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Modelling the effects of hypoxia on fish

Modelling the effects of hypoxia on fish. Kenneth Rose Dept. of Oceanography and Coastal Sciences Louisiana State University plus Many Co-authors. Aaron Adamack and Shaye Sable - Louisiana State University Cheryl A. Murphy – LSU, now University of Toronto

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Modelling the effects of hypoxia on fish

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  1. Modelling the effects of hypoxia on fish Kenneth Rose Dept. of Oceanography and Coastal Sciences Louisiana State University plus Many Co-authors

  2. Aaron Adamack and Shaye Sable - Louisiana State University • Cheryl A. Murphy – LSU, now University of Toronto • Peter Thomas and Saydur Rahman – University of Texas Marine Science Institute • Marius Brouwer and Nancy Brown-Peterson - University of Southern Mississippi • Ann O. Cheek - University of Texas Health Science Center • Carl Cerco - U.S. ACOE • Sandra Diamond, Texas Tech University

  3. Acknowledgements • EPA’s Science to Achieve Results (STAR) Program to University of Texas • STAR Estuarine and Great Lakes (EaGLe) Program through funding to the Consortium for Estuarine Ecoindicator Research for the Gulf of Mexico (CEER-GOM), US EPA Agreement (R 82945801) • NOAA Aquatic Research Consortium (ARC, Phase 2) to USM and Texas State University

  4. Introduction • Quantifying and forecasting effects of hypoxia is needed for effective management • Today: four examples • Physiological • Croaker matrix projection • Marsh community • Bay anchovy coupled to water quality

  5. Disclaimer No real data were harmed in the preparation of this presentation

  6. Gonadotropin pituitary Estradiol Estrogen receptor liver Testosterone ovary Vitellogenin 1. Physiological Model Relate physiological biomarkers to number of viable oocytes using ordinary differential equations Gonadotropin = GtH Testosterone = T Estradiol = E2 Estrogen Receptor = ER Vitellogenin = Vtg Gonadal Somatic Index = GSI Viable Oocytes

  7. SBP SBP (x3) (x3) Estradiol (x7) Estrogen Receptor Gonadotropin (driving) pituitary Synthesis T (GtH) k1 k1 Estradiol (x2) Testosterone (x1) k-1 k-1 Synthesis E2 (T) ovary k1 k-1 k1 k-1 Estrogen Receptor (x6) k2 liver k2 Vitellogenin (x8) blood

  8. 1. Baseline Simulation SPOTTED SEATROUT Smith and Thomas,1991 Gen. Comp. Endocrinol. 81:234-245 200 6 161 mg/ml 150 Year 1 4 Estrogen receptor (pmol/g) 100 Vitellogenin (mg/mL) cumulative Year 2 2 50 A M J J A S O 0 0 1500 1500 1000 1000 Total Estradiol (pg/mL) Total Testosterone (pg/mL) 500 500 A M J J A S O A M J J A S O 0 0 1 2 3 4 5 6 1 2 3 4 5 6 Time (months) Time (months)

  9. 1. Hypoxia Simulation • Simulate gonadotropin suppression • Multiply gonadotropin driving variable by 0.74 • Simulate aromatase impairment • Less Testosterone converted to estradiol • Small % of Estradiol sent to a “sink” every timestep • Total E2 is 41% of Control

  10. 1: Hypoxia Simulation Lab~ 62% decrease fecundity Lab~ 67 % decrease ER mRNA 200 6 Baseline 150 4 Vitellogenin (mg/mL) 73% Estrogen receptor (pmol/g) GtH 61% 100 2 50 GtH + aromatase 0 0 1 2 3 4 5 6 1 2 3 4 5 6 2000 2000 Lab~ 59% decrease 1500 1500 Total Testosterone (pg/mL) No change Total Estradiol (pg/mL) 59% 1000 1000 500 500 0 0 1 2 3 4 5 6 1 2 3 4 5 6 Time (months) Time (months)

  11. Simulated Field Laboratory results Simulated cumulative Vtg as % baseline cumulative Vtg GSI at field as % GSI control lab 2.7 ppm 4.6 ppm 1.7 ppm 1.4 4.9 1.9 ppm 1.2 3.0 1.3 Field Evaluation(Pensacola Bay) 100 100 80 80 60 60 40 40 20 20 0 0 0 20 40 60 80 100 Percent reduction of Total E2 (of control Lab)

  12. 2. Matrix Projection Model Classic formulation: Egg A1 A2 A3 A4 A5 A6 A7 Egg A1 A2 A3 A4 A5 A6 A7 t t+1 Stage duration and mortality are used to calculate P and G

  13. Late juvenile Early juvenile Estuary larva Adult Ocean larva Egg Yolk-sac

  14. Mid-Atlantic Bight (MAB) Age 12 11 10 9 8 7 6 5 4 3 2 1 Annual P Egg G Atlantic Bight Yolk-sac Daily P July Ocean Larva Estuary Larva Early Juvenile Late Juvenile Dec Ocean Larva Estuary Larva Early Juvenile Late Juvenile G North Carolina Transition July Estuary Larva Early Juvenile Late Juvenile Ocean Larva Dec Ocean Larva Estuary Larva Early Juvenile Late Juvenile Biweekly Monthly Virginia Transition

  15. Gulf of Mexico (GOM) Age 8 7 6 5 4 3 2 1 Annual P Egg G Gulf of Mexico Yolk-sac Daily P Ocean Larva Estuary Larva Early Juvenile Late Juvenile G Louisiana Transition Ocean Larva Estuary Larva Early Juvenile Late Juvenile Sept Texas Transition Biweekly Monthly

  16. GOM MAB 3.0 3.0 2.0 2.0 Total adult abundance (millions) Total adult abundance (millions) 1.0 1.0 0.0 0.0 20 40 60 80 100 20 40 60 80 100 Year of simulation Year of simulation 2. Baseline Simulations Reproductive output:

  17. Density dependence: spawner-recruit relationships MAB GOM 2.0 0.15 0.8 0.02 1.5 0.6 0.10 1.0 0.4 Age 1 recruits (millions) 0.01 0.05 0.5 0.2 Louisiana Virginia North Carolina Texas 12 12 0.0 0.00 0.0 Eggs (1 x 10 Eggs (1 x 10 ) ) 0.00 0.0 0.1 0.2 0.3 0.4 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.8 0.0 0.1 0.2 0.3 0.4 0.5 2. Baseline Simulations

  18. 1.3 1.0 Baseline 0.8 1.3 1.0 Hypoxia 0.8 0 20 40 60 80 100 Year 2. Hypoxia Simulation - GOM Reduced fecundity in lab exposure Total Population (millions)

  19. 3. Marsh Community Conditions Dissolved O2 Temperature Tidal Stage Prey Density Predator Density Individual Size Individual Processes Growth Movement Mortality Spawning Sheepshead Minnow Bay Anchovy Zooplankton (2 groups) Benthos (3 groups) Shrimp Silversides Killifish Blue Crab

  20. 200m T (oC) Prey (#/m2) 200m Stage Time Interior Marsh Marsh Edge Bay Channel Tidal Creek Marsh Pool 3. Marsh Habitat DO (mg/l)

  21. 3. Individual Processes • Growth: • bioenergetics • consumption based on prey and predator sizes; prey densities • Movement: • neighborhood depends on tidal stage and individual motility • move to cell with highest fitness score in neighborhood • Mortality: • predation; starvation; stranding; natural • Spawning: • temperature and weight-dependent fecundity • fractional spawning with brood intervals

  22. 3. Baseline Results: Densities Interior Marsh Marsh Edge Bay Channel Tidal Creek Marsh Pool Density (#/meter2) Days

  23. 3. Grass Shrimp Distribution Tidal Stage 0 0.01-1 1-2 2-5 5-10 10-20 20-30 30-40

  24. * Grass shrimp: Larval z (hour-1) = 5.5E-4*(DO) + 0.0007 * Grass shrimp: Brood interval (days) = 350.4 + 255.1/ln(DO) All species: Metabolism multiplier = 1.79*(DO)-0.362 when DO < 5 mg/l 3. Cyclic Dissolved Oxygen Stress * Grass shrimp functions fit to laboratory data provided by Brouwer & Brown-Peterson

  25. 3. DO Stress Gulf Killifish Blue Crab

  26. Frequency (x105) Length (mm) 3. DO Stress

  27. 3. Gene Chips • Episodes and fluctuations complicate exposure • Data • Grass shrimp and sheepshead minnow • Lab: DO, growth and fecundity, up/down regulation • Field: gene responses • Idea is to add damage-repair (Mancini; Breck) or vitality (Anderson) sub-model to individuals • Calibrate to lab • Apply to field exposures

  28. 4. Chesapeake Bay WQMDeveloped by the Army Corps • 3D hydrodynamic model (CH3D), eutrophication model (CE-QUAL-ICM), and sediment diagenesis model • Simulates 24 constituents • Forms of N, P, and Si • Spring and winter algae • Micro- and meso-zooplankton • DO and temperature • Bay is divided into 4073 cells • 729 surface cells • Minimum 2 layers thick • Maximum 15 layers thick

  29. 4. Bay Anchovy Model • Spatially-explicit, individual-based • Simulates growth, death, and movement • Dynamically coupled to the WQM • Temperature and DO affect anchovy growth and mortality • Micro- and meso-zooplankton affect anchovy growth • Consumption by anchovy is mortality on zooplankton • Movement depends on: • Horizontal: zooplankton density, temperature • Vertical: temperature, dissolved oxygen • Fixed recruitment each year

  30. 4. DO Effects DO effect on growth DO mortality

  31. 4. Results – Hypoxia(Normal Year, July, bottom layer) >3 mg/l Baseline 50% Increase 50% Reduce Nutrient Loading 0 mg/l

  32. 4. Results – Anchovy Spatial Distribution(Baseline year, late October) 35/m2 Normal Wet Dry 0/m2 Water Year

  33. 4. Results – Anchovy Biomass

  34. 4. Results – Zooplankton(Station C5.2 – mid-bay, with anchovy)

  35. 4. Results – YOY Anchovy Length(Late October)

  36. Overview of Models

  37. Concluding Remarks • Modeling techniques, measurements, and understanding are rapidly improving • Advances: • Scaled models • Spatial data • Exposure • Multiple stressors

  38. Concluding Remarks • Optimism for quantifying indirect effects? • Key will be movement

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