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2003 Progress Report: Application of Individual-based Fish Models to Regional Decision-making
EPA Grant Number: R830886Title: Application of Individual-based Fish Models to Regional Decision-making
Investigators: Lamberson, Roland H. , Railsback, Steven F.
Institution: Humboldt State University
EPA Project Officer: Goodman, Iris
Project Period: May 1, 2003 through April 30, 2006 (Extended to November 30, 2006)
Project Period Covered by this Report: May 1, 2003 through April 30, 2004
Project Amount: $418,710
RFA: Developing Regional-Scale Stressor-Response Models for Use in Environmental Decision-making (2002)
Research Category: Ecological Indicators/Assessment/Restoration
Description:
Objective:The objective of this research project is to gain an understanding of the dynamics that may emerge from combinations of stress on salmonids, particularly the impacts of variation in flow and temperature regimes, turbidity, and competing species. The impact of stress on fish populations emerges from the stress felt by individual fish and the ways that individual fish interact with each other and their habitat. Individual-based models (IBMs) are particularly well suited for use as stress-response models because they provide for the application of stress directly on virtual individuals and, as a result, the population level response emerges naturally. This approach provides the potential for complex and unexpected dynamics to emerge from the interaction of multiple stessors. An existing IBM for stream trout known as the Individual-Based Stream Trout Research and Assessment Model (inSTREAM) is being developed for routine applications to multiple-stressor assessments. In addition, we are developing methods for incorporating the local results of inSTREAM into a linked, larger scale model for use in regional decision-making.
Progress Summary:Progress in Year 1 of the project has focused on: revising and updating a stream trout IBM for use as a multistressor assessment model, analyzing the model's sensitivity to parameters, and developing calibration methods. Additional work has addressed the application of the model to regional decision-making and understanding of the interactions between wild and hatchery-reared fish.
We conducted a complete review and revision of our existing stream trout IBM and produced the version we call inSTREAM Version 4.0. Key revisions included: (1) new methods for modeling how fish food intake varies with fish size and water velocity and turbidity; (2) software improvements to make changes in mortality risks easy to implement; (3) new software to make it easy to change the kinds and types of fish population statistics reported as output; and (4) a tool allowing the sequence of simulated years to be shuffled randomly, so the effect of weather and flow history on model results can be analyzed or minimized.
The Version 4.0 software and draft documentation now are available for distribution. We currently are finalizing the documentation and software user guide.
In Year 1 of the project, we also conducted the first level of a complete analysis of inSTREAM's sensitivity to parameter values. This level examined single-parameter sensitivity; how key model results, especially mean biomass of adult trout, varied as parameters were varied one at a time. As expected, we found a small number of parameters (mainly those used for calibration) to which the model is strongly and consistently sensitive, while most parameters have relatively little effect under most conditions. (Many parameters are expected to have strong effects under some habitat conditions, for example, when temperatures are unusually high or low, or when flows are unusually variable.)
Progress also was made on two applications of inSTREAM to stress-response assessment. First, graduate student Eric Stewart is modifying the model to represent interactions between wild trout and introduced hatchery trout. Hatchery trout are being modeled as physiologically identical to wild trout, but lacking the ability to perceive how predation risk varies with habitat types in natural streams. Second, we began to examine how inSTREAM can be used to support regional decisionmaking. The model simulates trout population responses to stressors at individual study sites, but multiple sites can be simulated at the same time with fish able to move (in both directions, or downstream only) among sites. Key issues addressed in this work include how many study sites and what size streams are needed to represent watershed-scale responses, and how these sites should be linked within a simulation.
Future Activities:In the next year, we will focus on: (1) completing the parameter sensitivity analysis of inSTREAM, including analysis of interactions among the parameters with strongest effects; (2) investigating the sensitivity of inSTREAM to key structural assumptions, especially the assumption that fish feed only during daytime; (3) studying the impacts of turbidity and flow regimes on stream trout populations; and (4) developing methods for scaling results up to support regional-scale decision-making. We also expect to complete a full public release of inSTREAM, including software, model description, and user guide, by the end of 2004.
Journal Articles on this Report: 2 Displayed | Download in RIS Format
| Other project views: | All 28 publications | 6 publications in selected types | All 4 journal articles |
| Type | Citation | ||
|---|---|---|---|
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Harvey BC, Railsback SF. Elevated turbidity reduces abundance and biomass of stream trout in an individual-based model. Canadian Journal of Fisheries and Aquatic Sciences. |
R830886 (2003) |
not available |
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Railsback SF, Stauffer HB, Harvey BC. What can habitat preference models tell us? Tests using a virtual trout population. Ecological Applications 2003;13(6):1580-1594. |
R830886 (2003) |
not available |
watershed, water, ecological effects, aquatic, ecosystem, ecology, ecosystem protection, environmental exposure and risk, water, economics and decisionmaking, monitoring/modeling, regional/scaling, water and watershed, decisionmaking, Bayesian approach, Bayesian classifiers, TMDL, adaptive implementation modeling, aquatic ecosystem, assessment endpoint mechanistic research, decision analysis, decision support tool, ecological indicators, ecological models, ecological variation, ecology, ecology assessment models, ecosystem assessment, ecosystem modeling, ecosystem stress, environmental decisionmaking, environmental risk assessment, fish models, individual-based models, IBMs, regional scale impacts, risk assessment, stress response, stressor response model, water monitoring, habitat, economic, social, and behavioral science research program. , Ecosystem Protection/Environmental Exposure & Risk, Economic, Social, & Behavioral Science Research Program, Water, Scientific Discipline, RFA, Water & Watershed, Biology, decision-making, Economics & Decision Making, Watersheds, Regional/Scaling, Monitoring/Modeling, Ecology and Ecosystems, risk assessment, water quality, aquatic ecosystem, environmental risk assessment, decision support tool, watershed, ecosystem modeling, decision analysis, ecology assessment models, decision making, stressor response model, regional scale impacts, TMDL, ecological indicators, stress response, individual based models, ecology, ecosystem assessment, ecological variation, ecosystem stress, watershed assessment, ecological models, water monitoring, fish models, assessment endpoint mechanistic research, Bayesian approach, environmental decision making
Relevant Websites:
http://www.humboldt.edu/~ecomodel/ 
Progress and Final Reports:
Original Abstract
2004 Progress Report
Final Report