not reported Unknown 1.90 Kay et al. 2005 

White crappie (Pomoxis 

annularis) Not Reported Assume medium (spawning fish) 2 Neuman and Murphy 1992 

Bluegill (L. macrochirus) Not reported 

(adult females) Presume medium 2.1* Fischer et al. 1998 

Black crappie (Pomoxis 

nigromaculatus) 0.114 (±0.016) Medium 2.19 (±0.51) Sethajintanin et al. 2004 

Bluegill (L. macrochirus) Not reported Unknown 2.3 Sidwell 1981 

Black crappie 

(Pomoxis nigromaculatus) 0.111 (±0.015) Medium 2.54 (±1.85) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) Not reported Unknown 2.70 Kay et al. 2005 

Black crappie 

(Pomoxis nigromaculatus) 0.148 (±0.021) Medium 2.82 (±0.36) Sethajintanin et al. 2004 

White crappie 

(Pomoxis annularis) Not Reported Assume medium (spawning fish) 3 Neuman and Murphy 1992 

Smallmouth bass 

(Micropterus dolomieu) 0.395 (±0.222) Medium-Large 3.09 (±1.08) Sethajintanin et al. 2004 
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Black crappie 

(Pomoxis nigromaculatus) 0.0798 (±0.012) Medium 3.11 (±1.63) Sethajintanin et al. 2004 

Black crappie 

(Pomoxis nigromaculatus) 0.102 (±0.007) Medium 3.15 (±0.40) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) Not reported 

Medium-Large 

(defined based on 

length data) 

3.3 (±0.3) Kwon et al. 2006 

Smallmouth bass 

(Micropterus dolomieu) 0.154 (±0.0647) Medium 3.33 (±1.8) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) 0.326 (±0.177) Medium-Large 4.17 (±1.34) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) 0.870 (±0.0685) Medium-Large 4.93 (±0.33) Sethajintanin et al. 2004 

White crappie 

(Pomoxis annularis) Not Reported Assume medium (spawning fish) 5 Neuman and Murphy 1992 

White crappie 

(Pomoxis annularis) Not Reported Assume medium (spawning fish) 5 Neuman and Murphy 1992 

Smallmouth bass 

(Micropterus dolomieu) 0.277 (±0.0901) Medium-Large 5.03 (±0.358) Sethajintanin et al. 2004 

Smallmouth bass 

(Micropterus dolomieu) Not reported 

Medium-Large 

(defined based on 

length data) 

5.5 (±0.4) Kwon et al. 2006 

Smallmouth bass 

(Micropterus dolomieu) Not reported 

Medium-Large 

(defined based on 

length data) 

5.6 (±0.2) Kwon et al. 2006 

Smallmouth bass 

(Micropterus dolomieu) Not reported 

Medium-Large 

(defined based on 

length data) 

5.8 (±0.4) Kwon et al. 2006 

White crappie (Pomoxis 

annularis) Not Reported Assume medium (spawning fish) 6 Neuman and Murphy 1992 

Bluegill (L. macrochirus) 0.00972 (±0.00276) Small 7.9 (±0.14) Carr et al. 1997 

 

Average 2.9 

75th percentile 4.0 

90th percentile 5.5 

*Calculated from reported dry weight assuming that fish = 73% water (Table C18). 

The wet weight of an organism is the sum of the water, lipid, and NLOM content. By default, if 

the water content of fish is 73% of the wet weight, and the % lipid is known (default = 4%), the 

NLOM content of fish is the % remaining after subtracting the water and lipid content from 

100%. Therefore, the default parameter for NLOM composition is 23%. 
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Appendix D. Selection of Mammal Species of Concern and Corresponding Biological 

Parameters 

Mammal species of concern were defined for use as default species in KABAM. Mammals were 

considered to be of concern for pesticide exposures through aquatic bioaccumulation if their 

diets incorporated freshwater aquatic animals. Specific species were identified using a Field 

Guide to Mammals of North America (Reid 2006). This guide contains information on the 

ranges, taxonomy, habits, feeding preferences, and habitats of mammals located in the 

continental United States, Canada, and Alaska. 

A review of this source identified six species of mammals that consume aquatic animals. These 

include the American water shrew (Sorex palustris), the fog shrew (Sorex sonomae), the starnosed mole (Condylura cristata), the marsh rice rat (Oryzomys palustris), the American mink 

(Mustela vison), and the Northern river otter (Lontra canadensis). Additional references were 

sought to obtain data on the body weights and feeding preferences of these mammals. These 

species are used in KABAM to represent mammals of concern for risks of pesticide exposures 

through aquatic bioaccumulation. Descriptions of these species are provided below. Information 

from these species descriptions were used to define the default parameters used to represent 

mammals in the KABAM tool. 

D.1. Descriptions of Mammal Species 

D.1.1. American Water Shrew (Sorex palustris) 

The distribution of the American water shrew includes Canada, Alaska, and areas of the 

continental United States, including the West Coast, Rocky Mountains, Great Lakes, 

Appalachian, and New England areas. These shrews inhabit areas boarding fast and slow 

moving streams, marshes, creeks, and ponds. This species is primarily insectivorous, consuming 

aquatic invertebrates, such as stonefly nymphs, mayflies, caddisflies, and diptera. The American 

water shrew is also known to consume other animals, including fish, salamanders, leaches, and 

dead mice. Documented body weights range 0.008-0.018 kg, with males weighing more than 

females (Beneski and Stinson 1987). 

D.1.2. Fog Shrew (Sorex sonomae) 

Fog shrews inhabit parts of Oregon and California on the Pacific Coast in the “fog belt.” This 

species is found in marshes, near streams, and in forests. Their diet includes insects, earthworms, 

centipedes, slugs, snails, and amphibians. Their weight ranges 0.0055-0.015 kg (Reid 2006, 

Smithsonian 2008). 

D.1.3. Marsh Rice Rat (Oryzomys palustris) 

Marsh rice rats are distributed in states along the Gulf of Mexico and East Coast of the United 

States. This species inhabits wetlands, marshes, swamps, meadows, and areas along streams. Its 

diet includes insects, fiddler crabs, snails, fish, clams, arthropods, wetland plants, seeds, fungus, 
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baby turtles, bird eggs, and carrion (of mammals and birds). Their weight ranges 0.045-0.080 kg 

(Wolfe 1982). 

Six subspecies of O. palustris have been recognized (Wolfe 1982). One of these subspecies, 

Oryzomys palustris natator has been federally listed as endangered since 1991. This subspecies 

is known to occur in Florida and has a designated critical habitat (USFWS 1993). 

D.1.4. Star-nosed Mole (Condylura cristata) 

The star-nosed mole is distributed throughout the Eastern and Great Lakes regions of the United 

States and Canada. It inhabits marshy areas and streams. The diet of this species includes 

aquatic annelids, aquatic insects, small fish, mollusks, crustaceans, grubs, and earthworms. 

Reported body weights range 0.034-0.085 kg. The weights of these animals do not differ by sex 

but by location within their geographic distribution range, with smaller animals being observed 

in the southern parts of the range (e.g., Tennessee) (Petersen and Yates 1980, Reid 2006, 

Smithsonian 2008). 

D.1.5. American Mink (Mustela vison) 

The American mink is distributed throughout the United States and Canada, except in the dry 

areas of Arizona, Nevada, California, Utah, and Texas. Mink inhabit wetlands and marshes. 

Their diet is composed mostly of fish, amphibians (frogs), crustaceans (crayfish and crabs), 

muskrats, and other small mammals. They will also consume squirrels, birds, bird eggs, reptiles, 

aquatic insects, earthworms, and snails if given the opportunity. Individual body weights vary 

based on range and sex, with females weighing less than males. Documented body weights of 

this species range 0.45-1.8 kg (Larivière 1999, USEPA 1993). 

D.1.6. Northern River Otter (Lontra canadensis) 

The historical distribution of the Northern river otter includes most of the United States and 

Canada. The current distribution of this species in the United States includes states bordering the 

Gulf of Mexico and Great Lakes, the East Coast, New England, the West Coast and Alaska, as 

well as Canada (Larivière and Walton 1998, Reid 2006). Northern river otters inhabit lakes, 

swamps, marshes, streams, and ponds. The diet of this species is primarily fish, but also includes 

frogs, crayfish, small mollusks, reptiles, birds, and fruits. Body weights range 5-15 kg, with 

males weighing more than females (USEPA 1993, Larivière and Walton 1998). 

D.2. Determination of Mammalian Default Parameters for KABAM 

Tables 7 and 8 of the KABAM tool allow the user to identify six mammal species of concern, 

their body weights and their diets. For the purpose of KABAM, mammalian species of concern 

include those that consume aquatic animals. Based on the information above, relevant species in 

the United States include the American water shrew, the fog shrew, the star-nosed mole, the 

marsh rice rat, the American mink, and the Northern river otter. A detailed version (with specific 

mammals identified) of the conceptual model of the aquatic ecosystem depicted in Figure I of the 
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User’s Guide is provided in Figure D1. Default values representing the body weights and diets of 

these mammals are described below. 

Figure D1. Detailed conceptual model depicting aquatic food web, with mammals included. Arrows depict 

direction of trophic transfer of bioaccumulated pesticides from lower levels to higher levels of the food web. 

D.2.1. Identification of Default Body Weights for Mammalian Species 

Body weight and diet are the parameters that distinguish one mammalian species from another 

within KABAM. Two pairs of species have similar body weights and diets, such that they can be 

grouped together. These pairs are 1) the American water shrew and the fog shrew; and 2) the 

star-nosed mole and the marsh rice rat. The American mink and the northern river otter are 

Sediment

Benthic invertebrates:

Crayfish, chironomid 

larvae, mayfly larvae, 

snails 

Phytoplankton:

Algae, 

cyanobacteria, 

diatoms, 

dinoflagellates 

Zooplankton:

Cladocera, 

copepods, 

rotifers

Small fish: 

Young of 

the year, 

tadpoles 

Filter feeders:

Clams, mussels 

Medium fish:

Sunfish, bluegills, 

largemouth bass, 

frogs 

Large fish:

Largemouth bass 

Water column 

Northern 

River 

otter 

American 

mink

Rice rat, 

star-nosed 

mole

Fog and 

water 

shrews 
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sufficiently different in body weights to distinguish them as separate default species in the 

model. 

The selected body weight value influences the estimates of pesticide exposure through 

consumption of contaminated food items, as well as dose-adjusted toxicity values. Therefore, the 

magnitude of the body weight parameter has an effect on the magnitude of the RQ. Since higher 

body weight values result in higher dose-based RQs, the higher body weight values were 

selected to represent the four groups of mammals used in KABAM. In order to bound risk 

estimates for the two heaviest species of mammals (i.e., American mink and Northern river 

otter), default parameters are set to the minimum and maximum body weights. The following 

values are suggested for inclusion in Table 7 of the KABAM tool to represent mammals 1-6: 

Mammal # Name Body 

weight (kg) 

Mammal 1 Fog/Water Shrew 0.018 

Mammal 2 Rice Rat/Star-nosed mole 0.085 

Mammal 3 Small Mink 0.450 

Mammal 4 Large Mink 1.800 

Mammal 5 Small River Otter 5.000 

Mammal 6 Large River Otter 15.000 

D.2.2. Determination of Daily Food Intake 

If the weight of a food item (i.e., aquatic trophic level) is less than that of the amount of food 

consumed by the mammal in one day, then the food item is a reasonable assignment. In order to 

determine whether or not a particular trophic level is relevant to a mammal, the daily food intake 

is estimated. 

The dry food intake per day (Fdry, kg/day) for a mammal can be calculated according to Equation 

D1 (USEPA 1993). This value can be converted to represent food intake per day on a wet 

weight basis (Fwet, kg/day) by assuming that the diet of an organism is 75% water (Equation D2, 

see Appendix C for % water of aquatic organisms). 

0.822 Equation D1. Fdry = 0.0687 *BW

1 (% )

2. water of diet

F

Equation D F dry

wet − =

The resulting wet food intakes per day for the mammalian species of concern for KABAM are 

provided in Table D1. This table presents food intake per day for each species based on the low 

and high ranges of the body weights. These wet food intakes can be used to assign appropriate 

aquatic animals to the default diets of these mammals. 
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Table D1. Low- and high-end body weights and estimated food intake per day of mammals which consume 

aquatic animals. 

Species Body Weight 

(kg) 

Dry Food Intake 

per day (kg) 

Wet Food 

Intake per day 

(kg) 

Percent Body 

Weight 

Consumed Daily 

Shrew (Water and Fog) 0.006 0.001 0.004 67% 

Shrew (Water and Fog) 0.018 0.003 0.010 56% 

Rice Rat, Star-nosed mole 0.034 0.004 0.017 50% 

Rice Rat, Star-nosed mole 0.085 0.009 0.036 42% 

Mink 0.450 0.036 0.143 31% 

Mink 1.800 0.111 0.446 25% 

River Otter 5.000 0.258 1.032 20% 

River Otter 15.000 0.636 2.545 17% 

D.2.3. Definition of Default Diets of Mammals for use in KABAM 

Water/fog shrew 

The diets of the American water and fog shrews (see section D.1) include species that would be 

classified as benthic invertebrates (e.g., stonefly nymphs, mayflies, and snails) and fish (e.g., fish 

and amphibians) according to the trophic levels of KABAM. However, since these species are 

primarily insectivorous, the default diet is assigned as 100% benthic invertebrates. 

Based on the daily food intake for these two species (Table E.1), it is reasonable to assume that 

these shrews can consume organisms in the small fish category. If interested in the potential 

acute risk to water/fog shrews from pesticides through consumption of fish/amphibians, the 

model user could define the diet of these mammals as 100% small fish. Since this represents a 

higher trophic level in the aquatic ecosystem than the benthic invertebrates, this assumption may 

result in a higher RQ. 

 

Rice rat/star-nosed mole 

The diets of the rice rat and the star-nosed mole (see section D.1) include species that would be 

classified as benthic invertebrates (e.g., arthropods, snails), filter feeders (e.g., clams) and fish 

according to the trophic levels of KABAM. Based on the daily food intake for these two species 

(Table E.1), it is reasonable to assume that individuals of these species could consume organisms 

in the small fish category. Since no data are available to define feeding preferences of these 

two species, for the purpose of KABAM, the default diet composition of these mammals is 

equally distributed among these three trophic levels (i.e., 34% benthic invertebrates, 33% 

filter feeders, and 33% small fish). 

American mink 

The diet of the American mink (see section D.1) includes species that would be classified as 

benthic invertebrates (e.g., crayfish) and small/medium-sized fish according to the trophic levels 

of KABAM. Based on the daily food intake for this species (Table E.1), it is reasonable to 
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assume that these mammals could consume organisms in the small and medium fish category. 

The default diet for this mammal is 100% medium fish. 

Northern river otter 

The diet of the Northern river otter (see section D.1) is primarily fish, but also includes species 

that would be classified as benthic invertebrates (e.g., crayfish) according to the trophic levels of 

KABAM. Based on the daily food intake for this species (Table D.1), it is reasonable to assume 

that these mammals may consume organisms in the small, medium, and large fish categories. 

According to USEPA 1993, river otters have been documented as including various fish that 

would be classified in different trophic levels of KABAM, including sunfish and bass. 

Therefore, the default diet for this mammal is 100% medium fish for the small otter and 

100% large fish for the large otter. 
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Appendix E. Selection of Bird Species of Concern and Corresponding Biological 

Parameters 

Bird species of concern were identified in order to define default parameters (for body weight 

and diet composition) to represent birds in KABAM. Bird species were considered to be of 

concern for pesticide exposures through aquatic bioaccumulation if their diets incorporated 

freshwater aquatic animals. Specific species were identified using the Smithsonian handbooks’ 

Birds of North America (Eastern and Western Regions) (Alsop 2001a and 2001b). These 

handbooks contain information on the ranges, taxonomy, habits, feeding preferences, and 

habitats of birds located in the continental United States, Canada, and Alaska. 

A review of this source identified over 40 bird species of concern that fall into 11 families. These 

families include: Accipitridae (eagles, hawks and kites), Alcedinidae (kingfisher), Anatidae 

(ducks), Ardeidae (herons, egrets and bitterns), Gruidae (cranes), Pelecanidae (pelicans), 

Phalacrocoracidae (cormorants), Podicipedidae (grebes), Rallidae (rails), Scolopacidae 

(sandpiper) and Threskiornithidae (ibis). Descriptions of these families are provided below. 

It should be noted that this review was not intended to be inclusive of every relevant species or 

family of birds inhabiting North America. Rather, the intention of this review was to identify 

birds that may consume aquatic animals containing pesticides that bioaccumulate in aquatic 

ecosystems. Information from identified bird species and families was used to define the default 

parameters representing birds in the KABAM tool. These default parameters are described 

below. 

E.1. Bird Family Descriptions 

E.1.1. Accipitridae (Eagles, Hawks and Kites) 

Most species of this family prey upon terrestrial rodents; however, several rely upon aquatic 

animals for their diet (Table E1). These species include the osprey (Pandion haliaetus) and the 

bald eagle (Haliaeetus leucocephalus) (Alsop 2001a and 2001b). Ospreys fly over freshwater 

and saltwater areas and catch fish from the surface of the water using their feet. Body weights of 

osprey range from 1.25 to 2.0 kg (USEPA 1993). Bald eagles eat fish, rodents, birds, and 

carrion. Body weights of adult bald eagles range 3.0 – 5.8 kg (USEPA 1993). An additional 

member of this family, the snail kite (Rostrhamus sociabilis), has a subspecies that is federally 

listed as endangered (USFWS 2008). This species is known to occur in wetlands of Florida, 

where the bird eats snails (Alsop 2001a). The average body weight of this bird is 0.38 kg 

(Dunning 1984). 

Table E1. Body weights and diets of species of Accipitridae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg) Diet 

Snail kite (Rostrhamus sociabilis) 0.381 snails2

Osprey (Pandion haliaetus) 1.25-2.003 fish3

Bald eagle (Haliaeetus leucocephalus) 3.00 – 5.803 fish, rodents, birds, and carrion3

1

Dunning 1984; 2Alsop 2001a and 2001b; 3USEPA 1993 
98 of 123 

E.1.2. Alcedinidae (kingfisher) 

One species of this family, the belted kingfisher (Ceryle alcyon) is widely distributed throughout 

North America, inhabiting freshwater areas such as lakes, rivers, and ponds, as well as marine 

coastal areas. This species feeds primarily upon fish, but its diet also includes amphibians, 

insects, and crayfish. Body weights of this species range 0.13-0.22 kg (USEPA 1993; Table E2). 

Table E2. Body weights and diets of species of Alcedinidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg) Diet 

Belted kingfisher (Ceryle alcyon) 0.13-0.221 primarily fish, but also amphibians, insects and 

crayfish1

1

USEPA 1993 

E.1.3. Anatidae (Ducks) 

There are many species of ducks that are widely distributed in North America (Table E3). Ducks 

predominantly inhabit freshwater areas such as lakes, rivers, wetlands, and ponds. Their diets 

include a wide variety of aquatic organisms, such as aquatic insects, insect larvae, snails, 

amphibians, fish, crayfish, mollusks, plankton, and aquatic plants (Alsop 2001a and 2001b). 

Body weights of ducks vary based on the species, with a range of 0.3-2.0 kg for ducks inhabiting 

freshwater areas (Dunning 1984). 

Table E3. Body weights and diets of species of Anatidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Cinnamon teal (Anas cyanoptera) 0.36-0.41 seeds, aquatic insects, rice, algae, snails, 

crustaceans 

Bufflehead (Bucephala alboela) 0.30-0.55 aquatic insects and insect larvae, snails, small 

fish, seeds 

Wood duck (Aix sponsa) 0.64-0.91 plants, animals, snails, tadpoles, salamanders 

Hooded merganser (Lophodytes 

cuculatus) 0.54-0.91 fish, crustaceans, aquatic insects, aquatic animals 

Lesser scaup (Aythya affinis) 0.54-1.05 plants and animals 

Common goldeneye 

(Bucephala clangula) 0.80-1.40 mollusks, crustaceans, insects, aquatic plants 

Mallard (Anas platyrhynchos) 0.72-1.58 plants, insects, mollusks, crustaceans 

Red-breasted merganser (Mergus 

serrator) 0.91-1.31 fish 

Common merganser (Mergus 

merganser) 1.05-2.05 small fish, mollusks, crustaceans, aquatic insects, some plants 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.4. Ardeidae (Herons, Egrets and Bitterns) 

This family includes species of herons, bitterns and egrets, several of which inhabit waters of 

North America (Table E4). Their habitats include freshwater areas such as lakes, rivers, ponds, 

wetlands, and streams, as well as marine coastal areas. These birds wade through water to spear 
99 of 123 

their food with their beaks. Their diets include fish, crustaceans, amphibians, snakes, crayfish, 

and insects (Alsop 2001a and 2001b, USEPA 1993). Individuals of this family range in weight 

from 0.08 to 2.9 kg, depending upon the species (Dunning 1984, USEPA 1993). 

Table E4. Body weights and diets of species of Ardeidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Least bittern (Ixobrychus exilis) 0.08-0.09 fish, insects 

Green heron (Butorides virescens) 0.212 fish, aquatic invertebrates 

Snowy egret (Egretta thula) 0.35-0.40 crustaceans, insects, fish 

Little blue heron (Egretta caerulea) 0.32-0.45 small vertebrates, crustaceans, large insects 

American bittern (Botaurus 

lentiginosus) 0.52-1.07 

frogs, small eels, small fish, snakes, 

salamanders, crayfish, small rodents, water 

bugs 

Yellow-crowned night heron 

(Nyctanassa violacea) 0.72-0.85 crustaceans, fish, shellfish 

Black crowned night heron (Nycticorax 

nycticorax) 0.73-1.01 fish, mollusks, small rodents, frogs, snakes, crustaceans, plants, eggs, birds 

Great egret (Ardea alba) 0.80-1.07 fish, frogs, snakes, crayfish, large insects 

Great blue heron (Ardea herodias) 1.87-2.88 fish, other aquatic animals 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.5. Gruidae (Cranes) 

Cranes inhabit freshwater wetlands and marshes. These birds eat fish, frogs, small mammals, 

mollusks, crustaceans, and plants (Alsop 2001a and 2001b). Two species of cranes, i.e., the 

whooping crane (Grus americana) and the Mississippi sandhill crane (Grus canadensis pulla), 

are federally listed as endangered (USFWS 2008). Body weights of the whooping crane and 

sandhill crane range 2.5-6.7 kg (Dunning 1984) (Table E5). 

Table E5. Body weights and diets of species of Gruidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Whooping crane (Grus americana) 5.44-6.36 fish, frogs, small mammals, mollusks, 

crustaceans, and plants 

Sandhill crane (Grus canadensis) 2.45-6.70 plants and animals 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.6. Pelecanidae (Pelicans) 

There is one species of pelican that inhabits freshwater aquatic habitats of North America: the 

American white pelican (Pelecanus erythrorhynchos). Their habitats include freshwater areas 

such as lakes, rivers, ponds, wetlands and streams, as well as marine coastal areas. The diet of 

these birds includes fish (Alsop 2001a and 2001b). The average weight of the American white 

pelican is 7.5 kg (Dunning 1984) (Table E6). 
100 of 123 

Table E6. Body weights and diets of species of Pelecanidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg) Diet 

American white pelican (Pelecanus 

erythrorhynchos) 7.51 fish2

1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.7. Phalacrocoracidae (Cormorants) 

Of the species of cormorants inhabiting North America, the double-breasted cormorant 

(Phalacrocorax auritus) is the most widespread, inhabiting freshwater areas such as lakes, rivers, 

ponds, as well as marine coastal areas. Cormorants dive for their prey, which includes fish, 

crustaceans, and amphibians (Alsop 2001a and 2001b). The average weight of the doublebreasted cormorant is 1.8 kg (Dunning 1984). 

Table E7. Body weights and diets of species of Phalacrocoracidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg) Diet 

Double-breasted cormorant 

(Phalacrocorax auritus) 1.60-2.041 fish, crustaceans and amphibians2

1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.8. Podicipedidae (Grebes) 

Several species of grebes reside in the continental United States (Table E8). Their habitats 

include freshwater areas such as lakes, rivers, ponds, wetlands, and streams, as well as marine 

areas. These birds forage for aquatic insects, crustaceans, and fish by diving underwater (Alsop 

2001a and 2001b). They range in weight 0.2-1.8 kg (Alsop 2001a and 2001b, Dunning 1984). 

Table E8. Body weights and diets of species of Podicipedidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Eared grebe (Podiceps nigricollis) 0.22-0.37 aquatic insects 

Pied-billed grebe (Podilymbus 

podiceps)

0.34-0.55 aquatic insects, small fish, crustaceans 

Horned grebe (Podiceps auritus) 0.33-0.53 fish, crustaceans, aquatic insects 

Western grebe (Aechmorphorus 

occidentalis)

0.80-1.82 fish 

Clark's grebe (Aechmophorus clarkia) 1.502 fish 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.9. Rallidae (Rails) 

Rail species inhabit freshwater areas such as lakes, rivers, ponds, wetlands and streams as well as 

saltwater marshes of North America. These species feed upon crustaceans, aquatic insects, 

snails, fish, and plants (Alsop 2001a and 2001b). Individuals of this family range in weight from 

0.07 to 0.49 kg (Dunning 1984) (Table E9). One species from this family, the clapper rail (Rallus 

longirostris) is federally listed as an endangered species and is known to occur in Arizona, 

California, Nevada, and Utah (USFWS 2008). 
101 of 123 

Table E9. Body weights and diets of species of Rallidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Sora (Porzana carolina) 0.08 plants, insects, spiders, small crustaceans, 

snails 

Virginia rail (Rallus limicola) 0.07-0.12 insects (primarily), worms, crustaceans, small 

fish 

King rail (Rallus elegans) 0.25-0.49 plants, aquatic invertebrates, aquatic 

vertebrates 

Clapper rail (Rallus longirostris) 0.25-0.35 crabs, crustaceans, worms, amphibians, 

reptiles, mollusks, small fish, aquatic insects 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.10. Scolopacidae (Sandpipers) 

Many species of sandpipers inhabit freshwater aquatic habitats of North America (Table E10). 

These habitats include lakes, rivers, ponds, wetlands, and streams. Their diets include aquatic 

invertebrates, insects, crustaceans, small fish, amphibians, and mollusks (Alsop 2001a and 

2001b). Body weights of sandpipers range 0.02- 0.70 kg (Dunning 1984). 

Table E10. Body weights and diets of species of Scolopacidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

Least sandpiper (Calidris minutilla) 0.022 insects and larvae, crustaceans 

Spotted sandpiper (Actitis macularia) 0.03-0.06 invertebrates, small fish 

Wilson’s phalarope (Phalaropus 

tricolor)

0.072 larvae, crustaceans, seeds 

Greater yellow legs (Tringa 

melanoleca)

0.12-0.22 small fish, insects and larvae, crabs, snails 

Willet (Catoptrophorus semipalmatus) 0.22 aquatic insects, mollusks, small fish 

Long-billed curlew (Numenius 

americanus)

0.57-0.70 aquatic insects, larvae, mollusks, crustaceans, 

small amphibians 1

Dunning 1984; 2Alsop 2001a and 2001b 

E.1.11. Threskiornithidae (Ibis) 

Ibis inhabit freshwater areas such as lakes, rivers, ponds, wetlands, and streams, as well as 

marine coastal areas of North America. These species are wading birds that feed upon crayfish, 

aquatic invertebrates, fish, and frogs (Alsop 2001a and 2001b). Individuals of this family range 

in weight from 0.4 to 1.3 kg (Dunning 1984) (Table E11). 

Table E11. Body weights and diets of species of Threskiornithidae that prey upon aquatic animals. 

Species (scientific name) Body 

weight (kg)1 Diet2

White-faced ibis (Plegadis chihi) 0.43-0.81 crayfish, aquatic invertebrates, fish, frogs 

White ibis (Eudocimus albus) 0.59-1.28 not stated 1

Dunning 1984; 2Alsop 2001a and 2001b 
102 of 123 

E.2. Detailed Conceptual Model 

A detailed version of the conceptual model of the aquatic ecosystem depicted in Figure I of the 

User’s Guide, with specific birds identified, is provided in Figure E1. 

Figure E1. Detailed conceptual model depicting aquatic food web of KABAM. Arrows depict direction of 

trophic transfer of bioaccumulated pesticides from lower levels to higher levels of the food web. 

Sediment

Benthic invertebrates:

Crayfish, chironomid 

larvae, mayfly larvae, 

snails 

Phytoplankton:

Algae, 

cyanobacteria, 

diatoms, 

dinoflagellates

Zooplankton:

Cladocera, 

copepods, 

rotifers

Small fish: 

Young of the 

year, tadpoles

Filter feeders:

Clams, mussels

Medium fish:

Sunfish, bluegills, 

largemouth bass, 

frogs 

Large fish:

Largemouth bass 

Water column

Belted 

kingfisher, 

cormorant, 

grebes, ibis, 

herons, rails 

Ducks, 

cranes, 

sandpipers 

Bald eagle, 

osprey, 

pelican 
103 of 123 

E.3. Determination of Daily Food Intake 

If the weight of a food item (i.e., aquatic trophic level) is less than that of the amount of food 

consumed by the bird in one day, then the food item is a reasonable assignment. In order to 

determine whether or not a particular trophic level is relevant to a bird, the daily food intake is 

estimated. 

The dry food intake per day (Fdry, kg/day) for a bird can be calculated according to Equation E1 

(USEPA 1993). This value can be converted to represent food intake per day on a wet weight 

basis (Fwet, kg/day) by assuming that the diet of an organism is 75% water (Equation E2, see 

Appendix C for % water of aquatic organisms). 

0.651 Equation E1. Fdry = 0.0582*BW

1 (% )

2. water of diet

F

Equation E F dry

wet − =

Of the bird families described above, body weights range 0.02-7.5 kg. The resulting wet food 

intakes per day for birds of concern for KABAM are provided in Table E12. This table presents 

food intake values per day for each species based on body weight. These wet food intakes can be 

used to assign appropriate aquatic animals to the default diets of these birds. 

Table E12. Body weights representative of birds that consume aquatic animals and corresponding daily dry 

and wet food intakes. 

Family or species Body weight 

range (kg) 

Dry Food Intake 

per day (kg) 

Wet Food Intake per 

day (kg) 

Sandpipers 0.02- 0.70 0.005-0.046 0.018-0.185 

ducks 0.30-2.00 0.027-0.091 0.106-0.366 

cranes 2.45-6.70 0.104-0.201 0.417-0.803 

belted kingfisher 0.13-0.22 0.015-0.022 0.062-0.087 

rails 0.07-0.49 0.010-0.037 0.041-0.146 

ibis 0.43-1.28 0.034-0.068 0.134-0.273 

grebes 0.22-1.82 0.022-0.086 0.087-0.344 

Double-breasted cormorant 1.8 0.085 0.341 

Bitterns, egrets, herons 0.08-2.90 0.011-0.116 0.045-0.466 

osprey 1.25-2.00 0.067-0.091 0.269-0.366 

Bald eagle 3.00 – 5.80 0.119-0.183 0.476-0.731 

white pelican 7.5 0.216 0.864 
104 of 123 

E.4. Definition of Default Parameters to Represent Birds in KABAM 

Based on the species descriptions above, birds can be divided into three groups based on their 

diets. The three diets include: 1) filter feeders, benthic invertebrates and fish, 2) benthic 

invertebrates and fish and 3) fish. (Table E13). These three diets were used to define the default 

parameters representing birds in KABAM (Table E14), which are described below. 

Table E13. Summary of diets and body weights of families of birds defined as consuming aquatic animals. 

Diet Family or species Body weight range (kg) 

Sandpipers 0.02- 0.70 

Ducks 0.30-2.00 Filter feeders, benthic invertebrates, 

fish Cranes 2.45-6.70 

Belted kingfisher 0.13-0.22 

Rails 0.07-0.49 

Ibis 0.43-1.28 

Grebes 0.22-1.82 

Double-breasted cormorant 1.80 

Benthic invertebrates and fish 

Bitterns, egrets, herons 0.08-2.90 

Osprey 1.25-2.00 

Fish Bald eagle 3.00 – 5.80 

White pelican 7.50 

Table E14. Default body weights and diet parameters for use in KABAM to represent birds. 

Bird 

# Bird Name Relevant Families/species 

Default 

weight 

(kg) 

Default diet 

1 Sandpipers Sandpipers, ducks, cranes 0.02 

33% benthic invertebrates 

33% filter feeders 

34% small fish 

2 Cranes Sandpipers, ducks, cranes 6.70 

33% benthic invertebrates 

33% filter feeders 

34% medium fish 

3 Rails 

Belted kingfisher, rails, ibis, 

grebes, double-breasted 

cormorant, bitterns, egrets, herons 

0.07 50% benthic invertebrates 

50% small fish 

4 Herons 

Belted kingfisher, rails, ibis, 

grebes, double-breasted 

cormorant, bitterns, egrets, herons 

2.90 50% benthic invertebrates 

50% medium fish 

5 Small Osprey Osprey, bald eagle, white pelican 1.25 100% medium fish 

6 White pelican Osprey, bald eagle, white pelican 7.50 100% large fish 
105 of 123 

E.4.1. Birds Consuming Benthic Invertebrates, Filter Feeders, and Fish 

Because sandpipers, ducks, and cranes share similar diets (i.e., benthic invertebrates, filter 

feeders, and fish), they are considered as a group for defining input parameters for KABAM. 

Two of the default birds in KABAM (# 1 and 2) represent birds with a similar diet. 

Comparison of the daily wet food consumption for sandpipers (Table E12) to the weight of small 

and medium fish in KABAM (0.01 and 0.1 kg, respectively) indicates that not all of these 

species would be expected to consume medium-sized fish. Therefore, it is assumed that 

sandpipers consume small fish. All species of cranes are expected to be able to consume a 

medium-sized (0.1 kg) fish in one day. Therefore, it is assumed that the diet of cranes is 

composed of medium-sized fish. Since the relative proportion of benthic invertebrates, filter 

feeders and fish within the diets of these species is unknown, it is assumed that these prey items 

compose an equal share of the diet of these birds. 

The 1st default bird in KABAM has a diet of 33% benthic invertebrates, 33% filter feeders and 

34% small fish. This bird is intended to represent the low end of birds that consume benthic 

invertebrates, filter feeders, and small fish. Therefore, the default body weight of 0.02 kg was 

selected because it is consistent with the lowest body weight of birds that have this diet (Table 

E13). 

The 2nd default bird in KABAM has a diet of 33% benthic invertebrates, 33% filter feeders, and 

34% medium fish. This bird is intended to represent the high end of birds that consume benthic 

invertebrates, filter feeders, and medium-sized fish. Therefore, the default body weight of 6.7 kg 

was selected (Table E14). 

It should be noted that pesticide EECs and subsequent RQs for sandpipers, ducks, and cranes are 

bound by KABAM’s default birds 1 and 2. RQs for these two default birds are intended to 

represent birds with similar size and feeding habits as sandpipers, ducks, and cranes. These EECs 

and RQs can be refined by the model user to represent a specific bird species by entering specific 

body weights of individual species of concern and the appropriate species composition of their 

diet. 

E.4.2. Birds Consuming Benthic Invertebrates and Fish 

Because belted kingfisher, rails, ibis, grebes, double-breasted cormorants, bitterns, egrets, and 

herons share similar diets (i.e., benthic invertebrates and fish), they are considered as a group for 

defining input parameters for KABAM. Two of the default birds in KABAM (# 3 and 4) 

represent birds with a similar diet. 

Comparison of the daily wet food consumption for small rails, small grebes, and the belted 

kingfisher (Table E12) to the weight of small and medium-sized fish in KABAM (0.01 and 0.1 

kg, respectively) indicates that not all of these species would be expected to consume medium 

fish. Therefore, it is assumed that some of these species consume small fish. Species of rails, 

ibis, grebes, bitterns, egrets, herons and the double-breasted cormorant are expected to be able to 

consume a 0.1 kg fish per day. Therefore, it is assumed that the diet of these species is composed 
106 of 123 

of medium-sized fish. Since the relative proportion of benthic invertebrates and fish within the 

diets of these species is unknown, it is assumed that these prey items compose an equal share of 

the diet of these birds. 

The 3rd default bird in KABAM has a diet of 50% benthic invertebrates and 50% small fish. 

This bird is intended to represent the low end of birds that consume benthic invertebrates and 

small-sized fish. Therefore, the default body weight of 0.07 kg was selected because it is 

consistent with the lowest body weight of birds that have this diet (Table E13). 

The 4th default bird in KABAM has a diet of 50% benthic invertebrates and 50% medium fish. 

This bird is intended to represent the high end of birds that consume benthic invertebrates and 

medium-sized fish. Therefore, the default body weight of 2.9 kg was selected (Table E14). 

It should be noted that pesticide EECs and subsequent RQs for belted kingfisher, rails, ibis, 

grebes, double-breasted cormorants, bitterns, egrets, and herons are bounded for KABAM’s 

default birds 3 and 4. RQs for these two default birds are intended to represent birds with similar 

sizes and feeding habits. These EECs and RQs can be refined for specific bird species by 

entering specific body weights of individual species of concern and entering the appropriate diet. 

E.4.3. Birds Consuming Fish 

Because osprey, bald eagles, and white pelicans share similar diets (i.e., fish), they are 

considered as a group for defining input parameters for KABAM. Two of the default birds in 

KABAM (# 5 and 6) represent birds with a similar diet. 

Comparison of the daily wet food consumption for the lower end body weight (1.25 kg) of these 

birds to the weight of medium and large fish in KABAM (0.1 and 1.0 kg, respectively) indicates 

that the lower weight individuals of these bird species are able to consume medium fish, but 

unlikely to consume large fish. Therefore, it is assumed that the diet of default bird #5 (named 

osprey), can be represented by 100% medium-sized fish. Comparison of the daily wet food 

consumption (0.86 kg/day) for the higher end body weight (7.5 kg) of these birds to the weight 

of large fish in KABAM (1.0 kg) indicates that the higher weight individuals of these bird 

species are likely to consume large fish. Therefore, it is assumed that the diet of default bird #6 

(named white pelican), can be represented by 100% large-sized fish. 

In order to bound EECs and RQs for these three birds, the lowest and highest body weights were 

selected to represent KABAM’s default birds 5 and 6, respectively, in KABAM. These EECs 

and RQs can be refined for specific bird species by entering specific body weights of individual 

species of concern. 
107 of 123 

 

Appendix F. Description of Equations Used to Calculate the BCF, BAF, BMF, and BSAF 

Values 

Bioconcentration, bioaccumulation, and biomagnification factors are calculated in the “results” 

worksheet of the KABAM tool using data from the “parameters & calculations” worksheet. The 

equations for these calculations are described below. 

F.1. Bioconcentration 

Bioconcentration is a measure of the amount of pesticide residue in an organism’s tissue relative 

to the concentration in the organism’s environment (USEPA 2008c). This includes pesticide 

uptake through respiration and contact, not through dietary sources. Bioconcentration factors 

(BCFs) are calculated by considering pesticide tissue concentrations with respect to 

environmental pesticide concentrations. BCF values >1 indicate that the concentration in the 

organism is greater than that of the medium (e.g., soil or water) from which the pesticide was 

taken. BCFs can be calculated on a total organism basis or normalized to the lipid content of the 

organism. 

KABAM calculates the total (body weight) BCFs of a chemical for each aquatic organism 

according to Equation F1 (USEPA 2003). CBCF is calculated using equation A1 (see Table A.1 

of Appendix A for a full description) where CB = CBCF, when kD = kE = kM = kG = 0. The units of 

total BCF values are expressed as: (μg pesticide/kg wet weight)/(μg pesticide/L water). Total 

BCF values account for the total amount of the pesticide in the water (i.e., CWTO). 

 

WTO

BCF

C

C Equation F1 . Total BCF =

E G M

WTO P WDP D i Di

B k k k k

k m C m C k P C Eq A C

+ + +

Φ + + Σ =

2

1 0

*( * * * ) * ( * ) . 1

KABAM also calculates the lipid-normalized BCFs of a chemical for each aquatic organism 

according to Equation F2 (USEPA 2003). The units of lipid normalized BCF values are 

expressed as: (μg pesticide/kg lipid)/(μg pesticide/L water). VLB represents the fraction of lipid 

in the body of the organism for which the BCF is being derived. Lipid normalized BCF values 

account for the pesticide concentration that is freely dissolved in the water (i.e., CWTO*Φ). 

Φ

⎟

⎠

⎞ ⎜

⎝

⎛

= *2.

WTO

LB

BCF

C

V

C

Equation F Lipid normalized BCF
108 of 123 

F.2. Bioaccumulation 

Bioaccumulation is the net uptake of a pesticide from the environment by all possible routes 

(e.g., respiration, diet, dermal) from any source (e.g., water, sediment, and other organisms) 

(Spacie et al. 1995). Bioaccumulation factors (BAF) are calculated by considering pesticide 

tissue concentrations with respect to environmental pesticide concentrations. BAF values >1 

indicate that the accumulation in the organism is greater than that of the medium (e.g., soil or 

water) from which the pesticide was taken. These factors can be calculated on a total organism 

basis or normalized to the lipid content of the organism. 

KABAM calculates the total BAFs of a chemical for each aquatic organism according to 

Equation F3 (USEPA 2003). The units of total BAF values are expressed as: (μg pesticide/kg 

wet weight)/(μg pesticide/L water). CB is calculated according to Equation A1. Total BAF 

values account for the total amount of the pesticide in the water (i.e., CWTO). 

WTO

B

C

C Equation F3 . Total BAF =

Lipid-normalized BAFs of a chemical are calculated for each aquatic organism according to 

Equation F4 (USEPA 2003). The units of lipid normalized BAF values are expressed as: (μg 

pesticide/kg lipid)/(μg pesticide/L water). The variable CB is calculated according to Equation 

A1. The variable VLB represents the fraction of lipid in the body of the organism for which the 

BCF is being derived. Lipid normalized BAF values account for the pesticide concentration that 

is freely dissolved in the water (i.e., CWTO*Φ). 

Φ

⎟

⎠

⎞ ⎜

⎝

⎛

= *4.

WTO

LB

B

C

V

C

Equation F Lipid normalized BAF

Accumulation factors are also derived by considering pesticide tissue concentrations with respect 

to pesticide concentrations in sediment. Biota-sediment accumulation factors (BSAFs) are 

calculated by dividing the lipid normalized concentration of a chemical in an organism by the 

chemical concentration in the sediment (dry weight), normalized to the organic carbon content of 

the sediment (Equation F5) (USEPA 2003). The variable CSOC represents the pesticide 

concentration in the sediment, normalized to the organic carbon content of the sediment (units of 

g/kg OC). 

 

SOC

LB

B

C

V

C

Equation F BSAF

⎟

⎠

⎞ ⎜

⎝

⎛

5 . =
109 of 123 

F.3. Biomagnification 

Biomagnification is the increase of a pesticide concentration in the tissue of an organism 

compared to the tissue concentrations of its prey (USEPA 2008b). Biomagnification factors 

(BMFs) are calculated by considering lipid normalized pesticide tissue concentrations within an 

organism with respect to the lipid normalized concentrations of that pesticide in the prey of the 

organism. Factors >1 indicate the occurrence of biomagnification. 

KABAM calculates the BMFs of a chemical for each aquatic organism according to Equation 

F6 (USEPA 2003). The units of BMF values are expressed as: (μg pesticide/kg lipid)/(μg 

pesticide/kg lipid). The variable CB is calculated according to Equation A1. VLB represents the 

fraction of lipid in the body of the organism for which the BMF is being derived. Pi represents 

the fraction of diet containing prey item i. CDi represents the concentration of the pesticide in 

prey item i and VLBi represents the fraction of lipid in the body of the prey item i. It should be 

noted that although KABAM allows aquatic organisms to consume sediment, uptake of pesticide 

through consumption of sediment is not considered in the calculation of BMFs in the model tool. 

∑ ⎟

⎠

⎞ ⎜

⎝

⎛

⎟

⎠

⎞ ⎜

⎝

⎛

=

LBi

i Di

LB

B

V

P C

V

C

Equation F BMF

*

6.
110 of 123 

Appendix G. Description of Equations Used to Calculate Dietary-Based and Dose-Based 

EECs, Toxicity Values, and RQs for Mammals and Birds Consuming Contaminated 

Aquatic Organisms 

Exposures of birds and mammals to pesticides accumulated in tissues of aquatic organisms are 

calculated by the KABAM tool. Relevant toxicity data are also calculated by KABAM based on 

input data from toxicity studies for birds and mammals. The equations used to estimate exposure 

and to adjust toxicity values and to calculate RQs depicted in Tables 14-15 of the KABAM tool 

are described below. 

G.1. Food Ingestion Rates 

Dry food ingestion rates (FIdry) are estimated for mammals and birds using allometric equations 

that relate food intake with body weight (Equations G1 and G2, respectively). FI is calculated 

in kg dry food/kg-bw day and BW is animal body weight in kg. 

Food intake (FI) values are converted from food dry weight/kg-bw day to food wet weight/day 

using the wet weight of the assigned diet of each mammal and bird (Equation G3). The variable 

Pi represents the fraction of diet of the mammal or bird containing prey item i (an aquatic 

organism). The variable VWBi represents the fraction of water in the body of the prey item i. 

−∑( ) =

i WBi

dry

wet P V

FI

Equation G FI

1 *

3 .

G.2. Drinking Water Intake Rates 

Drinking water intake rates (DW) for mammals and birds are calculated based on Equations G4 

and G5 (USEPA 1993); where BW represents the body weight (in kg) of the animal for which 

the drinking water intake is being assessed. Resulting units of DW are L/day. 

4. (0.099* ) ( ) 0.90 Equation G DW = BW mammals

5. (0.059 * ) ( ) 0.67 Equation G DW = BW birds

( ) 0.0582*2.

0.651

birds

BW

BW Equation G FI dry =

( ) 0.0687 *1.

0.822

mammals

BW

BW Equation G FI dry =
111 of 123 

G.3. Dose-based EECs 

Dose-based EECs are estimated assuming that pesticide intake is a function of the amount of 

pesticide contained in the food and drinking water of an animal. The dose-based EEC is derived 

according to Equation G6. In this equation, pesticide intake through food is calculated as the 

sum of the products of the fraction of each prey item in the diet (Pi) and the pesticide tissue 

residue concentration for each prey item (CBi; μg/kg-ww). The sum of the pesticide residues 

ingested through food is converted into units of mg pesticide/kg food. This value is then 

multiplied by the intake rate for wet food (kg food/kg-bw day). The resulting value is in units of 

mg pesticide/kg-bw day. Pesticide intake through drinking water is calculated by multiplying the 

concentration of the pesticide in water (CWTO, mg/L) by the water intake (DW in units of L/d) 

and dividing by the bodyweight of the mammal or bird of concern. This results in units of mg 

pesticide/kg-bw day. The sum of pesticide intake through diet and through drinking water is the 

dose-based EEC. 

G.4. Dietary-based EECs 

Dietary-based EECs are estimated assuming that pesticide intake is a function of the amount of 

pesticide contained in the food of an animal. This differs from the dose-based EECs in that 

pesticide exposure through drinking water is not considered. In addition, the dietary-based 

exposure value is not adjusted for the relative amount of food consumed per day by animals of 

different sizes. The dietary-based EEC is derived according to Equation G7. In this equation, 

the pesticide intake through food is calculated as the sum of the products of the fraction of each 

prey item in the diet (Pi) and the pesticide tissue residue concentration for each prey item (CBi; 

μg/kg-ww). 

G.5. Adjusted Dose-based Toxicity Values 

Available dose-based toxicity values are adjusted for the weights of the animal tested (e.g.,

laboratory rat, mallard duck) and of the animal for which the risks are being assessed (e.g., mink, 

bald eagle). These adjustments are made for mammals and birds according to Equations G8 and 

G9, respectively (USEPA 2006). In these equations, AT = adjusted toxicity value; LD50 or 

NOAEL = endpoint reported by toxicity study; TW = body weight of tested animal (350g rat; 

1580g mallard, 178 g Northern bobwhite quail or weight defined by the model user for an 

alternative species); AW = body weight of assessed animal; x = Mineau scaling factor. 

Chemical specific values for x may be located in Mineau et al. 1996). If no chemical specific 

data are available, the default value of 1.15 should be used for this parameter. Methods for 

adjusting toxicity values are consistent with those used by T-REX (USEPA 2008a). 

( ) BW

C DW Equation G Dose based EEC P C FI WTO

i Bi wet

*

6. − =∑ * * +

− =∑( ) EEC Pi CBi Equation G7. Dietary based *
112 of 123 

8. ( ) ( )

0.25

50 mammals

AW

TW Equation G AT LD or NOAEL ⎟

⎠

⎞ ⎜

⎝

⎛ =

( )

9. ( )

1

50 birds

TW

AW Equation G AT LD

x−

⎟

⎠

⎞ ⎜

⎝

⎛ =
113 of 123 

Appendix H. Methods for Estimating Metabolism Rate Constant (kM) 

Generally, chemical-specific data are not available to determine the metabolism rate constant 

(kM) for aquatic organisms. However, this parameter can be estimated using data from available 

bioconcentration factor (BCF) studies, in combination with estimated rate constants. Two 

separate approaches can be employed to estimate kM. The first utilizes Equation A1 from Arnot 

and Gobas 2004. The second utilizes a method described by Arnot et al. 2008. These approaches 

are described below. 

H.1. Use of Equation A1 

In this approach, Equation A1 (see Table A1 of Appendix A) is rearranged to solve for kM

(Equation H1). In a BCF study, fish are fed uncontaminated food; therefore, uptake through the 

dietary pathway is assumed to be negligible. As a result, it is assumed that kD = 0. BCF studies 

with fish involve water-only exposures, so fish do not respire pore water. As a result, mO = 1 

and mP = 0. To calculate kM, the model user should use the measured concentration of pesticide 

in the test water. In this case, it is assumed that this value represents the freely dissolved 

pesticide in the water, and therefore, Ф = 1. Based on these assumptions, Equation H1 can be 

restated as Equation H2. 

E G M

WTO P WDP D i Di

B k k k k

k m C m C k P C Eq A C

+ + +

Φ + + Σ =

2

1 0

*( * * * ) * ( * ) . 1 (From Appendix A) 

( )

*( * * * ) * ( * ) . 1 2

1 0

E G

B

WTO P WDP D i Di

M k k k

C

k m C m C k P C Eq H k − + +

Φ + + Σ =

E G

B

WTO

M k k k

C

k C Eq H k = − 2 − − 1

*( ) . 2

Equation H2 can be used to estimate kM from available data from a BCF study. 

 Empirical estimates of k1 (L/kg*d), total pesticide concentration in fish tissues (CB; g/kgww) and CWTO (g/L) from the BCF study should be entered into this equation. 

 k2 (d-1) is calculated as k1(empirical)/KBW (see Table A6 of Appendix A). To calculate KBW, 

it is necessary to have estimates of % lipid, % non-lipid organic matter (NLOM), and % 

water of the test fish (VLB, VNB and VWB, respectively). 

o If % lipid data are not available for the test fish, this approach should not be 

used and it should be assumed that kM = 0. 

o If % lipid data are available, but % NLOM and % water are not available, it can 

be assumed that the fish are 73% water and that % NLOM is equal to 100-73-% 

lipid. 

 kE (d-1) can be estimated using the KABAM tool. The model user should use the large 

fish of KABAM to calculate kE. 
114 of 123 

o Body weight of the fish and water temperature should be set to mean reported 

values from the study. If body weight data are not available for the test fish, 

this approach should not be used and it should be assumed that kM = 0. 

o This constant is influenced by the % lipid, % NLOM, and % water of the diet 

(VLD, VND and VWD, respectively). Calculation of this constant requires input of 

diet of the large fish to be 100% medium fish (Table 6 of KABAM tool). If data 

are available from the BCF study report to define the % lipid, % NLOM, and % 

water of the feed of the test fish, the data should be entered in the appropriate 

columns of Table 5 of the KABAM tool for the medium fish. Otherwise, if these 

data are not available, the % lipid, % NLOM, and % water of the medium fish can 

be set to the default values of 4, 23, and 73%, respectively. 

 kG (d-1) can be estimated from empirical data on body weight over the study period. If kG

cannot be estimated, the model user can use kG from the large fish. 

H.2. Use of Arnot et al. 2008 

In this approach, it is assumed that the elimination rate constant measured during the BCF study 

(kT) is the sum of elimination through respiration, fecal elimination and metabolism of the 

pesticide by the fish as well as growth dilution (Equation H3, Arnot et al. 2008). Equation H3 

can be rearranged into Equation H4, to solve for kM. 

T E G M Eq H k = k + k + k + k 3 2 .

M T E G Eq H k = k − k − k − k 4 2 . 

Equation H4 can be used to estimate kM from available data from a BCF study. 

 kT (d-1) is the total elimination rate constant estimated from the depuration period of the 

BCF study. 

 As with the first approach, k2 (d-1) can be calculated as k1(empirical)/KBW (see table A6 of 

Appendix A). 

 kE (d-1) can be estimated using the KABAM tool. See discussion above on how to derive 

this constant value. 

 kG (d-1) can be estimated from empirical data on body weight over the study period. If kG

cannot be estimated, the model user can use kG from the large fish. 

H.3. Assumptions and Uncertainties 

If kM is calculated as a negative value, it should be assumed that no biotransformation of the 

chemical occurs and kM should be set to 0 in Table 2 of the KABAM tool. Since a negative 

biotransformation rate would indicate that the organism is creating the pesticide, it is assumed 

that this is not possible for a pesticide. 

There is some uncertainty in using the model estimated kG value (using Equation A7), as it may 

differ from the growth rate of the test species of the BCF study. 
115 of 123 

The first approach involves use of total pesticide concentration in fish tissues (CB; g/kg-ww) and 

CWTO (g/L). It would be appropriate to enter mean values for these parameters into equation H2. 

However, variability in these parameters can influence predictions of kM. Therefore, the model 

user should explore variability associated with these values by considering standard deviation, as 

well as minimum and maximum values for these parameters. This will result in a range of 

relevant kM values. 

Both approaches involve use of fish body composition data (VLB, VNB, and VWB). It would be 

appropriate to use mean values to calculate KBW (and ultimately k2). However, variability in 

these parameters can influence predictions of kM. Therefore, the model user should explore 

variability associated with these values by considering standard deviation, as well as minimum 

and maximum values for these parameters. This approach will result in a range of relevant kM

values. 

Both approaches involve using the KABAM tool to calculate kE. This involves the use of diet 

composition data (VLD, VND, and VWD). In the case that data are not available from the study 

report to define the % lipid, % NLOM, and % water of the diet of the test fish, there is 

uncertainty in using default values for these parameters, as they may differ from the diet of the 

test species of the BCF study. 
116 of 123 

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