1. General Model Information

Name: Bioaccumulation and Aquatic System Simulator

Acronym: BASS

Main medium: aquatic
Main subject: (eco)toxicology, biogeochemistry, population dynamics
Organization level: population, community/fish assemblage
Type of model: ordinary differential equations, age-structured population
Main application: research
Keywords: fish, growth, population dynamics, predator-prey, bioaccumulation, organic chemicals, metals


M. Craig Barber
Research Ecologist
960 College Station Road
Athens, GA 30605

Phone: 706-355-8110
Fax: 706-355-8104
email: barber.craig@epa.gov


M. Craig Barber


The Bioaccumulation and Aquatic System Simulator (BASS) is a Fortran 90 simulation model that predicts the population and bioaccumulation dynamics of age-structured fish assemblages which are exposed to hydrophobic organic pollutants and class B and borderline metals that complex with sulfhydryl groups (e.g., cadmium, copper, lead, mercury, nickel, silver, and zinc). BASS's bioaccumulation algorithms are based on diffusion kinetics and are coupled to a process-based model for the growth of individual fish. The model?s exchange algorithms consider both biological attributes of fishes and physico-chemical properties of the chemicals of concern that determine diffusive exchange across gill membranes and intestinal mucosa. Biological characteristics used by the model include the fish's gill morphometry, feeding and growth rate, and proximate composition (i.e., its fractional aqueous, lipid, and structural organic content). Relevant physico-chemical properties are the chemical's aqueous diffusivity, n-octanol/water partition coefficient (K_{ow}), and, for metals, binding coefficients to proteins and other organic matter. bass simulates the growth of individual fish using a standard mass balance, bioenergetic model (i.e., growth = ingestion - egestion - respiration - specific dynamic action - excretion). A fish's realized ingestion is calculated from its maximum consumption rate adjusted for the availability of prey of the appropriate size and taxonomy. The community?s food web is specified by defining one or more foraging classes for each fish species based on either its body weight, body length, or age. The dietary composition of each of these feeding classes is specified as a combination of benthos, incidental terrestrial insects, periphyton/attached algae, phytoplankton, zooplankton, and one or more fish species. Population dynamics are generated by predatory mortalities defined by community?s food web and standing stocks, size dependent physiological mortality rates, the maximum longevity of species, and toxicological responses to chemical exposures. The model's temporal and spatial scales of resolution are a day and a hectare, respectively. Currently, BASS ignores the migration of fish into and out of the simulated hectare.

Source of abstract: http://www.epa.gov/athens/staff/members/barbermahlonc/index.html

II. Technical Information

II.1 Executables:

Operating System(s): DOS

II.2 Source-code:

Programming Language(s): Fortran 95 (available from author on request)

II.3 Manuals:

Barber, M.C. 2001. Bioaccumulation and Aquatic System Simulator (BASS) User's Manual Beta Test Version 2.1. U.S. Environmental Protection Agency, National Exposure Research Laboratory, Ecosystems Research Division, Athens, GA. EPA 600/R-01/035.

II.4 Data:

III. Mathematical Information

III.1 Mathematics

Numerical integration of BASS's differential equations can be performed using either an Euler or Runge-Kutta integrator. These methods offer users two distinctly different options with respect to software performance and execution. Although Euler methods may often allow for fast model execution, they cannot assess the accuracy of their integration. Runge-Kutta methods, on the other hand, can monitor the accuracy of their integration but at the cost of increased execution time. Fortunately, however, this additional computational burden can often be significantly reduced by employing adaptive step sizing. BASS's Runge-Kutta integrator, which is the model's default integrator, is patterned on the fifth-order Cash-Karp Runge-Kutta algorithm outlined by Press et. al. (1992).

III.2 Quantities

III.2.1 Input

Input variables are broadly classified into three categories: simulation control parameters, chemical parameters, and fish parameters.

Simulation control parameters provide information that is applicable to the simulation as a whole, e.g., length of the simulation, the ambient water temperature, nonfish standing stocks, and user output options.

Chemical parameters specify the chemical's physico-chemical properties (e.g., the chemical's molecular weight, molecular volume, n-octanol/water partition coefficient, etc.) and exposure concentrations in the environment (i.e., in water, sediment, benthos, insects, etc.).

Fish parameters identify the fish's taxonomy (i.e., genus and species), feeding and metabolic demands, dietary composition,  predator-prey relationships, gill morphometrics, body composition, initial weight, initial whole body concentrations for each chemical, and initial population sizes.

III.2.2 Output

The model's output includes:

1) Summaries of all model input parameters and simulation controls.

2) Tabulated annual summaries for the bioenergetics of individual fish by species and age class.

3) Tabulated annual summaries for the chemical bioaccumulation within individual fish by species and age class.

4) Tabulated annual summaries for the community level consumption, production, and mortality of each fish species by age class.

5) Plotted annual dynamics of selected model variables as requested by the user.

IV. References

Barber, M.C., L.A. Suárez, and R.R. Lassiter. 1991. Modelling bioaccumulation of organic pollutants in fish with an application to PCBs in Lake Ontario salmonids. Can. J. Fish. Aquat. Sci. 48:318-337.

Barber, M.C. 2001. Bioaccumulation and Aquatic System Simulator (BASS) User's Manual Beta Test Version 2.1. U.S. Environmental Protection Agency, National Exposure Research Laboratory, Ecosystems Research Division, Athens, GA. EPA 600/R-01/035.

Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. 1992. Numerical Recipes in FORTRAN. Cambridge Univ. Press. pp 963.

V. Further information in the World-Wide-Web

VI. Additional remarks

September 27, 2001: A new model executable for BASS version 2.1 is available from the author.
This new executable has been recompiled using the latest release of the Lahey/Fujitsu Fortran 95 5.60 compiler (i.e., release 5.60g)

June 4, 2001:
BASS version 2.1 is a beta test version that is being released on a targeted basis to EPA Program and Regional Offices and to the academic research community for comment and testing. Although the model has not been extensively field-tested, its process-based algorithms for predicting chemical bioaccumulation, growth of individual fish, predator-prey interactions, and population dynamics either have been corroborated or have been formulated using widely accepted ecological and ecotoxicological principles. Even when a process-based model has undergone only limited field testing, it can be an extremely useful tool. Process-based models enable users to observe quantitatively the results of a particular abstraction of the real world. Moreover, such models can be argued to be the only objective method to make extrapolations to unobserved or unobservable conditions. If the conceptualization and construction of a process-based model are both comprehensive (i.e., holistic) and reasonable, then their output, validated or not, can still be used for comparative analyses. A model's ability to simulate trends and comparative dynamics are, in fact, often more important measures of a model's utility than is its ability to replicate a specific field or laboratory study. Although BASS can be used to analyze results from actual field studies, its principal intended use is to predict and compare the outcomes of alternative management options that are associated with pollution control or ecosystem management or restoration activities.
Last review of this document by:
Status of the document: contributed by: M. Craig Barber, June 4, 2001
last modified by Tobias Gabele Wed Aug 21 21:44:39 CEST 2002

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