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Fountain Darter Model

Fountain Darter Model. Bill Grant Hsiao- Hsuan Wang (Dr. Rose) University of Texas Texas A&M University May 12, 2014. Outline (Plan A). I. A bit of modeling philosophy II. The current model Model input / output Model structure Model function Model programming III. The future model

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Fountain Darter Model

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  1. Fountain Darter Model Bill Grant Hsiao-Hsuan Wang (Dr. Rose) University of Texas Texas A&M University May 12, 2014

  2. Outline (Plan A) I. A bit of modeling philosophy II. The current model • Model input / output • Model structure • Model function • Model programming III. The future model • Available data • UseS of data in model • Hypothesized relationships IV. Predicting the future? (Embracing uncertainty) • Parametric uncertainty • Structural uncertainty

  3. Outline (Plan A) I. A bit of modeling philosophy II. The current model • Model input / output • Model structure • Model function • Model programming III. The futuremodel • Available data • UseS of data in model • Hypothesized relationships IV. Predicting the future? (Embracing uncertainty) • Parametric uncertainty • Structural uncertainty

  4. Modeling Entailment Natural system Decoding Encoding Formal system Entailment Rosen (1991) in Saltelliet al. (2008)

  5. Outline (Plan A) I. A bit of modeling philosophy II. The current model • Model input / output • Model structure • Model function • Model programming III. The futuremodel • Available data • UseS of data in model • Hypothesized relationships IV. Predicting the future? (Embracing uncertainty) • Parametric uncertainty • Structural uncertainty

  6. Model Input Water flow Aquatic Habitat Fountain Darter Population Structure and Dynamics Model Output

  7. Model Structure Habitat Movement Life cycle Aquatic vegetation type Egg Larva Adult Juvenile Water depth Water velocity Water temperature

  8. Model Function Aquatic veg. type = f (season) Jan. 2003 Dec. 2008 Bio-West annual reports Water depth = f (flow) Old Channel 10 - 80 cfs Hardy et al. (2010) Water velocity = f(flow) Old Channel 10 - 80 cfs Hardy et al. (2010) Water temp. = f (hour) Jan. 2003 Dec. 2008 Bio-West annual reports Egg laying = f (month) Schenck and Whiteside (1977) Egg mort. = f (temp.) Larva mort. = f (temp.) Juvenile mort. = f (temp.) Adult mort. = f (temp.) Picher and Hart (1982) Brandt et al. (1993) Bonner et al. (1998) Density-dependent mort. = f (# veg. patches) Life cycle Movement= f (veg. type) Stay in habitat patch with vegetation Move toward habitat patch with vegetation Movement Habitat

  9. Model Programming 1 Initialization 2 Input 2.1 Input vegetation types 2.2 Input water temperatures 2.3 Input water depths and water velocities 3Submodels 3.1 Adjust vegetation types (seasonally) 3.2 Adjust water depths and water velocities (daily) 3.3 Adjust water temperatures (hourly) 3.4 Adjust fountain darter ages and developmental stages (daily) 3.5 Calculate fountain darter mortalities (daily) 3.6 Calculate fountain darter movements (hourly) 3.7 Calculate fountain darter egg laying (recruitment) (daily) 3.8 Update aggregated (output) variables (hourly)

  10. Outline (Plan A) I. A bit of modeling philosophy II. The current model • Model input / output • Model structure • Model function • Model programming III. The futuremodel • Available data • UseS of data in model • Hypothesized relationships IV. Predicting the future? (Embracing uncertainty) • Parametric uncertainty • Structural uncertainty

  11. Available Data UseS of Data in Model Aquatic Vegetation Data mapping of vegetation types plant growth = f (temp.) plant biomass = f (% veg. cover) (2014) plant growth rate and dispersal = f (substrate, depth, velocity) Fountain Darter Data density = f (veg. type) (Drop nets) Size class distribution (Dip nets) mortality = f (temp.) fecundity = f (habitat quality) predation = f(veg. density) movement = f (habitat preference, dispersal) Driving Variables (Input) Data Process Data (Functional Relationships for Model Equations) Evaluation (of Output) Data (2014)

  12. Hypothesized Relationships Flow Veg. restoration Recreation Veg. Movement = f (veg., flow) E L J A Depth Velocity Recr. & DO Mort. = fTemp. Food Cover/Pred. f (flow) Model Input Water Flow Aquatic Habitat Fountain Darter Dynamics Model Output f (veg.) Substrate Light Veg. = f (flow, veg. restoration, recreation, substrate, light)

  13. Outline (Plan A) I. A bit of modeling philosophy II. The current model • Model input / output • Model structure • Model function • Model programming III. The futuremodel • Available data • UseS of data in model • Hypothesized relationships IV. Predicting the future? (Embracing uncertainty) • Parametric uncertainty • Structural uncertainty

  14. Entailment Predicting the future?(Embracing uncertainty) Natural system Decoding Encoding “Oyefilósofo ¿va a lloverpor la noche?” “Tedigomañana.” (El Filósofo de Guemes) Formal system Entailment

  15. Parametric bootstrap version of uncertainty and sensitivity analyses Model Input data Estimation Estimated parameters Uncertainty and sensitivity analysis Inference Saltelliet al. (2008)

  16. Bootstrapping of the modeling process (Structural uncertainty) Model identification Model Loop on boot-replica of the input data Estimation Estimated parameters Bootstrap of the Modeling process Inference Chatfield (1993) in Saltelliet al. (2008)

  17. Bayesian model averaging Prior of model(s) Posterior of model(s) Data Inference Sampling Prior of parameters Posterior of parameters Saltelliet al. (2008)

  18. The End

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