EPA Report No. 600/R-01/035
update 2.3, October 2018
Bioaccumulation and Aquatic
System Simulator (bass)
User's Manual
Version 2.3
by
M. Craig Barber
Systems Exposure 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 Systems Exposure Division of the National Exposure Research
Laboratory in Athens, Georgia. Mention of trade names or commercial products does not constitute endorsement or recommendation
for use.
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Foreword
This report describes the theoretical development, parameterization, and application software of the BASS Bioaccumulation and Aquatic
System Simulator. This generalized, community-based simulation model is designed to predict the population and bioaccumulation
dynamics of age-structured fish communities exposed to hydrophobic organic chemicals and class B and borderline metals that
complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). This report is not a case study on the
application of BASS but a reference and user's guide. The intended audience of this report includes EPA Program and Regional
environmental engineers and scientists, technical staff in other state and federal agencies, and fisheries ecologists who routinely analyze
and estimate the bioaccumulation of chemicals in fish for ecological or human health exposure assessments.
Process-based models like bass 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 such as in the case of analyzing alternative management options for new or existing chemicals.
Jay Garland, Ph.D.
Director
Systems Exposure Division
Athens, GA 30605 USA
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Abstract
BASS (Bioaccumulation and Aquatic System Simulator) is a Fortran 95 simulation program that predicts growth, population, and
bioaccumulation dynamics of age-structured fish assemblages exposed to hydrophobic organic pollutants and class B or borderline
metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). The model's
bioaccumulation algorithms are based on diffusion kinetics and are coupled to a process-based model for the growth of individual fish.
These algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals that together determine
diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics used by the model include the fish's gill
morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and structural organic content).
Relevant physico-chemical properties include 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 delineated by defining one or more foraging classes for each fish species based on body weight, body
length, or age. The dietary composition of each of these foraging classes is specified as a combination of benthos, incidental terrestrial
insects, periphyton / attached algae, phytoplankton, zooplankton, and one or more fish species. Population dynamics are generated
by predatory mortalities defined by the community's food web and standing stocks, physiological mortality rates, maximum longevity
of species, toxicological responses to chemical exposures, and dispersal. The model's temporal and spatial scales are that of a day and
of a hectare, respectively.
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Table of Contents
Abstract iv
Figures vii
Tables viii
Acknowledgment ix
1. Introduction 1
2. Model Formulation 4
2.1. Modeling Internal Distribution of Chemicals 4
2.2. Modeling Exchange from Water 5
2.3. Modeling Exchange from Food 7
2.4. Modeling Chemical Biotransformation 9
2.5. Modeling Temperature Effects on Physiological Rates 9
2.6. Modeling Growth of Fish 10
2.7. Modeling Predator-Prey Interactions 12
2.8. Modeling Stable Isotopes and Trophic Position 14
2.9. Modeling Dispersal, Non-Predatory Mortalities, and Recruitment 15
2.10. Modeling Habitat Effects 16
2.11. Modeling Non-fish Compartments 16
2.12. Modeling Toxicological Effects 18
3. Model Parameterization 25
3.1. Parameterizing Kf 25
3.2. Parameters for Gill Exchange 25
3.3. Bioenergetic and Growth Parameters 26
3.4. Procedures Used to Generate the bass Database 26
3.5. Suggested Calibration Procedures 29
4. BASS User Guide 37
4.1. General Model Structure and Features 37
4.2. New Features 38
4.3. Input File Structure 38
4.3.1. Simulation Control Commands 39
4.3.2. Chemical Input Commands 42
4.3.3. Fish Input Commands 45
4.3.4. Non-fish Input Commands 51
4.4. Input Data Syntax 52
4.4.1. Units Recognized by BASS 52
4.4.2. User-specified Functions 52
4.4.3. User-specified Parameter Files 53
4.5. BASS Include File Structure 54
4.6. Output Files Generated by BASS 56
4.7. Command Line Options 57
5. bass Model Software and Graphical User Interface 61
5.1. Software Overview 61
5.2. Installation Procedures 62
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5.3. BASS GUI Operation 62
5.3.1. BASS File Editors 63
5.3.2. BASS Command Editors 64
5.3.3. Special Function Editors 65
5.3.4. File and Folder Operations 65
5.4. The BASS Output Analyzer 65
5.5. The BASS Parameterization Software 65
6. Example Applications 75
6.1. BASS Software Distribution Examples 75
6.2. Simulating Methylmercury Bioaccumulation in an Everglades Fish Community 76
6.3. Simulating PCB Bioaccumulation in a Fish Community Impacted by a Superfund Site 77
7. Model Quality Assurance 83
7.1. Questions Regarding QA of a Model's Scientific Foundations 83
7.2. Questions Regarding QA of a Model's Implementation 84
7.3. Questions Regarding QA of Model Documentation and Applications 89
REFERENCES 91
APPENDICES 112
Appendix A. Equilibrium complexation model for metals 112
Appendix B. Modeling diffusive chemical exchange across fish gills with ventilation and perfusion effects 115
Appendix C. Derivation of the consistency condition for feeding electivities 116
INDEX 117
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Figures
Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and
ventilation / perfusion ratio 22
Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and
ventilation / perfusion ratio 23
Figure 2.3 Functional behavior of Equation (2.53) 23
Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters
used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,
25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish's upper tolerance limit. 32
Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters
used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,
25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish's upper tolerance limit. 33
Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning bass's algorithm 34
Figure 5.1 BASS GUI Current BASS Directory window 62
Figure 5.2 General structure of BASS GUI file editors 63
Figure 5.3 Structure of BASS GUI project file editor 64
Figure 5.4 GUI command editor for simple strings 66
Figure 5.5 GUI command editor for simple strings with drop-down selection 66
Figure 5.6 GUI command editor for numeric data with user-specified units 67
Figure 5.7 GUI command editor for numeric data fixed units 67
Figure 5.8 GUI command editor for forcing functions 68
Figure 5.9 GUI command editor for feeding model options 68
Figure 5.10 GUI command editor for compositional and morphometric parameters 68
Figure 5.11 GUI command editor for nondiet ecological parameters 68
Figure 5.12 GUI command editor for fish diets 69
Figure 5.13 GUI command editor for physiological parameters 70
Figure 5.14 GUI command editor for cohort initial conditions 70
Figure 5.15 GUI command editor for spawning parameters 71
Figure 5.16 GUI command editor for fishery parameters 71
Figure 5.17 GUI command editor for non-fish biota as forcing functions 71
Figure 5.18 GUI command editor for non-fish biota as state variables 72
Figure 5.19 GUI command editor for non-fish bioaccumulation factors 72
Figure 5.20 GUI command editor for chemical biotransformation parameters 72
Figure 5.21 GUI command editor for chemical toxicity parameters 73
Figure 5.22 GUI command editor for automatic graphing selections 73
Figure 5.23 GUI Block comment editor 74
Figure 5.24 Data file editor for forcing functions specified as files 74
Figure 6.1 Simulated biomasses (kg wet wt/ha) of fishes in an Everglades canal 79
Figure 6.2 Simulated MeHg concentrations (mg/kg wet wt) of fishes in an Everglades canal 79
Figure 6.3 Simulated biomasses (kg wet wt/ha) of fishes in Twelve-Mile Creek, SC 80
Figure 6.4 Simulated total PCB concentrations (mg/kg wet wt) of fishes in Twelve-Mile Creek, SC 80
Figure 6.5 Simulated total PCB concentrations (mg/kg wet wt) of Bluegill by year class in Twelve-Mile Creek, SC 81
Figure 6.6 Simulated total PCB concentrations (mg/kg wet wt) of Channel catfish by year class in Twelve-Mile Creek, SC. ... 81
Figure 6.7 Simulated total PCB concentrations (mg/kg wet wt) of Largemouth bass by year class in Twelve-Mile Creek, SC. . 82
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Tables
Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental model
processes, rates, or rate coefficients 20
Table 3.1 Summary of NL2SOL regressions for Equation (3.34) fitted to maximum daily consumption rates and satiation meal
size reported in the literature 35
Table 3.2 Summary of NL2SOL regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by
the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily
consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit 36
Table 4.1 Valid Unit Prefixes 58
Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive and summarizes only
commonly used unit names that bass's units conversion program recognizes 59
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Acknowledgment
Many people have contributed to the development of the BASS bioaccumulation and community model through technical discussions,
software development and oversight, and peer reviews. This author wishes to thank Drs. Daniel Beyers (Colorado State University),
James E. Breck (University of Michigan), Lawrence Burkhard (USEPA, NHEERL), Lawrence Burns (USEPA, NERL deceased),
Michael Cyterski (USEPA, NERL), Russell Erickson (USEPA, NHEERL), Timothy Gleason (USEPA, NHEERL), John M. Johnston
(USEPA, NERL), Ray Lassiter (USEPA, NERL retired), Richard Lee (USEPA, OCSPP), David Mauriello (USEPA, OCSPP retired),
John W. Nichols (USEPA, NHEERL), Ross J. Norstrom (Environment Canada), Brenda Rashleigh (USEPA, NHEERL), David
Rodgers (Ontario Power Technologies), Dick Sijm [The Netherlands National Institute for Public Health and the Environment
(RVIM)], Luis Suarez (USEPA, NERL retired), Donald Rodier (USEPA, OCSPP retired), Robert R. Swank (USEPA, NERL retired),
and Anett Trebitz (USEPA, NHEERL). Thanks and appreciation are also given to Robert Ambrose (USEPA, NERL retired), Ron
Beloin (formerly of CSC Inc., Athens, GA), Sandra L. Bird (USEPA, NERL retired), Benjamin Daniel (formerly of CSC Inc., Athens,
GA), Mike Galvin (USEPA, NERL), Lourdes Prieto (USEPA, NERL), Fran Rauschenberg (USEPA, NERL), and Frank Standi
(USEPA, NERL retired).
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1. Introduction
Fish health can be defined from ecological and human health /
value perspectives in many ways. Questions relating to an
ecological perspective include:
1. Are individual fish growth and condition sufficient to
enable them to survive periods of natural (e.g.,
overwintering) and man-induced stress?
2. Are individual fish species able to maintain sustainable
populations? For example, is individual growth adequate
to attain the minimum body size required for
reproduction? Is there adequate physical environment for
successful spawning? Is there adequate physical habitat
for the survival of the young-of-year?
3. Do regional fish assemblages exhibit their expected
biodiversity or community structure based on
biogeographical and physical habitat considerations?
4. Are regional fish assemblages maintaining their expected
level of productivity based on biogeographical and
physical habitat considerations?
5. Are appropriately sized fish abundant enough to maintain
piscivorous wildlife (e.g., birds, mammals, and reptiles)
during breeding and non-breeding conditions?
6. Are potential fish prey sufficiently free of contaminants
(endocrine disruptors, heavy metals, etc.) so as not to
interfere with the growth and reproduction of piscivorous
wildlife?
7. How will native fishes respond to the introduction of
nonnative fish species, including those stocked for
recreational fishing?
From a human health or use perspective, another important
question related to fish health is:
8. Is the fish community / assemblage of concern fishable?
That is, are target fish species sufficiently abundant and of
the desired quality? Fish quality in this context can be
defined by desired body sizes (e.g., legal or trophy length)
and the absence of chemical contaminants.
Some important indicators that have been used often 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, and sediment loads); (2) community species
and functional diversity; (3) total community biomass (kg/ha or
kg/km); (4) population density (fish/ha or fish/km) and biomass
(kg/ha or kg/km) of dominant or valued species; (5) age and size
class structure of dominant or valued species; (6) annual
productivity of the community and its dominant species; (7)
individual growth rates and condition factors (i.e., the ratio of a
fish's current body weight to its expected body weight based on
its length); and (8) levels of chemical contaminants in muscle or
whole fish.
To evaluate alternative management options or to forecast
expected future consequences of existing conditions, however,
simulation models that can predict individual and population
growth of fish and their patterns of chemical bioaccumulation are
also important tools for assessing several of the aforementioned
dimensions of fish health.
Although the growth of individual fish has often been described
using empirical models such as the von Bertalanffy, logistic,
Gompertz, or Richards models [see for example Ricker (1979)
and Schnute (1981)], process-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), and van Nes
(2002) have become important tools for predicting fish growth.
Because these process-based models predict fish growth based on
the mass or energy balance of ingestion, egestion, respiration,
specific dynamic action, and excretion, they can generally be
parameterized independently of their current application.
Moreover, because of the inherent difficulties in obtaining
reliable field-based measurements of fish population dynamics
and productivity, researchers are increasingly using such
bioenergetic models to characterize these population and
community level endpoints. See for example Stewart and Ibarra
(1991) and Roell and Orth (1993).
The ability to predict accurately the bioaccumulation of
chemicals in fish has become an essential component of
ecological and human health risk assessments for chemical
pollutants. Not only are accurate estimates needed to predict
realistic dietary exposures to humans and piscivorous wildlife,
but they are also needed to assess potential ecological risks to
fish assemblages themselves more accurately. Although
exposure-referenced benchmarks such as LC50 and EC50 have
been widely used for hazard assessments, most deleterious
effects of chemical pollutants are caused by the internal
accumulation of those compounds, rather than their
environmental concentrations per se. Many authors (Neely 1984,
Friant and Henry 1985, McCarty et al. 1985, McCarty 1986,
Connell and Markwell 1992, McCarty and Mackay 1993,
Verhaar et al. 1995, van Loon et al. 1997) have discussed the
benefits of explicitly considering chemical bioaccumulation
when assessing expected ecological consequences of chemical
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pollutants in aquatic and marine ecosystems. Residue-based
toxicity studies confirm this supposition (Opperhuizen and
Schrap 1988, van Hoogen and Opperhuizen 1988, Donkin et al.
1989, Tas et al. 1991, van Wezel et al. 1995, Driscoll and
Landrum 1997).
Although concentrations of moderately hydrophobic chemicals
in fish can often be predicted accurately 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 either
considerably above or below those predicted by equilibrium
partitioning. Several factors can be identified to explain these
discrepancies.
Lower than expected contamination levels can result when the
length of exposure is insufficient to allow chemicals to
equilibrate. Because bioconcentration and bioaccumulation are
generally treated as first-order linear 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
(PCB s) and dioxins that have elimination half-lives ranging from
months to over a year, the time to equilibrium can be on the order
of years. If the fish species of concern is short lived, the time
needed for equilibrium can exceed the species' expected life
span. Even when time is sufficient for equilibration, whole-body
concentrations of fish can be much lower than those expected
from thermodynamic partitioning due to physical dilution of the
chemical that accompanies body growth or due to in situ
biotransformation of the parent compound.
One of two possible assumptions is implicitly made whenever
equilibrium-based estimators are used. The first of these is that
only the selected reference route of exposure is significant in
determining the total chemical accumulation in fish. The
alternative assumption is that there are multiple routes of
exposure that all covary with the chosen reference pathway in a
constant manner. For bioconcentration factors (BCFs), the
implicit assumption is that virtually the entire burden is
exchanged directly with the water across the fish's gills or
possibly across its skin. Although direct aqueous uptake is
certainly the most significant route of exchange for moderately
hydrophobic chemicals, dietary uptake accounts for most of a
fish's body burden for extremely hydrophobic chemicals. This
shift in the relative significance of the direct aqueous and dietary
pathways 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 (,ug/d), is the product of its
ventilation volume, Q (ml/d), and the chemical's aqueous
concentration, Cw (,ug/ml ). Similarly, the fish's dietary exposure,
DE (jjg/d), is the product of its feeding rate, Fw (g wet wt/d), and
the chemical's concentration in the fish's prey, Cp (ijg/g wet wt).
If the fish feeds only on one type of prey that has equilibrated
with the water, one can calculate when the fish's aqueous and
dietary exposures are equal using the equations
AE = DE
QCW = FWCP (1.1)
Q/Fw = BCF
Using data from Stewart et al. (1983) and Erickson and McKim
(1990), the ventilation-to-feeding ratio for a 1 kg trout would be
on the order of 1043 ml/g. Assuming that the quantitative
structure activity relationship (QS AR) for the trout's prey is BCF
= 0.048 Kow (Mackay 1982), one would conclude that food is the
trout's predominant route of exposure for any chemical whose
octanol / water partition coefficient is greater than 105 6.
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 chemical
concentrations of the fish's gut contents to increase (Connolly
andPedersen 1988, Gobas etal. 1988). Predicting residue levels
of 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
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 a fish's chemical exchanges
from food and water in concert with its growth, provide objective
and scientifically sound frameworks that can overcome many of
the aforementioned limitations of equilibrium-based BAFs and
BCFs. Although numerous models have been developed toward
this end (Norstrom et al. 1976, Thomann 1981, Jensen et al.
1982, Thomann and Connolly 1984, Barber et al. 1987, Gobas
et al. 1988, Barber et al. 1991, Borgmann and Whittle 1992,
Thomann et al. 1992, Gobas 1993, Madenjian et al. 1993,
Jackson 1996, Luk and Brockway 1997, Morrison et al. 1999,
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Arnot and Gobas 2004, Gewurtz et al. 2006, Park et al. 2008,
Lopes et al. 2012), they differ significantly regarding how food
web structure and dietary exposures are represented.
This report describes the theoretical framework,
parameterization, and use of BASS (Bioaccumulation and Aquatic
System Simulator). This generalized, process-based, Fortran 95
simulation model is designed to predict the growth of individuals
and populations within an age-structured fish community and the
bioaccumulation dynamics of those fish when exposed to
mixtures of metals and organic chemicals. The model is
formulated so that its parameterization does not rely upon
calibration data sets from specific toxicokinetic and population
field studies, but rather upon physical and chemical properties
that can be estimated using chemical property calculators such as
CLOGP (http://www.biobvte.com/bb/prod/clogp40.html) or the
Chemical Transformation Simulator (CTS) (Wolfe et al. 2016)
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 growth of
individuals and populations within an age-structured fish
community, BASS solves the following system of differential
equations for each age class or cohort of fish:
dB
—- = J-+ Jj ~ Jht
dt g '
dW'
dt
= F, - E. - R - EX - SDA
—=-EM-NM-PM
dt
(2.1)
(2.2)
(2.3)
where B, and Wd are the chemical body burden (jjg/fish) and dry
body weight (g dry wt/fish), respectively, of the average
individual within the cohort; and N is the cohort's population
density (fish/ha). In Equation (2.1), ./, is the net chemical
exchange (jjg/d ) across the fish's gills from the water;./, is the net
chemical exchange (|ig/d) across the fish's intestine from food;
and Jbt is the chemical's biotransformation rate (jjg/d). In
Equation (2.2), Fd, Ed, R, EX, and SDA are the fish's feeding,
egestion, routine respiration, excretion, and specific dynamic
action (i.e., the respiratory expenditure in excess of R required to
assimilate food), respectively, in units of g dry wt/d. Although
many physiologically based models for fish growth are
formulated in terms of energy content and flow (e.g., kcal/fish
and kcal/d), Equation (2.2) is fundamentally identical to these
bioenergetic models since energy densities of fish depend on
their dry weight (Kushlan et al. 1986, Hartman and Brandt
1995b, Schreckenbach et al. 2001). Finally, in Equation (2.3)
EM, NM and I'M are the cohort's rates (fish/ha/d) of
emigration/dispersal, non-predatory, and predatory mortality,
respectively. Although immigration can be a significant
determinant of population sizes, this process is not modeled in
BASS. Because cohort recruitment is treated as a boundary
condition, the right-hand side of Equation (2.3) does not require
a term for recruitment. Although it may not be immediately
apparent from the notation used, these equations are tightly
coupled. For example, the realized feeding of fish depends on the
availability (i.e., density and biomass) of suitable prey. The fish's
predatory mortality, in turn, is determined by 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 2.1 summarizes
the definitions of 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 unit conversion
is necessary to make the coupled system of equations
dimensionally consistent. Readers should also note that while the
growth of fish is modeled in terms of dry weight, a fish's
chemical bioaccumulation is formulated in terms of its wet body
weight since BASS models the chemical uptake and excretion by
fish as chemical diffusion between aqueous phases.
2.1. Modeling Internal Distribution of Chemicals
Chemical exchanges across gills of fish and from their food are
generally considered to occur by passive diffusion of chemicals
between a fish's internal aqueous phase and its external aqueous
environment, whether the latter is the surrounding ambient water
or the aqueous phase of the fish's own intestinal contents.
Consequently, to model these exchanges one must first consider
how chemicals distribute within the bodies of fish. If a fish is
conceptualized as a three-phase solvent consisting of water, lipid,
and non-lipid organic matter, then its whole-body chemical
concentration can be expressed as
Br
Cf= — =P„C„+P,C,+ Pn C„
f a a 11 o o
t \
c. c
Pn + P, ^ + P„ ~
a ' c ° c
(2.4)
<*/
where Ww is the fish's wet weight (g wet wt/fish); Pa, Pb and Pa
are the fractions of the whole fish that are water, lipid, and non-
lipid organic material, respectively; and Ca, Ch and Ca are the
chemical's concentrations in those respective phases. Because
the depuration rates of chemicals from different fish tissues often
do not differ significantly (Grzenda et al. 1970, van Veld et al.
1984, Branson et al. 1985, Norheim and Roald 1985, Kleeman
et al. 1986a, b), internal equilibration between these three phases
can be assumed to be rapid in comparison to external exchanges.
For organic chemicals, this assumption means that Equation (2.4)
simplifies to
Cf-K
PiKl+PoKo)Ca
(2.5)
where K, and Ka are the chemical's partition coefficients between
lipid and water and between organic carbon and water,
respectively.
For metals, however, Equation (2.4) is more complicated.
Although metals do partition into lipids (Simkiss 1983), their
accumulation within most other organic media occurs by
complexation reactions with specific binding sites. Consequently,
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for metals the term PaCJCa in Equation (2.4) could be
formulated as a function of an appropriate stability coefficient
and the availability of binding sites. Appendix A summarizes an
equilibrium complexation model that was initially formulated for
BASS. Despite its apparent correctness, however, this algorithm
greatly overestimated metal (particularly mercury)
bioaccumulation in fish. Although this overestimation can be
attributed to several factors, the most likely explanation for the
algorithm's unsatisfactory performance is that kinetics limits the
complexation of metal in fish. Because kinetic modeling was
considered incongruent with the time scales of most other major
processes represented elsewhere in bass, a much simpler
algorithm was adopted.
Because many fate and transport models (e.g., exams and
WASP) have successfully used operationally defined distribution
coefficients Kd to model the accumulation of metals in organic
media, a similar approach was adopted for BASS. Thus, for a
metal
Cf={Pa-PlKl^P0Kd)Ca
(2.6)
where Kt is again an appropriate partition coefficient between
lipid and water; and Kd is an appropriate metal-specific
distribution coefficient. Although this equation appears identical
to Equation (2.5) for organic contaminants, the relative values of
Kd and Ka in relation to A' can be remarkably different. See
Section 3.1.
J=Sak
g g g
/ \
Cf
C
w v
Kf
(2.9)
Although the chemical's conductance kg could be specified as a
ratio of the chemical's diffusivity to the thickness of an
associated boundary layer, implementation of this definition can
be problematic since the boundary layer thickness is a function
of the gill's ventilation velocity and varies along the length of the
gill's secondary lamellae. To avoid this problem, a fish's net
chemical exchange rate coefficient, Sg k„, can be estimated by
reformulating the gill's net chemical exchange as
Jg-Q(c„-ct)
(2.10)
where Q is the fish's ventilation volume (cm3/s); and CB is the
chemical's bulk concentration in the expired gill water. When
Equations (2.8) and (2.10) are equated, it follows that
Sk
g g
Q
C -C
c - c
(2.11)
Despite its appearance, the right-hand side of this equation can
be readily quantified. In particular, the ventilation volume of fish
can be estimated by
Q =
R.
02
a02 Cw,02
(2.12)
Because Ca equals the chemical's ambient environmental water
concentration Cw at equilibrium, it follows from Equations (2.4)
and (2.6) that a fish's thermodynamic bioconcentration factor (K,
= Cf/Cw at equilibrium) for a chemical pollutant of concern is
Kf =
Pa+P,K,+PoKo
Pa+PlKl+PoKd
for organics
for metalics
(2.7)
where R02 is the fish's rate of oxygen consumption (,ug/s); a02 is
the fish's oxygen assimilation efficiency; and Cw02 is the
environmental water's dissolved oxygen concentration (|ig/ml).
If one makes certain assumptions concerning the geometry of the
interlamellar spaces and the nature of mass transport between the
gill's secondary lamellae, the chemical's normalized bulk
concentration in the expired gill water (Cw-CB)/(Cw-Ca) can also
be calculated as outlined below.
2.2. Modeling Exchange from Water
Because chemical exchange (Jg) across the gills of fish occurs by
simple diffusion, it can be modeled by Fick's first law of
diffusion as
J =S k (C - C \
g g g\ W "I
(2.8)
where Sg is the fish's total gill area (cm2); k„ is the chemical's
conductance (cm/s) across the gills from the interlamellar water;
and Cw is the chemical's concentration (jjg/ml) in the
environmental water (Yalkowsky et al. 1973, Mackay 1982,
Mackay and Hughes 1984, Gobas etal. 1986, Gobas andMackay
1987, Barber et al. 1988, Erickson and McKim 1990). When
Equations (2.5), (2.6), and (2.7) are substituted into this equation,
one obtains
Because the gill's secondary lamellae form flat channels having
high aspect ratios (i.e., mean lamellar height / interlamellar
distance), they can be treated as parallel plates, and the flow of
water between them can be treated as Poiseuille slit flow (Hills
and Hughes 1970, Stevens and Lightfoot 1986). Under this
assumption, an expression for a chemical's concentration in the
bulk expired gill water can be obtained using the solutions of the
partial differential equation (PDE) that describes steady-state,
convective mass transport between parallel plates, i.e.,
r1 J dy
32 (
dx2
(2.13)
where Vis the gill's mean interlamellar flow velocity (cm/s); D
is the chemical's aqueous diffusivity (cm2/s); and x and v are the
lateral and longitudinal coordinates of the channel along which
October 2018
5
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diffusion and convection occurs, respectively. In this equation,
C = C(x, y) is the chemical's interlamellar concentration at the
distances x from the surface of the lamellae and y along its
length. The surfaces of adjacent lamellae are located at x = + r
where r is the hydraulic radius of the lamellar channel that equals
half the interlamellar distance d (cm). The midline between
adjacent lamellae is therefore denoted by x=0. The gill's mean
interlamellar flow velocity can be readily formulated as the ratio
of the fish's ventilation volume to the gill's cross-sectional pore
area, Xg (cm2). Because the gill's pore area is related to its
lamellar surface area by
V
/
(2.14)
where d is the mean interlamellar distance (cm); and / is the mean
lamellar length (cm) (Hills and Hughes 1970), a fish's mean
interlamellar flow velocity is given by
V =
QL
Sgd
(2.15)
To solve Equation (2.13), two boundary conditions must be
specified. Because adjacent lamellae presumably exchange the
chemical equally well, the solutions should be symmetrical about
the channel's midline. To insure this characteristic, the boundary
condition
8C
dx
= 0
(2.16)
the chemical's accumulated uptake (|ig/s) along the lamellar
segment [y, /]; and qp is the lamellar perfusion rate (cm3/s). If
both sides of the lamella uptake chemical, then Js(y, I) can be
formulated as
Js(y
,0-2//D
y o
dc
dx
= 2hD I —
dx
y
i
¦/
dudv
dv
(2.19)
where h is the height (cm) of the secondary lamella. Using this
expression, the boundary condition (2.18) can now be written as
D
8C
dx
= ~ k_
C(r,y) ~ Ca -
2 hD f BC
dx
f
dv
(2.20)
Once the solution of Equation (2.13) for these boundary
conditions has been obtained, the chemical's bulk concentration
in the expired gill water can be evaluated using the weighted
average
r
I
C(x,l)
C3 =
' 2N
1 - —
r2,
dx
*=o
r
I
(2.21)
1 - — dx
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
D
dc
dx
= ~ k.
,[C(r,y)-Ca]
(2.17)
where km is the permeability (cm/s) of the gill membrane.
Although this boundary condition has been used as is (Barber et
al. 1991), it can also be modified to address potential perfusion
limitations on gill uptake. To accomplish the latter task, a
formulation patterned after Erickson and McKim (1990) is used.
In particular, consider the following reformulation
D
dC
dx
= -km\c(r,y)-
ca(D
(2.18)
that scales each concentration profile C(x, I) by its relative
velocity.
A canonical solution to Equation (2.13) can be obtained by non-
dimensioning C(x, y), x, and y as follows
© =
C-C.
c -c
w a
x=-
yD
Vr1
(2.22)
(2.23)
(2.24)
When this is done, the chemical's dimensionless bulk
concentration is given by
where CJy) is the chemical's aqueous phase concentration at
point y along the length of a secondary lamella; Ca(l) = C„ is the
chemical's concentration in the afferent lamellar blood; Js(y, I) is
October 2018
6
-------
0 =.
^ D
cD-c
I&(X,N&)( 1 -X2)dX
B 0
c -c
w a
1
/-
(2.25)
X2dX
where N(, = (I D)/(V r2) is the gills' dimensionless lamellar
length or Graetz number. Two important features of this
expression can now be observed. First, one can easily verify that
1 - 0„ = ¦
cw-cB
c... - c
Consequently, Equation (2.11) can be rewritten as
Sgkg-Q( 1-0,)
(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
®b=L Bmexp(-%X^NaJ
(2.28)
i = 0
where the coefficients Bm and exponents are known functions
of the gills' dimensionless conductance or Sherwood number
*» = ¦
Ks
D
(2.29)
and the fish's ventilation /perfusion volume ratio. See Appendix
B. Although this infinite series solution does not have a
convenient convergence formula, for Sherwood numbers and
ventilation / perfusion ratios that are typical of fish gills, only the
first two terms of the series are needed to estimate 0„ with less
than 1% error (see Barber et al. 1991). See Figure 2.1 and
Figure 2.2 for displays of ^ and Bx and of and B2,
respectively.
2.3. Modeling Exchange from Food
Chemical exchange (J,) across the intestines of fish often has
been modeled as unidirectional chemical uptake assuming that
fish assimilate a constant fraction of the chemical that they
ingest, i.e.,
J. = a C F
I C p W
(2.30)
where ac is the assimilation efficiency (dimensionless) for the
chemical; Cp is the chemical's concentration (,ug/g wet wt) in the
ingested prey; and Fw is the fish's daily wet weight prey
consumption (g wet wt/d) (Norstrom et al. 1976, Jensen et al.
1982, Thomann and Connolly 1984, Niimi and Oliver 1987).
Because the chemical exchange across the intestine is driven by
diffusive gradients (Vetter et al. 1985, Clark et al. 1990, Gobas
et al. 1993), however, such formulations are thermodynamically
realistic only if ac is a decreasing function of the fish's total body
concentration C,.
A thermodynamically based description for the dietary exchange
of chemicals can be formulated using the simple mass balance
relationship
J. = C F -C E
i p w e w
(2.31)
where Ew is the fish's daily wet weight egestion (g wet wt/d) and
Ce is the pollutant's chemical concentration (,ug/g wet wt) in the
fish's feces. When this equation is reformulated in terms of dry
weight feeding and egestion (i.e., Fd = PdpFw and Ed = PdeEw
where Pdp and Pie denote the prey's and feces' dry weight
fractions, respectively) the fish's net dietary exchange becomes
CF.
t - p "
' P
dp
-Ice
\ ae a
CdeEd)
-C,F*
P*P
CnF'
- p d
dp
-C,F*
p*P
de
Pde-
CE;
CE.
(2.32)
CE,
where Cae and Cde are the pollutant's chemical concentrations in
the aqueous and dry phases of the fish's feces, respectively; Ea is
the mass / volume of the feces' aqueous phase; and Pae and Pap
are the aqueous fractions of the fish's feces and prey,
respectively. To parameterize Equation (2.32), two assumptions
are made.
The first assumption is that the concentrations of chemicals in the
fish's aqueous blood, intestinal fluids, and dry fecal matter
equilibrate with one another because the transit time through the
gastrointestinal tract is relatively slow; consequently, Cae = C'„.
Moreover, for organic chemicals, the concentration ratio Cie/Cae
can be replaced with an organic carbon / water partition
coefficient, Koc (e.g., Briggs 1981, Karickhoff 1981, Chiou et al.
1986), and for metals, this ratio can be substituted with a
distribution coefficient similar to that used in Equation (2.6).
Although reported values for the percent moisture of the
intestinal contents of fish vary typically between 50% and 80%
(Brett 1971, Marais and Erasmus 1977, Grabner and Hofer
October 2018
7
-------
1985), the second assumption made to parameterize Equation
(2.32) assumes that the fish's intestinal contents and whole body
are osmotically equilibrated; consequently, Pae = I',,. If this
assumption is reasonable, then meals with the same dry weights
but different wet weights should be processed by the fish at equal
rates and efficiencies since both will attain the same proximate
composition relatively soon after ingestion. Having the same
proximate composition implies that the concentrations of
digestive enzymes acting on the meals will be comparable and
that the physical forces exerted by the gut contents that control
gastric mobility will also be comparable. Because Bromley
(1980), Garber (1983), and Ruohonen et al. (1997) demonstrated
that initial dietary moisture content had no significant effect on
the assimilation efficiencies of turbot (Scophthalmus maximus)
or on gastric evacuation rates of yellow perch (Percaflavescens)
and rainbow trout (Oncorhynchus mykiss), respectively, the
assumption that Pae = I'a seems reasonable.
1991). Additionally, using in situ preparations of channel catfish
intestines Doi et al. (2000) have established clearly that pre-
exposures to 3,4,3',4'-tetrachlorobiphenyl decrease intestinal
uptake rates.
Although the preceding model development demonstrates the
potential logical inconsistency between an assumed constant
chemical assimilation efficiency model for dietary chemical
uptake and a thermodynamically based model, many researchers
continue to use the former assumption and model. Parameters for
these constant chemical assimilation efficiency models generally
have been estimated using the following equations proposed by
Bruggeman et al. (1981)
dC
dt
I=ac
-------
described by
exposures progressed.
dC
=[vw CP - K C/M exp[ ~K - Ml (2-4°)
where ac and k2 are the fish's assimilation efficiency and
apparent elimination rate coefficient that may require updating
for fj < t < t2. If an equation of the form of Equation (2.37) is
assumed to describe the fish's bioaccumulation dynamics over
the entire interval [f„, f2], then the derivatives specified by
Equations (2.39) and (2.40) must be equal when evaluated for t
= fj. This consistency condition, which is analogous to the
preservation of derivatives that occurs when approximating a
function with Bernstein polynomials, requires that
&c
-------
where p0 and p, are the reaction rates of the process at
temperatures T0 and respectively, such descriptions are
generally valid only within a range of the organism's thermal
tolerance. In many cases, however, a process's reaction rate
increases monotonically with temperature only up to a
temperature Tx after which it decreases. Moreover, the
temperature at which a process's rate is maximum is often very
close to the organism's upper thermal tolerance limit. To model
this behavior, Thornton and Lessem (1978) developed a logistic
multiplier to describe the temperature dependence of a wide
variety of physiological processes. Although this algorithm has
been used successfully in many fish bioenergetic models, BASS
uses an exponential-type formulation that responds
hyperbolically to increasing temperature. Importantly, such
algorithms can be easily parameterized.
Let P denote the rate of a physiological process, and letdenote
the temperature at which the rate is at its maximum value. If this
process generally exhibits an exponential response to
temperature changes well below Tl7 then Equation (2.48) can be
used to describe this process for T and T0« i.e.,
p = p0exp[6(r-r0)]
dP D
= E P
dT
(2.49)
(2.50)
where P0 is the process's rate at the low-end reference
temperature T0. To incorporate the adverse effects of high
temperatures on this process, the right-hand side of Equation
(2.50) can be multiplied by a hyperbolic temperature term that
approaches unity as temperature decreases well below Tx\ equals
zero at Tx\ and becomes increasingly negative as temperature
approaches the fish's upper thermal tolerance limit = T2.
Modifying Equation (2.50) in this fashion yields
dP D
— = eP
dT
r-r.
(2.51)
whose solution is
P=P0exp[e(r-r0)]
t2-t
T - T
12 0
T,)
(2.52)
If one assumes, without loss of generality, that T0 = 0, the
preceding equation can be simplified to
P = P0 exp(bT)
1
(2.53)
Figure 2.3 displays the behavior of this equation for P„ = 1 and
T2 = 36 Celsius as a function of e and Although these
equations apparently have not been used to describe
physiological responses of fish, their utility for doing so is
discussed in Section 3.3. For other applications of Equations
(2.52) and (2.53) see Lassiter and Kearns (1974), Lassiter
(1975), and Swartzman and Bentley (1979). Note that when
= T2, Equation (2.53) reduces to Equation (2.49).
2.6. Modeling Growth of Fish
Although the preceding algorithms for modeling chemical
bioaccumulation in fish depend on a fish's wet weight, BASS does
not directly simulate the wet 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,
W+W. + Wn
a I o
I w
*1 vrw
Pa = a0~aiPl
Pn= 1 -P„~P,
o a I
(2.54)
(2.55)
(2.56)
(2.57)
where Wa, Wd, Wb and Wa are the fish's aqueous, dry, lipid, and
non-lipid organic weights, respectively; and a0, a,, /,, and l2 are
empirical constants. Whereas Equations (2.54) and (2.57) are
simply assertions of mass conservation, Equations (2.55) and
(2.56) are purely empirical functions. Although Equation (2.55)
is assumed because simple power functions of this form
adequately describe many morphometric relationships for most
organisms, Equation (2.56) is based on the results of numerous
field and laboratory studies (Eschmeyer and Phillips 1965, Brett
et al. 1969, Groves 1970, Elliott 1976a, Staples and Nomura
1976, Craig 1977, Shubina and Rychagova 1981, Beamish and
Legrow 1983, Weatherley and Gill 1983, Flath and Diana 1985,
Lowe et al. 1985, Kunisaki et al. 1986, Morishita et al. 1987).
These equations yield an expression for a fish's wet weight that
is a monotonically increasing, but nonlinear, function of the
fish's dry weight.
BASS calculates a fish's realized feeding by first estimating its
expected consumption (F'd g dry wt/d) and then adjusting this
potential by the availability of appropriate prey as described in
the next section. Because a variety of models are commonly used
to describe the expected feeding of fish, bass is coded to allow
users the option of using any one of four different feeding models
for any particular age / size class of fish.
The first feeding model is a temperature-dependent power
function
K=fyWSMhT)
1 - —
(2.58)
October 2018 10
-------
where/i,/2,/3,and T2 are empirical constants specific to the
fish's feeding.
The second feeding model is the Rashevsky-Holling model that
is defined by the equations
dG
(2.59)
dt
= Fd~Ad~Ed
where (p.,., is the fish's ad libitum feeding rate (g dry wt/g dry
wt/d); is the maximum amount of food (g dry wt/fish) that
the fish's stomach / gut can hold; G is the actual amount of food
(g dry wt/fish) present in the gut; and Ad and Ed are the fish's
assimilation and egestion, respectively, in units of g dry wt/d
(Rashevsky 1959, Holling 1966). Given a fish's gut capacity
feeding time tmt to satiation, and satiating meal size Mmt,
rp,w can be estimated using the equations
t
^(0 = f
dt
(2.60)
The fourth feeding model back-calculates a fish's expected
feeding based on knowing the fish's expected growth and routine
respiratory demands. In particular, because assimilation,
egestion, specific dynamic action, and excretion are assumed to
be linear functions of feeding and routine respiration as discussed
subsequently, it is a straightforward matter to calculate a fish's
expected ingestion given its expected growth and respiration.
When users elect this feeding option, BASS assumes that the fish's
weight-specific growth rate y = Wwl dWw/dt (1/d) is given by
Y = g] Wv 2 exp(g3 T )
j1 | & (^2 ^'1)
(2.66)
where g1; g2, g3,and T2 are empirical constants specific to the
fish's growth rate. See Thomann and Connolly (1984) for
additional discussion of this feeding model.
When BASS estimates a fish's feeding rate using Equations
(2.58), (2.64), or (2.66), the fish's assimilation and egestion are
estimated as simple fractions of its realized ingestion Fd, i.e.,
Ad = afFd
(2.67)
dFl
dt
= (?dd[Gm^~Fd
-%dL, = ]n
1 -
(2.61)
(2.62)
where Fd (t) is the total food consumed during the interval (0, t)
and Msat = (tsa) (also see Dunbrack 1988). Alternatively, (pdd
can be estimated by simply assuming that Mmt = 0.95 x Gimn. in
which case
In 0.05
(2.63)
(2.68)
where a, is the fish's net food assimilation efficiency that is a
weighted average of its assimilation efficiencies for invertebrate,
piscine, and vegetative prey. When the Rashevsky-Holling
feeding model is used, however, bass calculates these fluxes by
substituting Fd with a function that describes the fish's pattern of
intestinal evacuation. The general form of this function is
assumed to be
EV=elGBlexp[eiT)
1-X
(2.69)
where eue2, e3,and T2 are empirical constants specific to the
fish's gastric evacuation.
The third feeding model, which is intended for planktivorous
species and larval/juvenile fish cohorts in general, is the
clearance volume model
Fd =vQcl
(2.64)
where is the plankton standing stock (g dry wt/L); and Qd is
the planktivore's clearance volume (L/d) that is assumed to be
given by
Qcl = ^WwqicxV(q3T)
(^2 ri)
1 " —
(2.65)
where ql7 q2. q ,,and i2 are empirical constants specific to the
fish's filtering rate.
The numerical value of this function's exponent, e2, depends on
characteristics of the food being consumed and on the
mechanisms that presumably control gastrointestinal motility and
digestion (Jobling 1981, 1986, 1987). For example, when gut
clearance is controlled by intestinal peristalsis, e2 should
approximately equal Vi since peristalsis is stimulated by
circumferential pressure exerted by the intestinal contents that,
in turn, is proportional to the square root of the contents mass.
On the other hand, when surface area controls the rate of
digestion, e2 should be approximately either % or unity. If the
fish consumes a small number of large-sized prey (e.g., a
piscivore), e2 = % may be the appropriate surface area model. On
the other hand, if the fish consumes a large number of smaller,
relatively uniform-sized prey (e.g., aplanktivore or drift feeder),
October 2018
11
-------
e2 = 1 is more appropriate since total surface area and total
volume of prey become almost directly proportional to one
another. When e2 = 1, the Rashevsky-Holling model [i.e.,
Equation (2.59)] is analogous to the Elliott-Persson model for
estimating daily rations of fish (Elliott and Persson 1978).
Finally, Olson and Mullen (1986) outlined a process-based
model that even suggests e2 = 0.
A fish's specific dynamic action, i.e., the respiratory expenditure
associated with the digestion and assimilation of food, is
modeled as a constant fraction of the fish's assimilation. In
particular,
SDA = cAd (2.70)
where o generally varies between 0.15 and 0.20 (Ware 1975,
Tandler and Beamish 1981, Beamish and MacMahon 1988).
to that of nitrogen.
2.7. Modeling Predator-Prey Interactions
bass simulates aquatic food webs in which each age class of a
species can feed upon other fish species, benthos, incidental
terrestrial insects, periphyton / attached algae, phytoplankton,
and zooplankton. The realized feeding of any given age class of
fish is determined by the expected feeding rate of individuals
within the cohort, the cohort's population size, and the biomass
of prey available to the cohort; the latter quantity is the sum of
the current biomass of potential prey minus the biomass of
potential prey expected to be consumed by other fish cohorts that
are more efficient foragers / competitors. BASS ranks the
competitive abilities of different cohorts using the following
assumptions:
bass assumes that body weight losses via metabolism are due
entirely to the respiration of carbon dioxide and the excretion of
ammonia. A fish's respiratory loss R is therefore calculated from
its routine oxygen consumption, R02 (g 02/d), using a respiratory
quotient, RQ (L C02 respired)/L 02 consumed), as follows
R
12 gC .
mole CO.
mole C02 22.4 L C02
22.4 L O, mole O,
'RQ
(2.71)
mole 02 32 g 02
R02 = T^mRQmR<
32
02
BASS calculates a fish's routine oxygen consumption as a
constant multiple RB of its standard basal oxygen consumption
(Ware 1975) that is assumed to be a temperature-dependent
power function. In particular,
RQ2=RBb1Ww
!exp (b3T)
1-r
(2.72)
where bu b2, b3,and T2 are empirical constants specific to the
fish's standard basal oxygen consumption. Although ammonia
excretion could be modeled using an analogous function
(Paulson 1980, du Preez and Cockroft 1988a, b), bass calculates
this flux as a constant fraction of the fish's total respiration since
excretion and oxygen consumption generally track one another.
For example, ammonia excretion increases after feeding, as does
oxygen consumption (Savitz 1969, Brett and Zala 1975,
Gallagher et al. 1984). Likewise, conditions that inhibit the
passive excretion of ammonia also depress carbon dioxide
excretion (Wright et al. 1989). Assuming that fish maintain a
constant nitrogen/carbon ratio NC (g N/g C), BASS estimates a
fish's excretory loss in body weight as
EX=eNC(R+SDA)
(2.73)
where s = 17/14 is the ratio of the molecular weight of ammonia
ASSUMPTION 1. The competitive abilities and efficiencies of
benthivores and piscivores are positively correlated with their
body sizes (Garman and Nielsen 1982, East and Magnan 1991).
Two general empirical trends support this assumption. The first
of these is the trend for the reactive distances, swimming speeds,
and territory sizes of fish to be positively correlated with their
body size (Minor and Crossman 1978, Breck and Gitter 1983,
WanzenbockandSchiemer 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 suggest that the larger predator is more likely to encounter
that prey than is the smaller. Having encountered the prey, the
other general trend for prey handling times to be inversely
correlated with body size (Werner 1974, Miller et al. 1992)
suggests that the larger predator could dispatch intercepted prey
and resume foraging more quickly than the smaller predator.
Also see Post et al. (1999) and Railsback et al. (2002).
ASSUMPTION 2. Unlike benthivores and piscivores, the
competitive abilities and efficiencies of planktivores are
inversely related to their body size due to their relative
morphologies (Lammens et al. 1985, Johnson and Vinyard 1987,
Wu and Culver 1992, Persson and Hansson 1999). Consequently,
"large" planktivores only have access to the leftovers of "small"
planktivores.
bass calculates the relative frequencies of the prey
consumed by a cohort using dietary electivities, i.e.,
drft
e, = •
! dt+f,
(2.74)
where./- is the relative availability of the i-th prey with respect to
all other prey consumed by the cohort. One can easily verify that
the range of dietary electivities is -1 < e(. < 1. One can also
October 2018
12
-------
verify that if the fish does not eat a potential food item i, ef = -1.
Similarly, if the fish consumes a potential prey item i in direct
proportion to the prey's relative abundance, then ej=0. BASS
actually allows users to specify a fish's diet as either a set of
fixed dietary frequencies {...,dja set of
electivities {..., ,...}, or a combination of fixed frequencies and
electivities To calculate a cohort's realized
dietary composition, however, BASS converts all user-specified
fixed dietary frequencies into their equivalent electivities using
the simulated relative abundances of the cohort's
potential prey. These electivities are then combined with user-
specified electivities to form a set of unadjusted
electivities ,...} that is subsequently converted into a
consistent set of realized electivities ,...}. Finally, BASS
then calculates the cohort's realized dietary frequencies using
d,=
1
(2.75)
The important step in this computational process is the
conversion of the unadjusted electivities into a set of realized
electivities. Although this conversion is sometimes unnecessary,
it is generally needed to insure that the sum of the dietary
frequencies calculated by Equation (2.75) equals 1. One can
verify that the condition that guarantees E dt = 1 is
« f
Ef = i
/-i 1 - e,
(2.76)
See Appendix C. When Equation (2.76) is not satisfied for a
given set of electivities and relative prey
availabilities BASS transforms the given electivities
using a linear transformation that maps ej = -1 into e: = -1
and max(...,e ,...) into e < 1. The general form of this
transformation is
el = m[el + l)-l (2.77)
where 0
-------
and sj2 = dl[Pl2/(Pil+Pi2)]. If only one age class of a forage
species is vulnerable to the cohort, then s = dr
If, while calculating the dietary frequencies of a piscivorous
cohort, BASS predicts that the cohort's available prey is
insufficient to satisfy its desired level of feeding, BASS reassigns
the cohort's unadjusted electivities to simulate prey
switching. These reassignments are based on the following
assumption:
ASSUMPTION 3. When forage fish become limiting, piscivores
switch to benthic macroinvertebrates or incidental terrestrial
insects as alternative prey. However, piscivores that must switch
to benthos or that routinely consume benthos in addition to fish
are less efficient benthivores than are obligate benthivores
(Hanson andLeggett 1986, Lacasse andMagnan 1992, Bergman
and Greenberg 1994). Consequently, only the leftovers of non-
piscivorous benthivores are available to benthos-feeding
piscivores. If such resources are still insufficient to satisfy the
piscivores' metabolic demands, piscivores are assumed to switch
to planktivory (Werner and Gilliam 1984, Magnan 1988,
Bergman and Greenberg 1994). In this case, piscivores have
access only to the leftovers of non-piscivorous planktivores.
Using this assumption, BASS first assigns the cohort's electivity
for benthos to zero regardless of its previous value, bass also
reassigns any other electivity that does not equal -1, to zero.
If benthos becomes limiting for benthivores, or if plankton
becomes limiting for planktivores, BASS assumes that benthivores
can shift their diets to include plankton and terrestrial insects and
that planktivores can shift their diets to include benthos and
terrestrial insects. See, for example, Ingram and Ziebell (1983).
After BASS has calculated a cohort's dietary composition, it then
assigns the cohort's individual realized feeding rate adjusted for
prey availability as
:min( F*, N~l £ ABj
\ V"1
(2.82)
where F*d is the cohort's expected individual ingestion (g dry
wt/fish); iVis the cohort's population size (fish/ha), and AH. is the
biomass (g dry wt/ha) of prey j that is available to that cohort.
Using its predicted dietary compositions and realized feeding
rates, bass then calculates the predatory mortalities for each fish
cohort and non-fish compartments.
2.8. Modeling Stable Isotopes and Trophic Position
To provide a summary output variable that integrates the
temporal and body size dynamics of a fish's dietary composition,
BASS simulates time dynamics of each cohort's carbon and
nitrogen stable isotopic signatures (i.e., 813C and 81SN).
Although other models have been proposed for this purpose (see
Hesslein et al. 1993, Harvey et al. 2002, Olive et al. 2003), BASS
assumes that the change in a fish's mean stable isotope
ratio during an arbitrarily "small" fraction or multiple of a day
while feeding on a single prey item can be described by
8At + h) ¦¦
8/Q W(t) + 8,(Q\ap{hF) - 8/Q\i{hR)
W(t)+ap{hF)-{hR)
(2.83)
where 8/0 and 8/0 denote the stable isotope ratios of the fish
and its prey, respectively, at time t; ^denotes the fish's
fractionation constant associated with prey assimilation; ap
denotes the fish's assimilation efficiency for that prey; F is the
fish's dry weight ingestion rate (g dry wt-d1); h is the length (d)
of the chosen time interval; |i is the fish's fractionation constant
associated with its routine metabolism (i.e., respiration and
excretion); and R is the fish's daily metabolic loss rate (g dry
wld ' ). One can verify that Equation (2.83) is also equivalent to
8/f + h) - &f(t)
>f\
= an^8„(0?
P P P K ^
n p 8/0 - [Op q> - P ] 8/f + h)
(2.84)
where q> = F! W(t) and p = R/W(t) are the fish's weight-specific
rates (g dry wtg dry wt -d ) of feeding and metabolism,
respectively. Passing to the limit (i.e., letting h approach zero)
then yields the differential equations
d8,
—t = a„ 1_ 8
dt
¦p-p-p*-\?pV
^ p) 8/
dt
—7T = Sp - (Y + ^ P) 5r
(2.85)
(2.86)
where y = ap cp - p is the fish's weight-specific growth rate (g dry
wt g dry wT'-d"1). Equation (2.86) can be reparameterized by
noting that most stable isotope studies assume that the difference
between the stable isotope signatures of any two successive
trophic levels is a constant. For Equation (2.86), this assumption
implies that
a k 8„ m
K = 8- ~ 8 = / p p -8„
f'q p (y + hp) p
(2.87)
where &f eq denotes the fish's steady-state stable isotope ratio.
Consequently,
A + i
8„
Y + HP
(2.88)
and Equation (2.86) can be rewritten as
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d&f .
~^ = (K-
dt y
^(y + UpMy + Hp)^
(2.89)
One can generalize Equations (2.86) and (2.89) to describe the
isotope dynamics of fish feeding on multiple prey or mixed
trophic levels as follows
—- = cp a p„ & - (y + (i p) 8,
dt /ri " " " " f
dhf
:(y + MP) E Pp(^ + 8p)-(y + I^p)8/
(2.90)
(2.91)
dt p = i
where is the frequency of thep-th prey in the fish's diet. BASS
calculates the trophic position of each fish cohort using the
cohort's predicted 81SN and the standard equation
TP = (Zf-ZmvJ/3A+2 (2.92)
(Vander Zanden and Rasmussen 2001).
2.9. Modeling Dispersal,
Mortalities, and Recruitment
Non-Predatory
The algorithm that BASS employs to simulate a species' dispersal
and non-predatory mortality is based on the general empirical
observation that population densities of most vertebrates can be
adequately characterized by the self-thinning power function
relationship
N = a W~
(2.93)
where Nis the species' or cohort's density (fish/ha) and Ww is the
species' or cohort's mean wet body weight (Damuth 1981, Peters
and Raelson 1984, Juanes 1986, Robinson and Redford 1986,
Dickie et al. 1987, Boudreau and Dickie 1989, Gordoa and
Duarte 1992, Randall et al. 1995, Dunham and Vinyard 1997,
Steingrfmsson and Grant 1999, Dunham et al. 2000, Guinez
2005). For fish the body weight exponent b generally varies from
0.75 to 1.5 (Boudreau and Dickie 1989, Grant and Kramer 1990,
Gordoa and Duarte 1992, Elliott 1993, Bohlin et al. 1994,
Randall et al. 1995, Dunham and Vinyard 1997, Grant et al.
1998, Dunham et al. 2000, Knouft 2002, Keeley 2003). Larger
exponents ranging from 1.5 to 3.0, however, have also been
reported (Steingrfmsson and Grant 1999). If Equation (2.93) is
differentiated with respect to time, it immediately follows that a
species' population dynamics can be modeled using the linear
time-varying differential equation
dN
dt
-a b W~ dW„,
W„,
dt
= - byN
(2.94)
where y = Ww dfVw/dt is the species' weight-specific growth
rate. Consequently, by corresponds to the cohort's total mortality
rate. Readers interested in detailed discussions concerning the
underlying process-based interpretation and general applicability
of this result should consult Peterson and Wroblewski (1984),
McGurk (1993, 1999), andLorenzen (1996).
Because Equations (2.93) and (2.94) encompass the cohort's
predatory and non-predatory mortality and dispersal, and because
BASS separately models the cohort's predatory mortality, BASS
assumes that the cohort's combined rate of dispersal (EM) and
non-predatory mortality (NM) is simply a fraction 5 of by. In
particular,
EM + NM=8byN
(2.95)
If community population dynamics are strongly dominated by
predation, the fraction 5 will be "small" (e.g., 5 < 0.5) for forage
fishes and "large" (e.g., 5 > 0.5) for predatory species. However,
if community population dynamics are dominated by dispersal
mechanisms related to competition for food, space, or other
limiting community resource, the fraction 5 will be large for
forage and predatory species alike.
Although BASS's basic self-thinning algorithm for predicting a
cohort's combined dispersal and non-predatory mortality (EM +
NM) may be sufficient to simulate realistically bounded
populations for most species, some species may tend to exhibit
unbounded or unrealistic population dynamics under certain
conditions. To correct such behaviors, a simulation option that
imposes an additional mortality and dispersal on all of a species'
cohorts as its total biomass approaches a user-defined biomass
carrying capacity has been implemented. This algorithm assumes
that a cohort's realized biomass derivative, adjusted for an
assumed carrying capacity, is given by
dX=dX
dt dt
S-K
X-K
.dW
N^jj~ W{EM + NM + PM + KM) =
N^Jt~ W^EM + NM + PA^
S-K
X-K
KM= W~l
1
S-K
X-K
dX
dt
(2.96)
(2.97)
(2.98)
where X = NWdenotes the cohort's biomass; KM is the cohort's
additional dispersal and mortality due to the species' biomass
carrying capacity constraint; S is the species total biomass; and
K is the species biomass carrying capacity. It is interesting to
note that Equation (2.96) can also be written as
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dX
dX
+
1
1
_ dX
/ \
\X-Kc\
dt
dt
I X-K J
dt
{ X-K)
where Kc= K - (S - X) corresponds to the cohort's instantaneous
carrying capacity given the species' existing age class structure.
That is, the cohort's biomass attains a local maximum when X =
Kc. Consequently, the hyperbolic multipliers used in these
equations are mathematically analogous to those used to model
temperature effects on the cohort's physiological functions (see
Section 2.5).
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' spawning biomass is defined as the total biomass of
all cohorts whose body lengths are greater than or equal to a
specified minimum value (tlr0) marking the species' sexual
maturation. When reproduction is simulated, the body weight of
each sexually mature cohort is decreased by its rbi, and the total
number of Y O Y recruited into the population as a new cohort is
calculated by dividing the species' total spawned biomass by its
characteristic YO Y 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).
2.10. Modeling Habitat Effects
Although BASS does not explicitly model physical habitat
features of the fish community of concern, it does allow users to
specify habitat suitability multipliers on the feeding, reproduction
/ recruitment, and dispersal / non-predatory mortality for any or
all species. Because these multipliers are assumed to be
analogous to subcomponents of habitat suitability indices, they
are assumed to take values from 0 to 1. If these multipliers are
not specified, bass assigns them the default value of 1.
When feeding habitat multipliers (HSIfeeding) are specified, BASS
uses the specified parameters as simple linear multipliers on the
fish's maximum rate of ingestion, i.e.,
Fd{habitat) = HSIfeeding F*d (2.100)
The resulting adjusted maximum feeding rate then replaces F*d
in Equation (2.82). These multipliers are assumed to modify the
fish's ability to perceive or to intercept prey by affecting the
fish's reactive distance, foraging patterns, etc. or by providing
modified refuges for its potential prey. Habitat interactions that
actually change the abundance of potential prey should not be
specified as feeding habitat multipliers since these interactions
are automatically addressed by the algorithms outlined in Section
2.7.
Like the aforementioned feeding habitat multipliers, BASS uses
any specified recruitment habitat multipliers (HSIrecmitment) as
simple linear multipliers on the number of young-of-year
recruited into the species population, i.e.,
N0(habitat) = HSIrecmilmenl N0 (2.101)
These multipliers can represent either the availability of suitable
spawning sites or the ability of otherwise successful spawns to
result in the expected numbers of young-of-year as discussed in
Section 2.8.
Finally, when habitat multipliers (HSIsurvival) are specified for
dispersal / non-predatory mortality, they are assumed to control
a species' self-thinning exponent b (see Section 2.8) so that the
exponent is maximum for HSIsurvival = 0 and minimum
for HSImrvjval = 1. Thus, as habitat suitability decreases, dispersal
and non-predatory mortality increase, and vice versa [see
Equation (2.95)]. Between this range, the species self-thinning
exponent is assumed to respond linearly to changing HSImrvival,
i.e.,
b(habitat) = (l - HSImrvival)(bm^ - bmin) + bmn (2.102)
Because constructing habitat suitability multipliers in a standard
way is not a trivial issue, BASS relegates their construction to the
user. Nevertheless, users might consider several obvious starting
points when simulating habitat effects with BASS. Turbidity, for
example, is known to affect the foraging abilities of both prey
and predatory fishes, and one could readily use results of
published studies (e.g., Vandenbyllaardt et al. 1991, Barrett
1992, Gregory 1993, Gregory and Northcote 1993, Miner and
Stein 1996, Reid et al. 1999, Vogel and Beauchamp 1999,
Bonner and Wilde 2002, de Robertis et al. 2003, Sweka and
Hartman 2003) to estimate feeding multipliers for Equation
(2.100) as power functions or polynomials of turbidity. Field-
based HSIs are often estimated by logistic regression of
presence-absence data without specifying the underlying
mechanisms that actually determine habitat suitability for a
species. Such HSIs could be used as habitat multipliers for
species' recruitment [Equation (2.101)] or persistence/survival
[Equations (2.95) and (2.102)] depending on the user's
interpretation of what the indices most likely represent.
2.11. Modeling Non-fish Compartments
BASS assumes that the non-fish components of a community of
concern can be treated as four lumped compartments, i.e.,
benthos, periphyton/attached algae, phytoplankton, and
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zooplankton. These compartments can be treated either as
community forcing functions or as state variables. In the latter
case, the required compartmental dynamics are simulated using
the simple mass balance model
dY
— = IP- R- F-M
dt
(2.103)
where Y is the compartment's biomass (g dry wt/m2); and IP,
R, F, and Mare the compartment's ingestion or photosynthesis,
respiration, mortality due to fish consumption, and non-
consumptive mortality and dispersal, respectively, all of which
have units of g dry wt/m2/d. Except for F, each of these fluxes is
modeled as a linear function of the compartment's biomass, i.e.,
IP = %d Y (2.104)
R = pY
M=\yY
(2.105)
(2.106)
where the rate coefficients (g dry wt/g dry wt/d) (p.,,,, p, and u are
minimally functions of temperature and time.
For benthos, phytoplankton, and zooplankton that can be
conceptualized as populations of organisms possessing similar
body sizes, the rate coefficients (p.,.,, p, and li are estimated using
temperature-dependent allometric relationships that describe
these processes for individuals comprising the compartment of
interest. For example, consider the following formulation of
benthos consumption. Assuming that Wd is the average dry
weight of individuals comprising the benthos compartment, it
follows that the expected density of individuals within the
compartment is simply
N=J-
WA
(2.107)
If the average consumption (g dry wt/d) of individual benthos is
then assumed to be
C = c1 w/1 exp(c3 rj
1-X
c3(V7-i)
(2.108)
it follows that the ingestion of the benthos compartment at large
can be modeled as
IP = C N
exp(c3 T)
'i-
C3(7-2-7-i)
Y (2.109)
1 - —
1 -¦
IJjPi-r,)
J1 \ ^3 (^2
(2.111)
(2.112)
(2.113)
The rationale for these formulations is based on the assumption
that the primary production, respiration, and mortality of
periphyton communities are generally limited by their surface-
volume relationships that are implicitly represented by these
equations.
Because (p.,,, is generally much greater than p, the astute reader
will recognize that Equations (2.103) - (2.106) will predict
unbounded autocatalytic growth for any non-fish compartment
whose predatory mortality and non-predatory mortality/dispersal
does not precisely balance its intrinsic growth rate. To prevent
such unrealistic dynamics, BASS internally estimates a
physiologically based carrying capacity for each non-fish
compartment based on its projected daily oxygen consumption
and the community's prevailing dissolved oxygen content. In
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particular, BASS assumes that compartmental oxygen
consumption cannot exceed the dissolved oxygen content
corresponding to the difference between the community's
prevailing dissolved oxygen concentration (DOC) and an
assumed hypoxic threshold of 4 mg 02 /L. When the
compartment's daily oxygen consumption is predicted to exceed
this available dissolved oxygen content, compartmental growth
is suspended by equating the compartment's
feeding/photosynthesis to its projected respiration.
BASS assumes that the rates of chemical bioaccumulation in non-
fish compartments are rapid enough to enable chemical
concentrations within these components to be calculated using
simple bioaccumulation factors. In particular,
Cnf = BAFnfCw
(2.114)
where Cnf is the chemical concentration (ug /g dry wt) in the
compartment of concern. BASS enables users to specify the
bioaccumulation factor BAFnf (ml/g dry wt) for Equation (2.114)
as an empirically derived constant, a quantitative structure
activity relationship (QSAR), or the ratio of the chemical's
uptake rate to the sum of its excretion rate and the compartment's
growth rate. When BAFnfis specified as a QSAR, BASS assumes
that
BAFnf= b, Kj
(2.115)
where bl and b2 are empirical constants. When BAFnf is specified
by the compartment's chemical exchange rates and growth rate,
bass assumes that
MFnf =
ki
K
k2 + y ki/Knf+y
(2.116)
where ku k2, and y are the rates of uptake, excretion, and growth,
respectively, by individuals comprising the compartment; and Knf
is the compartment's thermodynamic bioconcentration factor that
is defined analogously to Equation (2.7). For heterotrophs,
Equation (2.116) assumes that direct chemical uptake and
excretion with the ambient water are dominant over dietary
uptake and fecal excretion of the organisms of concern. Although
this assumption is not satisfied for all benthic or planktonic
heterotrophs, it does bypass the need to specify feeding rates,
assimilation efficiencies, and dietary compositions for
compartments that are actually mixed functional groups. For
further discussions of Equation (2.116) and its generalization,
readers should consult Connolly and Pedersen (1988), Thomann
(1989), and Arnot and Gobas (2004).
2.12. Modeling Toxicological Effects
Narcosis is defined as any reversible decrease in physiological
function induced by chemical agents. Because the potency of
narcotic agents was originally found to be correlated with their
olive oil / water partition coefficients (Meyer 1899, Overton
1901), it was long believed that the principal mechanism of
narcosis was the disruption of the transport functions of the lipid
bilayers of biomembranes (Mullins 1954, Miller et al. 1973,
Haydon et al. 1977, Janoff et al. 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 by changing their conformal structure or by changing
the configuration or availability of their active sites (Eyring et al.
1973, Adey et al. 1976, Middleton and Smith 1976, Richards et
al. 1978, Franks and Lieb 1982, 1984, Law et al. 1985, Lassiter
1990). In either case, the idea that the presence of narcotic
chemicals increases the physical dimensions of various
physiological targets to some "critical volume" that renders them
inactive is fundamental (Abernethy et al. 1988). Narcotic
chemicals can thus be treated as generalized physiological
toxicants, and narcosis itself can be considered to represent
baseline chemical toxicity for organisms. Although any particular
chemical can act by a more specific mode of action under acute
or chronic exposure conditions, all organic chemicals can be
assumed to act minimally as narcotics (Ferguson 1939, McCarty
and Mackay 1993).
Studies have shown that for narcotic chemicals there is a
relatively constant chemical activity within exposed organisms
associated with a given level of biological activity (Ferguson
1939, Brink and Posternak 1948, Veith et al. 1983). This
relationship holds true not only for exposures to a single
chemical but also for exposures to chemical mixtures. In the case
of a mixture of chemicals, the sum of the chemical activities for
each component chemical is constant for a given level of
biological activity. Because narcotic chemicals can be treated as
generalized physiological toxicants, it should not be surprising
that the effects of mixtures of chemicals possessing diverse
specific modes of action often not only resemble narcosis but
also appear to be additive in their toxic effects (Barber et al.
1987, McCarty and Mackay 1993). For example, although most
pesticides possess a specific mode of action during acute
exposures, the joint action of pesticides is often additive and
resembles narcosis (Hermanutz et al. 1985, Matthiessen et al.
1988, Bailey et al. 1997).
BASS simulates acute and chronic mortality assuming that the
chemicals of concern are an additive mixture of narcotics.
Because this assumption is the least conservative assumption that
one could make concerning the onset of effects, mortalities
predicted by BASS should signal immediate concern. When the
total chemical activity of a fish's aqueous phase exceeds its
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calculated lethal threshold, bass assumes that the fish dies and
eliminates that fish's age class from further consideration. The
total chemical activity of a fish's aqueous phase is simply the
sum of its aqueous phase chemical activity for each chemical.
bass calculates the aqueous phase chemical activity of each
chemical using the following formulae
A„ = v
a >a a
" 10 3MW 10 3MWKf
where Aa is the chemical's aqueous activity; y„ is the chemical's
aqueous activity coefficient (L/mol), the reciprocal of its sub-
cooled liquid solubility; Ma is the chemical's molarity within the
aqueous phase of the fish; and MW is the chemical's molecular
weight (g/mol).
BASS estimates the lethal chemical activity threshold for each
species as the geometric mean of the species' LA50, i.e., the
ambient aqueous chemical activity that causes 50% mortality in
an exposed population. These lethal thresholds are calculated
using the above formulae with user-specified /,C5„'s substituted
for Ca. These calculations are based on two important
assumptions. The first assumption is that the exposure time
associated with the specified LC50 is sufficient to allow almost
complete chemical equilibration between the fish and the water.
The second assumption is that the specified LCS0 is the minimum
LC50 that kills the fish during the associated exposure interval.
Fortunately, most reliable /,C5,/s 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. First, for narcotic
chemicals this approach is analogous to the toxic unit approach
for evaluating the toxicity of mixtures (Calamari and Alabaster
1980, Konemann 1981a, b, Hermens and Leeuwangh 1982,
Hermens et al. 1984a, Hermens et al. 1984b, Broderius and Kahl
1985, Hermens et al. 1985b, Hermens et al. 1985c, Hermens et
al. 1985a, Dawson 1994, Peterson 1994). Second, the approach
is also analogous to the critical body residue (CBR) and total
molar body residue (TB R) approaches proposed by McCarty and
Mackay (1993), Verhaar et al. (1995), and van Loon et al.
(1997). Third, although sublethal effects are not presently
modeled by bass, bass's simulation results can be used to
indicate when sublethal effects induced by narcotic agents would
be expected to occur. Results reported by Hermens etal. (1984b)
indicate that for Daphnia the ratio of the EC50 for reproductive
impairment to the LC50 is generally on the order of 0.15 - 0.30
for chemicals whose log Kow range from 4 to 8. For individual
growth inhibition, however, the mean EC50 to LC50 ratio for
Daphnia in 16 day chronic exposures was approximately 0.77
(Hermens et al. 1984a, Hermens et al. 1985b). Also see Roex et
al. (2000).
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Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental
model processes, rates, or rate coefficients.
Aa chemical activity in aqueous fraction of the fish (dimensionless)
Ad assimilation rate (g dry wt/d)
Bf chemical burden in whole fish (jjg/fish)
BAFnf bioaccumulation factor for non-fish prey (ml/g dry wt)
Ca chemical concentration in aqueous fraction of the fish (ijg/ml )
Cae chemical concentration in aqueous fraction of intestinal contents (jig/ml)
CB chemical concentration in bulk interlamellar water (jjg/ml )
Ce chemical concentration in egesta/feces (,ug/ml )
Cf chemical concentration in whole fish (,ug/g wet wt)
Cie chemical concentration in dry organic fraction of intestinal contents (ijg/g dry wt)
C; chemical concentration in lipid (ijg/g dry wt)
Cnf chemical concentration non-fish prey (,ug/g dry wt)
Ca chemical concentration in non lipid organic matter (,ug/g dry wt)
Cp chemical concentration in prey (ijg/g wet wt)
Cw chemical concentration in environmental water (jjg/ml )
Cm02 oxygen concentration in environmental water (,ug/ml )
d interlamellar distance (cm)
dt the relative frequency of prey i in a fish's diet (dimensionless)
I) aqueous diffusion coefficient (cm2/s)
e, the electivity prey i in a fish's diet (dimensionless)
Ed egestion rate (g dry wt/d)
Ew egestion rate (g wet wt/d)
EM emigration/dispersal (fish/ha/d)
EX excretory rate (g dry wt/d)
f the relative frequency of prey i in the field (dimensionless)
Fj expected feeding rate (g dry wt/d)
Fd realized feeding rate (g dry wt/d)
Fw realized feeding rate (g wet wt/d)
G mass of gut contents (g dry wt/fish)
h height of secondary lamellae (cm)
HSIfeeiing habitat suitability index for cohort feeding (dimensionless)
HSIrecmitment habitat suitability index for YOY recruitment (dimensionless)
HSImrvivcd habitat suitability index for cohort survival (dimensionless)
Jbt biotransformation of chemical (,ug/s)
Jg net chemical exchange across the gills (,ug/s)
./, net chemical exchange across the intestine (jjg/s)
k2 apparent elimination rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d), i.e., k2 = (y + kbt + kex)
khl chemical biotransformation rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)
ka chemical excretion rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)
k„ overall chemical conductance across the gill from the interlamellar water to the aqueous blood (cm/s)
km chemical conductance through the gill membrane (cm/s)
Ke partition coefficient for fecal matter (ml/g wet wt)
Kf thermodynamic bioconcentration factor (ml/g wet wt)
A' partition coefficient between generic lipid and water (ml/g dry wt)
Ka partition coefficient between non-lipid organic matter and water (ml/g dry wt)
Koc partition coefficient between organic carbon and water (ml/g dry wt)
Kow partition coefficient between n-octanol and water (ml/ml)
I lamellar length (cm)
L fish's body length (cm)
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Ma
chemical molarity in aqueous fraction of the fish (mol/L)
N
population density (fish/ha)
nGz
Graetz number (dimensionless) = (/ D)/(V r2)
Nsh
Sherwood number (dimensionless) = (km r)/D
NM
non-predatory mortality (fish/ha/d)
Pa
aqueous or moisture fraction of whole fish (g water/g wet wt = ml/g wet wt)
Pas
aqueous or moisture fraction of feces/egesta (g water/g wet wt = ml/g wet wt)
Pap
aqueous of moisture fraction of prey/food (g water/g wet wt = ml/g wet wt)
P ie
dry fraction of feces/egesta (g dry wt/g wet wt)
P'dp
dry fraction of prey/food (g dry wt/g wet wt)
Pd
dry fraction of whole fish (g dry wt/g wet wt), i.e., Pd = (1 - Pa) = (Pt + P0)
Pi
lipid fraction of whole fish (g dry wt/g wet wt)
Po
non-lipid organic fraction of whole fish (g dry wt/g wet wt)
PM
predatory mortality (fish/ha/d)
Q
ventilation volume (cm3/s)
r
hydraulic radius of interlamellar channels (cm), i.e., r = 0.5 d
R
routine respiratory rate (g dry wt/d)
P-02
oxygen consumption rate (mg 02/s or g 02/d)
SDA
specific dynamic action (g dry wt/d)
total gill surface area (cm2)
T
temperature (Celsius)
V
average velocity of interlamellar flow (cm/s)
weight/volume of fish's aqueous phase (g water/fish or ml/fish)
weight of fish (g dry wt/fish)
Wj
weight of fish's lipid phase (g dry wt/fish)
w
,r o
weight of fish's nonlipid organic phase (g dry wt/fish)
Ww
weight of fish (g wet wt/fish)
xs
cross sectional pore area of the gill (cm2)
a c
assimilation efficiency of chemical (dimensionless)
af
assimilation efficiency of food (g dry wt assimilated/g dry wt ingested)
a 02
oxygen assimilation efficiency of the gill (dimensionless)
1
specific growth rate (g wet wt/g wet wt/d), i.e., y = Wwl dWJdt
la
chemical aqueous phase activity coefficient (L/mol)
Za
aqueous phase biotransformation rate coefficient (1/d)
fdd
specific feeding rate (g dry wt/g dry wt/d)
WW
specific feeding rate (g wet wt/g wet wt/d)
T1
solution viscosity (poise)
V
molar volume (cmVmol)
P
lamellar density (lamellae/mm)
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Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a
function of gill Sherwood number and ventilation / perfusion ratio.
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Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a
function of gill Sherwood number and ventilation / perfusion ratio.
October 2018
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Figure 2.3 Functional behavior of Equation (2.53)
pt—EXP(0.1*T)*(1—1/36)0"1^*1-3®) pt-EXP(0.&>T)*(l-t/36)l"i
pt=EXP(O.4»TKl-t/3e)0*l(tl"M)
October 2018
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3. Model Parameterization
Because reliable application of a model depends not only on
the validity of its formulation but also on its parameterization,
important aspects regarding the parameterization of bass's
bioaccumulation and physiological algorithms are discussed
below.
3.1. Parameterizing Kf
Superficially, estimation of a fish's thermodynamic
bioconcentration factor Kf via Equation (2.7) appears to
require a great deal of information. This task, however, is
much simpler than it first appears. For example, given a fish's
lipid fraction [see Equation (2.56)], it is a straightforward
matter to calculate the fish's aqueous fraction using Equation
(2.55). One can then immediately calculate the fish's non-lipid
organic fraction since Pa, Pb and Pa must sum to unity [i.e.,
Equation (2.57)].
For an organic chemical, the partition coefficients A' and Ka
can be estimated using the chemical's octanol / water partition
coefficient^. Although triglycerides are the principal storage
lipids of fish and it would seem reasonable to estimate A' using
a triglyceride / water partition coefficient, BASS assumes that
A' equals Kow. To estimate Ka, BASS assumes that a fish's non-
lipid organic matter is equivalent to organic carbon and uses
Karickhoff s (1981) regression between the organic carbon /
water partition coefficient (Koc) and Kow to estimate this
parameter. Specifically,
K=Kn=QA\lK n i)
O OC OW v-'•J-/
For metals or metallo-organic compounds such as
methylmercury, the chemical's lipid partition coefficients, can
again be assumed to equal its octanol / water partition
coefficient Kow. A metal's distribution coefficient into non-lipid
organic matter, however, cannot be estimated using the Koc
relationship of Equation (3.1). For example, whereas the Kow
of methylmercury at physiological pH's is approximately 0.4
(Major et al. 1991), its distribution coefficient into
environmental organic matter is on the order of 104 - 106
(Benoit et al. 1999b, Benoit et al. 1999a). O'Loughlin et al.
(2000) report similar differences for organotin compounds.
Whereas distribution coefficients for metals into fecal matter
generally should be assigned values comparable to those used
to model the environmental fate and transport of metals,
distribution coefficients for metals into the non-lipid organic
matter of fish should be assigned values 10 to 100 times higher
to reflect the increased number and availability of sulfhydryl
binding sites.
3.2. Parameters for Gill Exchange
To parameterize the gill exchange model, the fish's total gill
area (Sg cm2), mean interlamellar distance (cl cm), and mean
lamellar length (/ cm) must be specified. Each of these
morphological variables is generally assumed to be an
allometric power function of the fish's body weight, i.e.,
Sg = SlWwh (3.2)
d = dlwJ2 (3.3)
/ = /j w} (3.4)
Although many authors have reported allometric coefficients
and exponents for total gill surface area, coefficients and
exponents for the latter two parameters are seldom available.
Parameters for a fish's mean interlamellar distance, however,
can be estimated if the allometric function for the density of
lamellae on the gill filaments, p (number of lamellae per mm
of gill filament), i.e.,
P = P1WJ2 (3-5)
is known. Fortunately, lamellar densities, like total gill areas,
are generally available in the literature. See Barber (2003).
BASS estimates d} and d2 from p, and p2 using the interspecies
regression (n = 28, r = -0.92)
= 0.118 p"119 (3.6)
To overcome the scarcity of published morphometries
relationships for lamellar lengths, bass uses the default
interspecific regression (n = 90, r = 0.92)
l = 0.0mww°294 (3.7)
Both of the preceding regressions are functional regressions
rather than simple linear regressions (Rayner 1985, Jensen
1986); the data used for their development are taken from
Saunders (1962), Hughes (1966), Steen andBerg (1966), Muir
andBrown (1971), Umezawaand Watanabe (1973), Galis and
Barel (1980), and Hughes et al. (1986).
To calculate lamellar Graetz and Sherwood numbers, bass
estimates a chemical's aqueous diffusivity (cm2/s), using the
empirical relationship,
D = 2.101xl0~7 ri~14v~0589 (3.8)
where r| is the viscosity (poise) of water; and v is the
chemical's molar volume (cmVmol) (Hayduk and Laudie
October 2018
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1974). The diffusivity of a chemical through the gill membrane
needed to estimate the membrane's permeability km is then
assumed to equal 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 epithelial layer. Although previous
versions of BASS assumed a constant water-blood barrier
thickness (\\,) equal to 0.0029 cm for all fish species, BASS now
uses the interspecies allometric relationship
Pe = 9.17* 10~5 Ww°'261 (3.9)
to estimate this parameter (Barber 2003).
To calculate ventilation / perfusion ratios, BASS estimates the
ventilation volumes (ml/hr) of fish from their oxygen
consumption rates assuming an extraction efficiency of 60%
and a saturated dissolved oxygen concentration [see Equation
(2.12)]. Perfusion rates (ml/hr) are estimated using
Qp = (0.23 T-0.78) 1.862 W™ (3.10)
as the default for all species. Although this expression, in units
of L/kg/hr, was developed by Erickson and McKim (1990) for
rainbow trout (Oncorhynchus mykiss), it has been successfully
applied to other fish species (Erickson and McKim 1990, Lien
and McKim 1993, Lien et al. 1994).
The eigenvalues and bulk mixing cup coefficients needed to
parameterize Equation (2.28) are interpolated internally by
BASS from matrices of tabulated eigenvalues and mixing cup
coefficients that encompass the range of Sherwood numbers
(1< Nsh <10) and ventilation / perfusion ratios (I < Q. / Q,, <
20) that are typical for fish (Hanson and Johansen 1970,
Barron 1990, McKim et al. 1994, Sijm et al. 1994). See
Figure 2.1 and Figure 2.2 of the previous chapter.
3.3. Bioenergetic and Growth Parameters
Parameterization of the physiological processes used by BASS
to simulate fish growth generally poses no special problems
since the literature abounds with studies that can be used for
this purpose. The BASS Data Supplement summarizes literature
data that have been analyzed to date for use by bass.
3.4. Procedures Used to Generate the bass
Database
bass's database for fish ecological, morphological, and
physiological parameters is generated by its own Fortran 95
software program. This program not only decodes functional
expressions for bass model parameters that have been reported
in the literature but also calculates its own regressions using
data reported in the literature. Each species within the BASS
database is assigned its own data file whose name corresponds
to its genus and species. Thus, all literature data and
regressions pertaining to largemouth bass are compiled into the
BASS database file micropterus_salmoides.dat. Literature
regressions are entered into bass database files using the
functional syntax outlined in chapter sections 4.3.3,4.4.1, and
4.4.2 herein. Except for this syntax, all literature regressions
are recorded as reported; any required unit conversions are
performed by the bass database generator.
When literature regres sions are not equivalent to the functional
forms used by BASS, and their associated primary data are not
reported, synthetic datasets are generated to estimate the
needed parameters. For example, when a fish's oxygen
consumption does not exhibit a temperature optimum, BASS
assumes that this parameter is given by
so[mg(o2)lhr\ = a W\g\b exp(c * t[celsius]) (3.11)
or, equivalently,
log so[mg(o2)/hr\ =
log a + b log W[g] + c t [celsius] ^ '
Although many researchers use similar expressions to report a
fish's oxygen consumption, some use the function
so[mg(o2)/hr] = a W[g]b t [celsius]0 (3.13)
or, equivalently,
log so[mg(o2)/hr\ =
log a + b log W[g] + c log t [celsius] ^ '
When such power functions of temperature are encountered,
synthetic datasets of "observed/predicted" oxygen
consumption are generated using the reported regressions for
the reported range of body weights and temperatures. These
synthetic data are then refitted to Equation (3.11).
A similar procedure for generating synthetic datasets is used to
convert the temperature-dependent functions (Kitchell et al.
1977, Thornton andLessem 1978) employed by the Wisconsin
Bioenergetics Fish Model (Hanson et al. 1997) into the
hyperbolic Arrhenius formulation assumed by BASS.
Although the bass database generator performs most
parameter estimations using univariate statistics or ordinary
linear least-squares regression analysis as appropriate,
nonlinear least-squares regression analysis is used to estimate
weight-specific growth rates and physiological functions that
are to be fitted to bass's hyperbolic Arrhenius formulation. In
these latter instances, bass's database generator uses the
NL2SOL Fortran 90 software that solves nonlinear
least-squares problems using a modified Newton's method
October 2018
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with analytic Jacobians and a secant-updating algorithm to
compute the required Hessian matrix. See Dennis etal. (1981).
Estimation of Weight-specific Growth Rates
BASS uses weight-specific growth rates (y = W'1 dW/df) not
only to estimate a cohort's rate of dispersal and non-predatory
mortality [see Equation (2.95)] but also as a parameter by
which a cohort's expected ingestion rate can be back-
calculated. Estimating weight-specific growth rates for BASS,
however, obviously depends on the underlying model used to
describe the fish's expected growth rate dynamics (i.e., dW/df)-
Selecting an appropriate growth model for use by the BASS
simulation software, like most model selections, was not a
trivial issue since at least four different models (i.e., von
Bertalanffy, Richards, Gompertz, and Parker-Larkin models)
have become standard tools for characterizing the growth of
fishes. See Ricker (1979) for a detailed discussion of these and
other less commonly used models.
According to the von Bertalanffy model, a fish's growth rate
is the simple mass balance of anabolic processes that are
directly proportional to the fish's surface area and of catabolic
processes that are directly proportional to the fish's body
weight. Consequently, the fish's growth dynamics are
governed by the following differential equation
K ~PWW (3.15)
at
where cp is the fish's rate of feeding and assimilation, and p is
the fish's total metabolic rate. Assuming isometric growth (i.e.,
Ww = /./;'), this model is also equivalent to
^ = f (Ana* - L) (3.16)
where L is the fish's body length; and = cp / (p is the
fish's "maximum" body length that is obtained by setting
Equation (3.15) to zero. For further discussion, see Parker and
Larkin (1959) and Paloheimo and Dickie (1965).
The Richards model (Richards 1959) is a generalization of the
von Bertalanffy model that relaxes the assumption of isometric
growth and strict proportionality between a fish's
feeding/assimilatory processes and its absorptive surface areas.
In this model, the fish's feeding is simply assumed to be a
power function of its body weight. The fish's growth is then
described by the differential equation
dw* *2
—^=%W^-pWw (3.17)
Although the von Bertalanffy and Richards models appear to
have strong physiological foundations, a critical analysis of
their parameters casts doubts on such assertions. One
particular point of contention is the assumption that a fish's
metabolism (i.e., respiration and excretion) is directly
proportional to its body weight. Although this assumption is
certainly satisfied or closely approximated for some fish
species, most species have metabolic demands that are best
described as power functions of their body weights.
Consequently, from a physiologically based perspective, a
better anabolic-catabolic process model for fish growth would
be
d W m p
—^=%wJ2-PlWwP2 (3.18)
See Paloheimo and Dickie (1965). Unlike the von Bertalanffy
and Richards models, however, this model generally does not
have a closed analytical solution. Furthermore, when this
model is fit to observed data, there is no a priori guarantee that
the fitted exponents will actually match expected physiological
exponents unless the analysis is suitably constrained.
In light of these criticisms, simpler empirical growth models
may be more than adequate for most applications. Two such
models that have proved useful in this regard are the Gompertz
and Parker-Larkin models. Both of these models are intended
to describe the growth of fishes that decreases with the age or
size of the individual. Whereas the Gompertz model describes
fish growth by
—~ = Ejexp(-£20 Ww (3.19)
at
the Parker-Larkin model (Parker and Larkin 1959) assumes
that
dW R
—f=aWj (3.20)
at
where the exponent P is less than 1.
Although each of the aforementioned models can describe very
different growth trajectories, much of the discussion
surrounding their use has focused on whether they predict
asymptotically zero or indeterminate growth (Parker and
Larkin 1959, Paloheimo and Dickie 1965, Knight 1968,
Schnute 1981). Although growth rates of individual fish almost
always decrease with increasing age or body size, Knight
(1968) argued that the traditional notion of asymptotically zero
growth is seldom, if ever, supported by studies that have
focused on actual growth increments rather than on size-at-age.
Because the Parker-Larkin model is the only model outlined
above that assumes fish growth is fundamentally
indeterminate, and because the Parker-Larkin model does not
October 2018
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rely on the a priori assumption that fish respiration is a linear
function of their body weight as do the von Bertalanffy and
Richards models, it is used exclusively by BASS when needed.
Three basic types of data have been used traditionally to
calculate fish growth rates; these are: (1) length at age or
capture, (2) back-calculated length at age for specific age
classes sampled over multiple years, and (3) back-calculated
length at age for specific year classes or cohorts. Back-
calculated body lengths for the latter two data types are
generally calculated by regression using measured growth
increments of body scales, otoliths, pectoral spines, or other
"hard" structures. Whereas for a length at age dataset each
individual fish contributes only one observation (i.e., its
current length), each individual fish contributes a time series
of body lengths for both of the remaining types of growth data.
To estimate weight-specific growth rates for fish, body lengths
at age that have been reported in the literature, whether back-
calculated or not, are converted into wet body weights using
weight-length regressions reported by the study of interest or
other published sources. Estimated wet body weights are then
fit to the analytical solution Parker-Larkin growth model,
dt
= rK = \g,wv
-Sl
W...
(3.21)
using the NL2SOLV nonlinear optimization software. The
explicit solution of the Parker-Larkin growth model for any
time interval [f„, t] is
wjt) = \glg2(t-t0) - wjt0y
Sl\ l,Z2
(3.22)
Because this expression is discontinuous atg2= 0, the growth
parameters gj and g2 are actually obtained by fitting calculated
body weights to the equivalent expression
WJt) = [gl exp (6) (/ - /Q) + Ww(t0)exp(A)
l/exp(£)
(3.23)
where g2 = exp (b).
Estimation of Hyperbolic Arrhenius Functions
When a fish's daily rate of maximum food ingestion, plankton
filtration, gastric evacuation, respiration, or growth exhibits a
temperature optimum, the bass database generator fits the
process's actual or synthetic data to the hyperbolic Arrhenius
function
P = Pi wj exp(p3 T)
P3{T2~Tl)
(3.24)
The bass database generator also fits actual or synthetic data
regarding satiation meal size and feeding times to satiation to
the above equation when these feeding parameters exhibit
temperature optima. Testing of the initial NL2SOL-based
procedure developed to estimate the parameters of Equation
(3.24) revealed that the convergence performance of NL2SOL
could be greatly improved by reconfiguring Equation (3.24) as
P = P\ wj exp(p3 T)
1-
+ 82
Mr»«*s2-r.
(3.25)
where 7mix is the maximum temperature of the dataset being
fitted, and T. = T „ + 82. Because estimations of nonlinear
' 2 max
parameters are frequently sensitive to their initial estimates, a
three-step procedure was developed to estimate the parameters
for Equation (3.25).
The first step estimates a mean body weight exponent p2 by
fitting repeated linear least-squares regressions
logPt = p2k log Wwk + pok (3.26)
to data subsets, indexed by k, whose temperature ranges are
less than 3 Celsius.
The second step uses NL2SOL to estimate the parameters of
the temperature response model
P =
W.
1T=P\ exp (p3 T)
Pi
1 -
+ 52
Mrm„*82-r1/
(3.27)
Multiple sets of initial parameters are sequentially supplied to
NL2SOL, and the set that produces the smallest sum of least-
squares is used in the third and final step in the estimation
process.
Initial parameter estimates for Equation (3.27) are generated
by first fitting P to the cubic polynomial
p = S3r3 + ^t2 + ^r + ^
(3.28)
using ordinary linear least-squares techniques. The initial value
offor each set of initial parameters is then assigned as the
local maximum of this polynomial, i.e.,
dP
dT
= 3 ^T1 + 2^r+ Sj = 0
(3.29)
T=T,
d2P
dT2
= 6^T + 2£2<0
(3.30)
T=T,
Initial estimates for 8 are assigned assuming that the fish's
upper tolerance temperature corresponds to equidistant
temperatures within the interval
October 2018
28
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T < r, < 43
max 2
(3.31)
Similarly, initial estimates of the process's temperature
coefficient p3 are assigned as equidistant values within the
interval
0.05 - Ww(tor =g0g2ml (3.38)
& -.
where
t,, * 365
1= | exp[g3(rm+ asin(pT+ cs))]
-------
where p = 1 - (1 - a)gl. Summing Equations (3.38) and (3.41)
appropriately, it follows that
Ww(t0 + 365 h)*2 +
£ K(t0 + 365 0
(3.42)
~Ww(t0)gl=g0g2nI
To calibrate a species growth rate using Equations (3.34),
(3.39) and (3.42), one must specify the parameters (Tm, a, p,
and co) describing the application's water temperatures and the
species' maximum age (n yr), mean age (in yr) of sexual
maturity, annual spawning times
[t = (t0 + 365m),(t0 + 365(w + 1)),... |, spawning loss constant
(o), initial body weight of young-of-year fish [ Ww{tQ)\, body
weight at maximum age [Ww(tQ + 365»)], and allometric
growth exponent (g2). The species' pre-spawn body weights
for Equation (3.41) can be estimated using Equation (3.22)
using the adjusted allometric growth coefficient
Si =
Ww(t0 + 365/j)?2 - Ww(tQ)f
S2arr
(3.43)
To demonstrate this procedure, growth rates estimated for
brook trout (Salvelinus fontinalis) from literature data will be
calibrated for a "typical" Mid-Atlantic trout stream whose
annual temperature regime is assumed to be given by
T[Celsius] = 10.8 + 8.8 sin(0.0172*f + 6.04) (3.44)
This temperature function assumes that the stream's annual
range of water temperatures is 2 to 19.5 Celsius, that April 1
corresponds to t = 0, and that January 15 is the coldest day of
the year. In this stream, brook trout are assumed to be recruited
into the population with an initial YOY body weight equal to
0.25 g wet wt/fish and to live a maximum of seven years. The
maximum size attained by these trout is assumed to be 825 g
wet wt/fish (i.e., -440 mm(TL) assuming
W[g\ = 0.148 xlO"4 TL[mm]2 93 ). Spawning and recruitment are
assumed to occur on October 30. Sexual maturity is reached
when trout attain a total body length of 157 mm (i.e., between
the ages of 2 and 3 years), and the trout's reproductive loss
constant is assumed to equal 0.2 g wet wt/g wet wt/spawn.
Finally, the trout's growth Q10 is assumed to equal 2
(i.e., g3 = 0.069). Using data compiled by Carlander (1969),
the bass database analysis program estimated the following
weight-specific growth rate for brook trout
y = 0.0196 Ww
(3.45)
maximum and YOY body weights and maximum age using
Equation (3.43) yields
^ 0.0178 W„,
(3.46)
When this adjusted growth rate is used to project pre-spawn
body weights for Equation (3.42) using Equation (3.22), the
weight-specific growth rate of brook trout calibrated for
reproductive losses and temperature dependencies is
y = 0.0107 Ww ¦
exp[0.069 (10.8 + 8.75 sin(0.0172f + 6.04))]
(3.47)
When weight-specific feeding rates ((p.,,, g dry wt/g dry wt/d)
are back-calculated monthly, using this equation and standard
salmonid metabolic relationships [i.e., food assimilation
efficiencies, specific dynamic action (SD A) to ingestion ratios,
oxygen consumption rates, respiratory quotients (RQ), and
ammonia excretion to oxygen consumption quotients (AO)] as
outlined by Barber (2003), the following allometric regression
can be calculated
q)^ =0.0251 Ww~0-205 exp( 0.064 T)
(» = 84;r2 = 0.98)
(3.48)
This regression agrees well with results of S weka and Hartman
(2001) who estimated the maximum consumption of brook
trout at 12 Celsius to be
^=0-13 Ww
(3.49)
Calibrating this growth rate to predict with the trout's assumed
Taken together, the preceding equations imply that the realized
ingestion rate of brook trout at 12 Celsius would be
approximately 42% of their maximum ingestion rate. This
result agrees well with that reported by Elliott and Hurley
(1998).
A Fortran 95 executable program (BASS_FILES.EXE) is
provided with the BASS simulation software to perform the
aforementioned growth rate calibration and back-calculated
feeding rate estimation. See Section 5.6.
Estimating Initial Conditions
Although most fish surveys typically report only total species
densities (fish/ha) or total species biomass (kg wet wt/ha), such
data can be easily converted into BASS initial conditions if one
assumes that the recruitment strength for each cohort of
observed population density has been relatively constant or has
been fluctuating around a long-term average. To perform this
conversion, bass's assumed self-thinning model Equation
(2.94), is first rewritten as
October 2018
30
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dN = b *K
N W...
This equation can then be reintegrated to obtain
(3.50)
In * = -6 In
W0)
N(t) = N(t0) exp
¦ b In
K(0
K(Oj
K(0
K«o)
(3.51)
A species total population density can be estimated by
applying Equation (3.51) to each of its cohorts, i.e.,
m=£ m
N(t) = £ W(.(/ - a,.) exp-j - b In
KM
KA<-°,)
(3.52)
where Nt, Wfr and a, denote the density, average wet body
weight, and age, respectively, of the ;'-th cohort. Assuming that
each cohort is recruited into the species' total population with
the same initial body weight [ Ww t(t - a;) = WQ | and population
density [N^t - a) = N0], the preceding equation can be
simplified to
^(0 = £ exP 1" b ln
K/0
Wn
(3.53)
If the growth rate trajectories of each cohort have also
remained relatively constant, it follows that an expected
decomposition of a species total population density into its
component cohort densities would be
N0) = N0 £ exP i -b ln
KM)
Wn
(3.54)
It also follows that an expected decomposition of a species
total biomass into its component cohort biomasses would be
B(t) = £ Wwi(at) NJt)
i
= Na £ KMi) exp 1-6 ln
KM)
wn
(3.55)
From Equations (3.54) and (3.55) it should be reasonably clear
that given a species total population density (N) or total
biomass (B) and given a model for the species body growth
[i.e., Equations (3.21) and (3.22)], one can straightforwardly
calculate the species' apparent long-term year-class strength
N0. Having done so, one can estimate the species' cohort
densities and also convert the species' total population density
into its expected total biomass and vice versa.
To corroborate the density-to-biomass conversion procedure
outlined above, a database of studies that have reported
measured fish densities and associated fish biomasses was
compiled from the literature (Miles 1978, Quinn 1988, Reed
and Rabeni 1989, Ensign etal. 1990, Buynaketal. 1991,Flick
and Webster 1992, Bettoli et al. 1993, Waters et al. 1993,
Maceina et al. 1995, Mueller 1996, Allen et al. 1998, Radwell
2000, Dettmers et al. 2001, Pierce et al. 2001, Habera et al.
2004). Reported fish densities were converted into estimated
biomasses assuming evenly spaced self-thinning exponents b
ranging from -0.5 to -1.0 at 0.025 increments. Reduced major
axis (RMA) regressions were then calculated for each assumed
self-thinning exponent. The self-thinning exponent that
minimized the intercurve area between the calculated RMA
regression line and the identity relationship Bobs = Baw was b =
-0.825. This regression was
ln B, = 0.827 ln B - 0.0528 (w = 512; r2 = 0.64)
Bobs = 0-949 5j 827
(3.56)
Figure 3.3 displays the data for the regression (3.56) and the
identity relationship Bobs = B
est'
In addition to calibrating fish growth rates and back-
calculating feeding rates, the auxiliary BASS program
BASS_FILES.EXE described in the preceding section estimates
initial body weights and cohort densities for users given a
target initial total species density or a target initial total species
biomass. See Section 5.6.
October 2018
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Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the Wisconsin Bioenergetics Model.
Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit.
*
14.40 21.60
T[Celsius]
Lepomis macrochirus (adult) (Fish Bioenergetics Model 3.0)
£
7.40 14.80 2220 89.60 37.00
T[Celsius]
Mlcropterus salmoidee (Fish Bioenergetics Model 3.0)
11.20 16.60
T[Celsius]
Perca flavescens (adult) (Fish Bioenergetics Model 3.0)
12.52 18.78
T[Celsius]
Morone spp. (Fish Bioenergetics Model 3.0)
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Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the Wisconsin Bioenergetics Model.
Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and
the fish's upper tolerance limit.
7.20 10.B0
T[Celsius]
Osmerus mordax (adult) (Fish Bioenergetics Model 3.0)
13.60 20.40
T[Celsius]
Esox masquinongy (Fish Bioenergetics Model 3.0)
9.60 14.40
T[Celsius]
Oncorhynchus mykiss (Fish Bioenergetics Model 3.0)
11.20 16.60
T[Celsius]
Stizostedion vitreum (adult) (Fish Bioenergetics Model 3.0)
October 2018
33
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Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning bass's algorithm.
jk
mr
JC x
>$<
x%
X X
_Q -0.B5
xxxx^g
X X
*xx X
I y /• >*>SK * XX
?/>$< >c X
-2.73
XX X
X X
-9.08
-5.77 -2.46 0.85
ln(biomassest)
density to biomass validation test
4.17
7.48
October 2018
34
-------
Table 3.1 Summary of NL2S0L regressions for Equation (3.24) fitted to maximum daily consumption rates and satiation meal
reported in the literature.
Species
Process
Pi
P2
P 3
r,
r,
1 Channa argus
2 Coregonus hoyi
3 Morone saxatilis
4 Morone saxatilis
5 Pomoxis annularis
6 Salmo trutta
I Salmo trutta
8 Salmo trutta
9 Salmo trutta
10 Salmo trutta
II Salmo trutta
12 Salvelinus alpinus
13 Salvelinus confluentus
14 Siniperca chuatsi
15 ¦
CnJg/d]
CnJg/g/d]
CnJg/g/d]
CnJg/g/d]
CnJg/d]
CmaxtKCal/d]
sm[mg(dw)]
sm[mg(dw)]
sm[mg(dw) ]
sm[mg(dw) ]
sm[mg(dw) ]
^max
[g(dw)/g/d]
CnJg/g/d]
CnJg/d]
' TttajnazMi^
2m
0.00741
0.159
0.000945
0.00542
0.00213
0.0100
1.54
0.731
0.843
1.72
0.906
0.00123
0.00840
0.0267
7.300E-07
0.52
-0.54
0.00
0.00
0.03
0.76
0.69
0.78
0.76
0.79
0.80
0.00
0.00
0.60
0.00
0.425
0.320
0.708
0.455
1.051
0.262
0.596
2.000
2.000
0.463
0.437
0.489
0.288
0.212
2.000
29.2
16.8
25.9
21.6
23.1
18.5
15.0
13.8
13.6
14.9
15.1
16.5
14.0
30.3
30.6
51.3
26.0
58.7
42.1
43.0
21.8
29.3
67.8
69.5
24.1
24.2
29.0
29.0
44.5
75.1
0.99
0.96
0.97
0.85
0.50
1.00
1.00
1.00
0.99
1.00
0.99
0.79
0.98
0.99
0.94
Data sources and notes
1 Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.
2 Binkowski and Rudstam (1994). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.
3 Cox and Coutant (1981). Rates as reported in Table 2 assuming no feeding or lethality at 43 Celsius.
4 Hartman and Brandt (1993). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.
5 Hayward and Arnold (1996). Rates as reported in Table 1 assuming no feeding or lethality at 43 Celsius.
6 Elliott (1976b). Rates generated by regressions reported in Table 2.
7 Elliott (1975). Data as reported in Table 4 for Baetis.
8 Elliott (1975). Data as reported in Table 4 for Hydropsyche.
9 Elliott (1975). Data as reported in Table 4 for chironomids.
10 Elliott (1975). Data as reported in Table 4 for mealworms (Tenebrio molitor).
11 Elliott (1975). Data as reported in Table 4 for oligochaetes.
12 Larsson and Berglund (1998). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.
13 Selong et al. (2001). Rates calculated from data reported in Table 2 assuming no assuming or lethality at 26 Celsius.
14 Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.
15 Piatt and Hauser (1978). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.
October 2018
35
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Table 3.2 Summary of NL2S0L regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by
the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily
consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the
fish's upper tolerance limit.
Species
/i
h
fs
Ti
t2
r2
Alosa psuedoharengus (adult)
0.102
0.70
0.426
15.5
29.3
0.99
Alosa psuedoharengus (juvenile)
0.112
0.70
0.214
19.6
27.3
0.98
Alosa psuedoharengus (yoy)
0.0919
0.70
0.196
21.8
29.2
0.99
Chrosomus spp.
0.0590
0.69
0.094
26.0
29.0
1.00
Clupea harengus (adult)
0.08
0.74
0.644
12.9
29.5
0.99
Clupea harengus (juvenile)
0.0808
0.74
0.535
14.4
31.5
0.99
Coregonus hoyi
0.159
0.46
0.320
16.8
26.0
1.00
Coregonus spp.
0.159
0.68
0.320
16.8
26.0
1.00
Esox masquinongy
0.0147
0.82
0.188
26.0
34.0
1.00
Lates niloticus
0.0112
0.73
0.235
27.5
38.0
1.00
Lepomis macrochirus (adult)
0.0150
0.73
0.172
27.0
36.0
1.00
Lepomis macrochirus (juvenile)
0.0113
0.73
0.138
31.0
37.0
1.00
Micropterus dolomieui
0.00139
0.69
0.296
29.0
36.0
1.00
Micropterus salmoides
0.0129
0.68
0.222
27.5
37.0
1.00
Morone saxatilis (adult)
0.0336
0.75
2.000
21.8
213.9
0.95
Morone saxatilis (age 0)
0.014
0.75
2.000
21.3
153.6
0.99
Morone saxatilis (age 1)
0.0310
0.75
2.000
22.4
221.1
0.98
Morone saxatilis (age 2)
0.0376
0.75
2.000
23.8
268.5
0.96
Morone spp.
0.0314
0.75
0.128
28.3
31.3
1.00
Oncorhynchus gorbuscha
0.142
0.73
0.102
17.0
25.9
0.99
Oncorhynchus kisutch
0.0460
0.73
0.320
15.6
25.8
0.98
Oncorhynchus mykiss
0.102
0.70
0.220
17.6
25.3
0.99
Oncorhynchus nerka
0.142
0.73
0.102
17.0
25.9
0.99
Oncorhynchus tshawytscha
0.0330
0.72
0.230
15.0
18.0
1.00
Osmerus mordax (adult)
0.0304
0.73
0.680
10.0
22.3
0.99
Osmerus mordax (juvenile)
0.0472
0.72
0.207
13.1
18.0
0.98
Osmerus mordax (yoy)
0.0587
0.73
0.143
17.9
26.1
0.98
Percaflavescens (adult)
0.0411
0.73
0.125
23.0
28.0
1.00
Percaflavescens (juvenile)
0.0317
0.73
0.094
29.0
32.0
1.00
Percaflavescens (larvae)
0.0647
0.58
0.094
29.0
32.0
1.00
Petromyzon marinus
0.0766
0.65
0.150
18.0
25.0
1.00
Sarotheradon spp.
0.00643
0.64
0.172
30.0
37.0
1.00
Stizostedion vitreum (adult)
0.0428
0.73
0.138
22.0
28.0
1.00
Stizostedion vitreum (juvenile)
0.0802
0.73
0.094
25.0
28.0
1.00
Theraga chalcogramma (adult)
0.146
0.41
0.270
8
15.0
1.00
Theraga chalcogramma (juvenile)
0.0994
0.41
0.461
8
15.0
1.00
October 2018
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4. bass User Guide
Although BASS versions 1.0 and 1.1 were written in Fortran 77,
BASS version 2.0 and higher are coded in Fortran 95. The model
enables users to simulate the population and bioaccumulation
dynamics of age-structured fish communities using the temporal
and spatial resolution of a day and a hectare, respectively.
Although BASS implicitly models the dispersal of fish out of the
simulated hectare, it does not explicitly simulate the immigration
of fish into the simulated hectare. Monthly or yearly age classes
can be used for any species. This flexibility enables users to
simulate small, short-lived species such as daces, live bearers,
and minnows together with larger, long-lived species such as
bass, perch, sunfishes, and trout. The community's food web is
specified by defining one or more foraging classes for each fish
species based on body weight, body length, or age. The user then
specifies the dietary composition of these foraging classes as a
combination of benthos, incidental terrestrial insects, periphyton,
phytoplankton, zooplankton, and/or other fish species, including
its own. Standing stocks of non-fish compartments can be
simulated as external forcing functions or as state variables.
Although bass was developed to simulate the bioaccumulation
of chemical pollutants within a community or ecosystem, it can
also 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 of chemical bioaccumulation
within individual fish by species and age class.
• Tabulated annual summaries for the community level
consumption, production, and mortality of each fish
species by age class.
• Comma-separated values (CSV) files that users can
import into Excel or other graphical software create
customized plots.
Please report any comments, criticisms, problems, or suggestions
regarding the model software or user manual to
Craig Barber
Systems Exposure Division
U.S. Environmental Protection Agency
960 College Station Road
Athens, GA 30605-2700
office: 706-355-8110
FAX: 706-355-8104
e-mail: barber.craig@epa.gov
4.1. General Model Structure and Features
The following features are available in BASS v2.3:
• There are no restrictions on the number of fish species
that can be simulated.
• There are no restrictions on the number of cohorts that a
fish species can have.
• There are no restrictions on the number of foraging
classes that a fish species can have, and seasonal diets can
be specified for any or all foraging classes. See the fish
command /ecological_parameters option
diet(.,.)={.»}¦
• Refuge levels at which cohorts of potential prey species
become unavailable to piscivores can be specified. See the
fish command /ECOLOGlCAL_PARAMETERS option
refugia[]=/«c.
• Size-dependent harvest and stocking functions can be
specified for any or all species to simulate fisheries
management practices. See the fish command
/FISHERY_PARAMETERS.
• Habitat suitability indices (HSI) can be specified to adjust
a fish's realized feeding/growth, recruitment/spawning,
and combined dispersal and non-predatory mortality. See
the fish command /HABITAT_PARAMETERS.
• Benthos, periphyton, phytoplankton, and zooplankton can
be simulated either as forcing functions or as state
variables. Incidental insects, however, can only be
simulated as a forcing function.
• There are no restrictions on the number of chemicals that
can be simulated.
October 2018
37
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• Biotransformation of chemicals can be simulated with or
without daughter products.
• Integration of bass's differential equations is performed
using a fifth-order Runge-Kutta method with adaptive step
sizing that monitors the accuracy of its integration. BASS' s
Runge-Kutta integrator is patterned on the fifth-order
Cash-Karp Runge-Kutta algorithm outlined by Press et al.
(1992).
4.2. New Features
The following features were not available in BASS v2.2 and
earlier
• Stable isotope dynamics of carbon and nitrogen are
simulated to estimate an operational trophic position of
each fish cohort within the community of interest; for
details see Section 2.8. Users can modify the parameters
of this algorithm using the new simulation control
command /isotope_parameters and the new arguments
del_cl3[-]=a and del_nl5[-]=p for the non-fish
commands /benthos, /terrestrial_insects,
/PERIPHYTON, /PHYTOPLANKTON, and /ZOOPLANKTON.
• BASS can simulate an additional mortality and dispersal
(KM), over and above a cohort's self-thinning mortality
and dispersal (NM and EM, respectively) and its predatory
mortality (PM), that are associated with a species-specific
biomass carrying capacity. Species-specific biomass
carrying capacities are calculated internally by BASS based
on each species initial relative biomasses and a user-
specified total fish biomass carrying capacity for the
community of interest. See the new simulation control
command /fish_carryin g_capacity .
• To facilitate its linkage to numerous fate and transport
models [e.g., see Johnson et al. (2011)], bass simulations
are now conducted to acknowledge when leap years
occur. See the new simulation control command
/FIRST_LEAP_YEAR.
• The BASS v2.1 plotting commands /ANNUAL_PLOTS and
/summary_plots and their associated code have been
deprecated. Users can now generate three different types
of CSV files which can be used to create even more
customized plots using Excel or other graphical software
programs. See Section 4.7. Command Line Options.
4.3. Input File Structure
The general structure of a bass's input or project file is:
/command! argument(s)
/command2 argument(s)
/command,, argument(s)
/end
The leading slash (/) identifies the line as a command. Blanks
or tabs before or after the slash are not significant. The keyword
or phrase (i.e., command,,) that follows each slash identifies the
type of data being specified by that record. Keywords must be
spelled in full without embedded blanks and must be separated
from the record's remaining information by at least one blank or
tab. Arguments are either integers (e.g., 7), real numbers (e.g., 0,
3.7e-2, 1.3, etc.), or character strings. If the command allows
multiple arguments or options, each argument must be separated
by a semicolon. Commands can be continued by appending an
ampersand (&) to the end of the record; therefore, the following
commands are equivalent
/command arg,; arg2; arg3; arg4; arg5; arg6
/command arg,; arg2; arg3; &
arg4; arg5; arg6
Because each record is transliterated to lowercase before being
decoded, the case of the input file is not significant. Likewise,
because consecutive blanks or tabs are collapsed into a single
blank, spacing within a command is not significant. The
maximum length of a command line, including continuation
lines, is 1024 characters.
An exclamation mark (!) in the first column of a line identifies
that line as a comment. An exclamation mark can be also placed
elsewhere within a record to start an end-of-line comment, i.e.,
the remainder of the line, including the exclamation mark, will be
ignored.
The last command in any BASS project file must be /END. This
command terminates program input and any text or commands
following it are ignored. BASS checks the syntactical accuracy of
each input command as it is read. If no syntax errors are
encountered, bass then checks the specified input parameters for
completeness and internal inconsistency.
BASS input data and commands are broadly classified into four
categories: simulation control parameters, chemical parameters,
fish parameters, and non-fish biotic parameters. Simulation
control parameters provide information that is applicable to the
simulation as a whole, e.g., length of the simulation, the ambient
water temperature, water column depth, and any desired output
options. Chemical parameters specify the chemical's physico-
chemical properties (e.g., the chemical's molecular weight,
October 2018
38
-------
molecular volume, n-octanol / water partition coefficient, etc.)
and the chemical's exposure concentrations in various media.
Fish parameters specify the fish's feeding and metabolic
demands, dietary composition, predator-prey relationships, gill
morphometries, body composition, and initial conditions for the
body weights, whole-body chemical concentrations, and
population sizes of a fish's cohorts. Non-fish biotic parameters
specify how benthos, terrestrial insects, periphyton, and plankton
will be simulated.
A bass project file is actually constructed and managed as a
series of include files which are blocks of closely related input
commands. These files are specified using the include statement
# include filename '
where filename is the name of the file containing the desired
commands. Each include file specifies data for either a chemical,
a fish species, or a non-fish biotic component. Consequently, a
typical BASS project file is structured as follows:
! file: bass_input_file.prj
! notes: a bass project file as specified by include files
!
/ command! simulation control_data
/ command2 simulation control_data
/ command3 simulation control_data
# include 'dataJor_chemical_l '
# include 'data^or_chemical_2 '
# include 'data.JorJish_l'
# include 'datajorjishjl'
# include 'data.JorJish_3 '
# include 'data_forJ'ish_4'
# include 'data_for_benthos '
# include 'dataJor_insects'
# include 'datajor_periphyton '
# include 'dataj'or_phytoplankton '
# include 'dataJ"or_zooplankton '
/end
bass's graphical user interface (GUI) enables users to create and
edit BASS project files and include files in a modular fashion. The
actual file structure used by the bass GUI is detailed in Section
4.5., following the discussion of the BASS input commands
below.
/SIMULATION_CONTROL
/ANNUAL_OUTPUTS
/BIOTA
/FIRST_LEAP_YEAR
no argument/option required
integer
string...; stringn
integer
/FISH_CARRYING_CAPACITY String
/FGETS no argument/option required
/HEADER string
/lSOPTOPE_PARAMETERS string/, String2
/LEN GTH_OF_S IMULATION String
/leslie_matrix_simulation no argument/option required
/MONTH_T0 string
/NONFISH_QSAR string/, ...; stringn
/TEMPERATURE string string2
/WATER_LEVEL stringstring2
Although the command /simulation_control must be the first
command in the block since it identifies the start of these data,
the order of the remaining commands is not significant. The use
of these commands is described below in alphabetical order.
¦ /ANNUAL_OUTPUTS integer
This command specifies the time interval, in years, between
bass's annual tabulated outputs. This number must be a
nonnegative integer, bass assumes a default value of zero that
signifies that no annual outputs will be generated.
¦/BIOTA stringy string,,
This BASS v2.1 command specifies non-fish standing stocks that
are to be generated as forcing functions rather than as simulated
state variables. Although this command has been superceded in
BASS v2.2 and higher by the commands /BENTHOS,
/TERRESTRIAL_INSECTS, /PERIPHYTON, /PHYTOPLANKTON, and
/ZOOPLANKTON (see Section 4.3.4), it has been retained for
upward compatibility. Valid options are:
• benthos[ vM/»
-------
string yunits must be dimensionally equivalent to g dry
wt/L.
• zooplankton[v'M/»Vv| = fnc to generate zooplankton
standing stocks according to the function fnc. The units
string yunits must be dimensionally equivalent to g dry
wt/L.
Valid specifications for the function strings fnc are :
• nonfish_name\yunits] = a to generate a constant
compartmental standing stock of a (yunits) for the
simulation.
• nonfish_name\yunits] = a + P*sin(e) + <$*t[xunits}) to
generate a sinusoidal compartmental standing stock for
the simulation where a is the mean standing stock for the
chosen time period, P is its amplitude (yunits), w is its
phase angle (radians), and cp = 2.7c / period is its frequency
(Hxunits).
• nonfish_name\yunits] = tile(filename) to read and
interpolate the specified compartmental standing stock
from the file filename. See Section 4.4.3.
Unless specified otherwise, bass assumes that the first day of
simulation is April 1 and that the 365-th simulation day is March
31. This assignment can be changed using the command
/MONTH_T0.
These options are only required when the user is simulating fish
that feed on these resources (see the "diet" option for
/ecological_parameters). Note, however, because bass
assumes that piscivorous fish switch to benthic invertebrates and
incidental terrestrial insects when appropriate forage fish are
unavailable, the benthos and insect options should be specified
even when simulating only piscivorous fish. Also note that if
project file uses the fgets option described below, the only
/BIOTA option that might be required is the
zooplankton|jwraiYs]=/rac option. This option is required only if
the user specifies a fish's feeding to be simulated using the
clearance model formulation described in Equation (2.64).
If multiple options are selected, each option must be separated by
a semicolon.
¦ /FGETS
This command enables users to run BASS without simulating the
assemblage's population dynamics, i.e., only the growth and
bioaccumulation of individual fish are simulated. The
command's function and name are based on the fgets (Food and
Gill Exchange of Toxic Substances) model (Barber et al. 1987,
1991) that is bass's predecessor.
¦ /first_leap_year integer
This command specifies which year of the simulation
corresponds to the first leap year, i.e., contains the first February
29. If not specified, BASS assumes that the fourth year of any
simulation is the first leap year.
¦ /fish_carrying_capacity string
This command specifies an optional total fish biomass carrying
capacity of the community of interest which is used to impose an
additional mortality and dispersal (KM) on all cohorts of each
species over and above their self-thinning mortality and dispersal
(NM and EM, respectively) and their predatory mortality (PA/)-
See Equations (2.96) - (2.98) in Section 2.9. The valid syntax for
string is
• «[ units |
where a is a nonnegative real value, and units is a string that
must be dimensionally equivalent to kg wet wt/ha. Using this
input and the initial relative biomass of each species, BASS then
internally estimates corresponding species-specific biomass
carrying capacities.
¦ /HEADER string
This is an optional command that specifies a title to be printed on
each page of the output file. The maximum length of the quoted
string is 80 characters.
¦ /isotope_parameters stringy string2
This command specifies how BASS will estimate a fish's
operational trophic position TP from its simulated stable isotope
fractions 813C and 51SN using the equations
® UCfish = a (TP^ - TPbenthJ) + 8 nCbmlhm (4.1)
® lSNflsh = p (TPjish ~ TP benthos) + ® ^benthos (4-2)
Valid options for this command are:
• del_cl3_tp[-]=a
• del_nl5_tp[-]=p
If not specified, bass assumes the defaults values of a = 0.8
and p = 3.4 (see Vander Zanden and Rasmussen 2001).
October 2018
40
-------
¦ /LENGTH_OF_SIMULATION String
This command specifies the desired length of the simulation. The
valid syntax for string is
• «[ units |
where a is a nonnegative real value. The time unit specified with
brackets is converted into days for internal use and subsequent
model output.
¦ /LESLIE_MATRIX_SIMULATION
This command enables users to run BASS in a mode that is
computationally intermediate between bass's fgets and full
community modes. When this option is specified, BASS simulates
fish population dynamics using the conceptual framework of a
multispecies Leslie matrix population model. A cohort's
mortality is predicted using a single, lumped, self-thinning
mortality rate [i.e., Equation (2.94)] without attempting to
partition its total mortality into predatory and non-predatory
mortality and dispersal as outlined in Sections 2.7 and 2.8.
Although predatory mortality is not simulated, the dietary
composition of each cohort is nevertheless predicted using the
methods described in Section 2.7. While this simulation option
is designed partially to lessen the need for detailed food web
information and the work required to calibrate a full community
simulation, it is also designed to simulate more realistically the
population dynamics of communities in which the dominant
process driving cohort mortality and self-thinning is dispersal
rather than predation.
¦ /NONFlSH_QSAR stringy stringn
This command specifies the quantitative structural activity
relationships for the bioconcentration / bioaccumulation factors
of the non-fish compartments benthos, periphyton,
phytoplankton, and zooplankton that are to be applied to all
chemicals. Valid string options are:
• BCF[ -\(nonfish_name)=a* Kow[ -1 A|i
where Kow[-] is the chemical's n-octanol / water partition
coefficient; and a and P are real or integer empirical constants.
Also see the chemical command /nonfish_bcf. When this
command is used, the specified QSARs supercede any BCFs
specified by /NONFlSH_BCF or exposures specified by /EXPOSURE.
¦ /month_tO string
This is an optional command that specifies the month that
corresponds to the start of the simulation. If not specified, BASS
assumes a default start time of April 1.
¦ /SIMULATION_CONTROL
This command specifies the beginning of input data that will
apply to the simulation at large, e.g., the type of simulation to be
performed, the length of the simulation, ambient water
temperature and depth, output options, etc.
¦ /TEMPERATURE Stringy String2
This command specifies a community's ambient water
temperatures. For an unstratified water body only one string
option is specified. In this case valid options for this command
are:
• temp[celsius]=a generates a constant ambient water
temperature for the simulation.
• temp[celsius]=a + P*sin(e) + ty*t\xunils\) generates a
sinusoidal ambient water temperature for the simulation
where a is the mean temperature for the chosen time
period, P is its amplitude (yunits), w is its phase angle
(radians), and cp=27t / period is its frequency (1 /xunits).
• tempi Celsius\=\\\v( file name) to read and interpolate the
ambient water temperature from the file filename. See
Section 4.4.3.
For a stratified water body, users must specify the temperature of
both the epilimion and hypolimnion. In this case valid options
are:
• temp_epilimnion[meter]=a
• temp_epilimnion[meter]=a + P*sin(to + ty*t\xunils\)
• temp_epilimnion[meter]=file(/i7eraa/rae)
• temp_hypolimnion[meter]=a
• temp_hypolimnion[meter]=a + p*sin(a) +
-------
• depth[meter]=a generates a constant water level for the
simulation.
• depth[meter]=a + P*sin(e) + <.\>*\\xunils\) generates a
sinusoidal water level for the simulation where a is the
mean water level for the chosen time period, P is its
amplitude (yunits), (o is its phase angle (radians), and
9=271 / period is its frequency (1 /xunits).
• depth[meter]=file(/i7eraa/rae) to read and interpolate the
water levels from the file filename. See Section 4.4.3.
For a stratified water body, users must specify the depth of both
the epilimion and the hypolimnion. In this case, valid options are:
• depth_epilimnion[meter]=a
• depth_epilimnion[meter]=a + p*sin(e) +
-------
concentrations according to the functionfnc. Note in BASS
2.1 the six-lettered name csdmnt was used to specify this
exposure function.
• cwater\yunits]=fnc generates aqueous exposure
concentrations according to the function fnc.
• czooplankton|jwraiYs]=/rac generates potential dietary
exposures to fish via zooplankton according to the
function fnc. Note in BASS 2.1 the six-lettered name
czplnk was used to specify this exposure function.
The concentration units for each exposure function are specified
within the indicated brackets. As previously noted for the
simulation control functions, unless specified otherwise, BASS
assumes that the first day of simulation is April 1 and that the
365-th simulation day is March 31 for all time-dependent
exposure functions discussed in the following. This assignment
can be changed using the command /monthjtO.
Valid expressions for dietary exposures via benthos, periphyton,
phytoplankton, or zooplankton and for benthic sediments are:
• nonfish_name\yunits]=a generates a constant
concentration of toxicant in benthos, periphyton,
phytoplankton, sediment, or zooplankton.
• nonfish_name[yunits]=a*cwater[xunits] generates
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 to the product of the
component's dry organic fraction and the chemical's Kow.
Also see /nonfish_bcf.
• nonfish_name\yunits]=iilt(filename) to read and
interpolate the concentration of toxicant in benthos,
periphyton, phytoplankton, sediment, or zooplankton from
the file filename. See Section 4.4.3.
Valid expressions for insect dietary exposures are:
• cinsectslyunits ]= a generates a constant concentration of
the toxicant in incidental terrestrial insects.
• cinsccts|vM'»'v \=\\\v(filename) to read and interpolate
the concentration of the toxicant in incidental terrestrial
insects from the file filename. See Section 4.4.3.
Valid expressions for direct aqueous exposures are:
• c water [v'mh/
-------
If the user desires, simulation of mortality associated with the
accumulation of a lethal aqueous chemical activity can be turned-
off by using the command line option "-1" as discussed in Section
4.5. When this is done, however, BASS still calculates the fish's
total aqueous phase chemical activity and reports it as a fraction
of the fish's estimated lethal chemical activity to provide the user
with a useful monitor of the total chemical status of the fish.
¦ /log_ac real number
This command specifies the log10 of the chemical's aqueous
activity coefficient. For organic chemicals, if this parameter is
not specified, BASS will estimate the chemical's activity
coefficient using its melting point and n-octanol / water partition
coefficient.
¦ /LOG_KBl real number
This command specifies the log10 of a metal's binding constant
for non-lipid organic matter [see Equation (2.6)]. This parameter
is input only for metals and organometals.
¦ /log_kb2 real number
This command specifies the log10 of a metal's binding constant
for refractory organic matter. This parameter is used to calculate
metal binding to the fish's dry fecal matter and input only for
metals and organometalics.
¦ /log_p real number
This command specifies the chemical's log10 Kow, where Kow is
the n-octanol / water partition coefficient. /LOG_P must be
specified for all organic chemicals.
¦ /melting_point real number
This command specifies the chemical's melting point (Celsius).
This datum, together with the chemical's logP, is used to
calculate the aqueous activity coefficient for organic chemicals
when that parameter is not specified by the user. See Yalkowsky
etal. (1983)
¦ /METABOLISM stringy string,,
This optional command specifies species-specific rates of
biotransformation for the chemical of concern either as a constant
rate or as a QSAR function. Valid string options are:
• IST[ units |(/i'.s7i_na»ie, chemical_name)=a
• IST[unils\(fisliname, chemical_name)=a*Kow[-]AP
• BT[units](fish_name, none)=a
• BT[units]{fish_name, none)=a*Kow[-]AP
where BT specifies the whole-body-referenced biotransformation
rate kbt in Equation (2.41); fish_name is the common name of the
fish species that can metabolize the chemical of concern;
chemical_name is the name of the daughter product generated by
the metabolism of the chemical of concern; Kow[-] is the
chemical's n-octanol / water partition coefficient: and a and P are
real or integer empirical constants. If the user does not wish to
simulate daughter products because they are insignificant or
assumed to be harmless, chemical_name can be assigned the
value none. When daughter products are specified, the user must
specify all physico-chemical properties of the identified by-
product in the same way that the physico-chemical properties of
the parent compound are specified.
¦ /molar_volume real number
This command specifies the chemical's molecular volume
(cmVmol) that is used to calculate the chemical's aqueous
diffusivity, i.e.,
D = 2.101x10"7ti14v"0589 (4.3)
where D is the toxicant's aqueous diffusivity (cm2/sec); r| is the
viscosity of water (poise); and v is the chemical's molecular
volume (cmVmol) (Hayduk and Laudie 1974). The viscosity of
water over its entire liquid range is represented with less than 1 %
error by
, %> 1.37 (r-20) + 8.36xl0"4(r-20)2
log./v — = ^ 1 L )_ (4 4)
10 rij. 109 + T K J
where % is the viscosity (centipoise) at temperature T (Celsius),
and r|20 is the viscosity of water at 20 Celsius (1.002 centipoise)
(Atkins 1978).
¦ /MOLAR_WElGHT real number
This command specifies the chemical's molecular weight
(g/mol).
¦ /NONFlSH_BCF stringy string„
This command specifies the bioconcentration / bioaccumulation
factors for the non-fish compartments benthos, periphyton,
phytoplankton, and zooplankton either as a numerical constant or
as a QSAR function. Valid string options are:
• BCF[-](nonfish_name)=a
October 2018
44
-------
• BCF[ -\(nonfish_name)=a* Kow[ -1 A|i
where Kow[-] is the chemical's n-octanol / water partition
coefficient; and a and P are real or integer empirical constants.
Note that this command or /NONFlSH_QSAR must be specifiedfor
any non-fish compartment that is simulated as a community state
variable.
4.3.3. Fish Input Commands
Model parameters for each fish species of interest are specified
by a block of thirteen commands, i.e.,
/COMMON_NAME String
/SPECIES string
/AGE_CLASS_DURATION String
/SPAWNING_PERIOD String
/FEEDING_OPTIONS stringy ...; stringn
/PREY_SWITCHING _OFF
/INITIAL_CONDITIONS string2; ...; stringn
/COMPOSITIONAL_PARAMETERS string/, Stringy
/ECOLOGICAL_PARAMETERS String/, Stringn
/MORPHOMETRIC_PARAMETERS string/, Stringn
/PHYSIOLOGICAL_PARAMETERS String2; Stringn
/FISHERY_PARAMETERS string/, ...; Stringy
/HABITAT_PARAMETERS string/, ...; stringn
The command /COMMON_NAME must be the first command in the
block since it is the identifier for the start of a new set of fish
parameters. The order of the remaining commands is not
significant. The use of these commands will now be described in
alphabetical order.
¦ /AGE_CLASS_DURATION String
This command specifies the duration of each age class. Two
character strings, "month" and "year", are recognized as valid
options.
¦ /COMMON_NAME String
This command specifies the start of input data for a fish species.
The command's specified common name string is used for model
output and as a label for specifying the dietary composition of
other fish species. Each common name must be a single character
string without embedded blanks. If a two-part name is desired,
the user should use an underscore as a separating blank. See
the diet option for the command /ecological_parameters .
¦ /compositional_parameters stringi, string,,
This command specifies aqueous and lipid fractions of the fish.
Valid options that must be separated by semicolons are:
• pa[-]=a + P*pl[-] specifies the fish's aqueous fraction as
a linear function of the fish's lipid fraction.
• pl[-\=u*\W\xunits | A|i 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 pl[ywraiYs]=a.
where a and P are real or integer empirical constants.
¦ /ecological_parameters stringy string„
This command specifies the ecological parameters describing the
fish's trophic interactions, non-predatory mortality, and
recruitment. Valid options that must be separated by semicolons
are:
• ast_yoy[-]=f(b[-]=a, yoy\x units\=\\, pop\y units\=y)
specifies parameters for implementing accelerated self-
thinning of young-of-year fish (YO Y), or more accurately
recently recruited cohorts, that often occurs due to
intraspecies competition for territories, refugia, or other
habitat resources. The functional argument b[-]=a
specifies the desired accelerated self-thinning exponent.
The functional argument yoy\x units |=P defines the age,
length, or wet weight threshold below which cohorts will
be subject to accelerated self-thinning. Valid expressions
foryoy are either "age", "tl", or "wt". The final functional
argument specifies the population threshold that triggers
accelerated self-thinning. Depending on the assumed
nature of the competition, this threshold can be specified
either as the total density of cohorts satisfying the
condition yoy[xunits] < p, or as the total density of cohorts
satisfying the condition yoy\xunits\>\\. For the former
case, pop equals "pop_yoy" whereas for the latter case,
pop equals "pop_adults".
• diet(c/ass, time) = {preyl = ,, ...,preyn = £ „} specifies
the dietary composition for fish of the age or size class
class during the months specified by time. The right-hand
side of the option specifies the prey items (preyn) and their
contribution (eJ to the fish's diet. Eachpreyn is either the
common name of a simulated fish species, "benthos",
"insects", "periphyton", "phytoplankton", or
"zooplankton" (see commands /biota and
/COMMON_NAME). Depending on its value, £n is
interpreted either as a constant percent contribution or as
a prey electivity. In particular, if l<£n<100, then £n
designates the relative frequency of that prey in the fish's
October 2018
45
-------
diet independent of its relative abundance in the field. On
the other hand, if -1<£„<1, then £n is considered a prey
electivity [see Equation (2.74)]. For any foraging class,
users can specify both constant dietary percentages and
prey electivities.
Valid expressions for class are:
a< u\x units |<|i for age-based dietary classes
a< \\x units 1 <|i for length-based dietary classes
ttHiVv|=« + \\*vx\i{'(H,\x units\)
where a, p, and 7 are real or integer empirical constants.
If a fish's minimum prey size is independent of its body
length (i.e., p equals zero), this parameter can be specified
simply as lp_min|jwrafe]=a. If not specified, BASS assigns
the default value lp_min[cm]=0.1*L[cm].
• mls| v'M/»''v |=« specifies the species' maximum longevity
or life span.
• nm[-]=a*b(P:7)*sg_mu[-] specifies a cohort's rate of
dispersal and non-predatory mortality as a function of its
habitat suitability and long-term weight-specific growth
rate sg_mu[-]. Whereas a specifies the fraction of the
species' total "mortality" that is attributable to dispersal
and non-predatory mortality, P and 7 specify the species'
minimum and maximum self-thinning exponents,
respectively. See Equations (2.95) and (2.102). If the user
elects not to simulate habitat effects on dispersal and non-
predatory mortality, this parameter can be specified
simply as
nm[-] = a*b(P)*sg_mu[-]
where P is the species' average self-thinning exponent.
Also see the /ecological_parameters option sg_mu[].
• rbi[-]=a specifies the species' reproductive biomass
investment (i.e., grams gametes per gram spawning fish)
where a is real empirical constant. If not specified, BASS
assigns the default value rbi[-]=0.2.
October 2018
46
-------
refugia[ywraiYs]=a specifies a refuge population size for
each cohort that can be prey for community piscivores
where a is real or integer constant. Yunits must be
dimensionally equivalent to fish/ha. If not specified, BASS
assumes no refuge level (i.e., refugia[ywraiYs]=0)
sg_mu[j>ttHi<.v\=(v\W\x units |A|i specifies the species'
mean long-term weight-specific growth rate where a and
P are real or integer empirical constants, yunits must be
dimensionally equivalent to day"1, and xunits must be
dimensionally equivalent to g wet wt/fish. If not specified,
bass can estimate this parameter provided that the user
specifies the species' expected body weight at its
maximum age. See /ECOLOGlCAL_PARAMETERS option
wt_max[] for details.
tl_rO|vM/»'v |=« specifies the species' minimum total
length when sexual maturity is reached where a is a real
or integer empirical constant.
w1[j>m/hVs |=
-------
The units of the specified stocking rate sunits must be
dimensionally equivalent to fish/ha. Valid expressions for
time are given below. Valid options for schedule are:
"weekly", biweekly", "monthly", or "one_time". If
schedule = weekly, then a new cohort of y individuals is
added to the species' population once a week throughout
the specified period. If schedule = monthly, then a new
cohort of y individuals is added to the species' population
once a month throughout the specified period.
• harvest[/iwraiYs](aln X = loge X
LN_1( X,) =>ln (Xi + 1) = loge (Xi + 1)
LOG( X,)=> log (X) = log|0 (X)
October 2018
48
-------
LOG_l( X,) =>log (X + 1) = log10 (X + 1)
SQRT( Xt) =>pT
ASIN_SQRT( X,) =>arcsin )
ASIN_SQRT_PCT( Z,) =>arcsin|y 0.01 X )
Habitat variables must be specified with units enclosed by
brackets, and must match in name and units to column variables
specified by the data filefilename. After evaluating the specified
logistic regression, BASS calculates the fish's HSI multiplier
using the standard equation
hsi_name = 1 / (1 + EXP (- hsi_name_equation))
If HSI functions are not specified, bass assigns the default value
of 1 to each unspecified HSI function.
¦ /initial_conditions stringy string„
This command specifies the species' initial ages, whole-body
chemical concentrations, wet body weights, and population sizes.
Valid options for this command are:
• age[w«iYs]={x1,xn} to initialize the age of each cohort
with the specified vector. The units enclosed by brackets
must be dimensionally equivalent to days.
• chemical_name\units |={ x,,xn} to initialize the whole-
body concentration of each cohort for the named chemical
by the specified vector. Each name must correspond
exactly to a name specified by one of the /chemical
commands. The units enclosed by brackets must be
dimensionally equivalent to jug/g wet wt.
• vvtj units |={\,,xn} to initialize the body size of each
age class with the specified vector. The units enclosed by
brackets must be dimensionally equivalent to g wet
wt/fish.
• pop[ units |={ x,,xn} to initialize the population density
of each age class with the specified vector. The units
enclosed by brackets must be dimensionally equivalent to
fish/ha.
¦ /morphometric_parameters stringy stringn
This command specifies the species' morphometric parameters
that are needed to describe the exchange of chemicals across its
gills. Each string specifies a required morphometric parameter
as a simple allometric power function of the fish's body weight.
Valid options, which must be separated by semicolons, are:
• ga\yunits]=a*W[xunits]A$ specifies the fish's total gill
surface area where a and p are real or integer empirical
constants, yunits must be dimensionally equivalent to cm2
or cm2/g wet wt.
• id[ywraiYs]=a*W[xwraiYs]AP specifies the interlamellar
distance between adjacent lamellae where a and p are real
or integer empirical constants, yunits must be
dimensionally equivalent to cm or cm/g wet wt.
• 1 (1 |vmnits |=« * W[xunite |A P specifies the density of
secondary lamellae on the primary gill filaments (i.e.,
number of lamellae per mm gill filament) where a and P
are real or integer empirical constants.
• 11[vm/»V,v\=(r'\W\xunits|Ap specifies the fish's lamellar
length where a and P are real or integer empirical
constants, yunits must be dimensionally equivalent to cm
or cm/g wet wt.
Note that if the exponent P equals zero for any of these
parameters, the resulting term W[xm/»7,v|a0 does not have to be
specified.
¦ /physiological_parameters stringy stringn
This command specifies the species' physiological parameters
for simulating growth. Each string specifies a physiological
parameter of the fish as a constant or temperature-dependent
power function of its body weight. In particular,
• ae_plant[-]=a specifies the fish's assimilation efficiency
for periphyton and phytoplankton where a is a real
empirical constant less than or equal to one.
• ae_invert[-]=a specifies the fish's assimilation efficiency
for benthos, insects, and zooplankton where a is a real
empirical constant less than or equal to one.
• ae_fish[-]=a specifies the fish's assimilation efficiency
for fish where a is real a empirical constant less than or
equal to one.
• ge[yM«iYs]=a*G[xM«iYs]AP*H(,y,T1,T2) specifies the fish's
gastric evacuation where G is the mass of food resident in
the intestine, and where a, p, 7, Th and T2 are real or
integer empirical constants, yunits must be dimensionally
equivalent to g dry wt/d. In general, y='/2, %, or 1 (Jobling
1981). This parameter is required only if the feeding
option holling(-) is used.
• kf_min[-]=a specifies the minimum condition factor for
October 2018
49
-------
a fish's continuing existence. In BASS, a fish's condition
factor is defined by the ratio
¥=
a Lf
(4.5)
where Wand L are the fish's current wet body weight and
total length, respectively; and a and p are the coefficient
and exponent for the fish's weight-length relationship (see
/PHYSIOLOGICAL_PARAMETERS option wl[ ]).
itiI'[vmh/',v\=u*W\xunits | AP*II(7,T,,T,) specifies the
fish's maximum filtering rate where a, p, 7, T,, and T2 are
real or integer empirical constants, yunits must be
dimensionally equivalent to L/d. This parameter is
required only if the feeding option clearance(-) is used.
ini|jwraiYs]=a*W[xwraiYs]AP*H(7,T1,T2) specifies the
fish's mean ingestion rate where a, p, 7, T,, and T2 are
real or integer empirical constants, yunits must be
dimensionally equivalent to g dry wt/d. This parameter is
required only if the feeding option allometric(-) is used.
rq[-]=a specifies the fish's respiratory quotient; (i.e.,
L(C02) respired/L(02) consumed) where a is a real
empirical constant.
rt:std[-]=a specifies the ratio of a fish's routine
respiration to its standard respiration where a is a real
empirical constant.
sda:in[-]=a specifies the ratio of a fish's SDA to its
ingestion where a is a real empirical constant.
sg[jwraiYs]=a*W[xwraiYs]AP*H(7,T1,T2) specifies the
fish's weight-specific growth rate where a, p, 7, T,, and
T2 are real or integer empirical constants, yunits must be
dimensionally equivalent to day"1. This parameter is
required only if the feeding option linear(•) is used.
sm\yunits]=a*W[xunits] AP*H(7,T1,T2) specifies the size
of the satiation meal consumed during the interval (0, st)
where a, p, 7, T,, and T2 are real or integer empirical
constants, yunits must be dimensionally equivalent to g
dry wt/d. See option st\yunits] below. This parameter is
required only if the feeding option holling(-) is used.
so[yM«iYs]=a*W[ArM«iYs]AP*H(7,T1,T2) specifies the
fish's standard oxygen consumption where a, p, 7, T h and
T2 are real or integer empirical constants, yunits must be
dimensionally equivalent to mg 02/hr or mg 02/g wet
wt/hr.
• st\yunits]=a*W[xunits] AP*H(7,T1,T2) specifies the time
to satiation when feeding with an initially empty stomach
where a, p, 7, T,, and T2 are real or integer empirical
constants. See option sm[ywraiYs] above. This parameter is
required only if the feeding option holling(-) is used.
For the options ge\yunits], mf[ywraiYs], mi\yunits], sglyunits],
sm\yunits], so\yunits], and st\yunits],
/f(Y,r1,r2) = exp(Yr)(i-^)Y(r2 (4.6)
12)
where Tx is the temperature at which each particular process's
rate is maximum and T2 is the upper temperature at which the
process is no longer operative. If the process does not exhibit a
temperature optimum, then the hyperbolic function H(7,TX,T2)
should be substituted with the exponential function
exp(7*T[celsius]). Consequently, each of these temperature-
dependent power functions can also be specified as
a*W[xwraiYs]AP*exp(7*T[celsius])
As noted for the fish's morphometric parameters, if the exponent
P equals zero for any of these temperature-dependent power
functions, the term W[xm/»Yv|a0 does not have to be specified.
If a required parameter is not specified, the program will
terminate with an appropriate error message.
¦ /PREY_SWITCHING_OFF
This command disables bass's prey-switching algorithms when
a cohort's expected feeding level cannot be satisfied using the
dietary compositions specified by the user. By default, bass's
prey-switching algorithms are enabled.
¦ /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_PERlOD April-June
The names of the months must be spelled-out in full.
October 2018
50
-------
¦ /SPECIES string
This optional command specifies the scientific name (genus and
species) of the fish to be simulated. Users should note, however,
that the BASS parameterization software (BASS_FILES .EXE) inserts
this command into all of its generated FSH files to inform users
what species data have been used to create the files of interest.
4.3.4. Non-fish Input Commands
These commands specify simulation parameters for benthos,
periphyton, incidental terrestrial insects, phytoplankton and
zooplankton. The syntax for these commands is as follows
/BENTHOS
String
.; string
/TERRES TRIAL_IN SECTS
string
.; string
/PERIPHYTON
string
.; string
/PHYTOPLANKTON
string
.; string
/ZOOPLANKTON
string..
.; string
Depending on the options selected, bass generates the standing
stocks of these non-fish compartments either as community
forcing functions or as community state variables. Although these
compartments can be simulated for any desired community, only
those identified as fish prey must be specified (see the
diet(.,.)={...} option for /ECOLOGICAL_PARAMETERS). Note,
however, that because piscivorous fish are assumed to switch to
benthic invertebrates and incidental terrestrial insects when
appropriate forage fish are unavailable, the benthos and insect
options should be specified even when simulating only
piscivorous fish.
When benthos, periphyton, incidental terrestrial insects,
phytoplankton or zooplankton are treated as community forcing
functions, a single option of the form
• biomass|vM/»''v|=,v/n'/ig
is specified. Valid expressions for this option are:
biomass| v'M/»'v |=« for a constant non-fish standing stock
biomass|v'M/»''v |=« + p*sin(e) + <$*t[xunits}) for a
sinusoidal non-fish standing stock where a is the mean
standing stock for the chosen time period, P is its
amplitude (yunits), w is its phase angle (radians), and
9=271 / period is its frequency (1 /xunits).
biomass\yunits]=rile(filename) to read and interpolate a
non-fish standing stock from the file filename. See
Section 4.4.3.
Whereas yunits must be dimensionally equivalent to g dry wt/m2
for benthos, incidental terrestrial insects, and periphyton, yunits
must be dimensionally equivalent to g dry wt/L for
phytoplankton and zooplankton. As previously noted, BASS
assumes that the first day of simulation is April 1 and that the
365-th simulation day is March 31. This assignment can be
changed using the command /MONTH_T0. This command-option
combination is equivalent to the BASS v2.1 simulation control
command /BIOTA
When benthos, periphyton, phytoplankton or zooplankton are
treated as community state variables, the following five options
must be specified:
• imti&\Joiom&ss\yunits]=number. This option specifies
the initial compartmental standing stock of the designated
component and is required to simulate the designated non-
fish compartment as a BASS state variable, yunits must be
dimensionally equivalent to g dry wt/m2.
• mean_weight|jwraiYs]=/rac. This option specifies the
average body weight of individuals within the designated
non-fish compartment. This parameter is required to
simulate the designated non-fish compartment as a bass
state variable, yunits must be dimensionally equivalent to
g dry wt/ind. Valid expressions for fnc are:
mean_weight|jwraiYs]=a generates a constant average
individual body weight for the designated prey.
mean_weight|jwraiYs]=a + P*sin(e) +
-------
simulate either benthos or zooplankton as a bass state
variable, y units must be dimensionally equivalent to g dry
wt/d, and xunits must be dimensionally equivalent to g
dry wt/ind.
• photosynthesis[yM«iYs]=a*W[ArM«iYs]AP*H('y,T1,T2)
specifies the photosynthetic rate of individuals within the
designated compartment as a function of their average
body weight and temperature where a, p, 7, Th and T2 are
real or integer empirical constants. This parameter is
required to simulate either periphyton or phytoplankton as
a BASS state variable, yunits must be dimensionally
equivalent to g dry wt/d, and xunits must be
dimensionally equivalent to g dry wt/ind. Currently,
photosynthesis is not treated as a function of nutrients and
light availability.
• respiration[jM«iVs]=a*W[ArM«iVs] aP*H('/,T1,T2)
specifies the rate of dry organic mater respiration for the
designated compartment as a function of average
individual body weight and temperature where a, p, 7, Th
and T2 are real or integer empirical constants. This
parameter is required to simulate the designated non-fish
compartment as a BASS state variable, yunits must be
dimensionally equivalent to g dry wt/d, and xunits must
be dimensionally equivalent to g dry wt/ind.
Although BASS enables users to simulate benthos, periphyton,
phytoplankton or zooplankton as community state variables,
incidental terrestrial insects are always treated as a community
forcing function.
Regardless of how the biomass of benthos, incidental terrestrial
insects, periphyton, phytoplankton, or zooplankton is specified
as BASS inputs, or not as in the case of the FGETS simulation
mode, users can specify the stable isotope fractions 813C
and 515N of these compartments using the optional arguments
• del_cl3[-]=a
• del_nl5[-]=p
If not specified, bass will assign default values to these
parameters and output those values in the project's message file.
4.4. Input Data Syntax
4.4.1. Units Recognized by BASS
Most BASS commands require the specification of units (or
combination of units) as part of an option. This section describes
the syntax for units that are recognized by bass's input
algorithms. The conversion of user-specified units to those
actually used by BASS is accomplished by referencing all units to
the MKS system (i.e., meter, kilogram, second). Table 4.1 and
Table 4.2 summarize prefixes and fundamental units,
respectively, that are recognized by bass's unit conversion
subroutines. Table 4.2 also summarizes the dimensionality and
the conversion factor to the MKS system standard unit. Table 4.3
summarizes units that are recognized by bass's unit conversion
subroutines for specifying ecological, morphometric, and
physiological units.
Units and their prefixes can be specified in either upper or lower
case. When prefixes are used, there must be no embedded blanks
between the prefix and the unit name, e.g., "milligram" is correct,
"milli gram" is incorrect. Unit names for wet and dry masses are
appended, without blanks, with the parenthetic identifiers "(ww)"
and "(dw)", respectively. Similarly, an analogous convention is
used to specify mass units of specific chemical compounds. For
example, "grams oxygen" is specified as "g(o2)". The circumflex
(A) denotes exponentiation (e.g., cm"2 is presented as cmA-2). The
slash (/) denotes division. If multiple slashes are used to specify
a unit, they are interpreted according to strict algebraic logic. For
example, both "mg/liter", and "mg literA-l" are equivalent
specifications. Similarly, the weight specific units "mg/g/day"
and "mg gA-l dayA-l" are equivalent.
4.4.2. JJser-specified Functions
The following syntax rules apply to specifying these options
• Brackets are used only to delineate units. Dimensionless
parameters like assimilation efficiency, lipid fraction, and
Kow must be specified with null units
• The order of addition and multiplication is not significant.
Thus, the following specifications are valid and
equivalent.
temp[celsius]=a+P*sin(co + cp*t[xunits]) <=>
temp[celsius]=P sin((p*t[xMra'fs]+co) + a
czplnk[yunits]=a*cwater[xunits] <=>
czplnk[yunits]=cwater[xunits] *a
• Options that are temperature-dependent or temperature-
independent power functions can be specified by log10 or
In transforms. For example, the following options are
valid
ln(so[yMMto])=a + P*ln(W [xunits]) + y*T[celsius]
log(so[yMMto])=a + P*log(W [xunits]) + y*T[celsius]
October 2018
52
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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|jMn;te]=a*W[xMn;te]AP*exp(y*(T[celsius]-20))
ln(so|jMn;te])=a+ P*ln(W [xunits]) + y*(T[celsius]-20)
log(so \yunits])= a+ P*log(W[xMra'tt]) + y*(T[celsius]-20)
If the temperature dependency is unknown, temperature-
dependent power functions can be input for a specific
temperature, y Celsius, in which case BASS assumes a
default Qio=2. If this feature is used, the reference
temperature must be enclosed by parentheses and follow
the units specification of the independent variable. For
example, the following specifications are valid
so[yM«to](y)=a*W[xM«/te]AP
ln(so[yM«to](y))=a + P*ln(W [xunits])
log(so[yMnto](y))=a + P*log(W[xM«to])
If either the slope of a linear function or the exponent of
a power function is zero, the function can be input as a
constant without specifying the expected independent
variable. For example, the following specifications are
equivalent
lp[cm]=4.5 <=> lp[cm]=4.5 + 0.0*L[cm]
pl[-]=0.05 <=> pl[-]=0.05*W[g(ww)]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(ww)]A-0.2
ln(so [mg(o2)/g/hr] )=
- 2.30+0.0693*T[celsius]+-0.2*ln(W[g(ww)])
The correct syntax for these options would be
so[mg(o2)/g/hr]=
0.1*exp(0.0693*T[celsius])*W[g(ww)]A(-0.2)
ln(so [mg(o2)/g/hr] )=
-2.30+0.0693*T[celsius]- 0.2*ln(W[g(ww)])
4.4.3. User-specified Parameter Files
If the user specifies a file option for the /EXPOSURE,
/TEMPERATURE, /WATER_LEVEL, /BIOTA, /BENTHOS,
/TERRES TRIAL_INSECTS, /PERIPHYTON, /PHYTOPLANKTON,
/zooplankton, or /habitat_parameters commands, the
designated files must exist and be supplied by the user. The
general format of a bass exposure file allows a user to specify
multiple exposure conditions within a single file. Each file record
specifies exposure conditions for a specific time. The general
format of a bass exposure file is as follows
! file: exposure.dat
!
/001 (ime|«/»7,v| ! see ensuing discussion
/CI string
/CM string
/START_DATA
vu Vj_2 ... v1MV ! comment
v2,i V22 ... v2MV ! comment
vnr,i vnr,2 ¦¦¦ vnr,mv ' comment
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, MV is the number of variables per record, and CI... CM are
the column positions of M exposure variables that are to be read.
Note, however, that MV can be greater than CM and that
CI...CM need not be consecutively numbered. To simplify the
reading of multiple exposure files, BASS requires that "time" be
the first column of any user-specified exposure file. Valid
character strings for specifying the remaining data columns
include:
• bbenthosl «/»'<,v I to read the standing stock of benthic
invertebrates;
• binsects[wra#s] to read the standing stock of incidental
terrestrial insects;
• bperiphyton[wraiYs] to read the standing stock of
periphyton or benthic algae;
• bphytoplankton[wraiYs] to read the standing stock of
phytoplankton;
• bzooplankton[ M/ifVv | to read the standing stock of
zooplankton;
October 2018
53
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• cbenthos [units](ChemicalName) to read the
concentration of Chemical Name in benthic invertebrates;
• cinsccts[ unils\(ChemicalName) to read the concentration
of ChemiealName in incidental terrestrial insects;
• cperiphyton[units](ChemicalName) to read the
concentration of ChemiealName in periphyton;
• cphytoplankton[wraiYs](C/8e/raicaZAtorae) to read the
concentration of ChemiealName in phytoplankton;
• csedimentlunils\(ChemicalName) to read the sediment
concentration of ChemiealName;
• cwaterlunils\(ChemicalName) to read the unbound,
aqueous concentration of ChemiealName;
• c/AH>\)\unkUm\units\(CliemicalName) to read the whole-
body concentration of ChemiealName in zooplankton;
• depth[wraiYs] to read water depth;
• hsi_['cc(lin^[-\(FishName) to read the feeding/growth
HSI for the fish species FishName;
• hsi rccruitmcntl-\{FishName) to read the recruitment/
spawning HSI for the fish species FishName;
• hsi survival[-|(/7v/iA'awjf) to read the dispersal/non-
predatory mortality HSI for the fish species FishName;
• tcmpcraturcl units | to read ambient water temperature;
• wbenthosl units | to read the mean body weight of benthic
invertebrates;
• winsectsl units | to read the mean body weight of
incidental terrestrial insects;
• wperiphyton[wraiYs] to read the mean body weight of
periphyton or benthic algae;
• wphytoplankton[wraiYs] to read the mean body weight of
phytoplankton;
• wzooplankton[ M/ifVv | to read the mean body weight of
zooplankton.
If column names other than those listed above are specified, bass
simply ignores them. Data records can be continued by
appending an ampersand (&) to the end of the record; for
example, the following data records are equivalent.
vi,l vi>2... vy &
Vi,j+1 Vi,j+2 - ¦ ¦ Vi,MV
File records must be sequenced so that time is non-decreasing
(i.e., tj < tl+|, i =1, 2, ..., N-l). The time increment between
consecutive records can be constant or variable, bass calculates
the exposure conditions between specified time points by simple
linear interpolation.
4.5. BASS Include File Structure
As mentioned in Section 4.1, bass's input processing routines
allow a bass project file to be specified using include files of
related parameters. This capability is the cornerstone upon which
the bass GUI has been developed.
To select an appropriate project / include file hierarchy for
implementation in the BASS GUI, careful consideration was given
to the perceived needs of researchers and environmental
regulators who would routinely analyze and evaluate similar
scenarios that might differ either in the chemical exposures of
interest or in the communities of concern. For example, the
USEPA Office of Chemical Safety and Pollution Prevention
(OCSPP) evaluates different pesticides for registration based on
their expected fate and effects in series of canonical aquatic
habitats / ecosystems. Similarly, OCSPP evaluates the pre-
manufacturing registration of industrial chemicals in much the
same way. Such examples suggested that a practical working
BASS project / include file hierarchy should be structured as
follows:
• All data specifying the bioenergetic, compositional, and
morphological parameters for a specific fish species that
can be considered to be independent of the particular
community in which the fish resides, should be contained
within a single include file that is assigned the reserved
extension FSH.
• All data specifying the structure and function of a
particular fish community should be contained within a
single include file that is assigned the reserved extension
cmm. These files should use fsh files (as include files)
interleaved with the necessary fish commands to define
each species' (1) dietary composition, (2) initial ages,
body weights, population densities, and chemical residues,
(3) habitat multipliers, (4) fishery parameters, and (5) any
fish commands contained within a FSH file that the user
wants to have superceded.
October 2018
54
-------
• All data specifying the physico-chemical properties for a
specific chemical of concern should be contained within
a single include file that is assigned the reserved extension
PRP.
• All data specifying a chemical exposure scenario should
be contained within a single include file that is assigned
the reserved extension chm. These files should use prp
files (as include files) interleaved with the necessary
chemical commands needed to specify each chemical's (1)
aqueous concentration, (2) dietary exposures via benthos,
insects, periphyton, phytoplankton, and zooplankton, (3)
effects concentrations for specific fish, and (4) relevant
rates of biotransformation by specific fish.
• Lastly, all bass project files should use cmm and chm
files (as include files) to specify the fish community and
the chemical exposures of concern, respectively. All such
project files will be assigned the reserved extension PRJ.
Based on these considerations, the general structure of a bass
project file is as follows:
! file: name.prj
! notes: general structure of a BASS project file
/ SIMULATION_CONTROL
/ HEADER
/ MONTH_TO
/ LENGTH_OF_SIMULATION a[year]
/ TEMPERATURE temp[celsius] = fnc
/ WATER_LEVEL depth[meter] = fnc
! specify chemical exposures (if any)
#include 'exposures.chm'
! specify fish community
#include 'community.cmm'
/ END
The chemical exposure scenario file EXPOSURES.CHM specified
in this project file has the following general form
file: exposures.chm
notes: general structure of a chemical
exposure scenario file
! specify physico-chemical parameters
#include ychemical_l.prp'
/ EXPOSURE cwater [pprn] = fnc; &
cbenthos[pprn] = fnc; &
cinsects[pprn] = fnc; &
cperiphyton [pprn] = fnc, &
cphytoplankton [ppm] = fnc, &
czooplankton[pprn] = fnc
I NONFISH_BCF &
bcf [-] (benthos) = fnc, &
bcf [-] (periphyton) = fnc, &
bcf [-] (phytoplankton) = fnc, &
bcf[-](zooplankton) = fnc
/ LETHALITY &
lc50[units] (fish_l) = fnc; &
lc50 [units] ( fish_2) = fnc
/ METABOLISM &
bt [units] ( fish_l, chem_n) = fnc, &
bt [units] ( fish_2, chem_n) = fnc
! repeat above chemical data block as needed
! end exposures.chm
The general structure of the chemical property file
CHEMICAL_ 1 .PRP specified in the above exposure scenario file is
file: chemical_i.prp
notes: general structure of a chemical
property file
/ CHEMICAL
/ LOG_AC creal number>
/ LOG_P creal number>
/ LOG_KBi creal number>
/ L0G_KB2 creal number>
/ MOLAR_WEIGHT creal number>
/ MOLAR_VOLUME creal number>
/ MELTING_POINT creal number>
! end chemical_i.prp
The community file COMMUNITY.CMM specified for the above
project file has the following general form
file: community.cmm
notes: general structure of a community file
#include yfish_l.fsh'
/ ECOLOGICAL_PARAMETERS &
diet (a
-------
/ COMMON_NAME
/ SPECIES
/ AGE_CLASS_DURATION
/ SPAWNING_PERIOD
/ FEEDING_OPTIONS &
allometric {a
-------
• an output file that tabulates selected results of the
simulation. Tabulated summaries include: (1) annual
bioenergetic fluxes and growth statistics (e.g., mean body
weight, mean growth rate, etc.) of individual fish by species
and age class; (2) annual bioaccumulation fluxes and
statistics (e.g., mean whole-body concentrations, mean B AF
and BMF, etc.) of individual fish by species and age class;
and (3) annual community fluxes and statistics (e.g., mean
population densities and biomasses, mean production, etc.)
of each fish species by age class. This file has the same
name of the executed project file with extension "B SS". For
example, when bass executes the project file input.prj, the
output file INPUT.BSS is generated. If this file already exists,
it is silently overwritten.
• one or more CSV files that enable users to create a variety
of custom figures or plots using Excel or other graphical
software. See the "-csv" command line options described in
the following section. If these files already exist, thev are
silently overwritten.
4.7. Command Line Options
To run a BASS simulation that is specified by the project file
INPUT.PRJ, BASS can be invoked either from the BASS GUI or
using the UNIX like command line
C:\BASS23> bass_V23 -i input.prj
Although the "-i filename" option is the only required command
line option, the following additional options are available
-a=> enable default accelerated YOY self-thinning;
-c => report distribution of cpu time in major subroutines;
-cc number => total fish carrying capacity as a multiple of
total initial biomass;
-cmm => generate updated cmm file using the ending valued
for age, weight, density, and cfish;
-csvl number => generate Excel CSV file of fish variables;
number=print interval in days;
-csv2 number => generate Excel CSV file of non-fish
variables; number=print interval in days;
-csv3 number => generate Excel CSV file of cfish X weight
for year=number;
-e => tabulate realized monthly diets for elective feeding;
-ef => tabulate realized monthly diets for elective feeding
and report FGETS style diets
-echo => echo input commands;
-f => turn off fishing;
-h => print this help list and stop (also see -?);
-hsi => turn off HSI functions (i.e. assume HSI=1);
-1 => turn off lethal effects;
-m => enable monthly spawning for species with annual age
classes;
-mba => output mass balance analysis with annual
summaries;
-n => internally calculate rate-based BCF for non-fish;
-p => turn on messages associated with feeding and predation;
-s => turn off fish stocking;
-t => run test of BASS integrators and stop;
-tp => report trophic positions;
-tte => report net trophic transfer efficiencies;
-w => write input data no execution attempted;
-? => print this help list and stop (also see -h)
For example, the command line
C:\bass22> bass_V23 -i input.prj -1 -c
will execute the project file INPUT.PRJ without simulating acute
or chronic chemical lethality and report the distribution of cpu
time spent within various key BASS subroutines.
October 2018
57
-------
Table 4.1 Valid Unit Prefixes.
Prefix Name Conversion
Factor
atto
1018
centi
10"02
deca
10+01
deci
10"01
exa
10+18
femto
1015
giga
10+0"
hecto
10+02
kilo
10+03
mega
10+06
micro
10"06
milli
10"03
myria
10+04
nano
10-09
peta
10+!5
pico
10"12
tera
10+12
October 2018
58
-------
Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive
and summarizes only commonly used unit names that bass's units conversion program recognizes.
Unit Name
Conversion
Factor to SI
Metre
Kg
Second
Description
acre
2.471xlO"04
2
0
0
4840 yards2
are
l.OOOxlO"02
2
0
0
100 meter2
btu
9.479xlO"04
2
1
-2
calorie
2.388xlO"01
2
1
-2
cc
l.OOOxlO"1"06
3
0
0
cm3
cm
l.OOOxlO"1"02
1
0
0
day
1.157xl005
0
0
1
decade
3.169xl009
0
0
1
10 years
erg
l.OOOxlO"1"07
2
1
-2
fathom
5.468xlO"01
1
0
0
6 feet
feet
3.281xlO+00
1
0
0
foot
3.281xlO+00
1
0
0
ft
3.281xlO+00
1
0
0
feet, foot
g
l.OOOxlO"1"03
0
1
0
grams
gallon
2.642xlO+02
3
0
0
3.785 liter
gm
1.000xl0+03
0
1
0
grams
gram
l.OOOxlO""03
0
1
0
gramme
l.OOOxlO""03
0
1
0
hectare
l.OOOxlO04
2
0
0
100 are
hour
2.778xlO"04
0
0
1
hr
2.778xlO"04
0
0
1
hour
imperialgallon
2.200xl0+°2
3
0
0
4.54 liter
inch
3.937xlO+01
1
0
0
joule
l.OOOxlO""00
2
1
-2
kg
l.OOOxlO""00
0
1
0
kilograms
km
l.OOOxlO03
1
0
0
kilometer
1
l.OOOxlO""03
3
0
0
liter
lb
2.205xl0+0°
0
1
0
pound
liter
l.OOOxlO"1"03
3
0
0
litre
l.OOOxlO"1"03
3
0
0
m
l.OOOxlO"1"00
1
0
0
meter
meter
l.OOOxlO"1"00
1
0
0
metre
l.OOOxlO"1"00
1
0
0
mg
l.OOOxlO"1"06
0
1
0
milligrams
micron
l.OOOxlO"1"06
1
0
0
10"6 meter
mile
6.214xlO"04
1
0
0
5280 feet
min
1.667xl002
0
0
1
minute
minute
1.667xl002
0
0
1
ml
l.OOOxlO"1"06
3
0
0
mm
l.OOOxlO"1"03
*59
0
0
-------
Table 4.3 Valid ecological, morphometric, and physiological units. BASS uses these units to convert user-specified units for
lamellar density, initial populations densities, fish body weights, and oxygen consumption to standard model units.
Unit Name
Conversion
Factor to SI
Metre
Kg
Second
Description
fish
n.a.
0
0
0
treated as an amount as is mole
individuals
n.a.
0
0
0
treated as an amount as is mole
inds
n.a.
0
0
0
treated as an amount as is mole
ha
l.OOOxlO"4
2
0
0
hectare
lamellae
n.a.
0
0
0
treated as an amount as is mole
g(dw)
242.5
0
1
0
used for wet-to-dry conversions
kg(dw)
0.2425
0
1
0
used for wet-to-dry conversions
mg(dw)
2.425x10s
0
1
0
used for wet-to-dry conversions
ug(dw)
2.425x10s
0
1
0
used for wet-to-dry conversions
g(ww)
l.OxlO3
0
1
0
used for wet-to-dry conversions
kg(ww)
1.0
0
1
0
used for wet-to-dry conversions
mg(ww)
l.OxlO6
0
1
0
used for wet-to-dry conversions
ug(ww)
1.0x10"
0
1
0
used for wet-to-dry conversions
g(02)
7.3718xl0"5
2
1
-2
gram of oxygen
mg(02)
7.3718xl0"2
2
1
-2
milligram of oxygen
ug(02)
7.3718x10
2
1
-2
microgram of oxygen
kcal
2.388xl0"4
2
1
-2
kilocalorie
ul(02)
5.1603x10
2
1
-2
microliter oxygen STP = micromole
ml(02)
5.1603xl0"2
2
1
-2
milliliter oxygen STP = millimole
1(02)
5.1603xl0"5
2
1
-2
22.4 liters STP = mole
umol(02)
2.3037
2
1
-2
micromole of oxygen
mmol(02)
2.3037xl0"3
2
1
-2
millimole of oxygen
mol(02)
2.3037xl0"6
2
1
-2
mole of oxygen
Notes:
1. For unit conversions, a fish's kilogram wet weight is treated as its SI weight. Therefore, kg(ww)=kg, g(ww)=g, and
mg(ww)=mg
2. For unit conversions, all units associated with oxygen consumption are treated dimensionally as joules.
October 2018
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5. bass Model Software and Graphical User Interface
5.1. Software Overview
The BASS v2.3 model and Graphical User's Interface (GUI)
software are provided via two downloads from the USEPA
Center for Exposure Assessment Modeling (CEAM) website
(http://www.epa.gov/ceampubl/). These downloads are:
1. BASS_V23.zip: A compressed WinZip file that installs only
the BASS modeling and parameterization software, user's
manual, and distribution examples for DOS/Windows
systems.
2. BASS_V23_GUI.zip: A compressed WinZip file that
installs the BASS modeling and parameterization software,
user's manual, GUI, and distribution examples for
DOS/Windows systems.
BASS_V23.zip creates a BASS modeling directory as shown
below
PATH\BASS_V2 3
BASS_FILES.EXE
BASS_FILES_ABS0FT_15.EXE
BAS S_FILES_LAHEY_77_AM.EXE
BASS_FILES_LAHEY_77_VS.EXE
BASS_V2 3.EXE
BASS_V23_ABSOFT_15.EXE
BAS S_V23_LAHEY_77_AM.EXE
BASS_V2 3_LAHEY_7 7_VS.EXE
BAS S_FIS H_DATA.DB
PISCES_DATA.DB
LIBGOMP.DLL
LIBGOMPX64.DLL
PTHREADVC2.DLL
PTHREADVC2_64.DLL
CLEAN_EXAMPLES.BAT
RUN_EXAMPLES.BAT
C OMPARE_BA S S _E XE s.BAT
BASS_CMD_OPTIONS.TXT
\BASS_DATABASES
\BASS_BD
\EIGEN
\OXYREF
\COMMUNITY
\DOCUMENTS
\BASS_FGETS_APPLICATIONS_REVIEWS
\FACT_SHEETS
\MANUAL
\PPT_PRESENTATIONS
\QA
\FISH
\PROPERTY
\PROJECTS
\OEX_EVERGLADES_CANAL_HG_ABSOFT_15
\0EX_EVERGLADES_CANAL_HG_LAHEY_77_AM
\0EX_EVERGLADES_CANAL_HG_LAHEY_77_VS
\EX_EVERGLADES_CANAL
\EX_EVERGLADES_CANAL_FISHING
\EX_EVERGLADES_CANAL_HG
\EX_EVERGLADE S_CANAL_HG_FGE T S
\EX_EVERGLADES_CANAL_HG_LESLIE
\EX_EVERGLADES_HG_HOLES
\EX_EVERGLADES_HG_MARSH
\EX_L_HARTWELL
\EX_L_HARTWELL_PCB
\EX_L_HARTWELL_PCB_TRANS
\EX_L_ONTAR10_P CB
\EX_SE_FARM_POND
\EX_SE_FARM_POND_XMS
\SOURCE_CODE
\BASS_FILES_ABS0FT_15
\BASS_FILES_LAHEY_7 7_AM
\BASS_FILES_LAHEY_7 7_AM
\BASS_v23_ABSOFT_15
\BASS_v23_LAHEY_7 7_AM
\BASS_v23_LAHEY_7 7_VS
where PATH = C:\USERS\USER_NAME unless changed by the user.
The contents of this directory include:
1. bass_v23.exe is the active bass model executable.
Although it is assigned to be BASS_v23_LAHEY_77_AM.EXE by
default, users can reassign it to be bass_v23_absoft_15.exe
or BASS_v23_LAHEY_77_VS.EXE to obtain faster simulations
depending on their CPU specifications.
2. bass_v23_absoft_15.exe is the most current bass model
executable that has been created with the Absoft version 2015
Fortran 95 compiler using a 64-byte Windows 10 operating
system. Depending on a user's CPU, this executable may run
faster or slower than the Lahey-Fujitsu executables below.
Also see Section 7.2.7.
3. bass_v23_lahey_77_AM.exe is the most current bass
model executable that has been created with the Lahey-Fujitsu
Fortran 95 version 7.7 compiler using its AutoMake software.
This executable is used as the default bass software executable
bass_v23.exe. Also see Section 7.2.7.
4. bass_v23_lahey_77_VS.exe is the most current bass
model executable that has been created with the Lahey-Fujitsu
Fortran 95 version 7.7 compiler using its Visual Studio shell.
Also see Section 7.2.7.
5. bass_files.exe is the active executable for the bass
parameterization software that uses the data files
bass_fish_data.db and pisces_data.db to enable users to
generate fish and community files. Although this executable
was created with the Lahey-Fujitsu 7.7 compiler using
AutoMake, executables compiled with Absoft 2015 and
Lahey-Fujitsu 7.7 using Visual Studio, are also available. See
Section 5.6.
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6. \bass_databases contain three data subdirectories with
associated Fortran 95 executables that generate input data for
bass_fish_data.db and Fortran 95 code for bass_files.exe
and BASS_v23.EXE. The directory \BASS_DB contains the
software and data subdirectory \bass_db\fish_data that
generates the bass database supplement and an internal
database of fish growth rates for bass_files.exe. The
directory \EIGEN contains the software that generates an
internal database of eigenvalues and mixing coefficients for
bass's gill exchange model described in Section 2.2. Lastly,
the directory \OXYREF contains the software and the OXYREF
database (Thurston and Gehrke 1993) that generates family-
specific oxygen consumption rates for BASS_FILES.EXE.
7. \community is the folder designed to be a repository of
community files (*.CMM) that the user wishes to save as a
canonical library for constructing future BASS projects.
Although this folder is empty, it must be present for the BASS
software to function correctly. See Section 4.5 (page 56).
8. \DOCUMENTS contains five document subdirectories related
to the application and use bass.
9. \fish is the folder designed to be a repository of fish files
(*.FSH) that the user wishes to save as a canonical library for
constructing future BASS projects. This folder must be present
for the BASS software to function correctly, and it is initially
populated with default FSH files generated by bass_files .exe
for 661 species of North American and European fishes. These
files can be regenerated by double clicking on the DOS batch
file regenerate_fsh_files.bat See Section 5.6.
10. \projects contains the BASS v2.3 distribution example
projects that are described in Section 6.1 (page 75). All of
these examples can be executed by double clicking on the
DOS batch file RUN_EXAMPLES.BAT. The three projects,
whose names begin with "0EX_", are also included to allow
users to compare the relative execution speeds of the Absoft
and Lahey-Fujitsu bass executables. These performance
projects can be executed by double clicking on the DOS batch
file COMPARE_BASS_EXES.BAT.
11. \PROPERTY is the folder designed to be a repository of
chemical property files (*.prp) that the user wishes to save as
a canonical library for constructing future BASS projects. This
folder must be present for the bass software to function
correctly, and it is initially populated with chemical property
files used by the BASS distribution examples. This folder also
contains the folder \barber_2003 which contains chemical
property files for the chemicals analyzed in Barber's review
paper of gill exchange models (Barber, M.C. 2003. Environ.
Toxicol. Chem. 22: 1963-1992). See Section 4.5 (page 56).
12. \source_code contains the current Fortran 95 source
code for bass_v23.exe and bass_files.exe. This folder is
included for those users who would like to review the BASS
code or to adapt it for other purposes.
In addition to the aforementioned BASS modeling directory, the
WinZip file BASS_V23_GUI.zip creates a \BASS GUI
subdirectory and installs the BASS GUI executable and its
associated dynamic link libraries (DLLs).
5.2. Installation Procedures
For complete installation procedures users are referred to the
BASS installation readme file at the USEPA Center for Exposure
Assessment Modeling (CEAM) website
(http://www.epa. gov/ceampubl/).
5.3. BASS GUI Operation
The bass GUI has been designed to emulate Microsoft's
Windows Explorer in much of its form and function. After the
BASS GUI is opened, the first window that users see is the GUI's
Current BASS Directory (see Figure 5.1). If this window is
inadvertently closed, it can be reopened using the View button
found on the toolbar of the GUI's host window.
Figure 5.1 BASS GUI Current BASS Directory window.
ni Current BASS Directory |- ||n|[x|
-¦I-I aUUHnlxld a -I B|-ne-l»lsal
b- IZH BASS_Root
Q community
1 fij fish
ex_Everglades_canal
ffi-CH ex_Everglades_canal_fishing
ffl-Q ex_Everglades_canal_hg
ffi-Cn ex_Everglades_canal_hg_leslie
ffl CH ex_Everglades_holes_hg
ffl-'CH ex_Everglades_marsh_hg
ex_L_Hartwell_pcb
ffi-CH ex_L_Hartwell_pcb_trans
Q ex_L_0ntario_pcb
ffi Q ex_SE_farrn_pond K
Q] property J
~
Double-clicking on a folder's name, icon, or directory node
expands or collapses the folder's contents into or out of the
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user's view, respectively. Double-clicking on a file name opens
the file with one of six GUI file editors based on the selected
file's extension. The GUI's file editors can also be invoked by:
1. Left-clicking on the file and pressing the Enter key.
2. Right-clicking on the file and then left-clicking on Edit.
3. Left-clicking on the file and left-clicking on the Edit
icon found on the Current BASS Directory toolbar.
When users are editing a BASS project file that contains include
files, users can also open file editors for those include files by
4. Left-clicking on the desired include command and then
left-clicking on the resulting activated Open Include File
link (see Section 5.4.1).
BASS output files (i.e., *.BSS, *.MSG, and *.XML), are not
displayed in the Current BASS Directory window. These files, if
they exist, are accessed via the project files (*.PRJ) that
generated them.
BASS message files (*.MSG) and simulation summary files
(*. B S S) can be reviewed by right-clicking on the relevant proj ect
file and then left-clicking on View Project Message File or View
BSS File, respectively. These files can also be reviewed by left-
clicking on the desired project file and then left-clicking on the
arrow of the File Viewing icon # - found on the Current
BASS Directory toolbar. The File Viewing icon has an
associated drop-down selection that enables users to specify
which output file type is to be viewed. If the File Viewing icon
is left-clicked directly, the project's message file is opened by
default.
BASS project files are executed either by right-clicking on the
desired project file and then left-clicking on Run Project or by
left-clicking on the desired project file and then left-clicking on
the arrow of the Execution icon - on the Current BASS
Directory toolbar. Like the File Viewing icon, the Execution icon
has an associated drop-down selection that enables users to
specify command line options as described in Section 4.7 (page
57). When a project file is being executed, all other GUI
functions are unavailable until the simulation is completed.
bass project files can be checked for their syntax and data
completeness before attempting execution either by right-clicking
on the desired project file and then left-clicking on Validate
Project or by left-clicking on the desired project file and then
left-clicking on the Validate Project icon on the Current
BASS Directory toolbar. If the project file has syntax errors or
missing input data, the GUI's Event Viewer will automatically
open and display validation status of the project as well as
associated errors and warnings. Most users, however, will find it
easier to review these errors by opening the project's MSG file,
as outlined previously, and search for the phrase "ERROR:" to
determine the needed corrective actions.
5.3.1. BASS File Editors
All six GUI file editors have the same essential format and
function as displayed in Figure 5.2. Commands, include files,
and comment blocks contained within the file being edited are
displayed in abbreviated form and in order of their appearance
within the Elements of This File box. The full details of these
elements can be viewed individually within the Element Value
box or as they appear within the file by left-clicking on the Show
Text View toggle button. Elements can be edited by either
double-clicking on the element name or by left-clicking on the
element and then left-clicking on the Open Editor... button.
Figure 5.2 General structure of BASS GUI file editors.
Elements of This File; Opor Editor...
comrnentBlock
comrnentBlock
ecologicaLpararneters
ecologicaLpararneters
initial_conditions
include
comments iock
ecologicaLpararneters
cological_parameters
nitial_conditions
elude
ommentBiock
cological_parameters
cological__pararneters
nitial_conditions
elude
ommentBiock
cologicaLpararneters
cological_parameters
nitial_conditions
elude
ommentBiock
cological_parameters
Show Text View »
Move Up Move Down
Remove
Insert Command
E lement v alue
ffinclude largemoum_ba$s.fsh
Click 'Show Text View' to see the full text
Apply
OK
Cancel
The position of elements can be changed by using the Move Up
and Move Down buttons. Existing elements can be removed and
new elements added by using the Remove button and Insert
Command box, respectively. When elements are either added,
removed, or reordered, however, users must first left-click on the
Apply button before opening any GUI command editor. The
Apply button is also used to save editorial changes at any time
during an editing session.
Because the typical Close "X" button has been disabled on all
October 2018
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GUI file editors, users can exit GUI file editors only by using the
OK and Cancel buttons. These buttons either save or cancel any
editorial changes since the last invocation of the Apply button.
This GUI behavior is designed to preserve the integrity of the
GUI's Document Object Model (DOM).
Figure 5.3 displays the structure of the BASS GUI project file
editor. This editor differs from the GUI's other five file editors
in two ways. First, this editor explicitly identifies all include files
that will be used by the project. Secondly, any include file that is
directly referenced by the project file can be opened and edited
by left-clicking on the Open Include File hyperlink that appears
below the Element Value box whenever an include statement is
highlighted in the Elements of This File box.
Figure 5.3 Structure of BASS GUI project file editor.
File Includes:
•community erglade:_c anal. cmTi
¦¦largemouth_bass, fsh
--florida_gar.fsh
-•yellow_bullhead. fsh
--bluegii_sunfi$h.fsh
comments lock
simulation_contfol
header
rnonthjO
length_of_sirnulation
temperature
waterjevel
annual_outputs
comments lock
commentB lock
comments lock
Show Text View >>
Cancel
5.3.2. BASS Command Editors
GUI command editors are opened from GUI file editors as
outlined in Section 5.4.1. In terms of their appearance and
functionality, there are 17 basic command editor types that are
described in the following:
• Simple String Editors that edit the commands /CHEMICAL,
/common_name, /header, /species, and include file
specifications (i.e., #INCLUDES . . .). See Figure 5.4.
• Simple String Editor with pull-down selection that edits the
commands /age_class_duration and /MONTH_T0. See
Figure 5.5.
• Numeric Editor with units that edits the command
/length_of_simulation. See Figure 5.6.
• Numeric Editor without units that edits the commands
/ANNUAL_OUTPUTS, /LOG_AC, /LOG_KB 1, /LOG_KB2, /LOG_P,
/melting_point, /molar_volume, and /molar_weight.
See Figure 5.7.
• Forcing Function Editor that edits the commands /BIOTA,
/EXPOSURE, /HABITAT_PARAMETERS, /TEMPERATURE, and
/WATER_LEVEL. See Figure 5.8.
• Feeding Model Editor that edits the command
/feeding_options. See Figure 5.9.
• Compositional and Morphometric Editor that edits the
commands /COMPOSITIONAL_PARAMETERS and
/morphometric_parameters. See Figure 5.10.
• Ecological Editor that edits the command
/ecological_parameters. See Figure 5.11 and Figure
5.12.
• Physiological and Growth Editor that edits the command
/PHYsiological_parameters . See Figure 5.13.
• Cohort Initial Conditions Editor that edits the command
/initial_conditions. See Figure 5.14.
• Spawning Period Editor that edits the command
/spawning_period See Figure 5.15.
• Fishery Editor that edits the command
/FISHERY_PARAMETERS. See Figure 5.16.
• Non-fish Biotic Editor that edits the commands /BENTHOS,
/PERIPHYTON, /PHYTOPLANKTON, /TERRESTRIAL_INSECTS,
and /ZOOPLANKTON. See Figure 5.17 and Figure 5.18.
• Non-fish B CF Editor that edits the command /NONFlSH_BCF.
See Figure 5.19.
• Chemical Metabolism Editor that edits the command
/METABOLISM. See Figure 5.20.
• Chemical Toxicity Editor that edits the command
/LETHALITY. See Figure 5.21.
• Plot Selection Editor that edits the commands
/annual_plots and /summary_plots . See Figure 5.22.
October 2018
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As noted with the GUI file editors, the typical Close "X" button
has been disabled on all GUI command editors. Users can only
exit or close a command editor by using the OK and Cancel
buttons. These buttons either save or cancel any editorial changes
since the editor was opened. This GUI behavior is designed to
preserve the integrity of the GUI's Document Object Model
(DOM).
5.3.3. Special Function Editors
In addition to the file and command editors described in the
previous section, the BASS GUI has two special function editors,
i.e.,
• Comment B lock Editor that is used to insert comment blocks
before or after bass commands, as opposed to end-of-line
comments associated with the individual options of BASS
commands. See Figure 5.23.
• Time Series Data Editor for editing external data files that
are specified as file functions (e.g., /BIOTA, /EXPOSURE,
/HABITAT_PARAMETERS, /TEMPERATURE, and
/WATER_LEVEL). See Figure 5.24.
5.3.4. File and Folder Operations
Using the GUI's Current BASS Directory window, users can
create new files and project folders either from scratch or from
existing files and project folders.
To create a BASS project or include file from scratch, users must
first left-click on the subdirectory (i.e., \COMMUNIY, VFISH, or
\PROPERTY) or project folder where the file is to be created.
The user then must left-click on the drop-down arrow head of the
Add New File icon - . When the Add New File drop-down
menu appears, the user must left-click on the desired file type to
be created. Finally, after the new file appears in the Current
BASS Directory window, the user must complete the naming of
the new file. New project folders can be created following these
same steps.
Users can create a file from an existing file by
1. Left-clicking on the desired file and then left-clicking on
the Copy icon
2. Left-clicking on the desired destination folder or
subdirectory and left-clicking on the Paste icon
Users can also create a new file from an existing file by
1. Right-clicking on the file to be copied and then left-
clicking on Copy.
2. Right-clicking on the destination folder or subdirectory
and then left-clicking on Paste.
Lastly users can create a new file from an existing file by
1. Left-clicking on the file to be copied and then pressing
CTRL-c.
2. Left-clicking on the destination folder or subdirectory and
then pressing CTRL-v
New project folders can be created from existing projects using
the same procedures.
5.4. The BASS Output Analyzer
The BASS Output Analyzer (OA) was a dual purpose post-
processor that enabled users to construct customized graphs and
tables. This software could be invoked either from within the
BASS GUI or as a standalone application. Using this software,
users could create two and three-dimensional graphs of any state
variable that is a valid option for the BASS v2.1 plotting
commands /ANNUAL_PLOTS or/SUMMARY_PLOTS. The BASS OA
also enabled users to create customized versions of the summary
tables generated for BSS output files. This software, however,
which was based on the Olectra Chart software (now
ComponentOne Chart), is no longer functional or supported by
the bass GUI.
To replace the lost plotting features of the bass OA, users can
now generate three different types of CSV files which can be
used to create even more customized plots using Excel or other
graphical software programs. See Section 4.7. Command Line
Options.
5.5. The BASS Parameterization Software
The bass parameterization software bass_files .exe was created
and is distributed with the BASS modeling software to assist users
in constructing BASS FSH files and CMM files. Using a
combination of an internal database of fish growth rates and two
external database files (bass_fish_codes.db and
PISCES_DATA.DB), this software currently can generate FSH files
for 661 species of North American and European freshwater fish.
The most current versions of these files are distributed with BASS
and reside in the data directory VFISH. All FSH files generated by
this software use bass's allometric feeding model. Users can also
use this software to construct a default CMM file and associate
FSH files for an arbitrary selection of the aforementioned 661
fish species. This software, however, does not have an associated
GUI and must be executed by the user from a DOS command
prompt.
October 2018
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To generate a FSH file for a single species of interest, the user
should open a DOS command prompt window and navigate to
the project folder in which they want the file to be generated.
Assuming that the user's BASS root directory is c:\BASS_v23, the
DOS command
>c:\bass_V23\bass_files.exe -g "lepomis macrochirus"
-m "January 15" -1 10 -u 33
will generate a FSH file for bluegill sunfish whose growth rate
has been calibrated to an annual sinusoidal water temperature
cycle that varies from 10 to 33 Celsius and whose minimum
annual temperature occurs on January 15.
To generate a CMM file and associated FSH files for a selection
of fish, the user should again open a DOS command prompt
window and navigate to the project folder in which the user
wants the files to be generated. Assuming that the user's bass
root directory is c:\BASS_v23, the DOS command
...>c:\bass_v23\bass_files.exe -i fishes.dat
will generate a CMM file and associated FSH files for the fish
species identified in the Tilefishes.dat. The file, fishes.dat must
reside in the desired proj ect folder and be structured as illustrated
below
! File:bass_bluegill_catfish.dat
CMM_FILE_NAME bass_bluegill_catfish.cmm
MONTH_TO August
COLDEST_DAY January 15
TEMPERATURE_MAXIMUM 3 0
TEMPERATURE_MINIMUM 10
FISH_START micropterus salmoides
COMMON_NAME largemouth bass
SPAWNING_PERIOD april-may
parameter_option_l; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number
or density[fish/ha]= number
FISH_END
FISH_START Lepomis macrochirus
COMMON_NAME bluegill sunfish
SPAWNING_PERIOD april-october
parameter_option_l; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number
or density[fish/ha]= number
FISH_END
FISH_START ictalurus puntatus
COMMON_NAME channel catfish
SPAWNING_PERIOD may-june
parameter_option_1; comment/reference
parameter_option_2; comment/reference
parameter_option_n; comment/reference
biomass[kg/ha]= number
or density [fish/ha]= number
FISH_END
where parameter_option_i is any valid option for the BASS fish
commands \COMPOSITIONAL_PARAMETERS,
\ECOLOGICAL_PARAMETERS, \MORPHOLOGICAL_PARAMETERS, or
\physiological_parameters that the user wants to supercede
the default assignment made by bass_files.exe. All CMM files
and associated FSH files used by the example BASS distribution
projects have been generated using this software.
Figure 5.4 GUI command editor for simple strings
Source: species of largemouth_bass.fsh
Value:
micropterus salmoides
Comment:
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Figure 5.5 GUI command editor for simple strings with drop-down selection.
Source; age_ciass_duration of largemouth_bass-.fsh
Figure 5.6 GUI command editor for numeric data with user-specified units.
Source; length_of sirriulation of everglades,prj
Comment; J
tengm_of_simuia?ion
Figure 5.7 GUI command editor for numeric data fixed units.
Source: annyal_outpu(s of everglades.prj
Value; pig
Zonmert
OK J Cancel
October 2018
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Figure 5.8 GUI command editor for forcing functions.
Source: temperature of everglades.prj
Comment i
; Parameter
Units : =
; Value ; Use File
Comments
;temp
celsius
22.5+7.5Ksin[0.172142e-01 Bt[day]-0.279 7311 ^
~
:temp_epilimnion
1
1
temp_hypolimnion
=
i
—J
OK j Cancel
Figure 5.9 GUI command editor for feeding model options.
ftrtrfiinp, uplioiis
Source; feeding_options of largernouth_bass.fsh
Comment j
3 Unls: P5"
Lower Boundary I Upper Boundary j Model Type j Comments
Cancel
Figure 5.10 GUI command editor for compositional and morphometric parameters.
Source: eomposftnal_parameter$ of largsmouth_hass.fsh
Comment j"
: Units j - ; Value
0.800-1.57*pi[-]
0.08001w[g]'s0.000
OK Cancel
October 2018
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Figure 5.11 GUI command editor for nondiet ecological parameters.
Source: ecologicai_parameters of taigernouth_hass.feh associated with
Comment: j
Ecological Parameters : Diet Editor
Parameter
Unit- =
Value
lp
mm =
0.3001mm]
lp_min
mm
0.0S01[mm]
ip_rnax
mm =
0.5001[mm]
mis
days =
2921.9
wt_max
g
1750
nm
=
sg_mu
g/g/day =
0.898e-01 xw[g{ f-0.698)
tl ro
=
rbi
=
0.2
wl
g
0.6?80e-05xl[mmr3.130
g
0.25
ast_yoy
tefugia
=
Comments
estimated from tinranons and shelton {1980) for lepomis
a:-3umed to allow 500 mm largemouOi to prey on 30 mm gambusi.
assumed
assumed
asummed see carlander 1.13?? pg 226)
long-term mean calibrated to wt_yoy, wt_max, and age_max
bass/carlander database default
5
Figure 5.12 GUI command editor for fish diets.
nf jMiviijjijk'r4:
Source: ecdogieal_parameters ot everglades, cmm associated with largemouth_bass.fsh
Comment: i
Ecological Parameters Diet Editor
Class Type: ;|ep1gth
; Units:
0-20
20-100
100-200
MIWBI;
300-1000
Prey Item
: benthos
i insects
i periphyton
phytoplankton
zooplankton
;feh
: largemouth_bass
lanuary-june
july-decernber
25
-1
-1
-1
-1
0
0
Add
Sort
Remove
Split Time Range ; Remove Range j Add Prey Fish ; Remove Prey Fish
OK
Refresh
Cancel
October 2018
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Figure 5.13 GUI command editor for physiological parameters.
Source: physiologicaLparameters of largermith_.bass.ffh
Comment: s
Log
Parameter
ae_pfant
aejrtvert
ae_fish
ge
mf
mi
rq
rt:std
sda:irt
sg
sm
so
st
kf rnin
Units temperature
g(dw'|/da
g/g/day
rng£o2J/h
Value
0.44
0.66
0.89
Comments
bass interspecies default
bass interspecies default
bass interspecies default
0.202e-01 xw[gf'0.557xexp{0 back-calculated from fish's
1
2
0,127
0.282e-01%[gn-0.E
Jexp
bass interspecies default
bass interspecies default
beamish (1374), tandler a
calibrated for specified tern
0.119*w(gf0.766Kexpf0.043 glass (1969), beamish (19
OK
Cancel
Figure 5.14 GUI command editor for cohort initial conditions.
initio! :.!)rn.litif;ji*;
Source: initiaI_condilbns of everglades.cmm
Comment: ft*ocn«sj;ki^'h«K C CC ;Wtrt',4!tthyir~2WG 0C463
Add Column 1 Remove Column
i Name Units : Comment
fish/m 2
grams
i wt
pop
213.0
578.0
943.0
1308.0
1673.0
2038.0
2403.0
2768.0
0.293e-02
0.719a-03
0.358e-03
0.225e-03
0.158e-03
0.119e-03
0.344e-04
0.771e-04
42.
173.9
348.7
555. S
789.7
1046.8
1324.6
1621.2
OK j Cancel
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Figure 5.15 GUI command editor for spawning parameters.
I Source- SDawnina period of taraemouth bass fsh 1
:
CoflW&frt: | assigned by user
Begin Month ; End Month
~ map june
| OK |
Cancel j
Figure 5.16 GUI command editor for fishery parameters.
liihery pdi-vnoter*
Source fisherjLpararnetert of everfltadesjishiracmrn
Comment: \
Faramstw i Units
~ ED stocking
Stocking Value?
Hafvest Parameter:
harvest Units:
1 /day
Min : Ma*
Length Units
Begin Month
; Begin uap
t nd Month
: liner yap
: Value Comment
~
10 14
inch
aprit
1
October
30
ln|0.80|/210
18 24
inch
aprit
1
October
30
ln{0.90)/210
*
OK
uancei
Figure 5.17 GUI command editor for non-fish biota as forcing functions.
Source: berthas of everglades-cmm
Comment j
<• Use Forcing Function
f" Use Communitji State Variables
Units !» j Value
g(d«)/m - nonfish.dat
Parameter
Use Fie
Comments
biomass
OK | Cancel j
October 2018 71
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Figure 5.18 GUI command editor for non-fish biota as state variables.
Source:
Comment:
:.cmm
r Use Forcing Function >'• Use Community State Variables
Log Parameter Units temperature = Value
; initial_biomas
: mean_weight =
; ingestion =
; respiration
Use File Comments
OK
Cancel
Figure 5.19 GUI command editor for non-fish bioaccumulation factors.
Source: nonfith_bcf of pcb_new.cta
Common*: j
Nonfish_bcf
i Value
; Comments j
~ benthos
0.02"kow[-I
assumed qsar !
periphyton
aorkowH
assumed qsar [
phytoplankton
0.01 "kowH
assumed qsar 1
zooplankton
0.05*kow[-l
assumed qsar |
QK
Cancel
Figure 5.20 GUI command editor for chemical biotransformation parameters.
Source metabolism of pcbjrare.chm
Comment: M i»« . 30 oay>
fish
j Units
I Value
I Dauohter Product
: Comment# i
~
largemouth bass
1/day
ln(2]/30
pcb metabolite
longnose gar
1/day
ln(2)/40
pcb metabolite
half life = 40 days I
channel catfish
1/day
ln(2)/60
pcbjnetabolite
half life = 60 days I
bluegill sunfish
1/day
ln(2l/30
none
half life = 30 days j
redear sunfish
Vd&y
200.0"kow[-rs(-0,9)
none
approximately 0,001 |
AddaFi*
Cancel
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Figure 5.21 GUI command editor for chemical toxicity parameters.
Source: lethality of pcbjrans.chro
Comment: J
I Fish ! Units Value
- Comments
~
jbass
> bluegill
molar
molar
0.5*0.135e-2xKow[-f [-0.871 ]
2.0xQ.135e-2xKow[-f[-Q.871)
half BASS default LC50
twice BASS default LC50
Add a Fish |
OK
Cancel
Figure 5.22 GUI command editor for automatic graphing selections
Source: surnmary_piot$ of l_ontario_pcb.prj
Comment: i
• Plot age • weight : length ; Comments
~ ; afish i i i
: baf i t i
: bmf i i i
djsh r ~ ^
pop
y ; . :
tvt V ™ f~
OK | Cancel
October 2018
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Figure 5.23 GUI Block comment editor.
inmnientlJlpi
Source: comments lock of largemouth_bsss.fsh
References:
Beamish, F.W.H. 1970. Oxygen consumption of Iargemouth 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 iargemouth bass, Micropterus
salmoides. J.Fish.Res.Bd.Can. 31:1763-1769.
Carlander, K.D. 1377. 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 Iargemouth bass. Trans.Am.Fish.Soc. 103:277-280.
Lowe, TP., 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 Iargemouth bass
(Micropterus salmoides) in relation to body weight and temperature. Can.J.Zool. 52:447-458.
Price, J ,W. 1931. G rowth and gill development in the small-mouthed black bass, M icropterus
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, andP.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 Iargemouth bass, Micropterus salmoides Lacepede.
Aquaculture 23:231 -242.
Timrnons, T.J. and W.L. Shelton. 1980. Differential growth of Iargemouth bass in West
Point Reservoir, Alabama-Georgia. Trans.Am.Fish.Soc. 109:176-186.
Figure 5.24 Data file editor for forcing functions specified as files.
OK
Cancel
tinibTiihlL-Dol-i
Source: timeTableData of water.dat
Add Lck.mr
Remove Column
Culunr Nai.ie
: :j time
2 I
Unit:
dd'r
^tlfLtuI
trre depth
OK | Cancel |
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6. Example Applications
6.1. BASS Software Distribution Examples
Thirteen example projects are provided with the BASS model
software and GUI. Each project resides in its own folder within
the \projects subdirectory. These projects have been updated
extensively for the October 2018 release of BASS V2.3 and now
use only FSH files generated by the BASS parameterization
software bass_files.exe.
The project EX_EVERGLADES_CANAL simulates the growth and
population dynamics of fish in an Everglades canal community
in Florida, US A using the proj ect file EVERGLADES_CANAL.PRJ.
The fish species in this community are bluegill (Lepomis
macrochirus), eastern mosquitofish (Gambusia holbrooki),
Florida gar (Lepisosteus platyrhincus), largemouth bass
(.Micropterus salmoides), redear sunfish (Lepomis
microlophus), and yellow bullhead (Ameiurus natalis). The
project's CMM file and associated FSH files are generated by
BASS_FILES.EXE using the input file
everglades_canal_species.dat. The canal's water depth
and non-fish biomasses are supplied by the data files
EVERGLADES_CANAL_WATER.DAT and
everglades_nonfish.dat, respectively.
The project ex_everglades_canal_fishing simulates the
growth and population dynamics of fish in the aforementioned
canal community assuming that bluegill, largemouth bass, and
redear sunfish are harvested by fishing. Its project file
EVERGLADES_CANAL_FISHING.PRJ uses the community file
Everglades_canal_fis hing . cmm , which was created by
inserting assumed fishing mortality rates into a CMM file that
is identical to the one used by the project
EX_EVERGLADES_CANAL. The canal's water depth and non-fish
biomasses are again specified using the data files
EVERGLADES_CANAL_WATER.DAT and
everglades_nonfish.dat, respectively.
The project EX_EVERGLADES_CANAL_HG simulates the growth,
population, and methylmercury (MeHg) bioaccumulation
dynamics of fish in an Everglades canal using the project file
EVERGLADES_CANAL_HG.PRJ. This project uses the same CMM
and FSH files as does the project ex_everglades_canal.
The community's MeHg exposures are supplied by the include
file EVERGLADES_MERCURY.CHM, which, in turn, uses the
chemical property file \PROPERTY\METYL_HG.PRP. The canal's
water depth and the non-fish biomasses are again supplied by
the data files everglades_canal_water.dat and
everglades_nonfish.dat, respectively.
The proj ect ex_everglades_canal_hg_fgets simulates the
growth and MeHg bioaccumulation dynamics of fish in an
Everglades canal using the FGETS simulation option. With the
exception of its PRJ and CMM files, this project uses the same
parameter files as the proj ect ex_everglades_canal_hg. To
create the project's CMM file, a project file identical to
everglades_canal_hg.prj was executed using the command
line option "-ef' which instructs BASS to output FGETS-style
diets for all fishes using the project's simulated trophic
dynamics. These diets were then inserted into the project's
CMM file which was renamed to
EVERGLADES_CANAL_FGETS. CMM.
The proj ect ex_everglades_canal_hg_leslie simulates the
growth, population, and MeHg bioaccumulation dynamics of
fish in an Everglades canal using the Leslie matrix simulation
option. This project uses the same CMM and FSH files as does
the project ex_everglades_canal_hg, and its MeHg
exposures and properties are again provided by
Everglades_mercury.chm. Similarly, the canal's water
depth and non-fish biomasses are again provided by the data
files EVERGLADES_CANAL_WATER.DAT and
everglades_nonfish.dat, respectively.
The proj ect EX_EVERGLADES_HG_HOLES simulates the growth,
population, and MeHg dynamics of fish in an Everglades
alligator hole community using the project file
EVERGLADES_HG_HOLES .PRJ. The fish species in these
communities are assumed to be bluegill, eastern mosquitofish,
Florida gar, largemouth bass, least killifish (Heterandria
formosa), redear sunfish, spotted sunfish (Lepomis puntatus),
warmouth (Lepomis gulosus), and yellow bullheads. The
project's CMM file and associated FSH files are generated by
BASS_FILES.EXE using the input file
everglades_holes_species.dat. The alligator hole's water
depth and non-fish biomasses are assigned in the project and
community files EVERGLADES_HG_HOLES.PRJ and
EVERGLADES_HG_HOLES. CMM, respectively. Lastly, MeHg
exposures and properties are assigned by the include file
Everglades_mercury.chm.
The project EX_EVERGLADES_HG_MARSH simulates the
growth, population, and MeHg dynamics of fish in an
Everglades marsh community using the project file
EVERGLADES_HG_MARSH.PRJ. The fish species in these
communities are assumed to be bluefin killifish (Lucania
goodei), eastern mosquitofish, Florida gar, golden topminnow
(Fundulus chrysotus), largemouth bass, least killifish, spotted
sunfish, warmouth, and yellow bullheads. The project's CMM
file and FSH files are generated by bass_files.exe using the
input file EVERGLADES_MARSH_SPECIES.DAT. The marsh's
October 2018
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water depth and non-fish biomasses are assigned by the project
and community files EVERGLADES_HG_MARSH.PRJ and
EVERGLADES_HG_MARSH.CMM, respectively. The community's
MeHg exposures are provided by the include file
Everglades_mercury.chm.
The project EX_L_HARTWELL simulates the growth and
population dynamics of fish in the Twelve-Mile Creek arm of
Lake Hartwell, SC, USA which was contaminated by the
Sangamo Weston Superfund site in Pickens, SC (USEPA
1994). The fish species simulated by this project are bluegill,
channel catfish (Ictaluruspunctatus), common carp (Cyprinus
carpio), gizzard shad (Dorosoma cepedianum), largemouth
bass, redbreast sunfish (Lepomis auritus), threadfin shad
(.Dorosoma petenense), and yellow perch (Perca flavescens).
The project's CMM file and associated FSH files are
generated by bass_files.exe using the input file
TWELVEMILE_CREEK_SPECIES .DAT.
The project f,x_t,_hartwf.t,t,_pcr simulates the growth,
population, and polychlorinated biphenyl (PCB) dynamics of
fish in the Twelve-Mile Creek arm of Lake Hartwell, SC, US A
which was contaminated by the Sangamo Weston Superfund
site in Pickens, SC (USEPA 1994). This project uses the same
CMM file and associated FSH files as does the project
ex_L_Hartwell. Total PCBs are modeled as the sum of
tetra-, penta-, hexa-, and hepta-PCB homologs. Exposure
concentrations and chemical properties of these PCB
homologs are provided by the include files
TWELVEMILE_CREEK. CHM \PROPERTY\PCB_TETRA.PRP,
\PROPERTY\PCB_PENTA.PRP, \PROPERTY\PCB_HEXA.PRP, and
\PROPERTY\PCB_HEPTA.PRP, respectively. This project
demonstrates BASS' s ability to simulate the bioaccumulation of
chemical mixtures.
The project ex_t,_hartwf.t,t,_pcb_trans simulates the
bioaccumulation of tetra-PCB by fish in the Twelve-Mile
Creek arm of Lake Hartwell assuming that tetra-PCB can be
metabolized by bluegill, channel catfish, common carp, and
largemouth bass. This project, however, is a contrived example
that is intended only to demonstrate how BASS simulates the
biotransformation of organic chemicals.
The project ex_l_ontario_pcb simulates the bioaccumu-
lation of tetra-, penta-, hexa-, and hepta-PCB s in Lake Ontario
salmonids and alewife using the FGETS option and the project
file barber_et_al_1991.prj. This project is a bass
implementation of the FGETS application published by Barber
et al. (1991). Whereas salmonid feeding is simulated using
bass's Holling feeding option, the feeding by alewife is
simulated using bass's clearance feeding option.
The project EX_SE_FARM_POND simulates the growth and
population dynamics of a typical southeastern US farm pond
community using the project file SE_FARM_POND.PRJ. The
principal fish species in these communities are assumed to be
bluegill, channel catfish, largemouth bass, and redear sunfish.
The project's CMM file and FSH files are generated by
BASS_FILES.EXE using the input file
SE_FARM_POND_SPEClES.DAT. The resulting community file
SE_FARM_POND.CMM assigns not only the ecological and
physiological parameters and the initial conditions for these
fishes but also the biomasses of benthos, periphyton, and
zooplankton.
The project EX_SE_FARM_POND_XMS simulates the growth,
population, and pesticide bioaccumulation dynamics of a
typical southeastern US farm pond community using the
project file SE_FARM_POND_XMS.PRJ. The project's CMM file
and FSH files are identical to those used by the project
ex_se_farm_pond. The project's pesticide exposure file was
generated by the USEPA's Office of Pesticide Programs using
the EXAMS fate and transport model (Burns 2004). The
identify of this pesticide is not specified since this project was
designed only to illustrate the functionality of an EXAMS-
BASS file transfer linkage.
6.2. Simulating Methylmercury Bioaccumulation in an
Everglades Fish Community
The bass example project ex_everglades_canal_hg
simulates methylmercury contamination in a canal fish
community of the Florida Everglades and is constructed as
outlined in Section 4.5. For this application bluegill, eastern
mosquitofish, Florida gar, largemouth bass, redear sunfish, and
yellow bullhead are assumed to be the dominant species in the
habitats of interest. The ecological, morphological, and
physiological parameters used by this example are documented
in the project's associated FSH files. Turner et al. (1999)
reported the mean biomass of large and small fishes across
various Everglades habitats to be approximately 60 kg wet
wt/ha. Initial biomasses of bluegill, eastern mosquitofish,
Florida gar, largemouth bass, redear sunfish, and yellow
bullhead were assigned to be 50, 5, 10, 5, 25, and 10 kg wet
wt/ha, respectively, for a total community biomass of 105 kg
wet wt/ha. The dissolved water concentration of MeHg for the
simulation was assigned to be a constant 0.2 ng/L (Stober et al.
1998) and the BAF's for benthos and zooplankton were
assigned to be lO609 and 105 90, respectively (Loftus et al.
1998). The simulation's length was set to be 24 years which
allowed transient dynamics associated with assumed initial
conditions to dissipate and a dynamic steady state to be
approximated (see Barber et al. 2016).
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Figure 6.1 displays the simulated time dynamics of each fish
species in this Everglades canal community. During the final
year of the simulation, the mean annual biomasses of bluegill,
eastern mosquitofish, Florida gar, largemouth bass, redear
sunfish, and yellow bullhead were 37.7,3.94,19.3,9.22,25.0,
and 9.18 kg wet wt/ha, respectively, for a total community
biomass of 104 kg wet wt/ha.
Figure 6.2 displays the time dynamics of each species' average
daily MeHg concentration which is weighted by the species'
cohort densities. During the final year of the simulation, the
mean annual MeHg concentrations for bluegill, eastern
mosquitofish, Florida gar, largemouth bass, redear sunfish, and
yellow bullhead were 0.195, 0.160, 0.456, 0.314, 0.224, and
0.196 mg/kg wet wt, respectively. These simulated
concentrations are intermediate to observed concentrations
reported for the Everglades National Park (ENP) and the
Everglades Water Conservation Areas (WCAs). Data reported
by Loftus et al. (1998) for the ENP yield average
concentrations of total mercury (T-Hg) in bluegill, eastern
mosquitofish, Florida gar, largemouth bass, redear sunfish, and
yellow bullhead equal to 0.550,0.313,1.20,0.967,0.247, and
0.547 mg/kg wet wt, respectively. Julian et al. (2015) report
that T-Hg concentration of sunfish, sampled across the ENP
and WCAs from 1999 to 2014, averaged 0.18 mg/kg wet wt
(SD=0.16; n=3440). Julian et al. (2015) also report that the
median T-Hg concentration of eastern mosquitofish, sampled
across the ENP and WCAs for the same period of record, was
0.06 mg/kg wet wt (n=685). Lastly, Julian et al. (2015) report
that the median T-Hg concentration of largemouth bass,
sampled across the ENP and WCAs from 1989 to 2014, was
0.54 mg/kg wet wt (n=4991). Regarding the results reported by
Julian et al. (2015), readers should note that whereas the
median and mean concentrations for eastern mosquitofish and
sunfish, respectively, are based on whole-body data, the
median concentration for largemouth bass is based on filet
data. Using the filet to whole-body conversion regression of
Peterson et al. (2007), the median whole-body T-Hg
concentration of largemouth bass would be estimated to be
0.322 mg/kg wet.
6.3. Simulating PCB Bioaccumulation in a Fish
Community Impacted by a Superfund Site
The project f,x_t,_hartwf.t,t,_pcr simulates the growth,
population, and polychlorinated biphenyl (PCB) dynamics of
fish in the Twelve-Mile Creek arm of Lake Hartwell, SC which
was contaminated by the Sangamo Weston Superfund site in
Pickens, SC (USEPA 1994). The fish species simulated by this
project are bluegill, channel catfish, common carp, gizzard
shad, largemouth bass, redbreast sunfish, redear sunfish,
threadfin shad, and yellow perch. Total PCBs are modeled as
the sum of tetra-, penta-, hexa-, and hepta-PCB homologs. The
ecological, morphological, and physiological parameters used
by this example are documented in the project's associated
FSH files. Using data from Lake Hartwell fish surveys
conducted by the Georgia Department of Natural Resources in
1987, 1990, and 1995, initial biomasses of bluegill, channel
catfish, common carp, gizzard shad, largemouth bass,
redbreast sunfish, threadfin shad, and yellow perch were
assumed to be 23.9,4.26,17.2,16.3,8.77,2.98,20.8, and 2.70
kg wet wt/ha, respectively, for a total community biomass of
96.8 kg wet wt/ha. The community's carrying capacity was
assumed to be three times its estimated initial biomass. The
dissolved water concentrations of tetra-, penta-, hexa-, and
hepta-PCB were back-calculated using their average
concentrations in Twelve-Mile Creek crayfish (Procambarus
spp.) reported by Brockway et al. (1996) and B AFs predicted
by the KABAM steady-state bioaccumulation model (Garber
2009). BAFs for benthos, periphyton, phytoplankton, and
zooplankton were assigned to be their KABAM-predicted
counterparts. The length of this simulation was set to be 24
years which allowed for transient dynamics associated with
assumed initial conditions to dissipate and for a dynamic
steady state to be approximated (see Barber et al. 2016).
Figure 6.3 displays the simulated biomass dynamics of each
fish species in this Twelve-Mile creek community. During the
final year of the simulation, the mean annual biomasses of
bluegill, channel catfish, common carp, gizzard shad,
largemouth bass, redbreast sunfish, threadfin shad, and yellow
perch were 64.7,9.14,43.1,38.0,24.1,6.87,21.6, and 7.73 kg
wet wt/ha, respectively, for a total community biomass of 215
kg wet wt/ha.
Figure 6.4 displays the time dynamics of each species' average
daily total PCB concentration which is weighted by the
species' cohort densities. During the simulation's final year,
the mean annual total PCB concentrations for bluegill, channel
catfish, common carp, gizzard shad, largemouth bass,
redbreast sunfish, threadfin shad, and yellow perch were 22.9,
16.9, 20.2, 33.7, 32.5, 5.16, 20.5, and 30.6 mg/kg wet wt,
respectively. Although data reported by Brockway etal. (1996)
yield average observed concentrations of total PCBs in
bluegill, channel catfish, and largemouth bass equal to 9.96
(SD=8.52; n=42), 8.05 (SD=5.85; n=14), and 21.4 (SD=15.9;
n=21) mg/kg wet wt, respectively, direct comparisons between
these observed and simulated concentrations are difficult due
to the range and distribution of body weights within each
dataset and due to that fact that the observed concentrations
include three lake sites and two creek sites which could not be
sampled equally well.
Figures 6.5, 6.6, and 6.7 display the time dynamics of total
October 2018
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PCB concentrations of bluegill, channel catfish, and
largemouth bass, respectively, plotted by year classes.
Considering only sampled and simulated fish having
comparable body weights, the ranges of observed and
simulated concentrations (mg/kg wet wt) for bluegill are
0.965-45.9 and 1.15-24.8, respectively. Similarly, ranges of
observed and simulated concentrations (mg/kg wet wt) for
channel catfish are 1.15-23.1 andO.562-18.8, respectively, and
the ranges of observed and simulated concentrations (mg/kg
wet wt) for largemouth bass are 1.72-64.7 and 0.651-33.5,
respectively.
October 2018
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60
50
40
30
.2 20
co
10
U/MiNI/sAi
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Time [day]
Figure 6.1 Simulated biomasses (kg wet wt/ha) of fishes in an Everglades canal.
0.8
0.7
0.6
ai
5 0.5
"SB
£ 0.4
c
o
00
X
0.3
0.2
0.1
0.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Time [day]
-Blue gill
- Eastern mosquitofish
Florida gar
Large mouth bass
- Redearsunfish
Yellow bullhead
-Bluegill
- Eastern mosquitofish
Florida gar
Largemouth bass
- Redearsunfish
Yellow bullhead
Figure 6.2 Simulated MeHg concentrations (mg/kg wet wt) of fishes in an Everglades canal.
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Figure 6.3 Simulated biomasses (kg wet wt/ha) of fishes in Twelve-Mile Creek, SC.
70
60
50
S 40
5
QJD
-id
« 30
l/l
ro
o
CO
20 '
10
An
r v\a
-1 nC
F=o~-C;
j-—.r-
-T
r\n-
rs
vy
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Time [day]
3 35
¦ Blue gill
Channel catfish
Common carp
Gizzard shad
Largemouth bass
- Redbreast sunfish
-Threadfin shad
-Yellow perch
- Bluegill
- Channel catfish
Common carp
Gizzard shad
Largemouth bass
• Redbreast sunfish
-Threadfin shad
-Yellow perch
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Time [day]
Figure 6.4 Simulated total PCB concentrations (mg/kg wet wt) of fishes in Twelve-Mile Creek, SC.
October 2018
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50
45
40
35
~5a
~~ 25
20
c
o
u
cq
<_>
a.
ss 15
o
10
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Time [day]
Figure 6.5 Simulated total PCB concentrations (mg/kg wet wt) of Bluegill by year class in Twelve-Mile Creek, SC.
70
60
5 50
P, 30
10
; * • * * * * : : : ; ; ; ~ ? * * *
* ~ ~
Figure 6.6 Simulated total PCB concentrations (mg/kg wet wt) of Channel catfish by year class in Twelve-Mile Creek, SC.
October 2018
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80
/ >S< : ; : ; ; : : I: : : ; ; : : : ; :
[n rrr,ririrrir,f"!*rrrrririrr'r'fr
. * ; : LLi : : : f ; : li : : : : : • : F : ;
Figure 6.7 Simulated total PCB concentrations (mg/kg wet wt) of Largemouth bass by year class in Twelve-Mile Creek, SC.
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7. Model Quality Assurance
Quality Assurance (QA) and Quality Control (QC) for the BASS
simulation model have 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 that has been published in the peer reviewed
literature (Barber etal. 1988,1991). These algorithms have been
reviewed and compared to other bioaccumulation models to
document their scientific foundation and to verify their predictive
performance (see Barber 2003, 2008). The bioenergetic
modeling paradigm that BASS uses to simulate fish growth has
been employed by many researchers in the peer-reviewed
literature (Norstrom et al. 1976, Kitchell etal. 1977, Minton and
McLean 1982, Stewart etal. 1983, Thomann and Connolly 1984,
Cuenco et al. 1985, Stewart and Binkowski 1986, Beauchamp et
al. 1989, Barber etal. 1991, Stewart and Ibarra 1991, Lantry and
Stewart 1993, Rand et al. 1993, Roell and Orth 1993, Hartman
and Brandt 1995a, Petersen and Ward 1999, Rose et al. 1999,
Schaeffer et al. 1999). Since their construction, BASS and FGETS
have been included in numerous reviews and discussions of
aquatic bioaccumulation models (Chapra and B oyer 1992, Olem
et al. 1992, Dixon and Florian 1993, Cowan et al. 1995,
Campfens and Mackay 1997, Feijtel et al. 1997, Exponent 1998,
Howgate 1998, Vorhees et al. 1998, Wania and Mackay 1999,
Mackay and Fraser 2000, Koelmans et al. 2001, Limno-Tech
2002, Nichols 2002, Barber 2003, Exponent 2003, Imhoff et al.
2004, Imhoff et al. 2005, Brooke and Crookes 2007, Barber
2008, Mackay and Milford 2008, Arnot 2009, Nichols et al.
2009, Brooke et al. 2012, EPRI2013, Radomyski et al. 2018).
Two criticisms have been lodged against FGETS in the literature.
The first of these is that fgets attempts to prove that the gill
exchange of chemicals is more important than other routes of
exchange (Madenjian et al. 1993). Madenjian et al. (1993) took
exception to FGETS predictions that "excretion of PCB through
the gills is an important flux in the PCB budget of lake trout".
Madenjian et al. claimed that this result was not supported by any
laboratory study on trout and cited Weininger (1978) as proof
that gill excretion was, in fact, negligible. Nevertheless,
Madenjian et al. used a single, unidentified excretion constant in
their model that simply lumps all excretion pathways (i.e., gill,
intestinal, urinary, and dermal) into one. Thus, what Madenjian
et al. are really questioning is not FGETS per se but rather the
need to use thermodynamically based diffusion models for
bioaccumulation in general.
The second criticism is that FGETS is overly complex and requires
too much additional data to parameterize (McKim et al. 1994,
Stow and Carpenter 1994, Jackson 1996). Since FGETS's
bioenergetic model for fish growth is not significantly different
from those used by several other authors (Norstrom et al. 1976,
Weininger 1978, Thomann and Connolly 1984, Madenjian et al.
1993, Luk and Brockway 1997), this criticism is also generally
aimed at bass's gill exchange model. A recent review and
comparison of gill exchange models, however, clearly
demonstrated that there is more than ample literature data to
parameterize the gill exchange formulations used by FGETS and
BASS (Barber 2003).
7.1.2. How has the model or its algorithms been corroborated or
used?
bass's dietary and gill exchange algorithms have been
corroborated by comparing its predicted dietary assimilation
efficiencies and gill uptake and excretion rates to those published
in the peer-reviewed literature (Barber et al. 1988, Barber 2003,
2008). bass's dietary exchange algorithms have also been cited
by other researchers to explain results of actual exposure studies
(e.g., Dabrowska et al. 1996, Doi et al. 2000). For validation of
bass's bioenergetic growth algorithms, the reader is referred to
Barber et al. (2016) and the examples herein.
bass and fgets have been used to predict the bioaccumulation
of persistent bioaccumulative toxicants (PB Ts) in both lakes and
rivers (Barber et al. 1991, Hunt et al. 1992, USEPA 1994, PNL
1995, Panzieri and Hallam 1999, Simon 1999, Marchettini et al.
2001, Panzieri et al. 2001, Murphy 2004, Rashleigh et al. 2004,
USEPA 2005, Knightes et al. 2009, Johnston et al. 2011,
Knightes et al. 2012, RTI International 2013, Reese et al. 2015a,
Reese et al. 2015b, Sokol 2015, Barber et al. 2016, Barber et al.
2017, Johnston etal. 2017). Additionally, both models have been
cited in numerous guidance documents issued by USEPA, ECHA
(The European Chemical Agency), OECD (The Organisation for
Economic Co-operation and Development) and other federal
agencies (USEPA 1991a, b, 1993, LaPoint et al. 1995, USEPA
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1995, 1998, ECOFRAM 1999, USEPA 2000, 2003, 2006,
ECHA 2008, USEPA 2008a, b, 2010, OECD 2011b, a, ECHA
2012b, a, OECD 2012b, c, a, 2013b, a, ECHA 2014a, d, c, b,
2016, OECD 2016b, a, ECHA 2017c, b, a).
Several researchers (Lassiter and Hallam 1990, ECOFRAM
Aquatic Effects Subcommittee et al. 1998, ECOFRAM 1999,
Boxall et al. 2001, Boxall et al. 2002, Reinert et al. 2002) have
used bass's predecessor, fgets, to predict acute and chronic
lethality, and the EPA's Office of Water's AQUATOX modeling
system uses the fgets/bass lethal effects algorithm as its
principal effects module (Park and Clough 2004). Additionally,
the Office of Water recognized bass as one of the leading
models available for simulating time dynamic bioaccumulation
for applications when steady-state methods (e.g., BAFs or
BSAFs) are considered insufficient (USEPA 2003). The
Commonwealth of Virginia identified BASS as an accepted tool
for its PCB bioaccumulation assessments (VDEQ 2005). BASS
has also been recommended to the states of Michigan and
Washington as an assessment tool (Exponent 1998, 2003).
Hallam and Deng (2006) implemented the FGETS/BASS
bioaccumulation framework within sophisticated McKendrick-
von Foerster partial differential equation models for age-
structured populations, and Cohen and Cooter (2002a, 2002b)
incorporated simpler forms of this framework into their fate and
transport exposure software. Lastly, Apeti et al. (2005) modified
FGETS to simulate metal bioaccumulation in shellfish.
7.1.3. What is the mathematical sensitivity of the model with
respect to parameters, state variables (initial value problems),
and forcing functions / boundary conditions? What is the
model's sensitivity to structural changes?
There are four major classes of mathematical sensitivity
regarding a model's behavior. These are the model's sensitivity
to parameter changes, forcing functions, initial state variables,
and structural configuration. The first three of these classes
generally are formally defined in terms of the following partial
derivatives
dXi dXt dXt
Ji/ Jz/ ex(0) (71)
where Xt is a state variable of interest; pj is some state parameter
of concern; Zj is some external forcing function; and X.(0) is the
initial value of some state variable of interest that may be Xt
itself. Structural sensitivity, which generally cannot be
formulated as a simple partial derivative, typically concerns the
number and connectivity between the system's state variables.
An excellent question regarding structural sensitivity for a model
like BASS might be how does a predator's population numbers or
growth rate change with the introduction or removal of new or
existing prey items?
Because sensitivity is simply a mathematical characteristic of a
model, model sensitivity in and of itself is neither good nor bad.
Sensitivity is desirable if the real system being modeled is itself
sensitive to the same parameters, forcing functions, initial state
perturbations, and structural changes to which the model is
sensitive. Even though model sensitivity can contribute to
undesirable model uncertainty or prediction error, it is important
to acknowledge that model sensitivity and uncertainty are not one
and the same (Summers et al. 1993, Wallach and Genard 1998).
Model uncertainty, or at least one of its most common
manifestations, is the product of both the model's sensitivity to
particular components and the statistical variability associated
with those components.
A generalized sensitivity analysis of BASS without explicit
specification of a fish community of concern is infeasible.
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.
7.2. Questions Regarding QA of a Model's Implementation
7.2.1. Did the input algorithms properly process all user input?
As part of its routine output, BASS generates a *.MSG file that
summarizes all input data 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 on the user to verify
that their input data has been properly processed. If not, the user
should report their problem to the technical contact identified in
the bass user's guide.
bass has a series of subroutines that check for the completeness
and consistency of the user's input data. When missing or
inconsistent data are detected, error messages are written to the
*.MSG file, and an error code is set to true. If this error code is
true after all input has been processed, BASS terminates without
attempting further execution.
To insure that all program subroutines, functions, and procedures
are transmitting and receiving the correct variables, all BASS
subroutines and functions are called using implicit interfaces
generated by the Absoft and Lahey-Fujitsu Fortran 95 compilers.
Subroutines and functions are packaged together within Fortran
95 modules according to their function and degree of interaction.
The BASS v2.3 software is coded with one main program
program bass_main (see bass_program.f90) and 30
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procedure modules. These modules are:
• MODULE ADAMS_GEAR - subroutines for performing
EXAMS Adams-Gear integrations (see
exams_adam_gear.f90) .
• module bass_alloc - subroutines for allocating and
reallocating derive type pointers (see bass_alloc.f90).
• module bass_check - subroutines for checking the
completeness and consistency of user input (see
bass_check.f90).
• module bass_debug - subroutines for program
debugging. Used only for program development (see
bass_debug.f90).
• MODULE BASS_DEFINED - functions for determining
whether program parameters and variables have been
initialized or assigned (see bass_defined.f90).
• MODULE BASS_EXP - subroutines for calculating chemical
exposures, community forcing functions, and habitat
suitability multipliers (see bass_exp.f90).
• MODULE BASS_INI - subroutines for initialization of
program variables (see bass_ini.f90).
• MODULE BASS_INPUT - subroutines for decoding user input
(see bass_input.f90).
• MODULE BASS_INT - subroutines for Adams-Gear, Euler,
and Runge-Kutta integrations (see bass_int.f90).
• module bass_int_loader - subroutines for loading BASS
derived type variables into standard integration vectors
(see bass_int_loader.f90).
• MODULE bass_io - subroutines for processing user input
and output (see bass_io_*.f90 for the Absoft and Lahey-
Fujitsu compilers).
• MODULE BASS_ODE - subroutines for the computational
kernel of the bass software (see bass_ode.f90).
• MODULE BASS_TABLES - subroutines for generating output
tables for bass v2.1 and earlier as well as for code
development and maintenance (see bass_tables.f90).
• module bass_write_csv - subroutines for generating
CSV output files for import into Excel workshets (see
bass_csv.f90).
• module bass_write_xml - subroutines for generating
XML output files for post processing by the BASS GUI
(see bass_xml.f90).
• module decode_functions - subroutines for decoding
constant, linear, and power functions from character
strings (see utl_dcod_fnc.f90).
• MODULE ERROR_MODULE - subroutines for printing error
codes encountered with general utility modules (see
utl_errors_v2.f90).
• MODULE F2KCLI - subroutines for extracting arguments
from a command line (see f2kcli.f90).
• MODULE files tuff - subroutines for parsing file names
and obtaining version numbers or time stamps (see
utl_filestuff_v2_*.f90 for the Absoft and Lahey-
Fujitsu compilers).
• 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_v2.f90).
• MODULE MODULO_XFREAD - subroutines for reading files
that contain comments, continuation lines, and include
files (see utl_xfread_v2_*.f90 for the Absoft and
Lahey-Fujitsu compilers).
• MODULE MSORT - subroutines for sorting and generating
permutation vectors for lists and vectors (see
utl_msort.f90).
• MODULE REALLOCATER - subroutines for allocating and
reallocating integer, logical, and real pointers (see
utl_alloc.f90).
• MODULE SEARCH - subroutines for finding the location of
a key phrase within a sorted list (see
utl_search_v2.f90).
• MODULE SEARCH_LISTS - subroutines for finding the
location of a value within a sorted list (see
utl_search_lists.f90).
• MODULE STRINGS - subroutines for character string
manipulations and printing multiline character text (see
utl_strings_*.f90 for the Absoft and Lahey-Fujitsu
compilers).
• MODULE TABLE_UTILS - subroutines for generating self-
formating tables (see utl_ptable.f90).
• MODULE UNITSLIBRARY - subroutines for defining and
performing units conversions (see utl_unitslib_*.f90
for the Absoft and Lahey-Fujitsu compilers).
In general, these procedure modules are coded with minimal
scoping units. Consequently, their component subroutines and
functions explicitly initialize all required internal variables. This
safeguard prevents inadvertent use of uninitialized variables.
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), data
exchanges between program procedures are performed via formal
subroutine / function parameters whenever possible. The only
notable exceptions to this coding policy are modules that must be
used to supply auxiliary parameters to "external" subroutines that
are used as arguments to certain mathematical subroutines (e.g.,
root finding subroutines). Working areas used by BASS are not
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used for data transfers between internal and external procedures.
To simplify the construction and maintenance of the formal
parameter lists of many BASS subroutines and functions and to
prevent the inadvertent transposition of formal parameters, BASS
makes extensive use of derived type data structures. Each derived
type definition is specified within its own module, and all derive
type definition modules are maintained in a single file
(bass_types.f90.) Derived types used by bass v2.3 are:
• MODULE BASS_TYPE_CHEM_PAR - type definition for
chemical parameters
• MODULE BASS_TYPE_DIET_MEAN - type definition used to
summarize average realized diets.
• MODULE BASS_TYPE_DIET_PAR - type definition used by
derived type bass_type_foodweb_par
• MODULE BASS_TYPE_DIETS - type definition used for input
processing of user-specified fish diets
• MODULE BAS S_TYPE_FIS H_INT - type definition for
integrated fish variables and fluxes
• MODULE BASS_TYPE_FISH_PAR - type definition for fish
parameters
• MODULE BAS S_TYPE_FIS H_VAR - type definition for
current fish variables and fluxes
• module bass_type_foodweb_par - type definition for
the decoded user-specified fish diets and community
trophic structure.
• MODULE BASS_TYPE_HSl_PAR - type definition for fish
habitat multipliers
• MODULE BASS_TYPE_NONFlSH_lNT - type definition for
integrated non-fish variables and fluxes
• MODULE BASS_TYPE_NONFlSH_PAR - type definition for
non-fish parameters
• module bass_type_nonfish_var - type definition for
current non-fish variables and fluxes
• MODULE BASS_TYPE_PREY_ITEMS - type definition used
by derived type bass_type_fish_var to store a fish's
currently realized dietary composition
• MODULE BASS_TYPE_QSAR_DATA - type definition for
linked list used during data input
• MODULE BASS_TYPE_QSAR_LINKED_LIST - type definition
for linked list used during data input
• MODULE BASS_TYPE_QSAR_NODE - type definition for
linked list used during data input
• MODULE bass_type_trophic - definition used for the
calculation of realized diet composition and consumption
• module bass_type_vmatrix_logical - type definition
for logical matrices having rows with varying number of
columns.
• MODULE BASS_TYPE_VMATRIX_REAL - type definition for
for real matrices having rows with varying number of
columns.
• module bass_type_zfunction_par - type definition for
user-specified exposure and forcing functions
A good example of bass's use of derived type data structures is
the derived type variable used to store and transfer the
ecological, physiological, and morphometric data for a particular
fish species. This derived type is defined by the following
module
MODULE bass_type_fish_par
USE bass_type_hsi_par
TYPE:: fish_par
CHARACTER (LEN=80) :: ageclass, ast_type, ast_var, commonjame, &
fmodel_var, genus_species, spawningjnterval, temp_var
INTEGER :: fmodel_cls=0, harvests=0, spawnings=0, &
stockings=0, temperatures=0
INTEGER,DIMENSIONC),POINTER:: fmodel=>NULL()
INTEGER,DIMENSION©,POINTER:: spawn_dates=>NULL()
INTEGER,DIMENSIONC),POINTER:: harvest_datel=>NULL()
INTEGER,DIMENSIONC),POINTER:: harvest_date2=>NULL()
INTEGER,DIMENSION©,POINTER:: stock_dates=>NULL()
LOGICAL:: bb_constant=.TRUE., prey_switching_on=.TRUE.
REAL :: ae_fish, aejnvert, ae_plant, ast_bb, ast_bnds, ast_pop, &
biomass_cc, dry21ive_ab, dry21ive_aa, dry21ive_bb, dry21ive_cc, &
gco2_d, kf_min, la, longevity, mgo2_s, rbi, refugia, rq, rt2std, &
sda2in, tl rO, wt_max, yoy
REAL, DIMENSIONS):: ga, id, Id, 11, lw, pa, pi, sg_mu, wl
REAL, DIMENSIONS):: nm
REAL, DIMENSIONS):: lp, lp_max, lp_min
REAL, DIMENSIONS):: ge, mf, mi, sg, sm, so, st
REAL, DIMENSION©, POINTER :: fmodel_bnds=>NULL()
REAL, DIMENSION©, POINTER :: harvestJenl=>NULL()
REAL, DIMENSION©, POINTER :: harvest_len2=>NULL()
REAL, DIMENSION©, POINTER :: harvest_rate=>NULL()
REAL, DIMENSION©, POINTER :: stock_age=>NULL()
REAL, DIMENSION©, POINTER :: stock_rate=>NULL()
REAL, DIMENSION©, POINTER :: stockJl=>NULL()
REAL, DIMENSION©, POINTER :: stock_wt=>NULL()
REAL, DIMENSION©, POINTER :: temp_bnds=>NULL()
REAL, DIMENSION©, POINTER :: temp_pref=>NULL()
TYPE(hsi_par) :: hsi_feed, hsi_persist, hsi_recruit
END TYPE fish_par
END MODULE bass_type_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 49). Other components
are secondary parameters that are calculated from the user's input
data. For example, dry21ive_ab, dry21ive_aa, dry21ive_bb, and
dry21ive_cc are constants that are used to calculate a fish's wet
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
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CALL subl(...., par,....)
without fear of data misalignment.
To insure that all program subroutines, functions, and procedures
use the same global constants or parameters, such constants are
declared and defined within a set of 15 data modules. These
modules include:
• module adam_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES .F90).
• MODULE BASS_CONSTANTS - specifies various biological
and physical constants used by bass's computational
subroutines (see bass_globals.f90).
• module bass_graetz - specifies parameters used to
calculate chemical exchange across the fish gills (see
bass_globals.f90).
• MODULE BASS_lOFlLES - specifies logical unit numbers for
input and output devices (see bass_globals.f90).
• module bass_names - stores user-specified fish and
chemical names (see bass_globals.f90).
• MODULE BASS_NOVALUE - specifies values for integer,
real, and character variables that have not been initialized
(see bass_globals.f90).
• MODULE bass_precision - specifies the precision of
floating point variables as either single, double, or quad
precision variables, (see bass_globals.f90).
• MODULE BASS_UNITS - specifies unit conversion factors
that are specific to bass for use by module unitslibrary
(see bass_units.f90).
• MODULE BASS_WORKING_DIMENSIONS - specifies
"standard" sizes for character variables, input records, etc.
(see bass_globals.f90).
• module constants - constants used by utility
subroutines (see utl_constants.f90).
• module gear_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES .F90).
• module local_gear_data - stores control parameters
for the exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES .F90).
• module step_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES .F90).
• module stiff_data - stores control parameters for the
exams Adams-Gear integrators (see
EXAMS_ADAM_GEAR_MODULES .F90).
• module units_parameters - specifies parameters used
by the units conversion subroutines (see
UTL_UPARAMS .F90)
BASS v2.3 uses the following modules (see
bass_work_areas.f90) to define work areas that are common
to two or more functions or subroutines:
• MODULE BASS_CPU_PERFORMANCE
• MODULE BASS_FOODWEB_WORK_AREA
• MODULE BASS_HSI_MEANS
• MODULE BASS_MULTISORT_WORK_AREA
• MODULE BASS_ODE_WORK_AREA
• MODULE BASS_OUTPUT_WORK_AREA
7.2.3. Is the developer reasonably confident that all program
subroutines, functions, and procedures are using the same
global constants or parameters?
All global constants are defined within their own individual
modules. These modules include:
• module bass_constants - constants used by bass's
computational subroutines (see bass_globals.f90).
• MODULE BASS_NOVALUE - specifies values for integer,
real, and character variables that have not been initialized
(see bass_globals.f90).
• MODULE bass_precision - specifies the precision of
floating point variables as either single, double, or quad
precision variables. This module also assigns certain
associated floating point constants (see
bass_globals.f90) .
• MODULE BASS_WORKING_DIMENSIONS - specifies
"standard" sizes for character variables, inputrecords, etc.
(see bass_globals.f90).
• module constants - constants used by utility
subroutines (see utl_constants.f90).
7.2.4. Do all strictly mathematical algorithms do what they are
supposed to? For example, are root finding algorithms
functioning properly?
During execution, BASS must employ root finding algorithms for
two important types of calculations. The first of these is the
calculation of a fish's wet weight from its dry weight given an
allometric relationship between its wet body weight and its
fraction lipid, and linear relationships between its moisture, lipid,
and non-lipid organic matter fractions. The second type of
calculation involves the linear transformation of unconditioned
dietary electivities into self-consistent sets of dietary electivities.
These calculations are performed using the combined bisection
/ Newton-Raphson algorithm outlined by Press et al. (1992).
BASS integrates its governing differential equations using a fifth-
order Runge-Kutta method with adaptive step sizing. This
integrator is patterned on the fifth-order Cash-Karp Runge-Kutta
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algorithm outlined by Press et. al. (1992) and was tested using
the following system of equations:
dyxldx = 1.0
dy2/dx = x
dy3/dx = cos(x)
dyjdx = cosh(x)
dyjdx = exp(x)
dy6ldx = 1,0/( 1.0 + x) (7.2)
cfyjcbc = 1.0/(1.0 + x2)
dyg/dx = 1.0/sJl.O +x2
dyjdx = -100 (y9 - sin(x)) y9(0) = 1
dulctx = 998 u + 1998 v u{ 0) = 1
dv/dx = -999 u-1999 v v(0) = 0
The analytical solution to this system of equations is
7.2.5. Are mathematical algorithms implemented correctly, i.e.,
are the assumptions of the procedure satisfied by the problem of
interest?
Because BASS is a differential equation model, a question of
paramount concern is how its integration between points of
discontinuity / nondifferentiability is controlled. Like many
ecological models, bass utilizes threshold responses, absolute
value functions, maximum and minimum functions, and linear
interpolations between time series in its formulation and
implementation. Although most BASS parameters are updated
continuously, some parameters that change very slowly and that
are computationally intensive to evaluate (e.g., dietary
compositions) are updated only daily. All of these features create
points of discontinuity or nondifferentiability. Although there is
nothing intrinsically wrong with using such formulations in
differential equation models, numerical integrations of such
models must proceed from one point of discontinuity /
nondifferentiability to another.
y i =x~xo
y2 = 0.5 (x2 - x2)
y3 = sin(x) - sin(x0)
y4 = sinh(x) - sinh(x0)
y5 = exp(x) - exp(x0)
y& = ln(l +x) - ln(l +x0)
y1 = arctan(x) - arctan(x0)
y% = asinh(x) - asinh(xQ)
10101
>V
(7.3)
10001
100
exp(-100x)
, , 10000 . , >
cos(x) + sin(x)
10001 10001
u = 2 exp(-x) - exp(-1000x)
v = -exp(-x) + exp(-1000x)
On the interval [0
-------
negative derivative. If the state variable is to remain non-
negative, then the largest allowable size for the integration step
can be calculated as follows
y(t+h) = y(t) + hy'(t)
0 h where y'(t) < 0
y'(t)
If h is greater than the numerical spacing of t (i.e., t + h*t), then
an integration step is possible. If the converse is true, however,
the function y(t) is approximating a step function in which case
the desired integration can simply be restarted with y(t) = 0.
There are at least two situations that can occur during a BASS
simulation that might necessitate this corrective action. The first
can occur when a cohort experiences intense predation or other
mortality that drives its population to extinction; the second can
occur when there is the rapid excretion of a hydrophilic
contaminant following the disappearance of an aqueous
exposure. When the derivative for a fish's body weight,
population density, or body burden is negative, BASS verifies
whether the current integration step will, in fact, yield non-
negative state values. If so, bass executes a simple Euler step of
the appropriate size and restarts the integration with the
appropriate state variables initialized to zero.
Using the "-mba" command line option, BASS performs a
comprehensive mass balance analysis of its fundamental
differential equations [i.e., Equations (2.1), (2.2), and (2.3)].
bass also calculates and reports mass balances for each cohort's
total biomass and the community's total predicted predatory
mortality and total predicted piscivorous consumption. For the
example projects ex_everglades_canal_hg and
ex_l_hartwell_pcb presented herein, these mass balances
were -5.239E-10 and -5.821E-11 g dry wt/ha/yr, respectively.
Since the total piscivory of these communities were 2.832E+04
and 3.124E+04 g dry wt/ha/yr, respectively, these mass balance
checks would have relative errors of less than 10"13.
7.2.7. Are simulation results consistent across machines or
compilers?
BASS was originally developed on a DEC 3000 workstation using
the DEC Fortran 90 compiler. In November 1999, it was ported
to the Windows operating system on the DELL OptiPlex using
the Lahey LF90 v4.5 compiler. Although the results of these two
implementations agree with one another up to single precision
accuracy, due to differences in compiler optimization, model
computations must be performed in double precision to obtain
this level of consistency. Since November 1999, BASS has also
been compiled and tested using the Lahey-Fujitsu Fortran LF95
versions 5.0, 5.5, 5.6, 5.7, 7.0, 7.1, and 7.6. Currently, BASS is
compiled on standard USEPA DELL laptops and desktops,
running 64-byte Windows 10, using the Lahey-Fujitsu 7.7
compiler.
In September 2004, BASS was ported to an IBM Intellistation A
Pro workstation equipped with dual 64-byte Opteron processors
and a Windows XP operating system. The BASS source code was
then recompiled using the Absoft multiprocessor Fortran 90/95
compilers 8.2 MP and 9.0 MP. Although initial compilations
using these compilers failed due to compiler bugs that were
acknowledged by Absoft Technical Support, workarounds for
these bugs were successfully implemented. Simulation results
using these executables were in excellent agreement with those
obtained using Lahey-Fujitsu single processor executables. BASS
has also been compiled on standard USEPA single processor
DELL laptops and desktops, running 32-byte Windows XP,
using the Absoft Pro Fortran 2012 compiler with similar results.
Currently, BASS is compiled on standard USEPA DELL laptops
and desktops, running 64-byte Windows 10, using the Absoft
15.0 compiler.
Finally, in June 2010, BASS ported to a DELL Latitude 8400
laptop equipped with Windows XP and a single dual core Intel
processor and recompiled using the Intel Fortran 95 compilers
11.0 and XE 2011.
7.2.8. Have test and reference / benchmark data sets been
documented and archived?
The 13 BASS projects discussed in Section 6.1 serve not only as
bass distribution examples but also as test projects that track
changes in the operation of bass associated with code
maintenance and updates. These project files are used as
benchmarks to verify that code modifications that should not
change bass's computational results also do not change bass's
simulation output.
7.3. Questions Regarding QA of Model Documentation and
Applications
7.3.1. Is the model intended for absolute or comparative
prediction?
Although bass can be used to analyze results from actual field
studies or predict the expected future condition of specific real
communities, its principal intended use is to predict and compare
outcomes of alterative management options associated with
pollution control, fisheries management, and / or ecosystem
restoration activities.
7.3.2. Does the User Guide provide the information needed to
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appropriately apply and use the model?
The BASS User's Guide summarizes the model's theoretical
foundations and assumptions, the model's input command
structure, issues related to user file and project management, and
software installation. The User's Guide also presents and
discusses the results of two of the 13 example projects that are
distributed with the BASS software.
7.3.3. What internal checking can be made to help insure that
the model is being used appropriately ?
Currently, the only internal checking performed by BASS is to
verify that all parameters needed by the model for a particular
simulation have, in fact, been specified by the user. Although
bass does assign default values for a limited number of
parameters, most unassigned parameters are fatal errors. Future
versions of BASS will perform bounds checking on many of its
physiological and morphological parameters.
7.3.4. Has the developer anticipated computational problem
areas that will cause the model to "bomb " ?
Several key mathematical calculations have been identified as
potential problem areas for a BASS simulation. In general, these
problem areas involve either the unsuccessful resolution of a root
of a nonlinear equation or the unsuccessful integration of BASS' s
basic state variables. Examples of the former include situations
when BASS' s calculated dietary compositions do not sum to unity
or when a fish's wet weight is calculated to be less or equal to its
dry weight. 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 such situations are encountered, BASS terminates execution
and issues an appropriate error message to the current *.MSG
file.
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APPENDICES
Appendix A. Equilibrium complexation model for metals
As reviewed by Mason and Jenkins (1995), metals can be
classified into three different categories based on their
complexation behavior and preference for different ligands.
These groups are generally designated as class A, class B, and
borderline metals. Of these, however, class B and borderline
metals are the most important from an ecotoxicological point of
view. Class B metals (e.g., Au, Ag, Cu, Hg, and Pb)
preferentially bind to marcromolecules such as proteins and
nucleotides that are rich in sulfhydryl groups and heterocyclic
nitrogen. Borderline metals (e.g., As, Cd, Co, Cr, Ni, Sn, andZn)
bind not only to the same sites as do class B metals but also to
those sites preferred by class A metals (i.e., carboxylates,
carbonyls, alcohols, phosphates, andphosphodiesters). Although
factors determining the preference of borderline metals for a
particular binding site are complex, the fact that the transport and
storage of these metals in fish and other biota are regulated by
metallothioneins via sulfhydryl complexation reactions suggests
that the total availability of sulfhydryl groups within organisms
plays a key role in their internal distribution and accumulation.
To formulate complexation reactions for class B and borderline
metals, one can assume that protein sulfhydryl groups are the
only significant ligand for these metals, i.e.,
RSH + M* RSM + H*
The stability constant for this reaction is
Kb _ [RSM] [/T]
[RSH] [AT]
RSM[H+]
RSH[M+]
(A. 1)
(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 is the moles
of metal bound to sulfhydryls; and RSH is the moles of free, non-
disassociated sulfhydryl. If a fish's metal concentrations (i.e., Ca
, C, , Ca , and Cf) are expressed on a molar basis, then the
following identities hold
C/ =
[M+] = Ca
RSM=CoPoWw
P +P,K +P —
a I aw 0 (2
(A3)
(A. 4)
(A. 5)
where Ww is the fish's kilogram wet weight. Substituting
Equations (A.3) and (A.4) into Equation (A.2), one can verify
that
and consequently
PoC„ = Kb RSH
Ca W [/T]
Cf= | Pa+PtZa* +
Kb RSH
WAH1
(A. 6)
(A. 7)
To parameterize Equation (A.7) for RSH, the following mass
balance for the fish's sulfhydryl content is then assumed
TS = RSH + RS~ +Y, RSMt
RSHK Kbt C RSH
RSH + ? + £ :
[/T]
RSH
[H1
\
(A. 8)
K Kb, C
[HI / [/T]
where TS is the total moles of sulfhydryl ligands; RS' is the
moles of disassociated sulfhydryls; and Ka is the sulfhydryl's
disassociation constant. Therefore,
RSH =
TS[H*]
[H^]+Ka^KbiCa
(A.9)
Using Equation (A.7), however, this expression can be rewritten
as
RSH =
TS
1 +
KbiBfi
.(A. 10)
[HI i [Pa+PlKo^Ww[H*]+KbiRSH
where Bf = Cf Ww is the fish's total burden (mol/fish) of metal i.
For most class B metals, however,
(Pa+Pi^W^H^KbiRSH
Consequendy, Equation (A. 10) can be simplified to
TS-T,Bf:
(A. 11)
RSH =
TS
K
1+77^+£
B,
A
1 +
K
(A. 12)
[H+] r RSH [H+]
This expression can then be substituted into Equation (A.7) to
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calculate the fish aqueous phase metal concentrations.
To use the aforementioned complexation model [i.e., Equation
(A. 12) substituted into Equation (A.7)], one must specify both
the metal's stability constant [see Equation (A.2)] and the total
concentration of sulfhydryl binding sites TS (mol SH/g dry wt)
within the fish. Although numerous studies have investigated the
sulfhydryl content of selected fish tissues, it appears that no study
has attempted to quantify the total sulfhydryl content of fish. A
reasonable approximation of this parameter, however, can still be
made since data do exist for the major tissues (i.e., muscle, liver,
kidney, gill, and intestine) typically associated with metal
bioaccumulation.
Itano and Sasaki (1983) reported the sulfhydryl content of
Japanese sea bass (Lateolabrax japonicus) muscle to be 11.5
(xmol SH/g(sacroplasmic protein) and 70.5 (xmol
SH/g(myofibrillar protein). Using these authors' reported values
of 0.0578 g(sarcoplasmic protein)/g(muscle) and 0.120
g(myofibrillar protein)/g(muscle), the total sulfhydryl content of
Japanese sea bass muscle is estimated to be 9.12
Limol(SII)/g(muscle) or 45.6 LimolCS I I)/g(dw muscle). Opstevedt
et al. (1984) reported the sulfhydryl content of Pacific mackerel
(Pneumataphorus japanicus) and Alaska pollock (Theragra
chalcogramma) muscle to be 6.6 and 6.2 mmol(SH)/16 g(muscle
N), respectively. Using conversion factors reported by these
authors, these values are equivalent to 48.7 and 56.7 Limol/g(dw
muscle). Chung et al. (2000) determined the sulfhydryl content
of mackerel (Scomber australasicus) muscle to be 88.2
Limol(SII)/g(protein). Using the conversion factor 0.83
g(protein)/g(dw muscle) (Opstevdt et al. 1984), this value is
equivalent to 73.2 |imol(SH)/g(dw muscle). Several studies have
determined sulfhydryl contents of the actomyosin and myosin
components of fish myofibrillar proteins (Connell and Howgate
1959, Buttkus 1967, 1971, Takashi 1973, Itoh et al. 1979,
Sompongseetal. 1996, Benjakuletal. 1997, Lin and Park 1998).
Because the results of these studies agree well with the
actomyosin analysis reported by Itano and Sasaki (1983), the
results of Itano and Sasaki (1983), Opstevedt et al. (1984), and
Chung et al. (2000) can be assumed to be representative of fish
in general. Consequently, the sulfhydryl content of fish muscle
can be assumed to be on the order of 45-70 LimolCS I I )/g(dw
muscle).
Although the sulfhydryl contents of liver, kidney, gills, and
intestine have not been measured directly, the sulfhydryl content
of these tissues can be estimated from their metallothionein
concentrations. Metallothioneins (MT) are sulfur-rich proteins
that are responsible for the transport and storage of heavy and
trace metals and that are also usually considered to be the
principal source of sulfhydryl binding sites in these tissues
(Hamilton and Mehrle 1986, Roesijadi 1992). Numerous
researchers have investigated the occurrence of 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 |imol(MT)/g(liver) (Brown
and Parsons 1978, Roch et al. 1982, Klaverkamp and Duncan
1987, Dutton et al. 1993). Using data from Takeda and Shimizu
(1982) who report the sulfhydryl content of skipjack tuna
(Katsuwonus pelamis) MTs to be approximately 25
mol(SH)/mol(MT) and assuming a dry to wet weight ratio equal
0.2, these MT concentrations would be equivalent to 3.75 - 37.5
|imol(SH)/g(dw liver). This range of values suggests that the
hepatic sulfhydryl content of fish, that includes both baseline MT
and cytoplasmic components that can be converted into MT,
might be on the order of 40 |imol(SH)/g(dw liver). This latter
value, however, is probably too conservative. Consider, for
example, the observation that the ratios of mercury
concentrations in liver to those in muscle often vary from 1.5 to
6 or more (Lockhart et al. 1972, Shultz et al. 1976, Sprenger et
al. 1988). If liver and muscle are equilibrating with the same
internal aqueous phase, then either the MT sulfhydryls are more
available than are the sacroplasmic and myofibrillar sulfhydryls
or the inducible concentrations of hepatic MT are much higher
than 40 |imol(SH)/g(dw liver). Of these two possibilities, the
latter appears more likely.
Although gill, kidney, and intestine MTs have not been studied
in the same detail as hepatic MTs, it appears thatMT, and hence
sulfhydryl, concentrations in gills and kidney are lower and not
as inducible as hepatic concentrations (Hamilton et al. 1987a, b,
Klaverkamp and Duncan 1987). Klaverkamp and Ducan (1987)
estimated the concentrations of gill MT in white suckers
(Catostomus commersoni) to be 33 ng(MT)/g(gill) which is
equivalent to 3.3 nmol(MT)/g(gill) or 0.0825 |imol(SH)/g(gill).
This latter value agrees well with the estimated concentrations of
unidentified binding sites [0.03 - 0.06 Limol/g(gill)| for copper on
the gills of rainbow trout (Oncorhynchus mykiss) and brook trout
(Salvelinus fontinalis) (MacRae et al. 1999), but is somewhat
higher than the concentration of unidentified binding sites [0.013
- 0.03 Limol/gCgi ll)| for copper, cadmium, and silver on the gills
of rainbow trout and fathead minnows (Pimephales promelas)
(Playle et al. 1993, Janes and Playle 1995).
Based on these considerations and the acknowledgment that
many other important organic compounds contain sulfhydryl
groups, e.g., enzymes involved in fatty acid synthesis,
glutathione, etc., it seems reasonable to assume that the
sulfhydryl content of fish is approximately 70 LimolCSII)/g dry
wt. Because Davis and Boyd (1978) reported the mean sulfur
content of 17 fish species to be 206 umoKSj/g dry wt, this
assumption implies that almost 1/3 of a fish's sulfur pool exists
as sulfhydryl groups.
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The aforementioned complexation model was implemented
within BASS using 70 umoKSIIj/g dry wt to calculate the fish's
total sulfhydryl content. The mean dissociation constant for
organic sulfhydryls was then assigned as pKa = 9.25 (i.e., the
SPARC estimated pKa for cysteine). Using literature values for
the stability constants of methylmercury, however, BASS over
predicted the bioaccumulation of methylmercury in fish by at
least an order of magnitude. Consequently, a much simpler
distribution coefficient algorithm was adopted.
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Appendix B. Modeling diffusive chemical exchange across fish gills with ventilation and perfusion effects. See Section 2.2 for
background information and notation.
If chemical exchange across fish gills is treated as steady-state,
convective mass transport between parallel plates, then the
following PDE and boundary conditions can be used to model
chemical uptake from and excretion to the interlamellar water:
i-*: v^-d^-
r2 J dy dx2
dC
dx
CB-D
(B.2)
x = 0
D
dC
dx
= -k_
C(r,y) -Ca- f —
a q J 8x
dv
(B.3)
To obtain a canonical solution for this gill model, these equations
can be nondimensionalized using the following transformations:
0 =
C-C.
c -c
w a
x=-
yD
Vr2
(B.4)
(B.5)
(B.6)
Applying these transformations, chemical exchange across a
fish's gills is described by the following dimensionless PDE and
boundary conditions:
-(l -X2)V— ~ d2®
2 dY~ dx2
00
dx
= o
x=o
d®
dX
= ~N.
Sh
X=l
Nn,
0(1 ,Y)~
2 rhV
f d&
J dX
qn J dX
ip Y
dv
x=i
(B.7)
(B.8)
(B.9)
where NSh = km r D 1 is the gills' dimensionless lamellar
permeability (i.e., Sherwood number); and N0z = IDV~1 r~2 is
the gills' dimensionless lamellar length (i.e., Graetz number).
The boundary condition (B.9) describing exchange across the
secondary lamellae, however, can be simplified by noting that the
solution of Equation (B.7) is separable, i.e., Q(X, Y) = <\HX)yY(Y)
and that qv = 2 rhV is the ventilation volume of an individual
interlamellar channel. Using these observations, one can then
write
aX
x=i
-N.
Sh
0(1)¥(T) -
gv dQ>
qB dX
x=i
"Oz
I
T(v) dv
(B.10)
that can then be differentiated with respect to Y to obtain
d*¥ d
dX
(B.14)
x=i
which is the boundary condition originally used by Barber et al.
(1991).
See Barber et al. (1991) for the method used to construct the
series solution for the dimensionless bulk concentration of the
aforementioned PDE gill exchange model [i.e., Equation (2.28)].
October 2018
115
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Appendix C. Derivation of the consistency condition for feeding electivities.
To derive a self consistency condition for a fish's electivities and When Equation (C.3) is substituted into Equation (C.5), one then
relative prey availabilities such that its calculated dietary obtains
frequencies will sum to unity, consider the following
dt-ft
e. =
' di+f
d,.=
( \
Ilfi
1 - e,.
f,
Summing Equation (C.2) over all i then yields
±fri'rtf,
' * /=1 /=1
E«/K+/) = °
i=i
(C.I)
(C.2)
(C.4)
(C.5)
E«,
c=i
or equivalently
1 + e.
1 - e
Lf,+ft
"2 e f
:Et^=0
/=i 1 - et
n e f
E-fi^L = o
/=1
1 - e,
(C.6)
(C.7)
(C.3) Finally, adding = 1 to each side of Equation (C.7), the
following consistency condition is obtained
E
i=l
eift
1 ~ e.
n f
E —= i
(C.8)
i=1
1 - e,
i=1
October 2018
116
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INDEX
chemical commands
/chemical 42
/exposure 42
/lethality 43
/log_ac 44
/log_kbl 44
/log_kb2 44
/log_p 44
/melting_point 44
/metabolism 44
/molar_volume 44
/molar_weight 44
/nonfish_bcf 44
chemical exposures
contaminated sediments 43
dietary exposure via benthos 43
dietary exposure via insects 43
dietary exposure via periphyton 43
dietary exposure via phytoplankton 43
direct aqueous exposures 43
files
chemical exposure files (.chm) 55, 56
chemical property files (.prp) 55
community files (.cmm) 54, 55
directory structure for BASS include files 56
fish files (.fsh) 54, 55
include files 39, 54
management 54
output file (.bss) 57
output file (.msg) 56
project files (.prj) 55, 56
user supplied parameter files 53
fish commands
/age_class_duration 45
/common_name 45
/compostional_parameters 45
/ecological_parameters 45
/feeding_options 47
/fishery_parameters 47
/habitat_parameters 48
/initial_conditions 49
/morphometric_parameters 49
/physiological_parameters 49
/prey_switching_off 50
/spawning_period 50
/species 51
restrictions
specifying chemical names 42
specifying common names 45
units recognized by BASS 52
simulation control commands
/annual_outputs 39
/biota 39
/fgets 40
/first_leap_year 40
/fish_carrying_capacity 40
/header 40
/isotope_parameters 40
/length_of_simulation 41
/leslie_matrix_simulation 41
/month_t0 41
/nonfish_qsar 41
/simulation_control 41
/temperature 41
/water_level 41
simulation options
defining community food web 37, 45
non-fish compartments as forcing functions 39, 51, 52
non-fish compartments as state variables 51
simulating bioaccumulation without community dynamics
40
simulating community dynamics without bioaccumulation
37
specifying fishery harvest and stocking 47
specifying habitat suitability multipliers 48
specifying non-fish BCFs 41, 44
specifying output 39
specifying water levels 41
turning off chemical lethality 44
turning off fishery stocking 48
turning off fishing harvest 48
specifying physical conditions
water depth 41
water temperature 41
syntax
commenting a line 38
continuing a line 38
specifying an include file 39
specifying units 52
user specified functions 52
technical support
reporting comments 37
reporting problems 37
reporting suggestions 37
October 2018
117
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Last Page (intentionally blank)
October 2018 118
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