EPA Report No. 600/R-01/035
update 2.2, March 2008
Bioaccumulation and Aquatic
System Simulator (bass)
User's Manual
Version 2.2
by
M. Craig Barber
Ecosystems Research Division
U.S. Environmental Protection Agency
960 College Station Road
Athens, GA 30605-2700
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
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Notice
The research described in this document was funded by the U.S. Environmental Protection Agency through the Office of Research
and Development. The research described herein was conducted at the Ecosystems Research Division of the U SEP A National Exposure
Research Laboratory in Athens, Georgia. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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Foreword
This report describes the theoretical development, parameterization, and application software of the bass Bioaccumulation and Aquatic
System Simulator. This generalized, community-based simulation model is designed to predict the population and bioaccumulation
dynamics of age-structured fish communities exposed to hydrophobic organic chemicals and class B and borderline metals that
complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). This report is not a case study on the
application of bass but a reference and user's guide. The intended audience of this report and associated software includes research
fisheries ecologists, bioaccumulation researchers, and EPA environmental scientists and ecologists who must routinely analyze and
estimate bioaccumulation of chemicals in fish for ecological or human health exposure assessments.
Although bass has not been extensively field-tested, its process-based algorithms for predicting chemical bioaccumulation, growth
of individual fish, predator-prey interactions, and population dynamics either have been corroborated or have been formulated using
widely accepted ecological and ecotoxicological principles. Even when a process-based model has undergone only limited field testing,
it can be an extremely useful tool. Process-based models enable users to observe quantitatively the results of a particular abstraction
of the real world. Moreover, such models can be argued to be the only objective method to make extrapolations to unobserved or
unobservable conditions. If the conceptualization and construction of process-based models are both comprehensive (i.e., holistic) and
reasonable, then their output, validated or not, can still be used for comparative analyses. A model's ability to simulate trends and
comparative dynamics are, in fact, often more important measures of a model's utility than is its ability to replicate a specific field or
laboratory study. Although bass can be used to analyze results from actual field studies, its principal intended use is to predict and
compare the outcomes of alternative management options associated with pollution control, ecosystem management, or restoration
activities.
Eric J. Weber, Ph.D.
Acting Director
Ecosystems Research Division
Athens, Georgia
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Abstract
bass (Bioaccumulation and Aquatic System Simulator) is a Fortran 95 simulation program that predicts the population and
bioaccumulation dynamics of age-structured fish assemblages exposed to hydrophobic organic pollutants and class B or borderline
metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). The model's
bioaccumulation algorithms are based on diffusion kinetics and are coupled to a process-based model for the growth of individual fish.
These algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals that together determine
diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics used by the model include the fish's gill
morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and structural organic content).
Relevant physico-chemical properties includes the chemical's aqueous diffusivity, n-octanol / water partition coefficient (K„J, and,
for metals, binding coefficients to proteins and other organic matter, bass simulates the growth of individual fish using a standard mass
balance, bioenergetic model (i.e., growth = ingestion - egestion - respiration - specific dynamic action - excretion). A fish's realized
ingestion is calculated from its maximum consumption rate adjusted for the availability of prey of the appropriate size and taxonomy.
The community's food web is delineated by defining one or more foraging classes for each fish species based on its body weight, body
length, or age. The dietary composition of each of these foraging classes is specified as a combination of benthos, incidental terrestrial
insects, periphyton / attached algae, phytoplankton, zooplankton, and one or more fish species. Population dynamics are generated
by predatory mortalities defined by the community's food web and standing stocks, physiological mortality rates, the maximum
longevity of species, toxicological responses to chemical exposures, and dispersal. The model's temporal and spatial scales are that
of a day and of a hectare, respectively.
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Table of Contents
Abstract 	iv
Figures	vii
Tables	 viii
Acknowledgment	ix
1.	Introduction 	 1
2.	Model Formulation	 4
2.1.	Modeling Internal Distribution of Chemicals 	 4
2.2.	Modeling Exchange from Water	 5
2.3.	Modeling Exchange from Food	 7
2.4.	Modeling Chemical Biotransformation	 9
2.5.	Modeling Temperature Effects on Physiological Rates	 9
2.6.	Modeling Growth of Fish	 10
2.7.	Modeling Predator-Prey Interactions	 12
2.8.	Modeling Dispersal, Non-Predatory Mortalities, and Recruitment	 14
2.9.	Modeling Habitat Effects	 15
2.10.	Modeling Nonfish Compartments 	 15
2.11.	Modeling Toxicological Effects 	 17
3.	Model Parameterization	 24
3.1.	Parameterizing Kf 		24
3.2.	Parameters for Gill Exchange 		24
3.3.	Bioenergetic and Growth Parameters 		25
3.4.	Procedures Used to Generate the bass Database		25
3.5.	Suggested Calibration Procedures		28
4.	bass User Guide	 36
4.1.	General Model Structure and Features 	 36
4.2.	New Features	 36
4.3.	Input File Structure 	 37
4.3.1.	Simulation Control Commands		38
4.3.2.	Chemical Input Commands		41
4.3.3.	Fish Input Commands		44
4.3.4.	Nonfish Input Commands		50
4.4.	Input Data Syntax 		52
4.4.1.	Units Recognized by bass 		52
4.4.2.	User Specified Functions 		52
4.4.3.	User Supplied Parameter Files 		53
4.5.	bass Include File Structure		54
4.6.	Output Files Generated by bass 		56
4.7.	Command Line Options		56
5.	bass Model Software and Graphical User Interface 	 61
5.1.	Software Overview 	 61
5.2.	Installation Notes	 62
5.3.	Installation Procedures	 62
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5.4.	bass GUI Operation 		62
5.4.1.	bass File Editors		63
5.4.2.	bass Command Editors		64
5.4.3.	Special Function Editors		64
5.4.4.	File and Folder Operations		65
5.5.	The bass Output Analyzer		65
5.6.	Auxiliary bass Software 		65
6.	Example Applications 	 75
6.1.	bass Software Distribution Examples	 75
6.2.	An Analysis of Everglades Mercury Bioaccumulation 	 76
7.	Model Quality Assurance	 84
7.1.	Questions Regarding QA of a Model's Scientific Foundations	 84
7.2.	Questions Regarding QA of a Model's Implementation 	 85
7.3.	Questions Regarding QA of Model Documentation and Applications 	 91
8.	Planned Future Features	 92
REFERENCES	 93
APPENDICES	 112
APPENDIX A. Equilibrium complexation model for metals	 112
APPENDIX B. Canonical equations for modeling diffusive chemical exchange across fish gills with ventilation and perfusion
effects. See Section 2.2 for background information and notation	 115
APPENDIX C. Derivation of the consistency condition for feeding electivities	 116
INDEX	 117
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Figures
Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and
ventilation / perfusion ratio	 21
Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and
ventilation / perfusion ratio	 22
Figure 2.3 Functional behavior of Equation (2.53)	 22
Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters
used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,
25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish's upper tolerance limit. 31
Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters
used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,
25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish's upper tolerance limit. 32
Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning bass's algorithm	 33
Figure 5.1 bass GUI Current BASS Directory window	 62
Figure 5.2 General structure of bass GUI file editors	 63
Figure 5.3 Structure of bass GUI project file editor	 64
Figure 5.4 GUI command editor for simple strings	 66
Figure 5.5 GUI command editor for simple strings with drop-down selection	 66
Figure 5.6 GUI command editor for numeric data with user specified units	 67
Figure 5.7 GUI command editor for numeric data fixed units	 67
Figure 5.8 GUI command editor for forcing functions	 68
Figure 5.9 GUI command editor for feeding model options	 68
Figure 5.10 GUI command editor for compositional and morphometric parameters	 68
Figure 5.11 GUI command editor for nondiet ecological parameters	 68
Figure 5.12 GUI command editor for fish diets	 69
Figure 5.13 GUI command editor for physiological parameters	 70
Figure 5.14 GUI command editor for cohort initial conditions	 70
Figure 5.15 GUI command editor for spawning parameters	 71
Figure 5.16 GUI command editor for fishery parameters	 71
Figure 5.17 GUI command editor for nonfish biota as forcing functions	 71
Figure 5.18 GUI command editor for nonfish biota as state variables	 72
Figure 5.19 GUI command editor for nonfish bioaccumulation factors	 72
Figure 5.20 GUI command editor for chemical biotransformation parameters	 72
Figure 5.21 GUI command editor for chemical toxicity parameters	 73
Figure 5.22 GUI command editor for automatic graphing selections	 73
Figure 5.23 GUI Block comment editor	 74
Figure 5.24 Data file editor for forcing functions specified as files	 74
Figure 6.1 Predicted and observed methylmercury concentrations of largemouth bass in Florida Everglades canals	78
Figure 6.2 Predicted and observed methylmercury concentrations of Florida gar in Florida Everglades canals	79
Figure 6.3 Predicted and observed methylmercury concentrations of yellow bullhead in Florida Everglades canals	 80
Figure 6.4 Predicted and observed methylmercury concentrations of bluegill sunfish in Florida Everglades canals	 81
Figure 6.5 Predicted and observed methylmercury concentrations of redear sunfish in Florida Everglades canals	 82
Figure 6.6 Predicted and observed methylmercury concentrations of Gambusia in Florida Everglades canals	 83
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Tables
Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental model
processes, rates, or rate coefficients	 19
Table 3.1 Summary of NL2SOL regressions for Equation (3.24) fitted to maximum daily consumption rates and satiation meal
size reported in the literature	 34
Table 3.2 Summary of NL2SOL regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by
the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily
consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit	 35
Table 4.1 Valid Unit Prefixes	 58
Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive and summarizes only
commonly used unit names that bass's units conversion program recognizes	 59
Table 4.3 Valid Ecological, Morphometric, and Physiological Unit Names	 60
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Acknowledgment
Many people have contributed to the development of the BASS bioaccumulation and community model through technical discussions,
software development and oversight, and peer reviews. This author wishes to thank Drs. Daniel Beyers (Colorado State University),
JamesE. Breck (University of Michigan), Lawrence Burkhard (USEPA, NHEERL), Lawrence Burns (USEPA, NERL retired), Michael
Cyterski (USEPA, NERL), Russell Erickson (USEPA, NHEERL), Timothy Gleason (USEPA, NHEERL), John M. Johnston (USEPA,
NERL), Ray Lassiter (USEPA, NERL retired), Richard Lee (USEPA, OPPTS), David Mauriello (USEPA, OPPTS retired), Ross J.
Norstrom (Environment Canada), Brenda Rashleigh (U SEP A, NERL), David Rodgers (Ontario Power T echnologies), Dick Sijm (The
Netherlands National Institute for Public Health and the Environment (RVIM)), Luis Suarez (USEPA, NERL), Donald Rodier
(USEPA, OPPTS), Robert R. Swank (USEPA, NERL retired), and Anett Trebitz (USEPA, NHEERL). Thanks and appreciation are
also given to Robert Ambrose (USEPA, NERL), Ron Beloin(CSC Inc., Athens, GA), Sandra L. Bird (USEPA, NERL), Benjamin
Daniel (CSC Inc., Athens, GA), Mike Galvin (CSC Inc., Athens, GA), Lourdes Prieto (USEPA, NERL), and Frank Standi (USEPA,
NERL).
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1. Introduction
Fish health can be defined from both an ecological and a human
health / value perspective in many ways. Questions relating to
fish health from an ecological perspective often include:
1)	Are individual fish growth and condition sufficient to
enable them to survive periods of natural (e.g.,
overwintering) and man induced stress?
2)	Are individual fish species able to maintain sustainable
populations? For example, is individual growth adequate
for fish to attain the minimum body size required for
reproduction? Is there adequate physical environment for
successful spawning? Is there adequate physical habitat
for the survival of the young-of-year?
3)	Do regional fish assemblages exhibit their expected
biodiversity or community structure based on
biogeographical and physical habitat considerations?
4)	Are regional fish assemblages maintaining their expected
level of productivity based on biogeographical and
physical habitat considerations?
5)	Are appropriately sized fish abundant enough to maintain
piscivorous wildlife (e.g., birds, mammals, and reptiles)
during breeding and non-breeding conditions?
6)	Are potential fish prey sufficiently free of contaminants
(endocrine disruptors, heavy metals, etc.) so as not to
interfere with the growth and reproduction of piscivorous
wildlife?
7)	How will native fishes respond to the introduction of
nonnative fish species, including those stocked for
recreational fishing?
From a human health or use perspective, another important
question related to fish health is:
8)	Is the fish community / assemblage of concern fishable?
That is, are target fish species sufficiently abundant and of
the desired quality? Fish quality is this context is often
defined in terms of desired body sizes (e.g., legal or
trophy length) and the absence of chemical contaminants.
Some of the important metrics or indicators that have often been
used to assess such questions include: 1) physical habitat
dimensions, e.g., bottom type and cover, occurrence of structural
elements such as woody debris or sand bars, mean and peak
current velocities, water temperature, sediment loading, etc., 2)
community species and functional diversity, 3) total community
biomass (kg/ha or kg/km), 4) the population density (fish/ha or
fish/km) or biomass (kg/ha or kg/km) of the community's
dominant species, 5) the age or size class structure of the
community's dominant species, 6) annual productivity of the
community and its dominant species, 7) individual growth rates
or condition factors (i.e., the fish's current body weight
normalized to an expected body weight based on its current
length), and 8) levels of chemical contaminants in muscle or
whole fish.
From the perspective of evaluating alternative management
options or of assessing expected future consequences of existing
conditions, simulation models that can predict the individual and
population growth of fish and their patterns of chemical
bioaccumulation are important tools for analyzing several of the
aforementioned dimensions of fish health.
Although the growth of individual fish has often been described
using empirical models such as the von Bertalanffy, logistic,
Gompertz, or Richards models (see for example Ricker (1979)
and Schnute (1981)), process-based bioenergetic models such as
those described by Kitchell et al. (1977), Minton and McLean
(1982), Stewart et al. (1983), Cuenco et al. (1985), Stewart and
Binkowski (1986), Beauchamp et al. (1989), Stewart and Ibarra
(1991), Lantry and Stewart (1993), Rand et al. (1993), Roell and
Orth (1993), Hartman and Brandt (1995a), Petersen and Ward
(1999), Rose et al. (1999), Schaeffer et al. (1999), and van Nes
(2002) are becoming the models of choice for predicting the
growth of fish. Because these process-based models predict fish
growth based on the mass or energy balance of ingestion,
egestion, respiration, specific dynamic action, and excretion, they
can generally be parameterized independently of their current
application. Moreover, because of the inherent difficulties in
obtaining reliable field-based measurements of fish population
dynamics and productivity, researchers are increasingly using
such bioenergetic models to characterize these population and
community level endpoints. See for example Stewart and Ibarra
(1991) and Roell and Orth (1993).
The ability to predict accurately the bioaccumulation of
chemicals in fish has become an essential component of
ecological and human health risk assessments for chemical
pollutants. Not only are accurate estimates needed to predict
realistic dietary exposures to humans and piscivorous wildlife,
but such estimates are also needed to assess more accurately
potential ecological risks to fish assemblages themselves.
Although exposure-referenced benchmarks such as LC50 and
EC50 have been widely used for hazard assessments, most
deleterious effects of chemical pollutants are caused by the
internal accumulation of those compounds, rather than their
environmental concentrations per se. Many authors (Neely 1984,
Friant and Henry 1985, McCarty et al. 1985, McCarty 1986,
Connell and Markwell 1992, McCarty and Mackay 1993,
Verhaar et al. 1995, van Loon et al. 1997) have discussed the
benefits of explicitly considering chemical bioaccumulation
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when assessing expected ecological consequences of chemical
pollutants in aquatic and marine ecosystems. Residue-based
toxicity studies confirm this supposition (Opperhuizen and
Schrap 1988, van Hoogen and Opperhuizen 1988, Donkin et al.
1989, Tas et al. 1991, van Wezel et al. 1995, Driscoll and
Landrum 1997).
Although concentrations of moderately hydrophobic chemicals
in fish can often be predicted accurately assuming equilibrium
partitioning of the chemicals between the fish's organic
constituents and the aqueous environment, this approach
frequently fails to predict observed concentrations of extremely
hydrophobic chemicals and metals that are often the chemicals
of greatest concern. Observed deviations can be either
considerably above or below those predicted by equilibrium
partitioning. Several factors can be identified to explain these
discrepancies.
Lower than expected contamination levels can result when the
length of exposure is insufficient to allow chemicals to
equilibrate. Because bioconcentration and bioaccumulation are
generally treated as linear, first order processes, the time needed
for chemicals to equilibrate between fish and their exposure
media is an increasing function of the elimination half lives of
those chemicals in fish. For example, the time required for
chemicals to achieve 95% of their equilibrium concentrations is
approximately 4.3 times their elimination half lives. Because the
elimination half lives of chemicals generally increase as their
hydrophobicities increase, the time needed for chemicals to reach
their equilibrium concentrations in fish also increases as a
function of chemical hydrophobicity. Consequently, for
extremely hydrophobic chemicals such as polychlorinated
biphenyls (PCBs) and dioxins that have elimination half lives
ranging from months to over a year, the time to equilibrium can
be on the order of years. If the fish species of concern is short
lived, the time needed for equilibrium can exceed the species
expected life span. Even when time is sufficient for equilibration,
whole-body concentrations of fish can be much lower than that
expected from thermodynamic partitioning due to physical
dilution of the chemical that accompanies body growth or due to
in situ biotransformation of the parent compound.
One of two possible assumptions is implicitly made whenever
equilibrium-based estimators are used. The first of these
assumptions is that only the selected reference route of exposure
is significant in determining the total chemical accumulation in
fish. The alternative assumption is that there are multiple routes
of exposure that all covary with the chosen reference pathway in
a constant manner. For bioconcentration factors (BCFs), the
implicit assumption is that virtually the entire burden is
exchanged directly with the water across the fish's gills or
possibly across its skin. Although direct aqueous uptake is
certainly the most significant route of exchange for moderately
hydrophobic chemicals, dietary uptake accounts for most of a
fish's body burden for extremely hydrophobic chemicals. This
shift in the relative significance of the direct aqueous versus the
dietary pathway is determined by the relative rates of exposure
via these media and by a fundamental difference in the nature of
chemical exchange from food and water. Consider, for example,
the relative absolute exposures to a fish via food and water. The
fish's direct aqueous exposure, AE (|ig/d), is the product of its
ventilation volume, Q (ml/d), and the chemical's aqueous
concentration, Cw ((ig/ml). Similarly, the fish's dietary exposure,
DE ((ig/d), is the product of its feeding rate, Fw (g wet wt/d), and
the chemical's concentration in the fish's prey, Cp (|ig/g wet wt).
If the fish feeds only on one type of prey that has equilibrated
with the water, one can calculate when the fish's aqueous and
dietary exposures are equal using the equations
AE = DE
QCW = FWCP	(1.1)
Q/Fw = BCF
Using data from Stewart et al. (1983) and Erickson and McKim
(1990), the ventilation-to-feeding ratio for a 1 kg trout would be
on the order of 104 3 ml/g. Assuming the quantitative structure
activity relationship (QSAR) for the trout's prey is BCF = 0.048
Kow (Mackay 1982), one would conclude that food is the trout's
predominant route of exposure for any chemical whose octanol
/ water partition coefficient is greater than 105 6. For extremely
hydrophobic chemicals, not only will fish be more exposed via
food but they probably will assimilate chemicals from food more
effectively than from the water. Although chemical exchange
from both food and water occur by passive diffusion, uptake
from food, unlike direct uptake from water, does not necessarily
relax the diffusion gradient into the fish. This fundamental
difference results from the digestion and assimilation of food that
can actually cause the chemical concentrations of the fish's gut
contents to increase (Connolly and Pedersen 1988, Gobas et al.
1988). Predicting residue levels for chemicals whose principal
route of exchange is dietary is further complicated since most
fish species demonstrate well-defined size-dependent, taxonomic,
and temporal trends regarding the prey they consume.
Consequently, one would not generally expect a single BAF to
be sufficiently accurate for risk assessments for all fish species
or even different sizes of the same species.
Process-based models that describe the kinetic exchange of
chemicals from food and water in concert with the growth of fish
provide objective and scientifically sound frameworks that can
overcome many of the aforementioned limitations of equilibrium-
based BAFs and BCFs. Although numerous models have been
developed toward this end (Norstrom et al. 1976, Thomann
1981, Jensen et al. 1982, Thomann and Connolly 1984, Barber
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etal. 1987, Gobasetal. 1988, Barber etal. 1991, Thomannetal.
1992, Gobas 1993, Madenjian et al. 1993), these models differ
significantly regarding how food web structure and dietary
exposures are represented.
This report describes the theoretical framework,
parameterization, and use of bass (Bioaccumulation and Aquatic
System Simulator). This generalized, process-based, Fortran 95
simulation model is designed to predict the growth of individuals
and populations within an age-structured fish community and the
bioaccumulation dynamics of those fish when exposed to
mixtures of metals and organic chemicals. The model is
formulated such that its parameterization does not rely upon
calibration data sets from specific toxicokinetic and population
field studies, but rather upon physical and chemical properties
that can be estimated using chemical property calculators such as
clogp (http://www.biobyte.com/bb/prod/clogp40.html). or sparc
(Carreira et al. 1 994) (also see http: //
ibmlc2. chem. uga. edu/sp arc/style/welcome. cfm) and on
ecological, morphological, and physiological parameters that can
be obtained from the published literature or computerized
databases.
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2. Model Formulation
To model the chemical bio accumulation and the growth of
individuals and populations within an age-structured fish
community, bass solves the following system of differential
equations for each age class or cohort of fish:
dB
dlV,
dt
—- = J. + Ji ~ Jht
dt g '
= F, - E. - R - EX - SDA
(2.1)
(2.2)
Because the system of units used to formulate chemical
exchanges is essentially the CGS-system (centimeter, gram,
second) and the system of units used to formulate a fish's growth
is the CGD-system (centimeter, gram, day), some units
conversion is necessary to make the coupled system of equations
dimensionally consistent. The reader should also note that
whereas the growth of fish is modeled in terms of dry weight, a
fish's chemical bioaccumulation is formulated in terms of its live
body weight since bass models chemical uptake and excretion by
fish as chemical diffusion between aqueous phases.
— = -EM-NM-PM
dt
(2.3)
where Bf and Wd are the chemical body burden ((ig/fish) and dry
body weight (g dry wt/fish), respectively, of the average
individual within the cohort; and N is the cohort's population
density (fish/ha). In Equation (2.1), Jg is the net chemical
exchange ((ig/d) across the fish's gills from the water; Jt is the net
chemical exchange ((ig/d) across the fish's intestine from food;
and Jbt is the chemical's biotransformation rate ((ig/d). In
Equation (2.2), Fd, Ed, R, EX, and SDA are the fish's feeding,
egestion, routine respiration, excretion, and specific dynamic
action (i. e., the respiratory expenditure in excess of R required to
assimilate food), respectively, in units of g dry wt/d. Although
many physiologically based models for fish growth are
formulated in terms of energy content and flow (e.g., kcal/fish
and kcal/d), Equation (2.2) is fundamentally identical to these
bioenergetic models since the energy densities of fish depend on
their dry weight (Kushlan et al. 1986, Hartman and Brandt
1995b, Schreckenbach et al. 2001). Finally, in Equation (2.3)
EM, NM and PM are the cohort's rates (fish/ha/d) of
emigration/dispersal, non-predatory, and predatory mortality,
respectively. Although immigration can be a significant process
in determining population sizes, this process is not presently
modeled in bass. Because cohort recruitment is treated as a
boundary condition, the right-hand side of Equation (2.3) does
not require a term for recruitment. Though it may not be
immediately apparent from the notation used, these equations are
tightly coupled to one another. For example, the realized feeding
of fish depends on the availability (i.e., density and biomass) of
suitable prey. The fish's predatory mortality, in turn, is
determined by the individual feeding levels and population
densities of its predators. Finally, the fish's dietary exposure is
determined by its rate of feeding and the levels of chemical
contamination in its prey.
The following sections describe how each mass flux in the above
system of equations is formulated in bass . T able 2.1 summarizes
the definitions of the variables used to develop these equations.
2.1. Modeling Internal Distribution of Chemicals
Chemical exchanges across gills of fish and from their food are
generally considered to occur by passive diffusion of chemicals
between a fish's internal aqueous phase and its external aqueous
environment, whether the latter is the surrounding ambient water
or the aqueous phase of the fish's intestinal contents.
Consequently, to model these exchanges one must first consider
how chemicals distribute within the bodies of fish. If a fish is
conceptualized as a three-phase solvent consisting of water, lipid,
and non-lipid organic matter, then its whole-body chemical
concentration can be expressed as
B,
Cf= — =P„C+P,C,+ Pn c
J	a a 11 o o
( \
c. c
P+P.— +P,—
(2.4)
where Ww is the fish's live weight (g wet wt/fish); Pa, Ph and Pa
are the fractions of the whole fish that are water, lipid, and non-
lipid organic material, respectively; and Ca, Cb and Ca are the
chemical's concentrations in those respective phases. Because
the depuration rates of chemicals from different fish tissues often
do not differ significantly (Grzenda et al. 1970, van Veld et al.
1984, Branson et al. 1985, Norheim and Roald 1985, Kleeman
et al. 1986a, b), internal equilibration between these three phases
can be assumed to be rapid in comparison to external exchanges.
For organic chemicals, this assumption means that Equation (2.4)
simplifies to

(2.5)
where K, and Ka are the chemical's partition coefficients between
lipid and water and between organic carbon and water,
respectively.
For metals, however, Equation (2.4) is more complicated.
Although metals do partition into lipids (Simkiss 1983), their
accumulation within most other organic media occurs by
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complexation reactions with specific binding sites. Consequently,
for metals the term PaCJCa in Equation (2.4) could be
formulated as a function of an appropriate stability coefficient
and the availability of binding sites. Appendix A summarizes an
equilibrium complexation model that was initially formulated for
bass. Despite its apparent correctness, this algorithm greatly
overestimated metal (in particular mercury) bioaccumulation in
fish. Although this overestimation can be attributed to several
factors, the most likely explanation for the algorithm's
unsatisfactory performance is that kinetics limits the
complexation of metal in fish. Because kinetic modeling was
considered incongruent with the time scales of most of the other
major processes represented elsewhere in bass, a much simpler
algorithm was adopted.
Because many fate and transport models (e.g., exams and
WASP) have successfully used operationally defined distribution
coefficients Kd to model the accumulation of metals in organic
media, a similar approach was adopted for bass. Thus, for a
metal
one then obtains
Cr{Pa^PlKl^P0Kd)Ca
(2.6)
where K, is again an appropriate partition coefficient between
lipid and water; and Kd is an appropriate metal-specific
distribution coefficient. Although this equation appears identical
to Equation (2.5) for organic contaminants, the relative values of
Kd and Ka in relation to K, can be remarkably different. See
Section 3.1.
J=Ska
g g g
C
w v
Kf,
(2.9)
Although the chemical's conductance kg could be specified as a
ratio of the chemical's diffusivity to the thickness of an
associated boundary layer, implementation of this definition can
be problematic since the boundary layer thickness is a function
of the gill's ventilation velocity and varies along the length of the
gill's secondary lamellae. To avoid this problem, a fish's net
chemical exchange rate coefficient, Sg kg , can be estimated by
reformulating the gill's net chemical exchange as

(2.10)
where Q is the fish's ventilation volume (cm3/s); and CB is the
chemical's bulk concentration in the expired gill water. When
Equations (2.8) and (2.10) are equated, it follows that
Sk-
g g
Q
C -C
c - c
(2.11)
Despite its appearance, the right-hand side of this equation can
be readily quantified. In particular, the ventilation volume of fish
can be estimated by
Q =
R.
02
a02 Cwm
(2.12)
Because Ca equals the chemical's ambient environmental water
concentration Cw at equilibrium, it follows from Equations (2.4)
and (2.6) that a fish's thermodynamic bioconcentration factor (Kf
= Cf/Cw at equilibrium) for a chemical pollutant of concern is
K,= -
Pa + PtKt + PoKo for organics
P+P,K, + PK,
a 11 o a
for metalics
(2.7)
where R01 is the fish's rate of oxygen consumption (|ig/s); aQ2 is
the fish's oxygen assimilation efficiency; and CwQ2 is the
environmental water's dissolved oxygen concentration ((ig/ml).
If one makes certain assumptions concerning the geometry of the
interlamellar spaces and the nature of mass transport between the
gill's secondary lamellae, the chemical's normalized bulk
concentration in the expired gill water (Cw-CB)/(Cw-Ca) can also
be calculated as outlined in the following.
2.2. Modeling Exchange from Water
Because chemical exchange across the gills of fish occurs by
simple diffusion, such exchanges can be modeled by Fick's first
law of diffusion as
J = S k (C - C )
g g g\ W "I
(2.8)
where Sg is the fish's total gill area (cm2); kg is the chemical's
conductance (cm/s) across the gills from the interlamellar water;
and Cw is the chemical's concentration ((ig/ml) in the
environmental water (Yalkowsky et al. 1973, Mackay 1982,
Mackay and Hughes 1984, Gobasetal. 1986, Gobas and Mackay
1987, Barber et al. 1988, Erickson and McKim 1990). When
Equations (2.5), (2.6), and (2.7) are substituted into this equation,
Because the gill's secondary lamellae form flat channels having
high aspect ratios (i.e., mean lamellar height / interlamellar
distance), they can be treated as parallel plates, and the flow of
water between them can be treated as Poiseuille slit flow (Hills
and Hughes 1970, Stevens and Lightfoot 1986). Under this
assumption, an expression for a chemical's concentration in the
bulk expired gill water can be obtained using the solutions of the
partial differential equation (PDE) that describes steady-state,
convective mass transport between parallel plates, i.e.,
Sy dx2
(2.13)
where Vis the gill's mean interlamellar flow velocity (cm/s); D
is the chemical's aqueous diffusivity (cm2/s); and x andy are the
BASS 2.2 March 2008
5

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lateral and longitudinal coordinates of the channel along which
diffusion and convection occurs, respectively. In this equation,
C = C(x, y) is the chemical's interlamellar concentration at the
distances x from the surface of the lamellae and y along its
length. The surfaces of adjacent lamellae are located at x = ±r
where r is the hydraulic radius of the lamellar channel that equals
half the interlamellar distance d (cm). The midline between
adjacent lamellae is therefore denoted by x=0. The gill's mean
interlamellar flow velocity can be readily formulated as the ratio
of the fish's ventilation volume to the gill's cross sectional pore
area, Xg (cm2). Because the gill's pore area is related to its
lamellar surface area by
V
/
(2.14)
where dis the mean interlamellar distance (cm); and I is the mean
lamellar length (cm) (Hills and Hughes 1970), a fish's mean
interlamellar flow velocity is given by
Qi
sgd
(2.15)
To solve Equation (2.13), two boundary conditions must be
specified. Because adjacent lamellae presumably exchange the
chemical equally well, the solutions should be symmetrical about
the channel's midline. To insure this characteristic, the boundary
condition
dc
dx
= 0
(2.16)
chemical's concentration in the afferent lamellar blood; Js(y, 1) is
the chemical's accumulated uptake ((ig/s) along the lamellar
segment \y, /]; and qp is the lamellar perfusion rate (cm3/s). If
both sides of the lamella uptake chemical, then Js(y, I) can be
formulated as
w2tID f
y o
= 2hD I —
dx
'/
dudv
dv
(2.19)
where h is the height (cm) of the secondary lamella. Using this
expression, the boundary condition (2.18) can now be written as
Dac
dx
= - k
C(r,y) - Ca -
2 hD
fdC
J dx
dv
(2.20)
Once the solution of Equation (2.13) for these boundary
conditions has been obtained, the chemical's bulk concentration
in the expired gill water can be evaluated using the weighted
average
r
I
C(x,l)
1 -
dx

x = 0
r
/
(2.21)
1 - — dx
r1
is assumed. The second necessary boundary condition must
describe how chemical exchange across the secondary lamellae
actually occurs. Assuming steady state diffusion from the
interlamellar water to the fish's aqueous blood, this boundary
condition can be formulated as
dx
= ~km[C(r,y)-Ca]
(2.17)
where km is the permeability (cm/s) of the gill membrane.
Although this boundary condition has been used as is (Barber et
al. 1991), it can also be modified to address potential perfusion
limitations on gill uptake. To accomplish this latter task, a
formulation patterned after Erickson and McKim (1990) is used.
In particular, consider the following reformulation
DdC
dx
kJc(r>y)
ca(D +
Js(y> 0
(2.18)
where Ca(y) is the chemical's aqueous phase concentration at
point y along the length of a secondary lamella; CJJ) = Ca is the
that scales each concentration profile C(x, /) by its relative
velocity.
A canonical solution to Equation (2.13) can be obtained by
nondimensioning C(x, y), x, and y as follows
© =
C-C
c -c
w a
x=
yD
Vr1
(2.22)
(2.23)
(2.24)
When this is done, the chemical's dimensionless bulk
concentration is given by
BASS 2.2 March 2008
6

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©D =
c„-c.
I&(X,N&)( 1 -X2)dX
c -c
w a
1
/-
(2.25)
X2dX
where NGz = (I D)/(V r2) is the gills' dimensionless lamellar
length or Graetz number. Two important features of this
expression can now be observed. First, one can easily verify that
1 - 0„ = ¦
CW~CB
c... - c
(2.26)
Consequently, Equation (2.11) can be rewritten as
Sgkg = Q(l-&B)	(2.27)
Secondly, analytical expressions for ®B are readily available
(Brown 1960, Grimsrud and Babb 1966, Colton et al. 1971,
Walker and Davies 1974). In particular, a chemical's
dimensionless bulk concentration can be evaluated by
©*=£ Bmexp(-%X2mNGz)
(2.28)
i = 0
where the coefficients Bm and exponents Xm are known functions
of the gills' dimensionless conductance or Sherwood number
*» = ¦
Ks
D
(2.29)
and the fish's ventilation / perfusion volume ratio. See Appendix
B. Although this infinite series solution does not have a
convenient convergence formula, for Sherwood numbers and
ventilation / perfusion ratios that are typical of fish gills, only the
first two terms of the series are needed to estimate ®B with less
than 1% error (see Barber et al. 1991). See Figure 2.1 and
Figure 2.2 for displays of Xj and Bx and of X2 and B2,
respectively.
2.3. Modeling Exchange from Food
Chemical uptake from food has usually been modeled assuming
that fish assimilate a constant fraction of the chemical that they
ingest, i.e.,
J. = a C F
I C p W
(2.30)
where ac is the assimilation efficiency (dimensionless) for the
chemical; Cp is the chemical's concentration (|ig/g wet wt) in the
ingested prey; and Fw is the fish's daily wet weight prey
consumption (g wet wt/d) (Norstrom et al. 1976, Jensen et al.
1982, Thomann and Connolly 1984, Niimi and Oliver 1987).
However, because the chemical exchange across the intestine is
driven by diffusive gradients (Vetter et al. 1985, Clark et al.
1990, Gobas et al. 1993), such formulations are
thermodynamically realistic only if ac is a decreasing function of
the fish's total body concentration Cf.
A thermodynamically based description for the dietary uptake of
chemicals can be formulated using the simple mass balance
relationship
J. = C F
I p W
¦CE
(2.31)
where Ew is the fish's daily wet weight egestion (g wet wt/d) and
Ce is the pollutant's chemical concentration (|ig/g wet wt) in the
fish's feces. When this equation is reformulated in terms of dry
weight feeding and egestion (i.e., Fd = PdpFw and Ed = PdeEw
where Pdp and Pde denote the prey's and feces' dry weight
fractions, respectively) the fish's net dietary exchange becomes
J =
CnF. ,
_p_d _ c
P	\ *
dp
CnF,
p <*
dp
CnF,
p <*
dp
CnF,
p <*
E+C
C
de
¦E*)
C,.E,
CE.,
(2.32)
dp
CE.
de
where Cae and Cde are the pollutant's chemical concentrations in
the aqueous and dry phases of the fish's feces, respectively; Ea is
the mass / volume of the feces' aqueous phase; and Pm and Pap
are the aqueous fractions of the fish's feces and prey,
respectively. To parameterize Equation (2.32), two assumptions
are made.
Because the transit time through the gastrointestinal tract is
relatively slow, the first of these assumptions is that the
concentrations of chemicals in the fish's aqueous blood,
intestinal fluids, and dry fecal matter equilibrate with one
another. Consequently, Cae = Ca. Moreover, for organic
chemicals the concentration ratio Cde / Cae can be replaced with
an organic carbon / water partition coefficient, Koc (e.g., Briggs
1981, Karickhoff 1981, Chiou et al. 1986), and for metals this
ratio can be substituted with a distribution coefficient similar to
the one used in Equation (2.6).
Although reported values for the percent moisture of the
intestinal contents of fish vary typically between 50 and 80%
(Brett 1971, Marais and Erasmus 1977, Grabner and Hofer
BASS 2.2 March 2008
7

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1985), the second assumption made to parameterize Equation
(2.32) assumes that the fish's intestinal contents and whole body
are osmotically equilibrated. Consequently, Pae = Pa. If this
assumption is reasonable, then meals with the same dry weights
but different wet weights should be processed by the fish at equal
rates and efficiencies since both will attain the same proximate
composition relatively soon after ingestion. Having the same
proximate composition implies that the concentrations of
digestive enzymes acting on the meals will be comparable and
that the physical forces exerted by the gut contents that control
gastric mobility will also be comparable. Because Bromley
(1980), Garber(1983), andRuohonenetal. (1997) demonstrated
that initial dietary moisture content had no significant effect on
the assimilation efficiencies of turbot (Scophthalmus maximus)
or on gastric evacuation rates of yellow perch (Perca jlavescens)
and rainbow trout (Oncorhynchus mykiss), respectively, the
assumption that Pae = Pa seems reasonable.
1991). Moreover, using in situ preparations of channel catfish
intestines Doi et al. (2000) have clearly established that
preexposures to 3,4,3',4'-tetrachlorobiphenyl do decrease
intestinal uptake rates.
Although the preceding model development demonstrates the
potential logical inconsistency between an assumed constant
chemical assimilation efficiency model for dietary chemical
uptake and a thermodynamically based model, many researchers
continue to use the former assumption and model. Parameters for
these constant chemical assimilation efficiency models generally
have been estimated using the following equations proposed by
Bruggeman et al. (1981)
dC
dt
I=ac
-------
described by
dC,
dtf-[&cV™^P ""i r^i ,v2
C-L Ct
(^)]exp[-4(?-^)] (2.40)
where ac and k2 are the fish's assimilation efficiency and
apparent elimination rate coefficient that may require updating
for tl < t < t2. If an equation of the form of Equation (2.37) is
assumed to describe the fish's bioaccumulation dynamics over
the entire interval [ta, t2\, then the derivatives specified by
Equations (2.39) and (2.40) must be equal when evaluated for t
= tx. This consistency condition, that is analogous to the
preservation of derivatives that occurs when approximating a
function with Bernstein polynomials, requires that
W™Cp-k2CAh)

which implies that
; Cp k2 Cf
¦(/o)] exp[
-K
'i-'o
(2.41)
k2 Cf(tx)
^ WW ^ p
a -
k2 Cy(tQ)
m C
T1W p
exp[-^2(?i -/0)] (2.42)
When the fish's initial whole-body concentration is zero, this
equation can be shown to reduce to
£
l --i
h)
exP[ ~^2 ('i " *o)] [ (2.43)
k2, chemical assimilation
This equation shows that unless k2
efficiencies estimated for different times and initial whole-body
concentration will be different. Phrased another way, this
equation implies that the fish's ability to excrete, biodilute, and
biotransform chemicals, as measured by k2 and k2, contributes
to the determination of the fish's realized chemical assimilation
efficiencies. Because specific growth rates (¦y = W~1 dWw Idt) and
chemical excretion rate coefficients (kex) for fish are generally
related to the fish's body size as allometric power functions, i.e.,
T = «i K
*«=pX
(2.44)
(2.45)
where a2<0 and P2<0 (Barber et al. 1988, Sijm et al. 1993,
Sijm and van der Linde 1995, Sijm et al. 1995), one would
expect that k2 < k2 when significant growth occurs during the
experiment. Consequently, one would also expect that ac
-------
where p0 and p1 are the reaction rates of the process at
temperatures T0 and Tu respectively, such descriptions are
generally valid only within a range of the organism's thermal
tolerance. In most cases, the process's reaction rate increases
exponentially with increasing temperature up to a temperature Tl
after which it decreases. Moreover, the temperature at which a
process's rate is maximum is often very close to the organism's
upper thermal tolerance limit. To model this behavior, Thornton
and Lessem (1978) developed a logistic multiplier to describe the
temperature dependence of a wide variety of physiological
processes. Although this algorithm has been used successfully in
many fish bioenergetic models, bass uses an exponential-type
formulation that responds hyperbolically to increasing
temperature. Importantly, such algorithms can be easily
parameterized.
Let P denote the rate of a physiological process, and let Tl denote
the temperature at which the rate is at its maximum value. If this
process generally exhibits an exponential response to
temperature changes well below Tu then Equation (2.48) can be
used to describe this process for T and T0 « Tu i.e.,
p = p0exp[6(r-r0)]
dP _
	 = E P
dT
(2.49)
(2.50)
where P0 is the process's rate at the low-end reference
temperature T0. To incorporate the adverse effects of high
temperatures on this process, the right-hand side of Equation
(2.50) can be multiplied by a hyperbolic temperature term that
approaches unity as temperature decreases well below Tu equals
zero at Tu and becomes increasingly negative as temperature
approaches the fish's upper thermal tolerance limit TL = T2.
Modifying Equation (2.50) in this fashion yields
dP _
— = eP
dT
r-r.
whose solution is
P = P0exp[e(r-r0)]
t2-t
T - T
12 0

(2.51)
(2.52)
If one assumes, without loss of generality, that T0 = 0, the
preceding equation can be simplified to
' f\e&~ ^i)
P = P0exp{£T)
1 -
(2.53)
Figure 2.3 displays the behavior of this equation for P0 = 1 and
T2 = 36 Celsius as a function of e and Tx. Although these
equations apparently have not been used to describe
physiological responses of fish, their utility for doing so is
discussed subsequently in Section 3.3. For other applications of
Equations (2.52) and (2.53) see Lassiter and Kearns (1974),
Lassiter (1975), and Swartzman and Bentley (1979). Note that
when Tl = T2, Equation (2.53) reduces to Equation (2.49).
2.6. Modeling Growth of Fish
Although the preceding algorithms for modeling chemical
bioaccumulation in fish depend on a fish's live weight, bass does
not directly simulate the live weight of fish. Instead, it simulates
the dry weight of fish as the mass balance of feeding, egestion,
respiration, and excretion and then calculates the fish's
associated wet weight using the following relationships
w= W„ + Wj
W+W. + wn
a I o
I w
*1 vrw
P. = an
a1Pl
P. = 1 - P.
(2.54)
(2.55)
(2.56)
(2.57)
where Wa, Wd, Wh and Wa are the fish's aqueous, dry, lipid, and
non-lipid organic weights, respectively; and a0, ab /b and l2 are
empirical constants. Whereas Equations (2.54) and (2.57) are
simply assertions of mass conservation, Equations (2.55) and
(2.56) are purely empirical functions. Although Equation (2.55)
is assumed because simple power functions of this form
adequately describe many morphometric relationships for most
organisms, Equation (2.56) is based on the results of numerous
field and laboratory studies (Eschmeyer and Phillips 1965, Brett
et al. 1969, Groves 1970, Elliott 1976a, Staples and Nomura
1976, Craig 1977, Shubina and Rychagova 1981, Beamish and
Legrow 1983, Weatherley and Gill 1983, Flath and Diana 1985,
Lowe et al. 1985, Kunisaki et al. 1986, Morishita et al. 1987).
These equations yield an expression for a fish's live weight that
is a monotonically increasing, but nonlinear, function of the
fish's dry weight.
bass calculates a fish's realized feeding by first estimating its
maximum ad libitum consumption (Frf* g dry wt/d) and then
adjusting this potential by the availability of appropriate prey as
described in the next section. Because a variety of models are
commonly used to describe the maximum feeding of fish, bass
is coded to allow users the option of using any one of four
different feeding models for any particular age / size class of fish.
The first formulation that can be used is a temperature-dependent
power function
F;=/X2exp(/3r)
l-T

(2.58)
BASS 2.2 March 2008
10

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where/i,/2,/3, Tu and T2 are empirical constants specific to the
fish's feeding.
A commonly used alternative to the preceding allometric model
is the Rashevsky-Holling model that is defined by the equations
K = ^{Gm-G)
dG
dt
= Fd~Ad~Ed
(2.59)
where cpdd is the fish's ad libitum feeding rate (g dry wt/g dry
wt/d); Gmax is the maximum amount of food (g dry wt/fish) that
the fish's stomach / gut can hold; G is the actual amount of food
(g dry wt/fish) present in the gut; and Ad and Ed are the fish's
assimilation and egestion, respectively, in units of g dry wt/d
(Rashevsky 1959, Holling 1966). Given a fish's gut capacity
Gmax, feeding time tsat to satiation, and satiating meal size Msat,
cpdd can be estimated using the equations
t
ck
dFl
dt
= (?dd[Gm^~Fd
-%dL, = ]n
l -

(2.60)
(2.61)
(2.62)
where Fd (f) is the total food consumed during the interval (0, t)
Msat = Fd(tsat) (also see Dunbrack 1988). Alternatively, (pdd
can be estimated by simply assuming thatMja( = 0.95 x Gmax in
which case
In 0.05
(2.63)
For planktivores, bass can also estimate a fish's maximum
ingestion using the clearance volume model
K = vqc1
(2.64)
where is the plankton standing stock (g dry wt/L); and Qd is
the planktivore's clearance volume (L/d) that is assumed to be
given by
Qci = ^w2exp(g3r)
1 -
(2.65)
where qx, q2, q3, Tx, and T2 are empirical constants specific to the
fish's filtering rate.
The fourth and final feeding model back-calculates a fish's
expected feeding based on knowing the fish's expected growth
and routine respiratory demands. In particular, because
assimilation, egestion, specific dynamic action, and excretion are
assumed to be linear functions of feeding and routine respiration
as discussed subsequently, it is a straightforward matter to
calculate a fish's expected ingestion given its expected growth
and respiration. When users elect this feeding option, bass
assumes that the fish's specific growth rate y = Ww1 dWw Idt (l/d)
is given by
\g^2-rt)
Y = giW„'exptg3T)
1 -
(2.66)
where gb g2, g3, Tx, and T2 are empirical constants specific to the
fish's growth rate. See Thomann and Connolly (1984) for
additional discussion of the use of this feeding model.
When bass estimates a fish's feeding rate using Equations
(2.58), (2.64), or (2.66), the fish's assimilation and egestion are
estimated as simple fractions of its realized ingestion Fd, i.e.,
Ad = afFd
Ed= 1
af)Fd
(2.67)
(2.68)
where ay is the fish's net food assimilation efficiency that is a
weighted average of its assimilation efficiencies for invertebrate,
piscine, and vegetative prey. However, when the Rashevsky-
Holling feeding model is used, bass calculates these fluxes by
substituting Fd with a function that describes the fish's pattern of
intestinal evacuation. The general form of this function is
assumed to be
EV = e^G'1 exp(e3r)
1 -

(2.69)
where eu e2, e3, Tu and T2 are empirical constants specific to the
fish's gastric evacuation.
The numerical value of this function's exponent, e2, depends both
on characteristics of the food item being consumed and on the
mechanisms that presumably control gastrointestinal motility and
digestion (Jobling 1981, 1986, 1987). For example, when gut
clearance is controlled by intestinal peristalsis, e2 should
approximately equal 14 since peristalsis is stimulated by
circumferential pressure exerted by the intestinal contents that,
in turn, is proportional to the square root of the contents mass.
On the other hand, when surface area controls the rate of
digestion, e2 should be approximately either % or unity. If the
fish consumes a small number of large-sized prey (e.g., a
piscivore), e2 = % may be the appropriate surface area model. On
the other hand, if the fish consumes a large number of smaller,
BASS 2.2 March 2008
11

-------
relatively uniform-sized prey (e.g., a planktivore or drift feeder),
e2 = 1 is more appropriate since total surface area and total
volume of prey become almost directly proportional to one
another. When e2 = 1, the Rashevsky-Holling model (i.e.,
Equation (2.59)) is analogous to the Elliott-Persson model for
estimating daily rations of fish (Elliott and Persson 1978).
Finally, Olson and Mullen (1986) outlined a process-based
model that even suggests e2 = 0.
A fish's specific dynamic action, i. e., the respiratory expenditure
associated with the digestion and assimilation of food, is
modeled as a constant fraction of the fish's assimilation. In
particular,
SDA - aA,
(2.70)
where o generally varies between 0.15 and 0.20 (Ware 1975,
Tandler and Beamish 1981, Beamish and MacMahon 1988).
where e = 17/14 is the ratio of the molecular weight of ammonia
to that of nitrogen.
2.7. Modeling Predator-Prey Interactions
bass is designed to simulate aquatic food webs in which each
age class of a species can feed upon other fish species, benthos,
incidental terrestrial insects, periphyton / attached algae,
phytoplankton, and zooplankton. The realized feeding of any
given age class of fish is determined by the estimated maximum
feeding rate of individuals within the cohort, the cohort's
population size, and the biomass of prey that is available to the
cohort; the latter quantity is the sum of the current biomass of
potential prey minus the biomass of potential prey expected to be
consumed by other fish cohorts that are more efficient foragers
/ competitors, bass ranks the competitive abilities of different
cohorts using the following assumptions:
bass assumes that body weight losses via metabolism are due
entirely to the respiration of carbon dioxide and the excretion of
ammonia. A fish's respiratory lossi? is therefore calculated from
its routine oxygen consumption, RQ2 (g 02/d), using a respiratory
quotient, RQ (L C02 respired)/L 02 consumed), as follows
R
_ 12 gC
mole CO.
mole C02 22A L C02
'RQ
22.4 L 0- mole O,
mole 02 32 g 02
•R02 = ^'RQ'R02
(2.71)
bass calculates a fish's routine oxygen consumption as a
constant multiple RB of its standard basal oxygen consumption
(Ware 1975) that is assumed to be temperature-dependent power
function. In particlar,
iMzi-r,)
R02 ~ RB bl Ww
' exP(bj T)
1-r
(2.72)
where bu b2, b3, Tu and T2 are empirical constants specific to the
fish's standard basal oxygen consumption. Although ammonia
excretion could be modeled using an analogous function
(Paulson 1980, duPreez and Cockroft 1988a, b), bass calculates
this flux as a constant fraction of the fish's total respiration since
excretion and oxygen consumption generally track one another.
For example, ammonia excretion increases after feeding, as does
oxygen consumption (Savitz 1969, Brett and Zala 1975,
Gallagher et al. 1984). Likewise, conditions that inhibit the
passive excretion of ammonia also depress carbon dioxide
excretion (Wright et al. 1989). Assuming that fish maintain a
constant nitrogen/carbon ratio NC (g N/g C), bass estimates a
fish's excretory loss in body weight as
EX=eNC{R+SDA)	(2.73)
ASSUMPTION 1. The competitive abilities and efficiencies of
benthivores and piscivores are positively correlated with their
body sizes (Garman and Nielsen 1982, East and Magnan 1991).
Two general empirical trends support this assumption. The first
of these is the trend for the reactive distances, swimming speeds,
and territory sizes of fish to be positively correlated with their
body size (Minor and Crossman 1978, Breck and Gitter 1983,
Wanzenbock and Schiemer 1989, Grant and Kramer 1990, Miller
et al. 1992, Keeley and Grant 1995, Minns 1995). Given two
differently sized predators of the same potential prey, these
trends would suggest that the larger predator is more likely to
encounter that prey than is the smaller. Having encountered the
prey, the other general trend for prey handling times to be
inversely correlated with body size (Werner 1974, Miller et al.
1992) suggests that the larger predator could dispatch intercepted
prey and resume foraging more quickly than the smaller predator.
Also see Post et al. (1999) and Railsback et al. (2002).
ASSUMPTION 2. Unlike benthivores and piscivores, the
competitive abilities and efficiencies of planktivores are
inversely related to their body size due to their relative
morphologies (Lammens et al. 1985, Johnson and Vinyard 1987,
Wu and Culver 1992, Persson and Hansson 1999). Consequently,
"large" planktivores only have access to the leftovers of "small"
planktivores.
bass calculates the relative frequencies	of the prey
consumed by a cohort using dietary electivities, i.e.,
drft
e. = •

(2.74)
where f is the relative availability of the z'-th prey with respect to
all other prey consumed by the cohort. One can easily verify that
BASS 2.2 March 2008
12

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the range of dietary electivities is -1 < e;. < 1. One can also
verify that if the fish does not eat a potential food item i
that et = -1. Similarly, if the fish consumes a potential prey item
i in direct proportion to the prey's relative abundance,
then et = 0. bass actually allows users to specify a fish's diet as
either a set of fixed dietary frequencies {...,dja set of
electivities {..., et,...}, or a combination of fixed frequencies and
electivities	To calculate a cohort's realized
dietary composition, however, bass converts all user supplied
fixed dietary frequencies into their equivalent electivities using
the simulated relative abundances {...JJ,...} of the cohort's
potential prey. These electivities are then combined with any user
specified electivities to form a set of unadjusted
electivities	that is subsequently converted into a
consistent set of realized electivities	. Using these
realized electivities, bass then calculates the cohort's realized
dietary frequencies using
d =
1
f,
(2.75)
The important step in this computational process is the
conversion of the unadjusted electivities into a set of realized
electivities. Although this conversion is sometimes unnecessary,
it is generally needed to insure that the sum of the dietary
frequencies calculated by Equation (2.75) equals 1. One can
verify that the condition that guarantees E d( = 1 is
» f
£— = i
1 - e.
(2.76)
See Appendix C. When Equation (2.76) is not satisfied for a
given set of electivities	and relative prey
availabilities {... ,ft,...}, bass transforms the given electivities
using a linear transformation that maps et = -1 into ef = -1
and max(...,e;.,...) into e; L
(L -L . ) (L - L ,)	J'j
v max mm' v max mode'
Because many studies have shown a strong positive correlation
between the body sizes of piscivorous fish and the forage fish
that they consume (Parsons 1971, Lewis et al. 1974, Timmons et
the relative frequencies of these two age classes in the cohort's
diet are calculated to be

BASS 2.2 March 2008
13

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and si2 = di[Pi2/(Pil + Pi2)]. If only one age class of a forage
species is vulnerable to the cohort, then s = dt.
If while calculating the dietary frequencies of a piscivorous
cohort bass predicts that the cohort's available prey is
insufficient to satisfy its desired level of feeding, bass reassigns
the cohort's unadjusted electivities ,...} in a manner to
simulate prey switching. These reassignments are based on the
following assumption:
ASSUMPTION 3. When forage fish become limiting, piscivores
switch to benthic macroinvertebrates or incidental terrestrial
insects as alternative prey. However, piscivores that must switch
to benthos or that routinely consume benthos in addition to fish
are less efficient benthivores than are obligate benthivores
(Hanson and Leggett 1986, Lacasse and Magnan 1992, Bergman
and Greenberg 1994). Consequently, only the leftovers of non-
piscivorous benthivores are available to benthic feeding
piscivores. If such resources are still insufficient to satisfy the
piscivores' metabolic demands, piscivores are assumed then to
switch to planktivory (Werner and Gilliam 1984, Magnan 1988,
Bergman and Greenberg 1994). In this case, piscivores have
access only to the leftovers of non-piscivorous planktivores.
Using this assumption, bass first assigns the cohort's electivity
for benthos to zero regardless of its previous value, bass also
reassigns any other electivity that does not equal -1, to zero.
If benthos becomes limiting for benthivores, or if plankton
becomes limiting for planktivores, bass assumes that benthivores
can shift their diets to include plankton and terrestrial insects and
that planktivores can shift their diets to include benthos and
terrestrial insects. See, for example, Ingram and Ziebell (1983).
After bass has calculated a cohort's dietary composition, it then
assigns the cohort's individual realized feeding rate adjusted for
prey availability as
Fd = max
Fd\ N~l £ ABj
(2.82)
where F*d is the cohort's maximum individual ingestion (g dry
wt/fish); N is the cohort's population size (fish/ha), and ABj is the
biomass (g dry wt/ha) of prey j that is available to that cohort.
Using its predicted dietary compositions and realized feeding
rates, bass then calculates the predatory mortalities for each fish
cohort and nonfish compartments.
2.8. Modeling Dispersal, Non-Predatory
Mortalities, and Recruitment
The algorithm that bass employs to simulate a species' dispersal
and non-predatory mortality is based on the general empirical
observation that population densities of most vertebrates can be
adequately characterized by the self-thinning power function
relationship
N=aW.
(2.83)
where Nis the species' or cohort's density (fish/ha) and Ww is the
species' or cohort's mean live body weight (Damuth 1981, Peters
and Raelson 1984, Juanes 1986, Robinson and Redford 1986,
Dickie et al. 1987, Boudreau and Dickie 1989, Gordoa and
Duarte 1992, Randall et al. 1995, Dunham and Vinyard 1997,
Steingrimsson and Grant 1999, Dunham et al. 2000, Guinez
2005). For fish the body weight exponent b generally varies from
0.75 to 1.5 (Boudreau and Dickie 1989, Grant and Kramer 1990,
Gordoa and Duarte 1992, Elliott 1993, Bohlin et al. 1994,
Randall et al. 1995, Dunham and Vinyard 1997, Grant et al.
1998, Dunham et al. 2000, Knouft 2002, Keeley 2003). Larger
exponents ranging from 1.5 to 3.0, however, have also been
reported (Steingrimsson and Grant 1999). If Equation (2.83) is
differentiated with respect to time, it immediately follows that a
species' population dynamics can be modeled using the time-
varying, linear differential equation
dN
dt
-a b W~b dW„,
W„.
dt
= - byN
(2.84)
where y=Ww dWw/dt is the species' specific growth rate.
Consequently, by corresponds to the cohort's total mortality rate.
Readers interested in detailed discussions concerning the
underlying process-based interpretation and general applicability
of this result should consult Peterson and Wroblewski (1984),
McGurk (1993, 1999), and Lorenzen (1996).
Because Equations (2.83) and (2.84) encompass the cohort's
predatory mortality, non-predatory mortality, and dispersal, and
because bass separately models the cohort's predatory mortality,
bass assumes that the cohort's combined rate of dispersal (EM)
and non-predatory mortality (NM) is simply a fraction 5 of by. In
particular,
EM + NM=5byN
(2.85)
If community population dynamics are strongly dominated by
predation, the fraction 5 will be "small" (e.g., 5 < 0.5) for forage
fishes and "large" (e.g., 5 > 0.5) for predatory species. However,
if community population dynamics are dominated by dispersal
mechanisms related to competition for food, space, or other
limiting community resource, the fraction 5 will be large for both
forage and predatory species alike.
bass estimates a species' recruitment by assuming that each
species turnsover a fixed percentage of its potential spawning
biomass into new young-of-year (YOY). This percentage is
BASS 2.2 March 2008
14

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referred to as the species' reproductive biomass investment (rbi).
The species' spawning biomass is defined to be the total biomass
of all cohorts whose body lengths are greater than or equal to a
specified minimum value (tlr0) marking the species' sexual
maturation. When reproduction is simulated, the body weight of
each sexually mature cohort is decreased by its rbi and the total
number of YOY that are recruited into the population as a new
cohort is estimated by simply dividing the species' total spawned
biomass by the species' characteristic YOY body weight.
Although this formulation does not address the myriad of factors
known to influence population recruitment, it is logically
consistent with the spawners' abundance model for fish
recruitment. See Myers and Barrowman (1996) and Myers
(1997).
2.9. Modeling Habitat Effects
Although bass does not explicitly model physical habitat
features of the fish community of concern, it does allow users to
specify habitat suitability multipliers on the feeding, reproduction
/ recruitment, and dispersal / non-predatory mortality for any or
all species. Because these multipliers are assumed to be
analogous to subcomponents of habitat suitability indices, they
are assumed to take values from 0 to 1. If these multipliers are
not specified by the user, bass assigns them the default value of
1.
When feeding habitat multipliers (HSlfeeding) are specified, bass
uses the specified parameters as simple linear multipliers on the
fish's maximum rate of ingestion, i.e.,
Fd(habitat) = HSIfeeding F*d (2.86)
The resulting adjusted maximum feeding rate then replaces F*d
in Equation (2.82). These multipliers are assumed to modify the
fish's ability to perceive or to intercept prey either by effecting
the fish's reactive distance, foraging patterns, etc. or by
providing modified refuges for its potential prey. Habitat
interactions that actually change the abundance of potential prey
should not be specified as feeding habitat multipliers since these
interactions are automatically addressed by the algorithms
outlined in Section 2.7.
Like the aforementioned feeding habitat multipliers, bass uses
any specified recruitment habitat multipliers (HSIrecruilmenl) as
simple linear multipliers on the number of young-of-year that is
recruited into the species population, i.e.,
N0(habitat) = HSIKcruitment N0	(2.87)
These multipliers can represent either the availability of suitable
spawning sites or the ability of the otherwise successful spawns
to result in the expected numbers of young-of-year as discussed
in Section 2.8.
Finally, when habitat multipliers (HS1 survival) are specified for
dispersal / non-predatory mortality, the specified values are
assumed to dynamically control the species self-thinning
exponent b (see Section 2.8) such that the exponent is maximum
for HSIsurvival = 0 and minimum for HSIsurvival = 1. Thus, as habitat
suitability decreases, dispersal and non-predatory mortality
increases, and vice versa (see Equation (2.85)). In between this
range the species self-thinning exponent is assumed to respond
linearly to changingHSImrvival, i.e.,
b{habitat) = (l - HSIsurvival)(bmax - b^) + bmm (2.88)
Because constructing the aforementioned habitat suitability
multipliers in a general or standard way is not a trivial issue,
bass relegates their construction to the user. Nevertheless, users
might consider several obvious starting points when simulating
habitat effects using bass. Turbidity, for example, is known to
affect the foraging abilities of both prey and predatory fishes, and
one could readily use results of published studies (e.g.,
Vandenbyllaardt et al. 1991, Barrett 1992, Gregory 1993,
Gregory and Northcote 1993, Miner and Stein 1996, Reid et al.
1999, Vogel and Beauchamp 1999, Bonner and Wilde 2002, de
Robertis et al. 2003, Sweka and Hartman 2003) to estimate
feeding multipliers for Equation (2.86) as power functions or
polynomials of turbidity. Field-based HSI's are often estimated
by logistic regression of presence-absence data without
specifying the underlying mechanisms that actually determine
habitat suitability for a species. Such HSI's could be used as
habitat multipliers for species' recruitment (Equation (2.87)) or
persistence/survival (Equations (2.85) and (2.88)) depending on
the user's own interpretation of what the indices most likely
represent.
2.10. Modeling Nonfish Compartments
bass assumes that the nonfish components of a community of
concern can be treated as four lumped compartments, i.e.,
benthos, periphyton/attached algae, phytoplankton, and
zooplankton. These compartments can be treated either as
community forcing functions or as bona fide state variables. In
the later case, the required compartmental dynamics are
simulated using the simple mass balance model
^=IP-R-F-M	(2.89)
where Y is the compartment's biomass (g dry wt/m2); and the
fluxes g dry wt/m2/d IP, R, F, and M are the compartment's
ingestion or photosynthesis, respiration, mortality due to fish
consumption, and non-consumptive mortality and dispersal,
BASS 2.2 March 2008
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respectively. Except for F, each of these fluxes is modeled as a
linear function of the compartment's biomass, i.e.,
R = pY
M=\iY
(2.90)
(2.91)
(2.92)
where the rate coefficients (g dry wt/g dry wt/d) cp^, p, and are
minimally functions of temperature and time.
For benthos, phytoplankton, and zooplankton that can be
conceptualized as populations of organisms possessing similar
body sizes, the rate coefficients 
-------
is suspended by equating the compartment's
feeding/photosynthesis to its projected respiration.
bass assumes that the rates of chemical bioaccumulation in
nonfish compartments are rapid enough to enable chemical
concentrations within these components to be calculated using
simple bioaccumulation factors. In particular,
Cnf = BAFnfCw
(2.100)
where Cnf is the chemical concentration ( g /g dry wt) in the
compartment of concern, bass enables users to specify the
bioaccumulation factor BAFnf(va\lg dry wt) for Equation (2.100)
either as an empirically derived constant, as a quantitative
structure activity relationship (QSAR), or as the ratio of the
chemical's uptake rate to the sum of its excretion rate and the
compartment's growth rate. When BAFnf is specified as a QSAR,
bass assumes that
BAFnf=b,Km
(2.101)
where bx and b2 are empirical constants. When_ft4Fn/is specified
by the compartment's chemical exchange rates and growth rate,
bass assumes that
BAF,
nf
k2+y klIKnf+V
(2.102)
where kx, k2, and y are the rates of uptake, excretion, and growth,
respectively, by individuals comprising the compartment; aa&Knf
is the compartment's thermodynamic bioconcentration factor that
is defined analogously to Equation (2.7). For heterotrophs,
Equation (2.102) assumes that direct chemical uptake and
excretion with the ambient water are dominant over the dietary
uptake and fecal excretion of the organisms of concern. Although
this assumption is not satisfied for all benthic or planktonic
heterotrophs, it does bypass the need to specify feeding rates,
assimilation efficiencies, and dietary compositions for
compartments that are actually mixed functional groups. For
further discussions of Equation (2.102) and its generalization,
readers should consult Connolly and Pedersen (1988), Thomann
(1989), and Arnot and Gobas (2004).
2.11. Modeling Toxicological Effects
Narcosis is defined to be any reversible decrease in physiological
function that is induced by chemical agents. Because the potency
of narcotic agents was originally found to be correlated with their
olive oil / water partition coefficients (Meyer 1899, Overton
1901), it was long believed that the principal mechanism of
narcosis was the disruption of the transport functions of the lipid
bilayers of biomembranes (Mullins 1954, Miller et al. 1973,
Haydonetal. 1977, Janoffetal. 1981,Pringleetal. 1981). More
recently, however, it has been acknowledged that narcotic
chemicals also partition into other macromolecular components
besides the lipid bilayers of membranes. It is now widely
accepted that partitioning of narcotic agents into hydrophobic
regions of proteins and enzymes inhibit their physiological
function either by changing their conformal structure or by
changing the configuration or availability of their active sites
(Eyring et al. 1973, Adey et al. 1976, Middleton and Smith 1976,
Richards et al. 1978, Franks and Lieb 1982, 1984, Law et al.
1985, Lassiter 1990). In either case, however, the idea that the
presence of narcotic chemicals increases the physical dimensions
of various physiological targets to some "critical volume" that
renders them inactive is fundamental (Abernethy et al. 1988).
Consequently, narcotic chemicals can be treated as generalized
physiological toxicants, and narcosis itself can be considered to
represent baseline chemical toxicity for organisms. Although any
particular chemical can act by a more specific mode of action
under acute or chronic exposure conditions, all organic chemicals
can be assumed to act minimally as narcotics (Ferguson 1939,
McCarty and Mackay 1993).
Studies have shown that for narcotic chemicals there is a
relatively constant chemical activity within exposed organisms
associated with any given level of biological activity (Ferguson
1939, Brink and Posternak 1948, Veith et al. 1983). This
relationship holds true not only for exposures to a single
chemical but also for exposures to chemical mixtures. In the case
of a mixture of chemicals, the sum of the chemical activities for
each component chemical is constant for a given level of
biological activity. Because narcotic chemicals can be treated as
generalized physiological toxicants as already noted, it should
not be too surprising that the effects of mixtures of chemicals
possessing diverse specific modes of action not only often
resemble narcosis but also appear to be additive in terms of their
toxic effects (Barber et al. 1987, McCarty and Mackay 1993).
For example, although most pesticides possess a specific mode
of action during acute exposures, the joint action of pesticides is
often additive and resembles narcosis (Hermanutz et al. 1985,
Matthiessen et al. 1988, Bailey et al. 1997).
bass simulates acute and chronic mortality assuming that the
chemicals of concern are an additive mixture of narcotics.
Because this assumption is the least conservative assumption that
one could make concerning the onset of effects, mortalities
predicted by bass should signal immediate concern. When the
total chemical activity of a fish's aqueous phase exceeds its
calculated lethal threshold, bass assumes that the fish dies and
then eliminates that fish's age class from further consideration.
The total chemical activity of a fish's aqueous phase is simply the
sum of the fish's aqueous phase chemical activity for each
chemical, bass calculates the aqueous phase chemical activity of
each chemical using the following formulae
BASS 2.2 March 2008
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A„ = ynM„
a *a a
based analysis of these issues.
" 10 3MW 10 3MWKf
where Aa is the chemical's aqueous activity; y„ is the chemical's
aqueous activity coefficient (L/mol), the reciprocal of its sub-
cooled liquid solubility; Ma is the chemical's molarity within the
aqueous phase of the fish; and MW is the chemical's molecular
weight (g/mol).
bass estimates the lethal chemical activity threshold for each
species as the geometric mean of the species' LA50, i.e., the
ambient aqueous chemical activity that causes 50% mortality in
an exposed population. These lethal thresholds are calculated
using the above formulae with user-specified LC}„ s substituted
for Ca. These calculations are based on two important
assumptions. The first assumption is that the exposure time
associated with the specified LC50 is sufficient to allow almost
complete chemical equilibration between the fish and the water.
The second assumption is that the specified LC50 is the minimum
LC50 that kills the fish during the associated exposure interval.
Fortunately, most reliable LC}„ s satisfy these two assumptions.
See Lassiter and Hallam (1990) for a comprehensive model-
Three points should be mentioned regarding the above approach
to modeling ecotoxicological effects. First, for narcotic
chemicals this approach is analogous to the toxic unit approach
for evaluating the toxicity of mixtures (Calamari and Alabaster
1980, Konemann 1981a, b, Hermens and Leeuwangh 1982,
Hermens et al. 1984a, Hermens et al. 1984b, Broderius and Kahl
1985, Hermens et al. 1985b, Hermens et al. 1985c, Hermens et
al. 1985a, Dawson 1994, Peterson 1994). Second, the approach
is also analogous to the critical body residue (CBR) and total
molar body residue (TBR) approaches proposed by McCarty and
Mackay (1993), Verhaar et al. (1995), and van Loon et al.
(1997). Lastly, although sublethal effects are not presently
modeled by bass, bass's simulation results can be used to
indicate when sublethal effects that are induced by narcotic
agents would be expected to occur. Results reported by Hermens
et al. (1984b) indicate that for Daphnia the ratio of the EC50 for
reproductive impairment to the LC50 is generally on the order of
0.15 - 0.30 for chemicals whose log Kow range from 4 to 8. For
individual growth inhibition, however, the mean EC50 to LC50
ratio for Daphnia in 16 day chronic exposures was
approximately 0.77 (Hermens et al. 1984a, Hermens et al.
1985b). Also see Roex et al. (2000).
BASS 2.2 March 2008
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Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental
model processes, rates, or rate coefficients.
Aa	chemical activity in aqueous fraction of the fish (dimensionless)
Ad	assimilation rate (g dry wt/d)
Bf	chemical burden in whole fish ((ig/fish)
BAFnf bioaccumulation factor for nonfish prey (ml/g dry wt)
Ca	chemical concentration in aqueous fraction of the fish ((ig/ml)
Cae	chemical concentration in aqueous fraction of intestinal contents ((ig/ml)
CB	chemical concentration in bulk interlamellar water ((ig/ml)
Ce	chemical concentration in egesta/feces ((ig/ml)
Cf	chemical concentration in whole fish (|ig/g wet wt)
Cde	chemical concentration in dry organic fraction of intestinal contents ((ig/g dry wt)
C,	chemical concentration in lipid ((ig/g dry wt)
Cnf	chemical concentration nonfish prey ((ig/g dry wt)
Ga	chemical concentration in non lipid organic matter (|ig/g dry wt)
Cp	chemical concentration in prey ((ig/g wet wt)
Cw	chemical concentration in environmental water ((ig/ml)
CwQ2	oxygen concentration in environmental water ((ig/ml)
d	interlamellar distance (cm)
dt	the relative frequency of prey i in a fish's diet (dimensionless)
D	aqueous diffusion coefficient (cm2/s)
e,	the electivity prey i in a fish's diet (dimensionless)
Ed	egestive rate (g dry wt/d)
Ew	egestive rate (g wet wt/d)
EM	emigration/dispersal (fish/ha/d)
EX	excretory rate (g dry wt/d)
f	the relative frequency of prey i in the field (dimensionless)
F*d	maximum feeding rate (g dry wt/d)
Fd	feeding rate (g dry wt/d)
Fw	feeding rate (g wet wt/d)
G	mass of gut contents (g dry wt/fish)
h	height of secondary lamellae (cm)
HSIfeeding habitat suitability index for cohort feeding (dimensionless)
FISIrecruitment habitat suitability index for YOY recruitment (dimensionless)
HSImrvivai habitat suitability index for cohort survival (dimensionless)
Jbt	biotransformation of chemical ((ig/s)
Jg	net chemical exchange across the gills ((ig/s)
Jt	net chemical exchange across the intestine ((ig/s)
k2	apparent elimination rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d), i.e., k2 = (y + kbt + kex)
kbt	chemical biotransformation rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)
kex	chemical excretion rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)
kg	overall chemical conductance across the gill from the interlamellar water to the aqueous blood (cm/s)
km	chemical conductance through the gill membrane (cm/s)
Ke	partition coefficient for fecal matter (ml/g wet wt)
Kf	thermodynamic bioconcentration factor (ml/g wet wt)
K,	partition coefficient between generic lipid and water (ml/g dry wt)
Ka	partition coefficient between non-lipid organic matter and water (ml/g dry wt)
Koc	partition coefficient between organic carbon and water (ml/g dry wt)
Kow	partition coefficient between n-octanol and water (ml/ml)
I	lamellar length (cm)
L	fish's body length (cm)
BASS 2.2 March 2008
19

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Ma
chemical molarity in aqueous fraction of the fish (mol/L)
N
population density (fish/ha)
nGz
Graetz number (dimensionless) = (I D)/(V r2)
Nsh
Sherwood number (dimensionless) = (km r)/D
NM
non-predatory mortality (fish/ha/d)
Pa
aqueous or moisture fraction of whole fish (g water/g wet wt = ml/g wet wt)
Pae
aqueous or moisture fraction of feces/egesta (g water/g wet wt = ml/g wet wt)
Pap
aqueous of moisture fraction of prey /food (g water/g wet wt = ml/g wet wt)
P de
dry fraction of feces/egesta (g dry wt/g wet wt)
P'dp
dry fraction of prey /food (g dry wt/g wet wt)
Pd
dry fraction of whole fish (g dry wt/g wet wt), i.e., Pd = (1 - Pa) = (Pt + P0)
Pi
lipid fraction of whole fish (g dry wt/g wet wt)
Po
non-lipid organic fraction of whole fish (g dry wt/g wet wt)
PM
predatory mortality (fish/ha/d)
Q
ventilation volume (cm3/s)
r
hydraulic radius of interlamellar channels (cm), i.e., r = 0.5 d
R
routine respiratory rate (g dry wt/d)
^02
oxygen consumption rate (mg 02/s or g 02/d)
SDA
specific dynamic action (g dry wt/d)
ss
total gill surface area (cm2)
T
temperature (Celsius)
V
average velocity of interlamellar flow (cm/s)
wa
weight/volume of fish's aqueous phase (g water/fish or ml/fish)

weight of fish (g dry wt/fish)
w,
weight of fish's lipid phase (g dry wt/fish)
K
weight of fish's nonlipid organic phase (g dry wt/fish)

weight of fish (g wet wt/fish)
Xz
cross sectional pore area of the gill (cm2)
ac
assimilation efficiency of chemical (dimensionless)
Of
assimilation efficiency of food (g dry wt assimilated/g dry wt ingested)
a02
oxygen assimilation efficiency of the gill (dimensionless)
1
specific growth rate (g wet wt/g wet wt/d), i.e., y = Ww1 dWJdt
la
chemical aqueous phase activity coefficient (L/mol)
S a
aqueous phase biotransformation rate coefficient (1/d)
tydd
specific feeding rate (g dry wt/g dry wt/d)
^Pmw
specific feeding rate (g wet wt/g wet wt/d)
T1
solution viscosity (poise)
V
molar volume (cm3/mol)
P
lamellar density (lamellae/mm)
BASS 2.2 March 2008
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Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a
function of gill Sherwood number and ventilation / perfusion ratio.
BASS 2.2 March 2008
21

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Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a
function of gill Sherwood number and ventilation / perfusion ratio.
BASS 2.2 March 2008
22

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Figure 2.3 Functional behavior of Equation (2.53)
pt—EXP(0.1*T)*(1—1/36)0"1^*1-3®)	pt-EXP(0.&>T)*(l-t/36)tui

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3. Model Parameterization
Because reliable application of a model depends not only on
the validity of its formulation but also on its parameterization,
important aspects regarding the parameterization of bass's
bioaccumulation and physiological algorithms are discussed
below.
3.1. Parameterizing Kf
Superficially, estimation of a fish's thermodynamic
bioconcentration factor Kf via Equation (2.7) appears to
require a great deal of information. This task, however, is
much simpler than it first appears. For example, given a fish's
lipid fraction (see Equation (2.56)), it is a straightforward
matter to calculate the fish's aqueous fraction using Equation
(2.55). Having done so, one can then immediately calculate the
fish's non-lipid organic fraction since Pa, Ph and Pa must sum
to unity (i.e., Equation (2.57)).
For an organic chemical, the partition coefficients K, and Ka
can be estimated using the chemical's octanol / water partition
coefficient Kow. Although triglycerides are the principal storage
lipids of fish and it would seem reasonable to estimate K, using
a triglyceride / water partition coefficient, bass assumes that
K, equals K^. To estimate Ka, bass assumes that a fish's non-
lipid organic matter is equivalent to organic carbon and uses
Karickhoff's (1981) regression between the organic carbon /
water partition coefficient (Koc) and K^ to estimate this
parameter. Specifically,
K=Knr = 0.411 £	(3 1)
O	OC	OW	• 1)
For metals or metallo-organic compounds such as
methylmercury, the chemical's lipid partition coefficient A, can
again be assumed to equal its octanol / water partition
coefficient^. A metal's distribution coefficient into non-lipid
organic matter, however, cannot be estimated using the Koc
relationship of Equation (3.1). For example, whereas the Kow
of methylmercury at physiological pH's is approximately 0.4
(Major et al. 1991), its distribution coefficient into
environmental organic matter is on the order of 104 - 106
(Benoit et al. 1999b, Benoit et al. 1999a). O'Loughlin et al.
(2000) report similar differences for organotin compounds.
Whereas distribution coefficients for metals into fecal matter
generally should be assigned values comparable to those used
to model the environmental fate and transport of metals,
distribution coefficients for metals into the non-lipid organic
matter of fish should be assigned values 10 to 100 times higher
to reflect the increased number and availability of sulfhydryl
binding sites.
3.2. Parameters for Gill Exchange
To parameterize the gill exchange model, the fish's total gill
area (Sg cm2), mean interlamellar distance (d cm), and mean
lamellar length (/ cm) must be specified. Each of these
morphological variables is generally assumed to be an
allometric power function of the fish's body weight, i.e.,
Sg = s1Wwh	(3.2)
d=diwj2	(3.3)
l = l,w}	(3.4)
Although many authors have reported allometric coefficients
and exponents for total gill surface area, coefficients and
exponents for the latter two parameters are seldom available.
Parameters for fish's mean interlamellar distance, however,
can be estimated if the allometric function for the density of
lamellae on the gill filaments, p (number of lamellae per mm
of gill filament), i.e.,
P = P1WJ2	(3.5)
is known. Fortunately, lamellar densities, like total gill areas,
are generally available in the literature. See Barber (2003).
bass estimates d, and d2 from p; and p2 using the interspecies
regression (n = 28, r = -0.92)
0.118 p"119	(3.6)
To overcome the scarcity of published morphometries
relationships for lamellar lengths, bass uses the default
interspecific regression (n = 90, r = 0.92)
l = 0.0mww°294	(3.7)
Both of the preceding regressions are functional regressions
rather than simple linear regressions (Rayner 1985, Jensen
1986); the data used for their development are taken from
Saunders (1962), Hughes (1966), Steen and Berg (1966), Muir
and Brown (1971), Umezawa and Watanabe (1973), Galis and
Barel (1980), and Hughes et al. (1986).
To calculate lamellar Graetz and Sherwood numbers, bass
estimates a chemical's aqueous diffusivity (cm2/s), using the
empirical relationship,
D = 2.101xl0~7 ri~14v~0589	(3 . 8)
where r| is the viscosity (poise) of water; and v is the
chemical's molar volume (cm3/mol) (Hayduk and Laudie
BASS 2.2 March 2008
24

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1974). The diffusivity of a chemical through the gill membrane
that is needed to estimate the membrane's permeability km is
then assumed to equal one half the chemical's aqueous
diffusivity (Piiper et al. 1986, Barber et al. 1988, Erickson and
McKim 1990). The other quantity needed to estimate km is the
thickness of the gill's epithelial layer. Although previous
versions of bass assumed a constant water-blood barrier
thickness fl3e) equal to 0.0029 cm for all fish species, bass now
uses the interspecies allometric relationship
pe = 9.17* 10~5 W^'261	(3.9)
to estimate this parameter (Barber 2003).
To calculate ventilation / perfusion ratios bass estimates the
ventilation volumes (ml/hr) of fish from their oxygen
consumption rates assuming an extraction efficiency of 60%
and a saturated dissolved oxygen concentration (see Equation
(2.12)). Perfusion rates (ml/hr) are estimated using
Qp = (0.23 T-0.78) 1.862 W™	(3.10)
as the default for all species. Although this expression, in units
of L/kg/hr, was developed by Erickson and McKim (1990) for
rainbow trout (Oncorhynchus mykiss), it has been successfully
applied to other fish species (Erickson and McKim 1990, Lien
and McKim 1993, Lien et al. 1994).
The eigenvalues and bulk mixing cup coefficients needed to
parameterize Equation (2.28) are interpolated internally by
bass from matrices of tabulated eigenvalues and mixing cup
coefficients that encompass the range of Sherwood numbers
(i.e., 1
-------
problems using a modified Newton's method with analytic
Jacobians and a secant updating algorithm to compute the
required Hessian matrix. See Dennis et al. (1981).
Estimation of Specific Growth Rates
bass uses specific growth rates (y = W'1 dW/dt) not only to
estimate a cohort's rate of dispersal and non-predatory
mortality (see Equation (2.85)) but also as a parameter by
which a cohort's expected ingestion rate can be back-
calculated, if desired. Estimating specific growth rates for
bass, however, obviously depends on the underlying model
used to describe the fish's expected growth rate dynamics (i.e.,
dW/dt). Selecting an appropriate growth model for use by the
bass simulation software, like most model selections, was not
a trivial issue since over the past 50 years at least four different
models (i.e., vonBertalanffy, Richards, Gompertz, andParker-
Larkin models) have become standard tools for characterizing
the growth of fishes. See Ricker (1979) for a detailed
discussion of these models and other less commonly used
models.
According to the von Bertalanffy model, a fish's growth rate
is the simple mass balance of anabolic processes that are
directly proportional to the fish's surface area and of catabolic
processes that are directly proportional to the fish's body
weight. Consequently, the fish's growth dynamics are
governed by the following differential equation
dW	2f3
—^=<9Wwm-pWw	(3.15)
where cp is the fish's rate of feeding and assimilation; and p is
the fish's total metabolic rate. Assuming isometric growth (i.e.,
Ww = XL3), this model is also equivalent to
= f - L)	(3.16)
where L is the fish's body length; and Lmax = cp / (p ?l1/3) is the
fish's "maximum" body length that is obtained by setting
Equation (3.15) to zero. For further discussion, see Parker and
Larkin (1959) and Paloheimo and Dickie (1965).
The Richards model (Richards 1959) is a generalization of the
von Bertalanffy model that relaxes the assumption of isometric
growth and strict proportionality between a fish's
feeding/assimilatory processes and its absorptive surface areas.
In this model, the fish's feeding is simply assumed to be a
power function of its body weight. The fish's growth is then
described by the differential equation
dW„	*2
—?L = <9lWw*1-pWw	(3.17)
Although the von Bertalanffy and the Richards models appear
to have strong physiological foundations, a critical analysis of
the parameters of these models casts doubts on such assertions.
One particular point of contention is the assumption that a
fish's metabolism (i.e., respiration and excretion) is directly
proportional to its body weight. Although this assumption is
certainly satisfied or closely approximated for some fish
species, most fish species have metabolic demands that are
best described as power functions of their body weights.
Consequently, from a purely physiologically-based
perspective, a better anabolic-catabolic process model for fish
growth would be
d W	m	p
—^ = %wJ2-PlWwP2	(3.18)
See Paloheimo and Dickie (1965). Unlike the von Bertalanffy
and Richards models, however, this model generally does not
have a closed analytical solution. Furthermore, when this
model is fit to observed data, there is no a priori guarantee that
the fitted exponents will actually match expected physiological
exponents unless the analysis is suitably constrained.
In light of these criticisms, simpler empirical growth models
may be more than adequate for most applications. Two such
models that have proved useful in this regard are the Gompertz
and Parker-Larkin models. Both of these models are intended
to describe the growth of fishes that decreases with the age or
size of the individual. Whereas the Gompertz model describes
fish growth by
dWw
= E1exp(-£20 Ww	(3.19)
the Parker-Larkin model (Parker and Larkin 1959) simply
assumes that
dW	R
~df = aWw	(120)
where the exponent p is less than 1.
Although each of the aforementioned models can describe very
different growth trajectories, much of the discussion
surrounding their use has focused on whether the models
predict asymptotically zero or indeterminate growth (Parker
and Larkin 1959, Paloheimo and Dickie 1965, Knight 1968,
Schnute 1981). Although growth rates of individual fish almost
always decrease with increasing age or body size, Knight
(1968) argued that the traditional notion of asymptotically zero
growth is seldom, if ever, supported by studies that have
focused on actual growth increments rather than on size-at-age.
Because the Parker-Larkin model is the only model outlined
above that assumes fish growth is fundamentally
BASS 2.2 March 2008
26

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indeterminate, and because the Parker-Larkin model does not
rely on the a priori assumption that fish respiration is a linear
function of their body weight as does the von Bertalanffy and
Richards models, this model is used exclusively by bass when
needed.
Three basic types of data have been traditionally used to
calculate fish growth rates; these are: 1) length at age or
capture, 2) back-calculated length at age for specific age
classes sampled over multiple years, and 3) back-calculated
length at age for specific year classes or cohorts. Back-
calculated body lengths for the latter two data types are
generally calculated by regression using measured growth
increments of body scales, otoliths, pectoral spines, or other
"hard" structures. Whereas for a length at age dataset each
individual fish contributes only one observation (i.e., its
current length), each individual fish contributes a time series
of body lengths for both of the remaining types of growth data.
To estimate specific growth rates for fish, body lengths at age
that have been reported in the literature, whether back-
calculated or not, are converted into live body weights using
weight-length regressions that were reported by either the
study of interest or other published sources. Estimated live
body weights are then fit to the analytical solution Parker-
Larkin growth model,
dWw
dt
rK
si <1 K
(3.21)
using the NL2SOLV nonlinear optimization software. The
explicit solution of the Parker-Larkin growth model for any
time interval [/„, /] is
wjt) = \glg2(t-t0) + wjt0y
gi] !/«2
(3.22)
However, because this expression is discontinuous at g2 = 0,
the growth parameters gj and g2 are actually obtained by fitting
calculated body weights to the equivalent expression
Ww{t) = [g, exp(i) (t -10) + WJe0) ] l/exp(A) (3.23)
where g2 = exp (b).
Estimation of Hyperbolic Arrhenius Functions
When a fish's daily rate of maximum food ingestion, plankton
filtration, gastric evacuation, respiration, or growth exhibits a
temperature optimum, the bass database generator fits the
process's actual or synthetic data to the hyperbolic Arrhenius
function
P =P\ wj2 exp(/>3 T)

P,(T1-Tl)
(3.24)
The bass database generator also fits actual or synthetic data
regarding satiation meal size and feeding times to satiation to
the above equation when these feeding parameters exhibit
temperature optima. Testing of the initial NL2SOL-based
procedure developed to estimate the parameters of Equation
(3.24) revealed that the convergence performance of NL2SOL
could be greatly improved by reconfiguring Equation (3.24) as
P=PlWwP2cxV(p3T)
1-
T + 5
max
Pi (^m;
(3.25)
where rmax is the maximum temperature of the dataset being
fitted, and, therefore, T' = r + 82. Because estimation of
' '	'2 max
nonlinear parameters are frequently sensitive to their required
initial estimates, a three-step procedure was developed to
estimate the parameters for Equation (3.25).
The first step in this procedure estimates a mean body weight
exponent p2 by using repeated linear least-squares regressions
log^ = p2,k[o&K,l +p0„
(3.26)
on data subsets whose maximum range of temperatures is less
than 3 Celsius.
The second step of the procedure then uses NL2SOL to
estimate the parameters of the reduced model
P =
W,
J~=P\ exp (/>3 T)
Pi
1 -
T + 8
max

(3.27)
To estimate these parameters, multiple sets of initial
parameters are sequentially supplied to NL2SOL, and the
parameter set that produces the smallest sum of least-squares
is used in the third and final step in the estimation process.
Initial parameter estimates for Equation (3.27) are generated
by first fitting the cubic polynomial
P = P3T3 + P2T2 + P\T + Pa
(3.28)
using ordinary linear least-squares techniques. The temperature
Te corresponding to the local maximum of the above cubic
polynomial, i.e.,
dP
dt
= 2p3Te + 2p1Te + pl = 0
d2P
dt2
= 6p3 T + 2p2 < 0
(3.29)
(3.30)
is then assigned as the process's optimum temperature Tl for
BASS 2.2 March 2008
27

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each set of initial parameter values. Initial estimates for 5 are
then assigned assuming that the fish's upper "tolerance"
temperature corresponds to equidistant temperatures within the
interval
Initial estimates of the process's temperature coefficients p3 are
similarly assigned as equidistant values within the interval
0.05 < p3 < 0.75	(3.32)
Having assigned initial estimates for p3, Tu and 5, the
process's rate at T0 = 0 is finally assigned as the back-
calculated mean
i "	P
1	i
P\ ~ 2-/	it - t\ (3.33)
n exp(/V,)(1
In the third and final step, the results of steps 1 and 2 are
supplied to NL2SOL as the "best" initial estimates of the
parameters for Equation (3.25). From this step, the final
parameters for Equation (3.24) are determined.
Table 3.1 summarizes the results obtained using the
aforementioned procedure to estimate maximum daily
consumption and maximum meal size for a variety of studies
reported in the open literature. Table 3.2 summarizes the
results of converting the maximum daily consumption
functions used by the Wisconsin Bioenergetics Model into
their "equivalent" hyperbolic Arrhenius form. Figure 3.1 and
Figure 3.2 display selected results from Table 3.2.
Readers interested in obtaining the Fortran 95 subroutines used
to implement this procedure can do so by simply requesting
this code from the author.
3.5. Suggested Calibration Procedures
Calibrating Fish Growth Rates
Because Equations (3.21) and (3.22) do not explicitly account
for either reproductive losses or temperature-dependent
growth, growth rates estimated by these equations generally
should be calibrated for the application at hand when back-
calculating fish ingestion rates from estimated growth rates.
Having estimated a long-term average growth rate
Y = g,Kg2	(3-34)
for a species of interest, the calibration procedure developed
for bass assumes that the fish's specific growth coefficient gj
is actually an exponential function the fish's ambient water
temperature that, in turn, is assumed to be a sinusoidal function
of the time of year. In particular,
gi=goexP[ft(rm + asin(P? + ®))] (3.35)
where g3 = 0.1 In(Q10 G) defines the fish's Q10 relationship
for growth; Tm is the mean annual water temperature
experienced by the fish; and a, p, and co are the coefficients
describing the amplitude, frequency, and phase shift of the
water temperatures experienced by the fish, respectively.
Under this assumption a fish's growth is therefore described by
dW f -	1
= (ft OexP[ft {Tm + «sin(pt+ <»))}) Ww (3.36)
If t0 is the day that the species' young-of-year are recruited into
the population, and m is the integer age in years when the fish
becomes sexually mature, it then follows that a fish's pre-
spawn body weight at the time of its first reproduction is given
by
WJt0 - 365 m)^ - WJt0)K* =
'0 +365m	(3.37)
g0S2 f exp[g3(r(B +asin(Pt + m))jdx
However, because the integrand of this equation is a harmonic
function possessing an annual period, the preceding equation
can be simplified to
Ww(t0 + 365 m)g> - Ww(t0)gl =g0g2ml (3.38)
where
t0 + 365
1= | exp[g3(rm+ asin(pT+ cs))]=g0g2 I
BASS 2.2 March 2008
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Ww(t0 + 365 0' +1))A'2 -
'l-p)Ww(t0 +365i)g>=g0g2I
(3.41)
where p = l - (1 -c)^2. Summing Equations (3.38) and (3.41)
appropriately, it then follows that
Ww(t0 + 365 n)s
£ WJt0 + 365 0A'2
-Ww(lo )gl=gQS2nI
(3.42)
To calibrate a species growth rate for a particular application
using Equations (3.34), (3.39) and (3.42), one must obviously
specify the parameters (Tm, a, p, and co) describing the
application's water temperatures and the species' maximum
age (amax = 365«), mean age of sexual maturity (365 m),
annual spawning times (t = (t0 + 365m),(t0 + 365(w + l)),...),
reproductive/spawning loss constant (a), initial body weight of
young-of-year fish (Wv(t0)\body weight of fish at maximum
age (Ww(tQ + 365 n)), and the species' allometric growth
exponent (g2). The species' pre-spawn body weights for
Equation (3.41) can be estimated using Equation (3.22) using
the adjusted allometric growth coefficient
Si
Ww(t0 +365 nfi-WJt0f
&2 flmax
(3.43)
To demonstrate this calibration procedure, growth rates
estimated for brook trout (Salvelinus fontinalis) from literature
data will how be calibrated for a "typical" Mid-Atlantic trout
stream whose annual temperature regime is assumed to be
given by
T[Celsius] = 10.8 + 8.8 sin(0.0172*f + 6.04) (3.44)
This temperature function assumes that the stream's annual
range of water temperatures is 2 to 19.5 Celsius, that April 1
corresponds to / = 0, and that January 15 is the coldest day of
the year. In this stream, brook trout are assumed to be recruited
into the population with an initial YOY body weight equal to
0.25 g wet wt/fish and live a maximum of seven years. The
maximum size attained by these trout is assumed to be 825 g
wet wt/fish (i.e., -440 mm(TL) assuming
W[g] = 0.148 xlO"4 TL[mm]293 ). Spawning and recruitment are
assumed to occur on October 30. Sexual maturity is reached
when trout attain a total body length of 157 mm (i.e., between
the ages of 2 and 3 years), and the trout's reproductive loss
constant is assumed to equal 0.2 g wet wt/g wet wt/spawn.
Finally, the trout's growth Q10 is assumed to equal 2
(i.e., g3 = 0.069). Using data compiled by Carlander (1969),
the bass database analysis program estimated the following
specific growth rate for brook trout
Y = 0.0196 Ww
(3.45)
Calibrating this growth rate to predict with the trout's assumed
maximum and YOY body weights and maximum age using
Equation (3.43) then yields
Y = 0.0178 Ww
(3.46)
When this adjusted growth rate is used to project pre-spawn
body weights for Equation (3.42) using Equation (3.22), the
specific growth rate of brook trout calibrated for reproductive
losses and temperature dependencies is
Y = 0.0107 Ww *
exp[0.069 (10.8 + 8.75 sin(0.0172f + 6.04))]
(3.47)
When specific feeding rates (cpdd g dry wt/g dry wt/d) are back-
calculated monthly using this equation and standard salmonid
metabolic relationships (i.e., food assimilation efficiencies,
specific dynamic action (SDA) to ingestion ratios, oxygen
consumption rates, respiratory quotients (RQ), and ammonia
excretion to oxygen consumption quotients(AO)) as outlined
by Barber (2003), the following allometric regression can be
calculated
=0.0251 WwQlas exp( 0.064 T)
(w = 84;r2 = 0.98)
(3.48)
This regression agrees well with results of Sweka and Hartman
(2001) who estimated the maximum consumption of brook
trout at 12 Celsius to be
^=0-13 Ww
(3.49)
Taken together, the proceeding equations imply that the
realized ingestion rate of brook trout at 12 Celsius would be
approximately 42% of their maximum ingestion rate. This
result agrees well with that reported by Elliott and Hurley
(1998).
A Fortran 95 executable program (bass_cmm_fsh.exe) is
provided with the bass simulation software to perform the
aforementioned growth calibration procedure and back-
calculated feeding rate estimation. See Section 5.6.
Estimating Initial Conditions
Although most fish surveys typically report only either total
BASS 2.2 March 2008
29

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species densities (fish/ha) or total species biomass (kg wet
wt/ha), such data can be easily converted into bass initial
conditions if one assumes that the recruitment strength for each
cohort of observed population density has been relatively
constant or has been fluctuating around a long term average.
To perform this conversion, bass's assumed self-thinning
model Equation (2.84), is first rewritten as
dN = b dw-
N
W„.
(3.50)
This equation can then be reintegrated to obtain
Wo)
N(t) = N(t0) exp
K(0
- b In
Kit)
WJ
(3.51)
A species total population density can then be estimated by
applying Equation (3.51) to each of its cohorts, i.e.,
N(t) = £ Nt(t)
N(t) = Y, Ni(t - ai) expj - b ln

KA'-a,)
(3.52)
where N„ Wfi, and a, denote the density, average wet body
weight, and age, respectively, of the z'-th cohort. Assuming that
each cohort is recruited into the species' total population with
the same initial body weight (Ww - a;) = W0) and population
density (N^t - a) = N0), the preceding equation can be
simplified to
N(t) = No £ exP 1" b ln
K/0
wn
(3.53)
If the growth rate trajectories of each cohort have also
remained relatively constant, it follows that an expected
decomposition of a species total population density into its
component cohort densities would be
N(t) = N0 Y, exp | - b ln

Wn
(3.54)
total biomass into its component cohort biomasses would be
B(t) - £ Wwi(at) Nt{t)
noY, KAaJ exPl^ln
KAaJ
wn
(3.55)
From Equations (3.54) and (3.55) it should be reasonably clear
that given a species total population density (AO or total
biomass (B) and given a model for the species body growth
(i.e., Equations (3.21) and (3.22)), one can straightforwardly
calculate the species' apparent long term year-class strength
N0. Having done so, one cannot only estimate the species'
cohort densities but also convert the species' total population
density into its expected total biomass and vice versa.
To corroborate the density-to-biomass conversion procedure
outlined above, a database of studies that have reported
measured fish densities and associated fish biomasses was
compiled from the literature (Miles 1978, Quinn 1988, Reed
and Rabeni 1989, Ensign et al. 1990, Buynaketal. 1991,Flick
and Webster 1992, Bettoli et al. 1993, Waters et al. 1993,
Maceina et al. 1995, Mueller 1996, Allen et al. 1998, Radwell
2000, Dettmers et al. 2001, Pierce et al. 2001, Habera et al.
2004). Reported fish densities were converted into estimated
biomasses assuming evenly spaced self-thinning exponents b
ranging from -0.5 to -1.0 at 0.025 increments. Reduced major
axis (RMA) regressions were then calculated for each assumed
self-thinning exponent. The self-thinning exponent that
minimized the intercurve area between the calculated RMA
regression line and the identity relationship Bgbs = Bwas b =
-0.825. This regression was
ln B. = 0.827 ln B - 0.0528 (w = 512; r2 = 0.64)
Bobs = 0-949 5j 827
(3.56)
Figure 3.3 displays the data for the regression (3.56) and the
identity relationship B
obs
B„
In addition to calibrating fish growth rates and back-
calculating feeding rates, the auxiliary bass program
bass_cmm_fsh.exe described in the preceding section
estimates initial body weights and cohort densities for users
given a target initial total species density or a target initial total
species biomass. See Section 5.6.
It also follows that an expected decomposition of a species
BASS 2.2 March 2008
30

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Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the Wisconsin Bioenergetics Model.
Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit.
T[Celsius]
Lepomis macrochirus (adult) (Fish Bioenergetics Model 3.0)
11.20	16.B0
T[Celsius]
Perca flavescens (adult) (Fish Bioenergetics Model 3.0)
7.40	14.80	2220	89.60	37.00
T[Celsius]
Mlcropterus salmoides (Fish Bioenergetics Model 3.0)
12.52	18.78
T[Celsius]
Morone spp. (Fish Bioenergetics Model 3.0)
BASS 2.2 March 2008
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Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the Wisconsin Bioenergetics Model.
Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit.
7.20	10.B0
T[Celsius]
Osmerus mordax (adult) (Fish Bioenergetics Model 3.0)
13.60	20.40
T[Celsius]
Esox masquinongy (Fish Bioenergetics Model 3.0)
9.60	14.40
T[Celsius]
Oncorhynchus mykiss (Fish Bioenergetics Model 3.0)
11.20	16.60
T[Celsius]
Stizostedion vitreum (adult) (Fish Bioenergetics Model 3.0)
BASS 2.2 March 2008
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Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning bass's algorithm.
-9.08
-5.77	-2.46	0.85
ln(biomassest)
density to biomass validation test
4.17
7.48
BASS 2.2 March 2008
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Table 3.1 Summary of NL2S0L regressions for Equation (3.24) fitted to maximum daily consumption rates and satiation meal
reported in the literature.
Species
Process
Pi
Pi
Pi
T,
t2
r2
1 Channa argus
CmJg/d]
0.00741
0.52
0.425
29.2
51.3
0.99
2 Coregonus hoyi
CmJg/g/d]
0.159
-0.54
0.320
16.8
26.0
0.96
3 Morone saxatilis
CmJg/g/d]
0.000945
0.00
0.708
25.9
58.7
0.97
4 Morone saxatilis
CmJg/g/d]
0.00542
0.00
0.455
21.6
42.1
0.85
5 Pomoxis annularis
CmJg/d]
0.00213
0.03
1.051
23.1
43.0
0.50
6 Salmo trutta
CmJKcal/d]
0.0100
0.76
0.262
18.5
21.8
1.00
1 Salmo trutta
sm[mg(dw)]
1.54
0.69
0.596
15.0
29.3
1.00
8 Salmo trutta
sm[mg(dw)]
0.731
0.78
2.000
13.8
67.8
1.00
9 Salmo trutta
sm[mg(dw) ]
0.843
0.76
2.000
13.6
69.5
0.99
10 Salmo trutta
sm[mg(dw) ]
1.72
0.79
0.463
14.9
24.1
1.00
11 Salmo trutta
sm[mg(dw) ]
0.906
0.80
0.437
15.1
24.2
0.99
12 Salvelinus alpinus
CmJg(dw)/g/d]
0.00123
0.00
0.489
16.5
29.0
0.79
13 Salvelinus confluentus
CmJg/g/d]
0.00840
0.00
0.288
14.0
29.0
0.98
14 Siniperca chuatsi
CmJg/d]
0.0267
0.60
0.212
30.3
44.5
0.99
15 Tilapia zillii

7.300E-07
0.00
2.000
30.6
75.1
0.94
Data sources and notes
1	Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.
2	Binkowski and Rudstam (1994). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.
3	Cox and Coutant (1981). Rates as reported in Table 2 assuming no feeding or lethality at 43 Celsius.
4	Hartman and Brandt (1993). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.
5	Hayward and Arnold (1996). Rates as reported in Table 1 assuming no feeding or lethality at 43 Celsius.
6	Elliott (1976b). Rates generated by regressions reported in Table 2.
7	Elliott (1975). Data as reported in Table 4 for Baetis.
8	Elliott (1975). Data as reported in Table 4 for Hydropsyche.
9	Elliott (1975). Data as reported in Table 4 for chironomids.
10	Elliott (1975). Data as reported in Table 4 for mealworms (Tenebrio molitor).
11	Elliott (1975). Data as reported in Table 4 for oligochaetes.
12	Larsson and Berglund (1998). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.
13	Selong et al. (2001). Rates calculated from data reported in Table 2 assuming no assuming or lethality at 26 Celsius.
14	Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.
15	Piatt and Hauser (1978). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.
BASS 2.2 March 2008
34

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Table 3.2 Summary of NL2S0L regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by
the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily
consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the
fish's upper tolerance limit.
Species
/i
A
f3
T,
t2
r2
Alosa psuedoharengus (adult)
0.102
0.70
0.426
15.5
29.3
0.99
Alosa psuedoharengus (juvenile)
0.112
0.70
0.214
19.6
27.3
0.98
Alosa psuedoharengus (yoy)
0.0919
0.70
0.196
21.8
29.2
0.99
Chrosomus spp.
0.0590
0.69
0.094
26.0
29.0
1.00
Clupea harengus (adult)
0.08
0.74
0.644
12.9
29.5
0.99
Clupea harengus (juvenile)
0.0808
0.74
0.535
14.4
31.5
0.99
Coregonus hoyi
0.159
0.46
0.320
16.8
26.0
1.00
Coregonus spp.
0.159
0.68
0.320
16.8
26.0
1.00
Esox masquinongy
0.0147
0.82
0.188
26.0
34.0
1.00
Lates niloticus
0.0112
0.73
0.235
27.5
38.0
1.00
Lepomis macrochirus (adult)
0.0150
0.73
0.172
27.0
36.0
1.00
Lepomis macrochirus (juvenile)
0.0113
0.73
0.138
31.0
37.0
1.00
Micropterus dolomieui
0.00139
0.69
0.296
29.0
36.0
1.00
Micropterus salmoides
0.0129
0.68
0.222
27.5
37.0
1.00
Morone saxatilis (adult)
0.0336
0.75
2.000
21.8
213.9
0.95
Morone saxatilis (age 0)
0.014
0.75
2.000
21.3
153.6
0.99
Morone saxatilis (age 1)
0.0310
0.75
2.000
22.4
221.1
0.98
Morone saxatilis (age 2)
0.0376
0.75
2.000
23.8
268.5
0.96
Morone spp.
0.0314
0.75
0.128
28.3
31.3
1.00
Oncorhynchus gorbuscha
0.142
0.73
0.102
17.0
25.9
0.99
Oncorhynchus kisutch
0.0460
0.73
0.320
15.6
25.8
0.98
Oncorhynchus mykiss
0.102
0.70
0.220
17.6
25.3
0.99
Oncorhynchus nerka
0.142
0.73
0.102
17.0
25.9
0.99
Oncorhynchus tshawytscha
0.0330
0.72
0.230
15.0
18.0
1.00
Osmerus mordax (adult)
0.0304
0.73
0.680
10.0
22.3
0.99
Osmerus mordax (juvenile)
0.0472
0.72
0.207
13.1
18.0
0.98
Osmerus mordax (yoy)
0.0587
0.73
0.143
17.9
26.1
0.98
Perca flavescens (adult)
0.0411
0.73
0.125
23.0
28.0
1.00
Perca flavescens (juvenile)
0.0317
0.73
0.094
29.0
32.0
1.00
Perca flavescens (larvae)
0.0647
0.58
0.094
29.0
32.0
1.00
Petromyzon marinus
0.0766
0.65
0.150
18.0
25.0
1.00
Sarotheradon spp.
0.00643
0.64
0.172
30.0
37.0
1.00
Stizostedion vitreum (adult)
0.0428
0.73
0.138
22.0
28.0
1.00
Stizostedion vitreum (juvenile)
0.0802
0.73
0.094
25.0
28.0
1.00
Theraga chalcogramma (adult)
0.146
0.41
0.270
8
15.0
1.00
Theraga chalcogramma (juvenile)
0.0994
0.41
0.461
8
15.0
1.00
BASS 2.2 March 2008	35

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4. bass User Guide
Although bass versions 1.0 and 1.1 were written in Fortran 77,
bass version 2.0 and higher are coded in Fortran 95. The model
enables users to simulate the population and bioaccumulation
dynamics of age-structured fish communities using the temporal
and spatial resolution of a day and a hectare, respectively.
Although bass implicitly models the dispersal of fish out of the
simulated hectare, it does not explicitly simulate the immigration
of fish into the simulated hectare. Monthly or yearly age classes
can be used for any species. This flexibility enables users to
simulate small, short-lived species such as daces, live bearers,
and minnows together with larger, long-lived species such as
bass, perch, sunfishes, and trout. The community's food web is
specified by defining one or more foraging classes for each fish
species based on either body weight, body length, or age. The
user then specifies the dietary composition of these foraging
classes as a combination of benthos, incidental terrestrial insects,
periphyton, phytoplankton, zooplankton, and/or other fish
species, including its own. Standing stocks of nonfish
compartments can be simulated either as external forcing
functions or as state variables.
Although bass was developed to simulate the bioaccumulation
of chemical pollutants within a community or ecosystem context,
it can also be used to simulate population and community
dynamics of fish assemblages that are not exposed to chemical
pollutants. For example, in its present form bass could be used
to simulate the population and community dynamics of fish
assemblages that are subjected to altered thermal regimes that
might be associated with a variety of hydrological alterations or
industrial activities, bass could also be used to investigate the
impacts of exotic species or sport fishery management programs
on population or community dynamics of native fish
assemblages.
The model's output includes:
•	Summaries of all model input parameters and simulation
controls.
•	Tabulated annual summaries for the bioenergetics of
individual fish by species and age class.
•	T abulated annual summaries of chemical bioaccumulation
within individual fish by species and age class.
•	Tabulated annual summaries for the community level
consumption, production, and mortality of each fish
species by age class.
•	Plotted annual dynamics of selected model variables as
requested by the user.
Please report any comments, criticisms, problems, or suggestions
regarding the model software or user manual to
Craig Barber
Ecosystems Research Division
U.S. Environmental Protection Agency
960 College Station Road
Athens, GA 30605-2700
office: 706-355-8110
FAX: 706-355-8104
e-mail: barber.craig@epa.gov
4.1.	General Model Structure and Features
The following features are available in bass v2.2:
•	There are no restrictions to the number of fish species that
can be simulated.
•	There are no restrictions to the number of cohorts that a
fish species can have.
•	There are no restrictions to the number of feeding classes
that a fish species can have (see the command
/feeding_options).
•	There are no restrictions to the number of foraging classes
that a fish species can have (see the command
/ecological_parameters).
•	There are no restrictions to the number of chemicals that
can be simulated.
•	Biotransformation of chemicals can be simulated with or
without daughter products.
•	Integration of bass's differential equations is performed
using a fifth-order Runge-Kutta method with adaptive step
sizing that monitors the accuracy of its integration, bass's
Runge-Kutta integrator is patterned on the fifth-order
Cash-Karp Runge-Kutta algorithm outlined by Press et al.
(1992).
4.2.	New Features
The following features were not available in bass v2.1 and
earlier
BASS 2.2 March 2008
36

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Users can now run bass in a mode that is computationally
intermediate between bass's fgets and full community
modes. In particular, users can simulate fish population
dynamics using the conceptual framework of a multi-
species Leslie matrix population model. See simulation
control command /leslie_matrix_simulation.
Benthos, periphyton, phytoplankton, and zooplankton can
now be simulated as state variables. To accommodate this
capability, the simulation control command /biota in
bass v2.1 has been augmented with five new commands,
i.e., /benthos, /terrestrial_insects, /periphyton,
/phytoplankton, and /zooplankton. Note, however,
bass v2.2 can still be executed using benthos, periphyton,
phytoplankton, and zooplankton as community forcing
functions as in bass v2. 1 and earlier.
Seasonal diets can be specified for any or all foraging
classes of a species. See the fish command
/ecological_parameters option diet(.,.)={...}¦
The mode of prey body lengths consumed by piscivores
can be specified either as a linear or exponential function
of their body lengths. See the fish command
/ecological_parameters option lp[]=/«c.
A fish's maximum and minimum prey lengths can now be
specified by the user as linear or exponential functions of
its body length. See the fish command
/ecological_parameters options lp_max[]=/«c and
l|)_min[]=/«t'
Refuge levels at which cohorts of potential prey species
become unavailable to piscivores can now be specified.
See the fish command /ecological_parameters option
refugia[]=/«c.
Dispersal and non-predatory mortality are now calculated
directly from a fish's expected specific growth rate; the
allometric power function formulation used in bass v2. 1
and earlier has been deleted. See the fish command
/ecological_parameters options nm []=fnc and
sg_mu[]=/«c.
Size dependent harvest and stocking functions can be
specified for any or all species to simulate fisheries
management practices. See the fish command
/FISHERY_P ARAMETERS.
Habitat suitability indices (HSI) can be specified to adjust
a fish's realized feeding/growth, recruitment/spawning,
and combined dispersal and non-predatory mortality. See
the fish command /habitat_parameters.
•	The syntax for specifying the temperature dependency of
a fish's rates of maximum daily consumption, filtering,
gastric evacuation, specific growth, and oxygen
consumption has been modified. See the fish command
/PHYSIOLOGICAL_P ARAMETERS.
•	An interspecies allometric function is now used to
estimate epithelial thicknesses for calculating gill
Sherwood numbers and chemical exchange. See Barber
(2003)
•	Because the new bass graphical user interface (GUI)
enables users to construct their own plots and tables of
simulation results, the simulation control commands
/annual_outputs, /annual_plots, and
/summary_plots should be considered by most users to
be vestigial commands. These commands have been
retained for the convenience of model refinement and
testing.
4.3. Input File Structure
The general structure of a bass's input or project file is:
/command! argument(s)
/command2 argument(s)
/command,, argument(s)
/end
The leading slash (/) identifies the line as a command. Blanks
or tabs before or after the slash are not significant. The keyword
or phrase (i.e., command,,) that follows each slash identifies the
type of data being specified by that record. Keywords must be
spelled in full without embedded blanks and must be separated
from the record's remaining information by at least one blank or
tab. Arguments are either integers (e.g., 7), real numbers (e.g., 0,
3.7e-2, 1.3, etc.), or character strings. If the command allows
multiple arguments or options, each argument must be separated
by a semicolon. Commands can be continued by appending an
ampersand (&) to the end of the record; therefore, the following
commands are equivalent
/command arg^ arg2; arg3; arg4; arg5; arg6
/command arg^ arg2; arg3; &
arg4; arg5; arg6
Because each record is transliterated to lowercase before being
decoded, the case of the input file is not significant. Likewise,
BASS 2.2 March 2008
37

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because consecutive blanks or tabs are collapsed into a single
blank, spacing within a command is not significant. The
maximum length of a command line, including continuation
lines, is 1024 characters.
An exclamation mark (!) in the first column of a line identifies
that line as a comment. An exclamation mark can be also placed
elsewhere within a record to start an end-of-line comment, i.e.,
the remainder of the line, including the exclamation mark, will be
ignored.
The last command in any bass project file must be /end. This
command terminates program input and any text or commands
following it are ignored, bass checks the syntactical accuracy of
each input command as it is read. If no syntax errors are
encountered, bass then checks the specified input parameters for
completeness and internal inconsistency.
bass input data and commands are broadly classified into four
categories: simulation control parameters, chemical parameters,
fish parameters, and nonfish biotic parameters. Simulation
control parameters provide information that is applicable to the
simulation as a whole, e.g., length of the simulation, the ambient
water temperature, water column depth, and any desired output
options. Chemical parameters specify the chemical's physico-
chemical properties (e.g., the chemical's molecular weight,
molecular volume, n-octanol / water partition coefficient, etc.)
and the chemical's exposure concentrations in various media.
Fish parameters specify the fish's feeding and metabolic
demands, dietary composition, predator-prey relationships, gill
morphometries, body composition, and initial conditions for the
body weights, whole-body chemical concentrations, and
population sizes of a fish's cohorts. Nonfish biotic parameters
specify how benthos, terrestrial insects, periphyton, and plankton
will be simulated.
A bass project file is actually constructed and managed as a
series of include files, i.e., blocks of closely related input
commands. These files are specified using the include statement
# include 'filename'
where filename is the name of the file containing the desired
commands. Each include file specifies data for either a chemical,
a fish species, or a nonfish biotic component. Consequently, a
typical bass project file is structured as follows:
file: bass_input_file.prj
notes: a bass project file as specified by include files
/ command] simulation controldata
/ command, simulation control data
/ command3 simulation control data
#	include 'data_forchemicall '
#	include 'data_for_chemical_2 '
#	include 'data JorJshl'
#	include 'data J'or Jish_2 '
#	include 'data J'or Jish_3 '
#	include 'data Jor Jsh_4 '
#	include 'data Jor benthos '
#	include 'data Jor insects '
#	include 'data Jor_periphyton '
#	include 'data Jor_phytoplankton '
#	include 'data Jor zooplankton '
/end
bass's graphical user interface (GUI) enables users to create and
edit bass project files and include files in a modular fashion. The
actual file structure used by the bass GUI is detailed in Section
4.5. following the discussion of the bass input commands
themselves below.
4.3.1. Simulation Control Commands
These commands establish the length of the simulation, the
ambient water temperature, the community's water level, and
other simulation and output options. These data are specified by
the following block of commands
/SIMULATION_CONTROL
/ ANNU AL_OUTPUTS
/ ANNU AL_PLOTS
/BIOTA
/FGETS
/HEADER
no argument/option required
integer
string,;...; stringn
string,;...; stringn
no argument/option required
string
/length_of_simulation string
/leslie_matrix_simulation no argument/option required
/month_t0	string
/nonfish_qsar	string,;...; stringn
/SUMMARY_PLOTS	string,;...; stringn
/temperature	string/, string2
/water_level	string,', string2
Although the command /simulation_control must be the first
command in the block since it identifies the start of these data,
the order of the remaining commands is not significant. The use
of these commands is described below in alphabetical order.
¦ /annualoutputs integer
This command specifies the time interval, in years, between
bass's annual tabulated and plotted outputs. This number must
be a nonnegative integer, bass assumes a default value of zero
that signifies that no annual outputs will be generated. Because
BASS 2.2 March 2008
38

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the bass v2.2 Output Analzyer enables users to generate
customized tables and plots, this command is actually a vestigial
option of bass v2.1.
¦ /annual_plots stringy stringn
This command specifies the variables whose annual dynamics
will be plotted for the years specified by command
/annual_outputs . Consequently, this command is also a
vestigial option of bass v2. 1. Valid options are:
•	afish(variable) generates plots of each species' total
aqueous phase chemical activity as a function of time (day
of year) and the species' age, length, or weight class;
•	baf(variable) generates plots of each species'
bioaccumulation factor (i.e., the ratio Cf/ Cw) for each
chemical as a function of time (day of year) and the
species' age, length, or weight class;
•	bmf(variable) generates plots of each species'
biomagnification factor (i.e., the ratio Cf/Cprey) for each
chemical as a function of time (day of year) and the
species' age, length, or weight class;
•	cfish(variable) generates plots of each species' whole-
body concentration (ppm) for each chemical as a function
of time (day of year) and the species' age, length, or
weight class;
•	pop (variable) generates plots of each species' population
density (fish/ha) as a function of time (day of year) and
the species' age, length, or weight class;
•	tl(variable) generates plots of each species' total body
length (cm/fish) as a function of time (day of year) and the
species' age or weight class;
•	wt(variable) generates plots of each species' body weight
(g wet wt/fish) as a function of time (day of year) and the
species' age or length class.
where variable equals "age", "length", or "weight". Each age
class or cohort of the species is assigned to one of five size
classes that are defined by bass based on the species' largest /
oldest and smallest / youngest individuals.
¦/biota stringystringn
This bass v2.1 command specifies nonfish standing stocks that
are to be generated as forcing functions rather than as simulated
state variables. Although this command has been superceded by
the bass v2.2 commands /benthos, /terrestrial_insects,
/PERIPHYTON, /PHYTOPLANKTON, and /ZOOPLANKTON (see
Section 4.3.4), it has been retained for upward compatibility.
Valid options are:
•	benthos \yunits\ =fnc to generate benthic standing stocks
according to the function fnc. The units stringyunits must
be dimensionally equivalent to g dry wt/m2.
•	insects \yunits\ = fnc to generate incidental terrestrial
insect standing stocks according to the function fnc. The
units string yunits must be dimensionally equivalent to g
dry wt/m2.
•	periphyton \yunits] =fnc to generate periphyton standing
stocks according to the function fnc. The units string
yunits must be dimensionally equivalent to g dry wt/m2.
•	p h y to p I an kto n \y units ] =fnc to generate phytoplankton
standing stocks according to the function fnc. The units
string yunits must be dimensionally equivalent to g dry
wt/L.
•	zo o p I an kto n \yunits \ = fnc to generate zooplankton
standing stocks according to the function fnc. The units
string yunits must be dimensionally equivalent to g dry
wt/L.
Valid specifications for the function strings fnc are :
•	nonfish name [yunits ] = a to generate a constant
compartmental standing stock of a (yunits) for the
simulation.
•	nonfish_name\yunits] = a + P*sin(a) + <$*t\xunits\) to
generate a sinusoidal compartmental standing stock for
the simulation where a is the mean standing stock for the
chosen time period, P is its amplitude (yunits), co is its
phase angle (radians), and (p = 2% / period is its frequency
(1 Ixunits).
•	n onfish_ n ante \yun its \ = \\\c(fHen ante) to read and
interpolate the specified compartmental standing stock
from the file filename. See Section 4.4.3.
Unless specified otherwise, bass assumes that the first day of
simulation is April 1 and that the 365-th simulation day is March
31. This assignment can be changed using the command
/month_tO.
These options are only required when the user is simulating fish
that feed on these resources (see the "diet" option for
BASS 2.2 March 2008
39

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/ecological_parameters). Note, however, because bass
assumes that piscivorous fish switch to benthic invertebrates and
incidental terrestrial insects when appropriate forage fish are
unavailable, the benthos and insect options should be specified
even when simulating only piscivorous fish. Also note that if
project file uses the fgets option described below, the only
/biota option that might be required is the
zoo plan kto n \yunits\=fnc option. This option is required only if
the user specifies a fish's feeding to be simulated using the
clearance model formulation described in Equation (2.64).
If multiple options are selected, each option must be separated by
a semicolon.
¦	/FGETS
This command enables users to run bass without simulating the
assemblage's population dynamics, i.e., only the growth and
bioaccumulation of individual fish are simulated. The
command's function and name are based on the fgets (Food and
Gill Exchange of Toxic Substances) model (Barber et al. 1987,
1991) that was bass's predecessor.
¦	/header string
This is an optional command that specifies a title to be printed on
each page of the output file. The maximum length of the quoted
string is 80 characters.
¦	/lengthofsimulation string
This command specifies the desired length of the simulation. The
valid syntax for string is
• a[units]
where a is a nonnegative real value. The time unit specified with
brackets is converted into days for internal use and subsequent
model output.
¦	/lesliematrixsimulation
This command enables users to run bass in a mode that is
computationally intermediate between bass's fgets and full
community modes. When this option is specified, bass simulates
fish population dynamics using the conceptual framework of a
multispecies Leslie matrix population model. A cohort's
mortality is predicted using a single, lumped, self-thinning
mortality rate (i.e., Equation (2.84)) without attempting to
partition its total mortality into predatory and non-predatory
mortality and dispersal as outlined in Sections 2.7 and 2.8.
Although predatory mortality is not simulated, the dietary
composition of each cohort is nevertheless predicted using the
methods described in Section 2.7. While this simulation option
is designed partially to lessen the need for detailed food web
information and the work required to calibrate a full community
simulation, it is also designed to simulate more realistically the
population dynamics of communities in which the dominant
process driving cohort mortality and self-thinning is dispersal
rather than predation.
¦	/nonfish_qsar stringy stringn
This command specifies the quantitative structural activity
relationships for the bioconcentration / bioaccumulation factors
of the nonfish compartments benthos, periphyton, phytoplankton,
and zooplankton that are to be applied to all chemicals. Valid
string options are:
•	BC F [-] (nonfish_name)=u* Kow |-]A |i
where Kow[-] is the chemical's n-octanol / water partition
coefficient; and a and p are real or integer empirical constants.
Also see the chemical command /nonfish_bcf. When this
command is used, the specified QSARs supercede any BCFs
specified by /nonfish_bcf or exposures specified by /exposure.
¦	/monthtO string
This is an optional command that specifies the month that
corresponds to the start of the simulation. If not specified, bass
assumes a default start time of April 1.
¦	/simulationcontrol
This command specifies the beginning of input data that will
apply to the simulation at large, i.e., the length of the simulation
and its integration step, the ambient water temperature,
community's water level, and various output options.
¦	/summary plots stringy stringn
This command specifies the variables whose temporal dynamics
will be plotted at the completion of the simulation. This
command, like /annual_plots, is a vestigial option of bass
v2.1. The options can be specified one per card, or all in one
card, separated by semicolons. Valid options are:
•	afish(variable) generates plots of each species' total
aqueous phase chemical activity as a function of time (day
of simulation) and the species' age, length, or weight
class;
•	baf(variable) generates plots of each species'
BASS 2.2 March 2008
40

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bioaccumulation factor (i.e., the ratio Cf/CJ for each
chemical as a function of time (day of simulation) and the
species' age, length, or weight class;
•	bmf(variable) generates plots of each species'
biomagnification factor (i.e., the ratio Cf/Cprey) for each
chemical as a function of time (day of simulation) and the
species' age, length, or weight class;
•	cfish(variable) generates plots of each species' whole-
body concentration (ppm) for each chemical as a function
of time (day of simulation) and the species' age, length, or
weight class;
•	pop (variable) generates plots of each species' population
density (fish/ha) as a function of time (day of simulation)
and the species' age, length, or weight class;
•	tl(variable) generates plots of each species' total body
length (cm/fish) as a function of time (day of year) and the
species' age or weight class;
•	wt (variable) generates plots of each species' body weight
(g wet wt/fish) as a function of time (day of year) and the
species' age or length class.
where variable equals "age", "length", or "weight". Each cohort
of the species is assigned to one of five size classes that are
defined by bass based on the species' largest / oldest and
smallest / youngest individuals.
¦ /temperature stringstring2
This command specifies a community's ambient water
temperatures. For an unstratified water body only one string
option is specified. In this case valid options for this command
are:
•	temp[celsius]=a generates a constant ambient water
temperature for the simulation.
•	temp[celsius]=a + P*sin(e) + <$H[xunits\) generates a
sinusoidal ambient water temperature for the simulation
where a is the mean temperature for the chosen time
period, P is its amplitude (yunits), co is its phase angle
(radians), and cp=27t / period is its frequency (1 Ixunits).
•	temp[celsius]=fi\c(flien ante) to read and interpolate the
ambient water temperature from the file filename. See
Section 4.4.3.
For a stratified water body, users must specify the temperature of
both the epilimion and the hypolimnion. In this case valid options
are:
•	temp_epilimnion[meter]=a
•	temp_epilimnion[meter]=a + P*sin(e) + <$*t[xunits\)
•	tc m pe p i I i m n io n [ m etc r ]=f i\c(filename)
•	temp_hypolimnion[meter]=a
•	temp_hypolimnion[meter]=a + P*sin(a) + <$*t[xunits\)
•	temp_hypolimnion[meter]=file(/i/e«a/Me)
Note that unless specified otherwise bass assumes that its first
day of simulation is April 1 and that the 365-th simulation day is
March 31. This assignment can be changed using the command
/month_tO.
¦ /water_level stringstring2
This command specifies a community's actual water level. For an
unstratified water body only one string option is specified. In this
case, valid options for this command are:
•	depth [meter]=a generates a constant water level for the
simulation.
•	depth [meter]=a + P*sin(e) + <$H[xunits\) generates a
sinusoidal water level for the simulation where a is the
mean water level for the chosen time period, p is its
amplitude (yunits), co is its phase angle (radians), and
cp=27t / period is its frequency (1 Ixunits).
•	depth [meter]=file(filename) to read and interpolate the
water levels from the file filename. See Section 4.4.3.
For a stratified water body, users must specify the depth of both
the epilimion and the hypolimnion. In this case, valid options are:
•	depth_epilimnion[meter]=a
•	depth_epilimnion[meter]=a + P*sin(a) + <$*t[xunits\)
•	depthepi limn ion [ meter ]=filc(/
-------
The physico-chemical properties and exposure concentrations of
each chemical of interest are specified by a block of twelve
commands, i.e.,
each option must be separated by a semicolon. Valid options are:
• c I) c n t h o s [v'mm its ] =fn c generates potential dietary
exposures to fish via benthic organisms according to the
function fnc. Note in bass 2.1 the six lettered name
cbnths was used to specify this exposure function.
/chemical string
/exposure string{,...; stringn
/lethality string{,...; stringn
/log_AC real number
/LOG_KBl real number
/log_KB2 real number
/log_P real number
/melting_point real number
/metabolism string{,...; stringn
/molar_volume real number
/molar_weight real number
/nonfish_bcf string{,...; stringn
•	cinsects|yunits]=fnc generates potential dietary exposures
to fish via incidental terrestrial insects according to the
functionfnc. Note in bass 2.1 the six lettered name cinsct
was used to specify this exposure function.
•	cperiphyton|yunits]=fnc generates potential dietary
exposures to fish via periphyton according to the function
fnc. Note in bass 2.1 the six lettered name cphytn was
used to specify this exposure function.
The command /chemical must be the first command in the block
since it identifies the start of a new set of chemical parameters.
The order of the remaining commands, however, is not
significant. The use of these commands will now be described in
alphabetical order.
¦	/chemical string
This command specifies the start of the input for a new chemical.
Each chemical name must be a single character string without
embedded blanks or hyphens. If a two-part name is desired, the
user should use an underscore as a separating character. This
command must precede the commands /exposure, /lethality,
/log_ac, /log_kb1, /log_kb2, /log_p, /metabolism,
/molar_weight, /molar_volume, and /melting_point. The
name specified by this command is used in conjunction with the
command /initial_conditions to input initial whole-body
concentrations of chemicals in each age class of the fish of
concern and with the command /metabolism to specify daughter
products of chemical biotransformation. If the user specifies
chemical exposures via the file option, the indicated name is also
used to direct reading of the specified exposure files. Otherwise,
this name is used only for output purposes; bass does not use
this name to link to any chemical data base.
¦	/exposure stringy stringn
This command enables the user to specify the temporal dynamics
of chemical exposures to fish via water or contaminated
sediments or via the ingestion of benthic invertebrates, incidental
terrestrial insects, or plankton. Exposure concentrations specified
by these options are assumed to be completely bioavailable to the
fish. For example, water concentrations are assumed to be actual
dissolved concentrations and not total water concentrations that
include particle-bound chemical. If multiple options are selected,
•	c p hy to p I a n kt o n [v'mm <7.v ] =fn c generates potential dietary
exposures to fish via phytoplankton according to the
function fnc. Note in bass 2.1 the six lettered name
cpplnk was used to specify this exposure function.
•	csediment[yw/«y.v]=/rtt' generates sediment exposure
concentrations according to the functionfnc. Note in bass
2.1 the six lettered name csdmnt was used to specify this
exposure function.
•	c\v Atiir\yunits\=fnc generates aqueous exposure
concentrations according to the function fnc.
•	czooplankton\yunits\=fnc generates potential dietary
exposures to fish via zooplankton according to the
function fnc. Note in bass 2.1 the six lettered name
czplnk was used to specify this exposure function.
The concentration units for each exposure function are specified
within the indicated brackets. As previously noted for the
simulation control functions, unless specified otherwise, bass
assumes that the first day of simulation is April 1 and that the
365-th simulation day is March 31 for all the time dependent
exposure functions discussed in the following. This assignment
can be changed using the command /month_tO.
Valid expressions for dietary exposures via benthos, periphyton,
phytoplankton, or zooplankton and for benthic sediments are:
•	nonfish_name\yunits]=a generates a constant
concentration of toxicant in benthos, periphyton,
phytoplankton, sediment, or zooplankton.
•	n onfish_ name\yun its ]=a * c\v at e r [xwm its ] generates
chemical concentrations in benthos, periphyton,
BASS 2.2 March 2008
42

-------
phytoplankton, sediment, or zooplankton as a chemical
equilibrium with the ambient environmental water. If this
equilibrium is assumed to be thermodynamic, then the
coefficient a generally is equal to the product of the
component's dry organic fraction and the chemical's Kow.
Also see /nonfish_bcf.
•	nonfish_name\yunits\=\\\c(filename) to read and
interpolate the concentration of toxicant in benthos,
periphyton, phytoplankton, sediment, or zooplankton from
the file filename. See Section 4.4.3.
Valid expressions for insect dietary exposures are:
•	cinsects \yunits ]= a generates a constant concentration of
the toxicant in incidental terrestrial insects.
•	cinsects \yunits ]=file(filename) to read and interpolate
the concentration of the toxicant in incidental terrestrial
insects from the file filename. See Section 4.4.3.
Valid expressions for direct aqueous exposures are:
•	cwater|jMMi£s]=a generates a constant aqueous
concentration for the chemical of concern.
•	c\v ate r[y]=u*csed i ment [xmmits ] generates aqueous
exposure concentrations as a chemical equilibrium with
the benthic sediments. If this equilibrium is assumed to be
thermodynamic, then the coefficient a generally is
assumed to equal the product of the sediment's organic
fraction and the chemical's Koc.
•	c\v ate r \y units |=«+ |i * e x p (7 * t \xunits ]) generates an
exponential dissolved chemical water concentration where
a and P have units of yunits and 7 has units of Mxunits.
This option can be used to simulate a chemical spill or
one time application of a pesticide.
•	c\v ate r\yunits]=«+ |i * s i n (to+9*t\xunits]) generates a
sinusoidal dissolved chemical water concentration where
a is the mean dissolved chemical water concentration
(yunits) (over one period), P is the amplitude (yunits), co
is its phase angle (radians), and cp=27t / period is its
frequency (Mxunits). This option might be used to
simulate the mobilization of sediment bound contaminants
during spring or fall turnover.
•	c\v ate r[v'Mrt<7.v]=fi te(filename) to read and interpolate the
dissolved aqueous concentration of toxicant from the file
filename. See Section 4.4.3.
Users should be very cautious and judicious when using more
than one of the above options since the user can easily construct
an exposure scenario that is inconsistent with theoretical
constraints on the fate and distribution of contaminants in aquatic
systems.
¦	/lethality stringstringn
This optional command specifies species-specific LC50's for the
chemicals of concern either as an actual concentration value or
as a QSAR function. Valid string options are:
•	lc50 [units] (fish_name)=a
•	LC50 [units] (fish_name)=u* Kow [-] AP
where fish name is the common name of the fish species to be
simulated; Kow[-] is the chemical's n-octanol / water partition
coefficient; and a and p are real or integer empirical constants.
bass converts user supplied LC50's into their corresponding
aqueous chemical activities and then uses the geometric mean of
these lethal activities to trigger mortality during the simulation.
If the user desires, simulation of mortality associated with the
accumulation of a lethal aqueous chemical activity can be turned-
off by using the command line option "-1" as discussed in Section
4.5. When this is done, however, bass still calculates the fish's
total aqueous phase chemical activity and reports it as a fraction
of the fish's estimated lethal chemical activity to provide the user
with a simple, but useful, monitor of the total chemical status of
the fish.
¦	/log_AC real number
This command specifies the log10 of the chemical's aqueous
activity coefficient. For organic chemicals, if this parameter is
not specified, bass will estimate the chemical's activity
coefficient using its melting point and n-octanol / water partition
coefficient.
¦	/LOG KBl real number
This command specifies the log10 of a metal's binding constant
for non-lipid organic matter (see Equation (2.6)). This parameter
is input only for metals and organometals.
¦	/log_KB2 real number
This command specifies the log10 of a metal's binding constant
for refractory organic matter. This parameter is used to calculate
metal binding to the fish's dry fecal matter and input only for
metals and organometalics.
BASS 2.2 March 2008
43

-------
I /LOG P real number
This command specifies the chemical's log10 K^,, where Kow is
the n-octanol / water partition coefficient. /log_p must be
specified for all organic chemicals.
¦	/melting point real number
This command specifies the chemical's melting point (Celsius).
This datum, together with the chemical's logP, is used to
calculate the aqueous activity coefficient for organic chemicals
when that parameter is not specified by the user. See Yalkowsky
etal. (1983)
¦	/metabolism stringstringn
This optional command specifies species-specific rates of
biotransformation for the chemical of concern either as an actual
rates or as a QSAR function. Valid string options are:
•	H I [units] (fishname, chemical_name)=u
•	H I [units](fish name, chemical_name)=u*Kmx\-\A\i
•	bt [units] (fish_name, none)=a
•	bt [units] (fish_name, none)=a*Kow[-]AP
where bt specifies the whole-body-referenced biotransformation
rate kbt in Equation (2A7);fish_name is the common name of the
fish species that can metabolize the chemical of concern;
chemicalname is the name of the daughter product generated by
the metabolism of the chemical of concern; Kow[-] is the
chemical's n-octanol / water partition coefficient: and a and p are
real or integer empirical constants. If the user does not wish to
simulate daughter products because they are insignificant or
assumed to be harmless, chemical name can be assigned the
value none. When daughter products are specified, the user must
specify all physico-chemical properties of the identified by-
product in the same way that the physico-chemical properties of
the parent compound are specified.
¦	/MOLAR_VOLUME real number
This command specifies the chemical's molecular volume
(cm3/mol) that is used to calculate the chemical's aqueous
diffusivity, i.e.,
£> = 2.101x10~7ti1V0589
(4.1)
where D is the toxicant's aqueous diffusivity (cm2/sec); r| is the
viscosity of water (poise); and v is the chemical's molecular
volume (cm3/mol) (Hayduk and Laudie 1974). The viscosity of
water over its entire liquid range is represented with less than 1 %
error by
, %> 1.37 (r-20) + 8.36xl0"4(r-20)2
login	 =	-	-	-	— (4 2)
10 v\T	109 + T	1¦ >
where % is the viscosity (centipoise) at temperature T (Celsius),
and r|20 is the viscosity of water at 20 Celsius (1.002 centipoise)
(Atkins 1978).
¦	/molar weight real number
This command specifies the chemical's molecular weight
(g/mol).
¦	/nonfish_bcf stringy stringn
This command specifies the bioconcentration / bioaccumulation
factors for the nonfish compartments benthos, periphyton,
phytoplankton, and zooplankton either as a numerical constant or
as a QSAR function. Valid string options are:
•	BC F [-] (nonfish_name)=u
•	BC F [-] (nonfish_name)=u* Kow [-1A |i
where Kow[-] is the chemical's n-octanol / water partition
coefficient; and a and p are real or integer empirical constants.
Note that this command or /nonfish_qsar must be specified for
any nonfish compartment that is simulated as a community state
variable.
4.3.3. Fish Input Commands
Model parameters for each fish species of interest are specified
by a block of thirteen commands, i.e.,
/ COMMON_N AME	String
/species	string
/age_class_duration string
/spawning_period string
/feeding_options stringy	...; stringn
/PREY_S WITCHING _OFF
/initial_conditions string{,...; stringn
/compositional_parameters	string/,...; stringn
/ecological_parameters	string/,...; stringn
/morphometric_parameters	string/,...; stringn
/physiological_parameters	string/,...; stringn
/fishery_parameters	string/,...; stringn
/habitat_parameters	string/,...; stringn
The command /common_name must be the first command in the
block since it is the identifier for the start of a new set of fish
BASS 2.2 March 2008
44

-------
parameters. The order of the remaining commands is not
significant. The use of these commands will now be described in
alphabetical order.
¦	/ageclassduration string
This command is used to specify the duration of each age class.
Two character strings, i.e., "month" and "year", are recognized
as valid options.
¦	/COMMON NAME String
This command specifies the start of input data for a fish species.
The command's specified common name string is used for model
output and as a label for specifying the dietary composition of
other fish species. Each common name must be a single character
string without embedded blanks. If a two-part name is desired,
the user should use an underscore as a separating blank. See
the diet option for the command /ecological_parameters.
¦	/compositional parameters strings stringn
This command specifies aqueous and lipid fractions of the fish.
Valid options that must be separated by semicolons are:
•	pa[-]=a + P*pl[-] specifies the fish's aqueous fraction as
a linear function of the fish's lipid fraction.
•	pl[-]=a*W[xM«i£s] AP specifies the fish's lipid fraction as
an allometric function of its body weight. If a fish's
average lipid content is independent of its body weight
(i.e., p equals zero), however, this parameter can be
specified simply as \}\\yunits\=a
where a and p are real or integer empirical constants.
¦	/ecological parameters stringy string,,
This command specifies the ecological parameters that describe
the fish's trophic interactions, non-predatory mortality, and
recruitment. Valid options that must be separated by semicolons
are:
•	ast_yoy[-]=f(b[-]=a, yoy\xunits\=\\, pop\yunits]=j)
specifies parameters for implementing accelerated self-
thinning ofyoung-of-year fish (YOY), or more accurately
recently recruited cohorts, that often occurs due to
intraspecies competition for territories, refugia, or other
habitat resources. The functional argument b[-]=a
specifies the desired accelerated self-thinning exponent.
The functional argument yoy[xunits\=$ defines the age,
length, or live weight threshold below which cohorts will
be subject to accelerated self-thinning. Valid expressions
foryoy are either "age", "tl", or "wt". The final functional
argument specifies the population threshold that triggers
accelerated self-thinning. Depending on the assumed
nature of the competition, this threshold can be specified
either as the total density of cohorts satisfying the
condition yoy[xunits\ §. For the former
case, pop equals "pop_yoy" whereas for the latter case
pop equals "popadults".
• dict(c7
-------
"january-december" is assumed.
The diet(.,.)={...} option can be repeated as many times as
needed in order to define a complete lifetime sequence of
diets for the fish.
\\>\yunits\= fnc specifies the average length of prey
consumed by a fish whose body length is L[xunits],
Unlike most fish command options, two valid function
strings are recognized, i.e.,
\[>\yunits\=a + |i*L[xw/«7.v] or
\[>\yunits\=a + p * c x p (7 * L \xunits ])
where a, p, and 7 are real or integer empirical constants.
If a fish's average prey size is independent of its body
length (i.e., Pequals zero), this parameter can be specified
simply as \^\yunits\=a
l|)_max[v'Mrt<7.v]= fnc specifies the maximum length of
prey consumed by a fish whose body length is L[xunits],
Like the option for a fish's average prey length, two valid
function strings are recognized, i.e.,
Ipmax\yunits\=a + |i*L[xw/«7.v] or
I pmax \yunits\=a + p * c x p (7 * L \xunits ])
where a, p, and 7 are real or integer empirical constants.
If a fish's maximum prey size is independent of its body
length (i.e., Pequals zero), this parameter can be specified
simply as lp_max|jwMi£s]=a. When this parameter is not
specified by the user, bass assigns the default value
lp_max[cm]=0.5*L[cm].
Ip_min[yw/«y.v]=fnc specifies the minimum length of prey
consumed by a fish whose body length is L[xunits], Like
the option for a fish's average prey length, two valid
function strings are recognized, i.e.,
I p_mi n \yunits]=a + |}*L[xw/«7.v] or
I p_m i n \y units ]=a + P * e x p (7 * L \xunits ])
where a, p, and 7 are real or integer empirical constants.
If a fish's minimum prey size is independent of its body
length (i.e., Pequals zero), thisparameter can be specified
simply as lp_min|jwMife]=a. When this parameter is not
specified by the user, bass assigns the default value
lp_min[cm]=0.1 *L[cm].
•	mls|jMMi£s]=a specifies the species' maximum longevity
or life span.
•	nm[-]=a*b(P:7)*sg_mu[-] specifies a cohort's rate of
dispersal and non-predatory mortality as a function of its
habitat suitability and its long-term specific growth rate
sg_mu[-]. Whereas a specifies the fraction of the species'
total "mortality" that is attributable to dispersal and non-
predatory mortality, P and 7 specify the species' minimum
and maximum self-thinning exponents, respectively. See
Equations (2.85) and (2.88). If the user elects not to
simulate habitat effects on dispersal and non-predatory
mortality, this parameter can be specified simply as
nm[-] = a*b(P)*sg_mu[-]
where p is the species' average self-thinning exponent.
Also see the /ecological_parameters option sg_mu[].
•	rbi[-]=a specifies the species' reproductive biomass
investment (i.e., grams gametes per gram spawning fish)
where a is real empirical constant.
•	refugia[yw/«y.v]=u specifies a refuge population size for
each cohort that can be prey for community piscivores
where a is real or integer constant. Yunits must be
dimensionally equivalent to fish/ha. If not specified, bass
assumes no refuge level (i.e., refugia|jwMi£s]=0)
•	sg_mu[vunits\=u*W\xunits\Ap specifies the species'
long-term mean specific growth rate where a and p are
real or integer empirical constants, yunits must be
dimensionally equivalent to day"1, and xunits must be
dimensionally equivalent to g wet wt/fish. If not specified,
bass can estimate this parameter provided that the user
specifies the species' expected body weight at its
maximum age. See /ecological_parameters option
wt_max[] for details.
•	tl_rO|jwMi£s]=a specifies the species' minimum total
length when sexual maturity is reached where a is a real
or integer empirical constant.
•	\v I \yunits\=a* L\xunits\A p specifies the fish's live weight
as an allometric function of its total length where a and p
are real or integer empirical constants.
•	\vt_max[yM/«y.v]=u specifies the species' expected live
body weight at its maximum age where a is a real or
integer empirical constant. This parameter is required only
when the user has not specified the species' long-term
mean specific growth rate using the
BASS 2.2 March 2008
46

-------
/ecological_parameters option sg_mu[]. When
sg_mu[] is not specified, bass will estimate the species'
long-term specific growth rate based on its maximum life
span	young-of-year body weight yoy[], and
wt_max[]. If the user has specified the species'
temperature/seasonal dependent specific growth rate
s g \yun its |=« * W \xunits ]A H (y,T, ,T2)
(see /PHYSIOLOGICAL_ PARAMETERS option Sg[]), BASS
estimates the species's long-term specific growth rate by
sg_mu[l/d] = a*WAP
where a is back-calculated as outlined by Equation
(3.43). If sg[] has not been specified, bass estimates the
species's long-term specific growth rate by
sg_mu[l/d] = a*WA(-0.732)
where a is back-calculated asoutlined by Equation (3.43)
usingthe mean interspecies specific growth exponent (i.e.,
-0.732) estimated from the bass model database. Also see
Barber (2003).
•	yoy|jwMi£s]=a specifies the live weight of fish recruited
into the population as age class 1 where a is a real or
integer empirical constant.
¦ /feeding options stringy string,,
This command instructs bass how to calculate ingestion for a
particular age or size range of fish. Valid options for this
command are :
•	allometric(c/
-------
See Section 4.5 for details.
¦ /habitat parameters stringy stringn
This command specifies habitat preferences, tolerances, and
suitability indices for the species.
Valid options for habitat preferences are:
• tpref[celsius] (class) = 7 specifies the preferred or optimum
temperature of the age or size class specified within the
parentheses.
Valid expressions for class are:
a < ii\xunits\ < p
a< \\xunits\ < p
a < w [xunits] < p
where a and P are real or integer empirical constants. This option
can be specified repeatedly as needed. Although for a given
species all class types must be the same type (i.e., age, length, or
weight), class types between species can be different.
Valid options for habitat suitability multipliers are:
•	hsi_feeding[-] = fnc specifies the species' HSI for
feeding by the time function fnc. This HSI is used as a
simple linear multiplier on a cohort's maximum ingestion
rate when feeding is modeled with either an allometric,
Holling, or clearance volume formulation. When a
cohort's ingestion is back-calculated from its expected
growth rate, the specified HSI is used as a simple linear
multiplier on the cohort's specific growth rate. See
Section 2.9.
•	hsi_recruitment[-] =fnc specifies the species's HSI for
recruitment by the time function fnc. This HSI is used as
a simple linear multiplier on the species' YOY
recruitment. See Section 2.9.
•	hsi_survival[-] = fnc specifies the species' HSI for
dispersal and non-predatory mortality by the time function
fnc. This HSI is used to control the species' self-thinning
exponent that determines, in combination with the fish's
growth rate, a cohort's estimated dispersal and non-
predatory mortality rate. See Section 2.9.
Valid expressions for these HSI functions are:
•	hsi_name[-]= a generates a constant HSI for the entire
simulation.
•	hsi_name\-\=ii\c(filename) generates time-varying HSIs
either by reading and interpolating HSIs specified by the
file filename or by reading and interpolating habitat
variables and then calculating HSIs using user-supplied
logistic regressions. See Sections 4.4.3.
When HSI multipliers are calculated using user-supplied logistic
regressions, the desired regressions are specified using the
following options:
•	hsi_feeding_equation[-] = regression
•	hsi_recruitment_equation[-] = regression
•	hsi_survival_equation[-] = regression
where regression specifies a linear combination of habitat
variables Xt that are transformed or raised to an integer or real
power. Transformations recognized by bass include:
LN(Xt) =>ln Xi = loge Xt
LN_1(X;) =>ln (X + 1) = loge (X,. + 1)
LOG( *;¦)=> log (X) = log10 (X)
LOG_l(X;) =>log (X + 1) = log10 (X + 1)
SQRT(X;) =>pT
ASIN_SQRT( A-,) => arcsin|vTX~)
ASIN_SQRT_PCT( X;) => arcsin(y 0.01 X )
Habitat variables must be specified with units inclosed within
brackets, and must match in name and units to column variables
specified by the data filefilename. After evaluating the specified
logistic regression, bass calculates the fish's HSI multiplier
using the standard equation
hsi name = 1 / (1 + EXP (- hsi name cqwAtwn))
When HSI functions are not specified by the user, bass assigns
the default value of 1 to each unspecified HSI function.
¦ /INITIAL CONDITIONS stringy stringn
This command specifies the species' initial ages, whole-body
chemical concentrations, live body weights, and population sizes.
Valid options for this command are:
• age[M«^]={x!,xn} to initialize the age of each cohort
with the specified vector. The units that are delineated by
brackets must be dimensionally equivalent to days.
BASS 2.2 March 2008
48

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•	chemical name\units] ='x,,xn} to initialize the whole-
body concentration of each cohort for the named chemical
by the specified vector. Each name must correspond
exactly to a name specified by one of the /chemical
commands. The units of measurement that must be
enclosed by brackets must be dimensionally equivalent to
(ig/g wet wt.
•	wt[M«iYs]={x!,xn} to initialize the body size of each
age class with the specified vector. The units delineated
by brackets must be dimensionally equivalent to g wet
wt/fish.
•	pop[MMite]={xj,xn} to initialize the population density
of each age class with the specified vector. The units
delineated by brackets must be dimensionally equivalent
to fish/ha.
¦	/morphometric parameters stringj; stringn
This command specifies the species' morphometric parameters
that are needed to describe the exchange of chemicals across its
gills. Each string specifies a required morphometric parameter
as a simple allometric power function of the fish's body weight.
Valid options, which must be separated by semicolons, are:
•	ga\yunits|=« * W [ jchmits]A|i specifies the fish's total gill
surface area where a and p are real or integer empirical
constants, yunits must be dimensionally equivalent to cm2
or cm2/g wet wt.
•	id \yunits|=« * W [ jchmits]A |i specifies the interlamellar
distance between adj acent lamellae where a and p are real
or integer empirical constants, yunits must be
dimensionally equivalent to cm or cm/g wet wt.
•	I d \yunits |=« * W [ jchmits ]A p specifies the density of
secondary lamellae on the primary gill filaments (i.e.,
number of lamellae per mm gill filament) where a and p
are real or integer empirical constants.
•	11 \yunits\=u*W\xunits\A p specifies the fish's lamellar
length where a and p are real or integer empirical
constants, yunits must be dimensionally equivalent to cm
or cm/g wet wt.
Note that if the exponent P equals zero for any of these
parameters, the resulting term W[xwm&s]a0 does not have to be
specified.
¦	/physiologicalparameters stringy stringn
This command specifies the species' physiological parameters
for simulating growth. Each string specifies a physiological
parameter of the fish as a constant or temperature-dependent
power function of its body weight. In particular,
•	ae_plant[-]=a specifies the fish's assimilation efficiency
for periphyton and phytoplankton where a is a real
empirical constant less than or equal to one.
•	ae_invert[-]=a specifies the fish's assimilation efficiency
for benthos, insects, and zooplankton where a is a real
empirical constant less than or equal to one.
•	ae_fish[-]=a specifies the fish's assimilation efficiency
for fish where a is real a empirical constant less than or
equal to one.
•	ge\yunits\=a*G\xunits]A p* H(y,T,,T2) specifies the fish's
gastric evacuation where G is the mass of food resident in
the intestine, and where a, P, y, and T2 are real or
integer empirical constants, yunits must be dimensionally
equivalent to g dry wt/d. In general, y=1/2, %, or 1 (Jobling
1981). This parameter is required only if the feeding
option holling(-) is used.
•	kf_min[-]=a specifies the minimum condition factor for
a fish's continuing existence. In bass, a fish's condition
factor is defined by the ratio
where W and L are the fish's current live body weight and
total length, respectively; and a and p are the coefficient
and exponent for the fish's weight-length relationship (see
/physiological parameters option wl[ ]).
•	m\\yunits ]=u*W [xM/«y.v ]A p* H(7,T, ,T2) specifies the
fish's maximum filtering rate where a, P, y, and T2 are
real or integer empirical constants, yunits must be
dimensionally equivalent to L/d. This parameter is
required only if the feeding option clearance(-) is used.
•	m i [yunits\=a*W \xunits ]A p* H (y,T, ,T2) specifies the
fish's maximum ingestion where a, P, y, and T2 are
real or integer empirical constants, yunits must be
dimensionally equivalent to g dry wt/d. This parameter is
required only if the feeding option allometric(-) is used.
•	rq[-]=a specifies the fish's respiratory quotient; (i.e.,
L(C02) respired/L(02) consumed) where a is a real
empirical constant.
BASS 2.2 March 2008
49

-------
•	rt:std[-]=a specifies the ratio of a fish's routine
respiration to its standard respiration where a is a real
empirical constant. If not specified by the user, bass
assigns a default value equal 2.
•	sda:in[-]=a specifies the ratio of a fish's SDA to its
ingestion where a is a real empirical constant. If not
specified by the user, bass assigns a default value equal
0.17.
•	s g \y u nits|=« * W [xunits ]A H (y,T, ,T 2) specifies the
fish's specific growth rate where a, P, y, and T2 are
real or integer empirical constants, yunits must be
dimensionally equivalent to day"1. This parameter is
required only if the feeding option linear(-) is used.
•	sm\yunits\=a*V] [xunits] AP*H(y,Tj,T2) specifiesthe size
of the satiation meal consumed during the interval (0, st)
where a, P, y, and T2 are real or integer empirical
constants, yunits must be dimensionally equivalent to g
dry wt/d. See option $t\yunits\ below. This parameter is
required only if the feeding option holling(-) is used.
•	so|jMMiYs]=a*W[xMMi£s]AP*H(y,T1,T2) specifies the
fish's standard oxygen consumption where a, P, y, and
T2 are real or integer empirical constants, yunits must be
dimensionally equivalent to mg 02/hr or mg 02/g wet
wt/hr.
•	st\yunits]=a*W[xunits] AP*H(y,Tj,T2) specifies the time
to satiation when feeding with an initially empty stomach
where a, p, y, and T2 are real or integer empirical
constants. See option sm\yunits] above. This parameter is
required only if the feeding option holling(-) is used.
For the options gt\yunits\, m\\yunits]. m\\y units]. $g\yunits\,
$m\yunits\, so\y units], and $t\y units],
/f(Y,r1,r2) = exp(Yr)(i-^)Y(r2	(4.4)
12
where Tl is the temperature at which each particular process's
rate is maximum and T2 is the upper temperature at which the
process is no longer operative. If the process does not exhibit a
temperature optimum, then the hyperbolic function H(y,T1,T2)
should be substituted with the exponential function
exp(y*T[celsius]). Consequently, each of these temperature-
dependent power functions can also be specified as
a*W[xwM&s]AP*exp(y*T[celsius])
As noted for the fish's morphometric parameters, if the exponent
P equals zero for any of these temperature-dependent power
functions, the term V/[xunits] A0 does not have to be specified.
If a required parameter is not specified, the program will
terminate with an appropriate error message.
¦	/PREYSWITCHINGOFF
This command disables bass's prey switching algorithms when
a cohort's expected feeding level cannot be satisfied using the
dietary compositions specified by the user. By default, bass's
prey switching algorithms are enabled.
¦	/SPAWNINGPERIOD String
This command specifies the months during which spawning
occurs. Valid character strings for this command are either the
name of a month or the names of two months separated by a
hyphen. For example,
/spawning_period may
OR
/spawning_period april-june
The names of the months must be spelled-out in full.
¦	/species string
This command specifies the scientific name (genus and species)
of the fish to be modeled. When this command is encountered,
bass uses the specified scientific name to assign default
ecological, morphological, and physiological parameters for the
species of interest. These default parameters are then updated
with the data that the user inputs via the
/ecological_parameters , /morphometric_parameters, and
/physiological_pARAMETERS commands. This option, however,
is not currently operational in bass v2.2.
4.3.4. Nonfish Input Commands
These commands specify simulation parameters for benthos,
periphyton, incidental terrestrial insects, phytoplankton and
zooplankton. The syntax for these commands is as follows
/benthos	string;,
/terrestrial_insects	string/,
/periphyton	string{,
/phytoplankton	string{,
/zooplankton	string{,
stringn
stringn
stringn
stringn
stringn
BASS 2.2 March 2008
50

-------
Depending on the options selected, bass generates the standing
stocks of these nonfish compartments either as community
forcing functions or as community state variables. Although these
compartments can be simulated for any desired community, only
those components identified as fish prey are required to be
specified (see the diet(.option for
/ecological_parameters). Note, however, because bass
assumes that piscivorous fish switch to benthic invertebrates and
incidental terrestrial insects when appropriate forage fish are
unavailable, the benthos and insect options should be specified
even when simulating only piscivorous fish.
When benthos, periphyton, incidental terrestrial insects,
phytoplankton or zooplankton are treated as community forcing
functions, a single option of the form
•	I) i o m as s \y units\=string
is specified. Valid expressions for this option are:
biomass|jMMi£s]=a for a constant nonfish standing stock
I)iomass[yunits]=u + p*sin(e) + <$*t[xunits\) for a
sinusoidal nonfish standing stock where a is the mean
standing stock for the chosen time period, P is its
amplitude (yunits), co is its phase angle (radians), and
cp=27t / period is its frequency (1 Ixunits).
I)iomass [v units] =fi\e(fHen ante) to read and interpolate a
nonfish standing stock from the file filename. See Section
4.4.3.
Whereas yunits must be dimensionally equivalent to g dry wt/m2
for benthos, incidental terrestrial insects, and periphyton, for
phytoplankton and zooplankton yunits must be dimensionally
equivalent to g dry wt/L. As previously noted, bass assumes that
the first day of simulation is April 1 and that the 365-th
simulation day is March 31. This assignment can be changed
using the command /month_tO. This command-option
combination is equivalent to the bass v2. 1 simulation control
command /biota
When benthos, periphyton, phytoplankton or zooplankton are
treated as community state variables, the following five options
must be specified:
•	i n itial_I)iomass [v'M/7its\ =number This option specifies
the initial compartmental standing stock of the designated
component and is required to simulate the designated
nonfish compartment as a bass state variable, yunits must
be dimensionally equivalent to g dry wt/m2.
•	mean_weight|jM«ite]=/«c. This option specifies the
average body weight of individuals within the designated
nonfish compartment. This parameter is required to
simulate the designated nonfish compartment as a bass
state variable, yunits must be dimensionally equivalent to
g dry wt/ind. Valid expressions for fnc are:
m eanw e i g h t \yunits ]=a generates a constant average
individual body weight for the designated prey.
me an_\v e i g h t [yunits\=a + p*sin(e) + <$*t[xunits\)
generates the average individual body weight of the
designated prey as a sinusoidal function of time where
a is the mean body weight for the chosen time period,
P is its amplitude (yunits), co is its phase angle
(radians), and cp=27t / period is its frequency (1 Ixunits).
mean_weight|jM«^]=file(/i/e«a/Me) to read and
interpolate the average individual body weight of the
designated prey from the file filename. See Section
4.4.3.
Unless specified otherwise, bass assumes that the first
day of simulation is April 1 and that the 365-th simulation
day is March 31. This assignment can be changed using
the command /month_tO.
•	ingestion|jM«ite]=a*W[xM«ite]AP*H('y,T1,T2) specifies
the ingestion rate of individuals within the designated
compartment as a function of their average body weight
and temperature where a, p, y, T1; and T2 are real or
integer empirical constants. This parameter is required to
simulate either benthos or zooplankton as a bass state
variable, yunits must be dimensionally equivalent to g dry
wt/d, and xunits must be dimensionally equivalent to g
dry wt/ind.
•	photosynthesis|j'MMi£s]=a*W[xMMi£s] AP*H(y,Tj,T2)
specifies the photosynthetic rate of individuals within the
designated compartment as a function of their average
body weight and temperature where a, p, y, and T2 are
real or integer empirical constants. This parameter is
required to simulate either periphyton or phytopalnkton as
a bass state variable, yunits must be dimensionally
equivalent to g dry wt/d, and xunits must be
dimensionally equivalent to g dry wt/ind. Currently,
photosynthesis is not treated as a function of nutrients and
light availability.
•	respiration|jM«i7s]=a*W[xM«i7s] AP*H(y,Tj,T2)
specifies the specific rate of dry organic mater respiration
for the designated compartment as a function of average
BASS 2.2 March 2008
51

-------
individual body weight and temperature where a, P, y, T1;
and T2 are real or integer empirical constants. This
parameter is required to simulate the designated nonfish
compartment as a bass state variable, yunits must be
dimensionally equivalent to g dry wt/d, and xunits must
be dimensionally equivalent to g dry wt/ind.
Although bass enables users to simulate benthos, periphyton,
phytoplankton or zooplankton as community state variables,
incidental terrestrial insects are always treated as a community
forcing function.
4.4. Input Data Syntax
4.4.1.	Units Recognized by BASS
Most bass commands require the specification of units (or
combination of units) as part of an option. This section describes
the syntax for units that are recognized by bass's input
algorithms. The conversion of user-supplied units to those
actually used by bass is accomplished by referencing all units to
the MKS system (i.e., meter, kilogram, second). Table 4.1 and
Table 4.2 summarize prefixes and fundamental units,
respectively, that are recognized by bass's unit conversion
subroutines. Table 4.2 also summarizes the dimensionality and
the conversion factor to the MKS system standard unit. Table 4.3
summarizes units that are recognized by bass's unit conversion
subroutines for specifying ecological, morphometric, and
physiological units.
Units and their prefixes can be specified in either upper or lower
case. When prefixes are used, there must be no embedded blanks
between the prefix and the unit name, e.g., "milligrams" is
correct, "milli grams" is incorrect. The circumflex (A) is used to
denote exponentiation (e.g., cm"2 is presented as cmA-2). The
slash (/) is used to denote division. If multiple slashes are used
to specify a unit, they are interpreted according to strict algebraic
logic. For example, both "mg/liter", and "mg literM" are
equivalent specifications. Similarly, the weight specific units
"mg/g/day" are "mg gA-l dayA-l" are equivalent.
4.4.2.	User Specified Functions
The following syntax rules apply to specifying these options
•	Brackets are used only to delineate units. Dimensionless
parameters like assimilation efficiency, lipid fraction, and
Kow must be specified with null units
•	The order of addition and multiplication is not significant.
Thus, the following specifications are valid and
equivalent.
temp(celsius)=a+P*sin(co + cp*t[xunits]) <=>
temp[celsius]=P sin((p*t[xMra'/s]+co) + a
czplnk[yMn/to]=a*cwater[xMn//s] <=>
czplnk[yMn//s]=cwater[xMn//s] *a
•	Options that are temperature-dependent or independent
power functions can be specified by log10 or In transforms.
For example, the following options are valid
ln(so[yunits])=a + P*ln(W [xunits]) +y*T[celsius]
log(so[yM«z'fa])=a + p*ln(W [xunits]) +y*T[celsius]
•	User-specified functions do not have to be in reduced
form. For example, temperature-dependent power
functions can be specified with a reference temperature
other than 0°Celsius. Thus, bass will correctly decode the
following functions
so[yMra'to]=a*W[xMra'to]AP*exp(y*(T[celsius]-20))
ln(so[yM«z'fa])=a+ p*ln(W[xM«/'/s]) + y*(T [celsius]-20)
log(so [yunits])= a+ P*log(W[xM«z'fa]) +y*(T [celsius]-20)
•	If the temperature dependency is unknown, temperature-
dependent power functions can be input for a specific
temperature, y° Celsius, in which case bass assumes a
default Qio=2. If this feature is used, the reference
temperature must be enclosed by parentheses and follow
the units specification of the independent variable. For
example, the following specifications are valid
so[yMn//s](y)=a*W[xMrato]AP
ln(so[yMra/s](y))=a + p*ln(W[xM«/'/s])
log(so[yMra/s](y))=a + P*log(W[xMra'/s])
•	If either the slope of a linear function or the exponent of
a power function is zero, the function can be input as a
constant without specifying the expected independent
variable. For example, the following specifications are
equivalent
lp[cm]=4.5 <=> lp[cm]=4.5 + 0.0*L[cm]
pl[-]=0.05 <=> pl[-]=0.05*W[g(fw)]A0.0
•	Operators (A*/+-) may not be concatenated. For example,
the following options have invalid syntax
BASS 2.2 March 2008
52

-------
so[mg(o2)/g/hr]=
0.1*exp(0.0693*T[celsius])*W[g(fw)]A-0.2
ln(so [mg(o2)/g/hr] )=
-2.30+0.0693*T [celsius]+-0.2 *ln( W[g(fw)])
The correct syntax for these options would be
so[mg(o2)/g/hr]=
0.1*exp(0.0693*T[celsius])*W[g(fw)]A(-0.2)
ln(so [mg(o2)/g/hr] )=
-2.30+0.0693 *T [celsius] - 0.2*ln(W[g(fw)])
4.4.3. User Supplied Parameter Files
If the user specifies a file option for the /exposure,
/temperature, /water_level, /biota, /benthos,
/terrestrial_insects, /periphyton, /phytoplankton,
/zooplankton, or /habitat_parameters commands, the
designated files must exist and be supplied by the user. The
general format of a bass exposure file allows a user to specify
multiple exposure conditions within a single file. Each file record
specifies exposure conditions for a specific time. The general
format of a bass exposure file is as follows
! file: exposure.dat
!
/001 time[M«z'fa] ! see ensuing discussion
/CI string
/CM string
/START_DATA
vu v1>2 ...	v1>MV ! comment
1 \'i2 ...	2 \ 1 \¦ ! comment
vnr,i vnr,2 ¦¦¦ vnr,mv ! comment
The records beginning with a slash (/) followed by an integer CJ
identify the type of data (time, exposure concentration,
temperature, etc.) contained in C J-th column of each data record.
In this example, NR is the total number of data records in the
file, MV is the number of variables per record, and CI... CM are
the column positions of M exposure variables that are to be read.
Note, however, that MV can be greater than CM and that
CI...CM need not be consecutively numbered. To simplify the
reading of multiple exposure files, bass requires that "time" be
specified as the first column of any user-supplied exposure file.
Valid character strings for specifying the remaining data columns
include:
bbenthos[M«/£s] to input the standing stock of benthic
invertebrates;
binsects [units] to input the standing stock of incidental terrestrial
insects;
bperiphyton[wMife] to input the standing stock of periphyton or
grazable algae;
I) p hyto p I an kto n [ units ] to input the standing stock of
phytoplankton;
bzooplankton[wMife] to input the standing stock of zooplankton;
c be nth0s [units ] (chemical name) to input the concentration of
chemical name in benthic invertebrates;
cmstcts[units](chemical name) to input the concentration of
chemical name in incidental terrestrial insects;
c pc ri p hyto n [mm<7.v] (chemical name) to input the concentration
of chemical name in periphyton;
cphy toplan kton [units] (chemical name) to input the
concentration of chemical name in phytoplankton;
csediment[units\(chemical name) to input the sediment
concentration of chemical name;
c\v ate r [ units \ (ch emical name) to input the unbound, aqueous
concentration of chemical name;
czooplan kton [units] (chemical name) to input the whole-body
concentration of chemical name in zooplankton;
depth [units] to input water depth.
hsi_feeding[-](/<.s7i name) to input the feeding/growth HSI for
the fish species identified within the parentheses.
hsi_recruitment[-](/<.v/i name) to input the recruitment/
spawning HSI for the fish species identified within the
parentheses.
hsi_survival[-](/<.v/i name) to input the dispersal/non-predatory
mortality HSI for the fish species identified within the
parentheses.
temperature[wMife] to input ambient water temperature.
wbenthos[M«/te] to input the mean body weight of benthic
invertebrates;
BASS 2.2 March 2008
53

-------
winsects[M«/£s] to input the mean body weight of incidental
terrestrial insects;
wperiphyton[wMife] to input the mean body weight of periphyton
or grazable algae;
\v p h vto p I an kto n [ units ] to input the mean body weight of
phytoplankton;
\v zoo plan kto n [ units ] to input the mean body weight of
zooplankton;
If column names other than those listed above are specified, bass
simply ignores them. Data records can be continued by
appending an ampersand (&) to the line, e.g., the following data
records are equivalent.
VU VU ... vy V1J+1 ... V1)MV
vuvu... vy&
Vij+1 Vij+2 ¦¦¦ Vi,MV
File records must be sequenced such that time is nondecreasing
(i.e., t; < tj+1, i =1, 2, ..., N-l). The time increment between
consecutive records can be either constant or variable, bass
calculates the exposure conditions between specified time points
by simple linear interpolation.
4.5. BASS Include File Structure
As mentioned in Section 4.1, bass's input processing routines
allow a bass project file to be specified using include files of
related parameters. This capability is the cornerstone upon which
the bass GUI has been developed.
In order to select an appropriate project / include file hierarchy
for implementation in the bass GUI, careful consideration was
given to the perceived needs of researchers and environmental
regulators who would routinely analyze and evaluate similar
scenarios that might differ either in terms of the chemical
exposures of interest or in terms of the communities of concern.
For example, the USEPA Office of Pesticide Programs routinely
evaluates different pesticides for registration based on their
expected fate and effects in series of canonical aquatic habitats
/ ecosystems. Similarly, the USEPA Office of Pollution
Prevention and Toxics evaluates the pre-manufacturing
registration of industrial chemicals in much the same way. These
considerations suggested that a practical working bass project /
include file hierarchy should be structured as follows:
• All data specifying the bioenergetic, compositional, and
morphological parameters for a specific fish species that
can be considered to be independent of the particular
community in which the fish resides, should be contained
within a single include file that is assigned the reserved
extension fsh.
•	All data specifying the structure and function of a
particular fish community should be contained within a
single include file that is assigned the reserved extension
cmm. These files should use the aforementioned *.fsh
files as include files intervened with the necessary fish
commands that are specific to the community of interest.
In general, these community-specific fish commands
define each species' 1) the dietary composition, 2) initial
ages, body weights, population densities, and chemical
residues, 3) habitat multipliers, 4) any desired fishery
parameters, and 5) any fish commands contained within
the specified *.fsh files that the user wants to have
superceded or updated.
•	All data specifying the physico-chemical properties for a
specific chemical of concern should be contained within
a single include file that is assigned the reserved extension
PRP.
•	All data specifying a chemical exposure scenario should
be contained within a single include file that is assigned
the reserved extension chm. These files should use the
aforementioned *.prp files intervened with the necessary
chemical commands needed to specify each chemical's 1)
aqueous concentration, 2) dietary exposures via benthos,
insects, periphyton, phytoplankton, and zooplankton, 3)
effects concentrations for specific fish, and 4) any relevant
rates of biotransformation by specific fish.
•	Lastly, all bass project files should use the
aforementioned *.cmm files and *.chm files to specify the
fish community and the chemical exposures of concern.
All such project files will be assigned the reserved
extension prj.
Based on these considerations, the general structure of a bass
project file is as follows:
1 file: name.prj
1 notes: general structure of a BASS project file
/ SIMULATION_CONTROL
/ HEADER 
/ MONTH_TO 
/ LENGTH_0F_SIMULATION a[year]
/ TEMPERATURE temp[celsius] = fnc
/ WATER_LEVEL depth[meter] = fnc
1 specify chemical exposures (if any)
#include xexposures.chm'
1 specify fish community
#include xcommunity.cmm'
BASS 2.2 March 2008
54

-------
/ END
The chemical exposure scenario file exposures.chm specified
in this project file has the following general form
file: exposures.chm
notes: general structure of a chemical
exposure scenario file
specify physico-chemical parameters
#include xchemical_l.prp'
/ EXPOSURE cwater[ppm] = fnc; &
cbenthos [ppm] = fnc, &
cinsects [ppm] = fnc, &
cperiphyton [ppm] = fnc, &
cphytoplankton [ppm] = fnc, &
czooplankton[ppm] = fnc
/ NONFISH_BCF &
bcf [-] (benthos) = fnc, &
bcf [-] (periphyton) = fnc, &
bcf [-] (phytoplankton) = fnc, &
bcf [-] (zooplankton) = fnc
/ LETHALITY &
lc50[units](fish_l) = fnc; &
lc50[units](fish_2) = fnc
/ METABOLISM &
bt [units] (fish_l, chem_n) = fnc, &
bt[units](fish_2, chem_n) = fnc
1 repeat above chemical data block as needed
1 end exposures.chm
The general structure of the chemical property file
chemical_1 .prp specified in the above exposure scenario file is
1 file: chemical_l.prp
1 notes: general structure of a chemical
1	property file
/ CHEMICAL
/ LOG_AC
/ LOG_P
/ L0G_KB1
/ L0G_KB2
/ MOLAR_WEIGHT
/ MOLAR_VOLUME
/ MELTING_POINT








1 end chemical_l.prp
The community file community.cmm specified for the above
project file has the following general form
1 file: community.cmm
1 notes: general structure of a community file
#include xfish_l.fsh'
/ ECOLOGICAL_PARAMETERS &
diet (a
/ SPECIES 
/ AGE_CLASS_DURATION 
/ SPAWNING_PERIOD 
/ FEEDING_OPTIONS &
allometric (a
-------
the bass installation software creates the directory structure
shown below
C:\BASS
|-- BASS_V22.EXE
I
|-- BASS GUI executables and DLLs
I
I-- \FISH -- *.FSH
I
|-- \COMMUNITY -- *.CMM
I
|-- \PROPERTY -- *.PRP
I
|-- \PROJECTS -- \projectl -- *.PRJ
|	*.CHM
|	* . DAT
I
\proj ect2
The \fish subdirectory contains canonical versions of the *.fsh
files that specify the bioenergetic, compositional, ecological, and
morphological parameters of individual fish species and that are
used as include files for constructing fish community files.
The \community subdirectory contains canonical versions of the
*.cmm files that specify the composition, trophic structure, and
initial conditions for the community's fishes as well as any
desired fishery and habitat suitability parameters.
The \property subdirectory contains canonical versions of the
*.prp files that specify the physico-chemical properties of
individual chemicals and that are used as include files for
chemical exposure files.
The \projects directory contains subdirectories that are created
by the user for a particular model application. In general, each
application should be assigned its own subdirectory. Three types
of bass data files will generally reside in each projects folder.
These file types are: 1) *.prj files that define the desired
application and any desired variants of the application, 2) *.chm
files that specify chemical exposures and properties, and 3)
*.dat files that specify chemical exposures, habitat suitability
multipliers, nonfish standing stocks, water temperature, and
water depth when these parameters are supplied by the "file"
option. A project subdirectory can also contain local copies of
either *.fsh, *.cmm, or *.prp files that have been created or
modified for a particular project. Such files may have been
created from scratch or may have been constructed from
canonical files residing in the \fish, \community, or \property
subdirectories.
bass's input subroutines process project files assuming that the
paths of all specified include files are relative to the project file
that is currently being read. Therefore, in the case of *.fsh,
*.cmm, or *.prp files, bass initially attempts to find these include
files in the current project file's subdirectory. If these files cannot
be found in the current subdirectory, bass uses the extension of
the specified include files to search the \fish, \community, or
\property subdirectories. This prioritized input processing
means that a user can specify a canonical *.cmm file that uses
both canonical and local *.fsh files.
4.6.	Output Files Generated by BASS
bass generates the following four types of output files
•	an output file that summarizes the user's input parameters,
any input errors detected by bass, and any warnings / errors
encountered during an actual simulation. This file has the
same name of the executed project file with extension
"msg". For example, when bass executes the project file
input, prj, the message file input, msg is generated. If this
message file already exists, it is silently overwritten.
•	an output file that tabulates selected results of the
simulation. Tabulated summaries include: 1) annual
bioenergetic fluxes and growth statistics (i.e., mean body
weight, mean growth rate) of individual fish by species and
age class, 2) annual bioaccumulation fluxes and statistics
(i.e., mean whole-body concentrations, BAF, and BMF) of
individual fish by species and age class, and 3) annual
community fluxes and statistics (i.e., mean population
densities and biomasses) of each fish species by age class.
This file has the same name of the executed project file with
extension "BSS". For example, when bass executes the
project file input.prj, the output file input.BSS is
generated. If this file already exists, it is silently overwritten.
•	a Post-script file that contains the plots that were requested
by the user using the commands /annual_plots and
/summary_plots . This file has the same name of the
executed project file with extension "PS". For example,
when bass executes the project file input.prj, the plot file
input, ps is generated. If this file already exists, it is silently
overwritten.
•	a XML file that outputs daily values of community state
variables as well as integrated annual flow summaries and
annual means for selected state variables. Users can import
this file into the bass Output Analyzer to generate their own
custom plots and tables.
4.7.	Command Line Options
To run a bass simulation that is specified by the project file
input.prj, bass can be invoked either from the bass GUI or
BASS 2.2 March 2008
56

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using the UNIX like command line
C:\bass22> bass_v22 -i input.prj
Although the "-i filename" option is the only required command
line option, the following additional options are available
-c => print distribution of cpu time in major subroutines
-e => output realized monthly dietary compositions for
electively feeding fish
-f => turn off fishing mortality
-h => print this help list and stop (also see -?)
-1 => calculate the total aqueous phase chemical activity of fish
but turn off associated incipient lethality
-m => enable monthly spawning for species with annual age
classes
-mba => output mass balance analysis associated with each
requested annual summary
-n=> internally calculate rate-based BCFs for nonfish (see
Equation (2.102))
-p => turn on messages associated with feeding and predation
-s => turn off fish stocking
-t => run test of the bass Runge-Kutta integrator and stop;
results outputted to file bass_int_test.out.
-w => read project file and generate associated message file
without attempting model execution
-z => output ending vectors for age, weight, density, and cfish
(See *.BSS)
-? => print this help list and stop (also see -h)
For example, the command line
C:\bass22> bass_v22 -i input.prj -1 -c
will execute the project file input.prj without simulating acute
or chronic chemical lethality and report the distribution of cpu
time spent within various key bass subroutines.
BASS 2.2 March 2008
57

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Table 4.1 Valid Unit Prefixes.
Prefix Name Conversion
Factor
atto
10"18
centi
10-02
deca
10+01
deci
10"01
exa
10+18
femto
10"15
giga
10+09
hecto
10+°2
kilo
10+03
mega
10+°6
micro
O
o
G\
milli
p
o
myria
10+°4
nano
O
o
peta
10+15
pico
10"12
tera
10+12
BASS 2.2 March 2008
58

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Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive
and summarizes only commonly used unit names that bass's units conversion program recognizes.
Unit Name
Conversion
Factor to SI
Metre
Kg
Second
Description
acre
2.471xlO"04
2
0
0
4840 yards2
are
l.OOOxlO"02
2
0
0
100 meter2
btu
9.479xlO"04
2
1
-2

calorie
2.388xlO"01
2
1
-2

cc
l,OOOxlO+06
3
0
0
cm3
cm
l,OOOxlO+02
1
0
0

day
1.157xlO"05
0
0
1

decade
3.169xlO"09
0
0
1
10 years
erg
l,OOOxlO+07
2
1
-2

fathom
5.468xlO"01
1
0
0
6 feet
feet
3.281xlO+00
1
0
0

foot
3.281xlO+00
1
0
0

ft
3.281xlO+00
1
0
0
feet, foot
g
l,OOOxlO+03
0
1
0
grams
gallon
2.642xlO+02
3
0
0
3.785 liter
gm
l,OOOxlO+03
0
1
0
grams
gram
l,OOOxlO+03
0
1
0

gramme
l,OOOxlO+03
0
1
0

hectare
l.OOOxlO"04
2
0
0
100 are
hour
2.778xlO"04
0
0
1

hr
2.778xlO"04
0
0
1
hour
imperialgallon
2.200xl0+02
3
0
0
4.54 liter
inch
3.937xlO+01
1
0
0

joule
1.000xl0+0°
2
1
-2

kg
1.000xl0+0°
0
1
0
kilograms
km
l.OOOxlO"03
1
0
0
kilometer
1
l,OOOxlO+03
3
0
0
liter
lb
2.205xl0+0°
0
1
0
pound
liter
l,OOOxlO+03
3
0
0

litre
l,OOOxlO+03
3
0
0

m
1.000xl0+0°
1
0
0
meter
meter
1.000xl0+0°
1
0
0

metre
1.000xl0+0°
1
0
0

mg
l,OOOxlO+06
0
1
0
milligrams
micron
l,OOOxlO+06
1
0
0
10"6 meter
mile
6.214xlO"04
1
0
0
5280 feet
min
1.667xlO"02
0
0
1
minute
minute
1.667xlO"02
0
0
1

ml
l,OOOxlO+06
3
0
0

mm
l,OOOxlO+03
1
0
0

BASS 2.2 March 2008

59




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Table 4.3 Valid Ecological, Morphometric, and Physiological Unit Names.
Unit Name
Conversion
Factor to SI
Metre
Kg
Second
Description
fish
n.a.
0
0
0
treated as information as is byte
g(02)
7.3718xl0"5
2
1
-2
gram of oxygen
mg(02)
7.3718xl0"2
2
1
-2
milligram of oxygen
ug(02)
7.3718x10
2
1
-2
microgram of oxygen
ha
l.OOOxlO"4
2
0
0
hectare
individuals
n.a.
0
0
0
treated as information as is byte
inds
n.a.
0
0
0
treated as information as is byte
kcal
2.388x 10"4
2
1
-2
kilocalorie
ul(02)
5.1603x10
2
1
-2
microliter oxygen STP = micromole
ml(02)
5.1603xl0"2
2
1
-2
milliliter oxygen STP = millimole
1(02)
5.1603xl0"5
2
1
-2
22.4 liters STP = mole
lamellae
n.a.
0
0
0
treated as information as is byte
umol(02)
2.3037
2
1
-2
micromole of oxygen
mmol(02)
2.3037x 10"3
2
1
-2
millimole of oxygen
mol(02)
2.3037xl0"6
2
1
-2
mole of oxygen
Note: For purposes of units conversion, all units for oxygen consumption are treated dimensionally as joules.
BASS 2.2 March 2008
60

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5. bass Model Software and Graphical User Interface
5.1. Software Overview
The bass v2.2 model and Graphical User's Interface (GUI)
software are provided via two downloads from the USEPA
Center for Exposure Assessment Modeling (CEAM) website
(http://www.epa.gov/ceampubl/). These downloads are:
1.	Install_BASS_v22.exe: An InstallShield executable file that
installs the bass model software, user's manual, distribution
examples, GUI, and Output Analyzerfor WindowsNT/2K/XP.
2.	Install_BASS_v22_ModelOnly.exe: A WinZip Self-
extracting file that installs only the bass model software,
user's manual, and distribution examples for all
DOS/Windows systems.
The installation program Install_BASSv22.exe creates and
installs the BASS model software, GUI, and Output Analyzer
into the installation directory shown below
PATH \BASS_V2 2
BASS_V2 2.EXE
BASS_V2 2_ABSOFT.EXE
BASS_V2 2_LAHEY.EXE
DISDLL.DLL
CLEAN_EXAMPLES.BAT
RUN_EXAMPLES.BAT
\BASS_CMM_FSH
BAS S_CMM_F SH.EXE
BASS_FISH_CODES.DB
BASS_FISHPAR.DB
\SOURCE_CODE
\BASS_GUI
\COMMUNITY
\DOCUMENTS
BAS S_MANUAL.WPD
BAS S_MANUAL.PDF
BASS_DATA_SUPPLEMENT.WPD
BASS_DATA_SUPPLEMENT.PDF
\FISH
\PROPERTY
\PROJECTS
\EX_EVERGLADES_CANAL
\EX_EVERGLADES_CANAL_FISHING
\EX_EVERGLADES_CANAL_HG
\EX_EVERGLADES_CANAL_LESLIE_HG
\EX_EVERGLADES_HOLES_HG
\EX_EVERGLADES_MARSH_HG
\EX_L_HARTWELL
\EX_L_HARTWELL_P CB
\EX_L_HARTWELL_P CB_TRANS
\EX_L_ONTARIO_PCB
\EX_SE_FARM_POND
\SOURCE_CODE
where PATH = c:\program files\bass unless changed by the
user. The contents of this directory are:
1.	bass_v22_absoft.exe is the most current bass model
executable that has been created with the Absoft MP version
9.0 Fortran 95 compiler using the standard 32-byte Windows
XP operating system running on an IBM IntelliStation A Pro
equipped with dual 64-byte Opteron processors. On single
processor machines, this executable will run approximately 3
times faster than the Lahey-Fujitsu executable; on dual
processor machines, this executable will run approximately 5-7
times faster than the Lahey-Fujitsu executable. Note, however,
that there are minor compiler bugs associated with this
executable that apparently do not affect its computational
accuracy. Also see Section 7.2.7.
2.	bass_v22_lahey.exe is the most current bass model
executable that has been created with the Lahey-Fujitsu
Fortran 95 version 5.7f compiler. This executable is used as
the default bass software executable bass_v22.exe.
3.	disdll.dll is a dynamic link library needed to execute the
DISLIN graphing software.
4.	\bass_gui contains the executables and associated library
and support files for the bass GUI and Output Analyzer.
5.	\bass_cmm_fsh contains the bass fish file and community
file generator, described in Section 5.6, with its associated
database files and source code.
6.	\community is the folder designed to be a repository of
community files (*.cmm) that the user wishes to save as a
canonical library for the construction of future bass projects.
Although this folder is empty, it must be present for the bass
software to function correctly. See Section 4.5 (page 56).
7.	\documents\bass_manual.wpd is the current bass
User's Manual in WordPerfect format.
8.	\documents\bass_manual.pdf is the current bass User's
Manual in PDF format.
9.	\documents\bass_v22_data_supplement.wpd is the
current compendium of fish data that can be used to
parameterize bass in WordPerfect format.
10.	\documents\bass_v22_data_supplement.pdf is the
current compendium of fish data that can be used to
parameterize bass in PDF format.
BASS 2.2 March 2008
61

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11.	\fish is the folder designed to be a repository of fish files
(*.fsh) that the user wishes to save as a canonical library for
the construction of future bass projects. Although this folder
is empty, it must be present for the bass software to function
correctly. See Section 4.5 (page 56).
12.	\projects contains the bass v2.2 distribution example
projects that are described in Section 6.1 (page 75). All of
these examples can executed by double clicking on the DOS
batch file RIIN EXAIY1PLES.BAT
13.	\property is the folder designed to be a repository of
chemical property files (*.prp) that the user wishes to save as
a canonical library forthe construction of future bass projects.
This folder must be present for the bass software to function
correctly, and it is initially populated with chemical property
files used by the bass distribution examples. This folder also
contains the folder \BARBER_2003 which contains chemical
property files for the chemicals analyzed in Barber's review
paper of gill exchange models (Barber, M.C. 2003. Environ.
Toxicol. Chem. 22: 1963-1992). See Section 4.5 (page 56).
14.	\source_code contains the current Fortran 95 source
code for bass v2.2. This folder is included for those users who
would like to review the bass code or to adapt it for other
purposes.
The installation program Install_BASS_v22_ModelOnly.exe
extracts a copy of the aforementioned installation directory
bass_v22 that does not include the \bass GUI subdirectory.
5.2.	Installation Notes
The bass model and GUI v2.2 has been installed and
successfully tested on systems running Win2000, WinNT4.0 and
WinXP operating systems with various configurations of each. If
users are running NT, 2000, or XP operating systems, they must
have Administrator privileges on their systems in order to install
the bass model and GUI software.
5.3.	Installation Procedures
For complete installation procedures users are referred to the
bass installation readme file at the USEPA Center for Exposure
Assessment Modeling (CEAM) website
(http://www.epa. gov/ceampubl/).
5.4.	BASS GUI Operation
The bass GUI has been designed to emulate Microsoft's
Windows Explorer in much of its form and function. After the
bass GUI is opened, the first window that users see is the GUI's
Current BASS Directory (see Figure 5.1). If this window is
inadvertently closed, it can be reopened using the View button
found on the toolbar of the GUI's host window BASS version 2.2
Figure 5.1 bass GUI Current BASS Directory window.
ni Current BASS Directory	|- ||n|[x|
-¦I-I aUUHnlxld a -I B|-He-I»lsal
b- IZH BASS_Root
Q community
Q fish
ex_Everglades_canal
ffl-Cn ex_Everglades_canal_fishing
ffl-Q ex_Everglades_canal_hg
ffi-Cn ex_Everglades_canal_hg_leslie
ffl Q] ex_Everglades_holes_hg
ffl-'CH ex_Everglades_marsh_hg
ffl-CH ex_L_Hartwell_pcb
ffi-CH ex_L_Hartwell_pcb_trans
ex_L_0ntario_pcb
ffi Q ex_SE_farrn_pond	K
Cn property	J

Double-clicking on a folder's name, icon, or directory node
expands or collapses the folder's contents into or out of the
user's view, respectively. Double-clicking on a file name opens
the file with one of six GUI file editors based on the selected
file's extension. The GUI's file editors can also be invoked by:
1.	Left-clicking on the file and pressing the Enter key.
2.	Right-clicking on the file and then left-clicking on Edit.
3.	Left-clicking on the file and left-clicking on the Edit
icon Ml found on the Current BASS Directory toolbar.
When users are editing a bass project file that contains include
files, users can also open file editors for those include files by
4.	Left-clicking on the desired include command and then
left-clicking on the resulting activated Open Include File
link (see Section 5.4.1).
bass output files (i.e., *.BSS, *.MSG, and *.XML), are not
displayed in the Current BASS Directory window. These files, if
they exist, are accessed via the project files (*.PRJ) that
generated them.
BASS 2.2 March 2008
62

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bass message files (*.MSG) and simulation summary files
(*.BSS) can be reviewed by right-clicking on the relevant project
file and then left-clicking on View Project Message File or View
BSSFile, respectively. These files can also be reviewed by left-
clicking on the desired project file and then left-clicking on the
arrow of the File Viewing icon	found on the Current
BASS Directory toolbar. The File Viewing icon has an
associated drop-down selection that enables users to specify
which output file type is to be viewed. If the File Viewing icon
is left-clicked directly, the project's message file is opened by
default.
bass XML files can be loaded into the bass Output Analyzer
either by right-clicking on the relevant project file and then left-
clicking on View Output Analyzer or by left-clicking on the
desired project file and then left-clicking on the Plotting
icon ££ found on the Current BASS Directory toolbar.
bass project files are executed either by right-clicking on the
desired project file and then left-clicking on Run Project or by
left-clicking on the desired project file and then left-clicking on
the arrow of the Execution icon on the Current BASS
Directory toolbar. Like the File Viewing icon, the Execution icon
has an associated drop-down selection that enables users to
specify command line options as described in Section 4.7 (page
56). When a project file is being executed, all other GUI
functions are unavailable until the simulation is completed.
bass project files can be checked for their syntax and data
completeness before attempting execution either by right-clicking
on the desired project file and then left-clicking on Validate
Project or by left-clicking on the desired project file and then
left-clicking on the Validate Project icon j3J on the Current
BASS Directory toolbar. If the project file has syntax errors or
missing input data, the GUI's Event Viewer will automatically
open and display validation status of the project as well as
associated errors and warnings. Most users, however, will find it
easier to review these errors by opening the project's MSG file,
as outlined previously, and search for the phrase "ERROR:" to
determine the needed corrective actions.
5.4.1. IS l.V.V File Editors
All six GUI file editors have the same essential format and
function as displayed in Figure 5.2. Commands, include files,
and comment blocks contained within the file being edited are
displayed in abbreviated form and in order of their appearance
within the Elements of This File box. The full details of these
elements can be viewed individually within the Element Value
box or as they appear within the file by left-clicking on the Show
Text View toggle button. Elements can be edited by either
double-clicking on the element name or by left-clicking on the
element and then left-clicking on the Open Editor... button.
Figure 5.2 General structure of bass GUI file editors.
community file: everglades_canal.cmm
Elements of This File:
Open Editor..,
commentBlock
commentBlock
ecological_parameters
ecological_parameters
initial_conditions
include
commentBlock
ecological_parameters
ecological_parameters
initial_conditions
include
commentBlock
ecological_parameters
ecological_parameters
initial_conditions
include
commentBlock
ecological_parameters
ecological_parameters
initial_conditions
include
commentBlock
ecological_parameters
Show Text View >>
Move Up Move Down
Remove
Insert Command
I	3
Element Value
ttinclude 'largemouth_bass.fsh'

Click 'Show Text View' to see the full text
Apply I	OK I Cancel
The position of elements can be changed by using the Move Up
and Move Down buttons. Existing elements can be removed and
new elements added by using the Remove button and Insert
Command box, respectively. When elements are either added,
removed, or reordered, however, users must first left-click on the
Apply button before opening any GUI command editor. The
Apply button is also used to save editorial changes at any time
during an editing session.
Because the typical Close "X" button has been disabled on all
GUI file editors, users can exit GUI file editors only by using the
OK and Cancel buttons. These buttons either save or cancel any
editorial changes since the last invocation of the Apply button.
This GUI behavior is designed to preserve the integrity of the
GUI's Document Object Model (DOM).
Figure 5.3 displays the structure of the bass GUI project file
editor. This editor differs from the GUI's other five file editors
in two ways. First, this editor explicitly identifies all include files
that will be used by the project. Secondly, any include file that is
directly referenced by the project file can be opened and edited
by left-clicking on the Open Include File hyperlink that appears
below the Element Value box whenever an include statement is
highlighted in the Elements of This File box.
BASS 2.2 March 2008
63

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Figure 5.3 Structure of bass GUI project file editor.
5.4.2. H l.V.V Command Editors
GUI command editors are opened from GUI file editors as
outlined in Section 5.4.1. In terms of their appearance and
functionality, there are 17 basic command editor types that are
described in the following:
•	Simple String Editors that edit the commands /chemical,
/common_name, /header, /species, and include file
specifications (i.e., #includes . . . ). See Figure 5.4.
•	Simple String Editor with pull-down selection that edits the
commands /age_class_duration and /month_tO. See
Figure 5.5.
•	Numeric Editor with units that edits the command
/length_of_simulation. See Figure 5.6.
•	Numeric Editor without units that edits the commands
/ANNU AL_OUTPUTS, /LOG_AC, /LOG_KB 1, /LOG_KB2, /LOG_P,
/melting_point, /molar_volume, and /molar_weight.
See Figure 5.7.
•	Forcing Function Editor that edits the commands /biota,
/exposure, /habitat_parameters, /temperature, and
/water_level. See Figure 5.8.
•	Feeding Model Editor that edits the command
/feeding_options. See Figure 5.9.
•	Compositional and Morphometric Editor that edits the
commands /compositional_parameters and
/morphometric_parameters. See Figure 5.10.
•	Ecological Editor that edits the command
/ecological_parameters. See Figure 5.11 and Figure
5.12
•	Physiological and Growth Editor that edits the command
/physiological_parameters. See Figure 5.13.
•	Cohort Initial Conditions Editor that edits the command
/initial_conditions. See Figure 5.14.
•	Spawning Period Editor that edits the command
/spawning_period See Figure 5.15.
•	Fishery Editor that edits the command
/fishery_parameters. See Figure 5.16.
•	Nonfish Biotic Editor that edits the commands /benthos,
/PERIPHYTON, /PHYTOPLANKTON, /TERRESTRIAL_INSECTS,
and /zooplankton. See Figure 5.17 and Figure 5.18.
•	Nonfish BCF Editor that edits the command /nonfish_bcf.
See Figure 5.19.
•	Chemical Metabolism Editor that edits the command
/metabolism. See Figure 5.20.
•	Chemical Toxicity Editor that edits the command
/lethality. See Figure 5.21.
•	Plot Selection Editor that edits the commands
/annual_plots and /summary_plots. See Figure 5.22.
As noted with the GUI file editors, the typical Close "X" button
has been disabled on all GUI command editors. Users can only
exit or close a command editor by using the OK and Cancel
buttons. These buttons either save or cancel any editorial changes
since the editor was opened. This GUI behavior is designed to
preserve the integrity of the GUI's Document Object Model
(DOM).
5.4.3. Special Function Editors
In addition to the file and command editors described in the
previous section, the bass GUI has two special function editors,
i.e.,
•	Comment Block Editor that is used to insert comment blocks
before or after bass commands, as opposed to end-of-line
project file: evergladescanal. prj
File Includes:
-communitv/everqlades canal, cmm Pt
m
--largemouth_bass. f sh

-florida_gar.fsh

-yellow_bullhead. f sh

-bluegill_sunf ish. f sh
V
Elements of This File:
commentBlock
sirnulation_control
header
month_t0
length_of_simulation
temperature
waterjevel
annual_outputs
commentBlock
commentBlock
commentBlock
Show Text View >>
~pen Editor...
Move Up Move Down
Insert Command
"3
Element Value
Open Include File
Click 'Show Text View1 to see the full text
Apply
OK
Cancel
BASS 2.2 March 2008
64

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comments associated with the individual options of bass
commands. See Figure 5.23.
• Time Series Data Editor for editing external data files that
are specified as file functions (e.g., /biota, /exposure,
/habitat_parameters, /temperature, and
/water_level). See Figure 5.24.
5.4.4. File and Folder Operations
Using the GUI's Current BASS Directory window, users can
create new files and project folders either from scratch or from
existing files and project folders.
To create a bass project or include file from scratch, users must
first left-click on the subdirectory (i.e., \COMMUNI Y, \FISH, or
\PROPERTY) or project folder where the file is to be created.
The user then must left-click on the drop-down arrow head of the
Add New File icon . - . When the Add New File drop-down
menu appears, the user must left-click on the desired file type to
be created. Finally, after the new file appears in the Current
BASS Directory window, the user must complete the naming of
the new file. New project folders can be created following these
same steps.
Users can create a file from an existing file by
1.	Left-clicking on the desired file and then left-clicking on
the Copy icon
2.	Left-clicking on the desired destination folder or
subdirectory and left-clicking on the Paste icon
Users can also create a new file from an existing file by
1.	Right-clicking on the file to be copied and then left-
clicking on Copy.
2.	Right-clicking on the destination folder or subdirectory
and then left-clicking on Paste.
Lastly users can create a new file from an existing file by
1.	Left-clicking on the file to be copied and then pressing
CTRL-c.
2.	Left-clicking on the destination folder or subdirectory and
then pressing CTRL-v
New project folders can be created from existing projects using
the same procedures.
5.5. The BASS Output Analyzer
The bass Output Analyzer (OA) is a dual purpose post-processor
that enables users to construct customized graphs and tables. This
software can be invoked either from within the bass GUI or as
a standalone application. Using this software, users can create
two and three-dimensional graphs of any state variable that is a
valid option for the plotting commands /annual_plots or
/summary_plots . Unlike the plots generated by these
commands, however, users can generate plots for only selected
species as desired. Additionally, users can specify arbitrary time
periods of interest as well as change the number of size or age
classes that are plotted. The bass OA also enables users to create
customized versions of the summary tables that are generated for
BSS output files. These tables can be copied and pasted into
either Word or WordPerfect documents.
When bass executes a user's project file, two XML files are
generated for use by the bass OA. Both of these files reside in
the project folder of the PRJ file that generated them. The first of
these files contains the actual data that the OA will use for
graphing and table construction. This file bears the same name as
its associated project file but possesses the extension XML.
Importantly, it is this file that users must open when using the
bass OA.
The second XML file generated for any particular bass
simulation contains general summary statistics of the simulation
and is loaded automatically into the OA when the
aforementioned XML file is opened. The name of this file is the
associated project file's name appended with the string incl.
5.6. Auxiliary BASS Software
To aid users in constructing bass fish files and community files,
an auxiliary piece of software named bass_cmm_fsh.exe is
distributed with the bass model and GUI software. Using a
combination of an internal database of fish growth rates and two
external database files (bass_fish_codes.db and
bass_fishpar.db), this software can generate default FSH files
for many North American freshwater fish. Although these
generated FSH files are setup to use bass 's linear feeding model,
users can easily edit these files to use bass's allometric feeding
model or a combination of both. Users can also construct
multiple FSH files and an associated, rudimentary CMM file for
an arbitrary selection of fish using this software. This software,
however, does not have a GUI and must be executed by the user
from a DOS command prompt.
To generate a FSH file for a single species of interest, the user
should open a DOS command prompt window and navigate to
the project folder in which they want the file to be generated.
Assuming that the user's bass root directory is c:\bass_v22, the
DOS command
BASS 2.2 March 2008
65

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...>c:\BASS_v22\BASS_CMM_FSH\bass_cmm_fsh.exe -f
"bluegill" -g "lepomis macrochirus" -m "January 10" -1 10 -u
33
will generate a fish file for bluegill sunfish whose growth rate has
been calibrated to an annual sinusoidal water temperature cycle
that varies from 10 to 33 Celsius and whose minimum annual
temperature occurs on January 10.
To generate multiple FSH files and an associated CMM file for
an arbitrary selection of fish, the user should again open a DOS
command prompt window and navigate to the project folder in
which the user wants the files to be generated. Assuming that the
user's bass root directory is c:\bass_v22, the DOS command
...>c:\BASS_v22\BASS_CMM_FSH\bass_cmm_fsh.exe -i
fishes. dat.O
will generate a FSH file for each fish species identified in the file
fishes. dat.O and an associated CMM file. The file fishes. dat.O must
reside in the desired project folder. An example of the general
structure of these input files is illustrated below
1 File:bass_bluegill_catfish.datO
CMM_FILE_NAME bass_bluegill_catfish.cmm
MONTH_TO August
COLDEST_DAY January 10
TEMPERATURE_MAXIMUM 3 0
TEMPERATURE_MINIMUM 10
FISH_START micropterus salmoides
COMMON_NAME largemouth bass
SPAWNING_PERIOD april-may
parameter_option_l; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number 1 or density[fish/ha]= number
FISH_END
FISH_START Lepomis macrochirus
COMMON_NAME bluegill sunfish
SPAWNING_PERIOD april-October
parameter_option_l; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number 1 or density[fish/ha]= number
FISH_END
FISH_START ictalurus puntatus
COMMON_NAME channe1 catfish
SPAWNING_PERIOD may-j une
parameter_option_l; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number 1 or density[fish/ha]= number
FISH_END
where parameter option i is any valid option for the bass fish
commands \compositional_parameters,
\ecological_parameters, \morphological_parameters, or
\PHYSiOLOGiCAL_PARAMETERS that the user wants to supercede
the default assignment made by bass_cmm_fsh.exe. Most of the
FSH files used by the example bass distribution projects have
been generated using earlier versions of this software.
Source: species of largernouth_bass.fsh
Va'ue: (micropterus salmoides
Comment:
OK
Cancel
Figure 5.4 GUI command editor for simple strings.
species
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66

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Figure 5.5 GUI command editor for simple strings with drop-down selection.
age_c [assduratio n
Source: age_class_duration of largemouth_bass.fsh
Value:
Comment:
"3
~K
Cancel
Figure 5.6 GUI command editor for numeric data with user specified units.
length_of_simulation
Source: length_of_sirnulation of everglades.prj
Comment: |
Figure 5.7 GUI command editor for numeric data fixed units.
annualoutputs
Source: annual_outputs of everglades.prj
Value: |20
Comment:
OK
Cancel
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Figure 5.8 GUI command editor for forcing functions.
temperature
Source: temperature of everglades.prj
Comment:

Parameter
| Units |
| Value
| Use File
Comments


temp
celsius
22.5+7.5xsin(0.172142e-01 xt[day]-0.279731)
r


~
temp_epiiimnion
i

r



ternp_hypolimnion


r


OK	| Cancel |
Figure 5.9 GUI command editor for feeding model options.
feeding options
Source: feedincLoptions of largemouth_bass.fsh
Comment: I
Class:
| age

Units: |day


Lower Boundary
| Upper Boundary
| Model Type | Comments

~
0
2931
linear

*




OK	| Cancel |
Figure 5.10 GUI command editor for compositional and morphometric parameters.
compositionalparameters
Source: compositional_parameters of largemouth_bass.fsh
Comment: |
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68

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Figure 5.11 GUI command editor for nondiet ecological parameters.
eco logical_pa ramete rs
Source: ecological_parameters of largemouth_bass.fsh associated with
Comment: F
Ecological Parameters | Diet Editor |

Log
Parameter
Units
Value
Comments
~

IP
mm =
0.300xl[rnmT
estimated from timmons and shelton (1980) for lepomis


lp_min
mm
0.060xl[mm]
assumed to allow 500 mm largemouth to prey on 30 mm gambusia


lp_max
mm =
0.500"l[mm]
assumed


mis
days =
2921.9
assumed


wt_max
g
1750
asummed see carlander (1977 pg 22G)

nm =


sgL_mu
g/g/day =
0.898e-01xw[gn-0.698)
long-term mean calibrated to wt_yoy, wt_max, and age_max


tl_ro
=




rbi
-
0.2
bass interspecies default


wl
g
0.6780e-051[mmr3.130
assigned using bass/carlander database


yoy
g
0.25
bass/carlander database default


ast_yoy
=




refugia
=


Figure 5.12 GUI command editor for fish diets.
ecologies Ipararneters
Source: ecological_parameters of everglades.cmrn associated with largemouth_bass.fsh
Comment: \
Ecological Parameters Diet Editor |
Class Type: ||ength
Units:
0-20
20-100
100-200
H
300-1000

Prey Item
january-june
july-december
~
benthos
25
25

insects
-1
-1

periphyton
-1
-1

phytoplankton
-1
-1

zooplankton
-1
-1

fish
0
0

largemouth_bass
0
0
Add
Sort
Remove
Split Time Range Remove Range Add Prey Fish Remove Prey Fish
Refresh
OK
Cancel
BASS 2.2 March 2008
69

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Figure 5.13 GUI command editor for physiological parameters.
p hysio logica Iparamete rs
Source: physiological_parameters of largennouth_bass.fsh
Comment: I
Log l Parameter
j Units temperature
= Value
Comments
~
ae_plant

= 0.44
bass interspecies default
ae invert

= 0.66
bass interspecies default
ae fish

= 0.89
bass interspecies default

ge




mf



mi
g(dw)/da
= 0.202e-01 xw[gf0.557xexp(0
back-calculated from fish's
rq

= 1
bass interspecies default
rt:std

= 2
bass interspecies default
sda:in

= 0.127
beamish (1974), tandler a
sg
g/g/day
= 0.282e-01*w[gf(-0.698rexp
calibrated for specified tern
sm



so
mg(o2)/'h
= 0.119xw[gr0.766xexp(0.043
glass (1969), beamish (19

st




kf_min



OK
Cancel
Figure 5.14 GUI command editor for cohort initial conditions.
initial_conditions
Source: initial_conditions of everglades.cmm
Comment: | biomass[kg/ha]=10.00;density[fish/mA2]=0.004G8
Add Column
Remove Column

Name
Units
Comment
~
age
days


pop
fish/rnA2


wt
grams

age
213.0
578.0
943.0
1308.0
1673.0
2038.0
2403.0
2768.0
pop
0.293e-02
0.719e-03
0.358e-03
0.225e-03
0.158e-03
0.119e-03
0.944e-04
0.771 e-04
wt
42.7
173.9
348.7
555.8
789.7
1046.8
1324.6
1621.2
OK
Cancel
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70

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Figure 5.15 GUI command editor for spawning parameters.
spawning_period
Source: spawningLperiod of largemouth_bass.fsh
Comment: | assigned by user
Figure 5.16 GUI command editor for fishery parameters.
fishery_parameters
Source: fishery_parameters of evergladesjishing.cmrn
Comment: |
OK | Cancel |
Figure 5.17 GUI command editor for nonfish biota as forcing functions.
benthos
Source: benthos of everglades.cmm
Comment:
Use Forcing Function	Use Community State Variables

Parameter
Units
Value
Use File
Comments
~
biomass
g(dw)/m =
nonfish.dat
F


OK
Cancel
BASS 2.2 March 2008
71

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Figure 5.18 GUI command editor for nonfish biota as state variables.
benthos
Source: benthos of everglades.cmm
Comment:
Use Forcing Function
Use Community State Variables
Figure 5.19 GUI command editor for nonfish bioaccumulation factors.
nonfishbcf
Source: nonfish_bcf of pcb_new.chm
Comment: j
Figure 5.20 GUI command editor for chemical biotransformation parameters.
metabolism
Source: metabolism of pcb_trans.chm
Comment: |half life = 30 days

Fish
Units
| Value
Daughter Product
Comments
~
largemouth_bass
1/day
ln(2)/30
pcb_metabolite


longnose_gar
1 /day
ln(2)/40
pcb_metabolite
half life = 40 days

channel catfish
1/day
ln(2)/G0
pcb_metabolite
half life = GO days

bluegill_sunfish
1/day
ln(2)/30
none
half life = 30 days
redear_sunfish
1/day
200.0xkow[-r(-0.9)
none
approximately 0.001
Add a Fish
OK
Cancel
BASS 2.2 March 2008
72

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Figure 5.21 GUI command editor for chemical toxicity parameters.
lethality
Source: lethality of pcb_trans.chm
Comment:
Figure 5.22 GUI command editor for automatic graphing selections.
summary_plots
Source: summary_plots of l_ontario_pcb.prj
Comment: [
BASS 2.2 March 2008
73

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Figure 5.23 GUI Block comment editor.
commentBlock
Source: commentBlock of largemouth_bass.fsh
References:
Beamish, F.W.H. 1970. Oxygen consumption of largemouth bass, Micropterus salmoides,
in relation to swimming speed and temperature. Can.J.Zool. 48:1221-1228.
Beamish, F.W.H. 1974. Apparent specific dynamic action of largemouth bass, Micropterus
salmoides. J.Fish.Res.Bd.Can. 31:1763-1769.
Carlander, K.D. 1977. Handbook of Freshwater Fishery Biology, vol 2. Iowa State University
Press. Ames, IA.
Glass, N.R. 1969. Discussion of the calculation of power function with special reference
to respiratory metabolism in fish. J.Fish.Res.Bd Can. 26:2643-2650.
Lewis, W.M., R. Heidinger, W. Kirk, W. Chapman, and D. Johnson. 1974. Food intake
of the largemouth bass. Trans.Am.Fish.Soc. 103:277-280.
Lowe, TP., T.W. May, W.G. Brumbaugh, and DA Kane. 1985. National Contaminant Biomonitoring
Program: concentrations of seven elements in freshwater fish, 1979-1981. Arch.Environ.Contam.Toxicol.
14:363-388.
Niimi, A.J. and F.W.H. Beamish. 1974. Bioenergetics and growth of largemouth bass
(Micropterus salmoides) in relation to body weight and temperature. Can.J.Zool. 52:447-456.
Price, J. W. 1931. Growth arid gill development in the small-mouthed black bass, Micropterus
dolomieu, Lacepede. Ohio State University, Franz Theodore Stone Laboratory 4:1-46.
Schmitt, C.J., and W.G. Brumbaugh. 1990. National Contaminant Biomonitoring Program:
Concentrations of arsenic, cadmium, lead, mercury, selenium, and zinc in U.S. freshwater
fish, 1976-1984. Arch.Environ.Contam.Toxicol. 19:731-747.
Schmitt, C.J., J.L. Zajicek, and P.H. Peterrnan. 1990. National Contaminant Biomonitoring
Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.
Arch. Environ.Contam.Toxicol. 19:748-781.
Tandler, A. and F.W.H. Beamish. 1981. Apparent specific dynamic action (SDA), fish
weight, arid level of caloric intake in largemouth bass, Micropterus salmoides Lacepede.
Aquaculture 23:231 -242.
Timmons, T.J. and W.L. Shelton. 1980. Differential growth of largemouth bass in West
Point Reservoir, Alabama-Georgia. Trans.Am.Fish.Soc. 109:176-186.
OK	Cancel
Figure 5.24 Data file editor for forcing functions specified as files.
timeTableData
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74

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6. Example Applications
6.1. BASS Software Distribution Examples
Several example projects are provided with the bass model
software and GUI. Each project resides in its own folder within
the \projects subdirectory.
The example project ex_everglades_canal simulates the
growth and population dynamics of a canal fish community in
the Florida Everglades, USA using the project file
everglades_canal.prj. The principal fish species in these
communities are assumed to be bluegill sunfish (.Lepomis
macrochirus), Florida gar (Lepisosteus platyrhincus),
largemouth bass (Micropterus salmoides), mosquito fish
(Gambusia holbrooki), redear sunfish (Lepomis microlophus),
and yellow bullheads (Ameiurus natalis). The community file
everglades_canal.cmm is used to specify the ecological and
physiological parameters and the initial conditions for these
species. Each species' daily consumption rate is back-
calculated from its expected growth rate using bass's linear
feeding option. The community's water depth and the standing
stocks of benthos, periphyton, and zooplankton are specified
using the data files everglades_canal_water.dat and
everglades_nonfish.dat, respectively.
The example project ex_everglades_canal_fishing
simulates the growth and population dynamics of the
aforementioned Everglades canal fish community assuming
that largemouth bass, bluegill sunfish, and redear sunfish are
harvested by fishing. This example's project file
everglades_canal_fis HiNG. prj uses the modified
community file everglades_canal_fishing.cmm to specify
the fishes' ecological and physiological parameters, initial
conditions, and assumed fishing mortalities. The community's
water depth and non-fish standing stocks are again specified
using the data files everglades_canal_water.dat and
everglades_nonfish.dat, respectively.
The example project ex_everglades_canal_hg simulates
the bioaccumulation of methylmercury in an Everglades canal
fish community using the project file
everglades_canal_hg.prj. This example's project file uses
the same community file as does the example project
everglades_canal to specify the ecological and
physiological parameters and initial conditions for the species
of interest. The community's chemical exposures to
methylmercury are provided by the include file
everglades_mercury. chm that in turn uses the include file
\property\metyl_hg.prp. As before, the community's water
depth and the non-fish standing stocks are specified using the
data files everglades canal water.dat and
everglades_nonfish . dat, respectively.
The example project ex_everglades_canal_leslie_hg
simulates the bioaccumulation of methylmercury in an
Everglades canal fish community using bass's Leslie matrix
option and the project file
F.VERGT ,APF,S_CANAT _T ,F,ST ,TF_HG. PRJ. This proj ect's ecological
and physiological data are provided by the community file
evergt,adf,s_canat _t,estje.cmm. Chemical exposures and
properties of methylmercury are provided by the include file
Everglades_mercury.chm that uses the include file
\property\methyl_hg.prp. Once again, the community's
water depth and non-fish standing stocks are provided by the
ancillary data files everglades_canal_water.dat and
everglades_nonfish . dat, respectively.
The example project ex_everglades_holes simulates the
growth, population, and methylmercury dynamics of an
alligator hole fish community in the Florida Everglades, USA
using the project file everglades_holes.prj. The principal
fish species in these communities are assumed to be bluegill
sunfish, Florida gar, largemouth bass, least killifish
(Heterandria formosa), mosquito fish, redear sunfish, spotted
sunfish (Lepomis puntatus), warmouth sunfish (Lepomis
gulosus), and yellow bullheads. The community file
everglades_holes.cmm is used to specify the ecological and
physiological parameters and the initial conditions for these
species. Each species' daily consumption rate is back-
calculated from its expected growth rate using bass's linear
feeding option. The community's water depth and the standing
stocks of benthos, periphyton, and zooplankton are specified
by the project and community files
EVERGLADES_HOLES_HG.PRJ and EVERGLADES_HOLES.CMM,
respectively. Methylmercury exposures are provided by the
include file Everglades_mercury.chm that uses the
property file \property\methyl_hg.prp as an include file.
The example project ex_everglades_marsh simulates the
growth, population, and methylmercury dynamics of an open
marsh fish community in the Florida Everglades, USA using
the project file everglades_marsh.prj. The principal fish
species in these communities are assumed to be bluefin
killifish (Lucania goodei), Florida gar, golden top minnow
(Fundulus chrysotus), largemouth bass, least killifish,
mosquito fish, spotted sunfish, warmouth sunfish, and yellow
bullheads. The community file everglades_marsh.cmm is
used to specify the ecological and physiological parameters
and the initial conditions for these species. Each species' daily
consumption rate is back-calculated from its expected growth
rate using bass's linear feeding option. The community's
BASS 2.2 March 2008
75

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water depth and the standing stocks of benthos, periphyton,
and zooplankton are specified by the project and community
files EVERGLADES_MARSH_HG. PRJ and
everglades_marsh. cmm, respectively. Methylmercury
exposures are provided by the include file
Everglades_mercury.chm that uses the property file
\property\methyl_hg.prp as an include file.
The example project ex_l_hartwell_pcb simulates the
bioaccumulation of tetra-, penta-, hexa-, and hepta-PCB in a
largemouth bass/ gizzard shad/ sunfish/catfish community in
the Twelve Mile Creek arm of Lake Hartwell, SC, USA using
the project file twelvemile_creek_pcb.prj. The
community's ecological and physiological parameters and
initial conditions are specified using the community file
Twelvemile_creek. cmm. Chemical exposures and properties
of tetra-, penta-, hexa-, and hepta-PCBs are provided by the
include files twelvemile_creek.chm
\property\pcb_tetra.prp, \property\pcb_penta.prp,
\property\pcb_hexa.prp, and \property\pcb_hepta.prp,
respectively. This example demonstrates bass's ability to
simulate the bioaccumulation of arbitrary mixtures.
The example project ex_l_hartwell_pcb_trans also
simulates the bioaccumulation of tetra-, penta-, hexa-, and
hepta-PCB in the aforementioned largemouth bass/ gizzard
shad/ sunfish/catfish community of Lake Hartwell, SC, USA
using the project file Twelvemile_creek_pcb_trans.prj.
This example, however, allows for the biotransformation of
selected PCB congeners by selected fish species. The
community file twelvemile_creek.cmm is again used to
specify the ecological and physiological parameters and the
initial conditions for this community.
The example project ex_l_ontario_pcb simulates the
bioaccumulation of tetra-, penta-, hexa-, and hepta-PCB in
Lake Ontario salmonids and alewife using bass's "fgets"
option and the project file barber_et_al_1991.prj. This
example is the bass implementation of the fgets application
published by Barber et al. (1991). Whereas salmonid feeding
is simulated using bass's Holling feeding option, the feeding
by alewife is simulated using bass's clearance feeding option.
The community file barber_et_al_ 1991. cmm is used to
specify the ecological and physiological parameters and the
initial conditions for this community.
The example project ex_se_farm_pond simulates the growth
and population dynamics of a typical southeastern US farm
pond community using the project file se_farm_pond.prj.
The principal fish species in these communities are assumed to
be largemouth bass (Micropterus salmoides), bluegill sunfish
(Lepomis macrochirus), redear sunfish (Lepomis
microlophus), redbreast sunfish (Lepomis auritus), channel
catfish (Ictalurus punctatus), and yellow bullheads (Ameiurus
natalis).The community file se_farm_pond.cmm is used to
specify the ecological and physiological parameters and the
initial conditions for these species as well as the standing
stocks of benthos, periphyton, and zooplankton. Each species'
daily consumption rate is back-calculated from its expected
growth rate using bass's linear feeding option.
6.2. An Analysis of Everglades Mercury
Bioaccumulation
The bass example project ex_everglades_canal_hg
simulates methyl mercury contamination in a canal fish
community of the Florida Everglades and is constructed as
outlined in Section 4.5. For this bass application largemouth
bass (Micropterus salmoides), Florida gar (Lepisosteus
platyrhincus), yellow bullheads (Ameiurus natalis), bluegill
sunfish (Lepomis macrochirus), redear sunfish (Lepomis
microlophus), and mosquito fish (Gambusia holbrooki) are
assumed to be the dominant species in the habitats of interest.
The sources of the ecological, morphological, and
physiological parameters used by this example are documented
in its associated FSH files. Turner et al. (1999) reported the
mean biomass of large and small fishes across a variety of
Everglades habitats to be approximately 60 kg wet wt/ha.
Initial standing stocks of the bass, gar, bullheads, bluegill,
redear sunfish, and mosquito fish were assigned to be 5, 10,
10, 50, 25, and 5 kg wet wt/ha, respectively, for a total
community biomass of 105 kg wet wt/ha. The water
concentration of methylmercury for the simulation was
assigned to be a constant 0.2 ng/L (Stober et al. 1998) and the
BAF's for benthos and zooplankton were assigned to be 106 09
and 105 90, respectively (Loftus et al. 1998).
At the end of the 10 year simulation, the mean annual standing
stocks of the bass, gar, bullheads, bluegill, redear sunfish, and
Gambusia are 0.867, 1.08, 4.79, 30.0, 35.4, and 2.55 kg wet
wt/ha, respectively, for a total community biomass of 74.7 kg
wet wt/ha.
The simulated whole-body concentrations of methyl mercury
in these species agree reasonably well with unpublished data
collected by Lange et al. and Loftus et al. (1998). See Figure
6.1 - Figure 6.6. bass's significant over prediction of the
whole-body methylmercury concentrations of redear sunfish is
probably due to the specialized feeding behavior of this
species. In particular, redear sunfish, which are also known as
shellcrackers, often feed almost exclusively on molluscs that
generally have significantly lower methylmercury
concentration than do other benthic macroinvertebrates. The
annual averaged concentrations of methylmercury in
BASS 2.2 March 2008
76

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largemouth, gar, bullhead, bluegill, redear and Gambusia
weighted by cohort biomasses were 0.842, 0.822, 0.580,
0.440,0.513, and 0.180 mg Hg/kg wet wt, respectively. When
weighted by cohort densities, the annual averaged
concentrations of methylmercury in largemouth, gar, bullhead,
bluegill and redear were 0.450,0.491,0.332,0.237,0.299, and
0.143 mg Hg/kg wet wt, respectively. Loftus et al. report
average whole-body concentrations of methylmercury in
largemouth, gar, bullhead, bluegill, redear, and Gambusia to
beO.967,1.16,0.443-0.755,0.478,0.247, and 0.247-0.321 mg
Hg/kg wet wt, respectively. The average body weights of
largemouth, gar, bullhead, bluegill, redear, and Gambusia
analyzed by Loftus etal. were 205,278,37.5-92.9,21.8,73.0,
and 0.0602-0.218 g wet wt/fish, respectively.
As is typically observed under field conditions (Forrester et al.
1972, Scott and Armstrong 1972, Cross et al. 1973, Akielaszek
and Haines 1981, Watling et al. 1981, Boush and Thieleke
1983a, b, MacCrimmon et al. 1983, Ueda and Takeda 1983,
Wren and MacCrimmon 1986, Braune 1987, Luten etal. 1987,
Moharram et al. 1987, Sprenger et al. 1988, Grieb et al. 1990,
Parks et al. 1991, Gutenmann et al. 1992, Lange et al. 1993,
Tracey 1993, Joiris et al. 1995, Munn and Short 1997, Stafford
and Haines 1997), Figure 6.1 - Figure 6.6 predicts a strong
interdependence between the body sizes of fish and their
whole-body mercury concentrations.
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Large mouth bass (Micropterus salmoides)
0	200	400	600	800	1000	1200	1400	1600	1800	2000
Wet Weight g(ww)
Figure 6.1 Predicted and observed methylmercury concentrations of largemouth bass in Florida Everglades canals.
~ observed
¦ canal
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Florida gar (Lepisosteusplatyrhincus)
~ observed
¦ canal
200
400
600	800
Wet Weight g(ww)
1000
1200
1400
Figure 6.2 Predicted and observed methylmercury concentrations of Florida gar in Florida Everglades canals.
BASS 2.2 March 2008
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Yellow bullhead (Ameiurus natalis)
1.8
1.6
1.4
1.2
at
3
U)
15 08
nj
0.6
0.4
0.2
200
400
600
Wet Weight g(ww)
800
1000
~ observed
¦ canal
1200
Figure 6.3 Predicted and observed methylmercury concentrations of yellow bullhead in Florida Everglades canals.
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Bluegill (Lepomis macrochirus)
Wet Weight g
Figure 6.4 Predicted and observed methylmercury concentrations of bluegill sunfish in Florida Everglades canals.
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Redear sunfish (Lepomis microlophus)
1.2
0.8
¦S1
O)
3
Ut
X
nj
o
0.6
0.4
0.2






/

~ observed
¦ canal
X ~ ~~ ~ . ~ ~
m ~ ~ ~
( ••• •
~ ~

50
100
150
Wet Weight g(ww)
200
250
300
Figure 6.5 Predicted and observed methylmercury concentrations of redear sunfish in Florida Everglades canals.
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Mosquiotfish (Gambusia holbrooki)
0.45
0.4
0.35
0.3
5 0.25
"3)
3
Ut
15 02
nj
0.15
0.1
0.05
0.1
0.2
0.3
Wet Weight g(ww)
0.4
0.5
~ observed
¦ canal
0.6
Figure 6.6 Predicted and observed methylmercury concentrations of Gambusia in Florida Everglades canals.
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7. Model Quality Assurance
Quality Assurance (QA) and Quality Control (QC) for the bass
simulation model has been addressed with respect to:
1)	The model's theoretical foundations, i.e., does the
model's conceptual and mathematical framework standup
to scientific / engineering peer view?
2)	The model's implementation, i.e., does the code actually
do what it is intended to do?
3)	The model's documentation and application, i.e., can the
model be used by the outside research and regulatory
community in a meaningful way?
7.1. Questions Regarding QA of a Model's Scientific
Foundations
7.1.1.1s the model's theoreticalfoundation published in the peer
reviewed literature?
With the exception of its population and trophodynamic
algorithms, bass is based on the fgets bioaccumulation and
bioenergetics model that has been published in the peer reviewed
literature (Barber etal. 1988,1991). These algorithms have been
reviewed and compared with other existing bioaccumulation
models to document their scientific foundation and to verify their
predictive performance (see Barber 2003, 2008). The
bioenergetic modeling paradigm that bass uses to simulate fish
growth has been employed by many researchers in the peer
reviewed literature (Norstrom et al. 1976, Kitchell et al. 1977,
Minton and McLean 1982, Stewart et al. 1983, Thomann and
Connolly 1984, Cuenco et al. 1985, Stewart and Binkowski
1986, Beauchamp et al. 1989, Barber et al. 1991, Stewart and
Ibarra 1991, Lantry and Stewart 1993, Rand et al. 1993, Roell
and Orth 1993, Hartman and Brandt 1995a, Petersen and Ward
1999, Rose et al. 1999, Schaeffer et al. 1999). Since its
construction, fgets has also been included in numerous reviews
of bioaccumulation models that are applicable for ecological risk
assessments and environmental management (Barron 1990, Jones
etal. 1991,Barnthouse 1992, ChapraandBoyer 1992,Landrum
et al. 1992, Olem et al. 1992, Dixon and Florian 1993, Wurbs
1994, Cowan etal. 1995, Campfens and Mackay 1997, Feijtel et
al. 1997, Deliman and Gerald 1998, Exponent 1998, Howgate
1998, Vorhees et al. 1998, Wania and Mackay 1999, Bartell et
al. 2000, Gobas and Morrison 2000, Mackay and Fraser 2000,
Bartell 2001, Limno-Tech 2002, Exponent 2003, Sood and
Bhagat 2005).
Two criticisms have been lodged against fgets in the literature.
The first of these is that fgets assumes or attempts to prove that
the gill exchange of chemicals is more important than other
routes of exchange (Madenjian et al. 1993). Madenjian et al.
(1993) took exception to fgets predictions that "excretion of
PCB through the gills is an important flux in the PCB budget of
lake trout". Madenjian et al. claimed that this result was not
supported by any laboratory study on trout and cited Weininger
(1978) as proof that gill excretion was, in fact, negligible.
Nevertheless, Madenjian et al. used a single, unidentified
excretion constant in their model that simply lumps all excretion
pathways (i.e., gill, intestinal, urinary, and dermal) into one.
Thus, what Madenjian et al. are essentially questioning is not
fgets per se but rather the need to use thermodynamically based
diffusion models for bioaccumulation in general.
The second criticism is that fgets is overly complex and requires
too much additional data to parameterize (McKim et al. 1994,
Stow and Carpenter 1994, Jackson 1996). Since fgets's
bioenergetic model for fish growth is not significantly different
from those used by several other authors (Norstrom et al. 1976,
Weininger 1978, Thomann and Connolly 1984, Madenjian et al.
1993, Luk and Brockway 1997), this criticism is also generally
aimed at bass's gill exchange model. A recent review and
comparison of gill exchange models, however, clearly
demonstrated that there is more than ample literature data to
parameterize the gill exchange formulations used by fgets and
bass (Barber 2003).
7.1.2. How has the model or its algorithms been corroborated or
used?
bass's dietary and gill exchange algorithms have been
corroborated by comparing its predicted dietary assimilation
efficiencies and gill uptake and excretion rates to those published
in the peer reviewed literature (Barber et al. 1988, Barber 2003,
2008). bass's dietary exchange algorithms have also been cited
by other researchers to explain results of actual exposure studies
(e.g., Dabrowska et al. 1996, Doi et al. 2000). For validation of
bass's bioenergetic growth algorithms, the reader is referred to
Barber et al. (1991) and the examples herein.
bass's predictive performance as an integrated bioaccumulation
model has been corroborated for organic chemicals by
simulations of PCBs dynamics in Lake Ontario salmonids,
various laboratory studies, largemouth bass-bluegill-catfish
communities of Lake Hartwell / Twelvemile Creek, SC, and
Tennessee stream fishes (Barber et al. 1991, USEPA 1994,
Brockway et al. 1996, Simon 1999, Marchettini et al. 2001,
USEPA 2004). Similarly, Hunt et al. (1992) used fgets to model
DDT bioaccumulation in caged channel catfish at Superfund
Sites. For sulfhydryl binding metals, bass's predictive
performance has been corroborated by simulations of
methylmercury bioaccumulation in Florida Everglades fish
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communities one of which is presented herein as a typical bass
application. Murphy (2004) also successfully used bass to model
and analyze mercury bioaccumulation in the South River and the
South Fork of the Shenandoah River in Virginia. More recently,
bass was used to estimate lag times of mercury residues in fish
responding to mercury load reductions as part of ORD's review
of the Agency's Clean Air Mercury Rule (CAMR, February 15,
2005). This work was subsequently incorporated into the
Regulatory Impact Analysis (RIA) that assessed the benefits of
atmospheric load reductions to aquatic ecosystems (USEPA
2005).
Several researchers (Lassiter and Hallam 1990, ECOFRAM
Aquatic Effects Subcommittee et al. 1998, ECOFRAM 1999,
Boxall et al. 2001, Boxall et al. 2002, Reinert et al. 2002) have
used bass's predecessor, fgets, to predict acute and chronic
lethality, and the EP A's Office of Water's AQUAT OX modeling
system uses the fgets/bass lethal effects algorithm as its
principal effects module (Park and Clough 2004). Additionally,
the Office of Water has recognized bass as one of the leading
models available for simulating time dynamic bioaccumulation
for applications when steady-state methods (e.g., BAFs or
BSAFs) are considered insufficient (USEPA 2003). The
Commonwealth of Virginia has identified bass as an accepted
tool for its PCB bioaccumulation assessments (VDEQ 2005).
bass has also been recommended to the states of Michigan and
Washington as an assessment tool (Exponent 1998, 2003).
Whereas Hallam and Deng (2006) implemented the fgets/bass
bioaccumulation framework within sophisticated McKendrick-
von Foerster partial differential equation models for age-
structured populations, Cohen and Cooter (2002a, 2002b)
incorporated simpler forms of this framework into more holistic
fate and transport exposure software. Lastly, Apeti et al. (2005)
modified fgets to simulate metal bioaccumulation in shellfish.
7.1.3. What is the mathematical sensitivity of the model with
respect to parameters, state variables (initial value problems),
and forcing functions / boundary conditions? What is the
model's sensitivity to structural changes?
There are four major classes of mathematical sensitivity
regarding a model's behavior. These are the model's sensitivity
to parameter changes, forcing functions, initial state variables,
and structural configuration. The first three of these classes
generally are formally defined in terms of the following partial
derivatives
ex ax. ex
Ji/ Jz/ ax.( o)	(71)
where X,. is a state variable of interest; pj is some state parameter
of concern; Z, is some external forcing function; and X/0) is the
initial value of some state variable of interest that may be Xt
itself. Structural sensitivity, which generally cannot be
formulated as a simple partial derivative, typically concerns the
number and connectivity between the system's state variables.
An excellent question regarding structural sensitivity for a model
like bass might be how does a predator's population numbers or
growth rate change with the introduction or removal of new or
existing prey items?
Because sensitivity is simply a mathematical characteristic of a
model, model sensitivity in and of itself is neither good nor bad.
Sensitivity is desirable if the real system being modeled is itself
sensitive to the same parameters, forcing functions, initial state
perturbations, and structural changes to which the model is
sensitive. Even though model sensitivity can contribute to
undesirable model uncertainty or prediction error, it is important
to acknowledge that model sensitivity and uncertainty are not one
and the same (Summers et al. 1993, Wallach and Genard 1998).
Model uncertainty, or at least one of its most common
manifestations, is the product of both the model's sensitivity to
particular components and the statistical variability associated
with those components.
A generalized sensitivity analysis of bass without explicit
specification of a fish community of concern is undoable.
Furthermore, the results of a sensitivity analysis for one
community generally cannot be extrapolated to other
communities. Issues related to bass's sensitivity must be
evaluated on a case by case basis by the users of the software.
Although procedures for enabling users to conduct a variety of
structured sensitivity analyses are currently being developed,
presently the onus of performing such analyses rests with the
user. Users interested in issues and techniques related to model
sensitivity and uncertainty should consult the following papers:
Giersch (1991), Elston (1992), Summers et al. (1993), Hakanson
(1995), Norton (1996), Loehle (1997), and Wallach and Genard
(1998).
7.2. Questions Regarding QA of a Model's Implementation
7.2.1. Did the input algorithms properly process all user input?
As part of its routine output, bass generates a *.MSG file that
summarizes all the input data that were used for a particular
simulation. This summary includes not only a line by line
summary of the user's input commands but also a complete
summary of all control, chemical and fish parameters that bass
assigned based on the user's specified input file(s). The onus is
then on the user to verify that their input data has been properly
processed. If not, the user should report their problem to the
technical contact identified in the bass user's guide.
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bass has a series of subroutines that check for the completeness
and consistency of the user's input data. When missing or
inconsistent data are detected, appropriate error messages are
written to the *.MSG file, and an error code is set to true. If this
error code is true after all the user's input has been processed,
bass terminates without attempting further program execution.
To insure that all program subroutines, functions, and procedures
are transmitting and receiving the correct variables, all bass
subroutines and functions are called using implicit interfaces
generated by the Lahey / Fujitsu Fortran 95 5.7f compiler.
Subroutines and functions are packaged together according to
their function and degree of interaction. The bass v2.2 software
is coded with one main program program bass_main (see
bass_program.f90) and 33 procedure modules. These modules
are:
•	module ADAMS_GEAR - subroutines for performing
exams Adams-Gear integrations (see
exams_adam_gear.f90) .
•	module bass_alloc - subroutines for allocating and
reallocating derive type pointers (see bass_alloc.f90).
•	module bass_check - subroutines for checking the
completeness and consistency of user input (see
bass_check.f90).
•	module bass_debug - subroutines for program
debugging. Used only for program development (see
bass_debug.f90).
•	module bass_defined - functions for determining
whether program parameters and variables have been
initialized or assigned (see bass_defined.f90).
•	module bass_exp - subroutines for calculating chemical
exposures, community forcing functions, and habitat
suitability multipliers (see bass_exp.f90).
•	module bass_ini - subroutines for initialization of
program variables (see bass_ini.f90).
•	module bass_input - subroutines for decoding user input
(see bass_input.f90).
•	module bass_int - subroutines for Adams-Gear, Euler,
and Runge-Kutta integrations (see bass_int.f90).
•	module bass_int_loader - subroutines for loading bass
derived type variables into standard integration vectors
(see bass_int_loader.f90).
•	module bass_io - subroutines for processing user input
and output (see bass_io.f90).
•	module bass_ode - subroutines for the computational
kernel of the bass software (see bass_ode.f90).
•	module bass_plots - subroutines for generating output
plots for bass v2.1 and earlier as well as for code
development and maintenance (see bass_plots.f90).
•	module bass_tables - subroutines for generating output
tables for bass v2.1 and earlier as well as for code
development and maintenance (see bass_tables.f90).
module bass_write_csv - subroutines for generating
CSV output files for import into Excel workshets (see
bass_csv.f90).
module bass_write_xml - subroutines for generating
XML output files for post processing by the bass GUI
(see bass_xml.f90).
module decode_functions - subroutines for decoding
constant, linear, and power functions from character
strings (see utl_dcod_fnc.f90).
module dislin - implicit interfaces for the DISLIN
graphics subroutines dislin.f90).
module dislin_plots - general utility subroutines for
generating 2 and 3-dimensional DISLIN plots (see
utl_plots.f90).
module error_module - subroutines for printing error
codes encountered with general utility modules (see
utl_errors.f90).
module filestuff - subroutines for parsing file names
and obtaining version numbers or time stamps (see
UTL_FILESTUFF. F90).
module floating_point_comparisons - operators for
testing equality or inequality of variables with explicit
consideration of their computer representation and
spacing characteristics (see utl_floatcmp.f90).
module getnumbers - subroutines for extracting
numbers from character strings (see utl_getnums.f90).
module iosubs - subroutines for assigning, opening, and
closing logical units (see utl_iosubs.f90).
module modulo_xfread - subroutines for reading files
that contain comments, continuation lines, and include
files (see utl_xfread.f90).
module msort - subroutines for sorting and generating
permutation vectors for lists and vectors (see
utl_msort.f90).
module mxgetargs - subroutines for extracting
arguments from a command line (see
UTL_MXGETARGS. F90).
module reallocater - subroutines for allocating and
reallocating integer, logical, and real pointers (see
utl_alloc.f90).
module search - subroutines for finding the location of
a key phrase within a sorted list (see utl_search.f90).
module search_lists - subroutines for finding the
location of a value within a sorted list (see
utl_search_lists.f90).
module strings - subroutines for character string
manipulations and printing multiline character text (see
utl_strings.f90).
module table_utils - subroutines for generating self-
formating tables (see utl_ptable.f90).
module unitslibrary - subroutines for defining and
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performing units conversions (see utl_unitslib.f90).
In general, these procedure modules are coded with minimal
scoping units. Consequently, their component subroutines and
functions explicitly initialize all required internal variables. This
safeguard is intended to prevent inadvertent use of uninitialized
variables. Wheneverpossible, subroutine and function arguments
are declared with INTENT (IN) and INTENT (OUT) declarations
to preclude unintentional reassignments.
Although global constants and Fortran parameters are supplied
to program procedures via modules (see question 7.2.3), data
exchanges between program procedures are performed via formal
subroutine / function parameters whenever possible. The only
notable exceptions to this coding policy are modules that must be
used to supply auxiliary parameters to "external" subroutines that
are used as arguments to certain mathematical subroutines (e.g.,
root finding subroutines). Working areas used by bass are not
used for data transfers between internal and external procedures.
To simplify the construction and maintenance of the formal
parameter lists of many bass subroutines and functions and to
help prevent the inadvertent transposition of formal parameters,
bass makes extensive use of derived type data structures. Each
derived type definition is specified within its own module, and all
derive type definition modules are maintained in a single file
(bass_types.f90.) Derived types used by bass v2.2 are:
•	module bas s_type_chem_par - type definition for
chemical parameters
•	module bas s_type_diet_mean - type definition used to
summarize average realized diets.
•	module bas s_type_diet_par - type definition used by
derived type bass_type_foodweb_par
•	module bas s_type_diets - type definition used for input
processing of user-specified fish diets
•	module bass_type_fish_int - type definition for
integrated fish variables and fluxes
•	module bass_type_fish_par - type definition for fish
parameters
•	module bass_type_fish_var - type definition for
current fish variables and fluxes
•	module bass_type_foodweb_par - type definition for
the decoded user-supplied fish diets and community
trophic structure.
•	module bas s_type_hsi_par - type definition for fish
habitat multipliers
•	module bass_type_nonfish_int - type definition for
integrated nonfish variables and fluxes
•	module bass_type_nonfish_par - type definition for
nonfish parameters
•	module bass_type_nonfish_var - type definition for
current nonfish variables and fluxes
•	module bass_type_plot_data - type definition for
user-specified plots
•	module bass_type_prey_items - type definition used
by derived type bass_type_fish_var to store a fish's
currently realized dietary composition
•	module bass_type_qsar_data - type definition for
linked list used during data input
•	module bass_type_qsar_linked_list - type definition
for linked list used during data input
•	module bass_type_qsar_node - type definition for
linked list used during data input
•	module bass_type_trophic - definition used for the
calculation of realized diet composition and consumption
•	module bass_type_vmatrix_logical - type definition
for DISLIN graphing matrices
•	module bass_type_vmatrix_real - type definition for
DISLIN graphing matrices
•	module bas s_type_zfunction_par - type definition for
user-supplied exposure and forcing functions
A good example of bass's use of derived type data structures is
the derived type variable used to store and transfer the
ecological, physiological, and morphometric data for a particular
fish species. This derived type is defined by following module
MODULE bass_type_fish_par
USE bass_type_hsi_par
TYPE:: fish_par
CHARACTER (LEN=80) :: ageclass, asttype, astvar, commonname, &
fmodelvar, genusspecies, spawninginterval, tempvar
INTEGER :: fmodel_cls=0, harvests=0, spawnings=0, &
stockings=0, temperatures=0
INTEGER,DIMENSION(:),POINTER: :fmodel=>NULL()
INTEGER,DIMENSION(:),POINTER:: spawn_dates=>NULL()
INTEGER,DIMENSION(:),POINTER:: harvest_datel=>NULL()
INTEGER,DIMENSION(:),POINTER:: harvest_date2=>NULL()
INTEGER,DIMENSION(:),POINTER:: stock_dates=>NULL()
LOGICAL:: bb_constant=.TRUE., prey_switching_on=.TRUE.
REAL :: aefish, aeinvert, ae_plant, astbb, astbnds, ast_pop, &
dry21ive_ab, dry21ive_aa, dry21ive_bb, dry21ive_cc, gco2_d, kf min, &
la, longevity, mgo2_s, rbi, refugia, rq, rt2std, sda2in, tlrO, wtmax, yoy
REAL, DIMENSIONS) :: ga, id, Id, 11, lw, pa, pi, sgmu, wl
REAL, DIMENSIONS) :: nm
REAL, DIMENSIONS) :: lp, lp max, lp min
REAL, DIMENSION(5) :: ge, mf, mi, sg, sm, so, st
REAL, DIMENSION^), POINTER :: fmodel_bnds=>NULL()
REAL, DIMENSION^), POINTER :: harvest_lenl=>NULL()
REAL, DIMENSION^), POINTER :: harvestJen2=>NULL()
REAL, DIMENSION^), POINTER :: harvest_rate=>NULL()
REAL, DIMENSION^), POINTER :: stock_age=>NULL()
REAL, DIMENSION^), POINTER :: stock_rate=>NULL()
REAL, DIMENSION^), POINTER :: stockJl=>NULL()
REAL, DIMENSION^), POINTER :: stock_wt=>NULL()
REAL, DIMENSION^), POINTER :: temp_bnds=>NULL()
REAL, DIMENSION^), POINTER :: temp_pref=>NULL()
TYPE(hsi_par):: hsifeed, hsi_persist, hsirecruit
END TYPE fish_par
END MODULE bass_type_fish_par
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Many components of this derived type are user input parameters
that have already been discussed. For example, the array ga(2)
stores the coefficient and exponent of a species' gill area function
(see /morphometric_parameters page 49). Other components
are secondary parameters that are calculated from the user's input
data. For example, dry21ive_ab, dry21ive_aa, dry21ive_bb, and
dry21ive_cc are constants that are used to calculate a fish's live
weight from its dry weight (see introduction to Section 2.6.
Modeling Growth of Fish). Using a declaration of the form
TYPE(fish_par), DIMENSION(nspecies):: par
all data defined by the above derived type can be passed to a
bass subroutine by the simple calling statement
CALL subl(...., par,....)
subroutines (see utl_constants.f90).
•	module gear_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES. F90).
•	module loc al_ge ar_d at a - stores control parameters
for the exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES. F90).
•	module step_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES. F90).
•	module stiff_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES. F90).
•	module units_parameters - specifies parameters used
by the units conversion subroutines (see
UTL_UP ARAMS. F90)
without fear of data misalignment.
To insure that all program subroutines, functions, and procedures
use the same global constants or parameters, such constants are
declared and defined within a set of 15 data modules. These
modules include:
•	module adam_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES. F90).
•	module bass_constants - specifies various biological
and physical constants used by bass's computational
subroutines (see bass_globals.f90).
•	module bass_graetz - specifies parameters used to
calculate chemical exchange across the fish gills (see
BAS S_GLOBALS. F90).
•	module bass_iofiles - specifies logical unit numbers for
input and output devices (see bass_globals.f90).
•	module bass_names - stores user-supplied fish and
chemical names (see bass_globals.f90).
•	module bas s_novalue - specifies values for integer,
real, and character variables that have not been initialized
(see bass_globals.f90).
•	module bass_precision - specifies the precision of
floating point variables as either single, double, or quad
precision variables. This module also assigns certain
associated floating point constants (see
BAS S_GLOBALS. F90).
•	module bass_units - specifies unit conversion factors
that are specific to bass for use by moduleunitslibrary
(see bass_units.f90).
•	MODULE BASS_WORKING_DIMENSIONS - specifies
"standard" sizes for character variables, input records, etc.
(see bass_globals.f90).
•	module constants - constants used by utility
bass v2.2 uses the following modules (see
bass_work_areas.f90) to define work areas that are common
to two or more functions or subroutines.

MODULE BASS_
MODULE BASS_
MODULE BASS_
MODULE BASS_
MODULE BASS_
MODULE BASS_
MODULE BASS
CPU_PERFORMANCE
FOODWEB_WORK_AREA
HSI_MEANS
MULTISORT_WORK_AREA
ODE_WORK_AREA
OUTPUT_W ORK_ARE A
PLOT WORK AREA
7.2.3. Is the developer reasonably confident that all program
subroutines, functions, and procedures are using the same
global constants or parameters?
All global constants are defined within their own individual
modules. These modules include
•	module bass_constants - constants used by bass's
computational subroutines (see bass_globals.f90).
•	module bass_novalue - specifies values for integer,
real, and character variables that have not been initialized
(see bass_globals.f90).
•	module bass_precision - specifies the precision of
floating point variables as either single, double, or quad
precision variables. This module also assigns certain
associated floating point constants (see
BASS_GLOBALS. F90).
•	MODULE BAS S_WORKING_DIMEN SION S - specifies
"standard" sizes for character variables, input records, etc.
(see bass_globals.f90).
•	module constants - constants used by utility
subroutines (see utl_constants.f90).
•	module units_parameters - specifies parameters used
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by the units conversion subroutines (see
UTL_UP ARAMS. F90)
7.2.4. Do all strictly mathematical algorithms do what they are
supposed to? For example, are root finding algorithms
functioning properly?
During execution, bass must employ root finding algorithms for
two important types of calculations. The first of these is the
calculation of a fish's live weight from its dry weight given an
allometric relationship between its live body weight and its
fraction lipid, and linear relationships between its moisture, lipid,
and non-lipid organic matter fractions. The second type of
calculation involves the linear transformation of unconditioned
dietary electivities into self-consistent sets of dietary electivities.
These calculations are performed using the combined bisection
/ Newton-Raphson algorithm outlined by Press et al. (1992).
y1 = arctan(x) - arctan(x0)
yt = asinh(x) - asinh(x0)
10101 , inA ,
y9 =	exp(-lOOx)
10001
100
, . 10000 . , .
cos(x) +	sin(jc)
10001	10001
u = 2 exp(-x) - exp(-1000 x)
v = -exp(-x) + exp(-1000x)
On the interval [0>3 = sin(x) - sin(x0)
y4 = sinh(x) - sinh(x0)
ys = exp(x) - exp(x0)
y6 = ln(l +x) - ln(l +x0)
(7.3)
7.2.5. Are mathematical algorithms implemented correctly, i.e.,
are the assumptions of the procedure satisfied by the problem of
interest?
Because bass is a differential equation model, a question of
paramount concern is how its integration between points of
discontinuity / nondifferentiability is controlled, bass, like many
ecological models, utilizes threshold responses, absolute value
functions, maximum and minimum functions, and linear
interpolations between time series in its formulation and
implementation. Although most of bass 's parameters are updated
continuously, some parameters that change very slowly and that
are computationally intensive to evaluate (e.g., dietary
compositions) are updated only daily. All of these features create
points of discontinuity or nondifferentiability. Although there is
nothing intrinsically wrong with using such formulations in
differential equation models, numerical integrations of such
models must proceed from one point of discontinuity /
nondifferentiability to another.
With these considerations in mind, bass's computational kernels
(subroutines bass_odesolvr and fgets_odesolvr) are
designed to integrate bass's differential equations for a single
day of the desired simulation period. Immediately following the
call of these computational kernels, bass calculates the dietary
composition of each fish that will be held constant for that day.
The progress of the subsequent numerical integration within the
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day is then controlled by any condition that results in a point of
nondifferentiability. The two most important conditions in this
regard occur when bass must read an exposure file to update the
parameters for the linear interpolation of one or more exposure
variables, or when one or more cohorts are eliminated from the
community. In the later case, bass also recalculates the dietary
compositions of the remaining fish that again will remain
constant for the remainder of the day. Note that recruitment of
new cohorts into the simulated community does not create a point
of nondifferentiability for bass since such amendments to the
community's structure are performed before calling the
computational kernels bass_odesolvr or fgets_odesolvr and,
therefore, constitutes a simple reinitialization problem.
7.2.6. Are simulated results consistentwith known mathematical
constraint of the model? For example, if state variables are
supposed to be non-negative, are they? Similarly, if the model is
supposed to mass balance, does it?
bass's state variables, like those of most physical or biological
models, must be by definition non-negative. However, insuring
that the numerical integration of a differential equation model
remains constrained to its appropriate state space is not a trivial
issue. Consider, for example, the case when one wants to take a
simple Eulerian step for a non-negative state variable that has a
negative derivative. If the state variable is to remain non-
negative, then the largest allowable size for the integration step
can be calculated as follows
y(t+h) = y(t) + hy'(t)
0  h where y'(t) < 0
y'(t)
If h is greater than the numerical spacing of t (i.e., t + h*t), then
an integration step is possible. If the converse is true, however,
the function y(t) is approximating a step function in which case
the desired integration can simply be restarted with y(t) = 0.
There are at least two types of situations that can occur during a
bass simulation that might necessitate this type of corrective
action. The first of these occurs when a cohort experiences
intense predation or other mortality that drives its population to
extinction whereas the second situation might occur when there
is the rapid excretion of a hydrophilic contaminant following the
disappearance of an aqueous exposure. When the derivative for
a fish's body weight, population density, or body burden is
negative, bass verifies whether the current integration step will,
in fact, yield non-negative state values. If not, bass either
executes a simple Euler step of the appropriate size or restarts the
integration with the appropriate state variables initialized to zero.
Using the "-mba" command line option, bass performs a
comprehensive mass balance analysis of its fundamental
differential equations (i.e., Equations (2.1), (2.2), and (2.3)).
bass also calculates and reports mass balances for each cohort's
total biomass and the community's total predicted predatory
mortality and its total predicted piscivorous consumption. For the
example presented herein, this mass balance is -2.950E-09 g dry
wt/ha/yr. Since this community's total piscivory is calculated to
be 8.850E+03 g dry wt/ha/yr, this mass balance check would
have a relative error of less than 10"11.
7.2.7.	Are simulation results consistent across machines or
compilers?
bass was originally developed on a DEC 3000 work station
using the DEC Fortran 90 compiler. In November 1999, it was
ported to the Windows operating system on the DELL OptiPlex
using the Lahey / Fujitsu Fortran 95 5.7f compiler. Although the
results of these two implementations agree with one another up
to single precision accuracy, due to differences in compiler
optimization, model computations must be performed in double
precision to obtain this level of consistency.
In September 2004, bass was ported to a IBM Intellistation A
Pro workstation equipped with dual 64-byte Opteron processors
and a Windows XP operating system. The bass source code was
then recompiled using the Absoft multiprocessor Fortran 90/95
compilers 8.2 MP and 9.0 MP. Although initial compilations
using these compilers failed due to compiler bugs that have been
acknowledged by Absoft Technical Support, workarounds for
these bugs were successfully implemented. Simulation results of
the bass Absoft MP dual processor executables were in excellent
agreement with those of the bass Lahey-Fujitsu single processor
executable. With respect to execution times:
1)	bass Lahey-Fujitsu executable runs on standard EPA, single
processor machines were approximately 1.5 times slower than
bass Lahey-Fujitsu executable runs on the dual processor
workstation.
2)	bass Lahey-Fujitsu executable runs on standard EPA, single
processor machines were approximately 2.9 times slower than
bass Absoft MP executable runs on standard EPA, single
processor machines
3)	bass Lahey-Fujitsu executable runs on standard EPA, single
processor machines were approximately 5.2 times slower than
bass Absoft MP SOF executable runs on the dual processor
workstation.
7.2.8.	Have test and reference / benchmark data sets been
documented and archived?
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The ten bass projects discussed in Section 6.1 serve not only as
bass distribution examples but also as test projects that track
changes in the operation of bass associated with code
maintenance and updates. These project files are used as
benchmarks to verify that code modifications that should not
change bass's computational results do not change bass's
simulation output.
7.3. Questions Regarding QA of Model Documentation and
Applications
7.3.1.	Is the model intended for absolute or comparative
prediction?
Although bass can be used to analyze results from actual field
studies or predict the expected future condition of specific real
communities, its principal intended use is to predict and compare
the outcomes of alterative management options that are
associated with pollution control, fisheries management, and / or
ecosystem restoration activities.
7.3.2.	Does the User Guide provide the information needed to
appropriately apply and use the model?
The bass User's Guide summarizes the model's theoretical
foundations and assumptions, the model's input command
structure, issues related to user file and project management, and
software installation. The User's Guide also presents and
discusses the results of one of eight example applications that are
distributed with the bass software.
7.3.3.	What internal checking can be made to help insure that
the model is being used appropriately?
Currently, the only internal checking performed by bass is to
verify that all parameters needed by the model for a particular
simulation have, in fact, been specified by the user. Although
bass does assign default values for a limited number of
parameters, most unassigned parameters are fatal errors. Future
versions of bass will perform bounds checking on many of its
physiological and morphological parameters.
7.3.4.	Has the developer anticipated computational problem
areas that will cause the model to "bomb "?
Several key mathematical calculations have been identified as
potential problem areas for a bass simulation. In general, these
problem areas involve either the unsuccessful resolution of a root
of a nonlinear equation or the unsuccessful integration of bass's
basic state variables. Examples of the former include situations
when bass's calculated dietary compositions do not sum to unity
or when a fish's live weight is calculated to be less or equal to its
dry weights. Examples of the latter include situations when the
current integration step is less than the numerical spacing of the
current time point, or when bass's integration error exceeds 10"5.
When these situations are encountered, bass terminates
execution and issues an appropriate error message to the current
*.MSG file.
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8. Planned Future Features
Presently, ten major program developments are planned for bass.
These include:
•	Development of canonical fish and community databases
(i.e., *.FSH and *.CMM files) to facilitate easier
application of bass.
•	Software to perform model sensitivity analyses.
•	Implementation of an option to read a simulated or
measured time series of dissolved oxygen concentrations
that are needed to calculate the fishes' ventilation
volumes. See Equation (2.12). Currently, bass uses
saturated dissolved oxygen concentrations that are
calculated as a function of water temperature.
•	Development of submodels for simulating the
physiological tolerances of fish to water quality
parameters other than toxic chemicals.
•	Incorporation of quantitative structure activity
relationships (QSAR's) to predict metabolism of organic
chemicals.
•	Development of immigration algorithms for simulating the
movement of fish into the simulated community based on
habitat parameters such as water depth, current velocity,
availability of prey, etc.
•	Development of subroutines to simulate sublethal,
residue-based effects.
•	Enable lipid fractions, fecundity, and physiological
mortality to be functionally dependent on the fish's
predicted growth rate and/or duration of fasting (see
Adams and Huntingford 1997, Simpkins et al. 2003).
•	Enable an option for specifying habitat suitability
multipliers on respiratory expenditures. See for example
Sweka and Hartman (2001) and Facey and Grossman
(1990).
•	Implementation of light and nutrient dependent primary
production by phytoplankton and periphyton.
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APPENDICES
APPENDIX A. Equilibrium complexation model for metals
As reviewed by Mason and Jenkins (1995), metals can be
classified into three different categories based on their
complexation behavior and preference for different ligands.
These groups are generally designated as class A, class B, and
borderline metals. Of these, however, class B and borderline
metals are the most important from an ecotoxicological point of
view. Class B metals (e.g., Au, Ag, Cu, Hg, and Pb)
preferentially bind to marcromolecules such as proteins and
nucleotides that are rich in sulfhydryl groups and heterocyclic
nitrogen. Borderline metals (e.g., As, Cd, Co, Cr, Ni, Sn, andZn)
bind not only to the same sites as do class B metals but also to
those sites preferred by class A metals (i.e., carboxylates,
carbonyls, alcohols, phosphates, andphosphodiesters). Although
factors determining the preference of borderline metals for a
particular binding site are complex, the fact that the transport and
storage of these metals in fish and other biota are regulated by
metallothioneins via sulfhydryl complexation reactions suggests
that the total availability of sulfhydryl groups within organisms
plays a key role in their internal distribution and accumulation.
To formulate complexation reactions for class B and borderline
metals, one can assume that protein sulfhydryl groups are the
only significant ligand for these metals, i.e.,
RSH + M* ** RSM + H*
The stability constant for this reaction is
RSM[H*]
Kb _ [RSM] [/T]
[RSH] [AT]
RSH[M+]
(A.l)
(A.2)
where [H +] is the hydrogen ion concentration (molar); [M+] is
the concentration of free metal (molar); [RSH] is the
concentration of reactive sulfhydryls (molar); [itlXW] is the
concentration of sulfur bound metal (molar); RSM is the moles
of metal bound to sulfhydryls; and RSH is the moles of free, non-
disassociated sulfhydryl. If a fish's metal concentrations (i.e., Ca
, C, , Ca , and CJ) are expressed on a molar basis, then the
following identities hold
C/ =
[M*] = Ca
RSM=CoPoWw
P +P,K +P —
a I aw 0 (2
(A. 3)
(A.4)
(A.5)
where Ww is the fish's kilogram live weight. Substituting
Equations (A.3) and (A.4) into Equation (A.2), one can verify
that
and consequently
PoC„ = Kb RSH
Ca W [/T]
Cf= | Pa+PtZa* +
Kb RSH
W\H+]
(A. 6)
(A. 7)
To parameterize Equation (A.7) for RSH, the following mass
balance for the fish's sulfhydryl content is then assumed
TS = RSH + RS~ +Y, RSMf
i
RSHK.
RSH +
[/T]
- + £
Kb, C RSH
i a.
RSH
[HI
\
(A. 8)
K	Kb,. C
[HI i [H+]
where TS is the total moles of sulfhydryl ligands; RS' is the
moles of disassociated sulfhydryls; and Ka is the sulfhydryl's
disassociation constant. Therefore,
RSH =
TS[H*]

(A. 9)
Using Equation (A.7), however, this expression can be rewritten
as
RSH =
TS
1 +
KbiBft
.(A. 10)
[/H < (P.+P^^WJH^+K^RSH
where Bf = Cf Ww is the fish's total burden (mol/fish) of metal i.
For most class B metals, however,
(Pa+Pi^KWUK^RSH
Consequently, Equation (A. 10) can be simplified to
TS ~ £ Bf
(All)
RSH =
TS
K
1+77^+£
B,
f,
1 +
K
(A. 12)
[JT] r RSH	[H+]
This expression can then be substituted into Equation (A.7) to
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calculate the fish aqueous phase metal concentrations.
To use the aforementioned complexation model (i.e., Equation
(A. 12) substituted into Equation (A. 7)), one must specify both
the metal's stability constant (see Equation (A.2)) and the total
concentration of sulfhydryl binding sites TS (mol SH/g dry wt)
within the fish. Although numerous studies have investigated the
sulfhydryl content of selected fish tissues, it appears that no study
has attempted to quantify the total sulfhydryl content of fish. A
reasonable approximation of this parameter, however, can still be
made since data do exist for the major tissues (i.e., muscle, liver,
kidney, gill, and intestine) typically associated with metal
bioaccumulation.
Itano and Sasaki (1983) reported the sulfhydryl content of
Japanese sea bass (Lateolabrax japonicus) muscle to be 11.5
mol SH/g(sacroplasmic protein) and 70.5 mol
SH/g(myofibrillar protein). Using these authors' reported values
of 0.0578 g(sarcoplasmic protein)/g(muscle) and 0.120
g(myofibrillar protein)/g(muscle), the total sulfhydryl content of
Japanese sea bass muscle is estimated to be 9.12
mol(SH)/g(muscle)or45.6 mol(SH)/g(dwmuscle). Opstevedt
et al. (1984) reported the sulfhydryl content of Pacific mackerel
(Pneumataphorus japanicus) and Alaska pollock (Theragra
chalcogramma) muscle to be 6.6 and 6.2 mmol(SH)/l 6 g(muscle
N), respectively. Using conversion factors reported by these
authors, these values are equivalent to 48.7 and 56.7 mol/g(dw
muscle). Chung et al. (2000) determined the sulfhydryl content
of mackerel (Scomber australasicus) muscle to be 88.2
mol(SH)/g(protein). Using the conversion factor 0.83
g(protein)/g(dw muscle) (Opstevdt et al. 1984), this value is
equivalentto73.2 mol(SH)/g(dwmuscle). Several studieshave
determined sulfhydryl contents of the actomyosin and myosin
components of fish myofibrillar proteins (Connell and Howgate
1959, Buttkus 1967, 1971, Takashi 1973, Itoh et al. 1979,
Sompongse et al. 1996, Benjakuletal. 1997, Lin and Park 1998).
Because the results of these studies agree well with the
actomyosin analysis reported by Itano and Sasaki (1983), the
results of Itano and Sasaki (1983), Opstevedt et al. (1984), and
Chung et al. (2000) can be assumed to be representative of fish
in general. Consequently, the sulfhydryl content of fish muscle
can be assumed to be on the order of 45-70 mol(SH)/g(dw
muscle).
Although the sulfhydryl contents of liver, kidney, gills, and
intestine have not been measured directly, the sulfhydryl content
of these tissues can be estimated from their metallothionein
concentrations. Metallothioneins (MT) are sulfur-rich proteins
that are responsible for the transport and storage of heavy and
trace metals and that are also usually considered to be the
principal source of sulfhydryl binding sites in these tissues
(Hamilton and Mehrle 1986, Roesijadi 1992). Numerous
researchers have investigated the occurrence of MT s in the liver,
kidney, and gills of fish, and most have shown that tissue
concentrations of MT s generally vary with metal exposures.
Under moderate exposures, typical hepatic MT concentrations in
fish are on the order of 0.03 - 0.30 mol(MT)/g(liver) (Brown
and Parsons 1978, Roch et al. 1982, Klaverkamp and Duncan
1987, Dutton et al. 1993). Using data from Takeda and Shimizu
(1982) who report the sulfhydryl content of skipjack tuna
(Katsuwonus pelamis) MTs to be approximately 25
mol(SH)/mol(MT) and assuming a dry to wet weight ratio equal
0.2, these MT concentrations would be equivalent to 3.75 - 37.5
mol(SH)/g(dw liver). This range of values suggests that the
hepatic sulfhydryl content of fish, that includes both baseline MT
and cytoplasmic components that can be converted into MT,
might be on the order of 40 mol(SH)/g(dw liver). This latter
value, however, is probably too conservative. Consider, for
example, the observation that the ratios of mercury
concentrations in liver to those in muscle often vary from 1.5 to
6 or more (Lockhart et al. 1972, Shultz et al. 1976, Sprenger et
al. 1988). If liver and muscle are equilibrating with the same
internal aqueous phase, then either the MT sulfhydryls are more
available than are the sacroplasmic and myofibrillar sulfhydryls
or the inducible concentrations of hepatic MT are much higher
than 40 mol(SH)/g(dw liver). Of these two possibilities, the
latter appears more likely.
Although gill, kidney, and intestine MTs have not been studied
in the same detail as hepatic MTs, it appears that MT, and hence
sulfhydryl, concentrations in gills and kidney are lower and not
as inducible as hepatic concentrations (Hamilton et al. 1987a, b,
Klaverkamp and Duncan 1987). Klaverkamp and Ducan (1987)
estimated the concentrations of gill MT in white suckers
(Catostomus commersoni) to be 33 g(MT)/g(gill) which is
equivalent to 3.3 nmol(MT)/g(gill) or0.0825 mol(SH)/g(gill).
This latter value agrees well with the estimated concentrations of
unidentifiedbinding sites (0.03 -0.06 mol/g(gill)) for copper on
the gills of rainbow trout (Oncorhynchus mykiss) and brook trout
(Salvelinus fontinalis) (MacRae et al. 1999), but is somewhat
higher than the concentration of unidentified binding sites (0.013
- 0.03 mol/g(gill)) for copper, cadmium, and silver on the gills
of rainbow trout and fathead minnows (Pimephales promelas)
(Playle et al. 1993, Janes and Playle 1995).
Based on these considerations and the acknowledgment that
many other important organic compounds contain sulfhydryl
groups, e.g., enzymes involved in fatty acid synthesis,
glutathione, etc., it seems reasonable to assume that the
sulfhydryl content of fish is approximately 70 mol(SH)/g dry
wt. Because Davis and Boyd (1978) reported the mean sulfur
content of 17 fish species to be 206 mol(S)/g dry wt, this
assumption implies that almost 1/3 of a fish's sulfur pool exists
as sulfhydryl groups.
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The aforementioned complexation model was implemented
within bass using 70 mol(SH)/g dry wt to calculate the fish's
total sulfhydryl content. The mean dissociation constant for
organic sulfhydryls was then assigned as pKa = 9.25 (i.e., the
SPARC estimated pKa for cysteine). Using literature values for
the stability constants of methylmercury, however, bass over
predicted the bioaccumulation of methylmercury in fish by at
least an order of magnitude. Consequently, a much simpler
distribution coefficient algorithm was adopted.
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APPENDIX B. Canonical equations for modeling diffusive chemical exchange across fish gills with ventilation and perfusion
effects. See Section 2.2 for background information and notation.
If chemical exchange across fish gills is treated as steady-state,
convective mass transport between parallel plates, then the
following PDE and boundary conditions can be used to model
chemical uptake from and excretion to the interlamellar water:
i-*: v^=d^
r2 J dy dx2
dC
dx
TT	(B-1)
(B.2)
x = 0
solution of Equation (B.7) is separable, i.e., ®(X, Y) =
and that qv = 2 rhV is the ventilation volume of an individual
interlamellar channel. Using these observations, one can then
write
dX
x=i
-N.
Sh
0(1)¥(T) -
gv dQ>
qB dX
x=i
*Gz
/
¥(v) dv
(B. 10)
D
dC
dx
= ~ k„
C(r,y) -Ca- f —
a q J 8x
dv
(B.3)
To obtain a canonical solution for this gill model, these equations
can be nondimensionalized using the following transformations:
0 =
C-C
c -c
w a
x=-
yD
Vr2
(B.4)
(B.5)
(B.6)
Applying these transformations, chemical exchange across a
fish's gills is described by the following dimensionless PDE and
boundary conditions:
-(l -x2)v— =
2	dY ~ 8X2
00
dx
= o
x=o
d®
dX
-N,
Sh
X=l
Nr.,
0(1 ,Y)~
2 rhV
f d&
J dX
qn J dX
ip Y
dv
x=i
(B.7)
(B.:
(B.9)
is the gills' dimensionless lamellar
where N„, = k rD 1
oft m
permeability (i.e., Sherwood number); and N0z = IDV~1 r~2 is
the gills' dimensionless lamellar length (i.e., Graetz number).
The boundary condition (B.9) describing exchange across the
secondary lamellae, however, can be simplified by noting that the
that can then be differentiated with respect to Y to obtain
d*¥ d
dX

(B. 14)
x=i
which is the boundary condition originally used by Barber et al.
(1991).
See Barber et al. (1991) for the method used to construct the
series solution for the dimensionless bulk concentration of the
aforementioned PDE gill exchange model (i.e., Equation (2.28)).
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APPENDIX C. Derivation of the consistency condition for feeding electivities.
To derive a self consistency condition for a fish's electivities and When Equation (C.3) is substituted into Equation (C.5), one then
relative prey availabilities such that its calculated dietary obtains
frequencies will sum to unity, consider the following
dt-ft
e. =
' di+ft
d..=
( \
Ilfi
1 - e.
ft
Summing Equation (C.2) over all i then yields
' *	J = 1	f = l
i=1
(C.l)
(C.2)
(C.4)
(C.5)
£
i=\
or equivalently
1 + e,.

	' f +f
1 - ef' J'
"2 e f
;=i 1 - e,
n e f
£-^ = 0
*=1
1 - e.
(C.6)
(C.l)
(C.3) Finally, adding = 1 to each side of Equation (C.l), the
following consistency condition is obtained
£
/=i
eifi
1 - et

11 f
£-^ = i
(C.8)
i=1
1 - e.
! = 1
BASS 2.2 March 2008
116

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INDEX
chemical exposures
contaminated sediments 	42
dietary exposure via benthos	42
dietary exposure via insects 	43
dietary exposure via periphyton	42
dietary exposure via phytoplankton 	42
direct aqueous exposures 	43
chemical parameters
/chemical	42
/exposure	42
/lethality 	43
/log_ac 	43
/log_kbl 	43
/log_kb2 	43
/log_p 	44
/melting_point	44
/metabolism	44
/molar_volume 	44
/molar_weight	44
/nonfish_bcf	44
files
chemical exposure files (.chm)	54-56
chemical property files (.prp)	54, 55
community files (.cmm) 	54, 55
directory structure for BASS include files 	56
fish files (.fsh)	54, 55
include files	38, 54
management 	54
output file (.bss) 	56
output file (.msg) 	56
output file (.ps)	56
output file (.xml)	56
project files (.prj) 	54, 56
user supplied parameter files 	53
fish parameters
/age_class_duration	45
/common_name	45
/compostional_parameters 	45
/ecological_parameters	45
/feeding_options	47
/fishery _parameters	47
/habitat_parameters	48
/initial_conditions	48
/morphometric_parameters	49
/physiological_parameters 	49
/prey_switching_off	50
/spawning_period	50
/species	50
future features	92
physical habitat conditions
specifying water depth 	41
specifying water temperature 	41
restrictions
specifying chemical names	42
specifying common names 	45
units recognized by BASS 	52
simulation controls
/annual_outputs	38
/annual_plots	39
/biota	39
/fgets	40
/header 	40
/length_of_simulation	40
/leslie_matrix_simulation	40
/month_t0 	40
/nonfish_qsar 	40
/simulation_control	40
/summary _plots	40
/temperature 	41
/water_level 	41
simulation options
defining community food web 	36, 45
non-fish compartments as forcing functions	39, 51, 52
non-fish compartments as state variables 	51
simulating bioaccumulation without community dynamics
	40
simulating community dynamics without bioaccumulation
	36
specifying fishery harvest and stocking 	47
specifying habitat suitability multipliers	48
specifying non-fish BCFs	40, 44
specifying output	38-40
specifying water levels	41
turning off chemical lethality 	43
turning off fishery stocking	47
turning off fishing harvest	47
syntax
commenting a line	38
continuing a line	37
specifying an include file 	38
specifying units	52
user specified functions 	52
technical support
reporting comments 	36
reporting problems	36
reporting suggestions 	36
BASS 2.2 March 2008
117

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BASS 2.2 March 2008

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