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R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2

Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models. R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2 1 Michigan State Univ., East Lansing, MI 2 Univ. Michigan, Ann Arbor, MI

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R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2

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  1. Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models R. Jan Stevenson1, M. J. Wiley2 D. Hyndman1, B. Pijanowski3, P. Seelbach2 1Michigan State Univ., East Lansing, MI 2Univ. Michigan, Ann Arbor, MI 3Purdue University, West Lafayette, IN Project Period: 5/1/2003-4/30/2006; NCX 4/30/2007 Project Cost: $748,527 Stevenson et al.

  2. Natural Ecosystems Are Complex but can be Organized for Management Septic Systems Silviculture Livestock Grazing Crop & Lawn Fertilizers Irrigation Construction Human Activities Sewers & Treatment Herb Buffer Strips Tree Canopy Ret. Basins & Wetlands Livestock Fences Other BMPs NH3 NOx PO4 Organic/ Part PNC Heat Light Sediments Hydrologic Variability Stressors Dissolved Oxygen Nitrifying Bacteria Other Bacteria Periphytic Microalgae Benthic Macroalgae Endpoints Benthic Invertebrates Fish Ecosystem Services Valued Ecological Attributes – Management Targets

  3. Understanding how it all works:Complicating Issues • Non-linearity and thresholds: • graded responses may be rare in complex systems. • thresholds complicate management choices. • Complex causation: • multiple actions simultaneously shape biological responses. • issues of direct and indirect causation (effects): spurious correlations • Scale and dynamics: • Potential stressors operate at different spatial and dynamic scales • Scales complicate the diagnosis of stressor-response relationships • obscure causal dependencies through time lags, ghosts of past events, and misidentification of natural spatial/temporal variability. Stevenson et al.

  4. G2M104070 Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models Goals • Relate patterns of human landscape activity to commonly co–varying stressors (nutrients, temperature, sediment load, DO, and hydrologic alterations) • Relate those stressors to valued fisheries capital and ecological integrity of stream ecosystems • Link empirical and mechanistic modeling approaches as a means to improving understanding and prediction Stevenson et al.

  5. Approach • Building on other regional assessment & modeling by our team (MI, IN, KY, OH, IL, WI) • Focus on basic interactions between landuse, hydrology, nutrients (CNP), and DO • Multi-scale Analysis: • Regional (Michigan) • (6) Focal Watersheds • Detailed Site monitoring • Modeling • empirical (statistical) • process-based (mechanistic) • hybrids ( a little of both!) using existing platforms and an integrated modeling system

  6. Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models G2M104070 Ecological significance • Our project is focused on the streams and rivers of the Lower Michigan Peninsula. • These are the veins and arteries of the Laurentian Great Lakes, the largest and most complex river-lake ecosystem in the world. • What we learn here about multiple stressors is applicable in fluvial ecosystems anywhere.

  7. Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models G2M104070 Key findings • Urban land use is a stronger stressor than agricultural land use but agricultural impacts are more widespread. • Legacy impacts of landuse can be as important as current impacts. • Agricultural impacts appear to occur through a complex but tractable interaction of nutrient, hydrologic and metabolic stressors. • Impacts of specific stressors and their interaction varies with ecological setting in general; and specific hydraulic setting in particular. • Management expectations (ecological targets and assessment scoring criteria) need to be conditioned by ecological context of the site in question.

  8. Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models G2M104070 Lessons Learned • Where exactly you look (sample locale), and at what scale you look (sample extent and frequency), affects what you can see (and model) • We need more concise language to talk about multiple stressors and stresses [incorporate concepts of frequency, duration, co-variation and interaction, contingency]

  9. Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models G2M104070 Interactions & Users • MDEQ nutrient criteria development • MDNR groundwater protection criteria • EPA nutrient criteria workgroups • MDNR Ecoregional management teams • GLFT Lake Michigan Tributary Assessments • Local watershed groups (MWA, HRWC, MiCORP)

  10. G2M104070 Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models Graduate students supported: total of 10 across all 3 institutions M.S. theses developed/completed: 4 Extensive linkage with other EPA-Star, NSF, Great Lakes Fisheries Trust, and Great Lakes Fisheries Commission projects

  11. 2006a Progress Report • Late start first year, 2004 first extensive field year, NCX to 2007 • Analyses of regional, aggregated data sets underway! {first looks} • Analysis of 2004 and 2005 focal basin surveys continues {some highlights} • intensive hydrologic and WQ monitoring continues in Cedar and Crane Creeks • Integrated process modeling running for Cedar, underway for Brooks, Bigelow, & Crane {description and early results}

  12. Large, Regional-Scale Statistical Modeling • Urban and agricultural land use as key multiple stressors • Relative impacts? • Direct and indirect effects? {watersheds and riparian buffers} • Causal relationships? Intervening variables? • Data assembled from MDEQ, Michigan Rivers Inventory, previous EPA-STAR, NSF, Muskegon River Assessment; registered on attributed NHD database (EPA-STAR/USGS AQGAP product) • Used regional Normalization approach to standardize datasets and metrics (fish and invertebrate)

  13. Regional modeling of Multiple-source assessment datasets: Patterns of human activities and fluvial ecosystem response Data coverage Fish & Invertebrate Multi-Metric

  14. Regional “~dose-response” relationships to Land use Stressors Indicator: normalized EPT score [(obs-exp)/sd] r= -.36 r= -.20 %Ag in riparian buffer %Urban in riparian buffer 5%, 50% 1%, 8% r= -.29 Noisey Linear(izable) Urb > Ag thresholds Urb and Ag: geom. mean

  15. Structural Equation Modeling to sort out direct, indirect and total effects • Issues of direct and indirect effects: • Urbanization of Ag areas • Multiple ways to represent land use/cover VEA:EPT score Riparian buffer Results: Overall Urban stronger than Ag Riparian Ag > than Basin AG Basin Urban > Riparian Urban watershed Standardized Total Effects - Estimates xWT_urb xWT_ag xRT_ag xRT_urb xWT_ag -0.152 0.000 0.000 0.000 xRT_ag -0.118 0.776 0.000 0.000 xRT_urb 0.923 0.000 0.000 0.000 nEPT -0.354 -0.189 -0.244 -0.023 Best fitting, structurally plausible model

  16. CART of normalized overall fish and invert multi-metric CART model fish & invert based Attainment Class Attainment class thresholds Basin Urban <= 5.5% or > 22.5% Basin Ag <=48.5%

  17. Statistical Modeling of Focal Basin dataset Agricultural impacts on Stream Ecosystems (6 )100-300 mi2 systems representing a targeted gradient of agricultural land cover • Cedar Creek • hIgh value fishery with Ag impacts, threatened by development • Bigelow • Pristine high value fishery resource • Mill Creek • Brooks Creek • threatened by developmentcurrently with signif agricultural • Crane Creek • Sycamore Creek • intensive agricultural impacts What is the nature of biological responses to agricultural land use? • The case for chronic metabolic stresses • Agricultural land use and nutrients • Agricultural land use and dissolved oxygen dynamics • Highly variable response tied to variation in hydrologic/hydraulic/DO regime

  18. Meso-scale empirical modeling (6) stream systems sampled across Ag and Hydrologic gradients EPT Taxa % metabolic conformers % surface breathers % Riparian Buffer area in Ag % Watershed area in Ag % Riparian Buffer area in Ag Biological response to indirect Landscape stressors Multiple Local (direct) Stressors response to Agriculture (indirect stressor) Organic Carbon (COD) Inorg Nitrogen (ppm) Phosphorus (ppm) PM oxygen (ppm) % Riparian Buffer area in Ag % Watershed in Ag % Watershed in Ag % Watershed Ag

  19. Early Morning D.O. levels

  20. Site-Intensive data collection &Integrated Mechanistic Modeling • Test hypothesis that cause-effect relations in regional statistical models are plausible • Understand how multiple stressors interact to cause biological response • Cedar Creek ** • Mill Creek* • Brooks Creek* • Crane Creek * • Sycamore Creek • Bigelow*

  21. Integrated Modeling of Cedar Creek - Spatially & temporally intensive water chemistry and biological sampling

  22. Holten to River Rd. Ratios Catchment area ratio= 26% Typical storm peak ratio = 80% Average flow ratio= 3% Runoff [ 67%] groundwater Holten Max Q = 250 cfs Mean Q =2cfs Runoff [ 5%] Groundwater [95%] Max Q = 200 cfs Mean Q =46cfs River Rd.

  23. Cedar Creek Bason Multi-Stressor Project Observed/Expected diversity Holten Gage Biological Quality Poor Below expectation Acceptable Excellent River Rd. Gage

  24. LTM2purdue Linking local-scale mechanistic models for Causal evaluation and modeling experiments Weather model* Landcover model* KendallPREPmsu HEC-HMSum HEC-HMSum SRTMum Surface abstraction Basin Routing transforms Channel Routing transforms Thermograph Groundwater Model MODFLOWmsu HEC-RASum MT3Dmsu QUAL2Kmsu Or Water Quality Data Channel hydraulics width depth velocity shear DOSMOSCum Habitat stress oxygen temperature bed transport Model accumulates hrs [or relative freq] of oxygen and bed mobilization stress over long period runs (e.g. 1-2 years) * or historical data

  25. Preprocessor & MODFLOW Inputs: Land Use (historical & LTM2) Regional Geology NEXRAD Precipitation NOAA Snow Depth MODIS LAI DEM Solar radiation HEC-HMS Surface Water and channel routing Hydrologic Modeling:Simulate Transient Fluxes to SW

  26. NEXRAD for Expanded Muskegon 10 yrs + 10 synth

  27. Monthly Vegetation Density Distribution in Expanded Muskegon and Cedar Creek Weekly Leaf Area Index Model Based on MODIS coverage

  28. Results Recharge Regional analyses indicate reduced recharge in agricultural vs forest watersheds • % of precipitation that becomes recharge • Landuse effects Cedar Creek well recharge monitoring

  29. Results MODFLOW • Observations All head observations: R2 = 0.81 Pre-1988: R2= 0.79 1988-2004: R2=0.89

  30. Results MODFLOW Upper Cedar Creek Lower Cedar Creek

  31. Nitrate Transport Simulation (MT3D) • Used GW model fluxes • Nitrate sources • Atmosphere • Agricultural lands • CAFOs • Septic systems • Nitrate fluxes exported to stream ecohydrology model NO3, mg/L

  32. 12 160 10 120 8 Simulated Dissolved Oxygen 80 Chlorophyll (ug/L) Observed Dissolved Oxygen Dissolved Oxygen (mg/L) Simulated Dissolved Oxygen Saturation 14 Observed Chlorophyll 6 40 13 4 0 0 5 10 15 20 12 Distance Downstream (km) 11 Water Temperature (°C) 10 Simulated Water Temperature 2000 9 Observed Water Temperature Observed Nitrate 8 Simulated Nitrate 1500 0 5 10 15 20 Distance Downstream (km) 1000 Nitrate + Nitrite (ugN/L) 500 0 0 5 10 15 20 Distance Downstream (km) Simulating Water Chemistry and Biological Response in Cedar Creek • Using nitrate fluxes to Cedar Creek calculated in transport model • QUAL2K

  33. Recharge Recharge Model Recharge Model MODFLOW Groundwater fluxes MT3D Nitrate fluxes QUAL2Kw Site Biological response (annual) Stream concentrations Coupling models to generate realistic processes (hr) (hr) Recharge MODFLOW (day) (day) Groundwater fluxes HEC-HMS (hr) (day) Watershed hydrology HEC-RAS (day) Channel hydraulics (hr) MRI-DOHSAM Cum metabolic stress

  34. MRI_DOHSAMcumulative DO & Hydraulic Stress Assessment Model 8 day simulation for Crane Creek Outlet channel using observed flow temp, depth and velocity data from an up-looking doppler sensor. Loading parameters BOD = 8 ppm, NH4=.2 ppm Exceedence frequencies for Dissolved oxygen and bed mobilization Stress summary: as % of period Scour_stress = 56.8 O2 stress = 2.5 Combined = 59.1 Simultaneous = <.1 CMSI 4 ppm d84 Specified stress thresholds: O2 : 4 ppm Incipient Bed mobilization : ratio of ave. shear to D84criticalshear/5

  35. Field data from our Biological Assessment 15 10 %MC = % of taxa that are Metabolic Conformers EPT = count (# species) of EPT Taxa 5 0 EPT 1 2 3 4 EPT 5 6 %MC EPT %MC %MC EPT Integrated Modeling of Cedar Creek Stress Assessment: year 2003 NexRAD with 1998 Landcover Modeling Multiple stressors: hydraulics, temp, NH4, TP, BOD cum O2 stress 1: .533 .0 .153 .00 .00 .031 cum bed mobil 2: .00 .003 .01 .02 .06 .00 % Ag in Basin 57% 42% 37% 18% 18% 15% % Ag in RT 41% 33% 29% 21% 21% 14% Number of genera @Brickyard @Crystal @M-120 @ Ryerson @Sweeter @River Rd Sensitive taxa %MC EPT Metabolic conformers

  36. y = 12.094 - 16.126x R= 0.96021 EPT Taxa y = 9.9731 - 13.722x R= 0.9414 Metabolic Conformers Cedar_metrics 14 12 10 Observed Number of genera 8 6 4 2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 Modeled cumulative oxygen stress

  37. COD TP NH4 Temp Hydraulics -53% Relative effect {as % reduction} in total stress score • Cedar Creek • e.g. Model “experiment” 1 • Cedar@Brickyard site • What are the individual • effects of each stressor • On cumulative stress? • Sum >100% • Hydraulics>temp>WQ -0% -4% -73% -81%

  38. @brickyard @crystal lake rd @m-120 @ryerson rd @sweeteer rd Below river rd e.g. Cedar Creek Modeling “experiment” 2 eliminating BOD and NH4 effects How do the sites respond to a TP gradient? How spatially variable is Cedar Creeks response to TP loading? [simulating simple response to a single stressor] 0.3 @brickyard 0.25 0.2 C&N set low BOD=1 NH4=.02 ppm Cumulative Metabolic Stress Index 0.15 0.1 @m-120 0.05 Below river rd 0 All others -0.05 0 50 100 150 200 250 300 350 TP ppb

  39. @brickyard @crystal lake rd @m-120 @ryerson rd @sweeteer rd Below river rd e.g. Cedar Creek Modeling “experiment” 3 Given current BOD and NH4 stressors How do the sites respond to a TP gradient? How spatially variable is Cedar Creeks response to TP loading? [simulating response to a single stressor in a Multi-Stressor setting] 0.6 @brickyard 0.5 Current elevated C and N concs 0.4 0.3 Cumulative Metabolic Stress Index Current concs 0.2 @m-120 0.1 Below river rd 0 All others -0.1 0 50 100 150 200 250 300 350 TP ppb

  40. @brickyard @crystal lake rd @m-120 @ryerson rd @sweeteer rd Below river rd e.g. Cedar Creek Modeling “experiment” 3 Response to TP relative to current conditions [simulating response to a single stressor in a multi-stressor setting] 40 Below river rd 20 @m-120 0 @brickyard All others -20 Cumulative Metabolic Stress Index -40 -60 -80 -100 -120 0 50 100 150 200 250 300 350 TP ppb

  41. Final Steps • Model refinements • Regional & focal watersheds • Complete model integration for focal watersheds • Validate using bio-assessment data • Re-visit regional empirical models based on mechanistic model insights; improve with stratification?

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