Stressor-response modeling is used when data are available to estimate a relationship between nutrient concentrations and a response measure. This relationship provides an empirical representation of the linkage between elevated concentrations of nutrients and the ecological effects that ultimately can influence whether a water body supports its designated uses. Appropriate stressor and response variables can be identified from the conceptual model developed during problem formulation. Once the stressor-response relationship is estimated, N and P concentrations that are protective of designated uses can be derived.

Seven distinct steps are involved in using stressor-response models to derive NNC:

  1. Identify variables for analysis based on linkages described in the conceptual model.
  2. Assemble available data on selected variables.
  3. Explore relationships across variables.
  4. Classify water bodies into groups in which stressor-response relationships are expected to be similar.
  5. Estimate stressor-response relationships within each group.
  6. Derive criteria based on the characteristics of the estimated relationship.
  7. Review, evaluate, and document analysis.

Step 1: Identify Variables for Analysis

Conceptual models developed during problem formulation represent the known relationships between increased levels of nutrients, ecological effects, and support for designated uses. Pathways describing these relationships can be simple (e.g., increased nutrient levels cause increased primary productivity) or they can involve many distinct steps (e.g., increased nutrient levels increase phytoplankton abundance in lakes, reducing available light, extirpating submerged macrophytes, and reducing available littoral habitat for juvenile fish and aquatic invertebrates). Regardless of the complexity of the relationship, the conceptual model provides insight into the variables that should be included in the stressor-response analysis. More specifically, the model structure and the linkages between variables can identify primary and classification variables.

Primary variables

Primary variables quantify the stressors and the responses in the analysis. In the context of deriving nutrient criteria, stressor variables are typically selected as nutrient concentrations (e.g., TN, TP). In some situations, however, other variables provide a more direct linkage to the chosen response variable. For example, increased nutrient loading increases organic matter in a lake, and the decomposition of that organic matter can cause hypoxia. In that case, the concentration of organic matter might be the more informative stressor variable. Response variables quantify or link directly to assessment endpoints. For example, a common assessment endpoint would be the health of the biological community, and the response variable could be a fish or an invertebrate index that quantifies the health of the community. In other cases, the response variable might quantify a stressor that arises directly from elevated concentrations of nutrients. For example, the extent of hypoxia in a lake is directly linked to the supply of organic material, part of which derives from primary productivity. That means you could use hypoxic extent as a response variable.

Classification variables

A flow chart connecting increased levels of nutrients to increased primary productivity.

Figure 1. Example of alternate path connecting increased levels of nutrients to increased primary productivity.

Other classification variables could be identified from an understanding of the processes by which elevated concentrations of nutrients are manifested as ecological effects. For example, the strength of stratification in a lake controls the rate at which oxygen can be diffused to deeper waters. That means stratification strength would potentially be a useful classification variable when you are modeling the relationship between nutrient levels and lake hypoxia.

Case Studies

Yaquina Estuary, OR

  • Looked at various response variables to nutrient inputs
  • Response variable included: seagrass, macroalgae, chlorophyll a, TSS, water clarity
  • Used a seagrass SRM to determine median percentile for water clarity

Pensacola Bay

  • The location of hypoxic waters is associated with stratification
  • Hypoxia extent not principally result of high DO demand
  • Water clarity and nutrient concentrations generally favorable to SAV growth

Coastal Bays in MD and VA

  • HABs (e.g., brown tide blooms) occur annually with increasing intensity
  • Chlorophyll a concentrations are low even during times of maximum summer biomass
  • Macroalgae are abundant and increasing in some areas
  • Hypoxia occurs in many locations

Barnegat Bay-Little Egg Harbor

  • Nutrient loading increases micro- and macroalgal growth that affect benthic habitats
  • High phytoplankton density causes shading effects detrimental to SAV beds
  • Increased macroalgae alters sediment chemistry and DO, affecting SAV and shellfish

Yaquina Estuary

  • Looked at nutrient responses on chlorophyll a, TSS, DO, and water clarity

San Francisco Bay

  • Agriculture and wastewater discharge contribute to high nutrient and sediment input
  • Suspended sediment limits phytoplankton and SAV growth
  • Spring diatom blooms routinely occur
  • New WWTP technology decreasing hypoxic and algal bloom events

Nutrients in Neuse River Estuary

  • Nutrient inputs from agriculture and hog operations and hydrodynamics trigger algal blooms
  • Phytoplankton, CDOM, and sediments cause low water clarity
  • Light attenuation prevents SAV growth

Nutrients in Chesapeake Bay

  • Increased nutrient inputs cause shifts in phytoplankton community and increases in HABs
  • Increased primary production affects DO levels, which affects benthic communities
  • Increased primary production affects water clarity, which affects SAV growth

Nutrients in Delaware Estuary

  • Continued nutrient inputs from agriculture and WWTPs
  • Typically no hypoxia and HABs in spite of nutrient inputs
  • Sediment resuspension from tidal currents cause turbid waters
  • Light limitation prevents phytoplankton, SAV, and macroalgal growth

Nutrients in Narragansett Bay

  • The bay ecosystem is undergoing change
  • Relationships between nutrients and phytoplankton and other endpoints still need to be developed

Nutrient Effects in CA Streams

  • Relationships between eutrophication stressors and BMI and algal community structure

Red River of the North

  • Stressor-response models used to look at relationships (nutrients, suspended sediment, biological response)
  • In upper reaches, results might be statistically significant
  • In downstream reaches, results might be limited

Recommended Criteria for WV Lakes

  • Average TP concentrations at which there is a substantial risk of DO dropping below 6 mg/L
  • Levels of TP that correspond to user perception that lake water is unsuitable for recreation
  • A regression analysis of TP and chlorophyll a

Virginia Freshwater Nutrient Criteria

  • For streams and rivers, define criteria to represent levels of algal biomass that impair the designated uses
  • Investigate nutrient-algal biomass relationships as an integral component of criteria development

Proposed Criteria for Tampa Bay

  • Stressor-response models developed based on nitrogen, phytoplankton, and water clarity
  • Incident light and seagrass depth relationship modeled to develop a light attenuation target

St. Louis Bay, MS

  • Empirical relationships between stressor and response variables
  • Development of predictive models for nutrient thresholds

Ontario Phosphorus Criteria

  • Proposed a water quality objective for TP based on modeled predevelopment P concentrations
  • Provide water quality managers with a constant assessment baseline
  • Create a buffer against incremental loss of water quality and variable water quality measurements

Estuarine Criteria in Florida

  • Considered several models using nutrients as causal variables and chlorophyll a as the response variable
  • Considered regressions to quantify relationships between light attenuation and chlorophyll a
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