TABLE CONTENTS
x
4. EXPOSURE ESTIMATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1
4.1. GENERAL DOSE EQUATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1
4.1.1. Drinking Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3
4.1.2. Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6
4.1.2.1. Dose Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-6
4.1.2.2. Energy Content and Assimilation Efficiencies . . . . . . . . 4-10
4.1.3. Soil and Sediment Ingestion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-16
4.1.3.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18
4.1.3.2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18
4.1.3.3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19
4.1.3.4. Dose Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21
4.1.4. Air . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-21
4.1.5. Dermal Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-23
4.2. ANALYSIS OF UNCERTAINTY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-23
4.2.1. Natural Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-24
4.2.2. Sampling Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25
4.2.3. Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26
2 4.3. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-26
APPENDIX: LITERATURE REVIEW DATABASE . . . . . . . . . . . . . . . . . . . . . . . See Volume II
LIST OF TABLES
xiii
Table 4-1. Gross Energy and Water Composition of Wildlife Foods:
Animal Prey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13
Table 4-2. Energy and Water Composition of Wildlife Foods:
Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-14
Table 4-3. General Assimilation Efficiency (AE) Values . . . . . . . . . . . . . . . . . . . . . . . 4-15
Table 4-4. Percent Soil or Sediment in Diet Estimated From
Acid-Insoluble Ash of Scat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-20
Table 4-5. Other Estimates of Percentage of Soil or Sediment in Diet . . . . . . . . . . . 4-21
LIST OF FIGURES
xv
Figure 4-1. Wildlife Dose Equations for Drinking Water Exposures . . . . . . . . . . . . . 4-4
Figure 4-2. Wildlife Dose Equations for Dietary Exposures . . . . . . . . . . . . . . . . . . . 4-6
Figure 4-3. Estimating NIR When Dietary Composition Is Known on ak
Wet-Weight Basis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8
Figure 4-4. Estimating NIR Based on Different ME Values Whenk
Dietary Composition Is Expressed as Percentage of
Total Prey Captured . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9
Figure 4-5. Utilization of Food Energy by Animals . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11
Figure 4-6. Metabolizable Energy (ME) Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12
Figure 4-7. Example of Estimating Food Ingestion Rates for
Wildlife Species From Free-Living Metabolic Rate and
Dietary Composition: Male Mink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17
Figure 4-8. Wildlife Oral Dose Equation for Soil or Sediment
Ingestion Exposures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-22
Dpot 


t2
22
t1
C(t) IR(t) dt
4-1
[4-1]
4. EXPOSURE ESTIMATES
This section provides equations to estimate oral doses of chemical contaminants
for wildlife, along with a discussion of dose estimates for other exposure routes. Section
4.1 provides general dose equations. Equations for drinking water exposures are
presented in Section 4.1.1, followed by equations for dietary exposures in Section 4.1.2. In
the dietary exposure section, data on the caloric and water content of various food types
and diet assimilation efficiencies are also provided. An equation and data to facilitate
estimating doses received through soil or sediment ingestion are discussed in Section
4.1.3. Sections 4.1.4 and 4.1.5 provide a qualitative discussion of inhalation and dermal
dose estimates. Section 4.2 describes considerations for analyses of uncertainty in
exposure assessments. References are provided in Section 4.3.
4.1. GENERAL DOSE EQUATIONS
EPA's (1992a) Framework for Ecological Risk Assessment defines exposure as the
co-occurrence of or contact between a stressor and an ecological component. When
assessing risks of exposure to chemical contaminants, potential dose is often the metric
used to quantify exposure. Potential dose is defined as the amount of chemical present in
food or water ingested, air inhaled, or material applied to the skin (U.S. EPA, 1992b).
Potential dose is analogous to the administered dose in a toxicity test. Because exposure
to chemicals in the environment is generally inadvertent, rather than administered, EPA's
(1992b) Guidelines for Exposure Assessment use the term potential dose rather than
administered dose.
A general equation for estimating dose for intake processes is:
4-2
where D is the total potential dose over time (e.g., total mg contaminant intake betweenpot
t1 and t2), C(t) is the contaminant concentration in the contacted medium at time t (e.g., mg
contaminant/kg medium), and IR(t) is the intake rate of the contaminated medium at time t
measured as mass ingested or inhaled by an animal per unit time (e.g., kg medium/day). If
C and IR are constant over time, then the total potential dose can be estimated as:
D = C × IR × ED [4-2]pot
where ED is the exposure duration and equals t2 - t1.
Therefore, if C and IR are constant, the potential average daily dose (ADD ) for thepot
duration of the exposure, normalized to the animal's body weight (e.g., mg/kg-day), is
estimated by dividing total potential dose by ED and by body weight (BW):
ADD = (C × IR × ED) / (BW × ED), or [4-3]pot
ADD = (C × IR) / BWpot
If C or IR vary over time, they may be averaged over ED. However, it is not always
appropriate to average intake over the entire exposure duration: For example, a given
quantity of a chemical might acutely poison an animal if ingested in a single event, but if
that amount is averaged over a longer period, effects might not be expected at all.
Similarly, developmental effects occur only during specific periods of gestation or
development. A toxicologist should be consulted to determine which effects may be of
concern given the exposure pattern and chemicals of interest. For carcinogenic
compounds, it may be more appropriate to average exposure over the animal's lifetime.
Again, address any questions to a toxicologist.
In addition, IR and BW can be combined into a normalized ingestion or inhalation
rate (NIR) (e.g., kg medium/kg body weight - day):
NIR = IR / BW [4-4]
The frequency term should be estimated with care. For example, if a feature attractive to wildlifea
is contaminated, an animal may spend a proportionally longer time in the contaminated area.
Similarly, if only part of an animal's theoretical foraging range has suitable habitat, the animal may
spend more time feeding in that habitat. Finally, animals may avoid areas or media with
contamination they can detect.
4-3
Therefore,
ADD = C × NIR [4-5]pot
It is important to remember that NIR can vary with changes in age, size, and reproductive
status of an animal.
Two other variables often are used in calculations of average daily dose. A
frequency term (FR) is used to denote the fraction of the time that an animal is exposed to
contaminated media. In ecological exposure assessments, this term often is used when
the foraging range of an animal is larger than the area of contamination. An absorptiona
factor (ABS) is used when an estimate of absorbed dose rather than potential dose is
desired. It is commonly assumed that absorption in the species of concern in the field is
the same as in the test organism, so no absorption factor is needed. However, if
absorption is expected to differ, a ratio of the absorption factors would be used in the
exposure equation.
4.1.1. Drinking Water
Figure 4-1 presents two wildlife oral exposure equations corresponding to two patterns of
contamination of water:
(1) the animal obtains some of its drinking water from a contaminated source
and the remainder from uncontaminated sources; and
(2) the animal consumes drinking water from several sources contaminated at
different levels.
4-4
One Source of Contamination
ADD = C × FR × NIR [4-6]pot
Different Sources With Varying Levels of Contamination
n
ADD =  (C × FR ) x NIR [4-7]pot i i
i=1
ADD = Potential average daily dose (e.g., in mg/kg-day).pot
C = Average contaminant concentration in a single water source (e.g., in mg/L or
in mg/kg, because 1 liter of water weighs 1 kg).
FR = Fraction of total water ingestion from the contaminated water source
(unitless).
NIR = Normalized water ingestion rate (i.e., fraction of body weight consumed as
water per unit time; e.g., in g/g-day)
and
C = Average contaminant concentration in the i water source (e.g., in mg/L).i
th
FR = Fraction of water consumed from the i water source (unitless).i
th
n = Number of contaminated water sources.
Figure 4-1. Wildlife Dose Equations for Drinking Water Exposures
In the first case, the distribution and mean value of the contaminant concentration in the
one source could be determined. In the second case, the different water sources are likely
to be characterized by different mean levels of contamination, and consumption from these
sources would be weighted by the fraction (FR ) of the animal's total daily water ingestioni
obtained from each source. FR (or FR ) in Figure 4-1 is a function of the degree of overlapi
of the contaminated water source(s) and the animal's home range. If the area of the
contaminated water source is larger than the typical home range for the species, FR could
4-5
equal one for many individuals. The number of individuals for which FR equals one could
be estimated from information on population density, distribution, and social structure.
For large, mobile animals, the area of contamination may be smaller than the area over
which a single animal is likely to move. In these cases, FR for an animal with the
contaminated area entirely within its home range can be estimated using information on
the home range, attributes of the contaminated area, and drinking behavior of the animal.
Home range estimates should be used with care because (1) the area in which an animal
moves varies with several factors, including reproductive status, season, and habitat
quality; (2) most animals do not drink or feed randomly within their home range; (3) the
term home range has been used inconsistently in the literature; and (4) estimates of home
range can vary substantially with the measurement technique used. In this Handbook and
accompanying Appendix, we have tried to identify clearly which estimates of home range
correspond to a daily activity and foraging home range.
When using home range data, we recommend that users consult the Appendix
tables for the species of interest to become familiar with how estimates of home range size
vary with geographic area, season, type of habitat, animal reproductive status, and
measurement technique. The Appendix tables provide both the sample size and a brief
description of the method used to estimate home range size, which can help indicate the
robustness of an estimate and whether it is likely to over- or underestimate home range
size. For mark-and-recapture studies, the number of recaptures per animal is provided
when possible to assist the user in determining the degree to which the reported values
may underestimate true home range size. If a study indicated that the home range
estimate is likely to include areas outside of the animals' usual activity range (e.g., distant
egg-laying sites used only once per season), this would be noted in the Appendix tables,
and the value would not be included in Chapter 2. Some animals use a fixed "home base"
some distance from feeding grounds such as a rookery. For these animals, we have
reported foraging radius (the distance they will travel to a feeding area). Foraging radius
can be used to determine whether the animal might feed or drink in a given contaminated
area.
4-6
m
ADD =  (C × FR × NIR ) [4-8]pot k k k
k=1
ADD = Potential average daily dose (e.g., in mg/kg-day).pot
C = Average contaminant concentration in the k type of food (e.g., in mg/kg wetk
th
weight).
FR = Fraction of intake of the k food type that is contaminated (unitless). Fork
th
example, if the k component of an animal's diet were salmon, FR for salmonth
k
would equal the fraction of the salmon consumed that is contaminated at level
C . If all of the salmon consumed were contaminated at level C , then FRk k k
would equal one.
NIR = Normalized ingestion rate of the k food type on a wet-weight basis (e.g., ink
th
g/g-day).
m = Number of contaminated food types.
Figure 4-2. Wildlife Dose Equations for Dietary Exposures
4.1.2. Diet
Wildlife can be exposed to contaminants in one or more components of their diet,
and different components can be contaminated at different levels. In this section, we
outline methods of estimating food ingestion rates that allow total doses to be estimated
when different components of the diet are contaminated, either at similar or different levels
(Section 4.1.2.1). We also provide data on caloric content of foods and assimilation
efficiencies that can be used in the dose equations provided (Section 4.1.2.2).
4.1.2.1. Dose Equations
Figure 4-2 presents a generic equation for estimating oral doses of contaminants in
food for wildlife species. FR is a function of the degree of overlap of the k type ofk
th
simplest case, the normalized ingestion rate for each food type, NIR , is known on a wet-k
4-7
contaminated forage or prey and the animal's home range (see Section 4.1.1). In the
weight basis, and Equation 4-8 can be used directly. In many cases, however, NIR isk
unknown or has been determined for laboratory diets that differ significantly from natural
diets in terms of caloric value per unit wet weight. Ingestion rates based on relatively dry
laboratory diets might underestimate the amount of food a free-living animal consumes.
There are several ways to estimate NIR , depending on the type of information thatk
is available. If dietary composition is expressed as the number of each prey type captured
on a daily basis (N ), estimating the normalized ingestion rate for each prey type (NIR )k k
requires only one step:
NIR = (N × Wt ) / BW [4-9]k k k
where Wt is the body weight of the k prey type and BW is the body weight of thek
th
predator.
Figure 4-3 presents a flow chart depicting equations that can be used if the
proportion of the diet for a given food type has been measured or estimated on a wetweight
basis. These equations may require estimates of the free-living metabolic rate
(FMR) of the organism and the metabolizable energy (ME) of the organism's forage or prey.
Estimated FMRs can be found in the species profiles in Chapter 2, and allometric equations
for estimating FMR on the basis of body weight are provided in Chapter 3 (Section 3.5). ME
should be averaged over the food types when ME on a wet-weight basis (e.g., cal/g wet
weight) differs substantially among the different foods. Section 4.1.2.2 describes how to
estimate ME.
A common situation facing someone conducting a wildlife exposure assessment for
predators is that in a key study, dietary composition is expressed as a percentage of the
total number of prey captured over a period of time instead of as a percentage of the total
wet weight of food ingested daily. Because some prey can be substantially larger than
others (e.g., rabbits compared with voles), and because ME of different types of prey may
4-9
Step 1: Calculate the metabolizable energy (ME) content of each prey or food type
on a wet-weight basis:
ME(wet wt) = GE(wet wt) × AE [4-13]k k k
Step 2: Estimate the average number of prey (or other food items) consumed each
day:
N = FMR / (weighted average prey ME)avg
m
N = FMR / ( PN × Wt × ME(wet wt) ) [4-14]avg k k k
k=1
Step 3: Calculate IR :k
IR = N × PN × Wt [4-15]k tot k k
Step 4: Normalize to body weight:
NIR = IR / BW [4-16]k k
ME(wet wt) = Metabolizable energy in the k prey or food type (e.g., in kcal/g wet weight).k
th
GE(wet wt) = Gross energy content of the k food type (e.g., in kcal/g wet weight).k
th
AE = Assimilation efficiency for the species for the k food type (unitless).k
th
N = Average number of prey (or other food items) eaten each day.avg
FMR = Free-living metabolic rate (e.g., in kcal/day).
m = Number of different types of prey or other foods.
PN = Proportion of the total number of prey that is composed of the k prey typek
th
(unitless). It often is the case that larger numbers of relatively small prey
and smaller numbers of relatively large prey are captured. (If the total
number of prey of each type captured each day are reported in the
literature, calculations of IR are very simple [i.e., N × Wt ] and steps 1 andk k k
2 are unnecessary.)
Wt = Body weight of an individual of the k food type (e.g., in g).k
th
IR = Ingestion rate of the k food type (e.g., in g/day).k
th
Figure 4-4. Estimating NIR Based on Different ME Values When Dietary Composition Isk
Expressed as Percentage of Total Prey Captured
Ash constituents typically include calcium carbonate (e.g., shell), calcium phosphate (vertebrateb
bone), and hydrated silica salts.
4-10
and the weighted average ME of the prey. Given N , the ingestion rate for each prey typeavg
(IR ) can be computed on a wet-weight basis and normalized to body weight (NIR ).k k
Because N is estimated using prey weight, different sizes of the same prey species (e.g.,avg
smaller and larger fish) should be separated into appropriate size intervals to reduce
uncertainty in the estimate.
4.1.2.2. Energy Content and Assimilation Efficiencies
The total or gross energy (GE) content of a food type is a function only of
characteristics of the food. On the other hand, metabolizable energy (ME) depends on
characteristics of both the food and the organism eating it. To clarify the meaning of ME,
Figure 4-5 presents a flow chart of energy utilization by animals. Digestible energy in a diet
is GE consumed minus the energy lost as feces; digestible energy efficiency (DE) is
digestible energy divided by GE. ME is GE consumed minus the energy lost as both feces
and urine. Assimilation efficiency (AE, also called metabolizable energy efficiency) is ME
divided by GE. Rearranging this relationship, ME is equal to GE of the diet multiplied by
the animal's AE for the diet as shown in Figure 4-6, Equation 4-17. General ME values can
be found in Table 3-1 or more specific ones calculated from GE content of the food and the
AE of the animal eating that food, as discussed below.
The GE content of food typically is reported using one (or more) of three measures:
(1) energy per unit total dry weight, (2) energy per unit ash-free dry weight, or (3) energy
per unit fresh biomass (i.e., per unit wet weight) (Górecki, 1975). Caloric content per unit
total dry weight is obtained directly from the combustion of dried material in a calorimeter.
Ash-free dry weight is the dry weight after subtracting the ash content. The ash-free dry-b
weight caloric value exceeds the total dry-weight caloric value by the ratio of the total dry
weight to the ash-free dry weight. Typically, animal (exclusive of thick shells) and plant
materials are 1 to 10 percent ash on a wet-weight basis and 5 to 30 percent ash on a dryweight
basis (Ashwell-Erickson and Elsner, 1981; Cummins and Wuycheck, 1971;
4-12
ME = GE × AE [4-17]
where:
ME = Metabolizable energy (e.g., in kcal/g)
GE = Gross energy (e.g., in kcal/g)
AE = Assimilation efficiency (unitless)
This Handbook assumes ME and GE are estimated on a wet-weight basis. To estimate ME or
GE of the k food type on a wet-weight basis from dry-weight measurements, the followingth
equations can be used:
GE(wet wt) = GE(dry wt) × (1 - proportion water ) or [4-18]k k k
ry wt) weight eight -19]
GE(wet wt) = GE(dk k k k× (dry /wet w ) [4
and
ME(wet wt) = ME(dry wt) × (1 - proportion water ) or [4-20]k k k
ry wt) weight eight -21]
ME(wet wt) = ME(dk k k k× (dry /wet w ) [4
Figure 4-6. Metabolizable Energy (ME) Equation
Hunt, 1972). The ash content of the diet is not metabolized and thus does not provide
energy to the animal. Figure 4-6 (Equations 4-18 through 4-21) illustrates how the caloric
content per unit of fresh biomass can be obtained by adjusting the dry-weight value based
on the water content of the biomass.
A summary of GE contents of many wildlife food types are presented in Tables 4-1
(animals) and 4-2 (plants), on both a wet-weight and a dry-weight basis. Caloric content of
a given species on a wet-weight basis tends to be more variable than caloric content on a
dry-weight basis because plants, and to a lesser degree animals, vary in their water
content depending on environmental conditions. Ash-free dry-weight caloric values are
not presented because it is not appropriate to use them with the equations and AEs in this
chapter. Ash contents are accounted for in the AEs presented in Table 4-3.
4-13
Table 4-1. Gross Energy and Water Composition of Wildlife Foods: Animal Prey (values
expressed as mean [standard deviation] where n = number of studies)n
Type of food
kcal/g
wet wt % H 02
kcal/g
dry wt References
Aquatic
invertebrates
bivalves (without shell)
crabs (with shell)
shrimp
isopods, amphipods
cladocerans
insect larvae
0.80
1.0 (0.21)5
1.1 (0.24)4
1.1
0.74
82 (4.5)3
74 (6.1)5
78 (3.3)7
71-80
79-87
4.6 (0.35)4
2.7 (0.45)4
4.8 (0.31)6
3.6 (0.78)3
4.8 (0.62)14
5.3 (0.37)8
1,2,3,4,5,6
1,2,3,7
1,3,4,6,7
4,6,7
2,4
1,4
vertebrates
bony fishes
Pacific herring
small fish (e.g., bluegill)
1.2 (0.24)18
2.0 (0.43)3
75 (5.1)18
68 (3.9)3
4.9 (0.38)18
6.1 (0.50)4
4.1 (0.47)3
7
8,9
1,7
Terrestrial
invertebrates
earthwormsa
grasshoppers, crickets
beetles (adult)
0.78-0.83
1.7 (0.26)3
1.5
84 (1.7)3
69 (5.6)11
61 (9.8)5
4.6 (0.36)4
5.4 (0.16)4
5.7-5.9
1,7
1,10,11
1,10,11
mammals
mice, voles, rabbits 1.7 (0.28)14
68 (1.6)4
5.0 (1.3)17
12,13,14
birds
passerines
with peak fat reservesb
with typical fat reserves
mallard (flesh only)
gulls, terns
1.9 (0.07)3
2.0
1.9
68
67
7.8 (0.18)10
5.6 (0.34)13
5.9
4.4
15
10,14,15,16
10
1
reptiles and amphibians
snake, lizards
frogs, toads
1.4
1.2
66
85 (4.7)3
4.5 (0.28)5
4.6 (0.45)3
14,17
12,14
Note: For Tables 4-1 and 4-2, a single value represents the results of a single study on one species,
and should not be interpreted as a mean value or a value indicating no variation in the category.
Two values separated by a hyphen indicate that values were obtained from only two studies.
Not including soil in gut, which can constitute one-third of the wet weight of an earthworm.a
Peak fat reserves occur just prior to migration. Typical fat reserves are for resident passerines orb
migratory species during nonmigratory seasons.
References: (1) Cummins and Wuycheck, 1971; (2) Golley, 1961; (3) Tyler, 1973; (4) Jorgensen et
al., 1991; (5) Pierotti and Annett, 1987; (6) Minnich, 1982; (7) Thayer et al., 1973; (8) Ashwell-Erickson
and Elsner, 1981; (9) Miller, 1978; (10) Collopy, 1975; (11) Bell, 1990; (12) Górecki, 1975; (13) Golley,
1960; (14) Koplin et al., 1980; (15) Odum et al., 1965; (16) Duke et al., 1987; (17) Congdon et al., 1982.
4-14
Table 4-2. Energy and Water Composition of Wildlife Foods: Plants (values expressed as
mean [standard deviation] where n = number of studies)n
Type of food
kcal/g
wet wta
% H 02
kcal/g
dry wt References
Aquatic
algae
aquatic macrophytes
emergent vegetation
0.41-0.61 84 (4.7)3
87 (3.1)3
[45-80]b
2.36 (0.64)4
4.0 (0.31)12
4.3 (0.13)3
1,2,3
1,2,4
1,2,4
Terrestrial
monocots
young grasses
mature dry grasses
1.3 70-88
7-10
4.2
4.3 (0.33)5
5,6
1,5,7,8
dicots
leaves
roots
bulbs, rhizomes
stems, branches
seeds
85 (3.5)3
9.3 (3.1)12
4.2 (0.49)57
4.7 (0.43)52
3.6 (0.68)3
4.3 (0.34)51
5.1 (1.1)57
9
9
2,7,10
9
6,9,11,12
fruit
pulp, skin
pulp, skin, seeds
1.1 (0.30)3
77 (3.6)3
2.0 (3.4)28
2.2 (1.6)10
10,13
10
Note: For Tables 4-1 and 4-2, a single value represents the results of a single study on one species,
and should not be interpreted as a mean value or a value indicating no variation in the category.
Two values separated by a hyphen indicate that values were obtained from only two studies.
Few determinations of the energy content of plants have been made on a wet-weight basisa
because plants fluctuate widely in water content depending on environmental conditions.
Values in brackets represent total range of field measurements, instead of values from only twob
studies, as for the remainder of the table. Buchsbaum and Valiela (1987) found the water content
of the emergent marsh vegetation Spartina alterniflora, S. patens, and Juncus gerardi to decrease
over a summer from 80 to 60 percent, 70 to 45 percent, and 78 to 61 percent, respectively, as the
marsh dried. In contrast, they found a submerged macrophyte to maintain water content within a
few percent throughout the season.
References: (1) Cummins and Wuycheck, 1971; (2) Jorgensen et al., 1991; (3) Minnich, 1982; (4)
Boyd and Goodyear, 1971; (5) Davis and Golley, 1963; (6) Drozdz, 1968; (7) Golley, 1960; (8)
Kendeigh and West, 1965; (9) Golley, 1961; (10) Karasov, 1990; (11) Dice, 1922; (12) Robel et al.,
1979; (13) Levey and Karasov, 1989.
4-15
Table 4-3. General Assimilation Efficiency (AE) Values (values expressed as mean [standard
deviation] where n = number of studies)n
Group Prey/Forage AE % Reference
Birds
birds of prey
eagles, seabirds
waterfowl
birds
animals
birds, small mammals
fish
aquatic invertebrates
terrestrial insects
78 (5.2)16
79 (4.5)9
77 (8.4)3
72 (5.1)16
1,2,3,4
1,2,4,5
1
1,5,6
passerines
non-passerines
birds
birds
birds
birds
grouse, ptarmigans
geese
ducks
geese, grouse
plants
wild seeds
wild seeds
cultivated seeds
fruit pulp, skin
fruit pulp, skin, seeds
grasses, leaves
stems, twigs, pine needles
emergents (e.g., spartina)
aquatic vegetation
bulbs, rhizomes
75 ( 9)11
59 (13)25
80 ( 8)17
64 (15)31
51 (15)22
47 ( 9.6)3
34 ( 5.3)8
39 ( 9.1)4
23 ( 5.3)5
56 (18)4
1
1
1
1
1
1*
1,1
1*
1*
1
Mammals
pinnipeds
mammals
mammals
small mammals
animals
fish
small birds, mammals
fish
insects
88 (1.1)5
84 (6.5)4
91
87 (4.9)6
7,8
9,10,11
12
11,13
voles, mice
lemmings, voles
rabbits, voles, mice
rabbits, voles, rats
plants
seeds, nuts
mature grasses
green forbs
"herbivory"
85 (7.3)8
41 (9.1)5
73 (7.6)8
76 (7.6)5
11,14
15
11,14,15
11,14,16
References: (1) Karasov, 1990; (1*) calculated from data presented in Appendix I of Karasov, 1990;
(2) Stalmaster and Gessaman, 1982; (3) Koplin et al., 1980; (4) Castro et al., 1989; (5) Ricklefs, 1974;
(6) Bryant and Bryant, 1988; (7) Ashwell-Erickson and Elsner, 1981; (8) Miller, 1978; (9) Litvaitis and
Mautz, 1976; (10) Vogtsberger and Barrett, 1973; (11) Grodzinski and Wunder, 1975; (12) estimated
by dividing 4.9 kcal/g gross energy for bony fishes (Table 4-1) by metabolizable energy of 4.47
reported for fish consumed by mammals (Nagy, 1987); (13) Barrett and Stueck, 1976; (14) Drozdz,
1968; (15) Batzli and Cole, 1979; (16) Drozdz et al., 1971.
4-16
Table 4-3 summarizes AEs for several different types of foods and species. Assimilation
efficiency is a function of both the consumer species' physiology and the type of diet.
Factors that reduce many species' ability to assimilate the energy contained in food
include the ash content of the diet and the percentage of relatively indigestible organic
materials such as chitin (arthropods) or cellulose (plants). The higher the ash content, the
lower the AE, all else being equal.
Fat content also influences GE. For example, carbohydrates (approximately 4.3
kcal/g) and proteins (approximately 5.7 kcal/g) typically provide about half as many
calories per gram as fat (approximately 9.5 kcal/g) (Peters, 1983). Thus, small changes in
fat content of animal tissues or plant seeds cause significant changes in their caloric value.
For example, just prior to fall migration, passerine birds have achieved peak fat deposition
and average 7.8 kcal/g dry weight. Non-migrating passerines (i.e., permanent residents or
migratory species during nonmigrating seasons) average only 5.6 kcal/g dry weight. Two
references with substantial compilation of data on caloric content of biological materials
are Jorgensen et al. (1991) and Cummins and Wuycheck (1971). The latter includes
extensive data on invertebrates.
Figure 4-7 provides a sample calculation of food ingestion rates using the
methodology outlined above.
4.1.3. Soil and Sediment Ingestion
In this section, we review information on the ingestion of soil and sediment for the
species included in this Handbook (and similar species). Despite the potential importance
of soil and sediment ingestion as a route of exposure of wildlife to environmental
contaminants, data to quantify these ingestion rates are limited at this time.
4-17
1. Estimate Field Metabolic FMR (kcal/day) = 0.6167 (g Wt)0.862
Rate (FMR) [Equation 3-47] = 0.6167 (1,040)0.862
= 246 (kcal/day)
2. Normalize to Body Weight NFMR (kcal/g-day) = 246 (kcal/day)/1,040 (g Wt)a
(Wt) [Equation 3-40] = 0.24 (kcal/g-day)
3. Estimate Average Metabolizable Energy (ME ) of Diet [Equation 4-12]avg
Dietary
Item
(k=5)
Proportion
of Diet
(P )k
b
Gross
Energy
(GE )k
c
(kcal/g wet
wt)
Assimil-
ation
Efficiency
(AE )k
d
Metabolizable
Energy (ME )k
(kcal/g wet wt)
(ME = GE × AE )k k k
(P × ME )k k
Fish 0.85 1.2 0.91 1.1 0.93
Crustacea 0.04 1.1 0.87 0.96 0.038
Amphibia 0.03 1.2 0.91 1.1 0.033
Birds/
Mammals
0.06 1.8 0.84 1.5 0.090
Vegetation 0.02 1.3 0.73 0.95 0.019
ME (kcal/g wet wt) = (P × ME ) = 1.1avg k k
e
4. Estimate Total NIR (g/g-day) = 0.24 (kcal/g-day)total
Normalized Ingestion Rate 1.1 (kcal/g wet wt) (i.e., ME )avg
(NIR ) [Equation 4-11] = 0.22 (g/g-day)total
5. Estimate Prey-specific NIR (g/g-day) = 0.85 (P ) × 0.22 (g/g-day)fish fish
Normalized Ingestion Rates = 0.19 (g/g-day)
(e.g., NIR ) [Equation 4-10]fish
Body weight for Montana population in the summer (Mitchell, 1961).a
Dietary composition based on Alexander (1977).b
Values from Tables 4-1 and 4-2 (for vegetation, assuming value for young grasses).c
Values from Table 4-3 (for vegetation, assuming green forbs; for crustacea, assuming equivalent AEd
for insects; for amphibia, assuming equivalent to mammals consuming fish).
In this example, ME is the same as the ME value for fish, which comprises 85 percent of the diet.e
avg
Figure 4-7. Example of Estimating Food Ingestion Rates for Wildlife Species From Free-Living
Metabolic Rate and Dietary Composition: Male Mink
Seed-eating birds often consume "grit" to aid in digestion, which makes them vulnerable toc
poisoning by granular formulations of pesticides and fertilizers. In this section, however, we
restrict our discussion to soils and sediments, which are composed of much smaller particle sizes.
4-18
4.1.3.1. Background
Soil is ingested both intentionally and incidentally by many species of wildlife and
can be a significant exposure pathway for some contaminants (Arthur and Alldredge, 1979;
Garten, 1980). Many ungulates deliberately eat soil to obtain nutrients; some may travel a
considerable distance to reach certain areas (salt licks) that are used by many animals.
Some birds gather mud in their beaks for nest-building, and others consume it for calcium
(Kreulen and Jager, 1984). Many animals can incidentally ingest soil while grooming,
digging, grazing close to the soil, or feeding on items that are covered with soil (such as
roots and tubers) or contain sediment (such as molluscs). Earthworms ingest soil directly;
the soil in their guts may be an important exposure medium for animals that eat these
organisms (Beyer et al., 1993).c
Soil ingestion rates have been estimated for only a few wildlife species and were not
available in the published literature for most of the animals in this Handbook. The
percentage of soil ingested is often estimated from the acid-insoluble ash content of
wildlife scats or digestive tract contents. Scat analysis on small animals is often difficult
because scat are small. Soil ingestion by large mammals also has been estimated using
insoluble chemical tracers (Mayland et al., 1977) and using standard x-ray diffraction
analysis (Garten, 1980).
4.1.3.2. Methods
Garten (1980) estimated the amount of soil in the gastrointestinal (GI) tract of a
small mammal (the hispid cotton rat) using the following equation:
I = (S - F)W [4-22]
4-19
where I equals the amount of soil in the GI tract, S equals the ratio of insoluble ash to dry
contents in the GI tract, F equals the ratio of insoluble ash to dry contents in fescue (the
dominant vegetation in the rat's habitat), and W equals the dry weight of GI-tract contents.
It is also possible to estimate soil ingestion rates from the acid-insoluble ash
content of the animal's scat because the percentage of acid-insoluble ash in mineral soil is
much higher (usually at least 90 percent) than in plant or animal tissue (usually no more
than a few percent). Beyer et al. (in press) used scat samples to estimate the fraction of
soil in the diet for several species. The equation for this estimation approach is slightly
more complicated than Equation 4-22, because it accounts for digestibility and the mineral
content of the soil. They found a significant correlation between the measured and
predicted relationships of the ratio of acid-insoluble ash to dry weight of scat and the
percentage of soil in the diet.
4.1.3.3. Results
Percent soil in the diet for some of the selected and similar species included in
Chapter 2 are included in Tables 4-4 and 4-5. Of the species studied, the sandpiper group,
which feeds on mud-dwelling invertebrates, was found to have the highest rates of
soil/sediment ingestion (30, 18, 17, and 7.3 percent of diet, respectively, for semipalmated,
western, stilt, and least sandpipers, although only a single sample was analyzed for each
species). Wood ducks also can ingest a high proportion of sediment (24 percent) with their
food. Relatively high soil intakes were estimated for the raccoon (9.4 percent), an
omnivore, and the woodcock (10.4 percent), which feeds extensively on earthworms.
Other species that eat earthworms might be expected to exhibit similarly high soil intakes.
The Canada goose, which browses on grasses, also exhibited a high percentage of soil in
its diet (8.2 percent). Soil ingestion was lowest for the white-footed mouse, meadow vole,
fox, and box turtle (<2, 2.4, 2.8, and 4.5 percent, respectively). Box turtles, tortoises, and
other reptiles, however, have been known to intentionally ingest soil, perhaps for its
nutrient content (Kramer, 1973; Sokal, 1971). Beyer et al.'s (in press) data should be used
with caution, because error was introduced by estimating variables in
4-20
Table 4-4. Percent Soil or Sediment in Diet Estimated From Acid-Insoluble Ash of Scat
Species
Scat
Samplesa
% Insoluble
Ash
Mean (SE) Range
Estimated
%
Digestibility
of Diet
Estimated
Percent Soil
in Diet
(dry weight)
Birds
Canada goose 23 12 (1.5) 3.9 - 38 25 8.2
Mallard 88 6.9 (1.1) 0.36 - 47 30 <2
Wood duck 7 24 (13) 0 - 75 60 11
Blue-winged teal 12 2.3 (0.36) 0.72 - 5.1 60 <2
Ring-necked duck 6 0.72 (5.5) 0.50 - 1.2 60 <2
American woodcock 7 22 (5.5) 6.3 - 40 55 10.4
Semipalmated
sandpiper
1 56 70 30
Western sandpiper 1 42 70 18
Stilt sandpiper 1 40 70 17
Least sandpiper 1 24 70 7.3
Mammals
Red fox 7 14 (2.6) 4.8 - 25 70 2.8
Raccoon 4 28 (8.9) 13 - 50 70 9.4
White-footed mouse 9 8.5 (0.71) 5.7 - 11 65 <2
Meadow vole 7 8.9 (1.2) 4.2 - 14 55 2.4
Reptiles and Amphibians
Eastern painted
turtle
9 21 (2.9) 11 - 41 70 5.9
Box turtle 8 18 (6.5) 3.6 - 49 70 4.5
For the sandpipers, the white-footed mouse, and the meadow vole, scat samples from more than onea
animal had to be combined into one sample to provide sufficient quantity for chemical analysis.
Source: Adapted from Beyer et al. (in press).
4-21
Species
Estimated % soil in diet
(dry weight) Reference
Jackrabbit 6.3 Arthur and Gates 1988
Hispid cotton rats 2.8 Garten 1980
Shorebirds 10-60 Reeder 1951
Table 4-5. Other Estimates of Percent Soil or Sediment in Diet
the equation (e.g., digestibility) and by the small samples they obtained from some of the
smaller animals.
Other studies of soil ingestion by species similar to those presented in this
Handbook are summarized in Table 4-5. Sediment has been found in the stomachs of
white-footed mice (Garten, 1980) and ruddy ducks and shovelers (Goodman and Fisher,
1962). Sediment in the gut of tadpoles inhabiting highway drainages may be responsible
for high concentrations of lead detected in these organisms (Birdsall et al., 1986).
4.1.3.4. Dose Equations
To estimate exposures to contaminants in soils or sediments from the data
provided in Tables 4-4 and 4-5, Equation 4-23 (Figure 4-8) can be used. If the percent soil
in the diet is measured on a dry-weight basis, as it usually is, total dietary intake should
also be expressed on a dry-weight basis.
4.1.4. Air
Inhalation toxicity values and exposure estimates are usually expressed in units of
concentration in air (e.g., mg/m ) rather than as average daily doses. Assessment of the3
inhalation pathway becomes complicated if the toxicity values must be extrapolated from a
test species (e.g., rat) to a different species (e.g., shrew). Inhalation toxicologists
extrapolate toxicity values from species to species on the basis of the dose deposited and
retained in the respiratory tract (the dose that is available for absorption, distribution,
4-22
m
ADD = ( (C × FS × IR (dry weight) × FR ))/BW [4-23]pot k total k
k=1
ADD = Potential average daily dose (e.g., in mg/kg-day).pot
C = Average contaminant concentration in soils in the k foraging area (e.g., ink
th
mg/kg dry weight).
FS = Fraction of soil in diet (as percentage of diet on a dry-weight basis divided
by 100; unitless).
IR = Food ingestion rate on a dry-weight basis (e.g., in kg/day). Nagy's (1987)total
equations for estimating FI rates on a dry-weight basis (presented in Section
3.1) can be used to estimate a value for this factor. If the equations for
estimating FI rates on a wet-weight basis presented in Section 4.2 are used,
conversion to ingestion rates on a dry-weight basis would be necessary.
FR = Fraction of total food intake from the k foraging area (unitless).k
th
BW = Body weight (e.g., in kg).
m = Total number of foraging areas.
Figure 4-8. Wildlife Oral Dose Equation for Soil or Sediment Ingestion Exposures
metabolism, and elimination). Once the appropriate toxicity benchmark (in terms of dose)
has been estimated for the species of concern (e.g., shrew), the corresponding air
concentration is estimated based on the respiratory physiology of that species. EPA uses
this approach because it can account for nonlinear relationships between exposure
concentrations, inhaled dose, and dose to the target organ(s). Because of the complexities
associated with the extrapolations, an inhalation toxicologist should be consulted when
assessing this pathway.
The dose deposited, retained, and absorbed in the respiratory tract is a function of
species anatomy and physiology as well as physicochemical properties of the
contaminant. The assessor will need to consider factors such as the target species' airway
4-23
size, branching pattern, breathing rate (volume and frequency), and clearance
mechanisms, as well as whether the contaminant is a gas or aerosol and whether its
effects are systemic or confined to the respiratory tract. Key information on the
contaminant includes particle size distribution (for aerosols), temperature and vapor
pressure (for gaseous agents), and pharmacokinetic data (e.g., air/blood partition
coefficients, metabolic parameters). While physiologically based pharmacokinetic models
have been useful for these calculations, they are available for only a few laboratory
species. These issues are discussed in detail in Interim Methods for Development of
Inhalation Reference Concentrations (U.S. EPA, 1990). Although the document specifically
describes how to calculate inhalation reference concentrations for humans, the principles
are useful for any air-breathing species.
4.1.5. Dermal Exposure
Dermal toxicity values and exposure estimates are usually expressed as an
absorbed dose resulting from skin contact with a contaminated medium. This exposure
pathway can be of great importance to wildlife, particularly when an animal is directly
sprayed (Driver et al., 1991). Dermal exposures may also be a concern for wildlife that
swim or burrow. Dermal absorption of contaminants is a function of chemical properties of
the contaminated medium, the permeability of the animals' integument, the area of
integument in contact with the contaminated medium, and the duration and pattern of
contact. A full discussion of quantifying absorbed dose through the skin is beyond the
scope of this document, and many of the required parameters have not been measured for
wildlife species. Readers interested in pursuing this exposure pathway may find useful
information in Dermal Exposure Assessment: Principles and Applications (U.S. EPA,
1992c).
4.2. ANALYSIS OF UNCERTAINTY
In the risk assessment process, several sources of uncertainty should be evaluated,
including the uncertainties associated with the exposure assessment and the toxicity
4-24
assessment. The following sections discuss three sources of uncertainty related to the
exposure assessment: (1) natural variability in the population in question, (2) uncertainty
about population parameters as a consequence of limits on sampling the population (i.e.,
sampling uncertainty), and (3) uncertainty about models used to estimate values. There
are other categories of uncertainties associated with site-specific risk assessments that
also need to be considered (e.g., selection of substances of concern, data gaps, toxicity
assessments). Additional discussion of sources and treatment of uncertainty is available
in Framework for Ecological Risk Assessment (U.S. EPA, 1992a) and Guidelines for
Exposure Assessment (U.S. EPA, 1992b). For treatment of site-specific uncertainties in
particular, see the Risk Assessment Guidance for Superfund, Volume I; Human Health
Evaluation Manual (Part A) Interim Final (U.S. EPA, 1989).
4.2.1. Natural Variation
As a review of the data provided in this Handbook makes clear, there is natural
variation in the values exhibited by populations for all exposure factors. Population values
for some parameters (e.g., body weight) can assume a normal distribution that can be
characterized by a mean and variance. We have provided the standard deviation (SD) as
the measure of population variance whenever possible. If a risk assessor is concerned
with exposures that might be experienced by animals exhibiting characteristics near the
extremes of the population's distribution, the SD can be used with the mean value for a
normally distributed population to estimate the parameter value for animals with
characteristics at specified points in the distribution (e.g., 95th percentile). We also have
provided the total range of values reported for each of the exposure factors whenever
possible. The ranges can be particularly helpful for parameters that are not normally
distributed, such as home-range size.
Another aspect of natural variation, however, is that different populations or the
same population at different times or locations can exhibit different mean values for any
parameter (e.g., body weight) and even different variances. We have tried to present
enough data to give users of the Handbook a feel for the range of values that different
populations can assume depending on geographic location, season, and other factors
4-25
(e.g., habitat quality). We recommend that risk assessors review the data presented in the
Appendix to appreciate the potential for variation in the parameters of interest.
Dietary composition, in particular, can vary markedly with season, location, and
availability of prey or forage. The latter factor varies with local conditions and usually is
not available for risk assessments. Thus, it can be one of the larger sources of uncertainty
in wildlife exposure assessments. State and local wildlife experts might be able to help
specify the local dietary habits of a species of concern and should be consulted if
screening analyses suggest that exposure at levels of concern is a possibility.
4.2.2. Sampling Uncertainty
Another source of uncertainty in exposure estimates results from limited sampling
of populations. Estimates of a population mean and variance become more accurate as
the number of samples taken from the population increases. With only a few samples from
a population, our confidence that the true population mean is near the estimated mean is
low; as the number of samples increases, our confidence increases. The standard error
(SE) of the mean is equal to the variance of the population () divided by the square root of
the sample size (n). SE can be estimated from the standard deviation of the population
divided by the square root of n. SE can be used to calculate confidence limits on an
estimate of the mean value for a population. For a normally distributed population, the 95percent
confidence limit of the mean is the estimated mean plus or minus approximately 2
SEs for reasonable sample sizes (e.g., n = at least 20).
Sampling uncertainty occurs in many areas of exposure assessment. Contaminant
concentration is one key parameter subject to sampling error. For site-specific risk
assessments, as the number of environmental samples increases, the uncertainty about
the true distribution of values decreases. Even with large sample sizes, however, this
uncertainty can dominate the total uncertainty in the exposure assessment. Other
parameters subject to sampling error are the exposure factors presented in this Handbook.
One of our criteria for selecting values from the Appendix to include in Chapter 2 was a
sample size large enough to ensure that SE was only a few percent of the mean value.
4-26
4.2.3. Model Uncertainty
Two main types of models are likely to be used in wildlife exposure assessments:
(1) allometric models to predict contact-rate parameters (e.g., food ingestion rates) and (2)
fate and transport models to predict contaminant concentrations to which wildlife are
exposed.
In this Handbook, we have tried to present statistical confidence limits associated
with allometric equations whenever possible. To reduce the confidence limits associated
with allometric models, it is important to use a model derived from the smallest and most
similar taxonomic/dietary group appropriate for the extrapolation. For example, to estimate
a metabolic rate for a red-winged blackbird, it is preferable to use a metabolic rate model
derived from data on passerines rather than a model derived from data on many different
groups of birds (e.g., raptors, seabirds, geese), and best to use a model for Icterids (the
subfamily to which the red-winged blackbird belongs) rather than a model derived from
data on passerines.
Uncertainties in exposure models can include how well the exposure model or its
mathematical expression approximates the true relationships in the field as well as how
realistic the exposure model assumptions are for the situation at hand. Judicious field
sampling (e.g., of contaminant concentrations in certain prey species) can help calibrate or
confirm estimates in the exposure model (e.g., food-chain exposures). Often a sensitivity
analysis can help a risk assessor identify which model parameters and assumptions are
most important in determining risk so that attention can be focused on reducing
uncertainty in these elements.
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Arthur, W. J., III; Alldredge, A. W. (1979) Soil ingestion by mule deer in north central
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4-27
Arthur W. J., III; Gates, R. J. (1988) Trace element intake via soil ingestion in pronghorns
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