EPA Report No. 600/R-01/035
April 2001
Bioaccumulation and Aquatic
System Simulator (BASS)
User's Manual
Beta Test Version 2.1
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 USEPA
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 a generalized, community-based,
bioaccumulation model called BASS (Bioaccumulation and Aquatic System Simulator). This model is designed to predict the
population and bioaccumulation dynamics of age-structured fish communities that are 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 rather a reference and user's guide. The intended audience of this report
and associated software is 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.
BASS version 2.1 is a beta test version that is being released on a targeted basis to EPA Program and Regional Offices and to the
academic research community for comment and testing. Although the model 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 canbe 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 canbe used to analyze results from
actual field studies, its principal intended use is to predict and compare the outcomes of alternative management options that are
associated with pollution control or ecosystem management or restoration activities.
Rosemarie C. Russo, Ph.D.
Director
Ecosystems Research Division
Athens, Georgia
in
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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 that are exposed to hydrophobic organic pollutants and class B and
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. The model's exchange algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals
of concern that 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 are the chemical's aqueous diffusivity, n-octanol/water partition
coefficient (Kow), 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 specified by defining one or more foraging classes for each fish species
based on either its body weight, body length, or age. The dietary composition of each of these feeding 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 community's food web and standing stocks, size
dependent physiological mortality rates, the maximum longevity of species, and lexicological responses to chemical exposures. The
model's temporal and spatial scales of resolution are a day and a hectare, respectively. Currently, BASS ignores the migration offish
into and out of the simulated hectare.
IV
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Table of Contents
Abstract iv
Figures vii
Tables vii
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 Trophic Interactions and Predatory Mortalities 11
2.8. Modeling Non Predatory Mortalities and Recruitment 13
2.9. Modeling Toxicological Effects 14
3. Model Parameterization 18
3.1. Parameterizing Kf 18
3.2. Parameters for Gill Exchange 18
3.3. Bioenergetic and Growth Parameters 19
4. BASS User Guide 31
4.1. Summary of New Features Available in BASS version 2.1 31
4.2. Input File Structure 32
4.2.1. Simulation Control Commands 33
4.2.2. Chemical Input Commands 35
4.2.3. Fish Input Commands 38
4.2.4. Units Recognized by BASS 41
4.2.5. Syntax for User Specified Functions 41
4.2.6. User Supplied Exposure Files 42
4.3. Output Files Generated by BASS 43
4.4. Include Files and General File Management 43
4.5. Command Line Options 45
4.6. Restrictions and Limitations 45
5. Software Installation and Management 50
5.1. MS-DOS Installation 50
5.2. Auxiliary Software 51
6. Example Application 52
7. Model Quality Assurance 54
7.1. Questions Regarding QA of a Model's Scientific Foundations 54
7.2. Questions Regarding QA of a Model's Implementation 55
7.3. Questions Regarding QA of Model Documentation and Applications 59
8. Planned Future Features 60
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References 61
APPENDICES 79
APPENDIX A. Equilibrium complexation model for metals 79
APPENDIX B. Nondimensionalization of chemical exchange equations for fish gills 82
APPENDIX C. Derivation of the consistency condition for feeding electivities 83
APPENDIX D. Example project file constructed using include files as discussed in Section 4.4 84
APPENDIX E. Example output file (filename.msg) that summarizes user input data, input data errors, and run time
warnings and errors 95
APPENDIX F. Example output file (filename.bss) that tabulates annual bioenergetic and contaminant fluxes 112
APPENDIX G. Example output file (filename.plx) that plots the variables requested by the user 122
VI
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Figures
Figure 1. Application of Eq.(2-51) to describe the temperature dependence of the maximum daily consumption of brown trout
(Salmo trutta) based on Elliott (1976b, Tables 2 and 9) 17
Figure 2. First eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio 27
Figure 3. Second eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio 28
Figure 4. First bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.29
Figure 5. Second bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion
ratio 30
Figure 6. Conceptual model for the primary food web of Everglades open water fish assemblage 53
Tables
Table 1. Symbols used for model development 16
Table 2. Summary of allometric coefficients and exponents for gill area and lamellar density for freshwater bony fishes and
agnatha 20
Table 3. Summary of allometric coefficients and exponents for gill area and lamellar density for cartilaginous and marine boney
fishes 22
Table 4. Summary of allometric coefficients and exponents for gill area and lamellar density for air-breathing fishes 23
Table 5. Summary of coefficients and exponents for lamellar lengths 24
Table 6. Summary of studies reporting water-blood barrier thickness for freshwater and marine fishes 25
Table 7. Sources of bioenergetic and growth for selected fish species 26
Table 8. Valid Unit Prefixes 46
Table 9. Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy 47
Table 10. Valid Ecological, Morphometric, and Physiological Unit Names 49
vn
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1. Introduction
Fish health can be defined from both an ecological and a human
health/value perspective in a wide variety of ways. Questions
relating to fish health from an ecological perspective often
include:
1) Is 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 the fish to attain it's 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 chemical considerations?
4) Are regional fish assemblages maintaining their
expected level of productivity based on
biogeographical and physical chemical 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 disrupters, heavy metals, etc.) so as not to
interfere with the growth and reproduction of
piscivorous wildlife?
From a human health or use perspective another important
question related to fish health is:
7) 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 been
typically 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 for human or ecological exposure assessments,
respectively.
From the perspective of evaluating alternative management
options orof 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
dimensions of fish health identified above.
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-basedbioenergetic 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), are
becoming the models of choice for predicting the growth of fish.
Because these models predict fish growth as 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 the population dynamics and productivity of
fish, researchers are increasingly using suchbioenergetic 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 in
assessing the ecological and human health risks of 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 lexicological benchmarks such as
the LC50 and the EC50 have been widely used to make hazard
assessments, most deleterious effects of chemical pollutants are
caused by the internal accumulation of those compounds, rather
than their environmental concentrations per se. Numerous
authors (Neely 1984; Friant and Henry 1985; McCarty et al.
1985;McCarty 1986; ConnellandMarkwell 1992; McCarty and
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Mackay 1993; Verhaar et al. 1995; van Loon et al. 1997) have
discussed the need to consider chemical bioaccumulation
explicitly when assessing expected ecological consequences of
chemical pollutants in aquatic and marine ecosystems. Residue-
based toxicity studies confirm this supposition (Opperhuizen and
Schrap 1988;vanHoogenand Opperhuizen 1988;Donkinetal.
1989; Tas et al. 1991; van Wezel et al. 1995; Driscoll and
Landrum 1997).
Although the concentrations of moderately hydrophobic
chemicals in fish often can be predicted accurately by 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 in either
direction, with calculated contamination levels being both
considerably above and 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 kinetic 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 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 species of concern is relatively
short lived, the time needed for equilibrium can exceed their
expected life span. Even when there is sufficient time 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 to the biotransformation and metabolism of the parent
compound.
One of two possible assumptions are 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 to this assumption is that there are actually
multiple routes of exposure which are all covariant with the
chosen reference pathway in a fixed and constant manner. In the
case of bioconcentration factors (BCFs), the implicit assumption
is that virtually all of the fish's accumulated body 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 burdens 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 //g/day, is the product of its
ventilation volume, Q mL/day, and the chemical's aqueous
concentration, Cw/j.g/mL. Similarly, the fish's dietary exposure,
DE //g/day, is the product of its feeding rate, F g/day, and the
chemical's concentration in the fish's prey, Cp /wg/g. Assuming
that 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
QCv=FCp
Q I F = BCF
(1-1)
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) forthe trout's prey is BCF= 0.048
^(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 and the growth of fish provide
objective and scientifically defensible tools that can overcome
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many of the limitations of equilibrium-based predictors of
bioaccumulation identified above. Although numerous models
have been developed to describe the dynamics of chemical
bioaccumulation in fish, (Norstrom et al. 1976; Thomann 1981,
1989; Jensen et al. 1982; Thomann and Connolly 1984; Gobas
et al. 1988; Barber et al. 1991; Thomann et al. 1992; Gobas
1993; Madenjian et al. 1993), these models differ significantly
with regard to how food web structure and dietary exposures are
represented.
This report describes the theoretical framework,
parameterization, and use of a generalized, community-based,
bioaccumulation model called BASS (Bioaccumulation and
Aquatic System Simulator). This 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 (hUE^/w^^bJobjJc^oi]3/bb_/Brod/clogB4(UiUiin , or
SPARC (Carreira et al. 1994;
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 bioaccumulation 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 of fish
dt
dB T T ,,
— = J + J - M
dt g
= F-E-R-EX- SDA
(2-1)
dN
dt
= - NM - PM
(2-3)
where B and Wd denote the chemical body burden (//g/fish) and
dry body weight (g(DW)/fish) of the average individual within
the age class and N denotes the age class's population density
(fish/ha). In Eq.(2-l) Jg and J, denote the net chemical exchange
across a fish' s gillfrom the water and across its intestine from
food, respectively, and M denotes the chemical's
biotransformation or metabolism. In Eq.(2-2) F, E, R, EX, and
SDA denote the fish' s feeding, egestion, routine respiration,
excretion, and specific dynamic action (i.e., the additional
respiratory expenditure in excess of R required to assimilate
food), respectively. Although many physiologically based
models for fish growth are formulated in terms of energy content
and fluxes (e.g., kcal/fish and kcal/d), formulating a
physiologically based growth model in terms of dry weight is
fundamentally identical to the former since the energy densities
of fish depend on their dry weight (Kushlan et al. 1986; Hartman
and Brandt 1995b). Finally, in Eq.(2-3) M/and PM denote the
age class's non-predatory and predatory mortality, respectively.
Although migration can be a significant process in determining
population sizes, this process is presently ignored in BASS.
Though it may not be immediately apparent from the above
notation, these equations are tightly coupled to one another. For
example, the realized feeding of fish depends on the availability
(i.e., density andbiomass) 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. Table 1 summarizes
the definitions of all the variables used to develop these
equations. 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 described in terms of dry weight,
modeling the bioaccumulation of chemicals in fish requires
knowledge of their live weights since the folio wing formulations
of the bioaccumulation process will be developed in terms of
diffusive exchange between aqueous phases.
(2-2) 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 it be the surrounding ambient water or the
aqueous phases of the fish's intestinal contents. Consequently,
to model these exchanges one must first consider how chemicals
distribute with the bodies offish. If individual fish are conceptu-
alized as a three-phase solvent consisting of water, lipid, and
non-lipid organic matter, then their whole body chemical
concentration can be expressed as
C, = — = P C + P,C, + P C
f TTT a a II o o
= P + P, —L+P -Ji C
(2-4)
where Wfis the fish's live weight (g(FW)); Pa, Ph and P0 are the
fractions of the whole fish that are water, lipid, and non-lipid
organic material, respectively; and Ca, C,, and C0 are the
chemical' s concentrations in those 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
etal. 1986a, 1986b), internal equilibration between these three
phases can be assumed to be rapid in comparison to external
exchanges. For organic chemicals this assumption means that
Eq.(2-4) simplifies to
Cf = (P.
(2-5)
where K, and K0 are partition coefficients between lipid and
water and between organic carbon and water, respectively.
For metals, however, Eq.(2-4) is in theory more complicated.
Although metals do partition into lipids (Simkiss 1983), their
accumulation within most other organic media occurs by
complexation reactions with specific binding sites.
Consequently, for metals it would seem that the term P0CJCa in
Eq.(2-4) should be formulated as a function of an appropriate
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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 to be inappropriate to 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, the same approach was adopted for BASS.
Thus, for a metal
cf =
p.
PK, C.
(2-6)
where K, is again an appropriate partition coefficients between
lipid and water and Kd is an appropriate metal specific
distribution coefficient. Although this equation appears identical
to Eq.(2-5) for organic contaminants, the relative values of Kd
and K0 in relation to K, can be remarkably different. See Section
3.1.
Because Cw equals Ca at equilibrium, it follows from Eq.(2-4)
that the thermodynamic bioconcentration factor (Kf = C/CW at
equilibrium) for a chemical in fish would be
Kf =
P
o^d
for organics
for metalics
(2-7)
2.2. Modeling Exchange from Water
Because chemical exchange across the gills of fish occurs by
simple diffusion, such exchanges can be modeled by Pick' s first
law of diffusion as follows
J
= S k
g g
- C,
(2-8)
where Sg is the fish' s total gill area (en), kg is the chemical' s
conductance (cm/s) across the gills from the interlamellar water,
and Cw is the chemical' s concentrations /4g/mL) in the
environmental water. See Yalkowsky et al. (1973), Mackay
(1982), Mackay and Hughes (1984), Gobas et al. (1986), Gobas
and Mackay (1987), and Erickson and McKim (1990). When
Eqs.(2-4) and (2-7) are substituted into this equation, one then
obtains
Cf
J = Ski C--i
(2-9)
Although according to Pick' s first law the conductanc&g of a
chemical across a fish's gill 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
because the thickness of the boundary layer varies along the
length of the gill's secondary lamellae and is a function of the
gill' s ventilation velocity. To circumvent this problem, a fish's
net chemical exchange rate, Sg kg, can be objectively estimated
by reformulating the gill' s net chemical exchange as
J = Q(C - CR)
Z ^ ^ W B'
(2-10)
where Q is the fish' s ventilation volume (cnis) and CB is the
bulk concentration of the chemical in the water expired from the
gills. When Eqs. (2-8) and (2-10) are equated, it follows that
S k = O
c,., - c.
(2-11)
Despite its appearance, the right hand side of this equation can
be readily quantified. In particular, the ventilation volume offish
can be estimated by
Q =
(2-12)
where O2 is the fish's rate of oxygen consumption (,ug/s), am is
the fish' s oxygen assimilation efficiency an^Cw02 is the water's
dissolved oxygen concentration (/^g/mL). And if one now makes
certain assumptions concerning the geometry of the interlamellar
spaces and the nature of mass transport between the secondary
lamellae, the normalized bulk concentration of the exhalant gill
water (Cw-CB)/(Cw-Ca)can also be formulated.
Because the gill's secondary lamellae form flat channels having
very high aspect ratios (i.e., mean lamellar height / interlamellar
distance), the lamellae can be considered 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 exhalant gill water can be obtained
using the solutions of the partial differential equation (PDE) that
describes steady-state convective mass transportbetweenparallel
plates, i.e.,
^-D—, (2-13)
dy
dx
where V (cm/s) is the gill's mean interlamellar flow velocity, D
(cnf/s) is the chemical's aqueous diffusivity, and x andy are the
lateral and longitudinal coordinates of the channel along which
diffusion and convection occurs, respectively. In this equation
C = C(x, y) denotes the chemical's interlamellar concentration at
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the distances x from the surface of the lamellae andy along its
length. The surfaces of adjacent lamellae are located at x = ±h
where h is the hydraulic radius of the lamellar channel which
equals one half of the interlamellar distance d (cm). The midline
between adjacent lamellae is therefore denoted by x=0. The
mean interlamellar flow velocity, V (cm/s), can be formulated as
the ratio of the fish's ventilation volume to the cross sectional
pore area, Xg (cm2), of its gills. Because this pore area is related
to the gill's lamellar surface area by
x -s'd
x'~~r
(2-14)
where d (cm) is the mean interlamellar distance and / (cm) is the
mean lamellar length (Hills and Hughes 1970), a fish's mean
interlamellar flow velocity is given by
(2-15)
S d
To solve the above PDE 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
3C
dx
(2-16)
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
f = -*- (CM - Ca) (2.17)
D-
where km is the permeability of the gill membrane (cm/s).
Although this boundary condition could be used as is (Barber et
al. 1991), it can also be modified to address potential perfusion
limitation of gill uptake. To accomplish this task a formulation
patterned after Erickson and McKim (1990) can be used. In
particular, consider the following reformulation
DdC
dx
= -km(C(h,y)-Ca(y))
(2-18)
where Ca(y) denotes the aqueous phase concentration of the
chemical at point y along the length of a secondary lamella, Ca(l)
= Ca denotes the chemical' s concentration in the afferent lamellar
blood, U(y, I) is the chemical's accumulated rate of uptake C^g/s)
along the lamellar segment [y, /], and qp is the lamellar perfusion
rate(cnrVs). If both sides of the lamella uptakes chemical, then
U(y, 1) can be formulated as
dzdy
dx
(2-19)
dy
where z denotes the height (cm) of the secondary lamella. Using
this expression, the boundary condition (2-18) can now be
written as
DdC
dx
2zD
= -tlc(M-cfl-^f'^
dx
dy
fy (2-20)
Once the solution of Eq.(2-13)forthese boundary conditions has
been obtained, the chemical's bulk concentration in the exhalant
gill water can be evaluated using the weighted average
fh C(x,l)(l -x2)dx
" —V— (2-21)
f*
Jo
that scales each concentration profile C(x, /) by its relative
velocity.
A canonical solution to Eq.(2-13) can be obtained by
nondimensioning C(x, y), x, and>- as follows
C - C.
@ =
Y =
cw-ca
' H
y D
Vh
(2-22)
(2-23)
(2-24)
where h is the hydraulic radius of the lamellar channel (i.e., one-
half the interlamellar distance). When this is done, the chemical's
dimensionless bulk concentration is given by
f1 @(X,NGz)(l -X2)dx
(2-25)
C,., - C
f1 (l -X2)dX
Jo
where N& = (ID) / (Vh2) is the lamellae's dimensionless length
or Graetz number. Two important points concerning this
expression can now be made. Firstly, one can easily verify that
1
c,., - c_
and therefore Eq. (2-11) can be rewritten as
(2-26)
(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
(2-28)
where the coefficients Bm and exponents Xm are known functions
of the lamellae's dimensionless conductance or Sherwood
number
jV = m (2-29^
sh D (2 ^]
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 offish gills, only the
first two terms of the series are needed to evaluate ®B with less
than 1% error (also see Barber et al. 1991).
2.3. Modeling Exchange from Food
Chemical uptake from food has usually been modeled by
assuming that a fish can assimilate a constant fraction of the
chemical it ingests, i.e.,
Jt = *c CP Ff (2-30)
where
-------
however, is not without problems since the fish's food
assimilation efficiency can vary significantly with feeding rate,
food quality, temperature and other factors. Nevertheless, the
results of studies by Lieb et al. (1974), Gruger et al. (1975), and
Opperhuizen and Schrap (1988) which are analyzed and
discussed in Barber et al. (1991) corroborate these predicted
tends. Recent studies by Dori et al. (2000) who used in situ
preparations of channel catfish intestines, have clearly
established that preexposures to 3,4,3',4'-tetrachlorobiphenyl
does indeed decrease intestinal uptake rates.
Muiretal. (1992), Dabrowska et al. (1996), and Fisketal.( 1998)
have investigated chemical assimilation efficiencies of rainbow
trout and channel catfish using a model proposed by Bruggeman
etal. (1981), i.e.,
dCf
~dT
= a f Cp - k2 Cf
= afC
1 -exp(-&2 f)
E
(2-34)
(2-35)
where a is a constant assimilation efficiency, / is the fish's
specific rate of feeding (g/g/d), and k2 is the chemical's apparent
elimination rate which necessarily must include actual excretion,
biotransformation, and growth dilution. Eq.(2-35), however, is
only the solution to Eq.(2-34) when C,(0) =0. The general
solution to Eq.(2-34) is actually
-exp(-&2
C,(0) exp(-£2 /)
k7
(2-36)
exp(-&2 f)
Acknowledging this fact is of paramount importance to interpret
the results reported by Muir et al. (1992), Dabrowska et al.
(1996), or Fisk et al. (1998) correctly in light of the fecal
partitioning model proposed herein. When this solution is
redifferentiated, one observes that
dC, afC
—T = ' C(0) ~
dt
/
0 (2-37)
Now let T denote the length of a bioaccumulation experiment in
which Cj(0)=0 and /and Cp are constant, i.e., such as those
studies cited above. Also let a and k2 denote the assimilation
efficiency and apparent depuration rate that were estimated for
this experiment. When the experiment is half over, the rate of
change in the fish's whole body concentration would be
calculated by Eq.(2-37) to be
dC
dt
= afCp exp(-£2 772)
(2-38)
If one now elects to arbitrary restart time, the bioaccumulation
dynamics for the second half of the experiment would be
described by
afC
c - p
f ~
afCp
1
exp(-£2T) (2-39)
where a and k2 denote updated estimates for the fish's
assimilation efficiency and apparent depuration rate for
0 < T < 772. This equation can also be differentiated to yield
dC,
di
afC }
Cf(T/2) - ——p- k2 exp(-£2 T)
£
which can be evaluated at T = 0 to yield
dC,
di
= &fCp -k2Cf(TI2)
(2-40)
(2-41)
For logical as well as mathematically consistency this derivative
should equal the derivative given by Eq.(2-38), i.e.,
&fCp-k2 C/772) = a f Cp exp(-£2 T/2) (2-42)
Solving for cc then yields
k2 Cf(T/2)
afCp exp(-£2 772)
Cf(TI2)
,(2-43)
= a exp(-&2772) + — (l - exp(-A:2 772))
This equation shows that unless k2=k2, chemical assimilation
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. Specific growth rates and chemical excretion rates
for fish, however, are generally related to the fish's body size as
allometric power functions, i.e.,
where in general p2<0(Barber et al. 1988; Sijm et al. 1993,
1995; Sijm and van der Linde 1995). Therefore, if any
-------
significant growth occurs during the experiment, which is often
the case, one would not expect that k2 = k2 and consequently one
would not expect a = a. In point of fact one would generally
expect a > a. Importantly, this simple analysis is corroborated
by findings of Ram and Gillet (1993) who showed that
assimilation efficiencies for a variety of organochlorines by
oligochaetes decreased as chemical exposures progressed.
In terms of application the above fecal partitioning model is best
suited to circumstances where its equilibrium assumptions are
best met such as the case herein where the object is to predict the
dietary exchange of average individual of an explicit or implicit
population. A more kinetically based approach may be needed,
however, when trying to describe the toxicokinetic of individual
fish. See for example Nichols et al. (1998).
2.4. Modeling Chemical Biotransformation
BASS assumes that the metabolism of xenobiotic chemicals in
fish is a simple first order reaction of the chemical's aqueous
phase concentration, i.e.,
M = -pca(par>
(2-45)
where Mis the total amount of chemical metabolized (ug / ml),
p is the fish's biotransformation rate (I/day), and (Pa W) is the
volume of the volume of the fish's aqueous phase. If Eqs. (2-9)
and (2-45) are used to described the bioconcentration of a
chemical in fish during a water only exposure without growth,
then a fish's whole body concentration would be modeled as
dCf _ i dBf
dt W dt
S k
g g
W
(2-46)
Kf
— /V W ^A- ^ /V J \^f-
where ku, ke, and km are the fish's uptake rate, elimination rate,
and biotransformation rate, respectively, which are often
reported in the literature. In terms of quantitative structure
activity relationships (QSARs), one should note that this model
predicts that the whole body biotransformation rate km should be
inversely proportional to the fish's thermodynamic
bioconcentration factor Kf which in turn is proportional to the
chemical's Kow. This relationship, however, will also be
influenced by any Q S AR dependencies which the fish' s aqueous
phase biotransformation rate p might have. See de Wolf et al.
(1992) and de Bruijn et al. (1993).
2.5. Modeling Temperature Effects on
Physiological Rates
Because temperature effects a fish's feeding, assimilation,
respiration, and egestion, a general discussion of how
temperature modulates these processes is in order before
describing how BASS actually models fish growth. Although the
temperature dependence of physiological processes are often
described using an exponential response equation, e.g.,
*, =k0ef^-T°> (2-47)
where k0 and k1 are the process's reaction rates at temperatures
T0 and T,, respectively, such descriptions are generally valid only
within a range of the organism's thermal tolerances. In most
cases, the process's reaction rate increases exponentially with
increasing temperature up to a temperature T1 after which it
decreases. Moreover, in most cases the temperature at which a
process's rate is maximal is very close to the organism's upper
thermal limit. To address this problem, 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 a variety of fish bioenergetic models, BASS uses an
exponential-type formulation that is assumed to response
hyperbolically to increasing temperature. Importantly, such
algorithms can be easily parameterized.
Let P denote the rate of a physiological process and T1 denote
the temperature at which this rate is maximal. If this process
generally exhibits an exponential response to temperature
changes well below Th then
P = P0e*(T~T^ (2-48)
— = Jp (2-49}
dT ^ ^)
where P0 is the process's rate at an appropriate lower-end
reference temperature T0. To incorporate the adverse effects of
high temperatures on this process, the right hand side of Eq.(2-
49) can be multiplied by a hyperbolic temperature term that
approaches unity as temperature decreases below Th equals zero
at Th and becomes increasingly negative as temperatures
approach the fish's upper thermal tolerance limit TL = T2.
Modifying Eq.(2-49) in this fashion subsequently yields
whose solution is
(2-50)
(2-51)
Figure 1 displays the predicted temperature response of the
maximum feeding of a 50 g brown trout (Salmo truttd) based on
data reported by Elliott (1976b, Tables 2 and 9). For this figure
-------
it is assumed that T0 = (3.8 + 6.6)12, 1\ = 17.8, and T2=25. The
parameters P0 = 340 and y = 0.50 were then calibrated using the
results of a non-linear least squares analysis as a starting point.
For other applications of this model see Lassiter and Kearns
(1974) and Swartzman and Bentley (1979). Note that when T, =
T2, the Eq.(2-51) reduces to Eq.(2-48).
2.6. Modeling Growth of Fish
Although the preceding formulations of the processes that
determine the bioaccumulation of chemicals 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 + w
a L
= w + w,
d
PI =
P=l-P-P,
(2-52)
(2-53)
(2-54)
(2-55)
where Wa, Wd, W,, and W0 denotes the fish's aqueous, dry, lipid,
and non-lipid organic weights, respectively. Whereas Eqs.(2-52)
and (2-55) are simply assertions of mass conservation, Eqs. (2-
53) and (2-54) are purely statistical in nature. Although Eq. (2-
53) is assumed because simple power functions of this form
generally describe a wide variety of morphometric relationships
for most organisms, the appropriateness of Eq. (2-54) is based
on the results of numerous field and laboratory studies
(Eschmeyer and Phillips 1965; Brett etal. 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 etal. 1985;Kunisaki
et al. 1985; Morishita et al. 1987). These equations yield an
expression for a fish's live weigh that is a monotonically
increasing but non-linear function of the fish's dry weight.
BASS calculates a fish's realized feeding by first estimating its
maximum ad libitum consumption and then adjusting this
potential by the availability of appropriate prey as described in
the next section. Because a wide variety of models and methods
have been used to describe maximum feeding of fish, BASS is
coded to allow a user the option of using any one of four
different models to simulate the feeding of any particular
age/size class of fish. The first formulation that can be used is a
temperature-dependent power function
max Cl
(2-56)
where the temperatures T0, Tlt and T2 are specific to the fish's
feeding. A commonly used alternative to this model is the
process-based Rashevsky-Holling model that is defined by the
equations
max T \ max /
dl
— = C
dt
where (j) is the fish's ad libitum feeding rate (day"1) that is
generally a temperature-dependent power function of body
weight, Imai is the maximum amount of food (g(DW)) that the
fish's stomach/intestine can hold, /is the actual amount of food
(g(DW)) present in the intestine, and A and E again are the fish's
assimilation (g(ow)/day) and egestion (g(ow)/day) , respectively
(Rashevsky 1959; Holling 1966). The feeding rate (f) can be
estimated using the following equations
M(t) = J'<|>(/max-M(T))rfT (2-58)
dt
(2-59)
(2-60)
where M(t) denotes the total amount of food consumed during
the interval (0,/J (also see Dunbrack 1988). Although given a
fish's gut capacity Imax, satiation meal size Msat, and time tsat
required to ingest Msat one can readily calculate (f), one can also
simply assume thatM^, = 0.95 x Imai in which case
ln(0.05)
(2-61)
For planktivores BASS can also estimate a fish's maximum
ingestion using the clearance volume model
C
Qd = 9i Wq* e-
(2-62)
where T is the plankton standing stock (g(DW)/L), Qcl is the
planktivore's clearance volume (L/day), and the temperatures T0,
T{, and T2 are specific to the fish's filtering rate. The fourth and
final option is based on knowing the fish's proj ected growth and
routine respiratory demands. In particular, because assimilation,
egestion, specific dynamic action, and excretion can be
calculated as linear functions of feeding and routine respiration
as discussed below, it is then a straightforward matter to
calculate a fish's expected ingestion given its projected growth
and respiration. When a user elects this feeding option, BASS
assumes that the fish's specific growth rate y (day"1) is given by
10
-------
I T — T \ 3 2 l
Y = r-i IE = gi w* e«
-------
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 Grossman 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 trend for prey handling times to be inversely correlated
with body size (Werner 1974; Miller et al. 1992) would also
suggest that the larger predator could dispatch the prey and
resume its foraging more quickly than the smaller predator.
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 {...,dt,...} of the
different prey consumed by a cohort using dietary electivities,
i.e.,
(2-71)
where/ is the relative availability of the i-th prey with respect to
all other prey consumed by the cohort. These electivities are
calculated dynamically by BASS using dietary data specified by
the user and the relative availabilities of the cohort's prey
currently predicted by BASS. As described in the discussion of
BASS' sdiet command (see page 38), BASS allows a user to
specify a fish' s diet as either a set of fixed dietary frequencies
{..., dj,...}, a set of electivities {...,e.,...}, or a combination of
fixed frequencies and electivities {..., d.,..., e},...}. In order to
calculate the cohort's realized dietary composition, BASS first
converts all fixed dietary frequencies specified by the user into
their equivalent electivities using Eq. (2-71) and the current
relative availabilities {...,fj,...} of all potential prey. These
electivities are then combined with any user specified electivities
to form a set of unadjusted electivities {...,et,...} which in
general must then be converted into a consistent set of realized
electivities {..., et,...}. Using these realized electivities, BASS
finally calculates the cohort's realized dietary frequencies using
1
The important step in this computational process is the
conversion of the unadjusted electivities {...,e.,...} intoasetof
realized electivities {...,e.,...}. Although this conversion is
sometimes unnecessary, it is generally needed to insure that the
sum of the dietary frequencies {..., df,...} calculated by Eq.(2-
72) equals 1. One can verify that the condition that
guarantees S d. = 1 is
E
1 -
= 1
(2-73)
See Appendix C. When this condition is not satisfied for a set of
electivities {...,et,...} and relative prey availabilities {...,fj,...},
BASS transforms the given electivities using a linear
transformation that maps e. = -1 into et = -l and max(..., e.,...)
into an e. < 1. The general form of this transformation is
ei = a (et + 1) - 1
(2-74)
where 0
-------
are more efficient piscivores (see assumption 1 above). If more
than one age class of species /' can be consumed by the cohort,
the relative frequencies of these age classes su in the cohort's
diet are calculated using the cohort's prey size distribution. For
example, let Ltl and La denote the body lengths of two age
classes of species /' that are prey for the cohort. If Pv denotes the
probabilistic density
1
exr
- Lpreyf
2 TZ O
(2-77)
the relative frequencies of these two age classes in the cohort's
diet are calculated to be Sj^d^Pjj/iPjj+P^)) and
si2 = di(Pi2/(Pil+Pi2)). If only one age class of a species is
vulnerable to the cohort, then sfj = di.
If during the calculation of 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 {...,ej*,...} in a
manner to simulate prey switching. These reassignments as
based on the following assumption:
AS SUMPTION 3. Whenforage 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 andMagnan 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 to then
switch to planktivory (Werner and Gilliam 1984; Magnan 1988;
Bergmann 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 0 regardless of its previous value. BASS also
reassigns any other electivity which does not equal -1, to 0.
After BASS has calculated a cohort's dietary composition, it then
assigns the realized feeding rate of cohort as
F = max
E
ABj,F^
(2-78)
where Fmca is the cohort's maximum or desired individual
ingestion, TV is the cohort's population size, and ABj is the
biomass 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 cohort and non-
fish biotic resource.
2.8. Modeling Non Predatory Mortalities and
Recruitment
Numerous studies (Damuth 1981; Peters and Raelson 1984;
Juanes 1986;RobinsonandRedford 1986;BoudreauandDickie
1989;GordoaandDuarte 1992; Randall etal. 1995 Dunham and
Vinyard 1997; Steingrimsson and Grant 1999) have shown that
the population densities of vertebrates are generally correlated
with their mean body size. In particular,
N = a W~b (2-79)
where N is the population density (inds/area) of the species or
cohort and Wis the mean body weight of that species or cohort.
Although an interspecific analysis of data for a variety of fish by
Randall et al. (1995) suggests a mean exponent close to unity,
data reported by Boudreau and Dickie (1989) and Gordoa and
Duarte (1992) for individual fish species suggest an average
exponent of approximately 0.75. An expression for a species'
total mortality rate can be obtained by differentiating Eq. (2-79)
as follows
— = -b a W \W —J = -b N y (2-80)
where y is the species specific growth rate. Based on this
equation, one could therefore conclude that a species' total
mortality rate is simply \a = b y . Readers interested in detailed
discussions concerning the underlying process-based
interpretation and general applicability of this result should
consult Peterson and Wroblewski (1984) and McGurk (1993,
1999). Because BASS assumes that the specific growth rates of
a species are allometric functions of its body sizes, it follows
that
V = bylWy> (2-81)
Also see Lorenzen (1996). Because this equation actually
includes both a species' predatory and non-predatory mortality,
BASS assumes that a species' non-predatory mortality rate is
simply some fraction 5 of u. In general, this fraction will be
small for forage fish and large for predatory species. During the
course of the simulation BASS calculates the daily non-predatory
mortality each cohort using Eq.(2-81) parameterized with the
cohort's current body weight.
BASS estimates a species' recruitment by assuming that each
species turns over a fixed percentage of its potential spawning
biomass into new young-of-year (YOY). This percentage is
referred to as the species' reproductive biomass investment (rbi).
The species' spawningbiomass is defined to be the total biomass
of all cohorts whose body length is are greater than or equal to
a specified minimum value (tl_rO) marking the species' sexual
maturation. When reproduction is simulated, the body weight of
each sexually mature cohort is decremented by its rbi and the
13
-------
total number of YOY which are recruited into the population as
a new cohort is estimated by simply dividing the species'
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 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 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 etal. 1973;Haydonetal. 1977; Janoffetal. 1981;Pringle
et al. 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; Franks and Lieb 1978, 1982, 1984; Richards et al.
1978; 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" which 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 may 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 (Fergusion
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 noted above, 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, even though most pesticides possess a specific
mode of action is during acute exposures, the joint action of
pesticides is often additive and resembles narcosis (Hermanutz
etal. 1985; Matthiessen etal. 1988; Bailey etal. 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 would 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 it's
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
A = y M
a * a a
C
103 MW
(2-82)
ca = 2.
Kf
where Aa is the chemical' s aqueous activity^ is the chemical' s
aqueous activity coefficient (L/mol) which is 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 causes 50% mortality in an
exposed population. These lethal thresholds are calculated using
the above formulae with user-specified LC50' substituted for Ca.
These calculations are based on two important assumptions. The
first assumption is that the exposure time associated with the
specified LCsg 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 LCsg satisfy these two assumptions.
See Lassiter and Hallam (1990) for a comprehensive model
based analysis of these issues.
Three points should be mentioned regarding the above approach
to modeling ecotoxicological effects. Firstly, it should be noted
that for narcotic chemicals this approach is analogous to the
14
-------
toxic unit approach for evaluating the toxicity of mixtures
(Calamari and Alabaster 1980; Konemann 1981a, 1981b;
Hermens and Leeuwangh 1982; Hermens et al. 1984a, 1984b,
1985a, 1985b, 1985c; Broderius andKahl 1985; Dawson 1994;
Peterson 1994). Secondly, 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 (1984a) 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 at.
1984a, 1985a). Also see Roex et al. (2000).
15
-------
-8
?
1
rt
o
IS}
j3
W
bolism of chemical Owg /s)
+3
8
llation density (inds/ha)
^
a
^
°\
^
i^
S"
^^
;tz number (dimensionless) =
re
r^
,~.
~^-
•«
^
^
II
wood number (dimensionless
^H
-.
•3
predatory mortality (inds-ha"1
i
S
c/}
i-^
13
o
a
y
s
2
U*l
^
o
43 T3
§ >^
-
§
"
H
3
bo t
3.
r-j -M
'Tj Cy
°^
s ^
•S a
a s
2 =3
k>
1^
^i
~Sf^i
,-s ^
C -B
II
3.
•i£
fraction of
1> CH
C C C
sccccccs
ooooooooo
.3222222222
1
c c c c c c
^ Q) Q) Q) Q) Q)
8 8 8 8 8 8
888888
re re re re re c^
o o o o o o
1> 1> 1> 1> 1> 1>
000000
^H H -s
° ^ ° i
llll
g bJ) O
il^l
g^ugl
O ^ g S
-------
Figure 1. Application of Eq.(2-51) to describe the temperature dependence of the maximum daily consumption of brown trout (Salmo
trutta) based on Elliott (1976b, Tables 2 and 9).
5275.00
4220.00 -
•§ 3165.00
O,
S
3
C/3
C
g 2110.00
£
3
* 1055.00
S3
0.00
2.00
6.60
11.20
15.80
20.40
25.00
Celsius
17
-------
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 of parameterizing the above equations are now
discussed.
3.1. Parameterizing Kf
Superficially, estimation of a fish's thermodynamic
bioconcentration factor Kfvia Eq. (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
Eq.(2-53)), it is a straightforward matter to calculate the fish's
aqueous fraction using Eq. (2-54). Having done so, one can then
immediately calculate the fish's non-lipid organic fraction since
the sum of Pa, P,, and P0 must be unity (i.e., Eq. (2-55)).
For an organic chemical the partition coefficients Kfand K0 can
be estimated using the chemical's octanol/water partition
coefficient Km. Although triglycerides are the principal storage
lipid of fish and it would seem reasonable to estimate K, using
a triglyceride/water partition coefficient, BASS assumes that K,
identically equals K^. To estimate K0 BASS assumes that a fish's
non-lipid organic matter is equivalent to organic carbon and uses
Karickhoffs (1981) regression between organic carbon/water
partition coefficients (Koc), and Km to estimate this parameter.
Specifically,
Ko =Koc = OAUKow (3_l)
For metals or metallo-organic compounds such as
methylmercury the chemical's lipid partition coefficient K, can
again be assumed to equal its octanol/water partition coefficient
K^. A metal's distribution coefficient into non-lipid organic
matter, however, cannot be estimated using the Koc relationship
given above. For example, whereas the Kow of methylmercury at
physiological pH's is on the order of 0.4 (Major et al. 1991), its
distribution coefficient into environmental organic matter is on
the order of 104 -106 (Benoit et al. 1999a, 1999b). O'Loughlin
et al. (2000) report similar discrepancies for organotin
compounds. In general distribution coefficients for metals into
fecal matter should be assigned values comparable to those used
to model the environmental fate and transport of metals whereas
metal distribution coefficients for metals into the non-lipid
organic matter of fish should be assigned values up to an order
of magnitude 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,
mean interlamellar distance, and mean lamellar length must be
specified. In general, each of these morphological variables is
dependent on the fish' s body size according to the allometric
functions,
Sg = SlW°* (3-2)
d = dlWd2 (3-3)
1 = 1^ (3-4)
Although many authors have reported allometric coefficients and
exponents fortotal gill surface areas, parameters forthe latter are
seldom available. Parameters for fish' s mean interlamellar
distance, however, canbe estimated if the allometric functionfor
the density of lamellae on the gill filaments, p (number of
lamellae per mm of gill filament), i.e.,
TT7 P2 /O C\
P = Pi w (-'•->)
is known. Fortunately, lamellar densities, like total gill areas, are
generally available in the literature. See Tables 2-4. BASS
estimates d1 and d2 from p, and p2 using the inter-specific re-
gression (n=28, r=-0.92)
d = O.llSp-119 (3-6)
To overcome the scarcity of published morphometric
relationships for lamellar lengths (see Table 5), BASS uses the
default inter-specific regression (n=90, r=0.92)
/ = 0.0188 W0294 (3-7)
Both of the preceding regressions are functional regressions
rather than simple linear regressions (Rayner 1985; Jensen
1986); the data used for their calculation were drawn 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 esti-
mates a chemical's aqueous diffusivity (cnf/s), using the
empirical relationship,
D = 2.101xlO-7Ti"14v^0589
(3-8)
where v (cmVmol) is the chemical's molar volume (Hayduk and
Laudie 1974). The diffusivity of chemicals through the gill
membrane which is needed to estimate the membrane's
permeability km is then assumed to equal one half of 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 water-blood barrier.
Based on the studies summarized in Table 6, BASS assumes a
default water-blood barrier thickness of approximately 0.0029
cm for all fish species and then calculates km as the ratio of the
chemical's membrane diffusivity to the thickness of the gill's
water-blood barrier. These assumptions imply that
18
-------
= 0.0116"^
(3-9)
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 Eq.(2-12)).
Perfusion rates (ml/hr) are estimated using
Qp = (0.23 T - 0.78) 1.862 Wc
(3-10)
as the default for all species. Although this expression, in units
of L/kg/kr, 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 Eq.(2-28) are interpolated internally by BASS from
matrices of tabulated eigenvalues and mixing cup coefficients
which encompass the range of Sherwood numbers (i.e.,
KNsh
-------
Table 2. Summary of allometric coefficients and exponents for gill area and lamellar density for freshwater bony fishes and agnatha.
species
Acipenser transmontanus
Botia dario
Botia lohachata
Catostomus commersoni
Cirrhinus mrigala
Comephorus dyoowski
Cottocomephorus grewingki
Cottocomephoms inermis
Cottus gobio
Cottus gobio
Ctenopharyngodon idella
Cyprinus carpio
Esox lucius
Fundulus chrysotus
Gambusia affinis
Glossogobius giuris
Hoplias lacerdae
Hoplias malabaricus
Hoplias malabaricus
Ictalurus nebulosus
Ictalums punctatus
L ampetra fluviatilis
Lampetra planeri
Leiopotherapon unicolor
Lepomis macrochims
Macrognathus aculeatum
Microptems dolomieui
Mystus cavasius
Oncorhynchus mykiss
Oncorhynchus mykiss
Oncorhynchus tshawytscha
Si
3.50
10.5
9.13
11.2
11.8
2.15
6.56
7.42
7.20
1.35
9.44
8.46
0.274
-
2.47
12.6
4.92
1.26
0.731
4.98
-
24.1
23.9
4.68
-
2.17
7.36
6.17
1.84
3.15
7.13
S2
0.849
0.716
0.700
0.587
0.816
0.675
0.91
0.918
0.849
1.29
0.774
0.794
1.24
1.18
0.842
0.516
0.81
1.14
1.25
0.728
-
1.03
0.689
1.04
-
0.733
0.819
0.915
1.13
0.932
0.922
f,
15.3
41.0
39.0
25.2
63.2
-
24.6
22.6
-
21.8
33.0
32.2
78.6
-
-
-
29.0
35.0
29.5
15.9
10.2
31.0
28.3
20.6
20.1
41.9
30.0
40.2
-
27.5
f2
-0.0475
-0.0460
-0.0055
-0.109
-0.129
~
-0.150
-0.110
~
-0.126
-0.0513
-0.0787
-0.222
~
~
~
-0.06
-0.090
-0.0600
-0.0917
-0.056
-0.123
-0.117
-0.0870
-0.098
-0.0690
-0.0615
-0.0970
~
-0.0639
source
Burggrenetal. (1979)
Singh etal. (1988)
Sharmaetal. (1982)
Saunders (1962)
Roy and Munshi (1986)
Jakubowski (1993)
Jakubowskietal. (1995)
Jakubowski et al. (1995)
Jakubowski etal. (1995)
Liszka (1969) and Starmach (1971)
Jakubowski (1982)
Oikawa and Itazawa (1985)
de Jager et al. (1977) and
Burnside (1976)
Murphy and Murphy (1971)
Singh and Munshi (1985)
Fernandes et al. (1994)
Fernandes et al. (1994)
Fernandes and Rantin (1985)
Saunders (1962)
Barber (2000)
Lewis and Potter (1976)
Lewis and Potter (1976)
Gehrke (1987)
Barber (2000)
Ojha and Munshi (1974)
Price (1931)
Ojha etal. (1985)
Niimi and Morgan (1980)
Hughes (1984)
Romough and Moroz (1990)
20
-------
Orechromis alcalicus
Orechromis niloticus
Oryzias latipes
Piaractus mesopotamicus
Plagioscion squamosissimus
Pomoxis nigromaculatus
Prochilodus scrofa
Stizostedion vitreum
Tinea tinea
Tinea tinea
11.1 0.789 38.4 -0.143 Hughes (1995)
6.35 0.777 32.9 -0.0545 Kisia and Hughes (1992)
4.65 0.446 43.5 0.0 Umezawa and Watanabe (1973)
5.65 0.769 40.2 -0.033 Seven etal. (1997)
12.0 0.70 37.0 -0.07 Mazonet al. (1998)
18.4 -.074 Barber (2000)
16.2 0.72 43.0 -0.12 Mazonet al. (1998)
0.796 1.13 - - Niimi and Morgan (1980)
28.5 0.522 20.3 0.0160 Hughes (1972)
0.698 25.5 -0.0300 Hughes (1972)
21
-------
Table 3. Summary of allometric coefficients and exponents for gill area and lamellar density for cartilaginous and marine boney
fishes.
species
Acanthopagrus australis
Alopias vulpinus
Blennius pholis
Carcharodon carcharias
Carcharhinus obscurus
Carcharhinus plumbeus
Coryphaena hippurus
Fundulus similis
Isurus oxyrinchus
Katsuwonus pelamis
Morone saxatilis
Opsanus tau
Platichthys flesus
Prionace glauca
Scomber scombrus
Scyliorhinus canicula
Scyliorhinus stellaris
Seriola quinqueradiata
Thunnus thynnus
Torpedo marmorata
Si
2.40
2512.
7.63
42.7
6.17
24.5
52.1
-
57.5
52.2
-
5.61
6.36
5.50
4.24
2.62
6.21
22.9
24.4
1.17
S2
0.788
0.410
0.849
0.770
0.880
0.740
0.713
0.850
0.740
0.850
-
0.790
0.824
0.880
0.997
0.961
0.779
0.686
0.901
0.937
ti
-
229.
28.3
27.5
33.8
23.4
33.8
-
50.0
59.0
17.0
16.0
-
12.9
27.1
17.1
30.3
38.5
63.2
34.2
12
-
-0.340
-0.139
-0.150
-0.160
-0.130
-0.0360
~
-0.200
-0.0759
-0.069
-0.0750
~
-0.0900
0.0230
-0.0710
-0.167
-0.0419
-0.0938
-0.167
source
Roubal (1987)
Emery and Szczepanski (1986)
Milton (1971)
Emery and Szczepanski (1986)
Emery and Szczepanski (1986)
Emery and Szczepanski (1986)
Hughes (1972)
Burnside (1976)
Emery and Szczepanski (1986)
Muir and Hughes (1969)
Barber (2000)
Hughes and Gray (1972)
Hughes and Al-Kadhomiy (1986)
Emery and Szczepanski (1986)
Hughes (1972)
Hughes (1972)
Hughes etal. (1986)
Kobayashietal. (1988)
Muir and Hughes (1969)
Hughes (1978)
22
-------
Table 4. Summary of allometric coefficients and exponents for gill area and lamellar density for air-breathing fishes.
species
Anabas testudineus
Boleophthalmus boddaerti
Boleophthalmus boddaerti
Boleophthalmus boddaerti
Channa punctata
Clarias batrachus
Clarias mossambicus
Cobitis taenia
Hoplerythrinus unitaeniatus
Hypostomus plecostomus
Lepidocephalichthys guntea
Lepisosteus oculatus
Lepisosteus osseus
Lepisosteus platostomus
Noemacheilus barbatulus
Periophthalmodon schlosseri
Periophthalmodon schlosseri
Periophthalmus chrysospilos
Rhinelepis strigosa
Saccobranchus fossilis
Si
5.56
2.81
0.927
6.79
4.70
2.28
0.958
4.67
5.99
4.36
4.94
3.35
4.77
3.01
3.60
3.00
1.00
0.976
6.25
1.86
S2
0.615
0.709
1.05
0.481
0.592
0.781
0.971
0.864
0.66
0.666
0.745
0.753
0.699
0.793
0.577
0.934
0.931
0.958
0.757
0.746
f,
36.5
24.6
26.6
23.1
36.0
25.4
30.7
45.5
48.0
17.3
45.0
18.1
20.9
15.3
36.4
27.0
47.9
30.2
12.3
31.6
f2
-0.152
-0.0830
-0.229
-0.0307
-0.138
-0.0830
0.0909
0.0
-0.16
0.081
-0.221
-0.0476
-0.0691
-0.0236
0.0
-0.0484
-0.0518
-0.237
0.020
-0.0950
source
Hughes etal. (1973)
Nivaetal. (1981)
Hughes and Al-Kadhomiy (1986)
Low etal. (1990)
Hakim etal. (1978)
Munshietal. (1980)
Maina and Maloiy (1986)
Robotham(1978)
Fernandes et al. (1994)
Perna and Fernandes (1996)
Singh etal. (1981)
Landolt and Hill (1975)
Landolt and Hill (1975)
Landolt and Hill (1975)
Robotham(1978)
Yadavetal. (1990)
Low etal. (1990)
Low etal. (1990)
Santos etal. (1994)
Hughes (1972)
23
-------
Table 5. Summary of coefficients and exponents for lamellar lengths.
species lj 12
Hoplias lacerdae 0.012 0.23
Hoplias malabaricus 0.006 0.36
Hoplerythrinus unitaeniatus 0.014 0.22
Ictalurus punctatus 0.00465 0.265
Lepomis macrochirus 0.00364 0.234
Morone saxatilis 0.00474 0.202
Piaractus mesopotamicus 0.0069 0.223
Pomoxis nigromaculatus 0.00255 0.257
Rhinelepis strigosa 0.0422 0.231
source
Fernandes et al (1994)
Fernandes et al (1994)
Fernandes et al. (1994)
Barber (2000)
Barber (2000)
Barber (2000)
Severietal. (1997)
Barber (2000)
Santos etal. (1994)
24
-------
Table 6. Summary of studies reporting water-blood barrier thickness for freshwater and marine fishes.
source
Dube and Munshi (1974)
Hughes (1972)
Hughes and Morgan (1973)
Hughes and Umezawa (1983)
Hughes etal. (1986)
Kobayashietal. (1988)
Munshi et al. (1980)
Ojha and Munshi (1974, 1976)
Ojhaetal. (1982)
Ojha etal. (1985)
Piiperetal. (1986)
Roy and Munshi (1987)
Sharmaetal. (1982)
Singh and Munshi (1985)
Singh etal. (1981)
Singh etal. (1988)
Steen and Berg (1966)
Stevens (1992)
Tuuralaetal. (1998)
species
Anabas testudineus
Tinea tinea
various species
Phrynelox tridens, Seriola quinqueradiata
Scyliorhinus stellaris
Seriola quinqueradiata
Glorias batrachus
Macrognathus aculeatum
Garra lamta
Mystus cavasius
Scyliorhinus stellaris
Cirrhinus mrigala
Botia lohachata
Glossogobius giuris
Lepidocephalichthys guntea
Botia dario
various species
Sciaenops ocellatus
Anguilla anguilla
25
-------
Table 7. Sources of bioenergetic and growth for selected fish species.
species
Alosa pseudoharengus
Ambloplites rupestris
Ameiurus sp.
Ctenopharyngodon idella
Cyprinodon sp.
Cyprinus carpio
Dorosoma cepedianum
Esox lucius
Gambusia affinis
Lepomis sp.
Micropterus salmoides
Micropterus dolomieu
Morone saxatilis
Oncorhynchus mykiss
Oncorhynchus nerka
Oncorhynchus tshawytscha
Osmerus mordax
Perca flavescens
Phoxinus phoxinus
Pimephales promelas
Ptychocheilus oregonensis
Pungitius pungitius
Pylodictis olivaris
Salmo trutta
Salvelinus namaycush
Stizostedion canadense
Stizostedion vitreum vitreum
source
Stewart and Binkowski (1986)
Roell and Orth (1993)
Glass (1969), Campbell and Branson (1978)
Wiley and Wike (1986)
Nordlie et al. (1991), Jordan et al. (1993)
Glass (1969), Oikawa and Itazawa (1984), Garcia and Adelman (1985)
Pierce et al. (1981), Drenner et al. (1982)
Diana (1982a, 1982b), Salam and Davies (1994)
Murphy and Murphy (1971), Shakuntala and Reddy (1977), Mitz and Newman (1989)
Wohlschlag and Juliano (1959), O'Hara (1968), Pierce and Wissing (1974), El-Shamy (1976),
Evans (1984)
Beamish (1970, 1974), Niimi and Beamish (1974), Tandler and Beamish (1981)
Roell and Orth (1993)
Hartman and Brandt (1995a)
Kutty (1968), Rao (1968), Staples and Nomura (1976), Muller-Feuga et al. (1978), Grove et al.
(1978), Rand etal. (1993)
Brett (1971), Beauchamp et al. (1989), Stewart and Ibarra (1991)
Stewart and Ibarra (1991)
Lantry and Stewart (1993)
Norstrom et al. (1976), Kitchell et al. (1977), Post (1990), Rose et al. (1999), Schaeffer et al.
(1999)
Wootton et al. (1980), Cui and Wootton (1988)
Wares and Igram (1979), Duffy (1998)
Petersen and Ward (1999)
Cameron etal. (1973)
Roell and Orth (1993)
Glass (1969), Elliott (1972, 1975a, 1975b, 1976b)
Stewart et al. (1983), Thomann and Connolly (1984)
Minton and McLean (1982)
Kitchell et al. (1977), Tarby (1980), Madon and Culver (1993), Rose et al. (1999)
26
-------
Figure 2. First eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
27
-------
Figure 3. Second eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
28
-------
Figure 4. First bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
29
-------
Figure 5. Second bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
30
-------
4. BASS User Guide
Although BASS versions 1.0 and 1.1 were written in Fortran 77,
BASS version 2.0 and higher is coded in Fortran 95. The model
enables users to simulate the population and bioaccumulation
dynamics of age-structured fish communities using a temporal
and spatial scale of resolution of a day and a hectare,
respectively. BASS currently ignores the migration of fish into
and out of this simulated hectare. The duration of any species'
age class can be specified as either a month or a year. This
flexibility enables users to simulate small, short-lived species
such as daces, live bearers, and minnows 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 each of these foraging classes as a combination
of benthos, incidental terrestrial insects, periphyton,
phytoplankton, zooplankton, and/or other fish species including
its own. Presently the standing stocks of all nonfish prey are
handled only as external forcing functions rather than as
simulated state variables.
Although BASS was developed to simulate the bioaccumulation
of chemical pollutants within a community or ecosystemcontext,
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.
• Tabulated annual summaries for the 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.
BASS version 2.1 is still a beta test version. 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. Summary of New Features Available in BASS
version 2.1
The following features that were unavailable in BASS versions
1.x are now active:
• There are now no restrictions to the number of
chemicals that can be simulated.
• There are now no restrictions to the number of fish
species that can be simulated.
• There are now no restrictions to the number of cohorts
that fish species may have.
• There are now no restrictions to the number of feeding
classes that fish species may have (see the command /
FEEDING_OPTIONS).
• There are now no restrictions to the number of foraging
classes that fish species may have (see the command /
ECOLOGICAL_PARAMETERS).
• Improved 3-dimensional and 2-dimensional plots of
selected state variables are available using the software
package DISLIN.
BASS'S output tabulations have also been reformatted, and
several input commands have been given new syntax.
New features of BASS version 2.1 that were unavailable in
version 2.0 include:
31
-------
• The ability to integrate BASS'S differential equations
using either a simple Euler method or a fifth-order
Runge-Kutta method with adaptive step sizing. In BASS
version 2.1 the default method of integration is the
Runge-Kutta method.
• The ability to simulate biotransformation of chemicals
with or without daughter products.
Regarding BASS'S Euler and Runge-Kutta integrators, the user
should realize that these methods offer the user two distinctly
different options with respect to software performance and
execution. Although Euler methods often allow for fast model
execution, these methods cannot assess the accuracy of their
integration. Runge-Kutta methods, on the other hand, can
monitor the accuracy of their integration but at the cost of
increased execution time. Fortunately, however, this additional
computational burden can often be significantly reduced by
employing adaptive step sizing. BASS'S Runge-Kutta integrator
is patterned on the fifth-order Cash-Karp Runge-Kutta algorithm
outlined by Press et. al. (1992).
4.2. Input File Structure
The general structure of a BASS' s input file is as follows
/ 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 (e.g., commandn ) 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. Argument may be an integer (e.g., 7), a real number (e.g., 0,
3.7e-2, 1.3, etc.), or a character string. If a command allows
multiple arguments, each argument must be separated by a
semicolon. Commands may be continued by appending an
ampersand (&) to the line, e.g., the following two commands
lines are equivalent
/ command arg^ arg2; arg3; &
arg4; arg5; arg6
/ command
^ arg2; arg3; arg4; arg5; arg6
Because each record is transliterated to lower case before being
decoded, the case of the input file is not significant. Likewise,
spacing within a command is not significant because consecutive
blanks or tabs are collapse into a single blank. The maximum
length of a command line, including continuation lines, is 1024
characters.
An exclamation mark (!) in the first column of a line identifies
the line as a comment. An exclamation mark can also be used
anywhere in the record field to start an end-of-line comment, i.e.,
the remainder of the line, including the exclamation mark, will
be ignored.
Commands are broadly classified into three categories:
simulation control parameters, chemical parameters, and fish
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, nonfish standing
stocks, and output options. Chemical parameters specify not only
the chemical's physico-chemical properties (e.g., the chemical's
molecular weight, molecular volume, n-octanol/water partition
coefficient, etc.) but also exposure concentrations in the
environment (i.e., in water, sediment, benthos, insects, etc.). Fish
parameters identify the fish's taxonomy (i.e., genus and species),
feeding and metabolic demands, dietary composition, predator-
prey relationships, gill morphometrics, body composition, initial
weight, initial whole body concentrations for each chemical, and
initial population sizes. In the following sections, these
commands are described alphabetically by class.
The last command in any BASS input file must be /END. This
command terminates program input and any text/commands
following it will be ignored. BASS checks the syntactical
accuracy of each input command as it is read. If no syntax errors
are encountered, B AS s then checks the specified input parameters
for completeness and internal inconsistency.
To facilitate easier data management when analyzing multiple
simulations of similar scenarios, a user can also specify blocks
of BASS input commands using include statements of the form
# include 'filename'
For example, a BASS input file that has all of its chemical and
fish data stored in separate files might appear as follows
! file: example file with include statements
I
/simulation_control
/ command argument, simulation control command 1
/ command argument, simulation control command 2
/ command argument, simulation control command 3
# include 'data^for_chemical_r
32
-------
# include 'data^for_chemical_2'
# include 'datajbrjish_r
# include 'datajbrjish_2'
# include 'data^for^fish_3'
# include 'data^for^fish_4'
/end
Users are strongly recommended to make use of BASS'S include
file capabilities. A recommended file and subdirectory structure
for using and managing BASS include files is discussed in detail
in Section 4.4.
4.2.1. Simulation Control Commands
These commands establish the length of the simulation and
BASS'S integration step, the ambient water temperature, the
availability of benthos, incidental terrestrial insects and
plankton, the community's water level, and various output
options. These data are specified by the following block of
twelve commands
/ SIMULATION_CONTROL
/HEADER
/ LENGTH_OF_SIMULATION
/ MONTHJTO
/ NSTEPS
/TEMPERATURE
/ WATER_LEVEL
/ BIOTA
/ ANNUAL_OUTPUTS
/ ANNUAL_PLOTS
/ SUMMARY_PLOTS
/FGETS
string
string
string
integer
string
string
stringy ... \stringn
integer
stringy ...; stringn
stringy ...;stringn
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, however, is not
significant. The use of these commands will now be described
in alphabetical order. See Appendix D for an example of the use
of these commands.
• /ANNUAL_OUTPUTS integer
This command specifies the time interval, in years, between
BASS'S annual tabulated and plotted outputs. This number must
be an non-negative integer. BASS assumes a default value of zero
which signifies that no annual outputs will be generated. This
command is optional.
• /ANNUAL_PLOTS string,;... ; stringn
This command specifies the variables whose annual dynamics
will be plotted for the years specified by command
/ANNUAL_OUTPUTS. The options may be specified one per card,
or all in one card, separated by semicolons. Valid options are:
afish(sfri«g) to generate 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;
\)af(string) to generate plots of each species' bioaccumulation
factor (i.e., the ratio Cf/ CJ for each chemical as a function of
time (day of year) and the species' age, length, or weight class;
bmf'(string) to generate 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(string) to generate 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 (string) to generate plots of each species' population density
(ind./ha) as a function of time (day of year) and the species' age,
length, or weight class;
wt(string) to generate plots of each species' whole body weight
(g(Fw)/fish) as a function of time (day of year) and the species'
age, length, or weight class;
where string 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 string,;... ; stringn
This command specifies nonfish standing stocks that are prey for
the simulated fish assemblage. Valid options are:
benthos|jw«&s] = string to generate benthic standing stocks
according to the function string whose units yunits must be
dimensionally equivalent to g(DW)/m2.
insects \yunits] = string to generate incidental terrestrial insect
standing stocks according to the function string whose units
yunits must be dimensionally equivalent to g(DW)/nf.
periphyton|jwmYs] = string to generate periphyton standing
stocks according to the function string whose units yunits must
be dimensionally equivalent to g(DW)/m2.
phytoplankton|jMwiYs] = string to generate phytoplankton
standing stocks according to the function string whose units
33
-------
yunits must be dimensionally equivalent to g(DW)/L.
zooplankton|jwwiYs] = string to generate zooplankton standing
stocks according to the function string whose units yunits must
be dimensionally equivalent to g(DW)/L.
Valid specifications for these biotic resource functions are
function_name\yunits] = a to generate the a constant prey
standing stock of a (yunits) for the simulation.
function_name\yunits] = a + P*sin(oo +
-------
pop(string) to generate plots of each species' population density
(ind./ha) as a function of time (day of simulation) and the
species' age, length, or weight class;
where string 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 String
The command specifies the ambient' s water temperature. Valid
options for this command are:
temp [celsius] = a to generate a constant ambient water tem-
perature for the simulation.
temp [celsius] = a, + p*sin(oo +
-------
sediments or via the ingestionof 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 which include particle-bound chemical. If
multiple options are selected, each option must be separated by
a semicolon. Valid options are:
cbnths|jM«&s] = string to generate potential dietary exposures
to fish via benthic organisms according to the function string.
cinsct|jM«&s] = stringto generate potential dietary exposures to
fish via incidental terrestrial insects according to the function
string.
cphytn|jww&s] = stringto generate potential dietary exposures
to fish via periphyton according to the function string.
cpplnk|jwmYs] = string to generate potential dietary exposures
to fish via phytoplankton according to the function string.
csdmnt|jwmYs] = string to generate sediment exposure concen-
trations according to the function string.
cwater|jwmYs] = string to generate aqueous exposure concen-
trations according to the function string.
czplnk\yunits] = string to generate potential dietary exposures
to fish via zooplankton according to the function string.
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 below. This assignment can be
changed using the command /MONTH_TO.
Valid expressions for dietary exposures viabenthos, periphyton,
phytoplankton, or zooplankton and for benthic sediments are:
function_name\yunits] = a to generate a constant concentration
of toxicant in benthos, periphyton, phytoplankton, sediment, or
zooplankton.
function_name\yunits\ = a*cwater[xunits] to generate
chemical concentrations in benthos, periphyton, 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 the
product of the component's dry organic fraction and the
chemical's Kow.
function_name\yunits\ = file(filename) to read and interpolate
the concentration of toxicant in benthos, periphyton,
phytoplankton, sediment, or zooplankton from the file filename.
Valid expressions for insect dietary exposures are:
cinsct\yunits ]= a to generate a constant concentrations of the
toxicant in incidental terrestrial insects.
cinsct|jM«&s ] = file(ftlename) to read and interpolate the
concentration of the toxicant in incidental terrestrial insects from
the file filename.
Valid expressions for direct aqueous exposures are:
cwater \yunits] = a to generate a constant aqueous concentration
for the chemical of concern.
cwater\yunits] = a*csdmnt[xunits] to generate aqueous
exposure concentrations as a chemical equilibrium with the
benthic sediments. If this equilibrium is assumed to be thermo-
dynamic, then the coefficient a generally is assumed to equal the
product of the sediment's organic fraction and the chemical's Koc.
cw&ter\yunits] = cc+p*exp(y*t[.v;wmYs]) to generate an
exponential dissolved chemical water concentration where a and
P have units ofyunits and y has units of l/xunits. This option
can be used to simulate a chemical spill or one time application
of a pesticide.
cw&ter\yunits] = a.+fi*sin((d+fy*t[xunits]) to generate a
sinusoidal dissolved chemical water concentrations where a is
the mean dissolved chemical water concentration (yunits) (over
one period), P is the amplitude (yunits), GO is its phase angle
(radians), and
-------
This optional command specifies species specific LC50's for the
chemicals of concern. Valid string options are:
LC50 [units] (fish_name) = a
LC50 [units] (fish_name) = a*Kow[-]Ay
where Kow[-] is the chemical's n-octanol/water partition
coefficient and fishjiame is the common name of the fish
species to be simulated. BASS converts these 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 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 simple but useful monitor of the total
chemical status of the fish.
• /LOG_AC real number
This command specifies the Iog10 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 Iog10 of metal's binding constant for
non-lipid organic matter (see Eq.(2-6)). This parameter is input
only for metals and organometals.
• /LOG_KB2 real number
This command specifies the Iog10 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 organometals.
• /LOG_P real number
This command specifies the chemical's Iog10 Kow, where Km is
the n-octanol/water partition coefficient. /LOG_P must be
specified for all organic chemicals.
• /MELTING POINT real number
The 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 string, ; ... ; stringn
This optional command specifies species specific rates of
biotransformation for the chemical of concern. Valid strings
options are:
BT [units] (fish_name, chemical_name) = a
BT [units] (fish_name, chemical_name) = a*Kow[-]Ay
BT [units] (fish_name, none) = a
BT [units] (fish_name, none) = a*Kow[-]Ay
where BT is the whole body referenced biotransformation rate
km in Eq.(2-46); Kow[-] is the chemical's n-octanol/water
partition coefficient; w&fishjiame is the common name of the
fish species that can metabolize the chemical of concern, and
chemical jiame is the name of the daughter product generatedby
the metabolism of chemical. 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
physical chemical properties of the identified by-product in the
same way that the physical chemicals properties of the parent
compound are specified.
• /MOLAR_VOLUME real number
The command specifies the chemical's molecular volume
(cmVmol) which is used to calculate the chemical's aqueous
diffusivity, i.e.,
2-101xl(r7
D -
1.4 V0.589
where D is the toxicant's aqueous diffusivity (cmVsec), t| is the
viscosity of water (poise), and v is the molecular volume of the
chemical (cnf/mol) (HaydukandLaudie 1974). The viscosity of
water over its entire liquid range is represented with less than
1% error by
f %,] 1.37(7--20) + 8.36xl(T4(r-20)2 , .
Sw (~^) 109^ (^'2)
where % is the viscosity (centipoise) at temperature T (Celsius),
and r|20 is the viscosity of water at 20 °C (1.002 centipoise)
(Atkins 1978).
37
-------
string
string
string,;
string,;
s
TERS
TERS
:ERS
...;stringn
...; string,,
string,; ..
string,; ..
string,; ..
string,; ..
.;stringn
.;stringn
.;stringn
:,stringn
• /MOLAR_WEIGHT real number
The command specifies the chemical's molecular weight (g/mol).
4.2.3. Fish Input Commands
Model parameters for each fish species of interest are specified
by a block of ten commands, i.e.,
/COMMON_NAME String
/SPECIES string
/AGE_CLAS S_DUR ATION
/SPAWNING_PERIOD
/FEEDING_OPTIONS
/INITIAL_CONDITIONS
/ECOLOGICAL_PARAMETERS
/COMPOSITIONAL_PARAMETERS
/MORPHOMETRIC_PARAMETERS
/PHYSIOLOGICAL_PARAMETERS
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
parameters. The order of the remaining commands is not
significant. See Appendix D for examples of the commands
described below.
• /AGE_CLASS_DURATION 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
separatingblank. See the diet optionforthe command/ECOLOGI-
CAL_PARAMETERS.
• /COMPOSITIONAL_PARAMETERS string,;... ; stringn
This command specifies aqueous and lipid fractions of the fish.
Valid options which must be separated by semicolons are:
pa[-] = a + P*pl[-] which specifies the fish's aqueous fraction
as a linear function of the fish's lipid fraction.
pl[-] = a*W[xunits] Ap which 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
\A\yunits] = a..
where a and p are integer or real numbers.
• /ECOLOGICAL_PARAMETERS string,;... ; 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:
diet(a< string
-------
mls\yunits] = a which specifies the species' maximum longevity
or life span.
nm\yunits] = a*W[jcw«&s]Ap which specifies a non-predatory
mortality rate for fish whose body weight is W[xunits]', yunits
must be dimensionally equivalent to I/year. If the mortality rate
offish is independent of their body weight (i.e., P equals zero),
however, this parameter can be specified simply as nm\yunits]
= a.
tl_ro\yunits] = a which specifies the species' minimum total
length when it reaches sexual maturity or its first reproduction.
rbi[-] = a which specifies the species' reproductive biomass
investment, i.e., grams gametes per gram spawning fish.
wl\yunits] = a*L[jcwmYs]Ap which specifies the fish' s live
weight as an allometric function of its total length.
yoy\yunits] = a which specifies the live weight offish recruited
into the population as age class 0.
• /FEEDING_OPTIONS string,;... ; 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(a< string
-------
I /PHYSIOLOGICAL_PARAMETERS string,;... ; stringn
Required only if the feeding option holling(-) is selected.
This command specifies the species' physiological parameters
for simulating its 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 which specifies the fish's assimilation efficiency
for periphyton and phytoplankton.
ae_invert[-] = a which specifies the fish's assimilation
efficiency for benthos, insects, and zooplankton.
ae_fish[-] = a which specifies the fish's assimilation efficiency
for fish.
= a*G[xunits] AP *exp(y*(T[celsius]-
T0))*h(T0,T1,T2) which specifies the fish's gastric evacuation
where G is the mass of food resident in the intestine, yunits must
be dimensionally equivalent to g(ow)/day. In general, y = !/2, %,
or 1 (Jobling 1981). This parameter is required only if the
feeding option holling(-) is selected.
mf[yunits] = a*W[jc«/nYs] Ap*exp(y*(T[celsius]-
T0))*h(T0,T1,T2) which specifies the fish's maximum filtering
rate, yunits must be dimensionally equivalent to L/day. Required
only if the feeding option clearance(-) is selected.
mi[yunits] = a*W[xunits] Ap*exp(y*(T[celsius]-
T0))*h(T0,T1,T2) which specifies the fish's maximum ingestion.
yunits must be dimensionally equivalent to g(DW)/day. Required
only if the feeding option allometric(-) is selected.
rq[-] = a which specifies the fish's respiratory quotient; rq =
L(CO2) respired / L(O2) consumed.
rt:std[-] = a which specifies the ratio of afish's routine respira-
tion to its standard respiration; rt:std = (routine O2 consumption)
/ (standard O2 consumption). BASS assumes a default value equal
2.
sda:in[-] = a which specifies the ratio of a fish's SDA to its
ingestion. BASS assumes a default value equal 0. 17.
sg[yunits] = a*W[jc«/nYs] AP *exp(y*(T[celsius]-
T0))*h(T0,Tj,T2) which specifies the fish's specific growth rate.
yunits must be dimensionally equivalent to day"1. Required only
if the feeding option linear(-) is selected.
so[yunits] = a*W[.x;wwi£s]Ap *exp(y*(T[celsius]-
T0))*h(T0,T!,T2) which specifies the fish's standard oxygen con-
sumption, yunits mustbe dimensionally equivalent to mg(O2)/ hr
ormg(O2)-g(FW)-1-hr'1.
st\yunits] = a*MV[xunits] Ap *exp(y*(T[celsius]-
T0))*h(T0,T!,T2) which specifies the time to satiation when
feeding with an initially empty stomach. See option sm [•] above.
Required only if the feeding option holling(-) is selected.
where
T2-T
T -T
-*- o -*- n
(4-3)
sm[yunits] = a*V/[xunits] AP *exp(y*(T[celsius]-
T0))*h(T0,Tj,T2) which specifies the size of the satiation meal
consumed during the interval (0, st]. See option "st[-]" below.
where T, is the temperature at which each particular process's
rate is maximal, T2 is the upper temperature at which the process
is no longer operative, and T0 is the low end reference
temperature that is used to specify the process's Q10 response.
Specification of the hyperbolic function hfT^TpT^ is optional
in which case the specification of the reference temperature T0
is also optional. Consequently, all of the above temperature
dependent power functions can also be specified simply as
oc*W[xM«/fc]Ap *exp(y*T[celsius])
As noted for the fish's morphometric parameters, if the exponent
P equals zero for any of parameters identified as being
allometric power functions, the resulting termW[.v;wmYs] A0 does
not have to be specified. If a required parameter is not specified,
the program will terminate with an appropriate message.
• /SPAWNING_PERIOD 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)
40
-------
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 implemented in BASS version 2.1.
4.2.4. Units Recognized by BASS
The many 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 B AS s is accomplished by referencing all units to
the MKS system (i.e., meter, kilogram, second). Tables 8 and 9
summarize prefixes and fundamental units, respectively, that are
recognized by BASS'S unit conversion subroutines. Table 9 also
summarizes the dimensionality and the conversion factor to the
MKS system of each unit. Table 10 summarizes units that are
recognized by B AS s ' s unit conversion subroutines for specifying
ecological, morphometric, and physiological units.
Units and their prefixes may be specified in either upper or
lower case. If 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. Only those units and their
plural form presented in Tables 9 and 10 are valid. 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 "nig/liter",
and "mg literA-l" are equivalent specifications. Similarly, the
weight specific units "mg/g/day" are "mg gA-l dayA-l" are
equivalent. The unit conversion factor (Tables 9 and 10)
converts from the given unit to the MKS system, e.g., 1 calorie
x 2.388x10"' =1 meter2 kilogram second"2.
4.2.5. Syntax for 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) =
temp[celsius] = P sin((f)*t[xMw/te]+(jo) + w7/fc]) = a + P*T[celsius] + y*
yw«/te]) = a + P*T[celsius] + y*log(W[xunits])
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\yunits] = a*exp(p*(T[celsius]-20))*W[xw«/fe]Ay
ln(so\yunits]) = w7/fe](p)) = a + Y*
log(so[yw«/te](P)) = lp[cm] = 4.5 + 0.0*L[cm]
41
-------
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
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)])
4.2.6. User Supplied Exposure Files
If the user specifies the file option for the /BIOTA,
/TEMPERATURE, /WATER_LEVEL, or /EXPOSURE 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
I
7001
/Cl
tim&[units]
string
! see ensuing discussion
/CM string
/START_DATA
VU V1>2
V2 1 V2 2
v1)MV ! comment
v2 MV ! comment
VNR,1
VNR,2
VNR,NV
! comment
The records beginning with a slash (/) followed by an integer CJ
identify the type of data (time, exposure concentration,
temperature, etc.) contained in CJ-th column of each data record.
In this example, NR is the total number of data records in the
file, NV is the number of variables per record, and C1...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
C1...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:
cbnths[units](chemical name) to input the concentration of
chemical name in benthic invertebrates;
cinsct[units](chemical name) to input the concentration of
chemical name in incidental terrestrial insects;
cphytn[units](chemical name) to input the concentration of
chemical name in periphyton;
cp])lnk[units](ctiemical name) to input the concentration of
chemical name in phytoplankton;
csdmnt[units](chemical name) to input the sediment
concentration of chemical name',
cw&ter[units](chemical name) to input the unbound, aqueous
concentration of chemical name',
czplnk[units](chemical name) to input the whole body
concentration of chemical name in zooplankton;
benthos[wmYs] to input the standing stock of benthic
invertebrates;
insects [units] to input the standing stock of incidental terrestrial
insects;
periphyton [units] to input the standing stock of periphyton or
grazable algae;
phytoplankton [units] to input the standing stock of
phytoplankton;
zooplankton [units] to input the standing stock of zooplankton;
temperature[wmYs] to input ambient water temperature.
depth [units] to input water depth.
If column names other than those listed above are specified BASS
simply ignores them. Data records may be continued by
appending an ampersand (&) to the line, e.g., the following data
records are equivalent.
vu
VI.MV
'U+i
Vi,2
V
y+2
Vy&
Vj,MV
42
-------
File records must be sequenced such that time is nondecreasing
(i.e., tj < ti+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.
strongly recommended since the graphical interface (GUI) that
is currently being developed for BASS uses this directory
structure. Using the installation procedures outlined in Section
5.1, the BASS installation software INSTBASS.EXE creates the
directory structure below
4.3. Output Files Generated by BASS
Given a user's input BASS generates the following three output
files
• an output file that summarizes the user's input
parameters, input errors detected by BASS, and
warnings/errors encountered during the actual
simulation. This file will have the name of the user' s
input command file, with extension "MSG"; e.g.,
INPUT.DAT will generate the file INPUT.MSG. If the
file already exists, it will be silently overwritten. See
Appendix E (page 95) for an example.
• 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) annualbioaccumulationfluxes
and statistics (i.e., mean whole body concentrations,
B AF, and BMP) of individual fish by species and age
class, and 3) annual community fluxes and statistics
(i.e., mean population densities andbiomasses) of each
fish species by age class. This file will have the name
of the user' s input command file, with extension
"BSS"; e.g., INPUT.DAT will generate the file
INPUT.BSS. If the file already exists, it will be silently
overwritten. See Appendix F (page 112) for an
example.
• a Post-script file that contains the plots that were
requested by the user. The file will have the name of
the user' s input command file, with extension "PLX";
e.g., INPUT.DAT will generate the file INPUT.PLX.
If the file already exists, it will be silently overwritten.
See Appendix G (page 122) for an example.
4.4. Include Files and General File Management
As mentioned previously BASS enables the user to construct BASS
simulation files using include files. Although the use of include
files was introduced in Section 4.1 as simply a matter of user
convenience, the installation software for BASS version 2.1
actually creates a specific subdirectory structure to help
construct and maintain user input files. Although users do not
have to use this subdirectory structure to run BASS, its use is
C:\BASS --+-- INSTBASS.EXE
I
+-- BASS_V2.EXE
I
+-- \DISLIN
I
+-- \FISH -- *.FHS
I
+-- \COMUNITY -- *.CMM
I
+-- \PROPERTY -- *.PRP
I
+-- \PROJECTS --+ \projectl -•
I
I
I
I
I
+ \project2
+ *.PRJ
+ *.CHM
+ * . DAT
+ *.BSS
+ *.MSG
+ *.PLX
Files within the subdirectory \FISH are all assigned the
extension FSH. These files specify the compositional,
ecological, morphological, and physiological parameters of a
fish species and are intended to be used as include files for
constructing fish community files which are discussed next. The
general structure of a *.FSH file is
! file: name.fsh
! date: June 20, 2000
i
! notes: structure of BASS fish file
i
/COMMON_NAME
/SPECIES
/AGE_CLASS_DURATION
/SPAWNING_PERIOD
/FEEDING_OPTIONS allometric(a
-------
11[cm]=a*w [g]^b
/PHYSIOLOGICAL_PARAMETERS &
ge[g/d]=a*w[g]^b*exp(c*t[celsius]); &
mf[1/d]=a*w[g]^b*exp(c*t[celsius]); &
mi[g/d]=a*w[g]^b*exp(c*t[celsius]); &
sg[1/d]=a*w[g]^b*exp(c*t [celsius]); &
sm[g]=a*w[g]^b*exp(c*t[celsius]); &
so[mg(O2)/h]=a*w[g]^b*exp(c*t[celsius]); &
st[min]=a*w[g]^b*exp(c*t[celsius]); &
ae_f ish [-] =a; &
ae_invert[-]=a; &
ae_plant[-]=a; &
sda: in [-] =a; &
rq[-] =a; &
rt:std[-] =a
! end c:\bass\fish\name.fsh
Files within the \COMUNITY subdirectory are all assigned the
extension CMM. These files specify the composition, trophic
structure, and initial conditions of a particular fish community.
These files will generally use FSH files from the \FISH
subdirectory as include files and are themselves used as include
files by PROJECTS files. The general form of a *.CMM file is
! file:c:\bass\comunity\name.cmm
! date: June 20,2000
! notes: structure of BASS community file
ttinclude 'namel.fsh'
/ECOLOGICAL_PARAMETERS &
diet (a
/LOG_AC
/LOG_P
/LOG_KB1
/LOG KB2
/MOLAR_WEIGHT
/MOLAR_VOLUME
/MELTING_POINT
! end c:\bass\chemical\name.prp
The PROJECTS directory contains subdirectories that are
created by the user for a particular model application. In general,
each application should be assigned to its own subdirectory. For
example, the BASS distribution example EVERGLD1.PRJ that
simulates mercury bioaccumulation in a deep-water Florida
Everglades community is assigned to the subdirectory
C:\B ASS\PROJECTS\EXAMPLE1. Six types of files will reside
in each PROJECTS subdirectory. These file types are: 1) *. PRJ
files that specify the simulation control parameters and chemical
and fish/community include files to be used for this particular
application, 2) *.CHM files that specify chemical exposures and
properties, 3) *.DAT files which specify actual chemical
exposures, nonfish standing stocks, water temperature, or water
depth when these functions supplied by the 'file' option, 4)
*.BSS which are the tabular output files generated by BASS, 5)
*.MSG which are the message output files generated by BASS,
and 6) *.PLX which are the Post Scrip plot files generated by
BASS. The recommended structure of a PRJ file is
! file: name.prj
! date: June 20, 2000
! notes: structure of BASS project file
/SIMULATION_CONTROL
/HEADER
/MONTHJTO
/LENGTH_OF_SIMULATION [year]
/TEMPERATURE temp[celsius]=
/WATER_LEVEL depth[meter]=
/BIOTA benthos[g/m*2]=; &
insects[g/m^2]=; &
periphyton[g/m^2]=; &
phytoplankton[mg/1]=; &
zooplankton[mg/1]=
/ANNUAL_OUTPUTS
/SUMMARY_PLOTS pop(length); cfish(length)
! specify chemical properties and exposures
ttinclude 'namel.chm'
! specify fish community
ttinclude 'name2.cmm'
/END
The chemical exposures and properties file NAME1.CHM
specified in the preceding project file has the following general
form
44
-------
! file: namel.chm
! date: June 20, 2000
! notes: structure of chemical exposures
! and properties file
! specify physico-chemical parameters
ttinclude 1chem_l.prp'
/EXPOSURE cwater[ppm]=; &
cbnths[ppm]=; &
cinsct[ppm]=; &
cphytn[ppm]=; &
cpplnk[ppm]=; &
czplnk[ppm]=
/LETHALITY Ic50[units](fish_l) = a; ....
/METABOLISM bt[units](fish_l,chem_n) = a;
! repeat above data block as needed
! for other chemicals of concern
! end namel.chm
The *.FSH, *.CMM, and *.PRP files within the subdirectories
\FISH, \COMUNITY, and \PROPERTY should be considered
by the user to be canonical "databases" for the construction of
new project files. If the user wishes to make changes to any of
these files, the user should either 1) edit the files as desired and
save the changes as a new *.FSH, *.CMM, and *.PRP file
within the subdirectories \FISH, \COMUNITY, and
\PROPERTY or 2) copy the desired files to a working project
subdirectory. Unless identified with an absolute path, any file
designated by an include command is assumed by default to
specify a path and file name relative to the project file specified
by the command line option "-i" when BASS is invoked. If a
specified *.FSH, *.CMM, or *.PRP file can not be found in the
subdirectory containing the user's project file, BASS then uses
the extension of the specified file to search the subdirectories
\FISH, \COMUNITY, or \PROPERTY.
4.5. Command Line Options
To run a BASS simulation which is specified by an input/project
file INPUT.PRJ, the BASS software is invoked using the UNIX
like command line shown below
C:\BASS2 I>bass_v21 -iinput.prj
Although the" -i filename" option is the only required command
line option, the following additional options are available
-a=> print abbreviated tabular output with minimal flux
summaries
-c => print distribution of cpu time in major subroutines
-e => integrate by Euler method
-h => print this help list and stop (also see -?)
-i filename => specify BASS input file (REQUIRED)
-1 => turn off lethal effects
-o filename => specify BASS_V21 output file
-p => print messages associated with prey
switching/limitation
-r=> integrate by Runge-Kutta method (DEFAULT)
-t => run test of BASS Runge-Kutta integrator and stop
-? => print this help list and stop (also see -h)
For example, the command line
C:\BASS2I>bass_v21 -iinput.prj -a-c
will execute the project file INPUT.PRJ and generate
abbreviated summary tables and a distribution of cpu time spent
within various key BASS subroutines.
4.6. Restrictions and Limitations
Commands may be presented in any order with the exceptions
noted below.
• The /CHEMICAL command must precede the commands
for any particular chemical since this command defines
a new chemical and increments the total number of
chemicals to be simulated.
• The /COMMON_NAME command must precede the
commands for the particular fish, since this command
essentially defines a (new) fish.
• Chemical commands must precede any fish commands.
• The /END command must be the last command. Any
other text or commands following it will be ignored.
45
-------
Table 8. Valid Unit Prefixes
Prefix Name Conversion Factor
atto lO'18
centi 10'02
deca 10+01
deci lO'01
exa 10+18
femto 10'15
giga 10+09
hecto 10+02
kilo 10+03
mega 10+06
micro lO'06
milli ID'03
myria 10+04
nano 10'09
peta 10+15
pico 10'12
tera 10+12
46
-------
Table 9. Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive and summaries only
commonly used unit names that BASS'S units conversion program recognizes.
Unit Name
acre
are
btu
calorie
cc
cm
day
decade
erg
fathom
feet
foot
ft
g
gallon
gm
gram
gramme
hectare
hour
hr
imperialgallon
inch
joule
kg
km
1
Ib
liter
litre
m
meter
metre
mg
micron
mile
min
minute
ml
mm
Conversion
Factor
2.471 xlO'04
i.oooxio-02
9.479X10'04
2.388X10'01
1.000xlO+06
1.000xlO+02
1.157X10'05
3.169X10'09
1.000xlO+07
5.468xlO-01
3.281xlO+0°
3.281xlO+0°
3.281xlO+0°
1.000xlO+03
2.642 xlO+02
1.000xlO+03
1.000xlO+03
1.000xlO+03
i.oooxio-04
2.778X10'04
2.778X10'04
2.200 xlO+02
3.937xlO+01
1.000xlO+0°
1.000xlO+0°
i.oooxio-03
1.000xlO+03
2.205 xlO+0°
1.000xlO+03
1.000xlO+03
1.000xlO+0°
1.000xlO+0°
1.000xlO+0°
1.000xlO+06
1.000xlO+06
6.214X10'04
1.667xlO-°2
1.667xlO-°2
1.000xlO+06
1.000xlO+03
Dimensions
Metre
2
2
2
2
3
1
0
0
2
1
1
1
1
0
o
J
0
0
0
2
0
0
3
1
2
0
1
o
J
0
3
3
0
0
0
o
J
1
Kg
0
0
1
1
0
0
0
0
1
0
0
0
0
1
0
1
1
1
0
0
0
0
0
1
1
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0
Second
0
0
-2
-2
0
0
1
1
-2
0
0
0
0
0
0
0
0
0
0
1
1
0
0
-2
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
Description
4840 yards2
100 meter2
cm3
10 years
6 feet
feet, foot
grams
3.785 liter
grams
100 are
hour
4.54 liter
kilograms
kilometer
liter
pound
meter
milligrams
10'6 meter
5280 feet
minute
47
-------
Table 9. Valid Unit Names (Continuation)
Unit Name
month
nauticalmile
ng
ounce
oz
pint
pound
ppb
ppm
ppq
ppt
quart
s
sec
second
ton
tonne
week
yard
year
Conversion
Factor
3.858X10'07
5.400xlO-°4
1.000xlO+12
3.527xlO+01
3.527xlO+01
2.113xlO+03
2.205 xlO+0°
1.000xlO+06
1.000xlO+03
1.000xlO+12
1.000xlO+09
1.057xlO+03
1.000xlO+0°
1.000xlO+0°
1.000xlO+0°
1.102X10'03
l.OOOxlO'03
1.653 xlO'06
1.094xlO+0°
3.169X10'08
Dimensions
Metre Kg Second
0 0 1
1 0 0
0
0
0
3 (
0
-3
-3
-3
-3
0
0
0
) 0
0
0
0
0
0
300
0 0 1
0 0 1
0 0 1
0 1 0
0 1 0
0 0 1
1 0 0
0 0 1
Description
1852 meter
nanograms
ounce
8 pint = 1 gallon
nanograms/mL
ugrams/mL
femtograms/mL
parts per trillion, picogram/mL
4 quarts = 1 gallon
second
second
2000 pounds
1000 kilograms
48
-------
Table 10. Valid Ecological, Morphometric, and Physiological Unit Names
Unit Name
fish
gram(O2)
g(02)
ha
individuals
inds
kcal
1(02)
lamellae
mg(02)
ml(O2)
mmole(O2)
mole(O2)
Conversion
Factor
n.a.
7.370X10'05
7.370X10'05
l.OOOxlO'04
n.a.
n.a.
2.388xlO-°4
5.159X10'05
n.a.
7.370X10'02
5.159xlO-°2
2.303 xlO'03
2.303 xlO'06
Dimensions
Metre Kg Second
000
2 1 -2
2 1 -2
200
000
000
2
2
0 (
2
2
2
2
-2
-2
) 0
-2
-2
-2
-2
Description
treated as information as is byte
gram of oxygen
gram of oxygen
hectare
treated as information as is byte
treated as information as is byte
kilocalorie
22.4 liters STP = mole
treated as information as is byte
milligram of oxygen = 3.24 calorie
milliliter of oxygen
millimole of oxygen
mole of oxygen
Note: For purposes of units conversion, units used to report oxygen consumption are treated dimensionally as joules.
49
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5. Software Installation and Management
5.1. MS-DOS Installation
The microcomputer ms-dos BASS 2.1 software is distributed with
1) a readme file, 2) the BASS 2.1 software, and 3) three example
project simulations. The BASS 2.1 executable and example
simulation files are compressed into a self extracting executable,
INSTBASS.EXE, using PKZIP and must be decompressed
before use. See instructions below.
BASS 2.1 is coded inFortran 95 and its executable, BASSJV21,
has been created using the Lahey/Fujitsu Fortran 95 5.60
compiler. Although BASS's source code is not included on its
software distribution diskette, it is available to any interested
party on request. Please note that there is a bug in the DISLIN
graphics software that BASS uses to generate 3 -dimensional plots
of model results as a function of age or size class and time. In
particular, there is a bug in DISLIN's hidden line removal
algorithm. This bug has been reported and is being investigated.
INSTBASS.EXE not only installs the BASS 2.1 executable but
also creates a subdirectory structure to organize and manage
project files and their associated data files. Following the
instructions givenbelow, INSTBASS.EXE creates the following
subdirectory structure
C:\BASS --+-- INSTBASS.EXE
I
+-- BASS_V21.EXE
I
+-- \DISLIN
I
+-- \FISH -- *.FHS
I
+-- \COMUNITY -- *.CMM
I
+-- \PROPERTY -- *.PRP
I
+-- \PROJECTS --+ \EXAMPLE1
I
+ \EXAMPLE2
I
+ \EXAMPLE3
The structure and use of the \FISH, \COMUNITY,
\PROPERTY, and \PROJECTS subdirectories are described in
Section 4.4 (page 43). The \DISLIN subdirectory contains the
*.DLL file needed to execute the DISLIN graphing software.
Three example BASS projects are provided in the \PROJECTS
subdirectory. Each example is allocated its own subdirectory. In
\PROJECTS\EXAMPLE1 the project file EVERGLD1.PRJ
simulates the bioaccumulation of methylmercury in a deep-water
fish community in the Florida Everglades, USA. The major fish
species in these communities are largemouth bass, Florida gar,
yellow bullhead, bluegill and red ear sunfish, and Gambusia.
EVERGLD1.PRJ uses the include file
\COMUNITY\EVERGLD 1. CMM to specify the ecological and
physiological dataforthese species. The chemical exposures and
properties of methylmercury are provide to EVERGLD1.PRJ
using the include file MERCURY. CHM which in turn uses the
include file \PROPERTY\METYL_HG.PRP. The community's
water depth and the standing stocks of benthos, periphyton, and
zooplankton are specified by NONFISH.DAT. This example is
presented in Section 6 of this user's manual.
In the subdirectory \PROJECTS\EXAMPLE2 the project file
EVERGLD2.PRJ also simulates the bioaccumulation of
methylmercury in a deep-water fish community in the Florida
Everglades, USA dominated by the same fish species. This
example, however, uses BASS'S "fgets" option to simulate only
the growth and bioaccumulation of individual fish. The
community's population dynamics are not simulated. The
ecological and physiological data for this example are provided
by the include file \COMUNITY\EVERGLD2.CMM. The
chemical exposures and properties of methylmercury are provide
to EVERGLD2.PRJ using the include file MERCURY.CHM
which in turn uses the include file
\PROPERTY\METYL_HG.PRP. The community's water depth
and the standing stocks of benthos, periphyton, and zooplankton
are specified by NONFISH.DAT. These files, however, are
simply copies of those found in \PROJECTS\EXAMPLE1.
Because the food web structure and dynamics specified and
implied by \COMUNITY\EVERGLD2.CMM can not be made
to coincide with that of \COMUNITY\EVERGLD1.CMM, the
output of EVERGLD2.PRJ will not match that of
EVERGLD1.PRJ.
In the subdirectory \PROJECTS\EXAMPLE3 the project file
HARTWELL.PRJ simulates the bioaccumulation of tetra-,
penta-, hexa-, and hepta-PCB in a largemouth/sunfish/catfish
community of the Twelve Mile Creek region of Lake Hartwell,
SC, USA which was a USEPA Superfund site. Because the
structure of the Twelve Mile Creek fish community, like many
other largemouth/sunfish/catfish communities throughout the
southeastern US A, closely resembles an Everglades deep-water
community, the project file HARTWELL.PRJ uses the
community file \COMUNITY\EVERGLD 1. CMM to model the
community of concern. This example is intended only to
demonstrate BASS's ability to simulate the bioaccumulation of
arbitrary mixtures and not what is actually occurring Lake
Hartwell fish communities. Despite this fact this simulation does
50
-------
predict some interesting results regarding largemouth bass. In
particular, largemouth bass are predicted to attain internal total
chemical activities on the order of 10% of their expected lethal
chemical activity threshold. As discussed earlier, one might
suspect that such accumulations would begin to produce
sublethal effects on these fish. Interestingly, biomarker studies
on Twelve Mile Creek largemouth bass indeed suggest this to be
the case.
To install the BASS software the user should first obtain a DOS
prompt and follow the instructions below.
• Select a default drive into which the BASS software is
to be installed (e.g., hard disk "C")
C:\WINDOWS> CD C:\
b. Create a directory for BASS software and then move to
that directory
C:\>MKDIRBASS21
C:\>CDBASS21\
c. Request verification of copy results
C:\BASS21> VERIFY ON
d. Transfer the files from the distribution diskette (e.g.,
drive "A") to the hard disk
C:\BASS21>COPYA:*.*
e. Execute the installation file INSTBASS.EXE to
recover files from the ZIP archives using the option -d
C:\BASS21> INSTBASS -d
f.. Edit your AUTOEXEC.BAT file as follows
SETBASS=C:\BASS21
SET PATH=%PATH%;%BASS%
SET PATH=%P ATH%;%B AS S%\DISLIN
to execute BASS from any directory and to enable the
BASS executable to find DISDLL.DLL which is needed
for DISLIN graphics.
g. To run one of the distribution examples move to the
desired PROJECTS subdirectory and invoke BASS
using the UNIX like command as shown in the example
below
C:\BASS21> CD PROJECTS\EXAMPLE 1
C:\BASS21\PROJECTS\EXAMPLE1> bass_v21 -i evergldl.prj
5.2. Auxiliary Software
To view and print BASS plot files the user will have to have some
type of PostScript previewing software installed on their system.
If the user does not have any such software, it is recommended
that the user obtain a copy of the Ghostscript/Ghostview/GSview
software. This freeware can be downloaded from the
Ghostscript, Ghostview and Gsview homepage:
http://www.cs.wisc.cdii/~ghost/.
BASS ' s input files and non-PostScript output files can be viewed
using a wide variety of editors. They can also be viewed using
word processing software such as WordPerfect or Microsoft
Word. When using a word processor, however, the user should
select a non-true type font (e.g., Courier) for viewing so that the
file's intended alignment is display properly. Using a word
processor to view non-PostScript BASS, has the added advantage
being able to compare similar files easily. For example, using
WordPerfect's Document/Compare feature one a easily view any
differences between two BASS output files resulting from a
parameter change.
51
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6. Example Application
Appendix D presents an example BASS project file that simulates
methyl mercury contamination in canals or open water habitats
in the south Florida Everglades. This proj ect file was constructed
as outlined in Section 4.4 (page 43) and is supplied with the
BASS distribution software as \EXAMPLE1. For this application
largemouth bass (Micropterus salmoides), Florida gar
(Lepisosteusplatyrhincus), yellow bullheads (Ameiums natalis),
bluegills (Lepomis macrochirus), redear sunfish (Lepomis
microlophus), and mosquito fish(Gambusia holbrooki) are
assumed to be the dominate fish in the habitats of interest and a
generalized food web of such assemblages is depicted in Figure
6. The sources of the ecological, morphological, and
physiological parameters used for this example are documented
by comment lines in the files presented in Appendix D. Total
fish biomass in canal and open water Everglades habitats vary
between 150 and 460 kg(FW)/ha (Frank Jordan unpublished
data). Using Jordan's relative abundance data as guidelines, the
initial standing stocks of the bass, gar, bullheads, bluegill, red
ear sunfish, and mosquito fish were assigned to be 20, 10, 20,
200,100, and 10 kg(FW)/ha, respectively, for a total community
biomass of 360 kg(FW)/ha. Based on Loftus et al. (1998) the
water concentration of methylmercury for the simulation was
assigned to be a constant 0.444 ng/L and the BAF's for benthos
and zooplankton were assigned to be 10617 and 10599,
respectively.
Appendices D, E, and F present the resulting output files
generated by BASS. At the end of the 10 year simulation the
mean annual standing stocks of the bass, gar, bullheads, bluegill,
red ear sunfish, and mosquito fish are 17.2,12.7,4.51,191,146,
and 0 kg(FW)/ha, respectively, for a total community biomass of
371 kg(Fw)/ha (see pages 120 and 121). Although the total
elimination of mosquito fish during the simulation may not be
particularly desirable, it is not unrealistic since bass and other
piscivores often exert intense predatory pressures on mosquito
fish and other small fishes in Everglades and other wetland or
swallow water communities.
The simulated whole body concentrations of methyl mercury in
these species agree also well with unpublished data collected by
Ted Lange et al. and Loftus et al. (1998). The annual averaged
concentrations of methylmercury in largemouth, gar, bullhead,
bluegill and red ear weighted by cohort biomasses were 0.817,
0.694,0.539,0.495, and 0.416 mgHg/kg(FW), respectively (see
pages 113, 115, 116, 117, and 118). When weighted by cohort
densities, the annual averaged concentrations of methylmercury
in largemouth, gar, bullhead, bluegill and red ear were 0.671,
0.615, 0.467,0.482, and 0.370 mgHg/kg(FW), respectively (see
pages 113, 115, 116, 117, and 118). Loftus et al. report whole
body concentrations of methylmercury in largemouth, gar,
bullhead, bluegill and red ear to be 0.967, 1.16, 0.443-0.755,
0.478, and 0.247 mg Hg/kg(FW), 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,1983b;MacCrimmonetal.l983;Ueda and Takeda 1983;
Wren and MacCrimmon 1986; Braune 1987; Luten et al. 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 ), BASS predicts a strong interdependence
between the body sizes of fish and their mercury whole body
mercury concentrations. For example, the mean annual mercury
concentration of newly recruited largemouth whose average
annual body weights is 86.9 g(FW) is 0.499 mg Hg/kg(FW).
However, the mean annual mercury concentration of oldest
largemouth whose average annual body weights is 1.09 kg(Fw)
is 1.03mgHg/kg(FW).
52
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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.Is the model's theoretical foundation 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 which has been published in the peer
reviewed literature (Barber etal. 1988, 1991). The bioenergetic
modeling paradigm employed by BASS to simulate fish growth
has been used by many researchers in the peer reviewed
literature (Norstrom etal. 1976 ;Kitchell etal. 1977;Mintonand
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). Moreover, since its
construction FGETS has been included in numerous reviews
bioaccumulation models that are applicable for ecological risk
assessments and environmental management decisions (Barren
et al. 1990; Jones et al. 1991; Barnthouse 1992; Chapra and
Boyer 1992; Landrum et al. 1992; Olem et al. 1992; Dixon and
Florian 1993; Cowan et al. 1995; Campfens and Mackay 1997;
Feijtel et al. 1997; Howgate 1998; Wania and Mackay 1999;
Mackay and Fraser 2000; Bartell et al. 2000).
Two criticisms have been lodged against FGETS in the
literature. The fist of these is that it assumes or attempts to prove
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 as 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 which simply lumps all
excretion pathways (i.e., gill, intestinal, urinary, and dermal)
into one. What Madejian 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 generally
aimed at BASS ' s gill exchange model. As indicated by Tables 2 -
6, however, there is in fact an abundance of gill morphometric
data available to estimate the parameters needed for this model.
7.7.2. How has the model or model algorithms been
corroborated / validated?
BASS'S bioconcentration and bioaccumulation algorithms have
been validated by comparing its predicted uptake and elimination
rates to published in the peer reviewed literature (Barber et al.
1988; Barber 2000). For organic chemicals these algorithms
have also been validated by simulations of mixtures of PCBs in
Lake Ontario salmonids and various laboratory studies (Barber
et al. 1991). For sulfhydryl binding metals, BASS'S
bioconcentration algorithms have been validated by simulations
of methylmercury bioaccumulation in Florida Everglades fish
communities one of which is presented herein as a typical BASS
application. For validation of BASS'S bioenergetic growth
algorithms the reader should refer to Barber et al. (1991) and the
example herein.
7.1.3. What is the mathematical sensitivity of the model with
respect to parameters, state variables (initial value problems),
and'forcingfunctions/boundary'conditions? What is the model's
sensitivity to structural changes?
There are four major class 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 term the following partial derivatives
££,
dp,
dX,
3Z.
dX.(Q)
where Jf, is a state variable of interest;/) is some state parameter
54
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of concern; Z, is some external forcing function; andJ^(O) is the
initial value of some state variable of interest which may be Xl
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 model sensitivity as defined above is simply a
mathematical characteristic of a model, model sensitivity in and
of itself is neither good nor bad. Sensitivity is desirable if the
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
etal. 1993; Wallachand Genard 1998). Model uncertainty, orat
least one of its most common manifestations, is the product of
both the model's sensitivity to particular components and the
statistically 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 be 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 etal. (1993),Hakanson
(1995), Norton (1996), Loehle (1997), and Wallachand 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 which
summarizes all the input data that was 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's should report their problem to the
technical contact identified in the BASS user's guide.
BASS has a series of subroutines that check for the completeness
and consistency of the user's input data. When missing or
inconsistent data is detected, an appropriate error message is
written to the above * .MSG file and a 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.
7.2.2. Is the developer reasonably confident that program
subroutines, functions, and procedures are transmitting and
receiving the correct variables? Similarly, is the developer
reasonably confident that program subroutines, functions, and
procedures are not inadvertently changing variable assignments
the shouldn 't be changed?
All BASS subroutines and functions are accessed using implicit
interface generated by the pertinent Fortran 95 compiler.
Subroutines and functions are packaged together according to
the function and degree of interaction. The BASS version 2.1
software is coded with one main program PROGRAM BASS_v21
(see BASS_v21.F90) and 25 procedure modules. These are
• 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 BAS S_DEFINED - functions for determining whether
program parameters and variables have been initialized or
assigned (see BASS_DEFiNED.F90).
• MODULE BASS_EXP - subroutines for calculating exposure
conditions (see BASS_EXP.F90).
• MODULE BASS_INI - subroutines for initialization of program
variables (see BASS_iNi.F90).
• MODULE B AS S_INPUT - subroutines for processing user input
(see BASSJNPUT.F90).
• MODULE BASS_ODE - subroutines for the computational
kernel of the BASS software (see BASS_ODE.F90).
• MODULE BASS_PLOTS - subroutines for generating BASS
Output plots (see BASS_PLOTS.F90).
• MODULE BASS_TABLES - subroutines for generating output
tables (see BASS_TABLES.F90).
• MODULE DECODE_FUNCTIONS - subroutines for decoding
constant, linear, and power functions from character strings
(see UTL_DCOD_FNC.F90).
• MODULE DISLIN_PLOTS - general 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).
55
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• 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
which 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 phase within a sorted list (see UTL_SEARCH2.F90).
• MODULE SEARCH_LISTS - subroutines for finding the
location of a value within a sorted list (see
UTL_SEARCHl.F90).
• MODULE STRFNGS - subroutines for character string
manipulations and printing multiline character text (see
UTL_STRINGS.F90).
• MODULE TABLEJJTILS - subroutines for generating self-
formating tables (see UTL_PTABLE.F90).
• MODULE UNITSLIBRARY - subroutines for defining and
performing units conversions (see UTL_UNITSLIB.F90).
In general these procedure modules are coded with minimal or
no scoping units. Also whenever possible 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 below),
data exchanges between program procedures are performed via
formal subroutine/function parameters whenever possible. The
only notably exception to this coding policy are modules which
must be used to supply auxiliary parameters to an "external"
subroutine which is used as an argument to certain mathematical
software packages. Working areas used by BASS are not used for
data transfers between internal or 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 subroutine or function
formal parameters, BASS makes extensive use of derive type data
structures. Each derived type definition is specified within its
own module and all derive type definition modules are
maintained in the file BASS_TYPES.F90. A good example of
BASS'S use of derive type data structures is the derive 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 dt_fish_par
TYPE:: fish_par
CHARACTER (LEN=80) :: ageclass, class_var, &
genus_species, spawning_interval
INTEGER :: classes=0, spawnings=0
INTEGER, DIMENSION(:), POINTER :: &
class_model=>NULL(), spawn_dates=>NULL()
REAL :: ae_fish, ae_invert, ae_plant, &
dry21ive_ab, dry21ive_aa, dry21ive_bb, &
dry21ive_cc, gco2_d, la, longevity, &
mgo2_s, rbi, rq, rt2std, sda2in, tl_rO, yoy
REAL, DIMENSIONS) :: &
ga, id, Id, 11, Ip, nm, pa, pi, wl
REAL, DIMENSIONS) :: ge, mf, mi, sg, sm, so, st
REAL, DIMENSION(:), POINTER :: class_bnds=>NULL()
END TYPE fish_par
END MODULE dt_fish_par
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 39). 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,....)
without fear of data misalignment.
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
56
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modules. These modules include
• MODULE BASS_CONSTANTS - constants used by BASS'S
computational subroutines (see BASS_CONSTANTS.F90).
• MODULE CONSTANTS - constants used by utility subroutines
(see UTL_CONSTANTS.F90).
• MODULE NOVALUE - specifies values for integer, real, and
character variables that have been been initialized (see
BASS_CONSTANTS.F90).
• MODULE SNGL_DBL_QUAD - specifies the precision of
floating point variables as either single, double, or quad
precision variables. This module also assigns certain
associate floating point constants (see
BASS_CONSTANTS.F90).
• MODULE WORKING_DIMENSIONS - specifies 'standard' sizes
for character variable, input records, etc. (see
BASS_CONSTANTS.F90).
• MODULE UNITS_PARAMETERS - specifies parameters used by
the units conversion subroutines (see UTLJJPARAMS.F90)
7.2.4. Do all strictly mathematical algorithms do what they are
suppose 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 percent water,
lipid, and non-lipid organic matter. 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).
As mentioned earlier, the BASS software allows the user to
integrate BASS'S differential equations using either a simple
Euler method or a fifth-order Runge-Kutta method with adaptive
step sizing. These methods offer the user two distinctly different
options with respect to software performance and execution.
Although Euler methods cannot assess the accuracy of their
integration, such methods often allow for fast model execution.
Runge-Kutta methods, on the other hand, can monitor the
accuracy of their integration but at the cost of increased
execution time. This additional computational burden, however,
can often be significantly reduced by employing adaptive step
sizing. BASS'S Runge-Kutta integrator is patterned on the fifth-
order Cash-Karp Runge-Kutta algorithm outlined by Press et. al.
(1992) and was tested using the following system of equations.
dyjdx = 1.0
dyjdx = x
dy3/dx =
dyjdx = cosh(X)
dys/dx =
dy&ldx = 1.07(1.0
dyjdx = l.U/Vl.U +x^
dyg/dx= -100(y9-sin(x))
duldx = 99%u + 1998v
dvldx= -999 u-1999 v
yg(0) = 1
u(Q) = 1
v(0) = 0
The analytical solution to this system of equations is
V, =
y2=
x-x0
0.5(x2-x02)
y6 =
y1 =
- sinh(x0)
exp(x) - exp(x0)
: arctan(x) - arctan(x0)
: asinh(x) - asinh(x0)
10101 , 1AA , 100
: exp(-lOOx)
10001 10001
^2exp(-x) -exp(-lOOOx)
= -exp(-x) +exp(-1000x)
/ N
cos(x)
10000
10001
. , ,
sm(x)
On the interval [0>5=0.220255E+05. Besides their large
numerical range, the last three equations in this system are
numerically stiff (Press et al. 1992; Ascher and Petzold 1998).
When integrated on the interval [0
-------
therefore step functions of time. 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 for 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
day is then controlled by any conditions 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 which 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 variable are
suppose to be non-negative, are they? Similarly, if the model is
suppose 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 which 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'(f)
0 < y(f) + hy'(t)
-y(t)
y'(t}
> h where y!(t) < 0
then an integration step is possible. If the converse is true,
however, the function y(f) 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 simulations 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. Regardless of the integration method used (i.e., Euler
or Runge-Kutta), 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.
When used it its full community mode (i.e., the non-FGETS
option), BASS calculates and reports the mass balance between
the community's total predicted predatory mortality and its total
predicted piscivorous consumption as a mass balance check on
its internal consistency and operation. Forthe example presented
herein this mass balance is -1.953E-02 [g(DW)/ha/yr]. Since this
community's total piscivory is calculated to be 2.778E+04
[g(DW)/ha/yr], this mass balance check would have a relative
error of less than 10"6. See page 121.
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. It has also been ported to
the Windows operating system on the DELL OptiPlex using the
Lahey/Fujitsu Fortran 95 5.60 compiler. Although the results of
these two implementations to 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.
7.2.8. Have test and reference/benchmark data sets been
documented and archived?
At least three different test project files are maintained to tract
changes in the operation of BASS associated with code
maintenance and updates. These files are used as benchmarks to
verify that code modifications that should not change BASS 's
computational results in point of fact do not change BASS'S
simulation output.
If h is greater than the numerical spacing of t (i.e., t + h * t),
58
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7.3. Questions Regarding QA
Documentation and Applications
of Model
7.3.7. 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 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
appropriate 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 userfile and project management, and
software installation. The User's Guide also presents and
discusses the results of one of the three 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 will assign a few default 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 f or a B AS s' s simulation. In general, these
problem areas involve either the unsuccessfully resolution of a
root of a nonlinear equation or the unsuccessfully 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 any of these situations are
encountered, BASS terminates execution and issues an
appropriate error message to the current *.MSG file.
59
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8. Planned Future Features
Presently, ten major program developments are planned for
BASS. These include:
• Development of a graphical user interface (GUI) for easy
construction of input files.
• Improved plotting capabilities including the generation of
output files that users can input to their own graphic
software.
• Development 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 of calculate the fishes' ventilation volumes.
See Eq.(2-12). Currently, BASS uses saturated dissolved
oxygen concentrations that are calculated as a function of
water temperature.
• Development of submodels for simulating the biomass
dynamics of benthos, periphyton, phytoplankton, and
zooplankton.
• 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 migration algorithms for simulating the
movement of fish into and out of 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.
60
-------
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78
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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 which include 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 which include As, Cd, Co, Cr, Ni,
Sn, and Zn bind not only to 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 is regulated by
metallothioneins via sulfhydryl complexation reactions certainly
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
The stability constant for this reaction is
Kb =
[RSM] [/T] = RSM [H *]
[RSH] [Mf] RSH [M1]
(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); [RSM] is the
concentration of sulfur bound metal (molar); RSM are the moles
of metal bound to sulfhydryls; and RSH are the moles of free
non-disassociated sulfhydryl. Metal complexation must be
constrained by mass balances for both the metal and sulfhydryl
binding sites. For the metal itself the following mass balance
must hold
TM = M + LM + RSM
Kb RSH
(A-3)
TM
(Pa
Kb RSH
where TM are the total moles of metal; LM are the moles of
metal that is partitioned into lipids; and W is the fish's volume
in liters which is approximately equivalent to its kilogram live
weight. The mass balance for the fish' s sulfhydryl content that
must is satisfied is
TS = RSH
= RSH 1
+ RSM
(A-4)
RSM
where TS denotes the total moles of sulfhydryl ligands; RS' are
the moles of disassociated sulfhydryls; and
Ka = [RS~][H+]/[RSH] is the sulfhydryl's disassociation
constant. In addition to the reaction specified in Eq. (A-l),
mixtures of metals interact by competing for the same binding
site, i.e.,
RSMt + M* - RSMj + M*
(A-5)
The stability constant for this reaction is
] Kb
T ~ ~Kb.
[RSMj]
[RSMt] [Mj]
From this expression it then follows
RSM. [M.+] Kb i = RSM,
TRSM, [M'] Kb, = ]
•*' * .7 ' * *-
TT (A-6)
Kb
t [M/] Kb,
[M;] Kb, = [M/] K
(A-7)
[M/] Kbj
RSH
If Eq.(A-3) is substituted in this equation, one then obtains
Kb, TM,
RSM. =
Kb, RSH/[H'}
Kbi TUi
(A-8)
[H*} W(Pa + P,Km) + Kb, RSH
79
-------
This equation in turn can be substituted into Eq.(A-4) to obtain
K
TS = RSH
1
Kb. TM.
(A-9)
i r/f+] W (P + P, K ) + Kb. RSH
L J v a i owj *
For most metals, however,
[H+] W(Pa + PtK ) « Kb RSH (A-10)
i
Therefore, the sulfhydryl balance equation is approximately
equal to
TS = RSH 1
Thus,
RSH =
(A-ll)
(A-12)
If the metal's aqueous and organic phase concentrations (i.e., Ca
and C0) are expressed on a molar basis, then
RSM = C0P0W
[Ml = Ca
(A-13)
(A-14)
When Eqs. (A-12), (A-13), and (A-14) are substituted into
Eq.(A-2), one then obtains
Kb =
P C
O O
C
Ka)
Ca(TS -
i
Kb (TS -
(A-15)
Ka)
which can then be substituted into the equation
C - °J.
0 P.+PlKm + ^
£
(A-16)
to calculated the fish's aqueous phase concentrations.
To use the above complexation model one must specify both the
metal's stability constant (seeEq.(A-2)) and the concentration of
sulfhydryl binding sites (mol SH/g(DW)) 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 offish. Despite
this situation, however, a reasonable approximation of this
parameter can still be made since data does exists 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 (Lateolabraxjaponicus) muscle to be 11.5
umol(SH)/ g(sacroplasmic protein) and 70.5 umol(SH) /
g(myofibrillar protein). Using the 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 would be estimated to be 9.12
umol(SH) / g(muscle) or 45.6 umol(SH) / g(dry muscle).
Opstevedt et al. (1984) reported the suldhydryl content of Pacific
mackerel (Pneumataphorus japanicus) and Alaska pollock
(Theragra chalcogramma) muscle to be 6.6 and 6.2 mmol(SH)
/ 16 g(muscle N), respectively. Using conversion factors
reported by these authors, these values are equivalent to 48.7 and
56.7 umol/g(dry muscle). Chung et al. (2000) determined the
sulfhydryl content of mackerel (Scomber australasicus) muscle
to be 88.2 umol(SH) / g(protein). Using the conversion factor
0.83 g(protein) / g(dry muscle) (Opstevedt et al. 1984) this value
is equivalent to 73.2 umol(SH) / g(dry muscle). Although few
other studies have investigated the sulfhydryl content of whole
fish muscle, several studies have reported on the sulfhydryl
content of the actomyosin and myosin components of fish
my ofibrillar proteins (Connell and Ho wgate 1959;Buttkus 1967,
1971; Takashi 1973; Itoh et al.1979; Sompongse et al. 1996;
Benjakul et al. 1997; Lin and Park 1998). Because the results of
these studies agree well with the actomyosin analysis reported by
Itano and Sasaki (1983), it would appear that the results of Itano
and Sasaki (1983), Opstevedt et al. (1984), and Chung et al.
(2000) can be applied to fish in general. Consequently, the
sulfhydryl content of fish muscle can be assumed to be on the
order of 45-70 umol(SH) / g(dry muscle)
Although the sulfhydryl content of liver, kidney, gills, and
intestine has not been measured directly, the sulfhydryl content
of these tissues can be estimated from their metallothionein
concentrations. Metallothioneins (MT) are sulfur-rich proteins
which are responsible for the transport and storage of heavy and
trace metals and which are also usually considered to be the
principle source of sulfhydryl binding sites in these tissues
(Hamilton and Mehrle 1986; Roesijadi 1992). Numerous
researchers have investigated the occurrence of MTs in the liver,
kidney, and gills of fish, and most have shown that tissue
concentrations of MTs generally vary with metal exposures.
Under moderate exposures typical hepatic MT concentrations in
fish are on the order of 0.03 - 0.30 umol(MT) / g(liver) (Brown
and Parsons 1978; Roch et al. 1982; Klaverkamp and Ducan
1987; Button et al. 1993). Using data from Takeda and Shimizu
80
-------
(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 umol(SH)
/ g(dry liver). These values suggest that the hepatic sulfhydryl
content of fish which would include both their baseline MT and
cytoplasmic components that can be converted into MT, might
be on the order of 40 umol(SH) / g(dry liver). This 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; Shultzetal. 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 umol(SH)
/ g(dry liver). Of these two possibilities the latter appears more
likely.
Although gill, kidney, and intestine MTs have not been studied
in the same detail that hepatic MTs have been, it appears that
MT and hence sulfhydryl concentrations in gills and kidney are
lower and not as inducible as hepatic concentrations
(Klaverkamp and Ducan 1987; Hamilton et al. 1987a,b).
Klaverkamp and Ducan (1987) estimated the concentrations of
gill MT in white suckers (Catostomus commersoni) to be 33
ug(MT) / g(gill) which is equivalent to 3.3 nmol(MT) / g(gill)
or 0.0825 umol(SH) / g(gill). This value agrees well the
estimated concentrations of unidentified binding sites (0.03 -
0.06 umol / g(gill)) for copper on the gills of rainbow trout
(Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis)
(MacRae et al. 1999) but is somewhat high for the concentration
of unidentified binding sites (0.013 - 0.03 umol / 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 such as those involved in fatty acid
synthesis, glutathione, etc., it seems reasonable to assume that
the sulfhydryl content of fish is approximately 70 umol(SH) /
g(DW). Because Davis and Boyd (1978) reported the mean sulfur
content of 17 fish species to be 206 umol(S) / g(DW), this
assumption implies that almost 1/3 of a fish's sulfur pool exists
as sulfhydryl groups.
The above complexation model was implemented within BASS
using 70 umol(SH) / g(DW) to calculate the total sulfhydryl
content offish and assuming that the mean dissociation constant
for organic sulfhydryls is pKa=9.25 (i.e., the SPARC estimated
pKa for cysteine). Using literature values for the stability
constants of methylmercury, however, BASS overpredicted the
bioammulation of methylmercury in fish by at least an order of
magnitude. Consequently, a much simpler distribution
coefficient algorithm was adapted.
81
-------
APPENDIX B. Nondimensionalization of chemical exchange equations for fish gills.
Using the transformations
n C ~ C«
® = n ^ (B-l)
interlamellar channel. Using these observations, one can then
write
h
Vh2
the PDE and boundary conditions
3 /. . 2\ #C
1 - X2 =
2 dy
DdC
dx
~/; , ~ 2zD ridC
C(hy) - C s, 4, —-
< ^ <3*
can be nondimensionalized into
3 /, ^ v2\ d® d20
— (1 - X2-} — v
2 dY gx2
d@
dX
dX
= -AU©(UO-
JY dX
(B-2)
(B-3)
(B-4)
(B-5)
}
dy\ (B-6)
(B-7)
(B-8)
7 (B-9)
The boundary condition (B-9) that describes exchange across the
secondary lamella, however, can be simplified by noting that the
solution of Eq.(B-7) is separable, i.e., ®(X, Y) = &(XyP(Y) and
that qv = h z V is the ventilation volume of an individual
u-^f^ (Y)^\ dY (B-10)
which can then be differentiated with respect to 7 to obtain
dY dX
TrJTOVf.
%
¥(7)
dX
¥(7)
(B-H)
(B-12)
Because 1P(7) = exp(-^'3^27) where -%^2 is the constant of
separation for Eq.(B-7), the preceding equation is equivalent to
dX
which can be manipulated to yield
%!2Nsh
dX
%l2-(2qv/q)N
(B-13)
(B-14)
p>"Sh>
Although this boundary condition is dependent on the eigenvalue
^, the eigenvalue expansion for the solution of Eq.(B-7) is still
straightforward (Walter 1973; Fulton 1977). Note that as the
fish's perfusion rate increases, this boundary condition
converges to
dX
' Jva
(B-15)
which is the boundary condition previously used by Barber et al.
(1991).
82
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APPENDIX C. Derivation of the consistency condition for feeding electivities.
To derive a self consistency condition on a fish'selectivities and Adding J^ ff = 1 to each side of the above equation one
relative prey availabilities such that its calculate dietary obtains the desired result, i.e.,
frequencies will sum to unity, consider the following
^ e'f< • Y f. = 1
Summing Eq. (C-2) over all / then yields
E«,-(^+^ = Ed, -Eft=° (C-4)
When Eq.(C-3) is substituted into this expression, one then
obtains
E = 0 (C-5)
"i ~ Ji 1 -e
e' = ~r—f (c-i)
(C-2) ~ l-et
(c-3) £ -T7.
83
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APPENDIX D. Example project file constructed using include files as discussed in Section 4.4
/ SIMULATION_CONTROL
/ HEADER methylmercury bioaccumulation in a "ponded" everglades community
/ MONTH_TO april
/ LENGTH_OF_SIMULATION 10[year]
/ TEMPERATURE temp [eelsius]=25.0+10.0*sin(0.172142e-01*t[days]+6.02497)
/ WATER_LEVEL depth [meter]=file(nonfish.dat)
/ BIOTA benthos [g/mA2]=file(nonfish.dat) ; &
periphyton[g/mA2]=file(nonfish.dat); &
zooplankton[mg/1]=file(nonfish.dat)
/ ANNUAL_OUTPUTS 10
/ SUMMARY_PLOTS pop(length); cfish(length)
! / SUMMARY_PLOTS afish(age); afish(length); afish(weight); &
! cfish(age); cfish(length); cfish(weight); &
! baf(age); baf(length); baf(weight); &
! bmf(age); bmf(length); bmf(weight); &
! pop(age); pop(length); pop(weight); &
! age(length); age(weight); tl(age); tl(weight); &
! wt(age); wt(length)
! specify chemical properties and exposures for methylmercury
#include 'mercury.chm'
! specify fish community; full community simulation
#include 'evergldl.cmm'
/ END
84
-------
APPENDIX D. (cont.) Include file MERCURY.CHM for methylmercury properties and exposures.
- Loftus, W.F., J.C. Trexler, and R. D. Jones. 1998 . Mercury transfer through an
everglades aquatic food web. final report contrat SP-329. Florida Department
of Environmental Protection.
- Stober, J. , D. Scheldt, R. Jones, K. Thornton, L. Gandy, J. Trexler, and
S. Rathbun. 1998. South Florida ecosystem assessment. EPA-904-R-002
- Watras, C. and N. Bloom. 1992 . Mercury and methylmercury in individual
zooplankton: implications for bioaccumulation. Limnol. Oceanogr. 37:1313-1318.
cwater [rig/1] = 1.13
= 0 . 15*1.13
cphytn[ng/g(fw) ] =76.11
57 . 85(n=4) utricularia
90 . 44 (n=5) diatoms
76.58(n=3) chlorophyta
cinsct [ng/g(fw)] = 212.17
258.04(n=9) dolomedes
148.95(n=6) hydracarina
304.29(n=12) tetragonids
136.23(n=15) unid spiders
czplnk[ng/g(fw)] = 54.60
46 . 35 (ri=10 )
62.90(n=12)
53.39(n=14) ostracoda
cbnths[ng/g(fw)] = 83.91
38.03(n=18) chironomids
38.89(n=9) gastropoda-littoridinops
8.92(n=5) gastropoda-melanoides
50.05(n=12) gastropoda-physella
14.21(n=9) gastropoda-planorbella
14.76(n=2) gastropoda-planorbella
19.26(n=13) gastropoda-pomacea
126.55(n=20) hemiptera-belostoma
95.98(n=22) hemiptera-pelocoris
44 . 85 (ri=23) hyalella
91.58(n=25) odonata-libellulidae
186.31(n=41) palaemonetes
18.90(n=8) pelycepoda-villosa
64.33(n=24) procambarus
g(dw)/g(fw) = 0.2 (Watras and Bloom 1992)
mehg/total hg = 0.15 in water (Stober et al. 1998)
mehg/total hg = 0.20 in phytoplankton (Watras and Bloom 1992)
mehg/total hg = 0.60 in zooplankton (Watras and Bloom 1992)
mehg/total hg > 0.90 in fish (Watras and Bloom 1992)
cphytn[ppb]=(0.2*16.74/0.2) /(1.13*0.15)*cwater[ng/1];
czplnk[ppb]=(0.6*54.60/0.2)/(1.13*0.15)*cwater[ng/1];
cbnths [ppb] = (0.6*83.91/0.2)/(I.13*0.15)*cwater[ng/1]
end mercury.chm
85
-------
APPENDIX D. (cont.) Include file METHYL_HG.PRP for methylmercury properties.
ref s :
- Arnold, A.P. and A.J. Canty. 1983 . Methylmercury(II) sulfhydryl interactions.
Potentiometric determinations of the formation constants for complexation of
methylmercury(II) by sulfhydryl containing amino acids and related molecules
including gltathione. Can.J.Chem. 61:1428-1434.
- Benoit, J.M., R.P. Mason, and C.C. Gilmore. 1999a. Estimation of mercury-sulfid
spedation in sediment pore waters using octanol-water partitioning and implications
for availability to methylating bacteria. Environ. Toxicol. Chem. 18:2138-2141.
- Benoit, J.M., C.C. Gilmore, R.P. Mason, and A. Heyes. 1999b. Sulfide controls on
mercury spedation and bioavailability to methylating bacteria in sediment pore
waters. Environ. Sci. Technol. 33:951-957.
- Major, M.A., D.H. Rosenblatt, and K.A. Bostian. 1991. The octanol/water
partition coeffiecent of methylmercury chloride and methylmercury hydroxide
in pure water and salt solutions. Environ.Toxicol.Chem. 10:5-8.
- Simpson, R.B. 1961 . Association constants of methylmercury with sulfhydryl and
other bases. J.Am.Chem.Soc. 83:4711-4717.
notes: Simpson (1961) reports that for cysteine log(kb)=log(k2)=7.1 and for
glutathione log(kb)=log(k2)=6.9. results of Arnold and Canty (1983) ,
however, estimate log(kb)=log(betallO)-pka=16.46-8.22=8.24. therefore
assume log(kb)=(7.1+8.24)/2=7.67
CHEMICAL methylmercury
LOG_KB1 6.00 ! assumed
LOG_KB2 5.00 ! assumed
LOG_P -0.4 ! kow = 0.4 at physiological pH; see Major et al (1991)
MOLAR_VOLUME 51 ! calculated using liquid referenced molar volume of dimethylmercury
MOLAR_WEIGHT 215.6
MELTING POINT 25
86
-------
APPENDIX D. (cont.) Include file EVERGLD1.CMM for community structure parameters.
#include 'Igmouth.fsh'
/ ECOLOGICAL_PARAMETERS &
diet(0<1[mm]<20)={zooplankton=100}; &
diet(20
-------
APPENDIX D. (cont.) Include file LGMOUTH.FSH for basic largemouth bass parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L. A. Suarez, and R.R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - 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, T.P., T.W. May, W.G. Brumbaugh, and D.A. 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.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
! - Price, J.W. 1931. Growth and 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. Peterman. 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,
! and 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.
/ COMMON_NAME bass
/ SPECIES Micropterus salmoides
/ AGE_CLASS_DURATION year
/ SPAWNING_PERIOD may-June
/ ECOLOGICAL_PARAMETERS &
lp[cm]=0.6 + 0.27*L[cm]; & ! estimated from Timmons and Shelton (1980) for Lepomis
wl [g]=0.0117*L[cm]A3.08; & ! Carlander (1977) 0.00543 adjusted such that 2.0kg = 50cm
tl_rO[mm]= 150; & ! Carlander (1977)
yoy[g]=25.0; &
mis [year] =8 ; &
see sg[] and assume exogenous mortality/total mortality = .9 and b=l
pi[-]=0.000121*W[g]A0.845 ! Lowe et al.
MORPHOMETRIC_PARAMETERS &
ga[cmA2]=7.32*W[g]A0.820; & ! Price (1931)
Id[lamellae/mm_per_side]=31.28*W[g]A(-.072); & ! Price (1931)
11[cm]=0.0188*W[g]A0.294 ! assumed (see Barber et al. 1991)
FEEDING_OPTIONS linear(l
-------
APPENDIX D. (cont.) Include file GAR.FSH for basic Florida gar parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L.A. Suarez, and R. R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - Brim et al. 1993 . Mercury concentrations in largemouth bass and other fishes of the
! Loxahatchee National Wildlife Refuge. U.S. Fish and Wildlife Service publ.no. PCFO-EC 93-02.
! - Carlander, K.D. 1969. Handbook of Freshwater Fishery Biology, vol 1. 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.
! - Landolt, J.C. and L.G. Hill. 1975. Observations on the gross structure and dimensions of the
! gills of three species of gars (Lepisosteidae) . Coepia 1975(3) :470-475.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
! - Rahn, H., K.B. Rahn, B.J. Howell, C. Cans, and S.M. Tenney. 1971. Air breathing of the
! garfish (Lepisosteus osseus). Respir.Physiol. 11:285-307.
! - Smatresk, N.J. and J.N. Cameron. 1982 . Respiration and acid-base physiology of the spotted
! gar, a bimodel breather II. responces to temperature change and hypercapnia. J.Exp.Biol. 96:281-293.
! - Winger, P.V. and J.K. Andreasen. 1985 . Contaminant residues in fish and sediments from
! lakes in the Atchafalaya River Basin (Louisiana). Arch.Environ.Contarn.Toxicol. 14:579-586.
/ COMMON_NAME gar
/ SPECIES Lepisosteus platyrhincus
/ AGE_CLASS_DURATION year
/ SPAWNING_PERIOD april-may
/ ECOLOGICAL_PARAMETERS &
Ip [cm]=0.15*L[cm] ; & ! assumed
wl [g]=0.00171*L [cm]A3.30; & ! Carlander (1969) for L. osseus 0.00065 adjusted such that 2.3 kg = 720 cm
tl_rO[mm]= 330; & ! Carlander (1969)
yoy[g]=25.0; &
mis [year] =5 ; &
nm[I/day]=1.0*0.882*W [g] A ( - 1 .048) ! see sg[], assume exogenous mortality/total mortality = 1 and b=l.0
/ COMPOSITIONAL_PARAMETERS &
pa[-] = 0.82-1.25*pl [-] ; & ! assumed (see Barber et al. 1991)
pl[-]=0.06 ! Winger and Andreasen (1985)
/ MORPHOMETRIC_PARAMETERS &
ga[cmA2] =3.94*W[g] A0.738; & ! Landolt and Hill(1975)
ld[lamellae/mm_per_side]=38.8*W[g]A(-.0603); & ! Landolt and Hill (1975)
11[cm]=0.0188*W[g]A0.294 ! assumed (see Barber et al. 1991)
/ FEEDING_OPTIONS linear(l
-------
APPENDIX D. (cont.) Include file BULLHEAD.FSH for basic bullhead parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L. A. Suarez, and R.R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - Campbell, R.D. and B.A. Branson. 1978 . Ecology and population dynamics of the black
! bullhead, Ictalurus melas (Rafinesque), in central Kentucky. Tulane Studies in Zoology
! and Botany 20:99-136.
! - Carlander, K.D. 1969 . Handbook of Freshwater Fishery Biology, vol 1. 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.
! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985 . National Contaminant
! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.
! Arch.Environ.Contam.Toxicol. 14:363-388.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
! - Saunders, R.L. 1962 . The irrigation of the gills in fishes II. Efficiency of oxygen uptake in
! relation to respiratory flow, activity and concentrations of oxygen and carbon dioxide.
! Can.J.Zool. 40:817-862.
! - 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. Peterman. 1990 . National Contaminant Biomonitoring
! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.
! Arch.Environ.Contam.Toxicol. 19:748-781.
/ COMMON_NAME bullhead ! yellow bullhead
/ SPECIES Ameiurus natalis
/ AGE_CLASS_DURATION year
/ SPAWNING_PERIOD march-april
/ ECOLOGICAL_PARAMETERS &
Ip[cm]=0.25*L[cm]; & ! assumed
wl [g]=0.0304*L[cm]A2.82; & ! Carlander (1969) adjusted such that 1kg = 40cm
tl_rO[mm] = 150; & ! Carlander (1969)
yoy[g]=10.0; & ! assumed
mis [year] =5 ; &
nm [I/day]=0.90*0.0382*W[g]A(-.537) ! see sg[] and assume exogenous mortality/total mortality = 0.9 and b=l
/ COMPOSITIONAL_PARAMETERS &
pa [-]=0.80-0.94*pl [-] ; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
pl[-]=0.08 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
/ MORPHOMETRIC_PARAMETERS &
ga [cmA2]=4.98*W[g]A0.728; & ! Saunders (1962) for brown bullhead
id[cm]=9.26e-4*W[g]A0.200; & ! Brockway et al. for channel catfish
Id[lamellae/mm_per_side]= 15.9*W[g]A(-0.00917); & ! Saunders (1962) for brown bullhead
11 [cm]=8.96e-3*W[g]A0.270 ! Brockway et al. for channel catfish
/ FEEDING_OPTIONS linear(l
-------
APPENDIX D. (cont.) Include file BLUEGILL.FSH for basic bluegill parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L. A. Suarez, and R.R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - Carlander, K.D. 1977 . Handbook of Freshwater Fishery Biology, vol 2. Iowa State University
! Press. Ames, IA.
! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985 . National Contaminant
! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.
! Arch.Environ.Contam.Toxicol. 14:363-388.
! - 0'Kara, J. The influence of weight and temperature on the metabolic rate of sunfish. Ecology
! 49 : 159-161.
! - Osenberg, C.W. M.H. Olson, and G.G. Mittelbach. 1994. Stage structure in fishes: Resource
! productivity and competition gradients. In: D.J. Stouder, K.L. Fresh, R.J. Feller (eds);
! M. Duke (ass.ed.). Theory and application in fish feeding ecology. University of South
! Carolina Press, p 151-170.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
! - Pierce, R.J. and T.E. Wissing. 1974. Energy cost of food utilization in the bluegill (Lepomis
! macrochirus) . Trans.Am.Fish.Soc. ??: 38-44 .
! - Price, J.W. 1931. Growth and 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. Peterman. 1990 . National Contaminant Biomonitoring
! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.
! Arch.Environ.Contam.Toxicol. 19:748-781.
! - Wohlschlag, D.E. and R.0. Juliano. Seasonal changes in bluegill metabolism. Limnog.
! Oceanog. 4:195-209.
/ COMMON_NAME bluegill
/ SPECIES Lepomis macrochirus
/ AGE_CLASS_DURATION year
/ SPAWNING_PERIOD april-june
/ ECOLOGICAL_PARAMETERS &
Ip [cm]=0.15*L[cm] ; & ! assumed
wl [g]=0.0209*L [cm]A3.06; & ! Carlander (1977) adjusted such that 200g = 20cm
tl_rO[mm]= 80; & ! Carlander (1977)
yoy[g]=5.0; & ! assumed
mis [year] =5 ; &
nm[I/day]=0.1*0.75*0.0208*W[g]A(-.615) ! see sg[] and assume exogenous mortality/total mortality = 0.1
/ COMPOSITIONAL_PARAMETERS &
pa [-]=0.781-0.94*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
pl[-]=0.0597 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
/ MORPHOMETRIC_PARAMETERS &
ga[cmA2]=7.32*W[g]A0.820; & ! Price (1931)
id[cm]=1.15e-3*W[g]A0.172; & ! Brockway et al.
11[cm]=6.55e-3*W[g]A0.259 ! Brockway et al.
/ FEEDING_OPTIONS linear(l
-------
APPENDIX D. (cont.) Include file REDEAR.FSH for basic redear sunfish (shell cracker) parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L.A. Suarez, and R. R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - Carlander, K.D. 1977 . Handbook of Freshwater Fishery Biology, vol 2. Iowa State University
! Press. Ames, IA.
! - Evans, D.0. 1984 . Temperature independence of the annual cycle of standard metabolism in
! the pumpkinseed. Trans.Amer.Fish.Soc. 113:494-512.
! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985 . National Contaminant
! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.
! Arch.Environ.Contarn.Toxicol. 14:363-388.
! - 0'Kara, J. The influence of weight and temperature on the metabolic rate of sunfish. Ecology
! 49:159-161.
! - Osenberg, C.W. M.H. Olson, and G.G. Mittelbach. 1994. Stage structure in fishes: Resource
! productivity and competition gradients. In: D.J. Stouder, K.L. Fresh, R.J. Feller (eds);
! M. Duke (ass.ed.). Theory and application in fish feeding ecology. University of South
! Carolina Press. p 151-170.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
! - Pierce, R.J. and T.E. Wissing. 1974. Energy cost of food utilization in the bluegill (Lepomis
! macrochirus) . Trans.Am.Fish.Soc. ??: 38-44 .
! - Price, J.W. 1931. Growth and 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. Peterman. 1990 . National Contaminant Biomonitoring
! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.
! Arch.Environ.Contam.Toxicol. 19:748-781.
! - Wilbur, R.L. 1969. The redear sunfish in Florida. Florida Game and Fresh Water Fish
! Commission. Fishery Bull no. 5.
/ COMMON_NAME redear ! shellcraker
/ SPECIES Lepomis microlophus
/ AGE_CLASS_DURATION year
/ SPAWNING_PERIOD may-June
/ ECOLOGICAL_PARAMETERS &
wl [g]=0.0148*L[cm]A3.08; & ! Carlander (1977) adjusted such that 300g = 25cm
tl_rO[mm]= 140; & ! Wilbur (1969)
yoy[g]=5.0; & ! assumed
mis [year] =5 ; &
nm[I/day]=0.3*0.75*0.0528*W[g]A(-.761) ! see sg[] and assume exogenous mortality/total mortality = 0.1
/ COMPOSITIONAL_PARAMETERS &
pa [-]=0.781-0.941*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
pl[-]=0.0597 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)
/ MORPHOMETRIC_PARAMETERS &
ga[cmA2]=7.32*W[g]A0.820; & ! Price (1931)
id[cm]=1.15e-3*W[g]A0.172; & ! Brockway et al.
11[cm]=6.55e-3*W[g]A0.259 ! Brockway et al.
/ FEEDING_OPTIONS linear(l
-------
APPENDIX D. (cont.) Include file GAMBUSIA.FSH for basic Gambusia parameters.
! notes: fish file (*.fsh) for BASS version 2.1
! refs:
! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991 . Modelling bioaccumulation of organic
! pollutants in fish with an application to PCBs in Lake Ontario salmonids.
! Can.J.Fish.Aquat.Sci. 48:318-337.
! - Haake, P.W. and J.M. Dean. 1983 . Age and growth of four Everglades fishes using otolith
! techniques. Everglades National Park. Tech. Rep. SFRC-83/03. pp 68.
! - Kushlan, J.A., S.A. Voorhees, W.F. Loftus, and P.C. Frohring. 1986 . Length, mass, and
! calorific relationships of Everglades animals. Fla. Sci. 49:65-79.
! - Meffe, G.K. and F.F. Snelson, jr. 1993. Lipid dynamics during reproduction in two
! livebearing fishes, Gambusia hoibrooki and Poecilia latipinna. Can.J.Fish.Aquat.Sci.
! 50 : 2185-2191.
! - Murphy, P.G. and J.V. Murphy. 1971 . Correlations between respiration and direct uptake of
! DDT in the mosquito fish Gambusia affinis. Bull.Environ.Contarn.Toxicol. 6:581-588.
! - Pandian, T.J. and F.J. Vernberg. 1987 . Animal Energetis - v. 2. Bivalvia through Reptilia.
! Academic Press.
/ COMMON_NAME gambusia ! mosquitofish
/ SPECIES Gambusia affinis
/ AGE_CLASS_DURATION month
/ SPAWNING_PERIOD march-october
/ COMPOSITIONAL_PARAMETERS &
pa[-] = 0.82-1.25*pl [-] ; & ! assumed (see Barber et al. 1991)
pl[-] = 0.125 ! Meffe and Snelson(1993)
/ ECOLOGICAL_PARAMETERS &
Ip [mm]= 0.2*L[mm] ; & ! assumed
log(wl[g])=-4.786+3.032*log(L[mm]); & ! std len Kushlan et al. (1986)
tl_rO[mm] = 35; & ! Carlander (1969)
yoy[g]=0.025; & ! assumed
mis[day] = 240; &
nm[I/day] = 0.1*0.75*0.0027*W[g]A(-0.693) ! see Haake and Dean below
/ MORPHOMETRIC_PARAMETERS &
ga[cmA2] = 2.606*W[g]A0.883; & ! Murphy and Murphy (1971)
Id[lamellae/mm_per_side] = 28.1*W[g]A(-0.0731); & ! interspecific geometric mean
11 [cm] = 0.0188*W[g]A0.294 ! assumed (Barber et al. 1991)
/ FEEDING_OPTIONS linear(0
-------
APPENDIX D. (cont.) Include file for nonfish prey and water level.
/OOl time [day]
/002 benthos[g/mA2]
/003 periphyton[g/mA2]
/004 zooplankton[mg/1]
/005 depth[meter]
/start_data
1 5.0 0.0 0.2
5000 5.0 0.0 0.2
94
-------
APPENDIX E. Example output file (filename.msg) that summarizes user input data, input data errors,
and run time warnings and errors.
GETINPT: summary of user commands in compressed format
/ simulation_control
/ header methylmercury bioaccumulation in a "ponded" everglades community
/ month_tO april
/ length_of_simulation 10[year]
/ temperature temp [celsius]= 25.0 + 10.0*sin(0.172142e-01*t[days]+6.02497)
/ water_level depth [meter]=file(nonfish.dat)
/ biota benthos [g/mA2]=file(nonfish.dat) ; periphyton[g/mA2]=file(nonfish.dat) ; zooplankton [mg/1]=file(nonfish.dat)
/ annual_outputs 10
/ summary_plots pop(length); cfish(length)
/ chemical methylmercury
/ log_kbl 6.00
/ Iog_kb2 5.00
/ log_p -0.4
/ molar_volume 51
/ molar_weight 215.6
/ melting_point 25
/ exposure cwater[ng/1]=0.444;
czplnk [ppb] = (0.6*54.60/0.2)/(
/ common_name bass
/ species micropterus salmoides
/ age_class_duration year
/ spawning_period may-June
/ ecological_parameters Ip [cm]=0.6 + 0.27*1[cm] ; wl[g]=0.0117*1[cm]A3 . 08 ; tl_rO[mm]= 150; yoy[g]=25.0; mis[year]=8;
nm[ I/day] =0. 9*1. 0*0. 0814 *w[g]A(-. 675)
/ compositional_parameters pa [-]=0.80-1.57*pl[-] ; pi[-]=0.000121*w[g]A0.845
/ morphometric_parameters ga[cmA2]=7.32*w[g]A0.820; Id[lamellae/mm_per_side]= 31.28*w[g]A(-.072);
ll[cm]=0.0188*w[g]A0.294
/ feeding_options linear(l
-------
/ species lepomis macrochirus
/ age_class_duration year
/ spawning_period april-June
/ ecological_parameters Ip [cm]=0.15*1 [cm] ; wl[g]=0.0209*1[cm]A3.06; tl_rO[mm]= 80; yoy[g]=5.0; mis[year]=5;
nm[ I/day] =0. 1*0. 75*0. 0208 *w[g]A(-. 615)
/ compositional_parameters pa [-]=0.781-0.94*pl[-] ; pi [-]=0.0597
/ morphometric_parameters ga[cmA2]=7.32*w[g]A0.820; id[cm]=1.15e-3*w[g]A0.172; ll[cm]=6.55e-3*w[g]A0.259
/ feeding_options linear(l
-------
97
-------
ekeeking user supplied control commands
CHKCTRL WARNING: insect standing stock not specified
CHKCTRL WARNING: phytoplankton standing stock not specified
CHKCTRL: no errors detected
98
-------
ambient water temperature temp [celsius] = 25.0 + 10.0*sin(6.02 + 1.721E-02*t[day] )
water level depth [meter] = C : \BASS\proj ects\examplel\nonf ish . dat, column5
benthos standing stock bnths[g(DW)/mA2] = C:\BASS\proj ects\examplel\nonfish.dat,column2
insect standing stock insct[g (DW) /mA2] = not_specif led
periphyton standing stock phytn[g(DW)/mA2] = C:\BASS\projects\examplel\nonfish.dat,columns
phytoplankton standing stock.... pplnk[g(DW)/I] = not_specifled
zooplankton standing stock zplnk[g(DW)/I] = C:\BASS\projects\examplel\nonfish.dat,column4
99
-------
100
-------
log_ac
log_kbl
Iog_kb2
log_p
melting_point.
molar_volume..
molar_weight..
biotransformation rate in bass
biotransformation rate in gar
biotransformation rate in bullhead.
biotransformation rate in bluegill.
biotransformation rate in redear...
biotransformation rate in gambusia.
LC50 for bass LC50[molar]=0.135E-02*KowA-0.871
LC50 for gar LC50[molar]=0.135E-02*KowA-0.871
LC50 for bullhead... LC50[molar]=0.135E-02*KowA-0.871
LC50 for bluegill... LC50[molar]=0.135E-02*KowA-0.871
LC50 for redear LC50[molar]=0.135E-02*KowA-0.871
LC50 for gambusia. . . LC50 [molar]=0.135E-02*KowA-0.871
benthos dietary exposure cbnths [ppm] = 1.485E + 06*cwater[ppm]
insect dietary exposure cinsct [ppm] = 1.06
periphytic dietary exposure cphytn[ppm] = 9.876E + 04*cwater[ppm]
phytoplankton dietary exposure. . . . cpplnk [ppm] = not_specifled
zooplankton dietary exposure czplnk [ppm] = 9.664E + 05*cwater[ppm]
sedimentary exposure csdmnt [ppm] = not_specif led
aqueous exposure cwater [ppm] = 4.440E-07
101
-------
CHKFISH WARNING: bullhead - default reproductive biomass investment assigned
CHKFISH WARNING: bluegill - default reproductive biomass investment assigned
CHKFISH WARNING: gambusia - default reproductive biomass investment assigned
102
-------
assimilation efficiency (fish)
assimilation efficiency (inverts)..
assimilation efficiency (plant)....
gill area ga [cmA2] = 7.320*W[g]A0.820
gastric evacuation 9e [9 (DW) /day] = not_specif led
inter lame liar distance id [cm] = 0.002*W[g]A0.086
lamellar density Id [lamellae/mm] = 31.280*W[g]A-0.072
lamellar length 11 [cm] = 0.019*W[g]A0.294
length of prey Ip [cm] = 0 . 600 + 0 . 270*L [cm]
maximum filtering mf [L/day] = not_specifled
maximum ingest ion nii[g (DW) /day] = not_specif led
maximum longevity mis [day] = 2922 .
non-predatory mortality nm [1/yr] = 26.8*W[g]A-0.675
fraction aqueous pa [-] = 0. 800-1. 570*pl[-]
fraction lipid pi [-] = 0.000*W[g]A0.845
reproductive biomass investment rbi[-] = 0.150
respiratory quotient rq [- ] = 1.000
routine : standard V0_2 rt: std [-] = 2.000
SDA: ingest ion ratio sda : in [- ] = 0.127
specific growth rate sg [I/day] = 0.014*W[g]A-0. 675*exp (0.069*t[celsius])
satiation meal size sm[g (DW) ] = not_specif led
standard V0_2 so [mg o2/hr] = 0.119*W[g] A0 . 766*exp(0.043*t [celsius] )
time to satiation st [minutes] = not_specif led
weight: length wl[g(FW)] = 0.012*L[cm]A3.080
length at first reproduction tl_rO[cm] = 15.0
weight of recruits yoy[g(FW)] =25.0
spawning interval may- June => day ( s) = 62 ,
initial standing stock ... 20.01 [kg(FW)/ha]
ecotoxicological parameters:
mean lethal activiy la[-] = 1.066E-03
103
-------
assimilation efficiency (fish)
assimilation efficiency (inverts) ..
assimilation efficiency (plant)
gill area ga [cmA2] = 3.940*W[g]A0.738
gastric evacuation 9e [9 (DW) /day] = not_specif led
inter lame liar distance id [cm] = 0.002*W[g]A0.072
lamellar density Id [lamellae/mm] = 38.800*W[g]A-0.060
lamellar length 11 [cm] = 0.019*W[g]A0.294
length of prey Ip [cm] = 0 . 000 + 0 . 150*L [cm]
maximum filtering mf [L/day] = not_specifled
maximum ingest ion nii[g (DW) /day] = not_specif led
maximum longevity mis [day] = 1826 .
non-predatory mortality nm [1/yr] = 322.*W[g]A-1.048
fraction aqueous pa [-] = 0. 820-1. 250*pl[-]
fraction lipid pi [-] = 0.060*W[g]A0.000
reproductive biomass investment rbi[-] = 0.150
respiratory quotient rq [- ] = 0.900
routine : standard V0_2 rt: std [-] = 2.000
SDA: ingest ion ratio sda : in [-] = 0.170
specific growth rate sg [I/day] = 0.156*W[g]A-l. 048*exp (0.069*t[celsius])
satiation meal size sm[g (DW) ] = not_specif led
standard V0_2 so [mg o2/hr] = 0 . 013 *W [g] Al . 000*exp (0.049*t[celsius])
time to satiation st [minutes] = not_specif led
weight: length wl[g(FW)] = 0.002*L[cm]A3.300
length at first reproduction tl_rO[cm] = 33.0
weight of recruits yoy[g(FW)] =25.0
spawning interval april-may => day (s) = 31,
initial standing stock ... 10.01 [kg(FW)/ha]
ecotoxicological parameters:
mean lethal activiy la[-] = 1.066E-03
104
-------
assimilation efficiency (fish)
assimilation efficiency (inverts) ..
assimilation efficiency (plant)
gill area ga [cmA2] = 4.980*W[g]A0.728
gastric evacuation 9e [9 (DW) /day] = not_specif led
inter lame liar distance id [cm] = 0.001*W[g]A0.200
lamellar density Id [lamellae/mm] = 15.900*W[g]A-0.009
lamellar length 11 [cm] = 0.009*W[g]A0.270
length of prey Ip [cm] = 0 . 000 + 0 . 250*L [cm]
maximum filtering mf [L/day] = not_specifled
maximum ingest ion nii[g (DW) /day] = not_specif led
maximum longevity mis [day] = 1826 .
non-predatory mortality nm [1/yr] = 12.6*W[g]A-0.537
fraction aqueous pa [-] = 0. 800-0. 940*pl[-]
fraction lipid pi [-] = 0.080*W[g]A0.000
reproductive biomass investment rbi[-] = 0.150
respiratory quotient rq [- ] = 1.000
routine : standard V0_2 rt: std [-] = 2.000
SDA: ingest ion ratio sda : in [-] = 0.170
specific growth rate sg [I/day] = 0.007*W[g]A-0. 537*exp (0.069*t[celsius])
satiation meal size sm[g (DW) ] = not_specif led
standard V0_2 so [mg o2/hr] = 0 . 001 *W [g] Al . 020*exp (0.184*t[celsius])
time to satiation st [minutes] = not_specif led
weight: length wl[g(FW)] = 0.030*L[cm]A2.820
length at first reproduction tl_rO[cm] = 15.0
weight of recruits yoy [g (FW) ] =10.0
spawning interval march-april => day(s) = 1,
initial conditions
initial standing stock ... 19.99 [kg(FW)/ha]
ecotoxicological parameters:
mean lethal activiy
105
-------
assimilation efficiency (fish) ae[-] = 0.890
assimilation efficiency (inverts) ... ae [-] = 0.660
assimilation efficiency (plant) ae[-] = 0.440
gill area ga [cmA2] = 7.320*W[g]A0.820
gastric evacuation 9e [9 (DW) /day] = not_specif led
inter lame liar distance id [cm] = 0.001*W[g]A0.172
lamellar density Id [lamellae/mm] = not_specif led
lamellar length 11 [cm] = 0.007*W[g]A0.259
length of prey Ip [cm] = 0 . 000 + 0 . 150*L [cm]
maximum filtering mf [L/day] = not_specifled
maximum ingest ion nii[g (DW) /day] = not_specif led
maximum longevity mis [day] = 1826 .
non-predatory mortality nm [1/yr] = 0.570*W[g]A-0.615
fraction aqueous pa [-] = 0. 781-0. 940*pl[-]
fraction lipid pi [-] = 0.060*W[g]A0.000
reproductive biomass investment rbi[-] = 0.150
respiratory quotient rq [- ] = 1.000
routine : standard V0_2 rt: std [-] = 2.000
SDA: ingest ion ratio sda : in [- ] = 0.127
specific growth rate sg [I/day] = 0.004*W[g]A-0. 615*exp (0.069*t[celsius])
satiation meal size sm[g (DW) ] = not_specif led
standard V0_2 so [mg o2/hr] = 0.024*W[g] A0 . 849*exp(0.141*t [celsius] )
time to satiation st [minutes] = not_specif led
weight: length wl[g(FW)] = 0.021*L[cm]A3.060
length at first reproduction tl_rO[cm] = 8.000
weight of recruits yoy[g(FW)] = 5.000
spawning interval april- June => day ( s) = 47 ,
initial conditions
initial standing stock ... 200.29 [kg(FW)/ha]
ecotoxicological parameters:
mean lethal activiy la[-] = 1.066E-03
106
-------
assimilation efficiency (fish)
assimilation efficiency (inverts)..
assimilation efficiency (plant)....
gill area ga [cmA2] = 7.320*W[g]A0.820
gastric evacuation 9e [9 (DW) /day] = not_specif led
inter lame liar distance id [cm] = 0.001*W[g]A0.172
lamellar density Id [lamellae/mm] = not_specif led
lamellar length 11 [cm] = 0.007*W[g]A0.259
length of prey Ip [cm] = not_specif led
maximum filtering mf [L/day] = not_specifled
maximum ingest ion nii[g (DW) /day] = not_specif led
maximum longevity mis [day] = 1826 .
non-predatory mortality nm [1/yr] = 4.34*W[g]A-0.761
fraction aqueous pa [-] = 0. 781-0. 941*pl[-]
fraction lipid pi [-] = 0.060*W[g]A0.000
reproductive biomass investment rbi[-] = 0.150
respiratory quotient rq [- ] = 1.000
routine : standard V0_2 rt: std [-] = 2.000
SDA: ingest ion ratio sda : in [- ] = 0.127
specific growth rate sg [I/day] = 0.009*W[g]A-0. 761*exp (0.069*t[celsius])
satiation meal size sm[g (DW) ] = not_specif led
standard V0_2 so [mg o2/hr] = 0 . 047 *W [g] A0 . 744*exp (0.044*t[celsius])
time to satiation st [minutes] = not_specif led
weight: length wl[g(FW)] = 0.015*L[cm]A3.080
length at first reproduction tl_rO[cm] = 14.0
weight of recruits yoy[g(FW)] = 5.000
spawning interval may- June => day ( s) = 62 ,
age/size
-1.
-1.
initial standing stock . . . 100 . 01 [kg(FW)/ha]
ecotoxicological parameters:
mean lethal activiy la[-] = 1.066E-03
107
-------
assimilation efficiency (fish)....
assimilation efficiency (inverts).
assimilation efficiency (plant)...
gill area
gastric evacuation
interlamellar distance
lamellar density
lamellar length
length of prey
maximum filtering
maximum ingestion
maximum longevity
non-predatory mortality
fraction aqueous
fraction lipid
reproductive biomass investment...
respiratory quotient
routine:standard V0_2
SDA:ingestion ratio
specific growth rate
satiation meal size
standard V0_2
time to satiation
weight:length
length at first reproduction
weight of recruits
spawning interval
ae [-] = 0.890
ae [-] = 0.660
ae [-] = 0 . 440
ga[cmA2] = 2.606*W[g]A0.883
ge[g(DW)/day] = not_specifled
id [cm] = 0.002*W[g]A0.087
Id [lamellae/mm] = 28.100*W[g]A-0.073
11 [cm] = 0.019*W[g]A0.294
Ip [cm] = 0.000 + 0.200*L[cm]
mf[L/day] = not_specifled
mi[g(DW)/day] = not_specifled
mis[day] = 240.
nm[l/yr] = 0 . 740E- 01*W [g] A- 0 . 693
pa[-] = 0.820-1.250*pl [-]
pi [-] = 0.125*W[g] A0.000
rbi[-] = 0.150
rq[-] = 1.000
rt:std[-] = 2.000
sda:in[-] = 0.170
sg [I/day] = 0.000*W[g]A-0. 693*exp (0.069*t[celsius])
sm[g(DW)] = not_specifled
so[mg o2/hr] = 0.022*W[g]A0.695*exp(0.055*t[celsius])
st [minutes] = not_specifled
wl[g(FW)] = 0.018*L[cm]A3.032
tl_rO[cm] = 3.500
yoy[g(FW)] = 0.025
march-October => day(s) = 15, 45, 75, 105, 135, 165, 1
initial conditions
initial standing stock ...
ecotoxicological parameters:
mean lethal activiy la[-] = 1.066E-03
108
-------
summary of special conditions during the simulation:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR :
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
RKINT RESTART:
RKINT RESTART:
RKINT_RESTART:
RKINT_RESTART:
RKINT RESTART:
BASS ODESOLVR:
BASS_ODESOLVR:
BASS_ODESOLVR:
BASS ODESOLVR:
BASS ODESOLVR:
BASS_ODESOLVR:
euler step
euler step
euler step
euler step
euler step
dn/dt for
euler step
euler step
euler step
euler step
euler step
euler step
dn/dt for
euler step
euler step
species 6
species 2
species 3
species 4
species 1
species 5
euler step
euler step
euler step
euler step
euler step
species 2
species 3
species 4
species 1
species 5
species 2
species 3
species 4
species 1
species 5
species 2
species 3
species 4
species 1
species 5
species 2
species 3
species 4
species 1
species 5
euler step
euler step
euler step
euler step
euler step
dn/dt for
species 3
species 2
species 1
species 3
species 2
species 1
species 4
species 5
euler step
euler step
euler step
euler step
dn/dt for
species 3
species 2
species 1
species 5
species 3
species 2
taken
taken
taken
taken
taken
species
taken
taken
taken
taken
taken
taken
species
taken
taken
species
cohort
cohort
cohort
cohort
cohort
cohort
taken
taken
taken
taken
taken
species
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
taken
taken
taken
taken
taken
species
cohort
cohort
cohort
cohort
cohort
cohort
cohort
cohort
taken
taken
taken
taken
species
cohort
cohort
cohort
cohort
cohort
cohort
at t= 4.50
at t= 4.62
at t= 4.63
at t= 4.63
at t= 4.63
6 cohort 1 approximates a
at t= 7.50
at t= 7.91
at t= 7.98
at t= 7.99
at t= 7.99
at t= 7.99
6 cohort 2 approximates a
at t= 9.50
at t =
4 dies
5 dies
5 dies
5 dies
8 dies
5 dies
at t =
at t =
at t =
at t =
at t =
4 dies
4 dies
4 dies
7 dies
4 dies
3 dies
3 dies
3 dies
6 dies
3 dies
2 dies
2 dies
2 dies
5 dies
2 dies
1 dies
1 dies
1 dies
4 dies
1 dies
at t =
at t =
at t =
at t =
at t =
10 . 0
on day= 12.0
on day=
on day=
on day=
on day=
on day=
350.
350.
350.
350.
350.
18.0
18.0
18 . 0
48.0
48.0
on day= 383.
on day=
on day=
on day=
on day=
on day=
on day
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
0.156E+04
0.156E+04
0 . 156E + 04
0 . 156E + 04
0.156E+04
383.
383.
413.
413.
748.
748 .
748.
778.
778.
0 . 111E+04
0 . 111E+04
0.111E+04
0.114E+04
0 . 114E + 04
0 . 148E+04
0.148E+04
0.148E+04
0 . 151E + 04
0 . 151E + 04
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
due
4 cohort 2 approximates a
1 dies
1 dies
3 dies
1 dies
1 dies
2 dies
1 dies
1 dies
at t =
at t =
at t =
at t =
on day=
on day=
on day=
on day=
on day=
on day=
on day=
on day=
0 . 226E + 04
0 . 226E + 04
0.226E+04
0.226E+04
0 . 183E + 04
0 . 186E + 04
0.187E+04
0.219E+04
0 . 222E+04
0 . 224E+04
0.224E+04
0.226E+04
4 cohort 2 approximat
1 dies
1 dies
1 dies
1 dies
1 dies
1 dies
on day=
on day=
on day=
on day=
on day=
on day=
0.256E+04
0.259E+04
0.260E+04
0 . 262E+04
0 . 292E + 04
0.295E+04
due
due
due
due
due
due
due
due
es a
due
due
due
due
due
due
step function
step function
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
to
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
step function
to
to
to
to
to
to
to
to
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
step function
to
to
to
to
to
to
exceeding
exceeding
exceeding
exceeding
exceeding
exceeding
for t =
for t =
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
for t =
maximum
maximum
maximum
maximum
maximum
maximum
maximum
maximum
for t =
maximum
maximum
maximum
maximum
maximum
maximum
4.63
7.99
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
longe
solution restarted
solution restarted
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vi tv
v _i_ L-y
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
vity
0.156E+04 solution restarted
longe
longe
longe
longe
longe
longe
longe
longe
0.226
longe
longe
longe
longe
longe
longe
vity
vity
vity
vity
vity
vity
vity
vity
E+04 solution restarted
vity
vity
vity
vity
vity
vity
109
-------
BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.297E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 1 cohort 1 dies on day= 0.299E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.299E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.329E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.332E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.334E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 1 cohort 1 dies on day= 0.335E+04 due to exceeding maximum longevity
BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.335E+04 due to exceeding maximum longevity
110
-------
total cpu =
bass_odesolvr cpu =
bass_dydt cpu =
dwdtfIx cpu =
dbdtfIx cpu =
bass_foodwebl cpu =
bass_foodwebO cpu =
ee_adj cpu =
dry21ive cpu =
R-K integrator cpu =
load/unload cpu =
bass_restart cpu =
mean h =
111
-------
APPENDIX F. Example output file (filename.bss) that tabulates annual bioenergetic and contaminant
fluxes.
112
-------
5.541E-03
2.696E-03
1 . 814E-03
1.418E-03
1.194E-03
1 . 051E-03
9 . 519E-04
8.798E-04
8.769E-04
[ug/g(FW)] log(BAF) log(BMP)
mean body cone. weighted by cohort biomasses = 0.817
mean body cone. weighted by cohort densities = 0.671
log mean BAF weighted by cohort biomasses = 6.26
log mean BAF weighted by cohort densities = 6.18
113
-------
9.307E-04
1.424E-03
1.586E-03
1.686E-03
1.764E-03
1.819E-03
1.872E-03
1.922E-03
2.028E-03
114
-------
155. 88.0
263 . 194.
347. 279.
417. 350.
476. 409.
45.4 40.0
mean body cone. weighted by cohort biomasses = 0.694
mean body cone. weighted by cohort densities = 0.615
log mean BAF weighted by cohort biomasses = 6.19
log mean BAF weighted by cohort densities = 6.14
1 9.945E-04
2 1.309E-03
3 1.429E-03
4 1.503E-03
5 1.548E-03
6 1.614E-03
76.9 0.00
243. 0.00
395. 0.00
530 . 0.00
644 . 0.00
64.0 0.00
115
-------
mean body cone. weighted by cohort biomasses = 0.539
mean body cone. weighted by cohort densities = 0.467
log mean BAF weighted by cohort biomasses = 6.08
log mean BAF weighted by cohort densities = 6.02
1 8.380E-04
2 1.017E-03
3 1.071E-03
4 1.141E-03
5 1 . 191E-03
6 1.242E-03
116
-------
mean body cone. weighted by cohort biomasses = 0.495
mean body cone. weighted by cohort densities = 0.482
log mean BAF weighted by cohort biomasses = 6.05
log mean BAF weighted by cohort densities = 6.04
117
-------
24.4 14.6
52.6 36.7
73.2 54.9
90.7 70.8
106. 84.6
22.5 18.2
mean body cone. weighted by cohort biomasses = 0.416
mean body cone. weighted by cohort densities = 0.370
log mean BAF weighted by cohort biomasses = 5.97
log mean BAF weighted by cohort densities = 5.92
118
-------
119
-------
mean
population
[#/ha]
6.853E+03
5.058E+03
3.542E+03
2.698E+03
2.177E+03
1.747E+03
1.483E+03
1.271E+03
240.
2.507E+04
1.141E+03
community level fluxes
120
-------
mean
population
[#/ha]
.478E+04
. 684E + 03
447.
.235E+03
.564E+03
101 .
. 144E + 04
6.110E+04
3.648E+04
2.917E+04
2.707E+04
1.701E+04
349.
1 . 712E + 05
6.717E+03 / 1.600E+03
2.231E+03 / 147.
606. / 20.4
241 . / 5.54
community consumption [g(DW)/ha/yr] of benthos
community consumption [g(DW)/ha/yr] of insects
community consumption [g(DW)/ha/yr] of periphyton....
community consumption [g(DW)/ha/yr] of phytoplankton.
community consumption [g(DW)/ha/yr] of zooplankton...
community consumption [g(DW)/ha/yr] of fish
(0.61 of total consumption)
(0.00 of total consumption)
(0.00 of total consumption)
(0.00 of total consumption)
121
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APPENDIX G. Example output file (filename.plx) that plots the variables requested by the user.
122
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INDEX
chemical parameters
/chemical 35
/exposure 35
/lethality 36
/log_ac 37
/log_kbl 37
/Iog_kb2 37
/log_p 37
/melting_point 37
/metabolism 37
/molar_volume 37
/molar_weight 38
files
chemical exposure files (.chm) 44
chemical property files (.prp) 44, 45
community files (.cmm) 44, 45
fishfiles (.fhs) 43, 45
generalized input file 32
include files 32, 43
management 43
output file (.bss) 43
output file (.msg) 43
output file (.plx) 43
project files (.prj) 44
fish parameters
/age_class_duration 38
/common_name 38
/compostional_parameters 38
/ecological_parameters 38
/feeding 39
/initial_conditions 39
/morphometric_parameters 39
/physiological_parameters 40
/spawning_period 40
/species 40
future features 60
restrictions
order of commands 45
specifying chemical names 35
specifying common names 38
units recognized by BASS 41
simulation controls
/annual_outputs 33
/annual_plots 33
/biota 33
/fgets 34
/header 34
/month_tO 34
/nsteps 34
/simulation_control 34
/simulation_interval 34
/summary_plots 34
/temperature 35
/water_level 35
simulation options
dietary exposure via benthos 36
dietary exposure via insects 36
dietary exposure via periphyton 36
dietary exposure via phytoplankton 36
dietary exposure via zooplankton 36
direct aqueous exposures 36
specifying non-fish prey 33,38
specifying output 33, 34
specifying water levels 35
specifying water temperatures 35
syntax
commenting a line 32
continuing a line 32
specifying an include file 32
specifying units 41
user specified functions 41
technical support
reporting comments 31
reporting problems 31
reporting suggestions 31
124
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