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

7. Testosterone (x1) Vitellogenin (x8) Estradiol (x2) Estrogen Receptor (x7) Estradiol Gonadotropin (driving) Estrogen Receptor (x 6 ) pituitary liver k 1 k -1 Synthesis E2 (T) k 1 k -1 k 2 ovary Synthesis T (GtH) blood SBP (x3) SBP (x3) k 1 k -1 k 1 k -1

8. Vitellogenin (mg/mL) 0 50 100 150 200 Estrogen receptor (pmol/g) 0 2 4 6 123456 Total Testosterone (pg/mL) 0 500 1000 1500 123456 Total Estradiol (pg/mL) 0 500 1000 1500 1. Baseline Simulation 1. Baseline Simulation Time (months) A M J J A S O SPOTTED SEATROUT Smith and Thomas,1991 Gen. Comp. Endocrinol. 81:234-245 Year 1 Year 2 161 mg/ml cumulative

9. 1. Hypoxia Simulation 1. Hypoxia Simulation 1.Simulate gonadotropin suppression Multiply gonadotropin driving variable by 0.74 2.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 1: Hypoxia Simulation 123456 0 500 1000 1500 2000 0 2 4 6 123456 0 500 1000 1500 2000 123456 123456 0 50 100 150 200 Total Testosterone (pg/mL) Total Estradiol (pg/mL) Vitellogenin (mg/mL) Estrogen receptor (pmol/g) Time (months) No change Lab~ 59% decrease Lab~ 67 % decrease ER mRNA Lab~ 62% decrease fecundity 61% 73% 59% GtH GtH + aromatase Baseline

11. 1.Field Evaluation (Pensacola Bay) Percent reduction of Total E2 (of control Lab) 020406080100 0 20 40 60 80 100 0 20 40 60 80 100 Simulated cumulative Vtg as % baseline cumulative Vtg Simulated 3.0 4.9 Field 1.2 1.9 ppm 1.3 1.4 4.6 ppm GSI at field as % GSI control lab Laboratory results 1.7 ppm 2.7 ppm

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

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

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

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

16. 2. Baseline Simulations Year of simulation 20406080100 Total adult abundance (millions) 0.0 1.0 2.0 3.0 Year of simulation 20406080100 Total adult abundance (millions) 0.0 1.0 2.0 3.0 GOM MAB Reproductive output:

17. 2. Baseline Simulations

18. Total Population (millions) 2. Hypoxia Simulation - GOM Reduced fecundity in lab exposure Baseline 0.8 1.0 1.3 Year Hypoxia 020406080100 0.8 1.0 1.3

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

20. 3. Marsh Habitat 200m Interior Marsh Marsh Edge Bay Channel Tidal Creek Marsh Pool Time T ( o C) Prey (#/m 2 ) Stage DO (mg/l)

21. 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 3. Individual Processes

22. Density (#/meter 2 ) Days 3. Baseline Results: Densities Interior Marsh Marsh Edge Bay Channel Tidal Creek Marsh Pool

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

24. 3. Cyclic Dissolved Oxygen Stress * 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 * Grass shrimp functions fit to laboratory data provided by Brouwer & Brown-Peterson

25. 3. DO Stress Gulf Killifish Blue Crab

26. 3. DO Stress Length (mm) Frequency (x10 5 )

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 WQM Developed 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 growthDO mortality

31. 4. Results – Hypoxia (Normal Year, July, bottom layer) 50% Increase50% Reduce Baseline Nutrient Loading 0 mg/l >3 mg/l

32. 4. Results – Anchovy Spatial Distribution (Baseline year, late October) WetDry Normal Water Year 35/m 2 0/m 2

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 TaxaLevelTimeSpace SciaenidsIndividualSingle fishDaily6 moPoint CroakerStage within age Population1,5,10, 30 d Annual 100 yrPoint within 2 nursery boxes GOM or MAB CommunityIndividualPopulationHourly1 yr2-D grid + water stage Marsh Bay anchovy IndividualPsuedo- population (fixed rec) 15 min, 1 hr 10 yr3-D gridChesapeake Bay

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