BASS 2.3 Sept 24, 2012

EPA Report No. 600/R-01/035

update 2.3, Sept 24, 2012

Bioaccumulation and Aquatic

System Simulator (BASS)

User’s Manual

Version 2.3

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
BASS 2.3 Sept 24, 2012 ii

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.
BASS 2.3 Sept 24, 2012 iii

Foreword

This report describes the theoretical development, parameterization, and application software of the BASSBioaccumulation and Aquatic

System Simulator. This generalized, community-based simulation model is designed to predict the population and bioaccumulation

dynamics of age-structured fish communities exposed to hydrophobic organic chemicals and class B and borderline metals that

complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). This report is not a case study on the

application of BASS but a reference and user’s guide. The intended audience of this report and associated software includes research

fisheries ecologists, bioaccumulation researchers, and EPA environmental scientists and ecologists who must routinely analyze and

estimate bioaccumulation of chemicals in fish for ecological or human health exposure assessments.

Although BASS has not been extensively field-tested, its process-based algorithms for predicting chemical bioaccumulation, growth

of individual fish, predator-prey interactions, and population dynamics either have been corroborated or have been formulated using

widely accepted ecological and ecotoxicological principles. Even when a process-based model has undergone only limited field testing,

it can be an extremely useful tool. Process-based models enable users to observe quantitatively the results of a particular abstraction

of the real world. Moreover, such models can be argued to be the only objective method to make extrapolations to unobserved or

unobservable conditions. If the conceptualization and construction of process-based models are both comprehensive (i.e., holistic) and

reasonable, then their output, validated or not, can still be used for comparative analyses. A model’s ability to simulate trends and

comparative dynamics are, in fact, often more important measures of a model’s utility than is its ability to replicate a specific field or

laboratory study. Although BASS can be used to analyze results from actual field studies, its principal intended use is to predict and

compare the outcomes of alternative management options associated with pollution control, ecosystem management, or restoration

activities.

Roy C. Sidle, Ph.D.

Director

Ecosystems Research Division

Athens, GA 30605 USA
BASS 2.3 Sept 24, 2012 iv

Abstract

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

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

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

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

These algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals that together determine

diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics used by the model include the fish’s gill

morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and structural organic content).

Relevant physico-chemical properties includes the chemical’s aqueous diffusivity, n-octanol / water partition coefficient (Kow), and,

for metals, binding coefficients to proteins and other organic matter. BASS simulates the growth of individual fish using a standard mass

balance, bioenergetic model (i.e., growth = ingestion - egestion - respiration - specific dynamic action - excretion). A fish’s realized

ingestion is calculated from its maximum consumption rate adjusted for the availability of prey of the appropriate size and taxonomy.

The community’s food web is delineated by defining one or more foraging classes for each fish species based on its body weight, body

length, or age. The dietary composition of each of these foraging classes is specified as a combination of benthos, incidental terrestrial

insects, periphyton / attached algae, phytoplankton, zooplankton, and one or more fish species. Population dynamics are generated

by predatory mortalities defined by the community’s food web and standing stocks, physiological mortality rates, the maximum

longevity of species, toxicological responses to chemical exposures, and dispersal. The model’s temporal and spatial scales are that

of a day and of a hectare, respectively.
BASS 2.3 Sept 24, 2012 v

Table of Contents

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

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

Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

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

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

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

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

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

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

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

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

2.7. Modeling Predator-Prey Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.8. Modeling Stable Isotopes and Trophic Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.9. Modeling Dispersal, Non-Predatory Mortalities, and Recruitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.10. Modeling Habitat Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.11. Modeling Nonfish Compartments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.12. Modeling Toxicological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3. Model Parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1. Parameterizing Kf

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2. Parameters for Gill Exchange . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3. Bioenergetic and Growth Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4. Procedures Used to Generate the BASS Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.5. Suggested Calibration Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4. BASS User Guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.1. General Model Structure and Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.2. New Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.3. Input File Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.3.1. Simulation Control Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

4.3.2. Chemical Input Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.3.3. Fish Input Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.3.4. Nonfish Input Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

4.4. Input Data Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.4.1. Units Recognized by BASS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.4.2. User Specified Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.4.3. User Supplied Parameter Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.5. BASS Include File Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

4.6. Output Files Generated by BASS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.7. Command Line Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5. BASS Model Software and Graphical User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.1. Software Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.2. Installation Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
BASS 2.3 Sept 24, 2012 vi

5.3. Installation Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.4. BASS GUI Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5.4.1. BASS File Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

5.4.2. BASS Command Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.4.3. Special Function Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.4.4. File and Folder Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.5. The BASS Output Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.6. Auxiliary BASS Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6. Example Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

6.1. BASS Software Distribution Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

6.2. An Analysis of Everglades Mercury Bioaccumulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

7. Model Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

7.1. Questions Regarding QA of a Model’s Scientific Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

7.2. Questions Regarding QA of a Model’s Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

7.3. Questions Regarding QA of Model Documentation and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

8. Planned Future Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

APPENDIX A. Equilibrium complexation model for metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

APPENDIX B. Canonical equations for modeling diffusive chemical exchange across fish gills with ventilation and perfusion

effects. See Section 2.2 for background information and notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

APPENDIX C. Derivation of the consistency condition for feeding electivities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
BASS 2.3 Sept 24, 2012 vii

Figures

Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and

ventilation / perfusion ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a function of gill Sherwood number and

ventilation / perfusion ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 2.3 Functional behavior of Equation (2.53) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters

used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,

25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish’s upper tolerance limit. 32

Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters

used by the Wisconsin Bioenergetics Model. Observed data corresponds to the maximum daily consumption of fish weighing 1,

25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the fish’s upper tolerance limit. 33

Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning BASS’s algorithm. . . . . . . . . . . . . . . . . 34

Figure 5.1 BASS GUI Current BASS Directory window. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Figure 5.2 General structure of BASS GUI file editors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Figure 5.3 Structure of BASS GUI project file editor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Figure 5.4 GUI command editor for simple strings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Figure 5.5 GUI command editor for simple strings with drop-down selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Figure 5.6 GUI command editor for numeric data with user specified units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Figure 5.7 GUI command editor for numeric data fixed units. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Figure 5.8 GUI command editor for forcing functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 5.9 GUI command editor for feeding model options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 5.10 GUI command editor for compositional and morphometric parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 5.11 GUI command editor for nondiet ecological parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Figure 5.12 GUI command editor for fish diets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Figure 5.13 GUI command editor for physiological parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 5.14 GUI command editor for cohort initial conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Figure 5.15 GUI command editor for spawning parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 5.16 GUI command editor for fishery parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 5.17 GUI command editor for nonfish biota as forcing functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Figure 5.18 GUI command editor for nonfish biota as state variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 5.19 GUI command editor for nonfish bioaccumulation factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 5.20 GUI command editor for chemical biotransformation parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Figure 5.21 GUI command editor for chemical toxicity parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 5.22 GUI command editor for automatic graphing selections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Figure 5.23 GUI Block comment editor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Figure 5.24 Data file editor for forcing functions specified as files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Figure 6.1 Predicted and observed methylmercury concentrations of largemouth bass in Florida Everglades canals. . . . . . . . . . 80

Figure 6.2 Predicted and observed methylmercury concentrations of Florida gar in Florida Everglades canals. . . . . . . . . . . . . . 81

Figure 6.3 Predicted and observed methylmercury concentrations of yellow bullhead in Florida Everglades canals. . . . . . . . . . 82

Figure 6.4 Predicted and observed methylmercury concentrations of bluegill sunfish in Florida Everglades canals. . . . . . . . . . . 83

Figure 6.5 Predicted and observed methylmercury concentrations of redear sunfish in Florida Everglades canals. . . . . . . . . . . . 84

Figure 6.6 Predicted and observed methylmercury concentrations of Gambusia in Florida Everglades canals. . . . . . . . . . . . . . . 85
BASS 2.3 Sept 24, 2012 viii

Tables

Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental model

processes, rates, or rate coefficients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Table 3.1 Summary of NL2SOL regressions for Equation (3.24) fitted to maximum daily consumption rates and satiation meal

size reported in the literature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Table 3.2 Summary of NL2SOL regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by

the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily

consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and

the fish’s upper tolerance limit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Table 4.1 Valid Unit Prefixes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive and summarizes only

commonly used unit names that BASS’s units conversion program recognizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
BASS 2.3 Sept 24, 2012 ix

Acknowledgment

Many people have contributed to the development of the BASS bioaccumulation and community model through technical discussions,

software development and oversight, and peer reviews. This author wishes to thank Drs. Daniel Beyers (Colorado State University),

James E. Breck (University of Michigan), Lawrence Burkhard (USEPA, NHEERL), Lawrence Burns (USEPA, NERL deceased),

Michael Cyterski (USEPA, NERL), Russell Erickson (USEPA, NHEERL), Timothy Gleason (USEPA, NHEERL), John M. Johnston

(USEPA, NERL), Ray Lassiter (USEPA, NERL retired), Richard Lee (USEPA, OCSPP), David Mauriello (USEPA, OCSPP, retired),

John W. Nichols (USEPA, NHEERL), Ross J. Norstrom (Environment Canada), Brenda Rashleigh (USEPA, NHEERL), David

Rodgers (Ontario Power Technologies), Dick Sijm (The Netherlands National Institute for Public Health and the Environment

(RVIM)), Luis Suárez (USEPA, NERL), Donald Rodier (USEPA, OCSPP, retired), Robert R. Swank (USEPA, NERL retired), and

Anett Trebitz (USEPA, NHEERL). Thanks and appreciation are also given to Robert Ambrose (USEPA, NERL, retired), Ron Beloin

(formerly of CSC Inc., Athens, GA), Sandra L. Bird (USEPA, NERL, retired), Benjamin Daniel (formerly of CSC Inc., Athens, GA),

Mike Galvin (USEPA, NERL), Lourdes Prieto (USEPA, NERL), and Frank Stancil (USEPA, NERL, retired).
BASS 2.3 Sept 24, 2012 1

1. Introduction

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

health / value perspective in many ways. Questions relating to

fish health from an ecological perspective often include:

1) Are individual fish growth and condition sufficient to

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

overwintering) and man induced stress?

2) Are individual fish species able to maintain sustainable

populations? For example, is individual growth adequate

for fish to attain the minimum body size required for

reproduction? Is there adequate physical environment for

successful spawning? Is there adequate physical habitat

for the survival of the young-of-year?

3) Do regional fish assemblages exhibit their expected

biodiversity or community structure based on

biogeographical and physical habitat considerations?

4) Are regional fish assemblages maintaining their expected

level of productivity based on biogeographical and

physical habitat considerations?

5) Are appropriately sized fish abundant enough to maintain

piscivorous wildlife (e.g., birds, mammals, and reptiles)

during breeding and non-breeding conditions?

6) Are potential fish prey sufficiently free of contaminants

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

interfere with the growth and reproduction of piscivorous

wildlife?

7) How will native fishes respond to the introduction of

nonnative fish species, including those stocked for

recreational fishing?

From a human health or use perspective, another important

question related to fish health is:

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

That is, are target fish species sufficiently abundant and of

the desired quality? Fish quality is this context is often

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

trophy length) and the absence of chemical contaminants.

Some of the important metrics or indicators that have often been

used to assess such questions include: 1) physical habitat

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

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

current velocities, water temperature, sediment loading, etc., 2)

community species and functional diversity, 3) total community

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

fish/km) or biomass (kg/ha or kg/km) of the community’s

dominant species, 5) the age or size class structure of the

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

community and its dominant species, 7) individual growth rates

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

normalized to an expected body weight based on its current

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

whole fish.

From the perspective of evaluating alternative management

options or of assessing expected future consequences of existing

conditions, simulation models that can predict the individual and

population growth of fish and their patterns of chemical

bioaccumulation are important tools for analyzing several of the

aforementioned dimensions of fish health.

Although the growth of individual fish has often been described

using empirical models such as the von Bertalanffy, logistic,

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

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

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

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

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

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

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

(1999), Rose et al. (1999), Schaeffer et al. (1999), and van Nes

(2002) are becoming the models of choice for predicting the

growth of fish. Because these process-based models predict fish

growth based on the mass or energy balance of ingestion,

egestion, respiration, specific dynamic action, and excretion, they

can generally be parameterized independently of their current

application. Moreover, because of the inherent difficulties in

obtaining reliable field-based measurements of fish population

dynamics and productivity, researchers are increasingly using

such bioenergetic models to characterize these population and

community level endpoints. See for example Stewart and Ibarra

(1991) and Roell and Orth (1993).

The ability to predict accurately the bioaccumulation of

chemicals in fish has become an essential component of

ecological and human health risk assessments for chemical

pollutants. Not only are accurate estimates needed to predict

realistic dietary exposures to humans and piscivorous wildlife,

but such estimates are also needed to assess more accurately

potential ecological risks to fish assemblages themselves.

Although exposure-referenced benchmarks such as LC50 and

EC50 have been widely used for hazard assessments, most

deleterious effects of chemical pollutants are caused by the

internal accumulation of those compounds, rather than their

environmental concentrations per se. Many authors (Neely 1984,

Friant and Henry 1985, McCarty et al. 1985, McCarty 1986,

Connell and Markwell 1992, McCarty and Mackay 1993,

Verhaar et al. 1995, van Loon et al. 1997) have discussed the

benefits of explicitly considering chemical bioaccumulation
BASS 2.3 Sept 24, 2012 2

when assessing expected ecological consequences of chemical

pollutants in aquatic and marine ecosystems. Residue-based

toxicity studies confirm this supposition (Opperhuizen and

Schrap 1988, van Hoogen and Opperhuizen 1988, Donkin et al.

1989, Tas et al. 1991, van Wezel et al. 1995, Driscoll and

Landrum 1997).

Although concentrations of moderately hydrophobic chemicals

in fish can often be predicted accurately assuming equilibrium

partitioning of the chemicals between the fish’s organic

constituents and the aqueous environment, this approach

frequently fails to predict observed concentrations of extremely

hydrophobic chemicals and metals that are often the chemicals

of greatest concern. Observed deviations can be either

considerably above or below those predicted by equilibrium

partitioning. Several factors can be identified to explain these

discrepancies.

Lower than expected contamination levels can result when the

length of exposure is insufficient to allow chemicals to

equilibrate. Because bioconcentration and bioaccumulation are

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

for chemicals to equilibrate between fish and their exposure

media is an increasing function of the elimination half lives of

those chemicals in fish. For example, the time required for

chemicals to achieve 95% of their equilibrium concentrations is

approximately 4.3 times their elimination half lives. Because the

elimination half lives of chemicals generally increase as their

hydrophobicities increase, the time needed for chemicals to reach

their equilibrium concentrations in fish also increases as a

function of chemical hydrophobicity. Consequently, for

extremely hydrophobic chemicals such as polychlorinated

biphenyls (PCBs) and dioxins that have elimination half lives

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

be on the order of years. If the fish species of concern is short

lived, the time needed for equilibrium can exceed the species

expected life span. Even when time is sufficient for equilibration,

whole-body concentrations of fish can be much lower than that

expected from thermodynamic partitioning due to physical

dilution of the chemical that accompanies body growth or due to

in situ biotransformation of the parent compound.

One of two possible assumptions is implicitly made whenever

equilibrium-based estimators are used. The first of these

assumptions is that only the selected reference route of exposure

is significant in determining the total chemical accumulation in

fish. The alternative assumption is that there are multiple routes

of exposure that all covary with the chosen reference pathway in

a constant manner. For bioconcentration factors (BCFs), the

implicit assumption is that virtually the entire burden is

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

possibly across its skin. Although direct aqueous uptake is

certainly the most significant route of exchange for moderately

hydrophobic chemicals, dietary uptake accounts for most of a

fish’s body burden for extremely hydrophobic chemicals. This

shift in the relative significance of the direct aqueous versus the

dietary pathway is determined by the relative rates of exposure

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

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

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

fish’s direct aqueous exposure, AE (μg/d), is the product of its

ventilation volume, Q (ml/d), and the chemical’s aqueous

concentration, Cw

 (μg/ml). Similarly, the fish’s dietary exposure,

DE (μg/d), is the product of its feeding rate, Fw

 (g wet wt/d), and

the chemical’s concentration in the fish’s prey, Cp (μg/g wet wt).

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

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

dietary exposures are equal using the equations

(1.1)

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

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

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

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

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

predominant route of exposure for any chemical whose octanol

/ water partition coefficient is greater than 105.6. For extremely

hydrophobic chemicals, not only will fish be more exposed via

food but they probably will assimilate chemicals from food more

effectively than from the water. Although chemical exchange

from both food and water occur by passive diffusion, uptake

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

relax the diffusion gradient into the fish. This fundamental

difference results from the digestion and assimilation of food that

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

contents to increase (Connolly and Pedersen 1988, Gobas et al.

1988). Predicting residue levels for chemicals whose principal

route of exchange is dietary is further complicated since most

fish species demonstrate well-defined size-dependent, taxonomic,

and temporal trends regarding the prey they consume.

Consequently, one would not generally expect a single BAF to

be sufficiently accurate for risk assessments for all fish species

or even different sizes of the same species.

Process-based models that describe the kinetic exchange of

chemicals from food and water in concert with the growth of fish

provide objective and scientifically sound frameworks that can

overcome many of the aforementioned limitations of equilibriumbased BAFs and BCFs. Although numerous models have been

developed toward this end (Norstrom et al. 1976, Thomann

1981, Jensen et al. 1982, Thomann and Connolly 1984, Barber
BASS 2.3 Sept 24, 2012 3

et al. 1987, Gobas et al. 1988, Barber et al. 1991, Thomann et al.

1992, Gobas 1993, Madenjian et al. 1993), these models differ

significantly regarding how food web structure and dietary

exposures are represented.

This report describes the theoretical framework,

parameterization, and use of BASS (Bioaccumulation and Aquatic

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

simulation model is designed to predict the growth of individuals

and populations within an age-structured fish community and the

bioaccumulation dynamics of those fish when exposed to

mixtures of metals and organic chemicals. The model is

formulated such that its parameterization does not rely upon

calibration data sets from specific toxicokinetic and population

field studies, but rather upon physical and chemical properties

that can be estimated using chemical property calculators such as

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

( C a r r e i r a e t a l . 1 9 9 4 ) ( a l s o s e e h t t p : / /

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.
BASS 2.3 Sept 24, 2012 4

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 or cohort of fish:

(2.1)

(2.2)

(2.3)

where Bf and Wd are the chemical body burden (μg/fish) and dry

body weight (g dry wt/fish), respectively, of the average

individual within the cohort; and N is the cohort’s population

density (fish/ha). In Equation (2.1), Jg is the net chemical

exchange (μg/d) across the fish’s gills from the water; Ji is the net

chemical exchange (μg/d) across the fish’s intestine from food;

and Jbt is the chemical’s biotransformation rate (μg/d). In

Equation (2.2), Fd, Ed, R, EX, and SDA are the fish’s feeding,

egestion, routine respiration, excretion, and specific dynamic

action (i.e., the respiratory expenditure in excess of R required to

assimilate food), respectively, in units of g dry wt/d. Although

many physiologically based models for fish growth are

formulated in terms of energy content and flow (e.g., kcal/fish

and kcal/d), Equation (2.2) is fundamentally identical to these

bioenergetic models since the energy densities of fish depend on

their dry weight (Kushlan et al. 1986, Hartman and Brandt

1995b, Schreckenbach et al. 2001). Finally, in Equation (2.3)

EM, NM and PM are the cohort’s rates (fish/ha/d) of

emigration/dispersal, non-predatory, and predatory mortality,

respectively. Although immigration can be a significant process

in determining population sizes, this process is not presently

modeled in BASS. Because cohort recruitment is treated as a

boundary condition, the right-hand side of Equation (2.3) does

not require a term for recruitment. Though it may not be

immediately apparent from the notation used, these equations are

tightly coupled to one another. For example, the realized feeding

of fish depends on the availability (i.e., density and biomass) of

suitable prey. The fish’s predatory mortality, in turn, is

determined by the individual feeding levels and population

densities of its predators. Finally, the fish’s dietary exposure is

determined by its rate of feeding and the levels of chemical

contamination in its prey.

The following sections describe how each mass flux in the above

system of equations is formulated in BASS. Table 2.1 summarizes

the definitions of the variables used to develop these equations.

Because the system of units used to formulate chemical

exchanges is essentially the CGS-system (centimeter, gram,

second) and the system of units used to formulate a fish’s growth

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

conversion is necessary to make the coupled system of equations

dimensionally consistent. The reader should also note that

whereas the growth of fish is modeled in terms of dry weight, a

fish’s chemical bioaccumulation is formulated in terms of its wet

body weight since BASS models chemical uptake and excretion by

fish as chemical diffusion between aqueous phases.

2.1. Modeling Internal Distribution of Chemicals

Chemical exchanges across gills of fish and from their food are

generally considered to occur by passive diffusion of chemicals

between a fish’s internal aqueous phase and its external aqueous

environment, whether the latter is the surrounding ambient water

or the aqueous phase of the fish’s intestinal contents.

Consequently, to model these exchanges one must first consider

how chemicals distribute within the bodies of fish. If a fish is

conceptualized as a three-phase solvent consisting of water, lipid,

and non-lipid organic matter, then its whole-body chemical

concentration can be expressed as

(2.4)

where Ww

 is the fish’s wet weight (g wet wt/fish); Pa, Pl, and Po

are the fractions of the whole fish that are water, lipid, and nonlipid organic material, respectively; and Ca

, Cl, and Co are the

chemical’s concentrations in those respective phases. Because

the depuration rates of chemicals from different fish tissues often

do not differ significantly (Grzenda et al. 1970, van Veld et al.

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

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

can be assumed to be rapid in comparison to external exchanges.

For organic chemicals, this assumption means that Equation (2.4)

simplifies to

(2.5)

where Kl and Ko are the chemical’s partition coefficients between

lipid and water and between organic carbon and water,

respectively.

For metals, however, Equation (2.4) is more complicated.

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

accumulation within most other organic media occurs by
BASS 2.3 Sept 24, 2012 5

complexation reactions with specific binding sites. Consequently,

for metals the term PoCo/Ca in Equation (2.4) could be

formulated as a function of an appropriate stability coefficient

and the availability of binding sites. Appendix A summarizes an

equilibrium complexation model that was initially formulated for

BASS. Despite its apparent correctness, this algorithm greatly

overestimated metal (in particular mercury) bioaccumulation in

fish. Although this overestimation can be attributed to several

factors, the most likely explanation for the algorithm’s

unsatisfactory performance is that kinetics limits the

complexation of metal in fish. Because kinetic modeling was

considered incongruent with the time scales of most of the other

major processes represented elsewhere in BASS, a much simpler

algorithm was adopted.

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

WASP) have successfully used operationally defined distribution

coefficients Kd to model the accumulation of metals in organic

media, a similar approach was adopted for BASS. Thus, for a

metal

(2.6)

where Kl is again an appropriate partition coefficient between

lipid and water; and Kd is an appropriate metal-specific

distribution coefficient. Although this equation appears identical

to Equation (2.5) for organic contaminants, the relative values of

Kd and Ko in relation to Kl can be remarkably different. See

Section 3.1.

Because Ca

 equals the chemical’s ambient environmental water

concentration Cw

 at equilibrium, it follows from Equations (2.4)

and (2.6) that a fish’s thermodynamic bioconcentration factor (Kf

= Cf /Cw at equilibrium) for a chemical pollutant of concern is

(2.7)

2.2. Modeling Exchange from Water

Because chemical exchange across the gills of fish occurs by

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

law of diffusion as

(2.8)

where Sg

 is the fish’s total gill area (cm2); kg is the chemical’s

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

and Cw is the chemical’s concentration (μg/ml) in the

environmental water (Yalkowsky et al. 1973, Mackay 1982,

Mackay and Hughes 1984, Gobas et al. 1986, Gobas and Mackay

1987, Barber et al. 1988, Erickson and McKim 1990). When

Equations (2.5), (2.6), and (2.7) are substituted into this equation,

one then obtains

(2.9)

Although the chemical’s conductance kg could be specified as a

ratio of the chemical’s diffusivity to the thickness of an

associated boundary layer, implementation of this definition can

be problematic since the boundary layer thickness is a function

of the gill’s ventilation velocity and varies along the length of the

gill’s secondary lamellae. To avoid this problem, a fish’s net

chemical exchange rate coefficient, Sg

 kg , can be estimated by

reformulating the gill’s net chemical exchange as

(2.10)

where Q is the fish’s ventilation volume (cm3/s); and CB is the

chemical’s bulk concentration in the expired gill water. When

Equations (2.8) and (2.10) are equated, it follows that

(2.11)

Despite its appearance, the right-hand side of this equation can

be readily quantified. In particular, the ventilation volume of fish

can be estimated by

(2.12)

where RO2 is the fish’s rate of oxygen consumption (μg/s); "O2 is

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

environmental water’s dissolved oxygen concentration (μg/ml).

If one makes certain assumptions concerning the geometry of the

interlamellar spaces and the nature of mass transport between the

gill’s secondary lamellae, the chemical’s normalized bulk

concentration in the expired gill water (Cw-CB)/(Cw-Ca) can also

be calculated as outlined in the following.

Because the gill’s secondary lamellae form flat channels having

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

distance), they can be treated as parallel plates, and the flow of

water between them can be treated as Poiseuille slit flow (Hills

and Hughes 1970, Stevens and Lightfoot 1986). Under this

assumption, an expression for a chemical’s concentration in the

bulk expired gill water can be obtained using the solutions of the

partial differential equation (PDE) that describes steady-state,

convective mass transport between parallel plates, i.e.,

(2.13)

where V is the gill’s mean interlamellar flow velocity (cm/s); D

is the chemical’s aqueous diffusivity (cm2/s); and x and y are the
BASS 2.3 Sept 24, 2012 6

lateral and longitudinal coordinates of the channel along which

diffusion and convection occurs, respectively. In this equation,

C = C(x, y) is the chemical’s interlamellar concentration at the

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

length. The surfaces of adjacent lamellae are located at x = ± r

where r is the hydraulic radius of the lamellar channel that equals

half the interlamellar distance d (cm). The midline between

adjacent lamellae is therefore denoted by x=0. The gill’s mean

interlamellar flow velocity can be readily formulated as the ratio

of the fish’s ventilation volume to the gill’s cross sectional pore

area, Xg

 (cm2). Because the gill’s pore area is related to its

lamellar surface area by

(2.14)

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

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

interlamellar flow velocity is given by

(2.15)

To solve Equation (2.13), two boundary conditions must be

specified. Because adjacent lamellae presumably exchange the

chemical equally well, the solutions should be symmetrical about

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

condition

(2.16)

is assumed. The second necessary boundary condition must

describe how chemical exchange across the secondary lamellae

actually occurs. Assuming steady state diffusion from the

interlamellar water to the fish’s aqueous blood, this boundary

condition can be formulated as

(2.17)

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

Although this boundary condition has been used as is (Barber et

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

limitations on gill uptake. To accomplish this latter task, a

formulation patterned after Erickson and McKim (1990) is used.

In particular, consider the following reformulation

(2.18)

where Ca(y) is the chemical’s aqueous phase concentration at

point y along the length of a secondary lamella; Ca

(l) = Ca is the

chemical’s concentration in the afferent lamellar blood; Js(y, l) is

the chemical’s accumulated uptake (μg/s) along the lamellar

segment [y, l]; and qp

 is the lamellar perfusion rate (cm3/s). If

both sides of the lamella uptake chemical, then Js(y, l) can be

formulated as

(2.19)

where h is the height (cm) of the secondary lamella. Using this

expression, the boundary condition (2.18) can now be written as

(2.20)

Once the solution of Equation (2.13) for these boundary

conditions has been obtained, the chemical’s bulk concentration

in the expired gill water can be evaluated using the weighted

average

(2.21)

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

velocity.

A canonical solution to Equation (2.13) can be obtained by

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

(2.22)

(2.23)

(2.24)

When this is done, the chemical’s dimensionless bulk

concentration is given by
BASS 2.3 Sept 24, 2012 7

(2.25)

where NGz = (l D)/(V r2) is the gills’ dimensionless lamellar

length or Graetz number. Two important features of this

expression can now be observed. First, one can easily verify that

(2.26)

Consequently, Equation (2.11) can be rewritten as

(2.27)

Secondly, analytical expressions for 1B are readily available

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

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

dimensionless bulk concentration can be evaluated by

(2.28)

where the coefficients Bm and exponents 8m are known functions

of the gills’ dimensionless conductance or Sherwood number

(2.29)

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

B. Although this infinite series solution does not have a

convenient convergence formula, for Sherwood numbers and

ventilation / perfusion ratios that are typical of fish gills, only the

first two terms of the series are needed to estimate 1B

 with less

than 1% error (see Barber et al. 1991). See Figure 2.1 and

Figure 2.2 for displays of 81 and B1 and of 82 and B2,

respectively.

2.3. Modeling Exchange from Food

Chemical uptake from food has usually been modeled assuming

that fish assimilate a constant fraction of the chemical that they

ingest, i.e.,

(2.30)

where "c is the assimilation efficiency (dimensionless) for the

chemical; Cp is the chemical’s concentration (μg/g wet wt) in the

ingested prey; and Fw

 is the fish’s daily wet weight prey

consumption (g wet wt/d) (Norstrom et al. 1976, Jensen et al.

1982, Thomann and Connolly 1984, Niimi and Oliver 1987).

However, because the chemical exchange across the intestine is

driven by diffusive gradients (Vetter et al. 1985, Clark et al.

1990, Gobas et al. 1993), such formulations are

thermodynamically realistic only if "c

 is a decreasing function of

the fish’s total body concentration Cf.

A thermodynamically based description for the dietary uptake of

chemicals can be formulated using the simple mass balance

relationship

(2.31)

where Ew

 is the fish’s daily wet weight egestion (g wet wt/d) and

Ce is the pollutant’s chemical concentration (μg/g wet wt) in the

fish’s feces. When this equation is reformulated in terms of dry

weight feeding and egestion (i.e., and 

where Pdp and Pde denote the prey’s and feces’ dry weight

fractions , respectively) the fish’s net dietary exchange becomes

(2.32)

where Cae and Cde are the pollutant’s chemical concentrations in

the aqueous and dry phases of the fish’s feces, respectively; Ea

 is

the mass / volume of the feces’ aqueous phase; and Pae and Pap

are the aqueous fractions of the fish’s feces and prey,

respectively. To parameterize Equation (2.32), two assumptions

are made.

Because the transit time through the gastrointestinal tract is

relatively slow, the first of these assumptions is that the

concentrations of chemicals in the fish’s aqueous blood,

intestinal fluids, and dry fecal matter equilibrate with one

another. Consequently, Cae = Ca. Moreover, for organic

chemicals the concentration ratio Cde / Cae can be replaced with

an organic carbon / water partition coefficient, Koc (e.g., Briggs

1981, Karickhoff 1981, Chiou et al. 1986), and for metals this

ratio can be substituted with a distribution coefficient similar to

the one used in Equation (2.6).

Although reported values for the percent moisture of the

intestinal contents of fish vary typically between 50 and 80%

(Brett 1971, Marais and Erasmus 1977, Grabner and Hofer
BASS 2.3 Sept 24, 2012 8

1985), the second assumption made to parameterize Equation

(2.32) assumes that the fish’s intestinal contents and whole body

are osmotically equilibrated. Consequently, Pae = Pa. If this

assumption is reasonable, then meals with the same dry weights

but different wet weights should be processed by the fish at equal

rates and efficiencies since both will attain the same proximate

composition relatively soon after ingestion. Having the same

proximate composition implies that the concentrations of

digestive enzymes acting on the meals will be comparable and

that the physical forces exerted by the gut contents that control

gastric mobility will also be comparable. Because Bromley

(1980), Garber (1983), and Ruohonen et al. (1997) demonstrated

that initial dietary moisture content had no significant effect on

the assimilation efficiencies of turbot (Scophthalmus maximus)

or on gastric evacuation rates of yellow perch (Perca flavescens)

and rainbow trout (Oncorhynchus mykiss), respectively, the

assumption that Pae = Pa

 seems reasonable.

Using the stated assumptions and Equations (2.5), (2.6), and

(2.7), Equation (2.32) now can be rewritten as

(2.33)

where Ke is the distribution coefficient describing the chemical

partitioning between the aqueous and dry organic matter phases

of the fish’s intestinal contents. Although BASS uses this equation

to calculate a fish’s net chemical dietary exchange, this equation

can also be further manipulated as follows

(2.34)

where is the fish’s food assimilation efficiency

(g dry wt assimilated/g dry wt ingested), and is the

fish’s dry fraction. In other words, a thermodynamically based

assimilation efficiency for Equation (2.30) corresponds to

(2.35)

Thus, Equation (2.33) is equivalent to Equation (2.30) with a

chemical assimilation efficiency that decreases as the fish’s

whole-body chemical burdens or concentrations increase. Studies

by Lieb et al. (1974), Gruger et al. (1975), and Opperhuizen and

Schrap (1988) corroborate this prediction (see Barber et al.

1991). Moreover, using in situ preparations of channel catfish

intestines Doi et al. (2000) have clearly established that

preexposures to 3,4,3',4'-tetrachlorobiphenyl do decrease

intestinal uptake rates.

Although the preceding model development demonstrates the

potential logical inconsistency between an assumed constant

chemical assimilation efficiency model for dietary chemical

uptake and a thermodynamically based model, many researchers

continue to use the former assumption and model. Parameters for

these constant chemical assimilation efficiency models generally

have been estimated using the following equations proposed by

Bruggeman et al. (1981)

(2.36)

(2.37)

where Nww is the fish’s specific feeding rate (g wet wt/g wet

wt/d); and k2 is the fish apparent elimination rate coefficient (g

wet wt/g wet wt/d or 1/d) that is the sum of its rate coefficients of

growth ((), biotransformation (kbt), and actual excretion (kex). See

for example Muir et al. (1992), Dabrowska et al. (1996), and

Fisk et al. (1998). Unfortunately, many researchers have failed

to acknowledge that Equation (2.37) is the solution to Equation

(2.36) only when initial time is t0 = 0 and the fish’s initial wholebody concentration is zero (i.e., Cf

 (t0) = 0). This fact, combined

with the ability of Equation (2.37) to fit experimental results

statistically, has been at least partially responsible for the

perpetuation of the idea of constant chemical assimilation

efficiencies.

The general solution to Equation (2.36) is actually

(2.38)

When this solution is re-differentiated, one observes that

(2.39)

Using this exponential form of Equation (2.36), one can analyze

the parameter behavior of a dietary exposure during consecutive

time segments for which Nww and Cp

 are constant. Therefore, let

T = (t2 - t0) denote the length of such a dietary exposure, and let

t1

 denote the time when the exposure is half over. During the first

half of the exposure (i.e., t0 < t < t1) the fish’s bioaccumulation

dynamics will be described by Equation (2.39). During the

second half of the exposure, however, these dynamics will be
BASS 2.3 Sept 24, 2012 9

described by

(2.40)

where and are the fish’s assimilation efficiency and

apparent elimination rate coefficient that may require updating

for t1 < t < t2. If an equation of the form of Equation (2.37) is

assumed to describe the fish’s bioaccumulation dynamics over

the entire interval [t0

, t2], then the derivatives specified by

Equations (2.39) and (2.40) must be equal when evaluated for t

= t1. This consistency condition, that is analogous to the

preservation of derivatives that occurs when approximating a

function with Bernstein polynomials, requires that

(2.41)

which implies that

(2.42)

When the fish’s initial whole-body concentration is zero, this

equation can be shown to reduce to

(2.43)

This equation shows that unless , chemical assimilation

efficiencies estimated for different times and initial whole-body

concentration will be different. Phrased another way, this

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

biotransform chemicals, as measured by and k2 , contributes

to the determination of the fish’s realized chemical assimilation

efficiencies. Because specific growth rates ( ) and

chemical excretion rate coefficients (kex) for fish are generally

related to the fish’s body size as allometric power functions, i.e.,

(2.44)

(2.45)

where and (Barber et al. 1988, Sijm et al. 1993,

Sijm and van der Linde 1995, Sijm et al. 1995), one would

expect that when significant growth occurs during the

experiment. Consequently, one would also expect that .

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 proposed fecal partitioning model

(i.e., Equation (2.33)) is best suited to circumstances where its

equilibrium assumptions are reasonably satisfied, as herein where

the object is to predict the dietary exchange of the average

individual of a population. A more kinetically based approach

may be needed, however, when trying to describe the

toxicokinetics of individual fish. See for example Nichols et al.

(1998). Readers are referred to Barber (Barber 2008) for a more

thorough discussion and analysis of dietary uptake algorithms

used to model chemical bioaccumulation in fish.

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.,

(2.46)

where ,a

 is the fish’s aqueous phase biotransformation rate

coefficient (1/d); and (Pa

 Ww) is the volume of the fish’s aqueous

phase. If Equations (2.9) and (2.46) are used to describe

chemical bioconcentration during a water-only exposure without

growth, then a fish’s whole-body concentration would be

modeled as

(2.47)

where ku, kex, and kbt are the fish’s rate coefficients of gill uptake,

excretion, and biotransformation, respectively. In terms of

quantitative structure activity relationships (QSARs), this

equation predicts that a fish’s whole-body biotransformation rate

kbt should be inversely proportional to its thermodynamic

bioconcentration factor Kf that in turn is proportional to the

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

influenced by any QSAR dependencies that the fish’s aqueous

phase biotransformation rate ,a

 might have. See de Wolf et al.

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

2.5. Modeling Temperature Effects on

Physiological Rates

Because temperature affects 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 is often

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

(2.48)
BASS 2.3 Sept 24, 2012 10

where p0

 and p1 are the reaction rates of the process at

temperatures T0

 and T1, respectively, such descriptions are

generally valid only within a range of the organism’s thermal

tolerance. In most cases, the process’s reaction rate increases

exponentially with increasing temperature up to a temperature T1

after which it decreases. Moreover, the temperature at which a

process’s rate is maximum is often very close to the organism’s

upper thermal tolerance limit. To model this behavior, Thornton

and Lessem (1978) developed a logistic multiplier to describe the

temperature dependence of a wide variety of physiological

processes. Although this algorithm has been used successfully in

many fish bioenergetic models, BASS uses an exponential-type

formulation that responds hyperbolically to increasing

temperature. Importantly, such algorithms can be easily

parameterized.

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

the temperature at which the rate is at its maximum value. If this

process generally exhibits an exponential response to

temperature changes well below T1

, then Equation (2.48) can be

used to describe this process for T and T0 << T1, i.e.,

(2.49)

(2.50)

where P0

 is the process’s rate at the low-end reference

temperature T0. To incorporate the adverse effects of high

temperatures on this process, the right-hand side of Equation

(2.50) can be multiplied by a hyperbolic temperature term that

approaches unity as temperature decreases well below T1, equals

zero at T1, and becomes increasingly negative as temperature

approaches the fish’s upper thermal tolerance limit TL

 = T2.

Modifying Equation (2.50) in this fashion yields

(2.51)

whose solution is

(2.52)

If one assumes, without loss of generality, that T0

 = 0, the

preceding equation can be simplified to

(2.53)

Figure 2.3 displays the behavior of this equation for P0

 = 1 and

T2

 = 36 Celsius as a function of , and T1. Although these

equations apparently have not been used to describe

physiological responses of fish, their utility for doing so is

discussed subsequently in Section 3.3. For other applications of

Equations (2.52) and (2.53) see Lassiter and Kearns (1974),

Lassiter (1975), and Swartzman and Bentley (1979). Note that

when T1 = T2, Equation (2.53) reduces to Equation (2.49).

2.6. Modeling Growth of Fish

Although the preceding algorithms for modeling chemical

bioaccumulation in fish depend on a fish’s wet weight, BASS does

not directly simulate the wet weight of fish. Instead, it simulates

the dry weight of fish as the mass balance of feeding, egestion,

respiration, and excretion and then calculates the fish’s

associated wet weight using the following relationships

(2.54)

(2.55)

(2.56)

(2.57)

where Wa

, Wd, Wl, and Wo are the fish’s aqueous, dry, lipid, and

non-lipid organic weights, respectively; and a0, a1, l1, and l2 are

empirical constants. Whereas Equations (2.54) and (2.57) are

simply assertions of mass conservation, Equations (2.55) and

(2.56) are purely empirical functions. Although Equation (2.55)

is assumed because simple power functions of this form

adequately describe many morphometric relationships for most

organisms, Equation (2.56) is based on the results of numerous

field and laboratory studies (Eschmeyer and Phillips 1965, Brett

et al. 1969, Groves 1970, Elliott 1976a, Staples and Nomura

1976, Craig 1977, Shubina and Rychagova 1981, Beamish and

Legrow 1983, Weatherley and Gill 1983, Flath and Diana 1985,

Lowe et al. 1985, Kunisaki et al. 1986, Morishita et al. 1987).

These equations yield an expression for a fish’s wet weight that

is a monotonically increasing, but nonlinear, function of the

fish’s dry weight.

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

maximum ad libitum consumption ( g dry wt/d) and then

adjusting this potential by the availability of appropriate prey as

described in the next section. Because a variety of models are

commonly used to describe the maximum feeding of fish, BASS

is coded to allow users the option of using any one of four

different feeding models for any particular age / size class of fish.

The first formulation that can be used is a temperature-dependent

power function

(2.58)
BASS 2.3 Sept 24, 2012 11

where f1

, f2, f3, T1, and T2 are empirical constants specific to the

fish’s feeding.

A commonly used alternative to the preceding allometric model

is the Rashevsky-Holling model that is defined by the equations

(2.59)

where Ndd is the fish’s ad libitum feeding rate (g dry wt/g dry

wt/d); Gmax is the maximum amount of food (g dry wt/fish) that

the fish’s stomach / gut can hold; G is the actual amount of food

(g dry wt/fish) present in the gut; and Ad and Ed are the fish’s

assimilation and egestion, respectively, in units of g dry wt/d

(Rashevsky 1959, Holling 1966). Given a fish’s gut capacity

Gmax, feeding time t

sat to satiation, and satiating meal size Msat ,

Ndd can be estimated using the equations

(2.60)

(2.61)

(2.62)

where is the total food consumed during the interval 

and (also see Dunbrack 1988). Alternatively, Ndd

can be estimated by simply assuming that Msat = 0.95 × Gmax in

which case

(2.63)

For planktivores, BASS can also estimate a fish’s maximum

ingestion using the clearance volume model

(2.64)

where Q is the plankton standing stock (g dry wt/L); and Qcl is

the planktivore’s clearance volume (L/d) that is assumed to be

given by

(2.65)

where q1

, q2, q3, T1, and T2 are empirical constants specific to the

fish’s filtering rate.

The fourth and final feeding model back-calculates a fish’s

expected feeding based on knowing the fish’s expected growth

and routine respiratory demands. In particular, because

assimilation, egestion, specific dynamic action, and excretion are

assumed to be linear functions of feeding and routine respiration

as discussed subsequently, it is a straightforward matter to

calculate a fish’s expected ingestion given its expected growth

and respiration. When users elect this feeding option, BASS

assumes that the fish’s specific growth rate (1/d)

is given by

(2.66)

where g1, g2, g3, T1, and T2 are empirical constants specific to the

fish’s growth rate. See Thomann and Connolly (1984) for

additional discussion of the use of this feeding model.

When BASS estimates a fish’s feeding rate using Equations

(2.58), (2.64), or (2.66), the fish’s assimilation and egestion are

estimated as simple fractions of its realized ingestion Fd

, i.e.,

(2.67)

(2.68)

where "f is the fish’s net food assimilation efficiency that is a

weighted average of its assimilation efficiencies for invertebrate,

piscine, and vegetative prey. However, when the RashevskyHolling feeding model is used, BASS calculates these fluxes by

substituting Fd with a function that describes the fish’s pattern of

intestinal evacuation. The general form of this function is

assumed to be

(2.69)

where e1, e2, e3, T1, and T2 are empirical constants specific to the

fish’s gastric evacuation.

The numerical value of this function’s exponent, e2

, depends both

on characteristics of the food item being consumed and on the

mechanisms that presumably control gastrointestinal motility and

digestion (Jobling 1981, 1986, 1987). For example, when gut

clearance is controlled by intestinal peristalsis, e2 should

approximately equal 1⁄2 since peristalsis is stimulated by

circumferential pressure exerted by the intestinal contents that,

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

On the other hand, when surface area controls the rate of

digestion, e2 should be approximately either b or unity. If the

fish consumes a small number of large-sized prey (e.g., a

piscivore), e2 = b may be the appropriate surface area model. On

the other hand, if the fish consumes a large number of smaller,
BASS 2.3 Sept 24, 2012 12

relatively uniform-sized prey (e.g., a planktivore or drift feeder),

e2 = 1 is more appropriate since total surface area and total

volume of prey become almost directly proportional to one

another. When e2 = 1, the Rashevsky-Holling model (i.e.,

Equation (2.59)) is analogous to the Elliott-Persson model for

estimating daily rations of fish (Elliott and Persson 1978).

Finally, Olson and Mullen (1986) outlined a process-based

model that even suggests e2 = 0.

A fish’s specific dynamic action, i.e., the respiratory expenditure

associated with the digestion and assimilation of food, is

modeled as a constant fraction of the fish’s assimilation. In

particular,

(2.70)

where F generally varies between 0.15 and 0.20 (Ware 1975,

Tandler and Beamish 1981, Beamish and MacMahon 1988).

BASS assumes that body weight losses via metabolism are due

entirely to the respiration of carbon dioxide and the excretion of

ammonia. A fish’s respiratory loss R is therefore calculated from

its routine oxygen consumption, RO2 (g O2

/d), using a respiratory

quotient, RQ (L CO2

 respired)/L O2 consumed), as follows

(2.71)

BASS calculates a fish’s routine oxygen consumption as a

constant multiple RB of its standard basal oxygen consumption

(Ware 1975) that is assumed to be temperature-dependent power

function. In particlar,

(2.72)

where b1, b2, b3, T1, and T2 are empirical constants specific to the

fish’s standard basal oxygen consumption. Although ammonia

excretion could be modeled using an analogous function

(Paulson 1980, du Preez and Cockroft 1988a, b), BASS calculates

this flux as a constant fraction of the fish’s total respiration since

excretion and oxygen consumption generally track one another.

For example, ammonia excretion increases after feeding, as does

oxygen consumption (Savitz 1969, Brett and Zala 1975,

Gallagher et al. 1984). Likewise, conditions that inhibit the

passive excretion of ammonia also depress carbon dioxide

excretion (Wright et al. 1989). Assuming that fish maintain a

constant nitrogen/carbon ratio NC (g N/g C), BASS estimates a

fish’s excretory loss in body weight as

(2.73)

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

to that of nitrogen.

2.7. Modeling Predator-Prey Interactions

BASS is designed to simulate aquatic food webs in which each

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

incidental terrestrial insects, periphyton / attached algae,

phytoplankton, and zooplankton. The realized feeding of any

given age class of fish is determined by the estimated maximum

feeding rate of individuals within the cohort, the cohort’s

population size, and the biomass of prey that is available to the

cohort; the latter quantity is the sum of the current biomass of

potential prey minus the biomass of potential prey expected to be

consumed by other fish cohorts that are more efficient foragers

/ competitors. BASS ranks the competitive abilities of different

cohorts using the following assumptions:

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 other general trend for prey handling times to be

inversely correlated with body size (Werner 1974, Miller et al.

1992) suggests that the larger predator could dispatch intercepted

prey and resume foraging more quickly than the smaller predator.

Also see Post et al. (1999) and Railsback et al. (2002).

ASSUMPTION 2. Unlike benthivores and piscivores, the

competitive abilities and efficiencies of planktivores are

inversely related to their body size due to their relative

morphologies (Lammens et al. 1985, Johnson and Vinyard 1987,

Wu and Culver 1992, Persson and Hansson 1999). Consequently,

“large” planktivores only have access to the leftovers of “small”

planktivores.

BASS calculates the relative frequencies of the prey

consumed by a cohort using dietary electivities, i.e.,

(2.74)

where f

i

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

all other prey consumed by the cohort. One can easily verify that
BASS 2.3 Sept 24, 2012 13

the range of dietary electivities is . One can also

verify that if the fish does not eat a potential food item i

that . Similarly, if the fish consumes a potential prey item

i in direct proportion to the prey’s relative abundance,

then . BASS actually allows users to specify a fish’s diet as

either a set of fixed dietary frequencies , a set of

electivities , or a combination of fixed frequencies and

electivities . To calculate a cohort’s realized

dietary composition, however, BASS converts all user supplied

fixed dietary frequencies into their equivalent electivities using

the simulated relative abundances of the cohort’s

potential prey. These electivities are then combined with any user

specified electivities to form a set of unadjusted

electivities that is subsequently converted into a

consistent set of realized electivities . Using these

realized electivities, BASS then calculates the cohort’s realized

dietary frequencies using

(2.75)

The important step in this computational process is the

conversion of the unadjusted electivities into a set of realized

electivities. Although this conversion is sometimes unnecessary,

it is generally needed to insure that the sum of the dietary

frequencies calculated by Equation (2.75) equals 1. One can

verify that the condition that guarantees is

(2.76)

See Appendix C. When Equation (2.76) is not satisfied for a

given set of electivities and relative prey

availabilities , BASS transforms the given electivities

using a linear transformation that maps into 

and into . The general form of this

transformation is

(2.77)

where . Besides insuring that

, this transformation also preserves the relative

preferences represented in the original base set .

Because many studies have shown a strong positive correlation

between the body sizes of piscivorous fish and the forage fish

that they consume (Parsons 1971, Lewis et al. 1974, Timmons et

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 aforementioned

procedure to calculate piscivorous interactions, only a specific

size range of forage fish is assumed to be available to a

piscivorous cohort. BASS characterizes this size spectrum by

using either linear or complementary exponential functions to

describe the maximum, minimum, and mode of prey body lengths

that a predator of a given body length will ingest. In particular,

these key features of a fish’s prey spectrum are described by

(2.78)

(2.79)

(2.80)

where "1, "2, and "3 are empirical constants describing the fish’s

maximum length of prey; $1, $2, and $3 are empirical constants

describing the fish’s minimal length of prey; and (1, (2, and (3

are empirical constants describing the mode of the length of prey

ingested by the fish. The relative frequencies f

i

 of forage fish

available to a piscivorous cohort are then calculated relative to

the sum of forage fish biomasses whose body lengths are both

greater than Lmin and less than Lmax minus the biomass of those

prey sizes predicted to be consumed by more efficient

piscivorous cohorts (see Assumption 1).

When two or more cohorts of a forage species i can be consumed

by a piscivore, the relative frequencies of those cohorts sij in the

piscivore’s diet are calculated assuming that prey sizes follow a

simple triangular distribution defined by Equations (2.78)

through (2.80). 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 is the triangular distribution function

(2.81)

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

diet are calculated to be 
BASS 2.3 Sept 24, 2012 14

and . If only one age class of a forage

species is vulnerable to the cohort, then .

If while calculating the dietary frequencies of a piscivorous

cohort BASS predicts that the cohort’s available prey is

insufficient to satisfy its desired level of feeding, BASS reassigns

the cohort’s unadjusted electivities in a manner to

simulate prey switching. These reassignments are based on the

following assumption:

ASSUMPTION 3. When forage fish become limiting, piscivores

switch to benthic macroinvertebrates or incidental terrestrial

insects as alternative prey. However, piscivores that must switch

to benthos or that routinely consume benthos in addition to fish

are less efficient benthivores than are obligate benthivores

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

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

piscivores. If such resources are still insufficient to satisfy the

piscivores’ metabolic demands, piscivores are assumed then to

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

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

access only to the leftovers of non-piscivorous planktivores.

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

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

reassigns any other electivity that does not equal -1, to zero.

If benthos becomes limiting for benthivores, or if plankton

becomes limiting for planktivores, BASS assumes that benthivores

can shift their diets to include plankton and terrestrial insects and

that planktivores can shift their diets to include benthos and

terrestrial insects. See, for example, Ingram and Ziebell (1983).

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

assigns the cohort’s individual realized feeding rate adjusted for

prey availability as

(2.82)

where is the cohort’s maximum individual ingestion (g dry

wt/fish); N is the cohort’s population size (fish/ha), and ABj is the

biomass (g dry wt/ha) of prey j that is available to that cohort.

Using its predicted dietary compositions and realized feeding

rates, BASS then calculates the predatory mortalities for each fish

cohort and nonfish compartments.

2.8. Modeling Stable Isotopes and Trophic Position

To provide a summary output variable that integrates the

temporal and body size dynamics of a fish’s dietary composition,

BASS simulates time dynamics of each cohort’s carbon and

nitrogen stable isotopic signatures (i.e., and ).

Although other models have been proposed for this purpose (see

Hesslein et al. 1993, Harvey et al. 2002, Olive et al. 2003), BASS

assumes that the change in a fish’s mean stable isotope

ratio during an arbitrarily “small” fraction or multiple of a day

while feeding on a single prey item can be described by

(2.83)

where and denote the stable isotope ratios of the fish

and its prey, respectively, at time t; denotes the fish’s

fractionation constant associated with prey assimilation; 

denotes the fish’s assimilation efficiency for that prey; is the

fish’s dry weight ingestion rate (g dry wtAd

!1

); is the length (d)

of the chosen time interval; is the fish’s fractionation constant

associated with its routine metabolism (i.e., respiration and

excretion); and is the fish’s daily metabolic loss rate (g dry

wtAd

!1

). One can straightforwardly verify that Equation (2.83) is

also equivalent to

(2.84)

where and are the fish’s specific rates

(g dry wtAg dry wt!1Ad

!1

) of feeding and metabolism, respectively.

Passing to the limit (i.e., letting h approach zero) then yields the

differential equations

(2.85)

(2.86)

where is the fish’s specific growth rate (g dry wtAg

dry wt!1Ad

!1

). Equation (2.86), can be reparameterized by noting

that most stable isotope studies assume that at or near steadystate the difference between the stable isotope signatures of any

two successive trophic levels is a constant. For Equation (2.86),

this assumption implies that

(2.87)

where denotes the fish’s steady-state stable isotope ratio.

Consequently,

(2.88)
BASS 2.3 Sept 24, 2012 15

and Equation (2.86) can be rewritten as

(2.89)

One can generalized Equations (2.86) and (2.89) to describe

the isotope dynamics of fish feeding on multiple prey or mixed

trophic levels as follows

(2.90)

(2.91)

where is the frequency of the p-th prey in the fish’s diet. BASS

calculates the trophic position of each fish cohort using the

cohort’s predicted and the standard equation

(2.92)

(Vander Zanden and Rasmussen 2001).

2.9. Modeling Dispersal, Non-Predatory

Mortalities, and Recruitment

The algorithm that BASS employs to simulate a species’ dispersal

and non-predatory mortality is based on the general empirical

observation that population densities of most vertebrates can be

adequately characterized by the self-thinning power function

relationship

(2.93)

where N is the species’ or cohort’s density (fish/ha) and Ww is the

species’ or cohort’s mean wet body weight (Damuth 1981, Peters

and Raelson 1984, Juanes 1986, Robinson and Redford 1986,

Dickie et al. 1987, Boudreau and Dickie 1989, Gordoa and

Duarte 1992, Randall et al. 1995, Dunham and Vinyard 1997,

Steingrímsson and Grant 1999, Dunham et al. 2000, Guiñez

2005). For fish the body weight exponent b generally varies from

0.75 to 1.5 (Boudreau and Dickie 1989, Grant and Kramer 1990,

Gordoa and Duarte 1992, Elliott 1993, Bohlin et al. 1994,

Randall et al. 1995, Dunham and Vinyard 1997, Grant et al.

1998, Dunham et al. 2000, Knouft 2002, Keeley 2003). Larger

exponents ranging from 1.5 to 3.0, however, have also been

reported (Steingrímsson and Grant 1999). If Equation (2.93) is

differentiated with respect to time, it immediately follows that a

species’ population dynamics can be modeled using the timevarying, linear differential equation

(2.94)

where is the species’ specific growth rate.

Consequently, b( corresponds to the cohort’s total mortality rate.

Readers interested in detailed discussions concerning the

underlying process-based interpretation and general applicability

of this result should consult Peterson and Wroblewski (1984),

McGurk (1993, 1999), and Lorenzen (1996).

Because Equations (2.93) and (2.94) encompass the cohort’s

predatory mortality, non-predatory mortality, and dispersal, and

because BASS separately models the cohort’s predatory mortality,

BASS assumes that the cohort’s combined rate of dispersal (EM)

and non-predatory mortality (NM) is simply a fraction * of b(.

In particular,

(2.95)

If community population dynamics are strongly dominated by

predation, the fraction * will be “small” (e.g., * < 0.5) for forage

fishes and “large” (e.g., * > 0.5) for predatory species. However,

if community population dynamics are dominated by dispersal

mechanisms related to competition for food, space, or other

limiting community resource, the fraction * will be large for both

forage and predatory species alike.

Although BASS’s basic self-thinning algorithm for predicting a

cohort’s combined dispersal and non-predatory mortality (EM +

NM) may be sufficient to simulate realistically bounded

populations for most species, under certain conditions some

species may tend to exhibit unbounded or unrealistic population

dynamics. To correct such behaviors, a simulation option has

been implemented that imposes an additional mortality and

dispersal on all of a species’ cohorts as that species’ total

biomass approaches a user-defined biomass carrying capacity.

This algorithm assumes that a cohort’s realized biomass

derivative adjusted for an assumed carrying capacity is given by

(2.96)

(2.97)

(2.98)

where X = N W denotes the cohort’s biomass; KM is the cohort’s

additional dispersal and mortality due to the species’ biomass

carrying capacity constraint; S is the species total biomass; and

K is the species biomass carrying capacity. It is interesting to

note that Equation (2.96) can also be written as
BASS 2.3 Sept 24, 2012 16

(2.99)

where corresponds to the cohort’s instantaneous

carrying capacity given the species’ existing age class structure.

That is, the cohort’s biomass attains a local maximum when X =

Kc

. Consequently, the hyperbolic multipliers used in these

equations are mathematically analogous to those used to model

temperature effects on the cohort’s physiological functions (see

Section 2.5).

BASS estimates a species’ recruitment by assuming that each

species turnsover 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 lengths are greater than or equal to a

specified minimum value (tlr0) marking the species’ sexual

maturation. When reproduction is simulated, the body weight of

each sexually mature cohort is decreased by its rbi and the total

number of YOY that are recruited into the population as a new

cohort is estimated by simply dividing the species’ total spawned

biomass by the species’ characteristic YOY body weight.

Although this formulation does not address the myriad of factors

known to influence population recruitment, it is logically

consistent with the spawners’ abundance model for fish

recruitment. See Myers and Barrowman (1996) and Myers

(1997).

2.10. Modeling Habitat Effects

Although BASS does not explicitly model physical habitat

features of the fish community of concern, it does allow users to

specify habitat suitability multipliers on the feeding, reproduction

/ recruitment, and dispersal / non-predatory mortality for any or

all species. Because these multipliers are assumed to be

analogous to subcomponents of habitat suitability indices, they

are assumed to take values from 0 to 1. If these multipliers are

not specified by the user, BASS assigns them the default value of

1.

When feeding habitat multipliers (HSIfeeding) are specified, BASS

uses the specified parameters as simple linear multipliers on the

fish’s maximum rate of ingestion, i.e.,

(2.100)

The resulting adjusted maximum feeding rate then replaces 

in Equation (2.82). These multipliers are assumed to modify the

fish’s ability to perceive or to intercept prey either by effecting

the fish’s reactive distance, foraging patterns, etc. or by

providing modified refuges for its potential prey. Habitat

interactions that actually change the abundance of potential prey

should not be specified as feeding habitat multipliers since these

interactions are automatically addressed by the algorithms

outlined in Section 2.7.

Like the aforementioned feeding habitat multipliers, BASS uses

any specified recruitment habitat multipliers (HSIrecruitment) as

simple linear multipliers on the number of young-of-year that is

recruited into the species population, i.e.,

(2.101)

These multipliers can represent either the availability of suitable

spawning sites or the ability of the otherwise successful spawns

to result in the expected numbers of young-of-year as discussed

in Section 2.8.

Finally, when habitat multipliers (HSIsurvival) are specified for

dispersal / non-predatory mortality, the specified values are

assumed to dynamically control the species self-thinning

exponent b (see Section 2.8) such that the exponent is maximum

for and minimum for . Thus, as habitat

suitability decreases, dispersal and non-predatory mortality

increases, and vice versa (see Equation (2.95)). In between this

range the species self-thinning exponent is assumed to respond

linearly to changing HSIsurvival, i.e.,

(2.102)

Because constructing the aforementioned habitat suitability

multipliers in a general or standard way is not a trivial issue,

BASS relegates their construction to the user. Nevertheless, users

might consider several obvious starting points when simulating

habitat effects using BASS. Turbidity, for example, is known to

affect the foraging abilities of both prey and predatory fishes, and

one could readily use results of published studies (e.g.,

Vandenbyllaardt et al. 1991, Barrett 1992, Gregory 1993,

Gregory and Northcote 1993, Miner and Stein 1996, Reid et al.

1999, Vogel and Beauchamp 1999, Bonner and Wilde 2002, de

Robertis et al. 2003, Sweka and Hartman 2003) to estimate

feeding multipliers for Equation (2.100) as power functions or

polynominals of turbidity. Field-based HSI’s are often estimated

by logistic regression of presence-absence data without

specifying the underlying mechanisms that actually determine

habitat suitability for a species. Such HSI’s could be used as

habitat multipliers for species’ recruitment (Equation (2.101)) or

persistence/survival (Equations (2.95) and (2.102)) depending on

the user’s own interpretation of what the indices most likely

represent.

2.11. Modeling Nonfish Compartments
BASS 2.3 Sept 24, 2012 17

BASS assumes that the nonfish components of a community of

concern can be treated as four lumped compartments, i.e.,

benthos, periphyton/attached algae, phytoplankton, and

zooplankton. These compartments can be treated either as

community forcing functions or as bona fide state variables. In

the later case, the required compartmental dynamics are

simulated using the simple mass balance model

(2.103)

where Y is the compartment’s biomass (g dry wt/m2); and the

fluxes g dry wt/m2/d IP, R, , and M are the compartment’s

ingestion or photosynthesis, respiration, mortality due to fish

consumption, and non-consumptive mortality and dispersal,

respectively. Except for , each of these fluxes is modeled as a

linear function of the compartment’s biomass, i.e.,

(2.104)

(2.105)

(2.106)

where the rate coefficients (g dry wt/g dry wt/d) Ndd, D, and : are

minimally functions of temperature and time.

For benthos, phytoplankton, and zooplankton that can be

conceptualized as populations of organisms possessing similar

body sizes, the rate coefficients Ndd, D, and : are estimated using

temperature-dependent allometric relationships that describe the

pertinent processes for the individuals comprising the

compartment of interest. For example, consider the following

formulation of benthos consumption. Assuming that is the

average dry weight of individuals comprising the benthos

compartment, it follows that the expected density of individuals

within the benthos compartment is simply

(2.107)

Given that the consumption (g dry wt/d) of the aforementioned

average benthic invertebrate is described by

(2.108)

it follows that the ingestion of the benthos compartment at large

can be modeled as

(2.109)

Formulating compartmental ingestion, photosynthesis, and

respiration using this method not only delineates an objective

procedure to parameterize BASS, but also yields production

relationships that are consistent with results reported by Plante

and Downing (1989), Stockwell and Johannsson (1997), and

Kuns and Sprules (2000). When estimating Ndd, D, and : for

benthos, phytoplankton, or zooplankton using this approach,

BASS assumes that

(2.110)

where P is the individual’s ingestion, photosynthesis, or

respiration in units of g dry wt/d; and p1, p2, p3, T1, and T2 are

empirical constants specific to the process of interest. Although

BASS does not attempt to simulate the average individual body

sizes of benthos, phytoplankton, or zooplankton, BASS does allow

the user to vary these parameters as functions of time.

Because periphyton communities are typically complex

amalgamations of filamentous and unicellular algae, it is difficult

to conceptualize this compartment as a population of archetypical

individuals and to employ the preceding model parameterization

scheme. Consequently, for periphyton BASS assumes that Ndd, D,

and : are generally temperature-dependent allometric functions

of the compartment’s biomass, i.e.,

(2.111)

(2.112)

(2.113)

The rational for these formulations is based on the assumption

that the primary production, respiration, and possibly mortality

of periphyton communities are generally limited by their surfacevolume relationships that are implicitly represented by these

equations.

Because Ndd is generally much greater than D, the astute reader

will recognize that Equations (2.103) - (2.106) will predict

unbounded autocatalytic growth for any nonfish compartment

whose predatory mortality and non-predatory mortality/dispersal

does not precisely balance its intrinsic growth rate. To prevent

such unrealistic dynamics, BASS internally estimates a

physiologically based carrying capacity for each nonfish

compartment based on its projected daily oxygen consumption

and the community’s prevailing dissolved oxygen content. In
BASS 2.3 Sept 24, 2012 18

particular, BASS assumes that compartmental oxygen

consumption cannot exceed the dissolved oxygen content

corresponding to the difference between the community’s

prevailing dissolved oxygen concentration (DOC) and an

assumed hypoxic threshold of 4 mg O2 /L. When the

compartment’s daily oxygen consumption is predicted to exceed

this available dissolved oxygen content, compartmental growth

is suspended by equating the compartment’s

feeding/photosynthesis to its projected respiration.

BASS assumes that the rates of chemical bioaccumulation in

nonfish compartments are rapid enough to enable chemical

concentrations within these components to be calculated using

simple bioaccumulation factors. In particular,

(2.114)

where Cnf is the chemical concentration (:g /g dry wt) in the

compartment of concern. BASS enables users to specify the

bioaccumulation factor BAFnf (ml/g dry wt) for Equation (2.114)

either as an empirically derived constant, as a quantitative

structure activity relationship (QSAR), or as the ratio of the

chemical’s uptake rate to the sum of its excretion rate and the

compartment’s growth rate. When BAFnf is specified as a QSAR,

BASS assumes that

(2.115)

where b1

 and b2 are empirical constants. When BAFnf is specified

by the compartment’s chemical exchange rates and growth rate,

BASS assumes that

(2.116)

where k1, k2, and ( are the rates of uptake, excretion, and growth,

respectively, by individuals comprising the compartment; and Knf

is the compartment’s thermodynamic bioconcentration factor that

is defined analogously to Equation (2.7). For heterotrophs,

Equation (2.116) assumes that direct chemical uptake and

excretion with the ambient water are dominant over the dietary

uptake and fecal excretion of the organisms of concern. Although

this assumption is not satisfied for all benthic or planktonic

heterotrophs, it does bypass the need to specify feeding rates,

assimilation efficiencies, and dietary compositions for

compartments that are actually mixed functional groups. For

further discussions of Equation (2.116) and its generalization,

readers should consult Connolly and Pedersen (1988), Thomann

(1989), and Arnot and Gobas (2004).

2.12. Modeling Toxicological Effects

Narcosis is defined to be any reversible decrease in physiological

function that is induced by chemical agents. Because the potency

of narcotic agents was originally found to be correlated with their

olive oil / water partition coefficients (Meyer 1899, Overton

1901), it was long believed that the principal mechanism of

narcosis was the disruption of the transport functions of the lipid

bilayers of biomembranes (Mullins 1954, Miller et al. 1973,

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,

Richards et al. 1978, Franks and Lieb 1982, 1984, Law et al.

1985, Lassiter 1990). In either case, however, the idea that the

presence of narcotic chemicals increases the physical dimensions

of various physiological targets to some “critical volume” that

renders them inactive is fundamental (Abernethy et al. 1988).

Consequently, narcotic chemicals can be treated as generalized

physiological toxicants, and narcosis itself can be considered to

represent baseline chemical toxicity for organisms. Although any

particular chemical can act by a more specific mode of action

under acute or chronic exposure conditions, all organic chemicals

can be assumed to act minimally as narcotics (Ferguson 1939,

McCarty and Mackay 1993).

Studies have shown that for narcotic chemicals there is a

relatively constant chemical activity within exposed organisms

associated with any given level of biological activity (Ferguson

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

relationship holds true not only for exposures to a single

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

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

each component chemical is constant for a given level of

biological activity. Because narcotic chemicals can be treated as

generalized physiological toxicants as already noted, it should

not be too surprising that the effects of mixtures of chemicals

possessing diverse specific modes of action not only often

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

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

For example, although most pesticides possess a specific mode

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

often additive and resembles narcosis (Hermanutz et al. 1985,

Matthiessen et al. 1988, Bailey et al. 1997).

BASS simulates acute and chronic mortality assuming that the

chemicals of concern are an additive mixture of narcotics.

Because this assumption is the least conservative assumption that

one could make concerning the onset of effects, mortalities

predicted by BASS should signal immediate concern. When the

total chemical activity of a fish’s aqueous phase exceeds its
BASS 2.3 Sept 24, 2012 19

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

(2.117)

where Aa

 is the chemical’s aqueous activity; (a is the chemical’s

aqueous activity coefficient (L/mol), the reciprocal of its subcooled liquid solubility; Ma

 is the chemical’s molarity within the

aqueous phase of the fish; and MW is the chemical’s molecular

weight (g/mol).

BASS estimates the lethal chemical activity threshold for each

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

ambient aqueous chemical activity that causes 50% mortality in

an exposed population. These lethal thresholds are calculated

using the above formulae with user-specified LC50's substituted

for Ca

. These calculations are based on two important

assumptions. The first assumption is that the exposure time

associated with the specified LC50 is sufficient to allow almost

complete chemical equilibration between the fish and the water.

The second assumption is that the specified LC50 is the minimum

LC50 that kills the fish during the associated exposure interval.

Fortunately, most reliable LC50's satisfy these two assumptions.

See Lassiter and Hallam (1990) for a comprehensive modelbased analysis of these issues.

Three points should be mentioned regarding the above approach

to modeling ecotoxicological effects. First, for narcotic

chemicals this approach is analogous to the toxic unit approach

for evaluating the toxicity of mixtures (Calamari and Alabaster

1980, Könemann 1981a, b, Hermens and Leeuwangh 1982,

Hermens et al. 1984a, Hermens et al. 1984b, Broderius and Kahl

1985, Hermens et al. 1985b, Hermens et al. 1985c, Hermens et

al. 1985a, Dawson 1994, Peterson 1994). Second, the approach

is also analogous to the critical body residue (CBR) and total

molar body residue (TBR) approaches proposed by McCarty and

Mackay (1993), Verhaar et al. (1995), and van Loon et al.

(1997). Lastly, although sublethal effects are not presently

modeled by BASS, BASS’s simulation results can be used to

indicate when sublethal effects that are induced by narcotic

agents would be expected to occur. Results reported by Hermens

et al. (1984b) indicate that for Daphnia the ratio of the EC50 for

reproductive impairment to the LC50 is generally on the order of

0.15 - 0.30 for chemicals whose log Kow range from 4 to 8. For

individual growth inhibition, however, the mean EC50 to LC50

ratio for Daphnia in 16 day chronic exposures was

approximately 0.77 (Hermens et al. 1984a, Hermens et al.

1985b). Also see Roex et al. (2000).
BASS 2.3 Sept 24, 2012 20

Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamental

model processes, rates, or rate coefficients.

Aa chemical activity in aqueous fraction of the fish (dimensionless)

Ad assimilation rate (g dry wt/d)

Bf chemical burden in whole fish (μg/fish)

BAFnf bioaccumulation factor for nonfish prey (ml/g dry wt)

Ca chemical concentration in aqueous fraction of the fish (μg/ml)

Cae chemical concentration in aqueous fraction of intestinal contents (μg/ml)

CB chemical concentration in bulk interlamellar water (μg/ml)

Ce chemical concentration in egesta/feces (μg/ml)

Cf chemical concentration in whole fish (μg/g wet wt)

Cde chemical concentration in dry organic fraction of intestinal contents (μg/g dry wt)

Cl chemical concentration in lipid (μg/g dry wt)

Cnf chemical concentration nonfish prey (μg/g dry wt)

Co chemical concentration in non lipid organic matter (μg/g dry wt)

Cp chemical concentration in prey (μg/g wet wt)

Cw chemical concentration in environmental water (μg/ml)

Cw,O2 oxygen concentration in environmental water (μg/ml)

d interlamellar distance (cm)

dithe relative frequency of prey i in a fish’s diet (dimensionless)

D aqueous diffusion coefficient (cm2

/s)

ei

the electivity prey i in a fish’s diet (dimensionless)

Ed egestive rate (g dry wt/d)

Ew egestive rate (g wet wt/d)

EM emigration/dispersal (fish/ha/d)

EX excretory rate (g dry wt/d)

f

i

the relative frequency of prey i in the field (dimensionless)

maximum feeding rate (g dry wt/d)

Fdfeeding rate (g dry wt/d)

Fw

feeding rate (g wet wt/d)

G mass of gut contents (g dry wt/fish)

h height of secondary lamellae (cm)

HSIfeeding habitat suitability index for cohort feeding (dimensionless)

HSIrecruitment habitat suitability index for YOY recruitment (dimensionless)

HSIsurvival habitat suitability index for cohort survival (dimensionless)

Jbt biotransformation of chemical (μg/s)

Jg net chemical exchange across the gills (μg/s)

Ji net chemical exchange across the intestine (μg/s)

k2 apparent elimination rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d), i.e., 

kbt chemical biotransformation rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)

kex chemical excretion rate coefficient (ml/g wet wt/d, g wet wt/g wet wt/d, or 1/d)

kg overall chemical conductance across the gill from the interlamellar water to the aqueous blood (cm/s)

km chemical conductance through the gill membrane (cm/s)

Ke partition coefficient for fecal matter (ml/g wet wt)

Kfthermodynamic bioconcentration factor (ml/g wet wt)

Kl

 partition coefficient between generic lipid and water (ml/g dry wt)

Ko partition coefficient between non-lipid organic matter and water (ml/g dry wt)

Koc partition coefficient between organic carbon and water (ml/g dry wt)

Kow partition coefficient between n-octanol and water (ml/ml)

l lamellar length (cm)

L fish’s body length (cm)
BASS 2.3 Sept 24, 2012 21

Ma chemical molarity in aqueous fraction of the fish (mol/L)

N population density (fish/ha)

NGz Graetz number (dimensionless) = (l D)/(V r2

)

NSh Sherwood number (dimensionless) = (km r)/D

NM non-predatory mortality (fish/ha/d)

Pa aqueous or moisture fraction of whole fish (g water/g wet wt = ml/g wet wt)

Pae aqueous or moisture fraction of feces/egesta (g water/g wet wt = ml/g wet wt)

Pap aqueous of moisture fraction of prey/food (g water/g wet wt = ml/g wet wt)

Pde dry fraction of feces/egesta (g dry wt/g wet wt)

Pdp dry fraction of prey/food (g dry wt/g wet wt)

Pd dry fraction of whole fish (g dry wt/g wet wt), i.e., 

Pllipid fraction of whole fish (g dry wt/g wet wt)

Po non-lipid organic fraction of whole fish (g dry wt/g wet wt)

PM predatory mortality (fish/ha/d)

Q ventilation volume (cm3/s)

r hydraulic radius of interlamellar channels (cm), i.e., 

R routine respiratory rate (g dry wt/d)

RO2 oxygen consumption rate (mg O2/s or g O2/d)

SDA specific dynamic action (g dry wt/d)

Sg

total gill surface area (cm2)

T temperature (Celsius)

V average velocity of interlamellar flow (cm/s)

Wa weight/volume of fish’s aqueous phase (g water/fish or ml/fish)

Wd weight of fish (g dry wt/fish)

Wl weight of fish’s lipid phase (g dry wt/fish)

Wo weight of fish’s nonlipid organic phase (g dry wt/fish)

Ww weight of fish (g wet wt/fish)

Xg cross sectional pore area of the gill (cm2

)

"c assimilation efficiency of chemical (dimensionless)

"f assimilation efficiency of food (g dry wt assimilated/g dry wt ingested)

"O2 oxygen assimilation efficiency of the gill (dimensionless)

( specific growth rate (g wet wt/g wet wt/d), i.e., 

(a chemical aqueous phase activity coefficient (L/mol)

,a aqueous phase biotransformation rate coefficient (1/d)

Ndd specific feeding rate (g dry wt/g dry wt/d)

Nww specific feeding rate (g wet wt/g wet wt/d)

0 solution viscosity (poise)

< molar volume (cm3/mol)

D lamellar density (lamellae/mm)
BASS 2.3 Sept 24, 2012 22

Figure 2.1 First eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a

function of gill Sherwood number and ventilation / perfusion ratio.
BASS 2.3 Sept 24, 2012 23

Figure 2.2 Second eigenvalue and bulk mixing cup coefficient for Equation (2.28) as a

function of gill Sherwood number and ventilation / perfusion ratio.
BASS 2.3 Sept 24, 2012 24

Figure 2.3 Functional behavior of Equation (2.53)
BASS 2.3 Sept 24, 2012 25

3. Model Parameterization

Because reliable application of a model depends not only on

the validity of its formulation but also on its parameterization,

important aspects regarding the parameterization of BASS’s

bioaccumulation and physiological algorithms are discussed

below.

3.1. Parameterizing Kf

Superficially, estimation of a fish’s thermodynamic

bioconcentration factor Kf

 via Equation (2.7) appears to

require a great deal of information. This task, however, is

much simpler than it first appears. For example, given a fish’s

lipid fraction (see Equation (2.56)), it is a straightforward

matter to calculate the fish’s aqueous fraction using Equation

(2.55). Having done so, one can then immediately calculate the

fish’s non-lipid organic fraction since Pa

, Pl, and Po must sum

to unity (i.e., Equation (2.57)).

For an organic chemical, the partition coefficients Kl

 and Ko

can be estimated using the chemical’s octanol / water partition

coefficient Kow. Although triglycerides are the principal storage

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

a triglyceride / water partition coefficient, BASS assumes that

Kl equals Kow. To estimate Ko, BASS assumes that a fish’s nonlipid organic matter is equivalent to organic carbon and uses

Karickhoff’s (1981) regression between the organic carbon /

water partition coefficient (Koc) and Kow to estimate this

parameter. Specifically,

(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 Kow. A metal’s distribution coefficient into non-lipid

organic matter, however, cannot be estimated using the Koc

relationship of Equation (3.1). For example, whereas the Kow

of methylmercury at physiological pH’s is approximately 0.4

(Major et al. 1991), its distribution coefficient into

environmental organic matter is on the order of 104

 - 106

(Benoit et al. 1999b, Benoit et al. 1999a). O’Loughlin et al.

(2000) report similar differences for organotin compounds.

Whereas distribution coefficients for metals into fecal matter

generally should be assigned values comparable to those used

to model the environmental fate and transport of metals,

distribution coefficients for metals into the non-lipid organic

matter of fish should be assigned values 10 to 100 times higher

to reflect the increased number and availability of sulfhydryl

binding sites.

3.2. Parameters for Gill Exchange

To parameterize the gill exchange model, the fish’s total gill

area (Sg

 cm2), mean interlamellar distance (d cm), and mean

lamellar length (l cm) must be specified. Each of these

morphological variables is generally assumed to be an

allometric power function of the fish’s body weight, i.e.,

(3.2)

(3.3)

(3.4)

Although many authors have reported allometric coefficients

and exponents for total gill surface area, coefficients and

exponents for the latter two parameters are seldom available.

Parameters for fish’s mean interlamellar distance, however,

can be estimated if the allometric function for the density of

lamellae on the gill filaments, D (number of lamellae per mm

of gill filament), i.e.,

(3.5)

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

are generally available in the literature. See Barber (2003).

BASS estimates d1 and d2 from D1 and D2 using the interspecies

regression (n = 28, r = -0.92)

(3.6)

To overcome the scarcity of published morphometrics

relationships for lamellar lengths, BASS uses the default

interspecific regression (n = 90, r = 0.92)

(3.7)

Both of the preceding regressions are functional regressions

rather than simple linear regressions (Rayner 1985, Jensen

1986); the data used for their development are taken from

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

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

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

To calculate lamellar Graetz and Sherwood numbers, BASS

estimates a chemical’s aqueous diffusivity (cm2/s), using the

empirical relationship,

(3.8)

where 0 is the viscosity (poise) of water; and < is the

chemical’s molar volume (cm3/mol) (Hayduk and Laudie
BASS 2.3 Sept 24, 2012 26

1974). The diffusivity of a chemical through the gill membrane

that is needed to estimate the membrane’s permeability km is

then assumed to equal one half the chemical’s aqueous

diffusivity (Piiper et al. 1986, Barber et al. 1988, Erickson and

McKim 1990). The other quantity needed to estimate km is the

thickness of the gill’s epithelial layer. Although previous

versions of BASS assumed a constant water-blood barrier

thickness ($e) equal to 0.0029 cm for all fish species, BASS

now uses the interspecies allometric relationship

(3.9)

to estimate this parameter (Barber 2003).

To calculate ventilation / perfusion ratios BASS estimates the

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

consumption rates assuming an extraction efficiency of 60%

and a saturated dissolved oxygen concentration (see Equation

(2.12)). Perfusion rates (ml/hr) are estimated using

(3.10)

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

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

rainbow trout (Oncorhynchus mykiss), it has been successfully

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

and McKim 1993, Lien et al. 1994).

The eigenvalues and bulk mixing cup coefficients needed to

parameterize Equation (2.28) are interpolated internally by

BASS from matrices of tabulated eigenvalues and mixing cup

coefficients that encompass the range of Sherwood numbers

(i.e., 1< NSh <10) and ventilation / perfusion ratios (i.e., 1 < Qv

/ Qp < 20) that are typical for fish (Hanson and Johansen 1970,

Barron 1990, McKim et al. 1994, Sijm et al. 1994). See

Figure 2.1 and Figure 2.2 of the previous chapter.

3.3. Bioenergetic and Growth Parameters

Parameterization of the physiological processes used by BASS

to simulate fish growth generally poses no special problems

since the literature abounds with studies that can be used for

this purpose. The BASS Data Supplement summarizes literature

data that have analyzed to date for use by BASS.

3.4. Procedures Used to Generate the BASS

Database

BASS’s database for fish ecological, morphological, and

physiological parameters is generated by its own Fortran 95

software program. This program not only decodes functional

expressions for BASS model parameters that have been reported

in the literature but also calculates its own regressions using

data reported in the literature. Each species within the BASS

database is assigned its own data file whose name corresponds

to its genus and species. Thus, all literature data and

regressions pertaining to largemouth bass are compiled into the

BASS database file micropterus_salmoides.dat. Literature

regressions are entered into BASS database files using the

functional syntax outlined in chapter sections 4.3.3, 4.4.1, and

4.4.2 herein. Except for this syntax, all literature regressions

are recorded as reported; any required units conversions are

performed by the BASS database generator.

When literature regressions are not equivalent to the functional

forms used by BASS, and their associated primary data are not

reported, synthetic datasets are generated to estimate the

needed parameters. For example, when a fish’s oxygen

consumption does not exhibit a temperature optimum, BASS

assumes that this parameter is given by

(3.11)

or equivalently

(3.12)

Although many researchers use similar expressions to report a

fish’s oxygen consumption, some researchers use the function

(3.13)

or equivalently

(3.14)

When such power functions of temperature are encountered,

synthetic datasets of “observed/predicted” oxygen

consumption are generated using the reported regressions for

the reported range of body weights and temperatures. These

synthetic data are then refitted to Equation (3.11).

A similar procedure for generating synthetic datasets is used to

convert the temperature-dependent functions (Kitchell et al.

1977, Thornton and Lessem 1978) employed by the widely

used Wisconsin Bioenergetics Fish Model (Hanson et al.

1997) into the hyperbolic Arrhenius formulation assumed by

BASS.

Although the BASS database generator performs most of its

parameter estimations using univariate statistics or ordinary

linear least-squares regression analysis as appropriate,

nonlinear least-squares regression analysis is used to estimate

specific growth rates and physiological functions that are to be

fitted to BASS’s hyperbolic Arrhenius formulation. In these

latter instances, BASS’s database generator uses the NL2SOL

Fortran 90 software that solves nonlinear least-squares
BASS 2.3 Sept 24, 2012 27

problems using a modified Newton’s method with analytic

Jacobians and a secant updating algorithm to compute the

required Hessian matrix. See Dennis et al. (1981).

Estimation of Specific Growth Rates

BASS uses specific growth rates (( = W -1 dW/dt) not only to

estimate a cohort’s rate of dispersal and non-predatory

mortality (see Equation (2.95)) but also as a parameter by

which a cohort’s expected ingestion rate can be backcalculated, if desired. Estimating specific growth rates for

BASS, however, obviously depends on the underlying model

used to describe the fish’s expected growth rate dynamics (i.e.,

dW/dt). Selecting an appropriate growth model for use by the

BASS simulation software, like most model selections, was not

a trivial issue since over the past 50 years at least four different

models (i.e., von Bertalanffy, Richards, Gompertz, and ParkerLarkin models) have become standard tools for characterizing

the growth of fishes. See Ricker (1979) for a detailed

discussion of these models and other less commonly used

models.

According to the von Bertalanffy model, a fish’s growth rate

is the simple mass balance of anabolic processes that are

directly proportional to the fish’s surface area and of catabolic

processes that are directly proportional to the fish’s body

weight. Consequently, the fish’s growth dynamics are

governed by the following differential equation

(3.15)

where N is the fish’s rate of feeding and assimilation; and D is

the fish’s total metabolic rate. Assuming isometric growth (i.e.,

Ww = 8L

3

), this model is also equivalent to

(3.16)

where L is the fish’s body length; and Lmax = N / (D 8

1/3) is the

fish’s “maximum” body length that is obtained by setting

Equation (3.15) to zero. For further discussion, see Parker and

Larkin (1959) and Paloheimo and Dickie (1965).

The Richards model (Richards 1959) is a generalization of the

von Bertalanffy model that relaxes the assumption of isometric

growth and strict proportionality between a fish’s

feeding/assimilatory processes and its absorptive surface areas.

In this model, the fish’s feeding is simply assumed to be a

power function of its body weight. The fish’s growth is then

described by the differential equation

(3.17)

Although the von Bertalanffy and the Richards models appear

to have strong physiological foundations, a critical analysis of

the parameters of these models casts doubts on such assertions.

One particular point of contention is the assumption that a

fish’s metabolism (i.e., respiration and excretion) is directly

proportional to its body weight. Although this assumption is

certainly satisfied or closely approximated for some fish

species, most fish species have metabolic demands that are

best described as power functions of their body weights.

Consequently, from a purely physiologically-based

perspective, a better anabolic-catabolic process model for fish

growth would be

(3.18)

See Paloheimo and Dickie (1965). Unlike the von Bertalanffy

and Richards models, however, this model generally does not

have a closed analytical solution. Furthermore, when this

model is fit to observed data, there is no a priori guarantee that

the fitted exponents will actually match expected physiological

exponents unless the analysis is suitably constrained.

In light of these criticisms, simpler empirical growth models

may be more than adequate for most applications. Two such

models that have proved useful in this regard are the Gompertz

and Parker-Larkin models. Both of these models are intended

to describe the growth of fishes that decreases with the age or

size of the individual. Whereas the Gompertz model describes

fish growth by

(3.19)

the Parker-Larkin model (Parker and Larkin 1959) simply

assumes that

(3.20)

where the exponent $ is less than 1.

Although each of the aforementioned models can describe very

different growth trajectories, much of the discussion

surrounding their use has focused on whether the models

predict asymptotically zero or indeterminate growth (Parker

and Larkin 1959, Paloheimo and Dickie 1965, Knight 1968,

Schnute 1981). Although growth rates of individual fish almost

always decrease with increasing age or body size, Knight

(1968) argued that the traditional notion of asymptotically zero

growth is seldom, if ever, supported by studies that have

focused on actual growth increments rather than on size-at-age.

Because the Parker-Larkin model is the only model outlined

above that assumes fish growth is fundamentally
BASS 2.3 Sept 24, 2012 28

indeterminate, and because the Parker-Larkin model does not

rely on the a priori assumption that fish respiration is a linear

function of their body weight as does the von Bertalanffy and

Richards models, this model is used exclusively by BASS when

needed.

Three basic types of data have been traditionally used to

calculate fish growth rates; these are: 1) length at age or

capture, 2) back-calculated length at age for specific age

classes sampled over multiple years, and 3) back-calculated

length at age for specific year classes or cohorts. Backcalculated body lengths for the latter two data types are

generally calculated by regression using measured growth

increments of body scales, otoliths, pectoral spines, or other

“hard” structures. Whereas for a length at age dataset each

individual fish contributes only one observation (i.e., its

current length), each individual fish contributes a time series

of body lengths for both of the remaining types of growth data.

To estimate specific growth rates for fish, body lengths at age

that have been reported in the literature, whether backcalculated or not, are converted into wet body weights using

weight-length regressions that were reported by either the

study of interest or other published sources. Estimated wet

body weights are then fit to the analytical solution ParkerLarkin growth model,

(3.21)

using the NL2SOLV nonlinear optimization software. The

explicit solution of the Parker-Larkin growth model for any

time interval [t0, t] is

(3.22)

However, because this expression is discontinuous at g2 = 0,

the growth parameters g1

 and g2 are actually obtained by fitting

calculated body weights to the equivalent expression

(3.23)

where g2

 = exp (b).

Estimation of Hyperbolic Arrhenius Functions

When a fish’s daily rate of maximum food ingestion, plankton

filtration, gastric evacuation, respiration, or growth exhibits a

temperature optimum, the BASS database generator fits the

process’s actual or synthetic data to the hyperbolic Arrhenius

function

(3.24)

The BASS database generator also fits actual or synthetic data

regarding satiation meal size and feeding times to satiation to

the above equation when these feeding parameters exhibit

temperature optima. Testing of the initial NL2SOL-based

procedure developed to estimate the parameters of Equation

(3.24) revealed that the convergence performance of NL2SOL

could be greatly improved by reconfiguring Equation (3.24) as

(3.25)

where Tmax is the maximum temperature of the dataset being

fitted, and, therefore, . Because estimation of

nonlinear parameters are frequently sensitive to their required

initial estimates, a three-step procedure was developed to

estimate the parameters for Equation (3.25).

The first step in this procedure estimates a mean body weight

exponent by using repeated linear least-squares regressions

(3.26)

on data subsets whose maximum range of temperatures is less

than 3 Celsius.

The second step of the procedure then uses NL2SOL to

estimate the parameters of the reduced model

(3.27)

To estimate these parameters, multiple sets of initial

parameters are sequentially supplied to NL2SOL, and the

parameter set that produces the smallest sum of least-squares

is used in the third and final step in the estimation process.

Initial parameter estimates for Equation (3.27) are generated

by first fitting the cubic polynomial

(3.28)

using ordinary linear least-squares techniques. The temperature

Te

 corresponding to the local maximum of the above cubic

polynomial, i.e.,

(3.29)

(3.30)

is then assigned as the process’s optimum temperature T1

 for
BASS 2.3 Sept 24, 2012 29

each set of initial parameter values. Initial estimates for * are

then assigned assuming that the fish’s upper “tolerance”

temperature corresponds to equidistant temperatures within the

interval

(3.31)

Initial estimates of the process’s temperature coefficients p3

 are

similarly assigned as equidistant values within the interval

(3.32)

Having assigned initial estimates for p3, T1, and *, the

process’s rate at T0 = 0 is finally assigned as the backcalculated mean

(3.33)

In the third and final step, the results of steps 1 and 2 are

supplied to NL2SOL as the “best” initial estimates of the

parameters for Equation (3.25). From this step, the final

parameters for Equation (3.24) are determined.

Table 3.1 summarizes the results obtained using the

aforementioned procedure to estimate maximum daily

consumption and maximum meal size for a variety of studies

reported in the open literature. Table 3.2 summarizes the

results of converting the maximum daily consumption

functions used by the Wisconsin Bioenergetics Model into

their “equivalent” hyperbolic Arrhenius form. Figure 3.1 and

Figure 3.2 display selected results from Table 3.2.

Readers interested in obtaining the Fortran 95 subroutines used

to implement this procedure can do so by simply requesting

this code from the author.

3.5. Suggested Calibration Procedures

Calibrating Fish Growth Rates

Because Equations (3.21) and (3.22) do not explicitly account

for either reproductive losses or temperature-dependent

growth, growth rates estimated by these equations generally

should be calibrated for the application at hand when backcalculating fish ingestion rates from estimated growth rates.

Having estimated a long-term average growth rate

(3.34)

for a species of interest, the calibration procedure developed

for BASS assumes that the fish’s specific growth coefficient g1

is actually an exponential function the fish’s ambient water

temperature that, in turn, is assumed to be a sinusoidal function

of the time of year. In particular,

(3.35)

where defines the fish’s Q10 relationship

for growth; Tm is the mean annual water temperature

experienced by the fish; and ", $, and T are the coefficients

describing the amplitude, frequency, and phase shift of the

water temperatures experienced by the fish, respectively.

Under this assumption a fish’s growth is therefore described by

(3.36)

If t0 is the day that the species’ young-of-year are recruited into

the population, and m is the integer age in years when the fish

becomes sexually mature, it then follows that a fish’s prespawn body weight at the time of its first reproduction is given

by

(3.37)

However, because the integrand of this equation is a harmonic

function possessing an annual period, the preceding equation

can be simplified to

(3.38)

where

(3.39)

Once fish reach sexual maturity, their underlying growth

equation (i.e., Equation (3.36)) is only piecewise differentiable

since fish are assumed to lose a constant fraction (F) of their

body weight during spawning due both to gamete production

and increased metabolic expenditures associated with

spawning behaviors. If n is the species’ maximum integer age

in years, Equation (3.36) can be integrated between any two

consecutive spawning events as

follows

(3.40)
BASS 2.3 Sept 24, 2012 30

(3.41)

where . Summing Equations (3.38) and (3.41)

appropriately, it then follows that

(3.42)

To calibrate a species growth rate for a particular application

using Equations (3.34), (3.39) and (3.42), one must obviously

specify the parameters (Tm, ", $, and T) describing the

application’s water temperatures and the species’ maximum

age ( ), mean age of sexual maturity (365 m),

annual spawning times ( ),

reproductive/spawning loss constant (F), initial body weight of

young-of-year fish ( ), body weight of fish at maximum

age ( ), and the species’ allometric growth

exponent (g2). The species’ pre-spawn body weights for

Equation (3.41) can be estimated using Equation (3.22) using

the adjusted allometric growth coefficient

(3.43)

To demonstrate this calibration procedure, growth rates

estimated for brook trout (Salvelinus fontinalis) from literature

data will how be calibrated for a “typical” Mid-Atlantic trout

stream whose annual temperature regime is assumed to be

given by

(3.44)

This temperature function assumes that the stream’s annual

range of water temperatures is 2 to 19.5 Celsius, that April 1

corresponds to t = 0, and that January 15 is the coldest day of

the year. In this stream, brook trout are assumed to be recruited

into the population with an initial YOY body weight equal to

0.25 g wet wt/fish and live a maximum of seven years. The

maximum size attained by these trout is assumed to be 825 g

wet wt/fish (i.e., .4 4 0 mm( T L) assuming

 ). Spawning and recruitment are

assumed to occur on October 30. Sexual maturity is reached

when trout attain a total body length of 157 mm (i.e., between

the ages of 2 and 3 years), and the trout’s reproductive loss

constant is assumed to equal 0.2 g wet wt/g wet wt/spawn.

Finally, the trout’s growth Q10 is assumed to equal 2

(i.e., ). Using data compiled by Carlander (1969),

the BASS database analysis program estimated the following

specific growth rate for brook trout

(3.45)

Calibrating this growth rate to predict with the trout’s assumed

maximum and YOY body weights and maximum age using

Equation (3.43) then yields

(3.46)

When this adjusted growth rate is used to project pre-spawn

body weights for Equation (3.42) using Equation (3.22), the

specific growth rate of brook trout calibrated for reproductive

losses and temperature dependencies is

(3.47)

When specific feeding rates (Ndd g dry wt/g dry wt/d) are backcalculated monthly using this equation and standard salmonid

metabolic relationships (i.e., food assimilation efficiencies,

specific dynamic action (SDA) to ingestion ratios, oxygen

consumption rates, respiratory quotients (RQ), and ammonia

excretion to oxygen consumption quotients(AO)) as outlined

by Barber (2003), the following allometric regression can be

calculated

(3.48)

This regression agrees well with results of Sweka and Hartman

(2001) who estimated the maximum consumption of brook

trout at 12 Celsius to be

(3.49)

Taken together, the proceeding equations imply that the

realized ingestion rate of brook trout at 12 Celsius would be

approximately 42% of their maximum ingestion rate. This

result agrees well with that reported by Elliott and Hurley

(1998).

A Fortran 95 executable program (BASS_FILES.EXE) is

provided with the BASS simulation software to perform the

aforementioned growth calibration procedure and backcalculated feeding rate estimation. See Section 5.6.

Estimating Initial Conditions

Although most fish surveys typically report only either total
BASS 2.3 Sept 24, 2012 31

species densities (fish/ha) or total species biomass (kg wet

wt/ha), such data can be easily converted into BASS initial

conditions if one assumes that the recruitment strength for each

cohort of observed population density has been relatively

constant or has been fluctuating around a long term average.

To perform this conversion, BASS’s assumed self-thinning

model Equation (2.94), is first rewritten as

(3.50)

This equation can then be reintegrated to obtain

(3.51)

A species total population density can then be estimated by

applying Equation (3.51) to each of its cohorts, i.e.,

(3.52)

where Ni, Wf,i, and ai denote the density, average wet body

weight, and age, respectively, of the i-th cohort. Assuming that

each cohort is recruited into the species’ total population with

the same initial body weight ( ) and population

density ( ), the preceding equation can be

simplified to

(3.53)

If the growth rate trajectories of each cohort have also

remained relatively constant, it follows that an expected

decomposition of a species total population density into its

component cohort densities would be

(3.54)

It also follows that an expected decomposition of a species

total biomass into its component cohort biomasses would be

(3.55)

From Equations (3.54) and (3.55) it should be reasonably clear

that given a species total population density (N) or total

biomass (B) and given a model for the species body growth

(i.e., Equations (3.21) and (3.22)), one can straightforwardly

calculate the species’ apparent long term year-class strength

N0. Having done so, one cannot only estimate the species’

cohort densities but also convert the species’ total population

density into its expected total biomass and vice versa.

To corroborate the density-to-biomass conversion procedure

outlined above, a database of studies that have reported

measured fish densities and associated fish biomasses was

compiled from the literature (Miles 1978, Quinn 1988, Reed

and Rabeni 1989, Ensign et al. 1990, Buynak et al. 1991, Flick

and Webster 1992, Bettoli et al. 1993, Waters et al. 1993,

Maceina et al. 1995, Mueller 1996, Allen et al. 1998, Radwell

2000, Dettmers et al. 2001, Pierce et al. 2001, Habera et al.

2004). Reported fish densities were converted into estimated

biomasses assuming evenly spaced self-thinning exponents b

ranging from -0.5 to -1.0 at 0.025 increments. Reduced major

axis (RMA) regressions were then calculated for each assumed

self-thinning exponent. The self-thinning exponent that

minimized the intercurve area between the calculated RMA

regression line and the identity relationship was b =

-0.825. This regression was

(3.56)

Figure 3.3 displays the data for the regression (3.56) and the

identity relationship .

In addition to calibrating fish growth rates and backcalculating feeding rates, the auxiliary BASS program

BASS_FILES.EXE described in the preceding section estimates

initial body weights and cohort densities for users given a

target initial total species density or a target initial total species

biomass. See Section 5.6.
BASS 2.3 Sept 24, 2012 32

Figure 3.1 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the 

Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced tem

the fish’s upper tolerance limit.
BASS 2.3 Sept 24, 2012 33

Figure 3.2 Selected results for fitting Equation (2.58) to maximum consumption rates calculated by the algorithms and parameters used by the 

Observed data corresponds to the maximum daily consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced tem

the fish’s upper tolerance limit.
BASS 2.3 Sept 24, 2012 34

Figure 3.3 Observed fish biomass versus fish biomass predicted by cohort self-thinning BASS’s algorithm.
BASS 2.3 Sept 24, 2012 35

Species Process p1 p2 p3 T1 T2r

2

1 Channa argus cmax[g/d] 0.00741 0.52 0.425 29.2 51.3 0.99

2 Coregonus hoyi cmax[g/g/d] 0.159 -0.54 0.320 16.8 26.0 0.96

3 Morone saxatilis cmax[g/g/d] 0.000945 0.00 0.708 25.9 58.7 0.97

4 Morone saxatilis cmax[g/g/d] 0.00542 0.00 0.455 21.6 42.1 0.85

5 Pomoxis annularis cmax[g/d] 0.00213 0.03 1.051 23.1 43.0 0.50

6

Salmo trutta cmax[Kcal/d] 0.0100 0.76 0.262 18.5 21.8 1.00

7

Salmo trutta sm[mg(dw)] 1.54 0.69 0.596 15.0 29.3 1.00

8

Salmo trutta sm[mg(dw)] 0.731 0.78 2.000 13.8 67.8 1.00

9

Salmo trutta sm[mg(dw) ] 0.843 0.76 2.000 13.6 69.5 0.99

10 Salmo trutta sm[mg(dw) ] 1.72 0.79 0.463 14.9 24.1 1.00

11 Salmo trutta sm[mg(dw) ] 0.906 0.80 0.437 15.1 24.2 0.99

12 Salvelinus alpinus cmax[g(dw)/g/d] 0.00123 0.00 0.489 16.5 29.0 0.79

13 Salvelinus confluentus cmax[g/g/d] 0.00840 0.00 0.288 14.0 29.0 0.98

14 Siniperca chuatsi cmax[g/d] 0.0267 0.60 0.212 30.3 44.5 0.99

15 Tilapia zillii cmax[g/g/d] 7.300E-07 0.00 2.000 30.6 75.1 0.94

Table 3.1 Summary of NL2SOL regressions for Equation (3.24) fitted to maximum daily consumption rates and satiation meal size

reported in the literature.

Data sources and notes

1

 Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.

2

 Binkowski and Rudstam (1994). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.

3

 Cox and Coutant (1981). Rates as reported in Table 2 assuming no feeding or lethality at 43 Celsius.

4

 Hartman and Brandt (1993). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.

5

 Hayward and Arnold (1996). Rates as reported in Table 1 assuming no feeding or lethality at 43 Celsius.

6

 Elliott (1976b). Rates generated by regressions reported in Table 2.

7

 Elliott (1975). Data as reported in Table 4 for Baetis.

8

 Elliott (1975). Data as reported in Table 4 for Hydropsyche.

9

 Elliott (1975). Data as reported in Table 4 for chironomids.

10 Elliott (1975). Data as reported in Table 4 for mealworms (Tenebrio molitor).

11 Elliott (1975). Data as reported in Table 4 for oligochaetes.

12 Larsson and Berglund (1998). Rates as reported in Table 1 assuming no feeding or lethality at 26 Celsius.

13 Selong et al. (2001). Rates calculated from data reported in Table 2 assuming no assuming or lethality at 26 Celsius.

14 Liu et al. (1998). Rates estimated by regression assuming no feeding or lethality at 43 Celsius.

15 Platt and Hauser (1978). Rates estimated from Figure 1 assuming no feeding or lethality at 43 Celsius.
BASS 2.3 Sept 24, 2012 36

Species f1f2f3 T1 T2r

2

Alosa psuedoharengus (adult) 0.102 0.70 0.426 15.5 29.3 0.99

Alosa psuedoharengus (juvenile) 0.112 0.70 0.214 19.6 27.3 0.98

Alosa psuedoharengus (yoy) 0.0919 0.70 0.196 21.8 29.2 0.99

Chrosomus spp. 0.0590 0.69 0.094 26.0 29.0 1.00

Clupea harengus (adult) 0.08 0.74 0.644 12.9 29.5 0.99

Clupea harengus (juvenile) 0.0808 0.74 0.535 14.4 31.5 0.99

Coregonus hoyi 0.159 0.46 0.320 16.8 26.0 1.00

Coregonus spp. 0.159 0.68 0.320 16.8 26.0 1.00

Esox masquinongy 0.0147 0.82 0.188 26.0 34.0 1.00

Lates niloticus 0.0112 0.73 0.235 27.5 38.0 1.00

Lepomis macrochirus (adult) 0.0150 0.73 0.172 27.0 36.0 1.00

Lepomis macrochirus (juvenile) 0.0113 0.73 0.138 31.0 37.0 1.00

Micropterus dolomieui 0.00139 0.69 0.296 29.0 36.0 1.00

Micropterus salmoides 0.0129 0.68 0.222 27.5 37.0 1.00

Morone saxatilis (adult) 0.0336 0.75 2.000 21.8 213.9 0.95

Morone saxatilis (age 0) 0.014 0.75 2.000 21.3 153.6 0.99

Morone saxatilis (age 1) 0.0310 0.75 2.000 22.4 221.1 0.98

Morone saxatilis (age 2) 0.0376 0.75 2.000 23.8 268.5 0.96

Morone spp. 0.0314 0.75 0.128 28.3 31.3 1.00

Oncorhynchus gorbuscha 0.142 0.73 0.102 17.0 25.9 0.99

Oncorhynchus kisutch 0.0460 0.73 0.320 15.6 25.8 0.98

Oncorhynchus mykiss 0.102 0.70 0.220 17.6 25.3 0.99

Oncorhynchus nerka 0.142 0.73 0.102 17.0 25.9 0.99

Oncorhynchus tshawytscha 0.0330 0.72 0.230 15.0 18.0 1.00

Osmerus mordax (adult) 0.0304 0.73 0.680 10.0 22.3 0.99

Osmerus mordax (juvenile) 0.0472 0.72 0.207 13.1 18.0 0.98

Osmerus mordax (yoy) 0.0587 0.73 0.143 17.9 26.1 0.98

Perca flavescens (adult) 0.0411 0.73 0.125 23.0 28.0 1.00

Perca flavescens (juvenile) 0.0317 0.73 0.094 29.0 32.0 1.00

Perca flavescens (larvae) 0.0647 0.58 0.094 29.0 32.0 1.00

Petromyzon marinus 0.0766 0.65 0.150 18.0 25.0 1.00

Sarotheradon spp. 0.00643 0.64 0.172 30.0 37.0 1.00

Stizostedion vitreum (adult) 0.0428 0.73 0.138 22.0 28.0 1.00

Stizostedion vitreum (juvenile) 0.0802 0.73 0.094 25.0 28.0 1.00

Theraga chalcogramma (adult) 0.146 0.41 0.270 8 15.0 1.00

Theraga chalcogramma (juvenile) 0.0994 0.41 0.461 8 15.0 1.00

Table 3.2 Summary of NL2SOL regressions for Equation (2.58) fitted to maximum consumption rates (g wet wt/day) estimated by

the Wisconsin Bioenergetics Model 3.0 and its distributed database. Observed data corresponds to the maximum daily

consumption of fish weighing 1, 25, 50, 75, and 100 g wet wt/fish at seven equally spaced temperatures between 0 Celsius and the

fish’s upper tolerance limit.
BASS 2.3 Sept 24, 2012 37
BASS 2.3 Sept 24, 2012 38

4. BASS User Guide

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

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

enables users to simulate the population and bioaccumulation

dynamics of age-structured fish communities using the temporal

and spatial resolution of a day and a hectare, respectively.

Although BASS implicitly models the dispersal of fish out of the

simulated hectare, it does not explicitly simulate the immigration

of fish into the simulated hectare. Monthly or yearly age classes

can be used for any species. This flexibility enables users to

simulate small, short-lived species such as daces, live bearers,

and minnows together with larger, long-lived species such as

bass, perch, sunfishes, and trout. The community’s food web is

specified by defining one or more foraging classes for each fish

species based on either body weight, body length, or age. The

user then specifies the dietary composition of these foraging

classes as a combination of benthos, incidental terrestrial insects,

periphyton, phytoplankton, zooplankton, and/or other fish

species, including its own. Standing stocks of nonfish

compartments can be simulated either as external forcing

functions or as state variables.

Although BASS was developed to simulate the bioaccumulation

of chemical pollutants within a community or ecosystem context,

it can also be used to simulate population and community

dynamics of fish assemblages that are not exposed to chemical

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

to simulate the population and community dynamics of fish

assemblages that are subjected to altered thermal regimes that

might be associated with a variety of hydrological alterations or

industrial activities. BASS could also be used to investigate the

impacts of exotic species or sport fishery management programs

on population or community dynamics of native fish

assemblages.

The model’s output includes:

! Summaries of all model input parameters and simulation

controls.

! Tabulated annual summaries for the bioenergetics of

individual fish by species and age class.

! Tabulated annual summaries of chemical bioaccumulation

within individual fish by species and age class.

! Tabulated annual summaries for the community level

consumption, production, and mortality of each fish

species by age class.

! Plotted annual dynamics of selected model variables as

requested by the user.

Please report any comments, criticisms, problems, or suggestions

regarding the model software or user manual to

Craig Barber

Ecosystems Research Division

U.S. Environmental Protection Agency

960 College Station Road

Athens, GA 30605-2700

office: 706-355-8110

FAX: 706-355-8104

e-mail: barber.craig@epa.gov

4.1. General Model Structure and Features

The following features are available in BASS v2.3:

! There are no restrictions to the number of fish species that

can be simulated.

! There are no restrictions to the number of cohorts that a

fish species can have.

! There are no restrictions to the number of foraging classes

that a fish species can have, and seasonal diets can be

specified for any or all foraging classes. See the fish

command /ECOLO GICAL_PARAMETERS option

diet(.,.)={...}.

! Refuge levels at which cohorts of potential prey species

become unavailable to piscivores can be specified. See the

fish command /ECOLOGICAL_PARAMETERS option

refugia[]=fnc.

! Size dependent harvest and stocking functions can be

specified for any or all species to simulate fisheries

management practices. See the fish command

/FISHERY_PARAMETERS.

! Habitat suitability indices (HSI) can be specified to adjust

a fish’s realized feeding/growth, recruitment/spawning,

and combined dispersal and non-predatory mortality. See

the fish command /HABITAT_PARAMETERS.

! Benthos, periphyton, phytoplankton, and zooplankton can

be simulated either as forcing functions or as state

variables. Incidental insects, however, can only be

simulated as a forcing function.
BASS 2.3 Sept 24, 2012 39

! There are no restrictions to the number of chemicals that

can be simulated.

! Biotransformation of chemicals can be simulated with or

without daughter products.

! Integration of BASS’s differential equations is performed

using a fifth-order Runge-Kutta method with adaptive step

sizing that monitors the accuracy of its integration. BASS’s

Runge-Kutta integrator is patterned on the fifth-order

Cash-Karp Runge-Kutta algorithm outlined by Press et al.

(1992).

4.2. New Features

The following features were not available in BASS v2.2 and

earlier

! Stable isotope dynamics of carbon and nitrogen are now

simulated for all fish cohorts as described in Section 2.8.

These dynamics are then used to estimate the operational

trophic position on each fish cohort within the community

of interest. See the new simulation control command

/ISOTOPE_PARAMETERS and the new arguments del_c13[-

]=" and del_n15[-]=$ for the nonfish commands 

/BENTHOS, /TERRESTRIAL_INSECTS, /PERIPHYTON,

/PHYTOPLANKTON, and /ZOOPLANKTON. 

! BASS can how simulate an additional mortality and

dispersal (KM), over and above a cohort’s self-thinning

mortality and dispersal (NM and EM, respectively) and its

predatory mortality (PM), that is associated with a

species-specific biomass carrying capacity. Currently,

these species-specific biomass carrying capacities are

calculate internally by BASS based on each species initial

relative biomasses and a user-specified total fish biomass

carrying capacity for the community of interest. See the

n e w s i m u l a t i o n c o n t r o l c o m m a n d

/FISH_CARRYING_CAPACITY .

! To facilitate its linkage to numerous fate & transport

models (e.g., see Johnson et al. (2011)), BASS simulations

are now conducted to acknowledge when leap years

occur. See the new simulation control command

/FIRST_LEAP_YEAR.

4.3. Input File Structure

The general structure of a BASS’s input or project file is:

/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 (i.e., 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. Arguments are either integers (e.g., 7), real numbers (e.g., 0,

3.7e-2, 1.3, etc.), or character strings. If the command allows

multiple arguments or options, each argument must be separated

by a semicolon. Commands can be continued by appending an

ampersand (&) to the end of the record; therefore, the following

commands are equivalent

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

/command arg1; arg2; arg3; &

arg4; arg5; arg6

Because each record is transliterated to lowercase before being

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

because consecutive blanks or tabs are collapsed into a single

blank, spacing within a command is not significant. The

maximum length of a command line, including continuation

lines, is 1024 characters.

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

that line as a comment. An exclamation mark can be also placed

elsewhere within a record to start an end-of-line comment, i.e.,

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

ignored.

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

command terminates program input and any text or commands

following it are ignored. BASS checks the syntactical accuracy of

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

encountered, BASS then checks the specified input parameters for

completeness and internal inconsistency.

BASS input data and commands are broadly classified into four

categories: simulation control parameters, chemical parameters,

fish parameters, and nonfish biotic parameters. Simulation

control parameters provide information that is applicable to the

simulation as a whole, e.g., length of the simulation, the ambient

water temperature, water column depth, and any desired output

options. Chemical parameters specify the chemical’s physicochemical properties (e.g., the chemical’s molecular weight,

molecular volume, n-octanol / water partition coefficient, etc.)

and the chemical’s exposure concentrations in various media.
BASS 2.3 Sept 24, 2012 40

Fish parameters specify the fish’s feeding and metabolic

demands, dietary composition, predator-prey relationships, gill

morphometrics, body composition, and initial conditions for the

body weights, whole-body chemical concentrations, and

population sizes of a fish’s cohorts. Nonfish biotic parameters

specify how benthos, terrestrial insects, periphyton, and plankton

will be simulated.

A BASS project file is actually constructed and managed as a

series of include files, i.e., blocks of closely related input

commands. These files are specified using the include statement

# include 'filename '

where filename is the name of the file containing the desired

commands. Each include file specifies data for either a chemical,

a fish species, or a nonfish biotic component. Consequently, a

typical BASS project file is structured as follows:

! file: bass_input_file.prj

! notes: a BASS project file as specified by include files

!

/ command1

simulation control_data

/ command2

simulation control_data

/ command3simulation control_data

# include 'data_for_chemical_1 '

# 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 '

# include 'data_for_benthos '

# include 'data_for_insects '

# include 'data_for_periphyton '

# include 'data_for_phytoplankton '

# include 'data_for_zooplankton '

/end

BASS’s graphical user interface (GUI) enables users to create and

edit BASS project files and include files in a modular fashion. The

actual file structure used by the BASS GUI is detailed in Section

4.5. following the discussion of the BASS input commands

themselves below.

4.3.1. Simulation Control Commands

These commands establish the length of the simulation, the

ambient water temperature, the community’s water level, and

other simulation and output options. These data are specified by

the following block of commands

/SIMULATION_CONTROL no argument/option required

/ANNUAL_OUTPUTS integer

/ANNUAL_PLOTS string1

; ...; stringn

/BIOTA string1; ...; stringn

/FIRST_LEAP_YEAR string

/FISH_CARRYING_CAPACITY string

/FGETS no argument/option required

/HEADER string

/ISOPTOPE_PARAMETERS string1; string2

/LENGTH_OF_SIMULATION string

/LESLIE_MATRIX_SIMULATION no argument/option required

/MONTH_T0 string

/NONFISH_QSAR string1

; ...; stringn

/SUMMARY_PLOTS string1; ...; stringn

/TEMPERATURE string1; string2

/WATER_LEVEL string1; string2

Although the command /SIMULATION_CONTROL must be the first

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

the order of the remaining commands is not significant. The use

of these commands is described below in alphabetical order.

O /ANNUAL_OUTPUTS integer

This command specifies the time interval, in years, between

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

be a nonnegative integer. BASS assumes a default value of zero

that signifies that no annual outputs will be generated. Because

the BASS Output Analzyer enables users to generate customized

tables and plots, this command is actually a vestigial option of

BASS v2.1.

O /ANNUAL_PLOTS string1; ...; stringn

This command specifies the variables whose annual dynamics

will be plotted for the years specified by command

/ANNUAL_OUTPUTS. Consequently, this command is also a

vestigial option of BASS v2.1. Valid options are:

! afish(variable) generates plots of each species’ total

aqueous phase chemical activity as a function of time (day

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

! baf(variable) generates plots of each species’

bioaccumulation factor (i.e., the ratio Cf / Cw) for each

chemical as a function of time (day of year) and the

species’ age, length, or weight class;

! bmf(variable) generates plots of each species’

biomagnification factor (i.e., the ratio Cf

 / Cprey) for each

chemical as a function of time (day of year) and the

species’ age, length, or weight class;
BASS 2.3 Sept 24, 2012 41

! cfish(variable) generates plots of each species’ wholebody concentration (ppm) for each chemical as a function

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

weight class;

! pop(variable) generates plots of each species’ population

density (fish/ha) as a function of time (day of year) and

the species’ age, length, or weight class;

! tl(variable) generates plots of each species’ total body

length (cm/fish) as a function of time (day of year) and the

species’ age or weight class;

! wt(variable) generates plots of each species’ body weight

(g wet wt/fish) as a function of time (day of year) and the

species’ age or length class.

where variable equals “age”, “length”, or “weight”. Each age

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

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

oldest and smallest / youngest individuals.

O/BIOTA string1; ...; stringn

This BASS v2.1 command specifies nonfish standing stocks that

are to be generated as forcing functions rather than as simulated

state variables. Although this command has been superceded in

BASS v2.2 and higher by the commands /BENTHOS,

/TERRESTRIAL_INSECTS, /PERIPHYTON, /PHYTOPLANKTON, and

/ZOOPLANKTON (see Section 4.3.4), it has been retained for

upward compatibility. Valid options are:

! benthos[yunits] = fnc to generate benthic standing stocks

according to the function fnc. The units string yunits must

be dimensionally equivalent to g dry wt/m2

.

! insects[yunits] = fnc to generate incidental terrestrial

insect standing stocks according to the function fnc. The

units string yunits must be dimensionally equivalent to g

dry wt/m2.

! periphyton[yunits] = fnc to generate periphyton standing

stocks according to the function fnc. The units string

yunits must be dimensionally equivalent to g dry wt/m2.

! phytoplankton[yunits] = fnc to generate phytoplankton

standing stocks according to the function fnc. The units

string yunits must be dimensionally equivalent to g dry

wt/L.

! zooplankton[yunits] = fnc to generate zooplankton

standing stocks according to the function fnc. The units

string yunits must be dimensionally equivalent to g dry

wt/L.

Valid specifications for the function strings fnc are :

! nonfish_name[yunits] = " to generate a constant

compartmental standing stock of " (yunits) for the

simulation.

! nonfish_name[yunits] = " + $*sin(T + N*t[xunits]) to

generate a sinusoidal compartmental standing stock for

the simulation where " is the mean standing stock for the

chosen time period, $ is its amplitude (yunits), T is its

phase angle (radians), and N = 2B / period is its frequency

(1/xunits).

! nonfish_name[yunits] = file(filename) to read and

interpolate the specified compartmental standing stock

from the file filename. See Section 4.4.3.

Unless specified otherwise, BASS assumes that the first day of

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

31. This assignment can be changed using the command

/MONTH_T0.

These options are only required when the user is simulating fish

that feed on these resources (see the “diet” option for

/ECOLOGICAL_PARAMETERS). Note, however, because BASS

assumes that piscivorous fish switch to benthic invertebrates and

incidental terrestrial insects when appropriate forage fish are

unavailable, the benthos and insect options should be specified

even when simulating only piscivorous fish. Also note that if

project file uses the FGETS option described below, the only

/BIOTA option that might be required is the

zooplankton[yunits]=fnc option. This option is required only if

the user specifies a fish’s feeding to be simulated using the

clearance model formulation described in Equation (2.64).

If multiple options are selected, each option must be separated by

a semicolon.

O /FGETS

This command enables users to run BASS without simulating the

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

bioaccumulation of individual fish are simulated. The

command’s function and name are based on the FGETS (Food and

Gill Exchange of Toxic Substances) model (Barber et al. 1987,

1991) that was BASS’s predecessor.
BASS 2.3 Sept 24, 2012 42

O /FIRST_LEAP_YEAR string

This command specifies which year of the simulation

corresponds to the first leap year, i.e., contains the first February

29. The valid syntax for string is

! year=1,2,3, or 4

If not specified, BASS assumes that the fourth year of any

simulation is corresponds to the first leap year as defined above.

O /FISH_CARRYING_CAPACITY string

This command specifies an optional total fish biomass carrying

capacity of the community of interest which is used to impose an

additional mortality and dispersal (KM) on all cohorts of each

species over and above their self-thinning mortality and dispersal

(NM and EM, respectively) and their predatory mortality (PM).

See Equations (2.96) - (2.98) in Section 2.9. The valid syntax for

string is

! "[units]

where " is a nonnegative real value, and units is a string that

must be dimensionally equivalent to kg wet wt/ha. Using this

input and the initial relative biomass of each species, BASS then

internally estimates corresponding species-specific biomass

carrying capacities. 

O /HEADER string

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

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

string is 80 characters.

O /ISOTOPE_PARAMETERS string1; string2

This command specifies how BASS will estimate a fish’s

operational trophic position TP from its simulated stable isotope

fractions and using the equations

(4.1)

(4.2)

Valid options for this command are:

! del_c13_tp[-]="

! del_n15_tp[-]=$

If not specified by the user, BASS assumes the defaults values

of and (see Vander Zanden and Rasmussen

2001). 

O /LENGTH_OF_SIMULATION string

This command specifies the desired length of the simulation. The

valid syntax for string is

! "[units]

where " is a nonnegative real value. The time unit specified with

brackets is converted into days for internal use and subsequent

model output.

O /LESLIE_MATRIX_SIMULATION

This command enables users to run BASS in a mode that is

computationally intermediate between BASS’s FGETS and full

community modes. When this option is specified, BASS simulates

fish population dynamics using the conceptual framework of a

multispecies Leslie matrix population model. A cohort’s

mortality is predicted using a single, lumped, self-thinning

mortality rate (i.e., Equation (2.94)) without attempting to

partition its total mortality into predatory and non-predatory

mortality and dispersal as outlined in Sections 2.7 and 2.8.

Although predatory mortality is not simulated, the dietary

composition of each cohort is nevertheless predicted using the

methods described in Section 2.7. While this simulation option

is designed partially to lessen the need for detailed food web

information and the work required to calibrate a full community

simulation, it is also designed to simulate more realistically the

population dynamics of communities in which the dominant

process driving cohort mortality and self-thinning is dispersal

rather than predation.

O /NONFISH_QSAR string1; ...; stringn

This command specifies the quantitative structural activity

relationships for the bioconcentration / bioaccumulation factors

of the nonfish compartments benthos, periphyton, phytoplankton,

and zooplankton that are to be applied to all chemicals. Valid

string options are:

! BCF[-](nonfish_name)="*Kow[-]^$

where Kow[-] is the chemical’s n-octanol / water partition

coefficient; and " and $ are real or integer empirical constants.

Also see the chemical command /NONFISH_BCF. When this

command is used, the specified QSARs supercede any BCFs

specified by /NONFISH_BCF or exposures specified by /EXPOSURE.

O /MONTH_T0 string

This is an optional command that specifies the month that
BASS 2.3 Sept 24, 2012 43

corresponds to the start of the simulation. If not specified, BASS

assumes a default start time of April 1.

O /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,

community’s water level, and various output options.

O /SUMMARY_PLOTS string1; ...; stringn

This command specifies the variables whose temporal dynamics

will be plotted at the completion of the simulation. This

command, like /ANNUAL_PLOTS, is a vestigial option of BASS

v2.1. The options can be specified one per card, or all in one

card, separated by semicolons. Valid options are:

! afish(variable) generates plots of each species’ total

aqueous phase chemical activity as a function of time (day

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

class;

! baf(variable) generates plots of each species’

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(variable) generates plots of each species’

biomagnification factor (i.e., the ratio Cf / Cprey) for each

chemical as a function of time (day of simulation) and the

species’ age, length, or weight class;

! cfish(variable) generates plots of each species’ wholebody concentration (ppm) for each chemical as a function

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

weight class;

! pop(variable) generates plots of each species’ population

density (fish/ha) as a function of time (day of simulation)

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

! tl(variable) generates plots of each species’ total body

length (cm/fish) as a function of time (day of year) and the

species’ age or weight class;

! wt(variable) generates plots of each species’ body weight

(g wet wt/fish) as a function of time (day of year) and the

species’ age or length class.

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

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

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

smallest / youngest individuals.

O /TEMPERATURE string1; string2

This command specifies a community’s ambient water

temperatures. For an unstratified water body only one string

option is specified. In this case valid options for this command

are:

! temp[celsius]=" generates a constant ambient water

temperature for the simulation.

! temp[celsius]=" + $*sin(T + N*t[xunits]) generates a

sinusoidal ambient water temperature for the simulation

where " is the mean temperature for the chosen time

period, $ is its amplitude (yunits), T is its phase angle

(radians), and N=2B / period is its frequency (1/xunits).

! temp[celsius]=file(filename) to read and interpolate the

ambient water temperature from the file filename. See

Section 4.4.3.

For a stratified water body, users must specify the temperature of

both the epilimion and the hypolimnion. In this case valid options

are:

! temp_epilimnion[meter]="

! temp_epilimnion[meter]=" + $*sin(T + N*t[xunits])

! temp_epilimnion[meter]=file(filename)

! temp_hypolimnion[meter]="

! temp_hypolimnion[meter]=" + $*sin(T + N*t[xunits])

! temp_hypolimnion[meter]=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.

O /WATER_LEVEL string1; string2

This command specifies a community’s actual water level. For an

unstratified water body only one string option is specified. In this

case, valid options for this command are:

! depth[meter]=" generates a constant water level for the

simulation.

! depth[meter]=" + $*sin(T + N*t[xunits]) generates a

sinusoidal water level for the simulation where " is the

mean water level for the chosen time period, $ is its
BASS 2.3 Sept 24, 2012 44

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

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

! depth[meter]=file(filename) to read and interpolate the

water levels from the file filename. See Section 4.4.3.

For a stratified water body, users must specify the depth of both

the epilimion and the hypolimnion. In this case, valid options are:

! depth_epilimnion[meter]="

! depth_epilimnion[meter]=" + $*sin(T + N*t[xunits])

! depth_epilimnion[meter]=file(filename)

! depth_hypolimnion[meter]="

! depth_hypolimnion[meter]=" + $*sin(T + N*t[xunits])

! depth_hypolimnion[meter]=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.

4.3.2. Chemical Input Commands

The physico-chemical properties and exposure concentrations of

each chemical of interest are specified by a block of twelve

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

/MELTING_POINT real number

/METABOLISM string1

; ...; stringn

/MOLAR_VOLUME real number

/MOLAR_WEIGHT real number

/NONFISH_BCF string1; ...; stringn

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.

O /CHEMICAL string

This command specifies the start of the input for a new chemical.

Each chemical name must be a single character string without

embedded blanks or hyphens. If a two-part name is desired, the

user should use an underscore “_” as a separating character. This

command must precede the commands /EXPOSURE, /LETHALITY,

/LOG_AC, /LOG_KB1, /LOG_KB2, /LOG_P, /METABOLISM,

/MOLAR_WEIGHT, /MOLAR_VOLUME, and /MELTING_POINT. The

name specified by this command is used in conjunction with the

command /INITIAL_CONDITIONS to input initial whole-body

concentrations of chemicals in each age class of the fish of

concern and with the command /METABOLISM to specify daughter

products of chemical biotransformation. If the user specifies

chemical exposures via the file option, the indicated name is also

used to direct reading of the specified exposure files. Otherwise,

this name is used only for output purposes; BASS does not use

this name to link to any chemical data base.

O /EXPOSURE string1; ...; stringn

This command enables the user to specify the temporal dynamics

of chemical exposures to fish via water or contaminated

sediments or via the ingestion of benthic invertebrates, incidental

terrestrial insects, or plankton. Exposure concentrations specified

by these options are assumed to be completely bioavailable to the

fish. For example, water concentrations are assumed to be actual

dissolved concentrations and not total water concentrations that

include particle-bound chemical. If multiple options are selected,

each option must be separated by a semicolon. Valid options are:

! cbenthos[yunits]=fnc generates potential dietary

exposures to fish via benthic organisms according to the

function fnc. Note in BASS 2.1 the six lettered name

cbnths was used to specify this exposure function.

! cinsects[yunits]=fnc generates potential dietary exposures

to fish via incidental terrestrial insects according to the

function fnc. Note in BASS 2.1 the six lettered name cinsct

was used to specify this exposure function.

! cperiphyton[yunits]=fnc generates potential dietary

exposures to fish via periphyton according to the function

fnc. Note in BASS 2.1 the six lettered name cphytn was

used to specify this exposure function.

! cphytoplankton[yunits]=fnc generates potential dietary

exposures to fish via phytoplankton according to the

function fnc. Note in BASS 2.1 the six lettered name

cpplnk was used to specify this exposure function.

! csediment[yunits]=fnc generates sediment exposure

concentrations according to the function fnc. Note in BASS

2.1 the six lettered name csdmnt was used to specify this

exposure function.

! cwater[yunits]=fnc generates aqueous exposure
BASS 2.3 Sept 24, 2012 45

concentrations according to the function fnc.

! czooplankton[yunits]=fnc generates potential dietary

exposures to fish via zooplankton according to the

function fnc. Note in BASS 2.1 the six lettered name

czplnk was used to specify this exposure function.

The concentration units for each exposure function are specified

within the indicated brackets. As previously noted for the

simulation control functions, unless specified otherwise, BASS

assumes that the first day of simulation is April 1 and that the

365-th simulation day is March 31 for all the time dependent

exposure functions discussed in the following. This assignment

can be changed using the command /MONTH_T0.

Valid expressions for dietary exposures via benthos, periphyton,

phytoplankton, or zooplankton and for benthic sediments are:

! nonfish_name[yunits]=" generates a constant

concentration of toxicant in benthos, periphyton,

phytoplankton, sediment, or zooplankton.

! nonfish_name[yunits]="*cwater[xunits] generates

chemical concentrations in benthos, periphyton,

phytoplankton, sediment, or zooplankton as a chemical

equilibrium with the ambient environmental water. If this

equilibrium is assumed to be thermodynamic, then the

coefficient " generally is equal to the product of the

component’s dry organic fraction and the chemical’s Kow.

Also see /NONFISH_BCF.

! nonfish_name[yunits]=file(filename) to read and

interpolate the concentration of toxicant in benthos,

periphyton, phytoplankton, sediment, or zooplankton from

the file filename. See Section 4.4.3.

Valid expressions for insect dietary exposures are:

! cinsects[yunits ]= " generates a constant concentration of

the toxicant in incidental terrestrial insects.

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

the concentration of the toxicant in incidental terrestrial

insects from the file filename. See Section 4.4.3.

Valid expressions for direct aqueous exposures are:

! cwater[yunits]=" generates a constant aqueous

concentration for the chemical of concern.

! cwater[yunits]="*csediment[xunits] generates 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 Koc.

! cwater[yunits]="+$*exp((*t[xunits]) generates 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+N*t[xunits]) generates a

sinusoidal dissolved chemical water concentration where

" is the mean dissolved chemical water concentration

(yunits) (over one period), $ is the amplitude (yunits), T

is its phase angle (radians), and N=2B / 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. See Section 4.4.3.

Users should be very cautious and judicious when using more

than one of the above options since the user can easily construct

an exposure scenario that is inconsistent with theoretical

constraints on the fate and distribution of contaminants in aquatic

systems.

O /LETHALITY string1; ...; stringn

This optional command specifies species-specific LC50's for the

chemicals of concern either as an actual concentration value or

as a QSAR function. Valid string options are:

! LC50[units](fish_name)="

! LC50[units](fish_name)="*Kow[-]^$

where fish_name is the common name of the fish species to be

simulated; Kow[-] is the chemical’s n-octanol / water partition

coefficient; and " and $ are real or integer empirical constants.

BASS converts user supplied LC50's into their corresponding

aqueous chemical activities and then uses the geometric mean of

these lethal activities to trigger mortality during the simulation.

If the user desires, simulation of mortality associated with the

accumulation of a lethal aqueous chemical activity can be turnedoff 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
BASS 2.3 Sept 24, 2012 46

of the fish’s estimated lethal chemical activity to provide the user

with a simple, but useful, monitor of the total chemical status of

the fish.

O /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.

O /LOG_KB1 real number

This command specifies the log10 of a metal’s binding constant

for non-lipid organic matter (see Equation (2.6)). This parameter

is input only for metals and organometals.

O /LOG_KB2 real number

This command specifies the log10 of a metal’s binding constant

for refractory organic matter. This parameter is used to calculate

metal binding to the fish’s dry fecal matter and input only for

metals and organometalics.

O /LOG_P real number

This command specifies the chemical’s log10 Kow, where Kow is

the n-octanol / water partition coefficient. /LOG_P must be

specified for all organic chemicals.

O /MELTING_POINT real number

This command specifies the chemical’s melting point (Celsius).

This datum, together with the chemical’s logP, is used to

calculate the aqueous activity coefficient for organic chemicals

when that parameter is not specified by the user. See Yalkowsky

et al. (1983)

O /METABOLISM string1; ...; stringn

This optional command specifies species-specific rates of

biotransformation for the chemical of concern either as an actual

rates or as a QSAR function. Valid string options are:

! 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 specifies the whole-body-referenced biotransformation

rate kbt in Equation (2.47); fish_name is the common name of the

fish species that can metabolize the chemical of concern;

chemical_name is the name of the daughter product generated by

the metabolism of the chemical of concern; Kow[-] is the

chemical’s n-octanol / water partition coefficient: and " and $

are real or integer empirical constants. If the user does not wish

to simulate daughter products because they are insignificant or

assumed to be harmless, chemical_name can be assigned the

value none. When daughter products are specified, the user must

specify all physico-chemical properties of the identified byproduct in the same way that the physico-chemical properties of

the parent compound are specified.

O /MOLAR_VOLUME real number

This command specifies the chemical’s molecular volume

(cm3

/mol) that is used to calculate the chemical’s aqueous

diffusivity, i.e.,

(4.3)

where D is the toxicant’s aqueous diffusivity (cm2

/sec); 0 is the

viscosity of water (poise); and < is the chemical’s molecular

volume (cm3

/mol) (Hayduk and Laudie 1974). The viscosity of

water over its entire liquid range is represented with less than 1%

error by

(4.4)

where 0T is the viscosity (centipoise) at temperature T (Celsius),

and 020 is the viscosity of water at 20 Celsius (1.002 centipoise)

(Atkins 1978).

O /MOLAR_WEIGHT real number

This command specifies the chemical’s molecular weight

(g/mol).

O /NONFISH_BCF string1; ...; stringn

This command specifies the bioconcentration / bioaccumulation

factors for the nonfish compartments benthos, periphyton,

phytoplankton, and zooplankton either as a numerical constant or

as a QSAR function. Valid string options are:

! BCF[-](nonfish_name)="

! BCF[-](nonfish_name)="*Kow[-]^$

where Kow[-] is the chemical’s n-octanol / water partition

coefficient; and " and $ are real or integer empirical constants.
BASS 2.3 Sept 24, 2012 47

Note that this command or /NONFISH_QSAR must be specified for

any nonfish compartment that is simulated as a community state

variable.

4.3.3. Fish Input Commands

Model parameters for each fish species of interest are specified

by a block of thirteen commands, i.e.,

/COMMON_NAME string

/SPECIES string

/AGE_CLASS_DURATION string

/SPAWNING_PERIOD string

/FEEDING_OPTIONS string1; ...; stringn

/PREY_SWITCHING _OFF

/INITIAL_CONDITIONS string1; ...; stringn

/COMPOSITIONAL_PARAMETERS string1; ...; stringn

/ECOLOGICAL_PARAMETERS string1; ...; stringn

/MORPHOMETRIC_PARAMETERS string1; ...; stringn

/PHYSIOLOGICAL_PARAMETERS string1; ...; stringn

/FISHERY_PARAMETERS string1; ...; stringn

/HABITAT_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. The use of these commands will now be described in

alphabetical order.

O /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.

O /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.

O /COMPOSITIONAL_PARAMETERS string1; ...; stringn

This command specifies aqueous and lipid fractions of the fish.

Valid options that must be separated by semicolons are:

! pa[-]=" + $*pl[-] specifies the fish’s aqueous fraction as

a linear function of the fish’s lipid fraction.

! pl[-]="*W[xunits]^$ 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 real or integer empirical constants.

O /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:

! ast_yoy[-]=f(b[-]=", yoy[xunits]=$, pop[yunits]=()

specifies parameters for implementing accelerated selfthinning of young-of-year fish (YOY), or more accurately

recently recruited cohorts, that often occurs due to

intraspecies competition for territories, refugia, or other

habitat resources. The functional argument b[-]="

specifies the desired accelerated self-thinning exponent.

The functional argument yoy[xunits]=$ defines the age,

length, or wet weight threshold below which cohorts will

be subject to accelerated self-thinning. Valid expressions

for yoy are either “age”, “tl”, or “wt”. The final functional

argument specifies the population threshold that triggers

accelerated self-thinning. Depending on the assumed

nature of the competition, this threshold can be specified

either as the total density of cohorts satisfying the

condition yoy[xunits]#$, or as the total density of cohorts

satisfying the condition yoy[xunits]>$. For the former

case, pop equals “pop_yoy” whereas for the latter case

pop equals “pop_adults”.

! diet(class, time) = {prey1 = , 1, ..., preyn = , n} specifies

the dietary composition for fish of the age or size class

class during the months specified by time. The right-hand

side of the option specifies the prey items (preyn) and their

contribution (,n

) to the fish’s diet. Each preyn is either the

common name of a simulated fish species, “benthos”,

“insects”, “periphyton”, “phytoplankton”, or

“zooplankton” (see commands /BIOTA and

/COMMON_NAME). Depending on its value, ,n is

interpreted either as a constant percent contribution or as

a prey electivity. In particular, if 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 ,n is considered a prey
BASS 2.3 Sept 24, 2012 48

electivity (see Equation (2.74)). For any given foraging

class, a user can specify both constant dietary percentages

and prey electivities.

Valid expressions for class are:

"< a[xunits]<$ for age based dietary classes

"< l[xunits]<$ for length based dietary classes

"<w[xunits]<$ for weight based dietary classes

where " and $ are real or integer empirical constants.

Although for a given species all class types must be the

same (i.e., age, length, or weight), the class types between

species can be different.

Valid expressions for time are either the name of a month

or the names of two months separated by a hyphen. For

example,

month1, e.g., july, or

month1-month2, e.g., july-december.

If the diet of a specified age / size class is independent of

time of year, “, time” can be omitted. In this case, time =

“january-december” is assumed.

The diet(.,.)={...} option can be repeated as many times as

needed in order to define a complete lifetime sequence of

diets for the fish.

! lp[yunits]= fnc specifies the average length of prey

consumed by a fish whose body length is L[xunits].

Unlike most fish command options, two valid function

strings are recognized, i.e.,

lp[yunits]=" + $*L[xunits] or

lp[yunits]=" + $*exp((*L[xunits])

where ", $, and ( are real or integer empirical constants.

If a fish’s average prey size is independent of its body

length (i.e., $ equals zero), this parameter can be specified

simply as lp[yunits]=".

! lp_max[yunits]= fnc specifies the maximum length of

prey consumed by a fish whose body length is L[xunits].

Like the option for a fish’s average prey length, two valid

function strings are recognized, i.e.,

lp_max[yunits]=" + $*L[xunits] or

lp_max[yunits]=" + $*exp((*L[xunits])

where ", $, and ( are real or integer empirical constants.

If a fish’s maximum prey size is independent of its body

length (i.e., $ equals zero), this parameter can be specified

simply as lp_max[yunits]=". When this parameter is not

specified by the user, BASS assigns the default value

lp_max[cm]=0.5*L[cm].

! lp_min[yunits]= fnc specifies the minimum length of prey

consumed by a fish whose body length is L[xunits]. Like

the option for a fish’s average prey length, two valid

function strings are recognized, i.e.,

lp_min[yunits]=" + $*L[xunits] or

lp_min[yunits]=" + $*exp((*L[xunits])

where ", $, and ( are real or integer empirical constants.

If a fish’s minimum prey size is independent of its body

length (i.e., $ equals zero), this parameter can be specified

simply as lp_min[yunits]=". When this parameter is not

specified by the user, BASS assigns the default value

lp_min[cm]=0.1*L[cm].

! mls[yunits]=" specifies the species’ maximum longevity

or life span.

! nm[-]="*b($:()*sg_mu[-] specifies a cohort’s rate of

dispersal and non-predatory mortality as a function of its

habitat suitability and its long-term specific growth rate

sg_mu[-]. Whereas " specifies the fraction of the species’

total “mortality” that is attributable to dispersal and nonpredatory mortality, $ and ( specify the species’

minimum and maximum self-thinning exponents,

respectively. See Equations (2.95) and (2.102). If the user

elects not to simulate habitat effects on dispersal and nonpredatory mortality, this parameter can be specified

simply as

nm[-] = "*b($)*sg_mu[-]

where $ is the species’ average self-thinning exponent.

Also see the /ECOLOGICAL_PARAMETERS option sg_mu[].

! rbi[-]=" specifies the species’ reproductive biomass

investment (i.e., grams gametes per gram spawning fish)

where " is real empirical constant.

! refugia[yunits]=" specifies a refuge population size for
BASS 2.3 Sept 24, 2012 49

each cohort that can be prey for community piscivores

where " is real or integer constant. Yunits must be

dimensionally equivalent to fish/ha. If not specified, BASS

assumes no refuge level (i.e., refugia[yunits]=0)

! sg_mu[yunits]="*W[xunits]^$ specifies the species’

long-term mean specific growth rate where " and $ are

real or integer empirical constants. yunits must be

dimensionally equivalent to day-1, and xunits must be

dimensionally equivalent to g wet wt/fish. If not specified,

BASS can estimate this parameter provided that the user

specifies the species’ expected body weight at its

maximum age. See /ECOLOGICAL_PARAMETERS option

wt_max[] for details.

! tl_r0[yunits]=" specifies the species’ minimum total

length when sexual maturity is reached where " is a real

or integer empirical constant.

! wl[yunits]="*L[xunits]^$ specifies the fish’s wet weight

as an allometric function of its total length where " and $

are real or integer empirical constants.

! wt_max[yunits]=" specifies the species’ expected wet

body weight at its maximum age where " is a real or

integer empirical constant. This parameter is required only

when the user has not specified the species’ long-term

me a n s p e c i f i c g r o wt h r a t e u s i n g t h e

/ECOLOGICAL_PARAMETERS option sg_mu[]. When

sg_mu[] is not specified, BASS will estimate the species’

long-term specific growth rate based on its maximum life

span mls[], young-of-year body weight yoy[], and

wt_max[]. If the user has specified the species’

temperature/seasonal dependent specific growth rate

sg[yunits]="*W[xunits]^$*H((,T1

,T2)

(see /PHYSIOLOGICAL_ PARAMETERS option sg[]), BASS

estimates the species’s long-term specific growth rate by

sg_mu[1/d] = *W^$

where is back-calculated as outlined by Equation

(3.43). If sg[] has not been specified, BASS estimates the

species’s long-term specific growth rate by

sg_mu[1/d] = *W^(-0.732)

where is back-calculated as outlined by Equation (3.43)

using the mean interspecies specific growth exponent (i.e.,

-0.732) estimated from the BASS model database. Also see

Barber (2003).

! yoy[yunits]=" specifies the wet weight of fish recruited

into the population as age class 1 where " is a real or

integer empirical constant.

O /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(class) to model expected feeding using

Equation (2.58).

! clearance(class) to model expected feeding using

Equation (2.64).

! holling(class) to model expected feeding using Equation

(2.59).

! linear(class) to model expected feeding using Equation

(2.66).

Valid expressions for class are:

" < 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.

where " and $ are real or integer empirical constants. Although

for a given species all class types must be the same type (i.e.,

age, length, or weight), class types between species can be

different. The parameters for these models are specified using the

/PHYSIOLOGICAL_PARAMETERS command.

O /FISHERY_PARAMETERS string1; ...; stringn

This command specifies stocking and harvest rates for sport

fishes. Valid options for this command are:

! stocking[sunits](age[aunits]=", size[bunits]=$,

season=time, frequency = schedule) = ( specifies the

stocking rate of fish of age " and body size $ (i.e., total

length or wet weight) during the time interval specified by

time and the stocking frequency specified by schedule.

The units of the specified stocking rate sunits must be

dimensionally equivalent to fish/ha. Valid expressions for
BASS 2.3 Sept 24, 2012 50

time are given below. Valid options for schedule are:

“weekly”, biweekly”, “monthly”, or “one_time”. If

schedule = weekly, then a new cohort of ( individuals is

added to the species’ population once a week throughout

the specified period. If schedule = monthly, then a new

cohort of ( individuals is added to the species’ population

once a month throughout the specified period.

! harvest[hunits]("<L[units]<$, time) = ( specifies the

fractional harvest rate of fish of the specified length class

during the indicated time period. The units of the

specified harvest rate hunits must be dimensionally

equivalent to 1/d.

Valid expressions for time are two month-day combinations

separated by a hyphen. For example,

march 15 - september 1.

Both of these fishery options can be repeated as many times as

needed to define the species stocking and harvest. Additionally,

only nonzero stocking and harvesting rates need to be specified.

Fishing mortality and harvest can be turned off without deleting

the user’s harvest parameters by using the command line option

“-f”. Similarly, stocking can be turned off without deleting the

user’s stocking parameters using the command line option “-s”.

See Section 4.5 for details.

O /HABITAT_PARAMETERS string1; ...; stringn

This command specifies habitat preferences, tolerances, and

suitability indices for the species.

Valid options for habitat preferences are:

! tpref[celsius](class) = ( specifies the preferred or optimum

temperature of the age or size class specified within the

parentheses.

Valid expressions for class are:

" < a[xunits] < $

"< l[xunits] < $

" < w[xunits] < $

where " and $ are real or integer empirical constants. This option

can be specified repeatedly as needed. Although for a given

species all class types must be the same type (i.e., age, length, or

weight), class types between species can be different.

Valid options for habitat suitability multipliers are:

! hsi_feeding[-] = fnc specifies the species’ HSI for

feeding by the time function fnc. This HSI is used as a

simple linear multiplier on a cohort’s maximum ingestion

rate when feeding is modeled with either an allometric,

Holling, or clearance volume formulation. When a

cohort’s ingestion is back-calculated from its expected

growth rate, the specified HSI is used as a simple linear

multiplier on the cohort’s specific growth rate. See

Section 2.9.

! hsi_recruitment[-] = fnc specifies the species’s HSI for

recruitment by the time function fnc. This HSI is used as

a simple linear multiplier on the species’ YOY

recruitment. See Section 2.9.

! hsi_survival[-] = fnc specifies the species’ HSI for

dispersal and non-predatory mortality by the time function

fnc. This HSI is used to control the species’ self-thinning

exponent that determines, in combination with the fish’s

growth rate, a cohort’s estimated dispersal and nonpredatory mortality rate. See Section 2.9.

Valid expressions for these HSI functions are:

! hsi_name[-]= " generates a constant HSI for the entire

simulation.

! hsi_name[-]=file(filename) generates time-varying HSIs

either by reading and interpolating HSIs specified by the

file filename or by reading and interpolating habitat

variables and then calculating HSIs using user-supplied

logistic regressions. See Sections 4.4.3.

When HSI multipliers are calculated using user-supplied logistic

regressions, the desired regressions are specified using the

following options:

! hsi_feeding_equation[-] = regression

! hsi_recruitment_equation[-] = regression

! hsi_survival_equation[-] = regression

where regression specifies a linear combination of habitat

variables Xi

 that are transformed or raised to an integer or real

power. Transformations recognized by BASS include:

LN( Xi

) =>

LN_1( Xi) =>

LOG( Xi) =>

LOG_1( Xi) =>
BASS 2.3 Sept 24, 2012 51

SQRT( Xi) =>

ASIN_SQRT( Xi) =>

ASIN_SQRT_PCT( Xi) =>

Habitat variables must be specified with units inclosed within

brackets, and must match in name and units to column variables

specified by the data file filename. After evaluating the specified

logistic regression, BASS calculates the fish’s HSI multiplier

using the standard equation

hsi_name = 1 / (1 + EXP (- hsi_name_equation))

When HSI functions are not specified by the user, BASS assigns

the default value of 1 to each unspecified HSI function.

O /INITIAL_CONDITIONS string1; ...; stringn

This command specifies the species’ initial ages, whole-body

chemical concentrations, wet body weights, and population sizes.

Valid options for this command are:

! age[units]={x1, ..., xn} to initialize the age of each cohort

with the specified vector. The units that are delineated by

brackets must be dimensionally equivalent to days.

! chemical_name[units]={x1, ..., xn} to initialize the wholebody concentration of each cohort for the named chemical

by the specified vector. Each name must correspond

exactly to a name specified by one of the /CHEMICAL

commands. The units of measurement that must be

enclosed by brackets must be dimensionally equivalent to

μg/g wet wt.

! wt[units]={x1, ..., xn} to initialize the body size of each

age class with the specified vector. The units delineated

by brackets must be dimensionally equivalent to g wet

wt/fish.

! pop[units]={x1, ..., xn} to initialize the population density

of each age class with the specified vector. The units

delineated by brackets must be dimensionally equivalent

to fish/ha.

O /MORPHOMETRIC_PARAMETERS string1; ...; stringn

This command specifies the species’ morphometric parameters

that are needed to describe the exchange of chemicals across its

gills. Each string specifies a required morphometric parameter

as a simple allometric power function of the fish’s body weight.

Valid options, which must be separated by semicolons, are:

! ga[yunits]="*W[xunits]^$ specifies the fish’s total gill

surface area where " and $ are real or integer empirical

constants. yunits must be dimensionally equivalent to cm2

or cm2/g wet wt.

! id[yunits]="*W[xunits]^$ specifies the interlamellar

distance between adjacent lamellae where " and $ are real

or integer empirical constants. yunits must be

dimensionally equivalent to cm or cm/g wet wt.

! ld[yunits]="*W[xunits]^$ specifies the density of

secondary lamellae on the primary gill filaments (i.e.,

number of lamellae per mm gill filament) where " and $

are real or integer empirical constants.

! ll[yunits]="*W[xunits]^$ specifies the fish’s lamellar

length where " and $ are real or integer empirical

constants. yunits must be dimensionally equivalent to cm

or cm/g wet wt.

Note that if the exponent $ equals zero for any of these

parameters, the resulting term W[xunits]^0 does not have to be

specified.

O /PHYSIOLOGICAL_PARAMETERS string1; ...; stringn

This command specifies the species’ physiological parameters

for simulating growth. Each string specifies a physiological

parameter of the fish as a constant or temperature-dependent

power function of its body weight. In particular,

! ae_plant[-]=" specifies the fish’s assimilation efficiency

for periphyton and phytoplankton where " is a real

empirical constant less than or equal to one.

! ae_invert[-]=" specifies the fish’s assimilation efficiency

for benthos, insects, and zooplankton where " is a real

empirical constant less than or equal to one.

! ae_fish[-]=" specifies the fish’s assimilation efficiency

for fish where " is real a empirical constant less than or

equal to one.

! ge[yunits]="*G[xunits]^$*H((,T1,T2) specifies the

fish’s gastric evacuation where G is the mass of food

resident in the intestine, and where ", $, (, T1

, and T2 are

real or integer empirical constants. yunits must be

dimensionally equivalent to g dry wt/d. In general, (=1⁄2,

b, or 1 (Jobling 1981). This parameter is required only if

the feeding option holling(A) is used.

! kf_min[-]=" specifies the minimum condition factor for
BASS 2.3 Sept 24, 2012 52

a fish’s continuing existence. In BASS, a fish’s condition

factor is defined by the ratio

(4.5)

where W and L are the fish’s current wet body weight and

total length, respectively; and " and $ are the coefficient

and exponent for the fish’s weight-length relationship (see

/PHYSIOLOGICAL_PARAMETERS option wl[A]).

! mf[yunits]="*W[xunits]^$*H((,T1,T2) specifies the

fish’s maximum filtering rate where ", $, (, T1

, and T2

are real or integer empirical constants. yunits must be

dimensionally equivalent to L/d. This parameter is

required only if the feeding option clearance(A) is used.

! mi[yunits]="*W[xunits]^$*H((,T1,T2) specifies the

fish’s maximum ingestion where ", $, (, T1

, and T2 are

real or integer empirical constants. yunits must be

dimensionally equivalent to g dry wt/d. This parameter is

required only if the feeding option allometric(A) is used.

! rq[-]=" specifies the fish’s respiratory quotient; (i.e.,

L(CO2) respired/L(O2) consumed) where " is a real

empirical constant.

! rt:std[-]=" specifies the ratio of a fish’s routine

respiration to its standard respiration where " is a real

empirical constant. If not specified by the user, BASS

assigns a default value equal 2.

! sda:in[-]=" specifies the ratio of a fish’s SDA to its

ingestion where " is a real empirical constant. If not

specified by the user, BASS assigns a default value equal

0.17.

! sg[yunits]="*W[xunits]^$*H((,T1,T2) specifies the

fish’s specific growth rate where ", $, (, T1

, and T2 are

real or integer empirical constants. yunits must be

dimensionally equivalent to day-1. This parameter is

required only if the feeding option linear(A) is used.

! sm[yunits]="*W[xunits]^$*H((,T1,T2) specifies the size

of the satiation meal consumed during the interval (0, st)

where ", $, (, T1, and T2 are real or integer empirical

constants. yunits must be dimensionally equivalent to g

dry wt/d. See option st[yunits] below. This parameter is

required only if the feeding option holling(A) is used.

! so[yunits]="*W[xunits]^$*H((,T1,T2) specifies the

fish’s standard oxygen consumption where ", $, (, T1,

and T2 are real or integer empirical constants. yunits must

be dimensionally equivalent to mg O2

/hr or mg O2/g wet

wt/hr.

! st[yunits]="*W[xunits]^$*H((,T1,T2) specifies the time

to satiation when feeding with an initially empty stomach

where ", $, (, T1, and T2 are real or integer empirical

constants. See option sm[yunits] above. This parameter is

required only if the feeding option holling(A) is used.

For the options ge[yunits], mf[yunits], mi[yunits], sg[yunits],

sm[yunits], so[yunits], and st[yunits],

(4.6)

where T1 is the temperature at which each particular process’s

rate is maximum and T2 is the upper temperature at which the

process is no longer operative. If the process does not exhibit a

temperature optimum, then the hyperbolic function H((,T1,T2)

should be substituted with the exponential function

exp((*T[celsius]). Consequently, each of these temperaturedependent power functions can also be specified as

"*W[xunits]^$*exp((*T[celsius])

As noted for the fish’s morphometric parameters, if the exponent

$ equals zero for any of these temperature-dependent power

functions, the term W[xunits]^0 does not have to be specified.

If a required parameter is not specified, the program will

terminate with an appropriate error message.

O /PREY_SWITCHING_OFF

This command disables BASS’s prey switching algorithms when

a cohort’s expected feeding level cannot be satisfied using the

dietary compositions specified by the user. By default, BASS’s

prey switching algorithms are enabled.

O /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.
BASS 2.3 Sept 24, 2012 53

O /SPECIES string

This command specifies the scientific name (genus and species)

of the fish to be modeled. When this command is encountered,

BASS uses the specified scientific name to assign default

ecological, morphological, and physiological parameters for the

species of interest. These default parameters are then updated

with the data that the user inputs via the

/ECOLOGICAL_PARAMETERS, /MORPHOMETRIC_PARAMETERS, and

/PHYSIOLOGICAL_PARAMETERS commands. The functionality of

this default parameterization, however, is not currently

operational in BASS v2.3.

4.3.4. Nonfish Input Commands

These commands specify simulation parameters for benthos,

periphyton, incidental terrestrial insects, phytoplankton and

zooplankton. The syntax for these commands is as follows

/BENTHOS string1

; ...; stringn

/TERRESTRIAL_INSECTS string1; ...; stringn

/PERIPHYTON string1; ...; stringn

/PHYTOPLANKTON string1; ...; stringn

/ZOOPLANKTON string1; ...; stringn

Depending on the options selected, BASS generates the standing

stocks of these nonfish compartments either as community

forcing functions or as community state variables. Although these

compartments can be simulated for any desired community, only

those components identified as fish prey are required to be

specified (see the diet(.,.)={...} o p tio n fo r

/ECOLOGICAL_PARAMETERS). Note, however, because BASS

assumes that piscivorous fish switch to benthic invertebrates and

incidental terrestrial insects when appropriate forage fish are

unavailable, the benthos and insect options should be specified

even when simulating only piscivorous fish.

When benthos, periphyton, incidental terrestrial insects,

phytoplankton or zooplankton are treated as community forcing

functions, a single option of the form

! biomass[yunits]=string

is specified. Valid expressions for this option are:

biomass[yunits]=" for a constant nonfish standing stock

biomass[yunits]=" + $*sin(T + N*t[xunits]) for a

sinusoidal nonfish standing stock where " is the mean

standing stock for the chosen time period, $ is its

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

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

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

nonfish standing stock from the file filename. See Section

4.4.3.

Whereas yunits must be dimensionally equivalent to g dry wt/m2

for benthos, incidental terrestrial insects, and periphyton, for

phytoplankton and zooplankton yunits must be dimensionally

equivalent to g dry wt/L. As previously noted, BASS assumes that

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

simulation day is March 31. This assignment can be changed

using the command /MONTH_T0. This command-option

combination is equivalent to the BASS v2.1 simulation control

command /BIOTA

When benthos, periphyton, phytoplankton or zooplankton are

treated as community state variables, the following five options

must be specified:

! initial_biomass[yunits]=number. This option specifies

the initial compartmental standing stock of the designated

component and is required to simulate the designated

nonfish compartment as a BASS state variable. yunits must

be dimensionally equivalent to g dry wt/m2

.

! mean_weight[yunits]=fnc. This option specifies the

average body weight of individuals within the designated

nonfish compartment. This parameter is required to

simulate the designated nonfish compartment as a BASS

state variable. yunits must be dimensionally equivalent to

g dry wt/ind. Valid expressions for fnc are:

mean_weight[yunits]=" generates a constant average

individual body weight for the designated prey.

mean_weight[yunits]=" + $*sin(T + N*t[xunits])

generates the average individual body weight of the

designated prey as a sinusoidal function of time where

" is the mean body weight for the chosen time period,

$ is its amplitude (yunits), T is its phase angle

(radians), and N=2B / period is its frequency (1/xunits).

mean_weight[yunits]=file(filename) to read and

interpolate the average individual body weight of the

designated prey from the file filename. See Section

4.4.3.

Unless specified otherwise, BASS assumes that the first

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

day is March 31. This assignment can be changed using

the command /MONTH_T0.

! ingestion[yunits]="*W[xunits]^$*H((,T1,T2) specifies
BASS 2.3 Sept 24, 2012 54

the ingestion rate of individuals within the designated

compartment as a function of their average body weight

and temperature where ", $, (, T1, and T2 are real or

integer empirical constants. This parameter is required to

simulate either benthos or zooplankton as a BASS state

variable. yunits must be dimensionally equivalent to g dry

wt/d, and xunits must be dimensionally equivalent to g

dry wt/ind.

! photosynthesis[yunits]="*W[xunits]^$*H((,T1,T2)

specifies the photosynthetic rate of individuals within the

designated compartment as a function of their average

body weight and temperature where ", $, (, T1

, and T2

are real or integer empirical constants. This parameter is

required to simulate either periphyton or phytopalnkton as

a BASS state variable. yunits must be dimensionally

equivalent to g dry wt/d, and xunits must be

dimensionally equivalent to g dry wt/ind. Currently,

photosynthesis is not treated as a function of nutrients and

light availability.

! respiration[yunits]="*W[xunits]^$*H((,T1,T2)

specifies the specific rate of dry organic mater respiration

for the designated compartment as a function of average

individual body weight and temperature where ", $, (, T1

,

and T2

 are real or integer empirical constants. This

parameter is required to simulate the designated nonfish

compartment as a BASS state variable. yunits must be

dimensionally equivalent to g dry wt/d, and xunits must

be dimensionally equivalent to g dry wt/ind.

Although BASS enables users to simulate benthos, periphyton,

phytoplankton or zooplankton as community state variables,

incidental terrestrial insects are always treated as a community

forcing function.

Regardless of how the biomass of benthos, incidental terrestrial

insects, periphyton, phytoplankton, or zooplankton is specified

as BASS inputs, or not as in the case of the FGETS simulation

mode, users can specify the stable isotope fractions 

and of these compartments using the optional arguments

! del_c13[-]="

! del_n15[-]=$

If the user does not specifies these arguments, BASS will assign

default values to these parameters and output those values in the

project’s message file. 

4.4. Input Data Syntax

4.4.1. Units Recognized by BASS

Most BASS commands require the specification of units (or

combination of units) as part of an option. This section describes

the syntax for units that are recognized by BASS’s input

algorithms. The conversion of user-supplied units to those

actually used by BASS is accomplished by referencing all units to

the MKS system (i.e., meter, kilogram, second). Table 4.1 and

Table 4.2 summarize prefixes and fundamental units,

respectively, that are recognized by BASS’s unit conversion

subroutines. Table 4.2 also summarizes the dimensionality and

the conversion factor to the MKS system standard unit. Table 4.3

summarizes units that are recognized by BASS’s unit conversion

subroutines for specifying ecological, morphometric, and

physiological units.

Units and their prefixes can be specified in either upper or lower

case. When prefixes are used, there must be no embedded blanks

between the prefix and the unit name, e.g., “milligram” is correct,

“milli gram” is incorrect. Unit names for wet and dry masses are

appended, without blanks, with the parenthetic identifiers “(ww)”

and “(dw)”, respectively. Similarly, an analogous convention is

used for specifying mass units of specific chemical compounds.

For example, “grams oxygen” is specified as “g(o2)”. 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.

4.4.2. User Specified Functions

The following syntax rules apply to specifying these options

! Brackets are used only to delineate units. Dimensionless

parameters like assimilation efficiency, lipid fraction, and

Kow must be specified with null units “[-]”.

! The order of addition and multiplication is not significant.

Thus, the following specifications are valid and

equivalent.

temp(celsius)="+$*sin(T + N*t[xunits]) <=>

temp[celsius]=$ sin(N*t[xunits]+T) + "

czplnk[yunits]="*cwater[xunits] <=>

czplnk[yunits]=cwater[xunits]*"

! Options that are temperature-dependent or independent

power functions can be specified by log10 or ln transforms.

For example, the following options are valid
BASS 2.3 Sept 24, 2012 55

ln(so[yunits])=" + $*ln(W[xunits]) + (*T[celsius]

log(so[yunits])=" + $*ln(W[xunits]) + (*T[celsius]

! User-specified functions do not have to be in reduced

form. For example, temperature-dependent power

functions can be specified with a reference temperature

other than 0"Celsius. Thus, BASS will correctly decode the

following functions

so[yunits]="*W[xunits]^$*exp((*(T[celsius]-20))

ln(so[yunits])="+ $*ln(W[xunits]) + (*(T[celsius]-20)

log(so[yunits])= "+ $*log(W[xunits]) + (*(T[celsius]-20)

! If the temperature dependency is unknown, temperaturedependent 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 function is zero, the function can be input as a

constant without specifying the expected independent

variable. For example, the following specifications are

equivalent

lp[cm]=4.5 <=> lp[cm]=4.5 + 0.0*L[cm]

pl[-]=0.05 <=> pl[-]=0.05*W[g(ww)]^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(ww)]^-0.2

ln(so[mg(o2)/g/hr])=

- 2.30+0.0693*T[celsius]+-0.2*ln(W[g(ww)])

The correct syntax for these options would be

so[mg(o2)/g/hr]=

0.1*exp(0.0693*T[celsius])*W[g(ww)]^(-0.2)

ln(so[mg(o2)/g/hr])=

-2.30+0.0693*T[celsius]- 0.2*ln(W[g(ww)])

4.4.3. User Supplied Parameter Files

If the user specifies a file option for the /EXPOSURE,

/TEMPERATURE, /WATER_LEVEL, /BIOTA, /BENTHOS,

/TERRESTRIAL_INSECTS, /PERIPHYTON, /PHYTOPLANKTON,

/ZOOPLANKTON, or /HABITAT_PARAMETERS commands, the

designated files must exist and be supplied by the user. The

general format of a BASS exposure file allows a user to specify

multiple exposure conditions within a single file. Each file record

specifies exposure conditions for a specific time. The general

format of a BASS exposure file is as follows

!

! file: exposure.dat

!

/001 time[units] ! see ensuing discussion

/C1 string

! !

/CM string

/START_DATA

v1,1 v1,2 ... v1,MV ! comment

v2,1 v2,2 ... v2,MV ! comment

! ! ... !

vNR,1 vNR,2 ... vNR,MV ! comment

The records beginning with a slash ( / ) followed by an integer CJ

identify the type of data (time, exposure concentration,

temperature, etc.) contained in CJ-th column of each data record.

In this example, NR is the total number of data records in the

file, MV is the number of variables per record, and 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:

! bbenthos[units] to read the standing stock of benthic

invertebrates;

! binsects[units] to read the standing stock of incidental

terrestrial insects;

! bperiphyton[units] to read the standing stock of

periphyton or benthic algae;
BASS 2.3 Sept 24, 2012 56

! bphytoplankton[units] to read the standing stock of

phytoplankton;

! bzooplankton[units] to read the standing stock of

zooplankton;

! cbenthos[units](ChemicalName) to read the

concentration of ChemicalName in benthic invertebrates;

! cinsects[units](ChemicalName) to read the concentration

of ChemicalName in incidental terrestrial insects;

! cperiphyton[units](ChemicalName) to read the

concentration of ChemicalName in periphyton;

! cphytoplankton[units](ChemicalName) to read the

concentration of ChemicalName in phytoplankton;

! csediment[units](ChemicalName) to read the sediment

concentration of ChemicalName;

! cwater[units](ChemicalName) to read the unbound,

aqueous concentration of ChemicalName;

! czooplankton[units](ChemicalName) to read the wholebody concentration of ChemicalName in zooplankton;

! depth[units] to read water depth;

! hsi_feeding[-](FishName) to read the feeding/growth

HSI for the fish species FishName;

! hsi_recruitment[-](FishName) to read the recruitment/

spawning HSI for the fish species FishName;

! hsi_survival[-](FishName) to read the dispersal/nonpredatory mortality HSI for the fish species FishName;

! temperature[units] to read ambient water temperature;

! wbenthos[units] to read the mean body weight of benthic

invertebrates;

! winsects[units] to read the mean body weight of

incidental terrestrial insects;

! wperiphyton[units] to read the mean body weight of

periphyton or benthic algae;

! wphytoplankton[units] to read the mean body weight of

phytoplankton;

! wzooplankton[units] to read the mean body weight of

zooplankton.

If column names other than those listed above are specified, BASS

simply ignores them. Data records can be continued by

appending an ampersand (&) to the line, e.g., the following data

records are equivalent.

vi,1 vi,2 ... vi,j vi,j+1 ... vi,MV

vi,1 vi,2 ... vi,j &

vi,j+1 vi,j+2 ... vi,MV

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

calculates the exposure conditions between specified time points

by simple linear interpolation.

4.5. BASS Include File Structure

As mentioned in Section 4.1, BASS’s input processing routines

allow a BASS project file to be specified using include files of

related parameters. This capability is the cornerstone upon which

the BASS GUI has been developed.

In order to select an appropriate project / include file hierarchy

for implementation in the BASS GUI, careful consideration was

given to the perceived needs of researchers and environmental

regulators who would routinely analyze and evaluate similar

scenarios that might differ either in terms of the chemical

exposures of interest or in terms of the communities of concern.

For example, the USEPA Office of Pesticide Programs routinely

evaluates different pesticides for registration based on their

expected fate and effects in series of canonical aquatic habitats

/ ecosystems. Similarly, the USEPA Office of Pollution

Prevention and Toxics evaluates the pre-manufacturing

registration of industrial chemicals in much the same way. These

considerations suggested that a practical working BASS project /

include file hierarchy should be structured as follows:

! All data specifying the bioenergetic, compositional, and

morphological parameters for a specific fish species that

can be considered to be independent of the particular

community in which the fish resides, should be contained

within a single include file that is assigned the reserved

extension FSH.

! All data specifying the structure and function of a

particular fish community should be contained within a

single include file that is assigned the reserved extension

CMM. These files should use the aforementioned *.FSH
BASS 2.3 Sept 24, 2012 57

files as include files intervened with the necessary fish

commands that are specific to the community of interest.

In general, these community-specific fish commands

define each species’ 1) the dietary composition, 2) initial

ages, body weights, population densities, and chemical

residues, 3) habitat multipliers, 4) any desired fishery

parameters, and 5) any fish commands contained within

the specified *.FSH files that the user wants to have

superceded or updated.

! All data specifying the physico-chemical properties for a

specific chemical of concern should be contained within

a single include file that is assigned the reserved extension

PRP.

! All data specifying a chemical exposure scenario should

be contained within a single include file that is assigned

the reserved extension CHM. These files should use the

aforementioned *.PRP files intervened with the necessary

chemical commands needed to specify each chemical’s 1)

aqueous concentration, 2) dietary exposures via benthos,

insects, periphyton, phytoplankton, and zooplankton, 3)

effects concentrations for specific fish, and 4) any relevant

rates of biotransformation by specific fish.

! Lastly, all BASS project files should use the

aforementioned *.CMM files and *.CHM files to specify the

fish community and the chemical exposures of concern.

All such project files will be assigned the reserved

extension PRJ.

Based on these considerations, the general structure of a BASS

project file is as follows:

! file: name.prj

! notes: general structure of a BASS project file

!

/ SIMULATION_CONTROL

/ HEADER <string>

/ MONTH_T0 <string>

/ LENGTH_OF_SIMULATION "[year]

/ TEMPERATURE temp[celsius] = fnc

/ WATER_LEVEL depth[meter] = fnc

! specify chemical exposures (if any)

#include ‘exposures.chm’

! specify fish community

#include ‘community.cmm’

/ END

The chemical exposure scenario file EXPOSURES.CHM specified

in this project file has the following general form

! file: exposures.chm

! notes: general structure of a chemical

! exposure scenario file

!

! specify physico-chemical parameters

#include ‘chemical_1.prp’

/ EXPOSURE cwater[ppm] = fnc; &

cbenthos[ppm] = fnc; &

cinsects[ppm] = fnc; &

cperiphyton[ppm] = fnc; &

cphytoplankton[ppm] = fnc; &

czooplankton[ppm] = fnc

/ NONFISH_BCF &

bcf[-](benthos) = fnc; &

bcf[-](periphyton) = fnc; &

bcf[-](phytoplankton) = fnc; &

bcf[-](zooplankton) = fnc

/ LETHALITY &

lc50[units](fish_1) = fnc; &

lc50[units](fish_2) = fnc

/ METABOLISM &

bt[units](fish_1,chem_n) = fnc; &

bt[units](fish_2,chem_n) = fnc

! repeat above chemical data block as needed

!

! end exposures.chm

The general structure of the chemical property file

CHEMICAL_1.PRP specified in the above exposure scenario file is

! file: chemical_1.prp

! notes: general structure of a chemical

! property 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 chemical_1.prp

The community file COMMUNITY.CMM specified for the above

project file has the following general form

! file: community.cmm

! notes: general structure of a community file

!

#include ‘fish_1.fsh’

/ ECOLOGICAL_PARAMETERS &

diet("<x[units]<$)={benthos=",ÿ,fish_n=$,ÿ}; &

diet("<x[units]<$)={benthos=",ÿ,fish_n=$,ÿ}; &

diet("<x[units]<$)={benthos=",ÿ,fish_n=$,ÿ}; &

diet("<x[units]<$)={benthos=",ÿ,fish_n=$,ÿ}; &

nm[-]="*b($:()*sg_mu[-]; &

ast_yoy[-]=f(bb[-]=", size=$, pop=()

/ INITIAL_CONDITIONS &

age[yr]={",ÿ,$}; &

wt[g(ww)]={",ÿ,$}; &

pop[fish/ha]={",ÿ,$}

/ FISHERY_PARAMETERS &

stocking[fish/ha](age[yr]=",size[g(ww)]=$,&

season=s1,frequency=s2)=(; &

harvest[1/yr]("<l[cm]<$,season_string)=(

/ HABITAT_PARAMETERS &

hsi_feeding[-]=fnc; &

hsi_recruitment[-]=fnc; &

hsi_survival[-]=fnc

! repeat above fish data block as needed

!

! specify nonfish compartments/forcing functions

/ BENTHOS &
BASS 2.3 Sept 24, 2012 58

initial_biomass[units]="; &

mean_weight[g(dw)]=fnc; &

ingestion[g(dw)/day]= "*w[g(dw)]^$*h((,t1,t2); &

respiration[g(dw)/day]="*w[g(dw)]^$*h((,t1,t2)

/ TERRESTRIAL_INSECTS biomass[units]=fnc

!

! end community.cmm

The general structure of the fish parameter file FISH_1.FSH

specified in the above community file is

! file: fish_1.fsh

! notes: general structure of a fish file

!

/ COMMON_NAME <string>

/ SPECIES <string>

/ AGE_CLASS_DURATION <string>

/ SPAWNING_PERIOD <string>

/ FEEDING_OPTIONS &

allometric("<x[units]<$); &

clearance("<x[units]<$); &

holling("<x[units]<$); &

linear("<x[units]<$)

/ COMPOSITIONAL_PARAMETERS &

pa[-]="*pl[-]+$; &

pl[-]="*w[g(ww)]^$

/ ECOLOGICAL_PARAMETERS &

yoy[g(ww)]="; wt_max[g(ww)]="; mls[yr]="; &

tl_r0[cm]="; rbi[-]="; &

lp[cm]="*l[cm]+$; &

lp_max[cm]="*l[cm]+$; &

lp_min[cm]="*l[cm]+$; &

sg_mu[1/d]="*w[g(ww)]^$; &

wl[g(ww)]="*l[cm]^$

/ MORPHOMETRIC_PARAMETERS &

ga[cm^2]="*w[g(ww)]^$; &

id[cm]="*w[g(ww)]^$; &

ld[cm]="*w[g(ww)]^$; &

ll[cm]="*w[g(ww)]^$

/ PHYSIOLOGICAL_PARAMETERS &

ae_fish[-]="; ae_invert[-]="; ae_plant[-]="; &

rq[-]="; rt:std[-]="; sda:in[-]="; &

ge[g(dw)/d]="*g[g(ww)]^$*h((,t1,t2); &

mf[l/d]="*w[g(ww)]^$*h((,t1,t2); &

mi[g(dw)/d]="*w[g(ww)]^$*h((,t1,t2); &

sg[1/d]="*w[g(ww)]^$*h((,t1,t2); &

sm[g(dw)]="*w[g(ww)]^$*h((,t1,t2); &

so[mg(o2)/h]="*w[g(ww)]^$*h((,t1,t2); &

st[minute]="*w[g(ww)]^$*h((,t1,t2)

! end fish_1.fsh

In addition to the file hierarchy outlined above, it was also

concluded that the BASS software and GUI should operate within

a fixed directory structure that would accommodate both projectspecific include files and canonical include files that could be

copied and edited as needed for new applications. Consequently,

the BASS installation software creates the directory structure

shown below

C:\BASS

|-- BASS_V23.EXE

|

|-- BASS GUI executables and DLLs

|

|-- \FISH -- *.FSH

|

|-- \COMMUNITY -- *.CMM

|

|-- \PROPERTY -- *.PRP

|

|-- \PROJECTS -- \project1 -- *.PRJ

| *.CHM

| *.DAT

|

\project2

|

!

The \FISH subdirectory contains canonical versions of the *.FSH

files that specify the bioenergetic, compositional, ecological, and

morphological parameters of individual fish species and that are

used as include files for constructing fish community files.

The \COMMUNITY subdirectory contains canonical versions of the

*.CMM files that specify the composition, trophic structure, and

initial conditions for the community’s fishes as well as any

desired fishery and habitat suitability parameters.

The \PROPERTY subdirectory contains canonical versions of the

*.PRP files that specify the physico-chemical properties of

individual chemicals and that are used as include files for

chemical exposure files.

The \PROJECTS directory contains subdirectories that are created

by the user for a particular model application. In general, each

application should be assigned its own subdirectory. Three types

of BASS data files will generally reside in each PROJECTS folder.

These file types are: 1) *.PRJ files that define the desired

application and any desired variants of the application, 2) *.CHM

files that specify chemical exposures and properties, and 3)

*.DAT files that specify chemical exposures, habitat suitability

multipliers, nonfish standing stocks, water temperature, and

water depth when these parameters are supplied by the “file”

option. A project subdirectory can also contain local copies of

either *.FSH, *.CMM, or *.PRP files that have been created or

modified for a particular project. Such files may have been

created from scratch or may have been constructed from

canonical files residing in the \FISH, \COMMUNITY, or \PROPERTY

subdirectories.

BASS’s input subroutines process project files assuming that the

paths of all specified include files are relative to the project file

that is currently being read. Therefore, in the case of *.FSH,

*.CMM, or *.PRP files, BASS initially attempts to find these include

files in the current project file’s subdirectory. If these files cannot

be found in the current subdirectory, BASS uses the extension of
BASS 2.3 Sept 24, 2012 59

the specified include files to search the \FISH, \COMMUNITY, or

\PROPERTY subdirectories. This prioritized input processing

means that a user can specify a canonical *.CMM file that uses

both canonical and local *.FSH files.

4.6. Output Files Generated by BASS

BASS generates the following four types of output files

! an output file that summarizes the user’s input parameters,

any input errors detected by BASS, and any warnings / errors

encountered during an actual simulation. This file has the

same name of the executed project file with extension

“MSG”. For example, when BASS executes the project file

INPUT.PRJ, the message file INPUT.MSG is generated. If this

message file already exists, it is silently overwritten.

! an output file that tabulates selected results of the

simulation. Tabulated summaries include: 1) annual

bioenergetic fluxes and growth statistics (i.e., mean body

weight, mean growth rate) of individual fish by species and

age class, 2) annual bioaccumulation fluxes and statistics

(i.e., mean whole-body concentrations, BAF, and BMF) of

individual fish by species and age class, and 3) annual

community fluxes and statistics (i.e., mean population

densities and biomasses) of each fish species by age class.

This file has the same name of the executed project file with

extension “BSS”. For example, when BASS executes the

project file INPUT.PRJ, the output file INPUT.BSS is

generated. If this file already exists, it is silently overwritten.

! a Post-script file that contains the plots that were requested

by the user using the commands /ANNUAL_PLOTS and

/SUMMARY_PLOTS. This file has the same name of the

executed project file with extension “PS”. For example,

when BASS executes the project file INPUT.PRJ, the plot file

INPUT.PS is generated. If this file already exists, it is silently

overwritten.

! a XML file that outputs daily values of community state

variables as well as integrated annual flow summaries and

annual means for selected state variables. Users can import

this file into the BASS Output Analyzer to generate their own

custom plots and tables.

4.7. Command Line Options

To run a BASS simulation that is specified by the project file

INPUT.PRJ, BASS can be invoked either from the BASS GUI or

using the UNIX like command line

C:\BASS23> bass_V23 -i input.prj

Although the “-i filename” option is the only required command

line option, the following additional options are available

-a => enable default accelerated YOY self-thinning;

-c => report distribution of cpu time in major subroutines;

-e => tabulate realized monthly diets for elective feeding;

-echo => echo input commands;

-f => turn off fishing;

-h => print this help list and stop (also see -?);

-l => turn off lethal effects;

-m => enable monthly spawning for species with annual age

classes;

-mba => output mass balance analysis with annual

summaries;

-n => internally calculate rate-based BCF for nonfish;

-p => turn on messages associated with feeding and predation;

-s => turn off fish stocking;

-t => run test of BASS integrators and stop;

-tp => report trophic positions;

-tte => report net trophic transfer efficiencies;

-t22 => output BASS v2.2 tables;

-w => write input data no execution attempted;

-xml number => specify daily interval for generating XML

output (DEFAULT=1)

-z => output ending vectors for age, weight, density, and cfish

(SEE *.BSS)

-? => print this help list and stop (also see -h)

For example, the command line

C:\BASS22> bass_V23 -i input.prj -l -c

will execute the project file INPUT.PRJ without simulating acute

or chronic chemical lethality and report the distribution of cpu

time spent within various key BASS subroutines.
BASS 2.3 Sept 24, 2012 60

Prefix Name Conversion

Factor

atto 10-18

centi 10-02

deca 10+01

deci 10-01

exa 10+18

femto 10-15

giga 10+09

hecto 10+02

kilo 10+03

mega 10+06

micro 10-06

milli 10-03

myria 10+04

nano 10-09

peta 10+15

pico 10-12

tera 10+12

Table 4.1 Valid Unit Prefixes.
BASS 2.3 Sept 24, 2012 61

Unit Name Conversion

Factor to SI

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

Table 4.2 Valid Unit Names for Length, Area, Volume, Mass, Time, and Energy. This list is not exhaustive

and summarizes only commonly used unit names that BASS’s units conversion program recognizes.
BASS 2.3 Sept 24, 2012 62

Unit Name Conversion

Factor to SI

Metre Kg Second Description

fish n.a. 0 0 0 treated as an amount as is mole

individuals n.a. 0 0 0 treated as an amount as is mole

inds n.a. 0 0 0 treated as an amount as is mole

ha 1.000×10-4 2 0 0 hectare

lamellae n.a. 0 0 0 treated as an amount as is mole

g(dw) 242.5 0 1 0 used for wet-to-dry conversions

kg(dw) 0.2425 0 1 0 used for wet-to-dry conversions

mg(dw) 2.425×105

0 1 0 used for wet-to-dry conversions

ug(dw) 2.425×1080 1 0 used for wet-to-dry conversions

g(ww) 1.0×103

0 1 0 used for wet-to-dry conversions

kg(ww) 1.0 0 1 0 used for wet-to-dry conversions

mg(ww) 1.0×106

0 1 0 used for wet-to-dry conversions

ug(ww) 1.0×109

0 1 0 used for wet-to-dry conversions

g(O2) 7.3718×10-5 2 1 -2 gram of oxygen 

mg(O2) 7.3718×10-2 2 1 -2 milligram of oxygen

ug(O2) 7.3718×10 2 1 -2 microgram of oxygen

kcal 2.388×10-4 2 1 -2 kilocalorie

ul(O2) 5.1603×10 2 1 -2 microliter oxygen STP = micromole

ml(O2) 5.1603×10-2 2 1 -2 milliliter oxygen STP = millimole

l(O2) 5.1603×10-5 2 1 -2 22.4 liters STP = mole

umol(O2) 2.3037 2 1 -2 micromole of oxygen

mmol(O2) 2.3037×10-3 2 1 -2 millimole of oxygen

mol(O2) 2.3037×10-6 2 1 -2 mole of oxygen

Notes:

1. For units conversions, a fish’s kilogram wet weight is treated as its SI weight. Therefore, kg(ww)/kg, g(ww)/g, and

mg(ww)/mg

2. For units conversion, all units associated with oxygen consumption are treated dimensionally as joules.

Table 4.3 Valid ecological, morphometric, and physiological units. BASS uses these units to convert user-specified units for

lamellar density, initial populations densities, fish body weights, and oxygen consumption to standard model units.
BASS 2.3 Sept 24, 2012 63

5. BASS Model Software and Graphical User Interface

5.1. Software Overview

The BASS v2.3 model and Graphical User’s Interface (GUI)

software are provided via two downloads from the USEPA

Center for Exposure Assessment Modeling (CEAM) website

(http://www.epa.gov/ceampubl/). These downloads are:

1. Install_BASS_V23.exe: An InstallShield executable file

that installs the BASS model software, user’s manual,

distribution examples, GUI, and Output Analyzer for Windows

NT/2K/XP.

2. Install_BASS_V23_ModelOnly.exe: A WinZip Selfextracting file that installs only the BASS model software,

user’s manual, and distribution examples for all

DOS/Windows systems.

The installation program Install_BASSV23.exe creates and

installs the BASS model software, GUI, and Output Analyzer

into the installation directory shown below

PATH \BASS_V23

BASS_V23.EXE

BASS_V23_ABSOFT.EXE

BASS_V23_INTEL.EXE

BASS_V23_LAHEY.EXE

BASS_FILES.EXE

BASS_FISH_CODES.DB

BASS_FISH_DATA.DB

DISDLL.DLL

CLEAN_EXAMPLES.BAT

RUN_EXAMPLES.BAT

\BASS_GUI

\COMMUNITY

\DOCUMENTS

BASS_MANUAL.PDF

BASS_DATA_SUPPLEMENT.PDF

\FISH

\PROPERTY

\PROJECTS

\EX_EVERGLADES_CANAL

\EX_EVERGLADES_CANAL_FISHING

\EX_EVERGLADES_CANAL_HG

\EX_EVERGLADES_CANAL_HG_LESLIE

\EX_EVERGLADES_HG_HOLES

\EX_EVERGLADES_HG_MARSH

\EX_L_HARTWELL

\EX_L_HARTWELL_PCB

\EX_L_HARTWELL_PCB_TRANS

\EX_L_ONTARIO_PCB

\EX_SE_FARM_POND

\SOURCE_CODE

\BASS_ABSOFT_90

\BASS_INTEL_110

\BASS_LAHEY_57F

\BASS_FILES

where PATH = C:\PROGRAM FILES\BASS unless changed by the

user. The contents of this directory are:

1. BASS_V23_ABSOFT.EXE is the most current BASS model

executable that has been created with the Absoft version 9.0

Fortran 95 compiler using the standard 32-byte Windows XP

operating system. This executable will run approximately 5

times faster than the Lahey-Fujitsu executable below. Note,

however, that there are minor compiler bugs associated with

this executable that apparently do not affect its computational

accuracy. This executable is used as the default BASS software

executable BASS_V23.EXE. Also see Section 7.2.7.

2. BASS_V23_INTEL.EXE is the most current BASS model

executable that has been created with the Intel version 11.0

Fortran 95 compiler using the standard 32-byte Windows XP

operating system. This executable will run approximately 9

times faster than the Lahey-Fujitsu executable below. Also see

Section 7.2.7.

3. BASS_V23_LAHEY.EXE is the most current BASS model

executable that has been created with the Lahey-Fujitsu

Fortran 95 version 7.1 compiler. Also see Section 7.2.7.

4. DISDLL.DLL is a dynamic link library needed to execute

the DISLIN graphing software.

5. \BASS_GUI contains the executables and associated library

and support files for the BASS GUI and Output Analyzer.

6. \BASS_FILES contains the BASS fish file and community

file generator, described in Section 5.6, with its associated

database files and source code.

7. \COMMUNITY is the folder designed to be a repository of

community files (*.CMM) that the user wishes to save as a

canonical library for the construction of future BASS

projects. Although this folder is empty, it must be present

for the BASS software to function correctly. See Section 4.5

(page 58).

8. \DOCUMENTS\BASS_MANUAL.WPD is the current BASS

User's Manual in WordPerfect format.

9. \DOCUMENTS\BASS_MANUAL.PDF is the current BASS

User's Manual in PDF format.

10. \DOCUMENTS\BASS_V23_DATA_SUPPLEMENT.WPD is

the current compendium of fish data that can be used to
BASS 2.3 Sept 24, 2012 64

parameterize BASS in WordPerfect format.

11. \DOCUMENTS\BASS_V23_DATA_SUPPLEMENT.PDF is the

current compendium of fish data that can be used to

parameterize BASS in PDF format.

12. \FISH is the folder designed to be a repository of fish

files (*.FSH) that the user wishes to save as a canonical

library for the construction of future BASS projects. Although

this folder is empty, it must be present for the BASS software

to function correctly. See Section 4.5 (page 58).

13. \PROJECTS contains the BASS v2.3 distribution example

projects that are described in Section 6.1 (page 77). All of

these examples can executed by double clicking on the DOS

batch file RUN_EXAMPLES.BAT.

14. \PROPERTY is the folder designed to be a repository of

chemical property files (*.PRP) that the user wishes to save

as a canonical library for the construction of future BASS

projects. This folder must be present for the BASS software

to function correctly, and it is initially populated with

chemical property files used by the BASS distribution

examples. This folder also contains the folder

\BARBER_2003 which contains chemical property files for

the chemicals analyzed in Barber’s review paper of gill

exchange models (Barber, M.C. 2003. Environ. Toxicol.

Chem. 22: 1963-1992). See Section 4.5 (page 58).

15. \SOURCE_CODE contains the current Fortran 95 source

code for BASS v2.3. This folder is included for those users

who would like to review the BASS code or to adapt it for

other purposes.

The installation program Install_BASS_V23_ModelOnly.exe

extracts a copy of the aforementioned installation directory

BASS_V23 that does not include the \BASS GUI subdirectory.

5.2. Installation Notes

The BASS model and GUI v2.3 has been installed and

successfully tested on systems running Win2000, WinNT4.0

and WinXP operating systems with various configurations of

each. If users are running NT, 2000, or XP operating systems,

they must have Administrator privileges on their systems in

order to install the BASS model and GUI software.

5.3. Installation Procedures

For complete installation procedures users are referred to the

BASS installation readme file at the USEPA Center for

Exposure Assessment Modeling (CEAM) website

(http://www.epa.gov/ceampubl/).

5.4. BASS GUI Operation

The BASS GUI has been designed to emulate Microsoft’s

Windows Explorer in much of its form and function. After the

BASS GUI is opened, the first window that users see is the

GUI’s Current BASS Directory (see Figure 5.1). If this

window is inadvertently closed, it can be reopened using the

View button found on the toolbar of the GUI’s host window

BASS version 2.2 ....

Figure 5.1 BASS GUI Current BASS Directory window.

Double-clicking on a folder’s name, icon, or directory node

expands or collapses the folder’s contents into or out of the

user’s view, respectively. Double-clicking on a file name

opens the file with one of six GUI file editors based on the

selected file’s extension. The GUI’s file editors can also be

invoked by:

1. Left-clicking on the file and pressing the Enter key.

2. Right-clicking on the file and then left-clicking on Edit.

3. Left-clicking on the file and left-clicking on the Edit

icon found on the Current BASS Directory toolbar.

When users are editing a BASS project file that contains include

files, users can also open file editors for those include files by

4. Left-clicking on the desired include command and then
BASS 2.3 Sept 24, 2012 65

left-clicking on the resulting activated Open Include

File link (see Section 5.4.1).

BASS output files (i.e., *.BSS, *.MSG, and *.XML), are not

displayed in the Current BASS Directory window. These files,

if they exist, are accessed via the project files (*.PRJ) that

generated them.

BASS message files (*.MSG) and simulation summary files

(*.BSS) can be reviewed by right-clicking on the relevant

project file and then left-clicking on View Project Message

File or View BSS File, respectively. These files can also be

reviewed by left-clicking on the desired project file and then

left-clicking on the arrow of the File Viewing icon 

found on the Current BASS Directory toolbar. The File

Viewing icon has an associated drop-down selection that

enables users to specify which output file type is to be viewed.

If the File Viewing icon is left-clicked directly, the project’s

message file is opened by default.

BASS XML files can be loaded into the BASS Output Analyzer

either by right-clicking on the relevant project file and then

left-clicking on View Output Analyzer or by left-clicking on

the desired project file and then left-clicking on the Plotting

icon found on the Current BASS Directory toolbar.

BASS project files are executed either by right-clicking on the

desired project file and then left-clicking on Run Project or by

left-clicking on the desired project file and then left-clicking

on the arrow of the Execution icon on the Current BASS

Directory toolbar. Like the File Viewing icon, the Execution

icon has an associated drop-down selection that enables users

to specify command line options as described in Section 4.7

(page 59). When a project file is being executed, all other GUI

functions are unavailable until the simulation is completed.

BASS project files can be checked for their syntax and data

completeness before attempting execution either by rightclicking on the desired project file and then left-clicking on

Validate Project or by left-clicking on the desired project file

and then left-clicking on the Validate Project icon on the

Current BASS Directory toolbar. If the project file has syntax

errors or missing input data, the GUI’s Event Viewer will

automatically open and display validation status of the project

as well as associated errors and warnings. Most users,

however, will find it easier to review these errors by opening

the project’s MSG file, as outlined previously, and search for

the phrase “ERROR:” to determine the needed corrective

actions.

5.4.1. BASS File Editors

All six GUI file editors have the same essential format and

function as displayed in Figure 5.2. Commands, include files,

and comment blocks contained within the file being edited are

displayed in abbreviated form and in order of their appearance

within the Elements of This File box. The full details of these

elements can be viewed individually within the Element Value

box or as they appear within the file by left-clicking on the

Show Text View toggle button. Elements can be edited by

either double-clicking on the element name or by left-clicking

on the element and then left-clicking on the Open Editor...

button.

Figure 5.2 General structure of BASS GUI file editors.

The position of elements can be changed by using the Move

Up and Move Down buttons. Existing elements can be

removed and new elements added by using the Remove button

and Insert Command box, respectively. When elements are

either added, removed, or reordered, however, users must first

left-click on the Apply button before opening any GUI

command editor. The Apply button is also used to save

editorial changes at any time during an editing session.

Because the typical Close “X” button has been disabled on all

GUI file editors, users can exit GUI file editors only by using

the OK and Cancel buttons. These buttons either save or

cancel any editorial changes since the last invocation of the

Apply button. This GUI behavior is designed to preserve the

integrity of the GUI’s Document Object Model (DOM).
BASS 2.3 Sept 24, 2012 66

Figure 5.3 displays the structure of the BASS GUI project file

editor. This editor differs from the GUI’s other five file editors

in two ways. First, this editor explicitly identifies all include

files that will be used by the project. Secondly, any include file

that is directly referenced by the project file can be opened and

edited by left-clicking on the Open Include File hyperlink that

appears below the Element Value box whenever an include

statement is highlighted in the Elements of This File box.

Figure 5.3 Structure of BASS GUI project file editor.

5.4.2. BASS Command Editors

GUI command editors are opened from GUI file editors as

outlined in Section 5.4.1. In terms of their appearance and

functionality, there are 17 basic command editor types that are

described in the following:

! Simple String Editors that edit the commands /CHEMICAL,

/COMMON_NAME, /HEADER, /SPECIES, and include file

specifications (i.e., #INCLUDES . . . ). See Figure 5.4.

! Simple String Editor with pull-down selection that edits

the commands /AGE_CLASS_DURATION and /MONTH_T0.

See Figure 5.5.

! Numeric Editor with units that edits the command

/LENGTH_OF_SIMULATION. See Figure 5.6.

! Numeric Editor without units that edits the commands

/ANNUAL_OUTPUTS, /LOG_AC, /LOG_KB1, /LOG_KB2,

/LOG_P, /MELTING_POINT, /MOLAR_VOLUME, and

/MOLAR_WEIGHT. See Figure 5.7.

! Forcing Function Editor that edits the commands /BIOTA,

/EXPOSURE, /HABITAT_PARAMETERS, /TEMPERATURE, and

/WATER_LEVEL. See Figure 5.8.

! Feeding Model Editor that edits the command

/FEEDING_OPTIONS. See Figure 5.9.

! Compositional and Morphometric Editor that edits the

commands /COMPOSITIONAL_PARAMETERS and

/MORPHOMETRIC_PARAMETERS. See Figure 5.10.

! Ecological Editor that edits the command

/ECOLOGICAL_PARAMETERS. See Figure 5.11 and Figure

5.12.

! Physiological and Growth Editor that edits the command

/PHYSIOLOGICAL_PARAMETERS. See Figure 5.13.

! Cohort Initial Conditions Editor that edits the command

/INITIAL_CONDITIONS. See Figure 5.14.

! Spawning Period Editor that edits the command

/SPAWNING_PERIOD See Figure 5.15.

! Fishery Editor that edits the command

/FISHERY_PARAMETERS. See Figure 5.16.

! Nonfish Biotic Editor that edits the commands /BENTHOS,

/PERIPHYTON, /PHYTOPLANKTON, /TERRESTRIAL_INSECTS,

and /ZOOPLANKTON. See Figure 5.17 and Figure 5.18.

! Nonfish BCF Editor that edits the command

/NONFISH_BCF. See Figure 5.19.

! Chemical Metabolism Editor that edits the command

/METABOLISM. See Figure 5.20.

! Chemical Toxicity Editor that edits the command

/LETHALITY. See Figure 5.21.

! Plot Selection Editor that edits the commands

/ANNUAL_PLOTS and /SUMMARY_PLOTS. See Figure 5.22.

As noted with the GUI file editors, the typical Close “X”

button has been disabled on all GUI command editors. Users

can only exit or close a command editor by using the OK and

Cancel buttons. These buttons either save or cancel any
BASS 2.3 Sept 24, 2012 67

editorial changes since the editor was opened. This GUI

behavior is designed to preserve the integrity of the GUI’s

Document Object Model (DOM).

5.4.3. Special Function Editors

In addition to the file and command editors described in the

previous section, the BASS GUI has two special function

editors, i.e.,

! Comment Block Editor that is used to insert comment

blocks before or after BASS commands, as opposed to endof-line comments associated with the individual options of

BASS commands. See Figure 5.23.

! Time Series Data Editor for editing external data files that

are specified as file functions (e.g., /BIOTA, /EXPOSURE,

/HABITAT_PARAMETERS, /TEMPERATURE, and

/WATER_LEVEL). See Figure 5.24.

5.4.4. File and Folder Operations

Using the GUI’s Current BASS Directory window, users can

create new files and project folders either from scratch or from

existing files and project folders.

To create a BASS project or include file from scratch, users

must first left-click on the subdirectory (i.e., \COMMUNIY,

\FISH, or \PROPERTY) or project folder where the file is to

be created. The user then must left-click on the drop-down

arrow head of the Add New File icon . When the Add

New File drop-down menu appears, the user must left-click on

the desired file type to be created. Finally, after the new file

appears in the Current BASS Directory window, the user must

complete the naming of the new file. New project folders can

be created following these same steps.

Users can create a file from an existing file by

1. Left-clicking on the desired file and then left-clicking

on the Copy icon .

2. Left-clicking on the desired destination folder or

subdirectory and left-clicking on the Paste icon .

Users can also create a new file from an existing file by

1. Right-clicking on the file to be copied and then leftclicking on Copy.

2. Right-clicking on the destination folder or subdirectory

and then left-clicking on Paste.

Lastly users can create a new file from an existing file by

1. Left-clicking on the file to be copied and then pressing

CTRL-c.

2. Left-clicking on the destination folder or subdirectory

and then pressing CTRL-v

New project folders can be created from existing projects

using the same procedures.

5.5. The BASS Output Analyzer

The BASS Output Analyzer (OA) is a dual purpose postprocessor that enables users to construct customized graphs

and tables. This software can be invoked either from within the

BASS GUI or as a standalone application. Using this software,

users can create two and three-dimensional graphs of any state

variable that is a valid option for the plotting commands

/ANNUAL_PLOTS or /SUMMARY_PLOTS. Unlike the plots

generated by these commands, however, users can generate

plots for only selected species as desired. Additionally, users

can specify arbitrary time periods of interest as well as change

the number of size or age classes that are plotted. The BASS

OA also enables users to create customized versions of the

summary tables that are generated for BSS output files. These

tables can be copied and pasted into either Word or

WordPerfect documents.

When BASS executes a user’s project file, two XML files are

generated for use by the BASS OA. Both of these files reside in

the project folder of the PRJ file that generated them. The first

of these files contains the actual data that the OA will use for

graphing and table construction. This file bears the same name

as its associated project file but possesses the extension XML.

Importantly, it is this file that users must open when using the

BASS OA.

The second XML file generated for any particular BASS

simulation contains general summary statistics of the

simulation and is loaded automatically into the OA when the

aforementioned XML file is opened. The name of this file is

the associated project file’s name appended with the string

_incl.

5.6. Auxiliary BASS Software

To aid users in constructing BASS fish files and community

files, an auxiliary piece of software named BASS_FILES.EXE is

distributed with the BASS model and GUI software. Using a

combination of an internal database of fish growth rates and

two external database files (BASS_FISH_CODES.DB and

BASS_FISH_DATA.DB), this software can generate default FSH
BASS 2.3 Sept 24, 2012 68

files for many North American freshwater fish. Although these

generated FSH files are setup to use BASS’s linear feeding

model, users can easily edit these files to use BASS’s allometric

feeding model or a combination of both. Users can also

construct multiple FSH files and an associated, rudimentary

CMM file for an arbitrary selection of fish using this software.

This software, however, does not have a GUI and must be

executed by the user from a DOS command prompt.

To generate a FSH file for a single species of interest, the user

should open a DOS command prompt window and navigate to

the project folder in which they want the file to be generated.

Assuming that the user’s BASS root directory is C:\BASS_V23,

the DOS command

...>c:\bass_V23\bass_files.exe -f “bluegill” -g

“lepomis macrochirus” -m “January 10” -l 10 -u 33

will generate a fish file for bluegill sunfish whose growth rate

has been calibrated to an annual sinusoidal water temperature

cycle that varies from 10 to 33 Celsius and whose minimum

annual temperature occurs on January 10.

To generate multiple FSH files and an associated CMM file

for an arbitrary selection of fish, the user should again open a

DOS command prompt window and navigate to the project

folder in which the user wants the files to be generated.

Assuming that the user’s BASS root directory is C:\BASS_V23,

the DOS command

...>c:\bass_v23\bass_files.exe -i fishes.dat0

will generate a FSH file for each fish species identified in the

file fishes.dat0 and an associated CMM file. The file

fishes.dat0 must reside in the desired project folder. An

example of the general structure of these input files is

illustrated below

! File:bass_bluegill_catfish.dat0

CMM_FILE_NAME bass_bluegill_catfish.cmm

MONTH_T0 August

COLDEST_DAY January 10

TEMPERATURE_MAXIMUM 30

TEMPERATURE_MINIMUM 10

FISH_START micropterus salmoides

COMMON_NAME largemouth bass

SPAWNING_PERIOD april-may

parameter_option_1; comment/reference

parameter_option_2; comment/reference

!

parameter_option_n; comment/reference

biomass[kg/ha]= number

or density[fish/ha]= number

FISH_END

FISH_START Lepomis macrochirus

COMMON_NAME bluegill sunfish

SPAWNING_PERIOD april-october

parameter_option_1; comment/reference

parameter_option_2; comment/reference

!

parameter_option_n; comment/reference

biomass[kg/ha]= number

or density[fish/ha]= number

FISH_END

FISH_START ictalurus puntatus

COMMON_NAME channel catfish

SPAWNING_PERIOD may-june

parameter_option_1; comment/reference

parameter_option_2; comment/reference

!

parameter_option_n; comment/reference

biomass[kg/ha]= number

or density[fish/ha]= number

FISH_END

where parameter_option_i is any valid option for the BASS fish

commands \COMPOSITIONAL_PARAMETERS,

\ECOLOGICAL_PARAMETERS, \MORPHOLOGICAL_PARAMETERS,

or \PHYSIOLOGICAL_PARAMETERS that the user wants to

supercede the default assignment made by BASS_FILES.EXE.

Most of the FSH files used by the example BASS distribution

projects have been generated using earlier versions of this

software.

Figure 5.4 GUI command editor for simple strings.
BASS 2.3 Sept 24, 2012 69

Figure 5.5 GUI command editor for simple strings with drop-down selection.

Figure 5.6 GUI command editor for numeric data with user specified units.

Figure 5.7 GUI command editor for numeric data fixed units.
BASS 2.3 Sept 24, 2012 70

Figure 5.8 GUI command editor for forcing functions.

Figure 5.9 GUI command editor for feeding model options.

Figure 5.10 GUI command editor for compositional and morphometric parameters.
BASS 2.3 Sept 24, 2012 71

Figure 5.11 GUI command editor for nondiet ecological parameters.

Figure 5.12 GUI command editor for fish diets.
BASS 2.3 Sept 24, 2012 72

Figure 5.13 GUI command editor for physiological parameters.

Figure 5.14 GUI command editor for cohort initial conditions.
BASS 2.3 Sept 24, 2012 73

Figure 5.15 GUI command editor for spawning parameters.

Figure 5.16 GUI command editor for fishery parameters.

Figure 5.17 GUI command editor for nonfish biota as forcing functions.
BASS 2.3 Sept 24, 2012 74

Figure 5.18 GUI command editor for nonfish biota as state variables.

Figure 5.19 GUI command editor for nonfish bioaccumulation factors.

Figure 5.20 GUI command editor for chemical biotransformation parameters.
BASS 2.3 Sept 24, 2012 75

Figure 5.21 GUI command editor for chemical toxicity parameters.

Figure 5.22 GUI command editor for automatic graphing selections.
BASS 2.3 Sept 24, 2012 76

Figure 5.23 GUI Block comment editor.

Figure 5.24 Data file editor for forcing functions specified as files.
BASS 2.3 Sept 24, 2012 77

6. Example Applications

6.1. BASS Software Distribution Examples

Several example projects are provided with the BASS model

software and GUI. Each project resides in its own folder

within the \PROJECTS subdirectory.

The example project EX_EVERGLADES_CANAL simulates the

growth and population dynamics of a canal fish community

in the Florida Everglades, USA using the project file

EVERGLADES_CANAL.PRJ. The principal fish species in these

communities are assumed to be bluegill sunfish (Lepomis

macrochirus), Florida gar (Lepisosteus platyrhincus),

largemouth bass (Micropterus salmoides), mosquito fish

(Gambusia holbrooki), redear sunfish (Lepomis

microlophus), and yellow bullheads (Ameiurus natalis). The

community file EVERGLADES_CANAL.CMM is used to specify

the ecological and physiological parameters and the initial

conditions for these species. Each species’ daily

consumption rate is back-calculated from its expected

growth rate using BASS’s linear feeding option. The

community’s water depth and the standing stocks of benthos,

periphyton, and zooplankton are specified using the data

files EVERGLADES_CANAL_WATER.DAT and

EVERGLADES_NONFISH.DAT, respectively.

The example project EX_EVERGLADES_CANAL_FISHING

simulates the growth and population dynamics of the

aforementioned Everglades canal fish community assuming

that largemouth bass, bluegill sunfish, and redear sunfish are

harvested by fishing. This example’s project file

EVERGLADES_CANAL_FISHING.PRJ uses the modified

community file EVERGLADES_CANAL_FISHING.CMM to

specify the fishes’ ecological and physiological parameters,

initial conditions, and assumed fishing mortalities. The

community’s water depth and non-fish standing stocks are

again specified using the data files

EVERGLADES_CANAL_WATER.DAT and

EVERGLADES_NONFISH.DAT, respectively.

The example project EX_EVERGLADES_CANAL_HG simulates

the bioaccumulation of methylmercury in an Everglades

canal fish community using the project file

EVERGLADES_CANAL_HG.PRJ. This example’s project file

uses the same community file as does the example project

EVERGLADES_CANAL to specify the ecological and

physiological parameters and initial conditions for the

species of interest. The community’s chemical exposures to

methylmercury are provided by the include file

EVERGLADES_MERCURY.CHM that in turn uses the include

file \PROPERTY\METYL_HG.PRP. As before, the community’s

water depth and the non-fish standing stocks are specified

using the data files EVERGLADES_CANAL_WATER.DAT and

EVERGLADES_NONFISH.DAT, respectively.

The example project EX_EVERGLADES_CANAL_HG_LESLIE

simulates the bioaccumulation of methylmercury in an

Everglades canal fish community using BASS’s Leslie matrix

option and the project file

EVERGLADES_CANAL_HG_LESLIE.PRJ. This project’s

ecological and physiological data are provided by the

community file EVERGLADES_CANAL_LESLIE.CMM. Chemical

exposures and properties of methylmercury are provided by

the include file EVERGLADES_MERCURY.CHM that uses the

include file \PROPERTY\METHYL_HG.PRP. Once again, the

community’s water depth and non-fish standing stocks are

provided by the ancillary data files

EVERGLADES_CANAL_WATER.DAT and

EVERGLADES_NONFISH.DAT, respectively.

The example project EX_EVERGLADES_HG_HOLES simulates

the growth, population, and methylmercury dynamics of an

alligator hole fish community in the Florida Everglades,

USA using the project file EVERGLADES_HG_HOLES.PRJ. The

principal fish species in these communities are assumed to

be bluegill sunfish, Florida gar, largemouth bass, least

killifish (Heterandria formosa), mosquito fish, redear

sunfish, spotted sunfish (Lepomis puntatus), warmouth

sunfish (Lepomis gulosus), and yellow bullheads. The

community file EVERGLADES_HG_HOLES.CMM is used to

specify the ecological and physiological parameters and the

initial conditions for these species. Each species’ daily

consumption rate is back-calculated from its expected

growth rate using BASS’s linear feeding option. The

community’s water depth and the standing stocks of benthos,

periphyton, and zooplankton are specified by the project and

community files EVERGLADES_HG_HOLES.PRJ and

EVERGLADES_HG_HOLES.CMM, respectively. Methylmercury

exposures are provided by the include file

EVERGLADES_MERCURY.CHM that uses the property file

\PROPERTY\METHYL_HG.PRP as an include file.

The example project EX_EVERGLADES_HG_MARSH simulates

the growth, population, and methylmercury dynamics of an

open marsh fish community in the Florida Everglades, USA

using the project file EVERGLADES_HG_MARSH.PRJ. The

principal fish species in these communities are assumed to

be bluefin killifish (Lucania goodei), Florida gar, golden top

minnow (Fundulus chrysotus), largemouth bass, least

killifish, mosquito fish, spotted sunfish, warmouth sunfish,

and yellow bullheads. The community file
BASS 2.3 Sept 24, 2012 78

EVERGLADES_HG_MARSH.CMM is used to specify the

ecological and physiological parameters and the initial

conditions for these species. Each species’ daily

consumption rate is back-calculated from its expected

growth rate using BASS’s linear feeding option. The

community’s water depth and the standing stocks of benthos,

periphyton, and zooplankton are specified by the project and

community files EVERGLADES_HG_MARSH.PRJ and

EVERGLADES_HG_MARSH.CMM, respectively. Methylmercury

exposures are provided by the include file

EVERGLADES_MERCURY.CHM that uses the property file

\PROPERTY\METHYL_HG.PRP as an include file.

The example project EX_L_HARTWELL_PCB simulates the

bioaccumulation of tetra-, penta-, hexa-, and hepta-PCB in a

largemouth bass/ gizzard shad/ sunfish/catfish community in

the Twelve Mile Creek arm of Lake Hartwell, SC, USA

using the project file TWELVEMILE_CREEK_PCB.PRJ. The

community’s ecological and physiological parameters and

initial conditions are specified using the community file

TWELVEMILE_CREEK.CMM. Chemical exposures and

properties of tetra-, penta-, hexa-, and hepta-PCBs are

provided by the include files TWELVEMILE_CREEK.CHM

\PROPERTY\PCB_TETRA.PRP, \PROPERTY\PCB_PENTA.PRP,

\PROPERTY\PCB_HEXA.PRP, and \PROPERTY\PCB_HEPTA.PRP,

respectively. This example demonstrates BASS’s ability to

simulate the bioaccumulation of arbitrary mixtures.

The example project EX_L_HARTWELL_PCB_TRANS also

simulates the bioaccumulation of tetra-, penta-, hexa-, and

hepta-PCB in the aforementioned largemouth bass/ gizzard

shad/ sunfish/catfish community of Lake Hartwell, SC,

USA using the project file

TWELVEMILE_CREEK_PCB_TRANS.PRJ. This example,

however, allows for the biotransformation of selected PCB

congeners by selected fish species. The community file

TWELVEMILE_CREEK.CMM is again used to specify the

ecological and physiological parameters and the initial

conditions for this community.

The example project EX_L_ONTARIO_PCB simulates the

bioaccumulation of tetra-, penta-, hexa-, and hepta-PCB in

Lake Ontario salmonids and alewife using BASS’s “fgets”

option and the project file BARBER_ET_AL_1991.PRJ. This

example is the BASS implementation of the FGETS

application published by Barber et al. (1991). Whereas

salmonid feeding is simulated using BASS’s Holling feeding

option, the feeding by alewife is simulated using BASS’s

clearance feeding option. The community file

BARBER_ET_AL_1991.CMM is used to specify the ecological

and physiological parameters and the initial conditions for

this community.

The example project EX_SE_FARM_POND simulates the

growth and population dynamics of a typical southeastern

US farm pond community using the project file

SE_FARM_POND.PRJ. The principal fish species in these

communities are assumed to be largemouth bass

(Micropterus salmoides), bluegill sunfish (Lepomis

macrochirus), redear sunfish (Lepomis microlophus),

redbreast sunfish (Lepomis auritus), channel catfish

(Ictalurus punctatus), and yellow bullheads (Ameiurus

natalis).The community file SE_FARM_POND.CMM is used to

specify the ecological and physiological parameters and the

initial conditions for these species as well as the standing

stocks of benthos, periphyton, and zooplankton. Each

species’ daily consumption rate is back-calculated from its

expected growth rate using BASS’s linear feeding option.

Users should note that for BASS version 2.2 this example

project was incorrectly parameterized for the largemouth

bass. To correct this error and to produce realistic biomass

dynamics, this project has been reformulated using the new

BASS version 2.3 biomass carrying capacity option. See

Section 2.8 regarding Equations (2.96) - (2.98).

6.2. An Analysis of Everglades Mercury

Bioaccumulation

The BASS example project EX_EVERGLADES_CANAL_HG

simulates methyl mercury contamination in a canal fish

community of the Florida Everglades and is constructed as

outlined in Section 4.5. For this BASS application largemouth

bass (Micropterus salmoides), Florida gar (Lepisosteus

platyrhincus), yellow bullheads (Ameiurus natalis), bluegill

sunfish (Lepomis macrochirus), redear sunfish (Lepomis

microlophus), and mosquito fish (Gambusia holbrooki) are

assumed to be the dominant species in the habitats of

interest. The sources of the ecological, morphological, and

physiological parameters used by this example are

documented in its associated FSH files. Turner et al. (1999)

reported the mean biomass of large and small fishes across a

variety of Everglades habitats to be approximately 60 kg wet

wt/ha. Initial standing stocks of the bass, gar, bullheads,

bluegill, redear sunfish, and mosquito fish were assigned to

be 5, 10, 10, 50, 25, and 5 kg wet wt/ha, respectively, for a

total community biomass of 105 kg wet wt/ha. The water

concentration of methylmercury for the simulation was

assigned to be a constant 0.2 ng/L (Stober et al. 1998) and

the BAF’s for benthos and zooplankton were assigned to be

106.09 and 105.90, respectively (Loftus et al. 1998).

At the end of the 10 year simulation, the mean annual

standing stocks of the bass, gar, bullheads, bluegill, redear

sunfish, and Gambusia are 0.867, 1.08, 4.79, 30.0, 35.4, and

2.55 kg wet wt/ha, respectively, for a total community
BASS 2.3 Sept 24, 2012 79

biomass of 74.7 kg wet wt/ha.

The simulated whole-body concentrations of methyl mercury

in these species agree reasonably well with unpublished data

collected by Lange et al. and Loftus et al. (1998). See

Figure 6.1 - Figure 6.6. BASS’s significant over prediction

of the whole-body methylmercury concentrations of redear

sunfish is probably due to the specialized feeding behavior

of this species. In particular, redear sunfish, which are also

known as shellcrackers, often feed almost exclusively on

molluscs that generally have significantly lower

methylmercury concentration than do other benthic

macroinvertebrates. The annual averaged concentrations of

methylmercury in largemouth, gar, bullhead, bluegill, redear

and Gambusia weighted by cohort biomasses were 0.842,

0.822, 0.580, 0.440, 0.513, and 0.180 mg Hg/kg wet wt,

respectively. When weighted by cohort densities, the annual

averaged concentrations of methylmercury in largemouth,

gar, bullhead, bluegill and redear were 0.450, 0.491, 0.332,

0.237, 0.299, and 0.143 mg Hg/kg wet wt, respectively.

Loftus et al. report average whole-body concentrations of

methylmercury in largemouth, gar, bullhead, bluegill,

redear, and Gambusia to be 0.967, 1.16, 0.443-0.755, 0.478,

0.247, and 0.247-0.321 mg Hg/kg wet wt, respectively. The

average body weights of largemouth, gar, bullhead, bluegill,

redear, and Gambusia analyzed by Loftus et al. were 205,

278, 37.5-92.9, 21.8, 73.0, and 0.0602-0.218 g wet wt/fish,

respectively.

As is typically observed under field conditions (Forrester et

al. 1972, Scott and Armstrong 1972, Cross et al. 1973,

Akielaszek and Haines 1981, Watling et al. 1981, Boush and

Thieleke 1983a, b, MacCrimmon et al. 1983, Ueda and

Takeda 1983, Wren and MacCrimmon 1986, Braune 1987,

Luten 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), Figure

6.1 - Figure 6.6 predicts a strong interdependence between

the body sizes of fish and their whole-body mercury

concentrations.
BASS 2.3 Sept 24, 2012 80

Largemouth bass (Micropterus salmoides)

0

0.20.40.60.8

1

1.21.41.61.8

2

0 200 400 600 800 1000 1200 1400 1600 1800 

Wet Weight g(ww)

Total Hg ug/g(ww) 

Figure 6.1 Predicted and observed methylmercury concentrations of largemouth bass in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 81

Florida gar (Lepisosteus platyrhincus)

0

0.5

1

1.5

2

2.5

0 200 400 600 800 1000 1200 

Wet Weight g(ww)

Total Hg ug/g(ww) 

Figure 6.2 Predicted and observed methylmercury concentrations of Florida gar in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 82

Yellow bullhead (Ameiurus natalis)

0

0.20.40.60.8

1

1.21.41.61.8

0 200 400 600 800 1000 

Wet Weight g(ww)

Total Hg ug/g(ww) 

Figure 6.3 Predicted and observed methylmercury concentrations of yellow bullhead in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 83

Bluegill (Lepomis macrochirus)

0

0.20.40.60.8

1

1.21.41.61.8

0 20 40 60 80 100 120 140 160 180 

Wet Weight g

Total Hg ug/g(ww) 

Figure 6.4 Predicted and observed methylmercury concentrations of bluegill sunfish in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 84

Redear sunfish (Lepomis microlophus)

0

0.20.40.60.8

1

1.2

0 50 100 150 200 250 

Wet Weight g(ww)

Total Hg ug/g(ww) 

Figure 6.5 Predicted and observed methylmercury concentrations of redear sunfish in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 85

Mosquiotfish (Gambusia holbrooki)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.1 0.2 0.3 0.4 0.5 

Wet Weight g(ww)

Total Hg ug/g(ww) 

Figure 6.6 Predicted and observed methylmercury concentrations of Gambusia in Florida Everglades canals.
BASS 2.3 Sept 24, 2012 86

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 that has been published in the peer

reviewed literature (Barber et al. 1988, 1991). These

algorithms have been reviewed and compared with other

existing bioaccumulation models to document their scientific

foundation and to verify their predictive performance (see

Barber 2003, 2008). The bioenergetic modeling paradigm that

BASS uses to simulate fish growth has been employed by many

researchers in the peer reviewed literature (Norstrom et al.

1976, Kitchell et al. 1977, Minton and McLean 1982, Stewart

et al. 1983, Thomann and Connolly 1984, Cuenco et al. 1985,

Stewart and Binkowski 1986, Beauchamp et al. 1989, Barber

et al. 1991, Stewart and Ibarra 1991, Lantry and Stewart 1993,

Rand et al. 1993, Roell and Orth 1993, Hartman and Brandt

1995a, Petersen and Ward 1999, Rose et al. 1999, Schaeffer et

al. 1999). Since its construction, FGETS has also been included

in numerous reviews of bioaccumulation models that are

applicable for ecological risk assessments and environmental

management (Barron 1990, Jones et al. 1991, Barnthouse

1992, Chapra and Boyer 1992, Landrum et al. 1992, Olem et

al. 1992, Dixon and Florian 1993, Wurbs 1994, Cowan et al.

1995, Campfens and Mackay 1997, Feijtel et al. 1997,

Deliman and Gerald 1998, Exponent 1998, Howgate 1998,

Vorhees et al. 1998, Wania and Mackay 1999, Bartell et al.

2000, Gobas and Morrison 2000, Mackay and Fraser 2000,

Bartell 2001, Limno-Tech 2002, Exponent 2003, Sood and

Bhagat 2005).

Two criticisms have been lodged against FGETS in the

literature. The first of these is that FGETS assumes or attempts

to prove that the gill exchange of chemicals is more important

than other routes of exchange (Madenjian et al. 1993).

Madenjian et al. (1993) took exception to FGETS predictions

that “excretion of PCB through the gills is an important flux in

the PCB budget of lake trout”. Madenjian et al. claimed that

this result was not supported by any laboratory study on trout

and cited Weininger (1978) as proof that gill excretion was, in

fact, negligible. Nevertheless, Madenjian et al. used a single,

unidentified excretion constant in their model that simply

lumps all excretion pathways (i.e., gill, intestinal, urinary, and

dermal) into one. Thus, what Madenjian et al. are essentially

questioning is not FGETS per se but rather the need to use

thermodynamically based diffusion models for

bioaccumulation in general.

The second criticism is that FGETS is overly complex and

requires too much additional data to parameterize (McKim et

al. 1994, Stow and Carpenter 1994, Jackson 1996). Since

FGETS’s bioenergetic model for fish growth is not significantly

different from those used by several other authors (Norstrom et

al. 1976, Weininger 1978, Thomann and Connolly 1984,

Madenjian et al. 1993, Luk and Brockway 1997), this criticism

is also generally aimed at BASS’s gill exchange model. A

recent review and comparison of gill exchange models,

however, clearly demonstrated that there is more than ample

literature data to parameterize the gill exchange formulations

used by FGETS and BASS (Barber 2003).

7.1.2. How has the model or its algorithms been corroborated

or used?

BASS’s dietary and gill exchange algorithms have been

corroborated by comparing its predicted dietary assimilation

efficiencies and gill uptake and excretion rates to those

published in the peer reviewed literature (Barber et al. 1988,

Barber 2003, 2008). BASS’s dietary exchange algorithms have

also been cited by other researchers to explain results of actual

exposure studies (e.g., Dabrowska et al. 1996, Doi et al. 2000).

For validation of BASS’s bioenergetic growth algorithms, the

reader is referred to Barber et al. (1991) and the examples

herein.

BASS’s predictive performance as an integrated

bioaccumulation model has been corroborated for organic

chemicals by simulations of PCBs dynamics in Lake Ontario

salmonids, various laboratory studies, largemouth bassbluegill-catfish communities of Lake Hartwell / Twelvemile

Creek, SC, and Tennessee stream fishes (Barber et al. 1991,

USEPA 1994, Brockway et al. 1996, Simon 1999, Marchettini

et al. 2001, USEPA 2004). Similarly, Hunt et al. (1992) used

FGETS to model DDT bioaccumulation in caged channel catfish
BASS 2.3 Sept 24, 2012 87

at Superfund Sites. For sulfhydryl binding metals, BASS’s

predictive performance has been corroborated by simulations

of methylmercury bioaccumulation in Florida Everglades fish

communities one of which is presented herein as a typical

BASS application. Murphy (2004) also successfully used BASS

to model and analyze mercury bioaccumulation in the South

River and the South Fork of the Shenandoah River in Virginia.

More recently, BASS was used to estimate lag times of mercury

residues in fish responding to mercury load reductions as part

of ORD’s review of the Agency’s Clean Air Mercury Rule

(CAMR, February 15, 2005). This work was subsequently

incorporated into the Regulatory Impact Analysis (RIA) that

assessed the benefits of atmospheric load reductions to aquatic

ecosystems (USEPA 2005, Knightes et al. 2009).

Several researchers (Lassiter and Hallam 1990, ECOFRAM

Aquatic Effects Subcommittee et al. 1998, ECOFRAM 1999,

Boxall et al. 2001, Boxall et al. 2002, Reinert et al. 2002) have

used BASS’s predecessor, FGETS, to predict acute and chronic

lethality, and the EPA’s Office of Water’s AQUATOX

modeling system uses the FGETS/BASS lethal effects algorithm

as its principal effects module (Park and Clough 2004).

Additionally, the Office of Water has recognized BASS as one

of the leading models available for simulating time dynamic

bioaccumulation for applications when steady-state methods

(e.g., BAFs or BSAFs) are considered insufficient (USEPA

2003). The Commonwealth of Virginia has identified BASS as

an accepted tool for its PCB bioaccumulation assessments

(VDEQ 2005). BASS has also been recommended to the states

of Michigan and Washington as an assessment tool (Exponent

1998, 2003).

Whereas Hallam and Deng (2006) implemented the

FGETS/BASS bioaccumulation framework within sophisticated

McKendrick-von Foerster partial differential equation models

for age-structured populations, Cohen and Cooter (2002a,

2002b) incorporated simpler forms of this framework into

more holistic fate and transport exposure software. Lastly,

Apeti et al. (2005) modified FGETS to simulate metal

bioaccumulation in shellfish.

7.1.3. What is the mathematical sensitivity of the model with

respect to parameters, state variables (initial value problems),

and forcing functions / boundary conditions? What is the

model’s sensitivity to structural changes?

There are four major classes of mathematical sensitivity

regarding a model’s behavior. These are the model’s

sensitivity to parameter changes, forcing functions, initial state

variables, and structural configuration. The first three of these

classes generally are formally defined in terms of the following

partial derivatives

(7.1)

where Xi

 is a state variable of interest; pj is some state

parameter of concern; Zj is some external forcing function; and

Xj(0) is the initial value of some state variable of interest that

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 sensitivity is simply a mathematical characteristic of a

model, model sensitivity in and of itself is neither good nor

bad. Sensitivity is desirable if the real system being modeled is

itself sensitive to the same parameters, forcing functions,

initial state perturbations, and structural changes to which the

model is sensitive. Even though model sensitivity can

contribute to undesirable model uncertainty or prediction

error, it is important to acknowledge that model sensitivity and

uncertainty are not one and the same (Summers et al. 1993,

Wallach and Genard 1998). Model uncertainty, or at least one

of its most common manifestations, is the product of both the

model’s sensitivity to particular components and the statistical

variability associated with those components.

A generalized sensitivity analysis of BASS without explicit

specification of a fish community of concern is undoable.

Furthermore, the results of a sensitivity analysis for one

community generally cannot be extrapolated to other

communities. Issues related to BASS’s sensitivity must be

evaluated on a case by case basis by the users of the software.

Although procedures for enabling users to conduct a variety of

structured sensitivity analyses are currently being developed,

presently the onus of performing such analyses rests with the

user. Users interested in issues and techniques related to model

sensitivity and uncertainty should consult the following papers:

Giersch (1991), Elston (1992), Summers et al. (1993),

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 that

summarizes all the input data that were used for a particular

simulation. This summary includes not only a line by line

summary of the user’s input commands but also a complete
BASS 2.3 Sept 24, 2012 88

summary of all control, chemical and fish parameters that BASS

assigned based on the user’s specified input file(s). The onus is

then on the user to verify that their input data has been

properly processed. If not, the user should report their problem

to the technical contact identified in the BASS user’s guide.

BASS has a series of subroutines that check for the

completeness and consistency of the user’s input data. When

missing or inconsistent data are detected, appropriate error

messages are written to the *.MSG file, and an error code is

set to true. If this error code is true after all the user’s input has

been processed, BASS terminates without attempting further

program execution.

To insure that all program subroutines, functions, and

procedures are transmitting and receiving the correct variables,

all BASS subroutines and functions are called using implicit

interfaces generated by the Absoft, Intel, and Lahey / Fujitsu

Fortran 95 compilers. Subroutines and functions are packaged

together within Fortran 95 modules according to their function

and degree of interaction. The BASS v2.3 software is coded

with one main program PROGRAM BASS_MAIN (see

BASS_PROGRAM.F90) and 33 procedure modules. These

modules are:

! MODULE ADAMS_GEAR - subroutines for performing

EXAMS Adams-Gear integrations (see

EXAMS_ADAM_GEAR.F90).

! MODULE BASS_ALLOC - subroutines for allocating and

reallocating derive type pointers (see BASS_ALLOC.F90).

! MODULE BASS_CHECK - subroutines for checking the

completeness and consistency of user input (see

BASS_CHECK.F90).

! MODULE BASS_DEBUG - subroutines for program

debugging. Used only for program development (see

BASS_DEBUG.F90).

! MODULE BASS_DEFINED - functions for determining

whether program parameters and variables have been

initialized or assigned (see BASS_DEFINED.F90).

! MODULE BASS_EXP - subroutines for calculating

chemical exposures, community forcing functions, and

habitat suitability multipliers (see BASS_EXP.F90).

! MODULE BASS_INI - subroutines for initialization of

program variables (see BASS_INI.F90).

! MODULE BASS_INPUT - subroutines for decoding user

input (see BASS_INPUT.F90).

! MODULE BASS_INT - subroutines for Adams-Gear, Euler,

and Runge-Kutta integrations (see BASS_INT.F90).

! MODULE BASS_INT_LOADER - subroutines for loading

BASS derived type variables into standard integration

vectors (see BASS_INT_LOADER.F90).

! MODULE BASS_IO - subroutines for processing user input

and output (see BASS_IO.F90).

! MODULE BASS_ODE - subroutines for the computational

kernel of the BASS software (see BASS_ODE.F90).

! MODULE BASS_PLOTS - subroutines for generating

output plots for BASS v2.1 and earlier as well as for

code development and maintenance (see

BASS_PLOTS.F90).

! MODULE BASS_TABLES - subroutines for generating

output tables for BASS v2.1 and earlier as well as for

code development and maintenance (see

BASS_TABLES.F90).

! MODULE BASS_WRITE_CSV - subroutines for generating

CSV output files for import into Excel workshets (see

BASS_CSV.F90).

! MODULE BASS_WRITE_XML - subroutines for generating

XML output files for post processing by the BASS GUI

(see BASS_XML.F90).

! MODULE DECODE_FUNCTIONS - subroutines for decoding

constant, linear, and power functions from character

strings (see UTL_DCOD_FNC.F90).

! MODULE DISLIN - implicit interfaces for the DISLIN

graphics subroutines DISLIN.F90).

! MODULE DISLIN_PLOTS - general utility subroutines for

generating 2 and 3-dimensional DISLIN plots (see

UTL_PLOTS.F90).

! MODULE ERROR_MODULE - subroutines for printing

error codes encountered with general utility modules

(see UTL_ERRORS.F90).

! MODULE FILESTUFF - subroutines for parsing file names

and obtaining version numbers or time stamps (see

UTL_FILESTUFF.F90).

! MODULE FLOATING_POINT_COMPARISONS - operators for

testing equality or inequality of variables with explicit

consideration of their computer representation and

spacing characteristics (see UTL_FLOATCMP.F90).

! MODULE GETNUMBERS - subroutines for extracting

numbers from character strings (see

UTL_GETNUMS.F90).

! MODULE IOSUBS - subroutines for assigning, opening,

and closing logical units (see UTL_IOSUBS.F90).

! MODULE MODULO_XFREAD - subroutines for reading

files that contain comments, continuation lines, and

include files (see UTL_XFREAD.F90).

! MODULE MSORT - subroutines for sorting and generating

permutation vectors for lists and vectors (see

UTL_MSORT.F90).

! MODULE MXGETARGS - subroutines for extracting

arguments from a command line (see

UTL_MXGETARGS.F90).

! MODULE REALLOCATER - subroutines for allocating and

reallocating integer, logical, and real pointers (see

UTL_ALLOC.F90).
BASS 2.3 Sept 24, 2012 89

! MODULE SEARCH - subroutines for finding the location

of a key phrase within a sorted list (see

UTL_SEARCH.F90).

! MODULE SEARCH_LISTS - subroutines for finding the

location of a value within a sorted list (see

UTL_SEARCH_LISTS.F90).

! MODULE STRINGS - subroutines for character string

manipulations and printing multiline character text (see

UTL_STRINGS.F90).

! MODULE TABLE_UTILS - subroutines for generating 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

scoping units. Consequently, their component subroutines and

functions explicitly initialize all required internal variables.

This safeguard is intended to prevent inadvertent use of

uninitialized variables. Whenever possible, subroutine and

function arguments are declared with INTENT(IN) and

INTENT(OUT) declarations to preclude unintentional

reassignments.

Although global constants and Fortran parameters are supplied

to program procedures via modules (see question 7.2.3), data

exchanges between program procedures are performed via

formal subroutine / function parameters whenever possible.

The only notable exceptions to this coding policy are modules

that must be used to supply auxiliary parameters to “external”

subroutines that are used as arguments to certain mathematical

subroutines (e.g., root finding subroutines). Working areas

used by BASS are not used for data transfers between internal

and external procedures.

To simplify the construction and maintenance of the formal

parameter lists of many BASS subroutines and functions and to

help prevent the inadvertent transposition of formal

parameters, BASS makes extensive use of derived type data

structures. Each derived type definition is specified within its

own module, and all derive type definition modules are

maintained in a single file (BASS_TYPES.F90.) Derived types

used by BASS v2.3 are:

! MODULE BASS_TYPE_CHEM_PAR - type definition for

chemical parameters

! MODULE BASS_TYPE_DIET_MEAN - type definition used

to summarize average realized diets.

! MODULE BASS_TYPE_DIET_PAR - type definition used by

derived type BASS_TYPE_FOODWEB_PAR

! MODULE BASS_TYPE_DIETS - type definition used for

input processing of user-specified fish diets

! MODULE BASS_TYPE_FISH_INT - type definition for

integrated fish variables and fluxes

! MODULE BASS_TYPE_FISH_PAR - type definition for fish

parameters

! MODULE BASS_TYPE_FISH_VAR - type definition for

current fish variables and fluxes

! MODULE BASS_TYPE_FOODWEB_PAR - type definition

for the decoded user-supplied fish diets and community

trophic structure.

! MODULE BASS_TYPE_HSI_PAR - type definition for fish

habitat multipliers

! MODULE BASS_TYPE_NONFISH_INT - type definition for

integrated nonfish variables and fluxes

! MODULE BASS_TYPE_NONFISH_PAR - type definition for

nonfish parameters

! MODULE BASS_TYPE_NONFISH_VAR - type definition for

current nonfish variables and fluxes

! MODULE BASS_TYPE_PLOT_DATA - type definition for

user-specified plots

! MODULE BASS_TYPE_PREY_ITEMS - type definition used

by derived type BASS_TYPE_FISH_VAR to store a fish’s

currently realized dietary composition

! MODULE BASS_TYPE_QSAR_DATA - type definition for

linked list used during data input

! MODULE BASS_TYPE_QSAR_LINKED_LIST - type

definition for linked list used during data input

! MODULE BASS_TYPE_QSAR_NODE - type definition for

linked list used during data input

! MODULE BASS_TYPE_TROPHIC - definition used for the

calculation of realized diet composition and

consumption

! MODULE BASS_TYPE_VMATRIX_LOGICAL - type

definition for DISLIN graphing matrices

! MODULE BASS_TYPE_VMATRIX_REAL - type definition

for DISLIN graphing matrices

! MODULE BASS_TYPE_ZFUNCTION_PAR - type definition

for user-supplied exposure and forcing functions

A good example of BASS’s use of derived type data structures

is the derived type variable used to store and transfer the

ecological, physiological, and morphometric data for a

particular fish species. This derived type is defined by

following module

MODULE bass_type_fish_par

USE bass_type_hsi_par

TYPE:: fish_par

CHARACTER (LEN=80) :: ageclass, ast_type, ast_var, common_name, &

fmodel_var, genus_species, spawning_interval, temp_var

INTEGER :: fmodel_cls=0, harvests=0, spawnings=0, &

stockings=0, temperatures=0

INTEGER,DIMENSION(:),POINTER:: fmodel=>NULL()

INTEGER,DIMENSION(:),POINTER:: spawn_dates=>NULL()

INTEGER,DIMENSION(:),POINTER:: harvest_date1=>NULL()

INTEGER,DIMENSION(:),POINTER:: harvest_date2=>NULL()

INTEGER,DIMENSION(:),POINTER:: stock_dates=>NULL()
BASS 2.3 Sept 24, 2012 90

LOGICAL :: bb_constant=.TRUE., prey_switching_on=.TRUE.

REAL :: ae_fish, ae_invert, ae_plant, ast_bb, ast_bnds, ast_pop, &

biomass_cc, dry2live_ab, dry2live_aa, dry2live_bb, dry2live_cc, &

gco2_d, kf_min, la, longevity, mgo2_s, rbi, refugia, rq, rt2std, &

sda2in, tl_r0, wt_max, yoy

REAL, DIMENSION(2) :: ga, id, ld, ll, lw, pa, pl, sg_mu, wl

REAL, DIMENSION(3) :: nm

REAL, DIMENSION(4) :: lp, lp_max, lp_min

REAL, DIMENSION(5) :: ge, mf, mi, sg, sm, so, st

REAL, DIMENSION(:), POINTER :: fmodel_bnds=>NULL()

REAL, DIMENSION(:), POINTER :: harvest_len1=>NULL()

REAL, DIMENSION(:), POINTER :: harvest_len2=>NULL()

REAL, DIMENSION(:), POINTER :: harvest_rate=>NULL()

REAL, DIMENSION(:), POINTER :: stock_age=>NULL()

REAL, DIMENSION(:), POINTER :: stock_rate=>NULL()

REAL, DIMENSION(:), POINTER :: stock_tl=>NULL()

REAL, DIMENSION(:), POINTER :: stock_wt=>NULL()

REAL, DIMENSION(:), POINTER :: temp_bnds=>NULL()

REAL, DIMENSION(:), POINTER :: temp_pref=>NULL()

TYPE(hsi_par) :: hsi_feed, hsi_persist, hsi_recruit

END TYPE fish_par

END MODULE bass_type_fish_par

Many components of this derived type are user input

parameters that have already been discussed. For example, the

array ga(2) stores the coefficient and exponent of a species’

gill area function (see /MORPHOMETRIC_PARAMETERS page

51). 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 wet weight from its

dry weight (see introduction to Section 2.6. Modeling Growth

of Fish). Using a declaration of the form

TYPE(fish_par), DIMENSION(nspecies) :: par

all data defined by the above derived type can be passed to a

BASS subroutine by the simple calling statement

CALL sub1(...., par, ....)

without fear of data misalignment.

To insure that all program subroutines, functions, and

procedures use the same global constants or parameters, such

constants are declared and defined within a set of 15 data

modules. These modules include:

! MODULE ADAM_DATA - stores control parameters for the

EXAMS Adams-Gear integrators (see

EXAMS_ADAM_GEAR_MODULES.F90).

! MODULE BASS_CONSTANTS - specifies various biological

and physical constants used by BASS’s computational

subroutines (see BASS_GLOBALS.F90).

! MODULE BASS_GRAETZ - specifies parameters used to

calculate chemical exchange across the fish gills (see

BASS_GLOBALS.F90).

! MODULE BASS_IOFILES - specifies logical unit numbers

for input and output devices (see BASS_GLOBALS.F90).

! MODULE BASS_NAMES - stores user-supplied fish and

chemical names (see BASS_GLOBALS.F90).

! MODULE BASS_NOVALUE - specifies values for integer,

real, and character variables that have not been

initialized (see BASS_GLOBALS.F90).

! MODULE BASS_PRECISION - specifies the precision of

floating point variables as either single, double, or quad

precision variables. This module also assigns certain

associated floating point constants (see

BASS_GLOBALS.F90).

! MODULE BASS_UNITS - specifies unit conversion factors

that are specific to BASS for use by MODULE

UNITSLIBRARY (see BASS_UNITS.F90).

! MODULE BASS_WORKING_DIMENSIONS - specifies

“standard” sizes for character variables, input records,

etc. (see BASS_GLOBALS.F90).

! MODULE CONSTANTS - constants used by utility

subroutines (see UTL_CONSTANTS.F90).

! MODULE GEAR_DATA - stores control parameters for the

EXAMS Adams-Gear integrators (see

EXAMS_ADAM_GEAR_MODULES.F90).

! MODULE LOCAL_GEAR_DATA - stores control parameters

for the EXAMS Adams-Gear integrators (see

EXAMS_ADAM_GEAR_MODULES.F90).

! MODULE STEP_DATA - stores control parameters for the

EXAMS Adams-Gear integrators (see

EXAMS_ADAM_GEAR_MODULES.F90).

! MODULE STIFF_DATA - stores control parameters for the

EXAMS Adams-Gear integrators (see

EXAMS_ADAM_GEAR_MODULES.F90).

! MODULE UNITS_PARAMETERS - specifies parameters

used by the units conversion subroutines (see

UTL_UPARAMS.F90)

BASS v2.3 uses the following modules (see

BASS_WORK_AREAS.F90) to define work areas that are

common to two or more functions or subroutines.

! MODULE BASS_CPU_PERFORMANCE

! MODULE BASS_FOODWEB_WORK_AREA

! MODULE BASS_HSI_MEANS

! MODULE BASS_MULTISORT_WORK_AREA

! MODULE BASS_ODE_WORK_AREA

! MODULE BASS_OUTPUT_WORK_AREA

! MODULE BASS_PLOT_WORK_AREA

7.2.3. Is the developer reasonably confident that all program

subroutines, functions, and procedures are using the same

global constants or parameters?
BASS 2.3 Sept 24, 2012 91

All global constants are defined within their own individual

modules. These modules include

! MODULE BASS_CONSTANTS - constants used by BASS’s

computational subroutines (see BASS_GLOBALS.F90).

! MODULE BASS_NOVALUE - specifies values for integer,

real, and character variables that have not been

initialized (see BASS_GLOBALS.F90).

! MODULE BASS_PRECISION - specifies the precision of

floating point variables as either single, double, or quad

precision variables. This module also assigns certain

associated floating point constants (see

BASS_GLOBALS.F90).

! MODULE BASS_WORKING_DIMENSIONS - specifies

“standard” sizes for character variables, input records,

etc. (see BASS_GLOBALS.F90).

! MODULE CONSTANTS - constants used by utility

subroutines (see UTL_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 supposed to? For example, are root finding algorithms

functioning properly?

During execution, BASS must employ root finding algorithms

for two important types of calculations. The first of these is the

calculation of a fish’s wet weight from its dry weight given an

allometric relationship between its wet body weight and its

fraction lipid, and linear relationships between its moisture,

lipid, and non-lipid organic matter fractions. The second type

of calculation involves the linear transformation of

unconditioned dietary electivities into self-consistent sets of

dietary electivities. These calculations are performed using the

combined bisection / Newton-Raphson algorithm outlined by

Press et al. (1992).

As mentioned earlier, BASS’s differential equations are

integrated using a fifth-order Runge-Kutta method with

adaptive step sizing that monitors the accuracy of its

integration. BASS’s Runge-Kutta integrator is patterned on the

fifth-order Cash-Karp Runge-Kutta algorithm outlined by

Press et. al. (1992) and was tested using the following system

of equations.

(7.2)

The analytical solution to this system of equations is

(7.3)

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.

BASS’s Runge-Kutta algorithm has also been compared to the

adaptive Adams-Gear algorithm employed by the widely used

EXAMS fate and transport model. These comparisons

demonstrated that BASS’s Runge-Kutta algorithm was not only

as accurate as the EXAMS Adams-Gear algorithm but was also

computationally faster.

7.2.5. Are mathematical algorithms implemented correctly,

i.e., are the assumptions of the procedure satisfied by the

problem of interest?

Because BASS is a differential equation model, a question of

paramount concern is how its integration between points of

discontinuity / nondifferentiability is controlled. BASS, like

many ecological models, utilizes threshold responses, absolute

value functions, maximum and minimum functions, and linear

interpolations between time series in its formulation and

implementation. Although most of BASS’s parameters are

updated continuously, some parameters that change very

slowly and that are computationally intensive to evaluate (e.g.,

dietary compositions) are updated only daily. All of these

features create points of discontinuity or nondifferentiability.

Although there is nothing intrinsically wrong with using such

formulations in differential equation models, numerical
BASS 2.3 Sept 24, 2012 92

integrations of such models must proceed from one point of

discontinuity / nondifferentiability to another.

With these considerations in mind, BASS’s computational

kernels (subroutines BASS_ODESOLVR and FGETS_ODESOLVR)

are designed to integrate BASS’s differential equations for a

single day of the desired simulation period. Immediately

following the call of these computational kernels, BASS

calculates the dietary composition of each fish that will be held

constant for that day. The progress of the subsequent

numerical integration within the day is then controlled by any

condition that results in a point of nondifferentiability. The

two most important conditions in this regard occur when BASS

must read an exposure file to update the parameters for the

linear interpolation of one or more exposure variables, or when

one or more cohorts are eliminated from the community. In the

later case, BASS also recalculates the dietary compositions of

the remaining fish that again will remain constant for the

remainder of the day. Note that recruitment of new cohorts

into the simulated community does not create a point of

nondifferentiability for BASS since such amendments to the

community’s structure are performed before calling the

computational kernels BASS_ODESOLVR or FGETS_ODESOLVR

and, therefore, constitutes a simple reinitialization problem.

7.2.6. Are simulated results consistent with known

mathematical constraint of the model? For example, if state

variables are supposed to be non-negative, are they?

Similarly, if the model is supposed to mass balance, does it?

BASS’s state variables, like those of most physical or biological

models, must be by definition non-negative. However, insuring

that the numerical integration of a differential equation model

remains constrained to its appropriate state space is not a

trivial issue. Consider, for example, the case when one wants

to take a simple Eulerian step for a non-negative state variable

that has a negative derivative. If the state variable is to remain

non-negative, then the largest allowable size for the integration

step can be calculated as follows

(7.4)

If h is greater than the numerical spacing of t (i.e., ),

then an integration step is possible. If the converse is true,

however, the function y(t) is approximating a step function in

which case the desired integration can simply be restarted with

y(t) = 0. There are at least two types of situations that can

occur during a BASS simulation that might necessitate this type

of corrective action. The first of these occurs when a cohort

experiences intense predation or other mortality that drives its

population to extinction whereas the second situation might

occur when there is the rapid excretion of a hydrophilic

contaminant following the disappearance of an aqueous

exposure. When the derivative for a fish’s body weight,

population density, or body burden is negative, BASS verifies

whether the current integration step will, in fact, yield 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.

Using the “-mba” command line option, BASS performs a

comprehensive mass balance analysis of its fundamental

differential equations (i.e., Equations (2.1), (2.2), and (2.3)).

BASS also calculates and reports mass balances for each

cohort’s total biomass and the community’s total predicted

predatory mortality and its total predicted piscivorous

consumption. For the example presented herein, this mass

balance is -2.950E-09 g dry wt/ha/yr. Since this community’s

total piscivory is calculated to be 8.850E+03 g dry wt/ha/yr,

this mass balance check would have a relative error of less

than 10-11.

7.2.7. Are simulation results consistent across machines or

compilers?

BASS was originally developed on a DEC 3000 work station

using the DEC Fortran 90 compiler. In November 1999, it was

ported to the Windows operating system on the DELL

OptiPlex using the Lahey LF90 v4.5 compiler. Although the

results of these two implementations agree with one another up

to single precision accuracy, due to differences in compiler

optimization, model computations must be performed in

double precision to obtain this level of consistency. Since

November 1999, BASS has also been compiled and tested using

the Lahey-Fujitsu Fortran LF95 compilers versions 5.0, 5.5,

5.6, 5.7, 7.0, and 7.1.

In September 2004, BASS was ported to an IBM Intellistation

A Pro workstation equipped with dual 64-byte Opteron

processors and a Windows XP operating system. The BASS

source code was then recompiled using the Absoft

multiprocessor Fortran 90/95 compilers 8.2 MP and 9.0 MP.

Although initial compilations using these compilers failed due

to compiler bugs that have been acknowledged by Absoft

Technical Support, workarounds for these bugs were

successfully implemented. Simulation results of the BASS

Absoft MP dual processor executables were in excellent

agreement with those of the BASS Lahey-Fujitsu single

processor executable. More recently, BASS also has been

recompiled on standard EPA single processor DELL laptop
BASS 2.3 Sept 24, 2012 93

and desktop machines, running 32-byte Windows XP, using

the Absoft Pro Fortran 90 EP compiler with similar results.

Finally, in June 2010, BASS ported to a DELL Latitude 8400

laptop equipped with Windows XP and a single dual core Intel

processor and recompiled using the Intel Fortran 95 compilers

11.0 and XE 2011.

7.2.8. Have test and reference / benchmark data sets been

documented and archived?

The eleven BASS projects discussed in Section 6.1 serve not

only as BASS distribution examples but also as test projects that

track changes in the operation of BASS associated with code

maintenance and updates. These project files are used as

benchmarks to verify that code modifications that should not

change BASS’s computational results do not change BASS’s

simulation output.

7.3. Questions Regarding QA of Model Documentation and

Applications

7.3.1. Is the model intended for absolute or comparative

prediction?

Although BASS can be used to analyze results from actual field

studies or predict the expected future condition of specific real

communities, its principal intended use is to predict and

compare the outcomes of alterative management options that

are associated with pollution control, fisheries management,

and / or ecosystem restoration activities.

7.3.2. Does the User Guide provide the information needed to

appropriately apply and use the model?

The BASS User’s Guide summarizes the model’s theoretical

foundations and assumptions, the model’s input command

structure, issues related to user file and project management,

and software installation. The User’s Guide also presents and

discusses the results of one of eleven example applications that

are distributed with the BASS software.

7.3.3. What internal checking can be made to help insure that

the model is being used appropriately?

Currently, the only internal checking performed by BASS is to

verify that all parameters needed by the model for a particular

simulation have, in fact, been specified by the user. Although

BASS does assign default values for a limited number of

parameters, most unassigned parameters are fatal errors.

Future versions of BASS will perform bounds checking on

many of its physiological and morphological parameters.

7.3.4. Has the developer anticipated computational problem

areas that will cause the model to “bomb”?

Several key mathematical calculations have been identified as

potential problem areas for a BASS simulation. In general, these

problem areas involve either the unsuccessful resolution of a

root of a nonlinear equation or the unsuccessful integration of

BASS’s basic state variables. Examples of the former include

situations when BASS’s calculated dietary compositions do not

sum to unity or when a fish’s wet weight is calculated to be

less or equal to its dry weight. Examples of the latter include

situations when the current integration step is less than the

numerical spacing of the current time point, or when BASS’s

integration error exceeds 10-5. When these situations are

encountered, BASS terminates execution and issues an

appropriate error message to the current *.MSG file.
BASS 2.3 Sept 24, 2012 94

8. Planned Future Features

Presently, ten major program developments are planned for

BASS. These include:

! Development of canonical fish and community

databases (i.e., *.FSH and *.CMM files) to facilitate

easier application of BASS.

! Software to perform model sensitivity analyses.

! Implementation of an option to read a simulated or

measured time series of dissolved oxygen

concentrations that are needed to calculate the fishes’

ventilation volumes. See Equation (2.12). Currently,

BASS uses saturated dissolved oxygen concentrations

that are calculated as a function of water temperature.

! Development of submodels for simulating the

physiological tolerances of fish to water quality

parameters other than toxic chemicals.

! Incorporation of quantitative structure activity

relationships (QSAR’s) to predict metabolism of

organic chemicals.

! Development of immigration algorithms for simulating

the movement of fish into the simulated community

based on habitat parameters such as water depth, current

velocity, availability of prey, etc.

! Development of subroutines to simulate sublethal,

residue-based effects.

! Enable lipid fractions, fecundity, and physiological

mortality to be functionally dependent on the fish’s

predicted growth rate and/or duration of fasting (see

Adams and Huntingford 1997, Simpkins et al. 2003).

! Enable an option for specifying habitat suitability

multipliers on respiratory expenditures. See for example

Sweka and Hartman (2001) and Facey and Grossman

(1990).

! Implementation of light and nutrient dependent primary

production by phytoplankton and periphyton.
BASS 2.3 Sept 24, 2012 95

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BASS 2.3 Sept 24, 2012 114

APPENDICES

APPENDIX A. Equilibrium complexation model for metals

As reviewed by Mason and Jenkins (1995), metals can be

classified into three different categories based on their

complexation behavior and preference for different ligands.

These groups are generally designated as class A, class B, and

borderline metals. Of these, however, class B and borderline

metals are the most important from an ecotoxicological point

of view. Class B metals (e.g., Au, Ag, Cu, Hg, and Pb)

preferentially bind to marcromolecules such as proteins and

nucleotides that are rich in sulfhydryl groups and heterocyclic

nitrogen. Borderline metals (e.g., As, Cd, Co, Cr, Ni, Sn, and

Zn) bind not only to the same sites as do class B metals but

also to those sites preferred by class A metals (i.e.,

carboxylates, carbonyls, alcohols, phosphates, 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 are regulated by metallothioneins via

sulfhydryl complexation reactions suggests that the total

availability of sulfhydryl groups within organisms plays a key

role in their internal distribution and accumulation.

To formulate complexation reactions for class B and

borderline metals, one can assume that protein sulfhydryl

groups are the only significant ligand for these metals, i.e.,

(A.1)

The stability constant for this reaction is

(A.2)

where [H

+

] is the hydrogen ion concentration (molar); [M +] is

the concentration of free metal (molar); [RSH] is the

concentration of reactive sulfhydryls (molar); [RSM] is the

concentration of sulfur bound metal (molar); RSM is the moles

of metal bound to sulfhydryls; and RSH is the moles of free,

non-disassociated sulfhydryl. If a fish’s metal concentrations

(i.e., Ca

 , Cl , Co , and Cf) are expressed on a molar basis, then

the following identities hold

(A.3)

(A.4)

(A.5)

where Ww is the fish’s kilogram wet weight. Substituting

Equations (A.3) and (A.4) into Equation (A.2), one can verify

that

(A.6)

and consequently

(A.7)

To parameterize Equation (A.7) for RSH, the following mass

balance for the fish’s sulfhydryl content is then assumed

(A.8)

where TS is the total moles of sulfhydryl ligands; RS - is the

moles of disassociated sulfhydryls; and Ka is the sulfhydryl’s

disassociation constant. Therefore,

(A.9)

Using Equation (A.7), however, this expression can be

rewritten as

(A.10)

where is the fish’s total burden (mol/fish) of metal

i. For most class B metals, however,

(A.11)

Consequently, Equation (A.10) can be simplified to

(A.12)
BASS 2.3 Sept 24, 2012 115

This expression can then be substituted into Equation (A.7) to

calculate the fish aqueous phase metal concentrations.

To use the aforementioned complexation model (i.e., Equation

(A.12) substituted into Equation (A.7)), one must specify both

the metal’s stability constant (see Equation (A.2)) and the total

concentration of sulfhydryl binding sites TS (mol SH/g dry wt)

within the fish. Although numerous studies have investigated

the sulfhydryl content of selected fish tissues, it appears that

no study has attempted to quantify the total sulfhydryl content

of fish. A reasonable approximation of this parameter,

however, can still be made since data do exist for the major

tissues (i.e., muscle, liver, kidney, gill, and intestine) typically

associated with metal bioaccumulation.

Itano and Sasaki (1983) reported the sulfhydryl content of

Japanese sea bass (Lateolabrax japonicus) muscle to be 11.5

:mol SH/g(sacroplasmic protein) and 70.5 :mol

SH/g(myofibrillar protein). Using these authors’ reported

values of 0.0578 g(sarcoplasmic protein)/g(muscle) and 0.120

g(myofibrillar protein)/g(muscle), the total sulfhydryl content

of Japanese sea bass muscle is estimated to be 9.12

:mol(SH)/g(muscle) or 45.6 :mol(SH)/g(dw muscle).

Opstevedt et al. (1984) reported the sulfhydryl content of

Pacific mackerel (Pneumataphorus japanicus) and Alaska

pollock (Theragra chalcogramma) muscle to be 6.6 and 6.2

mmol(SH)/16 g(muscle N), respectively. Using conversion

factors reported by these authors, these values are equivalent

to 48.7 and 56.7 :mol/g(dw muscle). Chung et al. (2000)

determined the sulfhydryl content of mackerel (Scomber

australasicus) muscle to be 88.2 :mol(SH)/g(protein). Using

the conversion factor 0.83 g(protein)/g(dw muscle) (Opstevdt

et al. 1984), this value is equivalent to 73.2 :mol(SH)/g(dw

muscle). Several studies have determined sulfhydryl contents

of the actomyosin and myosin components of fish myofibrillar

proteins (Connell and Howgate 1959, Buttkus 1967, 1971,

Takashi 1973, Itoh et al. 1979, 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), the results of Itano and

Sasaki (1983), Opstevedt et al. (1984), and Chung et al. (2000)

can be assumed to be representative of fish in general.

Consequently, the sulfhydryl content of fish muscle can be

assumed to be on the order of 45-70 :mol(SH)/g(dw muscle).

Although the sulfhydryl contents of liver, kidney, gills, and

intestine have not been measured directly, the sulfhydryl

content of these tissues can be estimated from their

metallothionein concentrations. Metallothioneins (MT) are

sulfur-rich proteins that are responsible for the transport and

storage of heavy and trace metals and that are also usually

considered to be the principal source of sulfhydryl binding

sites in these tissues (Hamilton and Mehrle 1986, Roesijadi

1992). Numerous researchers have investigated the occurrence

of 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

:mol(MT)/g(liver) (Brown and Parsons 1978, Roch et al.

1982, Klaverkamp and Duncan 1987, Dutton et al. 1993).

Using data from Takeda and Shimizu (1982) who report the

sulfhydryl content of skipjack tuna (Katsuwonus pelamis) MTs

to be approximately 25 mol(SH)/mol(MT) and assuming a dry

to wet weight ratio equal 0.2, these MT concentrations would

be equivalent to 3.75 - 37.5 :mol(SH)/g(dw liver). This range

of values suggests that the hepatic sulfhydryl content of fish,

that includes both baseline MT and cytoplasmic components

that can be converted into MT, might be on the order of 40

:mol(SH)/g(dw liver). This latter value, however, is probably

too conservative. Consider, for example, the observation that

the ratios of mercury concentrations in liver to those in muscle

often vary from 1.5 to 6 or more (Lockhart et al. 1972, Shultz

et al. 1976, Sprenger et al. 1988). If liver and muscle are

equilibrating with the same internal aqueous phase, then either

the MT sulfhydryls are more available than are the

sacroplasmic and myofibrillar sulfhydryls or the inducible

concentrations of hepatic MT are much higher than 40

:mol(SH)/g(dw liver). Of these two possibilities, the latter

appears more likely.

Although gill, kidney, and intestine MTs have not been studied

in the same detail as hepatic MTs, it appears that MT, and

hence sulfhydryl, concentrations in gills and kidney are lower

and not as inducible as hepatic concentrations (Hamilton et al.

1987a, b, Klaverkamp and Duncan 1987). Klaverkamp and

Ducan (1987) estimated the concentrations of gill MT in white

suckers (Catostomus commersoni) to be 33 :g(MT)/g(gill)

which is equilvalent to 3.3 nmol(MT)/g(gill) or 0.0825

:mol(SH)/g(gill). This latter value agrees well with the

estimated concentrations of unidentified binding sites (0.03 -

0.06 :mol/g(gill)) for copper on the gills of rainbow trout

(Oncorhynchus mykiss) and brook trout (Salvelinus fontinalis)

(MacRae et al. 1999), but is somewhat higher than the

concentration of unidentified binding sites (0.013 - 0.03

:mol/g(gill)) for copper, cadmium, and silver on the gills of

rainbow trout and fathead minnows (Pimephales promelas)

(Playle et al. 1993, Janes and Playle 1995).

Based on these considerations and the acknowledgment that

many other important organic compounds contain sulfhydryl

groups, e.g., enzymes involved in fatty acid synthesis,

glutathione, etc., it seems reasonable to assume that the

sulfhydryl content of fish is approximately 70 :mol(SH)/g dry

wt. Because Davis and Boyd (1978) reported the mean sulfur
BASS 2.3 Sept 24, 2012 116

content of 17 fish species to be 206 :mol(S)/g dry wt, this

assumption implies that almost 1/3 of a fish’s sulfur pool exists

as sulfhydryl groups.

The aforementioned complexation model was implemented

within BASS using 70 :mol(SH)/g dry wt to calculate the fish’s

total sulfhydryl content. The mean dissociation constant for

organic sulfhydryls was then assigned as pKa = 9.25 (i.e., the

SPARC estimated pKa

 for cysteine). Using literature values for

the stability constants of methylmercury, however, BASS over

predicted the bioaccumulation of methylmercury in fish by at

least an order of magnitude. Consequently, a much simpler

distribution coefficient algorithm was adopted.
BASS 2.3 Sept 24, 2012 117

APPENDIX B. Canonical equations for modeling diffusive chemical exchange across fish gills with ventilation and

perfusion effects. See Section 2.2 for background information and notation.

If chemical exchange across fish gills is treated as steady-state,

convective mass transport between parallel plates, then the

following PDE and boundary conditions can be used to model

chemical uptake from and excretion to the interlamellar water:

(B.1)

(B.2)

(B.3)

To obtain a canonical solution for this gill model, these

equations can be nondimensionalized using the following

transformations:

(B.4)

(B.5)

(B.6)

Applying these transformations, chemical exchange across a

fish’s gills is described by the following dimensionless PDE

and boundary conditions:

(B.7)

(B.8)

(B.9)

where is the gills’ dimensionless lamellar

permeability (i.e., Sherwood number); and

is the gills’ dimensionless lamellar length (i.e., Graetz

number).

The boundary condition (B.9) describing exchange across the

secondary lamellae, however, can be simplified by noting that

the solution of Equation (B.7) is separable, i.e., 1(X, Y) =

M(X)Q(Y) and that qv = 2 rhV is the ventilation volume of an

individual interlamellar channel. Using these observations, one

can then write

(B.10)

that can then be differentiated with respect to Y to obtain

(B.11)

Because where 8 is the constant of

separation for Equation (B.7), the preceding equation is

equivalent to

(B.12)

which can be rearranged to yield

(B.13)

Although this boundary condition is dependent on the

eigenvalue 8, the eigenvalue expansion for the solution of

Equation (B.7) is still straightforward (Walter 1973, Fulton

1977). Note that as the fish’s perfusion rate increases, this

boundary condition converges to

(B.14)

which is the boundary condition originally used by Barber et

al. (1991).

See Barber et al. (1991) for the method used to construct the
BASS 2.3 Sept 24, 2012 118

series solution for the dimensionless bulk concentration of the

aforementioned PDE gill exchange model (i.e., Equation

(2.28)).
BASS 2.3 Sept 24, 2012 119

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

To derive a self consistency condition for a fish’s electivities

and relative prey availabilities such that its calculated dietary

frequencies will sum to unity, consider the following

(C.1)

(C.2)

(C.3)

Summing Equation (C.2) over all i then yields

(C.4)

(C.5)

When Equation (C.3) is substituted into Equation (C.5), one

then obtains

(C.6)

or equivalently

(C.7)

Finally, adding to each side of Equation (C.7), the

following consistency condition is obtained

(C.8)
BASS 2.3 Sept 24, 2012 120
BASS 2.3 Sept 24, 2012 121

INDEX

chemical commands

/chemical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

/exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

/lethality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

/log_ac . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/log_kb1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/log_kb2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/log_p . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/melting_point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/molar_volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/molar_weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

/nonfish_bcf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

chemical exposures

contaminated sediments . . . . . . . . . . . . . . . . . . . . . . . . . . 45

dietary exposure via benthos . . . . . . . . . . . . . . . . . . . . . . . 45

dietary exposure via insects . . . . . . . . . . . . . . . . . . . . . . . 45

dietary exposure via periphyton . . . . . . . . . . . . . . . . . . . . 45

dietary exposure via phytoplankton . . . . . . . . . . . . . . . . . 45

direct aqueous exposures . . . . . . . . . . . . . . . . . . . . . . . . . 45

files

chemical exposure files (.chm) . . . . . . . . . . . . . . . . . . 57, 58

chemical property files (.prp) . . . . . . . . . . . . . . . . . . . . . . 57

community files (.cmm) . . . . . . . . . . . . . . . . . . . . . . . 56, 57

directory structure for BASS include files . . . . . . . . . . . . 58

fish files (.fsh) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56, 58

include files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 56

management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

output file (.bss) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

output file (.msg) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

output file (.ps) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

output file (.xml) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

project files (.prj) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57, 58

user supplied parameter files . . . . . . . . . . . . . . . . . . . . . . 55

fish commands

/age_class_duration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

/common_name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

/compostional_parameters . . . . . . . . . . . . . . . . . . . . . . . . 47

/ecological_parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

/feeding_options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

/fishery_parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

/habitat_parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

/initial_conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

/morphometric_parameters . . . . . . . . . . . . . . . . . . . . . . . . 51

/physiological_parameters . . . . . . . . . . . . . . . . . . . . . . . . 51

/prey_switching_off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

/spawning_period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

/species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

future features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

restrictions

specifying chemical names . . . . . . . . . . . . . . . . . . . . . . . . 44

specifying common names . . . . . . . . . . . . . . . . . . . . . . . . 47

units recognized by BASS . . . . . . . . . . . . . . . . . . . . . . . . 54

simulation control commands

/annual_outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

/annual_plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

/biota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

/fgets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

/first_leap_year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/fish_carrying_capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/header . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/isotope_parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/length_of_simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/leslie_matrix_simulation . . . . . . . . . . . . . . . . . . . . . . . . . 42

/month_t0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/nonfish_qsar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

/simulation_control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

/summary_plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

/temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

/water_level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

simulation options

defining community food web . . . . . . . . . . . . . . . . . . 38, 47

non-fish compartments as forcing functions . . . . . 41, 53, 54

non-fish compartments as state variables . . . . . . . . . . . . . 53

simulating bioaccumulation without community dynamics

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

simulating community dynamics without bioaccumulation

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

specifying fishery harvest and stocking . . . . . . . . . . . . . . 49

specifying habitat suitability multipliers . . . . . . . . . . . . . . 50

specifying non-fish BCFs . . . . . . . . . . . . . . . . . . . . . . 42, 46

specifying output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 43

specifying water levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

turning off chemical lethality . . . . . . . . . . . . . . . . . . . . . . 45

turning off fishery stocking . . . . . . . . . . . . . . . . . . . . . . . . 50

turning off fishing harvest . . . . . . . . . . . . . . . . . . . . . . . . . 50

specifying physical conditions 

water depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

water temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

syntax

commenting a line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

continuing a line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

specifying an include file . . . . . . . . . . . . . . . . . . . . . . . . . 40

specifying units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

user specified functions . . . . . . . . . . . . . . . . . . . . . . . . . . 54

technical support

reporting comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

reporting problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

reporting suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
BASS 2.3 Sept 24, 2012 122

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