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

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BASS 2.3 Sept 24, 2012 

## Notice

The research described in this document was funded by the U.S. Environmental Protection Agency throughthe Office of Research and Development. The research described herein was conducted at the Ecosystems Research Division of th e USEPA National Exposure Research Laboratory in Athens, Georgia. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

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BASS 2.3 Sept 24, 2012 

## Foreword

This report describes the theoretical development, parameterization, and application software of the BASS B ioaccumulation and A quatic S ystem S imulator. 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 b orderline metals that complex with sulfhydryl groups (e.g., cadmium, copp er, 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 aud ience of this report and associated software includ es research fisheries ecologists, bioaccumulation researchers, and EPA environmental scientists and ecologists who must routinely analyze and estimate bioaccumulation of chemicals in fish for e cological or human health exposure assessments. Although BASS has not been extensively field-tested, its process -based algorithms for predicting chemical bioaccumu lation, growth of individual fish, predator-prey interactions, and population dynamics either have been corroborated or have been formulated using widely accepted ecological and ecotoxicological pri nciples. Even when a process-based model has underg one only limited field testing, it can be an extremely useful tool. Process-based models enable users to observe quantitatively the re sults of a particular abstraction of the real world. Moreover, such models can be arg ued to be the only objective method to make extrapo lations to unobserved or unobservable conditions. If the conceptualization a nd construction of process-based models are both co mprehensive (i.e., holistic) and reasonable, then their output, validated or not, ca n still be used for comparative analyses. A model’s ability to simulate trends and comparative dynamics are, in fact, often more impor tant measures of a model’s utility than is its abil ity to replicate a specific field or laboratory study. Although BASS can be used to analyze results from actual field s tudies, its principal intended use is to predict an d compare the outcomes of alternative management opti ons associated with pollution control, ecosystem ma nagement, or restoration activities.

Roy C. Sidle, Ph.D.

Director Ecosystems Research Division Athens, GA 30605 USA

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BASS 2.3 Sept 24, 2012 

## Abstract

BASS ( B ioaccumulation and A quatic S ystem S imulator) is a Fortran 95 simulation program that p redicts growth, population, and bioaccumulation dynamics of age-structured fish ass emblages exposed to hydrophobic organic pollutants and class B or borderline metals that complex with sulfhydryl groups (e.g., c admium, 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 fo r the growth of individual fish. These algorithms consider both biological attribute s 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 c hemical’s aqueous diffusivity, n-octanol / water partition coefficient ( K ow), and, for metals, binding coefficients to proteins and other organic matter. BASS simulates the growth of individual fish using a st andard mass balance, bioenergetic model (i.e., growth = ingesti on - egestion - respiration - specific dynamic acti on - excretion). A fish’s realized ingestion is calculated from its maximum consumptio n 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 combinatio n of benthos, incidental terrestrial insects, periphyton / attached algae, phytoplankton , zooplankton, and one or more fish species. Popula tion dynamics are generated by predatory mortalities defined by the community’s food web and standing stocks, physiological mortal ity rates, the maximum longevity of species, toxicological responses to ch emical exposures, and dispersal. The model’s tempor al and spatial scales are that of a day and of a hectare, respectively.

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BASS 2.3 Sept 24, 2012 

## Table of Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v ii Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi ii Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i x 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 K f . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 9 4.3. Input File Structure . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1. Simulation Control Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 0 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 9 5. BASS Model Software and Graphical User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 5.1. Software Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 5.2. Installation Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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BASS 2.3 Sept 24, 2012 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 Implementa tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 meta ls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 APPENDIX B. Canonical equations for modeling diffus ive chemical exchange across fish gills with ventil ation and perfusion effects. See Section 2.2 for background informationand notation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 APPENDIX C. Derivation of the consistency conditionfor feeding electivities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

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BASS 2.3 Sept 24, 2012 

## Figures Figure 2.1 First eigenvalue and bulk mixing cup coefficient f or Equation (2.28) as a function of gill Sherwood n umber 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 ma ximum consumption rates calculated by the algorithm s 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 ma ximum consumption rates calculated by the algorithm s 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 predicte d 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-do wn selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Figure 5.6 GUI command editor for numeric data with user spec ified 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 paramete rs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 fu nctions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Figure 5.18 GUI command editor for nonfish biota as state vari ables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Figure 5.19 GUI command editor for nonfish bioaccumulation fac tors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Figure 5.20 GUI command editor for chemical biotransformation parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Figure 5.21 GUI command editor for chemical toxicity parameter s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Figure 5.22 GUI command editor for automatic graphing selectio ns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Figure 5.23 GUI Block comment editor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Figure 5.24 Data file editor for forcing functions specified a s files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Figure 6.1 Predicted and observed methylmercury concentration s of largemouth bass in Florida Everglades canals. . . . . . . . . . 80 Figure 6.2 Predicted and observed methylmercury concentration s of Florida gar in Florida Everglades canals. . . . . . . . . . . . . . 81 Figure 6.3 Predicted and observed methylmercury concentration s of yellow bullhead in Florida Everglades canals. . . . . . . . . . 82 Figure 6.4 Predicted and observed methylmercury concentration s of bluegill sunfish in Florida Everglades canals. . . . . . . . . . . 83 Figure 6.5 Predicted and observed methylmercury concentration s of redear sunfish in Florida Everglades canals. . . . . . . . . . . . 84 Figure 6.6 Predicted and observed methylmercury concentration s of Gambusia in Florida Everglades canals. . . . . . . . . . . . . . . 85

### viii

BASS 2.3 Sept 24, 2012 

## Tables
Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamen tal 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 satia tion 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 distr ibuted database. Observed data corresponds to the m aximum 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, T ime, and Energy. This list is not exhaustive and su mmarizes only commonly used unit names that BASS ’s units conversion program recognizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

### ix

BASS 2.3 Sept 24, 2012 

## Acknowledgment Many people have contributed to the development of the BASS bioaccumulation and community model throug h technical discussions, software development and oversight, and peer review s. This author wishes to thank Drs. Daniel Beyers ( Colorado State University), James E. Breck (University of Michigan), Lawrence B urkhard (USEPA, NHEERL), Lawrence Burns (USEPA, NER L deceased), Michael Cyterski (USEPA, NERL), Russell Erickson (U SEPA, NHEERL), Timothy Gleason (USEPA, NHEERL), Joh n 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, NHEER L), David Rodgers (Ontario Power Technologies), Dick Sijm (Th e Netherlands National Institute for Public Health and the Environment (RVIM)), Luis Suárez (USEPA, NERL), Donald Rodier ( USEPA, OCSPP, retired), Robert R. Swank (USEPA, NER L 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 o f CSC Inc., Athens, GA), Mike Galvin (USEPA, NERL), Lourdes Prieto (USEPA, N ERL), and Frank Stancil (USEPA, NERL, retired).

### 1

BASS 2.3 Sept 24, 2012 

## 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 in clude: 1) Are individual fish growth and condition sufficie nt to enable them to survive periods of natural (e.g., overwintering) and man induced stress? 2) Are individual fish species able to maintain sust ainable populations? For example, is individual growth adeq uate for fish to attain the minimum body size required f or reproduction? Is there adequate physical environmen t for successful spawning? Is there adequate physical hab itat for the survival of the young-of-year? 3) Do regional fish assemblages exhibit their expect ed biodiversity or community structure based on biogeographical and physical habitat considerations ? 4) Are regional fish assemblages maintaining their e xpected level of productivity based on biogeographical and physical habitat considerations? 5) Are appropriately sized fish abundant enough to m aintain piscivorous wildlife (e.g., birds, mammals, and rep tiles) during breeding and non-breeding conditions? 6) Are potential fish prey sufficiently free of cont aminants (endocrine disruptors, heavy metals, etc.) so as no t to interfere with the growth and reproduction of pisci vorous wildlife? 7) How will native fishes respond to the introductio n of nonnative fish species, including those stocked for recreational fishing? From a human health or use perspective, another imp ortant question related to fish health is: 8) Is the fish community / assemblage of concern fis hable? That is, are target fish species sufficiently abund ant and of the desired quality? Fish quality is this context i s often defined in terms of desired body sizes (e.g., legal or trophy length) and the absence of chemical contamin ants. Some of the important metrics or indicators that ha ve 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 an d peak current velocities, water temperature, sediment loa ding, etc., 2) community species and functional diversity, 3) tota l 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 communi ty’s dominant species, 5) the age or size class structur e of the community’s dominant species, 6) annual productivity of the community and its dominant species, 7) individual g rowth rates or condition factors (i.e., the fish’s current bodyweight 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 mana gement options or of assessing expected future consequence s of existing conditions, simulation models that can predict the individual and population growth of fish and their patterns of che mical bioaccumulation are important tools for analyzing s everal of the aforementioned dimensions of fish health. Although the growth of individual fish has often be en described using empirical models such as the von Bertalanffy, logistic, Gompertz, or Richards models (see for example Ricke r (1979) and Schnute (1981)), process-based bioenergetic mod els such as those described by Kitchell et al. (1977), Minton a nd 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. (199 3), Roell and Orth (1993), Hartman and Brandt (1995a), Petersen a nd Ward (1999), Rose et al. (1999), Schaeffer et al. (1999) , and van Nes (2002) are becoming the models of choice for predic ting the growth of fish. Because these process-based models predict fish growth based on the mass or energy balance of inges tion, egestion, respiration, specific dynamic action, and excretion, they can generally be parameterized independently of the ir current application. Moreover, because of the inherent diff iculties in obtaining reliable field-based measurements of fishpopulation dynamics and productivity, researchers are increasi ngly using such bioenergetic models to characterize these popu lation and community level endpoints. See for example Stewart and Ibarra (1991) and Roell and Orth (1993). The ability to predict accurately the bioaccumulati on of chemicals in fish has become an essential component of ecological and human health risk assessments for ch emical pollutants. Not only are accurate estimates needed to predict realistic dietary exposures to humans and piscivoro us wildlife, but such estimates are also needed to assess more a ccurately potential ecological risks to fish assemblages them selves. Although exposure-referenced benchmarks such as LC 50and EC 50have been widely used for hazard assessments, most deleterious effects of chemical pollutants are caus ed by the internal accumulation of those compounds, rather th an their environmental concentrations per se. Many authors ( Neely 1984, Friant and Henry 1985, McCarty et al. 1985, McCarty1986, Connell and Markwell 1992, McCarty and Mackay 1993, Verhaar et al. 1995, van Loon et al. 1997) have dis cussed the benefits of explicitly considering chemical bioaccu mulation

### 2

BASS 2.3 Sept 24, 2012 when assessing expected ecological consequences of chemical pollutants in aquatic and marine ecosystems. Residu e-based toxicity studies confirm this supposition (Opperhui zen and Schrap 1988, van Hoogen and Opperhuizen 1988, Donki n et al. 1989, Tas et al. 1991, van Wezel et al. 1995, Drisc oll and Landrum 1997). Although concentrations of moderately hydrophobic c hemicals in fish can often be predicted accurately assuming equilibrium partitioning of the chemicals between the fish’s or ganic constituents and the aqueous environment, this appr oach 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 equi librium 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 chemica ls to equilibrate. Because bioconcentration and bioaccumu lation are generally treated as linear, first order processes, the time needed for chemicals to equilibrate between fish and theirexposure media is an increasing function of the elimination half lives of those chemicals in fish. For example, the time requ ired for chemicals to achieve 95% of their equilibrium conce ntrations is approximately 4.3 times their elimination half live s. Because the elimination half lives of chemicals generally incre ase as their hydrophobicities increase, the time needed for chemicals to reach their equilibrium concentrations in fish also incre ases as a function of chemical hydrophobicity. Consequently, for extremely hydrophobic chemicals such as polychlorin ated biphenyls (PCBs) and dioxins that have elimination half lives ranging from months to over a year, the time to equ ilibrium can be on the order of years. If the fish species of co ncern is short lived, the time needed for equilibrium can exceed the species expected life span. Even when time is sufficient fo r equilibration, whole-body concentrations of fish can be much lowerthan that expected from thermodynamic partitioning due to phy sical dilution of the chemical that accompanies body grow th 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 rou te of exposure is significant in determining the total chemical ac cumulation in fish. The alternative assumption is that there are multiple routes of exposure that all covary with the chosen referen ce pathway in a constant manner. For bioconcentration factors (BC Fs), the implicit assumption is that virtually the entire bu rden is exchanged directly with the water across the fish’s gills or possibly across its skin. Although direct aqueous u ptake is certainly the most significant route of exchange fo r moderately hydrophobic chemicals, dietary uptake accounts for most of a fish’s body burden for extremely hydrophobic chemic als. This shift in the relative significance of the direct aq ueous 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, fo r 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, C w (μg/ml). Similarly, the fish’s dietary exposure, DE (μg/d), is the product of its feeding rate, F w (g wet wt/d), and the chemical’s concentration in the fish’s prey, C p (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 a queous 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 10 4.3 ml/g. Assuming the quantitative structure activity relationship (QSAR) for the trout’s prey i s BCF = 0.048 K ow (Mackay 1982), one would conclude that food is the trout’s predominant route of exposure for any chemical whos e octanol / water partition coefficient is greater than 10 5.6 . For extremely hydrophobic chemicals, not only will fish be more e xposed via food but they probably will assimilate chemicals fr om 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 no t necessarily relax the diffusion gradient into the fish. This fu ndamental 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, G obas et al. 1988). Predicting residue levels for chemicals whos e principal route of exchange is dietary is further complicated since most fish species demonstrate well-defined size-dependen t, taxonomic, and temporal trends regarding the prey they consume

. Consequently, one would not generally expect a sing le BAF to be sufficiently accurate for risk assessments for a ll fish species or even different sizes of the same species. Process-based models that describe the kinetic exch ange of chemicals from food and water in concert with the g rowth 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, Th omann 1981, Jensen et al. 1982, Thomann and Connolly 1984 , Barber

### 3

BASS 2.3 Sept 24, 2012 et al. 1987, Gobas et al. 1988, Barber et al. 1991, Thomann et al. 1992, Gobas 1993, Madenjian et al. 1993), these mod els differ significantly regarding how food web structure and dietary exposures are represented. This report describes the theoretical framework, parameterization, and use of BASS ( B ioaccumulation and A quatic S ystem S imulator). This generalized, process-based, Fortran95 simulation model is designed to predict the growth of individuals and populations within an age-structured fish commu nity 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 a nd population field studies, but rather upon physical and chemica l properties that can be estimated using chemical property calcu lators 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 parame ters that can be obtained from the published literature or comput erized databases.

### 4

BASS 2.3 Sept 24, 2012 

## 2. Model Formulation To model the chemical bioaccumulation and the growth of individuals and populations within an age-structure d 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 B

f and W

d are the chemical body burden (μg/fish) and dry body weight (g dry wt/fish), respectively, of the a verage individual within the cohort; and N is the cohort’s population density (fish/ha). In Equation (2.1), J g is the net chemical exchange (μg/d) across the fish’s gills from the wa ter; J iis the net chemical exchange (μg/d) across the fish’s intestin e from food; and J

bt is the chemical’s biotransformation rate (μg/d). I n Equation (2.2), F d , E d , R , EX , and SDA are the fish’s feeding, egestion, routine respiration, excretion, and speci fic dynamic action (i.e., the respiratory expenditure in excess of R required to assimilate food), respectively, in units of g dry w t/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 identi cal to these bioenergetic models since the energy densities of f ish depend on their dry weight (Kushlan et al. 1986, Hartman and Brandt 1995b, Schreckenbach et al. 2001). Finally, in Equa tion (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 signifi cant process in determining population sizes, this process is no t 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 n ot be immediately apparent from the notation used, these equations are tightly coupled to one another. For example, the re alized 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 pop ulation densities of its predators. Finally, the fish’s die tary 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 th ese equations. Because the system of units used to formulate chemi cal exchanges is essentially the CGS-system (centimeter , gram, second) and the system of units used to formulate afish’s growth is the CGD-system (centimeter, gram, day), some uni ts conversion is necessary to make the coupled system of equations dimensionally consistent. The reader should also no te that whereas the growth of fish is modeled in terms of d ry weight, a fish’s chemical bioaccumulation is formulated in te rms 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 th eir food are generally considered to occur by passive diffusion of chemicals between a fish’s internal aqueous phase and its ext ernal aqueous environment, whether the latter is the surrounding ambient water or the aqueous phase of the fish’s intestinal conte nts. Consequently, to model these exchanges one must fir st 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 c hemical concentration can be expressed as

(2.4)

where W

w is the fish’s wet weight (g wet wt/fish); P a , P l , and P o are the fractions of the whole fish that are water, lipid, and nonlipid organic material, respectively; and C a , C l , and C

o are the chemical’s concentrations in those respective phase s. Because the depuration rates of chemicals from different fi sh tissues often do not differ significantly (Grzenda et al. 1970, v an Veld et al. 1984, Branson et al. 1985, Norheim and Roald 1985, Kleeman et al. 1986a, b), internal equilibration between th ese three phases can be assumed to be rapid in comparison to externa l exchanges. For organic chemicals, this assumption means that E quation (2.4) simplifies to (2.5) where K

land K

o are the chemical’s partition coefficients between lipid and water and between organic carbon and wate r, respectively. For metals, however, Equation (2.4) is more complic ated. Although metals do partition into lipids (Simkiss 1 983), their accumulation within most other organic media occurs by

### 5

BASS 2.3 Sept 24, 2012 complexation reactions with specific binding sites. Consequently, for metals the term P o C o /C ain Equation (2.4) could be formulated as a function of an appropriate stability coefficient and the availability of binding sites. Appendix A s ummarizes an equilibrium complexation model that was initially f ormulated for BASS . Despite its apparent correctness, this algorithm greatly overestimated metal (in particular mercury) bioaccu mulation in fish. Although this overestimation can be attribute d to several factors, the most likely explanation for the algori thm’s unsatisfactory performance is that kinetics limits the complexation of metal in fish. Because kinetic mode ling 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 K

d to model the accumulation of metals in organic media, a similar approach was adopted for BASS . Thus, for a metal (2.6) where K

lis again an appropriate partition coefficient betw een lipid and water; and K d is an appropriate metal-specific distribution coefficient. Although this equation ap pears identical to Equation (2.5) for organic contaminants, the rel ative values of K d and K

o in relation to K lcan be remarkably different. See Section 3.1.
Because C

aequals the chemical’s ambient environmental water concentration C w at equilibrium, it follows from Equations (2.4) and (2.6) that a fish’s thermodynamic bioconcentration factor ( K f = C f /C w at equilibrium) for a chemical pollutant of concer n 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 ); k g is the chemical’s conductance (cm/s) across the gills from the interl amellar water; and C

w 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 an d Mackay 1987, Barber et al. 1988, Erickson and McKim 1990). When Equations (2.5), (2.6), and (2.7) are substituted i nto this equation, one then obtains
(2.9) Although the chemical’s conductance k g could be specified as a ratio of the chemical’s diffusivity to the thicknes s of an associated boundary layer, implementation of this d efinition can be problematic since the boundary layer thickness i s a function of the gill’s ventilation velocity and varies along the length of the gill’s secondary lamellae. To avoid this problem, afish’s net chemical exchange rate coefficient, S g k g , can be estimated by reformulating the gill’s net chemical exchange as (2.10) where Q is the fish’s ventilation volume (cm 3 /s); and C B is the chemical’s bulk concentration in the expired gill w ater. 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 ventilati on volume of fish can be estimated by (2.12)
where R O 2is the fish’s rate of oxygen consumption (μg/s); 

### " O2 is the fish’s oxygen assimilation efficiency; and C w,O2 is the environmental water’s dissolved oxygen concentratio n (μg/ml). If one makes certain assumptions concerning the geo metry of the interlamellar spaces and the nature of mass transpo rt between the gill’s secondary lamellae, the chemical’s normalize d bulk concentration in the expired gill water ( C w -C B )/( C w -C a ) can also be calculated as outlined in the following. Because the gill’s secondary lamellae form flat cha nnels having high aspect ratios (i.e., mean lamellar height / in terlamellar distance), they can be treated as parallel plates, and the flow of water between them can be treated as Poiseuille sli t flow (Hills and Hughes 1970, Stevens and Lightfoot 1986). Underthis assumption, an expression for a chemical’s concentr ation in the bulk expired gill water can be obtained using the s olutions 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

### 6

BASS 2.3 Sept 24, 2012 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 locat ed at x = ± r where r is the hydraulic radius of the lamellar channel th at 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 formulat ed as the ratio of the fish’s ventilation volume to the gill’s cros s sectional pore area, X

g (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 fis h’s mean interlamellar flow velocity is given by (2.15)

To solve Equation (2.13), two boundary conditions m ust be specified. Because adjacent lamellae presumably exc hange the chemical equally well, the solutions should be symmetrical about the channel’s midline. To insure this characteristi c, the boundary condition (2.16) is assumed. The second necessary boundary conditionmust describe how chemical exchange across the secondarylamellae actually occurs. Assuming steady state diffusion fr om the interlamellar water to the fish’s aqueous blood, th is boundary condition can be formulated as (2.17)
where k

mis the permeability (cm/s) of the gill membrane. Although this boundary condition has been used as i s (Barber et al. 1991), it can also be modified to address poten tial perfusion limitations on gill uptake. To accomplish this latt er task, a formulation patterned after Erickson and McKim (199 0) is used. In particular, consider the following reformulation
(2.18)
where C

a (y ) is the chemical’s aqueous phase concentration at point y along the length of a secondary lamella; C a ( l ) = C

ais the chemical’s concentration in the afferent lamellar b lood; J s (y, l ) is the chemical’s accumulated uptake (μg/s) along the lamellar segment [ y, l ]; and q p is the lamellar perfusion rate (cm 3 /s). If both sides of the lamella uptake chemical, then J s (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 b e written as
(2.20)
Once the solution of Equation (2.13) for these boun dary conditions has been obtained, the chemical’s bulk c oncentration in the expired gill water can be evaluated using th e 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 obta ined by nondimensioning C(x, y) , x , and y as follows (2.22)

(2.23)

(2.24) When this is done, the chemical’s dimensionless bul k concentration is given by

### 7

BASS 2.3 Sept 24, 2012 (2.25)

where N

Gz= ( l D )/( V r 2 ) is the gills’ dimensionless lamellar length or Graetz number. Two important features of this expression can now be observed. First, one can easi ly verify that (2.26) Consequently, Equation (2.11) can be rewritten as (2.27) Secondly, analytical expressions for 

### 1 B are readily available (Brown 1960, Grimsrud and Babb 1966, Colton et al. 1971, Walker and Davies 1974). In particular, a chemical’ s dimensionless bulk concentration can be evaluated b y (2.28)
where the coefficients B mand exponents 

### 8 mare 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 number s and ventilation / perfusion ratios that are typical of fish gills, only the first two terms of the series are needed to estimat e 

### 1 B with less than 1% error (see Barber et al. 1991). See Figure 2.1 and Figure 2.2 for displays of 

### 8 1 and B 1 and of 

### 8 2and B 2 , respectively. 

### 2.3. Modeling Exchange from Food Chemical uptake from food has usually been modeled assuming that fish assimilate a constant fraction of the che mical that they ingest, i.e., (2.30) where 

### " cis the assimilation efficiency (dimensionless) forthe chemical; C

p is the chemical’s concentration (μg/g wet wt) in the ingested prey; and F w is the fish’s daily wet weight prey consumption (g wet wt/d) (Norstrom et al. 1976, Jen sen et al. 1982, Thomann and Connolly 1984, Niimi and Oliver 1 987). However, because the chemical exchange across the i ntestine is driven by diffusive gradients (Vetter et al. 1985, Clark et al. 1990, Gobas et al. 1993), such formulations are thermodynamically realistic only if 

### " cis a decreasing function of the fish’s total body concentration C f . A thermodynamically based description for the dietary uptake of chemicals can be formulated using the simple mass b alance relationship (2.31) where E

w is the fish’s daily wet weight egestion (g wet wt/ d) and C e is the pollutant’s chemical concentration (μg/g we t wt) in the fish’s feces. When this equation is reformulated interms of dry weight feeding and egestion (i.e., and where P dp and P de denote the prey’s and feces’ dry weight fractions , respectively) the fish’s net dietary ex change becomes

(2.32)

where C

ae and C

de are the pollutant’s chemical concentrations in the aqueous and dry phases of the fish’s feces, res pectively; E ais the mass / volume of the feces’ aqueous phase; and P ae and P ap are the aqueous fractions of the fish’s feces and p rey, respectively. To parameterize Equation (2.32), two assumptions are made. Because the transit time through the gastrointestin al tract is relatively slow, the first of these assumptions is that the concentrations of chemicals in the fish’s aqueous b lood, intestinal fluids, and dry fecal matter equilibrate with one another. Consequently, C ae = C

a . Moreover, for organic chemicals the concentration ratio C de / C ae can be replaced with an organic carbon / water partition coefficient, K oc(e.g., Briggs 1981, Karickhoff 1981, Chiou et al. 1986), and for metals this ratio can be substituted with a distribution coeffi cient similar to the one used in Equation (2.6). Although reported values for the percent moisture o f the intestinal contents of fish vary typically between 50 and 80% (Brett 1971, Marais and Erasmus 1977, Grabner and H ofer

### 8

BASS 2.3 Sept 24, 2012 1985), the second assumption made to parameterize E quation (2.32) assumes that the fish’s intestinal contents and whole body are osmotically equilibrated. Consequently, P ae = P a . If this assumption is reasonable, then meals with the same dry weights but different wet weights should be processed by th e fish at equal rates and efficiencies since both will attain the s ame proximate composition relatively soon after ingestion. Having the same proximate composition implies that the concentratio ns of digestive enzymes acting on the meals will be comparable and that the physical forces exerted by the gut content s that control gastric mobility will also be comparable. Because B romley (1980), Garber (1983), and Ruohonen et al. (1997) d emonstrated that initial dietary moisture content had no signif icant 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 P ae = P aseems 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 K

e is the distribution coefficient describing the che mical partitioning between the aqueous and dry organic ma tter 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 thermodynami cally based assimilation efficiency for Equation (2.30) corresp onds 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 incre ase. Studies by Lieb et al. (1974), Gruger et al. (1975), and Op perhuizen and Schrap (1988) corroborate this prediction (see Barb er et al. 1991). Moreover, using in situ preparations of chan nel catfish intestines Doi et al. (2000) have clearly establish ed that preexposures to 3,4,3',4'-tetrachlorobiphenyl do de crease intestinal uptake rates. Although the preceding model development demonstrat es the potential logical inconsistency between an assumed constant chemical assimilation efficiency model for dietary chemical uptake and a thermodynamically based model, many re searchers continue to use the former assumption and model. Parameters for these constant chemical assimilation efficiency mod els generally have been estimated using the following equations p roposed by Bruggeman et al. (1981) (2.36)

(2.37)
where 

### N ww

is the fish’s specific feeding rate (g wet wt/g we t wt/d); and k

2 is the fish apparent elimination rate coefficient (g wet wt/g wet wt/d or 1/d) that is the sum of its ra te coefficients of growth ( 

### ( ), biotransformation ( k bt ), and actual excretion ( kex ). See for example Muir et al. (1992), Dabrowska et al. (1 996), and Fisk et al. (1998). Unfortunately, many researchers have failed to acknowledge that Equation (2.37) is the solutionto Equation (2.36) only when initial time is t 0= 0 and the fish’s initial wholebody concentration is zero (i.e., C f ( t 0 ) = 0). This fact, combined with the ability of Equation (2.37) to fit experime ntal results statistically, has been at least partially responsi ble 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 observ es 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 

### N ww and C

p are constant. Therefore, let T = ( t 2- t 0 ) denote the length of such a dietary exposure, and let t 1 denote the time when the exposure is half over. Du ring the first half of the exposure (i.e., t 0< t < t 1 ) the fish’s bioaccumulation dynamics will be described by Equation (2.39). Duri ng the second half of the exposure, however, these dynamic s will be

### 9

BASS 2.3 Sept 24, 2012 described by (2.40) where and are the fish’s assimilation efficiency and apparent elimination rate coefficient that may requ ire updating for t 1 < t < t 2 . If an equation of the form of Equation (2.37) is assumed to describe the fish’s bioaccumulation dyna mics over the entire interval [ t 0 , t 2 ], then the derivatives specified by Equations (2.39) and (2.40) must be equal when eval uated for t
= t 1 . This consistency condition, that is analogous to the preservation of derivatives that occurs when approx imating 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 assimila tion efficiencies estimated for different times and initial whole-body concentration will be different. Phrased another wa y, this equation implies that the fish’s ability to excrete , biodilute, and biotransform chemicals, as measured by and k 2 , contributes to the determination of the fish’s realized chemica l 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 powerfunctions, 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 tha t . Importantly, this simple analysis is corroborated b y findings of Ram and Gillet (1993) who showed that assimilation efficiencies for a variety of organochlorines by oligochaetes de creased as chemical exposures progressed. In terms of application, the proposed fecal partiti oning model (i.e., Equation (2.33)) is best suited to circumsta nces where its equilibrium assumptions are reasonably satisfied, a s herein where the object is to predict the dietary exchange of th e average individual of a population. A more kinetically base d 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 chemical s in fish is a simple, first order reaction of the chemical’s aqueous phase concentration, i.e., (2.46) where 

### , ais the fish’s aqueous phase biotransformation rate coefficient (1/d); and ( P aW w) is the volume of the fish’s aqueous phase. If Equations (2.9) and (2.46) are used to de scribe chemical bioconcentration during a water-only expos ure without growth, then a fish’s whole-body concentration woul d be modeled as

(2.47)

where k u , kex , and k bt are the fish’s rate coefficients of gill uptake, excretion, and biotransformation, respectively. In terms of quantitative structure activity relationships (QSAR s), this equation predicts that a fish’s whole-body biotrans formation rate k bt should be inversely proportional to its thermodyna mic bioconcentration factor K f that in turn is proportional to the chemical’s K

ow . This relationship, however, will also be influenced by any QSAR dependencies that the fish’s aqueous phase biotransformation rate 

### , amight 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 b efore describing how BASS actually models fish growth. Although the temperature dependence of physiological processes i s often described using an exponential response equation, e .g., (2.48)

### 10

BASS 2.3 Sept 24, 2012 where p 0and p 1 are the reaction rates of the process at temperatures T 0and T

1 , respectively, such descriptions are generally valid only within a range of the organism ’s thermal tolerance. In most cases, the process’s reaction ra te increases exponentially with increasing temperature up to a t emperature T 1 after which it decreases. Moreover, the temperature at which a process’s rate is maximum is often very close to th e organism’s upper thermal tolerance limit. To model this behavi or, Thornton and Lessem (1978) developed a logistic multiplier t o describe the temperature dependence of a wide variety of physiol ogical processes. Although this algorithm has been used su ccessfully in many fish bioenergetic models, BASS uses an exponential-type formulation that responds hyperbolically to increas ing temperature. Importantly, such algorithms can be ea sily parameterized.
Let Pdenote the rate of a physiological process, and le t T 1 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 T 1 , then Equation (2.48) can be used to describe this process for T and T

0<< T

1 , i.e., (2.49)
(2.50)
where P 0is the process’s rate at the low-end reference temperature T

0 . To incorporate the adverse effects of high temperatures on this process, the right-hand side o f Equation (2.50) can be multiplied by a hyperbolic temperatur e term that approaches unity as temperature decreases well belo w T 1 , equals zero at T1 , and becomes increasingly negative as temperature approaches the fish’s upper thermal tolerance limit T L= T 2 . Modifying Equation (2.50) in this fashion yields
(2.51)
whose solution is
(2.52) If one assumes, without loss of generality, that T 0= 0, the preceding equation can be simplified to (2.53) Figure 2.3 displays the behavior of this equation for P 0= 1 and T 2= 36 Celsius as a function of 

### , and T 1 . 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 ap plications of Equations (2.52) and (2.53) see Lassiter and Kearns (1974), Lassiter (1975), and Swartzman and Bentley (1979). Note that when T

1 = T 2 , 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 weig ht, BASS does not directly simulate the wet weight of fish. Inste ad, it simulates the dry weight of fish as the mass balance of feedi ng, egestion, respiration, and excretion and then calculates the fish’s associated wet weight using the following relations hips (2.54) (2.55) (2.56) (2.57) where W

a , W d , W l , and W

o are the fish’s aqueous, dry, lipid, and non-lipid organic weights, respectively; and a 0 , a 1 , l 1 , and l 2are 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 Equ ation (2.55) is assumed because simple power functions of this f orm adequately describe many morphometric relationships for most organisms, Equation (2.56) is based on the results of numerous field and laboratory studies (Eschmeyer and Phillip s 1965, Brett et al. 1969, Groves 1970, Elliott 1976a, Staples an d Nomura 1976, Craig 1977, Shubina and Rychagova 1981, Beami sh and Legrow 1983, Weatherley and Gill 1983, Flath and Di ana 1985, Lowe et al. 1985, Kunisaki et al. 1986, Morishita e t al. 1987). These equations yield an expression for a fish’s we t weight that is a monotonically increasing, but nonlinear, function of the fish’s dry weight.

BASS calculates a fish’s realized feeding by first esti mating its maximum ad libitum consumption ( g dry wt/d) and then adjusting this potential by the availability of app ropriate prey as described in the next section. Because a variety of models are commonly used to describe the maximum feeding of fi sh, BASS is coded to allow users the option of using any one of four different feeding models for any particular age / s ize class of fish. The first formulation that can be used is a tempera ture-dependent power function (2.58)

### 11

BASS 2.3 Sept 24, 2012 where f

1 , f 2 , f 3 , T 1 , and T

2are empirical constants specific to the fish’s feeding. A commonly used alternative to the preceding allome tric model is the Rashevsky-Holling model that is defined by the equations

(2.59)

where 

### N dd

is the fish’s ad libitum feeding rate (g dry wt/g dry wt/d); G max

is the maximum amount of food (g dry wt/fish) that the fish’s stomach / gut can hold; Gis the actual amount of food (g dry wt/fish) present in the gut; and A d and E

d are the fish’s assimilation and egestion, respectively, in units o f g dry wt/d (Rashevsky 1959, Holling 1966). Given a fish’s gut capacity G max , feeding time t sat to satiation, and satiating meal size M sat , 

### N dd can be estimated using the equations
(2.60)

(2.61)

(2.62) where is the total food consumed during the interv al and (also see Dunbrack 1988). Alternatively, 

### N dd can be estimated by simply assuming that M sat = 0.95 × G max

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 Q clis the planktivore’s clearance volume (L/d) that is as sumed to be given by (2.65)
where q 1 , q 2 , q 3 , T 1 , and T

2are 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 expect ed growth and routine respiratory demands. In particular, bec ause assimilation, egestion, specific dynamic action, an d excretion are assumed to be linear functions of feeding and routi ne respiration as discussed subsequently, it is a straightforward matter to calculate a fish’s expected ingestion given its exp ected growth and respiration. When users elect this feeding opti on, BASS assumes that the fish’s specific growth rate (1/d) is given by (2.66)
where g 1 , g 2 , g 3 , T 1 , and T

2are empirical constants specific to the fish’s growth rate. See Thomann and Connolly (1984) for additional discussion of the use of this feeding mo del. 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 inges tion F d , i.e., (2.67) (2.68) where 

### "

f is the fish’s net food assimilation efficiency tha t is a weighted average of its assimilation efficiencies f or invertebrate, piscine, and vegetative prey. However, when the Ras hevskyHolling feeding model is used, BASS calculates these fluxes by substituting F

d with a function that describes the fish’s pattern of intestinal evacuation. The general form of this fun ction is assumed to be (2.69)
where e 1 , e 2 , e 3 , T 1 , and T

2are empirical constants specific to the fish’s gastric evacuation. The numerical value of this function’s exponent, e 2 , depends both on characteristics of the food item being consumed and on the mechanisms that presumably control gastrointestinalmotility and digestion (Jobling 1981, 1986, 1987). For example, when gut clearance is controlled by intestinal peristalsis, e 2should approximately equal 1⁄2 since peristalsis is stimulat ed 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 r ate of digestion, e 2should be approximately either 

### b or unity. If the fish consumes a small number of large-sized prey (e .g., a piscivore), e 2= 

### b may be the appropriate surface area model. On the other hand, if the fish consumes a large numberof smaller,

### 12

BASS 2.3 Sept 24, 2012 relatively uniform-sized prey (e.g., a planktivore or drift feeder), e 2= 1 is more appropriate since total surface area a nd total volume of prey become almost directly proportional to one another. When e 2= 1, the Rashevsky-Holling model (i.e., Equation (2.59)) is analogous to the Elliott-Persso n model for estimating daily rations of fish (Elliott and Perss on 1978). Finally, Olson and Mullen (1986) outlined a process -based model that even suggests e 2= 0. A fish’s specific dynamic action, i.e., the respira tory expenditure associated with the digestion and assimilation of f ood, is modeled as a constant fraction of the fish’s assimi lation. 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, R O2 (g O 2/d), using a respiratory quotient, RQ (L CO 2respired)/L O 2consumed), 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-depen dent power function. In particlar, (2.72)
where b 1 , b 2 , b 3 , T 1 , and T

2are 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 tota l respiration since excretion and oxygen consumption generally track on e another. For example, ammonia excretion increases after feed ing, as does oxygen consumption (Savitz 1969, Brett and Zala 197 5, Gallagher et al. 1984). Likewise, conditions that i nhibit the passive excretion of ammonia also depress carbon di oxide 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 whicheach age class of a species can feed upon other fish spe cies, benthos, incidental terrestrial insects, periphyton / attach ed algae, phytoplankton, and zooplankton. The realized feedin g of any given age class of fish is determined by the estima ted maximum feeding rate of individuals within the cohort, the cohort’s population size, and the biomass of prey that is av ailable to the cohort; the latter quantity is the sum of the curre nt biomass of potential prey minus the biomass of potential prey expected to be consumed by other fish cohorts that are more effici ent foragers / competitors. BASS ranks the competitive abilities of different cohorts using the following assumptions: ASSUMPTION 1. The competitive abilities and efficie ncies of benthivores and piscivores are positively correlate d with their body sizes (Garman and Nielsen 1982, East and Magna n 1991). Two general empirical trends support this assumptio n. The first of these is the trend for the reactive distances, s wimming speeds, and territory sizes of fish to be positively correl ated with their body size (Minor and Crossman 1978, Breck and Gitte r 1983, Wanzenböck and Schiemer 1989, Grant and Kramer 1990 , Miller et al. 1992, Keeley and Grant 1995, Minns 1995). Gi ven two differently sized predators of the same potential p rey, these trends would suggest that the larger predator is mo re likely to encounter that prey than is the smaller. Having enc ountered the prey, the other general trend for prey handling times to be inversely correlated with body size (Werner 1974, M iller et al. 1992) suggests that the larger predator could dispa tch intercepted prey and resume foraging more quickly than the smal ler predator. Also see Post et al. (1999) and Railsback et al. (2 002). ASSUMPTION 2. Unlike benthivores and piscivores, th e competitive abilities and efficiencies of planktivo res are inversely related to their body size due to their r elative morphologies (Lammens et al. 1985, Johnson and Viny ard 1987, Wu and Culver 1992, Persson and Hansson 1999). Cons equently, “large” planktivores only have access to the leftov ers of “small” planktivores.

BASS calculates the relative frequencies of the prey consumed by a cohort using dietary electivities, i. e., (2.74)
where f

iis the relative availability of the i -th prey with respect to all other prey consumed by the cohort. One can easi ly verify that

### 13

BASS 2.3 Sept 24, 2012 the range of dietary electivities is . One can also verify that if the fish does not eat a potential fo od item i that . Similarly, if the fish consumes a potential prey item iin direct proportion to the prey’s relative abunda nce, then . BASS actually allows users to specify a fish’s diet as either a set of fixed dietary frequencies , a set o f electivities , or a combination of fixed frequencie s and electivities . To calculate a cohort’s realized dietary composition, however, BASS converts all user supplied fixed dietary frequencies into their equivalent ele ctivities using the simulated relative abundances of the cohort’s potential prey. These electivities are then combine d with any user specified electivities to form a set of unadjusted electivities that is subsequently converted into a consistent set of realized electivities . Using the se 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 se t of realized electivities. Although this conversion is sometimes unnecessary, it is generally needed to insure that the sum of th e 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 satisfi ed 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 c orrelation 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, M oore 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, Juan es et al. 1993, Mattingly and Butler 1994, Hale 1996, Madenji an 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, on ly a specific size range of forage fish is assumed to be availabl e to a piscivorous cohort. BASS characterizes this size spectrum by using either linear or complementary exponential fu nctions to describe the maximum, minimum, and mode of prey bod y lengths that a predator of a given body length will ingest. In particular, these key features of a fish’s prey spectrum are de scribed 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 iof forage fish available to a piscivorous cohort are then calculat ed relative to the sum of forage fish biomasses whose body lengths are both greater than Lmin

and less than Lmaxminus the biomass of those prey sizes predicted to be consumed by more efficie nt piscivorous cohorts (see Assumption 1). When two or more cohorts of a forage species ican be consumed by a piscivore, the relative frequencies of those c ohorts s ijin 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 L i1 and L i2 denote the body lengths of two age classes of species ithat are prey for the cohort. If P ijis the triangular distribution function

(2.81)

the relative frequencies of these two age classes i n the cohort’s diet are calculated to be

### 14

BASS 2.3 Sept 24, 2012 and . If only one age class of a forage species is vulnerable to the cohort, then . If while calculating the dietary frequencies of a p iscivorous cohort BASS predicts that the cohort’s available prey is insufficient to satisfy its desired level of feedin g, BASS reassigns the cohort’s unadjusted electivities in a manner t o simulate prey switching. These reassignments are ba sed on the following assumption: ASSUMPTION 3. When forage fish become limiting, pis civores switch to benthic macroinvertebrates or incidental terrestrial insects as alternative prey. However, piscivores th at must switch to benthos or that routinely consume benthos in add ition to fish are less efficient benthivores than are obligate be nthivores (Hanson and Leggett 1986, Lacasse and Magnan 1992, Bergman and Greenberg 1994). Consequently, only the leftove rs of nonpiscivorous benthivores are available to benthic fe eding piscivores. If such resources are still insufficien t to satisfy the piscivores’ metabolic demands, piscivores are assumed then to switch to planktivory (Werner and Gilliam 1984, Mag nan 1988, Bergman and Greenberg 1994). In this case, piscivor es have access only to the leftovers of non-piscivorous pla nktivores. Using this assumption, BASS first assigns the cohort’s electivity for benthos to zero regardless of its previous valu e. 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 terre strial insects and that planktivores can shift their diets to include benthos and terrestrial insects. See, for example, Ingram and Z iebell (1983). After BASS has calculated a cohort’s dietary composition, it then assigns the cohort’s individual realized feeding ra te 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 AB jis the biomass (g dry wt/ha) of prey jthat is available to that cohort. Using its predicted dietary compositions and realiz ed feeding rates, BASS then calculates the predatory mortalities for eachfish cohort and nonfish compartments. 

### 2.8. Modeling Stable Isotopes and Trophic Position To provide a summary output variable that integrate s the temporal and body size dynamics of a fish’s dietarycomposition, BASS simulates time dynamics of each cohort’s carbon an d nitrogen stable isotopic signatures (i.e., and ). Although other models have been proposed for this p urpose (see Hesslein et al. 1993, Harvey et al. 2002, Olive et al. 2003), BASS assumes that the change in a fish’s mean stable iso tope ratio during an arbitrarily “small” fraction or mul tiple of a day while feeding on a single prey item can be describe d 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 assimil ation; denotes the fish’s assimilation efficiency for that prey; is the fish’s dry weight ingestion rate (g dry wt 

### A d

! 1 ); is the length (d) of the chosen time interval; is the fish’s fractio nation constant associated with its routine metabolism (i.e., respi ration and excretion); and is the fish’s daily metabolic loss rate (g dry wt 

### A d

! 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 wt 

### A g dry wt

! 1


### A d

! 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 wt 

### A g dry wt

! 1


### A d

! 1 ). Equation (2.86), can be reparameterized by notin g that most stable isotope studies assume that at or near steadystate the difference between the stable isotope sig natures of any two successive trophic levels is a constant. For Eq uation (2.86), this assumption implies that (2.87) where denotes the fish’s steady-state stable isoto pe ratio. Consequently, (2.88)

### 15

BASS 2.3 Sept 24, 2012 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 pr ey 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 verte brates can be adequately characterized by the self-thinning powerfunction relationship (2.93) where N is the species’ or cohort’s density (fish/ha) and W w is the species’ or cohort’s mean wet body weight (Damuth 1 981, Peters and Raelson 1984, Juanes 1986, Robinson and Redford 1986, Dickie et al. 1987, Boudreau and Dickie 1989, Gordo a and Duarte 1992, Randall et al. 1995, Dunham and Vinyar d 1997, Steingrímsson and Grant 1999, Dunham et al. 2000, G uiñez 2005). For fish the body weight exponent b generally varies from 0.75 to 1.5 (Boudreau and Dickie 1989, Grant and Kr amer 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 al so been reported (Steingrímsson and Grant 1999). If Equatio n (2.93) is differentiated with respect to time, it immediatelyfollows 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 concerni ng the underlying process-based interpretation and general applicability of this result should consult Peterson and Wroblews ki (1984), McGurk (1993, 1999), and Lorenzen (1996). Because Equations (2.93) and (2.94) encompass the c ohort’s predatory mortality, non-predatory mortality, and d ispersal, and because BASS separately models the cohort’s predatory mortality , BASS assumes that the cohort’s combined rate of dispers al ( EM ) and non-predatory mortality ( NM) is simply a fraction 

### * of b 

### (
. In particular, (2.95) If community population dynamics are strongly domin ated 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 d ispersal 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 p redicting a cohort’s combined dispersal and non-predatory morta lity ( EM + NM ) may be sufficient to simulate realistically bound ed populations for most species, under certain conditi ons some species may tend to exhibit unbounded or unrealisti c population dynamics. To correct such behaviors, a simulation o ption has been implemented that imposes an additional mortali ty and dispersal on all of a species’ cohorts as that spec ies’ total biomass approaches a user-defined biomass carrying capacity. This algorithm assumes that a cohort’s realized bio mass 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 speci es’ biomass carrying capacity constraint; S is the species total biomass; and K is the species biomass carrying capacity. It is in teresting to note that Equation (2.96) can also be written as

### 16

BASS 2.3 Sept 24, 2012 (2.99) where corresponds to the cohort’s instantaneous carrying capacity given the species’ existing age c lass structure. That is, the cohort’s biomass attains a local maxim um when X

= K c . Consequently, the hyperbolic multipliers used in these equations are mathematically analogous to those use d to model temperature effects on the cohort’s physiological f unctions (see Section 2.5).

BASS estimates a species’ recruitment by assuming that each species turnsover a fixed percentage of its potenti al spawning biomass into new young-of-year (YOY). This percenta ge is referred to as the species’ reproductive biomass in vestment ( 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 ( tl r0 ) marking the species’ sexual maturation. When reproduction is simulated, the bod y weight of each sexually mature cohort is decreased by its rbi and the total number of YOY that are recruited into the populatio n as a new cohort is estimated by simply dividing the species’ total spawned biomass by the species’ characteristic YOY body wei ght. Although this formulation does not address the myri ad of factors known to influence population recruitment, it is lo gically consistent with the spawners’ abundance model for f ish recruitment. See Myers and Barrowman (1996) and Mye rs (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 feed ing, reproduction / recruitment, and dispersal / non-predatory mortal ity for any or all species. Because these multipliers are assumed to be analogous to subcomponents of habitat suitability i ndices, they are assumed to take values from 0 to 1. If these mu ltipliers are not specified by the user, BASS assigns them the default value of 1. When feeding habitat multipliers ( HSI feeding ) 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 re places in Equation (2.82). These multipliers are assumed t o modify the fish’s ability to perceive or to intercept prey either by effecting the fish’s reactive distance, foraging patterns, et c. 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 multipli ers since these interactions are automatically addressed by the alg orithms outlined in Section 2.7. Like the aforementioned feeding habitat multipliers , BASS uses any specified recruitment habitat multipliers ( HSI recruitment ) 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 availabi lity of suitable spawning sites or the ability of the otherwise succ essful spawns to result in the expected numbers of young-of-year as discussed in Section 2.8. Finally, when habitat multipliers ( HSI survival ) are specified for dispersal / non-predatory mortality, the specified values are assumed to dynamically control the species self-thi nning exponent b (see Section 2.8) such that the exponent is maximu m for and minimum for . Thus, as habitat suitability decreases, dispersal and non-predatory mortality increases, and vice versa (see Equation (2.95)). Inbetween this range the species self-thinning exponent is assumed to respond linearly to changing HSI survival , i.e., (2.102)
Because constructing the aforementioned habitat sui tability multipliers in a general or standard way is not a trivial issue, BASS relegates their construction to the user. Neverthe less, users might consider several obvious starting points whensimulating habitat effects using BASS . Turbidity, for example, is known to affect the foraging abilities of both prey and pred atory 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, R eid et al. 1999, Vogel and Beauchamp 1999, Bonner and Wilde 20 02, de Robertis et al. 2003, Sweka and Hartman 2003) to es timate feeding multipliers for Equation (2.100) as power f unctions or polynominals of turbidity. Field-based HSI’s are of ten 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

### 17

BASS 2.3 Sept 24, 2012 BASS assumes that the nonfish components of a communityof concern can be treated as four lumped compartments, i.e., benthos, periphyton/attached algae, phytoplankton, and zooplankton. These compartments can be treated eith er as community forcing functions or as bona fide state v ariables. 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, mortalitydue to fish consumption, and non-consumptive mortality and disp ersal, 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) 

### N dd , 

### D , and 

### : are minimally functions of temperature and time. For benthos, phytoplankton, and zooplankton that ca n be conceptualized as populations of organisms possessi ng similar body sizes, the rate coefficients 

### N dd , 

### 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 b enthos compartment, it follows that the expected density o f individuals within the benthos compartment is simply (2.107) Given that the consumption (g dry wt/d) of the afor ementioned average benthic invertebrate is described by (2.108) it follows that the ingestion of the benthos compar tment at large can be modeled as

(2.109) Formulating compartmental ingestion, photosynthesis , and respiration using this method not only delineates a n objective procedure to parameterize BASS , but also yields production relationships that are consistent with results repo rted by Plante and Downing (1989), Stockwell and Johannsson (1997) , and Kuns and Sprules (2000). When estimating 

### N dd , 

### D , and 

### : for benthos, phytoplankton, or zooplankton using this approach, BASS assumes that (2.110)
where Pis the individual’s ingestion, photosynthesis, or respiration in units of g dry wt/d; and p 1 , p 2 , p 3 , T 1 , and T

2are empirical constants specific to the process of inte rest. Although BASS does not attempt to simulate the average individua l 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 comple x amalgamations of filamentous and unicellular algae, it is difficult to conceptualize this compartment as a population o f archetypical individuals and to employ the preceding model parameterization scheme. Consequently, for periphyton BASS assumes that 

### N dd , 

### D , and 

### : are generally temperature-dependent allometric fun ctions 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 possi bly mortality of periphyton communities are generally limited by their surfacevolume relationships that are implicitly represente d by these equations.
Because 

### N dd

is generally much greater than 

### D , the astute reader will recognize that Equations (2.103) - (2.106) wil l predict unbounded autocatalytic growth for any nonfish comp artment whose predatory mortality and non-predatory mortali ty/dispersal does not precisely balance its intrinsic growth rat e. To prevent such unrealistic dynamics, BASS internally estimates a physiologically based carrying capacity for each no nfish compartment based on its projected daily oxygen con sumption and the community’s prevailing dissolved oxygen con tent. In

### 18

BASS 2.3 Sept 24, 2012 particular, BASS assumes that compartmental oxygen consumption cannot exceed the dissolved oxygen cont ent corresponding to the difference between the communi ty’s prevailing dissolved oxygen concentration (DOC) and an assumed hypoxic threshold of 4 mg O 2/L. When the compartment’s daily oxygen consumption is predicted to exceed this available dissolved oxygen content, compartmen tal growth is suspended by equating the compartment’s feeding/photosynthesis to its projected respiration

.
BASS assumes that the rates of chemical bioaccumulationin nonfish compartments are rapid enough to enable che mical concentrations within these components to be calcul ated using simple bioaccumulation factors. In particular, (2.114) where C

nf is the chemical concentration ( 

### : g /g dry wt) in the compartment of concern. BASS enables users to specify the bioaccumulation factor BAF nf (ml/g dry wt) for Equation (2.114) either as an empirically derived constant, as a qua ntitative structure activity relationship (QSAR), or as the r atio of the chemical’s uptake rate to the sum of its excretion rate and the compartment’s growth rate. When BAF nf is specified as a QSAR, BASS assumes that (2.115) where b 1 and b 2are empirical constants. When BAF nf is specified by the compartment’s chemical exchange rates and gr owth rate, BASS assumes that (2.116)
where k 1 , k2 , and 

### ( are the rates of uptake, excretion, and growth, respectively, by individuals comprising the compartment; and K nf is the compartment’s thermodynamic bioconcentrationfactor that is defined analogously to Equation (2.7). For heter otrophs, 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 conc ern. Although this assumption is not satisfied for all benthic orplanktonic heterotrophs, it does bypass the need to specify fe eding rates, assimilation efficiencies, and dietary compositions for compartments that are actually mixed functional gro ups. For further discussions of Equation (2.116) and its gen eralization, 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 i n physiological function that is induced by chemical agents. Becaus e the potency of narcotic agents was originally found to be corre lated with their olive oil / water partition coefficients (Meyer 189 9, Overton 1901), it was long believed that the principal mech anism of narcosis was the disruption of the transport functi ons of the lipid bilayers of biomembranes (Mullins 1954, Miller et a l. 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 physi ological function either by changing their conformal structu re or by changing the configuration or availability of theiractive sites (Eyring et al. 1973, Adey et al. 1976, Middleton an d Smith 1976, Richards et al. 1978, Franks and Lieb 1982, 1984, L aw et al. 1985, Lassiter 1990). In either case, however, the idea that the presence of narcotic chemicals increases the physic al 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 org anic chemicals can be assumed to act minimally as narcotics (Fergu son 1939, McCarty and Mackay 1993). Studies have shown that for narcotic chemicals ther e is a relatively constant chemical activity within expose d organisms associated with any given level of biological activ ity (Ferguson 1939, Brink and Posternak 1948, Veith et al. 1983). This relationship holds true not only for exposures to asingle chemical but also for exposures to chemical mixture s. In the case of a mixture of chemicals, the sum of the chemical activities for each component chemical is constant for a given lev el of biological activity. Because narcotic chemicals canbe treated as generalized physiological toxicants as already note d, it should not be too surprising that the effects of mixtures of chemicals possessing diverse specific modes of action not onl y often resemble narcosis but also appear to be additive interms of their toxic effects (Barber et al. 1987, McCarty and Mack ay 1993). For example, although most pesticides possess a spe cific 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 tha t the chemicals of concern are an additive mixture of nar cotics. Because this assumption is the least conservative a ssumption that one could make concerning the onset of effects, mor talities predicted by BASS should signal immediate concern. When the total chemical activity of a fish’s aqueous phase e xceeds its

### 19

BASS 2.3 Sept 24, 2012 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 pha se is simply the sum of the fish’s aqueous phase chemical activity f or each chemical. BASS calculates the aqueous phase chemical activity of each chemical using the following formulae

(2.117)

where A ais the chemical’s aqueous activity; 

### ( ais the chemical’s aqueous activity coefficient (L/mol), the reciproca l of its subcooled liquid solubility; M ais 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 f or each species as the geometric mean of the species’ LA 50 , i.e., the ambient aqueous chemical activity that causes 50% mortality in an exposed population. These lethal thresholds are calculated using the above formulae with user-specified LC 50 's substituted for C

a . These calculations are based on two important assumptions. The first assumption is that the expos ure time associated with the specified LC 50is sufficient to allow almost complete chemical equilibration between the fish an d the water. The second assumption is that the specified LC 50is the minimum LC 50that kills the fish during the associated exposure interval. Fortunately, most reliable LC 50 '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 abov e approach to modeling ecotoxicological effects. First, for narcotic chemicals this approach is analogous to the toxic u nit approach for evaluating the toxicity of mixtures (Calamari a nd Alabaster 1980, Könemann 1981a, b, Hermens and Leeuwangh 1982 , Hermens et al. 1984a, Hermens et al. 1984b, Broderi us and Kahl 1985, Hermens et al. 1985b, Hermens et al. 1985c, H ermens 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 McC arty 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 bynarcotic agents would be expected to occur. Results reported by Hermens et al. (1984b) indicate that for Daphnia the ratio of the EC 50for reproductive impairment to the LC 50is generally on the order of 0.15 - 0.30 for chemicals whose log K ow range from 4 to 8. For individual growth inhibition, however, the mean EC 50to LC 50 ratio for Daphnia in 16 day chronic exposures was approximately 0.77 (Hermens et al. 1984a, Hermens e t al. 1985b). Also see Roex et al. (2000).

### 20

BASS 2.3 Sept 24, 2012 Table 2.1 Summary of the notation used for model development excluding empirical parameters describing fundamen tal model processes, rates, or rate coefficients.
A a chemical activity in aqueous fraction of the fish ( dimensionless) A d assimilation rate (g dry wt/d) B f chemical burden in whole fish (μg/fish) BAF nf bioaccumulation factor for nonfish prey (ml/g dry w t) C a chemical concentration in aqueous fraction of the f ish (μg/ml) C ae chemical concentration in aqueous fraction of intes tinal contents (μg/ml) C B chemical concentration in bulk interlamellar water (μg/ml) C e chemical concentration in egesta/feces (μg/ml) C f chemical concentration in whole fish (μg/g wet wt) C de chemical concentration in dry organic fraction of i ntestinal contents (μg/g dry wt) C l chemical concentration in lipid (μg/g dry wt) C nf chemical concentration nonfish prey (μg/g dry wt) C o chemical concentration in non lipid organic matter (μg/g dry wt) C p chemical concentration in prey (μg/g wet wt) C w chemical concentration in environmental water (μg/ml) C w,O2 oxygen concentration in environmental water (μg/ml) d interlamellar distance (cm) d i the relative frequency of prey iin a fish’s diet (dimensionless) D aqueous diffusion coefficient (cm2/s) e i the electivity prey iin a fish’s diet (dimensionless) E d egestive rate (g dry wt/d) E w 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 iin the field (dimensionless) maximum feeding rate (g dry wt/d) F d feeding rate (g dry wt/d) F w feeding rate (g wet wt/d) G mass of gut contents (g dry wt/fish) h height of secondary lamellae (cm) HSI feeding habitat suitability index for cohort feeding (dime nsionless) HSI recruitment habitat suitability index for YOY recruitment (dimensionless) HSI survivalhabitat suitability index for cohort survival (dimensionless) J bt biotransformation of chemical (μg/s) J g net chemical exchange across the gills (μg/s) J i net chemical exchange across the intestine (μg/s) k 2 apparent elimination rate coefficient (ml/g wet wt/ d, g wet wt/g wet wt/d, or 1/d), i.e., k bt chemical biotransformation rate coefficient (ml/g w et 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) k g overall chemical conductance across the gill from the interlamellar water to the aqueous blood (cm/s) k m chemical conductance through the gill membrane (cm/ s) K e partition coefficient for fecal matter (ml/g wet wt ) K f thermodynamic bioconcentration factor (ml/g wet wt) K lpartition coefficient between generic lipid and wa ter (ml/g dry wt) K o partition coefficient between non-lipid organic matter and water (ml/g dry wt) K oc partition coefficient between organic carbon and wa ter (ml/g dry wt) K ow partition coefficient between n-octanol and water ( ml/ml) l lamellar length (cm) L fish’s body length (cm)

### 21

BASS 2.3 Sept 24, 2012 M a chemical molarity in aqueous fraction of the fish ( mol/L) N population density (fish/ha) N Gz Graetz number (dimensionless) = ( l D )/( V r 2 ) N Sh Sherwood number (dimensionless) = ( k mr)/ D NM non-predatory mortality (fish/ha/d) P a aqueous or moisture fraction of whole fish (g water /g wet wt = ml/g wet wt) P ae aqueous or moisture fraction of feces/egesta (g wat er/g wet wt = ml/g wet wt) P ap aqueous of moisture fraction of prey/food (g water/ g wet wt = ml/g wet wt) P de dry fraction of feces/egesta (g dry wt/g wet wt) P dp dry fraction of prey/food (g dry wt/g wet wt) P d dry fraction of whole fish (g dry wt/g wet wt), i.e ., P l lipid fraction of whole fish (g dry wt/g wet wt) P o non-lipid organic fraction of whole fish (g dry wt/ g wet wt) PM predatory mortality (fish/ha/d) Q ventilation volume (cm 3 /s) r hydraulic radius of interlamellar channels (cm), i. e., R routine respiratory rate (g dry wt/d) R O2 oxygen consumption rate (mg O 2/s or g O 2/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) W a weight/volume of fish’s aqueous phase (g water/fishor ml/fish) W d weight of fish (g dry wt/fish) W l weight of fish’s lipid phase (g dry wt/fish) W o weight of fish’s nonlipid organic phase (g dry wt/f ish) W w weight of fish (g wet wt/fish) X g cross sectional pore area of the gill (cm2 ) 

### " c assimilation efficiency of chemical (dimensionless) 

### " f assimilation efficiency of food (g dry wt assimilat ed/g dry wt ingested) 

### " O2 oxygen assimilation efficiency of the gill (dimensi onless) 

### ( 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) 

### N dd specific feeding rate (g dry wt/g dry wt/d) 

### N ww specific feeding rate (g wet wt/g wet wt/d) 

### 0 solution viscosity (poise) 

### < molar volume (cm 3 /mol) 

### D lamellar density (lamellae/mm)

### 22

BASS 2.3 Sept 24, 2012

Figure 2.1 First eigenvalue and bulk mixing cup coefficient f or Equation (2.28) as a function of gill Sherwood number and ventilation / perfusion ratio.

### 23

BASS 2.3 Sept 24, 2012

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.

### 24

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

### 25

BASS 2.3 Sept 24, 2012 

## 3. Model Parameterization Because reliable application of a model depends not only on the validity of its formulation but also on its par ameterization, important aspects regarding the parameterization of BASS ’s bioaccumulation and physiological algorithms are di scussed below. 

### 3.1. Parameterizing K f Superficially, estimation of a fish’s thermodynamic bioconcentration factor K f via Equation (2.7) appears to require a great deal of information. This task, how ever, is much simpler than it first appears. For example, gi ven a fish’s lipid fraction (see Equation (2.56)), it is a strai ghtforward matter to calculate the fish’s aqueous fraction usi ng Equation (2.55). Having done so, one can then immediately ca lculate the fish’s non-lipid organic fraction since P a , P l , and P o must sum to unity (i.e., Equation (2.57)). For an organic chemical, the partition coefficients K land K

o can be estimated using the chemical’s octanol / wat er partition coefficient K

ow . Although triglycerides are the principal storage lipids of fish and it would seem reasonable to esti mate K lusing a triglyceride / water partition coefficient, BASS assumes that K lequals K

ow . To estimate K o , BASS assumes that a fish’s nonlipid organic matter is equivalent to organic carbo n and uses Karickhoff’s (1981) regression between the organic carbon / water partition coefficient ( K oc ) and K

ow to estimate this parameter. Specifically, (3.1)
For metals or metallo-organic compounds such as methylmercury, the chemical’s lipid partition coeff icient K

l can again be assumed to equal its octanol / water parti tion coefficient K

ow . A metal’s distribution coefficient into non-lipid organic matter, however, cannot be estimated using the K oc relationship of Equation (3.1). For example, wherea s the K ow of methylmercury at physiological pH’s is approxima tely 0.4 (Major et al. 1991), its distribution coefficient i nto environmental organic matter is on the order of 10 4 - 10 6 (Benoit et al. 1999b, Benoit et al. 1999a). O’Lough lin et al. (2000) report similar differences for organotin com pounds. Whereas distribution coefficients for metals into f ecal matter generally should be assigned values comparable to those used to model the environmental fate and transport of me tals, distribution coefficients for metals into the non-l ipid 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 coef ficients and exponents for total gill surface area, coeffici ents 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 tot al gill areas, are generally available in the literature. See Barb er (2003). BASS estimates d 1 and d 2 from 

### D 1 and 

### D 2 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 re gressions rather than simple linear regressions (Rayner 1985, Jensen 1986); the data used for their development are take n from Saunders (1962), Hughes (1966), Steen and Berg (196 6), Muir and Brown (1971), Umezawa and Watanabe (1973), Gali s 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 (cm 3 /mol) (Hayduk and Laudie

### 26

BASS 2.3 Sept 24, 2012 1974). The diffusivity of a chemical through the gi ll membrane that is needed to estimate the membrane’s permeabil ity k mis then assumed to equal one half the chemical’s aqueo us diffusivity (Piiper et al. 1986, Barber et al. 1988 , Erickson and McKim 1990). The other quantity needed to estimate k mis 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 oxyg en consumption rates assuming an extraction efficiencyof 60% and a saturated dissolved oxygen concentration (see Equation (2.12)). Perfusion rates (ml/hr) are estimated usin g (3.10) as the default for all species. Although this expre ssion, in units of L/kg/hr, was developed by Erickson and McKim (19 90) for rainbow trout ( Oncorhynchus mykiss ), it has been successfully applied to other fish species (Erickson and McKim 1 990, Lien and McKim 1993, Lien et al. 1994). The eigenvalues and bulk mixing cup coefficients ne eded to parameterize Equation (2.28) are interpolated inter nally by BASS from matrices of tabulated eigenvalues and mixing cup coefficients that encompass the range of Sherwood n umbers (i.e., 1< N

Sh<10) and ventilation / perfusion ratios (i.e., 1 < Q v / Q p < 20) that are typical for fish (Hanson and Johans en 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 use d 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 Fo rtran 95 software program. This program not only decodes fun ctional expressions for BASS model parameters that have been reported in the literature but also calculates its own regre ssions using data reported in the literature. Each species withi n the BASS database is assigned its own data file whose name c orresponds to its genus and species. Thus, all literature dataand regressions pertaining to largemouth bass are compi led 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 literatur e regressions are recorded as reported; any required units conver sions 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 estim ate the needed parameters. For example, when a fish’s oxyge n 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 t o report a fish’s oxygen consumption, some researchers use the function (3.13) or equivalently (3.14) When such power functions of temperature are encoun tered, synthetic datasets of “observed/predicted” oxygen consumption are generated using the reported regres sions 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 datase ts is used to convert the temperature-dependent functions (Kitche ll et al. 1977, Thornton and Lessem 1978) employed by the wid ely used Wisconsin Bioenergetics Fish Model (Hanson et al. 1997) into the hyperbolic Arrhenius formulation ass umed by BASS
. Although the BASS database generator performs most of its parameter estimations using univariate statistics o r ordinary linear least-squares regression analysis as appropr iate, 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-squ ares

### 27

BASS 2.3 Sept 24, 2012 problems using a modified Newton’s method with anal ytic Jacobians and a secant updating algorithm to comput e the required Hessian matrix. See Dennis et al. (1981). Estimation of Specific Growth Rates

BASS uses specific growth rates ( 

### ( = W -1 d W /d t) not only to estimate a cohort’s rate of dispersal and non-preda tory mortality (see Equation (2.95)) but also as a parameter by which a cohort’s expected ingestion rate can be bac kcalculated, if desired. Estimating specific growth rates for BASS , however, obviously depends on the underlying mode l used to describe the fish’s expected growth rate dy namics (i.e., d W /d t). Selecting an appropriate growth model for use bythe BASS simulation software, like most model selections, w as not a trivial issue since over the past 50 years at lea st four different models (i.e., von Bertalanffy, Richards, Gompertz, and ParkerLarkin models) have become standard tools for chara cterizing the growth of fishes. See Ricker (1979) for a detai led discussion of these models and other less commonly used models. According to the von Bertalanffy model, a fish’s gr owth rate is the simple mass balance of anabolic processes th at are directly proportional to the fish’s surface area an d of catabolic processes that are directly proportional to the fis h’s body weight. Consequently, the fish’s growth dynamics ar e governed by the following differential equation (3.15)
where 

### N is the fish’s rate of feeding and assimilation; an d 

### D is the fish’s total metabolic rate. Assuming isometric growth (i.e., W w = 

### 8 L 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 se tting Equation (3.15) to zero. For further discussion, se e 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 o f isometric growth and strict proportionality between a fish’s feeding/assimilatory processes and its absorptive s urface 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 model s appear to have strong physiological foundations, a critica l analysis of the parameters of these models casts doubts on suchassertions. One particular point of contention is the assumptio n that a fish’s metabolism (i.e., respiration and excretion) is directly proportional to its body weight. Although this assu mption 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 wei ghts. Consequently, from a purely physiologically-based perspective, a better anabolic-catabolic process mo del for fish growth would be (3.18) See Paloheimo and Dickie (1965). Unlike the von Ber talanffy and Richards models, however, this model generally does not have a closed analytical solution. Furthermore, whe n this model is fit to observed data, there is no a priori guarantee that the fitted exponents will actually match expected p hysiological exponents unless the analysis is suitably constrain ed. In light of these criticisms, simpler empirical gro wth models may be more than adequate for most applications. Tw o 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) si mply assumes that (3.20) where the exponent 

### $ is less than 1. Although each of the aforementioned models can desc ribe very different growth trajectories, much of the discussi on surrounding their use has focused on whether the mo dels 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, K night (1968) argued that the traditional notion of asympt otically zero growth is seldom, if ever, supported by studies tha t have focused on actual growth increments rather than on size-at-age. Because the Parker-Larkin model is the only model o utlined above that assumes fish growth is fundamentally

### 28

BASS 2.3 Sept 24, 2012 indeterminate, and because the Parker-Larkin model does not rely on the a priori assumption that fish respirati on is a linear function of their body weight as does the von Berta lanffy and Richards models, this model is used exclusively by BASS when needed. Three basic types of data have been traditionally u sed to calculate fish growth rates; these are: 1) length a t age or capture, 2) back-calculated length at age for speci fic age classes sampled over multiple years, and 3) back-ca lculated length at age for specific year classes or cohorts. Backcalculated body lengths for the latter two data typ es are generally calculated by regression using measured g rowth increments of body scales, otoliths, pectoral spine s, or other “hard” structures. Whereas for a length at age data set each individual fish contributes only one observation (i .e., its current length), each individual fish contributes atime series of body lengths for both of the remaining types of growth data. To estimate specific growth rates for fish, body le ngths at age that have been reported in the literature, whether backcalculated or not, are converted into wet body weig hts using weight-length regressions that were reported by either the study of interest or other published sources. Estim ated wet body weights are then fit to the analytical solutio n ParkerLarkin growth model, (3.21) using the NL2SOLV nonlinear optimization software. The explicit solution of the Parker-Larkin growth model for any time interval [ t

0 , t] is (3.22) However, because this expression is discontinuous a t g 2 = 0, the growth parameters g 1 and g 2are actually obtained by fitting calculated body weights to the equivalent expressio n (3.23) where g 2= exp ( b ). Estimation of Hyperbolic Arrhenius Functions When a fish’s daily rate of maximum food ingestion, plankton filtration, gastric evacuation, respiration, or gro wth exhibits a temperature optimum, the BASS database generator fits the process’s actual or synthetic data to the hyperboli c Arrhenius function (3.24) The BASS database generator also fits actual or synthetic d ata regarding satiation meal size and feeding times to satiation to the above equation when these feeding parameters ex hibit temperature optima. Testing of the initial NL2SOL-b ased procedure developed to estimate the parameters of E quation (3.24) revealed that the convergence performance of NL2SOL could be greatly improved by reconfiguring Equation(3.24) as (3.25)
where T

maxis the maximum temperature of the dataset being fitted, and, therefore, . Because estimation of nonlinear parameters are frequently sensitive to th eir required initial estimates, a three-step procedure was devel oped to estimate the parameters for Equation (3.25). The first step in this procedure estimates a mean b ody weight exponent by using repeated linear least-squares re gressions (3.26) on data subsets whose maximum range of temperatures is less than 3 Celsius. The second step of the procedure then uses NL2SOL t o 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 lea st-squares is used in the third and final step in the estimati on process. Initial parameter estimates for Equation (3.27) are generated by first fitting the cubic polynomial (3.28) using ordinary linear least-squares techniques. Thetemperature T e corresponding to the local maximum of the above cu bic polynomial, i.e.,
(3.29)

(3.30) is then assigned as the process’s optimum temperatu re T1 for

### 29

BASS 2.3 Sept 24, 2012 each set of initial parameter values. Initial estim ates for 

### * are then assigned assuming that the fish’s upper “toler ance” temperature corresponds to equidistant temperatures within the interval (3.31) Initial estimates of the process’s temperature coef ficients p 3 are similarly assigned as equidistant values within the interval (3.32) Having assigned initial estimates for p 3 , T 1 , and 

### * , the process’s rate at T 0= 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 subr outines used to implement this procedure can do so by simply req uesting this code from the author. 

### 3.5. Suggested Calibration Procedures Calibrating Fish Growth Rates Because Equations (3.21) and (3.22) do not explicit ly account for either reproductive losses or temperature-depen dent growth, growth rates estimated by these equations g enerally should be calibrated for the application at hand wh en backcalculating fish ingestion rates from estimated gro wth rates. Having estimated a long-term average growth rate (3.34) for a species of interest, the calibration procedur e developed for BASS assumes that the fish’s specific growth coefficien t g 1 is actually an exponential function the fish’s ambi ent water temperature that, in turn, is assumed to be a sinus oidal function of the time of year. In particular, (3.35) where defines the fish’s Q 10relationship for growth; T mis the mean annual water temperature experienced by the fish; and 

### " , 

### $ , and 

### T are the coefficients describing the amplitude, frequency, and phase shif t of the water temperatures experienced by the fish, respectively. Under this assumption a fish’s growth is therefore described by (3.36)
If t 0is the day that the species’ young-of-year are rec ruited into the population, and m is the integer age in years when the fish becomes sexually mature, it then follows that a fis h’s prespawn body weight at the time of its first reproduc tion 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 g rowth 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 prod uction and increased metabolic expenditures associated with spawning behaviors. If n is the species’ maximum integer age in years, Equation (3.36) can be integrated betweenany two consecutive spawning events as follows

(3.40)

### 30

BASS 2.3 Sept 24, 2012 (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 particularapplication using Equations (3.34), (3.39) and (3.42), one must obviously specify the parameters ( T m , 

### " , 

### $ , and 

### T ) describing the application’s water temperatures and the species’ m aximum 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 maxim um age ( ), and the species’ allometric growth exponent ( g 2 ). 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 r ates estimated for brook trout ( Salvelinus fontinalis ) from literature data will how be calibrated for a “typical” Mid-Atl antic trout stream whose annual temperature regime is assumed t o 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 t o 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 year s. 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 reprodu ctive loss constant is assumed to equal 0.2 g wet wt/g wet wt/ spawn. Finally, the trout’s growth Q 10is 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 tr out’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 p re-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 ( 

### N ddg dry wt/g dry wt/d) are backcalculated monthly using this equation and standard salmonid metabolic relationships (i.e., food assimilation ef ficiencies, specific dynamic action (SDA) to ingestion ratios, oxygen consumption rates, respiratory quotients (RQ), and ammonia excretion to oxygen consumption quotients(AO)) as o utlined by Barber (2003), the following allometric regressi on can be calculated
(3.48) This regression agrees well with results of Sweka a nd Hartman (2001) who estimated the maximum consumption of bro ok trout at 12 Celsius to be (3.49) Taken together, the proceeding equations imply that the realized ingestion rate of brook trout at 12 Celsiu s would be approximately 42% of their maximum ingestion rate. This result agrees well with that reported by Elliott an d 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 bac kcalculated feeding rate estimation. See Section 5.6

. Estimating Initial Conditions Although most fish surveys typically report only ei ther total

### 31

BASS 2.3 Sept 24, 2012 species densities (fish/ha) or total species biomas s (kg wet wt/ha), such data can be easily converted into BASS initial conditions if one assumes that the recruitment stre ngth for each cohort of observed population density has been rela tively 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 esti mated by applying Equation (3.51) to each of its cohorts, i. e.,

(3.52)

where N

i , W f,i , and a idenote the density, average wet body weight, and age, respectively, of the i -th cohort. Assuming that each cohort is recruited into the species’ total po pulation 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 ex pected decomposition of a species total population densityinto 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 w ould be

(3.55)

From Equations (3.54) and (3.55) it should be reaso nably 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 straig htforwardly calculate the species’ apparent long term year-clas s strength N 0 . Having done so, one cannot only estimate the spec ies’ cohort densities but also convert the species’ tota l population density into its expected total biomass and vice ve rsa. To corroborate the density-to-biomass conversion pr ocedure outlined above, a database of studies that have rep orted measured fish densities and associated fish biomass es was compiled from the literature (Miles 1978, Quinn 198 8, 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. 199 8, Radwell 2000, Dettmers et al. 2001, Pierce et al. 2001, Hab era et al. 2004). Reported fish densities were converted into estimated biomasses assuming evenly spaced self-thinning expo nents b ranging from -0.5 to -1.0 at 0.025 increments. Redu ced major axis (RMA) regressions were then calculated for eac h assumed self-thinning exponent. The self-thinning exponent that minimized the intercurve area between the calculate d 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 th e identity relationship . In addition to calibrating fish growth rates and ba ckcalculating feeding rates, the auxiliary BASS program BASS

_ FILES
. EXEdescribed in the preceding section estimates initial body weights and cohort densities for users given a target initial total species density or a target in itial total species biomass. See Section 5.6.32BASS 2.3 Sept 24, 2012Figure 3.1Selected 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 andthe fish’s upper tolerance limit.33BASS 2.3 Sept 24, 2012Figure 3.2Selected 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 andthe fish’s upper tolerance limit.

### 34

BASS 2.3 Sept 24, 2012 Figure 3.3 Observed fish biomass versus fish biomass predicte d by cohort self-thinning BASS ’s algorithm.

### 35

BASS 2.3 Sept 24, 2012 Species Process p 1 p 2 p 3 T 1 T

2 r 2
1 Channa argus c max [g/d] 0.00741 0.52 0.425 29.2 51.3 0.99 2 Coregonus hoyi c max [g/g/d] 0.159 -0.54 0.320 16.8 26.0 0.96 3 Morone saxatilis c max [g/g/d] 0.000945 0.00 0.708 25.9 58.7 0.97 4 Morone saxatilis c max [g/g/d] 0.00542 0.00 0.455 21.6 42.1 0.85 5 Pomoxis annularis c max [g/d] 0.00213 0.03 1.051 23.1 43.0 0.50 6 Salmo trutta c max [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 c max [g(dw)/g/d] 0.00123 0.00 0.489 16.5 29.0 0.79 13 Salvelinus confluentus c max [g/g/d] 0.00840 0.00 0.288 14.0 29.0 0.98 14 Siniperca chuatsi c max [g/d] 0.0267 0.60 0.212 30.3 44.5 0.99 15 Tilapia zillii c max [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 satia tion meal size reported in the literature.

Data sources and notes

1 Liu et al. (1998). Rates estimated by regression a ssuming no feeding or lethality at 43 Celsius. 2Binkowski and Rudstam (1994). Rates as reported inTable 1 assuming no feeding or lethality at 26 Cel sius. 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 Fi gure 1 assuming no feeding or lethality at 43 Celsi us. 5 Hayward and Arnold (1996). Rates as reported in Table 1 assuming no feeding or lethality at 43 Celsiu s. 6 Elliott (1976b). Rates generated by regressions re ported 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 ch ironomids. 10Elliott (1975). Data as reported in Table 4 for me alworms ( Tenebrio molitor). 11 Elliott (1975). Data as reported in Table 4 for ol igochaetes. 12Larsson and Berglund (1998). Rates as reported in Table 1 assuming no feeding or lethality at 26 Cels ius. 13 Selong et al. (2001). Rates calculated from data r eported in Table 2 assuming no assuming or lethality at 26 Celsius. 14 Liu et al. (1998). Rates estimated by regression a ssuming no feeding or lethality at 43 Celsius. 15 Platt and Hauser (1978). Rates estimated from Figu re 1 assuming no feeding or lethality at 43 Celsius

.

### 36

BASS 2.3 Sept 24, 2012

Species f 1 f

2 f

3 T

1 T

2 r 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 distr ibuted database. Observed data corresponds to the m aximum 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.

### 37

BASS 2.3 Sept 24, 2012

### 38

BASS 2.3 Sept 24, 2012 

## 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. Th e model enables users to simulate the population and bioacc umulation dynamics of age-structured fish communities using the temporal and spatial resolution of a day and a hectare, resp ectively. 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 year ly age classes can be used for any species. This flexibility enabl es users to simulate small, short-lived species such as daces, live bearers, and minnows together with larger, long-lived specie s 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, o r age. The user then specifies the dietary composition of thes e foraging classes as a combination of benthos, incidental terrestrial insects, periphyton, phytoplankton, zooplankton, and/or othe r fish species, including its own. Standing stocks of nonf ish compartments can be simulated either as external fo rcing functions or as state variables. Although BASS was developed to simulate the bioaccumulation of chemical pollutants within a community or ecosys tem context, it can also be used to simulate population and comm unity dynamics of fish assemblages that are not exposed t o chemical pollutants. For example, in its present form BASS could be used to simulate the population and community dynamics o f fish assemblages that are subjected to altered thermal r egimes 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 manageme nt 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, o r 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 spe cies 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 ca n 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 sp ecies become unavailable to piscivores can be specified. See the fish command / ECOLOGICAL

_ PARAMETERS option refugia[]=

fnc

.


### ! Size dependent harvest and stocking functions can b e specified for any or all species to simulate fisher ies management practices. See the fish command /FISHERY

_ PARAMETERS
.


### ! Habitat suitability indices (HSI) can be specified to adjust a fish’s realized feeding/growth, recruitment/spawn ing, and combined dispersal and non-predatory mortality. See the fish command /HABITAT

_ PARAMETERS
.


### ! Benthos, periphyton, phytoplankton, and zooplanktoncan be simulated either as forcing functions or as stat e variables. Incidental insects, however, can only be simulated as a forcing function.

### 39

BASS 2.3 Sept 24, 2012 

### ! There are no restrictions to the number of chemical s 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 adaptiv e step sizing that monitors the accuracy of its integratio n. BASS ’s Runge-Kutta integrator is patterned on the fifth-or der Cash-Karp Runge-Kutta algorithm outlined by Press e t 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. Current ly, 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. Se e 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 & transp ort 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: /command 1 argument(s) /command 2 argument(s) 

### ! ! /command n argument(s) /end The leading slash ( / ) identifies the line as a co mmand. Blanks or tabs before or after the slash are not significa nt. The keyword or phrase (i.e., command n ) that follows each slash identifies the type of data being specified by that record. Keywor ds 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 co mmand allows multiple arguments or options, each argument must b e separated by a semicolon. Commands can be continued by append ing an ampersand (&) to the end of the record; therefore, the following commands are equivalent /command arg 1 ; arg 2 ; arg 3 ; arg 4 ; arg 5 ; arg 6 /command arg 1 ; arg 2 ; arg 3 ; &

arg 4 ; arg 5 ; arg 6 Because each record is transliterated to lowercase before being decoded, the case of the input file is not signific ant. Likewise, because consecutive blanks or tabs are collapsed in to a single blank, spacing within a command is not significant. The maximum length of a command line, including continu ation lines, is 1024 characters. An exclamation mark (!) in the first column of a li ne identifies that line as a comment. An exclamation mark can be also placed elsewhere within a record to start an end-of-line c omment, i.e., the remainder of the line, including the exclamatio n mark, will be ignored. The last command in any BASS project file must be /END . This command terminates program input and any text or co mmands following it are ignored. BASS checks the syntactical accuracy of each input command as it is read. If no syntax erro rs are encountered, BASS then checks the specified input parameters for completeness and internal inconsistency.

BASS input data and commands are broadly classified int o four categories: simulation control parameters, chemical parameters, fish parameters, and nonfish biotic parameters. Sim ulation control parameters provide information that is appl icable to the simulation as a whole, e.g., length of the simulati on, the ambient water temperature, water column depth, and any desi red output options. Chemical parameters specify the chemical’s physicochemical properties (e.g., the chemical’s molecularweight, molecular volume, n-octanol / water partition coeff icient, etc.) and the chemical’s exposure concentrations in vario us media.

### 40

BASS 2.3 Sept 24, 2012 Fish parameters specify the fish’s feeding and metabolic demands, dietary composition, predator-prey relatio nships, gill morphometrics, body composition, and initial conditions for the body weights, whole-body chemical concentrations, a nd population sizes of a fish’s cohorts. Nonfish bioti c parameters specify how benthos, terrestrial insects, periphyto n, and plankton will be simulated.
A BASS project file is actually constructed and managed a s a series of include files, i.e., blocks of closely re lated input commands. These files are specified using the inclu de statement # include '

filename ' where filename is the name of the file containing the desired commands. Each include file specifies data for eith er a chemical, a fish species, or a nonfish biotic component. Cons equently, a typical BASS project file is structured as follows: ! file: bass_input_file.prj ! notes: a BASS project file as specified by include files ! / command 1 simulation control_data / command 2 simulation control_data / command 3 simulation 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 fashi on. 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 simulati on, the ambient water temperature, the community’s water le vel, 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 string 1 ; ...; string n /BIOTA string 1 ; ...; string n /FIRST

_ LEAP

_ YEAR string /FISH

_ CARRYING

_ CAPACITY string /FGETS no argument/option required /HEADER string /ISOPTOPE

_ PARAMETERS string 1 ; string 2 /LENGTH

_ OF

_ SIMULATION string /LESLIE

_ MATRIX

_ SIMULATION no argument/option required /MONTH

_ T 0 string /NONFISH

_ QSAR string 1 ; ...; string n / SUMMARY

_ PLOTS string 1 ; ...; string n /TEMPERATURE string 1 ; string 2 / WATER

_ LEVEL string 1 ; string 2 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 signific ant. The use of these commands is described below in alphabetica l order.


### O / ANNUAL

_ OUTPUTS integer This command specifies the time interval, in years, between BASS ’s annual tabulated and plotted outputs. This numbe r must be a nonnegative integer. BASS assumes a default value of zero that signifies that no annual outputs will be gener ated. Because the BASS Output Analzyer enables users to generate customiz ed tables and plots, this command is actually a vestig ial option of BASS v2.1.


### O / ANNUAL

_ PLOTS string 1 ; ...; string n This command specifies the variables whose annual d ynamics 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 ti me (day of year) and the species’ age, length, or weight cl ass;



### ! baf( variable ) generates plots of each species’ bioaccumulation factor (i.e., the ratio C f / C w) for each chemical as a function of time (day of year) and th e species’ age, length, or weight class;



### ! bmf( variable ) generates plots of each species’ biomagnification factor (i.e., the ratio C f / C prey ) for each chemical as a function of time (day of year) and th e species’ age, length, or weight class;

### 41

BASS 2.3 Sept 24, 2012 

### ! cfish( variable ) generates plots of each species’ wholebody concentration (ppm) for each chemical as a fun ction 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 o f five size classes that are defined by BASS based on the species’ largest / oldest and smallest / youngest individuals.


### O / BIOTA string 1 ; ...; string n This BASS v2.1 command specifies nonfish standing stocks tha t are to be generated as forcing functions rather tha n as simulated state variables. Although this command has been sup erceded 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 functionfnc . 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 = 2 

### B / period is its frequency (1/ xunits ).



### ! nonfish_name [

yunits ] = file(

filename ) to read and interpolate the specified compartmental standing st ock 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 simulatio n day is March 31. This assignment can be changed using the comman d /MONTH

_ T 0. These options are only required when the user is si mulating fish that feed on these resources (see the “diet” optionfor /ECOLOGICAL

_ PARAMETERS ). Note, however, because BASS assumes that piscivorous fish switch to benthic inv ertebrates and incidental terrestrial insects when appropriate for age fish are unavailable, the benthos and insect options should be specified even when simulating only piscivorous fish. Also no te 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 gr owth and bioaccumulation of individual fish are simulated. T he 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.

### 42

BASS 2.3 Sept 24, 2012 

### 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 bioma ss 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 v alid 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. U sing this input and the initial relative biomass of each spec ies, 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 string 1 ; string 2 This command specifies how BASS will estimate a fish’s operational trophic position TPfrom 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 si mulation. The valid syntax for string is



### ! 

### " [ units ] where 

### " is a nonnegative real value. The time unit specifi ed with brackets is converted into days for internal use an d 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 frame work of a multispecies Leslie matrix population model. A coho rt’s mortality is predicted using a single, lumped, self -thinning mortality rate (i.e., Equation (2.94)) without atte mpting to partition its total mortality into predatory and no n-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 predicte d using the methods described in Section 2.7. While this simula tion option is designed partially to lessen the need for detail ed food web information and the work required to calibrate a fu ll community simulation, it is also designed to simulate more re alistically the population dynamics of communities in which the dominant process driving cohort mortality and self-thinning is dispersal rather than predation.


### O / NONFISH

_ QSAR string 1 ; ...; string n This command specifies the quantitative structural activity relationships for the bioconcentration / bioaccumul ation factors of the nonfish compartments benthos, periphyton, ph ytoplankton, and zooplankton that are to be applied to all chemi cals . 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

_ BCFor exposures specified by / EXPOSURE
. 

### O / MONTH

_ T 0 string This is an optional command that specifies the month that

### 43

BASS 2.3 Sept 24, 2012 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 tempera ture, community’s water level, and various output options

. 

### O / SUMMARY

_ PLOTS string 1 ; ...; string n 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, orall 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 ti me (day of simulation) and the species’ age, length, or wei ght class;



### ! baf( variable ) generates plots of each species’ bioaccumulation factor (i.e., the ratio C f / C w ) 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 C f / C prey ) 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 fun ction of time (day of simulation) and the species’ age, l ength, or weight class;



### ! pop( variable ) generates plots of each species’ population density (fish/ha) as a function of time (day of sim ulation) 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 clas ses that are defined by BASS based on the species’ largest / oldest and smallest / youngest individuals.


### O / TEMPERATURE string 1 ; string 2 This command specifies a community’s ambient water temperatures. For an unstratified water body only o ne string option is specified. In this case valid options forthis 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 simula tion where 

### " is the mean temperature for the chosen time period, 

### $ is its amplitude (yunits ), 

### T is its phase angle (radians), and 

### N =2 

### B / 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 cas e 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 si mulation day is March 31. This assignment can be changed using the command /MONTH

_ T 0.


### O / WATER

_ LEVEL string 1 ; string 2 This command specifies a community’s actual water l evel. For an unstratified water body only one string option is s pecified. 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

### 44

BASS 2.3 Sept 24, 2012 amplitude (yunits ), 

### T is its phase angle (radians), and 

### N =2 

### B / 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, va lid 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 si mulation day is March 31. This assignment can be changed using the command /MONTH

_ T 0. 4.3.2. Chemical Input Commands The physico-chemical properties and exposure concen trations of each chemical of interest are specified by a block of twelve commands, i.e.,
/ CHEMICAL string /EXPOSURE string 1 ; ...; string n /LETHALITY string 1 ; ...; string n /LOG

_ AC real number /LOG

_ KB 1 real number /LOG

_ KB 2 real number /LOG

_ P real number /MELTING

_ POINT real number /METABOLISM string 1 ; ...; string n /MOLAR

_ VOLUME real number /MOLAR

_WEIGHT real number /NONFISH

_ BCF string 1 ; ...; string n The command / CHEMICAL must be the first command in the block since it identifies the start of a new set of chemi cal parameters. The order of the remaining commands, however, is no t 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 anew chemical. Each chemical name must be a single character strin g without embedded blanks or hyphens. If a two-part name is d esired, the user should use an underscore “_” as a separating c haracter. This command must precede the commands /EXPOSURE , /LETHALITY , /LOG

_ AC , /LOG

_ KB 1, /LOG

_ KB 2, /LOG

_ P , /METABOLISM , /MOLAR

_WEIGHT , /MOLAR

_ VOLUME , and /MELTING

_ POINT . The name specified by this command is used in conjuncti on with the command /INITIAL

_ CONDITIONS to input initial whole-body concentrations of chemicals in each age class of th e fish of concern and with the command /METABOLISM to specify daughter products of chemical biotransformation. If the userspecifies chemical exposures via the file option, the indicat ed name is also used to direct reading of the specified exposure fi les. Otherwise, this name is used only for output purposes; BASS does not use this name to link to any chemical data base.


### O / EXPOSURE string 1 ; ...; string n This command enables the user to specify the tempor al dynamics of chemical exposures to fish via water or contamin ated sediments or via the ingestion of benthic invertebr ates, incidental terrestrial insects, or plankton. Exposure concentr ations specified by these options are assumed to be completely bioav ailable to the fish. For example, water concentrations are assumed to be actual dissolved concentrations and not total water concen trations that include particle-bound chemical. If multiple option s 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 t o 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 accordin g to the functionfnc . Note in BASS 2.1 the six lettered name cinsct was used to specify this exposure function.



### ! cperiphyton[

yunits ]= fnc generates potential dietary exposures to fish via periphyton according to the f unction 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 th e function fnc . Note in BASS 2.1 the six lettered name cpplnkwas 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 csdmntwas used to specify this exposure function.



### ! cwater[

yunits ]= fnc generates aqueous exposure

### 45

BASS 2.3 Sept 24, 2012 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 czplnkwas 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 othe rwise, 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

_ T 0. 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 chemic al equilibrium with the ambient environmental water. I f this equilibrium is assumed to be thermodynamic, then th e coefficient 

### " generally is equal to the product of the component’s dry organic fraction and the chemical’s K ow . Also see /NONFISH

_ BCF
.


### ! nonfish_name [

yunits ]=file(

filename ) to read and interpolate the concentration of toxicant in bentho s, 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 w ith 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 orga nic fraction and the chemical’s K oc .


### ! 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 spil l 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 =2 

### B / period is its frequency (1/

xunits ). This option might be used to simulate the mobilization of sediment bound contami nants during spring or fall turnover.



### ! cwater[

yunits ]=file(

filename ) to read and interpolate the dissolved aqueous concentration of toxicant from th e file filename . See Section 4.4.3. Users should be very cautious and judicious when us ing more than one of the above options since the user can ea sily construct an exposure scenario that is inconsistent with theo retical constraints on the fate and distribution of contami nants in aquatic systems. 

### O / LETHALITY string 1 ; ...; string n This optional command specifies species-specific LC 50 's for the chemicals of concern either as an actual concentration value or as a QSAR function. Valid string options are:



### ! LC 50[units](

fish_name )= 

### "

! LC 50[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 / wat er partition coefficient; and 

### " and 

### $ are real or integer empirical constants. BASS converts user supplied LC 50 's into their corresponding aqueous chemical activities and then uses the geome tric mean of these lethal activities to trigger mortality during the simulation. If the user desires, simulation of mortality associ ated with the accumulation of a lethal aqueous chemical activity can be turnedoff by using the command line option “-l” as discus sed in Section 4.5. When this is done, however, BASS still calculates the fish’s total aqueous phase chemical activity and reports i t as a fraction

### 46

BASS 2.3 Sept 24, 2012 of the fish’s estimated lethal chemical activity to provide the user with a simple, but useful, monitor of the total che mical status of the fish.


### O / LOG

_ AC real number This command specifies the log 10of the chemical’s aqueous activity coefficient. For organic chemicals, if thi s parameter is not specified, BASS will estimate the chemical’s activity coefficient using its melting point and n-octanol / water partition coefficient.


### O / LOG

_ KB 1 real number This command specifies the log 10of 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

_ KB 2 real number This command specifies the log 10of a metal’s binding constant for refractory organic matter. This parameter is us ed 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 log 10 K ow , where K

ow is the n-octanol / water partition coefficient. /LOG

_ Pmust 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 u sed to calculate the aqueous activity coefficient for orga nic chemicals when that parameter is not specified by the user. S ee Yalkowsky et al. (1983)


### O / METABOLISM string 1 ; ...; string n This optional command specifies species-specific ra tes of biotransformation for the chemical of concern eithe r 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 BTspecifies the whole-body-referenced biotransformation rate k bt in Equation (2.47); fish_name is the common name of the fish species that can metabolize the chemical of co ncern; chemical_name is the name of the daughter product generated by the metabolism of the chemical of concern; Kow[-] i s the chemical’s n-octanol / water partition coefficient: and 

### " and 

### $ are real or integer empirical constants. If the use r does not wish to simulate daughter products because they are insi gnificant or assumed to be harmless, chemical_name can be assigned the value none . When daughter products are specified, the user mu st specify all physico-chemical properties of the iden tified byproduct in the same way that the physico-chemical p roperties of the parent compound are specified.


### O / MOLAR

_ VOLUME real number This command specifies the chemical’s molecular vol ume (cm 3 /mol) that is used to calculate the chemical’s aque ous 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 (cm 3 /mol) (Hayduk and Laudie 1974). The viscosity of water over its entire liquid range is represented w ith less than 1% error by (4.4)
where 

### 0 T is the viscosity (centipoise) at temperature T (Celsius), and 

### 0 20is the viscosity of water at 20 Celsius (1.002 cen tipoise) (Atkins 1978).


### O / MOLAR

_ WEIGHT real number This command specifies the chemical’s molecular wei ght (g/mol).


### O / NONFISH

_ BCF string 1 ; ...; string n This command specifies the bioconcentration / bioac cumulation factors for the nonfish compartments benthos, perip hyton, phytoplankton, and zooplankton either as a numerica l 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.

### 47

BASS 2.3 Sept 24, 2012 Note that this command or / NONFISH

_ QSARmust be specified for any nonfish compartment that is simulated as a comm unity 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 string 1 ; ...; string n /PREY

_ SWITCHING

_ OFF /INITIAL

_ CONDITIONS string 1 ; ...; string n / COMPOSITIONAL

_ PARAMETERS string 1 ; ...; string n /ECOLOGICAL

_ PARAMETERS string 1 ; ...; string n /MORPHOMETRIC

_ PARAMETERS string 1 ; ...; string n /PHYSIOLOGICAL

_ PARAMETERS string 1 ; ...; string n /FISHERY

_ PARAMETERS string 1 ; ...; string n /HABITAT

_ PARAMETERS string 1 ; ...; string n The command / COMMON

_ NAMEmust be the first command in the block since it is the identifier for the start of anew 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 eac h age class. Two character strings, i.e., “month” and “year”, ar e 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 co mposition of other fish species. Each common name must be a sing le character string without embedded blanks. If a two-part name is desired, the user should use an underscore “_” as a separati ng blank. See the dietoption for the command /ECOLOGICAL

_ PARAMETERS
. 

### O / COMPOSITIONAL

_ PARAMETERS string 1 ; ...; string n 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 fis h’s average lipid content is independent of its body we ight (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 string 1 ; ...; string n This command specifies the ecological parameters th at describe the fish’s trophic interactions, non-predatory mort ality, and recruitment. Valid options that must be separated b y semicolons are:



### ! ast_yoy[-]=f(b[-]= 

### " , yoy [xunits ]= 

### $ , pop [ yunits ]= 

### ( ) specifies parameters for implementing accelerated s elfthinning of young-of-year fish (YOY), or more accur ately recently recruited cohorts, that often occurs due t o intraspecies competition for territories, refugia, or other habitat resources. The functional argument b[-]= 

### " specifies the desired accelerated self-thinning exp onent. The functional argument yoy [ xunits ]= 

### $ defines the age, length, or wet weight threshold below which cohorts will be subject to accelerated self-thinning. Valid expr essions for yoy are either “ age ”, “ tl ”, or “ wt ”. The final functional argument specifies the population threshold that tr iggers accelerated self-thinning. Depending on the assumed nature of the competition, this threshold can be sp ecified either as the total density of cohorts satisfying t he 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 ) = {

prey 1 = 

### , 1 , ..., prey n = 

### , 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 ( prey n ) and their contribution ( 

### , n ) to the fish’s diet. Each prey n 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, 

### , nis interpreted either as a constant percent contributi on 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 f ield. On the other hand, if -1< 

### , n <1, then 

### , nis considered a prey

### 48

BASS 2.3 Sept 24, 2012 electivity (see Equation (2.74)). For any given for aging class, a user can specify both constant dietary per centages 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. F or example, month1 , e.g., july , or month1-month2 , e.g., july-december

. If the diet of a specified age / size class is inde pendent 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 seque nce 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 functio n 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, t wo 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 va lid 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 functionof its habitat suitability and its long-term specific grow th 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 specifie d 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 f ish) where 

### " is real empirical constant.



### ! refugia[

yunits ]= 

### " specifies a refuge population size for

### 49

BASS 2.3 Sept 24, 2012 each cohort that can be prey for community piscivor es where 

### " is real or integer constant. Yunits must be dimensionally equivalent to fish/ha. If not specifi ed, 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 s pecified, 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 requi red only when the user has not specified the species’ long-t erm 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( 

### ( ,T 1 ,T 2 ) (see /PHYSIOLOGICAL

_PARAMETERS optionsg[] ), BASS estimates the species’s long-term specific growth r ate 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 exponen t (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 string 1 ; ...; string n 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 specie s can be different. The parameters for these models are spec ified using the /PHYSIOLOGICAL

_ PARAMETERS command.



### O / FISHERY

_ PARAMETERS string 1 ; ...; string n This command specifies stocking and harvest rates f or 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 spec ified 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 expressi ons for

### 50

BASS 2.3 Sept 24, 2012 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 throug hout 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 le ngth 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 ma ny 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 o ption “-s”. See Section 4.5 for details.


### O /HABITAT

_ PARAMETERS string 1 ; ...; string n This command specifies habitat preferences, toleran ces, 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 with in the parentheses. Valid expressions for class are:



### " < a[ xunits ] < 

### $ " < l[ xunits ] < 

### $ " < w[ xunits ] < 

### $
where 

### " and 

### $ are real or integer empirical constants. This opti on can be specified repeatedly as needed. Although fora given species all class types must be the same type (i.e. , age, length, or weight), class types between species can be differe nt. 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 inge stion rate when feeding is modeled with either an allometric, Holling, or clearance volume formulation. When a cohort’s ingestion is back-calculated from its expe cted growth rate, the specified HSI is used as a simple linear multiplier on the cohort’s specific growth rate. Se e 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 f unction fnc . This HSI is used to control the species’ self-thi nning exponent that determines, in combination with the f ish’s growth rate, a cohort’s estimated dispersal and non

predatory mortality rate. See Section 2.9. Valid expressions for these HSI functions are:



### ! hsi_name [-]= 

### " 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-supp lied logistic regressions. See Sections 4.4.3. When HSI multipliers are calculated using user-supp lied 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 X

ithat are transformed or raised to an integer or re al power. Transformations recognized by BASS include: LN( X

i ) => LN_1( X

i ) => LOG( X

i ) => LOG_1( X

i ) =>

### 51

BASS 2.3 Sept 24, 2012 SQRT( X

i ) => ASIN_SQRT( X i ) => ASIN_SQRT_PCT( X i ) => Habitat variables must be specified with units incl osed within brackets, and must match in name and units to column variables specified by the data filefilename . After evaluating the specified logistic regression, BASS calculates the fish’s HSI multiplier using the standard equation hsi_name = 1 / (1 + EXP (- hsi_name_ equation))
When HSI functions are not specified by the user, BASS assigns the default value of 1 to each unspecified HSI func tion.


### O /INITIAL

_ CONDITIONS string 1 ; ...; string n This command specifies the species’ initial ages, whole-body chemical concentrations, wet body weights, and popu lation sizes. Valid options for this command are:



### ! age[ units ]={x 1 , ..., x n } to initialize the age of each cohort with the specified vector. The units that are delin eated by brackets must be dimensionally equivalent to days.



### ! chemical_name [ units ]={x 1 , ..., x n } to initialize the wholebody concentration of each cohort for the named che mical 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 equivale nt to μg/g wet wt.



### ! wt[ units ]={x 1 , ..., x n } to initialize the body size of each age class with the specified vector. The units deli neated by brackets must be dimensionally equivalent to g w et wt/fish.



### ! pop[ units ]={x 1 , ..., x n } to initialize the population density of each age class with the specified vector. The un its delineated by brackets must be dimensionally equiva lent to fish/ha.


### O / MORPHOMETRIC

_ PARAMETERS string 1 ; ...; string n This command specifies the species’ morphometric parameters that are needed to describe the exchange of chemica ls 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 semicolon s, 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 string 1 ; ...; string n This command specifies the species’ physiological p arameters 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( 

### ( ,T 1 ,T 2 ) specifies the fish’s gastric evacuation where G is the mass of food resident in the intestine, and where 

### " , 

### $ , 

### ( , T 1 , and T 2are 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 o nly if the feeding option holling( 

### A ) is used.



### ! kf_min[-]= 

### " specifies the minimum condition factor for

### 52

BASS 2.3 Sept 24, 2012 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 relations hip (see / PHYSIOLOGICAL

_ PARAMETERS option wl[ 

### A ] ).



### ! mf[ yunits ]= 

### " *W[

xunits ]^ 

### $ *H( 

### ( ,T 1 ,T 2 ) specifies the fish’s maximum filtering rate where 

### " , 

### $ , 

### ( , T 1 , and T 2 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( 

### ( ,T 1 ,T 2 ) specifies the fish’s maximum ingestion where 

### " , 

### $ , 

### ( , T 1 , and T 2are real or integer empirical constants. yunits must be dimensionally equivalent to g dry wt/d. This parame ter is required only if the feeding option allometric( 

### A ) is used.



### ! rq[-]= 

### " specifies the fish’s respiratory quotient; (i.e., L(CO 2 ) respired/L(O 2 ) 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( 

### ( ,T 1 ,T 2 ) specifies the fish’s specific growth rate where 

### " , 

### $ , 

### ( , T 1 , and T 2are 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( 

### ( ,T 1 ,T 2 ) specifies the size of the satiation meal consumed during the interval (0, st) where 

### " , 

### $ , 

### ( , T 1 , and T 2are 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( 

### ( ,T 1 ,T 2 ) specifies the fish’s standard oxygen consumption where 

### " , 

### $ , 

### ( , T 1 , and T 2are real or integer empirical constants. yunits must be dimensionally equivalent to mg O 2/hr or mg O 2/g wet wt/hr.



### ! st[ yunits ]= 

### " *W[

xunits ]^ 

### $ *H( 

### ( ,T 1 ,T 2 ) specifies the time to satiation when feeding with an initially empty s tomach where 

### " , 

### $ , 

### ( , T 1 , and T 2are 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 T

1 is the temperature at which each particular proces s’s rate is maximum and T 2is 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( 

### ( ,T 1 ,T 2 ) 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 progr am 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 satisfi ed 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 spaw ning occurs. Valid character strings for this command ar e either the name of a month or the names of two months separate d by a hyphen. For example,
/ SPAWNING

_ PERIOD may OR / SPAWNING

_ PERIOD april-june The names of the months must be spelled-out in full

.

### 53

BASS 2.3 Sept 24, 2012 

### O / SPECIES string This command specifies the scientific name (genus a nd species) of the fish to be modeled. When this command is enc ountered, BASS uses the specified scientific name to assign defau lt ecological, morphological, and physiological parame ters 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 curr ently operational in BASS v2.3. 4.3.4. Nonfish Input Commands These commands specify simulation parameters for be nthos, periphyton, incidental terrestrial insects, phytopl ankton and zooplankton. The syntax for these commands is as fo llows
/BENTHOS string 1 ; ...; string n /TERRESTRIAL

_ INSECTS string 1 ; ...; string n /PERIPHYTON string 1 ; ...; string n /PHYTOPLANKTON string 1 ; ...; string n / ZOOPLANKTON string 1 ; ...; string n Depending on the options selected, BASS generates the standing stocks of these nonfish compartments either as comm unity forcing functions or as community state variables. Although these compartments can be simulated for any desired commu nity, only those components identified as fish prey are requir ed 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 inv ertebrates and incidental terrestrial insects when appropriate for age fish are unavailable, the benthos and insect options should be specified even when simulating only piscivorous fish. When benthos, periphyton, incidental terrestrial in sects, phytoplankton or zooplankton are treated as communi ty 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 =2 

### B / 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 pe riphyton, 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

_ T 0. 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 des ignated component and is required to simulate the designate d 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 desig nated 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 wh ere 

### " is the mean body weight for the chosen time period , 

### $ is its amplitude (yunits ), 

### T is its phase angle (radians), and 

### N =2 

### B / 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 si mulation day is March 31. This assignment can be changed usi ng the command /MONTH

_ T 0.



### ! ingestion[

yunits ]= 

### " *W[

xunits ]^ 

### $ *H( 

### ( ,T 1 ,T 2 ) specifies

### 54

BASS 2.3 Sept 24, 2012 the ingestion rate of individuals within the design ated compartment as a function of their average body wei ght and temperature where 

### " , 

### $ , 

### ( , T 1 , and T 2are real or integer empirical constants. This parameter is requ ired 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( 

### ( ,T 1 ,T 2 ) specifies the photosynthetic rate of individuals wi thin the designated compartment as a function of their avera ge body weight and temperature where 

### " , 

### $ , 

### ( , T 1 , and T 2 are real or integer empirical constants. This parameter is required to simulate either periphyton or phytopaln kton 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 nutr ients and light availability.



### ! respiration[yunits ]= 

### " *W[ xunits ]^ 

### $ *H( 

### ( ,T 1 ,T 2 ) specifies the specific rate of dry organic mater re spiration for the designated compartment as a function of ave rage individual body weight and temperature where 

### " , 

### $ , 

### ( , T 1 , and T 2are real or integer empirical constants. This parameter is required to simulate the designated no nfish 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 var iables, incidental terrestrial insects are always treated a s a community forcing function. Regardless of how the biomass of benthos, incidenta l 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 fraction s 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 se ction describes the syntax for units that are recognized by BASS ’s input algorithms. The conversion of user-supplied units t o 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 un it. 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 eitherupper or lower case. When prefixes are used, there must be no embe dded blanks between the prefix and the unit name, e.g., “millig ram” is correct, “milli gram” is incorrect. Unit names for wet and d ry masses are appended, without blanks, with the parenthetic iden tifiers “(ww)” and “(dw)”, respectively. Similarly, an analogous c onvention 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-2is presented as cm^-2). The slash ( / ) is used to den ote division. If multiple slashes are used to specify a unit, they are interpreted according to strict algebraic logic. For example, b oth “mg/liter”, and “mg liter^-1” are equivalent specifications. Si milarly, 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 thes e options



### ! Brackets are used only to delineate units. Dimensio nless parameters like assimilation efficiency, lipid frac tion, and K ow must be specified with null units “[-]”.



### ! The order of addition and multiplication is not sig nificant. 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 independe nt power functions can be specified by log 10or ln transforms. For example, the following options are valid

### 55

BASS 2.3 Sept 24, 2012 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 reduc ed form. For example, temperature-dependent power functions can be specified with a reference tempera ture 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, temperatu redependent power functions can be input for a specif ic temperature, 

### ( " Celsius, in which case BASS assumes a default Q 10 =2. If this feature is used, the reference temperature must be enclosed by parentheses and fol low 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 exp onent of a power function is zero, the function can be input as a constant without specifying the expected independen t 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 ex ample, 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 v 1,1 v 1,2 ... v 1,MV ! comment v 2,1 v 2,2 ... v 2,MV ! comment 

### ! !
... 

### ! v NR,1 v NR,2 ... v NR,MV ! comment The records beginning with a slash ( / ) followed b y an integer CJ identify the type of data (time, exposure concentra tion, temperature, etc.) contained in CJ-th column of eac h data record. In this example, NR is the total number of data rec ords 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 simp lify 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 remainin g 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;

### 56

BASS 2.3 Sept 24, 2012 

### ! 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 s pecified, BASS simply ignores them. Data records can be continued by appending an ampersand (&) to the line, e.g., the f ollowing data records are equivalent.

v i,1 v i,2... v i,j v i,j+1 ... v i,MV

v i,1 v i,2 ... v i,j & v i,j+1 v i,j+2 ... v i,MV File records must be sequenced such that time is no ndecreasing (i.e., t i 

### # 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 specifie d 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 o f related parameters. This capability is the cornerst one 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 env ironmental 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 communitie s of concern. For example, the USEPA Office of Pesticide Programs routinely evaluates different pesticides for registration bas ed on their expected fate and effects in series of canonical aq uatic habitats / ecosystems. Similarly, the USEPA Office of Pollution Prevention and Toxics evaluates the pre-manufacturi ng registration of industrial chemicals in much the sa me way. These considerations suggested that a practical working BASS project / include file hierarchy should be structured as foll ows:



### ! All data specifying the bioenergetic, compositional , and morphological parameters for a specific fish specie s that can be considered to be independent of the particul ar community in which the fish resides, should be cont ained within a single include file that is assigned the r eserved extension FSH
.


### ! All data specifying the structure and function of a particular fish community should be contained withi n a single include file that is assigned the reserved e xtension CMM . These files should use the aforementioned *. FSH

### 57

BASS 2.3 Sept 24, 2012 files as include files intervened with the necessar y fish commands that are specific to the community of inte rest. In general, these community-specific fish commands define each species’ 1) the dietary composition, 2) initial ages, body weights, population densities, and chemi cal residues, 3) habitat multipliers, 4) any desired fi shery parameters, and 5) any fish commands contained with in 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 wi thin a single include file that is assigned the reserved extension PRP
.


### ! All data specifying a chemical exposure scenario sh ould be contained within a single include file that is a ssigned the reserved extension CHM . These files should use the aforementioned *. PRPfiles intervened with the necessary chemical commands needed to specify each chemical’s 1) aqueous concentration, 2) dietary exposures via ben thos, insects, periphyton, phytoplankton, and zooplankton , 3) effects concentrations for specific fish, and 4) an y 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 concer n. All such project files will be assigned the reserve d extension PRJ
. Based on these considerations, the general structur e 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. PRPspecified 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 &

### 58

BASS 2.3 Sept 24, 2012 initial_biomass[units]= " ; & mean_weight[g(dw)]= fnc ; & ingestion[g(dw)/day]= " *w[g(dw)]^ $ *h( ( ,t 1 ,t 2 ); & respiration[g(dw)/day]= " *w[g(dw)]^ $ *h( ( ,t 1 ,t 2 ) / 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( ( ,t 1 ,t 2 ); & mf[l/d]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ); & mi[g(dw)/d]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ); & sg[1/d]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ); & sm[g(dw)]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ); & so[mg(o2)/h]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ); & st[minute]= " *w[g(ww)]^ $ *h( ( ,t 1 ,t 2 ) ! end fish_1.fsh In addition to the file hierarchy outlined above, i t 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. C onsequently, 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 communi ty 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 we ll as any desired fishery and habitat suitability parameters. The \PROPERTY subdirectory contains canonical versions of the *. PRPfiles that specify the physico-chemical properties of individual chemicals and that are used as include f iles 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 applica tion, 2) *. CHM files that specify chemical exposures and propertie s, and 3) *. DATfiles that specify chemical exposures, habitat sui tability multipliers, nonfish standing stocks, water tempera ture, and water depth when these parameters are supplied by the “file” option. A project subdirectory can also contain loc al copies of either *. FSH , *. CMM , or *. PRPfiles that have been created or modified for a particular project. Such files may h ave been created from scratch or may have been constructed f rom 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 projec t file that is currently being read. Therefore, in the cas e of *. FSH , *. CMM , or *. PRPfiles, BASS initially attempts to find these include files in the current project file’s subdirectory. I f these files cannot be found in the current subdirectory, BASS uses the extension of

### 59

BASS 2.3 Sept 24, 2012 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 par ameters, 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 extensi on
“ MSG ”. For example, when BASS executes the project file INPUT
. PRJ , the message file INPUT
. MSGis generated. If this message file already exists, it is silently overwri tten

.


### ! an output file that tabulates selected results of the simulation. Tabulated summaries include: 1) annual bioenergetic fluxes and growth statistics (i.e., me an body weight, mean growth rate) of individual fish by spe cies and age class, 2) annual bioaccumulation fluxes and sta tistics (i.e., mean whole-body concentrations, BAF, and BMF ) of individual fish by species and age class, and 3) an nual community fluxes and statistics (i.e., mean population densities and biomasses) of each fish species by ag e 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 silen tly overwritten

.


### ! a Post-script file that contains the plots that wer e 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 exam ple, when BASS executes the project file INPUT
. PRJ , the plot file INPUT
. PS is generated. If this file already exists, it is s ilently overwritten

.


### ! a XML file that outputs daily values of community s tate variables as well as integrated annual flow summari es and annual means for selected state variables. Users ca n 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:\BASS 23> bass_V23 -i input.prj Although the “-i filename” option is the only requi red command line option, the following additional options are a vailable -a => enable default accelerated YOY self-thinning; -c => report distribution of cpu time in major subro utines; -e => tabulate realized monthly diets for elective f eeding; -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 annua l age classes; -mba => output mass balance analysis with annual

summaries; -n => internally calculate rate-based BCF for nonfis h; -p => turn on messages associated with feeding and p redation; -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:\BASS 22> bass_V23 -i input.prj -l -c will execute the project file INPUT
. PRJ without simulating acute or chronic chemical lethality and report the distri bution of cpu time spent within various key BASS subroutines.

### 60

BASS 2.3 Sept 24, 2012 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.

### 61

BASS 2.3 Sept 24, 2012

Unit Name Conversion

Factor to SI Metre Kg Second Description
acre 2.471×10 -04 2 0 0 4840 yards 2 are 1.000×10 -02 2 0 0 100 meter 2 btu 9.479×10 -04 2 1 -2 calorie 2.388×10 -01 2 1 -2 cc 1.000×10 +06 3 0 0 cm 3 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, T ime, and Energy. This list is not exhaustive and summarizes only commonly used unit names that BASS ’s units conversion program recognizes.

### 62

BASS 2.3 Sept 24, 2012

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) 2.425×10 5 0 1 0 used for wet-to-dry conversions ug(dw) 2.425×10 8 0 1 0 used for wet-to-dry conversions g(ww) 1.0×10 3 0 1 0 used for wet-to-dry conversions kg(ww) 1.0 0 1 0 used for wet-to-dry conversions mg(ww) 1.0×10 6 0 1 0 used for wet-to-dry conversions ug(ww) 1.0×10 9 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 = micr omole 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 wei ght 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 jou les. Table 4.3 Valid ecological, morphometric, and physiological units. BASS uses these units to convert user-specified units f or lamellar density, initial populations densities, fi sh body weights, and oxygen consumption to standard model units.

### 63

BASS 2.3 Sept 24, 2012



## 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 US EPA Center for Exposure Assessment Modeling (CEAM) webs ite ( 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 forWindows 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 A nalyzer 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

_ V 23_ ABSOFT . EXE is the most current BASS model executable that has been created with the Absoft ve rsion 9.0 Fortran 95 compiler using the standard 32-byte Wind ows XP operating system. This executable will run approxim ately 5 times faster than the Lahey-Fujitsu executable belo w. Note, however, that there are minor compiler bugs associa ted with this executable that apparently do not affect its c omputational accuracy. This executable is used as the default BASS software executable BASS

_ V 23. EXE . Also see Section 7.2.7.
2. BASS

_ V 23_ INTEL . EXE is the most current BASS model executable that has been created with the Intel ver sion 11.0 Fortran 95 compiler using the standard 32-byte Wind ows XP operating system. This executable will run approxim ately 9 times faster than the Lahey-Fujitsu executable belo w. Also see Section 7.2.7.
3. BASS

_ V 23_ LAHEY . EXE is the most current BASS model executable that has been created with the Lahey-Fuj itsu 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. \ COMMUNITYis 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

_ V 23_ DATA

_ SUPPLEMENT . WPD is the current compendium of fish data that can be use d to

### 64

BASS 2.3 Sept 24, 2012 parameterize BASS in WordPerfect format.
11. \ DOCUMENTS \ BASS

_ V 23_ DATA

_ SUPPLEMENT . PDF is the current compendium of fish data that can be used to parameterize BASS in PDF format.
12. \ FISHis 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. \ PROPERTYis 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 futu re BASS projects. This folder must be present for the BASS software to function correctly, and it is initially populate d 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. Toxico l. 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

_ V 23 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, Win NT4.0 and WinXP operating systems with various configurations of each. If users are running NT, 2000, or XP operatin g systems, they must have Administrator privileges on their sy stems in order to install the BASS model and GUI software. 

### 5.3. Installation Procedures For complete installation procedures users are refe rred 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 wind ow BASS version 2.2 ...

. Figure 5.1 BASS GUI Current BASS Directory window.

Double-clicking on a folder’s name, icon, or direct ory node expands or collapses the folder’s contents into or out of the user’s view, respectively. Double-clicking on a fil e name opens the file with one of six GUI file editors bas ed on the selected file’s extension. The GUI’s file editors c an 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 th e 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 i nclude files by 4. Left-clicking on the desired include command and then

### 65

BASS 2.3 Sept 24, 2012 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 re levant 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 fi le 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 fi le 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-clickin g 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 Sec tion 4.7 (page 59). When a project file is being executed, a ll other GUI functions are unavailable until the simulation is c ompleted.

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 use rs, 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 fo rmat 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 theirappearance 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 le ft-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 th e 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 ses sion. Because the typical Close “X” button has been disabled on all GUI file editors, users can exit GUI file editors o nly 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).

### 66

BASS 2.3 Sept 24, 2012 Figure 5.3 displays the structure of the BASS GUI project file editor. This editor differs from the GUI’s other fi ve file editors in two ways. First, this editor explicitly identifi es all include files that will be used by the project. Secondly, a ny include file that is directly referenced by the project file canbe 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 editor s as outlined in Section 5.4.1. In terms of their appear ance 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

_ T 0. 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 command s /ANNUAL

_ OUTPUTS , /LOG

_ AC , /LOG

_ KB 1, /LOG

_ KB 2, /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 th e 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 comm and /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 th e OK and Cancel buttons. These buttons either save or cancel any

### 67

BASS 2.3 Sept 24, 2012 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 describ ed 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 f iles 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 sc ratch 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., \C OMMUNIY, \FISH, or \PROPERTY) or project folder where the fi le is to be created. The user then must left-click on the dr op-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, afterthe new file appears in the Current BASS Directory window, the user must complete the naming of the new file. New project fo lders 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 o r subdirectory and left-clicking on the Paste icon . Users can also create a new file from an existing f ile by 1. Right-clicking on the file to be copied and then leftclicking on Copy

. 2. Right-clicking on the destination folder or subdi rectory 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 p ressing CTRL-c. 2. Left-clicking on the destination folder or subdir ectory and then pressing CTRL-v New project folders can be created from existing pr ojects 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 customize d graphs and tables. This software can be invoked either fro m within the BASS GUI or as a standalone application. Using this sof tware, users can create two and three-dimensional graphs o f any state variable that is a valid option for the plotting co mmands /ANNUAL

_ PLOTS or / SUMMARY

_ PLOTS . Unlike the plots generated by these commands, however, users can gen erate plots for only selected species as desired. Additio nally, users can specify arbitrary time periods of interest as w ell 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 fi les. 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 OAwill use for graphing and table construction. This file bears th e same name as its associated project file but possesses the ex tension 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 th e 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
. EXEis 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

### 68

BASS 2.3 Sept 24, 2012 files for many North American freshwater fish. Alth ough 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 a lso construct multiple FSH files and an associated, rud imentary CMM file for an arbitrary selection of fish using this software. This software, however, does not have a GUI and mus t be executed by the user from a DOS command prompt. To generate a FSH file for a single species of inte rest, the user should open a DOS command prompt window and navigat e to the project folder in which they want the file to b e generated. Assuming that the user’s BASS root directory is C :\BASS

_ V 23, 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 whos e growth rate has been calibrated to an annual sinusoidal water t emperature 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 CM M file for an arbitrary selection of fish, the user should again open a DOS command prompt window and navigate to the proje ct folder in which the user wants the files to be gene rated. Assuming that the user’s BASS root directory is C :\BASS

_ V 23, the DOS command ...>c:\bass_v23\bass_files.exe -i fishes.dat0 will generate a FSH file for each fish species iden tified in the file fishes.dat0and an associated CMM file. The file fishes.dat0must reside in the desired project folder. An example of the general structure of these input fil es 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_iis 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.

### 69

BASS 2.3 Sept 24, 2012 Figure 5.5 GUI command editor for simple strings with drop-do wn selection.

Figure 5.6 GUI command editor for numeric data with user spec ified units.

Figure 5.7 GUI command editor for numeric data fixed units.

### 70

BASS 2.3 Sept 24, 2012 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.

### 71

BASS 2.3 Sept 24, 2012 Figure 5.11 GUI command editor for nondiet ecological paramete rs.

Figure 5.12 GUI command editor for fish diets.

### 72

BASS 2.3 Sept 24, 2012 Figure 5.13 GUI command editor for physiological parameters.

Figure 5.14 GUI command editor for cohort initial conditions.

### 73

BASS 2.3 Sept 24, 2012 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 fu nctions.

### 74

BASS 2.3 Sept 24, 2012 Figure 5.18 GUI command editor for nonfish biota as state vari ables.

Figure 5.19 GUI command editor for nonfish bioaccumulation fac tors.

Figure 5.20 GUI command editor for chemical biotransformation parameters.

### 75

BASS 2.3 Sept 24, 2012 Figure 5.21 GUI command editor for chemical toxicity parameter s.

Figure 5.22 GUI command editor for automatic graphing selectio ns.

### 76

BASS 2.3 Sept 24, 2012 Figure 5.23 GUI Block comment editor.

Figure 5.24 Data file editor for forcing functions specified a s files.

### 77

BASS 2.3 Sept 24, 2012 

## 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 f older within the \PROJECTS subdirectory. The example project EX

_ EVERGLADES

_ CANAL simulates the growth and population dynamics of a canal fish comm unity in the Florida Everglades, USA using the project fi le 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 expect ed 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
. DATand EVERGLADES

_ NONFISH
. DAT , respectively. The example project EX

_ EVERGLADES

_ CANAL

_ FISHING simulates the growth and population dynamics of the aforementioned Everglades canal fish community assu ming 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 stock s are again specified using the data files EVERGLADES

_ CANAL

_WATER
. DATand EVERGLADES

_ NONFISH
. DAT , respectively. The example project EX

_ EVERGLADES

_ CANAL

_ HGsimulates the bioaccumulation of methylmercury in an Everglad es 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 pr oject EVERGLADES

_ CANAL

to specify the ecological and physiological parameters and initial conditions forthe species of interest. The community’s chemical expos ures 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 sp ecified using the data files EVERGLADES

_ CANAL

_WATER
. DATand EVERGLADES

_ NONFISH
. DAT , respectively. The example project EX

_ EVERGLADES

_ CANAL

_ HG

_ LESLIE simulates the bioaccumulation of methylmercury in a n 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 provi ded by the include file E VERGLADES

_ MERCURY
. CHM that uses the include file \PROPERTY \ METHYL

_ HG
. PRP . Once again, the community’s water depth and non-fish standing stock s are provided by the ancillary data files EVERGLADES

_ CANAL

_WATER
. DATand 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 Evergl ades, USA using the project file EVERGLADES

_ HG

_ HOLES
. PRJ . The principal fish species in these communities are ass umed 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 expect ed 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 pr oject and community files EVERGLADES

_ HG

_ HOLES
. PRJ and EVERGLADES

_ HG

_ HOLES
. CMM , respectively. Methylmercury exposures are provided by the include file E VERGLADES

_ MERCURY
. CHM that uses the property file \PROPERTY \

METHYL

_ HG
. PRPas 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 ass umed to be bluefin killifish ( Lucania goodei ), Florida gar, golden top minnow ( Fundulus chrysotus ), largemouth bass, least killifish, mosquito fish, spotted sunfish, warmouthsunfish, and yellow bullheads. The community file

### 78

BASS 2.3 Sept 24, 2012 EVERGLADES

_ HG

_ MARSH
. CMM is used to specify the ecological and physiological parameters and the ini tial conditions for these species. Each species’ daily consumption rate is back-calculated from its expect ed 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 pr oject and community files EVERGLADES

_ HG

_ MARSH
. PRJ and EVERGLADES

_ HG

_ MARSH
. CMM , respectively. Methylmercury exposures are provided by the include file E VERGLADES

_ MERCURY
. CHM that uses the property file \PROPERTY \

METHYL

_ HG
. PRPas 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 comm unity 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 T WELVEMILE

_ 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-, he xa-, and hepta-PCB in the aforementioned largemouth bass/ gi zzard shad/ sunfish/catfish community of Lake Hartwell, S C, USA using the project file T WELVEMILE

_ CREEK

_ PCB

_ TRANS
. PRJ . This example, however, allows for the biotransformation of select ed PCB congeners by selected fish species. The community f ile TWELVEMILE

_ CREEK
. CMM is again used to specify the ecological and physiological parameters and the ini tial 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). Wher eas 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 southea stern 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. Eac h 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 large mouth bass. To correct this error and to produce realisti c 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 f ish community of the Florida Everglades and is construc ted 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, morphologi cal, and physiological parameters used by this example are documented in its associated FSH files. Turner et a l. (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, bu llheads, bluegill, redear sunfish, and mosquito fish were as signed to be 5, 10, 10, 50, 25, and 5 kg wet wt/ha, respectiv ely, for a total community biomass of 105 kg wet wt/ha. The wa ter concentration of methylmercury for the simulation w as assigned to be a constant 0.2 ng/L (Stober et al. 1 998) and the BAF’s for benthos and zooplankton were assigned to be 10 6.09 and 10 5.90 , respectively (Loftus et al. 1998). At the end of the 10 year simulation, the mean annu al standing stocks of the bass, gar, bullheads, bluegi ll, 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 commun ity

### 79

BASS 2.3 Sept 24, 2012 biomass of 74.7 kg wet wt/ha. The simulated whole-body concentrations of methyl mercury in these species agree reasonably well with unpubli shed 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 r edear sunfish is probably due to the specialized feeding behavior of this species. In particular, redear sunfish, whi ch are also known as shellcrackers, often feed almost exclusive ly on molluscs that generally have significantly lower methylmercury concentration than do other benthic macroinvertebrates. The annual averaged concentrati ons of methylmercury in largemouth, gar, bullhead, bluegil l, 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, th e annual averaged concentrations of methylmercury in largemo uth, gar, bullhead, bluegill and redear were 0.450, 0.49 1, 0.332, 0.237, 0.299, and 0.143 mg Hg/kg wet wt, respective ly. Loftus et al. report average whole-body concentrati ons of methylmercury in largemouth, gar, bullhead, bluegil l, 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, respectivel y. 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 (Fo rrester et al. 1972, Scott and Armstrong 1972, Cross et al. 19 73, Akielaszek and Haines 1981, Watling et al. 1981, Bo ush 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 e t al. 1988, Grieb et al. 1990, Parks et al. 1991, Gutenma nn 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.80BASS 2.3 Sept 24, 2012Largemouth bass (Micropterus salmoides)00.20.40.60.811.21.41.61.820 200 400 600 800 1000 1200 1400 1600 1800 2000Wet Weight g(ww)

Total Hg ug/g(ww)observedcanalFigure 6.1Predicted and observed methylmercury concentrations of largemouth bass in Florida Everglades canals.81BASS 2.3 Sept 24, 2012Florida gar (Lepisosteus platyrhincus)00.511.522.50 200 400 600 800 1000 1200 1400Wet Weight g(ww)

Total Hg ug/g(ww)observedcanalFigure 6.2Predicted and observed methylmercury concentrations of Florida gar in Florida Everglades canals.82BASS 2.3 Sept 24, 2012Yellow bullhead (Ameiurus natalis)00.20.40.60.811.21.41.61.80 200 400 600 800 1000 1200Wet Weight g(ww)

Total Hg ug/g(ww)observedcanalFigure 6.3Predicted and observed methylmercury concentrations of yellow bullhead in Florida Everglades canals.83BASS 2.3 Sept 24, 2012Bluegill (Lepomis macrochirus)00.20.40.60.811.21.41.61.80 20 40 60 80 100 120 140 160 180 200Wet Weight g

Total Hg ug/g(ww)observedcanalFigure 6.4Predicted and observed methylmercury concentrations of bluegill sunfish in Florida Everglades canals.84BASS 2.3 Sept 24, 2012Redear sunfish (Lepomis microlophus)00.20.40.60.811.20 50 100 150 200 250 300Wet Weight g(ww)

Total Hg ug/g(ww)observedcanalFigure 6.5Predicted and observed methylmercury concentrations of redear sunfish in Florida Everglades canals.85BASS 2.3 Sept 24, 2012Mosquiotfish (Gambusia holbrooki)00.050.10.150.20.250.30.350.40.450 0.1 0.2 0.3 0.4 0.5 0.6Wet Weight g(ww)

Total Hg ug/g(ww)observedcanalFigure 6.6Predicted and observed methylmercury concentrations ofGambusiain Florida Everglades canals.

### 86

BASS 2.3 Sept 24, 2012 

## 7. Model Quality Assurance Quality Assurance (QA) and Quality Control (QC) forthe BASS simulation model has been addressed with respect t o: 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 publis hed in the peer reviewed literature? With the exception of its population and trophodyna mic 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). The se algorithms have been reviewed and compared with oth er existing bioaccumulation models to document their s cientific foundation and to verify their predictive performan ce (see Barber 2003, 2008). The bioenergetic modeling parad igm that BASS uses to simulate fish growth has been employed by many researchers in the peer reviewed literature (Norstr om et al. 1976, Kitchell et al. 1977, Minton and McLean 1982, Stewart et al. 1983, Thomann and Connolly 1984, Cuenco et a l. 1985, Stewart and Binkowski 1986, Beauchamp et al. 1989, Barber et al. 1991, Stewart and Ibarra 1991, Lantry and St ewart 1993, Rand et al. 1993, Roell and Orth 1993, Hartman and Brandt 1995a, Petersen and Ward 1999, Rose et al. 1999, Sc haeffer 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 envi ronmental management (Barron 1990, Jones et al. 1991, Barntho use 1992, Chapra and Boyer 1992, Landrum et al. 1992, O lem et al. 1992, Dixon and Florian 1993, Wurbs 1994, Cowanet al. 1995, Campfens and Mackay 1997, Feijtel et al. 1997 , Deliman and Gerald 1998, Exponent 1998, Howgate 199 8, Vorhees et al. 1998, Wania and Mackay 1999, Bartell et al. 2000, Gobas and Morrison 2000, Mackay and Fraser 20 00, 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 mor e important than other routes of exchange (Madenjian et al. 199 3). Madenjian et al. (1993) took exception to FGETS predictions that “excretion of PCB through the gills is an impo rtant flux in the PCB budget of lake trout”. Madenjian et al. cla imed that this result was not supported by any laboratory stu dy on trout and cited Weininger (1978) as proof that gill excre tion was, in fact, negligible. Nevertheless, Madenjian et al. us ed a single, unidentified excretion constant in their model that simply lumps all excretion pathways (i.e., gill, intestina l, urinary, and dermal) into one. Thus, what Madenjian et al. are e ssentially 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). S ince FGETS ’s bioenergetic model for fish growth is not signif icantly 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 model s, however, clearly demonstrated that there is more th an ample literature data to parameterize the gill exchange f ormulations used by FGETS and BASS (Barber 2003). 7.1.2. How has the model or its algorithms been cor roborated or used?

BASS ’s dietary and gill exchange algorithms have been corroborated by comparing its predicted dietary ass imilation efficiencies and gill uptake and excretion rates to those published in the peer reviewed literature (Barber e t al. 1988, Barber 2003, 2008). BASS ’s dietary exchange algorithms have also been cited by other researchers to explain res ults 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 org anic chemicals by simulations of PCBs dynamics in Lake O ntario salmonids, various laboratory studies, largemouth b assbluegill-catfish communities of Lake Hartwell / Twe lvemile Creek, SC, and Tennessee stream fishes (Barber et a l. 1991, USEPA 1994, Brockway et al. 1996, Simon 1999, March ettini et al. 2001, USEPA 2004). Similarly, Hunt et al. (1 992) used FGETS to model DDT bioaccumulation in caged channel catf ish

### 87

BASS 2.3 Sept 24, 2012 at Superfund Sites. For sulfhydryl binding metals, BASS ’s predictive performance has been corroborated by sim ulations of methylmercury bioaccumulation in Florida Evergla des 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 inVirginia. More recently, BASS was used to estimate lag times of mercury residues in fish responding to mercury load reducti ons as part of ORD’s review of the Agency’s Clean Air Mercury Rule (CAMR, February 15, 2005). This work was subsequent ly incorporated into the Regulatory Impact Analysis (R IA) that assessed the benefits of atmospheric load reduction s to aquatic ecosystems (USEPA 2005, Knightes et al. 2009). Several researchers (Lassiter and Hallam 1990, ECOF RAM Aquatic Effects Subcommittee et al. 1998, ECOFRAM 1 999, 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 20 04). 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 assess ments (VDEQ 2005). BASS has also been recommended to the states of Michigan and Washington as an assessment tool (E xponent 1998, 2003). Whereas Hallam and Deng (2006) implemented the FGETS /BASS bioaccumulation framework within sophisticated McKendrick-von Foerster partial differential equati on models for age-structured populations, Cohen and Cooter (2 002a, 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 val ue problems), and forcing functions / boundary conditions? What i s 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 X

iis a state variable of interest; p jis some state parameter of concern; Z jis some external forcing function; and X j (0) is the initial value of some state variable of interest that may be X

iitself. Structural sensitivity, which generally ca nnot be formulated as a simple partial derivative, typic ally concerns the number and connectivity between the system’s st ate variables. An excellent question regarding structur al 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 ite ms? Because sensitivity is simply a mathematical charac teristic of a model, model sensitivity in and of itself is neithe r good nor bad. Sensitivity is desirable if the real system be ing modeled is itself sensitive to the same parameters, forcing fu nctions, initial state perturbations, and structural changes to which the model is sensitive. Even though model sensitivity c an contribute to undesirable model uncertainty or pred iction error, it is important to acknowledge that model se nsitivity 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 o f both the model’s sensitivity to particular components and th e statistical variability associated with those components. A generalized sensitivity analysis of BASS without explicit specification of a fish community of concern is und oable. 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 avariety of structured sensitivity analyses are currently being developed, presently the onus of performing such analyses rest s with the user. Users interested in issues and techniques rel ated to model sensitivity and uncertainty should consult the foll owing 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 Implementa tion 7.2.1. Did the input algorithms properly process al l 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 b y line summary of the user’s input commands but also a com plete

### 88

BASS 2.3 Sept 24, 2012 summary of all control, chemical and fish parameter s 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 ha s 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 da ta. When missing or inconsistent data are detected, appropri ate error messages are written to the *.MSG file, and an erro r 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 corre ct variables, all BASS subroutines and functions are called using implici t interfaces generated by the Absoft, Intel, and Lahe y / Fujitsu Fortran 95 compilers. Subroutines and functions are packaged together within Fortran 95 modules according to the ir function and degree of interaction. The BASS v2.3 software is coded with one main program PROGRAM BASS

_ MAIN (see BASS

_ PROGRAM
. F 90) and 33 procedure modules. These modules are:



### ! MODULE ADAMS

_ GEAR- subroutines for performing EXAMS Adams-Gear integrations (see EXAMS

_ ADAM

_ GEAR
. F 90). 

### ! MODULE BASS

_ ALLOC - subroutines for allocating and reallocating derive type pointers (see BASS

_ ALLOC
. F 90). 

### ! MODULE BASS

_ CHECK - subroutines for checking the completeness and consistency of user input (see BASS

_ CHECK
. F 90). 

### ! MODULE BASS

_ DEBUG- subroutines for program debugging. Used only for program development (see BASS

_ DEBUG
. F 90). 

### ! MODULE BASS

_ DEFINED - functions for determining whether program parameters and variables have been initialized or assigned (see BASS

_ DEFINED
. F 90). 

### ! MODULE BASS

_ EXP- subroutines for calculating chemical exposures, community forcing functions, an d habitat suitability multipliers (see BASS

_ EXP
. F 90). 

### ! MODULE BASS

_ INI

- subroutines for initialization of program variables (see BASS

_ INI
. F 90). 

### ! MODULE BASS

_ INPUT- subroutines for decoding user input (see BASS

_ INPUT
. F 90). 

### ! MODULE BASS

_ INT- subroutines for Adams-Gear, Euler, and Runge-Kutta integrations (see BASS

_ INT
. F 90). 

### ! MODULE BASS

_ INT

_ LOADER- subroutines for loading BASS derived type variables into standard integration vectors (see BASS

_ INT

_ LOADER
. F 90). 

### ! MODULE BASS

_ IO - subroutines for processing user input and output (see BASS

_ IO
. F 90). 

### ! MODULE BASS

_ ODE- subroutines for the computational kernel of the BASS software (see BASS

_ ODE
. F 90). 

### ! 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
. F 90). 

### ! 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
. F 90). 

### ! MODULE BASS

_WRITE

_ CSV- subroutines for generating CSV output files for import into Excel workshets (s ee BASS

_ CSV
. F 90). 

### ! MODULE BASS

_WRITE

_ XML - subroutines for generating XML output files for post processing by the BASS GUI (see BASS

_ XML
. F 90). 

### ! MODULE DECODE

_ FUNCTIONS - subroutines for decoding constant, linear, and power functions from characte r strings (see UTL

_ DCOD

_ FNC
. F 90). 

### ! MODULE DISLIN - implicit interfaces for the DISLIN graphics subroutines DISLIN
. F 90). 

### ! MODULE DISLIN

_ PLOTS - general utility subroutines for generating 2 and 3-dimensional DISLIN plots (see UTL

_ PLOTS
. F 90). 

### ! MODULE ERROR

_ MODULE- subroutines for printing error codes encountered with general utility module s (see UTL

_ ERRORS
. F 90). 

### ! MODULE FILESTUFF- subroutines for parsing file names and obtaining version numbers or time stamps (see UTL

_ FILESTUFF
. F 90). 

### ! MODULE FLOATING

_ POINT

_ COMPARISONS - operators for testing equality or inequality of variables with ex plicit consideration of their computer representation and spacing characteristics (see UTL

_ FLOATCMP
. F 90). 

### ! MODULE GETNUMBERS - subroutines for extracting numbers from character strings (see UTL

_ GETNUMS
. F 90). 

### ! MODULE IOSUBS - subroutines for assigning, opening, and closing logical units (see UTL

_ IOSUBS
. F 90). 

### ! MODULE MODULO

_ XFREAD - subroutines for reading files that contain comments, continuation lines, an d include files (see UTL

_ XFREAD
. F 90). 

### ! MODULE MSORT- subroutines for sorting and generating permutation vectors for lists and vectors (see UTL

_ MSORT
. F 90). 

### ! MODULE MXGETARGS - subroutines for extracting arguments from a command line (see UTL

_ MXGETARGS
. F 90). 

### ! MODULE REALLOCATER- subroutines for allocating and reallocating integer, logical, and real pointers (s ee UTL

_ ALLOC
. F 90).

### 89

BASS 2.3 Sept 24, 2012 

### ! MODULE SEARCH - subroutines for finding the location of a key phrase within a sorted list (see UTL

_ SEARCH
. F 90). 

### ! MODULE SEARCH

_ LISTS - subroutines for finding the location of a value within a sorted list (see UTL

_ SEARCH

_ LISTS
. F 90). 

### ! MODULE STRINGS - subroutines for character string manipulations and printing multiline character text (see UTL

_ STRINGS
. F 90). 

### ! MODULE TABLE

_ UTILS - subroutines for generating selfformating tables (see UTL

_ PTABLE
. F 90). 

### ! MODULE UNITSLIBRARY - subroutines for defining and performing units conversions (see UTL

_ UNITSLIB
. F 90). In general, these procedure modules are coded with minimal scoping units. Consequently, their component subrou tines and functions explicitly initialize all required intern al variables. This safeguard is intended to prevent inadvertent u se 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 ar e supplied to program procedures via modules (see question 7.2 .3), data exchanges between program procedures are performed via formal subroutine / function parameters whenever po ssible. 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 m athematical subroutines (e.g., root finding subroutines). Worki ng 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 forma l parameters, BASS makes extensive use of derived type data structures. Each derived type definition is specifi ed within its own module, and all derive type definition modules are maintained in a single file ( BASS

_ TYPES
. F 90.) 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 commun ity 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

_ VARto 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 tran sfer the ecological, physiological, and morphometric data fo r a particular fish species. This derived type is defin ed 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()

### 90

BASS 2.3 Sept 24, 2012 LOGICAL :: bb_constant=.TRUE., prey_switching_on=.T RUE. REAL :: ae_fish, ae_invert, ae_plant, ast_bb, ast_b nds, 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, s g_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 ex ample, 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 w eight from its dry weight (see introduction to Section 2.6. Modeli ng 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 p assed 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 paramet ers, 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
. F 90). 

### ! MODULE BASS

_ CONSTANTS - specifies various biological and physical constants used by BASS ’s computational subroutines (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ GRAETZ - specifies parameters used to calculate chemical exchange across the fish gills ( see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ IOFILES - specifies logical unit numbers for input and output devices (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ NAMES - stores user-supplied fish and chemical names (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ NOVALUE- specifies values for integer, real, and character variables that have not been initialized (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ PRECISION - specifies the precision of floating point variables as either single, double, or quad precision variables. This module also assigns certa in associated floating point constants (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ UNITS - specifies unit conversion factors that are specific to BASS for use by MODULE UNITSLIBRARY (see BASS

_ UNITS
. F 90). 

### ! MODULE BASS

_WORKING

_ DIMENSIONS - specifies “standard” sizes for character variables, input rec ords, etc. (see BASS

_ GLOBALS
. F 90). 

### ! MODULE CONSTANTS - constants used by utility subroutines (see UTL

_ CONSTANTS
. F 90). 

### ! MODULE GEAR

_ DATA- stores control parameters for the EXAMS Adams-Gear integrators (see EXAMS

_ ADAM

_ GEAR

_ MODULES
. F 90). 

### ! MODULE LOCAL

_ GEAR

_ DATA- stores control parameters for the EXAMS Adams-Gear integrators (see EXAMS

_ ADAM

_ GEAR

_ MODULES
. F 90). 

### ! MODULE STEP

_ DATA- stores control parameters for the EXAMS Adams-Gear integrators (see EXAMS

_ ADAM

_ GEAR

_ MODULES
. F 90). 

### ! MODULE STIFF

_ DATA- stores control parameters for the EXAMS Adams-Gear integrators (see EXAMS

_ ADAM

_ GEAR

_ MODULES
. F 90). 

### ! MODULE UNITS

_ PARAMETERS - specifies parameters used by the units conversion subroutines (see UTL

_ UPARAMS
. F 90)

BASS v2.3 uses the following modules (see BASS

_WORK

_ AREAS
. F 90) 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 a ll program subroutines, functions, and procedures are using th e same global constants or parameters?

### 91

BASS 2.3 Sept 24, 2012 All global constants are defined within their own i ndividual modules. These modules include



### ! MODULE BASS

_ CONSTANTS - constants used by BASS ’s computational subroutines (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ NOVALUE- specifies values for integer, real, and character variables that have not been initialized (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_ PRECISION - specifies the precision of floating point variables as either single, double, or quad precision variables. This module also assigns certa in associated floating point constants (see BASS

_ GLOBALS
. F 90). 

### ! MODULE BASS

_WORKING

_ DIMENSIONS - specifies “standard” sizes for character variables, input rec ords, etc. (see BASS

_ GLOBALS
. F 90). 

### ! MODULE CONSTANTS - constants used by utility subroutines (see UTL

_ CONSTANTS
. F 90). 

### ! MODULE UNITS

_ PARAMETERS - specifies parameters used by the units conversion subroutines (see UTL

_ UPARAMS
. F 90) 7.2.4. Do all strictly mathematical algorithms do w hat they are supposed to? For example, are root finding algo rithms 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 wei ght given an allometric relationship between its wet body weight and its fraction lipid, and linear relationships between it s moisture, lipid, and non-lipid organic matter fractions. The second type of calculation involves the linear transformation o f unconditioned dietary electivities into self-consis tent sets of dietary electivities. These calculations are perfor med using the combined bisection / Newton-Raphson algorithm outli ned by Press et al. (1992). As mentioned earlier, BASS ’s differential equations are integrated using a fifth-order Runge-Kutta method w ith 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 outline d by Press et. al. (1992) and was tested using the follo wing 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 y 5 =0.220255E+05. Besides their large numerical range, the last three equations in this system are numerically stiff (Press et al. 1992, Ascher an d Petzold 1998). When integrated on the interval [0<x <10], the ratio of the numerical solutions and the corresponding analy tical 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 widel y 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 satisfie d by the problem of interest?
Because BASS is a differential equation model, a question of paramount concern is how its integration between po ints of discontinuity / nondifferentiability is controlled. BASS , like many ecological models, utilizes threshold response s, absolute value functions, maximum and minimum functions, and linear interpolations between time series in its formulati on and implementation. Although most of BASS ’s parameters are updated continuously, some parameters that change v ery slowly and that are computationally intensive to ev aluate (e.g., dietary compositions) are updated only daily. All o f these features create points of discontinuity or nondiffe rentiability. Although there is nothing intrinsically wrong with using such formulations in differential equation models, numer ical

### 92

BASS 2.3 Sept 24, 2012 integrations of such models must proceed from one p oint of discontinuity / nondifferentiability to another. With these considerations in mind, BASS ’s computational kernels (subroutines BASS

_ ODESOLVRand FGETS

_ ODESOLVR ) are designed to integrate BASS ’s differential equations for a single day of the desired simulation period. Immedi ately following the call of these computational kernels, BASS calculates the dietary composition of each fish tha t will be held constant for that day. The progress of the subseque nt numerical integration within the day is then contro lled 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 variab les, or when one or more cohorts are eliminated from the communi ty. 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 poin t of nondifferentiability for BASS since such amendments to the community’s structure are performed before calling the computational kernels BASS

_ ODESOLVRor FGETS

_ ODESOLVR and, therefore, constitutes a simple reinitializati on 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 eq uation model remains constrained to its appropriate state space is not a trivial issue. Consider, for example, the case whenone wants to take a simple Eulerian step for a non-negative s tate variable that has a negative derivative. If the state variab le 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 conver se is true, however, the function y ( t) is approximating a step function in which case the desired integration can simply be re started 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 whe n a cohort experiences intense predation or other mortality th at drives its population to extinction whereas the second situati on might occur when there is the rapid excretion of a hydrop hilic contaminant following the disappearance of an aqueo us exposure. When the derivative for a fish’s body wei ght, 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 fundamen tal 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 pr edicted predatory mortality and its total predicted piscivo rous consumption. For the example presented herein, this mass balance is -2.950E-09 g dry wt/ha/yr. Since this co mmunity’s total piscivory is calculated to be 8.850E+03 g drywt/ha/yr, this mass balance check would have a relative errorof less than 10 -11

. 7.2.7. Are simulation results consistent across mac hines or compilers?

BASS was originally developed on a DEC 3000 work statio n 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. Althou gh the results of these two implementations agree with one another up to single precision accuracy, due to differences incompiler optimization, model computations must be performed in double precision to obtain this level of consistenc y. 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 Optero n 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 Abs oft Technical Support, workarounds for these bugs were successfully implemented. Simulation results of the BASS Absoft MP dual processor executables were in excell ent 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

### 93

BASS 2.3 Sept 24, 2012 and desktop machines, running 32-byte Windows XP, u sing 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 c ore Intel processor and recompiled using the Intel Fortran 95 compilers 11.0 and XE 2011. 7.2.8. Have test and reference / benchmark data set s 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 th at track changes in the operation of BASS associated with code maintenance and updates. These project files are us ed as benchmarks to verify that code modifications that s hould 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 compar ative 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 predi ct and compare the outcomes of alterative management optio ns that are associated with pollution control, fisheries ma nagement, 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 comm and structure, issues related to user file and project management, and software installation. The User’s Guide also pr esents and discusses the results of one of eleven example appl ications that are distributed with the BASS software. 7.3.3. What internal checking can be made to help i nsure 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 use r. 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 paramet ers. 7.3.4. Has the developer anticipated computational problem areas that will cause the model to “bomb”? Several key mathematical calculations have been ide ntified as potential problem areas for a BASS simulation. In general, these problem areas involve either the unsuccessful resol ution of a root of a nonlinear equation or the unsuccessful in tegration of BASS ’s basic state variables. Examples of the former in clude situations when BASS ’s calculated dietary compositions do not sum to unity or when a fish’s wet weight is calcula ted to be less or equal to its dry weight. Examples of the la tter include situations when the current integration step is les s than the numerical spacing of the current time point, or whe n 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

.

### 94

BASS 2.3 Sept 24, 2012 

## 8. Planned Future Features Presently, ten major program developments are plann ed for BASS . These include:



### ! Development of canonical fish and community databases (i.e., *.FSH and *.CMM files) to facilita te 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 fis hes’ 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 simulatin g the movement of fish into the simulated community based on habitat parameters such as water depth, cu rrent velocity, availability of prey, etc.



### ! Development of subroutines to simulate sublethal, residue-based effects.



### ! Enable lipid fractions, fecundity, and physiologica l mortality to be functionally dependent on the fish’ s predicted growth rate and/or duration of fasting (s ee Adams and Huntingford 1997, Simpkins et al. 2003).



### ! Enable an option for specifying habitat suitability multipliers on respiratory expenditures. See for ex ample Sweka and Hartman (2001) and Facey and Grossman (1990).



### ! Implementation of light and nutrient dependent prim ary production by phytoplankton and periphyton.

### 95

BASS 2.3 Sept 24, 2012 

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## APPENDICES APPENDIX A. Equilibrium complexation model for meta ls As reviewed by Mason and Jenkins (1995), metals canbe classified into three different categories based ontheir complexation behavior and preference for different ligands. These groups are generally designated as class A, c lass B, and borderline metals. Of these, however, class B and b orderline metals are the most important from an ecotoxicologi cal point of view. Class B metals (e.g., Au, Ag, Cu, Hg, and Pb) preferentially bind to marcromolecules such as prot eins 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 me tals in fish and other biota are regulated by metallothioneins v ia sulfhydryl complexation reactions suggests that the total availability of sulfhydryl groups within organisms plays a key role in their internal distribution and accumulatio n. To formulate complexation reactions for class B and borderline metals, one can assume that protein sulf hydryl groups are the only significant ligand for these me tals, 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 con centrations (i.e., C

a, C l, C o , and C

f ) are expressed on a molar basis, then the following identities hold (A.3) (A.4)

(A.5)
where W

w 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 a ssumed

(A.8)

where TS is the total moles of sulfhydryl ligands; RS - is the moles of disassociated sulfhydryls; and K ais 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 met al i . For most class B metals, however, (A.11) Consequently, Equation (A.10) can be simplified to

(A.12)

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BASS 2.3 Sept 24, 2012 This expression can then be substituted into Equati on (A.7) to calculate the fish aqueous phase metal concentratio ns. To use the aforementioned complexation model (i.e., Equation (A.12) substituted into Equation (A.7)), one must s pecify 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 inv estigated the sulfhydryl content of selected fish tissues, it appears that no study has attempted to quantify the total sulfhy dryl content of fish. A reasonable approximation of this paramet er, however, can still be made since data do exist for the major tissues (i.e., muscle, liver, kidney, gill, and int estine) typically associated with metal bioaccumulation. Itano and Sasaki (1983) reported the sulfhydryl con tent 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’ re ported values of 0.0578 g(sarcoplasmic protein)/g(muscle) and 0.120 g(myofibrillar protein)/g(muscle), the total sulfhy dryl 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 con tent 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 conver sion 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 myo fibrillar proteins (Connell and Howgate 1959, Buttkus 1967, 1 971, Takashi 1973, Itoh et al. 1979, Sompongse et al. 19 96, Benjakul et al. 1997, Lin and Park 1998). Because the results of these studies agree well with the actomyosin ana lysis reported by Itano and Sasaki (1983), the results of Itano and Sasaki (1983), Opstevedt et al. (1984), and Chung e t al. (2000) can be assumed to be representative of fish in gene ral. 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 sulf hydryl content of these tissues can be estimated from thei r metallothionein concentrations. Metallothioneins (M T) 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, R oesijadi 1992). Numerous researchers have investigated the o ccurrence of MTs in the liver, kidney, and gills of fish, and most have shown that tissue concentrations of MTs generally v ary 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 a l. 1982, Klaverkamp and Duncan 1987, Dutton et al. 199 3). Using data from Takeda and Shimizu (1982) who repor t 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 concentrati ons would be equivalent to 3.75 - 37.5 

### : mol(SH)/g(dw liver). This range of values suggests that the hepatic sulfhydryl cont ent of fish, that includes both baseline MT and cytoplasmic comp onents that can be converted into MT, might be on the orde r of 40 

### : mol(SH)/g(dw liver). This latter value, however, is probably too conservative. Consider, for example, the observ ation that the ratios of mercury concentrations in liver to th ose in muscle often vary from 1.5 to 6 or more (Lockhart et al. 1 972, Shultz et al. 1976, Sprenger et al. 1988). If liver and mu scle 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 in ducible concentrations of hepatic MT are much higher than 4 0 

### : mol(SH)/g(dw liver). Of these two possibilities, th e latter appears more likely. Although gill, kidney, and intestine MTs have not b een studied in the same detail as hepatic MTs, it appears that MT, and hence sulfhydryl, concentrations in gills and kidne y are lower and not as inducible as hepatic concentrations (Hamilton et al. 1987a, b, Klaverkamp and Duncan 1987). Klaverkamp a nd Ducan (1987) estimated the concentrations of gill M T in white suckers ( Catostomus commersoni ) to be 33 

### : g(MT)/g(gill) which is equilvalent to 3.3 nmol(MT)/g(gill) or 0.0 825 

### : mol(SH)/g(gill). This latter value agrees well withthe estimated concentrations of unidentified binding si tes (0.03 - 0.06 

### : mol/g(gill)) for copper on the gills of rainbow tro ut ( 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 acknowledgmen t that many other important organic compounds contain sulf hydryl groups, e.g., enzymes involved in fatty acid synthe sis, glutathione, etc., it seems reasonable to assume th at the sulfhydryl content of fish is approximately 70 

### : mol(SH)/g dry wt. Because Davis and Boyd (1978) reported the meansulfur

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BASS 2.3 Sept 24, 2012 content of 17 fish species to be 206 

### : mol(S)/g dry wt, this assumption implies that almost 1/3 of a fish’s sulf ur pool exists as sulfhydryl groups. The aforementioned complexation model was implement ed within BASS using 70 

### : mol(SH)/g dry wt to calculate the fish’s total sulfhydryl content. The mean dissociation con stant for organic sulfhydryls was then assigned as pK a = 9.25 (i.e., the SPARC estimated pK a

for cysteine). Using literature values for the stability constants of methylmercury, however, BASS over predicted the bioaccumulation of methylmercury in f ish by at least an order of magnitude. Consequently, a much s impler distribution coefficient algorithm was adopted.

### 117

BASS 2.3 Sept 24, 2012 APPENDIX B. Canonical equations for modeling diffus ive chemical exchange across fish gills with ventil ation and perfusion effects. See Section 2.2 for background i nformation and notation. If chemical exchange across fish gills is treated a s steady-state, convective mass transport between parallel plates, then the following PDE and boundary conditions can be used t o model chemical uptake from and excretion to the interlame llar water: (B.1)

(B.2)

(B.3)
To obtain a canonical solution for this gill model, these equations can be nondimensionalized using the follo wing transformations: (B.4)

(B.5)

(B.6) Applying these transformations, chemical exchange a cross a fish’s gills is described by the following dimensio nless 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 ac ross the secondary lamellae, however, can be simplified by n oting that the solution of Equation (B.7) is separable, i.e., 

### 1 (X, Y ) = 

### M (X ) 

### Q ( Y ) and that q v= 2 rhV

is the ventilation volume of an individual interlamellar channel. Using these obser vations, 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 equati on is equivalent to

(B.12)

which can be rearranged to yield
(B.13) Although this boundary condition is dependent on th e eigenvalue 

### 8 , the eigenvalue expansion for the solution of Equation (B.7) is still straightforward (Walter 197 3, Fulton 1977). Note that as the fish’s perfusion rate incre ases, 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 con struct the

### 118

BASS 2.3 Sept 24, 2012 series solution for the dimensionless bulk concentr ation of the aforementioned PDE gill exchange model (i.e., Equation (2.28)).

### 119

BASS 2.3 Sept 24, 2012 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 calc ulated dietary frequencies will sum to unity, consider the followi ng (C.1)
(C.2)

(C.3)

Summing Equation (C.2) over all ithen 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), th e following consistency condition is obtained

(C.8)

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BASS 2.3 Sept 24, 2012

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BASS 2.3 Sept 24, 2012 

## 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, 5 6 management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 output file (.bss) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 9 output file (.msg) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 output file (.ps) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 9 output file (.xml) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 9 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 /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 . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 /header . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 /isotope_parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 /length_of_simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 /leslie_matrix_simulation . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 /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 dynami cs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 simulating community dynamics without bioaccumulati on . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 specifying fishery harvest and stocking . . . . . . . . . . . . . . 49 specifying habitat suitability multipliers . . . . . . . . . . . . . . 50 specifying non-fish BCFs . . . . . . . . . . . . . . . . . . . . . . 42, 4 6 specifying output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40, 4 3 specifying water levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 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 . . . . . . . . . . . . . . . . . . . . . . . . . . 5 4 technical support reporting comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 reporting problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 reporting suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

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