EPA Report No. 600/R-01/035 

April 2001 

Bioaccumulation and Aquatic 

System Simulator (BASS) 

User's Manual 

Beta Test Version 2.1 

by 

M. Craig Barber

Ecosystems Research Division 

U.S. Environmental Protection Agency 

960 College Station Road 

Athens, GA 30605-2700 

National Exposure Research Laboratory 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 27711 
Notice


The research described in this document was funded by the U.S. Environmental Protection Agency through the Office of 

Research and Development. The research described herein was conducted at the Ecosystems Research Division of the USEPA 

National Exposure Research Laboratory in Athens, Georgia. Mention of trade names or commercial products does not constitute 

endorsement or recommendation for use. 

ii 
Foreword


This report describes the theoretical development, parameterization, and application software of a generalized, community-based, 

bioaccumulation model called BASS (Bioaccumulation and Aquatic System Simulator). This model is designed to predict the 

population and bioaccumulation dynamics of age-structured fish communities that are exposed to hydrophobic organic chemicals and 

class B and borderline metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). 

This report is not a case study on the application of BASS but rather a reference and user’s guide. The intended audience of this report 

and associated software is research fisheries ecologists, bioaccumulation researchers, and EPA environmental scientists and ecologists 

who must routinely analyze and estimate bioaccumulation of chemicals in fish for ecological or human health exposure assessments. 

BASS version 2.1 is a beta test version that is being released on a targeted basis to EPA Program and Regional Offices and to the 

academic research community for comment and testing. Although the model has not been extensively field-tested, its process-based 

algorithms for predicting chemical bioaccumulation, growth of individual fish, predator-prey interactions, and population dynamics 

either have been corroborated or have been formulated using widely accepted ecological and ecotoxicological principles. Even when 

a process-based model has undergone only limited field testing, it can be an extremely useful tool. Process-based models enable users 

to observe quantitatively the results of a particular abstraction of the real world. Moreover, such models can be argued to be the only 

objective method to make extrapolations to unobserved or unobservable conditions. If the conceptualization and construction of 

process-based models are both comprehensive (i.e., holistic) and reasonable, then their output, validated or not, can still be used for 

comparative analyses. A model’s ability to simulate trends and comparative dynamics are, in fact, often more important measures of 

a model’s utility than is its ability to replicate a specific field or laboratory study. Although BASS can be used to analyze results from 

actual field studies, its principal intended use is to predict and compare the outcomes of alternative management options that are 

associated with pollution control or ecosystem management or restoration activities. 

Rosemarie C. Russo, Ph.D. 

Director 

Ecosystems Research Division 

Athens, Georgia 

iii 
Abstract


BASS (Bioaccumulation and Aquatic System Simulator) is a Fortran 95 simulation program that predicts the population and 

bioaccumulation dynamics of age-structured fish assemblages that are exposed to hydrophobic organic pollutants and class B and 

borderline metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). The model's 

bioaccumulation algorithms are based on diffusion kinetics and are coupled to a process-based model for the growth of individual 

fish. The model’s exchange algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals 

of concern that determine diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics used by the 

model include the fish's gill morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and 

structural organic content). Relevant physico-chemical properties are the chemical's aqueous diffusivity, n-octanol/water partition 

coefficient ( Kow ), and, for metals, binding coefficients to proteins and other organic matter. BASS simulates the growth of individual 

fish using a standard mass balance, bioenergetic model (i.e., growth = ingestion - egestion - respiration - specific dynamic action ­

excretion). A fish's realized ingestion is calculated from its maximum consumption rate adjusted for the availability of prey of the 

appropriate size and taxonomy. The community’s food web is specified by defining one or more foraging classes for each fish species 

based on either its body weight, body length, or age. The dietary composition of each of these feeding classes is specified as a 

combination of benthos, incidental terrestrial insects, periphyton/attached algae, phytoplankton, zooplankton, and one or more fish 

species. Population dynamics are generated by predatory mortalities defined by community’s food web and standing stocks, size 

dependent physiological mortality rates, the maximum longevity of species, and toxicological responses to chemical exposures. The 

model’s temporal and spatial scales of resolution are a day and a hectare, respectively. Currently, BASS ignores the migration of fish 

into and out of the simulated hectare. 

iv 
Table of Contents


Abstract .............................................................................................. iv


Figures .............................................................................................. vii


Tables ............................................................................................... vii


1. Introduction ......................................................................................... 1


2. Model Formulation ................................................................................... 4


2.1. Modeling Internal Distribution of Chemicals ....................................................... 4


2.2. Modeling Exchange from Water ................................................................. 5


2.3. Modeling Exchange from Food ................................................................. 7


2.4. Modeling Chemical Biotransformation ........................................................... 9


2.5. Modeling Temperature Effects on Physiological Rates ............................................... 9


2.6. Modeling Growth of Fish ..................................................................... 10


2.7. Modeling Trophic Interactions and Predatory Mortalities ............................................ 11


2.8. Modeling Non Predatory Mortalities and Recruitment ............................................... 13


2.9. Modeling Toxicological Effects ................................................................ 14


3. Model Parameterization ............................................................................... 18


3.1. Parameterizing K ........................................................................... 18
 f 

3.2. Parameters for Gill Exchange .................................................................. 18


3.3. Bioenergetic and Growth Parameters ............................................................ 19


4. BASS User Guide .................................................................................... 31


4.1. Summary of New Features Available in BASS version 2.1 ............................................ 31


4.2. Input File Structure .......................................................................... 32


4.2.1. Simulation Control Commands ........................................................ 33


4.2.2. Chemical Input Commands ........................................................... 35


4.2.3. Fish Input Commands ............................................................... 38


4.2.4. Units Recognized by BASS ............................................................ 41


4.2.5. Syntax for User Specified Functions .................................................... 41


4.2.6. User Supplied Exposure Files ......................................................... 42


4.3. Output Files Generated by BASS ................................................................ 43


4.4. Include Files and General File Management ...................................................... 43


4.5. Command Line Options ...................................................................... 45


4.6. Restrictions and Limitations ................................................................... 45


5. Software Installation and Management ................................................................... 50


5.1. MS-DOS Installation ........................................................................ 50


5.2. Auxiliary Software .......................................................................... 51


6. Example Application ................................................................................. 52


7. Model Quality Assurance .............................................................................. 54


7.1. Questions Regarding QA of a Model’s Scientific Foundations ........................................ 54


7.2. Questions Regarding QA of a Model’s Implementation ............................................. 55


7.3. Questions Regarding QA of Model Documentation and Applications .................................. 59


8. Planned Future Features ............................................................................... 60


v 
References ........................................................................................... 61


APPENDICES ........................................................................................ 79


APPENDIX A. Equilibrium complexation model for metals ............................................. 79


APPENDIX B. Nondimensionalization of chemical exchange equations for fish gills. ......................... 82


APPENDIX C. Derivation of the consistency condition for feeding electivities. ............................. 83


APPENDIX D. Example project file constructed using include files as discussed in Section 4.4 ................. 84


APPENDIX E. Example output file (filename.msg) that summarizes user input data, input data errors, and run time


warnings and errors. ..................................................................... 95


APPENDIX F. Example output file (filename.bss) that tabulates annual bioenergetic and contaminant fluxes. ..... 112


APPENDIX G. Example output file (filename.plx) that plots the variables requested by the user. ............... 122


vi 
Figures 

Figure 1. Application of Eq.(2-51) to describe the temperature dependence of the maximum daily consumption of brown trout 

(Salmo trutta) based on Elliott (1976b, Tables 2 and 9). ................................................. 17


Figure 2. First eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio. ............ 27


Figure 3. Second eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio. .......... 28


Figure 4. First bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.29


Figure 5. Second bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion


ratio. ......................................................................................... 30


Figure 6. Conceptual model for the primary food web of Everglades open water fish assemblage. ....................... 53


Tables 

Table 1. Symbols used for model development ............................................................... 16


Table 2. Summary of allometric coefficients and exponents for gill area and lamellar density for freshwater bony fishes and


agnatha. ...................................................................................... 20


Table 3. Summary of allometric coefficients and exponents for gill area and lamellar density for cartilaginous and marine boney


fishes. ........................................................................................ 22


Table 4. Summary of allometric coefficients and exponents for gill area and lamellar density for air-breathing fishes. ....... 23


Table 5. Summary of coefficients and exponents for lamellar lengths. ............................................. 24


Table 6. Summary of studies reporting water-blood barrier thickness for freshwater and marine fishes. ................... 25


Table 7. Sources of bioenergetic and growth for selected fish species. ............................................. 26


Table 8. Valid Unit Prefixes ............................................................................. 46


Table 9. Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy ..................................... 47


Table 10. Valid Ecological, Morphometric, and Physiological Unit Names ......................................... 49


vii 
1. Introduction


Fish health can be defined from both an ecological and a human 

health/value perspective in a wide variety of ways. Questions 

relating to fish health from an ecological perspective often 

include: 

1) Is individual fish growth and condition sufficient to 

enable them to survive periods of natural (e.g., 

overwintering) and man induced stress? 

2) Are individual fish species able to maintain sustainable 

populations? For example, is individual growth 

adequate for the fish to attain it’s minimum body size 

required for reproduction? Is there adequate physical 

environment for successful spawning? Is there 

adequate physical habitat for the survival of the youngof-year? 

3) Do regional fish assemblages exhibit their expected 

biodiversity or community structure based on 

biogeographical and physical chemical considerations? 

4) Are regional fish assemblages maintaining their 

expected level of productivity based on 

biogeographical and physical chemical considerations? 

5) Are appropriately sized fish abundant enough to 

maintain piscivorous wildlife (e.g., birds, mammals, 

and reptiles) during breeding and non-breeding 

conditions? 

6) Are potential fish prey sufficiently free of contaminants 

(endocrine disruptors, heavy metals, etc.) so as not to 

interfere with the growth and reproduction of 

piscivorous wildlife? 

From a human health or use perspective another important 

question related to fish health is: 

7)	 Is the fish community/assemblage of concern fishable? 

That is are target fish species sufficiently abundant and 

of the desired quality? Fish quality is this context is 

often defined in terms of desired body sizes (e.g., legal 

or trophy length) and the absence of chemical 

contaminants. 

Some of the important metrics or indicators that have been 

typically used to assess such questions include 1) physical 

habitat dimensions, e.g., bottom type and cover, occurrence of 

structural elements such as woody debris or sand bars, mean and 

peak current velocities, water temperature, sediment loading, 

etc., 2) community species and functional diversity, 3) total 

community biomass (kg/ha or kg/km), 4) the population density 

(fish/ha or fish/km) or biomass (kg/ha or kg/km) of the 

community’s dominant species, 5) the age or size class structure 

of the community’s dominant species, 6) annual productivity of 

the community and its dominant species, 7) individual growth 

rates or condition factors (i.e., the fish’s current body weight 

normalized to an expected body weight based on its current 

length), and 8) levels of chemical contaminants in muscle or 

whole fish for human or ecological exposure assessments, 

respectively. 

From the perspective of evaluating alternative management 

options or of assessing expected future consequences of existing 

conditions,simulation models that can predict the individual and 

population growth of fish and their patterns of chemical 

bioaccumulation are important tools for analyzing several of the 

dimensions of fish health identified above. 

Although the growth of individual fish has often been described 

using empirical models such as the von Bertalanffy, logistic, 

Gompertz, or Richards models (see for example Ricker (1979) 

and Schnute (1981)), process-based bioenergetic models such as 

those described by Kitchell et al. (1977), Minton and McLean 

(1982), Stewart et al. (1983), Cuenco et al. (1985), Stewart and 

Binkowski (1986), Beauchamp et al. (1989), Stewart and Ibarra 

(1991), Lantry and Stewart (1993), Rand et al. (1993), Roell and 

Orth (1993), Hartman and Brandt (1995a), Petersen and Ward 

(1999), Rose et al. (1999) , Schaeffer et al. (1999), are 

becoming the models of choice for predicting the growth of fish. 

Because these models predict fish growth as the mass or energy 

balance of ingestion, egestion, respiration, specific dynamic 

action, and excretion, they can generally be parameterized 

independently of their current application. Moreover, because of 

the inherent difficulties in obtaining reliable field-based 

measurements of the population dynamics and productivity of 

fish,researchers are increasingly using 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 in 

assessing the ecological and human health risks of chemical 

pollutants. Not only are accurate estimates needed to predict 

realistic dietary exposures to humans and piscivorous wildlife 

but such estimates are also needed to assess more accurately 

potential ecological risks to fish assemblages themselves. 

Although exposure-referenced toxicological benchmarks such as 

the LC50 and the EC50 have been widely used to make hazard 

assessments, most deleterious effects of chemical pollutants are 

caused by the internal accumulation of those compounds, rather 

than their environmental concentrations per se. Numerous 

authors (Neely 1984; Friant and Henry 1985; McCarty et al. 

1985; McCarty 1986; Connell and Markwell 1992; McCarty and 

1

Mackay 1993; Verhaar et al. 1995; van Loon et al. 1997) have 

discussed the need to consider chemical bioaccumulation 

explicitly when assessing expected ecological consequences of 

chemical pollutants in aquatic and marine ecosystems. Residuebased 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 the concentrations of moderately hydrophobic 

chemicals in fish often can be predicted accurately by assuming 

equilibrium partitioning of the chemicals between the fish’s 

organic constituents and the aqueous environment, this approach 

frequently fails to predict observed concentrations of extremely 

hydrophobic chemicals and metals that are often the chemicals 

of greatest concern. Observed deviations can be in either 

direction, with calculated contamination levels being both 

considerably above and below those predicted by equilibrium 

partitioning. Several factors can be identified to explain these 

discrepancies. 

Lower than expected contamination levels can result when the 

length of exposure is insufficient to allow chemicals to 

equilibrate. Because bioconcentration and bioaccumulation are 

generally treated as linear, first order kinetic processes, the time 

needed for chemicals to equilibrate between fish and their 

exposure media is an increasing function of the elimination half 

lives of those chemicals in fish. For example, the time required 

for chemicals to achieve 95% of their equilibrium concentrations 

is approximately 4.3 times their elimination half lives. Because 

the elimination half lives of chemicals generally increase as their 

hydrophobicities increase, the time needed for chemicals to 

reach equilibrium concentrations in fish also increases as a 

function of chemical hydrophobicity. Consequently, for 

extremely hydrophobic chemicals such as polychlorinated 

biphenyls (PCBs) and dioxins that have elimination half lives 

ranging from months to over a year, the time to equilibrium can 

be on the order of years. If the species of concern is relatively 

short lived, the time needed for equilibrium can exceed their 

expected life span. Even when there is sufficient time for 

equilibration, whole body concentrations of fish can be much 

lower than that expected from thermodynamic partitioning due 

to physical dilution of the chemical that accompanies body 

growth or to the biotransformation and metabolism of the parent 

compound. 

One of two possible assumptions are implicitly made whenever 

equilibrium-based estimators are used. The first of these 

assumptions is that only the selected reference route of exposure 

is significant in determining the total chemical accumulation in 

fish. The alternative to this assumption is that there are actually 

multiple routes of exposure which are all covariant with the 

chosen reference pathway in a fixed and constant manner. In the 

case of bioconcentration factors (BCFs), the implicit assumption 

is that virtually all of the fish’s accumulated body burden is 

exchanged directly with the water across the fish’s gills or 

possibly across its skin. Although direct aqueous uptake is 

certainly the most significant route of exchange for moderately 

hydrophobic chemicals, dietary uptake accounts for most of a 

fish’s body burdens for extremely hydrophobic chemicals. This 

shift in the relative significance of the direct aqueous versus the 

dietary pathway is determined by the relative rates of exposure 

via these media and by a fundamental difference in the nature of 

chemical exchange from food and water. Consider, for example, 

the relative absolute exposures to a fish via food and water. The 

fish’s direct aqueous exposure, AE � g/day, is the product of its 

ventilation volume, Q mL/day, and the chemical’s aqueous 

concentration, Cw � g/mL. Similarly, the fish’s dietary exposure, 

DE � 

g/day, is the product of its feeding rate, F g/day, and the 

chemical’s concentration in the fish’s prey, Cp � g/g. Assuming 

that the fish feeds only on one type of prey that has equilibrated 

with the water, one can calculate when the fish’s aqueous and 

dietary exposures are equal using the equations 

AE  DE 

Q Cw  F Cp (1-1) 

Q / F  BCF 

K

Using data from Stewart et al. (1983) and Erickson and McKim 

(1990) the ventilation-to-feeding ratio for a 1 kg trout would be 

on the order of 104.3 mL/g. Assuming the quantitative structure 

activity relationship (QSAR) for the trout’s prey is BCF = 0.048 

ow(Mackay 1982), one would conclude that food is the trout’s 

predominant route of exposure for any chemical whose octanol/ 

water partition coefficient is greater than 105.6. For extremely 

hydrophobic chemicals, not only will fish be more exposed via 

food but they probably will assimilate chemicals from food more 

effectively than from the water. Although chemical exchange 

from both food and water occur by passive diffusion, uptake 

from food, unlike direct uptake from water, does not necessarily 

relax the diffusion gradient into the fish. This fundamental 

difference results from the digestion and assimilation of food 

that can actually cause the chemical concentrations of the fish’s 

gut contents to increase (Connolly and Pedersen 1988; Gobas et 

al. 1988). Predicting residue levels for chemicals whose 

principal route of exchange is dietary is further complicated 

sincemost fish species demonstrate well defined size dependent, 

taxonomic, and temporal trends regarding the prey they 

consume.Consequently, one would not generally expect a single 

BAF to be sufficiently accurate for risk assessments for all fish 

species or even different sizes of the same species. 

Process-based models that describe the kinetic exchange of 

chemicals from food and water and the growth of fish provide 

objective and scientifically defensible tools that can overcome 

2

many of the limitations of equilibrium-based predictors of 

bioaccumulation identified above. Although numerous models 

have been developed to describe the dynamics of chemical 

bioaccumulation in fish, (Norstrom et al. 1976; Thomann 1981, 

1989; Jensen et al. 1982; Thomann and Connolly 1984; Gobas 

et al. 1988; Barber et al. 1991; Thomann et al. 1992; Gobas 

1993; Madenjian et al. 1993), these models differ significantly 

with regard to how food web structure and dietary exposures are 

represented. 

This report describes the theoretical framework, 

parameterization, and use of a generalized, community-based, 

bioaccumulation model called BASS (Bioaccumulation and 

Aquatic System Simulator). This process-based, Fortran 95 

simulationmodel is designed to predict the growth of individuals 

and populations within an age-structured fish community and the 

bioaccumulation dynamics of those fish when exposed to 

mixtures of metals and organic chemicals. The model is 

formulated such that its parameterization does not rely upon 

calibration data sets from specific toxicokinetic and population 

field studies but rather upon physical and chemical properties 

that can be estimated using chemical property calculators such 

as CLOGP (http://www.biobyte.com/bb/prod/clogp40.html) , or 

SP A RC ( C a r r e i r a e t a l . 1 9 9 4 ; 

http://ibmlc2.chem.uga.edu/sparc/style/welcome.cfm), and on 

ecological,morphological, and physiological parameters that can 

be obtained from the published literature or computerized 

databases. 

3

2. Model Formulation


To model the chemical bioaccumulation and the growth of 

individuals and populations within an age-structured fish 

community, BASS solves the following system of differential 

equations for each age class of fish 

dB 

dt � 

J g  Ji M (2-1) 

d Wd 

d t  

F E R EX SDA (2-2) 

d N 

d t  

NM PM (2-3) 

where B and Wd denote the chemical body burden ( g/fish) and 

dry body weight (g(DW)/fish) of the average individual within 

the age class and N denotes the age class’s population density 

(fish/ha). In Eq.(2-1) Jg and Ji denote the net chemical exchange 

across a fish' s gillsfrom the water and across its intestine from 

food, respectively, and M denotes the chemical’s 

biotransformation or metabolism. In Eq.(2-2) F, E, R, EX, and 

SDA denote the fish' s feeding, egestion, routine respiration, 

excretion, and specific dynamic action (i.e., the additional 

respiratory expenditure in excess of R required to assimilate 

food), respectively. Although many physiologically based 

modelsfor fish growth are formulated in terms of energy content 

and fluxes (e.g., kcal/fish and kcal/d), formulating a 

physiologically based growth model in terms of dry weight is 

fundamentally identical to the former since the energy densities 

of fish depend on their dry weight (Kushlan et al. 1986; Hartman 

and Brandt 1995b). Finally, in Eq.(2-3) NM and PM denote the 

age class’s non-predatory and predatory mortality, respectively. 

Although migration can be a significant process in determining 

population sizes, this process is presently ignored in BASS. 

Though it may not be immediately apparent from the above 

notation, these equations are tightly coupled to one another. For 

example, the realized feeding of fish depends on the availability 

(i.e., density and biomass) of suitable prey. The fish’s predatory 

mortality in turn is determined by the individual feeding levels 

and population densities of its predators. Finally, the fish’s 

dietary exposure is determined by its rate of feeding and the 

levels of chemical contamination in its prey. 

The following sections describe how each mass flux in the above 

system of equations is formulated in BASS. Table 1 summarizes 

the definitions of all the variables used to develop these 

equations. Because the system of units used to formulate 

chemical exchanges is essentially the CGS-system (centimeter, 

gram, second) and the system of units used to formulate a fish' s 

growth is the CGD-system (centimeter, gram, day), some units 

conversion is necessary to make the coupled system of equations 

dimensionally consistent. The reader should also note that 

whereas the growth of fish is described in terms of dry weight, 

modeling the bioaccumulation of chemicals in fish requires 

knowledge of their live weights since the following formulations 

of the bioaccumulation process will be developed in terms of 

diffusive exchange 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 it be the surrounding ambient water or the 

aqueous phases of the fish’s intestinal contents. Consequently, 

to model these exchanges one must first consider how chemicals 

distribute with the bodies of fish. If individual fish are conceptualized as a three-phase solvent consisting of water, lipid, and 

non-lipid organic matter, then their whole body chemical 

concentration can be expressed as 

Cf  

B  

P C  PlCl P C 

Wf

a a o o 

(2-4) 

C 

P  Pl  

Cl 

P o C a a C o C a a 

where Wf is the fish’s live weight (g(FW)); Pa, Pl

, and P are the o 

fractions of the whole fish that are water, lipid, and non-lipid 

organic material, respectively; and Ca, Cl, and C are the 

chemical' s concentrations in those phases. Because the 

depuration rates of chemicals from different fish tissues often do 

not differ significantly (Grzenda et al. 1970; van Veld et al. 

1984; Branson et al. 1985; Norheim and Roald 1985; Kleeman 

et al. 1986a, 1986b), internal equilibration between these three 

phases can be assumed to be rapid in comparison to external 

exchanges. For organic chemicals this assumption means that 

Eq.(2-4) simplifies to 

o 



Cf  P  PlKl P K Ca (2-5) a o o 

where Kl and K are partition coefficients between lipid and o 

water and between organic carbon and water, respectively. 

For metals, however, Eq.(2-4) is in theory more complicated. 

Although metals do partition into lipids (Simkiss 1983), their 

accumulation within most other organic media occurs by 

complexation reactions with specific binding sites. 

Consequently, for metals it would seem that the term P C /C in 

Eq.(2-4) should be formulated as a function of an appropriate o o a 

4 
stability coefficient and the availability of binding sites. 

Appendix A. summarizes an equilibrium complexation model 

that was initially formulated for BASS. Despite its apparent 

correctness, this algorithm greatly overestimated metal (in 

particular mercury) bioaccumulation in fish. Although this 

overestimation can be attributed to several factors, the most 

likelyexplanation for the algorithm’s unsatisfactory performance 

is that kinetics limits the complexation of metal in fish. Because 

kinetic modeling was considered to be inappropriate to the time 

scales of most of the other major processes represented 

elsewhere in BASS, a much simpler algorithm was adopted. 

Because many fate and transport models (e.g., EXAMS and 

WASP) have successfully used operationally defined 

distribution coefficients Kd to model the accumulation of metals 

in organic media, the same approach was adopted for BASS. 

Thus, for a metal 

Cf � P  PlKl PoKd Ca (2-6) a 

where Kl is again an appropriate partition coefficients between 

lipid and water and Kd is an appropriate metal specific 

distribution coefficient. Although this equation appears identical 

to Eq.(2-5) for organic contaminants, the relative values of Kd 

and K in relation to Kl can be remarkably different. See Section o 

3.1. 

Because Cw equals C at equilibrium, it follows from Eq.(2-4) a 

that the thermodynamic bioconcentration factor (Kf = Cf

/C at w 

equilibrium) for a chemical in fish would be 

P  PlKl P K for organics a o o 

Kf � 

(2-7) 

P  PlKl for metalics a PoKd 

2.2. Modeling Exchange from Water 

Because chemical exchange across the gills of fish occurs by 

simple diffusion, such exchanges can be modeled by Fick' s first 

law of diffusion as follows 

J C � S k C (2-8) g g g w a 

where S is the fish' s total gill area (cm2), kg is the chemical' s 

conductance (cm/s) across the gills from the interlamellar water, g 

and C is the chemical' s concentrations  

(g/mL) in the 

environmental water. See Yalkowsky et al. (1973), Mackay 

(1982), Mackay and Hughes (1984), Gobas et al. (1986), Gobas 

and Mackay (1987), and Erickson and McKim (1990). When 

Eqs.(2-4) and (2-7) are substituted into this equation, one then 

obtains 

w 

Cf J � 

S k C 

(2-9) g g g w Kf 

Although according to Fick' s first law the conductance kg of a 

chemical across a fish’s gill could be specified as a ratio of the 

chemical’s diffusivity to the thickness of an associated boundary 

layer, implementation of this definition can be problematic 

because the thickness of the boundary layer varies along the 

length of the gill’s secondary lamellae and is a function of the 

gill' s ventilation velocity. To circumvent this problem, a fish’s 

net chemical exchange rate, Sg kg , can be objectively estimated 

by reformulating the gill' s net chemical exchange as 

Jg � Q (Cw CB) (2-10) 

3 where Q is the fish' s ventilation volume (cm/s) and CB is the 

bulk concentration of the chemical in the water expired from the 

gills. When Eqs. (2-8) and (2-10) are equated, it follows that 

C w CB S k � 

Q (2-11) g g C 

C w a 

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 

O2 Q �  (2-12) 

O C

2 w,O2 

where O2 is the fish’s rate of oxygen consumption ( g/s),  

O2 is 

the fish' s oxygen assimilation efficiency and Cw,O2 is the water’s 

dissolved oxygen concentration ( g/mL). And if one now makes 

certain assumptions concerning the geometry of the interlamellar 

spaces and the nature of mass transport between the secondary 

lamellae, the normalized bulk concentration of the exhalant gill 

water (C -CB)/(C -Ca)can also be formulated. w w 

Because the gill’s secondary lamellae form flat channels having 

very high aspect ratios (i.e., mean lamellar height / interlamellar 

distance), the lamellae can be considered as parallel plates and 

the flow of water between them can be treated as Poiseuille slit 

flow (Hills and Hughes 1970; Stevens and Lightfoot 1986). 

Under this assumption, an expression for a chemical' s 

concentration in the bulk exhalant gill water can be obtained 

using the solutions of the partial differential equation (PDE) that 

describessteady-state convective mass transport between parallel 

plates, i.e., 

 

3 C  

2

C 1  x 2 V 

 

D 

 (2-13) 2 y x 2 

where V (cm/s) is the gill's mean interlamellar flow velocity, D 

(cm2/s) is the chemical's aqueous diffusivity, and x and y are the 

lateral and longitudinal coordinates of the channel along which 

diffusion and convection occurs, respectively. In this equation 

C = C(x, y) denotes the chemical's interlamellar concentration at 

5 




the distances x from the surface of the lamellae and y along its 

length. The surfaces of adjacent lamellae are located at x � ± h 

where h is the hydraulic radius of the lamellar channel which 

equals one half of the interlamellar distance d (cm). The midline 

between adjacent lamellae is therefore denoted by x=0. The 

mean interlamellar flow velocity, V (cm/s), can be formulated as 

the ratio of the fish’s ventilation volume to the cross sectional 

pore area, Xg (cm2), of its gills. Because this pore area is related 

to the gill’s lamellar surface area by 

S d g X � 

g (2-14) l 

where d (cm) is the mean interlamellar distance and l (cm) is the 

mean lamellar length (Hills and Hughes 1970), a fish’s mean 

interlamellar flow velocity is given by 

V � 

Q l 

S d (2-15) g 

To solve the above PDE two boundary conditions must be 

specified. Because adjacent lamellae presumably exchange the 

chemical equally well, the solutions should be symmetrical about 

the channel' s midline. To insure this characteristic, the boundary 

condition 

 

C 

 

0 (2-16) x x  0  

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 

 

C D 

 



km C(h, y) Ca (2-17) x x  h  	 	 

where k is the permeability of the gill membrane (cm/s). 

Although this boundary condition could be used as is (Barber et 

al. 1991), it can also be modified to address potential perfusion 

limitation of gill uptake. To accomplish this task a formulation 

patterned after Erickson and McKim (1990) can be used. In 

particular, consider the following reformulation 

m 

 

C D 

 



k C(h,y) Ca(y) m x x  h  	 	 

(2-18) 

C (l) 
 

U(y, l ) k C(h,y) m a q  	 	 p

where Ca(y) denotes the aqueous phase concentration of the 

chemical at point y along the length of a secondary lamella, Ca(l) 

= C denotes the chemical' s concentration in the afferent lamellar a

blood, U(y, l)is the chemical’s accumulated rate of uptake ( g/s) 

along the lamellar segment [y, l], and qp is the lamellar perfusion 

rate(cm3/s). If both sides of the lamella uptakes chemical, then 

U(y, l) can be formulated as 

 

l z C U(y, l ) 2   D 

 




dzdy y 0 x x  h 

(2-19)  

l C 2 z D  

 




dy y x x  h 

where z denotes the height (cm) of the secondary lamella. Using 

this expression, the boundary condition (2-18) can now be 

written as 

 


D 

 

C 


   

k
 C(h,y)  Ca  

2 z D  

l 

 

C 






 dy m (2-20) x x  h qp y x x  h 

Once the solution of Eq.(2-13) for these boundary conditions has 

been obtained, the chemical's bulk concentration in the exhalant 

gill water can be evaluated using the weighted average 

h 2  C(x, l) 1  

x dx 

CB  

0 (2-21) h 2  1  

x dx 

0 

that scales each concentration profile C(x, l) by its relative 

velocity. 

A canonical solution to Eq.(2-13) can be obtained by 

nondimensioning C(x, y), x, and y as follows 

  

C  Ca 

C  C (2-22) w a 

X  

x (2-23) h 

 

y D Y 

V h 2 (2-24) 

where h is the hydraulic radius of the lamellar channel (i.e., onehalf the interlamellar distance). When this is done, the chemical's 

dimensionless bulk concentration is given by 

1 

1  X 2 (X, N dx Gz ) 

  

CB  C a
  0

B 1
 (2-25) Cw  Ca 1  X 2 dX 0 

where NGz = (l D) / (V h2) is the lamellae's dimensionless length 

or Graetz number. Two important points concerning this 

expression can now be made. Firstly, one can easily verify that 

1   

B  

C w  CB

C  C (2-26) w a 

and therefore Eq. (2-11) can be rewritten as 

 

S k Q (1   

B) g g (2-27)

6 
� 

Secondly, analytical expressions for are readily available B 

(Brown 1960; Grimsrud and Babb 1966; Colton et al. 1971; 

Walker and Davies 1974). In particular, a chemical's 

dimensionless bulk concentration can be evaluated by 

� 

  

2 Bm exp   

B  m NGz (2-28) m  0 

where the coefficients Bm and exponents  

m are known functions 

of the lamellae's dimensionless conductance or Sherwood 

number 

k h m (2-29) 

and the fish’s ventilation/perfusion volume ratio. See Appendix 

NSh  

D 

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 evaluate 	 

B with less 

than 1% error (also see Barber et al. 1991). 

2.3. Modeling Exchange from Food 

Chemical uptake from food has usually been modeled by 

assuming that a fish can assimilate a constant fraction of the 

chemical it ingests, i.e., 

Ji  
 c Cp Ff (2-30) 

where 
 

c is an assimilation efficiency (dimensionless) for the 

chemical, Cp is the chemical's concentration ( g/g(FW)) in the 

ingested prey, and Ff is the fish’s wet weight consumption 

(Norstrom et al. 1976; Jensen et al. 1982; Thomann and 

Connolly 1984; Niimi and Oliver 1987). However, because the 

chemical exchange across the intestine is driven by diffusive 

gradients (Vetter et al. 1985; Clark et al. 1990; Gobas et al. 

1993), such formulations would be thermodynamically realistic 

only if 
 

c is a decreasing function of the fish's total body 

concentration Cf.A thermodynamically sound description for the 

dietary uptake of chemicals can be formulated using the simple 

mass balance relationship 

Ji  Cp Ff  Ce Ef (2-31) 

where Ef is the fish’s daily wet weight egestion and C is the e 

chemical's concentration ( g/g(FW)) in the fish's feces. 

Because the transit time through the gastrointestinal tract is 

relatively slow, it is reasonable to assume that the concentrations 

of chemicals in the fish's aqueous blood, intestinal fluids, and 

dry fecal matter equilibrate with one another. Connell (1989) 

made similar assumptions to analyze the ratio of a predator's 

chemical concentration to that of its prey. Using this assumption, 

a fish’s total fecal elimination of a chemical can be calculated as 

C E Cd C E e Ef  a a 
 

Cd 

 

Ea 
 E Ca C a 

Cf (2-32) Pia Cd E  
 

Kf 1  Pia Ca 

K e Cf E  

Kf 

where Ea is the aqueous phase volume of the fish’s feces Pia = 

Ea a /(E +E) is the aqueous fraction of the fish’s feces, and Cd is 

the chemical’s concentrations in the feces’s dry organic phase. 

For organic chemicals, the concentration ratio Cd/C can be a 

replaced with an organic carbon/water partition coefficient Koc 

P

(e.g., Karickhoff 1981; Briggs 1981; Chiou et al. 1986) whereas 

for metals, this ratio can be substituted with the distribution 

coefficientsimilar to the one used in Eq.(2-6). Although reported 

values for the percent moisture of the intestinal contents of fish 

vary between 50 and 80% (Brett 1971; Marais and Erasmus 

1977; Grabner and Hofer 1985), in general one can assume that 

ia = P due to rapid osmotic equilibration between the fish’s a

intestinal contents and its whole body. If this assumption is 

indeed reasonable, then meals with the same dry weight but 

different moisture contents should be processed by the fish at the 

same rate and efficiency since they will attain the same 

proximate composition relatively soon after ingestion. Having 

the same proximate composition implies not only that the 

concentrations of digestive enzymes acting on such meals should 

be comparable but also that physical forces exerted by the 

volume of the gut contents which controls peristalsis and gastric 

mobility should likewise be comparable. Because Bromley 

(1980) and Garber (1983) demonstrated that initial dietary 

moisture content had no significant effect on the assimilation 

efficiencies of turbot (Scophthalmus maximus) or gastric 

evacuation rates of yellow perch (Perca flavescens), 

respectively, the assumption that Pia = P does seem to be a

reasonable. 

When Eq.(2-32) is substituted into Eq.(2-31) and the resulting 

expression is equated to Eq.(2-30), one can verify that. 

Ke Cf (1 
 c  

1   
 f (2-33) ) 

Kf Cp 

where 
 f is the fish's food assimilation efficiency. This 

expression predicts that a fish's chemical assimilation efficiency 

decreases as its whole body chemical burden or concentration 

increases and increases as the whole body concentration of its 

prey increases. Observing these predictions experimentally, 

7















however, is not without problems since the fish's food 

assimilation efficiency can vary significantly with feeding rate, 

food quality, temperature and other factors. Nevertheless, the 

results of studies by Lieb et al. (1974), Gruger et al. (1975) , and 

Opperhuizen and Schrap (1988) which are analyzed and 

discussed in Barber et al. (1991) corroborate these predicted 

tends. Recent studies by Dori et al. (2000) who used in situ 

preparations of channel catfish intestines, have clearly 

established that preexposures to 3,4,3',4'-tetrachlorobiphenyl 

does indeed decrease intestinal uptake rates. 

Muir et al. (1992), Dabrowska et al. (1996), and Fisk et al.(1998) 

have investigated chemical assimilation efficiencies of rainbow 

trout and channel catfish using a model proposed by Bruggeman 

et al. (1981), i.e., 

d Cf �  

f Cp k2 Cf (2-34) dt 

Cf �  

1 exp( k2 t) f C p (2-35) k2 

where  

is a constant assimilation efficiency, f is the fish's 

specific rate of feeding (g/g/d), and k2 is the chemical's apparent 

elimination rate which necessarily must include actual excretion, 

biotransformation, and growth dilution. Eq.(2-35), however, is 

only the solution to Eq.(2-34) when Cf (0) � 0. The general 

solution to Eq.(2-34) is actually 

Cf �  

1 exp( k2 t)
 f C
  

Cf (0) exp( k2 t) p k2 

(2-36) 

 

f C  f C � 

p  p 

exp( k2 t) k2 

Cf (0) k2 

Acknowledging this fact is of paramount importance to interpret 

the results reported by Muir et al. (1992), Dabrowska et al. 

(1996), or Fisk et al. (1998) correctly in light of the fecal 

partitioning model proposed herein. When this solution is 

redifferentiated, one observes that 

 

d Cf � f Cp 

k2 exp( k2 C t) (2-37) f (0) d t k2 

Now let T denote the length of a bioaccumulation experiment in 

which Cf(0)=0 and f and Cp are constant, i.e., such as those 

studies cited above. Also let  

and k2 denote the assimilation 

efficiency and apparent depuration rate that were estimated for 

this experiment. When the experiment is half over, the rate of 

change in the fish’s whole body concentration would be 

calculated by Eq.(2-37) to be 

If one now elects to arbitrary restart time, the bioaccumulation 

dynamics for the second half of the experiment would be 

described by 



ˆ f C ˆ f C 

Cf � 

p  

C 

p 

f (T/2) exp( k

ˆ

2  

) (2-39) k

ˆ

2 k

ˆ

2 

where ˆ and k

ˆ

2 denote updated estimates for the fish’s 

assimilation efficiency and apparent depuration rate for 

0 	 
 	 T/2. This equation can also be differentiated to yield 




d Cf ˆ f Cp Cf (T/2) k

ˆ

2 exp( k

ˆ

2 
 ) (2-40) d 
     

k

ˆ

2 

which can be evaluated at � � 0 to yield 



d Cf ˆ f Cp  

k

ˆ

2 Cf

(T/2) (2-41) d   

0  

For logical as well as mathematically consistency this derivative 

should equal the derivative given by Eq.(2-38), i.e., 



ˆ f Cp  

k

ˆ

2 Cf

(T/2)  

f Cp exp(  k2 T/2) (2-42)  

Solving for ˆ then yields 

 

f Cp exp( T/2)  Cf

(T/2)  

k2 k

ˆ

2 ˆ 

f C  p 

k

ˆ  

2

exp( T/2)  Cf(T/2)  

k2 f C  p 

(2-43) 

k exp( T/2) ˆ

2 1   

k

 

2

exp(  

k2 T/2)  f Cp f C  p k2 

k

ˆ  

2

exp( T/2)  exp( T/2)  

k2 1   k2
 


k2 

This equation shows that unless ˆ

k , chemical assimilation 2 k2

efficiencies estimated for different time  

s and initial whole body 

concentration will be different. Phrased another way, this 

equation implies that the fish’s ability to excrete, biodilute, and 

biotransform chemicals, as measured by ˆ and k2 k , contributes 2 

to the determination of the fish’s realized chemical assimilation 

efficiencies. Specific growth rates and chemical excretion rates 

for fish, however, are generally related to the fish’s body size as 

allometric power functions, i.e., 

2 r W  

1   

where in general  

2 < 0(Barber et al. 1988; Sijm et al. 1993, 

1995; Sijm and van der Linde 1995). Therefore, if any 

d Cf �  

f Cp exp( k2 T/2) (2-38) d t t  T/2 8 
significant growth occurs during the experiment, which is often 

the case, one would not expect that k2 � 

k

ˆ

2 and consequently one � 

would not expect  ˆ . In point of fact one would generally 

expect  > ˆ . Importantly, this simple analysis is corroborated 

by findings of Ram and Gillet (1993) who showed that 

assimilation efficiencies for a variety of organochlorines by 

oligochaetes decreased as chemical exposures progressed. 

In terms of application the above fecal partitioning model is best 

suited to circumstances where its equilibrium assumptions are 

best met such as the case herein where the object is to predict the 

dietary exchange of average individual of an explicit or implicit 

population. A more kinetically based approach may be needed, 

however, when trying to describe the toxicokinetic of individual 

fish. See for example Nichols et al. (1998). 

2.4. Modeling Chemical Biotransformation 

BASS assumes that the metabolism of xenobiotic chemicals in 

fish is a simple first order reaction of the chemical’s aqueous 

phase concentration, i.e., 

M �  C (Pa W) a (2-45) 

where M is the total amount of chemical metabolized (μg / ml), 

 

is the fish’s biotransformation rate (1/day), and (P W) is the a 

volume of the volume of the fish’s aqueous phase. If Eqs. (2-9) 

and (2-45) are used to described the bioconcentration of a 

chemical in fish during a water only exposure without growth, 

then a fish’s whole body concentration would be modeled as 

dCf 1 dBf


dt W dt
  



S k Cf P  

g g a Cf (2-46) C w   

W Kf Kf  

k C (ke u w   k m) Cf

where ku, ke, and km are the fish’s uptake rate, elimination rate, 

and biotransformation rate, respectively, which are often 

reported in the literature. In terms of quantitative structure 

activity relationships (QSARs), one should note that this model 

predictsthat the whole body biotransformation rate km should be 

inversely proportional to the fish’s thermodynamic 

bioconcentration factor Kf which in turn is proportional to the 

chemical’s Kow. This relationship, however, will also be 

influenced by any QSAR dependencies which the fish’s aqueous 

phase biotransformation rate  

might have. See de Wolf et al. 

(1992) and de Bruijn et al. (1993). 

2.5. Modeling Temperature Effects on 

Physiological Rates 

Because temperature effects a fish’s feeding, assimilation, 

respiration, and egestion, a general discussion of how 

temperature modulates these processes is in order before 

describing how BASS actually models fish growth. Although the 

temperature dependence of physiological processes are often 

described using an exponential response equation, e.g., 

k1  k0 e  

(T1  

T0) 

T

(2-47) 

where k0 and k1 are the process’s reaction rates at temperatures 

0 and T1,respectively, such descriptions are generally valid only 

within a range of the organism’s thermal tolerances. In most 

cases, the process’s reaction rate increases exponentially with 

increasing temperature up to a temperature T1 after which it 

decreases. Moreover, in most cases the temperature at which a 

process’s rate is maximal is very close to the organism’s upper 

thermal limit. To address this problem, Thornton and Lessem 

(1978) developed a logistic multiplier to describe the 

temperature dependence of a wide variety of physiological 

processes. Although this algorithm has been used successfully 

in a variety of fish bioenergetic models, BASS uses an 

exponential-type formulation that is assumed to response 

hyperbolically to increasing temperature. Importantly, such 

algorithms can be easily parameterized. 

Let P denote the rate of a physiological process and T1 denote 

the temperature at which this rate is maximal. If this process 

generally exhibits an exponential response to temperature 

changes well below T1, then 

P 	 P0 e 
 

( T  T0 ) (2-48) 

d P  
 

P (2-49) dT 

where P0 is the process’s rate at an appropriate lower-end 

reference temperature T0. To incorporate the adverse effects of 

high temperatures on this process, the right hand side of Eq.(2-

49) can be multiplied by a hyperbolic temperature term that 

approaches unity as temperature decreases below T1, equals zero 

at T1, and becomes increasingly negative as temperatures 

approach the fish’s upper thermal tolerance limit TL = T2. 

Modifying Eq.(2-49) in this fashion subsequently yields 

T  T d P  
 

1 P (2-50) d T T  T2 

whose solution is 

T2  T  

(T2  T1) 

P  P0 e  

(T  T0 ) (2-51) T2  

T0 

Figure 1 displays the predicted temperature response of the 

maximum feeding of a 50 g brown trout (Salmo trutta) based on 

data reported by Elliott (1976b, Tables 2 and 9). For this figure 

9

it is assumed that T0 = (3.8 + 6.6)/2, T1 = 17.8, and T2=25. The 

parameters P0 = 340 and � 

= 0.50 were then calibrated using the 

results of a non-linear least squares analysis as a starting point. 

For other applications of this model see Lassiter and Kearns 

(1974) and Swartzman and Bentley (1979). Note that when T1 = 

T2, the Eq.(2-51) reduces to Eq.(2-48). 

2.6. Modeling Growth of Fish 

Although the preceding formulations of the processes that 

determine the bioaccumulation of chemicals in fish depend on a 

fish’slive weight, BASS does not directly simulate the live weight 

of fish. Instead, it simulates the dry weight of fish as the mass 

balance of feeding, egestion, respiration, and excretion and then 

calculates the fish’s associated wet weight using the following 

relationships 

W  Wa Wd  Wa 

Wl Wo 

(2-52) 

 

2 Pl   

0 W (2-53) 

Pa   0   1 Pl (2-54) 

P 1 Pa  P o   l (2-55) 

where Wa, Wd, Wl, and Wo denotes the fish’s aqueous, dry, lipid, 

and non-lipid organic weights, respectively. Whereas Eqs.(2-52) 

and (2-55) are simply assertions of mass conservation, Eqs. (2­

53) and (2-54) are purely statistical in nature. Although Eq. (2­

53) is assumed because simple power functions of this form 

generally describe a wide variety of morphometric relationships 

for most organisms, the appropriateness of Eq. (2-54) is based 

on the results of numerous field and laboratory studies 

(Eschmeyer and Phillips 1965; Brett 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. 1985; Morishita et al. 1987). These equations yield an 

expression for a fish’s live weigh that is a monotonically 

increasing but non-linear function of the fish’s dry weight. 

BASS calculates a fish’s realized feeding by first estimating its 

maximum ad libitum consumption and then adjusting this 

potential by the availability of appropriate prey as described in 

the next section. Because a wide variety of models and methods 

have been used to describe maximum feeding of fish, BASS is 

coded to allow a user the option of using any one of four 

different models to simulate the feeding of any particular 

age/size class of fish. The first formulation that can be used is a 

temperature-dependent power function 

T2  

c3 (T2  T1) T 

C c1 W c2 c3 (T  T0 ) e (2-56) max  

T2  T0 

where the temperatures T0, T1, and T2 are specific to the fish’s 

feeding. A commonly used alternative to this model is the 

process-based Rashevsky-Holling model that is defined by the 

equations 

C I I max  	 

max 
 

(2-57) dI  

C A E max 
 
 

dt 

	 

where is the fish's ad libitum feeding rate (day-1) that is 

generally a temperature-dependent power function of body 

weight, Im is the maximum amount of food (g(DW)) that the ax 

fish’s stomach/intestine can hold, I is the actual amount of food 

(g(DW)) present in the intestine, and A and E again are the fish's 

assimilation (g(DW)/day) and egestion (g(DW)/day) , respectively 

	 

(Rashevsky 1959; Holling 1966). The feeding rate can be 

estimated using the following equations 

	


M (t)  
 t I M (
 ) d 
 

max 
 (2-58) 0 

dM (t)  	 

I M (t) max 
 

(2-59) dt 


 

M (t) t  

ln 1 
 (2-60) 	 

I max 

where M(t) denotes the total amount of food consumed during 

the interval (0,t] (also see Dunbrack 1988). Although given a 

fish’s gut capacity Imax, satiation meal size Msat, and time t

	 

sat 

required to ingest Msat one can readily calculate , one can also 

simply assume that Msat = 0.95 × Im in which case ax 	 


 

ln(0.05) (2-61) t

sat 

For planktivores BASS can also estimate a fish’s maximum 

ingestion using the clearance volume model 

C   

max Qcl 

q3 (T2  T1) (2-62) T 

q1 W q2 q3 (T  T0 ) T2  Q e cl  

T2  T0 

where  

is the plankton standing stock (g(DW)/L), Qcl is the 

planktivore's clearance volume (L/day), and the temperatures T0, 

T1, and T2 are specific to the fish’s filtering rate. The fourth and 

final option is based on knowing the fish’s projected growth and 

routine respiratory demands. In particular, because assimilation, 

egestion, specific dynamic action, and excretion can be 

calculated as linear functions of feeding and routine respiration 

as discussed below, it is then a straightforward matter to 

calculate a fish’s expected ingestion given its projected growth 

and respiration. When a user elects this feeding option, BASS 

assumes that the fish’s specific growth rate  (day-1) is given by 

10

r

T 

2  

T g3 (T2 T1) 

�  

W 

1 dW  

g1 W g2 g3 (T T0 ) e 

(2-63) dt T2  

T0 

where the temperatures T0, T1, and T2 are specific to the fish’s 

growth rate. See Thomann and Connolly (1984) for additional 

discussion of the use of this feeding model. 

If Eqs. (2-56), (2-62), or (2-63) are used to estimate a fish' s 

maximum consumption, then BASS calculates the fish’s 

assimilation and egestion as a simple fraction of its realized 

ingestion F, i.e., 

A   

f F (2-64) 

E  

1  

f F (2-65) 

where  

f is the fish' s net assimilation efficiency which is a 

weighted average of the fish’s assimilation efficiencies for 

invertebrate, piscine, and vegetative prey. However, when the 

Rashevsky-Holling model is used for this purpose, BASS 

calculates these fluxes by substituting F with a function that 

describes the fish' s pattern of intestinal evacuation. The general 

form of this function is assumed to be 

 

d3 (T T0 ) T2  

T d3 (T2 T1) 

D  d1 I

d2 e 

(2-66) 

d

T2  T0 

The numerical value of this function’s exponent, d2, depends 

both on characteristics of the food item being consumed and on 

the mechanisms that presumably control gastro-intestinal 

motility and digestion (Jobling 1981, 1986, 1987). For example, 

when gut clearance is controlled by intestinal peristalsis, d2 

should approximately equal 1⁄2 since peristalsis is stimulated by 

circumferential pressure exerted by the intestinal contents which, 

in turn, is proportional to the square root of its mass. On the 

other hand, when surface area controls the rate of digestion, d2 

should be approximately either  or unity. If the fish consumes 

a small number of large-sized prey (e.g., a piscivore), d2 =  

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., a planktivore or drift feeder), d2 = 1 is 

more appropriate since total surface area and total volume of 

prey become almost directly proportional to one another. When 

2 = 1, the above Rashevsky-Holling model is analogous to the 

Elliott-Persson model for estimating daily rations of fish (Elliott 

and Persson 1978). Finally, Olson and Mullen (1986) outlined 

a hypothetical, process-based model that even suggests d2 = 0 as 

an appropriate model. 

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   A (2-67) 

where  

is generally on the order of 0.15 to 0.20 (Ware 1975; 

Tandler and Beamish 1981; Beamish and MacMahon 1988). 

In BASS, it is assumed that body weight losses via metabolism 

are due entirely to the respiration of carbon dioxide and the 

excretion of ammonia. A fish’s respiratory losses are therefore 

calculated from its routine oxygen consumption, Or (g O2/day) 

using respiratory quotients RQ (L (CO2 ) respired)/ L (O2) 

consumed) as follows 

R  

12 gC  

moleCO2  

RQ  

22.4 L O2  

moleO2  

O 

moleCO2 22.4 L CO2 moleO2 32 gO2 (2-68) 	 

12  

RQ  

Or 32 

BASS then calculates a fish’s routine oxygen consumption as a 

constant multiple of its basal or standard oxygen consumption 

(Ware 1975) which is specified using the temperature-dependent 

power function 

T2  

b3 (T2 
 T1) T 

Ob 	 

b3 (T 
 T0 ) b1 W b2 e (2-69) T2  

T0 

Although the ammonia excretion could be modeled using an 

analogous function (Paulson 1980; du Preez and Cockroft 

1988a, 1988b), in BASS this flux is formulated 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 	  NC ( R 
 SDA ) (2-70) 

where  

= 17/14 is the ratio of the molecular weight of ammonia 

to that of nitrogen. 

2.7. Modeling Trophic Interactions and Predatory 

Mortalities 

BASS is designed to simulate aquatic food webs in which each 

age class of a species can feed upon other fish species, benthos, 

incidental terrestrial insects, periphyton / attached algae, 

phytoplankton, and zooplankton. The realized feeding of any 

given age class of fish is determined by the maximum or desired 

feeding rate of an individual of that cohort, the cohort's 

population size, and the biomass of prey available to the cohort 

which is the sum of the prey's compartmental biomasses minus 

the biomass of those components which are expected to be 

consumed by other cohorts that are more efficient 

foragers/competitors. BASS ranks the competitive abilities of 

different cohorts using the following assumptions: 

11

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; 

Wanzenböck and Schiemer 1989; Grant and Kramer 1990; 

Miller et al. 1992; Keeley and Grant 1995; Minns 1995). Given 

two differently sized predators of the same potential prey, these 

trends would suggest that the larger predator is more likely to 

encounter that prey than is the smaller. Having encountered the 

prey, the trend for prey handling times to be inversely correlated 

with body size (Werner 1974; Miller et al. 1992) would also 

suggest that the larger predator could dispatch the prey and 

resume its foraging more quickly than the smaller predator. 

ASSUMPTION 2. Unlike benthivores and piscivores, the 

competitive abilities and efficiencies of planktivores are 

inversely related to their body size due to their relative 

morphologies (Lammens et. al. 1985; Johnson and Vinyard 

1987; Wu and Culver 1992; Persson and Hansson 1999). 

Consequently, “large” planktivores only have access to the 

leftovers of “small” planktivores. 

BASS calculates the relative frequencies {..., di ,...} of the 

different prey consumed by a cohort using dietary electivities, 

i.e., 

di  f � i ei di 

(2-71) f

i 

where fi is the relative availability of the i-th prey with respect to 

all other prey consumed by the cohort. These electivities are 

calculated dynamically by BASS using dietary data specified by 

the user and the relative availabilities of the cohort’s prey 

currently predicted by BASS. As described in the discussion of 

BASS' ds iet command (see page 38), BASS allows a user to 

specify a fish' s diet as either a set of fixed dietary frequencies 

{..., dˆ

i ,...}, a set of electivities {..., eˆi ,...}, or a combination of 

fixed frequencies and electivities {..., dˆ

i , ..., eˆj ,...}. In order to 

calculate the cohort’s realized dietary composition, BASS first 

converts all fixed dietary frequencies specified by the user into 

their equivalent electivities using Eq. (2-71) and the current 

relative availabilities {..., f

i ,...} of all potential prey. These 

electivities are then combined with any user specified electivities 

to form a set of unadjusted electivities {..., eˆi ,...} which in 

general must then be converted into a consistent set of realized 

electivities {..., ei ,...}. Using these realized electivities, BASS 

finally calculates the cohort’s realized dietary frequencies using 

1 

ei di � 

f

i (2-72) 

1 ei 

The important step in this computational process is the 

conversion of the unadjusted electivities {..., eˆi ,...} into a set of 

realized electivities {..., ei ,...}. Although this conversion is 

sometimes unnecessary, it is generally needed to insure that the 

sum of the dietary frequencies {..., di ,...} calculated by Eq.(2-

72) equals 1. One can verify that the condition that 

 

guarantees d 1 is i   

f

i 

1  ei  

1 (2-73) 

See Appendix C. When this condition is not satisfied for a set of 

electivities {..., eˆi ,...} and relative prey availabilities {..., f

i ,...}, 

BASS transforms the given electivities using a linear 

transformation that maps ˆ  1 into ei 

 1 and max(..., eˆi e ,...) i  

into an ei < 1. The general form of this transformation is 

ei   

(eˆi  1)  1 (2-74) 

where 0<  <2/(max(..., eˆi ,...) 1). Besides insuring that 



 

d 1, this transformation also preserves the relative i  

preferences represented in the original base set {..., eˆi ,...}. 

Because numerous food web studies have shown that there is 

generally a strong positive correlation between the body sizes of 

piscivorous fish and the forage fish that they consume (Parsons 

1971; Lewis et al. 1974; Timmons et al. 1980; Gillen et al. 1981; 

Knight et al. 1984; Moore et al. 1985; Stiefvater and Malvestuto 

1985; Storck 1986; Jude et al. 1987; Johnson et al. 1988; Yang 

and Livingston 1988; Brodeur 1991; Elrod and O’Gorman 1991; 

Hambright 1991; Juanes et al. 1993; Mattingly and Butler 1994; 

Hale 1996; Madenjian et al. 1998; Margenau et al. 1998; 

Mittelbach and Persson 1998; Bozek et al. 1999), when BASS 

uses the above procedure to calculate piscivorous interactions, 

only a specific size range of forage fish are assumed to be 

available to a piscivorous cohort. More specifically, BASS 

assumes that the body lengths of forage fish available to such a 

cohort are distributed normally with mean 

L L prey    	 predator (2-75) 

BASS estimates the variance of this distribution by assuming that 

the body length of the largest prey typically taken by a piscivore 

approximately equals 50% of its own body length (Juanes 1994). 

If less than 1% of a predator' s prey exceeds this upper limit, the 

variance of the predator’s prey size distribution can be calculated 

from the corresponding standardized Z-score as 

0.5 L  L




 

predator prey (2-76) 2.33 

When BASS calculates the relative frequency di of a forage fish 

species i in a cohort’s diet, the relative availability of that species 

is calculated as the sum of all cohort biomasses whose body 

lengths are less than 0.5 Lpredator minus the biomass of those 

cohorts that are calculated to be consumed by other cohorts that 

12

are more efficient piscivores (see assumption 1 above). If more 

than one age class of species i can be consumed by the cohort, 

the relative frequencies of these age classes sij in the cohort’s 

diet are calculated using the cohort’s prey size distribution . For 

example, let Li1 and Li2 denote the body lengths of two age 

classes of species i that are prey for the cohort. If Pij denotes the 

probabilistic density  L )

2 (Lij prey P � 

1 

exp  

(2-77) ij 2 

2 2  

the relative frequencies of these two age classes in the cohort’s 

diet are calculated to be si1 di (Pi1 /(Pi1  

Pi2 )) and  

s / . If only one age class of a species is i2 di (Pi2 (Pi1  Pi2 ))


vulnerable to the cohort, then sij  

di .


If during the calculation of the dietary frequencies of a 

piscivorous cohort BASS predicts that the cohort’s available prey 

is insufficient to satisfy its desired level of feeding, BASS  

reassigns the cohort’s unadjusted electivities {..., e ,...} in a i 

manner to simulate prey switching. These reassignments as 

based on the following assumption: 

ASSUMPTION 3. When forage fish become limiting, piscivores 

switch to benthic macroinvertebrates or incidental terrestrial 

insects as alternative prey. However, piscivores that must switch 

to benthos or that routinely consume benthos in addition to fish, 

are less efficient benthivores than are obligate benthivores 

(Hanson and Leggett 1986; Lacasse and Magnan 1992; Bergman 

and Greenberg 1994). Consequently, only the leftovers of nonpiscivorous benthivores are available to benthic feeding 

piscivores. If such resources are still insufficient to satisfy the 

piscivores’ metabolic demands, piscivores are assumed to then 

switch to planktivory (Werner and Gilliam 1984; Magnan 1988; 

Bergmann and Greenberg 1994). In this case, piscivores have 

access only to the leftovers of non-piscivorous planktivores. 

Using this assumption, BASS first assigns the cohort’s electivity 

for benthos to 0 regardless of its previous value. BASS also 

reassigns any other electivity which does not equal -1, to 0. 

After BASS has calculated a cohort’s dietary composition, it then 

assigns the realized feeding rate of cohort as 

F  max N  

1 	 

ABj , F max (2-78) e 1 j 
  

where F is the cohort’s maximum or desired individual 

ingestion, N is the cohort’s population size, and ABj is the 

biomass of prey j that is available to that cohort. Using its 

predicted dietary compositions and realized feeding rates, BASS 

then calculates the predatory mortalities for each cohort and nonfish biotic resource. 

max 

2.8. Modeling Non Predatory Mortalities and 

Recruitment 

Numerous studies (Damuth 1981; Peters and Raelson 1984; 

Juanes 1986; Robinson and Redford 1986; Boudreau and Dickie 

1989; Gordoa and Duarte 1992; Randall et al. 1995 Dunham and 

Vinyard 1997; Steingrímsson and Grant 1999) have shown that 

the population densities of vertebrates are generally correlated 

with their mean body size. In particular, 

b N  a W  

(2-79) 

where N is the population density (inds/area) of the species or 

cohort and W is the mean body weight of that species or cohort. 

Although an interspecific analysis of data for a variety of fish by 

Randall et al. (1995) suggests a mean exponent close to unity, 

data reported by Boudreau and Dickie (1989) and Gordoa and 

Duarte (1992) for individual fish species suggest an average 

exponent of approximately 0.75. An expression for a species’ 

total mortality rate can be obtained by differentiating Eq. (2-79) 

as follows 

W  

dN 1 dW  
 b a W  

b  
 b N  

(2-80) dt dt 

where  is the species specific growth rate. Based on this 

equation, one could therefore conclude that a species’ total 

mortality rate is simply μ = b  . Readers interested in detailed 

discussions concerning the underlying process-based 

interpretation and general applicability of this result should 

consult Peterson and Wroblewski (1984) and McGurk (1993, 

1999). Because BASS assumes that the specific growth rates of 

a species are allometric functions of its body sizes, it follows 

that 

2 μ  b  

1 W  

(2-81) 

Also see Lorenzen (1996). Because this equation actually 

includes both a species’ predatory and non-predatory mortality, 

BASS assumes that a species’ non-predatory mortality rate is 

simply some fraction  of μ. In general, this fraction will be 

small for forage fish and large for predatory species. During the 

course of the simulation BASS calculates the daily non-predatory 

mortality each cohort using Eq.(2-81) parameterized with the 

cohort’s current body weight. 

BASS estimates a species’ recruitment by assuming that each 

species turns over a fixed percentage of its potential spawning 

biomass into new young-of-year (YOY). This percentage is 

referred to as the species’ reproductive biomass investment (rbi). 

The species’ spawning biomass is defined to be the total biomass 

of all cohorts whose body length is are greater than or equal to 

a specified minimum value (tl_r0) marking the species’ sexual 

maturation. When reproduction is simulated, the body weight of 

each sexually mature cohort is decremented by its rbi and the 

13

total number of YOY which are recruited into the population as 

a new cohort is estimated by simply dividing the species’ 

spawned biomass by the species’ characteristic YOY body 

weight. Although this formulation does not address the myriad 

of factors known to influence population recruitment, it is 

logically consistent with the spawners abundance model for fish 

recruitment (see Myers and Barrowman(1996) and 

Myers(1997)). 

2.9. Modeling Toxicological Effects 

Narcosis is defined to be any reversible decrease in 

physiological function that is induced by chemical agents. 

Because the potency of narcotic agents was originally found to 

be correlated their olive oil / water partition coefficients (Meyer 

1899; Overton 1901), it was long believed that the principal 

mechanism of narcosis was the disruption of the transport 

functions of the lipid bilayers of biomembranes (Mullins 1954; 

Miller 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 either by changing their conformal 

structure or by changing the configuration or availability of their 

active sites (Eyring et al 1973; Adey et al. 1976; Middleton and 

Smith 1976; Franks and Lieb 1978, 1982, 1984; Richards et al. 

1978; Law et al. 1985; Lassiter 1990). In either case, however, 

the idea that the presence of narcotic chemicals increases the 

physical dimensions of various physiological targets to some 

“critical volume” which renders them inactive is fundamental 

(Abernethy et al. 1988). Consequently, narcotic chemicals can 

be treated as generalized physiological toxicants and narcosis 

itself can be considered to represent baseline chemical toxicity 

for organisms. Although any particular chemical may act by a 

more specific mode of action under acute or chronic exposure 

conditions, all organic chemicals can be assumed to act 

minimally as narcotics (Ferguson 1939; McCarty and Mackay 

1993). 

Studies have shown that for narcotic chemicals there is a 

relatively constant chemical activity within exposed organisms 

associated with any given level of biological activity (Fergusion 

1939; Brink and Posternak 1948; Veith et al. 1983). This 

relationship holds true not only for exposures to a single 

chemical but also for exposures to chemical mixtures. In the case 

of a mixture of chemicals, the sum of the chemical activities for 

each component chemical is constant for a given level of 

biological activity. Because narcotic chemicals can be treated as 

generalized physiological toxicants as noted above, it should not 

be too surprising that the effects of mixtures of chemicals 

possessing diverse specific modes of action not only often 

resemble narcosis but also appear to be additive in terms of their 

toxic effects (Barber et al. 1987; McCarty and Mackay 1993). 

For example, even though most pesticides possess a specific 

mode of action is during acute exposures, the joint action of 

pesticides is often additive and resembles narcosis (Hermanutz 

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 would make concerning the onset of effects, mortalities 

predicted by BASS should signal immediate concern. When the 

total chemical activity of a fish’s aqueous phase exceeds it’s 

calculated lethal threshold, BASS assumes that the fish dies and 

then eliminates that fish’s age class from further consideration. 

The total chemical activity of a fish’s aqueous phase is simply 

the sum of the fish’s aqueous phase chemical activity for each 

chemical. BASS calculates the aqueous phase chemical activity of 

each chemical using the following formulae 

A �  M a a a 

C a M � 

a 103 MW (2-82) 

Cf C � 

a Kf 

where A is the chemical' s aqueous activity,  

is the chemical' s 

aqueous activity coefficient (L/mol) which is the reciprocal of its 

sub-cooled liquid solubility, Ma is the chemical’s molarity within 

the aqueous phase of the fish, and MW is the chemical’s 

molecular weight (g/mol). 

a a 

BASS estimates the lethal chemical activity threshold for each 

species as the geometric mean of the species’ LA50, i.e., the 

ambient aqueous chemical activity causes 50% mortality in an 

exposed population. These lethal thresholds are calculated using 

the above formulae with user-specified LC50 ' substituted for C . 

These calculations are based on two important assumptions. The 

first assumption is that the exposure time associated with the 

specified LC50 is sufficient to allow almost complete chemical 

equilibration between the fish and the water. The second 

assumption is that the specified LC50 is the minimum LC50 that 

kills the fish during the associated exposure interval. 

Fortunately, most reliable LC50 ' satisfy these two assumptions. 

See Lassiter and Hallam (1990) for a comprehensive model 

based analysis of these issues. 

a

Three points should be mentioned regarding the above approach 

to modeling ecotoxicological effects. Firstly, it should be noted 

that for narcotic chemicals this approach is analogous to the 

14

toxic unit approach for evaluating the toxicity of mixtures 

(Calamari and Alabaster 1980; Könemann 1981a, 1981b; 

Hermens and Leeuwangh 1982; Hermens et al. 1984a, 1984b, 

1985a, 1985b, 1985c; Broderius and Kahl 1985; Dawson 1994; 

Peterson 1994). Secondly, the approach is also analogous to the 

critical body residue (CBR) and total molar body residue (TBR) 

approaches proposed by McCarty and Mackay (1993), Verhaar 

et al. (1995), and van Loon et al. (1997). Lastly, although 

sublethal effects are not presently modeled by BASS, BASS’s 

LC

simulation results can be used to indicate when sublethal effects 

that are induced by narcotic agents would be expected to occur. 

Results reported by Hermens et al (1984a) indicate that for 

Daphnia the ratio of the EC50 for reproductive impairment to the 

50 is generally on the order of 0.15 - 0.30 for chemicals whose 

log K range from 4 to 8. For individual growth inhibition, ow 

however, the mean EC50 to LC50 ratio for Daphnia in 16 day 

chronic exposures was approximately 0.77 (Hermens et at. 

1984a, 1985a). Also see Roex et al. (2000). 

15

C 

C C C I l 

Table 1. Symbols used for model development 

B chemical burden in whole fish ( g) �L fish’s body length (cm) 

chemical concentration in aqueous fraction of the fish ( a g/mL) M metabolism of chemical ( �g �/s) 

CB chemical concentration in bulk interlamellar water ( g/mL) N population density (inds/ha) �

chemical concentration in egesta/feces ( e g/mL) NGz Graetz number (dimensionless) = (l D)/(V r2 ) �

Cf chemical concentration in whole fish ( g/g(FW)) NSh Sherwood number (dimensionless) = (k h) D / m

ha-1 day-1) 

�

C ia chemical concentration in aqueous fraction of intestinal contents ( g/mL) NM non-predatory mortality (inds �C io chemical concentration in organic fraction of intestinal contents ( g/mL) P fraction of whole fish that is aqueous (dimensionless) a �Cl chemical concentration in lipid ( g/g(FW)) Pia fraction of intestinal contents that is aqueous (dimensionless) �

chemical concentration in non lipid organic matter ( o g/g(FW)) Pio fraction of intestinal contents that is organic (dimensionless) �chemical concentration in environmental water ( w g/mL) Pl fraction of whole fish that is lipid (dimensionless) �

D aqueous diffusion coefficient (cm2/s) PM predatory mortality (inds d interlamellar distance (cm) P fraction of whole fish that is non-lipid organic matter (dimensionless) 

ha-1 day-1) 

o 



E egestive flux (g(FW)/day) O2 oxygen consumption (mg/s)
EX excretory flux (g(FW)/day) Q ventilation volume (cm3/s)
F feeding flux (g(FW)/day) R routine respiratory flux (g(FW)/day)
h hydraulic radius of interlamellar channels (cm) = 0.5d SDA specific dynamic action (g(FW)/day)


mass of food resident in the intestine (g(FW)) Sg total gill surface area (cm2) 

Jg net chemical exchange across the gills ( �g /s) T temperature (Celsius) Ji net chemical exchange across the intestine ( �g /s) V average velocity of interlamellar flow (cm/s) Kl partition coefficient between generic lipid and water (dimensionless) W weight of fish (g(FW) or g(DW)) K partition coefficient for fecal matter (dimensionless) Xg cross sectional pore area of the gill (cm2) e Kf thermodynamic bioconcentration factor (dimensionless) assimilation efficiency of chemical (dimensionless) c K partition coefficient between organic carbon and water (dimensionless) oc f assimilation efficiency of food (dimensionless) K ow partition coefficient between n-octanol and water (dimensionless) K partition coefficient between non-lipid organic matter and water (dimensionless) o k conductance in interlamellar water (cm/s) gk conductance of the gill membrane (cm/s) 

lamellar length (cm) 

m 





 

fish's specific growth rate (day-1) = W-1 dW/dt solution viscosity (poise) molar volume (cm3 mol) /lamellar density (lamellae/mm) 

16 
Figure 1. Application of Eq.(2-51) to describe the temperature dependence of the maximum daily consumption of brown trout (Salmo 

trutta) based on Elliott (1976b, Tables 2 and 9). 

17

3. Model Parameterization


Because reliable application of a model depends not only on the 

validity of its formulation but also on its parameterization, 

important aspects of parameterizing the above equations are now 

discussed. 

3.1. Parameterizing Kf 

Superficially, estimation of a fish's thermodynamic 

bioconcentration factor Kf via Eq. (2-7) appears to require a great 

deal of information. This task, however, is much simpler than 

it first appears. For example, given a fish’s lipid fraction (see 

Eq.(2-53)), it is a straightforward matter to calculate the fish’s 

aqueous fraction using Eq. (2-54). Having done so, one can then 

immediately calculate the fish’s non-lipid organic fraction since 

the sum of Pa, Pl, and Po must be unity (i.e., Eq. (2-55)). 

For an organic chemical the partition coefficients Kf and K can o 

be estimated using the chemical’s octanol/water partition 

coefficient K . Although triglycerides are the principal storage ow

lipid of fish and it would seem reasonable to estimate Kl using 

a triglyceride/water partition coefficient, BASS assumes that Kl 

ow o identically equals K . To estimate K BASS assumes that a fish's 

non-lipid organic matter is equivalent to organic carbon and uses 

Karickhoff's (1981) regression between organic carbon/water 

partition coefficients (Koc), and Kow to estimate this parameter. 

Specifically, �

Ko � Koc 0.411 Kow (3-1) 

For metals or metallo-organic compounds such as 

methylmercury the chemical’s lipid partition coefficient Kl can 

again be assumed to equal its octanol/water partition coefficient 

K . A metal’s distribution coefficient into non-lipid organic ow

matter, however, cannot be estimated using the Koc relationship 

given above. For example, whereas the Kow of methylmercury at 

physiological pH’s is on the order of 0.4 (Major et al. 1991), its 

distribution coefficient into environmental organic matter is on 

the order of 104 - 106 (Benoit et al. 1999a, 1999b). O’Loughlin 

et al. (2000) report similar discrepancies for organotin 

compounds. In general distribution coefficients for metals into 

fecal matter should be assigned values comparable to those used 

to model the environmental fate and transport of metals whereas 

metal distribution coefficients for metals into the non-lipid 

organic matter of fish should be assigned values up to an order 

of magnitude higher to reflect the increased number and 

availability of sulfhydryl binding sites. 

3.2. Parameters for Gill Exchange 

To parameterize the gill exchange model the fish' s total gill area, 

mean interlamellar distance, and mean lamellar length must be 

specified. In general, each of these morphological variables is 

dependent on the fish' s body size according to the allometric 

functions, 

s1 W s2 S � 

(3-2) g 

d � d1 W d2 (3-3) 

l � l

1 W l

2 (3-4) 

Although many authors have reported allometric coefficients and 

exponentsfor total gill surface areas, parameters for the latter are 

seldom available. Parameters for fish' s mean interlamellar 

distance, however, can be estimated if the allometric function for 

the density of lamellae on the gill filaments,  

(number of 

lamellae per mm of gill filament), i.e., 

   

W 2 (3-5) 1 

is known. Fortunately, lamellar densities, like total gill areas, are 

generally available in the literature. See Tables 2-4. BASS 

estimates d1 and d2 from  

1 and  2 using the inter-specific regression (n=28, r=-0.92) 

d  0.118 

1.19  

(3-6) 

To overcome the scarcity of published morphometric 

relationships for lamellar lengths (see Table 5), BASS uses the 

default inter-specific regression (n=90, r=0.92) 

l  0.0188 W 0.294 (3-7) 

Both of the preceding regressions are functional regressions 

rather than simple linear regressions (Rayner 1985; Jensen 

1986); the data used for their calculation were drawn from 

Saunders (1962), Hughes (1966), Steen and Berg (1966), Muir 

and Brown (1971), Umezawa and Watanabe (1973), Galis and 

Barel (1980), and Hughes et al. (1986). 

To calculate lamellar Graetz and Sherwood numbers, BASS estimates a chemical's aqueous diffusivity (cm2

/s), using the 

empirical relationship, 

D  2.101×10 7  	 1.4 
  0.589  

(3-8) 

where 
 

(cm3

/mol) is the chemical's molar volume (Hayduk and 

Laudie 1974). The diffusivity of chemicals through the gill 

membrane which is needed to estimate the membrane’s 

permeability k is then assumed to equal one half of the 

chemical’s aqueous diffusivity (Piiper et al. 1986; Barber et al. 

1988; Erickson and McKim 1990). The other quantity needed to 

estimate k is the thickness of the gill’s water-blood barrier. 

m 

m 

Based on the studies summarized in Table 6, BASS assumes a 

default water-blood barrier thickness of approximately 0.0029 

cm for all fish species and then calculates km as the ratio of the 

chemical’s membrane diffusivity to the thickness of the gill’s 

water-blood barrier. These assumptions imply that 

18

�

NSh 0.0116  

1 d (3-9) 3.3. Bioenergetic and Growth Parameters 

To calculate ventilation/perfusion ratios BASS estimates the 

ventilation volumes (ml/hr) of fish from their oxygen 

consumption rates assuming an extraction efficiency of 60% and 

a saturated dissolved oxygen concentration (see Eq.(2-12)). 

Perfusion rates (ml/hr) are estimated using �

Qp (0.23 T 

0.78) 1.862 W 0.9 (3-10) 

as the default for all species. Although this expression, in units 

of L/kg/kr, was developed by Erickson and McKim (1990) for 

rainbow trout (Oncorhynchus mykiss), it has been successfully 

applied to other fish species (Erickson and McKim 1990; Lien 

and McKim 1993; Lien et al.1994). 

The eigenvalues and bulk mixing cup coefficients needed to 

parameterize Eq.(2-28) are interpolated internally by BASS from 

matrices of tabulated eigenvalues and mixing cup coefficients 

which encompass the range of Sherwood numbers (i.e., 

1 < N < 10) and ventilation/perfusion ratios (i.e., Sh 

1 < Qv / Qp < 20) that are typical for fish (Hanson and Johansen 

1970; Barron 1990; McKim et al. 1994; Sijm et al. 1994). See 

Figures 2-5. 

In general parameterization of the physiological processes used 

by BASS to simulate fish growth poses no special problems since 

the literature abounds with studies that can be used for this 

purpose. Table 7 presents a very brief and cursory survey of data 

sources that can be used to parameterize BASS for a number of 

common and important fish species. The database that is 

distributed with the Wisconsin Bioenergetics Fish Model 

(Hanson et al. 1997) can also be used for this purpose. In 

addition to these sources, however, the reader should become 

familiar with Carlander’s classic three volume work that 

summarizes allometric, growth, and natural history data for 

hundreds of North American fish species. See Carlander (1969, 

1977, 1997). For oxygen consumption data the reader should 

also be aware of the computerized OXYREF database that has 

been complied by Thurston and Gerke (1993) . This database 

can be downloaded from the USEPA Center for Exposure 

A s s e s s m e n t M o d e l i n g w e b s it e a t 

http://www.epa.gov/ceampubl/oxyref.htm. 

19

-- --

-- --

-- -- --

-- --

-- --

-- --

-- --

-- --

-- --

Table 2. Summary of allometric coefficients and exponents for gill area and lamellar density for freshwater bony fishes and agnatha. 

species s1 

Acipenser transmontanus 3.50 

Botia dario 10.5 

Botia lohachata 9.13 

Catostomus commersoni 11.2 

Cirrhinus mrigala 11.8 

Comephorus dyoowski 2.15 

Cottocomephorus grewingki 6.56 

Cottocomephorus inermis 7.42 

Cottus gobio 7.20 

Cottus gobio 1.35 

Ctenopharyngodon idella 9.44 

Cyprinus carpio 8.46 

Esox lucius 0.274 

Fundulus chrysotus 

Gambusia affinis 2.47 

Glossogobius giuris 12.6 

Hoplias lacerdae 4.92 

Hoplias malabaricus 1.26 

Hoplias malabaricus 0.731 

Ictalurus nebulosus 4.98 

Ictalurus punctatus 

Lampetra fluviatilis 24.1 

Lampetra planeri 23.9 

Leiopotherapon unicolor 4.68 

Lepomis macrochirus 

Macrognathus aculeatum 2.17 

Micropterus dolomieui 7.36 

Mystus cavasius 6.17 

Oncorhynchus mykiss 1.84 

Oncorhynchus mykiss 3.15 

Oncorhynchus tshawytscha 7.13 

s2 f1 f2 source 

0.849 15.3 -0.0475 Burggren et al. (1979) 

0.716 41.0 -0.0460 Singh et al. (1988) 

0.700 39.0 -0.0055 Sharma et al. (1982) 

0.587 25.2 -0.109 Saunders (1962) 

0.816 63.2 -0.129 Roy and Munshi (1986) 

0.675 Jakubowski (1993) 

0.91 24.6 -0.150 Jakubowski et al. (1995) 

0.918 22.6 -0.110 Jakubowski et al. (1995) 

0.849 Jakubowski et al. (1995) 

1.29 21.8 -0.126 Liszka (1969) and Starmach (1971) 

0.774 33.0 -0.0513 Jakubowski (1982) 

0.794 32.2 -0.0787 Oikawa and Itazawa (1985) 

1.24 78.6 -0.222 de Jager et al. (1977) and 

1.18 Burnside (1976) 

0.842 Murphy and Murphy (1971) 

0.516 Singh and Munshi (1985) 

0.81 29.0 -0.06 Fernandes et al. (1994) 

1.14 35.0 -0.090 Fernandes et al. (1994) 

1.25 29.5 -0.0600 Fernandes and Rantin (1985) 

0.728 15.9 -0.0917 Saunders (1962) 

10.2 -0.056 Barber (2000) 

1.03 31.0 -0.123 Lewis and Potter (1976) 

0.689 28.3 -0.117 Lewis and Potter (1976) 

1.04 20.6 -0.0870 Gehrke (1987) 

20.1 -0.098 Barber (2000) 

0.733 41.9 -0.0690 Ojha and Munshi (1974) 

0.819 30.0 -0.0615 Price (1931) 

0.915 40.2 -0.0970 Ojha et al. (1985) 

1.13 Niimi and Morgan (1980) 

0.932 27.5 -0.0639 Hughes (1984) 

0.922 Romough and Moroz (1990) 

20

-- --

-- --

Orechromis alcalicus 11.1 0.789 38.4 -0.143 Hughes (1995) 

Orechromis niloticus 6.35 0.777 32.9 -0.0545 Kisia and Hughes (1992) 

Oryzias latipes 4.65 0.446 43.5 0.0 Umezawa and Watanabe (1973) 

Piaractus mesopotamicus 5.65 0.769 40.2 -0.033 Severi et al. (1997) 

Plagioscion squamosissimus 12.0 0.70 37.0 -0.07 Mazon et al. (1998) 

Pomoxis nigromaculatus 18.4 -.074 Barber (2000) 

Prochilodus scrofa 16.2 0.72 43.0 -0.12 Mazon et al. (1998) 

Stizostedion vitreum 0.796 1.13 Niimi and Morgan (1980) 

Tinca tinca 28.5 0.522 20.3 0.0160 Hughes (1972) 

Tinca tinca 8.67 0.698 25.5 -0.0300 Hughes (1972) 

21

Table 3. Summary of allometric coefficients and exponents for gill area and lamellar density for cartilaginous and marine boney 

fishes. 

species s1 s2 f1 f2 source 

Acanthopagrus australis 2.40 0.788 -- -- Roubal (1987)


Alopias vulpinus 2512. 0.410 229. -0.340 Emery and Szczepanski (1986)


Blennius pholis 7.63 0.849 28.3 -0.139 Milton (1971)


Carcharodon carcharias 42.7 0.770 27.5 -0.150 Emery and Szczepanski (1986)


Carcharhinus obscurus 6.17 0.880 33.8 -0.160 Emery and Szczepanski (1986)


Carcharhinus plumbeus 24.5 0.740 23.4 -0.130 Emery and Szczepanski (1986)


Coryphaena hippurus 52.1 0.713 33.8 -0.0360 Hughes (1972)


Fundulus similis -- 0.850 -- -- Burnside (1976)


Isurus oxyrinchus 57.5 0.740 50.0 -0.200 Emery and Szczepanski (1986)


Katsuwonus pelamis 52.2 0.850 59.0 -0.0759 Muir and Hughes (1969)


Morone saxatilis -- -- 17.0 -0.069 Barber (2000)


Opsanus tau 5.61 0.790 16.0 -0.0750 Hughes and Gray (1972)


Platichthys flesus 6.36 0.824 -- -- Hughes and Al-Kadhomiy (1986)


Prionace glauca 5.50 0.880 12.9 -0.0900 Emery and Szczepanski (1986)


Scomber scombrus 4.24 0.997 27.1 0.0230 Hughes (1972)


Scyliorhinus canicula 2.62 0.961 17.1 -0.0710 Hughes (1972)


Scyliorhinus stellaris 6.21 0.779 30.3 -0.167 Hughes et al. (1986)


Seriola quinqueradiata 22.9 0.686 38.5 -0.0419 Kobayashi et al. (1988)


Thunnus thynnus 24.4 0.901 63.2 -0.0938 Muir and Hughes (1969)


Torpedo marmorata 1.17 0.937 34.2 -0.167 Hughes (1978)


22

Table 4. Summary of allometric coefficients and exponents for gill area and lamellar density for air-breathing fishes. 

species s1 s2 f1 f2 source 

Anabas testudineus 5.56 0.615 36.5 -0.152 Hughes et al. (1973)


Boleophthalmus boddaerti 2.81 0.709 24.6 -0.0830 Niva et al. (1981)


Boleophthalmus boddaerti 0.927 1.05 26.6 -0.229 Hughes and Al-Kadhomiy (1986)


Boleophthalmus boddaerti 6.79 0.481 23.1 -0.0307 Low et al. (1990)


Channa punctata 4.70 0.592 36.0 -0.138 Hakim et al. (1978)


Clarias batrachus 2.28 0.781 25.4 -0.0830 Munshi et al. (1980)


Clarias mossambicus 0.958 0.971 30.7 0.0909 Maina and Maloiy (1986)


Cobitis taenia 4.67 0.864 45.5 0.0 Robotham (1978)


Hoplerythrinus unitaeniatus 5.99 0.66 48.0 -0.16 Fernandes et al. (1994)


Hypostomus plecostomus 4.36 0.666 17.3 0.081 Perna and Fernandes (1996)


Lepidocephalichthys guntea 4.94 0.745 45.0 -0.221 Singh et al. (1981)


Lepisosteus oculatus 3.35 0.753 18.1 -0.0476 Landolt and Hill (1975)


Lepisosteus osseus 4.77 0.699 20.9 -0.0691 Landolt and Hill (1975)


Lepisosteus platostomus 3.01 0.793 15.3 -0.0236 Landolt and Hill (1975)


Noemacheilus barbatulus 3.60 0.577 36.4 0.0 Robotham (1978)


Periophthalmodon schlosseri 3.00 0.934 27.0 -0.0484 Yadav et al. (1990)


Periophthalmodon schlosseri 1.00 0.931 47.9 -0.0518 Low et al. (1990)


Periophthalmus chrysospilos 0.976 0.958 30.2 -0.237 Low et al. (1990)


Rhinelepis strigosa 6.25 0.757 12.3 0.020 Santos et al. (1994)


Saccobranchus fossilis 1.86 0.746 31.6 -0.0950 Hughes (1972)


23

Table 5. Summary of coefficients and exponents for lamellar lengths. 

species 

Hoplias lacerdae 

Hoplias malabaricus 

Hoplerythrinus unitaeniatus 

Ictalurus punctatus 

Lepomis macrochirus 

Morone saxatilis 

Piaractus mesopotamicus 

Pomoxis nigromaculatus 

Rhinelepis strigosa 

l1 l2 

0.012 0.23 

0.006 0.36 

0.014 0.22 

0.00465 0.265 

0.00364 0.234 

0.00474 0.202 

0.0069 0.223 

0.00255 0.257 

0.0422 0.231 

source


Fernandes et al (1994)


Fernandes et al (1994)


Fernandes et al. (1994)


Barber (2000)


Barber (2000)


Barber (2000)


Severi et al. (1997)


Barber (2000)


Santos et al. (1994)


24

Table 6. Summary of studies reporting water-blood barrier thickness for freshwater and marine fishes. 

source species 

Dube and Munshi (1974) Anabas testudineus 

Hughes (1972) Tinca tinca 

Hughes and Morgan (1973) various species 

Hughes and Umezawa (1983) Phrynelox tridens, Seriola quinqueradiata 

Hughes et al. (1986) Scyliorhinus stellaris 

Kobayashi et al. (1988) Seriola quinqueradiata 

Munshi et al. (1980) Clarias batrachus 

Ojha and Munshi (1974, 1976) Macrognathus aculeatum 

Ojha et al. (1982) Garra lamta 

Ojha et al. (1985) Mystus cavasius 

Piiper et al. (1986) Scyliorhinus stellaris 

Roy and Munshi (1987) Cirrhinus mrigala 

Sharma et al. (1982) Botia lohachata 

Singh and Munshi (1985) Glossogobius giuris 

Singh et al. (1981) Lepidocephalichthys guntea 

Singh et al. (1988) Botia dario 

Steen and Berg (1966) various species 

Stevens (1992) Sciaenops ocellatus 

Tuurala et al. (1998) Anguilla anguilla 

25

Table 7. Sources of bioenergetic and growth for selected fish species. 

species source 

Alosa pseudoharengus Stewart and Binkowski (1986) 

Ambloplites rupestris Roell and Orth (1993) 

Ameiurus sp. Glass (1969), Campbell and Branson (1978) 

Ctenopharyngodon idella Wiley and Wike (1986) 

Cyprinodon sp. Nordlie et al. (1991), Jordan et al. (1993) 

Cyprinus carpio Glass (1969), Oikawa and Itazawa (1984), Garcia and Adelman (1985) 

Dorosoma cepedianum Pierce et al. (1981), Drenner et al. (1982) 

Esox lucius Diana (1982a, 1982b), Salam and Davies (1994) 

Gambusia affinis Murphy and Murphy (1971), Shakuntala and Reddy (1977), Mitz and Newman (1989) 

Lepomis sp. Wohlschlag and Juliano (1959), O’Hara (1968), Pierce and Wissing (1974), El-Shamy (1976), 

Evans (1984) 

Micropterus salmoides Beamish (1970, 1974), Niimi and Beamish (1974), Tandler and Beamish (1981) 

Micropterus dolomieu Roell and Orth (1993) 

Morone saxatilis Hartman and Brandt (1995a) 

Oncorhynchus mykiss Kutty (1968), Rao (1968), Staples and Nomura (1976), Muller-Feuga et al. (1978), Grove et al. 

(1978), Rand et al. (1993) 

Oncorhynchus nerka Brett (1971), Beauchamp et al. (1989), Stewart and Ibarra (1991) 

Oncorhynchus tshawytscha Stewart and Ibarra (1991) 

Osmerus mordax Lantry and Stewart (1993) 

Perca flavescens Norstrom et al. (1976), Kitchell et al. (1977), Post (1990), Rose et al. (1999), Schaeffer et al. 

(1999) 

Phoxinus phoxinus Wootton et al. (1980), Cui and Wootton (1988) 

Pimephales promelas Wares and Igram (1979), Duffy (1998) 

Ptychocheilus oregonensis Petersen and Ward (1999) 

Pungitius pungitius Cameron et al. (1973) 

Pylodictis olivaris Roell and Orth (1993) 

Salmo trutta Glass (1969), Elliott (1972, 1975a, 1975b, 1976b) 

Salvelinus namaycush Stewart et al. (1983), Thomann and Connolly (1984) 

Stizostedion canadense Minton and McLean (1982) 

Stizostedion vitreum vitreum Kitchell et al. (1977), Tarby (1980), Madon and Culver (1993), Rose et al. (1999) 

26

27

Figure 2. First eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
28

Figure 3. Second eigenvalue for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
29

Figure 4. First bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
30

Figure 5. Second bulk mixing cup coefficient for Eq.(2-28) as a function of gill Sherwood number and ventilation/perfusion ratio.
4. BASS User Guide


Although BASS versions 1.0 and 1.1 were written in Fortran 77, 

BASS version 2.0 and higher is coded in Fortran 95. The model 

enables users to simulate the population and bioaccumulation 

dynamics of age-structured fish communities using a temporal 

and spatial scale of resolution of a day and a hectare, 

respectively. BASS currently ignores the migration of fish into 

and out of this simulated hectare. The duration of any species’ 

age class can be specified as either a month or a year. This 

flexibility enables users to simulate small, short-lived species 

such as daces, live bearers, and minnows with larger, long-lived 

species such as bass, perch, sunfishes, and trout. The 

community’s food web is specified by defining one or more 

foraging classes for each fish species based on either body 

weight, body length, or age. The user then specifies the dietary 

composition of each of these foraging classes as a combination 

of benthos, incidental terrestrial insects, periphyton, 

phytoplankton, zooplankton, and/or other fish species including 

its own. Presently the standing stocks of all nonfish prey are 

handled only as external forcing functions rather than as 

simulated state variables. 

Although BASS was developed to simulate the bioaccumulation 

of chemical pollutants within a community or ecosystem context, 

it can also be used to simulate population and community 

dynamics of fish assemblages that are not exposed to chemical 

pollutants. For example, in its present form BASS could be used 

to simulate the population and community dynamics of fish 

assemblages that are subjected to altered thermal regimes that 

might be associated with a variety of hydrological alterations or 

industrial activities. BASS could also be used to investigate the 

impacts of exotic species or sport fishery management programs 

on population or community dynamics of native fish 

assemblages. 

The model’s output includes: 

� 

Summaries of all model input parameters and 

simulation controls. 

� 

Tabulated annual summaries for the bioenergetics of 

individual fish by species and age class. 

� 

Tabulated annual summaries for the chemical 

bioaccumulation within individual fish by species and 

age class. 

� 

Tabulated annual summaries for the community level 

consumption, production, and mortality of each fish 

species by age class. 

� 

Plotted annual dynamics of selected model variables as 

requested by the user. 

BASS version 2.1 is still a beta test version. Please report any 

comments, criticisms, problems, or suggestions regarding the 

model software or user manual to 

Craig Barber


Ecosystems Research Division


U.S. Environmental Protection Agency 

960 College Station Road 

Athens, GA 30605-2700 

office: 706-355-8110 

FAX: 706-355-8104 

e-mail: barber.craig@epa.gov 

4.1. Summary of New Features Available in BASS 

version 2.1 

The following features that were unavailable in BASS versions 

1.x are now active: 

� 

There are now no restrictions to the number of 

chemicals that can be simulated. 

� 

There are now no restrictions to the number of fish 

species that can be simulated. 

� 

There are now no restrictions to the number of cohorts 

that fish species may have. 

� 

There are now no restrictions to the number of feeding 

classes that fish species may have (see the command / 

FEEDING_OPTIONS). � 

There are now no restrictions to the number of foraging 

classes that fish species may have (see the command / 

ECOLOGICAL_PARAMETERS). � 

Improved 3-dimensional and 2-dimensional plots of 

selected state variables are available using the software 

package DISLIN. 

BASS’s output tabulations have also been reformatted, and 

several input commands have been given new syntax. 

New features of BASS version 2.1 that were unavailable in 

version 2.0 include: 

31

� 

The ability to integrate BASS’s differential equations 

using either a simple Euler method or a fifth-order 

Runge-Kutta method with adaptive step sizing. In BASS 

version 2.1 the default method of integration is the 

Runge-Kutta method. 

� 

The ability to simulate biotransformation of chemicals 

with or without daughter products. 

Regarding BASS’s Euler and Runge-Kutta integrators, the user 

should realize that these methods offer the user two distinctly 

different options with respect to software performance and 

execution. Although Euler methods often allow for fast model 

execution, these methods cannot assess the accuracy of their 

integration. Runge-Kutta methods, on the other hand, can 

monitor the accuracy of their integration but at the cost of 

increased execution time. Fortunately, however, this additional 

computational burden can often be significantly reduced by 

employing adaptive step sizing. BASS’s Runge-Kutta integrator 

is patterned on the fifth-order Cash-Karp Runge-Kutta algorithm 

outlined by Press et. al. (1992). 

4.2. Input File Structure 

The general structure of a BASS' s input file is as follows 

/ command1 argument(s)


/ command2 argument(s)


 

/ commandn argument(s)


/ end


The leading slash (/) identifies the line as a command. Blanks or 

tabs before or after the slash are not significant. The keyword or 

phrase (e.g., commandn ) that follows each slash identifies the 

type of data being specified by that record. Keywords must be 

spelled in full without embedded blanks and must be separated 

from the record's remaining information by at least one blank or 

tab. Argument may be an integer (e.g., 7), a real number (e.g., 0, 

3.7e-2, 1.3, etc.), or a character string. If a command allows 

multiple arguments, each argument must be separated by a 

semicolon. Commands may be continued by appending an 

ampersand (&) to the line, e.g., the following two commands 

lines are equivalent 

/ command	 arg1; arg2; arg3; &


arg4; arg5; arg6


/ command	 arg1; arg2; arg3; arg4; arg5; arg6 

Because each record is transliterated to lower case before being 

decoded, the case of the input file is not significant. Likewise, 

spacingwithin a command is not significant because consecutive 

blanks or tabs are collapse into a single blank. The maximum 

length of a command line, including continuation lines, is 1024 

characters. 

An exclamation mark (!) in the first column of a line identifies 

the line as a comment. An exclamation mark can also be used 

anywhere in the record field to start an end-of-line comment, i.e., 

the remainder of the line, including the exclamation mark, will 

be ignored. 

Commands are broadly classified into three categories: 

simulation control parameters, chemical parameters, and fish 

parameters. Simulation control parameters provide information 

that is applicable to the simulation as a whole, e.g., length of the 

simulation, the ambient water temperature, nonfish standing 

stocks, and output options. Chemical parameters specify not only 

the chemical's physico-chemical properties (e.g., the chemical's 

molecular weight, molecular volume, n-octanol/water partition 

coefficient, etc.) but also exposure concentrations in the 

environment(i.e., in water, sediment, benthos, insects, etc.). Fish 

parametersidentify the fish's taxonomy (i.e., genus and species), 

feeding and metabolic demands, dietary composition, predatorprey relationships, gill morphometrics, body composition, initial 

weight, initial whole body concentrations for each chemical, and 

initial population sizes. In the following sections, these 

commands are described alphabetically by class. 

The last command in any BASS input file must be /END. This 

command terminates program input and any text/commands 

following it will be ignored. BASS checks the syntactical 

accuracy of each input command as it is read. If no syntax errors 

are encountered, BASS then checks the specified input parameters 

for completeness and internal inconsistency. 

To facilitate easier data management when analyzing multiple 

simulations of similar scenarios, a user can also specify blocks 

of BASS input commands using include statements of the form 

# include ‘filename’ 

For example, a BASS input file that has all of its chemical and 

fish data stored in separate files might appear as follows 

! 

! file: example file with include statements 

! 

/simulation_control 

/ command argument! simulation control command 1 

/ command argument! simulation control command 2 

/ command argument! simulation control command 3 

# include 'data_for_chemical_1' 

32

# include 'data_for_chemical_2'


# include 'data_for_fish_1'


# include 'data_for_fish_2'


# include 'data_for_fish_3'


# include 'data_for_fish_4'


/ end


Users are strongly recommended to make use of BASS’s include 

file capabilities. A recommended file and subdirectory structure 

for using and managing BASS include files is discussed in detail 

in Section 4.4. 

4.2.1. Simulation Control Commands 

These commands establish the length of the simulation and 

BASS’s integration step, the ambient water temperature, the 

availability of benthos, incidental terrestrial insects and 

plankton, the community’s water level, and various output 

options. These data are specified by the following block of 

twelve commands 

/ SIMULATION_CONTROL 

/ HEADER string 

/ LENGTH_OF_SIMULATION string 

/ MONTH_T0 string 

/ NSTEPS integer 

/ TEMPERATURE string 

/ WATER_LEVEL string 

/ BIOTA string1; ... ;stringn 

/ ANNUAL_OUTPUTS integer 

/ ANNUAL_PLOTS string1; ...; stringn 

/ SUMMARY_PLOTS string1; ...; stringn 

/ FGETS 

The command /SIMULATION_CONTROL must be the first 

command in the block since it identifies the start of these data. 

The order of the remaining commands, however, is not 

significant. The use of these commands will now be described 

in alphabetical order. See Appendix D for an example of the use 

of these commands. 

� 

/ANNUAL_OUTPUTS integer 

This command specifies the time interval, in years, between 

BASS’s annual tabulated and plotted outputs. This number must 

be an non-negative integer. BASS assumes a default value of zero 

which signifies that no annual outputs will be generated. This 

command is optional. 

� 

/ANNUAL_PLOTS string1 ; ... ; stringn 

This command specifies the variables whose annual dynamics 

will be plotted for the years specified by command 

/ANNUAL_OUTPUTS. The options may be specified one per card, 

or all in one card, separated by semicolons. Valid options are: 

afish(string) to generate plots of each species’ total aqueous 

phase chemical activity as a function of time (day of year) and 

the species’ age, length, or weight class; 

baf(string) to generate plots of each species’ bioaccumulation 

factor (i.e., the ratio Cf / Cw) for each chemical as a function of 

time (day of year) and the species’ age, length, or weight class; 

bmf(string) to generate plots of each species’ biomagnification 

factor (i.e., the ratio Cf / Cprey) for each chemical as a function of 

time (day of year) and the species’ age, length, or weight class; 

cfish(string) to generate plots of each species’ whole body 

concentration (ppm) for each chemical as a function of time (day 

of year) and the species’ age, length, or weight class; 

pop(string) to generate plots of each species’ population density 

(ind./ha) as a function of time (day of year) and the species’ age, 

length, or weight class; 

wt(string) to generate plots of each species’ whole body weight 

(g(FW)/fish) as a function of time (day of year) and the species’ 

age, length, or weight class; 

where string equals “age”, “length” or “weight”. Each age class 

or cohort of the species is assigned to one of five size classes 

that are defined by BASS based on the species’ largest/oldest and 

smallest/youngest individuals. 

� 

/BIOTA string1 ; ... ; stringn 

This command specifies nonfish standing stocks that are prey for 

the simulated fish assemblage. Valid options are: 

benthos[yunits] = string to generate benthic standing stocks 

according to the function string whose units yunits must be 

dimensionally equivalent to g(DW)/m2. 

insects[yunits] = string to generate incidental terrestrial insect 

standing stocks according to the function string whose units 2 yunits must be dimensionally equivalent to g(DW)/m . 

periphyton[yunits] = string to generate periphyton standing 

stocks according to the function string whose units yunits must 

be dimensionally equivalent to g(DW)/m2. 

phytoplankton[yunits] = string to generate phytoplankton 

standing stocks according to the function string whose units 

33

yunits must be dimensionally equivalent to g(DW)/L. 

zooplankton[yunits] = string to generate zooplankton standing 

stocks according to the function string whose units yunits must 

be dimensionally equivalent to g(DW)/L. 

Valid specifications for these biotic resource functions are 

function_name[yunits] = �� 

to generate the a constant prey 

standing stock of �� 

(yunits) for the simulation. 



function_name[yunits] = �� 

+ *sin(+  *t[xunits]) to 

generate a sinusoidal prey standing stock for the simulation 

where  is the mean standing stock for the chosen time period, 

 

is its amplitude (yunits),  is its phase angle (radians), and  

= 2 /period is its frequency (1/xunits). 

function_name[yunits] = file(filename) to read and interpolate 

the specified prey standing stock from the file filename. 

Note that 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. If multiple options are selected, 

each option must be separated by a semicolon. 

 

/FGETS 

This command enables a user to run BASS without simulating the 

assemblage’s population dynamics, i.e., only the growth and 

bioaccumulation of individual fish are simulated. 

 

/HEADER string 

This is an optional command that specifies a title to printed on 

each page of the output file. The maximum length of the quoted 

string is 80 characters. 

 

/LENGTH_OF_SIMULATION string 

This command specifies the ending time of the simulation. The 

valid syntax for string is 

		 

[units] 

where 		 

is non-negative real value. The time unit specified with 

brackets is converted into days for internal use and subsequent 

model output. 

 

/MONTH_T0 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. 

 

/NSTEPS number 

This command specifies the number of steps per day used by 

BASS’s Euler numerical integrator and is optional since BASS’s 

default integrator is a fifth-order Runge-Kutta method with 

adaptive step sizing. When used, the specified number should be 

greater than or equal to one. 

 

/SIMULATION_CONTROL 

This command specifies the beginning of input data that will 

apply to the simulation at large, i.e., the length of the simulation 

and its integration step, the ambient water temperature, the 

availability of benthos, incidental terrestrial insects and 

plankton, the community’s water level, and various output 

options. 

 

/SUMMARY_PLOTS string1 ; ... ; stringn 

This command specifies the variables whose temporal dynamics 

will be plotted at the completion of the simulation. The options 

may be specified one per card, or all in one card, separated by 

semicolons. Valid options are: 

afish(string) to generate plots of each species’ total aqueous 

phase chemical activity as a function of time (day of simulation) 

and the species’ age, length, or weight class; 

baf(string) to generate plots of each species’ bioaccumulation 

factor (i.e., the ratio Cf / Cw) for each chemical as a function of 

time (day of simulation) and the species’ age, length, or weight 

class; 

bmf(string) to generate plots of each species’ biomagnification 

factor (i.e., the ratio Cf / Cprey) for each chemical as a function of 

time (day of simulation) and the species’ age, length, or weight 

class; 

cfish(string) to generate plots of each species’ whole body 

concentration (ppm) for each chemical as a function of time (day 

of simulation) and the species’ age, length, or weight class; 

34

pop(string) to generate plots of each species’ population density 

(ind./ha) as a function of time (day of simulation) and the 

species’ age, length, or weight class; 

where string equals “age”, “length” or “weight”. Each cohort of 

the species is assigned to one of five size classes that are defined 

by BASS based on the species’ largest/oldest and 

smallest/youngest individuals. 

� 

/TEMPERATURE string 

The command specifies the ambient' s water temperature. Valid 

options for this command are: 

temp[celsius] =  to generate a constant ambient water temperature for the simulation. 

temp[celsius] =  + *sin( +  *t[xunits]) to generate a 

sinusoidal ambient water temperature for the simulation where 

 is the mean temperature for the chosen time period, is its 

amplitude (yunits),  is its phase angle (radians), and  = 

2 /period is its frequency (1/xunits). 

temp[celsius] = file(filename) to read and interpolate the ambient water temperature from the file filename. 

Note that unless specified otherwise BASS assumes that its first 

day of simulation is April 1 and that the 365-th simulation day 

is March 31. This assignment can be changed using the 

command /MONTH_T0. 

� 

/WATER_LEVEL string 

For shallow water communities, this command specifies a 

community’s actual water level. For deep water communities, 

however, this command specifies the depth of the community’s 

productive plankton layer. Valid options for this command are: 

depth[meter] =  to generate a constant water level for the 

simulation. 

depth[meter] =  + *sin( +  *t[xunits]) to generate a 

sinusoidal water level for the simulation where  is the mean 

water level for the chosen time period, is its amplitude 

(yunits),  is its phase angle (radians), and  = 2 /period is its 

frequency (1/xunits). 

depth[meter] = file(filename) to read and interpolate the water 

levels from the file filename. 

Note that unless specified otherwise BASS assumes that its first 

day of simulation is April 1 and that the 365-th simulation day 

is March 31. This assignment can be changed using the 

command /MONTH_T0. 

4.2.2. Chemical Input Commands 

The physico-chemical properties and exposure concentrations of 

each chemical of interest are specified by a block of eleven 

commands, i.e., 

/CHEMICAL string 

/EXPOSURE string1 ; ... ; stringn 

/LETHALITY string1 ; ... ; stringn 

/LOG_AC real number 

/LOG_KB1 real number 

/LOG_KB2 real number 

/LOG_P real number 

/METABOLISM string1 ; ... ; stringn 

/MOLAR_WEIGHT real number


/MOLAR_VOLUME real number


/MELTING_POINT real number


The command /CHEMICAL must be the first command in the 

block since it identifies the start of a new set of chemical 

parameters. The order of the remaining commands, however, is 

not significant. The use of these commands will now be 

described in alphabetical order. See Appendix D for an example 

of the use of these commands. 

� 

/CHEMICAL string 

This command specifies the start of the input for a new 

chemical. Each chemical name must be a single character string 

without embedded blanks or hyphens. If a two part name is 

desired, the user should use an underscore "_" as a separating 

character. This command must precede the commands 

/EXPOSURE, /LETHALITY, /LOG_AC, /LOG_KB1, /LOG_KB2, /LOG_P, 

/METABOLISM, /MOLAR_WEIGHT, /MOLAR_VOLUME, and /MELTING_POINT. The name specified by this command is used in 

conjunction with the command /INITIAL_CONDITIONS to input 

initial whole body concentrations of chemicals in each age class 

of the fish of concern and with the command /METABOLISM to 

specify daughter products of chemical biotransformation. If the 

user specifies chemical exposures via by the file option, the 

indicated name is also used to direct reading of the specified 

exposure files. Otherwise this name is used only for output 

purposes; BASS does not use this name to link to any chemical 

data base. 

� 

/EXPOSURE string1 ; ... ; stringn 

This command enables the user to specify the temporal dynamics 

of chemical exposures to fish via the water or contaminated 

35

sediments or via the ingestion of benthic invertebrates, incidental 

terrestrial insects, or plankton. Exposure concentrations 

specified by these options are assumed to be completely 

bioavailable to the fish. For example, water concentrations are 

assumed to be actual dissolved concentrations and not total 

water concentrations which include particle-bound chemical. If 

multiple options are selected, each option must be separated by 

a semicolon. Valid options are: 

cbnths[yunits] = string to generate potential dietary exposures 

to fish via benthic organisms according to the function string. 

cinsct[yunits] = string to generate potential dietary exposures to 

fish via incidental terrestrial insects according to the function 

string. 

cphytn[yunits] = string to generate potential dietary exposures 

to fish via periphyton according to the function string. 

cpplnk[yunits] = string to generate potential dietary exposures 

to fish via phytoplankton according to the function string. 

csdmnt[yunits] = string to generate sediment exposure concentrations according to the function string. 

cwater[yunits] = string to generate aqueous exposure concentrations according to the function string. 

czplnk[yunits] = string to generate potential dietary exposures 

to fish via zooplankton according to the function string. 

The concentration units for each exposure function are specified 

within the indicated brackets. As previously noted for the 

simulation control functions, unless specified otherwise BASS 

assumes that the first day of simulation is April 1 and that the 

365-th simulation day is March 31 for all the time dependent 

exposure functions discussed below. This assignment can be 

changed using the command /MONTH_T0. 

Valid expressions for dietary exposures via benthos, periphyton, 

phytoplankton, or zooplankton and for benthic sediments are: 

function_name[yunits] = �� 

to generate a constant concentration 

of toxicant in benthos, periphyton, phytoplankton, sediment, or 

zooplankton. 

function_name[yunits] = �� 

*cwater[xunits] to generate 

chemical concentrations in benthos, periphyton, phytoplankton, 

sediment, or zooplankton as a chemical equilibrium with the 

ambient environmental water. If this equilibrium is assumed to 

be thermodynamic, then the coefficient �� 

generally is equal the 

product of the component's dry organic fraction and the 

chemical's K . ow

function_name[yunits] = file(filename) to read and interpolate 

the concentration of toxicant in benthos, periphyton, 

phytoplankton, sediment, or zooplankton from the file filename. 

Valid expressions for insect dietary exposures are: 

cinsct[yunits ]= �� 

to generate a constant concentrations of the 

toxicant in incidental terrestrial insects. 

cinsct[yunits ] = file(filename) to read and interpolate the 

concentration of the toxicant in incidental terrestrial insects from 

the file filename. 

Valid expressions for direct aqueous exposures are: 

cwater[yunits] = �� 

to generate a constant aqueous concentration 

for the chemical of concern. 

cwater[yunits] = �� *csdmnt[xunits] to generate aqueous 

exposure concentrations as a chemical equilibrium with the 

benthic sediments. If this equilibrium is assumed to be thermodynamic, then the coefficient �� 

generally is assumed to equal the 

product of the sediment's organic fraction and the chemical's K . oc 

cwater[yunits] = �� + *exp(*t[xunits]) to generate an 

exponential dissolved chemical water concentration where  and 



 have units of yunits and has units of 1/xunits. This option 

can be used to simulate a chemical spill or one time application 

of a pesticide. 

cwater[yunits] =  +  *sin( + *t[xunits]) to generate a 

sinusoidal dissolved chemical water concentrations where  is 

the mean dissolved chemical water concentration (yunits) (over 

 

one period), is the amplitude (yunits),  is its phase angle 

(radians), and  = 2 /period is its frequency (1/xunits). This 

option might be used to simulate the mobilization of sediment 

bound contaminants during spring or fall turnover. 

cwater[yunits] = file(filename) to read and interpolate the dissolved aqueous concentration of toxicant from the file filename. 

This option is currently inactive. 

The user should be very cautious and judicious when using more 

than one of the above options since the user can easily construct 

an exposure scenario which is inconsistent with theoretical 

constraints on the fate and distribution of contaminants in 

aquatic systems. 

 

/LETHALITY string1 ; ... ; stringn 

36 
This optional command specifies species specific LC50's for the 

chemicals of concern. Valid string options are: 

LC50[units](fish_name) = �� 

LC50[units](fish_name) = �� 

*Kow[-]^ 

where Kow[-] is the chemical’s n-octanol/water partition 

coefficient and fish_name is the common name of the fish 

species to be simulated. BASS converts these user supplied LC50's 

into their corresponding aqueous chemical activities and then 

uses the geometric mean of these lethal activities to trigger 

mortality during the simulation. 

If the user desires, simulation of mortality associated with the 

accumulation a lethal aqueous chemical activity can be turned 

off by using the command line option “-l” as discussed in 

Section 4.5. When this is done, however, BASS still calculates 

the fish’s total aqueous phase chemical activity and reports it as 

a fraction of the fish’s estimated lethal chemical activity to 

provide the user with simple but useful monitor of the total 

chemical status of the fish. 



/LOG_AC real number 

This command specifies the 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_KB1 real number 

This command specifies the log10 of metal’s binding constant for 

non-lipid organic matter (see Eq.(2-6)). This parameter is input 

only for metals and organometals. 



/LOG_KB2 real number 

This command specifies the 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 organometals. 



/LOG_P real number 

This command specifies the chemical's log10 Kow, where K is ow 

the n-octanol/water partition coefficient. /LOG_P must be 

specified for all organic chemicals. 



/MELTING_POINT real number 

The command specifies the chemical's melting point (Celsius). 

This datum, together with the chemical's logP, is used to 

calculate the aqueous activity coefficient for organic chemicals 

when that parameter is not specified by the user. See Yalkowsky 

et al. (1983) 



/METABOLISM string1 ; ... ; stringn 

This optional command specifies species specific rates of 

biotransformation for the chemical of concern. Valid strings 

options are: 

BT[units](fish_name, chemical_name) = �� 

BT[units](fish_name, chemical_name) = �� 

*Kow[-]^ 

BT[units](fish_name, none) = �� 

BT[units](fish_name, none) = �� 

*Kow[-]^ 

where BT is the whole body referenced biotransformation rate 

km in Eq.(2-46); Kow[-] is the chemical’s n-octanol/water 

partition coefficient; and fish_name is the common name of the 

fish species that can metabolize the chemical of concern, and 

chemical_name is the name of the daughter product generated by 

the metabolism of chemical. If the user does not wish to simulate 

daughter products because they are insignificant or assumed to 

be harmless, chemical_name can be assigned the value none. 

When daughter products are specified, the user must specify all 

physical chemical properties of the identified by-product in the 

same way that the physical chemicals properties of the parent 

compound are specified. 



/MOLAR_VOLUME real number 

The command specifies the chemical's molecular volume 

(cm3/mol) which is used to calculate the chemical's aqueous 

diffusivity, i.e., 

2.101×10  

7 

D  

(4-1) 

 

1.4  0.589 

where D is the toxicant's aqueous diffusivity (cm2/sec),  

is the 

viscosity of water (poise), and  

is the molecular volume of the 

chemical(cm3/mol) (Hayduk and Laudie 1974). The viscosity of 

water over its entire liquid range is represented with less than 

1% error by 

 

1.37( T  20)  8.36×10 	 

4 ( T 
 20)2 

(4-2) 20 Log10   

109  T T 

where  

T is the viscosity (centipoise) at temperature T (Celsius), 

and  

20 is the viscosity of water at 20  C (1.002 centipoise) 

(Atkins 1978). 

37

� 

/MOLAR_WEIGHT real number 

The command specifies the chemical's molecular weight (g/mol). 

4.2.3. Fish Input Commands 

Model parameters for each fish species of interest are specified 

by a block of ten commands, i.e., 

/COMMON_NAME string 

/SPECIES string 

/AGE_CLASS_DURATION string 

/SPAWNING_PERIOD string 

/FEEDING_OPTIONS string1; ...; stringn 

/INITIAL_CONDITIONS string1; ...; stringn 

/ECOLOGICAL_PARAMETERS string1; ...; stringn 

/COMPOSITIONAL_PARAMETERS string1; ...; stringn 

/MORPHOMETRIC_PARAMETERS string1; ...; stringn 

/PHYSIOLOGICAL_PARAMETERS string1; ...; 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. See Appendix D for examples of the commands 

described below. 

� 

/AGE_CLASS_DURATION string 

This command is used to specify the duration of each age class. 

Two character strings, i.e., "month" and "year", are recognized 

as valid options. 

� 

/COMMON_NAME string 

This command specifies the start of input data for a fish species. 

The command’s specified common name string is used for 

model output and as a label for specifying the dietary 

composition of other fish species. Each common name must be 

a single character string without embedded blanks. If a two-part 

name is desired, the user should use an underscore "_" as a 

separating blank. See the diet option for the command /ECOLOGICAL_PARAMETERS. � 

/COMPOSITIONAL_PARAMETERS string1 ; ... ; stringn 

This command specifies aqueous and lipid fractions of the fish. 

Valid options which must be separated by semicolons are: 

pa[-] =  + *pl[-] which specifies the fish's aqueous fraction 

as a linear function of the fish's lipid fraction. 

pl[-] =  *W[xunits]^which specifies the fish's lipid fraction 

as an allometric function of its body weight. If a fish’s average 

lipid content is independent of its body weight (i.e., equals 

zero), however, this parameter can be specified simply as 

pl[yunits] =  . 

where  and are integer or real numbers. 

� 

/ECOLOGICAL_PARAMETERS string1 ; ... ; stringn 

This command specifies the ecological parameters that describe 

the fish's trophic interactions, non-predatory mortality, and 

recruitment. Valid options that must be separated by semicolons 

are: 

diet( < string <) = {string1 =  

1 , ... , stringn =  n} which 

specifies the dietary composition for fish of the age or size range 

( [xunits], [xunits]) where[xunits] must be dimensionally 

equivalent to either yr, g(FW), or cm. The right hand side of the 

option specifies the prey items (stringn) and their contribution 

( 

n) to the fish's diet. Each stringn is either the common name of 

one of the fish species to be simulated, "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 1< 

n<100, then  n 

designates the relative frequency of that prey in the fish's diet 

independent of its relative abundance in the field. On the other 

hand, if -1< 

n<1, then  is considered a prey electivity (see n 

Eq.(2-71)). For any given foraging class, a user can specify both 

constant dietary percentages and prey electivities. Valid syntax 

for specifying the size or age range of the fish are 

 

< a[xunits]<

if the fish's age determines its dietary 

composition; 

 

< l[xunits]<

if the fish's length determines its dietary 

composition; 

 

<w[xunits]<

if the fish's weight determines its dietary 

composition. 

Although for a given species all range types must be the same 

(i.e., age, length, or weight), the range types between species 

may be different. The diet(·)={·} option can be repeated as many 

times as needed in order to define a complete lifetime sequence 

of diets for the fish. 

lp[yunits] =  

+  *L[xunits] which specifies the average length 

of prey consumed by a fish whose body length is L[xunits]. If a 

fish’s average prey size is independent of its body length (i.e.,  

equals zero), however, this parameter can be specified simply as 

lp[yunits] =  

. 

38 
mls[yunits] = �� 

which specifies the species’ maximum longevity 

or life span. 



nm[yunits] = �� *W[xunits]^ which specifies a non-predatory 

mortality rate for fish whose body weight is W[xunits]; yunits 

must be dimensionally equivalent to 1/year. If the mortality rate 

 

of fish is independent of their body weight (i.e., equals zero), 

however, this parameter can be specified simply as nm[yunits] 

�

= . 

tl_ro[yunits] = �� 

which specifies the species’ minimum total 

length when it reaches sexual maturity or its first reproduction. 

rbi[-] = �� 

which specifies the species’ reproductive biomass 

investment, i.e., grams gametes per gram spawning fish. 



wl[yunits] = �� *L[xunits]^ which specifies the fish' s live 

weight as an allometric function of its total length. 

yoy[yunits] = �� 

which specifies the live weight of fish recruited 

into the population as age class 0. 



/FEEDING_OPTIONS string1 ; ... ; stringn 

This command instructs BASS how to calculate ingestion for a 

particular age or size range of fish. Valid options for this 

command are 



allometric(�� < string < ) to model expected feeding using 

Eq.(2-56). 



clearance(�� < string < ) to model expected feeding using Eq.(2-

62). 



holling(�� < string < ) to model expected feeding using Eqs.(2-

57). 



linear(�� < string < ) to model expected feeding using Eq.(2-63). 

where  and  are integer or real numbers and string equals one 

of the following 

a[xunits] if the fish' s age determines its feeding algorithm; 

l[xunits] if the fish' s length determines its feeding algorithm; 

w[xunits] if the fish' s weight determines its feeding algorithm. 

Although for a given species all range types must be the same 

type (i.e., age, length, or weight), the range types between 

species may be different. The parameters for these models are 

specified using the /PHYSIOLOGICAL_PARAMETERS command. 



/INITIAL_CONDITIONS string1 ; ... ; stringn 

This command specifies the species’ initial ages, whole body 

chemical concentrations, live body weights, and population 

sizes. Valid options for this command are: 

age[units] = {n1 , ... , nage_class} to initialize the age of each 

cohort with the specified vector. The units which are delineated 

by brackets must be dimensionally equivalent to days. 

chemical_name[units]= {n1 , ... , nage_class} to initialize the whole 

body concentration of each cohort for the named chemical by the 

specified vector. Each name must correspond exactly to a name 

specified by one of the /CHEMICAL commands. The units of measurement which must be enclosed by brackets must be 

dimensionally equivalent to  g/ g(FW). 

wt[units] = {n1 , ... , nage_class} to initialize the body size of each 

age class with the specified vector. The units which are delineated by brackets must be dimensionally equivalent to g(FW). 

pop[units] = {n1 , ... , nage_class} to initialize the population 

density of each age class with the specified vector. The units 

which are delineated by brackets must be dimensionally 

equivalent to inds/ ha. 

 

/MORPHOMETRIC_PARAMETERS string1 ; ... ; stringn 

This command specifies the species' morphometric parameters 

that describe the exchange of chemicals across its gills. Each 

string specifies a required morphometric parameter as a simple 

allometric power function of the fish’s body weight. Valid 

options, which must be separated by semicolons, are: 

ga[yunits] =  *W[xunits]^ which specifies the fish's total gill 

surface area. yunits must be dimensionally equivalent to cm2 or 

cm2/g(FW). 

id[yunits] =  *W[xunits]^ which specifies the interlamellar 

distance between adjacent lamellae. 

ld[yunits] =  *W[xunits]^ which specifies the density of 

secondary lamellae on the primary gill filaments, i.e., number of 

lamellae per mm gill filament. 

ll[yunits] =  *W[xunits]^ which specifies the fish's lamellar 

length. yunits must be dimensionally equivalent to cm or cm 

/g(FW). 

Note that if the exponent  equals zero for any of these 

parameters, the resulting term W[xunits]^0 does not have to be 

specified. 

39

 

/PHYSIOLOGICAL_PARAMETERS string1 ; ... ; stringn 

This command specifies the species' physiological parameters 

for simulating its growth. Each string specifies a physiological 

parameter of the fish as a constant or temperature-dependent 

power function of its body weight. In particular, 

ae_plant[-] =  which specifies the fish's assimilation efficiency 

for periphyton and phytoplankton. 

ae_invert[-] =  which specifies the fish's assimilation 

efficiency for benthos, insects, and zooplankton. 

ae_fish[-] =  which specifies the fish's assimilation efficiency 

for fish. 

ge[yunits] =  *G[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the fish's gastric evacuation 

where G is the mass of food resident in the intestine. yunits must 

be dimensionally equivalent to g(DW)/day. In general,  = 1⁄2, 	 , 

or 1 (Jobling 1981). This parameter is required only if the 

feeding option holling(·) is selected. 

mf[yunits] = 

 *W[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the fish's maximum filtering 

rate. yunits must be dimensionally equivalent to L/day. Required 

only if the feeding option clearance(·) is selected. 

m i [ yu nits]= 

 *W [ xu nits] ^  * e x p (  * ( T [ c e l s i u s ]-

T0))*h(T0,T1,T2) which specifies the fish's maximum ingestion. 

yunits must be dimensionally equivalent to g(DW)/day. Required 

only if the feeding option allometric(·) is selected. 

rq[-] = 

 which specifies the fish's respiratory quotient; rq = 

L(CO2) respired / L(O2) consumed. 

rt:std[-] = 

 which specifies the ratio of a fish's routine respiration to its standard respiration; rt:std = (routine O2 consumption) 

/ (standard O2 consumption). BASS assumes a default value equal 

2. 

sda:in[-] = 

 which specifies the ratio of a fish's SDA to its 

ingestion. BASS assumes a default value equal 0.17. 

sg[yunits] = 

 *W[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the fish's specific growth rate. 

yunits must be dimensionally equivalent to day-1

. Required only 

if the feeding option linear(·) is selected. 

sm[yunits] = 

 *W[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the size of the satiation meal 

consumed during the interval (0, st]. See option “st[·]” below. 

Required only if the feeding option holling(·) is selected. 

so[yunits] = 

 *W[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the fish's standard oxygen consumption. yunitsmust be dimensionally equivalent to mg(O2)/ hr 

or mg(O2) 
 g(FW)

-1 
 hr-1

. 

st[yunits] =  *W[xunits]^  *exp(  *(T[celsius]-

T0))*h(T0,T1,T2) which specifies the time to satiation when 

feeding with an initially empty stomach. See option sm[·] above. 

Required only if the feeding option holling(·) is selected. 

where 



T2  

(T2  

T1) T 

h( T0, T1, T2 ) � 

(4-3) T2  

T0 

where T1 is the temperature at which each particular process’s 

rate is maximal, T2 is the upper temperature at which the process 

is no longer operative, and T0 is the low end reference 

temperature that is used to specify the process’s Q10 response. 

Specification of the hyperbolic function h(T0,T1,T2) is optional 

in which case the specification of the reference temperature T0 

is also optional. Consequently, all of the above temperature 

dependent power functions can also be specified simply as 

 

*W[xunits]^ *exp( *T[celsius]) 

As noted for the fish’s morphometric parameters, if the exponent 

 equals zero for any of parameters identified as being 

allometric power functions, the resulting term W[xunits]^0 does 

not have to be specified. If a required parameter is not specified, 

the program will terminate with an appropriate message. 

 

/SPAWNING_PERIOD string 

This command specifies the months during which spawning 

occurs. Valid character strings for this command are either the 

name of a month or the names of two months separated by a 

hyphen. For example, 

/SPAWNING_PERIOD may OR 

/SPAWNING_PERIOD april-june 

The names of the months must be spelled out in full. 

 

/SPECIES string 

This command specifies the scientific name (genus and species) 

40 
of the fish to be modeled. When this command is encountered, 

BASS uses the specified scientific name to assign default 

ecological, morphological, and physiological parameters for the 

species of interest. These default parameters are then updated 

with the data that the user inputs via the 

/ECOLOGICAL_PARAMETERS, /MORPHOMETRIC_PARAMETERS, and 

/PHYSIOLOGICAL_PARAMETERS commands. This option,  

however, is not implemented in BASS version 2.1. 

4.2.4. Units Recognized by BASS 

The many BASS commands require the specification of units (or 

combination of units) as part of an option. This section describes 

the syntax for units that are recognized by BASS’s input 

 

algorithms. The conversion of user supplied units to those 

actually used by BASS is accomplished by referencing all units to 

the MKS system (i.e., meter, kilogram, second). Tables 8 and 9 

summarize prefixes and fundamental units, respectively, that are 

recognized by BASS’s unit conversion subroutines. Table 9 also 

summarizes the dimensionality and the conversion factor to the 

MKS system of each unit. Table 10 summarizes units that are 

recognized by BASS’s unit conversion subroutines for specifying 

ecological, morphometric, and physiological units. 

Units and their prefixes may be specified in either upper or 

lower case. If prefixes are used, there must be no embedded 

blanks between the prefix and the unit name, e.g., "milligrams" 

is correct, "milli grams" is incorrect. Only those units and their 

 

plural form presented in Tables 9 and 10 are valid. The 

circumflex (^) is used to denote exponentiation (e.g., cm-2 is 

presented as cm^-2). The slash (/) is used to denote division. If 

multiple slashes are used to specify a unit, they are interpreted 

according to strict algebraic logic. For example, both "mg/liter", 

and "mg liter^-1" are equivalent specifications. Similarly, the 

weight specific units "mg/g/day" are "mg g^-1 day^-1" are 

equivalent. The unit conversion factor (Tables 9 and 10) 

converts from the given unit to the MKS system, e.g., 1 calorie �

× 2.388×10-1 1 meter2 kilogram second-2. 

4.2.5. Syntax for User Specified Functions 

The following syntax rules apply to specifying these options 

  

Brackets are used only to delineate units. 

Dimensionless parameters like assimilation efficiency, 

lipid fraction, and Kow must be specified with null units 

"[-]". 

 

The order of addition and multiplication is not 

significant. Thus, the following specifications are valid 

and equivalent. 

temp(celsius) = +  *sin( +  *t[xunits]) <=> 

 

temp[celsius] = sin( *t[xunits]+ ) + 

czplnk[yunits] = *cwater[xunits] <=> 

czplnk[yunits] = cwater[xunits]*

Optionsthat are temperature dependent or independent 

power functions may be specified by their log10 or ln 

transforms. For example, the following options are 

valid 

ln(so[yunits]) = +  *T[celsius] +  *ln(W[xunits]) 

log(so[yunits]) = +  *T[celsius] +  *log(W[xunits]) 

User specified functions do not have to be in reduced 

form. For example, temperature-dependent power 

functions can be specified with a reference temperature 

other than 0 Celsius. Thus, BASS will correctly decode 

the following functions 

so[yunits] =  *exp(	 *(T[celsius]-20))*W[xunits]^
 

ln(so[yunits]) =  + 	 *(T[celsius]-20) 

+ 
 *ln(W[xunits]) 

log(so[yunits])=  +	 *(T[celsius]-20) 

+ 
 *log(W[xunits]) 

If the temperature dependency is unknown, 

temperature-dependent power functions can be input 

for a specific temperature, 	  Celsius, in which case 

BASS assumes a default Q10=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[yunits](	 ) =  *W[xunits]^
 

ln(so[yunits](	 )) =  + 
 *ln(W[xunits]) 

log(so[yunits](	 )) =  + 
 *log(W[xunits]) 

If either the slope of a linear function or the exponent 

of a power functions is zero, the function can be input 

as a constant function 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] 

41 
pl[-] = 0.05 <=> pl[-] = 0.05*W[g(FW)]^0.0 

� 

Operators (^*/+-) may not be concatenated. For 

example, the following options have invalid syntax 

so[mg(o2)/g/hr]=0.1*exp(0.0693*T[celsius])

 *W[g(FW)]^-0.2 

ln(so[mg(o2)/g/hr])=- 2.30+0.0693*T[celsius] 

+-0.2*ln(W[g(FW)]) 

The correct syntax for these options would be 

so[mg(o2)/g/hr]=0.1*exp(0.0693*T[celsius])

 *W[g(FW)]^(-0.2) 

ln(so[mg(o2)/g/hr])= -2.30+0.0693*T[celsius] 

- 0.2*ln(W[g(FW)]) 

4.2.6. User Supplied Exposure Files 

If the user specifies the file option for the /BIOTA, 

/TEMPERATURE, /WATER_LEVEL, or /EXPOSURE commands, the 

designated files must exist and be supplied by the user. The 

general format of a BASS exposure file allows a user to specify 

multiple exposure conditions within a single file. Each file 

record specifies exposure conditions for a specific time The 

general format of a BASS exposure file is as follows 

! 

! file: exposure.dat 

! 

/001 time[units] ! see ensuing discussion 

/C1 string 

  

v

v

/CM string 

/START_DATA 

1,1 v1,2 ... v1,MV ! comment 

2,1 v2,2 ... v2,MV ! comment 

   

v

...


NR,1 vNR,2 ... vNR,NV ! comment


The records beginning with a slash (/) followed by an integer CJ 

identify the type of data (time, exposure concentration, 

temperature, etc.) contained in CJ-th column of each data record. 

In this example, NR is the total number of data records in the 

file, NV is the number of variables per record, and C1...CM are 

the column positions of M exposure variables that are to be 

read. Note, however, that MV can be greater than CM and that 

C1...CM need not be consecutively numbered. To simplify the 

reading of multiple exposure files, BASS requires that “time” be 

specified as the first column of any user-supplied exposure file. 

Valid character strings for specifying the remaining data 

columns include: 

cbnths[units](chemical name) to input the concentration of 

chemical name in benthic invertebrates; 

cinsct[units](chemical name) to input the concentration of 

chemical name in incidental terrestrial insects; 

cphytn[units](chemical name) to input the concentration of 

chemical name in periphyton; 

cpplnk[units](chemical name) to input the concentration of 

chemical name in phytoplankton; 

csdmnt[units](chemical name) to input the sediment 

concentration of chemical name; 

cwater[units](chemical name) to input the unbound, aqueous 

concentration of chemical name; 

czplnk[units](chemical name) to input the whole body 

concentration of chemical name in zooplankton; 

benthos[units] to input the standing stock of benthic 

invertebrates; 

insects[units] to input the standing stock of incidental terrestrial 

insects; 

periphyton[units] to input the standing stock of periphyton or 

grazable algae; 

phytoplankton[units] to input the standing stock of 

phytoplankton; 

zooplankton[units] to input the standing stock of zooplankton; 

temperature[units] to input ambient water temperature. 

depth[units] to input water depth. 

If column names other than those listed above are specified BASS 

simply ignores them. Data records may be continued by 

appending an ampersand (&) to the line, e.g., the following data 

records are equivalent. 

... vi,j vi,j+1 vi,1 vi,2 ... vi,MV 

v

vi,1 vi,2 ... vi,j &


i,j+1 vi,j+2 
... vi,MV 

42 
File records must be sequenced such that time is nondecreasing 

(i.e., ti � 

ti+1, I = 1, 2, ..., N-1). The time increment between 

consecutive records can be either constant or variable. BASS 

calculatesthe exposure conditions between specified time points 

by simple linear interpolation. 

4.3. Output Files Generated by BASS 

Given a user’s input BASS generates the following three output 

files 

 

an output file that summarizes the user’s input 

parameters, input errors detected by BASS, and 

warnings/errors encountered during the actual 

simulation. This file will have the name of the user' s 

input command file, with extension "MSG"; e.g., 

INPUT.DAT will generate the file INPUT.MSG.If the 

file already exists, it will be silently overwritten. See 

Appendix E (page 95) for an example. 

 

an output file that tabulates selected results of the 

simulation. Tabulated summaries include 1) annual 

bioenergetic fluxes and growth statistics (i.e., mean 

body weight, mean growth rate) of individual fish by 

species and age class, 2) annual bioaccumulation fluxes 

and statistics (i.e., mean whole body concentrations, 

BAF, and BMF) of individual fish by species and age 

class, and 3) annual community fluxes and statistics 

(i.e., mean population densities and biomasses) of each 

fish species by age class. This file will have the name 

of the user' s input command file, with extension 

"BSS"; e.g., INPUT.DAT will generate the file 

INPUT.BSS. If the file already exists, it will be silently 

overwritten. See Appendix F (page 112) for an 

example. 

 

a Post-script file that contains the plots that were 

requested by the user. The file will have the name of 

the user' s input command file, with extension "PLX"; 

e.g., INPUT.DAT will generate the file INPUT.PLX. 

If the file already exists, it will be silently overwritten. 

See Appendix G (page 122) for an example. 

4.4. Include Files and General File Management 

Asmentioned previously BASS enables the user to construct BASS 

simulation files using include files. Although the use of include 

files was introduced in Section 4.1 as simply a matter of user 

convenience, the installation software for BASS version 2.1 

actually creates a specific subdirectory structure to help 

construct and maintain user input files. Although users do not 

have to use this subdirectory structure to run BASS, its use is 

strongly recommended since the graphical interface (GUI) that 

is currently being developed for BASS uses this directory 

structure. Using the installation procedures outlined in Section 

5.1, the BASS installation software INSTBASS.EXE creates the 

directory structure below 

C:\BASS --+-- INSTBASS.EXE


 |


 +-- BASS_V2.EXE


 |


 +-- \DISLIN


 |


 +-- \FISH -- *.FHS


 |


 +-- \COMUNITY -- *.CMM


 |


 +-- \PROPERTY -- *.PRP


 |


 +-- \PROJECTS --+ \project1 --+ *.PRJ


 | + *.CHM

 | + *.DAT

 | + *.BSS

 | + *.MSG

 | + *.PLX

 + \project2 



Files within the subdirectory \FISH are all assigned the 

extension FSH. These files specify the compositional, 

ecological, morphological, and physiological parameters of a 

fish species and are intended to be used as include files for 

constructing fish community files which are discussed next. The 

general structure of a *.FSH file is 

! file: name.fsh


! date: june 20, 2000


!


! notes: structure of BASS fish file


!


/COMMON_NAME <string>


/SPECIES <string>


/AGE_CLASS_DURATION <string>


/SPAWNING_PERIOD <string>


/FEEDING_OPTIONS allometric(a<x[units]<b); &


 clearance(a<x[units]<b); &


 holling(a<x[units]<b); &


 linear(a<x[units]<b)


/COMPOSITIONAL_PARAMETERS pa[-]=a*pl[-]+b; &


 pl[-]=a*w[g]^b


/ECOLOGICAL_PARAMETERS lp[cm]=a*l[cm]+b; &


 wl[g]=a*l[cm]^b; &


 tl_r0[cm]=a; &


 rbi[-]=a; &


 yoy[g]=a; &


 mls[yr]=a; &


 nm[1/yr]=a*w[g]^b


/MORPHOMETRIC_PARAMETERS ga[cm^2]=a*w[g]^b; &


 id[cm]=a*w[g]^b; &


 ld[cm]=a*w[g]^b; &


43

 ll[cm]=a*w[g]^b


/PHYSIOLOGICAL_PARAMETERS &


 ge[g/d]=a*w[g]^b*exp(c*t[celsius]); &


 mf[l/d]=a*w[g]^b*exp(c*t[celsius]); &


 mi[g/d]=a*w[g]^b*exp(c*t[celsius]); &


 sg[1/d]=a*w[g]^b*exp(c*t[celsius]); &


 sm[g]=a*w[g]^b*exp(c*t[celsius]); &


 so[mg(O2)/h]=a*w[g]^b*exp(c*t[celsius]); &


 st[min]=a*w[g]^b*exp(c*t[celsius]); &


 ae_fish[-]=a; &


 ae_invert[-]=a; &


 ae_plant[-]=a; &


 sda:in[-]=a; &


 rq[-]=a; &


 rt:std[-]=a


! end c:\bass\fish\name.fsh


Files within the \COMUNITY subdirectory are all assigned the 

extension CMM. These files specify the composition, trophic 

structure, and initial conditions of a particular fish community. 

These files will generally use FSH files from the \FISH 

subdirectory as include files and are themselves used as include 

files by PROJECTS files. The general form of a *.CMM file is 

! file:c:\bass\comunity\name.cmm


! date: june 20,2000


!


! notes: structure of BASS community file


!


#include ‘name1.fsh’


/ECOLOGICAL_PARAMETERS &


 diet(a<x[units]<b)={benthos=a,�,name1=b,� }; &


diet(a<x[units]<b)={benthos=a,�,name1=b,�}; &


diet(a<x[units]<b)={benthos=a,�,name1=b,�}; &


diet(a<x[units]<b)={benthos=a,�,name1=b,�}


/INITIAL_CONDITIONS age[yr]={a,�,b}; &


wt[g]={a, �,b}; &


pop[inds/ha]={a, �,b}


!


! repeat above fish data block as needed


!


 

! end c:\bass\comunity\name.cmm


Files within the PROPERTY subdirectory are all assigned the 

extension PRP and specify the physico-chemical properties of 

individual chemicals. These files serve as include files for 

chemical exposure files. The general structure of *.PRP files is 

! file: name.prp


! date: june 20, 2000


!


! notes: structure of BASS chemical file


!


/CHEMICAL <string>


/LOG_AC <real number>


/LOG_P <real number>


/LOG_KB1 <real number>


/LOG_KB2 <real number>


/MOLAR_WEIGHT <real number>


/MOLAR_VOLUME <real number>


/MELTING_POINT <real number>


! end c:\bass\chemical\name.prp


The PROJECTS directory contains subdirectories that are 

created by the user for a particular model application. In general, 

each application should be assigned to its own subdirectory. For 

example, the BASS distribution example EVERGLD1.PRJ that 

simulates mercury bioaccumulation in a deep-water Florida 

Everglades community is assigned to the subdirectory 

C:\BASS\PROJECTS\EXAMPLE1. Six types of files will reside 

in each PROJECTS subdirectory. These file types are: 1) *. PRJ 

filesthat specify the simulation control parameters and chemical 

and fish/community include files to be used for this particular 

application, 2) *.CHM files that specify chemical exposures and 

properties, 3) *.DAT files which specify actual chemical 

exposures, nonfish standing stocks, water temperature, or water 

depth when these functions supplied by the ‘file’ option, 4) 

*.BSS which are the tabular output files generated by BASS, 5) 

*.MSG which are the message output files generated by BASS, 

and 6) *.PLX which are the Post Scrip plot files generated by 

BASS. The recommended structure of a PRJ file is 

! file: name.prj


! date: june 20, 2000


!


! notes: structure of BASS project file


!


/SIMULATION_CONTROL


/HEADER <string>


/MONTH_T0 <string>


/LENGTH_OF_SIMULATION <number>[year]


/TEMPERATURE temp[celsius]=<string>


/WATER_LEVEL depth[meter]=<string>


/BIOTA benthos[g/m^2]=<string>; &


 insects[g/m^2]=<string>; &


 periphyton[g/m^2]=<string>; &


 phytoplankton[mg/l]=<string>; &


 zooplankton[mg/l]=<string>


/ANNUAL_OUTPUTS <integer number>


/SUMMARY_PLOTS pop(length); cfish(length)


!


! specify chemical properties and exposures


!


#include ‘name1.chm’ 


!


! specify fish community


!


#include 'name2.cmm'


/END


The chemical exposures and properties file NAME1.CHM 

specified in the preceding project file has the following general 

form 

44

! file: name1.chm


! date: june 20, 2000


!


! notes: structure of chemical exposures


! and properties file


!


! specify physico-chemical parameters


!


#include ‘chem_1.prp’ 


/EXPOSURE cwater[ppm]=<string>; &


 cbnths[ppm]=<string>; &


 cinsct[ppm]=<string>; &


 cphytn[ppm]=<string>; &


 cpplnk[ppm]=<string>; &


 czplnk[ppm]=<string>


/LETHALITY lc50[units](fish_1) = a; .... 

/METABOLISM bt[units](fish_1,chem_n) = a; ... 

! 

! repeat above data block as needed


! for other chemicals of concern


� 

! end name1.chm


The *.FSH, *.CMM, and *.PRP files within the subdirectories 

\FISH, \COMUNITY, and \PROPERTY should be considered 

by the user to be canonical “databases” for the construction of 

new project files. If the user wishes to make changes to any of 

these files, the user should either 1) edit the files as desired and 

save the changes as a new *.FSH, *.CMM, and *.PRP file 

within the subdirectories \FISH, \COMUNITY, and 

\PROPERTY or 2) copy the desired files to a working project 

subdirectory. Unless identified with an absolute path, any file 

designated by an include command is assumed by default to 

specify a path and file name relative to the project file specified 

by the command line option “-i” when BASS is invoked. If a 

specified *.FSH, *.CMM, or *.PRP file can not be found in the 

subdirectory containing the user’s project file, BASS then uses 

the extension of the specified file to search the subdirectories 

\FISH, \COMUNITY, or \PROPERTY. 

4.5. Command Line Options 

To run a BASS simulation which is specified by an input/project 

file INPUT.PRJ, the BASS software is invoked using the UNIX 

like command line shown below 

C:\BASS21> bass_v21 -i input.prj 

Although the "-i filename " option is the only required command 

line option, the following additional options are available 

-a => print abbreviated tabular output with minimal flux 

summaries 

-c => print distribution of cpu time in major subroutines 

-e => integrate by Euler method 

-h => print this help list and stop (also see -?) 

-i filename => specify BASS input file (REQUIRED) 

-l => turn off lethal effects 

-o filename => specify BASS_V21 output file 

-p => p r i n t m e s s a g e s a s s o c i a t e d w it h p r e y 

switching/limitation 

-r => integrate by Runge-Kutta method (DEFAULT) 

-t => run test of BASS Runge-Kutta integrator and stop 

-? => print this help list and stop (also see -h) 

For example, the command line 

C:\BASS21> bass_v21 -i input.prj -a -c 

will execute the project file INPUT.PRJ and generate 

abbreviated summary tables and a distribution of cpu time spent 

within various key BASS subroutines. 

4.6. Restrictions and Limitations 

Commands may be presented in any order with the exceptions 

noted below. 

 

The /CHEMICAL command must precede the commands 

for any particular chemical since this command defines 

a new chemical and increments the total number of 

chemicals to be simulated. 

 

The /COMMON_NAME command must precede the 

commands for the particular fish, since this command 

essentially defines a (new) fish. 

 

Chemical commands must precede any fish commands. 

 

The /END command must be the last command. Any 

other text or commands following it will be ignored. 

45

Prefix Name 

atto 

centi 

deca 

deci 

exa 

femto 

giga 

hecto 

kilo 

mega 

micro 

milli 

myria 

nano 

peta 

pico 

tera 

Table 8. Valid Unit Prefixes 

Conversion Factor 

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10

10-18 

-02 

+01 

-01 

+18 

-15 

+09 

+02 

+03 

+06 

-06 

-03 

+04 

-09 

+15 

-12 

+12 

46

Table 9. Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive and summaries only 

commonly used unit names that BASS’s units conversion program recognizes. 

Conversion Dimensions 

Unit Name Factor Metre Kg Second Description 

acre 2.471×10-04 2 0 0 4840 yards2 

are 1.000×10-02 2 0 0 100 meter2 

btu 9.479×10-04 2 1 -2 

calorie 2.388×10-01 2 1 -2 

cc 1.000×10+06 3 0 0 cm3 

cm 1.000×10+02 1 0 0 

day 1.157×10-05 0 0 1 

decade 3.169×10-09 0 0 1 10 years 

erg 1.000×10+07 2 1 -2 

fathom 5.468×10-01 1 0 0 6 feet 

feet 3.281×10+00 1 0 0 

foot 3.281×10+00 1 0 0 

ft 3.281×10+00 1 0 0 feet, foot 

g 1.000×10+03 0 1 0 grams 

gallon 2.642×10+02 3 0 0 3.785 liter 

gm 1.000×10+03 0 1 0 grams 

gram 1.000×10+03 0 1 0 

gramme 1.000×10+03 0 1 0 

hectare 1.000×10-04 2 0 0 100 are 

hour 2.778×10-04 0 0 1 

hr 2.778×10-04 0 0 1 hour 

imperialgallon 2.200×10+02 3 0 0 4.54 liter 

inch 3.937×10+01 1 0 0 

joule 1.000×10+00 2 1 -2 

kg 1.000×10+00 0 1 0 kilograms 

km 1.000×10-03 1 0 0 kilometer 

l 1.000×10+03 3 0 0 liter 

lb 2.205×10+00 0 1 0 pound 

liter 1.000×10+03 3 0 0 

litre 1.000×10+03 3 0 0 

m 1.000×10+00 1 0 0 meter 

meter 1.000×10+00 1 0 0 

metre 1.000×10+00 1 0 0 

mg 1.000×10+06 0 1 0 milligrams 

micron 1.000×10+06 1 0 0 10-6 meter 

mile 6.214×10-04 1 0 0 5280 feet 

min 1.667×10-02 0 0 1 minute 

minute 1.667×10-02 0 0 1 

ml 1.000×10+06 3 0 0 

mm 1.000×10+03 1 0 0 

47

Table 9. Valid Unit Names (Continuation) 

Conversion Dimensions 

Unit Name Factor Metre Kg Second Description 

month 3.858×10-07 0 0 1 

nauticalmile 5.400×10-04 1 0 0 1852 meter 

ng 1.000×10+12 0 1 0 nanograms 

ounce 3.527×10+01 0 1 0 

oz 3.527×10+01 0 1 0 ounce 

pint 2.113×10+03 3 0 0 8 pint � 1 gallon 

pound 2.205×10+00 0 1 0 

ppb 1.000×10+06 -3 1 0 nanograms/mL 

ppm 1.000×10+03 -3 1 0 μgrams/mL 

ppq 1.000×10+12 -3 1 0 femtograms/mL 

ppt 1.000×10+09 -3 1 0 parts per trillion, picogram/mL 

quart 1.057×10+03 3 0 0 4 quarts � 1 gallon 

s 1.000×10+00 0 0 1 second 

sec 1.000×10+00 0 0 1 second 

second 1.000×10+00 0 0 1 

ton 1.102×10-03 0 1 0 2000 pounds 

tonne 1.000×10-03 0 1 0 1000 kilograms 

week 1.653×10-06 0 0 1 

yard 1.094×10+00 1 0 0 

year 3.169×10-08 0 0 1 

48

Table 10. Valid Ecological, Morphometric, and Physiological Unit Names 

Conversion Dimensions 

Unit Name Factor Metre Kg Second Description 

fish n.a. 0 0 0 t r e a t e d a s i n f o r m a ti on a s i s b y t e 

gram(O2) 7.370×10-05 2 1 -2 gram of oxygen 

g(O2) 7.370×10-05 2 1 -2 gram of oxygen 

ha 1.000×10-04 2 0 0 hectare 

individuals n.a. 0 0 0 treated as information as is byte 

inds n.a. 0 0 0 treated as information as is byte 

kcal 2.388×10-04 2 1 -2 kilocalorie 

l(O2) 5.159×10-05 2 1 -2 22.4 liters STP = mole 

lamellae n.a. 0 0 0 t r e a t e d a s i n f o r m a ti on a s i s b y t e 

mg(O2) 7.370×10-02 2 1 -2 milligram of oxygen = 3.24 calorie 

ml(O2) 5.159×10-02 2 1 -2 milliliter of oxygen 

mmole(O2) 2.303×10-03 2 1 -2 millimole of oxygen 

mole(O2) 2.303×10-06 2 1 -2 mole of oxygen 

Note: For purposes of units conversion, units used to report oxygen consumption are treated dimensionally as joules. 

49

5. Software Installation and Management


5.1. MS-DOS Installation 

Themicrocomputer ms-dos BASS 2.1 software is distributed with 

1) a readme file, 2) the BASS 2.1 software, and 3) three example 

project simulations. The BASS 2.1 executable and example 

simulation files are compressed into a self extracting executable, 

INSTBASS.EXE, using PKZIP and must be decompressed 

before use. See instructions below. 

BASS 2.1 is coded in Fortran 95 and its executable, BASS_V21, 

has been created using the Lahey/Fujitsu Fortran 95 5.60 

compiler. Although BASS's source code is not included on its 

software distribution diskette, it is available to any interested 

party on request. Please note that there is a bug in the DISLIN 

graphics software that BASS uses to generate 3-dimensional plots 

of model results as a function of age or size class and time. In 

particular, there is a bug in DISLIN's hidden line removal 

algorithm. This bug has been reported and is being investigated. 

INSTBASS.EXE not only installs the BASS 2.1 executable but 

also creates a subdirectory structure to organize and manage 

project files and their associated data files. Following the 

instructions given below, INSTBASS.EXE creates the following 

subdirectory structure 

C:\BASS --+-- INSTBASS.EXE


 |


 +-- BASS_V21.EXE


 |


 +-- \DISLIN


 |


 +-- \FISH -- *.FHS


 |


 +-- \COMUNITY -- *.CMM


 |


 +-- \PROPERTY -- *.PRP


 |


 +-- \PROJECTS --+ \EXAMPLE1


 |


 + \EXAMPLE2

 |


 + \EXAMPLE3

The structure and use of the \FISH, \COMUNITY, 

\PROPERTY, and \PROJECTS subdirectories are described in 

Section 4.4 (page 43). The \DISLIN subdirectory contains the 

*.DLL file needed to execute the DISLIN graphing software. 

Three example BASS projects are provided in the \PROJECTS 

subdirectory. Each example is allocated its own subdirectory. In 

\PROJECTS\EXAMPLE1 the project file EVERGLD1.PRJ 

simulates the bioaccumulation of methylmercury in a deep-water 

fish community in the Florida Everglades, USA. The major fish 

species in these communities are largemouth bass, Florida gar, 

yellow bullhead, bluegill and red ear sunfish, and Gambusia. 

E V E R G L D 1 . P R J u s e s t h e i n c l u d e f i l e 

\COMUNITY\EVERGLD1.CMM to specify the ecological and 

physiological data for these species. The chemical exposures and 

properties of methylmercury are provide to EVERGLD1.PRJ 

using the include file MERCURY.CHM which in turn uses the 

include file \PROPERTY\METYL_HG.PRP. The community's 

water depth and the standing stocks of benthos, periphyton, and 

zooplankton are specified by NONFISH.DAT. This example is 

presented in Section 6 of this user's manual. 

In the subdirectory \PROJECTS\EXAMPLE2 the project file 

EVERGLD2.PRJ also simulates the bioaccumulation of 

methylmercury in a deep-water fish community in the Florida 

Everglades, USA dominated by the same fish species. This 

example, however, uses BASS's "fgets" option to simulate only 

the growth and bioaccumulation of individual fish. The 

community's population dynamics are not simulated. The 

ecological and physiological data for this example are provided 

by the include file \COMUNITY\EVERGLD2.CMM. The 

chemical exposures and properties of methylmercury are provide 

to EVERGLD2.PRJ using the include file MERCURY.CHM 

w h i c h i n t u r n u s e s t h e i n c l ud e f i l e 

\PROPERTY\METYL_HG.PRP. The community's water depth 

and the standing stocks of benthos, periphyton, and zooplankton 

are specified by NONFISH.DAT. These files, however, are 

simply copies of those found in \PROJECTS\EXAMPLE1. 

Because the food web structure and dynamics specified and 

implied by \COMUNITY\EVERGLD2.CMM can not be made 

to coincide with that of \COMUNITY\EVERGLD1.CMM, the 

output of EVERGLD2.PRJ will not match that of 

EVERGLD1.PRJ. 

In the subdirectory \PROJECTS\EXAMPLE3 the project file 

HARTWELL.PRJ simulates the bioaccumulation of tetra-, 

penta-, hexa-, and hepta-PCB in a largemouth/sunfish/catfish 

community of the Twelve Mile Creek region of Lake Hartwell, 

SC, USA which was a USEPA Superfund site. Because the 

structure of the Twelve Mile Creek fish community, like many 

other largemouth/sunfish/catfish communities throughout the 

southeastern USA, closely resembles an Everglades deep-water 

community, the project file HARTWELL.PRJ uses the 

community file \COMUNITY\EVERGLD1.CMM to model the 

community of concern. This example is intended only to 

demonstrate BASS's ability to simulate the bioaccumulation of 

arbitrary mixtures and not what is actually occurring Lake 

Hartwellfish communities. Despite this fact this simulation does 

50

predict some interesting results regarding largemouth bass. In 

particular, largemouth bass are predicted to attain internal total 

chemical activities on the order of 10% of their expected lethal 

chemical activity threshold. As discussed earlier, one might 

suspect that such accumulations would begin to produce 

sublethal effects on these fish. Interestingly, biomarker studies 

on Twelve Mile Creek largemouth bass indeed suggest this to be 

the case. 

To install the BASS software the user should first obtain a DOS 

prompt and follow the instructions below. 

� 

Select a default drive into which the BASS software is 

to be installed (e.g., hard disk "C") 

C:\WINDOWS> CD C:\ 

b.	 Create a directory for BASS software and then move to 

that directory 

C:\> MKDIR BASS21 

C:\> CD BASS21\ 

c.	 Request verification of copy results 

C:\BASS21> VERIFY ON 

d.	 Transfer the files from the distribution diskette (e.g., 

drive "A") to the hard disk 

C:\BASS21> COPY A:*.* 

e.	 Execute the installation file INSTBASS.EXE to 

recover files from the ZIP archives using the option -d 

C:\BASS21> INSTBASS -d 

f..	 Edit your AUTOEXEC.BAT file as follows 

SET BASS=C:\BASS21


SET PATH=%PATH%;%BASS%


SET PATH=%PATH%;%BASS%\DISLIN


to execute BASS from any directory and to enable the 

BASS executable to find DISDLL.DLL which is needed 

for DISLIN graphics. 

g.	 To run one of the distribution examples move to the 

desired PROJECTS subdirectory and invoke BASS 

using the UNIX like command as shown in the example 

below 

C:\BASS21> CD PROJECTS\EXAMPLE1 

C:\BASS21\PROJECTS\EXAMPLE1> bass_v21 -i evergld1.prj 

5.2. Auxiliary Software 

To view and print BASS plot files the user will have to have some 

type of PostScript previewing software installed on their system. 

If the user does not have any such software, it is recommended 

that the user obtain a copy of the Ghostscript/Ghostview/GSview 

software. This freeware can be downloaded from the 

Ghostscript, Ghostview and Gsview homepage: 

http://www.cs.wisc.edu/~ghost/. 

BASS’s input files and non-PostScript output files can be viewed 

using a wide variety of editors. They can also be viewed using 

word processing software such as WordPerfect or Microsoft 

Word. When using a word processor, however, the user should 

select a non-true type font (e.g., Courier) for viewing so that the 

file’s intended alignment is display properly. Using a word 

processor to view non-PostScript BASS, has the added advantage 

being able to compare similar files easily. For example, using 

WordPerfect’s Document/Compare feature one a easily view any 

differences between two BASS output files resulting from a 

parameter change. 

51

6. Example Application


Appendix D presents an example BASS project file that simulates 

methyl mercury contamination in canals or open water habitats 

in the south Florida Everglades. This project file was constructed 

as outlined in Section 4.4 (page 43) and is supplied with the 

BASS distribution software as \EXAMPLE1. For this application 

largemouth bass (Micropterus salmoides), Florida gar 

(Lepisosteus platyrhincus), yellow bullheads (Ameiurus natalis), 

bluegills (Lepomis macrochirus), redear sunfish (Lepomis 

microlophus), and mosquito fish(Gambusia holbrooki) are 

assumed to be the dominate fish in the habitats of interest and a 

generalized food web of such assemblages is depicted in Figure 

6. The sources of the ecological, morphological, and 

physiological parameters used for this example are documented 

by comment lines in the files presented in Appendix D. Total 

fish biomass in canal and open water Everglades habitats vary 

between 150 and 460 kg(FW)/ha (Frank Jordan unpublished 

data). Using Jordan’s relative abundance data as guidelines, the 

initial standing stocks of the bass, gar, bullheads, bluegill, red 

ear sunfish, and mosquito fish were assigned to be 20, 10, 20, 

200, 100, and 10 kg(FW)/ha, respectively, for a total community 

biomass of 360 kg(FW)/ha. Based on Loftus et al. (1998) the 

water concentration of methylmercury for the simulation was 

assigned to be a constant 0.444 ng/L and the BAF’s for benthos 

106.17 and zooplankton were assigned to be and 105.99, 

respectively. 

Appendices D, E, and F present the resulting output files 

generated by BASS. At the end of the 10 year simulation the 

mean annual standing stocks of the bass, gar, bullheads, bluegill, 

red ear sunfish, and mosquito fish are 17.2, 12.7, 4.51, 191, 146, 

and 0 kg(FW)/ha, respectively, for a total community biomass of 

371 kg(FW)/ha (see pages 120 and 121). Although the total 

elimination of mosquito fish during the simulation may not be 

particularly desirable, it is not unrealistic since bass and other 

piscivores often exert intense predatory pressures on mosquito 

fish and other small fishes in Everglades and other wetland or 

swallow water communities. 

The simulated whole body concentrations of methyl mercury in 

these species agree also well with unpublished data collected by 

Ted Lange et al. and Loftus et al. (1998). The annual averaged 

concentrations of methylmercury in largemouth, gar, bullhead, 

bluegill and red ear weighted by cohort biomasses were 0.817, 

0.694, 0.539, 0.495, and 0.416 mg Hg/kg(FW), respectively (see 

pages 113, 115, 116, 117, and 118). When weighted by cohort 

densities, the annual averaged concentrations of methylmercury 

in largemouth, gar, bullhead, bluegill and red ear were 0.671, 

0.615, 0.467, 0.482, and 0.370 mg Hg/kg(FW), respectively (see 

pages 113, 115, 116, 117, and 118). Loftus et al. report whole 

body concentrations of methylmercury in largemouth, gar, 

bullhead, bluegill and red ear to be 0.967, 1.16, 0.443-0.755, 

0.478, and 0.247 mg Hg/kg(FW), respectively. 

As is typically observed under field conditions (Forrester et al. 

1972; Scott and Armstrong 1972; Cross et al. 1973; Akielaszek 

and Haines 1981; Watling et al. 1981; Boush and Thieleke 

1983a ,1983b; MacCrimmon et al.1983; Ueda and Takeda 1983; 

Wren and MacCrimmon 1986; Braune 1987; Luten et al. 1987; 

Moharram et al. 1987; Sprenger et al.1988; Grieb et al. 1990; 

Parks et al. 1991; Gutenmann et al. 1992; Lange et al. 1993; 

Tracey 1993; Joiris et al. 1995; Munn and Short 1997; Stafford 

and Haines 1997 ), BASS predicts a strong interdependence 

between the body sizes of fish and their mercury whole body 

mercury concentrations. For example, the mean annual mercury 

concentration of newly recruited largemouth whose average 

annual body weights is 86.9 g(FW) is 0.499 mg Hg/kg(FW). 

However, the mean annual mercury concentration of oldest 

largemouth whose average annual body weights is 1.09 kg(FW) 

is 1.03 mg Hg/kg(FW). 

52

53

Figure 6. Conceptual model for the primary food web of Everglades open water fish assemblage.
7. Model Quality Assurance


Quality Assurance (QA) and Quality Control (QC) for the BASS 

simulation model has been addressed with respect to: 

1) The model’s theoretical foundations, i.e., does the model’s 

conceptual and mathematical framework standup to 

scientific/engineering peer view? 

2) The model’s implementation, i.e., does the code actually do 

what it is intended to do? 

3) The model’s documentation and application, i.e., can the 

model be used by the outside research and regulatory 

community in a meaningful way? 

7.1. Questions Regarding QA of a Model’s 

Scientific Foundations 

7.1.1.Is the model’s theoretical foundation published in the peer 

reviewed literature? 

With the exception of its population and trophodynamic 

algorithms, BASS is based on the FGETS bioaccumulation and 

bioenergetics model which has been published in the peer 

reviewed literature (Barber et al. 1988, 1991). The bioenergetic 

modeling paradigm employed by BASS to simulate fish growth 

has been used by many researchers in the peer reviewed 

literature (Norstrom et al. 1976; Kitchell et al. 1977; Minton and 

McLean 1982; Stewart et al. 1983; Thomann and Connolly 

1984; Cuenco et al. 1985; Stewart and Binkowski 1986; 

Beauchamp et al. 1989; Barber et al. 1991; Stewart and Ibarra 

1991; Lantry and Stewart 1993; Rand et al. 1993; Roell and Orth 

1993; Hartman and Brandt 1995a; Petersen and Ward 1999; 

Rose et al. 1999; Schaeffer et al. 1999). Moreover, since its 

construction FGETS has been included in numerous reviews 

bioaccumulation models that are applicable for ecological risk 

assessments and environmental management decisions (Barron 

et al. 1990; Jones et al. 1991; Barnthouse 1992; Chapra and 

Boyer 1992; Landrum et al. 1992; Olem et al. 1992; Dixon and 

Florian 1993; Cowan et al. 1995; Campfens and Mackay 1997; 

Feijtel et al. 1997; Howgate 1998; Wania and Mackay 1999; 

Mackay and Fraser 2000; Bartell et al. 2000). 

Two criticisms have been lodged against FGETS in the 

literature. The fist of these is that it assumes or attempts to prove 

the gill exchange of chemicals is more important than other 

routes of exchange (Madenjian et al. 1993). Madenjian et al. 

(1993) took exception to FGETS predictions that “excretion of 

PCB through the gills is an important flux in the PCB budget of 

lake trout”. Madenjian et al. claimed that this result as not 

supported by any laboratory study on trout and cited Weininger 

(1978) as proof that gill excretion was in fact negligible. 

Nevertheless Madenjian et al. used a single, unidentified 

excretion constant in their model which simply lumps all 

excretion pathways ( i.e., gill, intestinal, urinary, and dermal) 

into one. What Madejian et al. are essentially questioning is not 

FGETS per se but rather the need to use thermodynamically 

based diffusion models for bioaccumulation in general. 

The second criticism is that FGETS is overly complex and 

requires too much additional data to parameterize (McKim et al. 

1994; Stow and Carpenter 1994; Jackson 1996). Since FGETS’s 

bioenergetic model for fish growth is not significantly different 

from those used by several other authors (Norstrom et al. 1976; 

Weininger 1978; Thomann and Connolly 1984; Madenjian et al. 

1993; Luk and Brockway 1997) , this criticism is generally 

aimed at BASS’s gill exchange model. As indicated by Tables 2 ­

6, however, there is in fact an abundance of gill morphometric 

data available to estimate the parameters needed for this model. 

7.1.2. How has the model or model algorithms been 

corroborated / validated? 

BASS’s bioconcentration and bioaccumulation algorithms have 

been validated by comparing its predicted uptake and elimination 

rates to published in the peer reviewed literature (Barber et al. 

1988; Barber 2000). For organic chemicals these algorithms 

have also been validated by simulations of mixtures of PCBs in 

Lake Ontario salmonids and various laboratory studies (Barber 

et al. 1991). For sulfhydryl binding metals, BASS’s 

bioconcentration algorithms have been validated by simulations 

of methylmercury bioaccumulation in Florida Everglades fish 

communities one of which is presented herein as a typical BASS 

application. For validation of BASS’s bioenergetic growth 

algorithms the reader should refer to Barber et al. (1991) and the 

example herein. 

7.1.3. What is the mathematical sensitivity of the model with 

respect to parameters, state variables (initial value problems), 

and forcing functions/boundary conditions? What is the model’s 

sensitivity to structural changes? 

There are four major class of mathematical sensitivity regarding 

a model’s behavior. These are the model’s sensitivity to 

parameter changes, forcing functions, initial state variables, and 

structural configuration. The first three of these classes generally 

are formally defined in term the following partial derivatives 

� � �

Xi Xi Xi � 

; � ; � 

pj Z X (0) j j 

where Xi is a state variable of interest; pj is some state parameter 

54 
of concern; Zj is some external forcing function; and Xj

(0) is the 

initial value of some state variable of interest which may be Xi 

itself. Structural sensitivity, which generally cannot be 

formulated as a simple partial derivative, typically concerns the 

number and connectivity between the system’s state variables. 

An excellent question regarding structural sensitivity for a model 

like BASS might be how does a predator’s population numbers or 

growth rate change with the introduction or removal of new or 

existing prey items? 

Because model sensitivity as defined above is simply a 

mathematical characteristic of a model, model sensitivity in and 

of itself is neither good nor bad. Sensitivity is desirable if the 

system being modeled is itself sensitive to the same parameters, 

forcing functions, initial state perturbations, and structural 

changes to which the model is sensitive. Even though model 

sensitivity can contribute to undesirable model uncertainty or 

prediction error, it is important to acknowledge that model 

sensitivity and uncertainty are not one and the same (Summers 

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 

statistically variability associated with those components. 

A generalized sensitivity analysis of BASS without explicit 

specification of a fish community of concern is undoable. 

Furthermore, the results of a sensitivity analysis for one 

community generally cannot be extrapolated to other 

communities. Issues related to BASS’s sensitivity must be 

evaluated on a case by case basis by the users of the software. 

Although procedures for enabling users to conduct a variety of 

structured sensitivity analyses are currently be developed, 

presently the onus of performing such analyses rests with the 

user. Users interested in issues and techniques related to model 

sensitivity and uncertainty should consult the following papers: 

Giersch (1991), Elston (1992), Summers et al. (1993), Håkanson 

(1995), Norton (1996), Loehle (1997), and Wallach and Genard 

(1998). 

7.2. Questions Regarding QA of a Model’s 

Implementation 

7.2.1. Did the input algorithms properly process all user input? 

As part of its routine output BASS generates a *.MSG file which 

summarizes all the input data that was used for a particular 

simulation. This summary includes not only a line by line 

summary of the user’s input commands but also a complete 

summary of all control, chemical and fish parameters that BASS 

assigned based on the user’s specified input file(s). The onus is 

then on the user to verify that their input data has been properly 

processed. If not, the user’s should report their problem to the 

technical contact identified in the BASS user’s guide. 

BASS has a series of subroutines that check for the completeness 

and consistency of the user’s input data. When missing or 

inconsistent data is detected, an appropriate error message is 

written to the above *.MSG file and a error code is set to true. If 

this error code is true after all the user’s input has been 

processed, BASS terminates without attempting further program 

execution. 

7.2.2. Is the developer reasonably confident that program 

subroutines, functions, and procedures are transmitting and 

receiving the correct variables? Similarly, is the developer 

reasonably confident that program subroutines, functions, and 

procedures are not inadvertently changing variable assignments 

the shouldn’t be changed? 

All BASS subroutines and functions are accessed using implicit 

interface generated by the pertinent Fortran 95 compiler. 

Subroutines and functions are packaged together according to 

the function and degree of interaction. The BASS version 2.1 

software is coded with one main program PROGRAM BASS_V21 

(see BASS_V21.F90) and 25 procedure modules. These are 

� 

MODULE BASS_ALLOC - subroutines for allocating and 

reallocating derive type pointers (see BASS_ALLOC.F90). 

� 

MODULE BASS_CHECK - subroutines for checking the 

completeness and consistency of user input (see 

BASS_CHECK.F90). 

� 

MODULEBASS_DEFINED - functions for determining whether 

program parameters and variables have been initialized or 

assigned (see BASS_DEFINED.F90). 

� 

MODULE BASS_EXP - subroutines for calculating exposure 

conditions (see BASS_EXP.F90). 

� 

MODULE BASS_INI- subroutines for initialization of program 

variables (see BASS_INI.F90). 

� 

MODULEBASS_INPUT - subroutines for processing user input 

(see BASS_INPUT.F90). 

� 

MODULE BASS_ODE - subroutines for the computational 

kernel of the BASS software (see BASS_ODE.F90). 

� 

MODULE BASS_PLOTS - subroutines for generating BASS 

output plots (see BASS_PLOTS.F90). 

� 

MODULE BASS_TABLES - subroutines for generating output 

tables (see BASS_TABLES.F90). 

� 

MODULE DECODE_FUNCTIONS - subroutines for decoding 

constant, linear, and power functions from character strings 

(see UTL_DCOD_FNC.F90). 

� 

MODULE DISLIN_PLOTS - general subroutines for generating 

2 and 3-dimensional DISLIN plots (see UTL_PLOTS.F90). 

� 

MODULE ERROR_MODULE - subroutines for printing error 

codes encountered with general utility modules (see 

UTL_ERRORS.F90). 

55

� 

MODULE FILESTUFF - subroutines for parsing file names and 

obtaining version numbers or time stamps (see 

UTL_FILESTUFF.F90). 

� 

MODULE FLOATING_POINT_COMPARISONS - operators for 

testing equality or inequality of variables with explicit 

consideration of their computer representation and spacing 

characteristics (see UTL_FLOATCMP.F90). 

� 

MODULE GETNUMBERS - subroutines for extracting numbers 

from character strings (see UTL_GETNUMS.F90). 

� 

MODULE IOSUBS - subroutines for assigning, opening, and 

closing logical units (see UTL_IOSUBS.F90). 

� 

MODULE MODULO_XFREAD - subroutines for reading files 

which contain comments, continuation lines, and include 

files (see UTL_XFREAD.F90). 

� 

MODULE MSORT - subroutines for sorting and generating 

permutation vectors for lists and vectors (see 

UTL_MSORT.F90). 

� 

MODULE MXGETARGS - subroutines for extracting arguments 

from a command line (see UTL_MXGETARGS.F90). 

� 

MODULE REALLOCATER - subroutines for allocating and 

reallocating integer, logical, and real pointers (see 

UTL_ALLOC.F90). 

� 

MODULE SEARCH - subroutines for finding the location of a 

key phase within a sorted list (see UTL_SEARCH2.F90). 

� 

MODULE SEARCH_LISTS - subroutines for finding the 

location of a value within a sorted list (see 

UTL_SEARCH1.F90). 

� 

MODULE STRINGS - subroutines for character string 

manipulations and printing multiline character text (see 

UTL_STRINGS.F90). 

� 

MODULE TABLE_UTILS - subroutines for generating selfformating tables (see UTL_PTABLE.F90). 

� 

MODULE UNITSLIBRARY - subroutines for defining and 

performing units conversions (see UTL_UNITSLIB.F90). 

In general these procedure modules are coded with minimal or 

no scoping units. Also whenever possible subroutine and 

function arguments are declared with INTENT(IN) and 

INTENT(OUT) declarations to preclude unintentional 

reassignments. 

Although global constants and Fortran parameters are supplied 

to program procedures via modules (see question 7.2.3 below), 

data exchanges between program procedures are performed via 

formal subroutine/function parameters whenever possible. The 

only notably exception to this coding policy are modules which 

must be used to supply auxiliary parameters to an “external” 

subroutine which is used as an argument to certain mathematical 

software packages. Working areas used by BASS are not used for 

data transfers between internal or external procedures. 

To simplify the construction and maintenance of the formal 

parameter lists of many BASS subroutines and functions and to 

help prevent the inadvertent transposition subroutine or function 

formal parameters, BASS makes extensive use of derive type data 

structures. Each derived type definition is specified within its 

own module and all derive type definition modules are 

maintained in the file BASS_TYPES.F90. A good example of 

BASS’s use of derive type data structures is the derive type 

variable used to store and transfer the ecological, physiological, 

and morphometric data for a particular fish species. This derived 

type is defined by following module 

MODULE dt_fish_par


TYPE:: fish_par


 CHARACTER (LEN=80) :: ageclass, class_var, &


 genus_species, spawning_interval


 INTEGER :: classes=0, spawnings=0


 INTEGER, DIMENSION(:), POINTER :: &


 class_model=>NULL(), spawn_dates=>NULL()


 REAL :: ae_fish, ae_invert, ae_plant, &


 dry2live_ab, dry2live_aa, dry2live_bb, &


 dry2live_cc, gco2_d, la, longevity, &


 mgo2_s, rbi, rq, rt2std, sda2in, tl_r0, yoy


 REAL, DIMENSION(2) :: &


 ga, id, ld, ll, lp, nm, pa, pl, wl


 REAL, DIMENSION(6) :: ge, mf, mi, sg, sm, so, st


 REAL, DIMENSION(:), POINTER :: class_bnds=>NULL()


END TYPE fish_par


END MODULE dt_fish_par


Many components of this derived type are user input parameters 

that have already been discussed. For example, the array ga(2) 

stores the coefficient and exponent of a species’ gill area 

function (see /MORPHOMETRIC_PARAMETERS page 39). Other 

components are secondary parameters that are calculated from 

the user’s input data. For example, dry2live_ab, dry2live_aa, 

dry2live_bb, and dry2live_cc are constants that are used to 

calculate a fish’s live weight from its dry weight (see 

introduction to Section 2.6. Modeling Growth of Fish). Using a 

declaration of the form 

TYPE(fish_par), DIMENSION(nspecies) :: par 

all data defined by the above derived type can be passed to a 

BASS subroutine by the simple calling statement 

CALL sub1(...., par, ....) 

without fear of data misalignment. 

7.2.3. Is the developer reasonably confident that all program 

subroutines, functions, and procedures are using the same 

global constants or parameters? 

All global constants are defined within their own individual 

56 
modules. These modules include 

� 

MODULE BASS_CONSTANTS - constants used by BASS’s 

computational subroutines (see BASS_CONSTANTS.F90). 

� 

MODULE CONSTANTS - constants used by utility subroutines 

(see UTL_CONSTANTS.F90). 

� 

MODULE NOVALUE - specifies values for integer, real, and 

character variables that have been been initialized (see 

BASS_CONSTANTS.F90). 

� 

MODULE SNGL_DBL_QUAD - specifies the precision of 

floating point variables as either single, double, or quad 

precision variables. This module also assigns certain 

a s s o c i a t e f l o a t i n g p o i n t c o n s t a n t s ( s e e 

BASS_CONSTANTS.F90). 

� 

MODULE WORKING_DIMENSIONS - specifies ‘standard’ sizes 

for character variable, input records, etc. (see 

BASS_CONSTANTS.F90). 

� 

MODULE UNITS_PARAMETERS - specifies parameters used by 

the units conversion subroutines (see UTL_UPARAMS.F90) 

7.2.4. Do all strictly mathematical algorithms do what they are 

suppose to? For example are root finding algorithms 

functioning properly? 

During execution BASS must employ root finding algorithms for 

two important types of calculations. The first of these is the 

calculation of a fish’s live weight from its dry weight given an 

allometric relationship between its live body weight and its 

fraction lipid and linear relationships between its percent water, 

lipid, and non-lipid organic matter. The second type of 

calculation involves the linear transformation of unconditioned 

dietary electivities into self consistent sets of dietary electivities. 

These calculations are performed using the combined 

bisection/Newton-Raphson algorithm outlined by Press et al. 

(1992). 

As mentioned earlier, the BASS software allows the user to 

integrate BASS’s differential equations using either a simple 

Eulermethod or a fifth-order Runge-Kutta method with adaptive 

step sizing. These methods offer the user two distinctly different 

options with respect to software performance and execution. 

Although Euler methods cannot assess the accuracy of their 

integration, such methods often allow for fast model execution. 

Runge-Kutta methods, on the other hand, can monitor the 

accuracy of their integration but at the cost of increased 

execution time. This additional computational burden, however, 

can often be significantly reduced by employing adaptive step 

sizing. BASS’s Runge-Kutta integrator is patterned on the fifthorder Cash-Karp Runge-Kutta algorithm outlined by Press et. al. 

(1992) and was tested using the following system of equations. 

dy1/dx  1.0 

dy2/dx  x 

dy3/dx  cos(x)

dy4/dx  cosh(x)

dy5/dx  exp(x)

dy  

6/dx 1.0/(1.0 x) 

dy 

7/dx 1.0/(1.0 x 2

) 

dy 

8/dx 1.0/ 1.0 x 2 

dy   

9/dx 100 (y9 sin(x)) y9(0)  1 

du/dx  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 

y1  x  x0 

y2  0.5 (x 2  x0

2

) 

y3  sin(x) sin(x0  )

y4  sinh(x) sinh(x0  )

y5  exp(x) exp(x0  )

y6  ln(1 x) ln(1 x0    )

y7  arctan(x) arctan(x0  )

y8  asinh(x) asinh(x0  )

10101 100 10000 

y9  exp(  100 x)  cos(x)  sin(x) 10001 10001 10001 

u  2 exp(  x)  exp(  1000 x)


v   exp(  x)  exp( 

1000 x) 

On the interval [0<x<10], the above solutions range in value 

from v=0.453999E-04 to y5=0.220255E+05. Besides their large 

numerical range, the last three equations in this system are 

numerically stiff (Press et al. 1992; Ascher and Petzold 1998). 

When integrated on the interval [0<x<10], the ratio of the 

numerical solutions and the corresponding analytical solutions 

equaled unity with an absolute error of <10-6. 

7.2.5. Are mathematical algorithms implemented correctly, i.e., 

are the assumptions of the procedure satisfied by the problem of 

interest? 

Because BASS is a differential equation model, a question of 

paramount concern is how its integration between points of 

discontinuity/ nondifferentiability is controlled. BASS, like many 

ecological models, utilizes threshold responses, absolute value 

functions, maximum and minimum functions, and linear 

interpolations between time series in its formulation and 

implementation. Although most of BASS’s parameters are 

updated continuously, a few parameters (e.g., dietary 

compositions) which are computationally intensive to evaluate 

and which change very slowly are updated only daily and are 

57

therefore step functions of time. All of these features create 

points of discontinuity or nondifferentiability. Although there is 

nothing intrinsically wrong with using such formulations in 

differential equation models, numerical integrations of such 

models must proceed for one point of discontinuity / 

nondifferentiability to another. 

With these considerations in mind, BASS’s computational kernels 

(subroutines BASS_ODESOLVR and FGETS_ODESOLVR) are 

designed to integrate BASS’s differential equations for a single 

day of the desired simulation period. Immediately following the 

call of these computational kernels, BASS calculates the dietary 

composition of each fish that will be held constant for that day. 

The progress of the subsequent numerical integration within the 

day is then controlled by any conditions that results in a point 

of nondifferentiability. The two most important conditions in 

this regard occur when BASS must read an exposure file to 

update the parameters for the linear interpolation of one or more 

exposure variables or when one or more cohorts are eliminated 

from the community. In the later case, BASS also recalculates the 

dietary compositions of the remaining fish which again will 

remain constant for the remainder of the day. Note that 

recruitment of new cohorts into the simulated community does 

not create a point of nondifferentiability for BASS since such 

amendments to the community’s structure are performed before 

calling the computational kernels BASS_ODESOLVR or 

FGETS_ODESOLVR and therefore constitutes a simple 

reinitialization problem. 

7.2.6.Are simulated results consistent with known mathematical 

constraint of the model? For example, if state variable are 

suppose to be non-negative, are they? Similarly, if the model is 

suppose to mass balance, does it? 

BASS‘s state variables, like those of most physical or biological 

models, must be by definition non-negative. However, insuring 

that the numerical integration of a differential equation model 

remains constrained to its appropriate state space is not a trivial 

issue. Consider, for example, the case when one wants to take a 

simple Eulerian step for a non-negative state variable which has 

a negative derivative. If the state variable is to remain nonnegative, then the largest allowable size for the integration step 

can be calculated as follows 

� 

y (t � h)  y (t) h y (t) 



0 < y (t)  h y (t)  

y (t) > h where y (t) < 0 

y (t) 

If h is greater than the numerical spacing of t (i.e., t  h  

t ), 

then an integration step is possible. If the converse is true, 

however, the function y(t) is approximating a step function in 

which case the desired integration can simply be restarted with 

y (t)  0. There are at least two types of situations that can occur 

during a BASS simulations that might necessitate this type of 

corrective action. The first of these occurs when a cohort 

experiences intense predation or other mortality that drives its 

population to extinction whereas the second situation might 

occur when there is the rapid excretion of a hydrophilic 

contaminant following the disappearance of an aqueous 

exposure. Regardless of the integration method used (i.e., Euler 

or Runge-Kutta), when the derivative for a fish’s body weight, 

population density, or body burden is negative, BASS verifies 

whether the current integration step will in fact yield nonnegative state values. If not, BASS either executes a simple Euler 

step of the appropriate size or restarts the integration with the 

appropriate state variables initialized to zero. 

When used it its full community mode (i.e., the non-FGETS 

option), BASS calculates and reports the mass balance between 

the community’s total predicted predatory mortality and its total 

predicted piscivorous consumption as a mass balance check on 

itsinternal consistency and operation. For the example presented 

herein this mass balance is -1.953E-02 [g(DW)/ha/yr]. Since this 

community’s total piscivory is calculated to be 2.778E+04 

[g(DW)/ha/yr], this mass balance check would have a relative 

error of less than 10-6. See page 121. 

7.2.7. Are simulation results consistent across machines or 

compilers? 

BASS was originally developed on a DEC 3000 work station 

using the DEC Fortran 90 compiler. It has also been ported to 

the Windows operating system on the DELL OptiPlex using the 

Lahey/Fujitsu Fortran 95 5.60 compiler. Although the results of 

these two implementations to agree with one another up to single 

precision accuracy, due to differences in compiler optimization, 

model computations must be performed in double precision to 

obtain this level of consistency. 

7.2.8. Have test and reference/benchmark data sets been 

documented and archived? 

At least three different test project files are maintained to tract 

changes in the operation of BASS associated with code 

maintenance and updates. These files are used as benchmarks to 

verify that code modifications that should not change BASS ‘s 

computational results in point of fact do not change BASS’s 

simulation output. 

58

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 specific real 

communities, its principal intended use is to predict and compare 

the outcomes of alterative management options that are 

associated with pollution control, fisheries management, and/or 

ecosystem restoration activities. 

7.3.2. Does the User Guide provide the information needed to 

appropriate apply and use the model? 

The BASS User’s Guide summarizes the model’s theoretical 

foundations and assumptions, the model’s input command 

structure, issues related to user file and project management, and 

software installation. The User’s Guide also presents and 

discusses the results of one of the three example applications 

that are distributed with the BASS software. 

7.3.3. What internal checking can be made to help insure that 

the model is being used appropriately? 

Currently the only internal checking performed by BASS is to 

verify that all parameters needed by the model for a particular 

simulation have in fact been specified by the user. Although 

BASS will assign a few default parameters, most unassigned 

parameters are fatal errors. Future versions of BASS will perform 

bounds checking on many of its physiological and 

morphological parameters. 

7.3.4. Has the developer anticipated computational problem 

areas that will cause the model to “bomb”? 

Several key mathematical calculations have been identified as 

potential problem areas for a BASS’s simulation. In general, these 

problem areas involve either the unsuccessfully resolution of a 

root of a nonlinear equation or the unsuccessfully integration of 

BASS’s basic state variables. Examples of the former include 

situations when BASS’s calculated dietary compositions do not 

sum to unity or when a fish’s live weight is calculated to be less 

or equal to its dry weights. Examples of the latter include 

situations when the current integration step is less than the 

numerical spacing of the current time point or when BASS’s 

integration error exceeds 10-5. When any of these situations are 

encountered, BASS terminates execution and issues an 

appropriate error message to the current *.MSG file. 

59

8. Planned Future Features


Presently, ten major program developments are planned for water temperature. 

BASS. These include: 

� 

Development of submodels for simulating the biomass 

� 

Development of a graphical user interface (GUI) for easy dynamics of benthos, periphyton, phytoplankton, and 

construction of input files. zooplankton. 

� 

Improved plotting capabilities including the generation of � Development of submodels for simulating the physiological 

output files that users can input to their own graphic tolerances of fish to water quality parameters other than 

software. toxic chemicals. 

� 

Development canonical fish and community databases (i.e., � Incorporation of quantitative structure activity relationships 

*.FSH and *.CMM files) to facilitate easier application of (QSAR’s) to predict metabolism of organic chemicals. 

BASS. � 

Development of migration algorithms for simulating the 

� 

Software to perform model sensitivity analyses.	 movement of fish into and out of the simulated community 

based on habitat parameters such as water depth, current 

� 

Implementation of an option to read a simulated or velocity, availability of prey, etc.


measured time series of dissolved oxygen concentrations


that are needed of calculate the fishes’ ventilation volumes. � Development of subroutines to simulate sublethal, residue-


See Eq.(2-12). Currently, BASS uses saturated dissolved based effects.


oxygen concentrations that are calculated as a function of


60

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77 
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78

APPENDICES 

APPENDIX A. Equilibrium complexation model for metals 

As reviewed by Mason and Jenkins (1995), metals can be 

classified into three different categories based on their 

complexation behavior and preference for different ligands. 

These groups are generally designated as class A, class B, and 

borderline metals. Of these, however, class B and borderline 

metals are the most important from an ecotoxicological point of 

view. Class B metals which include Au, Ag, Cu, Hg, and Pb 

preferentially bind to marcromolecules such as proteins and 

nucleotides that are rich in sulfhydryl groups and heterocyclic 

nitrogen. Borderline metals which include As, Cd, Co, Cr, Ni, 

Sn, and Zn bind not only to same sites as do class B metals but 

also to those sites preferred by class A metals (i.e., carboxylates, 

carbonyls, alcohols, phosphates, and phosphodiesters). Although 

factors determining the preference of borderline metals for a 

particular binding site are complex, the fact that the transport 

and storage of these metals in fish and other biota is regulated by 

metallothioneins via sulfhydryl complexation reactions certainly 

suggests that the total availability of sulfhydryl groups within 

organisms plays a key role in their internal distribution and 

accumulation. To formulate complexation reactions for class B 

and borderline metals, one can assume that protein sulfhydryl 

groups are the only significant ligand for these metals, i.e., 

RSH � M  RSM  H  

(A-1) 

The stability constant for this reaction is   

Kb  

[RSM] [H ]  

RSM [H ]   

(A-2) [RSH] [M ] RSH [M ] 

where [H +] is the hydrogen ion concentration (molar); [M +] is 

the concentration of free metal (molar); [RSH] is the 

concentration of reactive sulfhydryls (molar); [RSM] is the 

concentration of sulfur bound metal (molar); RSM are the moles 

of metal bound to sulfhydryls; and RSH are the moles of free 

non-disassociated sulfhydryl. Metal complexation must be 

constrained by mass balances for both the metal and sulfhydryl 

binding sites. For the metal itself the following mass balance 

must hold 

TM  M  LM  RSM 

 

[M 	 ] 
 P a W  [M  ] K ow Pl W  

Kb RSH [M  

[H  ] 

] 


 

[M  ] P a W  Pl W K ow  

Kb RSH 

[H  ] 

(A-3) 

[M  ] 
 

W (P a  Pl 

TM 

K ow )  

Kb RSH 

[H  ] 

where TM are the total moles of metal; LM are the moles of 

metal that is partitioned into lipids; and W is the fish’s volume 

in liters which is approximately equivalent to its kilogram live 

weight. The mass balance for the fish' s sulfhydryl content that 

must is satisfied is 

TS  RSH  RS    RSMi i 

(A-4)  

K a 

RSH 1   RSMi [H  ] i 

- where TS denotes the total moles of sulfhydryl ligands; RS are 

t h e mo l e s of d is ass ociated s u lf h yd r yl s ; a n d 

K  

[RS  ][H  ]/[RSH] is the sulfhydryl's disassociation a 

constant. In addition to the reaction specified in Eq. (A-1), 

mixtures of metals interact by competing for the same binding 

site, i.e., 

RSMi  Mj 

  

RSM  Mi  

(A-5) j 

The stability constant for this reaction is 

[RSMj] [Mi  ] Kb  

j Kb  

ij 

[RSMi

] [M  ] Kbi 

(A-6) 

j 

From this expression it then follows 

RSM [Mi  

] Kbi  

RSMi [Mj  

] Kb j j 

 

RSM [Mi  

] Kbi   

RSMi [Mj  

] Kb j j i i 

RSM [Mi  

] Kbi  [Mj  ] Kb  

j j RSMi i i (A-7)  

RSMj  

[Mi  ] Kbi  

RSMi 

[Mj  ] Kb i i j 

RSH  

[Mi  ] Kbi  

RSMi [H  ] i i 

If Eq.(A-3) is substituted in this equation, one then obtains 

 

RSH  

Kbi TMi RSMi  

i [H  ] i P W  Pl W Kowi  Kbi RSH/[H  ] a 

(A-8)  

RSH  

Kbi TMi 

i [H  ] W (Pa  )  Kbi P RSH l Kowi 

79 
This equation in turn can be substituted into Eq.(A-4) to obtain 

K a TS � 

RSH 1  

[H ] 

Kbi TMi (A-9)  

RSH  

i [H ] W (Pa  

)  

Kbi P RSH l Kowi 

For most metals, however, 

[H ] W (Pa  ) « Kbi RSH P (A-10) l Kowi 

Therefore, the sulfhydryl balance equation is approximately 

equal to 

K  

a 

TS � 

RSH 1  TMi (A-11) 

[H ] i 

Thus, 

 

TS  

RSH � 

1  

K 

TMi i (A-12) 

/[H ] a 

If the metal’s aqueous and organic phase concentrations (i.e., Ca 

and Co) are expressed on a molar basis, then 

RSM � C P W o o (A-13) 



[M ] � 

Ca (A-14) 

When Eqs. (A-12), (A-13), and (A-14) are substituted into 

Eq.(A-2), one then obtains 

P C W ([H ]  Ka) o o Kb �  

C ( TS  TMi

) a i  

(A-15) 

Kb ( TS  TMi

) PoCo � 

i 

Ca W ([H 

]  

Ka) 

which can then be substituted into the equation 

Cf C � 

a 

P  Pl K  

Po Co (A-16) a ow C a 

to calculated the fish’s aqueous phase concentrations. 

To use the above complexation model one must specify both the 

metal’sstability constant (see Eq.(A-2)) and the concentration of 

sulfhydryl binding sites (mol SH/g(DW)) within the fish. 

Although numerous studies have investigated the sulfhydryl 

content of selected fish tissues, it appears that no study has 

attempted to quantify the total sulfhydryl content of fish. Despite 

this situation, however, a reasonable approximation of this 

parameter can still be made since data does exists for the major 

tissues (i.e., muscle, liver, kidney, gill, and intestine) typically 

associated with metal bioaccumulation. 

Itano and Sasaki (1983) reported the sulfhydryl content of 

Japanese sea bass (Lateolabrax japonicus) muscle to be 11.5 

μmol(SH)/ g(sacroplasmic protein) and 70.5 μmol(SH) / 

g(myofibrillar protein). Using the authors reported values of 

0.0578 g(sarcoplasmic protein) / g(muscle) and 0.120 

g(myofibrillar protein) / g(muscle) the total sulfhydryl content of 

Japanese sea bass muscle would be estimated to be 9.12 

μmol(SH) / g(muscle) or 45.6 μmol(SH) / g(dry muscle). 

Opstevedt et al. (1984) reported the suldhydryl content of Pacific 

mackerel (Pneumataphorus japanicus) and Alaska pollock 

(Theragra chalcogramma) muscle to be 6.6 and 6.2 mmol(SH) 

/ 16 g(muscle N), respectively. Using conversion factors 

reported by these authors, these values are equivalent to 48.7 and 

56.7 μmol/g(dry muscle). Chung et al. (2000) determined the 

sulfhydryl content of mackerel (Scomber australasicus) muscle 

to be 88.2 μmol(SH) / g(protein). Using the conversion factor 

0.83 g(protein) / g(dry muscle) (Opstevedt et al. 1984) this value 

is equivalent to 73.2 μmol(SH) / g(dry muscle). Although few 

other studies have investigated the sulfhydryl content of whole 

fish muscle, several studies have reported on the sulfhydryl 

content of the actomyosin and myosin components of fish 

myofibrillar proteins (Connell and Howgate 1959; Buttkus 1967, 

1971; Takashi 1973; Itoh et al.1979; Sompongse et al. 1996; 

Benjakul et al. 1997; Lin and Park 1998). Because the results of 

these studies agree well with the actomyosin analysis reported by 

Itano and Sasaki (1983), it would appear that the results of Itano 

and Sasaki (1983), Opstevedt et al. (1984), and Chung et al. 

(2000) can be applied to fish in general. Consequently, the 

sulfhydryl content of fish muscle can be assumed to be on the 

order of 45-70 μmol(SH) / g(dry muscle) 

Although the sulfhydryl content of liver, kidney, gills, and 

intestine has not been measured directly, the sulfhydryl content 

of these tissues can be estimated from their metallothionein 

concentrations. Metallothioneins (MT) are sulfur-rich proteins 

which are responsible for the transport and storage of heavy and 

trace metals and which are also usually considered to be the 

principle source of sulfhydryl binding sites in these tissues 

(Hamilton and Mehrle 1986; Roesijadi 1992). Numerous 

researchers have investigated the occurrence of MTs in the liver, 

kidney, and gills of fish, and most have shown that tissue 

concentrations of MTs generally vary with metal exposures. 

Under moderate exposures typical hepatic MT concentrations in 

fish are on the order of 0.03 - 0.30 μmol(MT) / g(liver) (Brown 

and Parsons 1978; Roch et al. 1982; Klaverkamp and Ducan 

1987; Dutton et al. 1993). Using data from Takeda and Shimizu 

80

(1982) who report the sulfhydryl content of skipjack tuna 

(Katsuwonus pelamis) MTs to be approximately 25 mol(SH) / 

mol(MT) and assuming a dry to wet weight ratio equal 0.2, these 

MT concentrations would be equivalent to 3.75 - 37.5 μmol(SH) 

/ g(dry liver). These values suggest that the hepatic sulfhydryl 

content of fish which would include both their baseline MT and 

cytoplasmic components that can be converted into MT, might 

be on the order of 40 μmol(SH) / g(dry liver). This value, 

however, is probably too conservative. Consider, for example, 

the observation that the ratios of mercury concentrations in liver 

to those in muscle often vary from 1.5 to 6 or more (Lockhart et 

al. 1972; 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 μmol(SH) 

/ g(dry liver). Of these two possibilities the latter appears more 

likely. 

Although gill, kidney, and intestine MTs have not been studied 

in the same detail that hepatic MTs have been, it appears that 

MT and hence sulfhydryl concentrations in gills and kidney are 

lower and not as inducible as hepatic concentrations 

(Klaverkamp and Ducan 1987; Hamilton et al. 1987a,b). 

Klaverkamp and Ducan (1987) estimated the concentrations of 

gill MT in white suckers (Catostomus commersoni) to be 33 

μg(MT) / g(gill) which is equilvalent to 3.3 nmol(MT) / g(gill) 

or 0.0825 μmol(SH) / g(gill). This value agrees well the 

estimated concentrations of unidentified binding sites (0.03 -

0.06 μmol / g(gill)) for copper on the gills of rainbow trout 

(Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis) 

(MacRae et al. 1999) but is somewhat high for the concentration 

of unidentified binding sites (0.013 - 0.03 μmol / g(gill)) for 

copper, cadmium, and silver on the gills of rainbow trout and 

fathead minnows (Pimephales promelas) (Playle et al. 1993; 

Janes and Playle 1995). 

Based on these considerations and the acknowledgment that 

many other important organic compounds contain sulfhydryl 

groups, e.g., enzymes such as those involved in fatty acid 

synthesis, glutathione, etc., it seems reasonable to assume that 

the sulfhydryl content of fish is approximately 70 μmol(SH) / 

g(DW).Because Davis and Boyd (1978) reported the mean sulfur 

content of 17 fish species to be 206 μmol(S) / g(DW), this 

assumption implies that almost 1/3 of a fish’s sulfur pool exists 

as sulfhydryl groups. 

The above complexation model was implemented within BASS 

using 70 μmol(SH) / g(DW) to calculate the total sulfhydryl 

content of fish and assuming that the mean dissociation constant 

for organic sulfhydryls is pKa=9.25 (i.e., the SPARC estimated 

pKa for cysteine). Using literature values for the stability 

constants of methylmercury, however, BASS overpredicted the 

bioammulation of methylmercury in fish by at least an order of 

magnitude. Consequently, a much simpler distribution 

coefficient algorithm was adapted. 

81





"

"

'

' -

-

-

-

-

-

= =

=

=

x 

APPENDIX B. Nondimensionalization of chemical exchange equations for fish gills. 

Using the transformations	 interlamellar channel. Using these observations, one can then 

�  C C	 write a 

C C	 (B-1)  d 



w a (Y) 



NSh  (1)  (Y)  

2 

q

q

p

v Y

NGz (Y) d

d 

# 

X ! 

X $ 1 

dY (B-10) 

X  x 

(B-2) dX X  1   

h  y D which can then be differentiated with respect to Y to obtain 

Y (B-3) V h 2	

d % d ( , d 2 qv d (1) (Y) , /0

0

N (B-11) Sh the PDE and boundary conditions	 dY dX &

X ) 1 * dY . q + p dX X 1 1 

3 

2  C D  

2

C 1 x  y  x

(B-4) 2	 1 d d (1) d 2 qv d , /0

0

2 , /0

,

0

N (B-12) Sh (Y) dY (Y) dY . qp dX X 1 1 dX X 1 1 2 + 

dC 3

0 (B-5) Because (Y) 4 exp( 5 6 7 2 Y) where 8 9 7 2 is the constant of 

dx x  0  separation for Eq.(B-7), the preceding equation is equivalent to 


  

2 < 

2 qv d < :;

C 2 

;

z D l C 8 9 7 2 d <

;

;

:

D 8 9 7 (1) ? (B-13)


 	

k C(h,y) Ca  dy (B-6) 8 NSh 	 m y dX X 1 > qp dX X 1 x  h     qp  x 


 x  h 

can be nondimensionalized into	 which can be manipulated to yield 

  2	 9 7 2 

3 NSh 1  X 8

2  < (1) 

	

(B-7) (B-14) 2 9 7 2 8 (2 qv Y  X / 2	

d

d 

< 

X 

:;

;

x 1 > qp) NSh 

d  Although this boundary condition is dependent on the eigenvalue 

0 (B-8) 7 , the eigenvalue expansion for the solution of Eq.(B-7) is still dX 


 X   0	 straightforward (Walter 1973; Fulton 1977). Note that as the 

fish’s perfusion rate increases, this boundary condition 

   converges to (1,Y)  

2 h z V NGz   NSh  dY (B-9) 




X 

X  1 qp Y X 
 X  1 d < :;

;

8 NSh < (1) (B-15)

The boundary condition (B-9) that describes exchange across the dX x 1 > 

secondary lamella, however, can be simplified by noting that the  

solution of Eq.(B-7) is separable, i.e., (X, Y) =  (X) (Y) and which is the boundary condition previously used by Barber et al. 

that qv = h z V is the ventilation volume of an individual (1991). 

82

APPENDIX C. Derivation of the consistency condition for feeding electivities. 

 

f

i �

To derive a self consistency condition on a fish’s electivities and Adding 1 to each side of the above equation one


relative prey availabilities such that its calculate dietary obtains the desired result, i.e.,


frequencies will sum to unity, consider the following


 

ei f

i 

f

i � 

1 di  

�

ei di 

f

f

i

i (C-1) 1 ei  

ei (di f

i

) � 

di  f

i (C-2) 1 

ei  f

e

i

i 



f

i � 

1 (C-6) 

 

f 1 i �



ei 1 di � 

f

i (C-3) 1  

ei 

1 ei 

Summing Eq. (C-2) over all i then yields 

   

ei (di f

i

) � 

di  f

i � 

0 (C-4) 

When Eq.(C-3) is substituted into this expression, one then 

obtains 

 

1 

ei f

f � 

e 0 i f  i 1 ei  

2 ei f

i
 � 

0 (C-5)
 

1 ei  

ei f

i � 

0 

1 ei 

83 
APPENDIX D. Example project file constructed using include files as discussed in Section 4.4. 

!


! file: evergld1.prj


! date: sept. 19, 2000


!


! notes: project file (*.prj) for BASS version 2.1. constructed to simulate


! methylmercury bioaccumulation in a 'typical' deep-water fish community 

! of the Florida Everglades, USA. 

! 

! specify control parameters


/ SIMULATION_CONTROL


/ HEADER methylmercury bioaccumulation in a "ponded" everglades community


/ MONTH_T0 april


/ LENGTH_OF_SIMULATION 10[year]


/ TEMPERATURE temp[celsius]=25.0+10.0*sin(0.172142e-01*t[days]+6.02497)


/ WATER_LEVEL depth[meter]=file(nonfish.dat)


/ BIOTA benthos[g/m^2]=file(nonfish.dat); &


 periphyton[g/m^2]=file(nonfish.dat); &


 zooplankton[mg/l]=file(nonfish.dat)


/ ANNUAL_OUTPUTS 10


/ SUMMARY_PLOTS pop(length); cfish(length)


!


! other available plots include:


!


! / SUMMARY_PLOTS afish(age); afish(length); afish(weight); &


! cfish(age); cfish(length); cfish(weight); &


! baf(age); baf(length); baf(weight); &


! bmf(age); bmf(length); bmf(weight); &


! pop(age); pop(length); pop(weight); &


! age(length); age(weight); tl(age); tl(weight); &


! wt(age); wt(length)


! specify chemical properties and exposures for methylmercury


#include 'mercury.chm'


! specify fish community; full community simulation


#include 'evergld1.cmm'


/ END


84 
APPENDIX D. (cont.) Include file MERCURY.CHM for methylmercury properties and exposures. 

!


! file: mercury.chm


! date: sept. 19, 2000


!


! notes: chemical properties and exposure file (*.chm) for BASS version 2.1. constructed


! to simulate methylmercury bioaccumulation in a 'typical' deep-water fish community


! in the Florida Everglades, USA.


#include 'methyl_hg.prp'


!


! refs:


!


! - Loftus, W.F., J.C. Trexler, and R.D. Jones. 1998. Mercury transfer through an


! everglades aquatic food web. final report contrat SP-329. Florida Department


! of Environmental Protection.


! - Stober, J., D. Scheidt, R. Jones, K. Thornton, L. Gandy, J. Trexler, and


! S. Rathbun. 1998. South Florida ecosystem assessment. EPA-904-R-002


! - Watras, C. and N. Bloom. 1992. Mercury and methylmercury in individual


! zooplankton: implications for bioaccumulation. Limnol. Oceanogr. 37:1313-1318.


!


! based on Loftus et al. (1998) total mercury concentrations are


! 

! cwater[ng/l] = 1.13 for total mercury 

! = 0.15*1.13 for methymercury 

! cphytn[ng/g(fw)] = 76.11 

! 57.85(n=4) utricularia


! 90.44(n=5) diatoms


! 76.58(n=3) chlorophyta


! cinsct[ng/g(fw)] = 212.17


! 258.04(n=9) dolomedes


! 148.95(n=6) hydracarina


! 304.29(n=12) tetragonids


! 136.23(n=15) unid spiders


! czplnk[ng/g(fw)] = 54.60


! 46.35(n=10) cladocera


! 62.90(n=12) copepoda


! 53.39(n=14) ostracoda


! cbnths[ng/g(fw)] = 83.91


! 38.03(n=18) chironomids


! 38.89(n=9) gastropoda-littoridinops


! 8.92(n=5) gastropoda-melanoides


! 50.05(n=12) gastropoda-physella


! 14.21(n=9) gastropoda-planorbella


! 14.76(n=2) gastropoda-planorbella


! 19.26(n=13) gastropoda-pomacea


! 126.55(n=20) hemiptera-belostoma


! 95.98(n=22) hemiptera-pelocoris


! 44.85(n=23) hyalella


! 91.58(n=25) odonata-libellulidae


! 186.31(n=41) palaemonetes


! 18.90(n=8) pelycepoda-villosa


! 64.33(n=24) procambarus


!


! assume


!


! g(dw)/g(fw) = 0.2 (Watras and Bloom 1992)


! mehg/total hg = 0.15 in water (Stober et al. 1998)


! mehg/total hg = 0.20 in phytoplankton (Watras and Bloom 1992)


! mehg/total hg = 0.60 in zooplankton (Watras and Bloom 1992)


! mehg/total hg > 0.90 in fish (Watras and Bloom 1992)


!


/ EXPOSURE cwater[ng/l]=0.444; cinsct[ppb]=212.17/0.2; &


 cphytn[ppb]=(0.2*16.74/0.2)/(1.13*0.15)*cwater[ng/l]; &


 czplnk[ppb]=(0.6*54.60/0.2)/(1.13*0.15)*cwater[ng/l]; &


 cbnths[ppb]=(0.6*83.91/0.2)/(1.13*0.15)*cwater[ng/l]


! end mercury.chm


85 
APPENDIX D. (cont.) Include file METHYL_HG.PRP for methylmercury properties. 

!


! file: methyl_hg.prp


! date: sept. 19, 2000


!


! specify chemical properties for methylmercury


!


! refs:


! - Arnold, A.P. and A.J. Canty. 1983. Methylmercury(II) sulfhydryl interactions.


! Potentiometric determinations of the formation constants for complexation of 

! methylmercury(II) by sulfhydryl containing amino acids and related molecules 

! including gltathione. Can.J.Chem. 61:1428-1434. 

! - Benoit, J.M., R.P. Mason, and C.C. Gilmore. 1999a. Estimation of mercury-sulfid


! speciation in sediment pore waters using octanol-water partitioning and implications


! for availability to methylating bacteria. Environ. Toxicol. Chem. 18:2138-2141.


! - Benoit, J.M., C.C. Gilmore, R.P. Mason, and A. Heyes. 1999b. Sulfide controls on


! mercury speciation and bioavailability to methylating bacteria in sediment pore


! waters. Environ. Sci. Technol. 33:951-957.


! - Major, M.A., D.H. Rosenblatt, and K.A. Bostian. 1991. The octanol/water


! partition coeffiecent of methylmercury chloride and methylmercury hydroxide


! in pure water and salt solutions. Environ.Toxicol.Chem. 10:5-8.


! - Simpson, R.B. 1961. Association constants of methylmercury with sulfhydryl and


! other bases. J.Am.Chem.Soc. 83:4711-4717.


!


! notes: Simpson (1961) reports that for cysteine log(kb)=log(k2)=7.1 and for


! glutathione log(kb)=log(k2)=6.9. results of Arnold and Canty (1983),


! however, estimate log(kb)=log(beta_110)-pka=16.46-8.22=8.24. therefore


! assume log(kb)=(7.1+8.24)/2=7.67


!


/ CHEMICAL methylmercury


/ LOG_KB1 6.00 ! assumed


/ LOG_KB2 5.00 ! assumed


/ LOG_P -0.4 ! kow = 0.4 at physiological pH; see Major et al (1991)


/ MOLAR_VOLUME 51 ! calculated using liquid referenced molar volume of dimethylmercury


/ MOLAR_WEIGHT 215.6


/ MELTING_POINT 25


! end methyl_hg.prp


86 
APPENDIX D. (cont.) Include file EVERGLD1.CMM for community structure parameters. 

! file: evergld1.cmm


! date: sept. 19, 2000


!


! notes: community file (*.cmm) for BASS version 2.1. constructed to simulate methylmercury


! bioaccumulation in a 'typical' deep-water fish community in the Florida Everglades, 

! USA. 

! 

! specify fish community


!


#include 'lgmouth.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(0<l[mm]<20)={zooplankton=100}; &


 diet(20<l[mm]<100)={zooplankton=35, benthos=35, bluegill=0, redear=0, gambusia=0}; &


 diet(100<l[mm]<200)={benthos=50, bass=0, bluegill=0, redear=0, gambusia=0}; &


 diet(200<l[mm]<600)={benthos=25, bass=0, bullhead=0, bluegill=0, redear=0}


/ INITIAL_CONDITIONS &


 age[day]={ 320., 685., 1050., 1415., 1780., 2145., 2510., 2875.}; &


 wt[g]={ 127., 294., 501., 740., 1008., 1302., 1618., 1957.}; &


 pop[fish/ha]={ 12.56, 6.70, 4.49, 3.35, 2.66, 2.19, 1.86, 1.62} ! 20.00[kg/ha]


#include 'gar.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(0<l[mm]<20)={zooplankton=100}; &


 diet(20<l[mm]<100)={zooplankton=25,benthos=25, bass=0, bluegill=0, redear=0, gambusia=0}; &


 diet(100<l[mm]<1000)={benthos=25, bass=0, bluegill=0, redear=0, gambusia=0}


/ INITIAL_CONDITIONS &


 age[day]={ 350., 715., 1080., 1445., 1810.}; &


 wt[g]={ 269., 511., 747., 980., 1210.}; &


 pop[fish/ha]={ 5.90, 3.65, 2.74, 2.24, 1.91} ! 10.00[kg/ha]


#include 'bullhead.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(0<l[mm]<50)={benthos=100}; &


 diet(50<l[mm]<500)={benthos=0, bullhead=0, redear=0}


/ INITIAL_CONDITIONS &


 age[day]={ 350., 715., 1080., 1445., 1810.}; &


 wt[g]={ 81., 219., 418., 674., 986.}; &


 pop[fish/ha]={ 33.53, 15.90, 9.80, 6.85, 5.15} ! 20.00[kg/ha]


#include 'bluegill.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(00<l[mm]<50)={zooplankton=100}; &


 diet(50<l[mm]<150)={zooplankton=0, gambusia=0, benthos=20}


/ INITIAL_CONDITIONS &


 age[day]={ 350., 715., 1080., 1445., 1810.}; &


 wt[g]={ 25., 55., 95., 143., 198.}; &


 pop[fish/ha]={ 1187.79, 643.79, 429.04, 316.59, 248.26} ! 200.00[kg/ha]


#include 'redear.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(00<l[mm]<50)={zooplankton=100}; &


 diet(50<l[mm]<60)={zooplankton=90, benthos=10}; &


 diet(60<l[mm]<70)={zooplankton=60, benthos=40}; &


 diet(70<l[mm]<80)={zooplankton=30, benthos=70}; &


 diet(80<l[mm]<150)={zooplankton=20, benthos=80}


/ INITIAL_CONDITIONS &


 age[day]={ 320., 685., 1050., 1415., 1780.}; &


 wt[g]={ 39., 91., 151., 218., 291.}; &


 pop[fish/ha]={ 375.86, 199.22, 135.84, 103.17, 83.23} ! 100.00[kg/ha]


#include 'gambusia.fsh'


/ ECOLOGICAL_PARAMETERS &


 diet(0<l[mm]<10)={zooplankton=100}; &


 diet(10<l[mm]<40)={zooplankton=0, gambusia=0}


/ INITIAL_CONDITIONS &


 age[day]={ 20., 170., 200., 230.}; &


 wt[g]={0.043, 0.260, 0.315, 0.374}; &


 pop[fish/ha]={39159.31, 10158.52, 8794.47, 7743.90} ! 10.00[kg/ha]


! end evergld1.cmm


87 
APPENDIX D. (cont.) Include file LGMOUTH.FSH for basic largemouth bass parameters. 

! file: lgmouth.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Beamish, F.W.H. 1970. Oxygen consumption of largemouth bass, Micropterus salmoides, in


! relation to swimming speed and temperature. Can.J.Zool. 48:1221-1228.


! - Beamish, F.W.H. 1974. Apparent specific dynamic action of largemouth bass, Micropterus


! salmoides. J.Fish.Res.Bd.Can. 31:1763-1769.


! - Carlander, K.D. 1977. Handbook of Freshwater Fishery Biology, vol 2. Iowa State University


! Press. Ames, IA.


! - Glass, N.R. 1969. Discussion of the calculation of power function with special reference to


! respiratory metabolism in fish. J.Fish.Res.Bd Can. 26:2643-2650.


! - Lewis, W.M., R. Heidinger, W. Kirk, W. Chapman, and D. Johnson. 1974. Food intake of the


! largemouth bass. Trans.Am.Fish.Soc. 103:277-280.


! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985. National Contaminant


! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.


! Arch.Environ.Contam.Toxicol. 14:363-388.


! - Niimi, A.J. and F.W.H. Beamish. 1974. Bioenergetics and growth of largemouth bass


! (Micropterus salmoides) in relation to body weight and temperature. Can.J.Zool. 52:447-456.


! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


! - Price, J.W. 1931. Growth and gill development in the small-mouthed black bass, Micropterus


! dolomieu, Lacepede. Ohio State University, Franz Theodore Stone Laboratory 4:1-46.


! - Schmitt, C.J., and W.G. Brumbaugh. 1990. National Contaminant Biomonitoring Program:


! Concentrations of arsenic, cadmium, lead, mercury, selenium, and zinc in U.S. freshwater


! fish, 1976-1984. Arch.Environ.Contam.Toxicol. 19:731-747.


! - Schmitt, C.J., J.L. Zajicek, and P.H. Peterman. 1990. National Contaminant Biomonitoring


! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984. Arch.


! Environ.Contam.Toxicol. 19:748-781.


! - Tandler, A. and F.W.H. Beamish. 1981. Apparent specific dynamic action (SDA), fish weight,


! and level of caloric intake in largemouth bass, Micropterus salmoides Lacepede.


! Aquaculture 23:231-242.


! - Timmons, T.J. and W.L. Shelton. 1980. Differential growth of largemouth bass in West Point


! Reservoir, Alabama-Georgia. Trans.Am.Fish.Soc. 109:176-186.


/ COMMON_NAME bass


/ SPECIES Micropterus salmoides


/ AGE_CLASS_DURATION year


/ SPAWNING_PERIOD may-june


/ ECOLOGICAL_PARAMETERS &


 lp[cm]=0.6+0.27*L[cm]; & ! estimated from Timmons and Shelton (1980) for Lepomis


 wl[g]=0.0117*L[cm]^3.08; & ! Carlander (1977) 0.00543 adjusted such that 2.0kg = 50cm


 tl_r0[mm]= 150; & ! Carlander (1977)


 yoy[g]=25.0; &


 mls[year]=8; &


 nm[1/day]=0.9*1.0*0.0814*W[g]^(-.675) ! see sg[] and assume exogenous mortality/total mortality = .9 and b=1


/ COMPOSITIONAL_PARAMETERS &


 pa[-]=0.80-1.57*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


 pl[-]=0.000121*W[g]^0.845 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2]=7.32*W[g]^0.820; & ! Price (1931)


 ld[lamellae/mm_per_side]=31.28*W[g]^(-.072); & ! Price (1931)


 ll[cm]=0.0188*W[g]^0.294 ! assumed (see Barber et al. 1991)


/ FEEDING_OPTIONS linear(1<a[yr]<10)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=1.0; & ! assumed (see Barber et al. 1991)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.127; & ! Beamish (1974), Tandler and Beamish (1981)


 sg[g/g/day](25)=0.0814*W[g]^(-.675); & ! Carlander (1977) assuming wt(yoy)=25 and wt(8)=2000


 so[mg(o2)/hr]=0.1187*EXP(0.0428*t[celsius])*W[g]^0.766 ! Glass (1969), Beamish (1970), Niimi and Beamish (1974)


! end lgmouth.fsh


88 
APPENDIX D. (cont.) Include file GAR.FSH for basic Florida gar parameters. 

! file: gar.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Brim et al. 1993. Mercury concentrations in largemouth bass and other fishes of the


! Loxahatchee National Wildlife Refuge. U.S. Fish and Wildlife Service publ.no. PCFO-EC 93-02.


! - Carlander, K.D. 1969. Handbook of Freshwater Fishery Biology, vol 1. Iowa State University


! Press. Ames, IA.


! - Glass, N.R. 1969. Discussion of the calculation of power function with special reference to


! respiratory metabolism in fish. J.Fish.Res.Bd Can. 26:2643-2650.


! - Landolt, J.C. and L.G. Hill. 1975. Observations on the gross structure and dimensions of the


! gills of three species of gars (Lepisosteidae). Coepia 1975(3):470-475.


! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


! - Rahn, H., K.B. Rahn, B.J. Howell, C. Gans, and S.M. Tenney. 1971. Air breathing of the


! garfish (Lepisosteus osseus). Respir.Physiol. 11:285-307.


! - Smatresk, N.J. and J.N. Cameron. 1982. Respiration and acid-base physiology of the spotted


! gar, a bimodel breather II. responces to temperature change and hypercapnia. J.Exp.Biol. 96:281-293.


! - Winger, P.V. and J.K. Andreasen. 1985. Contaminant residues in fish and sediments from


! lakes in the Atchafalaya River Basin (Louisiana). Arch.Environ.Contam.Toxicol. 14:579-586.


/ COMMON_NAME gar


/ SPECIES Lepisosteus platyrhincus


/ AGE_CLASS_DURATION year


/ SPAWNING_PERIOD april-may


/ ECOLOGICAL_PARAMETERS &


 lp[cm]=0.15*L[cm]; & ! assumed


 wl[g]=0.00171*L[cm]^3.30; & ! Carlander (1969) for L. osseus 0.00065 adjusted such that 2.3 kg=720 cm


 tl_r0[mm]= 330; & ! Carlander (1969)


 yoy[g]=25.0; &


 mls[year]=5; &


 nm[1/day]=1.0*0.882*W[g]^(-1.048) ! see sg[], assume exogenous mortality/total mortality = 1 and b=1.0


/ COMPOSITIONAL_PARAMETERS &


 pa[-] = 0.82-1.25*pl[-]; & ! assumed (see Barber et al. 1991)


 pl[-]=0.06 ! Winger and Andreasen (1985)


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2]=3.94*W[g]^0.738; & ! Landolt and Hill(1975)


 ld[lamellae/mm_per_side]=38.8*W[g]^(-.0603); & ! Landolt and Hill (1975)


 ll[cm]=0.0188*W[g]^0.294 ! assumed (see Barber et al. 1991)


/ FEEDING_OPTIONS linear(1<a[yr]<10)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=0.9; & ! Rahn et al. (1971) and Smatresk and Cameron (1982)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.17; & ! assumed (see Barber et al. 1991)


 sg[g/g/day](25)=.882*W[g]^(-1.048); & ! Carlander (1969) and Hunt (1952) assuming wt(yoy)=25 and wt(5)=1219


 so[ml(o2)/kg/minute]=.43*exp(ln(.70/.43)/10*(t[celsius]-22)) ! Smatresk and Cameron (1982) for L. oculatus


! end gar.fsh


89 
APPENDIX D. (cont.) Include file BULLHEAD.FSH for basic bullhead parameters. 

! file: bullhead.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Campbell, R.D. and B.A. Branson. 1978. Ecology and population dynamics of the black


! bullhead, Ictalurus melas (Rafinesque), in central Kentucky. Tulane Studies in Zoology


! and Botany 20:99-136.


! - Carlander, K.D. 1969. Handbook of Freshwater Fishery Biology, vol 1. Iowa State University


! Press. Ames, IA.


! - Glass, N.R. 1969. Discussion of the calculation of power function with special reference to


! respiratory metabolism in fish. J.Fish.Res.Bd.Can. 26:2643-2650.


! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985. National Contaminant


! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.


! Arch.Environ.Contam.Toxicol. 14:363-388.


! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


! - Saunders, R.L. 1962. The irrigation of the gills in fishes II. Efficiency of oxygen uptake in


! relation to respiratory flow, activity and concentrations of oxygen and carbon dioxide.


! Can.J.Zool. 40:817-862.


! - Schmitt, C.J., and W.G. Brumbaugh. 1990. National Contaminant Biomonitoring Program:


! Concentrations of arsenic, cadmium, lead, mercury, selenium, and zinc in U.S. freshwater


! fish, 1976-1984. Arch.Environ.Contam.Toxicol. 19:731-747.


! - Schmitt, C.J., J.L. Zajicek, and P.H. Peterman. 1990. National Contaminant Biomonitoring


! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.


! Arch.Environ.Contam.Toxicol. 19:748-781.


/ COMMON_NAME bullhead ! yellow bullhead


/ SPECIES Ameiurus natalis


/ AGE_CLASS_DURATION year


/ SPAWNING_PERIOD march-april


/ ECOLOGICAL_PARAMETERS &


 lp[cm]=0.25*L[cm]; & ! assumed


 wl[g]=0.0304*L[cm]^2.82; & ! Carlander (1969) adjusted such that 1kg = 40cm


 tl_r0[mm] = 150; & ! Carlander (1969)


 yoy[g]=10.0; & ! assumed


 mls[year]=5; &


 nm[1/day]=0.90*0.0382*W[g]^(-.537) ! see sg[] and assume exogenous mortality/total mortality = 0.9 and b=1


/ COMPOSITIONAL_PARAMETERS &


 pa[-]=0.80-0.94*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


 pl[-]=0.08 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2]=4.98*W[g]^0.728; & ! Saunders (1962) for brown bullhead


 id[cm]=9.26e-4*W[g]^0.200; & ! Brockway et al. for channel catfish


 ld[lamellae/mm_per_side]=15.9*W[g]^(-0.00917); & ! Saunders (1962) for brown bullhead


 ll[cm]=8.96e-3*W[g]^0.270 ! Brockway et al. for channel catfish


/ FEEDING_OPTIONS linear(1<a[yr]<5)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=1.0; & ! assumed (see Barber et al. 1991)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.17; & ! assumed (see Barber et al. 1991)


 sg[g/g/day](25)=0.0382*W[g]^(-.537); & ! Carlander (1969) assuming wt(yoy)=10 and wt(5)=1000.0


 so[mg(o2)/hr]=0.0012*EXP(0.1838*t[celsius])*W[g]^1.02 ! Campbell and Branson (1978) Glass (1969)


! end bullhead.fsh


90 
APPENDIX D. (cont.) Include file BLUEGILL.FSH for basic bluegill parameters. 

! file: bluegill.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Carlander, K.D. 1977. Handbook of Freshwater Fishery Biology, vol 2. Iowa State University


! Press. Ames, IA.


! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985. National Contaminant


! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.


! Arch.Environ.Contam.Toxicol. 14:363-388.


! - O'Hara, J. The influence of weight and temperature on the metabolic rate of sunfish. Ecology


! 49:159-161.


! - Osenberg, C.W. M.H. Olson, and G.G. Mittelbach. 1994. Stage structure in fishes: Resource


! productivity and competition gradients. In: D.J. Stouder, K.L. Fresh, R.J. Feller (eds); 

! M. Duke (ass.ed.). Theory and application in fish feeding ecology. University of South 

! Carolina Press. p 151-170. 

! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


! - Pierce, R.J. and T.E. Wissing. 1974. Energy cost of food utilization in the bluegill (Lepomis


! macrochirus). Trans.Am.Fish.Soc. ??:38-44.


! - Price, J.W. 1931. Growth and gill development in the small-mouthed black bass, Micropterus


! dolomieu, Lacepede. Ohio State University, Franz Theodore Stone Laboratory 4:1-46.


! - Schmitt, C.J., and W.G. Brumbaugh. 1990. National Contaminant Biomonitoring Program:


! Concentrations of arsenic, cadmium, lead, mercury, selenium, and zinc in U.S. freshwater


! fish, 1976-1984. Arch.Environ.Contam.Toxicol. 19:731-747.


! - Schmitt, C.J., J.L. Zajicek, and P.H. Peterman. 1990. National Contaminant Biomonitoring


! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.


! Arch.Environ.Contam.Toxicol. 19:748-781.


! - Wohlschlag, D.E. and R.O. Juliano. Seasonal changes in bluegill metabolism. Limnog.


! Oceanog. 4:195-209.


/ COMMON_NAME bluegill


/ SPECIES Lepomis macrochirus


/ AGE_CLASS_DURATION year


/ SPAWNING_PERIOD april-june


/ ECOLOGICAL_PARAMETERS &


 lp[cm]=0.15*L[cm]; & ! assumed


 wl[g]=0.0209*L[cm]^3.06; & ! Carlander (1977) adjusted such that 200g = 20cm


 tl_r0[mm]= 80; & ! Carlander (1977)


 yoy[g]=5.0; & ! assumed


 mls[year]=5; &


 nm[1/day]=0.1*0.75*0.0208*W[g]^(-.615) ! see sg[] and assume exogenous mortality/total mortality = 0.1


/ COMPOSITIONAL_PARAMETERS &


 pa[-]=0.781-0.94*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


 pl[-]=0.0597 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2]=7.32*W[g]^0.820; & ! Price (1931)


 id[cm]=1.15e-3*W[g]^0.172; & ! Brockway et al.


 ll[cm]=6.55e-3*W[g]^0.259 ! Brockway et al.


/ FEEDING_OPTIONS linear(1<a[yr]<5)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=1.0; & ! assumed (see Barber et al. 1991)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.127; & ! Pierce and Wissing (1974)


 sg[g/g/day](25)=0.0208*W[g]^(-.615);& ! Carlander (1977) assuming wt(yoy)=5 and wt(5)=200


 so[mg(o2)/hr]=0.0243*EXP(0.1409*t[celsius])*W[g]^0.849 ! o'Hara (1968), Wohlschlag and Juliano (1959)


! end bluegill.fsh


91 
APPENDIX D. (cont.) Include file REDEAR.FSH for basic redear sunfish (shell cracker) parameters. 

! file: redear.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Carlander, K.D. 1977. Handbook of Freshwater Fishery Biology, vol 2. Iowa State University


! Press. Ames, IA.


! - Evans, D.O. 1984. Temperature independence of the annual cycle of standard metabolism in


! the pumpkinseed. Trans.Amer.Fish.Soc. 113:494-512.


! - Lowe, T.P., T.W. May, W.G. Brumbaugh, and D.A. Kane. 1985. National Contaminant


! Biomonitoring Program: concentrations of seven elements in freshwater fish, 1979-1981.


! Arch.Environ.Contam.Toxicol. 14:363-388.


! - O'Hara, J. The influence of weight and temperature on the metabolic rate of sunfish. Ecology


! 49:159-161.


! - Osenberg, C.W. M.H. Olson, and G.G. Mittelbach. 1994. Stage structure in fishes: Resource


! productivity and competition gradients. In: D.J. Stouder, K.L. Fresh, R.J. Feller (eds); 

! M. Duke (ass.ed.). Theory and application in fish feeding ecology. University of South 

! Carolina Press. p 151-170. 

! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


! - Pierce, R.J. and T.E. Wissing. 1974. Energy cost of food utilization in the bluegill (Lepomis


! macrochirus). Trans.Am.Fish.Soc. ??:38-44.


! - Price, J.W. 1931. Growth and gill development in the small-mouthed black bass, Micropterus


! dolomieu, Lacepede. Ohio State University, Franz Theodore Stone Laboratory 4:1-46.


! - Schmitt, C.J., and W.G. Brumbaugh. 1990. National Contaminant Biomonitoring Program:


! Concentrations of arsenic, cadmium, lead, mercury, selenium, and zinc in U.S. freshwater


! fish, 1976-1984. Arch.Environ.Contam.Toxicol. 19:731-747.


! - Schmitt, C.J., J.L. Zajicek, and P.H. Peterman. 1990. National Contaminant Biomonitoring


! Program: Residues of organochlorine chemicals in U.S. freshwater fish, 1976-1984.


! Arch.Environ.Contam.Toxicol. 19:748-781.


! - Wilbur, R.L. 1969. The redear sunfish in Florida. Florida Game and Fresh Water Fish


! Commission. Fishery Bull no. 5.


/ COMMON_NAME redear ! shellcraker


/ SPECIES Lepomis microlophus


/ AGE_CLASS_DURATION year


/ SPAWNING_PERIOD may-june


/ ECOLOGICAL_PARAMETERS &


 wl[g]=0.0148*L[cm]^3.08; & ! Carlander (1977) adjusted such that 300g = 25cm


 tl_r0[mm]= 140; & ! Wilbur (1969)


 yoy[g]=5.0; & ! assumed


 mls[year]=5; &


 nm[1/day]=0.3*0.75*0.0528*W[g]^(-.761) ! see sg[] and assume exogenous mortality/total mortality = 0.1


/ COMPOSITIONAL_PARAMETERS &


 pa[-]=0.781-0.941*pl[-]; & ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


 pl[-]=0.0597 ! Lowe et al. (1985), Schmitt and Brumbaugh (1990), Schmitt et al. (1990)


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2]=7.32*W[g]^0.820; & ! Price (1931)


 id[cm]=1.15e-3*W[g]^0.172; & ! Brockway et al.


 ll[cm]=6.55e-3*W[g]^0.259 ! Brockway et al.


/ FEEDING_OPTIONS linear(1<a[yr]<5)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=1.0; & ! assumed (see Barber et al. 1991)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.127; & ! Pierce and Wissing (1974)


 sg[g/g/day](25)=0.0528*W[g]^(-.761);& ! Carlander (1972) assuming wt(yoy)=5 and wt(5)=300


 so[mg(o2)/hr]=0.0474*EXP(0.0438*t[celsius])*W[g]^0.744 ! Evans (1984), o'Hara (1968)


! end redear.fsh


92 
APPENDIX D. (cont.) Include file GAMBUSIA.FSH for basic Gambusia parameters. 

! file: gambusi1.fsh


! date: sept. 19, 2000


!


! notes: fish file (*.fsh) for BASS version 2.1


!


! refs:


! - Barber, M.C., L.A. Suarez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic


! pollutants in fish with an application to PCBs in Lake Ontario salmonids.


! Can.J.Fish.Aquat.Sci. 48:318-337.


! - Haake, P.W. and J.M. Dean. 1983. Age and growth of four Everglades fishes using otolith


! techniques. Everglades National Park. Tech. Rep. SFRC-83/03. pp 68.


! - Kushlan, J.A., S.A. Voorhees, W.F. Loftus, and P.C. Frohring. 1986. Length, mass, and


! calorific relationships of Everglades animals. Fla. Sci. 49:65-79.


! - Meffe, G.K. and F.F. Snelson, jr. 1993. Lipid dynamics during reproduction in two


! livebearing fishes, Gambusia holbrooki and Poecilia latipinna. Can.J.Fish.Aquat.Sci.


! 50:2185-2191.


! - Murphy, P.G. and J.V. Murphy. 1971. Correlations between respiration and direct uptake of


! DDT in the mosquito fish Gambusia affinis. Bull.Environ.Contam.Toxicol. 6:581-588.


! - Pandian, T.J. and F.J. Vernberg. 1987. Animal Energetis - v. 2. Bivalvia through Reptilia. 


! Academic Press.


/ COMMON_NAME gambusia ! mosquitofish


/ SPECIES Gambusia affinis


/ AGE_CLASS_DURATION month


/ SPAWNING_PERIOD march-october


/ COMPOSITIONAL_PARAMETERS &


 pa[-] = 0.82-1.25*pl[-]; & ! assumed (see Barber et al. 1991)


 pl[-] = 0.125 ! Meffe and Snelson(1993)


/ ECOLOGICAL_PARAMETERS &


 lp[mm]= 0.2*L[mm]; & ! assumed


 log(wl[g])=-4.786+3.032*log(L[mm]); & ! std len Kushlan et al. (1986)


 tl_r0[mm] = 35; & ! Carlander (1969)


 yoy[g]=0.025; & ! assumed


 mls[day] = 240; &


 nm[1/day] = 0.1*0.75*0.0027*W[g]^(-0.693) ! see Haake and Dean below


/ MORPHOMETRIC_PARAMETERS &


 ga[cm^2] = 2.606*W[g]^0.883; & ! Murphy and Murphy (1971)


 ld[lamellae/mm_per_side] = 28.1*W[g]^(-0.0731); & ! interspecific geometric mean


 ll[cm] = 0.0188*W[g]^0.294 ! assumed (Barber et al. 1991)


/ FEEDING_OPTIONS linear(0<a[year]<1)


/ PHYSIOLOGICAL_PARAMETERS &


 ae_fish[-]=0.89; & ! assumed (see Pandian and Verberg 1987)


 ae_invert[-]=0.66; & ! assumed (see Pandian and Verberg 1987)


 ae_plant[-]=0.44; & ! assumed (see Pandian and Verberg 1987)


 rq[-]=1.0; & ! assumed (see Barber et al. 1991)


 rt:std[-]=2.0; & ! BASS default


 sda:in[-]=0.17; & ! assumed (see Barber et al. 1991)


 sg[g/g/day](25)=0.0027*W[g]^(-.693);& ! Haake and Dean (1983) assuming wt(yoy)=0.025 wt(8)=0.4


 so[mg(o2)/hr] = 0.0223*EXP(0.0552*t[celsius])*W[g]^0.695 ! Murphy and Murphy (1971)


! end gambusia.fsh


93 
APPENDIX D. (cont.) Include file for nonfish prey and water level. 

!


! file: nonfish.dat


! date: Tue Apr 11 13:49:08 2000


!


! notes: BASS 2.1 demonstration file showing the use and structure of


! a BASS exposure file. this file is equivalent to the following 

! BASS commands 

! 

! /BIOTA benthos[g/m^2]=5.0 ;& 

! periphyton[g/m^2]=0.0 ; & 

! zooplankton[mg/l]=0.2 

! /WATER_LEVEL depth[meter]=2.0 

! 

/001 time[day]


/002 benthos[g/m^2]


/003 periphyton[g/m^2]


/004 zooplankton[mg/l]


/005 depth[meter]


/start_data


1 5.0 0.0 0.2 2.0


5000 5.0 0.0 0.2 2.0


94

APPENDIX E. Example output file (filename.msg) that summarizes user input data, input data errors, 

and run time warnings and errors. 

! file : evergld1.msg


! input file : evergld1.prj (Tue Dec 05 15:58:40 2000)


! program file: C:\BASS\BASS_V21.EXE (Thu Jan 11 11:28:48 2001)


!


 GETINPT: summary of user commands in compressed format


 / simulation_control


 / header methylmercury bioaccumulation in a "ponded" everglades community


 / month_t0 april


 / length_of_simulation 10[year]


 / temperature temp[celsius]=25.0+10.0*sin(0.172142e-01*t[days]+6.02497)


 / water_level depth[meter]=file(nonfish.dat)


 / biota benthos[g/m^2]=file(nonfish.dat); periphyton[g/m^2]=file(nonfish.dat); zooplankton[mg/l]=file(nonfish.dat)


 / annual_outputs 10


 / summary_plots pop(length); cfish(length)


 / chemical methylmercury


 / log_kb1 6.00


 / log_kb2 5.00


 / log_p -0.4


 / molar_volume 51


 / molar_weight 215.6


 / melting_point 25


 / exposure cwater[ng/l]=0.444; cinsct[ppb]=212.17/0.2; cphytn[ppb]=(0.2*16.74/0.2)/(1.13*0.15)*cwater[ng/l];


 czplnk[ppb]=(0.6*54.60/0.2)/(1.13*0.15)*cwater[ng/l]; cbnths[ppb]=(0.6*83.91/0.2)/(1.13*0.15)*cwater[ng/l]


 / common_name bass


 / species micropterus salmoides


 / age_class_duration year


 / spawning_period may-june


 / ecological_parameters lp[cm]=0.6+0.27*l[cm]; wl[g]=0.0117*l[cm]^3.08; tl_r0[mm]= 150; yoy[g]=25.0; mls[year]=8;


 nm[1/day]=0.9*1.0*0.0814*w[g]^(-.675)


 / compositional_parameters pa[-]=0.80-1.57*pl[-]; pl[-]=0.000121*w[g]^0.845


 / morphometric_parameters ga[cm^2]=7.32*w[g]^0.820; ld[lamellae/mm_per_side]=31.28*w[g]^(-.072);


 ll[cm]=0.0188*w[g]^0.294


 / feeding_options linear(1<a[yr]<10)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=1.0; rt:std[-]=2.0;


 sda:in[-]=0.127; sg[g/g/day](25)=0.0814*w[g]^(-.675); so[mg(o2)/hr]=0.1187*exp(0.0428*t[celsius])*w[g]^0.766


 / ecological_parameters diet(0<l[mm]<20)={zooplankton=100}; diet(20<l[mm]<100)={zooplankton=35, benthos=35,


 bluegill=0, redear=0, gambusia=0}; diet(100<l[mm]<200)={benthos=50, bass=0, bluegill=0, redear=0, gambusia=0};


 diet(200<l[mm]<600)={benthos=25, bass=0, bullhead=0, bluegill=0, redear=0}


 / initial_conditions age[day]={ 320., 685., 1050., 1415., 1780., 2145., 2510., 2875.}; wt[g]={ 127., 294., 501.,


 740., 1008., 1302., 1618., 1957.}; pop[fish/ha]={ 12.56, 6.70, 4.49, 3.35, 2.66, 2.19, 1.86, 1.62}


 / common_name gar


 / species lepisosteus platyrhincus


 / age_class_duration year


 / spawning_period april-may


 / ecological_parameters lp[cm]=0.15*l[cm]; wl[g]=0.00171*l[cm]^3.30; tl_r0[mm]= 330; yoy[g]=25.0; mls[year]=5;


 nm[1/day]=1.0*0.882*w[g]^(-1.048)


 / compositional_parameters pa[-] = 0.82-1.25*pl[-]; pl[-]=0.06


 / morphometric_parameters ga[cm^2]=3.94*w[g]^0.738; ld[lamellae/mm_per_side]=38.8*w[g]^(-.0603);


 ll[cm]=0.0188*w[g]^0.294


 / feeding_options linear(1<a[yr]<10)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=0.9; rt:std[-]=2.0;


 sda:in[-]=0.17; sg[g/g/day](25)=.882*w[g]^(-1.048); so[ml(o2)/kg/minute]=.43*exp(ln(.70/.43)/10*(t[celsius]-22))


 / ecological_parameters diet(0<l[mm]<20)={zooplankton=100}; diet(20<l[mm]<100)={zooplankton=25,benthos=25, bass=0,


 bluegill=0, redear=0, gambusia=0}; diet(100<l[mm]<1000)={benthos=25, bass=0, bluegill=0, redear=0, gambusia=0}


 / initial_conditions age[day]={ 350., 715., 1080., 1445., 1810.}; wt[g]={ 269., 511., 747., 980., 1210.};


 pop[fish/ha]={ 5.90, 3.65, 2.74, 2.24, 1.91}


 / common_name bullhead


 / species ameiurus natalis


 / age_class_duration year


 / spawning_period march-april


 / ecological_parameters lp[cm]=0.25*l[cm]; wl[g]=0.0304*l[cm]^2.82; tl_r0[mm] = 150; yoy[g]=10.0; mls[year]=5;


 nm[1/day]=0.90*0.0382*w[g]^(-.537)


 / compositional_parameters pa[-]=0.80-0.94*pl[-]; pl[-]=0.08


 / morphometric_parameters ga[cm^2]=4.98*w[g]^0.728; id[cm]=9.26e-4*w[g]^0.200;


 ld[lamellae/mm_per_side]=15.9*w[g]^(-0.00917); ll[cm]=8.96e-3*w[g]^0.270


 / feeding_options linear(1<a[yr]<5)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=1.0; rt:std[-]=2.0;


 sda:in[-]=0.17; sg[g/g/day](25)=0.0382*w[g]^(-.537); so[mg(o2)/hr]=0.0012*exp(0.1838*t[celsius])*w[g]^1.02


 / ecological_parameters diet(0<l[mm]<50)={benthos=100}; diet(50<l[mm]<500)={benthos=0, bullhead=0, redear=0}


 / initial_conditions age[day]={ 350., 715., 1080., 1445., 1810.}; wt[g]={ 81., 219., 418., 674., 986.};


 pop[fish/ha]={ 33.53, 15.90, 9.80, 6.85, 5.15}


 / common_name bluegill


95

 / species lepomis macrochirus


 / age_class_duration year


 / spawning_period april-june


 / ecological_parameters lp[cm]=0.15*l[cm]; wl[g]=0.0209*l[cm]^3.06; tl_r0[mm]= 80; yoy[g]=5.0; mls[year]=5;


 nm[1/day]=0.1*0.75*0.0208*w[g]^(-.615)


 / compositional_parameters pa[-]=0.781-0.94*pl[-]; pl[-]=0.0597


 / morphometric_parameters ga[cm^2]=7.32*w[g]^0.820; id[cm]=1.15e-3*w[g]^0.172; ll[cm]=6.55e-3*w[g]^0.259


 / feeding_options linear(1<a[yr]<5)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=1.0; rt:std[-]=2.0;


 sda:in[-]=0.127; sg[g/g/day](25)=0.0208*w[g]^(-.615);so[mg(o2)/hr]=0.0243*exp(0.1409*t[celsius])*w[g]^0.849


 / ecological_parameters diet(00<l[mm]<50)={zooplankton=100}; diet(50<l[mm]<150)={zooplankton=0, gambusia=0,


 benthos=20}


 / initial_conditions age[day]={ 350., 715., 1080., 1445., 1810.}; wt[g]={ 25., 55., 95., 143., 198.}; pop[fish/ha]={


 1187.79, 643.79, 429.04, 316.59, 248.26}


 / common_name redear


 / species lepomis microlophus


 / age_class_duration year


 / spawning_period may-june


 / ecological_parameters wl[g]=0.0148*l[cm]^3.08; tl_r0[mm]= 140; yoy[g]=5.0; mls[year]=5;


 nm[1/day]=0.3*0.75*0.0528*w[g]^(-.761)


 / compositional_parameters pa[-]=0.781-0.941*pl[-]; pl[-]=0.0597


 / morphometric_parameters ga[cm^2]=7.32*w[g]^0.820; id[cm]=1.15e-3*w[g]^0.172; ll[cm]=6.55e-3*w[g]^0.259


 / feeding_options linear(1<a[yr]<5)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=1.0; rt:std[-]=2.0;


 sda:in[-]=0.127; sg[g/g/day](25)=0.0528*w[g]^(-.761);so[mg(o2)/hr]=0.0474*exp(0.0438*t[celsius])*w[g]^0.744


 / ecological_parameters diet(00<l[mm]<50)={zooplankton=100}; diet(50<l[mm]<60)={zooplankton=90, benthos=10};


 diet(60<l[mm]<70)={zooplankton=60, benthos=40}; diet(70<l[mm]<80)={zooplankton=30, benthos=70};


 diet(80<l[mm]<150)={zooplankton=20, benthos=80}


 / initial_conditions age[day]={ 320., 685., 1050., 1415., 1780.}; wt[g]={ 39., 91., 151., 218., 291.};


 pop[fish/ha]={ 375.86, 199.22, 135.84, 103.17, 83.23}


 / common_name gambusia


 / species gambusia affinis


 / age_class_duration month


 / spawning_period march-october


 / compositional_parameters pa[-] = 0.82-1.25*pl[-]; pl[-] = 0.125


 / ecological_parameters lp[mm]= 0.2*l[mm]; log(wl[g])=-4.786+3.032*log(l[mm]); tl_r0[mm] = 35; yoy[g]=0.025;


 mls[day] = 240; nm[1/day] = 0.1*0.75*0.0027*w[g]^(-0.693)


 / morphometric_parameters ga[cm^2] = 2.606*w[g]^0.883; ld[lamellae/mm_per_side] = 28.1*w[g]^(-0.0731); ll[cm] =


 0.0188*w[g]^0.294


 / feeding_options linear(0<a[year]<1)


 / physiological_parameters ae_fish[-]=0.89; ae_invert[-]=0.66; ae_plant[-]=0.44; rq[-]=1.0; rt:std[-]=2.0;


 sda:in[-]=0.17; sg[g/g/day](25)=0.0027*w[g]^(-.693);so[mg(o2)/hr] = 0.0223*exp(0.0552*t[celsius])*w[g]^0.695


 / ecological_parameters diet(0<l[mm]<10)={zooplankton=100}; diet(10<l[mm]<40)={zooplankton=0, gambusia=0}


 / initial_conditions age[day]={ 20., 170., 200., 230.}; wt[g]={0.043, 0.260, 0.315, 0.374}; pop[fish/ha]={39159.31,


 10158.52, 8794.47, 7743.90}


 / end


96
 decoding and initializing exposure files as required


97
 ckecking user supplied control commands


 CHKCTRL WARNING: insect standing stock not specified


 CHKCTRL WARNING: phytoplankton standing stock not specified


 CHKCTRL: no errors detected


98
 methylmercury bioaccumulation in a "ponded" everglades community 


start time[day]................. april 1


 end time[day]................... 3652.


 integration steps per day....... 8


 ambient water temperature....... temp[celsius] = 25.0+10.0*sin(6.02+1.721E-02*t[day])


 water level..................... depth[meter] = C:\BASS\projects\example1\nonfish.dat,column5


 benthos standing stock.......... bnths[g(DW)/m^2] = C:\BASS\projects\example1\nonfish.dat,column2


 insect standing stock........... insct[g(DW)/m^2] = not_specified


 periphyton standing stock....... phytn[g(DW)/m^2] = C:\BASS\projects\example1\nonfish.dat,column3


 phytoplankton standing stock.... pplnk[g(DW)/l] = not_specified


 zooplankton standing stock...... zplnk[g(DW)/l] = C:\BASS\projects\example1\nonfish.dat,column4


99

 ckecking user supplied chemical commands


 CHKCHEM WARNING: methylmercury - dietary exposure via phytoplankton not specified


 CHKCHEM: no errors detected


100

 methylmercury bioaccumulation in a "ponded" everglades community 


chemical........ methylmercury


 log_ac.......... -0.451 


log_kb1......... 6.00 


log_kb2......... 5.00 


log_p........... -0.400 


melting_point... 25.0 

molar_volume.... 51.0 

molar_weight.... 216. 

biotransformation rate in bass....... bt[1/d]=0.00


 biotransformation rate in gar........ bt[1/d]=0.00


 biotransformation rate in bullhead... bt[1/d]=0.00


 biotransformation rate in bluegill... bt[1/d]=0.00


 biotransformation rate in redear..... bt[1/d]=0.00


 biotransformation rate in gambusia... bt[1/d]=0.00


 LC50 for bass....... LC50[molar]=0.135E-02*Kow^-0.871


 LC50 for gar........ LC50[molar]=0.135E-02*Kow^-0.871


 LC50 for bullhead... LC50[molar]=0.135E-02*Kow^-0.871


 LC50 for bluegill... LC50[molar]=0.135E-02*Kow^-0.871


 LC50 for redear..... LC50[molar]=0.135E-02*Kow^-0.871


 LC50 for gambusia... LC50[molar]=0.135E-02*Kow^-0.871


 benthos dietary exposure.......... cbnths[ppm] = 1.485E+06*cwater[ppm]


 insect dietary exposure........... cinsct[ppm] = 1.06


 periphytic dietary exposure....... cphytn[ppm] = 9.876E+04*cwater[ppm]


 phytoplankton dietary exposure.... cpplnk[ppm] = not_specified


 zooplankton dietary exposure...... czplnk[ppm] = 9.664E+05*cwater[ppm]


 sedimentary exposure.............. csdmnt[ppm] = not_specified


 aqueous exposure.................. cwater[ppm] = 4.440E-07


101
 ckecking user supplied fish commands


 CHKFISH WARNING: bass - default reproductive biomass investment assigned


 CHKFISH WARNING: gar - default reproductive biomass investment assigned


 CHKFISH WARNING: bullhead - default reproductive biomass investment assigned


 CHKFISH WARNING: bluegill - default reproductive biomass investment assigned


 CHKFISH WARNING: redear - default reproductive biomass investment assigned


 CHKFISH WARNING: gambusia - default reproductive biomass investment assigned


 CHKFISH: no errors detected


102

 methylmercury bioaccumulation in a "ponded" everglades community 


common name... bass


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 7.320*W[g]^0.820


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.002*W[g]^0.086


 lamellar density.................... ld[lamellae/mm] = 31.280*W[g]^-0.072


 lamellar length..................... ll[cm] = 0.019*W[g]^0.294


 length of prey...................... lp[cm] = 0.600+0.270*L[cm]


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 2922.


 non-predatory mortality............. nm[1/yr] = 26.8*W[g]^-0.675


 fraction aqueous.................... pa[-] = 0.800-1.570*pl[-]


 fraction lipid...................... pl[-] = 0.000*W[g]^0.845


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 1.000


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.127


 specific growth rate................ sg[1/day] = 0.014*W[g]^-0.675*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.119*W[g]^0.766*exp(0.043*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.012*L[cm]^3.080


 length at first reproduction........ tl_r0[cm] = 15.0


 weight of recruits.................. yoy[g(FW)] = 25.0


 spawning interval................... may-june => day(s) = 62,


 selected feeding models as a function of age or size:


 A[year]< 10.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 2.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00


 L[cm]< 10.0 -1.00 -1.00 -1.00 0.00 0.00 0.00 35.00 -1.00 -1.00 -1.00 35.00


 L[cm]< 20.0 0.00 -1.00 -1.00 0.00 0.00 0.00 50.00 -1.00 -1.00 -1.00 -1.00


 L[cm]< 60.0 0.00 -1.00 0.00 0.00 0.00 -1.00 25.00 -1.00 -1.00 -1.00 -1.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 320. 127.0 12.6 0.000


 2 685. 294.0 6.7 0.000


 3 1050. 501.0 4.5 0.000


 4 1415. 740.0 3.3 0.000


 5 1780. 1008.0 2.7 0.000


 6 2145. 1302.0 2.2 0.000


 7 2510. 1618.0 1.9 0.000


 8 2875. 1957.0 1.6 0.000


 initial standing stock ... 20.01 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


103
 methylmercury bioaccumulation in a "ponded" everglades community 


common name... gar


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 3.940*W[g]^0.738


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.002*W[g]^0.072


 lamellar density.................... ld[lamellae/mm] = 38.800*W[g]^-0.060


 lamellar length..................... ll[cm] = 0.019*W[g]^0.294


 length of prey...................... lp[cm] = 0.000+0.150*L[cm]


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 1826.


 non-predatory mortality............. nm[1/yr] = 322.*W[g]^-1.048


 fraction aqueous.................... pa[-] = 0.820-1.250*pl[-]


 fraction lipid...................... pl[-] = 0.060*W[g]^0.000


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 0.900


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.170


 specific growth rate................ sg[1/day] = 0.156*W[g]^-1.048*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.013*W[g]^1.000*exp(0.049*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.002*L[cm]^3.300


 length at first reproduction........ tl_r0[cm] = 33.0


 weight of recruits.................. yoy[g(FW)] = 25.0


 spawning interval................... april-may => day(s) = 31,


 selected feeding models as a function of age or size:


 A[year]< 10.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 2.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00


 L[cm]< 10.0 0.00 -1.00 -1.00 0.00 0.00 0.00 25.00 -1.00 -1.00 -1.00 25.00


 L[cm]< 100.0 0.00 -1.00 -1.00 0.00 0.00 0.00 25.00 -1.00 -1.00 -1.00 -1.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 350. 269.0 5.9 0.000


 2 715. 511.0 3.7 0.000


 3 1080. 747.0 2.7 0.000


 4 1445. 980.0 2.2 0.000


 5 1810. 1210.0 1.9 0.000


 initial standing stock ... 10.01 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


104
 methylmercury bioaccumulation in a "ponded" everglades community 


common name... bullhead


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 4.980*W[g]^0.728


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.001*W[g]^0.200


 lamellar density.................... ld[lamellae/mm] = 15.900*W[g]^-0.009


 lamellar length..................... ll[cm] = 0.009*W[g]^0.270


 length of prey...................... lp[cm] = 0.000+0.250*L[cm]


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 1826.


 non-predatory mortality............. nm[1/yr] = 12.6*W[g]^-0.537


 fraction aqueous.................... pa[-] = 0.800-0.940*pl[-]


 fraction lipid...................... pl[-] = 0.080*W[g]^0.000


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 1.000


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.170


 specific growth rate................ sg[1/day] = 0.007*W[g]^-0.537*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.001*W[g]^1.020*exp(0.184*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.030*L[cm]^2.820


 length at first reproduction........ tl_r0[cm] = 15.0


 weight of recruits.................. yoy[g(FW)] = 10.0


 spawning interval................... march-april => day(s) = 1,


 selected feeding models as a function of age or size:


 A[year]< 5.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 5.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00 -1.00 -1.00 -1.00 -1.00


 L[cm]< 50.0 -1.00 -1.00 0.00 -1.00 0.00 -1.00 0.00 -1.00 -1.00 -1.00 -1.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 350. 81.0 33.5 0.000


 2 715. 219.0 15.9 *******


 3 1080. 418.0 9.8 0.000


 4 1445. 674.0 6.8 0.000


 5 1810. 986.0 5.2 0.000


 initial standing stock ... 19.99 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


105
 methylmercury bioaccumulation in a "ponded" everglades community 


common name... bluegill


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 7.320*W[g]^0.820


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.001*W[g]^0.172


 lamellar density.................... ld[lamellae/mm] = not_specified


 lamellar length..................... ll[cm] = 0.007*W[g]^0.259


 length of prey...................... lp[cm] = 0.000+0.150*L[cm]


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 1826.


 non-predatory mortality............. nm[1/yr] = 0.570*W[g]^-0.615


 fraction aqueous.................... pa[-] = 0.781-0.940*pl[-]


 fraction lipid...................... pl[-] = 0.060*W[g]^0.000


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 1.000


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.127


 specific growth rate................ sg[1/day] = 0.004*W[g]^-0.615*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.024*W[g]^0.849*exp(0.141*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.021*L[cm]^3.060


 length at first reproduction........ tl_r0[cm] = 8.000


 weight of recruits.................. yoy[g(FW)] = 5.000


 spawning interval................... april-june => day(s) = 47,


 selected feeding models as a function of age or size:


 A[year]< 5.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 5.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00


 L[cm]< 15.0 -1.00 -1.00 -1.00 -1.00 -1.00 0.00 20.00 -1.00 -1.00 -1.00 0.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 350. 25.0 1187.8 0.000


 2 715. 55.0 643.8 0.000


 3 1080. 95.0 429.0 0.000


 4 1445. 143.0 316.6 0.000


 5 1810. 198.0 248.3 0.000


 initial standing stock ... 200.29 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


106
 methylmercury bioaccumulation in a "ponded" everglades community 


common name... redear


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 7.320*W[g]^0.820


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.001*W[g]^0.172


 lamellar density.................... ld[lamellae/mm] = not_specified


 lamellar length..................... ll[cm] = 0.007*W[g]^0.259


 length of prey...................... lp[cm] = not_specified


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 1826.


 non-predatory mortality............. nm[1/yr] = 4.34*W[g]^-0.761


 fraction aqueous.................... pa[-] = 0.781-0.941*pl[-]


 fraction lipid...................... pl[-] = 0.060*W[g]^0.000


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 1.000


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.127


 specific growth rate................ sg[1/day] = 0.009*W[g]^-0.761*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.047*W[g]^0.744*exp(0.044*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.015*L[cm]^3.080


 length at first reproduction........ tl_r0[cm] = 14.0


 weight of recruits.................. yoy[g(FW)] = 5.000


 spawning interval................... may-june => day(s) = 62,


 selected feeding models as a function of age or size:


 A[year]< 5.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 5.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00


 L[cm]< 6.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 10.00 -1.00 -1.00 -1.00 90.00


 L[cm]< 7.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 40.00 -1.00 -1.00 -1.00 60.00


 L[cm]< 8.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 70.00 -1.00 -1.00 -1.00 30.00


 L[cm]< 15.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 80.00 -1.00 -1.00 -1.00 20.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 320. 39.0 375.9 0.000


 2 685. 91.0 199.2 0.000


 3 1050. 151.0 135.8 0.000


 4 1415. 218.0 103.2 0.000


 5 1780. 291.0 83.2 0.000


 initial standing stock ... 100.01 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


107
 methylmercury bioaccumulation in a "ponded" everglades community 


common name... gambusia


 ecological, morphological, and physiological parameters:


 assimilation efficiency (fish)...... ae[-] = 0.890


 assimilation efficiency (inverts)... ae[-] = 0.660


 assimilation efficiency (plant)..... ae[-] = 0.440


 gill area........................... ga[cm^2] = 2.606*W[g]^0.883


 gastric evacuation.................. ge[g(DW)/day] = not_specified


 interlamellar distance.............. id[cm] = 0.002*W[g]^0.087


 lamellar density.................... ld[lamellae/mm] = 28.100*W[g]^-0.073


 lamellar length..................... ll[cm] = 0.019*W[g]^0.294


 length of prey...................... lp[cm] = 0.000+0.200*L[cm]


 maximum filtering................... mf[L/day] = not_specified


 maximum ingestion................... mi[g(DW)/day] = not_specified


 maximum longevity................... mls[day] = 240.


 non-predatory mortality............. nm[1/yr] = 0.740E-01*W[g]^-0.693


 fraction aqueous.................... pa[-] = 0.820-1.250*pl[-]


 fraction lipid...................... pl[-] = 0.125*W[g]^0.000


 reproductive biomass investment..... rbi[-] = 0.150


 respiratory quotient................ rq[-] = 1.000


 routine:standard VO_2............... rt:std[-] = 2.000


 SDA:ingestion ratio................. sda:in[-] = 0.170


 specific growth rate................ sg[1/day] = 0.000*W[g]^-0.693*exp(0.069*t[celsius])


 satiation meal size................. sm[g(DW)] = not_specified


 standard VO_2....................... so[mg o2/hr] = 0.022*W[g]^0.695*exp(0.055*t[celsius])


 time to satiation................... st[minutes] = not_specified


 weight:length....................... wl[g(FW)] = 0.018*L[cm]^3.032


 length at first reproduction........ tl_r0[cm] = 3.500


 weight of recruits.................. yoy[g(FW)] = 0.025


 spawning interval................... march-october => day(s) = 15, 45, 75, 105, 135, 165, 195, 345,


 selected feeding models as a function of age or size:


 A[year]< 1.0 linear 


dietary composition as a function of age or size (entries between 1 and 100 represent relative frequencies


 whereas entries between -1 and 1 represent electivities. a -1 entry signifies that the item is not utilized):


 age/size bass gar bullhead bluegill redear gambusia benthos insects periphyton phytoplankton zooplankton


 -------- ------ ------ -------- -------- ------ -------- ------- ------- ---------- ------------- ----------­


L[cm]< 1.0 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 100.00


 L[cm]< 4.0 -1.00 -1.00 -1.00 -1.00 -1.00 0.00 -1.00 -1.00 -1.00 -1.00 0.00


 initial conditions:


 age age body weight population density methylmercury


 class [days] [g(FW)] [#/ha] [ug/g(FW)]


 ----- ------ ----------- ------------------ ------------­


1 20. 0.0 39159.3 0.000


 2 170. 0.3 10158.5 0.000


 3 200. 0.3 8794.5 0.000


 4 230. 0.4 7743.9 0.000


 initial standing stock ... 9.99 [kg(FW)/ha]


 ecotoxicological parameters:


 mean lethal activiy...... la[-] = 1.066E-03


108
 summary of special conditions during the simulation:


 RKINT_RESTART: euler step taken at t= 4.50 


RKINT_RESTART: euler step taken at t= 4.62 


RKINT_RESTART: euler step taken at t= 4.63 


RKINT_RESTART: euler step taken at t= 4.63 


RKINT_RESTART: euler step taken at t= 4.63 


RKINT_RESTART: dn/dt for species 6 cohort 1 approximates a step function for t= 4.63 solution restarted


 RKINT_RESTART: euler step taken at t= 7.50 


RKINT_RESTART: euler step taken at t= 7.91 


RKINT_RESTART: euler step taken at t= 7.98 


RKINT_RESTART: euler step taken at t= 7.99 


RKINT_RESTART: euler step taken at t= 7.99 


RKINT_RESTART: euler step taken at t= 7.99 


RKINT_RESTART: dn/dt for species 6 cohort 2 approximates a step function for t= 7.99 solution restarted


 RKINT_RESTART: euler step taken at t= 9.50 


RKINT_RESTART: euler step taken at t= 10.0 


RKINT_RESTART: dn/dt for species 6 cohort 3 approximates a step function for t= 10.0 solution restarted


 BASS_ODESOLVR: species 6 cohort 4 dies on day= 12.0 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 5 dies on day= 18.0 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 5 dies on day= 18.0 due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 5 dies on day= 18.0 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 8 dies on day= 48.0 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 5 dies on day= 48.0 due to exceeding maximum longevity


 RKINT_RESTART: euler step taken at t= 350. 


RKINT_RESTART: euler step taken at t= 350. 


RKINT_RESTART: euler step taken at t= 350. 


RKINT_RESTART: euler step taken at t= 350. 


RKINT_RESTART: euler step taken at t= 350. 


RKINT_RESTART: dn/dt for species 5 cohort 6 approximates a step function for t= 350. solution restarted


 BASS_ODESOLVR: species 2 cohort 4 dies on day= 383. due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 4 dies on day= 383. due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 4 dies on day= 383. due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 7 dies on day= 413. due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 4 dies on day= 413. due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 3 dies on day= 748. due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 3 dies on day= 748. due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 3 dies on day= 748. due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 6 dies on day= 778. due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 3 dies on day= 778. due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 2 dies on day= 0.111E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 2 dies on day= 0.111E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 2 dies on day= 0.111E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 5 dies on day= 0.114E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 2 dies on day= 0.114E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.148E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.148E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.148E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 4 dies on day= 0.151E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.151E+04 due to exceeding maximum longevity


 RKINT_RESTART: euler step taken at t= 0.156E+04


 RKINT_RESTART: euler step taken at t= 0.156E+04


 RKINT_RESTART: euler step taken at t= 0.156E+04


 RKINT_RESTART: euler step taken at t= 0.156E+04


 RKINT_RESTART: euler step taken at t= 0.156E+04


 RKINT_RESTART: dn/dt for species 4 cohort 2 approximates a step function for t= 0.156E+04 solution restarted


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.183E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.186E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 3 dies on day= 0.187E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.219E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.222E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 2 dies on day= 0.224E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.224E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.226E+04 due to exceeding maximum longevity


 RKINT_RESTART: euler step taken at t= 0.226E+04


 RKINT_RESTART: euler step taken at t= 0.226E+04


 RKINT_RESTART: euler step taken at t= 0.226E+04


 RKINT_RESTART: euler step taken at t= 0.226E+04


 RKINT_RESTART: dn/dt for species 4 cohort 2 approximates a step function for t= 0.226E+04 solution restarted


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.256E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.259E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 1 dies on day= 0.260E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.262E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.292E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.295E+04 due to exceeding maximum longevity


109

 BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.297E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 1 dies on day= 0.299E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.299E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 3 cohort 1 dies on day= 0.329E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 2 cohort 1 dies on day= 0.332E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 4 cohort 1 dies on day= 0.334E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 1 cohort 1 dies on day= 0.335E+04 due to exceeding maximum longevity


 BASS_ODESOLVR: species 5 cohort 1 dies on day= 0.335E+04 due to exceeding maximum longevity


110

 total cpu = 511. 

bass_odesolvr cpu = 503. 

bass_dydt cpu = 473. 

dwdtflx cpu = 126. 

dbdtflx cpu = 108. 

bass_foodweb1 cpu = 116. 

bass_foodweb0 cpu = 57.4 

ee_adj cpu = 28.9 

dry2live cpu = 46.3 

R-K integrator cpu = 490. 

load/unload cpu = 70.4 

bass_restart cpu = 1.80 

mean h = 0.491 (n= 7445) 

111

APPENDIX F. Example output file (filename.bss) that tabulates annual bioenergetic and contaminant 

fluxes. 

! page 1 

! file : evergld1.bss 

! input file : evergld1.prj (Tue Dec 05 15:58:40 2000) 

! program file: C:\BASS\BASS_V21.EXE (Thu Jan 11 11:28:48 2001) 

! 

112

 methylmercury bioaccumulation in a "ponded" everglades community page 2


 *** SUMMARY FOR YEAR 10 *** 


bioenergetics of a representative individual bass:


 residence mean mean mean weight


 time body weight body weight growth rate gain/loss ingestion assimilation metabolism


 cohort [days] [g(FW)] [g(DW)] [1/day] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr]


 ------ --------- ----------- ----------- ----------- ----------- ---------- ------------ ---------­


1 303. 86.9 18.2 5.541E-03 22.1 145. 111. 88.4


 2 365. 208. 45.4 2.696E-03 39.4 314. 259. 220.


 3 365. 355. 80.8 1.814E-03 49.6 464. 384. 334.


 4 365. 501. 118. 1.418E-03 58.0 597. 495. 437.


 5 365. 639. 156. 1.194E-03 65.2 715. 593. 528.


 6 365. 768. 194. 1.051E-03 71.5 819. 680. 608.


 7 365. 885. 229. 9.519E-04 77.0 910. 756. 679.


 8 365. 992. 263. 8.798E-04 81.8 990. 823. 741.


 9 64.0 1.088E+03 294. 8.769E-04 16.5 205. 170. 154.


 exchange of methylmercury by a representative individual bass:


 residence mean metabolically egested metabolically


 time body conc. mean mean gill uptake ingested generated & excreted degraded


 cohort [days] [ug/g(FW)] log(BAF) log(BMF) [ug/yr] [ug/yr] [ug/yr] [ug/yr] [ug/yr]


 ------ --------- ---------- -------- -------- ----------- ----------- ------------- ---------- ------------­


1 303. 0.499 6.05 0.326 13.0 158. 0.00 78.3 0.00


 2 365. 0.758 6.23 0.415 33.7 423. 0.00 295. 0.00


 3 365. 0.837 6.28 0.429 51.9 634. 0.00 494. 0.00


 4 365. 0.883 6.30 0.432 68.2 824. 0.00 676. 0.00


 5 365. 0.918 6.32 0.431 82.7 995. 0.00 841. 0.00


 6 365. 0.940 6.33 0.429 95.5 1.136E+03 0.00 983. 0.00


 7 365. 0.962 6.34 0.426 107. 1.268E+03 0.00 1.113E+03 0.00


 8 365. 0.983 6.35 0.424 117. 1.387E+03 0.00 1.231E+03 0.00


 9 64.0 1.03 6.37 0.420 24.7 299. 0.00 270. 0.00


 mean body conc. weighted by cohort biomasses = 0.817 


mean body conc. weighted by cohort densities = 0.671 


log mean BAF weighted by cohort biomasses = 6.26 


log mean BAF weighted by cohort densities = 6.18 


113
 methylmercury bioaccumulation in a "ponded" everglades community page 3


 *** SUMMARY FOR YEAR 10 *** 


total aqueous phase chemical activity in a representative individual bass:


 as a fraction of


 cohort lethal narcotic activity


 ------ -----------------------­


1 9.307E-04


 2 1.424E-03


 3 1.586E-03


 4 1.686E-03


 5 1.764E-03


 6 1.819E-03


 7 1.872E-03


 8 1.922E-03


 9 2.028E-03


114
 methylmercury bioaccumulation in a "ponded" everglades community page 4


 *** SUMMARY FOR YEAR 10 *** 


bioenergetics of a representative individual gar:


 residence mean mean mean weight


 time body weight body weight growth rate gain/loss ingestion assimilation metabolism


 cohort [days] [g(FW)] [g(DW)] [1/day] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr]


 ------ --------- ----------- ----------- ----------- ----------- ---------- ------------ ---------­


1 334. 190. 48.4 7.322E-03 67.2 188. 155. 88.0


 2 365. 409. 104. 2.026E-03 69.2 317. 263. 194.


 3 365. 599. 153. 1.292E-03 67.9 417. 347. 279.


 4 365. 758. 193. 9.932E-04 67.0 501. 417. 350.


 5 365. 890. 227. 8.319E-04 66.5 572. 476. 409.


 6 33.0 985. 251. 6.485E-04 5.36 54.8 45.4 40.0


 exchange of methylmercury by a representative individual gar:


 residence mean metabolically egested metabolically


 time body conc. mean mean gill uptake ingested generated & excreted degraded


 cohort [days] [ug/g(FW)] log(BAF) log(BMF) [ug/yr] [ug/yr] [ug/yr] [ug/yr] [ug/yr]


 ------ --------- ---------- -------- -------- ----------- ----------- ------------- ---------- ------------­


1 334. 0.517 6.07 0.174 11.1 254. 0.00 76.9 0.00


 2 365. 0.680 6.18 0.288 28.8 433. 0.00 243. 0.00


 3 365. 0.742 6.22 0.319 43.3 579. 0.00 395. 0.00


 4 365. 0.781 6.25 0.338 55.2 703. 0.00 530. 0.00


 5 365. 0.804 6.26 0.349 65.2 806. 0.00 644. 0.00


 6 33.0 0.838 6.28 0.346 6.19 81.1 0.00 64.0 0.00


 mean body conc. weighted by cohort biomasses = 0.694 


mean body conc. weighted by cohort densities = 0.615 


log mean BAF weighted by cohort biomasses = 6.19 


log mean BAF weighted by cohort densities = 6.14 


total aqueous phase chemical activity in a representative individual gar:


 as a fraction of


 cohort lethal narcotic activity


 ------ -----------------------­


1 9.945E-04


 2 1.309E-03


 3 1.429E-03


 4 1.503E-03


 5 1.548E-03


 6 1.614E-03


115
 methylmercury bioaccumulation in a "ponded" everglades community page 5


 *** SUMMARY FOR YEAR 10 *** 


bioenergetics of a representative individual bullhead:


 residence mean mean mean weight


 time body weight body weight growth rate gain/loss ingestion assimilation metabolism


 cohort [days] [g(FW)] [g(DW)] [1/day] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr]


 ------ --------- ----------- ----------- ----------- ----------- ---------- ------------ ---------­


1 364. 56.4 15.5 6.277E-03 24.3 210. 139. 114.


 2 365. 172. 47.3 2.923E-03 43.8 679. 466. 422.


 3 365. 330. 90.8 1.986E-03 60.4 1.331E+03 927. 866.


 4 365. 516. 142. 1.538E-03 75.1 2.047E+03 1.479E+03 1.404E+03


 5 365. 720. 198. 1.275E-03 88.0 2.865E+03 2.092E+03 2.004E+03


 6 3.00 774. 213. 7.992E-04 0.509 9.65 7.06 6.55


 exchange of methylmercury by a representative individual bullhead:


 residence mean metabolically egested metabolically


 time body conc. mean mean gill uptake ingested generated & excreted degraded


 cohort [days] [ug/g(FW)] log(BAF) log(BMF) [ug/yr] [ug/yr] [ug/yr] [ug/yr] [ug/yr]


 ------ --------- ---------- -------- -------- ----------- ----------- ------------- ---------- ------------­


1 364. 0.429 5.98 0.373 17.3 139. 0.00 109. 0.00


 2 365. 0.520 6.07 0.432 66.6 473. 0.00 447. 0.00


 3 365. 0.548 6.09 0.421 139. 956. 0.00 951. 0.00


 4 365. 0.584 6.12 0.411 226. 1.573E+03 0.00 1.609E+03 0.00


 5 365. 0.609 6.14 0.412 324. 2.306E+03 0.00 2.394E+03 0.00


 6 3.00 0.635 6.16 0.404 0.871 8.80 0.00 7.23 0.00


 mean body conc. weighted by cohort biomasses = 0.539 


mean body conc. weighted by cohort densities = 0.467 


log mean BAF weighted by cohort biomasses = 6.08 


log mean BAF weighted by cohort densities = 6.02 


total aqueous phase chemical activity in a representative individual bullhead:


 as a fraction of


 cohort lethal narcotic activity


 ------ -----------------------­


1 8.380E-04


 2 1.017E-03


 3 1.071E-03


 4 1.141E-03


 5 1.191E-03


 6 1.242E-03


116
 methylmercury bioaccumulation in a "ponded" everglades community page 6


 *** SUMMARY FOR YEAR 10 *** 


bioenergetics of a representative individual bluegill:


 residence mean mean mean weight


 time body weight body weight growth rate gain/loss ingestion assimilation metabolism


 cohort [days] [g(FW)] [g(DW)] [1/day] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr]


 ------ --------- ----------- ----------- ----------- ----------- ---------- ------------ ---------­


1 318. 16.4 4.50 5.023E-03 5.42 150. 98.9 93.5


 2 365. 16.4 4.52 -2.006E-04 -0.285 196. 129. 130.


 3 365. 23.1 6.36 2.252E-04 0.694 290. 191. 191.


 4 365. 32.2 8.87 1.823E-03 5.54 366. 242. 236.


 5 365. 50.8 14.0 1.475E-03 7.14 527. 348. 341.


 6 49.0 60.4 16.6 3.520E-04 0.277 75.6 49.9 49.7


 exchange of methylmercury by a representative individual bluegill:


 residence mean metabolically egested metabolically


 time body conc. mean mean gill uptake ingested generated & excreted degraded


 cohort [days] [ug/g(FW)] log(BAF) log(BMF) [ug/yr] [ug/yr] [ug/yr] [ug/yr] [ug/yr]


 ------ --------- ---------- -------- -------- ----------- ----------- ------------- ---------- ------------­


1 318. 0.452 6.01 0.494 15.7 79.0 0.00 82.3 0.00


 2 365. 0.486 6.04 0.517 22.2 106. 0.00 129. 0.00


 3 365. 0.512 6.06 0.520 33.0 176. 0.00 208. 0.00


 4 365. 0.519 6.07 0.520 40.5 226. 0.00 255. 0.00


 5 365. 0.528 6.08 0.524 58.3 327. 0.00 371. 0.00


 6 49.0 0.526 6.07 0.526 7.93 44.2 0.00 50.1 0.00


 mean body conc. weighted by cohort biomasses = 0.495 


mean body conc. weighted by cohort densities = 0.482 


log mean BAF weighted by cohort biomasses = 6.05 


log mean BAF weighted by cohort densities = 6.04 


total aqueous phase chemical activity in a representative individual bluegill:


 as a fraction of


 cohort lethal narcotic activity


 ------ -----------------------­


1 8.893E-04


 2 9.553E-04


 3 1.007E-03


 4 1.020E-03


 5 1.038E-03


 6 1.034E-03


117
 methylmercury bioaccumulation in a "ponded" everglades community page 7


 *** SUMMARY FOR YEAR 10 *** 


bioenergetics of a representative individual redear:


 residence mean mean mean weight


 time body weight body weight growth rate gain/loss ingestion assimilation metabolism


 cohort [days] [g(FW)] [g(DW)] [1/day] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr] [g(DW)/yr]


 ------ --------- ----------- ----------- ----------- ----------- ---------- ------------ ---------­


1 303. 26.4 7.27 6.921E-03 9.83 37.0 24.4 14.6


 2 365. 68.0 18.7 2.645E-03 15.9 79.7 52.6 36.7


 3 365. 117. 32.2 1.668E-03 18.3 111. 73.2 54.9


 4 365. 165. 45.5 1.261E-03 20.0 137. 90.7 70.8


 5 365. 211. 58.1 1.039E-03 21.2 160. 106. 84.6


 6 64.0 243. 66.9 9.883E-04 4.24 34.1 22.5 18.2


 exchange of methylmercury by a representative individual redear:


 residence mean metabolically egested metabolically


 time body conc. mean mean gill uptake ingested generated & excreted degraded


 cohort [days] [ug/g(FW)] log(BAF) log(BMF) [ug/yr] [ug/yr] [ug/yr] [ug/yr] [ug/yr]


 ------ --------- ---------- -------- -------- ----------- ----------- ------------- ---------- ------------­


1 303. 0.320 5.86 0.279 1.95 22.6 0.00 8.37 0.00


 2 365. 0.409 5.96 0.385 5.34 48.9 0.00 27.8 0.00


 3 365. 0.435 5.99 0.411 8.21 68.0 0.00 44.8 0.00


 4 365. 0.450 6.01 0.426 10.7 84.3 0.00 60.0 0.00


 5 365. 0.460 6.02 0.435 12.9 98.4 0.00 73.7 0.00


 6 64.0 0.473 6.03 0.447 2.85 20.9 0.00 16.6 0.00


 mean body conc. weighted by cohort biomasses = 0.416 


mean body conc. weighted by cohort densities = 0.370 


log mean BAF weighted by cohort biomasses = 5.97 


log mean BAF weighted by cohort densities = 5.92 


total aqueous phase chemical activity in a representative individual redear:


 as a fraction of


 cohort lethal narcotic activity


 ------ -----------------------­


1 6.298E-04


 2 8.051E-04


 3 8.562E-04


 4 8.850E-04


 5 9.045E-04


 6 9.296E-04


118
 methylmercury bioaccumulation in a "ponded" everglades community page 8


 *** SUMMARY FOR YEAR 10 *** 


all cohorts of gambusia have been exterminated


119
 methylmercury bioaccumulation in a "ponded" everglades community page 9


 *** SUMMARY FOR YEAR 10 *** 


community level fluxes for bass:


 prey endogenous mean mean


 consumption predatory mortality exogenous mortality productivity standing stock population


 cohort [g(DW)/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] [g(DW)/ha] / [g(FW)/ha] [#/ha]


 ------ ------------- ------------------------- ------------------------- ------------- ----------------------- ---------­


1 6.853E+03 118. / 10.1 848. / 63.3 1.141E+03 600. / 2.879E+03 38.4


 2 5.058E+03 17.8 / 0.514 510. / 12.4 656. 683. / 3.141E+03 15.9


 3 3.542E+03 1.70 / 2.583E-02 304. / 3.92 387. 592. / 2.605E+03 7.50


 4 2.698E+03 0.00 / 0.00 210. / 1.81 266. 519. / 2.195E+03 4.44


 5 2.177E+03 0.00 / 0.00 159. / 1.03 201. 464. / 1.898E+03 2.99


 6 1.747E+03 0.00 / 0.00 122. / 0.638 154. 404. / 1.604E+03 2.10


 7 1.483E+03 0.00 / 0.00 101. / 0.442 127. 366. / 1.415E+03 1.61


 8 1.271E+03 0.00 / 0.00 84.2 / 0.323 106. 331. / 1.251E+03 1.26


 9 240. 0.00 / 0.00 14.4 / 4.894E-02 19.2 60.2 / 223. 0.205


 total 2.507E+04 138. / 10.7 2.353E+03 / 83.9 3.056E+03 4.019E+03 / 1.721E+04 74.3


 community level fluxes for gar:


 prey endogenous mean mean


 consumption predatory mortality exogenous mortality productivity standing stock population


 cohort [g(DW)/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] [g(DW)/ha] / [g(FW)/ha] [#/ha]


 ------ ------------- ------------------------- ------------------------- ------------- ----------------------- ---------­


1 4.259E+03 0.00 / 0.00 1.303E+03 / 58.3 1.703E+03 778. / 3.050E+03 20.0


 2 2.466E+03 0.00 / 0.00 470. / 4.90 556. 767. / 3.007E+03 7.62


 3 1.836E+03 0.00 / 0.00 261. / 1.76 304. 651. / 2.553E+03 4.32


 4 1.454E+03 0.00 / 0.00 171. / 0.898 197. 547. / 2.145E+03 2.85


 5 1.188E+03 0.00 / 0.00 122. / 0.542 140. 463. / 1.814E+03 2.05


 6 81.4 0.00 / 0.00 7.93 / 3.160E-02 7.96 33.7 / 132. 0.134


 total 1.128E+04 0.00 / 0.00 2.335E+03 / 66.4 2.907E+03 3.239E+03 / 1.270E+04 36.9


 community level fluxes for bullhead:


 prey endogenous mean mean


 consumption predatory mortality exogenous mortality productivity standing stock population


 cohort [g(DW)/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] [g(DW)/ha] / [g(FW)/ha] [#/ha]


 ------ ------------- ------------------------- ------------------------- ------------- ----------------------- ---------­


1 4.863E+03 90.2 / 11.4 409. / 50.6 541. 249. / 905. 23.1


 2 4.261E+03 1.69 / 5.952E-02 207. / 5.00 262. 254. / 924. 5.79


 3 4.131E+03 0.00 / 0.00 143. / 1.68 180. 253. / 919. 2.89


 4 3.876E+03 0.00 / 0.00 109. / 0.802 137. 248. / 900. 1.78


 5 3.631E+03 0.00 / 0.00 86.5 / 0.449 108. 234. / 852. 1.20


 6 11.6 0.00 / 0.00 0.741 / 3.489E-03 0.611 2.10 / 7.64 9.866E-03


 total 2.077E+04 91.9 / 11.5 955. / 58.6 1.228E+03 1.241E+03 / 4.508E+03 34.8


120

 methylmercury bioaccumulation in a "ponded" everglades community page 10


 *** SUMMARY FOR YEAR 10 *** 


community level fluxes for bluegill:


 prey endogenous mean mean


 consumption predatory mortality exogenous mortality productivity standing stock population


 cohort [g(DW)/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] [g(DW)/ha] / [g(FW)/ha] [#/ha]


 ------ ------------- ------------------------- ------------------------- ------------- ----------------------- ---------­


1 7.052E+05 6.218E+03 / 2.086E+03 1.708E+03 / 458. 2.478E+04 1.655E+04 / 6.015E+04 3.883E+03


 2 5.618E+05 4.009E+03 / 977. 1.192E+03 / 281. -2.684E+03 1.201E+04 / 4.365E+04 2.609E+03


 3 4.029E+05 3.111E+03 / 499. 673. / 107. 447. 8.228E+03 / 2.991E+04 1.283E+03


 4 3.538E+05 2.343E+03 / 282. 547. / 63.6 5.235E+03 8.066E+03 / 2.932E+04 924.


 5 2.734E+05 1.722E+03 / 133. 353. / 25.9 3.564E+03 6.897E+03 / 2.507E+04 500.


 6 2.693E+04 221. / 13.3 36.5 / 2.20 101. 797. / 2.895E+03 48.0


 total 2.324E+06 1.762E+04 / 3.990E+03 4.508E+03 / 938. 3.144E+04 5.255E+04 / 1.910E+05 9.246E+03


 community level fluxes for redear:


 prey endogenous mean mean


 consumption predatory mortality exogenous mortality productivity standing stock population


 cohort [g(DW)/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] / [#/ha/yr] [g(DW)/ha/yr] [g(DW)/ha] / [g(FW)/ha] [#/ha]


 ------ ------------- ------------------------- ------------------------- ------------- ----------------------- ---------­


1 6.110E+04 6.717E+03 / 1.600E+03 3.268E+03 / 676. 1.734E+04 8.406E+03 / 3.055E+04 1.331E+03


 2 3.648E+04 2.231E+03 / 147. 1.461E+03 / 85.0 7.399E+03 8.198E+03 / 2.979E+04 453.


 3 2.917E+04 606. / 20.4 968. / 30.8 4.844E+03 8.341E+03 / 3.031E+04 261.


 4 2.707E+04 241. / 5.54 791. / 17.6 3.946E+03 8.879E+03 / 3.227E+04 196.


 5 1.701E+04 118. / 2.08 452. / 7.84 2.256E+03 6.119E+03 / 2.223E+04 105.


 6 349. 15.0 / 0.224 7.97 / 0.119 43.3 120. / 437. 1.80


 total 1.712E+05 9.929E+03 / 1.776E+03 6.949E+03 / 817. 3.583E+04 4.006E+04 / 1.456E+05 2.347E+03


 all cohorts of gambusia have been exterminated


 community consumption [g(DW)/ha/yr] of benthos......... 1.566E+06 (0.61 of total consumption)


 community consumption [g(DW)/ha/yr] of insects......... 0.00 (0.00 of total consumption)


 community consumption [g(DW)/ha/yr] of periphyton...... 0.00 (0.00 of total consumption)


 community consumption [g(DW)/ha/yr] of phytoplankton... 0.00 (0.00 of total consumption)


 community consumption [g(DW)/ha/yr] of zooplankton..... 9.588E+05 (0.38 of total consumption)


 community consumption [g(DW)/ha/yr] of fish............ 2.778E+04 (0.01 of total consumption)


 community mass balances 


piscivory - predatory mortality [g(DW)/ha/yr]..........-1.953E-02


121

APPENDIX G. Example output file (filename.plx) that plots the variables requested by the user. 

122

123

population dynamics of bass
Legend 

class 1: 0.00 < total length class 2: 9.93 < total length class 3: 19.9 < total length class 4: 29.8 < total length class 5: 39.7 < total length [cm] [cm] [cm] [cm] [cm] < 9.93 < 19.9 < 29.8 < 39.7 < 49.7 

p o p u l a t i o n d e n s i t y [ i n d . / h a ] 

81.63 65.30 48.98 32.65 16.33 

0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of bass 

9.98 
methylmercury dynamics in bass
Legend 

class 1: 0.00 < total length class 2: 9.93 < total length class 3: 19.9 < total length class 4: 29.8 < total length class 5: 39.7 < total length [cm] [cm] [cm] [cm] [cm] < 9.93 < 19.9 < 29.8 < 39.7 < 49.7 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

1.03 0.83 0.62 0.41 0.21 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in bass 

9.98 
population dynamics of gar
Legend 

class 1: 0.00 < total length class 2: 11.9 < total length class 3: 23.7 < total length class 4: 35.6 < total length class 5: 47.4 < total length [cm] [cm] [cm] [cm] [cm] < 11.9 < 23.7 < 35.6 < 47.4 < 59.3 

p o p u l a t i o n d e n s i t y [ i n d . / h a ] 

30.89 24.71 18.53 12.36 

6.18 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of gar 

9.98 
methylmercury dynamics in gar
Legend 

class 1: 0.00 < total length class 2: 11.9 < total length class 3: 23.7 < total length class 4: 35.6 < total length class 5: 47.4 < total length [cm] [cm] [cm] [cm] [cm] < 11.9 < 23.7 < 35.6 < 47.4 < 59.3 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

0.83 0.67 0.50 0.33 0.17 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in gar 

9.98 
population dynamics of bullhead
Legend 

class 1: 0.00 < total length class 2: 7.96 < total length class 3: 15.9 < total length class 4: 23.9 < total length class 5: 31.8 < total length [cm] [cm] [cm] [cm] [cm] < 7.96 < 15.9 < 23.9 < 31.8 < 39.8 

p o p u l a t i o n d e n s i t y [ i n d . / h a ] 

86.05 68.84 51.63 34.42 17.21 

0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of bullhead 

9.98 
methylmercury dynamics in bullhead
Legend 

class 1: 0.00 < total length class 2: 7.96 < total length class 3: 15.9 < total length class 4: 23.9 < total length class 5: 31.8 < total length [cm] [cm] [cm] [cm] [cm] < 7.96 < 15.9 < 23.9 < 31.8 < 39.8 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

0.65 0.52 0.39 0.26 0.13 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in bullhead 

9.98 
population dynamics of bluegill
Legend 

class 1: 0.00 < total length class 2: 3.99 < total length class 3: 7.97 < total length class 4: 12.0 < total length class 5: 15.9 < total length [cm] [cm] [cm] [cm] [cm] < 3.99 < 7.97 < 12.0 < 15.9 < 19.9 

p o p u l a t i o n d e n s i t y [ i n d . / h a ] 

5.48*103 4.38*103 3.29*103 2.19*103 

1.10*103

0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of bluegill 

9.98 
methylmercury dynamics in bluegill
Legend 

class 1: 0.00 < total length class 2: 3.99 < total length class 3: 7.97 < total length class 4: 12.0 < total length class 5: 15.9 < total length [cm] [cm] [cm] [cm] [cm] < 3.99 < 7.97 < 12.0 < 15.9 < 19.9 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

0.66 0.52 0.39 0.26 0.13 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in bluegill 

9.98 
population dynamics of redear
Legend 

class 1: 0.00 < total length class 2: 4.96 < total length class 3: 9.91 < total length class 4: 14.9 < total length class 5: 19.8 < total length [cm] [cm] [cm] [cm] [cm] < 4.96 < 9.91 < 14.9 < 19.8 < 24.8 

p o p u l a t i o n d e n s i t y [ i n d . / h a ] 

2.78*103 2.22*103 1.67*103 1.11*103 

5.56*102

0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of redear 

9.98 
methylmercury dynamics in redear
Legend 

class 1: 0.00 < total length class 2: 4.96 < total length class 3: 9.91 < total length class 4: 14.9 < total length class 5: 19.8 < total length [cm] [cm] [cm] [cm] [cm] < 4.96 < 9.91 < 14.9 < 19.8 < 24.8 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

0.47 0.38 0.28 0.19 0.09 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in redear 

9.98 
population dynamics of gambusia
Legend 

class 1: 0.00 < total length class 2: 5.49 < total length class 3: 11.0 < total length class 4: 16.5 < total length class 5: 21.9 < total length [mm] [mm] [mm] [mm] [mm] < 5.49 < 11.0 < 16.5 < 21.9 < 27.4 

p o p u l a t i o n d e n s i t y [ i n d . / m 2 ] 

2.63 2.11 1.58 1.05 0.53 0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

population dynamics of gambusia 

9.98 
methylmercury dynamics in gambusia
Legend 

class 1: 0.00 < total length class 2: 5.49 < total length class 3: 11.0 < total length class 4: 16.5 < total length class 5: 21.9 < total length [mm] [mm] [mm] [mm] [mm] < 5.49 < 11.0 < 16.5 < 21.9 < 27.4 

w h o l e b o d y c o n c . [ m g / k g ( F W ) ] 

1.02*10-2 8.19*10-3 6.14*10-3 4.09*10-3 

2.05*10-3 

0.00 

0.00 2.00 3.99 5.99 7.98 

time[yr]

methylmercury dynamics in gambusia 

9.98 
INDEX


chemical parameters


/chemical ...................................... 35


/exposure ..................................... 35


/lethality ...................................... 36


/log_ac ....................................... 37


/log_kb1 ...................................... 37


/log_kb2 ...................................... 37


/log_p ........................................ 37


/melting_point .................................. 37


/metabolism ................................... 37


/molar_volume ................................. 37


/molar_weight .................................. 38


files


chemical exposure files (.chm) ..................... 44


chemical property files (.prp) ................... 44, 45


community files (.cmm) ....................... 44, 45


fish files (.fhs) .............................. 43, 45


generalized input file ............................ 32


include files ................................ 32, 43


management ................................... 43


output file (.bss) ................................ 43


output file (.msg) ............................... 43


output file (.plx) ................................ 43


project files (.prj) ............................... 44


fish parameters


/age_class_duration ............................. 38


/common_name ................................. 38


/compostional_parameters ........................ 38


/ecological_parameters ........................... 38


/feeding ....................................... 39


/initial_conditions ............................... 39


/morphometric_parameters ........................ 39


/physiological_parameters ........................ 40


/spawning_period ............................... 40


/species ....................................... 40


future features ................................... 60


restrictions


order of commands .............................. 45


specifying chemical names ........................ 35


specifying common names ........................ 38


units recognized by BASS ........................ 41


simulation controls


/annual_outputs ................................ 33


/annual_plots .................................. 33


/biota ......................................... 33


/fgets ......................................... 34


/header ....................................... 34


/month_t0 ..................................... 34


/nsteps ........................................ 34


/simulation_control .............................. 34


/simulation_interval ............................. 34


/summary_plots ................................. 34


/temperature ................................... 35


/water_level ................................... 35


simulation options


dietary exposure via benthos ...................... 36


dietary exposure via insects ....................... 36


dietary exposure via periphyton .................... 36


dietary exposure via phytoplankton ................. 36


dietary exposure via zooplankton ................... 36


direct aqueous exposures ......................... 36


specifying non-fish prey ....................... 33, 38


specifying output ............................ 33, 34


specifying water levels ........................... 35


specifying water temperatures ..................... 35


syntax


commenting a line ............................... 32


continuing a line ................................ 32


specifying an include file ......................... 32


specifying units ................................. 41


user specified functions .......................... 41


technical support


reporting comments ............................. 31


reporting problems .............................. 31


reporting suggestions ............................ 31


124